wip

runner/.gitignore

@@ -0,0 +1,2 @@
+model.bin
+runner

runner/darwin.go

@@ -0,0 +1,39 @@
+package main
+
+import (
+	"embed"
+	"io"
+	"os"
+	"path/filepath"
+)
+
+//go:embed ggml-metal.metal
+var fs embed.FS
+
+func init() {
+	exec, err := os.Executable()
+	if err != nil {
+		return
+	}
+
+	exec, err = filepath.EvalSymlinks(exec)
+	if err != nil {
+		return
+	}
+
+	dst, err := os.Create(filepath.Join(filepath.Dir(exec), "ggml-metal.metal"))
+	if err != nil {
+		return
+	}
+	defer dst.Close()
+
+	src, err := fs.Open("ggml-metal.metal")
+	if err != nil {
+		return
+	}
+	defer src.Close()
+
+	if _, err := io.Copy(dst, src); err != nil {
+		return
+	}
+}

runner/ggml-alloc.c

@@ -0,0 +1,620 @@
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#include "ggml-alloc.h"
+#include "ggml-backend.h"
+#include "ggml.h"
+#include <assert.h>
+#include <stdarg.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+
+#define UNUSED(x) (void)(x)
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+#define GGML_MAX_CONCUR (2*GGML_MAX_NODES)
+
+//#define GGML_ALLOCATOR_DEBUG
+
+//#define AT_PRINTF printf
+#define AT_PRINTF(...) ((void)0)
+
+struct hash_node {
+    struct ggml_tensor * t;
+    int n_children;
+    int n_views;
+};
+
+static size_t hash(void * p) {
+    return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE;
+}
+
+static struct hash_node * hash_get(struct hash_node hash_table[], struct ggml_tensor * t) {
+    size_t h = hash(t);
+
+    // linear probing
+    size_t i = h;
+    while (hash_table[i].t != NULL) {
+        if (hash_table[i].t == t) {
+            return &hash_table[i];
+        }
+        i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE;
+        if (i == h) {
+            // hash table is full
+            GGML_ASSERT(false);
+        }
+    }
+
+    hash_table[i].t = t;
+    return &hash_table[i];
+}
+
+// TODO: GGML_PAD ?
+static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
+    assert(alignment && !(alignment & (alignment - 1))); // power of 2
+    size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
+    return offset + align;
+}
+
+struct free_block {
+    void * addr;
+    size_t size;
+};
+
+#define MAX_FREE_BLOCKS 256
+
+struct ggml_allocr {
+    struct ggml_backend_buffer * buffer;
+    bool buffer_owned;
+    void * data;
+    size_t alignment;
+    int n_free_blocks;
+    struct free_block free_blocks[MAX_FREE_BLOCKS];
+    struct hash_node hash_table[GGML_GRAPH_HASHTABLE_SIZE];
+    size_t max_size;
+    bool measure;
+    int parse_seq[GGML_MAX_CONCUR];
+    int parse_seq_len;
+
+#ifdef GGML_ALLOCATOR_DEBUG
+    struct ggml_tensor * allocated_tensors[1024];
+#endif
+};
+
+#ifdef GGML_ALLOCATOR_DEBUG
+static void add_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
+    for (int i = 0; i < 1024; i++) {
+        if (alloc->allocated_tensors[i] == NULL) {
+            alloc->allocated_tensors[i] = tensor;
+            return;
+        }
+    }
+    GGML_ASSERT(!"out of allocated_tensors");
+}
+static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
+    for (int i = 0; i < 1024; i++) {
+        if (alloc->allocated_tensors[i] == tensor ||
+            (alloc->allocated_tensors[i] != NULL && alloc->allocated_tensors[i]->data == tensor->data)) {
+            alloc->allocated_tensors[i] = NULL;
+            return;
+        }
+    }
+    printf("tried to free tensor %s not found\n", tensor->name);
+    GGML_ASSERT(!"tensor not found");
+}
+#endif
+
+// check if a tensor is allocated by this buffer
+static bool ggml_allocr_is_own(struct ggml_allocr * alloc, const struct ggml_tensor * tensor) {
+    return tensor->buffer == alloc->buffer;
+}
+
+static bool ggml_is_view(struct ggml_tensor * t) {
+    return t->view_src != NULL;
+}
+
+void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
+    GGML_ASSERT(!ggml_is_view(tensor)); // views generally get data pointer from one of their sources
+    GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated
+
+    size_t size = ggml_backend_buffer_get_alloc_size(alloc->buffer, tensor);
+    size = aligned_offset(NULL, size, alloc->alignment);
+
+    AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
+
+    size_t max_avail = 0;
+
+    // find the best fitting free block besides the last block
+    int best_fit_block = -1;
+    size_t best_fit_size = SIZE_MAX;
+    for (int i = 0; i < alloc->n_free_blocks - 1; i++) {
+        struct free_block * block = &alloc->free_blocks[i];
+        max_avail = MAX(max_avail, block->size);
+        if (block->size >= size && block->size <= best_fit_size) {
+            best_fit_block = i;
+            best_fit_size = block->size;
+        }
+    }
+
+    AT_PRINTF("block %d\n", best_fit_block);
+
+    if (best_fit_block == -1) {
+        // the last block is our last resort
+        struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1];
+        max_avail = MAX(max_avail, block->size);
+        if (block->size >= size) {
+            best_fit_block = alloc->n_free_blocks - 1;
+        } else {
+            fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n",
+                    __func__, size, max_avail);
+            GGML_ASSERT(!"not enough space in the buffer");
+            return;
+        }
+    }
+    struct free_block * block = &alloc->free_blocks[best_fit_block];
+    void * addr = block->addr;
+    block->addr = (char*)block->addr + size;
+    block->size -= size;
+    if (block->size == 0) {
+        // remove block if empty
+        alloc->n_free_blocks--;
+        for (int j = best_fit_block; j < alloc->n_free_blocks; j++) {
+            alloc->free_blocks[j] = alloc->free_blocks[j+1];
+        }
+    }
+
+    tensor->data = addr;
+    AT_PRINTF("%s: allocated data at %p\n", __func__, tensor->data);
+    tensor->buffer = alloc->buffer;
+    ggml_backend_buffer_init_tensor(alloc->buffer, tensor);
+
+#ifdef GGML_ALLOCATOR_DEBUG
+    add_allocated_tensor(alloc, tensor);
+    size_t cur_max = (char*)addr - (char*)alloc->data + size;
+    if (cur_max > alloc->max_size) {
+        printf("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
+        for (int i = 0; i < 1024; i++) {
+            if (alloc->allocated_tensors[i]) {
+                printf("%s (%.2f MB) ", alloc->allocated_tensors[i]->name, ggml_nbytes(alloc->allocated_tensors[i]) / 1024.0 / 1024.0);
+            }
+        }
+        printf("\n");
+    }
+#endif
+
+    alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->data + size);
+}
+
+// this is a very naive implementation, but for our case the number of free blocks should be very small
+static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
+    if (ggml_allocr_is_own(alloc, tensor) == false) {
+        // the tensor was not allocated in this buffer
+        // this can happen because the graph allocator will try to free weights and other tensors from different buffers
+        // the easiest way to deal with this is just to ignore it
+        AT_PRINTF("ignoring %s (their buffer: %p, our buffer: %p)\n", tensor->name, (void *)tensor->buffer, (void *)alloc->buffer);
+        return;
+    }
+
+    void * ptr = tensor->data;
+
+    size_t size = ggml_backend_buffer_get_alloc_size(alloc->buffer, tensor);
+    size = aligned_offset(NULL, size, alloc->alignment);
+    AT_PRINTF("%s: freeing %s at %p (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, ptr, size, alloc->n_free_blocks);
+
+    ggml_backend_buffer_free_tensor(alloc->buffer, tensor);
+
+#ifdef GGML_ALLOCATOR_DEBUG
+    remove_allocated_tensor(alloc, tensor);
+#endif
+
+    // see if we can merge with an existing block
+    for (int i = 0; i < alloc->n_free_blocks; i++) {
+        struct free_block * block = &alloc->free_blocks[i];
+        // check if ptr is at the end of the block
+        if ((char*)block->addr + block->size == ptr) {
+            block->size += size;
+            // check if we can merge with the next block
+            if (i < alloc->n_free_blocks - 1 && (char*)block->addr + block->size == alloc->free_blocks[i+1].addr) {
+                block->size += alloc->free_blocks[i+1].size;
+                alloc->n_free_blocks--;
+                for (int j = i+1; j < alloc->n_free_blocks; j++) {
+                    alloc->free_blocks[j] = alloc->free_blocks[j+1];
+                }
+            }
+            return;
+        }
+        // check if ptr is at the beginning of the block
+        if ((char*)ptr + size == block->addr) {
+            block->addr = ptr;
+            block->size += size;
+            // check if we can merge with the previous block
+            if (i > 0 && (char*)alloc->free_blocks[i-1].addr + alloc->free_blocks[i-1].size == block->addr) {
+                alloc->free_blocks[i-1].size += block->size;
+                alloc->n_free_blocks--;
+                for (int j = i; j < alloc->n_free_blocks; j++) {
+                    alloc->free_blocks[j] = alloc->free_blocks[j+1];
+                }
+            }
+            return;
+        }
+    }
+    // otherwise, add a new block
+    GGML_ASSERT(alloc->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks");
+    // insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster)
+    int insert_pos = 0;
+    while (insert_pos < alloc->n_free_blocks && alloc->free_blocks[insert_pos].addr < ptr) {
+        insert_pos++;
+    }
+    // shift all blocks from insert_pos onward to make room for the new block
+    for (int i = alloc->n_free_blocks; i > insert_pos; i--) {
+        alloc->free_blocks[i] = alloc->free_blocks[i-1];
+    }
+    // insert the new block
+    alloc->free_blocks[insert_pos].addr = ptr;
+    alloc->free_blocks[insert_pos].size = size;
+    alloc->n_free_blocks++;
+}
+
+void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n) {
+    for (int i = 0; i < n; i++) {
+        alloc->parse_seq[i] = list[i];
+    }
+    alloc->parse_seq_len = n;
+}
+
+void ggml_allocr_reset(struct ggml_allocr * alloc) {
+    alloc->n_free_blocks = 1;
+    size_t align_offset = aligned_offset(alloc->data, 0, alloc->alignment);
+    alloc->free_blocks[0].addr = (char *)alloc->data + align_offset;
+    alloc->free_blocks[0].size = ggml_backend_buffer_get_size(alloc->buffer) - align_offset;
+}
+
+struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) {
+    struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(NULL, data, size);
+
+    struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr));
+
+    *alloc = (struct ggml_allocr){
+        /*.buffer        = */ buffer,
+        /*.buffer_owned  = */ true,
+        /*.base          = */ ggml_backend_buffer_get_base(buffer),
+        /*.alignment     = */ alignment,
+        /*.n_free_blocks = */ 0,
+        /*.free_blocks   = */ {{0}},
+        /*.hash_table    = */ {{0}},
+        /*.max_size      = */ 0,
+        /*.measure       = */ false,
+        /*.parse_seq     = */ {0},
+        /*.parse_seq_len = */ 0,
+#ifdef GGML_ALLOCATOR_DEBUG
+        /*.allocated_tensors = */ {0},
+#endif
+    };
+
+    ggml_allocr_reset(alloc);
+
+    return alloc;
+}
+
+struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
+    struct ggml_allocr * alloc = ggml_allocr_new((void *)0x1000, (size_t)-0x1001, alignment);
+    alloc->measure = true;
+
+    return alloc;
+}
+
+struct ggml_allocr * ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer) {
+    struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr));
+
+    *alloc = (struct ggml_allocr){
+        /*.buffer        = */ buffer,
+        /*.buffer_owned  = */ false,
+        /*.base          = */ ggml_backend_buffer_get_base(buffer),
+        /*.alignment     = */ ggml_backend_buffer_get_alignment(buffer),
+        /*.n_free_blocks = */ 0,
+        /*.free_blocks   = */ {{0}},
+        /*.hash_table    = */ {{0}},
+        /*.max_size      = */ 0,
+        /*.measure       = */ false,
+        /*.parse_seq     = */ {0},
+        /*.parse_seq_len = */ 0,
+#ifdef GGML_ALLOCATOR_DEBUG
+        /*.allocated_tensors = */ {0},
+#endif
+    };
+
+    ggml_allocr_reset(alloc);
+
+    return alloc;
+}
+
+void ggml_allocr_free(struct ggml_allocr * alloc) {
+    if (alloc->buffer_owned) {
+        ggml_backend_buffer_free(alloc->buffer);
+    }
+    free(alloc);
+}
+
+bool ggml_allocr_is_measure(struct ggml_allocr * alloc) {
+    return alloc->measure;
+}
+
+//////////// compute graph allocator
+
+static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
+    if (a->type != b->type) {
+        return false;
+    }
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        if (a->ne[i] != b->ne[i]) {
+            return false;
+        }
+        if (a->nb[i] != b->nb[i]) {
+            return false;
+        }
+    }
+    return true;
+}
+
+static bool ggml_op_can_inplace(enum ggml_op op) {
+    switch (op) {
+        case GGML_OP_SCALE:
+        case GGML_OP_DIAG_MASK_ZERO:
+        case GGML_OP_DIAG_MASK_INF:
+        case GGML_OP_ADD:
+        case GGML_OP_ADD1:
+        case GGML_OP_SUB:
+        case GGML_OP_MUL:
+        case GGML_OP_DIV:
+        case GGML_OP_SQR:
+        case GGML_OP_SQRT:
+        case GGML_OP_LOG:
+        case GGML_OP_UNARY:
+        case GGML_OP_ROPE:
+        case GGML_OP_RMS_NORM:
+        case GGML_OP_SOFT_MAX:
+            return true;
+
+        default:
+            return false;
+    }
+}
+
+static void init_view(struct ggml_allocr * alloc, struct ggml_tensor * view) {
+    assert(view->view_src != NULL && view->view_src->data != NULL);
+    view->backend = view->view_src->backend;
+    view->buffer  = view->view_src->buffer;
+    view->data    = (char *)view->view_src->data + view->view_offs;
+
+    // FIXME: the view should be initialized by the owning buffer, but currently this breaks the CUDA backend
+    // due to the ggml_tensor_extra_gpu ring buffer overwriting the KV cache extras
+    assert(ggml_allocr_is_measure(alloc) || !view->buffer || view->buffer->backend == alloc->buffer->backend);
+    ggml_backend_buffer_init_tensor(alloc->buffer, view);
+}
+
+static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) {
+    struct hash_node * ht = alloc->hash_table;
+    if (node->data == NULL) {
+        if (ggml_is_view(node)) {
+            init_view(alloc, node);
+        } else {
+            // see if we can reuse a parent's buffer (inplace)
+            if (ggml_op_can_inplace(node->op)) {
+                for (int i = 0; i < GGML_MAX_SRC; i++) {
+                    struct ggml_tensor * parent = node->src[i];
+                    if (parent == NULL) {
+                        break;
+                    }
+
+                    // if the node's data is external, then we cannot re-use it
+                    if (ggml_allocr_is_own(alloc, parent) == false) {
+                        AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data);
+                        continue;
+                    }
+
+                    struct hash_node * p_hn = hash_get(ht, parent);
+                    if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) {
+                        if (ggml_is_view(parent)) {
+                            struct ggml_tensor * view_src = parent->view_src;
+                            struct hash_node * view_src_hn = hash_get(ht, view_src);
+                            if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
+                                // TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite
+                                // the parent's data that it will need later (same layout requirement). the problem is that then
+                                // we cannot free the tensor because the original address of the allocation is lost.
+                                // adding a view_src pointer to the tensor would solve this and simplify the code dealing with views
+                                // for now, we only reuse the parent's data if the offset is zero (view_src->data == parent->data)
+                                AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
+                                node->view_src = view_src;
+                                view_src_hn->n_views += 1;
+                                init_view(alloc, node);
+                                return;
+                            }
+                        }
+                        else {
+                            AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
+                            node->view_src = parent;
+                            p_hn->n_views += 1;
+                            init_view(alloc, node);
+                            return;
+                        }
+                    }
+                }
+            }
+            ggml_allocr_alloc(alloc, node);
+        }
+    }
+}
+
+size_t ggml_allocr_alloc_graph_n(
+    struct ggml_allocr * alloc,
+    struct ggml_cgraph ** graphs, int n_graphs,
+    struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) {
+
+    // reset hash table
+    struct hash_node * ht = alloc->hash_table;
+    memset(ht, 0, sizeof(struct hash_node) * GGML_GRAPH_HASHTABLE_SIZE);
+
+    // count number of children and views
+    for (int g = 0; g < n_graphs; g++) {
+        struct ggml_cgraph * gf = graphs[g];
+        for (int i = 0; i < gf->n_nodes; i++) {
+            struct ggml_tensor * node = gf->nodes[i];
+
+            if (ggml_is_view(node)) {
+                struct ggml_tensor * view_src = node->view_src;
+                hash_get(ht, view_src)->n_views += 1;
+                if (node->buffer == NULL && node->data != NULL) {
+                    // view of a pre-allocated tensor, didn't call init_view() yet
+                    init_view(alloc, node);
+                }
+            }
+
+            for (int j = 0; j < GGML_MAX_SRC; j++) {
+                struct ggml_tensor * parent = node->src[j];
+                if (parent == NULL) {
+                    break;
+                }
+                hash_get(ht, parent)->n_children += 1;
+                if (ggml_is_view(parent) && parent->buffer == NULL && parent->data != NULL) {
+                    init_view(alloc, parent);
+                }
+            }
+        }
+    }
+
+    // allocate tensors
+    for (int g = 0; g < n_graphs; g++) {
+        struct ggml_cgraph * gf = graphs[g];
+        AT_PRINTF("####### graph %d/%d\n", g, n_graphs);
+        // graph inputs are allocated first to ensure that they are not overwritten by each other
+        if (inputs != NULL && inputs[g] != NULL) {
+            for (int i = 0; inputs[g][i] != NULL; i++) {
+                struct ggml_tensor * input = inputs[g][i];
+                AT_PRINTF("input: %s\n", input->name);
+                allocate_node(alloc, input);
+            }
+        }
+        // if we have parse_seq then we allocate nodes following the list, and we only free nodes at barriers
+        int last_barrier_pos = 0;
+        int n_nodes = alloc->parse_seq_len ? alloc->parse_seq_len : gf->n_nodes;
+
+        for (int ind = 0; ind < n_nodes; ind++) {
+            // allocate a node if there is no parse_seq or this is not a barrier
+            if ((alloc->parse_seq_len==0) || alloc->parse_seq[ind] != -1) {
+                int i = alloc->parse_seq_len ? alloc->parse_seq[ind] : ind;
+                struct ggml_tensor * node = gf->nodes[i];
+
+                // allocate parents (leafs)
+                for (int j = 0; j < GGML_MAX_SRC; j++) {
+                    struct ggml_tensor * parent = node->src[j];
+                    if (parent == NULL) {
+                        break;
+                    }
+                    allocate_node(alloc, parent);
+                }
+
+                // allocate node
+                allocate_node(alloc, node);
+
+                AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name);
+                for (int j = 0; j < GGML_MAX_SRC; j++) {
+                    struct ggml_tensor * parent = node->src[j];
+                    if (parent == NULL) {
+                        break;
+                    }
+                    AT_PRINTF("%s", parent->name);
+                    if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) {
+                        AT_PRINTF(", ");
+                    }
+                }
+                AT_PRINTF("\n");
+            }
+
+            // update parents
+            // update immediately if there is no parse_seq
+            // update only at barriers if there is parse_seq
+            if ((alloc->parse_seq_len == 0) || alloc->parse_seq[ind] == -1) {
+                int update_start = alloc->parse_seq_len ? last_barrier_pos : ind;
+                int update_end   = alloc->parse_seq_len ? ind              : ind + 1;
+                for (int i = update_start; i < update_end; i++) {
+                    int node_i = alloc->parse_seq_len ? alloc->parse_seq[i] : i;
+                    struct ggml_tensor * node = gf->nodes[node_i];
+
+                    for (int j = 0; j < GGML_MAX_SRC; j++) {
+                        struct ggml_tensor * parent = node->src[j];
+                        if (parent == NULL) {
+                            break;
+                        }
+                        struct hash_node * p_hn = hash_get(ht, parent);
+                        p_hn->n_children -= 1;
+
+                        //AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views);
+
+                        if (p_hn->n_children == 0 && p_hn->n_views == 0) {
+                            if (ggml_is_view(parent)) {
+                                struct ggml_tensor * view_src = parent->view_src;
+                                struct hash_node * view_src_hn = hash_get(ht, view_src);
+                                view_src_hn->n_views -= 1;
+                                AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views);
+                                if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
+                                    ggml_allocr_free_tensor(alloc, view_src);
+                                }
+                            }
+                            else {
+                                if (parent->data != node->data) {
+                                    ggml_allocr_free_tensor(alloc, parent);
+                                }
+                            }
+                        }
+                    }
+                }
+                AT_PRINTF("\n");
+                if (alloc->parse_seq_len) {
+                    last_barrier_pos = ind + 1;
+                }
+            }
+        }
+        // free graph outputs here that wouldn't be freed otherwise because they have no children
+        if (outputs != NULL && outputs[g] != NULL) {
+            for (int i = 0; outputs[g][i] != NULL; i++) {
+                struct ggml_tensor * output = outputs[g][i];
+                AT_PRINTF("output: %s\n", output->name);
+                ggml_allocr_free_tensor(alloc, output);
+            }
+        }
+    }
+
+    return alloc->max_size;
+}
+
+size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) {
+    return ggml_allocr_alloc_graph_n(alloc, &graph, 1, NULL, NULL);
+}
+
+size_t ggml_allocr_max_size(struct ggml_allocr * alloc) {
+    return alloc->max_size;
+}

runner/ggml-alloc.h

@@ -0,0 +1,59 @@
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#pragma once
+
+#include "ggml.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+struct ggml_backend_buffer;
+
+GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment);
+GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);
+GGML_API struct ggml_allocr * ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer);
+
+// tell the allocator to parse nodes following the order described in the list
+// you should call this if your graph are optimized to execute out-of-order
+GGML_API void   ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n);
+
+GGML_API void   ggml_allocr_free       (struct ggml_allocr * alloc);
+GGML_API bool   ggml_allocr_is_measure (struct ggml_allocr * alloc);
+GGML_API void   ggml_allocr_reset      (struct ggml_allocr * alloc);
+GGML_API void   ggml_allocr_alloc      (struct ggml_allocr * alloc, struct ggml_tensor * tensor);
+GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph);
+GGML_API size_t ggml_allocr_max_size   (struct ggml_allocr * alloc);
+
+GGML_API size_t ggml_allocr_alloc_graph_n(
+                    struct ggml_allocr * alloc,
+                    struct ggml_cgraph ** graphs, int n_graphs,
+                    struct ggml_tensor *** inputs, struct ggml_tensor *** outputs);
+
+#ifdef  __cplusplus
+}
+#endif

runner/ggml-backend.c

@@ -0,0 +1,411 @@
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#include "ggml-backend.h"
+#include "ggml-alloc.h"
+
+#include <assert.h>
+#include <stdarg.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#define UNUSED GGML_UNUSED
+
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+
+// backend buffer
+
+ggml_backend_buffer_t ggml_backend_buffer_init(
+        struct ggml_backend                  * backend,
+        struct ggml_backend_buffer_i           iface,
+               ggml_backend_buffer_context_t   context,
+               size_t                          size) {
+    ggml_backend_buffer_t buffer = malloc(sizeof(struct ggml_backend_buffer));
+
+    GGML_ASSERT(iface.get_base != NULL);
+
+    (*buffer) = (struct ggml_backend_buffer) {
+        /* .interface = */ iface,
+        /* .backend   = */ backend,
+        /* .context   = */ context,
+        /* .size      = */ size,
+    };
+
+    return buffer;
+}
+
+void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) {
+    if (buffer->iface.free_buffer != NULL) {
+        buffer->iface.free_buffer(buffer);
+    }
+    free(buffer);
+}
+
+size_t ggml_backend_buffer_get_alignment(ggml_backend_buffer_t buffer) {
+    return ggml_backend_get_alignment(buffer->backend);
+}
+
+void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
+    return buffer->iface.get_base(buffer);
+}
+
+size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) {
+    return buffer->size;
+}
+
+size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
+    if (buffer->iface.get_alloc_size) {
+        return buffer->iface.get_alloc_size(buffer, tensor);
+    }
+    return ggml_nbytes(tensor);
+}
+
+void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
+    if (buffer->iface.init_tensor) {
+        buffer->iface.init_tensor(buffer, tensor);
+    }
+}
+
+void ggml_backend_buffer_free_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
+    if (buffer->iface.free_tensor) {
+        buffer->iface.free_tensor(buffer, tensor);
+    }
+}
+
+// backend
+
+ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor) {
+    return tensor->buffer->backend;
+}
+
+const char * ggml_backend_name(ggml_backend_t backend) {
+    return backend->iface.get_name(backend);
+}
+
+void ggml_backend_free(ggml_backend_t backend) {
+    backend->iface.free(backend);
+}
+
+ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size) {
+    return backend->iface.alloc_buffer(backend, size);
+}
+
+size_t ggml_backend_get_alignment(ggml_backend_t backend) {
+    return backend->iface.get_alignment(backend);
+}
+
+void ggml_backend_tensor_set_async(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    ggml_get_backend(tensor)->iface.set_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size);
+}
+
+void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    ggml_get_backend(tensor)->iface.get_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size);
+}
+
+void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    ggml_get_backend(tensor)->iface.set_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size);
+    ggml_get_backend(tensor)->iface.synchronize(ggml_get_backend(tensor));
+}
+
+void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    ggml_get_backend(tensor)->iface.get_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size);
+    ggml_get_backend(tensor)->iface.synchronize(ggml_get_backend(tensor));
+}
+
+void ggml_backend_synchronize(ggml_backend_t backend) {
+    backend->iface.synchronize(backend);
+}
+
+ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    return backend->iface.graph_plan_create(backend, cgraph);
+}
+
+void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    backend->iface.graph_plan_free(backend, plan);
+}
+
+void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    backend->iface.graph_plan_compute(backend, plan);
+}
+
+void ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    backend->iface.graph_compute(backend, cgraph);
+}
+
+bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+    return backend->iface.supports_op(backend, op);
+}
+
+// backend copy
+
+static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
+    if (a->type != b->type) {
+        return false;
+    }
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        if (a->ne[i] != b->ne[i]) {
+            return false;
+        }
+        if (a->nb[i] != b->nb[i]) {
+            return false;
+        }
+    }
+    return true;
+}
+
+void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst) {
+    //printf("src: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", src->name, (int)src->ne[0], (int)src->ne[1], (int)src->ne[2], (int)src->ne[3], (int)src->nb[0], (int)src->nb[1], (int)src->nb[2], (int)src->nb[3]);
+    //printf("dst: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", dst->name, (int)dst->ne[0], (int)dst->ne[1], (int)dst->ne[2], (int)dst->ne[3], (int)dst->nb[0], (int)dst->nb[1], (int)dst->nb[2], (int)dst->nb[3]);
+    GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
+
+    // printf("cpy tensor %s from %s to %s (%lu bytes)\n", src->name, ggml_backend_name(src->backend), ggml_backend_name(dst->backend), ggml_nbytes(src));
+
+    if (src == dst) {
+        return;
+    }
+
+    // TODO: allow backends to support copy to/from same backend
+
+    if (ggml_get_backend(dst)->iface.cpy_tensor_from != NULL) {
+        ggml_get_backend(dst)->iface.cpy_tensor_from(ggml_get_backend(dst)->context, src, dst);
+    } else if (ggml_get_backend(src)->iface.cpy_tensor_to != NULL) {
+        ggml_get_backend(src)->iface.cpy_tensor_to(ggml_get_backend(src)->context, src, dst);
+    } else {
+        // shouldn't be hit when copying from/to CPU
+        #ifndef NDEBUG
+        fprintf(stderr, "ggml_backend_tensor_copy: neither cpy_tensor_from nor cpy_tensor_to are implemented for backends %s and %s, falling back to get/set\n", ggml_backend_name(src->buffer->backend), ggml_backend_name(dst->buffer->backend));
+        #endif
+        size_t nbytes = ggml_nbytes(src);
+        void * data = malloc(nbytes);
+        ggml_backend_tensor_get(src, data, 0, nbytes);
+        ggml_backend_tensor_set(dst, data, 0, nbytes);
+        free(data);
+    }
+}
+
+// backend CPU
+
+struct ggml_backend_cpu_context {
+    int n_threads;
+    void * work_data;
+    size_t work_size;
+};
+
+static const char * ggml_backend_cpu_name(ggml_backend_t backend) {
+    return "CPU";
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_cpu_free(ggml_backend_t backend) {
+    struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
+    free(cpu_ctx->work_data);
+    free(cpu_ctx);
+    free(backend);
+}
+
+static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
+    return (void *)buffer->context;
+}
+
+static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    free(buffer->context);
+    UNUSED(buffer);
+}
+
+static struct ggml_backend_buffer_i cpu_backend_buffer_i = {
+    /* .free_buffer    = */ ggml_backend_cpu_buffer_free_buffer,
+    /* .get_base       = */ ggml_backend_cpu_buffer_get_base,
+    /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
+    /* .init_tensor    = */ NULL, // no initialization required
+    /* .free_tensor    = */ NULL, // no cleanup required
+};
+
+// for buffers from ptr, free is not called
+static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = {
+    /* .free_buffer    = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed
+    /* .get_base       = */ ggml_backend_cpu_buffer_get_base,
+    /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
+    /* .init_tensor    = */ NULL,
+    /* .free_tensor    = */ NULL,
+};
+
+static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512
+
+static ggml_backend_buffer_t ggml_backend_cpu_alloc_buffer(ggml_backend_t backend, size_t size) {
+    size += TENSOR_ALIGNMENT;   // malloc may return an address that is not aligned
+    void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC?
+
+    return ggml_backend_buffer_init(backend, cpu_backend_buffer_i, data, size);
+}
+
+static size_t ggml_backend_cpu_get_alignment(ggml_backend_t backend) {
+    return TENSOR_ALIGNMENT;
+    UNUSED(backend);
+}
+
+static void ggml_backend_cpu_set_tensor_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+
+    memcpy((char *)tensor->data + offset, data, size);
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_cpu_get_tensor_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+
+    memcpy(data, (const char *)tensor->data + offset, size);
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_cpu_synchronize(ggml_backend_t backend) {
+    UNUSED(backend);
+}
+
+static void ggml_backend_cpu_cpy_tensor_from(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
+    ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_cpu_cpy_tensor_to(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
+    // for a backend such as CUDA that can queue async calls, it is ok to do this asynchronously, but it may not be the case for other backends
+    ggml_backend_tensor_set_async(dst, src->data, 0, ggml_nbytes(src));
+
+    UNUSED(backend);
+}
+
+struct ggml_backend_plan_cpu {
+    struct ggml_cplan cplan;
+    struct ggml_cgraph cgraph;
+};
+
+static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
+
+    struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu));
+
+    cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
+    cpu_plan->cgraph = *cgraph;
+
+    if (cpu_plan->cplan.work_size > 0) {
+        cpu_plan->cplan.work_data = malloc(cpu_plan->cplan.work_size);
+    }
+
+    return cpu_plan;
+}
+
+static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
+
+    free(cpu_plan->cplan.work_data);
+    free(cpu_plan);
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
+
+    ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
+
+    struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
+
+    if (cpu_ctx->work_size < cplan.work_size) {
+        // TODO: may be faster to free and use malloc to avoid the copy
+        cpu_ctx->work_data = realloc(cpu_ctx->work_data, cplan.work_size);
+        cpu_ctx->work_size = cplan.work_size;
+    }
+
+    cplan.work_data = cpu_ctx->work_data;
+
+    ggml_graph_compute(cgraph, &cplan);
+}
+
+static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+    return true;
+    UNUSED(backend);
+    UNUSED(op);
+}
+
+static struct ggml_backend_i cpu_backend_i = {
+    /* .get_name            = */ ggml_backend_cpu_name,
+    /* .free                = */ ggml_backend_cpu_free,
+    /* .alloc_buffer        = */ ggml_backend_cpu_alloc_buffer,
+    /* .get_alignment       = */ ggml_backend_cpu_get_alignment,
+    /* .set_tensor_async    = */ ggml_backend_cpu_set_tensor_async,
+    /* .get_tensor_async    = */ ggml_backend_cpu_get_tensor_async,
+    /* .synchronize         = */ ggml_backend_cpu_synchronize,
+    /* .cpy_tensor_from     = */ ggml_backend_cpu_cpy_tensor_from,
+    /* .cpy_tensor_to       = */ ggml_backend_cpu_cpy_tensor_to,
+    /* .graph_plan_create   = */ ggml_backend_cpu_graph_plan_create,
+    /* .graph_plan_free     = */ ggml_backend_cpu_graph_plan_free,
+    /* .graph_plan_compute  = */ ggml_backend_cpu_graph_plan_compute,
+    /* .graph_compute       = */ ggml_backend_cpu_graph_compute,
+    /* .supports_op         = */ ggml_backend_cpu_supports_op,
+};
+
+ggml_backend_t ggml_backend_cpu_init(void) {
+    struct ggml_backend_cpu_context * ctx = malloc(sizeof(struct ggml_backend_cpu_context));
+
+    ctx->n_threads = GGML_DEFAULT_N_THREADS;
+    ctx->work_data = NULL;
+    ctx->work_size = 0;
+
+    ggml_backend_t cpu_backend = malloc(sizeof(struct ggml_backend));
+
+    *cpu_backend = (struct ggml_backend) {
+        /* .interface = */ cpu_backend_i,
+        /* .context   = */ ctx
+    };
+    return cpu_backend;
+}
+
+bool ggml_backend_is_cpu(ggml_backend_t backend) {
+    return backend->iface.get_name == ggml_backend_cpu_name;
+}
+
+void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) {
+    GGML_ASSERT(ggml_backend_is_cpu(backend_cpu));
+
+    struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context;
+    ctx->n_threads = n_threads;
+}
+
+ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size) {
+    return ggml_backend_buffer_init(backend_cpu, cpu_backend_buffer_i_from_ptr, ptr, size);
+}

runner/ggml-backend.h

@@ -0,0 +1,169 @@
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#pragma once
+
+#include "ggml.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+    struct ggml_backend;
+    struct ggml_backend_buffer;
+
+    // type-erased backend-specific types / wrappers
+    typedef void * ggml_backend_context_t;
+    typedef void * ggml_backend_graph_plan_t;
+    typedef void * ggml_backend_buffer_context_t;
+
+    // avoid accessing internals of these types
+    typedef struct ggml_backend        * ggml_backend_t;
+    typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
+
+    //
+    // backend buffer
+    //
+
+    struct ggml_backend_buffer_i {
+        void   (*free_buffer)   (ggml_backend_buffer_t buffer);
+        void * (*get_base)      (ggml_backend_buffer_t buffer); // get base pointer
+        size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback
+        void   (*init_tensor)   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback
+        void   (*free_tensor)   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback
+    };
+
+    // TODO: hide behind API
+    struct ggml_backend_buffer {
+        struct ggml_backend_buffer_i iface;
+
+        ggml_backend_t                backend;
+        ggml_backend_buffer_context_t context;
+
+        size_t size;
+    };
+
+    // backend buffer functions
+    GGML_API ggml_backend_buffer_t ggml_backend_buffer_init(
+            struct ggml_backend                  * backend,
+            struct ggml_backend_buffer_i           iface,
+                   ggml_backend_buffer_context_t   context,
+                   size_t                          size);
+
+    GGML_API void   ggml_backend_buffer_free          (ggml_backend_buffer_t buffer);
+    GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
+    GGML_API void * ggml_backend_buffer_get_base      (ggml_backend_buffer_t buffer);
+    GGML_API size_t ggml_backend_buffer_get_size      (ggml_backend_buffer_t buffer);
+    GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API void   ggml_backend_buffer_init_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API void   ggml_backend_buffer_free_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+
+    //
+    // backend
+    //
+
+    struct ggml_backend_i {
+        const char * (*get_name)(ggml_backend_t backend);
+
+        void (*free)(ggml_backend_t backend);
+
+        // buffer allocation
+        ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size);
+
+        // get buffer alignment
+        size_t (*get_alignment)(ggml_backend_t backend);
+
+        // tensor data access
+        // these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize
+        void (*set_tensor_async)(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+        void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+        void (*synchronize)     (ggml_backend_t backend);
+
+        // (optional) copy tensor between different backends, allow for single-copy tranfers
+        void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
+        void (*cpy_tensor_to)  (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
+
+        // compute graph with a plan
+        ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+        void                      (*graph_plan_free)   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+        void                      (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+
+        // compute graph without a plan
+        void (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+
+        // check if the backend supports an operation
+        bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
+    };
+
+    // TODO: hide behind API
+    struct ggml_backend {
+        struct ggml_backend_i iface;
+
+        ggml_backend_context_t context;
+    };
+
+    // backend helper functions
+    GGML_API ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor);
+
+    GGML_API const char * ggml_backend_name(ggml_backend_t backend);
+    GGML_API void         ggml_backend_free(ggml_backend_t backend);
+
+    GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
+
+    GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend);
+
+    GGML_API void ggml_backend_tensor_set_async(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+
+    GGML_API void ggml_backend_tensor_set(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+
+    GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
+
+    GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+
+    GGML_API void ggml_backend_graph_plan_free   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+    GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+    GGML_API void ggml_backend_graph_compute     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API bool ggml_backend_supports_op       (ggml_backend_t backend, const struct ggml_tensor * op);
+
+    // tensor copy between different backends
+    GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
+
+    //
+    // CPU backend
+    //
+
+    GGML_API ggml_backend_t ggml_backend_cpu_init(void);
+
+    GGML_API bool ggml_backend_is_cpu(ggml_backend_t backend);
+
+    GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
+
+    GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size);
+
+#ifdef  __cplusplus
+}
+#endif

runner/ggml-cuda.cu

@@ -0,0 +1,7850 @@
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#include <algorithm>
+#include <cstddef>
+#include <cstdint>
+#include <limits>
+#include <stdint.h>
+#include <stdio.h>
+#include <atomic>
+#include <assert.h>
+
+#if defined(GGML_USE_HIPBLAS)
+#include <hip/hip_runtime.h>
+#include <hipblas/hipblas.h>
+#include <hip/hip_fp16.h>
+#ifdef __HIP_PLATFORM_AMD__
+// for rocblas_initialize()
+#include "rocblas/rocblas.h"
+#endif // __HIP_PLATFORM_AMD__
+#define CUBLAS_COMPUTE_16F HIPBLAS_R_16F
+#define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
+#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
+#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
+#define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT
+#define CUBLAS_OP_N HIPBLAS_OP_N
+#define CUBLAS_OP_T HIPBLAS_OP_T
+#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
+#define CUBLAS_TF32_TENSOR_OP_MATH 0
+#define CUDA_R_16F  HIPBLAS_R_16F
+#define CUDA_R_32F  HIPBLAS_R_32F
+#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
+#define cublasCreate hipblasCreate
+#define cublasGemmEx hipblasGemmEx
+#define cublasHandle_t hipblasHandle_t
+#define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
+#define cublasSetStream hipblasSetStream
+#define cublasSgemm hipblasSgemm
+#define cublasStatus_t hipblasStatus_t
+#define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer
+#define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
+#define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
+#define cudaDeviceProp hipDeviceProp_t
+#define cudaDeviceSynchronize hipDeviceSynchronize
+#define cudaError_t hipError_t
+#define cudaEventCreateWithFlags hipEventCreateWithFlags
+#define cudaEventDisableTiming hipEventDisableTiming
+#define cudaEventRecord hipEventRecord
+#define cudaEvent_t hipEvent_t
+#define cudaEventDestroy hipEventDestroy
+#define cudaFree hipFree
+#define cudaFreeHost hipHostFree
+#define cudaGetDevice hipGetDevice
+#define cudaGetDeviceCount hipGetDeviceCount
+#define cudaGetDeviceProperties hipGetDeviceProperties
+#define cudaGetErrorString hipGetErrorString
+#define cudaGetLastError hipGetLastError
+#define cudaMalloc hipMalloc
+#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
+#define cudaMemcpy hipMemcpy
+#define cudaMemcpy2DAsync hipMemcpy2DAsync
+#define cudaMemcpyAsync hipMemcpyAsync
+#define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice
+#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost
+#define cudaMemcpyHostToDevice hipMemcpyHostToDevice
+#define cudaMemcpyKind hipMemcpyKind
+#define cudaMemset hipMemset
+#define cudaMemsetAsync hipMemsetAsync
+#define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize
+#define cudaSetDevice hipSetDevice
+#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
+#define cudaStreamNonBlocking hipStreamNonBlocking
+#define cudaStreamSynchronize hipStreamSynchronize
+#define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
+#define cudaStream_t hipStream_t
+#define cudaSuccess hipSuccess
+#else
+#include <cuda_runtime.h>
+#include <cublas_v2.h>
+#include <cuda_fp16.h>
+#endif // defined(GGML_USE_HIPBLAS)
+
+#include "ggml-cuda.h"
+#include "ggml.h"
+
+#define MIN_CC_DP4A   610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
+#define CC_VOLTA      700
+#define CC_OFFSET_AMD 1000000
+#define CC_RDNA2      (CC_OFFSET_AMD + 1030)
+
+#if defined(GGML_USE_HIPBLAS)
+#define __CUDA_ARCH__ 1300
+
+#if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \
+    defined(__gfx1150__) || defined(__gfx1151__)
+#define RDNA3
+#endif
+
+#if defined(__gfx1030__) || defined(__gfx1031__) || defined(__gfx1032__) || defined(__gfx1033__) || \
+    defined(__gfx1034__) || defined(__gfx1035__) || defined(__gfx1036__) || defined(__gfx1037__)
+#define RDNA2
+#endif
+
+#ifndef __has_builtin
+    #define __has_builtin(x) 0
+#endif
+
+typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
+static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
+    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
+    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
+#if __has_builtin(__builtin_elementwise_sub_sat)
+    const int8x4_t c = __builtin_elementwise_sub_sat(va, vb);
+    return reinterpret_cast<const int&>(c);
+#else
+    int8x4_t c;
+    int16_t tmp;
+#pragma unroll
+    for (int i = 0; i < 4; i++) {
+        tmp = va[i] - vb[i];
+        if(tmp > std::numeric_limits<int8_t>::max()) tmp = std::numeric_limits<int8_t>::max();
+        if(tmp < std::numeric_limits<int8_t>::min()) tmp = std::numeric_limits<int8_t>::min();
+        c[i] = tmp;
+    }
+    return reinterpret_cast<int&>(c);
+#endif // __has_builtin(__builtin_elementwise_sub_sat)
+}
+
+static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
+#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
+    c = __builtin_amdgcn_sdot4(a, b, c, false);
+#elif defined(__gfx1100__)
+    c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
+#elif defined(__gfx1010__) || defined(__gfx900__)
+    int tmp1;
+    int tmp2;
+    asm("\n \
+        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
+        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
+        v_add3_u32 %0, %1, %2, %0 \n \
+        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
+        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
+        v_add3_u32 %0, %1, %2, %0 \n \
+        "
+        : "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
+        : "v"(a), "v"(b)
+    );
+#else
+    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
+    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
+    c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
+#endif
+    return c;
+}
+#endif // defined(GGML_USE_HIPBLAS)
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
+
+#define CUDA_CHECK(err)                                                                 \
+    do {                                                                                \
+        cudaError_t err_ = (err);                                                       \
+        if (err_ != cudaSuccess) {                                                      \
+            int id;                                                                     \
+            cudaGetDevice(&id);                                                         \
+            fprintf(stderr, "\nCUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \
+                cudaGetErrorString(err_));                                              \
+            fprintf(stderr, "current device: %d\n", id);                                \
+            exit(1);                                                                    \
+        }                                                                               \
+    } while (0)
+
+#if CUDART_VERSION >= 12000
+#define CUBLAS_CHECK(err)                                                               \
+    do {                                                                                \
+        cublasStatus_t err_ = (err);                                                    \
+        if (err_ != CUBLAS_STATUS_SUCCESS) {                                            \
+            int id;                                                                     \
+            cudaGetDevice(&id);                                                         \
+            fprintf(stderr, "\ncuBLAS error %d at %s:%d: %s\n",                         \
+                    err_, __FILE__, __LINE__, cublasGetStatusString(err_));             \
+            fprintf(stderr, "current device: %d\n", id);                                \
+            exit(1);                                                                    \
+        }                                                                               \
+    } while (0)
+#else
+#define CUBLAS_CHECK(err)                                                               \
+    do {                                                                                \
+        cublasStatus_t err_ = (err);                                                    \
+        if (err_ != CUBLAS_STATUS_SUCCESS) {                                            \
+            int id;                                                                     \
+            cudaGetDevice(&id);                                                         \
+            fprintf(stderr, "\ncuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__);  \
+            fprintf(stderr, "current device: %d\n", id);                                \
+            exit(1);                                                                    \
+        }                                                                               \
+    } while (0)
+#endif // CUDART_VERSION >= 11
+
+#if CUDART_VERSION >= 11100
+#define GGML_CUDA_ASSUME(x) __builtin_assume(x)
+#else
+#define GGML_CUDA_ASSUME(x)
+#endif // CUDART_VERSION >= 11100
+
+#ifdef GGML_CUDA_F16
+typedef half dfloat; // dequantize float
+typedef half2 dfloat2;
+#else
+typedef float dfloat; // dequantize float
+typedef float2 dfloat2;
+#endif //GGML_CUDA_F16
+
+static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) {
+    const uint16_t * x16 = (uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
+
+    int x32 = 0;
+    x32 |= x16[0] <<  0;
+    x32 |= x16[1] << 16;
+
+    return x32;
+}
+
+static __device__ __forceinline__ int get_int_from_uint8(const uint8_t * x8, const int & i32) {
+    const uint16_t * x16 = (uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
+
+    int x32 = 0;
+    x32 |= x16[0] <<  0;
+    x32 |= x16[1] << 16;
+
+    return x32;
+}
+
+static __device__ __forceinline__ int get_int_from_int8_aligned(const int8_t * x8, const int & i32) {
+    return *((int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
+}
+
+static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t * x8, const int & i32) {
+    return *((int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
+}
+
+template<typename T>
+using to_t_cuda_t = void (*)(const void * __restrict__ x, T * __restrict__ y, int k, cudaStream_t stream);
+typedef to_t_cuda_t<float> to_fp32_cuda_t;
+typedef to_t_cuda_t<half> to_fp16_cuda_t;
+
+typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v);
+typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v);
+typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
+typedef void (*ggml_cuda_func_t)(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+typedef void (*ggml_cuda_op_mul_mat_t)(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, const cudaStream_t & stream);
+typedef void (*ggml_cuda_op_flatten_t)(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream);
+
+// QK = number of values after dequantization
+// QR = QK / number of values before dequantization
+// QI = number of 32 bit integers before dequantization
+
+#define QK4_0 32
+#define QR4_0 2
+#define QI4_0 (QK4_0 / (4 * QR4_0))
+typedef struct {
+    half    d;              // delta
+    uint8_t qs[QK4_0 / 2];  // nibbles / quants
+} block_q4_0;
+static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
+
+#define QK4_1 32
+#define QR4_1 2
+#define QI4_1 (QK4_1 / (4 * QR4_1))
+typedef struct {
+    half2   dm;             // dm.x = delta, dm.y = min
+    uint8_t qs[QK4_1 / 2];  // nibbles / quants
+} block_q4_1;
+static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
+
+#define QK5_0 32
+#define QR5_0 2
+#define QI5_0 (QK5_0 / (4 * QR5_0))
+typedef struct {
+    half d;                 // delta
+    uint8_t qh[4];          // 5-th bit of quants
+    uint8_t qs[QK5_0 / 2];  // nibbles / quants
+} block_q5_0;
+static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
+
+#define QK5_1 32
+#define QR5_1 2
+#define QI5_1 (QK5_1 / (4 * QR5_1))
+typedef struct {
+    half2 dm;               // dm.x = delta, dm.y = min
+    uint8_t qh[4];          // 5-th bit of quants
+    uint8_t qs[QK5_1 / 2];  // nibbles / quants
+} block_q5_1;
+static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
+
+#define QK8_0 32
+#define QR8_0 1
+#define QI8_0 (QK8_0 / (4 * QR8_0))
+typedef struct {
+    half    d;              // delta
+    int8_t  qs[QK8_0];      // quants
+} block_q8_0;
+static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
+
+#define QK8_1 32
+#define QR8_1 1
+#define QI8_1 (QK8_1 / (4 * QR8_1))
+typedef struct {
+    half2   ds;             // ds.x = delta, ds.y = sum
+    int8_t  qs[QK8_0];      // quants
+} block_q8_1;
+static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_fp16_t) + QK8_0, "wrong q8_1 block size/padding");
+
+typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs);
+typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc);
+typedef void (*load_tiles_cuda_t)(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row);
+typedef float (*vec_dot_q_mul_mat_cuda_t)(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k);
+
+//================================= k-quants
+
+#ifdef GGML_QKK_64
+#define QK_K 64
+#define K_SCALE_SIZE 4
+#else
+#define QK_K 256
+#define K_SCALE_SIZE 12
+#endif
+
+#define QR2_K 4
+#define QI2_K (QK_K / (4*QR2_K))
+typedef struct {
+    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
+    uint8_t qs[QK_K/4];      // quants
+    half2 dm;                // super-block scale for quantized scales/mins
+} block_q2_K;
+static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
+
+#define QR3_K 4
+#define QI3_K (QK_K / (4*QR3_K))
+typedef struct {
+    uint8_t hmask[QK_K/8];     // quants - high bit
+    uint8_t qs[QK_K/4];        // quants - low 2 bits
+#ifdef GGML_QKK_64
+    uint8_t scales[2]; // scales, quantized with 8 bits
+#else
+    uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits
+#endif
+    half d;             // super-block scale
+} block_q3_K;
+//static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + K_SCALE_SIZE, "wrong q3_K block size/padding");
+
+#define QR4_K 2
+#define QI4_K (QK_K / (4*QR4_K))
+#ifdef GGML_QKK_64
+typedef struct {
+    half    dm[2];             // super-block scales/mins
+    uint8_t scales[2];         // 4-bit block scales/mins
+    uint8_t qs[QK_K/2];        // 4--bit quants
+} block_q4_K;
+static_assert(sizeof(block_q4_K) == sizeof(half2) + QK_K/2 + 2, "wrong q4_K block size/padding");
+#else
+typedef struct {
+    half2 dm;                  // super-block scale for quantized scales/mins
+    uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
+    uint8_t qs[QK_K/2];        // 4--bit quants
+} block_q4_K;
+static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_K block size/padding");
+#endif
+
+#define QR5_K 2
+#define QI5_K (QK_K / (4*QR5_K))
+#ifdef GGML_QKK_64
+typedef struct {
+    half d;                  // super-block scale
+    int8_t scales[QK_K/16];  // block scales
+    uint8_t qh[QK_K/8];      // quants, high bit
+    uint8_t qs[QK_K/2];      // quants, low 4 bits
+} block_q5_K;
+static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
+#else
+typedef struct {
+    half2 dm;                     // super-block scale for quantized scales/mins
+    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
+    uint8_t qh[QK_K/8];           // quants, high bit
+    uint8_t qs[QK_K/2];           // quants, low 4 bits
+} block_q5_K;
+static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
+#endif
+
+#define QR6_K 2
+#define QI6_K (QK_K / (4*QR6_K))
+typedef struct {
+    uint8_t ql[QK_K/2];   // quants, lower 4 bits
+    uint8_t qh[QK_K/4];   // quants, upper 2 bits
+    int8_t  scales[QK_K/16]; // scales
+    half    d;         // delta
+} block_q6_K;
+static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_K block size/padding");
+
+#define WARP_SIZE 32
+#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
+
+#define CUDA_ADD_BLOCK_SIZE 256
+#define CUDA_MUL_BLOCK_SIZE 256
+#define CUDA_GELU_BLOCK_SIZE 256
+#define CUDA_SILU_BLOCK_SIZE 256
+#define CUDA_CPY_BLOCK_SIZE 32
+#define CUDA_SCALE_BLOCK_SIZE 256
+#define CUDA_CLAMP_BLOCK_SIZE 256
+#define CUDA_ROPE_BLOCK_SIZE 256
+#define CUDA_ALIBI_BLOCK_SIZE 32
+#define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
+#define CUDA_QUANTIZE_BLOCK_SIZE 256
+#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
+#define CUDA_GET_ROWS_BLOCK_SIZE 256
+
+// dmmv = dequantize_mul_mat_vec
+#ifndef GGML_CUDA_DMMV_X
+#define GGML_CUDA_DMMV_X 32
+#endif
+#ifndef GGML_CUDA_MMV_Y
+#define GGML_CUDA_MMV_Y 1
+#endif
+
+#ifndef K_QUANTS_PER_ITERATION
+#define K_QUANTS_PER_ITERATION 2
+#else
+static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
+#endif
+
+#ifndef GGML_CUDA_PEER_MAX_BATCH_SIZE
+#define GGML_CUDA_PEER_MAX_BATCH_SIZE 128
+#endif // GGML_CUDA_PEER_MAX_BATCH_SIZE
+
+#define MUL_MAT_SRC1_COL_STRIDE 128
+
+#define MAX_STREAMS 8
+static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_DEVICES][MAX_STREAMS] = { nullptr };
+
+struct ggml_tensor_extra_gpu {
+    void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
+    cudaEvent_t events[GGML_CUDA_MAX_DEVICES][MAX_STREAMS]; // events for synchronizing multiple GPUs
+};
+
+// this is faster on Windows
+// probably because the Windows CUDA libraries forget to make this check before invoking the drivers
+inline cudaError_t ggml_cuda_set_device(const int device) {
+    int current_device;
+    CUDA_CHECK(cudaGetDevice(&current_device));
+
+    if (device == current_device) {
+        return cudaSuccess;
+    }
+
+    return cudaSetDevice(device);
+}
+
+static int g_device_count = -1;
+static int g_main_device = 0;
+static int g_compute_capabilities[GGML_CUDA_MAX_DEVICES];
+static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0};
+static bool g_mul_mat_q = true;
+
+static void * g_scratch_buffer = nullptr;
+static size_t g_scratch_size = 0; // disabled by default
+static size_t g_scratch_offset = 0;
+
+static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
+
+static __global__ void add_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= kx) {
+        return;
+    }
+    dst[i] = x[i] + y[i%ky];
+}
+
+static __global__ void add_f16_f32_f16(const half * x, const float * y, half * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = __hadd(x[i], __float2half(y[i]));
+}
+
+static __global__ void mul_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= kx) {
+        return;
+    }
+    dst[i] = x[i] * y[i%ky];
+}
+
+static __global__ void gelu_f32(const float * x, float * dst, const int k) {
+    const float GELU_COEF_A    = 0.044715f;
+    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    float xi = x[i];
+    dst[i] = 0.5f*xi*(1.0f + tanhf(SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi)));
+}
+
+static __global__ void silu_f32(const float * x, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] / (1.0f + expf(-x[i]));
+}
+
+static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
+        a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
+    }
+    return a;
+}
+
+template <int block_size>
+static __global__ void norm_f32(const float * x, float * dst, const int ncols) {
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    const int tid = threadIdx.x;
+
+    const float eps = 1e-5f;
+
+    float2 mean_var = make_float2(0.f, 0.f);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const float xi = x[row*ncols + col];
+        mean_var.x += xi;
+        mean_var.y += xi * xi;
+    }
+
+    // sum up partial sums
+    mean_var = warp_reduce_sum(mean_var);
+    if (block_size > WARP_SIZE) {
+        __shared__ float2 s_sum[32];
+        int warp_id = threadIdx.x / WARP_SIZE;
+        int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = mean_var;
+        }
+        __syncthreads();
+        mean_var = s_sum[lane_id];
+        mean_var = warp_reduce_sum(mean_var);
+    }
+
+    const float mean = mean_var.x / ncols;
+    const float var = mean_var.y / ncols - mean * mean;
+    const float inv_std = rsqrtf(var + eps);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std;
+    }
+}
+
+static __device__ __forceinline__ float warp_reduce_sum(float x) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        x += __shfl_xor_sync(0xffffffff, x, mask, 32);
+    }
+    return x;
+}
+
+template <int block_size>
+static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) {
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    const int tid = threadIdx.x;
+
+    float tmp = 0.0f; // partial sum for thread in warp
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const float xi = x[row*ncols + col];
+        tmp += xi * xi;
+    }
+
+    // sum up partial sums
+    tmp = warp_reduce_sum(tmp);
+    if (block_size > WARP_SIZE) {
+        __shared__ float s_sum[32];
+        int warp_id = threadIdx.x / WARP_SIZE;
+        int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        __syncthreads();
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp);
+    }
+
+    const float mean = tmp / ncols;
+    const float scale = rsqrtf(mean + eps);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        dst[row*ncols + col] = scale * x[row*ncols + col];
+    }
+}
+
+static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const block_q4_0 * x = (const block_q4_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    const int vui = x[ib].qs[iqs];
+
+    v.x = vui & 0xF;
+    v.y = vui >> 4;
+
+#ifdef GGML_CUDA_F16
+    v = __hsub2(v, {8.0f, 8.0f});
+    v = __hmul2(v, {d, d});
+#else
+    v.x = (v.x - 8.0f) * d;
+    v.y = (v.y - 8.0f) * d;
+#endif // GGML_CUDA_F16
+}
+
+static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const block_q4_1 * x = (const block_q4_1 *) vx;
+
+    const dfloat d = __low2half(x[ib].dm);
+    const dfloat m = __high2half(x[ib].dm);
+
+    const int vui = x[ib].qs[iqs];
+
+    v.x = vui & 0xF;
+    v.y = vui >> 4;
+
+#ifdef GGML_CUDA_F16
+    v = __hmul2(v, {d, d});
+    v = __hadd2(v, {m, m});
+#else
+    v.x = (v.x * d) + m;
+    v.y = (v.y * d) + m;
+#endif // GGML_CUDA_F16
+}
+
+static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const block_q5_0 * x = (const block_q5_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    uint32_t qh;
+    memcpy(&qh, x[ib].qh, sizeof(qh));
+
+    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
+    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
+
+    v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
+    v.y = ((x[ib].qs[iqs] >>  4) | xh_1);
+
+#ifdef GGML_CUDA_F16
+    v = __hsub2(v, {16.0f, 16.0f});
+    v = __hmul2(v, {d, d});
+#else
+    v.x = (v.x - 16.0f) * d;
+    v.y = (v.y - 16.0f) * d;
+#endif // GGML_CUDA_F16
+}
+
+static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const block_q5_1 * x = (const block_q5_1 *) vx;
+
+    const dfloat d = __low2half(x[ib].dm);
+    const dfloat m = __high2half(x[ib].dm);
+
+    uint32_t qh;
+    memcpy(&qh, x[ib].qh, sizeof(qh));
+
+    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
+    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
+
+    v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
+    v.y = ((x[ib].qs[iqs] >>  4) | xh_1);
+
+#ifdef GGML_CUDA_F16
+    v = __hmul2(v, {d, d});
+    v = __hadd2(v, {m, m});
+#else
+    v.x = (v.x * d) + m;
+    v.y = (v.y * d) + m;
+#endif // GGML_CUDA_F16
+}
+
+static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const block_q8_0 * x = (const block_q8_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    v.x = x[ib].qs[iqs + 0];
+    v.y = x[ib].qs[iqs + 1];
+
+#ifdef GGML_CUDA_F16
+    v = __hmul2(v, {d, d});
+#else
+    v.x *= d;
+    v.y *= d;
+#endif // GGML_CUDA_F16
+}
+
+//================================== k-quants
+
+template<typename dst_t>
+static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int i   = blockIdx.x;
+    const block_q2_K * x = (const block_q2_K *) vx;
+
+    const int tid = threadIdx.x;
+#if QK_K == 256
+    const int n   = tid/32;
+    const int l   = tid - 32*n;
+    const int is  = 8*n + l/16;
+
+    const uint8_t q = x[i].qs[32*n + l];
+    dst_t * y = yy + i*QK_K + 128*n;
+
+    float dall = __low2half(x[i].dm);
+    float dmin = __high2half(x[i].dm);
+    y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
+    y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
+    y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
+    y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);
+#else
+    const int is = tid/16;  // 0 or 1
+    const int il = tid%16;  // 0...15
+    const uint8_t q = x[i].qs[il] >> (2*is);
+    dst_t * y = yy + i*QK_K + 16*is + il;
+    float dall = __low2half(x[i].dm);
+    float dmin = __high2half(x[i].dm);
+    y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
+    y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int i = blockIdx.x;
+    const block_q3_K * x = (const block_q3_K *) vx;
+
+#if QK_K == 256
+    const int r = threadIdx.x/4;
+    const int tid = r/2;
+    const int is0 = r%2;
+    const int l0 = 16*is0 + 4*(threadIdx.x%4);
+    const int n = tid / 4;
+    const int j = tid - 4*n;
+
+    uint8_t m = 1 << (4*n + j);
+    int is = 8*n + 2*j + is0;
+    int shift = 2*j;
+
+    int8_t us = is <  4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) :
+                is <  8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) :
+                is < 12 ? (x[i].scales[is-8] >>  4) | (((x[i].scales[is+0] >> 4) & 3) << 4) :
+                          (x[i].scales[is-8] >>  4) | (((x[i].scales[is-4] >> 6) & 3) << 4);
+    float d_all = x[i].d;
+    float dl = d_all * (us - 32);
+
+    dst_t * y = yy + i*QK_K + 128*n + 32*j;
+    const uint8_t * q = x[i].qs + 32*n;
+    const uint8_t * hm = x[i].hmask;
+
+    for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
+#else
+    const int tid = threadIdx.x;
+    const int is  = tid/16;  // 0 or 1
+    const int il  = tid%16;  // 0...15
+    const int im  = il/8;    // 0...1
+    const int in  = il%8;    // 0...7
+
+    dst_t * y = yy + i*QK_K + 16*is + il;
+
+    const uint8_t q = x[i].qs[il] >> (2*is);
+    const uint8_t h = x[i].hmask[in] >> (2*is + im);
+    const float   d = (float)x[i].d;
+
+    if (is == 0) {
+        y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
+        y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
+    } else {
+        y[ 0] = d * ((x[i].scales[0] >>  4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
+        y[32] = d * ((x[i].scales[1] >>  4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
+    }
+#endif
+
+}
+
+#if QK_K == 256
+static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
+    if (j < 4) {
+        d = q[j] & 63; m = q[j + 4] & 63;
+    } else {
+        d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
+        m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
+    }
+}
+#endif
+
+template<typename dst_t>
+static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const block_q4_K * x = (const block_q4_K *) vx;
+
+    const int i = blockIdx.x;
+
+#if QK_K == 256
+    // assume 32 threads
+    const int tid = threadIdx.x;
+    const int il  = tid/8;
+    const int ir  = tid%8;
+    const int is  = 2*il;
+    const int n   = 4;
+
+    dst_t * y = yy + i*QK_K + 64*il + n*ir;
+
+    const float dall = __low2half(x[i].dm);
+    const float dmin = __high2half(x[i].dm);
+
+    const uint8_t * q = x[i].qs + 32*il + n*ir;
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, x[i].scales, sc, m);
+    const float d1 = dall * sc; const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, x[i].scales, sc, m);
+    const float d2 = dall * sc; const float m2 = dmin * m;
+    for (int l = 0; l < n; ++l) {
+        y[l + 0] = d1 * (q[l] & 0xF) - m1;
+        y[l +32] = d2 * (q[l] >>  4) - m2;
+    }
+#else
+    const int tid = threadIdx.x;
+    const uint8_t * q = x[i].qs;
+    dst_t * y = yy + i*QK_K;
+    const float d = (float)x[i].dm[0];
+    const float m = (float)x[i].dm[1];
+    y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
+    y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >>  4) - m * (x[i].scales[1] >> 4);
+#endif
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const block_q5_K * x = (const block_q5_K *) vx;
+
+    const int i = blockIdx.x;
+
+#if QK_K == 256
+    // assume 64 threads - this is very slightly better than the one below
+    const int tid = threadIdx.x;
+    const int il  = tid/16;   // il is in 0...3
+    const int ir  = tid%16;   // ir is in 0...15
+    const int is  = 2*il;     // is is in 0...6
+
+    dst_t * y = yy + i*QK_K + 64*il + 2*ir;
+
+    const float dall = __low2half(x[i].dm);
+    const float dmin = __high2half(x[i].dm);
+
+    const uint8_t * ql = x[i].qs + 32*il + 2*ir;
+    const uint8_t * qh = x[i].qh + 2*ir;
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, x[i].scales, sc, m);
+    const float d1 = dall * sc; const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, x[i].scales, sc, m);
+    const float d2 = dall * sc; const float m2 = dmin * m;
+
+    uint8_t   hm  = 1 << (2*il);
+    y[ 0] = d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1;
+    y[ 1] = d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1;
+    hm <<= 1;
+    y[32] = d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2;
+    y[33] = d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2;
+#else
+    const int tid = threadIdx.x;
+    const uint8_t q = x[i].qs[tid];
+    const int im = tid/8;  // 0...3
+    const int in = tid%8;  // 0...7
+    const int is = tid/16; // 0 or 1
+    const uint8_t h = x[i].qh[in] >> im;
+    const float d = x[i].d;
+    dst_t * y = yy + i*QK_K + tid;
+    y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16));
+    y[32] = d * x[i].scales[is+2] * ((q >>  4) - ((h >> 4) & 1 ? 0 : 16));
+#endif
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const block_q6_K * x = (const block_q6_K *) vx;
+
+    const int i = blockIdx.x;
+#if QK_K == 256
+
+    // assume 64 threads - this is very slightly better than the one below
+    const int tid = threadIdx.x;
+    const int ip  = tid/32;   // ip is 0 or 1
+    const int il  = tid - 32*ip; // 0...32
+    const int is  = 8*ip + il/16;
+
+    dst_t * y = yy + i*QK_K + 128*ip + il;
+
+    const float d = x[i].d;
+
+    const uint8_t * ql = x[i].ql + 64*ip + il;
+    const uint8_t   qh = x[i].qh[32*ip + il];
+    const int8_t  * sc = x[i].scales + is;
+
+    y[ 0] = d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
+    y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
+    y[64] = d * sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
+    y[96] = d * sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh >> 6) & 3) << 4)) - 32);
+#else
+
+    // assume 32 threads
+    const int tid = threadIdx.x;
+    const int ip  = tid/16;         // 0 or 1
+    const int il  = tid - 16*ip;    // 0...15
+
+    dst_t * y = yy + i*QK_K + 16*ip + il;
+
+    const float d = x[i].d;
+
+    const uint8_t   ql = x[i].ql[16*ip + il];
+    const uint8_t   qh = x[i].qh[il] >> (2*ip);
+    const int8_t  * sc = x[i].scales;
+
+    y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
+    y[32] = d * sc[ip+2] * ((int8_t)((ql  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
+#endif
+}
+
+static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
+
+    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
+
+    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q2_K * x = (const block_q2_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...15
+    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
+
+    const int step = 16/K_QUANTS_PER_ITERATION;
+
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0...15 or 0...7
+
+    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15 or 0...14 in steps of 2
+    const int q_offset = 32*im + l0;
+    const int s_offset = 8*im;
+    const int y_offset = 128*im + l0;
+
+    uint32_t aux[4];
+    const uint8_t * d = (const uint8_t *)aux;
+    const uint8_t * m = (const uint8_t *)(aux + 2);
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + y_offset;
+        const uint8_t * q = x[i].qs + q_offset;
+
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
+
+        const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
+        aux[0] = a[0] & 0x0f0f0f0f;
+        aux[1] = a[1] & 0x0f0f0f0f;
+        aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
+        aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
+
+        float sum1 = 0, sum2 = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
+                  + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
+                  + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
+                  + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
+                  + y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
+                  + y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
+                  + y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
+                  +y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
+            sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
+                  + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
+
+        }
+        tmp += dall * sum1 - dmin * sum2;
+
+    }
+#else
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
+    const int offset = tid * K_QUANTS_PER_ITERATION;
+
+    uint32_t uaux[2];
+    const uint8_t * d = (const uint8_t *)uaux;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + offset;
+        const uint8_t * q = x[i].qs + offset;
+        const uint32_t * s = (const uint32_t *)x[i].scales;
+
+        uaux[0] = s[0] & 0x0f0f0f0f;
+        uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
+
+        const float2 dall = __half22float2(x[i].dm);
+
+        float sum1 = 0, sum2 = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            const uint8_t ql = q[l];
+            sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3)
+                  + y[l+16] * d[1] * ((ql >> 2) & 3)
+                  + y[l+32] * d[2] * ((ql >> 4) & 3)
+                  + y[l+48] * d[3] * ((ql >> 6) & 3);
+            sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7];
+        }
+        tmp += dall.x * sum1 - dall.y * sum2;
+    }
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    }
+
+    if (threadIdx.x == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
+
+    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q3_K * x = (const block_q3_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+
+    const uint16_t kmask1 = 0x0303;
+    const uint16_t kmask2 = 0x0f0f;
+
+    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
+
+    const int n  = K_QUANTS_PER_ITERATION;               // iterations in the inner loop
+    const int step = 16/K_QUANTS_PER_ITERATION;
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0....15 or 0...7
+
+    const uint8_t m = 1 << (4*im);
+
+    const int l0 = n*in;                                 // 0...15 or 0...14 in steps of 2
+    const int q_offset =  32*im + l0;
+    const int y_offset = 128*im + l0;
+
+    uint16_t utmp[4];
+    const int8_t * s = (const int8_t *)utmp;
+
+    const uint16_t s_shift = 4*im;
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + y_offset;
+        const uint8_t * q = x[i].qs + q_offset;
+        const uint8_t * h = x[i].hmask + l0;
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
+        utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
+        utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
+        utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
+
+        const float d = x[i].d;
+
+        float sum = 0;
+        for (int l = 0; l < n; ++l) {
+            sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
+                 + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
+                 + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
+                 + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
+            sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
+                 + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
+                 + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
+                + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
+        }
+        tmp += d * sum;
+
+    }
+#else
+
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
+    const int offset = tid * K_QUANTS_PER_ITERATION;         // 0...15 or 0...14
+    const int in = offset/8;                                 // 0 or 1
+    const int im = offset%8;                                 // 0...7
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + offset;
+        const uint8_t * q = x[i].qs + offset;
+        const uint8_t * s = x[i].scales;
+
+        const float dall = (float)x[i].d;
+
+        float sum = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            const uint8_t hl = x[i].hmask[im+l] >> in;
+            const uint8_t ql = q[l];
+            sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4))
+                 + y[l+16] * dall * ((s[0] >>  4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4))
+                 + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4))
+                 + y[l+48] * dall * ((s[1] >>  4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4));
+        }
+        tmp += sum;
+    }
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    }
+
+    if (threadIdx.x == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
+
+    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+    if (row > nrows) return;
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q4_K * x = (const block_q4_K *)vx + ib0;
+
+#if QK_K == 256
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
+    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
+
+    const int step = 8/K_QUANTS_PER_ITERATION;           // 8 or 4
+
+    const int il  = tid/step;                            // 0...3
+    const int ir  = tid - step*il;                       // 0...7 or 0...3
+    const int n   = 2 * K_QUANTS_PER_ITERATION;          // 2 or 4
+
+    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const int in = il%2;
+
+    const int l0 = n*(2*ir + in);
+    const int q_offset = 32*im + l0;
+    const int y_offset = 64*im + l0;
+
+    uint16_t aux[4];
+    const uint8_t * sc = (const uint8_t *)aux;
+
+#if K_QUANTS_PER_ITERATION == 2
+    uint32_t q32[4];
+    const uint8_t * q4 = (const uint8_t *)q32;
+#else
+    uint16_t q16[4];
+    const uint8_t * q4 = (const uint8_t *)q16;
+#endif
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y1 = yy + i*QK_K + y_offset;
+        const float   * y2 = y1 + 128;
+
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux[0] = a[im+0] & kmask1;
+        aux[1] = a[im+2] & kmask1;
+        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
+        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
+
+#if K_QUANTS_PER_ITERATION == 2
+        const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset);
+        const uint32_t * q2 = q1 + 16;
+
+        q32[0] = q1[0] & 0x0f0f0f0f;
+        q32[1] = q1[0] & 0xf0f0f0f0;
+        q32[2] = q2[0] & 0x0f0f0f0f;
+        q32[3] = q2[0] & 0xf0f0f0f0;
+
+        float4 s = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        for (int l = 0; l < 4; ++l) {
+            s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+ 4];
+            s.z += y2[l] * q4[l+8]; s.w += y2[l+32] * q4[l+12];
+            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
+        }
+        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
+#else
+        const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset);
+        const uint16_t * q2 = q1 + 32;
+
+        q16[0] = q1[0] & 0x0f0f;
+        q16[1] = q1[0] & 0xf0f0;
+        q16[2] = q2[0] & 0x0f0f;
+        q16[3] = q2[0] & 0xf0f0;
+
+        float4 s = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        for (int l = 0; l < 2; ++l) {
+            s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2];
+            s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6];
+            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
+        }
+        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
+#endif
+
+    }
+#else
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
+
+    const int step = tid * K_QUANTS_PER_ITERATION;
+
+    uint16_t aux16[2];
+    const uint8_t * s = (const uint8_t *)aux16;
+
+    float tmp = 0;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+        const uint8_t * q = x[i].qs + step;
+        const float   * y = yy + i*QK_K + step;
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux16[0] = a[0] & 0x0f0f;
+        aux16[1] = (a[0] >> 4) & 0x0f0f;
+        const float d = (float)x[i].dm[0];
+        const float m = (float)x[i].dm[1];
+        float sum = 0.f;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
+                 + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
+                 + y[j+32] * (d * s[1] * (q[j+ 0] >>  4) - m * s[3])
+                 + y[j+48] * (d * s[1] * (q[j+16] >>  4) - m * s[3]);
+        }
+        tmp += sum;
+    }
+
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    }
+
+    if (tid == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols) {
+
+    const int row = blockIdx.x;
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q5_K * x = (const block_q5_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
+    const int tid = threadIdx.x/2;  // 0...15
+    const int ix  = threadIdx.x%2;
+
+    const int il  = tid/4;     // 0...3
+    const int ir  = tid - 4*il;// 0...3
+    const int n   = 2;
+
+    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const int in = il%2;
+
+    const int l0 = n*(2*ir + in);
+    const int q_offset = 32*im + l0;
+    const int y_offset = 64*im + l0;
+
+    const uint8_t hm1  = 1 << (2*im);
+    const uint8_t hm2  = hm1 << 4;
+
+    uint16_t aux[4];
+    const uint8_t * sc = (const uint8_t *)aux;
+
+    uint16_t q16[8];
+    const uint8_t * q4 = (const uint8_t *)q16;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2) {
+
+        const uint8_t * ql1 = x[i].qs + q_offset;
+        const uint8_t * qh  = x[i].qh + l0;
+        const float   * y1  = yy + i*QK_K + y_offset;
+        const float   * y2  = y1 + 128;
+
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux[0] = a[im+0] & kmask1;
+        aux[1] = a[im+2] & kmask1;
+        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
+        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
+
+        float4 sum = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        const uint16_t * q1 = (const uint16_t *)ql1;
+        const uint16_t * q2 = q1 + 32;
+        q16[0] = q1[0] & 0x0f0f;
+        q16[1] = q1[8] & 0x0f0f;
+        q16[2] = (q1[0] >> 4) & 0x0f0f;
+        q16[3] = (q1[8] >> 4) & 0x0f0f;
+        q16[4] = q2[0] & 0x0f0f;
+        q16[5] = q2[8] & 0x0f0f;
+        q16[6] = (q2[0] >> 4) & 0x0f0f;
+        q16[7] = (q2[8] >> 4) & 0x0f0f;
+        for (int l = 0; l < n; ++l) {
+            sum.x += y1[l+ 0] * (q4[l +0] + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
+                   + y1[l+16] * (q4[l +2] + (qh[l+16] & (hm1 << 0) ? 16 : 0));
+            sum.y += y1[l+32] * (q4[l +4] + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
+                   + y1[l+48] * (q4[l +6] + (qh[l+16] & (hm1 << 1) ? 16 : 0));
+            sum.z += y2[l+ 0] * (q4[l +8] + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
+                   + y2[l+16] * (q4[l+10] + (qh[l+16] & (hm2 << 0) ? 16 : 0));
+            sum.w += y2[l+32] * (q4[l+12] + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
+                   + y2[l+48] * (q4[l+14] + (qh[l+16] & (hm2 << 1) ? 16 : 0));
+            smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
+                  + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
+        }
+        tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
+    }
+
+#else
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
+    const int step = tid * K_QUANTS_PER_ITERATION;
+    const int im = step/8;
+    const int in = step%8;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+        const uint8_t * q = x[i].qs + step;
+        const int8_t  * s = x[i].scales;
+        const float   * y = yy + i*QK_K + step;
+        const float     d = x[i].d;
+        float sum = 0.f;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            const uint8_t h = x[i].qh[in+j] >> im;
+            sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16))
+                 + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16))
+                 + y[j+32] * d * s[2] * ((q[j+ 0] >>  4) - ((h >> 4) & 1 ? 0 : 16))
+                 + y[j+48] * d * s[3] * ((q[j+16] >>  4) - ((h >> 6) & 1 ? 0 : 16));
+        }
+        tmp += sum;
+    }
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    }
+
+    if (threadIdx.x == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
+
+    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
+
+    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q6_K * x = (const block_q6_K *)vx + ib0;
+
+#if QK_K == 256
+
+    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const int step = 16/K_QUANTS_PER_ITERATION;          // 16 or 8
+
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0...15 or 0...7
+
+#if K_QUANTS_PER_ITERATION == 1
+    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15
+    const int is = 0;
+#else
+    const int l0 = 4 * in;                               // 0, 4, 8, ..., 28
+    const int is = in / 4;
+#endif
+    const int ql_offset = 64*im + l0;
+    const int qh_offset = 32*im + l0;
+    const int s_offset  =  8*im + is;
+    const int y_offset = 128*im + l0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + y_offset;
+        const uint8_t * ql = x[i].ql + ql_offset;
+        const uint8_t * qh = x[i].qh + qh_offset;
+        const int8_t  * s  = x[i].scales + s_offset;
+
+        const float d = x[i].d;
+
+#if K_QUANTS_PER_ITERATION == 1
+        float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
+                  + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
+                  + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
+                  + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
+                  + y[64] * s[4] * d * ((int8_t)((ql[ 0]  >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
+                  + y[80] * s[5] * d * ((int8_t)((ql[16]  >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
+                  + y[96] * s[6] * d * ((int8_t)((ql[32]  >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
+                  +y[112] * s[7] * d * ((int8_t)((ql[48]  >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
+        tmp += sum;
+#else
+        float sum = 0;
+        for (int l = 0; l < 4; ++l) {
+            sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
+                 + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
+                 + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
+                 + y[l+96] * s[6] * d * ((int8_t)((ql[l+32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
+        }
+        tmp += sum;
+#endif
+
+    }
+
+#else
+
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...7
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0...3
+
+    const int step = tid * K_QUANTS_PER_ITERATION;
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + step;
+        const uint8_t * ql = x[i].ql + step;
+        const uint8_t * qh = x[i].qh + step;
+        const int8_t  * s  = x[i].scales;
+
+        const float d = x[i+0].d;
+
+        float sum = 0;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
+                 + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
+                 + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >>  4) | ((qh[j] & 0x30) >> 0)) - 32)
+                 + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >>  4) | ((qh[j] & 0xc0) >> 2)) - 32);
+        }
+        tmp += sum;
+
+    }
+
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    }
+
+    if (tid == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __device__ void convert_f16(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const half * x = (const half *) vx;
+
+    // automatic half -> float type cast if dfloat == float
+    v.x = x[ib + iqs + 0];
+    v.y = x[ib + iqs + 1];
+}
+
+static __device__ void convert_f32(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const float * x = (const float *) vx;
+
+    // automatic half -> float type cast if dfloat == float
+    v.x = x[ib + iqs + 0];
+    v.y = x[ib + iqs + 1];
+}
+
+static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded) {
+    const int ix = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (ix >= kx_padded) {
+        return;
+    }
+
+    const int iy = blockDim.y*blockIdx.y + threadIdx.y;
+
+    const int i_padded = iy*kx_padded + ix;
+
+    block_q8_1 * y = (block_q8_1 *) vy;
+
+    const int ib = i_padded / QK8_1; // block index
+    const int iqs = i_padded % QK8_1; // quant index
+
+    const float xi = ix < kx ? x[iy*kx + ix] : 0.0f;
+    float amax = fabsf(xi);
+    float sum = xi;
+
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        amax = fmaxf(amax, __shfl_xor_sync(0xffffffff, amax, mask, 32));
+        sum += __shfl_xor_sync(0xffffffff, sum, mask, 32);
+    }
+
+    const float d = amax / 127;
+    const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
+
+    y[ib].qs[iqs] = q;
+
+    if (iqs > 0) {
+        return;
+    }
+
+    reinterpret_cast<half&>(y[ib].ds.x) = d;
+    reinterpret_cast<half&>(y[ib].ds.y) = sum;
+}
+
+template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static __global__ void k_get_rows(const void * x, const int32_t * y, dst_t * dst, const int ncols) {
+    const int col = (blockIdx.x*blockDim.x + threadIdx.x)*2;
+    const int row = blockDim.y*blockIdx.y + threadIdx.y;
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int r = y[row];
+
+    // copy x[r*ncols + col] to dst[row*ncols + col]
+    const int xi = r*ncols + col;
+    const int di = row*ncols + col;
+
+    const int ib = xi/qk; // block index
+    const int iqs = (xi%qk)/qr; // quant index
+    const int iybs = di - di%qk; // y block start index
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+    // dequantize
+    dfloat2 v;
+    dequantize_kernel(x, ib, iqs, v);
+
+    dst[iybs + iqs + 0]        = v.x;
+    dst[iybs + iqs + y_offset] = v.y;
+}
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
+    const int i = blockDim.x*blockIdx.x + 2*threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    const int ib = i/qk; // block index
+    const int iqs = (i%qk)/qr; // quant index
+    const int iybs = i - i%qk; // y block start index
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+    // dequantize
+    dfloat2 v;
+    dequantize_kernel(vx, ib, iqs, v);
+
+    y[iybs + iqs + 0]        = v.x;
+    y[iybs + iqs + y_offset] = v.y;
+}
+
+// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
+// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
+
+#define VDR_Q4_0_Q8_1_MMVQ 2
+#define VDR_Q4_0_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl(
+    const int * v, const int * u, const float & d4, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
+        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
+
+        // SIMD dot product of quantized values
+        sumi = __dp4a(vi0, u[2*i+0], sumi);
+        sumi = __dp4a(vi1, u[2*i+1], sumi);
+    }
+
+    const float2 ds8f = __half22float2(ds8);
+
+    // second part effectively subtracts 8 from each quant value
+    return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y);
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q4_1_Q8_1_MMVQ 2
+#define VDR_Q4_1_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl(
+    const int * v, const int * u, const half2 & dm4, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
+        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
+
+        // SIMD dot product of quantized values
+        sumi = __dp4a(vi0, u[2*i+0], sumi);
+        sumi = __dp4a(vi1, u[2*i+1], sumi);
+    }
+
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm4, ds8));
+    const float d4d8 = tmp.x;
+    const float m4s8 = tmp.y;
+#else
+    const float2 dm4f = __half22float2(dm4);
+    const float2 ds8f = __half22float2(ds8);
+    const float d4d8 = dm4f.x * ds8f.x;
+    const float m4s8 = dm4f.y * ds8f.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
+    return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q5_0_Q8_1_MMVQ 2
+#define VDR_Q5_0_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl(
+    const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
+        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
+        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
+        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
+        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
+        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
+
+        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
+        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
+        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
+        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
+        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
+        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
+    }
+
+    const float2 ds8f = __half22float2(ds8);
+
+    // second part effectively subtracts 16 from each quant value
+    return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y);
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q5_1_Q8_1_MMVQ 2
+#define VDR_Q5_1_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl(
+    const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
+        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
+        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
+        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
+        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
+        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
+
+        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
+        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
+        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
+        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
+        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
+        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
+    }
+
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm5, ds8));
+    const float d5d8 = tmp.x;
+    const float m5s8 = tmp.y;
+#else
+    const float2 dm5f = __half22float2(dm5);
+    const float2 ds8f = __half22float2(ds8);
+    const float d5d8 = dm5f.x * ds8f.x;
+    const float m5s8 = dm5f.y * ds8f.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
+    return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
+
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q8_0_Q8_1_MMVQ 2
+#define VDR_Q8_0_Q8_1_MMQ 8
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl(
+    const int * v, const int * u, const float & d8_0, const float & d8_1) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        // SIMD dot product of quantized values
+        sumi = __dp4a(v[i], u[i], sumi);
+    }
+
+    return d8_0*d8_1 * sumi;
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl(
+    const int * v, const int * u, const half2 & dm8, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        // SIMD dot product of quantized values
+        sumi = __dp4a(v[i], u[i], sumi);
+    }
+
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm8, ds8));
+    const float d8d8 = tmp.x;
+    const float m8s8 = tmp.y;
+#else
+    const float2 dm8f = __half22float2(dm8);
+    const float2 ds8f = __half22float2(ds8);
+    const float d8d8 = dm8f.x * ds8f.x;
+    const float m8s8 = dm8f.y * ds8f.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
+    return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q2_K_Q8_1_MMVQ 1
+#define VDR_Q2_K_Q8_1_MMQ  2
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
+    const int & v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
+    const half2 & dm2, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR2_K; ++i) {
+        const int sc = scales[2*i];
+
+        const int vi = (v >> (2*i)) & 0x03030303;
+
+        sumf_d += d8[i] * (__dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product
+
+        // fill int with 4x m
+        int m = sc >> 4;
+        m |= m <<  8;
+        m |= m << 16;
+        sumf_m += d8[i] * __dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values
+    }
+
+    const float2 dm2f = __half22float2(dm2);
+
+    return dm2f.x*sumf_d - dm2f.y*sumf_m;
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
+    const half2 & dm2, const float & d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi_d = 0;
+    int sumi_m = 0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) {
+        int sumi_d_sc = 0;
+
+        const int sc = scales[i0 / (QI8_1/2)];
+
+        // fill int with 4x m
+        int m = sc >> 4;
+        m |= m <<  8;
+        m |= m << 16;
+
+#pragma unroll
+        for (int i = i0; i < i0 + QI8_1/2; ++i) {
+            sumi_d_sc = __dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product
+            sumi_m    = __dp4a(m,    u[i], sumi_m); // multiply sum of q8_1 values with m
+        }
+
+        sumi_d += sumi_d_sc * (sc & 0xF);
+    }
+
+    const float2 dm2f = __half22float2(dm2);
+
+    return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m);
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q3_K_Q8_1_MMVQ 1
+#define VDR_Q3_K_Q8_1_MMQ  2
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
+    const int & vl, const int & vh, const int * __restrict__ u, const uint8_t * __restrict__ scales,
+    const int & scale_offset, const float & d3, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR3_K; ++i) {
+        const int isc = scale_offset + 2*i;
+
+        const int isc_low = isc % (QK_K/32);
+        const int sc_shift_low = 4 * (isc / (QK_K/32));
+        const int sc_low  = (scales[isc_low] >> sc_shift_low) & 0xF;
+
+        const int isc_high = isc % (QK_K/64);
+        const int sc_shift_high = 2 * (isc / (QK_K/64));
+        const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;
+
+        const int sc = (sc_low | sc_high) - 32;
+
+        const int vil = (vl >> (2*i)) & 0x03030303;
+
+        const int vih = ((vh >> i) << 2) & 0x04040404;
+
+        const int vi = __vsubss4(vil, vih);
+
+        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
+    }
+
+    return d3 * sumf;
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales,
+    const float & d3, const float & d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) {
+        int sumi_sc = 0;
+
+        for (int i = i0; i < i0 + QI8_1/2; ++i) {
+            sumi_sc = __dp4a(v[i], u[i], sumi_sc); // SIMD dot product
+        }
+
+        sumi += sumi_sc * scales[i0 / (QI8_1/2)];
+    }
+
+    return d3*d8 * sumi;
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q4_K_Q8_1_MMVQ 2
+#define VDR_Q4_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm4, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR4_K; ++i) {
+        const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F;
+        const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F;
+
+        const int dot1 = __dp4a(v1i, u[2*i+1], __dp4a(v0i, u[2*i+0], 0)); // SIMD dot product
+        const int dot2 = __dp4a(0x01010101, u[2*i+1], __dp4a(0x01010101, u[2*i+0], 0)); // sum of u
+
+        sumf_d += d8[i] * (dot1 * sc[i]);
+        sumf_m += d8[i] * (dot2 * m[i]);  // multiply constant part of q4_K with sum of q8_1 values
+    }
+
+    const float2 dm4f = __half22float2(dm4);
+
+    return dm4f.x*sumf_d - dm4f.y*sumf_m;
+
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) {
+        int sumi_d = 0;
+
+#pragma unroll
+        for (int j = 0; j < QI8_1; ++j) {
+            sumi_d = __dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product
+        }
+
+        const float2 ds8f = __half22float2(ds8[i]);
+
+        sumf_d += ds8f.x * (sc[i] * sumi_d);
+        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
+    }
+
+    const float2 dm4f = __half22float2(dm4);
+
+    return dm4f.x*sumf_d - dm4f.y*sumf_m;
+
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q5_K_Q8_1_MMVQ 2
+#define VDR_Q5_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
+    const int * __restrict__ vl, const int * __restrict__ vh, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm5, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K; ++i) {
+        const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F;
+        const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F;
+
+        const int vh0i = ((vh[0] >> i) << 4) & 0x10101010;
+        const int vh1i = ((vh[1] >> i) << 4) & 0x10101010;
+
+        const int v0i = vl0i | vh0i;
+        const int v1i = vl1i | vh1i;
+
+        const int dot1 = __dp4a(v0i, u[2*i+0], __dp4a(v1i, u[2*i+1], 0)); // SIMD dot product
+        const int dot2 = __dp4a(0x01010101, u[2*i+0], __dp4a(0x01010101, u[2*i+1], 0)); // sum of u
+
+        sumf_d += d8[i] * (dot1 * sc[i]);
+        sumf_m += d8[i] * (dot2 * m[i]);
+
+    }
+
+    const float2 dm5f = __half22float2(dm5);
+
+    return dm5f.x*sumf_d - dm5f.y*sumf_m;
+
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) {
+        int sumi_d = 0;
+
+#pragma unroll
+        for (int j = 0; j < QI8_1; ++j) {
+            sumi_d = __dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product
+        }
+
+        const float2 ds8f = __half22float2(ds8[i]);
+
+        sumf_d += ds8f.x * (sc[i] * sumi_d);
+        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
+    }
+
+    const float2 dm4f = __half22float2(dm4);
+
+    return dm4f.x*sumf_d - dm4f.y*sumf_m;
+
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q6_K_Q8_1_MMVQ 1
+#define VDR_Q6_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
+    const int & vl, const int & vh, const int * __restrict__ u, const int8_t * __restrict__ scales,
+    const float & d, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR6_K; ++i) {
+        const int sc = scales[4*i];
+
+        const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
+
+        const int vih = ((vh >> (4*i)) << 4) & 0x30303030;
+
+        const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32
+
+        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
+    }
+
+    return d*sumf;
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc,
+    const float & d6, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+
+#pragma unroll
+    for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
+        int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale
+
+#pragma unroll
+        for (int i = i0; i < i0 + 2; ++i) {
+            sumi_d.x = __dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product
+            sumi_d.x = __dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product
+
+            sumi_d.y = __dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product
+            sumi_d.y = __dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product
+        }
+
+        sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y);
+    }
+
+    return d6 * sumf_d;
+
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
+
+    int v[VDR_Q4_0_Q8_1_MMVQ];
+    int u[2*VDR_Q4_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
+        v[i]     = get_int_from_uint8(bq4_0->qs, iqs + i);
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
+    }
+
+    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int  tile_x_qs[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI4_0) + mmq_y/QI4_0];
+
+    *x_ql = tile_x_qs;
+    *x_dm = (half2 *) tile_x_d;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_0;
+    const int kqsx = k % QI4_0;
+
+    const block_q4_0 * bx0 = (block_q4_0 *) vx;
+
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
+        // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) {
+        int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q4_0_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const float * x_dmf = (float *) x_dm;
+
+    int u[2*VDR_Q4_0_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE];
+    }
+
+    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0],
+         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
+
+    int v[VDR_Q4_1_Q8_1_MMVQ];
+    int u[2*VDR_Q4_1_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
+        v[i]    = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
+    }
+
+    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_qs[mmq_y * (WARP_SIZE) +     + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_1) + mmq_y/QI4_1];
+
+    *x_ql = tile_x_qs;
+    *x_dm = tile_x_dm;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_1;
+    const int kqsx = k % QI4_1;
+
+    const block_q4_1 * bx0 = (block_q4_1 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) {
+        int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q4_1_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+
+    int u[2*VDR_Q4_1_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE];
+    }
+
+    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1],
+         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
+
+    int vl[VDR_Q5_0_Q8_1_MMVQ];
+    int vh[VDR_Q5_0_Q8_1_MMVQ];
+    int  u[2*VDR_Q5_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
+        vl[i]    = get_int_from_uint8(bq5_0->qs, iqs + i);
+        vh[i]    = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0);
+    }
+
+    return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, bq5_0->d, bq8_1->ds);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int  tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
+    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI5_0) + mmq_y/QI5_0];
+
+    *x_ql = tile_x_ql;
+    *x_dm = (half2 *) tile_x_d;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_0;
+    const int kqsx = k % QI5_0;
+
+    const block_q5_0 * bx0 = (block_q5_0 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        const int ql = get_int_from_uint8(bxi->qs, kqsx);
+        const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0));
+
+        int qs0 = (ql >>  0)   & 0x0F0F0F0F;
+        qs0    |= (qh <<  4)   & 0x00000010;  // 0 ->  4
+        qs0    |= (qh << 11)   & 0x00001000;  // 1 -> 12
+        qs0    |= (qh << 18)   & 0x00100000;  // 2 -> 20
+        qs0    |= (qh << 25)   & 0x10000000;  // 3 -> 28
+        qs0     = __vsubss4(qs0, 0x10101010); // subtract 16
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
+
+        int qs1 = (ql >>  4)   & 0x0F0F0F0F;
+        qs1    |= (qh >> 12)   & 0x00000010;  // 16 ->  4
+        qs1    |= (qh >>  5)   & 0x00001000;  // 17 -> 12
+        qs1    |= (qh <<  2)   & 0x00100000;  // 18 -> 20
+        qs1    |= (qh <<  9)   & 0x10000000;  // 19 -> 28
+        qs1     = __vsubss4(qs1, 0x10101010); // subtract 16
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) {
+        int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0;
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    int u[2*VDR_Q5_0_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE];
+    }
+
+    return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
+        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
+
+    int vl[VDR_Q5_1_Q8_1_MMVQ];
+    int vh[VDR_Q5_1_Q8_1_MMVQ];
+    int  u[2*VDR_Q5_1_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
+        vl[i]   = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
+        vh[i]   = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
+    }
+
+    return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_1) + mmq_y/QI5_1];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset < nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_1;
+    const int kqsx = k % QI5_1;
+
+    const block_q5_1 * bx0 = (block_q5_1 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
+        const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));
+
+        int qs0 = (ql >>  0) & 0x0F0F0F0F;
+        qs0    |= (qh <<  4) & 0x00000010; // 0 ->  4
+        qs0    |= (qh << 11) & 0x00001000; // 1 -> 12
+        qs0    |= (qh << 18) & 0x00100000; // 2 -> 20
+        qs0    |= (qh << 25) & 0x10000000; // 3 -> 28
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
+
+        int qs1 = (ql >>  4) & 0x0F0F0F0F;
+        qs1    |= (qh >> 12) & 0x00000010; // 16 ->  4
+        qs1    |= (qh >>  5) & 0x00001000; // 17 -> 12
+        qs1    |= (qh <<  2) & 0x00100000; // 18 -> 20
+        qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) {
+        int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q5_1_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1;
+
+    int u[2*VDR_Q5_1_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE];
+    }
+
+    return vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
+        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
+
+    int v[VDR_Q8_0_Q8_1_MMVQ];
+    int u[VDR_Q8_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
+        v[i] = get_int_from_int8(bq8_0->qs, iqs + i);
+        u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+    }
+
+    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, __low2half(bq8_1->ds));
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int  tile_x_qs[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI8_0) + mmq_y/QI8_0];
+
+    *x_ql = tile_x_qs;
+    *x_dm = (half2 *) tile_x_d;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI8_0;
+    const int kqsx = k % QI8_0;
+    float * x_dmf = (float *) x_dm;
+
+    const block_q8_0 * bx0 = (block_q8_0 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) {
+        int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
+         y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
+}
+
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q2_K * bq2_K = (const block_q2_K *) vbq;
+
+    const int bq8_offset = QR2_K * (iqs / QI8_1);
+    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+
+    const uint8_t * scales = bq2_K->scales + scale_offset;
+
+    const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs);
+    int    u[QR2_K];
+    float d8[QR2_K];
+
+#pragma unroll
+    for (int i = 0; i < QR2_K; ++ i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        d8[i] = __low2half(bq8_1[bq8_offset + i].ds);
+    }
+
+    return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q2_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI2_K) + mmq_y/QI2_K];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/4)     + mmq_y/4];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI2_K;
+    const int kqsx = k % QI2_K;
+
+    const block_q2_K * bx0 = (block_q2_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI2_K;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) {
+        int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
+        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4);
+
+        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4));
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    const int kbx = k / QI2_K;
+    const int ky  = (k % QI2_K) * QR2_K;
+    const float * y_df = (const float *) y_ds;
+
+    int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];
+
+    const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2);
+    const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2));
+
+#pragma unroll
+    for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) {
+        v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303;
+    }
+
+    const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;
+
+    const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE;
+    return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]);
+}
+
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q3_K * bq3_K = (const block_q3_K *) vbq;
+
+    const int bq8_offset = QR3_K * (iqs / (QI3_K/2));
+    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+
+    const float d = bq3_K->d;
+
+    const int vl = get_int_from_uint8(bq3_K->qs, iqs);
+
+    // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
+    const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;
+
+    int    u[QR3_K];
+    float d8[QR3_K];
+
+#pragma unroll
+    for (int i = 0; i < QR3_K; ++i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        d8[i] = __low2half(bq8_1[bq8_offset + i].ds);
+    }
+
+    return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q3_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI3_K) + mmq_y/QI3_K];
+    __shared__ int   tile_x_qh[mmq_y * (WARP_SIZE/2)     + mmq_y/2];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/4)     + mmq_y/4];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_qh = tile_x_qh;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI3_K;
+    const int kqsx = k % QI3_K;
+
+    const block_q3_K * bx0 = (block_q3_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
+    const int kbxd = k % blocks_per_tile_x_row;
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) {
+        int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) {
+        int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2);
+
+        // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
+        x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2));
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
+        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4);
+
+        const int ksc = k % (QI3_K/4);
+
+        const int ksc_low = ksc % (QI3_K/8);
+        const int shift_low = 4 * (ksc / (QI3_K/8));
+        const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;
+
+        const int ksc_high = QI3_K/8;
+        const int shift_high = 2 * ksc;
+        const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;
+
+        const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
+
+        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    const int kbx  = k / QI3_K;
+    const int ky  = (k % QI3_K) * QR3_K;
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    const int8_t * scales = ((int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
+
+    int v[QR3_K*VDR_Q3_K_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) {
+        const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2);
+        const int shift = 2 * ((ky % 32) / 8);
+        const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303;
+
+        const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8);
+        const int vlh = (vh << 2) & 0x04040404;
+
+        v[l] = __vsubss4(vll, vlh);
+    }
+
+    const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE;
+    return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]);
+}
+
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+#ifndef GGML_QKK_64
+    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
+
+    int    v[2];
+    int    u[2*QR4_K];
+    float d8[QR4_K];
+
+    // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
+    const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));
+
+    // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
+    // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
+    // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
+    // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
+
+    const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
+    v[0] = q4[0];
+    v[1] = q4[4];
+
+    const uint16_t * scales = (const uint16_t *)bq4_K->scales;
+    uint16_t aux[2];
+    const int j = bq8_offset/2;
+    if (j < 2) {
+        aux[0] = scales[j+0] & 0x3f3f;
+        aux[1] = scales[j+2] & 0x3f3f;
+    } else {
+        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
+        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+    }
+    const uint8_t * sc = (const uint8_t *)aux;
+    const uint8_t * m  = sc + 2;
+
+    for (int i = 0; i < QR4_K; ++i) {
+        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
+        d8[i] = __low2half(bq8i->ds);
+
+        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
+        u[2*i+0] = q8[0];
+        u[2*i+1] = q8[4];
+    }
+
+    return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8);
+
+#else
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+    uint16_t aux16[2];
+    const uint8_t * s = (const uint8_t *)aux16;
+
+    const uint16_t * a = (const uint16_t *)bq4_K->scales;
+    aux16[0] = a[0] & 0x0f0f;
+    aux16[1] = (a[0] >> 4) & 0x0f0f;
+
+    const float dall = bq4_K->dm[0];
+    const float dmin = bq4_K->dm[1];
+
+    const float d8_1 = __low2float(bq8_1[0].ds);
+    const float d8_2 = __low2float(bq8_1[1].ds);
+
+    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
+    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
+    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
+    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
+
+    const int * q4 = (const int *)bq4_K->qs + (iqs/2);
+    const int v1 = q4[0];
+    const int v2 = q4[4];
+
+    const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0));
+    const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0));
+    const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
+    const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0));
+
+    sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]);
+    sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]);
+
+    return dall * sumf_d - dmin * sumf_m;
+
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+
+#endif
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_K) + mmq_y/QI4_K];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI4_K; // == k if QK_K == 256
+
+    const block_q4_K * bx0 = (block_q4_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) {
+        int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+#if QK_K == 256
+        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
+#else
+        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]};
+#endif
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8);
+
+        const int * scales = (int *) bxi->scales;
+
+        const int ksc = k % (WARP_SIZE/8);
+
+        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
+        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
+        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8);
+
+    const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE;
+    return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8,
+                                      x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]);
+}
+
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+#ifndef GGML_QKK_64
+    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
+
+    int   vl[2];
+    int   vh[2];
+    int    u[2*QR5_K];
+    float d8[QR5_K];
+
+    const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2));
+    const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
+    const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4));
+
+    vl[0] = ql[0];
+    vl[1] = ql[4];
+
+    vh[0] = qh[0] >> bq8_offset;
+    vh[1] = qh[4] >> bq8_offset;
+
+    const uint16_t * scales = (const uint16_t *)bq5_K->scales;
+    uint16_t aux[2];
+    const int j = bq8_offset/2;
+    if (j < 2) {
+        aux[0] = scales[j+0] & 0x3f3f;
+        aux[1] = scales[j+2] & 0x3f3f;
+    } else {
+        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
+        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+    }
+    const uint8_t * sc = (const uint8_t *)aux;
+    const uint8_t * m  = sc + 2;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K; ++i) {
+        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
+        d8[i] = __low2float(bq8i->ds);
+
+        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
+        u[2*i+0] = q8[0];
+        u[2*i+1] = q8[4];
+    }
+
+    return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8);
+
+#else
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
+
+    const int8_t * s = bq5_K->scales;
+
+    const float d = bq5_K->d;
+
+    const float d8_1 = __low2half(bq8_1[0].ds);
+    const float d8_2 = __low2half(bq8_1[1].ds);
+
+    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
+    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
+    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
+    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
+
+    const int * ql = (const int *)bq5_K->qs + (iqs/2);
+    const int vl1 = ql[0];
+    const int vl2 = ql[4];
+
+    const int step = 4 * (iqs/2); // 0, 4, 8, 12
+    const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6
+    const int in = step%8; // 0, 4, 0, 4
+    const int vh = (*((const int *)(bq5_K->qh + in))) >> im;
+
+    const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f);
+    const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f);
+    const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f);
+    const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f);
+
+    const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1])
+                       + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]);
+
+    return d * sumf_d;
+
+#else
+    assert(false);
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+
+#endif
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_K) + mmq_y/QI5_K];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI5_K; // == k if QK_K == 256
+
+    const block_q5_K * bx0 = (block_q5_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx;
+        const int ky = QR5_K*kqsx;
+
+        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
+        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
+        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
+
+        const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4));
+        const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
+        const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;
+
+        const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0;
+        const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4);
+
+        x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
+        x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) {
+        int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+#if QK_K == 256
+        x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
+#endif
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8);
+
+        const int * scales = (int *) bxi->scales;
+
+        const int ksc = k % (WARP_SIZE/8);
+
+        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
+        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
+        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8);
+
+    const int index_x = i * (QR5_K*WARP_SIZE + 1) +  QR5_K*k;
+    const int index_y = j * WARP_SIZE             + (QR5_K*k) % WARP_SIZE;
+    return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8,
+                                      x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]);
+}
+
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q6_K * bq6_K = (const block_q6_K *) vbq;
+
+    const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4);
+    const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
+    const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));
+
+    const int vl = get_int_from_uint8(bq6_K->ql, iqs);
+    const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;
+
+    const int8_t * scales = bq6_K->scales + scale_offset;
+
+    int    u[QR6_K];
+    float d8[QR6_K];
+
+#pragma unroll
+    for (int i = 0; i < QR6_K; ++i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
+        d8[i] = __low2half(bq8_1[bq8_offset + 2*i].ds);
+    }
+
+    return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q6_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI6_K) + mmq_y/QI6_K];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI6_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI6_K; // == k if QK_K == 256
+
+    const block_q6_K * bx0 = (block_q6_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx;
+        const int ky = QR6_K*kqsx;
+
+        const int ql = get_int_from_uint8(bxi->ql, kqsx);
+        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
+        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
+
+        const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
+        const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
+        const int qh1 =  (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4))))       & 0x30303030;
+
+        const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0;
+        const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2);
+
+        x_ql[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
+        x_ql[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) {
+        int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4;
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8));
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]);
+
+    const int index_x = i * (QR6_K*WARP_SIZE + 1) +  QR6_K*k;
+    const int index_y = j * WARP_SIZE             + (QR6_K*k) % WARP_SIZE;
+    return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
+}
+
+template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x, int mmq_y, int nwarps,
+              allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles, int vdr, vec_dot_q_mul_mat_cuda_t vec_dot>
+static __device__ __forceinline__ void mul_mat_q(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    const int blocks_per_row_x = ncols_x / qk;
+    const int blocks_per_col_y = nrows_y / QK8_1;
+    const int blocks_per_warp = WARP_SIZE / qi;
+
+    const int & ncols_dst = ncols_y;
+
+    const int row_dst_0 = blockIdx.x*mmq_y;
+    const int & row_x_0 = row_dst_0;
+
+    const int col_dst_0 = blockIdx.y*mmq_x;
+    const int & col_y_0 = col_dst_0;
+
+    int   * tile_x_ql = nullptr;
+    half2 * tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+    allocate_tiles(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc);
+
+    __shared__ int    tile_y_qs[mmq_x * WARP_SIZE];
+    __shared__ half2  tile_y_ds[mmq_x * WARP_SIZE/QI8_1];
+
+    float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {0.0f};
+
+    for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {
+
+        load_tiles(x + row_x_0*blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc,
+                   threadIdx.y, nrows_x-row_x_0-1, threadIdx.x, blocks_per_row_x);
+
+#pragma unroll
+        for (int ir = 0; ir < qr; ++ir) {
+            const int kqs = ir*WARP_SIZE + threadIdx.x;
+            const int kbxd = kqs / QI8_1;
+
+#pragma unroll
+            for (int i = 0; i < mmq_x; i += nwarps) {
+                const int col_y_eff = min(col_y_0 + threadIdx.y + i, ncols_y-1); // to prevent out-of-bounds memory accesses
+
+                const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd];
+
+                const int index_y = (threadIdx.y + i) * WARP_SIZE + kqs % WARP_SIZE;
+                tile_y_qs[index_y] = get_int_from_int8_aligned(by0->qs, threadIdx.x % QI8_1);
+            }
+
+#pragma unroll
+            for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) {
+                const int ids = (ids0 + threadIdx.y * QI8_1 + threadIdx.x / (WARP_SIZE/QI8_1)) % mmq_x;
+                const int kby = threadIdx.x % (WARP_SIZE/QI8_1);
+                const int col_y_eff = min(col_y_0 + ids, ncols_y-1);
+
+                // if the sum is not needed it's faster to transform the scale to f32 ahead of time
+                const half2 * dsi_src = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + ir*(WARP_SIZE/QI8_1) + kby].ds;
+                half2       * dsi_dst = &tile_y_ds[ids * (WARP_SIZE/QI8_1) + kby];
+                if (need_sum) {
+                    *dsi_dst = *dsi_src;
+                } else {
+                    float * dfi_dst = (float *) dsi_dst;
+                    *dfi_dst = __low2half(*dsi_src);
+                }
+            }
+
+            __syncthreads();
+
+// #pragma unroll // unrolling this loop causes too much register pressure
+            for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) {
+#pragma unroll
+                for (int j = 0; j < mmq_x; j += nwarps) {
+#pragma unroll
+                    for (int i = 0; i < mmq_y; i += WARP_SIZE) {
+                        sum[i/WARP_SIZE][j/nwarps] += vec_dot(
+                            tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs, tile_y_ds,
+                            threadIdx.x + i, threadIdx.y + j, k);
+                    }
+                }
+            }
+
+            __syncthreads();
+        }
+    }
+
+#pragma unroll
+    for (int j = 0; j < mmq_x; j += nwarps) {
+        const int col_dst = col_dst_0 + j + threadIdx.y;
+
+        if (col_dst >= ncols_dst) {
+            return;
+        }
+
+#pragma unroll
+        for (int i = 0; i < mmq_y; i += WARP_SIZE) {
+            const int row_dst = row_dst_0 + threadIdx.x + i;
+
+            if (row_dst >= nrows_dst) {
+                continue;
+            }
+
+            dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps];
+        }
+    }
+}
+
+#define  MMQ_X_Q4_0_RDNA2  64
+#define  MMQ_Y_Q4_0_RDNA2  128
+#define NWARPS_Q4_0_RDNA2  8
+#define  MMQ_X_Q4_0_RDNA1  64
+#define  MMQ_Y_Q4_0_RDNA1  64
+#define NWARPS_Q4_0_RDNA1  8
+#define  MMQ_X_Q4_0_AMPERE 64
+#define  MMQ_Y_Q4_0_AMPERE 128
+#define NWARPS_Q4_0_AMPERE 4
+#define  MMQ_X_Q4_0_PASCAL 64
+#define  MMQ_Y_Q4_0_PASCAL 64
+#define NWARPS_Q4_0_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_0_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    mul_mat_q4_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q4_0_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q4_0_RDNA2;
+    const int nwarps = NWARPS_Q4_0_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q4_0_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q4_0_RDNA1;
+    const int nwarps = NWARPS_Q4_0_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
+        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q4_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_0_AMPERE;
+    const int nwarps = NWARPS_Q4_0_AMPERE;
+
+    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
+        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q4_0_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q4_0_PASCAL;
+    const int nwarps = NWARPS_Q4_0_PASCAL;
+
+    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
+        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    (void) vec_dot_q4_0_q8_1_mul_mat;
+    assert(false);
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q4_1_RDNA2  64
+#define  MMQ_Y_Q4_1_RDNA2  128
+#define NWARPS_Q4_1_RDNA2  8
+#define  MMQ_X_Q4_1_RDNA1  64
+#define  MMQ_Y_Q4_1_RDNA1  64
+#define NWARPS_Q4_1_RDNA1  8
+#define  MMQ_X_Q4_1_AMPERE 64
+#define  MMQ_Y_Q4_1_AMPERE 128
+#define NWARPS_Q4_1_AMPERE 4
+#define  MMQ_X_Q4_1_PASCAL 64
+#define  MMQ_Y_Q4_1_PASCAL 64
+#define NWARPS_Q4_1_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#elif __CUDA_ARCH__ < CC_VOLTA
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_PASCAL, 2)
+#endif // __CUDA_ARCH__ < CC_VOLTA
+    mul_mat_q4_1(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q4_1_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q4_1_RDNA2;
+    const int nwarps = NWARPS_Q4_1_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q4_1_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q4_1_RDNA1;
+    const int nwarps = NWARPS_Q4_1_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
+        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q4_1_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_1_AMPERE;
+    const int nwarps = NWARPS_Q4_1_AMPERE;
+
+    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
+        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q4_1_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q4_1_PASCAL;
+    const int nwarps = NWARPS_Q4_1_PASCAL;
+
+    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
+        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    (void) vec_dot_q4_1_q8_1_mul_mat;
+    assert(false);
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q5_0_RDNA2  64
+#define  MMQ_Y_Q5_0_RDNA2  128
+#define NWARPS_Q5_0_RDNA2  8
+#define  MMQ_X_Q5_0_RDNA1  64
+#define  MMQ_Y_Q5_0_RDNA1  64
+#define NWARPS_Q5_0_RDNA1  8
+#define  MMQ_X_Q5_0_AMPERE 128
+#define  MMQ_Y_Q5_0_AMPERE 64
+#define NWARPS_Q5_0_AMPERE 4
+#define  MMQ_X_Q5_0_PASCAL 64
+#define  MMQ_Y_Q5_0_PASCAL 64
+#define NWARPS_Q5_0_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q5_0_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    mul_mat_q5_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q5_0_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q5_0_RDNA2;
+    const int nwarps = NWARPS_Q5_0_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q5_0_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q5_0_RDNA1;
+    const int nwarps = NWARPS_Q5_0_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
+        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q5_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_0_AMPERE;
+    const int nwarps = NWARPS_Q5_0_AMPERE;
+
+    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
+        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q5_0_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q5_0_PASCAL;
+    const int nwarps = NWARPS_Q5_0_PASCAL;
+
+    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
+        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    (void) vec_dot_q5_0_q8_1_mul_mat;
+    assert(false);
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q5_1_RDNA2  64
+#define  MMQ_Y_Q5_1_RDNA2  128
+#define NWARPS_Q5_1_RDNA2  8
+#define  MMQ_X_Q5_1_RDNA1  64
+#define  MMQ_Y_Q5_1_RDNA1  64
+#define NWARPS_Q5_1_RDNA1  8
+#define  MMQ_X_Q5_1_AMPERE 128
+#define  MMQ_Y_Q5_1_AMPERE 64
+#define NWARPS_Q5_1_AMPERE 4
+#define  MMQ_X_Q5_1_PASCAL 64
+#define  MMQ_Y_Q5_1_PASCAL 64
+#define NWARPS_Q5_1_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q5_1_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+mul_mat_q5_1(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q5_1_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q5_1_RDNA2;
+    const int nwarps = NWARPS_Q5_1_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q5_1_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q5_1_RDNA1;
+    const int nwarps = NWARPS_Q5_1_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
+        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q5_1_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_1_AMPERE;
+    const int nwarps = NWARPS_Q5_1_AMPERE;
+
+    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
+        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q5_1_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q5_1_PASCAL;
+    const int nwarps = NWARPS_Q5_1_PASCAL;
+
+    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
+        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    (void) vec_dot_q5_1_q8_1_mul_mat;
+    assert(false);
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q8_0_RDNA2  64
+#define  MMQ_Y_Q8_0_RDNA2  128
+#define NWARPS_Q8_0_RDNA2  8
+#define  MMQ_X_Q8_0_RDNA1  64
+#define  MMQ_Y_Q8_0_RDNA1  64
+#define NWARPS_Q8_0_RDNA1  8
+#define  MMQ_X_Q8_0_AMPERE 128
+#define  MMQ_Y_Q8_0_AMPERE 64
+#define NWARPS_Q8_0_AMPERE 4
+#define  MMQ_X_Q8_0_PASCAL 64
+#define  MMQ_Y_Q8_0_PASCAL 64
+#define NWARPS_Q8_0_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q8_0_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    mul_mat_q8_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q8_0_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q8_0_RDNA2;
+    const int nwarps = NWARPS_Q8_0_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q8_0_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q8_0_RDNA1;
+    const int nwarps = NWARPS_Q8_0_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
+        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q8_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q8_0_AMPERE;
+    const int nwarps = NWARPS_Q8_0_AMPERE;
+
+    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
+        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q8_0_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q8_0_PASCAL;
+    const int nwarps = NWARPS_Q8_0_PASCAL;
+
+    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
+        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    (void) vec_dot_q8_0_q8_1_mul_mat;
+    assert(false);
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q2_K_RDNA2  64
+#define  MMQ_Y_Q2_K_RDNA2  128
+#define NWARPS_Q2_K_RDNA2  8
+#define  MMQ_X_Q2_K_RDNA1  128
+#define  MMQ_Y_Q2_K_RDNA1  32
+#define NWARPS_Q2_K_RDNA1  8
+#define  MMQ_X_Q2_K_AMPERE 64
+#define  MMQ_Y_Q2_K_AMPERE 128
+#define NWARPS_Q2_K_AMPERE 4
+#define  MMQ_X_Q2_K_PASCAL 64
+#define  MMQ_Y_Q2_K_PASCAL 64
+#define NWARPS_Q2_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q2_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+mul_mat_q2_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q2_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q2_K_RDNA2;
+    const int nwarps = NWARPS_Q2_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q2_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q2_K_RDNA1;
+    const int nwarps = NWARPS_Q2_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
+        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q2_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q2_K_AMPERE;
+    const int nwarps = NWARPS_Q2_K_AMPERE;
+
+    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
+        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q2_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q2_K_PASCAL;
+    const int nwarps = NWARPS_Q2_K_PASCAL;
+
+    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
+        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    (void) vec_dot_q2_K_q8_1_mul_mat;
+    assert(false);
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q3_K_RDNA2  128
+#define  MMQ_Y_Q3_K_RDNA2  64
+#define NWARPS_Q3_K_RDNA2  8
+#define  MMQ_X_Q3_K_RDNA1  32
+#define  MMQ_Y_Q3_K_RDNA1  128
+#define NWARPS_Q3_K_RDNA1  8
+#define  MMQ_X_Q3_K_AMPERE 128
+#define  MMQ_Y_Q3_K_AMPERE 128
+#define NWARPS_Q3_K_AMPERE 4
+#define  MMQ_X_Q3_K_PASCAL 64
+#define  MMQ_Y_Q3_K_PASCAL 64
+#define NWARPS_Q3_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#elif __CUDA_ARCH__ < CC_VOLTA
+    __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_PASCAL, 2)
+#endif // __CUDA_ARCH__ < CC_VOLTA
+    mul_mat_q3_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q3_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q3_K_RDNA2;
+    const int nwarps = NWARPS_Q3_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q3_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q3_K_RDNA1;
+    const int nwarps = NWARPS_Q3_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
+        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q3_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q3_K_AMPERE;
+    const int nwarps = NWARPS_Q3_K_AMPERE;
+
+    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
+        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q3_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q3_K_PASCAL;
+    const int nwarps = NWARPS_Q3_K_PASCAL;
+
+    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
+        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    (void) vec_dot_q3_K_q8_1_mul_mat;
+    assert(false);
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q4_K_RDNA2  64
+#define  MMQ_Y_Q4_K_RDNA2  128
+#define NWARPS_Q4_K_RDNA2  8
+#define  MMQ_X_Q4_K_RDNA1  32
+#define  MMQ_Y_Q4_K_RDNA1  64
+#define NWARPS_Q4_K_RDNA1  8
+#define  MMQ_X_Q4_K_AMPERE 64
+#define  MMQ_Y_Q4_K_AMPERE 128
+#define NWARPS_Q4_K_AMPERE 4
+#define  MMQ_X_Q4_K_PASCAL 64
+#define  MMQ_Y_Q4_K_PASCAL 64
+#define NWARPS_Q4_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#elif __CUDA_ARCH__ < CC_VOLTA
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_PASCAL, 2)
+#endif // __CUDA_ARCH__ < CC_VOLTA
+    mul_mat_q4_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q4_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q4_K_RDNA2;
+    const int nwarps = NWARPS_Q4_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q4_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q4_K_RDNA1;
+    const int nwarps = NWARPS_Q4_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
+        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q4_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_K_AMPERE;
+    const int nwarps = NWARPS_Q4_K_AMPERE;
+
+    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
+        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q4_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q4_K_PASCAL;
+    const int nwarps = NWARPS_Q4_K_PASCAL;
+
+    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
+        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    (void) vec_dot_q4_K_q8_1_mul_mat;
+    assert(false);
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q5_K_RDNA2  64
+#define  MMQ_Y_Q5_K_RDNA2  128
+#define NWARPS_Q5_K_RDNA2  8
+#define  MMQ_X_Q5_K_RDNA1  32
+#define  MMQ_Y_Q5_K_RDNA1  64
+#define NWARPS_Q5_K_RDNA1  8
+#define  MMQ_X_Q5_K_AMPERE 64
+#define  MMQ_Y_Q5_K_AMPERE 128
+#define NWARPS_Q5_K_AMPERE 4
+#define  MMQ_X_Q5_K_PASCAL 64
+#define  MMQ_Y_Q5_K_PASCAL 64
+#define NWARPS_Q5_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q5_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+mul_mat_q5_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q5_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q5_K_RDNA2;
+    const int nwarps = NWARPS_Q5_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q5_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q5_K_RDNA1;
+    const int nwarps = NWARPS_Q5_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
+        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q5_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_K_AMPERE;
+    const int nwarps = NWARPS_Q5_K_AMPERE;
+
+    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
+        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q5_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q5_K_PASCAL;
+    const int nwarps = NWARPS_Q5_K_PASCAL;
+
+    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
+        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    (void) vec_dot_q5_K_q8_1_mul_mat;
+    assert(false);
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q6_K_RDNA2  64
+#define  MMQ_Y_Q6_K_RDNA2  128
+#define NWARPS_Q6_K_RDNA2  8
+#define  MMQ_X_Q6_K_RDNA1  32
+#define  MMQ_Y_Q6_K_RDNA1  64
+#define NWARPS_Q6_K_RDNA1  8
+#define  MMQ_X_Q6_K_AMPERE 64
+#define  MMQ_Y_Q6_K_AMPERE 64
+#define NWARPS_Q6_K_AMPERE 4
+#define  MMQ_X_Q6_K_PASCAL 64
+#define  MMQ_Y_Q6_K_PASCAL 64
+#define NWARPS_Q6_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#elif __CUDA_ARCH__ < CC_VOLTA
+    __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_PASCAL, 2)
+#endif // __CUDA_ARCH__ < CC_VOLTA
+    mul_mat_q6_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q6_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q6_K_RDNA2;
+    const int nwarps = NWARPS_Q6_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q6_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q6_K_RDNA1;
+    const int nwarps = NWARPS_Q6_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
+        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q6_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q6_K_AMPERE;
+    const int nwarps = NWARPS_Q6_K_AMPERE;
+
+    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
+        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q6_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q6_K_PASCAL;
+    const int nwarps = NWARPS_Q6_K_PASCAL;
+
+    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
+        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    (void) vec_dot_q6_K_q8_1_mul_mat;
+    assert(false);
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
+static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows) {
+    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = 0; i < blocks_per_row; i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i + threadIdx.x / (qi/vdr); // x block index
+
+        const int iby = (i + threadIdx.x / (qi/vdr)) * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs  = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    }
+
+    if (threadIdx.x == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
+static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) {
+    // qk = quantized weights per x block
+    // qr = number of quantized weights per data value in x block
+    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int tid = threadIdx.x;
+
+    const int iter_stride = 2*GGML_CUDA_DMMV_X;
+    const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+// partial sum for each thread
+#ifdef GGML_CUDA_F16
+    half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics
+#else
+    float tmp = 0.0f;
+#endif // GGML_CUDA_F16
+
+    for (int i = 0; i < ncols; i += iter_stride) {
+        const int col = i + vals_per_iter*tid;
+        const int ib = (row*ncols + col)/qk; // x block index
+        const int iqs = (col%qk)/qr; // x quant index
+        const int iybs = col - col%qk; // y block start index
+
+// processing >2 values per i iter is faster for fast GPUs
+#pragma unroll
+        for (int j = 0; j < vals_per_iter; j += 2) {
+            // process 2 vals per j iter
+
+            // dequantize
+            // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
+            dfloat2 v;
+            dequantize_kernel(vx, ib, iqs + j/qr, v);
+
+            // matrix multiplication
+            // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
+#ifdef GGML_CUDA_F16
+            tmp += __hmul2(v, {
+                y[iybs + iqs + j/qr + 0],
+                y[iybs + iqs + j/qr + y_offset]
+            });
+#else
+            tmp += v.x * y[iybs + iqs + j/qr + 0];
+            tmp += v.y * y[iybs + iqs + j/qr + y_offset];
+#endif // GGML_CUDA_F16
+        }
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    }
+
+    if (tid == 0) {
+#ifdef GGML_CUDA_F16
+        dst[row] = tmp.x + tmp.y;
+#else
+        dst[row] = tmp;
+#endif // GGML_CUDA_F16
+    }
+}
+
+static __global__ void mul_mat_p021_f16_f32(
+    const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y) {
+
+    const half * x = (const half *) vx;
+
+    const int row_x = blockDim.y*blockIdx.y + threadIdx.y;
+    const int channel = blockDim.z*blockIdx.z + threadIdx.z;
+    const int channel_x = channel / (nchannels_y / nchannels_x);
+
+    const int nrows_y = ncols_x;
+    const int nrows_dst = nrows_x;
+    const int row_dst = row_x;
+
+    float tmp = 0.0f;
+
+    for (int col_x0 = 0; col_x0 < ncols_x; col_x0 += blockDim.x) {
+        const int col_x = col_x0 + threadIdx.x;
+
+        if (col_x >= ncols_x) {
+            break;
+        }
+
+        // x is transposed and permuted
+        const int ix = row_x*nchannels_x*ncols_x + channel_x*ncols_x + col_x;
+        const float xi = __half2float(x[ix]);
+
+        const int row_y = col_x;
+
+
+        // y is not transposed but permuted
+        const int iy = channel*nrows_y + row_y;
+
+        tmp += xi * y[iy];
+    }
+
+    // dst is not transposed and not permuted
+    const int idst = channel*nrows_dst + row_dst;
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    }
+
+    if (threadIdx.x == 0) {
+        dst[idst] = tmp;
+    }
+}
+
+static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
+    const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, const int ncols_x, const int nrows_x,
+    const int row_stride_x, const int channel_stride_x, const int channel_x_divisor) {
+
+    const half * x = (const half *) vx;
+
+    const int row_x = blockDim.y*blockIdx.y + threadIdx.y;
+    const int channel = blockDim.z*blockIdx.z + threadIdx.z;
+    const int channel_x = channel / channel_x_divisor;
+
+    const int nrows_y = ncols_x;
+    const int nrows_dst = nrows_x;
+    const int row_dst = row_x;
+
+    const int idst = channel*nrows_dst + row_dst;
+
+    float tmp = 0.0f;
+
+    for (int col_x0 = 0; col_x0 < ncols_x; col_x0 += blockDim.x) {
+        const int col_x = col_x0 + threadIdx.x;
+
+        if (col_x >= ncols_x) {
+            break;
+        }
+
+        const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x;
+        const float xi = __half2float(x[ix]);
+
+        const int row_y = col_x;
+
+        const int iy = channel*nrows_y + row_y;
+
+        tmp += xi * y[iy];
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    }
+
+    if (threadIdx.x == 0) {
+        dst[idst] = tmp;
+    }
+}
+
+static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    float * dsti = (float *) cdsti;
+
+    *dsti = *xi;
+}
+
+static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    half * dsti = (half *) cdsti;
+
+    *dsti = __float2half(*xi);
+}
+
+template <cpy_kernel_t cpy_1>
+static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
+                                   const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
+                                   const int ne10, const int ne11, const int nb10, const int nb11, const int nb12) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= ne) {
+        return;
+    }
+
+    // determine indices i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor
+    // then combine those indices with the corresponding byte offsets to get the total offsets
+    const int i02 = i / (ne00*ne01);
+    const int i01 = (i - i02*ne01*ne00) / ne00;
+    const int i00 = i - i02*ne01*ne00 - i01*ne00;
+    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02;
+
+    const int i12 = i / (ne10*ne11);
+    const int i11 = (i - i12*ne10*ne11) / ne10;
+    const int i10 = i - i12*ne10*ne11 - i11*ne10;
+    const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12;
+
+    cpy_1(cx + x_offset, cdst + dst_offset);
+}
+
+// rope == RoPE == rotary positional embedding
+
+template<typename T, bool has_pos>
+static __global__ void rope(const T * x, T * dst, const int ncols, const int32_t * pos, const float freq_scale,
+                            const int p_delta_rows, const float theta_scale) {
+    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int i = row*ncols + col;
+    const int i2 = row/p_delta_rows;
+
+    const int p = has_pos ? pos[i2] : 0;
+    const float p0 = p*freq_scale;
+    const float theta = p0*powf(theta_scale, col/2);
+    const float sin_theta = sinf(theta);
+    const float cos_theta = cosf(theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + 1];
+
+    dst[i + 0] = x0*cos_theta - x1*sin_theta;
+    dst[i + 1] = x0*sin_theta + x1*cos_theta;
+}
+
+template<typename T, bool has_pos>
+static __global__ void rope_neox(const T * x, T * dst, const int ncols, const int32_t * pos, const float freq_scale,
+                                 const int p_delta_rows, const float theta_scale) {
+    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int i = row*ncols + col/2;
+    const int i2 = row/p_delta_rows;
+
+    const int p = has_pos ? pos[i2] : 0;
+    const float p0 = p*freq_scale;
+    const float theta = p0*powf(theta_scale, col/2);
+    const float sin_theta = sinf(theta);
+    const float cos_theta = cosf(theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + ncols/2];
+
+    dst[i + 0]       = x0*cos_theta - x1*sin_theta;
+    dst[i + ncols/2] = x0*sin_theta + x1*cos_theta;
+}
+
+static __global__ void rope_glm_f32(const float * x, float * dst, const int ncols, const int32_t * pos, const float freq_scale,
+                                    const int p_delta_rows, const float theta_scale, const int n_ctx) {
+    const int col = blockDim.x*blockIdx.x + threadIdx.x;
+    const int half_n_dims = ncols/4;
+
+    if (col >= half_n_dims) {
+        return;
+    }
+
+    const int row = blockDim.y*blockIdx.y + threadIdx.y;
+    const int i = row*ncols + col;
+    const int i2 = row/p_delta_rows;
+
+    const float col_theta_scale = powf(theta_scale, col);
+     // FIXME: this is likely wrong
+    const int p = pos != nullptr ? pos[i2] : 0;
+
+    const float theta = min(p, n_ctx - 2)*freq_scale*col_theta_scale;
+    const float sin_theta = sinf(theta);
+    const float cos_theta = cosf(theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + half_n_dims];
+
+    dst[i + 0]           = x0*cos_theta - x1*sin_theta;
+    dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta;
+
+    const float block_theta = ((float)max(p - n_ctx - 2, 0))*col_theta_scale;
+    const float sin_block_theta = sinf(block_theta);
+    const float cos_block_theta = cosf(block_theta);
+
+    const float x2 = x[i + half_n_dims * 2];
+    const float x3 = x[i + half_n_dims * 3];
+
+    dst[i + half_n_dims * 2] = x2*cos_block_theta - x3*sin_block_theta;
+    dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta;
+}
+
+static __global__ void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows,
+                                 const int n_heads_log2_floor, const float m0, const float m1) {
+    const int col = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int row = blockDim.y*blockIdx.y + threadIdx.y;
+    const int i = row*ncols + col;
+
+    const int k = row/k_rows;
+
+    float m_k;
+    if (k < n_heads_log2_floor) {
+        m_k = powf(m0, k + 1);
+    } else {
+        m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
+    }
+
+    dst[i] = col * m_k + x[i];
+}
+
+static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past) {
+    const int col = blockDim.y*blockIdx.y + threadIdx.y;
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int i = row*ncols + col;
+    // dst[i] = col > n_past + row ? -INFINITY : x[i];
+    dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU
+}
+
+// the CUDA soft max implementation differs from the CPU implementation
+// instead of doubles floats are used
+static __global__ void soft_max_f32(const float * x, float * dst, const int ncols) {
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int block_size = blockDim.y;
+    const int tid = threadIdx.y;
+
+    float max_val = -INFINITY;
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const int i = row*ncols + col;
+        max_val = max(max_val, x[i]);
+    }
+
+    // find the max value in the block
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        max_val = max(max_val, __shfl_xor_sync(0xffffffff, max_val, mask, 32));
+    }
+
+    float tmp = 0.f;
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const int i = row*ncols + col;
+        const float val = expf(x[i] - max_val);
+        tmp += val;
+        dst[i] = val;
+    }
+
+    // sum up partial sums
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    }
+
+    const float inv_tmp = 1.f / tmp;
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const int i = row*ncols + col;
+        dst[i] *= inv_tmp;
+    }
+}
+
+static __global__ void scale_f32(const float * x, float * dst, const float scale, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    dst[i] = scale * x[i];
+}
+
+static __global__ void clamp_f32(const float * x, float * dst, const float min, const float max, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
+}
+
+template<int qk, int qr, dequantize_kernel_t dq>
+static void get_rows_cuda(const void * x, const int32_t * y, float * dst, const int nrows, const int ncols, cudaStream_t stream) {
+    const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
+    const int block_num_x = (ncols + 2*CUDA_GET_ROWS_BLOCK_SIZE - 1) / (2*CUDA_GET_ROWS_BLOCK_SIZE);
+    const dim3 block_nums(block_num_x, nrows, 1);
+    k_get_rows<qk, qr, dq><<<block_nums, block_dims, 0, stream>>>(x, y, dst, ncols);
+}
+
+static void add_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) {
+    const int num_blocks = (kx + CUDA_ADD_BLOCK_SIZE - 1) / CUDA_ADD_BLOCK_SIZE;
+    add_f32<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky);
+}
+
+static void add_f16_f32_f16_cuda(const half * x, const float * y, half * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_ADD_BLOCK_SIZE - 1) / CUDA_ADD_BLOCK_SIZE;
+    add_f16_f32_f16<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, k);
+}
+
+static void mul_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) {
+    const int num_blocks = (kx + CUDA_MUL_BLOCK_SIZE - 1) / CUDA_MUL_BLOCK_SIZE;
+    mul_f32<<<num_blocks, CUDA_MUL_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky);
+}
+
+static void gelu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
+    gelu_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_SILU_BLOCK_SIZE - 1) / CUDA_SILU_BLOCK_SIZE;
+    silu_f32<<<num_blocks, CUDA_SILU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % WARP_SIZE == 0);
+    if (ncols < 1024) {
+        const dim3 block_dims(WARP_SIZE, 1, 1);
+        norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols);
+    } else {
+        const dim3 block_dims(1024, 1, 1);
+        norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols);
+    }
+}
+
+static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
+    GGML_ASSERT(ncols % WARP_SIZE == 0);
+    if (ncols < 1024) {
+        const dim3 block_dims(WARP_SIZE, 1, 1);
+        rms_norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+    } else {
+        const dim3 block_dims(1024, 1, 1);
+        rms_norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+    }
+}
+
+static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream) {
+    const int block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
+    const dim3 num_blocks(block_num_x, ky, 1);
+    const dim3 block_size(CUDA_DEQUANTIZE_BLOCK_SIZE, 1, 1);
+    quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx, kx_padded);
+}
+
+template<typename dst_t>
+static void dequantize_row_q4_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
+    dequantize_block<QK4_0, QR4_0, dequantize_q4_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
+}
+
+template<typename dst_t>
+static void dequantize_row_q4_1_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
+    dequantize_block<QK4_1, QR4_1, dequantize_q4_1><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
+}
+
+template<typename dst_t>
+static void dequantize_row_q5_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
+    dequantize_block<QK5_0, QR5_0, dequantize_q5_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
+}
+
+template<typename dst_t>
+static void dequantize_row_q5_1_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
+    dequantize_block<QK5_1, QR5_1, dequantize_q5_1><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
+}
+
+template<typename dst_t>
+static void dequantize_row_q8_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
+    dequantize_block<QK8_0, QR8_0, dequantize_q8_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
+}
+
+template<typename dst_t>
+static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    dequantize_block_q2_K<<<nb, 64, 0, stream>>>(vx, y);
+#else
+    dequantize_block_q2_K<<<nb, 32, 0, stream>>>(vx, y);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    dequantize_block_q3_K<<<nb, 64, 0, stream>>>(vx, y);
+#else
+    dequantize_block_q3_K<<<nb, 32, 0, stream>>>(vx, y);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_q4_K<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    dequantize_block_q5_K<<<nb, 64, 0, stream>>>(vx, y);
+#else
+    dequantize_block_q5_K<<<nb, 32, 0, stream>>>(vx, y);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    dequantize_block_q6_K<<<nb, 64, 0, stream>>>(vx, y);
+#else
+    dequantize_block_q6_K<<<nb, 32, 0, stream>>>(vx, y);
+#endif
+}
+
+static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(32, ny, 1);
+    dequantize_mul_mat_vec_q2_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(32, ny, 1);
+    dequantize_mul_mat_vec_q3_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(32, ny, 1);
+    dequantize_mul_mat_vec_q4_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const dim3 block_dims(32, 1, 1);
+    dequantize_mul_mat_vec_q5_k<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols);
+}
+
+static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(32, ny, 1);
+    dequantize_mul_mat_vec_q6_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK4_0 == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK4_1 == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK5_0 == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK5_1 == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK8_0 == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
+    dequantize_block<1, 1, convert_f16><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
+}
+
+static void convert_fp32_to_fp16_cuda(const void * vx, half * y, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
+    dequantize_block<1, 1, convert_f32><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
+}
+
+static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<1, 1, convert_f16>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+            return dequantize_row_q4_0_cuda;
+        case GGML_TYPE_Q4_1:
+            return dequantize_row_q4_1_cuda;
+        case GGML_TYPE_Q5_0:
+            return dequantize_row_q5_0_cuda;
+        case GGML_TYPE_Q5_1:
+            return dequantize_row_q5_1_cuda;
+        case GGML_TYPE_Q8_0:
+            return dequantize_row_q8_0_cuda;
+        case GGML_TYPE_Q2_K:
+            return dequantize_row_q2_K_cuda;
+        case GGML_TYPE_Q3_K:
+            return dequantize_row_q3_K_cuda;
+        case GGML_TYPE_Q4_K:
+            return dequantize_row_q4_K_cuda;
+        case GGML_TYPE_Q5_K:
+            return dequantize_row_q5_K_cuda;
+        case GGML_TYPE_Q6_K:
+            return dequantize_row_q6_K_cuda;
+        case GGML_TYPE_F32:
+            return convert_fp32_to_fp16_cuda;
+        default:
+            return nullptr;
+    }
+}
+
+static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+            return dequantize_row_q4_0_cuda;
+        case GGML_TYPE_Q4_1:
+            return dequantize_row_q4_1_cuda;
+        case GGML_TYPE_Q5_0:
+            return dequantize_row_q5_0_cuda;
+        case GGML_TYPE_Q5_1:
+            return dequantize_row_q5_1_cuda;
+        case GGML_TYPE_Q8_0:
+            return dequantize_row_q8_0_cuda;
+        case GGML_TYPE_Q2_K:
+            return dequantize_row_q2_K_cuda;
+        case GGML_TYPE_Q3_K:
+            return dequantize_row_q3_K_cuda;
+        case GGML_TYPE_Q4_K:
+            return dequantize_row_q4_K_cuda;
+        case GGML_TYPE_Q5_K:
+            return dequantize_row_q5_K_cuda;
+        case GGML_TYPE_Q6_K:
+            return dequantize_row_q6_K_cuda;
+        case GGML_TYPE_F16:
+            return convert_fp16_to_fp32_cuda;
+        default:
+            return nullptr;
+    }
+}
+
+static void ggml_mul_mat_q4_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = g_compute_capabilities[id];
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q4_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_0_RDNA2;
+        nwarps = NWARPS_Q4_0_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q4_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_0_RDNA1;
+        nwarps = NWARPS_Q4_0_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q4_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_0_AMPERE;
+        nwarps = NWARPS_Q4_0_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q4_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_0_PASCAL;
+        nwarps = NWARPS_Q4_0_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q4_1_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = g_compute_capabilities[id];
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q4_1_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_1_RDNA2;
+        nwarps = NWARPS_Q4_1_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q4_1_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_1_RDNA1;
+        nwarps = NWARPS_Q4_1_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q4_1_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_1_AMPERE;
+        nwarps = NWARPS_Q4_1_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q4_1_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_1_PASCAL;
+        nwarps = NWARPS_Q4_1_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q5_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = g_compute_capabilities[id];
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q5_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_0_RDNA2;
+        nwarps = NWARPS_Q5_0_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q5_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_0_RDNA1;
+        nwarps = NWARPS_Q5_0_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q5_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_0_AMPERE;
+        nwarps = NWARPS_Q5_0_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q5_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_0_PASCAL;
+        nwarps = NWARPS_Q5_0_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q5_1_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = g_compute_capabilities[id];
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q5_1_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_1_RDNA2;
+        nwarps = NWARPS_Q5_1_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q5_1_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_1_RDNA1;
+        nwarps = NWARPS_Q5_1_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q5_1_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_1_AMPERE;
+        nwarps = NWARPS_Q5_1_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q5_1_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_1_PASCAL;
+        nwarps = NWARPS_Q5_1_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q8_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = g_compute_capabilities[id];
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q8_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q8_0_RDNA2;
+        nwarps = NWARPS_Q8_0_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q8_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q8_0_RDNA1;
+        nwarps = NWARPS_Q8_0_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q8_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q8_0_AMPERE;
+        nwarps = NWARPS_Q8_0_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q8_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q8_0_PASCAL;
+        nwarps = NWARPS_Q8_0_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q2_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = g_compute_capabilities[id];
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q2_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q2_K_RDNA2;
+        nwarps = NWARPS_Q2_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q2_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q2_K_RDNA1;
+        nwarps = NWARPS_Q2_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q2_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q2_K_AMPERE;
+        nwarps = NWARPS_Q2_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q2_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q2_K_PASCAL;
+        nwarps = NWARPS_Q2_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q3_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+#if QK_K == 256
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = g_compute_capabilities[id];
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q3_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q3_K_RDNA2;
+        nwarps = NWARPS_Q3_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q3_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q3_K_RDNA1;
+        nwarps = NWARPS_Q3_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q3_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q3_K_AMPERE;
+        nwarps = NWARPS_Q3_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q3_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q3_K_PASCAL;
+        nwarps = NWARPS_Q3_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+#endif
+}
+
+static void ggml_mul_mat_q4_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = g_compute_capabilities[id];
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q4_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_K_RDNA2;
+        nwarps = NWARPS_Q4_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q4_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_K_RDNA1;
+        nwarps = NWARPS_Q4_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q4_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_K_AMPERE;
+        nwarps = NWARPS_Q4_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q4_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_K_PASCAL;
+        nwarps = NWARPS_Q4_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q5_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = g_compute_capabilities[id];
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q5_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_K_RDNA2;
+        nwarps = NWARPS_Q5_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q5_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_K_RDNA1;
+        nwarps = NWARPS_Q5_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q5_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_K_AMPERE;
+        nwarps = NWARPS_Q5_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q5_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_K_PASCAL;
+        nwarps = NWARPS_Q5_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q6_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = g_compute_capabilities[id];
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q6_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q6_K_RDNA2;
+        nwarps = NWARPS_Q6_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q6_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q6_K_RDNA1;
+        nwarps = NWARPS_Q6_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q6_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q6_K_AMPERE;
+        nwarps = NWARPS_Q6_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q6_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q6_K_PASCAL;
+        nwarps = NWARPS_Q6_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_p021_f16_f32_cuda(
+    const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x,
+    const int nchannels_x, const int nchannels_y, cudaStream_t stream) {
+
+    const dim3 block_nums(1, nrows_x, nchannels_y);
+    const dim3 block_dims(WARP_SIZE, 1, 1);
+    mul_mat_p021_f16_f32<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols_x, nrows_x, nchannels_x, nchannels_y);
+}
+
+static void ggml_mul_mat_vec_nc_f16_f32_cuda(
+    const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, const int row_stride_x,
+    const int nchannels_x, const int nchannels_y, const int channel_stride_x, cudaStream_t stream) {
+
+    const dim3 block_nums(1, nrows_x, nchannels_y);
+    const dim3 block_dims(WARP_SIZE, 1, 1);
+    mul_mat_vec_nc_f16_f32<<<block_nums, block_dims, 0, stream>>>
+        (vx, y, dst, ncols_x, nrows_x, row_stride_x, channel_stride_x, nchannels_y/nchannels_x);
+}
+
+static void ggml_cpy_f32_f32_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
+    const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
+
+    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
+    cpy_f32_f16<cpy_1_f32_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
+}
+
+static void ggml_cpy_f32_f16_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
+    const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
+
+    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
+    cpy_f32_f16<cpy_1_f32_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
+}
+
+static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
+    scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
+}
+
+static void clamp_f32_cuda(const float * x, float * dst, const float min, const float max, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_CLAMP_BLOCK_SIZE - 1) / CUDA_CLAMP_BLOCK_SIZE;
+    clamp_f32<<<num_blocks, CUDA_CLAMP_BLOCK_SIZE, 0, stream>>>(x, dst, min, max, k);
+}
+
+template<typename T>
+static void rope_cuda(const T * x, T * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale,
+                          const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
+    GGML_ASSERT(ncols % 2 == 0);
+    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
+    const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const dim3 block_nums(nrows, num_blocks_x, 1);
+    if (pos == nullptr) {
+        rope<T, false><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale);
+    } else {
+        rope<T, true><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale);
+    }
+}
+
+template<typename T>
+static void rope_neox_cuda(const T * x, T * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale,
+                          const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
+    GGML_ASSERT(ncols % 2 == 0);
+    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
+    const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const dim3 block_nums(nrows, num_blocks_x, 1);
+    if (pos == nullptr) {
+        rope_neox<T, false><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale);
+    } else {
+        rope_neox<T, true><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale);
+    }
+}
+
+static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale,
+                              const int p_delta_rows, const float theta_scale, const int n_ctx, cudaStream_t stream) {
+    GGML_ASSERT(ncols % 4 == 0);
+    const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1);
+    const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE;
+    const dim3 block_nums(num_blocks_x, nrows, 1);
+    rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale, n_ctx);
+}
+
+static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows,
+                           const int k_rows, const int n_heads_log2_floor, const float m0,
+                           const float m1, cudaStream_t stream) {
+    const dim3 block_dims(CUDA_ALIBI_BLOCK_SIZE, 1, 1);
+    const int num_blocks_x = (ncols + CUDA_ALIBI_BLOCK_SIZE - 1) / (CUDA_ALIBI_BLOCK_SIZE);
+    const dim3 block_nums(num_blocks_x, nrows, 1);
+    alibi_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, k_rows, n_heads_log2_floor, m0, m1);
+}
+
+static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, const int rows_per_channel, const int n_past, cudaStream_t stream) {
+    const dim3 block_dims(1, CUDA_DIAG_MASK_INF_BLOCK_SIZE, 1);
+    const int block_num_x = (ncols_x + CUDA_DIAG_MASK_INF_BLOCK_SIZE - 1) / CUDA_DIAG_MASK_INF_BLOCK_SIZE;
+    const dim3 block_nums(nrows_x, block_num_x, 1);
+    diag_mask_inf_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x, rows_per_channel, n_past);
+}
+
+static void soft_max_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, cudaStream_t stream) {
+    const dim3 block_dims(1, WARP_SIZE, 1);
+    const dim3 block_nums(nrows_x, 1, 1);
+    soft_max_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x);
+}
+
+// buffer pool for cuda
+#define MAX_CUDA_BUFFERS 256
+
+struct scoped_spin_lock {
+    std::atomic_flag& lock;
+    scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
+        while (lock.test_and_set(std::memory_order_acquire)) {
+            ; // spin
+        }
+    }
+    ~scoped_spin_lock() {
+        lock.clear(std::memory_order_release);
+    }
+    scoped_spin_lock(const scoped_spin_lock&) = delete;
+    scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
+};
+
+struct cuda_buffer {
+    void * ptr = nullptr;
+    size_t size = 0;
+};
+
+static cuda_buffer g_cuda_buffer_pool[GGML_CUDA_MAX_DEVICES][MAX_CUDA_BUFFERS];
+static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT;
+
+static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
+    scoped_spin_lock lock(g_cuda_pool_lock);
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+#ifdef DEBUG_CUDA_MALLOC
+    int nnz = 0;
+    size_t max_size = 0, tot_size = 0;
+#endif
+    size_t best_diff = 1ull << 36;
+    int ibest = -1;
+    for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
+        cuda_buffer& b = g_cuda_buffer_pool[id][i];
+        if (b.ptr != nullptr) {
+#ifdef DEBUG_CUDA_MALLOC
+            ++nnz;
+            tot_size += b.size;
+            if (b.size > max_size) max_size = b.size;
+#endif
+            if (b.size >= size) {
+                size_t diff = b.size - size;
+                if (diff < best_diff) {
+                    best_diff = diff;
+                    ibest = i;
+                    if (!best_diff) {
+                        void * ptr = b.ptr;
+                        *actual_size = b.size;
+                        b.ptr = nullptr;
+                        b.size = 0;
+                        return ptr;
+                    }
+                }
+            }
+        }
+    }
+    if (ibest >= 0) {
+        cuda_buffer& b = g_cuda_buffer_pool[id][ibest];
+        void * ptr = b.ptr;
+        *actual_size = b.size;
+        b.ptr = nullptr;
+        b.size = 0;
+        return ptr;
+    }
+#ifdef DEBUG_CUDA_MALLOC
+    fprintf(stderr, "%s: %d buffers, max_size = %u MB, tot_size = %u MB, requested %u MB\n", __func__, nnz,
+            (uint32_t)(max_size/1024/1024), (uint32_t)(tot_size/1024/1024), (uint32_t)(size/1024/1024));
+#endif
+    void * ptr;
+    size_t look_ahead_size = (size_t) (1.05 * size);
+    look_ahead_size = 256 * ((look_ahead_size + 255)/256);
+    CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size));
+    *actual_size = look_ahead_size;
+    return ptr;
+}
+
+static void ggml_cuda_pool_free(void * ptr, size_t size) {
+    scoped_spin_lock lock(g_cuda_pool_lock);
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+
+    for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
+        cuda_buffer& b = g_cuda_buffer_pool[id][i];
+        if (b.ptr == nullptr) {
+            b.ptr = ptr;
+            b.size = size;
+            return;
+        }
+    }
+    fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
+    CUDA_CHECK(cudaFree(ptr));
+}
+
+
+void ggml_init_cublas() {
+    static bool initialized = false;
+
+    if (!initialized) {
+
+#ifdef __HIP_PLATFORM_AMD__
+        // Workaround for a rocBLAS bug when using multiple graphics cards:
+        // https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346
+        rocblas_initialize();
+        CUDA_CHECK(cudaDeviceSynchronize());
+#endif
+
+        CUDA_CHECK(cudaGetDeviceCount(&g_device_count));
+        GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES);
+        int64_t total_vram = 0;
+        fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, g_device_count);
+        for (int64_t id = 0; id < g_device_count; ++id) {
+            cudaDeviceProp prop;
+            CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
+            fprintf(stderr, "  Device %ld: %s, compute capability %d.%d\n", id, prop.name, prop.major, prop.minor);
+
+            g_tensor_split[id] = total_vram;
+            total_vram += prop.totalGlobalMem;
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+            g_compute_capabilities[id] = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD;
+#else
+            g_compute_capabilities[id] = 100*prop.major + 10*prop.minor;
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+        }
+        for (int64_t id = 0; id < g_device_count; ++id) {
+            g_tensor_split[id] /= total_vram;
+        }
+
+        for (int64_t id = 0; id < g_device_count; ++id) {
+            CUDA_CHECK(ggml_cuda_set_device(id));
+
+            // create cuda streams
+            for (int64_t is = 0; is < MAX_STREAMS; ++is) {
+                CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams[id][is], cudaStreamNonBlocking));
+            }
+
+            // create cublas handle
+            CUBLAS_CHECK(cublasCreate(&g_cublas_handles[id]));
+            CUBLAS_CHECK(cublasSetMathMode(g_cublas_handles[id], CUBLAS_TF32_TENSOR_OP_MATH));
+        }
+
+        // configure logging to stdout
+        // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
+
+        initialized = true;
+    }
+}
+
+void ggml_cuda_set_tensor_split(const float * tensor_split) {
+    if (tensor_split == nullptr) {
+        return;
+    }
+    bool all_zero = true;
+    for (int i = 0; i < g_device_count; ++i) {
+        if (tensor_split[i] != 0.0f) {
+            all_zero = false;
+            break;
+        }
+    }
+    if (all_zero) {
+        return;
+    }
+    float split_sum = 0.0f;
+    for (int i = 0; i < g_device_count; ++i) {
+        g_tensor_split[i] = split_sum;
+        split_sum += tensor_split[i];
+    }
+    for (int i = 0; i < g_device_count; ++i) {
+        g_tensor_split[i] /= split_sum;
+    }
+}
+
+void * ggml_cuda_host_malloc(size_t size) {
+    if (getenv("GGML_CUDA_NO_PINNED") != nullptr) {
+        return nullptr;
+    }
+
+    void * ptr = nullptr;
+    cudaError_t err = cudaMallocHost((void **) &ptr, size);
+    if (err != cudaSuccess) {
+        // The allocation error can be bypassed. A null ptr will assigned out of this function.
+        // This can fixed the OOM error in WSL.
+        cudaGetLastError();
+        fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
+            size/1024.0/1024.0, cudaGetErrorString(err));
+        return nullptr;
+    }
+
+    return ptr;
+}
+
+void ggml_cuda_host_free(void * ptr) {
+    CUDA_CHECK(cudaFreeHost(ptr));
+}
+
+static cudaError_t ggml_cuda_cpy_tensor_2d(
+    void * dst, const struct ggml_tensor * src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, cudaStream_t stream) {
+
+    cudaMemcpyKind kind;
+    char * src_ptr;
+    if (src->backend == GGML_BACKEND_CPU) {
+        kind = cudaMemcpyHostToDevice;
+        src_ptr = (char *) src->data;
+    } else if (src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT) {
+        GGML_ASSERT(src->backend != GGML_BACKEND_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
+        kind = cudaMemcpyDeviceToDevice;
+        ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
+        int id;
+        CUDA_CHECK(cudaGetDevice(&id));
+        src_ptr = (char *) extra->data_device[id];
+    } else {
+        GGML_ASSERT(false);
+    }
+    char * dst_ptr = (char *) dst;
+
+    const int64_t ne0 = src->ne[0];
+    const int64_t nb0 = src->nb[0];
+    const int64_t nb1 = src->nb[1];
+    const int64_t nb2 = src->nb[2];
+    const int64_t nb3 = src->nb[3];
+    const enum ggml_type type = src->type;
+    const int64_t ts = ggml_type_size(type);
+    const int64_t bs = ggml_blck_size(type);
+    int64_t i1_diff = i1_high - i1_low;
+
+    const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3;
+    if (nb0 == ts && nb1 == ts*ne0/bs) {
+        return cudaMemcpyAsync(dst_ptr, x, i1_diff*nb1, kind, stream);
+    } else if (nb0 == ts) {
+        return cudaMemcpy2DAsync(dst_ptr, ts*ne0/bs, x, nb1, ts*ne0/bs, i1_diff, kind, stream);
+    } else {
+        for (int64_t i1 = 0; i1 < i1_diff; i1++) {
+            const void * rx = (const void *) ((const char *) x + i1*nb1);
+            void * rd = (void *) (dst_ptr + i1*ts*ne0/bs);
+            // pretend the row is a matrix with cols=1
+            cudaError_t r = cudaMemcpy2DAsync(rd, ts/bs, rx, nb0, ts/bs, ne0, kind, stream);
+            if (r != cudaSuccess) return r;
+        }
+        return cudaSuccess;
+    }
+}
+
+static void ggml_cuda_op_repeat(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_d, const float * src1_d, float * dst_d, const cudaStream_t & stream) {
+    // guaranteed to be an integer due to the check in ggml_can_repeat
+    const int64_t ne0 = dst->ne[0];
+    const int64_t ne1 = dst->ne[1];
+    const int64_t ne2 = dst->ne[2];
+    const int64_t ne3 = dst->ne[3];
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
+
+    const size_t nb0 = dst->nb[0];
+    const size_t nb1 = dst->nb[1];
+    const size_t nb2 = dst->nb[2];
+    const size_t nb3 = dst->nb[3];
+
+    const size_t nb00 = src0->nb[0];
+    const size_t nb01 = src0->nb[1];
+    const size_t nb02 = src0->nb[2];
+    const size_t nb03 = src0->nb[3];
+
+    const int nr0 = (int)(ne0/ne00);
+    const int nr1 = (int)(ne1/ne01);
+    const int nr2 = (int)(ne2/ne02);
+    const int nr3 = (int)(ne3/ne03);
+
+    // TODO: support for transposed / permuted tensors
+    GGML_ASSERT(nb0  == sizeof(float));
+    GGML_ASSERT(nb00 == sizeof(float));
+
+    // TODO: very inefficient, implement in a kernel, or fewer cudaMemcpyAsync calls for contiguous tensors
+    for                         (int i3 = 0; i3 < nr3;  i3++) {
+        for                     (int k3 = 0; k3 < ne03; k3++) {
+            for                 (int i2 = 0; i2 < nr2;  i2++) {
+                for             (int k2 = 0; k2 < ne02; k2++) {
+                    for         (int i1 = 0; i1 < nr1;  i1++) {
+                        for     (int k1 = 0; k1 < ne01; k1++) {
+                            for (int i0 = 0; i0 < nr0;  i0++) {
+                                CUDA_CHECK(cudaMemcpyAsync(
+                                              (char *)  dst_d + (i3*ne03 + k3)*nb3  + (i2*ne02 + k2)*nb2  + (i1*ne01 + k1)*nb1  + (i0*ne00)*nb0,
+                                        (const char *) src0_d + (          k3)*nb03 + (          k2)*nb02 + (          k1)*nb01,
+                                        ne00*nb0, cudaMemcpyDeviceToDevice, stream));
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+
+    (void) src1;
+    (void) src1_d;
+}
+
+static void ggml_cuda_op_get_rows(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_d, const float * src1_d, float * dst_d, const cudaStream_t & stream) {
+
+    GGML_ASSERT(src1->type == GGML_TYPE_I32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src1));
+    GGML_ASSERT(ggml_is_contiguous(dst));
+
+    const int ncols = src0->ne[0];
+    const int nrows = ggml_nelements(src1);
+
+    const int32_t * src1_i32 = (const int32_t *) src1_d;
+
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            get_rows_cuda<1, 1, convert_f16>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
+            break;
+        case GGML_TYPE_F32:
+            get_rows_cuda<1, 1, convert_f32>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
+            break;
+        case GGML_TYPE_Q4_0:
+            get_rows_cuda<QK4_0, QR4_0, dequantize_q4_0>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            get_rows_cuda<QK4_1, QR4_1, dequantize_q4_1>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            get_rows_cuda<QK5_0, QR5_0, dequantize_q5_0>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            get_rows_cuda<QK5_1, QR5_1, dequantize_q5_1>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            get_rows_cuda<QK8_0, QR8_0, dequantize_q8_0>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
+            break;
+        default:
+            // TODO: k-quants
+            GGML_ASSERT(false);
+            break;
+    }
+}
+
+inline void ggml_cuda_op_add(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
+
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+
+    if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+        add_f32_cuda(src0_dd, src1_dd, dst_dd, ggml_nelements(src0), ne10*ne11, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
+        add_f16_f32_f16_cuda((const half *) src0_dd, src1_dd, (half *) dst_dd, ggml_nelements(src0), main_stream);
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    (void) src1;
+    (void) dst;
+}
+
+inline void ggml_cuda_op_mul(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+
+    mul_f32_cuda(src0_dd, src1_dd, dst_dd, ggml_nelements(src0), ne10*ne11, main_stream);
+
+    (void) dst;
+}
+
+inline void ggml_cuda_op_gelu(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    gelu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_cuda_op_silu(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    silu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_cuda_op_norm(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    norm_f32_cuda(src0_dd, dst_dd, ne00, nrows, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_cuda_op_rms_norm(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    rms_norm_f32_cuda(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_cuda_op_mul_mat_q(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, const cudaStream_t & stream) {
+
+    const int64_t ne00 = src0->ne[0];
+
+    const int64_t ne10 = src1->ne[0];
+    GGML_ASSERT(ne10 % QK8_1 == 0);
+
+    const int64_t ne0 = dst->ne[0];
+
+    const int64_t row_diff = row_high - row_low;
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into
+    const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            ggml_mul_mat_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            ggml_mul_mat_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            ggml_mul_mat_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            ggml_mul_mat_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            ggml_mul_mat_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            ggml_mul_mat_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            ggml_mul_mat_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            ggml_mul_mat_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            ggml_mul_mat_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            ggml_mul_mat_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+
+    (void) src1;
+    (void) dst;
+    (void) src1_ddf_i;
+}
+
+static int64_t get_row_rounding(ggml_type type) {
+    int64_t min_compute_capability = INT_MAX;
+    int64_t max_compute_capability = INT_MIN;
+    for (int64_t id = 0; id < g_device_count; ++id) {
+        if (g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
+            if (min_compute_capability > g_compute_capabilities[id]) {
+                min_compute_capability = g_compute_capabilities[id];
+            }
+            if (max_compute_capability < g_compute_capabilities[id]) {
+                max_compute_capability = g_compute_capabilities[id];
+            }
+        }
+    }
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    switch(type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+            return max_compute_capability >= CC_RDNA2 ? 128 : 64;
+        case GGML_TYPE_F16:
+            return 1;
+        case GGML_TYPE_Q2_K:
+            return max_compute_capability >= CC_RDNA2 ? 128 : 32;
+        case GGML_TYPE_Q3_K:
+            return min_compute_capability < CC_RDNA2 ? 128 : 64;
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            return max_compute_capability >= CC_RDNA2 ? 128 : 64;
+        default:
+            GGML_ASSERT(false);
+    }
+#else
+    switch(type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+            return max_compute_capability >= CC_VOLTA ? 128 : 64;
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+            return 64;
+        case GGML_TYPE_F16:
+            return 1;
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+            return max_compute_capability >= CC_VOLTA ? 128 : 64;
+        case GGML_TYPE_Q6_K:
+            return 64;
+        default:
+            GGML_ASSERT(false);
+    }
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+}
+
+inline void ggml_cuda_op_mul_mat_vec_q(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, const cudaStream_t & stream) {
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t row_diff = row_high - row_low;
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            mul_mat_vec_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            mul_mat_vec_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            mul_mat_vec_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            mul_mat_vec_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            mul_mat_vec_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            mul_mat_vec_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            mul_mat_vec_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            mul_mat_vec_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            mul_mat_vec_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            mul_mat_vec_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+
+    (void) src1;
+    (void) dst;
+    (void) src1_ddf_i;
+    (void) src1_ncols;
+    (void) src1_padded_row_size;
+}
+
+inline void ggml_cuda_op_dequantize_mul_mat_vec(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, const cudaStream_t & stream) {
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t row_diff = row_high - row_low;
+
+    // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
+#ifdef GGML_CUDA_F16
+    size_t ash;
+    dfloat * src1_dfloat = nullptr; // dfloat == half
+
+    bool src1_convert_f16 = src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
+        src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
+        src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
+
+    if (src1_convert_f16) {
+        src1_dfloat = (half *) ggml_cuda_pool_malloc(ne00*sizeof(half), &ash);
+        ggml_cpy_f32_f16_cuda((const char *) src1_ddf_i, (char *) src1_dfloat, ne00,
+                                ne00, 1, sizeof(float), 0, 0,
+                                ne00, 1, sizeof(half),  0, 0, stream);
+    }
+#else
+    const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion
+#endif // GGML_CUDA_F16
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            dequantize_mul_mat_vec_q4_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            dequantize_mul_mat_vec_q4_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            dequantize_mul_mat_vec_q5_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            dequantize_mul_mat_vec_q5_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            dequantize_mul_mat_vec_q8_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            dequantize_mul_mat_vec_q2_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            dequantize_mul_mat_vec_q3_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            dequantize_mul_mat_vec_q4_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            dequantize_mul_mat_vec_q5_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            dequantize_mul_mat_vec_q6_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_F16:
+            convert_mul_mat_vec_f16_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+
+#ifdef GGML_CUDA_F16
+    if (src1_convert_f16) {
+        ggml_cuda_pool_free(src1_dfloat, ash);
+    }
+#endif // GGML_CUDA_F16
+
+    (void) src1;
+    (void) dst;
+    (void) src1_ddq_i;
+    (void) src1_ncols;
+    (void) src1_padded_row_size;
+}
+
+inline void ggml_cuda_op_mul_mat_cublas(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, const cudaStream_t & stream) {
+
+    GGML_ASSERT(src0_dd_i != nullptr);
+    GGML_ASSERT(src1_ddf_i != nullptr);
+    GGML_ASSERT(dst_dd_i != nullptr);
+
+
+    const int64_t ne00 = src0->ne[0];
+
+    const int64_t ne10 = src1->ne[0];
+
+    const int64_t ne0 = dst->ne[0];
+    const int64_t row_diff = row_high - row_low;
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // ldc == nrows of the matrix that cuBLAS writes into
+    int ldc = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
+
+    const int compute_capability = g_compute_capabilities[id];
+
+    if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1]) {
+        // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32
+        half * src0_as_f16 = nullptr;
+        size_t src0_as = 0;
+        if (src0->type != GGML_TYPE_F16) {
+            const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type);
+            GGML_ASSERT(to_fp16_cuda != nullptr);
+            size_t ne = row_diff*ne00;
+            src0_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src0_as);
+            to_fp16_cuda(src0_dd_i, src0_as_f16, ne, stream);
+        }
+        const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16;
+
+        half * src1_as_f16 = nullptr;
+        size_t src1_as = 0;
+        if (src1->type != GGML_TYPE_F16) {
+            const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
+            GGML_ASSERT(to_fp16_cuda != nullptr);
+            size_t ne = src1_ncols*ne10;
+            src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src1_as);
+            to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream);
+        }
+        const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddq_i : src1_as_f16;
+
+        size_t dst_as = 0;
+        half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as);
+
+        const half alpha_f16 = 1.0f;
+        const half beta_f16 = 0.0f;
+
+        CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream));
+        CUBLAS_CHECK(
+            cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
+                    row_diff, src1_ncols, ne10,
+                    &alpha_f16, src0_ptr, CUDA_R_16F, ne00,
+                                src1_ptr, CUDA_R_16F, ne10,
+                    &beta_f16,   dst_f16, CUDA_R_16F, ldc,
+                    CUBLAS_COMPUTE_16F,
+                    CUBLAS_GEMM_DEFAULT_TENSOR_OP));
+
+        const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
+        to_fp32_cuda(dst_f16, dst_dd_i, row_diff*src1_ncols, stream);
+
+        ggml_cuda_pool_free(dst_f16, dst_as);
+
+        if (src0_as != 0) {
+            ggml_cuda_pool_free(src0_as_f16, src0_as);
+        }
+
+        if (src1_as != 0) {
+            ggml_cuda_pool_free(src1_as_f16, src1_as);
+        }
+    }
+    else {
+        float * src0_ddq_as_f32 = nullptr;
+        size_t src0_as = 0;
+
+        if (src0->type != GGML_TYPE_F32) {
+            const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type);
+            GGML_ASSERT(to_fp32_cuda != nullptr);
+            src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc(row_diff*ne00 * sizeof(float), &src0_as); // NOLINT
+            to_fp32_cuda(src0_dd_i, src0_ddq_as_f32, row_diff*ne00, stream);
+        }
+        const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32;
+
+        const float alpha = 1.0f;
+        const float beta = 0.0f;
+
+        CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream));
+        CUBLAS_CHECK(
+            cublasSgemm(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
+                    row_diff, src1_ncols, ne10,
+                    &alpha, src0_ddf_i, ne00,
+                            src1_ddf_i,  ne10,
+                    &beta,  dst_dd_i,   ldc));
+
+        if (src0_as != 0) {
+            ggml_cuda_pool_free(src0_ddq_as_f32, src0_as);
+        }
+    }
+
+    (void) dst;
+    (void) src1_ddq_i;
+    (void) src1_padded_row_size;
+}
+
+inline void ggml_cuda_op_rope(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
+    GGML_ASSERT(src0->type == dst->type);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne2 = dst->ne[2];
+    const int64_t nrows = ggml_nrows(src0);
+
+    //const int n_past = ((int32_t *) dst->op_params)[0];
+    const int n_dims = ((int32_t *) dst->op_params)[1];
+    const int mode   = ((int32_t *) dst->op_params)[2];
+    const int n_ctx  = ((int32_t *) dst->op_params)[3];
+    // RoPE alteration for extended context
+
+    float freq_base, freq_scale;
+    memcpy(&freq_base,  (int32_t *) dst->op_params + 4, sizeof(float));
+    memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    const int32_t * pos = nullptr;
+    if ((mode & 1) == 0) {
+        GGML_ASSERT(src1->type == GGML_TYPE_I32);
+        GGML_ASSERT(src1->ne[0] == ne2);
+        pos = (const int32_t *) src1_dd;
+    }
+
+    const bool is_neox = mode & 2;
+    const bool is_glm  = mode & 4;
+
+    // compute
+    if (is_glm) {
+        GGML_ASSERT(false);
+        rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, n_ctx, main_stream);
+    } else if (is_neox) {
+        GGML_ASSERT(ne00 == n_dims && "ne00 != n_dims is not implemented for CUDA yet");
+        if (src0->type == GGML_TYPE_F32) {
+            rope_neox_cuda((const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream);
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_neox_cuda((const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream);
+        } else {
+            GGML_ASSERT(false);
+        }
+    } else {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_cuda((const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream);
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_cuda((const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream);
+        } else {
+            GGML_ASSERT(false);
+        }
+    }
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_cuda_op_alibi(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t nrows = ggml_nrows(src0);
+
+    //const int n_past = ((int32_t *) dst->op_params)[0];
+    const int n_head = ((int32_t *) dst->op_params)[1];
+    float max_bias;
+    memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
+
+    //GGML_ASSERT(ne01 + n_past == ne00);
+    GGML_ASSERT(n_head == ne02);
+
+    const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
+
+    const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
+
+    alibi_f32_cuda(src0_dd, dst_dd, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, main_stream);
+
+    (void) src1;
+    (void) src1_dd;
+}
+
+inline void ggml_cuda_op_diag_mask_inf(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int nrows0 = ggml_nrows(src0);
+
+    const int n_past = ((int32_t *) dst->op_params)[0];
+
+    diag_mask_inf_f32_cuda(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_cuda_op_soft_max(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    soft_max_f32_cuda(src0_dd, dst_dd, ne00, nrows, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_cuda_op_scale(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    float scale;
+    // HACK: support for ggml backend interface
+    if (src1->backend == GGML_BACKEND_CPU) {
+        scale = ((float *) src1->data)[0];
+    } else {
+        // TODO: pass pointer to kernel instead of copying to host
+        CUDA_CHECK(cudaMemcpy(&scale, src1->data, sizeof(float), cudaMemcpyDeviceToHost));
+    }
+
+    scale_f32_cuda(src0_dd, dst_dd, scale, ggml_nelements(src0), main_stream);
+    CUDA_CHECK(cudaGetLastError());
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_cuda_op_clamp(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const float min = ((float *) dst->op_params)[0];
+    const float max = ((float *) dst->op_params)[1];
+
+    clamp_f32_cuda(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream);
+    CUDA_CHECK(cudaGetLastError());
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const ggml_cuda_op_flatten_t op) {
+    const int64_t nrows0 = ggml_nrows(src0);
+
+    const bool use_src1 = src1 != nullptr;
+    const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
+
+    GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(              dst->backend != GGML_BACKEND_GPU_SPLIT);
+
+    ggml_tensor_extra_gpu * src0_extra =            (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
+    ggml_tensor_extra_gpu * dst_extra  =            (ggml_tensor_extra_gpu *)  dst->extra;
+
+    const bool src0_on_device =             src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU;
+    const bool  dst_on_device =              dst->backend == GGML_BACKEND_GPU;
+
+    const bool src1_stays_on_host = use_src1 && dst->op == GGML_OP_SCALE;
+
+    // dd = data device
+    float * src0_ddf = nullptr;
+    float * src1_ddf = nullptr;
+    float *  dst_ddf = nullptr;
+
+    // as = actual size
+    size_t src0_asf = 0;
+    size_t src1_asf = 0;
+    size_t  dst_asf = 0;
+
+    ggml_cuda_set_device(g_main_device);
+    const cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
+
+    if (src0_on_device) {
+        src0_ddf = (float *) src0_extra->data_device[g_main_device];
+    } else {
+        src0_ddf = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src0), &src0_asf);
+        CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_ddf, src0, 0, 0, 0, nrows0, main_stream));
+    }
+
+    if (use_src1 && !src1_stays_on_host) {
+        if (src1_on_device) {
+            src1_ddf = (float *) src1_extra->data_device[g_main_device];
+        } else {
+            src1_ddf = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src1), &src1_asf);
+            CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src1_ddf, src1, 0, 0, 0, nrows1, main_stream));
+        }
+    }
+    if (dst_on_device) {
+        dst_ddf = (float *) dst_extra->data_device[g_main_device];
+    } else {
+        dst_ddf = (float *) ggml_cuda_pool_malloc(ggml_nbytes(dst), &dst_asf);
+    }
+
+    // do the computation
+    op(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream);
+    CUDA_CHECK(cudaGetLastError());
+
+    // copy dst to host if necessary
+    if (!dst_on_device) {
+        CUDA_CHECK(cudaMemcpyAsync(dst->data, dst_ddf, ggml_nbytes(dst), cudaMemcpyDeviceToHost, main_stream));
+    }
+
+    if (src0_asf > 0) {
+        ggml_cuda_pool_free(src0_ddf, src0_asf);
+    }
+    if (src1_asf > 0) {
+        ggml_cuda_pool_free(src1_ddf, src1_asf);
+    }
+    if (dst_asf > 0) {
+        ggml_cuda_pool_free(dst_ddf, dst_asf);
+    }
+
+    if (dst->backend == GGML_BACKEND_CPU) {
+        CUDA_CHECK(cudaDeviceSynchronize());
+    }
+}
+
+static void ggml_cuda_set_peer_access(const int n_tokens) {
+    static bool peer_access_enabled = false;
+
+    const bool enable_peer_access = n_tokens <= GGML_CUDA_PEER_MAX_BATCH_SIZE;
+
+    if (peer_access_enabled == enable_peer_access) {
+        return;
+    }
+
+#ifdef NDEBUG
+    for (int id = 0; id < g_device_count; ++id) {
+        CUDA_CHECK(ggml_cuda_set_device(id));
+
+        for (int id_other = 0; id_other < g_device_count; ++id_other) {
+            if (id == id_other) {
+                continue;
+            }
+            if (id != g_main_device && id_other != g_main_device) {
+                continue;
+            }
+
+            int can_access_peer;
+            CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, id_other));
+            if (can_access_peer) {
+                if (enable_peer_access) {
+                    CUDA_CHECK(cudaDeviceEnablePeerAccess(id_other, 0));
+                } else {
+                    CUDA_CHECK(cudaDeviceDisablePeerAccess(id_other));
+                }
+            }
+        }
+    }
+#endif // NDEBUG
+
+    peer_access_enabled = enable_peer_access;
+}
+
+static void ggml_cuda_op_mul_mat(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_cuda_op_mul_mat_t op,
+    const bool convert_src1_to_q8_1) {
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
+    const int64_t nrows0 = ggml_nrows(src0);
+
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    const int64_t ne12 = src1->ne[2];
+    const int64_t ne13 = src1->ne[3];
+    const int64_t nrows1 = ggml_nrows(src1);
+
+    GGML_ASSERT(ne03 == ne13);
+
+    const int64_t ne0 = dst->ne[0];
+    const int64_t ne1 = dst->ne[1];
+
+    const int nb2 = dst->nb[2];
+    const int nb3 = dst->nb[3];
+
+    ggml_cuda_set_peer_access(ne11);
+
+    GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT);
+
+    GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
+
+    const int64_t i02_divisor = ne12 / ne02;
+
+    const size_t src0_ts = ggml_type_size(src0->type);
+    const size_t src0_bs = ggml_blck_size(src0->type);
+    const size_t q8_1_ts = sizeof(block_q8_1);
+    const size_t q8_1_bs = QK8_1;
+
+    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
+    ggml_tensor_extra_gpu *  dst_extra = (ggml_tensor_extra_gpu *)  dst->extra;
+
+    const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool src0_is_contiguous = ggml_is_contiguous(src0);
+
+    const bool src1_is_contiguous = ggml_is_contiguous(src1);
+    const int64_t src1_padded_col_size = ne10 % MATRIX_ROW_PADDING == 0 ?
+        ne10 : ne10 - ne10 % MATRIX_ROW_PADDING + MATRIX_ROW_PADDING;
+
+    const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
+    GGML_ASSERT(!(split && ne02 > 1));
+    GGML_ASSERT(!(split && ne03 > 1));
+    GGML_ASSERT(!(split && ne02 < ne12));
+
+    // dd = data device
+    char  *  src0_dd[GGML_CUDA_MAX_DEVICES] = {nullptr};
+    float * src1_ddf[GGML_CUDA_MAX_DEVICES] = {nullptr}; // float
+    char  * src1_ddq[GGML_CUDA_MAX_DEVICES] = {nullptr}; // q8_1
+    float *   dst_dd[GGML_CUDA_MAX_DEVICES] = {nullptr};
+
+    // as = actual size
+    size_t  src0_as[GGML_CUDA_MAX_DEVICES] = {0};
+    size_t src1_asf[GGML_CUDA_MAX_DEVICES] = {0};
+    size_t src1_asq[GGML_CUDA_MAX_DEVICES] = {0};
+    size_t   dst_as[GGML_CUDA_MAX_DEVICES] = {0};
+
+    int64_t  row_low[GGML_CUDA_MAX_DEVICES];
+    int64_t row_high[GGML_CUDA_MAX_DEVICES];
+
+    for (int64_t id = 0; id < g_device_count; ++id) {
+        // by default, use all rows
+        row_low[id]  = 0;
+        row_high[id] = ne01;
+
+        // for multi GPU, get the row boundaries from tensor split
+        // and round to mul_mat_q tile sizes
+        if (split) {
+            const int64_t rounding = get_row_rounding(src0->type);
+
+            if (id != 0) {
+                row_low[id]  = ne01*g_tensor_split[id];
+                row_low[id] -= row_low[id] % rounding;
+            }
+
+            if (id != g_device_count - 1) {
+                row_high[id]  = ne01*g_tensor_split[id + 1];
+                row_high[id] -= row_high[id] % rounding;
+            }
+        }
+    }
+
+    for (int64_t id = 0; id < g_device_count; ++id) {
+        if ((!split && id != g_main_device) || row_low[id] == row_high[id]) {
+            continue;
+        }
+
+        const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device;
+        const bool  dst_on_device =  dst->backend == GGML_BACKEND_GPU && id == g_main_device;
+
+        ggml_cuda_set_device(id);
+        const cudaStream_t stream = g_cudaStreams[id][0];
+
+        if (src0_on_device && src0_is_contiguous) {
+            src0_dd[id] = (char *) src0_extra->data_device[id];
+        } else {
+            const size_t size_src0_ddq = split ? (row_high[id]-row_low[id])*ne00 * src0_ts/src0_bs : ggml_nbytes(src0);
+            src0_dd[id] = (char *) ggml_cuda_pool_malloc(ggml_nbytes(src0), &src0_as[id]);
+        }
+
+        if (src1_on_device && src1_is_contiguous) {
+            src1_ddf[id] = (float *) src1_extra->data_device[id];
+        } else {
+            src1_ddf[id] = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src1), &src1_asf[id]);
+        }
+
+        if (convert_src1_to_q8_1) {
+            src1_ddq[id] = (char *) ggml_cuda_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]);
+
+            if (src1_on_device && src1_is_contiguous) {
+                quantize_row_q8_1_cuda(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream);
+                CUDA_CHECK(cudaGetLastError());
+            }
+        }
+
+        if (dst_on_device) {
+            dst_dd[id] = (float *) dst_extra->data_device[id];
+        } else {
+            const size_t size_dst_ddf = split ? (row_high[id]-row_low[id])*ne1*sizeof(float) : ggml_nbytes(dst);
+            dst_dd[id] = (float *) ggml_cuda_pool_malloc(size_dst_ddf, &dst_as[id]);
+        }
+    }
+
+    // if multiple devices are used they need to wait for the main device
+    // here an event is recorded that signals that the main device has finished calculating the input data
+    if (split && g_device_count > 1) {
+        CUDA_CHECK(ggml_cuda_set_device(g_main_device));
+        CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device][0], g_cudaStreams[g_main_device][0]));
+    }
+
+    const int64_t src1_col_stride = split && g_device_count > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11;
+    for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) {
+        const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0;
+        const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride;
+
+        for (int64_t id = 0; id < g_device_count; ++id) {
+            if ((!split && id != g_main_device) || row_low[id] == row_high[id]) {
+                continue;
+            }
+
+            const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device;
+            const bool  dst_on_device =  dst->backend == GGML_BACKEND_GPU && id == g_main_device;
+            const int64_t row_diff = row_high[id] - row_low[id];
+
+            ggml_cuda_set_device(id);
+            const cudaStream_t stream = g_cudaStreams[id][is];
+
+            // wait for main GPU data if necessary
+            if (split && (id != g_main_device || is != 0)) {
+                CUDA_CHECK(cudaStreamWaitEvent(stream, src0_extra->events[g_main_device][0], 0));
+            }
+
+            for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) {
+                const int64_t i03 = i0 / ne12;
+                const int64_t i02 = i0 % ne12;
+
+                const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs;
+
+                // for split tensors the data begins at i0 == i0_offset_low
+                char  *  src0_dd_i =  src0_dd[id] + (i0/i02_divisor) * ne01*ne00*src0_ts/src0_bs;
+                float * src1_ddf_i = src1_ddf[id] + (i0*ne11 + src1_col_0) * ne10;
+                char  * src1_ddq_i = src1_ddq[id] +  src1_ddq_i_offset;
+                float *   dst_dd_i =   dst_dd[id] + (i0*ne1  + src1_col_0) * (dst_on_device ? ne0 : row_diff);
+
+                // the main device memory buffer can be on VRAM scratch, with space for all partial results
+                // in that case an offset on dst_ddf_i is needed
+                if (dst->backend == GGML_BACKEND_GPU && id == g_main_device) {
+                    dst_dd_i += row_low[id]; // offset is 0 if no tensor split
+                }
+
+                // copy src0, src1 to device if necessary
+                if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) {
+                    if (id != g_main_device) {
+                        if (convert_src1_to_q8_1) {
+                            char * src1_ddq_i_source = src1_ddq[g_main_device] + src1_ddq_i_offset;
+                            CUDA_CHECK(cudaMemcpyAsync(src1_ddq_i, src1_ddq_i_source, src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs,
+                                                    cudaMemcpyDeviceToDevice, stream));
+                        } else {
+                            float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device];
+                            src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10;
+                            CUDA_CHECK(cudaMemcpyAsync(src1_ddf_i, src1_ddf_i_source, src1_ncols*ne10*sizeof(float),
+                                                    cudaMemcpyDeviceToDevice, stream));
+                        }
+                    }
+                } else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) {
+                    CUDA_CHECK(ggml_cuda_cpy_tensor_2d(
+                                   src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
+                } else {
+                    GGML_ASSERT(false);
+                }
+
+                if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_CPU || !src1_is_contiguous)) {
+                    quantize_row_q8_1_cuda(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
+                    CUDA_CHECK(cudaGetLastError());
+                }
+
+                if (src1_col_0 == 0 && (!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) {
+                    CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, row_low[id], row_high[id], stream));
+                }
+
+                // do the computation
+                op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i,
+                   row_low[id], row_high[id], src1_ncols, src1_padded_col_size, stream);
+                CUDA_CHECK(cudaGetLastError());
+
+                // copy dst to host or other device if necessary
+                if (!dst_on_device) {
+                    void * dst_off_device;
+                    cudaMemcpyKind kind;
+                    if (dst->backend == GGML_BACKEND_CPU) {
+                        dst_off_device = dst->data;
+                        kind = cudaMemcpyDeviceToHost;
+                    } else if (dst->backend == GGML_BACKEND_GPU) {
+                        dst_off_device = dst_extra->data_device[g_main_device];
+                        kind = cudaMemcpyDeviceToDevice;
+                    } else {
+                        GGML_ASSERT(false);
+                    }
+                    if (split) {
+                        // src0 = weight matrix is saved as a transposed matrix for better memory layout.
+                        // dst is NOT transposed.
+                        // The outputs of matrix matrix multiplications can therefore NOT simply be concatenated for >1 GPU.
+                        // Instead they need to be copied to the correct slice in ne0 = dst row index.
+                        // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results.
+                        float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
+                        GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
+                        dhf_dst_i += src1_col_0*ne0 + row_low[id];
+                        CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float), dst_dd_i, row_diff*sizeof(float),
+                                                    row_diff*sizeof(float), src1_ncols, kind, stream));
+                    } else {
+                        float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
+                        GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
+                        dhf_dst_i += src1_col_0*ne0;
+                        CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_dd_i, src1_ncols*ne0*sizeof(float), kind, stream));
+                    }
+                }
+
+                // add event for the main device to wait on until other device is done
+                if (split && (id != g_main_device || is != 0)) {
+                    CUDA_CHECK(cudaEventRecord(src0_extra->events[id][is], stream));
+                }
+            }
+        }
+    }
+
+    for (int64_t id = 0; id < g_device_count; ++id) {
+        CUDA_CHECK(ggml_cuda_set_device(id));
+
+        // free buffers again when done
+        if (src0_as[id] > 0) {
+            ggml_cuda_pool_free(src0_dd[id], src0_as[id]);
+        }
+        if (src1_asf[id] > 0) {
+            ggml_cuda_pool_free(src1_ddf[id], src1_asf[id]);
+        }
+        if (src1_asq[id] > 0) {
+            ggml_cuda_pool_free(src1_ddq[id], src1_asq[id]);
+        }
+        if (dst_as[id] > 0) {
+            ggml_cuda_pool_free(dst_dd[id], dst_as[id]);
+        }
+    }
+
+    // main device waits for all other devices to be finished
+    if (split && g_device_count > 1) {
+        int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE;
+        is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS;
+
+        CUDA_CHECK(ggml_cuda_set_device(g_main_device));
+        for (int64_t id = 0; id < g_device_count; ++id) {
+            for (int64_t is = 0; is < is_max; ++is) {
+                CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[g_main_device][0], src0_extra->events[id][is], 0));
+            }
+        }
+    }
+
+    if (dst->backend == GGML_BACKEND_CPU) {
+        CUDA_CHECK(ggml_cuda_set_device(g_main_device));
+        CUDA_CHECK(cudaDeviceSynchronize());
+    }
+}
+
+static void ggml_cuda_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_repeat);
+}
+
+static void ggml_cuda_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_get_rows);
+}
+
+static void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_add);
+}
+
+static void ggml_cuda_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_mul);
+}
+
+static void ggml_cuda_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_gelu);
+}
+
+static void ggml_cuda_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_silu);
+}
+
+static void ggml_cuda_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_norm);
+}
+
+static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm);
+}
+
+bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
+    const int64_t ne10 = src1->ne[0];
+
+    const int64_t ne0 = dst->ne[0];
+    const int64_t ne1 = dst->ne[1];
+
+    // TODO: find the optimal values for these
+    return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
+            src1->type == GGML_TYPE_F32 &&
+             dst->type == GGML_TYPE_F32 &&
+            (ne0 >= 32 && ne1 >= 32 && ne10 >= 32);
+}
+
+static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
+    GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
+    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
+    GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+
+    const int64_t ne12 = src1->ne[2];
+
+    CUDA_CHECK(ggml_cuda_set_device(g_main_device));
+    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
+
+    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
+    void * src0_ddq = src0_extra->data_device[g_main_device];
+
+    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
+    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
+
+    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
+    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
+
+    ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream);
+}
+
+static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
+    GGML_ASSERT(!ggml_is_contiguous(src0) && ggml_is_contiguous(src1));
+    GGML_ASSERT(!ggml_is_permuted(src0));
+    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+
+    const int64_t ne12 = src1->ne[2];
+
+    const int64_t nb01 = src0->nb[1];
+    const int64_t nb02 = src0->nb[2];
+
+    CUDA_CHECK(ggml_cuda_set_device(g_main_device));
+    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
+
+    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
+    void * src0_ddq = src0_extra->data_device[g_main_device];
+
+    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
+    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
+
+    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
+    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
+
+    const int64_t row_stride_x = nb01 / sizeof(half);
+    const int64_t channel_stride_x = nb02 / sizeof(half);
+
+    ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream);
+}
+
+static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    bool all_on_device = (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) &&
+        src1->backend == GGML_BACKEND_GPU && dst->backend == GGML_BACKEND_GPU;
+
+    int64_t min_compute_capability = INT_MAX;
+    for (int64_t id = 0; id < g_device_count; ++id) {
+        if (min_compute_capability > g_compute_capabilities[id]
+                && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
+            min_compute_capability = g_compute_capabilities[id];
+        }
+    }
+
+    if (all_on_device && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
+        ggml_cuda_mul_mat_vec_p021(src0, src1, dst);
+    } else if (all_on_device && !ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && src1->ne[1] == 1) {
+        ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
+    } else if (src0->type == GGML_TYPE_F32) {
+        ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
+    } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) {
+        if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0) {
+
+#ifdef GGML_CUDA_FORCE_DMMV
+            const bool use_mul_mat_vec_q = false;
+#else
+            const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type);
+#endif // GGML_CUDA_FORCE_DMMV
+
+            if (use_mul_mat_vec_q) {
+                ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
+            } else {
+                ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false);
+            }
+        } else {
+            if (g_mul_mat_q && ggml_is_quantized(src0->type) && min_compute_capability >= MIN_CC_DP4A) {
+                ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true);
+            } else {
+                ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
+            }
+        }
+    } else {
+        GGML_ASSERT(false);
+    }
+}
+
+static void ggml_cuda_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_scale);
+}
+
+static void ggml_cuda_clamp(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_clamp);
+}
+
+static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const int64_t ne = ggml_nelements(src0);
+    GGML_ASSERT(ne == ggml_nelements(src1));
+
+    GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
+
+    GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
+    GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    GGML_ASSERT(src0->ne[3] == 1);
+
+    const int64_t nb00 = src0->nb[0];
+    const int64_t nb01 = src0->nb[1];
+    const int64_t nb02 = src0->nb[2];
+
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    GGML_ASSERT(src1->ne[3] == 1);
+
+    const int64_t nb10 = src1->nb[0];
+    const int64_t nb11 = src1->nb[1];
+    const int64_t nb12 = src1->nb[2];
+
+    CUDA_CHECK(ggml_cuda_set_device(g_main_device));
+    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
+
+    const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
+    const ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
+
+    char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
+    char * src1_ddc = (char *) src1_extra->data_device[g_main_device];
+
+    if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_f32_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02,
+                              ne10, ne11, nb10, nb11, nb12, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
+        ggml_cpy_f32_f16_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02,
+                              ne10, ne11, nb10, nb11, nb12, main_stream);
+    } else {
+        fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
+                ggml_type_name(src0->type), ggml_type_name(src1->type));
+        GGML_ASSERT(false);
+    }
+
+    (void) dst;
+}
+
+static void ggml_cuda_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_cpy(src0, dst, nullptr);
+    (void) src1;
+}
+
+static void ggml_cuda_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_diag_mask_inf);
+}
+
+static void ggml_cuda_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_soft_max);
+}
+
+static void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rope);
+}
+
+static void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_alibi);
+}
+
+static void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    (void) src0;
+    (void) src1;
+    (void) dst;
+}
+
+void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
+    const int64_t nrows = ggml_nrows(tensor);
+
+    const int64_t ne0 = tensor->ne[0];
+
+    const size_t nb1 = tensor->nb[1];
+
+    ggml_backend_type backend = tensor->backend;
+    ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu;
+    memset(extra, 0, sizeof(*extra));
+
+    for (int64_t id = 0; id < g_device_count; ++id) {
+        if (backend == GGML_BACKEND_GPU && id != g_main_device) {
+            continue;
+        }
+
+        ggml_cuda_set_device(id);
+
+        int64_t row_low, row_high;
+        if (backend == GGML_BACKEND_GPU) {
+            row_low = 0;
+            row_high = nrows;
+        } else if (backend == GGML_BACKEND_GPU_SPLIT) {
+            const int64_t rounding = get_row_rounding(tensor->type);
+
+            row_low = id == 0 ? 0 : nrows*g_tensor_split[id];
+            row_low -= row_low % rounding;
+
+            if (id == g_device_count - 1) {
+                row_high = nrows;
+            } else {
+                row_high = nrows*g_tensor_split[id + 1];
+                row_high -= row_high % rounding;
+            }
+        } else {
+            GGML_ASSERT(false);
+        }
+        if (row_low == row_high) {
+            continue;
+        }
+
+        int64_t nrows_split = row_high - row_low;
+
+        const size_t offset_split = row_low*nb1;
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += (MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING)
+                * ggml_type_size(tensor->type)/ggml_blck_size(tensor->type);
+        }
+
+        char * buf;
+        CUDA_CHECK(cudaMalloc(&buf, size));
+        char * buf_host = (char*)data + offset_split;
+
+        // set padding to 0 to avoid possible NaN values
+        if (size > original_size) {
+            CUDA_CHECK(cudaMemset(buf + original_size, 0, size - original_size));
+        }
+
+        CUDA_CHECK(cudaMemcpy(buf, buf_host, original_size, cudaMemcpyHostToDevice));
+
+        extra->data_device[id] = buf;
+
+        if (backend == GGML_BACKEND_GPU_SPLIT) {
+            for (int64_t is = 0; is < MAX_STREAMS; ++is) {
+                CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming));
+            }
+        }
+    }
+
+    tensor->extra = extra;
+}
+
+void ggml_cuda_free_data(struct ggml_tensor * tensor) {
+    if (!tensor || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) {
+        return;
+    }
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+    for (int64_t id = 0; id < g_device_count; ++id) {
+        if (extra->data_device[id] != nullptr) {
+            CUDA_CHECK(ggml_cuda_set_device(id));
+            CUDA_CHECK(cudaFree(extra->data_device[id]));
+        }
+
+        for (int64_t is = 0; is < MAX_STREAMS; ++is) {
+            if (extra->events[id][is] != nullptr) {
+                CUDA_CHECK(ggml_cuda_set_device(id));
+                CUDA_CHECK(cudaEventDestroy(extra->events[id][is]));
+            }
+        }
+    }
+
+    delete extra;
+}
+
+static ggml_tensor_extra_gpu * g_temp_tensor_extras = nullptr;
+static size_t g_temp_tensor_extra_index = 0;
+
+static ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
+    if (g_temp_tensor_extras == nullptr) {
+        g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_MAX_NODES];
+    }
+
+    size_t alloc_index = g_temp_tensor_extra_index;
+    g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_MAX_NODES;
+    ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index];
+    memset(extra, 0, sizeof(*extra));
+
+    return extra;
+}
+
+static void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace, bool no_alloc) {
+    if (scratch && g_scratch_size == 0) {
+        return;
+    }
+
+    tensor->backend = GGML_BACKEND_GPU;
+
+    // recursively assign CUDA buffers until a compute tensor is found
+    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) {
+        const ggml_op src0_op = tensor->src[0]->op;
+        if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) {
+            ggml_cuda_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc);
+        }
+    }
+    if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_CPU) {
+        ggml_cuda_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc);
+    }
+
+    if (scratch && no_alloc) {
+        return;
+    }
+
+    ggml_tensor_extra_gpu * extra;
+
+    const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) ||
+        tensor->op == GGML_OP_VIEW ||
+        force_inplace;
+    const size_t size = ggml_nbytes(tensor);
+
+    CUDA_CHECK(ggml_cuda_set_device(g_main_device));
+    if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) {
+        ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
+        char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
+        size_t offset = 0;
+        if (tensor->op == GGML_OP_VIEW) {
+            memcpy(&offset, tensor->op_params, sizeof(size_t));
+        }
+        extra = ggml_cuda_alloc_temp_tensor_extra();
+        extra->data_device[g_main_device] = src0_ddc + offset;
+    } else if (tensor->op == GGML_OP_CPY) {
+        ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra;
+        void * src1_ddv = src1_extra->data_device[g_main_device];
+        extra = ggml_cuda_alloc_temp_tensor_extra();
+        extra->data_device[g_main_device] = src1_ddv;
+    } else if (scratch) {
+        GGML_ASSERT(size <= g_scratch_size);
+        if (g_scratch_offset + size > g_scratch_size) {
+            g_scratch_offset = 0;
+        }
+
+        char * data = (char *) g_scratch_buffer;
+        if (data == nullptr) {
+            CUDA_CHECK(cudaMalloc(&data, g_scratch_size));
+            g_scratch_buffer = data;
+        }
+        extra = ggml_cuda_alloc_temp_tensor_extra();
+        extra->data_device[g_main_device] = data + g_scratch_offset;
+
+        g_scratch_offset += size;
+
+        GGML_ASSERT(g_scratch_offset <= g_scratch_size);
+    } else { // allocate new buffers outside of scratch
+        void * data;
+        CUDA_CHECK(cudaMalloc(&data, size));
+        CUDA_CHECK(cudaMemset(data, 0, size));
+        extra = new ggml_tensor_extra_gpu;
+        memset(extra, 0, sizeof(*extra));
+        extra->data_device[g_main_device] = data;
+    }
+
+    tensor->extra = extra;
+}
+
+void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset) {
+    if (g_scratch_size == 0) {
+        return;
+    }
+    if (g_scratch_buffer == nullptr) {
+        ggml_cuda_set_device(g_main_device);
+        CUDA_CHECK(cudaMalloc(&g_scratch_buffer, g_scratch_size));
+    }
+
+    ggml_tensor_extra_gpu * extra = ggml_cuda_alloc_temp_tensor_extra();
+
+    const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) ||
+        tensor->op == GGML_OP_VIEW;
+
+    if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) {
+        ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
+        char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
+        size_t view_offset = 0;
+        if (tensor->op == GGML_OP_VIEW) {
+            memcpy(&view_offset, tensor->op_params, sizeof(size_t));
+        }
+        extra->data_device[g_main_device] = src0_ddc + view_offset;
+    } else {
+        extra->data_device[g_main_device] = (char *) g_scratch_buffer + offset;
+    }
+
+    tensor->extra = extra;
+}
+
+void ggml_cuda_copy_to_device(struct ggml_tensor * tensor) {
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(ggml_is_contiguous(tensor));
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+    CUDA_CHECK(ggml_cuda_set_device(g_main_device));
+    CUDA_CHECK(cudaMemcpy(extra->data_device[g_main_device], tensor->data, ggml_nbytes(tensor), cudaMemcpyHostToDevice));
+}
+
+void ggml_cuda_assign_buffers(struct ggml_tensor * tensor) {
+    ggml_cuda_assign_buffers_impl(tensor, true, false, false);
+}
+
+void ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor) {
+    ggml_cuda_assign_buffers_impl(tensor, true, false, true);
+}
+
+void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor) {
+    ggml_cuda_assign_buffers_impl(tensor, false, false, false);
+}
+
+void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor) {
+    ggml_cuda_assign_buffers_impl(tensor, false, true, false);
+}
+
+void ggml_cuda_set_main_device(const int main_device) {
+    if (main_device >= g_device_count) {
+        fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n",
+                main_device, g_device_count, g_main_device);
+        return;
+    }
+    g_main_device = main_device;
+    if (g_device_count > 1) {
+        cudaDeviceProp prop;
+        CUDA_CHECK(cudaGetDeviceProperties(&prop, g_main_device));
+        fprintf(stderr, "%s: using device %d (%s) as main device\n", __func__, g_main_device, prop.name);
+    }
+}
+
+void ggml_cuda_set_mul_mat_q(const bool mul_mat_q) {
+    g_mul_mat_q = mul_mat_q;
+}
+
+void ggml_cuda_set_scratch_size(const size_t scratch_size) {
+    // this is a hack to not completely break llama.cpp when using multiple models or contexts simultaneously
+    // it still won't always work as expected, but it's better than nothing
+    if (scratch_size > g_scratch_size) {
+        ggml_cuda_free_scratch();
+    }
+    g_scratch_size = std::max(g_scratch_size, scratch_size);
+}
+
+void ggml_cuda_free_scratch() {
+    if (g_scratch_buffer == nullptr) {
+        return;
+    }
+
+    CUDA_CHECK(cudaFree(g_scratch_buffer));
+    g_scratch_buffer = nullptr;
+}
+
+bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
+    ggml_cuda_func_t func;
+    const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
+        || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
+        || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
+
+    if (!any_on_device && tensor->op != GGML_OP_MUL_MAT) {
+        return false;
+    }
+
+    switch (tensor->op) {
+        case GGML_OP_REPEAT:
+            func = ggml_cuda_repeat;
+            break;
+        case GGML_OP_GET_ROWS:
+            func = ggml_cuda_get_rows;
+            break;
+        case GGML_OP_DUP:
+            func = ggml_cuda_dup;
+            break;
+        case GGML_OP_ADD:
+            func = ggml_cuda_add;
+            break;
+        case GGML_OP_MUL:
+            func = ggml_cuda_mul;
+            break;
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(tensor)) {
+                case GGML_UNARY_OP_GELU:
+                    func = ggml_cuda_gelu;
+                    break;
+                case GGML_UNARY_OP_SILU:
+                    func = ggml_cuda_silu;
+                    break;
+                default:
+                    return false;
+            } break;
+        case GGML_OP_NORM:
+            func = ggml_cuda_norm;
+            break;
+        case GGML_OP_RMS_NORM:
+            func = ggml_cuda_rms_norm;
+            break;
+        case GGML_OP_MUL_MAT:
+            if (!any_on_device && !ggml_cuda_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) {
+                return false;
+            }
+            func = ggml_cuda_mul_mat;
+            break;
+        case GGML_OP_SCALE:
+            func = ggml_cuda_scale;
+            break;
+        case GGML_OP_CLAMP:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cuda_clamp;
+            break;
+        case GGML_OP_CPY:
+            func = ggml_cuda_cpy;
+            break;
+        case GGML_OP_CONT:
+            func = ggml_cuda_dup;
+            break;
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+            func = ggml_cuda_nop;
+            break;
+        case GGML_OP_DIAG_MASK_INF:
+            func = ggml_cuda_diag_mask_inf;
+            break;
+        case GGML_OP_SOFT_MAX:
+            func = ggml_cuda_soft_max;
+            break;
+        case GGML_OP_ROPE:
+            func = ggml_cuda_rope;
+            break;
+        case GGML_OP_ALIBI:
+            func = ggml_cuda_alibi;
+            break;
+        default:
+            return false;
+    }
+
+    if (params->ith != 0) {
+        return true;
+    }
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return true;
+    }
+    func(tensor->src[0], tensor->src[1], tensor);
+    return true;
+}
+
+int ggml_cuda_get_device_count() {
+    int device_count;
+    CUDA_CHECK(cudaGetDeviceCount(&device_count));
+    return device_count;
+}
+
+void ggml_cuda_get_device_description(int device, char * description, size_t description_size) {
+    cudaDeviceProp prop;
+    CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
+    snprintf(description, description_size, "%s", prop.name);
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+// backend interface
+
+#define UNUSED GGML_UNUSED
+
+struct ggml_backend_context_cuda {
+};
+
+static const char * ggml_backend_cuda_name(ggml_backend_t backend) {
+    return GGML_CUDA_NAME;
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_cuda_free(ggml_backend_t backend) {
+    ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
+    delete cuda_ctx;
+    delete backend;
+}
+
+struct ggml_backend_buffer_context_cuda {
+    void * device;
+
+    ggml_tensor_extra_gpu * temp_tensor_extras = nullptr;
+    size_t temp_tensor_extra_index = 0;
+
+    ~ggml_backend_buffer_context_cuda() {
+        delete[] temp_tensor_extras;
+    }
+
+    ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
+        if (temp_tensor_extras == nullptr) {
+            temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_MAX_NODES];
+        }
+
+        size_t alloc_index = temp_tensor_extra_index;
+        temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_MAX_NODES;
+        ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index];
+        memset(extra, 0, sizeof(*extra));
+
+        return extra;
+    }
+};
+
+static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
+    CUDA_CHECK(cudaFree(ctx->device));
+    delete ctx;
+}
+
+static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) {
+    ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
+    return ctx->device;
+}
+
+static size_t ggml_backend_cuda_buffer_get_alloc_size(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+    int64_t row_low = 0;
+    int64_t row_high = ggml_nrows(tensor);
+    int64_t nrows_split = row_high - row_low;
+
+    size_t size = ggml_nbytes_split(tensor, nrows_split);
+
+    int64_t ne0 = tensor->ne[0];
+
+    if (ggml_is_quantized(tensor->type)) {
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += (MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING)
+                * ggml_type_size(tensor->type)/ggml_blck_size(tensor->type);
+        }
+    }
+
+    return size;
+
+    UNUSED(buffer);
+}
+
+static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+    ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
+
+    if (tensor->view_src != NULL && tensor->view_offs == 0) {
+        assert(tensor->view_src->buffer->backend == buffer->backend);
+        tensor->backend = tensor->view_src->backend;
+        tensor->extra = tensor->view_src->extra;
+        return;
+    }
+
+    ggml_tensor_extra_gpu * extra = ctx->ggml_cuda_alloc_temp_tensor_extra();
+
+    extra->data_device[g_main_device] = tensor->data;
+
+    tensor->backend = GGML_BACKEND_GPU;
+    tensor->extra = extra;
+
+    if (ggml_is_quantized(tensor->type)) {
+        // initialize padding to 0 to avoid possible NaN values
+        int64_t row_low = 0;
+        int64_t row_high = ggml_nrows(tensor);
+        int64_t nrows_split = row_high - row_low;
+
+        size_t original_size = ggml_nbytes_split(tensor, nrows_split);
+        size_t padded_size = ggml_backend_cuda_buffer_get_alloc_size(tensor->buffer, tensor);
+
+        if (padded_size > original_size && tensor->view_src == nullptr) {
+            CUDA_CHECK(cudaMemsetAsync((char *)tensor->data + original_size, 0, padded_size - original_size, g_cudaStreams[g_main_device][0]));
+        }
+    }
+
+    UNUSED(buffer);
+}
+
+static struct ggml_backend_buffer_i cuda_backend_buffer_interface = {
+    /* .free_buffer    = */ ggml_backend_cuda_buffer_free_buffer,
+    /* .get_base       = */ ggml_backend_cuda_buffer_get_base,
+    /* .get_alloc_size = */ ggml_backend_cuda_buffer_get_alloc_size,
+    /* .init_tensor    = */ ggml_backend_cuda_buffer_init_tensor,
+    /* .free_tensor    = */ NULL,
+};
+
+static ggml_backend_buffer_t ggml_backend_cuda_alloc_buffer(ggml_backend_t backend, size_t size) {
+    ggml_cuda_set_device(g_main_device);
+
+    ggml_backend_buffer_context_cuda * ctx = new ggml_backend_buffer_context_cuda;
+    CUDA_CHECK(cudaMalloc(&ctx->device, size));
+    return ggml_backend_buffer_init(backend, cuda_backend_buffer_interface, ctx, size);
+}
+
+static size_t ggml_backend_cuda_get_alignment(ggml_backend_t backend) {
+    return 128;
+    UNUSED(backend);
+}
+
+static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+
+    CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[g_main_device][0]));
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+
+    CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0]));
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
+    CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0]));
+
+    UNUSED(backend);
+}
+
+static ggml_backend_graph_plan_t ggml_backend_cuda_graph_plan_create(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    GGML_ASSERT(!"not implemented");
+
+    return nullptr;
+
+    UNUSED(backend);
+    UNUSED(cgraph);
+}
+
+static void ggml_backend_cuda_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    GGML_ASSERT(!"not implemented");
+
+    UNUSED(backend);
+    UNUSED(plan);
+}
+
+static void ggml_backend_cuda_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    GGML_ASSERT(!"not implemented");
+
+    UNUSED(backend);
+    UNUSED(plan);
+}
+
+static void ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    ggml_cuda_set_device(g_main_device);
+
+    ggml_compute_params params = {};
+    params.type = GGML_TASK_COMPUTE;
+    params.ith = 0;
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_tensor * node = cgraph->nodes[i];
+
+        assert(node->backend == GGML_BACKEND_GPU);
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            if (node->src[j] != nullptr) {
+                assert(node->src[j]->backend == GGML_BACKEND_GPU);
+            }
+        }
+
+        bool ok = ggml_cuda_compute_forward(&params, node);
+        if (!ok) {
+            fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
+        }
+        GGML_ASSERT(ok);
+
+#if 0
+        if (node->type == GGML_TYPE_F32) {
+            cudaDeviceSynchronize();
+            std::vector<float> tmp(ggml_nelements(node), 0.0f);
+            cudaMemcpy(tmp.data(), node->data, ggml_nelements(node)*sizeof(float), cudaMemcpyDeviceToHost);
+            printf("\n%s (%s) (%s %s) (%s %s): ", node->name, ggml_op_name(node->op),
+                ggml_type_name(node->src[0]->type),
+                node->src[1] ? ggml_type_name(node->src[1]->type) : "none",
+                node->src[0]->name,
+                node->src[1] ? node->src[1]->name : "none");
+            double sum = 0.0;
+            double sq_sum = 0.0;
+            for (int i = 0; i < ggml_nelements(node); i++) {
+                printf("%f ", tmp[i]);
+                sum += tmp[i];
+                sq_sum += tmp[i]*tmp[i];
+            }
+            printf("\n");
+            printf("sum: %f, ", sum);
+            printf("sq_sum: %f\n", sq_sum);
+        }
+#endif
+    }
+
+    UNUSED(backend);
+}
+
+static ggml_backend_i cuda_backend_i = {
+    /* .get_name            = */ ggml_backend_cuda_name,
+    /* .free                = */ ggml_backend_cuda_free,
+    /* .alloc_buffer        = */ ggml_backend_cuda_alloc_buffer,
+    /* .get_alignment       = */ ggml_backend_cuda_get_alignment,
+    /* .set_tensor_async    = */ ggml_backend_cuda_set_tensor_async,
+    /* .get_tensor_async    = */ ggml_backend_cuda_get_tensor_async,
+    /* .synchronize         = */ ggml_backend_cuda_synchronize,
+    /* .cpy_tensor_from     = */ nullptr,
+    /* .cpy_tensor_to       = */ nullptr,
+    /* .graph_plan_create   = */ ggml_backend_cuda_graph_plan_create,
+    /* .graph_plan_free     = */ ggml_backend_cuda_graph_plan_free,
+    /* .graph_plan_compute  = */ ggml_backend_cuda_graph_plan_compute,
+    /* .graph_compute       = */ ggml_backend_cuda_graph_compute,
+    /* .supports_op         = */ nullptr,
+};
+
+ggml_backend_t ggml_backend_cuda_init() {
+    ggml_init_cublas(); // TODO: remove from ggml.c
+
+    ggml_backend_context_cuda * ctx = new ggml_backend_context_cuda;
+
+    ggml_backend_t cuda_backend = new ggml_backend {
+        /* .interface = */ cuda_backend_i,
+        /* .context   = */ ctx
+    };
+
+    return cuda_backend;
+}

runner/ggml-cuda.h

@@ -0,0 +1,77 @@
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef GGML_USE_HIPBLAS
+#define GGML_CUDA_NAME "ROCm"
+#define GGML_CUBLAS_NAME "hipBLAS"
+#else
+#define GGML_CUDA_NAME "CUDA"
+#define GGML_CUBLAS_NAME "cuBLAS"
+#endif
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+#define GGML_CUDA_MAX_DEVICES       16
+
+GGML_API void   ggml_init_cublas(void);
+GGML_API void * ggml_cuda_host_malloc(size_t size);
+GGML_API void   ggml_cuda_host_free(void * ptr);
+
+GGML_API bool   ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+GGML_API void   ggml_cuda_set_tensor_split(const float * tensor_split);
+GGML_API void   ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
+GGML_API void   ggml_cuda_free_data(struct ggml_tensor * tensor);
+
+GGML_API void   ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
+GGML_API void   ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
+GGML_API void   ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
+
+GGML_API void   ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor);
+GGML_API void   ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset);
+GGML_API void   ggml_cuda_copy_to_device(struct ggml_tensor * tensor);
+
+GGML_API void   ggml_cuda_set_main_device(int main_device);
+GGML_API void   ggml_cuda_set_mul_mat_q(bool mul_mat_q);
+GGML_API void   ggml_cuda_set_scratch_size(size_t scratch_size);
+GGML_API void   ggml_cuda_free_scratch(void);
+GGML_API bool   ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
+
+GGML_API int    ggml_cuda_get_device_count(void);
+GGML_API void   ggml_cuda_get_device_description(int device, char * description, size_t description_size);
+
+// backend API
+GGML_API ggml_backend_t ggml_backend_cuda_init(void); // TODO: take a list of devices to use
+
+#ifdef  __cplusplus
+}
+#endif

runner/ggml-metal.h

@@ -0,0 +1,134 @@
+//go:build darwin
+
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+// An interface allowing to compute ggml_cgraph with Metal
+//
+// This is a fully functional interface that extends ggml with GPU support for Apple devices.
+// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, OpenCL, etc.)
+//
+// How it works?
+//
+// As long as your program can create and evaluate a ggml_cgraph on the CPU, you can use this
+// interface to evaluate the same graph on the GPU. Instead of using ggml_graph_compute(), you
+// use ggml_metal_graph_compute() (or ggml_vulkan_graph_compute(), etc.)
+//
+// You only need to make sure that all memory buffers that you used during the graph creation
+// are mapped to the device memory with the ggml_metal_add_buffer() function. This mapping is
+// used during the graph evaluation to determine the arguments of the compute kernels.
+//
+// Synchronization between device and host memory (for example for input and output tensors)
+// is done with the ggml_metal_set_tensor() and ggml_metal_get_tensor() functions.
+//
+
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#include <stddef.h>
+#include <stdbool.h>
+
+// max memory buffers that can be mapped to the device
+#define GGML_METAL_MAX_BUFFERS 16
+#define GGML_METAL_MAX_COMMAND_BUFFERS 32
+
+struct ggml_tensor;
+struct ggml_cgraph;
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+//
+// internal API
+// temporary exposed to user-code
+//
+
+struct ggml_metal_context;
+
+void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_data);
+
+// number of command buffers to use
+struct ggml_metal_context * ggml_metal_init(int n_cb);
+void ggml_metal_free(struct ggml_metal_context * ctx);
+
+void * ggml_metal_host_malloc(size_t n);
+void   ggml_metal_host_free  (void * data);
+
+// set the number of command buffers to use
+void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb);
+
+// creates a mapping between a host memory buffer and a device memory buffer
+// - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
+// - the mapping is used during computation to determine the arguments of the compute kernels
+// - you don't need to keep the host memory buffer allocated as it is never accessed by Metal
+// - max_size specifies the maximum size of a tensor and is used to create shared views such
+//   that it is guaranteed that the tensor will fit in at least one of the views
+//
+bool ggml_metal_add_buffer(
+        struct ggml_metal_context * ctx,
+                       const char * name,
+                             void * data,
+                           size_t   size,
+                           size_t   max_size);
+
+// set data from host memory into the device
+void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
+
+// get data from the device into host memory
+void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
+
+// try to find operations that can be run concurrently in the graph
+// you should run it again if the topology of your graph changes
+void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf, bool check_mem);
+
+// if the graph has been optimized for concurrently dispatch, return length of the concur_list if optimized
+int ggml_metal_if_optimized(struct ggml_metal_context * ctx);
+
+// output the concur_list for ggml_alloc
+int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx);
+
+// same as ggml_graph_compute but uses Metal
+// creates gf->n_threads command buffers in parallel
+void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
+
+//
+// backend API
+// user-code should use only these functions
+//
+
+GGML_API ggml_backend_t ggml_backend_metal_init(void);
+
+GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);
+
+GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb);
+
+#ifdef __cplusplus
+}
+#endif
+

runner/ggml-metal.m

@@ -0,0 +1,1670 @@
+//go:build darwin
+
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#import "ggml-metal.h"
+
+#import "ggml.h"
+
+#import <Foundation/Foundation.h>
+
+#import <Metal/Metal.h>
+
+#undef MIN
+#undef MAX
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+
+#ifdef GGML_METAL_NDEBUG
+#define GGML_METAL_LOG_INFO(...)
+#define GGML_METAL_LOG_WARN(...)
+#define GGML_METAL_LOG_ERROR(...)
+#else
+#define GGML_METAL_LOG_INFO(...)  ggml_metal_log(GGML_LOG_LEVEL_INFO, __VA_ARGS__)
+#define GGML_METAL_LOG_WARN(...)  ggml_metal_log(GGML_LOG_LEVEL_WARN, __VA_ARGS__)
+#define GGML_METAL_LOG_ERROR(...) ggml_metal_log(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
+#endif
+
+#define UNUSED(x) (void)(x)
+
+#define GGML_MAX_CONCUR (2*GGML_MAX_NODES)
+
+struct ggml_metal_buffer {
+    const char * name;
+
+    void   * data;
+    size_t   size;
+
+    id<MTLBuffer> metal;
+};
+
+struct ggml_metal_context {
+    int n_cb;
+
+    id<MTLDevice>       device;
+    id<MTLCommandQueue> queue;
+    id<MTLLibrary>      library;
+
+    id<MTLCommandBuffer>         command_buffers [GGML_METAL_MAX_COMMAND_BUFFERS];
+    id<MTLComputeCommandEncoder> command_encoders[GGML_METAL_MAX_COMMAND_BUFFERS];
+
+    dispatch_queue_t d_queue;
+
+    int n_buffers;
+    struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
+
+    int concur_list[GGML_MAX_CONCUR];
+    int concur_list_len;
+
+    // custom kernels
+#define GGML_METAL_DECL_KERNEL(name) \
+    id<MTLFunction>             function_##name; \
+    id<MTLComputePipelineState> pipeline_##name
+
+    GGML_METAL_DECL_KERNEL(add);
+    GGML_METAL_DECL_KERNEL(add_row); // TODO: avoid this extra kernel, instead extend the "add" kernel to support broadcast
+    GGML_METAL_DECL_KERNEL(mul);
+    GGML_METAL_DECL_KERNEL(mul_row); // TODO: avoid this extra kernel, instead extend the "mul" kernel to support broadcast
+    GGML_METAL_DECL_KERNEL(scale);
+    GGML_METAL_DECL_KERNEL(silu);
+    GGML_METAL_DECL_KERNEL(relu);
+    GGML_METAL_DECL_KERNEL(gelu);
+    GGML_METAL_DECL_KERNEL(soft_max);
+    GGML_METAL_DECL_KERNEL(soft_max_4);
+    GGML_METAL_DECL_KERNEL(diag_mask_inf);
+    GGML_METAL_DECL_KERNEL(diag_mask_inf_8);
+    GGML_METAL_DECL_KERNEL(get_rows_f32);
+    GGML_METAL_DECL_KERNEL(get_rows_f16);
+    GGML_METAL_DECL_KERNEL(get_rows_q4_0);
+    GGML_METAL_DECL_KERNEL(get_rows_q4_1);
+    GGML_METAL_DECL_KERNEL(get_rows_q5_0);
+    GGML_METAL_DECL_KERNEL(get_rows_q5_1);
+    GGML_METAL_DECL_KERNEL(get_rows_q8_0);
+    GGML_METAL_DECL_KERNEL(get_rows_q2_K);
+    GGML_METAL_DECL_KERNEL(get_rows_q3_K);
+    GGML_METAL_DECL_KERNEL(get_rows_q4_K);
+    GGML_METAL_DECL_KERNEL(get_rows_q5_K);
+    GGML_METAL_DECL_KERNEL(get_rows_q6_K);
+    GGML_METAL_DECL_KERNEL(rms_norm);
+    GGML_METAL_DECL_KERNEL(norm);
+    GGML_METAL_DECL_KERNEL(mul_mv_f32_f32);
+    GGML_METAL_DECL_KERNEL(mul_mv_f16_f32);
+    GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_1row);
+    GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_l4);
+    GGML_METAL_DECL_KERNEL(mul_mv_q4_0_f32);
+    GGML_METAL_DECL_KERNEL(mul_mv_q4_1_f32);
+    GGML_METAL_DECL_KERNEL(mul_mv_q5_0_f32);
+    GGML_METAL_DECL_KERNEL(mul_mv_q5_1_f32);
+    GGML_METAL_DECL_KERNEL(mul_mv_q8_0_f32);
+    GGML_METAL_DECL_KERNEL(mul_mv_q2_K_f32);
+    GGML_METAL_DECL_KERNEL(mul_mv_q3_K_f32);
+    GGML_METAL_DECL_KERNEL(mul_mv_q4_K_f32);
+    GGML_METAL_DECL_KERNEL(mul_mv_q5_K_f32);
+    GGML_METAL_DECL_KERNEL(mul_mv_q6_K_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_f32_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_f16_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_q4_0_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_q4_1_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_q5_0_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_q5_1_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_q8_0_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_q2_K_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_q3_K_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_q4_K_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_q5_K_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_q6_K_f32);
+    GGML_METAL_DECL_KERNEL(rope_f32);
+    GGML_METAL_DECL_KERNEL(rope_f16);
+    GGML_METAL_DECL_KERNEL(alibi_f32);
+    GGML_METAL_DECL_KERNEL(cpy_f32_f16);
+    GGML_METAL_DECL_KERNEL(cpy_f32_f32);
+    GGML_METAL_DECL_KERNEL(cpy_f16_f16);
+    GGML_METAL_DECL_KERNEL(concat);
+    GGML_METAL_DECL_KERNEL(sqr);
+
+#undef GGML_METAL_DECL_KERNEL
+};
+
+// MSL code
+// TODO: move the contents here when ready
+//       for now it is easier to work in a separate file
+static NSString * const msl_library_source = @"see metal.metal";
+
+// Here to assist with NSBundle Path Hack
+@interface GGMLMetalClass : NSObject
+@end
+@implementation GGMLMetalClass
+@end
+
+ggml_log_callback ggml_metal_log_callback = NULL;
+void * ggml_metal_log_user_data = NULL;
+
+void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) {
+    ggml_metal_log_callback  = log_callback;
+    ggml_metal_log_user_data = user_data;
+}
+
+static void ggml_metal_log(enum ggml_log_level level, const char* format, ...){
+    if (ggml_metal_log_callback != NULL) {
+        va_list args;
+        va_start(args, format);
+        char buffer[128];
+        int len = vsnprintf(buffer, 128, format, args);
+        if (len < 128) {
+            ggml_metal_log_callback(level, buffer, ggml_metal_log_user_data);
+        } else {
+            char* buffer2 = malloc(len+1);
+            vsnprintf(buffer2, len+1, format, args);
+            buffer2[len] = 0;
+            ggml_metal_log_callback(level, buffer2, ggml_metal_log_user_data);
+            free(buffer2);
+        }
+        va_end(args);
+    }
+}
+
+
+
+struct ggml_metal_context * ggml_metal_init(int n_cb) {
+    GGML_METAL_LOG_INFO("%s: allocating\n", __func__);
+
+    id <MTLDevice> device;
+    NSString * s;
+
+#if TARGET_OS_OSX
+    // Show all the Metal device instances in the system
+    NSArray * devices = MTLCopyAllDevices();
+    for (device in devices) {
+        s = [device name];
+        GGML_METAL_LOG_INFO("%s: found device: %s\n", __func__, [s UTF8String]);
+    }
+#endif
+
+    // Pick and show default Metal device
+    device = MTLCreateSystemDefaultDevice();
+    s = [device name];
+    GGML_METAL_LOG_INFO("%s: picking default device: %s\n", __func__, [s UTF8String]);
+
+    // Configure context
+    struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
+    ctx->device = device;
+    ctx->n_cb   = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
+    ctx->queue  = [ctx->device newCommandQueue];
+    ctx->n_buffers = 0;
+    ctx->concur_list_len = 0;
+
+    ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT);
+
+    // load library
+    {
+        NSBundle * bundle = nil;
+#ifdef SWIFT_PACKAGE
+        bundle = SWIFTPM_MODULE_BUNDLE;
+#else
+        bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
+#endif
+        NSError * error = nil;
+        NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"];
+        if (libPath != nil) {
+            NSURL * libURL = [NSURL fileURLWithPath:libPath];
+            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]);
+            ctx->library = [ctx->device newLibraryWithURL:libURL error:&error];
+        } else {
+            GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
+
+            NSString * sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
+            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [sourcePath UTF8String]);
+            NSString * src = [NSString stringWithContentsOfFile:sourcePath encoding:NSUTF8StringEncoding error:&error];
+            if (error) {
+                GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
+                return NULL;
+            }
+
+            MTLCompileOptions* options = nil;
+#ifdef GGML_QKK_64
+            options = [MTLCompileOptions new];
+            options.preprocessorMacros = @{ @"QK_K" : @(64) };
+#endif
+            ctx->library = [ctx->device newLibraryWithSource:src options:options error:&error];
+        }
+
+        if (error) {
+            GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
+            return NULL;
+        }
+    }
+
+    // load kernels
+    {
+        NSError * error = nil;
+#define GGML_METAL_ADD_KERNEL(name) \
+        ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \
+        ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \
+        GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name, \
+                (int) ctx->pipeline_##name.maxTotalThreadsPerThreadgroup, \
+                (int) ctx->pipeline_##name.threadExecutionWidth); \
+        if (error) { \
+          GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
+            return NULL; \
+        }
+
+        GGML_METAL_ADD_KERNEL(add);
+        GGML_METAL_ADD_KERNEL(add_row);
+        GGML_METAL_ADD_KERNEL(mul);
+        GGML_METAL_ADD_KERNEL(mul_row);
+        GGML_METAL_ADD_KERNEL(scale);
+        GGML_METAL_ADD_KERNEL(silu);
+        GGML_METAL_ADD_KERNEL(relu);
+        GGML_METAL_ADD_KERNEL(gelu);
+        GGML_METAL_ADD_KERNEL(soft_max);
+        GGML_METAL_ADD_KERNEL(soft_max_4);
+        GGML_METAL_ADD_KERNEL(diag_mask_inf);
+        GGML_METAL_ADD_KERNEL(diag_mask_inf_8);
+        GGML_METAL_ADD_KERNEL(get_rows_f32);
+        GGML_METAL_ADD_KERNEL(get_rows_f16);
+        GGML_METAL_ADD_KERNEL(get_rows_q4_0);
+        GGML_METAL_ADD_KERNEL(get_rows_q4_1);
+        GGML_METAL_ADD_KERNEL(get_rows_q5_0);
+        GGML_METAL_ADD_KERNEL(get_rows_q5_1);
+        GGML_METAL_ADD_KERNEL(get_rows_q8_0);
+        GGML_METAL_ADD_KERNEL(get_rows_q2_K);
+        GGML_METAL_ADD_KERNEL(get_rows_q3_K);
+        GGML_METAL_ADD_KERNEL(get_rows_q4_K);
+        GGML_METAL_ADD_KERNEL(get_rows_q5_K);
+        GGML_METAL_ADD_KERNEL(get_rows_q6_K);
+        GGML_METAL_ADD_KERNEL(rms_norm);
+        GGML_METAL_ADD_KERNEL(norm);
+        GGML_METAL_ADD_KERNEL(mul_mv_f32_f32);
+        GGML_METAL_ADD_KERNEL(mul_mv_f16_f32);
+        GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_1row);
+        GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_l4);
+        GGML_METAL_ADD_KERNEL(mul_mv_q4_0_f32);
+        GGML_METAL_ADD_KERNEL(mul_mv_q4_1_f32);
+        GGML_METAL_ADD_KERNEL(mul_mv_q5_0_f32);
+        GGML_METAL_ADD_KERNEL(mul_mv_q5_1_f32);
+        GGML_METAL_ADD_KERNEL(mul_mv_q8_0_f32);
+        GGML_METAL_ADD_KERNEL(mul_mv_q2_K_f32);
+        GGML_METAL_ADD_KERNEL(mul_mv_q3_K_f32);
+        GGML_METAL_ADD_KERNEL(mul_mv_q4_K_f32);
+        GGML_METAL_ADD_KERNEL(mul_mv_q5_K_f32);
+        GGML_METAL_ADD_KERNEL(mul_mv_q6_K_f32);
+        if ([ctx->device supportsFamily:MTLGPUFamilyApple7]) {
+            GGML_METAL_ADD_KERNEL(mul_mm_f32_f32);
+            GGML_METAL_ADD_KERNEL(mul_mm_f16_f32);
+            GGML_METAL_ADD_KERNEL(mul_mm_q4_0_f32);
+            GGML_METAL_ADD_KERNEL(mul_mm_q4_1_f32);
+            GGML_METAL_ADD_KERNEL(mul_mm_q5_0_f32);
+            GGML_METAL_ADD_KERNEL(mul_mm_q5_1_f32);
+            GGML_METAL_ADD_KERNEL(mul_mm_q8_0_f32);
+            GGML_METAL_ADD_KERNEL(mul_mm_q2_K_f32);
+            GGML_METAL_ADD_KERNEL(mul_mm_q3_K_f32);
+            GGML_METAL_ADD_KERNEL(mul_mm_q4_K_f32);
+            GGML_METAL_ADD_KERNEL(mul_mm_q5_K_f32);
+            GGML_METAL_ADD_KERNEL(mul_mm_q6_K_f32);
+        }
+        GGML_METAL_ADD_KERNEL(rope_f32);
+        GGML_METAL_ADD_KERNEL(rope_f16);
+        GGML_METAL_ADD_KERNEL(alibi_f32);
+        GGML_METAL_ADD_KERNEL(cpy_f32_f16);
+        GGML_METAL_ADD_KERNEL(cpy_f32_f32);
+        GGML_METAL_ADD_KERNEL(cpy_f16_f16);
+        GGML_METAL_ADD_KERNEL(concat);
+        GGML_METAL_ADD_KERNEL(sqr);
+
+#undef GGML_METAL_ADD_KERNEL
+    }
+
+#if TARGET_OS_OSX
+    // print MTL GPU family:
+    GGML_METAL_LOG_INFO("%s: GPU name:   %s\n", __func__, [[ctx->device name] UTF8String]);
+
+    // determine max supported GPU family
+    // https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf
+    // https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
+    for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) {
+        if ([ctx->device supportsFamily:i]) {
+            GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - MTLGPUFamilyApple1 + 1, i);
+            break;
+        }
+    }
+
+    GGML_METAL_LOG_INFO("%s: hasUnifiedMemory              = %s\n",       __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
+    GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize  = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
+    if (ctx->device.maxTransferRate != 0) {
+        GGML_METAL_LOG_INFO("%s: maxTransferRate               = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
+    } else {
+        GGML_METAL_LOG_INFO("%s: maxTransferRate               = built-in GPU\n", __func__);
+    }
+#endif
+
+    return ctx;
+}
+
+void ggml_metal_free(struct ggml_metal_context * ctx) {
+    GGML_METAL_LOG_INFO("%s: deallocating\n", __func__);
+#define GGML_METAL_DEL_KERNEL(name) \
+    [ctx->function_##name release]; \
+    [ctx->pipeline_##name release];
+
+    GGML_METAL_DEL_KERNEL(add);
+    GGML_METAL_DEL_KERNEL(add_row);
+    GGML_METAL_DEL_KERNEL(mul);
+    GGML_METAL_DEL_KERNEL(mul_row);
+    GGML_METAL_DEL_KERNEL(scale);
+    GGML_METAL_DEL_KERNEL(silu);
+    GGML_METAL_DEL_KERNEL(relu);
+    GGML_METAL_DEL_KERNEL(gelu);
+    GGML_METAL_DEL_KERNEL(soft_max);
+    GGML_METAL_DEL_KERNEL(soft_max_4);
+    GGML_METAL_DEL_KERNEL(diag_mask_inf);
+    GGML_METAL_DEL_KERNEL(diag_mask_inf_8);
+    GGML_METAL_DEL_KERNEL(get_rows_f32);
+    GGML_METAL_DEL_KERNEL(get_rows_f16);
+    GGML_METAL_DEL_KERNEL(get_rows_q4_0);
+    GGML_METAL_DEL_KERNEL(get_rows_q4_1);
+    GGML_METAL_DEL_KERNEL(get_rows_q5_0);
+    GGML_METAL_DEL_KERNEL(get_rows_q5_1);
+    GGML_METAL_DEL_KERNEL(get_rows_q8_0);
+    GGML_METAL_DEL_KERNEL(get_rows_q2_K);
+    GGML_METAL_DEL_KERNEL(get_rows_q3_K);
+    GGML_METAL_DEL_KERNEL(get_rows_q4_K);
+    GGML_METAL_DEL_KERNEL(get_rows_q5_K);
+    GGML_METAL_DEL_KERNEL(get_rows_q6_K);
+    GGML_METAL_DEL_KERNEL(rms_norm);
+    GGML_METAL_DEL_KERNEL(norm);
+    GGML_METAL_DEL_KERNEL(mul_mv_f32_f32);
+    GGML_METAL_DEL_KERNEL(mul_mv_f16_f32);
+    GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_1row);
+    GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_l4);
+    GGML_METAL_DEL_KERNEL(mul_mv_q4_0_f32);
+    GGML_METAL_DEL_KERNEL(mul_mv_q4_1_f32);
+    GGML_METAL_DEL_KERNEL(mul_mv_q5_0_f32);
+    GGML_METAL_DEL_KERNEL(mul_mv_q5_1_f32);
+    GGML_METAL_DEL_KERNEL(mul_mv_q8_0_f32);
+    GGML_METAL_DEL_KERNEL(mul_mv_q2_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mv_q3_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mv_q4_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mv_q5_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mv_q6_K_f32);
+    if ([ctx->device supportsFamily:MTLGPUFamilyApple7]) {
+        GGML_METAL_DEL_KERNEL(mul_mm_f32_f32);
+        GGML_METAL_DEL_KERNEL(mul_mm_f16_f32);
+        GGML_METAL_DEL_KERNEL(mul_mm_q4_0_f32);
+        GGML_METAL_DEL_KERNEL(mul_mm_q4_1_f32);
+        GGML_METAL_DEL_KERNEL(mul_mm_q5_0_f32);
+        GGML_METAL_DEL_KERNEL(mul_mm_q5_1_f32);
+        GGML_METAL_DEL_KERNEL(mul_mm_q8_0_f32);
+        GGML_METAL_DEL_KERNEL(mul_mm_q2_K_f32);
+        GGML_METAL_DEL_KERNEL(mul_mm_q3_K_f32);
+        GGML_METAL_DEL_KERNEL(mul_mm_q4_K_f32);
+        GGML_METAL_DEL_KERNEL(mul_mm_q5_K_f32);
+        GGML_METAL_DEL_KERNEL(mul_mm_q6_K_f32);
+    }
+    GGML_METAL_DEL_KERNEL(rope_f32);
+    GGML_METAL_DEL_KERNEL(rope_f16);
+    GGML_METAL_DEL_KERNEL(alibi_f32);
+    GGML_METAL_DEL_KERNEL(cpy_f32_f16);
+    GGML_METAL_DEL_KERNEL(cpy_f32_f32);
+    GGML_METAL_DEL_KERNEL(cpy_f16_f16);
+    GGML_METAL_DEL_KERNEL(concat);
+    GGML_METAL_DEL_KERNEL(sqr);
+
+#undef GGML_METAL_DEL_KERNEL
+
+    for (int i = 0; i < ctx->n_buffers; ++i) {
+        [ctx->buffers[i].metal release];
+    }
+
+    [ctx->library release];
+    [ctx->queue release];
+    [ctx->device release];
+
+    dispatch_release(ctx->d_queue);
+
+    free(ctx);
+}
+
+void * ggml_metal_host_malloc(size_t n) {
+    void * data = NULL;
+    const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n);
+    if (result != 0) {
+        GGML_METAL_LOG_ERROR("%s: error: posix_memalign failed\n", __func__);
+        return NULL;
+    }
+
+    return data;
+}
+
+void ggml_metal_host_free(void * data) {
+    free(data);
+}
+
+void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) {
+    ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
+}
+
+int ggml_metal_if_optimized(struct ggml_metal_context * ctx) {
+    return ctx->concur_list_len;
+}
+
+int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) {
+    return ctx->concur_list;
+}
+
+// finds the Metal buffer that contains the tensor data on the GPU device
+// the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the
+// Metal buffer based on the host memory pointer
+//
+static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, struct ggml_tensor * t, size_t * offs) {
+    //GGML_METAL_LOG_INFO("%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);
+
+    const int64_t tsize = ggml_nbytes(t);
+
+    // find the view that contains the tensor fully
+    for (int i = 0; i < ctx->n_buffers; ++i) {
+        const int64_t ioffs = (int64_t) t->data - (int64_t) ctx->buffers[i].data;
+
+        //GGML_METAL_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, ctx->buffers[%d].size = %10ld, name = %s\n", ioffs, tsize, ioffs + tsize, i, ctx->buffers[i].size, ctx->buffers[i].name);
+        if (ioffs >= 0 && ioffs + tsize <= (int64_t) ctx->buffers[i].size) {
+            *offs = (size_t) ioffs;
+
+            //GGML_METAL_LOG_INFO("%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs);
+
+            return ctx->buffers[i].metal;
+        }
+    }
+
+    GGML_METAL_LOG_ERROR("%s: error: buffer is nil\n", __func__);
+
+    return nil;
+}
+
+bool ggml_metal_add_buffer(
+        struct ggml_metal_context * ctx,
+                     const char * name,
+                           void * data,
+                         size_t   size,
+                         size_t   max_size) {
+    if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) {
+        GGML_METAL_LOG_ERROR("%s: error: too many buffers\n", __func__);
+        return false;
+    }
+
+    if (data) {
+        // verify that the buffer does not overlap with any of the existing buffers
+        for (int i = 0; i < ctx->n_buffers; ++i) {
+            const int64_t ioffs = (int64_t) data - (int64_t) ctx->buffers[i].data;
+
+            if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) {
+                GGML_METAL_LOG_ERROR("%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name);
+                return false;
+            }
+        }
+
+        const size_t size_page = sysconf(_SC_PAGESIZE);
+
+        size_t size_aligned = size;
+        if ((size_aligned % size_page) != 0) {
+            size_aligned += (size_page - (size_aligned % size_page));
+        }
+
+        // the buffer fits into the max buffer size allowed by the device
+        if (size_aligned <= ctx->device.maxBufferLength) {
+            ctx->buffers[ctx->n_buffers].name = name;
+            ctx->buffers[ctx->n_buffers].data = data;
+            ctx->buffers[ctx->n_buffers].size = size;
+
+            ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];
+
+            if (ctx->buffers[ctx->n_buffers].metal == nil) {
+                GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0);
+                return false;
+            }
+
+            GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0);
+
+            ++ctx->n_buffers;
+        } else {
+            // this overlap between the views will guarantee that the tensor with the maximum size will fully fit into
+            // one of the views
+            const size_t size_ovlp = ((max_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case
+            const size_t size_step = ctx->device.maxBufferLength - size_ovlp;
+            const size_t size_view = ctx->device.maxBufferLength;
+
+            for (size_t i = 0; i < size; i += size_step) {
+                const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i);
+
+                ctx->buffers[ctx->n_buffers].name = name;
+                ctx->buffers[ctx->n_buffers].data = (void *) ((uint8_t *) data + i);
+                ctx->buffers[ctx->n_buffers].size = size_step_aligned;
+
+                ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil];
+
+                if (ctx->buffers[ctx->n_buffers].metal == nil) {
+                    GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0);
+                    return false;
+                }
+
+                GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i);
+                if (i + size_step < size) {
+                    GGML_METAL_LOG_INFO("\n");
+                }
+
+                ++ctx->n_buffers;
+            }
+        }
+
+#if TARGET_OS_OSX
+        GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)",
+                ctx->device.currentAllocatedSize / 1024.0 / 1024.0,
+                ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
+
+        if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) {
+            GGML_METAL_LOG_WARN(", warning: current allocated size is greater than the recommended max working set size\n", __func__);
+        } else {
+            GGML_METAL_LOG_INFO("\n");
+        }
+#else
+        GGML_METAL_LOG_INFO(", (%8.2f)\n", ctx->device.currentAllocatedSize / 1024.0 / 1024.0);
+#endif
+    }
+
+    return true;
+}
+
+void ggml_metal_set_tensor(
+        struct ggml_metal_context * ctx,
+        struct ggml_tensor * t) {
+    size_t offs;
+    id<MTLBuffer> id_dst = ggml_metal_get_buffer(ctx, t, &offs);
+
+    memcpy((void *) ((uint8_t *) id_dst.contents + offs), t->data, ggml_nbytes(t));
+}
+
+void ggml_metal_get_tensor(
+        struct ggml_metal_context * ctx,
+        struct ggml_tensor * t) {
+    size_t offs;
+    id<MTLBuffer> id_src = ggml_metal_get_buffer(ctx, t, &offs);
+
+    memcpy(t->data, (void *) ((uint8_t *) id_src.contents + offs), ggml_nbytes(t));
+}
+
+void ggml_metal_graph_find_concurrency(
+        struct ggml_metal_context * ctx,
+        struct ggml_cgraph * gf, bool check_mem) {
+    int search_depth = gf->n_nodes; //we only find concurrency in this range to avoid wasting too much time
+    int nodes_unused[GGML_MAX_CONCUR];
+
+    for (int i = 0; i < GGML_MAX_CONCUR; i++) { ctx->concur_list[i] = 0; }
+    for (int i = 0; i < gf->n_nodes;     i++) { nodes_unused[i]     = 1; }
+    ctx->concur_list_len = 0;
+
+    int n_left    = gf->n_nodes;
+    int n_start   = 0; // all nodes before n_start at nodes_unused array have been sorted and store back to ctx->concur_list
+    int level_pos = 0; // at ctx->concur_list, the last layer (level) ends at level_pos
+
+    while (n_left > 0) {
+        // number of nodes at a layer (that can be issued concurrently)
+        int concurrency = 0;
+        for (int i = n_start; i < ((n_start + search_depth > gf->n_nodes) ? gf->n_nodes : n_start + search_depth); i++) {
+            if (nodes_unused[i]) {
+                // if the requirements for gf->nodes[i] are satisfied
+                int exe_flag = 1;
+
+                // scan all srcs
+                for (int src_ind = 0; src_ind < GGML_MAX_SRC; src_ind++) {
+                    struct ggml_tensor * src_cur = gf->nodes[i]->src[src_ind];
+                    if (src_cur) {
+                        // if is leaf nodes it's satisfied.
+                        // TODO: ggml_is_leaf()
+                        if (src_cur->op == GGML_OP_NONE && src_cur->grad == NULL) {
+                            continue;
+                        }
+
+                        // otherwise this src should be the output from previous nodes.
+                        int is_found = 0;
+
+                        // scan 2*search_depth back because we inserted barrier.
+                        //for (int j = ((level_pos - 2*search_depth) < 0 ? 0 : (level_pos - 2*search_depth)); j < level_pos; j++) {
+                        for (int j = MAX(0, level_pos - 2*search_depth); j < level_pos; j++) {
+                            if (ctx->concur_list[j] >= 0 && gf->nodes[ctx->concur_list[j]] == src_cur) {
+                                is_found = 1;
+                                break;
+                            }
+                        }
+                        if (is_found == 0) {
+                            exe_flag = 0;
+                            break;
+                        }
+                    }
+                }
+                if (exe_flag && check_mem) {
+                    // check if nodes[i]'s data will be overwritten by a node before nodes[i].
+                    // if node[5] and node[3] write to the same memory region, then we can't issue node[5] before node[3]
+                    int64_t data_start = (int64_t) gf->nodes[i]->data;
+                    int64_t length     = (int64_t) ggml_nbytes(gf->nodes[i]);
+                    for (int j = n_start; j < i; j++) {
+                        if (nodes_unused[j] && gf->nodes[j]->op != GGML_OP_RESHAPE \
+                                            && gf->nodes[j]->op != GGML_OP_VIEW \
+                                            && gf->nodes[j]->op != GGML_OP_TRANSPOSE \
+                                            && gf->nodes[j]->op != GGML_OP_PERMUTE) {
+                            if (((int64_t)gf->nodes[j]->data) >= data_start + length || \
+                                ((int64_t)gf->nodes[j]->data) + (int64_t) ggml_nbytes(gf->nodes[j]) <= data_start) {
+                                continue;
+                            }
+
+                            exe_flag = 0;
+                        }
+                    }
+                }
+                if (exe_flag) {
+                    ctx->concur_list[level_pos + concurrency] = i;
+                    nodes_unused[i] = 0;
+                    concurrency++;
+                    ctx->concur_list_len++;
+                }
+            }
+        }
+        n_left -= concurrency;
+        // adding a barrier different layer
+        ctx->concur_list[level_pos + concurrency] = -1;
+        ctx->concur_list_len++;
+        // jump all sorted nodes at nodes_bak
+        while (!nodes_unused[n_start]) {
+            n_start++;
+        }
+        level_pos += concurrency + 1;
+    }
+
+    if (ctx->concur_list_len > GGML_MAX_CONCUR) {
+        GGML_METAL_LOG_WARN("%s: too many elements for metal ctx->concur_list!\n", __func__);
+    }
+}
+
+void ggml_metal_graph_compute(
+        struct ggml_metal_context * ctx,
+               struct ggml_cgraph * gf) {
+    @autoreleasepool {
+
+    // if there is ctx->concur_list, dispatch concurrently
+    // else fallback to serial dispatch
+    MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor;
+
+    const bool has_concur = ctx->concur_list_len && ctx->concur_list_len <= GGML_MAX_CONCUR;
+
+    const int n_nodes  = has_concur ? ctx->concur_list_len      : gf->n_nodes;
+    edesc.dispatchType = has_concur ? MTLDispatchTypeConcurrent : MTLDispatchTypeSerial;
+
+    // create multiple command buffers and enqueue them
+    // then, we encode the graph into the command buffers in parallel
+
+    const int n_cb = ctx->n_cb;
+
+    for (int i = 0; i < n_cb; ++i) {
+        ctx->command_buffers[i] = [ctx->queue commandBuffer];
+
+        // enqueue the command buffers in order to specify their execution order
+        [ctx->command_buffers[i] enqueue];
+
+        ctx->command_encoders[i] = [ctx->command_buffers[i] computeCommandEncoderWithDescriptor: edesc];
+    }
+
+    for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
+        const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb;
+
+        dispatch_async(ctx->d_queue, ^{
+            size_t offs_src0 = 0;
+            size_t offs_src1 = 0;
+            size_t offs_dst  = 0;
+
+            id<MTLCommandBuffer> command_buffer  = ctx->command_buffers[cb_idx];
+            id<MTLComputeCommandEncoder> encoder = ctx->command_encoders[cb_idx];
+
+            const int node_start =                                      (cb_idx + 0) * n_nodes_per_cb;
+            const int node_end   = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes);
+
+            for (int ind = node_start; ind < node_end; ++ind) {
+                const int i = has_concur ? ctx->concur_list[ind] : ind;
+
+                if (i == -1) {
+                    [encoder memoryBarrierWithScope:MTLBarrierScopeBuffers];
+                    continue;
+                }
+
+                //GGML_METAL_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
+
+                struct ggml_tensor * src0 = gf->nodes[i]->src[0];
+                struct ggml_tensor * src1 = gf->nodes[i]->src[1];
+                struct ggml_tensor * dst  = gf->nodes[i];
+
+                const int64_t  ne00 = src0 ? src0->ne[0] : 0;
+                const int64_t  ne01 = src0 ? src0->ne[1] : 0;
+                const int64_t  ne02 = src0 ? src0->ne[2] : 0;
+                const int64_t  ne03 = src0 ? src0->ne[3] : 0;
+
+                const uint64_t nb00 = src0 ? src0->nb[0] : 0;
+                const uint64_t nb01 = src0 ? src0->nb[1] : 0;
+                const uint64_t nb02 = src0 ? src0->nb[2] : 0;
+                const uint64_t nb03 = src0 ? src0->nb[3] : 0;
+
+                const int64_t  ne10 = src1 ? src1->ne[0] : 0;
+                const int64_t  ne11 = src1 ? src1->ne[1] : 0;
+                const int64_t  ne12 = src1 ? src1->ne[2] : 0;
+                const int64_t  ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13);
+
+                const uint64_t nb10 = src1 ? src1->nb[0] : 0;
+                const uint64_t nb11 = src1 ? src1->nb[1] : 0;
+                const uint64_t nb12 = src1 ? src1->nb[2] : 0;
+                const uint64_t nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13);
+
+                const int64_t  ne0  = dst ? dst->ne[0] : 0;
+                const int64_t  ne1  = dst ? dst->ne[1] : 0;
+                const int64_t  ne2  = dst ? dst->ne[2] : 0;
+                const int64_t  ne3  = dst ? dst->ne[3] : 0;
+
+                const uint64_t nb0  = dst ? dst->nb[0] : 0;
+                const uint64_t nb1  = dst ? dst->nb[1] : 0;
+                const uint64_t nb2  = dst ? dst->nb[2] : 0;
+                const uint64_t nb3  = dst ? dst->nb[3] : 0;
+
+                const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
+                const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;
+                const enum ggml_type dstt  = dst  ? dst->type  : GGML_TYPE_COUNT;
+
+                id<MTLBuffer> id_src0 = src0 ? ggml_metal_get_buffer(ctx, src0, &offs_src0) : nil;
+                id<MTLBuffer> id_src1 = src1 ? ggml_metal_get_buffer(ctx, src1, &offs_src1) : nil;
+                id<MTLBuffer> id_dst  = dst  ? ggml_metal_get_buffer(ctx, dst,  &offs_dst)  : nil;
+
+                //GGML_METAL_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op));
+                //if (src0) {
+                //    GGML_METAL_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02,
+                //            ggml_is_contiguous(src0), src0->name);
+                //}
+                //if (src1) {
+                //    GGML_METAL_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12,
+                //            ggml_is_contiguous(src1), src1->name);
+                //}
+                //if (dst) {
+                //    GGML_METAL_LOG_INFO("%s: dst  - %4s [%5lld, %5lld, %5lld], 1, %s\n",  __func__, ggml_type_name(dstt),  ne0,  ne1,  ne2,
+                //            dst->name);
+                //}
+
+                switch (dst->op) {
+                    case GGML_OP_NONE:
+                    case GGML_OP_RESHAPE:
+                    case GGML_OP_VIEW:
+                    case GGML_OP_TRANSPOSE:
+                    case GGML_OP_PERMUTE:
+                        {
+                            // noop
+                        } break;
+                    case GGML_OP_CONCAT:
+                        {
+                            const int64_t nb = ne00;
+
+                            [encoder setComputePipelineState:ctx->pipeline_concat];
+                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                            [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
+                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
+                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
+                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
+                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
+                            [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6];
+                            [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7];
+                            [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8];
+                            [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9];
+                            [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10];
+                            [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11];
+                            [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12];
+                            [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13];
+                            [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14];
+                            [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15];
+                            [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16];
+                            [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17];
+                            [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18];
+                            [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:19];
+                            [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:20];
+                            [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:21];
+                            [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:22];
+                            [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:23];
+                            [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:24];
+                            [encoder setBytes:&nb2  length:sizeof(nb2)  atIndex:25];
+                            [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:26];
+                            [encoder setBytes:&nb   length:sizeof(nb)   atIndex:27];
+
+                            const int nth = MIN(1024, ne0);
+
+                            [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                        } break;
+                    case GGML_OP_ADD:
+                        {
+                            GGML_ASSERT(ggml_is_contiguous(src0));
+                            GGML_ASSERT(ggml_is_contiguous(src1));
+
+                            bool bcast_row = false;
+
+                            int64_t nb = ne00;
+
+                            if (ggml_nelements(src1) == ne10 && ne00 % 4 == 0) {
+                                // src1 is a row
+                                GGML_ASSERT(ne11 == 1);
+
+                                nb = ne00 / 4;
+                                [encoder setComputePipelineState:ctx->pipeline_add_row];
+
+                                bcast_row = true;
+                            } else {
+                                [encoder setComputePipelineState:ctx->pipeline_add];
+                            }
+                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                            [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
+                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
+                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
+                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
+                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
+                            [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6];
+                            [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7];
+                            [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8];
+                            [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9];
+                            [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10];
+                            [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11];
+                            [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12];
+                            [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13];
+                            [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14];
+                            [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15];
+                            [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16];
+                            [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17];
+                            [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18];
+                            [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:19];
+                            [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:20];
+                            [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:21];
+                            [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:22];
+                            [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:23];
+                            [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:24];
+                            [encoder setBytes:&nb2  length:sizeof(nb2)  atIndex:25];
+                            [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:26];
+                            [encoder setBytes:&nb   length:sizeof(nb)   atIndex:27];
+
+                            if (bcast_row) {
+                                const int64_t n = ggml_nelements(dst)/4;
+
+                                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                            } else {
+                                const int nth = MIN(1024, ne0);
+
+                                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                            }
+                        } break;
+                    case GGML_OP_MUL:
+                        {
+                            GGML_ASSERT(ggml_is_contiguous(src0));
+                            GGML_ASSERT(ggml_is_contiguous(src1));
+
+                            // utilize float4
+                            GGML_ASSERT(ne00 % 4 == 0);
+                            const int64_t nb = ne00/4;
+
+                            if (ggml_nelements(src1) == ne10) {
+                                // src1 is a row
+                                GGML_ASSERT(ne11 == 1);
+                                [encoder setComputePipelineState:ctx->pipeline_mul_row];
+                            } else {
+                                [encoder setComputePipelineState:ctx->pipeline_mul];
+                            }
+                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                            [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
+                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
+                            [encoder setBytes:&nb     length:sizeof(nb) atIndex:3];
+
+                            const int64_t n = ggml_nelements(dst)/4;
+
+                            [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                        } break;
+                    case GGML_OP_SCALE:
+                        {
+                            GGML_ASSERT(ggml_is_contiguous(src0));
+
+                            const float scale = *(const float *) src1->data;
+
+                            [encoder setComputePipelineState:ctx->pipeline_scale];
+                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                            [encoder setBytes:&scale length:sizeof(scale) atIndex:2];
+
+                            const int64_t n = ggml_nelements(dst);
+                            GGML_ASSERT(n % 4 == 0);
+
+                            [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                        } break;
+                    case GGML_OP_UNARY:
+                        switch (ggml_get_unary_op(gf->nodes[i])) {
+                            case GGML_UNARY_OP_SILU:
+                                {
+                                    [encoder setComputePipelineState:ctx->pipeline_silu];
+                                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+
+                                    const int64_t n = ggml_nelements(dst);
+                                    GGML_ASSERT(n % 4 == 0);
+
+                                    [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                                } break;
+                            case GGML_UNARY_OP_RELU:
+                                {
+                                    [encoder setComputePipelineState:ctx->pipeline_relu];
+                                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+
+                                    const int64_t n = ggml_nelements(dst);
+
+                                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                                } break;
+                            case GGML_UNARY_OP_GELU:
+                                {
+                                    [encoder setComputePipelineState:ctx->pipeline_gelu];
+                                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+
+                                    const int64_t n = ggml_nelements(dst);
+                                    GGML_ASSERT(n % 4 == 0);
+
+                                    [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                                } break;
+                            default:
+                                {
+                                    GGML_METAL_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
+                                    GGML_ASSERT(false);
+                                }
+                        } break;
+                    case GGML_OP_SQR:
+                        {
+                            GGML_ASSERT(ggml_is_contiguous(src0));
+
+                            [encoder setComputePipelineState:ctx->pipeline_sqr];
+                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst atIndex:1];
+
+                            const int64_t n = ggml_nelements(dst);
+                            [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                        } break;
+                    case GGML_OP_SOFT_MAX:
+                        {
+                            const int nth = MIN(32, ne00);
+
+                            if (ne00%4 == 0) {
+                                [encoder setComputePipelineState:ctx->pipeline_soft_max_4];
+                            } else {
+                                [encoder setComputePipelineState:ctx->pipeline_soft_max];
+                            }
+                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
+                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
+                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
+
+                            [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                        } break;
+                    case GGML_OP_DIAG_MASK_INF:
+                        {
+                            const int n_past = ((int32_t *)(dst->op_params))[0];
+
+                            if (ne00%8 == 0) {
+                                [encoder setComputePipelineState:ctx->pipeline_diag_mask_inf_8];
+                            } else {
+                                [encoder setComputePipelineState:ctx->pipeline_diag_mask_inf];
+                            }
+                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                            [encoder setBytes:&ne00   length:sizeof(ne00) atIndex:2];
+                            [encoder setBytes:&ne01   length:sizeof(ne01) atIndex:3];
+                            [encoder setBytes:&n_past length:sizeof(int)  atIndex:4];
+
+                            if (ne00%8 == 0) {
+                                [encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                            }
+                            else {
+                                [encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                            }
+                        } break;
+                    case GGML_OP_MUL_MAT:
+                        {
+                            GGML_ASSERT(ne00 == ne10);
+                            GGML_ASSERT(ne03 == ne13);
+
+                            const uint gqa = ne12/ne02;
+
+                            // find the break-even point where the matrix-matrix kernel becomes more efficient compared
+                            // to the matrix-vector kernel
+                            int ne11_mm_min = 1;
+
+#if 0
+                            // the numbers below are measured on M2 Ultra for 7B and 13B models
+                            // these numbers do not translate to other devices or model sizes
+                            // TODO: need to find a better approach
+                            if ([ctx->device.name isEqualToString:@"Apple M2 Ultra"]) {
+                                switch (src0t) {
+                                    case GGML_TYPE_F16:  ne11_mm_min = 2;  break;
+                                    case GGML_TYPE_Q8_0: ne11_mm_min = 7;  break;
+                                    case GGML_TYPE_Q2_K: ne11_mm_min = 15; break;
+                                    case GGML_TYPE_Q3_K: ne11_mm_min = 7;  break;
+                                    case GGML_TYPE_Q4_0:
+                                    case GGML_TYPE_Q4_1: ne11_mm_min = 15; break;
+                                    case GGML_TYPE_Q4_K: ne11_mm_min = 11; break;
+                                    case GGML_TYPE_Q5_0:                          // not tested yet
+                                    case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet
+                                    case GGML_TYPE_Q5_K: ne11_mm_min = 7;  break;
+                                    case GGML_TYPE_Q6_K: ne11_mm_min = 7;  break;
+                                    default:             ne11_mm_min = 1;  break;
+                                }
+                            }
+#endif
+
+                            // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
+                            // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
+                            if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
+                                !ggml_is_transposed(src0) &&
+                                !ggml_is_transposed(src1) &&
+                                src1t == GGML_TYPE_F32 &&
+                                ne00 % 32 == 0 && ne00 >= 64 &&
+                                ne11 > ne11_mm_min) {
+                                //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
+                                switch (src0->type) {
+                                    case GGML_TYPE_F32:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_f32_f32];  break;
+                                    case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32];  break;
+                                    case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_0_f32]; break;
+                                    case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_1_f32]; break;
+                                    case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_0_f32]; break;
+                                    case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_1_f32]; break;
+                                    case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q8_0_f32]; break;
+                                    case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q2_K_f32]; break;
+                                    case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q3_K_f32]; break;
+                                    case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_K_f32]; break;
+                                    case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_K_f32]; break;
+                                    case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q6_K_f32]; break;
+                                    default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
+                                }
+                                [encoder setBuffer:id_src0 offset:offs_src0    atIndex:0];
+                                [encoder setBuffer:id_src1 offset:offs_src1    atIndex:1];
+                                [encoder setBuffer:id_dst  offset:offs_dst     atIndex:2];
+                                [encoder setBytes:&ne00    length:sizeof(ne00) atIndex:3];
+                                [encoder setBytes:&ne02    length:sizeof(ne02) atIndex:4];
+                                [encoder setBytes:&nb01    length:sizeof(nb01) atIndex:5];
+                                [encoder setBytes:&nb02    length:sizeof(nb02) atIndex:6];
+                                [encoder setBytes:&ne12    length:sizeof(ne12) atIndex:7];
+                                [encoder setBytes:&nb10    length:sizeof(nb10) atIndex:8];
+                                [encoder setBytes:&nb11    length:sizeof(nb11) atIndex:9];
+                                [encoder setBytes:&nb12    length:sizeof(nb12) atIndex:10];
+                                [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:11];
+                                [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:12];
+                                [encoder setBytes:&gqa     length:sizeof(gqa)  atIndex:13];
+                                [encoder setThreadgroupMemoryLength:8192 atIndex:0];
+                                [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
+                            } else {
+                                int nth0 = 32;
+                                int nth1 = 1;
+                                int nrows = 1;
+                                //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
+
+                                // use custom matrix x vector kernel
+                                switch (src0t) {
+                                    case GGML_TYPE_F32:
+                                        {
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_f32_f32];
+                                            nrows = 4;
+                                        } break;
+                                    case GGML_TYPE_F16:
+                                        {
+                                            nth0 = 32;
+                                            nth1 = 1;
+                                            if (ne11 * ne12 < 4) {
+                                                [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_1row];
+                                            } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
+                                                [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_l4];
+                                                nrows = ne11;
+                                            } else {
+                                                [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32];
+                                                nrows = 4;
+                                            }
+                                        } break;
+                                    case GGML_TYPE_Q4_0:
+                                        {
+                                            GGML_ASSERT(ne02 == 1);
+                                            GGML_ASSERT(ne12 == 1);
+
+                                            nth0 = 8;
+                                            nth1 = 8;
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_0_f32];
+                                        } break;
+                                    case GGML_TYPE_Q4_1:
+                                        {
+                                            GGML_ASSERT(ne02 == 1);
+                                            GGML_ASSERT(ne12 == 1);
+
+                                            nth0 = 8;
+                                            nth1 = 8;
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_1_f32];
+                                        } break;
+                                    case GGML_TYPE_Q5_0:
+                                        {
+                                            GGML_ASSERT(ne02 == 1);
+                                            GGML_ASSERT(ne12 == 1);
+
+                                            nth0 = 8;
+                                            nth1 = 8;
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_0_f32];
+                                        } break;
+                                    case GGML_TYPE_Q5_1:
+                                        {
+                                            GGML_ASSERT(ne02 == 1);
+                                            GGML_ASSERT(ne12 == 1);
+
+                                            nth0 = 8;
+                                            nth1 = 8;
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_1_f32];
+                                        } break;
+                                    case GGML_TYPE_Q8_0:
+                                        {
+                                            GGML_ASSERT(ne02 == 1);
+                                            GGML_ASSERT(ne12 == 1);
+
+                                            nth0 = 8;
+                                            nth1 = 8;
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q8_0_f32];
+                                        } break;
+                                    case GGML_TYPE_Q2_K:
+                                        {
+                                            GGML_ASSERT(ne02 == 1);
+                                            GGML_ASSERT(ne12 == 1);
+
+                                            nth0 = 2;
+                                            nth1 = 32;
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q2_K_f32];
+                                        } break;
+                                    case GGML_TYPE_Q3_K:
+                                        {
+                                            GGML_ASSERT(ne02 == 1);
+                                            GGML_ASSERT(ne12 == 1);
+
+                                            nth0 = 2;
+                                            nth1 = 32;
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q3_K_f32];
+                                        } break;
+                                    case GGML_TYPE_Q4_K:
+                                        {
+                                            GGML_ASSERT(ne02 == 1);
+                                            GGML_ASSERT(ne12 == 1);
+
+                                            nth0 = 4; //1;
+                                            nth1 = 8; //32;
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_K_f32];
+                                        } break;
+                                    case GGML_TYPE_Q5_K:
+                                        {
+                                            GGML_ASSERT(ne02 == 1);
+                                            GGML_ASSERT(ne12 == 1);
+
+                                            nth0 = 2;
+                                            nth1 = 32;
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_K_f32];
+                                        } break;
+                                    case GGML_TYPE_Q6_K:
+                                        {
+                                            GGML_ASSERT(ne02 == 1);
+                                            GGML_ASSERT(ne12 == 1);
+
+                                            nth0 = 2;
+                                            nth1 = 32;
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q6_K_f32];
+                                        } break;
+                                    default:
+                                        {
+                                            GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t);
+                                            GGML_ASSERT(false && "not implemented");
+                                        }
+                                };
+
+                                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                                [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
+                                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
+                                [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
+                                [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
+                                [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
+                                [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
+                                [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
+                                [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
+                                [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9];
+                                [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10];
+                                [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:11];
+                                [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12];
+                                [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13];
+                                [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14];
+                                [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:15];
+                                [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:16];
+                                [encoder setBytes:&gqa  length:sizeof(gqa)  atIndex:17];
+
+                                if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
+                                    src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 ||
+                                    src0t == GGML_TYPE_Q2_K) { // || src0t == GGML_TYPE_Q4_K) {
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                }
+                                else if (src0t == GGML_TYPE_Q4_K) {
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                }
+                                else if (src0t == GGML_TYPE_Q3_K) {
+#ifdef GGML_QKK_64
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+#else
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+#endif
+                                }
+                                else if (src0t == GGML_TYPE_Q5_K) {
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                }
+                                else if (src0t == GGML_TYPE_Q6_K) {
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                } else {
+                                    int64_t ny = (ne11 + nrows - 1)/nrows;
+                                    [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                }
+                            }
+                        } break;
+                    case GGML_OP_GET_ROWS:
+                        {
+                            switch (src0->type) {
+                                case GGML_TYPE_F32:  [encoder setComputePipelineState:ctx->pipeline_get_rows_f32];  break;
+                                case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_get_rows_f16];  break;
+                                case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
+                                case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_1]; break;
+                                case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_0]; break;
+                                case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_1]; break;
+                                case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q8_0]; break;
+                                case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_K]; break;
+                                case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_K]; break;
+                                case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_K]; break;
+                                case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_K]; break;
+                                case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_K]; break;
+                                default: GGML_ASSERT(false && "not implemented");
+                            }
+
+                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                            [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
+                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
+                            [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3];
+                            [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:4];
+                            [encoder setBytes:&nb1  length:sizeof(uint64_t) atIndex:5];
+
+                            const int64_t n = ggml_nelements(src1);
+
+                            [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                        } break;
+                    case GGML_OP_RMS_NORM:
+                        {
+                            GGML_ASSERT(ne00 % 4 == 0);
+
+                            float eps;
+                            memcpy(&eps, dst->op_params, sizeof(float));
+
+                            const int nth = MIN(512, ne00);
+
+                            [encoder setComputePipelineState:ctx->pipeline_rms_norm];
+                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                            [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
+                            [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3];
+                            [encoder setBytes:&eps  length:sizeof(   float) atIndex:4];
+                            [encoder setThreadgroupMemoryLength:nth/32*sizeof(float) atIndex:0];
+
+                            const int64_t nrows = ggml_nrows(src0);
+
+                            [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                        } break;
+                    case GGML_OP_NORM:
+                        {
+                            float eps;
+                            memcpy(&eps, dst->op_params, sizeof(float));
+
+                            const int nth = MIN(256, ne00);
+
+                            [encoder setComputePipelineState:ctx->pipeline_norm];
+                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
+                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
+                            [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:3];
+                            [encoder setBytes:&eps     length:sizeof(   float) atIndex:4];
+                            [encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0];
+
+                            const int64_t nrows = ggml_nrows(src0);
+
+                            [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                        } break;
+                    case GGML_OP_ALIBI:
+                        {
+                            GGML_ASSERT((src0t == GGML_TYPE_F32));
+
+                            const int nth = MIN(1024, ne00);
+
+                            //const int n_past = ((int32_t *) dst->op_params)[0];
+                            const int n_head = ((int32_t *) dst->op_params)[1];
+                            float max_bias;
+                            memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
+
+                            const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
+                            const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
+                            const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
+
+                            [encoder setComputePipelineState:ctx->pipeline_alibi_f32];
+                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                            [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
+                            [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
+                            [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
+                            [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
+                            [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
+                            [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
+                            [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
+                            [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
+                            [encoder setBytes:&ne0  length:sizeof( int64_t) atIndex:10];
+                            [encoder setBytes:&ne1  length:sizeof( int64_t) atIndex:11];
+                            [encoder setBytes:&ne2  length:sizeof( int64_t) atIndex:12];
+                            [encoder setBytes:&ne3  length:sizeof( int64_t) atIndex:13];
+                            [encoder setBytes:&nb0  length:sizeof(uint64_t) atIndex:14];
+                            [encoder setBytes:&nb1  length:sizeof(uint64_t) atIndex:15];
+                            [encoder setBytes:&nb2  length:sizeof(uint64_t) atIndex:16];
+                            [encoder setBytes:&nb3  length:sizeof(uint64_t) atIndex:17];
+                            [encoder setBytes:&m0   length:sizeof(   float) atIndex:18];
+                            [encoder setBytes:&m1   length:sizeof(   float) atIndex:19];
+                            [encoder setBytes:&n_heads_log2_floor   length:sizeof(int) atIndex:20];
+
+                            [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                        } break;
+                    case GGML_OP_ROPE:
+                        {
+                            GGML_ASSERT(ne10 == ne02);
+
+                            const int nth = MIN(1024, ne00);
+
+                            const int n_past = ((int32_t *) dst->op_params)[0];
+                            const int n_dims = ((int32_t *) dst->op_params)[1];
+                            const int mode   = ((int32_t *) dst->op_params)[2];
+
+                            float freq_base;
+                            float freq_scale;
+                            memcpy(&freq_base,  (int32_t *) dst->op_params + 4, sizeof(float));
+                            memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
+
+                            switch (src0->type) {
+                                case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_rope_f32]; break;
+                                case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_rope_f16]; break;
+                                default: GGML_ASSERT(false);
+                            };
+
+                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
+                            [encoder setBuffer:id_src1 offset:offs_src1        atIndex:1];
+                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:2];
+                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:3];
+                            [encoder setBytes:&ne01    length:sizeof( int64_t) atIndex:4];
+                            [encoder setBytes:&ne02    length:sizeof( int64_t) atIndex:5];
+                            [encoder setBytes:&ne03    length:sizeof( int64_t) atIndex:6];
+                            [encoder setBytes:&nb00    length:sizeof(uint64_t) atIndex:7];
+                            [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:8];
+                            [encoder setBytes:&nb02    length:sizeof(uint64_t) atIndex:9];
+                            [encoder setBytes:&nb03    length:sizeof(uint64_t) atIndex:10];
+                            [encoder setBytes:&ne0     length:sizeof( int64_t) atIndex:11];
+                            [encoder setBytes:&ne1     length:sizeof( int64_t) atIndex:12];
+                            [encoder setBytes:&ne2     length:sizeof( int64_t) atIndex:13];
+                            [encoder setBytes:&ne3     length:sizeof( int64_t) atIndex:14];
+                            [encoder setBytes:&nb0     length:sizeof(uint64_t) atIndex:15];
+                            [encoder setBytes:&nb1     length:sizeof(uint64_t) atIndex:16];
+                            [encoder setBytes:&nb2     length:sizeof(uint64_t) atIndex:17];
+                            [encoder setBytes:&nb3     length:sizeof(uint64_t) atIndex:18];
+                            [encoder setBytes:&n_past  length:sizeof(     int) atIndex:19];
+                            [encoder setBytes:&n_dims  length:sizeof(     int) atIndex:20];
+                            [encoder setBytes:&mode    length:sizeof(     int) atIndex:21];
+                            [encoder setBytes:&freq_base  length:sizeof(float) atIndex:22];
+                            [encoder setBytes:&freq_scale length:sizeof(float) atIndex:23];
+
+                            [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                        } break;
+                    case GGML_OP_DUP:
+                    case GGML_OP_CPY:
+                    case GGML_OP_CONT:
+                        {
+                            const int nth = MIN(1024, ne00);
+
+                            switch (src0t) {
+                                case GGML_TYPE_F32:
+                                    {
+                                        switch (dstt) {
+                                            case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f16]; break;
+                                            case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f32]; break;
+                                            default: GGML_ASSERT(false && "not implemented");
+                                        };
+                                    } break;
+                                case GGML_TYPE_F16:
+                                    {
+                                        switch (dstt) {
+                                            case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_cpy_f16_f16]; break;
+                                            case GGML_TYPE_F32: GGML_ASSERT(false && "cpy_f16_f32 not implemented"); break;
+                                            default: GGML_ASSERT(false && "not implemented");
+                                        };
+                                    } break;
+                                default: GGML_ASSERT(false && "not implemented");
+                            }
+
+                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
+                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
+                            [encoder setBytes:&ne01    length:sizeof( int64_t) atIndex:3];
+                            [encoder setBytes:&ne02    length:sizeof( int64_t) atIndex:4];
+                            [encoder setBytes:&ne03    length:sizeof( int64_t) atIndex:5];
+                            [encoder setBytes:&nb00    length:sizeof(uint64_t) atIndex:6];
+                            [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:7];
+                            [encoder setBytes:&nb02    length:sizeof(uint64_t) atIndex:8];
+                            [encoder setBytes:&nb03    length:sizeof(uint64_t) atIndex:9];
+                            [encoder setBytes:&ne0     length:sizeof( int64_t) atIndex:10];
+                            [encoder setBytes:&ne1     length:sizeof( int64_t) atIndex:11];
+                            [encoder setBytes:&ne2     length:sizeof( int64_t) atIndex:12];
+                            [encoder setBytes:&ne3     length:sizeof( int64_t) atIndex:13];
+                            [encoder setBytes:&nb0     length:sizeof(uint64_t) atIndex:14];
+                            [encoder setBytes:&nb1     length:sizeof(uint64_t) atIndex:15];
+                            [encoder setBytes:&nb2     length:sizeof(uint64_t) atIndex:16];
+                            [encoder setBytes:&nb3     length:sizeof(uint64_t) atIndex:17];
+
+                            [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                        } break;
+                    default:
+                        {
+                            GGML_METAL_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
+                            GGML_ASSERT(false);
+                        }
+                }
+            }
+
+            if (encoder != nil) {
+                [encoder endEncoding];
+                encoder = nil;
+            }
+
+            [command_buffer commit];
+        });
+    }
+
+    // wait for all threads to finish
+    dispatch_barrier_sync(ctx->d_queue, ^{});
+
+    // check status of command buffers
+    // needed to detect if the device ran out-of-memory for example (#1881)
+    for (int i = 0; i < n_cb; i++) {
+        [ctx->command_buffers[i] waitUntilCompleted];
+
+        MTLCommandBufferStatus status = (MTLCommandBufferStatus) [ctx->command_buffers[i] status];
+        if (status != MTLCommandBufferStatusCompleted) {
+            GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
+            GGML_ASSERT(false);
+        }
+    }
+
+    }
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+// backend interface
+
+static const char * ggml_backend_metal_name(ggml_backend_t backend) {
+    return "Metal";
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_metal_free(ggml_backend_t backend) {
+    struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
+    ggml_metal_free(ctx);
+    free(backend);
+}
+
+static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) {
+    return (void *)buffer->context;
+}
+
+static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    free(buffer->context);
+    UNUSED(buffer);
+}
+
+static struct ggml_backend_buffer_i metal_backend_buffer_i = {
+    /* .free_buffer    = */ ggml_backend_metal_buffer_free_buffer,
+    /* .get_base       = */ ggml_backend_metal_buffer_get_base,
+    /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
+    /* .init_tensor    = */ NULL, // no initialization required
+    /* .free_tensor    = */ NULL, // no cleanup required
+};
+
+static ggml_backend_buffer_t ggml_backend_metal_alloc_buffer(ggml_backend_t backend, size_t size) {
+    struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
+
+    void * data = ggml_metal_host_malloc(size);
+
+    // TODO: set proper name of the buffers
+    ggml_metal_add_buffer(ctx, "backend", data, size, 0);
+
+    return ggml_backend_buffer_init(backend, metal_backend_buffer_i, data, size);
+}
+
+static size_t ggml_backend_metal_get_alignment(ggml_backend_t backend) {
+    return 32;
+    UNUSED(backend);
+}
+
+static void ggml_backend_metal_set_tensor_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+
+    memcpy((char *)tensor->data + offset, data, size);
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_metal_get_tensor_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+
+    memcpy(data, (const char *)tensor->data + offset, size);
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_metal_synchronize(ggml_backend_t backend) {
+    UNUSED(backend);
+}
+
+static void ggml_backend_metal_cpy_tensor_from(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
+    ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_metal_cpy_tensor_to(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
+    ggml_backend_tensor_set_async(dst, src->data, 0, ggml_nbytes(src));
+
+    UNUSED(backend);
+}
+
+static void ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context;
+
+    ggml_metal_graph_compute(metal_ctx, cgraph);
+}
+
+static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+    return true;
+    UNUSED(backend);
+    UNUSED(op);
+}
+
+static struct ggml_backend_i metal_backend_i = {
+    /* .get_name            = */ ggml_backend_metal_name,
+    /* .free                = */ ggml_backend_metal_free,
+    /* .alloc_buffer        = */ ggml_backend_metal_alloc_buffer,
+    /* .get_alignment       = */ ggml_backend_metal_get_alignment,
+    /* .set_tensor_async    = */ ggml_backend_metal_set_tensor_async,
+    /* .get_tensor_async    = */ ggml_backend_metal_get_tensor_async,
+    /* .synchronize         = */ ggml_backend_metal_synchronize,
+    /* .cpy_tensor_from     = */ ggml_backend_metal_cpy_tensor_from,
+    /* .cpy_tensor_to       = */ ggml_backend_metal_cpy_tensor_to,
+    /* .graph_plan_create   = */ NULL, // the metal implementation does not require creating graph plans atm
+    /* .graph_plan_free     = */ NULL,
+    /* .graph_plan_compute  = */ NULL,
+    /* .graph_compute       = */ ggml_backend_metal_graph_compute,
+    /* .supports_op         = */ ggml_backend_metal_supports_op,
+};
+
+ggml_backend_t ggml_backend_metal_init(void) {
+    struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
+
+    ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS);
+
+    ggml_backend_t metal_backend = malloc(sizeof(struct ggml_backend));
+
+    *metal_backend = (struct ggml_backend) {
+        /* .interface = */ metal_backend_i,
+        /* .context   = */ ctx,
+    };
+
+    return metal_backend;
+}
+
+bool ggml_backend_is_metal(ggml_backend_t backend) {
+    return backend->iface.get_name == ggml_backend_metal_name;
+}
+
+void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
+    struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
+
+    ggml_metal_set_n_cb(ctx, n_cb);
+}

runner/ggml-mpi.c

@@ -0,0 +1,244 @@
+//go:build mpi
+
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#include "ggml-mpi.h"
+
+#include "ggml.h"
+
+#include <mpi.h>
+
+#include <stdio.h>
+#include <stdlib.h>
+
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+
+#define UNUSED GGML_UNUSED
+
+struct ggml_mpi_context {
+    int rank;
+    int size;
+};
+
+void ggml_mpi_backend_init(void) {
+    MPI_Init(NULL, NULL);
+}
+
+void ggml_mpi_backend_free(void) {
+    MPI_Finalize();
+}
+
+struct ggml_mpi_context * ggml_mpi_init(void) {
+    struct ggml_mpi_context * ctx = calloc(1, sizeof(struct ggml_mpi_context));
+
+    MPI_Comm_rank(MPI_COMM_WORLD, &ctx->rank);
+    MPI_Comm_size(MPI_COMM_WORLD, &ctx->size);
+
+    return ctx;
+}
+
+void ggml_mpi_free(struct ggml_mpi_context * ctx) {
+    free(ctx);
+}
+
+int ggml_mpi_rank(struct ggml_mpi_context * ctx) {
+    return ctx->rank;
+}
+
+void ggml_mpi_eval_init(
+        struct ggml_mpi_context * ctx_mpi,
+                            int * n_tokens,
+                            int * n_past,
+                            int * n_threads) {
+    UNUSED(ctx_mpi);
+
+    // synchronize the worker node parameters with the root node
+    MPI_Barrier(MPI_COMM_WORLD);
+
+    MPI_Bcast(n_tokens,  1, MPI_INT, 0, MPI_COMM_WORLD);
+    MPI_Bcast(n_past,    1, MPI_INT, 0, MPI_COMM_WORLD);
+    MPI_Bcast(n_threads, 1, MPI_INT, 0, MPI_COMM_WORLD);
+}
+
+static int ggml_graph_get_node_idx(struct ggml_cgraph * gf, const char * name) {
+    struct ggml_tensor * t = ggml_graph_get_tensor(gf, name);
+    if (t == NULL) {
+        fprintf(stderr, "%s: tensor %s not found\n", __func__, name);
+        return -1;
+    }
+
+    for (int i = 0; i < gf->n_nodes; i++) {
+        if (gf->nodes[i] == t) {
+            return i;
+        }
+    }
+
+    fprintf(stderr, "%s: tensor %s not found in graph (should not happen)\n", __func__, name);
+    return -1;
+}
+
+static void ggml_mpi_tensor_send(struct ggml_tensor * t, int mpi_rank_dst) {
+    MPI_Datatype mpi_type;
+
+    switch (t->type) {
+        case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break;
+        case GGML_TYPE_F32: mpi_type = MPI_FLOAT;   break;
+        default: GGML_ASSERT(false && "not implemented");
+    }
+
+    const int retval = MPI_Send(t->data, ggml_nelements(t), mpi_type, mpi_rank_dst, 0, MPI_COMM_WORLD);
+    GGML_ASSERT(retval == MPI_SUCCESS);
+}
+
+static void ggml_mpi_tensor_recv(struct ggml_tensor * t, int mpi_rank_src) {
+    MPI_Datatype mpi_type;
+
+    switch (t->type) {
+        case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break;
+        case GGML_TYPE_F32: mpi_type = MPI_FLOAT;   break;
+        default: GGML_ASSERT(false && "not implemented");
+    }
+
+    MPI_Status status; UNUSED(status);
+
+    const int retval = MPI_Recv(t->data, ggml_nelements(t), mpi_type, mpi_rank_src, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
+    GGML_ASSERT(retval == MPI_SUCCESS);
+}
+
+// TODO: there are many improvements that can be done to this implementation
+void ggml_mpi_graph_compute_pre(
+        struct ggml_mpi_context * ctx_mpi,
+             struct ggml_cgraph * gf,
+                            int   n_layers) {
+    const int mpi_rank = ctx_mpi->rank;
+    const int mpi_size = ctx_mpi->size;
+
+    struct ggml_tensor * inp_tokens = ggml_graph_get_tensor(gf, "inp_tokens");
+    if (inp_tokens == NULL) {
+        fprintf(stderr, "%s: tensor 'inp_tokens' not found\n", __func__);
+        return;
+    }
+
+    struct ggml_tensor * inp0 = ggml_graph_get_tensor(gf, "layer_inp_0");
+    if (inp0 == NULL) {
+        fprintf(stderr, "%s: tensor 'inp0' not found\n", __func__);
+        return;
+    }
+
+    GGML_ASSERT(inp0 == gf->nodes[0]);
+
+    // distribute the compute graph into slices across the MPI nodes
+    //
+    // the main node (0) processes the last layers + the remainder of the compute graph
+    // and is responsible to pass the input tokens to the first node (1)
+    //
+    // node 1:   [(  0) * n_per_node, (  1) * n_per_node)
+    // node 2:   [(  1) * n_per_node, (  2) * n_per_node)
+    // ...
+    // node n-1: [(n-2) * n_per_node, (n-1) * n_per_node)
+    // node 0:   [(n-1) * n_per_node,            n_nodes)
+    //
+    if (mpi_rank > 0) {
+        if (mpi_rank == 1) {
+            // the first node (1) receives the input tokens from the main node (0)
+            ggml_mpi_tensor_recv(inp_tokens, 0);
+        } else {
+            // recv input data for each node into the "inp0" tensor (i.e. the first node in the compute graph)
+            ggml_mpi_tensor_recv(inp0, mpi_rank - 1);
+        }
+    } else if (mpi_size > 1) {
+        // node 0 sends the input tokens to node 1
+        ggml_mpi_tensor_send(inp_tokens, 1);
+
+        // recv the output data from the last node
+        ggml_mpi_tensor_recv(inp0, mpi_size - 1);
+    }
+
+    {
+        const int n_per_node = (n_layers + (mpi_size - 1)) / mpi_size;
+
+        const int mpi_idx = mpi_rank > 0 ? mpi_rank - 1 : mpi_size - 1;
+
+        const int il0 =               (mpi_idx + 0) * n_per_node;
+        const int il1 = MIN(n_layers, (mpi_idx + 1) * n_per_node);
+
+        char name_l0[GGML_MAX_NAME];
+        char name_l1[GGML_MAX_NAME];
+
+        snprintf(name_l0, sizeof(name_l0), "layer_inp_%d", il0);
+        snprintf(name_l1, sizeof(name_l1), "layer_inp_%d", il1);
+
+        const int idx_l0 =                ggml_graph_get_node_idx(gf, name_l0);
+        const int idx_l1 = mpi_rank > 0 ? ggml_graph_get_node_idx(gf, name_l1) + 1 : gf->n_nodes;
+
+        if (idx_l0 < 0 || idx_l1 < 0) {
+            fprintf(stderr, "%s: layer input nodes not found\n", __func__);
+            return;
+        }
+
+        // attach the input data to all nodes that need it
+        // TODO: not great - should be able to do this without modifying the compute graph (see next TODO below)
+        for (int i = idx_l0; i < idx_l1; i++) {
+            if (gf->nodes[i]->src[0] == gf->nodes[idx_l0]) {
+                gf->nodes[i]->src[0] =  inp0;
+            }
+            if (gf->nodes[i]->src[1] == gf->nodes[idx_l0]) {
+                gf->nodes[i]->src[1] =  inp0;
+            }
+        }
+
+        // TODO: instead of rearranging the nodes, we should be able to execute a subset of the compute graph
+        for (int i = 1; i < idx_l1 - idx_l0; i++) {
+            gf->nodes[i] = gf->nodes[idx_l0 + i];
+            gf->grads[i] = gf->grads[idx_l0 + i];
+        }
+
+        // the first node performs the "get_rows" operation, the rest of the nodes get the data from the previous node
+        if (mpi_idx != 0) {
+            gf->nodes[0]->op = GGML_OP_NONE;
+        }
+
+        gf->n_nodes = idx_l1 - idx_l0;
+
+        //fprintf(stderr, "%s: node %d: processing %d nodes [%d, %d)\n", __func__, mpi_rank, gf->n_nodes, il0, il1);
+    }
+}
+
+void ggml_mpi_graph_compute_post(
+        struct ggml_mpi_context * ctx_mpi,
+             struct ggml_cgraph * gf,
+                            int   n_layers) {
+    UNUSED(n_layers);
+
+    const int mpi_rank = ctx_mpi->rank;
+    const int mpi_size = ctx_mpi->size;
+
+    // send the output data to the next node
+    if (mpi_rank > 0) {
+        ggml_mpi_tensor_send(gf->nodes[gf->n_nodes - 1], (mpi_rank + 1) % mpi_size);
+    }
+}

runner/ggml-mpi.h

@@ -0,0 +1,67 @@
+//go:build mpi
+
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#pragma once
+
+struct ggml_context;
+struct ggml_tensor;
+struct ggml_cgraph;
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+struct ggml_mpi_context;
+
+void ggml_mpi_backend_init(void);
+void ggml_mpi_backend_free(void);
+
+struct ggml_mpi_context * ggml_mpi_init(void);
+void ggml_mpi_free(struct ggml_mpi_context * ctx);
+
+int ggml_mpi_rank(struct ggml_mpi_context * ctx);
+
+void ggml_mpi_eval_init(
+        struct ggml_mpi_context * ctx_mpi,
+                            int * n_tokens,
+                            int * n_past,
+                            int * n_threads);
+
+void ggml_mpi_graph_compute_pre(
+        struct ggml_mpi_context * ctx_mpi,
+             struct ggml_cgraph * gf,
+                            int   n_layers);
+
+void ggml_mpi_graph_compute_post(
+        struct ggml_mpi_context * ctx_mpi,
+             struct ggml_cgraph * gf,
+                            int   n_layers);
+
+#ifdef __cplusplus
+}
+#endif

runner/ggml-opencl.cpp

@@ -0,0 +1,1927 @@
+//go:build opencl
+
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#include "ggml-opencl.h"
+
+#include <array>
+#include <atomic>
+#include <sstream>
+#include <vector>
+#include <limits>
+
+#define CL_TARGET_OPENCL_VERSION 110
+#include <clblast.h>
+
+#include <stdlib.h>
+#include <stdio.h>
+#include <string.h>
+
+#include "ggml.h"
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+#define CL_DMMV_LOCAL_SIZE 32
+
+#ifndef K_QUANTS_PER_ITERATION
+#define K_QUANTS_PER_ITERATION 1
+#else
+static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
+#endif
+
+#define MULTILINE_QUOTE(...) #__VA_ARGS__
+static std::string program_source = MULTILINE_QUOTE(
+
+typedef char int8_t;
+typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+struct __attribute__ ((packed)) block_q4_0
+{
+    half d;
+    uint8_t qs[QK4_0 / 2];
+};
+
+struct __attribute__ ((packed)) block_q4_1
+{
+    half d;
+    half m;
+    uint8_t qs[QK4_1 / 2];
+};
+
+struct __attribute__ ((packed)) block_q5_0
+{
+    half d;
+    uint32_t qh;
+    uint8_t qs[QK5_0 / 2];
+};
+
+struct __attribute__ ((packed)) block_q5_1
+{
+    half d;
+    half m;
+    uint32_t qh;
+    uint8_t qs[QK5_1 / 2];
+};
+
+struct __attribute__ ((packed)) block_q8_0
+{
+    half d;
+    int8_t qs[QK8_0];
+};
+
+struct __attribute__((packed)) block_q2_K
+{
+    uint8_t scales[16];
+    uint8_t qs[64];
+    half d;
+    half dmin;
+};
+
+struct __attribute__((packed)) block_q3_K
+{
+    uint8_t hmask[32];
+    uint8_t qs[64];
+    uint8_t scales[12];
+    half d;
+};
+
+struct __attribute__((packed)) block_q4_K
+{
+    half d;
+    half dmin;
+    uint8_t scales[12];
+    uint8_t qs[128];
+};
+
+struct __attribute__((packed)) block_q5_K
+{
+    half d;
+    half dmin;
+    uint8_t scales[12];
+    uint8_t qh[32];
+    uint8_t qs[128];
+};
+
+struct __attribute__((packed)) block_q6_K
+{
+    uint8_t ql[128];
+    uint8_t qh[64];
+    int8_t scales[16];
+    half d;
+};
+
+__kernel void convert_fp16_to_fp32(__global half* x, __global float* y) {
+    const uint i = get_global_id(0);
+
+    y[i] = vload_half(0, &x[i]);
+}
+
+void dequantize_q4_0(__global const struct block_q4_0* x, const int ib, const int iqs, float* v0, float* v1) {
+    const float d = vload_half(0, &x[ib].d);
+
+    const uint8_t vui = x[ib].qs[iqs];
+
+    const int8_t vi0 = vui & 0xF;
+    const int8_t vi1 = vui >> 4;
+
+    *v0 = (vi0 - 8)*d;
+    *v1 = (vi1 - 8)*d;
+}
+void dequantize_q4_1(__global const struct block_q4_1* x, const int ib, const int iqs, float* v0, float* v1) {
+    const float d = vload_half(0, &x[ib].d);
+    const float m = vload_half(0, &x[ib].m);
+
+    const uint8_t vui = x[ib].qs[iqs];
+
+    const int8_t vi0 = vui & 0xF;
+    const int8_t vi1 = vui >> 4;
+
+    *v0 = vi0*d + m;
+    *v1 = vi1*d + m;
+}
+void dequantize_q5_0(__global const struct block_q5_0* x, const int ib, const int iqs, float* v0, float* v1) {
+    const float d = vload_half(0, &x[ib].d);
+
+    uint32_t qh = x[ib].qh;
+
+    const uint8_t xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
+    const uint8_t xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
+
+    const int32_t x0 = ((x[ib].qs[iqs] & 0xf) | xh_0) - 16;
+    const int32_t x1 = ((x[ib].qs[iqs] >>  4) | xh_1) - 16;
+
+    *v0 = x0*d;
+    *v1 = x1*d;
+}
+void dequantize_q5_1(__global const struct block_q5_1* x, const int ib, const int iqs, float* v0, float* v1) {
+    const float d = vload_half(0, &x[ib].d);
+    const float m = vload_half(0, &x[ib].m);
+
+    uint32_t qh = x[ib].qh;
+
+    const uint8_t xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
+    const uint8_t xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
+
+    const int32_t x0 = ((x[ib].qs[iqs] & 0xf) | xh_0);
+    const int32_t x1 = ((x[ib].qs[iqs] >>  4) | xh_1);
+
+    *v0 = x0*d + m;
+    *v1 = x1*d + m;
+}
+void dequantize_q8_0(__global const struct block_q8_0* x, const int ib, const int iqs, float* v0, float* v1) {
+    const float d = vload_half(0, &x[ib].d);
+
+    const int8_t vi0 = x[ib].qs[iqs + 0];
+    const int8_t vi1 = x[ib].qs[iqs + 1];
+
+    *v0 = vi0*d;
+    *v1 = vi1*d;
+}
+void convert_f16(__global half* x, const int ib, const int iqs, float* v0, float* v1){
+    *v0 = vload_half(0, &x[ib + 0]);
+    *v1 = vload_half(0, &x[ib + 1]);
+}
+);
+
+static std::string k_quants_source = MULTILINE_QUOTE(
+inline void get_scale_min_k4(int j, const __global uint8_t *q, uint8_t *d, uint8_t *m)
+{
+    if (j < 4)
+    {
+        *d = q[j] & 63;
+        *m = q[j + 4] & 63;
+    }
+    else
+    {
+        *d = (q[j + 4] & 0xF) | ((q[j - 4] >> 6) << 4);
+        *m = (q[j + 4] >> 4) | ((q[j - 0] >> 6) << 4);
+    }
+}
+
+__kernel void dequantize_block_q2_K(__global const struct block_q2_K *x, __global float *yy)
+{
+    const int i = get_group_id(0) + get_global_offset(0);
+    const int tid = get_local_id(0);
+    const int n = tid / 32;
+    const int l = tid - 32 * n;
+    const int is = 8 * n + l / 16;
+
+    const uint8_t q = x[i].qs[32 * n + l];
+    __global float *y = yy + get_group_id(0) * QK_K + 128 * n;
+
+    const float dall = vload_half(0, &x[i].d);
+    const float dmin = vload_half(0, &x[i].dmin);
+
+    y[l + 0] = dall * (x[i].scales[is + 0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is + 0] >> 4);
+    y[l + 32] = dall * (x[i].scales[is + 2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is + 2] >> 4);
+    y[l + 64] = dall * (x[i].scales[is + 4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is + 4] >> 4);
+    y[l + 96] = dall * (x[i].scales[is + 6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is + 6] >> 4);
+}
+
+__kernel void dequantize_block_q3_K(__global const struct block_q3_K *x, __global float *yy)
+{
+    int r = get_local_id(0) / 4;
+    int i = get_group_id(0) + get_global_offset(0);
+    int tid = r / 2;
+    int is0 = r % 2;
+    int l0 = 16 * is0 + 4 * (get_local_id(0) % 4);
+    int n = tid / 4;
+    int j = tid - 4 * n;
+
+    uint8_t m = 1 << (4 * n + j);
+    int is = 8 * n + 2 * j + is0;
+    int shift = 2 * j;
+
+    int8_t us = is < 4 ? (x[i].scales[is - 0] & 0xF) | (((x[i].scales[is + 8] >> 0) & 3) << 4)
+              : is < 8 ? (x[i].scales[is - 0] & 0xF) | (((x[i].scales[is + 4] >> 2) & 3) << 4)
+              : is < 12  ? (x[i].scales[is - 8] >> 4) | (((x[i].scales[is + 0] >> 4) & 3) << 4)
+              : (x[i].scales[is - 8] >> 4) | (((x[i].scales[is - 4] >> 6) & 3) << 4);
+    float d_all = vload_half(0, &x[i].d);
+    float dl = d_all * (us - 32);
+
+    __global float *y = yy + get_group_id(0) * QK_K + 128 * n + 32 * j;
+    const __global uint8_t *q = x[i].qs + 32 * n;
+    const __global uint8_t *hm = x[i].hmask;
+
+    for (int l = l0; l < l0 + 4; ++l)
+        y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
+}
+
+__kernel void dequantize_block_q4_K(__global const struct block_q4_K *x, __global float *yy)
+{
+    const int i = get_group_id(0) + get_global_offset(0);
+    const int tid = get_local_id(0);
+    const int il = tid / 8;
+    const int ir = tid % 8;
+    const int is = 2 * il;
+    const int n = 4;
+
+    __global float *y = yy + get_group_id(0) * QK_K + 64 * il + n * ir;
+
+    const float dall = vload_half(0, &x[i].d);
+    const float dmin = vload_half(0, &x[i].dmin);
+
+    __global const uint8_t *q = x[i].qs + 32 * il + n * ir;
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
+    float d1 = dall * sc;
+    float m1 = dmin * m;
+    get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
+    float d2 = dall * sc;
+    float m2 = dmin * m;
+    for (int l = 0; l < n; ++l)
+    {
+        y[l + 0] = d1 * (q[l] & 0xF) - m1;
+        y[l + 32] = d2 * (q[l] >> 4) - m2;
+    }
+}
+
+__kernel void dequantize_block_q5_K(__global const struct block_q5_K *x, __global float *yy)
+{
+    const int i = get_group_id(0) + get_global_offset(0);
+    const int tid = get_local_id(0);
+    const int il = tid / 16;
+    const int ir = tid % 16;
+    const int is = 2 * il;
+
+    __global float *y = yy + get_group_id(0) * QK_K + 64 * il + 2 * ir;
+
+    const float dall = vload_half(0, &x[i].d);
+    const float dmin = vload_half(0, &x[i].dmin);
+
+    __global const uint8_t *ql = x[i].qs + 32 * il + 2 * ir;
+    __global const uint8_t *qh = x[i].qh + 2 * ir;
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
+    const float d1 = dall * sc;
+    const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
+    const float d2 = dall * sc;
+    const float m2 = dmin * m;
+
+    uint8_t hm = 1 << (2 * il);
+    y[0] = d1 * ((ql[0] & 0xF) + (qh[0] & hm ? 16 : 0)) - m1;
+    y[1] = d1 * ((ql[1] & 0xF) + (qh[1] & hm ? 16 : 0)) - m1;
+    hm <<= 1;
+    y[32] = d2 * ((ql[0] >> 4) + (qh[0] & hm ? 16 : 0)) - m2;
+    y[33] = d2 * ((ql[1] >> 4) + (qh[1] & hm ? 16 : 0)) - m2;
+}
+
+__kernel void dequantize_block_q6_K(__global const struct block_q6_K *x, __global float *yy)
+{
+    const int i = get_group_id(0) + get_global_offset(0);
+    const int tid = get_local_id(0);
+    const int ip = tid / 32;
+    const int il = tid - 32 * ip;
+    const int is = 8 * ip + il / 16;
+
+    __global float *y = yy + get_group_id(0) * QK_K + 128 * ip + il;
+
+    const float d = vload_half(0, &x[i].d);
+
+    __global const uint8_t *ql = x[i].ql + 64 * ip + il;
+    const uint8_t qh = x[i].qh[32 * ip + il];
+    __global const int8_t *sc = x[i].scales + is;
+
+    y[0] = d * sc[0] * ((int8_t)((ql[0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
+    y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
+    y[64] = d * sc[4] * ((int8_t)((ql[0] >> 4) | (((qh >> 4) & 3) << 4)) - 32);
+    y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32);
+}
+
+__kernel void dequantize_mul_mat_vec_q2_K(__global const struct block_q2_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
+
+    const int row = get_group_id(0);
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
+
+    __global const struct block_q2_K * x = xx + ib0;
+
+    const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION;  // 0...31 or 0...15
+    const int ix  = get_local_id(0)%K_QUANTS_PER_ITERATION;  // 0 or 0,1
+
+    const int step = 16/K_QUANTS_PER_ITERATION;
+
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0...15 or 0...7
+
+    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15 or 0...14 in steps of 2
+    const int q_offset = 32*im + l0;
+    const int s_offset = 8*im;
+    const int y_offset = 128*im + l0;
+
+    tmp[16 * ix + tid] = 0;
+
+    uint32_t aux[4];
+    const uint8_t * d = (const uint8_t *)aux;
+    const uint8_t * m = (const uint8_t *)(aux + 2);
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        __global const float   * y = yy + i * QK_K + y_offset;
+        __global const uint8_t * q = x[i].qs + q_offset;
+
+        const float dall = vload_half(0, &x[i].d);
+        const float dmin = vload_half(0, &x[i].dmin);
+
+        __global const uint32_t * a = (__global const uint32_t *)(x[i].scales + s_offset);
+        aux[0] = a[0] & 0x0f0f0f0f;
+        aux[1] = a[1] & 0x0f0f0f0f;
+        aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
+        aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
+
+        float sum1 = 0, sum2 = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
+                  + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
+                  + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
+                  + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
+                  + y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
+                  + y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
+                  + y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
+                  +y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
+            sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
+                  + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
+
+        }
+        tmp[16 * ix + tid] += dall * sum1 - dmin * sum2;
+
+    }
+
+    // sum up partial sums and write back result
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (int s=16; s>0; s>>=1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if (tid == 0) {
+        dst[row] = tmp[0];
+    }
+}
+
+__kernel void dequantize_mul_mat_vec_q3_K(__global const struct block_q3_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
+    const uint16_t kmask1 = 0x0303;
+    const uint16_t kmask2 = 0x0f0f;
+
+    const int row = get_group_id(0);
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
+
+    __global const struct block_q3_K * x = xx + ib0;
+
+    const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const int ix  = get_local_id(0)%K_QUANTS_PER_ITERATION;  // 0 or 0,1
+
+    const int n  = K_QUANTS_PER_ITERATION;               // iterations in the inner loop
+    const int step = 16/K_QUANTS_PER_ITERATION;
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0....15 or 0...7
+
+    const uint8_t m = 1 << (4*im);
+
+    const int l0 = n*in;                                 // 0...15 or 0...14 in steps of 2
+    const int q_offset =  32*im + l0;
+    const int y_offset = 128*im + l0;
+
+    uint16_t utmp[4];
+    const int8_t * s = (const int8_t *)utmp;
+
+    const uint16_t s_shift = 4*im;
+
+    tmp[16 * ix + tid] = 0;
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        __global const float   * y  = yy + i * QK_K + y_offset;
+        __global const uint8_t * q = x[i].qs + q_offset;
+        __global const uint8_t * h = x[i].hmask + l0;
+
+        __global const uint16_t * a = (__global const uint16_t *)x[i].scales;
+        utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
+        utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
+        utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
+        utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
+
+        const float d = vload_half(0, &x[i].d);
+
+        float sum = 0;
+        for (int l = 0; l < n; ++l) {
+            sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
+                 + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
+                 + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
+                 + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
+            sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
+                 + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
+                 + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
+                + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
+        }
+        tmp[16 * ix + tid] += d * sum;
+
+    }
+
+    // sum up partial sums and write back result
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (int s=16; s>0; s>>=1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if (tid == 0) {
+        dst[row] = tmp[0];
+    }
+}
+
+__kernel void dequantize_mul_mat_vec_q4_K(__global const struct block_q4_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
+
+    //to rename it later, just to test now
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
+    const int row = get_group_id(0);
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
+
+    const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION;  // 0...15
+    const int ix  = get_local_id(0)%K_QUANTS_PER_ITERATION;
+
+    const int step = 8/K_QUANTS_PER_ITERATION;
+
+    const int il  = tid/step;     // 0...3
+    const int ir  = tid - step*il;// 0...3
+    const int n   = 2*K_QUANTS_PER_ITERATION;
+
+    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const int in = il%2;
+
+    const int l0 = n*(2*ir + in);
+    const int q_offset = 32*im + l0;
+    const int y_offset = 64*im + l0;
+
+    uint16_t aux[4];
+    const uint8_t * sc = (const uint8_t *)aux;
+
+    __global const struct block_q4_K * x = xx + ib0;
+
+    tmp[16 * ix + tid] = 0;
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        __global const uint8_t * q1 = x[i].qs + q_offset;
+        __global const uint8_t * q2 = q1 + 64;
+        __global const float   * y1 = yy + i*QK_K + y_offset;
+        __global const float   * y2 = y1 + 128;
+
+        const float dall = vload_half(0, &x[i].d);
+        const float dmin = vload_half(0, &x[i].dmin);
+
+        __global const uint16_t * a = (__global const uint16_t *)x[i].scales;
+        aux[0] = a[im+0] & kmask1;
+        aux[1] = a[im+2] & kmask1;
+        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
+        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
+
+        float4 s = (float4)(0.f);
+        float smin = 0;
+        for (int l = 0; l < n; ++l) {
+            s.x += y1[l] * (q1[l] & 0xF); s.y += y1[l+32] * (q1[l] >> 4);
+            s.z += y2[l] * (q2[l] & 0xF); s.w += y2[l+32] * (q2[l] >> 4);
+            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
+        }
+        tmp[16 * ix + tid] += dall * (s.x * sc[0] + s.y * sc[1] + s.z * sc[4] + s.w * sc[5]) - dmin * smin;
+
+    }
+
+    // sum up partial sums and write back result
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (int s=16; s>0; s>>=1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if (tid == 0) {
+        dst[row] = tmp[0];
+    }
+}
+
+__kernel void dequantize_mul_mat_vec_q5_K(__global const struct block_q5_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
+
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
+    const int row = get_group_id(0);
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
+
+    const int tid = get_local_id(0)/2;  // 0...15
+    const int ix  = get_local_id(0)%2;
+
+    const int il  = tid/4;     // 0...3
+    const int ir  = tid - 4*il;// 0...3
+    const int n   = 2;
+
+    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const int in = il%2;
+
+    const int l0 = n*(2*ir + in);
+    const int q_offset = 32*im + l0;
+    const int y_offset = 64*im + l0;
+
+    const uint8_t hm1  = 1 << (2*im);
+    const uint8_t hm2  = hm1 << 4;
+
+    uint16_t aux[4];
+    const uint8_t * sc = (const uint8_t *)aux;
+
+    __global const struct block_q5_K * x = xx + ib0;
+
+    tmp[16 * ix + tid] = 0;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2) {
+
+        __global const uint8_t * ql1 = x[i].qs + q_offset;
+        __global const uint8_t * ql2 = ql1 + 64;
+        __global const uint8_t * qh  = x[i].qh + l0;
+        __global const float   * y1  = yy + i*QK_K + y_offset;
+        __global const float   * y2  = y1 + 128;
+
+        const float dall = vload_half(0, &x[i].d);
+        const float dmin = vload_half(0, &x[i].dmin);
+
+        __global const uint16_t * a = (__global const uint16_t *)x[i].scales;
+        aux[0] = a[im+0] & kmask1;
+        aux[1] = a[im+2] & kmask1;
+        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
+        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
+
+        float4 sum = (float4)(0.f);
+        float smin = 0;
+        for (int l = 0; l < n; ++l) {
+            sum.x += y1[l+ 0] * ((ql1[l+ 0] & 0xF) + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
+                   + y1[l+16] * ((ql1[l+16] & 0xF) + (qh[l+16] & (hm1 << 0) ? 16 : 0));
+            sum.y += y1[l+32] * ((ql1[l+ 0] >>  4) + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
+                   + y1[l+48] * ((ql1[l+16] >>  4) + (qh[l+16] & (hm1 << 1) ? 16 : 0));
+            sum.z += y2[l+ 0] * ((ql2[l+ 0] & 0xF) + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
+                   + y2[l+16] * ((ql2[l+16] & 0xF) + (qh[l+16] & (hm2 << 0) ? 16 : 0));
+            sum.w += y2[l+32] * ((ql2[l+ 0] >>  4) + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
+                   + y2[l+48] * ((ql2[l+16] >>  4) + (qh[l+16] & (hm2 << 1) ? 16 : 0));
+            smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
+                  + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
+        }
+        tmp[16 * ix + tid] += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
+
+    }
+
+    // sum up partial sums and write back result
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (int s=16; s>0; s>>=1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if (tid == 0) {
+        dst[row] = tmp[0];
+    }
+}
+
+__kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx, __local float* tmp, __global const float * yy, __global float * dst, const int ncols) {
+
+    const int row = get_group_id(0);
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
+
+    __global const struct block_q6_K * x = xx + ib0;
+
+    const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const int ix  = get_local_id(0)%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const int step = 16/K_QUANTS_PER_ITERATION;          // 16 or 8
+
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0...15 or 0...7
+
+\n#if K_QUANTS_PER_ITERATION == 1\n
+    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15
+    const int is = 0;
+
+\n#else\n
+
+    const int l0 = 4 * in;                               // 0, 4, 8, ..., 28
+    const int is = in / 4;
+
+\n#endif\n
+
+    const int ql_offset = 64*im + l0;
+    const int qh_offset = 32*im + l0;
+    const int s_offset  =  8*im + is;
+    const int y_offset = 128*im + l0;
+
+    tmp[16 * ix + tid] = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        __global const float   * y  = yy + i * QK_K + y_offset;
+        __global const uint8_t * ql = x[i].ql + ql_offset;
+        __global const uint8_t * qh = x[i].qh + qh_offset;
+        __global const int8_t  * s  = x[i].scales + s_offset;
+
+        const float d = vload_half(0, &x[i].d);
+
+\n#if K_QUANTS_PER_ITERATION == 1\n
+        float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
+                  + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
+                  + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
+                  + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
+                  + y[64] * s[4] * d * ((int8_t)((ql[ 0]  >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
+                  + y[80] * s[5] * d * ((int8_t)((ql[16]  >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
+                  + y[96] * s[6] * d * ((int8_t)((ql[32]  >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
+                  +y[112] * s[7] * d * ((int8_t)((ql[48]  >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
+        tmp[16 * ix + tid] += sum;
+\n#else\n
+        float sum = 0;
+        for (int l = 0; l < 4; ++l) {
+            sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
+                 + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
+                 + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
+                 + y[l+96] * s[6] * d * ((int8_t)((ql[l+32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
+        }
+        tmp[16 * ix + tid] += sum;
+\n#endif\n
+
+    }
+
+    // sum up partial sums and write back result
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (int s=16; s>0; s>>=1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if (tid == 0) {
+        dst[row] = tmp[0];
+    }
+}
+
+);
+
+
+std::string dequant_template = MULTILINE_QUOTE(
+__kernel void KERNEL_NAME(__global X_TYPE* x, __global float* y) {
+    const int i = get_group_id(0)*get_local_size(0) + get_local_id(0)*2;
+
+    if (i >= get_global_size(0)) {
+        return;
+    }
+
+    const uint qk = QUANT_K;
+    const uint qr = QUANT_R;
+
+    const int ib = i/qk + get_global_offset(0); // block index
+    const int iqs = (i%qk)/qr; // quant index
+    const int iybs = i - i%qk; // y block start index
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+    // dequantize
+    float v0, v1;
+    DEQUANT_FUNC(x, ib, iqs, &v0, &v1);
+    y[iybs + iqs + 0] = v0;
+    y[iybs + iqs + y_offset] = v1;
+}
+);
+
+std::string dequant_mul_mat_vec_template = MULTILINE_QUOTE(
+__kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float* y, __global float* dst, const int ncols) {
+    const int local_size = get_local_size(0);
+    const int row = get_group_id(0);
+    const int tid = get_local_id(0);
+
+    const uint qk = QUANT_K;
+    const uint qr = QUANT_R;
+
+    const int col_step = local_size * 2;
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+    x += get_global_offset(0);
+
+    tmp[tid] = 0;
+
+    for (int col = tid*2; col < ncols; col += col_step) {
+        const int ib = (row*ncols + col)/qk; // block index
+        const int iqs = (col%qk)/qr; // quant index
+        const int iybs = col - col%qk; // y block start index
+
+        // dequantize
+        float v0, v1;
+        DEQUANT_FUNC(x, ib, iqs, &v0, &v1);
+
+        // matrix multiplication
+        tmp[tid] += v0 * y[iybs + iqs + 0];
+        tmp[tid] += v1 * y[iybs + iqs + y_offset];
+    }
+
+    // sum up partial sums and write back result
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (int s=local_size/2; s>0; s>>=1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if (tid == 0) {
+        dst[row] = tmp[0];
+    }
+}
+);
+
+
+std::string mul_template = MULTILINE_QUOTE(
+__kernel void KERNEL_NAME(__global TYPE* x, const int x_offset, __global TYPE* y, const int y_offset, __global TYPE* dst, const int dst_offset, const int ky) {
+    const int i = get_group_id(0)*get_local_size(0) + get_local_id(0);
+
+    if (i >= get_global_size(0)) {
+        return;
+    }
+
+    dst[dst_offset + i] = x[x_offset + i] * y[y_offset + i%ky];
+}
+);
+
+#define CL_CHECK(err)                                               \
+    do {                                                            \
+        cl_int err_ = (err);                                        \
+        if (err_ != CL_SUCCESS) {                                   \
+            fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n",  \
+                #err, err_, __FILE__, __LINE__);                    \
+            exit(1);                                                \
+        }                                                           \
+    } while (0)
+
+#define CLBLAST_CHECK(err)                                          \
+    do {                                                            \
+        CLBlastStatusCode err_ = (err);                             \
+        if (err_ != CLBlastSuccess) {                               \
+            fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n",  \
+                #err, err_, __FILE__, __LINE__);                    \
+            exit(1);                                                \
+        }                                                           \
+    } while (0)
+
+std::array<std::string, 5> dequant_str_keys = {
+    "KERNEL_NAME", "X_TYPE", "QUANT_K", "QUANT_R", "DEQUANT_FUNC"
+};
+
+std::array<std::string, 30> dequant_str_values = {
+    "dequantize_row_q4_0", "struct block_q4_0", "QK4_0", "QR4_0", "dequantize_q4_0",
+    "dequantize_row_q4_1", "struct block_q4_1", "QK4_1", "QR4_1", "dequantize_q4_1",
+    "dequantize_row_q5_0", "struct block_q5_0", "QK5_0", "QR5_0", "dequantize_q5_0",
+    "dequantize_row_q5_1", "struct block_q5_1", "QK5_1", "QR5_1", "dequantize_q5_1",
+    "dequantize_row_q8_0", "struct block_q8_0", "QK8_0", "QR8_0", "dequantize_q8_0",
+    "convert_row_f16", "half", "1", "1", "convert_f16"
+};
+
+std::array<std::string, 30> dequant_mul_mat_vec_str_values = {
+    "dequantize_mul_mat_vec_q4_0", "struct block_q4_0", "QK4_0", "QR4_0", "dequantize_q4_0",
+    "dequantize_mul_mat_vec_q4_1", "struct block_q4_1", "QK4_1", "QR4_1", "dequantize_q4_1",
+    "dequantize_mul_mat_vec_q5_0", "struct block_q5_0", "QK5_0", "QR5_0", "dequantize_q5_0",
+    "dequantize_mul_mat_vec_q5_1", "struct block_q5_1", "QK5_1", "QR5_1", "dequantize_q5_1",
+    "dequantize_mul_mat_vec_q8_0", "struct block_q8_0", "QK8_0", "QR8_0", "dequantize_q8_0",
+    "convert_mul_mat_vec_f16", "half", "1", "1", "convert_f16"
+};
+
+std::array<std::string, 2> mul_str_keys = {
+    "KERNEL_NAME", "TYPE"
+};
+std::array<std::string, 2> mul_str_values = {
+    "mul_f32", "float"
+};
+
+static std::string& replace(std::string& s, const std::string& from, const std::string& to) {
+    size_t pos = 0;
+    while ((pos = s.find(from, pos)) != std::string::npos) {
+         s.replace(pos, from.length(), to);
+         pos += to.length();
+    }
+    return s;
+}
+
+static std::string generate_kernels() {
+    std::stringstream src;
+    src << program_source << '\n';
+    src << k_quants_source << '\n';
+    for (size_t i = 0; i < dequant_str_values.size(); i += dequant_str_keys.size()) {
+        std::string dequant_kernel = dequant_template;
+        std::string dmmv_kernel = dequant_mul_mat_vec_template;
+        for (size_t j = 0; j < dequant_str_keys.size(); j++) {
+            replace(dequant_kernel, dequant_str_keys[j], dequant_str_values[i + j]);
+            replace(dmmv_kernel, dequant_str_keys[j], dequant_mul_mat_vec_str_values[i + j]);
+        }
+        src << dequant_kernel << '\n';
+        src << dmmv_kernel << '\n';
+    }
+    for (size_t i = 0; i < mul_str_values.size(); i += mul_str_keys.size()) {
+        std::string mul_kernel = mul_template;
+        for (size_t j = 0; j < mul_str_keys.size(); j++) {
+            replace(mul_kernel, mul_str_keys[j], mul_str_values[i + j]);
+        }
+        src << mul_kernel << '\n';
+    }
+
+    return src.str();
+}
+
+static cl_platform_id platform;
+static cl_device_id device;
+static cl_context context;
+static cl_command_queue queue;
+static cl_program program;
+static cl_kernel convert_row_f16_cl;
+static cl_kernel dequantize_row_q4_0_cl, dequantize_row_q4_1_cl, dequantize_row_q5_0_cl, dequantize_row_q5_1_cl, dequantize_row_q8_0_cl;
+static cl_kernel dequantize_mul_mat_vec_q4_0_cl, dequantize_mul_mat_vec_q4_1_cl, dequantize_mul_mat_vec_q5_0_cl, dequantize_mul_mat_vec_q5_1_cl, dequantize_mul_mat_vec_q8_0_cl, convert_mul_mat_vec_f16_cl;
+static cl_kernel dequantize_block_q2_k_cl, dequantize_block_q3_k_cl, dequantize_block_q4_k_cl, dequantize_block_q5_k_cl, dequantize_block_q6_k_cl;
+static cl_kernel dequantize_mul_mat_vec_q2_K_cl, dequantize_mul_mat_vec_q3_K_cl, dequantize_mul_mat_vec_q4_K_cl, dequantize_mul_mat_vec_q5_K_cl, dequantize_mul_mat_vec_q6_K_cl;
+static cl_kernel mul_f32_cl;
+static bool fp16_support;
+
+static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) {
+    cl_program p;
+    char *program_log;
+    size_t program_size;
+    size_t log_size;
+    int err;
+
+    program_size = strlen(program_buffer);
+
+    p = clCreateProgramWithSource(ctx, 1, (const char**)&program_buffer, &program_size, &err);
+    if(err < 0) {
+        fprintf(stderr, "OpenCL error creating program");
+        exit(1);
+    }
+
+    std::string compile_opts = "-cl-mad-enable -cl-unsafe-math-optimizations -cl-finite-math-only -cl-fast-relaxed-math "
+                               "-DQK4_0=32 -DQR4_0=2 -DQK4_1=32 -DQR4_1=2 -DQK5_0=32 -DQR5_0=2 -DQK5_1=32 -DQR5_1=2 -DQK8_0=32 -DQR8_0=1 "
+                               "-DQK_K=256 -DK_QUANTS_PER_ITERATION=" + std::to_string(K_QUANTS_PER_ITERATION);
+
+    err = clBuildProgram(p, 0, NULL, compile_opts.c_str(), NULL, NULL);
+    if(err < 0) {
+
+        clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size);
+        program_log = (char*) malloc(log_size + 1);
+        program_log[log_size] = '\0';
+        clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, log_size + 1, program_log, NULL);
+        fprintf(stderr, "ggml_opencl: kernel compile error:\n\n%s\n", program_log);
+        free(program_log);
+        exit(1);
+    }
+
+    return p;
+}
+
+void ggml_cl_init(void) {
+    cl_int err;
+
+    struct cl_device;
+    struct cl_platform {
+        cl_platform_id id;
+        unsigned number;
+        char name[128];
+        char vendor[128];
+        struct cl_device * devices;
+        unsigned n_devices;
+        struct cl_device * default_device;
+    };
+
+    struct cl_device {
+        struct cl_platform * platform;
+        cl_device_id id;
+        unsigned number;
+        cl_device_type type;
+        char name[128];
+    };
+
+    enum { NPLAT = 16, NDEV = 16 };
+
+    struct cl_platform platforms[NPLAT];
+    unsigned n_platforms = 0;
+    struct cl_device devices[NDEV];
+    unsigned n_devices = 0;
+    struct cl_device * default_device = NULL;
+
+    platform = NULL;
+    device = NULL;
+
+    cl_platform_id platform_ids[NPLAT];
+    CL_CHECK(clGetPlatformIDs(NPLAT, platform_ids, &n_platforms));
+
+    for (unsigned i = 0; i < n_platforms; i++) {
+        struct cl_platform * p = &platforms[i];
+        p->number = i;
+        p->id = platform_ids[i];
+        CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_NAME, sizeof(p->name), &p->name, NULL));
+        CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_VENDOR, sizeof(p->vendor), &p->vendor, NULL));
+
+        cl_device_id device_ids[NDEV];
+        cl_int clGetDeviceIDsError = clGetDeviceIDs(p->id, CL_DEVICE_TYPE_ALL, NDEV, device_ids, &p->n_devices);
+        if (clGetDeviceIDsError == CL_DEVICE_NOT_FOUND) {
+            p->n_devices = 0;
+        } else {
+            CL_CHECK(clGetDeviceIDsError);
+        }
+        p->devices = p->n_devices > 0 ? &devices[n_devices] : NULL;
+        p->default_device = NULL;
+
+        for (unsigned j = 0; j < p->n_devices; j++) {
+            struct cl_device * d = &devices[n_devices];
+            d->number = n_devices++;
+            d->id = device_ids[j];
+            d->platform = p;
+            CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_NAME, sizeof(d->name), &d->name, NULL));
+            CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_TYPE, sizeof(d->type), &d->type, NULL));
+
+            if (p->default_device == NULL && d->type == CL_DEVICE_TYPE_GPU) {
+                p->default_device = d;
+            }
+        }
+
+        if (default_device == NULL && p->default_device != NULL) {
+            default_device = p->default_device;
+        }
+    }
+
+    if (n_devices == 0) {
+        fprintf(stderr, "ggml_opencl: could find any OpenCL devices.\n");
+        exit(1);
+    }
+
+    char * user_platform_string = getenv("GGML_OPENCL_PLATFORM");
+    char * user_device_string = getenv("GGML_OPENCL_DEVICE");
+    int user_platform_number = -1;
+    int user_device_number = -1;
+
+    unsigned n;
+    if (user_platform_string != NULL && sscanf(user_platform_string, " %u", &n) == 1 && n < n_platforms) {
+        user_platform_number = (int)n;
+    }
+    if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1 && n < n_devices) {
+        user_device_number = (int)n;
+    }
+    if (user_platform_number != -1 && user_device_number != -1) {
+        cl_platform* platform = &platforms[user_platform_number];
+        if ((unsigned)user_device_number >= platform->n_devices) {
+            fprintf(stderr, "ggml_opencl: invalid device number %d\n", user_device_number);
+            exit(1);
+        }
+        default_device = &platform->devices[user_device_number];
+    } else {
+
+        struct cl_device * selected_devices = devices;
+        unsigned n_selected_devices = n_devices;
+
+        if (user_platform_number == -1 && user_platform_string != NULL && user_platform_string[0] != 0) {
+            for (unsigned i = 0; i < n_platforms; i++) {
+                struct cl_platform * p = &platforms[i];
+                if (strstr(p->name, user_platform_string) != NULL ||
+                    strstr(p->vendor, user_platform_string) != NULL) {
+                    user_platform_number = (int)i;
+                    break;
+                }
+            }
+            if (user_platform_number == -1) {
+                fprintf(stderr, "ggml_opencl: no platform matching '%s' was found.\n", user_platform_string);
+                exit(1);
+            }
+        }
+        if (user_platform_number != -1) {
+            struct cl_platform * p = &platforms[user_platform_number];
+            selected_devices = p->devices;
+            n_selected_devices = p->n_devices;
+            default_device = p->default_device;
+            if (n_selected_devices == 0) {
+                fprintf(stderr, "ggml_opencl: selected platform '%s' does not have any devices.\n", p->name);
+                exit(1);
+            }
+        }
+
+        if (user_device_number == -1 && user_device_string != NULL && user_device_string[0] != 0) {
+            for (unsigned i = 0; i < n_selected_devices; i++) {
+                struct cl_device * d = &selected_devices[i];
+                if (strstr(d->name, user_device_string) != NULL) {
+                    user_device_number = d->number;
+                    break;
+                }
+            }
+            if (user_device_number == -1) {
+                fprintf(stderr, "ggml_opencl: no device matching '%s' was found.\n", user_device_string);
+                exit(1);
+            }
+        }
+        if (user_device_number != -1) {
+            selected_devices = &devices[user_device_number];
+            n_selected_devices = 1;
+            default_device = &selected_devices[0];
+        }
+
+        GGML_ASSERT(n_selected_devices > 0);
+
+        if (default_device == NULL) {
+            default_device = &selected_devices[0];
+        }
+    }
+
+    fprintf(stderr, "ggml_opencl: selecting platform: '%s'\n", default_device->platform->name);
+    fprintf(stderr, "ggml_opencl: selecting device: '%s'\n", default_device->name);
+    if (default_device->type != CL_DEVICE_TYPE_GPU) {
+        fprintf(stderr, "ggml_opencl: warning, not a GPU: '%s'.\n", default_device->name);
+    }
+
+    platform = default_device->platform->id;
+    device = default_device->id;
+
+    size_t ext_str_size;
+    clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, 0, NULL, &ext_str_size);
+    char *ext_buffer = (char *)alloca(ext_str_size + 1);
+    clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, ext_str_size, ext_buffer, NULL);
+    ext_buffer[ext_str_size] = '\0'; // ensure it is null terminated
+    // Check if ext_buffer contains cl_khr_fp16
+    fp16_support = strstr(ext_buffer, "cl_khr_fp16") != NULL;
+    fprintf(stderr, "ggml_opencl: device FP16 support: %s\n", fp16_support ? "true" : "false");
+
+    cl_context_properties properties[] = {
+        (intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)platform, 0
+    };
+
+    CL_CHECK((context = clCreateContext(properties, 1, &device, NULL, NULL, &err), err));
+
+    CL_CHECK((queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err),
+        (err != CL_INVALID_QUEUE_PROPERTIES && err != CL_INVALID_VALUE ? err :
+        (queue = clCreateCommandQueue(context, device, 0, &err), err)
+    )));
+
+    const std::string kernel_src = generate_kernels();
+
+    program = build_program_from_source(context, device, kernel_src.c_str());
+
+    // FP16 to FP32 kernel
+    CL_CHECK((convert_row_f16_cl = clCreateKernel(program, "convert_row_f16", &err), err));
+
+    // Dequantize kernels
+    CL_CHECK((dequantize_row_q4_0_cl = clCreateKernel(program, "dequantize_row_q4_0", &err), err));
+    CL_CHECK((dequantize_row_q4_1_cl = clCreateKernel(program, "dequantize_row_q4_1", &err), err));
+    CL_CHECK((dequantize_row_q5_0_cl = clCreateKernel(program, "dequantize_row_q5_0", &err), err));
+    CL_CHECK((dequantize_row_q5_1_cl = clCreateKernel(program, "dequantize_row_q5_1", &err), err));
+    CL_CHECK((dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err), err));
+    CL_CHECK((dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err), err));
+    CL_CHECK((dequantize_block_q2_k_cl = clCreateKernel(program, "dequantize_block_q2_K", &err), err));
+    CL_CHECK((dequantize_block_q3_k_cl = clCreateKernel(program, "dequantize_block_q3_K", &err), err));
+    CL_CHECK((dequantize_block_q4_k_cl = clCreateKernel(program, "dequantize_block_q4_K", &err), err));
+    CL_CHECK((dequantize_block_q5_k_cl = clCreateKernel(program, "dequantize_block_q5_K", &err), err));
+    CL_CHECK((dequantize_block_q6_k_cl = clCreateKernel(program, "dequantize_block_q6_K", &err), err));
+
+    // dequant mul mat kernel
+    CL_CHECK((dequantize_mul_mat_vec_q4_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_0", &err), err));
+    CL_CHECK((dequantize_mul_mat_vec_q4_1_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_1", &err), err));
+    CL_CHECK((dequantize_mul_mat_vec_q5_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_0", &err), err));
+    CL_CHECK((dequantize_mul_mat_vec_q5_1_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_1", &err), err));
+    CL_CHECK((dequantize_mul_mat_vec_q8_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q8_0", &err), err));
+    CL_CHECK((convert_mul_mat_vec_f16_cl = clCreateKernel(program, "convert_mul_mat_vec_f16", &err), err));
+    CL_CHECK((dequantize_mul_mat_vec_q2_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q2_K", &err), err));
+    CL_CHECK((dequantize_mul_mat_vec_q3_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q3_K", &err), err));
+    CL_CHECK((dequantize_mul_mat_vec_q4_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_K", &err), err));
+    CL_CHECK((dequantize_mul_mat_vec_q5_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_K", &err), err));
+    CL_CHECK((dequantize_mul_mat_vec_q6_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q6_K", &err), err));
+
+    // mul kernel
+    CL_CHECK((mul_f32_cl = clCreateKernel(program, "mul_f32", &err), err));
+}
+
+static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+            return &dequantize_row_q4_0_cl;
+        case GGML_TYPE_Q4_1:
+            return &dequantize_row_q4_1_cl;
+        case GGML_TYPE_Q5_0:
+            return &dequantize_row_q5_0_cl;
+        case GGML_TYPE_Q5_1:
+            return &dequantize_row_q5_1_cl;
+        case GGML_TYPE_Q8_0:
+            return &dequantize_row_q8_0_cl;
+        case GGML_TYPE_Q2_K:
+            return &dequantize_block_q2_k_cl;
+        case GGML_TYPE_Q3_K:
+            return &dequantize_block_q3_k_cl;
+        case GGML_TYPE_Q4_K:
+            return &dequantize_block_q4_k_cl;
+        case GGML_TYPE_Q5_K:
+            return &dequantize_block_q5_k_cl;
+        case GGML_TYPE_Q6_K:
+            return &dequantize_block_q6_k_cl;
+        case GGML_TYPE_F16:
+            return &convert_row_f16_cl;
+        default:
+            return nullptr;
+    }
+}
+
+static size_t ggml_cl_global_denom(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+            return 1;
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+            return 4;
+        case GGML_TYPE_Q4_K:
+            return 8;
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            return 4;
+        case GGML_TYPE_F16:
+        default:
+            return 1;
+    }
+}
+
+static size_t ggml_cl_local_size(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+            return 0;
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+            return 64;
+        case GGML_TYPE_Q4_K:
+            return 32;
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            return 64;
+        case GGML_TYPE_F16:
+        default:
+            return 0;
+    }
+}
+
+static cl_kernel* ggml_get_dequantize_mul_mat_vec_cl(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+            return &dequantize_mul_mat_vec_q4_0_cl;
+        case GGML_TYPE_Q4_1:
+            return &dequantize_mul_mat_vec_q4_1_cl;
+        case GGML_TYPE_Q5_0:
+            return &dequantize_mul_mat_vec_q5_0_cl;
+        case GGML_TYPE_Q5_1:
+            return &dequantize_mul_mat_vec_q5_1_cl;
+        case GGML_TYPE_Q8_0:
+            return &dequantize_mul_mat_vec_q8_0_cl;
+        case GGML_TYPE_F16:
+            return &convert_mul_mat_vec_f16_cl;
+        case GGML_TYPE_Q2_K:
+            return &dequantize_mul_mat_vec_q2_K_cl;
+        case GGML_TYPE_Q3_K:
+            return &dequantize_mul_mat_vec_q3_K_cl;
+        case GGML_TYPE_Q4_K:
+            return &dequantize_mul_mat_vec_q4_K_cl;
+        case GGML_TYPE_Q5_K:
+            return &dequantize_mul_mat_vec_q5_K_cl;
+        case GGML_TYPE_Q6_K:
+            return &dequantize_mul_mat_vec_q6_K_cl;
+        default:
+            return nullptr;
+    }
+}
+
+// buffer pool for cl
+#define MAX_CL_BUFFERS 256
+
+struct scoped_spin_lock {
+    std::atomic_flag& lock;
+    scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
+        while (lock.test_and_set(std::memory_order_acquire)) {
+            ; // spin
+        }
+    }
+    ~scoped_spin_lock() {
+        lock.clear(std::memory_order_release);
+    }
+    scoped_spin_lock(const scoped_spin_lock&) = delete;
+    scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
+};
+
+struct cl_buffer {
+    cl_mem mem;
+    size_t size = 0;
+};
+
+static cl_buffer g_cl_buffer_pool[MAX_CL_BUFFERS];
+static std::atomic_flag g_cl_pool_lock = ATOMIC_FLAG_INIT;
+
+static cl_mem ggml_cl_pool_malloc(size_t size, size_t * actual_size) {
+    scoped_spin_lock lock(g_cl_pool_lock);
+    cl_int err;
+
+    int best_i = -1;
+    size_t best_size = std::numeric_limits<size_t>::max(); //smallest unused buffer that fits our needs
+    int worst_i = -1;
+    size_t worst_size = 0; //largest unused buffer seen so far
+    for (int i = 0; i < MAX_CL_BUFFERS; ++i) {
+        cl_buffer &b = g_cl_buffer_pool[i];
+        if (b.size > 0 && b.size >= size && b.size < best_size)
+        {
+            best_i = i;
+            best_size = b.size;
+        }
+        if (b.size > 0 && b.size > worst_size)
+        {
+            worst_i = i;
+            worst_size = b.size;
+        }
+    }
+    if(best_i!=-1) //found the smallest buffer that fits our needs
+    {
+        cl_buffer& b = g_cl_buffer_pool[best_i];
+        cl_mem mem = b.mem;
+        *actual_size = b.size;
+        b.size = 0;
+        return mem;
+    }
+    if(worst_i!=-1) //no buffer that fits our needs, resize largest one to save memory
+    {
+         cl_buffer& b = g_cl_buffer_pool[worst_i];
+         cl_mem mem = b.mem;
+         b.size = 0;
+         clReleaseMemObject(mem);
+    }
+    cl_mem mem;
+    CL_CHECK((mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err), err));
+    *actual_size = size;
+    return mem;
+}
+
+static void ggml_cl_pool_free(cl_mem mem, size_t size) {
+    scoped_spin_lock lock(g_cl_pool_lock);
+
+    for (int i = 0; i < MAX_CL_BUFFERS; ++i) {
+        cl_buffer& b = g_cl_buffer_pool[i];
+        if (b.size == 0) {
+            b.mem = mem;
+            b.size = size;
+            return;
+        }
+    }
+    fprintf(stderr, "WARNING: cl buffer pool full, increase MAX_CL_BUFFERS\n");
+    clReleaseMemObject(mem);
+}
+
+void ggml_cl_free_data(const struct ggml_tensor* tensor) {
+    if (tensor->backend != GGML_BACKEND_GPU) {
+        return;
+    }
+
+    cl_mem mem = (cl_mem)tensor->extra;
+    clReleaseMemObject(mem);
+}
+
+static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t offset, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cl_event* ev) {
+    cl_int err;
+    const uint64_t ne0 = src->ne[0];
+    const uint64_t ne1 = src->ne[1];
+    const uint64_t nb0 = src->nb[0];
+    const uint64_t nb1 = src->nb[1];
+    const uint64_t nb2 = src->nb[2];
+    const uint64_t nb3 = src->nb[3];
+    const enum ggml_type type = src->type;
+    const size_t ts = ggml_type_size(type);
+    const size_t bs = ggml_blck_size(type);
+    const uint64_t row_size = ts*ne0/bs;
+
+    const char * x = (const char *) src->data + i2*nb2 + i3*nb3;
+    if (nb0 == ts && nb1 == row_size) {
+        return clEnqueueWriteBuffer(queue, dst, CL_FALSE, offset, ne1*row_size, x, 0, NULL, ev);
+    }
+    if (nb0 == ts) {
+        const size_t buffer_origin[3] = { offset, 0, 0 };
+        const size_t host_origin[3] = { 0, 0, 0 };
+        const size_t region[3] = { row_size, ne1, 1 };
+        return clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, row_size, 0, nb1, 0, x, 0, NULL, ev);
+    }
+    std::vector<cl_event> events;
+    if (ev && ne1>1) events.reserve(ne1-1);
+    for (uint64_t i1 = 0; i1 < ne1; i1++) {
+        // pretend the row is a matrix with cols=1
+        const size_t buffer_origin[3] = { offset + i1*row_size, 0, 0 };
+        const size_t host_origin[3] = { 0, 0, 0 };
+        const size_t region[3] = { ts, ne0/bs, 1 };
+        // if an event is requested, make the last write wait for all previous writes to complete
+        if (ev && i1) {
+            events.push_back(*ev);
+        }
+        cl_uint nevents = i1 == ne1-1 ? events.size() : 0U;
+        err = clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, ts, 0, nb0, 0, x + i1*nb1, nevents, nevents ? events.data() : nullptr, ev);
+        if (err != CL_SUCCESS) {
+            for (auto event : events) {
+                clReleaseEvent(event);
+            }
+            return err;
+        }
+    }
+    for (auto event : events) {
+        CL_CHECK(clReleaseEvent(event));
+    }
+    return CL_SUCCESS;
+}
+
+static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    const int64_t ne12 = src1->ne[2];
+    const int64_t ne13 = src1->ne[3];
+    const int nb2  = dst->nb[2];
+    const int nb3  = dst->nb[3];
+    size_t x_size;
+    size_t d_size;
+
+    cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0
+    cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted.
+    cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst
+
+
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        for (int64_t i02 = 0; i02 < ne02; i02++) {
+            cl_event ev;
+
+            // copy src0 to device
+            CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev));
+
+            const int64_t i13 = i03%ne13;
+            const int64_t i12 = i02%ne12;
+            const int i1 = i13*ne12*ne11 + i12*ne11;
+
+            cl_int x_offset = 0;
+            cl_int y_offset = i1*ne10;
+            cl_int d_offset = 0;
+
+            size_t global = ne00 * ne01;
+            cl_int ky = ne10 * ne11;
+
+            CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X));
+            CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset));
+            CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y));
+            CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset));
+            CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D));
+            CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset));
+            CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky));
+            CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
+
+            CL_CHECK(clReleaseEvent(ev));
+            CL_CHECK(clFinish(queue));
+
+            // copy dst to host
+            float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
+            CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL));
+        }
+    }
+    ggml_cl_pool_free(d_X, x_size);
+    ggml_cl_pool_free(d_D, d_size);
+}
+
+void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
+    ggml_cl_mul_f32(src0, src1, dst);
+}
+
+static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
+
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    const int64_t ne12 = src1->ne[2];
+    const int64_t ne13 = src1->ne[3];
+
+    const int nb2  = dst->nb[2];
+    const int nb3  = dst->nb[3];
+
+    const int64_t r2 = ne12 / ne02;
+    const int64_t r3 = ne13 / ne03;
+
+    const float alpha = 1.0f;
+    const float beta = 0.0f;
+    const int x_ne = ne01 * ne00;
+    const int y_ne = ne11 * ne10;
+    const int d_ne = ne11 * ne01;
+
+    size_t x_size;
+    size_t y_size;
+    size_t d_size;
+    cl_mem d_X;
+    if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
+        d_X = (cl_mem) src0->extra;
+    } else {
+        d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size);
+    }
+    cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
+    cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
+
+    size_t x_offset = 0;
+
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        // TODO: copy src0 here when r3>1
+        for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                if (src0->backend == GGML_BACKEND_GPU) {
+                    x_offset = (i03 * ne02 + i02) * x_ne;
+                } else {
+                    // copy src0 to device
+                    CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
+                }
+
+                for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
+                    // copy src1 to device
+                    CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
+
+                    CL_CHECK(clFinish(queue));
+
+                    // compute
+                    cl_event ev_sgemm;
+                    clblast::StatusCode status = clblast::Gemm<cl_float>(clblast::Layout::kColMajor,
+                                                               clblast::Transpose::kYes, clblast::Transpose::kNo,
+                                                               ne01, ne11, ne10,
+                                                               alpha,
+                                                               d_X, x_offset, ne00,
+                                                               d_Y, 0, ne10,
+                                                               beta,
+                                                               d_D, 0, ne01,
+                                                               &queue, &ev_sgemm);
+
+                    if (status != clblast::StatusCode::kSuccess) {
+                        GGML_ASSERT(false);
+                    }
+
+                    // copy dst to host
+                    float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
+                    CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL));
+                }
+            }
+        }
+    }
+
+    if (src0->backend != GGML_BACKEND_GPU) {
+        ggml_cl_pool_free(d_X, x_size);
+    }
+    ggml_cl_pool_free(d_Y, y_size);
+    ggml_cl_pool_free(d_D, d_size);
+}
+
+static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t wsize) {
+    GGML_ASSERT(fp16_support);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
+
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    const int64_t ne12 = src1->ne[2];
+    const int64_t ne13 = src1->ne[3];
+
+    const int nb10 = src1->nb[0];
+    const int nb11 = src1->nb[1];
+    const int nb12 = src1->nb[2];
+    const int nb13 = src1->nb[3];
+
+    const int nb2  = dst->nb[2];
+    const int nb3  = dst->nb[3];
+
+    const int64_t r2 = ne12 / ne02;
+    const int64_t r3 = ne13 / ne03;
+
+    const ggml_fp16_t alpha = ggml_fp32_to_fp16(1.0f);
+    const ggml_fp16_t beta = ggml_fp32_to_fp16(0.0f);
+    const int x_ne = ne01 * ne00;
+    const int y_ne = ne11 * ne10;
+    const int d_ne = ne11 * ne01;
+
+    GGML_ASSERT(wsize >= sizeof(ggml_fp16_t) * y_ne);
+    GGML_ASSERT(wsize >= sizeof(ggml_fp16_t) * d_ne);
+    ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata;
+
+    size_t x_size;
+    size_t y_size;
+    size_t d_size;
+    cl_mem d_X;
+    if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
+        d_X = (cl_mem) src0->extra;
+    } else {
+        d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
+    }
+    cl_mem d_Y = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * y_ne, &y_size);
+    cl_mem d_D = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * d_ne, &d_size);
+
+    bool src1_cont_rows = nb10 == sizeof(float);
+    bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);
+
+    size_t x_offset = 0;
+
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        // TODO: copy src0 here when r3>1
+        for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                if (src0->backend == GGML_BACKEND_GPU) {
+                    x_offset = (i03 * ne02 + i02) * x_ne;
+                } else {
+                    // copy src0 to device
+                    CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
+                }
+
+                for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
+                    // convert src1 to fp16
+                    // TODO: use multiple threads
+                    char * src1i = (char *) src1->data + i13*nb13 + i12*nb12;
+                    if (src1_cont_rows) {
+                        if (src1_cont_cols) {
+                            ggml_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11);
+                        }
+                        else {
+                            for (int64_t i11 = 0; i11 < ne11; i11++) {
+                                ggml_fp32_to_fp16_row((float *) (src1i + i11*nb11), tmp + i11*ne10, ne10);
+                            }
+                        }
+                    }
+                    else {
+                        for (int64_t i11 = 0; i11 < ne11; i11++) {
+                            for (int64_t i10 = 0; i10 < ne10; i10++) {
+                                // very slow due to no inlining
+                                tmp[i11*ne10 + i10] = ggml_fp32_to_fp16(*(float *) (src1i + i11*nb11 + i10*nb10));
+                            }
+                        }
+                    }
+
+                    // copy src1 to device
+                    CL_CHECK(clEnqueueWriteBuffer(queue, d_Y, false, 0, sizeof(ggml_fp16_t) * y_ne, tmp, 0, NULL, NULL));
+
+                    CL_CHECK(clFinish(queue));
+
+                    // compute
+                    cl_event ev_sgemm;
+                    clblast::StatusCode status = clblast::Gemm<cl_half>(clblast::Layout::kColMajor,
+                                                               clblast::Transpose::kYes, clblast::Transpose::kNo,
+                                                               ne01, ne11, ne10,
+                                                               alpha,
+                                                               d_X, x_offset, ne00,
+                                                               d_Y, 0, ne10,
+                                                               beta,
+                                                               d_D, 0, ne01,
+                                                               &queue, &ev_sgemm);
+
+                    if (status != clblast::StatusCode::kSuccess) {
+                        GGML_ASSERT(false);
+                    }
+
+                    // copy dst to host, then convert to float
+                    CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL));
+
+                    float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
+
+                    ggml_fp16_to_fp32_row(tmp, d, d_ne);
+                }
+            }
+        }
+    }
+
+    if (src0->backend != GGML_BACKEND_GPU) {
+        ggml_cl_pool_free(d_X, x_size);
+    }
+    ggml_cl_pool_free(d_Y, y_size);
+    ggml_cl_pool_free(d_D, d_size);
+}
+
+static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
+
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    const int64_t ne12 = src1->ne[2];
+    const int64_t ne13 = src1->ne[3];
+
+    const int nb2  = dst->nb[2];
+    const int nb3  = dst->nb[3];
+    const ggml_type type = src0->type;
+    const bool mul_mat_vec = ne11 == 1 && ne00%2 == 0;
+
+    const int64_t r2 = ne12 / ne02;
+    const int64_t r3 = ne13 / ne03;
+
+    const float alpha = 1.0f;
+    const float beta = 0.0f;
+    const int x_ne = ne01 * ne00;
+    const int y_ne = ne11 * ne10;
+    const int d_ne = ne11 * ne01;
+    const int x_bps = x_ne / ggml_blck_size(type); // blocks per 2D slice
+    const size_t q_sz = ggml_type_size(type) * x_bps;
+
+    size_t x_size;
+    size_t y_size;
+    size_t d_size;
+    size_t q_size;
+    cl_mem d_X;
+    if (!mul_mat_vec) {
+        d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size);
+    }
+    cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
+    cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
+    cl_mem d_Q;
+    if (src0->backend == GGML_BACKEND_CPU) {
+        d_Q = ggml_cl_pool_malloc(q_sz, &q_size);
+    }
+
+    cl_kernel* to_fp32_cl = ggml_get_to_fp32_cl(type);
+    cl_kernel* dmmv = ggml_get_dequantize_mul_mat_vec_cl(type);
+    GGML_ASSERT(to_fp32_cl != nullptr);
+
+    const size_t global_denom = ggml_cl_global_denom(type);
+    const size_t local = mul_mat_vec ? CL_DMMV_LOCAL_SIZE : ggml_cl_local_size(type);
+
+    size_t ev_idx = 0;
+    std::vector<cl_event> events;
+
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        // TODO: copy and dequantize src0 here when r3>1
+        for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                // copy src0 to device if necessary
+                if (src0->backend == GGML_BACKEND_CPU) {
+                    events.emplace_back();
+                    CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
+                } else if (src0->backend == GGML_BACKEND_GPU) {
+                    d_Q = (cl_mem) src0->extra;
+                } else {
+                    GGML_ASSERT(false);
+                }
+
+                if (!mul_mat_vec) {
+                    // convert src0 to fp32 on device
+                    const size_t global = x_ne / global_denom;
+                    const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
+                    CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
+                    CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
+                    CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
+                }
+
+                for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
+                    if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel
+                        // copy src1 to device
+                        events.emplace_back();
+                        CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, events.data() + ev_idx++));
+
+                        // compute
+                        const size_t global = ne01 * local;
+                        const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
+                        const cl_int ncols = ne00;
+                        events.emplace_back();
+                        CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q));
+                        CL_CHECK(clSetKernelArg(*dmmv, 1, sizeof(float) * local, NULL));
+                        CL_CHECK(clSetKernelArg(*dmmv, 2, sizeof(cl_mem), &d_Y));
+                        CL_CHECK(clSetKernelArg(*dmmv, 3, sizeof(cl_mem), &d_D));
+                        CL_CHECK(clSetKernelArg(*dmmv, 4, sizeof(cl_int), &ncols));
+                        CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, &offset, &global, &local, events.size() - 1, events.data(), events.data() + ev_idx++));
+                    } else { // CLBlast matrix matrix multiplication
+                        // copy src1 to device
+                        CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
+
+                        // wait for conversion
+                        CL_CHECK(clFinish(queue));
+
+                        // compute
+                        events.emplace_back();
+                        clblast::StatusCode status = clblast::Gemm<cl_float>(clblast::Layout::kColMajor,
+                                                                   clblast::Transpose::kYes, clblast::Transpose::kNo,
+                                                                   ne01, ne11, ne10,
+                                                                   alpha,
+                                                                   d_X, 0, ne00,
+                                                                   d_Y, 0, ne10,
+                                                                   beta,
+                                                                   d_D, 0, ne01,
+                                                                   &queue, events.data() + ev_idx++);
+
+                        if (status != clblast::StatusCode::kSuccess) {
+                            GGML_ASSERT(false);
+                        }
+                    }
+
+                    // copy dst to host
+                    float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
+                    CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &events[events.size() - 1], NULL));
+                    for (auto *event : events) {
+                        clReleaseEvent(event);
+                    }
+
+                    ev_idx = 0;
+                    events.clear();
+                }
+            }
+        }
+    }
+
+    if (!mul_mat_vec) {
+        ggml_cl_pool_free(d_X, x_size);
+    }
+    ggml_cl_pool_free(d_Y, y_size);
+    ggml_cl_pool_free(d_D, d_size);
+    if (src0->backend == GGML_BACKEND_CPU) {
+        ggml_cl_pool_free(d_Q, q_size);
+    }
+}
+
+
+bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
+    const int64_t ne10 = src1->ne[0];
+
+    const int64_t ne0 = dst->ne[0];
+    const int64_t ne1 = dst->ne[1];
+
+    // TODO: find the optimal values for these
+    if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
+        src1->type == GGML_TYPE_F32 &&
+        dst->type == GGML_TYPE_F32 &&
+        ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU)) {
+        return true;
+    }
+
+    return false;
+}
+
+static bool ggml_cl_mul_mat_use_f16(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * /* dst */) {
+    // If device doesn't support FP16
+    if (!fp16_support) {
+        return false;
+    }
+
+    size_t src0_sz = ggml_nbytes(src0);
+    size_t src1_sz = ggml_nbytes(src1);
+
+    // mul_mat_q: src0 is converted to fp32 on device
+    size_t mul_mat_q_transfer = src0_sz + src1_sz;
+
+    // mul_mat_f16: src1 is converted to fp16 on cpu
+    size_t mul_mat_f16_transfer = src0_sz + sizeof(ggml_fp16_t) * ggml_nelements(src1);
+
+    // choose the smaller one to transfer to the device
+    // TODO: this is not always the best choice due to the overhead of converting to fp16
+    return mul_mat_f16_transfer < mul_mat_q_transfer;
+}
+
+void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize) {
+    GGML_ASSERT(ggml_cl_can_mul_mat(src0, src1, dst));
+
+    if (src0->type == GGML_TYPE_F32) {
+        ggml_cl_mul_mat_f32(src0, src1, dst);
+    }
+    else if (src0->type == GGML_TYPE_F16) {
+        if (ggml_cl_mul_mat_use_f16(src0, src1, dst)) {
+            ggml_cl_mul_mat_f16(src0, src1, dst, wdata, wsize);
+        }
+        else {
+            ggml_cl_mul_mat_q_f32(src0, src1, dst);
+        }
+    }
+    else if (ggml_is_quantized(src0->type)) {
+        ggml_cl_mul_mat_q_f32(src0, src1, dst);
+    }
+    else {
+        GGML_ASSERT(false);
+    }
+}
+
+size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
+    if (src0->type == GGML_TYPE_F16 && ggml_cl_mul_mat_use_f16(src0, src1, dst)) {
+        return sizeof(ggml_fp16_t) * std::max(src1->ne[0] * src1->ne[1], dst->ne[0] * dst->ne[1]);
+    }
+    return 0;
+}
+
+void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) {
+    const int64_t ne0 = tensor->ne[0];
+    const int64_t ne1 = tensor->ne[1];
+    const int64_t ne2 = tensor->ne[2];
+    const int64_t ne3 = tensor->ne[3];
+
+    const ggml_type type = tensor->type;
+    const size_t s_sz = ggml_type_size(type) * (size_t) (ne0 * ne1 / ggml_blck_size(type));
+    const size_t q_sz = s_sz * (size_t) (ne2 * ne3);
+
+    size_t q_size;
+    cl_mem dst = ggml_cl_pool_malloc(q_sz, &q_size);
+
+    tensor->data = data;
+    // copy tensor to device
+    size_t offset = 0;
+    for (int64_t i3 = 0; i3 < ne3; i3++) {
+        for (int64_t i2 = 0; i2 < ne2; i2++) {
+            CL_CHECK(ggml_cl_h2d_tensor_2d(queue, dst, offset, tensor, i3, i2, NULL));
+            offset += s_sz;
+        }
+    }
+
+    CL_CHECK(clFinish(queue));
+
+    tensor->extra = dst;
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+}

runner/ggml-opencl.h

@@ -0,0 +1,53 @@
+//go:build opencl
+
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#pragma once
+
+#include "ggml.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+void ggml_cl_init(void);
+
+void   ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+bool   ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+void   ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
+
+void * ggml_cl_host_malloc(size_t size);
+void   ggml_cl_host_free(void * ptr);
+
+void ggml_cl_free_data(const struct ggml_tensor* tensor);
+
+void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor);
+
+#ifdef  __cplusplus
+}
+#endif

runner/ggml.c

@@ -0,0 +1,22071 @@
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows
+
+#include "ggml.h"
+
+#ifdef GGML_USE_K_QUANTS
+#include "k_quants.h"
+#endif
+
+#if defined(_MSC_VER) || defined(__MINGW32__)
+#include <malloc.h> // using malloc.h with MSC/MINGW
+#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
+#include <alloca.h>
+#endif
+
+#include <assert.h>
+#include <errno.h>
+#include <time.h>
+#include <math.h>
+#include <stdlib.h>
+#include <string.h>
+#include <stdint.h>
+#include <inttypes.h>
+#include <stdio.h>
+#include <float.h>
+#include <limits.h>
+#include <stdarg.h>
+#include <signal.h>
+
+#ifdef GGML_USE_METAL
+#include <unistd.h>
+#endif
+
+// static_assert should be a #define, but if it's not,
+// fall back to the _Static_assert C11 keyword.
+// if C99 - static_assert is noop
+// ref: https://stackoverflow.com/a/53923785/4039976
+#ifndef static_assert
+#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
+#define static_assert(cond, msg) _Static_assert(cond, msg)
+#else
+#define static_assert(cond, msg) struct global_scope_noop_trick
+#endif
+#endif
+
+#if defined(_MSC_VER)
+// disable "possible loss of data" to avoid hundreds of casts
+// we should just be careful :)
+#pragma warning(disable: 4244 4267)
+
+// disable POSIX deprecation warnigns
+// these functions are never going away, anyway
+#pragma warning(disable: 4996)
+#endif
+
+#if defined(_WIN32)
+
+#include <windows.h>
+
+typedef volatile LONG atomic_int;
+typedef atomic_int atomic_bool;
+
+static void atomic_store(atomic_int * ptr, LONG val) {
+    InterlockedExchange(ptr, val);
+}
+static LONG atomic_load(atomic_int * ptr) {
+    return InterlockedCompareExchange(ptr, 0, 0);
+}
+static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) {
+    return InterlockedExchangeAdd(ptr, inc);
+}
+static LONG atomic_fetch_sub(atomic_int * ptr, LONG dec) {
+    return atomic_fetch_add(ptr, -(dec));
+}
+
+typedef HANDLE pthread_t;
+
+typedef DWORD thread_ret_t;
+static int pthread_create(pthread_t * out, void * unused, thread_ret_t(*func)(void *), void * arg) {
+    (void) unused;
+    HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL);
+    if (handle == NULL)
+    {
+        return EAGAIN;
+    }
+
+    *out = handle;
+    return 0;
+}
+
+static int pthread_join(pthread_t thread, void * unused) {
+    (void) unused;
+    int ret = (int) WaitForSingleObject(thread, INFINITE);
+    CloseHandle(thread);
+    return ret;
+}
+
+static int sched_yield (void) {
+    Sleep (0);
+    return 0;
+}
+#else
+#include <pthread.h>
+#include <stdatomic.h>
+
+typedef void * thread_ret_t;
+
+#include <sys/types.h>
+#include <sys/stat.h>
+#include <unistd.h>
+
+#endif
+#ifdef GGML_USE_CPU_HBM
+#include <hbwmalloc.h>
+#endif
+
+// __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512
+#if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__))
+#ifndef __FMA__
+#define __FMA__
+#endif
+#ifndef __F16C__
+#define __F16C__
+#endif
+#ifndef __SSE3__
+#define __SSE3__
+#endif
+#endif
+
+/*#define GGML_PERF*/
+#define GGML_DEBUG 0
+#define GGML_GELU_FP16
+#define GGML_GELU_QUICK_FP16
+#define GGML_SILU_FP16
+// #define GGML_CROSS_ENTROPY_EXP_FP16
+// #define GGML_FLASH_ATTN_EXP_FP16
+
+#define GGML_SOFT_MAX_UNROLL 4
+#define GGML_VEC_DOT_UNROLL  2
+#define GGML_VEC_MAD_UNROLL  32
+
+//
+// logging
+//
+
+#if (GGML_DEBUG >= 1)
+#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
+#else
+#define GGML_PRINT_DEBUG(...)
+#endif
+
+#if (GGML_DEBUG >= 5)
+#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
+#else
+#define GGML_PRINT_DEBUG_5(...)
+#endif
+
+#if (GGML_DEBUG >= 10)
+#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
+#else
+#define GGML_PRINT_DEBUG_10(...)
+#endif
+
+#define GGML_PRINT(...) printf(__VA_ARGS__)
+
+//
+// end of logging block
+//
+
+#ifdef GGML_USE_ACCELERATE
+// uncomment to use vDSP for soft max computation
+// note: not sure if it is actually faster
+//#define GGML_SOFT_MAX_ACCELERATE
+#endif
+
+#if defined(_MSC_VER) || defined(__MINGW32__)
+#define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN)
+#define GGML_ALIGNED_FREE(ptr)    _aligned_free(ptr)
+#else
+inline static void * ggml_aligned_malloc(size_t size) {
+    if (size == 0) {
+        GGML_PRINT("WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_aligned_malloc!\n");
+        return NULL;
+    }
+    void * aligned_memory = NULL;
+#ifdef GGML_USE_CPU_HBM
+    int result = hbw_posix_memalign(&aligned_memory, 16, size);
+#elif GGML_USE_METAL
+    int result = posix_memalign(&aligned_memory, sysconf(_SC_PAGESIZE), size);
+#else
+    int result = posix_memalign(&aligned_memory, GGML_MEM_ALIGN, size);
+#endif
+    if (result != 0) {
+        // Handle allocation failure
+        const char *error_desc = "unknown allocation error";
+        switch (result) {
+            case EINVAL:
+                error_desc = "invalid alignment value";
+                break;
+            case ENOMEM:
+                error_desc = "insufficient memory";
+                break;
+        }
+        GGML_PRINT("%s: %s (attempted to allocate %6.2f MB)\n", __func__, error_desc, size/(1024.0*1024.0));
+        return NULL;
+    }
+    return aligned_memory;
+}
+#define GGML_ALIGNED_MALLOC(size) ggml_aligned_malloc(size)
+#ifdef GGML_USE_CPU_HBM
+#define GGML_ALIGNED_FREE(ptr)    if(NULL != ptr) hbw_free(ptr)
+#else
+#define GGML_ALIGNED_FREE(ptr)    free(ptr)
+#endif
+#endif
+
+#define UNUSED GGML_UNUSED
+#define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0)
+
+//
+// tensor access macros
+//
+
+#define GGML_TENSOR_UNARY_OP_LOCALS \
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
+#define GGML_TENSOR_BINARY_OP_LOCALS \
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
+#if defined(GGML_USE_ACCELERATE)
+#include <Accelerate/Accelerate.h>
+#if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions
+#include "ggml-opencl.h"
+#endif
+#elif defined(GGML_USE_OPENBLAS)
+#if defined(GGML_BLAS_USE_MKL)
+#include <mkl.h>
+#else
+#include <cblas.h>
+#endif
+#elif defined(GGML_USE_CUBLAS)
+#include "ggml-cuda.h"
+#elif defined(GGML_USE_CLBLAST)
+#include "ggml-opencl.h"
+#endif
+
+#undef MIN
+#undef MAX
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+
+// floating point type used to accumulate sums
+typedef double ggml_float;
+
+// 16-bit float
+// on Arm, we use __fp16
+// on x86, we use uint16_t
+#if defined(__ARM_NEON) && !defined(_MSC_VER)
+
+// if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
+//
+//   $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/
+//
+#include <arm_neon.h>
+
+#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x))
+#define GGML_COMPUTE_FP32_TO_FP16(x) (x)
+
+#define GGML_FP16_TO_FP32(x) ((float) (x))
+#define GGML_FP32_TO_FP16(x) (x)
+
+#else
+
+#ifdef __wasm_simd128__
+#include <wasm_simd128.h>
+#else
+#ifdef __POWER9_VECTOR__
+#include <altivec.h>
+#undef bool
+#define bool _Bool
+#else
+#if defined(_MSC_VER) || defined(__MINGW32__)
+#include <intrin.h>
+#else
+#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__)
+#if !defined(__riscv)
+#include <immintrin.h>
+#endif
+#endif
+#endif
+#endif
+#endif
+
+#ifdef __riscv_v_intrinsic
+#include <riscv_vector.h>
+#endif
+
+#ifdef __F16C__
+
+#ifdef _MSC_VER
+#define GGML_COMPUTE_FP16_TO_FP32(x) _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128(x)))
+#define GGML_COMPUTE_FP32_TO_FP16(x) _mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(x), 0), 0)
+#else
+#define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x)
+#define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0)
+#endif
+
+#elif defined(__POWER9_VECTOR__)
+
+#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
+#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
+/* the inline asm below is about 12% faster than the lookup method */
+#define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x)
+#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
+
+static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
+    register float f;
+    register double d;
+    __asm__(
+        "mtfprd %0,%2\n"
+        "xscvhpdp %0,%0\n"
+        "frsp %1,%0\n" :
+        /* temp */ "=d"(d),
+        /* out */  "=f"(f):
+        /* in */   "r"(h));
+    return f;
+}
+
+static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
+    register double d;
+    register ggml_fp16_t r;
+    __asm__( /* xscvdphp can work on double or single precision */
+        "xscvdphp %0,%2\n"
+        "mffprd %1,%0\n" :
+        /* temp */ "=d"(d),
+        /* out */  "=r"(r):
+        /* in */   "f"(f));
+    return r;
+}
+
+#else
+
+// FP16 <-> FP32
+// ref: https://github.com/Maratyszcza/FP16
+
+static inline float fp32_from_bits(uint32_t w) {
+    union {
+        uint32_t as_bits;
+        float as_value;
+    } fp32;
+    fp32.as_bits = w;
+    return fp32.as_value;
+}
+
+static inline uint32_t fp32_to_bits(float f) {
+    union {
+        float as_value;
+        uint32_t as_bits;
+    } fp32;
+    fp32.as_value = f;
+    return fp32.as_bits;
+}
+
+static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
+    const uint32_t w = (uint32_t) h << 16;
+    const uint32_t sign = w & UINT32_C(0x80000000);
+    const uint32_t two_w = w + w;
+
+    const uint32_t exp_offset = UINT32_C(0xE0) << 23;
+#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)
+    const float exp_scale = 0x1.0p-112f;
+#else
+    const float exp_scale = fp32_from_bits(UINT32_C(0x7800000));
+#endif
+    const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale;
+
+    const uint32_t magic_mask = UINT32_C(126) << 23;
+    const float magic_bias = 0.5f;
+    const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias;
+
+    const uint32_t denormalized_cutoff = UINT32_C(1) << 27;
+    const uint32_t result = sign |
+        (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value));
+    return fp32_from_bits(result);
+}
+
+static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
+#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)
+    const float scale_to_inf = 0x1.0p+112f;
+    const float scale_to_zero = 0x1.0p-110f;
+#else
+    const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000));
+    const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000));
+#endif
+    float base = (fabsf(f) * scale_to_inf) * scale_to_zero;
+
+    const uint32_t w = fp32_to_bits(f);
+    const uint32_t shl1_w = w + w;
+    const uint32_t sign = w & UINT32_C(0x80000000);
+    uint32_t bias = shl1_w & UINT32_C(0xFF000000);
+    if (bias < UINT32_C(0x71000000)) {
+        bias = UINT32_C(0x71000000);
+    }
+
+    base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base;
+    const uint32_t bits = fp32_to_bits(base);
+    const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00);
+    const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF);
+    const uint32_t nonsign = exp_bits + mantissa_bits;
+    return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign);
+}
+
+#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
+#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
+
+#endif // __F16C__
+
+#endif // __ARM_NEON
+
+//
+// global data
+//
+
+// precomputed gelu table for f16 (128 KB)
+static ggml_fp16_t table_gelu_f16[1 << 16];
+
+// precomputed quick gelu table for f16 (128 KB)
+static ggml_fp16_t table_gelu_quick_f16[1 << 16];
+
+// precomputed silu table for f16 (128 KB)
+static ggml_fp16_t table_silu_f16[1 << 16];
+
+// precomputed exp table for f16 (128 KB)
+static ggml_fp16_t table_exp_f16[1 << 16];
+
+// precomputed f32 table for f16 (256 KB)
+static float table_f32_f16[1 << 16];
+
+#if defined(__ARM_NEON) || defined(__wasm_simd128__)
+#define B1(c,s,n)  0x ## n ## c ,  0x ## n ## s
+#define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s)
+#define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s)
+#define B4(c,s,n) B3(c,s,n ## c), B3(c,s,n ## s)
+#define B5(c,s,n) B4(c,s,n ## c), B4(c,s,n ## s)
+#define B6(c,s,n) B5(c,s,n ## c), B5(c,s,n ## s)
+#define B7(c,s,n) B6(c,s,n ## c), B6(c,s,n ## s)
+#define B8(c,s  ) B7(c,s,     c), B7(c,s,     s)
+
+// precomputed tables for expanding 8bits to 8 bytes:
+static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4
+static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4
+#endif
+
+// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
+// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON.
+// This is also true for POWER9.
+#if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16)
+
+inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
+    uint16_t s;
+    memcpy(&s, &f, sizeof(uint16_t));
+    return table_f32_f16[s];
+}
+
+#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x)
+#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
+
+#endif
+
+// note: do not use these inside ggml.c
+// these are meant to be used via the ggml.h API
+float ggml_fp16_to_fp32(ggml_fp16_t x) {
+    return (float) GGML_FP16_TO_FP32(x);
+}
+
+ggml_fp16_t ggml_fp32_to_fp16(float x) {
+    return GGML_FP32_TO_FP16(x);
+}
+
+void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n) {
+    for (int i = 0; i < n; i++) {
+        y[i] = GGML_FP16_TO_FP32(x[i]);
+    }
+}
+
+void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n) {
+    int i = 0;
+#if defined(__F16C__)
+    for (; i + 7 < n; i += 8) {
+        __m256 x_vec = _mm256_loadu_ps(x + i);
+        __m128i y_vec = _mm256_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT);
+        _mm_storeu_si128((__m128i *)(y + i), y_vec);
+    }
+    for(; i + 3 < n; i += 4) {
+        __m128 x_vec = _mm_loadu_ps(x + i);
+        __m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT);
+        _mm_storel_epi64((__m128i *)(y + i), y_vec);
+    }
+#endif
+    for (; i < n; i++) {
+        y[i] = GGML_FP32_TO_FP16(x[i]);
+    }
+}
+
+//
+// timing
+//
+
+#if defined(_MSC_VER) || defined(__MINGW32__)
+static int64_t timer_freq, timer_start;
+void ggml_time_init(void) {
+    LARGE_INTEGER t;
+    QueryPerformanceFrequency(&t);
+    timer_freq = t.QuadPart;
+
+    // The multiplication by 1000 or 1000000 below can cause an overflow if timer_freq
+    // and the uptime is high enough.
+    // We subtract the program start time to reduce the likelihood of that happening.
+    QueryPerformanceCounter(&t);
+    timer_start = t.QuadPart;
+}
+int64_t ggml_time_ms(void) {
+    LARGE_INTEGER t;
+    QueryPerformanceCounter(&t);
+    return ((t.QuadPart-timer_start) * 1000) / timer_freq;
+}
+int64_t ggml_time_us(void) {
+    LARGE_INTEGER t;
+    QueryPerformanceCounter(&t);
+    return ((t.QuadPart-timer_start) * 1000000) / timer_freq;
+}
+#else
+void ggml_time_init(void) {}
+int64_t ggml_time_ms(void) {
+    struct timespec ts;
+    clock_gettime(CLOCK_MONOTONIC, &ts);
+    return (int64_t)ts.tv_sec*1000 + (int64_t)ts.tv_nsec/1000000;
+}
+
+int64_t ggml_time_us(void) {
+    struct timespec ts;
+    clock_gettime(CLOCK_MONOTONIC, &ts);
+    return (int64_t)ts.tv_sec*1000000 + (int64_t)ts.tv_nsec/1000;
+}
+#endif
+
+int64_t ggml_cycles(void) {
+    return clock();
+}
+
+int64_t ggml_cycles_per_ms(void) {
+    return CLOCKS_PER_SEC/1000;
+}
+
+#ifdef GGML_PERF
+#define ggml_perf_time_ms()       ggml_time_ms()
+#define ggml_perf_time_us()       ggml_time_us()
+#define ggml_perf_cycles()        ggml_cycles()
+#define ggml_perf_cycles_per_ms() ggml_cycles_per_ms()
+#else
+#define ggml_perf_time_ms()       0
+#define ggml_perf_time_us()       0
+#define ggml_perf_cycles()        0
+#define ggml_perf_cycles_per_ms() 0
+#endif
+
+
+//
+// cache line
+//
+
+#if defined(__cpp_lib_hardware_interference_size)
+#define CACHE_LINE_SIZE hardware_destructive_interference_size
+#else
+#if defined(__POWER9_VECTOR__)
+#define CACHE_LINE_SIZE 128
+#else
+#define CACHE_LINE_SIZE 64
+#endif
+#endif
+
+static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float);
+
+//
+// quantization
+//
+
+#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1)
+
+#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
+// multiply int8_t, add results pairwise twice
+static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) {
+    // Get absolute values of x vectors
+    const __m128i ax = _mm_sign_epi8(x, x);
+    // Sign the values of the y vectors
+    const __m128i sy = _mm_sign_epi8(y, x);
+    // Perform multiplication and create 16-bit values
+    const __m128i dot = _mm_maddubs_epi16(ax, sy);
+    const __m128i ones = _mm_set1_epi16(1);
+    return _mm_madd_epi16(ones, dot);
+}
+
+#if __AVX__ || __AVX2__ || __AVX512F__
+// horizontally add 8 floats
+static inline float hsum_float_8(const __m256 x) {
+    __m128 res = _mm256_extractf128_ps(x, 1);
+    res = _mm_add_ps(res, _mm256_castps256_ps128(x));
+    res = _mm_add_ps(res, _mm_movehl_ps(res, res));
+    res = _mm_add_ss(res, _mm_movehdup_ps(res));
+    return _mm_cvtss_f32(res);
+}
+
+// horizontally add 8 int32_t
+static inline int hsum_i32_8(const __m256i a) {
+    const __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1));
+    const __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128);
+    const __m128i sum64 = _mm_add_epi32(hi64, sum128);
+    const __m128i hi32  = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1));
+    return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32));
+}
+
+// horizontally add 4 int32_t
+static inline int hsum_i32_4(const __m128i a) {
+    const __m128i hi64 = _mm_unpackhi_epi64(a, a);
+    const __m128i sum64 = _mm_add_epi32(hi64, a);
+    const __m128i hi32  = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1));
+    return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32));
+}
+
+#if defined(__AVX2__) || defined(__AVX512F__)
+// spread 32 bits to 32 bytes { 0x00, 0xFF }
+static inline __m256i bytes_from_bits_32(const uint8_t * x) {
+    uint32_t x32;
+    memcpy(&x32, x, sizeof(uint32_t));
+    const __m256i shuf_mask = _mm256_set_epi64x(
+            0x0303030303030303, 0x0202020202020202,
+            0x0101010101010101, 0x0000000000000000);
+    __m256i bytes = _mm256_shuffle_epi8(_mm256_set1_epi32(x32), shuf_mask);
+    const __m256i bit_mask = _mm256_set1_epi64x(0x7fbfdfeff7fbfdfe);
+    bytes = _mm256_or_si256(bytes, bit_mask);
+    return _mm256_cmpeq_epi8(bytes, _mm256_set1_epi64x(-1));
+}
+
+// Unpack 32 4-bit fields into 32 bytes
+// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
+static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi)
+{
+    const __m128i tmp = _mm_loadu_si128((const __m128i *)rsi);
+    const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp);
+    const __m256i lowMask = _mm256_set1_epi8( 0xF );
+    return _mm256_and_si256(lowMask, bytes);
+}
+
+// add int16_t pairwise and return as float vector
+static inline __m256 sum_i16_pairs_float(const __m256i x) {
+    const __m256i ones = _mm256_set1_epi16(1);
+    const __m256i summed_pairs = _mm256_madd_epi16(ones, x);
+    return _mm256_cvtepi32_ps(summed_pairs);
+}
+
+static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
+#if __AVXVNNI__
+    const __m256i zero = _mm256_setzero_si256();
+    const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy);
+    return _mm256_cvtepi32_ps(summed_pairs);
+#else
+    // Perform multiplication and create 16-bit values
+    const __m256i dot = _mm256_maddubs_epi16(ax, sy);
+    return sum_i16_pairs_float(dot);
+#endif
+}
+
+// multiply int8_t, add results pairwise twice and return as float vector
+static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
+#if __AVXVNNIINT8__
+    const __m256i zero = _mm256_setzero_si256();
+    const __m256i summed_pairs = _mm256_dpbssd_epi32(zero, x, y);
+    return _mm256_cvtepi32_ps(summed_pairs);
+#else
+    // Get absolute values of x vectors
+    const __m256i ax = _mm256_sign_epi8(x, x);
+    // Sign the values of the y vectors
+    const __m256i sy = _mm256_sign_epi8(y, x);
+    return mul_sum_us8_pairs_float(ax, sy);
+#endif
+}
+
+static inline __m128i packNibbles( __m256i bytes )
+{
+    // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
+#if __AVX512F__
+    const __m256i bytes_srli_4 = _mm256_srli_epi16(bytes, 4);   // 0000_0000_abcd_0000
+    bytes = _mm256_or_si256(bytes, bytes_srli_4);               // 0000_abcd_abcd_efgh
+    return _mm256_cvtepi16_epi8(bytes);                         // abcd_efgh
+#else
+    const __m256i lowByte = _mm256_set1_epi16( 0xFF );
+    __m256i high = _mm256_andnot_si256( lowByte, bytes );
+    __m256i low = _mm256_and_si256( lowByte, bytes );
+    high = _mm256_srli_epi16( high, 4 );
+    bytes = _mm256_or_si256( low, high );
+
+    // Compress uint16_t lanes into bytes
+    __m128i r0 = _mm256_castsi256_si128( bytes );
+    __m128i r1 = _mm256_extracti128_si256( bytes, 1 );
+    return _mm_packus_epi16( r0, r1 );
+#endif
+}
+#elif defined(__AVX__)
+// spread 32 bits to 32 bytes { 0x00, 0xFF }
+static inline __m256i bytes_from_bits_32(const uint8_t * x) {
+    uint32_t x32;
+    memcpy(&x32, x, sizeof(uint32_t));
+    const __m128i shuf_maskl = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000);
+    const __m128i shuf_maskh = _mm_set_epi64x(0x0303030303030303, 0x0202020202020202);
+    __m128i bytesl = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskl);
+    __m128i bytesh = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskh);
+    const __m128i bit_mask = _mm_set1_epi64x(0x7fbfdfeff7fbfdfe);
+    bytesl = _mm_or_si128(bytesl, bit_mask);
+    bytesh = _mm_or_si128(bytesh, bit_mask);
+    bytesl = _mm_cmpeq_epi8(bytesl, _mm_set1_epi64x(-1));
+    bytesh = _mm_cmpeq_epi8(bytesh, _mm_set1_epi64x(-1));
+    return MM256_SET_M128I(bytesh, bytesl);
+}
+
+// Unpack 32 4-bit fields into 32 bytes
+// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
+static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi)
+{
+    // Load 16 bytes from memory
+    __m128i tmpl = _mm_loadu_si128((const __m128i *)rsi);
+    __m128i tmph = _mm_srli_epi16(tmpl, 4);
+    const __m128i lowMask = _mm_set1_epi8(0xF);
+    tmpl = _mm_and_si128(lowMask, tmpl);
+    tmph = _mm_and_si128(lowMask, tmph);
+    return MM256_SET_M128I(tmph, tmpl);
+}
+
+// add int16_t pairwise and return as float vector
+static inline __m256 sum_i16_pairs_float(const __m128i xh, const __m128i xl) {
+    const __m128i ones = _mm_set1_epi16(1);
+    const __m128i summed_pairsl = _mm_madd_epi16(ones, xl);
+    const __m128i summed_pairsh = _mm_madd_epi16(ones, xh);
+    const __m256i summed_pairs = MM256_SET_M128I(summed_pairsh, summed_pairsl);
+    return _mm256_cvtepi32_ps(summed_pairs);
+}
+
+static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
+    const __m128i axl = _mm256_castsi256_si128(ax);
+    const __m128i axh = _mm256_extractf128_si256(ax, 1);
+    const __m128i syl = _mm256_castsi256_si128(sy);
+    const __m128i syh = _mm256_extractf128_si256(sy, 1);
+    // Perform multiplication and create 16-bit values
+    const __m128i dotl = _mm_maddubs_epi16(axl, syl);
+    const __m128i doth = _mm_maddubs_epi16(axh, syh);
+    return sum_i16_pairs_float(doth, dotl);
+}
+
+// multiply int8_t, add results pairwise twice and return as float vector
+static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
+    const __m128i xl = _mm256_castsi256_si128(x);
+    const __m128i xh = _mm256_extractf128_si256(x, 1);
+    const __m128i yl = _mm256_castsi256_si128(y);
+    const __m128i yh = _mm256_extractf128_si256(y, 1);
+    // Get absolute values of x vectors
+    const __m128i axl = _mm_sign_epi8(xl, xl);
+    const __m128i axh = _mm_sign_epi8(xh, xh);
+    // Sign the values of the y vectors
+    const __m128i syl = _mm_sign_epi8(yl, xl);
+    const __m128i syh = _mm_sign_epi8(yh, xh);
+    // Perform multiplication and create 16-bit values
+    const __m128i dotl = _mm_maddubs_epi16(axl, syl);
+    const __m128i doth = _mm_maddubs_epi16(axh, syh);
+    return sum_i16_pairs_float(doth, dotl);
+}
+
+static inline __m128i packNibbles( __m128i bytes1, __m128i bytes2 )
+{
+    // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
+    const __m128i lowByte = _mm_set1_epi16( 0xFF );
+    __m128i high = _mm_andnot_si128( lowByte, bytes1 );
+    __m128i low = _mm_and_si128( lowByte, bytes1 );
+    high = _mm_srli_epi16( high, 4 );
+    bytes1 = _mm_or_si128( low, high );
+    high = _mm_andnot_si128( lowByte, bytes2 );
+    low = _mm_and_si128( lowByte, bytes2 );
+    high = _mm_srli_epi16( high, 4 );
+    bytes2 = _mm_or_si128( low, high );
+
+    return _mm_packus_epi16( bytes1, bytes2);
+}
+#endif
+#elif defined(__SSSE3__)
+// horizontally add 4x4 floats
+static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 c, const __m128 d) {
+    __m128 res_0 =_mm_hadd_ps(a, b);
+    __m128 res_1 =_mm_hadd_ps(c, d);
+    __m128 res =_mm_hadd_ps(res_0, res_1);
+    res =_mm_hadd_ps(res, res);
+    res =_mm_hadd_ps(res, res);
+
+    return _mm_cvtss_f32(res);
+}
+#endif // __AVX__ || __AVX2__ || __AVX512F__
+#endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
+
+#if defined(__ARM_NEON)
+
+#if !defined(__aarch64__)
+
+inline static int32_t vaddvq_s32(int32x4_t v) {
+    return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
+}
+
+inline static float vaddvq_f32(float32x4_t v) {
+    return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3);
+}
+
+inline static float vmaxvq_f32(float32x4_t v) {
+    return
+        MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)),
+            MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3)));
+}
+
+inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) {
+    int32x4_t res;
+
+    res[0] = roundf(vgetq_lane_f32(v, 0));
+    res[1] = roundf(vgetq_lane_f32(v, 1));
+    res[2] = roundf(vgetq_lane_f32(v, 2));
+    res[3] = roundf(vgetq_lane_f32(v, 3));
+
+    return res;
+}
+
+#endif
+#endif
+
+#define QK4_0 32
+typedef struct {
+    ggml_fp16_t d;          // delta
+    uint8_t qs[QK4_0 / 2];  // nibbles / quants
+} block_q4_0;
+static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
+
+#define QK4_1 32
+typedef struct {
+    ggml_fp16_t d;          // delta
+    ggml_fp16_t m;          // min
+    uint8_t qs[QK4_1 / 2];  // nibbles / quants
+} block_q4_1;
+static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");
+
+#define QK5_0 32
+typedef struct {
+    ggml_fp16_t d;         // delta
+    uint8_t qh[4];         // 5-th bit of quants
+    uint8_t qs[QK5_0 / 2]; // nibbles / quants
+} block_q5_0;
+static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
+
+#define QK5_1 32
+typedef struct {
+    ggml_fp16_t d;         // delta
+    ggml_fp16_t m;         // min
+    uint8_t qh[4];         // 5-th bit of quants
+    uint8_t qs[QK5_1 / 2]; // nibbles / quants
+} block_q5_1;
+static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
+
+#define QK8_0 32
+typedef struct {
+    ggml_fp16_t d;         // delta
+    int8_t  qs[QK8_0];     // quants
+} block_q8_0;
+static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
+
+#define QK8_1 32
+typedef struct {
+    float d;               // delta
+    float s;               // d * sum(qs[i])
+    int8_t  qs[QK8_1];     // quants
+} block_q8_1;
+static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding");
+
+// reference implementation for deterministic creation of model files
+static void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k) {
+    static const int qk = QK4_0;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        float amax = 0.0f; // absolute max
+        float max  = 0.0f;
+
+        for (int j = 0; j < qk; j++) {
+            const float v = x[i*qk + j];
+            if (amax < fabsf(v)) {
+                amax = fabsf(v);
+                max  = v;
+            }
+        }
+
+        const float d  = max / -8;
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = GGML_FP32_TO_FP16(d);
+
+        for (int j = 0; j < qk/2; ++j) {
+            const float x0 = x[i*qk + 0    + j]*id;
+            const float x1 = x[i*qk + qk/2 + j]*id;
+
+            const uint8_t xi0 = MIN(15, (int8_t)(x0 + 8.5f));
+            const uint8_t xi1 = MIN(15, (int8_t)(x1 + 8.5f));
+
+            y[i].qs[j]  = xi0;
+            y[i].qs[j] |= xi1 << 4;
+        }
+    }
+}
+
+static void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) {
+    quantize_row_q4_0_reference(x, y, k);
+}
+
+static void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k) {
+    const int qk = QK4_1;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        float min = FLT_MAX;
+        float max = -FLT_MAX;
+
+        for (int j = 0; j < qk; j++) {
+            const float v = x[i*qk + j];
+
+            if (v < min) min = v;
+            if (v > max) max = v;
+        }
+
+        const float d  = (max - min) / ((1 << 4) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = GGML_FP32_TO_FP16(d);
+        y[i].m = GGML_FP32_TO_FP16(min);
+
+        for (int j = 0; j < qk/2; ++j) {
+            const float x0 = (x[i*qk + 0    + j] - min)*id;
+            const float x1 = (x[i*qk + qk/2 + j] - min)*id;
+
+            const uint8_t xi0 = MIN(15, (int8_t)(x0 + 0.5f));
+            const uint8_t xi1 = MIN(15, (int8_t)(x1 + 0.5f));
+
+            y[i].qs[j]  = xi0;
+            y[i].qs[j] |= xi1 << 4;
+        }
+    }
+}
+
+static void quantize_row_q4_1(const float * restrict x, void * restrict y, int k) {
+    quantize_row_q4_1_reference(x, y, k);
+}
+
+static void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k) {
+    static const int qk = QK5_0;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        float amax = 0.0f; // absolute max
+        float max  = 0.0f;
+
+        for (int j = 0; j < qk; j++) {
+            const float v = x[i*qk + j];
+            if (amax < fabsf(v)) {
+                amax = fabsf(v);
+                max  = v;
+            }
+        }
+
+        const float d  = max / -16;
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = GGML_FP32_TO_FP16(d);
+
+        uint32_t qh = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const float x0 = x[i*qk + 0    + j]*id;
+            const float x1 = x[i*qk + qk/2 + j]*id;
+
+            const uint8_t xi0 = MIN(31, (int8_t)(x0 + 16.5f));
+            const uint8_t xi1 = MIN(31, (int8_t)(x1 + 16.5f));
+
+            y[i].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4);
+
+            // get the 5-th bit and store it in qh at the right position
+            qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+            qh |= ((xi1 & 0x10u) >> 4) << (j + qk/2);
+        }
+
+        memcpy(&y[i].qh, &qh, sizeof(qh));
+    }
+}
+
+static void quantize_row_q5_0(const float * restrict x, void * restrict y, int k) {
+    quantize_row_q5_0_reference(x, y, k);
+}
+
+static void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k) {
+    const int qk = QK5_1;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        float min = FLT_MAX;
+        float max = -FLT_MAX;
+
+        for (int j = 0; j < qk; j++) {
+            const float v = x[i*qk + j];
+
+            if (v < min) min = v;
+            if (v > max) max = v;
+        }
+
+        const float d  = (max - min) / ((1 << 5) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = GGML_FP32_TO_FP16(d);
+        y[i].m = GGML_FP32_TO_FP16(min);
+
+        uint32_t qh = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const float x0 = (x[i*qk + 0    + j] - min)*id;
+            const float x1 = (x[i*qk + qk/2 + j] - min)*id;
+
+            const uint8_t xi0 = (uint8_t)(x0 + 0.5f);
+            const uint8_t xi1 = (uint8_t)(x1 + 0.5f);
+
+            y[i].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4);
+
+            // get the 5-th bit and store it in qh at the right position
+            qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+            qh |= ((xi1 & 0x10u) >> 4) << (j + qk/2);
+        }
+
+        memcpy(&y[i].qh, &qh, sizeof(y[i].qh));
+    }
+}
+
+static void quantize_row_q5_1(const float * restrict x, void * restrict y, int k) {
+    quantize_row_q5_1_reference(x, y, k);
+}
+
+// reference implementation for deterministic creation of model files
+static void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k) {
+    assert(k % QK8_0 == 0);
+    const int nb = k / QK8_0;
+
+    for (int i = 0; i < nb; i++) {
+        float amax = 0.0f; // absolute max
+
+        for (int j = 0; j < QK8_0; j++) {
+            const float v = x[i*QK8_0 + j];
+            amax = MAX(amax, fabsf(v));
+        }
+
+        const float d = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = GGML_FP32_TO_FP16(d);
+
+        for (int j = 0; j < QK8_0; ++j) {
+            const float x0 = x[i*QK8_0 + j]*id;
+
+            y[i].qs[j] = roundf(x0);
+        }
+    }
+}
+
+static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) {
+    assert(QK8_0 == 32);
+    assert(k % QK8_0 == 0);
+    const int nb = k / QK8_0;
+
+    block_q8_0 * restrict y = vy;
+
+#if defined(__ARM_NEON)
+    for (int i = 0; i < nb; i++) {
+        float32x4_t srcv [8];
+        float32x4_t asrcv[8];
+        float32x4_t amaxv[8];
+
+        for (int j = 0; j < 8; j++) srcv[j]  = vld1q_f32(x + i*32 + 4*j);
+        for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]);
+
+        for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]);
+        for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]);
+        for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]);
+
+        const float amax = vmaxvq_f32(amaxv[0]);
+
+        const float d = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = GGML_FP32_TO_FP16(d);
+
+        for (int j = 0; j < 8; j++) {
+            const float32x4_t v  = vmulq_n_f32(srcv[j], id);
+            const int32x4_t   vi = vcvtnq_s32_f32(v);
+
+            y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0);
+            y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1);
+            y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2);
+            y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3);
+        }
+    }
+#elif defined(__wasm_simd128__)
+    for (int i = 0; i < nb; i++) {
+        v128_t srcv [8];
+        v128_t asrcv[8];
+        v128_t amaxv[8];
+
+        for (int j = 0; j < 8; j++) srcv[j]  = wasm_v128_load(x + i*32 + 4*j);
+        for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]);
+
+        for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]);
+        for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]);
+        for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]);
+
+        const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0),
+                                   wasm_f32x4_extract_lane(amaxv[0], 1)),
+                               MAX(wasm_f32x4_extract_lane(amaxv[0], 2),
+                                   wasm_f32x4_extract_lane(amaxv[0], 3)));
+
+        const float d = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = GGML_FP32_TO_FP16(d);
+
+        for (int j = 0; j < 8; j++) {
+            const v128_t v  = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id));
+            const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v);
+
+            y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0);
+            y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1);
+            y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2);
+            y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3);
+        }
+    }
+#elif defined(__AVX2__) || defined(__AVX__)
+    for (int i = 0; i < nb; i++) {
+        // Load elements into 4 AVX vectors
+        __m256 v0 = _mm256_loadu_ps( x );
+        __m256 v1 = _mm256_loadu_ps( x + 8 );
+        __m256 v2 = _mm256_loadu_ps( x + 16 );
+        __m256 v3 = _mm256_loadu_ps( x + 24 );
+        x += 32;
+
+        // Compute max(abs(e)) for the block
+        const __m256 signBit = _mm256_set1_ps( -0.0f );
+        __m256 maxAbs = _mm256_andnot_ps( signBit, v0 );
+        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) );
+        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) );
+        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) );
+
+        __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) );
+        max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) );
+        max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) );
+        const float maxScalar = _mm_cvtss_f32( max4 );
+
+        // Quantize these floats
+        const float d = maxScalar / 127.f;
+        y[i].d = GGML_FP32_TO_FP16(d);
+        const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f;
+        const __m256 mul = _mm256_set1_ps( id );
+
+        // Apply the multiplier
+        v0 = _mm256_mul_ps( v0, mul );
+        v1 = _mm256_mul_ps( v1, mul );
+        v2 = _mm256_mul_ps( v2, mul );
+        v3 = _mm256_mul_ps( v3, mul );
+
+        // Round to nearest integer
+        v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST );
+        v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST );
+        v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST );
+        v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST );
+
+        // Convert floats to integers
+        __m256i i0 = _mm256_cvtps_epi32( v0 );
+        __m256i i1 = _mm256_cvtps_epi32( v1 );
+        __m256i i2 = _mm256_cvtps_epi32( v2 );
+        __m256i i3 = _mm256_cvtps_epi32( v3 );
+
+#if defined(__AVX2__)
+        // Convert int32 to int16
+        i0 = _mm256_packs_epi32( i0, i1 );	// 0, 1, 2, 3,  8, 9, 10, 11,  4, 5, 6, 7, 12, 13, 14, 15
+        i2 = _mm256_packs_epi32( i2, i3 );	// 16, 17, 18, 19,  24, 25, 26, 27,  20, 21, 22, 23, 28, 29, 30, 31
+                                            // Convert int16 to int8
+        i0 = _mm256_packs_epi16( i0, i2 );	// 0, 1, 2, 3,  8, 9, 10, 11,  16, 17, 18, 19,  24, 25, 26, 27,  4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31
+
+        // We got our precious signed bytes, but the order is now wrong
+        // These AVX2 pack instructions process 16-byte pieces independently
+        // The following instruction is fixing the order
+        const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 );
+        i0 = _mm256_permutevar8x32_epi32( i0, perm );
+
+        _mm256_storeu_si256((__m256i *)y[i].qs, i0);
+#else
+        // Since we don't have in AVX some necessary functions,
+        // we split the registers in half and call AVX2 analogs from SSE
+        __m128i ni0 = _mm256_castsi256_si128( i0 );
+        __m128i ni1 = _mm256_extractf128_si256( i0, 1);
+        __m128i ni2 = _mm256_castsi256_si128( i1 );
+        __m128i ni3 = _mm256_extractf128_si256( i1, 1);
+        __m128i ni4 = _mm256_castsi256_si128( i2 );
+        __m128i ni5 = _mm256_extractf128_si256( i2, 1);
+        __m128i ni6 = _mm256_castsi256_si128( i3 );
+        __m128i ni7 = _mm256_extractf128_si256( i3, 1);
+
+        // Convert int32 to int16
+        ni0 = _mm_packs_epi32( ni0, ni1 );
+        ni2 = _mm_packs_epi32( ni2, ni3 );
+        ni4 = _mm_packs_epi32( ni4, ni5 );
+        ni6 = _mm_packs_epi32( ni6, ni7 );
+        // Convert int16 to int8
+        ni0 = _mm_packs_epi16( ni0, ni2 );
+        ni4 = _mm_packs_epi16( ni4, ni6 );
+
+        _mm_storeu_si128((__m128i *)(y[i].qs +  0), ni0);
+        _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4);
+#endif
+    }
+#elif defined(__riscv_v_intrinsic)
+
+    size_t vl = __riscv_vsetvl_e32m4(QK8_0);
+
+    for (int i = 0; i < nb; i++) {
+        // load elements
+        vfloat32m4_t v_x   = __riscv_vle32_v_f32m4(x+i*QK8_0, vl);
+
+        vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl);
+        vfloat32m1_t tmp   = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+        vfloat32m1_t vmax  = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl);
+        float amax = __riscv_vfmv_f_s_f32m1_f32(vmax);
+
+        const float d = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = GGML_FP32_TO_FP16(d);
+
+        vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl);
+
+        // convert to integer
+        vint16m2_t   vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl);
+        vint8m1_t    vs = __riscv_vncvt_x_x_w_i8m1(vi, vl);
+
+        // store result
+        __riscv_vse8_v_i8m1(y[i].qs , vs, vl);
+    }
+#else
+    // scalar
+    quantize_row_q8_0_reference(x, y, k);
+#endif
+}
+
+// reference implementation for deterministic creation of model files
+static void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k) {
+    assert(QK8_1 == 32);
+    assert(k % QK8_1 == 0);
+    const int nb = k / QK8_1;
+
+    for (int i = 0; i < nb; i++) {
+        float amax = 0.0f; // absolute max
+
+        for (int j = 0; j < QK8_1; j++) {
+            const float v = x[i*QK8_1 + j];
+            amax = MAX(amax, fabsf(v));
+        }
+
+        const float d = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = d;
+
+        int sum = 0;
+
+        for (int j = 0; j < QK8_1/2; ++j) {
+            const float v0 = x[i*QK8_1           + j]*id;
+            const float v1 = x[i*QK8_1 + QK8_1/2 + j]*id;
+
+            y[i].qs[          j] = roundf(v0);
+            y[i].qs[QK8_1/2 + j] = roundf(v1);
+
+            sum += y[i].qs[          j];
+            sum += y[i].qs[QK8_1/2 + j];
+        }
+
+        y[i].s = sum*d;
+    }
+}
+
+static void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
+    assert(k % QK8_1 == 0);
+    const int nb = k / QK8_1;
+
+    block_q8_1 * restrict y = vy;
+
+#if defined(__ARM_NEON)
+    for (int i = 0; i < nb; i++) {
+        float32x4_t srcv [8];
+        float32x4_t asrcv[8];
+        float32x4_t amaxv[8];
+
+        for (int j = 0; j < 8; j++) srcv[j]  = vld1q_f32(x + i*32 + 4*j);
+        for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]);
+
+        for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]);
+        for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]);
+        for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]);
+
+        const float amax = vmaxvq_f32(amaxv[0]);
+
+        const float d = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = d;
+
+        int32x4_t accv = vdupq_n_s32(0);
+
+        for (int j = 0; j < 8; j++) {
+            const float32x4_t v  = vmulq_n_f32(srcv[j], id);
+            const int32x4_t   vi = vcvtnq_s32_f32(v);
+
+            y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0);
+            y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1);
+            y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2);
+            y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3);
+
+            accv = vaddq_s32(accv, vi);
+        }
+
+        y[i].s = d * vaddvq_s32(accv);
+    }
+#elif defined(__wasm_simd128__)
+    for (int i = 0; i < nb; i++) {
+        v128_t srcv [8];
+        v128_t asrcv[8];
+        v128_t amaxv[8];
+
+        for (int j = 0; j < 8; j++) srcv[j]  = wasm_v128_load(x + i*32 + 4*j);
+        for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]);
+
+        for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]);
+        for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]);
+        for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]);
+
+        const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0),
+                                   wasm_f32x4_extract_lane(amaxv[0], 1)),
+                               MAX(wasm_f32x4_extract_lane(amaxv[0], 2),
+                                   wasm_f32x4_extract_lane(amaxv[0], 3)));
+
+        const float d = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = d;
+
+        v128_t accv = wasm_i32x4_splat(0);
+
+        for (int j = 0; j < 8; j++) {
+            const v128_t v  = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id));
+            const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v);
+
+            y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0);
+            y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1);
+            y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2);
+            y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3);
+
+            accv = wasm_i32x4_add(accv, vi);
+        }
+
+        y[i].s = d * (wasm_i32x4_extract_lane(accv, 0) +
+                      wasm_i32x4_extract_lane(accv, 1) +
+                      wasm_i32x4_extract_lane(accv, 2) +
+                      wasm_i32x4_extract_lane(accv, 3));
+    }
+#elif defined(__AVX2__) || defined(__AVX__)
+    for (int i = 0; i < nb; i++) {
+        // Load elements into 4 AVX vectors
+        __m256 v0 = _mm256_loadu_ps( x );
+        __m256 v1 = _mm256_loadu_ps( x + 8 );
+        __m256 v2 = _mm256_loadu_ps( x + 16 );
+        __m256 v3 = _mm256_loadu_ps( x + 24 );
+        x += 32;
+
+        // Compute max(abs(e)) for the block
+        const __m256 signBit = _mm256_set1_ps( -0.0f );
+        __m256 maxAbs = _mm256_andnot_ps( signBit, v0 );
+        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) );
+        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) );
+        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) );
+
+        __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) );
+        max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) );
+        max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) );
+        const float maxScalar = _mm_cvtss_f32( max4 );
+
+        // Quantize these floats
+        const float d = maxScalar / 127.f;
+        y[i].d = d;
+        const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f;
+        const __m256 mul = _mm256_set1_ps( id );
+
+        // Apply the multiplier
+        v0 = _mm256_mul_ps( v0, mul );
+        v1 = _mm256_mul_ps( v1, mul );
+        v2 = _mm256_mul_ps( v2, mul );
+        v3 = _mm256_mul_ps( v3, mul );
+
+        // Round to nearest integer
+        v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST );
+        v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST );
+        v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST );
+        v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST );
+
+        // Convert floats to integers
+        __m256i i0 = _mm256_cvtps_epi32( v0 );
+        __m256i i1 = _mm256_cvtps_epi32( v1 );
+        __m256i i2 = _mm256_cvtps_epi32( v2 );
+        __m256i i3 = _mm256_cvtps_epi32( v3 );
+
+#if defined(__AVX2__)
+        // Compute the sum of the quants and set y[i].s
+        y[i].s = d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3)));
+
+        // Convert int32 to int16
+        i0 = _mm256_packs_epi32( i0, i1 );	// 0, 1, 2, 3,  8, 9, 10, 11,  4, 5, 6, 7, 12, 13, 14, 15
+        i2 = _mm256_packs_epi32( i2, i3 );	// 16, 17, 18, 19,  24, 25, 26, 27,  20, 21, 22, 23, 28, 29, 30, 31
+                                            // Convert int16 to int8
+        i0 = _mm256_packs_epi16( i0, i2 );	// 0, 1, 2, 3,  8, 9, 10, 11,  16, 17, 18, 19,  24, 25, 26, 27,  4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31
+
+        // We got our precious signed bytes, but the order is now wrong
+        // These AVX2 pack instructions process 16-byte pieces independently
+        // The following instruction is fixing the order
+        const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 );
+        i0 = _mm256_permutevar8x32_epi32( i0, perm );
+
+        _mm256_storeu_si256((__m256i *)y[i].qs, i0);
+#else
+        // Since we don't have in AVX some necessary functions,
+        // we split the registers in half and call AVX2 analogs from SSE
+        __m128i ni0 = _mm256_castsi256_si128( i0 );
+        __m128i ni1 = _mm256_extractf128_si256( i0, 1);
+        __m128i ni2 = _mm256_castsi256_si128( i1 );
+        __m128i ni3 = _mm256_extractf128_si256( i1, 1);
+        __m128i ni4 = _mm256_castsi256_si128( i2 );
+        __m128i ni5 = _mm256_extractf128_si256( i2, 1);
+        __m128i ni6 = _mm256_castsi256_si128( i3 );
+        __m128i ni7 = _mm256_extractf128_si256( i3, 1);
+
+        // Compute the sum of the quants and set y[i].s
+        const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3));
+        const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7));
+        y[i].s = d * hsum_i32_4(_mm_add_epi32(s0, s1));
+
+        // Convert int32 to int16
+        ni0 = _mm_packs_epi32( ni0, ni1 );
+        ni2 = _mm_packs_epi32( ni2, ni3 );
+        ni4 = _mm_packs_epi32( ni4, ni5 );
+        ni6 = _mm_packs_epi32( ni6, ni7 );
+        // Convert int16 to int8
+        ni0 = _mm_packs_epi16( ni0, ni2 );
+        ni4 = _mm_packs_epi16( ni4, ni6 );
+
+        _mm_storeu_si128((__m128i *)(y[i].qs +  0), ni0);
+        _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4);
+#endif
+    }
+#elif defined(__riscv_v_intrinsic)
+
+    size_t vl = __riscv_vsetvl_e32m4(QK8_1);
+
+    for (int i = 0; i < nb; i++) {
+        // load elements
+        vfloat32m4_t v_x   = __riscv_vle32_v_f32m4(x+i*QK8_1, vl);
+
+        vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl);
+        vfloat32m1_t tmp   = __riscv_vfmv_v_f_f32m1(0.0, vl);
+        vfloat32m1_t vmax  = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl);
+        float amax = __riscv_vfmv_f_s_f32m1_f32(vmax);
+
+        const float d  = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = d;
+
+        vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl);
+
+        // convert to integer
+        vint16m2_t   vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl);
+        vint8m1_t    vs = __riscv_vncvt_x_x_w_i8m1(vi, vl);
+
+        // store result
+        __riscv_vse8_v_i8m1(y[i].qs , vs, vl);
+
+        // compute sum for y[i].s
+        vint16m1_t tmp2 = __riscv_vmv_v_x_i16m1(0, vl);
+        vint16m1_t vwrs = __riscv_vwredsum_vs_i8m1_i16m1(vs, tmp2, vl);
+
+        // set y[i].s
+        int sum = __riscv_vmv_x_s_i16m1_i16(vwrs);
+        y[i].s = sum*d;
+    }
+#else
+    // scalar
+    quantize_row_q8_1_reference(x, y, k);
+#endif
+}
+
+static void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k) {
+    static const int qk = QK4_0;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int x0 = (x[i].qs[j] & 0x0F) - 8;
+            const int x1 = (x[i].qs[j] >>   4) - 8;
+
+            y[i*qk + j + 0   ] = x0*d;
+            y[i*qk + j + qk/2] = x1*d;
+        }
+    }
+}
+
+static void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k) {
+    static const int qk = QK4_1;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+        const float m = GGML_FP16_TO_FP32(x[i].m);
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int x0 = (x[i].qs[j] & 0x0F);
+            const int x1 = (x[i].qs[j] >>   4);
+
+            y[i*qk + j + 0   ] = x0*d + m;
+            y[i*qk + j + qk/2] = x1*d + m;
+        }
+    }
+}
+
+static void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k) {
+    static const int qk = QK5_0;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+
+        uint32_t qh;
+        memcpy(&qh, x[i].qh, sizeof(qh));
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
+            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
+
+            const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16;
+            const int32_t x1 = ((x[i].qs[j] >>   4) | xh_1) - 16;
+
+            y[i*qk + j + 0   ] = x0*d;
+            y[i*qk + j + qk/2] = x1*d;
+        }
+    }
+}
+
+static void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k) {
+    static const int qk = QK5_1;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+        const float m = GGML_FP16_TO_FP32(x[i].m);
+
+        uint32_t qh;
+        memcpy(&qh, x[i].qh, sizeof(qh));
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
+            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
+
+            const int x0 = (x[i].qs[j] & 0x0F) | xh_0;
+            const int x1 = (x[i].qs[j] >>   4) | xh_1;
+
+            y[i*qk + j + 0   ] = x0*d + m;
+            y[i*qk + j + qk/2] = x1*d + m;
+        }
+    }
+}
+
+static void dequantize_row_q8_0(const void * restrict vx, float * restrict y, int k) {
+    static const int qk = QK8_0;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    const block_q8_0 * restrict x = vx;
+
+    for (int i = 0; i < nb; i++) {
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+
+        for (int j = 0; j < qk; ++j) {
+            y[i*qk + j] = x[i].qs[j]*d;
+        }
+    }
+}
+
+static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y);
+static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y);
+static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+
+static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
+    [GGML_TYPE_I8] = {
+        .type_name                = "i8",
+        .blck_size                = 1,
+        .type_size                = sizeof(int8_t),
+        .is_quantized             = false,
+    },
+    [GGML_TYPE_I16] = {
+        .type_name                = "i16",
+        .blck_size                = 1,
+        .type_size                = sizeof(int16_t),
+        .is_quantized             = false,
+    },
+    [GGML_TYPE_I32] = {
+        .type_name                = "i32",
+        .blck_size                = 1,
+        .type_size                = sizeof(int32_t),
+        .is_quantized             = false,
+    },
+    [GGML_TYPE_F32] = {
+        .type_name                = "f32",
+        .blck_size                = 1,
+        .type_size                = sizeof(float),
+        .is_quantized             = false,
+        .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f32,
+        .vec_dot_type             = GGML_TYPE_F32,
+    },
+    [GGML_TYPE_F16] = {
+        .type_name                = "f16",
+        .blck_size                = 1,
+        .type_size                = sizeof(ggml_fp16_t),
+        .is_quantized             = false,
+        .to_float                 = (ggml_to_float_t) ggml_fp16_to_fp32_row,
+        .from_float               = (ggml_from_float_t) ggml_fp32_to_fp16_row,
+        .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_fp16_row,
+        .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f16,
+        .vec_dot_type             = GGML_TYPE_F16,
+    },
+    [GGML_TYPE_Q4_0] = {
+        .type_name                = "q4_0",
+        .blck_size                = QK4_0,
+        .type_size                = sizeof(block_q4_0),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q4_0,
+        .from_float               = quantize_row_q4_0,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_0_reference,
+        .vec_dot                  = ggml_vec_dot_q4_0_q8_0,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+    },
+    [GGML_TYPE_Q4_1] = {
+        .type_name                = "q4_1",
+        .blck_size                = QK4_1,
+        .type_size                = sizeof(block_q4_1),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q4_1,
+        .from_float               = quantize_row_q4_1,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_1_reference,
+        .vec_dot                  = ggml_vec_dot_q4_1_q8_1,
+        .vec_dot_type             = GGML_TYPE_Q8_1,
+    },
+    [GGML_TYPE_Q5_0] = {
+        .type_name                = "q5_0",
+        .blck_size                = QK5_0,
+        .type_size                = sizeof(block_q5_0),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q5_0,
+        .from_float               = quantize_row_q5_0,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_0_reference,
+        .vec_dot                  = ggml_vec_dot_q5_0_q8_0,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+    },
+    [GGML_TYPE_Q5_1] = {
+        .type_name                = "q5_1",
+        .blck_size                = QK5_1,
+        .type_size                = sizeof(block_q5_1),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q5_1,
+        .from_float               = quantize_row_q5_1,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_1_reference,
+        .vec_dot                  = ggml_vec_dot_q5_1_q8_1,
+        .vec_dot_type             = GGML_TYPE_Q8_1,
+    },
+    [GGML_TYPE_Q8_0] = {
+        .type_name                = "q8_0",
+        .blck_size                = QK8_0,
+        .type_size                = sizeof(block_q8_0),
+        .is_quantized             = true,
+        .to_float                 = dequantize_row_q8_0,
+        .from_float               = quantize_row_q8_0,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q8_0_reference,
+        .vec_dot                  = ggml_vec_dot_q8_0_q8_0,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+    },
+    [GGML_TYPE_Q8_1] = {
+        .type_name                = "q8_1",
+        .blck_size                = QK8_1,
+        .type_size                = sizeof(block_q8_1),
+        .is_quantized             = true,
+        .from_float               = quantize_row_q8_1,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q8_1_reference,
+        .vec_dot_type             = GGML_TYPE_Q8_1,
+    },
+#ifdef GGML_USE_K_QUANTS
+    [GGML_TYPE_Q2_K] = {
+        .type_name                = "q2_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q2_K),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q2_K,
+        .from_float               = quantize_row_q2_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q2_K_reference,
+        .vec_dot                  = ggml_vec_dot_q2_K_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+    },
+    [GGML_TYPE_Q3_K] = {
+        .type_name                = "q3_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q3_K),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q3_K,
+        .from_float               = quantize_row_q3_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q3_K_reference,
+        .vec_dot                  = ggml_vec_dot_q3_K_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+    },
+    [GGML_TYPE_Q4_K] = {
+        .type_name                = "q4_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q4_K),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q4_K,
+        .from_float               = quantize_row_q4_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_K_reference,
+        .vec_dot                  = ggml_vec_dot_q4_K_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+    },
+    [GGML_TYPE_Q5_K] = {
+        .type_name                = "q5_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q5_K),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q5_K,
+        .from_float               = quantize_row_q5_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_K_reference,
+        .vec_dot                  = ggml_vec_dot_q5_K_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+    },
+    [GGML_TYPE_Q6_K] = {
+        .type_name                = "q6_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q6_K),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q6_K,
+        .from_float               = quantize_row_q6_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q6_K_reference,
+        .vec_dot                  = ggml_vec_dot_q6_K_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+    },
+    [GGML_TYPE_Q8_K] = {
+        .type_name                = "q8_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q8_K),
+        .is_quantized             = true,
+        .from_float               = quantize_row_q8_K,
+    }
+#endif
+};
+
+// For internal test use
+ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
+    GGML_ASSERT(type < GGML_TYPE_COUNT);
+    return type_traits[type];
+}
+
+
+//
+// simd mappings
+//
+
+// we define a common set of C macros which map to specific intrinsics based on the current architecture
+// we then implement the fundamental computation operations below using only these macros
+// adding support for new architectures requires to define the corresponding SIMD macros
+//
+// GGML_F32_STEP / GGML_F16_STEP
+//   number of elements to process in a single step
+//
+// GGML_F32_EPR / GGML_F16_EPR
+//   number of elements to fit in a single register
+//
+
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA)
+
+#define GGML_SIMD
+
+// F32 NEON
+
+#define GGML_F32_STEP 16
+#define GGML_F32_EPR  4
+
+#define GGML_F32x4              float32x4_t
+#define GGML_F32x4_ZERO         vdupq_n_f32(0.0f)
+#define GGML_F32x4_SET1(x)      vdupq_n_f32(x)
+#define GGML_F32x4_LOAD         vld1q_f32
+#define GGML_F32x4_STORE        vst1q_f32
+#define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c)
+#define GGML_F32x4_ADD          vaddq_f32
+#define GGML_F32x4_MUL          vmulq_f32
+#define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x)
+#define GGML_F32x4_REDUCE(res, x)              \
+{                                              \
+    int offset = GGML_F32_ARR >> 1;            \
+    for (int i = 0; i < offset; ++i) {         \
+        x[i] = vaddq_f32(x[i], x[offset+i]);   \
+    }                                          \
+    offset >>= 1;                              \
+    for (int i = 0; i < offset; ++i) {         \
+        x[i] = vaddq_f32(x[i], x[offset+i]);   \
+    }                                          \
+    offset >>= 1;                              \
+    for (int i = 0; i < offset; ++i) {         \
+        x[i] = vaddq_f32(x[i], x[offset+i]);   \
+    }                                          \
+    res = GGML_F32x4_REDUCE_ONE(x[0]);         \
+}
+
+#define GGML_F32_VEC        GGML_F32x4
+#define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x4_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x4_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x4_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x4_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x4_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x4_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
+
+// F16 NEON
+
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+    #define GGML_F16_STEP 32
+    #define GGML_F16_EPR  8
+
+    #define GGML_F16x8              float16x8_t
+    #define GGML_F16x8_ZERO         vdupq_n_f16(0.0f)
+    #define GGML_F16x8_SET1(x)      vdupq_n_f16(x)
+    #define GGML_F16x8_LOAD         vld1q_f16
+    #define GGML_F16x8_STORE        vst1q_f16
+    #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c)
+    #define GGML_F16x8_ADD          vaddq_f16
+    #define GGML_F16x8_MUL          vmulq_f16
+    #define GGML_F16x8_REDUCE(res, x)                             \
+    do {                                                          \
+        int offset = GGML_F16_ARR >> 1;                           \
+        for (int i = 0; i < offset; ++i) {                        \
+            x[i] = vaddq_f16(x[i], x[offset+i]);                  \
+        }                                                         \
+        offset >>= 1;                                             \
+        for (int i = 0; i < offset; ++i) {                        \
+            x[i] = vaddq_f16(x[i], x[offset+i]);                  \
+        }                                                         \
+        offset >>= 1;                                             \
+        for (int i = 0; i < offset; ++i) {                        \
+            x[i] = vaddq_f16(x[i], x[offset+i]);                  \
+        }                                                         \
+        const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 (x[0])); \
+        const float32x4_t t1 = vcvt_f32_f16(vget_high_f16(x[0])); \
+        res = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1));         \
+    } while (0)
+
+    #define GGML_F16_VEC                GGML_F16x8
+    #define GGML_F16_VEC_ZERO           GGML_F16x8_ZERO
+    #define GGML_F16_VEC_SET1           GGML_F16x8_SET1
+    #define GGML_F16_VEC_LOAD(p, i)     GGML_F16x8_LOAD(p)
+    #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE(p, r[i])
+    #define GGML_F16_VEC_FMA            GGML_F16x8_FMA
+    #define GGML_F16_VEC_ADD            GGML_F16x8_ADD
+    #define GGML_F16_VEC_MUL            GGML_F16x8_MUL
+    #define GGML_F16_VEC_REDUCE         GGML_F16x8_REDUCE
+#else
+    // if FP16 vector arithmetic is not supported, we use FP32 instead
+    // and take advantage of the vcvt_ functions to convert to/from FP16
+
+    #define GGML_F16_STEP 16
+    #define GGML_F16_EPR  4
+
+    #define GGML_F32Cx4              float32x4_t
+    #define GGML_F32Cx4_ZERO         vdupq_n_f32(0.0f)
+    #define GGML_F32Cx4_SET1(x)      vdupq_n_f32(x)
+    #define GGML_F32Cx4_LOAD(x)      vcvt_f32_f16(vld1_f16(x))
+    #define GGML_F32Cx4_STORE(x, y)  vst1_f16(x, vcvt_f16_f32(y))
+    #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c)
+    #define GGML_F32Cx4_ADD          vaddq_f32
+    #define GGML_F32Cx4_MUL          vmulq_f32
+    #define GGML_F32Cx4_REDUCE       GGML_F32x4_REDUCE
+
+    #define GGML_F16_VEC                GGML_F32Cx4
+    #define GGML_F16_VEC_ZERO           GGML_F32Cx4_ZERO
+    #define GGML_F16_VEC_SET1           GGML_F32Cx4_SET1
+    #define GGML_F16_VEC_LOAD(p, i)     GGML_F32Cx4_LOAD(p)
+    #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i])
+    #define GGML_F16_VEC_FMA            GGML_F32Cx4_FMA
+    #define GGML_F16_VEC_ADD            GGML_F32Cx4_ADD
+    #define GGML_F16_VEC_MUL            GGML_F32Cx4_MUL
+    #define GGML_F16_VEC_REDUCE         GGML_F32Cx4_REDUCE
+#endif
+
+#elif defined(__AVX__)
+
+#define GGML_SIMD
+
+// F32 AVX
+
+#define GGML_F32_STEP 32
+#define GGML_F32_EPR  8
+
+#define GGML_F32x8         __m256
+#define GGML_F32x8_ZERO    _mm256_setzero_ps()
+#define GGML_F32x8_SET1(x) _mm256_set1_ps(x)
+#define GGML_F32x8_LOAD    _mm256_loadu_ps
+#define GGML_F32x8_STORE   _mm256_storeu_ps
+#if defined(__FMA__)
+    #define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a)
+#else
+    #define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a)
+#endif
+#define GGML_F32x8_ADD     _mm256_add_ps
+#define GGML_F32x8_MUL     _mm256_mul_ps
+#define GGML_F32x8_REDUCE(res, x)                                 \
+do {                                                              \
+    int offset = GGML_F32_ARR >> 1;                               \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm256_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm256_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm256_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]),    \
+                                 _mm256_extractf128_ps(x[0], 1)); \
+    const __m128 t1 = _mm_hadd_ps(t0, t0);                        \
+    res = _mm_cvtss_f32(_mm_hadd_ps(t1, t1));                     \
+} while (0)
+// TODO: is this optimal ?
+
+#define GGML_F32_VEC        GGML_F32x8
+#define GGML_F32_VEC_ZERO   GGML_F32x8_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x8_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x8_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x8_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x8_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x8_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x8_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE
+
+// F16 AVX
+
+#define GGML_F16_STEP 32
+#define GGML_F16_EPR  8
+
+// F16 arithmetic is not supported by AVX, so we use F32 instead
+
+#define GGML_F32Cx8             __m256
+#define GGML_F32Cx8_ZERO        _mm256_setzero_ps()
+#define GGML_F32Cx8_SET1(x)     _mm256_set1_ps(x)
+
+#if defined(__F16C__)
+// the  _mm256_cvt intrinsics require F16C
+#define GGML_F32Cx8_LOAD(x)     _mm256_cvtph_ps(_mm_loadu_si128((__m128i *)(x)))
+#define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0))
+#else
+static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) {
+    float tmp[8];
+
+    for (int i = 0; i < 8; i++) {
+        tmp[i] = GGML_FP16_TO_FP32(x[i]);
+    }
+
+    return _mm256_loadu_ps(tmp);
+}
+static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) {
+    float arr[8];
+
+    _mm256_storeu_ps(arr, y);
+
+    for (int i = 0; i < 8; i++)
+        x[i] = GGML_FP32_TO_FP16(arr[i]);
+}
+#define GGML_F32Cx8_LOAD(x)     __avx_f32cx8_load(x)
+#define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y)
+#endif
+
+#define GGML_F32Cx8_FMA         GGML_F32x8_FMA
+#define GGML_F32Cx8_ADD         _mm256_add_ps
+#define GGML_F32Cx8_MUL         _mm256_mul_ps
+#define GGML_F32Cx8_REDUCE      GGML_F32x8_REDUCE
+
+#define GGML_F16_VEC                GGML_F32Cx8
+#define GGML_F16_VEC_ZERO           GGML_F32Cx8_ZERO
+#define GGML_F16_VEC_SET1           GGML_F32Cx8_SET1
+#define GGML_F16_VEC_LOAD(p, i)     GGML_F32Cx8_LOAD(p)
+#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i])
+#define GGML_F16_VEC_FMA            GGML_F32Cx8_FMA
+#define GGML_F16_VEC_ADD            GGML_F32Cx8_ADD
+#define GGML_F16_VEC_MUL            GGML_F32Cx8_MUL
+#define GGML_F16_VEC_REDUCE         GGML_F32Cx8_REDUCE
+
+#elif defined(__POWER9_VECTOR__)
+
+#define GGML_SIMD
+
+// F32 POWER9
+
+#define GGML_F32_STEP 32
+#define GGML_F32_EPR  4
+
+#define GGML_F32x4              vector float
+#define GGML_F32x4_ZERO         0.0f
+#define GGML_F32x4_SET1         vec_splats
+#define GGML_F32x4_LOAD(p)      vec_xl(0, p)
+#define GGML_F32x4_STORE(p, r)  vec_xst(r, 0, p)
+#define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a)
+#define GGML_F32x4_ADD          vec_add
+#define GGML_F32x4_MUL          vec_mul
+#define GGML_F32x4_REDUCE(res, x)              \
+{                                              \
+    int offset = GGML_F32_ARR >> 1;            \
+    for (int i = 0; i < offset; ++i) {         \
+        x[i] = vec_add(x[i], x[offset+i]);     \
+    }                                          \
+    offset >>= 1;                              \
+    for (int i = 0; i < offset; ++i) {         \
+        x[i] = vec_add(x[i], x[offset+i]);     \
+    }                                          \
+    offset >>= 1;                              \
+    for (int i = 0; i < offset; ++i) {         \
+        x[i] = vec_add(x[i], x[offset+i]);     \
+    }                                          \
+    res = vec_extract(x[0], 0) +               \
+          vec_extract(x[0], 1) +               \
+          vec_extract(x[0], 2) +               \
+          vec_extract(x[0], 3);                \
+}
+
+#define GGML_F32_VEC        GGML_F32x4
+#define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x4_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x4_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x4_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x4_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x4_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x4_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
+
+// F16 POWER9
+#define GGML_F16_STEP       GGML_F32_STEP
+#define GGML_F16_EPR        GGML_F32_EPR
+#define GGML_F16_VEC        GGML_F32x4
+#define GGML_F16_VEC_ZERO   GGML_F32x4_ZERO
+#define GGML_F16_VEC_SET1   GGML_F32x4_SET1
+#define GGML_F16_VEC_FMA    GGML_F32x4_FMA
+#define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE
+// Use vec_xl, not vec_ld, in case the load address is not aligned.
+#define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ?                   \
+  vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \
+  vec_extract_fp32_from_shortl(vec_xl(0, p))
+#define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i]
+#define GGML_F16_VEC_STORE(p, r, i)                             \
+  if (i & 0x1)                                                  \
+    vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)],  \
+                                   r[i - GGML_ENDIAN_BYTE(0)]), \
+            0, p - GGML_F16_EPR)
+
+#elif defined(__wasm_simd128__)
+
+#define GGML_SIMD
+
+// F32 WASM
+
+#define GGML_F32_STEP 16
+#define GGML_F32_EPR  4
+
+#define GGML_F32x4              v128_t
+#define GGML_F32x4_ZERO         wasm_f32x4_splat(0.0f)
+#define GGML_F32x4_SET1(x)      wasm_f32x4_splat(x)
+#define GGML_F32x4_LOAD         wasm_v128_load
+#define GGML_F32x4_STORE        wasm_v128_store
+#define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a)
+#define GGML_F32x4_ADD          wasm_f32x4_add
+#define GGML_F32x4_MUL          wasm_f32x4_mul
+#define GGML_F32x4_REDUCE(res, x)                  \
+{                                                  \
+    int offset = GGML_F32_ARR >> 1;                \
+    for (int i = 0; i < offset; ++i) {             \
+        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
+    }                                              \
+    offset >>= 1;                                  \
+    for (int i = 0; i < offset; ++i) {             \
+        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
+    }                                              \
+    offset >>= 1;                                  \
+    for (int i = 0; i < offset; ++i) {             \
+        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
+    }                                              \
+    res = wasm_f32x4_extract_lane(x[0], 0) +       \
+          wasm_f32x4_extract_lane(x[0], 1) +       \
+          wasm_f32x4_extract_lane(x[0], 2) +       \
+          wasm_f32x4_extract_lane(x[0], 3);        \
+}
+
+#define GGML_F32_VEC        GGML_F32x4
+#define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x4_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x4_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x4_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x4_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x4_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x4_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
+
+// F16 WASM
+
+#define GGML_F16_STEP 16
+#define GGML_F16_EPR  4
+
+inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) {
+    float tmp[4];
+
+    tmp[0] = GGML_FP16_TO_FP32(p[0]);
+    tmp[1] = GGML_FP16_TO_FP32(p[1]);
+    tmp[2] = GGML_FP16_TO_FP32(p[2]);
+    tmp[3] = GGML_FP16_TO_FP32(p[3]);
+
+    return wasm_v128_load(tmp);
+}
+
+inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) {
+    float tmp[4];
+
+    wasm_v128_store(tmp, x);
+
+    p[0] = GGML_FP32_TO_FP16(tmp[0]);
+    p[1] = GGML_FP32_TO_FP16(tmp[1]);
+    p[2] = GGML_FP32_TO_FP16(tmp[2]);
+    p[3] = GGML_FP32_TO_FP16(tmp[3]);
+}
+
+#define GGML_F16x4             v128_t
+#define GGML_F16x4_ZERO        wasm_f32x4_splat(0.0f)
+#define GGML_F16x4_SET1(x)     wasm_f32x4_splat(x)
+#define GGML_F16x4_LOAD(x)     __wasm_f16x4_load(x)
+#define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y)
+#define GGML_F16x4_FMA         GGML_F32x4_FMA
+#define GGML_F16x4_ADD         wasm_f32x4_add
+#define GGML_F16x4_MUL         wasm_f32x4_mul
+#define GGML_F16x4_REDUCE(res, x)                  \
+{                                                  \
+    int offset = GGML_F16_ARR >> 1;                \
+    for (int i = 0; i < offset; ++i) {             \
+        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
+    }                                              \
+    offset >>= 1;                                  \
+    for (int i = 0; i < offset; ++i) {             \
+        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
+    }                                              \
+    offset >>= 1;                                  \
+    for (int i = 0; i < offset; ++i) {             \
+        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
+    }                                              \
+    res = wasm_f32x4_extract_lane(x[0], 0) +       \
+          wasm_f32x4_extract_lane(x[0], 1) +       \
+          wasm_f32x4_extract_lane(x[0], 2) +       \
+          wasm_f32x4_extract_lane(x[0], 3);        \
+}
+
+#define GGML_F16_VEC                GGML_F16x4
+#define GGML_F16_VEC_ZERO           GGML_F16x4_ZERO
+#define GGML_F16_VEC_SET1           GGML_F16x4_SET1
+#define GGML_F16_VEC_LOAD(p, i)     GGML_F16x4_LOAD(p)
+#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i])
+#define GGML_F16_VEC_FMA            GGML_F16x4_FMA
+#define GGML_F16_VEC_ADD            GGML_F16x4_ADD
+#define GGML_F16_VEC_MUL            GGML_F16x4_MUL
+#define GGML_F16_VEC_REDUCE         GGML_F16x4_REDUCE
+
+#elif defined(__SSE3__)
+
+#define GGML_SIMD
+
+// F32 SSE
+
+#define GGML_F32_STEP 32
+#define GGML_F32_EPR  4
+
+#define GGML_F32x4         __m128
+#define GGML_F32x4_ZERO    _mm_setzero_ps()
+#define GGML_F32x4_SET1(x) _mm_set1_ps(x)
+#define GGML_F32x4_LOAD    _mm_loadu_ps
+#define GGML_F32x4_STORE   _mm_storeu_ps
+#if defined(__FMA__)
+    // TODO: Does this work?
+    #define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a)
+#else
+    #define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a)
+#endif
+#define GGML_F32x4_ADD     _mm_add_ps
+#define GGML_F32x4_MUL     _mm_mul_ps
+#define GGML_F32x4_REDUCE(res, x)                                 \
+{                                                                 \
+    int offset = GGML_F32_ARR >> 1;                               \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm_add_ps(x[i], x[offset+i]);                     \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm_add_ps(x[i], x[offset+i]);                     \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm_add_ps(x[i], x[offset+i]);                     \
+    }                                                             \
+    const __m128 t0 = _mm_hadd_ps(x[0], x[0]);                    \
+    res = _mm_cvtss_f32(_mm_hadd_ps(t0, t0));                     \
+}
+// TODO: is this optimal ?
+
+#define GGML_F32_VEC        GGML_F32x4
+#define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x4_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x4_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x4_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x4_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x4_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x4_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
+
+// F16 SSE
+
+#define GGML_F16_STEP 32
+#define GGML_F16_EPR  4
+
+static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) {
+    float tmp[4];
+
+    tmp[0] = GGML_FP16_TO_FP32(x[0]);
+    tmp[1] = GGML_FP16_TO_FP32(x[1]);
+    tmp[2] = GGML_FP16_TO_FP32(x[2]);
+    tmp[3] = GGML_FP16_TO_FP32(x[3]);
+
+    return _mm_loadu_ps(tmp);
+}
+
+static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) {
+    float arr[4];
+
+    _mm_storeu_ps(arr, y);
+
+    x[0] = GGML_FP32_TO_FP16(arr[0]);
+    x[1] = GGML_FP32_TO_FP16(arr[1]);
+    x[2] = GGML_FP32_TO_FP16(arr[2]);
+    x[3] = GGML_FP32_TO_FP16(arr[3]);
+}
+
+#define GGML_F32Cx4             __m128
+#define GGML_F32Cx4_ZERO        _mm_setzero_ps()
+#define GGML_F32Cx4_SET1(x)     _mm_set1_ps(x)
+#define GGML_F32Cx4_LOAD(x)     __sse_f16x4_load(x)
+#define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y)
+#define GGML_F32Cx4_FMA         GGML_F32x4_FMA
+#define GGML_F32Cx4_ADD         _mm_add_ps
+#define GGML_F32Cx4_MUL         _mm_mul_ps
+#define GGML_F32Cx4_REDUCE      GGML_F32x4_REDUCE
+
+#define GGML_F16_VEC                 GGML_F32Cx4
+#define GGML_F16_VEC_ZERO            GGML_F32Cx4_ZERO
+#define GGML_F16_VEC_SET1            GGML_F32Cx4_SET1
+#define GGML_F16_VEC_LOAD(p, i)      GGML_F32Cx4_LOAD(p)
+#define GGML_F16_VEC_STORE(p, r, i)  GGML_F32Cx4_STORE(p, r[i])
+#define GGML_F16_VEC_FMA             GGML_F32Cx4_FMA
+#define GGML_F16_VEC_ADD             GGML_F32Cx4_ADD
+#define GGML_F16_VEC_MUL             GGML_F32Cx4_MUL
+#define GGML_F16_VEC_REDUCE          GGML_F32Cx4_REDUCE
+
+#endif
+
+// GGML_F32_ARR / GGML_F16_ARR
+//   number of registers to use per step
+#ifdef GGML_SIMD
+#define GGML_F32_ARR (GGML_F32_STEP/GGML_F32_EPR)
+#define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR)
+#endif
+
+//
+// fundamental operations
+//
+
+inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
+
+inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
+
+inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
+
+inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
+
+inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i] + y[i]; }
+inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float   v) { for (int i = 0; i < n; ++i) z[i]  = x[i] + v;    }
+inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x)                  { for (int i = 0; i < n; ++i) y[i] += x[i];        }
+inline static void ggml_vec_acc1_f32(const int n, float * y, const float   v)                  { for (int i = 0; i < n; ++i) y[i] += v;           }
+inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i] - y[i]; }
+inline static void ggml_vec_set_f32 (const int n, float * x, const float   v)                  { for (int i = 0; i < n; ++i) x[i]  = v;           }
+inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x)                  { for (int i = 0; i < n; ++i) y[i]  = x[i];        }
+inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x)                  { for (int i = 0; i < n; ++i) y[i]  = -x[i];       }
+inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i]*y[i];   }
+inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i]/y[i];   }
+
+static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) {
+#ifdef GGML_SIMD
+    float sumf = 0.0f;
+    const int np = (n & ~(GGML_F32_STEP - 1));
+
+    GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
+
+    GGML_F32_VEC ax[GGML_F32_ARR];
+    GGML_F32_VEC ay[GGML_F32_ARR];
+
+    for (int i = 0; i < np; i += GGML_F32_STEP) {
+        for (int j = 0; j < GGML_F32_ARR; j++) {
+            ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
+            ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
+
+            sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]);
+        }
+    }
+
+    // reduce sum0..sum3 to sum0
+    GGML_F32_VEC_REDUCE(sumf, sum);
+
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        sumf += x[i]*y[i];
+    }
+#else
+    // scalar
+    ggml_float sumf = 0.0;
+    for (int i = 0; i < n; ++i) {
+        sumf += (ggml_float)(x[i]*y[i]);
+    }
+#endif
+
+    *s = sumf;
+}
+
+static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) {
+    ggml_float sumf = 0.0;
+
+#if defined(GGML_SIMD)
+    const int np = (n & ~(GGML_F16_STEP - 1));
+
+    GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO };
+
+    GGML_F16_VEC ax[GGML_F16_ARR];
+    GGML_F16_VEC ay[GGML_F16_ARR];
+
+    for (int i = 0; i < np; i += GGML_F16_STEP) {
+        for (int j = 0; j < GGML_F16_ARR; j++) {
+            ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
+            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
+
+            sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]);
+        }
+    }
+
+    // reduce sum0..sum3 to sum0
+    GGML_F16_VEC_REDUCE(sumf, sum);
+
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]));
+    }
+#else
+    for (int i = 0; i < n; ++i) {
+        sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]));
+    }
+#endif
+
+    *s = sumf;
+}
+
+static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+
+    assert(n % qk == 0);
+
+    const block_q4_0 * restrict x = vx;
+    const block_q8_0 * restrict y = vy;
+
+#if defined(__ARM_NEON)
+    float32x4_t sumv0 = vdupq_n_f32(0.0f);
+    float32x4_t sumv1 = vdupq_n_f32(0.0f);
+
+    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
+    for (int i = 0; i < nb; i += 2) {
+        const block_q4_0 * restrict x0 = &x[i + 0];
+        const block_q4_0 * restrict x1 = &x[i + 1];
+        const block_q8_0 * restrict y0 = &y[i + 0];
+        const block_q8_0 * restrict y1 = &y[i + 1];
+
+        const uint8x16_t m4b = vdupq_n_u8(0x0F);
+        const int8x16_t  s8b = vdupq_n_s8(0x8);
+
+        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
+        const uint8x16_t v0_1 = vld1q_u8(x1->qs);
+
+        // 4-bit -> 8-bit
+        const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8  (v0_0, m4b));
+        const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
+        const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8  (v0_1, m4b));
+        const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
+
+        // sub 8
+        const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b);
+        const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b);
+        const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b);
+        const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b);
+
+        // load y
+        const int8x16_t v1_0l = vld1q_s8(y0->qs);
+        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
+        const int8x16_t v1_1l = vld1q_s8(y1->qs);
+        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
+
+#if defined(__ARM_FEATURE_DOTPROD)
+        // dot product into int32x4_t
+        const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h);
+        const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h);
+
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+#else
+        const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0l));
+        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0l));
+        const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hs), vget_low_s8 (v1_0h));
+        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hs), vget_high_s8(v1_0h));
+
+        const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1ls), vget_low_s8 (v1_1l));
+        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1ls), vget_high_s8(v1_1l));
+        const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hs), vget_low_s8 (v1_1h));
+        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hs), vget_high_s8(v1_1h));
+
+        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
+        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
+        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
+        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
+
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+#endif
+    }
+
+    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
+#elif defined(__AVX2__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
+
+    // Main loop
+    for (int i = 0; i < nb; ++i) {
+        /* Compute combined scale for the block */
+        const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
+
+        __m256i bx = bytes_from_nibbles_32(x[i].qs);
+
+        // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
+        const __m256i off = _mm256_set1_epi8( 8 );
+        bx = _mm256_sub_epi8( bx, off );
+
+        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+
+        const __m256 q = mul_sum_i8_pairs_float(bx, by);
+
+        /* Multiply q with scale and accumulate */
+        acc = _mm256_fmadd_ps( d, q, acc );
+    }
+
+    *s = hsum_float_8(acc);
+#elif defined(__AVX__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
+
+    // Main loop
+    for (int i = 0; i < nb; ++i) {
+        // Compute combined scale for the block
+        const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
+
+        const __m128i lowMask = _mm_set1_epi8(0xF);
+        const __m128i off = _mm_set1_epi8(8);
+
+        const __m128i tmp = _mm_loadu_si128((const __m128i *)x[i].qs);
+
+        __m128i bx = _mm_and_si128(lowMask, tmp);
+        __m128i by = _mm_loadu_si128((const __m128i *)y[i].qs);
+        bx = _mm_sub_epi8(bx, off);
+        const __m128i i32_0 = mul_sum_i8_pairs(bx, by);
+
+        bx = _mm_and_si128(lowMask, _mm_srli_epi64(tmp, 4));
+        by = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
+        bx = _mm_sub_epi8(bx, off);
+        const __m128i i32_1 = mul_sum_i8_pairs(bx, by);
+
+        // Convert int32_t to float
+        __m256 p = _mm256_cvtepi32_ps(MM256_SET_M128I(i32_0, i32_1));
+
+        // Apply the scale, and accumulate
+        acc = _mm256_add_ps(_mm256_mul_ps( d, p ), acc);
+    }
+
+    *s = hsum_float_8(acc);
+#elif defined(__SSSE3__)
+    // set constants
+    const __m128i lowMask = _mm_set1_epi8(0xF);
+    const __m128i off = _mm_set1_epi8(8);
+
+    // Initialize accumulator with zeros
+    __m128 acc_0 = _mm_setzero_ps();
+    __m128 acc_1 = _mm_setzero_ps();
+    __m128 acc_2 = _mm_setzero_ps();
+    __m128 acc_3 = _mm_setzero_ps();
+
+    // First round without accumulation
+    {
+        _mm_prefetch(&x[0] + sizeof(block_q4_0), _MM_HINT_T0);
+        _mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0);
+
+        // Compute combined scale for the block 0 and 1
+        const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[0].d) * GGML_FP16_TO_FP32(y[0].d) );
+
+        const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[0].qs);
+
+        __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1);
+        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[0].qs);
+        bx_0 = _mm_sub_epi8(bx_0, off);
+        const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
+
+        __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4));
+        __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[0].qs + 16));
+        bx_1 = _mm_sub_epi8(bx_1, off);
+        const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
+
+        _mm_prefetch(&x[1] + sizeof(block_q4_0), _MM_HINT_T0);
+        _mm_prefetch(&y[1] + sizeof(block_q8_0), _MM_HINT_T0);
+
+        // Compute combined scale for the block 2 and 3
+        const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[1].d) * GGML_FP16_TO_FP32(y[1].d) );
+
+        const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[1].qs);
+
+        __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3);
+        __m128i by_2 = _mm_loadu_si128((const __m128i *)y[1].qs);
+        bx_2 = _mm_sub_epi8(bx_2, off);
+        const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
+
+        __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4));
+        __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[1].qs + 16));
+        bx_3 = _mm_sub_epi8(bx_3, off);
+        const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
+
+        // Convert int32_t to float
+        __m128 p0 = _mm_cvtepi32_ps(i32_0);
+        __m128 p1 = _mm_cvtepi32_ps(i32_1);
+        __m128 p2 = _mm_cvtepi32_ps(i32_2);
+        __m128 p3 = _mm_cvtepi32_ps(i32_3);
+
+        // Apply the scale
+        acc_0 = _mm_mul_ps( d_0_1, p0 );
+        acc_1 = _mm_mul_ps( d_0_1, p1 );
+        acc_2 = _mm_mul_ps( d_2_3, p2 );
+        acc_3 = _mm_mul_ps( d_2_3, p3 );
+    }
+
+    // Main loop
+    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
+    for (int i = 2; i < nb; i+=2) {
+        _mm_prefetch(&x[i] + sizeof(block_q4_0), _MM_HINT_T0);
+        _mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0);
+
+        // Compute combined scale for the block 0 and 1
+        const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
+
+        const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[i].qs);
+
+        __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1);
+        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[i].qs);
+        bx_0 = _mm_sub_epi8(bx_0, off);
+        const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
+
+        __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4));
+        __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
+        bx_1 = _mm_sub_epi8(bx_1, off);
+        const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
+
+        _mm_prefetch(&x[i] + 2 * sizeof(block_q4_0), _MM_HINT_T0);
+        _mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
+
+        // Compute combined scale for the block 2 and 3
+        const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i + 1].d) * GGML_FP16_TO_FP32(y[i + 1].d) );
+
+        const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[i + 1].qs);
+
+        __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3);
+        __m128i by_2 = _mm_loadu_si128((const __m128i *)y[i + 1].qs);
+        bx_2 = _mm_sub_epi8(bx_2, off);
+        const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
+
+        __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4));
+        __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[i + 1].qs + 16));
+        bx_3 = _mm_sub_epi8(bx_3, off);
+        const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
+
+        // Convert int32_t to float
+        __m128 p0 = _mm_cvtepi32_ps(i32_0);
+        __m128 p1 = _mm_cvtepi32_ps(i32_1);
+        __m128 p2 = _mm_cvtepi32_ps(i32_2);
+        __m128 p3 = _mm_cvtepi32_ps(i32_3);
+
+        // Apply the scale
+        __m128 p0_d = _mm_mul_ps( d_0_1, p0 );
+        __m128 p1_d = _mm_mul_ps( d_0_1, p1 );
+        __m128 p2_d = _mm_mul_ps( d_2_3, p2 );
+        __m128 p3_d = _mm_mul_ps( d_2_3, p3 );
+
+        // Acummulate
+        acc_0 = _mm_add_ps(p0_d, acc_0);
+        acc_1 = _mm_add_ps(p1_d, acc_1);
+        acc_2 = _mm_add_ps(p2_d, acc_2);
+        acc_3 = _mm_add_ps(p3_d, acc_3);
+    }
+
+    *s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
+#elif defined(__riscv_v_intrinsic)
+    float sumf = 0.0;
+
+    size_t vl = __riscv_vsetvl_e8m1(qk/2);
+
+    for (int i = 0; i < nb; i++) {
+        // load elements
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
+
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
+
+        // mask and store lower part of x, and then upper part
+        vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
+        vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
+
+        vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a);
+        vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l);
+
+        // subtract offset
+        vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 8, vl);
+        vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 8, vl);
+
+        vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl);
+        vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl);
+
+        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);
+
+        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl);
+        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl);
+
+        int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
+
+        sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d);
+    }
+
+    *s = sumf;
+#else
+    // scalar
+    float sumf = 0.0;
+
+    for (int i = 0; i < nb; i++) {
+        int sumi = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int v0 = (x[i].qs[j] & 0x0F) - 8;
+            const int v1 = (x[i].qs[j] >>   4) - 8;
+
+            sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
+        }
+
+        sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d);
+    }
+
+    *s = sumf;
+#endif
+}
+
+static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    const int qk = QK8_1;
+    const int nb = n / qk;
+
+    assert(n % qk == 0);
+
+    const block_q4_1 * restrict x = vx;
+    const block_q8_1 * restrict y = vy;
+
+    // TODO: add WASM SIMD
+#if defined(__ARM_NEON)
+    float32x4_t sumv0 = vdupq_n_f32(0.0f);
+    float32x4_t sumv1 = vdupq_n_f32(0.0f);
+
+    float summs = 0;
+
+    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
+    for (int i = 0; i < nb; i += 2) {
+        const block_q4_1 * restrict x0 = &x[i + 0];
+        const block_q4_1 * restrict x1 = &x[i + 1];
+        const block_q8_1 * restrict y0 = &y[i + 0];
+        const block_q8_1 * restrict y1 = &y[i + 1];
+
+        summs += GGML_FP16_TO_FP32(x0->m) * y0->s + GGML_FP16_TO_FP32(x1->m) * y1->s;
+
+        const uint8x16_t m4b = vdupq_n_u8(0x0F);
+
+        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
+        const uint8x16_t v0_1 = vld1q_u8(x1->qs);
+
+        // 4-bit -> 8-bit
+        const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8  (v0_0, m4b));
+        const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
+        const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8  (v0_1, m4b));
+        const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
+
+        // load y
+        const int8x16_t v1_0l = vld1q_s8(y0->qs);
+        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
+        const int8x16_t v1_1l = vld1q_s8(y1->qs);
+        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
+
+#if defined(__ARM_FEATURE_DOTPROD)
+        // dot product into int32x4_t
+        const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h);
+        const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h);
+
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*y0->d);
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*y1->d);
+#else
+        const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0l), vget_low_s8 (v1_0l));
+        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0l), vget_high_s8(v1_0l));
+        const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0h), vget_low_s8 (v1_0h));
+        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0h), vget_high_s8(v1_0h));
+
+        const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1l), vget_low_s8 (v1_1l));
+        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1l), vget_high_s8(v1_1l));
+        const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1h), vget_low_s8 (v1_1h));
+        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1h), vget_high_s8(v1_1h));
+
+        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
+        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
+        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
+        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
+
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d);
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d);
+#endif
+    }
+
+    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs;
+#elif defined(__AVX2__) || defined(__AVX__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
+
+    float summs = 0;
+
+    // Main loop
+    for (int i = 0; i < nb; ++i) {
+        const float d0 = GGML_FP16_TO_FP32(x[i].d);
+        const float d1 = y[i].d;
+
+        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
+
+        const __m256 d0v = _mm256_set1_ps( d0 );
+        const __m256 d1v = _mm256_set1_ps( d1 );
+
+        // Compute combined scales
+        const __m256 d0d1 = _mm256_mul_ps( d0v, d1v );
+
+        // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes
+        const __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs );
+
+        const __m256 xy = mul_sum_us8_pairs_float(bx, by);
+
+        // Accumulate d0*d1*x*y
+#if defined(__AVX2__)
+        acc = _mm256_fmadd_ps( d0d1, xy, acc );
+#else
+        acc = _mm256_add_ps( _mm256_mul_ps( d0d1, xy ), acc );
+#endif
+    }
+
+    *s = hsum_float_8(acc) + summs;
+#elif defined(__riscv_v_intrinsic)
+    float sumf = 0.0;
+
+    size_t vl = __riscv_vsetvl_e8m1(qk/2);
+
+    for (int i = 0; i < nb; i++) {
+        // load elements
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
+
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
+
+        // mask and store lower part of x, and then upper part
+        vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
+        vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
+
+        vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a);
+        vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l);
+
+        vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl);
+        vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl);
+
+        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);
+
+        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl);
+        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl);
+
+        int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
+
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
+    }
+
+    *s = sumf;
+#else
+    // scalar
+    float sumf = 0.0;
+
+    for (int i = 0; i < nb; i++) {
+        int sumi = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int v0 = (x[i].qs[j] & 0x0F);
+            const int v1 = (x[i].qs[j] >>   4);
+
+            sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
+        }
+
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
+    }
+
+    *s = sumf;
+#endif
+}
+
+static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+
+    assert(n % qk == 0);
+    assert(qk == QK5_0);
+
+    const block_q5_0 * restrict x = vx;
+    const block_q8_0 * restrict y = vy;
+
+#if defined(__ARM_NEON)
+    float32x4_t sumv0 = vdupq_n_f32(0.0f);
+    float32x4_t sumv1 = vdupq_n_f32(0.0f);
+
+    uint32_t qh0;
+    uint32_t qh1;
+
+    uint64_t tmp0[4];
+    uint64_t tmp1[4];
+
+    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
+    for (int i = 0; i < nb; i += 2) {
+        const block_q5_0 * restrict x0 = &x[i];
+        const block_q5_0 * restrict x1 = &x[i + 1];
+        const block_q8_0 * restrict y0 = &y[i];
+        const block_q8_0 * restrict y1 = &y[i + 1];
+
+        const uint8x16_t m4b = vdupq_n_u8(0x0F);
+
+        // extract the 5th bit via lookup table ((!b) << 4)
+        memcpy(&qh0, x0->qh, sizeof(qh0));
+        memcpy(&qh1, x1->qh, sizeof(qh1));
+
+        tmp0[0] = table_b2b_1[(qh0 >>  0) & 0xFF];
+        tmp0[1] = table_b2b_1[(qh0 >>  8) & 0xFF];
+        tmp0[2] = table_b2b_1[(qh0 >> 16) & 0xFF];
+        tmp0[3] = table_b2b_1[(qh0 >> 24)       ];
+
+        tmp1[0] = table_b2b_1[(qh1 >>  0) & 0xFF];
+        tmp1[1] = table_b2b_1[(qh1 >>  8) & 0xFF];
+        tmp1[2] = table_b2b_1[(qh1 >> 16) & 0xFF];
+        tmp1[3] = table_b2b_1[(qh1 >> 24)       ];
+
+        const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0));
+        const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2));
+        const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0));
+        const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2));
+
+        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
+        const uint8x16_t v0_1 = vld1q_u8(x1->qs);
+
+        // 4-bit -> 8-bit
+        int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8  (v0_0, m4b));
+        int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
+        int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8  (v0_1, m4b));
+        int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
+
+        // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero)
+        const int8x16_t v0_0lf = vsubq_s8(v0_0l, qhl0);
+        const int8x16_t v0_0hf = vsubq_s8(v0_0h, qhh0);
+        const int8x16_t v0_1lf = vsubq_s8(v0_1l, qhl1);
+        const int8x16_t v0_1hf = vsubq_s8(v0_1h, qhh1);
+
+        // load y
+        const int8x16_t v1_0l = vld1q_s8(y0->qs);
+        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
+        const int8x16_t v1_1l = vld1q_s8(y1->qs);
+        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
+
+#if defined(__ARM_FEATURE_DOTPROD)
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
+                        vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
+                        vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
+                        vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
+                        vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+#else
+        const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l));
+        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
+        const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hf), vget_low_s8 (v1_0h));
+        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hf), vget_high_s8(v1_0h));
+
+        const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lf), vget_low_s8 (v1_1l));
+        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lf), vget_high_s8(v1_1l));
+        const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hf), vget_low_s8 (v1_1h));
+        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hf), vget_high_s8(v1_1h));
+
+        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
+        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
+        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
+        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
+
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+#endif
+    }
+
+    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
+#elif defined(__wasm_simd128__)
+    v128_t sumv = wasm_f32x4_splat(0.0f);
+
+    uint32_t qh;
+    uint64_t tmp[4];
+
+    // TODO: check if unrolling this is better
+    for (int i = 0; i < nb; ++i) {
+        const block_q5_0 * restrict x0 = &x[i];
+        const block_q8_0 * restrict y0 = &y[i];
+
+        const v128_t m4b  = wasm_i8x16_splat(0x0F);
+
+        // extract the 5th bit
+        memcpy(&qh, x0->qh, sizeof(qh));
+
+        tmp[0] = table_b2b_1[(qh >>  0) & 0xFF];
+        tmp[1] = table_b2b_1[(qh >>  8) & 0xFF];
+        tmp[2] = table_b2b_1[(qh >> 16) & 0xFF];
+        tmp[3] = table_b2b_1[(qh >> 24)       ];
+
+        const v128_t qhl = wasm_v128_load(tmp + 0);
+        const v128_t qhh = wasm_v128_load(tmp + 2);
+
+        const v128_t v0 = wasm_v128_load(x0->qs);
+
+        // 4-bit -> 8-bit
+        const v128_t v0l = wasm_v128_and (v0, m4b);
+        const v128_t v0h = wasm_u8x16_shr(v0, 4);
+
+        // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero)
+        const v128_t v0lf = wasm_i8x16_sub(v0l, qhl);
+        const v128_t v0hf = wasm_i8x16_sub(v0h, qhh);
+
+        // load y
+        const v128_t v1l = wasm_v128_load(y0->qs);
+        const v128_t v1h = wasm_v128_load(y0->qs + 16);
+
+        // int8x16 -> int16x8
+        const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf);
+        const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf);
+        const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf);
+        const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf);
+
+        const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l);
+        const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l);
+        const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h);
+        const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h);
+
+        // dot product
+        sumv = wasm_f32x4_add(sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4(
+                        wasm_i32x4_add(
+                            wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll),
+                                           wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
+                            wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl),
+                                           wasm_i32x4_dot_i16x8(v0hfh, v1hh)))),
+                    wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d))));
+    }
+
+    *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
+         wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3);
+#elif defined(__AVX2__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
+
+    // Main loop
+    for (int i = 0; i < nb; i++) {
+        /* Compute combined scale for the block */
+        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
+
+        __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0));
+        bx = _mm256_or_si256(bx, bxhi);
+
+        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+
+        const __m256 q = mul_sum_i8_pairs_float(bx, by);
+
+        /* Multiply q with scale and accumulate */
+        acc = _mm256_fmadd_ps(d, q, acc);
+    }
+
+    *s = hsum_float_8(acc);
+#elif defined(__AVX__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
+    __m128i mask = _mm_set1_epi8((char)0xF0);
+
+    // Main loop
+    for (int i = 0; i < nb; i++) {
+        /* Compute combined scale for the block */
+        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
+
+        __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        const __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        __m128i bxhil = _mm256_castsi256_si128(bxhi);
+        __m128i bxhih = _mm256_extractf128_si256(bxhi, 1);
+        bxhil = _mm_andnot_si128(bxhil, mask);
+        bxhih = _mm_andnot_si128(bxhih, mask);
+        __m128i bxl = _mm256_castsi256_si128(bx);
+        __m128i bxh = _mm256_extractf128_si256(bx, 1);
+        bxl = _mm_or_si128(bxl, bxhil);
+        bxh = _mm_or_si128(bxh, bxhih);
+        bx = MM256_SET_M128I(bxh, bxl);
+
+        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+
+        const __m256 q = mul_sum_i8_pairs_float(bx, by);
+
+        /* Multiply q with scale and accumulate */
+        acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc);
+    }
+
+    *s = hsum_float_8(acc);
+#elif defined(__riscv_v_intrinsic)
+    float sumf = 0.0;
+
+    uint32_t qh;
+
+    size_t vl = __riscv_vsetvl_e8m1(qk/2);
+
+    // These tempory registers are for masking and shift operations
+    vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl);
+    vuint32m2_t vt_2 = __riscv_vsll_vv_u32m2(__riscv_vmv_v_x_u32m2(1, vl), vt_1, vl);
+
+    vuint32m2_t vt_3 = __riscv_vsll_vx_u32m2(vt_2, 16, vl);
+    vuint32m2_t vt_4 = __riscv_vadd_vx_u32m2(vt_1, 12, vl);
+
+    for (int i = 0; i < nb; i++) {
+        memcpy(&qh, x[i].qh, sizeof(uint32_t));
+
+        // ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
+        vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(vt_2, qh, vl);
+        vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(xha_0, vt_1, vl);
+        vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl);
+
+        // ((qh & (1u << (j + 16))) >> (j + 12));
+        vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(vt_3, qh, vl);
+        vuint32m2_t xhl_1 = __riscv_vsrl_vv_u32m2(xha_1, vt_4, vl);
+
+        // narrowing
+        vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xhl_0, vl);
+        vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl);
+
+        vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xhl_1, vl);
+        vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl);
+
+        // load
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
+
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
+
+        vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
+        vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
+
+        vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl);
+        vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl);
+
+        vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a);
+        vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l);
+
+        vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 16, vl);
+        vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 16, vl);
+
+        vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl);
+        vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl);
+
+        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);
+
+        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl);
+        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl);
+
+        int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
+
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi;
+    }
+
+    *s = sumf;
+#else
+    // scalar
+    float sumf = 0.0;
+
+    for (int i = 0; i < nb; i++) {
+        uint32_t qh;
+        memcpy(&qh, x[i].qh, sizeof(qh));
+
+        int sumi = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
+            const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));
+
+            const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16;
+            const int32_t x1 = ((x[i].qs[j] >>   4) | xh_1) - 16;
+
+            sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
+        }
+
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi;
+    }
+
+    *s = sumf;
+#endif
+}
+
+static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    const int qk = QK8_1;
+    const int nb = n / qk;
+
+    assert(n % qk == 0);
+    assert(qk == QK5_1);
+
+    const block_q5_1 * restrict x = vx;
+    const block_q8_1 * restrict y = vy;
+
+#if defined(__ARM_NEON)
+    float32x4_t sumv0 = vdupq_n_f32(0.0f);
+    float32x4_t sumv1 = vdupq_n_f32(0.0f);
+
+    float summs0 = 0.0f;
+    float summs1 = 0.0f;
+
+    uint32_t qh0;
+    uint32_t qh1;
+
+    uint64_t tmp0[4];
+    uint64_t tmp1[4];
+
+    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
+    for (int i = 0; i < nb; i += 2) {
+        const block_q5_1 * restrict x0 = &x[i];
+        const block_q5_1 * restrict x1 = &x[i + 1];
+        const block_q8_1 * restrict y0 = &y[i];
+        const block_q8_1 * restrict y1 = &y[i + 1];
+
+        const uint8x16_t m4b = vdupq_n_u8(0x0F);
+
+        summs0 += GGML_FP16_TO_FP32(x0->m) * y0->s;
+        summs1 += GGML_FP16_TO_FP32(x1->m) * y1->s;
+
+        // extract the 5th bit via lookup table ((b) << 4)
+        memcpy(&qh0, x0->qh, sizeof(qh0));
+        memcpy(&qh1, x1->qh, sizeof(qh1));
+
+        tmp0[0] = table_b2b_0[(qh0 >>  0) & 0xFF];
+        tmp0[1] = table_b2b_0[(qh0 >>  8) & 0xFF];
+        tmp0[2] = table_b2b_0[(qh0 >> 16) & 0xFF];
+        tmp0[3] = table_b2b_0[(qh0 >> 24)       ];
+
+        tmp1[0] = table_b2b_0[(qh1 >>  0) & 0xFF];
+        tmp1[1] = table_b2b_0[(qh1 >>  8) & 0xFF];
+        tmp1[2] = table_b2b_0[(qh1 >> 16) & 0xFF];
+        tmp1[3] = table_b2b_0[(qh1 >> 24)       ];
+
+        const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0));
+        const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2));
+        const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0));
+        const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2));
+
+        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
+        const uint8x16_t v0_1 = vld1q_u8(x1->qs);
+
+        // 4-bit -> 8-bit
+        const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8  (v0_0, m4b));
+        const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
+        const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8  (v0_1, m4b));
+        const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
+
+        // add high bit
+        const int8x16_t v0_0lf = vorrq_s8(v0_0l, qhl0);
+        const int8x16_t v0_0hf = vorrq_s8(v0_0h, qhh0);
+        const int8x16_t v0_1lf = vorrq_s8(v0_1l, qhl1);
+        const int8x16_t v0_1hf = vorrq_s8(v0_1h, qhh1);
+
+        // load y
+        const int8x16_t v1_0l = vld1q_s8(y0->qs);
+        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
+        const int8x16_t v1_1l = vld1q_s8(y1->qs);
+        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
+
+#if defined(__ARM_FEATURE_DOTPROD)
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
+                        vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
+                        vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d);
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
+                        vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
+                        vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d);
+#else
+        const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l));
+        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
+        const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hf), vget_low_s8 (v1_0h));
+        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hf), vget_high_s8(v1_0h));
+
+        const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lf), vget_low_s8 (v1_1l));
+        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lf), vget_high_s8(v1_1l));
+        const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hf), vget_low_s8 (v1_1h));
+        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hf), vget_high_s8(v1_1h));
+
+        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
+        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
+        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
+        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
+
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d);
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d);
+#endif
+    }
+
+    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1;
+#elif defined(__wasm_simd128__)
+    v128_t sumv = wasm_f32x4_splat(0.0f);
+
+    float summs = 0.0f;
+
+    uint32_t qh;
+    uint64_t tmp[4];
+
+    // TODO: check if unrolling this is better
+    for (int i = 0; i < nb; ++i) {
+        const block_q5_1 * restrict x0 = &x[i];
+        const block_q8_1 * restrict y0 = &y[i];
+
+        summs += GGML_FP16_TO_FP32(x0->m) * y0->s;
+
+        const v128_t m4b = wasm_i8x16_splat(0x0F);
+
+        // extract the 5th bit
+        memcpy(&qh, x0->qh, sizeof(qh));
+
+        tmp[0] = table_b2b_0[(qh >>  0) & 0xFF];
+        tmp[1] = table_b2b_0[(qh >>  8) & 0xFF];
+        tmp[2] = table_b2b_0[(qh >> 16) & 0xFF];
+        tmp[3] = table_b2b_0[(qh >> 24)       ];
+
+        const v128_t qhl = wasm_v128_load(tmp + 0);
+        const v128_t qhh = wasm_v128_load(tmp + 2);
+
+        const v128_t v0 = wasm_v128_load(x0->qs);
+
+        // 4-bit -> 8-bit
+        const v128_t v0l = wasm_v128_and (v0, m4b);
+        const v128_t v0h = wasm_u8x16_shr(v0, 4);
+
+        // add high bit
+        const v128_t v0lf = wasm_v128_or(v0l, qhl);
+        const v128_t v0hf = wasm_v128_or(v0h, qhh);
+
+        // load y
+        const v128_t v1l = wasm_v128_load(y0->qs);
+        const v128_t v1h = wasm_v128_load(y0->qs + 16);
+
+        // int8x16 -> int16x8
+        const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf);
+        const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf);
+        const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf);
+        const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf);
+
+        const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l);
+        const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l);
+        const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h);
+        const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h);
+
+        // dot product
+        sumv = wasm_f32x4_add(sumv,
+                wasm_f32x4_mul(wasm_f32x4_convert_i32x4(wasm_i32x4_add(
+                            wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll),
+                                           wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
+                            wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl),
+                                           wasm_i32x4_dot_i16x8(v0hfh, v1hh)))),
+                    wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * y0->d)));
+    }
+
+    *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
+         wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs;
+#elif defined(__AVX2__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
+
+    float summs = 0.0f;
+
+    // Main loop
+    for (int i = 0; i < nb; i++) {
+        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
+
+        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
+
+        __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10));
+        bx = _mm256_or_si256(bx, bxhi);
+
+        const __m256 dy = _mm256_set1_ps(y[i].d);
+        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+
+        const __m256 q = mul_sum_us8_pairs_float(bx, by);
+
+        acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc);
+    }
+
+    *s = hsum_float_8(acc) + summs;
+#elif defined(__AVX__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
+    __m128i mask = _mm_set1_epi8(0x10);
+
+    float summs = 0.0f;
+
+    // Main loop
+    for (int i = 0; i < nb; i++) {
+        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
+
+        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
+
+        __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        const __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        __m128i bxhil = _mm256_castsi256_si128(bxhi);
+        __m128i bxhih = _mm256_extractf128_si256(bxhi, 1);
+        bxhil = _mm_and_si128(bxhil, mask);
+        bxhih = _mm_and_si128(bxhih, mask);
+        __m128i bxl = _mm256_castsi256_si128(bx);
+        __m128i bxh = _mm256_extractf128_si256(bx, 1);
+        bxl = _mm_or_si128(bxl, bxhil);
+        bxh = _mm_or_si128(bxh, bxhih);
+        bx = MM256_SET_M128I(bxh, bxl);
+
+        const __m256 dy = _mm256_set1_ps(y[i].d);
+        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+
+        const __m256 q = mul_sum_us8_pairs_float(bx, by);
+
+        acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc);
+    }
+
+    *s = hsum_float_8(acc) + summs;
+#elif defined(__riscv_v_intrinsic)
+    float sumf = 0.0;
+
+    uint32_t qh;
+
+    size_t vl = __riscv_vsetvl_e8m1(qk/2);
+
+    // temporary registers for shift operations
+    vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl);
+    vuint32m2_t vt_2 = __riscv_vadd_vx_u32m2(vt_1, 12, vl);
+
+    for (int i = 0; i < nb; i++) {
+        memcpy(&qh, x[i].qh, sizeof(uint32_t));
+
+        // load qh
+        vuint32m2_t vqh = __riscv_vmv_v_x_u32m2(qh, vl);
+
+        // ((qh >> (j +  0)) << 4) & 0x10;
+        vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(vqh, vt_1, vl);
+        vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl);
+        vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(xhl_0, 0x10, vl);
+
+        // ((qh >> (j + 12))     ) & 0x10;
+        vuint32m2_t xhr_1 = __riscv_vsrl_vv_u32m2(vqh, vt_2, vl);
+        vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(xhr_1, 0x10, vl);
+
+        // narrowing
+        vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xha_0, vl);
+        vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl);
+
+        vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xha_1, vl);
+        vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl);
+
+        // load
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
+
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
+
+        vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
+        vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
+
+        vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl);
+        vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl);
+
+        vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a);
+        vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l);
+
+        vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl);
+        vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl);
+
+        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);
+
+        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl);
+        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl);
+
+        int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
+
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
+    }
+
+    *s = sumf;
+#else
+    // scalar
+    float sumf = 0.0;
+
+    for (int i = 0; i < nb; i++) {
+        uint32_t qh;
+        memcpy(&qh, x[i].qh, sizeof(qh));
+
+        int sumi = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
+            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
+
+            const int32_t x0 = (x[i].qs[j] & 0xF) | xh_0;
+            const int32_t x1 = (x[i].qs[j] >>  4) | xh_1;
+
+            sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
+        }
+
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
+    }
+
+    *s = sumf;
+#endif
+}
+
+static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+
+    assert(n % qk == 0);
+
+    const block_q8_0 * restrict x = vx;
+    const block_q8_0 * restrict y = vy;
+
+#if defined(__ARM_NEON)
+    float32x4_t sumv0 = vdupq_n_f32(0.0f);
+    float32x4_t sumv1 = vdupq_n_f32(0.0f);
+
+    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
+    for (int i = 0; i < nb; i += 2) {
+        const block_q8_0 * restrict x0 = &x[i + 0];
+        const block_q8_0 * restrict x1 = &x[i + 1];
+        const block_q8_0 * restrict y0 = &y[i + 0];
+        const block_q8_0 * restrict y1 = &y[i + 1];
+
+        const int8x16_t x0_0 = vld1q_s8(x0->qs);
+        const int8x16_t x0_1 = vld1q_s8(x0->qs + 16);
+        const int8x16_t x1_0 = vld1q_s8(x1->qs);
+        const int8x16_t x1_1 = vld1q_s8(x1->qs + 16);
+
+        // load y
+        const int8x16_t y0_0 = vld1q_s8(y0->qs);
+        const int8x16_t y0_1 = vld1q_s8(y0->qs + 16);
+        const int8x16_t y1_0 = vld1q_s8(y1->qs);
+        const int8x16_t y1_1 = vld1q_s8(y1->qs + 16);
+
+#if defined(__ARM_FEATURE_DOTPROD)
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
+                        vdotq_s32(vdupq_n_s32(0), x0_0, y0_0),
+                        vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
+                        vdotq_s32(vdupq_n_s32(0), x1_0, y1_0),
+                        vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+
+#else
+        const int16x8_t p0_0 = vmull_s8(vget_low_s8 (x0_0), vget_low_s8 (y0_0));
+        const int16x8_t p0_1 = vmull_s8(vget_high_s8(x0_0), vget_high_s8(y0_0));
+        const int16x8_t p0_2 = vmull_s8(vget_low_s8 (x0_1), vget_low_s8 (y0_1));
+        const int16x8_t p0_3 = vmull_s8(vget_high_s8(x0_1), vget_high_s8(y0_1));
+
+        const int16x8_t p1_0 = vmull_s8(vget_low_s8 (x1_0), vget_low_s8 (y1_0));
+        const int16x8_t p1_1 = vmull_s8(vget_high_s8(x1_0), vget_high_s8(y1_0));
+        const int16x8_t p1_2 = vmull_s8(vget_low_s8 (x1_1), vget_low_s8 (y1_1));
+        const int16x8_t p1_3 = vmull_s8(vget_high_s8(x1_1), vget_high_s8(y1_1));
+
+        const int32x4_t p0 = vaddq_s32(vpaddlq_s16(p0_0), vpaddlq_s16(p0_1));
+        const int32x4_t p1 = vaddq_s32(vpaddlq_s16(p0_2), vpaddlq_s16(p0_3));
+        const int32x4_t p2 = vaddq_s32(vpaddlq_s16(p1_0), vpaddlq_s16(p1_1));
+        const int32x4_t p3 = vaddq_s32(vpaddlq_s16(p1_2), vpaddlq_s16(p1_3));
+
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(p0, p1)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(p2, p3)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+#endif
+    }
+
+    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
+#elif defined(__AVX2__) || defined(__AVX__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
+
+    // Main loop
+    for (int i = 0; i < nb; ++i) {
+        // Compute combined scale for the block
+        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
+        __m256i bx = _mm256_loadu_si256((const __m256i *)x[i].qs);
+        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+
+        const __m256 q = mul_sum_i8_pairs_float(bx, by);
+
+        // Multiply q with scale and accumulate
+#if defined(__AVX2__)
+        acc = _mm256_fmadd_ps( d, q, acc );
+#else
+        acc = _mm256_add_ps( _mm256_mul_ps( d, q ), acc );
+#endif
+    }
+
+    *s = hsum_float_8(acc);
+#elif defined(__riscv_v_intrinsic)
+    float sumf = 0.0;
+    size_t vl = __riscv_vsetvl_e8m1(qk);
+
+    for (int i = 0; i < nb; i++) {
+        // load elements
+        vint8m1_t bx = __riscv_vle8_v_i8m1(x[i].qs, vl);
+        vint8m1_t by = __riscv_vle8_v_i8m1(y[i].qs, vl);
+
+        vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx, by, vl);
+
+        vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, vl);
+        vint32m1_t v_sum = __riscv_vwredsum_vs_i16m2_i32m1(vw_mul, v_zero, vl);
+
+        int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum);
+
+        sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d));
+    }
+
+    *s = sumf;
+#else
+    // scalar
+    float sumf = 0.0;
+
+    for (int i = 0; i < nb; i++) {
+        int sumi = 0;
+
+        for (int j = 0; j < qk; j++) {
+            sumi += x[i].qs[j]*y[i].qs[j];
+        }
+
+        sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d));
+    }
+
+    *s = sumf;
+#endif
+}
+
+// compute GGML_VEC_DOT_UNROLL dot products at once
+// xs - x row stride in bytes
+inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv, ggml_fp16_t * restrict y) {
+    ggml_float sumf[GGML_VEC_DOT_UNROLL] = { 0.0 };
+
+    ggml_fp16_t * restrict x[GGML_VEC_DOT_UNROLL];
+
+    for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) {
+        x[i] = (ggml_fp16_t *) ((char *) xv + i*xs);
+    }
+
+#if defined(GGML_SIMD)
+    const int np = (n & ~(GGML_F16_STEP - 1));
+
+    GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } };
+
+    GGML_F16_VEC ax[GGML_F16_ARR];
+    GGML_F16_VEC ay[GGML_F16_ARR];
+
+    for (int i = 0; i < np; i += GGML_F16_STEP) {
+        for (int j = 0; j < GGML_F16_ARR; j++) {
+            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
+
+            for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
+                ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j);
+
+                sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]);
+            }
+        }
+    }
+
+    // reduce sum0..sum3 to sum0
+    for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
+        GGML_F16_VEC_REDUCE(sumf[k], sum[k]);
+    }
+
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
+            sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]));
+        }
+    }
+#else
+    for (int i = 0; i < n; ++i) {
+        for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
+            sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]));
+        }
+    }
+#endif
+
+    for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) {
+        s[i] = sumf[i];
+    }
+}
+
+inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float * restrict x, const float v) {
+#if defined(GGML_SIMD)
+    const int np = (n & ~(GGML_F32_STEP - 1));
+
+    GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
+
+    GGML_F32_VEC ax[GGML_F32_ARR];
+    GGML_F32_VEC ay[GGML_F32_ARR];
+
+    for (int i = 0; i < np; i += GGML_F32_STEP) {
+        for (int j = 0; j < GGML_F32_ARR; j++) {
+            ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
+            ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
+            ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx);
+
+            GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
+        }
+    }
+
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        y[i] += x[i]*v;
+    }
+#else
+    // scalar
+    for (int i = 0; i < n; ++i) {
+        y[i] += x[i]*v;
+    }
+#endif
+}
+
+// xs and vs are byte strides of x and v
+inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int vs, float * restrict y, const float * restrict xv, const float * restrict vv) {
+
+    const float * restrict x[GGML_VEC_MAD_UNROLL];
+    const float * restrict v[GGML_VEC_MAD_UNROLL];
+
+    for (int i = 0; i < GGML_VEC_MAD_UNROLL; ++i) {
+        x[i] = (const float *) ((const char *) xv + i*xs);
+        v[i] = (const float *) ((const char *) vv + i*vs);
+    }
+
+#if defined(GGML_SIMD)
+    const int np = (n & ~(GGML_F32_STEP - 1));
+
+    GGML_F32_VEC vx[GGML_VEC_MAD_UNROLL];
+
+    for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
+        vx[k] = GGML_F32_VEC_SET1(v[k][0]);
+    }
+
+    GGML_F32_VEC ax[GGML_VEC_MAD_UNROLL][GGML_F32_ARR];
+    GGML_F32_VEC ay[GGML_F32_ARR];
+
+    for (int i = 0; i < np; i += GGML_F32_STEP) {
+        for (int j = 0; j < GGML_F32_ARR; j++) {
+            ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
+
+            for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
+                ax[k][j] = GGML_F32_VEC_LOAD(x[k] + i + j*GGML_F32_EPR);
+                ay[j] = GGML_F32_VEC_FMA(ay[j], ax[k][j], vx[k]);
+            }
+
+            GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
+        }
+    }
+
+    // leftovers
+    for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
+        for (int i = np; i < n; ++i) {
+            y[i] += x[k][i]*v[k][0];
+        }
+    }
+#else
+    // scalar
+    for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
+        for (int i = 0; i < n; ++i) {
+            y[i] += x[k][i]*v[k][0];
+        }
+    }
+#endif
+}
+
+//inline static void ggml_vec_scale_f32(const int n, float * y, const float   v) { for (int i = 0; i < n; ++i) y[i] *= v;          }
+inline static void ggml_vec_scale_f32(const int n, float * y, const float   v) {
+#if defined(GGML_USE_ACCELERATE)
+    vDSP_vsmul(y, 1, &v, y, 1, n);
+#elif defined(GGML_SIMD)
+    const int np = (n & ~(GGML_F32_STEP - 1));
+
+    GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
+
+    GGML_F32_VEC ay[GGML_F32_ARR];
+
+    for (int i = 0; i < np; i += GGML_F32_STEP) {
+        for (int j = 0; j < GGML_F32_ARR; j++) {
+            ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
+            ay[j] = GGML_F32_VEC_MUL(ay[j], vx);
+
+            GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
+        }
+    }
+
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        y[i] *= v;
+    }
+#else
+    // scalar
+    for (int i = 0; i < n; ++i) {
+        y[i] *= v;
+    }
+#endif
+}
+
+inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, x, x); *s = sqrtf(*s);   }
+inline static void ggml_vec_sqr_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i];   }
+inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); }
+inline static void ggml_vec_log_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]);   }
+inline static void ggml_vec_abs_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); }
+inline static void ggml_vec_sgn_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); }
+inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; }
+inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]);  }
+inline static void ggml_vec_elu_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; }
+inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; }
+
+static const float GELU_COEF_A     = 0.044715f;
+static const float GELU_QUICK_COEF = -1.702f;
+static const float SQRT_2_OVER_PI  = 0.79788456080286535587989211986876f;
+
+inline static float ggml_gelu_f32(float x) {
+    return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
+}
+
+inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
+    const uint16_t * i16 = (const uint16_t *) x;
+    for (int i = 0; i < n; ++i) {
+        y[i] = table_gelu_f16[i16[i]];
+    }
+}
+
+#ifdef GGML_GELU_FP16
+inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) {
+    uint16_t t;
+    for (int i = 0; i < n; ++i) {
+        ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
+        memcpy(&t, &fp16, sizeof(uint16_t));
+        y[i] = GGML_FP16_TO_FP32(table_gelu_f16[t]);
+    }
+}
+#else
+inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) {
+    for (int i = 0; i < n; ++i) {
+        y[i] = ggml_gelu_f32(x[i]);
+    }
+}
+#endif
+
+inline static float ggml_gelu_quick_f32(float x) {
+    return x*(1.0f/(1.0f+expf(GELU_QUICK_COEF*x)));
+}
+
+//inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
+//    const uint16_t * i16 = (const uint16_t *) x;
+//    for (int i = 0; i < n; ++i) {
+//        y[i] = table_gelu_quick_f16[i16[i]];
+//    }
+//}
+
+#ifdef GGML_GELU_QUICK_FP16
+inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) {
+    uint16_t t;
+    for (int i = 0; i < n; ++i) {
+        ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
+        memcpy(&t, &fp16, sizeof(uint16_t));
+        y[i] = GGML_FP16_TO_FP32(table_gelu_quick_f16[t]);
+    }
+}
+#else
+inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) {
+    for (int i = 0; i < n; ++i) {
+        y[i] = ggml_gelu_quick_f32(x[i]);
+    }
+}
+#endif
+
+// Sigmoid Linear Unit (SiLU) function
+inline static float ggml_silu_f32(float x) {
+    return x/(1.0f + expf(-x));
+}
+
+//inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
+//    const uint16_t * i16 = (const uint16_t *) x;
+//    for (int i = 0; i < n; ++i) {
+//        y[i] = table_silu_f16[i16[i]];
+//    }
+//}
+
+#ifdef GGML_SILU_FP16
+inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) {
+    uint16_t t;
+    for (int i = 0; i < n; ++i) {
+        ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
+        memcpy(&t, &fp16, sizeof(uint16_t));
+        y[i] = GGML_FP16_TO_FP32(table_silu_f16[t]);
+    }
+}
+#else
+inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) {
+    for (int i = 0; i < n; ++i) {
+        y[i] = ggml_silu_f32(x[i]);
+    }
+}
+#endif
+
+inline static float ggml_silu_backward_f32(float x, float dy) {
+    const float s = 1.0f/(1.0f + expf(-x));
+    return dy*s*(1.0f + x*(1.0f - s));
+}
+
+#ifdef GGML_SILU_FP16
+inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) {
+    for (int i = 0; i < n; ++i) {
+        // we did not use x[i] to compute forward silu but its f16 equivalent
+        // take derivative at f16 of x[i]:
+        ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
+        float usedx = GGML_FP16_TO_FP32(fp16);
+        dx[i] = ggml_silu_backward_f32(usedx, dy[i]);
+    }
+}
+#else
+inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) {
+    for (int i = 0; i < n; ++i) {
+        dx[i] = ggml_silu_backward_f32(x[i], dy[i]);
+    }
+}
+#endif
+
+inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) {
+#ifndef GGML_USE_ACCELERATE
+    ggml_float sum = 0.0;
+    for (int i = 0; i < n; ++i) {
+        sum += (ggml_float)x[i];
+    }
+    *s = sum;
+#else
+    vDSP_sve(x, 1, s, n);
+#endif
+}
+
+inline static void ggml_vec_sum_f32_ggf(const int n, ggml_float * s, const float * x) {
+    ggml_float sum = 0.0;
+    for (int i = 0; i < n; ++i) {
+        sum += (ggml_float)x[i];
+    }
+    *s = sum;
+}
+
+inline static void ggml_vec_sum_f16_ggf(const int n, float * s, const ggml_fp16_t * x) {
+    float sum = 0.0f;
+    for (int i = 0; i < n; ++i) {
+        sum += GGML_FP16_TO_FP32(x[i]);
+    }
+    *s = sum;
+}
+
+inline static void ggml_vec_max_f32(const int n, float * s, const float * x) {
+#ifndef GGML_USE_ACCELERATE
+    float max = -INFINITY;
+    for (int i = 0; i < n; ++i) {
+        max = MAX(max, x[i]);
+    }
+    *s = max;
+#else
+    vDSP_maxv(x, 1, s, n);
+#endif
+}
+
+inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) {
+    ggml_vec_norm_f32(n, s, x);
+    *s = 1.f/(*s);
+}
+
+inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) {
+    float max = -INFINITY;
+    int idx = 0;
+    for (int i = 0; i < n; ++i) {
+        max = MAX(max, x[i]);
+        if (max == x[i]) { idx = i; }
+    }
+    *s = idx;
+}
+
+//
+// data types
+//
+
+static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
+    "NONE",
+
+    "DUP",
+    "ADD",
+    "ADD1",
+    "ACC",
+    "SUB",
+    "MUL",
+    "DIV",
+    "SQR",
+    "SQRT",
+    "LOG",
+    "SUM",
+    "SUM_ROWS",
+    "MEAN",
+    "ARGMAX",
+    "REPEAT",
+    "REPEAT_BACK",
+    "CONCAT",
+    "SILU_BACK",
+    "NORM",
+    "RMS_NORM",
+    "RMS_NORM_BACK",
+    "GROUP_NORM",
+
+    "MUL_MAT",
+    "OUT_PROD",
+
+    "SCALE",
+    "SET",
+    "CPY",
+    "CONT",
+    "RESHAPE",
+    "VIEW",
+    "PERMUTE",
+    "TRANSPOSE",
+    "GET_ROWS",
+    "GET_ROWS_BACK",
+    "DIAG",
+    "DIAG_MASK_INF",
+    "DIAG_MASK_ZERO",
+    "SOFT_MAX",
+    "SOFT_MAX_BACK",
+    "ROPE",
+    "ROPE_BACK",
+    "ALIBI",
+    "CLAMP",
+    "CONV_1D",
+    "CONV_TRANSPOSE_1D",
+    "CONV_2D",
+    "CONV_TRANSPOSE_2D",
+    "POOL_1D",
+    "POOL_2D",
+    "UPSCALE",
+
+    "CONV_1D_STAGE_0",
+    "CONV_1D_STAGE_1",
+
+    "FLASH_ATTN",
+    "FLASH_FF",
+    "FLASH_ATTN_BACK",
+    "WIN_PART",
+    "WIN_UNPART",
+    "GET_REL_POS",
+    "ADD_REL_POS",
+
+    "UNARY",
+
+    "MAP_UNARY",
+    "MAP_BINARY",
+
+    "MAP_CUSTOM1_F32",
+    "MAP_CUSTOM2_F32",
+    "MAP_CUSTOM3_F32",
+
+    "MAP_CUSTOM1",
+    "MAP_CUSTOM2",
+    "MAP_CUSTOM3",
+
+    "CROSS_ENTROPY_LOSS",
+    "CROSS_ENTROPY_LOSS_BACK",
+};
+
+static_assert(GGML_OP_COUNT == 71, "GGML_OP_COUNT != 71");
+
+static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
+    "none",
+
+    "x",
+    "x+y",
+    "x+y",
+    "view(x,nb,offset)+=y->x",
+    "x-y",
+    "x*y",
+    "x/y",
+    "x^2",
+    "√x",
+    "log(x)",
+    "Σx",
+    "Σx_k",
+    "Σx/n",
+    "argmax(x)",
+    "repeat(x)",
+    "repeat_back(x)",
+    "concat(x, y)",
+    "silu_back(x)",
+    "norm(x)",
+    "rms_norm(x)",
+    "rms_norm_back(x)",
+    "group_norm(x)",
+
+    "X*Y",
+    "X*Y",
+
+    "x*v",
+    "y-\\>view(x)",
+    "x-\\>y",
+    "cont(x)",
+    "reshape(x)",
+    "view(x)",
+    "permute(x)",
+    "transpose(x)",
+    "get_rows(x)",
+    "get_rows_back(x)",
+    "diag(x)",
+    "diag_mask_inf(x)",
+    "diag_mask_zero(x)",
+    "soft_max(x)",
+    "soft_max_back(x)",
+    "rope(x)",
+    "rope_back(x)",
+    "alibi(x)",
+    "clamp(x)",
+    "conv_1d(x)",
+    "conv_transpose_1d(x)",
+    "conv_2d(x)",
+    "conv_transpose_2d(x)",
+    "pool_1d(x)",
+    "pool_2d(x)",
+    "upscale(x)",
+
+    "conv_1d_stage_0(x)",
+    "conv_1d_stage_1(x)",
+
+    "flash_attn(x)",
+    "flash_ff(x)",
+    "flash_attn_back(x)",
+    "win_part(x)",
+    "win_unpart(x)",
+    "get_rel_pos(x)",
+    "add_rel_pos(x)",
+
+    "unary(x)",
+
+    "f(x)",
+    "f(x,y)",
+
+    "custom_f32(x)",
+    "custom_f32(x,y)",
+    "custom_f32(x,y,z)",
+
+    "custom(x)",
+    "custom(x,y)",
+    "custom(x,y,z)",
+
+    "cross_entropy_loss(x,y)",
+    "cross_entropy_loss_back(x,y)",
+};
+
+static_assert(GGML_OP_COUNT == 71, "GGML_OP_COUNT != 71");
+
+static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
+
+static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
+static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
+
+// WARN:
+// Mis-confguration can lead to problem that's hard to reason about:
+// * At best  it crash or talks nosense.
+// * At worst it talks slightly difference but hard to perceive.
+//
+// An op has to enable INIT or FINALIZE when any of it's branch needs that pass.
+// Take care about compile options (e.g., GGML_USE_xxx).
+static bool GGML_OP_HAS_INIT    [GGML_OP_COUNT] = { 0 };
+static bool GGML_OP_HAS_FINALIZE[GGML_OP_COUNT] = { 0 };
+
+static void ggml_setup_op_has_task_pass(void) {
+    {   // INIT
+        bool * p = GGML_OP_HAS_INIT;
+
+        p[GGML_OP_ACC                    ] = true;
+        p[GGML_OP_MUL_MAT                ] = true;
+        p[GGML_OP_OUT_PROD               ] = true;
+        p[GGML_OP_SET                    ] = true;
+        p[GGML_OP_GET_ROWS_BACK          ] = true;
+        p[GGML_OP_DIAG_MASK_INF          ] = true;
+        p[GGML_OP_DIAG_MASK_ZERO         ] = true;
+        p[GGML_OP_CONV_1D                ] = true;
+        p[GGML_OP_CONV_1D_STAGE_0        ] = true;
+        p[GGML_OP_CONV_1D_STAGE_1        ] = true;
+        p[GGML_OP_CONV_2D                ] = true;
+        p[GGML_OP_CONV_TRANSPOSE_1D      ] = true;
+        p[GGML_OP_CONV_TRANSPOSE_2D      ] = true;
+        p[GGML_OP_FLASH_ATTN_BACK        ] = true;
+        p[GGML_OP_CROSS_ENTROPY_LOSS     ] = true;
+        p[GGML_OP_ADD_REL_POS            ] = true;
+    }
+
+    {   // FINALIZE
+        bool * p = GGML_OP_HAS_FINALIZE;
+
+        p[GGML_OP_CROSS_ENTROPY_LOSS     ] = true;
+    }
+}
+
+//
+// ggml context
+//
+
+struct ggml_context {
+    size_t mem_size;
+    void * mem_buffer;
+    bool   mem_buffer_owned;
+    bool   no_alloc;
+    bool   no_alloc_save; // this is used to save the no_alloc state when using scratch buffers
+
+    int    n_objects;
+
+    struct ggml_object * objects_begin;
+    struct ggml_object * objects_end;
+
+    struct ggml_scratch scratch;
+    struct ggml_scratch scratch_save;
+};
+
+struct ggml_context_container {
+    bool used;
+
+    struct ggml_context context;
+};
+
+//
+// NUMA support
+//
+
+#define GGML_NUMA_MAX_NODES 8
+#define GGML_NUMA_MAX_CPUS 512
+
+struct ggml_numa_node {
+    uint32_t cpus[GGML_NUMA_MAX_CPUS]; // hardware threads on this node
+    uint32_t n_cpus;
+};
+
+struct ggml_numa_nodes {
+    struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES];
+    uint32_t n_nodes;
+    uint32_t total_cpus; // hardware threads on system
+};
+
+//
+// ggml state
+//
+
+struct ggml_state {
+    struct ggml_context_container contexts[GGML_MAX_CONTEXTS];
+    struct ggml_numa_nodes numa;
+};
+
+// global state
+static struct ggml_state g_state;
+static atomic_int g_state_barrier = 0;
+
+// barrier via spin lock
+inline static void ggml_critical_section_start(void) {
+    int processing = atomic_fetch_add(&g_state_barrier, 1);
+
+    while (processing > 0) {
+        // wait for other threads to finish
+        atomic_fetch_sub(&g_state_barrier, 1);
+        sched_yield(); // TODO: reconsider this
+        processing = atomic_fetch_add(&g_state_barrier, 1);
+    }
+}
+
+// TODO: make this somehow automatically executed
+//       some sort of "sentry" mechanism
+inline static void ggml_critical_section_end(void) {
+    atomic_fetch_sub(&g_state_barrier, 1);
+}
+
+void ggml_numa_init(void) {
+    if (g_state.numa.n_nodes > 0) {
+        fprintf(stderr, "ggml_numa_init: NUMA already initialized\n");
+
+        return;
+    }
+
+#ifdef __linux__
+    struct stat st;
+    char path[256];
+    int rv;
+
+    // enumerate nodes
+    while (g_state.numa.n_nodes < GGML_NUMA_MAX_NODES) {
+        rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u", g_state.numa.n_nodes);
+        GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path));
+        if (stat(path, &st) != 0) { break; }
+        ++g_state.numa.n_nodes;
+    }
+
+    // enumerate CPUs
+    while (g_state.numa.total_cpus < GGML_NUMA_MAX_CPUS) {
+        rv = snprintf(path, sizeof(path), "/sys/devices/system/cpu/cpu%u", g_state.numa.total_cpus);
+        GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path));
+        if (stat(path, &st) != 0) { break; }
+        ++g_state.numa.total_cpus;
+    }
+
+    GGML_PRINT_DEBUG("found %u numa nodes, %u CPUs\n", g_state.numa.n_nodes, g_state.numa.total_cpus);
+
+    if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1) {
+        g_state.numa.n_nodes = 0;
+        return;
+    }
+
+    for (uint32_t n = 0; n < g_state.numa.n_nodes; ++n) {
+        struct ggml_numa_node * node = &g_state.numa.nodes[n];
+        GGML_PRINT_DEBUG("CPUs on node %u:", n);
+        node->n_cpus = 0;
+        for (uint32_t c = 0; c < g_state.numa.total_cpus; ++c) {
+            rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u/cpu%u", n, c);
+            GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path));
+            if (stat(path, &st) == 0) {
+                node->cpus[node->n_cpus++] = c;
+                GGML_PRINT_DEBUG(" %u", c);
+            }
+        }
+        GGML_PRINT_DEBUG("\n");
+    }
+
+    if (ggml_is_numa()) {
+        FILE *fptr = fopen("/proc/sys/kernel/numa_balancing", "r");
+        if (fptr != NULL) {
+            char buf[42];
+            if (fgets(buf, sizeof(buf), fptr) && strncmp(buf, "0\n", sizeof(buf)) != 0) {
+                GGML_PRINT("WARNING: /proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n");
+            }
+            fclose(fptr);
+        }
+    }
+#else
+    // TODO
+#endif
+}
+
+bool ggml_is_numa(void) {
+    return g_state.numa.n_nodes > 1;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+void ggml_print_object(const struct ggml_object * obj) {
+    GGML_PRINT(" - ggml_object: type = %d, offset = %zu, size = %zu, next = %p\n",
+            obj->type, obj->offs, obj->size, (const void *) obj->next);
+}
+
+void ggml_print_objects(const struct ggml_context * ctx) {
+    struct ggml_object * obj = ctx->objects_begin;
+
+    GGML_PRINT("%s: objects in context %p:\n", __func__, (const void *) ctx);
+
+    while (obj != NULL) {
+        ggml_print_object(obj);
+        obj = obj->next;
+    }
+
+    GGML_PRINT("%s: --- end ---\n", __func__);
+}
+
+int64_t ggml_nelements(const struct ggml_tensor * tensor) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
+}
+
+int64_t ggml_nrows(const struct ggml_tensor * tensor) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
+}
+
+size_t ggml_nbytes(const struct ggml_tensor * tensor) {
+    size_t nbytes;
+    size_t blck_size = ggml_blck_size(tensor->type);
+    if (blck_size == 1) {
+        nbytes = ggml_type_size(tensor->type);
+        for (int i = 0; i < GGML_MAX_DIMS; ++i) {
+            nbytes += (tensor->ne[i] - 1)*tensor->nb[i];
+        }
+    }
+    else {
+        nbytes = tensor->ne[0]*tensor->nb[0]/blck_size;
+        for (int i = 1; i < GGML_MAX_DIMS; ++i) {
+            nbytes += (tensor->ne[i] - 1)*tensor->nb[i];
+        }
+    }
+
+    return nbytes;
+}
+
+size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) {
+    return GGML_PAD(ggml_nbytes(tensor), GGML_MEM_ALIGN);
+}
+
+size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return (nrows_split*tensor->ne[0]*ggml_type_size(tensor->type))/ggml_blck_size(tensor->type);
+}
+
+int ggml_blck_size(enum ggml_type type) {
+    return type_traits[type].blck_size;
+}
+
+size_t ggml_type_size(enum ggml_type type) {
+    return type_traits[type].type_size;
+}
+
+float ggml_type_sizef(enum ggml_type type) {
+    return ((float)(type_traits[type].type_size))/type_traits[type].blck_size;
+}
+
+const char * ggml_type_name(enum ggml_type type) {
+    return type_traits[type].type_name;
+}
+
+bool ggml_is_quantized(enum ggml_type type) {
+    return type_traits[type].is_quantized;
+}
+
+const char * ggml_op_name(enum ggml_op op) {
+    return GGML_OP_NAME[op];
+}
+
+const char * ggml_op_symbol(enum ggml_op op) {
+    return GGML_OP_SYMBOL[op];
+}
+
+size_t ggml_element_size(const struct ggml_tensor * tensor) {
+    return ggml_type_size(tensor->type);
+}
+
+static inline bool ggml_is_scalar(const struct ggml_tensor * tensor) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return tensor->ne[0] == 1 && tensor->ne[1] == 1 && tensor->ne[2] == 1 && tensor->ne[3] == 1;
+}
+
+static inline bool ggml_is_vector(const struct ggml_tensor * tensor) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return tensor->ne[1] == 1 && tensor->ne[2] == 1 && tensor->ne[3] == 1;
+}
+
+static inline bool ggml_is_matrix(const struct ggml_tensor * tensor) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return tensor->ne[2] == 1 && tensor->ne[3] == 1;
+}
+
+static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return (t0->ne[0]           == t1->ne[0])  &&
+           (t1->ne[2]%t0->ne[2] == 0)          && // verify t0 is broadcastable
+           (t1->ne[3]%t0->ne[3] == 0);
+}
+
+static inline bool ggml_can_out_prod(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return (t0->ne[1] == t1->ne[1])   &&
+           (t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable
+           (t1->ne[3]%t0->ne[3] == 0);
+}
+
+enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
+    enum ggml_type wtype = GGML_TYPE_COUNT;
+
+    switch (ftype) {
+        case GGML_FTYPE_ALL_F32:              wtype = GGML_TYPE_F32;   break;
+        case GGML_FTYPE_MOSTLY_F16:           wtype = GGML_TYPE_F16;   break;
+        case GGML_FTYPE_MOSTLY_Q4_0:          wtype = GGML_TYPE_Q4_0;  break;
+        case GGML_FTYPE_MOSTLY_Q4_1:          wtype = GGML_TYPE_Q4_1;  break;
+        case GGML_FTYPE_MOSTLY_Q5_0:          wtype = GGML_TYPE_Q5_0;  break;
+        case GGML_FTYPE_MOSTLY_Q5_1:          wtype = GGML_TYPE_Q5_1;  break;
+        case GGML_FTYPE_MOSTLY_Q8_0:          wtype = GGML_TYPE_Q8_0;  break;
+        case GGML_FTYPE_MOSTLY_Q2_K:          wtype = GGML_TYPE_Q2_K;  break;
+        case GGML_FTYPE_MOSTLY_Q3_K:          wtype = GGML_TYPE_Q3_K;  break;
+        case GGML_FTYPE_MOSTLY_Q4_K:          wtype = GGML_TYPE_Q4_K;  break;
+        case GGML_FTYPE_MOSTLY_Q5_K:          wtype = GGML_TYPE_Q5_K;  break;
+        case GGML_FTYPE_MOSTLY_Q6_K:          wtype = GGML_TYPE_Q6_K;  break;
+        case GGML_FTYPE_UNKNOWN:              wtype = GGML_TYPE_COUNT; break;
+        case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break;
+    }
+
+    GGML_ASSERT(wtype != GGML_TYPE_COUNT);
+
+    return wtype;
+}
+
+size_t ggml_tensor_overhead(void) {
+    return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE;
+}
+
+bool ggml_is_transposed(const struct ggml_tensor * tensor) {
+    return tensor->nb[0] > tensor->nb[1];
+}
+
+bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return
+        tensor->nb[0] == ggml_type_size(tensor->type) &&
+        tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) &&
+        tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
+        tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
+}
+
+static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * tensor) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return
+        tensor->nb[0] == ggml_type_size(tensor->type) &&
+        tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
+        tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
+}
+
+bool ggml_is_permuted(const struct ggml_tensor * tensor) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return tensor->nb[0] > tensor->nb[1] || tensor->nb[1] > tensor->nb[2] || tensor->nb[2] > tensor->nb[3];
+}
+
+static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return
+        tensor->nb[0] == ggml_type_size(tensor->type) &&
+        tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
+        tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
+}
+
+bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return
+        (t0->ne[0] == t1->ne[0] ) &&
+        (t0->ne[1] == t1->ne[1] ) &&
+        (t0->ne[2] == t1->ne[2] ) &&
+        (t0->ne[3] == t1->ne[3] );
+}
+
+// check if t1 can be represented as a repeatition of t0
+static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return
+        (t1->ne[0]%t0->ne[0] == 0) &&
+        (t1->ne[1]%t0->ne[1] == 0) &&
+        (t1->ne[2]%t0->ne[2] == 0) &&
+        (t1->ne[3]%t0->ne[3] == 0);
+}
+
+static inline bool ggml_can_repeat_rows(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return (t0->ne[0] == t1->ne[0]) && ggml_can_repeat(t0, t1);
+}
+
+static inline int ggml_up32(int n) {
+    return (n + 31) & ~31;
+}
+
+//static inline int ggml_up64(int n) {
+//    return (n + 63) & ~63;
+//}
+
+static inline int ggml_up(int n, int m) {
+    // assert m is a power of 2
+    GGML_ASSERT((m & (m - 1)) == 0);
+    return (n + m - 1) & ~(m - 1);
+}
+
+// assert that pointer is aligned to GGML_MEM_ALIGN
+#define ggml_assert_aligned(ptr) \
+    GGML_ASSERT(((uintptr_t) (ptr))%GGML_MEM_ALIGN == 0)
+
+////////////////////////////////////////////////////////////////////////////////
+
+struct ggml_context * ggml_init(struct ggml_init_params params) {
+    // make this function thread safe
+    ggml_critical_section_start();
+
+    static bool is_first_call = true;
+
+    if (is_first_call) {
+        // initialize time system (required on Windows)
+        ggml_time_init();
+
+        // initialize GELU, Quick GELU, SILU and EXP F32 tables
+        {
+            const uint64_t t_start = ggml_time_us(); UNUSED(t_start);
+
+            ggml_fp16_t ii;
+            for (int i = 0; i < (1 << 16); ++i) {
+                uint16_t ui = i;
+                memcpy(&ii, &ui, sizeof(ii));
+                const float f = table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii);
+                table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f));
+                table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f));
+                table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f));
+                table_exp_f16[i]  = GGML_FP32_TO_FP16(expf(f));
+            }
+
+            const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
+
+            GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
+        }
+
+        // initialize g_state
+        {
+            const uint64_t t_start = ggml_time_us(); UNUSED(t_start);
+
+            g_state = (struct ggml_state) {
+                /*.contexts =*/ { { 0 } },
+                /*.numa =*/ {
+                    .n_nodes = 0,
+                    .total_cpus = 0,
+                },
+            };
+
+            for (int i = 0; i < GGML_MAX_CONTEXTS; ++i) {
+                g_state.contexts[i].used = false;
+            }
+
+            const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
+
+            GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
+        }
+
+#if defined(GGML_USE_CUBLAS)
+        ggml_init_cublas();
+#elif defined(GGML_USE_CLBLAST)
+        ggml_cl_init();
+#endif
+
+        ggml_setup_op_has_task_pass();
+
+        is_first_call = false;
+    }
+
+    // find non-used context in g_state
+    struct ggml_context * ctx = NULL;
+
+    for (int i = 0; i < GGML_MAX_CONTEXTS; i++) {
+        if (!g_state.contexts[i].used) {
+            g_state.contexts[i].used = true;
+            ctx = &g_state.contexts[i].context;
+
+            GGML_PRINT_DEBUG("%s: found unused context %d\n", __func__, i);
+            break;
+        }
+    }
+
+    if (ctx == NULL) {
+        GGML_PRINT_DEBUG("%s: no unused context found\n", __func__);
+
+        ggml_critical_section_end();
+
+        return NULL;
+    }
+
+    // allow to call ggml_init with 0 size
+    if (params.mem_size == 0) {
+        params.mem_size = GGML_MEM_ALIGN;
+    }
+
+    const size_t mem_size = params.mem_buffer ? params.mem_size : GGML_PAD(params.mem_size, GGML_MEM_ALIGN);
+
+    *ctx = (struct ggml_context) {
+        /*.mem_size           =*/ mem_size,
+        /*.mem_buffer         =*/ params.mem_buffer ? params.mem_buffer : GGML_ALIGNED_MALLOC(mem_size),
+        /*.mem_buffer_owned   =*/ params.mem_buffer ? false : true,
+        /*.no_alloc           =*/ params.no_alloc,
+        /*.no_alloc_save      =*/ params.no_alloc,
+        /*.n_objects          =*/ 0,
+        /*.objects_begin      =*/ NULL,
+        /*.objects_end        =*/ NULL,
+        /*.scratch            =*/ { 0, 0, NULL, },
+        /*.scratch_save       =*/ { 0, 0, NULL, },
+    };
+
+    GGML_ASSERT(ctx->mem_buffer != NULL);
+
+    ggml_assert_aligned(ctx->mem_buffer);
+
+    GGML_PRINT_DEBUG("%s: context initialized\n", __func__);
+
+    ggml_critical_section_end();
+
+    return ctx;
+}
+
+void ggml_free(struct ggml_context * ctx) {
+    // make this function thread safe
+    ggml_critical_section_start();
+
+    bool found = false;
+
+    for (int i = 0; i < GGML_MAX_CONTEXTS; i++) {
+        if (&g_state.contexts[i].context == ctx) {
+            g_state.contexts[i].used = false;
+
+            GGML_PRINT_DEBUG("%s: context %d has been freed. memory used = %zu\n",
+                    __func__, i, ggml_used_mem(ctx));
+
+            if (ctx->mem_buffer_owned) {
+                GGML_ALIGNED_FREE(ctx->mem_buffer);
+            }
+
+            found = true;
+            break;
+        }
+    }
+
+    if (!found) {
+        GGML_PRINT_DEBUG("%s: context not found\n", __func__);
+    }
+
+    ggml_critical_section_end();
+}
+
+size_t ggml_used_mem(const struct ggml_context * ctx) {
+    return ctx->objects_end == NULL ? 0 : ctx->objects_end->offs + ctx->objects_end->size;
+}
+
+size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch) {
+    const size_t result = ctx->scratch.data ? ctx->scratch.offs : 0;
+
+    ctx->scratch = scratch;
+
+    return result;
+}
+
+bool ggml_get_no_alloc(struct ggml_context * ctx) {
+    return ctx->no_alloc;
+}
+
+void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc) {
+    ctx->no_alloc = no_alloc;
+}
+
+void * ggml_get_mem_buffer(const struct ggml_context * ctx) {
+    return ctx->mem_buffer;
+}
+
+size_t ggml_get_mem_size(const struct ggml_context * ctx) {
+    return ctx->mem_size;
+}
+
+size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) {
+    size_t max_size = 0;
+
+    struct ggml_object * obj = ctx->objects_begin;
+
+    while (obj != NULL) {
+        if (obj->type == GGML_OBJECT_TENSOR) {
+            struct ggml_tensor * tensor = (struct ggml_tensor *) ((char *) ctx->mem_buffer + obj->offs);
+
+            const size_t size = ggml_nbytes(tensor);
+
+            if (max_size < size) {
+                max_size = size;
+            }
+        }
+
+        obj = obj->next;
+    }
+
+    return max_size;
+}
+
+// IMPORTANT:
+// when creating "opt" tensors, always save and load the scratch buffer
+// this is an error prone process, but it is necessary to support inplace
+// operators when using scratch buffers
+// TODO: implement a better way
+static void ggml_scratch_save(struct ggml_context * ctx) {
+    // this is needed to allow opt tensors to store their data
+    // TODO: again, need to find a better way
+    ctx->no_alloc_save = ctx->no_alloc;
+    ctx->no_alloc      = false;
+
+    ctx->scratch_save = ctx->scratch;
+    ctx->scratch.data = NULL;
+}
+
+static void ggml_scratch_load(struct ggml_context * ctx) {
+    ctx->no_alloc = ctx->no_alloc_save;
+
+    ctx->scratch = ctx->scratch_save;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml_object_type type, size_t size) {
+    // always insert objects at the end of the context's memory pool
+    struct ggml_object * obj_cur = ctx->objects_end;
+
+    const size_t cur_offs = obj_cur == NULL ? 0 : obj_cur->offs;
+    const size_t cur_size = obj_cur == NULL ? 0 : obj_cur->size;
+    const size_t cur_end  = cur_offs + cur_size;
+
+    // align to GGML_MEM_ALIGN
+    size_t size_needed = GGML_PAD(size, GGML_MEM_ALIGN);
+
+    char * const mem_buffer = ctx->mem_buffer;
+    struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end);
+
+    if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) {
+        GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
+                __func__, cur_end + size_needed, ctx->mem_size);
+        assert(false);
+        return NULL;
+    }
+
+    *obj_new = (struct ggml_object) {
+        .offs = cur_end + GGML_OBJECT_SIZE,
+        .size = size_needed,
+        .next = NULL,
+        .type = type,
+    };
+
+    ggml_assert_aligned(mem_buffer + obj_new->offs);
+
+    if (obj_cur != NULL) {
+        obj_cur->next = obj_new;
+    } else {
+        // this is the first object in this context
+        ctx->objects_begin = obj_new;
+    }
+
+    ctx->objects_end = obj_new;
+
+    //printf("%s: inserted new object at %zu, size = %zu\n", __func__, cur_end, obj_new->size);
+
+    return obj_new;
+}
+
+static struct ggml_tensor * ggml_new_tensor_impl(
+        struct ggml_context * ctx,
+        enum   ggml_type      type,
+        int                   n_dims,
+        const int64_t       * ne,
+        struct ggml_tensor  * view_src,
+        size_t                view_offs) {
+
+    assert(n_dims >= 1 && n_dims <= GGML_MAX_DIMS);
+
+    // find the base tensor and absolute offset
+    if (view_src != NULL && view_src->view_src != NULL) {
+        view_offs += view_src->view_offs;
+        view_src   = view_src->view_src;
+    }
+
+    size_t data_size = ggml_type_size(type)*(ne[0]/ggml_blck_size(type));
+    for (int i = 1; i < n_dims; i++) {
+        data_size *= ne[i];
+    }
+
+    GGML_ASSERT(view_src == NULL || data_size + view_offs <= ggml_nbytes(view_src));
+
+    void * data = view_src != NULL ? view_src->data : NULL;
+    if (data != NULL) {
+        data = (char *) data + view_offs;
+    }
+
+    size_t obj_alloc_size = 0;
+
+    if (view_src == NULL && !ctx->no_alloc) {
+        if (ctx->scratch.data != NULL) {
+            // allocate tensor data in the scratch buffer
+            if (ctx->scratch.offs + data_size > ctx->scratch.size) {
+                GGML_PRINT("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n",
+                        __func__, ctx->scratch.offs + data_size, ctx->scratch.size);
+                assert(false);
+                return NULL;
+            }
+
+            data = (char * const) ctx->scratch.data + ctx->scratch.offs;
+
+            ctx->scratch.offs += data_size;
+        } else {
+            // allocate tensor data in the context's memory pool
+            obj_alloc_size = data_size;
+        }
+    }
+
+    struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size);
+
+    // TODO: for recoverable errors, we would need to free the data allocated from the scratch buffer here
+
+    struct ggml_tensor * const result = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj_new->offs);
+
+    *result = (struct ggml_tensor) {
+        /*.type         =*/ type,
+        /*.backend      =*/ GGML_BACKEND_CPU,
+        /*.buffer       =*/ NULL,
+        /*.n_dims       =*/ n_dims,
+        /*.ne           =*/ { 1, 1, 1, 1 },
+        /*.nb           =*/ { 0, 0, 0, 0 },
+        /*.op           =*/ GGML_OP_NONE,
+        /*.op_params    =*/ { 0 },
+        /*.is_param     =*/ false,
+        /*.grad         =*/ NULL,
+        /*.src          =*/ { NULL },
+        /*.perf_runs    =*/ 0,
+        /*.perf_cycles  =*/ 0,
+        /*.perf_time_us =*/ 0,
+        /*.view_src     =*/ view_src,
+        /*.view_offs    =*/ view_offs,
+        /*.data         =*/ obj_alloc_size > 0 ? (void *)(result + 1) : data,
+        /*.name         =*/ { 0 },
+        /*.extra        =*/ NULL,
+        /*.padding      =*/ { 0 },
+    };
+
+    // TODO: this should not be needed as long as we don't rely on aligned SIMD loads
+    //ggml_assert_aligned(result->data);
+
+    for (int i = 0; i < n_dims; i++) {
+        result->ne[i] = ne[i];
+    }
+
+    result->nb[0] = ggml_type_size(type);
+    result->nb[1] = result->nb[0]*(result->ne[0]/ggml_blck_size(type));
+    for (int i = 2; i < GGML_MAX_DIMS; i++) {
+        result->nb[i] = result->nb[i - 1]*result->ne[i - 1];
+    }
+
+    ctx->n_objects++;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_new_tensor(
+        struct ggml_context * ctx,
+        enum   ggml_type      type,
+        int                   n_dims,
+        const int64_t       * ne) {
+    return ggml_new_tensor_impl(ctx, type, n_dims, ne, NULL, 0);
+}
+
+struct ggml_tensor * ggml_new_tensor_1d(
+        struct ggml_context * ctx,
+        enum   ggml_type      type,
+        int64_t ne0) {
+    return ggml_new_tensor(ctx, type, 1, &ne0);
+}
+
+struct ggml_tensor * ggml_new_tensor_2d(
+        struct ggml_context * ctx,
+        enum   ggml_type      type,
+        int64_t ne0,
+        int64_t ne1) {
+    const int64_t ne[2] = { ne0, ne1 };
+    return ggml_new_tensor(ctx, type, 2, ne);
+}
+
+struct ggml_tensor * ggml_new_tensor_3d(
+        struct ggml_context * ctx,
+        enum   ggml_type      type,
+        int64_t ne0,
+        int64_t ne1,
+        int64_t ne2) {
+    const int64_t ne[3] = { ne0, ne1, ne2 };
+    return ggml_new_tensor(ctx, type, 3, ne);
+}
+
+struct ggml_tensor * ggml_new_tensor_4d(
+        struct ggml_context * ctx,
+        enum   ggml_type type,
+        int64_t ne0,
+        int64_t ne1,
+        int64_t ne2,
+        int64_t ne3) {
+    const int64_t ne[4] = { ne0, ne1, ne2, ne3 };
+    return ggml_new_tensor(ctx, type, 4, ne);
+}
+
+struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) {
+    ggml_scratch_save(ctx);
+
+    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1);
+
+    ggml_scratch_load(ctx);
+
+    ggml_set_i32(result, value);
+
+    return result;
+}
+
+struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) {
+    ggml_scratch_save(ctx);
+
+    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+
+    ggml_scratch_load(ctx);
+
+    ggml_set_f32(result, value);
+
+    return result;
+}
+
+struct ggml_tensor * ggml_dup_tensor(struct ggml_context * ctx, const struct ggml_tensor * src) {
+    return ggml_new_tensor(ctx, src->type, src->n_dims, src->ne);
+}
+
+static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) {
+    GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings
+    assert(params_size <= GGML_MAX_OP_PARAMS);
+    memcpy(tensor->op_params, params, params_size);
+}
+
+static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_t i) {
+    assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
+    return ((const int32_t *)(tensor->op_params))[i];
+}
+
+static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) {
+    assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
+    ((int32_t *)(tensor->op_params))[i] = value;
+}
+
+struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) {
+    memset(tensor->data, 0, ggml_nbytes(tensor));
+    return tensor;
+}
+
+struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) {
+    const int n     = ggml_nrows(tensor);
+    const int nc    = tensor->ne[0];
+    const size_t n1 = tensor->nb[1];
+
+    char * const data = tensor->data;
+
+    switch (tensor->type) {
+        case GGML_TYPE_I8:
+            {
+                assert(tensor->nb[0] == sizeof(int8_t));
+                for (int i = 0; i < n; i++) {
+                    ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value);
+                }
+            } break;
+        case GGML_TYPE_I16:
+            {
+                assert(tensor->nb[0] == sizeof(int16_t));
+                for (int i = 0; i < n; i++) {
+                    ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value);
+                }
+            } break;
+        case GGML_TYPE_I32:
+            {
+                assert(tensor->nb[0] == sizeof(int32_t));
+                for (int i = 0; i < n; i++) {
+                    ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value);
+                }
+            } break;
+        case GGML_TYPE_F16:
+            {
+                assert(tensor->nb[0] == sizeof(ggml_fp16_t));
+                for (int i = 0; i < n; i++) {
+                    ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value));
+                }
+            } break;
+        case GGML_TYPE_F32:
+            {
+                assert(tensor->nb[0] == sizeof(float));
+                for (int i = 0; i < n; i++) {
+                    ggml_vec_set_f32(nc, (float *)(data + i*n1), value);
+                }
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+
+    return tensor;
+}
+
+struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) {
+    const int n     = ggml_nrows(tensor);
+    const int nc    = tensor->ne[0];
+    const size_t n1 = tensor->nb[1];
+
+    char * const data = tensor->data;
+
+    switch (tensor->type) {
+        case GGML_TYPE_I8:
+            {
+                assert(tensor->nb[0] == sizeof(int8_t));
+                for (int i = 0; i < n; i++) {
+                    ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value);
+                }
+            } break;
+        case GGML_TYPE_I16:
+            {
+                assert(tensor->nb[0] == sizeof(int16_t));
+                for (int i = 0; i < n; i++) {
+                    ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value);
+                }
+            } break;
+        case GGML_TYPE_I32:
+            {
+                assert(tensor->nb[0] == sizeof(int32_t));
+                for (int i = 0; i < n; i++) {
+                    ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value);
+                }
+            } break;
+        case GGML_TYPE_F16:
+            {
+                assert(tensor->nb[0] == sizeof(ggml_fp16_t));
+                for (int i = 0; i < n; i++) {
+                    ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value));
+                }
+            } break;
+        case GGML_TYPE_F32:
+            {
+                assert(tensor->nb[0] == sizeof(float));
+                for (int i = 0; i < n; i++) {
+                    ggml_vec_set_f32(nc, (float *)(data + i*n1), value);
+                }
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+
+    return tensor;
+}
+
+void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3) {
+    const int64_t ne2 = tensor->ne[2];
+    const int64_t ne1 = tensor->ne[1];
+    const int64_t ne0 = tensor->ne[0];
+
+    const int64_t i3_ = (i/(ne2*ne1*ne0));
+    const int64_t i2_ = (i - i3_*ne2*ne1*ne0)/(ne1*ne0);
+    const int64_t i1_ = (i - i3_*ne2*ne1*ne0 - i2_*ne1*ne0)/ne0;
+    const int64_t i0_ = (i - i3_*ne2*ne1*ne0 - i2_*ne1*ne0 - i1_*ne0);
+
+    if (i0) {
+        * i0 = i0_;
+    }
+    if (i1) {
+        * i1 = i1_;
+    }
+    if (i2) {
+        * i2 = i2_;
+    }
+    if (i3) {
+        * i3 = i3_;
+    }
+}
+
+int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) {
+    if (!ggml_is_contiguous(tensor)) {
+        int64_t id[4] = { 0, 0, 0, 0 };
+        ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]);
+        return ggml_get_i32_nd(tensor, id[0], id[1], id[2], id[3]);
+    }
+    switch (tensor->type) {
+        case GGML_TYPE_I8:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(int8_t));
+                return ((int8_t *)(tensor->data))[i];
+            }
+        case GGML_TYPE_I16:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
+                return ((int16_t *)(tensor->data))[i];
+            }
+        case GGML_TYPE_I32:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
+                return ((int32_t *)(tensor->data))[i];
+            }
+        case GGML_TYPE_F16:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
+                return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
+            }
+        case GGML_TYPE_F32:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(float));
+                return ((float *)(tensor->data))[i];
+            }
+        default:
+            {
+                GGML_ASSERT(false);
+            }
+    }
+
+    return 0.0f;
+}
+
+void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) {
+    if (!ggml_is_contiguous(tensor)) {
+        int64_t id[4] = { 0, 0, 0, 0 };
+        ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]);
+        ggml_set_i32_nd(tensor, id[0], id[1], id[2], id[3], value);
+        return;
+    }
+    switch (tensor->type) {
+        case GGML_TYPE_I8:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(int8_t));
+                ((int8_t *)(tensor->data))[i] = value;
+            } break;
+        case GGML_TYPE_I16:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
+                ((int16_t *)(tensor->data))[i] = value;
+            } break;
+        case GGML_TYPE_I32:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
+                ((int32_t *)(tensor->data))[i] = value;
+            } break;
+        case GGML_TYPE_F16:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
+                ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(float));
+                ((float *)(tensor->data))[i] = value;
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) {
+    void * data   = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3];
+    switch (tensor->type) {
+        case GGML_TYPE_I8:
+            return ((int8_t *) data)[0];
+        case GGML_TYPE_I16:
+            return ((int16_t *) data)[0];
+        case GGML_TYPE_I32:
+            return ((int32_t *) data)[0];
+        case GGML_TYPE_F16:
+            return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]);
+        case GGML_TYPE_F32:
+            return ((float *) data)[0];
+        default:
+            GGML_ASSERT(false);
+    }
+
+    return 0.0f;
+}
+
+void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value) {
+    void * data   = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3];
+    switch (tensor->type) {
+        case GGML_TYPE_I8:
+            {
+                ((int8_t *)(data))[0] = value;
+            } break;
+        case GGML_TYPE_I16:
+            {
+                ((int16_t *)(data))[0] = value;
+            } break;
+        case GGML_TYPE_I32:
+            {
+                ((int32_t *)(data))[0] = value;
+            } break;
+        case GGML_TYPE_F16:
+            {
+                ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ((float *)(data))[0] = value;
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) {
+    if (!ggml_is_contiguous(tensor)) {
+        int64_t id[4] = { 0, 0, 0, 0 };
+        ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]);
+        return ggml_get_f32_nd(tensor, id[0], id[1], id[2], id[3]);
+    }
+    switch (tensor->type) {
+        case GGML_TYPE_I8:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(int8_t));
+                return ((int8_t *)(tensor->data))[i];
+            }
+        case GGML_TYPE_I16:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
+                return ((int16_t *)(tensor->data))[i];
+            }
+        case GGML_TYPE_I32:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
+                return ((int32_t *)(tensor->data))[i];
+            }
+        case GGML_TYPE_F16:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
+                return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
+            }
+        case GGML_TYPE_F32:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(float));
+                return ((float *)(tensor->data))[i];
+            }
+        default:
+            {
+                GGML_ASSERT(false);
+            }
+    }
+
+    return 0.0f;
+}
+
+void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) {
+    if (!ggml_is_contiguous(tensor)) {
+        int64_t id[4] = { 0, 0, 0, 0 };
+        ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]);
+        ggml_set_f32_nd(tensor, id[0], id[1], id[2], id[3], value);
+        return;
+    }
+    switch (tensor->type) {
+        case GGML_TYPE_I8:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(int8_t));
+                ((int8_t *)(tensor->data))[i] = value;
+            } break;
+        case GGML_TYPE_I16:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
+                ((int16_t *)(tensor->data))[i] = value;
+            } break;
+        case GGML_TYPE_I32:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
+                ((int32_t *)(tensor->data))[i] = value;
+            } break;
+        case GGML_TYPE_F16:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
+                ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                GGML_ASSERT(tensor->nb[0] == sizeof(float));
+                ((float *)(tensor->data))[i] = value;
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) {
+    void * data   = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3];
+    switch (tensor->type) {
+        case GGML_TYPE_I8:
+            return ((int8_t *) data)[0];
+        case GGML_TYPE_I16:
+            return ((int16_t *) data)[0];
+        case GGML_TYPE_I32:
+            return ((int32_t *) data)[0];
+        case GGML_TYPE_F16:
+            return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]);
+        case GGML_TYPE_F32:
+            return ((float *) data)[0];
+        default:
+            GGML_ASSERT(false);
+    }
+
+    return 0.0f;
+}
+
+void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value) {
+    void * data   = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3];
+    switch (tensor->type) {
+        case GGML_TYPE_I8:
+            {
+                ((int8_t *)(data))[0] = value;
+            } break;
+        case GGML_TYPE_I16:
+            {
+                ((int16_t *)(data))[0] = value;
+            } break;
+        case GGML_TYPE_I32:
+            {
+                ((int32_t *)(data))[0] = value;
+            } break;
+        case GGML_TYPE_F16:
+            {
+                ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ((float *)(data))[0] = value;
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+void * ggml_get_data(const struct ggml_tensor * tensor) {
+    return tensor->data;
+}
+
+float * ggml_get_data_f32(const struct ggml_tensor * tensor) {
+    assert(tensor->type == GGML_TYPE_F32);
+    return (float *)(tensor->data);
+}
+
+enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) {
+    GGML_ASSERT(tensor->op == GGML_OP_UNARY);
+    return (enum ggml_unary_op) ggml_get_op_params_i32(tensor, 0);
+}
+
+const char * ggml_get_name(const struct ggml_tensor * tensor) {
+    return tensor->name;
+}
+
+struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name) {
+    strncpy(tensor->name, name, sizeof(tensor->name));
+    tensor->name[sizeof(tensor->name) - 1] = '\0';
+    return tensor;
+}
+
+struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * fmt, ...) {
+    va_list args;
+    va_start(args, fmt);
+    vsnprintf(tensor->name, sizeof(tensor->name), fmt, args);
+    va_end(args);
+    return tensor;
+}
+
+struct ggml_tensor * ggml_view_tensor(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * src) {
+    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, src, 0);
+    ggml_format_name(result, "%s (view)", src->name);
+
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        result->nb[i] = src->nb[i];
+    }
+
+    return result;
+}
+
+struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx) {
+    struct ggml_object * obj = ctx->objects_begin;
+
+    char * const mem_buffer = ctx->mem_buffer;
+
+    while (obj != NULL) {
+        if (obj->type == GGML_OBJECT_TENSOR) {
+            return (struct ggml_tensor *)(mem_buffer + obj->offs);
+        }
+
+        obj = obj->next;
+    }
+
+    return NULL;
+}
+
+struct ggml_tensor * ggml_get_next_tensor(struct ggml_context * ctx, struct ggml_tensor * tensor) {
+    struct ggml_object * obj = (struct ggml_object *) ((char *)tensor - GGML_OBJECT_SIZE);
+    obj = obj->next;
+
+    char * const mem_buffer = ctx->mem_buffer;
+
+    while (obj != NULL) {
+        if (obj->type == GGML_OBJECT_TENSOR) {
+            return (struct ggml_tensor *)(mem_buffer + obj->offs);
+        }
+
+        obj = obj->next;
+    }
+
+    return NULL;
+}
+
+struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name) {
+    struct ggml_object * obj = ctx->objects_begin;
+
+    char * const mem_buffer = ctx->mem_buffer;
+
+    while (obj != NULL) {
+        if (obj->type == GGML_OBJECT_TENSOR) {
+            struct ggml_tensor * cur = (struct ggml_tensor *)(mem_buffer + obj->offs);
+            if (strcmp(cur->name, name) == 0) {
+                return cur;
+            }
+        }
+
+        obj = obj->next;
+    }
+
+    return NULL;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+// ggml_dup
+
+static struct ggml_tensor * ggml_dup_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        bool inplace) {
+    bool is_node = false;
+
+    if (!inplace && (a->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_DUP;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_dup(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a) {
+    return ggml_dup_impl(ctx, a, false);
+}
+
+struct ggml_tensor * ggml_dup_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a) {
+    return ggml_dup_impl(ctx, a, true);
+}
+
+// ggml_add
+
+static struct ggml_tensor * ggml_add_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b,
+        bool inplace) {
+    // TODO: support less-strict constraint
+    //       GGML_ASSERT(ggml_can_repeat(b, a));
+    GGML_ASSERT(ggml_can_repeat_rows(b, a));
+
+    bool is_node = false;
+
+    if (!inplace && (a->grad || b->grad)) {
+        // TODO: support backward pass for broadcasting
+        GGML_ASSERT(ggml_are_same_shape(a, b));
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_ADD;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_add(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b) {
+    return ggml_add_impl(ctx, a, b, false);
+}
+
+struct ggml_tensor * ggml_add_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b) {
+    return ggml_add_impl(ctx, a, b, true);
+}
+
+// ggml_add_cast
+
+static struct ggml_tensor * ggml_add_cast_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b,
+        enum   ggml_type     type) {
+    // TODO: support less-strict constraint
+    //       GGML_ASSERT(ggml_can_repeat(b, a));
+    GGML_ASSERT(ggml_can_repeat_rows(b, a));
+    GGML_ASSERT(ggml_is_quantized(a->type)); // currently only supported for quantized input
+
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        // TODO: support backward pass for broadcasting
+        GGML_ASSERT(ggml_are_same_shape(a, b));
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = ggml_new_tensor(ctx, type, a->n_dims, a->ne);
+
+    result->op   = GGML_OP_ADD;
+    result->grad = is_node ? ggml_new_tensor(ctx, GGML_TYPE_F32, a->n_dims, a->ne) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_add_cast(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b,
+        enum   ggml_type     type) {
+    return ggml_add_cast_impl(ctx, a, b, type);
+}
+
+// ggml_add1
+
+static struct ggml_tensor * ggml_add1_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b,
+        bool inplace) {
+    GGML_ASSERT(ggml_is_scalar(b));
+    GGML_ASSERT(ggml_is_padded_1d(a));
+
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_ADD1;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_add1(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b) {
+    return ggml_add1_impl(ctx, a, b, false);
+}
+
+struct ggml_tensor * ggml_add1_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b) {
+    return ggml_add1_impl(ctx, a, b, true);
+}
+
+// ggml_acc
+
+static struct ggml_tensor * ggml_acc_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b,
+        size_t               nb1,
+        size_t               nb2,
+        size_t               nb3,
+        size_t               offset,
+        bool inplace) {
+    GGML_ASSERT(ggml_nelements(b) <= ggml_nelements(a));
+    GGML_ASSERT(ggml_is_contiguous(a));
+    GGML_ASSERT(a->type == GGML_TYPE_F32);
+    GGML_ASSERT(b->type == GGML_TYPE_F32);
+
+    bool is_node = false;
+
+    if (!inplace && (a->grad || b->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op   = GGML_OP_ACC;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_acc(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b,
+        size_t               nb1,
+        size_t               nb2,
+        size_t               nb3,
+        size_t               offset) {
+    return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, false);
+}
+
+struct ggml_tensor * ggml_acc_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b,
+        size_t               nb1,
+        size_t               nb2,
+        size_t               nb3,
+        size_t               offset) {
+    return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, true);
+}
+
+// ggml_sub
+
+static struct ggml_tensor * ggml_sub_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b,
+        bool inplace) {
+    GGML_ASSERT(ggml_are_same_shape(a, b));
+
+    bool is_node = false;
+
+    if (!inplace && (a->grad || b->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_SUB;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_sub(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b) {
+    return ggml_sub_impl(ctx, a, b, false);
+}
+
+struct ggml_tensor * ggml_sub_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b) {
+    return ggml_sub_impl(ctx, a, b, true);
+}
+
+// ggml_mul
+
+static struct ggml_tensor * ggml_mul_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b,
+        bool inplace) {
+    // TODO: support less-strict constraint
+    //       GGML_ASSERT(ggml_can_repeat(b, a));
+    GGML_ASSERT(ggml_can_repeat_rows(b, a));
+
+    bool is_node = false;
+
+    if (!inplace && (a->grad || b->grad)) {
+        // TODO: support backward pass for broadcasting
+        GGML_ASSERT(ggml_are_same_shape(a, b));
+        is_node = true;
+    }
+
+    if (inplace) {
+        GGML_ASSERT(!is_node);
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_MUL;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_mul(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b) {
+    return ggml_mul_impl(ctx, a, b, false);
+}
+
+struct ggml_tensor * ggml_mul_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b) {
+    return ggml_mul_impl(ctx, a, b, true);
+}
+
+// ggml_div
+
+static struct ggml_tensor * ggml_div_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b,
+        bool inplace) {
+    GGML_ASSERT(ggml_are_same_shape(a, b));
+
+    bool is_node = false;
+
+    if (!inplace && (a->grad || b->grad)) {
+        is_node = true;
+    }
+
+    if (inplace) {
+        GGML_ASSERT(!is_node);
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_DIV;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_div(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b) {
+    return ggml_div_impl(ctx, a, b, false);
+}
+
+struct ggml_tensor * ggml_div_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b) {
+    return ggml_div_impl(ctx, a, b, true);
+}
+
+// ggml_sqr
+
+static struct ggml_tensor * ggml_sqr_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        bool inplace) {
+    bool is_node = false;
+
+    if (!inplace && (a->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_SQR;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_sqr(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_sqr_impl(ctx, a, false);
+}
+
+struct ggml_tensor * ggml_sqr_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_sqr_impl(ctx, a, true);
+}
+
+// ggml_sqrt
+
+static struct ggml_tensor * ggml_sqrt_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        bool inplace) {
+    bool is_node = false;
+
+    if (!inplace && (a->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_SQRT;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_sqrt(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_sqrt_impl(ctx, a, false);
+}
+
+struct ggml_tensor * ggml_sqrt_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_sqrt_impl(ctx, a, true);
+}
+
+
+// ggml_log
+
+static struct ggml_tensor * ggml_log_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        bool inplace) {
+    bool is_node = false;
+
+    if (!inplace && (a->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_LOG;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_log(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_log_impl(ctx, a, false);
+}
+
+struct ggml_tensor * ggml_log_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_log_impl(ctx, a, true);
+}
+
+// ggml_sum
+
+struct ggml_tensor * ggml_sum(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a) {
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1);
+
+    result->op   = GGML_OP_SUM;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+
+// ggml_sum_rows
+
+struct ggml_tensor * ggml_sum_rows(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a) {
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    int64_t ne[4] = {1,1,1,1};
+    for (int i=1; i<a->n_dims; ++i) {
+        ne[i] = a->ne[i];
+    }
+
+    struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, a->n_dims, ne);
+
+    result->op   = GGML_OP_SUM_ROWS;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_mean
+
+struct ggml_tensor * ggml_mean(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a) {
+    bool is_node = false;
+
+    if (a->grad) {
+        GGML_ASSERT(false); // TODO: implement
+        is_node = true;
+    }
+
+    int64_t ne[GGML_MAX_DIMS] = { 1, a->ne[1], a->ne[2], a->ne[3] };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, a->n_dims, ne);
+
+    result->op   = GGML_OP_MEAN;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_argmax
+
+struct ggml_tensor * ggml_argmax(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a) {
+    GGML_ASSERT(ggml_is_matrix(a));
+    bool is_node = false;
+
+    if (a->grad) {
+        GGML_ASSERT(false);
+        is_node = true;
+    }
+
+    int64_t ne[GGML_MAX_DIMS] = { a->ne[1], 1, 1, 1 };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, a->n_dims, ne);
+
+    result->op   = GGML_OP_ARGMAX;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_repeat
+
+struct ggml_tensor * ggml_repeat(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b) {
+    GGML_ASSERT(ggml_can_repeat(a, b));
+
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, b->n_dims, b->ne);
+
+    result->op   = GGML_OP_REPEAT;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_repeat_back
+
+struct ggml_tensor * ggml_repeat_back(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b) {
+    GGML_ASSERT(ggml_can_repeat(b, a));
+
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    if (ggml_are_same_shape(a, b) && !is_node) {
+        return a;
+    }
+
+    struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, b->n_dims, b->ne);
+
+    result->op   = GGML_OP_REPEAT_BACK;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_concat
+
+struct ggml_tensor * ggml_concat(
+    struct ggml_context* ctx,
+    struct ggml_tensor* a,
+    struct ggml_tensor* b) {
+    GGML_ASSERT(a->ne[0] == b->ne[0] && a->ne[1] == b->ne[1] && a->ne[3] == b->ne[3]);
+
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, a->ne[0], a->ne[1], a->ne[2] + b->ne[2], a->ne[3]);
+
+    result->op = GGML_OP_CONCAT;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// ggml_abs
+
+struct ggml_tensor * ggml_abs(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_ABS);
+}
+
+struct ggml_tensor * ggml_abs_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ABS);
+}
+
+// ggml_sgn
+
+struct ggml_tensor * ggml_sgn(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_SGN);
+}
+
+struct ggml_tensor * ggml_sgn_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SGN);
+}
+
+// ggml_neg
+
+struct ggml_tensor * ggml_neg(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_NEG);
+}
+
+struct ggml_tensor * ggml_neg_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_NEG);
+}
+
+// ggml_step
+
+struct ggml_tensor * ggml_step(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_STEP);
+}
+
+struct ggml_tensor * ggml_step_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_STEP);
+}
+
+// ggml_tanh
+
+struct ggml_tensor * ggml_tanh(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_TANH);
+}
+
+struct ggml_tensor * ggml_tanh_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_TANH);
+}
+
+// ggml_elu
+
+struct ggml_tensor * ggml_elu(
+    struct ggml_context * ctx,
+    struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_ELU);
+}
+
+struct ggml_tensor * ggml_elu_inplace(
+    struct ggml_context * ctx,
+    struct ggml_tensor  * a) {
+    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ELU);
+}
+
+// ggml_relu
+
+struct ggml_tensor * ggml_relu(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_RELU);
+}
+
+struct ggml_tensor * ggml_relu_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_RELU);
+}
+
+// ggml_gelu
+
+struct ggml_tensor * ggml_gelu(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_GELU);
+}
+
+struct ggml_tensor * ggml_gelu_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_GELU);
+}
+
+// ggml_gelu_quick
+
+struct ggml_tensor * ggml_gelu_quick(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_GELU_QUICK);
+}
+
+struct ggml_tensor * ggml_gelu_quick_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_GELU_QUICK);
+}
+
+// ggml_silu
+
+struct ggml_tensor * ggml_silu(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_SILU);
+}
+
+struct ggml_tensor * ggml_silu_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SILU);
+}
+
+// ggml_silu_back
+
+struct ggml_tensor * ggml_silu_back(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b) {
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        // TODO: implement backward
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_SILU_BACK;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// ggml_norm
+
+static struct ggml_tensor * ggml_norm_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        float eps,
+        bool inplace) {
+    bool is_node = false;
+
+    if (!inplace && (a->grad)) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    ggml_set_op_params(result, &eps, sizeof(eps));
+
+    result->op   = GGML_OP_NORM;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_norm(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        float eps) {
+    return ggml_norm_impl(ctx, a, eps, false);
+}
+
+struct ggml_tensor * ggml_norm_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        float eps) {
+    return ggml_norm_impl(ctx, a, eps, true);
+}
+
+// ggml_rms_norm
+
+static struct ggml_tensor * ggml_rms_norm_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        float eps,
+        bool inplace) {
+    bool is_node = false;
+
+    if (!inplace && (a->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    ggml_set_op_params(result, &eps, sizeof(eps));
+
+    result->op   = GGML_OP_RMS_NORM;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_rms_norm(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        float  eps) {
+    return ggml_rms_norm_impl(ctx, a, eps, false);
+}
+
+struct ggml_tensor * ggml_rms_norm_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        float eps) {
+    return ggml_rms_norm_impl(ctx, a, eps, true);
+}
+
+// ggml_rms_norm_back
+
+struct ggml_tensor * ggml_rms_norm_back(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        float  eps) {
+    bool is_node = false;
+
+    if (a->grad) {
+        // TODO: implement backward
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
+
+    ggml_set_op_params(result, &eps, sizeof(eps));
+
+    result->op   = GGML_OP_RMS_NORM_BACK;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// ggml_group_norm
+
+static struct ggml_tensor * ggml_group_norm_impl(
+    struct ggml_context * ctx,
+    struct ggml_tensor * a,
+    int n_groups,
+    bool inplace) {
+
+    bool is_node = false;
+    if (!inplace && (a->grad)) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op = GGML_OP_GROUP_NORM;
+    result->op_params[0] = n_groups;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = NULL; // TODO: maybe store epsilon here?
+
+    return result;
+}
+
+struct ggml_tensor * ggml_group_norm(
+    struct ggml_context * ctx,
+    struct ggml_tensor * a,
+    int n_groups) {
+    return ggml_group_norm_impl(ctx, a, n_groups, false);
+}
+
+struct ggml_tensor * ggml_group_norm_inplace(
+    struct ggml_context * ctx,
+    struct ggml_tensor * a,
+    int n_groups) {
+    return ggml_group_norm_impl(ctx, a, n_groups, true);
+}
+
+// ggml_mul_mat
+
+struct ggml_tensor * ggml_mul_mat(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b) {
+    GGML_ASSERT(ggml_can_mul_mat(a, b));
+    GGML_ASSERT(!ggml_is_transposed(a));
+
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        is_node = true;
+    }
+
+    const int64_t ne[4] = { a->ne[1], b->ne[1], b->ne[2], b->ne[3] };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, MAX(a->n_dims, b->n_dims), ne);
+
+    result->op   = GGML_OP_MUL_MAT;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// ggml_out_prod
+
+struct ggml_tensor * ggml_out_prod(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b) {
+    GGML_ASSERT(ggml_can_out_prod(a, b));
+    GGML_ASSERT(!ggml_is_transposed(a));
+
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        is_node = true;
+    }
+
+    // a is broadcastable to b for ne[2] and ne[3] -> use b->ne[2] and b->ne[3]
+    const int64_t ne[4] = { a->ne[0], b->ne[0], b->ne[2], b->ne[3] };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, MAX(a->n_dims, b->n_dims), ne);
+
+    result->op   = GGML_OP_OUT_PROD;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// ggml_scale
+
+static struct ggml_tensor * ggml_scale_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        bool inplace) {
+    GGML_ASSERT(ggml_is_scalar(b));
+    GGML_ASSERT(ggml_is_padded_1d(a));
+
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_SCALE;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_scale(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b) {
+    return ggml_scale_impl(ctx, a, b, false);
+}
+
+struct ggml_tensor * ggml_scale_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b) {
+    return ggml_scale_impl(ctx, a, b, true);
+}
+
+// ggml_set
+
+static struct ggml_tensor * ggml_set_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        size_t                nb1,
+        size_t                nb2,
+        size_t                nb3,
+        size_t                offset,
+        bool inplace) {
+    GGML_ASSERT(ggml_nelements(a) >= ggml_nelements(b));
+
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        is_node = true;
+    }
+
+    // make a view of the destination
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op   = GGML_OP_SET;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_set(
+        struct ggml_context * ctx,
+        struct ggml_tensor *  a,
+        struct ggml_tensor *  b,
+        size_t                nb1,
+        size_t                nb2,
+        size_t                nb3,
+        size_t                offset) {
+    return ggml_set_impl(ctx, a, b, nb1, nb2, nb3, offset, false);
+}
+
+struct ggml_tensor * ggml_set_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor *  a,
+        struct ggml_tensor *  b,
+        size_t                nb1,
+        size_t                nb2,
+        size_t                nb3,
+        size_t                offset) {
+    return ggml_set_impl(ctx, a, b, nb1, nb2, nb3, offset, true);
+}
+
+struct ggml_tensor * ggml_set_1d(
+        struct ggml_context * ctx,
+        struct ggml_tensor *  a,
+        struct ggml_tensor *  b,
+        size_t                offset) {
+    return ggml_set_impl(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], offset, false);
+}
+
+struct ggml_tensor * ggml_set_1d_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor *  a,
+        struct ggml_tensor *  b,
+        size_t                offset) {
+    return ggml_set_impl(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], offset, true);
+}
+
+struct ggml_tensor * ggml_set_2d(
+        struct ggml_context * ctx,
+        struct ggml_tensor *  a,
+        struct ggml_tensor *  b,
+        size_t                nb1,
+        size_t                offset) {
+    return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, false);
+}
+
+struct ggml_tensor * ggml_set_2d_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor *  a,
+        struct ggml_tensor *  b,
+        size_t                nb1,
+        size_t                offset) {
+    return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, false);
+}
+
+
+// ggml_cpy
+
+static struct ggml_tensor * ggml_cpy_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        bool inplace) {
+    GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b));
+
+    bool is_node = false;
+
+    if (!inplace && (a->grad || b->grad)) {
+        is_node = true;
+    }
+
+    // make a view of the destination
+    struct ggml_tensor * result = ggml_view_tensor(ctx, b);
+    if (strlen(b->name) > 0) {
+        ggml_format_name(result, "%s (copy of %s)", b->name, a->name);
+    } else {
+        ggml_format_name(result, "%s (copy)", a->name);
+    }
+
+    result->op   = GGML_OP_CPY;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_cpy(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b) {
+    return ggml_cpy_impl(ctx, a, b, false);
+}
+
+struct ggml_tensor * ggml_cpy_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b) {
+    return ggml_cpy_impl(ctx, a, b, true);
+}
+
+// ggml_cont
+
+static struct ggml_tensor * ggml_cont_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        bool inplace) {
+    bool is_node = false;
+
+    if (!inplace && a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+    ggml_format_name(result, "%s (cont)", a->name);
+
+    result->op   = GGML_OP_CONT;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_cont(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a) {
+    return ggml_cont_impl(ctx, a, false);
+}
+
+struct ggml_tensor * ggml_cont_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a) {
+    return ggml_cont_impl(ctx, a, true);
+}
+
+
+// make contiguous, with new shape
+GGML_API struct ggml_tensor * ggml_cont_1d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0) {
+    return ggml_cont_4d(ctx, a, ne0, 1, 1, 1);
+}
+
+GGML_API struct ggml_tensor * ggml_cont_2d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        int64_t               ne1) {
+    return ggml_cont_4d(ctx, a, ne0, ne1, 1, 1);
+}
+
+GGML_API struct ggml_tensor * ggml_cont_3d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        int64_t               ne1,
+        int64_t               ne2) {
+    return ggml_cont_4d(ctx, a, ne0, ne1, ne2, 1);
+}
+
+struct ggml_tensor * ggml_cont_4d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        int64_t               ne1,
+        int64_t               ne2,
+        int64_t               ne3) {
+    GGML_ASSERT(ggml_nelements(a) == (ne0*ne1*ne2*ne3));
+
+    bool is_node = false;
+
+    struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3);
+    ggml_format_name(result, "%s (cont)", a->name);
+
+    result->op   = GGML_OP_CONT;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_reshape
+
+struct ggml_tensor * ggml_reshape(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        struct ggml_tensor * b) {
+    GGML_ASSERT(ggml_is_contiguous(a));
+    // as only the shape of b is relevant, and not its memory layout, b is allowed to be non contiguous.
+    GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b));
+
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    if (b->grad) {
+        // gradient propagation is not supported
+        //GGML_ASSERT(false);
+    }
+
+    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, b->n_dims, b->ne, a, 0);
+    ggml_format_name(result, "%s (reshaped)", a->name);
+
+    result->op   = GGML_OP_RESHAPE;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_reshape_1d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0) {
+    GGML_ASSERT(ggml_is_contiguous(a));
+    GGML_ASSERT(ggml_nelements(a) == ne0);
+
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    const int64_t ne[1] = { ne0 };
+    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, ne, a, 0);
+    ggml_format_name(result, "%s (reshaped)", a->name);
+
+    result->op   = GGML_OP_RESHAPE;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_reshape_2d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        int64_t               ne1) {
+    GGML_ASSERT(ggml_is_contiguous(a));
+    GGML_ASSERT(ggml_nelements(a) == ne0*ne1);
+
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    const int64_t ne[2] = { ne0, ne1 };
+    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a, 0);
+    ggml_format_name(result, "%s (reshaped)", a->name);
+
+    result->op   = GGML_OP_RESHAPE;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_reshape_3d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        int64_t               ne1,
+        int64_t               ne2) {
+    GGML_ASSERT(ggml_is_contiguous(a));
+    GGML_ASSERT(ggml_nelements(a) == ne0*ne1*ne2);
+
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    const int64_t ne[3] = { ne0, ne1, ne2 };
+    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, a, 0);
+    ggml_format_name(result, "%s (reshaped)", a->name);
+
+    result->op   = GGML_OP_RESHAPE;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_reshape_4d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        int64_t               ne1,
+        int64_t               ne2,
+        int64_t               ne3) {
+    GGML_ASSERT(ggml_is_contiguous(a));
+    GGML_ASSERT(ggml_nelements(a) == ne0*ne1*ne2*ne3);
+
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    const int64_t ne[4] = { ne0, ne1, ne2, ne3 };
+    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, a, 0);
+    ggml_format_name(result, "%s (reshaped)", a->name);
+
+    result->op   = GGML_OP_RESHAPE;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+static struct ggml_tensor * ggml_view_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   n_dims,
+        const int64_t       * ne,
+        size_t                offset) {
+
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, n_dims, ne, a, offset);
+    ggml_format_name(result, "%s (view)", a->name);
+
+    ggml_set_op_params(result, &offset, sizeof(offset));
+
+    result->op   = GGML_OP_VIEW;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_view_1d
+
+struct ggml_tensor * ggml_view_1d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        size_t                offset) {
+
+    struct ggml_tensor * result = ggml_view_impl(ctx, a, 1, &ne0, offset);
+
+    return result;
+}
+
+// ggml_view_2d
+
+struct ggml_tensor * ggml_view_2d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        int64_t               ne1,
+        size_t                nb1,
+        size_t                offset) {
+
+    const int64_t ne[2] = { ne0, ne1 };
+
+    struct ggml_tensor * result = ggml_view_impl(ctx, a, 2, ne, offset);
+
+    result->nb[1] = nb1;
+    result->nb[2] = result->nb[1]*ne1;
+    result->nb[3] = result->nb[2];
+
+    return result;
+}
+
+// ggml_view_3d
+
+struct ggml_tensor * ggml_view_3d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        int64_t               ne1,
+        int64_t               ne2,
+        size_t                nb1,
+        size_t                nb2,
+        size_t                offset) {
+
+    const int64_t ne[3] = { ne0, ne1, ne2 };
+
+    struct ggml_tensor * result = ggml_view_impl(ctx, a, 3, ne, offset);
+
+    result->nb[1] = nb1;
+    result->nb[2] = nb2;
+    result->nb[3] = result->nb[2]*ne2;
+
+    return result;
+}
+
+// ggml_view_4d
+
+struct ggml_tensor * ggml_view_4d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        int64_t               ne1,
+        int64_t               ne2,
+        int64_t               ne3,
+        size_t                nb1,
+        size_t                nb2,
+        size_t                nb3,
+        size_t                offset) {
+
+    const int64_t ne[4] = { ne0, ne1, ne2, ne3 };
+
+    struct ggml_tensor * result = ggml_view_impl(ctx, a, 4, ne, offset);
+
+    result->nb[1] = nb1;
+    result->nb[2] = nb2;
+    result->nb[3] = nb3;
+
+    return result;
+}
+
+// ggml_permute
+
+struct ggml_tensor * ggml_permute(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   axis0,
+        int                   axis1,
+        int                   axis2,
+        int                   axis3) {
+    GGML_ASSERT(axis0 >= 0 && axis0 < GGML_MAX_DIMS);
+    GGML_ASSERT(axis1 >= 0 && axis1 < GGML_MAX_DIMS);
+    GGML_ASSERT(axis2 >= 0 && axis2 < GGML_MAX_DIMS);
+    GGML_ASSERT(axis3 >= 0 && axis3 < GGML_MAX_DIMS);
+
+    GGML_ASSERT(axis0 != axis1);
+    GGML_ASSERT(axis0 != axis2);
+    GGML_ASSERT(axis0 != axis3);
+    GGML_ASSERT(axis1 != axis2);
+    GGML_ASSERT(axis1 != axis3);
+    GGML_ASSERT(axis2 != axis3);
+
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = ggml_view_tensor(ctx, a);
+    ggml_format_name(result, "%s (permuted)", a->name);
+
+    int ne[GGML_MAX_DIMS];
+    int nb[GGML_MAX_DIMS];
+
+    ne[axis0] = a->ne[0];
+    ne[axis1] = a->ne[1];
+    ne[axis2] = a->ne[2];
+    ne[axis3] = a->ne[3];
+
+    nb[axis0] = a->nb[0];
+    nb[axis1] = a->nb[1];
+    nb[axis2] = a->nb[2];
+    nb[axis3] = a->nb[3];
+
+    result->ne[0] = ne[0];
+    result->ne[1] = ne[1];
+    result->ne[2] = ne[2];
+    result->ne[3] = ne[3];
+
+    result->nb[0] = nb[0];
+    result->nb[1] = nb[1];
+    result->nb[2] = nb[2];
+    result->nb[3] = nb[3];
+
+    result->op   = GGML_OP_PERMUTE;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    int32_t params[] = { axis0, axis1, axis2, axis3 };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    return result;
+}
+
+// ggml_transpose
+
+struct ggml_tensor * ggml_transpose(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = ggml_view_tensor(ctx, a);
+    ggml_format_name(result, "%s (transposed)", a->name);
+
+    result->ne[0] = a->ne[1];
+    result->ne[1] = a->ne[0];
+
+    result->nb[0] = a->nb[1];
+    result->nb[1] = a->nb[0];
+
+    result->op   = GGML_OP_TRANSPOSE;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_get_rows
+
+struct ggml_tensor * ggml_get_rows(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b) {
+    GGML_ASSERT(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32);
+
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        is_node = true;
+    }
+
+    // TODO: implement non F32 return
+    //struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]);
+    struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, a->ne[0], b->ne[0]);
+
+    result->op   = GGML_OP_GET_ROWS;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// ggml_get_rows_back
+
+struct ggml_tensor * ggml_get_rows_back(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        struct ggml_tensor  * c) {
+    GGML_ASSERT(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32);
+    GGML_ASSERT(ggml_is_matrix(c) && (a->ne[0] == c->ne[0]));
+
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        is_node = true;
+    }
+
+    // TODO: implement non F32 return
+    //struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]);
+    struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, c->ne[0], c->ne[1]);
+
+    result->op   = GGML_OP_GET_ROWS_BACK;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// ggml_diag
+
+struct ggml_tensor * ggml_diag(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    GGML_ASSERT(a->ne[1] == 1);
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    const int64_t ne[4] = { a->ne[0], a->ne[0], a->ne[2], a->ne[3] };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, MAX(a->n_dims, 2), ne);
+
+    result->op   = GGML_OP_DIAG;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+
+// ggml_diag_mask_inf
+
+static struct ggml_tensor * ggml_diag_mask_inf_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   n_past,
+        bool                  inplace) {
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    int32_t params[] = { n_past };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op   = GGML_OP_DIAG_MASK_INF;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_diag_mask_inf(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   n_past) {
+    return ggml_diag_mask_inf_impl(ctx, a, n_past, false);
+}
+
+struct ggml_tensor * ggml_diag_mask_inf_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   n_past) {
+    return ggml_diag_mask_inf_impl(ctx, a, n_past, true);
+}
+
+// ggml_diag_mask_zero
+
+static struct ggml_tensor * ggml_diag_mask_zero_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   n_past,
+        bool                  inplace) {
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    int32_t params[] = { n_past };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op   = GGML_OP_DIAG_MASK_ZERO;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_diag_mask_zero(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   n_past) {
+    return ggml_diag_mask_zero_impl(ctx, a, n_past, false);
+}
+
+struct ggml_tensor * ggml_diag_mask_zero_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   n_past) {
+    return ggml_diag_mask_zero_impl(ctx, a, n_past, true);
+}
+
+// ggml_soft_max
+
+static struct ggml_tensor * ggml_soft_max_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        bool                  inplace) {
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_SOFT_MAX;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_soft_max(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_soft_max_impl(ctx, a, false);
+}
+
+struct ggml_tensor * ggml_soft_max_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_soft_max_impl(ctx, a, true);
+}
+
+
+// ggml_soft_max_back
+
+static struct ggml_tensor * ggml_soft_max_back_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        bool                  inplace) {
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        is_node = true; // TODO : implement backward pass
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_SOFT_MAX_BACK;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_soft_max_back(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b) {
+    return ggml_soft_max_back_impl(ctx, a, b, false);
+}
+
+struct ggml_tensor * ggml_soft_max_back_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b) {
+    return ggml_soft_max_back_impl(ctx, a, b, true);
+}
+
+// ggml_rope
+
+static struct ggml_tensor * ggml_rope_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   n_dims,
+        int                   mode,
+        int                   n_ctx,
+        float                 freq_base,
+        float                 freq_scale,
+        float                 xpos_base,
+        bool                  xpos_down,
+        bool                  inplace) {
+    GGML_ASSERT(ggml_is_vector(b));
+    GGML_ASSERT(b->type == GGML_TYPE_I32);
+    GGML_ASSERT(a->ne[2] == b->ne[0]);
+
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx };
+    memcpy(params + 4, &freq_base,  sizeof(float));
+    memcpy(params + 5, &freq_scale, sizeof(float));
+    memcpy(params + 6, &xpos_base,  sizeof(float));
+    memcpy(params + 7, &xpos_down,  sizeof(bool));
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op   = GGML_OP_ROPE;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_rope(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   n_dims,
+        int                   mode,
+        int                   n_ctx) {
+    return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, false);
+}
+
+struct ggml_tensor * ggml_rope_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   n_dims,
+        int                   mode,
+        int                   n_ctx) {
+    return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, true);
+}
+
+struct ggml_tensor * ggml_rope_custom(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   n_dims,
+        int                   mode,
+        int                   n_ctx,
+        float                 freq_base,
+        float                 freq_scale) {
+    return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, false);
+}
+
+struct ggml_tensor * ggml_rope_custom_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   n_dims,
+        int                   mode,
+        int                   n_ctx,
+        float                 freq_base,
+        float                 freq_scale) {
+    return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, true);
+}
+
+struct ggml_tensor * ggml_rope_xpos_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   n_dims,
+        float                 base,
+        bool                  down) {
+    return ggml_rope_impl(ctx, a, b, n_dims, 0, 0, 10000.0f, 1.0f, base, down, true);
+}
+
+// ggml_rope_back
+
+struct ggml_tensor * ggml_rope_back(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   n_dims,
+        int                   mode,
+        int                   n_ctx,
+        float                 freq_base,
+        float                 freq_scale,
+        float                 xpos_base,
+        bool                  xpos_down) {
+    GGML_ASSERT(ggml_is_vector(b));
+    GGML_ASSERT(b->type == GGML_TYPE_I32);
+    GGML_ASSERT(a->ne[2] == b->ne[0]);
+
+    GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet");
+
+    bool is_node = false;
+
+    if (a->grad) {
+        is_node = false; // TODO: implement backward
+    }
+
+    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
+
+    int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx };
+    memcpy(params + 4, &freq_base,  sizeof(float));
+    memcpy(params + 5, &freq_scale, sizeof(float));
+    memcpy(params + 6, &xpos_base,  sizeof(float));
+    memcpy(params + 7, &xpos_down,  sizeof(bool));
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op   = GGML_OP_ROPE_BACK;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// ggml_alibi
+
+struct ggml_tensor * ggml_alibi(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   n_past,
+        int                   n_head,
+        float                 bias_max) {
+    GGML_ASSERT(n_past >= 0);
+    bool is_node = false;
+
+    if (a->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    // TODO: when implement backward, fix this:
+    //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+    struct ggml_tensor * result = ggml_view_tensor(ctx, a);
+
+    int32_t op_params[3] = { n_past, n_head };
+    memcpy(op_params + 2, &bias_max, sizeof(float));
+    ggml_set_op_params(result, op_params, sizeof(op_params));
+
+    result->op   = GGML_OP_ALIBI;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_clamp
+
+struct ggml_tensor * ggml_clamp(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        float                 min,
+        float                 max) {
+    bool is_node = false;
+
+    if (a->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    // TODO: when implement backward, fix this:
+    struct ggml_tensor * result = ggml_view_tensor(ctx, a);
+
+    float params[] = { min, max };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op   = GGML_OP_CLAMP;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_conv_1d
+
+static int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d) {
+    return (ins + 2 * p - d * (ks - 1) - 1) / s + 1;
+}
+
+// im2col: [N, IC, IL] => [N, OL, IC*K]
+// a: [OC，IC, K]
+// b: [N, IC, IL]
+// result: [N, OL, IC*K]
+static struct ggml_tensor * ggml_conv_1d_stage_0(
+    struct ggml_context * ctx,
+    struct ggml_tensor  * a,
+    struct ggml_tensor  * b,
+    int                   s0,
+    int                   p0,
+    int                   d0) {
+    GGML_ASSERT(a->ne[1] == b->ne[1]);
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    const int64_t OL = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0);
+
+    const int64_t ne[4] = {
+        a->ne[1] * a->ne[0],
+        OL,
+        b->ne[2],
+        1,
+    };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne);
+
+    int32_t params[] = { s0, p0, d0 };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op = GGML_OP_CONV_1D_STAGE_0;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// ggml_conv_1d_stage_1
+
+// gemm: [N, OC, OL] = [OC, IC * K] x [N*OL, IC * K]
+// a: [OC, IC, K]
+// b: [N, OL, IC * K]
+// result: [N, OC, OL]
+static struct ggml_tensor * ggml_conv_1d_stage_1(
+    struct ggml_context * ctx,
+    struct ggml_tensor  * a,
+    struct ggml_tensor  * b) {
+
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    const int64_t ne[4] = {
+        b->ne[1],
+        a->ne[2],
+        b->ne[2],
+        1,
+    };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+
+    result->op = GGML_OP_CONV_1D_STAGE_1;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// ggml_conv_1d
+
+GGML_API struct ggml_tensor * ggml_conv_1d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   s0,
+        int                   p0,
+        int                   d0) {
+    struct ggml_tensor * result = ggml_conv_1d_stage_0(ctx, a, b, s0, p0, d0);
+    result = ggml_conv_1d_stage_1(ctx, a, result);
+    return result;
+}
+
+// GGML_API struct ggml_tensor * ggml_conv_1d(
+//         struct ggml_context * ctx,
+//         struct ggml_tensor  * a,
+//         struct ggml_tensor  * b,
+//         int                   s0,
+//         int                   p0,
+//         int                   d0) {
+//     GGML_ASSERT(ggml_is_matrix(b));
+//     GGML_ASSERT(a->ne[1] == b->ne[1]);
+//     bool is_node = false;
+
+//     if (a->grad || b->grad) {
+//         GGML_ASSERT(false); // TODO: implement backward
+//         is_node = true;
+//     }
+
+//     const int64_t ne[4] = {
+//         ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0),
+//         a->ne[2], 1, 1,
+//     };
+//     struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
+
+//     int32_t params[] = { s0, p0, d0 };
+//     ggml_set_op_params(result, params, sizeof(params));
+
+//     result->op = GGML_OP_CONV_1D;
+//     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+//     result->src[0] = a;
+//     result->src[1] = b;
+
+//     return result;
+// }
+
+// ggml_conv_1d_ph
+
+struct ggml_tensor* ggml_conv_1d_ph(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   s,
+        int                   d) {
+    return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d);
+}
+
+// ggml_conv_transpose_1d
+
+static int64_t ggml_calc_conv_transpose_1d_output_size(int64_t ins, int64_t ks, int s, int p, int d) {
+    return (ins - 1) * s - 2 * p + d * (ks - 1) + 1;
+}
+
+GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   s0,
+        int                   p0,
+        int                   d0) {
+    GGML_ASSERT(ggml_is_matrix(b));
+    GGML_ASSERT(a->ne[2] == b->ne[1]);
+    GGML_ASSERT(a->ne[3] == 1);
+
+    GGML_ASSERT(p0 == 0);
+    GGML_ASSERT(d0 == 1);
+
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    const int64_t ne[4] = {
+        ggml_calc_conv_transpose_1d_output_size(b->ne[0], a->ne[0], s0, 0 /*p0*/, 1 /*d0*/),
+        a->ne[1], b->ne[2], 1,
+    };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+
+    int32_t params[] = { s0, p0, d0 };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op = GGML_OP_CONV_TRANSPOSE_1D;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// ggml_conv_2d
+
+struct ggml_tensor * ggml_conv_2d(
+    struct ggml_context * ctx,
+    struct ggml_tensor  * a,
+    struct ggml_tensor  * b,
+    int                  s0,
+    int                  s1,
+    int                  p0,
+    int                  p1,
+    int                  d0,
+    int                  d1) {
+
+    GGML_ASSERT(a->ne[2] == b->ne[2]);
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    const int64_t ne[4] = {
+        ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0),
+        ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1),
+        a->ne[3], b->ne[3],
+    };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+
+    int32_t params[] = { s0, s1, p0, p1, d0, d1 };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op = GGML_OP_CONV_2D;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+
+}
+
+// ggml_conv_2d_sk_p0
+
+struct ggml_tensor * ggml_conv_2d_sk_p0(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b) {
+    return ggml_conv_2d(ctx, a, b, a->ne[0], a->ne[1], 0, 0, 1, 1);
+}
+
+// ggml_conv_2d_s1_ph
+
+struct ggml_tensor * ggml_conv_2d_s1_ph(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b) {
+    return ggml_conv_2d(ctx, a, b, 1, 1, a->ne[0] / 2, a->ne[1] / 2, 1, 1);
+}
+
+// ggml_conv_transpose_2d_p0
+
+static int64_t ggml_calc_conv_transpose_output_size(int64_t ins, int64_t ks, int s, int p) {
+    return (ins - 1) * s - 2 * p + ks;
+}
+
+struct ggml_tensor * ggml_conv_transpose_2d_p0(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   stride) {
+    GGML_ASSERT(a->ne[3] == b->ne[2]);
+
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    const int64_t ne[4] = {
+        ggml_calc_conv_transpose_output_size(b->ne[0], a->ne[0], stride, 0 /*p0*/),
+        ggml_calc_conv_transpose_output_size(b->ne[1], a->ne[1], stride, 0 /*p1*/),
+        a->ne[2], b->ne[3],
+    };
+
+    struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+
+    ggml_set_op_params_i32(result, 0, stride);
+
+    result->op = GGML_OP_CONV_TRANSPOSE_2D;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// ggml_pool_*
+
+static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, int p) {
+    return (ins + 2 * p - ks) / s + 1;
+}
+
+// ggml_pool_1d
+
+struct ggml_tensor * ggml_pool_1d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        enum ggml_op_pool     op,
+        int                   k0,
+        int                   s0,
+        int                   p0) {
+
+    bool is_node = false;
+
+    if (a->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    const int64_t ne[3] = {
+        ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
+        a->ne[1],
+    };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
+
+    int32_t params[] = { op, k0, s0, p0 };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op = GGML_OP_POOL_1D;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_pool_2d
+
+struct ggml_tensor * ggml_pool_2d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        enum ggml_op_pool     op,
+        int                   k0,
+        int                   k1,
+        int                   s0,
+        int                   s1,
+        int                   p0,
+        int                   p1) {
+
+    bool is_node = false;
+
+    if (a->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    const int64_t ne[3] = {
+        ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
+        ggml_calc_pool_output_size(a->ne[1], k1, s1, p1),
+        a->ne[2],
+    };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
+
+    int32_t params[] = { op, k0, k1, s0, s1, p0, p1 };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op = GGML_OP_POOL_2D;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_upscale
+
+static struct ggml_tensor * ggml_upscale_impl(
+    struct ggml_context * ctx,
+    struct ggml_tensor * a,
+    int scale_factor) {
+    bool is_node = false;
+
+    if (a->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type,
+            a->ne[0] * scale_factor,
+            a->ne[1] * scale_factor,
+            a->ne[2], a->ne[3]);
+
+    result->op = GGML_OP_UPSCALE;
+    result->op_params[0] = scale_factor;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = NULL;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_upscale(
+    struct ggml_context * ctx,
+    struct ggml_tensor * a,
+    int scale_factor) {
+    return ggml_upscale_impl(ctx, a, scale_factor);
+}
+
+// ggml_flash_attn
+
+struct ggml_tensor * ggml_flash_attn(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * q,
+        struct ggml_tensor  * k,
+        struct ggml_tensor  * v,
+        bool                  masked) {
+    GGML_ASSERT(ggml_can_mul_mat(k, q));
+    // TODO: check if vT can be multiplied by (k*qT)
+
+    bool is_node = false;
+
+    if (q->grad || k->grad || v->grad) {
+        is_node = true;
+    }
+
+    //struct ggml_tensor * result = ggml_dup_tensor(ctx, q);
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, q->n_dims, q->ne);
+
+    int32_t t = masked ? 1 : 0;
+    ggml_set_op_params(result, &t, sizeof(t));
+
+    result->op   = GGML_OP_FLASH_ATTN;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = q;
+    result->src[1] = k;
+    result->src[2] = v;
+
+    return result;
+}
+
+// ggml_flash_ff
+
+struct ggml_tensor * ggml_flash_ff(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b0,
+        struct ggml_tensor  * b1,
+        struct ggml_tensor  * c0,
+        struct ggml_tensor  * c1) {
+    GGML_ASSERT(ggml_can_mul_mat(b0, a));
+    // TODO: more checks
+
+    bool is_node = false;
+
+    if (a->grad || b0->grad || b1->grad || c0->grad || c1->grad) {
+        is_node = true;
+    }
+
+    //struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, a->n_dims, a->ne);
+
+    result->op   = GGML_OP_FLASH_FF;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b0;
+    result->src[2] = b1;
+    result->src[3] = c0;
+    result->src[4] = c1;
+
+    return result;
+}
+
+// ggml_flash_attn_back
+
+struct ggml_tensor * ggml_flash_attn_back(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * q,
+        struct ggml_tensor  * k,
+        struct ggml_tensor  * v,
+        struct ggml_tensor  * d,
+        bool                  masked) {
+    GGML_ASSERT(ggml_can_mul_mat(k, q));
+    // TODO: check if vT can be multiplied by (k*qT)
+
+    // d shape [D,N,ne2,ne3]
+    // q shape [D,N,ne2,ne3]
+    // k shape [D,M,kvne2,ne3]
+    // v shape [M,D,kvne2,ne3]
+
+    const int64_t     D = q->ne[0];
+    const int64_t     N = q->ne[1];
+    const int64_t     M = k->ne[1];
+    const int64_t   ne2 = q->ne[2];
+    const int64_t   ne3 = q->ne[3];
+    const int64_t kvne2 = k->ne[2];
+
+    GGML_ASSERT(k->ne[0] == D);
+    GGML_ASSERT(v->ne[0] == M);
+    GGML_ASSERT(v->ne[1] == D);
+    GGML_ASSERT(d->ne[0] == D);
+    GGML_ASSERT(d->ne[1] == N);
+    GGML_ASSERT(k->ne[2] == kvne2);
+    GGML_ASSERT(k->ne[3] == ne3);
+    GGML_ASSERT(v->ne[2] == kvne2);
+    GGML_ASSERT(v->ne[3] == ne3);
+    GGML_ASSERT(d->ne[2] == ne2);
+    GGML_ASSERT(d->ne[3] == ne3);
+
+    GGML_ASSERT(ne2 % kvne2 == 0);
+
+    bool is_node = false;
+
+    if (q->grad || k->grad || v->grad) {
+        // when using this operation (in backwards pass) these grads are set.
+        // we don't want to create (big) grad of our result, so is_node is false.
+        is_node = false;
+    }
+
+    // store gradients of q, k and v as continuous tensors concatenated in result.
+    // note: v and gradv are actually transposed, i.e. v->ne[0] != D.
+    const int64_t elem_q = ggml_nelements(q);
+    const int64_t elem_k = ggml_nelements(k);
+    const int64_t elem_v = ggml_nelements(v);
+
+    enum ggml_type result_type = GGML_TYPE_F32;
+    GGML_ASSERT(ggml_blck_size(result_type) == 1);
+    const size_t tsize = ggml_type_size(result_type);
+
+    const size_t offs_q = 0;
+    const size_t offs_k = offs_q + GGML_PAD(elem_q * tsize, GGML_MEM_ALIGN);
+    const size_t offs_v = offs_k + GGML_PAD(elem_k * tsize, GGML_MEM_ALIGN);
+    const size_t end    = offs_v + GGML_PAD(elem_v * tsize, GGML_MEM_ALIGN);
+
+    const size_t nelements = (end + tsize - 1)/tsize;
+
+    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nelements);
+
+    int32_t masked_i = masked ? 1 : 0;
+    ggml_set_op_params(result, &masked_i, sizeof(masked_i));
+
+    result->op   = GGML_OP_FLASH_ATTN_BACK;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = q;
+    result->src[1] = k;
+    result->src[2] = v;
+    result->src[3] = d;
+
+    return result;
+}
+
+// ggml_win_part
+
+struct ggml_tensor * ggml_win_part(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   w) {
+    GGML_ASSERT(a->ne[3] == 1);
+    GGML_ASSERT(a->type  == GGML_TYPE_F32);
+
+    bool is_node = false;
+
+    if (a->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    // padding
+    const int px = (w - a->ne[1]%w)%w;
+    const int py = (w - a->ne[2]%w)%w;
+
+    const int npx = (px + a->ne[1])/w;
+    const int npy = (py + a->ne[2])/w;
+    const int np  = npx*npy;
+
+    const int64_t ne[4] = { a->ne[0], w, w, np, };
+
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+
+    int32_t params[] = { npx, npy, w };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op   = GGML_OP_WIN_PART;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_win_unpart
+
+struct ggml_tensor * ggml_win_unpart(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   w0,
+        int                   h0,
+        int                   w) {
+    GGML_ASSERT(a->type == GGML_TYPE_F32);
+
+    bool is_node = false;
+
+    if (a->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    const int64_t ne[4] = { a->ne[0], w0, h0, 1, };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
+
+    int32_t params[] = { w };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op   = GGML_OP_WIN_UNPART;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_get_rel_pos
+
+struct ggml_tensor * ggml_get_rel_pos(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   qh,
+        int                   kh) {
+    GGML_ASSERT(qh == kh);
+    GGML_ASSERT(2*MAX(qh, kh) - 1 == a->ne[1]);
+
+    bool is_node = false;
+
+    if (a->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    const int64_t ne[4] = { a->ne[0], kh, qh, 1, };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 3, ne);
+
+    result->op   = GGML_OP_GET_REL_POS;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = NULL;
+
+    return result;
+}
+
+// ggml_add_rel_pos
+
+static struct ggml_tensor * ggml_add_rel_pos_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * pw,
+        struct ggml_tensor  * ph,
+        bool                  inplace) {
+    GGML_ASSERT(ggml_are_same_shape(pw, ph));
+    GGML_ASSERT(ggml_is_contiguous(a));
+    GGML_ASSERT(ggml_is_contiguous(pw));
+    GGML_ASSERT(ggml_is_contiguous(ph));
+    GGML_ASSERT(ph->type == GGML_TYPE_F32);
+    GGML_ASSERT(pw->type == GGML_TYPE_F32);
+    GGML_ASSERT(pw->ne[3] == a->ne[2]);
+    GGML_ASSERT(pw->ne[0]*pw->ne[0] == a->ne[0]);
+    GGML_ASSERT(pw->ne[1]*pw->ne[2] == a->ne[1]);
+
+    bool is_node = false;
+
+    if (!inplace && (a->grad || pw->grad || ph->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+    ggml_set_op_params_i32(result, 0, inplace ? 1 : 0);
+
+    result->op   = GGML_OP_ADD_REL_POS;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = pw;
+    result->src[2] = ph;
+
+    return result;
+}
+
+
+struct ggml_tensor * ggml_add_rel_pos(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * pw,
+        struct ggml_tensor  * ph) {
+    return ggml_add_rel_pos_impl(ctx, a, pw, ph, false);
+}
+
+struct ggml_tensor * ggml_add_rel_pos_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * pw,
+        struct ggml_tensor  * ph) {
+    return ggml_add_rel_pos_impl(ctx, a, pw, ph, true);
+}
+
+// gmml_unary
+
+static struct ggml_tensor * ggml_unary_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        enum ggml_unary_op op,
+        bool inplace) {
+    bool is_node = false;
+
+    if (!inplace && (a->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    ggml_set_op_params_i32(result, 0, (int32_t) op);
+
+    result->op   = GGML_OP_UNARY;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_unary(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        enum ggml_unary_op op) {
+    return ggml_unary_impl(ctx, a, op, false);
+}
+
+struct ggml_tensor * ggml_unary_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        enum ggml_unary_op op) {
+    return ggml_unary_impl(ctx, a, op, true);
+}
+
+// ggml_map_unary
+
+static struct ggml_tensor * ggml_map_unary_impl_f32(
+        struct ggml_context        * ctx,
+        struct ggml_tensor         * a,
+        const  ggml_unary_op_f32_t fun,
+        bool   inplace) {
+    bool is_node = false;
+
+    if (!inplace && a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
+
+    result->op = GGML_OP_MAP_UNARY;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_map_unary_f32(
+        struct ggml_context        * ctx,
+        struct ggml_tensor         * a,
+        const  ggml_unary_op_f32_t fun) {
+    return ggml_map_unary_impl_f32(ctx, a, fun, false);
+}
+
+struct ggml_tensor * ggml_map_unary_inplace_f32(
+        struct ggml_context        * ctx,
+        struct ggml_tensor         * a,
+        const  ggml_unary_op_f32_t fun) {
+    return ggml_map_unary_impl_f32(ctx, a, fun, true);
+}
+
+// ggml_map_binary
+
+static struct ggml_tensor * ggml_map_binary_impl_f32(
+        struct ggml_context         * ctx,
+        struct ggml_tensor          * a,
+        struct ggml_tensor          * b,
+        const  ggml_binary_op_f32_t fun,
+        bool   inplace) {
+    GGML_ASSERT(ggml_are_same_shape(a, b));
+
+    bool is_node = false;
+
+    if (!inplace && (a->grad || b->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
+
+    result->op = GGML_OP_MAP_BINARY;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_map_binary_f32(
+        struct ggml_context         * ctx,
+        struct ggml_tensor          * a,
+        struct ggml_tensor          * b,
+        const  ggml_binary_op_f32_t fun) {
+    return ggml_map_binary_impl_f32(ctx, a, b, fun, false);
+}
+
+struct ggml_tensor * ggml_map_binary_inplace_f32(
+        struct ggml_context         * ctx,
+        struct ggml_tensor          * a,
+        struct ggml_tensor          * b,
+        const  ggml_binary_op_f32_t fun) {
+    return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
+}
+
+// ggml_map_custom1_f32
+
+static struct ggml_tensor * ggml_map_custom1_impl_f32(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        const  ggml_custom1_op_f32_t   fun,
+        bool   inplace) {
+    bool is_node = false;
+
+    if (!inplace && a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
+
+    result->op = GGML_OP_MAP_CUSTOM1_F32;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_map_custom1_f32(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        const  ggml_custom1_op_f32_t   fun) {
+    return ggml_map_custom1_impl_f32(ctx, a, fun, false);
+}
+
+struct ggml_tensor * ggml_map_custom1_inplace_f32(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        const  ggml_custom1_op_f32_t   fun) {
+    return ggml_map_custom1_impl_f32(ctx, a, fun, true);
+}
+
+// ggml_map_custom2_f32
+
+static struct ggml_tensor * ggml_map_custom2_impl_f32(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        const  ggml_custom2_op_f32_t   fun,
+        bool   inplace) {
+    bool is_node = false;
+
+    if (!inplace && (a->grad || b->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
+
+    result->op = GGML_OP_MAP_CUSTOM2_F32;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_map_custom2_f32(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        const  ggml_custom2_op_f32_t   fun) {
+    return ggml_map_custom2_impl_f32(ctx, a, b, fun, false);
+}
+
+struct ggml_tensor * ggml_map_custom2_inplace_f32(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        const  ggml_custom2_op_f32_t   fun) {
+    return ggml_map_custom2_impl_f32(ctx, a, b, fun, true);
+}
+
+// ggml_map_custom3_f32
+
+static struct ggml_tensor * ggml_map_custom3_impl_f32(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        struct ggml_tensor           * c,
+        const  ggml_custom3_op_f32_t   fun,
+        bool   inplace) {
+    bool is_node = false;
+
+    if (!inplace && (a->grad || b->grad || c->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
+
+    result->op = GGML_OP_MAP_CUSTOM3_F32;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+    result->src[2] = c;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_map_custom3_f32(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        struct ggml_tensor           * c,
+        const  ggml_custom3_op_f32_t   fun) {
+    return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, false);
+}
+
+struct ggml_tensor * ggml_map_custom3_inplace_f32(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        struct ggml_tensor           * c,
+        const  ggml_custom3_op_f32_t   fun) {
+    return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, true);
+}
+
+// ggml_map_custom1
+struct ggml_map_custom1_op_params {
+    ggml_custom1_op_t fun;
+    int n_tasks;
+    void * userdata;
+};
+
+static struct ggml_tensor * ggml_map_custom1_impl(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        const  ggml_custom1_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata,
+        bool                           inplace) {
+    GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
+
+    bool is_node = false;
+
+    if (!inplace && a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    struct ggml_map_custom1_op_params params = {
+        /*.fun      =*/ fun,
+        /*.n_tasks  =*/ n_tasks,
+        /*.userdata =*/ userdata
+    };
+    ggml_set_op_params(result, (const void *) &params, sizeof(params));
+
+    result->op = GGML_OP_MAP_CUSTOM1;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_map_custom1(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        const  ggml_custom1_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata) {
+    return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, false);
+}
+
+struct ggml_tensor * ggml_map_custom1_inplace(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        const  ggml_custom1_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata) {
+    return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, true);
+}
+
+// ggml_map_custom2
+
+struct ggml_map_custom2_op_params {
+    ggml_custom2_op_t fun;
+    int n_tasks;
+    void * userdata;
+};
+
+static struct ggml_tensor * ggml_map_custom2_impl(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        const  ggml_custom2_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata,
+        bool                           inplace) {
+    GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
+
+    bool is_node = false;
+
+    if (!inplace && (a->grad || b->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    struct ggml_map_custom2_op_params params = {
+        /*.fun      =*/ fun,
+        /*.n_tasks  =*/ n_tasks,
+        /*.userdata =*/ userdata
+    };
+    ggml_set_op_params(result, (const void *) &params, sizeof(params));
+
+    result->op = GGML_OP_MAP_CUSTOM2;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_map_custom2(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        const  ggml_custom2_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata) {
+    return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, false);
+}
+
+struct ggml_tensor * ggml_map_custom2_inplace(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        const  ggml_custom2_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata) {
+    return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, true);
+}
+
+// ggml_map_custom3
+
+struct ggml_map_custom3_op_params {
+    ggml_custom3_op_t fun;
+    int n_tasks;
+    void * userdata;
+};
+
+static struct ggml_tensor * ggml_map_custom3_impl(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        struct ggml_tensor           * c,
+        const  ggml_custom3_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata,
+        bool                           inplace) {
+    GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
+
+    bool is_node = false;
+
+    if (!inplace && (a->grad || b->grad || c->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    struct ggml_map_custom3_op_params params = {
+        /*.fun      =*/ fun,
+        /*.n_tasks  =*/ n_tasks,
+        /*.userdata =*/ userdata
+    };
+    ggml_set_op_params(result, (const void *) &params, sizeof(params));
+
+    result->op = GGML_OP_MAP_CUSTOM3;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+    result->src[2] = c;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_map_custom3(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        struct ggml_tensor           * c,
+        const  ggml_custom3_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata) {
+    return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, false);
+}
+
+struct ggml_tensor * ggml_map_custom3_inplace(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        struct ggml_tensor           * c,
+        const  ggml_custom3_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata) {
+    return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, true);
+}
+
+
+
+// ggml_cross_entropy_loss
+
+struct ggml_tensor * ggml_cross_entropy_loss(
+        struct ggml_context         * ctx,
+        struct ggml_tensor          * a,
+        struct ggml_tensor          * b) {
+    GGML_ASSERT(ggml_are_same_shape(a, b));
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1);
+
+    result->op   = GGML_OP_CROSS_ENTROPY_LOSS;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// ggml_cross_entropy_loss_back
+
+struct ggml_tensor * ggml_cross_entropy_loss_back(
+        struct ggml_context         * ctx,
+        struct ggml_tensor          * a,
+        struct ggml_tensor          * b,
+        struct ggml_tensor          * c) {
+    GGML_ASSERT(ggml_are_same_shape(a, b));
+    GGML_ASSERT(ggml_is_scalar(c));
+
+    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_CROSS_ENTROPY_LOSS_BACK;
+    result->grad = NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+    result->src[2] = c;
+
+    return result;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+void ggml_set_param(
+        struct ggml_context * ctx,
+        struct ggml_tensor * tensor) {
+    tensor->is_param = true;
+
+    GGML_ASSERT(tensor->grad == NULL);
+    tensor->grad = ggml_dup_tensor(ctx, tensor);
+    ggml_format_name(tensor->grad, "%s (grad)", tensor->name);
+}
+
+// ggml_compute_forward_dup
+
+static void ggml_compute_forward_dup_same_cont(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
+    GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
+    GGML_ASSERT(src0->type == dst->type);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const size_t nb00 = src0->nb[0];
+    const size_t nb0 = dst->nb[0];
+
+    const int ith = params->ith; // thread index
+    const int nth = params->nth; // number of threads
+
+    // parallelize by elements
+    const int ne = ggml_nelements(dst);
+    const int dr = (ne + nth - 1) / nth;
+    const int ie0 = dr * ith;
+    const int ie1 = MIN(ie0 + dr, ne);
+
+    if (ie0 < ie1) {
+        memcpy(
+            ((char *)  dst->data + ie0*nb0),
+            ((char *) src0->data + ie0*nb00),
+            (ie1 - ie0) * ggml_type_size(src0->type));
+    }
+
+}
+static void ggml_compute_forward_dup_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    const int ith = params->ith; // thread index
+    const int nth = params->nth; // number of threads
+
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
+        ggml_compute_forward_dup_same_cont(params, src0, dst);
+        return;
+    }
+
+    // parallelize by rows
+    const int nr = ne01;
+    // number of rows per thread
+    const int dr = (nr + nth - 1) / nth;
+    // row range for this thread
+    const int ir0 = dr * ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    if (src0->type == dst->type &&
+        ne00 == ne0 &&
+        nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) {
+        // copy by rows
+        const size_t rs = ne00*nb00;
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                    memcpy(
+                        ((char *)  dst->data + i01*nb1  + i02*nb2  + i03*nb3),
+                        ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03),
+                        rs);
+                }
+            }
+        }
+        return;
+    }
+
+    // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy
+
+    if (ggml_is_contiguous(dst)) {
+        if (nb00 == sizeof(ggml_fp16_t)) {
+            if (dst->type == GGML_TYPE_F16) {
+                size_t id = 0;
+                const size_t rs = ne00 * nb00;
+                char * dst_ptr = (char *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += rs * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
+                            memcpy(dst_ptr + id, src0_ptr, rs);
+                            id += rs;
+                        }
+                        id += rs * (ne01 - ir1);
+                    }
+                }
+            } else if (dst->type == GGML_TYPE_F32) {
+                size_t id = 0;
+                float * dst_ptr = (float *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += ne00 * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                dst_ptr[id] = GGML_FP16_TO_FP32(src0_ptr[i00]);
+                                id++;
+                            }
+                        }
+                        id += ne00 * (ne01 - ir1);
+                    }
+                }
+            } else if (type_traits[dst->type].from_float) {
+                ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float;
+                float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith;
+
+                size_t id = 0;
+                size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type));
+                char * dst_ptr = (char *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += rs * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                src0_f32[i00] = GGML_FP16_TO_FP32(src0_ptr[i00]);
+                            }
+
+                            quantize_row_q(src0_f32, dst_ptr + id, ne00);
+                            id += rs;
+                        }
+                        id += rs * (ne01 - ir1);
+                    }
+                }
+            } else {
+                GGML_ASSERT(false); // TODO: implement
+            }
+        } else {
+            //printf("%s: this is not optimal - fix me\n", __func__);
+
+            if (dst->type == GGML_TYPE_F32) {
+                size_t id = 0;
+                float * dst_ptr = (float *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += ne00 * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+
+                                dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr);
+                                id++;
+                            }
+                        }
+                        id += ne00 * (ne01 - ir1);
+                    }
+                }
+            } else if (dst->type == GGML_TYPE_F16) {
+                size_t id = 0;
+                ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += ne00 * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+
+                                dst_ptr[id] = *src0_ptr;
+                                id++;
+                            }
+                        }
+                        id += ne00 * (ne01 - ir1);
+                    }
+                }
+            } else {
+                GGML_ASSERT(false); // TODO: implement
+            }
+        }
+        return;
+    }
+
+    // dst counters
+    int64_t i10 = 0;
+    int64_t i11 = 0;
+    int64_t i12 = 0;
+    int64_t i13 = 0;
+
+    if (dst->type == GGML_TYPE_F16) {
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                i10 += ne00 * ir0;
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+                for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                              char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
+
+                        memcpy(dst_ptr, src0_ptr, sizeof(ggml_fp16_t));
+
+                        if (++i10 == ne00) {
+                            i10 = 0;
+                            if (++i11 == ne01) {
+                                i11 = 0;
+                                if (++i12 == ne02) {
+                                    i12 = 0;
+                                    if (++i13 == ne03) {
+                                        i13 = 0;
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+                i10 += ne00 * (ne01 - ir1);
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    } else if (dst->type == GGML_TYPE_F32) {
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                i10 += ne00 * ir0;
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+                for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                              char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
+
+                        *(float *) dst_ptr = GGML_FP16_TO_FP32(*(const ggml_fp16_t *) src0_ptr);
+
+                        if (++i10 == ne0) {
+                            i10 = 0;
+                            if (++i11 == ne1) {
+                                i11 = 0;
+                                if (++i12 == ne2) {
+                                    i12 = 0;
+                                    if (++i13 == ne3) {
+                                        i13 = 0;
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+                i10 += ne00 * (ne01 - ir1);
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    } else {
+        GGML_ASSERT(false); // TODO: implement
+    }
+}
+
+static void ggml_compute_forward_dup_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    const int ith = params->ith; // thread index
+    const int nth = params->nth; // number of threads
+
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
+        ggml_compute_forward_dup_same_cont(params, src0, dst);
+        return;
+    }
+
+    // parallelize by rows
+    const int nr = ne01;
+    // number of rows per thread
+    const int dr = (nr + nth - 1) / nth;
+    // row range for this thread
+    const int ir0 = dr * ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    if (src0->type == dst->type &&
+        ne00 == ne0 &&
+        nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) {
+        // copy by rows
+        const size_t rs = ne00*nb00;
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                    memcpy(
+                        ((char *)  dst->data + i01*nb1  + i02*nb2  + i03*nb3),
+                        ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03),
+                        rs);
+                }
+            }
+        }
+        return;
+    }
+
+    if (ggml_is_contiguous(dst)) {
+        // TODO: simplify
+        if (nb00 == sizeof(float)) {
+            if (dst->type == GGML_TYPE_F32) {
+                size_t id = 0;
+                const size_t rs = ne00 * nb00;
+                char * dst_ptr = (char *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += rs * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
+                            memcpy(dst_ptr + id, src0_ptr, rs);
+                            id += rs;
+                        }
+                        id += rs * (ne01 - ir1);
+                    }
+                }
+            } else if (type_traits[dst->type].from_float) {
+                ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float;
+
+                size_t id = 0;
+                size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type));
+                char * dst_ptr = (char *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += rs * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            const float * src0_ptr = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+                            quantize_row_q(src0_ptr, dst_ptr + id, ne00);
+                            id += rs;
+                        }
+                        id += rs * (ne01 - ir1);
+                    }
+                }
+            } else {
+                GGML_ASSERT(false); // TODO: implement
+            }
+        } else {
+            //printf("%s: this is not optimal - fix me\n", __func__);
+
+            if (dst->type == GGML_TYPE_F32) {
+                size_t id = 0;
+                float * dst_ptr = (float *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += ne00 * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+
+                                dst_ptr[id] = *src0_ptr;
+                                id++;
+                            }
+                        }
+                        id += ne00 * (ne01 - ir1);
+                    }
+                }
+            } else if (dst->type == GGML_TYPE_F16) {
+                size_t id = 0;
+                ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        id += ne00 * ir0;
+                        for (int i01 = ir0; i01 < ir1; i01++) {
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+
+                                dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
+                                id++;
+                            }
+                        }
+                        id += ne00 * (ne01 - ir1);
+                    }
+                }
+            } else {
+                GGML_ASSERT(false); // TODO: implement
+            }
+        }
+
+        return;
+    }
+
+    // dst counters
+
+    int64_t i10 = 0;
+    int64_t i11 = 0;
+    int64_t i12 = 0;
+    int64_t i13 = 0;
+
+    if (dst->type == GGML_TYPE_F32) {
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                i10 += ne00 * ir0;
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+                for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                              char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
+
+                        memcpy(dst_ptr, src0_ptr, sizeof(float));
+
+                        if (++i10 == ne0) {
+                            i10 = 0;
+                            if (++i11 == ne1) {
+                                i11 = 0;
+                                if (++i12 == ne2) {
+                                    i12 = 0;
+                                    if (++i13 == ne3) {
+                                        i13 = 0;
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+                i10 += ne00 * (ne01 - ir1);
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    } else if (dst->type == GGML_TYPE_F16) {
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                i10 += ne00 * ir0;
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+                for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                              char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
+
+                        *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(*(const float *) src0_ptr);
+
+                        if (++i10 == ne0) {
+                            i10 = 0;
+                            if (++i11 == ne1) {
+                                i11 = 0;
+                                if (++i12 == ne2) {
+                                    i12 = 0;
+                                    if (++i13 == ne3) {
+                                        i13 = 0;
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+                i10 += ne00 * (ne01 - ir1);
+                while (i10 >= ne0) {
+                    i10 -= ne0;
+                    if (++i11 == ne1) {
+                        i11 = 0;
+                        if (++i12 == ne2) {
+                            i12 = 0;
+                            if (++i13 == ne3) {
+                                i13 = 0;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    } else {
+        GGML_ASSERT(false); // TODO: implement
+    }
+}
+
+static void ggml_compute_forward_dup(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
+        ggml_compute_forward_dup_same_cont(params, src0, dst);
+        return;
+    }
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_dup_f16(params, src0, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_dup_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_add
+
+static void ggml_compute_forward_add_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    GGML_ASSERT( nb0 == sizeof(float));
+    GGML_ASSERT(nb00 == sizeof(float));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    if (nb10 == sizeof(float)) {
+        for (int ir = ir0; ir < ir1; ++ir) {
+            // src1 is broadcastable across src0 and dst in i1, i2, i3
+            const int64_t i03 = ir/(ne02*ne01);
+            const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
+            const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
+
+            const int64_t i13 = i03 % ne13;
+            const int64_t i12 = i02 % ne12;
+            const int64_t i11 = i01 % ne11;
+
+            float * dst_ptr  = (float *) ((char *) dst->data  + i03*nb3  + i02*nb2  + i01*nb1 );
+            float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
+            float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
+
+#ifdef GGML_USE_ACCELERATE
+            vDSP_vadd(src0_ptr, 1, src1_ptr, 1, dst_ptr, 1, ne00);
+#else
+            ggml_vec_add_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
+#endif
+        }
+    } else {
+        // src1 is not contiguous
+        for (int ir = ir0; ir < ir1; ++ir) {
+            // src1 is broadcastable across src0 and dst in i1, i2, i3
+            const int64_t i03 = ir/(ne02*ne01);
+            const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
+            const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
+
+            const int64_t i13 = i03 % ne13;
+            const int64_t i12 = i02 % ne12;
+            const int64_t i11 = i01 % ne11;
+
+            float * dst_ptr  = (float *) ((char *) dst->data  + i03*nb3  + i02*nb2  + i01*nb1 );
+            float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
+
+            for (int i0 = 0; i0 < ne0; i0++) {
+                float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i0*nb10);
+
+                dst_ptr[i0] = src0_ptr[i0] + *src1_ptr;
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_add_f16_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F16);
+
+    GGML_ASSERT( nb0 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    if (nb10 == sizeof(float)) {
+        for (int ir = ir0; ir < ir1; ++ir) {
+            // src0, src1 and dst are same shape => same indices
+            const int i3 = ir/(ne2*ne1);
+            const int i2 = (ir - i3*ne2*ne1)/ne1;
+            const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+            ggml_fp16_t * dst_ptr  = (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1);
+            ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
+            float *       src1_ptr = (float *)       ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11);
+
+            for (int i = 0; i < ne0; i++) {
+                dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]);
+            }
+        }
+    }
+    else {
+        // src1 is not contiguous
+        GGML_ASSERT(false);
+    }
+}
+
+static void ggml_compute_forward_add_f16_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F16);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F16);
+
+    GGML_ASSERT( nb0 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    if (nb10 == sizeof(ggml_fp16_t)) {
+        for (int ir = ir0; ir < ir1; ++ir) {
+            // src0, src1 and dst are same shape => same indices
+            const int i3 = ir/(ne2*ne1);
+            const int i2 = (ir - i3*ne2*ne1)/ne1;
+            const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+            ggml_fp16_t * dst_ptr  = (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1);
+            ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
+            ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11);
+
+            for (int i = 0; i < ne0; i++) {
+                dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + GGML_FP16_TO_FP32(src1_ptr[i]));
+            }
+        }
+    }
+    else {
+        // src1 is not contiguous
+        GGML_ASSERT(false);
+    }
+}
+
+static void ggml_compute_forward_add_q_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const enum ggml_type type = src0->type;
+    const enum ggml_type dtype = dst->type;
+    ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
+    ggml_from_float_t const quantize_row_q = type_traits[dtype].from_float;
+
+    // we don't support permuted src0 or src1
+    GGML_ASSERT(nb00 == ggml_type_size(type));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    GGML_ASSERT(ggml_is_quantized(src0->type));
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    float * wdata = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith;
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // src0 indices
+        const int i03 = ir/(ne02*ne01);
+        const int i02 = (ir - i03*ne02*ne01)/ne01;
+        const int i01 = (ir - i03*ne02*ne01 - i02*ne01);
+
+        // src1 and dst are same shape as src0 => same indices
+        const int i13 = i03;
+        const int i12 = i02;
+        const int i11 = i01;
+
+        const int i3 = i03;
+        const int i2 = i02;
+        const int i1 = i01;
+
+        void  * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03));
+        float * src1_row = (float *)((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13));
+        void  * dst_row  = (void *) ((char *)  dst->data + ( i1*nb1  +  i2*nb2  +  i3*nb3));
+
+        assert(ne00 % 32 == 0);
+
+        // unquantize row from src0 to temp buffer
+        dequantize_row_q(src0_row, wdata, ne00);
+        // add src1
+        ggml_vec_acc_f32(ne00, wdata, src1_row);
+        // quantize row to dst
+        if (quantize_row_q != NULL) {
+            quantize_row_q(wdata, dst_row, ne00);
+        } else {
+            memcpy(dst_row, wdata, ne0*nb0);
+        }
+    }
+}
+
+static void ggml_compute_forward_add(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_add_f32(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F16:
+            {
+                if (src1->type == GGML_TYPE_F16) {
+                    ggml_compute_forward_add_f16_f16(params, src0, src1, dst);
+                }
+                else if (src1->type == GGML_TYPE_F32) {
+                    ggml_compute_forward_add_f16_f32(params, src0, src1, dst);
+                }
+                else {
+                    GGML_ASSERT(false);
+                }
+            } break;
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            {
+                ggml_compute_forward_add_q_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_add1
+
+static void ggml_compute_forward_add1_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_is_scalar(src1));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    GGML_ASSERT( nb0 == sizeof(float));
+    GGML_ASSERT(nb00 == sizeof(float));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // src0 and dst are same shape => same indices
+        const int i3 = ir/(ne2*ne1);
+        const int i2 = (ir - i3*ne2*ne1)/ne1;
+        const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+#ifdef GGML_USE_ACCELERATE
+        UNUSED(ggml_vec_add1_f32);
+
+        vDSP_vadd(
+                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1,
+                (float *) ((char *) src1->data), 0,
+                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 ), 1,
+                ne0);
+#else
+        ggml_vec_add1_f32(ne0,
+                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 ),
+                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01),
+               *(float *) src1->data);
+#endif
+    }
+}
+
+static void ggml_compute_forward_add1_f16_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_is_scalar(src1));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // scalar to add
+    const float v = *(float *) src1->data;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F16);
+
+    GGML_ASSERT( nb0 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // src0 and dst are same shape => same indices
+        const int i3 = ir/(ne2*ne1);
+        const int i2 = (ir - i3*ne2*ne1)/ne1;
+        const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+        ggml_fp16_t * dst_ptr  = (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 );
+        ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
+        for (int i = 0; i < ne0; i++) {
+            dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v);
+        }
+    }
+}
+
+static void ggml_compute_forward_add1_f16_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_is_scalar(src1));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // scalar to add
+    const float v = GGML_FP16_TO_FP32(*(ggml_fp16_t *) src1->data);
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F16);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F16);
+
+    GGML_ASSERT( nb0 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // src0 and dst are same shape => same indices
+        const int i3 = ir/(ne2*ne1);
+        const int i2 = (ir - i3*ne2*ne1)/ne1;
+        const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+        ggml_fp16_t * dst_ptr  = (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 );
+        ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
+        for (int i = 0; i < ne0; i++) {
+            dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v);
+        }
+    }
+}
+
+static void ggml_compute_forward_add1_q_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_is_scalar(src1));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // scalar to add
+    const float v = *(float *) src1->data;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    const enum ggml_type type = src0->type;
+    ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
+    ggml_from_float_t const quantize_row_q = type_traits[type].from_float;
+
+    // we don't support permuted src0
+    GGML_ASSERT(nb00 == ggml_type_size(type));
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    GGML_ASSERT(ggml_is_quantized(src0->type));
+    GGML_ASSERT(dst->type == src0->type);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith;
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // src0 and dst are same shape => same indices
+        const int i3 = ir/(ne2*ne1);
+        const int i2 = (ir - i3*ne2*ne1)/ne1;
+        const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+        void  * src0_row = (void *) ((char *) src0->data + (i1*nb01 + i2*nb02 + i3*nb03));
+        void  * dst_row  = (void *) ((char *)  dst->data + (i1*nb1  + i2*nb2  + i3*nb0 ));
+
+        assert(ne0 % 32 == 0);
+
+        // unquantize row from src0 to temp buffer
+        dequantize_row_q(src0_row, wdata, ne0);
+        // add src1
+        ggml_vec_acc1_f32(ne0, wdata, v);
+        // quantize row to dst
+        quantize_row_q(wdata, dst_row, ne0);
+    }
+}
+
+static void ggml_compute_forward_add1(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_add1_f32(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F16:
+            {
+                if (src1->type == GGML_TYPE_F16) {
+                    ggml_compute_forward_add1_f16_f16(params, src0, src1, dst);
+                }
+                else if (src1->type == GGML_TYPE_F32) {
+                    ggml_compute_forward_add1_f16_f32(params, src0, src1, dst);
+                }
+                else {
+                    GGML_ASSERT(false);
+                }
+            } break;
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q8_1:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            {
+                ggml_compute_forward_add1_q_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+
+// ggml_compute_forward_acc
+
+static void ggml_compute_forward_acc_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
+
+    // view src0 and dst with these strides and data offset inbytes during acc
+    // nb0 is implicitely element_size because src0 and dst are contiguous
+    size_t nb1     = ((int32_t *) dst->op_params)[0];
+    size_t nb2     = ((int32_t *) dst->op_params)[1];
+    size_t nb3     = ((int32_t *) dst->op_params)[2];
+    size_t offset  = ((int32_t *) dst->op_params)[3];
+    bool   inplace = (bool) ((int32_t *) dst->op_params)[4];
+
+    if (!inplace && (params->type == GGML_TASK_INIT)) {
+        // memcpy needs to be synchronized across threads to avoid race conditions.
+        // => do it in INIT phase
+        memcpy(
+            ((char *)  dst->data),
+            ((char *) src0->data),
+            ggml_nbytes(dst));
+    }
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr = ggml_nrows(src1);
+    const int nc = src1->ne[0];
+
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb)
+
+    // src0 and dst as viewed during acc
+    const size_t nb0 = ggml_element_size(src0);
+
+    const size_t nb00 = nb0;
+    const size_t nb01 = nb1;
+    const size_t nb02 = nb2;
+    const size_t nb03 = nb3;
+
+    GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb0  + (ne11 == 0 ? 0 : ne11-1)*nb1  + (ne12 == 0 ? 0 : ne12-1)*nb2  + (ne13 == 0 ? 0 : ne13-1)*nb3  < ggml_nbytes(dst));
+    GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb00 + (ne11 == 0 ? 0 : ne11-1)*nb01 + (ne12 == 0 ? 0 : ne12-1)*nb02 + (ne13 == 0 ? 0 : ne13-1)*nb03 < ggml_nbytes(src0));
+
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // src0 and dst are viewed with shape of src1 and offset
+        // => same indices
+        const int i3 = ir/(ne12*ne11);
+        const int i2 = (ir - i3*ne12*ne11)/ne11;
+        const int i1 = (ir - i3*ne12*ne11 - i2*ne11);
+
+#ifdef GGML_USE_ACCELERATE
+        vDSP_vadd(
+                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), 1,
+                (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1,
+                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1  + offset), 1, nc);
+#else
+        ggml_vec_add_f32(nc,
+                (float *) ((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + offset),
+                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset),
+                (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11));
+#endif
+    }
+}
+
+static void ggml_compute_forward_acc(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_acc_f32(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F16:
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q8_1:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_sub
+
+static void ggml_compute_forward_sub_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    GGML_ASSERT( nb0 == sizeof(float));
+    GGML_ASSERT(nb00 == sizeof(float));
+
+    if (nb10 == sizeof(float)) {
+        for (int ir = 0; ir < nr; ++ir) {
+            // src0, src1 and dst are same shape => same indices
+            const int i3 = ir/(ne2*ne1);
+            const int i2 = (ir - i3*ne2*ne1)/ne1;
+            const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+
+#ifdef GGML_USE_ACCELERATE
+            vDSP_vsub(
+                    (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1,
+                    (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1,
+                    (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 ), 1,
+                    ne0);
+#else
+            ggml_vec_sub_f32(ne0,
+                    (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 ),
+                    (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01),
+                    (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11));
+#endif
+                // }
+            // }
+        }
+    } else {
+        // src1 is not contiguous
+        for (int ir = 0; ir < nr; ++ir) {
+            // src0, src1 and dst are same shape => same indices
+            const int i3 = ir/(ne2*ne1);
+            const int i2 = (ir - i3*ne2*ne1)/ne1;
+            const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+            float * dst_ptr  = (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 );
+            float * src0_ptr = (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
+            for (int i0 = 0; i0 < ne0; i0++) {
+                float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11 + i0*nb10);
+
+                dst_ptr[i0] = src0_ptr[i0] - *src1_ptr;
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_sub(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_sub_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_mul
+
+static void ggml_compute_forward_mul_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+#ifdef GGML_USE_CLBLAST
+    if (src1->backend == GGML_BACKEND_GPU) {
+        if (ith == 0) {
+            ggml_cl_mul(src0, src1, dst);
+        }
+        return;
+    }
+#endif
+
+    const int64_t nr = ggml_nrows(src0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    GGML_ASSERT( nb0 == sizeof(float));
+    GGML_ASSERT(nb00 == sizeof(float));
+    GGML_ASSERT(ne00 == ne10);
+
+    if (nb10 == sizeof(float)) {
+        for (int64_t ir = ith; ir < nr; ir += nth) {
+            // src0 and dst are same shape => same indices
+            const int64_t i03 = ir/(ne02*ne01);
+            const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
+            const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
+
+            const int64_t i13 = i03 % ne13;
+            const int64_t i12 = i02 % ne12;
+            const int64_t i11 = i01 % ne11;
+
+            float * dst_ptr  = (float *) ((char *) dst->data  + i03*nb3  + i02*nb2  + i01*nb1 );
+            float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
+            float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
+
+#ifdef GGML_USE_ACCELERATE
+            UNUSED(ggml_vec_mul_f32);
+
+            vDSP_vmul( src0_ptr, 1, src1_ptr, 1, dst_ptr,  1, ne00);
+#else
+            ggml_vec_mul_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
+#endif
+                // }
+            // }
+        }
+    } else {
+        // src1 is not contiguous
+        for (int64_t ir = ith; ir < nr; ir += nth) {
+            // src0 and dst are same shape => same indices
+            // src1 is broadcastable across src0 and dst in i1, i2, i3
+            const int64_t i03 = ir/(ne02*ne01);
+            const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
+            const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
+
+            const int64_t i13 = i03 % ne13;
+            const int64_t i12 = i02 % ne12;
+            const int64_t i11 = i01 % ne11;
+
+            float * dst_ptr  = (float *) ((char *) dst->data  + i03*nb3  + i02*nb2  + i01*nb1 );
+            float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
+
+            for (int64_t i0 = 0; i0 < ne00; i0++) {
+                float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i0*nb10);
+
+                dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr);
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_mul(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(src1->type == GGML_TYPE_F32 && "only f32 src1 supported for now");
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_mul_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_div
+
+static void ggml_compute_forward_div_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    GGML_ASSERT( nb0 == sizeof(float));
+    GGML_ASSERT(nb00 == sizeof(float));
+
+    if (nb10 == sizeof(float)) {
+        for (int ir = 0; ir < nr; ++ir) {
+            // src0, src1 and dst are same shape => same indices
+            const int i3 = ir/(ne2*ne1);
+            const int i2 = (ir - i3*ne2*ne1)/ne1;
+            const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+
+#ifdef GGML_USE_ACCELERATE
+            UNUSED(ggml_vec_div_f32);
+
+            vDSP_vdiv(
+                    (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1,
+                    (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1,
+                    (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 ), 1,
+                    ne0);
+#else
+            ggml_vec_div_f32(ne0,
+                    (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 ),
+                    (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01),
+                    (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11));
+#endif
+                // }
+            // }
+        }
+    } else {
+        // src1 is not contiguous
+        for (int ir = 0; ir < nr; ++ir) {
+            // src0, src1 and dst are same shape => same indices
+            const int i3 = ir/(ne2*ne1);
+            const int i2 = (ir - i3*ne2*ne1)/ne1;
+            const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+            float * dst_ptr  = (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 );
+            float * src0_ptr = (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
+            for (int i0 = 0; i0 < ne0; i0++) {
+                float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11 + i0*nb10);
+
+                dst_ptr[i0] = src0_ptr[i0] / (*src1_ptr);
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_div(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_div_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_sqr
+
+static void ggml_compute_forward_sqr_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n     = ggml_nrows(src0);
+    const int nc    = src0->ne[0];
+
+    assert( dst->nb[0] == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_sqr_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_sqr(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_sqr_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_sqrt
+
+static void ggml_compute_forward_sqrt_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert( dst->nb[0] == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_sqrt_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_sqrt(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_sqrt_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+
+// ggml_compute_forward_log
+
+static void ggml_compute_forward_log_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(params->ith == 0);
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    GGML_ASSERT( dst->nb[0] == sizeof(float));
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_log_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_log(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_log_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_sum
+
+static void ggml_compute_forward_sum_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_is_scalar(dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    assert(ggml_is_scalar(dst));
+    assert(src0->nb[0] == sizeof(float));
+
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+
+    ggml_float sum     = 0;
+    ggml_float row_sum = 0;
+
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        for (int64_t i02 = 0; i02 < ne02; i02++) {
+            for (int64_t i01 = 0; i01 < ne01; i01++) {
+                ggml_vec_sum_f32_ggf(ne00,
+                        &row_sum,
+                        (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03));
+                sum += row_sum;
+            }
+        }
+    }
+    ((float *) dst->data)[0] = sum;
+}
+
+static void ggml_compute_forward_sum_f16(
+    const struct ggml_compute_params * params,
+    const struct ggml_tensor * src0,
+          struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_is_scalar(dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    assert(src0->nb[0] == sizeof(ggml_fp16_t));
+
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+
+    float sum = 0;
+    float row_sum = 0;
+
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        for (int64_t i02 = 0; i02 < ne02; i02++) {
+            for (int64_t i01 = 0; i01 < ne01; i01++) {
+                ggml_vec_sum_f16_ggf(ne00,
+                    &row_sum,
+                    (ggml_fp16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03));
+                sum += row_sum;
+            }
+        }
+    }
+    ((ggml_fp16_t *) dst->data)[0] = GGML_FP32_TO_FP16(sum);
+}
+
+static void ggml_compute_forward_sum(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_sum_f32(params, src0, dst);
+            } break;
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_sum_f16(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_sum_rows
+
+static void ggml_compute_forward_sum_rows_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+    GGML_ASSERT(dst->nb[0] == sizeof(float));
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    GGML_ASSERT(ne0 == 1);
+    GGML_ASSERT(ne1 == ne01);
+    GGML_ASSERT(ne2 == ne02);
+    GGML_ASSERT(ne3 == ne03);
+
+    for (int64_t i3 = 0; i3 < ne03; i3++) {
+        for (int64_t i2 = 0; i2 < ne02; i2++) {
+            for (int64_t i1 = 0; i1 < ne01; i1++) {
+                float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03);
+                float * dst_row = (float *) ((char *) dst->data  + i1*nb1  + i2*nb2  + i3*nb3);
+                float row_sum = 0;
+                ggml_vec_sum_f32(ne00, &row_sum, src_row);
+                dst_row[0] = row_sum;
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_sum_rows(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_sum_rows_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_mean
+
+static void ggml_compute_forward_mean_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    assert(src0->nb[0] == sizeof(float));
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    assert(ne0 == 1);
+    assert(ne1 == ne01);
+    assert(ne2 == ne02);
+    assert(ne3 == ne03);
+
+    UNUSED(ne0);
+    UNUSED(ne1);
+    UNUSED(ne2);
+    UNUSED(ne3);
+
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        for (int64_t i02 = 0; i02 < ne02; i02++) {
+            for (int64_t i01 = 0; i01 < ne01; i01++) {
+                ggml_vec_sum_f32(ne00,
+                        (float *) ((char *)  dst->data + i01*nb1  + i02*nb2  + i03*nb3),
+                        (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03));
+
+                *(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3) /= (float) ne00;
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_mean(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_mean_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_argmax
+
+static void ggml_compute_forward_argmax_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    assert(src0->nb[0] == sizeof(float));
+    assert(dst->nb[0] == sizeof(float));
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+
+    const size_t nb01 = src0->nb[1];
+    const size_t nb0 = dst->nb[0];
+
+    for (int64_t i1 = 0; i1 < ne01; i1++) {
+        float * src = (float *) ((char *) src0->data + i1*nb01);
+        int32_t * dst_ = (int32_t *) ((char *)  dst->data + i1*nb0);
+        int v = 0;
+        ggml_vec_argmax_f32(ne00, &v, src);
+        dst_[0] = v;
+    }
+}
+
+static void ggml_compute_forward_argmax(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_argmax_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_repeat
+
+static void ggml_compute_forward_repeat_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(params->ith == 0);
+    GGML_ASSERT(ggml_can_repeat(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    // guaranteed to be an integer due to the check in ggml_can_repeat
+    const int nr0 = (int)(ne0/ne00);
+    const int nr1 = (int)(ne1/ne01);
+    const int nr2 = (int)(ne2/ne02);
+    const int nr3 = (int)(ne3/ne03);
+
+    // TODO: support for transposed / permuted tensors
+    GGML_ASSERT(nb0  == sizeof(float));
+    GGML_ASSERT(nb00 == sizeof(float));
+
+    // TODO: maybe this is not optimal?
+    for                         (int i3 = 0; i3 < nr3;  i3++) {
+        for                     (int k3 = 0; k3 < ne03; k3++) {
+            for                 (int i2 = 0; i2 < nr2;  i2++) {
+                for             (int k2 = 0; k2 < ne02; k2++) {
+                    for         (int i1 = 0; i1 < nr1;  i1++) {
+                        for     (int k1 = 0; k1 < ne01; k1++) {
+                            for (int i0 = 0; i0 < nr0;  i0++) {
+                                ggml_vec_cpy_f32(ne00,
+                                        (float *) ((char *)  dst->data + (i3*ne03 + k3)*nb3  + (i2*ne02 + k2)*nb2  + (i1*ne01 + k1)*nb1  + (i0*ne00)*nb0),
+                                        (float *) ((char *) src0->data + (          k3)*nb03 + (          k2)*nb02 + (          k1)*nb01));
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_repeat_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(params->ith == 0);
+    GGML_ASSERT(ggml_can_repeat(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_TENSOR_UNARY_OP_LOCALS;
+
+    // guaranteed to be an integer due to the check in ggml_can_repeat
+    const int nr0 = (int)(ne0/ne00);
+    const int nr1 = (int)(ne1/ne01);
+    const int nr2 = (int)(ne2/ne02);
+    const int nr3 = (int)(ne3/ne03);
+
+    // TODO: support for transposed / permuted tensors
+    GGML_ASSERT(nb0  == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+
+    // TODO: maybe this is not optimal?
+    for                         (int i3 = 0; i3 < nr3;  i3++) {
+        for                     (int k3 = 0; k3 < ne03; k3++) {
+            for                 (int i2 = 0; i2 < nr2;  i2++) {
+                for             (int k2 = 0; k2 < ne02; k2++) {
+                    for         (int i1 = 0; i1 < nr1;  i1++) {
+                        for     (int k1 = 0; k1 < ne01; k1++) {
+                            for (int i0 = 0; i0 < nr0;  i0++) {
+                                ggml_fp16_t * y = (ggml_fp16_t *) ((char *)  dst->data + (i3*ne03 + k3)*nb3  + (i2*ne02 + k2)*nb2  + (i1*ne01 + k1)*nb1  + (i0*ne00)*nb0);
+                                ggml_fp16_t * x = (ggml_fp16_t *) ((char *) src0->data + (          k3)*nb03 + (          k2)*nb02 + (          k1)*nb01);
+                                // ggml_vec_cpy_f16(ne00, y, x)
+                                for (int i = 0; i < ne00; ++i) {
+                                    y[i]  = x[i];
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_repeat(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_repeat_f16(params, src0, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_repeat_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_repeat_back
+
+static void ggml_compute_forward_repeat_back_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(params->ith == 0);
+    GGML_ASSERT(ggml_can_repeat(dst, src0));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    // guaranteed to be an integer due to the check in ggml_can_repeat
+    const int nr0 = (int)(ne00/ne0);
+    const int nr1 = (int)(ne01/ne1);
+    const int nr2 = (int)(ne02/ne2);
+    const int nr3 = (int)(ne03/ne3);
+
+    // TODO: support for transposed / permuted tensors
+    GGML_ASSERT(nb0  == sizeof(float));
+    GGML_ASSERT(nb00 == sizeof(float));
+
+    if (ggml_is_contiguous(dst)) {
+        ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0);
+    } else {
+        for         (int k3 = 0; k3 < ne3; k3++) {
+            for     (int k2 = 0; k2 < ne2; k2++) {
+                for (int k1 = 0; k1 < ne1; k1++) {
+                    ggml_vec_set_f32(ne0,
+                        (float *) ((char *) dst->data + k1*nb1 + k2*nb2 + k3*nb3),
+                        0);
+                }
+            }
+        }
+    }
+
+    // TODO: maybe this is not optimal?
+    for                         (int i3 = 0; i3 < nr3; i3++) {
+        for                     (int k3 = 0; k3 < ne3; k3++) {
+            for                 (int i2 = 0; i2 < nr2; i2++) {
+                for             (int k2 = 0; k2 < ne2; k2++) {
+                    for         (int i1 = 0; i1 < nr1; i1++) {
+                        for     (int k1 = 0; k1 < ne1; k1++) {
+                            for (int i0 = 0; i0 < nr0; i0++) {
+                                ggml_vec_acc_f32(ne0,
+                                        (float *) ((char *)  dst->data + (         k3)*nb3  + (         k2)*nb2  + (         k1)*nb1),
+                                        (float *) ((char *) src0->data + (i3*ne3 + k3)*nb03 + (i2*ne2 + k2)*nb02 + (i1*ne1 + k1)*nb01 + (i0*ne0)*nb00));
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_repeat_back(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_repeat_back_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_concat
+
+static void ggml_compute_forward_concat_f32(
+    const struct ggml_compute_params * params,
+    const struct ggml_tensor * src0,
+    const struct ggml_tensor * src1,
+    struct ggml_tensor * dst) {
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+
+    const int ith = params->ith;
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    // TODO: support for transposed / permuted tensors
+    GGML_ASSERT(nb0  == sizeof(float));
+    GGML_ASSERT(nb00 == sizeof(float));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    for (int i3 = 0; i3 < ne3; i3++) {
+        for (int i2 = ith; i2 < ne2; i2++) {
+            if (i2 < ne02) { // src0
+                for (int i1 = 0; i1 < ne1; i1++) {
+                    for (int i0 = 0; i0 < ne0; i0++) {
+                        const float * x = (float *)((char *) src0->data + i0 * nb00 + i1 * nb01 + i2 * nb02 + i3 * nb03);
+
+                        float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3);
+                        *y = *x;
+                    }
+                }
+            } // src1
+            else {
+                for (int i1 = 0; i1 < ne1; i1++) {
+                    for (int i0 = 0; i0 < ne0; i0++) {
+                        const float * x = (float *)((char *) src1->data + i0 * nb10 + i1 * nb11 + (i2 - ne02) * nb12 + i3 * nb13);
+
+                        float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3);
+                        *y = *x;
+                    }
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_concat(
+    const struct ggml_compute_params* params,
+    const struct ggml_tensor* src0,
+    const struct ggml_tensor* src1,
+    struct ggml_tensor* dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_concat_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_abs
+
+static void ggml_compute_forward_abs_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert(dst->nb[0]  == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_abs_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_abs(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_abs_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_sgn
+
+static void ggml_compute_forward_sgn_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert(dst->nb[0]  == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_sgn_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_sgn(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_sgn_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_neg
+
+static void ggml_compute_forward_neg_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert(dst->nb[0]  == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_neg_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_neg(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_neg_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_step
+
+static void ggml_compute_forward_step_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert(dst->nb[0]  == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_step_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_step(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_step_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_tanh
+
+static void ggml_compute_forward_tanh_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert(dst->nb[0]  == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_tanh_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_tanh(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_tanh_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_elu
+
+static void ggml_compute_forward_elu_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert(dst->nb[0]  == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_elu_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_elu(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_elu_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_relu
+
+static void ggml_compute_forward_relu_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert(dst->nb[0]  == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_relu_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_relu(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_relu_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_gelu
+
+static void ggml_compute_forward_gelu_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
+    GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nc = src0->ne[0];
+    const int nr = ggml_nrows(src0);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        ggml_vec_gelu_f32(nc,
+                (float *) ((char *) dst->data  + i1*( dst->nb[1])),
+                (float *) ((char *) src0->data + i1*(src0->nb[1])));
+
+#ifndef NDEBUG
+        for (int k = 0; k < nc; k++) {
+            const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k];
+            UNUSED(x);
+            assert(!isnan(x));
+            assert(!isinf(x));
+        }
+#endif
+    }
+}
+
+static void ggml_compute_forward_gelu(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_gelu_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_gelu_quick
+
+static void ggml_compute_forward_gelu_quick_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
+    GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nc = src0->ne[0];
+    const int nr = ggml_nrows(src0);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        ggml_vec_gelu_quick_f32(nc,
+                (float *) ((char *) dst->data  + i1*( dst->nb[1])),
+                (float *) ((char *) src0->data + i1*(src0->nb[1])));
+
+#ifndef NDEBUG
+        for (int k = 0; k < nc; k++) {
+            const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k];
+            UNUSED(x);
+            assert(!isnan(x));
+            assert(!isinf(x));
+        }
+#endif
+    }
+}
+
+static void ggml_compute_forward_gelu_quick(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_gelu_quick_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_silu
+
+static void ggml_compute_forward_silu_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
+    GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nc = src0->ne[0];
+    const int nr = ggml_nrows(src0);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        ggml_vec_silu_f32(nc,
+                (float *) ((char *) dst->data  + i1*( dst->nb[1])),
+                (float *) ((char *) src0->data + i1*(src0->nb[1])));
+
+#ifndef NDEBUG
+        for (int k = 0; k < nc; k++) {
+            const float x = ((float *) ((char *) dst->data + i1*(dst->nb[1])))[k];
+            UNUSED(x);
+            assert(!isnan(x));
+            assert(!isinf(x));
+        }
+#endif
+    }
+}
+
+static void ggml_compute_forward_silu(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_silu_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_silu_back
+
+static void ggml_compute_forward_silu_back_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * grad,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous_except_dim_1(grad));
+    GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
+    GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_are_same_shape(src0, grad));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nc = src0->ne[0];
+    const int nr = ggml_nrows(src0);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        ggml_vec_silu_backward_f32(nc,
+                (float *) ((char *) dst->data  + i1*( dst->nb[1])),
+                (float *) ((char *) src0->data + i1*(src0->nb[1])),
+                (float *) ((char *) grad->data + i1*(grad->nb[1])));
+
+#ifndef NDEBUG
+        for (int k = 0; k < nc; k++) {
+            const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k];
+            UNUSED(x);
+            assert(!isnan(x));
+            assert(!isinf(x));
+        }
+#endif
+    }
+}
+
+static void ggml_compute_forward_silu_back(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * grad,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_silu_back_f32(params, src0, grad, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_norm
+
+static void ggml_compute_forward_norm_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    // TODO: optimize
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        for (int64_t i02 = 0; i02 < ne02; i02++) {
+            for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
+                const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+
+                ggml_float sum = 0.0;
+                for (int64_t i00 = 0; i00 < ne00; i00++) {
+                    sum += (ggml_float)x[i00];
+                }
+
+                float mean = sum/ne00;
+
+                float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
+
+                ggml_float sum2 = 0.0;
+                for (int64_t i00 = 0; i00 < ne00; i00++) {
+                    float v = x[i00] - mean;
+                    y[i00] = v;
+                    sum2 += (ggml_float)(v*v);
+                }
+
+                float variance = sum2/ne00;
+                const float scale = 1.0f/sqrtf(variance + eps);
+
+                ggml_vec_scale_f32(ne00, y, scale);
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_norm(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_norm_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_group_rms_norm
+
+static void ggml_compute_forward_rms_norm_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    // TODO: optimize
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        for (int64_t i02 = 0; i02 < ne02; i02++) {
+            for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
+                const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+
+                ggml_float sum = 0.0;
+                for (int64_t i00 = 0; i00 < ne00; i00++) {
+                    sum += (ggml_float)(x[i00] * x[i00]);
+                }
+
+                const float mean = sum/ne00;
+
+                float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
+
+                memcpy(y, x, ne00 * sizeof(float));
+                // for (int i00 = 0; i00 < ne00; i00++) {
+                //     y[i00] = x[i00];
+                // }
+
+                const float scale = 1.0f/sqrtf(mean + eps);
+
+                ggml_vec_scale_f32(ne00, y, scale);
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_rms_norm(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_rms_norm_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+static void ggml_compute_forward_rms_norm_back_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    // TODO: optimize
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        for (int64_t i02 = 0; i02 < ne02; i02++) {
+            for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
+                // src1 is same shape as src0 => same indices
+                const int64_t i11 = i01;
+                const int64_t i12 = i02;
+                const int64_t i13 = i03;
+
+                const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+                const float * dz = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13);
+
+                ggml_float sum_xx  = 0.0;
+                ggml_float sum_xdz = 0.0;
+
+                for (int64_t i00 = 0; i00 < ne00; i00++) {
+                    sum_xx  += (ggml_float)(x[i00] * x[i00]);
+                    sum_xdz += (ggml_float)(x[i00] * dz[i00]);
+                }
+
+                //const float mean     = (float)(sum_xx)/ne00;
+                const float mean_eps = (float)(sum_xx)/ne00 + eps;
+                const float sum_eps  = (float)(sum_xx) + eps*ne00;
+                //const float mean_xdz = (float)(sum_xdz)/ne00;
+                // we could cache rms from forward pass to improve performance.
+                // to do this implement ggml_rms and compose ggml_rms_norm using ggml_rms.
+                //const float rms      = sqrtf(mean_eps);
+                const float rrms     = 1.0f / sqrtf(mean_eps);
+                //const float scale    = -rrms/(ne00 * mean_eps); // -1/(n*rms**3)
+
+                {
+                    // z = rms_norm(x)
+                    //
+                    // rms_norm(src0) =
+                    //     scale(
+                    //         src0,
+                    //         div(
+                    //             1,
+                    //             sqrt(
+                    //                 add(
+                    //                     scale(
+                    //                         sum(
+                    //                             sqr(
+                    //                                 src0)),
+                    //                         (1.0/N)),
+                    //                     eps))));
+
+                    // postorder:
+                    // ## op    args         grad
+                    // 00 param src0         grad[#00]
+                    // 01 const 1
+                    // 02 sqr   (#00)        grad[#02]
+                    // 03 sum   (#02)        grad[#03]
+                    // 04 const 1/N
+                    // 05 scale (#03, #04)   grad[#05]
+                    // 06 const eps
+                    // 07 add   (#05, #06)   grad[#07]
+                    // 08 sqrt  (#07)        grad[#08]
+                    // 09 div   (#01,#08)    grad[#09]
+                    // 10 scale (#00,#09)    grad[#10]
+                    //
+                    // backward pass, given grad[#10]
+                    // #10: scale
+                    // grad[#00] += scale(grad[#10],#09)
+                    // grad[#09] += sum(mul(grad[#10],#00))
+                    // #09: div
+                    // grad[#08] += neg(mul(grad[#09], div(#09,#08)))
+                    // #08: sqrt
+                    // grad[#07] += mul(grad[#08], div(0.5, #08))
+                    // #07: add
+                    // grad[#05] += grad[#07]
+                    // #05: scale
+                    // grad[#03] += scale(grad[#05],#04)
+                    // #03: sum
+                    // grad[#02] += repeat(grad[#03], #02)
+                    // #02:
+                    // grad[#00] += scale(mul(#00, grad[#02]), 2.0)
+                    //
+                    // substitute and simplify:
+                    // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0)
+                    // grad[#02] = repeat(grad[#03], #02)
+                    // grad[#02] = repeat(scale(grad[#05],#04), #02)
+                    // grad[#02] = repeat(scale(grad[#07],#04), #02)
+                    // grad[#02] = repeat(scale(mul(grad[#08], div(0.5, #08)),#04), #02)
+                    // grad[#02] = repeat(scale(mul(neg(mul(grad[#09], div(#09,#08))), div(0.5, #08)),#04), #02)
+                    // grad[#02] = repeat(scale(mul(neg(mul(sum(mul(grad[#10],#00)), div(#09,#08))), div(0.5, #08)),#04), #02)
+                    // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(#09,#08) * div(0.5, #08) * (1/N)), #02)
+                    // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(div(#01,#08),#08) * div(0.5, #08) * (1/N)), #02)
+                    // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#08*#08) * div(0.5, #08) * (1/N)), #02)
+                    // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02)
+                    // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0)
+                    // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02)), 2.0)
+                    // grad[#00] = scale(grad(#10), #09) + scale(scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N))), 2.0)
+                    // grad[#00] = scale(grad(#10), #09) + scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(1,#08) * (1/N)))
+                    // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N))
+                    // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N))
+                    // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,mean_eps*rms) * (-1/N))
+                    // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*mean_eps))
+                    // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*(sum_xx/N+eps)))
+                    // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*sum_xx+rms*N*eps))
+                    // grad[#00] = scale(dz, rrms) + scale(x, sum(mul(dz,x)) * div(-1,rms*N*mean_eps))
+                    // grad[#00] = scale(dz, rrms) + scale(x, sum_xdz * div(-1,rms*N*mean_eps))
+                    // a = b*c + d*e
+                    // a = b*c*f/f + d*e*f/f
+                    // a = (b*c*f + d*e*f)*(1/f)
+                    // a = (b*c*(1/c) + d*e*(1/c))*(1/(1/c))
+                    // a = (b + d*e/c)*c
+                    // b = dz, c = rrms, d = x, e = sum_xdz * div(-1,rms*N*mean_eps)
+                    // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)/rrms)*rrms
+                    // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)*rms)*rrms
+                    // a = (dz + x*sum_xdz * div(-rms,rms*N*mean_eps))*rrms
+                    // a = (dz + x*sum_xdz * div(-1,N*mean_eps))*rrms
+                    // a = (dz + x*div(-sum_xdz,N*mean_eps))*rrms
+                    // a = (dz + x*div(-mean_xdz,mean_eps))*rrms
+                    // grad[#00] = scale(dz + scale(x, div(-mean_xdz,mean_eps)),rrms)
+                    // grad[#00] = scale(dz + scale(x, -mean_xdz/mean_eps),rrms)
+                    // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms)
+                }
+                // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms)
+                // post-order:
+                // dx := x
+                // dx := scale(dx,-mean_xdz/mean_eps)
+                // dx := add(dx, dz)
+                // dx := scale(dx, rrms)
+                float * dx = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
+
+                ggml_vec_cpy_f32  (ne00, dx, x);
+                // ggml_vec_scale_f32(ne00, dx, -mean_xdz/mean_eps);
+                ggml_vec_scale_f32(ne00, dx, (float)(-sum_xdz)/sum_eps);
+                ggml_vec_acc_f32  (ne00, dx, dz);
+                ggml_vec_scale_f32(ne00, dx, rrms);
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_rms_norm_back(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_rms_norm_back_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_group_norm
+
+static void ggml_compute_forward_group_norm_f32(
+    const struct ggml_compute_params * params,
+    const struct ggml_tensor * src0,
+    struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    const float eps = 1e-6f; // TODO: make this a parameter
+
+    // TODO: optimize
+
+    int n_channels = src0->ne[2];
+    int n_groups = dst->op_params[0];
+    int n_channels_per_group = (n_channels + n_groups - 1) / n_groups;
+    for (int i = ith; i < n_groups; i+=nth) {
+        int start = i * n_channels_per_group;
+        int end = start + n_channels_per_group;
+        if (end > n_channels) {
+            end = n_channels;
+        }
+        int step = end - start;
+
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            ggml_float sum = 0.0;
+            for (int64_t i02 = start; i02 < end; i02++) {
+                for (int64_t i01 = 0; i01 < ne01; i01++) {
+                    const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);
+
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        sum += (ggml_float)x[i00];
+                    }
+                }
+            }
+            float mean = sum / (ne00 * ne01 * step);
+            ggml_float sum2 = 0.0;
+
+            for (int64_t i02 = start; i02 < end; i02++) {
+                for (int64_t i01 = 0; i01 < ne01; i01++) {
+                    const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);
+
+                    float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3);
+
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        float v = x[i00] - mean;
+                        y[i00] = v;
+                        sum2 += (ggml_float)(v * v);
+                    }
+                }
+            }
+            float variance = sum2 / (ne00 * ne01 * step);
+            const float scale = 1.0f / sqrtf(variance + eps);
+
+            for (int64_t i02 = start; i02 < end; i02++) {
+                for (int64_t i01 = 0; i01 < ne01; i01++) {
+                    float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3);
+                    ggml_vec_scale_f32(ne00, y, scale);
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_group_norm(
+    const struct ggml_compute_params * params,
+    const struct ggml_tensor * src0,
+    struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_group_norm_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_mul_mat
+
+#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
+// helper function to determine if it is better to use BLAS or not
+// for large matrices, BLAS is faster
+static bool ggml_compute_forward_mul_mat_use_blas(
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    //const int64_t ne00 = src0->ne[0];
+    //const int64_t ne01 = src0->ne[1];
+
+    const int64_t ne10 = src1->ne[0];
+
+    const int64_t ne0 = dst->ne[0];
+    const int64_t ne1 = dst->ne[1];
+
+    // TODO: find the optimal values for these
+    if (ggml_is_contiguous(src0) &&
+        ggml_is_contiguous(src1) &&
+        (ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {
+
+        /*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/
+        return true;
+    }
+
+    return false;
+}
+#endif
+
+static void ggml_compute_forward_mul_mat(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const enum ggml_type type = src0->type;
+
+    const bool src1_cont = ggml_is_contiguous(src1);
+
+    ggml_vec_dot_t    const vec_dot               = type_traits[type].vec_dot;
+    enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
+    ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
+
+    GGML_ASSERT(ne0 == ne01);
+    GGML_ASSERT(ne1 == ne11);
+    GGML_ASSERT(ne2 == ne12);
+    GGML_ASSERT(ne3 == ne13);
+
+    // we don't support permuted src0 or src1
+    GGML_ASSERT(nb00 == ggml_type_size(type));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    // broadcast factors
+    const int64_t r2 = ne12/ne02;
+    const int64_t r3 = ne13/ne03;
+
+    // nb01 >= nb00 - src0 is not transposed
+    //   compute by src0 rows
+
+#if defined(GGML_USE_CLBLAST)
+    if (ggml_cl_can_mul_mat(src0, src1, dst)) {
+        if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
+            ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
+        }
+        return;
+    }
+#endif
+
+#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
+    if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
+        if (params->ith != 0) {
+            return;
+        }
+
+        if (params->type == GGML_TASK_INIT) {
+            return;
+        }
+
+        if (params->type == GGML_TASK_FINALIZE) {
+            return;
+        }
+
+        for (int64_t i13 = 0; i13 < ne13; i13++) {
+            for (int64_t i12 = 0; i12 < ne12; i12++) {
+                // broadcast src0 into src1 across 2nd,3rd dimension
+                const int64_t i03 = i13/r3;
+                const int64_t i02 = i12/r2;
+
+                const void  * x = (char *)            src0->data + i02*nb02 + i03*nb03;
+                const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13);
+
+                float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
+
+                if (type != GGML_TYPE_F32) {
+                            float * const wdata    = params->wdata;
+                    ggml_to_float_t const to_float = type_traits[type].to_float;
+
+                    size_t id = 0;
+                    for (int64_t i01 = 0; i01 < ne01; ++i01) {
+                        to_float((const char *) x + i01*nb01, wdata + id, ne00);
+                        id += ne00;
+                    }
+
+                    assert(id*sizeof(float) <= params->wsize);
+                    x = wdata;
+                }
+
+                cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
+                        ne11, ne01, ne10,
+                        1.0f,    y, ne10,
+                                 x, ne00,
+                        0.0f,    d, ne01);
+            }
+        }
+
+        //printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
+
+        return;
+    }
+#endif
+
+    if (params->type == GGML_TASK_INIT) {
+        if (src1->type != vec_dot_type) {
+            char * wdata = params->wdata;
+            const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
+
+            for (int64_t i13 = 0; i13 < ne13; ++i13) {
+                for (int64_t i12 = 0; i12 < ne12; ++i12) {
+                    for (int64_t i11 = 0; i11 < ne11; ++i11) {
+                        from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
+                        wdata += row_size;
+                    }
+                }
+            }
+        }
+
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const void * wdata    = (src1->type == vec_dot_type) ? src1->data : params->wdata;
+    const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
+
+    const int64_t nr0 = ne01;           // src0 rows
+    const int64_t nr1 = ne11*ne12*ne13; // src1 rows
+
+    //printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
+
+    // distribute the thread work across the inner or outer loop based on which one is larger
+
+    const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
+    const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows
+
+    const int64_t ith0 = ith % nth0;
+    const int64_t ith1 = ith / nth0;
+
+    const int64_t dr0 = (nr0 + nth0 - 1)/nth0;
+    const int64_t dr1 = (nr1 + nth1 - 1)/nth1;
+
+    const int64_t ir010 = dr0*ith0;
+    const int64_t ir011 = MIN(ir010 + dr0, nr0);
+
+    const int64_t ir110 = dr1*ith1;
+    const int64_t ir111 = MIN(ir110 + dr1, nr1);
+
+    //printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111);
+
+    // threads with no work simply yield (not sure if it helps)
+    if (ir010 >= ir011 || ir110 >= ir111) {
+        sched_yield();
+        return;
+    }
+
+    assert(ne12 % ne02 == 0);
+    assert(ne13 % ne03 == 0);
+
+    // block-tiling attempt
+    const int64_t blck_0 = 16;
+    const int64_t blck_1 = 16;
+
+    // attempt to reduce false-sharing (does not seem to make a difference)
+    float tmp[16];
+
+    for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
+        for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
+            for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
+                const int64_t i13 = (ir1/(ne12*ne11));
+                const int64_t i12 = (ir1 - i13*ne12*ne11)/ne11;
+                const int64_t i11 = (ir1 - i13*ne12*ne11 - i12*ne11);
+
+                // broadcast src0 into src1
+                const int64_t i03 = i13/r3;
+                const int64_t i02 = i12/r2;
+
+                const int64_t i1 = i11;
+                const int64_t i2 = i12;
+                const int64_t i3 = i13;
+
+                const char * src0_row = (const char *) src0->data + (0 + i02*nb02 + i03*nb03);
+
+                // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
+                //       if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
+                //       the original src1 data pointer, so we should index using the indices directly
+                // TODO: this is a bit of a hack, we should probably have a better way to handle this
+                const char * src1_col = (const char *) wdata +
+                    (src1_cont || src1->type != vec_dot_type
+                     ? (i11      + i12*ne11 + i13*ne12*ne11)*row_size
+                     : (i11*nb11 + i12*nb12 + i13*nb13));
+
+                float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3));
+
+                //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
+                //    vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
+                //}
+
+                for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
+                    vec_dot(ne00, &tmp[ir0 - iir0], src0_row + ir0*nb01, src1_col);
+                }
+                memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float));
+            }
+        }
+    }
+}
+
+// ggml_compute_forward_out_prod
+
+static void ggml_compute_forward_out_prod_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    // int64_t t0 = ggml_perf_time_us();
+    // UNUSED(t0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    GGML_ASSERT(ne02 == ne12);
+    GGML_ASSERT(ne03 == ne13);
+    GGML_ASSERT(ne2  == ne12);
+    GGML_ASSERT(ne3  == ne13);
+
+    // we don't support permuted src0 or src1
+    GGML_ASSERT(nb00 == sizeof(float));
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    // GGML_ASSERT(nb0 <= nb1);
+    // GGML_ASSERT(nb1 <= nb2);
+    // GGML_ASSERT(nb2 <= nb3);
+
+    GGML_ASSERT(ne0 == ne00);
+    GGML_ASSERT(ne1 == ne10);
+    GGML_ASSERT(ne2 == ne02);
+    GGML_ASSERT(ne3 == ne03);
+
+    // nb01 >= nb00 - src0 is not transposed
+    //   compute by src0 rows
+
+    // TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod
+    // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
+
+    if (params->type == GGML_TASK_INIT) {
+        ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0);
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // dst[:,:,:,:] = 0
+    // for i2,i3:
+    //   for i1:
+    //     for i01:
+    //       for i0:
+    //         dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3]
+
+    // parallelize by last three dimensions
+
+    // total rows in dst
+    const int64_t nr = ne1*ne2*ne3;
+
+    // rows per thread
+    const int64_t dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int64_t ir0 = dr*ith;
+    const int64_t ir1 = MIN(ir0 + dr, nr);
+
+    // block-tiling attempt
+    const int64_t blck_0 = MAX(GGML_VEC_MAD_UNROLL, 32);
+    const int64_t blck_1 = 16;
+
+    for (int64_t bir = ir0; bir < ir1; bir += blck_1) {
+        const int64_t bir1 = MIN(bir + blck_1, ir1);
+        for (int64_t bi01 = 0; bi01 < ne01; bi01 += blck_0) {
+            const int64_t bne01 = MIN(bi01 + blck_0, ne01);
+            for (int64_t ir = bir; ir < bir1; ++ir) {
+                // dst indices
+                const int64_t i3 = ir/(ne2*ne1);
+                const int64_t i2 = (ir - i3*ne2*ne1)/ne1;
+                const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+                const int64_t i02 = i2;
+                const int64_t i03 = i3;
+
+                //const int64_t i10 = i1;
+                const int64_t i12 = i2;
+                const int64_t i13 = i3;
+
+#if GGML_VEC_MAD_UNROLL > 2
+                const int64_t bne01_unroll = bne01 - (bne01 % GGML_VEC_MAD_UNROLL);
+                for (int64_t i01 = bi01; i01 < bne01_unroll; i01 += GGML_VEC_MAD_UNROLL) {
+                    const int64_t i11 = i01;
+
+                    float * s0 = (float *) ((char *) src0->data + (          i01*nb01 + i02*nb02 + i03*nb03));
+                    float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13));
+                    float * d  = (float *) ((char *)  dst->data + (          i1*nb1 + i2*nb2 + i3*nb3));
+
+                    ggml_vec_mad_f32_unroll(ne0, nb01, nb11, d, s0, s1);
+                }
+                for (int64_t i01 = bne01_unroll; i01 < bne01; ++i01) {
+                    const int64_t i11 = i01;
+
+                    float * s0 = (float *) ((char *) src0->data + (          i01*nb01 + i02*nb02 + i03*nb03));
+                    float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13));
+                    float * d  = (float *) ((char *)  dst->data + (          i1*nb1 + i2*nb2 + i3*nb3));
+
+                    ggml_vec_mad_f32(ne0, d, s0, *s1);
+                }
+#else
+                for (int64_t i01 = bi01; i01 < bne01; ++i01) {
+                    const int64_t i11 = i01;
+
+                    float * s0 = (float *) ((char *) src0->data + (          i01*nb01 + i02*nb02 + i03*nb03));
+                    float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13));
+                    float * d  = (float *) ((char *)  dst->data + (          i1*nb1 + i2*nb2 + i3*nb3));
+
+                    ggml_vec_mad_f32(ne0, d, s0, *s1);
+                }
+#endif
+            }
+        }
+    }
+
+
+    //int64_t t1 = ggml_perf_time_us();
+    //static int64_t acc = 0;
+    //acc += t1 - t0;
+    //if (t1 - t0 > 10) {
+    //    printf("\n");
+    //    printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
+    //    printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
+    //    printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
+    //    printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13);
+
+    //    printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
+    //}
+}
+
+static void ggml_compute_forward_out_prod_q_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    // int64_t t0 = ggml_perf_time_us();
+    // UNUSED(t0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const enum ggml_type type = src0->type;
+    ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
+
+    GGML_ASSERT(ne02 == ne12);
+    GGML_ASSERT(ne03 == ne13);
+    GGML_ASSERT(ne2  == ne12);
+    GGML_ASSERT(ne3  == ne13);
+
+    // we don't support permuted src0 dim0
+    GGML_ASSERT(nb00 == ggml_type_size(type));
+
+    // dst dim0 cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    // GGML_ASSERT(nb0 <= nb1);
+    // GGML_ASSERT(nb1 <= nb2);
+    // GGML_ASSERT(nb2 <= nb3);
+
+    GGML_ASSERT(ne0 == ne00);
+    GGML_ASSERT(ne1 == ne10);
+    GGML_ASSERT(ne2 == ne02);
+    GGML_ASSERT(ne3 == ne03);
+
+    // nb01 >= nb00 - src0 is not transposed
+    //   compute by src0 rows
+
+    // TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod
+    // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
+
+    if (params->type == GGML_TASK_INIT) {
+        ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0);
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // parallelize by last three dimensions
+
+    // total rows in dst
+    const int64_t nr = ne1*ne2*ne3;
+
+    // rows per thread
+    const int64_t dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int64_t ir0 = dr*ith;
+    const int64_t ir1 = MIN(ir0 + dr, nr);
+
+    // dst[:,:,:,:] = 0
+    // for i2,i3:
+    //   for i1:
+    //     for i01:
+    //       for i0:
+    //         dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3]
+
+    float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith;
+
+    for (int64_t ir = ir0; ir < ir1; ++ir) {
+        // dst indices
+        const int64_t i3 = ir/(ne2*ne1);
+        const int64_t i2 = (ir - i3*ne2*ne1)/ne1;
+        const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+        const int64_t i02 = i2;
+        const int64_t i03 = i3;
+
+        //const int64_t i10 = i1;
+        const int64_t i12 = i2;
+        const int64_t i13 = i3;
+
+        for (int64_t i01 = 0; i01 < ne01; ++i01) {
+            const int64_t i11 = i01;
+
+            float * s0 = (float *) ((char *) src0->data + (          i01*nb01 + i02*nb02 + i03*nb03));
+            float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13));
+            float * d  = (float *) ((char *)  dst->data + (          i1*nb1 + i2*nb2 + i3*nb3));
+
+            dequantize_row_q(s0, wdata, ne0);
+            ggml_vec_mad_f32(ne0, d, wdata, *s1);
+        }
+    }
+
+    //int64_t t1 = ggml_perf_time_us();
+    //static int64_t acc = 0;
+    //acc += t1 - t0;
+    //if (t1 - t0 > 10) {
+    //    printf("\n");
+    //    printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
+    //    printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
+    //    printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
+    //    printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13);
+
+    //    printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
+    //}
+}
+
+static void ggml_compute_forward_out_prod(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            {
+                ggml_compute_forward_out_prod_q_f32(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F16:
+            {
+                GGML_ASSERT(false); // todo
+                // ggml_compute_forward_out_prod_f16_f32(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_out_prod_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_scale
+
+static void ggml_compute_forward_scale_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(dst));
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_is_scalar(src1));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // scale factor
+    const float v = *(float *) src1->data;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nc = src0->ne[0];
+    const int nr = ggml_nrows(src0);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    const size_t nb01 = src0->nb[1];
+
+    const size_t nb1 = dst->nb[1];
+
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        if (dst->data != src0->data) {
+            // src0 is same shape as dst => same indices
+            memcpy((char *)dst->data + i1*nb1, (char *)src0->data + i1*nb01, nc * sizeof(float));
+        }
+        ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*nb1), v);
+    }
+}
+
+static void ggml_compute_forward_scale(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_scale_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_set
+
+static void ggml_compute_forward_set_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
+
+    // view src0 and dst with these strides and data offset inbytes during set
+    // nb0 is implicitely element_size because src0 and dst are contiguous
+    size_t nb1     = ((int32_t *) dst->op_params)[0];
+    size_t nb2     = ((int32_t *) dst->op_params)[1];
+    size_t nb3     = ((int32_t *) dst->op_params)[2];
+    size_t offset  = ((int32_t *) dst->op_params)[3];
+    bool   inplace = (bool) ((int32_t *) dst->op_params)[4];
+
+    if (!inplace && (params->type == GGML_TASK_INIT)) {
+        // memcpy needs to be synchronized across threads to avoid race conditions.
+        // => do it in INIT phase
+        memcpy(
+            ((char *)  dst->data),
+            ((char *) src0->data),
+            ggml_nbytes(dst));
+    }
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr = ggml_nrows(src1);
+    const int nc = src1->ne[0];
+
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb)
+
+    // src0 and dst as viewed during set
+    const size_t nb0 = ggml_element_size(src0);
+
+    const int im0 = (ne10 == 0 ? 0 : ne10-1);
+    const int im1 = (ne11 == 0 ? 0 : ne11-1);
+    const int im2 = (ne12 == 0 ? 0 : ne12-1);
+    const int im3 = (ne13 == 0 ? 0 : ne13-1);
+
+    GGML_ASSERT(offset + im0*nb0  + im1*nb1  + im2*nb2  + im3*nb3  <= ggml_nbytes(dst));
+
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // src0 and dst are viewed with shape of src1 and offset
+        // => same indices
+        const int i3 = ir/(ne12*ne11);
+        const int i2 = (ir - i3*ne12*ne11)/ne11;
+        const int i1 = (ir - i3*ne12*ne11 - i2*ne11);
+
+        ggml_vec_cpy_f32(nc,
+                (float *) ((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + offset),
+                (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11));
+    }
+}
+
+static void ggml_compute_forward_set(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_set_f32(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F16:
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q8_1:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_cpy
+
+static void ggml_compute_forward_cpy(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    ggml_compute_forward_dup(params, src0, dst);
+}
+
+// ggml_compute_forward_cont
+
+static void ggml_compute_forward_cont(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    ggml_compute_forward_dup(params, src0, dst);
+}
+
+// ggml_compute_forward_reshape
+
+static void ggml_compute_forward_reshape(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    // NOP
+    UNUSED(params);
+    UNUSED(src0);
+    UNUSED(dst);
+}
+
+// ggml_compute_forward_view
+
+static void ggml_compute_forward_view(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0) {
+    // NOP
+    UNUSED(params);
+    UNUSED(src0);
+}
+
+// ggml_compute_forward_permute
+
+static void ggml_compute_forward_permute(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0) {
+    // NOP
+    UNUSED(params);
+    UNUSED(src0);
+}
+
+// ggml_compute_forward_transpose
+
+static void ggml_compute_forward_transpose(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0) {
+    // NOP
+    UNUSED(params);
+    UNUSED(src0);
+}
+
+// ggml_compute_forward_get_rows
+
+static void ggml_compute_forward_get_rows_q(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int nc = src0->ne[0];
+    const int nr = ggml_nelements(src1);
+    const enum ggml_type type = src0->type;
+    ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
+
+    assert( dst->ne[0] == nc);
+    assert( dst->ne[1] == nr);
+    assert(src0->nb[0] == ggml_type_size(type));
+
+    for (int i = 0; i < nr; ++i) {
+        const int r = ((int32_t *) src1->data)[i];
+
+        dequantize_row_q(
+                (const void *) ((char *) src0->data + r*src0->nb[1]),
+                     (float *) ((char *)  dst->data + i*dst->nb[1]), nc);
+    }
+}
+
+static void ggml_compute_forward_get_rows_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int nc = src0->ne[0];
+    const int nr = ggml_nelements(src1);
+
+    assert( dst->ne[0] == nc);
+    assert( dst->ne[1] == nr);
+    assert(src0->nb[0] == sizeof(ggml_fp16_t));
+
+    for (int i = 0; i < nr; ++i) {
+        const int r = ((int32_t *) src1->data)[i];
+
+        for (int j = 0; j < nc; ++j) {
+            ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + r*src0->nb[1]))[j];
+            ((float *) ((char *)  dst->data + i*dst->nb[1]))[j] = GGML_FP16_TO_FP32(v);
+        }
+    }
+}
+
+static void ggml_compute_forward_get_rows_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int nc = src0->ne[0];
+    const int nr = ggml_nelements(src1);
+
+    assert( dst->ne[0] == nc);
+    assert( dst->ne[1] == nr);
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < nr; ++i) {
+        const int r = ((int32_t *) src1->data)[i];
+
+        ggml_vec_cpy_f32(nc,
+                (float *) ((char *)  dst->data + i*dst->nb[1]),
+                (float *) ((char *) src0->data + r*src0->nb[1]));
+    }
+}
+
+static void ggml_compute_forward_get_rows(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q8_1:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            {
+                ggml_compute_forward_get_rows_q(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_get_rows_f16(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_get_rows_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+
+    //static bool first = true;
+    //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]);
+    //if (first) {
+    //    first = false;
+    //} else {
+    //    for (int k = 0; k < dst->ne[1]; ++k) {
+    //        for (int j = 0; j < dst->ne[0]/16; ++j) {
+    //            for (int i = 0; i < 16; ++i) {
+    //                printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]);
+    //            }
+    //            printf("\n");
+    //        }
+    //        printf("\n");
+    //    }
+    //    printf("\n");
+    //    exit(0);
+    //}
+}
+
+// ggml_compute_forward_get_rows_back
+
+static void ggml_compute_forward_get_rows_back_f32_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    GGML_ASSERT(params->ith == 0);
+    GGML_ASSERT(ggml_is_contiguous(dst));
+
+    // ggml_compute_forward_dup_same_cont(params, opt0, dst);
+
+    if (params->type == GGML_TASK_INIT) {
+        memset(dst->data, 0, ggml_nbytes(dst));
+    }
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int nc = src0->ne[0];
+    const int nr = ggml_nelements(src1);
+
+    GGML_ASSERT( dst->ne[0] == nc);
+    GGML_ASSERT(src0->nb[0] == sizeof(ggml_fp16_t));
+
+    for (int i = 0; i < nr; ++i) {
+        const int r = ((int32_t *) src1->data)[i];
+
+        for (int j = 0; j < nc; ++j) {
+            ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + i*src0->nb[1]))[j];
+            ((float *) ((char *) dst->data + r*dst->nb[1]))[j] += GGML_FP16_TO_FP32(v);
+        }
+    }
+}
+
+static void ggml_compute_forward_get_rows_back_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    GGML_ASSERT(params->ith == 0);
+    GGML_ASSERT(ggml_is_contiguous(dst));
+
+    // ggml_compute_forward_dup_same_cont(params, opt0, dst);
+
+    if (params->type == GGML_TASK_INIT) {
+        memset(dst->data, 0, ggml_nbytes(dst));
+    }
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int nc = src0->ne[0];
+    const int nr = ggml_nelements(src1);
+
+    GGML_ASSERT( dst->ne[0] == nc);
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < nr; ++i) {
+        const int r = ((int32_t *) src1->data)[i];
+
+        ggml_vec_add_f32(nc,
+                (float *) ((char *)  dst->data + r*dst->nb[1]),
+                (float *) ((char *)  dst->data + r*dst->nb[1]),
+                (float *) ((char *) src0->data + i*src0->nb[1]));
+    }
+}
+
+
+static void ggml_compute_forward_get_rows_back(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_get_rows_back_f32_f16(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_get_rows_back_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+
+    //static bool first = true;
+    //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]);
+    //if (first) {
+    //    first = false;
+    //} else {
+    //    for (int k = 0; k < dst->ne[1]; ++k) {
+    //        for (int j = 0; j < dst->ne[0]/16; ++j) {
+    //            for (int i = 0; i < 16; ++i) {
+    //                printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]);
+    //            }
+    //            printf("\n");
+    //        }
+    //        printf("\n");
+    //    }
+    //    printf("\n");
+    //    exit(0);
+    //}
+}
+
+// ggml_compute_forward_diag
+
+static void ggml_compute_forward_diag_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // TODO: handle transposed/permuted matrices
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    GGML_ASSERT(ne00 == ne0);
+    GGML_ASSERT(ne00 == ne1);
+    GGML_ASSERT(ne01 == 1);
+    GGML_ASSERT(ne02 == ne2);
+    GGML_ASSERT(ne03 == ne3);
+
+    GGML_ASSERT(nb00 == sizeof(float));
+    GGML_ASSERT(nb0  == sizeof(float));
+
+    for (int i3 = 0; i3 < ne3; i3++) {
+        for (int i2 = 0; i2 < ne2; i2++) {
+            for (int i1 = 0; i1 < ne1; i1++) {
+                float * d = (float *)((char *)  dst->data + i3*nb3  + i2*nb2 + i1*nb1);
+                float * s = (float *)((char *) src0->data + i3*nb03 + i2*nb02);
+                for (int i0 = 0; i0 < i1; i0++) {
+                    d[i0] = 0;
+                }
+                d[i1] = s[i1];
+                for (int i0 = i1+1; i0 < ne0; i0++) {
+                    d[i0] = 0;
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_diag(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_diag_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_diag_mask_inf
+
+static void ggml_compute_forward_diag_mask_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst,
+        const float value) {
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int  n_past  = ((int32_t *) dst->op_params)[0];
+    const bool inplace = src0->data == dst->data;
+
+    GGML_ASSERT(n_past >= 0);
+
+    if (!inplace && (params->type == GGML_TASK_INIT)) {
+        // memcpy needs to be synchronized across threads to avoid race conditions.
+        // => do it in INIT phase
+        GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
+        GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
+        memcpy(
+            ((char *)  dst->data),
+            ((char *) src0->data),
+            ggml_nbytes(dst));
+    }
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // TODO: handle transposed/permuted matrices
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+    const int nr = src0->ne[1];
+    const int nz = n/nr;
+
+    GGML_ASSERT( dst->nb[0] == sizeof(float));
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+
+    for (int k = 0; k < nz; k++) {
+        for (int j = ith; j < nr; j += nth) {
+            for (int i = n_past; i < nc; i++) {
+                if (i > n_past + j) {
+                    *(float *)((char *) dst->data + k*dst->nb[2] + j*dst->nb[1] + i*dst->nb[0]) = value;
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_diag_mask_inf(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_diag_mask_f32(params, src0, dst, -INFINITY);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+static void ggml_compute_forward_diag_mask_zero(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_diag_mask_f32(params, src0, dst, 0);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_soft_max
+
+static void ggml_compute_forward_soft_max_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(dst));
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // TODO: handle transposed/permuted matrices
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nc = src0->ne[0];
+    const int nr = ggml_nrows(src0);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        float *sp = (float *)((char *) src0->data + i1*src0->nb[1]);
+        float *dp = (float *)((char *)  dst->data +  i1*dst->nb[1]);
+
+#ifndef NDEBUG
+        for (int i = 0; i < nc; ++i) {
+            //printf("p[%d] = %f\n", i, p[i]);
+            assert(!isnan(sp[i]));
+        }
+#endif
+
+        float max = -INFINITY;
+        ggml_vec_max_f32(nc, &max, sp);
+
+        ggml_float sum = 0.0;
+
+        uint16_t scvt;
+        for (int i = 0; i < nc; i++) {
+            if (sp[i] == -INFINITY) {
+                dp[i] = 0.0f;
+            } else {
+                // const float val = (sp[i] == -INFINITY) ? 0.0 : exp(sp[i] - max);
+                ggml_fp16_t s = GGML_FP32_TO_FP16(sp[i] - max);
+                memcpy(&scvt, &s, sizeof(scvt));
+                const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
+                sum += (ggml_float)val;
+                dp[i] = val;
+            }
+        }
+
+        assert(sum > 0.0);
+
+        sum = 1.0/sum;
+        ggml_vec_scale_f32(nc, dp, sum);
+
+#ifndef NDEBUG
+        for (int i = 0; i < nc; ++i) {
+            assert(!isnan(dp[i]));
+            assert(!isinf(dp[i]));
+        }
+#endif
+    }
+}
+
+static void ggml_compute_forward_soft_max(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_soft_max_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_soft_max_back
+
+static void ggml_compute_forward_soft_max_back_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src1));
+    GGML_ASSERT(ggml_is_contiguous(dst));
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_are_same_shape(src1, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // TODO: handle transposed/permuted matrices
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nc = src0->ne[0];
+    const int nr = ggml_nrows(src0);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        float *dy = (float *)((char *) src0->data + i1*src0->nb[1]);
+        float *y  = (float *)((char *) src1->data + i1*src1->nb[1]);
+        float *dx = (float *)((char *) dst->data  + i1*dst->nb[1]);
+
+#ifndef NDEBUG
+        for (int i = 0; i < nc; ++i) {
+            //printf("p[%d] = %f\n", i, p[i]);
+            assert(!isnan(dy[i]));
+            assert(!isnan(y[i]));
+        }
+#endif
+        // Jii = yi - yi*yi
+        // Jij = -yi*yj
+        // J = diag(y)-y.T*y
+        // dx = J * dy
+        // dxk = sum_i(Jki * dyi)
+        // dxk = sum_i(-yk*yi * dyi) - (-yk*yk)*dyk + (yk - yk*yk)*dyk
+        // dxk = sum_i(-yk*yi * dyi) + yk*yk*dyk + yk*dyk - yk*yk*dyk
+        // dxk = sum_i(-yk*yi * dyi) + yk*dyk
+        // dxk = -yk * sum_i(yi * dyi) + yk*dyk
+        // dxk = -yk * dot(y, dy) + yk*dyk
+        // dxk = yk * (- dot(y, dy) + dyk)
+        // dxk = yk * (dyk - dot(y, dy))
+        //
+        // post-order:
+        // dot_y_dy := dot(y, dy)
+        // dx := dy
+        // dx := dx - dot_y_dy
+        // dx := dx * y
+
+        // linear runtime, no additional memory
+        float dot_y_dy = 0;
+        ggml_vec_dot_f32 (nc, &dot_y_dy, y, dy);
+        ggml_vec_cpy_f32 (nc, dx, dy);
+        ggml_vec_acc1_f32(nc, dx, -dot_y_dy);
+        ggml_vec_mul_f32 (nc, dx, dx, y);
+
+#ifndef NDEBUG
+        for (int i = 0; i < nc; ++i) {
+            assert(!isnan(dx[i]));
+            assert(!isinf(dx[i]));
+        }
+#endif
+    }
+}
+
+static void ggml_compute_forward_soft_max_back(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_soft_max_back_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_alibi
+
+static void ggml_compute_forward_alibi_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    //const int n_past = ((int32_t *) dst->op_params)[0];
+    const int n_head = ((int32_t *) dst->op_params)[1];
+    float max_bias;
+    memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
+
+    const int64_t ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
+    const int64_t ne1 = src0->ne[1]; // seq_len_without_past
+    const int64_t ne2 = src0->ne[2]; // n_head -> this is k
+    //const int64_t ne3 = src0->ne[3]; // 1 -> bsz
+
+    const int64_t n  = ggml_nrows(src0);
+    const int64_t ne2_ne3 = n/ne1; // ne2*ne3
+
+    const size_t nb0 = src0->nb[0];
+    const size_t nb1 = src0->nb[1];
+    const size_t nb2 = src0->nb[2];
+    //const int nb3 = src0->nb[3];
+
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(n_head == ne2);
+
+    // add alibi to src0 (KQ_scaled)
+    const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
+
+    const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
+
+    for (int64_t i = 0; i < ne0; i++) {
+        for (int64_t j = 0; j < ne1; j++) {
+            for (int64_t k = 0; k < ne2_ne3; k++) {
+                float * const src = (float *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2);
+                float *      pdst = (float *)((char *)  dst->data + i*nb0 + j*nb1 + k*nb2);
+
+                // TODO: k*nb2 or k*nb3
+
+                float m_k;
+
+                if (k < n_heads_log2_floor) {
+                    m_k = powf(m0, k + 1);
+                } else {
+                    m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
+                }
+
+                pdst[0] = i * m_k + src[0];
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_alibi_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    //const int n_past = ((int32_t *) dst->op_params)[0];
+    const int n_head = ((int32_t *) dst->op_params)[1];
+    float max_bias;
+    memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
+
+    const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
+    const int ne1 = src0->ne[1]; // seq_len_without_past
+    const int ne2 = src0->ne[2]; // n_head -> this is k
+    //const int ne3 = src0->ne[3]; // 1 -> bsz
+
+    const int n  = ggml_nrows(src0);
+    const int ne2_ne3 = n/ne1; // ne2*ne3
+
+    const int nb0 = src0->nb[0];
+    const int nb1 = src0->nb[1];
+    const int nb2 = src0->nb[2];
+    //const int nb3 = src0->nb[3];
+
+    GGML_ASSERT(nb0 == sizeof(ggml_fp16_t));
+    //GGML_ASSERT(ne1 + n_past == ne0); (void) n_past;
+    GGML_ASSERT(n_head == ne2);
+
+    // add alibi to src0 (KQ_scaled)
+    const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
+
+    const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
+
+    for (int i = 0; i < ne0; i++) {
+        for (int j = 0; j < ne1; j++) {
+            for (int k = 0; k < ne2_ne3; k++) {
+                ggml_fp16_t * const src  = (ggml_fp16_t *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2);
+                      float *      pdst  =       (float *)((char *)  dst->data + i*nb0 + j*nb1 + k*nb2);
+
+                // TODO: k*nb2 or k*nb3
+
+                float m_k;
+
+                if (k < n_heads_log2_floor) {
+                    m_k = powf(m0, k + 1);
+                } else {
+                    m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
+                }
+
+                // we return F32
+                pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]);
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_alibi(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_alibi_f16(params, src0, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_alibi_f32(params, src0, dst);
+            } break;
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q8_1:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_Q8_K:
+        case GGML_TYPE_I8:
+        case GGML_TYPE_I16:
+        case GGML_TYPE_I32:
+        case GGML_TYPE_COUNT:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_clamp
+
+static void ggml_compute_forward_clamp_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    float min;
+    float max;
+    memcpy(&min, (float *) dst->op_params + 0, sizeof(float));
+    memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    const size_t nb00 = src0->nb[0];
+    const size_t nb01 = src0->nb[1];
+
+    const size_t nb0 = dst->nb[0];
+    const size_t nb1 = dst->nb[1];
+
+    GGML_ASSERT( nb0 == sizeof(float));
+    GGML_ASSERT(nb00 == sizeof(float));
+
+    for (int j = ith; j < n; j += nth) {
+        float * dst_ptr  = (float *) ((char *)  dst->data + j*nb1);
+        float * src0_ptr = (float *) ((char *) src0->data + j*nb01);
+
+        for (int i = 0; i < nc; i++) {
+            dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min);
+        }
+    }
+}
+
+static void ggml_compute_forward_clamp(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_clamp_f32(params, src0, dst);
+            } break;
+        case GGML_TYPE_F16:
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q8_1:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_Q8_K:
+        case GGML_TYPE_I8:
+        case GGML_TYPE_I16:
+        case GGML_TYPE_I32:
+        case GGML_TYPE_COUNT:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_rope
+
+static void ggml_compute_forward_rope_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    float freq_base;
+    float freq_scale;
+
+    // these two only relevant for xPos RoPE:
+    float xpos_base;
+    bool  xpos_down;
+
+    //const int n_past = ((int32_t *) dst->op_params)[0];
+    const int n_dims = ((int32_t *) dst->op_params)[1];
+    const int mode   = ((int32_t *) dst->op_params)[2];
+    const int n_ctx  = ((int32_t *) dst->op_params)[3];
+    memcpy(&freq_base,  (int32_t *) dst->op_params + 4, sizeof(float));
+    memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
+    memcpy(&xpos_base,  (int32_t *) dst->op_params + 6, sizeof(float));
+    memcpy(&xpos_down,  (int32_t *) dst->op_params + 7, sizeof(bool));
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
+    //printf("n_past = %d, ne2 = %d\n", n_past, ne2);
+
+    GGML_ASSERT(nb00 == sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr = ggml_nrows(dst);
+
+    GGML_ASSERT(n_dims <= ne0);
+    GGML_ASSERT(n_dims % 2 == 0);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    // row index used to determine which thread to use
+    int ir = 0;
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    const bool is_neox = mode & 2;
+    const bool is_glm  = mode & 4;
+
+    const int32_t * pos = (const int32_t *) src1->data;
+
+    for (int64_t i3 = 0; i3 < ne3; i3++) {
+        for (int64_t i2 = 0; i2 < ne2; i2++) {
+            const int64_t p = pos[i2];
+            for (int64_t i1 = 0; i1 < ne1; i1++) {
+                if (ir++ < ir0) continue;
+                if (ir   > ir1) break;
+
+                float theta = freq_scale * (float)p;
+
+                if (is_glm) {
+                    theta = MIN(p, n_ctx - 2);
+                    float block_theta = MAX(p - (n_ctx - 2), 0);
+                    for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
+                        const float cos_theta = cosf(theta);
+                        const float sin_theta = sinf(theta);
+                        const float cos_block_theta = cosf(block_theta);
+                        const float sin_block_theta = sinf(block_theta);
+
+                        theta *= theta_scale;
+                        block_theta *= theta_scale;
+
+                        const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                              float * dst_data  = (float *)((char *)  dst->data +  i3*nb3 + i2*nb2  + i1*nb1  + i0*nb0);
+
+                        const float x0 = src[0];
+                        const float x1 = src[n_dims/2];
+                        const float x2 = src[n_dims];
+                        const float x3 = src[n_dims/2*3];
+
+                        dst_data[0]          = x0*cos_theta - x1*sin_theta;
+                        dst_data[n_dims/2]   = x0*sin_theta + x1*cos_theta;
+                        dst_data[n_dims]     = x2*cos_block_theta - x3*sin_block_theta;
+                        dst_data[n_dims/2*3] = x2*sin_block_theta + x3*cos_block_theta;
+                    }
+                } else if (!is_neox) {
+                    for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
+                        const float cos_theta = cosf(theta);
+                        const float sin_theta = sinf(theta);
+                        // zeta scaling for xPos only:
+                        float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
+                        if (xpos_down) zeta = 1.0f / zeta;
+
+                        theta *= theta_scale;
+
+                        const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                              float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+                        const float x0 = src[0];
+                        const float x1 = src[1];
+
+                        dst_data[0] = x0*cos_theta*zeta - x1*sin_theta*zeta;
+                        dst_data[1] = x0*sin_theta*zeta + x1*cos_theta*zeta;
+                    }
+                } else {
+                    // TODO: this might be wrong for ne0 != n_dims - need double check
+                    // ref:  https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28
+                    for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
+                        for (int64_t ic = 0; ic < n_dims; ic += 2) {
+                            const float cos_theta = cosf(theta);
+                            const float sin_theta = sinf(theta);
+
+                            theta *= theta_scale;
+
+                            const int64_t i0 = ib*n_dims + ic/2;
+
+                            const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                                  float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+                            const float x0 = src[0];
+                            const float x1 = src[n_dims/2];
+
+                            dst_data[0]        = x0*cos_theta - x1*sin_theta;
+                            dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_rope_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    float freq_base;
+    float freq_scale;
+
+    //const int n_past = ((int32_t *) dst->op_params)[0];
+    const int n_dims = ((int32_t *) dst->op_params)[1];
+    const int mode   = ((int32_t *) dst->op_params)[2];
+    const int n_ctx  = ((int32_t *) dst->op_params)[3];
+    memcpy(&freq_base,  (int32_t *) dst->op_params + 4, sizeof(float));
+    memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
+    //printf("n_past = %d, ne2 = %d\n", n_past, ne2);
+
+    GGML_ASSERT(nb0 == sizeof(ggml_fp16_t));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr = ggml_nrows(dst);
+
+    GGML_ASSERT(n_dims <= ne0);
+    GGML_ASSERT(n_dims % 2 == 0);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    // row index used to determine which thread to use
+    int ir = 0;
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    const bool is_neox = mode & 2;
+    const bool is_glm  = mode & 4;
+
+    const int32_t * pos = (const int32_t *) src1->data;
+
+    for (int64_t i3 = 0; i3 < ne3; i3++) {
+        for (int64_t i2 = 0; i2 < ne2; i2++) {
+            const int64_t p = pos[i2];
+            for (int64_t i1 = 0; i1 < ne1; i1++) {
+                if (ir++ < ir0) continue;
+                if (ir   > ir1) break;
+
+                float theta = freq_scale * (float)p;
+
+                if (is_glm) {
+                    theta = MIN(p, n_ctx - 2);
+                    float block_theta = MAX(p - (n_ctx - 2), 0);
+                    for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
+                        const float cos_theta = cosf(theta);
+                        const float sin_theta = sinf(theta);
+                        const float cos_block_theta = cosf(block_theta);
+                        const float sin_block_theta = sinf(block_theta);
+
+                        theta *= theta_scale;
+                        block_theta *= theta_scale;
+
+                        const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                              ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data +  i3*nb3 + i2*nb2  + i1*nb1  + i0*nb0);
+
+                        const float x0 = GGML_FP16_TO_FP32(src[0]);
+                        const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]);
+                        const float x2 = GGML_FP16_TO_FP32(src[n_dims]);
+                        const float x3 = GGML_FP16_TO_FP32(src[n_dims/2*3]);
+
+                        dst_data[0]          = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
+                        dst_data[n_dims/2]   = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
+                        dst_data[n_dims]     = GGML_FP32_TO_FP16(x2*cos_block_theta - x3*sin_block_theta);
+                        dst_data[n_dims/2*3] = GGML_FP32_TO_FP16(x2*sin_block_theta + x3*cos_block_theta);
+                    }
+                } else if (!is_neox) {
+                    for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
+                        const float cos_theta = cosf(theta);
+                        const float sin_theta = sinf(theta);
+
+                        theta *= theta_scale;
+
+                        const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                              ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+                        const float x0 = GGML_FP16_TO_FP32(src[0]);
+                        const float x1 = GGML_FP16_TO_FP32(src[1]);
+
+                        dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
+                        dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
+                    }
+                } else {
+                    // TODO: this might be wrong for ne0 != n_dims - need double check
+                    // ref:  https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28
+                    for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
+                        for (int64_t ic = 0; ic < n_dims; ic += 2) {
+                            const float cos_theta = cosf(theta);
+                            const float sin_theta = sinf(theta);
+
+                            theta *= theta_scale;
+
+                            const int64_t i0 = ib*n_dims + ic/2;
+
+                            const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                                  ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+                            const float x0 = GGML_FP16_TO_FP32(src[0]);
+                            const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]);
+
+                            dst_data[0]        = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
+                            dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_rope(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_rope_f16(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_rope_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_rope_back
+
+static void ggml_compute_forward_rope_back_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // y = rope(x, src1)
+    // dx = rope_back(dy, src1)
+    // src0 is dy, src1 contains options
+
+    float freq_base;
+    float freq_scale;
+
+    // these two only relevant for xPos RoPE:
+    float xpos_base;
+    bool xpos_down;
+
+    //const int n_past = ((int32_t *) dst->op_params)[0];
+    const int n_dims = ((int32_t *) dst->op_params)[1];
+    const int mode   = ((int32_t *) dst->op_params)[2];
+    const int n_ctx  = ((int32_t *) dst->op_params)[3]; UNUSED(n_ctx);
+    memcpy(&freq_base,  (int32_t *) dst->op_params + 4, sizeof(float));
+    memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
+    memcpy(&xpos_base,  (int32_t *) dst->op_params + 6, sizeof(float));
+    memcpy(&xpos_down,  (int32_t *) dst->op_params + 7, sizeof(bool));
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
+    //printf("n_past = %d, ne2 = %d\n", n_past, ne2);
+
+    assert(nb0 == sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr = ggml_nrows(dst);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    // row index used to determine which thread to use
+    int ir = 0;
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    const bool is_neox = mode & 2;
+
+    const int32_t * pos = (const int32_t *) src1->data;
+
+    for (int64_t i3 = 0; i3 < ne3; i3++) {
+        for (int64_t i2 = 0; i2 < ne2; i2++) {
+            const int64_t p = pos[i2];
+            for (int64_t i1 = 0; i1 < ne1; i1++) {
+                if (ir++ < ir0) continue;
+                if (ir   > ir1) break;
+
+                float theta = freq_scale * (float)p;
+
+                if (!is_neox) {
+                    for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
+                        const float cos_theta = cosf(theta);
+                        const float sin_theta = sinf(theta);
+                        // zeta scaling for xPos only:
+                        float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
+                        if (xpos_down) zeta = 1.0f / zeta;
+
+                        theta *= theta_scale;
+
+                        const float * const dy  = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                              float *       dx  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+                        const float dy0 = dy[0];
+                        const float dy1 = dy[1];
+
+                        dx[0] =   dy0*cos_theta*zeta + dy1*sin_theta*zeta;
+                        dx[1] = - dy0*sin_theta*zeta + dy1*cos_theta*zeta;
+                    }
+                } else {
+                    for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
+                        for (int64_t ic = 0; ic < n_dims; ic += 2) {
+                            const float cos_theta = cosf(theta);
+                            const float sin_theta = sinf(theta);
+
+                            theta *= theta_scale;
+
+                            const int64_t i0 = ib*n_dims + ic/2;
+
+                            const float * const dy  = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                                  float *       dx  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+                            const float dy0 = dy[0];
+                            const float dy1 = dy[n_dims/2];
+
+                            dx[0]        =   dy0*cos_theta + dy1*sin_theta;
+                            dx[n_dims/2] = - dy0*sin_theta + dy1*cos_theta;
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_rope_back_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // y = rope(x, src1)
+    // dx = rope_back(dy, src1)
+    // src0 is dy, src1 contains options
+
+    //const int n_past = ((int32_t *) dst->op_params)[0];
+    const int n_dims = ((int32_t *) dst->op_params)[1];
+    const int mode   = ((int32_t *) dst->op_params)[2];
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
+    //printf("n_past = %d, ne2 = %d\n", n_past, ne2);
+
+    assert(nb0 == sizeof(ggml_fp16_t));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr = ggml_nrows(dst);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    // row index used to determine which thread to use
+    int ir = 0;
+
+    const float theta_scale = powf(10000.0, -2.0f/n_dims);
+
+    const bool is_neox = mode & 2;
+
+    const int32_t * pos = (const int32_t *) src1->data;
+
+    for (int64_t i3 = 0; i3 < ne3; i3++) {
+        for (int64_t i2 = 0; i2 < ne2; i2++) {
+            const int64_t p = pos[i2];
+            for (int64_t i1 = 0; i1 < ne1; i1++) {
+                if (ir++ < ir0) continue;
+                if (ir   > ir1) break;
+
+                float theta = (float)p;
+
+                if (!is_neox) {
+                    for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
+                        const float cos_theta = cosf(theta);
+                        const float sin_theta = sinf(theta);
+
+                        theta *= theta_scale;
+
+                        const ggml_fp16_t * const dy  = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                              ggml_fp16_t *       dx  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+                        const float dy0 = GGML_FP16_TO_FP32(dy[0]);
+                        const float dy1 = GGML_FP16_TO_FP32(dy[1]);
+
+                        dx[0] = GGML_FP32_TO_FP16( dy0*cos_theta + dy1*sin_theta);
+                        dx[1] = GGML_FP32_TO_FP16(-dy0*sin_theta + dy1*cos_theta);
+                    }
+                } else {
+                    for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
+                        for (int64_t ic = 0; ic < n_dims; ic += 2) {
+                            const float cos_theta = cosf(theta);
+                            const float sin_theta = sinf(theta);
+
+                            theta *= theta_scale;
+
+                            const int64_t i0 = ib*n_dims + ic/2;
+
+                            const ggml_fp16_t * const dy  = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                                  ggml_fp16_t *       dx  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+                            const float dy0 = GGML_FP16_TO_FP32(dy[0]);
+                            const float dy1 = GGML_FP16_TO_FP32(dy[n_dims/2]);
+
+                            dx[0]        = GGML_FP32_TO_FP16( dy0*cos_theta + dy1*sin_theta);
+                            dx[n_dims/2] = GGML_FP32_TO_FP16(-dy0*sin_theta + dy1*cos_theta);
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_rope_back(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_rope_back_f16(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_rope_back_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_conv_1d
+
+static void ggml_compute_forward_conv_1d_f16_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nk = ne00;
+
+    // size of the convolution row - the kernel size unrolled across all input channels
+    const int ew0 = nk*ne01;
+
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+    const int32_t p0 = ((const int32_t*)(dst->op_params))[1];
+    const int32_t d0 = ((const int32_t*)(dst->op_params))[2];
+
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    if (params->type == GGML_TASK_INIT) {
+        memset(params->wdata, 0, params->wsize);
+
+        ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
+
+        for (int64_t i11 = 0; i11 < ne11; i11++) {
+            const float * const src = (float *)((char *) src1->data + i11*nb11);
+            ggml_fp16_t * dst_data = wdata;
+
+            for (int64_t i0 = 0; i0 < ne0; i0++) {
+                for (int64_t ik = 0; ik < nk; ik++) {
+                    const int idx0 = i0*s0 + ik*d0 - p0;
+
+                    if(!(idx0 < 0 || idx0 >= ne10)) {
+                        dst_data[i0*ew0 + i11*nk + ik] = GGML_FP32_TO_FP16(src[idx0]);
+                    }
+                }
+            }
+        }
+
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // total rows in dst
+    const int nr = ne2;
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
+
+    for (int i2 = 0; i2 < ne2; i2++) {
+        for (int i1 = ir0; i1 < ir1; i1++) {
+            float * dst_data = (float *)((char *) dst->data + i2*nb2 + i1*nb1);
+
+            for (int i0 = 0; i0 < ne0; i0++) {
+                ggml_vec_dot_f16(ew0, dst_data + i0,
+                        (ggml_fp16_t *) ((char *) src0->data + i1*nb02),
+                        (ggml_fp16_t *)                wdata + i2*nb2 + i0*ew0);
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_conv_1d_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nk = ne00;
+
+    const int ew0 = nk*ne01;
+
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+    const int32_t p0 = ((const int32_t*)(dst->op_params))[1];
+    const int32_t d0 = ((const int32_t*)(dst->op_params))[2];
+
+    GGML_ASSERT(nb00 == sizeof(float));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    if (params->type == GGML_TASK_INIT) {
+        memset(params->wdata, 0, params->wsize);
+
+        float * const wdata = (float *) params->wdata + 0;
+
+        for (int64_t i11 = 0; i11 < ne11; i11++) {
+            const float * const src = (float *)((char *) src1->data + i11*nb11);
+            float * dst_data = wdata;
+
+            for (int64_t i0 = 0; i0 < ne0; i0++) {
+                for (int64_t ik = 0; ik < nk; ik++) {
+                    const int idx0 = i0*s0 + ik*d0 - p0;
+
+                    if(!(idx0 < 0 || idx0 >= ne10)) {
+                        dst_data[i0*ew0 + i11*nk + ik] = src[idx0];
+                    }
+                }
+            }
+        }
+
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // total rows in dst
+    const int nr = ne02;
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    float * const wdata = (float *) params->wdata + 0;
+
+    for (int i2 = 0; i2 < ne2; i2++) {
+        for (int i1 = ir0; i1 < ir1; i1++) {
+            float * dst_data = (float *)((char *) dst->data + i2*nb2 + i1*nb1);
+
+            for (int i0 = 0; i0 < ne0; i0++) {
+                ggml_vec_dot_f32(ew0, dst_data + i0,
+                        (float *) ((char *) src0->data + i1*nb02),
+                        (float *)                wdata + i2*nb2 + i0*ew0);
+            }
+        }
+    }
+}
+
+static void gemm_f16_out_f32(int64_t m, int64_t n, int64_t k,
+                             ggml_fp16_t * A,
+                             ggml_fp16_t * B,
+                             float * C,
+                             const int ith, const int nth) {
+    // does not seem to make a difference
+    int64_t m0, m1, n0, n1;
+    // patches per thread
+    if (m > n) {
+        n0 = 0;
+        n1 = n;
+
+        // total patches in dst
+        const int np = m;
+
+        // patches per thread
+        const int dp = (np + nth - 1)/nth;
+
+        // patch range for this thread
+        m0 = dp*ith;
+        m1 = MIN(m0 + dp, np);
+    } else {
+        m0 = 0;
+        m1 = m;
+
+        // total patches in dst
+        const int np = n;
+
+        // patches per thread
+        const int dp = (np + nth - 1)/nth;
+
+        // patch range for this thread
+        n0 = dp*ith;
+        n1 = MIN(n0 + dp, np);
+    }
+
+    // block-tiling attempt
+    int64_t blck_n = 16;
+    int64_t blck_m = 16;
+
+    // int64_t CACHE_SIZE = 2 * 1024 * 1024; // 2MB
+    // int64_t blck_size = CACHE_SIZE / (sizeof(float) + 2 * sizeof(ggml_fp16_t) * K);
+    // if (blck_size > 0) {
+    //     blck_0 = 4;
+    //     blck_1 = blck_size / blck_0;
+    //     if (blck_1 < 0) {
+    //         blck_1 = 1;
+    //     }
+    //     // blck_0 = (int64_t)sqrt(blck_size);
+    //     // blck_1 = blck_0;
+    // }
+    // // printf("%zd %zd %zd %zd\n", blck_size, K, blck_0, blck_1);
+
+    for (int j = n0; j < n1; j+=blck_n) {
+        for (int i = m0; i < m1; i+=blck_m) {
+            // printf("i j k => %d %d %d\n", i, j, K);
+            for (int ii = i; ii < i + blck_m && ii < m1; ii++) {
+                for (int jj = j; jj < j + blck_n && jj < n1; jj++) {
+                    ggml_vec_dot_f16(k,
+                                    C + ii*n + jj,
+                                    A + ii * k,
+                                    B + jj * k);
+                }
+            }
+        }
+    }
+}
+
+// src0: kernel [OC, IC, K]
+// src1: signal [N, IC, IL]
+// dst:  result [N, OL, IC*K]
+static void ggml_compute_forward_conv_1d_stage_0_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F16);
+
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    const int64_t N  = ne12;
+    const int64_t IC = ne11;
+    const int64_t IL = ne10;
+
+    const int64_t K = ne00;
+
+    const int64_t OL = ne1;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+    const int32_t p0 = ((const int32_t*)(dst->op_params))[1];
+    const int32_t d0 = ((const int32_t*)(dst->op_params))[2];
+
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    if (params->type == GGML_TASK_INIT) {
+        memset(dst->data, 0, ggml_nbytes(dst));
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // im2col: [N, IC, IL] => [N, OL, IC*K]
+    {
+        ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data;
+
+        for (int64_t in = 0; in < N; in++) {
+            for (int64_t iol = 0; iol < OL; iol++) {
+                for (int64_t iic = ith; iic < IC; iic+=nth) {
+
+                    // micro kernel
+                    ggml_fp16_t * dst_data = wdata + (in*OL + iol)*(IC*K); // [IC, K]
+                    const float * const src_data = (float *)((char *) src1->data + in*nb12 + iic*nb11); // [IL]
+
+                    for (int64_t ik = 0; ik < K; ik++) {
+                        const int64_t iil = iol*s0 + ik*d0 - p0;
+
+                        if (!(iil < 0 || iil >= IL)) {
+                            dst_data[iic*K + ik] = GGML_FP32_TO_FP16(src_data[iil]);
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+// gemm: [N, OC, OL] = [OC, IC * K] x [N*OL, IC * K]
+// src0: [OC, IC, K]
+// src1: [N, OL, IC * K]
+// result: [N, OC, OL]
+static void ggml_compute_forward_conv_1d_stage_1_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F16);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    if (params->type == GGML_TASK_INIT) {
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb10 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb0  == sizeof(float));
+
+    const int N = ne12;
+    const int OL = ne11;
+
+    const int OC = ne02;
+    const int IC = ne01;
+    const int K  = ne00;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    int64_t m = OC;
+    int64_t n = OL;
+    int64_t k = IC * K;
+
+    // [N, OC, OL] = [OC, IC * K] x [N*OL, IC * K]
+    for (int i = 0; i < N; i++) {
+        ggml_fp16_t * A = (ggml_fp16_t *)src0->data; // [m, k]
+        ggml_fp16_t * B = (ggml_fp16_t *)src1->data + i * m * k; // [n, k]
+        float * C = (float *)dst->data + i * m * n; // [m, n]
+
+        gemm_f16_out_f32(m, n, k, A, B, C, ith, nth);
+    }
+}
+
+static void ggml_compute_forward_conv_1d(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    switch(src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_conv_1d_f16_f32(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_conv_1d_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+static void ggml_compute_forward_conv_1d_stage_0(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    switch(src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_conv_1d_stage_0_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+static void ggml_compute_forward_conv_1d_stage_1(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    switch(src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_conv_1d_stage_1_f16(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_conv_transpose_1d
+
+static void ggml_compute_forward_conv_transpose_1d_f16_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nk = ne00*ne01*ne02;
+
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    if (params->type == GGML_TASK_INIT) {
+        memset(params->wdata, 0, params->wsize);
+
+        // permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout)
+        {
+            ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
+
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                for (int64_t i01 = 0; i01 < ne01; i01++) {
+                    const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01);
+                    ggml_fp16_t * dst_data = wdata + i01*ne00*ne02;
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        dst_data[i00*ne02 + i02] = src[i00];
+                    }
+                }
+            }
+        }
+
+        // permute source data (src1) from (L x Cin) to (Cin x L)
+        {
+            ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk;
+            ggml_fp16_t * dst_data = wdata;
+
+            for (int64_t i11 = 0; i11 < ne11; i11++) {
+                const float * const src = (float *)((char *) src1->data + i11*nb11);
+                for (int64_t i10 = 0; i10 < ne10; i10++) {
+                    dst_data[i10*ne11 + i11] = GGML_FP32_TO_FP16(src[i10]);
+                }
+            }
+        }
+
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+
+    // total rows in dst
+    const int nr = ne1;
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    ggml_fp16_t * const wdata     = (ggml_fp16_t *) params->wdata + 0;
+    ggml_fp16_t * const wdata_src = wdata + nk;
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        float * dst_data = (float *)((char *) dst->data + i1*nb1);
+        ggml_fp16_t * wdata_kernel = wdata + i1*ne02*ne00;
+        for (int i10 = 0; i10 < ne10; i10++) {
+            const int i1n = i10*ne11;
+            for (int i00 = 0; i00 < ne00; i00++) {
+                float v = 0;
+                ggml_vec_dot_f16(ne02, &v,
+                        (ggml_fp16_t *)    wdata_src + i1n,
+                        (ggml_fp16_t *) wdata_kernel + i00*ne02);
+                dst_data[i10*s0 + i00] += v;
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_conv_transpose_1d_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nk = ne00*ne01*ne02;
+
+    GGML_ASSERT(nb00 == sizeof(float));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    if (params->type == GGML_TASK_INIT) {
+        memset(params->wdata, 0, params->wsize);
+
+        // prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout)
+        {
+            float * const wdata = (float *) params->wdata + 0;
+
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                for (int64_t i01 = 0; i01 < ne01; i01++) {
+                    const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01);
+                    float * dst_data = wdata + i01*ne00*ne02;
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        dst_data[i01*ne00*ne02 + i00*ne02 + i02] = src[i00];
+                    }
+                }
+            }
+        }
+
+        // prepare source data (src1)
+        {
+            float * const wdata = (float *) params->wdata + nk;
+            float * dst_data = wdata;
+
+            for (int64_t i11 = 0; i11 < ne11; i11++) {
+                const float * const src = (float *)((char *) src1->data + i11*nb11);
+                for (int64_t i10 = 0; i10 < ne10; i10++) {
+                    dst_data[i10*ne11 + i11] = src[i10];
+                }
+            }
+        }
+
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+
+    // total rows in dst
+    const int nr = ne1;
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    float * const wdata     = (float *) params->wdata + 0;
+    float * const wdata_src = wdata + nk;
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        float * dst_data = (float *)((char *) dst->data + i1*nb1);
+        float * wdata_kernel = wdata + i1*ne02*ne00;
+        for (int i10 = 0; i10 < ne10; i10++) {
+            const int i1n = i10*ne11;
+            for (int i00 = 0; i00 < ne00; i00++) {
+                float v = 0;
+                ggml_vec_dot_f32(ne02, &v,
+                        wdata_src + i1n,
+                        wdata_kernel + i00*ne02);
+                dst_data[i10*s0 + i00] += v;
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_conv_transpose_1d(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_conv_transpose_1d_f16_f32(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_conv_transpose_1d_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_conv_2d
+
+static void ggml_compute_forward_conv_2d_f16_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nk0 = ne00;
+    const int nk1 = ne01;
+
+    // size of the convolution row - the kernel size unrolled across all channels
+    const int ew0 = nk0*nk1*ne02;
+
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+    const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
+    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
+    const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
+    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
+    const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
+
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    if (params->type == GGML_TASK_INIT) {
+        memset(params->wdata, 0, params->wsize);
+
+        // prepare source data (src1)
+        {
+            ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
+
+            for (int i13 = 0; i13 < ne13; i13++) {
+                for (int i12 = 0; i12 < ne12; i12++) {
+                    const float * const src = (float *)((char *) src1->data + i13*nb13 + i12*nb12);
+                    ggml_fp16_t * dst_data = wdata + i13*(ne1*ne0*ew0);
+
+                    for (int i1 = 0; i1 < ne1; i1++) {
+                        for (int i0 = 0; i0 < ne0; i0++) {
+                            for (int ik1 = 0; ik1 < nk1; ik1++) {
+                                for (int ik0 = 0; ik0 < nk0; ik0++) {
+                                    const int idx0 = i0*s0 + ik0*d0 - p0;
+                                    const int idx1 = i1*s1 + ik1*d1 - p1;
+
+                                    if (!(idx1 < 0 || idx1 >= ne11 || idx0 < 0 || idx0 >= ne10)) {
+                                        dst_data[(i1*ne0 + i0)*ew0 + i12*(nk0*nk1) + ik1*nk0 + ik0] =
+                                            GGML_FP32_TO_FP16(src[idx1*ne10 + idx0]);
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // total patches in dst
+    const int np = ne2;
+
+    // patches per thread
+    const int dp = (np + nth - 1)/nth;
+
+    // patch range for this thread
+    const int ip0 = dp*ith;
+    const int ip1 = MIN(ip0 + dp, np);
+
+    ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
+
+    for (int i3 = 0; i3 < ne3; i3++) {
+        for (int i2 = ip0; i2 < ip1; i2++) {
+            float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2);
+
+            for (int i1 = 0; i1 < ne1; ++i1) {
+                for (int i0 = 0; i0 < ne0; ++i0) {
+                    ggml_vec_dot_f16(ew0, dst_data + i1*ne0 + i0,
+                            (ggml_fp16_t *) ((char *) src0->data + i2*nb03),
+                            (ggml_fp16_t *)                wdata + i3*nb3 + (i1*ne0 + i0)*ew0);
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_conv_2d(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_conv_2d_f16_f32(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                //ggml_compute_forward_conv_2d_f32(params, src0, src1, dst);
+                GGML_ASSERT(false);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_conv_transpose_2d
+
+static void ggml_compute_forward_conv_transpose_2d(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nk = ne00*ne01*ne02*ne03;
+
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    if (params->type == GGML_TASK_INIT) {
+        memset(params->wdata, 0, params->wsize);
+
+        // permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout)
+        {
+            ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
+
+            for (int64_t i03 = 0; i03 < ne03; i03++) {
+                for (int64_t i02 = 0; i02 < ne02; i02++) {
+                    const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i03*nb03 + i02*nb02);
+                    ggml_fp16_t * dst_data = wdata + i02*ne01*ne00*ne03;
+                    for (int64_t i01 = 0; i01 < ne01; i01++) {
+                        for (int64_t i00 = 0; i00 < ne00; i00++) {
+                            dst_data[i01*ne00*ne03 + i00*ne03 + i03] = src[i01 * ne00 + i00];
+                        }
+                    }
+                }
+            }
+        }
+
+        // permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh)
+        {
+            ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk;
+            for (int i12 = 0; i12 < ne12; i12++) {
+                for (int i11 = 0; i11 < ne11; i11++) {
+                    const float * const src = (float *)((char *) src1->data + i12*nb12 + i11*nb11);
+                    ggml_fp16_t * dst_data = wdata + i11*ne10*ne12;
+                    for (int i10 = 0; i10 < ne10; i10++) {
+                        dst_data[i10*ne12 + i12] = GGML_FP32_TO_FP16(src[i10]);
+                    }
+                }
+            }
+        }
+
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int32_t stride = ggml_get_op_params_i32(dst, 0);
+
+    // total patches in dst
+    const int np = ne2;
+
+    // patches per thread
+    const int dp = (np + nth - 1)/nth;
+
+    // patch range for this thread
+    const int ip0 = dp*ith;
+    const int ip1 = MIN(ip0 + dp, np);
+
+    ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
+    ggml_fp16_t * const wdata_src = wdata + nk;
+
+    for (int i2 = ip0; i2 < ip1; i2++) { // Cout
+        float * dst_data = (float *)((char *) dst->data + i2*nb2);
+        ggml_fp16_t * wdata_kernel = wdata + i2*ne01*ne00*ne03;
+        for (int i11 = 0; i11 < ne11; i11++) {
+            for (int i10 = 0; i10 < ne10; i10++) {
+                const int i1n = i11*ne10*ne12 + i10*ne12;
+                for (int i01 = 0; i01 < ne01; i01++) {
+                    for (int i00 = 0; i00 < ne00; i00++) {
+                        float v = 0;
+                        ggml_vec_dot_f16(ne03, &v,
+                                wdata_src + i1n,
+                                wdata_kernel + i01*ne00*ne03 + i00*ne03);
+                        dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v;
+                    }
+                }
+            }
+        }
+    }
+}
+
+// ggml_compute_forward_pool_1d_sk_p0
+
+static void ggml_compute_forward_pool_1d_sk_p0(
+        const struct ggml_compute_params * params,
+        const enum ggml_op_pool op,
+        const struct ggml_tensor * src,
+        const int k,
+        struct ggml_tensor * dst) {
+    assert(src->type == GGML_TYPE_F32);
+    assert(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const char * cdata = (const char *)src->data;
+    const char * const data_end = cdata + ggml_nbytes(src);
+    float * drow = (float *)dst->data;
+
+    const int64_t rs = dst->ne[0];
+
+    while (cdata < data_end) {
+        const float * const srow = (const float *)cdata;
+
+        int j = 0;
+
+        for (int64_t i = 0; i < rs; ++i) {
+            switch (op) {
+                case GGML_OP_POOL_AVG:   drow[i] = 0;        break;
+                case GGML_OP_POOL_MAX:   drow[i] = -FLT_MAX; break;
+                case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break;
+            }
+            for (int ki = 0; ki < k; ++ki) {
+                switch (op) {
+                    case GGML_OP_POOL_AVG:                          drow[i] += srow[j]; break;
+                    case GGML_OP_POOL_MAX:   if (srow[j] > drow[i]) drow[i]  = srow[j]; break;
+                    case GGML_OP_POOL_COUNT:                        GGML_ASSERT(false); break;
+                }
+                ++j;
+            }
+            switch (op) {
+                case GGML_OP_POOL_AVG:         drow[i] /= k; break;
+                case GGML_OP_POOL_MAX:                       break;
+                case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break;
+            }
+        }
+
+        cdata += src->nb[1];
+        drow  += rs;
+    }
+}
+
+// ggml_compute_forward_pool_1d
+
+static void ggml_compute_forward_pool_1d(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+              struct ggml_tensor * dst) {
+
+    const int32_t * opts = (const int32_t *)dst->op_params;
+    enum ggml_op_pool op = opts[0];
+    const int k0 = opts[1];
+    const int s0 = opts[2];
+    const int p0 = opts[3];
+    GGML_ASSERT(p0 == 0); // padding not supported
+    GGML_ASSERT(k0 == s0); // only s = k supported
+
+    ggml_compute_forward_pool_1d_sk_p0(params, op, src0, k0, dst);
+}
+
+// ggml_compute_forward_pool_2d_sk_p0
+
+static void ggml_compute_forward_pool_2d_sk_p0(
+        const struct ggml_compute_params * params,
+        const enum   ggml_op_pool op,
+        const struct ggml_tensor * src,
+        const int k0,
+        const int k1,
+        struct ggml_tensor * dst) {
+    assert(src->type == GGML_TYPE_F32);
+    assert(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const char * cdata = (const char*)src->data;
+    const char * const data_end = cdata + ggml_nbytes(src);
+
+    const int64_t px = dst->ne[0];
+    const int64_t py = dst->ne[1];
+    const int64_t pa = px * py;
+
+    float * dplane = (float *)dst->data;
+
+    const int ka = k0 * k1;
+
+    while (cdata < data_end) {
+        for (int oy = 0; oy < py; ++oy) {
+            float * const drow = dplane + oy * px;
+            for (int ox = 0; ox < px; ++ox) {
+                float * const out =  drow + ox;
+                switch (op) {
+                    case GGML_OP_POOL_AVG:     *out = 0;        break;
+                    case GGML_OP_POOL_MAX:     *out = -FLT_MAX; break;
+                    case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break;
+                }
+
+                const int ix = ox * k0;
+                const int iy = oy * k1;
+
+                for (int ky = 0; ky < k1; ++ky) {
+                    const float * const srow = (const float *)(cdata + src->nb[1] * (iy + ky));
+                    for (int kx = 0; kx < k0; ++kx) {
+                        int j = ix + kx;
+                        switch (op) {
+                            case GGML_OP_POOL_AVG:                     *out += srow[j]; break;
+                            case GGML_OP_POOL_MAX: if (srow[j] > *out) *out  = srow[j]; break;
+                            case GGML_OP_POOL_COUNT:                GGML_ASSERT(false); break;
+                        }
+                    }
+                }
+                switch (op) {
+                    case GGML_OP_POOL_AVG:           *out /= ka; break;
+                    case GGML_OP_POOL_MAX:                       break;
+                    case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break;
+                }
+            }
+        }
+
+        cdata  += src->nb[2];
+        dplane += pa;
+    }
+}
+
+// ggml_compute_forward_pool_2d
+
+static void ggml_compute_forward_pool_2d(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+              struct ggml_tensor * dst) {
+
+    const int32_t * opts = (const int32_t *)dst->op_params;
+    enum ggml_op_pool op = opts[0];
+    const int k0 = opts[1];
+    const int k1 = opts[2];
+    const int s0 = opts[3];
+    const int s1 = opts[4];
+    const int p0 = opts[5];
+    const int p1 = opts[6];
+    GGML_ASSERT(p0 == 0);
+    GGML_ASSERT(p1 == 0); // padding not supported
+    GGML_ASSERT(k0 == s0);
+    GGML_ASSERT(k1 == s1); // only s = k supported
+
+    ggml_compute_forward_pool_2d_sk_p0(params, op, src0, k0, k1, dst);
+}
+
+// ggml_compute_forward_upscale
+
+static void ggml_compute_forward_upscale_f32(
+    const struct ggml_compute_params * params,
+    const struct ggml_tensor * src0,
+    struct ggml_tensor * dst) {
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+
+    const int ith = params->ith;
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    const int scale_factor = dst->op_params[0];
+
+    // TODO: optimize
+
+    for (int i03 = 0; i03 < ne03; i03++) {
+        for (int i02 = ith; i02 < ne02; i02++) {
+            for (int m = 0; m < dst->ne[1]; m++) {
+                int i01 = m / scale_factor;
+                for (int n = 0; n < dst->ne[0]; n++) {
+                    int i00 = n / scale_factor;
+
+                    const float * x = (float *)((char *) src0->data + i00 * nb00 +i01 * nb01 + i02 * nb02 + i03 * nb03);
+
+                    float * y = (float *)((char *) dst->data + n * dst->nb[0] + m * dst->nb[1] + i02 * dst->nb[2] + i03 * dst->nb[3]);
+
+                    *y = *x;
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_upscale(
+    const struct ggml_compute_params * params,
+    const struct ggml_tensor * src0,
+    struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_upscale_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_flash_attn
+
+static void ggml_compute_forward_flash_attn_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * q,
+        const struct ggml_tensor * k,
+        const struct ggml_tensor * v,
+        const bool masked,
+        struct ggml_tensor * dst) {
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
+    GGML_TENSOR_LOCALS(int64_t, nek, k,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbk, k,   nb)
+    GGML_TENSOR_LOCALS(int64_t, nev, v,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbv, v,   nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst, nb)
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t D = neq0;
+    const int64_t N = neq1;
+    const int64_t P = nek1 - N;
+    const int64_t M = P + N;
+
+    const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL);
+
+    GGML_ASSERT(ne0 == D);
+    GGML_ASSERT(ne1 == N);
+    GGML_ASSERT(P >= 0);
+
+    GGML_ASSERT(nbq0 == sizeof(float));
+    GGML_ASSERT(nbk0 == sizeof(float));
+    GGML_ASSERT(nbv0 == sizeof(float));
+
+    GGML_ASSERT(neq0 == D);
+    GGML_ASSERT(nek0 == D);
+    GGML_ASSERT(nev1 == D);
+
+    GGML_ASSERT(neq1 == N);
+    GGML_ASSERT(nek1 == N + P);
+    GGML_ASSERT(nev1 == D);
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    if (params->type == GGML_TASK_INIT) {
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // parallelize by q rows using ggml_vec_dot_f32
+
+    // total rows in q
+    const int nr = neq1*neq2*neq3;
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    const float scale = 1.0f/sqrtf(D);
+
+    //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale);
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // q indices
+        const int iq3 = ir/(neq2*neq1);
+        const int iq2 = (ir - iq3*neq2*neq1)/neq1;
+        const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1);
+
+        float * S = (float *) params->wdata + ith*(Mup + CACHE_LINE_SIZE_F32);
+
+        for (int i = M; i < Mup; ++i) {
+            S[i] = -INFINITY;
+        }
+
+        const int64_t masked_begin = masked ? (P + iq1 + 1) : M;
+        for (int64_t ic = 0; ic < masked_begin; ++ic) {
+            // k indices
+            const int ik3 = iq3;
+            const int ik2 = iq2 % nek2;
+            const int ik1 = ic;
+
+            // S indices
+            const int i1 = ik1;
+
+            ggml_vec_dot_f32(neq0,
+                    S + i1,
+                    (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)),
+                    (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)));
+        }
+
+        // scale
+        ggml_vec_scale_f32(masked_begin, S, scale);
+
+        for (int64_t i = masked_begin; i < M; i++) {
+            S[i] = -INFINITY;
+        }
+
+        // softmax
+        // exclude known -INF S[..] values from max and loop
+        // dont forget to set their SW values to zero
+        {
+            float max = -INFINITY;
+            ggml_vec_max_f32(masked_begin, &max, S);
+
+            ggml_float sum = 0.0;
+            {
+#ifdef GGML_SOFT_MAX_ACCELERATE
+                max = -max;
+                vDSP_vsadd(S, 1, &max, S, 1, Mup);
+                vvexpf(S, S, &Mup);
+                ggml_vec_sum_f32(Mup, &sum, S);
+#else
+                uint16_t   scvt[GGML_SOFT_MAX_UNROLL]; UNUSED(scvt);
+                ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 };
+
+                for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) {
+                    if (i >= masked_begin) {
+                        break;
+                    }
+                    float * SS = S + i;
+
+                    for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) {
+                        if (i + j >= masked_begin) {
+                            break;
+                        } else if (SS[j] == -INFINITY) {
+                            SS[j] = 0.0f;
+                        } else {
+#ifndef GGML_FLASH_ATTN_EXP_FP16
+                            const float val = expf(SS[j] - max);
+#else
+                            ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max);
+                            memcpy(&scvt[j], &s, sizeof(uint16_t));
+                            const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
+#endif
+                            sump[j] += (ggml_float)val;
+                            SS[j] = val;
+                        }
+                    }
+                }
+
+                for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) {
+                    sum += sump[i];
+                }
+#endif
+            }
+
+            assert(sum > 0.0);
+
+            sum = 1.0/sum;
+            ggml_vec_scale_f32(masked_begin, S, sum);
+
+#ifndef NDEBUG
+            for (int i = 0; i < masked_begin; ++i) {
+                assert(!isnan(S[i]));
+                assert(!isinf(S[i]));
+            }
+#endif
+        }
+
+        for (int64_t ic = 0; ic < nev1; ++ic) {
+            // dst indices
+            const int i1 = iq1;
+            const int i2 = iq2;
+            const int i3 = iq3;
+
+            // v indices
+            const int iv2 = iq2 % nev2;
+            const int iv3 = iq3;
+
+            ggml_vec_dot_f32(masked_begin,
+                    (float *) ((char *) dst->data + (ic*nb0 + i1*nb1  + i2*nb2   + i3*nb3)),
+                    (float *) ((char *) v->data   + (         ic*nbv1 + iv2*nbv2 + iv3*nbv3)),
+                    S);
+        }
+    }
+}
+
+static void ggml_compute_forward_flash_attn_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * q,
+        const struct ggml_tensor * k,
+        const struct ggml_tensor * v,
+        const bool masked,
+        struct ggml_tensor * dst) {
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
+    GGML_TENSOR_LOCALS(int64_t, nek, k,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbk, k,   nb)
+    GGML_TENSOR_LOCALS(int64_t, nev, v,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbv, v,   nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst, nb)
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t D = neq0;
+    const int64_t N = neq1;
+    const int64_t P = nek1 - N;
+    const int64_t M = P + N;
+
+    const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL);
+
+    GGML_ASSERT(ne0 == D);
+    GGML_ASSERT(ne1 == N);
+    GGML_ASSERT(P >= 0);
+
+    GGML_ASSERT(nbq0 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t));
+
+    GGML_ASSERT(neq0 == D);
+    GGML_ASSERT(nek0 == D);
+    GGML_ASSERT(nev1 == D);
+
+    GGML_ASSERT(neq1 == N);
+    GGML_ASSERT(nek1 == N + P);
+    GGML_ASSERT(nev1 == D);
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    if (params->type == GGML_TASK_INIT) {
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // parallelize by q rows using ggml_vec_dot_f32
+
+    // total rows in q
+    const int nr = neq1*neq2*neq3;
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    const float scale = 1.0f/sqrtf(D);
+
+    //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale);
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // q indices
+        const int iq3 = ir/(neq2*neq1);
+        const int iq2 = (ir - iq3*neq2*neq1)/neq1;
+        const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1);
+
+        float * S = (float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32);
+
+        for (int i = M; i < Mup; ++i) {
+            S[i] = -INFINITY;
+        }
+
+        if (GGML_VEC_DOT_UNROLL > 2 || nek1 % GGML_VEC_DOT_UNROLL != 0) {
+            for (int64_t ic = 0; ic < nek1; ++ic) {
+                // k indices
+                const int ik3 = iq3;
+                const int ik2 = iq2 % nek2;
+                const int ik1 = ic;
+
+                // S indices
+                const int i1 = ik1;
+
+                ggml_vec_dot_f16(neq0,
+                        S + i1,
+                        (ggml_fp16_t *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)),
+                        (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)));
+            }
+        } else {
+            for (int64_t ic = 0; ic < nek1; ic += GGML_VEC_DOT_UNROLL) {
+                // k indices
+                const int ik3 = iq3;
+                const int ik2 = iq2 % nek2;
+                const int ik1 = ic;
+
+                // S indices
+                const int i1 = ik1;
+
+                ggml_vec_dot_f16_unroll(neq0, nbk1,
+                        S + i1,
+                        ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)),
+                        (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)));
+            }
+        }
+
+        // scale
+        ggml_vec_scale_f32(nek1, S, scale);
+
+        if (masked) {
+            for (int64_t i = P; i < M; i++) {
+                if (i > P + iq1) {
+                    S[i] = -INFINITY;
+                }
+            }
+        }
+
+        // softmax
+        // todo: exclude known -INF S[..] values from max and loop, assuming their results to be zero.
+        // dont forget to set their S values to zero
+        {
+            float max = -INFINITY;
+            ggml_vec_max_f32(M, &max, S);
+
+            ggml_float sum = 0.0;
+            {
+#ifdef GGML_SOFT_MAX_ACCELERATE
+                max = -max;
+                vDSP_vsadd(S, 1, &max, S, 1, Mup);
+                vvexpf(S, S, &Mup);
+                ggml_vec_sum_f32(Mup, &sum, S);
+#else
+                uint16_t   scvt[GGML_SOFT_MAX_UNROLL];
+                ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 };
+
+                for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) {
+                    float * SS = S + i;
+
+                    for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) {
+                        if (SS[j] == -INFINITY) {
+                            SS[j] = 0.0f;
+                        } else {
+                            ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max);
+                            memcpy(&scvt[j], &s, sizeof(uint16_t));
+                            const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
+                            sump[j] += (ggml_float)val;
+                            SS[j] = val;
+                        }
+                    }
+                }
+
+                for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) {
+                    sum += sump[i];
+                }
+#endif
+            }
+
+            assert(sum > 0.0);
+
+            sum = 1.0/sum;
+            ggml_vec_scale_f32(M, S, sum);
+
+#ifndef NDEBUG
+            for (int i = 0; i < M; ++i) {
+                assert(!isnan(S[i]));
+                assert(!isinf(S[i]));
+            }
+#endif
+        }
+
+        ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32) + Mup);
+
+        for (int64_t i = 0; i < M; i++) {
+            S16[i] = GGML_FP32_TO_FP16(S[i]);
+        }
+
+        // todo: exclude known zero S[..] values from dot (reducing nev0 and increasing begin of v and S16).
+        if (GGML_VEC_DOT_UNROLL == 1 || (nev1 % GGML_VEC_DOT_UNROLL != 0)) {
+            for (int64_t ic = 0; ic < nev1; ++ic) {
+                // dst indices
+                const int i1 = iq1;
+                const int i2 = iq2;
+                const int i3 = iq3;
+
+                // v indices
+                const int iv2 = iq2 % nev2;
+                const int iv3 = iq3;
+
+                ggml_vec_dot_f16(nev0,
+                        (float *)       ((char *) dst->data + (ic*nb0 + i1*nb1  + i2*nb2   + i3*nb3)),
+                        (ggml_fp16_t *) ((char *) v->data   + (         ic*nbv1 + iv2*nbv2 + iv3*nbv3)),
+                        S16);
+            }
+        } else {
+            for (int64_t ic = 0; ic < nev1; ic += GGML_VEC_DOT_UNROLL) {
+                // dst indices
+                const int i1 = iq1;
+                const int i2 = iq2;
+                const int i3 = iq3;
+
+                // v indices
+                const int iv2 = iq2 % nev2;
+                const int iv3 = iq3;
+
+                ggml_vec_dot_f16_unroll(nev0, nbv1,
+                        (float *) ((char *) dst->data + (ic*nb0 + i1*nb1  + i2*nb2   + i3*nb3)),
+                        ((char *)             v->data + (         ic*nbv1 + iv2*nbv2 + iv3*nbv3)),
+                        S16);
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_flash_attn(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * q,
+        const struct ggml_tensor * k,
+        const struct ggml_tensor * v,
+        const bool masked,
+        struct ggml_tensor * dst) {
+    switch (q->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_flash_attn_f16(params, q, k, v, masked, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_flash_attn_f32(params, q, k, v, masked, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_flash_ff
+
+static void ggml_compute_forward_flash_ff_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * a,  // F16
+        const struct ggml_tensor * b0, // F16 fc_w
+        const struct ggml_tensor * b1, // F32 fc_b
+        const struct ggml_tensor * c0, // F16 proj_w
+        const struct ggml_tensor * c1, // F32 proj_b
+        struct ggml_tensor * dst) {
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_LOCALS(int64_t, nea,  a,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nba,  a,   nb)
+    GGML_TENSOR_LOCALS(int64_t, neb0, b0,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nbb0, b0,  nb)
+    GGML_TENSOR_LOCALS(int64_t, neb1, b1,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nbb1, b1,  nb)
+    GGML_TENSOR_LOCALS(int64_t, nec0, c0,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nbc0, c0,  nb)
+    GGML_TENSOR_LOCALS(int64_t, nec1, c1,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nbc1, c1,  nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,   dst, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,   dst, nb)
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t D = nea0;
+    //const int64_t N = nea1;
+    const int64_t M = neb01;
+
+    GGML_ASSERT(ne0 == nea0);
+    GGML_ASSERT(ne1 == nea1);
+    GGML_ASSERT(ne2 == nea2);
+
+    GGML_ASSERT(nba0  == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nbb00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nbb10 == sizeof(float));
+    GGML_ASSERT(nbc00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nbc10 == sizeof(float));
+
+    GGML_ASSERT(neb00 == D);
+    GGML_ASSERT(neb01 == M);
+    GGML_ASSERT(neb10 == M);
+    GGML_ASSERT(neb11 == 1);
+
+    GGML_ASSERT(nec00 == M);
+    GGML_ASSERT(nec01 == D);
+    GGML_ASSERT(nec10 == D);
+    GGML_ASSERT(nec11 == 1);
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    if (params->type == GGML_TASK_INIT) {
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // parallelize by a rows using ggml_vec_dot_f32
+
+    // total rows in a
+    const int nr = nea1*nea2*nea3;
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // a indices
+        const int ia3 = ir/(nea2*nea1);
+        const int ia2 = (ir - ia3*nea2*nea1)/nea1;
+        const int ia1 = (ir - ia3*nea2*nea1 - ia2*nea1);
+
+        float * S = (float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32);
+
+        for (int64_t ic = 0; ic < neb01; ++ic) {
+            // b0 indices
+            const int ib03 = ia3;
+            const int ib02 = ia2;
+            const int ib01 = ic;
+
+            // S indices
+            const int i1 = ib01;
+
+            ggml_vec_dot_f16(nea0,
+                    S + i1,
+                    (ggml_fp16_t *) ((char *) b0->data + (ib01*nbb01 + ib02*nbb02 + ib03*nbb03)),
+                    (ggml_fp16_t *) ((char *)  a->data + ( ia1*nba1  +  ia2*nba2  +  ia3*nba3)));
+        }
+
+        ggml_vec_add_f32(neb01, S, S, (float *) b1->data);
+        //ggml_vec_gelu_f32(neb01, S, S);
+
+        ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32) + M);
+
+        for (int64_t i = 0; i < M; i++) {
+            S16[i] = GGML_FP32_TO_FP16(S[i]);
+        }
+
+        ggml_vec_gelu_f16(neb01, S16, S16);
+
+        {
+            // dst indices
+            const int i1 = ia1;
+            const int i2 = ia2;
+            const int i3 = ia3;
+
+            for (int64_t ic = 0; ic < nec01; ++ic) {
+
+                ggml_vec_dot_f16(neb01,
+                        (float *)       ((char *) dst->data + (ic*nb0 + i1*nb1   + i2*nb2   + i3*nb3)),
+                        (ggml_fp16_t *) ((char *) c0->data  + (         ic*nbc01 + i2*nbc02 + i3*nbc03)),
+                        S16);
+            }
+
+            ggml_vec_add_f32(nec01,
+                    (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)),
+                    (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)),
+                    (float *) c1->data);
+        }
+    }
+}
+
+static void ggml_compute_forward_flash_ff(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * a,
+        const struct ggml_tensor * b0,
+        const struct ggml_tensor * b1,
+        const struct ggml_tensor * c0,
+        const struct ggml_tensor * c1,
+        struct ggml_tensor * dst) {
+    switch (b0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_flash_ff_f16(params, a, b0, b1, c0, c1, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                GGML_ASSERT(false); // TODO
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_flash_attn_back
+
+static void ggml_compute_forward_flash_attn_back_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * q,
+        const struct ggml_tensor * k,
+        const struct ggml_tensor * v,
+        const struct ggml_tensor * d,
+        const bool masked,
+              struct ggml_tensor * dst) {
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
+    GGML_TENSOR_LOCALS(int64_t, nek, k,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbk, k,   nb)
+    GGML_TENSOR_LOCALS(int64_t, nev, v,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbv, v,   nb)
+    GGML_TENSOR_LOCALS(int64_t, ned, d,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbd, d,   nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst, nb)
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t D = neq0;
+    const int64_t N = neq1;
+    const int64_t P = nek1 - N;
+    const int64_t M = P + N;
+
+    const int Mup  = ggml_up(M, GGML_SOFT_MAX_UNROLL);
+    const int mxDM = MAX(D, Mup);
+
+    // GGML_ASSERT(ne0 == D);
+    // GGML_ASSERT(ne1 == N);
+    GGML_ASSERT(P >= 0);
+
+    GGML_ASSERT(nbq0 == sizeof(float));
+    GGML_ASSERT(nbk0 == sizeof(float));
+    GGML_ASSERT(nbv0 == sizeof(float));
+
+    GGML_ASSERT(neq0 == D);
+    GGML_ASSERT(nek0 == D);
+    GGML_ASSERT(nev1 == D);
+    GGML_ASSERT(ned0 == D);
+
+    GGML_ASSERT(neq1 == N);
+    GGML_ASSERT(nek1 == N + P);
+    GGML_ASSERT(nev1 == D);
+    GGML_ASSERT(ned1 == N);
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    if (params->type == GGML_TASK_INIT) {
+        if (ith == 0) {
+            memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3);
+        }
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int64_t elem_q = ggml_nelements(q);
+    const int64_t elem_k = ggml_nelements(k);
+
+    enum ggml_type result_type = dst->type;
+    GGML_ASSERT(ggml_blck_size(result_type) == 1);
+    const size_t tsize = ggml_type_size(result_type);
+
+    const size_t offs_q = 0;
+    const size_t offs_k = offs_q + GGML_PAD(elem_q * tsize, GGML_MEM_ALIGN);
+    const size_t offs_v = offs_k + GGML_PAD(elem_k * tsize, GGML_MEM_ALIGN);
+
+    void * grad_q = (char *) dst->data;
+    void * grad_k = (char *) dst->data + offs_k;
+    void * grad_v = (char *) dst->data + offs_v;
+
+    const size_t nbgq1 = nb0*neq0;
+    const size_t nbgq2 = nb0*neq0*neq1;
+    const size_t nbgq3 = nb0*neq0*neq1*neq2;
+
+    const size_t nbgk1 = nb0*nek0;
+    const size_t nbgk2 = nb0*nek0*nek1;
+    const size_t nbgk3 = nb0*nek0*nek1*neq2;
+
+    const size_t nbgv1 = nb0*nev0;
+    const size_t nbgv2 = nb0*nev0*nev1;
+    const size_t nbgv3 = nb0*nev0*nev1*neq2;
+
+    // parallelize by k rows using ggml_vec_dot_f32
+
+    // total rows in k
+    const int nr = nek2*nek3;
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    const float scale = 1.0f/sqrtf(D);
+
+    //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale);
+
+    // how often k2 (and v2) is repeated in q2
+    int nrep = neq2/nek2;
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // q indices
+        const int ik3 = ir/(nek2);
+        const int ik2 = ir - ik3*nek2;
+
+        const int iq3 = ik3;
+        const int id3 = ik3;
+        const int iv3 = ik3;
+        const int iv2 = ik2;
+
+        for (int irep = 0; irep < nrep; ++irep) {
+            const int iq2 = ik2 + irep*nek2;
+            const int id2 = iq2;
+
+            // (ik2 + irep*nek2) % nek2 == ik2
+            for (int iq1 = 0; iq1 < neq1; ++iq1) {
+                const int id1 = iq1;
+
+                // not sure about CACHE_LINE_SIZE_F32..
+                // - maybe it must not be multiplied by 2 and excluded from .. in SM 1*(..) offset?
+                float * S  = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 0*(mxDM+CACHE_LINE_SIZE_F32);
+                float * SM = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 1*(mxDM+CACHE_LINE_SIZE_F32);
+
+                for (int i = M; i < Mup; ++i) {
+                    S[i] = -INFINITY;
+                }
+
+                const int64_t masked_begin = masked ? (P + iq1 + 1) : M;
+                for (int64_t ic = 0; ic < masked_begin; ++ic) {
+                    // k indices
+                    const int ik1 = ic;
+
+                    // S indices
+                    const int i1 = ik1;
+
+                    ggml_vec_dot_f32(neq0,
+                            S + i1,
+                            (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)),
+                            (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)));
+                }
+
+                // scale
+                ggml_vec_scale_f32(masked_begin, S, scale);
+
+                for (int64_t i = masked_begin; i < M; i++) {
+                    S[i] = -INFINITY;
+                }
+
+                // softmax
+                // exclude known -INF S[..] values from max and loop
+                // dont forget to set their SM values to zero
+                {
+                    float max = -INFINITY;
+                    ggml_vec_max_f32(masked_begin, &max, S);
+
+                    ggml_float sum = 0.0;
+                    {
+#ifdef GGML_SOFT_MAX_ACCELERATE
+                        max = -max;
+                        vDSP_vsadd(SM, 1, &max, SM, 1, Mup);
+                        vvexpf(SM, SM, &Mup);
+                        ggml_vec_sum_f32(Mup, &sum, SM);
+#else
+                        uint16_t   scvt[GGML_SOFT_MAX_UNROLL]; UNUSED(scvt);
+                        ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 };
+
+                        for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) {
+                            if (i >= masked_begin) {
+                                break;
+                            }
+                            float * SR =  S + i;
+                            float * SW = SM + i;
+
+                            for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) {
+                                if (i + j >= masked_begin) {
+                                    break;
+                                } else if (SR[j] == -INFINITY) {
+                                    SW[j] = 0.0f;
+                                } else {
+#ifndef GGML_FLASH_ATTN_EXP_FP16
+                                    const float val = expf(SR[j] - max);
+#else
+                                    ggml_fp16_t s = GGML_FP32_TO_FP16(SR[j] - max);
+                                    memcpy(&scvt[j], &s, sizeof(uint16_t));
+                                    const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
+#endif
+                                    sump[j] += (ggml_float)val;
+                                    SW[j] = val;
+                                }
+                            }
+                        }
+
+                        for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) {
+                            sum += sump[i];
+                        }
+#endif
+                    }
+
+                    assert(sum > 0.0);
+
+                    sum = 1.0/sum;
+                    ggml_vec_scale_f32(masked_begin, SM, sum);
+
+                }
+
+                // step-by-step explanation
+                {
+                    // forward-process                    shape      grads from backward process
+                    // parallel_for ik2,ik3:
+                    //  for irep:
+                    //   iq2 = ik2 + irep*nek2
+                    //   k[:D,:M,:,:]                     [D,M,:,:]  grad[k][:D,:M,ik2,ik3]  += grad[kcur]
+                    //   q[:D,:N,:,:]                     [D,N,:,:]  grad[q][:D,iq1,iq2,iq3] += grad[qcur]
+                    //   v[:M,:D,:,:]                     [M,D,:,:]  grad[v][:M,:D,iv2,iv3]  += grad[vcur]
+                    //   for iq1:
+                    //    kcur   = k[:D,:M,ik2,ik3]       [D,M,1,1]  grad[kcur] = grad[S1].T @ qcur
+                    //    qcur   = q[:D,iq1,iq2,iq3]      [D,1,1,1]  grad[qcur] = grad[S1]   @ kcur
+                    //    vcur   = v[:M,:D,iv2,iv3]       [M,D,1,1]  grad[vcur] = grad[S5].T @ S4
+                    //    S0     = -Inf                   [D,1,1,1]
+                    //   ~S1[i]  = dot(kcur[:D,i], qcur)
+                    //    S1     = qcur @ kcur.T          [M,1,1,1]  grad[S1]   = grad[S2] * scale
+                    //    S2     = S1 * scale             [M,1,1,1]  grad[S2]   = diag_mask_zero(grad[S3], P)
+                    //    S3     = diag_mask_inf(S2, P)   [M,1,1,1]  grad[S3]   = S4 * (grad[S4] - dot(S4, grad[S4]))
+                    //    S4     = softmax(S3)            [M,1,1,1]  grad[S4]   = grad[S5] @ vcur
+                    //   ~S5[i]  = dot(vcur[:,i], S4)
+                    //    S5     = S4 @ vcur.T            [D,1,1,1]  grad[S5]   = d[:D,id1,id2,id3]
+                    //   ~dst[i,iq1,iq2,iq3]  = S5[i]              ^
+                    //    dst[:D,iq1,iq2,iq3] = S5                 | grad[dst[:D,iq1,iq2,iq3]] = d[:D,id1,id2,id3]
+                    // dst                               backward-/ grad[dst]                 = d
+                    //
+                    // output gradients with their dependencies:
+                    //
+                    // grad[kcur] = grad[S1].T @ qcur
+                    // grad[S1]   = diag_mask_zero(grad[S3], P) * scale
+                    // grad[S3]   = S4 * (grad[S4] - dot(S4, grad[S4]))
+                    // grad[S4]   = grad[S5] @ vcur
+                    // grad[S4]   = d[:D,id1,id2,id3] @ vcur
+                    // grad[qcur] = grad[S1]   @ kcur
+                    // grad[vcur] = grad[S5].T @ S4
+                    // grad[vcur] = d[:D,id1,id2,id3].T @ S4
+                    //
+                    // in post-order:
+                    //
+                    // S1         = qcur @ kcur.T
+                    // S2         = S1 * scale
+                    // S3         = diag_mask_inf(S2, P)
+                    // S4         = softmax(S3)
+                    // grad[S4]   = d[:D,id1,id2,id3] @ vcur
+                    // grad[S3]   = S4 * (grad[S4] - dot(S4, grad[S4]))
+                    // grad[S1]   = diag_mask_zero(grad[S3], P) * scale
+                    // grad[qcur] = grad[S1]   @ kcur
+                    // grad[kcur] = grad[S1].T @ qcur
+                    // grad[vcur] = d[:D,id1,id2,id3].T @ S4
+                    //
+                    // using less variables (SM=S4):
+                    //
+                    // S             = diag_mask_inf(qcur @ kcur.T * scale, P)
+                    // SM            = softmax(S)
+                    // S             = d[:D,iq1,iq2,iq3] @ vcur
+                    // dot_SM_gradSM = dot(SM, S)
+                    // S             = SM * (S - dot(SM, S))
+                    // S             = diag_mask_zero(S, P) * scale
+                    //
+                    // grad[q][:D,iq1,iq2,iq3] += S   @ kcur
+                    // grad[k][:D,:M,ik2,ik3]  += S.T @ qcur
+                    // grad[v][:M,:D,iv2,iv3]  += d[:D,id1,id2,id3].T @ SM
+                }
+
+                // S = gradSM = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3]
+                // S = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3]
+                // for ic:
+                //   S[:M] += vcur[:M,ic,iv2,iv3] * d[ic,id1,id2,id3]
+                // exclude known future zero S[..] values from operation
+                ggml_vec_set_f32(masked_begin, S, 0);
+                for (int64_t ic = 0; ic < D; ++ic) {
+                    ggml_vec_mad_f32(masked_begin,
+                            S,
+                             (float *) ((char *) v->data + (          ic*nbv1  + iv2*nbv2 + iv3*nbv3)),
+                            *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3)));
+                }
+
+                // S = SM * (S - dot(SM, S))
+                float dot_SM_gradSM = 0;
+                ggml_vec_dot_f32 (masked_begin, &dot_SM_gradSM, SM, S);
+                ggml_vec_acc1_f32(M, S, -dot_SM_gradSM);
+                ggml_vec_mul_f32 (masked_begin, S, S, SM);
+
+                // S = diag_mask_zero(S, P) * scale
+                // already done by above ggml_vec_set_f32
+
+                // exclude known zero S[..] values from operation
+                ggml_vec_scale_f32(masked_begin, S, scale);
+
+                // S    shape [M,1]
+                // SM   shape [M,1]
+                // kcur shape [D,M]
+                // qcur shape [D,1]
+                // vcur shape [M,D]
+
+                // grad[q][:D,iq1,iq2,iq3] += S @ kcur
+                // grad[q][:D,iq1,iq2,iq3] += shape[M,1] @ shape[D,M]
+                // for ic:
+                //  grad[q][:D,iq1,iq2,iq3] += S[ic] * kcur[:D,ic,ik2,ik3]
+                // exclude known zero S[..] values from loop
+                for (int64_t ic = 0; ic < masked_begin; ++ic) {
+                    ggml_vec_mad_f32(D,
+                            (float *) ((char *) grad_q  + (iq1*nbgq1 + iq2*nbgq2  + iq3*nbgq3)),
+                            (float *) ((char *) k->data + (ic*nbk1   + ik2*nbk2   + ik3*nbk3)),
+                            S[ic]);
+                }
+
+                // grad[k][:D,:M,iq2,iq3] += S.T @ qcur
+                // for ic:
+                //  grad[k][:D,ic,iq2,iq3] += S.T[0,ic] * qcur[:D,0]
+                //  grad[k][:D,ic,iq2,iq3] += S[ic]     * qcur[:D,0]
+                // exclude known zero S[..] values from loop
+                for (int64_t ic = 0; ic < masked_begin; ++ic) {
+                    ggml_vec_mad_f32(D,
+                            (float *) ((char *) grad_k  + (ic*nbgk1  + ik2*nbgk2  + ik3*nbgk3)),
+                            (float *) ((char *) q->data + (iq1*nbq1  + iq2*nbq2   + iq3*nbq3)),
+                            S[ic]);
+                }
+
+                // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T       @ SM
+                // for ic:
+                //  grad[v][:M,ic,iv2,iv3] += d[:D,id1,id2,id3].T[0,ic] * SM[:M]
+                //  grad[v][:M,ic,iv2,iv3] += d[ic,id1,id2,id3]         * SM[:M]
+                // exclude known zero SM[..] values from mad
+                for (int64_t ic = 0; ic < D; ++ic) {
+                    ggml_vec_mad_f32(masked_begin,
+                            (float *) ((char *) grad_v   + (          ic*nbgv1 + iv2*nbgv2 + iv3*nbgv3)),
+                            SM,
+                            *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2  + id3*nbd3)));
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_flash_attn_back(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * q,
+        const struct ggml_tensor * k,
+        const struct ggml_tensor * v,
+        const struct ggml_tensor * d,
+        const bool masked,
+        struct ggml_tensor * dst) {
+    switch (q->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_flash_attn_back_f32(params, q, k, v, d, masked, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_win_part
+
+static void ggml_compute_forward_win_part_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+
+    const int32_t nep0 = ((const int32_t *)(dst->op_params))[0];
+    const int32_t nep1 = ((const int32_t *)(dst->op_params))[1];
+    const int32_t w    = ((const int32_t *)(dst->op_params))[2];
+
+    assert(ne00 == ne0);
+    assert(ne3  == nep0*nep1);
+
+    // TODO: optimize / multi-thread
+    for (int py = 0; py < nep1; ++py) {
+        for (int px = 0; px < nep0; ++px) {
+            const int64_t i3 = py*nep0 + px;
+            for (int64_t i2 = 0; i2 < ne2; ++i2) {
+                for (int64_t i1 = 0; i1 < ne1; ++i1) {
+                    for (int64_t i0 = 0; i0 < ne0; ++i0) {
+                        const int64_t i02 = py*w + i2;
+                        const int64_t i01 = px*w + i1;
+                        const int64_t i00 = i0;
+
+                        const int64_t i = i3*ne2*ne1*ne0 + i2*ne1*ne0    + i1*ne0   + i0;
+                        const int64_t j =                  i02*ne01*ne00 + i01*ne00 + i00;
+
+                        if (py*w + i2 >= ne02 || px*w + i1 >= ne01) {
+                            ((float *) dst->data)[i] = 0.0f;
+                        } else {
+                            ((float *) dst->data)[i] = ((float *) src0->data)[j];
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_win_part(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_win_part_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_win_unpart
+
+static void ggml_compute_forward_win_unpart_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+
+    const int32_t w = ((const int32_t *)(dst->op_params))[0];
+
+    // padding
+    const int px = (w - ne1%w)%w;
+    //const int py = (w - ne2%w)%w;
+
+    const int npx = (px + ne1)/w;
+    //const int npy = (py + ne2)/w;
+
+    assert(ne0 == ne00);
+
+    // TODO: optimize / multi-thread
+    for (int64_t i2 = 0; i2 < ne2; ++i2) {
+        for (int64_t i1 = 0; i1 < ne1; ++i1) {
+            for (int64_t i0 = 0; i0 < ne0; ++i0) {
+                const int ip2 = i2/w;
+                const int ip1 = i1/w;
+
+                const int64_t i02 = i2%w;
+                const int64_t i01 = i1%w;
+                const int64_t i00 = i0;
+
+                const int64_t i = (ip2*npx + ip1)*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00 + i00;
+                const int64_t j =                                  i2*ne1*ne0    + i1*ne0   + i0;
+
+                ((float *) dst->data)[j] = ((float *) src0->data)[i];
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_win_unpart(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_win_unpart_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+//gmml_compute_forward_unary
+
+static void ggml_compute_forward_unary(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    const enum ggml_unary_op op = ggml_get_unary_op(dst);
+
+    switch (op) {
+        case GGML_UNARY_OP_ABS:
+            {
+                ggml_compute_forward_abs(params, src0, dst);
+            } break;
+        case GGML_UNARY_OP_SGN:
+            {
+                ggml_compute_forward_sgn(params, src0, dst);
+            } break;
+        case GGML_UNARY_OP_NEG:
+            {
+                ggml_compute_forward_neg(params, src0, dst);
+            } break;
+        case GGML_UNARY_OP_STEP:
+            {
+                ggml_compute_forward_step(params, src0, dst);
+            } break;
+        case GGML_UNARY_OP_TANH:
+            {
+                ggml_compute_forward_tanh(params, src0, dst);
+            } break;
+        case GGML_UNARY_OP_ELU:
+            {
+                ggml_compute_forward_elu(params, src0, dst);
+            } break;
+        case GGML_UNARY_OP_RELU:
+            {
+                ggml_compute_forward_relu(params, src0, dst);
+            } break;
+        case GGML_UNARY_OP_GELU:
+            {
+                ggml_compute_forward_gelu(params, src0, dst);
+            } break;
+        case GGML_UNARY_OP_GELU_QUICK:
+            {
+                ggml_compute_forward_gelu_quick(params, src0, dst);
+            } break;
+        case GGML_UNARY_OP_SILU:
+            {
+                ggml_compute_forward_silu(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_get_rel_pos
+
+static void ggml_compute_forward_get_rel_pos_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L292-L322
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    const int64_t w = ne1;
+
+    ggml_fp16_t * src0_data = (ggml_fp16_t *) src0->data;
+    ggml_fp16_t * dst_data  = (ggml_fp16_t *) dst->data;
+
+    for (int64_t i2 = 0; i2 < ne2; ++i2) {
+        for (int64_t i1 = 0; i1 < ne1; ++i1) {
+            const int64_t pos = (w - i1 - 1) + i2;
+            for (int64_t i0 = 0; i0 < ne0; ++i0) {
+                dst_data[i2*ne1*ne0 + i1*ne0 + i0] = src0_data[pos*ne00 + i0];
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_get_rel_pos(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_get_rel_pos_f16(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_add_rel_pos
+
+static void ggml_compute_forward_add_rel_pos_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        const struct ggml_tensor * src2,
+        struct ggml_tensor * dst) {
+
+    const bool inplace = (bool) ((int32_t *) dst->op_params)[0];
+    if (!inplace && params->type == GGML_TASK_INIT) {
+        memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst));
+        return;
+    }
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L357-L359
+
+    float * src1_data = (float *) src1->data;
+    float * src2_data = (float *) src2->data;
+    float * dst_data  = (float *) dst->data;
+
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    const int64_t ne12 = src1->ne[2];
+    const int64_t ne13 = src1->ne[3];
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    // total patches in dst
+    const int np = ne13;
+
+    // patches per thread
+    const int dp = (np + nth - 1)/nth;
+
+    // patch range for this thread
+    const int ip0 = dp*ith;
+    const int ip1 = MIN(ip0 + dp, np);
+
+
+    for (int64_t i13 = ip0; i13 < ip1; ++i13) {
+        for (int64_t i12 = 0; i12 < ne12; ++i12) {
+            for (int64_t i11 = 0; i11 < ne11; ++i11) {
+                const int64_t jp1 = i13*ne12*ne11*ne10 + i12*ne11*ne10 + i11*ne10;
+                for (int64_t i10 = 0; i10 < ne10; ++i10) {
+                    const int64_t jp0  = jp1 + i10;
+                    const float src1_e = src1_data[jp0];
+                    const float src2_e = src2_data[jp0];
+
+                    const int64_t jdh = jp0 * ne10;
+                    const int64_t jdw = jdh - (ne10 - 1) * i10;
+
+                    for (int64_t j = 0; j < ne10; ++j) {
+                        dst_data[jdh + j     ] += src2_e;
+                        dst_data[jdw + j*ne10] += src1_e;
+                    }
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_add_rel_pos(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        const struct ggml_tensor * src2,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_add_rel_pos_f32(params, src0, src1, src2, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_map_unary
+
+static void ggml_compute_forward_map_unary_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst,
+        const ggml_unary_op_f32_t fun) {
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert( dst->nb[0] == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        fun(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+
+static void ggml_compute_forward_map_unary(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst,
+        const ggml_unary_op_f32_t fun) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_map_unary_f32(params, src0, dst, fun);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_map_binary
+
+static void ggml_compute_forward_map_binary_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst,
+        const ggml_binary_op_f32_t fun) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert( dst->nb[0] == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+    assert(src1->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        fun(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])),
+                (float *) ((char *) src1->data + i*(src1->nb[1])));
+    }
+}
+
+
+static void ggml_compute_forward_map_binary(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst,
+        const ggml_binary_op_f32_t fun) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_map_binary_f32(params, src0, src1, dst, fun);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_map_custom1
+
+static void ggml_compute_forward_map_custom1_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * a,
+        struct ggml_tensor * dst,
+        const ggml_custom1_op_f32_t fun) {
+    assert(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    fun(dst, a);
+}
+
+// ggml_compute_forward_map_custom2
+
+static void ggml_compute_forward_map_custom2_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * a,
+        const struct ggml_tensor * b,
+        struct ggml_tensor * dst,
+        const ggml_custom2_op_f32_t fun) {
+    assert(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    fun(dst, a, b);
+}
+
+
+// ggml_compute_forward_map_custom3
+
+static void ggml_compute_forward_map_custom3_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * a,
+        const struct ggml_tensor * b,
+        const struct ggml_tensor * c,
+        struct ggml_tensor * dst,
+        const ggml_custom3_op_f32_t fun) {
+    assert(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    fun(dst, a, b, c);
+}
+
+// ggml_compute_forward_map_custom1
+
+static void ggml_compute_forward_map_custom1(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * a,
+              struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) dst->op_params;
+
+    p->fun(dst, a, params->ith, params->nth, p->userdata);
+}
+
+// ggml_compute_forward_map_custom2
+
+static void ggml_compute_forward_map_custom2(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * a,
+        const struct ggml_tensor * b,
+              struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) dst->op_params;
+
+    p->fun(dst, a, b, params->ith, params->nth, p->userdata);
+}
+
+// ggml_compute_forward_map_custom3
+
+static void ggml_compute_forward_map_custom3(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * a,
+        const struct ggml_tensor * b,
+        const struct ggml_tensor * c,
+              struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) dst->op_params;
+
+    p->fun(dst, a, b, c, params->ith, params->nth, p->userdata);
+}
+
+// ggml_compute_forward_cross_entropy_loss
+
+static void ggml_compute_forward_cross_entropy_loss_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src1));
+    GGML_ASSERT(ggml_is_scalar(dst));
+    GGML_ASSERT(ggml_are_same_shape(src0, src1));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    float * sums = (float *) params->wdata;
+
+    // TODO: handle transposed/permuted matrices
+    const int nc = src0->ne[0];
+    const int nr = ggml_nrows(src0);
+
+    GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc));
+
+    if (params->type == GGML_TASK_INIT) {
+        if (ith == 0) {
+            memset(sums, 0, sizeof(float) * (nth + nth * nc));
+        }
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        if (ith == 0) {
+            float * dp = (float *) dst->data;
+            ggml_vec_sum_f32(nth, dp, sums);
+            dp[0] *= -1.0f / (float) nr;
+        }
+        return;
+    }
+
+    const double eps = 1e-9;
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]);
+        float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]);
+        float * st = ((float *) params->wdata) + nth + ith*nc;
+
+#ifndef NDEBUG
+        for (int i = 0; i < nc; ++i) {
+            //printf("p[%d] = %f\n", i, p[i]);
+            assert(!isnan(s0[i]));
+            assert(!isnan(s1[i]));
+        }
+#endif
+        // soft_max
+        ggml_float sum = 0.0;
+        {
+            float max = -INFINITY;
+            ggml_vec_max_f32(nc, &max, s0);
+
+            uint16_t scvt; UNUSED(scvt);
+            for (int i = 0; i < nc; i++) {
+                if (s0[i] == -INFINITY) {
+                    st[i] = 0.0f;
+                } else {
+#ifndef GGML_CROSS_ENTROPY_EXP_FP16
+                    const float s = s0[i] - max;
+                    const float val = expf(s);
+#else
+                    ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max);
+                    memcpy(&scvt, &s, sizeof(scvt));
+                    const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
+#endif
+                    sum += (ggml_float)val;
+                    st[i] = val;
+                }
+            }
+
+            assert(sum > 0.0);
+            // sum = 1.0/sum;
+        }
+        // avoid log(0) by rescaling from [0..1] to [eps..1]
+        sum = (1.0 - eps) / sum;
+        ggml_vec_scale_f32(nc, st, sum);
+        ggml_vec_add1_f32(nc, st, st, eps);
+        ggml_vec_log_f32(nc, st, st);
+        ggml_vec_mul_f32(nc, st, st, s1);
+
+        float st_sum = 0;
+        ggml_vec_sum_f32(nc, &st_sum, st);
+        sums[ith] += st_sum;
+
+#ifndef NDEBUG
+        for (int i = 0; i < nc; ++i) {
+            assert(!isnan(st[i]));
+            assert(!isinf(st[i]));
+        }
+#endif
+    }
+
+}
+
+static void ggml_compute_forward_cross_entropy_loss(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_cross_entropy_loss_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_cross_entropy_loss_back
+
+static void ggml_compute_forward_cross_entropy_loss_back_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        const struct ggml_tensor * opt0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(dst));
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src1));
+    GGML_ASSERT(ggml_is_contiguous(opt0));
+    GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+
+    const int64_t ith = params->ith;
+    const int64_t nth = params->nth;
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const double eps = 1e-9;
+
+    // TODO: handle transposed/permuted matrices
+    const int64_t nc = src0->ne[0];
+    const int64_t nr = ggml_nrows(src0);
+
+    // rows per thread
+    const int64_t dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int64_t ir0 = dr*ith;
+    const int64_t ir1 = MIN(ir0 + dr, nr);
+
+    float * d   = (float *) opt0->data;
+
+    for (int64_t i1 = ir0; i1 < ir1; i1++) {
+        float * ds0 = (float *)((char *) dst->data  + i1*dst->nb[1]);
+        float * s0  = (float *)((char *) src0->data + i1*src0->nb[1]);
+        float * s1  = (float *)((char *) src1->data + i1*src1->nb[1]);
+
+#ifndef NDEBUG
+        for (int i = 0; i < nc; ++i) {
+            //printf("p[%d] = %f\n", i, p[i]);
+            assert(!isnan(s0[i]));
+            assert(!isnan(s1[i]));
+        }
+#endif
+
+        // soft_max
+        ggml_float sum = 0.0;
+        {
+            float max = -INFINITY;
+            ggml_vec_max_f32(nc, &max, s0);
+
+            uint16_t scvt; UNUSED(scvt);
+            for (int i = 0; i < nc; i++) {
+                if (s0[i] == -INFINITY) {
+                    ds0[i] = 0.0f;
+                } else {
+#ifndef GGML_CROSS_ENTROPY_EXP_FP16
+                    const float s = s0[i] - max;
+                    const float val = expf(s);
+#else
+                    ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max);
+                    memcpy(&scvt, &s, sizeof(scvt));
+                    const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
+#endif
+                    sum += (ggml_float)val;
+                    ds0[i] = val;
+                }
+            }
+
+            assert(sum > 0.0);
+            sum = (1.0 - eps)/sum;
+        }
+
+        // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr
+        ggml_vec_scale_f32(nc, ds0, sum);
+        ggml_vec_add1_f32(nc, ds0, ds0, eps);
+        ggml_vec_sub_f32(nc, ds0, ds0, s1);
+        ggml_vec_scale_f32(nc, ds0, d[0] / (float) nr);
+
+
+#ifndef NDEBUG
+        for (int i = 0; i < nc; ++i) {
+            assert(!isnan(ds0[i]));
+            assert(!isinf(ds0[i]));
+        }
+#endif
+    }
+}
+
+static void ggml_compute_forward_cross_entropy_loss_back(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        const struct ggml_tensor * opt0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_cross_entropy_loss_back_f32(params, src0, src1, opt0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+
+/////////////////////////////////
+
+static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
+    GGML_ASSERT(params);
+
+#ifdef GGML_USE_CUBLAS
+    bool skip_cpu = ggml_cuda_compute_forward(params, tensor);
+    if (skip_cpu) {
+        return;
+    }
+    GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU);
+    GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU);
+#endif // GGML_USE_CUBLAS
+
+    switch (tensor->op) {
+        case GGML_OP_DUP:
+            {
+                ggml_compute_forward_dup(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_ADD:
+            {
+                ggml_compute_forward_add(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_ADD1:
+            {
+                ggml_compute_forward_add1(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_ACC:
+            {
+                ggml_compute_forward_acc(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_SUB:
+            {
+                ggml_compute_forward_sub(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_MUL:
+            {
+                ggml_compute_forward_mul(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_DIV:
+            {
+                ggml_compute_forward_div(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_SQR:
+            {
+                ggml_compute_forward_sqr(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_SQRT:
+            {
+                ggml_compute_forward_sqrt(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_LOG:
+            {
+                ggml_compute_forward_log(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_SUM:
+            {
+                ggml_compute_forward_sum(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_SUM_ROWS:
+            {
+                ggml_compute_forward_sum_rows(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_MEAN:
+            {
+                ggml_compute_forward_mean(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_ARGMAX:
+            {
+                ggml_compute_forward_argmax(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_REPEAT:
+            {
+                ggml_compute_forward_repeat(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_REPEAT_BACK:
+            {
+                ggml_compute_forward_repeat_back(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_CONCAT:
+            {
+                ggml_compute_forward_concat(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_SILU_BACK:
+            {
+                ggml_compute_forward_silu_back(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_NORM:
+            {
+                ggml_compute_forward_norm(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_RMS_NORM:
+            {
+                ggml_compute_forward_rms_norm(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_RMS_NORM_BACK:
+            {
+                ggml_compute_forward_rms_norm_back(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_GROUP_NORM:
+            {
+                ggml_compute_forward_group_norm(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_MUL_MAT:
+            {
+                ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_OUT_PROD:
+            {
+                ggml_compute_forward_out_prod(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_SCALE:
+            {
+                ggml_compute_forward_scale(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_SET:
+            {
+                ggml_compute_forward_set(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_CPY:
+            {
+                ggml_compute_forward_cpy(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_CONT:
+            {
+                ggml_compute_forward_cont(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_RESHAPE:
+            {
+                ggml_compute_forward_reshape(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_VIEW:
+            {
+                ggml_compute_forward_view(params, tensor->src[0]);
+            } break;
+        case GGML_OP_PERMUTE:
+            {
+                ggml_compute_forward_permute(params, tensor->src[0]);
+            } break;
+        case GGML_OP_TRANSPOSE:
+            {
+                ggml_compute_forward_transpose(params, tensor->src[0]);
+            } break;
+        case GGML_OP_GET_ROWS:
+            {
+                ggml_compute_forward_get_rows(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_GET_ROWS_BACK:
+            {
+                ggml_compute_forward_get_rows_back(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_DIAG:
+            {
+                ggml_compute_forward_diag(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_DIAG_MASK_INF:
+            {
+                ggml_compute_forward_diag_mask_inf(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_DIAG_MASK_ZERO:
+            {
+                ggml_compute_forward_diag_mask_zero(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_SOFT_MAX:
+            {
+                ggml_compute_forward_soft_max(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_SOFT_MAX_BACK:
+            {
+                ggml_compute_forward_soft_max_back(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_ROPE:
+            {
+                ggml_compute_forward_rope(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_ROPE_BACK:
+            {
+                ggml_compute_forward_rope_back(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_ALIBI:
+            {
+                ggml_compute_forward_alibi(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_CLAMP:
+            {
+                ggml_compute_forward_clamp(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_CONV_1D:
+            {
+                ggml_compute_forward_conv_1d(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_CONV_1D_STAGE_0:
+            {
+                ggml_compute_forward_conv_1d_stage_0(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_CONV_1D_STAGE_1:
+            {
+                ggml_compute_forward_conv_1d_stage_1(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_CONV_TRANSPOSE_1D:
+            {
+                ggml_compute_forward_conv_transpose_1d(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_CONV_2D:
+            {
+                ggml_compute_forward_conv_2d(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_CONV_TRANSPOSE_2D:
+            {
+                ggml_compute_forward_conv_transpose_2d(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_POOL_1D:
+            {
+                ggml_compute_forward_pool_1d(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_POOL_2D:
+            {
+                ggml_compute_forward_pool_2d(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_UPSCALE:
+            {
+                ggml_compute_forward_upscale(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_FLASH_ATTN:
+            {
+                const int32_t t = ggml_get_op_params_i32(tensor, 0);
+                GGML_ASSERT(t == 0 || t == 1);
+                const bool masked = t != 0;
+                ggml_compute_forward_flash_attn(params, tensor->src[0], tensor->src[1], tensor->src[2], masked, tensor);
+            } break;
+        case GGML_OP_FLASH_FF:
+            {
+                ggml_compute_forward_flash_ff(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor->src[4], tensor);
+            } break;
+        case GGML_OP_FLASH_ATTN_BACK:
+            {
+                int32_t t = ggml_get_op_params_i32(tensor, 0);
+                GGML_ASSERT(t == 0 || t == 1);
+                bool masked = t != 0;
+                ggml_compute_forward_flash_attn_back(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], masked, tensor);
+            } break;
+        case GGML_OP_WIN_PART:
+            {
+                ggml_compute_forward_win_part(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_WIN_UNPART:
+            {
+                ggml_compute_forward_win_unpart(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_UNARY:
+            {
+                ggml_compute_forward_unary(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_GET_REL_POS:
+            {
+                ggml_compute_forward_get_rel_pos(params, tensor->src[0], tensor);
+            } break;
+        case GGML_OP_ADD_REL_POS:
+            {
+                ggml_compute_forward_add_rel_pos(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
+            } break;
+        case GGML_OP_MAP_UNARY:
+            {
+                ggml_unary_op_f32_t fun;
+                memcpy(&fun, tensor->op_params, sizeof(fun));
+                ggml_compute_forward_map_unary(params, tensor->src[0], tensor, fun);
+            }
+            break;
+        case GGML_OP_MAP_BINARY:
+            {
+                ggml_binary_op_f32_t fun;
+                memcpy(&fun, tensor->op_params, sizeof(fun));
+                ggml_compute_forward_map_binary(params, tensor->src[0], tensor->src[1], tensor, fun);
+            }
+            break;
+        case GGML_OP_MAP_CUSTOM1_F32:
+            {
+                ggml_custom1_op_f32_t fun;
+                memcpy(&fun, tensor->op_params, sizeof(fun));
+                ggml_compute_forward_map_custom1_f32(params, tensor->src[0], tensor, fun);
+            }
+            break;
+        case GGML_OP_MAP_CUSTOM2_F32:
+            {
+                ggml_custom2_op_f32_t fun;
+                memcpy(&fun, tensor->op_params, sizeof(fun));
+                ggml_compute_forward_map_custom2_f32(params, tensor->src[0], tensor->src[1], tensor, fun);
+            }
+            break;
+        case GGML_OP_MAP_CUSTOM3_F32:
+            {
+                ggml_custom3_op_f32_t fun;
+                memcpy(&fun, tensor->op_params, sizeof(fun));
+                ggml_compute_forward_map_custom3_f32(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun);
+            }
+            break;
+        case GGML_OP_MAP_CUSTOM1:
+            {
+                ggml_compute_forward_map_custom1(params, tensor->src[0], tensor);
+            }
+            break;
+        case GGML_OP_MAP_CUSTOM2:
+            {
+                ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor);
+            }
+            break;
+        case GGML_OP_MAP_CUSTOM3:
+            {
+                ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
+            }
+            break;
+        case GGML_OP_CROSS_ENTROPY_LOSS:
+            {
+                ggml_compute_forward_cross_entropy_loss(params, tensor->src[0], tensor->src[1], tensor);
+            }
+            break;
+        case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
+            {
+                ggml_compute_forward_cross_entropy_loss_back(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
+            }
+            break;
+        case GGML_OP_NONE:
+            {
+                // nop
+            } break;
+        case GGML_OP_COUNT:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+static_assert(GGML_GRAPH_HASHTABLE_SIZE > GGML_MAX_NODES * 2, "GGML_GRAPH_HT_SIZE is too small");
+
+static size_t hash(void * p) {
+    return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE;
+}
+
+static size_t hash_find(void * hash_table[], void * p) {
+    size_t h = hash(p);
+
+    // linear probing
+    size_t i = h;
+    while (hash_table[i] != NULL && hash_table[i] != p) {
+        i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE;
+        if (i == h) {
+            // visited all hash table entries -> not found
+            return GGML_GRAPH_HASHTABLE_SIZE;
+        }
+    }
+    return i;
+}
+
+static bool hash_insert(void * hash_table[], void * p) {
+    size_t i = hash_find(hash_table, p);
+
+    GGML_ASSERT(i < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full
+
+    if (hash_table[i] == p) {
+        return true;
+    }
+
+    // insert
+    GGML_ASSERT(hash_table[i] == NULL);
+    hash_table[i] = p;
+    return false;
+}
+
+static bool hash_contains(void * hash_table[], void * p) {
+    size_t i = hash_find(hash_table, p);
+    return (i < GGML_GRAPH_HASHTABLE_SIZE) && (hash_table[i] == p);
+}
+
+struct hash_map {
+    void * keys[GGML_GRAPH_HASHTABLE_SIZE];
+    void * vals[GGML_GRAPH_HASHTABLE_SIZE];
+};
+
+static struct hash_map * new_hash_map(void) {
+    struct hash_map * result = malloc(sizeof(struct hash_map));
+    for (int i=0; i<GGML_GRAPH_HASHTABLE_SIZE; ++i) {
+        result->keys[i] = NULL;
+        result->vals[i] = NULL;
+    }
+    return result;
+}
+
+static void free_hash_map(struct hash_map * map) {
+    free(map);
+}
+
+// gradient checkpointing
+
+static struct ggml_tensor * ggml_recompute_graph_node(
+        struct ggml_context * ctx,
+        struct ggml_cgraph  * graph,
+        struct hash_map     * replacements,
+        struct ggml_tensor  * node) {
+
+    if (node == NULL) {
+        return NULL;
+    }
+
+    if (node->is_param) {
+        return node;
+    }
+
+    if (!hash_contains(graph->visited_hash_table, node)) {
+        return node;
+    }
+
+    int count_children = 0;
+    for (int k = 0; k < GGML_MAX_SRC; ++k) {
+        if (node->src[k]) {
+            ++count_children;
+        }
+    }
+
+    if (count_children == 0) {
+        return node;
+    }
+
+    size_t i = hash_find(replacements->keys, node);
+    GGML_ASSERT(i < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full
+    if (replacements->keys[i] == node) {
+        return (struct ggml_tensor *) replacements->vals[i];
+    }
+
+    struct ggml_tensor * clone = ggml_new_tensor(ctx, node->type, node->n_dims, node->ne);
+
+    // insert clone into replacements
+    GGML_ASSERT(replacements->keys[i] == NULL); // assert that we don't overwrite
+    replacements->keys[i] = node;
+    replacements->vals[i] = clone;
+
+    clone->op       = node->op;
+    clone->grad     = node->grad;
+    clone->is_param = node->is_param;
+    clone->extra    = node->extra;
+    for (int k = 0; k < GGML_MAX_DIMS; ++k) {
+        clone->nb[k] = node->nb[k];
+    }
+    for (int k = 0; k < GGML_MAX_SRC; ++k) {
+        clone->src[k] = ggml_recompute_graph_node(ctx, graph, replacements, node->src[k]);
+    }
+    if (node->view_src != NULL) {
+        clone->data = (node->view_src->data == NULL)
+                        ? NULL // view_src not yet allocated
+                        : (char *) node->view_src->data // view_src already allocated
+                                 + node->view_offs;
+        clone->view_src  = node->view_src;
+        clone->view_offs = node->view_offs;
+    }
+
+    GGML_ASSERT(sizeof(node->op_params) == sizeof(int32_t) * (GGML_MAX_OP_PARAMS / sizeof(int32_t)));
+    GGML_ASSERT(sizeof(node->name)      == GGML_MAX_NAME);
+    memcpy(clone->op_params, node->op_params, sizeof(node->op_params));
+    ggml_format_name(clone, "%s (clone)", ggml_get_name(node));
+
+    return clone;
+}
+
+void ggml_build_backward_gradient_checkpointing(
+        struct ggml_context   * ctx,
+        struct ggml_cgraph    * gf,
+        struct ggml_cgraph    * gb,
+        struct ggml_cgraph    * gb_tmp,
+        struct ggml_tensor  * * checkpoints,
+        int                     n_checkpoints) {
+    *gb_tmp = *gf;
+    ggml_build_backward_expand(ctx, gf, gb_tmp, true);
+
+    if (n_checkpoints <= 0) {
+        *gb = *gb_tmp;
+        return;
+    }
+
+    struct hash_map * replacements = new_hash_map();
+
+    // insert checkpoints in replacements
+    for (int i = 0; i < n_checkpoints; ++i) {
+        size_t k = hash_find(replacements->keys, checkpoints[i]);
+        GGML_ASSERT(k < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full
+        GGML_ASSERT(replacements->keys[k] == NULL); // assert that we don't overwrite
+        replacements->keys[k] = checkpoints[i];
+        replacements->vals[k] = checkpoints[i];
+    }
+
+    *gb = *gf;
+    // rewrite gb_tmp->nodes[gf->n_nodes:gb_tmp->n_nodes],
+    // replacing references to gb_tmp->nodes[0:gf->n_nodes] ( == gf->nodes[0:gf->n_nodes]),
+    // by recomputing them from checkpoints
+    for (int i = gf->n_nodes; i<gb_tmp->n_nodes; ++i) {
+        struct ggml_tensor * node = gb_tmp->nodes[i];
+        for (int k = 0; k < GGML_MAX_SRC; ++k) {
+            // insert new tensors recomputing src, reusing already made replacements,
+            // remember replacements: remember new tensors with mapping from corresponding gf nodes
+            // recurse for input tensors,
+            // unless (i.e. terminating when) input tensors are replacments (like checkpoints)
+            node->src[k] = ggml_recompute_graph_node(ctx, gf, replacements, node->src[k]);
+        }
+        // insert rewritten backward node with replacements made into resulting backward graph gb
+        ggml_build_forward_expand(gb, node);
+    }
+
+    free_hash_map(replacements);
+}
+
+// functions to change gradients considering the case that input a might be initial gradient with zero value
+
+static struct ggml_tensor * ggml_add_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, void * zero_table[]) {
+    if (hash_contains(zero_table, a)) {
+        return b;
+    } else {
+        return ggml_add_impl(ctx, a, b, false);
+    }
+}
+
+static struct ggml_tensor * ggml_acc_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset, void * zero_table[]) {
+    if (hash_contains(zero_table, a)) {
+        struct ggml_tensor * a_zero = ggml_scale(ctx, a, ggml_new_f32(ctx, 0));
+        return ggml_acc_impl(ctx, a_zero, b, nb1, nb2, nb3, offset, false);
+    } else {
+        return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, false);
+    }
+}
+
+static struct ggml_tensor * ggml_add1_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, void * zero_table[]) {
+    if (hash_contains(zero_table, a)) {
+        return ggml_repeat(ctx, b, a);
+    } else {
+        return ggml_add1_impl(ctx, a, b, false);
+    }
+}
+
+static struct ggml_tensor * ggml_sub_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, void * zero_table[]) {
+    if (hash_contains(zero_table, a)) {
+        return ggml_neg(ctx, b);
+    } else {
+        return ggml_sub_impl(ctx, a, b, false);
+    }
+}
+
+static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, void * zero_table[]) {
+    struct ggml_tensor * src0 = tensor->src[0];
+    struct ggml_tensor * src1 = tensor->src[1];
+
+    switch (tensor->op) {
+        case GGML_OP_DUP:
+            {
+                if (src0->grad) {
+                    src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table);
+                }
+            } break;
+        case GGML_OP_ADD:
+            {
+                if (src0->grad) {
+                    src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table);
+                }
+                if (src1->grad) {
+                    src1->grad = ggml_add_or_set(ctx, src1->grad, tensor->grad, zero_table);
+                }
+            } break;
+        case GGML_OP_ADD1:
+            {
+                if (src0->grad) {
+                    src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table);
+                }
+                if (src1->grad) {
+                    src1->grad = ggml_add_or_set(ctx,
+                        src1->grad,
+                        ggml_mean(ctx, tensor->grad), // TODO: should probably be sum instead of mean
+                        zero_table);
+                }
+            } break;
+        case GGML_OP_ACC:
+            {
+                if (src0->grad) {
+                    src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table);
+                }
+                if (src1->grad) {
+                    const size_t nb1     = ((int32_t *) tensor->op_params)[0];
+                    const size_t nb2     = ((int32_t *) tensor->op_params)[1];
+                    const size_t nb3     = ((int32_t *) tensor->op_params)[2];
+                    const size_t offset  = ((int32_t *) tensor->op_params)[3];
+
+                    struct ggml_tensor * tensor_grad_view = ggml_view_4d(ctx,
+                        tensor->grad,
+                        src1->grad->ne[0],
+                        src1->grad->ne[1],
+                        src1->grad->ne[2],
+                        src1->grad->ne[3],
+                        nb1, nb2, nb3, offset);
+
+                    src1->grad =
+                        ggml_add_or_set(ctx,
+                            src1->grad,
+                            ggml_reshape(ctx,
+                                ggml_cont(ctx, tensor_grad_view),
+                                src1->grad),
+                            zero_table);
+                }
+            } break;
+        case GGML_OP_SUB:
+            {
+                if (src0->grad) {
+                    src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table);
+                }
+                if (src1->grad) {
+                    src1->grad = ggml_sub_or_set(ctx, src1->grad, tensor->grad, zero_table);
+                }
+            } break;
+        case GGML_OP_MUL:
+            {
+                if (src0->grad) {
+                    src0->grad =
+                        ggml_add_or_set(ctx,
+                                src0->grad,
+                                ggml_mul(ctx, src1, tensor->grad),
+                                zero_table);
+                }
+                if (src1->grad) {
+                    src1->grad =
+                        ggml_add_or_set(ctx,
+                                src1->grad,
+                                ggml_mul(ctx, src0, tensor->grad),
+                                zero_table);
+                }
+            } break;
+        case GGML_OP_DIV:
+            {
+                if (src0->grad) {
+                    src0->grad =
+                        ggml_add_or_set(ctx,
+                                src0->grad,
+                                ggml_div(ctx, tensor->grad, src1),
+                                zero_table);
+                }
+                if (src1->grad) {
+                    src1->grad =
+                        ggml_sub_or_set(ctx,
+                                src1->grad,
+                                ggml_mul(ctx,
+                                    tensor->grad,
+                                    ggml_div(ctx, tensor, src1)),
+                                zero_table);
+                }
+            } break;
+        case GGML_OP_SQR:
+            {
+                if (src0->grad) {
+                    src0->grad =
+                        ggml_add_or_set(ctx,
+                                src0->grad,
+                                ggml_scale(ctx,
+                                    ggml_mul(ctx, src0, tensor->grad),
+                                    ggml_new_f32(ctx, 2.0f)),
+                                zero_table);
+                }
+            } break;
+        case GGML_OP_SQRT:
+            {
+                if (src0->grad) {
+                    src0->grad =
+                        ggml_add_or_set(ctx,
+                                src0->grad,
+                                ggml_scale(ctx,
+                                    ggml_div(ctx,
+                                        tensor->grad,
+                                        tensor),
+                                    ggml_new_f32(ctx, 0.5f)),
+                                zero_table);
+                }
+            } break;
+        case GGML_OP_LOG:
+            {
+                if (src0->grad) {
+                    src0->grad =
+                        ggml_add_or_set(ctx,
+                                src0->grad,
+                                ggml_div(ctx,
+                                    tensor->grad,
+                                    src0),
+                                zero_table);
+                }
+            } break;
+        case GGML_OP_SUM:
+            {
+                if (src0->grad) {
+                    src0->grad =
+                        ggml_add1_or_set(ctx,
+                                src0->grad,
+                                tensor->grad,
+                                zero_table);
+                }
+            } break;
+        case GGML_OP_SUM_ROWS:
+            {
+                if (src0->grad) {
+                    src0->grad =
+                        ggml_add_or_set(ctx,
+                                src0->grad,
+                                ggml_repeat(ctx,
+                                    tensor->grad,
+                                    src0->grad),
+                                zero_table);
+                }
+            } break;
+        case GGML_OP_MEAN:
+        case GGML_OP_ARGMAX:
+            {
+                GGML_ASSERT(false); // TODO: implement
+            } break;
+        case GGML_OP_REPEAT:
+            {
+                // necessary for llama
+                if (src0->grad) {
+                    src0->grad = ggml_add_or_set(ctx,
+                            src0->grad,
+                            ggml_repeat_back(ctx, tensor->grad, src0->grad),
+                            zero_table);
+                }
+            } break;
+        case GGML_OP_REPEAT_BACK:
+            {
+                if (src0->grad) {
+                    // TODO: test this
+                    src0->grad = ggml_add_or_set(ctx,
+                            src0->grad,
+                            ggml_repeat(ctx, tensor->grad, src0->grad),
+                            zero_table);
+                }
+            } break;
+        case GGML_OP_CONCAT:
+            {
+                GGML_ASSERT(false); // TODO: implement
+            } break;
+        case GGML_OP_SILU_BACK:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_NORM:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_RMS_NORM:
+            {
+                // necessary for llama
+                if (src0->grad) {
+                    float eps;
+                    memcpy(&eps, tensor->op_params, sizeof(float));
+
+                    src0->grad = ggml_add_or_set(ctx,
+                            src0->grad,
+                            ggml_rms_norm_back(ctx, src0, tensor->grad, eps),
+                            zero_table);
+                }
+            } break;
+        case GGML_OP_RMS_NORM_BACK:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_GROUP_NORM:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_MUL_MAT:
+            {
+                // https://cs231n.github.io/optimization-2/#staged
+                // # forward pass
+                // s0 = np.random.randn(5, 10)
+                // s1 = np.random.randn(10, 3)
+                // t = s0.dot(s1)
+
+                // # now suppose we had the gradient on t from above in the circuit
+                // dt = np.random.randn(*t.shape) # same shape as t
+                // ds0 = dt.dot(s1.T) #.T gives the transpose of the matrix
+                // ds1 = t.T.dot(dt)
+
+                // tensor.shape [m,p,qq,rr]
+                // src0.shape   [n,m,q1,r1]
+                // src1.shape   [n,p,qq,rr]
+
+                // necessary for llama
+                if (src0->grad) {
+                    struct ggml_tensor * s1_tg =
+                        ggml_out_prod(ctx, // [n,m,qq,rr]
+                            src1,          // [n,p,qq,rr]
+                            tensor->grad); // [m,p,qq,rr]
+                    const int64_t qq = s1_tg->ne[2];
+                    const int64_t rr = s1_tg->ne[3];
+                    const int64_t q1 = src0->ne[2];
+                    const int64_t r1 = src0->ne[3];
+                    const bool ne2_broadcasted = qq > q1;
+                    const bool ne3_broadcasted = rr > r1;
+                    if (ne2_broadcasted || ne3_broadcasted) {
+                        // sum broadcast repetitions of s1_tg into shape of src0
+                        s1_tg = ggml_repeat_back(ctx, s1_tg, src0);
+                    }
+                    src0->grad =
+                        ggml_add_or_set(ctx,
+                                src0->grad, // [n,m,q1,r1]
+                                s1_tg,      // [n,m,q1,r1]
+                                zero_table);
+                }
+                if (src1->grad) {
+                    src1->grad =
+                        ggml_add_or_set(ctx,
+                                src1->grad,                            // [n,p,qq,rr]
+                                // ggml_mul_mat(ctx,                   // [n,p,qq,rr]
+                                //     ggml_cont(ctx,                  // [m,n,q1,r1]
+                                //         ggml_transpose(ctx, src0)), // [m,n,q1,r1]
+                                //     tensor->grad),                  // [m,p,qq,rr]
+
+                                // // when src0 is bigger than tensor->grad (this is mostly the case in llama),
+                                // // avoid transpose of src0, rather transpose smaller tensor->grad
+                                // // and then use ggml_out_prod
+                                ggml_out_prod(ctx,                  // [n,p,qq,rr]
+                                    src0,                           // [n,m,q1,r1]
+                                    ggml_transpose(ctx,             // [p,m,qq,rr]
+                                        tensor->grad)),             // [m,p,qq,rr]
+                                zero_table);
+                }
+            } break;
+        case GGML_OP_OUT_PROD:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_SCALE:
+            {
+                // necessary for llama
+                if (src0->grad) {
+                    src0->grad =
+                        ggml_add_or_set(ctx,
+                            src0->grad,
+                            ggml_scale_impl(ctx, tensor->grad, src1, false),
+                            zero_table);
+                }
+                if (src1->grad) {
+                    src1->grad =
+                        ggml_add_or_set(ctx,
+                            src1->grad,
+                            ggml_sum(ctx, ggml_mul_impl(ctx, tensor->grad, src0, false)),
+                            zero_table);
+                }
+            } break;
+        case GGML_OP_SET:
+            {
+                const size_t nb1     = ((int32_t *) tensor->op_params)[0];
+                const size_t nb2     = ((int32_t *) tensor->op_params)[1];
+                const size_t nb3     = ((int32_t *) tensor->op_params)[2];
+                const size_t offset  = ((int32_t *) tensor->op_params)[3];
+
+                struct ggml_tensor * tensor_grad_view = NULL;
+
+                if (src0->grad || src1->grad) {
+                    GGML_ASSERT(src0->type == tensor->type);
+                    GGML_ASSERT(tensor->grad->type == tensor->type);
+                    GGML_ASSERT(tensor->grad->type == src1->grad->type);
+
+                    tensor_grad_view = ggml_view_4d(ctx,
+                        tensor->grad,
+                        src1->grad->ne[0],
+                        src1->grad->ne[1],
+                        src1->grad->ne[2],
+                        src1->grad->ne[3],
+                        nb1, nb2, nb3, offset);
+                }
+
+                if (src0->grad) {
+                    src0->grad = ggml_add_or_set(ctx,
+                        src0->grad,
+                        ggml_acc_impl(ctx,
+                            tensor->grad,
+                            ggml_neg(ctx, tensor_grad_view),
+                            nb1, nb2, nb3, offset, false),
+                        zero_table);
+                }
+
+                if (src1->grad) {
+                    src1->grad =
+                        ggml_add_or_set(ctx,
+                            src1->grad,
+                            ggml_reshape(ctx,
+                                ggml_cont(ctx, tensor_grad_view),
+                                src1->grad),
+                            zero_table);
+                }
+            } break;
+        case GGML_OP_CPY:
+            {
+                // necessary for llama
+                // cpy overwrites value of src1 by src0 and returns view(src1)
+                // the overwriting is mathematically equivalent to:
+                // tensor = src0 * 1 + src1 * 0
+                if (src0->grad) {
+                    // dsrc0 = dtensor * 1
+                    src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table);
+                }
+                if (src1->grad) {
+                    // dsrc1 = dtensor * 0 -> noop
+                }
+            } break;
+        case GGML_OP_CONT:
+            {
+                // same as cpy
+                if (src0->grad) {
+                    GGML_ASSERT(ggml_is_contiguous(src0->grad));
+                    GGML_ASSERT(ggml_is_contiguous(tensor->grad));
+                    src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table);
+                }
+            } break;
+        case GGML_OP_RESHAPE:
+            {
+                // necessary for llama
+                if (src0->grad) {
+                    src0->grad =
+                        ggml_add_or_set(ctx, src0->grad,
+                            ggml_reshape(ctx,
+                                ggml_is_contiguous(tensor->grad)
+                                    ? tensor->grad
+                                    : ggml_cont(ctx, tensor->grad),
+                                src0->grad),
+                        zero_table);
+                }
+            } break;
+        case GGML_OP_VIEW:
+            {
+                // necessary for llama
+                if (src0->grad) {
+                    size_t offset;
+
+                    memcpy(&offset, tensor->op_params, sizeof(offset));
+
+                    size_t nb1     = tensor->nb[1];
+                    size_t nb2     = tensor->nb[2];
+                    size_t nb3     = tensor->nb[3];
+
+                    if (src0->type != src0->grad->type) {
+                        // gradient is typically F32, but src0 could be other type
+                        size_t ng = ggml_element_size(src0->grad);
+                        size_t n0 = ggml_element_size(src0);
+                        GGML_ASSERT(offset % n0 == 0);
+                        GGML_ASSERT(nb1 % n0 == 0);
+                        GGML_ASSERT(nb2 % n0 == 0);
+                        GGML_ASSERT(nb3 % n0 == 0);
+                        offset = (offset / n0) * ng;
+                        nb1 = (nb1 / n0) * ng;
+                        nb2 = (nb2 / n0) * ng;
+                        nb3 = (nb3 / n0) * ng;
+                    }
+
+                    src0->grad = ggml_acc_or_set(ctx, src0->grad, tensor->grad, nb1, nb2, nb3, offset, zero_table);
+                }
+            } break;
+        case GGML_OP_PERMUTE:
+            {
+                // necessary for llama
+                if (src0->grad) {
+                    int32_t * axes = (int32_t *) tensor->op_params;
+                    int axis0 = axes[0] & 0x3;
+                    int axis1 = axes[1] & 0x3;
+                    int axis2 = axes[2] & 0x3;
+                    int axis3 = axes[3] & 0x3;
+                    int axes_backward[4] = {0,0,0,0};
+                    axes_backward[axis0] = 0;
+                    axes_backward[axis1] = 1;
+                    axes_backward[axis2] = 2;
+                    axes_backward[axis3] = 3;
+                    src0->grad =
+                        ggml_add_or_set(ctx, src0->grad,
+                            ggml_permute(ctx,
+                                tensor->grad,
+                                axes_backward[0],
+                                axes_backward[1],
+                                axes_backward[2],
+                                axes_backward[3]),
+                            zero_table);
+                }
+            } break;
+        case GGML_OP_TRANSPOSE:
+            {
+                // necessary for llama
+                if (src0->grad) {
+                    src0->grad =
+                        ggml_add_or_set(ctx, src0->grad,
+                            ggml_transpose(ctx, tensor->grad),
+                        zero_table);
+                }
+            } break;
+        case GGML_OP_GET_ROWS:
+            {
+                // necessary for llama (only for tokenizer)
+                if (src0->grad) {
+                    src0->grad =
+                        ggml_add_or_set(ctx, src0->grad,
+                            // last ggml_get_rows_back argument src0->grad is only
+                            // necessary to setup correct output shape
+                            ggml_get_rows_back(ctx, tensor->grad, src1, src0->grad),
+                        zero_table);
+                }
+                if (src1->grad) {
+                    // noop
+                }
+            } break;
+        case GGML_OP_GET_ROWS_BACK:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_DIAG:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_DIAG_MASK_INF:
+            {
+                // necessary for llama
+                if (src0->grad) {
+                    const int n_past = ((int32_t *) tensor->op_params)[0];
+                    src0->grad =
+                        ggml_add_or_set(ctx, src0->grad,
+                            ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false),
+                        zero_table);
+                }
+            } break;
+        case GGML_OP_DIAG_MASK_ZERO:
+            {
+                // necessary for llama
+                if (src0->grad) {
+                    const int n_past = ((int32_t *) tensor->op_params)[0];
+                    src0->grad =
+                        ggml_add_or_set(ctx, src0->grad,
+                            ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false),
+                        zero_table);
+                }
+            } break;
+        case GGML_OP_SOFT_MAX:
+            {
+                // necessary for llama
+                if (src0->grad) {
+                    src0->grad =
+                        ggml_add_or_set(ctx, src0->grad,
+                            ggml_soft_max_back(ctx, tensor->grad, tensor),
+                        zero_table);
+                }
+
+            } break;
+        case GGML_OP_SOFT_MAX_BACK:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_ROPE:
+            {
+                // necessary for llama
+                if (src0->grad) {
+                    //const int n_past = ((int32_t *) tensor->op_params)[0];
+                    const int n_dims = ((int32_t *) tensor->op_params)[1];
+                    const int mode   = ((int32_t *) tensor->op_params)[2];
+                    const int n_ctx  = ((int32_t *) tensor->op_params)[3];
+                    float freq_base;
+                    float freq_scale;
+                    float xpos_base;
+                    bool  xpos_down;
+                    memcpy(&freq_base,  (int32_t *) tensor->op_params + 4, sizeof(float));
+                    memcpy(&freq_scale, (int32_t *) tensor->op_params + 5, sizeof(float));
+                    memcpy(&xpos_base,  (int32_t *) tensor->op_params + 6, sizeof(float));
+                    memcpy(&xpos_down,  (int32_t *) tensor->op_params + 7, sizeof(bool));
+
+                    src0->grad = ggml_add_or_set(ctx,
+                            src0->grad,
+                            ggml_rope_back(ctx,
+                                tensor->grad,
+                                src1,
+                                n_dims,
+                                mode,
+                                n_ctx,
+                                freq_base,
+                                freq_scale,
+                                xpos_base,
+                                xpos_down),
+                            zero_table);
+                }
+            } break;
+        case GGML_OP_ROPE_BACK:
+            {
+                if (src0->grad) {
+                    //const int n_past = ((int32_t *) tensor->op_params)[0];
+                    const int n_dims = ((int32_t *) tensor->op_params)[1];
+                    const int mode   = ((int32_t *) tensor->op_params)[2];
+                    const int n_ctx  = ((int32_t *) tensor->op_params)[3];
+                    float freq_base;
+                    float freq_scale;
+                    float xpos_base;
+                    bool  xpos_down;
+                    memcpy(&freq_base,  (int32_t *) tensor->op_params + 4, sizeof(float));
+                    memcpy(&freq_scale, (int32_t *) tensor->op_params + 5, sizeof(float));
+                    memcpy(&xpos_base,  (int32_t *) tensor->op_params + 6, sizeof(float));
+                    memcpy(&xpos_down,  (int32_t *) tensor->op_params + 7, sizeof(bool));
+
+                    src0->grad = ggml_add_or_set(ctx,
+                            src0->grad,
+                            ggml_rope_impl(ctx,
+                                tensor->grad,
+                                src1,
+                                n_dims,
+                                mode,
+                                n_ctx,
+                                freq_base,
+                                freq_scale,
+                                xpos_base,
+                                xpos_down,
+                                false),
+                            zero_table);
+                }
+            } break;
+        case GGML_OP_ALIBI:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_CLAMP:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_CONV_1D:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_CONV_1D_STAGE_0:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_CONV_1D_STAGE_1:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_CONV_2D:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_CONV_TRANSPOSE_1D:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_CONV_TRANSPOSE_2D:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_POOL_1D:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_POOL_2D:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_UPSCALE:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_FLASH_ATTN:
+            {
+                struct ggml_tensor * flash_grad = NULL;
+                if (src0->grad || src1->grad || tensor->src[2]->grad) {
+                    int32_t t = ggml_get_op_params_i32(tensor, 0);
+                    GGML_ASSERT(t == 0 || t == 1);
+                    bool masked = t != 0;
+                    flash_grad =
+                        ggml_flash_attn_back(ctx,
+                            src0,
+                            src1,
+                            tensor->src[2],
+                            tensor->grad,
+                            masked);
+                }
+
+                struct ggml_tensor * src2 = tensor->src[2];
+                const int64_t elem_q = ggml_nelements(src0);
+                const int64_t elem_k = ggml_nelements(src1);
+                const int64_t elem_v = ggml_nelements(src2);
+
+                enum ggml_type result_type = flash_grad->type;
+                GGML_ASSERT(ggml_blck_size(result_type) == 1);
+                const size_t tsize = ggml_type_size(result_type);
+
+                const size_t offs_q = 0;
+                const size_t offs_k = offs_q + GGML_PAD(elem_q * tsize, GGML_MEM_ALIGN);
+                const size_t offs_v = offs_k + GGML_PAD(elem_k * tsize, GGML_MEM_ALIGN);
+
+                if (src0->grad) {
+                    struct ggml_tensor * view_q = ggml_view_1d(ctx, flash_grad, elem_q, offs_q);
+                    struct ggml_tensor * grad_q = ggml_reshape(ctx, view_q, src0);
+                    src0->grad = ggml_add_or_set(ctx,
+                            src0->grad,
+                            grad_q,
+                            zero_table);
+                }
+                if (src1->grad) {
+                    struct ggml_tensor * view_k = ggml_view_1d(ctx, flash_grad, elem_k, offs_k);
+                    struct ggml_tensor * grad_k = ggml_reshape(ctx, view_k, src1);
+                    src1->grad = ggml_add_or_set(ctx,
+                            src1->grad,
+                            grad_k,
+                            zero_table);
+                }
+                if (src2->grad) {
+                    struct ggml_tensor * view_v = ggml_view_1d(ctx, flash_grad, elem_v, offs_v);
+                    struct ggml_tensor * grad_v = ggml_reshape(ctx, view_v, src2);
+                    src2->grad = ggml_add_or_set(ctx,
+                            src2->grad,
+                            grad_v,
+                            zero_table);
+                }
+            } break;
+        case GGML_OP_FLASH_FF:
+            {
+                GGML_ASSERT(false); // not supported
+            } break;
+        case GGML_OP_FLASH_ATTN_BACK:
+            {
+                GGML_ASSERT(false); // not supported
+            } break;
+        case GGML_OP_WIN_PART:
+        case GGML_OP_WIN_UNPART:
+        case GGML_OP_UNARY:
+            {
+                switch (ggml_get_unary_op(tensor)) {
+                    case GGML_UNARY_OP_ABS:
+                        {
+                            if (src0->grad) {
+                                src0->grad =
+                                    ggml_add_or_set(ctx,
+                                            src0->grad,
+                                            ggml_mul(ctx,
+                                                ggml_sgn(ctx, src0),
+                                                tensor->grad),
+                                            zero_table);
+                            }
+                        } break;
+                    case GGML_UNARY_OP_SGN:
+                        {
+                            if (src0->grad) {
+                                // noop
+                            }
+                        } break;
+                    case GGML_UNARY_OP_NEG:
+                        {
+                            if (src0->grad) {
+                                src0->grad = ggml_sub_or_set(ctx, src0->grad, tensor->grad, zero_table);
+                            }
+                        } break;
+                    case GGML_UNARY_OP_STEP:
+                        {
+                            if (src0->grad) {
+                                // noop
+                            }
+                        } break;
+                    case GGML_UNARY_OP_TANH:
+                        {
+                            GGML_ASSERT(false); // TODO: not implemented
+                        } break;
+                    case GGML_UNARY_OP_ELU:
+                        {
+                            GGML_ASSERT(false); // TODO: not implemented
+                        } break;
+                    case GGML_UNARY_OP_RELU:
+                        {
+                            if (src0->grad) {
+                                src0->grad = ggml_add_or_set(ctx,
+                                        src0->grad,
+                                        ggml_mul(ctx,
+                                            ggml_step(ctx, src0),
+                                            tensor->grad),
+                                        zero_table);
+                            }
+                        } break;
+                    case GGML_UNARY_OP_GELU:
+                        {
+                            GGML_ASSERT(false); // TODO: not implemented
+                        } break;
+                    case GGML_UNARY_OP_GELU_QUICK:
+                        {
+                            GGML_ASSERT(false); // TODO: not implemented
+                        } break;
+                    case GGML_UNARY_OP_SILU:
+                        {
+                            // necessary for llama
+                            if (src0->grad) {
+                                src0->grad = ggml_add_or_set(ctx,
+                                        src0->grad,
+                                        ggml_silu_back(ctx, src0, tensor->grad),
+                                        zero_table);
+                            }
+                        } break;
+                    default:
+                        GGML_ASSERT(false);
+                }
+            } break;
+        case GGML_OP_GET_REL_POS:
+        case GGML_OP_ADD_REL_POS:
+        case GGML_OP_MAP_UNARY:
+        case GGML_OP_MAP_BINARY:
+        case GGML_OP_MAP_CUSTOM1_F32:
+        case GGML_OP_MAP_CUSTOM2_F32:
+        case GGML_OP_MAP_CUSTOM3_F32:
+        case GGML_OP_MAP_CUSTOM1:
+        case GGML_OP_MAP_CUSTOM2:
+        case GGML_OP_MAP_CUSTOM3:
+            {
+                GGML_ASSERT(false); // not supported
+            } break;
+        case GGML_OP_CROSS_ENTROPY_LOSS:
+            {
+                if (src0->grad) {
+                    src0->grad = ggml_add_or_set(ctx,
+                                src0->grad,
+                                ggml_cross_entropy_loss_back(ctx,
+                                    src0,
+                                    src1,
+                                    tensor->grad),
+                                zero_table);
+                }
+            } break;
+        case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
+            {
+                GGML_ASSERT(false); // not supported
+            } break;
+        case GGML_OP_NONE:
+            {
+                // nop
+            } break;
+        case GGML_OP_COUNT:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+
+    for (int i = 0; i < GGML_MAX_SRC; ++i) {
+        if (tensor->src[i] && tensor->src[i]->grad) {
+            GGML_ASSERT(ggml_are_same_shape(tensor->src[i], tensor->src[i]->grad));
+        }
+    }
+}
+
+static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * node) {
+    if (node->grad == NULL) {
+        // this usually happens when we generate intermediate nodes from constants in the backward pass
+        // it can also happen during forward pass, if the user performs computations with constants
+        if (node->op != GGML_OP_NONE) {
+            //GGML_PRINT_DEBUG("%s: warning: node %p has no grad, but op %d\n", __func__, (void *) node, node->op);
+        }
+    }
+
+    // check if already visited
+    if (hash_insert(cgraph->visited_hash_table, node)) {
+        return;
+    }
+
+    for (int i = 0; i < GGML_MAX_SRC; ++i) {
+        const int k =
+            (cgraph->order == GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT) ? i :
+            (cgraph->order == GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT) ? (GGML_MAX_SRC-1-i) :
+            /* unknown order, just fall back to using i*/ i;
+        if (node->src[k]) {
+            ggml_visit_parents(cgraph, node->src[k]);
+        }
+    }
+
+    if (node->op == GGML_OP_NONE && node->grad == NULL) {
+        // reached a leaf node, not part of the gradient graph (e.g. a constant)
+        GGML_ASSERT(cgraph->n_leafs < GGML_MAX_NODES);
+
+        if (strlen(node->name) == 0) {
+            ggml_format_name(node, "leaf_%d", cgraph->n_leafs);
+        }
+
+        cgraph->leafs[cgraph->n_leafs] = node;
+        cgraph->n_leafs++;
+    } else {
+        GGML_ASSERT(cgraph->n_nodes < GGML_MAX_NODES);
+
+        if (strlen(node->name) == 0) {
+            ggml_format_name(node, "node_%d", cgraph->n_nodes);
+        }
+
+        cgraph->nodes[cgraph->n_nodes] = node;
+        cgraph->grads[cgraph->n_nodes] = node->grad;
+        cgraph->n_nodes++;
+    }
+}
+
+static void ggml_build_forward_impl(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor, bool expand) {
+    if (!expand) {
+        cgraph->n_nodes = 0;
+        cgraph->n_leafs = 0;
+    }
+
+    const int n0 = cgraph->n_nodes;
+    UNUSED(n0);
+
+    ggml_visit_parents(cgraph, tensor);
+
+    const int n_new = cgraph->n_nodes - n0;
+    GGML_PRINT_DEBUG("%s: visited %d new nodes\n", __func__, n_new);
+
+    if (n_new > 0) {
+        // the last added node should always be starting point
+        GGML_ASSERT(cgraph->nodes[cgraph->n_nodes - 1] == tensor);
+    }
+}
+
+void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor) {
+    ggml_build_forward_impl(cgraph, tensor, true);
+}
+
+struct ggml_cgraph ggml_build_forward(struct ggml_tensor * tensor) {
+    struct ggml_cgraph result = {
+        /*.n_nodes      =*/ 0,
+        /*.n_leafs      =*/ 0,
+        /*.nodes        =*/ { NULL },
+        /*.grads        =*/ { NULL },
+        /*.leafs        =*/ { NULL },
+        /*.hash_table   =*/ { NULL },
+        /*.order        =*/ GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT,
+        /*.perf_runs    =*/ 0,
+        /*.perf_cycles  =*/ 0,
+        /*.perf_time_us =*/ 0,
+    };
+
+    ggml_build_forward_impl(&result, tensor, false);
+
+    return result;
+}
+
+void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep) {
+    GGML_ASSERT(gf->n_nodes > 0);
+
+    // if we are keeping the gradient graph, we have to detach the gradient nodes from the original graph
+    if (keep) {
+        for (int i = 0; i < gf->n_nodes; i++) {
+            struct ggml_tensor * node = gf->nodes[i];
+
+            if (node->grad) {
+                node->grad = ggml_dup_tensor(ctx, node);
+                gf->grads[i] = node->grad;
+            }
+        }
+    }
+
+    // remember original gradients which start with zero values
+    void ** zero_table = malloc(sizeof(void *) * GGML_GRAPH_HASHTABLE_SIZE);
+    memset(zero_table, 0, sizeof(void*) * GGML_GRAPH_HASHTABLE_SIZE);
+    for (int i = 0; i < gf->n_nodes; i++) {
+        if (gf->grads[i]) {
+            hash_insert(zero_table, gf->grads[i]);
+        }
+    }
+
+    for (int i = gf->n_nodes - 1; i >= 0; i--) {
+        struct ggml_tensor * node = gf->nodes[i];
+
+        // inplace operations to add gradients are not created by ggml_compute_backward
+        // use allocator to automatically make inplace operations
+        if (node->grad) {
+            ggml_compute_backward(ctx, node, zero_table);
+        }
+    }
+
+    for (int i = 0; i < gf->n_nodes; i++) {
+        struct ggml_tensor * node = gf->nodes[i];
+
+        if (node->is_param) {
+            GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *) node);
+            ggml_build_forward_expand(gb, node->grad);
+        }
+    }
+
+    free(zero_table);
+}
+
+struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep) {
+    struct ggml_cgraph result = *gf;
+    ggml_build_backward_expand(ctx, gf, &result, keep);
+    return result;
+}
+
+struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) {
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, GGML_GRAPH_SIZE);
+    struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs);
+
+    *cgraph = (struct ggml_cgraph) {
+        /*.n_nodes      =*/ 0,
+        /*.n_leafs      =*/ 0,
+        /*.nodes        =*/ { NULL },
+        /*.grads        =*/ { NULL },
+        /*.leafs        =*/ { NULL },
+        /*.hash_table   =*/ { NULL },
+        /*.order        =*/ GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT,
+        /*.perf_runs    =*/ 0,
+        /*.perf_cycles  =*/ 0,
+        /*.perf_time_us =*/ 0,
+    };
+
+    return cgraph;
+}
+
+struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor) {
+    struct ggml_cgraph * cgraph = ggml_new_graph(ctx);
+    ggml_build_forward_impl(cgraph, tensor, false);
+    return cgraph;
+}
+
+size_t ggml_graph_overhead(void) {
+    return GGML_OBJECT_SIZE + GGML_PAD(GGML_GRAPH_SIZE, GGML_MEM_ALIGN);
+}
+
+//
+// thread data
+//
+// synchronization is done via busy loops
+// I tried using spin locks, but not sure how to use them correctly - the things I tried were slower than busy loops
+//
+
+#ifdef __APPLE__
+
+//#include <os/lock.h>
+//
+//typedef os_unfair_lock ggml_lock_t;
+//
+//#define ggml_lock_init(x)    UNUSED(x)
+//#define ggml_lock_destroy(x) UNUSED(x)
+//#define ggml_lock_lock       os_unfair_lock_lock
+//#define ggml_lock_unlock     os_unfair_lock_unlock
+//
+//#define GGML_LOCK_INITIALIZER OS_UNFAIR_LOCK_INIT
+
+typedef int ggml_lock_t;
+
+#define ggml_lock_init(x)    UNUSED(x)
+#define ggml_lock_destroy(x) UNUSED(x)
+#define ggml_lock_lock(x)    UNUSED(x)
+#define ggml_lock_unlock(x)  UNUSED(x)
+
+#define GGML_LOCK_INITIALIZER 0
+
+typedef pthread_t ggml_thread_t;
+
+#define ggml_thread_create pthread_create
+#define ggml_thread_join   pthread_join
+
+#else
+
+//typedef pthread_spinlock_t ggml_lock_t;
+
+//#define ggml_lock_init(x) pthread_spin_init(x, PTHREAD_PROCESS_PRIVATE)
+//#define ggml_lock_destroy pthread_spin_destroy
+//#define ggml_lock_lock    pthread_spin_lock
+//#define ggml_lock_unlock  pthread_spin_unlock
+
+typedef int ggml_lock_t;
+
+#define ggml_lock_init(x)    UNUSED(x)
+#define ggml_lock_destroy(x) UNUSED(x)
+#if defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64))
+#define ggml_lock_lock(x)    _mm_pause()
+#else
+#define ggml_lock_lock(x)    UNUSED(x)
+#endif
+#define ggml_lock_unlock(x)  UNUSED(x)
+
+#define GGML_LOCK_INITIALIZER 0
+
+typedef pthread_t ggml_thread_t;
+
+#define ggml_thread_create pthread_create
+#define ggml_thread_join   pthread_join
+
+#endif
+
+// Android's libc implementation "bionic" does not support setting affinity
+#if defined(__linux__) && !defined(__BIONIC__)
+static void set_numa_thread_affinity(int thread_n, int n_threads) {
+    if (!ggml_is_numa()) {
+        return;
+    }
+
+    // run thread on node_num thread_n / (threads per node)
+    const int node_num = thread_n / ((n_threads + g_state.numa.n_nodes - 1) / g_state.numa.n_nodes);
+    struct ggml_numa_node * node = &g_state.numa.nodes[node_num];
+    size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus);
+
+    cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus);
+    CPU_ZERO_S(setsize, cpus);
+    for (size_t i = 0; i < node->n_cpus; ++i) {
+        CPU_SET_S(node->cpus[i], setsize, cpus);
+    }
+
+    int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus);
+    if (rv) {
+            fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",
+                    strerror(rv));
+    }
+
+    CPU_FREE(cpus);
+}
+
+static void clear_numa_thread_affinity(void) {
+    if (!ggml_is_numa()) {
+        return;
+    }
+
+    size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus);
+
+    cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus);
+    CPU_ZERO_S(setsize, cpus);
+    for (unsigned i = 0; i < g_state.numa.total_cpus; ++i) {
+        CPU_SET_S(i, setsize, cpus);
+    }
+
+    int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus);
+    if (rv) {
+        fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",
+            strerror(rv));
+    }
+
+    CPU_FREE(cpus);
+}
+#else
+// TODO: Windows etc.
+// (the linux implementation may also work on BSD, someone should test)
+static void set_numa_thread_affinity(int thread_n, int n_threads) { UNUSED(thread_n); UNUSED(n_threads);  }
+static void clear_numa_thread_affinity(void) {}
+#endif
+
+struct ggml_compute_state_shared {
+    const struct ggml_cgraph * cgraph;
+    const struct ggml_cplan  * cplan;
+
+    int64_t perf_node_start_cycles;
+    int64_t perf_node_start_time_us;
+
+    const int n_threads;
+
+    // synchronization primitives
+    atomic_int n_active; // num active threads
+    atomic_int node_n;   // active graph node
+
+    bool (*abort_callback)(void * data); // abort ggml_graph_compute when true
+    void * abort_callback_data;
+};
+
+struct ggml_compute_state {
+    ggml_thread_t thrd;
+    int ith;
+    struct ggml_compute_state_shared * shared;
+};
+
+static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) {
+    int64_t cycles_cur  = ggml_perf_cycles()  - st->perf_node_start_cycles;
+    int64_t time_us_cur = ggml_perf_time_us() - st->perf_node_start_time_us;
+
+    node->perf_runs++;
+    node->perf_cycles  += cycles_cur;
+    node->perf_time_us += time_us_cur;
+}
+
+static thread_ret_t ggml_graph_compute_thread(void * data) {
+    struct ggml_compute_state * state = (struct ggml_compute_state *) data;
+
+    const struct ggml_cgraph * cgraph = state->shared->cgraph;
+    const struct ggml_cplan  * cplan  = state->shared->cplan;
+
+    const int * n_tasks_arr = cplan->n_tasks;
+    const int   n_threads   = state->shared->n_threads;
+
+    set_numa_thread_affinity(state->ith, n_threads);
+
+    int node_n = -1;
+
+    while (true) {
+        if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
+            state->shared->node_n += 1;
+            return (thread_ret_t) GGML_EXIT_ABORTED;
+        }
+        if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
+            // all other threads are finished and spinning
+            // do finalize and init here so we don't have synchronize again
+            struct ggml_compute_params params = {
+                /*.type  =*/ GGML_TASK_FINALIZE,
+                /*.ith   =*/ 0,
+                /*.nth   =*/ 0,
+                /*.wsize =*/ cplan->work_size,
+                /*.wdata =*/ cplan->work_data,
+            };
+
+            if (node_n != -1) {
+                /* FINALIZE */
+                struct ggml_tensor * node = state->shared->cgraph->nodes[node_n];
+                if (GGML_OP_HAS_FINALIZE[node->op]) {
+                    params.nth = n_tasks_arr[node_n];
+                    ggml_compute_forward(&params, node);
+                }
+                ggml_graph_compute_perf_stats_node(node, state->shared);
+            }
+
+            // distribute new work or execute it direct if 1T
+            while (++node_n < cgraph->n_nodes) {
+                GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes);
+
+                struct ggml_tensor * node = cgraph->nodes[node_n];
+                const int n_tasks = n_tasks_arr[node_n];
+
+                state->shared->perf_node_start_cycles  = ggml_perf_cycles();
+                state->shared->perf_node_start_time_us = ggml_perf_time_us();
+
+                params.nth = n_tasks;
+
+                /* INIT */
+                if (GGML_OP_HAS_INIT[node->op]) {
+                    params.type = GGML_TASK_INIT;
+                    ggml_compute_forward(&params, node);
+                }
+
+                if (n_tasks == 1) {
+                    // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1,
+                    // they do something more efficient than spinning (?)
+                    params.type = GGML_TASK_COMPUTE;
+                    ggml_compute_forward(&params, node);
+
+                    if (GGML_OP_HAS_FINALIZE[node->op]) {
+                        params.type = GGML_TASK_FINALIZE;
+                        ggml_compute_forward(&params, node);
+                    }
+
+                    ggml_graph_compute_perf_stats_node(node, state->shared);
+                } else {
+                    break;
+                }
+
+                if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
+                    break;
+                }
+            }
+
+            atomic_store(&state->shared->n_active, n_threads);
+            atomic_store(&state->shared->node_n,   node_n);
+        } else {
+            // wait for other threads to finish
+            const int last = node_n;
+            while (true) {
+                // TODO: this sched_yield can have significant impact on the performance - either positive or negative
+                //       depending on the workload and the operating system.
+                //       since it is not clear what is the best approach, it should potentially become user-configurable
+                //       ref: https://github.com/ggerganov/ggml/issues/291
+#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
+                sched_yield();
+#endif
+
+                node_n = atomic_load(&state->shared->node_n);
+                if (node_n != last) break;
+            };
+        }
+
+        // check if we should stop
+        if (node_n >= cgraph->n_nodes) break;
+
+        /* COMPUTE */
+        struct ggml_tensor * node = cgraph->nodes[node_n];
+        const int n_tasks = n_tasks_arr[node_n];
+
+        struct ggml_compute_params params = {
+            /*.type  =*/ GGML_TASK_COMPUTE,
+            /*.ith   =*/ state->ith,
+            /*.nth   =*/ n_tasks,
+            /*.wsize =*/ cplan->work_size,
+            /*.wdata =*/ cplan->work_data,
+        };
+
+        if (state->ith < n_tasks) {
+            ggml_compute_forward(&params, node);
+        }
+    }
+
+    return GGML_EXIT_SUCCESS;
+}
+
+struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
+    if (n_threads <= 0) {
+        n_threads = GGML_DEFAULT_N_THREADS;
+    }
+
+    size_t work_size = 0;
+
+    struct ggml_cplan cplan;
+    memset(&cplan, 0, sizeof(struct ggml_cplan));
+
+    // thread scheduling for the different operations + work buffer size estimation
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        int n_tasks = 1;
+
+        struct ggml_tensor * node = cgraph->nodes[i];
+
+        switch (node->op) {
+            case GGML_OP_CPY:
+            case GGML_OP_DUP:
+                {
+                    n_tasks = n_threads;
+
+                    size_t cur = 0;
+                    if (ggml_is_quantized(node->type)) {
+                        cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
+                    }
+
+                    work_size = MAX(work_size, cur);
+                } break;
+            case GGML_OP_ADD:
+            case GGML_OP_ADD1:
+                {
+                    n_tasks = n_threads;
+
+                    size_t cur = 0;
+
+                    if (ggml_is_quantized(node->src[0]->type)) {
+                        cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
+                    }
+
+                    work_size = MAX(work_size, cur);
+                } break;
+            case GGML_OP_ACC:
+                {
+                    n_tasks = n_threads;
+
+                    size_t cur = 0;
+
+                    if (ggml_is_quantized(node->src[0]->type)) {
+                        cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks;
+                    }
+
+                    work_size = MAX(work_size, cur);
+                } break;
+            case GGML_OP_SUB:
+            case GGML_OP_DIV:
+            case GGML_OP_SQR:
+            case GGML_OP_SQRT:
+            case GGML_OP_LOG:
+            case GGML_OP_SUM:
+            case GGML_OP_SUM_ROWS:
+            case GGML_OP_MEAN:
+            case GGML_OP_ARGMAX:
+            case GGML_OP_REPEAT:
+            case GGML_OP_REPEAT_BACK:
+            {
+                    n_tasks = 1;
+                } break;
+
+            case GGML_OP_UNARY:
+                {
+                    switch (ggml_get_unary_op(node)) {
+                        case GGML_UNARY_OP_ABS:
+                        case GGML_UNARY_OP_SGN:
+                        case GGML_UNARY_OP_NEG:
+                        case GGML_UNARY_OP_STEP:
+                        case GGML_UNARY_OP_TANH:
+                        case GGML_UNARY_OP_ELU:
+                        case GGML_UNARY_OP_RELU:
+                            {
+                                n_tasks = 1;
+                            } break;
+
+                        case GGML_UNARY_OP_GELU:
+                        case GGML_UNARY_OP_GELU_QUICK:
+                        case GGML_UNARY_OP_SILU:
+                            {
+                                n_tasks = n_threads;
+                            } break;
+                    }
+                } break;
+            case GGML_OP_SILU_BACK:
+            case GGML_OP_MUL:
+            case GGML_OP_NORM:
+            case GGML_OP_RMS_NORM:
+            case GGML_OP_RMS_NORM_BACK:
+            case GGML_OP_GROUP_NORM:
+                {
+                    n_tasks = n_threads;
+                } break;
+            case GGML_OP_CONCAT:
+            case GGML_OP_MUL_MAT:
+                {
+                    n_tasks = n_threads;
+
+                    // TODO: use different scheduling for different matrix sizes
+                    //const int nr0 = ggml_nrows(node->src[0]);
+                    //const int nr1 = ggml_nrows(node->src[1]);
+
+                    //n_tasks = MIN(n_threads, MAX(1, nr0/128));
+                    //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks%d\n", nr0, nr1, nr0*nr1, n_tasks);
+
+                    size_t cur = 0;
+                    const enum ggml_type vec_dot_type = type_traits[node->src[0]->type].vec_dot_type;
+
+#if defined(GGML_USE_CUBLAS)
+                    if (ggml_cuda_can_mul_mat(node->src[0], node->src[1], node)) {
+                        n_tasks = 1; // TODO: this actually is doing nothing
+                                     //       the threads are still spinning
+                    } else
+#elif defined(GGML_USE_CLBLAST)
+                    if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) {
+                        n_tasks = 1; // TODO: this actually is doing nothing
+                                     //       the threads are still spinning
+                        cur = ggml_cl_mul_mat_get_wsize(node->src[0], node->src[1], node);
+                    } else
+#endif
+#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
+                    if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) {
+                        n_tasks = 1; // TODO: this actually is doing nothing
+                                     //       the threads are still spinning
+                        if (node->src[0]->type != GGML_TYPE_F32) {
+                            // here we need memory just for single 2D matrix from src0
+                            cur = ggml_type_size(GGML_TYPE_F32)*(node->src[0]->ne[0]*node->src[0]->ne[1]);
+                        }
+                    } else
+#endif
+                    if (node->src[1]->type != vec_dot_type) {
+                        cur = ggml_type_size(vec_dot_type)*ggml_nelements(node->src[1])/ggml_blck_size(vec_dot_type);
+                    } else {
+                        cur = 0;
+                    }
+
+                    work_size = MAX(work_size, cur);
+                } break;
+            case GGML_OP_OUT_PROD:
+                {
+                    n_tasks = n_threads;
+
+                    size_t cur = 0;
+
+                    if (ggml_is_quantized(node->src[0]->type)) {
+                        cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
+                    }
+
+                    work_size = MAX(work_size, cur);
+                } break;
+            case GGML_OP_SCALE:
+                {
+                    n_tasks = 1;
+                } break;
+            case GGML_OP_SET:
+            case GGML_OP_CONT:
+            case GGML_OP_RESHAPE:
+            case GGML_OP_VIEW:
+            case GGML_OP_PERMUTE:
+            case GGML_OP_TRANSPOSE:
+            case GGML_OP_GET_ROWS:
+            case GGML_OP_GET_ROWS_BACK:
+            case GGML_OP_DIAG:
+                {
+                    n_tasks = 1;
+                } break;
+            case GGML_OP_DIAG_MASK_ZERO:
+            case GGML_OP_DIAG_MASK_INF:
+            case GGML_OP_SOFT_MAX:
+            case GGML_OP_SOFT_MAX_BACK:
+            case GGML_OP_ROPE:
+            case GGML_OP_ROPE_BACK:
+            case GGML_OP_ADD_REL_POS:
+                {
+                    n_tasks = n_threads;
+                } break;
+            case GGML_OP_ALIBI:
+                {
+                    n_tasks = 1; //TODO
+                } break;
+            case GGML_OP_CLAMP:
+                {
+                    n_tasks = 1; //TODO
+                } break;
+            case GGML_OP_CONV_1D:
+                {
+                    n_tasks = n_threads;
+
+                    GGML_ASSERT(node->src[0]->ne[3] == 1);
+                    GGML_ASSERT(node->src[1]->ne[2] == 1);
+                    GGML_ASSERT(node->src[1]->ne[3] == 1);
+
+                    const int64_t ne00 = node->src[0]->ne[0];
+                    const int64_t ne01 = node->src[0]->ne[1];
+                    const int64_t ne02 = node->src[0]->ne[2];
+
+                    const int64_t ne10 = node->src[1]->ne[0];
+                    const int64_t ne11 = node->src[1]->ne[1];
+
+                    const int64_t ne0 = node->ne[0];
+                    const int64_t ne1 = node->ne[1];
+                    const int64_t nk  = ne00;
+                    const int64_t ew0 = nk * ne01;
+
+                    UNUSED(ne02);
+                    UNUSED(ne10);
+                    UNUSED(ne11);
+
+                    size_t cur = 0;
+
+                    if (node->src[0]->type == GGML_TYPE_F16 &&
+                        node->src[1]->type == GGML_TYPE_F32) {
+                        cur = sizeof(ggml_fp16_t)*(ne0*ne1*ew0);
+                    } else if (node->src[0]->type == GGML_TYPE_F32 &&
+                               node->src[1]->type == GGML_TYPE_F32) {
+                        cur = sizeof(float)*(ne0*ne1*ew0);
+                    } else {
+                        GGML_ASSERT(false);
+                    }
+
+                    work_size = MAX(work_size, cur);
+                } break;
+            case GGML_OP_CONV_1D_STAGE_0:
+                {
+                    n_tasks = n_threads;
+                } break;
+            case GGML_OP_CONV_1D_STAGE_1:
+                {
+                    n_tasks = n_threads;
+                } break;
+            case GGML_OP_CONV_TRANSPOSE_1D:
+                {
+                    n_tasks = n_threads;
+
+                    GGML_ASSERT(node->src[0]->ne[3] == 1);
+                    GGML_ASSERT(node->src[1]->ne[2] == 1);
+                    GGML_ASSERT(node->src[1]->ne[3] == 1);
+
+                    const int64_t ne00 = node->src[0]->ne[0];  // K
+                    const int64_t ne01 = node->src[0]->ne[1];  // Cout
+                    const int64_t ne02 = node->src[0]->ne[2];  // Cin
+
+                    const int64_t ne10 = node->src[1]->ne[0];  // L
+                    const int64_t ne11 = node->src[1]->ne[1];  // Cin
+
+                    size_t cur = 0;
+                    if (node->src[0]->type == GGML_TYPE_F16 &&
+                        node->src[1]->type == GGML_TYPE_F32) {
+                        cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02;
+                        cur += sizeof(ggml_fp16_t)*ne10*ne11;
+                    } else if (node->src[0]->type == GGML_TYPE_F32 &&
+                               node->src[1]->type == GGML_TYPE_F32) {
+                        cur += sizeof(float)*ne00*ne01*ne02;
+                        cur += sizeof(float)*ne10*ne11;
+                    } else {
+                        GGML_ASSERT(false);
+                    }
+
+                    work_size = MAX(work_size, cur);
+                } break;
+            case GGML_OP_CONV_2D:
+                {
+                    n_tasks = n_threads;
+
+                    const int64_t ne00 = node->src[0]->ne[0]; // W
+                    const int64_t ne01 = node->src[0]->ne[1]; // H
+                    const int64_t ne02 = node->src[0]->ne[2]; // C
+                    const int64_t ne03 = node->src[0]->ne[3]; // N
+
+                    const int64_t ne10 = node->src[1]->ne[0]; // W
+                    const int64_t ne11 = node->src[1]->ne[1]; // H
+                    const int64_t ne12 = node->src[1]->ne[2]; // C
+
+                    const int64_t ne0 = node->ne[0];
+                    const int64_t ne1 = node->ne[1];
+                    const int64_t ne2 = node->ne[2];
+                    const int64_t nk = ne00*ne01;
+                    const int64_t ew0 = nk * ne02;
+
+                    UNUSED(ne03);
+                    UNUSED(ne2);
+
+                    size_t cur = 0;
+
+                    if (node->src[0]->type == GGML_TYPE_F16 &&
+                        node->src[1]->type == GGML_TYPE_F32) {
+                        cur = sizeof(ggml_fp16_t)*(ne0*ne1*ew0);
+                    } else if (node->src[0]->type == GGML_TYPE_F32 &&
+                               node->src[1]->type == GGML_TYPE_F32) {
+                        cur = sizeof(float)*      (ne10*ne11*ne12);
+                    } else {
+                        GGML_ASSERT(false);
+                    }
+
+                    work_size = MAX(work_size, cur);
+                } break;
+            case GGML_OP_CONV_TRANSPOSE_2D:
+                {
+                    n_tasks = n_threads;
+
+                    const int64_t ne00 = node->src[0]->ne[0]; // W
+                    const int64_t ne01 = node->src[0]->ne[1]; // H
+                    const int64_t ne02 = node->src[0]->ne[2]; // Channels Out
+                    const int64_t ne03 = node->src[0]->ne[3]; // Channels In
+
+                    const int64_t ne10 = node->src[1]->ne[0]; // W
+                    const int64_t ne11 = node->src[1]->ne[1]; // H
+                    const int64_t ne12 = node->src[1]->ne[2]; // Channels In
+
+                    size_t cur = 0;
+                    cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03;
+                    cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12;
+
+                    work_size = MAX(work_size, cur);
+                } break;
+            case GGML_OP_POOL_1D:
+            case GGML_OP_POOL_2D:
+                {
+                    n_tasks = 1;
+                } break;
+            case GGML_OP_UPSCALE:
+                {
+                    n_tasks = n_threads;
+                } break;
+            case GGML_OP_FLASH_ATTN:
+                {
+                    n_tasks = n_threads;
+
+                    size_t cur = 0;
+
+                    const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL);
+
+                    if (node->src[1]->type == GGML_TYPE_F32) {
+                        cur  = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1)
+                        cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2
+                    }
+
+                    if (node->src[1]->type == GGML_TYPE_F16) {
+                        cur  = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1)
+                        cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2
+                    }
+
+                    work_size = MAX(work_size, cur);
+                } break;
+            case GGML_OP_FLASH_FF:
+                {
+                    n_tasks = n_threads;
+
+                    size_t cur = 0;
+
+                    if (node->src[1]->type == GGML_TYPE_F32) {
+                        cur  = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1)
+                        cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2
+                    }
+
+                    if (node->src[1]->type == GGML_TYPE_F16) {
+                        cur  = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1)
+                        cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2
+                    }
+
+                    work_size = MAX(work_size, cur);
+                } break;
+            case GGML_OP_FLASH_ATTN_BACK:
+                {
+                    n_tasks = n_threads;
+
+                    size_t cur = 0;
+
+                    const int64_t    D = node->src[0]->ne[0];
+                    const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL);
+                    const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back
+                    if (node->src[1]->type == GGML_TYPE_F32) {
+                        cur  = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1)
+                        cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2
+                    }
+
+                    if (node->src[1]->type == GGML_TYPE_F16) {
+                        cur  = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1)
+                        cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2
+                    }
+
+                    work_size = MAX(work_size, cur);
+                } break;
+            case GGML_OP_WIN_PART:
+            case GGML_OP_WIN_UNPART:
+            case GGML_OP_GET_REL_POS:
+            case GGML_OP_MAP_UNARY:
+            case GGML_OP_MAP_BINARY:
+            case GGML_OP_MAP_CUSTOM1_F32:
+            case GGML_OP_MAP_CUSTOM2_F32:
+            case GGML_OP_MAP_CUSTOM3_F32:
+                {
+                    n_tasks = 1;
+                } break;
+            case GGML_OP_MAP_CUSTOM1:
+                {
+                    struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params;
+                    if (p->n_tasks == GGML_N_TASKS_MAX) {
+                        n_tasks = n_threads;
+                    } else {
+                        n_tasks = MIN(p->n_tasks, n_threads);
+                    }
+                } break;
+            case GGML_OP_MAP_CUSTOM2:
+                {
+                    struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params;
+                    if (p->n_tasks == GGML_N_TASKS_MAX) {
+                        n_tasks = n_threads;
+                    } else {
+                        n_tasks = MIN(p->n_tasks, n_threads);
+                    }
+                } break;
+            case GGML_OP_MAP_CUSTOM3:
+                {
+                    struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params;
+                    if (p->n_tasks == GGML_N_TASKS_MAX) {
+                        n_tasks = n_threads;
+                    } else {
+                        n_tasks = MIN(p->n_tasks, n_threads);
+                    }
+                } break;
+            case GGML_OP_CROSS_ENTROPY_LOSS:
+                {
+                    n_tasks = n_threads;
+
+                    size_t cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks);
+
+                    work_size = MAX(work_size, cur);
+                } break;
+            case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
+                {
+                    n_tasks = n_threads;
+                } break;
+            case GGML_OP_NONE:
+                {
+                    n_tasks = 1;
+                } break;
+            case GGML_OP_COUNT:
+                {
+                    GGML_ASSERT(false);
+                } break;
+        }
+
+        cplan.n_tasks[i] = n_tasks;
+    }
+
+    if (work_size > 0) {
+        work_size += CACHE_LINE_SIZE*(n_threads - 1);
+    }
+
+    cplan.n_threads = n_threads;
+    cplan.work_size = work_size;
+    cplan.work_data = NULL;
+
+    return cplan;
+}
+
+int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
+    {
+        GGML_ASSERT(cplan);
+        GGML_ASSERT(cplan->n_threads > 0);
+
+        if (cplan->work_size > 0) {
+            GGML_ASSERT(cplan->work_data);
+        }
+
+        for (int i = 0; i < cgraph->n_nodes; ++i) {
+            if (cgraph->nodes[i]->op != GGML_OP_NONE) {
+                GGML_ASSERT(cplan->n_tasks[i] > 0);
+            }
+        }
+    }
+
+    const int n_threads = cplan->n_threads;
+
+    struct ggml_compute_state_shared state_shared = {
+        /*.cgraph                  =*/ cgraph,
+        /*.cgraph_plan             =*/ cplan,
+        /*.perf_node_start_cycles  =*/ 0,
+        /*.perf_node_start_time_us =*/ 0,
+        /*.n_threads               =*/ n_threads,
+        /*.n_active                =*/ n_threads,
+        /*.node_n                  =*/ -1,
+        /*.abort_callback          =*/ NULL,
+        /*.abort_callback_data     =*/ NULL,
+    };
+    struct ggml_compute_state * workers = alloca(sizeof(struct ggml_compute_state)*n_threads);
+
+    // create thread pool
+    if (n_threads > 1) {
+        for (int j = 1; j < n_threads; ++j) {
+            workers[j] = (struct ggml_compute_state) {
+                .thrd   = 0,
+                .ith = j,
+                .shared = &state_shared,
+            };
+
+            const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]);
+            GGML_ASSERT(rc == 0);
+            UNUSED(rc);
+        }
+    }
+
+    workers[0].ith = 0;
+    workers[0].shared = &state_shared;
+
+    const int64_t perf_start_cycles  = ggml_perf_cycles();
+    const int64_t perf_start_time_us = ggml_perf_time_us();
+
+    // this is a work thread too
+    int compute_status = (size_t) ggml_graph_compute_thread(&workers[0]);
+
+    // don't leave affinity set on the main thread
+    clear_numa_thread_affinity();
+
+    // join or kill thread pool
+    if (n_threads > 1) {
+        for (int j = 1; j < n_threads; j++) {
+            const int rc = ggml_thread_join(workers[j].thrd, NULL);
+            GGML_ASSERT(rc == 0);
+        }
+    }
+
+    // performance stats (graph)
+    {
+        int64_t perf_cycles_cur  = ggml_perf_cycles()  - perf_start_cycles;
+        int64_t perf_time_us_cur = ggml_perf_time_us() - perf_start_time_us;
+
+        cgraph->perf_runs++;
+        cgraph->perf_cycles  += perf_cycles_cur;
+        cgraph->perf_time_us += perf_time_us_cur;
+
+        GGML_PRINT_DEBUG("%s: perf (%d) - cpu = %.3f / %.3f ms, wall = %.3f / %.3f ms\n",
+                __func__, cgraph->perf_runs,
+                (double) perf_cycles_cur      / (double) ggml_cycles_per_ms(),
+                (double) cgraph->perf_cycles  / (double) ggml_cycles_per_ms() / (double) cgraph->perf_runs,
+                (double) perf_time_us_cur     / 1000.0,
+                (double) cgraph->perf_time_us / 1000.0 / cgraph->perf_runs);
+    }
+
+    return compute_status;
+}
+
+void ggml_graph_reset(struct ggml_cgraph * cgraph) {
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        struct ggml_tensor * grad = cgraph->grads[i];
+
+        if (grad) {
+            ggml_set_zero(grad);
+        }
+    }
+}
+
+void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) {
+    struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads);
+
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
+
+    cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
+
+    ggml_graph_compute(cgraph, &cplan);
+}
+
+struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name) {
+    for (int i = 0; i < cgraph->n_leafs; i++) {
+        struct ggml_tensor * leaf = cgraph->leafs[i];
+
+        if (strcmp(leaf->name, name) == 0) {
+            return leaf;
+        }
+    }
+
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        struct ggml_tensor * node = cgraph->nodes[i];
+
+        if (strcmp(node->name, name) == 0) {
+            return node;
+        }
+    }
+
+    return NULL;
+}
+
+static void ggml_graph_export_leaf(const struct ggml_tensor * tensor, FILE * fout) {
+    const int64_t * ne = tensor->ne;
+    const size_t  * nb = tensor->nb;
+
+    fprintf(fout, "%-6s %-12s %8d %" PRId64 " %" PRId64 " %" PRId64 " %" PRId64 " %16zu %16zu %16zu %16zu %16p %32s\n",
+            ggml_type_name(tensor->type),
+            ggml_op_name  (tensor->op),
+            tensor->n_dims,
+            ne[0], ne[1], ne[2], ne[3],
+            nb[0], nb[1], nb[2], nb[3],
+            tensor->data,
+            tensor->name);
+}
+
+static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char * arg, FILE * fout) {
+    const int64_t * ne = tensor->ne;
+    const size_t  * nb = tensor->nb;
+
+    fprintf(fout, "%-6s %-6s %-12s %8d %" PRId64 " %" PRId64 " %" PRId64 " %" PRId64 " %16zu %16zu %16zu %16zu %16p %32s\n",
+            arg,
+            ggml_type_name(tensor->type),
+            ggml_op_name  (tensor->op),
+            tensor->n_dims,
+            ne[0], ne[1], ne[2], ne[3],
+            nb[0], nb[1], nb[2], nb[3],
+            tensor->data,
+            tensor->name);
+}
+
+void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
+    uint64_t size_eval = 0;
+
+    // compute size of intermediate results
+    // TODO: does not take into account scratch buffers !!!!
+    for (int i = 0; i < cgraph->n_nodes; ++i) {
+        size_eval += ggml_nbytes_pad(cgraph->nodes[i]);
+    }
+
+    // print
+    {
+        FILE * fout = stdout;
+
+        fprintf(fout, "\n");
+        fprintf(fout, "%-16s %8x\n", "magic",        GGML_FILE_MAGIC);
+        fprintf(fout, "%-16s %8d\n", "version",      GGML_FILE_VERSION);
+        fprintf(fout, "%-16s %8d\n", "leafs",        cgraph->n_leafs);
+        fprintf(fout, "%-16s %8d\n", "nodes",        cgraph->n_nodes);
+        fprintf(fout, "%-16s %" PRIu64 "\n", "eval", size_eval);
+
+        // header
+        fprintf(fout, "\n");
+        fprintf(fout, "%-6s %-12s %8s %8s %8s %8s %8s %16s %16s %16s %16s %16s %16s\n",
+                "TYPE", "OP", "NDIMS", "NE0", "NE1", "NE2", "NE3", "NB0", "NB1", "NB2", "NB3", "DATA", "NAME");
+
+        for (int i = 0; i < cgraph->n_leafs; ++i) {
+            ggml_graph_export_leaf(cgraph->leafs[i], fout);
+
+            GGML_ASSERT(cgraph->leafs[i]->op   == GGML_OP_NONE);
+            GGML_ASSERT(cgraph->leafs[i]->src[0] == NULL);
+            GGML_ASSERT(cgraph->leafs[i]->src[1] == NULL);
+        }
+
+        // header
+        fprintf(fout, "\n");
+        fprintf(fout, "%-6s %-6s %-12s %8s %8s %8s %8s %8s %16s %16s %16s %16s %8s %16s %16s\n",
+                "ARG", "TYPE", "OP", "NDIMS", "NE0", "NE1", "NE2", "NE3", "NB0", "NB1", "NB2", "NB3", "NTASKS", "DATA", "NAME");
+
+        for (int i = 0; i < cgraph->n_nodes; ++i) {
+            ggml_graph_export_node(cgraph->nodes[i], "DST", fout);
+
+            for (int j = 0; j < GGML_MAX_SRC; ++j) {
+                if (cgraph->nodes[i]->src[j]) {
+                    ggml_graph_export_node(cgraph->nodes[i]->src[j], "SRC", fout);
+                }
+            }
+
+            fprintf(fout, "\n");
+        }
+
+        fprintf(fout, "\n");
+    }
+
+    // write binary data
+    {
+        FILE * fout = fopen(fname, "wb");
+
+        if (!fout) {
+            fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
+            return;
+        }
+
+        // header
+        {
+            const uint32_t magic   = GGML_FILE_MAGIC;
+            const uint32_t version = GGML_FILE_VERSION;
+            const uint32_t n_leafs = cgraph->n_leafs;
+            const uint32_t nodes   = cgraph->n_nodes;
+
+            fwrite(&magic,     sizeof(uint32_t), 1, fout);
+            fwrite(&version,   sizeof(uint32_t), 1, fout);
+            fwrite(&n_leafs,   sizeof(uint32_t), 1, fout);
+            fwrite(&nodes,     sizeof(uint32_t), 1, fout);
+            fwrite(&size_eval, sizeof(uint64_t), 1, fout);
+        }
+
+        // leafs
+        {
+            for (int i = 0; i < cgraph->n_leafs; ++i) {
+                const struct ggml_tensor * tensor = cgraph->leafs[i];
+
+                const uint32_t type   = tensor->type;
+                const uint32_t op     = tensor->op;
+                const uint32_t n_dims = tensor->n_dims;
+
+                fwrite(&type,   sizeof(uint32_t), 1, fout);
+                fwrite(&op,     sizeof(uint32_t), 1, fout);
+                fwrite(&n_dims, sizeof(uint32_t), 1, fout);
+
+                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
+                    const uint64_t ne = tensor->ne[j];
+                    const uint64_t nb = tensor->nb[j];
+
+                    fwrite(&ne, sizeof(uint64_t), 1, fout);
+                    fwrite(&nb, sizeof(uint64_t), 1, fout);
+                }
+
+                fwrite(tensor->name,      sizeof(char), GGML_MAX_NAME,      fout);
+                fwrite(tensor->op_params, sizeof(char), GGML_MAX_OP_PARAMS, fout);
+
+                // dump the data
+                // TODO: pad this to 32 byte boundary
+                {
+                    const size_t size = ggml_nbytes(tensor);
+
+                    fwrite(tensor->data, sizeof(char), size, fout);
+                }
+            }
+        }
+
+        // nodes
+        {
+            for (int i = 0; i < cgraph->n_nodes; ++i) {
+                const struct ggml_tensor * tensor = cgraph->nodes[i];
+
+                const uint32_t type   = tensor->type;
+                const uint32_t op     = tensor->op;
+                const uint32_t n_dims = tensor->n_dims;
+
+                fwrite(&type,   sizeof(uint32_t), 1, fout);
+                fwrite(&op,     sizeof(uint32_t), 1, fout);
+                fwrite(&n_dims, sizeof(uint32_t), 1, fout);
+
+                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
+                    const uint64_t ne = tensor->ne[j];
+                    const uint64_t nb = tensor->nb[j];
+
+                    fwrite(&ne, sizeof(uint64_t), 1, fout);
+                    fwrite(&nb, sizeof(uint64_t), 1, fout);
+                }
+
+                fwrite(tensor->name,      sizeof(char), GGML_MAX_NAME,      fout);
+                fwrite(tensor->op_params, sizeof(char), GGML_MAX_OP_PARAMS, fout);
+
+                // output the op arguments
+                {
+                    struct ggml_tensor * args[GGML_MAX_SRC] = { NULL };
+
+                    for (int j = 0; j < GGML_MAX_SRC; ++j) {
+                        args[j] = tensor->src[j];
+                    }
+
+                    for (int j = 0; j < GGML_MAX_SRC; ++j) {
+                        if (args[j]) {
+                            int32_t idx = -1;
+
+                            // check if leaf
+                            {
+                                for (int k = 0; k < cgraph->n_leafs; ++k) {
+                                    if (args[j] == cgraph->leafs[k]) {
+                                        idx = k;
+                                        break;
+                                    }
+                                }
+                            }
+
+                            // check if node
+                            if (idx == -1) {
+                                for (int k = 0; k < cgraph->n_nodes; ++k) {
+                                    if (args[j] == cgraph->nodes[k]) {
+                                        idx = GGML_MAX_NODES + k;
+                                        break;
+                                    }
+                                }
+                            }
+
+                            if (idx == -1) {
+                                fprintf(stderr, "%s: failed to find tensor, arg = %d, node = %d\n", __func__, j, i);
+                                fclose(fout);
+                                return;
+                            }
+
+                            fwrite(&idx, sizeof(int32_t), 1, fout);
+                        } else {
+                            const int32_t nul = -1;
+
+                            fwrite(&nul, sizeof(int32_t), 1, fout);
+                        }
+                    }
+                }
+            }
+        }
+
+        fclose(fout);
+    }
+}
+
+struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval) {
+    assert(*ctx_data == NULL);
+    assert(*ctx_eval == NULL);
+
+    struct ggml_cgraph result = { 0 };
+
+    struct ggml_tensor * data = NULL;
+
+    // read file into data
+    {
+        FILE * fin = fopen(fname, "rb");
+        if (!fin) {
+            fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
+            return result;
+        }
+
+        size_t fsize = 0;
+
+        fseek(fin, 0, SEEK_END);
+        fsize = ftell(fin);
+        fseek(fin, 0, SEEK_SET);
+
+        // create the data context
+        {
+            const size_t overhead = 1*ggml_tensor_overhead();
+
+            struct ggml_init_params params = {
+                .mem_size   = fsize + overhead,
+                .mem_buffer = NULL,
+                .no_alloc   = false,
+            };
+
+            *ctx_data = ggml_init(params);
+
+            if (!*ctx_data) {
+                fprintf(stderr, "%s: failed to create ggml context\n", __func__);
+                fclose(fin);
+                return result;
+            }
+        }
+
+        data = ggml_new_tensor_1d(*ctx_data, GGML_TYPE_I8, fsize);
+
+        {
+            const size_t ret = fread(data->data, sizeof(char), fsize, fin);
+            if (ret != fsize) {
+                fprintf(stderr, "%s: failed to read %s\n", __func__, fname);
+                fclose(fin);
+                return result;
+            }
+        }
+
+        fclose(fin);
+    }
+
+    // populate result
+    {
+        char * ptr = (char *) data->data;
+
+        const uint32_t magic = *(const uint32_t *) ptr; ptr += sizeof(magic);
+
+        if (magic != GGML_FILE_MAGIC) {
+            fprintf(stderr, "%s: invalid magic number, got %08x\n", __func__, magic);
+            return result;
+        }
+
+        const uint32_t version = *(const uint32_t *) ptr; ptr += sizeof(version);
+
+        if (version != GGML_FILE_VERSION) {
+            fprintf(stderr, "%s: invalid version number\n", __func__);
+            return result;
+        }
+
+        const uint32_t n_leafs   = *(const uint32_t *) ptr; ptr += sizeof(n_leafs);
+        const uint32_t n_nodes   = *(const uint32_t *) ptr; ptr += sizeof(n_nodes);
+        const uint64_t size_eval = *(const uint64_t *) ptr; ptr += sizeof(size_eval);
+
+        result.n_leafs = n_leafs;
+        result.n_nodes = n_nodes;
+
+        // create the data context
+        {
+            const size_t overhead = (n_leafs + n_nodes)*ggml_tensor_overhead();
+
+            struct ggml_init_params params = {
+                .mem_size   = size_eval + overhead,
+                .mem_buffer = NULL,
+                .no_alloc   = true,
+            };
+
+            *ctx_eval = ggml_init(params);
+
+            if (!*ctx_eval) {
+                fprintf(stderr, "%s: failed to create ggml context\n", __func__);
+                return result;
+            }
+        }
+
+        // leafs
+        {
+            uint32_t type;
+            uint32_t op;
+            uint32_t n_dims;
+
+            for (uint32_t i = 0; i < n_leafs; ++i) {
+                type   = *(const uint32_t *) ptr; ptr += sizeof(type);
+                op     = *(const uint32_t *) ptr; ptr += sizeof(op);
+                n_dims = *(const uint32_t *) ptr; ptr += sizeof(n_dims);
+
+                int64_t ne[GGML_MAX_DIMS];
+                size_t  nb[GGML_MAX_DIMS];
+
+                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
+                    uint64_t ne_cur;
+                    uint64_t nb_cur;
+
+                    ne_cur = *(const uint64_t *) ptr; ptr += sizeof(ne_cur);
+                    nb_cur = *(const uint64_t *) ptr; ptr += sizeof(nb_cur);
+
+                    ne[j] = ne_cur;
+                    nb[j] = nb_cur;
+                }
+
+                struct ggml_tensor * tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, n_dims, ne);
+
+                tensor->op = (enum ggml_op) op;
+
+                memcpy(tensor->name,      ptr, GGML_MAX_NAME);      ptr += GGML_MAX_NAME;
+                memcpy(tensor->op_params, ptr, GGML_MAX_OP_PARAMS); ptr += GGML_MAX_OP_PARAMS;
+
+                tensor->data = (void *) ptr;
+
+                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
+                    tensor->nb[j] = nb[j];
+                }
+
+                result.leafs[i] = tensor;
+
+                ptr += ggml_nbytes(tensor);
+
+                fprintf(stderr, "%s: loaded leaf %d: '%16s', %3d dims, %9zu bytes\n", __func__, i, tensor->name, n_dims, ggml_nbytes(tensor));
+            }
+        }
+
+        ggml_set_no_alloc(*ctx_eval, false);
+
+        // nodes
+        {
+            uint32_t type;
+            uint32_t op;
+            uint32_t n_dims;
+
+            for (uint32_t i = 0; i < n_nodes; ++i) {
+                type   = *(const uint32_t *) ptr; ptr += sizeof(type);
+                op     = *(const uint32_t *) ptr; ptr += sizeof(op);
+                n_dims = *(const uint32_t *) ptr; ptr += sizeof(n_dims);
+
+                enum ggml_op eop = (enum ggml_op) op;
+
+                int64_t ne[GGML_MAX_DIMS];
+                size_t  nb[GGML_MAX_DIMS];
+
+                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
+                    uint64_t ne_cur;
+                    uint64_t nb_cur;
+
+                    ne_cur = *(const uint64_t *) ptr; ptr += sizeof(ne_cur);
+                    nb_cur = *(const uint64_t *) ptr; ptr += sizeof(nb_cur);
+
+                    ne[j] = ne_cur;
+                    nb[j] = nb_cur;
+                }
+
+                const char * ptr_name      = ptr; ptr += GGML_MAX_NAME;
+                const char * ptr_op_params = ptr; ptr += GGML_MAX_OP_PARAMS;
+
+                const int32_t * ptr_arg_idx = (const int32_t *) ptr; ptr += GGML_MAX_SRC*sizeof(int32_t);
+
+                struct ggml_tensor * args[GGML_MAX_SRC] = { NULL };
+
+                // parse args
+                for (int j = 0; j < GGML_MAX_SRC; ++j) {
+                    const int32_t arg_idx = ptr_arg_idx[j];
+
+                    if (arg_idx == -1) {
+                        continue;
+                    }
+
+                    if (arg_idx < GGML_MAX_NODES) {
+                        args[j] = result.leafs[arg_idx];
+                    } else {
+                        args[j] = result.nodes[arg_idx - GGML_MAX_NODES];
+                    }
+                }
+
+                // create the tensor
+                // "view" operations are handled differently
+                // TODO: handle inplace ops - currently a copy is always made
+
+                struct ggml_tensor * tensor = NULL;
+
+                switch (eop) {
+                    // TODO: implement other view ops
+                    case GGML_OP_RESHAPE:
+                        {
+                            tensor = ggml_reshape_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3]);
+                        } break;
+                    case GGML_OP_VIEW:
+                        {
+                            tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0);
+
+                            size_t offs;
+                            memcpy(&offs, ptr_op_params, sizeof(offs));
+
+                            tensor->data = ((char *) tensor->data) + offs;
+                        } break;
+                    case GGML_OP_TRANSPOSE:
+                        {
+                            tensor = ggml_transpose(*ctx_eval, args[0]);
+                        } break;
+                    case GGML_OP_PERMUTE:
+                        {
+                            tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0);
+                        } break;
+                    default:
+                        {
+                            tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, n_dims, ne);
+
+                            tensor->op = eop;
+                        } break;
+                }
+
+                memcpy(tensor->name,      ptr_name,      GGML_MAX_NAME);
+                memcpy(tensor->op_params, ptr_op_params, GGML_MAX_OP_PARAMS);
+
+                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
+                    tensor->nb[j] = nb[j];
+                }
+
+                for (int j = 0; j < GGML_MAX_SRC; ++j) {
+                    tensor->src[j] = args[j];
+                }
+
+                result.nodes[i] = tensor;
+
+                fprintf(stderr, "%s: loaded node %d: '%16s', %3d dims, %9zu bytes\n", __func__, i, tensor->name, n_dims, ggml_nbytes(tensor));
+            }
+        }
+    }
+
+    return result;
+}
+
+void ggml_graph_print(const struct ggml_cgraph * cgraph) {
+    int64_t perf_total_per_op_us[GGML_OP_COUNT] = {0};
+
+    GGML_PRINT("=== GRAPH ===\n");
+
+    GGML_PRINT("n_nodes = %d\n", cgraph->n_nodes);
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        struct ggml_tensor * node = cgraph->nodes[i];
+
+        perf_total_per_op_us[node->op] += MAX(1, node->perf_time_us);
+
+        GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s (%3d) cpu = %7.3f / %7.3f ms, wall = %7.3f / %7.3f ms\n",
+                i,
+                node->ne[0], node->ne[1], node->ne[2],
+                ggml_op_name(node->op), node->is_param ? "x" : node->grad ? "g" : " ", node->perf_runs,
+                (double) node->perf_cycles  / (double) ggml_cycles_per_ms(),
+                (double) node->perf_cycles  / (double) ggml_cycles_per_ms() / (double) node->perf_runs,
+                (double) node->perf_time_us / 1000.0,
+                (double) node->perf_time_us / 1000.0 / node->perf_runs);
+    }
+
+    GGML_PRINT("n_leafs = %d\n", cgraph->n_leafs);
+    for (int i = 0; i < cgraph->n_leafs; i++) {
+        struct ggml_tensor * node = cgraph->leafs[i];
+
+        GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 "] %8s %16s\n",
+                i,
+                node->ne[0], node->ne[1],
+                ggml_op_name(node->op),
+                ggml_get_name(node));
+    }
+
+    for (int i = 0; i < GGML_OP_COUNT; i++) {
+        if (perf_total_per_op_us[i] == 0) {
+            continue;
+        }
+
+        GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", ggml_op_name(i), (double) perf_total_per_op_us[i] / 1000.0);
+    }
+
+    GGML_PRINT("========================================\n");
+}
+
+// check if node is part of the graph
+static bool ggml_graph_find(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) {
+    if (cgraph == NULL) {
+        return true;
+    }
+
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        if (cgraph->nodes[i] == node) {
+            return true;
+        }
+    }
+
+    return false;
+}
+
+static struct ggml_tensor * ggml_graph_get_parent(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) {
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        struct ggml_tensor * parent = cgraph->nodes[i];
+
+        if (parent->grad == node) {
+            return parent;
+        }
+    }
+
+    return NULL;
+}
+
+static void ggml_graph_dump_dot_node_edge(FILE * fp, const struct ggml_cgraph * gb, struct ggml_tensor * node, struct ggml_tensor * parent, const char * label)  {
+    struct ggml_tensor * gparent = ggml_graph_get_parent(gb, node);
+    struct ggml_tensor * gparent0 = ggml_graph_get_parent(gb, parent);
+    fprintf(fp, "  \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"%s\"; ]\n",
+            gparent0 ? (void *) gparent0 : (void *) parent,
+            gparent0 ? "g" : "x",
+            gparent ? (void *) gparent : (void *) node,
+            gparent ? "g" : "x",
+            gparent ? "empty" : "vee",
+            gparent ? "dashed" : "solid",
+            label);
+}
+
+static void ggml_graph_dump_dot_leaf_edge(FILE * fp, struct ggml_tensor * node, struct ggml_tensor * parent, const char * label)  {
+    fprintf(fp, "  \"%p\":%s -> \"%p\":%s [ label = \"%s\"; ]\n",
+            (void *) parent, "x",
+            (void *) node, "x",
+            label);
+}
+
+void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename) {
+    char color[16];
+
+    FILE * fp = fopen(filename, "w");
+    GGML_ASSERT(fp);
+
+    fprintf(fp, "digraph G {\n");
+    fprintf(fp, "  newrank = true;\n");
+    fprintf(fp, "  rankdir = LR;\n");
+
+    for (int i = 0; i < gb->n_nodes; i++) {
+        struct ggml_tensor * node = gb->nodes[i];
+
+        if (ggml_graph_get_parent(gb, node) != NULL) {
+            continue;
+        }
+
+        if (node->is_param) {
+            snprintf(color, sizeof(color), "yellow");
+        } else if (node->grad) {
+            if (ggml_graph_find(gf, node)) {
+                snprintf(color, sizeof(color), "green");
+            } else {
+                snprintf(color, sizeof(color), "lightblue");
+            }
+        } else {
+            snprintf(color, sizeof(color), "white");
+        }
+
+        fprintf(fp, "  \"%p\" [ "
+                    "style = filled; fillcolor = %s; shape = record; "
+                    "label=\"",
+                (void *) node, color);
+
+        if (strlen(node->name) > 0) {
+            fprintf(fp, "%s (%s)|", node->name, ggml_type_name(node->type));
+        } else {
+            fprintf(fp, "(%s)|", ggml_type_name(node->type));
+        }
+
+        if (node->n_dims == 2) {
+            fprintf(fp, "%d [%" PRId64 ", %" PRId64 "] | <x>%s", i, node->ne[0], node->ne[1], ggml_op_symbol(node->op));
+        } else {
+            fprintf(fp, "%d [%" PRId64 ", %" PRId64 ", %" PRId64 "] | <x>%s", i, node->ne[0], node->ne[1], node->ne[2], ggml_op_symbol(node->op));
+        }
+
+        if (node->grad) {
+            fprintf(fp, " | <g>%s\"; ]\n", ggml_op_symbol(node->grad->op));
+        } else {
+            fprintf(fp, "\"; ]\n");
+        }
+    }
+
+    for (int i = 0; i < gb->n_leafs; i++) {
+        struct ggml_tensor * node = gb->leafs[i];
+
+        snprintf(color, sizeof(color), "pink");
+
+        fprintf(fp, "  \"%p\" [ "
+                    "style = filled; fillcolor = %s; shape = record; "
+                    "label=\"<x>",
+                (void *) node, color);
+
+        if (strlen(node->name) > 0) {
+            fprintf(fp, "%s (%s)|", node->name, ggml_type_name(node->type));
+        } else {
+            fprintf(fp, "(%s)|", ggml_type_name(node->type));
+        }
+
+        fprintf(fp, "CONST %d [%" PRId64 ", %" PRId64 "]", i, node->ne[0], node->ne[1]);
+        if (ggml_nelements(node) < 5) {
+            fprintf(fp, " | (");
+            for (int j = 0; j < ggml_nelements(node); j++) {
+                if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) {
+                    fprintf(fp, "%d", ggml_get_i32_1d(node, j));
+                }
+                else if (node->type == GGML_TYPE_F32 || node->type == GGML_TYPE_F16) {
+                    fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, j));
+                }
+                else {
+                    fprintf(fp, "#");
+                }
+                if (j < ggml_nelements(node) - 1) {
+                    fprintf(fp, ", ");
+                }
+            }
+            fprintf(fp, ")");
+        }
+        fprintf(fp, "\"; ]\n");
+    }
+
+    for (int i = 0; i < gb->n_nodes; i++) {
+        struct ggml_tensor * node = gb->nodes[i];
+
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            if (node->src[j]) {
+                char label[16];
+                snprintf(label, sizeof(label), "src %d", j);
+                ggml_graph_dump_dot_node_edge(fp, gb, node, node->src[j], label);
+            }
+        }
+    }
+
+    for (int i = 0; i < gb->n_leafs; i++) {
+        struct ggml_tensor * node = gb->leafs[i];
+
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            if (node->src[j]) {
+                char label[16];
+                snprintf(label, sizeof(label), "src %d", j);
+                ggml_graph_dump_dot_leaf_edge(fp, node, node->src[j], label);
+            }
+        }
+    }
+
+    fprintf(fp, "}\n");
+
+    fclose(fp);
+
+    GGML_PRINT("%s: dot -Tpng %s -o %s.png && open %s.png\n", __func__, filename, filename, filename);
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+static void ggml_opt_set_params(int np, struct ggml_tensor * const ps[], const float * x) {
+    int i = 0;
+    for (int p = 0; p < np; ++p) {
+        const int64_t ne = ggml_nelements(ps[p]) ;
+        // TODO: add function to set tensor from array
+        for (int64_t j = 0; j < ne; ++j) {
+            ggml_set_f32_1d(ps[p], j, x[i++]);
+        }
+    }
+}
+
+static void ggml_opt_get_params(int np, struct ggml_tensor * const ps[], float * x) {
+    int i = 0;
+    for (int p = 0; p < np; ++p) {
+        const int64_t ne = ggml_nelements(ps[p]) ;
+        // TODO: add function to get all elements at once
+        for (int64_t j = 0; j < ne; ++j) {
+            x[i++] = ggml_get_f32_1d(ps[p], j);
+        }
+    }
+}
+
+static void ggml_opt_get_grad(int np, struct ggml_tensor * const ps[], float * g) {
+    int64_t i = 0;
+    for (int p = 0; p < np; ++p) {
+        const int64_t ne = ggml_nelements(ps[p]) ;
+        // TODO: add function to get all elements at once
+        for (int64_t j = 0; j < ne; ++j) {
+            g[i++] = ggml_get_f32_1d(ps[p]->grad, j);
+        }
+    }
+}
+
+static void ggml_opt_acc_grad(int np, struct ggml_tensor * const ps[], float * g, float scale) {
+    int64_t i = 0;
+    for (int p = 0; p < np; ++p) {
+        const int64_t ne = ggml_nelements(ps[p]) ;
+        // TODO: add function to get all elements at once
+        for (int64_t j = 0; j < ne; ++j) {
+            g[i++] += ggml_get_f32_1d(ps[p]->grad, j) * scale;
+        }
+    }
+}
+
+//
+// ADAM
+//
+//   ref: https://arxiv.org/pdf/1412.6980.pdf
+//
+
+static enum ggml_opt_result ggml_opt_adam(
+        struct ggml_context * ctx,
+        struct ggml_opt_context * opt,
+        struct ggml_opt_params params,
+        struct ggml_tensor * f,
+        struct ggml_cgraph * gf,
+        struct ggml_cgraph * gb,
+        ggml_opt_callback callback,
+        void * callback_data) {
+    GGML_ASSERT(ggml_is_scalar(f));
+
+    // these will store the parameters we want to optimize
+    struct ggml_tensor * ps[GGML_MAX_PARAMS];
+
+    int np = 0;
+    int64_t nx = 0;
+    for (int i = 0; i < gf->n_nodes; ++i) {
+        if (gf->nodes[i]->is_param) {
+            GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op);
+
+            GGML_ASSERT(np < GGML_MAX_PARAMS);
+
+            ps[np++] = gf->nodes[i];
+            nx += ggml_nelements(gf->nodes[i]);
+        }
+    }
+
+    if ((opt->params.type != params.type) || (opt->nx != nx) || (opt->params.past != params.past)) {
+        int iter = opt->iter;
+        ggml_opt_init(opt->ctx, opt, params, nx);
+        opt->iter = iter;
+    }
+
+    // constants
+    float sched = params.adam.sched;
+    const float alpha = params.adam.alpha;
+    const float decay = params.adam.decay * alpha;
+    const float beta1 = params.adam.beta1;
+    const float beta2 = params.adam.beta2;
+    const float eps   = params.adam.eps;
+    const float gclip = params.adam.gclip;
+    const int decay_min_ndim = params.adam.decay_min_ndim;
+    const int n_accum = MAX(1, params.n_gradient_accumulation);
+    const float accum_norm = 1.0f / (float) n_accum;
+
+    float * g  = opt->adam.g->data;  // gradients
+    float * m  = opt->adam.m->data;  // first moment
+    float * v  = opt->adam.v->data;  // second moment
+
+    float * pf = params.past > 0 ? opt->adam.pf->data : NULL; // past function values
+
+    struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads);
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
+    cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
+
+    bool cancel = false;
+
+    // compute the function value
+    float fx = 0;
+    ggml_set_zero(opt->adam.g);
+    for (int accum_step = 0; accum_step < n_accum; ++accum_step) {
+        if (callback) {
+            callback(callback_data, accum_step, &sched, &cancel);
+            if (cancel) {
+                return GGML_OPT_CANCEL;
+            }
+        }
+        // ggml_graph_reset  (gf);
+        ggml_set_f32      (f->grad, 1.0f);
+        ggml_graph_compute(gb, &cplan);
+        ggml_opt_acc_grad(np, ps, g, accum_norm);
+        fx += ggml_get_f32_1d(f, 0);
+    }
+    fx *= accum_norm;
+
+    opt->adam.fx_prev = fx;
+    opt->adam.fx_best = opt->adam.fx_prev;
+    if (pf) {
+        pf[opt->iter % params.past] = opt->adam.fx_prev;
+    }
+
+    opt->loss_before = opt->adam.fx_prev;
+    opt->loss_after  = opt->adam.fx_prev;
+
+    // initialize
+    if (opt->just_initialized) {
+        opt->adam.n_no_improvement = 0;
+        opt->just_initialized = false;
+    }
+
+    float * fx_best = &opt->adam.fx_best;
+    float * fx_prev = &opt->adam.fx_prev;
+    int * n_no_improvement = &opt->adam.n_no_improvement;
+
+    int iter0 = opt->iter;
+
+    // run the optimizer
+    for (int t = 0; t < params.adam.n_iter; ++t) {
+        opt->iter = iter0 + t + 1;
+        GGML_PRINT_DEBUG  ("=== iter %d ===\n", t);
+
+        GGML_PRINT_DEBUG  ("f      = %10.6f\n", ggml_get_f32_1d(f, 0));
+        GGML_PRINT_DEBUG_5("df/dx0 = %10.6f\n", ggml_get_f32_1d(ps[0]->grad, 0));
+        GGML_PRINT_DEBUG_5("df/dx1 = %10.6f\n", ggml_get_f32_1d(ps[1]->grad, 0));
+
+        for (int i = 0; i < np; ++i) {
+            GGML_PRINT_DEBUG("param %d: %10.6f, g = %10.6f\n", i,
+                    ggml_get_f32_1d(ps[i], 0), ggml_get_f32_1d(ps[i]->grad, 0));
+        }
+
+        const int64_t t_start_wall = ggml_time_us();
+        const int64_t t_start_cpu = ggml_cycles();
+        UNUSED(t_start_wall);
+        UNUSED(t_start_cpu);
+
+        {
+            float gnorm = 1.0f;
+            if (gclip > 0.0f) {
+                // gradient clipping
+                ggml_float sum = 0.0;
+                for (int64_t i = 0; i < nx; ++i) {
+                    sum += (ggml_float)(g[i]*g[i]);
+                }
+                ggml_float norm = sqrt(sum);
+                if (norm > (ggml_float) gclip) {
+                    gnorm = (float) ((ggml_float) gclip / norm);
+                }
+            }
+            const float beta1h = alpha*sched/(1.0f - powf(beta1, opt->iter));
+            const float beta2h =        1.0f/(1.0f - powf(beta2, opt->iter));
+            int64_t i = 0;
+            for (int p = 0; p < np; ++p) {
+                const int64_t ne = ggml_nelements(ps[p]);
+                const float p_decay = ((ps[p]->n_dims >= decay_min_ndim) ? decay : 0.0f) * sched;
+                for (int64_t j = 0; j < ne; ++j) {
+                    float x  = ggml_get_f32_1d(ps[p], j);
+                    float g_ = g[i]*gnorm;
+                    m[i] = m[i]*beta1 +    g_*(1.0f - beta1);
+                    v[i] = v[i]*beta2 + g_*g_*(1.0f - beta2);
+                    float mh = m[i]*beta1h;
+                    float vh = v[i]*beta2h;
+                    vh = sqrtf(vh) + eps;
+                    x  = x*(1.0f - p_decay) - mh/vh;
+                    ggml_set_f32_1d(ps[p], j, x);
+                    ++i;
+                }
+            }
+        }
+
+        fx = 0;
+        ggml_set_zero(opt->adam.g);
+        for (int accum_step = 0; accum_step < n_accum; ++accum_step) {
+            if (callback) {
+                callback(callback_data, accum_step, &sched, &cancel);
+                if (cancel) {
+                    return GGML_OPT_CANCEL;;
+                }
+            }
+            // ggml_graph_reset  (gf);
+            ggml_set_f32      (f->grad, 1.0f);
+            ggml_graph_compute(gb, &cplan);
+            ggml_opt_acc_grad(np, ps, g, accum_norm);
+            fx += ggml_get_f32_1d(f, 0);
+        }
+        fx *= accum_norm;
+
+        opt->loss_after = fx;
+
+
+        // check convergence
+        if (fabsf(fx - fx_prev[0])/fx < params.adam.eps_f) {
+            GGML_PRINT_DEBUG("converged\n");
+
+            return GGML_OPT_OK;
+        }
+
+        // delta-based convergence test
+        if (pf != NULL) {
+            // need at least params.past iterations to start checking for convergence
+            if (params.past <= iter0 + t) {
+                const float rate = (pf[(iter0 + t)%params.past] - fx)/fx;
+
+                if (fabsf(rate) < params.delta) {
+                    return GGML_OPT_OK;
+                }
+            }
+
+            pf[(iter0 + t)%params.past] = fx;
+        }
+
+        // check for improvement
+        if (params.max_no_improvement > 0) {
+            if (fx_best[0] > fx) {
+                fx_best[0] = fx;
+                n_no_improvement[0] = 0;
+            } else {
+                ++n_no_improvement[0];
+
+                if (n_no_improvement[0] >= params.max_no_improvement) {
+                    return GGML_OPT_OK;
+                }
+            }
+        }
+
+        fx_prev[0] = fx;
+
+        {
+            const int64_t t_end_cpu = ggml_cycles();
+            GGML_PRINT_DEBUG("time iter:      %5.3f s\n", ((float)(t_end_cpu - t_start_cpu))/CLOCKS_PER_SEC);
+            UNUSED(t_end_cpu);
+
+            const int64_t t_end_wall = ggml_time_us();
+            GGML_PRINT_DEBUG("wall time iter: %5.3f s\n", (t_end_wall - t_start_wall)/1e6);
+            UNUSED(t_end_wall);
+        }
+    }
+
+    return GGML_OPT_DID_NOT_CONVERGE;
+}
+
+//
+// L-BFGS
+//
+// the L-BFGS implementation below is based on the following implementation:
+//
+//   https://github.com/chokkan/liblbfgs
+//
+
+struct ggml_lbfgs_iteration_data {
+    float alpha;
+    float ys;
+    float * s;
+    float * y;
+};
+
+static enum ggml_opt_result linesearch_backtracking(
+        const struct ggml_opt_params * params,
+        int nx,
+        float * x,
+        float * fx,
+        float * g,
+        float * d,
+        float * step,
+        const float * xp,
+        struct ggml_tensor * f,
+        struct ggml_cgraph * gb,
+        struct ggml_cplan  * cplan,
+        const int np,
+        struct ggml_tensor * ps[],
+        bool * cancel,
+        ggml_opt_callback callback,
+        void * callback_data) {
+    int count = 0;
+
+    float width  = 0.0f;
+    float dg     = 0.0f;
+    float finit  = 0.0f;
+    float dginit = 0.0f;
+    float dgtest = 0.0f;
+
+    const float dec = 0.5f;
+    const float inc = 2.1f;
+
+    const int n_accum = MAX(1, params->n_gradient_accumulation);
+    const float accum_norm = 1.0f / (float) n_accum;
+
+    if (*step <= 0.f) {
+        return GGML_LINESEARCH_INVALID_PARAMETERS;
+    }
+
+    // compute the initial gradient in the search direction
+    ggml_vec_dot_f32(nx, &dginit, g, d);
+
+    // make sure that d points to a descent direction
+    if (0 < dginit) {
+        return GGML_LINESEARCH_FAIL;
+    }
+
+    // initialize local variables
+    finit = *fx;
+    dgtest = params->lbfgs.ftol*dginit;
+
+    while (true) {
+        ggml_vec_cpy_f32(nx, x, xp);
+        ggml_vec_mad_f32(nx, x, d, *step);
+
+        // evaluate the function and gradient values
+        {
+            ggml_opt_set_params(np, ps, x);
+
+            *fx = 0;
+            memset(g, 0, sizeof(float)*nx);
+            for (int accum_step = 0; accum_step < n_accum; ++accum_step) {
+                if (callback) {
+                    // LBFG-S does not support learning rate -> ignore learning schedule
+                    float sched = 0;
+                    callback(callback_data, accum_step, &sched, cancel);
+                    if (*cancel) {
+                        return GGML_OPT_CANCEL;
+                    }
+                }
+                // ggml_graph_reset  (gf);
+                ggml_set_f32      (f->grad, 1.0f);
+                ggml_graph_compute(gb, cplan);
+                ggml_opt_acc_grad(np, ps, g, accum_norm);
+                *fx += ggml_get_f32_1d(f, 0);
+            }
+            *fx *= accum_norm;
+
+        }
+
+        ++count;
+
+        if (*fx > finit + (*step)*dgtest) {
+            width = dec;
+        } else {
+            // Armijo condition is satisfied
+            if (params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_ARMIJO) {
+                return count;
+            }
+
+            ggml_vec_dot_f32(nx, &dg, g, d);
+
+            // check the Wolfe condition
+            if (dg < params->lbfgs.wolfe * dginit) {
+                width = inc;
+            } else {
+                if(params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE) {
+                    // regular Wolfe conditions
+                    return count;
+                }
+
+                if(dg > -params->lbfgs.wolfe*dginit) {
+                    width = dec;
+                } else {
+                    // strong Wolfe condition (GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE)
+                    return count;
+                }
+            }
+        }
+
+        if (*step < params->lbfgs.min_step) {
+            return GGML_LINESEARCH_MINIMUM_STEP;
+        }
+        if (*step > params->lbfgs.max_step) {
+            return GGML_LINESEARCH_MAXIMUM_STEP;
+        }
+        if (params->lbfgs.max_linesearch <= count) {
+            return GGML_LINESEARCH_MAXIMUM_ITERATIONS;
+        }
+
+        (*step) *= width;
+    }
+
+    GGML_UNREACHABLE();
+}
+
+static enum ggml_opt_result ggml_opt_lbfgs(
+        struct ggml_context * ctx,
+        struct ggml_opt_context * opt,
+        struct ggml_opt_params params,
+        struct ggml_tensor * f,
+        struct ggml_cgraph * gf,
+        struct ggml_cgraph * gb,
+        ggml_opt_callback callback,
+        void * callback_data) {
+    if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE ||
+        params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) {
+        if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1.f <= params.lbfgs.wolfe) {
+            return GGML_OPT_INVALID_WOLFE;
+        }
+    }
+
+    const int m = params.lbfgs.m;
+
+    // these will store the parameters we want to optimize
+    struct ggml_tensor * ps[GGML_MAX_PARAMS];
+
+    int np = 0;
+    int nx = 0;
+    for (int i = 0; i < gf->n_nodes; ++i) {
+        if (gf->nodes[i]->is_param) {
+            GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op);
+
+            GGML_ASSERT(np < GGML_MAX_PARAMS);
+
+            ps[np++] = gf->nodes[i];
+            nx += ggml_nelements(gf->nodes[i]);
+        }
+    }
+
+    if ((opt->params.type != params.type) || (opt->nx != nx) || (opt->params.past != params.past) || (opt->params.lbfgs.m != params.lbfgs.m)) {
+        int iter = opt->iter;
+        ggml_opt_init(ctx, opt, params, nx);
+        opt->iter = iter;
+    }
+
+    struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads);
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
+    cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
+
+    float * x  = opt->lbfgs.x->data;  // current parameters
+    float * xp = opt->lbfgs.xp->data; // previous parameters
+    float * g  = opt->lbfgs.g->data;  // current gradient
+    float * gp = opt->lbfgs.gp->data; // previous gradient
+    float * d  = opt->lbfgs.d->data;  // search direction
+
+    float * pf = params.past > 0 ? opt->lbfgs.pf->data : NULL; // past function values
+
+    const int n_accum = MAX(1, params.n_gradient_accumulation);
+    const float accum_norm = 1.0f / (float) n_accum;
+
+    float fx    = 0.0f; // cost function value
+    float xnorm = 0.0f; // ||x||
+    float gnorm = 0.0f; // ||g||
+
+    // initialize x from the graph nodes
+    ggml_opt_get_params(np, ps, x);
+
+    // the L-BFGS memory
+    float * lm_alpha = opt->lbfgs.lmal->data;
+    float * lm_ys    = opt->lbfgs.lmys->data;
+    float * lm_s     = opt->lbfgs.lms->data;
+    float * lm_y     = opt->lbfgs.lmy->data;
+
+    bool cancel = false;
+
+    // evaluate the function value and its gradient
+    {
+        ggml_opt_set_params(np, ps, x);
+
+        fx = 0;
+        memset(g, 0, sizeof(float)*nx);
+        for (int accum_step = 0; accum_step < n_accum; ++accum_step) {
+            if (callback) {
+                // LBFG-S does not support learning rate -> ignore learning schedule
+                float sched = 0;
+                callback(callback_data, accum_step, &sched, &cancel);
+                if (cancel) {
+                    return GGML_OPT_CANCEL;
+                }
+            }
+            // ggml_graph_reset  (gf);
+            ggml_set_f32      (f->grad, 1.0f);
+            ggml_graph_compute(gb, &cplan);
+            ggml_opt_acc_grad(np, ps, g, accum_norm);
+            fx += ggml_get_f32_1d(f, 0);
+        }
+        fx *= accum_norm;
+
+        opt->loss_before = fx;
+        opt->loss_after  = fx;
+    }
+
+    // search direction = -gradient
+    ggml_vec_neg_f32(nx, d, g);
+
+    // ||x||, ||g||
+    ggml_vec_norm_f32(nx, &xnorm, x);
+    ggml_vec_norm_f32(nx, &gnorm, g);
+
+    if (xnorm < 1.0f) {
+        xnorm = 1.0f;
+    }
+
+    // already optimized
+    if (gnorm/xnorm <= params.lbfgs.eps) {
+        return GGML_OPT_OK;
+    }
+
+    if (opt->just_initialized) {
+        if (pf) {
+            pf[0] = fx;
+        }
+        opt->lbfgs.fx_best = fx;
+
+        // initial step
+        ggml_vec_norm_inv_f32(nx, &opt->lbfgs.step, d);
+        opt->lbfgs.j                = 0;
+        opt->lbfgs.k                = 1;
+        opt->lbfgs.end              = 0;
+        opt->lbfgs.n_no_improvement = 0;
+        opt->just_initialized       = false;
+    }
+
+    float * fx_best        = &opt->lbfgs.fx_best;
+    float * step           = &opt->lbfgs.step;
+    int * j                = &opt->lbfgs.j;
+    int * k                = &opt->lbfgs.k;
+    int * end              = &opt->lbfgs.end;
+    int * n_no_improvement = &opt->lbfgs.n_no_improvement;
+
+    int ls     = 0;
+    int bound  = 0;
+
+    float ys   = 0.0f;
+    float yy   = 0.0f;
+    float beta = 0.0f;
+
+    int it = 0;
+
+    while (true) {
+        // store the current position and gradient vectors
+        ggml_vec_cpy_f32(nx, xp, x);
+        ggml_vec_cpy_f32(nx, gp, g);
+
+        // TODO: instead of passing &cancel here, use the return code of the linesearch
+        //       to determine if the optimization should be cancelled
+        //       this is a simple change, but not doing this atm, since I don't have a nice
+        //       way to test and don't want to break something with so many changes lined up
+        ls = linesearch_backtracking(&params, nx, x, &fx, g, d, step, xp, f, gb, &cplan, np, ps, &cancel, callback, callback_data);
+        if (cancel) {
+            return GGML_OPT_CANCEL;
+        }
+
+        if (ls < 0) {
+            // linesearch failed - go back to the previous point and return
+            ggml_vec_cpy_f32(nx, x, xp);
+            ggml_vec_cpy_f32(nx, g, gp);
+
+            return ls;
+        }
+
+        opt->loss_after = fx;
+
+        ggml_vec_norm_f32(nx, &xnorm, x);
+        ggml_vec_norm_f32(nx, &gnorm, g);
+
+        GGML_PRINT_DEBUG("f = %10.6f\n", ggml_get_f32_1d(f, 0));
+
+        if (xnorm < 1.0f) {
+            xnorm = 1.0f;
+        }
+        if (gnorm/xnorm <= params.lbfgs.eps) {
+            // converged
+            return GGML_OPT_OK;
+        }
+
+        // delta-based convergence test
+        if (pf != NULL) {
+            // need at least params.past iterations to start checking for convergence
+            if (params.past <= k[0]) {
+                const float rate = (pf[k[0]%params.past] - fx)/fx;
+
+                if (fabsf(rate) < params.delta) {
+                    return GGML_OPT_OK;
+                }
+            }
+
+            pf[k[0]%params.past] = fx;
+        }
+
+        // check for improvement
+        if (params.max_no_improvement > 0) {
+            if (fx < fx_best[0]) {
+                fx_best[0] = fx;
+                n_no_improvement[0] = 0;
+            } else {
+                n_no_improvement[0]++;
+
+                if (n_no_improvement[0] >= params.max_no_improvement) {
+                    return GGML_OPT_OK;
+                }
+            }
+        }
+
+        if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < it + 1) {
+            // reached the maximum number of iterations
+            return GGML_OPT_DID_NOT_CONVERGE;
+        }
+
+        // update vectors s and y:
+        //   s_{k+1} = x_{k+1} - x_{k} = \step * d_{k}.
+        //   y_{k+1} = g_{k+1} - g_{k}.
+        //
+        ggml_vec_sub_f32(nx, &lm_s[end[0]*nx], x, xp);
+        ggml_vec_sub_f32(nx, &lm_y[end[0]*nx], g, gp);
+
+        // compute scalars ys and yy:
+        //     ys = y^t \cdot s    -> 1 / \rho.
+        //     yy = y^t \cdot y.
+        //
+        ggml_vec_dot_f32(nx, &ys, &lm_y[end[0]*nx], &lm_s[end[0]*nx]);
+        ggml_vec_dot_f32(nx, &yy, &lm_y[end[0]*nx], &lm_y[end[0]*nx]);
+
+        lm_ys[end[0]] = ys;
+
+        // find new search direction
+        //   ref: https://en.wikipedia.org/wiki/Limited-memory_BFGS
+
+        bound = (m <= k[0]) ? m : k[0];
+        k[0]++;
+        it++;
+        end[0] = (end[0] + 1)%m;
+
+        // initialize search direction with -g
+        ggml_vec_neg_f32(nx, d, g);
+
+        j[0] = end[0];
+        for (int i = 0; i < bound; ++i) {
+            j[0] = (j[0] + m - 1) % m;
+            // \alpha_{j} = \rho_{j} s^{t}_{j} \cdot q_{k+1}
+            ggml_vec_dot_f32(nx, &lm_alpha[j[0]], &lm_s[j[0]*nx], d);
+            lm_alpha[j[0]] /= lm_ys[j[0]];
+            // q_{i} = q_{i+1} - \alpha_{i} y_{i}
+            ggml_vec_mad_f32(nx, d, &lm_y[j[0]*nx], -lm_alpha[j[0]]);
+        }
+
+        ggml_vec_scale_f32(nx, d, ys/yy);
+
+        for (int i = 0; i < bound; ++i) {
+            // \beta_{j} = \rho_{j} y^t_{j} \cdot \gamma_{i}
+            ggml_vec_dot_f32(nx, &beta, &lm_y[j[0]*nx], d);
+            beta /= lm_ys[j[0]];
+            // \gamma_{i+1} = \gamma_{i} + (\alpha_{j} - \beta_{j}) s_{j}
+            ggml_vec_mad_f32(nx, d, &lm_s[j[0]*nx], lm_alpha[j[0]] - beta);
+            j[0] = (j[0] + 1)%m;
+        }
+
+        step[0] = 1.0;
+    }
+
+    GGML_UNREACHABLE();
+}
+
+struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) {
+    struct ggml_opt_params result;
+
+    switch (type) {
+        case GGML_OPT_ADAM:
+            {
+                result = (struct ggml_opt_params) {
+                    .type      = GGML_OPT_ADAM,
+                    .n_threads = 1,
+                    .past      = 0,
+                    .delta     = 1e-5f,
+
+                    .max_no_improvement = 100,
+
+                    .print_forward_graph  = true,
+                    .print_backward_graph = true,
+
+                    .n_gradient_accumulation = 1,
+
+                    .adam = {
+                        .n_iter = 10000,
+                        .sched  = 1.000f,
+                        .decay  = 0.0f,
+                        .decay_min_ndim = 2,
+                        .alpha  = 0.001f,
+                        .beta1  = 0.9f,
+                        .beta2  = 0.999f,
+                        .eps    = 1e-8f,
+                        .eps_f  = 1e-5f,
+                        .eps_g  = 1e-3f,
+                        .gclip  = 0.0f,
+                    },
+                };
+            } break;
+        case GGML_OPT_LBFGS:
+            {
+                result = (struct ggml_opt_params) {
+                    .type      = GGML_OPT_LBFGS,
+                    .n_threads = 1,
+                    .past      = 0,
+                    .delta     = 1e-5f,
+
+                    .max_no_improvement = 0,
+
+                    .print_forward_graph  = true,
+                    .print_backward_graph = true,
+
+                    .n_gradient_accumulation = 1,
+
+                    .lbfgs = {
+                        .m              = 6,
+                        .n_iter         = 100,
+                        .max_linesearch = 20,
+
+                        .eps      = 1e-5f,
+                        .ftol     = 1e-4f,
+                        .wolfe    = 0.9f,
+                        .min_step = 1e-20f,
+                        .max_step = 1e+20f,
+
+                        .linesearch = GGML_LINESEARCH_DEFAULT,
+                    },
+                };
+            } break;
+    }
+
+    return result;
+}
+
+GGML_API void ggml_opt_init(
+        struct ggml_context * ctx,
+        struct ggml_opt_context * opt,
+        struct ggml_opt_params params,
+        int64_t nx) {
+    opt->ctx = ctx;
+    opt->params = params;
+    opt->iter = 0;
+    opt->nx = nx;
+    opt->just_initialized = true;
+    if (opt->ctx == NULL) {
+        struct ggml_init_params ctx_opt_params;
+        if (opt->params.type == GGML_OPT_ADAM) {
+            ctx_opt_params.mem_size = GGML_MEM_ALIGN*3 + ggml_tensor_overhead()*3 + ggml_type_size(GGML_TYPE_F32)*nx*3;
+            if (opt->params.past > 0) {
+                ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past;
+            }
+        } else if (opt->params.type == GGML_OPT_LBFGS) {
+            ctx_opt_params.mem_size = GGML_MEM_ALIGN*9 + ggml_tensor_overhead()*9 + ggml_type_size(GGML_TYPE_F32)*(nx*5 + opt->params.lbfgs.m*2 + nx*opt->params.lbfgs.m*2);
+            if (opt->params.past > 0) {
+                ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past;
+            }
+        }
+        ctx_opt_params.mem_buffer = NULL;
+        ctx_opt_params.no_alloc   = false;
+
+        opt->ctx = ggml_init(ctx_opt_params);
+    }
+    switch (opt->params.type) {
+        case GGML_OPT_ADAM:
+            {
+                opt->adam.g  = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
+                opt->adam.m  = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
+                opt->adam.v  = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
+                opt->adam.pf = params.past > 0
+                    ? ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.past)
+                    : NULL;
+                ggml_set_zero(opt->adam.m);
+                ggml_set_zero(opt->adam.v);
+                if (opt->adam.pf) {
+                    ggml_set_zero(opt->adam.pf);
+                }
+            } break;
+        case GGML_OPT_LBFGS:
+            {
+                opt->lbfgs.x  = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
+                opt->lbfgs.xp = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
+                opt->lbfgs.g  = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
+                opt->lbfgs.gp = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
+                opt->lbfgs.d  = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
+                opt->lbfgs.pf = params.past > 0
+                    ? ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.past)
+                    : NULL;
+                opt->lbfgs.lmal = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.lbfgs.m);
+                opt->lbfgs.lmys = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.lbfgs.m);
+                opt->lbfgs.lms  = ggml_new_tensor_2d(opt->ctx, GGML_TYPE_F32, nx, params.lbfgs.m);
+                opt->lbfgs.lmy  = ggml_new_tensor_2d(opt->ctx, GGML_TYPE_F32, nx, params.lbfgs.m);
+                ggml_set_zero(opt->lbfgs.x);
+                ggml_set_zero(opt->lbfgs.xp);
+                ggml_set_zero(opt->lbfgs.g);
+                ggml_set_zero(opt->lbfgs.gp);
+                ggml_set_zero(opt->lbfgs.d);
+                if (opt->lbfgs.pf) {
+                    ggml_set_zero(opt->lbfgs.pf);
+                }
+                ggml_set_zero(opt->lbfgs.lmal);
+                ggml_set_zero(opt->lbfgs.lmys);
+                ggml_set_zero(opt->lbfgs.lms);
+                ggml_set_zero(opt->lbfgs.lmy);
+            } break;
+    }
+}
+
+enum ggml_opt_result ggml_opt(
+        struct ggml_context * ctx,
+        struct ggml_opt_params params,
+        struct ggml_tensor * f) {
+    bool free_ctx = false;
+    if (ctx == NULL) {
+        struct ggml_init_params params_ctx = {
+            .mem_size   = 16*1024*1024,
+            .mem_buffer = NULL,
+            .no_alloc   = false,
+        };
+
+        ctx = ggml_init(params_ctx);
+        if (ctx == NULL) {
+            return GGML_OPT_NO_CONTEXT;
+        }
+
+        free_ctx = true;
+    }
+
+    enum ggml_opt_result result = GGML_OPT_OK;
+
+    struct ggml_opt_context * opt = (struct ggml_opt_context *) alloca(sizeof(struct ggml_opt_context));
+
+    ggml_opt_init(ctx, opt, params, 0);
+    result = ggml_opt_resume(ctx, opt, f);
+
+    if (free_ctx) {
+        ggml_free(ctx);
+    }
+
+    return result;
+}
+
+enum ggml_opt_result ggml_opt_resume(
+        struct ggml_context * ctx,
+        struct ggml_opt_context * opt,
+        struct ggml_tensor * f) {
+
+    // build forward + backward compute graphs
+    struct ggml_tensor * gfbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32)+ (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0));
+    struct ggml_tensor * gbbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32)+ (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0));
+
+    struct ggml_cgraph * gf = (struct ggml_cgraph *) gfbuf->data;
+    struct ggml_cgraph * gb = (struct ggml_cgraph *) gbbuf->data;
+
+    *gf = ggml_build_forward (f);
+    *gb = ggml_build_backward(ctx, gf, true);
+
+    return ggml_opt_resume_g(ctx, opt, f, gf, gb, NULL, NULL);
+}
+
+enum ggml_opt_result ggml_opt_resume_g(
+        struct ggml_context * ctx,
+        struct ggml_opt_context * opt,
+        struct ggml_tensor * f,
+        struct ggml_cgraph * gf,
+        struct ggml_cgraph * gb,
+        ggml_opt_callback callback,
+        void * callback_data) {
+
+    // build forward + backward compute graphs
+    enum ggml_opt_result result = GGML_OPT_OK;
+
+    switch (opt->params.type) {
+        case GGML_OPT_ADAM:
+            {
+                result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb, callback, callback_data);
+            } break;
+        case GGML_OPT_LBFGS:
+            {
+                result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb, callback, callback_data);
+            } break;
+    }
+
+    if (opt->params.print_forward_graph) {
+        ggml_graph_print   (gf);
+        ggml_graph_dump_dot(gf, NULL, "opt-forward.dot");
+    }
+
+    if (opt->params.print_backward_graph) {
+        ggml_graph_print   (gb);
+        ggml_graph_dump_dot(gb, gf, "opt-backward.dot");
+    }
+
+    return result;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist) {
+    assert(k % QK4_0 == 0);
+    const int nb = k / QK4_0;
+
+    for (int b = 0; b < n; b += k) {
+        block_q4_0 * restrict y = (block_q4_0 *) dst + b/QK4_0;
+
+        quantize_row_q4_0_reference(src + b, y, k);
+
+        for (int i = 0; i < nb; i++) {
+            for (int j = 0; j < QK4_0; j += 2) {
+                const uint8_t vi0 = y[i].qs[j/2] & 0x0F;
+                const uint8_t vi1 = y[i].qs[j/2] >> 4;
+
+                hist[vi0]++;
+                hist[vi1]++;
+            }
+        }
+    }
+
+    return (n/QK4_0*sizeof(block_q4_0));
+}
+
+size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist) {
+    assert(k % QK4_1 == 0);
+    const int nb = k / QK4_1;
+
+    for (int b = 0; b < n; b += k) {
+        block_q4_1 * restrict y = (block_q4_1 *) dst + b/QK4_1;
+
+        quantize_row_q4_1_reference(src + b, y, k);
+
+        for (int i = 0; i < nb; i++) {
+            for (int j = 0; j < QK4_1; j += 2) {
+                const uint8_t vi0 = y[i].qs[j/2] & 0x0F;
+                const uint8_t vi1 = y[i].qs[j/2] >> 4;
+
+                hist[vi0]++;
+                hist[vi1]++;
+            }
+        }
+    }
+
+    return (n/QK4_1*sizeof(block_q4_1));
+}
+
+size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist) {
+    assert(k % QK5_0 == 0);
+    const int nb = k / QK5_0;
+
+    for (int b = 0; b < n; b += k) {
+        block_q5_0 * restrict y = (block_q5_0 *)dst + b/QK5_0;
+
+        quantize_row_q5_0_reference(src + b, y, k);
+
+        for (int i = 0; i < nb; i++) {
+            uint32_t qh;
+            memcpy(&qh, &y[i].qh, sizeof(qh));
+
+            for (int j = 0; j < QK5_0; j += 2) {
+                const uint8_t vh0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
+                const uint8_t vh1 = ((qh & (1u << (j + 16))) >> (j + 12));
+
+                // cast to 16 bins
+                const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2;
+                const uint8_t vi1 = ((y[i].qs[j/2] >>   4) | vh1) / 2;
+
+                hist[vi0]++;
+                hist[vi1]++;
+            }
+        }
+    }
+
+    return (n/QK5_0*sizeof(block_q5_0));
+}
+
+size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist) {
+    assert(k % QK5_1 == 0);
+    const int nb = k / QK5_1;
+
+    for (int b = 0; b < n; b += k) {
+        block_q5_1 * restrict y = (block_q5_1 *)dst + b/QK5_1;
+
+        quantize_row_q5_1_reference(src + b, y, k);
+
+        for (int i = 0; i < nb; i++) {
+            uint32_t qh;
+            memcpy(&qh, &y[i].qh, sizeof(qh));
+
+            for (int j = 0; j < QK5_1; j += 2) {
+                const uint8_t vh0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
+                const uint8_t vh1 = ((qh & (1u << (j + 16))) >> (j + 12));
+
+                // cast to 16 bins
+                const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2;
+                const uint8_t vi1 = ((y[i].qs[j/2] >>   4) | vh1) / 2;
+
+                hist[vi0]++;
+                hist[vi1]++;
+            }
+        }
+    }
+
+    return (n/QK5_1*sizeof(block_q5_1));
+}
+
+size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist) {
+    assert(k % QK8_0 == 0);
+    const int nb = k / QK8_0;
+
+    for (int b = 0; b < n; b += k) {
+        block_q8_0 * restrict y = (block_q8_0 *)dst + b/QK8_0;
+
+        quantize_row_q8_0_reference(src + b, y, k);
+
+        for (int i = 0; i < nb; i++) {
+            for (int j = 0; j < QK8_0; ++j) {
+                const int8_t vi = y[i].qs[j];
+
+                hist[vi/16 + 8]++;
+            }
+        }
+    }
+
+    return (n/QK8_0*sizeof(block_q8_0));
+}
+
+size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist) {
+    size_t result = 0;
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+            {
+                GGML_ASSERT(start % QK4_0 == 0);
+                block_q4_0 * block = (block_q4_0*)dst + start / QK4_0;
+                result = ggml_quantize_q4_0(src + start, block, n, n, hist);
+            } break;
+        case GGML_TYPE_Q4_1:
+            {
+                GGML_ASSERT(start % QK4_1 == 0);
+                block_q4_1 * block = (block_q4_1*)dst + start / QK4_1;
+                result = ggml_quantize_q4_1(src + start, block, n, n, hist);
+            } break;
+        case GGML_TYPE_Q5_0:
+            {
+                GGML_ASSERT(start % QK5_0 == 0);
+                block_q5_0 * block = (block_q5_0*)dst + start / QK5_0;
+                result = ggml_quantize_q5_0(src + start, block, n, n, hist);
+            } break;
+        case GGML_TYPE_Q5_1:
+            {
+                GGML_ASSERT(start % QK5_1 == 0);
+                block_q5_1 * block = (block_q5_1*)dst + start / QK5_1;
+                result = ggml_quantize_q5_1(src + start, block, n, n, hist);
+            } break;
+        case GGML_TYPE_Q8_0:
+            {
+                GGML_ASSERT(start % QK8_0 == 0);
+                block_q8_0 * block = (block_q8_0*)dst + start / QK8_0;
+                result = ggml_quantize_q8_0(src + start, block, n, n, hist);
+            } break;
+#ifdef GGML_USE_K_QUANTS
+        case GGML_TYPE_Q2_K:
+            {
+                GGML_ASSERT(start % QK_K == 0);
+                block_q2_K * block = (block_q2_K*)dst + start / QK_K;
+                result = ggml_quantize_q2_K(src + start, block, n, n, hist);
+            } break;
+        case GGML_TYPE_Q3_K:
+            {
+                GGML_ASSERT(start % QK_K == 0);
+                block_q3_K * block = (block_q3_K*)dst + start / QK_K;
+                result = ggml_quantize_q3_K(src + start, block, n, n, hist);
+            } break;
+        case GGML_TYPE_Q4_K:
+            {
+                GGML_ASSERT(start % QK_K == 0);
+                block_q4_K * block = (block_q4_K*)dst + start / QK_K;
+                result = ggml_quantize_q4_K(src + start, block, n, n, hist);
+            } break;
+        case GGML_TYPE_Q5_K:
+            {
+                GGML_ASSERT(start % QK_K == 0);
+                block_q5_K * block = (block_q5_K*)dst + start / QK_K;
+                result = ggml_quantize_q5_K(src + start, block, n, n, hist);
+            } break;
+        case GGML_TYPE_Q6_K:
+            {
+                GGML_ASSERT(start % QK_K == 0);
+                block_q6_K * block = (block_q6_K*)dst + start / QK_K;
+                result = ggml_quantize_q6_K(src + start, block, n, n, hist);
+            } break;
+#endif
+        case GGML_TYPE_F16:
+            {
+                int elemsize = sizeof(ggml_fp16_t);
+                ggml_fp32_to_fp16_row(src + start, (ggml_fp16_t *)dst + start, n);
+                result = n * elemsize;
+            } break;
+        case GGML_TYPE_F32:
+            {
+                int elemsize = sizeof(float);
+                result = n * elemsize;
+                memcpy((uint8_t *)dst + start * elemsize, src + start, result);
+            } break;
+        default:
+            assert(false);
+    }
+    return result;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+struct gguf_str {
+    uint64_t n;  // GGUFv2
+    char * data;
+};
+
+static const size_t GGUF_TYPE_SIZE[GGUF_TYPE_COUNT] = {
+    [GGUF_TYPE_UINT8]   = sizeof(uint8_t),
+    [GGUF_TYPE_INT8]    = sizeof(int8_t),
+    [GGUF_TYPE_UINT16]  = sizeof(uint16_t),
+    [GGUF_TYPE_INT16]   = sizeof(int16_t),
+    [GGUF_TYPE_UINT32]  = sizeof(uint32_t),
+    [GGUF_TYPE_INT32]   = sizeof(int32_t),
+    [GGUF_TYPE_FLOAT32] = sizeof(float),
+    [GGUF_TYPE_BOOL]    = sizeof(bool),
+    [GGUF_TYPE_STRING]  = sizeof(struct gguf_str),
+    [GGUF_TYPE_UINT64]  = sizeof(uint64_t),
+    [GGUF_TYPE_INT64]   = sizeof(int64_t),
+    [GGUF_TYPE_FLOAT64] = sizeof(double),
+    [GGUF_TYPE_ARRAY]   = 0, // undefined
+};
+static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13");
+
+static const char * GGUF_TYPE_NAME[GGUF_TYPE_COUNT] = {
+    [GGUF_TYPE_UINT8]   = "u8",
+    [GGUF_TYPE_INT8]    = "i8",
+    [GGUF_TYPE_UINT16]  = "u16",
+    [GGUF_TYPE_INT16]   = "i16",
+    [GGUF_TYPE_UINT32]  = "u32",
+    [GGUF_TYPE_INT32]   = "i32",
+    [GGUF_TYPE_FLOAT32] = "f32",
+    [GGUF_TYPE_BOOL]    = "bool",
+    [GGUF_TYPE_STRING]  = "str",
+    [GGUF_TYPE_ARRAY]   = "arr",
+    [GGUF_TYPE_UINT64]  = "u64",
+    [GGUF_TYPE_INT64]   = "i64",
+    [GGUF_TYPE_FLOAT64] = "f64",
+};
+static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13");
+
+union gguf_value {
+    uint8_t  uint8;
+    int8_t   int8;
+    uint16_t uint16;
+    int16_t  int16;
+    uint32_t uint32;
+    int32_t  int32;
+    float    float32;
+    uint64_t uint64;
+    int64_t  int64;
+    double   float64;
+    bool     bool_;
+
+    struct gguf_str str;
+
+    struct {
+        enum gguf_type type;
+
+        uint64_t n;  // GGUFv2
+        void * data;
+    } arr;
+};
+
+struct gguf_kv {
+    struct gguf_str key;
+
+    enum  gguf_type  type;
+    union gguf_value value;
+};
+
+struct gguf_header {
+    char magic[4];
+    uint32_t version;
+    uint64_t n_tensors; // GGUFv2
+    uint64_t n_kv;      // GGUFv2
+};
+
+struct gguf_tensor_info {
+    struct gguf_str name;
+
+    uint32_t n_dims;
+    uint64_t ne[GGML_MAX_DIMS];
+
+    enum ggml_type type;
+
+    uint64_t offset; // offset from start of `data`, must be a multiple of `ALIGNMENT`
+
+    // for writing API
+    const void * data;
+    size_t size;
+};
+
+struct gguf_context {
+    struct gguf_header header;
+
+    struct gguf_kv          * kv;
+    struct gguf_tensor_info * infos;
+
+    size_t alignment;
+    size_t offset;    // offset of `data` from beginning of file
+    size_t size;      // size of `data` in bytes
+
+    //uint8_t * padding;
+    void * data;
+};
+
+static bool gguf_fread_el(FILE * file, void * dst, size_t size, size_t * offset) {
+    const size_t n = fread(dst, 1, size, file);
+    *offset += n;
+    return n == size;
+}
+
+// NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
+static bool gguf_fread_str_cur(FILE * file, struct gguf_str * p, size_t * offset) {
+    p->n    = 0;
+    p->data = NULL;
+
+    bool ok = true;
+
+    ok = ok && gguf_fread_el(file, &p->n,    sizeof(p->n), offset); p->data = calloc(p->n + 1, 1);
+    ok = ok && gguf_fread_el(file,  p->data, p->n,         offset);
+
+    return ok;
+}
+
+static bool gguf_fread_str_v1(FILE * file, struct gguf_str * p, size_t * offset) {
+    p->n    = 0;
+    p->data = NULL;
+
+    bool ok = true;
+
+    uint32_t n = 0;
+    ok = ok && gguf_fread_el(file, &n,       sizeof(n), offset); p->data = calloc(n + 1, 1); p->n = n;
+    ok = ok && gguf_fread_el(file,  p->data, p->n,      offset);
+
+    return ok;
+}
+
+struct gguf_context * gguf_init_empty(void) {
+    struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));
+
+    memcpy(ctx->header.magic, GGUF_MAGIC, sizeof(ctx->header.magic));
+    ctx->header.version   = GGUF_VERSION;
+    ctx->header.n_tensors = 0;
+    ctx->header.n_kv      = 0;
+
+    ctx->kv    = NULL;
+    ctx->infos = NULL;
+
+    ctx->alignment = GGUF_DEFAULT_ALIGNMENT;
+    ctx->offset    = 0;
+    ctx->size      = 0;
+
+    ctx->data = NULL;
+
+    return ctx;
+}
+
+struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
+    FILE * file = fopen(fname, "rb");
+    if (!file) {
+        return NULL;
+    }
+
+    // offset from start of file
+    size_t offset = 0;
+
+    char magic[4];
+
+    // check the magic before making allocations
+    {
+        gguf_fread_el(file, &magic, sizeof(magic), &offset);
+
+        for (uint32_t i = 0; i < sizeof(magic); i++) {
+            if (magic[i] != GGUF_MAGIC[i]) {
+                fprintf(stderr, "%s: invalid magic characters %s.\n", __func__, magic);
+                fclose(file);
+                return NULL;
+            }
+        }
+    }
+
+    bool ok = true;
+
+    struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));
+
+    // read the header
+    {
+        strncpy(ctx->header.magic, magic, 4);
+
+
+        ctx->kv    = NULL;
+        ctx->infos = NULL;
+        ctx->data  = NULL;
+
+        ok = ok && gguf_fread_el(file, &ctx->header.version,   sizeof(ctx->header.version),   &offset);
+
+        if (ctx->header.version == 1) {
+            // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
+            uint32_t n_tensors = 0;
+            uint32_t n_kv      = 0;
+
+            ok = ok && gguf_fread_el(file, &n_tensors, sizeof(n_tensors), &offset);
+            ok = ok && gguf_fread_el(file, &n_kv,      sizeof(n_kv),      &offset);
+
+            ctx->header.n_tensors = n_tensors;
+            ctx->header.n_kv      = n_kv;
+        } else {
+            ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset);
+            ok = ok && gguf_fread_el(file, &ctx->header.n_kv,      sizeof(ctx->header.n_kv),      &offset);
+        }
+
+        if (!ok) {
+            fprintf(stderr, "%s: failed to read header\n", __func__);
+            fclose(file);
+            gguf_free(ctx);
+            return NULL;
+        }
+    }
+
+    // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
+    bool (* gguf_fread_str)(FILE *, struct gguf_str *, size_t *) = gguf_fread_str_cur;
+    if (ctx->header.version == 1) {
+        gguf_fread_str = gguf_fread_str_v1;
+    }
+
+    // read the kv pairs
+    {
+        ctx->kv = malloc(ctx->header.n_kv * sizeof(struct gguf_kv));
+
+        for (uint32_t i = 0; i < ctx->header.n_kv; ++i) {
+            struct gguf_kv * kv = &ctx->kv[i];
+
+            //fprintf(stderr, "%s: reading kv %d\n", __func__, i);
+
+            ok = ok && gguf_fread_str(file, &kv->key,                    &offset);
+            ok = ok && gguf_fread_el (file, &kv->type, sizeof(kv->type), &offset);
+
+            //fprintf(stderr, "%s: reading kv with key %s\n", __func__, kv->key.data);
+
+            switch (kv->type) {
+                case GGUF_TYPE_UINT8:   ok = ok && gguf_fread_el (file, &kv->value.uint8,   sizeof(kv->value.uint8),   &offset); break;
+                case GGUF_TYPE_INT8:    ok = ok && gguf_fread_el (file, &kv->value.int8,    sizeof(kv->value.int8),    &offset); break;
+                case GGUF_TYPE_UINT16:  ok = ok && gguf_fread_el (file, &kv->value.uint16,  sizeof(kv->value.uint16),  &offset); break;
+                case GGUF_TYPE_INT16:   ok = ok && gguf_fread_el (file, &kv->value.int16,   sizeof(kv->value.int16),   &offset); break;
+                case GGUF_TYPE_UINT32:  ok = ok && gguf_fread_el (file, &kv->value.uint32,  sizeof(kv->value.uint32),  &offset); break;
+                case GGUF_TYPE_INT32:   ok = ok && gguf_fread_el (file, &kv->value.int32,   sizeof(kv->value.int32),   &offset); break;
+                case GGUF_TYPE_FLOAT32: ok = ok && gguf_fread_el (file, &kv->value.float32, sizeof(kv->value.float32), &offset); break;
+                case GGUF_TYPE_UINT64:  ok = ok && gguf_fread_el (file, &kv->value.uint64,  sizeof(kv->value.uint64),  &offset); break;
+                case GGUF_TYPE_INT64:   ok = ok && gguf_fread_el (file, &kv->value.int64,   sizeof(kv->value.int64),   &offset); break;
+                case GGUF_TYPE_FLOAT64: ok = ok && gguf_fread_el (file, &kv->value.float64, sizeof(kv->value.float64), &offset); break;
+                case GGUF_TYPE_BOOL:    ok = ok && gguf_fread_el (file, &kv->value.bool_,   sizeof(kv->value.bool_),   &offset); break;
+                case GGUF_TYPE_STRING:  ok = ok && gguf_fread_str(file, &kv->value.str,                                &offset); break;
+                case GGUF_TYPE_ARRAY:
+                    {
+                        ok = ok && gguf_fread_el(file, &kv->value.arr.type, sizeof(kv->value.arr.type), &offset);
+
+                        if (ctx->header.version == 1) {
+                            // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
+                            uint32_t n = 0;
+                            ok = ok && gguf_fread_el(file, &n, sizeof(n), &offset);
+                            kv->value.arr.n = n;
+                        } else {
+                            ok = ok && gguf_fread_el(file, &kv->value.arr.n, sizeof(kv->value.arr.n), &offset);
+                        }
+
+                        switch (kv->value.arr.type) {
+                            case GGUF_TYPE_UINT8:
+                            case GGUF_TYPE_INT8:
+                            case GGUF_TYPE_UINT16:
+                            case GGUF_TYPE_INT16:
+                            case GGUF_TYPE_UINT32:
+                            case GGUF_TYPE_INT32:
+                            case GGUF_TYPE_FLOAT32:
+                            case GGUF_TYPE_UINT64:
+                            case GGUF_TYPE_INT64:
+                            case GGUF_TYPE_FLOAT64:
+                            case GGUF_TYPE_BOOL:
+                                {
+                                    kv->value.arr.data = malloc(kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type]);
+                                    ok = ok && gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type], &offset);
+                                } break;
+                            case GGUF_TYPE_STRING:
+                                {
+                                    kv->value.arr.data = malloc(kv->value.arr.n * sizeof(struct gguf_str));
+                                    for (uint32_t j = 0; j < kv->value.arr.n; ++j) {
+                                        ok = ok && gguf_fread_str(file, &((struct gguf_str *) kv->value.arr.data)[j], &offset);
+                                    }
+                                } break;
+                            case GGUF_TYPE_ARRAY:
+                            case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type"); break;
+                        }
+                    } break;
+                case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type");
+            }
+
+            if (!ok) {
+                break;
+            }
+        }
+
+        if (!ok) {
+            fprintf(stderr, "%s: failed to read key-value pairs\n", __func__);
+            fclose(file);
+            gguf_free(ctx);
+            return NULL;
+        }
+    }
+
+    // read the tensor infos
+    {
+        ctx->infos = malloc(ctx->header.n_tensors * sizeof(struct gguf_tensor_info));
+
+        for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
+            struct gguf_tensor_info * info = &ctx->infos[i];
+
+            for (int j = 0; j < GGML_MAX_DIMS; ++j) {
+                info->ne[j] = 1;
+            }
+
+            ok = ok && gguf_fread_str(file, &info->name,                          &offset);
+            ok = ok && gguf_fread_el (file, &info->n_dims, sizeof(info->n_dims),  &offset);
+            for (uint32_t j = 0; j < info->n_dims; ++j) {
+                if (ctx->header.version == 1) {
+                    // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
+                    uint32_t t = 0;
+                    ok = ok && gguf_fread_el(file, &t, sizeof(t), &offset);
+                    info->ne[j] = t;
+                } else {
+                    ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset);
+                }
+            }
+            ok = ok && gguf_fread_el (file, &info->type,   sizeof(info->type),    &offset);
+            ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset),  &offset);
+
+            if (!ok) {
+                fprintf(stderr, "%s: failed to read tensor info\n", __func__);
+                fclose(file);
+                gguf_free(ctx);
+                return NULL;
+            }
+        }
+    }
+
+    ctx->alignment = GGUF_DEFAULT_ALIGNMENT;
+
+    int alignment_idx = gguf_find_key(ctx, "general.alignment");
+    if (alignment_idx != -1) {
+        ctx->alignment = gguf_get_val_u32(ctx, alignment_idx);
+    }
+
+    // we require the data section to be aligned, so take into account any padding
+    {
+        const size_t offset_pad = offset % ctx->alignment;
+
+        if (offset_pad != 0) {
+            offset += ctx->alignment - offset_pad;
+            fseek(file, offset, SEEK_SET);
+        }
+    }
+
+    // store the current file offset - this is where the data section starts
+    ctx->offset = offset;
+
+    // compute the total size of the data section, taking into account the alignment
+    {
+        ctx->size = 0;
+        for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
+            struct gguf_tensor_info * info = &ctx->infos[i];
+
+            const int64_t ne =
+                (int64_t) info->ne[0] *
+                (int64_t) info->ne[1] *
+                (int64_t) info->ne[2] *
+                (int64_t) info->ne[3];
+
+            if (ne % ggml_blck_size(info->type) != 0) {
+                fprintf(stderr, "%s: tensor '%s' number of elements (%" PRId64 ") is not a multiple of block size (%d)\n",
+                        __func__, info->name.data, ne, ggml_blck_size(info->type));
+                fclose(file);
+                gguf_free(ctx);
+                return NULL;
+            }
+
+            const size_t size_cur = (ne*ggml_type_size(info->type))/ggml_blck_size(info->type);
+
+            ctx->size += GGML_PAD(size_cur, ctx->alignment);
+        }
+    }
+
+    // load the tensor data only if requested
+    if (params.ctx != NULL) {
+        // if the provided gguf_context is no_alloc, then we create "empty" tensors and do not read the binary blob
+        // otherwise, we load the binary blob into the created ggml_context as well, and point the "data" members of
+        // the ggml_tensor structs to the appropriate locations in the binary blob
+
+        // compute the exact size needed for the new ggml_context
+        const size_t mem_size =
+            params.no_alloc ?
+            (ctx->header.n_tensors    )*ggml_tensor_overhead() :
+            (ctx->header.n_tensors + 1)*ggml_tensor_overhead() + ctx->size;
+
+        struct ggml_init_params pdata = {
+            .mem_size   = mem_size,
+            .mem_buffer = NULL,
+            .no_alloc   = params.no_alloc,
+        };
+
+        *params.ctx = ggml_init(pdata);
+
+        struct ggml_context * ctx_data = *params.ctx;
+
+        struct ggml_tensor * data = NULL;
+
+        if (!params.no_alloc) {
+            data = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I8, ctx->size);
+
+            ok = ok && data != NULL;
+
+            // read the binary blob with the tensor data
+            ok = ok && gguf_fread_el(file, data->data, ctx->size, &offset);
+
+            if (!ok) {
+                fprintf(stderr, "%s: failed to read tensor data\n", __func__);
+                fclose(file);
+                ggml_free(ctx_data);
+                gguf_free(ctx);
+                return NULL;
+            }
+
+            ctx->data = data->data;
+        }
+
+        ggml_set_no_alloc(ctx_data, true);
+
+        // create the tensors
+        for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
+            const int64_t ne[GGML_MAX_DIMS] = {
+                ctx->infos[i].ne[0],
+                ctx->infos[i].ne[1],
+                ctx->infos[i].ne[2],
+                ctx->infos[i].ne[3],
+            };
+
+            struct ggml_tensor * cur = ggml_new_tensor(ctx_data, ctx->infos[i].type, ctx->infos[i].n_dims, ne);
+
+            ok = ok && cur != NULL;
+
+            ggml_set_name(cur, ctx->infos[i].name.data);
+
+            if (!ok) {
+                break;
+            }
+
+            // point the data member to the appropriate location in the binary blob using the tensor infos
+            if (!params.no_alloc) {
+              //cur->data = (char *) data->data + ctx->infos[i].offset - ctx->offset; // offset from start of file
+                cur->data = (char *) data->data + ctx->infos[i].offset;               // offset from data
+            }
+        }
+
+        if (!ok) {
+            fprintf(stderr, "%s: failed to read the tensor data\n", __func__);
+            fclose(file);
+            ggml_free(ctx_data);
+            gguf_free(ctx);
+            return NULL;
+        }
+
+        ggml_set_no_alloc(ctx_data, params.no_alloc);
+    }
+
+    fclose(file);
+
+    return ctx;
+}
+
+void gguf_free(struct gguf_context * ctx) {
+    if (ctx == NULL) {
+        return;
+    }
+
+    if (ctx->kv) {
+        // free string memory - not great..
+        for (uint32_t i = 0; i < ctx->header.n_kv; ++i) {
+            struct gguf_kv * kv = &ctx->kv[i];
+
+            if (kv->key.data) {
+                free(kv->key.data);
+            }
+
+            if (kv->type == GGUF_TYPE_STRING) {
+                if (kv->value.str.data) {
+                    free(kv->value.str.data);
+                }
+            }
+
+            if (kv->type == GGUF_TYPE_ARRAY) {
+                if (kv->value.arr.data) {
+                    if (kv->value.arr.type == GGUF_TYPE_STRING) {
+                        for (uint32_t j = 0; j < kv->value.arr.n; ++j) {
+                            struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j];
+                            if (str->data) {
+                                free(str->data);
+                            }
+                        }
+                    }
+                    free(kv->value.arr.data);
+                }
+            }
+        }
+
+        free(ctx->kv);
+    }
+
+    if (ctx->infos) {
+        for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
+            struct gguf_tensor_info * info = &ctx->infos[i];
+
+            if (info->name.data) {
+                free(info->name.data);
+            }
+        }
+
+        free(ctx->infos);
+    }
+
+    GGML_ALIGNED_FREE(ctx);
+}
+
+const char * gguf_type_name(enum gguf_type type) {
+    return GGUF_TYPE_NAME[type];
+}
+
+int gguf_get_version(const struct gguf_context * ctx) {
+    return ctx->header.version;
+}
+
+size_t gguf_get_alignment(const struct gguf_context * ctx) {
+    return ctx->alignment;
+}
+
+size_t gguf_get_data_offset(const struct gguf_context * ctx) {
+    return ctx->offset;
+}
+
+void * gguf_get_data(const struct gguf_context * ctx) {
+    return ctx->data;
+}
+
+int gguf_get_n_kv(const struct gguf_context * ctx) {
+    return ctx->header.n_kv;
+}
+
+int gguf_find_key(const struct gguf_context * ctx, const char * key) {
+    // return -1 if key not found
+    int keyfound = -1;
+
+    const int n_kv = gguf_get_n_kv(ctx);
+
+    for (int i = 0; i < n_kv; ++i) {
+        if (strcmp(key, gguf_get_key(ctx, i)) == 0) {
+            keyfound = i;
+            break;
+        }
+    }
+
+    return keyfound;
+}
+
+const char * gguf_get_key(const struct gguf_context * ctx, int key_id) {
+    return ctx->kv[key_id].key.data;
+}
+
+enum gguf_type gguf_get_kv_type(const struct gguf_context * ctx, int key_id) {
+    return ctx->kv[key_id].type;
+}
+
+enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY);
+    return ctx->kv[key_id].value.arr.type;
+}
+
+const void * gguf_get_arr_data(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY);
+    return ctx->kv[key_id].value.arr.data;
+}
+
+const char * gguf_get_arr_str(const struct gguf_context * ctx, int key_id, int i) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY);
+    struct gguf_kv * kv = &ctx->kv[key_id];
+    struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[i];
+    return str->data;
+}
+
+int gguf_get_arr_n(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY);
+    return ctx->kv[key_id].value.arr.n;
+}
+
+uint8_t gguf_get_val_u8(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT8);
+    return ctx->kv[key_id].value.uint8;
+}
+
+int8_t gguf_get_val_i8(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT8);
+    return ctx->kv[key_id].value.int8;
+}
+
+uint16_t gguf_get_val_u16(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT16);
+    return ctx->kv[key_id].value.uint16;
+}
+
+int16_t gguf_get_val_i16(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT16);
+    return ctx->kv[key_id].value.int16;
+}
+
+uint32_t gguf_get_val_u32(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT32);
+    return ctx->kv[key_id].value.uint32;
+}
+
+int32_t gguf_get_val_i32(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT32);
+    return ctx->kv[key_id].value.int32;
+}
+
+float gguf_get_val_f32(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_FLOAT32);
+    return ctx->kv[key_id].value.float32;
+}
+
+uint64_t gguf_get_val_u64(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT64);
+    return ctx->kv[key_id].value.uint64;
+}
+
+int64_t gguf_get_val_i64(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT64);
+    return ctx->kv[key_id].value.int64;
+}
+
+double gguf_get_val_f64(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_FLOAT64);
+    return ctx->kv[key_id].value.float64;
+}
+
+bool gguf_get_val_bool(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_BOOL);
+    return ctx->kv[key_id].value.bool_;
+}
+
+const char * gguf_get_val_str(const struct gguf_context * ctx, int key_id) {
+    GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_STRING);
+    return ctx->kv[key_id].value.str.data;
+}
+
+int gguf_get_n_tensors(const struct gguf_context * ctx) {
+    return ctx->header.n_tensors;
+}
+
+int gguf_find_tensor(const struct gguf_context * ctx, const char * name) {
+    // return -1 if tensor not found
+    int tensorfound = -1;
+
+    const int n_tensors = gguf_get_n_tensors(ctx);
+
+    for (int i = 0; i < n_tensors; ++i) {
+        if (strcmp(name, gguf_get_tensor_name(ctx, i)) == 0) {
+            tensorfound = i;
+            break;
+        }
+    }
+
+    return tensorfound;
+}
+
+size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int i) {
+    return ctx->infos[i].offset;
+}
+
+char * gguf_get_tensor_name(const struct gguf_context * ctx, int i) {
+    return ctx->infos[i].name.data;
+}
+
+// returns the index
+static int gguf_get_or_add_key(struct gguf_context * ctx, const char * key) {
+    const int idx = gguf_find_key(ctx, key);
+    if (idx >= 0) {
+        return idx;
+    }
+
+    const int n_kv = gguf_get_n_kv(ctx);
+
+    ctx->kv = realloc(ctx->kv, (n_kv + 1) * sizeof(struct gguf_kv));
+    ctx->kv[n_kv].key.n    = strlen(key);
+    ctx->kv[n_kv].key.data = strdup(key);
+    ctx->header.n_kv++;
+
+    return n_kv;
+}
+
+void gguf_set_val_u8(struct gguf_context * ctx, const char * key, uint8_t val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type        = GGUF_TYPE_UINT8;
+    ctx->kv[idx].value.uint8 = val;
+}
+
+void gguf_set_val_i8(struct gguf_context * ctx, const char * key, int8_t val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type       = GGUF_TYPE_INT8;
+    ctx->kv[idx].value.int8 = val;
+}
+
+void gguf_set_val_u16(struct gguf_context * ctx, const char * key, uint16_t val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type         = GGUF_TYPE_UINT16;
+    ctx->kv[idx].value.uint16 = val;
+}
+
+void gguf_set_val_i16(struct gguf_context * ctx, const char * key, int16_t val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type        = GGUF_TYPE_INT16;
+    ctx->kv[idx].value.int16 = val;
+}
+
+void gguf_set_val_u32(struct gguf_context * ctx, const char * key, uint32_t val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type         = GGUF_TYPE_UINT32;
+    ctx->kv[idx].value.uint32 = val;
+}
+
+void gguf_set_val_i32(struct gguf_context * ctx, const char * key, int32_t val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type        = GGUF_TYPE_INT32;
+    ctx->kv[idx].value.int32 = val;
+}
+
+void gguf_set_val_f32(struct gguf_context * ctx, const char * key, float val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type          = GGUF_TYPE_FLOAT32;
+    ctx->kv[idx].value.float32 = val;
+}
+
+void gguf_set_val_u64(struct gguf_context * ctx, const char * key, uint64_t val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type         = GGUF_TYPE_UINT64;
+    ctx->kv[idx].value.uint64 = val;
+}
+
+void gguf_set_val_i64(struct gguf_context * ctx, const char * key, int64_t val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type        = GGUF_TYPE_INT64;
+    ctx->kv[idx].value.int64 = val;
+}
+
+void gguf_set_val_f64(struct gguf_context * ctx, const char * key, double val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type          = GGUF_TYPE_FLOAT64;
+    ctx->kv[idx].value.float64 = val;
+}
+
+void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type        = GGUF_TYPE_BOOL;
+    ctx->kv[idx].value.bool_ = val;
+}
+
+void gguf_set_val_str(struct gguf_context * ctx, const char * key, const char * val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type           = GGUF_TYPE_STRING;
+    ctx->kv[idx].value.str.n    = strlen(val);
+    ctx->kv[idx].value.str.data = strdup(val);
+}
+
+void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type           = GGUF_TYPE_ARRAY;
+    ctx->kv[idx].value.arr.type = type;
+    ctx->kv[idx].value.arr.n    = n;
+    ctx->kv[idx].value.arr.data = malloc(n*GGUF_TYPE_SIZE[type]);
+    memcpy(ctx->kv[idx].value.arr.data, data, n*GGUF_TYPE_SIZE[type]);
+}
+
+void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char ** data, int n) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type           = GGUF_TYPE_ARRAY;
+    ctx->kv[idx].value.arr.type = GGUF_TYPE_STRING;
+    ctx->kv[idx].value.arr.n    = n;
+    ctx->kv[idx].value.arr.data = malloc(n*sizeof(struct gguf_str));
+    for (int i = 0; i < n; i++) {
+        struct gguf_str * str = &((struct gguf_str *)ctx->kv[idx].value.arr.data)[i];
+        str->n    = strlen(data[i]);
+        str->data = strdup(data[i]);
+    }
+}
+
+// set or add KV pairs from another context
+void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) {
+    for (uint32_t i = 0; i < src->header.n_kv; i++) {
+        switch (src->kv[i].type) {
+            case GGUF_TYPE_UINT8:   gguf_set_val_u8  (ctx, src->kv[i].key.data, src->kv[i].value.uint8);    break;
+            case GGUF_TYPE_INT8:    gguf_set_val_i8  (ctx, src->kv[i].key.data, src->kv[i].value.int8);     break;
+            case GGUF_TYPE_UINT16:  gguf_set_val_u16 (ctx, src->kv[i].key.data, src->kv[i].value.uint16);   break;
+            case GGUF_TYPE_INT16:   gguf_set_val_i16 (ctx, src->kv[i].key.data, src->kv[i].value.int16);    break;
+            case GGUF_TYPE_UINT32:  gguf_set_val_u32 (ctx, src->kv[i].key.data, src->kv[i].value.uint32);   break;
+            case GGUF_TYPE_INT32:   gguf_set_val_i32 (ctx, src->kv[i].key.data, src->kv[i].value.int32);    break;
+            case GGUF_TYPE_FLOAT32: gguf_set_val_f32 (ctx, src->kv[i].key.data, src->kv[i].value.float32);  break;
+            case GGUF_TYPE_UINT64:  gguf_set_val_u64 (ctx, src->kv[i].key.data, src->kv[i].value.uint64);   break;
+            case GGUF_TYPE_INT64:   gguf_set_val_i64 (ctx, src->kv[i].key.data, src->kv[i].value.int64);    break;
+            case GGUF_TYPE_FLOAT64: gguf_set_val_f64 (ctx, src->kv[i].key.data, src->kv[i].value.float64);  break;
+            case GGUF_TYPE_BOOL:    gguf_set_val_bool(ctx, src->kv[i].key.data, src->kv[i].value.bool_);    break;
+            case GGUF_TYPE_STRING:  gguf_set_val_str (ctx, src->kv[i].key.data, src->kv[i].value.str.data); break;
+            case GGUF_TYPE_ARRAY:
+                {
+                    if (src->kv[i].value.arr.type == GGUF_TYPE_STRING) {
+                        const char ** data = malloc(src->kv[i].value.arr.n*sizeof(char *));
+                        for (uint32_t j = 0; j < src->kv[i].value.arr.n; j++) {
+                            data[j] = ((struct gguf_str *)src->kv[i].value.arr.data)[j].data;
+                        }
+                        gguf_set_arr_str(ctx, src->kv[i].key.data, data, src->kv[i].value.arr.n);
+                        free(data);
+                    } else if (src->kv[i].value.arr.type == GGUF_TYPE_ARRAY) {
+                        GGML_ASSERT(false && "nested arrays not supported");
+                    } else {
+                        gguf_set_arr_data(ctx, src->kv[i].key.data, src->kv[i].value.arr.type, src->kv[i].value.arr.data, src->kv[i].value.arr.n);
+                    }
+                } break;
+            case GGUF_TYPE_COUNT:  GGML_ASSERT(false && "invalid type"); break;
+        }
+    }
+}
+
+void gguf_add_tensor(
+             struct gguf_context * ctx,
+        const struct ggml_tensor * tensor) {
+    const int idx = ctx->header.n_tensors;
+    ctx->infos = realloc(ctx->infos, (idx + 1)*sizeof(struct gguf_tensor_info));
+
+    ctx->infos[idx].name.n    = strlen(tensor->name);
+    ctx->infos[idx].name.data = strdup(tensor->name);
+
+    for (int i = 0; i < GGML_MAX_DIMS; ++i) {
+        ctx->infos[idx].ne[i] = 1;
+    }
+
+    ctx->infos[idx].n_dims = tensor->n_dims;
+    for (int i = 0; i < tensor->n_dims; i++) {
+        ctx->infos[idx].ne[i] = tensor->ne[i];
+    }
+
+    ctx->infos[idx].type   = tensor->type;
+    ctx->infos[idx].offset = 0;
+    ctx->infos[idx].data   = tensor->data;
+    ctx->infos[idx].size   = ggml_nbytes(tensor);
+
+    if (ctx->header.n_tensors > 0) {
+        ctx->infos[idx].offset = ctx->infos[idx - 1].offset + GGML_PAD(ctx->infos[idx - 1].size, ctx->alignment);
+    }
+
+    ctx->header.n_tensors++;
+}
+
+void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type) {
+    const int idx = gguf_find_tensor(ctx, name);
+    if (idx < 0) {
+        GGML_ASSERT(false && "tensor not found");
+    }
+
+    ctx->infos[idx].type = type;
+}
+
+void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size) {
+    const int idx = gguf_find_tensor(ctx, name);
+    if (idx < 0) {
+        GGML_ASSERT(false && "tensor not found");
+    }
+
+    ctx->infos[idx].data = data;
+    ctx->infos[idx].size = size;
+
+    // update offsets
+    for (uint32_t i = idx + 1; i < ctx->header.n_tensors; ++i) {
+        ctx->infos[i].offset = ctx->infos[i - 1].offset + GGML_PAD(ctx->infos[i - 1].size, ctx->alignment);
+    }
+}
+
+//static void gguf_fwrite_str(FILE * file, const struct gguf_str * val) {
+//    fwrite(&val->n,   sizeof(val->n),    1, file);
+//    fwrite(val->data, sizeof(char), val->n, file);
+//}
+//
+//static void gguf_fwrite_el(FILE * file, const void * val, size_t size) {
+//    fwrite(val, sizeof(char), size, file);
+//}
+
+struct gguf_buf {
+    void * data;
+    size_t size;
+    size_t offset;
+};
+
+static struct gguf_buf gguf_buf_init(size_t size) {
+    struct gguf_buf buf = {
+        /*buf.data   =*/ size == 0 ? NULL : malloc(size),
+        /*buf.size   =*/ size,
+        /*buf.offset =*/ 0,
+    };
+
+    return buf;
+}
+
+static void gguf_buf_free(struct gguf_buf buf) {
+    if (buf.data) {
+        free(buf.data);
+    }
+}
+
+static void gguf_buf_grow(struct gguf_buf * buf, size_t size) {
+    if (buf->offset + size > buf->size) {
+        buf->size = 1.5*(buf->offset + size);
+        if (buf->data) {
+            buf->data = realloc(buf->data, buf->size);
+        }
+    }
+}
+
+static void gguf_bwrite_str(struct gguf_buf * buf, const struct gguf_str * val) {
+    gguf_buf_grow(buf, sizeof(val->n) + val->n);
+
+    if (buf->data) {
+        memcpy((char *) buf->data + buf->offset, &val->n, sizeof(val->n));
+    }
+    buf->offset += sizeof(val->n);
+
+    if (buf->data) {
+        memcpy((char *) buf->data + buf->offset, val->data, val->n);
+    }
+    buf->offset += val->n;
+}
+
+static void gguf_bwrite_el(struct gguf_buf * buf, const void * val, size_t el_size) {
+    gguf_buf_grow(buf, el_size);
+
+    if (buf->data) {
+        memcpy((char *) buf->data + buf->offset, val, el_size);
+    }
+    buf->offset += el_size;
+}
+
+static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta) {
+    // write header
+    gguf_bwrite_el(buf, &ctx->header.magic,     sizeof(ctx->header.magic));
+    gguf_bwrite_el(buf, &ctx->header.version,   sizeof(ctx->header.version));
+    gguf_bwrite_el(buf, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors));
+    gguf_bwrite_el(buf, &ctx->header.n_kv,      sizeof(ctx->header.n_kv));
+
+    // write key-value pairs
+    for (uint32_t i = 0; i < ctx->header.n_kv; ++i) {
+        struct gguf_kv * kv = &ctx->kv[i];
+
+        gguf_bwrite_str(buf, &kv->key);
+        gguf_bwrite_el (buf, &kv->type, sizeof(kv->type));
+
+        switch (kv->type) {
+            case GGUF_TYPE_UINT8:   gguf_bwrite_el( buf, &kv->value.uint8,   sizeof(kv->value.uint8)  ); break;
+            case GGUF_TYPE_INT8:    gguf_bwrite_el (buf, &kv->value.int8,    sizeof(kv->value.int8)   ); break;
+            case GGUF_TYPE_UINT16:  gguf_bwrite_el (buf, &kv->value.uint16,  sizeof(kv->value.uint16) ); break;
+            case GGUF_TYPE_INT16:   gguf_bwrite_el (buf, &kv->value.int16,   sizeof(kv->value.int16)  ); break;
+            case GGUF_TYPE_UINT32:  gguf_bwrite_el (buf, &kv->value.uint32,  sizeof(kv->value.uint32) ); break;
+            case GGUF_TYPE_INT32:   gguf_bwrite_el (buf, &kv->value.int32,   sizeof(kv->value.int32)  ); break;
+            case GGUF_TYPE_FLOAT32: gguf_bwrite_el (buf, &kv->value.float32, sizeof(kv->value.float32)); break;
+            case GGUF_TYPE_UINT64:  gguf_bwrite_el (buf, &kv->value.uint64,  sizeof(kv->value.uint64) ); break;
+            case GGUF_TYPE_INT64:   gguf_bwrite_el (buf, &kv->value.int64,   sizeof(kv->value.int64)  ); break;
+            case GGUF_TYPE_FLOAT64: gguf_bwrite_el (buf, &kv->value.float64, sizeof(kv->value.float64)); break;
+            case GGUF_TYPE_BOOL:    gguf_bwrite_el (buf, &kv->value.bool_,   sizeof(kv->value.bool_)  ); break;
+            case GGUF_TYPE_STRING:  gguf_bwrite_str(buf, &kv->value.str                               ); break;
+            case GGUF_TYPE_ARRAY:
+                {
+                    gguf_bwrite_el(buf, &kv->value.arr.type, sizeof(kv->value.arr.type));
+                    gguf_bwrite_el(buf, &kv->value.arr.n,    sizeof(kv->value.arr.n)   );
+
+                    switch (kv->value.arr.type) {
+                        case GGUF_TYPE_UINT8:
+                        case GGUF_TYPE_INT8:
+                        case GGUF_TYPE_UINT16:
+                        case GGUF_TYPE_INT16:
+                        case GGUF_TYPE_UINT32:
+                        case GGUF_TYPE_INT32:
+                        case GGUF_TYPE_FLOAT32:
+                        case GGUF_TYPE_UINT64:
+                        case GGUF_TYPE_INT64:
+                        case GGUF_TYPE_FLOAT64:
+                        case GGUF_TYPE_BOOL:
+                            {
+                                gguf_bwrite_el(buf, kv->value.arr.data, kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type]);
+                            } break;
+                        case GGUF_TYPE_STRING:
+                            {
+                                for (uint32_t j = 0; j < kv->value.arr.n; ++j) {
+                                    gguf_bwrite_str(buf, &((struct gguf_str *) kv->value.arr.data)[j]);
+                                }
+                            } break;
+                        case GGUF_TYPE_ARRAY:
+                        case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type"); break;
+                    }
+                } break;
+            case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type");
+        }
+    }
+
+    // write tensor infos
+    for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
+        struct gguf_tensor_info * info = &ctx->infos[i];
+
+        gguf_bwrite_str(buf, &info->name);
+        gguf_bwrite_el (buf, &info->n_dims, sizeof(info->n_dims));
+        for (uint32_t j = 0; j < info->n_dims; ++j) {
+            gguf_bwrite_el(buf, &info->ne[j], sizeof(info->ne[j]));
+        }
+        gguf_bwrite_el(buf, &info->type,   sizeof(info->type));
+        gguf_bwrite_el(buf, &info->offset, sizeof(info->offset));
+    }
+
+    // we require the data section to be aligned, so take into account any padding
+    {
+        const size_t offset     = buf->offset;
+        const size_t offset_pad = GGML_PAD(offset, ctx->alignment);
+
+        if (offset_pad != offset) {
+            uint8_t pad = 0;
+            for (size_t i = 0; i < offset_pad - offset; ++i) {
+                gguf_bwrite_el(buf, &pad, sizeof(pad));
+            }
+        }
+    }
+
+    if (only_meta) {
+        return;
+    }
+
+    size_t offset = 0;
+
+    // write tensor data
+    for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
+        struct gguf_tensor_info * info = &ctx->infos[i];
+
+        const size_t size     = info->size;
+        const size_t size_pad = GGML_PAD(size, ctx->alignment);
+
+        gguf_bwrite_el(buf, info->data, size);
+
+        if (size_pad != size) {
+            uint8_t pad = 0;
+            for (size_t j = 0; j < size_pad - size; ++j) {
+                gguf_bwrite_el(buf, &pad, sizeof(pad));
+            }
+        }
+
+        GGML_ASSERT(offset == info->offset);
+
+        offset += size_pad;
+    }
+}
+
+void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) {
+    FILE * file = fopen(fname, "wb");
+    if (!file) {
+        GGML_ASSERT(false && "failed to open file for writing");
+    }
+
+    struct gguf_buf buf = gguf_buf_init(16*1024);
+
+    gguf_write_to_buf(ctx, &buf, only_meta);
+
+    fwrite(buf.data, 1, buf.offset, file);
+
+    gguf_buf_free(buf);
+
+    fclose(file);
+}
+
+size_t gguf_get_meta_size(const struct gguf_context * ctx) {
+    // no allocs - only compute size
+    struct gguf_buf buf = gguf_buf_init(0);
+
+    gguf_write_to_buf(ctx, &buf, true);
+
+    return buf.offset;
+}
+
+void gguf_get_meta_data(const struct gguf_context * ctx, void * data) {
+    struct gguf_buf buf = gguf_buf_init(16*1024);
+
+    gguf_write_to_buf(ctx, &buf, true);
+
+    memcpy(data, buf.data, buf.offset);
+
+    gguf_buf_free(buf);
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+int ggml_cpu_has_avx(void) {
+#if defined(__AVX__)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_avx2(void) {
+#if defined(__AVX2__)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_avx512(void) {
+#if defined(__AVX512F__)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_avx512_vbmi(void) {
+#if defined(__AVX512VBMI__)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_avx512_vnni(void) {
+#if defined(__AVX512VNNI__)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_fma(void) {
+#if defined(__FMA__)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_neon(void) {
+#if defined(__ARM_NEON)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_arm_fma(void) {
+#if defined(__ARM_FEATURE_FMA)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_metal(void) {
+#if defined(GGML_USE_METAL)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_f16c(void) {
+#if defined(__F16C__)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_fp16_va(void) {
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_wasm_simd(void) {
+#if defined(__wasm_simd128__)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_blas(void) {
+#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_cublas(void) {
+#if defined(GGML_USE_CUBLAS)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_clblast(void) {
+#if defined(GGML_USE_CLBLAST)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_gpublas(void) {
+    return ggml_cpu_has_cublas() || ggml_cpu_has_clblast();
+}
+
+int ggml_cpu_has_sse3(void) {
+#if defined(__SSE3__)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_ssse3(void) {
+#if defined(__SSSE3__)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_vsx(void) {
+#if defined(__POWER9_VECTOR__)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+////////////////////////////////////////////////////////////////////////////////

runner/ggml.h

@@ -0,0 +1,2141 @@
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#pragma once
+
+//
+// GGML Tensor Library
+//
+// This documentation is still a work in progress.
+// If you wish some specific topics to be covered, feel free to drop a comment:
+//
+//   https://github.com/ggerganov/whisper.cpp/issues/40
+//
+// ## Overview
+//
+// This library implements:
+//
+//  - a set of tensor operations
+//  - automatic differentiation
+//  - basic optimization algorithms
+//
+// The aim of this library is to provide a minimalistic approach for various machine learning tasks. This includes,
+// but is not limited to, the following:
+//
+//  - linear regression
+//  - support vector machines
+//  - neural networks
+//
+// The library allows the user to define a certain function using the available tensor operations. This function
+// definition is represented internally via a computation graph. Each tensor operation in the function definition
+// corresponds to a node in the graph. Having the computation graph defined, the user can choose to compute the
+// function's value and/or its gradient with respect to the input variables. Optionally, the function can be optimized
+// using one of the available optimization algorithms.
+//
+// For example, here we define the function: f(x) = a*x^2 + b
+//
+//   {
+//       struct ggml_init_params params = {
+//           .mem_size   = 16*1024*1024,
+//           .mem_buffer = NULL,
+//       };
+//
+//       // memory allocation happens here
+//       struct ggml_context * ctx = ggml_init(params);
+//
+//       struct ggml_tensor * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+//
+//       ggml_set_param(ctx, x); // x is an input variable
+//
+//       struct ggml_tensor * a  = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+//       struct ggml_tensor * b  = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+//       struct ggml_tensor * x2 = ggml_mul(ctx, x, x);
+//       struct ggml_tensor * f  = ggml_add(ctx, ggml_mul(ctx, a, x2), b);
+//
+//       ...
+//   }
+//
+// Notice that the function definition above does not involve any actual computation. The computation is performed only
+// when the user explicitly requests it. For example, to compute the function's value at x = 2.0:
+//
+//   {
+//       ...
+//
+//       struct ggml_cgraph gf = ggml_build_forward(f);
+//
+//       // set the input variable and parameter values
+//       ggml_set_f32(x, 2.0f);
+//       ggml_set_f32(a, 3.0f);
+//       ggml_set_f32(b, 4.0f);
+//
+//       ggml_graph_compute_with_ctx(ctx, &gf, n_threads);
+//
+//       printf("f = %f\n", ggml_get_f32_1d(f, 0));
+//
+//       ...
+//   }
+//
+// The actual computation is performed in the ggml_graph_compute() function.
+//
+// The ggml_new_tensor_...() functions create new tensors. They are allocated in the memory buffer provided to the
+// ggml_init() function. You have to be careful not to exceed the memory buffer size. Therefore, you have to know
+// in advance how much memory you need for your computation. Alternatively, you can allocate a large enough memory
+// and after defining the computation graph, call the ggml_used_mem() function to find out how much memory was
+// actually needed.
+//
+// The ggml_set_param() function marks a tensor as an input variable. This is used by the automatic
+// differentiation and optimization algorithms.
+//
+// The described approach allows to define the function graph once and then compute its forward or backward graphs
+// multiple times. All computations will use the same memory buffer allocated in the ggml_init() function. This way
+// the user can avoid the memory allocation overhead at runtime.
+//
+// The library supports multi-dimensional tensors - up to 4 dimensions. The FP16 and FP32 data types are first class
+// citizens, but in theory the library can be extended to support FP8 and integer data types.
+//
+// Each tensor operation produces a new tensor. Initially the library was envisioned to support only the use of unary
+// and binary operations. Most of the available operations fall into one of these two categories. With time, it became
+// clear that the library needs to support more complex operations. The way to support these operations is not clear
+// yet, but a few examples are demonstrated in the following operations:
+//
+//   - ggml_permute()
+//   - ggml_conv_1d_1s()
+//   - ggml_conv_1d_2s()
+//
+// For each tensor operator, the library implements a forward and backward computation function. The forward function
+// computes the output tensor value given the input tensor values. The backward function computes the adjoint of the
+// input tensors given the adjoint of the output tensor. For a detailed explanation of what this means, take a
+// calculus class, or watch the following video:
+//
+//   What is Automatic Differentiation?
+//   https://www.youtube.com/watch?v=wG_nF1awSSY
+//
+//
+// ## Tensor data (struct ggml_tensor)
+//
+// The tensors are stored in memory via the ggml_tensor struct. The structure provides information about the size of
+// the tensor, the data type, and the memory buffer where the tensor data is stored. Additionally, it contains
+// pointers to the "source" tensors - i.e. the tensors that were used to compute the current tensor. For example:
+//
+//   {
+//       struct ggml_tensor * c = ggml_add(ctx, a, b);
+//
+//       assert(c->src[0] == a);
+//       assert(c->src[1] == b);
+//   }
+//
+// The multi-dimensional tensors are stored in row-major order. The ggml_tensor struct contains fields for the
+// number of elements in each dimension ("ne") as well as the number of bytes ("nb", a.k.a. stride). This allows
+// to store tensors that are not contiguous in memory, which is useful for operations such as transposition and
+// permutation. All tensor operations have to take the stride into account and not assume that the tensor is
+// contiguous in memory.
+//
+// The data of the tensor is accessed via the "data" pointer. For example:
+//
+//   {
+//       const int nx = 2;
+//       const int ny = 3;
+//
+//       struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, ny);
+//
+//       for (int y = 0; y < ny; y++) {
+//           for (int x = 0; x < nx; x++) {
+//               *(float *) ((char *) a->data + y*a->nb[1] + x*a->nb[0]) = x + y;
+//           }
+//       }
+//
+//       ...
+//   }
+//
+// Alternatively, there are helper functions, such as ggml_get_f32_1d() and ggml_set_f32_1d() that can be used.
+//
+// ## The matrix multiplication operator (ggml_mul_mat)
+//
+// TODO
+//
+//
+// ## Multi-threading
+//
+// TODO
+//
+//
+// ## Overview of ggml.c
+//
+// TODO
+//
+//
+// ## SIMD optimizations
+//
+// TODO
+//
+//
+// ## Debugging ggml
+//
+// TODO
+//
+//
+
+#ifdef GGML_SHARED
+#    if defined(_WIN32) && !defined(__MINGW32__)
+#        ifdef GGML_BUILD
+#            define GGML_API __declspec(dllexport)
+#        else
+#            define GGML_API __declspec(dllimport)
+#        endif
+#    else
+#        define GGML_API __attribute__ ((visibility ("default")))
+#    endif
+#else
+#    define GGML_API
+#endif
+
+// TODO: support for clang
+#ifdef __GNUC__
+#    define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
+#elif defined(_MSC_VER)
+#    define GGML_DEPRECATED(func, hint) __declspec(deprecated(hint)) func
+#else
+#    define GGML_DEPRECATED(func, hint) func
+#endif
+
+#ifndef __GNUC__
+#    define GGML_ATTRIBUTE_FORMAT(...)
+#elif defined(__MINGW32__)
+#    define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
+#else
+#    define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
+#endif
+
+#include <stdint.h>
+#include <stddef.h>
+#include <stdbool.h>
+
+#define GGML_FILE_MAGIC   0x67676d6c // "ggml"
+#define GGML_FILE_VERSION 1
+
+#define GGML_QNT_VERSION        2    // bump this on quantization format changes
+#define GGML_QNT_VERSION_FACTOR 1000 // do not change this
+
+#define GGML_MAX_DIMS          4
+#define GGML_MAX_NODES         16384
+#define GGML_MAX_PARAMS        1024
+#define GGML_MAX_CONTEXTS      64
+#define GGML_MAX_SRC           6
+#define GGML_MAX_NAME          64
+#define GGML_MAX_OP_PARAMS     32
+#define GGML_DEFAULT_N_THREADS 4
+
+#if UINTPTR_MAX == 0xFFFFFFFF
+    #define GGML_MEM_ALIGN 4
+#else
+    #define GGML_MEM_ALIGN 16
+#endif
+
+#define GGML_EXIT_SUCCESS 0
+#define GGML_EXIT_ABORTED 1
+
+#define GGUF_MAGIC "GGUF"
+
+#define GGUF_VERSION 3
+
+#define GGUF_DEFAULT_ALIGNMENT 32
+
+#define GGML_UNUSED(x) (void)(x)
+
+#define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
+
+#define GGML_ASSERT(x) \
+    do { \
+        if (!(x)) { \
+            fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
+            abort(); \
+        } \
+    } while (0)
+
+#ifndef NDEBUG
+#define GGML_UNREACHABLE() GGML_ASSERT(!"statement should not be reached")
+#elif defined(__GNUC__)
+#define GGML_UNREACHABLE() __builtin_unreachable()
+#else
+#define GGML_UNREACHABLE() ((void) 0)
+#endif
+
+// used to copy the number of elements and stride in bytes of tensors into local variables.
+// main purpose is to reduce code duplication and improve readability.
+//
+// example:
+//
+//    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
+//    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb);
+//
+#define GGML_TENSOR_LOCALS_1(type, prefix, pointer, array) \
+    const type prefix##0 = (pointer)->array[0]; \
+    GGML_UNUSED(prefix##0);
+#define GGML_TENSOR_LOCALS_2(type, prefix, pointer, array) \
+    GGML_TENSOR_LOCALS_1    (type, prefix, pointer, array) \
+    const type prefix##1 = (pointer)->array[1]; \
+    GGML_UNUSED(prefix##1);
+#define GGML_TENSOR_LOCALS_3(type, prefix, pointer, array) \
+    GGML_TENSOR_LOCALS_2    (type, prefix, pointer, array) \
+    const type prefix##2 = (pointer)->array[2]; \
+    GGML_UNUSED(prefix##2);
+#define GGML_TENSOR_LOCALS(type, prefix, pointer, array) \
+    GGML_TENSOR_LOCALS_3  (type, prefix, pointer, array) \
+    const type prefix##3 = (pointer)->array[3]; \
+    GGML_UNUSED(prefix##3);
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+#if defined(__ARM_NEON) && defined(__CUDACC__)
+    typedef half ggml_fp16_t;
+#elif defined(__ARM_NEON)
+    typedef __fp16 ggml_fp16_t;
+#else
+    typedef uint16_t ggml_fp16_t;
+#endif
+
+    // convert FP16 <-> FP32
+    GGML_API float       ggml_fp16_to_fp32(ggml_fp16_t x);
+    GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x);
+
+    GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n);
+    GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n);
+
+    struct ggml_object;
+    struct ggml_context;
+
+    enum ggml_type {
+        GGML_TYPE_F32  = 0,
+        GGML_TYPE_F16  = 1,
+        GGML_TYPE_Q4_0 = 2,
+        GGML_TYPE_Q4_1 = 3,
+        // GGML_TYPE_Q4_2 = 4, support has been removed
+        // GGML_TYPE_Q4_3 (5) support has been removed
+        GGML_TYPE_Q5_0 = 6,
+        GGML_TYPE_Q5_1 = 7,
+        GGML_TYPE_Q8_0 = 8,
+        GGML_TYPE_Q8_1 = 9,
+        // k-quantizations
+        GGML_TYPE_Q2_K = 10,
+        GGML_TYPE_Q3_K = 11,
+        GGML_TYPE_Q4_K = 12,
+        GGML_TYPE_Q5_K = 13,
+        GGML_TYPE_Q6_K = 14,
+        GGML_TYPE_Q8_K = 15,
+        GGML_TYPE_I8,
+        GGML_TYPE_I16,
+        GGML_TYPE_I32,
+        GGML_TYPE_COUNT,
+    };
+
+    enum ggml_backend_type {
+        GGML_BACKEND_CPU = 0,
+        GGML_BACKEND_GPU = 10,
+        GGML_BACKEND_GPU_SPLIT = 20,
+    };
+
+    // model file types
+    enum ggml_ftype {
+        GGML_FTYPE_UNKNOWN     = -1,
+        GGML_FTYPE_ALL_F32     = 0,
+        GGML_FTYPE_MOSTLY_F16  = 1,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_0 = 2,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_1 = 3,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
+        GGML_FTYPE_MOSTLY_Q8_0 = 7,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_0 = 8,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_1 = 9,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors
+    };
+
+    // available tensor operations:
+    enum ggml_op {
+        GGML_OP_NONE = 0,
+
+        GGML_OP_DUP,
+        GGML_OP_ADD,
+        GGML_OP_ADD1,
+        GGML_OP_ACC,
+        GGML_OP_SUB,
+        GGML_OP_MUL,
+        GGML_OP_DIV,
+        GGML_OP_SQR,
+        GGML_OP_SQRT,
+        GGML_OP_LOG,
+        GGML_OP_SUM,
+        GGML_OP_SUM_ROWS,
+        GGML_OP_MEAN,
+        GGML_OP_ARGMAX,
+        GGML_OP_REPEAT,
+        GGML_OP_REPEAT_BACK,
+        GGML_OP_CONCAT,
+        GGML_OP_SILU_BACK,
+        GGML_OP_NORM, // normalize
+        GGML_OP_RMS_NORM,
+        GGML_OP_RMS_NORM_BACK,
+        GGML_OP_GROUP_NORM,
+
+        GGML_OP_MUL_MAT,
+        GGML_OP_OUT_PROD,
+
+        GGML_OP_SCALE,
+        GGML_OP_SET,
+        GGML_OP_CPY,
+        GGML_OP_CONT,
+        GGML_OP_RESHAPE,
+        GGML_OP_VIEW,
+        GGML_OP_PERMUTE,
+        GGML_OP_TRANSPOSE,
+        GGML_OP_GET_ROWS,
+        GGML_OP_GET_ROWS_BACK,
+        GGML_OP_DIAG,
+        GGML_OP_DIAG_MASK_INF,
+        GGML_OP_DIAG_MASK_ZERO,
+        GGML_OP_SOFT_MAX,
+        GGML_OP_SOFT_MAX_BACK,
+        GGML_OP_ROPE,
+        GGML_OP_ROPE_BACK,
+        GGML_OP_ALIBI,
+        GGML_OP_CLAMP,
+        GGML_OP_CONV_1D,
+        GGML_OP_CONV_2D,
+        GGML_OP_CONV_TRANSPOSE_1D,
+        GGML_OP_CONV_TRANSPOSE_2D,
+        GGML_OP_POOL_1D,
+        GGML_OP_POOL_2D,
+
+        GGML_OP_CONV_1D_STAGE_0,  // internal
+        GGML_OP_CONV_1D_STAGE_1,  // internal
+
+        GGML_OP_UPSCALE, // nearest interpolate
+
+        GGML_OP_FLASH_ATTN,
+        GGML_OP_FLASH_FF,
+        GGML_OP_FLASH_ATTN_BACK,
+        GGML_OP_WIN_PART,
+        GGML_OP_WIN_UNPART,
+        GGML_OP_GET_REL_POS,
+        GGML_OP_ADD_REL_POS,
+
+        GGML_OP_UNARY,
+
+        GGML_OP_MAP_UNARY,
+        GGML_OP_MAP_BINARY,
+
+        GGML_OP_MAP_CUSTOM1_F32,
+        GGML_OP_MAP_CUSTOM2_F32,
+        GGML_OP_MAP_CUSTOM3_F32,
+
+        GGML_OP_MAP_CUSTOM1,
+        GGML_OP_MAP_CUSTOM2,
+        GGML_OP_MAP_CUSTOM3,
+
+        GGML_OP_CROSS_ENTROPY_LOSS,
+        GGML_OP_CROSS_ENTROPY_LOSS_BACK,
+
+        GGML_OP_COUNT,
+    };
+
+    enum ggml_unary_op {
+        GGML_UNARY_OP_ABS,
+        GGML_UNARY_OP_SGN,
+        GGML_UNARY_OP_NEG,
+        GGML_UNARY_OP_STEP,
+        GGML_UNARY_OP_TANH,
+        GGML_UNARY_OP_ELU,
+        GGML_UNARY_OP_RELU,
+        GGML_UNARY_OP_GELU,
+        GGML_UNARY_OP_GELU_QUICK,
+        GGML_UNARY_OP_SILU,
+    };
+
+    enum ggml_object_type {
+        GGML_OBJECT_TENSOR,
+        GGML_OBJECT_GRAPH,
+        GGML_OBJECT_WORK_BUFFER
+    };
+
+    enum ggml_log_level {
+        GGML_LOG_LEVEL_ERROR = 2,
+        GGML_LOG_LEVEL_WARN = 3,
+        GGML_LOG_LEVEL_INFO = 4
+    };
+
+    // ggml object
+    struct ggml_object {
+        size_t offs;
+        size_t size;
+
+        struct ggml_object * next;
+
+        enum ggml_object_type type;
+
+        char padding[4];
+    };
+
+    static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object);
+
+    // n-dimensional tensor
+    struct ggml_tensor {
+        enum ggml_type         type;
+        enum ggml_backend_type backend;
+
+        struct ggml_backend_buffer * buffer;
+
+        int     n_dims;
+        int64_t ne[GGML_MAX_DIMS]; // number of elements
+        size_t  nb[GGML_MAX_DIMS]; // stride in bytes:
+                                   // nb[0] = ggml_type_size(type)
+                                   // nb[1] = nb[0]   * (ne[0] / ggml_blck_size(type)) + padding
+                                   // nb[i] = nb[i-1] * ne[i-1]
+
+        // compute data
+        enum ggml_op op;
+
+        // op params - allocated as int32_t for alignment
+        int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
+
+        bool is_param;
+
+        struct ggml_tensor * grad;
+        struct ggml_tensor * src[GGML_MAX_SRC];
+
+        // performance
+        int     perf_runs;
+        int64_t perf_cycles;
+        int64_t perf_time_us;
+
+        struct ggml_tensor * view_src;
+        size_t               view_offs;
+
+        void * data;
+
+        char name[GGML_MAX_NAME];
+
+        void * extra; // extra things e.g. for ggml-cuda.cu
+
+        char padding[12];
+    };
+
+    static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
+
+    // the compute plan that needs to be prepared for ggml_graph_compute()
+    // since https://github.com/ggerganov/ggml/issues/287
+    struct ggml_cplan {
+        size_t    work_size; // size of work buffer, calculated by `ggml_graph_plan()`
+        uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()`
+
+        int n_threads;
+
+        // the `n_tasks` of nodes, 1:1 mapping to cgraph nodes
+        int n_tasks[GGML_MAX_NODES];
+
+        // abort ggml_graph_compute when true
+        bool (*abort_callback)(void * data);
+        void * abort_callback_data;
+    };
+
+    // next prime after GGML_MAX_NODES
+    // #define GGML_GRAPH_HASHTABLE_SIZE 4099
+    // next prime after GGML_MAX_NODES * 2 (nodes + leafs)
+    // #define GGML_GRAPH_HASHTABLE_SIZE 8273
+    // #define GGML_GRAPH_HASHTABLE_SIZE 16411
+    #define GGML_GRAPH_HASHTABLE_SIZE 32771
+
+    enum ggml_cgraph_eval_order {
+        GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT = 0,
+        GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT,
+        GGML_CGRAPH_EVAL_ORDER_COUNT
+    };
+
+    // computation graph
+    struct ggml_cgraph {
+        int n_nodes;
+        int n_leafs;
+
+        struct ggml_tensor * nodes[GGML_MAX_NODES];
+        struct ggml_tensor * grads[GGML_MAX_NODES];
+        struct ggml_tensor * leafs[GGML_MAX_NODES];
+
+        void * visited_hash_table[GGML_GRAPH_HASHTABLE_SIZE];
+
+        enum ggml_cgraph_eval_order order;
+
+        // performance
+        int     perf_runs;
+        int64_t perf_cycles;
+        int64_t perf_time_us;
+    };
+
+    static const size_t GGML_GRAPH_SIZE = sizeof(struct ggml_cgraph);
+
+    // scratch buffer
+    struct ggml_scratch {
+        size_t offs;
+        size_t size;
+        void * data;
+    };
+
+    struct ggml_init_params {
+        // memory pool
+        size_t mem_size;   // bytes
+        void * mem_buffer; // if NULL, memory will be allocated internally
+        bool   no_alloc;   // don't allocate memory for the tensor data
+    };
+
+
+    // compute types
+
+    // NOTE: the INIT or FINALIZE pass is not scheduled unless explicitly enabled.
+    // This behavior was changed since https://github.com/ggerganov/llama.cpp/pull/1995.
+    enum ggml_task_type {
+        GGML_TASK_INIT = 0,
+        GGML_TASK_COMPUTE,
+        GGML_TASK_FINALIZE,
+    };
+
+    struct ggml_compute_params {
+        enum ggml_task_type type;
+
+        // ith = thread index, nth = number of threads
+        int ith, nth;
+
+        // work buffer for all threads
+        size_t wsize;
+        void * wdata;
+    };
+
+    // misc
+
+    GGML_API void    ggml_time_init(void); // call this once at the beginning of the program
+    GGML_API int64_t ggml_time_ms(void);
+    GGML_API int64_t ggml_time_us(void);
+    GGML_API int64_t ggml_cycles(void);
+    GGML_API int64_t ggml_cycles_per_ms(void);
+
+    GGML_API void    ggml_numa_init(void); // call once for better performance on NUMA systems
+    GGML_API bool    ggml_is_numa(void); // true if init detected that system has >1 NUMA node
+
+    GGML_API void    ggml_print_object (const struct ggml_object * obj);
+    GGML_API void    ggml_print_objects(const struct ggml_context * ctx);
+
+    GGML_API int64_t ggml_nelements   (const struct ggml_tensor * tensor);
+    GGML_API int64_t ggml_nrows       (const struct ggml_tensor * tensor);
+    GGML_API size_t  ggml_nbytes      (const struct ggml_tensor * tensor);
+    GGML_API size_t  ggml_nbytes_pad  (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
+    GGML_API size_t  ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split);
+
+    GGML_API int     ggml_blck_size (enum ggml_type type);
+    GGML_API size_t  ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block
+    GGML_API float   ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float
+
+    GGML_API const char * ggml_type_name(enum ggml_type type);
+    GGML_API const char * ggml_op_name  (enum ggml_op   op);
+    GGML_API const char * ggml_op_symbol(enum ggml_op   op);
+
+    GGML_API size_t  ggml_element_size(const struct ggml_tensor * tensor);
+
+    GGML_API bool    ggml_is_quantized(enum ggml_type type);
+
+    // TODO: temporary until model loading of ggml examples is refactored
+    GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
+
+    GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor);
+    GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
+    GGML_API bool ggml_is_permuted  (const struct ggml_tensor * tensor);
+
+    GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
+
+    // use this to compute the memory overhead of a tensor
+    GGML_API size_t ggml_tensor_overhead(void);
+
+    // main
+
+    GGML_API struct ggml_context * ggml_init(struct ggml_init_params params);
+    GGML_API void                  ggml_free(struct ggml_context * ctx);
+
+    GGML_API size_t  ggml_used_mem(const struct ggml_context * ctx);
+
+    GGML_API size_t  ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch);
+    GGML_API bool    ggml_get_no_alloc(struct ggml_context * ctx);
+    GGML_API void    ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);
+
+    GGML_API void *  ggml_get_mem_buffer     (const struct ggml_context * ctx);
+    GGML_API size_t  ggml_get_mem_size       (const struct ggml_context * ctx);
+    GGML_API size_t  ggml_get_max_tensor_size(const struct ggml_context * ctx);
+
+    GGML_API struct ggml_tensor * ggml_new_tensor(
+            struct ggml_context * ctx,
+            enum   ggml_type type,
+            int    n_dims,
+            const int64_t *ne);
+
+    GGML_API struct ggml_tensor * ggml_new_tensor_1d(
+            struct ggml_context * ctx,
+            enum   ggml_type type,
+            int64_t ne0);
+
+    GGML_API struct ggml_tensor * ggml_new_tensor_2d(
+            struct ggml_context * ctx,
+            enum   ggml_type type,
+            int64_t ne0,
+            int64_t ne1);
+
+    GGML_API struct ggml_tensor * ggml_new_tensor_3d(
+            struct ggml_context * ctx,
+            enum   ggml_type type,
+            int64_t ne0,
+            int64_t ne1,
+            int64_t ne2);
+
+    GGML_API struct ggml_tensor * ggml_new_tensor_4d(
+            struct ggml_context * ctx,
+            enum   ggml_type type,
+            int64_t ne0,
+            int64_t ne1,
+            int64_t ne2,
+            int64_t ne3);
+
+    GGML_API struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value);
+    GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
+
+    GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);
+    GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src);
+
+    // Context tensor enumeration and lookup
+    GGML_API struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx);
+    GGML_API struct ggml_tensor * ggml_get_next_tensor (struct ggml_context * ctx, struct ggml_tensor * tensor);
+    GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
+
+    GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
+    GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);
+    GGML_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value);
+
+    // Converts a flat index into coordinates
+    GGML_API void    ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3);
+
+    GGML_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i);
+    GGML_API void    ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value);
+
+    GGML_API int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3);
+    GGML_API void    ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value);
+
+    GGML_API float   ggml_get_f32_1d(const struct ggml_tensor * tensor, int i);
+    GGML_API void    ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value);
+
+    GGML_API float   ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3);
+    GGML_API void    ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value);
+
+    GGML_API void *  ggml_get_data    (const struct ggml_tensor * tensor);
+    GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
+
+    GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
+
+    GGML_API const char *         ggml_get_name   (const struct ggml_tensor * tensor);
+    GGML_API struct ggml_tensor * ggml_set_name   (      struct ggml_tensor * tensor, const char * name);
+    GGML_ATTRIBUTE_FORMAT(2, 3)
+    GGML_API struct ggml_tensor * ggml_format_name(      struct ggml_tensor * tensor, const char * fmt, ...);
+
+    //
+    // operations on tensors with backpropagation
+    //
+
+    GGML_API struct ggml_tensor * ggml_dup(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_dup_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_add(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_add_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_add_cast(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            enum   ggml_type      type);
+
+    GGML_API struct ggml_tensor * ggml_add1(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_add1_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_acc(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                nb1,
+            size_t                nb2,
+            size_t                nb3,
+            size_t                offset);
+
+    GGML_API struct ggml_tensor * ggml_acc_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                nb1,
+            size_t                nb2,
+            size_t                nb3,
+            size_t                offset);
+
+    GGML_API struct ggml_tensor * ggml_sub(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_sub_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_mul(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_mul_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_div(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_div_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_sqr(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sqr_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sqrt(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sqrt_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_log(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_log_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // return scalar
+    GGML_API struct ggml_tensor * ggml_sum(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // sums along rows, with input shape [a,b,c,d] return shape [1,b,c,d]
+    GGML_API struct ggml_tensor * ggml_sum_rows(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // mean along rows
+    GGML_API struct ggml_tensor * ggml_mean(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // argmax along rows
+    GGML_API struct ggml_tensor * ggml_argmax(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // if a is the same shape as b, and a is not parameter, return a
+    // otherwise, return a new tensor: repeat(a) to fit in b
+    GGML_API struct ggml_tensor * ggml_repeat(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // sums repetitions in a into shape of b
+    GGML_API struct ggml_tensor * ggml_repeat_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // concat a and b on dim 2
+    // used in stable-diffusion
+    GGML_API struct ggml_tensor * ggml_concat(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_abs(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_abs_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sgn(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sgn_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_neg(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_neg_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_step(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_step_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_tanh(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_tanh_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_elu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_elu_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_relu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_relu_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // TODO: double-check this computation is correct
+    GGML_API struct ggml_tensor * ggml_gelu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_gelu_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_gelu_quick(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_gelu_quick_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_silu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_silu_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // a - x
+    // b - dy
+    GGML_API struct ggml_tensor * ggml_silu_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // normalize along rows
+    GGML_API struct ggml_tensor * ggml_norm(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 eps);
+
+    GGML_API struct ggml_tensor * ggml_norm_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 eps);
+
+    GGML_API struct ggml_tensor * ggml_rms_norm(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 eps);
+
+    GGML_API struct ggml_tensor * ggml_rms_norm_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 eps);
+
+    // group normalize along ne0*ne1*n_groups
+    // used in stable-diffusion
+    // TODO: eps is hardcoded to 1e-6 for now
+    GGML_API struct ggml_tensor * ggml_group_norm(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_groups);
+
+    GGML_API struct ggml_tensor * ggml_group_norm_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_groups);
+
+    // a - x
+    // b - dy
+    GGML_API struct ggml_tensor * ggml_rms_norm_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            float                 eps);
+
+    // A: n columns, m rows
+    // B: n columns, p rows  (i.e. we transpose it internally)
+    // result is m columns, p rows
+    GGML_API struct ggml_tensor * ggml_mul_mat(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // A: m columns, n rows,
+    // B: p columns, n rows,
+    // result is m columns, p rows
+    GGML_API struct ggml_tensor * ggml_out_prod(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    //
+    // operations on tensors without backpropagation
+    //
+
+    GGML_API struct ggml_tensor * ggml_scale(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_scale_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // b -> view(a,offset,nb1,nb2,3), return modified a
+    GGML_API struct ggml_tensor * ggml_set(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                nb1,
+            size_t                nb2,
+            size_t                nb3,
+            size_t                offset);
+
+    // b -> view(a,offset,nb1,nb2,3), return view(a)
+    GGML_API struct ggml_tensor * ggml_set_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                nb1,
+            size_t                nb2,
+            size_t                nb3,
+            size_t                offset);
+
+    GGML_API struct ggml_tensor * ggml_set_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                offset);
+
+    GGML_API struct ggml_tensor * ggml_set_1d_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                offset);
+
+    // b -> view(a,offset,nb1,nb2,3), return modified a
+    GGML_API struct ggml_tensor * ggml_set_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                nb1,
+            size_t                offset);
+
+    // b -> view(a,offset,nb1,nb2,3), return view(a)
+    GGML_API struct ggml_tensor * ggml_set_2d_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                nb1,
+            size_t                offset);
+
+    // a -> b, return view(b)
+    GGML_API struct ggml_tensor * ggml_cpy(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // a -> b, in-place, return view(b)
+    GGML_API struct ggml_tensor * ggml_cpy_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // make contiguous
+    GGML_API struct ggml_tensor * ggml_cont(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // make contiguous, in-place
+    GGML_API struct ggml_tensor * ggml_cont_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // make contiguous, with new shape
+    GGML_API struct ggml_tensor * ggml_cont_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0);
+
+    GGML_API struct ggml_tensor * ggml_cont_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1);
+
+    GGML_API struct ggml_tensor * ggml_cont_3d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2);
+
+    GGML_API struct ggml_tensor * ggml_cont_4d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2,
+            int64_t               ne3);
+
+    // return view(a), b specifies the new shape
+    // TODO: when we start computing gradient, make a copy instead of view
+    GGML_API struct ggml_tensor * ggml_reshape(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // return view(a)
+    // TODO: when we start computing gradient, make a copy instead of view
+    GGML_API struct ggml_tensor * ggml_reshape_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0);
+
+    GGML_API struct ggml_tensor * ggml_reshape_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1);
+
+    // return view(a)
+    // TODO: when we start computing gradient, make a copy instead of view
+    GGML_API struct ggml_tensor * ggml_reshape_3d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2);
+
+    GGML_API struct ggml_tensor * ggml_reshape_4d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2,
+            int64_t               ne3);
+
+    // offset in bytes
+    GGML_API struct ggml_tensor * ggml_view_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            size_t                offset);
+
+    GGML_API struct ggml_tensor * ggml_view_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            size_t                nb1, // row stride in bytes
+            size_t                offset);
+
+    GGML_API struct ggml_tensor * ggml_view_3d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2,
+            size_t                nb1, // row   stride in bytes
+            size_t                nb2, // slice stride in bytes
+            size_t                offset);
+
+    GGML_API struct ggml_tensor * ggml_view_4d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2,
+            int64_t               ne3,
+            size_t                nb1, // row   stride in bytes
+            size_t                nb2, // slice stride in bytes
+            size_t                nb3,
+            size_t                offset);
+
+    GGML_API struct ggml_tensor * ggml_permute(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   axis0,
+            int                   axis1,
+            int                   axis2,
+            int                   axis3);
+
+    // alias for ggml_permute(ctx, a, 1, 0, 2, 3)
+    GGML_API struct ggml_tensor * ggml_transpose(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_get_rows(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_get_rows_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * c);
+
+    GGML_API struct ggml_tensor * ggml_diag(
+        struct ggml_context     * ctx,
+        struct ggml_tensor      * a);
+
+    // set elements above the diagonal to -INF
+    GGML_API struct ggml_tensor * ggml_diag_mask_inf(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_past);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_diag_mask_inf_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_past);
+
+    // set elements above the diagonal to 0
+    GGML_API struct ggml_tensor * ggml_diag_mask_zero(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_past);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_diag_mask_zero_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_past);
+
+    GGML_API struct ggml_tensor * ggml_soft_max(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_soft_max_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_soft_max_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_soft_max_back_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // rotary position embedding
+    // if mode & 1 == 1, skip n_past elements (DEPRECATED)
+    // if mode & 2 == 1, GPT-NeoX style
+    // if mode & 4 == 1, ChatGLM style
+    //
+    // b is an int32 vector with size a->ne[2], it contains the positions
+    GGML_API struct ggml_tensor * ggml_rope(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   n_dims,
+            int                   mode,
+            int                   n_ctx);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_rope_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   n_dims,
+            int                   mode,
+            int                   n_ctx);
+
+    // custom RoPE
+    GGML_API struct ggml_tensor * ggml_rope_custom(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   n_dims,
+            int                   mode,
+            int                   n_ctx,
+            float                 freq_base,
+            float                 freq_scale);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   n_dims,
+            int                   mode,
+            int                   n_ctx,
+            float                 freq_base,
+            float                 freq_scale);
+
+    // xPos RoPE, in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_rope_xpos_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   n_dims,
+            float                 base,
+            bool                  down);
+
+    // rotary position embedding backward, i.e compute dx from dy
+    // a - dy
+    GGML_API struct ggml_tensor * ggml_rope_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   n_dims,
+            int                   mode,
+            int                   n_ctx,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 xpos_base,
+            bool                  xpos_down);
+
+    // alibi position embedding
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_alibi(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_past,
+            int                   n_head,
+            float                 bias_max);
+
+    // clamp
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_clamp(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 min,
+            float                 max);
+
+    GGML_API struct ggml_tensor * ggml_conv_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   s0,  // stride
+            int                   p0,  // padding
+            int                   d0); // dilation
+
+    // conv_1d with padding = half
+    // alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
+    GGML_API struct ggml_tensor* ggml_conv_1d_ph(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   s,
+            int                   d);
+
+    GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   s0,
+            int                   p0,
+            int                   d0);
+
+    GGML_API struct ggml_tensor * ggml_conv_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   s0,
+            int                   s1,
+            int                   p0,
+            int                   p1,
+            int                   d0,
+            int                   d1);
+
+
+    // kernel size is a->ne[0] x a->ne[1]
+    // stride is equal to kernel size
+    // padding is zero
+    // example:
+    // a:     16   16    3  768
+    // b:   1024 1024    3    1
+    // res:   64   64  768    1
+    // used in sam
+    GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // kernel size is a->ne[0] x a->ne[1]
+    // stride is 1
+    // padding is half
+    // example:
+    // a:      3    3    256  256
+    // b:     64   64    256    1
+    // res:   64   64    256    1
+    // used in sam
+    GGML_API struct ggml_tensor * ggml_conv_2d_s1_ph(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   stride);
+
+    enum ggml_op_pool {
+        GGML_OP_POOL_MAX,
+        GGML_OP_POOL_AVG,
+        GGML_OP_POOL_COUNT,
+    };
+
+    GGML_API struct ggml_tensor * ggml_pool_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            enum ggml_op_pool     op,
+            int                   k0, // kernel size
+            int                   s0, // stride
+            int                   p0); // padding
+
+    GGML_API struct ggml_tensor * ggml_pool_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            enum ggml_op_pool     op,
+            int                   k0,
+            int                   k1,
+            int                   s0,
+            int                   s1,
+            int                   p0,
+            int                   p1);
+
+    // nearest interpolate
+    // used in stable-diffusion
+    GGML_API struct ggml_tensor * ggml_upscale(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   scale_factor);
+
+    GGML_API struct ggml_tensor * ggml_flash_attn(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * q,
+            struct ggml_tensor  * k,
+            struct ggml_tensor  * v,
+            bool                  masked);
+
+    GGML_API struct ggml_tensor * ggml_flash_attn_back(
+           struct ggml_context * ctx,
+           struct ggml_tensor  * q,
+           struct ggml_tensor  * k,
+           struct ggml_tensor  * v,
+           struct ggml_tensor  * d,
+           bool                  masked);
+
+    GGML_API struct ggml_tensor * ggml_flash_ff(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b0,
+            struct ggml_tensor  * b1,
+            struct ggml_tensor  * c0,
+            struct ggml_tensor  * c1);
+
+    // partition into non-overlapping windows with padding if needed
+    // example:
+    // a:   768   64   64    1
+    // w:    14
+    // res: 768   14   14    25
+    // used in sam
+    GGML_API struct ggml_tensor * ggml_win_part(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   w);
+
+    // reverse of ggml_win_part
+    // used in sam
+    GGML_API struct ggml_tensor * ggml_win_unpart(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   w0,
+            int                   h0,
+            int                   w);
+
+    GGML_API struct ggml_tensor * ggml_unary(
+            struct ggml_context * ctx,
+             struct ggml_tensor * a,
+             enum ggml_unary_op op);
+
+    GGML_API struct ggml_tensor * ggml_unary_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        enum ggml_unary_op op);
+
+    // used in sam
+    GGML_API struct ggml_tensor * ggml_get_rel_pos(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   qh,
+            int                   kh);
+
+    // used in sam
+
+    GGML_API struct ggml_tensor * ggml_add_rel_pos(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * pw,
+            struct ggml_tensor  * ph);
+
+    GGML_API struct ggml_tensor * ggml_add_rel_pos_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * pw,
+            struct ggml_tensor  * ph);
+
+    // custom operators
+
+    typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
+    typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
+
+    typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
+    typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
+    typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
+
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_f32(
+            struct ggml_context        * ctx,
+            struct ggml_tensor         * a,
+                   ggml_unary_op_f32_t   fun),
+        "use ggml_map_custom1 instead");
+
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
+            struct ggml_context        * ctx,
+            struct ggml_tensor         * a,
+                   ggml_unary_op_f32_t   fun),
+        "use ggml_map_custom1_inplace instead");
+
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_f32(
+            struct ggml_context         * ctx,
+            struct ggml_tensor          * a,
+            struct ggml_tensor          * b,
+                   ggml_binary_op_f32_t   fun),
+        "use ggml_map_custom2 instead");
+
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
+            struct ggml_context         * ctx,
+            struct ggml_tensor          * a,
+            struct ggml_tensor          * b,
+                   ggml_binary_op_f32_t   fun),
+        "use ggml_map_custom2_inplace instead");
+
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_f32(
+            struct ggml_context          * ctx,
+            struct ggml_tensor           * a,
+                   ggml_custom1_op_f32_t   fun),
+        "use ggml_map_custom1 instead");
+
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
+            struct ggml_context          * ctx,
+            struct ggml_tensor           * a,
+                   ggml_custom1_op_f32_t   fun),
+        "use ggml_map_custom1_inplace instead");
+
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_f32(
+            struct ggml_context          * ctx,
+            struct ggml_tensor           * a,
+            struct ggml_tensor           * b,
+                   ggml_custom2_op_f32_t   fun),
+        "use ggml_map_custom2 instead");
+
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
+            struct ggml_context          * ctx,
+            struct ggml_tensor           * a,
+            struct ggml_tensor           * b,
+                   ggml_custom2_op_f32_t   fun),
+        "use ggml_map_custom2_inplace instead");
+
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_f32(
+            struct ggml_context          * ctx,
+            struct ggml_tensor           * a,
+            struct ggml_tensor           * b,
+            struct ggml_tensor           * c,
+                   ggml_custom3_op_f32_t   fun),
+        "use ggml_map_custom3 instead");
+
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
+            struct ggml_context          * ctx,
+            struct ggml_tensor           * a,
+            struct ggml_tensor           * b,
+            struct ggml_tensor           * c,
+                   ggml_custom3_op_f32_t   fun),
+        "use ggml_map_custom3_inplace instead");
+
+    // custom operators v2
+
+    typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata);
+    typedef void (*ggml_custom2_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata);
+    typedef void (*ggml_custom3_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata);
+
+    #define GGML_N_TASKS_MAX -1
+
+    GGML_API struct ggml_tensor * ggml_map_custom1(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            ggml_custom1_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom1_inplace(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            ggml_custom1_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom2(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            ggml_custom2_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom2_inplace(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            ggml_custom2_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom3(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            struct ggml_tensor    * c,
+            ggml_custom3_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom3_inplace(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            struct ggml_tensor    * c,
+            ggml_custom3_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    // loss function
+
+    GGML_API struct ggml_tensor * ggml_cross_entropy_loss(
+            struct ggml_context         * ctx,
+            struct ggml_tensor          * a,
+            struct ggml_tensor          * b);
+
+    GGML_API struct ggml_tensor * ggml_cross_entropy_loss_back(
+            struct ggml_context         * ctx,
+            struct ggml_tensor          * a,
+            struct ggml_tensor          * b,
+            struct ggml_tensor          * c);
+
+    //
+    // automatic differentiation
+    //
+
+    GGML_API void ggml_set_param(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * tensor);
+
+
+    GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
+    GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);
+
+    GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
+    GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
+
+    // graph allocation in a context
+    GGML_API struct ggml_cgraph * ggml_new_graph        (struct ggml_context * ctx);
+    GGML_API struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor);
+    GGML_API size_t ggml_graph_overhead(void);
+
+    // ggml_graph_plan() has to be called before ggml_graph_compute()
+    // when plan.work_size > 0, caller must allocate memory for plan.work_data
+    GGML_API struct ggml_cplan ggml_graph_plan   (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
+    GGML_API               int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
+    GGML_API              void ggml_graph_reset  (struct ggml_cgraph * cgraph);
+
+    // same as ggml_graph_compute() but the work data is allocated as a part of the context
+    // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
+    GGML_API void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
+
+    GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name);
+
+    GGML_API void               ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname);
+    GGML_API struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval);
+
+    // print info and performance information for the graph
+    GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph);
+
+    // dump the graph into a file using the dot format
+    GGML_API void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename);
+
+    // build gradient checkpointing backward graph gb for gf using provided checkpoints
+    // gb_tmp will contain original backward graph with rewritten backward process nodes,
+    // but without the second forward pass nodes.
+    GGML_API void ggml_build_backward_gradient_checkpointing(
+            struct ggml_context   * ctx,
+            struct ggml_cgraph    * gf,
+            struct ggml_cgraph    * gb,
+            struct ggml_cgraph    * gb_tmp,
+            struct ggml_tensor  * * checkpoints,
+            int                     n_checkpoints);
+    //
+    // optimization
+    //
+
+    // optimization methods
+    enum ggml_opt_type {
+        GGML_OPT_ADAM,
+        GGML_OPT_LBFGS,
+    };
+
+    // linesearch methods
+    enum ggml_linesearch {
+        GGML_LINESEARCH_DEFAULT = 1,
+
+        GGML_LINESEARCH_BACKTRACKING_ARMIJO       = 0,
+        GGML_LINESEARCH_BACKTRACKING_WOLFE        = 1,
+        GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 2,
+    };
+
+    // optimization return values
+    enum ggml_opt_result {
+        GGML_OPT_OK = 0,
+        GGML_OPT_DID_NOT_CONVERGE,
+        GGML_OPT_NO_CONTEXT,
+        GGML_OPT_INVALID_WOLFE,
+        GGML_OPT_FAIL,
+        GGML_OPT_CANCEL,
+
+        GGML_LINESEARCH_FAIL = -128,
+        GGML_LINESEARCH_MINIMUM_STEP,
+        GGML_LINESEARCH_MAXIMUM_STEP,
+        GGML_LINESEARCH_MAXIMUM_ITERATIONS,
+        GGML_LINESEARCH_INVALID_PARAMETERS,
+    };
+
+    typedef void (*ggml_opt_callback)(void * data, int accum_step, float * sched, bool * cancel);
+    typedef void (*ggml_log_callback)(enum ggml_log_level level, const char * text, void * user_data);
+
+    // optimization parameters
+    //
+    //   see ggml.c (ggml_opt_default_params) for default values
+    //
+    struct ggml_opt_params {
+        enum ggml_opt_type type;
+
+        int n_threads;
+
+        // delta-based convergence test
+        //
+        //   if past == 0 - disabled
+        //   if past > 0:
+        //     stop if |f(x) - f(x_past)| < delta * max(1, |f(x)|)
+        //
+        int past;
+        float delta;
+
+        // maximum number of iterations without improvement
+        //
+        //   if 0 - disabled
+        //   if > 0:
+        //     assume convergence if no cost improvement in this number of iterations
+        //
+        int max_no_improvement;
+
+        bool print_forward_graph;
+        bool print_backward_graph;
+
+        int n_gradient_accumulation;
+
+        // ADAM parameters
+        struct {
+            int n_iter;
+
+            float sched; // schedule multiplier (fixed, decay or warmup)
+            float decay; // weight decay for AdamW, use 0.0f to disable
+            int   decay_min_ndim; // minimum number of tensor dimension to apply weight decay
+            float alpha; // learning rate
+            float beta1;
+            float beta2;
+            float eps;   // epsilon for numerical stability
+            float eps_f; // epsilon for convergence test
+            float eps_g; // epsilon for convergence test
+            float gclip; // gradient clipping
+        } adam;
+
+        // LBFGS parameters
+        struct {
+            int m; // number of corrections to approximate the inv. Hessian
+            int n_iter;
+            int max_linesearch;
+
+            float eps;      // convergence tolerance
+            float ftol;     // line search tolerance
+            float wolfe;
+            float min_step;
+            float max_step;
+
+            enum ggml_linesearch linesearch;
+        } lbfgs;
+    };
+
+    struct ggml_opt_context {
+        struct ggml_context * ctx;
+        struct ggml_opt_params params;
+
+        int iter;
+        int64_t nx; // number of parameter elements
+
+        bool just_initialized;
+
+        float loss_before;
+        float loss_after;
+
+        struct {
+            struct ggml_tensor * g;  // current gradient
+            struct ggml_tensor * m;  // first moment
+            struct ggml_tensor * v;  // second moment
+            struct ggml_tensor * pf; // past function values
+            float fx_best;
+            float fx_prev;
+            int n_no_improvement;
+        } adam;
+
+        struct {
+            struct ggml_tensor * x;    // current parameters
+            struct ggml_tensor * xp;   // previous parameters
+            struct ggml_tensor * g;    // current gradient
+            struct ggml_tensor * gp;   // previous gradient
+            struct ggml_tensor * d;    // search direction
+            struct ggml_tensor * pf;   // past function values
+            struct ggml_tensor * lmal; // the L-BFGS memory alpha
+            struct ggml_tensor * lmys; // the L-BFGS memory ys
+            struct ggml_tensor * lms;  // the L-BFGS memory s
+            struct ggml_tensor * lmy;  // the L-BFGS memory y
+            float fx_best;
+            float step;
+            int j;
+            int k;
+            int end;
+            int n_no_improvement;
+        } lbfgs;
+    };
+
+    GGML_API struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type);
+
+    // optimize the function defined by the tensor f
+    GGML_API enum ggml_opt_result ggml_opt(
+            struct ggml_context * ctx,
+            struct ggml_opt_params params,
+            struct ggml_tensor * f);
+
+    // initialize optimizer context
+    GGML_API void ggml_opt_init(
+            struct ggml_context     * ctx,
+            struct ggml_opt_context * opt,
+            struct ggml_opt_params    params,
+            int64_t                   nx);
+
+    // continue optimizing the function defined by the tensor f
+    GGML_API enum ggml_opt_result ggml_opt_resume(
+            struct ggml_context * ctx,
+            struct ggml_opt_context * opt,
+            struct ggml_tensor * f);
+
+    // continue optimizing the function defined by the tensor f
+    GGML_API enum ggml_opt_result ggml_opt_resume_g(
+            struct ggml_context * ctx,
+            struct ggml_opt_context * opt,
+            struct ggml_tensor * f,
+            struct ggml_cgraph * gf,
+            struct ggml_cgraph * gb,
+            ggml_opt_callback callback,
+            void * callback_data);
+
+    //
+    // quantization
+    //
+
+    GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist);
+
+    GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
+
+    //
+    // gguf
+    //
+
+    enum gguf_type {
+        GGUF_TYPE_UINT8   = 0,
+        GGUF_TYPE_INT8    = 1,
+        GGUF_TYPE_UINT16  = 2,
+        GGUF_TYPE_INT16   = 3,
+        GGUF_TYPE_UINT32  = 4,
+        GGUF_TYPE_INT32   = 5,
+        GGUF_TYPE_FLOAT32 = 6,
+        GGUF_TYPE_BOOL    = 7,
+        GGUF_TYPE_STRING  = 8,
+        GGUF_TYPE_ARRAY   = 9,
+        GGUF_TYPE_UINT64  = 10,
+        GGUF_TYPE_INT64   = 11,
+        GGUF_TYPE_FLOAT64 = 12,
+        GGUF_TYPE_COUNT,       // marks the end of the enum
+    };
+
+    struct gguf_context;
+
+    struct gguf_init_params {
+        bool no_alloc;
+
+        // if not NULL, create a ggml_context and allocate the tensor data in it
+        struct ggml_context ** ctx;
+    };
+
+    GGML_API struct gguf_context * gguf_init_empty(void);
+    GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params);
+    //GGML_API struct gguf_context * gguf_init_from_buffer(..);
+
+    GGML_API void gguf_free(struct gguf_context * ctx);
+
+    GGML_API const char * gguf_type_name(enum gguf_type type);
+
+    GGML_API int    gguf_get_version    (const struct gguf_context * ctx);
+    GGML_API size_t gguf_get_alignment  (const struct gguf_context * ctx);
+    GGML_API size_t gguf_get_data_offset(const struct gguf_context * ctx);
+    GGML_API void * gguf_get_data       (const struct gguf_context * ctx);
+
+    GGML_API int          gguf_get_n_kv(const struct gguf_context * ctx);
+    GGML_API int          gguf_find_key(const struct gguf_context * ctx, const char * key);
+    GGML_API const char * gguf_get_key (const struct gguf_context * ctx, int key_id);
+
+    GGML_API enum gguf_type gguf_get_kv_type (const struct gguf_context * ctx, int key_id);
+    GGML_API enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int key_id);
+
+    // will abort if the wrong type is used for the key
+    GGML_API uint8_t      gguf_get_val_u8  (const struct gguf_context * ctx, int key_id);
+    GGML_API int8_t       gguf_get_val_i8  (const struct gguf_context * ctx, int key_id);
+    GGML_API uint16_t     gguf_get_val_u16 (const struct gguf_context * ctx, int key_id);
+    GGML_API int16_t      gguf_get_val_i16 (const struct gguf_context * ctx, int key_id);
+    GGML_API uint32_t     gguf_get_val_u32 (const struct gguf_context * ctx, int key_id);
+    GGML_API int32_t      gguf_get_val_i32 (const struct gguf_context * ctx, int key_id);
+    GGML_API float        gguf_get_val_f32 (const struct gguf_context * ctx, int key_id);
+    GGML_API uint64_t     gguf_get_val_u64 (const struct gguf_context * ctx, int key_id);
+    GGML_API int64_t      gguf_get_val_i64 (const struct gguf_context * ctx, int key_id);
+    GGML_API double       gguf_get_val_f64 (const struct gguf_context * ctx, int key_id);
+    GGML_API bool         gguf_get_val_bool(const struct gguf_context * ctx, int key_id);
+    GGML_API const char * gguf_get_val_str (const struct gguf_context * ctx, int key_id);
+    GGML_API int          gguf_get_arr_n   (const struct gguf_context * ctx, int key_id);
+    GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int key_id);
+    GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int key_id, int i);
+
+    GGML_API int    gguf_get_n_tensors    (const struct gguf_context * ctx);
+    GGML_API int    gguf_find_tensor      (const struct gguf_context * ctx, const char * name);
+    GGML_API size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int i);
+    GGML_API char * gguf_get_tensor_name  (const struct gguf_context * ctx, int i);
+
+    // overrides existing values or adds a new one
+    GGML_API void gguf_set_val_u8  (struct gguf_context * ctx, const char * key, uint8_t  val);
+    GGML_API void gguf_set_val_i8  (struct gguf_context * ctx, const char * key, int8_t   val);
+    GGML_API void gguf_set_val_u16 (struct gguf_context * ctx, const char * key, uint16_t val);
+    GGML_API void gguf_set_val_i16 (struct gguf_context * ctx, const char * key, int16_t  val);
+    GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val);
+    GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t  val);
+    GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float    val);
+    GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t val);
+    GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t  val);
+    GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double   val);
+    GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool     val);
+    GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);
+    GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n);
+    GGML_API void gguf_set_arr_str (struct gguf_context * ctx, const char * key, const char ** data, int n);
+
+    // set or add KV pairs from another context
+    GGML_API void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src);
+
+    // manage tensor info
+    GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor);
+    GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type);
+    GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size);
+
+    // writing gguf files can be done in 2 ways:
+    //
+    // - write the entire gguf_context to a binary file in a single pass:
+    //
+    //   gguf_write_to_file(ctx, fname);
+    //
+    // - first prepare a file with a placeholder for the meta data, write the tensor data, then write the meta data:
+    //
+    //   FILE * f = fopen(fname, "wb");
+    //   fseek(f, gguf_get_meta_size(ctx), SEEK_SET);
+    //   fwrite(f, ...);
+    //   void * data = gguf_meta_get_meta_data(ctx);
+    //   fseek(f, 0, SEEK_SET);
+    //   fwrite(f, data, gguf_get_meta_size(ctx));
+    //   free(data);
+    //   fclose(f);
+    //
+
+    // write the entire context to a binary file
+    GGML_API void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta);
+
+    // get the size in bytes of the meta data (header, kv pairs, tensor info) including padding
+    GGML_API size_t gguf_get_meta_size(const struct gguf_context * ctx);
+    GGML_API void   gguf_get_meta_data(const struct gguf_context * ctx, void * data);
+
+    //
+    // system info
+    //
+
+    GGML_API int ggml_cpu_has_avx        (void);
+    GGML_API int ggml_cpu_has_avx2       (void);
+    GGML_API int ggml_cpu_has_avx512     (void);
+    GGML_API int ggml_cpu_has_avx512_vbmi(void);
+    GGML_API int ggml_cpu_has_avx512_vnni(void);
+    GGML_API int ggml_cpu_has_fma        (void);
+    GGML_API int ggml_cpu_has_neon       (void);
+    GGML_API int ggml_cpu_has_arm_fma    (void);
+    GGML_API int ggml_cpu_has_metal      (void);
+    GGML_API int ggml_cpu_has_f16c       (void);
+    GGML_API int ggml_cpu_has_fp16_va    (void);
+    GGML_API int ggml_cpu_has_wasm_simd  (void);
+    GGML_API int ggml_cpu_has_blas       (void);
+    GGML_API int ggml_cpu_has_cublas     (void);
+    GGML_API int ggml_cpu_has_clblast    (void);
+    GGML_API int ggml_cpu_has_gpublas    (void);
+    GGML_API int ggml_cpu_has_sse3       (void);
+    GGML_API int ggml_cpu_has_ssse3      (void);
+    GGML_API int ggml_cpu_has_vsx        (void);
+
+    //
+    // Internal types and functions exposed for tests and benchmarks
+    //
+
+#ifdef  __cplusplus
+// restrict not standard in C++
+#define GGML_RESTRICT
+#else
+#define GGML_RESTRICT restrict
+#endif
+    typedef void (*ggml_to_float_t)  (const void  * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+    typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void  * GGML_RESTRICT y, int k);
+    typedef void (*ggml_vec_dot_t)   (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
+
+    typedef struct {
+        const char      * type_name;
+        int               blck_size;
+        size_t            type_size;
+        bool              is_quantized;
+        ggml_to_float_t   to_float;
+        ggml_from_float_t from_float;
+        ggml_from_float_t from_float_reference;
+        ggml_vec_dot_t    vec_dot;
+        enum ggml_type    vec_dot_type;
+    } ggml_type_traits_t;
+
+    GGML_API ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);
+
+#ifdef  __cplusplus
+}
+#endif

runner/k_quants.c

@@ -0,0 +1,5078 @@
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#include "k_quants.h"
+#include "ggml.h"
+
+#include <math.h>
+#include <string.h>
+#include <assert.h>
+
+#ifdef __ARM_NEON
+
+// if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
+//
+//   $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/
+//
+#include <arm_neon.h>
+
+#if !defined(__aarch64__)
+inline static int32_t vaddvq_s16(int16x8_t v) {
+    return
+        (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) +
+        (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) +
+        (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) +
+        (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7);
+}
+
+inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) {
+    int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a));
+    int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b));
+    return vcombine_s16(a0, b0);
+}
+
+inline static int32_t vaddvq_s32(int32x4_t v) {
+    return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
+}
+#endif
+
+#else
+
+#ifdef __wasm_simd128__
+#include <wasm_simd128.h>
+#else
+#ifdef __POWER9_VECTOR__
+#include <altivec.h>
+#undef bool
+#define bool _Bool
+#else
+#if defined(_MSC_VER) || defined(__MINGW32__)
+#include <intrin.h>
+#else
+#if !defined(__riscv) && !defined(__s390__)
+#include <immintrin.h>
+#endif
+#endif
+#endif
+#endif
+#endif
+
+#ifdef __riscv_v_intrinsic
+#include <riscv_vector.h>
+#endif
+
+#undef MIN
+#undef MAX
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+
+#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1)
+
+//
+// 2-6 bit quantization in super-blocks
+//
+
+//
+// ===================== Helper functions
+//
+static inline int nearest_int(float fval) {
+    assert(fval <= 4194303.f);
+    float val = fval + 12582912.f;
+    int i; memcpy(&i, &val, sizeof(int));
+    return (i & 0x007fffff) - 0x00400000;
+}
+
+static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, int rmse_type) {
+    float max = 0;
+    float amax = 0;
+    for (int i = 0; i < n; ++i) {
+        float ax = fabsf(x[i]);
+        if (ax > amax) { amax = ax; max = x[i]; }
+    }
+    if (amax < 1e-30f) { // all zero
+        for (int i = 0; i < n; ++i) {
+            L[i] = 0;
+        }
+        return 0.f;
+    }
+    float iscale = -nmax / max;
+    if (rmse_type == 0) {
+        for (int i = 0; i < n; ++i) {
+            int l = nearest_int(iscale * x[i]);
+            L[i] = nmax + MAX(-nmax, MIN(nmax-1, l));
+        }
+        return 1/iscale;
+    }
+    bool return_early = false;
+    if (rmse_type < 0) {
+        rmse_type = -rmse_type;
+        return_early = true;
+    }
+    int weight_type = rmse_type%2;
+    float sumlx = 0;
+    float suml2 = 0;
+    for (int i = 0; i < n; ++i) {
+        int l = nearest_int(iscale * x[i]);
+        l = MAX(-nmax, MIN(nmax-1, l));
+        L[i] = l + nmax;
+        float w = weight_type == 1 ? x[i] * x[i] : 1;
+        sumlx += w*x[i]*l;
+        suml2 += w*l*l;
+    }
+    float scale = sumlx/suml2;
+    if (return_early) return suml2 > 0 ? 0.5f*(scale + 1/iscale) : 1/iscale;
+    float best = scale * sumlx;
+    for (int is = -9; is <= 9; ++is) {
+        if (is == 0) {
+            continue;
+        }
+        iscale = -(nmax + 0.1f*is) / max;
+        sumlx = suml2 = 0;
+        for (int i = 0; i < n; ++i) {
+            int l = nearest_int(iscale * x[i]);
+            l = MAX(-nmax, MIN(nmax-1, l));
+            float w = weight_type == 1 ? x[i] * x[i] : 1;
+            sumlx += w*x[i]*l;
+            suml2 += w*l*l;
+        }
+        if (suml2 > 0 && sumlx*sumlx > best*suml2) {
+            for (int i = 0; i < n; ++i) {
+                int l = nearest_int(iscale * x[i]);
+                L[i] = nmax + MAX(-nmax, MIN(nmax-1, l));
+            }
+            scale = sumlx/suml2; best = scale*sumlx;
+        }
+    }
+    return scale;
+}
+
+static float make_q3_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, bool do_rmse) {
+    float max = 0;
+    float amax = 0;
+    for (int i = 0; i < n; ++i) {
+        float ax = fabsf(x[i]);
+        if (ax > amax) { amax = ax; max = x[i]; }
+    }
+    if (!amax) { // all zero
+        for (int i = 0; i < n; ++i) { L[i] = 0; }
+        return 0.f;
+    }
+    float iscale = -nmax / max;
+    if (do_rmse) {
+        float sumlx = 0;
+        float suml2 = 0;
+        for (int i = 0; i < n; ++i) {
+            int l = nearest_int(iscale * x[i]);
+            l = MAX(-nmax, MIN(nmax-1, l));
+            L[i] = l;
+            float w = x[i]*x[i];
+            sumlx += w*x[i]*l;
+            suml2 += w*l*l;
+        }
+        for (int itry = 0; itry < 5; ++itry) {
+            int n_changed = 0;
+            for (int i = 0; i < n; ++i) {
+                float w = x[i]*x[i];
+                float slx = sumlx - w*x[i]*L[i];
+                if (slx > 0) {
+                    float sl2 = suml2 - w*L[i]*L[i];
+                    int new_l = nearest_int(x[i] * sl2 / slx);
+                    new_l = MAX(-nmax, MIN(nmax-1, new_l));
+                    if (new_l != L[i]) {
+                        slx += w*x[i]*new_l;
+                        sl2 += w*new_l*new_l;
+                        if (sl2 > 0 && slx*slx*suml2 > sumlx*sumlx*sl2) {
+                            L[i] = new_l; sumlx = slx; suml2 = sl2;
+                            ++n_changed;
+                        }
+                    }
+                }
+            }
+            if (!n_changed) {
+                break;
+            }
+        }
+        for (int i = 0; i < n; ++i) {
+            L[i] += nmax;
+        }
+        return sumlx / suml2;
+    }
+    for (int i = 0; i < n; ++i) {
+        int l = nearest_int(iscale * x[i]);
+        l = MAX(-nmax, MIN(nmax-1, l));
+        L[i] = l + nmax;
+    }
+    return 1/iscale;
+}
+
+static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t * restrict L, float * restrict the_min,
+        int ntry, float alpha) {
+    float min = x[0];
+    float max = x[0];
+    for (int i = 1; i < n; ++i) {
+        if (x[i] < min) min = x[i];
+        if (x[i] > max) max = x[i];
+    }
+    if (max == min) {
+        for (int i = 0; i < n; ++i) L[i] = 0;
+        *the_min = 0;
+        return 0.f;
+    }
+    if (min > 0) min = 0;
+    float iscale = nmax/(max - min);
+    float scale = 1/iscale;
+    for (int itry = 0; itry < ntry; ++itry) {
+        float sumlx = 0; int suml2 = 0;
+        bool did_change = false;
+        for (int i = 0; i < n; ++i) {
+            int l = nearest_int(iscale*(x[i] - min));
+            l = MAX(0, MIN(nmax, l));
+            if (l != L[i]) {
+                L[i] = l;
+                did_change = true;
+            }
+            sumlx += (x[i] - min)*l;
+            suml2 += l*l;
+        }
+        scale = sumlx/suml2;
+        float sum = 0;
+        for (int i = 0; i < n; ++i) {
+            sum += x[i] - scale*L[i];
+        }
+        min = alpha*min + (1 - alpha)*sum/n;
+        if (min > 0) min = 0;
+        iscale = 1/scale;
+        if (!did_change) break;
+    }
+    *the_min = -min;
+    return scale;
+}
+
+static float make_qkx2_quants(int n, int nmax, const float * restrict x, const float * restrict weights,
+        uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux,
+        float rmin, float rdelta, int nstep, bool use_mad) {
+    float min = x[0];
+    float max = x[0];
+    float sum_w = weights[0];
+    float sum_x = sum_w * x[0];
+    for (int i = 1; i < n; ++i) {
+        if (x[i] < min) min = x[i];
+        if (x[i] > max) max = x[i];
+        float w = weights[i];
+        sum_w += w;
+        sum_x += w * x[i];
+    }
+    if (min > 0) min = 0;
+    if (max == min) {
+        for (int i = 0; i < n; ++i) L[i] = 0;
+        *the_min = -min;
+        return 0.f;
+    }
+    float iscale = nmax/(max - min);
+    float scale = 1/iscale;
+    float best_mad = 0;
+    for (int i = 0; i < n; ++i) {
+        int l = nearest_int(iscale*(x[i] - min));
+        L[i] = MAX(0, MIN(nmax, l));
+        float diff = scale * L[i] + min - x[i];
+        diff = use_mad ? fabsf(diff) : diff * diff;
+        float w = weights[i];
+        best_mad += w * diff;
+    }
+    if (nstep < 1) {
+        *the_min = -min;
+        return scale;
+    }
+    for (int is = 0; is <= nstep; ++is) {
+        iscale = (rmin + rdelta*is + nmax)/(max - min);
+        float sum_l = 0, sum_l2 = 0, sum_xl = 0;
+        for (int i = 0; i < n; ++i) {
+            int l = nearest_int(iscale*(x[i] - min));
+            l = MAX(0, MIN(nmax, l));
+            Laux[i] = l;
+            float w = weights[i];
+            sum_l += w*l;
+            sum_l2 += w*l*l;
+            sum_xl += w*l*x[i];
+        }
+        float D = sum_w * sum_l2 - sum_l * sum_l;
+        if (D > 0) {
+            float this_scale = (sum_w * sum_xl - sum_x * sum_l)/D;
+            float this_min   = (sum_l2 * sum_x - sum_l * sum_xl)/D;
+            if (this_min > 0) {
+                this_min = 0;
+                this_scale = sum_xl / sum_l2;
+            }
+            float mad = 0;
+            for (int i = 0; i < n; ++i) {
+                float diff = this_scale * Laux[i] + this_min - x[i];
+                diff = use_mad ? fabsf(diff) : diff * diff;
+                float w = weights[i];
+                mad += w * diff;
+            }
+            if (mad < best_mad) {
+                for (int i = 0; i < n; ++i) {
+                    L[i] = Laux[i];
+                }
+                best_mad = mad;
+                scale = this_scale;
+                min = this_min;
+            }
+        }
+    }
+    *the_min = -min;
+    return scale;
+}
+
+#if QK_K == 256
+static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * restrict d, uint8_t * restrict m) {
+    if (j < 4) {
+        *d = q[j] & 63; *m = q[j + 4] & 63;
+    } else {
+        *d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
+        *m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
+    }
+}
+#endif
+
+//========================- 2-bit (de)-quantization
+
+void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    uint8_t L[QK_K];
+    uint8_t Laux[16];
+    float   weights[16];
+    float mins[QK_K/16];
+    float scales[QK_K/16];
+
+    const float q4scale = 15.f;
+
+    for (int i = 0; i < nb; i++) {
+        float max_scale = 0; // as we are deducting the min, scales are always positive
+        float max_min = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            for (int l = 0; l < 16; ++l) weights[l] = fabsf(x[16*j + l]);
+            scales[j] = make_qkx2_quants(16, 3, x + 16*j, weights, L + 16*j, &mins[j], Laux, -0.5f, 0.1f, 15, true);
+            float scale = scales[j];
+            if (scale > max_scale) {
+                max_scale = scale;
+            }
+            float min = mins[j];
+            if (min > max_min) {
+                max_min = min;
+            }
+        }
+
+        if (max_scale > 0) {
+            float iscale = q4scale/max_scale;
+            for (int j = 0; j < QK_K/16; ++j) {
+                int l = nearest_int(iscale*scales[j]);
+                y[i].scales[j] = l;
+            }
+            y[i].d = ggml_fp32_to_fp16(max_scale/q4scale);
+        } else {
+            for (int j = 0; j < QK_K/16; ++j) y[i].scales[j] = 0;
+            y[i].d = ggml_fp32_to_fp16(0.f);
+        }
+        if (max_min > 0) {
+            float iscale = q4scale/max_min;
+            for (int j = 0; j < QK_K/16; ++j) {
+                int l = nearest_int(iscale*mins[j]);
+                y[i].scales[j] |= (l << 4);
+            }
+            y[i].dmin = ggml_fp32_to_fp16(max_min/q4scale);
+        } else {
+            y[i].dmin = ggml_fp32_to_fp16(0.f);
+        }
+        for (int j = 0; j < QK_K/16; ++j) {
+            const float d = ggml_fp16_to_fp32(y[i].d) * (y[i].scales[j] & 0xF);
+            if (!d) continue;
+            const float dm = ggml_fp16_to_fp32(y[i].dmin) * (y[i].scales[j] >> 4);
+            for (int ii = 0; ii < 16; ++ii) {
+                int l = nearest_int((x[16*j + ii] + dm)/d);
+                l = MAX(0, MIN(3, l));
+                L[16*j + ii] = l;
+            }
+        }
+
+#if QK_K == 256
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6);
+            }
+        }
+#else
+        for (int l = 0; l < 16; ++l) {
+            y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6);
+        }
+#endif
+
+        x += QK_K;
+
+    }
+}
+
+void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d = ggml_fp16_to_fp32(x[i].d);
+        const float min = ggml_fp16_to_fp32(x[i].dmin);
+
+        const uint8_t * q = x[i].qs;
+
+#if QK_K == 256
+        int is = 0;
+        float dl, ml;
+        for (int n = 0; n < QK_K; n += 128) {
+            int shift = 0;
+            for (int j = 0; j < 4; ++j) {
+
+                uint8_t sc = x[i].scales[is++];
+                dl = d * (sc & 0xF); ml = min * (sc >> 4);
+                for (int l = 0; l < 16; ++l) *y++ = dl * ((int8_t)((q[l] >> shift) & 3)) - ml;
+
+                sc = x[i].scales[is++];
+                dl = d * (sc & 0xF); ml = min * (sc >> 4);
+                for (int l = 0; l < 16; ++l) *y++ = dl * ((int8_t)((q[l+16] >> shift) & 3)) - ml;
+
+                shift += 2;
+            }
+            q += 32;
+        }
+#else
+        float dl1 = d * (x[i].scales[0] & 0xF), ml1 = min * (x[i].scales[0] >> 4);
+        float dl2 = d * (x[i].scales[1] & 0xF), ml2 = min * (x[i].scales[1] >> 4);
+        float dl3 = d * (x[i].scales[2] & 0xF), ml3 = min * (x[i].scales[2] >> 4);
+        float dl4 = d * (x[i].scales[3] & 0xF), ml4 = min * (x[i].scales[3] >> 4);
+        for (int l = 0; l < 16; ++l) {
+            y[l+ 0] = dl1 * ((int8_t)((q[l] >> 0) & 3)) - ml1;
+            y[l+16] = dl2 * ((int8_t)((q[l] >> 2) & 3)) - ml2;
+            y[l+32] = dl3 * ((int8_t)((q[l] >> 4) & 3)) - ml3;
+            y[l+48] = dl4 * ((int8_t)((q[l] >> 6) & 3)) - ml4;
+        }
+        y += QK_K;
+#endif
+    }
+}
+
+void quantize_row_q2_K(const float * restrict x, void * restrict vy, int k) {
+    quantize_row_q2_K_reference(x, vy, k);
+}
+
+size_t ggml_quantize_q2_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
+    (void)hist; // TODO: collect histograms
+
+    for (int j = 0; j < n; j += k) {
+        block_q2_K * restrict y = (block_q2_K *)dst + j/QK_K;
+        quantize_row_q2_K_reference(src + j, y, k);
+    }
+    return (n/QK_K*sizeof(block_q2_K));
+}
+
+//========================= 3-bit (de)-quantization
+
+void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    int8_t L[QK_K];
+    float scales[QK_K / 16];
+
+    for (int i = 0; i < nb; i++) {
+
+        float max_scale = 0;
+        float amax = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            scales[j] = make_q3_quants(16, 4, x + 16*j, L + 16*j, true);
+            float scale = fabsf(scales[j]);
+            if (scale > amax) {
+                amax = scale; max_scale = scales[j];
+            }
+        }
+
+#if QK_K == 256
+        memset(y[i].scales, 0, 12);
+        if (max_scale) {
+            float iscale = -32.f/max_scale;
+            for (int j = 0; j < QK_K/16; ++j) {
+                int8_t l = nearest_int(iscale*scales[j]);
+                l = MAX(-32, MIN(31, l)) + 32;
+                if (j < 8) {
+                    y[i].scales[j] = l & 0xF;
+                } else {
+                    y[i].scales[j-8] |= ((l & 0xF) << 4);
+                }
+                l >>= 4;
+                y[i].scales[j%4 + 8] |= (l << (2*(j/4)));
+            }
+            y[i].d = ggml_fp32_to_fp16(1/iscale);
+        } else {
+            y[i].d = ggml_fp32_to_fp16(0.f);
+        }
+
+        int8_t sc;
+        for (int j = 0; j < QK_K/16; ++j) {
+            sc = j < 8 ? y[i].scales[j] & 0xF : y[i].scales[j-8] >> 4;
+            sc = (sc | (((y[i].scales[8 + j%4] >> (2*(j/4))) & 3) << 4)) - 32;
+            float d = ggml_fp16_to_fp32(y[i].d) * sc;
+            if (!d) {
+                continue;
+            }
+            for (int ii = 0; ii < 16; ++ii) {
+                int l = nearest_int(x[16*j + ii]/d);
+                l = MAX(-4, MIN(3, l));
+                L[16*j + ii] = l + 4;
+            }
+        }
+#else
+        if (max_scale) {
+            float iscale = -8.f/max_scale;
+            for (int j = 0; j < QK_K/16; j+=2) {
+                int l1 = nearest_int(iscale*scales[j]);
+                l1 = 8 + MAX(-8, MIN(7, l1));
+                int l2 = nearest_int(iscale*scales[j+1]);
+                l2 = 8 + MAX(-8, MIN(7, l2));
+                y[i].scales[j/2] = l1 | (l2 << 4);
+            }
+            y[i].d = ggml_fp32_to_fp16(1/iscale);
+        } else {
+            for (int j = 0; j < QK_K/16; j+=2) {
+                y[i].scales[j/2] = 0;
+            }
+            y[i].d = ggml_fp32_to_fp16(0.f);
+        }
+        for (int j = 0; j < QK_K/16; ++j) {
+            int s = j%2 == 0 ? y[i].scales[j/2] & 0xF : y[i].scales[j/2] >> 4;
+            float d = ggml_fp16_to_fp32(y[i].d) * (s - 8);
+            if (!d) {
+                continue;
+            }
+            for (int ii = 0; ii < 16; ++ii) {
+                int l = nearest_int(x[16*j + ii]/d);
+                l = MAX(-4, MIN(3, l));
+                L[16*j + ii] = l + 4;
+            }
+        }
+#endif
+
+        memset(y[i].hmask, 0, QK_K/8);
+        // We put the high-bit for the 1st 8 quants into bit 0, the next 8 into bit 1, etc.
+        int m = 0;
+        uint8_t hm = 1;
+        for (int j = 0; j < QK_K; ++j) {
+            if (L[j] > 3) {
+                y[i].hmask[m] |= hm;
+                L[j] -= 4;
+            }
+            if (++m == QK_K/8) {
+                m = 0; hm <<= 1;
+            }
+        }
+#if QK_K == 256
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6);
+            }
+        }
+#else
+        for (int l = 0; l < 16; ++l) {
+            y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6);
+        }
+#endif
+
+        x += QK_K;
+    }
+}
+
+#if QK_K == 256
+void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    const uint32_t kmask1 = 0x03030303;
+    const uint32_t kmask2 = 0x0f0f0f0f;
+
+    uint32_t aux[4];
+    const int8_t * scales = (const int8_t*)aux;
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d_all = ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict q = x[i].qs;
+        const uint8_t * restrict hm = x[i].hmask;
+        uint8_t m = 1;
+
+        memcpy(aux, x[i].scales, 12);
+        uint32_t tmp = aux[2];
+        aux[2] = ((aux[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+        aux[3] = ((aux[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+        aux[0] = (aux[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
+        aux[1] = (aux[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+
+        int is = 0;
+        float dl;
+        for (int n = 0; n < QK_K; n += 128) {
+            int shift = 0;
+            for (int j = 0; j < 4; ++j) {
+
+                dl = d_all * (scales[is++] - 32);
+                for (int l = 0; l < 16; ++l) {
+                    *y++ = dl * ((int8_t)((q[l+ 0] >> shift) & 3) - ((hm[l+ 0] & m) ? 0 : 4));
+                }
+
+                dl = d_all * (scales[is++] - 32);
+                for (int l = 0; l < 16; ++l) {
+                    *y++ = dl * ((int8_t)((q[l+16] >> shift) & 3) - ((hm[l+16] & m) ? 0 : 4));
+                }
+
+                shift += 2;
+                m <<= 1;
+            }
+            q += 32;
+        }
+
+    }
+}
+#else
+void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    assert(QK_K == 64);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d_all = ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict q = x[i].qs;
+        const uint8_t * restrict hm = x[i].hmask;
+
+        const float d1 = d_all * ((x[i].scales[0] & 0xF) - 8);
+        const float d2 = d_all * ((x[i].scales[0] >>  4) - 8);
+        const float d3 = d_all * ((x[i].scales[1] & 0xF) - 8);
+        const float d4 = d_all * ((x[i].scales[1] >>  4) - 8);
+
+        for (int l=0; l<8; ++l) {
+            uint8_t h = hm[l];
+            y[l+ 0] = d1 * ((int8_t)((q[l+0] >> 0) & 3) - ((h & 0x01) ? 0 : 4));
+            y[l+ 8] = d1 * ((int8_t)((q[l+8] >> 0) & 3) - ((h & 0x02) ? 0 : 4));
+            y[l+16] = d2 * ((int8_t)((q[l+0] >> 2) & 3) - ((h & 0x04) ? 0 : 4));
+            y[l+24] = d2 * ((int8_t)((q[l+8] >> 2) & 3) - ((h & 0x08) ? 0 : 4));
+            y[l+32] = d3 * ((int8_t)((q[l+0] >> 4) & 3) - ((h & 0x10) ? 0 : 4));
+            y[l+40] = d3 * ((int8_t)((q[l+8] >> 4) & 3) - ((h & 0x20) ? 0 : 4));
+            y[l+48] = d4 * ((int8_t)((q[l+0] >> 6) & 3) - ((h & 0x40) ? 0 : 4));
+            y[l+56] = d4 * ((int8_t)((q[l+8] >> 6) & 3) - ((h & 0x80) ? 0 : 4));
+        }
+        y += QK_K;
+    }
+}
+#endif
+
+void quantize_row_q3_K(const float * restrict x, void * restrict vy, int k) {
+    quantize_row_q3_K_reference(x, vy, k);
+}
+
+size_t ggml_quantize_q3_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
+    (void)hist; // TODO: collect histograms
+
+    for (int j = 0; j < n; j += k) {
+        block_q3_K * restrict y = (block_q3_K *)dst + j/QK_K;
+        quantize_row_q3_K_reference(src + j, y, k);
+    }
+    return (n/QK_K*sizeof(block_q3_K));
+}
+
+// ====================== 4-bit (de)-quantization
+
+void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    uint8_t L[QK_K];
+    uint8_t Laux[32];
+    float   weights[32];
+    float mins[QK_K/32];
+    float scales[QK_K/32];
+
+    for (int i = 0; i < nb; i++) {
+
+        float max_scale = 0; // as we are deducting the min, scales are always positive
+        float max_min = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            //scales[j] = make_qkx1_quants(32, 15, x + 32*j, L + 32*j, &mins[j], 9, 0.5f);
+            float sum_x2 = 0;
+            for (int l = 0; l < 32; ++l) sum_x2 += x[32*j + l] * x[32*j + l];
+            float av_x = sqrtf(sum_x2/32);
+            for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
+            scales[j] = make_qkx2_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -1.f, 0.1f, 20, false);
+            float scale = scales[j];
+            if (scale > max_scale) {
+                max_scale = scale;
+            }
+            float min = mins[j];
+            if (min > max_min) {
+                max_min = min;
+            }
+        }
+
+#if QK_K == 256
+        float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f;
+        float inv_min   = max_min   > 0 ? 63.f/max_min   : 0.f;
+        for (int j = 0; j < QK_K/32; ++j) {
+            uint8_t ls = nearest_int(inv_scale*scales[j]);
+            uint8_t lm = nearest_int(inv_min*mins[j]);
+            ls = MIN(63, ls);
+            lm = MIN(63, lm);
+            if (j < 4) {
+                y[i].scales[j] = ls;
+                y[i].scales[j+4] = lm;
+            } else {
+                y[i].scales[j+4] = (ls & 0xF) | ((lm & 0xF) << 4);
+                y[i].scales[j-4] |= ((ls >> 4) << 6);
+                y[i].scales[j-0] |= ((lm >> 4) << 6);
+            }
+        }
+        y[i].d = ggml_fp32_to_fp16(max_scale/63.f);
+        y[i].dmin = ggml_fp32_to_fp16(max_min/63.f);
+
+        uint8_t sc, m;
+        for (int j = 0; j < QK_K/32; ++j) {
+            get_scale_min_k4(j, y[i].scales, &sc, &m);
+            const float d = ggml_fp16_to_fp32(y[i].d) * sc;
+            if (!d) continue;
+            const float dm = ggml_fp16_to_fp32(y[i].dmin) * m;
+            for (int ii = 0; ii < 32; ++ii) {
+                int l = nearest_int((x[32*j + ii] + dm)/d);
+                l = MAX(0, MIN(15, l));
+                L[32*j + ii] = l;
+            }
+        }
+#else
+        const float s_factor = 15.f;
+        float inv_scale = max_scale > 0 ? s_factor/max_scale : 0.f;
+        float inv_min   = max_min   > 0 ? s_factor/max_min   : 0.f;
+        int d1 = nearest_int(inv_scale*scales[0]);
+        int m1 = nearest_int(inv_min*mins[0]);
+        int d2 = nearest_int(inv_scale*scales[1]);
+        int m2 = nearest_int(inv_min*mins[1]);
+        y[i].scales[0] = d1 | (m1 << 4);
+        y[i].scales[1] = d2 | (m2 << 4);
+        y[i].d[0] = ggml_fp32_to_fp16(max_scale/s_factor);
+        y[i].d[1] = ggml_fp32_to_fp16(max_min/s_factor);
+
+        float sumlx = 0;
+        int   suml2 = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            const uint8_t sd = y[i].scales[j] & 0xF;
+            const uint8_t sm = y[i].scales[j] >>  4;
+            const float d = ggml_fp16_to_fp32(y[i].d[0]) * sd;
+            if (!d) continue;
+            const float m = ggml_fp16_to_fp32(y[i].d[1]) * sm;
+            for (int ii = 0; ii < 32; ++ii) {
+                int l = nearest_int((x[32*j + ii] + m)/d);
+                l = MAX(0, MIN(15, l));
+                L[32*j + ii] = l;
+                sumlx += (x[32*j + ii] + m)*l*sd;
+                suml2 += l*l*sd*sd;
+            }
+        }
+        if (suml2) {
+            y[i].d[0] = ggml_fp32_to_fp16(sumlx/suml2);
+        }
+#endif
+        uint8_t * q = y[i].qs;
+        for (int j = 0; j < QK_K; j += 64) {
+            for (int l = 0; l < 32; ++l) q[l] = L[j + l] | (L[j + l + 32] << 4);
+            q += 32;
+        }
+
+        x += QK_K;
+
+    }
+}
+
+void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const uint8_t * q = x[i].qs;
+
+#if QK_K == 256
+
+        const float d   = ggml_fp16_to_fp32(x[i].d);
+        const float min = ggml_fp16_to_fp32(x[i].dmin);
+
+        int is = 0;
+        uint8_t sc, m;
+        for (int j = 0; j < QK_K; j += 64) {
+            get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
+            const float d1 = d * sc; const float m1 = min * m;
+            get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
+            const float d2 = d * sc; const float m2 = min * m;
+            for (int l = 0; l < 32; ++l) *y++ = d1 * (q[l] & 0xF) - m1;
+            for (int l = 0; l < 32; ++l) *y++ = d2 * (q[l]  >> 4) - m2;
+            q += 32; is += 2;
+        }
+#else
+        const float dall = ggml_fp16_to_fp32(x[i].d[0]);
+        const float mall = ggml_fp16_to_fp32(x[i].d[1]);
+        const float d1 = dall * (x[i].scales[0] & 0xF), m1 = mall * (x[i].scales[0] >> 4);
+        const float d2 = dall * (x[i].scales[1] & 0xF), m2 = mall * (x[i].scales[1] >> 4);
+        for (int l = 0; l < 32; ++l) {
+            y[l+ 0] = d1 * (q[l] & 0xF) - m1;
+            y[l+32] = d2 * (q[l] >>  4) - m2;
+        }
+        y += QK_K;
+#endif
+
+    }
+}
+
+void quantize_row_q4_K(const float * restrict x, void * restrict vy, int k) {
+    assert(k % QK_K == 0);
+    block_q4_K * restrict y = vy;
+    quantize_row_q4_K_reference(x, y, k);
+}
+
+size_t ggml_quantize_q4_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
+    assert(k % QK_K == 0);
+    (void)hist; // TODO: collect histograms
+
+    for (int j = 0; j < n; j += k) {
+        block_q4_K * restrict y = (block_q4_K *)dst + j/QK_K;
+        quantize_row_q4_K_reference(src + j, y, k);
+    }
+    return (n/QK_K*sizeof(block_q4_K));
+}
+
+// ====================== 5-bit (de)-quantization
+
+void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+#if QK_K == 256
+    uint8_t L[QK_K];
+    float mins[QK_K/32];
+    float scales[QK_K/32];
+    float weights[32];
+    uint8_t Laux[32];
+#else
+    int8_t L[QK_K];
+    float scales[QK_K/16];
+#endif
+
+    for (int i = 0; i < nb; i++) {
+
+#if QK_K == 256
+
+        float max_scale = 0; // as we are deducting the min, scales are always positive
+        float max_min = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            //scales[j] = make_qkx1_quants(32, 31, x + 32*j, L + 32*j, &mins[j], 9, 0.5f);
+            float sum_x2 = 0;
+            for (int l = 0; l < 32; ++l) sum_x2 += x[32*j + l] * x[32*j + l];
+            float av_x = sqrtf(sum_x2/32);
+            for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
+            scales[j] = make_qkx2_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.5f, 0.1f, 15, false);
+            float scale = scales[j];
+            if (scale > max_scale) {
+                max_scale = scale;
+            }
+            float min = mins[j];
+            if (min > max_min) {
+                max_min = min;
+            }
+        }
+
+        float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f;
+        float inv_min   = max_min   > 0 ? 63.f/max_min   : 0.f;
+        for (int j = 0; j < QK_K/32; ++j) {
+            uint8_t ls = nearest_int(inv_scale*scales[j]);
+            uint8_t lm = nearest_int(inv_min*mins[j]);
+            ls = MIN(63, ls);
+            lm = MIN(63, lm);
+            if (j < 4) {
+                y[i].scales[j] = ls;
+                y[i].scales[j+4] = lm;
+            } else {
+                y[i].scales[j+4] = (ls & 0xF) | ((lm & 0xF) << 4);
+                y[i].scales[j-4] |= ((ls >> 4) << 6);
+                y[i].scales[j-0] |= ((lm >> 4) << 6);
+            }
+        }
+        y[i].d = ggml_fp32_to_fp16(max_scale/63.f);
+        y[i].dmin = ggml_fp32_to_fp16(max_min/63.f);
+
+        uint8_t sc, m;
+        for (int j = 0; j < QK_K/32; ++j) {
+            get_scale_min_k4(j, y[i].scales, &sc, &m);
+            const float d = ggml_fp16_to_fp32(y[i].d) * sc;
+            if (!d) continue;
+            const float dm = ggml_fp16_to_fp32(y[i].dmin) * m;
+            for (int ii = 0; ii < 32; ++ii) {
+                int l = nearest_int((x[32*j + ii] + dm)/d);
+                l = MAX(0, MIN(31, l));
+                L[32*j + ii] = l;
+            }
+        }
+
+        uint8_t * restrict qh = y[i].qh;
+        uint8_t * restrict ql = y[i].qs;
+        memset(qh, 0, QK_K/8);
+
+        uint8_t m1 = 1, m2 = 2;
+        for (int n = 0; n < QK_K; n += 64) {
+            for (int j = 0; j < 32; ++j) {
+                int l1 = L[n + j];
+                if (l1 > 15) {
+                    l1 -= 16; qh[j] |= m1;
+                }
+                int l2 = L[n + j + 32];
+                if (l2 > 15) {
+                    l2 -= 16; qh[j] |= m2;
+                }
+                ql[j] = l1 | (l2 << 4);
+            }
+            m1 <<= 2; m2 <<= 2;
+            ql += 32;
+        }
+#else
+        float max_scale = 0, amax = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            scales[j] = make_qx_quants(16, 16, x + 16*j, L + 16*j, 1);
+            float abs_scale = fabsf(scales[j]);
+            if (abs_scale > amax) {
+                amax = abs_scale;
+                max_scale = scales[j];
+            }
+        }
+
+        float iscale = -128.f/max_scale;
+        for (int j = 0; j < QK_K/16; ++j) {
+            int l = nearest_int(iscale*scales[j]);
+            y[i].scales[j] = MAX(-128, MIN(127, l));
+        }
+        y[i].d = ggml_fp32_to_fp16(1/iscale);
+
+        for (int j = 0; j < QK_K/16; ++j) {
+            const float d = ggml_fp16_to_fp32(y[i].d) * y[i].scales[j];
+            if (!d) continue;
+            for (int ii = 0; ii < 16; ++ii) {
+                int l = nearest_int(x[16*j + ii]/d);
+                l = MAX(-16, MIN(15, l));
+                L[16*j + ii] = l + 16;
+            }
+        }
+
+        uint8_t * restrict qh = y[i].qh;
+        uint8_t * restrict ql = y[i].qs;
+        memset(qh, 0, QK_K/8);
+
+        for (int j = 0; j < 32; ++j) {
+            int jm = j%8;
+            int is = j/8;
+            int l1 = L[j];
+            if (l1 > 15) {
+                l1 -= 16; qh[jm] |= (1 << is);
+            }
+            int l2 = L[j + 32];
+            if (l2 > 15) {
+                l2 -= 16; qh[jm] |= (1 << (4 + is));
+            }
+            ql[j] = l1 | (l2 << 4);
+        }
+#endif
+
+        x += QK_K;
+
+    }
+}
+
+void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const uint8_t * ql = x[i].qs;
+        const uint8_t * qh = x[i].qh;
+
+#if QK_K == 256
+
+        const float d = ggml_fp16_to_fp32(x[i].d);
+        const float min = ggml_fp16_to_fp32(x[i].dmin);
+
+        int is = 0;
+        uint8_t sc, m;
+        uint8_t u1 = 1, u2 = 2;
+        for (int j = 0; j < QK_K; j += 64) {
+            get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
+            const float d1 = d * sc; const float m1 = min * m;
+            get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
+            const float d2 = d * sc; const float m2 = min * m;
+            for (int l = 0; l < 32; ++l) *y++ = d1 * ((ql[l] & 0xF) + (qh[l] & u1 ? 16 : 0)) - m1;
+            for (int l = 0; l < 32; ++l) *y++ = d2 * ((ql[l]  >> 4) + (qh[l] & u2 ? 16 : 0)) - m2;
+            ql += 32; is += 2;
+            u1 <<= 2; u2 <<= 2;
+        }
+#else
+        float d = ggml_fp16_to_fp32(x[i].d);
+        const int8_t * restrict s = x[i].scales;
+        for (int l = 0; l < 8; ++l) {
+            y[l+ 0] = d * s[0] * ((ql[l+ 0] & 0xF) - (qh[l] & 0x01 ? 0 : 16));
+            y[l+ 8] = d * s[0] * ((ql[l+ 8] & 0xF) - (qh[l] & 0x02 ? 0 : 16));
+            y[l+16] = d * s[1] * ((ql[l+16] & 0xF) - (qh[l] & 0x04 ? 0 : 16));
+            y[l+24] = d * s[1] * ((ql[l+24] & 0xF) - (qh[l] & 0x08 ? 0 : 16));
+            y[l+32] = d * s[2] * ((ql[l+ 0] >>  4) - (qh[l] & 0x10 ? 0 : 16));
+            y[l+40] = d * s[2] * ((ql[l+ 8] >>  4) - (qh[l] & 0x20 ? 0 : 16));
+            y[l+48] = d * s[3] * ((ql[l+16] >>  4) - (qh[l] & 0x40 ? 0 : 16));
+            y[l+56] = d * s[3] * ((ql[l+24] >>  4) - (qh[l] & 0x80 ? 0 : 16));
+        }
+        y += QK_K;
+#endif
+    }
+}
+
+void quantize_row_q5_K(const float * restrict x, void * restrict vy, int k) {
+    assert(k % QK_K == 0);
+    block_q5_K * restrict y = vy;
+    quantize_row_q5_K_reference(x, y, k);
+}
+
+size_t ggml_quantize_q5_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
+    assert(k % QK_K == 0);
+    (void)hist; // TODO: collect histograms
+
+    for (int j = 0; j < n; j += k) {
+        block_q5_K * restrict y = (block_q5_K *)dst + j/QK_K;
+        quantize_row_q5_K_reference(src + j, y, k);
+    }
+    return (n/QK_K*sizeof(block_q5_K));
+}
+
+// ====================== 6-bit (de)-quantization
+
+void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    int8_t L[QK_K];
+    float   scales[QK_K/16];
+
+    for (int i = 0; i < nb; i++) {
+
+        float max_scale = 0;
+        float max_abs_scale = 0;
+
+        for (int ib = 0; ib < QK_K/16; ++ib) {
+
+            const float scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1);
+            scales[ib] = scale;
+
+            const float abs_scale = fabsf(scale);
+            if (abs_scale > max_abs_scale) {
+                max_abs_scale = abs_scale;
+                max_scale = scale;
+            }
+
+        }
+
+        if (!max_abs_scale) {
+            memset(&y[i], 0, sizeof(block_q6_K));
+            y[i].d = ggml_fp32_to_fp16(0.f);
+            x += QK_K;
+            continue;
+        }
+
+        float iscale = -128.f/max_scale;
+        y[i].d = ggml_fp32_to_fp16(1/iscale);
+        for (int ib = 0; ib < QK_K/16; ++ib) {
+            y[i].scales[ib] = MIN(127, nearest_int(iscale*scales[ib]));
+        }
+
+        for (int j = 0; j < QK_K/16; ++j) {
+            float d = ggml_fp16_to_fp32(y[i].d) * y[i].scales[j];
+            if (!d) {
+                continue;
+            }
+            for (int ii = 0; ii < 16; ++ii) {
+                int l = nearest_int(x[16*j + ii]/d);
+                l = MAX(-32, MIN(31, l));
+                L[16*j + ii] = l + 32;
+            }
+        }
+
+        uint8_t * restrict ql = y[i].ql;
+        uint8_t * restrict qh = y[i].qh;
+#if QK_K == 256
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                const uint8_t q1 = L[j + l +  0] & 0xF;
+                const uint8_t q2 = L[j + l + 32] & 0xF;
+                const uint8_t q3 = L[j + l + 64] & 0xF;
+                const uint8_t q4 = L[j + l + 96] & 0xF;
+                ql[l+ 0] = q1 | (q3 << 4);
+                ql[l+32] = q2 | (q4 << 4);
+                qh[l] = (L[j + l] >> 4) | ((L[j + l + 32] >> 4) << 2) | ((L[j + l + 64] >> 4) << 4) | ((L[j + l + 96] >> 4) << 6);
+            }
+            ql += 64;
+            qh += 32;
+        }
+#else
+        for (int l = 0; l < 32; ++l) {
+            const uint8_t q1 = L[l +  0] & 0xF;
+            const uint8_t q2 = L[l + 32] & 0xF;
+            ql[l] = q1 | (q2 << 4);
+        }
+        for (int l = 0; l < 16; ++l) {
+            qh[l] = (L[l] >> 4) | ((L[l + 16] >> 4) << 2) | ((L[l + 32] >> 4) << 4) | ((L[l + 48] >> 4) << 6);
+        }
+#endif
+
+        x += QK_K;
+
+    }
+}
+
+void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d = ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict ql = x[i].ql;
+        const uint8_t * restrict qh = x[i].qh;
+        const int8_t  * restrict sc = x[i].scales;
+
+#if QK_K == 256
+        for (int n = 0; n < QK_K; n += 128) {
+            for (int l = 0; l < 32; ++l) {
+                int is = l/16;
+                const int8_t q1 = (int8_t)((ql[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+                const int8_t q2 = (int8_t)((ql[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+                const int8_t q3 = (int8_t)((ql[l +  0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+                const int8_t q4 = (int8_t)((ql[l + 32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+                y[l +  0] = d * sc[is + 0] * q1;
+                y[l + 32] = d * sc[is + 2] * q2;
+                y[l + 64] = d * sc[is + 4] * q3;
+                y[l + 96] = d * sc[is + 6] * q4;
+            }
+            y  += 128;
+            ql += 64;
+            qh += 32;
+            sc += 8;
+        }
+#else
+        for (int l = 0; l < 16; ++l) {
+            const int8_t q1 = (int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+            const int8_t q2 = (int8_t)((ql[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+            const int8_t q3 = (int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+            const int8_t q4 = (int8_t)((ql[l+16]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+            y[l+ 0] = d * sc[0] * q1;
+            y[l+16] = d * sc[1] * q2;
+            y[l+32] = d * sc[2] * q3;
+            y[l+48] = d * sc[3] * q4;
+        }
+        y  += 64;
+#endif
+
+    }
+}
+
+void quantize_row_q6_K(const float * restrict x, void * restrict vy, int k) {
+    assert(k % QK_K == 0);
+    block_q6_K * restrict y = vy;
+    quantize_row_q6_K_reference(x, y, k);
+}
+
+size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist) {
+    assert(k % QK_K == 0);
+    (void)hist; // TODO: collect histograms
+
+    for (int j = 0; j < n; j += k) {
+        block_q6_K * restrict y = (block_q6_K *)dst + j/QK_K;
+        quantize_row_q6_K_reference(src + j, y, k);
+    }
+    return (n/QK_K*sizeof(block_q6_K));
+}
+
+//===================================== Q8_K ==============================================
+
+void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        float max = 0;
+        float amax = 0;
+        for (int j = 0; j < QK_K; ++j) {
+            float ax = fabsf(x[j]);
+            if (ax > amax) {
+                amax = ax; max = x[j];
+            }
+        }
+        if (!amax) {
+            y[i].d = 0;
+            memset(y[i].qs, 0, QK_K);
+            x += QK_K;
+            continue;
+        }
+        const float iscale = -128.f/max;
+        for (int j = 0; j < QK_K; ++j) {
+            int v = nearest_int(iscale*x[j]);
+            y[i].qs[j] = MIN(127, v);
+        }
+        for (int j = 0; j < QK_K/16; ++j) {
+            int sum = 0;
+            for (int ii = 0; ii < 16; ++ii) {
+                sum += y[i].qs[j*16 + ii];
+            }
+            y[i].bsums[j] = sum;
+        }
+        y[i].d = 1/iscale;
+        x += QK_K;
+    }
+}
+
+void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+        for (int j = 0; j < QK_K; ++j) {
+            *y++ = x[i].d * x[i].qs[j];
+        }
+    }
+}
+
+void quantize_row_q8_K(const float * restrict x, void * restrict y, int k) {
+    quantize_row_q8_K_reference(x, y, k);
+}
+
+//===================================== Dot ptoducts =================================
+
+//
+// Helper functions
+//
+#if __AVX__ || __AVX2__ || __AVX512F__
+
+// horizontally add 8 floats
+static inline float hsum_float_8(const __m256 x) {
+    __m128 res = _mm256_extractf128_ps(x, 1);
+    res = _mm_add_ps(res, _mm256_castps256_ps128(x));
+    res = _mm_add_ps(res, _mm_movehl_ps(res, res));
+    res = _mm_add_ss(res, _mm_movehdup_ps(res));
+    return _mm_cvtss_f32(res);
+}
+
+// shuffles to pick the required scales in dot products
+static inline __m256i get_scale_shuffle_q3k(int i) {
+    static const uint8_t k_shuffle[128] = {
+         0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,     2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,
+         4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5,     6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7,
+         8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9,    10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,
+        12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,    14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,
+    };
+    return _mm256_loadu_si256((const __m256i*)k_shuffle + i);
+}
+static inline __m256i get_scale_shuffle_k4(int i) {
+    static const uint8_t k_shuffle[256] = {
+         0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
+         2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,
+         4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5,
+         6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7,
+         8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9,
+        10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,
+        12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,
+        14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15
+    };
+    return _mm256_loadu_si256((const __m256i*)k_shuffle + i);
+}
+static inline __m128i get_scale_shuffle(int i) {
+    static const uint8_t k_shuffle[128] = {
+         0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+         2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,
+         4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5,
+         6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7,
+         8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9,
+        10,10,10,10,10,10,10,10, 11,11,11,11,11,11,11,11,
+        12,12,12,12,12,12,12,12, 13,13,13,13,13,13,13,13,
+        14,14,14,14,14,14,14,14, 15,15,15,15,15,15,15,15
+    };
+    return _mm_loadu_si128((const __m128i*)k_shuffle + i);
+}
+#endif
+
+#if QK_K == 256
+void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+
+    const block_q2_K * restrict x = vx;
+    const block_q8_K * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#ifdef __ARM_NEON
+
+    const uint8x16_t m3 = vdupq_n_u8(0x3);
+    const uint8x16_t m4 = vdupq_n_u8(0xF);
+#if defined(__ARM_FEATURE_DOTPROD)
+    const int32x4_t  vzero = vdupq_n_s32(0);
+#endif
+
+    int8x16x2_t q2bytes;
+    uint8_t aux[16];
+
+    float sum = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        const uint8_t * restrict q2 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+        const uint8_t * restrict sc = x[i].scales;
+
+        const uint8x16_t mins_and_scales = vld1q_u8(sc);
+        const uint8x16_t scales = vandq_u8(mins_and_scales, m4);
+        vst1q_u8(aux, scales);
+
+        const uint8x16_t mins = vshrq_n_u8(mins_and_scales, 4);
+        const int16x8x2_t q8sums = vld1q_s16_x2(y[i].bsums);
+        const int16x8x2_t mins16 = {vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))};
+        const int32x4_t s0 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[0]), vget_low_s16 (q8sums.val[0])),
+                                       vmull_s16(vget_high_s16(mins16.val[0]), vget_high_s16(q8sums.val[0])));
+        const int32x4_t s1 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[1]), vget_low_s16 (q8sums.val[1])),
+                                       vmull_s16(vget_high_s16(mins16.val[1]), vget_high_s16(q8sums.val[1])));
+        sum += dmin * vaddvq_s32(vaddq_s32(s0, s1));
+
+        int isum = 0;
+        int is = 0;
+
+// We use this macro instead of a function call because for some reason
+// the code runs 2-3% slower, even if the function is declared inline
+#if defined(__ARM_FEATURE_DOTPROD)
+#define MULTIPLY_ACCUM_WITH_SCALE(index)\
+        isum += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * aux[is+(index)];\
+        isum += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * aux[is+1+(index)];
+#else
+#define MULTIPLY_ACCUM_WITH_SCALE(index)\
+        {\
+    const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[0]), vget_low_s8 (q8bytes.val[0])),\
+                                   vmull_s8(vget_high_s8(q2bytes.val[0]), vget_high_s8(q8bytes.val[0])));\
+    const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[1]), vget_low_s8 (q8bytes.val[1])),\
+                                   vmull_s8(vget_high_s8(q2bytes.val[1]), vget_high_s8(q8bytes.val[1])));\
+    isum += vaddvq_s16(p1) * aux[is+(index)] + vaddvq_s16(p2) * aux[is+1+(index)];\
+        }
+#endif
+
+#define SHIFT_MULTIPLY_ACCUM_WITH_SCALE(shift, index)\
+        q8bytes = vld1q_s8_x2(q8); q8 += 32;\
+        q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[0], (shift)), m3));\
+        q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[1], (shift)), m3));\
+        MULTIPLY_ACCUM_WITH_SCALE((index));
+
+
+        for (int j = 0; j < QK_K/128; ++j) {
+
+            const uint8x16x2_t q2bits = vld1q_u8_x2(q2); q2 += 32;
+
+            int8x16x2_t q8bytes = vld1q_s8_x2(q8); q8 += 32;
+            q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[0], m3));
+            q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[1], m3));
+            MULTIPLY_ACCUM_WITH_SCALE(0);
+
+            SHIFT_MULTIPLY_ACCUM_WITH_SCALE(2, 2);
+
+            SHIFT_MULTIPLY_ACCUM_WITH_SCALE(4, 4);
+
+            SHIFT_MULTIPLY_ACCUM_WITH_SCALE(6, 6);
+
+            is += 8;
+        }
+        sum += d * isum;
+
+    }
+
+    *s = sum;
+
+#elif defined __AVX2__
+
+    const __m256i m3 = _mm256_set1_epi8(3);
+    const __m128i m4 = _mm_set1_epi8(0xF);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        const uint8_t * restrict q2 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const __m128i mins_and_scales = _mm_loadu_si128((const __m128i*)x[i].scales);
+        const __m128i scales8 = _mm_and_si128(mins_and_scales, m4);
+        const __m128i mins8 = _mm_and_si128(_mm_srli_epi16(mins_and_scales, 4), m4);
+        const __m256i mins = _mm256_cvtepi8_epi16(mins8);
+        const __m256i prod = _mm256_madd_epi16(mins, _mm256_loadu_si256((const __m256i*)y[i].bsums));
+
+        acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(prod), acc);
+
+        const __m256i all_scales = _mm256_cvtepi8_epi16(scales8);
+        const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0);
+        const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1);
+        const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)};
+
+        __m256i sumi = _mm256_setzero_si256();
+
+        for (int j = 0; j < QK_K/128; ++j) {
+
+            const __m256i q2bits = _mm256_loadu_si256((const __m256i*)q2); q2 += 32;
+
+            const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+
+            const __m256i q2_0 = _mm256_and_si256(q2bits, m3);
+            const __m256i q2_1 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 2), m3);
+            const __m256i q2_2 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 4), m3);
+            const __m256i q2_3 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 6), m3);
+
+            __m256i p0 = _mm256_maddubs_epi16(q2_0, q8_0);
+            __m256i p1 = _mm256_maddubs_epi16(q2_1, q8_1);
+            __m256i p2 = _mm256_maddubs_epi16(q2_2, q8_2);
+            __m256i p3 = _mm256_maddubs_epi16(q2_3, q8_3);
+
+            p0 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(0)), p0);
+            p1 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(1)), p1);
+            p2 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(2)), p2);
+            p3 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(3)), p3);
+
+            p0 = _mm256_add_epi32(p0, p1);
+            p2 = _mm256_add_epi32(p2, p3);
+
+            sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p0, p2));
+        }
+
+        acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc);
+
+    }
+
+    *s = hsum_float_8(acc);
+
+#elif defined __AVX__
+
+    const __m128i m3 = _mm_set1_epi8(0x3);
+    const __m128i m4 = _mm_set1_epi8(0xF);
+    const __m128i m2 = _mm_set1_epi8(0x2);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float dall = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        const uint8_t * restrict q2 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        // load mins and scales from block_q2_K.scales[QK_K/16]
+        const __m128i mins_and_scales = _mm_loadu_si128((const __m128i*)x[i].scales);
+        const __m128i scales16 = _mm_and_si128(mins_and_scales, m4);
+        const __m128i mins16 = _mm_and_si128(_mm_srli_epi16(mins_and_scales, 4), m4);
+        const __m128i mins_0 = _mm_cvtepi8_epi16(mins16);
+        const __m128i mins_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(mins16, mins16));
+
+        // summs = y[i].bsums * (x[i].scales >> 4) in 16bits*8*2 to 32bits*4*2
+        const __m128i summs_0 = _mm_madd_epi16(mins_0, _mm_loadu_si128((const __m128i*)&y[i].bsums[0]));
+        const __m128i summs_1 = _mm_madd_epi16(mins_1, _mm_loadu_si128((const __m128i*)&y[i].bsums[8]));
+
+        // sumf += -dmin * summs in 32bits*8
+        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(MM256_SET_M128I(summs_1, summs_0))), acc);
+
+        const __m128i scales_0 = _mm_cvtepi8_epi16(scales16);
+        const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales16, scales16));
+        const __m128i scales[2] = { scales_0, scales_1 };
+
+        __m128i sumi_0 = _mm_setzero_si128();
+        __m128i sumi_1 = _mm_setzero_si128();
+
+        for (int j = 0; j < QK_K/128; ++j) {
+
+            // load Q8 quants int8*16*8 from block_q8_K.qs[QK_K]
+            const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+
+            // load 2bits*16*8 from block_q2_K.qs[QK_K/4]
+            __m128i q2bits = _mm_loadu_si128((const __m128i*)q2); q2 += 16;
+            const __m128i q2_0 = _mm_and_si128(q2bits, m3);
+            const __m128i q2_2 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3);
+            const __m128i q2_4 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3);
+            const __m128i q2_6 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3);
+            q2bits = _mm_loadu_si128((const __m128i*)q2); q2 += 16;
+            const __m128i q2_1 = _mm_and_si128(q2bits, m3);
+            const __m128i q2_3 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3);
+            const __m128i q2_5 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3);
+            const __m128i q2_7 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3);
+
+            // isuml = q8[l] * ((q2[l] >> shift) & 3) in 8bits*16*8 to 16bits*8*8
+            __m128i p0 = _mm_maddubs_epi16(q2_0, q8_0);
+            __m128i p1 = _mm_maddubs_epi16(q2_1, q8_1);
+            __m128i p2 = _mm_maddubs_epi16(q2_2, q8_2);
+            __m128i p3 = _mm_maddubs_epi16(q2_3, q8_3);
+            __m128i p4 = _mm_maddubs_epi16(q2_4, q8_4);
+            __m128i p5 = _mm_maddubs_epi16(q2_5, q8_5);
+            __m128i p6 = _mm_maddubs_epi16(q2_6, q8_6);
+            __m128i p7 = _mm_maddubs_epi16(q2_7, q8_7);
+
+            // isum += (x[i].scales[is++] & 0xF) * isuml in 16bits*8*8 to 32bits*4*8
+            __m128i shuffle = _mm_set1_epi16(0x0100);
+            p0 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p0);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            p1 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p1);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            p2 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p2);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            p3 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p3);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            p4 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p4);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            p5 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p5);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            p6 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p6);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            p7 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p7);
+
+            p0 = _mm_add_epi32(p0, p1);
+            p2 = _mm_add_epi32(p2, p3);
+            p4 = _mm_add_epi32(p4, p5);
+            p6 = _mm_add_epi32(p6, p7);
+
+            // isum in 32bits*4*2
+            sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p0, p2));
+            sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p4, p6));
+        }
+
+        // sumf += dall * isum - dmin * summs in 32bits
+        __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
+        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dall), _mm256_cvtepi32_ps(sumi)), acc);
+    }
+
+    *s = hsum_float_8(acc);
+
+#elif defined __riscv_v_intrinsic
+
+    float sumf = 0;
+    uint8_t temp_01[32] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                            1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+
+    for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * q2 = x[i].qs;
+        const  int8_t * q8 = y[i].qs;
+        const uint8_t * sc = x[i].scales;
+
+        const float dall = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        size_t vl = 16;
+
+        vuint8m1_t scales = __riscv_vle8_v_u8m1(sc, vl);
+        vuint8m1_t aux = __riscv_vand_vx_u8m1(scales, 0x0F, vl);
+
+        vint16m1_t q8sums = __riscv_vle16_v_i16m1(y[i].bsums, vl);
+
+        vuint8mf2_t scales_2 = __riscv_vle8_v_u8mf2(sc, vl);
+        vuint8mf2_t mins8 = __riscv_vsrl_vx_u8mf2(scales_2, 0x4, vl);
+        vint16m1_t mins = __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vzext_vf2_u16m1(mins8, vl));
+        vint32m2_t prod = __riscv_vwmul_vv_i32m2(q8sums, mins, vl);
+        vint32m1_t vsums = __riscv_vredsum_vs_i32m2_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl);
+
+        sumf  += dmin * __riscv_vmv_x_s_i32m1_i32(vsums);
+
+        vl = 32;
+
+        vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1);
+        vuint8m1_t v_b = __riscv_vle8_v_u8m1(temp_01, vl);
+
+        uint8_t is=0;
+        int isum=0;
+
+        for (int j = 0; j < QK_K/128; ++j) {
+            // load Q2
+            vuint8m1_t q2_x = __riscv_vle8_v_u8m1(q2, vl);
+
+            vuint8m1_t q2_0 = __riscv_vand_vx_u8m1(q2_x, 0x03, vl);
+            vuint8m1_t q2_1 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x2, vl), 0x03 , vl);
+            vuint8m1_t q2_2 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x4, vl), 0x03 , vl);
+            vuint8m1_t q2_3 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x6, vl), 0x03 , vl);
+
+            // duplicate scale elements for product
+            vuint8m1_t sc0 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 0+is, vl), vl);
+            vuint8m1_t sc1 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 2+is, vl), vl);
+            vuint8m1_t sc2 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 4+is, vl), vl);
+            vuint8m1_t sc3 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 6+is, vl), vl);
+
+            vint16m2_t p0 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_0, sc0, vl));
+            vint16m2_t p1 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_1, sc1, vl));
+            vint16m2_t p2 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_2, sc2, vl));
+            vint16m2_t p3 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_3, sc3, vl));
+
+            // load Q8
+            vint8m1_t q8_0 = __riscv_vle8_v_i8m1(q8, vl);
+            vint8m1_t q8_1 = __riscv_vle8_v_i8m1(q8+32, vl);
+            vint8m1_t q8_2 = __riscv_vle8_v_i8m1(q8+64, vl);
+            vint8m1_t q8_3 = __riscv_vle8_v_i8m1(q8+96, vl);
+
+            vint32m4_t s0 = __riscv_vwmul_vv_i32m4(p0, __riscv_vwcvt_x_x_v_i16m2(q8_0, vl), vl);
+            vint32m4_t s1 = __riscv_vwmul_vv_i32m4(p1, __riscv_vwcvt_x_x_v_i16m2(q8_1, vl), vl);
+            vint32m4_t s2 = __riscv_vwmul_vv_i32m4(p2, __riscv_vwcvt_x_x_v_i16m2(q8_2, vl), vl);
+            vint32m4_t s3 = __riscv_vwmul_vv_i32m4(p3, __riscv_vwcvt_x_x_v_i16m2(q8_3, vl), vl);
+
+            vint32m1_t isum0 = __riscv_vredsum_vs_i32m4_i32m1(__riscv_vadd_vv_i32m4(s0, s1, vl), vzero, vl);
+            vint32m1_t isum1 = __riscv_vredsum_vs_i32m4_i32m1(__riscv_vadd_vv_i32m4(s2, s3, vl), isum0, vl);
+
+            isum += __riscv_vmv_x_s_i32m1_i32(isum1);
+
+            q2+=32;  q8+=128;  is=8;
+
+        }
+
+        sumf += dall * isum;
+
+    }
+
+    *s = sumf;
+
+#else
+
+    float sumf = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * q2 = x[i].qs;
+        const  int8_t * q8 = y[i].qs;
+        const uint8_t * sc = x[i].scales;
+
+        int summs = 0;
+        for (int j = 0; j < 16; ++j) {
+            summs += y[i].bsums[j] * (sc[j] >> 4);
+        }
+
+        const float dall = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        int isum = 0;
+        int is = 0;
+        int d;
+        for (int k = 0; k < QK_K/128; ++k) {
+            int shift = 0;
+            for (int j = 0; j < 4; ++j) {
+                d = sc[is++] & 0xF;
+                int isuml = 0;
+                for (int l =  0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
+                isum += d * isuml;
+                d = sc[is++] & 0xF;
+                isuml = 0;
+                for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
+                isum += d * isuml;
+                shift += 2;
+                q8 += 32;
+            }
+            q2 += 32;
+        }
+        sumf += dall * isum - dmin * summs;
+    }
+    *s = sumf;
+#endif
+}
+
+#else
+
+void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+
+    const block_q2_K * restrict x = vx;
+    const block_q8_K * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#ifdef __ARM_NEON
+
+    const uint8x16_t m3 = vdupq_n_u8(0x3);
+#if defined(__ARM_FEATURE_DOTPROD)
+    const int32x4_t  vzero = vdupq_n_s32(0);
+#endif
+
+    int8x16x4_t q2bytes;
+
+    uint32_t aux32[2];
+    const uint8_t * scales = (const uint8_t *)aux32;
+
+    float sum = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * (float)x[i].d;
+        const float dmin = -y[i].d * (float)x[i].dmin;
+
+        const uint8_t * restrict q2 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+        const uint32_t * restrict sc = (const uint32_t *)x[i].scales;
+
+        aux32[0] = sc[0] & 0x0f0f0f0f;
+        aux32[1] = (sc[0] >> 4) & 0x0f0f0f0f;
+
+        sum += dmin * (scales[4] * y[i].bsums[0] + scales[5] * y[i].bsums[1] + scales[6] * y[i].bsums[2] + scales[7] * y[i].bsums[3]);
+
+        int isum1 = 0, isum2 = 0;
+
+        const uint8x16_t q2bits = vld1q_u8(q2);
+
+        const int8x16x4_t q8bytes = vld1q_s8_x4(q8);
+
+        q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits, m3));
+        q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 2), m3));
+        q2bytes.val[2] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 4), m3));
+        q2bytes.val[3] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 6), m3));
+
+#if defined(__ARM_FEATURE_DOTPROD)
+        isum1 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * scales[0];
+        isum2 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * scales[1];
+        isum1 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[2], q8bytes.val[2])) * scales[2];
+        isum2 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[3], q8bytes.val[3])) * scales[3];
+#else
+        const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
+                                       vmull_s8(vget_high_s8(q2bytes.val[0]), vget_high_s8(q8bytes.val[0])));
+        const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
+                                       vmull_s8(vget_high_s8(q2bytes.val[1]), vget_high_s8(q8bytes.val[1])));
+        isum1 += vaddvq_s16(p1) * scales[0];
+        isum2 += vaddvq_s16(p2) * scales[1];
+
+        const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[2]), vget_low_s8 (q8bytes.val[2])),
+                                       vmull_s8(vget_high_s8(q2bytes.val[2]), vget_high_s8(q8bytes.val[2])));
+        const int16x8_t p4 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[3]), vget_low_s8 (q8bytes.val[3])),
+                                       vmull_s8(vget_high_s8(q2bytes.val[3]), vget_high_s8(q8bytes.val[3])));
+        isum1 += vaddvq_s16(p3) * scales[2];
+        isum2 += vaddvq_s16(p4) * scales[3];
+#endif
+        sum += d * (isum1 + isum2);
+
+    }
+
+    *s = sum;
+
+#elif defined __AVX2__
+
+    const __m256i m3 = _mm256_set1_epi8(3);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    uint32_t ud, um;
+    const uint8_t * restrict db = (const uint8_t *)&ud;
+    const uint8_t * restrict mb = (const uint8_t *)&um;
+
+    float summs = 0;
+
+    // TODO: optimize this
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        const uint8_t * restrict q2 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const uint32_t * restrict sc = (const uint32_t *)x[i].scales;
+        ud = (sc[0] >> 0) & 0x0f0f0f0f;
+        um = (sc[0] >> 4) & 0x0f0f0f0f;
+
+        int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3];
+        summs += dmin * smin;
+
+        const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2);
+        const __m256i q2_0 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 2), q2bits), m3);
+        const __m256i q2_1 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 6), _mm_srli_epi16(q2bits, 4)), m3);
+
+        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
+        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
+
+        const __m256i p0 = _mm256_maddubs_epi16(q2_0, q8_0);
+        const __m256i p1 = _mm256_maddubs_epi16(q2_1, q8_1);
+
+        const __m256i p_0 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p0, 0));
+        const __m256i p_1 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p0, 1));
+        const __m256i p_2 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p1, 0));
+        const __m256i p_3 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p1, 1));
+
+        acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0), acc);
+        acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1), acc);
+        acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[2]), _mm256_cvtepi32_ps(p_2), acc);
+        acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[3]), _mm256_cvtepi32_ps(p_3), acc);
+    }
+
+    *s = hsum_float_8(acc) + summs;
+
+#elif defined __AVX__
+
+    const __m128i m3 = _mm_set1_epi8(3);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    uint32_t ud, um;
+    const uint8_t * restrict db = (const uint8_t *)&ud;
+    const uint8_t * restrict mb = (const uint8_t *)&um;
+
+    float summs = 0;
+
+    // TODO: optimize this
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        const uint8_t * restrict q2 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const uint32_t * restrict sc = (const uint32_t *)x[i].scales;
+        ud = (sc[0] >> 0) & 0x0f0f0f0f;
+        um = (sc[0] >> 4) & 0x0f0f0f0f;
+
+        int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3];
+        summs += dmin * smin;
+
+        const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2);
+        const __m128i q2_0 = _mm_and_si128(q2bits, m3);
+        const __m128i q2_1 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3);
+        const __m128i q2_2 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3);
+        const __m128i q2_3 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3);
+
+        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
+        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
+
+        const __m128i p0 = _mm_maddubs_epi16(q2_0, _mm256_extractf128_si256(q8_0, 0));
+        const __m128i p1 = _mm_maddubs_epi16(q2_1, _mm256_extractf128_si256(q8_0, 1));
+        const __m128i p2 = _mm_maddubs_epi16(q2_2, _mm256_extractf128_si256(q8_1, 0));
+        const __m128i p3 = _mm_maddubs_epi16(q2_3, _mm256_extractf128_si256(q8_1, 1));
+
+        const __m256i p_0 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p0, p0)), _mm_cvtepi16_epi32(p0));
+        const __m256i p_1 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p1, p1)), _mm_cvtepi16_epi32(p1));
+        const __m256i p_2 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p2, p2)), _mm_cvtepi16_epi32(p2));
+        const __m256i p_3 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p3, p3)), _mm_cvtepi16_epi32(p3));
+
+        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0)), acc);
+        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1)), acc);
+        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[2]), _mm256_cvtepi32_ps(p_2)), acc);
+        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[3]), _mm256_cvtepi32_ps(p_3)), acc);
+    }
+
+    *s = hsum_float_8(acc) + summs;
+
+#elif defined __riscv_v_intrinsic
+
+    uint32_t aux32[2];
+    const uint8_t * scales = (const uint8_t *)aux32;
+
+    float sumf = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * (float)x[i].d;
+        const float dmin = -y[i].d * (float)x[i].dmin;
+
+        const uint8_t * restrict q2 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+        const uint32_t * restrict sc = (const uint32_t *)x[i].scales;
+
+        aux32[0] = sc[0] & 0x0f0f0f0f;
+        aux32[1] = (sc[0] >> 4) & 0x0f0f0f0f;
+
+        sumf += dmin * (scales[4] * y[i].bsums[0] + scales[5] * y[i].bsums[1] + scales[6] * y[i].bsums[2] + scales[7] * y[i].bsums[3]);
+
+        int isum1 = 0;
+        int isum2 = 0;
+
+        size_t vl = 16;
+
+        vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1);
+
+        // load Q2
+        vuint8mf2_t q2_x = __riscv_vle8_v_u8mf2(q2, vl);
+
+        vint8mf2_t q2_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q2_x, 0x03, vl));
+        vint8mf2_t q2_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x2, vl), 0x03 , vl));
+        vint8mf2_t q2_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x4, vl), 0x03 , vl));
+        vint8mf2_t q2_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x6, vl), 0x03 , vl));
+
+        // load Q8, and take product with Q2
+        vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q2_0, __riscv_vle8_v_i8mf2(q8, vl), vl);
+        vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q2_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl);
+        vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q2_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl);
+        vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q2_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl);
+
+        vint16m1_t vs_0 = __riscv_vredsum_vs_i16m1_i16m1(p0, vzero, vl);
+        vint16m1_t vs_1 = __riscv_vredsum_vs_i16m1_i16m1(p1, vzero, vl);
+        vint16m1_t vs_2 = __riscv_vredsum_vs_i16m1_i16m1(p2, vzero, vl);
+        vint16m1_t vs_3 = __riscv_vredsum_vs_i16m1_i16m1(p3, vzero, vl);
+
+        isum1 += __riscv_vmv_x_s_i16m1_i16(vs_0) * scales[0];
+        isum2 += __riscv_vmv_x_s_i16m1_i16(vs_1) * scales[1];
+        isum1 += __riscv_vmv_x_s_i16m1_i16(vs_2) * scales[2];
+        isum2 += __riscv_vmv_x_s_i16m1_i16(vs_3) * scales[3];
+
+        sumf += d * (isum1 + isum2);
+
+    }
+
+    *s = sumf;
+
+#else
+
+    float sumf = 0;
+
+    int isum[4];
+
+    for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * q2 = x[i].qs;
+        const  int8_t * q8 = y[i].qs;
+        const uint8_t * sc = x[i].scales;
+
+        int summs = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            summs += y[i].bsums[j] * (sc[j] >> 4);
+        }
+
+        const float dall = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        isum[0] = isum[1] = isum[2] = isum[3] = 0;
+        for (int l =  0; l < 16; ++l) {
+            isum[0] += q8[l+ 0] * ((q2[l] >> 0) & 3);
+            isum[1] += q8[l+16] * ((q2[l] >> 2) & 3);
+            isum[2] += q8[l+32] * ((q2[l] >> 4) & 3);
+            isum[3] += q8[l+48] * ((q2[l] >> 6) & 3);
+        }
+        for (int l = 0; l < 4; ++l) {
+            isum[l] *= (sc[l] & 0xF);
+        }
+        sumf += dall * (isum[0] + isum[1] + isum[2] + isum[3]) - dmin * summs;
+    }
+    *s = sumf;
+#endif
+}
+#endif
+
+#if QK_K == 256
+void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    assert(n % QK_K == 0);
+
+    const uint32_t kmask1 = 0x03030303;
+    const uint32_t kmask2 = 0x0f0f0f0f;
+
+    const block_q3_K * restrict x = vx;
+    const block_q8_K * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#ifdef __ARM_NEON
+
+    uint32_t aux[3];
+    uint32_t utmp[4];
+
+    const uint8x16_t m3b = vdupq_n_u8(0x3);
+#ifdef __ARM_FEATURE_DOTPROD
+    const int32x4_t  vzero = vdupq_n_s32(0);
+#endif
+
+    const uint8x16_t m0 = vdupq_n_u8(1);
+    const uint8x16_t m1 = vshlq_n_u8(m0, 1);
+    const uint8x16_t m2 = vshlq_n_u8(m0, 2);
+    const uint8x16_t m3 = vshlq_n_u8(m0, 3);
+    const int8_t m32 = 32;
+
+    int8x16x4_t q3bytes;
+
+    float sum = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict q3 = x[i].qs;
+        const uint8_t * restrict qh = x[i].hmask;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        uint8x16x2_t qhbits = vld1q_u8_x2(qh);
+
+        uint8x16x4_t q3h;
+
+        int32_t isum = 0;
+
+        // Set up scales
+        memcpy(aux, x[i].scales, 12);
+        utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4);
+        utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4);
+        utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4);
+        utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4);
+
+        int8_t * scale = (int8_t *)utmp;
+        for (int j = 0; j < 16; ++j) scale[j] -= m32;
+
+        for (int j = 0; j < QK_K/128; ++j) {
+
+            const uint8x16x2_t q3bits = vld1q_u8_x2(q3); q3 += 32;
+            const int8x16x4_t q8bytes_1 = vld1q_s8_x4(q8); q8 += 64;
+            const int8x16x4_t q8bytes_2 = vld1q_s8_x4(q8); q8 += 64;
+
+            q3h.val[0] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[0]), 2);
+            q3h.val[1] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[1]), 2);
+            q3h.val[2] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[0]), 1);
+            q3h.val[3] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[1]), 1);
+
+            q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[0], m3b)), vreinterpretq_s8_u8(q3h.val[0]));
+            q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[1], m3b)), vreinterpretq_s8_u8(q3h.val[1]));
+            q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 2), m3b)), vreinterpretq_s8_u8(q3h.val[2]));
+            q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 2), m3b)), vreinterpretq_s8_u8(q3h.val[3]));
+
+#if defined(__ARM_FEATURE_DOTPROD)
+            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0];
+            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1];
+            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2];
+            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3];
+#else
+            int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[0]), vget_low_s8 (q8bytes_1.val[0])),
+                                     vmull_s8(vget_high_s8(q3bytes.val[0]), vget_high_s8(q8bytes_1.val[0])));
+            int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[1]), vget_low_s8 (q8bytes_1.val[1])),
+                                     vmull_s8(vget_high_s8(q3bytes.val[1]), vget_high_s8(q8bytes_1.val[1])));
+            int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[2]), vget_low_s8 (q8bytes_1.val[2])),
+                                     vmull_s8(vget_high_s8(q3bytes.val[2]), vget_high_s8(q8bytes_1.val[2])));
+            int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[3]), vget_low_s8 (q8bytes_1.val[3])),
+                                     vmull_s8(vget_high_s8(q3bytes.val[3]), vget_high_s8(q8bytes_1.val[3])));
+            isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1] + vaddvq_s16(p2) * scale[2] + vaddvq_s16(p3) * scale[3];
+#endif
+            scale += 4;
+
+            q3h.val[0] = vbicq_u8(m2, qhbits.val[0]);
+            q3h.val[1] = vbicq_u8(m2, qhbits.val[1]);
+            q3h.val[2] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[0]), 1);
+            q3h.val[3] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[1]), 1);
+
+            q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 4), m3b)), vreinterpretq_s8_u8(q3h.val[0]));
+            q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 4), m3b)), vreinterpretq_s8_u8(q3h.val[1]));
+            q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 6), m3b)), vreinterpretq_s8_u8(q3h.val[2]));
+            q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 6), m3b)), vreinterpretq_s8_u8(q3h.val[3]));
+
+#if defined(__ARM_FEATURE_DOTPROD)
+            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0];
+            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1];
+            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2];
+            isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3];
+#else
+            p0 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[0]), vget_low_s8 (q8bytes_2.val[0])),
+                           vmull_s8(vget_high_s8(q3bytes.val[0]), vget_high_s8(q8bytes_2.val[0])));
+            p1 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[1]), vget_low_s8 (q8bytes_2.val[1])),
+                           vmull_s8(vget_high_s8(q3bytes.val[1]), vget_high_s8(q8bytes_2.val[1])));
+            p2 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[2]), vget_low_s8 (q8bytes_2.val[2])),
+                           vmull_s8(vget_high_s8(q3bytes.val[2]), vget_high_s8(q8bytes_2.val[2])));
+            p3 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[3]), vget_low_s8 (q8bytes_2.val[3])),
+                           vmull_s8(vget_high_s8(q3bytes.val[3]), vget_high_s8(q8bytes_2.val[3])));
+            isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1] + vaddvq_s16(p2) * scale[2] + vaddvq_s16(p3) * scale[3];
+#endif
+            scale += 4;
+
+            if (j == 0) {
+                qhbits.val[0] = vshrq_n_u8(qhbits.val[0], 4);
+                qhbits.val[1] = vshrq_n_u8(qhbits.val[1], 4);
+            }
+
+        }
+        sum += d * isum;
+
+    }
+
+    *s = sum;
+
+#elif defined __AVX2__
+
+    const __m256i m3 = _mm256_set1_epi8(3);
+    const __m256i mone = _mm256_set1_epi8(1);
+    const __m128i m32 = _mm_set1_epi8(32);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    uint32_t aux[3];
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict q3 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        // Set up scales
+        memcpy(aux, x[i].scales, 12);
+        __m128i scales128 = _mm_set_epi32(
+                ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4),
+                ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4),
+                (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4),
+                (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4));
+        scales128 = _mm_sub_epi8(scales128, m32);
+        const __m256i all_scales = _mm256_cvtepi8_epi16(scales128);
+        const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0);
+        const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1);
+        const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)};
+
+        // high bit
+        const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].hmask);
+
+        // integer accumulator
+        __m256i sumi = _mm256_setzero_si256();
+
+        int bit = 0;
+        int is  = 0;
+
+        for (int j = 0; j < QK_K/128; ++j) {
+            // load low 2 bits
+            const __m256i q3bits = _mm256_loadu_si256((const __m256i*)q3); q3 += 32;
+
+            // prepare low and high bits
+            const __m256i q3l_0 = _mm256_and_si256(q3bits, m3);
+            const __m256i q3h_0 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2);
+            ++bit;
+
+            const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 2), m3);
+            const __m256i q3h_1 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2);
+            ++bit;
+
+            const __m256i q3l_2 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 4), m3);
+            const __m256i q3h_2 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2);
+            ++bit;
+
+            const __m256i q3l_3 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 6), m3);
+            const __m256i q3h_3 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2);
+            ++bit;
+
+            // load Q8 quants
+            const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+
+            // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16,
+            // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set,
+            // and 2 if the high bit was set)
+            __m256i q8s_0 = _mm256_maddubs_epi16(q3h_0, q8_0);
+            __m256i q8s_1 = _mm256_maddubs_epi16(q3h_1, q8_1);
+            __m256i q8s_2 = _mm256_maddubs_epi16(q3h_2, q8_2);
+            __m256i q8s_3 = _mm256_maddubs_epi16(q3h_3, q8_3);
+
+            __m256i p16_0 = _mm256_maddubs_epi16(q3l_0, q8_0);
+            __m256i p16_1 = _mm256_maddubs_epi16(q3l_1, q8_1);
+            __m256i p16_2 = _mm256_maddubs_epi16(q3l_2, q8_2);
+            __m256i p16_3 = _mm256_maddubs_epi16(q3l_3, q8_3);
+
+            p16_0 = _mm256_sub_epi16(p16_0, q8s_0);
+            p16_1 = _mm256_sub_epi16(p16_1, q8s_1);
+            p16_2 = _mm256_sub_epi16(p16_2, q8s_2);
+            p16_3 = _mm256_sub_epi16(p16_3, q8s_3);
+
+            // multiply with scales
+            p16_0 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 0)), p16_0);
+            p16_1 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 1)), p16_1);
+            p16_2 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 2)), p16_2);
+            p16_3 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 3)), p16_3);
+
+            // accumulate
+            p16_0 = _mm256_add_epi32(p16_0, p16_1);
+            p16_2 = _mm256_add_epi32(p16_2, p16_3);
+            sumi  = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_2));
+
+        }
+
+        // multiply with block scale and accumulate
+        acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc);
+
+    }
+
+    *s = hsum_float_8(acc);
+
+#elif defined __AVX__
+
+    const __m128i m3 = _mm_set1_epi8(3);
+    const __m128i mone = _mm_set1_epi8(1);
+    const __m128i m32 = _mm_set1_epi8(32);
+    const __m128i m2 = _mm_set1_epi8(2);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    const uint32_t *aux;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict q3 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        // Set up scales
+        aux = (const uint32_t *)x[i].scales;
+        __m128i scales128 = _mm_set_epi32(
+                ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4),
+                ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4),
+                (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4),
+                (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4));
+        scales128 = _mm_sub_epi8(scales128, m32);
+        const __m128i scales_0 = _mm_cvtepi8_epi16(scales128);
+        const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales128, scales128));
+        const __m128i scales[2] = { scales_0, scales_1 };
+
+        // high bit *128*2 from block_q3_K.hmask[QK_K/8]
+        const __m128i hbits_0 = _mm_loadu_si128((const __m128i*)&x[i].hmask[0]);
+        const __m128i hbits_1 = _mm_loadu_si128((const __m128i*)&x[i].hmask[16]);
+
+        // integer accumulator
+        __m128i sumi_0 = _mm_setzero_si128();
+        __m128i sumi_1 = _mm_setzero_si128();
+
+        for (int j = 0; j < QK_K/128; ++j) {
+            // load low 2 bits *64*2 from block_q3_K.qs[QK_K/4]
+            const __m128i q3bits_0 = _mm_loadu_si128((const __m128i*)q3); q3 += 16;
+            const __m128i q3bits_1 = _mm_loadu_si128((const __m128i*)q3); q3 += 16;
+
+            // prepare low and high bits
+            const int bit = j << 2;
+
+            const __m128i q3l_0 = _mm_and_si128(q3bits_0, m3);
+            const __m128i q3l_1 = _mm_and_si128(q3bits_1, m3);
+            const __m128i q3h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit)), bit), 2);
+            const __m128i q3h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit)), bit), 2);
+
+            const __m128i q3l_2 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 2), m3);
+            const __m128i q3l_3 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 2), m3);
+            const __m128i q3h_2 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+1)), bit+1), 2);
+            const __m128i q3h_3 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+1)), bit+1), 2);
+
+            const __m128i q3l_4 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 4), m3);
+            const __m128i q3l_5 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 4), m3);
+            const __m128i q3h_4 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+2)), bit+2), 2);
+            const __m128i q3h_5 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+2)), bit+2), 2);
+
+            const __m128i q3l_6 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 6), m3);
+            const __m128i q3l_7 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 6), m3);
+            const __m128i q3h_6 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+3)), bit+3), 2);
+            const __m128i q3h_7 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+3)), bit+3), 2);
+
+            // load Q8 quants from block_q8_K.qs[QK_K]
+            const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+
+            // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16,
+            // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set,
+            // and 2 if the high bit was set)
+            __m128i q8s_0 = _mm_maddubs_epi16(q3h_0, q8_0);
+            __m128i q8s_1 = _mm_maddubs_epi16(q3h_1, q8_1);
+            __m128i q8s_2 = _mm_maddubs_epi16(q3h_2, q8_2);
+            __m128i q8s_3 = _mm_maddubs_epi16(q3h_3, q8_3);
+            __m128i q8s_4 = _mm_maddubs_epi16(q3h_4, q8_4);
+            __m128i q8s_5 = _mm_maddubs_epi16(q3h_5, q8_5);
+            __m128i q8s_6 = _mm_maddubs_epi16(q3h_6, q8_6);
+            __m128i q8s_7 = _mm_maddubs_epi16(q3h_7, q8_7);
+
+            __m128i p16_0 = _mm_maddubs_epi16(q3l_0, q8_0);
+            __m128i p16_1 = _mm_maddubs_epi16(q3l_1, q8_1);
+            __m128i p16_2 = _mm_maddubs_epi16(q3l_2, q8_2);
+            __m128i p16_3 = _mm_maddubs_epi16(q3l_3, q8_3);
+            __m128i p16_4 = _mm_maddubs_epi16(q3l_4, q8_4);
+            __m128i p16_5 = _mm_maddubs_epi16(q3l_5, q8_5);
+            __m128i p16_6 = _mm_maddubs_epi16(q3l_6, q8_6);
+            __m128i p16_7 = _mm_maddubs_epi16(q3l_7, q8_7);
+
+            p16_0 = _mm_sub_epi16(p16_0, q8s_0);
+            p16_1 = _mm_sub_epi16(p16_1, q8s_1);
+            p16_2 = _mm_sub_epi16(p16_2, q8s_2);
+            p16_3 = _mm_sub_epi16(p16_3, q8s_3);
+            p16_4 = _mm_sub_epi16(p16_4, q8s_4);
+            p16_5 = _mm_sub_epi16(p16_5, q8s_5);
+            p16_6 = _mm_sub_epi16(p16_6, q8s_6);
+            p16_7 = _mm_sub_epi16(p16_7, q8s_7);
+
+            // multiply with scales
+            __m128i shuffle = _mm_set1_epi16(0x0100);
+            p16_0 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_0);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            p16_1 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_1);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            p16_2 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_2);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            p16_3 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_3);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            p16_4 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_4);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            p16_5 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_5);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            p16_6 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_6);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            p16_7 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_7);
+
+            // accumulate
+            p16_0 = _mm_add_epi32(p16_0, p16_1);
+            p16_2 = _mm_add_epi32(p16_2, p16_3);
+            p16_4 = _mm_add_epi32(p16_4, p16_5);
+            p16_6 = _mm_add_epi32(p16_6, p16_7);
+            sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2));
+            sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_4, p16_6));
+
+        }
+
+        // multiply with block scale and accumulate
+        __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
+        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc);
+
+    }
+
+    *s = hsum_float_8(acc);
+
+#elif defined __riscv_v_intrinsic
+
+    uint32_t aux[3];
+    uint32_t utmp[4];
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * restrict q3 = x[i].qs;
+        const uint8_t * restrict qh = x[i].hmask;
+        const  int8_t * restrict q8 = y[i].qs;
+
+        memcpy(aux, x[i].scales, 12);
+        utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4);
+        utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4);
+        utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4);
+        utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4);
+
+        int8_t * scale = (int8_t *)utmp;
+        for (int j = 0; j < 16; ++j) scale[j] -= 32;
+
+
+        size_t vl = 32;
+        uint8_t m =  1;
+
+        vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1);
+        vuint8m1_t vqh = __riscv_vle8_v_u8m1(qh, vl);
+
+        int sum_t = 0;
+
+        for (int j = 0; j < QK_K; j += 128) {
+
+            vl = 32;
+
+            // load Q3
+            vuint8m1_t q3_x = __riscv_vle8_v_u8m1(q3, vl);
+
+            vint8m1_t q3_0 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q3_x, 0x03, vl));
+            vint8m1_t q3_1 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x2, vl), 0x03 , vl));
+            vint8m1_t q3_2 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x4, vl), 0x03 , vl));
+            vint8m1_t q3_3 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x6, vl), 0x03 , vl));
+
+            // compute mask for subtraction
+            vuint8m1_t qh_m0 = __riscv_vand_vx_u8m1(vqh, m, vl);
+            vbool8_t vmask_0 = __riscv_vmseq_vx_u8m1_b8(qh_m0, 0, vl);
+            vint8m1_t q3_m0 = __riscv_vsub_vx_i8m1_m(vmask_0, q3_0, 0x4, vl);
+            m <<= 1;
+
+            vuint8m1_t qh_m1 = __riscv_vand_vx_u8m1(vqh, m, vl);
+            vbool8_t vmask_1 = __riscv_vmseq_vx_u8m1_b8(qh_m1, 0, vl);
+            vint8m1_t q3_m1 = __riscv_vsub_vx_i8m1_m(vmask_1, q3_1, 0x4, vl);
+            m <<= 1;
+
+            vuint8m1_t qh_m2 = __riscv_vand_vx_u8m1(vqh, m, vl);
+            vbool8_t vmask_2 = __riscv_vmseq_vx_u8m1_b8(qh_m2, 0, vl);
+            vint8m1_t q3_m2 = __riscv_vsub_vx_i8m1_m(vmask_2, q3_2, 0x4, vl);
+            m <<= 1;
+
+            vuint8m1_t qh_m3 = __riscv_vand_vx_u8m1(vqh, m, vl);
+            vbool8_t vmask_3 = __riscv_vmseq_vx_u8m1_b8(qh_m3, 0, vl);
+            vint8m1_t q3_m3 = __riscv_vsub_vx_i8m1_m(vmask_3, q3_3, 0x4, vl);
+            m <<= 1;
+
+            // load Q8 and take product with Q3
+            vint16m2_t a0 = __riscv_vwmul_vv_i16m2(q3_m0, __riscv_vle8_v_i8m1(q8, vl), vl);
+            vint16m2_t a1 = __riscv_vwmul_vv_i16m2(q3_m1, __riscv_vle8_v_i8m1(q8+32, vl), vl);
+            vint16m2_t a2 = __riscv_vwmul_vv_i16m2(q3_m2, __riscv_vle8_v_i8m1(q8+64, vl), vl);
+            vint16m2_t a3 = __riscv_vwmul_vv_i16m2(q3_m3, __riscv_vle8_v_i8m1(q8+96, vl), vl);
+
+            vl = 16;
+
+            // retreive lane to multiply with scale
+            vint32m2_t aux0_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 0), (scale[0]), vl);
+            vint32m2_t aux0_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 1), (scale[1]), vl);
+            vint32m2_t aux1_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 0), (scale[2]), vl);
+            vint32m2_t aux1_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 1), (scale[3]), vl);
+            vint32m2_t aux2_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a2, 0), (scale[4]), vl);
+            vint32m2_t aux2_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a2, 1), (scale[5]), vl);
+            vint32m2_t aux3_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a3, 0), (scale[6]), vl);
+            vint32m2_t aux3_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a3, 1), (scale[7]), vl);
+
+            vint32m1_t isum0 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux0_0, aux0_1, vl), vzero, vl);
+            vint32m1_t isum1 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux1_0, aux1_1, vl), isum0, vl);
+            vint32m1_t isum2 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux2_0, aux2_1, vl), isum1, vl);
+            vint32m1_t isum3 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux3_0, aux3_1, vl), isum2, vl);
+
+            sum_t +=  __riscv_vmv_x_s_i32m1_i32(isum3);
+
+            q3 += 32;    q8 += 128;   scale += 8;
+
+        }
+
+        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+
+        sumf += d*sum_t;
+
+    }
+
+    *s = sumf;
+
+#else
+    // scalar version
+    // This function is written like this so the compiler can manage to vectorize most of it
+    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
+    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
+    // The ideal situation would be if we could just write the code once, and the compiler would
+    // automatically produce the best possible set of machine instructions, instead of us having to manually
+    // write vectorized versions for AVX, ARM_NEON, etc.
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    uint32_t auxs[4];
+    const int8_t * scales = (const int8_t*)auxs;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * restrict q3 = x[i].qs;
+        const uint8_t * restrict hm = x[i].hmask;
+        const  int8_t * restrict q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * restrict a = aux8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            q3 += 32;
+        }
+        a = aux8;
+
+        memcpy(auxs, x[i].scales, 12);
+        uint32_t tmp = auxs[2];
+        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
+        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+        for (int j = 0; j < QK_K/16; ++j) {
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
+
+#endif
+
+}
+
+#else
+
+void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    assert(n % QK_K == 0);
+
+    const block_q3_K * restrict x = vx;
+    const block_q8_K * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#ifdef __ARM_NEON
+
+#ifdef __ARM_FEATURE_DOTPROD
+    const int32x4_t  vzero = vdupq_n_s32(0);
+#endif
+
+    const uint8x16_t m3b = vdupq_n_u8(0x3);
+    const uint8x16_t mh  = vdupq_n_u8(4);
+
+    int8x16x4_t q3bytes;
+
+    uint16_t aux16[2];
+    int8_t * scales = (int8_t *)aux16;
+
+    float sum = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        uint8x16x4_t q3h;
+
+        const uint8x8_t  hbits    = vld1_u8(x[i].hmask);
+        const uint8x16_t q3bits   = vld1q_u8(x[i].qs);
+        const int8x16x4_t q8bytes = vld1q_s8_x4(y[i].qs);
+
+        const uint16_t a = *(const uint16_t *)x[i].scales;
+        aux16[0] = a & 0x0f0f;
+        aux16[1] = (a >> 4) & 0x0f0f;
+
+        for (int j = 0; j < 4; ++j) scales[j] -= 8;
+
+        int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]);
+
+        const float d = y[i].d * (float)x[i].d;
+
+        const uint8x16_t htmp = vcombine_u8(hbits, vshr_n_u8(hbits, 1));
+        q3h.val[0] = vandq_u8(mh, vshlq_n_u8(htmp, 2));
+        q3h.val[1] = vandq_u8(mh, htmp);
+        q3h.val[2] = vandq_u8(mh, vshrq_n_u8(htmp, 2));
+        q3h.val[3] = vandq_u8(mh, vshrq_n_u8(htmp, 4));
+
+        q3bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q3bits, m3b),                q3h.val[0]));
+        q3bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 2), m3b), q3h.val[1]));
+        q3bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 4), m3b), q3h.val[2]));
+        q3bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q3bits, 6),                q3h.val[3]));
+
+#if defined(__ARM_FEATURE_DOTPROD)
+        isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes.val[0])) * scales[0];
+        isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes.val[1])) * scales[2];
+        isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes.val[2])) * scales[1];
+        isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes.val[3])) * scales[3];
+#else
+        const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
+                                       vmull_s8(vget_high_s8(q3bytes.val[0]), vget_high_s8(q8bytes.val[0])));
+        const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
+                                       vmull_s8(vget_high_s8(q3bytes.val[1]), vget_high_s8(q8bytes.val[1])));
+        const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[2]), vget_low_s8 (q8bytes.val[2])),
+                                       vmull_s8(vget_high_s8(q3bytes.val[2]), vget_high_s8(q8bytes.val[2])));
+        const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[3]), vget_low_s8 (q8bytes.val[3])),
+                                       vmull_s8(vget_high_s8(q3bytes.val[3]), vget_high_s8(q8bytes.val[3])));
+        isum += vaddvq_s16(p0) * scales[0] + vaddvq_s16(p1) * scales[2] + vaddvq_s16(p2) * scales[1] + vaddvq_s16(p3) * scales[3];
+#endif
+
+        sum += d * isum;
+
+    }
+
+    *s = sum;
+
+#elif defined __AVX2__
+
+    const __m256i m3 = _mm256_set1_epi8(3);
+    const __m256i m1 = _mm256_set1_epi8(1);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    uint64_t aux64;
+
+    uint16_t aux16[2];
+    const int8_t * aux8 = (const int8_t *)aux16;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict q3 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const uint16_t a = *(const uint16_t *)x[i].scales;
+        aux16[0] = a & 0x0f0f;
+        aux16[1] = (a >> 4) & 0x0f0f;
+
+        const __m256i scale_0 = MM256_SET_M128I(_mm_set1_epi16(aux8[2] - 8), _mm_set1_epi16(aux8[0] - 8));
+        const __m256i scale_1 = MM256_SET_M128I(_mm_set1_epi16(aux8[3] - 8), _mm_set1_epi16(aux8[1] - 8));
+
+        memcpy(&aux64, x[i].hmask, 8);
+
+        const __m128i haux = _mm_set_epi64x(aux64 >> 1, aux64 >> 0);
+        __m256i q3h_0 = MM256_SET_M128I(_mm_srli_epi16(haux, 2), haux);
+        __m256i q3h_1 = _mm256_srli_epi16(q3h_0, 4);
+        q3h_0 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_0, m1), 2);
+        q3h_1 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_1, m1), 2);
+
+        // load low 2 bits
+        const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3);
+
+        // prepare low and high bits
+        const __m256i q3aux  = MM256_SET_M128I(_mm_srli_epi16(q3bits, 2), q3bits);
+        const __m256i q3l_0 = _mm256_and_si256(q3aux, m3);
+        const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3aux, 4), m3);
+
+        // load Q8 quants
+        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
+        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
+
+        // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16,
+        // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set,
+        // and 2 if the high bit was set)
+        const __m256i q8s_0 = _mm256_maddubs_epi16(q3h_0, q8_0);
+        const __m256i q8s_1 = _mm256_maddubs_epi16(q3h_1, q8_1);
+
+        __m256i p16_0 = _mm256_maddubs_epi16(q3l_0, q8_0);
+        __m256i p16_1 = _mm256_maddubs_epi16(q3l_1, q8_1);
+
+        p16_0 = _mm256_sub_epi16(p16_0, q8s_0);
+        p16_1 = _mm256_sub_epi16(p16_1, q8s_1);
+
+        // multiply with scales
+        p16_0 = _mm256_madd_epi16(scale_0, p16_0);
+        p16_1 = _mm256_madd_epi16(scale_1, p16_1);
+
+        p16_0 = _mm256_add_epi32(p16_0, p16_1);
+
+        // multiply with block scale and accumulate
+        acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16_0), acc);
+
+    }
+
+    *s = hsum_float_8(acc);
+
+#elif defined __AVX__
+
+    const __m128i m3 = _mm_set1_epi8(3);
+    const __m128i m1 = _mm_set1_epi8(1);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    uint64_t aux64;
+
+    uint16_t aux16[2];
+    const int8_t * aux8 = (const int8_t *)aux16;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict q3 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const uint16_t a = *(const uint16_t *)x[i].scales;
+        aux16[0] = a & 0x0f0f;
+        aux16[1] = (a >> 4) & 0x0f0f;
+
+        const __m128i scale_0 = _mm_set1_epi16(aux8[0] - 8);
+        const __m128i scale_1 = _mm_set1_epi16(aux8[2] - 8);
+        const __m128i scale_2 = _mm_set1_epi16(aux8[1] - 8);
+        const __m128i scale_3 = _mm_set1_epi16(aux8[3] - 8);
+
+        memcpy(&aux64, x[i].hmask, 8);
+
+        __m128i q3h_0 = _mm_set_epi64x(aux64 >> 1, aux64 >> 0);
+        __m128i q3h_1 = _mm_srli_epi16(q3h_0, 2);
+        __m128i q3h_2 = _mm_srli_epi16(q3h_0, 4);
+        __m128i q3h_3 = _mm_srli_epi16(q3h_0, 6);
+        q3h_0 = _mm_slli_epi16(_mm_andnot_si128(q3h_0, m1), 2);
+        q3h_1 = _mm_slli_epi16(_mm_andnot_si128(q3h_1, m1), 2);
+        q3h_2 = _mm_slli_epi16(_mm_andnot_si128(q3h_2, m1), 2);
+        q3h_3 = _mm_slli_epi16(_mm_andnot_si128(q3h_3, m1), 2);
+
+        // load low 2 bits
+        const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3);
+
+        // prepare low and high bits
+        const __m128i q3l_0 = _mm_and_si128(q3bits, m3);
+        const __m128i q3l_1 = _mm_and_si128(_mm_srli_epi16(q3bits, 2), m3);
+        const __m128i q3l_2 = _mm_and_si128(_mm_srli_epi16(q3bits, 4), m3);
+        const __m128i q3l_3 = _mm_and_si128(_mm_srli_epi16(q3bits, 6), m3);
+
+        // load Q8 quants
+        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
+        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
+
+        // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm_maddubs_epi16,
+        // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set,
+        // and 2 if the high bit was set)
+        const __m128i q8s_0 = _mm_maddubs_epi16(q3h_0, _mm256_extractf128_si256(q8_0, 0));
+        const __m128i q8s_1 = _mm_maddubs_epi16(q3h_1, _mm256_extractf128_si256(q8_0, 1));
+        const __m128i q8s_2 = _mm_maddubs_epi16(q3h_2, _mm256_extractf128_si256(q8_1, 0));
+        const __m128i q8s_3 = _mm_maddubs_epi16(q3h_3, _mm256_extractf128_si256(q8_1, 1));
+
+        __m128i p16_0 = _mm_maddubs_epi16(q3l_0, _mm256_extractf128_si256(q8_0, 0));
+        __m128i p16_1 = _mm_maddubs_epi16(q3l_1, _mm256_extractf128_si256(q8_0, 1));
+        __m128i p16_2 = _mm_maddubs_epi16(q3l_2, _mm256_extractf128_si256(q8_1, 0));
+        __m128i p16_3 = _mm_maddubs_epi16(q3l_3, _mm256_extractf128_si256(q8_1, 1));
+
+        p16_0 = _mm_sub_epi16(p16_0, q8s_0);
+        p16_1 = _mm_sub_epi16(p16_1, q8s_1);
+        p16_2 = _mm_sub_epi16(p16_2, q8s_2);
+        p16_3 = _mm_sub_epi16(p16_3, q8s_3);
+
+        // multiply with scales
+        p16_0 = _mm_madd_epi16(scale_0, p16_0);
+        p16_1 = _mm_madd_epi16(scale_1, p16_1);
+        p16_2 = _mm_madd_epi16(scale_2, p16_2);
+        p16_3 = _mm_madd_epi16(scale_3, p16_3);
+
+        p16_0 = _mm_add_epi32(p16_0, p16_2);
+        p16_1 = _mm_add_epi32(p16_1, p16_3);
+        __m256i p16 = MM256_SET_M128I(p16_1, p16_0);
+
+        // multiply with block scale and accumulate
+        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16)), acc);
+
+    }
+
+    *s = hsum_float_8(acc);
+
+#elif defined __riscv_v_intrinsic
+
+    uint16_t aux16[2];
+    int8_t * scales = (int8_t *)aux16;
+
+    float sumf = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * restrict q3 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const uint16_t a = *(const uint16_t *)x[i].scales;
+        aux16[0] = a & 0x0f0f;
+        aux16[1] = (a >> 4) & 0x0f0f;
+
+        for (int j = 0; j < 4; ++j) scales[j] -= 8;
+
+        int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]);
+
+        const float d = y[i].d * (float)x[i].d;
+
+        vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1);
+
+        // load qh
+        vuint8mf4_t qh_x1   = __riscv_vle8_v_u8mf4(x[i].hmask, 8);
+        vuint8mf2_t qh_x2   = __riscv_vlmul_ext_v_u8mf4_u8mf2(__riscv_vsrl_vx_u8mf4(qh_x1, 1, 8));
+
+        size_t vl = 16;
+
+        // extend and combine both qh_x1 and qh_x2
+        vuint8mf2_t qh_x = __riscv_vslideup_vx_u8mf2(__riscv_vlmul_ext_v_u8mf4_u8mf2(qh_x1), qh_x2, vl/2, vl);
+
+        vuint8mf2_t qh_0 = __riscv_vand_vx_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x2, vl), 0x4, vl);
+        vuint8mf2_t qh_1 = __riscv_vand_vx_u8mf2(qh_x, 0x4, vl);
+        vuint8mf2_t qh_2 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl), 0x4, vl);
+        vuint8mf2_t qh_3 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x4, vl), 0x4, vl);
+
+        // load Q3
+        vuint8mf2_t q3_x  = __riscv_vle8_v_u8mf2(q3, vl);
+
+        vuint8mf2_t q3h_0 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q3_x, 0x3, vl), qh_0, vl);
+        vuint8mf2_t q3h_1 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 2, vl), 0x3, vl), qh_1, vl);
+        vuint8mf2_t q3h_2 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 4, vl), 0x3, vl), qh_2, vl);
+        vuint8mf2_t q3h_3 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 0x6, vl), qh_3, vl);
+
+        vint8mf2_t q3_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_0);
+        vint8mf2_t q3_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_1);
+        vint8mf2_t q3_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_2);
+        vint8mf2_t q3_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_3);
+
+        // load Q8 and take product with Q3
+        vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q3_0, __riscv_vle8_v_i8mf2(q8, vl), vl);
+        vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q3_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl);
+        vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q3_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl);
+        vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q3_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl);
+
+        vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl);
+        vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl);
+        vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl);
+        vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl);
+
+        isum += __riscv_vmv_x_s_i32m1_i32(vs_0) * scales[0];
+        isum += __riscv_vmv_x_s_i32m1_i32(vs_1) * scales[2];
+        isum += __riscv_vmv_x_s_i32m1_i32(vs_2) * scales[1];
+        isum += __riscv_vmv_x_s_i32m1_i32(vs_3) * scales[3];
+
+        sumf += d * isum;
+
+    }
+
+    *s = sumf;
+
+#else
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    int32_t scales[4];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * restrict q3 = x[i].qs;
+        const uint8_t * restrict hm = x[i].hmask;
+        const  int8_t * restrict q8 = y[i].qs;
+        int8_t * restrict a = aux8;
+        for (int l = 0; l < 8; ++l) {
+            a[l+ 0] = (int8_t)((q3[l+0] >> 0) & 3) - (hm[l] & 0x01 ? 0 : 4);
+            a[l+ 8] = (int8_t)((q3[l+8] >> 0) & 3) - (hm[l] & 0x02 ? 0 : 4);
+            a[l+16] = (int8_t)((q3[l+0] >> 2) & 3) - (hm[l] & 0x04 ? 0 : 4);
+            a[l+24] = (int8_t)((q3[l+8] >> 2) & 3) - (hm[l] & 0x08 ? 0 : 4);
+            a[l+32] = (int8_t)((q3[l+0] >> 4) & 3) - (hm[l] & 0x10 ? 0 : 4);
+            a[l+40] = (int8_t)((q3[l+8] >> 4) & 3) - (hm[l] & 0x20 ? 0 : 4);
+            a[l+48] = (int8_t)((q3[l+0] >> 6) & 3) - (hm[l] & 0x40 ? 0 : 4);
+            a[l+56] = (int8_t)((q3[l+8] >> 6) & 3) - (hm[l] & 0x80 ? 0 : 4);
+        }
+
+        scales[0] = (x[i].scales[0] & 0xF) - 8;
+        scales[1] = (x[i].scales[0] >>  4) - 8;
+        scales[2] = (x[i].scales[1] & 0xF) - 8;
+        scales[3] = (x[i].scales[1] >>  4) - 8;
+
+        memset(aux32, 0, 8*sizeof(int32_t));
+        for (int j = 0; j < QK_K/16; ++j) {
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] += q8[l] * a[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux32[l] += scales[j] * aux16[l];
+        }
+        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
+
+#endif
+
+}
+#endif
+
+#if QK_K == 256
+void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    assert(n % QK_K == 0);
+
+    const block_q4_K * restrict x = vx;
+    const block_q8_K * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+    static const uint32_t kmask1 = 0x3f3f3f3f;
+    static const uint32_t kmask2 = 0x0f0f0f0f;
+    static const uint32_t kmask3 = 0x03030303;
+
+    uint32_t utmp[4];
+
+#ifdef __ARM_NEON
+
+    const uint8x16_t m4b = vdupq_n_u8(0xf);
+#ifdef __ARM_FEATURE_DOTPROD
+    const int32x4_t mzero = vdupq_n_s32(0);
+#endif
+
+    int8x16x2_t q4bytes;
+    int8x16x2_t q8bytes;
+
+    float sumf = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8));
+
+        memcpy(utmp, x[i].scales, 12);
+
+        uint32x2_t mins8 = { 0 };
+        mins8 = vset_lane_u32(utmp[1] & kmask1, mins8, 0);
+        mins8 = vset_lane_u32(((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4), mins8, 1);
+
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[0] &= kmask1;
+
+        const int16x8_t mins = vreinterpretq_s16_u16(vmovl_u8(vreinterpret_u8_u32(mins8)));
+        const int32x4_t prod = vaddq_s32(vmull_s16(vget_low_s16 (q8sums), vget_low_s16 (mins)),
+                                         vmull_s16(vget_high_s16(q8sums), vget_high_s16(mins)));
+        sumf -= dmin * vaddvq_s32(prod);
+
+        const uint8_t * scales = (const uint8_t *)utmp;
+
+        const uint8_t * restrict q4 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        int32_t sumi1 = 0;
+        int32_t sumi2 = 0;
+
+        for (int j = 0; j < QK_K/64; ++j) {
+
+            const uint8x16x2_t q4bits = vld1q_u8_x2(q4); q4 += 32;
+
+#ifdef __ARM_FEATURE_DOTPROD
+            q8bytes = vld1q_s8_x2(q8); q8 += 32;
+            q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8  (q4bits.val[0], m4b));
+            q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8  (q4bits.val[1], m4b));
+
+            const int32x4_t p1 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]);
+            sumi1 += vaddvq_s32(p1) * scales[2*j+0];
+
+            q8bytes = vld1q_s8_x2(q8); q8 += 32;
+            q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4));
+            q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4));
+
+            const int32x4_t p2 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]);
+
+            sumi2 += vaddvq_s32(p2) * scales[2*j+1];
+#else
+            q8bytes = vld1q_s8_x2(q8); q8 += 32;
+            q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8  (q4bits.val[0], m4b));
+            q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8  (q4bits.val[1], m4b));
+            const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
+                                           vmull_s8(vget_high_s8(q4bytes.val[0]), vget_high_s8(q8bytes.val[0])));
+            const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
+                                           vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[1])));
+            sumi1 += vaddvq_s16(vaddq_s16(p0, p1)) * scales[2*j+0];
+
+            q8bytes = vld1q_s8_x2(q8); q8 += 32;
+            q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4));
+            q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4));
+            const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
+                                           vmull_s8(vget_high_s8(q4bytes.val[0]), vget_high_s8(q8bytes.val[0])));
+            const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
+                                           vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[1])));
+            sumi2 += vaddvq_s16(vaddq_s16(p2, p3)) * scales[2*j+1];
+
+#endif
+        }
+
+        sumf += d * (sumi1 + sumi2);
+
+    }
+
+    *s = sumf;
+
+#elif defined __AVX2__
+
+    const __m256i m4 = _mm256_set1_epi8(0xF);
+
+    __m256 acc = _mm256_setzero_ps();
+    __m128 acc_m = _mm_setzero_ps();
+
+   for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        const uint8_t * restrict q4 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]));
+
+        const __m256i q8sums = _mm256_loadu_si256((const __m256i*)y[i].bsums);
+        const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1));
+        const __m128i prod = _mm_madd_epi16(_mm256_extracti128_si256(mins_and_scales, 1), q8s);
+        acc_m = _mm_fmadd_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod), acc_m);
+
+        const __m128i sc128  = _mm256_extracti128_si256(mins_and_scales, 0);
+        const __m256i scales = MM256_SET_M128I(sc128, sc128);
+
+        __m256i sumi = _mm256_setzero_si256();
+
+        for (int j = 0; j < QK_K/64; ++j) {
+
+            const __m256i scale_l = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+0));
+            const __m256i scale_h = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+1));
+
+            const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4); q4 += 32;
+            const __m256i q4l = _mm256_and_si256(q4bits, m4);
+            const __m256i q4h = _mm256_and_si256(_mm256_srli_epi16(q4bits, 4), m4);
+
+            const __m256i q8l = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            __m256i p16l = _mm256_maddubs_epi16(q4l, q8l);
+            p16l = _mm256_madd_epi16(scale_l, p16l);
+
+            const __m256i q8h = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            __m256i p16h = _mm256_maddubs_epi16(q4h, q8h);
+            p16h = _mm256_madd_epi16(scale_h, p16h);
+            const __m256i sumj = _mm256_add_epi32(p16l, p16h);
+
+            sumi = _mm256_add_epi32(sumi, sumj);
+        }
+
+        __m256 vd = _mm256_set1_ps(d);
+        acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(sumi), acc);
+
+    }
+
+    acc_m = _mm_add_ps(acc_m, _mm_movehl_ps(acc_m, acc_m));
+    acc_m = _mm_add_ss(acc_m, _mm_movehdup_ps(acc_m));
+
+    *s = hsum_float_8(acc) + _mm_cvtss_f32(acc_m);
+
+#elif defined __AVX__
+
+    const __m128i m4 = _mm_set1_epi8(0xF);
+    const __m128i m2 = _mm_set1_epi8(0x2);
+
+    __m256 acc = _mm256_setzero_ps();
+    __m128 acc_m = _mm_setzero_ps();
+
+   for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        const uint8_t * restrict q4 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        const __m128i utmps = _mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]);
+        const __m128i scales = _mm_cvtepu8_epi16(utmps);
+        const __m128i mins = _mm_cvtepu8_epi16(_mm_unpackhi_epi64(utmps, utmps));
+
+        const __m128i q8sums_0 = _mm_loadu_si128((const __m128i*)&y[i].bsums[0]);
+        const __m128i q8sums_1 = _mm_loadu_si128((const __m128i*)&y[i].bsums[8]);
+        const __m128i q8s = _mm_hadd_epi16(q8sums_0, q8sums_1);
+        const __m128i prod = _mm_madd_epi16(mins, q8s);
+        acc_m = _mm_add_ps(_mm_mul_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod)), acc_m);
+
+        __m128i sumi_0 = _mm_setzero_si128();
+        __m128i sumi_1 = _mm_setzero_si128();
+
+        __m128i shuffle = _mm_set1_epi16(0x0100);
+        for (int j = 0; j < QK_K/64; ++j) {
+
+            const __m128i scale_l = _mm_shuffle_epi8(scales, shuffle);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            const __m128i scale_h = _mm_shuffle_epi8(scales, shuffle);
+            shuffle = _mm_add_epi16(shuffle, m2);
+
+            __m128i q4bits = _mm_loadu_si128((const __m128i*)q4); q4 += 16;
+            const __m128i q4l_0 = _mm_and_si128(q4bits, m4);
+            const __m128i q4h_0 = _mm_and_si128(_mm_srli_epi16(q4bits, 4), m4);
+            q4bits = _mm_loadu_si128((const __m128i*)q4); q4 += 16;
+            const __m128i q4l_1 = _mm_and_si128(q4bits, m4);
+            const __m128i q4h_1 = _mm_and_si128(_mm_srli_epi16(q4bits, 4), m4);
+
+            const __m128i q8l_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            __m128i p16l = _mm_maddubs_epi16(q4l_0, q8l_0);
+            p16l = _mm_madd_epi16(scale_l, p16l);
+            sumi_0 = _mm_add_epi32(sumi_0, p16l);
+            const __m128i q8l_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            p16l = _mm_maddubs_epi16(q4l_1, q8l_1);
+            p16l = _mm_madd_epi16(scale_l, p16l);
+            sumi_1 = _mm_add_epi32(sumi_1, p16l);
+
+            const __m128i q8h_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            __m128i p16h = _mm_maddubs_epi16(q4h_0, q8h_0);
+            p16h = _mm_madd_epi16(scale_h, p16h);
+            sumi_0 = _mm_add_epi32(sumi_0, p16h);
+            const __m128i q8h_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            p16h = _mm_maddubs_epi16(q4h_1, q8h_1);
+            p16h = _mm_madd_epi16(scale_h, p16h);
+            sumi_1 = _mm_add_epi32(sumi_1, p16h);
+
+        }
+
+        __m256 vd = _mm256_set1_ps(d);
+        __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
+        acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc);
+
+    }
+
+    acc_m = _mm_add_ps(acc_m, _mm_movehl_ps(acc_m, acc_m));
+    acc_m = _mm_add_ss(acc_m, _mm_movehdup_ps(acc_m));
+
+    *s = hsum_float_8(acc) + _mm_cvtss_f32(acc_m);
+
+#elif defined __riscv_v_intrinsic
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    float sumf = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        size_t vl = 8;
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        vint16mf2_t q8sums_0 = __riscv_vlse16_v_i16mf2(y[i].bsums, 4, vl);
+        vint16mf2_t q8sums_1 = __riscv_vlse16_v_i16mf2(y[i].bsums+1, 4, vl);
+        vint16mf2_t q8sums   = __riscv_vadd_vv_i16mf2(q8sums_0, q8sums_1, vl);
+
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        vuint8mf4_t mins8  = __riscv_vle8_v_u8mf4(mins, vl);
+        vint16mf2_t v_mins = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vzext_vf2_u16mf2(mins8, vl));
+        vint32m1_t  prod   = __riscv_vwmul_vv_i32m1(q8sums, v_mins, vl);
+
+        vint32m1_t sumi = __riscv_vredsum_vs_i32m1_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl);
+        sumf -= dmin * __riscv_vmv_x_s_i32m1_i32(sumi);
+
+        const uint8_t * restrict q4 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        vl = 32;
+
+        int32_t sum_1 = 0;
+        int32_t sum_2 = 0;
+
+        vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1);
+
+        for (int j = 0; j < QK_K/64; ++j) {
+            // load Q4
+            vuint8m1_t q4_x = __riscv_vle8_v_u8m1(q4, vl);
+
+            // load Q8 and multiply it with lower Q4 nibble
+            vint8m1_t  q8_0 = __riscv_vle8_v_i8m1(q8, vl);
+            vint8m1_t  q4_0 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q4_x, 0x0F, vl));
+            vint16m2_t qv_0 = __riscv_vwmul_vv_i16m2(q4_0, q8_0, vl);
+            vint16m1_t vs_0 = __riscv_vredsum_vs_i16m2_i16m1(qv_0, vzero, vl);
+
+            sum_1 += __riscv_vmv_x_s_i16m1_i16(vs_0) * scales[2*j+0];
+
+            // load Q8 and multiply it with upper Q4 nibble
+            vint8m1_t  q8_1 = __riscv_vle8_v_i8m1(q8+32, vl);
+            vint8m1_t  q4_1 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q4_x, 0x04, vl));
+            vint16m2_t qv_1 = __riscv_vwmul_vv_i16m2(q4_1, q8_1, vl);
+            vint16m1_t vs_1 = __riscv_vredsum_vs_i16m2_i16m1(qv_1, vzero, vl);
+
+            sum_2 += __riscv_vmv_x_s_i16m1_i16(vs_1) * scales[2*j+1];
+
+            q4 += 32;    q8 += 64;
+
+        }
+
+        sumf += d*(sum_1 + sum_2);
+
+    }
+
+    *s = sumf;
+
+#else
+
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * restrict q4 = x[i].qs;
+        const  int8_t * restrict q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * restrict a = aux8;
+        for (int j = 0; j < QK_K/64; ++j) {
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
+            a += 32;
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
+            a += 32; q4 += 32;
+        }
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        int sumi = 0;
+        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            int32_t scale = scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = ggml_fp16_to_fp32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
+#endif
+}
+#else
+void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    assert(n % QK_K == 0);
+
+    const block_q4_K * restrict x = vx;
+    const block_q8_K * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#ifdef __ARM_NEON
+
+    const uint8x16_t m4b = vdupq_n_u8(0xf);
+
+#ifdef __ARM_FEATURE_DOTPROD
+    const int32x4_t mzero = vdupq_n_s32(0);
+#endif
+
+    float sumf = 0;
+
+    int8x16x2_t q4bytes;
+    int8x16x4_t q8bytes;
+
+    float sum_mins = 0.f;
+
+    uint16_t aux16[2];
+    const uint8_t * restrict scales = (const uint8_t *)aux16;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * restrict q4 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const uint16_t * restrict a = (const uint16_t *)x[i].scales;
+        aux16[0] = a[0] & 0x0f0f;
+        aux16[1] = (a[0] >> 4) & 0x0f0f;
+
+        const int32_t summi = scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]);
+        sum_mins += y[i].d * (float)x[i].d[1] * summi;
+
+        const float d = y[i].d * (float)x[i].d[0];
+
+        const uint8x16x2_t q4bits = vld1q_u8_x2(q4);
+
+#ifdef __ARM_FEATURE_DOTPROD
+        q8bytes = vld1q_s8_x4(q8);
+        q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8  (q4bits.val[0], m4b));
+        q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8  (q4bits.val[1], m4b));
+
+        const int32x4_t p1 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]);
+        const int32_t sumi1 = vaddvq_s32(p1) * scales[0];
+
+        q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4));
+        q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4));
+
+        const int32x4_t p2 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[2]), q4bytes.val[1], q8bytes.val[3]);
+        const int32_t sumi2 = vaddvq_s32(p2) * scales[1];
+
+#else
+        q8bytes = vld1q_s8_x4(q8);
+        q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8  (q4bits.val[0], m4b));
+        q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8  (q4bits.val[1], m4b));
+        const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
+                                       vmull_s8(vget_high_s8(q4bytes.val[0]), vget_high_s8(q8bytes.val[0])));
+        const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
+                                       vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[1])));
+        int32_t sumi1 = vaddvq_s16(vaddq_s16(p0, p1)) * scales[0];
+
+        q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4));
+        q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4));
+        const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[2])),
+                                       vmull_s8(vget_high_s8(q4bytes.val[0]), vget_high_s8(q8bytes.val[2])));
+        const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[1]), vget_low_s8 (q8bytes.val[3])),
+                                       vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[3])));
+        int32_t sumi2 = vaddvq_s16(vaddq_s16(p2, p3)) * scales[1];
+
+#endif
+        sumf += d * (sumi1 + sumi2);
+
+    }
+
+    *s = sumf - sum_mins;
+
+#elif defined __AVX2__
+
+    const __m256i m4 = _mm256_set1_epi8(0xF);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    float summs = 0;
+
+    uint16_t aux16[2];
+    const uint8_t * scales = (const uint8_t *)aux16;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = ggml_fp16_to_fp32(x[i].d[0]) * y[i].d;
+        const float m = ggml_fp16_to_fp32(x[i].d[1]) * y[i].d;
+        const __m256 vd = _mm256_set1_ps(d);
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux16[0] = a[0] & 0x0f0f;
+        aux16[1] = (a[0] >> 4) & 0x0f0f;
+
+        summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]));
+
+        const uint8_t * restrict q4 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4);
+        const __m256i q4l = _mm256_and_si256(q4bits, m4);
+        const __m256i q4h = _mm256_and_si256(_mm256_srli_epi16(q4bits, 4), m4);
+
+        const __m256i q8l = _mm256_loadu_si256((const __m256i*)(q8+ 0));
+        const __m256i q8h = _mm256_loadu_si256((const __m256i*)(q8+32));
+
+        const __m256i p16l = _mm256_maddubs_epi16(q4l, q8l);
+        const __m256i p16h = _mm256_maddubs_epi16(q4h, q8h);
+
+        const __m256i p32l = _mm256_madd_epi16(_mm256_set1_epi16(scales[0]), p16l);
+        acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(p32l), acc);
+
+        const __m256i p32h = _mm256_madd_epi16(_mm256_set1_epi16(scales[1]), p16h);
+        acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(p32h), acc);
+
+    }
+
+    *s = hsum_float_8(acc) - summs;
+
+#elif defined __AVX__
+
+    const __m128i m4 = _mm_set1_epi8(0xF);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    float summs = 0;
+
+    uint16_t aux16[2];
+    const uint8_t * scales = (const uint8_t *)aux16;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = ggml_fp16_to_fp32(x[i].d[0]) * y[i].d;
+        const float m = ggml_fp16_to_fp32(x[i].d[1]) * y[i].d;
+        const __m256 vd = _mm256_set1_ps(d);
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux16[0] = a[0] & 0x0f0f;
+        aux16[1] = (a[0] >> 4) & 0x0f0f;
+
+        summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]));
+
+        const uint8_t * restrict q4 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4);
+        const __m128i q4bits_0 = _mm256_extractf128_si256(q4bits, 0);
+        const __m128i q4bits_1 = _mm256_extractf128_si256(q4bits, 1);
+        const __m128i q4_0 = _mm_and_si128(q4bits_0, m4);
+        const __m128i q4_1 = _mm_and_si128(q4bits_1, m4);
+        const __m128i q4_2 = _mm_and_si128(_mm_srli_epi16(q4bits_0, 4), m4);
+        const __m128i q4_3 = _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4);
+
+        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
+        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
+
+        const __m128i p16_0 = _mm_maddubs_epi16(q4_0, _mm256_extractf128_si256(q8_0, 0));
+        const __m128i p16_1 = _mm_maddubs_epi16(q4_1, _mm256_extractf128_si256(q8_0, 1));
+        const __m128i p16_2 = _mm_maddubs_epi16(q4_2, _mm256_extractf128_si256(q8_1, 0));
+        const __m128i p16_3 = _mm_maddubs_epi16(q4_3, _mm256_extractf128_si256(q8_1, 1));
+
+        const __m128i p32_0 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_0);
+        const __m128i p32_1 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_1);
+        acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_1, p32_0))), acc);
+
+        const __m128i p32_2 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_2);
+        const __m128i p32_3 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_3);
+        acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_3, p32_2))), acc);
+
+    }
+
+    *s = hsum_float_8(acc) - summs;
+
+#elif defined __riscv_v_intrinsic
+
+    uint16_t s16[2];
+    const uint8_t * restrict scales = (const uint8_t *)s16;
+
+    float sumf = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * restrict q4 = x[i].qs;
+        const  int8_t * restrict q8 = y[i].qs;
+
+        const uint16_t * restrict b = (const uint16_t *)x[i].scales;
+        s16[0] = b[0] & 0x0f0f;
+        s16[1] = (b[0] >> 4) & 0x0f0f;
+
+        sumf -= y[i].d * ggml_fp16_to_fp32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]));
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d[0]);
+
+        size_t vl = 32;
+
+        vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1);
+
+        // load Q4
+        vuint8m1_t q4_x = __riscv_vle8_v_u8m1(q4, vl);
+
+        // load Q8 and multiply it with lower Q4 nibble
+        vint8m1_t  q4_a = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q4_x, 0x0F, vl));
+        vint16m2_t va_0 = __riscv_vwmul_vv_i16m2(q4_a, __riscv_vle8_v_i8m1(q8, vl), vl);
+        vint16m1_t aux1 = __riscv_vredsum_vs_i16m2_i16m1(va_0, vzero, vl);
+
+        sumf += d*scales[0]*__riscv_vmv_x_s_i16m1_i16(aux1);
+
+        // load Q8 and multiply it with upper Q4 nibble
+        vint8m1_t  q4_s = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q4_x, 0x04, vl));
+        vint16m2_t va_1 = __riscv_vwmul_vv_i16m2(q4_s, __riscv_vle8_v_i8m1(q8+32, vl), vl);
+        vint16m1_t aux2 = __riscv_vredsum_vs_i16m2_i16m1(va_1, vzero, vl);
+
+        sumf += d*scales[1]*__riscv_vmv_x_s_i16m1_i16(aux2);
+
+    }
+
+    *s = sumf;
+
+#else
+
+    uint8_t aux8[QK_K];
+    int16_t aux16[16];
+    float   sums [8];
+    memset(sums, 0, 8*sizeof(float));
+
+    uint16_t s16[2];
+    const uint8_t * restrict scales = (const uint8_t *)s16;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * restrict q4 = x[i].qs;
+        const  int8_t * restrict q8 = y[i].qs;
+        uint8_t * restrict a = aux8;
+        for (int l = 0; l < 32; ++l) a[l+ 0] = q4[l] & 0xF;
+        for (int l = 0; l < 32; ++l) a[l+32] = q4[l]  >> 4;
+
+        const uint16_t * restrict b = (const uint16_t *)x[i].scales;
+        s16[0] = b[0] & 0x0f0f;
+        s16[1] = (b[0] >> 4) & 0x0f0f;
+
+        sumf -= y[i].d * ggml_fp16_to_fp32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]));
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d[0]);
+
+        for (int j = 0; j < QK_K/32; ++j) {
+            for (int l = 0; l < 16; ++l) aux16[l] = q8[l] * a[l];
+            q8 += 16; a += 16;
+            for (int l = 0; l < 16; ++l) aux16[l] += q8[l] * a[l];
+            q8 += 16; a += 16;
+            const float dl = d * scales[j];
+            for (int l = 0; l < 8; ++l) sums[l] += dl * (aux16[l] + aux16[l+8]);
+        }
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
+#endif
+}
+#endif
+
+#if QK_K == 256
+void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    assert(n % QK_K == 0);
+
+    const block_q5_K * restrict x = vx;
+    const block_q8_K * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+    static const uint32_t kmask1 = 0x3f3f3f3f;
+    static const uint32_t kmask2 = 0x0f0f0f0f;
+    static const uint32_t kmask3 = 0x03030303;
+
+    uint32_t utmp[4];
+
+
+#ifdef __ARM_NEON
+
+    const uint8x16_t m4b = vdupq_n_u8(0xf);
+    const uint8x16_t mone = vdupq_n_u8(1);
+    const uint8x16_t mtwo = vdupq_n_u8(2);
+#if defined(__ARM_FEATURE_DOTPROD)
+    const int32x4_t mzero = vdupq_n_s32(0);
+#endif
+
+    int8x16x4_t q5bytes;
+
+    float sumf = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8));
+
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        const uint8x8_t mins8 = vld1_u8((const uint8_t*)utmp + 8);
+        const int16x8_t mins = vreinterpretq_s16_u16(vmovl_u8(mins8));
+        const int32x4_t prod = vaddq_s32(vmull_s16(vget_low_s16 (q8sums), vget_low_s16 (mins)),
+                                         vmull_s16(vget_high_s16(q8sums), vget_high_s16(mins)));
+        int32_t sumi_mins = vaddvq_s32(prod);
+
+        const uint8_t * scales = (const uint8_t *)utmp;
+
+        const uint8_t * restrict q5 = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        uint8x16x2_t qhbits = vld1q_u8_x2(qh);
+
+        uint8x16x4_t q5h;
+
+        int32_t sumi = 0;
+
+        for (int j = 0; j < QK_K/64; ++j) {
+
+            const uint8x16x2_t q5bits = vld1q_u8_x2(q5); q5 += 32;
+            const int8x16x4_t q8bytes = vld1q_s8_x4(q8); q8 += 64;
+
+            q5h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4);
+            q5h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4);
+            q5h.val[2] = vshlq_n_u8(vandq_u8(mtwo, qhbits.val[0]), 3);
+            q5h.val[3] = vshlq_n_u8(vandq_u8(mtwo, qhbits.val[1]), 3);
+            qhbits.val[0] = vshrq_n_u8(qhbits.val[0], 2);
+            qhbits.val[1] = vshrq_n_u8(qhbits.val[1], 2);
+
+            q5bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q5bits.val[0], m4b), q5h.val[0]));
+            q5bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q5bits.val[1], m4b), q5h.val[1]));
+            q5bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[0], 4), q5h.val[2]));
+            q5bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[1], 4), q5h.val[3]));
+
+#if defined(__ARM_FEATURE_DOTPROD)
+
+            sumi += vaddvq_s32(vdotq_s32(vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]), q5bytes.val[1], q8bytes.val[1])) * *scales++;
+            sumi += vaddvq_s32(vdotq_s32(vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]), q5bytes.val[3], q8bytes.val[3])) * *scales++;
+#else
+
+            const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
+                                           vmull_s8(vget_high_s8(q5bytes.val[0]), vget_high_s8(q8bytes.val[0])));
+            const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
+                                           vmull_s8(vget_high_s8(q5bytes.val[1]), vget_high_s8(q8bytes.val[1])));
+            sumi += vaddvq_s16(vaddq_s16(p0, p1)) * *scales++;
+
+            const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[2]), vget_low_s8 (q8bytes.val[2])),
+                                           vmull_s8(vget_high_s8(q5bytes.val[2]), vget_high_s8(q8bytes.val[2])));
+            const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[3]), vget_low_s8 (q8bytes.val[3])),
+                                           vmull_s8(vget_high_s8(q5bytes.val[3]), vget_high_s8(q8bytes.val[3])));
+            sumi += vaddvq_s16(vaddq_s16(p2, p3)) * *scales++;
+#endif
+        }
+
+        sumf += d * sumi - dmin * sumi_mins;
+
+    }
+
+    *s = sumf;
+
+#elif defined __AVX2__
+
+    const __m256i m4 = _mm256_set1_epi8(0xF);
+    const __m128i mzero = _mm_setzero_si128();
+    const __m256i mone  = _mm256_set1_epi8(1);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    float summs = 0.f;
+
+   for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * restrict q5 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+#if QK_K == 256
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+#else
+        // TODO
+        const float d = 0, dmin = 0;
+#endif
+
+        const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]));
+
+        const __m256i q8sums = _mm256_loadu_si256((const __m256i*)y[i].bsums);
+        const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1));
+        const __m128i prod = _mm_madd_epi16(_mm256_extracti128_si256(mins_and_scales, 1), q8s);
+        const __m128i hsum = _mm_hadd_epi32(_mm_hadd_epi32(prod, mzero), mzero);
+        summs += dmin * _mm_extract_epi32(hsum, 0);
+
+        const __m128i sc128  = _mm256_extracti128_si256(mins_and_scales, 0);
+        const __m256i scales = MM256_SET_M128I(sc128, sc128);
+
+        const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].qh);
+        __m256i hmask = mone;
+
+        __m256i sumi = _mm256_setzero_si256();
+
+        int bit = 0;
+
+        for (int j = 0; j < QK_K/64; ++j) {
+
+            const __m256i scale_0 = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+0));
+            const __m256i scale_1 = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+1));
+
+            const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5); q5 += 32;
+
+            const __m256i q5l_0 = _mm256_and_si256(q5bits, m4);
+            const __m256i q5h_0 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_and_si256(hbits, hmask), bit++), 4);
+            const __m256i q5_0  = _mm256_add_epi8(q5l_0, q5h_0);
+            hmask = _mm256_slli_epi16(hmask, 1);
+
+            const __m256i q5l_1 = _mm256_and_si256(_mm256_srli_epi16(q5bits, 4), m4);
+            const __m256i q5h_1 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_and_si256(hbits, hmask), bit++), 4);
+            const __m256i q5_1  = _mm256_add_epi8(q5l_1, q5h_1);
+            hmask = _mm256_slli_epi16(hmask, 1);
+
+            const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+
+            __m256i p16_0 = _mm256_maddubs_epi16(q5_0, q8_0);
+            __m256i p16_1 = _mm256_maddubs_epi16(q5_1, q8_1);
+
+            p16_0 = _mm256_madd_epi16(scale_0, p16_0);
+            p16_1 = _mm256_madd_epi16(scale_1, p16_1);
+
+            sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1));
+
+        }
+
+        __m256 vd = _mm256_set1_ps(d);
+        acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(sumi), acc);
+
+    }
+
+    *s = hsum_float_8(acc) + summs;
+
+#elif defined __AVX__
+
+    const __m128i m4 = _mm_set1_epi8(0xF);
+    const __m128i mzero = _mm_setzero_si128();
+    const __m128i mone  = _mm_set1_epi8(1);
+    const __m128i m2 = _mm_set1_epi8(2);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    float summs = 0.f;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+
+        const uint8_t * restrict q5 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        const __m128i utmps = _mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]);
+        const __m128i scales = _mm_cvtepu8_epi16(utmps);
+        const __m128i mins = _mm_cvtepu8_epi16(_mm_unpackhi_epi64(utmps, utmps));
+
+        const __m128i q8sums_0 = _mm_loadu_si128((const __m128i*)&y[i].bsums[0]);
+        const __m128i q8sums_1 = _mm_loadu_si128((const __m128i*)&y[i].bsums[8]);
+        const __m128i q8s = _mm_hadd_epi16(q8sums_0, q8sums_1);
+        const __m128i prod = _mm_madd_epi16(mins, q8s);
+        const __m128i hsum = _mm_hadd_epi32(_mm_hadd_epi32(prod, mzero), mzero);
+        summs += dmin * _mm_extract_epi32(hsum, 0);
+
+        const __m128i hbits_0 = _mm_loadu_si128((const __m128i*)&x[i].qh[0]);
+        const __m128i hbits_1 = _mm_loadu_si128((const __m128i*)&x[i].qh[16]);
+        __m128i hmask = mone;
+
+        __m128i sumi_0 = _mm_setzero_si128();
+        __m128i sumi_1 = _mm_setzero_si128();
+
+        int bit = 0;
+
+        __m128i shuffle = _mm_set1_epi16(0x0100);
+        for (int j = 0; j < QK_K/64; ++j) {
+
+            const __m128i scale_0 = _mm_shuffle_epi8(scales, shuffle);
+            shuffle = _mm_add_epi16(shuffle, m2);
+            const __m128i scale_1 = _mm_shuffle_epi8(scales, shuffle);
+            shuffle = _mm_add_epi16(shuffle, m2);
+
+            const __m128i q5bits_0 = _mm_loadu_si128((const __m128i*)q5); q5 += 16;
+            const __m128i q5bits_1 = _mm_loadu_si128((const __m128i*)q5); q5 += 16;
+
+            __m128i q5l_0 = _mm_and_si128(q5bits_0, m4);
+            __m128i q5l_1 = _mm_and_si128(q5bits_1, m4);
+            __m128i q5h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_0, hmask), bit), 4);
+            __m128i q5h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_1, hmask), bit++), 4);
+            __m128i q5_0  = _mm_add_epi8(q5l_0, q5h_0);
+            __m128i q5_1  = _mm_add_epi8(q5l_1, q5h_1);
+            hmask = _mm_slli_epi16(hmask, 1);
+
+            __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            __m128i p16_0 = _mm_maddubs_epi16(q5_0, q8_0);
+            __m128i p16_1 = _mm_maddubs_epi16(q5_1, q8_1);
+            p16_0 = _mm_madd_epi16(scale_0, p16_0);
+            p16_1 = _mm_madd_epi16(scale_0, p16_1);
+
+            q5l_0 = _mm_and_si128(_mm_srli_epi16(q5bits_0, 4), m4);
+            q5l_1 = _mm_and_si128(_mm_srli_epi16(q5bits_1, 4), m4);
+            q5h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_0, hmask), bit), 4);
+            q5h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_1, hmask), bit++), 4);
+            q5_0  = _mm_add_epi8(q5l_0, q5h_0);
+            q5_1  = _mm_add_epi8(q5l_1, q5h_1);
+            hmask = _mm_slli_epi16(hmask, 1);
+
+            q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            __m128i p16_2 = _mm_maddubs_epi16(q5_0, q8_0);
+            __m128i p16_3 = _mm_maddubs_epi16(q5_1, q8_1);
+            p16_2 = _mm_madd_epi16(scale_1, p16_2);
+            p16_3 = _mm_madd_epi16(scale_1, p16_3);
+
+            sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2));
+            sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3));
+
+        }
+
+        __m256 vd = _mm256_set1_ps(d);
+        __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
+        acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc);
+
+    }
+
+    *s = hsum_float_8(acc) + summs;
+
+#elif defined __riscv_v_intrinsic
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    float sumf = 0;
+    float sums = 0.0;
+
+    size_t vl;
+
+    for (int i = 0; i < nb; ++i) {
+
+        vl = 8;
+
+        const uint8_t * restrict q5 = x[i].qs;
+        const uint8_t * restrict hm = x[i].qh;
+        const  int8_t * restrict q8 = y[i].qs;
+
+        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        const float dmin = ggml_fp16_to_fp32(x[i].dmin) * y[i].d;
+
+        vint16mf2_t q8sums_0 = __riscv_vlse16_v_i16mf2(y[i].bsums, 4, vl);
+        vint16mf2_t q8sums_1 = __riscv_vlse16_v_i16mf2(y[i].bsums+1, 4, vl);
+        vint16mf2_t q8sums = __riscv_vadd_vv_i16mf2(q8sums_0, q8sums_1, vl);
+
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        vuint8mf4_t mins8 = __riscv_vle8_v_u8mf4(mins, vl);
+        vint16mf2_t v_mins = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vzext_vf2_u16mf2(mins8, vl));
+        vint32m1_t prod = __riscv_vwmul_vv_i32m1(q8sums, v_mins, vl);
+
+        vint32m1_t sumi = __riscv_vredsum_vs_i32m1_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl);
+        sumf -= dmin * __riscv_vmv_x_s_i32m1_i32(sumi);
+
+        vl = 32;
+        int32_t aux32 = 0;
+        int is = 0;
+
+        uint8_t m = 1;
+        vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1);
+        vuint8m1_t vqh = __riscv_vle8_v_u8m1(hm, vl);
+
+        for (int j = 0; j < QK_K/64; ++j) {
+            // load Q5 and Q8
+            vuint8m1_t q5_x = __riscv_vle8_v_u8m1(q5, vl);
+            vint8m1_t  q8_y1 = __riscv_vle8_v_i8m1(q8, vl);
+            vint8m1_t  q8_y2 = __riscv_vle8_v_i8m1(q8+32, vl);
+
+            // compute mask for addition
+            vint8m1_t q5_a = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q5_x, 0x0F, vl));
+            vuint8m1_t qh_m1 = __riscv_vand_vx_u8m1(vqh, m, vl);
+            vbool8_t vmask_1 = __riscv_vmsne_vx_u8m1_b8(qh_m1, 0, vl);
+            vint8m1_t q5_m1 = __riscv_vadd_vx_i8m1_m(vmask_1, q5_a, 16, vl);
+            m <<= 1;
+
+            vint8m1_t q5_l = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q5_x, 0x04, vl));
+            vuint8m1_t qh_m2 = __riscv_vand_vx_u8m1(vqh, m, vl);
+            vbool8_t vmask_2 = __riscv_vmsne_vx_u8m1_b8(qh_m2, 0, vl);
+            vint8m1_t q5_m2 = __riscv_vadd_vx_i8m1_m(vmask_2, q5_l, 16, vl);
+            m <<= 1;
+
+            vint16m2_t v0 = __riscv_vwmul_vv_i16m2(q5_m1, q8_y1, vl);
+            vint16m2_t v1 = __riscv_vwmul_vv_i16m2(q5_m2, q8_y2, vl);
+
+            vint32m4_t vs1 = __riscv_vwmul_vx_i32m4(v0, scales[is++], vl);
+            vint32m4_t vs2 = __riscv_vwmul_vx_i32m4(v1, scales[is++], vl);
+
+            vint32m1_t vacc1 = __riscv_vredsum_vs_i32m4_i32m1(vs1, vzero, vl);
+            vint32m1_t vacc2 = __riscv_vredsum_vs_i32m4_i32m1(vs2, vzero, vl);
+
+            aux32 += __riscv_vmv_x_s_i32m1_i32(vacc1) + __riscv_vmv_x_s_i32m1_i32(vacc2);
+            q5 += 32;    q8 += 64;
+
+        }
+
+        vfloat32m1_t vaux = __riscv_vfmul_vf_f32m1(__riscv_vfmv_v_f_f32m1(aux32, 1), d, 1);
+        sums += __riscv_vfmv_f_s_f32m1_f32(vaux);
+
+    }
+
+    *s = sumf+sums;
+
+#else
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * restrict q4 = x[i].qs;
+        const uint8_t * restrict hm = x[i].qh;
+        const  int8_t * restrict q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * restrict a = aux8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K/64; ++j) {
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
+            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
+            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
+            a += 32; m <<= 1;
+            q4 += 32;
+        }
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        int sumi = 0;
+        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            int32_t scale = scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = ggml_fp16_to_fp32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
+#endif
+}
+
+#else
+
+void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    assert(n % QK_K == 0);
+
+    const block_q5_K * restrict x = vx;
+    const block_q8_K * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#ifdef __ARM_NEON
+
+    const uint8x16_t m4b = vdupq_n_u8(0xf);
+    const uint8x16_t mh = vdupq_n_u8(16);
+#if defined(__ARM_FEATURE_DOTPROD)
+    const int32x4_t mzero = vdupq_n_s32(0);
+#endif
+
+    int8x16x4_t q5bytes;
+    uint8x16x4_t q5h;
+
+    float sumf = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * (float)x[i].d;
+        const int8_t * sc = x[i].scales;
+
+        const uint8_t * restrict q5 = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const uint8x8_t qhbits = vld1_u8(qh);
+
+        const uint8x16x2_t q5bits = vld1q_u8_x2(q5);
+        const int8x16x4_t q8bytes = vld1q_s8_x4(q8);
+
+        const uint8x16_t htmp = vcombine_u8(qhbits, vshr_n_u8(qhbits, 1));
+        q5h.val[0] = vbicq_u8(mh, vshlq_n_u8(htmp, 4));
+        q5h.val[1] = vbicq_u8(mh, vshlq_n_u8(htmp, 2));
+        q5h.val[2] = vbicq_u8(mh, htmp);
+        q5h.val[3] = vbicq_u8(mh, vshrq_n_u8(htmp, 2));
+
+        q5bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q5bits.val[0], m4b)), vreinterpretq_s8_u8(q5h.val[0]));
+        q5bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q5bits.val[1], m4b)), vreinterpretq_s8_u8(q5h.val[1]));
+        q5bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[0], 4)), vreinterpretq_s8_u8(q5h.val[2]));
+        q5bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[1], 4)), vreinterpretq_s8_u8(q5h.val[3]));
+
+#if defined(__ARM_FEATURE_DOTPROD)
+
+        int32_t sumi1 = sc[0] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]));
+        int32_t sumi2 = sc[1] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[1], q8bytes.val[1]));
+        int32_t sumi3 = sc[2] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]));
+        int32_t sumi4 = sc[3] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[3], q8bytes.val[3]));
+
+        sumf += d * (sumi1 + sumi2 + sumi3 + sumi4);
+
+#else
+
+        const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
+                                       vmull_s8(vget_high_s8(q5bytes.val[0]), vget_high_s8(q8bytes.val[0])));
+        const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
+                                       vmull_s8(vget_high_s8(q5bytes.val[1]), vget_high_s8(q8bytes.val[1])));
+        int32_t sumi = sc[0] * vaddvq_s16(p0) + sc[1] * vaddvq_s16(p1);
+
+        const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[2]), vget_low_s8 (q8bytes.val[2])),
+                                       vmull_s8(vget_high_s8(q5bytes.val[2]), vget_high_s8(q8bytes.val[2])));
+        const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[3]), vget_low_s8 (q8bytes.val[3])),
+                                       vmull_s8(vget_high_s8(q5bytes.val[3]), vget_high_s8(q8bytes.val[3])));
+        sumi += sc[2] * vaddvq_s16(p2) + sc[3] * vaddvq_s16(p3);
+
+        sumf += d*sumi;
+#endif
+
+    }
+
+    *s = sumf;
+
+#elif defined __AVX2__
+
+    const __m256i m4 = _mm256_set1_epi8(0xF);
+    const __m256i mone  = _mm256_set1_epi8(1);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * restrict q5 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+
+        const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5);
+
+        const __m256i scale_l = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[1]), _mm_set1_epi16(x[i].scales[0]));
+        const __m256i scale_h = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[3]), _mm_set1_epi16(x[i].scales[2]));
+
+        int64_t aux64;
+        memcpy(&aux64, x[i].qh, 8);
+        const __m128i haux128 = _mm_set_epi64x(aux64 >> 1, aux64);
+        const __m256i haux256 = MM256_SET_M128I(_mm_srli_epi16(haux128, 2), haux128);
+
+        const __m256i q5h_0 = _mm256_slli_epi16(_mm256_andnot_si256(haux256, mone), 4);
+        const __m256i q5h_1 = _mm256_slli_epi16(_mm256_andnot_si256(_mm256_srli_epi16(haux256, 4), mone), 4);
+
+        const __m256i q5l_0 = _mm256_and_si256(q5bits, m4);
+        const __m256i q5l_1 = _mm256_and_si256(_mm256_srli_epi16(q5bits, 4), m4);
+
+        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
+        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
+
+        const __m256i p16_0 = _mm256_madd_epi16(scale_l, _mm256_maddubs_epi16(q5l_0, q8_0));
+        const __m256i p16_1 = _mm256_madd_epi16(scale_h, _mm256_maddubs_epi16(q5l_1, q8_1));
+        const __m256i s16_0 = _mm256_madd_epi16(scale_l, _mm256_maddubs_epi16(q5h_0, q8_0));
+        const __m256i s16_1 = _mm256_madd_epi16(scale_h, _mm256_maddubs_epi16(q5h_1, q8_1));
+
+        const __m256i dot = _mm256_sub_epi32(_mm256_add_epi32(p16_0, p16_1), _mm256_add_epi32(s16_0, s16_1));
+
+        acc = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(dot), acc);
+
+    }
+
+    *s = hsum_float_8(acc);
+
+#elif defined __AVX__
+
+    const __m128i m4 = _mm_set1_epi8(0xF);
+    const __m128i mone  = _mm_set1_epi8(1);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * restrict q5 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+
+        const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5);
+
+        const __m128i scale_0 = _mm_set1_epi16(x[i].scales[0]);
+        const __m128i scale_1 = _mm_set1_epi16(x[i].scales[1]);
+        const __m128i scale_2 = _mm_set1_epi16(x[i].scales[2]);
+        const __m128i scale_3 = _mm_set1_epi16(x[i].scales[3]);
+
+        int64_t aux64;
+        memcpy(&aux64, x[i].qh, 8);
+        const __m128i haux128_0 = _mm_set_epi64x(aux64 >> 1, aux64);
+        const __m128i haux128_1 = _mm_srli_epi16(haux128_0, 2);
+
+        const __m128i q5h_0 = _mm_slli_epi16(_mm_andnot_si128(haux128_0, mone), 4);
+        const __m128i q5h_1 = _mm_slli_epi16(_mm_andnot_si128(haux128_1, mone), 4);
+        const __m128i q5h_2 = _mm_slli_epi16(_mm_andnot_si128(_mm_srli_epi16(haux128_0, 4), mone), 4);
+        const __m128i q5h_3 = _mm_slli_epi16(_mm_andnot_si128(_mm_srli_epi16(haux128_1, 4), mone), 4);
+
+        const __m128i q5l_0 = _mm_and_si128(_mm256_extractf128_si256(q5bits, 0), m4);
+        const __m128i q5l_1 = _mm_and_si128(_mm256_extractf128_si256(q5bits, 1), m4);
+        const __m128i q5l_2 = _mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q5bits, 0), 4), m4);
+        const __m128i q5l_3 = _mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q5bits, 1), 4), m4);
+
+        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
+        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
+
+        const __m128i p16_0 = _mm_madd_epi16(scale_0, _mm_maddubs_epi16(q5l_0, _mm256_extractf128_si256(q8_0, 0)));
+        const __m128i p16_1 = _mm_madd_epi16(scale_1, _mm_maddubs_epi16(q5l_1, _mm256_extractf128_si256(q8_0, 1)));
+        const __m128i p16_2 = _mm_madd_epi16(scale_2, _mm_maddubs_epi16(q5l_2, _mm256_extractf128_si256(q8_1, 0)));
+        const __m128i p16_3 = _mm_madd_epi16(scale_3, _mm_maddubs_epi16(q5l_3, _mm256_extractf128_si256(q8_1, 1)));
+        const __m128i s16_0 = _mm_madd_epi16(scale_0, _mm_maddubs_epi16(q5h_0, _mm256_extractf128_si256(q8_0, 0)));
+        const __m128i s16_1 = _mm_madd_epi16(scale_1, _mm_maddubs_epi16(q5h_1, _mm256_extractf128_si256(q8_0, 1)));
+        const __m128i s16_2 = _mm_madd_epi16(scale_2, _mm_maddubs_epi16(q5h_2, _mm256_extractf128_si256(q8_1, 0)));
+        const __m128i s16_3 = _mm_madd_epi16(scale_3, _mm_maddubs_epi16(q5h_3, _mm256_extractf128_si256(q8_1, 1)));
+
+        const __m128i dot_0 = _mm_sub_epi32(_mm_add_epi32(p16_0, p16_2), _mm_add_epi32(s16_0, s16_2));
+        const __m128i dot_1 = _mm_sub_epi32(_mm_add_epi32(p16_1, p16_3), _mm_add_epi32(s16_1, s16_3));
+
+        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(dot_1, dot_0))), acc);
+
+    }
+
+    *s = hsum_float_8(acc);
+
+#elif defined __riscv_v_intrinsic
+
+    float sumf = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * (float)x[i].d;
+        const int8_t * sc = x[i].scales;
+
+        const uint8_t * restrict q5 = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1);
+
+        // load qh
+        vuint8mf4_t qh_x1   = __riscv_vle8_v_u8mf4(qh, 8);
+        vuint8mf2_t qh_x2   = __riscv_vlmul_ext_v_u8mf4_u8mf2(__riscv_vsrl_vx_u8mf4(qh_x1, 1, 8));
+
+        size_t vl = 16;
+
+        // combine both qh_1 and qh_2
+        vuint8mf2_t qh_x = __riscv_vslideup_vx_u8mf2(__riscv_vlmul_ext_v_u8mf4_u8mf2(qh_x1), qh_x2, vl/2, vl);
+
+        vuint8mf2_t qh_h0 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x4, vl), vl), 16, vl);
+        vuint8mf2_t qh_h1 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x2, vl), vl), 16, vl);
+        vuint8mf2_t qh_h2 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(qh_x, vl), 16, vl);
+        vuint8mf2_t qh_h3 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x4, vl), vl), 16, vl);
+
+        vint8mf2_t qh_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h0);
+        vint8mf2_t qh_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h1);
+        vint8mf2_t qh_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h2);
+        vint8mf2_t qh_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h3);
+
+        // load q5
+        vuint8mf2_t q5_x1  = __riscv_vle8_v_u8mf2(q5, vl);
+        vuint8mf2_t q5_x2  = __riscv_vle8_v_u8mf2(q5+16, vl);
+
+        vint8mf2_t q5s_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q5_x1, 0xF, vl));
+        vint8mf2_t q5s_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q5_x2, 0xF, vl));
+        vint8mf2_t q5s_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vsrl_vx_u8mf2(q5_x1, 0x4, vl));
+        vint8mf2_t q5s_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vsrl_vx_u8mf2(q5_x2, 0x4, vl));
+
+        vint8mf2_t q5_0 = __riscv_vsub_vv_i8mf2(q5s_0, qh_0, vl);
+        vint8mf2_t q5_1 = __riscv_vsub_vv_i8mf2(q5s_1, qh_1, vl);
+        vint8mf2_t q5_2 = __riscv_vsub_vv_i8mf2(q5s_2, qh_2, vl);
+        vint8mf2_t q5_3 = __riscv_vsub_vv_i8mf2(q5s_3, qh_3, vl);
+
+        // load Q8 and multiply it with Q5
+        vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q5_0, __riscv_vle8_v_i8mf2(q8, vl), vl);
+        vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q5_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl);
+        vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q5_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl);
+        vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q5_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl);
+
+        vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl);
+        vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl);
+        vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl);
+        vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl);
+
+        int32_t sumi1 = sc[0] * __riscv_vmv_x_s_i32m1_i32(vs_0);
+        int32_t sumi2 = sc[1] * __riscv_vmv_x_s_i32m1_i32(vs_1);
+        int32_t sumi3 = sc[2] * __riscv_vmv_x_s_i32m1_i32(vs_2);
+        int32_t sumi4 = sc[3] * __riscv_vmv_x_s_i32m1_i32(vs_3);
+
+        sumf += d * (sumi1 + sumi2 + sumi3 + sumi4);
+
+    }
+
+    *s = sumf;
+
+#else
+
+    int8_t aux8[QK_K];
+    int16_t aux16[16];
+    float   sums [8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * restrict q4 = x[i].qs;
+        const uint8_t * restrict hm = x[i].qh;
+        const  int8_t * restrict q8 = y[i].qs;
+        int8_t * restrict a = aux8;
+        for (int l = 0; l < 32; ++l) {
+            a[l+ 0] = q4[l] & 0xF;
+            a[l+32] = q4[l]  >> 4;
+        }
+        for (int is = 0; is < 8; ++is) {
+            uint8_t m = 1 << is;
+            for (int l = 0; l < 8; ++l) a[8*is + l] -= (hm[l] & m ? 0 : 16);
+        }
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const int8_t * restrict sc = x[i].scales;
+
+        for (int j = 0; j < QK_K/16; ++j) {
+            const float dl = d * sc[j];
+            for (int l = 0; l < 16; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l <  8; ++l) sums[l] += dl * (aux16[l] + aux16[8+l]);
+            q8 += 16; a += 16;
+        }
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
+#endif
+}
+#endif
+
+
+#if QK_K == 256
+void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    assert(n % QK_K == 0);
+
+    const block_q6_K * restrict x = vx;
+    const block_q8_K * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#ifdef __ARM_NEON
+
+    float sum = 0;
+
+    const uint8x16_t m4b = vdupq_n_u8(0xF);
+#if defined(__ARM_FEATURE_DOTPROD)
+    const int32x4_t  vzero = vdupq_n_s32(0);
+#endif
+    //const int8x16_t  m32s = vdupq_n_s8(32);
+
+    const uint8x16_t mone = vdupq_n_u8(3);
+
+    int8x16x4_t q6bytes;
+    uint8x16x4_t q6h;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d_all = ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict q6 = x[i].ql;
+        const uint8_t * restrict qh = x[i].qh;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const int8_t * restrict scale = x[i].scales;
+
+        const int16x8x2_t q8sums = vld1q_s16_x2(y[i].bsums);
+        const int8x16_t scales = vld1q_s8(scale);
+        const int16x8x2_t q6scales = {vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))};
+
+        const int32x4_t prod = vaddq_s32(vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[0]), vget_low_s16 (q6scales.val[0])),
+                                                   vmull_s16(vget_high_s16(q8sums.val[0]), vget_high_s16(q6scales.val[0]))),
+                                         vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[1]), vget_low_s16 (q6scales.val[1])),
+                                                   vmull_s16(vget_high_s16(q8sums.val[1]), vget_high_s16(q6scales.val[1]))));
+        int32_t isum_mins = vaddvq_s32(prod);
+
+        int32_t isum = 0;
+
+        for (int j = 0; j < QK_K/128; ++j) {
+
+            uint8x16x2_t qhbits = vld1q_u8_x2(qh); qh += 32;
+            uint8x16x4_t q6bits = vld1q_u8_x4(q6); q6 += 64;
+            int8x16x4_t q8bytes = vld1q_s8_x4(q8); q8 += 64;
+
+            q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4);
+            q6h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4);
+            uint8x16_t shifted = vshrq_n_u8(qhbits.val[0], 2);
+            q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4);
+            shifted = vshrq_n_u8(qhbits.val[1], 2);
+            q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4);
+
+            //q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])), m32s);
+            //q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])), m32s);
+            //q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2])), m32s);
+            //q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3])), m32s);
+            q6bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0]));
+            q6bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1]));
+            q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2]));
+            q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3]));
+
+#if defined(__ARM_FEATURE_DOTPROD)
+
+            isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] +
+                    vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] +
+                    vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] +
+                    vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3];
+            scale += 4;
+
+#else
+
+            int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
+                                     vmull_s8(vget_high_s8(q6bytes.val[0]), vget_high_s8(q8bytes.val[0])));
+            int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
+                                     vmull_s8(vget_high_s8(q6bytes.val[1]), vget_high_s8(q8bytes.val[1])));
+            isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1];
+            scale += 2;
+
+            int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[2]), vget_low_s8 (q8bytes.val[2])),
+                                     vmull_s8(vget_high_s8(q6bytes.val[2]), vget_high_s8(q8bytes.val[2])));
+            int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[3]), vget_low_s8 (q8bytes.val[3])),
+                                     vmull_s8(vget_high_s8(q6bytes.val[3]), vget_high_s8(q8bytes.val[3])));
+            isum += vaddvq_s16(p2) * scale[0] + vaddvq_s16(p3) * scale[1];
+            scale += 2;
+#endif
+
+            q8bytes = vld1q_s8_x4(q8); q8 += 64;
+
+            shifted = vshrq_n_u8(qhbits.val[0], 4);
+            q6h.val[0] = vshlq_n_u8(vandq_u8(mone, shifted), 4);
+            shifted = vshrq_n_u8(qhbits.val[1], 4);
+            q6h.val[1] = vshlq_n_u8(vandq_u8(mone, shifted), 4);
+            shifted = vshrq_n_u8(qhbits.val[0], 6);
+            q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4);
+            shifted = vshrq_n_u8(qhbits.val[1], 6);
+            q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4);
+
+            //q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[0])), m32s);
+            //q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[1])), m32s);
+            //q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2])), m32s);
+            //q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3])), m32s);
+            q6bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[0]));
+            q6bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[1]));
+            q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2]));
+            q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3]));
+
+#if defined(__ARM_FEATURE_DOTPROD)
+
+            isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] +
+                    vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] +
+                    vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] +
+                    vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3];
+            scale += 4;
+
+            //for (int l = 0; l < 4; ++l) {
+            //    const int32x4_t p = vdotq_s32(vzero, q6bytes.val[l], q8bytes.val[l]);
+            //    isum += vaddvq_s32(p) * *scale++;
+            //}
+#else
+            p0 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
+                                    vmull_s8(vget_high_s8(q6bytes.val[0]), vget_high_s8(q8bytes.val[0])));
+            p1 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
+                                    vmull_s8(vget_high_s8(q6bytes.val[1]), vget_high_s8(q8bytes.val[1])));
+            isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1];
+            scale += 2;
+
+            p2 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[2]), vget_low_s8 (q8bytes.val[2])),
+                                    vmull_s8(vget_high_s8(q6bytes.val[2]), vget_high_s8(q8bytes.val[2])));
+            p3 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[3]), vget_low_s8 (q8bytes.val[3])),
+                                    vmull_s8(vget_high_s8(q6bytes.val[3]), vget_high_s8(q8bytes.val[3])));
+            isum += vaddvq_s16(p2) * scale[0] + vaddvq_s16(p3) * scale[1];
+            scale += 2;
+#endif
+
+        }
+        //sum += isum * d_all * y[i].d;
+        sum += d_all * y[i].d * (isum - 32 * isum_mins);
+
+    }
+    *s = sum;
+
+#elif defined __AVX2__
+
+    const __m256i m4 = _mm256_set1_epi8(0xF);
+    const __m256i m2 = _mm256_set1_epi8(3);
+    const __m256i m32s = _mm256_set1_epi8(32);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict q4 = x[i].ql;
+        const uint8_t * restrict qh = x[i].qh;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const __m128i scales = _mm_loadu_si128((const __m128i*)x[i].scales);
+
+        __m256i sumi = _mm256_setzero_si256();
+
+        int is = 0;
+
+        for (int j = 0; j < QK_K/128; ++j) {
+
+            const __m128i scale_0 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 0));
+            const __m128i scale_1 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 1));
+            const __m128i scale_2 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 2));
+            const __m128i scale_3 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 3));
+            is += 4;
+
+            const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4); q4 += 32;
+            const __m256i q4bits2 = _mm256_loadu_si256((const __m256i*)q4); q4 += 32;
+            const __m256i q4bitsH = _mm256_loadu_si256((const __m256i*)qh); qh += 32;
+
+            const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(q4bitsH, m2), 4);
+            const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 2), m2), 4);
+            const __m256i q4h_2 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 4), m2), 4);
+            const __m256i q4h_3 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 6), m2), 4);
+
+            const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0);
+            const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(q4bits2, m4), q4h_1);
+            const __m256i q4_2 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_2);
+            const __m256i q4_3 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits2, 4), m4), q4h_3);
+
+            const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+
+            __m256i q8s_0 = _mm256_maddubs_epi16(m32s, q8_0);
+            __m256i q8s_1 = _mm256_maddubs_epi16(m32s, q8_1);
+            __m256i q8s_2 = _mm256_maddubs_epi16(m32s, q8_2);
+            __m256i q8s_3 = _mm256_maddubs_epi16(m32s, q8_3);
+
+            __m256i p16_0 = _mm256_maddubs_epi16(q4_0, q8_0);
+            __m256i p16_1 = _mm256_maddubs_epi16(q4_1, q8_1);
+            __m256i p16_2 = _mm256_maddubs_epi16(q4_2, q8_2);
+            __m256i p16_3 = _mm256_maddubs_epi16(q4_3, q8_3);
+
+            p16_0 = _mm256_sub_epi16(p16_0, q8s_0);
+            p16_1 = _mm256_sub_epi16(p16_1, q8s_1);
+            p16_2 = _mm256_sub_epi16(p16_2, q8s_2);
+            p16_3 = _mm256_sub_epi16(p16_3, q8s_3);
+
+            p16_0 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_0), p16_0);
+            p16_1 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_1), p16_1);
+            p16_2 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_2), p16_2);
+            p16_3 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_3), p16_3);
+
+            sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1));
+            sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_2, p16_3));
+
+        }
+
+        acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc);
+    }
+
+    *s = hsum_float_8(acc);
+
+#elif defined __AVX__
+
+    const __m128i m4 = _mm_set1_epi8(0xF);
+    const __m128i m3 = _mm_set1_epi8(3);
+    const __m128i m32s = _mm_set1_epi8(32);
+    const __m128i m2 = _mm_set1_epi8(2);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict q4 = x[i].ql;
+        const uint8_t * restrict qh = x[i].qh;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const __m128i scales = _mm_loadu_si128((const __m128i*)x[i].scales);
+
+        __m128i sumi_0 = _mm_setzero_si128();
+        __m128i sumi_1 = _mm_setzero_si128();
+
+        __m128i shuffle = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000);
+        for (int j = 0; j < QK_K/128; ++j) {
+
+            const __m128i q4bitsH_0 = _mm_loadu_si128((const __m128i*)qh); qh += 16;
+            const __m128i q4bitsH_1 = _mm_loadu_si128((const __m128i*)qh); qh += 16;
+
+            const __m128i q4h_0 = _mm_slli_epi16(_mm_and_si128(q4bitsH_0, m3), 4);
+            const __m128i q4h_1 = _mm_slli_epi16(_mm_and_si128(q4bitsH_1, m3), 4);
+            const __m128i q4h_2 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_0, 2), m3), 4);
+            const __m128i q4h_3 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_1, 2), m3), 4);
+            const __m128i q4h_4 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_0, 4), m3), 4);
+            const __m128i q4h_5 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_1, 4), m3), 4);
+            const __m128i q4h_6 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_0, 6), m3), 4);
+            const __m128i q4h_7 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_1, 6), m3), 4);
+
+            const __m128i q4bits1_0 = _mm_loadu_si128((const __m128i*)q4); q4 += 16;
+            const __m128i q4bits1_1 = _mm_loadu_si128((const __m128i*)q4); q4 += 16;
+            const __m128i q4bits2_0 = _mm_loadu_si128((const __m128i*)q4); q4 += 16;
+            const __m128i q4bits2_1 = _mm_loadu_si128((const __m128i*)q4); q4 += 16;
+
+            const __m128i q4_0 = _mm_or_si128(_mm_and_si128(q4bits1_0, m4), q4h_0);
+            const __m128i q4_1 = _mm_or_si128(_mm_and_si128(q4bits1_1, m4), q4h_1);
+            const __m128i q4_2 = _mm_or_si128(_mm_and_si128(q4bits2_0, m4), q4h_2);
+            const __m128i q4_3 = _mm_or_si128(_mm_and_si128(q4bits2_1, m4), q4h_3);
+            const __m128i q4_4 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits1_0, 4), m4), q4h_4);
+            const __m128i q4_5 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits1_1, 4), m4), q4h_5);
+            const __m128i q4_6 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits2_0, 4), m4), q4h_6);
+            const __m128i q4_7 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits2_1, 4), m4), q4h_7);
+
+            const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+            const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16;
+
+            __m128i q8s_0 = _mm_maddubs_epi16(m32s, q8_0);
+            __m128i q8s_1 = _mm_maddubs_epi16(m32s, q8_1);
+            __m128i q8s_2 = _mm_maddubs_epi16(m32s, q8_2);
+            __m128i q8s_3 = _mm_maddubs_epi16(m32s, q8_3);
+            __m128i q8s_4 = _mm_maddubs_epi16(m32s, q8_4);
+            __m128i q8s_5 = _mm_maddubs_epi16(m32s, q8_5);
+            __m128i q8s_6 = _mm_maddubs_epi16(m32s, q8_6);
+            __m128i q8s_7 = _mm_maddubs_epi16(m32s, q8_7);
+
+            __m128i p16_0 = _mm_maddubs_epi16(q4_0, q8_0);
+            __m128i p16_1 = _mm_maddubs_epi16(q4_1, q8_1);
+            __m128i p16_2 = _mm_maddubs_epi16(q4_2, q8_2);
+            __m128i p16_3 = _mm_maddubs_epi16(q4_3, q8_3);
+            __m128i p16_4 = _mm_maddubs_epi16(q4_4, q8_4);
+            __m128i p16_5 = _mm_maddubs_epi16(q4_5, q8_5);
+            __m128i p16_6 = _mm_maddubs_epi16(q4_6, q8_6);
+            __m128i p16_7 = _mm_maddubs_epi16(q4_7, q8_7);
+
+            p16_0 = _mm_sub_epi16(p16_0, q8s_0);
+            p16_1 = _mm_sub_epi16(p16_1, q8s_1);
+            p16_2 = _mm_sub_epi16(p16_2, q8s_2);
+            p16_3 = _mm_sub_epi16(p16_3, q8s_3);
+            p16_4 = _mm_sub_epi16(p16_4, q8s_4);
+            p16_5 = _mm_sub_epi16(p16_5, q8s_5);
+            p16_6 = _mm_sub_epi16(p16_6, q8s_6);
+            p16_7 = _mm_sub_epi16(p16_7, q8s_7);
+
+            const __m128i scale_0 = _mm_shuffle_epi8(scales, shuffle);
+            shuffle = _mm_add_epi8(shuffle, m2);
+            const __m128i scale_1 = _mm_shuffle_epi8(scales, shuffle);
+            shuffle = _mm_add_epi8(shuffle, m2);
+            const __m128i scale_2 = _mm_shuffle_epi8(scales, shuffle);
+            shuffle = _mm_add_epi8(shuffle, m2);
+            const __m128i scale_3 = _mm_shuffle_epi8(scales, shuffle);
+            shuffle = _mm_add_epi8(shuffle, m2);
+
+            p16_0 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_0), p16_0);
+            p16_1 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_0, scale_0)), p16_1);
+            p16_2 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_1), p16_2);
+            p16_3 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_1, scale_1)), p16_3);
+            p16_4 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_2), p16_4);
+            p16_5 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_2, scale_2)), p16_5);
+            p16_6 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_3), p16_6);
+            p16_7 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_3, scale_3)), p16_7);
+
+            sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2));
+            sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3));
+            sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_4, p16_6));
+            sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_5, p16_7));
+
+        }
+
+        __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
+        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc);
+    }
+
+    *s = hsum_float_8(acc);
+
+#elif defined __riscv_v_intrinsic
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+
+        const uint8_t * restrict q6 = x[i].ql;
+        const uint8_t * restrict qh = x[i].qh;
+        const  int8_t * restrict q8 = y[i].qs;
+
+        const int8_t * restrict scale = x[i].scales;
+
+        size_t vl;
+
+        vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1);
+
+        int sum_t = 0;
+        int is = 0;
+
+        for (int j = 0; j < QK_K/128; ++j) {
+
+            vl = 32;
+
+            // load qh
+            vuint8m1_t qh_x = __riscv_vle8_v_u8m1(qh, vl);
+
+            // load Q6
+            vuint8m1_t q6_0 = __riscv_vle8_v_u8m1(q6, vl);
+            vuint8m1_t q6_1 = __riscv_vle8_v_u8m1(q6+32, vl);
+
+            vuint8m1_t q6a_0 = __riscv_vand_vx_u8m1(q6_0, 0x0F, vl);
+            vuint8m1_t q6a_1 = __riscv_vand_vx_u8m1(q6_1, 0x0F, vl);
+            vuint8m1_t q6s_0 = __riscv_vsrl_vx_u8m1(q6_0, 0x04, vl);
+            vuint8m1_t q6s_1 = __riscv_vsrl_vx_u8m1(q6_1, 0x04, vl);
+
+            vuint8m1_t qh_0 = __riscv_vand_vx_u8m1(qh_x, 0x03, vl);
+            vuint8m1_t qh_1 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x2, vl), 0x03 , vl);
+            vuint8m1_t qh_2 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x4, vl), 0x03 , vl);
+            vuint8m1_t qh_3 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x6, vl), 0x03 , vl);
+
+            vuint8m1_t qhi_0 = __riscv_vor_vv_u8m1(q6a_0, __riscv_vsll_vx_u8m1(qh_0, 0x04, vl), vl);
+            vuint8m1_t qhi_1 = __riscv_vor_vv_u8m1(q6a_1, __riscv_vsll_vx_u8m1(qh_1, 0x04, vl), vl);
+            vuint8m1_t qhi_2 = __riscv_vor_vv_u8m1(q6s_0, __riscv_vsll_vx_u8m1(qh_2, 0x04, vl), vl);
+            vuint8m1_t qhi_3 = __riscv_vor_vv_u8m1(q6s_1, __riscv_vsll_vx_u8m1(qh_3, 0x04, vl), vl);
+
+            vint8m1_t a_0 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_0), 32, vl);
+            vint8m1_t a_1 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_1), 32, vl);
+            vint8m1_t a_2 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_2), 32, vl);
+            vint8m1_t a_3 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_3), 32, vl);
+
+            // load Q8 and take product
+            vint16m2_t va_q_0 = __riscv_vwmul_vv_i16m2(a_0, __riscv_vle8_v_i8m1(q8, vl), vl);
+            vint16m2_t va_q_1 = __riscv_vwmul_vv_i16m2(a_1, __riscv_vle8_v_i8m1(q8+32, vl), vl);
+            vint16m2_t va_q_2 = __riscv_vwmul_vv_i16m2(a_2, __riscv_vle8_v_i8m1(q8+64, vl), vl);
+            vint16m2_t va_q_3 = __riscv_vwmul_vv_i16m2(a_3, __riscv_vle8_v_i8m1(q8+96, vl), vl);
+
+            vl = 16;
+
+            vint32m2_t vaux_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_0, 0), scale[is+0], vl);
+            vint32m2_t vaux_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_0, 1), scale[is+1], vl);
+            vint32m2_t vaux_2 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_1, 0), scale[is+2], vl);
+            vint32m2_t vaux_3 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_1, 1), scale[is+3], vl);
+            vint32m2_t vaux_4 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_2, 0), scale[is+4], vl);
+            vint32m2_t vaux_5 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_2, 1), scale[is+5], vl);
+            vint32m2_t vaux_6 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_3, 0), scale[is+6], vl);
+            vint32m2_t vaux_7 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_3, 1), scale[is+7], vl);
+
+            vint32m1_t isum0 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_0, vaux_1, vl), vzero, vl);
+            vint32m1_t isum1 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_2, vaux_3, vl), isum0, vl);
+            vint32m1_t isum2 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_4, vaux_5, vl), isum1, vl);
+            vint32m1_t isum3 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_6, vaux_7, vl), isum2, vl);
+
+            sum_t += __riscv_vmv_x_s_i32m1_i32(isum3);
+
+            q6 += 64;   qh += 32;   q8 += 128;   is=8;
+
+        }
+
+        sumf += d * sum_t;
+
+    }
+
+    *s = sumf;
+
+#else
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * restrict q4 = x[i].ql;
+        const uint8_t * restrict qh = x[i].qh;
+        const  int8_t * restrict q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * restrict a = aux8;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+            }
+            a  += 128;
+            q4 += 64;
+            qh += 32;
+        }
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            int scale = x[i].scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
+#endif
+}
+
+#else
+
+void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    assert(n % QK_K == 0);
+
+    const block_q6_K * restrict x = vx;
+    const block_q8_K * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#ifdef __ARM_NEON
+
+    float sum = 0;
+
+    const uint8x16_t m4b = vdupq_n_u8(0xF);
+    const int8x16_t  m32s = vdupq_n_s8(32);
+#if defined(__ARM_FEATURE_DOTPROD)
+    const int32x4_t  vzero = vdupq_n_s32(0);
+#endif
+
+    const uint8x16_t mone = vdupq_n_u8(3);
+
+    int8x16x4_t q6bytes;
+    uint8x16x4_t q6h;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d_all = (float)x[i].d;
+
+        const uint8_t * restrict q6 = x[i].ql;
+        const uint8_t * restrict qh = x[i].qh;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const int8_t * restrict scale = x[i].scales;
+
+        int32_t isum = 0;
+
+        uint8x16_t   qhbits = vld1q_u8(qh);
+        uint8x16x2_t q6bits = vld1q_u8_x2(q6);
+        int8x16x4_t q8bytes = vld1q_s8_x4(q8);
+
+        q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits), 4);
+        uint8x16_t shifted = vshrq_n_u8(qhbits, 2);
+        q6h.val[1] = vshlq_n_u8(vandq_u8(mone, shifted), 4);
+        shifted = vshrq_n_u8(qhbits, 4);
+        q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4);
+        shifted = vshrq_n_u8(qhbits, 6);
+        q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4);
+
+        q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])), m32s);
+        q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])), m32s);
+        q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[2])), m32s);
+        q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[3])), m32s);
+
+#if defined(__ARM_FEATURE_DOTPROD)
+
+        isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] +
+                vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] +
+                vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] +
+                vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3];
+#else
+
+        int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
+                                 vmull_s8(vget_high_s8(q6bytes.val[0]), vget_high_s8(q8bytes.val[0])));
+        int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[1]), vget_low_s8 (q8bytes.val[1])),
+                                 vmull_s8(vget_high_s8(q6bytes.val[1]), vget_high_s8(q8bytes.val[1])));
+        isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1];
+
+        int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[2]), vget_low_s8 (q8bytes.val[2])),
+                                 vmull_s8(vget_high_s8(q6bytes.val[2]), vget_high_s8(q8bytes.val[2])));
+        int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[3]), vget_low_s8 (q8bytes.val[3])),
+                                 vmull_s8(vget_high_s8(q6bytes.val[3]), vget_high_s8(q8bytes.val[3])));
+        isum += vaddvq_s16(p2) * scale[2] + vaddvq_s16(p3) * scale[3];
+#endif
+
+        sum += isum * d_all * y[i].d;
+
+    }
+    *s = sum;
+
+#elif defined __AVX2__
+
+    const __m256i m4 = _mm256_set1_epi8(0xF);
+    const __m256i m2 = _mm256_set1_epi8(3);
+    const __m256i m32s = _mm256_set1_epi8(32);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict q4 = x[i].ql;
+        const uint8_t * restrict qh = x[i].qh;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const __m64 scales_1 = _mm_set1_pi8(x[i].scales[0]);
+        const __m64 scales_2 = _mm_set1_pi8(x[i].scales[1]);
+        const __m64 scales_3 = _mm_set1_pi8(x[i].scales[2]);
+        const __m64 scales_4 = _mm_set1_pi8(x[i].scales[3]);
+
+        __m256i sumi = _mm256_setzero_si256();
+
+        const __m128i scale_0 = _mm_set_epi64(scales_2, scales_1);
+        const __m128i scale_1 = _mm_set_epi64(scales_4, scales_3);
+
+        const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4);
+        const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh);
+
+        const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 2), q4bitsH), m2), 4);
+        const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 6), _mm_srli_epi16(q4bitsH, 4)), m2), 4);
+
+        const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0);
+        const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_1);
+
+        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
+        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
+
+        __m256i q8s_0 = _mm256_maddubs_epi16(m32s, q8_0);
+        __m256i q8s_1 = _mm256_maddubs_epi16(m32s, q8_1);
+
+        __m256i p16_0 = _mm256_maddubs_epi16(q4_0, q8_0);
+        __m256i p16_1 = _mm256_maddubs_epi16(q4_1, q8_1);
+
+        p16_0 = _mm256_sub_epi16(p16_0, q8s_0);
+        p16_1 = _mm256_sub_epi16(p16_1, q8s_1);
+
+        p16_0 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_0), p16_0);
+        p16_1 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_1), p16_1);
+
+        sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1));
+
+        acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc);
+    }
+
+    *s = hsum_float_8(acc);
+
+#elif defined __AVX__
+
+    const __m128i m4 = _mm_set1_epi8(0xF);
+    const __m128i m2 = _mm_set1_epi8(3);
+    const __m128i m32s = _mm_set1_epi8(32);
+
+    __m256 acc = _mm256_setzero_ps();
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict q4 = x[i].ql;
+        const uint8_t * restrict qh = x[i].qh;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const __m64 scales_1 = _mm_set1_pi8(x[i].scales[0]);
+        const __m64 scales_2 = _mm_set1_pi8(x[i].scales[1]);
+        const __m64 scales_3 = _mm_set1_pi8(x[i].scales[2]);
+        const __m64 scales_4 = _mm_set1_pi8(x[i].scales[3]);
+
+        __m128i sumi_0 = _mm_setzero_si128();
+        __m128i sumi_1 = _mm_setzero_si128();
+
+        const __m128i scale_0 = _mm_set_epi64(scales_2, scales_1);
+        const __m128i scale_1 = _mm_set_epi64(scales_4, scales_3);
+
+        const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4);
+        const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh);
+
+        const __m128i q4h_0 = _mm_slli_epi16(_mm_and_si128(q4bitsH, m2), 4);
+        const __m128i q4h_1 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 2), m2), 4);
+        const __m128i q4h_2 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 4), m2), 4);
+        const __m128i q4h_3 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 6), m2), 4);
+
+        const __m128i q4_0 = _mm_or_si128(_mm_and_si128(_mm256_extractf128_si256(q4bits1, 0), m4), q4h_0);
+        const __m128i q4_1 = _mm_or_si128(_mm_and_si128(_mm256_extractf128_si256(q4bits1, 1), m4), q4h_1);
+        const __m128i q4_2 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q4bits1, 0), 4), m4), q4h_2);
+        const __m128i q4_3 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q4bits1, 1), 4), m4), q4h_3);
+
+        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
+        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
+
+        __m128i q8s_0 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_0, 0));
+        __m128i q8s_1 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_0, 1));
+        __m128i q8s_2 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_1, 0));
+        __m128i q8s_3 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_1, 1));
+
+        __m128i p16_0 = _mm_maddubs_epi16(q4_0, _mm256_extractf128_si256(q8_0, 0));
+        __m128i p16_1 = _mm_maddubs_epi16(q4_1, _mm256_extractf128_si256(q8_0, 1));
+        __m128i p16_2 = _mm_maddubs_epi16(q4_2, _mm256_extractf128_si256(q8_1, 0));
+        __m128i p16_3 = _mm_maddubs_epi16(q4_3, _mm256_extractf128_si256(q8_1, 1));
+
+        p16_0 = _mm_sub_epi16(p16_0, q8s_0);
+        p16_1 = _mm_sub_epi16(p16_1, q8s_1);
+        p16_2 = _mm_sub_epi16(p16_2, q8s_2);
+        p16_3 = _mm_sub_epi16(p16_3, q8s_3);
+
+        p16_0 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_0), p16_0);
+        p16_1 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_0, scale_0)), p16_1);
+        p16_2 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_1), p16_2);
+        p16_3 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_1, scale_1)), p16_3);
+
+        sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2));
+        sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3));
+
+        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(MM256_SET_M128I(sumi_1, sumi_0))), acc);
+    }
+
+    *s = hsum_float_8(acc);
+
+#elif defined __riscv_v_intrinsic
+
+    float sumf = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d_all = (float)x[i].d;
+
+        const uint8_t * restrict q6 = x[i].ql;
+        const uint8_t * restrict qh = x[i].qh;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const int8_t * restrict scale = x[i].scales;
+
+        int32_t isum = 0;
+
+        size_t vl = 16;
+
+        vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1);
+
+        // load Q6
+        vuint8mf2_t q6_0 = __riscv_vle8_v_u8mf2(q6, vl);
+        vuint8mf2_t q6_1 = __riscv_vle8_v_u8mf2(q6+16, vl);
+
+        // load qh
+        vuint8mf2_t qh_x = __riscv_vle8_v_u8mf2(qh, vl);
+
+        vuint8mf2_t qh0 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl);
+        qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl);
+        vuint8mf2_t qh1 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl);
+        qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl);
+        vuint8mf2_t qh2 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl);
+        qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl);
+        vuint8mf2_t qh3 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl);
+
+        vuint8mf2_t q6h_0 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q6_0, 0xF, vl), qh0, vl);
+        vuint8mf2_t q6h_1 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q6_1, 0xF, vl), qh1, vl);
+        vuint8mf2_t q6h_2 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q6_0, 0x4, vl), qh2, vl);
+        vuint8mf2_t q6h_3 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q6_1, 0x4, vl), qh3, vl);
+
+        vint8mf2_t q6v_0 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_0), 32, vl);
+        vint8mf2_t q6v_1 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_1), 32, vl);
+        vint8mf2_t q6v_2 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_2), 32, vl);
+        vint8mf2_t q6v_3 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_3), 32, vl);
+
+        // load Q8 and take product
+        vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q6v_0, __riscv_vle8_v_i8mf2(q8, vl), vl);
+        vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q6v_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl);
+        vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q6v_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl);
+        vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q6v_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl);
+
+        vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl);
+        vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl);
+        vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl);
+        vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl);
+
+        isum += __riscv_vmv_x_s_i32m1_i32(vs_0) * scale[0];
+        isum += __riscv_vmv_x_s_i32m1_i32(vs_1) * scale[1];
+        isum += __riscv_vmv_x_s_i32m1_i32(vs_2) * scale[2];
+        isum += __riscv_vmv_x_s_i32m1_i32(vs_3) * scale[3];
+
+        sumf += isum * d_all * y[i].d;
+
+    }
+
+    *s = sumf;
+
+#else
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * restrict q4 = x[i].ql;
+        const uint8_t * restrict qh = x[i].qh;
+        const  int8_t * restrict q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * restrict a = aux8;
+        for (int l = 0; l < 16; ++l) {
+            a[l+ 0] = (int8_t)((q4[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+            a[l+16] = (int8_t)((q4[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+            a[l+32] = (int8_t)((q4[l+ 0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+            a[l+48] = (int8_t)((q4[l+16] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+        }
+        int is = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            int scale = x[i].scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
+#endif
+}
+
+#endif

runner/k_quants.h

@@ -0,0 +1,191 @@
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#pragma once
+
+#include "ggml.h"
+
+#include <stdint.h>
+#include <assert.h>
+#include <stddef.h>
+
+// Super-block size
+#ifdef GGML_QKK_64
+#define QK_K 64
+#define K_SCALE_SIZE 4
+#else
+#define QK_K 256
+#define K_SCALE_SIZE 12
+#endif
+
+#ifndef static_assert
+#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
+#define static_assert(cond, msg) _Static_assert(cond, msg)
+#else
+#define static_assert(cond, msg) struct global_scope_noop_trick
+#endif
+#endif
+
+//
+// Super-block quantization structures
+//
+
+// 2-bit quantization
+// weight is represented as x = a * q + b
+// 16 blocks of 16 elements each
+// Effectively 2.5625 bits per weight
+typedef struct {
+    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
+    uint8_t qs[QK_K/4];      // quants
+    ggml_fp16_t d;           // super-block scale for quantized scales
+    ggml_fp16_t dmin;        // super-block scale for quantized mins
+} block_q2_K;
+static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
+
+// 3-bit quantization
+// weight is represented as x = a * q
+// 16 blocks of 16 elements each
+// Effectively 3.4375 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    uint8_t hmask[QK_K/8];     // quants - high bit
+    uint8_t qs[QK_K/4];        // quants - low 2 bits
+    uint8_t scales[2];
+    ggml_fp16_t d;             // super-block scale
+} block_q3_K;
+static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
+#else
+typedef struct {
+    uint8_t hmask[QK_K/8];     // quants - high bit
+    uint8_t qs[QK_K/4];        // quants - low 2 bits
+    uint8_t scales[12];        // scales, quantized with 6 bits
+    ggml_fp16_t d;             // super-block scale
+} block_q3_K;
+static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
+#endif
+
+// 4-bit quantization
+// 8 blocks of 32 elements each
+// weight is represented as x = a * q + b
+// Effectively 4.5 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    ggml_fp16_t d[2];          // super-block scales/mins
+    uint8_t scales[2];         // 4-bit block scales/mins
+    uint8_t qs[QK_K/2];        // 4--bit quants
+} block_q4_K;
+static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
+#else
+typedef struct {
+    ggml_fp16_t d;             // super-block scale for quantized scales
+    ggml_fp16_t dmin;          // super-block scale for quantized mins
+    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
+    uint8_t qs[QK_K/2];        // 4--bit quants
+} block_q4_K;
+static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
+#endif
+
+// 5-bit quantization
+// 8 blocks of 32 elements each
+// weight is represented as x = a * q + b
+// Effectively 5.5 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    ggml_fp16_t d;               // super-block scale
+    int8_t  scales[QK_K/16];     // 8-bit block scales
+    uint8_t qh[QK_K/8];          // quants, high bit
+    uint8_t qs[QK_K/2];          // quants, low 4 bits
+} block_q5_K;
+static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
+#else
+typedef struct {
+    ggml_fp16_t d;               // super-block scale for quantized scales
+    ggml_fp16_t dmin;            // super-block scale for quantized mins
+    uint8_t scales[K_SCALE_SIZE];   // scales and mins, quantized with 6 bits
+    uint8_t qh[QK_K/8];          // quants, high bit
+    uint8_t qs[QK_K/2];          // quants, low 4 bits
+} block_q5_K;
+static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
+#endif
+
+// 6-bit quantization
+// weight is represented as x = a * q
+// 16 blocks of 16 elements each
+// Effectively 6.5625 bits per weight
+typedef struct {
+    uint8_t ql[QK_K/2];      // quants, lower 4 bits
+    uint8_t qh[QK_K/4];      // quants, upper 2 bits
+    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
+    ggml_fp16_t d;           // super-block scale
+} block_q6_K;
+static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");
+
+// This is only used for intermediate quantization and dot products
+typedef struct {
+    float   d;              // delta
+    int8_t  qs[QK_K];       // quants
+    int16_t bsums[QK_K/16]; // sum of quants in groups of 16
+} block_q8_K;
+static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
+
+
+// Quantization
+void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k);
+void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k);
+void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k);
+void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k);
+void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k);
+void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k);
+
+void quantize_row_q2_K(const float * restrict x, void * restrict y, int k);
+void quantize_row_q3_K(const float * restrict x, void * restrict y, int k);
+void quantize_row_q4_K(const float * restrict x, void * restrict y, int k);
+void quantize_row_q5_K(const float * restrict x, void * restrict y, int k);
+void quantize_row_q6_K(const float * restrict x, void * restrict y, int k);
+void quantize_row_q8_K(const float * restrict x, void * restrict y, int k);
+
+// Dequantization
+void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k);
+void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k);
+void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k);
+void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k);
+void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k);
+void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k);
+
+// Dot product
+void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+
+// Quantization with histogram collection
+size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist);
+size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist);
+size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);
+size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);
+size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);
+

runner/llama.cpp

@@ -0,0 +1,9942 @@
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#define LLAMA_API_INTERNAL
+#include "llama.h"
+
+#include "unicode.h"
+
+#include "ggml.h"
+
+#include "ggml-alloc.h"
+
+#ifdef GGML_USE_CUBLAS
+#  include "ggml-cuda.h"
+#elif defined(GGML_USE_CLBLAST)
+#  include "ggml-opencl.h"
+#endif
+
+#ifdef GGML_USE_METAL
+#  include "ggml-metal.h"
+#endif
+#ifdef GGML_USE_MPI
+#  include "ggml-mpi.h"
+#endif
+#ifdef GGML_USE_K_QUANTS
+#  ifndef QK_K
+#    ifdef GGML_QKK_64
+#      define QK_K 64
+#    else
+#      define QK_K 256
+#    endif
+#  endif
+#endif
+
+#ifdef __has_include
+    #if __has_include(<unistd.h>)
+        #include <unistd.h>
+        #if defined(_POSIX_MAPPED_FILES)
+            #include <sys/mman.h>
+        #endif
+        #if defined(_POSIX_MEMLOCK_RANGE)
+            #include <sys/resource.h>
+        #endif
+    #endif
+#endif
+
+#if defined(_WIN32)
+    #define WIN32_LEAN_AND_MEAN
+    #ifndef NOMINMAX
+        #define NOMINMAX
+    #endif
+    #include <windows.h>
+    #include <io.h>
+    #include <stdio.h> // for _fseeki64
+#endif
+
+#include <algorithm>
+#include <array>
+#include <cassert>
+#include <cinttypes>
+#include <climits>
+#include <cstdarg>
+#include <cstddef>
+#include <cstdint>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <fstream>
+#include <initializer_list>
+#include <map>
+#include <memory>
+#include <mutex>
+#include <numeric>
+#include <queue>
+#include <random>
+#include <regex>
+#include <sstream>
+#include <thread>
+#include <unordered_map>
+#include <set>
+#include <forward_list>
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+#ifdef __GNUC__
+#ifdef __MINGW32__
+#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
+#else
+#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
+#endif
+#else
+#define LLAMA_ATTRIBUTE_FORMAT(...)
+#endif
+
+//
+// logging
+//
+
+LLAMA_ATTRIBUTE_FORMAT(2, 3)
+static void llama_log_internal        (ggml_log_level level, const char* format, ...);
+static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data);
+
+#define LLAMA_LOG_INFO(...)  llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
+#define LLAMA_LOG_WARN(...)  llama_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
+#define LLAMA_LOG_ERROR(...) llama_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
+
+//
+// helpers
+//
+
+static size_t utf8_len(char src) {
+    const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
+    uint8_t highbits = static_cast<uint8_t>(src) >> 4;
+    return lookup[highbits];
+}
+
+static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
+    std::string result;
+    for (size_t pos = 0; ; pos += search.length()) {
+        auto new_pos = s.find(search, pos);
+        if (new_pos == std::string::npos) {
+            result += s.substr(pos, s.size() - pos);
+            break;
+        }
+        result += s.substr(pos, new_pos - pos) + replace;
+        pos = new_pos;
+    }
+    s = std::move(result);
+}
+
+static bool is_float_close(float a, float b, float abs_tol) {
+    // Check for non-negative tolerance
+    if (abs_tol < 0.0) {
+        throw std::invalid_argument("Tolerance must be non-negative");
+    }
+
+    // Exact equality check
+    if (a == b) {
+        return true;
+    }
+
+    // Check for infinities
+    if (std::isinf(a) || std::isinf(b)) {
+        return false;
+    }
+
+    // Regular comparison using the provided absolute tolerance
+    return std::fabs(b - a) <= abs_tol;
+}
+
+#ifdef GGML_USE_CPU_HBM
+#include <hbwmalloc.h>
+#endif
+
+static void zeros(std::ofstream & file, size_t n) {
+    char zero = 0;
+    for (size_t i = 0; i < n; ++i) {
+        file.write(&zero, 1);
+    }
+}
+
+LLAMA_ATTRIBUTE_FORMAT(1, 2)
+static std::string format(const char * fmt, ...) {
+    va_list ap;
+    va_list ap2;
+    va_start(ap, fmt);
+    va_copy(ap2, ap);
+    int size = vsnprintf(NULL, 0, fmt, ap);
+    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
+    std::vector<char> buf(size + 1);
+    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
+    GGML_ASSERT(size2 == size);
+    va_end(ap2);
+    va_end(ap);
+    return std::string(buf.data(), size);
+}
+
+//
+// gguf constants (sync with gguf.py)
+//
+
+enum llm_arch {
+    LLM_ARCH_LLAMA,
+    LLM_ARCH_FALCON,
+    LLM_ARCH_BAICHUAN,
+    LLM_ARCH_GPT2,
+    LLM_ARCH_GPTJ,
+    LLM_ARCH_GPTNEOX,
+    LLM_ARCH_MPT,
+    LLM_ARCH_STARCODER,
+    LLM_ARCH_PERSIMMON,
+    LLM_ARCH_REFACT,
+    LLM_ARCH_BLOOM,
+    LLM_ARCH_UNKNOWN,
+};
+
+static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
+    { LLM_ARCH_LLAMA,           "llama"     },
+    { LLM_ARCH_FALCON,          "falcon"    },
+    { LLM_ARCH_GPT2,            "gpt2"      },
+    { LLM_ARCH_GPTJ,            "gptj"      },
+    { LLM_ARCH_GPTNEOX,         "gptneox"   },
+    { LLM_ARCH_MPT,             "mpt"       },
+    { LLM_ARCH_BAICHUAN,        "baichuan"  },
+    { LLM_ARCH_STARCODER,       "starcoder" },
+    { LLM_ARCH_PERSIMMON,       "persimmon" },
+    { LLM_ARCH_REFACT,          "refact"    },
+    { LLM_ARCH_BLOOM,           "bloom"     },
+};
+
+enum llm_kv {
+    LLM_KV_GENERAL_ARCHITECTURE,
+    LLM_KV_GENERAL_QUANTIZATION_VERSION,
+    LLM_KV_GENERAL_ALIGNMENT,
+    LLM_KV_GENERAL_NAME,
+    LLM_KV_GENERAL_AUTHOR,
+    LLM_KV_GENERAL_URL,
+    LLM_KV_GENERAL_DESCRIPTION,
+    LLM_KV_GENERAL_LICENSE,
+    LLM_KV_GENERAL_SOURCE_URL,
+    LLM_KV_GENERAL_SOURCE_HF_REPO,
+
+    LLM_KV_CONTEXT_LENGTH,
+    LLM_KV_EMBEDDING_LENGTH,
+    LLM_KV_BLOCK_COUNT,
+    LLM_KV_FEED_FORWARD_LENGTH,
+    LLM_KV_USE_PARALLEL_RESIDUAL,
+    LLM_KV_TENSOR_DATA_LAYOUT,
+
+    LLM_KV_ATTENTION_HEAD_COUNT,
+    LLM_KV_ATTENTION_HEAD_COUNT_KV,
+    LLM_KV_ATTENTION_MAX_ALIBI_BIAS,
+    LLM_KV_ATTENTION_CLAMP_KQV,
+    LLM_KV_ATTENTION_LAYERNORM_EPS,
+    LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
+
+    LLM_KV_ROPE_DIMENSION_COUNT,
+    LLM_KV_ROPE_FREQ_BASE,
+    LLM_KV_ROPE_SCALE_LINEAR,
+
+    LLM_KV_TOKENIZER_MODEL,
+    LLM_KV_TOKENIZER_LIST,
+    LLM_KV_TOKENIZER_TOKEN_TYPE,
+    LLM_KV_TOKENIZER_SCORES,
+    LLM_KV_TOKENIZER_MERGES,
+    LLM_KV_TOKENIZER_BOS_ID,
+    LLM_KV_TOKENIZER_EOS_ID,
+    LLM_KV_TOKENIZER_UNK_ID,
+    LLM_KV_TOKENIZER_SEP_ID,
+    LLM_KV_TOKENIZER_PAD_ID,
+    LLM_KV_TOKENIZER_HF_JSON,
+    LLM_KV_TOKENIZER_RWKV,
+};
+
+static std::map<llm_kv, std::string> LLM_KV_NAMES = {
+    { LLM_KV_GENERAL_ARCHITECTURE,          "general.architecture"                  },
+    { LLM_KV_GENERAL_QUANTIZATION_VERSION,  "general.quantization_version"          },
+    { LLM_KV_GENERAL_ALIGNMENT,             "general.alignment"                     },
+    { LLM_KV_GENERAL_NAME,                  "general.name"                          },
+    { LLM_KV_GENERAL_AUTHOR,                "general.author"                        },
+    { LLM_KV_GENERAL_URL,                   "general.url"                           },
+    { LLM_KV_GENERAL_DESCRIPTION,           "general.description"                   },
+    { LLM_KV_GENERAL_LICENSE,               "general.license"                       },
+    { LLM_KV_GENERAL_SOURCE_URL,            "general.source.url"                    },
+    { LLM_KV_GENERAL_SOURCE_HF_REPO,        "general.source.huggingface.repository" },
+
+    { LLM_KV_CONTEXT_LENGTH,                "%s.context_length"        },
+    { LLM_KV_EMBEDDING_LENGTH,              "%s.embedding_length"      },
+    { LLM_KV_BLOCK_COUNT,                   "%s.block_count"           },
+    { LLM_KV_FEED_FORWARD_LENGTH,           "%s.feed_forward_length"   },
+    { LLM_KV_USE_PARALLEL_RESIDUAL,         "%s.use_parallel_residual" },
+    { LLM_KV_TENSOR_DATA_LAYOUT,            "%s.tensor_data_layout"    },
+
+    { LLM_KV_ATTENTION_HEAD_COUNT,          "%s.attention.head_count"             },
+    { LLM_KV_ATTENTION_HEAD_COUNT_KV,       "%s.attention.head_count_kv"          },
+    { LLM_KV_ATTENTION_MAX_ALIBI_BIAS,      "%s.attention.max_alibi_bias"         },
+    { LLM_KV_ATTENTION_CLAMP_KQV,           "%s.attention.clamp_kqv"              },
+    { LLM_KV_ATTENTION_LAYERNORM_EPS,       "%s.attention.layer_norm_epsilon"     },
+    { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,   "%s.attention.layer_norm_rms_epsilon" },
+
+    { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count" },
+    { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"       },
+    { LLM_KV_ROPE_SCALE_LINEAR,             "%s.rope.scale_linear"    },
+
+    { LLM_KV_TOKENIZER_MODEL,               "tokenizer.ggml.model"              },
+    { LLM_KV_TOKENIZER_LIST,                "tokenizer.ggml.tokens"             },
+    { LLM_KV_TOKENIZER_TOKEN_TYPE,          "tokenizer.ggml.token_type"         },
+    { LLM_KV_TOKENIZER_SCORES,              "tokenizer.ggml.scores"             },
+    { LLM_KV_TOKENIZER_MERGES,              "tokenizer.ggml.merges"             },
+    { LLM_KV_TOKENIZER_BOS_ID,              "tokenizer.ggml.bos_token_id"       },
+    { LLM_KV_TOKENIZER_EOS_ID,              "tokenizer.ggml.eos_token_id"       },
+    { LLM_KV_TOKENIZER_UNK_ID,              "tokenizer.ggml.unknown_token_id"   },
+    { LLM_KV_TOKENIZER_SEP_ID,              "tokenizer.ggml.seperator_token_id" },
+    { LLM_KV_TOKENIZER_PAD_ID,              "tokenizer.ggml.padding_token_id"   },
+    { LLM_KV_TOKENIZER_HF_JSON,             "tokenizer.huggingface.json"        },
+    { LLM_KV_TOKENIZER_RWKV,                "tokenizer.rwkv.world"              },
+};
+
+struct LLM_KV {
+    LLM_KV(llm_arch arch) : arch(arch) {}
+
+    llm_arch arch;
+
+    std::string operator()(llm_kv kv) const {
+        return ::format(LLM_KV_NAMES[kv].c_str(), LLM_ARCH_NAMES[arch].c_str());
+    }
+};
+
+enum llm_tensor {
+    LLM_TENSOR_TOKEN_EMBD,
+    LLM_TENSOR_TOKEN_EMBD_NORM,
+    LLM_TENSOR_POS_EMBD,
+    LLM_TENSOR_OUTPUT,
+    LLM_TENSOR_OUTPUT_NORM,
+    LLM_TENSOR_ROPE_FREQS,
+    LLM_TENSOR_ATTN_Q,
+    LLM_TENSOR_ATTN_K,
+    LLM_TENSOR_ATTN_V,
+    LLM_TENSOR_ATTN_QKV,
+    LLM_TENSOR_ATTN_OUT,
+    LLM_TENSOR_ATTN_NORM,
+    LLM_TENSOR_ATTN_NORM_2,
+    LLM_TENSOR_ATTN_ROT_EMBD,
+    LLM_TENSOR_FFN_GATE,
+    LLM_TENSOR_FFN_DOWN,
+    LLM_TENSOR_FFN_UP,
+    LLM_TENSOR_FFN_NORM,
+    LLM_TENSOR_ATTN_Q_NORM,
+    LLM_TENSOR_ATTN_K_NORM,
+};
+
+static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
+    {
+        LLM_ARCH_LLAMA,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_BAICHUAN,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_FALCON,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_NORM_2,     "blk.%d.attn_norm_2" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_GPT2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+        },
+    },
+    {
+        LLM_ARCH_GPTJ,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+        },
+    },
+    {
+        LLM_ARCH_GPTNEOX,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_PERSIMMON,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd"},
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm"},
+            { LLM_TENSOR_OUTPUT,          "output"},
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm"},
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv"},
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output"},
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm"},
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm"},
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm"},
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down"},
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up"},
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd"},
+        },
+    },
+    {
+        LLM_ARCH_MPT,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_STARCODER,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_POS_EMBD,        "position_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+        },
+    },
+    {
+        LLM_ARCH_REFACT,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_BLOOM,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+        },
+    },
+    {
+        LLM_ARCH_UNKNOWN,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+        },
+    },
+};
+
+static llm_arch llm_arch_from_string(const std::string & name) {
+    for (const auto & kv : LLM_ARCH_NAMES) { // NOLINT
+        if (kv.second == name) {
+            return kv.first;
+        }
+    }
+
+    return LLM_ARCH_UNKNOWN;
+}
+
+// helper to handle gguf constants
+// usage:
+//
+//   const auto tn = LLM_TN(LLM_ARCH_LLAMA);
+//
+//   std::string name = tn(LLM_TENSOR_OUTPUT);                     -> "output"
+//   std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias");         -> "token_embd.bias"
+//   std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3);     -> "blk.3.attn_norm.weight"
+//
+struct LLM_TN {
+    LLM_TN(llm_arch arch) : arch(arch) {}
+
+    llm_arch arch;
+
+    std::string operator()(llm_tensor tensor) const {
+        return LLM_TENSOR_NAMES[arch].at(tensor);
+    }
+
+    std::string operator()(llm_tensor tensor, const std::string & suffix) const {
+        return LLM_TENSOR_NAMES[arch].at(tensor) + "." + suffix;
+    }
+
+    std::string operator()(llm_tensor tensor, int bid) const {
+        return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid);
+    }
+
+    std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
+        return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid) + "." + suffix;
+    }
+};
+
+//
+// gguf helpers
+//
+
+#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \
+do { \
+    const std::string skey(key); \
+    const int kid = gguf_find_key(ctx, skey.c_str()); \
+    if (kid >= 0) { \
+        enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \
+        if (ktype != (type)) { \
+            throw std::runtime_error(format("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype))); \
+        } \
+        (dst) = func(ctx, kid); \
+    } else if (req) { \
+        throw std::runtime_error(format("key not found in model: %s", skey.c_str())); \
+    } \
+} while (0)
+
+//
+// ggml helpers
+//
+
+static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
+    struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
+
+    if (plan.work_size > 0) {
+        buf.resize(plan.work_size);
+        plan.work_data = buf.data();
+    }
+
+    ggml_graph_compute(graph, &plan);
+}
+
+//
+// llama helpers
+//
+
+#ifdef GGML_USE_CUBLAS
+#   define llama_host_malloc(n)  ggml_cuda_host_malloc(n)
+#   define llama_host_free(data) ggml_cuda_host_free(data)
+#elif GGML_USE_METAL
+#   define llama_host_malloc(n)  ggml_metal_host_malloc(n)
+#   define llama_host_free(data) ggml_metal_host_free(data)
+#elif GGML_USE_CPU_HBM
+#   define llama_host_malloc(n)  hbw_malloc(n)
+#   define llama_host_free(data) if (data != NULL) hbw_free(data)
+#else
+#   define llama_host_malloc(n)  malloc(n)
+#   define llama_host_free(data) free(data)
+#endif
+
+#if defined(_WIN32)
+static std::string llama_format_win_err(DWORD err) {
+    LPSTR buf;
+    size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
+                                 NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
+    if (!size) {
+        return "FormatMessageA failed";
+    }
+    std::string ret(buf, size);
+    LocalFree(buf);
+    return ret;
+}
+#endif
+
+struct llama_buffer {
+    void * data = NULL;
+    size_t size = 0;
+
+    // fallback to malloc / free
+    // useful in cases where CUDA can try to allocate PINNED memory
+    bool fallback = false;
+
+    void resize(size_t n) {
+        llama_host_free(data);
+
+        data = llama_host_malloc(n);
+        if (!data) {
+            fallback = true;
+            data = malloc(n);
+        } else {
+            fallback = false;
+        }
+
+        GGML_ASSERT(data);
+        size = n;
+    }
+
+    ~llama_buffer() {
+        if (data) {
+            if (fallback) { // NOLINT
+                free(data);
+            } else {
+                llama_host_free(data);
+            }
+        }
+
+        data = NULL;
+    }
+};
+
+struct llama_file {
+    // use FILE * so we don't have to re-open the file to mmap
+    FILE * fp;
+    size_t size;
+
+    llama_file(const char * fname, const char * mode) {
+        fp = std::fopen(fname, mode);
+        if (fp == NULL) {
+            throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
+        }
+        seek(0, SEEK_END);
+        size = tell();
+        seek(0, SEEK_SET);
+    }
+
+    size_t tell() const {
+#ifdef _WIN32
+        __int64 ret = _ftelli64(fp);
+#else
+        long ret = std::ftell(fp);
+#endif
+        GGML_ASSERT(ret != -1); // this really shouldn't fail
+        return (size_t) ret;
+    }
+
+    void seek(size_t offset, int whence) const {
+#ifdef _WIN32
+        int ret = _fseeki64(fp, (__int64) offset, whence);
+#else
+        int ret = std::fseek(fp, (long) offset, whence);
+#endif
+        GGML_ASSERT(ret == 0); // same
+    }
+
+    void read_raw(void * ptr, size_t len) const {
+        if (len == 0) {
+            return;
+        }
+        errno = 0;
+        std::size_t ret = std::fread(ptr, len, 1, fp);
+        if (ferror(fp)) {
+            throw std::runtime_error(format("read error: %s", strerror(errno)));
+        }
+        if (ret != 1) {
+            throw std::runtime_error(std::string("unexpectedly reached end of file"));
+        }
+    }
+
+    uint32_t read_u32() const {
+        uint32_t ret;
+        read_raw(&ret, sizeof(ret));
+        return ret;
+    }
+
+    void write_raw(const void * ptr, size_t len) const {
+        if (len == 0) {
+            return;
+        }
+        errno = 0;
+        size_t ret = std::fwrite(ptr, len, 1, fp);
+        if (ret != 1) {
+            throw std::runtime_error(format("write error: %s", strerror(errno)));
+        }
+    }
+
+    void write_u32(std::uint32_t val) const {
+        write_raw(&val, sizeof(val));
+    }
+
+    ~llama_file() {
+        if (fp) {
+            std::fclose(fp);
+        }
+    }
+};
+
+struct llama_mmap {
+    void * addr;
+    size_t size;
+
+    llama_mmap(const llama_mmap &) = delete;
+
+#ifdef _POSIX_MAPPED_FILES
+    static constexpr bool SUPPORTED = true;
+
+    llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
+        size = file->size;
+        int fd = fileno(file->fp);
+        int flags = MAP_SHARED;
+        // prefetch/readahead impairs performance on NUMA systems
+        if (numa) { prefetch = 0; }
+#ifdef __linux__
+        if (prefetch) { flags |= MAP_POPULATE; }
+#endif
+        addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
+        if (addr == MAP_FAILED) {
+            throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
+        }
+
+        if (prefetch > 0) {
+            // Advise the kernel to preload the mapped memory
+            if (posix_madvise(addr, std::min(file->size, prefetch), POSIX_MADV_WILLNEED)) {
+                fprintf(stderr, "warning: posix_madvise(.., POSIX_MADV_WILLNEED) failed: %s\n",
+                        strerror(errno));
+            }
+        }
+        if (numa) {
+            // advise the kernel not to use readahead
+            // (because the next page might not belong on the same node)
+            if (posix_madvise(addr, file->size, POSIX_MADV_RANDOM)) {
+                fprintf(stderr, "warning: posix_madvise(.., POSIX_MADV_RANDOM) failed: %s\n",
+                        strerror(errno));
+            }
+        }
+    }
+
+    ~llama_mmap() {
+        munmap(addr, size);
+    }
+#elif defined(_WIN32)
+    static constexpr bool SUPPORTED = true;
+
+    llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) {
+        (void) numa;
+
+        size = file->size;
+
+        HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
+
+        HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
+        DWORD error = GetLastError();
+
+        if (hMapping == NULL) {
+            throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
+        }
+
+        addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
+        error = GetLastError();
+        CloseHandle(hMapping);
+
+        if (addr == NULL) {
+            throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
+        }
+
+        if (prefetch) {
+            // PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it
+            BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG);
+            HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll");
+
+            // may fail on pre-Windows 8 systems
+            pPrefetchVirtualMemory = reinterpret_cast<decltype(pPrefetchVirtualMemory)> (GetProcAddress(hKernel32, "PrefetchVirtualMemory"));
+
+            if (pPrefetchVirtualMemory) {
+                // advise the kernel to preload the mapped memory
+                WIN32_MEMORY_RANGE_ENTRY range;
+                range.VirtualAddress = addr;
+                range.NumberOfBytes = (SIZE_T)size;
+                if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
+                    fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
+                            llama_format_win_err(GetLastError()).c_str());
+                }
+            }
+        }
+    }
+
+    ~llama_mmap() {
+        if (!UnmapViewOfFile(addr)) {
+            fprintf(stderr, "warning: UnmapViewOfFile failed: %s\n",
+                    llama_format_win_err(GetLastError()).c_str());
+        }
+    }
+#else
+    static constexpr bool SUPPORTED = false;
+
+    llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) {
+        (void) file;
+        (void) prefetch;
+        (void) numa;
+
+        throw std::runtime_error(std::string("mmap not supported"));
+    }
+#endif
+};
+
+// Represents some region of memory being locked using mlock or VirtualLock;
+// will automatically unlock on destruction.
+struct llama_mlock {
+    void * addr = NULL;
+    size_t size = 0;
+
+    bool failed_already = false;
+
+    llama_mlock() {}
+    llama_mlock(const llama_mlock &) = delete;
+
+    ~llama_mlock() {
+        if (size) {
+            raw_unlock(addr, size);
+        }
+    }
+
+    void init(void * ptr) {
+        GGML_ASSERT(addr == NULL && size == 0); // NOLINT
+        addr = ptr;
+    }
+
+    void grow_to(size_t target_size) {
+        GGML_ASSERT(addr);
+        if (failed_already) {
+            return;
+        }
+        size_t granularity = lock_granularity();
+        target_size = (target_size + granularity - 1) & ~(granularity - 1);
+        if (target_size > size) {
+            if (raw_lock((uint8_t *) addr + size, target_size - size)) {
+                size = target_size;
+            } else {
+                failed_already = true;
+            }
+        }
+    }
+
+#ifdef _POSIX_MEMLOCK_RANGE
+    static constexpr bool SUPPORTED = true;
+
+    static size_t lock_granularity() {
+        return (size_t) sysconf(_SC_PAGESIZE);
+    }
+
+    #ifdef __APPLE__
+        #define MLOCK_SUGGESTION \
+            "Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
+            "decreasing 'vm.global_no_user_wire_amount'.  Also try increasing RLIMIT_MLOCK (ulimit -l).\n"
+    #else
+        #define MLOCK_SUGGESTION \
+            "Try increasing RLIMIT_MLOCK ('ulimit -l' as root).\n"
+    #endif
+
+    bool raw_lock(const void * addr, size_t size) const {
+        if (!mlock(addr, size)) {
+            return true;
+        }
+
+        char* errmsg = std::strerror(errno);
+        bool suggest = (errno == ENOMEM);
+
+        // Check if the resource limit is fine after all
+        struct rlimit lock_limit;
+        if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit)) {
+            suggest = false;
+        }
+        if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size)) {
+            suggest = false;
+        }
+
+        fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
+                size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
+        return false;
+    }
+
+    #undef MLOCK_SUGGESTION
+
+    static void raw_unlock(void * addr, size_t size) {
+        if (munlock(addr, size)) {
+            fprintf(stderr, "warning: failed to munlock buffer: %s\n", std::strerror(errno));
+        }
+    }
+#elif defined(_WIN32)
+    static constexpr bool SUPPORTED = true;
+
+    static size_t lock_granularity() {
+        SYSTEM_INFO si;
+        GetSystemInfo(&si);
+        return (size_t) si.dwPageSize;
+    }
+
+    bool raw_lock(void * ptr, size_t len) const {
+        for (int tries = 1; ; tries++) {
+            if (VirtualLock(ptr, len)) {
+                return true;
+            }
+            if (tries == 2) {
+                fprintf(stderr, "warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
+                    len, size, llama_format_win_err(GetLastError()).c_str());
+                return false;
+            }
+
+            // It failed but this was only the first try; increase the working
+            // set size and try again.
+            SIZE_T min_ws_size, max_ws_size;
+            if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
+                fprintf(stderr, "warning: GetProcessWorkingSetSize failed: %s\n",
+                        llama_format_win_err(GetLastError()).c_str());
+                return false;
+            }
+            // Per MSDN: "The maximum number of pages that a process can lock
+            // is equal to the number of pages in its minimum working set minus
+            // a small overhead."
+            // Hopefully a megabyte is enough overhead:
+            size_t increment = len + 1048576;
+            // The minimum must be <= the maximum, so we need to increase both:
+            min_ws_size += increment;
+            max_ws_size += increment;
+            if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
+                fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n",
+                        llama_format_win_err(GetLastError()).c_str());
+                return false;
+            }
+        }
+    }
+
+    static void raw_unlock(void * ptr, size_t len) {
+        if (!VirtualUnlock(ptr, len)) {
+            fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n",
+                    llama_format_win_err(GetLastError()).c_str());
+        }
+    }
+#else
+    static constexpr bool SUPPORTED = false;
+
+    static size_t lock_granularity() {
+        return (size_t) 65536;
+    }
+
+    bool raw_lock(const void * addr, size_t len) const {
+        fprintf(stderr, "warning: mlock not supported on this system\n");
+        return false;
+    }
+
+    static void raw_unlock(const void * addr, size_t len) {}
+#endif
+};
+
+typedef void (*offload_func_t)(struct ggml_tensor * tensor);
+
+static void llama_nop(struct ggml_tensor * tensor) { // don't offload by default
+    (void) tensor;
+}
+
+static std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
+    std::vector<char> result(8, 0);
+    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+    if (n_tokens < 0) {
+        result.resize(-n_tokens);
+        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+        GGML_ASSERT(check == -n_tokens);
+    } else {
+        result.resize(n_tokens);
+    }
+
+    return std::string(result.data(), result.size());
+}
+
+//
+// globals
+//
+
+struct llama_state {
+    // We save the log callback globally
+    ggml_log_callback log_callback = llama_log_callback_default;
+    void * log_callback_user_data = nullptr;
+};
+
+static llama_state g_state;
+
+// available llama models
+enum e_model {
+    MODEL_UNKNOWN,
+    MODEL_1B,
+    MODEL_3B,
+    MODEL_7B,
+    MODEL_8B,
+    MODEL_13B,
+    MODEL_15B,
+    MODEL_30B,
+    MODEL_34B,
+    MODEL_40B,
+    MODEL_65B,
+    MODEL_70B,
+};
+
+static const size_t kB = 1024;
+static const size_t MB = 1024*kB;
+static const size_t GB = 1024*MB;
+
+struct llama_hparams {
+    bool     vocab_only;
+    uint32_t n_vocab;
+    uint32_t n_ctx_train; // context size the model was trained on
+    uint32_t n_embd;
+    uint32_t n_head;
+    uint32_t n_head_kv;
+    uint32_t n_layer;
+    uint32_t n_rot;
+    uint32_t n_ff;
+
+    float f_norm_eps;
+    float f_norm_rms_eps;
+
+    float rope_freq_base_train;
+    float rope_freq_scale_train;
+
+    float f_clamp_kqv;
+    float f_max_alibi_bias;
+
+    bool operator!=(const llama_hparams & other) const {
+        if (this->vocab_only  != other.vocab_only)  return true;
+        if (this->n_vocab     != other.n_vocab)     return true;
+        if (this->n_ctx_train != other.n_ctx_train) return true;
+        if (this->n_embd      != other.n_embd)      return true;
+        if (this->n_head      != other.n_head)      return true;
+        if (this->n_head_kv   != other.n_head_kv)   return true;
+        if (this->n_layer     != other.n_layer)     return true;
+        if (this->n_rot       != other.n_rot)       return true;
+        if (this->n_ff        != other.n_ff)        return true;
+
+        const float EPSILON = 1e-9;
+
+        if (!is_float_close(this->f_norm_eps,            other.f_norm_eps,            EPSILON)) return true;
+        if (!is_float_close(this->f_norm_rms_eps,        other.f_norm_rms_eps,        EPSILON)) return true;
+        if (!is_float_close(this->rope_freq_base_train,  other.rope_freq_base_train,  EPSILON)) return true;
+        if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true;
+
+        return false;
+    }
+
+    uint32_t n_gqa() const {
+        return n_head/n_head_kv;
+    }
+
+    uint32_t n_embd_head() const {
+        return n_embd/n_head;
+    }
+
+    uint32_t n_embd_gqa() const {
+        return n_embd/n_gqa();
+    }
+};
+
+struct llama_cparams {
+    uint32_t n_ctx;       // context size used during inference
+    uint32_t n_batch;
+    uint32_t n_threads;       // number of threads to use for generation
+    uint32_t n_threads_batch; // number of threads to use for batch processing
+
+    float rope_freq_base;
+    float rope_freq_scale;
+
+    bool mul_mat_q;
+};
+
+struct llama_layer {
+    // normalization
+    struct ggml_tensor * attn_norm;
+    struct ggml_tensor * attn_norm_b;
+    struct ggml_tensor * attn_norm_2;
+    struct ggml_tensor * attn_norm_2_b;
+    struct ggml_tensor * attn_q_norm;
+    struct ggml_tensor * attn_q_norm_b;
+    struct ggml_tensor * attn_k_norm;
+    struct ggml_tensor * attn_k_norm_b;
+
+    // attention
+    struct ggml_tensor * wq;
+    struct ggml_tensor * wk;
+    struct ggml_tensor * wv;
+    struct ggml_tensor * wo;
+    struct ggml_tensor * wqkv;
+
+    // attention bias
+    struct ggml_tensor * bo;
+    struct ggml_tensor * bqkv;
+
+    // normalization
+    struct ggml_tensor * ffn_norm;
+    struct ggml_tensor * ffn_norm_b;
+
+    // ff
+    struct ggml_tensor * w1; // ffn_gate
+    struct ggml_tensor * w2; // ffn_down
+    struct ggml_tensor * w3; // ffn_up
+
+    // ff bias
+    struct ggml_tensor * b2; // ffn_down
+    struct ggml_tensor * b3; // ffn_up
+};
+
+struct llama_kv_cell {
+    llama_pos pos   = -1;
+    llama_pos delta = 0;
+
+    std::set<llama_seq_id> seq_id;
+
+    bool has_seq_id(const llama_seq_id & id) const {
+        return seq_id.find(id) != seq_id.end();
+    }
+};
+
+// ring-buffer of cached KV data
+struct llama_kv_cache {
+    bool has_shift = false;
+
+    // Note: The value of head isn't only used to optimize searching
+    // for a free KV slot. llama_decode_internal also uses it, so it
+    // cannot be freely changed after a slot has been allocated.
+    uint32_t head = 0;
+    uint32_t size = 0;
+
+    // computed before each graph build
+    uint32_t n = 0;
+
+    std::vector<llama_kv_cell> cells;
+
+    struct ggml_tensor * k = NULL;
+    struct ggml_tensor * v = NULL;
+
+    struct ggml_context * ctx = NULL;
+
+    llama_buffer buf;
+
+    ~llama_kv_cache() {
+        if (ctx) {
+            ggml_free(ctx);
+        }
+
+#ifdef GGML_USE_CUBLAS
+        ggml_cuda_free_data(k);
+        ggml_cuda_free_data(v);
+#endif // GGML_USE_CUBLAS
+    }
+};
+
+struct llama_vocab {
+    using id    = int32_t;
+    using token = std::string;
+    using ttype = llama_token_type;
+
+    struct token_data {
+        token text;
+        float score;
+        ttype type;
+    };
+
+    enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM;
+
+    std::unordered_map<token, id> token_to_id;
+    std::vector<token_data>       id_to_token;
+
+    std::unordered_map<token, id> special_tokens_cache;
+
+    std::map<std::pair<std::string, std::string>, int> bpe_ranks;
+
+    // default LLaMA special tokens
+    id special_bos_id = 1;
+    id special_eos_id = 2;
+    id special_unk_id = 0;
+    id special_sep_id = -1;
+    id special_pad_id = -1;
+
+    id linefeed_id       = 13;
+    id special_prefix_id = 32007;
+    id special_middle_id = 32009;
+    id special_suffix_id = 32008;
+    id special_eot_id    = 32010;
+
+    int find_bpe_rank(std::string token_left, std::string token_right) const {
+        replace_all(token_left,  " ",  "\u0120");
+        replace_all(token_left,  "\n", "\u010A");
+        replace_all(token_right, " ",  "\u0120");
+        replace_all(token_right, "\n", "\u010A");
+
+        auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
+        if (it == bpe_ranks.end()) {
+            return -1;
+        }
+
+        return it->second;
+    }
+};
+
+struct llama_model {
+    e_model     type  = MODEL_UNKNOWN;
+    llm_arch    arch  = LLM_ARCH_UNKNOWN;
+    llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
+
+    std::string name = "n/a";
+
+    llama_hparams hparams = {};
+    llama_vocab   vocab;
+
+    struct ggml_tensor * tok_embeddings;
+    struct ggml_tensor * pos_embeddings;
+    struct ggml_tensor * tok_norm;
+    struct ggml_tensor * tok_norm_b;
+
+    struct ggml_tensor * output_norm;
+    struct ggml_tensor * output_norm_b;
+    struct ggml_tensor * output;
+
+    std::vector<llama_layer> layers;
+
+    int n_gpu_layers;
+
+    // context
+    struct ggml_context * ctx = NULL;
+
+    // the model memory buffer
+    llama_buffer buf;
+
+    // model memory mapped file
+    std::unique_ptr<llama_mmap> mapping;
+
+    // objects representing data potentially being locked in memory
+    llama_mlock mlock_buf;
+    llama_mlock mlock_mmap;
+
+    // for quantize-stats only
+    std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
+
+    int64_t t_load_us = 0;
+    int64_t t_start_us = 0;
+
+    ~llama_model() {
+        if (ctx) {
+            ggml_free(ctx);
+        }
+
+#ifdef GGML_USE_CUBLAS
+        for (size_t i = 0; i < tensors_by_name.size(); ++i) {
+            ggml_cuda_free_data(tensors_by_name[i].second);
+        }
+        ggml_cuda_free_scratch();
+#elif defined(GGML_USE_CLBLAST)
+        for (size_t i = 0; i < tensors_by_name.size(); ++i) {
+            ggml_cl_free_data(tensors_by_name[i].second);
+        }
+#endif
+    }
+};
+
+struct llama_context {
+    llama_context(const llama_model & model) : model(model), t_start_us(model.t_start_us), t_load_us(model.t_load_us) {}
+    ~llama_context() {
+#ifdef GGML_USE_METAL
+        if (ctx_metal) {
+            ggml_metal_free(ctx_metal);
+        }
+#endif
+        if (alloc) {
+            ggml_allocr_free(alloc);
+        }
+    }
+
+    llama_cparams cparams;
+
+    const llama_model & model;
+
+    // key + value cache for the self attention
+    struct llama_kv_cache kv_self;
+
+    std::mt19937 rng;
+
+    bool has_evaluated_once = false;
+
+    int64_t t_start_us;
+    int64_t t_load_us;
+    int64_t t_sample_us = 0;
+    int64_t t_p_eval_us = 0;
+    int64_t t_eval_us   = 0;
+
+    int32_t n_sample = 0; // number of tokens sampled
+    int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
+    int32_t n_eval   = 0; // number of eval calls
+
+    // decode output (2-dimensional array: [n_tokens][n_vocab])
+    std::vector<float> logits;
+    bool logits_all = false;
+
+    // input embedding (1-dimensional array: [n_embd])
+    std::vector<float> embedding;
+
+    // reusable buffer for `struct ggml_graph_plan.work_data`
+    std::vector<uint8_t> work_buffer;
+
+    // memory buffers used to evaluate the model
+    llama_buffer buf_compute;
+
+    llama_buffer buf_alloc;
+    ggml_allocr * alloc = NULL;
+
+#ifdef GGML_USE_METAL
+    ggml_metal_context * ctx_metal = NULL;
+#endif
+
+#ifdef GGML_USE_MPI
+    ggml_mpi_context * ctx_mpi = NULL;
+#endif
+};
+
+//
+// kv cache helpers
+//
+
+static bool llama_kv_cache_init(
+        const struct llama_hparams & hparams,
+             struct llama_kv_cache & cache,
+                         ggml_type   wtype,
+                          uint32_t   n_ctx,
+                               int   n_gpu_layers) {
+    const uint32_t n_embd  = hparams.n_embd_gqa();
+    const uint32_t n_layer = hparams.n_layer;
+
+    const int64_t n_mem      = n_layer*n_ctx;
+    const int64_t n_elements = n_embd*n_mem;
+
+    cache.has_shift = false;
+
+    cache.head = 0;
+    cache.size = n_ctx;
+
+    cache.cells.clear();
+    cache.cells.resize(n_ctx);
+
+    cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*ggml_tensor_overhead());
+    memset(cache.buf.data, 0, cache.buf.size);
+
+    struct ggml_init_params params;
+    params.mem_size   = cache.buf.size;
+    params.mem_buffer = cache.buf.data;
+    params.no_alloc   = false;
+
+    cache.ctx = ggml_init(params);
+
+    if (!cache.ctx) {
+        LLAMA_LOG_ERROR("%s: failed to allocate memory for kv cache\n", __func__);
+        return false;
+    }
+
+    cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
+    cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
+    ggml_set_name(cache.k, "cache_k");
+    ggml_set_name(cache.v, "cache_v");
+
+    (void) n_gpu_layers;
+#ifdef GGML_USE_CUBLAS
+    size_t vram_kv_cache = 0;
+
+    if (n_gpu_layers > (int)n_layer + 1) {
+        ggml_cuda_assign_buffers_no_scratch(cache.v);
+        LLAMA_LOG_INFO("%s: offloading v cache to GPU\n", __func__);
+        vram_kv_cache += ggml_nbytes(cache.v);
+    }
+    if (n_gpu_layers > (int)n_layer + 2) {
+        ggml_cuda_assign_buffers_no_scratch(cache.k);
+        LLAMA_LOG_INFO("%s: offloading k cache to GPU\n", __func__);
+        vram_kv_cache += ggml_nbytes(cache.k);
+    }
+    if (vram_kv_cache > 0) {
+        LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0);
+    }
+#endif // GGML_USE_CUBLAS
+
+    return true;
+}
+
+// find an empty slot of size "n_tokens" in the cache
+// updates the cache head
+// Note: On success, it's important that cache.head points
+// to the first cell of the slot.
+static bool llama_kv_cache_find_slot(
+           struct llama_kv_cache & cache,
+        const struct llama_batch & batch) {
+    const uint32_t n_ctx    = cache.size;
+    const uint32_t n_tokens = batch.n_tokens;
+
+    if (n_tokens > n_ctx) {
+        LLAMA_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx);
+        return false;
+    }
+
+    uint32_t n_tested = 0;
+
+    while (true) {
+        if (cache.head + n_tokens > n_ctx) {
+            n_tested += n_ctx - cache.head;
+            cache.head = 0;
+            continue;
+        }
+
+        bool found = true;
+        for (uint32_t i = 0; i < n_tokens; i++) {
+            if (cache.cells[cache.head + i].pos >= 0) {
+                found = false;
+                cache.head += i + 1;
+                n_tested   += i + 1;
+                break;
+            }
+        }
+
+        if (found) {
+            break;
+        }
+
+        if (n_tested >= n_ctx) {
+            //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
+            return false;
+        }
+    }
+
+    for (uint32_t i = 0; i < n_tokens; i++) {
+        cache.cells[cache.head + i].pos = batch.pos[i];
+
+        for (int32_t j = 0; j < batch.n_seq_id[i]; j++) {
+            cache.cells[cache.head + i].seq_id.insert(batch.seq_id[i][j]);
+        }
+    }
+
+    return true;
+}
+
+// find how many cells are currently in use
+static int32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
+    for (uint32_t i = cache.size - 1; i > 0; --i) {
+        if (cache.cells[i].pos >= 0 && !cache.cells[i].seq_id.empty()) {
+            return i + 1;
+        }
+    }
+
+    return 0;
+}
+
+static void llama_kv_cache_tokens_rm(struct llama_kv_cache & cache, int32_t c0, int32_t c1) {
+    if (c0 < 0) c0 = 0;
+    if (c1 < 0) c1 = cache.size;
+
+    for (int32_t i = c0; i < c1; ++i) {
+        cache.cells[i].pos = -1;
+        cache.cells[i].seq_id.clear();
+    }
+
+    // Searching for a free slot can start here since we know it will be empty.
+    cache.head = uint32_t(c0);
+}
+
+static void llama_kv_cache_seq_rm(
+        struct llama_kv_cache & cache,
+                 llama_seq_id   seq_id,
+                    llama_pos   p0,
+                    llama_pos   p1) {
+    uint32_t new_head = cache.size;
+
+    if (p0 < 0) p0 = 0;
+    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
+
+    for (uint32_t i = 0; i < cache.size; ++i) {
+        if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
+            cache.cells[i].seq_id.erase(seq_id);
+            if (cache.cells[i].seq_id.empty()) {
+                cache.cells[i].pos = -1;
+                if (new_head == cache.size) new_head = i;
+            }
+        }
+    }
+
+    // If we freed up a slot, set head to it so searching can start there.
+    if (new_head != cache.size) cache.head = new_head;
+}
+
+static void llama_kv_cache_seq_cp(
+        struct llama_kv_cache & cache,
+                 llama_seq_id   seq_id_src,
+                 llama_seq_id   seq_id_dst,
+                    llama_pos   p0,
+                    llama_pos   p1) {
+    if (p0 < 0) p0 = 0;
+    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
+
+    cache.head = 0;
+
+    for (uint32_t i = 0; i < cache.size; ++i) {
+        if (cache.cells[i].has_seq_id(seq_id_src) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
+            cache.cells[i].seq_id.insert(seq_id_dst);
+        }
+    }
+}
+
+static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id seq_id) {
+    uint32_t new_head = cache.size;
+
+    for (uint32_t i = 0; i < cache.size; ++i) {
+        if (!cache.cells[i].has_seq_id(seq_id)) {
+            cache.cells[i].pos = -1;
+            cache.cells[i].seq_id.clear();
+            if (new_head == cache.size) new_head = i;
+        } else {
+            cache.cells[i].seq_id.clear();
+            cache.cells[i].seq_id.insert(seq_id);
+        }
+    }
+
+    // If we freed up a slot, set head to it so searching can start there.
+    if (new_head != cache.size) cache.head = new_head;
+}
+
+static void llama_kv_cache_seq_shift(
+        struct llama_kv_cache & cache,
+                 llama_seq_id   seq_id,
+                    llama_pos   p0,
+                    llama_pos   p1,
+                    llama_pos   delta) {
+    uint32_t new_head = cache.size;
+
+    if (p0 < 0) p0 = 0;
+    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
+
+    for (uint32_t i = 0; i < cache.size; ++i) {
+        if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
+            cache.cells[i].pos += delta;
+            if (cache.cells[i].pos < 0) {
+                cache.cells[i].pos = -1;
+                cache.cells[i].seq_id.clear();
+                if (new_head == cache.size) new_head = i;
+            } else {
+                cache.has_shift = true;
+                cache.cells[i].delta = delta;
+            }
+        }
+    }
+
+    // If we freed up a slot, set head to it so searching can start there.
+    // Otherwise we just start the next search from the beginning.
+    cache.head = new_head != cache.size ? new_head : 0;
+}
+
+//
+// model loading and saving
+//
+
+enum llama_fver {
+    GGUF_FILE_VERSION_V1 = 1,
+    GGUF_FILE_VERSION_V2 = 2,
+};
+
+static const char * llama_file_version_name(llama_fver version) {
+    switch (version) {
+        case GGUF_FILE_VERSION_V1: return "GGUF V1 (support until nov 2023)";
+        case GGUF_FILE_VERSION_V2: return "GGUF V2 (latest)";
+    }
+
+    return "unknown";
+}
+
+static std::string llama_format_tensor_shape(const std::vector<int64_t> & ne) {
+    char buf[256];
+    snprintf(buf, sizeof(buf), "%5" PRId64, ne.at(0));
+    for (size_t i = 1; i < ne.size(); i++) {
+        snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, ne.at(i));
+    }
+    return buf;
+}
+
+static std::string llama_format_tensor_shape(const struct ggml_tensor * t) {
+    char buf[256];
+    snprintf(buf, sizeof(buf), "%5" PRId64, t->ne[0]);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, t->ne[i]);
+    }
+    return buf;
+}
+
+struct llama_model_loader {
+    int n_kv      = 0;
+    int n_tensors = 0;
+    int n_created = 0;
+
+    int64_t n_elements = 0;
+    size_t  n_bytes    = 0;
+
+    bool use_mmap = false;
+
+    llama_file  file;
+    llama_ftype ftype;
+    llama_fver  fver;
+
+    std::unique_ptr<llama_mmap> mapping;
+
+    struct gguf_context * ctx_gguf = NULL;
+    struct ggml_context * ctx_meta = NULL;
+
+    llama_model_loader(const std::string & fname, bool use_mmap) : file(fname.c_str(), "rb") {
+        struct gguf_init_params params = {
+            /*.no_alloc = */ true,
+            /*.ctx      = */ &ctx_meta,
+        };
+
+        ctx_gguf = gguf_init_from_file(fname.c_str(), params);
+        if (!ctx_gguf) {
+            throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str()));
+        }
+
+        n_kv      = gguf_get_n_kv(ctx_gguf);
+        n_tensors = gguf_get_n_tensors(ctx_gguf);
+
+        fver = (enum llama_fver ) gguf_get_version(ctx_gguf);
+
+        for (int i = 0; i < n_tensors; i++) {
+            const char * name = gguf_get_tensor_name(ctx_gguf, i);
+            struct ggml_tensor * t = ggml_get_tensor(ctx_meta, name);
+            n_elements += ggml_nelements(t);
+            n_bytes    += ggml_nbytes(t);
+        }
+
+        LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
+                __func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver));
+
+        // determine file type based on the number of tensors for each quantization and print meta data
+        // TODO: make optional
+        {
+            std::map<enum ggml_type, uint32_t> n_type;
+
+            uint32_t n_type_max = 0;
+            enum ggml_type type_max = GGML_TYPE_F32;
+
+            for (int i = 0; i < n_tensors; i++) {
+                const char * name = gguf_get_tensor_name(ctx_gguf, i);
+                struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, name);
+
+                n_type[meta->type]++;
+
+                if (n_type_max < n_type[meta->type]) {
+                    n_type_max = n_type[meta->type];
+                    type_max   = meta->type;
+                }
+
+                LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, name, ggml_type_name(meta->type), llama_format_tensor_shape(meta).c_str());
+            }
+
+            switch (type_max) {
+                case GGML_TYPE_F32:  ftype = LLAMA_FTYPE_ALL_F32;       break;
+                case GGML_TYPE_F16:  ftype = LLAMA_FTYPE_MOSTLY_F16;    break;
+                case GGML_TYPE_Q4_0: ftype = LLAMA_FTYPE_MOSTLY_Q4_0;   break;
+                case GGML_TYPE_Q4_1: ftype = LLAMA_FTYPE_MOSTLY_Q4_1;   break;
+                case GGML_TYPE_Q5_0: ftype = LLAMA_FTYPE_MOSTLY_Q5_0;   break;
+                case GGML_TYPE_Q5_1: ftype = LLAMA_FTYPE_MOSTLY_Q5_1;   break;
+                case GGML_TYPE_Q8_0: ftype = LLAMA_FTYPE_MOSTLY_Q8_0;   break;
+                case GGML_TYPE_Q2_K: ftype = LLAMA_FTYPE_MOSTLY_Q2_K;   break;
+                case GGML_TYPE_Q3_K: ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M; break;
+                case GGML_TYPE_Q4_K: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M; break;
+                case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break;
+                case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K;   break;
+                default:
+                     {
+                         LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
+                         ftype = LLAMA_FTYPE_ALL_F32;
+                     } break;
+            }
+
+            // this is a way to mark that we have "guessed" the file type
+            ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);
+
+            {
+                const int kid = gguf_find_key(ctx_gguf, "general.file_type");
+                if (kid >= 0) {
+                    ftype = (llama_ftype) gguf_get_val_u32(ctx_gguf, kid);
+                }
+            }
+
+            for (int i = 0; i < n_kv; i++) {
+                const char * name         = gguf_get_key(ctx_gguf, i);
+                const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
+
+                LLAMA_LOG_INFO("%s: - kv %3d: %42s %-8s\n", __func__, i, name, gguf_type_name(type));
+            }
+
+            // print type counts
+            for (auto & kv : n_type) {
+                if (kv.second == 0) {
+                    continue;
+                }
+
+                LLAMA_LOG_INFO("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
+            }
+        }
+
+        if (!llama_mmap::SUPPORTED) {
+            LLAMA_LOG_WARN("%s: mmap is not supported on this platform\n", __func__);
+            use_mmap = false;
+        }
+
+        this->use_mmap = use_mmap;
+    }
+
+    ~llama_model_loader() {
+        if (ctx_gguf) {
+            gguf_free(ctx_gguf);
+        }
+        if (ctx_meta) {
+            ggml_free(ctx_meta);
+        }
+    }
+
+    std::string get_arch_name() const {
+        const auto kv = LLM_KV(LLM_ARCH_UNKNOWN);
+
+        std::string arch_name;
+        GGUF_GET_KEY(ctx_gguf, arch_name, gguf_get_val_str, GGUF_TYPE_STRING, false, kv(LLM_KV_GENERAL_ARCHITECTURE));
+
+        return arch_name;
+    }
+
+    enum llm_arch get_arch() const {
+        const std::string arch_name = get_arch_name();
+
+        return llm_arch_from_string(arch_name);
+    }
+
+    const char * get_tensor_name(int i) const {
+        return gguf_get_tensor_name(ctx_gguf, i);
+    }
+
+    struct ggml_tensor * get_tensor_meta(int i) const {
+        return ggml_get_tensor(ctx_meta, get_tensor_name(i));
+    }
+
+    void calc_sizes(size_t & ctx_size_p, size_t & mmapped_size_p) const {
+        ctx_size_p     = 0;
+        mmapped_size_p = 0;
+
+        for (int i = 0; i < n_tensors; i++) {
+            struct ggml_tensor * meta = get_tensor_meta(i);
+            ctx_size_p += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE;
+            (use_mmap ? mmapped_size_p : ctx_size_p) += ggml_nbytes_pad(meta);
+        }
+    }
+
+    struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta, ggml_backend_type backend) {
+        if (backend != GGML_BACKEND_CPU) {
+            ggml_set_no_alloc(ctx, true);
+        }
+
+        struct ggml_tensor * tensor = ggml_dup_tensor(ctx, meta);
+        tensor->backend = backend; // TODO: ggml_set_backend
+        ggml_set_name(tensor, ggml_get_name(meta));
+
+        if (backend != GGML_BACKEND_CPU) {
+            ggml_set_no_alloc(ctx, use_mmap);
+        }
+
+        n_created++;
+
+        return tensor;
+    }
+
+    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, ggml_backend_type backend) {
+        struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str());
+
+        if (cur == NULL) {
+            throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
+        }
+
+        {
+            bool is_ok = true;
+            for (size_t i = 0; i < ne.size(); ++i) {
+                if (ne[i] != cur->ne[i]) {
+                    is_ok = false;
+                    break;
+                }
+            }
+            if (!is_ok) {
+                throw std::runtime_error(
+                        format("%s: tensor '%s' has wrong shape; expected %s, got %s",
+                            __func__, name.c_str(),
+                            llama_format_tensor_shape(ne).c_str(),
+                            llama_format_tensor_shape(cur).c_str()));
+            }
+        }
+
+        return create_tensor_for(ctx, cur, backend);
+    }
+
+    void done_getting_tensors() const {
+        if (n_created != n_tensors) {
+            throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
+        }
+    }
+
+    size_t file_offset(const char * name) const {
+        const int idx = gguf_find_tensor(ctx_gguf, name);
+
+        if (idx < 0) {
+            throw std::runtime_error(format("%s: tensor '%s' not found in the file", __func__, name));
+        }
+
+        return gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, idx);
+    }
+
+    void load_data_for(struct ggml_tensor * cur) const {
+        const size_t offs = file_offset(ggml_get_name(cur));
+
+        if (use_mmap) {
+            cur->data = (uint8_t *) mapping->addr + offs;
+        } else {
+            file.seek(offs, SEEK_SET);
+            file.read_raw(cur->data, ggml_nbytes(cur));
+        }
+    }
+
+    void load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, llama_mlock * lmlock) {
+        size_t size_data = 0;
+        size_t size_lock = 0;
+        size_t size_pref = 0; // prefetch
+
+        for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
+            struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
+            size_data += ggml_nbytes(cur);
+            if (cur->backend == GGML_BACKEND_CPU) {
+                size_pref += ggml_nbytes(cur);
+            }
+        }
+
+        if (use_mmap) {
+            mapping.reset(new llama_mmap(&file, size_pref, ggml_is_numa()));
+            if (lmlock) {
+                lmlock->init(mapping->addr);
+            }
+        }
+
+        size_t done_size = 0;
+        for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
+            struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
+            GGML_ASSERT(cur); // unused tensors should have been caught by load_data already
+
+            if (progress_callback) {
+                progress_callback((float) done_size / size_data, progress_callback_user_data);
+            }
+
+            // allocate temp buffer if not using mmap
+            if (!use_mmap && cur->data == NULL) {
+                GGML_ASSERT(cur->backend != GGML_BACKEND_CPU);
+                #ifdef GGML_USE_CPU_HBM
+                cur->data = (uint8_t*)hbw_malloc(ggml_nbytes(cur));
+                #else
+                cur->data = (uint8_t*)malloc(ggml_nbytes(cur));
+                #endif
+            }
+
+            load_data_for(cur);
+
+            switch (cur->backend) {
+                case GGML_BACKEND_CPU:
+                    if (use_mmap && lmlock) {
+                        size_lock += ggml_nbytes(cur);
+                        lmlock->grow_to(size_lock);
+                    }
+                    break;
+#ifdef GGML_USE_CUBLAS
+                case GGML_BACKEND_GPU:
+                case GGML_BACKEND_GPU_SPLIT:
+                    // old code:
+                    //ggml_cuda_transform_tensor(lt.data, lt.ggml_tensor);
+
+                    // TODO: test if this works !!
+                    ggml_cuda_transform_tensor(cur->data, cur);
+                    if (!use_mmap) {
+                        free(cur->data);
+                    }
+                    break;
+#elif defined(GGML_USE_CLBLAST)
+                case GGML_BACKEND_GPU:
+                    ggml_cl_transform_tensor(cur->data, cur);
+                    if (!use_mmap) {
+                        free(cur->data);
+                    }
+                    break;
+#endif
+                default:
+                    continue;
+            }
+
+            done_size += ggml_nbytes(cur);
+        }
+    }
+};
+
+//
+// load LLaMA models
+//
+
+static std::string llama_model_arch_name(llm_arch arch) {
+    auto it = LLM_ARCH_NAMES.find(arch);
+    if (it == LLM_ARCH_NAMES.end()) {
+        return "unknown";
+    }
+    return it->second;
+}
+
+static std::string llama_model_ftype_name(llama_ftype ftype) {
+    if (ftype & LLAMA_FTYPE_GUESSED) {
+        return llama_model_ftype_name((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) + " (guessed)";
+    }
+
+    switch (ftype) {
+        case LLAMA_FTYPE_ALL_F32:     return "all F32";
+        case LLAMA_FTYPE_MOSTLY_F16:  return "mostly F16";
+        case LLAMA_FTYPE_MOSTLY_Q4_0: return "mostly Q4_0";
+        case LLAMA_FTYPE_MOSTLY_Q4_1: return "mostly Q4_1";
+        case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
+                                      return "mostly Q4_1, some F16";
+        case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0";
+        case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1";
+        case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0";
+
+        // K-quants
+        case LLAMA_FTYPE_MOSTLY_Q2_K:   return "mostly Q2_K";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "mostly Q3_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "mostly Q3_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "mostly Q3_K - Large";
+        case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "mostly Q4_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "mostly Q4_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "mostly Q5_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "mostly Q5_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q6_K:   return "mostly Q6_K";
+
+        default: return "unknown, may not work";
+    }
+}
+
+static const char * llama_model_type_name(e_model type) {
+    switch (type) {
+        case MODEL_1B:  return "1B";
+        case MODEL_3B:  return "3B";
+        case MODEL_7B:  return "7B";
+        case MODEL_8B:  return "8B";
+        case MODEL_13B: return "13B";
+        case MODEL_15B: return "15B";
+        case MODEL_30B: return "30B";
+        case MODEL_34B: return "34B";
+        case MODEL_40B: return "40B";
+        case MODEL_65B: return "65B";
+        case MODEL_70B: return "70B";
+        default:        return "?B";
+    }
+}
+
+static void llm_load_arch(llama_model_loader & ml, llama_model & model) {
+    model.arch = ml.get_arch();
+    if (model.arch == LLM_ARCH_UNKNOWN) {
+        throw std::runtime_error("unknown model architecture: '" + ml.get_arch_name() + "'");
+    }
+}
+
+static void llm_load_hparams(
+        llama_model_loader & ml,
+        llama_model & model) {
+    struct gguf_context * ctx = ml.ctx_gguf;
+
+    const auto kv = LLM_KV(model.arch);
+
+    auto & hparams = model.hparams;
+
+    // get general kv
+    GGUF_GET_KEY(ctx, model.name, gguf_get_val_str, GGUF_TYPE_STRING, false, kv(LLM_KV_GENERAL_NAME));
+
+    // get hparams kv
+    GGUF_GET_KEY(ctx, hparams.n_vocab,        gguf_get_arr_n,   GGUF_TYPE_ARRAY,  true, kv(LLM_KV_TOKENIZER_LIST));
+    GGUF_GET_KEY(ctx, hparams.n_ctx_train,    gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_CONTEXT_LENGTH));
+    GGUF_GET_KEY(ctx, hparams.n_embd,         gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_EMBEDDING_LENGTH));
+    GGUF_GET_KEY(ctx, hparams.n_ff,           gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_FEED_FORWARD_LENGTH));
+    GGUF_GET_KEY(ctx, hparams.n_head,         gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_ATTENTION_HEAD_COUNT));
+    GGUF_GET_KEY(ctx, hparams.n_layer,        gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_BLOCK_COUNT));
+
+    // n_head_kv is optional, default to n_head
+    hparams.n_head_kv = hparams.n_head;
+    GGUF_GET_KEY(ctx, hparams.n_head_kv, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV));
+
+    // rope_freq_base (optional)
+    hparams.rope_freq_base_train = 10000.0f;
+    GGUF_GET_KEY(ctx, hparams.rope_freq_base_train, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));
+
+    // rope_freq_scale (inverse of the kv) is optional
+    float ropescale = 1.0f;
+    GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
+    hparams.rope_freq_scale_train = 1.0f/ropescale;
+
+    // sanity check for n_rot (optional)
+    {
+        hparams.n_rot = hparams.n_embd / hparams.n_head;
+
+        GGUF_GET_KEY(ctx, hparams.n_rot, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ROPE_DIMENSION_COUNT));
+
+        if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) {
+            if (hparams.n_rot != hparams.n_embd / hparams.n_head) {
+                throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head));
+            }
+        }
+        // gpt-neox n_rot = rotary_pct * (n_embd / n_head)
+        // gpt-j n_rot = rotary_dim
+    }
+
+    // arch-specific KVs
+    switch (model.arch) {
+        case LLM_ARCH_LLAMA:
+            {
+                GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
+
+                switch (hparams.n_layer) {
+                    case 26: model.type = e_model::MODEL_3B; break;
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 40: model.type = e_model::MODEL_13B; break;
+                    case 48: model.type = e_model::MODEL_34B; break;
+                    case 60: model.type = e_model::MODEL_30B; break;
+                    case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_FALCON:
+            {
+                GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));
+
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 60: model.type = e_model::MODEL_40B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_BAICHUAN:
+            {
+                GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 40: model.type = e_model::MODEL_13B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_STARCODER:
+            {
+                GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1B; break;
+                    case 36: model.type = e_model::MODEL_3B; break;
+                    case 42: model.type = e_model::MODEL_7B; break;
+                    case 40: model.type = e_model::MODEL_15B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_PERSIMMON:
+            {
+                GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));
+                switch (hparams.n_layer) {
+                    case 36: model.type = e_model::MODEL_8B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_REFACT:
+            {
+                GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_1B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_BLOOM:
+            {
+                GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));
+
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1B; break;
+                    case 30:
+                        switch (hparams.n_embd) {
+                            case 2560: model.type = e_model::MODEL_3B; break;
+                            case 4096: model.type = e_model::MODEL_7B; break;
+                        } break;
+                }
+            } break;
+        case LLM_ARCH_MPT:
+            {
+                hparams.f_clamp_kqv = 0.0f;
+
+                GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));
+                GGUF_GET_KEY(ctx, hparams.f_clamp_kqv, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_CLAMP_KQV));
+                GGUF_GET_KEY(ctx, hparams.f_max_alibi_bias, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_MAX_ALIBI_BIAS));
+
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 48: model.type = e_model::MODEL_30B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        default: (void)0;
+    }
+
+    model.ftype = ml.ftype;
+}
+
+// TODO: This should probably be in llama.h
+static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special = false);
+static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch);
+
+static void llm_load_vocab(
+        llama_model_loader & ml,
+        llama_model & model) {
+    auto & vocab = model.vocab;
+
+    struct gguf_context * ctx = ml.ctx_gguf;
+
+    const auto kv = LLM_KV(model.arch);
+
+    const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
+    if (token_idx == -1) {
+        throw std::runtime_error("cannot find tokenizer vocab in model file\n");
+    }
+
+    const float * scores = nullptr;
+    const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
+    if (score_idx != -1) {
+        scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
+    }
+
+    const int * toktypes = nullptr;
+    const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
+    if (toktype_idx != -1) {
+        toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
+    }
+
+    // determine vocab type
+    {
+        std::string tokenizer_name;
+
+        GGUF_GET_KEY(ctx, tokenizer_name, gguf_get_val_str, GGUF_TYPE_STRING, true, kv(LLM_KV_TOKENIZER_MODEL));
+
+        if (tokenizer_name == "llama") {
+            vocab.type = LLAMA_VOCAB_TYPE_SPM;
+
+            // default special tokens
+            vocab.special_bos_id = 1;
+            vocab.special_eos_id = 2;
+            vocab.special_unk_id = 0;
+            vocab.special_sep_id = -1;
+            vocab.special_pad_id = -1;
+        } else if (tokenizer_name == "gpt2") {
+            vocab.type = LLAMA_VOCAB_TYPE_BPE;
+
+            // read bpe merges and populate bpe ranks
+            const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
+            if (merges_keyidx == -1) {
+                throw std::runtime_error("cannot find tokenizer merges in model file\n");
+            }
+
+            const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
+
+            for (int i = 0; i < n_merges; i++) {
+                const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
+                GGML_ASSERT(codepoints_from_utf8(word).size() > 0);
+
+                std::string first;
+                std::string second;
+
+                const size_t pos = word.find(' ', 1);
+
+                if (pos != std::string::npos) {
+                    first  = word.substr(0, pos);
+                    second = word.substr(pos + 1);
+                }
+
+                vocab.bpe_ranks.emplace(std::make_pair(first, second), i);
+            }
+
+            // default special tokens
+            vocab.special_bos_id = 11;
+            vocab.special_eos_id = 11;
+            vocab.special_unk_id = -1;
+            vocab.special_sep_id = -1;
+            vocab.special_pad_id = -1;
+        } else {
+            LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str());
+            LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__);
+
+            vocab.type = LLAMA_VOCAB_TYPE_SPM;
+        }
+    }
+
+    const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
+
+    vocab.id_to_token.resize(n_vocab);
+
+    for (uint32_t i = 0; i < n_vocab; i++) {
+        std::string word = gguf_get_arr_str(ctx, token_idx, i);
+        GGML_ASSERT(codepoints_from_utf8(word).size() > 0);
+
+        vocab.token_to_id[word] = i;
+
+        auto & token_data = vocab.id_to_token[i];
+        token_data.text  = std::move(word);
+        token_data.score = scores ? scores[i] : 0.0f;
+        token_data.type  = toktypes ? (llama_token_type) toktypes[i] : LLAMA_TOKEN_TYPE_NORMAL;
+    }
+    GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size());
+
+    // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
+    if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
+        vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
+    } else {
+        vocab.linefeed_id = llama_tokenize_internal(vocab, "\u010A", false)[0];
+    }
+
+    // special tokens
+    GGUF_GET_KEY(ctx, vocab.special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID));
+    GGUF_GET_KEY(ctx, vocab.special_eos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_EOS_ID));
+    GGUF_GET_KEY(ctx, vocab.special_unk_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_UNK_ID));
+    GGUF_GET_KEY(ctx, vocab.special_sep_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_SEP_ID));
+    GGUF_GET_KEY(ctx, vocab.special_pad_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_PAD_ID));
+
+    // build special tokens cache
+    {
+        // TODO: It is unclear (to me) at this point, whether special tokes are guaranteed to be of a deterministic type,
+        //  and will always be correctly labeled in 'added_tokens.json' etc.
+        // The assumption is, since special tokens aren't meant to be exposed to end user, they are designed
+        //  to be unmatchable by the tokenizer, therefore tokens from the vocab, which are unmatchable by the tokenizer
+        //  are special tokens.
+        // From testing, this appears to corelate 1:1 with special tokens.
+        //
+
+        // Counting special tokens and verifying in only one direction
+        //  is sufficient to detect difference in those two sets.
+        //
+        uint32_t special_tokens_count_by_type = 0;
+        uint32_t special_tokens_count_from_verification = 0;
+
+        bool special_tokens_definition_mismatch = false;
+
+        for (const auto & t : vocab.token_to_id) {
+            const auto & token = t.first;
+            const auto & id    = t.second;
+
+            // Count all non-normal tokens in the vocab while iterating
+            if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) {
+                special_tokens_count_by_type++;
+            }
+
+            // Skip single character tokens
+            if (token.length() > 1) {
+                bool is_tokenizable = false;
+
+                // Split token string representation in two, in all possible ways
+                //  and check if both halves can be matched to a valid token
+                for (unsigned i = 1; i < token.length();) {
+                    const auto left  = token.substr(0, i);
+                    const auto right = token.substr(i);
+
+                    // check if we didnt partition in the middle of a utf sequence
+                    auto utf = utf8_len(left.at(left.length() - 1));
+
+                    if (utf == 1) {
+                        if (vocab.token_to_id.find(left)  != vocab.token_to_id.end() &&
+                            vocab.token_to_id.find(right) != vocab.token_to_id.end() ) {
+                            is_tokenizable = true;
+                            break;
+                        }
+                        i++;
+                    } else {
+                        // skip over the rest of multibyte utf sequence
+                        i += utf - 1;
+                    }
+                }
+
+                if (!is_tokenizable) {
+                    // Some tokens are multibyte, but they are utf sequences with equivalent text length of 1
+                    //  it's faster to re-filter them here, since there are way less candidates now
+
+                    // Calculate a total "utf" length of a token string representation
+                    size_t utf8_str_len = 0;
+                    for (unsigned i = 0; i < token.length();) {
+                        utf8_str_len++;
+                        i += utf8_len(token.at(i));
+                    }
+
+                    // And skip the ones which are one character
+                    if (utf8_str_len > 1) {
+                        // At this point what we have left are special tokens only
+                        vocab.special_tokens_cache[token] = id;
+
+                        // Count manually found special tokens
+                        special_tokens_count_from_verification++;
+
+                        // If this manually found special token is not marked as such, flag a mismatch
+                        if (vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL) {
+                            special_tokens_definition_mismatch = true;
+                        }
+                    }
+                }
+            }
+        }
+
+        if (special_tokens_definition_mismatch || special_tokens_count_from_verification != special_tokens_count_by_type) {
+            LLAMA_LOG_WARN("%s: mismatch in special tokens definition ( %u/%zu vs %u/%zu ).\n",
+                __func__,
+                special_tokens_count_from_verification, vocab.id_to_token.size(),
+                special_tokens_count_by_type, vocab.id_to_token.size()
+            );
+        } else {
+            LLAMA_LOG_INFO("%s: special tokens definition check successful ( %u/%zu ).\n",
+                __func__,
+                special_tokens_count_from_verification, vocab.id_to_token.size()
+            );
+        }
+    }
+}
+
+static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
+    const auto & hparams = model.hparams;
+    const auto & vocab   = model.vocab;
+
+    // hparams
+    LLAMA_LOG_INFO("%s: format           = %s\n",     __func__, llama_file_version_name(ml.fver));
+    LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, LLM_ARCH_NAMES.at(model.arch).c_str());
+    LLAMA_LOG_INFO("%s: vocab type       = %s\n",     __func__, vocab.type == LLAMA_VOCAB_TYPE_SPM ? "SPM" : "BPE"); // TODO: fix
+    LLAMA_LOG_INFO("%s: n_vocab          = %u\n",     __func__, hparams.n_vocab);
+    LLAMA_LOG_INFO("%s: n_merges         = %u\n",     __func__, (int) vocab.bpe_ranks.size());
+    LLAMA_LOG_INFO("%s: n_ctx_train      = %u\n",     __func__, hparams.n_ctx_train);
+    LLAMA_LOG_INFO("%s: n_embd           = %u\n",     __func__, hparams.n_embd);
+    LLAMA_LOG_INFO("%s: n_head           = %u\n",     __func__, hparams.n_head);
+    LLAMA_LOG_INFO("%s: n_head_kv        = %u\n",     __func__, hparams.n_head_kv);
+    LLAMA_LOG_INFO("%s: n_layer          = %u\n",     __func__, hparams.n_layer);
+    LLAMA_LOG_INFO("%s: n_rot            = %u\n",     __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim
+    LLAMA_LOG_INFO("%s: n_gqa            = %u\n",     __func__, hparams.n_gqa());
+    LLAMA_LOG_INFO("%s: f_norm_eps       = %.1e\n",   __func__, hparams.f_norm_eps);
+    LLAMA_LOG_INFO("%s: f_norm_rms_eps   = %.1e\n",   __func__, hparams.f_norm_rms_eps);
+    LLAMA_LOG_INFO("%s: f_clamp_kqv      = %.1e\n",   __func__, hparams.f_clamp_kqv);
+    LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n",   __func__, hparams.f_max_alibi_bias);
+    LLAMA_LOG_INFO("%s: n_ff             = %u\n",     __func__, hparams.n_ff);
+    LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
+    LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
+    LLAMA_LOG_INFO("%s: model type       = %s\n",     __func__, llama_model_type_name(model.type));
+    LLAMA_LOG_INFO("%s: model ftype      = %s\n",     __func__, llama_model_ftype_name(model.ftype).c_str());
+    LLAMA_LOG_INFO("%s: model params     = %.2f B\n", __func__, ml.n_elements*1e-9);
+    if (ml.n_bytes < GB) {
+        LLAMA_LOG_INFO("%s: model size       = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
+    } else {
+        LLAMA_LOG_INFO("%s: model size       = %.2f GiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
+    }
+
+    // general kv
+    LLAMA_LOG_INFO("%s: general.name   = %s\n",    __func__, model.name.c_str());
+
+    // special tokens
+    if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
+    if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
+    if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
+    if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
+    if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
+    if (vocab.linefeed_id    != -1) { LLAMA_LOG_INFO( "%s: LF token  = %d '%s'\n", __func__, vocab.linefeed_id,    vocab.id_to_token[vocab.linefeed_id].text.c_str() );    }
+}
+
+static void llm_load_tensors(
+        llama_model_loader & ml,
+        llama_model & model,
+        int n_gpu_layers,
+        int main_gpu,
+        const float * tensor_split,
+        bool use_mlock,
+        llama_progress_callback progress_callback,
+        void * progress_callback_user_data) {
+    model.t_start_us = ggml_time_us();
+
+    auto & ctx     = model.ctx;
+    auto & hparams = model.hparams;
+
+    model.n_gpu_layers = n_gpu_layers;
+
+    size_t ctx_size;
+    size_t mmapped_size;
+
+    ml.calc_sizes(ctx_size, mmapped_size);
+
+    LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/1024.0/1024.0);
+
+    // create the ggml context
+    {
+        model.buf.resize(ctx_size);
+        if (use_mlock) {
+            model.mlock_buf.init   (model.buf.data);
+            model.mlock_buf.grow_to(model.buf.size);
+        }
+
+        struct ggml_init_params params = {
+            /*.mem_size   =*/ model.buf.size,
+            /*.mem_buffer =*/ model.buf.data,
+            /*.no_alloc   =*/ ml.use_mmap,
+        };
+
+        model.ctx = ggml_init(params);
+        if (!model.ctx) {
+            throw std::runtime_error(format("ggml_init() failed"));
+        }
+    }
+
+    (void) main_gpu;
+#ifdef GGML_USE_CUBLAS
+    LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__);
+    ggml_cuda_set_main_device(main_gpu);
+#define LLAMA_BACKEND_OFFLOAD       GGML_BACKEND_GPU
+#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT
+#elif defined(GGML_USE_CLBLAST)
+    LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__);
+#define LLAMA_BACKEND_OFFLOAD       GGML_BACKEND_GPU
+#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU
+#else
+#define LLAMA_BACKEND_OFFLOAD       GGML_BACKEND_CPU
+#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_CPU
+#endif
+
+    // prepare memory for the weights
+    size_t vram_weights = 0;
+    {
+        const int64_t n_embd     = hparams.n_embd;
+        const int64_t n_embd_gqa = hparams.n_embd_gqa();
+        const int64_t n_layer    = hparams.n_layer;
+        const int64_t n_vocab    = hparams.n_vocab;
+
+        const auto tn = LLM_TN(model.arch);
+        switch (model.arch) {
+            case LLM_ARCH_LLAMA:
+            case LLM_ARCH_REFACT:
+                {
+                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+
+                    // output
+                    {
+                        ggml_backend_type backend_norm;
+                        ggml_backend_type backend_output;
+
+                        if (n_gpu_layers > int(n_layer)) {
+                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
+                            // on Windows however this is detrimental unless everything is on the GPU
+#ifndef _WIN32
+                            backend_norm = LLAMA_BACKEND_OFFLOAD;
+#else
+                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
+#endif // _WIN32
+
+                            backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
+                        } else {
+                            backend_norm   = GGML_BACKEND_CPU;
+                            backend_output = GGML_BACKEND_CPU;
+                        }
+
+                        model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
+                        model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+
+                        if (backend_norm == GGML_BACKEND_GPU) {
+                            vram_weights += ggml_nbytes(model.output_norm);
+                        }
+                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
+                            vram_weights += ggml_nbytes(model.output);
+                        }
+                    }
+
+                    const uint32_t n_ff = hparams.n_ff;
+
+                    const int i_gpu_start = n_layer - n_gpu_layers;
+
+                    model.layers.resize(n_layer);
+
+                    for (uint32_t i = 0; i < n_layer; ++i) {
+                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
+                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
+
+                        layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd},     backend_split);
+                        layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
+                        layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
+                        layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},     backend_split);
+
+                        layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
+
+                        layer.w1 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
+                        layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+
+                        if (backend == GGML_BACKEND_GPU) {
+                            vram_weights +=
+                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk)       +
+                                ggml_nbytes(layer.wv)        + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
+                                ggml_nbytes(layer.w1)        + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
+                        }
+                    }
+                } break;
+            case LLM_ARCH_BAICHUAN:
+                {
+                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    {
+                        ggml_backend_type backend_norm;
+                        ggml_backend_type backend_output;
+
+                        if (n_gpu_layers > int(n_layer)) {
+                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
+                            // on Windows however this is detrimental unless everything is on the GPU
+#ifndef _WIN32
+                            backend_norm = LLAMA_BACKEND_OFFLOAD;
+#else
+                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
+#endif // _WIN32
+
+                            backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
+                        } else {
+                            backend_norm   = GGML_BACKEND_CPU;
+                            backend_output = GGML_BACKEND_CPU;
+                        }
+
+                        model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
+                        model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+
+                        if (backend_norm == GGML_BACKEND_GPU) {
+                            vram_weights += ggml_nbytes(model.output_norm);
+                        }
+                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
+                            vram_weights += ggml_nbytes(model.output);
+                        }
+                    }
+
+                    const uint32_t n_ff = hparams.n_ff;
+
+                    const int i_gpu_start = n_layer - n_gpu_layers;
+
+                    model.layers.resize(n_layer);
+
+                    for (uint32_t i = 0; i < n_layer; ++i) {
+                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
+                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
+
+                        layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd},     backend_split);
+                        layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
+                        layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
+                        layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},     backend_split);
+
+                        layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
+
+                        layer.w1 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
+                        layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+
+                        if (backend == GGML_BACKEND_GPU) {
+                            vram_weights +=
+                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk)       +
+                                ggml_nbytes(layer.wv)        + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
+                                ggml_nbytes(layer.w1)        + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
+                        }
+                    }
+                } break;
+            case LLM_ARCH_FALCON:
+                {
+                    // TODO: CPU-only for now
+
+                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+
+                    // output
+                    {
+                        ggml_backend_type backend_norm;
+                        ggml_backend_type backend_output;
+
+                        if (n_gpu_layers > int(n_layer)) {
+                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
+                            // on Windows however this is detrimental unless everything is on the GPU
+#ifndef _WIN32
+                            backend_norm = LLAMA_BACKEND_OFFLOAD;
+#else
+                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
+#endif // _WIN32
+
+                            backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
+                        } else {
+                            backend_norm   = GGML_BACKEND_CPU;
+                            backend_output = GGML_BACKEND_CPU;
+                        }
+
+                        model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
+                        model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
+                        model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+
+                        if (backend_norm == GGML_BACKEND_GPU) {
+                            vram_weights += ggml_nbytes(model.output_norm);
+                            vram_weights += ggml_nbytes(model.output_norm_b);
+                        }
+                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
+                            vram_weights += ggml_nbytes(model.output);
+                        }
+                    }
+
+                    const uint32_t n_ff = hparams.n_ff;
+
+                    const int i_gpu_start = n_layer - n_gpu_layers;
+
+                    model.layers.resize(n_layer);
+
+                    for (uint32_t i = 0; i < n_layer; ++i) {
+                        const ggml_backend_type backend       = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
+                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
+                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, backend);
+
+                        if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i).c_str()) >= 0) {
+                            layer.attn_norm_2   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, backend);
+                            layer.attn_norm_2_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, backend);
+
+                            if (backend == GGML_BACKEND_GPU) {
+                                vram_weights += ggml_nbytes(layer.attn_norm_2);
+                                vram_weights += ggml_nbytes(layer.attn_norm_2_b);
+                            }
+                        }
+
+                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
+                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},                backend_split);
+
+                        layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
+                        layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+
+                        if (backend == GGML_BACKEND_GPU) {
+                            vram_weights +=
+                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) +
+                                ggml_nbytes(layer.wqkv)      + ggml_nbytes(layer.wo)          +
+                                ggml_nbytes(layer.w2)        + ggml_nbytes(layer.w3);
+                        }
+                    }
+                } break;
+            case LLM_ARCH_STARCODER:
+                {
+                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.pos_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
+
+                    // output
+                    {
+                        ggml_backend_type backend_norm;
+                        ggml_backend_type backend_output;
+
+                        if (n_gpu_layers > int(n_layer)) {
+                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
+                            // on Windows however this is detrimental unless everything is on the GPU
+#ifndef _WIN32
+                            backend_norm = LLAMA_BACKEND_OFFLOAD;
+#else
+                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
+#endif // _WIN32
+
+                            backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
+                        } else {
+                            backend_norm   = GGML_BACKEND_CPU;
+                            backend_output = GGML_BACKEND_CPU;
+                        }
+
+                        model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
+                        model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
+                        model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+
+                        if (backend_norm == GGML_BACKEND_GPU) {
+                            vram_weights += ggml_nbytes(model.output_norm);
+                            vram_weights += ggml_nbytes(model.output_norm_b);
+                        }
+                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
+                            vram_weights += ggml_nbytes(model.output);
+                        }
+                    }
+
+                    const uint32_t n_ff = hparams.n_ff;
+
+                    const int i_gpu_start = n_layer - n_gpu_layers;
+
+                    model.layers.resize(n_layer);
+
+                    for (uint32_t i = 0; i < n_layer; ++i) {
+                        const ggml_backend_type backend       = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
+                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
+                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, backend);
+
+                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
+                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa},         backend_split);
+
+                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},   backend_split);
+                        layer.bo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd},           backend_split);
+
+                        layer.ffn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
+                        layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, backend);
+
+                        layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
+                        layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend_split);
+
+                        layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend_split);
+
+                        if (backend == GGML_BACKEND_GPU) {
+                            vram_weights +=
+                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) +
+                                ggml_nbytes(layer.wqkv)      + ggml_nbytes(layer.bqkv)        +
+                                ggml_nbytes(layer.wo)        + ggml_nbytes(layer.bo)          +
+                                ggml_nbytes(layer.ffn_norm)  + ggml_nbytes(layer.ffn_norm_b)  +
+                                ggml_nbytes(layer.w2)        + ggml_nbytes(layer.b2)          +
+                                ggml_nbytes(layer.w3)        + ggml_nbytes(layer.b3);
+                        }
+                    }
+                } break;
+            case LLM_ARCH_PERSIMMON:
+                {
+                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"),  {n_embd, n_vocab}, GGML_BACKEND_CPU);
+
+                    {
+                        ggml_backend_type backend_norm;
+                        ggml_backend_type backend_output;
+
+                        if (n_gpu_layers > int(n_layer)) {
+                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
+                            // on Windows however this is detrimental unless everything is on the GPU
+#ifndef _WIN32
+                            backend_norm = LLAMA_BACKEND_OFFLOAD;
+#else
+                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
+#endif // _WIN32
+
+                            backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
+                        } else {
+                            backend_norm   = GGML_BACKEND_CPU;
+                            backend_output = GGML_BACKEND_CPU;
+                        }
+
+                        model.output_norm    = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
+                        model.output_norm_b  = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
+                        model.output         = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+
+                        if (backend_norm == GGML_BACKEND_GPU) {
+                            vram_weights += ggml_nbytes(model.output_norm);
+                            vram_weights += ggml_nbytes(model.output_norm_b);
+                        }
+                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
+                            vram_weights += ggml_nbytes(model.output);
+                        }
+                    }
+
+                    const uint32_t n_ff = hparams.n_ff;
+                    const int i_gpu_start = n_layer - n_gpu_layers;
+                    model.layers.resize(n_layer);
+                    for (uint32_t i = 0; i < n_layer; ++i) {
+                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
+                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT;
+                        auto & layer = model.layers[i];
+                        layer.attn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
+                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, backend);
+                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
+                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa},         backend_split);
+                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},   backend_split);
+                        layer.bo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd},           backend_split);
+                        layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
+                        layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend_split);
+                        layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend_split);
+                        layer.ffn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
+                        layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, backend);
+                        layer.attn_q_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}, backend);
+                        layer.attn_q_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i),   {64}, backend);
+                        layer.attn_k_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64}, backend);
+                        layer.attn_k_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i),   {64}, backend);
+                    }
+                } break;
+            case LLM_ARCH_BLOOM:
+                {
+                    // TODO: CPU-only for now
+
+                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_norm       = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd},          GGML_BACKEND_CPU);
+                    model.tok_norm_b     = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd},          GGML_BACKEND_CPU);
+
+                    // output
+                    {
+                        ggml_backend_type backend_norm;
+                        ggml_backend_type backend_output;
+
+                        if (n_gpu_layers > int(n_layer)) {
+                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
+                            // on Windows however this is detrimental unless everything is on the GPU
+#ifndef _WIN32
+                            backend_norm = LLAMA_BACKEND_OFFLOAD;
+#else
+                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
+#endif // _WIN32
+
+                            backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
+                        } else {
+                            backend_norm   = GGML_BACKEND_CPU;
+                            backend_output = GGML_BACKEND_CPU;
+                        }
+
+                        model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
+                        model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
+                        model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+
+                        if (backend_norm == GGML_BACKEND_GPU) {
+                            vram_weights += ggml_nbytes(model.output_norm);
+                            vram_weights += ggml_nbytes(model.output_norm_b);
+                        }
+                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
+                            vram_weights += ggml_nbytes(model.output);
+                        }
+                    }
+
+                    const uint32_t n_ff = hparams.n_ff;
+
+                    const int i_gpu_start = n_layer - n_gpu_layers;
+
+                    model.layers.resize(n_layer);
+
+                    for (uint32_t i = 0; i < n_layer; ++i) {
+                        const ggml_backend_type backend       = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
+                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
+                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, backend);
+
+                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
+                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa},         backend_split);
+
+                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},                backend_split);
+                        layer.bo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd},                        backend_split);
+
+                        layer.ffn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
+                        layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, backend);
+
+                        layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
+                        layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend_split);
+
+                        layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend_split);
+
+                        if (backend == GGML_BACKEND_GPU) {
+                            vram_weights +=
+                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) +
+                                ggml_nbytes(layer.wqkv)      + ggml_nbytes(layer.bqkv)        +
+                                ggml_nbytes(layer.wo)        + ggml_nbytes(layer.bo)          +
+                                ggml_nbytes(layer.ffn_norm)  + ggml_nbytes(layer.ffn_norm_b)  +
+                                ggml_nbytes(layer.w3)        + ggml_nbytes(layer.b3)          +
+                                ggml_nbytes(layer.w2)        + ggml_nbytes(layer.b2);
+                        }
+                    }
+                } break;
+            case LLM_ARCH_MPT:
+                {
+                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+
+                    // output
+                    {
+                        ggml_backend_type backend_norm;
+                        ggml_backend_type backend_output;
+
+                        if (n_gpu_layers > int(n_layer)) {
+                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
+                            // on Windows however this is detrimental unless everything is on the GPU
+#ifndef _WIN32
+                            backend_norm = LLAMA_BACKEND_OFFLOAD;
+#else
+                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
+#endif // _WIN32
+
+                            backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
+                        } else {
+                            backend_norm   = GGML_BACKEND_CPU;
+                            backend_output = GGML_BACKEND_CPU;
+                        }
+
+                        model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
+                        model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+
+                        if (backend_norm == GGML_BACKEND_GPU) {
+                            vram_weights += ggml_nbytes(model.output_norm);
+                        }
+                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
+                            vram_weights += ggml_nbytes(model.output);
+                        }
+                    }
+
+                    const uint32_t n_ff = hparams.n_ff;
+
+                    const int i_gpu_start = n_layer - n_gpu_layers;
+
+                    model.layers.resize(n_layer);
+
+                    for (uint32_t i = 0; i < n_layer; ++i) {
+                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
+                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
+                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
+                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},                backend_split);
+
+                        layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
+
+                        layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
+                        layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+
+                        if (backend == GGML_BACKEND_GPU) {
+                            vram_weights +=
+                                ggml_nbytes(layer.attn_norm) +
+                                ggml_nbytes(layer.wqkv)      +
+                                ggml_nbytes(layer.wo)        +
+                                ggml_nbytes(layer.ffn_norm)  +
+                                ggml_nbytes(layer.w2)        +
+                                ggml_nbytes(layer.w3);
+                        }
+                    }
+                } break;
+            default:
+                throw std::runtime_error("unknown architecture");
+        }
+    }
+
+    ml.done_getting_tensors();
+
+    // print memory requirements
+    {
+        // this is the total memory required to run the inference
+        size_t mem_required =
+            ctx_size +
+            mmapped_size - vram_weights; // weights in VRAM not in memory
+
+        LLAMA_LOG_INFO("%s: mem required  = %7.2f MB\n", __func__, mem_required / 1024.0 / 1024.0);
+
+#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
+        const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
+
+        LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
+        if (n_gpu_layers > (int) hparams.n_layer) {
+            LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU\n", __func__);
+        }
+
+#ifdef GGML_USE_CUBLAS
+        const int max_backend_supported_layers = hparams.n_layer + 3;
+        const int max_offloadable_layers = hparams.n_layer + 3;
+#elif defined(GGML_USE_CLBLAST)
+        const int max_backend_supported_layers = hparams.n_layer + 1;
+        const int max_offloadable_layers = hparams.n_layer + 1;
+#endif // GGML_USE_CUBLAS
+
+        LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
+        LLAMA_LOG_INFO("%s: VRAM used: %.2f MB\n", __func__, vram_weights / 1024.0 / 1024.0);
+#else
+        (void) n_gpu_layers;
+#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
+    }
+
+    // populate `tensors_by_name`
+    for (int i = 0; i < ml.n_tensors; ++i) {
+        struct ggml_tensor * cur = ggml_get_tensor(ctx, ml.get_tensor_name(i));
+        model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
+    }
+
+    (void) tensor_split;
+#ifdef GGML_USE_CUBLAS
+    {
+        ggml_cuda_set_tensor_split(tensor_split);
+    }
+#endif
+
+    ml.load_all_data(ctx, progress_callback, progress_callback_user_data, use_mlock ? &model.mlock_mmap : NULL);
+
+    if (progress_callback) {
+        progress_callback(1.0f, progress_callback_user_data);
+    }
+
+    model.mapping = std::move(ml.mapping);
+
+    // loading time will be recalculate after the first eval, so
+    // we take page faults deferred by mmap() into consideration
+    model.t_load_us = ggml_time_us() - model.t_start_us;
+}
+
+static bool llama_model_load(
+        const std::string & fname,
+        llama_model & model,
+        int n_gpu_layers,
+        int main_gpu,
+        const float * tensor_split,
+        bool use_mmap,
+        bool use_mlock,
+        bool vocab_only,
+        llama_progress_callback progress_callback,
+        void *progress_callback_user_data) {
+    try {
+        llama_model_loader ml(fname, use_mmap);
+
+        model.hparams.vocab_only = vocab_only;
+
+        llm_load_arch   (ml, model);
+        llm_load_hparams(ml, model);
+        llm_load_vocab  (ml, model);
+
+        llm_load_print_meta(ml, model);
+
+        if (model.hparams.n_vocab != model.vocab.id_to_token.size()) {
+            throw std::runtime_error("vocab size mismatch");
+        }
+
+        if (vocab_only) {
+            LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
+            return true;
+        }
+
+        llm_load_tensors(
+                ml, model, n_gpu_layers,
+                main_gpu, tensor_split,
+                use_mlock, progress_callback, progress_callback_user_data);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("error loading model: %s\n", err.what());
+        return false;
+    }
+
+    return true;
+}
+
+static struct ggml_cgraph * llm_build_llama(
+    llama_context & lctx,
+    const llama_batch & batch) {
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+    const auto & cparams = lctx.cparams;
+
+    const auto & kv_self = lctx.kv_self;
+
+    GGML_ASSERT(!!kv_self.ctx);
+
+    const int64_t n_embd      = hparams.n_embd;
+    const int64_t n_layer     = hparams.n_layer;
+    const int64_t n_ctx       = cparams.n_ctx;
+    const int64_t n_head      = hparams.n_head;
+    const int64_t n_head_kv   = hparams.n_head_kv;
+    const int64_t n_embd_head = hparams.n_embd_head();
+    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    const float freq_base    = cparams.rope_freq_base;
+    const float freq_scale   = cparams.rope_freq_scale;
+    const float norm_rms_eps = hparams.f_norm_rms_eps;
+
+    const int n_gpu_layers = model.n_gpu_layers;
+
+    const int32_t n_tokens = batch.n_tokens;
+    const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
+    const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+
+    const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
+
+    //printf("n_kv = %d\n", n_kv);
+
+    auto & buf_compute = lctx.buf_compute;
+
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ buf_compute.size,
+        /*.mem_buffer =*/ buf_compute.data,
+        /*.no_alloc   =*/ true,
+    };
+
+    struct ggml_context * ctx0 = ggml_init(params);
+
+    ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+    struct ggml_tensor * cur;
+    struct ggml_tensor * inpL;
+
+    if (batch.token) {
+        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, inp_tokens);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
+        }
+        ggml_set_name(inp_tokens, "inp_tokens");
+
+        inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
+    } else {
+#ifdef GGML_USE_MPI
+        GGML_ASSERT(false && "not implemented");
+#endif
+
+        inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, inpL);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
+        }
+    }
+
+    const int i_gpu_start = n_layer - n_gpu_layers;
+    (void) i_gpu_start;
+
+    // offload functions set the tensor output backend to GPU
+    // tensors are GPU-accelerated if any input or the output has been offloaded
+    offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
+    offload_func_t offload_func_kq = llama_nop;
+    offload_func_t offload_func_v  = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+    if (n_gpu_layers > n_layer) {
+        offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
+    }
+    if (n_gpu_layers > n_layer + 1) {
+        offload_func_v  = ggml_cuda_assign_buffers_no_alloc;
+    }
+    if (n_gpu_layers > n_layer + 2) {
+        offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
+    }
+#endif // GGML_USE_CUBLAS
+
+    // KQ_scale
+    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
+    ggml_allocr_alloc(lctx.alloc, KQ_scale);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
+    }
+
+    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+    offload_func_kq(KQ_mask);
+    ggml_set_name(KQ_mask, "KQ_mask");
+    ggml_allocr_alloc(lctx.alloc, KQ_mask);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        float * data = (float *) KQ_mask->data;
+        memset(data, 0, ggml_nbytes(KQ_mask));
+
+        for (int h = 0; h < 1; ++h) {
+            for (int j = 0; j < n_tokens; ++j) {
+                const llama_pos    pos    = batch.pos[j];
+                const llama_seq_id seq_id = batch.seq_id[j][0];
+
+                for (int i = 0; i < n_kv; ++i) {
+                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
+                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
+                    }
+                }
+            }
+        }
+    }
+
+    // KQ_pos - contains the positions
+    struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    offload_func_kq(KQ_pos);
+    ggml_set_name(KQ_pos, "KQ_pos");
+    ggml_allocr_alloc(lctx.alloc, KQ_pos);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        int * data = (int *) KQ_pos->data;
+        for (int i = 0; i < n_tokens; ++i) {
+            data[i] = batch.pos[i];
+        }
+    }
+
+    // shift the entire K-cache if needed
+    if (do_rope_shift) {
+        struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
+        offload_func_kq(K_shift);
+        ggml_set_name(K_shift, "K_shift");
+        ggml_allocr_alloc(lctx.alloc, K_shift);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            int * data = (int *) K_shift->data;
+            for (int i = 0; i < n_ctx; ++i) {
+                data[i] = kv_self.cells[i].delta;
+            }
+        }
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * tmp =
+                    ggml_rope_custom_inplace(ctx0,
+                        ggml_view_3d(ctx0, kv_self.k,
+                            n_embd_head, n_head_kv, n_ctx,
+                            ggml_element_size(kv_self.k)*n_embd_head,
+                            ggml_element_size(kv_self.k)*n_embd_gqa,
+                            ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il),
+                        K_shift, n_embd_head, 0, 0, freq_base, freq_scale);
+            offload_func_kq(tmp);
+            ggml_build_forward_expand(gf, tmp);
+        }
+    }
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_format_name(inpL, "layer_inp_%d", il);
+
+        offload_func_t offload_func = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+        if (il >= i_gpu_start) {
+            offload_func = ggml_cuda_assign_buffers_no_alloc;
+        }
+#endif // GGML_USE_CUBLAS
+
+        struct ggml_tensor * inpSA = inpL;
+
+        // norm
+        {
+            cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
+            offload_func(cur);
+            ggml_set_name(cur, "rms_norm_0");
+
+            // cur = cur*attn_norm(broadcasted)
+            cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
+            offload_func(cur);
+            ggml_set_name(cur, "attention_norm_0");
+        }
+
+        // self-attention
+        {
+            // compute Q and K and RoPE them
+            struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+            offload_func_kq(tmpk);
+            ggml_set_name(tmpk, "tmpk");
+
+            struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+            offload_func_kq(tmpq);
+            ggml_set_name(tmpq, "tmpq");
+
+            struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+            offload_func_kq(Kcur);
+            ggml_set_name(Kcur, "Kcur");
+
+            struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head,    n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+            offload_func_kq(Qcur);
+            ggml_set_name(Qcur, "Qcur");
+
+            // store key and value to memory
+            {
+                // compute the transposed [n_tokens, n_embd] V matrix
+
+                struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                offload_func_v(tmpv);
+                ggml_set_name(tmpv, "tmpv");
+
+                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
+                offload_func_v(Vcur);
+                ggml_set_name(Vcur, "Vcur");
+
+                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
+                offload_func_kq(k);
+                ggml_set_name(k, "k");
+
+                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
+                        (   n_ctx)*ggml_element_size(kv_self.v),
+                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
+                offload_func_v(v);
+                ggml_set_name(v, "v");
+
+                // important: storing RoPE-ed version of K in the KV cache!
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
+            }
+
+            struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+            offload_func_kq(Q);
+            ggml_set_name(Q, "Q");
+
+            struct ggml_tensor * K =
+                ggml_view_3d(ctx0, kv_self.k,
+                        n_embd_head, n_kv, n_head_kv,
+                        ggml_element_size(kv_self.k)*n_embd_gqa,
+                        ggml_element_size(kv_self.k)*n_embd_head,
+                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
+            offload_func_kq(K);
+            ggml_set_name(K, "K");
+
+            // K * Q
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+            offload_func_kq(KQ);
+            ggml_set_name(KQ, "KQ");
+
+            // KQ_scaled = KQ / sqrt(n_embd_head)
+            // KQ_scaled shape [n_kv, n_tokens, n_head, 1]
+            struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
+            offload_func_kq(KQ_scaled);
+            ggml_set_name(KQ_scaled, "KQ_scaled");
+
+            // KQ_masked = mask_past(KQ_scaled)
+            struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
+            offload_func_kq(KQ_masked);
+            ggml_set_name(KQ_masked, "KQ_masked");
+
+            // KQ = soft_max(KQ_masked)
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
+            offload_func_v(KQ_soft_max);
+            ggml_set_name(KQ_soft_max, "KQ_soft_max");
+
+            // split cached V into n_head heads
+            struct ggml_tensor * V =
+                ggml_view_3d(ctx0, kv_self.v,
+                        n_kv, n_embd_head, n_head_kv,
+                        ggml_element_size(kv_self.v)*n_ctx,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
+            offload_func_v(V);
+            ggml_set_name(V, "V");
+
+#if 1
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
+            offload_func_v(KQV);
+            ggml_set_name(KQV, "KQV");
+#else
+            // make V contiguous in memory to speed up the matmul, however we waste time on the copy
+            // on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
+            // is there a better way?
+            struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_ctx, n_embd_head, n_head));
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max);
+#endif
+
+            // KQV_merged = KQV.permute(0, 2, 1, 3)
+            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+            offload_func_v(KQV_merged);
+            ggml_set_name(KQV_merged, "KQV_merged");
+
+            // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
+            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
+            offload_func_v(cur);
+            ggml_set_name(cur, "KQV_merged_contiguous");
+
+            // projection (no bias)
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].wo,
+                    cur);
+            offload_func(cur);
+            ggml_set_name(cur, "result_wo");
+        }
+
+        struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
+        offload_func(inpFF);
+        ggml_set_name(inpFF, "inpFF");
+
+        // feed-forward network
+        {
+            // norm
+            {
+                cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps);
+                offload_func(cur);
+                ggml_set_name(cur, "rms_norm_1");
+
+                // cur = cur*ffn_norm(broadcasted)
+                cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
+                offload_func(cur);
+                ggml_set_name(cur, "ffn_norm");
+            }
+
+            struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
+                    model.layers[il].w3,
+                    cur);
+            offload_func(tmp);
+            ggml_set_name(tmp, "result_w3");
+
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].w1,
+                    cur);
+            offload_func(cur);
+            ggml_set_name(cur, "result_w1");
+
+            // SILU activation
+            cur = ggml_silu(ctx0, cur);
+            offload_func(cur);
+            ggml_set_name(cur, "silu");
+
+            cur = ggml_mul(ctx0, cur, tmp);
+            offload_func(cur);
+            ggml_set_name(cur, "silu_x_result_w3");
+
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].w2,
+                    cur);
+            offload_func(cur);
+            ggml_set_name(cur, "result_w2");
+        }
+
+        cur = ggml_add(ctx0, cur, inpFF);
+        offload_func(cur);
+        ggml_set_name(cur, "inpFF_+_result_w2");
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    // norm
+    {
+        cur = ggml_rms_norm(ctx0, cur, norm_rms_eps);
+        offload_func_nr(cur);
+        ggml_set_name(cur, "rms_norm_2");
+
+        // cur = cur*norm(broadcasted)
+        cur = ggml_mul(ctx0, cur, model.output_norm);
+        // offload_func_nr(cur); // TODO CPU + GPU mirrored backend
+        ggml_set_name(cur, "result_norm");
+    }
+
+    // lm_head
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+    ggml_set_name(cur, "result_output");
+
+    ggml_build_forward_expand(gf, cur);
+
+    ggml_free(ctx0);
+
+    return gf;
+}
+
+static struct ggml_cgraph * llm_build_baichaun(
+         llama_context & lctx,
+     const llama_batch & batch) {
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+    const auto & cparams = lctx.cparams;
+
+    const auto & kv_self = lctx.kv_self;
+
+    GGML_ASSERT(!!kv_self.ctx);
+
+    const int64_t n_embd      = hparams.n_embd;
+    const int64_t n_layer     = hparams.n_layer;
+    const int64_t n_ctx       = cparams.n_ctx;
+    const int64_t n_head      = hparams.n_head;
+    const int64_t n_head_kv   = hparams.n_head_kv;
+    const int64_t n_embd_head = hparams.n_embd_head();
+    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    const float freq_base    = cparams.rope_freq_base;
+    const float freq_scale   = cparams.rope_freq_scale;
+    const float norm_rms_eps = hparams.f_norm_rms_eps;
+
+    const int n_gpu_layers = model.n_gpu_layers;
+
+    const int32_t n_tokens = batch.n_tokens;
+    const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
+    const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+
+    const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
+
+    auto & buf_compute = lctx.buf_compute;
+
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ buf_compute.size,
+        /*.mem_buffer =*/ buf_compute.data,
+        /*.no_alloc   =*/ true,
+    };
+
+    struct ggml_context * ctx0 = ggml_init(params);
+
+    ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+    struct ggml_tensor * cur;
+    struct ggml_tensor * inpL;
+
+    if (batch.token) {
+        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, inp_tokens);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
+        }
+        ggml_set_name(inp_tokens, "inp_tokens");
+
+        inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
+    } else {
+#ifdef GGML_USE_MPI
+        GGML_ASSERT(false && "not implemented");
+#endif
+
+        inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, inpL);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
+        }
+    }
+
+    const int i_gpu_start = n_layer - n_gpu_layers;
+    (void) i_gpu_start;
+
+    // offload functions set the tensor output backend to GPU
+    // tensors are GPU-accelerated if any input or the output has been offloaded
+    offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
+    offload_func_t offload_func_kq = llama_nop;
+    offload_func_t offload_func_v  = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+    if (n_gpu_layers > n_layer) {
+        offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
+    }
+    if (n_gpu_layers > n_layer + 1) {
+        offload_func_v  = ggml_cuda_assign_buffers_no_alloc;
+    }
+    if (n_gpu_layers > n_layer + 2) {
+        offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
+    }
+#endif // GGML_USE_CUBLAS
+
+    // KQ_scale
+    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
+    ggml_allocr_alloc(lctx.alloc, KQ_scale);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
+    }
+
+    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+    offload_func_kq(KQ_mask);
+    ggml_set_name(KQ_mask, "KQ_mask");
+    ggml_allocr_alloc(lctx.alloc, KQ_mask);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        float * data = (float *) KQ_mask->data;
+        memset(data, 0, ggml_nbytes(KQ_mask));
+
+        for (int h = 0; h < 1; ++h) {
+            for (int j = 0; j < n_tokens; ++j) {
+                const llama_pos    pos    = batch.pos[j];
+                const llama_seq_id seq_id = batch.seq_id[j][0];
+
+                for (int i = 0; i < n_kv; ++i) {
+                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
+                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
+                    }
+                }
+            }
+        }
+    }
+
+    // KQ_pos - contains the positions
+    struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    offload_func_kq(KQ_pos);
+    ggml_set_name(KQ_pos, "KQ_pos");
+    ggml_allocr_alloc(lctx.alloc, KQ_pos);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        int * data = (int *) KQ_pos->data;
+        for (int i = 0; i < n_tokens; ++i) {
+            data[i] = batch.pos[i];
+        }
+    }
+
+    // shift the entire K-cache if needed
+    if (do_rope_shift) {
+        struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
+        offload_func_kq(K_shift);
+        ggml_set_name(K_shift, "K_shift");
+        ggml_allocr_alloc(lctx.alloc, K_shift);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            int * data = (int *) K_shift->data;
+            for (int i = 0; i < n_ctx; ++i) {
+                data[i] = kv_self.cells[i].delta;
+            }
+        }
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * tmp =
+                    ggml_rope_custom_inplace(ctx0,
+                        ggml_view_3d(ctx0, kv_self.k,
+                            n_embd_head, n_head_kv, n_ctx,
+                            ggml_element_size(kv_self.k)*n_embd_head,
+                            ggml_element_size(kv_self.k)*n_embd_gqa,
+                            ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il),
+                        K_shift, n_embd_head, 0, 0, freq_base, freq_scale);
+            offload_func_kq(tmp);
+            ggml_build_forward_expand(gf, tmp);
+        }
+    }
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_format_name(inpL, "layer_inp_%d", il);
+
+        offload_func_t offload_func = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+        if (il >= i_gpu_start) {
+            offload_func = ggml_cuda_assign_buffers_no_alloc;
+        }
+#endif // GGML_USE_CUBLAS
+
+        struct ggml_tensor * inpSA = inpL;
+
+        // norm
+        {
+            cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
+            offload_func(cur);
+            ggml_set_name(cur, "rms_norm_0");
+
+            // cur = cur*attn_norm(broadcasted)
+            cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
+            offload_func(cur);
+            ggml_set_name(cur, "attention_norm_0");
+        }
+
+        // self-attention
+        {
+            // compute Q and K and RoPE them
+            struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+            offload_func_kq(tmpk);
+            ggml_set_name(tmpk, "tmpk");
+
+            struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+            offload_func_kq(tmpq);
+            ggml_set_name(tmpq, "tmpq");
+
+            struct ggml_tensor * Kcur;
+            struct ggml_tensor * Qcur;
+            switch (model.type) {
+                case MODEL_7B:
+                    Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+                    Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens),    KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+                    break;
+                case MODEL_13B:
+                    Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, n_tokens);
+                    Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, n_tokens);
+                    break;
+                default:
+                    GGML_ASSERT(false);
+            }
+
+            offload_func_kq(Kcur);
+            ggml_set_name(Kcur, "Kcur");
+
+            offload_func_kq(Qcur);
+            ggml_set_name(Qcur, "Qcur");
+
+            // store key and value to memory
+            {
+                // compute the transposed [n_tokens, n_embd] V matrix
+
+                struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                offload_func_v(tmpv);
+                ggml_set_name(tmpv, "tmpv");
+
+                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
+                offload_func_v(Vcur);
+                ggml_set_name(Vcur, "Vcur");
+
+                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
+                offload_func_kq(k);
+                ggml_set_name(k, "k");
+
+                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
+                        (   n_ctx)*ggml_element_size(kv_self.v),
+                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
+                offload_func_v(v);
+                ggml_set_name(v, "v");
+
+                // important: storing RoPE-ed version of K in the KV cache!
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
+            }
+
+            struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+            offload_func_kq(Q);
+            ggml_set_name(Q, "Q");
+
+            struct ggml_tensor * K =
+                ggml_view_3d(ctx0, kv_self.k,
+                        n_embd_head, n_kv, n_head_kv,
+                        ggml_element_size(kv_self.k)*n_embd_gqa,
+                        ggml_element_size(kv_self.k)*n_embd_head,
+                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
+            offload_func_kq(K);
+            ggml_set_name(K, "K");
+
+            // K * Q
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+            offload_func_kq(KQ);
+            ggml_set_name(KQ, "KQ");
+
+            // KQ_scaled = KQ / sqrt(n_embd_head)
+            // KQ_scaled shape [n_past + n_tokens, n_tokens, n_head, 1]
+            struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
+            offload_func_kq(KQ_scaled);
+            ggml_set_name(KQ_scaled, "KQ_scaled");
+
+            struct ggml_tensor * KQ_masked;
+            struct ggml_tensor * KQ_scaled_alibi;
+
+            switch (model.type) {
+                case MODEL_7B:
+                    KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
+                    break;
+                case MODEL_13B:
+                    // TODO: replace with ggml_add()
+                    KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ 0, n_head, 8);
+                    ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
+                    KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
+                    break;
+                default:
+                    GGML_ASSERT(false);
+            }
+
+            // KQ = soft_max(KQ_masked)
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
+            offload_func_v(KQ_soft_max);
+            ggml_set_name(KQ_soft_max, "KQ_soft_max");
+
+            // split cached V into n_head heads
+            struct ggml_tensor * V =
+                ggml_view_3d(ctx0, kv_self.v,
+                        n_kv, n_embd_head, n_head_kv,
+                        ggml_element_size(kv_self.v)*n_ctx,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
+            offload_func_v(V);
+            ggml_set_name(V, "V");
+
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
+            offload_func_v(KQV);
+            ggml_set_name(KQV, "KQV");
+
+            // KQV_merged = KQV.permute(0, 2, 1, 3)
+            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+            offload_func_v(KQV_merged);
+            ggml_set_name(KQV_merged, "KQV_merged");
+
+            // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
+            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
+            offload_func_v(cur);
+            ggml_set_name(cur, "KQV_merged_contiguous");
+
+            // projection (no bias)
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].wo,
+                    cur);
+            offload_func(cur);
+            ggml_set_name(cur, "result_wo");
+        }
+
+        struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
+        offload_func(inpFF);
+        ggml_set_name(inpFF, "inpFF");
+
+        // feed-forward network
+        {
+            // norm
+            {
+                cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps);
+                offload_func(cur);
+                ggml_set_name(cur, "rms_norm_1");
+
+                // cur = cur*ffn_norm(broadcasted)
+                cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
+                offload_func(cur);
+                ggml_set_name(cur, "ffn_norm");
+            }
+
+            struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
+                    model.layers[il].w3,
+                    cur);
+            offload_func(tmp);
+            ggml_set_name(tmp, "result_w3");
+
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].w1,
+                    cur);
+            offload_func(cur);
+            ggml_set_name(cur, "result_w1");
+
+            // SILU activation
+            cur = ggml_silu(ctx0, cur);
+            offload_func(cur);
+            ggml_set_name(cur, "silu");
+
+            cur = ggml_mul(ctx0, cur, tmp);
+            offload_func(cur);
+            ggml_set_name(cur, "silu_x_result_w3");
+
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].w2,
+                    cur);
+            offload_func(cur);
+            ggml_set_name(cur, "result_w2");
+        }
+
+        cur = ggml_add(ctx0, cur, inpFF);
+        offload_func(cur);
+        ggml_set_name(cur, "inpFF_+_result_w2");
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    // norm
+    {
+        cur = ggml_rms_norm(ctx0, cur, norm_rms_eps);
+        offload_func_nr(cur);
+        ggml_set_name(cur, "rms_norm_2");
+
+        // cur = cur*norm(broadcasted)
+        cur = ggml_mul(ctx0, cur, model.output_norm);
+        // offload_func_nr(cur); // TODO CPU + GPU mirrored backend
+        ggml_set_name(cur, "result_norm");
+    }
+
+    // lm_head
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+    ggml_set_name(cur, "result_output");
+
+    ggml_build_forward_expand(gf, cur);
+
+    ggml_free(ctx0);
+
+    return gf;
+}
+
+static struct ggml_cgraph * llm_build_refact(
+         llama_context & lctx,
+     const llama_batch & batch) {
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+    const auto & cparams = lctx.cparams;
+
+    const auto & kv_self = lctx.kv_self;
+
+    GGML_ASSERT(!!kv_self.ctx);
+
+    const int64_t n_embd      = hparams.n_embd;
+    const int64_t n_layer     = hparams.n_layer;
+    const int64_t n_ctx       = cparams.n_ctx;
+    const int64_t n_head      = hparams.n_head;
+    const int64_t n_head_kv   = hparams.n_head_kv;
+    const int64_t n_embd_head = hparams.n_embd_head();
+    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
+
+    const float norm_rms_eps = hparams.f_norm_rms_eps;
+
+    const int n_gpu_layers = model.n_gpu_layers;
+
+    const int32_t n_tokens = batch.n_tokens;
+    const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
+    const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+
+    // printf("n_kv = %d\n", n_kv);
+
+    auto & buf_compute = lctx.buf_compute;
+
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ buf_compute.size,
+        /*.mem_buffer =*/ buf_compute.data,
+        /*.no_alloc   =*/ true,
+    };
+
+    struct ggml_context * ctx0 = ggml_init(params);
+
+    ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+    struct ggml_tensor * cur;
+    struct ggml_tensor * inpL;
+
+    if (batch.token) {
+        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, inp_tokens);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
+        }
+        ggml_set_name(inp_tokens, "inp_tokens");
+
+        inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
+    } else {
+#ifdef GGML_USE_MPI
+        GGML_ASSERT(false && "not implemented");
+#endif
+
+        inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, inpL);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
+        }
+    }
+
+    const int i_gpu_start = n_layer - n_gpu_layers;
+    (void) i_gpu_start;
+
+    // offload functions set the tensor output backend to GPU
+    // tensors are GPU-accelerated if any input or the output has been offloaded
+    offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
+    offload_func_t offload_func_kq = llama_nop;
+    offload_func_t offload_func_v  = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+    if (n_gpu_layers > n_layer) {
+        offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
+    }
+    if (n_gpu_layers > n_layer + 1) {
+        offload_func_v  = ggml_cuda_assign_buffers_no_alloc;
+    }
+    if (n_gpu_layers > n_layer + 2) {
+        offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
+    }
+#endif // GGML_USE_CUBLAS
+
+    // KQ_scale
+    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
+    ggml_allocr_alloc(lctx.alloc, KQ_scale);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
+    }
+
+    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+    offload_func_kq(KQ_mask);
+    ggml_set_name(KQ_mask, "KQ_mask");
+    ggml_allocr_alloc(lctx.alloc, KQ_mask);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        float * data = (float *) KQ_mask->data;
+        memset(data, 0, ggml_nbytes(KQ_mask));
+
+        for (int h = 0; h < 1; ++h) {
+            for (int j = 0; j < n_tokens; ++j) {
+                const llama_pos    pos    = batch.pos[j];
+                const llama_seq_id seq_id = batch.seq_id[j][0];
+
+                for (int i = 0; i < n_kv; ++i) {
+                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
+                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
+                    }
+                }
+            }
+        }
+    }
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_format_name(inpL, "layer_inp_%d", il);
+
+        offload_func_t offload_func = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+        if (il >= i_gpu_start) {
+            offload_func = ggml_cuda_assign_buffers_no_alloc;
+        }
+#endif // GGML_USE_CUBLAS
+
+        struct ggml_tensor * inpSA = inpL;
+
+        // norm
+        {
+            cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
+            offload_func(cur);
+            ggml_set_name(cur, "rms_norm_0");
+
+            // cur = cur*attn_norm(broadcasted)
+            cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
+            offload_func(cur);
+            ggml_set_name(cur, "attention_norm_0");
+        }
+
+        // self-attention
+        {
+            // compute Q and K
+            struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+            offload_func_kq(tmpk);
+            ggml_set_name(tmpk, "tmpk");
+
+            struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+            offload_func_kq(tmpq);
+            ggml_set_name(tmpq, "tmpq");
+
+            struct ggml_tensor * Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens);
+            offload_func_kq(Kcur);
+            ggml_set_name(Kcur, "Kcur");
+
+            struct ggml_tensor * Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head,    n_tokens);
+            offload_func_kq(Qcur);
+            ggml_set_name(Qcur, "Qcur");
+
+            // store key and value to memory
+            {
+                // compute the transposed [n_tokens, n_embd] V matrix
+
+                struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                offload_func_v(tmpv);
+                ggml_set_name(tmpv, "tmpv");
+
+                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
+                offload_func_v(Vcur);
+                ggml_set_name(Vcur, "Vcur");
+
+                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
+                offload_func_kq(k);
+                ggml_set_name(k, "k");
+
+                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
+                        (   n_ctx)*ggml_element_size(kv_self.v),
+                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
+                offload_func_v(v);
+                ggml_set_name(v, "v");
+
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
+            }
+
+            struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+            offload_func_kq(Q);
+            ggml_set_name(Q, "Q");
+
+            struct ggml_tensor * K =
+                ggml_view_3d(ctx0, kv_self.k,
+                        n_embd_head, n_kv, n_head_kv,
+                        ggml_element_size(kv_self.k)*n_embd_gqa,
+                        ggml_element_size(kv_self.k)*n_embd_head,
+                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
+            offload_func_kq(K);
+            ggml_set_name(K, "K");
+
+            // K * Q
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+            offload_func_kq(KQ);
+            ggml_set_name(KQ, "KQ");
+
+            // KQ_scaled = KQ / sqrt(n_embd_head)
+            // KQ_scaled shape [n_kv, n_tokens, n_head, 1]
+            struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
+            offload_func_kq(KQ_scaled);
+            ggml_set_name(KQ_scaled, "KQ_scaled");
+
+            // KQ_masked = mask_past(KQ_scaled)
+            struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ 0, n_head, 8);
+            ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
+
+            struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
+            offload_func_kq(KQ_masked);
+            ggml_set_name(KQ_masked, "KQ_masked");
+
+            // KQ = soft_max(KQ_masked)
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
+            offload_func_v(KQ_soft_max);
+            ggml_set_name(KQ_soft_max, "KQ_soft_max");
+
+            // split cached V into n_head heads
+            struct ggml_tensor * V =
+                ggml_view_3d(ctx0, kv_self.v,
+                        n_kv, n_embd_head, n_head_kv,
+                        ggml_element_size(kv_self.v)*n_ctx,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
+            offload_func_v(V);
+            ggml_set_name(V, "V");
+
+#if 1
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
+            offload_func_v(KQV);
+            ggml_set_name(KQV, "KQV");
+#else
+            // make V contiguous in memory to speed up the matmul, however we waste time on the copy
+            // on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
+            // is there a better way?
+            struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_ctx, n_embd_head, n_head));
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max);
+#endif
+
+            // KQV_merged = KQV.permute(0, 2, 1, 3)
+            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+            offload_func_v(KQV_merged);
+            ggml_set_name(KQV_merged, "KQV_merged");
+
+            // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
+            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
+            offload_func_v(cur);
+            ggml_set_name(cur, "KQV_merged_contiguous");
+
+            // projection (no bias)
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].wo,
+                    cur);
+            offload_func(cur);
+            ggml_set_name(cur, "result_wo");
+        }
+
+        struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
+        offload_func(inpFF);
+        ggml_set_name(inpFF, "inpFF");
+
+        // feed-forward network
+        {
+            // norm
+            {
+                cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps);
+                offload_func(cur);
+                ggml_set_name(cur, "rms_norm_1");
+
+                // cur = cur*ffn_norm(broadcasted)
+                cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
+                offload_func(cur);
+                ggml_set_name(cur, "ffn_norm");
+            }
+
+            struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
+                    model.layers[il].w3,
+                    cur);
+            offload_func(tmp);
+            ggml_set_name(tmp, "result_w3");
+
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].w1,
+                    cur);
+            offload_func(cur);
+            ggml_set_name(cur, "result_w1");
+
+            // SILU activation
+            cur = ggml_silu(ctx0, cur);
+            offload_func(cur);
+            ggml_set_name(cur, "silu");
+
+            cur = ggml_mul(ctx0, cur, tmp);
+            offload_func(cur);
+            ggml_set_name(cur, "silu_x_result_w3");
+
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].w2,
+                    cur);
+            offload_func(cur);
+            ggml_set_name(cur, "result_w2");
+        }
+
+        cur = ggml_add(ctx0, cur, inpFF);
+        offload_func(cur);
+        ggml_set_name(cur, "inpFF_+_result_w2");
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    // norm
+    {
+        cur = ggml_rms_norm(ctx0, cur, norm_rms_eps);
+        offload_func_nr(cur);
+        ggml_set_name(cur, "rms_norm_2");
+
+        // cur = cur*norm(broadcasted)
+        cur = ggml_mul(ctx0, cur, model.output_norm);
+        // offload_func_nr(cur); // TODO CPU + GPU mirrored backend
+        ggml_set_name(cur, "result_norm");
+    }
+
+    // lm_head
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+    ggml_set_name(cur, "result_output");
+
+    ggml_build_forward_expand(gf, cur);
+
+    ggml_free(ctx0);
+
+    return gf;
+}
+
+static struct ggml_cgraph * llm_build_falcon(
+         llama_context & lctx,
+     const llama_batch & batch) {
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+    const auto & cparams = lctx.cparams;
+
+    const auto & kv_self = lctx.kv_self;
+
+    GGML_ASSERT(!!kv_self.ctx);
+
+    const int64_t n_embd      = hparams.n_embd;
+    const int64_t n_layer     = hparams.n_layer;
+    const int64_t n_ctx       = cparams.n_ctx;
+    const int64_t n_head      = hparams.n_head;
+    const int64_t n_head_kv   = hparams.n_head_kv;
+    const int64_t n_embd_head = hparams.n_embd_head();
+    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    const float freq_base  = cparams.rope_freq_base;
+    const float freq_scale = cparams.rope_freq_scale;
+    const float norm_eps   = hparams.f_norm_eps;
+
+    const int n_gpu_layers = model.n_gpu_layers;
+
+    const int32_t n_tokens = batch.n_tokens;
+    const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
+    const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+
+    const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
+
+    //printf("kv_head = %d, n_kv = %d, n_tokens = %d, n_ctx = %d, is_measure = %d, has_shift = %d\n",
+    //        kv_head, n_kv, n_tokens, n_ctx, ggml_allocr_is_measure(lctx.alloc), kv_self.has_shift);
+
+    auto & buf_compute = lctx.buf_compute;
+
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ buf_compute.size,
+        /*.mem_buffer =*/ buf_compute.data,
+        /*.no_alloc   =*/ true,
+    };
+
+    struct ggml_context * ctx0 = ggml_init(params);
+
+    ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+    struct ggml_tensor * cur;
+    struct ggml_tensor * inpL;
+
+    if (batch.token) {
+        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, inp_tokens);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
+        }
+        ggml_set_name(inp_tokens, "inp_tokens");
+
+        inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
+    } else {
+#ifdef GGML_USE_MPI
+        GGML_ASSERT(false && "not implemented");
+#endif
+
+        inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, inpL);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
+        }
+    }
+
+    const int i_gpu_start = n_layer - n_gpu_layers;
+    (void) i_gpu_start;
+
+    // offload functions set the tensor output backend to GPU
+    // tensors are GPU-accelerated if any input or the output has been offloaded
+    offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
+    offload_func_t offload_func_kq = llama_nop;
+    offload_func_t offload_func_v  = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+    if (n_gpu_layers > n_layer) {
+        offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
+    }
+    if (n_gpu_layers > n_layer + 1) {
+        offload_func_v  = ggml_cuda_assign_buffers_no_alloc;
+    }
+    if (n_gpu_layers > n_layer + 2) {
+        offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
+    }
+#endif // GGML_USE_CUBLAS
+
+    // KQ_scale
+    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
+    ggml_allocr_alloc(lctx.alloc, KQ_scale);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
+    }
+
+    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+    offload_func_kq(KQ_mask);
+    ggml_set_name(KQ_mask, "KQ_mask");
+    ggml_allocr_alloc(lctx.alloc, KQ_mask);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        float * data = (float *) KQ_mask->data;
+        memset(data, 0, ggml_nbytes(KQ_mask));
+
+        for (int h = 0; h < 1; ++h) {
+            for (int j = 0; j < n_tokens; ++j) {
+                const llama_pos    pos    = batch.pos[j];
+                const llama_seq_id seq_id = batch.seq_id[j][0];
+
+                for (int i = 0; i < n_kv; ++i) {
+                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
+                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
+                    }
+                }
+            }
+        }
+    }
+
+    // KQ_pos - contains the positions
+    struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    offload_func_kq(KQ_pos);
+    ggml_set_name(KQ_pos, "KQ_pos");
+    ggml_allocr_alloc(lctx.alloc, KQ_pos);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        int * data = (int *) KQ_pos->data;
+        for (int i = 0; i < n_tokens; ++i) {
+            data[i] = batch.pos[i];
+        }
+    }
+
+    // shift the entire K-cache if needed
+    if (do_rope_shift) {
+        struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
+        offload_func_kq(K_shift);
+        ggml_set_name(K_shift, "K_shift");
+        ggml_allocr_alloc(lctx.alloc, K_shift);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            int * data = (int *) K_shift->data;
+            for (int i = 0; i < n_ctx; ++i) {
+                data[i] = kv_self.cells[i].delta;
+            }
+        }
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * tmp =
+                    ggml_rope_custom_inplace(ctx0,
+                        ggml_view_3d(ctx0, kv_self.k,
+                            n_embd_head, n_head_kv, n_ctx,
+                            ggml_element_size(kv_self.k)*n_embd_head,
+                            ggml_element_size(kv_self.k)*n_embd_gqa,
+                            ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il),
+                        K_shift, n_embd_head, 2, 0, freq_base, freq_scale);
+            offload_func_kq(tmp);
+            ggml_build_forward_expand(gf, tmp);
+        }
+    }
+
+    for (int il = 0; il < n_layer; ++il) {
+        struct ggml_tensor * attn_norm;
+
+        offload_func_t offload_func = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+        if (il >= i_gpu_start) {
+            offload_func = ggml_cuda_assign_buffers_no_alloc;
+        }
+#endif // GGML_USE_CUBLAS
+
+        // self-attention
+        // TODO: refactor into common function (shared with LLaMA)
+        {
+            attn_norm = ggml_norm(ctx0, inpL, norm_eps);
+            offload_func(attn_norm);
+
+            attn_norm = ggml_add(ctx0,
+                    ggml_mul(ctx0, attn_norm, model.layers[il].attn_norm),
+                    model.layers[il].attn_norm_b);
+            offload_func(attn_norm->src[0]);
+            offload_func(attn_norm);
+
+            if (model.layers[il].attn_norm_2) { // Falcon-40B
+                cur = ggml_norm(ctx0, inpL, norm_eps);
+                offload_func(cur);
+
+                cur = ggml_add(ctx0,
+                        ggml_mul(ctx0, cur, model.layers[il].attn_norm_2),
+                        model.layers[il].attn_norm_2_b);
+                offload_func(cur->src[0]);
+                offload_func(cur);
+            } else { // Falcon 7B
+                cur = attn_norm;
+            }
+
+            // compute QKV
+
+            cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+            offload_func_kq(cur);
+
+            // Note that the strides for Kcur, Vcur are set up so that the
+            // resulting views are misaligned with the tensor's storage
+            // (by applying the K/V offset we shift the tensor's original
+            // view to stick out behind the viewed QKV tensor's allocated
+            // memory, so to say). This is ok because no actual accesses
+            // happen to that out-of-range memory, but it can require some
+            // trickery when trying to accurately dump these views for
+            // debugging.
+
+            const size_t wsize = ggml_type_size(cur->type);
+
+            // TODO: these 2 ggml_conts are technically not needed, but we add them until CUDA support for
+            //       non-contiguous views is added for the rope operator
+            struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_3d(
+                ctx0, cur, n_embd_head, n_head, n_tokens,
+                wsize * n_embd_head,
+                wsize * n_embd_head * (n_head + 2 * n_head_kv),
+                0));
+            offload_func_kq(tmpq);
+
+            struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_3d(
+                ctx0, cur, n_embd_head, n_head_kv, n_tokens,
+                wsize * n_embd_head,
+                wsize * n_embd_head * (n_head + 2 * n_head_kv),
+                wsize * n_embd_head *  n_head));
+            offload_func_kq(tmpk);
+
+            struct ggml_tensor * tmpv = ggml_view_3d(
+                ctx0, cur, n_embd_head, n_head_kv, n_tokens,
+                wsize * n_embd_head,
+                wsize * n_embd_head * (n_head + 2 * n_head_kv),
+                wsize * n_embd_head * (n_head +     n_head_kv));
+            offload_func_v(tmpv);
+
+            // using mode = 2 for neox mode
+            struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, tmpq, KQ_pos, n_embd_head, 2, 0, freq_base, freq_scale);
+            offload_func_kq(Qcur);
+            struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, tmpk, KQ_pos, n_embd_head, 2, 0, freq_base, freq_scale);
+            offload_func_kq(Kcur);
+
+            {
+                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens));
+                offload_func_v(Vcur);
+                offload_func_v(Vcur->src[0]->src[0]);
+                ggml_set_name(Vcur, "Vcur");
+
+                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
+                offload_func_kq(k);
+                ggml_set_name(k, "k");
+
+                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
+                        (   n_ctx)*ggml_element_size(kv_self.v),
+                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
+                offload_func_v(v);
+
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
+            }
+
+            struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+            offload_func_kq(Q);
+            ggml_set_name(Q, "Q");
+
+            struct ggml_tensor * K =
+                ggml_view_3d(ctx0, kv_self.k,
+                        n_embd_head, n_kv, n_head_kv,
+                        ggml_element_size(kv_self.k)*n_embd_gqa,
+                        ggml_element_size(kv_self.k)*n_embd_head,
+                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
+            offload_func_kq(K);
+            ggml_set_name(K, "K");
+
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+            offload_func_kq(KQ);
+            ggml_set_name(KQ, "KQ");
+
+            struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
+            offload_func_kq(KQ_scaled);
+            ggml_set_name(KQ_scaled, "KQ_scaled");
+
+            struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
+            offload_func_kq(KQ_masked);
+            ggml_set_name(KQ_masked, "KQ_masked");
+
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
+            offload_func_v(KQ_soft_max);
+            ggml_set_name(KQ_soft_max, "KQ_soft_max");
+
+            struct ggml_tensor * V =
+                ggml_view_3d(ctx0, kv_self.v,
+                        n_kv, n_embd_head, n_head_kv,
+                        ggml_element_size(kv_self.v)*n_ctx,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
+            offload_func_v(V);
+            ggml_set_name(V, "V");
+
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
+            offload_func_v(KQV);
+            ggml_set_name(KQV, "KQV");
+
+            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+            offload_func_v(KQV_merged);
+            ggml_set_name(KQV_merged, "KQV_merged");
+
+            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
+            offload_func_v(cur);
+            ggml_set_name(cur, "KQV_merged_contiguous");
+
+            cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
+            offload_func(cur);
+            ggml_set_name(cur, "result_wo");
+        }
+
+        struct ggml_tensor * attn_out = cur;
+
+        // feed forward
+        {
+            struct ggml_tensor * inpFF = attn_norm;
+
+            cur = ggml_mul_mat(ctx0, model.layers[il].w3, inpFF);
+            offload_func(cur);
+
+            cur = ggml_gelu(ctx0, cur);
+            offload_func(cur);
+            cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
+            offload_func(cur);
+        }
+
+        cur = ggml_add(ctx0, cur, attn_out);
+        offload_func(cur);
+        cur = ggml_add(ctx0, cur, inpL);
+        offload_func(cur);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    // norm
+    {
+        cur = ggml_norm(ctx0, cur, norm_eps);
+        offload_func_nr(cur);
+
+        cur = ggml_add(ctx0,
+                ggml_mul(ctx0, cur, model.output_norm),
+                model.output_norm_b);
+        ggml_set_name(cur, "result_norm");
+    }
+
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+    ggml_set_name(cur, "result_output");
+
+    ggml_build_forward_expand(gf, cur);
+
+    ggml_free(ctx0);
+
+    return gf;
+}
+
+static struct ggml_cgraph * llm_build_starcoder(
+         llama_context & lctx,
+     const llama_batch & batch) {
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+    const auto & cparams = lctx.cparams;
+
+    const auto & kv_self = lctx.kv_self;
+
+    GGML_ASSERT(!!kv_self.ctx);
+
+    const int64_t n_embd      = hparams.n_embd;
+    const int64_t n_layer     = hparams.n_layer;
+    const int64_t n_ctx       = cparams.n_ctx;
+    const int64_t n_head      = hparams.n_head;
+    const int64_t n_head_kv   = hparams.n_head_kv;
+    const int64_t n_embd_head = hparams.n_embd_head();
+    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    const float norm_eps = hparams.f_norm_eps;
+
+    const int32_t n_tokens = batch.n_tokens;
+    const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
+    const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+
+    auto & buf_compute = lctx.buf_compute;
+
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ buf_compute.size,
+        /*.mem_buffer =*/ buf_compute.data,
+        /*.no_alloc   =*/ true,
+    };
+
+    struct ggml_context * ctx0 = ggml_init(params);
+
+    ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+    struct ggml_tensor * cur;
+    struct ggml_tensor * token;
+    struct ggml_tensor * position;
+    struct ggml_tensor * inpL;
+
+    if (batch.token) {
+        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, inp_tokens);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
+        }
+        ggml_set_name(inp_tokens, "inp_tokens");
+
+        token = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
+    } else {
+#ifdef GGML_USE_MPI
+        GGML_ASSERT(false && "not implemented");
+#endif
+
+        token = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, token);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(token->data, batch.embd, n_tokens * n_embd * ggml_element_size(token));
+        }
+    }
+
+    {
+        // Compute position embeddings.
+        struct ggml_tensor * inp_positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+        ggml_allocr_alloc(lctx.alloc, inp_positions);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            for (int i = 0; i < n_tokens; ++i) {
+                ((int32_t *) inp_positions->data)[i] = batch.pos[i];
+            }
+        }
+        ggml_set_name(inp_positions, "inp_positions");
+
+        position = ggml_get_rows(ctx0, model.pos_embeddings, inp_positions);
+    }
+
+    // KQ_scale
+    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
+    ggml_allocr_alloc(lctx.alloc, KQ_scale);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
+    }
+
+    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+    ggml_set_name(KQ_mask, "KQ_mask");
+    ggml_allocr_alloc(lctx.alloc, KQ_mask);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        float * data = (float *) KQ_mask->data;
+        memset(data, 0, ggml_nbytes(KQ_mask));
+
+        for (int h = 0; h < 1; ++h) {
+            for (int j = 0; j < n_tokens; ++j) {
+                const llama_pos    pos    = batch.pos[j];
+                const llama_seq_id seq_id = batch.seq_id[j][0];
+
+                for (int i = 0; i < n_kv; ++i) {
+                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
+                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
+                    }
+                }
+            }
+        }
+    }
+
+    inpL = ggml_add(ctx0, token, position);
+    ggml_set_name(inpL, "inpL");
+
+    for (int il = 0; il < n_layer; ++il) {
+        {
+            // Norm
+            cur = ggml_norm(ctx0, inpL, norm_eps);
+            cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].attn_norm), model.layers[il].attn_norm_b);
+        }
+
+        {
+            // Self Attention
+            cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wqkv, cur), model.layers[il].bqkv);
+
+            struct ggml_tensor * tmpq = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*n_embd);
+            struct ggml_tensor * tmpk = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*n_embd);
+            struct ggml_tensor * tmpv = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*(n_embd + n_embd_gqa));
+
+            struct ggml_tensor * Qcur = tmpq;
+            struct ggml_tensor * Kcur = tmpk;
+
+            {
+                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens));
+                ggml_set_name(Vcur, "Vcur");
+
+                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
+                ggml_set_name(k, "k");
+
+                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
+                        (   n_ctx)*ggml_element_size(kv_self.v),
+                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
+
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
+            }
+
+            struct ggml_tensor * Q =
+                ggml_permute(ctx0,
+                        ggml_cpy(ctx0,
+                            Qcur,
+                            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd_head, n_head, n_tokens)),
+                        0, 2, 1, 3);
+            ggml_set_name(Q, "Q");
+
+            struct ggml_tensor * K =
+                ggml_view_3d(ctx0, kv_self.k,
+                        n_embd_head, n_kv, n_head_kv,
+                        ggml_element_size(kv_self.k)*n_embd_gqa,
+                        ggml_element_size(kv_self.k)*n_embd_head,
+                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
+            ggml_set_name(K, "K");
+
+            // K * Q
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+            ggml_set_name(KQ, "KQ");
+
+            // KQ_scaled = KQ / sqrt(n_embd_head)
+            // KQ_scaled shape [n_past + n_tokens, n_tokens, n_head, 1]
+            struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
+            ggml_set_name(KQ_scaled, "KQ_scaled");
+
+            // KQ_masked = mask_past(KQ_scaled)
+            struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
+            ggml_set_name(KQ_masked, "KQ_masked");
+
+            // KQ = soft_max(KQ_masked)
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
+            ggml_set_name(KQ_soft_max, "KQ_soft_max");
+
+            // split cached V into n_head heads
+            struct ggml_tensor * V =
+                ggml_view_3d(ctx0, kv_self.v,
+                        n_kv, n_embd_head, n_head_kv,
+                        ggml_element_size(kv_self.v)*n_ctx,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
+            ggml_set_name(V, "V");
+
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
+            ggml_set_name(KQV, "KQV");
+
+            // KQV_merged = KQV.permute(0, 2, 1, 3)
+            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+            ggml_set_name(KQV_merged, "KQV_merged");
+
+            // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
+            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
+            ggml_set_name(cur, "KQV_merged_contiguous");
+        }
+
+        // Projection
+        cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wo, cur), model.layers[il].bo);
+
+        // Add the input
+        cur = ggml_add(ctx0, cur, inpL);
+
+        struct ggml_tensor * inpFF = cur;
+
+        // FF
+        {
+            // Norm
+            {
+                cur = ggml_norm(ctx0, inpFF, norm_eps);
+                cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ffn_norm), model.layers[il].ffn_norm_b);
+            }
+
+            cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w3, cur), model.layers[il].b3);
+
+            // GELU activation
+            cur = ggml_gelu(ctx0, cur);
+
+            // Projection
+            cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w2, cur), model.layers[il].b2);
+        }
+
+        inpL = ggml_add(ctx0, cur, inpFF);
+    }
+
+    // Output Norm
+    {
+        cur = ggml_norm(ctx0, inpL, norm_eps);
+        cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.output_norm), model.output_norm_b);
+    }
+    ggml_set_name(cur, "result_norm");
+
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+    ggml_set_name(cur, "result_output");
+
+    ggml_build_forward_expand(gf, cur);
+    ggml_free(ctx0);
+
+    return gf;
+}
+
+static struct ggml_cgraph * llm_build_persimmon(
+         llama_context & lctx,
+     const llama_batch & batch) {
+    const auto & model = lctx.model;
+    const auto & hparams = model.hparams;
+
+    const auto & kv_self = lctx.kv_self;
+
+    GGML_ASSERT(!!kv_self.ctx);
+
+    const auto & cparams = lctx.cparams;
+    const int64_t n_embd      = hparams.n_embd;
+    const int64_t n_layer     = hparams.n_layer;
+    const int64_t n_ctx       = cparams.n_ctx;
+    const int64_t n_head_kv   = hparams.n_head_kv;
+    const int64_t n_head      = hparams.n_head;
+    const int64_t n_embd_head = hparams.n_embd_head();
+    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
+    const size_t n_rot        = n_embd_head / 2;
+
+    const float freq_base  = cparams.rope_freq_base;
+    const float freq_scale = cparams.rope_freq_scale;
+    const float norm_eps = hparams.f_norm_eps;
+
+    const int n_gpu_layers = model.n_gpu_layers;
+
+
+    const int32_t n_tokens    = batch.n_tokens;
+    const int32_t n_kv        = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
+    const int32_t kv_head     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+
+    const bool do_rope_shift  = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
+
+    auto & buf_compute = lctx.buf_compute;
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ buf_compute.size,
+        /*.mem_buffer =*/ buf_compute.data,
+        /*.no_alloc   =*/ true,
+    };
+
+    struct ggml_context * ctx0 = ggml_init(params);
+
+    ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+    struct ggml_tensor * cur;
+    struct ggml_tensor * inpL;
+
+    if (batch.token) {
+        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, inp_tokens);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
+        }
+        ggml_set_name(inp_tokens, "inp_tokens");
+        inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
+    } else {
+        inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+        ggml_allocr_alloc(lctx.alloc, inpL);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
+        }
+    }
+    const int i_gpu_start = n_layer - n_gpu_layers;
+    (void) i_gpu_start;
+    offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
+    offload_func_t offload_func_kq = llama_nop;
+    offload_func_t offload_func_v  = llama_nop;
+    // KQ_scale
+    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+    ggml_allocr_alloc(lctx.alloc, KQ_scale);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
+    }
+    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
+    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+    offload_func_kq(KQ_mask);
+    ggml_set_name(KQ_mask, "KQ_mask");
+    ggml_allocr_alloc(lctx.alloc, KQ_mask);
+
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        float * data = (float *) KQ_mask->data;
+        memset(data, 0, ggml_nbytes(KQ_mask));
+        for (int h = 0; h < 1; ++h) {
+            for (int j = 0; j < n_tokens; ++j) {
+                const llama_pos    pos    = batch.pos[j];
+                const llama_seq_id seq_id = batch.seq_id[j][0];
+                for (int i = 0; i < n_kv; ++i) {
+                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
+                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
+                    }
+                }
+            }
+        }
+    }
+
+    struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    offload_func_kq(KQ_pos);
+    ggml_set_name(KQ_pos, "KQ_pos");
+    ggml_allocr_alloc(lctx.alloc, KQ_pos);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        int * data = (int *) KQ_pos->data;
+        for (int i = 0; i < n_tokens; ++i) {
+            data[i] = batch.pos[i];
+        }
+    }
+    if (do_rope_shift) {
+        struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
+        offload_func_kq(K_shift);
+        ggml_set_name(K_shift, "K_shift");
+        ggml_allocr_alloc(lctx.alloc, K_shift);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            int * data = (int *) K_shift->data;
+            for (int i = 0; i < n_ctx; ++i) {
+                data[i] = kv_self.cells[i].delta;
+            }
+        }
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * tmp =
+                    // we rotate only the first n_rot dimensions.
+                    ggml_rope_custom_inplace(ctx0,
+                        ggml_view_3d(ctx0, kv_self.k,
+                            n_rot, n_head, n_ctx,
+                            ggml_element_size(kv_self.k)*n_embd_gqa,
+                            ggml_element_size(kv_self.k)*n_embd_head,
+                            ggml_element_size(kv_self.k)*(n_embd_head*n_ctx*il)
+                        ),
+                        K_shift, n_rot, 2, 0, freq_base, freq_scale);
+            offload_func_kq(tmp);
+            ggml_build_forward_expand(gf, tmp);
+        }
+    }
+    for (int il=0; il < n_layer; ++il) {
+        struct ggml_tensor * residual = inpL;
+        offload_func_t offload_func = llama_nop;
+        {
+            cur = ggml_norm(ctx0, inpL, norm_eps);
+            offload_func(cur);
+            cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
+            offload_func(cur);
+            cur = ggml_add(ctx0, cur, model.layers[il].attn_norm_b);
+            offload_func(cur);
+            ggml_format_name(cur, "input_layernorm_%d", il);
+        }
+        // self attention
+        {
+            cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+            offload_func_kq(cur);
+            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+            offload_func_kq(cur);
+
+            // split qkv
+            GGML_ASSERT(n_head_kv == n_head);
+            ggml_set_name(cur, format("qkv_%d", il).c_str());
+            struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens);
+            offload_func_kq(tmpqkv);
+            struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
+            offload_func_kq(tmpqkv_perm);
+            ggml_format_name(tmpqkv_perm, "tmpqkv_perm_%d", il);
+            struct ggml_tensor * tmpq = ggml_view_3d(
+                    ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
+                    ggml_element_size(tmpqkv_perm) * n_embd_head,
+                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
+                    0
+                );
+            offload_func_kq(tmpq);
+            struct ggml_tensor * tmpk = ggml_view_3d(
+                    ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
+                    ggml_element_size(tmpqkv_perm) * n_embd_head,
+                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
+                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens
+                );
+            offload_func_kq(tmpk);
+            // Q/K Layernorm
+            tmpq = ggml_norm(ctx0, tmpq, norm_eps);
+            offload_func_kq(tmpq);
+            tmpq =  ggml_mul(ctx0, tmpq, model.layers[il].attn_q_norm);
+            offload_func_kq(tmpq);
+            tmpq =  ggml_add(ctx0, tmpq, model.layers[il].attn_q_norm_b);
+            offload_func_kq(tmpq);
+
+            tmpk = ggml_norm(ctx0, tmpk, norm_eps);
+            offload_func_v(tmpk);
+            tmpk =  ggml_mul(ctx0, tmpk, model.layers[il].attn_k_norm);
+            offload_func_v(tmpk);
+            tmpk =  ggml_add(ctx0, tmpk, model.layers[il].attn_k_norm_b);
+            offload_func_v(tmpk);
+
+            // RoPE the first n_rot of q/k, pass the other half, and concat.
+            struct ggml_tensor * qrot = ggml_view_3d(
+                ctx0, tmpq, n_rot, n_head, n_tokens,
+                ggml_element_size(tmpq) * n_embd_head,
+                ggml_element_size(tmpq) * n_embd_head * n_head,
+                0
+            );
+            offload_func_kq(qrot);
+            ggml_format_name(qrot, "qrot_%d", il);
+            struct ggml_tensor * krot = ggml_view_3d(
+                ctx0, tmpk, n_rot, n_head, n_tokens,
+                ggml_element_size(tmpk) * n_embd_head,
+                ggml_element_size(tmpk) * n_embd_head * n_head,
+                0
+            );
+            offload_func_kq(krot);
+            ggml_format_name(krot, "krot_%d", il);
+
+            // get the second half of tmpq, e.g tmpq[n_rot:, :, :]
+            struct ggml_tensor * qpass = ggml_view_3d(
+                ctx0, tmpq, n_rot, n_head, n_tokens,
+                ggml_element_size(tmpq) * n_embd_head,
+                ggml_element_size(tmpq) * n_embd_head * n_head,
+                ggml_element_size(tmpq) * n_rot
+            );
+            offload_func_kq(qpass);
+            ggml_format_name(qpass, "qpass_%d", il);
+            struct ggml_tensor * kpass = ggml_view_3d(
+                ctx0, tmpk, n_rot, n_head, n_tokens,
+                ggml_element_size(tmpk) * n_embd_head,
+                ggml_element_size(tmpk) * n_embd_head * n_head,
+                ggml_element_size(tmpk) * n_rot
+            );
+            offload_func_kq(kpass);
+            ggml_format_name(kpass, "kpass_%d", il);
+
+            struct ggml_tensor * qrotated =  ggml_rope_custom(
+                    ctx0, qrot, KQ_pos, n_rot, 2, 0, freq_base, freq_scale
+            );
+            offload_func_kq(qrotated);
+            struct ggml_tensor * krotated = ggml_rope_custom(
+                    ctx0, krot, KQ_pos, n_rot, 2, 0, freq_base, freq_scale
+            );
+            offload_func_kq(krotated);
+            // ggml currently only supports concatenation on dim=2
+            // so we need to permute qrot, qpass, concat, then permute back.
+            qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
+            offload_func_kq(qrotated);
+            krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
+            offload_func_kq(krotated);
+
+            qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
+            offload_func_kq(qpass);
+            kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
+            offload_func_kq(kpass);
+
+            struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
+            offload_func_kq(Qcur);
+            struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
+            offload_func_kq(Kcur);
+
+            struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 1, 2, 0, 3));
+            offload_func_kq(Q);
+
+            Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
+            offload_func_kq(Kcur);
+            {
+                struct ggml_tensor * tmpv = ggml_view_3d(
+                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
+                        ggml_element_size(tmpqkv_perm) * n_embd_head,
+                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
+                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2
+                    );
+                offload_func_v(tmpv);
+                // store K, V in cache
+                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
+                offload_func_v(Vcur);
+                ggml_set_name(Vcur, "Vcur");
+
+                struct ggml_tensor * k = ggml_view_1d(
+                    ctx0, kv_self.k, n_tokens*n_embd_gqa,
+                    (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head)
+                );
+                offload_func_kq(k);
+                ggml_set_name(k, "k");
+
+                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
+                        (   n_ctx)*ggml_element_size(kv_self.v),
+                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
+                offload_func_v(v);
+                ggml_set_name(v, "v");
+
+                // important: storing RoPE-ed version of K in the KV cache!
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
+            }
+            struct ggml_tensor * K = ggml_view_3d(ctx0, kv_self.k,
+                    n_embd_head, n_kv, n_head_kv,
+                    ggml_element_size(kv_self.k)*n_embd_gqa,
+                    ggml_element_size(kv_self.k)*n_embd_head,
+                    ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
+
+            offload_func_kq(K);
+            ggml_format_name(K, "K_%d", il);
+
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+            offload_func_kq(KQ);
+            ggml_set_name(KQ, "KQ");
+
+            struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
+            offload_func_kq(KQ_scaled);
+            ggml_set_name(KQ_scaled, "KQ_scaled");
+
+            struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
+            offload_func_kq(KQ_masked);
+            ggml_set_name(KQ_masked, "KQ_masked");
+
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
+            offload_func_kq(KQ_soft_max);
+            ggml_set_name(KQ_soft_max, "KQ_soft_max");
+
+            struct ggml_tensor * V =
+                ggml_view_3d(ctx0, kv_self.v,
+                        n_kv, n_embd_head, n_head_kv,
+                        ggml_element_size(kv_self.v)*n_ctx,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
+            offload_func_v(V);
+            ggml_set_name(V, "V");
+
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
+            offload_func_v(KQV);
+            ggml_set_name(KQV, "KQV");
+
+            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+            offload_func_v(KQV_merged);
+            ggml_set_name(KQV_merged, "KQV_merged");
+
+            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
+            offload_func_v(cur);
+            ggml_set_name(cur, "KQV_merged_contiguous");
+
+            cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
+            offload_func(cur);
+            cur = ggml_add(ctx0, cur, model.layers[il].bo);
+            offload_func(cur);
+            ggml_set_name(cur, "result_wo");
+        }
+
+        struct ggml_tensor * inpFF = ggml_add(ctx0, residual, cur);
+        offload_func(inpFF);
+        ggml_set_name(inpFF, "inpFF");
+        {
+            // MLP
+            {
+                // Norm
+                cur = ggml_norm(ctx0, inpFF, norm_eps);
+                offload_func(cur);
+                cur = ggml_add(ctx0,
+                    ggml_mul(ctx0, cur, model.layers[il].ffn_norm),
+                    model.layers[il].ffn_norm_b
+                );
+                ggml_set_name(cur, "ffn_norm");
+                offload_func(cur);
+            }
+            cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur);
+            offload_func(cur);
+
+            cur = ggml_add(ctx0, cur, model.layers[il].b3);
+            offload_func(cur);
+            ggml_set_name(cur, "result_ffn_up");
+
+            cur = ggml_sqr(ctx0, ggml_relu(ctx0, cur));
+            ggml_set_name(cur, "result_ffn_act");
+            offload_func(cur);
+            offload_func(cur->src[0]);
+
+            cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
+            offload_func(cur);
+            cur = ggml_add(ctx0,
+                cur,
+                model.layers[il].b2);
+            offload_func(cur);
+            ggml_set_name(cur, "outFF");
+        }
+        cur = ggml_add(ctx0, cur, inpFF);
+        offload_func(cur);
+        ggml_set_name(cur, "inpFF_+_outFF");
+        inpL = cur;
+    }
+    cur = inpL;
+    {
+        cur = ggml_norm(ctx0, cur, norm_eps);
+        offload_func_nr(cur);
+        cur = ggml_mul(ctx0, cur, model.output_norm);
+        offload_func_nr(cur);
+
+        cur = ggml_add(ctx0, cur, model.output_norm_b);
+        // offload_func_nr(cur);
+
+        ggml_set_name(cur, "result_norm");
+    }
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+    ggml_set_name(cur, "result_output");
+    ggml_build_forward_expand(gf, cur);
+    ggml_free(ctx0);
+    return gf;
+}
+
+static struct ggml_cgraph * llm_build_bloom(
+         llama_context & lctx,
+     const llama_batch & batch) {
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+    const auto & cparams = lctx.cparams;
+
+    const auto & kv_self = lctx.kv_self;
+
+    GGML_ASSERT(!!kv_self.ctx);
+
+    const int64_t n_embd      = hparams.n_embd;
+    const int64_t n_layer     = hparams.n_layer;
+    const int64_t n_ctx       = cparams.n_ctx;
+    const int64_t n_head      = hparams.n_head;
+    const int64_t n_head_kv   = hparams.n_head_kv;
+    const int64_t n_embd_head = hparams.n_embd_head();
+    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    const float norm_eps = hparams.f_norm_eps;
+
+    const int32_t n_tokens = batch.n_tokens;
+    const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
+    const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+
+    auto & buf_compute = lctx.buf_compute;
+
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ buf_compute.size,
+        /*.mem_buffer =*/ buf_compute.data,
+        /*.no_alloc   =*/ false,
+    };
+
+    params.no_alloc = true;
+
+    struct ggml_context * ctx0 = ggml_init(params);
+
+    ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+    struct ggml_tensor * cur;
+    struct ggml_tensor * token;
+    struct ggml_tensor * inpL;
+
+    if (batch.token) {
+        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, inp_tokens);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
+        }
+        ggml_set_name(inp_tokens, "inp_tokens");
+
+        token = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
+    } else {
+#ifdef GGML_USE_MPI
+        GGML_ASSERT(false && "not implemented");
+#endif
+
+        token = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, token);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(token->data, batch.embd, n_tokens * n_embd * ggml_element_size(token));
+        }
+    }
+
+    // KQ_scale
+    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
+    ggml_allocr_alloc(lctx.alloc, KQ_scale);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
+    }
+
+    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+    ggml_set_name(KQ_mask, "KQ_mask");
+    ggml_allocr_alloc(lctx.alloc, KQ_mask);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        float * data = (float *) KQ_mask->data;
+        memset(data, 0, ggml_nbytes(KQ_mask));
+
+        for (int h = 0; h < 1; ++h) {
+            for (int j = 0; j < n_tokens; ++j) {
+                const llama_pos    pos    = batch.pos[j];
+                const llama_seq_id seq_id = batch.seq_id[j][0];
+
+                for (int i = 0; i < n_kv; ++i) {
+                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
+                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
+                    }
+                }
+            }
+        }
+    }
+
+    // norm
+    {
+        inpL = ggml_norm(ctx0, token, norm_eps);
+        inpL = ggml_add(ctx0, ggml_mul(ctx0, inpL, model.tok_norm), model.tok_norm_b);
+    }
+
+    ggml_set_name(inpL, "inpL");
+
+    for (int il = 0; il < n_layer; ++il) {
+        {
+            // Norm
+            cur = ggml_norm(ctx0, inpL, norm_eps);
+            cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].attn_norm), model.layers[il].attn_norm_b);
+        }
+
+        {
+            // Self Attention
+            cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wqkv, cur), model.layers[il].bqkv);
+
+            struct ggml_tensor * tmpq = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*n_embd);
+            struct ggml_tensor * tmpk = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*n_embd);
+            struct ggml_tensor * tmpv = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*(n_embd + n_embd_gqa));
+
+            struct ggml_tensor * Qcur = tmpq;
+            struct ggml_tensor * Kcur = tmpk;
+
+            // store key and value to memory
+            {
+                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens));
+                ggml_set_name(Vcur, "Vcur");
+
+                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
+                ggml_set_name(k, "k");
+
+                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
+                        (   n_ctx)*ggml_element_size(kv_self.v),
+                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
+
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
+            }
+
+            struct ggml_tensor * Q =
+                ggml_permute(ctx0,
+                        ggml_cpy(ctx0,
+                            Qcur,
+                            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd_head, n_head, n_tokens)),
+                        0, 2, 1, 3);
+            ggml_set_name(Q, "Q");
+
+            struct ggml_tensor * K =
+                ggml_view_3d(ctx0, kv_self.k,
+                        n_embd_head, n_kv, n_head_kv,
+                        ggml_element_size(kv_self.k)*n_embd_gqa,
+                        ggml_element_size(kv_self.k)*n_embd_head,
+                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
+            ggml_set_name(K, "K");
+
+            // K * Q
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+            ggml_set_name(KQ, "KQ");
+
+            // KQ_scaled = KQ / sqrt(n_embd_head)
+            // KQ_scaled shape [n_past + n_tokens, n_tokens, n_head, 1]
+            struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
+            ggml_set_name(KQ_scaled, "KQ_scaled");
+
+            struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ kv_head, n_head, 8);
+            ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
+
+            // KQ_masked = mask_past(KQ_scaled)
+            struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
+            ggml_set_name(KQ_masked, "KQ_masked");
+
+            // KQ = soft_max(KQ_masked)
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
+            ggml_set_name(KQ_soft_max, "KQ_soft_max");
+
+            // split cached V into n_head heads
+            struct ggml_tensor * V =
+                ggml_view_3d(ctx0, kv_self.v,
+                        n_kv, n_embd_head, n_head_kv,
+                        ggml_element_size(kv_self.v)*n_ctx,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
+            ggml_set_name(V, "V");
+
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
+            ggml_set_name(KQV, "KQV");
+
+            // KQV_merged = KQV.permute(0, 2, 1, 3)
+            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+            ggml_set_name(KQV_merged, "KQV_merged");
+
+            // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
+            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
+            ggml_set_name(cur, "KQV_merged_contiguous");
+        }
+
+        // Projection
+        cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wo, cur), model.layers[il].bo);
+
+        // Add the input
+        cur = ggml_add(ctx0, cur, inpL);
+
+        struct ggml_tensor * inpFF = cur;
+
+        // FF
+        {
+            // Norm
+            {
+                cur = ggml_norm(ctx0, inpFF, norm_eps);
+                cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ffn_norm), model.layers[il].ffn_norm_b);
+            }
+
+            cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w3, cur), model.layers[il].b3);
+
+            // GELU activation
+            cur = ggml_gelu(ctx0, cur);
+
+            // Projection
+            cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w2, cur), model.layers[il].b2);
+        }
+
+        inpL = ggml_add(ctx0, cur, inpFF);
+    }
+
+    // Output Norm
+    {
+        cur = ggml_norm(ctx0, inpL, norm_eps);
+        cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.output_norm), model.output_norm_b);
+    }
+    ggml_set_name(cur, "result_norm");
+
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+    ggml_set_name(cur, "result_output");
+
+    ggml_build_forward_expand(gf, cur);
+
+    ggml_free(ctx0);
+
+    return gf;
+}
+
+static struct ggml_cgraph * llm_build_mpt(
+         llama_context & lctx,
+     const llama_batch & batch) {
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+    const auto & cparams = lctx.cparams;
+
+    const auto & kv_self = lctx.kv_self;
+
+    GGML_ASSERT(!!kv_self.ctx);
+
+    const int64_t n_embd      = hparams.n_embd;
+    const int64_t n_layer     = hparams.n_layer;
+    const int64_t n_ctx       = cparams.n_ctx;
+    const int64_t n_head      = hparams.n_head;
+    const int64_t n_head_kv   = hparams.n_head_kv;
+    const int64_t n_embd_head = hparams.n_embd_head();
+    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
+
+    const float norm_eps       = hparams.f_norm_eps;
+    const float clamp_kqv      = hparams.f_clamp_kqv;
+    const float max_alibi_bias = hparams.f_max_alibi_bias;
+
+    const int n_gpu_layers = model.n_gpu_layers;
+
+    const int32_t n_tokens = batch.n_tokens;
+    const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
+    const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+
+    auto & buf_compute = lctx.buf_compute;
+
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ buf_compute.size,
+        /*.mem_buffer =*/ buf_compute.data,
+        /*.no_alloc   =*/ false,
+    };
+
+    params.no_alloc = true;
+
+    struct ggml_context * ctx0 = ggml_init(params);
+
+    ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+    struct ggml_tensor * cur;
+    struct ggml_tensor * inpL;
+
+    //int warmup = 0;
+    if (batch.token) {
+        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, inp_tokens);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
+            //warmup = ((uint32_t*) inp_tokens->data)[0] == 0;
+        }
+
+        ggml_set_name(inp_tokens, "inp_tokens");
+
+        inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
+    } else {
+#ifdef GGML_USE_MPI
+        GGML_ASSERT(false && "not implemented");
+#endif
+
+        inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+
+        ggml_allocr_alloc(lctx.alloc, inpL);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
+        }
+    }
+
+    const int i_gpu_start = n_layer - n_gpu_layers;
+    (void) i_gpu_start;
+
+    // offload functions set the tensor output backend to GPU
+    // tensors are GPU-accelerated if any input or the output has been offloaded
+    offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
+    offload_func_t offload_func_kq = llama_nop;
+    offload_func_t offload_func_v  = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+    if (n_gpu_layers > n_layer) {
+        offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
+    }
+    if (n_gpu_layers > n_layer + 1) {
+        offload_func_v  = ggml_cuda_assign_buffers_no_alloc;
+    }
+    if (n_gpu_layers > n_layer + 2) {
+        offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
+    }
+#endif // GGML_USE_CUBLAS
+
+    // KQ_scale
+    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
+    ggml_allocr_alloc(lctx.alloc, KQ_scale);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
+    }
+
+    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+    offload_func_kq(KQ_mask);
+    ggml_set_name(KQ_mask, "KQ_mask");
+    ggml_allocr_alloc(lctx.alloc, KQ_mask);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        float * data = (float *) KQ_mask->data;
+        memset(data, 0, ggml_nbytes(KQ_mask));
+
+        for (int h = 0; h < 1; ++h) {
+            for (int j = 0; j < n_tokens; ++j) {
+                const llama_pos    pos    = batch.pos[j];
+                const llama_seq_id seq_id = batch.seq_id[j][0];
+
+                for (int i = 0; i < n_kv; ++i) {
+                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
+                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
+                    }
+                }
+            }
+        }
+    }
+
+    for (int il = 0; il < n_layer; ++il) {
+        struct ggml_tensor * attn_norm;
+
+        offload_func_t offload_func = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+        if (il >= i_gpu_start) {
+            offload_func = ggml_cuda_assign_buffers_no_alloc;
+        }
+#endif // GGML_USE_CUBLAS
+
+        // self-attention
+        // TODO: refactor into common function (shared with LLaMA)
+        {
+            attn_norm = ggml_norm(ctx0, inpL, norm_eps);
+            offload_func(attn_norm);
+
+            attn_norm = ggml_mul(ctx0, attn_norm, model.layers[il].attn_norm);
+            offload_func(attn_norm);
+
+            if (1) {
+                cur = attn_norm;
+            }
+
+            // compute QKV
+
+            cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+            offload_func_kq(cur);
+
+            if (clamp_kqv > 0.0f) {
+                cur = ggml_clamp(ctx0, cur, -clamp_kqv, clamp_kqv);
+                offload_func_kq(cur);
+            }
+
+            const size_t wsize = ggml_type_size(cur->type);
+
+            struct ggml_tensor * Qcur = ggml_view_3d(
+                ctx0, cur, n_embd_head, n_head, n_tokens,
+                wsize * n_embd_head,
+                wsize * n_embd_head * (n_head + 2 * n_head_kv),
+                0);
+            offload_func_kq(Qcur);
+
+            struct ggml_tensor * Kcur = ggml_view_3d(
+                ctx0, cur, n_embd_head, n_head_kv, n_tokens,
+                wsize * n_embd_head,
+                wsize * n_embd_head * (n_head + 2 * n_head_kv),
+                wsize * n_embd_head *  n_head);
+            offload_func_kq(Kcur);
+
+            struct ggml_tensor * tmpv = ggml_view_3d(
+                ctx0, cur, n_embd_head, n_head_kv, n_tokens,
+                wsize * n_embd_head,
+                wsize * n_embd_head * (n_head + 2 * n_head_kv),
+                wsize * n_embd_head * (n_head +     n_head_kv));
+            offload_func_kq(Kcur);
+
+            ggml_set_name(Qcur, "Qcur");
+            ggml_set_name(Kcur, "Kcur");
+
+            {
+                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens));
+                offload_func_v(Vcur);
+                offload_func_v(Vcur->src[0]->src[0]);
+                ggml_set_name(Vcur, "Vcur");
+
+                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
+                offload_func_kq(k);
+                ggml_set_name(k, "k");
+
+                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
+                        (   n_ctx)*ggml_element_size(kv_self.v),
+                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
+                offload_func_v(v);
+
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
+            }
+
+            struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+            offload_func_kq(Q);
+            ggml_set_name(Q, "Q");
+
+            struct ggml_tensor * K =
+                ggml_view_3d(ctx0, kv_self.k,
+                        n_embd_head, n_kv, n_head_kv,
+                        ggml_element_size(kv_self.k)*n_embd_gqa,
+                        ggml_element_size(kv_self.k)*n_embd_head,
+                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
+            offload_func_kq(K);
+            ggml_set_name(K, "K");
+
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+            offload_func_kq(KQ);
+            ggml_set_name(KQ, "KQ");
+
+            struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
+            offload_func_kq(KQ_scaled);
+            ggml_set_name(KQ_scaled, "KQ_scaled");
+
+            // TODO: replace with ggml_add()
+            struct ggml_tensor * KQ_scaled_alibi =
+                ggml_alibi(ctx0, KQ_scaled, 0, n_head, max_alibi_bias);
+            offload_func_kq(KQ_scaled_alibi);
+            ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
+
+            struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
+            offload_func_kq(KQ_masked);
+            ggml_set_name(KQ_masked, "KQ_masked");
+
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
+            offload_func_v(KQ_soft_max);
+            ggml_set_name(KQ_soft_max, "KQ_soft_max");
+
+            struct ggml_tensor * V =
+                ggml_view_3d(ctx0, kv_self.v,
+                        n_kv, n_embd_head, n_head_kv,
+                        ggml_element_size(kv_self.v)*n_ctx,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
+                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
+            offload_func_v(V);
+            ggml_set_name(V, "V");
+
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
+            offload_func_v(KQV);
+            ggml_set_name(KQV, "KQV");
+
+            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+            offload_func_v(KQV_merged);
+            ggml_set_name(KQV_merged, "KQV_merged");
+
+            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
+            offload_func_v(cur);
+            ggml_set_name(cur, "KQV_merged_contiguous");
+
+            cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
+            offload_func(cur);
+            ggml_set_name(cur, "result_wo");
+        }
+
+        // Add the input
+        cur = ggml_add(ctx0, cur, inpL);
+        offload_func(cur);
+
+        struct ggml_tensor * attn_out = cur;
+
+        // feed forward
+        {
+            // Norm
+            {
+                cur = ggml_norm(ctx0, attn_out, norm_eps);
+                offload_func(cur);
+
+                cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
+                offload_func(cur);
+            }
+
+            cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur);
+            offload_func(cur);
+
+            cur = ggml_gelu(ctx0, cur);
+            offload_func(cur);
+            cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
+            offload_func(cur);
+        }
+
+        cur = ggml_add(ctx0, cur, attn_out);
+        offload_func(cur);
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    // norm
+    {
+        cur = ggml_norm(ctx0, cur, norm_eps);
+        offload_func_nr(cur);
+
+        cur = ggml_mul(ctx0, cur, model.output_norm);
+        ggml_set_name(cur, "result_norm");
+    }
+
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+    ggml_set_name(cur, "result_output");
+
+    ggml_build_forward_expand(gf, cur);
+
+    ggml_free(ctx0);
+
+    return gf;
+}
+
+static struct ggml_cgraph * llama_build_graph(
+         llama_context & lctx,
+     const llama_batch & batch) {
+    const auto & model = lctx.model;
+
+    struct ggml_cgraph * result = NULL;
+
+    switch (model.arch) {
+        case LLM_ARCH_LLAMA:
+            {
+                result = llm_build_llama(lctx, batch);
+            } break;
+        case LLM_ARCH_BAICHUAN:
+            {
+                result = llm_build_baichaun(lctx, batch);
+            } break;
+        case LLM_ARCH_FALCON:
+            {
+                result = llm_build_falcon(lctx, batch);
+            } break;
+        case LLM_ARCH_STARCODER:
+            {
+                result = llm_build_starcoder(lctx, batch);
+            } break;
+        case LLM_ARCH_PERSIMMON:
+            {
+                result = llm_build_persimmon(lctx, batch);
+            } break;
+        case LLM_ARCH_REFACT:
+            {
+                result = llm_build_refact(lctx, batch);
+            } break;
+        case LLM_ARCH_BLOOM:
+            {
+                result = llm_build_bloom(lctx, batch);
+            } break;
+        case LLM_ARCH_MPT:
+            {
+                result = llm_build_mpt(lctx, batch);
+            } break;
+        default:
+            GGML_ASSERT(false);
+    }
+
+    return result;
+}
+
+// decode a batch of tokens by evaluating the transformer
+//
+//   - lctx:      llama context
+//   - batch:     batch to evaluate
+//
+// return 0 on success
+// return positive int on warning
+// return negative int on error
+//
+static int llama_decode_internal(
+         llama_context & lctx,
+           llama_batch   batch) {
+    const uint32_t n_tokens = batch.n_tokens;
+
+    if (n_tokens == 0) {
+        LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__);
+        return -1;
+    }
+
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+    const auto & cparams = lctx.cparams;
+
+    const auto n_batch = cparams.n_batch;
+
+    GGML_ASSERT(n_tokens <= n_batch);
+
+    int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
+    GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
+
+    const int64_t t_start_us = ggml_time_us();
+
+#ifdef GGML_USE_MPI
+    // TODO: needs fix after #3228
+    GGML_ASSERT(false && "not implemented");
+    //ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
+#endif
+
+    GGML_ASSERT(n_threads > 0);
+
+    auto & kv_self = lctx.kv_self;
+
+    GGML_ASSERT(!!kv_self.ctx);
+
+    const int64_t n_embd  = hparams.n_embd;
+    const int64_t n_vocab = hparams.n_vocab;
+
+    // helpers for smoother batch API transistion
+    // after deprecating the llama_eval calls, these will be removed
+    std::vector<llama_pos> pos;
+
+    std::vector<int32_t>                   n_seq_id;
+    std::vector<llama_seq_id *>            seq_id_arr;
+    std::vector<std::vector<llama_seq_id>> seq_id;
+
+    if (batch.pos == nullptr) {
+        pos.resize(n_tokens);
+        for (uint32_t i = 0; i < n_tokens; i++) {
+            pos[i] = batch.all_pos_0 + i*batch.all_pos_1;
+        }
+
+        batch.pos = pos.data();
+    }
+
+    if (batch.seq_id == nullptr) {
+        n_seq_id.resize(n_tokens);
+        seq_id.resize(n_tokens);
+        seq_id_arr.resize(n_tokens);
+        for (uint32_t i = 0; i < n_tokens; i++) {
+            n_seq_id[i] = 1;
+            seq_id[i].resize(1);
+            seq_id[i][0] = batch.all_seq_id;
+            seq_id_arr[i] = seq_id[i].data();
+        }
+
+        batch.n_seq_id = n_seq_id.data();
+        batch.seq_id = seq_id_arr.data();
+    }
+
+    if (!llama_kv_cache_find_slot(kv_self, batch)) {
+        return 1;
+    }
+
+    // a heuristic, to avoid attending the full cache if it is not yet utilized
+    // after enough generations, the benefit from this heuristic disappears
+    // if we start defragmenting the cache, the benefit from this will be more important
+    //kv_self.n = std::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32));   // TODO: this might be better for CUDA?
+    kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, llama_kv_cache_cell_max(kv_self)));
+
+    //printf("kv_self.n = %d\n", kv_self.n);
+
+    ggml_allocr_reset(lctx.alloc);
+
+    ggml_cgraph * gf = llama_build_graph(lctx, batch);
+
+    ggml_allocr_alloc_graph(lctx.alloc, gf);
+
+    struct ggml_tensor * res        = gf->nodes[gf->n_nodes - 1];
+    struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
+
+    GGML_ASSERT(strcmp(res->name,        "result_output") == 0);
+    GGML_ASSERT(strcmp(embeddings->name, "result_norm")   == 0);
+
+
+#ifdef GGML_USE_CUBLAS
+    for (int i = 0; i < gf->n_leafs; i++) {
+        ggml_tensor * node = gf->leafs[i];
+        if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) {
+            ggml_cuda_assign_scratch_offset(node, (char*)node->data - (char *) lctx.buf_alloc.data);
+            ggml_cuda_copy_to_device(node);
+        }
+    }
+
+    for (int i = 0; i < gf->n_nodes; i++) {
+        ggml_tensor * node = gf->nodes[i];
+        if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) {
+            ggml_cuda_assign_scratch_offset(node, (char*)node->data - (char *) lctx.buf_alloc.data);
+        }
+    }
+
+    ggml_cuda_set_mul_mat_q(cparams.mul_mat_q);
+
+    // HACK: ggml-alloc may change the tensor backend when reusing a parent, so force output to be on the CPU here if needed
+    if (!lctx.embedding.empty()) {
+        embeddings->backend = GGML_BACKEND_CPU;
+    }
+    res->backend = GGML_BACKEND_CPU;
+#endif
+
+    // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
+
+    // for big prompts, if BLAS is enabled, it is better to use only one thread
+    // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
+    // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well
+    //       we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering
+    //       with the BLAS calls. need a better solution
+    if (n_tokens >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
+        n_threads = std::min(4, n_threads);
+    }
+
+    // If all tensors can be run on the GPU then using more than 1 thread is detrimental.
+    const bool full_offload_supported = model.arch == LLM_ARCH_LLAMA ||
+        model.arch == LLM_ARCH_BAICHUAN ||
+        model.arch == LLM_ARCH_FALCON ||
+        model.arch == LLM_ARCH_REFACT ||
+        model.arch == LLM_ARCH_MPT;
+    const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
+    if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
+        n_threads = 1;
+    }
+
+#if GGML_USE_MPI
+    const int64_t n_layer = hparams.n_layer;
+    ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
+#endif
+
+#ifdef GGML_USE_METAL
+    if (lctx.ctx_metal) {
+        ggml_metal_set_n_cb     (lctx.ctx_metal, n_threads);
+        ggml_metal_graph_compute(lctx.ctx_metal, gf);
+    } else {
+        ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads);
+    }
+#else
+    ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads);
+#endif
+
+#if GGML_USE_MPI
+    ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
+#endif
+
+    // update the kv ring buffer
+    lctx.kv_self.has_shift  = false;
+    lctx.kv_self.head      += n_tokens;
+    // Ensure kv cache head points to a valid index.
+    if (lctx.kv_self.head >= lctx.kv_self.size) {
+        lctx.kv_self.head = 0;
+    }
+
+#ifdef GGML_PERF
+    // print timing information per ggml operation (for debugging purposes)
+    // requires GGML_PERF to be defined
+    ggml_graph_print(gf);
+#endif
+
+    // plot the computation graph in dot format (for debugging purposes)
+    //if (n_past%100 == 0) {
+    //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
+    //}
+
+    // extract logits
+    {
+        auto & logits_out = lctx.logits;
+
+        if (batch.logits) {
+            logits_out.resize(n_vocab * n_tokens);
+            for (uint32_t i = 0; i < n_tokens; i++) {
+                if (batch.logits[i] == 0) {
+                    continue;
+                }
+                memcpy(logits_out.data() + (n_vocab*i), (float *) ggml_get_data(res) + (n_vocab*i), sizeof(float)*n_vocab);
+            }
+        } else if (lctx.logits_all) {
+            logits_out.resize(n_vocab * n_tokens);
+            memcpy(logits_out.data(), (float *) ggml_get_data(res), sizeof(float)*n_vocab*n_tokens);
+        } else {
+            logits_out.resize(n_vocab);
+            memcpy(logits_out.data(), (float *) ggml_get_data(res) + (n_vocab*(n_tokens - 1)), sizeof(float)*n_vocab);
+        }
+    }
+
+    // extract embeddings
+    if (!lctx.embedding.empty()) {
+        auto & embedding_out = lctx.embedding;
+
+        embedding_out.resize(n_embd);
+        memcpy(embedding_out.data(), (float *) ggml_get_data(embeddings) + (n_embd*(n_tokens - 1)), sizeof(float)*n_embd);
+    }
+
+    // measure the performance only for the single-token evals
+    if (n_tokens == 1) {
+        lctx.t_eval_us += ggml_time_us() - t_start_us;
+        lctx.n_eval++;
+    }
+    else if (n_tokens > 1) {
+        lctx.t_p_eval_us += ggml_time_us() - t_start_us;
+        lctx.n_p_eval += n_tokens;
+    }
+
+    // get a more accurate load time, upon first eval
+    // TODO: fix this
+    if (!lctx.has_evaluated_once) {
+        lctx.t_load_us = ggml_time_us() - lctx.t_start_us;
+        lctx.has_evaluated_once = true;
+    }
+
+    return 0;
+}
+
+//
+// tokenizer
+//
+
+static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
+    return vocab.type;
+}
+
+static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
+    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL;
+}
+
+static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
+    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN;
+}
+
+static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
+    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
+}
+
+static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
+    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE;
+}
+
+static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) {
+    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED;
+}
+
+static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
+    GGML_ASSERT(llama_is_byte_token(vocab, id));
+    const auto& token_data = vocab.id_to_token.at(id);
+    switch (llama_vocab_get_type(vocab)) {
+    case LLAMA_VOCAB_TYPE_SPM: {
+        auto buf = token_data.text.substr(3, 2);
+        return strtol(buf.c_str(), NULL, 16);
+    }
+    case LLAMA_VOCAB_TYPE_BPE: {
+        GGML_ASSERT(false);
+        return unicode_to_bytes_bpe(token_data.text);
+    }
+    default:
+        GGML_ASSERT(false);
+    }
+}
+
+static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
+    switch (llama_vocab_get_type(vocab)) {
+    case LLAMA_VOCAB_TYPE_SPM: {
+        char buf[7];
+        int result = snprintf(buf, sizeof(buf), "<0x%02X>", ch);
+        GGML_ASSERT(0 <= result && result < 7);
+        return vocab.token_to_id.at(buf);
+    }
+    case LLAMA_VOCAB_TYPE_BPE: {
+        return vocab.token_to_id.at(bytes_to_unicode_bpe(ch));
+    }
+    default:
+        GGML_ASSERT(false);
+    }
+}
+
+static void llama_escape_whitespace(std::string & text) {
+    replace_all(text, " ", "\xe2\x96\x81");
+}
+
+static void llama_unescape_whitespace(std::string & word) {
+    replace_all(word, "\xe2\x96\x81", " ");
+}
+
+struct llm_symbol {
+    using index = int;
+    index prev;
+    index next;
+    const char * text;
+    size_t n;
+};
+
+static_assert(std::is_trivially_copyable<llm_symbol>::value, "llm_symbol is not trivially copyable");
+
+// SPM tokenizer
+// original implementation:
+// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
+
+struct llm_bigram_spm {
+    struct comparator {
+        bool operator()(llm_bigram_spm & l, llm_bigram_spm & r) {
+            return (l.score < r.score) || (l.score == r.score && l.left > r.left);
+        }
+    };
+    using queue_storage = std::vector<llm_bigram_spm>;
+    using queue = std::priority_queue<llm_bigram_spm, queue_storage, comparator>;
+    llm_symbol::index left;
+    llm_symbol::index right;
+    float score;
+    size_t size;
+};
+
+struct llm_tokenizer_spm {
+    llm_tokenizer_spm(const llama_vocab & vocab): vocab(vocab) {}
+
+    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
+        // split string into utf8 chars
+        int index = 0;
+        size_t offs = 0;
+        while (offs < text.size()) {
+            llm_symbol sym;
+            size_t len = utf8_len(text[offs]);
+            sym.text = text.c_str() + offs;
+            sym.n = std::min(len, text.size() - offs);
+            offs += sym.n;
+            sym.prev = index - 1;
+            sym.next = offs == text.size() ? -1 : index + 1;
+            index++;
+            symbols.emplace_back(sym);
+        }
+
+        // seed the work queue with all possible 2-character tokens.
+        for (size_t i = 1; i < symbols.size(); ++i) {
+            try_add_bigram(i - 1, i);
+        }
+
+        // keep substituting the highest frequency pairs for as long as we can.
+        while (!work_queue.empty()) {
+            auto bigram = work_queue.top();
+            work_queue.pop();
+
+            auto & left_sym = symbols[bigram.left];
+            auto & right_sym = symbols[bigram.right];
+
+            // if one of the symbols already got merged, skip it.
+            if (left_sym.n == 0 || right_sym.n == 0 ||
+                left_sym.n + right_sym.n != bigram.size) {
+                continue;
+            }
+
+            // merge the right sym into the left one
+            left_sym.n += right_sym.n;
+            right_sym.n = 0;
+
+            //LLAMA_LOG_INFO("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
+
+            // remove the right sym from the chain
+            left_sym.next = right_sym.next;
+            if (right_sym.next >= 0) {
+                symbols[right_sym.next].prev = bigram.left;
+            }
+
+            // find more substitutions
+            try_add_bigram(left_sym.prev, bigram.left);
+            try_add_bigram(bigram.left, left_sym.next);
+        }
+
+        for (int i = 0; i != -1; i = symbols[i].next) {
+            auto & symbol = symbols[i];
+            resegment(symbol, output);
+        }
+    }
+
+private:
+    void resegment(llm_symbol & symbol, std::vector<llama_vocab::id> & output) {
+        auto text = std::string(symbol.text, symbol.n);
+        auto token = vocab.token_to_id.find(text);
+
+        // Do we need to support is_unused?
+        if (token != vocab.token_to_id.end()) {
+            output.push_back((*token).second);
+            return;
+        }
+
+        const auto p = rev_merge.find(text);
+
+        if (p == rev_merge.end()) {
+            // output any symbols that did not form tokens as bytes.
+            for (int j = 0; j < (int)symbol.n; ++j) {
+                llama_vocab::id token_id = llama_byte_to_token(vocab, symbol.text[j]);
+                output.push_back(token_id);
+            }
+            return;
+        }
+
+        resegment(symbols[p->second.first],  output);
+        resegment(symbols[p->second.second], output);
+    }
+
+    void try_add_bigram(int left, int right) {
+        if (left == -1 || right == -1) {
+            return;
+        }
+
+        const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n);
+        auto token = vocab.token_to_id.find(text);
+
+        if (token == vocab.token_to_id.end()) {
+            return;
+        }
+
+        if (static_cast<size_t>((*token).second) >= vocab.id_to_token.size()) {
+            return;
+        }
+
+        const auto & tok_data = vocab.id_to_token[(*token).second];
+
+        llm_bigram_spm bigram;
+        bigram.left  = left;
+        bigram.right = right;
+        bigram.score = tok_data.score;
+        bigram.size  = text.size();
+
+        work_queue.push(bigram);
+
+        // Do we need to support is_unused?
+        rev_merge[text] = std::make_pair(left, right);
+    }
+
+    const llama_vocab & vocab;
+
+    std::vector<llm_symbol> symbols;
+    llm_bigram_spm::queue work_queue;
+
+    std::map<std::string, std::pair<int, int>> rev_merge;
+};
+
+// BPE tokenizer
+// adapted from https://github.com/cmp-nct/ggllm.cpp [MIT License]
+// tried to simplify unicode stuff, so most likely does not work 100% correctly!
+
+// TODO: there are a lot of common parts between spm and bpe tokenizers, should be refactored and reused
+
+struct llm_bigram_bpe {
+    struct comparator {
+        bool operator()(const llm_bigram_bpe & l, const llm_bigram_bpe & r) const {
+            return l.rank > r.rank || (l.rank == r.rank && l.left > r.left);
+        }
+    };
+
+    using queue_storage = std::vector<llm_bigram_bpe>;
+    using queue = std::priority_queue<llm_bigram_bpe, queue_storage, comparator>;
+    llm_symbol::index left;
+    llm_symbol::index right;
+    std::string text;
+    int rank;
+    size_t size;
+};
+
+struct llm_tokenizer_bpe {
+    llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {}
+
+    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
+        int final_prev_index = -1;
+        auto word_collection = bpe_gpt2_preprocess(text);
+
+        symbols_final.clear();
+
+        for (auto & word : word_collection) {
+            work_queue = llm_bigram_bpe::queue();
+            symbols.clear();
+
+            int index = 0;
+            size_t offset = 0;
+
+            while (offset < word.size()) {
+                llm_symbol sym;
+                size_t char_len = std::min(word.size() - offset, (size_t) ::utf8_len(word[offset]));
+                sym.text = word.c_str() + offset;
+                sym.n = char_len;
+                offset += sym.n;
+                sym.prev = index - 1;
+                sym.next = offset == word.size() ? -1 : index + 1;
+                index++;
+                symbols.emplace_back(sym);
+            }
+            for (size_t i = 1; i < symbols.size(); ++i) {
+                add_new_bigram(i - 1, i);
+            }
+
+            // build token(s)
+            while (!work_queue.empty()) {
+                auto bigram = work_queue.top();
+                work_queue.pop();
+
+                auto & left_symbol = symbols[bigram.left];
+                auto & right_symbol = symbols[bigram.right];
+
+                if (left_symbol.n == 0 || right_symbol.n == 0) {
+                    continue;
+                }
+                std::string left_token = std::string(left_symbol.text, left_symbol.n);
+                std::string right_token = std::string(right_symbol.text, right_symbol.n);
+                if (left_token + right_token != bigram.text) {
+                    continue;  // Skip this bigram if it's outdated
+                }
+
+                // merge the right sym into the left one
+                left_symbol.n += right_symbol.n;
+                right_symbol.n = 0;
+
+                // remove the right sym from the chain
+                left_symbol.next = right_symbol.next;
+                if (right_symbol.next >= 0) {
+                    symbols[right_symbol.next].prev = bigram.left;
+                }
+
+                add_new_bigram(left_symbol.prev, bigram.left);  // left side of current symbol
+                add_new_bigram(bigram.left, left_symbol.next);  // right side of current symbol
+            }
+
+            // add the fnished tokens to the final list keeping correct order for next and prev
+            for (auto & sym : symbols) {
+                if (sym.n > 0) {
+                    sym.prev = final_prev_index;
+                    sym.next = -1;
+                    if (final_prev_index != -1) {
+                        symbols_final[final_prev_index].next = symbols_final.size();
+                    }
+                    symbols_final.emplace_back(sym);
+                    final_prev_index = symbols_final.size() - 1;
+                }
+            }
+        }
+
+        symbols = symbols_final;
+
+        if (!symbols.empty()) {
+            for (int i = 0; i != -1; i = symbols[i].next) {
+                auto & symbol = symbols[i];
+                if (symbol.n == 0) {
+                    continue;
+                }
+
+                const std::string str = std::string(symbol.text, symbol.n);
+                const auto token = vocab.token_to_id.find(str);
+
+                if (token == vocab.token_to_id.end()) {
+                    for (auto j = str.begin(); j != str.end(); ++j) {
+                        std::string byte_str(1, *j);
+                        auto token_multibyte = vocab.token_to_id.find(byte_str);
+                        if (token_multibyte == vocab.token_to_id.end()) {
+                            throw std::runtime_error("ERROR: byte not found in vocab");
+                        }
+                        output.push_back((*token_multibyte).second);
+                    }
+                } else {
+                    output.push_back((*token).second);
+                }
+            }
+        }
+    }
+
+private:
+    void add_new_bigram(int left, int right) {
+        if (left == -1 || right == -1) {
+            return;
+        }
+
+        std::string left_token  = std::string(symbols[left].text,  symbols[left].n);
+        std::string right_token = std::string(symbols[right].text, symbols[right].n);
+
+        int rank_found = -1;
+
+        rank_found = vocab.find_bpe_rank(left_token, right_token);
+
+        if (rank_found < 0) {
+            return;
+        }
+
+        llm_bigram_bpe bigram;
+
+        bigram.left  = left;
+        bigram.right = right;
+        bigram.text  = left_token + right_token;
+        bigram.size  = left_token.size() + right_token.size();
+        bigram.rank  = rank_found;
+
+        work_queue.push(bigram);
+    }
+
+    std::vector<std::string> bpe_gpt2_preprocess(const std::string & text) {
+        std::vector<std::string> bpe_words;
+        std::vector<std::string> bpe_encoded_words;
+
+        std::string token = "";
+        // GPT2 system regex:  's|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+
+        bool collecting_numeric = false;
+        bool collecting_letter = false;
+        bool collecting_special = false;
+        bool collecting_whitespace_lookahead = false;
+        bool collecting = false;
+
+        std::vector<std::string> text_utf;
+        text_utf.reserve(text.size());
+        bpe_words.reserve(text.size());
+        bpe_encoded_words.reserve(text.size());
+
+        auto cps = codepoints_from_utf8(text);
+        for (size_t i = 0; i < cps.size(); ++i)
+            text_utf.emplace_back(codepoint_to_utf8(cps[i]));
+
+        for (int i = 0; i < (int)text_utf.size(); i++) {
+            const std::string & utf_char = text_utf[i];
+            bool split_condition = false;
+            int bytes_remain = text_utf.size() - i;
+            // forward backward lookups
+            const std::string & utf_char_next = (i + 1 < (int)text_utf.size()) ? text_utf[i + 1] : "";
+            const std::string & utf_char_next_next = (i + 2 < (int)text_utf.size()) ? text_utf[i + 2] : "";
+
+            // handling contractions
+            if (!split_condition && bytes_remain >= 2) {
+                // 's|'t|'m|'d
+                if (utf_char == "\'" && (utf_char_next == "s" || utf_char_next == "t" || utf_char_next == "m" || utf_char_next == "d")) {
+                    split_condition = true;
+                }
+                if (split_condition) {
+                    if (token.size()) {
+                        bpe_words.emplace_back(token); // push previous content as token
+                    }
+                    token = utf_char + utf_char_next;
+                    bpe_words.emplace_back(token);
+                    token = "";
+                    i++;
+                    continue;
+                }
+            }
+            if (!split_condition && bytes_remain >= 3) {
+                // 're|'ve|'ll
+                if (utf_char == "\'" && (
+                    (utf_char_next == "r" && utf_char_next_next == "e") ||
+                    (utf_char_next == "v" && utf_char_next_next == "e") ||
+                    (utf_char_next == "l" && utf_char_next_next == "l"))
+                    ) {
+                    split_condition = true;
+                }
+                if (split_condition) {
+                    // current token + next token can be defined
+                    if (token.size()) {
+                        bpe_words.emplace_back(token); // push previous content as token
+                    }
+                    token = utf_char + utf_char_next + utf_char_next_next;
+                    bpe_words.emplace_back(token); // the contraction
+                    token = "";
+                    i += 2;
+                    continue;
+                }
+            }
+
+            if (!split_condition && !collecting) {
+                if (codepoint_type(utf_char) == CODEPOINT_TYPE_LETTER || (!token.size() && utf_char == " " && codepoint_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) {
+                    collecting_letter = true;
+                    collecting = true;
+                }
+                else if (codepoint_type(utf_char) == CODEPOINT_TYPE_DIGIT || (!token.size() && utf_char == " " && codepoint_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
+                    collecting_numeric = true;
+                    collecting = true;
+                }
+                else if (
+                    ((codepoint_type(utf_char) != CODEPOINT_TYPE_LETTER && codepoint_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (codepoint_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) ||
+                    (!token.size() && utf_char == " " && codepoint_type(utf_char_next) != CODEPOINT_TYPE_LETTER && codepoint_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && codepoint_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE)
+                    ) {
+                    collecting_special = true;
+                    collecting = true;
+                }
+                else if (codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && codepoint_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) {
+                    collecting_whitespace_lookahead = true;
+                    collecting = true;
+                }
+                else if (codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) {
+                    split_condition = true;
+                }
+            }
+            else if (!split_condition && collecting) {
+                if (collecting_letter && codepoint_type(utf_char) != CODEPOINT_TYPE_LETTER) {
+                    split_condition = true;
+                }
+                else if (collecting_numeric && codepoint_type(utf_char) != CODEPOINT_TYPE_DIGIT) {
+                    split_condition = true;
+                }
+                else if (collecting_special && (codepoint_type(utf_char) == CODEPOINT_TYPE_LETTER || codepoint_type(utf_char) == CODEPOINT_TYPE_DIGIT || codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) {
+                    split_condition = true;
+                }
+                else if (collecting_whitespace_lookahead && (codepoint_type(utf_char_next) == CODEPOINT_TYPE_LETTER || codepoint_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
+                    split_condition = true;
+                }
+            }
+
+            if (utf_char_next == "") {
+                split_condition = true; // final
+                token += utf_char;
+            }
+
+            if (split_condition) {
+                if (token.size()) {
+                    bpe_words.emplace_back(token);
+                }
+                token = utf_char;
+                collecting = false;
+                collecting_letter = false;
+                collecting_numeric = false;
+                collecting_special = false;
+                collecting_whitespace_lookahead = false;
+            }
+            else {
+                token += utf_char;
+            }
+        }
+
+        for (std::string & word : bpe_words) {
+            std::string encoded_token = "";
+            for (char & c : word) {
+                encoded_token += bytes_to_unicode_bpe(c);
+            }
+            bpe_encoded_words.emplace_back(encoded_token);
+        }
+
+        return bpe_encoded_words;
+    }
+
+    const llama_vocab & vocab;
+
+    std::vector<llm_symbol> symbols;
+    std::vector<llm_symbol> symbols_final;
+
+    llm_bigram_bpe::queue work_queue;
+};
+
+typedef enum FRAGMENT_BUFFER_VARIANT_TYPE{
+    FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN,
+    FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT
+} FRAGMENT_BUFFER_VARIANT_TYPE;
+
+struct fragment_buffer_variant{
+    fragment_buffer_variant(llama_vocab::id _token)
+    :
+        type(FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN),
+        token(_token),
+        raw_text(_dummy),
+        offset(0),
+        length(0){}
+    fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length)
+    :
+        type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT),
+        token((llama_vocab::id)-1),
+        raw_text(_raw_text),
+        offset(_offset),
+        length(_length){
+            GGML_ASSERT( _offset >= 0 );
+            GGML_ASSERT( _length >= 1 );
+            GGML_ASSERT( offset + length <= raw_text.length() );
+        }
+
+    const FRAGMENT_BUFFER_VARIANT_TYPE type;
+    const llama_vocab::id token;
+    const std::string _dummy;
+    const std::string & raw_text;
+    const uint64_t offset;
+    const uint64_t length;
+};
+
+// #define PRETOKENIZERDEBUG
+
+static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer)
+{
+    // for each special token
+    for (const auto & st: vocab.special_tokens_cache) {
+        const auto & special_token = st.first;
+        const auto & special_id    = st.second;
+
+        // for each text fragment
+        std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
+        while (it != buffer.end()) {
+            auto & fragment = (*it);
+
+            // if a fragment is text ( not yet processed )
+            if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                auto * raw_text = &(fragment.raw_text);
+
+                auto raw_text_base_offset = fragment.offset;
+                auto raw_text_base_length = fragment.length;
+
+                // loop over the text
+                while (true) {
+                    // find the first occurence of a given special token in this fragment
+                    //  passing offset argument only limit the "search area" but match coordinates
+                    //  are still relative to the source full raw_text
+                    auto match = raw_text->find(special_token, raw_text_base_offset);
+
+                    // no occurences found, stop processing this fragment for a given special token
+                    if (match == std::string::npos) break;
+
+                    // check if match is within bounds of offset <-> length
+                    if (match + special_token.length() > raw_text_base_offset + raw_text_base_length) break;
+
+#ifdef PRETOKENIZERDEBUG
+                    fprintf(stderr, "FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
+#endif
+                    auto source = std::distance(buffer.begin(), it);
+
+                    // if match is further than base offset
+                    //  then we have some text to the left of it
+                    if (match > raw_text_base_offset) {
+                        // left
+                        const int64_t left_reminder_offset = raw_text_base_offset + 0;
+                        const int64_t left_reminder_length = match - raw_text_base_offset;
+                        buffer.emplace_after(it, (*raw_text), left_reminder_offset, left_reminder_length);
+
+#ifdef PRETOKENIZERDEBUG
+                        fprintf(stderr, "FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
+#endif
+                        it++;
+                    }
+
+                    // special token
+                    buffer.emplace_after(it, special_id);
+                    it++;
+
+                    // right
+                    if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) {
+                        const int64_t right_reminder_offset = match + special_token.length();
+                        const int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
+                        buffer.emplace_after(it, (*raw_text), right_reminder_offset, right_reminder_length);
+
+#ifdef PRETOKENIZERDEBUG
+                        fprintf(stderr, "FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
+#endif
+
+                        it++;
+
+                        if (source == 0) {
+                            buffer.erase_after(buffer.before_begin());
+                        } else {
+                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
+                        }
+
+                        // repeat for the right side
+                        raw_text_base_offset = right_reminder_offset;
+                        raw_text_base_length = right_reminder_length;
+
+#ifdef PRETOKENIZERDEBUG
+                        fprintf(stderr, "RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
+#endif
+                    } else {
+                        if (source == 0) {
+                            buffer.erase_after(buffer.before_begin());
+                        } else {
+                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
+                        }
+                        break;
+                    }
+                }
+            }
+            it++;
+        }
+    }
+}
+
+static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special) {
+    std::vector<llama_vocab::id> output;
+
+    // OG tokenizer behavior:
+    //
+    // tokenizer.encode('', add_bos=True)  returns [1]
+    // tokenizer.encode('', add_bos=False) returns []
+
+    if (bos && vocab.special_bos_id != -1) {
+        output.push_back(vocab.special_bos_id);
+    }
+
+    if (raw_text.empty()) {
+        return output;
+    }
+
+    std::forward_list<fragment_buffer_variant> fragment_buffer;
+    fragment_buffer.emplace_front( raw_text, 0, raw_text.length() );
+
+    if (special) tokenizer_st_partition( vocab, fragment_buffer );
+
+    switch (vocab.type) {
+        case LLAMA_VOCAB_TYPE_SPM:
+            {
+                for (const auto & fragment: fragment_buffer)
+                {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT)
+                    {
+                        // without adding this leading whitespace, we do not get the same results as the original tokenizer
+
+                        // TODO: It's likely possible to get rid of this string copy entirely
+                        //  by modifying llm_tokenizer_x to operate with string offsets like pre-tokenizer
+                        //  and passing 'add space prefix' as bool argument
+                        //
+                        auto raw_text = (special ? "" : " ") + fragment.raw_text.substr(fragment.offset, fragment.length);
+
+#ifdef PRETOKENIZERDEBUG
+                        fprintf(stderr,"TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
+#endif
+                        llm_tokenizer_spm tokenizer(vocab);
+                        llama_escape_whitespace(raw_text);
+                        tokenizer.tokenize(raw_text, output);
+                    }
+                    else // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                    {
+                        output.push_back(fragment.token);
+                    }
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_BPE:
+            {
+                for (const auto & fragment: fragment_buffer)
+                {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT)
+                    {
+                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
+
+#ifdef PRETOKENIZERDEBUG
+                        fprintf(stderr,"TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
+#endif
+                        llm_tokenizer_bpe tokenizer(vocab);
+                        tokenizer.tokenize(raw_text, output);
+                    }
+                    else // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                    {
+                        output.push_back(fragment.token);
+                    }
+                }
+            } break;
+    }
+
+    return output;
+}
+
+//
+// grammar - internal
+//
+
+struct llama_partial_utf8 {
+    uint32_t value;    // bit value so far (unshifted)
+    int      n_remain; // num bytes remaining; -1 indicates invalid sequence
+};
+
+struct llama_grammar {
+    const std::vector<std::vector<llama_grammar_element>>   rules;
+    std::vector<std::vector<const llama_grammar_element *>> stacks;
+
+    // buffer for partially generated UTF-8 sequence from accepted tokens
+    llama_partial_utf8                                      partial_utf8;
+};
+
+struct llama_grammar_candidate {
+    size_t               index;
+    const uint32_t     * code_points;
+    llama_partial_utf8   partial_utf8;
+};
+
+// Decodes a UTF-8 string which may end in an incomplete sequence. Adds a terminating 0 for use as
+// pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`.
+static std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
+        const char         * src,
+        llama_partial_utf8   partial_start) {
+    static const int      lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
+    const char          * pos      = src;
+    std::vector<uint32_t> code_points;
+    uint32_t              value    = partial_start.value;
+    int                   n_remain = partial_start.n_remain;
+
+    // continue previous decode, if applicable
+    while (*pos != 0 && n_remain > 0) {
+        uint8_t next_byte = static_cast<uint8_t>(*pos);
+        if ((next_byte >> 6) != 2) {
+            // invalid sequence, abort
+            code_points.push_back(0);
+            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, -1 });
+        }
+        value = (value << 6) + (next_byte & 0x3F);
+        ++pos;
+        --n_remain;
+    }
+
+    if (partial_start.n_remain > 0 && n_remain == 0) {
+        code_points.push_back(value);
+    }
+
+    // decode any subsequent utf-8 sequences, which may end in an incomplete one
+    while (*pos != 0) {
+        uint8_t  first_byte = static_cast<uint8_t>(*pos);
+        uint8_t  highbits   = first_byte >> 4;
+                 n_remain   = lookup[highbits] - 1;
+
+        if (n_remain < 0) {
+            // invalid sequence, abort
+            code_points.clear();
+            code_points.push_back(0);
+            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, n_remain });
+        }
+
+        uint8_t  mask       = (1 << (7 - n_remain)) - 1;
+                 value      = first_byte & mask;
+        ++pos;
+        while (*pos != 0 && n_remain > 0) {
+            value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
+            ++pos;
+            --n_remain;
+        }
+        if (n_remain == 0) {
+            code_points.push_back(value);
+        }
+    }
+    code_points.push_back(0);
+
+    return std::make_pair(std::move(code_points), llama_partial_utf8{ value, n_remain });
+}
+
+// returns true iff pos points to the end of one of the definitions of a rule
+static bool llama_grammar_is_end_of_sequence(const llama_grammar_element * pos) {
+    switch (pos->type) {
+        case LLAMA_GRETYPE_END: return true;  // NOLINT
+        case LLAMA_GRETYPE_ALT: return true;  // NOLINT
+        default:                return false;
+    }
+}
+
+// returns true iff chr satisfies the char range at pos (regular or inverse range)
+// asserts that pos is pointing to a char range element
+static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
+        const llama_grammar_element * pos,
+        const uint32_t                chr) {
+
+    bool found            = false;
+    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
+
+    GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT); // NOLINT
+
+    do {
+        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
+            // inclusive range, e.g. [a-z]
+            found = found || (pos->value <= chr && chr <= pos[1].value);
+            pos += 2;
+        } else {
+            // exact char match, e.g. [a] or "a"
+            found = found || pos->value == chr;
+            pos += 1;
+        }
+    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
+
+    return std::make_pair(found == is_positive_char, pos);
+}
+
+// returns true iff some continuation of the given partial UTF-8 sequence could satisfy the char
+// range at pos (regular or inverse range)
+// asserts that pos is pointing to a char range element
+static bool llama_grammar_match_partial_char(
+        const llama_grammar_element * pos,
+        const llama_partial_utf8      partial_utf8) {
+
+    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
+    GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);
+
+    uint32_t partial_value = partial_utf8.value;
+    int      n_remain      = partial_utf8.n_remain;
+
+    // invalid sequence or 7-bit char split across 2 bytes (overlong)
+    if (n_remain < 0 || (n_remain == 1 && partial_value < 2)) {
+        return false;
+    }
+
+    // range of possible code points this partial UTF-8 sequence could complete to
+    uint32_t low  = partial_value << (n_remain * 6);
+    uint32_t high = low | ((1 << (n_remain * 6)) - 1);
+
+    if (low == 0) {
+        if (n_remain == 2) {
+            low = 1 << 11;
+        } else if (n_remain == 3) {
+            low = 1 << 16;
+        }
+    }
+
+    do {
+        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
+            // inclusive range, e.g. [a-z]
+            if (pos->value <= high && low <= pos[1].value) {
+                return is_positive_char;
+            }
+            pos += 2;
+        } else {
+            // exact char match, e.g. [a] or "a"
+            if (low <= pos->value && pos->value <= high) {
+                return is_positive_char;
+            }
+            pos += 1;
+        }
+    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
+
+    return !is_positive_char;
+}
+
+
+// transforms a grammar pushdown stack into N possible stacks, all ending
+// at a character range (terminal element)
+static void llama_grammar_advance_stack(
+        const std::vector<std::vector<llama_grammar_element>>   & rules,
+        const std::vector<const llama_grammar_element *>        & stack,
+        std::vector<std::vector<const llama_grammar_element *>> & new_stacks) {
+
+    if (stack.empty()) {
+        new_stacks.emplace_back(stack);
+        return;
+    }
+
+    const llama_grammar_element * pos = stack.back();
+
+    switch (pos->type) {
+        case LLAMA_GRETYPE_RULE_REF: {
+            const size_t                  rule_id = static_cast<size_t>(pos->value);
+            const llama_grammar_element * subpos  = rules[rule_id].data();
+            do {
+                // init new stack without the top (pos)
+                std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
+                if (!llama_grammar_is_end_of_sequence(pos + 1)) {
+                    // if this rule ref is followed by another element, add that to stack
+                    new_stack.push_back(pos + 1);
+                }
+                if (!llama_grammar_is_end_of_sequence(subpos)) {
+                    // if alternate is nonempty, add to stack
+                    new_stack.push_back(subpos);
+                }
+                llama_grammar_advance_stack(rules, new_stack, new_stacks);
+                while (!llama_grammar_is_end_of_sequence(subpos)) {
+                    // scan to end of alternate def
+                    subpos++;
+                }
+                if (subpos->type == LLAMA_GRETYPE_ALT) {
+                    // there's another alternate def of this rule to process
+                    subpos++;
+                } else {
+                    break;
+                }
+            } while (true);
+            break;
+        }
+        case LLAMA_GRETYPE_CHAR:
+        case LLAMA_GRETYPE_CHAR_NOT:
+            new_stacks.emplace_back(stack);
+            break;
+        default:
+            // end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range
+            // (LLAMA_GRETYPE_CHAR_ALT, LLAMA_GRETYPE_CHAR_RNG_UPPER); stack should never be left on
+            // those
+            GGML_ASSERT(false);
+    }
+}
+
+// takes a set of possible pushdown stacks on a grammar, which are required to
+// be positioned at a character range (see `llama_grammar_advance_stack`), and
+// produces the N possible stacks if the given char is accepted at those
+// positions
+static std::vector<std::vector<const llama_grammar_element *>> llama_grammar_accept(
+        const std::vector<std::vector<llama_grammar_element>>         & rules,
+        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
+        const uint32_t                                                  chr) {
+
+    std::vector<std::vector<const llama_grammar_element *>> new_stacks;
+
+    for (const auto & stack : stacks) {
+        if (stack.empty()) {
+            continue;
+        }
+
+        auto match = llama_grammar_match_char(stack.back(), chr);
+        if (match.first) {
+            const llama_grammar_element * pos = match.second;
+
+            // update top of stack to next element, if any
+            std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
+            if (!llama_grammar_is_end_of_sequence(pos)) {
+                new_stack.push_back(pos);
+            }
+            llama_grammar_advance_stack(rules, new_stack, new_stacks);
+        }
+    }
+
+    return new_stacks;
+}
+
+static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
+        const std::vector<std::vector<llama_grammar_element>>         & rules,
+        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
+        const std::vector<llama_grammar_candidate>                    & candidates);
+
+static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
+        const std::vector<std::vector<llama_grammar_element>> & rules,
+        const std::vector<const llama_grammar_element *>      & stack,
+        const std::vector<llama_grammar_candidate>            & candidates) {
+
+    std::vector<llama_grammar_candidate> rejects;
+
+    if (stack.empty()) {
+        for (const auto & tok : candidates) {
+            if (*tok.code_points != 0 || tok.partial_utf8.n_remain != 0) {
+                rejects.push_back(tok);
+            }
+        }
+        return rejects;
+    }
+
+    const llama_grammar_element * stack_pos = stack.back();
+
+    std::vector<llama_grammar_candidate> next_candidates;
+    for (const auto & tok : candidates) {
+        if (*tok.code_points == 0) {
+            // reached end of full codepoints in token, reject iff it ended in a partial sequence
+            // that cannot satisfy this position in grammar
+            if (tok.partial_utf8.n_remain != 0 &&
+                    !llama_grammar_match_partial_char(stack_pos, tok.partial_utf8)) {
+                rejects.push_back(tok);
+            }
+        } else if (llama_grammar_match_char(stack_pos, *tok.code_points).first) {
+            next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8 });
+        } else {
+            rejects.push_back(tok);
+        }
+    }
+
+    const auto * stack_pos_after = llama_grammar_match_char(stack_pos, 0).second;
+
+    // update top of stack to next element, if any
+    std::vector<const llama_grammar_element *> stack_after(stack.begin(), stack.end() - 1);
+    if (!llama_grammar_is_end_of_sequence(stack_pos_after)) {
+        stack_after.push_back(stack_pos_after);
+    }
+    std::vector<std::vector<const llama_grammar_element *>> next_stacks;
+    llama_grammar_advance_stack(rules, stack_after, next_stacks);
+
+    auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates);
+    for (const auto & tok : next_rejects) {
+        rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 });
+    }
+
+    return rejects;
+}
+
+static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
+        const std::vector<std::vector<llama_grammar_element>>         & rules,
+        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
+        const std::vector<llama_grammar_candidate>                    & candidates) {
+    GGML_ASSERT(!stacks.empty()); // REVIEW
+
+    if (candidates.empty()) {
+        return std::vector<llama_grammar_candidate>();
+    }
+
+    auto rejects = llama_grammar_reject_candidates_for_stack(rules, stacks.front(), candidates);
+
+    for (size_t i = 1, size = stacks.size(); i < size; ++i) {
+        rejects = llama_grammar_reject_candidates_for_stack(rules, stacks[i], rejects);
+    }
+    return rejects;
+}
+
+//
+// grammar - external
+//
+
+struct llama_grammar * llama_grammar_init(
+            const llama_grammar_element ** rules,
+                                 size_t    n_rules,
+                                 size_t    start_rule_index) {
+    const llama_grammar_element * pos;
+
+    // copy rule definitions into vectors
+    std::vector<std::vector<llama_grammar_element>> vec_rules(n_rules);
+    for (size_t i = 0; i < n_rules; i++) {
+        for (pos = rules[i]; pos->type != LLAMA_GRETYPE_END; pos++) {
+            vec_rules[i].push_back(*pos);
+        }
+        vec_rules[i].push_back({LLAMA_GRETYPE_END, 0});
+    }
+
+    // loop over alternates of start rule to build initial stacks
+    std::vector<std::vector<const llama_grammar_element *>> stacks;
+    pos = rules[start_rule_index];
+    do {
+        std::vector<const llama_grammar_element *> stack;
+        if (!llama_grammar_is_end_of_sequence(pos)) {
+            // if alternate is nonempty, add to stack
+            stack.push_back(pos);
+        }
+        llama_grammar_advance_stack(vec_rules, stack, stacks);
+        while (!llama_grammar_is_end_of_sequence(pos)) {
+            // scan to end of alternate def
+            pos++;
+        }
+        if (pos->type == LLAMA_GRETYPE_ALT) {
+            // there's another alternate def of this rule to process
+            pos++;
+        } else {
+            break;
+        }
+    } while (true);
+
+    return new llama_grammar{ std::move(vec_rules), std::move(stacks), {} };
+}
+
+void llama_grammar_free(struct llama_grammar * grammar) {
+    delete grammar;
+}
+
+struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar) {
+    llama_grammar * result = new llama_grammar{ grammar->rules, grammar->stacks, grammar->partial_utf8 };
+
+    // redirect elements in stacks to point to new rules
+    for (size_t is = 0; is < result->stacks.size(); is++) {
+        for (size_t ie = 0; ie < result->stacks[is].size(); ie++) {
+            for (size_t ir0 = 0; ir0 < grammar->rules.size(); ir0++) {
+                for (size_t ir1 = 0; ir1 < grammar->rules[ir0].size(); ir1++) {
+                    if (grammar->stacks[is][ie] == &grammar->rules[ir0][ir1]) {
+                         result->stacks[is][ie]  =  &result->rules[ir0][ir1];
+                    }
+                }
+            }
+        }
+    }
+
+    return result;
+}
+
+//
+// sampling
+//
+
+void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed) {
+    if (seed == LLAMA_DEFAULT_SEED) {
+        seed = time(NULL);
+    }
+    ctx->rng.seed(seed);
+}
+
+void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates) {
+    GGML_ASSERT(candidates->size > 0);
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Sort the logits in descending order
+    if (!candidates->sorted) {
+        std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
+            return a.logit > b.logit;
+        });
+        candidates->sorted = true;
+    }
+
+    float max_l = candidates->data[0].logit;
+    float cum_sum = 0.0f;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        float p = expf(candidates->data[i].logit - max_l);
+        candidates->data[i].p = p;
+        cum_sum += p;
+    }
+    for (size_t i = 0; i < candidates->size; ++i) {
+        candidates->data[i].p /= cum_sum;
+    }
+
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep) {
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    k = std::max(k, (int) min_keep);
+    k = std::min(k, (int) candidates->size);
+
+    // Sort scores in descending order
+    if (!candidates->sorted) {
+        auto comp = [](const llama_token_data & a, const llama_token_data & b) {
+            return a.logit > b.logit;
+        };
+        if (k == (int) candidates->size) {
+            std::sort(candidates->data, candidates->data + candidates->size, comp);
+        } else {
+            std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
+        }
+        candidates->sorted = true;
+    }
+    candidates->size = k;
+
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
+    if (p >= 1.0f) {
+        return;
+    }
+
+    llama_sample_softmax(ctx, candidates);
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Compute the cumulative probabilities
+    float cum_sum = 0.0f;
+    size_t last_idx = candidates->size;
+
+    for (size_t i = 0; i < candidates->size; ++i) {
+        cum_sum += candidates->data[i].p;
+
+        // Check if the running sum is at least p or if we have kept at least min_keep tokens
+        // we set the last index to i+1 to indicate that the current iterate should be included in the set
+        if (cum_sum >= p && i + 1 >= min_keep) {
+            last_idx = i + 1;
+            break;
+        }
+    }
+
+    // Resize the output vector to keep only the top-p tokens
+    candidates->size = last_idx;
+
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
+    if (z >= 1.0f || candidates->size <= 2) {
+        return;
+    }
+
+    llama_sample_softmax(nullptr, candidates);
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Compute the first and second derivatives
+    std::vector<float> first_derivatives(candidates->size - 1);
+    std::vector<float> second_derivatives(candidates->size - 2);
+
+    for (size_t i = 0; i < first_derivatives.size(); ++i) {
+        first_derivatives[i] = candidates->data[i].p - candidates->data[i + 1].p;
+    }
+    for (size_t i = 0; i < second_derivatives.size(); ++i) {
+        second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
+    }
+
+    // Calculate absolute value of second derivatives
+    for (size_t i = 0; i < second_derivatives.size(); ++i) {
+        second_derivatives[i] = std::abs(second_derivatives[i]);
+    }
+
+    // Normalize the second derivatives
+    {
+        const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
+
+        if (second_derivatives_sum > 1e-6f) {
+            for (float & value : second_derivatives) {
+                value /= second_derivatives_sum;
+            }
+        } else {
+            for (float & value : second_derivatives) {
+                value = 1.0f / second_derivatives.size();
+            }
+        }
+    }
+
+    float cum_sum = 0.0f;
+    size_t last_idx = candidates->size;
+    for (size_t i = 0; i < second_derivatives.size(); ++i) {
+        cum_sum += second_derivatives[i];
+
+        // Check if the running sum is greater than z or if we have kept at least min_keep tokens
+        if (cum_sum > z && i >= min_keep) {
+            last_idx = i;
+            break;
+        }
+    }
+
+    // Resize the output vector to keep only the tokens above the tail location
+    candidates->size = last_idx;
+
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
+    // Reference implementation:
+    // https://github.com/huggingface/transformers/compare/main...cimeister:typical-sampling:typical-pr
+    if (p >= 1.0f) {
+        return;
+    }
+
+    // Compute the softmax of logits and calculate entropy
+    llama_sample_softmax(nullptr, candidates);
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    float entropy = 0.0f;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        entropy += -candidates->data[i].p * logf(candidates->data[i].p);
+    }
+
+    // Compute the absolute difference between negative log probability and entropy for each candidate
+    std::vector<float> shifted_scores;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        float shifted_score = fabsf(-logf(candidates->data[i].p) - entropy);
+        shifted_scores.push_back(shifted_score);
+    }
+
+    // Sort tokens based on the shifted_scores and their corresponding indices
+    std::vector<size_t> indices(candidates->size);
+    std::iota(indices.begin(), indices.end(), 0);
+
+    std::sort(indices.begin(), indices.end(), [&](size_t a, size_t b) {
+        return shifted_scores[a] < shifted_scores[b];
+    });
+
+    // Compute the cumulative probabilities
+    float cum_sum = 0.0f;
+    size_t last_idx = indices.size();
+
+    for (size_t i = 0; i < indices.size(); ++i) {
+        size_t idx = indices[i];
+        cum_sum += candidates->data[idx].p;
+
+        // Check if the running sum is greater than typical or if we have kept at least min_keep tokens
+        if (cum_sum > p && i >= min_keep - 1) {
+            last_idx = i + 1;
+            break;
+        }
+    }
+
+    // Resize the output vector to keep only the locally typical tokens
+    std::vector<llama_token_data> new_candidates;
+    for (size_t i = 0; i < last_idx; ++i) {
+        size_t idx = indices[i];
+        new_candidates.push_back(candidates->data[idx]);
+    }
+
+    // Replace the data in candidates with the new_candidates data
+    std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
+    candidates->size = new_candidates.size();
+
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    for (size_t i = 0; i < candidates_p->size; ++i) {
+        candidates_p->data[i].logit /= temp;
+    }
+
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
+    llama_sample_temp(ctx, candidates_p, temp);
+}
+
+void llama_sample_repetition_penalties(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+               const llama_token * last_tokens,
+                          size_t   penalty_last_n,
+                           float   penalty_repeat,
+                           float   penalty_freq,
+                           float   penalty_present) {
+    if (penalty_last_n == 0 || (penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f)) {
+        return;
+    }
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Create a frequency map to count occurrences of each token in last_tokens
+    std::unordered_map<llama_token, int> token_count;
+    for (size_t i = 0; i < penalty_last_n; ++i) {
+        token_count[last_tokens[i]]++;
+    }
+
+    // Apply frequency and presence penalties to the candidates
+    for (size_t i = 0; i < candidates->size; ++i) {
+        const auto token_iter = token_count.find(candidates->data[i].id);
+        if (token_iter == token_count.end()) {
+            continue;
+        }
+
+        const int count = token_iter->second;
+
+        // The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
+        // This is common fix for this problem, which is to multiply by the penalty instead of dividing.
+        if (candidates->data[i].logit <= 0) {
+            candidates->data[i].logit *= penalty_repeat;
+        } else {
+            candidates->data[i].logit /= penalty_repeat;
+        }
+
+        candidates->data[i].logit -= float(count) * penalty_freq + float(count > 0) * penalty_present;
+    }
+
+    candidates->sorted = false;
+
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
+    GGML_ASSERT(ctx);
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    bool allow_eos = false;
+    for (const auto & stack : grammar->stacks) {
+        if (stack.empty()) {
+            allow_eos = true;
+            break;
+        }
+    }
+
+    const llama_token eos = llama_token_eos(ctx);
+
+    std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
+    std::vector<llama_grammar_candidate>                              candidates_grammar;
+
+    for (size_t i = 0; i < candidates->size; ++i) {
+        const llama_token id    = candidates->data[i].id;
+        const std::string piece = llama_token_to_str(ctx, id);
+        if (id == eos) {
+            if (!allow_eos) {
+                candidates->data[i].logit = -INFINITY;
+            }
+        } else if (piece.empty() || piece[0] == 0) {
+            candidates->data[i].logit = -INFINITY;
+        } else {
+            candidates_decoded.push_back(decode_utf8(piece.c_str(), grammar->partial_utf8));
+            candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
+        }
+    }
+
+    const auto rejects = llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar);
+    for (const auto & reject : rejects) {
+        candidates->data[reject.index].logit = -INFINITY;
+    }
+
+    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+}
+
+static void llama_log_softmax(float * array, size_t size) {
+    float max_l = *std::max_element(array, array + size);
+    float sum = 0.f;
+    for (size_t i = 0; i < size; ++i) {
+        float p = expf(array[i] - max_l);
+        sum += p;
+        array[i] = p;
+    }
+
+    for (size_t i = 0; i < size; ++i) {
+        array[i] = logf(array[i] / sum);
+    }
+}
+
+void llama_sample_classifier_free_guidance(
+          struct llama_context * ctx,
+        llama_token_data_array * candidates,
+          struct llama_context * guidance_ctx,
+                         float   scale) {
+    int64_t t_start_sample_us = ggml_time_us();
+
+    GGML_ASSERT(ctx);
+
+    auto n_vocab = llama_n_vocab(llama_get_model(ctx));
+
+    GGML_ASSERT(n_vocab == (int)candidates->size);
+    GGML_ASSERT(!candidates->sorted);
+
+    std::vector<float> logits_base;
+    logits_base.reserve(candidates->size);
+    for (size_t i = 0; i < candidates->size; ++i) {
+        logits_base.push_back(candidates->data[i].logit);
+    }
+    llama_log_softmax(logits_base.data(), candidates->size);
+
+    float* logits_guidance = llama_get_logits(guidance_ctx);
+    llama_log_softmax(logits_guidance, n_vocab);
+
+    for (int i = 0; i < n_vocab; ++i) {
+        float logit_guidance = logits_guidance[i];
+        float logit_base = logits_base[i];
+        candidates->data[i].logit = scale * (logit_base - logit_guidance) + logit_guidance;
+    }
+
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu) {
+    GGML_ASSERT(ctx);
+
+    auto N = float(llama_n_vocab(llama_get_model(ctx)));
+    int64_t t_start_sample_us;
+    t_start_sample_us = ggml_time_us();
+
+    llama_sample_softmax(nullptr, candidates);
+
+    // Estimate s_hat using the most probable m tokens
+    float s_hat = 0.0;
+    float sum_ti_bi = 0.0;
+    float sum_ti_sq = 0.0;
+    for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
+        float t_i = logf(float(i + 2) / float(i + 1));
+        float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
+        sum_ti_bi += t_i * b_i;
+        sum_ti_sq += t_i * t_i;
+    }
+    s_hat = sum_ti_bi / sum_ti_sq;
+
+    // Compute k from the estimated s_hat and target surprise value
+    float epsilon_hat = s_hat - 1;
+    float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(N, -epsilon_hat)), 1 / s_hat);
+
+    // Sample the next word X using top-k sampling
+    llama_sample_top_k(nullptr, candidates, int(k), 1);
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+    llama_token X = llama_sample_token(ctx, candidates);
+    t_start_sample_us = ggml_time_us();
+
+    // Compute error as the difference between observed surprise and target surprise value
+    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
+        return candidate.id == X;
+    }));
+    float observed_surprise = -log2f(candidates->data[X_idx].p);
+    float e = observed_surprise - tau;
+
+    // Update mu using the learning rate and error
+    *mu = *mu - eta * e;
+
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+    return X;
+}
+
+llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
+    int64_t t_start_sample_us;
+    t_start_sample_us = ggml_time_us();
+
+    llama_sample_softmax(ctx, candidates);
+
+    // Truncate the words with surprise values greater than mu
+    candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
+        return -log2f(candidate.p) > *mu;
+    }));
+
+    if (candidates->size == 0) {
+        candidates->size = 1;
+    }
+
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+
+    // Normalize the probabilities of the remaining words
+    llama_sample_softmax(ctx, candidates);
+
+    // Sample the next word X from the remaining words
+    llama_token X = llama_sample_token(ctx, candidates);
+    t_start_sample_us = ggml_time_us();
+
+    // Compute error as the difference between observed surprise and target surprise value
+    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
+        return candidate.id == X;
+    }));
+    float observed_surprise = -log2f(candidates->data[X_idx].p);
+    float e = observed_surprise - tau;
+
+    // Update mu using the learning rate and error
+    *mu = *mu - eta * e;
+
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+    return X;
+}
+
+llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates) {
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Find max element
+    auto * max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
+        return a.logit < b.logit;
+    });
+
+    llama_token result = max_iter->id;
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+        ctx->n_sample++;
+    }
+    return result;
+}
+
+llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
+    GGML_ASSERT(ctx);
+
+    const int64_t t_start_sample_us = ggml_time_us();
+    llama_sample_softmax(nullptr, candidates);
+
+    std::vector<float> probs;
+    probs.reserve(candidates->size);
+    for (size_t i = 0; i < candidates->size; ++i) {
+        probs.push_back(candidates->data[i].p);
+    }
+
+    std::discrete_distribution<> dist(probs.begin(), probs.end());
+    auto & rng = ctx->rng;
+    int idx = dist(rng);
+
+    llama_token result = candidates->data[idx].id;
+
+    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    ctx->n_sample++;
+    return result;
+}
+
+void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    if (token == llama_token_eos(ctx)) {
+        for (const auto & stack : grammar->stacks) {
+            if (stack.empty()) {
+                return;
+            }
+        }
+        GGML_ASSERT(false);
+    }
+
+    const std::string piece = llama_token_to_str(ctx, token);
+
+    // Note terminating 0 in decoded string
+    const auto   decoded     = decode_utf8(piece.c_str(), grammar->partial_utf8);
+    const auto & code_points = decoded.first;
+    for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
+        grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
+    }
+    grammar->partial_utf8 = decoded.second;
+    GGML_ASSERT(!grammar->stacks.empty());
+
+    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+}
+
+//
+// Beam search
+//
+
+struct llama_beam {
+    std::vector<llama_token> tokens;
+    float p;  // Cumulative beam probability (renormalized relative to all beams)
+    bool eob; // Initialize end-of-beam to false. Callback sets this to true.
+    // Sort beams by probability. In case of ties, prefer beams at eob.
+    bool operator<(const llama_beam & rhs) const {
+        return std::make_pair(p, eob) < std::make_pair(rhs.p, rhs.eob);
+    }
+    // Shift off first n tokens and discard them.
+    void shift_tokens(const size_t n) {
+        if (n) {
+            std::copy(tokens.begin() + n, tokens.end(), tokens.begin());
+            tokens.resize(tokens.size() - n);
+        }
+    }
+    llama_beam_view view() const { return {tokens.data(), tokens.size(), p, eob}; }
+};
+
+// A struct for calculating logit-related info.
+struct llama_logit_info {
+    const float * const logits;
+    const int n_vocab;
+    const float max_l;
+    const float normalizer;
+    struct sum_exp {
+        float max_l;
+        float operator()(float sum, float l) const { return sum + std::exp(l - max_l); }
+    };
+    llama_logit_info(llama_context * ctx)
+      : logits(llama_get_logits(ctx))
+      , n_vocab(llama_n_vocab(llama_get_model(ctx)))
+      , max_l(*std::max_element(logits, logits + n_vocab))
+      , normalizer(1.0f / std::accumulate(logits, logits + n_vocab, 0.0f, sum_exp{max_l}))
+      { }
+    llama_token_data get_token_data(const llama_token token_id) const {
+        constexpr auto p = std::numeric_limits<float>::quiet_NaN();  // never used
+        return {token_id, logits[token_id], p};
+    }
+    // Return top k token_data by logit.
+    std::vector<llama_token_data> top_k(size_t k) {
+        std::vector<llama_token_data> min_heap;  // min-heap by logit
+        const llama_token k_min = std::min(static_cast<llama_token>(k), n_vocab);
+        min_heap.reserve(k_min);
+        for (llama_token token_id = 0 ; token_id < k_min ; ++token_id) {
+            min_heap.push_back(get_token_data(token_id));
+        }
+        auto comp = [](const llama_token_data & a, const llama_token_data & b) { return a.logit > b.logit; };
+        std::make_heap(min_heap.begin(), min_heap.end(), comp);
+        for (llama_token token_id = k_min ; token_id < n_vocab ; ++token_id) {
+            if (min_heap.front().logit < logits[token_id]) {
+                std::pop_heap(min_heap.begin(), min_heap.end(), comp);
+                min_heap.back().id = token_id;
+                min_heap.back().logit = logits[token_id];
+                std::push_heap(min_heap.begin(), min_heap.end(), comp);
+            }
+        }
+        return min_heap;
+    }
+    float probability_from_logit(float logit) const {
+        return normalizer * std::exp(logit - max_l);
+    }
+};
+
+struct llama_beam_search_data {
+    llama_context * ctx;
+    size_t n_beams;
+    int n_past;
+    int n_predict;
+    std::vector<llama_beam> beams;
+    std::vector<llama_beam> next_beams;
+
+    // Re-calculated on each loop iteration
+    size_t common_prefix_length;
+
+    // Used to communicate to/from callback on beams state.
+    std::vector<llama_beam_view> beam_views;
+
+    llama_beam_search_data(llama_context * ctx, size_t n_beams, int n_past, int n_predict)
+      : ctx(ctx)
+      , n_beams(n_beams)
+      , n_past(n_past)
+      , n_predict(n_predict)
+      , beam_views(n_beams) {
+        beams.reserve(n_beams);
+        next_beams.reserve(n_beams);
+    }
+
+    // Collapse beams to a single beam given by index.
+    void collapse_beams(const size_t beam_idx) {
+        if (0u < beam_idx) {
+            std::swap(beams[0], beams[beam_idx]);
+        }
+        beams.resize(1);
+    }
+
+    // Min-heaps are used to efficiently collect the top-k elements (k=n_beams).
+    // The repetative patterns below reflect the 2 stages of heaps:
+    //  * Gather elements until the vector is full, then call std::make_heap() on it.
+    //  * If the heap is full and a new element is found that should be included, pop the
+    //    least element to the back(), replace it with the new, then push it into the heap.
+    void fill_next_beams_by_top_probabilities(llama_beam & beam) {
+        // Min-heaps use a greater-than comparator.
+        const auto comp = [](const llama_beam & a, const llama_beam & b) { return a.p > b.p; };
+        if (beam.eob) {
+            // beam is at end-of-sentence, so just copy it to next_beams if its probability is high enough.
+            if (next_beams.size() < n_beams) {
+                next_beams.push_back(std::move(beam));
+                if (next_beams.size() == n_beams) {
+                    std::make_heap(next_beams.begin(), next_beams.end(), comp);
+                }
+            } else if (next_beams.front().p < beam.p) {
+                std::pop_heap(next_beams.begin(), next_beams.end(), comp);
+                next_beams.back() = std::move(beam);
+                std::push_heap(next_beams.begin(), next_beams.end(), comp);
+            }
+        } else {
+            // beam is not at end-of-sentence, so branch with next top_k tokens.
+            if (!beam.tokens.empty()) {
+                llama_decode(ctx, llama_batch_get_one(beam.tokens.data(), beam.tokens.size(), n_past, 0));
+            }
+            llama_logit_info logit_info(ctx);
+            std::vector<llama_token_data> next_tokens = logit_info.top_k(n_beams);
+            size_t i=0;
+            if (next_beams.size() < n_beams) {
+                for (; next_beams.size() < n_beams ; ++i) {
+                    llama_beam next_beam = beam;
+                    next_beam.tokens.push_back(next_tokens[i].id);
+                    next_beam.p *= logit_info.probability_from_logit(next_tokens[i].logit);
+                    next_beams.push_back(std::move(next_beam));
+                }
+                std::make_heap(next_beams.begin(), next_beams.end(), comp);
+            } else {
+                for (; next_beams.front().p == 0.0f ; ++i) {
+                    std::pop_heap(next_beams.begin(), next_beams.end(), comp);
+                    next_beams.back() = beam;
+                    next_beams.back().tokens.push_back(next_tokens[i].id);
+                    next_beams.back().p *= logit_info.probability_from_logit(next_tokens[i].logit);
+                    std::push_heap(next_beams.begin(), next_beams.end(), comp);
+                }
+            }
+            for (; i < n_beams ; ++i) {
+                const float next_p = beam.p * logit_info.probability_from_logit(next_tokens[i].logit);
+                if (next_beams.front().p < next_p) {
+                    std::pop_heap(next_beams.begin(), next_beams.end(), comp);
+                    next_beams.back() = beam;
+                    next_beams.back().tokens.push_back(next_tokens[i].id);
+                    next_beams.back().p = next_p;
+                    std::push_heap(next_beams.begin(), next_beams.end(), comp);
+                }
+            }
+        }
+    }
+
+    // Find common_prefix_length based on beams.
+    // Requires beams is not empty.
+    size_t find_common_prefix_length() {
+        size_t common_prefix_length = beams[0].tokens.size();
+        for (size_t i = 1 ; i < beams.size() ; ++i) {
+            common_prefix_length = std::min(common_prefix_length, beams[i].tokens.size());
+            for (size_t j = 0 ; j < common_prefix_length ; ++j) {
+                if (beams[0].tokens[j] != beams[i].tokens[j]) {
+                    common_prefix_length = j;
+                    break;
+                }
+            }
+        }
+        return common_prefix_length;
+    }
+
+    // Construct beams_state to send back to caller via the callback function.
+    // Side effect: set common_prefix_length = find_common_prefix_length();
+    llama_beams_state get_beams_state(const bool last_call) {
+        for (size_t i = 0 ; i < beams.size() ; ++i) {
+            beam_views[i] = beams[i].view();
+        }
+        common_prefix_length = find_common_prefix_length();
+        return {beam_views.data(), beams.size(), common_prefix_length, last_call};
+    }
+
+    // Loop:
+    //  * while i < n_predict, AND
+    //  * any of the beams have not yet reached end-of-beam (eob), AND
+    //  * the highest probability beam(s) (plural in case of ties) are not at end-of-sentence
+    //    (since all other beam probabilities can only decrease)
+    void loop(const llama_beam_search_callback_fn_t callback, void * const callback_data) {
+        beams.push_back({{}, 1.0f, false});  // Start with one empty beam w/ probability = 1.0 and !eob.
+        const auto not_eob = [](const llama_beam & beam) { return !beam.eob; };
+        for (int i = 0 ; i < n_predict && std::any_of(beams.begin(),beams.end(),not_eob) &&
+                       !beams[top_beam_index()].eob ; ++i) {
+            callback(callback_data, get_beams_state(false));  // Sets common_prefix_length
+            update_beams_from_beam_views();   // Update values (p,eob) that callback may have changed.
+            if (common_prefix_length) {
+                llama_decode(ctx, llama_batch_get_one(beams[0].tokens.data(), common_prefix_length, n_past, 0));
+                n_past += common_prefix_length;
+            }
+            // Zero-out next_beam probabilities to place them last in following min-heap.
+            std::for_each(next_beams.begin(), next_beams.end(), [](llama_beam & beam) { beam.p = 0.0f; });
+            for (llama_beam & beam : beams) {
+                beam.shift_tokens(common_prefix_length);
+                fill_next_beams_by_top_probabilities(beam);
+            }
+            // next_beams become the beams of next/final iteration. Swap them to re-use memory.
+            beams.swap(next_beams);
+            renormalize_beam_probabilities(beams);
+        }
+        collapse_beams(top_beam_index());
+        callback(callback_data, get_beams_state(true));
+    }
+
+    // As beams grow, the cumulative probabilities decrease.
+    // Renormalize them to avoid floating point underflow.
+    static void renormalize_beam_probabilities(std::vector<llama_beam> & beams) {
+        const auto sum_p = [](float sum, llama_beam & beam) { return sum + beam.p; };
+        const float inv_sum = 1.0f / std::accumulate(beams.begin(), beams.end(), 0.0f, sum_p);
+        std::for_each(beams.begin(), beams.end(), [=](llama_beam & beam) { beam.p *= inv_sum; });
+    }
+
+    // Assumes beams is non-empty.  Uses llama_beam::operator<() for ordering.
+    size_t top_beam_index() {
+        return std::max_element(beams.begin(), beams.end()) - beams.begin();
+    }
+
+    // Copy (p,eob) for each beam which may have been changed by the callback.
+    void update_beams_from_beam_views() {
+        for (size_t i = 0 ; i < beams.size() ; ++i) {
+            beams[i].p = beam_views[i].p;
+            beams[i].eob = beam_views[i].eob;
+        }
+    }
+};
+
+void llama_beam_search(llama_context * ctx,
+                       llama_beam_search_callback_fn_t callback, void * callback_data,
+                       size_t n_beams, int n_past, int n_predict) {
+    assert(ctx);
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    llama_beam_search_data beam_search_data(ctx, n_beams, n_past, n_predict);
+
+    beam_search_data.loop(callback, callback_data);
+
+    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    ctx->n_sample++;
+}
+
+//
+// quantization
+//
+
+template <typename T>
+struct no_init {
+    T value;
+    no_init() { /* do nothing */ }
+};
+
+static void llama_convert_tensor_internal(
+    struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
+    const size_t nelements, const int nthread
+) {
+    if (output.size() < nelements) {
+        output.resize(nelements);
+    }
+    float * f32_output = (float *) output.data();
+
+    ggml_type_traits_t qtype;
+    if (ggml_is_quantized(tensor->type)) {
+        qtype = ggml_internal_get_type_traits(tensor->type);
+        if (qtype.to_float == NULL) {
+            throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor->type)));
+        }
+    } else if (tensor->type != GGML_TYPE_F16) {
+        throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(tensor->type)));
+    }
+
+    if (nthread < 2) {
+        if (tensor->type == GGML_TYPE_F16) {
+            ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor->data, f32_output, nelements);
+        } else if (ggml_is_quantized(tensor->type)) {
+            qtype.to_float(tensor->data, f32_output, nelements);
+        } else {
+            GGML_ASSERT(false); // unreachable
+        }
+        return;
+    }
+
+    auto block_size = tensor->type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor->type);
+    auto block_size_bytes = ggml_type_size(tensor->type);
+
+    GGML_ASSERT(nelements % block_size == 0);
+    auto nblocks = nelements / block_size;
+    auto blocks_per_thread = nblocks / nthread;
+    auto spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
+
+    for (auto tnum = 0, in_buff_offs = 0, out_buff_offs = 0; tnum < nthread; tnum++) {
+        auto thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
+        auto thr_elems = thr_blocks * block_size; // number of elements for this thread
+        auto thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
+
+        auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
+            if (typ == GGML_TYPE_F16) {
+                ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
+            } else {
+                qtype.to_float(inbuf, outbuf, nels);
+            }
+        };
+        workers.emplace_back(compute, tensor->type, (uint8_t *) tensor->data + in_buff_offs, f32_output + out_buff_offs, thr_elems);
+        in_buff_offs += thr_block_bytes;
+        out_buff_offs += thr_elems;
+    }
+    for (auto & w : workers) { w.join(); }
+    workers.clear();
+}
+
+#ifdef GGML_USE_K_QUANTS
+static ggml_type get_k_quant_type(
+    ggml_type new_type, const ggml_tensor * tensor, const llama_model & model, llama_ftype ftype, int * i_attention_wv,
+    int n_attention_wv, int * i_feed_forward_w2, int n_feed_forward_w2
+) {
+    const std::string name = ggml_get_name(tensor);
+    // TODO: avoid hardcoded tensor names - use the TN_* constants
+    const auto tn = LLM_TN(model.arch);
+
+    auto use_more_bits = [](int i_layer, int num_layers) -> bool {
+        return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2;
+    };
+
+    if (name == tn(LLM_TENSOR_OUTPUT, "weight")) {
+        int nx = tensor->ne[0];
+        if (model.arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
+            new_type = GGML_TYPE_Q8_0;
+        }
+        else if (new_type != GGML_TYPE_Q8_0) {
+            new_type = GGML_TYPE_Q6_K;
+        }
+    } else if (name.find("attn_v.weight") != std::string::npos) {
+        if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
+            new_type = *i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
+        else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
+                use_more_bits(*i_attention_wv, n_attention_wv)) new_type = GGML_TYPE_Q6_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && *i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
+        else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
+                (*i_attention_wv < n_attention_wv/8 || *i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
+        if (model.type == MODEL_70B) {
+            // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
+            // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
+            // nearly negligible increase in model size by quantizing this tensor with more bits:
+            if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
+        }
+        ++*i_attention_wv;
+    } else if (name.find("ffn_down.weight") != std::string::npos) {
+        if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
+            new_type = *i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K
+                     : model.arch != LLM_ARCH_FALCON || use_more_bits(*i_feed_forward_w2, n_feed_forward_w2) ? GGML_TYPE_Q4_K
+                     : GGML_TYPE_Q3_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
+            new_type = model.arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
+            if (model.arch == LLM_ARCH_FALCON) {
+                new_type = *i_feed_forward_w2 < 2 ? GGML_TYPE_Q6_K :
+                           use_more_bits(*i_feed_forward_w2, n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
+            } else {
+                if (use_more_bits(*i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
+            }
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(*i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && model.arch != LLM_ARCH_FALCON && *i_feed_forward_w2 < 4) {
+            new_type = GGML_TYPE_Q5_K;
+        }
+        ++*i_feed_forward_w2;
+    } else if (name.find("attn_output.weight") != std::string::npos) {
+        if (model.arch != LLM_ARCH_FALCON) {
+            if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K  ) new_type = GGML_TYPE_Q3_K;
+            else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K;
+            else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
+        } else {
+            if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
+        }
+    }
+    else if (name.find("attn_qkv.weight") != std::string::npos) {
+        if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
+    }
+    else if (name.find("ffn_gate.weight") != std::string::npos || name.find("ffn_up.weight") != std::string::npos) {
+        if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
+    }
+    // This can be used to reduce the size of the Q5_K_S model.
+    // The associated PPL increase is fully in line with the size reduction
+    //else {
+    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q4_K;
+    //}
+    bool convert_incompatible_tensor = false;
+    if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
+        new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) {
+        int nx = tensor->ne[0];
+        int ny = tensor->ne[1];
+        if (nx % QK_K != 0) {
+            LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for k-quants\n", __func__, nx, ny, QK_K);
+            convert_incompatible_tensor = true;
+        }
+    }
+    if (convert_incompatible_tensor) {
+        if (name == tn(LLM_TENSOR_OUTPUT, "weight")) {
+            new_type = GGML_TYPE_F16; //fall back to F16 instead of just failing.
+            LLAMA_LOG_WARN("F16 will be used for this tensor instead.\n");
+        } else if (name == tn(LLM_TENSOR_TOKEN_EMBD, "weight")) {
+            new_type = GGML_TYPE_Q4_0; //fall back to Q4_0 instead of just failing.
+            LLAMA_LOG_WARN("Q4_0 will be used for this tensor instead.\n");
+        } else {
+            throw std::runtime_error("Unsupported tensor size encountered\n");
+        }
+    }
+
+    return new_type;
+}
+#endif
+
+static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
+    ggml_type quantized_type;
+    llama_ftype ftype = params->ftype;
+
+    switch (params->ftype) {
+        case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
+        case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
+        case LLAMA_FTYPE_MOSTLY_F16:  quantized_type = GGML_TYPE_F16;  break;
+        case LLAMA_FTYPE_ALL_F32:     quantized_type = GGML_TYPE_F32;  break;
+
+#ifdef GGML_USE_K_QUANTS
+        // K-quants
+        case LLAMA_FTYPE_MOSTLY_Q2_K:   quantized_type = GGML_TYPE_Q2_K; break;
+        case LLAMA_FTYPE_MOSTLY_Q3_K_S:
+        case LLAMA_FTYPE_MOSTLY_Q3_K_M:
+        case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_K_S:
+        case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_K_S:
+        case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break;
+        case LLAMA_FTYPE_MOSTLY_Q6_K:   quantized_type = GGML_TYPE_Q6_K; break;
+#endif
+        default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
+    }
+
+    int nthread = params->nthread;
+
+    if (nthread <= 0) {
+        nthread = std::thread::hardware_concurrency();
+    }
+
+    // mmap consistently increases speed Linux, and also increases speed on Windows with
+    // hot cache. It may cause a slowdown on macOS, possibly related to free memory.
+#if defined(__linux__) || defined(_WIN32)
+    constexpr bool use_mmap = true;
+#else
+    constexpr bool use_mmap = false;
+#endif
+
+    llama_model_loader ml(fname_inp, use_mmap);
+    if (ml.use_mmap) {
+        ml.mapping.reset(new llama_mmap(&ml.file, /* prefetch */ 0, ggml_is_numa()));
+    }
+
+    llama_model model;
+    llm_load_arch(ml, model);
+    llm_load_hparams(ml, model);
+
+    if (params->only_copy) {
+        ftype = model.ftype;
+    }
+
+    const size_t align = GGUF_DEFAULT_ALIGNMENT;
+    struct gguf_context * ctx_out = gguf_init_empty();
+
+    // copy the KV pairs from the input file
+    gguf_set_kv     (ctx_out, ml.ctx_gguf);
+    gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
+    gguf_set_val_u32(ctx_out, "general.file_type", ftype);
+
+#ifdef GGML_USE_K_QUANTS
+    int n_attention_wv    = 0;
+    int n_feed_forward_w2 = 0;
+
+    for (int i = 0; i < ml.n_tensors; ++i) {
+        struct ggml_tensor * meta = ml.get_tensor_meta(i);
+
+        const std::string name = ggml_get_name(meta);
+
+        // TODO: avoid hardcoded tensor names - use the TN_* constants
+        if (name.find("attn_v.weight") != std::string::npos || name.find("attn_qkv.weight") != std::string::npos) {
+            ++n_attention_wv;
+        }
+        else if (name.find("ffn_down.weight") != std::string::npos) {
+            ++n_feed_forward_w2;
+        }
+    }
+    if (n_attention_wv != n_feed_forward_w2 || (uint32_t)n_attention_wv != model.hparams.n_layer) {
+        LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n",
+                __func__, n_attention_wv, n_feed_forward_w2, model.hparams.n_layer);
+    }
+
+    int i_attention_wv = 0;
+    int i_feed_forward_w2 = 0;
+#endif
+
+    size_t total_size_org = 0;
+    size_t total_size_new = 0;
+    std::vector<int64_t> hist_all(1 << 4, 0);
+
+    std::vector<std::thread> workers;
+    workers.reserve(nthread);
+    std::mutex mutex;
+
+    int idx = 0;
+
+    std::vector<no_init<uint8_t>> read_data;
+    std::vector<no_init<uint8_t>> work;
+    std::vector<no_init<float>> f32_conv_buf;
+
+    // populate the original tensors so we get an initial meta data
+    for (int i = 0; i < ml.n_tensors; ++i) {
+        struct ggml_tensor * meta = ml.get_tensor_meta(i);
+        gguf_add_tensor(ctx_out, meta);
+    }
+
+    std::ofstream fout(fname_out, std::ios::binary);
+    fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+
+    const size_t meta_size = gguf_get_meta_size(ctx_out);
+
+    LLAMA_LOG_INFO("%s: meta size = %zu bytes\n", __func__, meta_size);
+
+    // placeholder for the meta data
+    ::zeros(fout, meta_size);
+
+    for (int i = 0; i < ml.n_tensors; ++i) {
+        struct ggml_tensor * tensor = ml.get_tensor_meta(i);
+
+        const std::string name = ggml_get_name(tensor);
+
+        if (!ml.use_mmap) {
+            if (read_data.size() < ggml_nbytes(tensor)) {
+                read_data.resize(ggml_nbytes(tensor));
+            }
+            tensor->data = read_data.data();
+        }
+        ml.load_data_for(tensor);
+
+        LLAMA_LOG_INFO("[%4d/%4d] %36s - [%s], type = %6s, ",
+               ++idx, ml.n_tensors,
+               ggml_get_name(tensor),
+               llama_format_tensor_shape(tensor).c_str(),
+               ggml_type_name(tensor->type));
+
+        // This used to be a regex, but <regex> has an extreme cost to compile times.
+        bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
+
+        // quantize only 2D tensors
+        quantize &= (tensor->n_dims == 2);
+        quantize &= params->quantize_output_tensor || name != "output.weight";
+        quantize &= !params->only_copy;
+
+        enum ggml_type new_type;
+        void * new_data;
+        size_t new_size;
+
+        if (quantize) {
+            new_type = quantized_type;
+#ifdef GGML_USE_K_QUANTS
+            new_type = get_k_quant_type(
+                new_type, tensor, model, ftype, &i_attention_wv, n_attention_wv, &i_feed_forward_w2, n_feed_forward_w2
+            );
+#endif
+            // If we've decided to quantize to the same type the tensor is already
+            // in then there's nothing to do.
+            quantize = tensor->type != new_type;
+        }
+        if (!quantize) {
+            new_type = tensor->type;
+            new_data = tensor->data;
+            new_size = ggml_nbytes(tensor);
+            LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0);
+        } else {
+            const size_t nelements = ggml_nelements(tensor);
+
+            float * f32_data;
+
+            if (tensor->type == GGML_TYPE_F32) {
+                f32_data = (float *) tensor->data;
+            } else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
+                throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
+            } else {
+                llama_convert_tensor_internal(tensor, f32_conv_buf, workers, nelements, nthread);
+                f32_data = (float *) f32_conv_buf.data();
+            }
+
+            LLAMA_LOG_INFO("quantizing to %s .. ", ggml_type_name(new_type));
+            fflush(stdout);
+
+            if (work.size() < nelements * 4) {
+                work.resize(nelements * 4); // upper bound on size
+            }
+            new_data = work.data();
+            std::array<int64_t, 1 << 4> hist_cur = {};
+
+            static const int chunk_size = 32 * 512;
+            const int nchunk = (nelements + chunk_size - 1)/chunk_size;
+            const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
+            if (nthread_use < 2) {
+                new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nelements, hist_cur.data());
+            } else {
+                size_t counter = 0;
+                new_size = 0;
+                auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements]() {
+                    std::array<int64_t, 1 << 4> local_hist = {};
+                    size_t local_size = 0;
+                    while (true) {
+                        std::unique_lock<std::mutex> lock(mutex);
+                        size_t first = counter; counter += chunk_size;
+                        if (first >= nelements) {
+                            if (local_size > 0) {
+                                for (int j=0; j<int(local_hist.size()); ++j) {
+                                    hist_cur[j] += local_hist[j];
+                                }
+                                new_size += local_size;
+                            }
+                            break;
+                        }
+                        lock.unlock();
+                        size_t last = std::min(nelements, first + chunk_size);
+                        local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first, last - first, local_hist.data());
+                    }
+                };
+                for (int it = 0; it < nthread_use - 1; ++it) {
+                    workers.emplace_back(compute);
+                }
+                compute();
+                for (auto & w : workers) { w.join(); }
+                workers.clear();
+            }
+
+            LLAMA_LOG_INFO("size = %8.2f MB -> %8.2f MB | hist: ", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
+            int64_t tot_count = 0;
+            for (size_t i = 0; i < hist_cur.size(); i++) {
+                hist_all[i] += hist_cur[i];
+                tot_count += hist_cur[i];
+            }
+
+            if (tot_count > 0) {
+                for (size_t i = 0; i < hist_cur.size(); i++) {
+                    LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements));
+                }
+            }
+            LLAMA_LOG_INFO("\n");
+        }
+        total_size_org += ggml_nbytes(tensor);
+        total_size_new += new_size;
+
+        // update the gguf meta data as we go
+        gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
+        gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size);
+
+        // write tensor data + padding
+        fout.write((const char *) new_data, new_size);
+        zeros(fout, GGML_PAD(new_size, align) - new_size);
+    }
+
+    // go back to beginning of file and write the updated meta data
+    {
+        fout.seekp(0);
+        std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
+        gguf_get_meta_data(ctx_out, data.data());
+        fout.write((const char *) data.data(), data.size());
+    }
+
+    fout.close();
+
+    gguf_free(ctx_out);
+
+    LLAMA_LOG_INFO("%s: model size  = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
+    LLAMA_LOG_INFO("%s: quant size  = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
+
+    // print histogram for all tensors
+    {
+        int64_t sum_all = 0;
+        for (size_t i = 0; i < hist_all.size(); i++) {
+            sum_all += hist_all[i];
+        }
+
+        if (sum_all > 0) {
+            LLAMA_LOG_INFO("%s: hist: ", __func__);
+            for (size_t i = 0; i < hist_all.size(); i++) {
+                LLAMA_LOG_INFO("%5.3f ", hist_all[i] / float(sum_all));
+            }
+            LLAMA_LOG_INFO("\n");
+        }
+    }
+}
+
+static int llama_apply_lora_from_file_internal(
+    const struct llama_model & model, const char * path_lora, float scale, const char * path_base_model, int n_threads
+) {
+    LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
+
+    const int64_t t_start_lora_us = ggml_time_us();
+
+    auto fin = std::ifstream(path_lora, std::ios::binary);
+    if (!fin) {
+        LLAMA_LOG_ERROR("%s: failed to open '%s'\n", __func__, path_lora);
+        return 1;
+    }
+
+    // verify magic and version
+    {
+        uint32_t magic;
+        fin.read((char *) &magic, sizeof(magic));
+        uint32_t format_version;
+        fin.read((char *) &format_version, sizeof(format_version));
+
+        if (format_version != 1) {
+            LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
+            return 1;
+        }
+    }
+
+    int32_t lora_r;
+    int32_t lora_alpha;
+    fin.read((char *) &lora_r, sizeof(lora_r));
+    fin.read((char *) &lora_alpha, sizeof(lora_alpha));
+    float scaling = scale * (float)lora_alpha / (float)lora_r;
+
+    LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
+
+    // create a temporary ggml context to store the lora tensors
+    // todo: calculate size from biggest possible tensor
+    std::vector<uint8_t> lora_buf(1024ull * 1024ull * 1024ull);
+    struct ggml_init_params params;
+    params.mem_size   = lora_buf.size();
+    params.mem_buffer = lora_buf.data();
+    params.no_alloc   = false;
+
+    ggml_context * lora_ctx = ggml_init(params);
+    std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
+
+    // create a name -> tensor map of the model to accelerate lookups
+    std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
+    for (const auto & kv : model.tensors_by_name) {
+        model_tensors.insert(kv);
+    }
+
+    // load base model
+    std::unique_ptr<llama_model_loader> ml;
+    ggml_context * base_ctx = NULL;
+    std::vector<uint8_t> base_buf;
+    if (path_base_model) {
+        LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
+        ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true));
+
+        size_t ctx_size;
+        size_t mmapped_size;
+        ml->calc_sizes(ctx_size, mmapped_size);
+        base_buf.resize(ctx_size);
+
+        ggml_init_params base_params;
+        base_params.mem_size   = base_buf.size();
+        base_params.mem_buffer = base_buf.data();
+        base_params.no_alloc   = ml->use_mmap;
+
+        base_ctx = ggml_init(base_params);
+
+        // maybe this should in llama_model_loader
+        if (ml->use_mmap) {
+            ml->mapping.reset(new llama_mmap(&ml->file, /* prefetch */ 0, ggml_is_numa()));
+        }
+    }
+
+    // read tensors and apply
+    bool warned = false;
+    int n_tensors = 0;
+
+    std::vector<uint8_t> work_buffer;
+
+    while (true) {
+        int32_t n_dims;
+        int32_t length;
+        int32_t ftype;
+
+        fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+        fin.read(reinterpret_cast<char *>(&length), sizeof(length));
+        fin.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
+        if (fin.eof()) {
+            break;
+        }
+
+        int32_t ne[2] = { 1, 1 };
+        for (int i = 0; i < n_dims; ++i) {
+            fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
+        }
+
+        std::string name;
+        {
+            char buf[1024];
+            fin.read(buf, length);
+            name = std::string(buf, length);
+        }
+
+        // check for lora suffix and get the type of tensor
+        const std::string lora_suffix = ".lora";
+        size_t pos = name.rfind(lora_suffix);
+        if (pos == std::string::npos) {
+            LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
+            return 1;
+        }
+
+        std::string lora_type = name.substr(pos + lora_suffix.length());
+        std::string base_name = name;
+        base_name.erase(pos);
+        // LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(),base_name.c_str(), lora_type.c_str());
+
+        if (model_tensors.find(base_name) == model_tensors.end()) {
+            LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
+            return 1;
+        }
+
+        // create ggml tensor
+        ggml_type wtype;
+        switch (ftype) {
+            case 0: wtype = GGML_TYPE_F32;  break;
+            case 1: wtype = GGML_TYPE_F16;  break;
+            default:
+                    {
+                        LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n",
+                                __func__, ftype);
+                        return false;
+                    }
+        }
+        ggml_tensor * lora_tensor;
+        if (n_dims == 2) {
+            lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]);
+        }
+        else {
+            LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
+            return 1;
+        }
+        ggml_set_name(lora_tensor, "lora_tensor");
+
+        // load tensor data
+        size_t offset = fin.tellg();
+        size_t tensor_data_size = ggml_nbytes(lora_tensor);
+        offset = (offset + 31) & -32;
+        fin.seekg(offset);
+        fin.read((char*)lora_tensor->data, tensor_data_size);
+
+        lora_tensors[name] = lora_tensor;
+
+        // check if we have both A and B tensors and apply
+        if (lora_tensors.find(base_name + ".loraA") != lora_tensors.end() &&
+            lora_tensors.find(base_name + ".loraB") != lora_tensors.end()) {
+
+            ggml_tensor * dest_t = model_tensors[base_name];
+
+            offload_func_t offload_func = llama_nop;
+            offload_func_t offload_func_force_inplace = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+            if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) {
+                if (dest_t->type != GGML_TYPE_F16) {
+                    throw std::runtime_error(format(
+                        "%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models", __func__));
+                }
+                offload_func = ggml_cuda_assign_buffers;
+                offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace;
+            }
+#endif // GGML_USE_CUBLAS
+
+            ggml_tensor * base_t;
+            if (ml) {
+                struct gguf_context * ctx_gguf = ml->ctx_gguf;
+
+                // load from base model
+                if (gguf_find_tensor(ctx_gguf, base_name.c_str()) < 0) {
+                    // TODO: throw
+                    LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
+                    return 1;
+                }
+
+                // TODO: not tested!! maybe not working!
+                base_t = ml->create_tensor(base_ctx, base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
+                ml->load_data_for(base_t);
+            } else {
+                base_t = dest_t;
+            }
+
+            if (ggml_is_quantized(base_t->type)) {
+                if (!warned) {
+                    LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, "
+                                   "use a f16 or f32 base model with --lora-base\n", __func__);
+                    warned = true;
+                }
+            }
+
+            ggml_tensor * loraA = lora_tensors[base_name + ".loraA"];
+            GGML_ASSERT(loraA->type == GGML_TYPE_F32);
+            ggml_set_name(loraA, "loraA");
+
+            ggml_tensor * loraB = lora_tensors[base_name + ".loraB"];
+            GGML_ASSERT(loraB->type == GGML_TYPE_F32);
+            ggml_set_name(loraB, "loraB");
+
+            if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
+                LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
+                                " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
+                return 1;
+            }
+
+            // w = w + BA*s
+            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
+            offload_func(BA);
+            ggml_set_name(BA, "BA");
+
+            if (scaling != 1.0f) {
+                ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
+                ggml_set_name(scale_tensor, "scale_tensor");
+
+                BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor);
+                offload_func(BA);
+                ggml_set_name(BA, "BA_scaled");
+            }
+
+            ggml_tensor * r;
+            if (base_t == dest_t) {
+                r = ggml_add_inplace(lora_ctx, dest_t, BA);
+                offload_func_force_inplace(r);
+                ggml_set_name(r, "r_add_inplace");
+            }
+            else {
+                r = ggml_add(lora_ctx, base_t, BA);
+                offload_func(r);
+                ggml_set_name(r, "r_add");
+
+                r = ggml_cpy(lora_ctx, r, dest_t);
+                offload_func(r);
+                ggml_set_name(r, "r_cpy");
+            }
+
+            struct ggml_cgraph * gf = ggml_new_graph(lora_ctx);
+            ggml_build_forward_expand(gf, r);
+
+            ggml_graph_compute_helper(work_buffer, gf, n_threads);
+
+            // we won't need these tensors again, reset the context to save memory
+            ggml_free(lora_ctx);
+            lora_ctx = ggml_init(params);
+            lora_tensors.clear();
+
+            n_tensors++;
+            if (n_tensors % 4 == 0) {
+                LLAMA_LOG_INFO(".");
+            }
+        }
+    }
+
+    // TODO: this should be in a destructor, it will leak on failure
+    ggml_free(lora_ctx);
+    if (base_ctx) {
+        ggml_free(base_ctx);
+    }
+
+    const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
+    LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);
+
+    return 0;
+}
+
+//
+// interface implementation
+//
+struct llama_model_params llama_model_default_params() {
+    struct llama_model_params result = {
+        /*.n_gpu_layers                =*/ 0,
+        /*.main_gpu                    =*/ 0,
+        /*.tensor_split                =*/ nullptr,
+        /*.progress_callback           =*/ nullptr,
+        /*.progress_callback_user_data =*/ nullptr,
+        /*.vocab_only                  =*/ false,
+        /*.use_mmap                    =*/ true,
+        /*.use_mlock                   =*/ false,
+    };
+
+#ifdef GGML_USE_METAL
+    result.n_gpu_layers = 1;
+#endif
+
+    return result;
+}
+
+struct llama_context_params llama_context_default_params() {
+    struct llama_context_params result = {
+        /*.seed                        =*/ LLAMA_DEFAULT_SEED,
+        /*.n_ctx                       =*/ 512,
+        /*.n_batch                     =*/ 512,
+        /*.n_threads                   =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
+        /*.n_threads_batch             =*/ GGML_DEFAULT_N_THREADS,
+        /*.rope_freq_base              =*/ 0.0f,
+        /*.rope_freq_scale             =*/ 0.0f,
+        /*.mul_mat_q                   =*/ true,
+        /*.f16_kv                      =*/ true,
+        /*.logits_all                  =*/ false,
+        /*.embedding                   =*/ false,
+    };
+
+    return result;
+}
+
+struct llama_model_quantize_params llama_model_quantize_default_params() {
+    struct llama_model_quantize_params result = {
+        /*.nthread                     =*/ 0,
+        /*.ftype                       =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
+        /*.allow_requantize            =*/ false,
+        /*.quantize_output_tensor      =*/ true,
+        /*.only_copy                   =*/ false,
+    };
+
+    return result;
+}
+
+int llama_max_devices(void) {
+    return LLAMA_MAX_DEVICES;
+}
+
+bool llama_mmap_supported(void) {
+    return llama_mmap::SUPPORTED;
+}
+
+bool llama_mlock_supported(void) {
+    return llama_mlock::SUPPORTED;
+}
+
+void llama_backend_init(bool numa) {
+    ggml_time_init();
+
+    // needed to initialize f16 tables
+    {
+        struct ggml_init_params params = { 0, NULL, false };
+        struct ggml_context * ctx = ggml_init(params);
+        ggml_free(ctx);
+    }
+
+    if (numa) {
+        ggml_numa_init();
+    }
+
+#ifdef GGML_USE_MPI
+    ggml_mpi_backend_init();
+#endif
+}
+
+void llama_backend_free(void) {
+#ifdef GGML_USE_MPI
+    ggml_mpi_backend_free();
+#endif
+}
+
+int64_t llama_time_us(void) {
+    return ggml_time_us();
+}
+
+struct llama_model * llama_load_model_from_file(
+                             const char * path_model,
+              struct llama_model_params   params) {
+    ggml_time_init();
+
+    llama_model * model = new llama_model;
+
+    unsigned cur_percentage = 0;
+    if (params.progress_callback == NULL) {
+        params.progress_callback_user_data = &cur_percentage;
+        params.progress_callback = [](float progress, void * ctx) {
+            unsigned * cur_percentage_p = (unsigned *) ctx;
+            unsigned percentage = (unsigned) (100 * progress);
+            while (percentage > *cur_percentage_p) {
+                *cur_percentage_p = percentage;
+                LLAMA_LOG_INFO(".");
+                if (percentage >= 100) {
+                    LLAMA_LOG_INFO("\n");
+                }
+            }
+        };
+    }
+
+    if (!llama_model_load(path_model, *model, params.n_gpu_layers,
+                params.main_gpu, params.tensor_split,
+                params.use_mmap, params.use_mlock, params.vocab_only,
+                params.progress_callback, params.progress_callback_user_data)) {
+        LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
+        delete model;
+        return nullptr;
+    }
+
+    return model;
+}
+
+void llama_free_model(struct llama_model * model) {
+    delete model;
+}
+
+struct llama_context * llama_new_context_with_model(
+                 struct llama_model * model,
+        struct llama_context_params   params) {
+
+    if (!model) {
+        return nullptr;
+    }
+
+    llama_context * ctx = new llama_context(*model);
+
+    const auto & hparams = model->hparams;
+    auto       & cparams = ctx->cparams;
+
+    cparams.n_batch         = params.n_batch;
+    cparams.n_ctx           = params.n_ctx == 0           ? hparams.n_ctx_train           : params.n_ctx;
+    cparams.rope_freq_base  = params.rope_freq_base == 0  ? hparams.rope_freq_base_train  : params.rope_freq_base;
+    cparams.rope_freq_scale = params.rope_freq_scale == 0 ? hparams.rope_freq_scale_train : params.rope_freq_scale;
+    cparams.n_threads       = params.n_threads;
+    cparams.n_threads_batch = params.n_threads_batch;
+    cparams.mul_mat_q       = params.mul_mat_q;
+
+    if (params.seed == LLAMA_DEFAULT_SEED) {
+        params.seed = time(NULL);
+    }
+
+    LLAMA_LOG_INFO("%s: n_ctx      = %u\n",     __func__, cparams.n_ctx);
+    LLAMA_LOG_INFO("%s: freq_base  = %.1f\n",   __func__, cparams.rope_freq_base);
+    LLAMA_LOG_INFO("%s: freq_scale = %g\n",     __func__, cparams.rope_freq_scale);
+
+    ctx->rng = std::mt19937(params.seed);
+    ctx->logits_all = params.logits_all;
+
+    ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
+
+    // reserve memory for context buffers
+    if (!hparams.vocab_only) {
+        if (!llama_kv_cache_init(ctx->model.hparams, ctx->kv_self, memory_type, cparams.n_ctx, model->n_gpu_layers)) {
+            LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
+            llama_free(ctx);
+            return nullptr;
+        }
+
+        {
+            const size_t memory_size = ggml_nbytes(ctx->kv_self.k) + ggml_nbytes(ctx->kv_self.v);
+            LLAMA_LOG_INFO("%s: kv self size  = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
+        }
+
+        // resized during inference
+        if (params.logits_all) {
+            ctx->logits.reserve(cparams.n_ctx*hparams.n_vocab);
+        } else {
+            ctx->logits.reserve(hparams.n_vocab);
+        }
+
+        if (params.embedding){
+            ctx->embedding.resize(hparams.n_embd);
+        }
+
+        {
+            static const size_t tensor_alignment = 32;
+            // the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data
+            ctx->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead());
+
+            // create measure allocator
+            ctx->alloc = ggml_allocr_new_measure(tensor_alignment);
+
+            // build worst-case graph
+            int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_batch);
+            int n_past = cparams.n_ctx - n_tokens;
+            llama_token token = llama_token_bos(ctx); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
+            ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0));
+
+#ifdef GGML_USE_METAL
+            if (model->n_gpu_layers > 0) {
+                ggml_metal_log_set_callback(llama_log_callback_default, NULL);
+
+                ctx->ctx_metal = ggml_metal_init(1);
+                if (!ctx->ctx_metal) {
+                    LLAMA_LOG_ERROR("%s: ggml_metal_init() failed\n", __func__);
+                    llama_free(ctx);
+                    return NULL;
+                }
+                //ggml_metal_graph_find_concurrency(ctx->ctx_metal, gf, false);
+                //ggml_allocr_set_parse_seq(ctx->alloc, ggml_metal_get_concur_list(ctx->ctx_metal), ggml_metal_if_optimized(ctx->ctx_metal));
+            }
+#endif
+            // measure memory requirements for the graph
+            size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment;
+
+            LLAMA_LOG_INFO("%s: compute buffer total size = %.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0);
+
+            // recreate allocator with exact memory requirements
+            ggml_allocr_free(ctx->alloc);
+
+            ctx->buf_alloc.resize(alloc_size);
+            ctx->alloc = ggml_allocr_new(ctx->buf_alloc.data, ctx->buf_alloc.size, tensor_alignment);
+#ifdef GGML_USE_METAL
+            if (ctx->ctx_metal) {
+                //ggml_allocr_set_parse_seq(ctx->alloc, ggml_metal_get_concur_list(ctx->ctx_metal), ggml_metal_if_optimized(ctx->ctx_metal));
+            }
+#endif
+#ifdef GGML_USE_CUBLAS
+            ggml_cuda_set_scratch_size(alloc_size);
+            LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MB\n", __func__, alloc_size / 1024.0 / 1024.0);
+
+            // calculate total VRAM usage
+            auto add_tensor = [](const ggml_tensor * t, size_t & size) {
+                if (t->backend == GGML_BACKEND_GPU || t->backend == GGML_BACKEND_GPU_SPLIT) {
+                    size += ggml_nbytes(t);
+                }
+            };
+            size_t model_vram_size = 0;
+            for (const auto & kv : model->tensors_by_name) {
+                add_tensor(kv.second, model_vram_size);
+            }
+
+            size_t kv_vram_size = 0;
+            add_tensor(ctx->kv_self.k, kv_vram_size);
+            add_tensor(ctx->kv_self.v, kv_vram_size);
+
+            size_t ctx_vram_size = alloc_size + kv_vram_size;
+            size_t total_vram_size = model_vram_size + ctx_vram_size;
+
+            LLAMA_LOG_INFO("%s: total VRAM used: %.2f MB (model: %.2f MB, context: %.2f MB)\n", __func__,
+                    total_vram_size / 1024.0 / 1024.0,
+                    model_vram_size / 1024.0 / 1024.0,
+                    ctx_vram_size / 1024.0 / 1024.0);
+#endif
+        }
+
+#ifdef GGML_USE_METAL
+        if (model->n_gpu_layers > 0) {
+            // this allocates all Metal resources and memory buffers
+
+            void * data_ptr  = NULL;
+            size_t data_size = 0;
+
+            if (ctx->model.mapping) {
+                data_ptr  = ctx->model.mapping->addr;
+                data_size = ctx->model.mapping->size;
+            } else {
+                data_ptr  = ggml_get_mem_buffer(ctx->model.ctx);
+                data_size = ggml_get_mem_size  (ctx->model.ctx);
+            }
+
+            const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx);
+
+            LLAMA_LOG_INFO("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0);
+
+#define LLAMA_METAL_CHECK_BUF(result)                            \
+            if (!(result)) {                                             \
+                LLAMA_LOG_ERROR("%s: failed to add buffer\n", __func__); \
+                llama_free(ctx);                                         \
+                return NULL;                                             \
+            }
+
+            LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data",  data_ptr, data_size, max_size));
+            LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv",    ctx->kv_self.buf.data, ctx->kv_self.buf.size, 0));
+            LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "alloc", ctx->buf_alloc.data, ctx->buf_alloc.size, 0));
+#undef LLAMA_METAL_CHECK_BUF
+        }
+#endif
+    }
+
+#ifdef GGML_USE_MPI
+    ctx->ctx_mpi = ggml_mpi_init();
+
+    if (ggml_mpi_rank(ctx->ctx_mpi) > 0) {
+        // Enter a blocking eval loop with dummy input, letting rank=0 drive the process
+        // TODO: needs fix after #3228
+        GGML_ASSERT(false && "not implemented");
+        //const std::vector<llama_token> tmp(ctx->model.hparams.n_ctx, llama_token_bos(ctx));
+        //while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {};
+        llama_backend_free();
+        exit(1);
+    }
+#endif
+
+    return ctx;
+}
+
+void llama_free(struct llama_context * ctx) {
+    delete ctx;
+}
+
+const llama_model * llama_get_model(const struct llama_context * ctx) {
+    return &ctx->model;
+}
+
+int llama_n_ctx(const struct llama_context * ctx) {
+    return ctx->cparams.n_ctx;
+}
+
+enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
+    return model->vocab.type;
+}
+
+int llama_n_vocab(const struct llama_model * model) {
+    return model->vocab.id_to_token.size();
+}
+
+int llama_n_ctx_train(const struct llama_model * model) {
+    return model->hparams.n_ctx_train;
+}
+
+int llama_n_embd(const struct llama_model * model) {
+    return model->hparams.n_embd;
+}
+
+float llama_rope_freq_scale_train(const struct llama_model * model) {
+    return model->hparams.rope_freq_scale_train;
+}
+
+int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
+    return snprintf(buf, buf_size, "%s %s %s",
+            llama_model_arch_name(model->arch).c_str(),
+            llama_model_type_name(model->type),
+            llama_model_ftype_name(model->ftype).c_str());
+}
+
+uint64_t llama_model_size(const struct llama_model * model) {
+    uint64_t size = 0;
+    for (const auto & it : model->tensors_by_name) {
+        size += ggml_nbytes(it.second);
+    }
+    return size;
+}
+
+uint64_t llama_model_n_params(const struct llama_model * model) {
+    uint64_t nparams = 0;
+    for (const auto & it : model->tensors_by_name) {
+        nparams += ggml_nelements(it.second);
+    }
+    return nparams;
+}
+
+struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name) {
+    return ggml_get_tensor(model->ctx, name);
+}
+
+int llama_model_quantize(
+        const char * fname_inp,
+        const char * fname_out,
+        const llama_model_quantize_params * params) {
+    try {
+        llama_model_quantize_internal(fname_inp, fname_out, params);
+        return 0;
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
+        return 1;
+    }
+}
+
+int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int n_threads) {
+    try {
+        return llama_apply_lora_from_file_internal(ctx->model, path_lora, scale, path_base_model, n_threads);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
+        return 1;
+    }
+}
+
+int llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int n_threads) {
+    try {
+        return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
+        return 1;
+    }
+}
+
+int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
+    return ctx->kv_self.head;
+}
+
+void llama_kv_cache_tokens_rm(struct llama_context * ctx, int32_t c0, int32_t c1) {
+    llama_kv_cache_tokens_rm(ctx->kv_self, c0, c1);
+}
+
+void llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+    llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1);
+}
+
+void llama_kv_cache_seq_cp(struct llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
+    if (seq_id_src == seq_id_dst) {
+        return;
+    }
+    llama_kv_cache_seq_cp(ctx->kv_self, seq_id_src, seq_id_dst, p0, p1);
+}
+
+void llama_kv_cache_seq_keep(struct llama_context * ctx, llama_seq_id seq_id) {
+    llama_kv_cache_seq_keep(ctx->kv_self, seq_id);
+}
+
+void llama_kv_cache_seq_shift(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
+    llama_kv_cache_seq_shift(ctx->kv_self, seq_id, p0, p1, delta);
+}
+
+// Returns the *maximum* size of the state
+size_t llama_get_state_size(const struct llama_context * ctx) {
+    // we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
+    // for reference, std::mt19937(1337) serializes to 6701 bytes.
+    const size_t s_rng_size        = sizeof(size_t);
+    const size_t s_rng             = LLAMA_MAX_RNG_STATE;
+    const size_t s_logits_capacity = sizeof(size_t);
+    const size_t s_logits_size     = sizeof(size_t);
+    const size_t s_logits          = ctx->logits.capacity() * sizeof(float);
+    const size_t s_embedding_size  = sizeof(size_t);
+    const size_t s_embedding       = ctx->embedding.size() * sizeof(float);
+    const size_t s_kv_size         = sizeof(size_t);
+    const size_t s_kv_ntok         = sizeof(int);
+    const size_t s_kv              = ctx->kv_self.buf.size;
+
+    const size_t s_total = (
+        + s_rng_size
+        + s_rng
+        + s_logits_capacity
+        + s_logits_size
+        + s_logits
+        + s_embedding_size
+        + s_embedding
+        + s_kv_size
+        + s_kv_ntok
+        + s_kv
+    );
+
+    return s_total;
+}
+
+// llama_context_data
+struct llama_data_context {
+    virtual void write(const void * src, size_t size) = 0;
+    virtual size_t get_size_written() = 0;
+    virtual ~llama_data_context() = default;
+};
+
+struct llama_data_buffer_context : llama_data_context {
+    uint8_t * ptr;
+    size_t size_written = 0;
+
+    llama_data_buffer_context(uint8_t * p) : ptr(p) {}
+
+    void write(const void * src, size_t size) override {
+        memcpy(ptr, src, size);
+        ptr += size;
+        size_written += size;
+    }
+
+    size_t get_size_written() override {
+        return size_written;
+    }
+};
+
+struct llama_data_file_context : llama_data_context {
+    llama_file * file;
+    size_t size_written = 0;
+
+    llama_data_file_context(llama_file * f) : file(f) {}
+
+    void write(const void * src, size_t size) override {
+        file->write_raw(src, size);
+        size_written += size;
+    }
+
+    size_t get_size_written() override {
+        return size_written;
+    }
+};
+
+/** copy state data into either a buffer or file depending on the passed in context
+ *
+ * file context:
+ * llama_file file("/path", "wb");
+ * llama_data_file_context data_ctx(&file);
+ * llama_copy_state_data(ctx, &data_ctx);
+ *
+ * buffer context:
+ * std::vector<uint8_t> buf(max_size, 0);
+ * llama_data_buffer_context data_ctx(&buf.data());
+ * llama_copy_state_data(ctx, &data_ctx);
+ *
+*/
+static void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
+    // copy rng
+    {
+        std::stringstream rng_ss;
+        rng_ss << ctx->rng;
+
+        const size_t rng_size = rng_ss.str().size();
+        char rng_buf[LLAMA_MAX_RNG_STATE];
+
+        memset(&rng_buf[0], 0, LLAMA_MAX_RNG_STATE);
+        memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
+
+        data_ctx->write(&rng_size,   sizeof(rng_size));
+        data_ctx->write(&rng_buf[0], LLAMA_MAX_RNG_STATE);
+    }
+
+    // copy logits
+    {
+        const size_t logits_cap  = ctx->logits.capacity();
+        const size_t logits_size = ctx->logits.size();
+
+        data_ctx->write(&logits_cap,  sizeof(logits_cap));
+        data_ctx->write(&logits_size, sizeof(logits_size));
+
+        if (logits_size) {
+            data_ctx->write(ctx->logits.data(), logits_size * sizeof(float));
+        }
+
+        // If there is a gap between the size and the capacity, write padding
+        size_t padding_size = (logits_cap - logits_size) * sizeof(float);
+        if (padding_size > 0) {
+            std::vector<uint8_t> padding(padding_size, 0); // Create a buffer filled with zeros
+            data_ctx->write(padding.data(), padding_size);
+        }
+    }
+
+    // copy embeddings
+    {
+        const size_t embedding_size = ctx->embedding.size();
+
+        data_ctx->write(&embedding_size, sizeof(embedding_size));
+
+        if (embedding_size) {
+            data_ctx->write(ctx->embedding.data(), embedding_size * sizeof(float));
+        }
+    }
+
+    // copy kv cache
+    {
+        const auto & kv_self = ctx->kv_self;
+        const auto & hparams = ctx->model.hparams;
+        const auto & cparams = ctx->cparams;
+
+        const auto   n_layer = hparams.n_layer;
+        const auto   n_embd  = hparams.n_embd_gqa();
+        const auto   n_ctx   = cparams.n_ctx;
+
+        const size_t   kv_buf_size = kv_self.buf.size;
+        const uint32_t kv_head     = kv_self.head;
+        const uint32_t kv_size     = kv_self.size;
+
+        data_ctx->write(&kv_buf_size, sizeof(kv_buf_size));
+        data_ctx->write(&kv_head,     sizeof(kv_head));
+        data_ctx->write(&kv_size,     sizeof(kv_size));
+
+        if (kv_buf_size) {
+            const size_t elt_size = ggml_element_size(kv_self.k);
+
+            ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true });
+            ggml_cgraph gf{};
+
+            ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer);
+            std::vector<uint8_t> kout3d_data(ggml_nbytes(kout3d), 0);
+            kout3d->data = kout3d_data.data();
+
+            ggml_tensor * vout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_head, n_embd, n_layer);
+            std::vector<uint8_t> vout3d_data(ggml_nbytes(vout3d), 0);
+            vout3d->data = vout3d_data.data();
+
+            ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
+                n_embd, kv_head, n_layer,
+                elt_size*n_embd, elt_size*n_embd*n_ctx, 0);
+
+            ggml_tensor * v3d = ggml_view_3d(cpy_ctx, kv_self.v,
+                kv_head, n_embd, n_layer,
+                elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);
+
+            ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d));
+            ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, v3d, vout3d));
+            ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1);
+
+            ggml_free(cpy_ctx);
+
+            // our data is now in the kout3d_data and vout3d_data buffers
+            // write them to file
+            data_ctx->write(kout3d_data.data(), kout3d_data.size());
+            data_ctx->write(vout3d_data.data(), vout3d_data.size());
+        }
+
+        for (uint32_t i = 0; i < kv_size; ++i) {
+            const auto & cell = kv_self.cells[i];
+
+            const llama_pos pos         = cell.pos;
+            const size_t    seq_id_size = cell.seq_id.size();
+
+            data_ctx->write(&pos,         sizeof(pos));
+            data_ctx->write(&seq_id_size, sizeof(seq_id_size));
+
+            for (auto seq_id : cell.seq_id) {
+                data_ctx->write(&seq_id, sizeof(seq_id));
+            }
+        }
+    }
+}
+
+size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
+    llama_data_buffer_context data_ctx(dst);
+    llama_copy_state_data_internal(ctx, &data_ctx);
+
+    return data_ctx.get_size_written();
+}
+
+// Sets the state reading from the specified source address
+size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
+    uint8_t * inp = src;
+
+    // set rng
+    {
+        size_t rng_size;
+        char   rng_buf[LLAMA_MAX_RNG_STATE];
+
+        memcpy(&rng_size,   inp, sizeof(rng_size));    inp += sizeof(rng_size);
+        memcpy(&rng_buf[0], inp, LLAMA_MAX_RNG_STATE); inp += LLAMA_MAX_RNG_STATE;
+
+        std::stringstream rng_ss;
+        rng_ss.str(std::string(&rng_buf[0], rng_size));
+        rng_ss >> ctx->rng;
+
+        GGML_ASSERT(!rng_ss.fail());
+    }
+
+    // set logits
+    {
+        size_t logits_cap;
+        size_t logits_size;
+
+        memcpy(&logits_cap,  inp, sizeof(logits_cap));  inp += sizeof(logits_cap);
+        memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);
+
+        GGML_ASSERT(ctx->logits.capacity() == logits_cap);
+
+        if (logits_size) {
+            ctx->logits.resize(logits_size);
+            memcpy(ctx->logits.data(), inp, logits_size * sizeof(float));
+        }
+
+        inp += logits_cap * sizeof(float);
+    }
+
+    // set embeddings
+    {
+        size_t embedding_size;
+
+        memcpy(&embedding_size, inp, sizeof(embedding_size)); inp += sizeof(embedding_size);
+
+        GGML_ASSERT(ctx->embedding.capacity() == embedding_size);
+
+        if (embedding_size) {
+            memcpy(ctx->embedding.data(), inp, embedding_size * sizeof(float));
+            inp += embedding_size * sizeof(float);
+        }
+    }
+
+    // set kv cache
+    {
+        const auto & kv_self = ctx->kv_self;
+        const auto & hparams = ctx->model.hparams;
+        const auto & cparams = ctx->cparams;
+
+        const int    n_layer = hparams.n_layer;
+        const int    n_embd  = hparams.n_embd_gqa();
+        const int    n_ctx   = cparams.n_ctx;
+
+        size_t   kv_buf_size;
+        uint32_t kv_head;
+        uint32_t kv_size;
+
+        memcpy(&kv_buf_size, inp, sizeof(kv_buf_size)); inp += sizeof(kv_buf_size);
+        memcpy(&kv_head,     inp, sizeof(kv_head));     inp += sizeof(kv_head);
+        memcpy(&kv_size,     inp, sizeof(kv_size));     inp += sizeof(kv_size);
+
+        if (kv_buf_size) {
+            GGML_ASSERT(kv_self.buf.size == kv_buf_size);
+
+            const size_t elt_size = ggml_element_size(kv_self.k);
+
+            ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true });
+            ggml_cgraph gf{};
+
+            ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer);
+            kin3d->data = (void *) inp;
+            inp += ggml_nbytes(kin3d);
+
+            ggml_tensor * vin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_head, n_embd, n_layer);
+            vin3d->data = (void *) inp;
+            inp += ggml_nbytes(vin3d);
+
+            ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
+                n_embd, kv_head, n_layer,
+                elt_size*n_embd, elt_size*n_embd*n_ctx, 0);
+
+            ggml_tensor * v3d = ggml_view_3d(cpy_ctx, kv_self.v,
+                kv_head, n_embd, n_layer,
+                elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);
+
+            ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, kin3d, k3d));
+            ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, vin3d, v3d));
+            ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1);
+
+            ggml_free(cpy_ctx);
+        }
+
+        ctx->kv_self.head = kv_head;
+        ctx->kv_self.size = kv_size;
+
+        ctx->kv_self.cells.resize(kv_size);
+
+        for (uint32_t i = 0; i < kv_size; ++i) {
+            llama_pos pos;
+            size_t    seq_id_size;
+
+            memcpy(&pos,         inp, sizeof(pos));         inp += sizeof(pos);
+            memcpy(&seq_id_size, inp, sizeof(seq_id_size)); inp += sizeof(seq_id_size);
+
+            ctx->kv_self.cells[i].pos = pos;
+
+            llama_seq_id seq_id;
+
+            for (size_t j = 0; j < seq_id_size; ++j) {
+                memcpy(&seq_id, inp, sizeof(seq_id)); inp += sizeof(seq_id);
+                ctx->kv_self.cells[i].seq_id.insert(seq_id);
+            }
+        }
+    }
+
+    const size_t nread    = inp - src;
+    const size_t max_size = llama_get_state_size(ctx);
+
+    GGML_ASSERT(nread <= max_size);
+
+    return nread;
+}
+
+static bool llama_load_session_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    llama_file file(path_session, "rb");
+
+    // sanity checks
+    {
+        const uint32_t magic   = file.read_u32();
+        const uint32_t version = file.read_u32();
+
+        if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
+            LLAMA_LOG_ERROR("%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
+            return false;
+        }
+
+        llama_hparams session_hparams;
+        file.read_raw(&session_hparams, sizeof(llama_hparams));
+
+        if (session_hparams != ctx->model.hparams) {
+            LLAMA_LOG_INFO("%s : model hparams didn't match from session file!\n", __func__);
+            return false;
+        }
+    }
+
+    // load the prompt
+    {
+        const uint32_t n_token_count = file.read_u32();
+
+        if (n_token_count > n_token_capacity) {
+            LLAMA_LOG_ERROR("%s : token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
+            return false;
+        }
+
+        file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
+        *n_token_count_out = n_token_count;
+    }
+
+    // restore the context state
+    {
+        const size_t n_state_size_cur = file.size - file.tell();
+        const size_t n_state_size_max = llama_get_state_size(ctx);
+
+        if (n_state_size_cur > n_state_size_max) {
+            LLAMA_LOG_ERROR("%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur);
+            return false;
+        }
+
+        std::vector<uint8_t> state_data(n_state_size_max);
+        file.read_raw(state_data.data(), n_state_size_cur);
+
+        llama_set_state_data(ctx, state_data.data());
+    }
+
+    return true;
+}
+
+bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    try {
+        return llama_load_session_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("error loading session file: %s\n", err.what());
+        return false;
+    }
+}
+
+bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+    llama_file file(path_session, "wb");
+
+    file.write_u32(LLAMA_SESSION_MAGIC);
+    file.write_u32(LLAMA_SESSION_VERSION);
+
+    file.write_raw(&ctx->model.hparams, sizeof(llama_hparams));
+
+    // save the prompt
+    file.write_u32((uint32_t) n_token_count);
+    file.write_raw(tokens, sizeof(llama_token) * n_token_count);
+
+    // save the context state using stream saving
+    llama_data_file_context data_ctx(&file);
+    llama_copy_state_data_internal(ctx, &data_ctx);
+
+    return true;
+}
+
+int llama_eval(
+        struct llama_context * ctx,
+                 llama_token * tokens,
+                     int32_t   n_tokens,
+                         int   n_past) {
+    llama_kv_cache_tokens_rm(ctx->kv_self, n_past, -1);
+
+    const int ret = llama_decode_internal(*ctx, llama_batch_get_one(tokens, n_tokens, n_past, 0));
+    if (ret < 0) {
+        LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
+    }
+
+    return ret;
+}
+
+int llama_eval_embd(
+            struct llama_context * ctx,
+                           float * embd,
+                         int32_t   n_tokens,
+                             int   n_past) {
+    llama_kv_cache_tokens_rm(ctx->kv_self, n_past, -1);
+
+    llama_batch batch = { n_tokens, nullptr, embd, nullptr, nullptr, nullptr, nullptr, n_past, 1, 0, };
+
+    const int ret = llama_decode_internal(*ctx, batch);
+    if (ret < 0) {
+        LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
+    }
+
+    return ret;
+}
+
+void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch) {
+    ctx->cparams.n_threads       = n_threads;
+    ctx->cparams.n_threads_batch = n_threads_batch;
+}
+
+struct llama_batch llama_batch_get_one(
+             llama_token * tokens,
+                 int32_t   n_tokens,
+               llama_pos   pos_0,
+            llama_seq_id   seq_id) {
+    return {
+        /*n_tokens       =*/ n_tokens,
+        /*tokens         =*/ tokens,
+        /*embd           =*/ nullptr,
+        /*pos            =*/ nullptr,
+        /*n_seq_id       =*/ nullptr,
+        /*seq_id         =*/ nullptr,
+        /*logits         =*/ nullptr,
+        /*all_pos_0      =*/ pos_0,
+        /*all_pos_1      =*/ 1,
+        /*all_seq_id     =*/ seq_id,
+    };
+}
+
+struct llama_batch llama_batch_init(int32_t n_tokens, int32_t embd, int32_t n_seq_max) {
+    llama_batch batch = { 0, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, };
+
+    if (embd) {
+        batch.embd = (float *) malloc(sizeof(float) * n_tokens * embd);
+    } else {
+        batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens);
+    }
+
+    batch.pos      = (llama_pos *)     malloc(sizeof(llama_pos)      * n_tokens);
+    batch.n_seq_id = (int32_t *)       malloc(sizeof(int32_t)        * n_tokens);
+    batch.seq_id   = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens);
+    for (int i = 0; i < n_tokens; ++i) {
+        batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
+    }
+    batch.logits   = (int8_t *)        malloc(sizeof(int8_t)         * n_tokens);
+
+    return batch;
+}
+
+void llama_batch_free(struct llama_batch batch) {
+    if (batch.token)    free(batch.token);
+    if (batch.embd)     free(batch.embd);
+    if (batch.pos)      free(batch.pos);
+    if (batch.n_seq_id) free(batch.n_seq_id);
+    if (batch.seq_id) {
+        for (int i = 0; i < batch.n_tokens; ++i) {
+            free(batch.seq_id[i]);
+        }
+        free(batch.seq_id);
+    }
+    if (batch.logits)   free(batch.logits);
+}
+
+int llama_decode(
+        struct llama_context * ctx,
+          struct llama_batch   batch) {
+    const int ret = llama_decode_internal(*ctx, batch);
+    if (ret < 0) {
+        LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
+    }
+
+    return ret;
+}
+
+float * llama_get_logits(struct llama_context * ctx) {
+    return ctx->logits.data();
+}
+
+float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
+    return ctx->logits.data() + i*ctx->model.hparams.n_vocab;
+}
+
+float * llama_get_embeddings(struct llama_context * ctx) {
+    return ctx->embedding.data();
+}
+
+const char * llama_token_get_text(const struct llama_context * ctx, llama_token token) {
+    return ctx->model.vocab.id_to_token[token].text.c_str();
+}
+
+float llama_token_get_score(const struct llama_context * ctx, llama_token token) {
+    return ctx->model.vocab.id_to_token[token].score;
+}
+
+llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token) {
+    return ctx->model.vocab.id_to_token[token].type;
+}
+
+llama_token llama_token_bos(const struct llama_context * ctx) {
+    return ctx->model.vocab.special_bos_id;
+}
+
+llama_token llama_token_eos(const struct llama_context * ctx) {
+    return ctx->model.vocab.special_eos_id;
+}
+
+llama_token llama_token_nl(const struct llama_context * ctx) {
+    return ctx->model.vocab.linefeed_id;
+}
+llama_token llama_token_prefix(const struct llama_context * ctx) {
+    return ctx->model.vocab.special_prefix_id;
+}
+
+llama_token llama_token_middle(const struct llama_context * ctx) {
+    return ctx->model.vocab.special_middle_id;
+}
+
+llama_token llama_token_suffix(const struct llama_context * ctx) {
+    return ctx->model.vocab.special_suffix_id;
+}
+
+llama_token llama_token_eot(const struct llama_context * ctx) {
+    return ctx->model.vocab.special_eot_id;
+}
+
+int llama_tokenize(
+    const struct llama_model * model,
+                  const char * text,
+                         int   text_len,
+                 llama_token * tokens,
+                         int   n_max_tokens,
+                        bool   add_bos,
+                        bool   special) {
+    auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special);
+
+    if (n_max_tokens < (int) res.size()) {
+        // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
+        return -((int) res.size());
+    }
+
+    for (size_t i = 0; i < res.size(); i++) {
+        tokens[i] = res[i];
+    }
+
+    return res.size();
+}
+
+static std::string llama_decode_text(const std::string & text) {
+    std::string decoded_text;
+    auto unicode_sequences = codepoints_from_utf8(text);
+    for (auto& unicode_sequence : unicode_sequences) {
+        decoded_text += unicode_to_bytes_bpe(codepoint_to_utf8(unicode_sequence));
+    }
+
+    return decoded_text;
+}
+
+// does not write null-terminator to buf
+int llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int length) {
+    if (0 <= token && token < llama_n_vocab(model)) {
+        switch (llama_vocab_get_type(model->vocab)) {
+        case LLAMA_VOCAB_TYPE_SPM: {
+            if (llama_is_normal_token(model->vocab, token)) {
+                std::string result = model->vocab.id_to_token[token].text;
+                llama_unescape_whitespace(result);
+                if (length < (int) result.length()) {
+                    return -result.length();
+                }
+                memcpy(buf, result.c_str(), result.length());
+                return result.length();
+            } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
+                if (length < 3) {
+                    return -3;
+                }
+                memcpy(buf, "\xe2\x96\x85", 3);
+                return 3;
+            } else if (llama_is_control_token(model->vocab, token)) {
+                ;
+            } else if (llama_is_byte_token(model->vocab, token)) {
+                if (length < 1) {
+                    return -1;
+                }
+                buf[0] = llama_token_to_byte(model->vocab, token);
+                return 1;
+            } else {
+                // TODO: for now we accept all unsupported token types,
+                // suppressing them like CONTROL tokens.
+                // GGML_ASSERT(false);
+            }
+            break;
+        }
+        case LLAMA_VOCAB_TYPE_BPE: {
+            if (llama_is_normal_token(model->vocab, token)) {
+                std::string result = model->vocab.id_to_token[token].text;
+                result = llama_decode_text(result);
+                if (length < (int) result.length()) {
+                    return -result.length();
+                }
+                memcpy(buf, result.c_str(), result.length());
+                return result.length();
+            } else if (llama_is_control_token(model->vocab, token)) {
+                ;
+            } else {
+                // TODO: for now we accept all unsupported token types,
+                // suppressing them like CONTROL tokens.
+                // GGML_ASSERT(false);
+            }
+            break;
+        }
+        default:
+            GGML_ASSERT(false);
+        }
+    }
+    return 0;
+}
+
+struct llama_timings llama_get_timings(struct llama_context * ctx) {
+    struct llama_timings result = {
+        /*.t_start_ms  =*/ 1e-3 * ctx->t_start_us,
+        /*.t_end_ms    =*/ 1.00 * ggml_time_ms(),
+        /*.t_load_ms   =*/ 1e-3 * ctx->t_load_us,
+        /*.t_sample_ms =*/ 1e-3 * ctx->t_sample_us,
+        /*.t_p_eval_ms =*/ 1e-3 * ctx->t_p_eval_us,
+        /*.t_eval_ms   =*/ 1e-3 * ctx->t_eval_us,
+
+        /*.n_sample =*/ std::max(1, ctx->n_sample),
+        /*.n_p_eval =*/ std::max(1, ctx->n_p_eval),
+        /*.n_eval   =*/ std::max(1, ctx->n_eval),
+    };
+
+    return result;
+}
+
+void llama_print_timings(struct llama_context * ctx) {
+    const llama_timings timings = llama_get_timings(ctx);
+
+    LLAMA_LOG_INFO("\n");
+    LLAMA_LOG_INFO("%s:        load time = %10.2f ms\n", __func__, timings.t_load_ms);
+    LLAMA_LOG_INFO("%s:      sample time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
+            __func__, timings.t_sample_ms, timings.n_sample, timings.t_sample_ms / timings.n_sample, 1e3 / timings.t_sample_ms * timings.n_sample);
+    LLAMA_LOG_INFO("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
+            __func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval);
+    LLAMA_LOG_INFO("%s:        eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
+            __func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval);
+    LLAMA_LOG_INFO("%s:       total time = %10.2f ms\n", __func__, (timings.t_end_ms - timings.t_start_ms));
+}
+
+void llama_reset_timings(struct llama_context * ctx) {
+    ctx->t_start_us = ggml_time_us();
+    ctx->t_sample_us = ctx->n_sample = 0;
+    ctx->t_eval_us   = ctx->n_eval   = 0;
+    ctx->t_p_eval_us = ctx->n_p_eval = 0;
+}
+
+const char * llama_print_system_info(void) {
+    static std::string s;
+
+    s  = "";
+    s += "AVX = "         + std::to_string(ggml_cpu_has_avx())         + " | ";
+    s += "AVX2 = "        + std::to_string(ggml_cpu_has_avx2())        + " | ";
+    s += "AVX512 = "      + std::to_string(ggml_cpu_has_avx512())      + " | ";
+    s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | ";
+    s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | ";
+    s += "FMA = "         + std::to_string(ggml_cpu_has_fma())         + " | ";
+    s += "NEON = "        + std::to_string(ggml_cpu_has_neon())        + " | ";
+    s += "ARM_FMA = "     + std::to_string(ggml_cpu_has_arm_fma())     + " | ";
+    s += "F16C = "        + std::to_string(ggml_cpu_has_f16c())        + " | ";
+    s += "FP16_VA = "     + std::to_string(ggml_cpu_has_fp16_va())     + " | ";
+    s += "WASM_SIMD = "   + std::to_string(ggml_cpu_has_wasm_simd())   + " | ";
+    s += "BLAS = "        + std::to_string(ggml_cpu_has_blas())        + " | ";
+    s += "SSE3 = "        + std::to_string(ggml_cpu_has_sse3())        + " | ";
+    s += "SSSE3 = "       + std::to_string(ggml_cpu_has_ssse3())       + " | ";
+    s += "VSX = "         + std::to_string(ggml_cpu_has_vsx())         + " | ";
+
+    return s.c_str();
+}
+
+void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) {
+    fprintf(stream, "\n");
+    fprintf(stream, "###########\n");
+    fprintf(stream, "# Timings #\n");
+    fprintf(stream, "###########\n");
+    fprintf(stream, "\n");
+
+    fprintf(stream, "mst_eval: %.2f  # ms / token during generation\n",
+            1.0e-3 * ctx->t_eval_us / ctx->n_eval);
+    fprintf(stream, "mst_p_eval: %.2f  # ms / token during prompt processing\n",
+            1.0e-3 * ctx->t_p_eval_us / ctx->n_p_eval);
+    fprintf(stream, "mst_sample: %.2f  # ms / token during sampling\n",
+            1.0e-3 * ctx->t_sample_us / ctx->n_sample);
+    fprintf(stream, "n_eval: %d  # number of tokens generated (excluding the first one)\n", ctx->n_eval);
+    fprintf(stream, "n_p_eval: %d  # number of tokens processed in batches at the beginning\n", ctx->n_p_eval);
+    fprintf(stream, "n_sample: %d  # number of sampled tokens\n", ctx->n_sample);
+    fprintf(stream, "t_eval_us: %" PRId64 "  # total microseconds spent generating tokens\n", ctx->t_eval_us);
+    fprintf(stream, "t_load_us: %" PRId64 "  # total microseconds spent loading the model\n", ctx->t_load_us);
+    fprintf(stream, "t_p_eval_us: %" PRId64 "  # total microseconds spent prompt processing\n", ctx->t_p_eval_us);
+    fprintf(stream, "t_sample_us: %" PRId64 "  # total microseconds spent sampling\n", ctx->t_sample_us);
+    fprintf(stream, "ts_eval: %.2f  # tokens / second during generation\n",
+            1.0e6 * ctx->n_eval / ctx->t_eval_us);
+    fprintf(stream, "ts_p_eval: %.2f  # tokens / second during prompt processing\n",
+            1.0e6 * ctx->n_p_eval / ctx->t_p_eval_us);
+    fprintf(stream, "ts_sample: %.2f  # tokens / second during sampling\n",
+            1.0e6 * ctx->n_sample / ctx->t_sample_us);
+}
+
+// For internal test use
+const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
+    struct llama_context * ctx
+) {
+    return ctx->model.tensors_by_name;
+}
+
+void llama_log_set(ggml_log_callback log_callback, void * user_data) {
+    g_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
+    g_state.log_callback_user_data = user_data;
+}
+
+static void llama_log_internal_v(ggml_log_level level, const char * format, va_list args) {
+    va_list args_copy;
+    va_copy(args_copy, args);
+    char buffer[128];
+    int len = vsnprintf(buffer, 128, format, args);
+    if (len < 128) {
+        g_state.log_callback(level, buffer, g_state.log_callback_user_data);
+    } else {
+        char* buffer2 = new char[len+1];
+        vsnprintf(buffer2, len+1, format, args_copy);
+        buffer2[len] = 0;
+        g_state.log_callback(level, buffer2, g_state.log_callback_user_data);
+        delete[] buffer2;
+    }
+    va_end(args_copy);
+}
+
+static void llama_log_internal(ggml_log_level level, const char * format, ...) {
+    va_list args;
+    va_start(args, format);
+    llama_log_internal_v(level, format, args);
+    va_end(args);
+}
+
+static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) user_data;
+    fputs(text, stderr);
+    fflush(stderr);
+}

runner/llama.h

@@ -0,0 +1,778 @@
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#ifndef LLAMA_H
+#define LLAMA_H
+
+#include "ggml.h"
+#ifdef GGML_USE_CUBLAS
+#include "ggml-cuda.h"
+#define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES
+#else
+#define LLAMA_MAX_DEVICES 1
+#endif // GGML_USE_CUBLAS
+#include <stddef.h>
+#include <stdint.h>
+#include <stdio.h>
+#include <stdbool.h>
+
+#ifdef LLAMA_SHARED
+#    if defined(_WIN32) && !defined(__MINGW32__)
+#        ifdef LLAMA_BUILD
+#            define LLAMA_API __declspec(dllexport)
+#        else
+#            define LLAMA_API __declspec(dllimport)
+#        endif
+#    else
+#        define LLAMA_API __attribute__ ((visibility ("default")))
+#    endif
+#else
+#    define LLAMA_API
+#endif
+
+#ifdef __GNUC__
+#    define DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
+#elif defined(_MSC_VER)
+#    define DEPRECATED(func, hint) __declspec(deprecated(hint)) func
+#else
+#    define DEPRECATED(func, hint) func
+#endif
+
+#define LLAMA_DEFAULT_SEED 0xFFFFFFFF
+
+#define LLAMA_MAX_RNG_STATE (64*1024)
+
+#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
+
+#define LLAMA_SESSION_MAGIC   LLAMA_FILE_MAGIC_GGSN
+#define LLAMA_SESSION_VERSION 2
+
+#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)
+// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
+#define LLAMA_SUPPORTS_GPU_OFFLOAD
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+    //
+    // C interface
+    //
+    // TODO: show sample usage
+    //
+
+    struct llama_model;
+    struct llama_context;
+
+    typedef int32_t llama_pos;
+    typedef int32_t llama_token;
+    typedef int32_t llama_seq_id;
+
+    enum llama_vocab_type {
+        LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
+        LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding
+    };
+
+    enum llama_token_type {
+        LLAMA_TOKEN_TYPE_UNDEFINED    = 0,
+        LLAMA_TOKEN_TYPE_NORMAL       = 1,
+        LLAMA_TOKEN_TYPE_UNKNOWN      = 2,
+        LLAMA_TOKEN_TYPE_CONTROL      = 3,
+        LLAMA_TOKEN_TYPE_USER_DEFINED = 4,
+        LLAMA_TOKEN_TYPE_UNUSED       = 5,
+        LLAMA_TOKEN_TYPE_BYTE         = 6,
+    };
+
+    // model file types
+    enum llama_ftype {
+        LLAMA_FTYPE_ALL_F32              = 0,
+        LLAMA_FTYPE_MOSTLY_F16           = 1,  // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_0          = 2,  // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_1          = 3,  // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4,  // tok_embeddings.weight and output.weight are F16
+        // LLAMA_FTYPE_MOSTLY_Q4_2       = 5,  // support has been removed
+        // LLAMA_FTYPE_MOSTLY_Q4_3       = 6,  // support has been removed
+        LLAMA_FTYPE_MOSTLY_Q8_0          = 7,  // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q5_0          = 8,  // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q5_1          = 9,  // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q2_K          = 10, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q3_K_S        = 11, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q3_K_M        = 12, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q3_K_L        = 13, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_K_S        = 14, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_K_M        = 15, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q5_K_S        = 16, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q5_K_M        = 17, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q6_K          = 18, // except 1d tensors
+
+        LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
+    };
+
+    typedef struct llama_token_data {
+        llama_token id; // token id
+        float logit;    // log-odds of the token
+        float p;        // probability of the token
+    } llama_token_data;
+
+    typedef struct llama_token_data_array {
+        llama_token_data * data;
+        size_t size;
+        bool sorted;
+    } llama_token_data_array;
+
+    typedef void (*llama_progress_callback)(float progress, void *ctx);
+
+    // Input data for llama_decode
+    // A llama_batch object can contain input about one or many sequences
+    // The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
+    //
+    // - token  : the token ids of the input (used when embd is NULL)
+    // - embd   : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
+    // - pos    : the positions of the respective token in the sequence
+    // - seq_id : the sequence to which the respective token belongs
+    // - logits : if zero, the logits for the respective token will not be output
+    //
+    typedef struct llama_batch {
+        int32_t n_tokens;
+
+        llama_token  *  token;
+        float        *  embd;
+        llama_pos    *  pos;
+        int32_t      *  n_seq_id;
+        llama_seq_id ** seq_id;
+        int8_t       *  logits;
+
+        // NOTE: helpers for smooth API transition - can be deprecated in the future
+        //       for future-proof code, use the above fields instead and ignore everything below
+        //
+        // pos[i] = all_pos_0 + i*all_pos_1
+        //
+        llama_pos    all_pos_0;  // used if pos == NULL
+        llama_pos    all_pos_1;  // used if pos == NULL
+        llama_seq_id all_seq_id; // used if seq_id == NULL
+    } llama_batch;
+
+    struct llama_model_params {
+        int32_t n_gpu_layers; // number of layers to store in VRAM
+        int32_t main_gpu;     // the GPU that is used for scratch and small tensors
+        const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)
+
+        // called with a progress value between 0 and 1, pass NULL to disable
+        llama_progress_callback progress_callback;
+        // context pointer passed to the progress callback
+        void * progress_callback_user_data;
+
+        // Keep the booleans together to avoid misalignment during copy-by-value.
+        bool vocab_only; // only load the vocabulary, no weights
+        bool use_mmap;   // use mmap if possible
+        bool use_mlock;  // force system to keep model in RAM
+    };
+
+    struct llama_context_params {
+        uint32_t seed;            // RNG seed, -1 for random
+        uint32_t n_ctx;           // text context, 0 = from model
+        uint32_t n_batch;         // prompt processing maximum batch size
+        uint32_t n_threads;       // number of threads to use for generation
+        uint32_t n_threads_batch; // number of threads to use for batch processing
+
+        // ref: https://github.com/ggerganov/llama.cpp/pull/2054
+        float rope_freq_base;  // RoPE base frequency, 0 = from model
+        float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model
+
+        // Keep the booleans together to avoid misalignment during copy-by-value.
+        bool mul_mat_q;  // if true, use experimental mul_mat_q kernels
+        bool f16_kv;     // use fp16 for KV cache, fp32 otherwise
+        bool logits_all; // the llama_eval() call computes all logits, not just the last one
+        bool embedding;  // embedding mode only
+    };
+
+    // model quantization parameters
+    typedef struct llama_model_quantize_params {
+        int nthread;                 // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
+        enum llama_ftype ftype;      // quantize to this llama_ftype
+        bool allow_requantize;       // allow quantizing non-f32/f16 tensors
+        bool quantize_output_tensor; // quantize output.weight
+        bool only_copy;              // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
+    } llama_model_quantize_params;
+
+    // grammar types
+    struct llama_grammar;
+
+    // grammar element type
+    enum llama_gretype {
+        // end of rule definition
+        LLAMA_GRETYPE_END            = 0,
+
+        // start of alternate definition for rule
+        LLAMA_GRETYPE_ALT            = 1,
+
+        // non-terminal element: reference to rule
+        LLAMA_GRETYPE_RULE_REF       = 2,
+
+        // terminal element: character (code point)
+        LLAMA_GRETYPE_CHAR           = 3,
+
+        // inverse char(s) ([^a], [^a-b] [^abc])
+        LLAMA_GRETYPE_CHAR_NOT       = 4,
+
+        // modifies a preceding LLAMA_GRETYPE_CHAR or LLAMA_GRETYPE_CHAR_ALT to
+        // be an inclusive range ([a-z])
+        LLAMA_GRETYPE_CHAR_RNG_UPPER = 5,
+
+        // modifies a preceding LLAMA_GRETYPE_CHAR or
+        // LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
+        LLAMA_GRETYPE_CHAR_ALT       = 6,
+    };
+
+    typedef struct llama_grammar_element {
+        enum llama_gretype type;
+        uint32_t           value; // Unicode code point or rule ID
+    } llama_grammar_element;
+
+    // performance timing information
+    struct llama_timings {
+        double t_start_ms;
+        double t_end_ms;
+        double t_load_ms;
+        double t_sample_ms;
+        double t_p_eval_ms;
+        double t_eval_ms;
+
+        int32_t n_sample;
+        int32_t n_p_eval;
+        int32_t n_eval;
+    };
+
+    // Helpers for getting default parameters
+    LLAMA_API struct llama_model_params llama_model_default_params(void);
+    LLAMA_API struct llama_context_params llama_context_default_params(void);
+    LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);
+
+    // Initialize the llama + ggml backend
+    // If numa is true, use NUMA optimizations
+    // Call once at the start of the program
+    LLAMA_API void llama_backend_init(bool numa);
+
+    // Call once at the end of the program - currently only used for MPI
+    LLAMA_API void llama_backend_free(void);
+
+    LLAMA_API struct llama_model * llama_load_model_from_file(
+                             const char * path_model,
+            struct llama_model_params     params);
+
+    LLAMA_API void llama_free_model(struct llama_model * model);
+
+    LLAMA_API struct llama_context * llama_new_context_with_model(
+                     struct llama_model * model,
+            struct llama_context_params   params);
+
+    // Frees all allocated memory
+    LLAMA_API void llama_free(struct llama_context * ctx);
+
+    LLAMA_API int64_t llama_time_us(void);
+
+    LLAMA_API int  llama_max_devices    (void);
+    LLAMA_API bool llama_mmap_supported (void);
+    LLAMA_API bool llama_mlock_supported(void);
+
+    LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
+
+    LLAMA_API int llama_n_ctx      (const struct llama_context * ctx);
+
+    LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
+
+    LLAMA_API int llama_n_vocab    (const struct llama_model * model);
+    LLAMA_API int llama_n_ctx_train(const struct llama_model * model);
+    LLAMA_API int llama_n_embd     (const struct llama_model * model);
+
+    // Get the model's RoPE frequency scaling factor
+    LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
+
+    // Get a string describing the model type
+    LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
+
+    // Returns the total size of all the tensors in the model in bytes
+    LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
+
+    // Returns the total number of parameters in the model
+    LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
+
+    // Get a llama model tensor
+    LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
+
+    // Returns 0 on success
+    LLAMA_API int llama_model_quantize(
+            const char * fname_inp,
+            const char * fname_out,
+            const llama_model_quantize_params * params);
+
+    // Apply a LoRA adapter to a loaded model
+    // path_base_model is the path to a higher quality model to use as a base for
+    // the layers modified by the adapter. Can be NULL to use the current loaded model.
+    // The model needs to be reloaded before applying a new adapter, otherwise the adapter
+    // will be applied on top of the previous one
+    // Returns 0 on success
+    LLAMA_API DEPRECATED(int llama_apply_lora_from_file(
+            struct llama_context * ctx,
+                      const char * path_lora,
+                           float   scale,
+                      const char * path_base_model,
+                             int   n_threads),
+            "use llama_model_apply_lora_from_file instead");
+
+    LLAMA_API int llama_model_apply_lora_from_file(
+            const struct llama_model * model,
+                      const char * path_lora,
+                           float   scale,
+                      const char * path_base_model,
+                             int   n_threads);
+
+    //
+    // KV cache
+    //
+
+    // Returns the number of tokens in the KV cache
+    LLAMA_API DEPRECATED(int llama_get_kv_cache_token_count(const struct llama_context * ctx),
+            "avoid using this, it will be removed in the future, instead - count the tokens in user code");
+
+    // Remove all tokens data of cells in [c0, c1)
+    // c0 < 0 : [0,  c1]
+    // c1 < 0 : [c0, inf)
+    LLAMA_API void llama_kv_cache_tokens_rm(
+            struct llama_context * ctx,
+                         int32_t   c0,
+                         int32_t   c1);
+
+    // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
+    // p0 < 0 : [0,  p1]
+    // p1 < 0 : [p0, inf)
+    LLAMA_API void llama_kv_cache_seq_rm(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id,
+                       llama_pos   p0,
+                       llama_pos   p1);
+
+    // Copy all tokens that belong to the specified sequence to another sequence
+    // Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence
+    // p0 < 0 : [0,  p1]
+    // p1 < 0 : [p0, inf)
+    LLAMA_API void llama_kv_cache_seq_cp(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id_src,
+                    llama_seq_id   seq_id_dst,
+                       llama_pos   p0,
+                       llama_pos   p1);
+
+    // Removes all tokens that do not belong to the specified sequence
+    LLAMA_API void llama_kv_cache_seq_keep(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id);
+
+    // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
+    // If the KV cache is RoPEd, the KV data is updated accordingly
+    // p0 < 0 : [0,  p1]
+    // p1 < 0 : [p0, inf)
+    LLAMA_API void llama_kv_cache_seq_shift(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id,
+                       llama_pos   p0,
+                       llama_pos   p1,
+                       llama_pos   delta);
+
+    //
+    // State / sessions
+    //
+
+    // Returns the maximum size in bytes of the state (rng, logits, embedding
+    // and kv_cache) - will often be smaller after compacting tokens
+    LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
+
+    // Copies the state to the specified destination address.
+    // Destination needs to have allocated enough memory.
+    // Returns the number of bytes copied
+    LLAMA_API size_t llama_copy_state_data(
+            struct llama_context * ctx,
+                         uint8_t * dst);
+
+    // Set the state reading from the specified address
+    // Returns the number of bytes read
+    LLAMA_API size_t llama_set_state_data(
+            struct llama_context * ctx,
+                         uint8_t * src);
+
+    // Save/load session file
+    LLAMA_API bool llama_load_session_file(
+            struct llama_context * ctx,
+                      const char * path_session,
+                     llama_token * tokens_out,
+                          size_t   n_token_capacity,
+                          size_t * n_token_count_out);
+
+    LLAMA_API bool llama_save_session_file(
+            struct llama_context * ctx,
+                      const char * path_session,
+               const llama_token * tokens,
+                          size_t   n_token_count);
+
+    //
+    // Decoding
+    //
+
+    // Run the llama inference to obtain the logits and probabilities for the next token(s).
+    // tokens + n_tokens is the provided batch of new tokens to process
+    // n_past is the number of tokens to use from previous eval calls
+    // Returns 0 on success
+    // DEPRECATED: use llama_decode() instead
+    LLAMA_API DEPRECATED(int llama_eval(
+            struct llama_context * ctx,
+                     llama_token * tokens,
+                         int32_t   n_tokens,
+                             int   n_past),
+            "use llama_decode() instead");
+
+    // Same as llama_eval, but use float matrix input directly.
+    // DEPRECATED: use llama_decode() instead
+    LLAMA_API DEPRECATED(int llama_eval_embd(
+            struct llama_context * ctx,
+                           float * embd,
+                         int32_t   n_tokens,
+                             int   n_past),
+            "use llama_decode() instead");
+
+    // Return batch for single sequence of tokens starting at pos_0
+    //
+    // NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
+    //
+    LLAMA_API struct llama_batch llama_batch_get_one(
+                  llama_token * tokens,
+                      int32_t   n_tokens,
+                    llama_pos   pos_0,
+                 llama_seq_id   seq_id);
+
+    // Allocates a batch of tokens on the heap that can hold a maximum of n_tokens
+    // Each token can be assigned up to n_seq_max sequence ids
+    // The batch has to be freed with llama_batch_free()
+    // If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float)
+    // Otherwise, llama_batch.token will be allocated to store n_tokens llama_token
+    // The rest of the llama_batch members are allocated with size n_tokens
+    // All members are left uninitialized
+    LLAMA_API struct llama_batch llama_batch_init(
+            int32_t n_tokens,
+            int32_t embd,
+            int32_t n_seq_max);
+
+    // Frees a batch of tokens allocated with llama_batch_init()
+    LLAMA_API void llama_batch_free(struct llama_batch batch);
+
+    // Positive return values does not mean a fatal error, but rather a warning.
+    //   0 - success
+    //   1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
+    // < 0 - error
+    LLAMA_API int llama_decode(
+            struct llama_context * ctx,
+              struct llama_batch   batch);
+
+    // Set the number of threads used for decoding
+    // n_threads is the number of threads used for generation (single token)
+    // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
+    LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);
+
+    // Token logits obtained from the last call to llama_eval()
+    // The logits for the last token are stored in the last row
+    // Logits for which llama_batch.logits[i] == 0 are undefined
+    // Rows: n_tokens provided with llama_batch
+    // Cols: n_vocab
+    LLAMA_API float * llama_get_logits(struct llama_context * ctx);
+
+    // Logits for the ith token. Equivalent to:
+    // llama_get_logits(ctx) + i*n_vocab
+    LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);
+
+    // Get the embeddings for the input
+    // shape: [n_embd] (1-dimensional)
+    LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
+
+    //
+    // Vocab
+    //
+
+    LLAMA_API const char * llama_token_get_text(const struct llama_context * ctx, llama_token token);
+
+    LLAMA_API float llama_token_get_score(const struct llama_context * ctx, llama_token token);
+
+    LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token);
+
+    // Special tokens
+    LLAMA_API llama_token llama_token_bos(const struct llama_context * ctx);  // beginning-of-sentence
+    LLAMA_API llama_token llama_token_eos(const struct llama_context * ctx);  // end-of-sentence
+    LLAMA_API llama_token llama_token_nl (const struct llama_context * ctx);  // next-line
+    // codellama infill tokens
+    LLAMA_API llama_token llama_token_prefix(const struct llama_context * ctx); // Beginning of infill prefix
+    LLAMA_API llama_token llama_token_middle(const struct llama_context * ctx); // Beginning of infill middle
+    LLAMA_API llama_token llama_token_suffix(const struct llama_context * ctx); // Beginning of infill suffix
+    LLAMA_API llama_token llama_token_eot   (const struct llama_context * ctx); // End of infill middle
+
+    //
+    // Tokenization
+    //
+
+    /// @details Convert the provided text into tokens.
+    /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
+    /// @return Returns the number of tokens on success, no more than n_max_tokens
+    /// @return Returns a negative number on failure - the number of tokens that would have been returned
+    /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
+    ///                Does not insert a leading space.
+    LLAMA_API int llama_tokenize(
+        const struct llama_model * model,
+                      const char * text,
+                             int   text_len,
+                     llama_token * tokens,
+                             int   n_max_tokens,
+                            bool   add_bos,
+                            bool   special);
+
+    // Token Id -> Piece.
+    // Uses the vocabulary in the provided context.
+    // Does not write null terminator to the buffer.
+    // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
+    LLAMA_API int llama_token_to_piece(
+              const struct llama_model * model,
+                           llama_token   token,
+                                  char * buf,
+                                  int    length);
+
+    //
+    // Grammar
+    //
+
+    LLAMA_API struct llama_grammar * llama_grammar_init(
+            const llama_grammar_element ** rules,
+                                 size_t    n_rules,
+                                 size_t    start_rule_index);
+
+    LLAMA_API void llama_grammar_free(struct llama_grammar * grammar);
+
+    LLAMA_API struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar);
+
+    //
+    // Sampling functions
+    //
+
+    // Sets the current rng seed.
+    LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
+
+    /// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
+    /// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
+    LLAMA_API void llama_sample_repetition_penalties(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+               const llama_token * last_tokens,
+                          size_t   penalty_last_n,
+                           float   penalty_repeat,
+                           float   penalty_freq,
+                           float   penalty_present);
+
+    /// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
+    /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
+    /// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
+    /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
+    LLAMA_API void llama_sample_classifier_free_guidance(
+              struct llama_context * ctx,
+            llama_token_data_array * candidates,
+              struct llama_context * guidance_ctx,
+                             float   scale);
+
+    /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
+    LLAMA_API void llama_sample_softmax(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates);
+
+    /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
+    LLAMA_API void llama_sample_top_k(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+                             int   k,
+                          size_t   min_keep);
+
+    /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
+    LLAMA_API void llama_sample_top_p(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+                           float   p,
+                          size_t   min_keep);
+
+    /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
+    LLAMA_API void llama_sample_tail_free(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+                           float   z,
+                          size_t   min_keep);
+
+    /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
+    LLAMA_API void llama_sample_typical(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+                           float   p,
+                          size_t   min_keep);
+
+    LLAMA_API void llama_sample_temp(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+                           float   temp);
+
+    LLAMA_API DEPRECATED(void llama_sample_temperature(
+                struct llama_context * ctx,
+              llama_token_data_array * candidates,
+                               float   temp),
+            "use llama_sample_temp instead");
+
+    /// @details Apply constraints from grammar
+    LLAMA_API void llama_sample_grammar(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+      const struct llama_grammar * grammar);
+
+    /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
+    /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
+    /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
+    /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
+    /// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
+    /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
+    LLAMA_API llama_token llama_sample_token_mirostat(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+                           float   tau,
+                           float   eta,
+                             int   m,
+                           float * mu);
+
+    /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
+    /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
+    /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
+    /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
+    /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
+    LLAMA_API llama_token llama_sample_token_mirostat_v2(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+                           float   tau,
+                           float   eta,
+                           float * mu);
+
+    /// @details Selects the token with the highest probability.
+    LLAMA_API llama_token llama_sample_token_greedy(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates);
+
+    /// @details Randomly selects a token from the candidates based on their probabilities.
+    LLAMA_API llama_token llama_sample_token(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates);
+
+    /// @details Accepts the sampled token into the grammar
+    LLAMA_API void llama_grammar_accept_token(
+            struct llama_context * ctx,
+            struct llama_grammar * grammar,
+                     llama_token   token);
+
+    //
+    // Beam search
+    //
+
+    struct llama_beam_view {
+        const llama_token * tokens;
+
+        size_t n_tokens;
+        float  p;        // Cumulative beam probability (renormalized relative to all beams)
+        bool   eob;      // Callback should set this to true when a beam is at end-of-beam.
+    };
+
+    // Passed to beam_search_callback function.
+    // Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams
+    // (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.
+    // These pointers are valid only during the synchronous callback, so should not be saved.
+    struct llama_beams_state {
+        struct llama_beam_view * beam_views;
+
+        size_t n_beams;               // Number of elements in beam_views[].
+        size_t common_prefix_length;  // Current max length of prefix tokens shared by all beams.
+        bool   last_call;             // True iff this is the last callback invocation.
+    };
+
+    // Type of pointer to the beam_search_callback function.
+    // void* callback_data is any custom data passed to llama_beam_search, that is subsequently
+    // passed back to beam_search_callback. This avoids having to use global variables in the callback.
+    typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);
+
+    /// @details Deterministically returns entire sentence constructed by a beam search.
+    /// @param ctx Pointer to the llama_context.
+    /// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state.
+    /// @param callback_data A pointer that is simply passed back to callback.
+    /// @param n_beams Number of beams to use.
+    /// @param n_past Number of tokens already evaluated.
+    /// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
+    LLAMA_API void llama_beam_search(
+                   struct llama_context * ctx,
+        llama_beam_search_callback_fn_t   callback,
+                                   void * callback_data,
+                                 size_t   n_beams,
+                                    int   n_past,
+                                    int   n_predict);
+
+    // Performance information
+    LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
+
+    LLAMA_API void llama_print_timings(struct llama_context * ctx);
+    LLAMA_API void llama_reset_timings(struct llama_context * ctx);
+
+    // Print system information
+    LLAMA_API const char * llama_print_system_info(void);
+
+    // Set callback for all future logging events.
+    // If this is not called, or NULL is supplied, everything is output on stderr.
+    LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data);
+
+    LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);
+
+#ifdef __cplusplus
+}
+#endif
+
+// Internal API to be implemented by llama.cpp and used by tests/benchmarks only
+#ifdef LLAMA_API_INTERNAL
+
+#include <vector>
+#include <string>
+
+struct ggml_tensor;
+
+const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
+    struct llama_context * ctx
+);
+
+#endif // LLAMA_API_INTERNAL
+
+#endif // LLAMA_H

runner/main.cpp

@@ -0,0 +1,272 @@
+#include <cmath>
+#include <cstdio>
+#include <string>
+#include <vector>
+#include <thread>
+
+#include "llama.h"
+#include "main.h"
+
+std::vector<llama_token> tokenize(const struct llama_model * model, const std::string & text, bool add_bos, bool special = false) {
+    // upper limit for the number of tokens
+    int n_tokens = text.length() + add_bos;
+    std::vector<llama_token> result(n_tokens);
+    n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special);
+    if (n_tokens < 0) {
+        result.resize(-n_tokens);
+        int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special);
+        GGML_ASSERT(check == -n_tokens);
+    } else {
+        result.resize(n_tokens);
+    }
+    return result;
+}
+
+std::string token_to_piece(const struct llama_context * ctx, llama_token token) {
+    std::vector<char> result(8, 0);
+    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+    if (n_tokens < 0) {
+        result.resize(-n_tokens);
+        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+        GGML_ASSERT(check == -n_tokens);
+    } else {
+        result.resize(n_tokens);
+    }
+
+    return std::string(result.data(), result.size());
+}
+
+void batch_add(
+                 struct llama_batch & batch,
+                        llama_token   id,
+                          llama_pos   pos,
+    const std::vector<llama_seq_id> & seq_ids,
+                               bool   logits) {
+    batch.token   [batch.n_tokens] = id;
+    batch.pos     [batch.n_tokens] = pos,
+    batch.n_seq_id[batch.n_tokens] = seq_ids.size();
+    for (size_t i = 0; i < seq_ids.size(); ++i) {
+        batch.seq_id[batch.n_tokens][i] = seq_ids[i];
+    }
+    batch.logits  [batch.n_tokens] = logits;
+
+    batch.n_tokens++;
+}
+
+void batch_clear(struct llama_batch & batch) {
+    batch.n_tokens = 0;
+}
+
+int generate(const char *model_path, const char *prompt) {
+    // number of parallel batches
+    int n_parallel = 1;
+
+    // total length of the sequences including the prompt
+    int n_len = 32;
+
+    // init LLM
+    llama_backend_init(true);
+
+    // initialize the model
+
+    llama_model_params model_params = llama_model_default_params();
+
+    // model_params.n_gpu_layers = 99; // offload all layers to the GPU
+
+    llama_model * model = llama_load_model_from_file(model_path, model_params);
+
+    if (model == NULL) {
+        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
+        return 1;
+    }
+
+    // tokenize the prompt
+
+    std::vector<llama_token> tokens_list;
+    tokens_list = tokenize(model, std::string(prompt), true);
+    const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;
+
+    // initialize the context
+
+    llama_context_params ctx_params = llama_context_default_params();
+
+    ctx_params.seed  = 1234;
+    ctx_params.n_ctx = n_kv_req;
+    ctx_params.n_batch = std::max(n_len, n_parallel);
+    ctx_params.n_threads       = std::thread::hardware_concurrency();
+    ctx_params.n_threads_batch = ctx_params.n_threads;
+
+    llama_context * ctx = llama_new_context_with_model(model, ctx_params);
+
+    if (ctx == NULL) {
+        fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
+        return 1;
+    }
+
+    const int n_ctx    = llama_n_ctx(ctx);
+
+    printf("\n%s: n_len = %d, n_ctx = %d, n_batch = %d, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);
+
+    // make sure the KV cache is big enough to hold all the prompt and generated tokens
+    if (n_kv_req > n_ctx) {
+        printf("%s: error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\n", __func__,  n_kv_req);
+        printf("%s:        either reduce n_parallel or increase n_ctx\n", __func__);
+        return 1;
+    }
+
+    // print the prompt token-by-token
+
+    fprintf(stderr, "\n");
+
+    for (auto id : tokens_list) {
+        fprintf(stderr, "%s", token_to_piece(ctx, id).c_str());
+    }
+
+    fflush(stderr);
+
+    // create a llama_batch
+    // we use this object to submit token data for decoding
+    llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t)n_parallel), 0, 1);
+
+    // evaluate the initial prompt
+    for (size_t i = 0; i < tokens_list.size(); ++i) {
+        batch_add(batch, tokens_list[i], i, { 0 }, false);
+    }
+    GGML_ASSERT(batch.n_tokens == (int) tokens_list.size());
+
+    // llama_decode will output logits only for the last token of the prompt
+    batch.logits[batch.n_tokens - 1] = true;
+
+    if (llama_decode(ctx, batch) != 0) {
+        printf("%s: llama_decode() failed\n", __func__);
+        return 1;
+    }
+
+    // assign the system KV cache to all parallel sequences
+    // this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
+    for (int32_t i = 1; i < n_parallel; ++i) {
+        llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens);
+    }
+
+    if (n_parallel > 1) {
+        printf("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
+    }
+
+    // main loop
+
+    // we will store the parallel decoded sequences in this vector
+    std::vector<std::string> streams(n_parallel);
+
+    // remember the batch index of the last token for each parallel sequence
+    // we need this to determine which logits to sample from
+    std::vector<int32_t> i_batch(n_parallel, batch.n_tokens - 1);
+
+    int n_cur    = batch.n_tokens;
+    int n_decode = 0;
+
+    const auto t_main_start = ggml_time_us();
+
+    while (n_cur <= n_len) {
+        // prepare the next batch
+        batch_clear(batch);
+
+        // sample the next token for each parallel sequence / stream
+        for (int32_t i = 0; i < n_parallel; ++i) {
+            if (i_batch[i] < 0) {
+                // the stream has already finished
+                continue;
+            }
+
+            auto   n_vocab = llama_n_vocab(model);
+            auto * logits  = llama_get_logits_ith(ctx, i_batch[i]);
+
+            std::vector<llama_token_data> candidates;
+            candidates.reserve(n_vocab);
+
+            for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+                candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
+            }
+
+            llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+
+            const int   top_k = 40;
+            const float top_p = 0.9f;
+            const float temp  = 0.4f;
+
+            llama_sample_top_k(ctx, &candidates_p, top_k, 1);
+            llama_sample_top_p(ctx, &candidates_p, top_p, 1);
+            llama_sample_temp (ctx, &candidates_p, temp);
+
+            const llama_token new_token_id = llama_sample_token(ctx, &candidates_p);
+
+            //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
+
+            // is it an end of stream? -> mark the stream as finished
+            if (new_token_id == llama_token_eos(ctx) || n_cur == n_len) {
+                i_batch[i] = -1;
+                printf("\n");
+                if (n_parallel > 1) {
+                    printf("%s: stream %d finished at n_cur = %d", __func__, i, n_cur);
+                }
+
+                continue;
+            }
+
+            // if there is only one stream, we print immediately to stdout
+            if (n_parallel == 1) {
+                printf("%s", token_to_piece(ctx, new_token_id).c_str());
+                fflush(stdout);
+            }
+
+            streams[i] += token_to_piece(ctx, new_token_id);
+
+            i_batch[i] = batch.n_tokens;
+
+            // push this new token for next evaluation
+            batch_add(batch, new_token_id, n_cur, { i }, true);
+
+            n_decode += 1;
+        }
+
+        // all streams are finished
+        if (batch.n_tokens == 0) {
+            break;
+        }
+
+        n_cur += 1;
+
+        // evaluate the current batch with the transformer model
+        if (llama_decode(ctx, batch)) {
+            fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
+            return 1;
+        }
+    }
+
+    printf("\n");
+
+    if (n_parallel > 1) {
+        printf("\n");
+
+        for (int32_t i = 0; i < n_parallel; ++i) {
+            printf("sequence %d:\n\n%s%s\n\n", i, prompt, streams[i].c_str());
+        }
+    }
+
+    const auto t_main_end = ggml_time_us();
+
+    printf("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
+            __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
+
+    llama_print_timings(ctx);
+
+    fprintf(stderr, "\n");
+
+    llama_batch_free(batch);
+
+    llama_free(ctx);
+    llama_free_model(model);
+
+    llama_backend_free();
+
+    return 0;
+}

runner/main.go

@@ -0,0 +1,32 @@
+package main
+
+/*
+#cgo CFLAGS: -Ofast -std=c11 -fPIC -Wno-deprecated-declarations -Wno-unused-but-set-variable
+#cgo CPPFLAGS: -Ofast -Wall -Wextra -Wno-unused-function -Wno-unused-variable -Wno-deprecated-declarations -Wno-unused-but-set-variable -DNDEBUG -DGGML_USE_K_QUANTS
+#cgo CXXFLAGS: -std=c++11 -fPIC
+#cgo darwin CPPFLAGS:  -DGGML_USE_ACCELERATE
+#cgo darwin,arm64 CPPFLAGS: -DGGML_USE_METAL
+#cgo darwin LDFLAGS: -framework Accelerate -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
+#include <stdlib.h>
+#include "main.h"
+*/
+import "C"
+import (
+	"fmt"
+	"os"
+	"unsafe"
+)
+
+func main() {
+	if len(os.Args) < 2 {
+		fmt.Println("No prompt provided")
+		return
+	}
+
+	prompt := C.CString(os.Args[1])
+	defer C.free(unsafe.Pointer(prompt))
+	model := C.CString("./model.bin")
+	defer C.free(unsafe.Pointer(model))
+
+	C.generate(model, prompt)
+}

runner/main.h

@@ -0,0 +1,14 @@
+#ifndef MAIN_H
+#define MAIN_H
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+int generate(const char *model_path, const char *prompt);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif // MAIN_H

runner/unicode.h

@@ -0,0 +1,488 @@
+/**
+ * llama.cpp - git 465219b9143ac01db0990bbcb0a081ef72ec2008
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#pragma once
+
+#include <cassert>
+#include <stdexcept>
+#include <vector>
+#include <unordered_map>
+
+static const std::vector<std::pair<uint32_t, uint32_t>> digit_ranges = {
+{0x30, 0x39}, {0xB2, 0xB3}, {0xB9, 0xB9}, {0x660, 0x669}, {0x6F0, 0x6F9}, {0x7C0, 0x7C9}, {0x966, 0x96F}, {0x9E6, 0x9EF}, {0xA66, 0xA6F}, {0xAE6, 0xAEF}, {0xB66, 0xB6F}, {0xBE6, 0xBEF}, {0xC66, 0xC6F},
+{0xCE6, 0xCEF}, {0xD66, 0xD6F}, {0xDE6, 0xDEF}, {0xE50, 0xE59}, {0xED0, 0xED9}, {0xF20, 0xF29}, {0x1040, 0x1049}, {0x1090, 0x1099}, {0x1369, 0x1371}, {0x17E0, 0x17E9}, {0x1810, 0x1819}, {0x1946, 0x194F},
+{0x19D0, 0x19DA}, {0x1A80, 0x1A89}, {0x1A90, 0x1A99}, {0x1B50, 0x1B59}, {0x1BB0, 0x1BB9}, {0x1C40, 0x1C49}, {0x1C50, 0x1C59}, {0x2070, 0x2070}, {0x2074, 0x2079}, {0x2080, 0x2089}, {0x2460, 0x2468},
+{0x2474, 0x247C}, {0x2488, 0x2490}, {0x24EA, 0x24EA}, {0x24F5, 0x24FD}, {0x24FF, 0x24FF}, {0x2776, 0x277E}, {0x2780, 0x2788}, {0x278A, 0x2792}, {0xA620, 0xA629}, {0xA8D0, 0xA8D9}, {0xA900, 0xA909},
+{0xA9D0, 0xA9D9}, {0xA9F0, 0xA9F9}, {0xAA50, 0xAA59}, {0xABF0, 0xABF9}, {0xFF10, 0xFF19}, {0x104A0, 0x104A9}, {0x10A40, 0x10A43}, {0x10D30, 0x10D39}, {0x10E60, 0x10E68}, {0x11052, 0x1105A},
+{0x11066, 0x1106F}, {0x110F0, 0x110F9}, {0x11136, 0x1113F}, {0x111D0, 0x111D9}, {0x112F0, 0x112F9}, {0x11450, 0x11459}, {0x114D0, 0x114D9}, {0x11650, 0x11659}, {0x116C0, 0x116C9}, {0x11730, 0x11739},
+{0x118E0, 0x118E9}, {0x11950, 0x11959}, {0x11C50, 0x11C59}, {0x11D50, 0x11D59}, {0x11DA0, 0x11DA9}, {0x16A60, 0x16A69}, {0x16B50, 0x16B59}, {0x1D7CE, 0x1D7FF}, {0x1E140, 0x1E149}, {0x1E2F0, 0x1E2F9},
+{0x1E950, 0x1E959}, {0x1F100, 0x1F10A}, {0x1FBF0, 0x1FBF9},
+};
+
+static const std::vector<std::pair<uint32_t, uint32_t>> letter_ranges = {
+{0x41, 0x5A}, {0x61, 0x7A}, {0xAA, 0xAA}, {0xB5, 0xB5}, {0xBA, 0xBA}, {0xC0, 0xD6}, {0xD8, 0xF6}, {0xF8, 0x2C1}, {0x2C6, 0x2D1}, {0x2E0, 0x2E4}, {0x2EC, 0x2EC}, {0x2EE, 0x2EE}, {0x370, 0x374},
+{0x376, 0x377}, {0x37A, 0x37D}, {0x37F, 0x37F}, {0x386, 0x386}, {0x388, 0x38A}, {0x38C, 0x38C}, {0x38E, 0x3A1}, {0x3A3, 0x3F5}, {0x3F7, 0x481}, {0x48A, 0x52F}, {0x531, 0x556}, {0x559, 0x559},
+{0x560, 0x588}, {0x5D0, 0x5EA}, {0x5EF, 0x5F2}, {0x620, 0x64A}, {0x66E, 0x66F}, {0x671, 0x6D3}, {0x6D5, 0x6D5}, {0x6E5, 0x6E6}, {0x6EE, 0x6EF}, {0x6FA, 0x6FC}, {0x6FF, 0x6FF}, {0x710, 0x710},
+{0x712, 0x72F}, {0x74D, 0x7A5}, {0x7B1, 0x7B1}, {0x7CA, 0x7EA}, {0x7F4, 0x7F5}, {0x7FA, 0x7FA}, {0x800, 0x815}, {0x81A, 0x81A}, {0x824, 0x824}, {0x828, 0x828}, {0x840, 0x858}, {0x860, 0x86A},
+{0x8A0, 0x8B4}, {0x8B6, 0x8C7}, {0x904, 0x939}, {0x93D, 0x93D}, {0x950, 0x950}, {0x958, 0x961}, {0x971, 0x980}, {0x985, 0x98C}, {0x98F, 0x990}, {0x993, 0x9A8}, {0x9AA, 0x9B0}, {0x9B2, 0x9B2},
+{0x9B6, 0x9B9}, {0x9BD, 0x9BD}, {0x9CE, 0x9CE}, {0x9DC, 0x9DD}, {0x9DF, 0x9E1}, {0x9F0, 0x9F1}, {0x9FC, 0x9FC}, {0xA05, 0xA0A}, {0xA0F, 0xA10}, {0xA13, 0xA28}, {0xA2A, 0xA30}, {0xA32, 0xA33},
+{0xA35, 0xA36}, {0xA38, 0xA39}, {0xA59, 0xA5C}, {0xA5E, 0xA5E}, {0xA72, 0xA74}, {0xA85, 0xA8D}, {0xA8F, 0xA91}, {0xA93, 0xAA8}, {0xAAA, 0xAB0}, {0xAB2, 0xAB3}, {0xAB5, 0xAB9}, {0xABD, 0xABD},
+{0xAD0, 0xAD0}, {0xAE0, 0xAE1}, {0xAF9, 0xAF9}, {0xB05, 0xB0C}, {0xB0F, 0xB10}, {0xB13, 0xB28}, {0xB2A, 0xB30}, {0xB32, 0xB33}, {0xB35, 0xB39}, {0xB3D, 0xB3D}, {0xB5C, 0xB5D}, {0xB5F, 0xB61},
+{0xB71, 0xB71}, {0xB83, 0xB83}, {0xB85, 0xB8A}, {0xB8E, 0xB90}, {0xB92, 0xB95}, {0xB99, 0xB9A}, {0xB9C, 0xB9C}, {0xB9E, 0xB9F}, {0xBA3, 0xBA4}, {0xBA8, 0xBAA}, {0xBAE, 0xBB9}, {0xBD0, 0xBD0},
+{0xC05, 0xC0C}, {0xC0E, 0xC10}, {0xC12, 0xC28}, {0xC2A, 0xC39}, {0xC3D, 0xC3D}, {0xC58, 0xC5A}, {0xC60, 0xC61}, {0xC80, 0xC80}, {0xC85, 0xC8C}, {0xC8E, 0xC90}, {0xC92, 0xCA8}, {0xCAA, 0xCB3},
+{0xCB5, 0xCB9}, {0xCBD, 0xCBD}, {0xCDE, 0xCDE}, {0xCE0, 0xCE1}, {0xCF1, 0xCF2}, {0xD04, 0xD0C}, {0xD0E, 0xD10}, {0xD12, 0xD3A}, {0xD3D, 0xD3D}, {0xD4E, 0xD4E}, {0xD54, 0xD56}, {0xD5F, 0xD61},
+{0xD7A, 0xD7F}, {0xD85, 0xD96}, {0xD9A, 0xDB1}, {0xDB3, 0xDBB}, {0xDBD, 0xDBD}, {0xDC0, 0xDC6}, {0xE01, 0xE30}, {0xE32, 0xE33}, {0xE40, 0xE46}, {0xE81, 0xE82}, {0xE84, 0xE84}, {0xE86, 0xE8A},
+{0xE8C, 0xEA3}, {0xEA5, 0xEA5}, {0xEA7, 0xEB0}, {0xEB2, 0xEB3}, {0xEBD, 0xEBD}, {0xEC0, 0xEC4}, {0xEC6, 0xEC6}, {0xEDC, 0xEDF}, {0xF00, 0xF00}, {0xF40, 0xF47}, {0xF49, 0xF6C}, {0xF88, 0xF8C},
+{0x1000, 0x102A}, {0x103F, 0x103F}, {0x1050, 0x1055}, {0x105A, 0x105D}, {0x1061, 0x1061}, {0x1065, 0x1066}, {0x106E, 0x1070}, {0x1075, 0x1081}, {0x108E, 0x108E}, {0x10A0, 0x10C5}, {0x10C7, 0x10C7},
+{0x10CD, 0x10CD}, {0x10D0, 0x10FA}, {0x10FC, 0x1248}, {0x124A, 0x124D}, {0x1250, 0x1256}, {0x1258, 0x1258}, {0x125A, 0x125D}, {0x1260, 0x1288}, {0x128A, 0x128D}, {0x1290, 0x12B0}, {0x12B2, 0x12B5},
+{0x12B8, 0x12BE}, {0x12C0, 0x12C0}, {0x12C2, 0x12C5}, {0x12C8, 0x12D6}, {0x12D8, 0x1310}, {0x1312, 0x1315}, {0x1318, 0x135A}, {0x1380, 0x138F}, {0x13A0, 0x13F5}, {0x13F8, 0x13FD}, {0x1401, 0x166C},
+{0x166F, 0x167F}, {0x1681, 0x169A}, {0x16A0, 0x16EA}, {0x16F1, 0x16F8}, {0x1700, 0x170C}, {0x170E, 0x1711}, {0x1720, 0x1731}, {0x1740, 0x1751}, {0x1760, 0x176C}, {0x176E, 0x1770}, {0x1780, 0x17B3},
+{0x17D7, 0x17D7}, {0x17DC, 0x17DC}, {0x1820, 0x1878}, {0x1880, 0x1884}, {0x1887, 0x18A8}, {0x18AA, 0x18AA}, {0x18B0, 0x18F5}, {0x1900, 0x191E}, {0x1950, 0x196D}, {0x1970, 0x1974}, {0x1980, 0x19AB},
+{0x19B0, 0x19C9}, {0x1A00, 0x1A16}, {0x1A20, 0x1A54}, {0x1AA7, 0x1AA7}, {0x1B05, 0x1B33}, {0x1B45, 0x1B4B}, {0x1B83, 0x1BA0}, {0x1BAE, 0x1BAF}, {0x1BBA, 0x1BE5}, {0x1C00, 0x1C23}, {0x1C4D, 0x1C4F},
+{0x1C5A, 0x1C7D}, {0x1C80, 0x1C88}, {0x1C90, 0x1CBA}, {0x1CBD, 0x1CBF}, {0x1CE9, 0x1CEC}, {0x1CEE, 0x1CF3}, {0x1CF5, 0x1CF6}, {0x1CFA, 0x1CFA}, {0x1D00, 0x1DBF}, {0x1E00, 0x1F15}, {0x1F18, 0x1F1D},
+{0x1F20, 0x1F45}, {0x1F48, 0x1F4D}, {0x1F50, 0x1F57}, {0x1F59, 0x1F59}, {0x1F5B, 0x1F5B}, {0x1F5D, 0x1F5D}, {0x1F5F, 0x1F7D}, {0x1F80, 0x1FB4}, {0x1FB6, 0x1FBC}, {0x1FBE, 0x1FBE}, {0x1FC2, 0x1FC4},
+{0x1FC6, 0x1FCC}, {0x1FD0, 0x1FD3}, {0x1FD6, 0x1FDB}, {0x1FE0, 0x1FEC}, {0x1FF2, 0x1FF4}, {0x1FF6, 0x1FFC}, {0x2071, 0x2071}, {0x207F, 0x207F}, {0x2090, 0x209C}, {0x2102, 0x2102}, {0x2107, 0x2107},
+{0x210A, 0x2113}, {0x2115, 0x2115}, {0x2119, 0x211D}, {0x2124, 0x2124}, {0x2126, 0x2126}, {0x2128, 0x2128}, {0x212A, 0x212D}, {0x212F, 0x2139}, {0x213C, 0x213F}, {0x2145, 0x2149}, {0x214E, 0x214E},
+{0x2183, 0x2184}, {0x2C00, 0x2C2E}, {0x2C30, 0x2C5E}, {0x2C60, 0x2CE4}, {0x2CEB, 0x2CEE}, {0x2CF2, 0x2CF3}, {0x2D00, 0x2D25}, {0x2D27, 0x2D27}, {0x2D2D, 0x2D2D}, {0x2D30, 0x2D67}, {0x2D6F, 0x2D6F},
+{0x2D80, 0x2D96}, {0x2DA0, 0x2DA6}, {0x2DA8, 0x2DAE}, {0x2DB0, 0x2DB6}, {0x2DB8, 0x2DBE}, {0x2DC0, 0x2DC6}, {0x2DC8, 0x2DCE}, {0x2DD0, 0x2DD6}, {0x2DD8, 0x2DDE}, {0x2E2F, 0x2E2F}, {0x3005, 0x3006},
+{0x3031, 0x3035}, {0x303B, 0x303C}, {0x3041, 0x3096}, {0x309D, 0x309F}, {0x30A1, 0x30FA}, {0x30FC, 0x30FF}, {0x3105, 0x312F}, {0x3131, 0x318E}, {0x31A0, 0x31BF}, {0x31F0, 0x31FF}, {0x3400, 0x4DBF},
+{0x4E00, 0x9FFC}, {0xA000, 0xA48C}, {0xA4D0, 0xA4FD}, {0xA500, 0xA60C}, {0xA610, 0xA61F}, {0xA62A, 0xA62B}, {0xA640, 0xA66E}, {0xA67F, 0xA69D}, {0xA6A0, 0xA6E5}, {0xA717, 0xA71F}, {0xA722, 0xA788},
+{0xA78B, 0xA7BF}, {0xA7C2, 0xA7CA}, {0xA7F5, 0xA801}, {0xA803, 0xA805}, {0xA807, 0xA80A}, {0xA80C, 0xA822}, {0xA840, 0xA873}, {0xA882, 0xA8B3}, {0xA8F2, 0xA8F7}, {0xA8FB, 0xA8FB}, {0xA8FD, 0xA8FE},
+{0xA90A, 0xA925}, {0xA930, 0xA946}, {0xA960, 0xA97C}, {0xA984, 0xA9B2}, {0xA9CF, 0xA9CF}, {0xA9E0, 0xA9E4}, {0xA9E6, 0xA9EF}, {0xA9FA, 0xA9FE}, {0xAA00, 0xAA28}, {0xAA40, 0xAA42}, {0xAA44, 0xAA4B},
+{0xAA60, 0xAA76}, {0xAA7A, 0xAA7A}, {0xAA7E, 0xAAAF}, {0xAAB1, 0xAAB1}, {0xAAB5, 0xAAB6}, {0xAAB9, 0xAABD}, {0xAAC0, 0xAAC0}, {0xAAC2, 0xAAC2}, {0xAADB, 0xAADD}, {0xAAE0, 0xAAEA}, {0xAAF2, 0xAAF4},
+{0xAB01, 0xAB06}, {0xAB09, 0xAB0E}, {0xAB11, 0xAB16}, {0xAB20, 0xAB26}, {0xAB28, 0xAB2E}, {0xAB30, 0xAB5A}, {0xAB5C, 0xAB69}, {0xAB70, 0xABE2}, {0xAC00, 0xD7A3}, {0xD7B0, 0xD7C6}, {0xD7CB, 0xD7FB},
+{0xF900, 0xFA6D}, {0xFA70, 0xFAD9}, {0xFB00, 0xFB06}, {0xFB13, 0xFB17}, {0xFB1D, 0xFB1D}, {0xFB1F, 0xFB28}, {0xFB2A, 0xFB36}, {0xFB38, 0xFB3C}, {0xFB3E, 0xFB3E}, {0xFB40, 0xFB41}, {0xFB43, 0xFB44},
+{0xFB46, 0xFBB1}, {0xFBD3, 0xFD3D}, {0xFD50, 0xFD8F}, {0xFD92, 0xFDC7}, {0xFDF0, 0xFDFB}, {0xFE70, 0xFE74}, {0xFE76, 0xFEFC}, {0xFF21, 0xFF3A}, {0xFF41, 0xFF5A}, {0xFF66, 0xFFBE}, {0xFFC2, 0xFFC7},
+{0xFFCA, 0xFFCF}, {0xFFD2, 0xFFD7}, {0xFFDA, 0xFFDC}, {0x10000, 0x1000B}, {0x1000D, 0x10026}, {0x10028, 0x1003A}, {0x1003C, 0x1003D}, {0x1003F, 0x1004D}, {0x10050, 0x1005D}, {0x10080, 0x100FA},
+{0x10280, 0x1029C}, {0x102A0, 0x102D0}, {0x10300, 0x1031F}, {0x1032D, 0x10340}, {0x10342, 0x10349}, {0x10350, 0x10375}, {0x10380, 0x1039D}, {0x103A0, 0x103C3}, {0x103C8, 0x103CF}, {0x10400, 0x1049D},
+{0x104B0, 0x104D3}, {0x104D8, 0x104FB}, {0x10500, 0x10527}, {0x10530, 0x10563}, {0x10600, 0x10736}, {0x10740, 0x10755}, {0x10760, 0x10767}, {0x10800, 0x10805}, {0x10808, 0x10808}, {0x1080A, 0x10835},
+{0x10837, 0x10838}, {0x1083C, 0x1083C}, {0x1083F, 0x10855}, {0x10860, 0x10876}, {0x10880, 0x1089E}, {0x108E0, 0x108F2}, {0x108F4, 0x108F5}, {0x10900, 0x10915}, {0x10920, 0x10939}, {0x10980, 0x109B7},
+{0x109BE, 0x109BF}, {0x10A00, 0x10A00}, {0x10A10, 0x10A13}, {0x10A15, 0x10A17}, {0x10A19, 0x10A35}, {0x10A60, 0x10A7C}, {0x10A80, 0x10A9C}, {0x10AC0, 0x10AC7}, {0x10AC9, 0x10AE4}, {0x10B00, 0x10B35},
+{0x10B40, 0x10B55}, {0x10B60, 0x10B72}, {0x10B80, 0x10B91}, {0x10C00, 0x10C48}, {0x10C80, 0x10CB2}, {0x10CC0, 0x10CF2}, {0x10D00, 0x10D23}, {0x10E80, 0x10EA9}, {0x10EB0, 0x10EB1}, {0x10F00, 0x10F1C},
+{0x10F27, 0x10F27}, {0x10F30, 0x10F45}, {0x10FB0, 0x10FC4}, {0x10FE0, 0x10FF6}, {0x11003, 0x11037}, {0x11083, 0x110AF}, {0x110D0, 0x110E8}, {0x11103, 0x11126}, {0x11144, 0x11144}, {0x11147, 0x11147},
+{0x11150, 0x11172}, {0x11176, 0x11176}, {0x11183, 0x111B2}, {0x111C1, 0x111C4}, {0x111DA, 0x111DA}, {0x111DC, 0x111DC}, {0x11200, 0x11211}, {0x11213, 0x1122B}, {0x11280, 0x11286}, {0x11288, 0x11288},
+{0x1128A, 0x1128D}, {0x1128F, 0x1129D}, {0x1129F, 0x112A8}, {0x112B0, 0x112DE}, {0x11305, 0x1130C}, {0x1130F, 0x11310}, {0x11313, 0x11328}, {0x1132A, 0x11330}, {0x11332, 0x11333}, {0x11335, 0x11339},
+{0x1133D, 0x1133D}, {0x11350, 0x11350}, {0x1135D, 0x11361}, {0x11400, 0x11434}, {0x11447, 0x1144A}, {0x1145F, 0x11461}, {0x11480, 0x114AF}, {0x114C4, 0x114C5}, {0x114C7, 0x114C7}, {0x11580, 0x115AE},
+{0x115D8, 0x115DB}, {0x11600, 0x1162F}, {0x11644, 0x11644}, {0x11680, 0x116AA}, {0x116B8, 0x116B8}, {0x11700, 0x1171A}, {0x11800, 0x1182B}, {0x118A0, 0x118DF}, {0x118FF, 0x11906}, {0x11909, 0x11909},
+{0x1190C, 0x11913}, {0x11915, 0x11916}, {0x11918, 0x1192F}, {0x1193F, 0x1193F}, {0x11941, 0x11941}, {0x119A0, 0x119A7}, {0x119AA, 0x119D0}, {0x119E1, 0x119E1}, {0x119E3, 0x119E3}, {0x11A00, 0x11A00},
+{0x11A0B, 0x11A32}, {0x11A3A, 0x11A3A}, {0x11A50, 0x11A50}, {0x11A5C, 0x11A89}, {0x11A9D, 0x11A9D}, {0x11AC0, 0x11AF8}, {0x11C00, 0x11C08}, {0x11C0A, 0x11C2E}, {0x11C40, 0x11C40}, {0x11C72, 0x11C8F},
+{0x11D00, 0x11D06}, {0x11D08, 0x11D09}, {0x11D0B, 0x11D30}, {0x11D46, 0x11D46}, {0x11D60, 0x11D65}, {0x11D67, 0x11D68}, {0x11D6A, 0x11D89}, {0x11D98, 0x11D98}, {0x11EE0, 0x11EF2}, {0x11FB0, 0x11FB0},
+{0x12000, 0x12399}, {0x12480, 0x12543}, {0x13000, 0x1342E}, {0x14400, 0x14646}, {0x16800, 0x16A38}, {0x16A40, 0x16A5E}, {0x16AD0, 0x16AED}, {0x16B00, 0x16B2F}, {0x16B40, 0x16B43}, {0x16B63, 0x16B77},
+{0x16B7D, 0x16B8F}, {0x16E40, 0x16E7F}, {0x16F00, 0x16F4A}, {0x16F50, 0x16F50}, {0x16F93, 0x16F9F}, {0x16FE0, 0x16FE1}, {0x16FE3, 0x16FE3}, {0x17000, 0x187F7}, {0x18800, 0x18CD5}, {0x18D00, 0x18D08},
+{0x1B000, 0x1B11E}, {0x1B150, 0x1B152}, {0x1B164, 0x1B167}, {0x1B170, 0x1B2FB}, {0x1BC00, 0x1BC6A}, {0x1BC70, 0x1BC7C}, {0x1BC80, 0x1BC88}, {0x1BC90, 0x1BC99}, {0x1D400, 0x1D454}, {0x1D456, 0x1D49C},
+{0x1D49E, 0x1D49F}, {0x1D4A2, 0x1D4A2}, {0x1D4A5, 0x1D4A6}, {0x1D4A9, 0x1D4AC}, {0x1D4AE, 0x1D4B9}, {0x1D4BB, 0x1D4BB}, {0x1D4BD, 0x1D4C3}, {0x1D4C5, 0x1D505}, {0x1D507, 0x1D50A}, {0x1D50D, 0x1D514},
+{0x1D516, 0x1D51C}, {0x1D51E, 0x1D539}, {0x1D53B, 0x1D53E}, {0x1D540, 0x1D544}, {0x1D546, 0x1D546}, {0x1D54A, 0x1D550}, {0x1D552, 0x1D6A5}, {0x1D6A8, 0x1D6C0}, {0x1D6C2, 0x1D6DA}, {0x1D6DC, 0x1D6FA},
+{0x1D6FC, 0x1D714}, {0x1D716, 0x1D734}, {0x1D736, 0x1D74E}, {0x1D750, 0x1D76E}, {0x1D770, 0x1D788}, {0x1D78A, 0x1D7A8}, {0x1D7AA, 0x1D7C2}, {0x1D7C4, 0x1D7CB}, {0x1E100, 0x1E12C}, {0x1E137, 0x1E13D},
+{0x1E14E, 0x1E14E}, {0x1E2C0, 0x1E2EB}, {0x1E800, 0x1E8C4}, {0x1E900, 0x1E943}, {0x1E94B, 0x1E94B}, {0x1EE00, 0x1EE03}, {0x1EE05, 0x1EE1F}, {0x1EE21, 0x1EE22}, {0x1EE24, 0x1EE24}, {0x1EE27, 0x1EE27},
+{0x1EE29, 0x1EE32}, {0x1EE34, 0x1EE37}, {0x1EE39, 0x1EE39}, {0x1EE3B, 0x1EE3B}, {0x1EE42, 0x1EE42}, {0x1EE47, 0x1EE47}, {0x1EE49, 0x1EE49}, {0x1EE4B, 0x1EE4B}, {0x1EE4D, 0x1EE4F}, {0x1EE51, 0x1EE52},
+{0x1EE54, 0x1EE54}, {0x1EE57, 0x1EE57}, {0x1EE59, 0x1EE59}, {0x1EE5B, 0x1EE5B}, {0x1EE5D, 0x1EE5D}, {0x1EE5F, 0x1EE5F}, {0x1EE61, 0x1EE62}, {0x1EE64, 0x1EE64}, {0x1EE67, 0x1EE6A}, {0x1EE6C, 0x1EE72},
+{0x1EE74, 0x1EE77}, {0x1EE79, 0x1EE7C}, {0x1EE7E, 0x1EE7E}, {0x1EE80, 0x1EE89}, {0x1EE8B, 0x1EE9B}, {0x1EEA1, 0x1EEA3}, {0x1EEA5, 0x1EEA9}, {0x1EEAB, 0x1EEBB}, {0x20000, 0x2A6DD}, {0x2A700, 0x2B734},
+{0x2B740, 0x2B81D}, {0x2B820, 0x2CEA1}, {0x2CEB0, 0x2EBE0}, {0x2F800, 0x2FA1D}, {0x30000, 0x3134A},
+};
+
+static const std::vector<std::pair<uint32_t, uint32_t>> whitespace_ranges = {
+{0x9, 0xD}, {0x1C, 0x20}, {0x85, 0x85}, {0xA0, 0xA0}, {0x1680, 0x1680}, {0x2000, 0x200A}, {0x2028, 0x2029}, {0x202F, 0x202F}, {0x205F, 0x205F}, {0x3000, 0x3000},
+};
+
+static const std::vector<std::pair<uint32_t, uint32_t>> accent_mark_ranges = {
+{0x300, 0x36F}, {0x483, 0x489}, {0x591, 0x5BD}, {0x5BF, 0x5BF}, {0x5C1, 0x5C2}, {0x5C4, 0x5C5}, {0x5C7, 0x5C7}, {0x610, 0x61A}, {0x64B, 0x65F}, {0x670, 0x670}, {0x6D6, 0x6DC}, {0x6DF, 0x6E4},
+{0x6E7, 0x6E8}, {0x6EA, 0x6ED}, {0x711, 0x711}, {0x730, 0x74A}, {0x7A6, 0x7B0}, {0x7EB, 0x7F3}, {0x7FD, 0x7FD}, {0x816, 0x819}, {0x81B, 0x823}, {0x825, 0x827}, {0x829, 0x82D}, {0x859, 0x85B},
+{0x8D3, 0x8E1}, {0x8E3, 0x903}, {0x93A, 0x93C}, {0x93E, 0x94F}, {0x951, 0x957}, {0x962, 0x963}, {0x981, 0x983}, {0x9BC, 0x9BC}, {0x9BE, 0x9C4}, {0x9C7, 0x9C8}, {0x9CB, 0x9CD}, {0x9D7, 0x9D7},
+{0x9E2, 0x9E3}, {0x9FE, 0x9FE}, {0xA01, 0xA03}, {0xA3C, 0xA3C}, {0xA3E, 0xA42}, {0xA47, 0xA48}, {0xA4B, 0xA4D}, {0xA51, 0xA51}, {0xA70, 0xA71}, {0xA75, 0xA75}, {0xA81, 0xA83}, {0xABC, 0xABC},
+{0xABE, 0xAC5}, {0xAC7, 0xAC9}, {0xACB, 0xACD}, {0xAE2, 0xAE3}, {0xAFA, 0xAFF}, {0xB01, 0xB03}, {0xB3C, 0xB3C}, {0xB3E, 0xB44}, {0xB47, 0xB48}, {0xB4B, 0xB4D}, {0xB55, 0xB57}, {0xB62, 0xB63},
+{0xB82, 0xB82}, {0xBBE, 0xBC2}, {0xBC6, 0xBC8}, {0xBCA, 0xBCD}, {0xBD7, 0xBD7}, {0xC00, 0xC04}, {0xC3E, 0xC44}, {0xC46, 0xC48}, {0xC4A, 0xC4D}, {0xC55, 0xC56}, {0xC62, 0xC63}, {0xC81, 0xC83},
+{0xCBC, 0xCBC}, {0xCBE, 0xCC4}, {0xCC6, 0xCC8}, {0xCCA, 0xCCD}, {0xCD5, 0xCD6}, {0xCE2, 0xCE3}, {0xD00, 0xD03}, {0xD3B, 0xD3C}, {0xD3E, 0xD44}, {0xD46, 0xD48}, {0xD4A, 0xD4D}, {0xD57, 0xD57},
+{0xD62, 0xD63}, {0xD81, 0xD83}, {0xDCA, 0xDCA}, {0xDCF, 0xDD4}, {0xDD6, 0xDD6}, {0xDD8, 0xDDF}, {0xDF2, 0xDF3}, {0xE31, 0xE31}, {0xE34, 0xE3A}, {0xE47, 0xE4E}, {0xEB1, 0xEB1}, {0xEB4, 0xEBC},
+{0xEC8, 0xECD}, {0xF18, 0xF19}, {0xF35, 0xF35}, {0xF37, 0xF37}, {0xF39, 0xF39}, {0xF3E, 0xF3F}, {0xF71, 0xF84}, {0xF86, 0xF87}, {0xF8D, 0xF97}, {0xF99, 0xFBC}, {0xFC6, 0xFC6}, {0x102B, 0x103E},
+{0x1056, 0x1059}, {0x105E, 0x1060}, {0x1062, 0x1064}, {0x1067, 0x106D}, {0x1071, 0x1074}, {0x1082, 0x108D}, {0x108F, 0x108F}, {0x109A, 0x109D}, {0x135D, 0x135F}, {0x1712, 0x1714}, {0x1732, 0x1734},
+{0x1752, 0x1753}, {0x1772, 0x1773}, {0x17B4, 0x17D3}, {0x17DD, 0x17DD}, {0x180B, 0x180D}, {0x1885, 0x1886}, {0x18A9, 0x18A9}, {0x1920, 0x192B}, {0x1930, 0x193B}, {0x1A17, 0x1A1B}, {0x1A55, 0x1A5E},
+{0x1A60, 0x1A7C}, {0x1A7F, 0x1A7F}, {0x1AB0, 0x1AC0}, {0x1B00, 0x1B04}, {0x1B34, 0x1B44}, {0x1B6B, 0x1B73}, {0x1B80, 0x1B82}, {0x1BA1, 0x1BAD}, {0x1BE6, 0x1BF3}, {0x1C24, 0x1C37}, {0x1CD0, 0x1CD2},
+{0x1CD4, 0x1CE8}, {0x1CED, 0x1CED}, {0x1CF4, 0x1CF4}, {0x1CF7, 0x1CF9}, {0x1DC0, 0x1DF9}, {0x1DFB, 0x1DFF}, {0x20D0, 0x20F0}, {0x2CEF, 0x2CF1}, {0x2D7F, 0x2D7F}, {0x2DE0, 0x2DFF}, {0x302A, 0x302F},
+{0x3099, 0x309A}, {0xA66F, 0xA672}, {0xA674, 0xA67D}, {0xA69E, 0xA69F}, {0xA6F0, 0xA6F1}, {0xA802, 0xA802}, {0xA806, 0xA806}, {0xA80B, 0xA80B}, {0xA823, 0xA827}, {0xA82C, 0xA82C}, {0xA880, 0xA881},
+{0xA8B4, 0xA8C5}, {0xA8E0, 0xA8F1}, {0xA8FF, 0xA8FF}, {0xA926, 0xA92D}, {0xA947, 0xA953}, {0xA980, 0xA983}, {0xA9B3, 0xA9C0}, {0xA9E5, 0xA9E5}, {0xAA29, 0xAA36}, {0xAA43, 0xAA43}, {0xAA4C, 0xAA4D},
+{0xAA7B, 0xAA7D}, {0xAAB0, 0xAAB0}, {0xAAB2, 0xAAB4}, {0xAAB7, 0xAAB8}, {0xAABE, 0xAABF}, {0xAAC1, 0xAAC1}, {0xAAEB, 0xAAEF}, {0xAAF5, 0xAAF6}, {0xABE3, 0xABEA}, {0xABEC, 0xABED}, {0xFB1E, 0xFB1E},
+{0xFE00, 0xFE0F}, {0xFE20, 0xFE2F}, {0x101FD, 0x101FD}, {0x102E0, 0x102E0}, {0x10376, 0x1037A}, {0x10A01, 0x10A03}, {0x10A05, 0x10A06}, {0x10A0C, 0x10A0F}, {0x10A38, 0x10A3A}, {0x10A3F, 0x10A3F},
+{0x10AE5, 0x10AE6}, {0x10D24, 0x10D27}, {0x10EAB, 0x10EAC}, {0x10F46, 0x10F50}, {0x11000, 0x11002}, {0x11038, 0x11046}, {0x1107F, 0x11082}, {0x110B0, 0x110BA}, {0x11100, 0x11102}, {0x11127, 0x11134},
+{0x11145, 0x11146}, {0x11173, 0x11173}, {0x11180, 0x11182}, {0x111B3, 0x111C0}, {0x111C9, 0x111CC}, {0x111CE, 0x111CF}, {0x1122C, 0x11237}, {0x1123E, 0x1123E}, {0x112DF, 0x112EA}, {0x11300, 0x11303},
+{0x1133B, 0x1133C}, {0x1133E, 0x11344}, {0x11347, 0x11348}, {0x1134B, 0x1134D}, {0x11357, 0x11357}, {0x11362, 0x11363}, {0x11366, 0x1136C}, {0x11370, 0x11374}, {0x11435, 0x11446}, {0x1145E, 0x1145E},
+{0x114B0, 0x114C3}, {0x115AF, 0x115B5}, {0x115B8, 0x115C0}, {0x115DC, 0x115DD}, {0x11630, 0x11640}, {0x116AB, 0x116B7}, {0x1171D, 0x1172B}, {0x1182C, 0x1183A}, {0x11930, 0x11935}, {0x11937, 0x11938},
+{0x1193B, 0x1193E}, {0x11940, 0x11940}, {0x11942, 0x11943}, {0x119D1, 0x119D7}, {0x119DA, 0x119E0}, {0x119E4, 0x119E4}, {0x11A01, 0x11A0A}, {0x11A33, 0x11A39}, {0x11A3B, 0x11A3E}, {0x11A47, 0x11A47},
+{0x11A51, 0x11A5B}, {0x11A8A, 0x11A99}, {0x11C2F, 0x11C36}, {0x11C38, 0x11C3F}, {0x11C92, 0x11CA7}, {0x11CA9, 0x11CB6}, {0x11D31, 0x11D36}, {0x11D3A, 0x11D3A}, {0x11D3C, 0x11D3D}, {0x11D3F, 0x11D45},
+{0x11D47, 0x11D47}, {0x11D8A, 0x11D8E}, {0x11D90, 0x11D91}, {0x11D93, 0x11D97}, {0x11EF3, 0x11EF6}, {0x16AF0, 0x16AF4}, {0x16B30, 0x16B36}, {0x16F4F, 0x16F4F}, {0x16F51, 0x16F87}, {0x16F8F, 0x16F92},
+{0x16FE4, 0x16FE4}, {0x16FF0, 0x16FF1}, {0x1BC9D, 0x1BC9E}, {0x1D165, 0x1D169}, {0x1D16D, 0x1D172}, {0x1D17B, 0x1D182}, {0x1D185, 0x1D18B}, {0x1D1AA, 0x1D1AD}, {0x1D242, 0x1D244}, {0x1DA00, 0x1DA36},
+{0x1DA3B, 0x1DA6C}, {0x1DA75, 0x1DA75}, {0x1DA84, 0x1DA84}, {0x1DA9B, 0x1DA9F}, {0x1DAA1, 0x1DAAF}, {0x1E000, 0x1E006}, {0x1E008, 0x1E018}, {0x1E01B, 0x1E021}, {0x1E023, 0x1E024}, {0x1E026, 0x1E02A},
+{0x1E130, 0x1E136}, {0x1E2EC, 0x1E2EF}, {0x1E8D0, 0x1E8D6}, {0x1E944, 0x1E94A}, {0xE0100, 0xE01EF},
+};
+
+static const std::vector<std::pair<uint32_t, uint32_t>> punctuation_ranges = {
+{0x21, 0x23}, {0x25, 0x2A}, {0x2C, 0x2F}, {0x3A, 0x3B}, {0x3F, 0x40}, {0x5B, 0x5D}, {0x5F, 0x5F}, {0x7B, 0x7B}, {0x7D, 0x7D}, {0xA1, 0xA1}, {0xA7, 0xA7}, {0xAB, 0xAB}, {0xB6, 0xB7}, {0xBB, 0xBB},
+{0xBF, 0xBF}, {0x37E, 0x37E}, {0x387, 0x387}, {0x55A, 0x55F}, {0x589, 0x58A}, {0x5BE, 0x5BE}, {0x5C0, 0x5C0}, {0x5C3, 0x5C3}, {0x5C6, 0x5C6}, {0x5F3, 0x5F4}, {0x609, 0x60A}, {0x60C, 0x60D},
+{0x61B, 0x61B}, {0x61E, 0x61F}, {0x66A, 0x66D}, {0x6D4, 0x6D4}, {0x700, 0x70D}, {0x7F7, 0x7F9}, {0x830, 0x83E}, {0x85E, 0x85E}, {0x964, 0x965}, {0x970, 0x970}, {0x9FD, 0x9FD}, {0xA76, 0xA76},
+{0xAF0, 0xAF0}, {0xC77, 0xC77}, {0xC84, 0xC84}, {0xDF4, 0xDF4}, {0xE4F, 0xE4F}, {0xE5A, 0xE5B}, {0xF04, 0xF12}, {0xF14, 0xF14}, {0xF3A, 0xF3D}, {0xF85, 0xF85}, {0xFD0, 0xFD4}, {0xFD9, 0xFDA},
+{0x104A, 0x104F}, {0x10FB, 0x10FB}, {0x1360, 0x1368}, {0x1400, 0x1400}, {0x166E, 0x166E}, {0x169B, 0x169C}, {0x16EB, 0x16ED}, {0x1735, 0x1736}, {0x17D4, 0x17D6}, {0x17D8, 0x17DA}, {0x1800, 0x180A},
+{0x1944, 0x1945}, {0x1A1E, 0x1A1F}, {0x1AA0, 0x1AA6}, {0x1AA8, 0x1AAD}, {0x1B5A, 0x1B60}, {0x1BFC, 0x1BFF}, {0x1C3B, 0x1C3F}, {0x1C7E, 0x1C7F}, {0x1CC0, 0x1CC7}, {0x1CD3, 0x1CD3}, {0x2010, 0x2027},
+{0x2030, 0x2043}, {0x2045, 0x2051}, {0x2053, 0x205E}, {0x207D, 0x207E}, {0x208D, 0x208E}, {0x2308, 0x230B}, {0x2329, 0x232A}, {0x2768, 0x2775}, {0x27C5, 0x27C6}, {0x27E6, 0x27EF}, {0x2983, 0x2998},
+{0x29D8, 0x29DB}, {0x29FC, 0x29FD}, {0x2CF9, 0x2CFC}, {0x2CFE, 0x2CFF}, {0x2D70, 0x2D70}, {0x2E00, 0x2E2E}, {0x2E30, 0x2E4F}, {0x2E52, 0x2E52}, {0x3001, 0x3003}, {0x3008, 0x3011}, {0x3014, 0x301F},
+{0x3030, 0x3030}, {0x303D, 0x303D}, {0x30A0, 0x30A0}, {0x30FB, 0x30FB}, {0xA4FE, 0xA4FF}, {0xA60D, 0xA60F}, {0xA673, 0xA673}, {0xA67E, 0xA67E}, {0xA6F2, 0xA6F7}, {0xA874, 0xA877}, {0xA8CE, 0xA8CF},
+{0xA8F8, 0xA8FA}, {0xA8FC, 0xA8FC}, {0xA92E, 0xA92F}, {0xA95F, 0xA95F}, {0xA9C1, 0xA9CD}, {0xA9DE, 0xA9DF}, {0xAA5C, 0xAA5F}, {0xAADE, 0xAADF}, {0xAAF0, 0xAAF1}, {0xABEB, 0xABEB}, {0xFD3E, 0xFD3F},
+{0xFE10, 0xFE19}, {0xFE30, 0xFE52}, {0xFE54, 0xFE61}, {0xFE63, 0xFE63}, {0xFE68, 0xFE68}, {0xFE6A, 0xFE6B}, {0xFF01, 0xFF03}, {0xFF05, 0xFF0A}, {0xFF0C, 0xFF0F}, {0xFF1A, 0xFF1B}, {0xFF1F, 0xFF20},
+{0xFF3B, 0xFF3D}, {0xFF3F, 0xFF3F}, {0xFF5B, 0xFF5B}, {0xFF5D, 0xFF5D}, {0xFF5F, 0xFF65}, {0x10100, 0x10102}, {0x1039F, 0x1039F}, {0x103D0, 0x103D0}, {0x1056F, 0x1056F}, {0x10857, 0x10857},
+{0x1091F, 0x1091F}, {0x1093F, 0x1093F}, {0x10A50, 0x10A58}, {0x10A7F, 0x10A7F}, {0x10AF0, 0x10AF6}, {0x10B39, 0x10B3F}, {0x10B99, 0x10B9C}, {0x10EAD, 0x10EAD}, {0x10F55, 0x10F59}, {0x11047, 0x1104D},
+{0x110BB, 0x110BC}, {0x110BE, 0x110C1}, {0x11140, 0x11143}, {0x11174, 0x11175}, {0x111C5, 0x111C8}, {0x111CD, 0x111CD}, {0x111DB, 0x111DB}, {0x111DD, 0x111DF}, {0x11238, 0x1123D}, {0x112A9, 0x112A9},
+{0x1144B, 0x1144F}, {0x1145A, 0x1145B}, {0x1145D, 0x1145D}, {0x114C6, 0x114C6}, {0x115C1, 0x115D7}, {0x11641, 0x11643}, {0x11660, 0x1166C}, {0x1173C, 0x1173E}, {0x1183B, 0x1183B}, {0x11944, 0x11946},
+{0x119E2, 0x119E2}, {0x11A3F, 0x11A46}, {0x11A9A, 0x11A9C}, {0x11A9E, 0x11AA2}, {0x11C41, 0x11C45}, {0x11C70, 0x11C71}, {0x11EF7, 0x11EF8}, {0x11FFF, 0x11FFF}, {0x12470, 0x12474}, {0x16A6E, 0x16A6F},
+{0x16AF5, 0x16AF5}, {0x16B37, 0x16B3B}, {0x16B44, 0x16B44}, {0x16E97, 0x16E9A}, {0x16FE2, 0x16FE2}, {0x1BC9F, 0x1BC9F}, {0x1DA87, 0x1DA8B}, {0x1E95E, 0x1E95F},
+};
+
+static const std::vector<std::pair<uint32_t, uint32_t>> symbol_ranges = {
+{0x24, 0x24}, {0x2B, 0x2B}, {0x3C, 0x3E}, {0x5E, 0x5E}, {0x60, 0x60}, {0x7C, 0x7C}, {0x7E, 0x7E}, {0xA2, 0xA6}, {0xA8, 0xA9}, {0xAC, 0xAC}, {0xAE, 0xB1}, {0xB4, 0xB4}, {0xB8, 0xB8}, {0xD7, 0xD7},
+{0xF7, 0xF7}, {0x2C2, 0x2C5}, {0x2D2, 0x2DF}, {0x2E5, 0x2EB}, {0x2ED, 0x2ED}, {0x2EF, 0x2FF}, {0x375, 0x375}, {0x384, 0x385}, {0x3F6, 0x3F6}, {0x482, 0x482}, {0x58D, 0x58F}, {0x606, 0x608},
+{0x60B, 0x60B}, {0x60E, 0x60F}, {0x6DE, 0x6DE}, {0x6E9, 0x6E9}, {0x6FD, 0x6FE}, {0x7F6, 0x7F6}, {0x7FE, 0x7FF}, {0x9F2, 0x9F3}, {0x9FA, 0x9FB}, {0xAF1, 0xAF1}, {0xB70, 0xB70}, {0xBF3, 0xBFA},
+{0xC7F, 0xC7F}, {0xD4F, 0xD4F}, {0xD79, 0xD79}, {0xE3F, 0xE3F}, {0xF01, 0xF03}, {0xF13, 0xF13}, {0xF15, 0xF17}, {0xF1A, 0xF1F}, {0xF34, 0xF34}, {0xF36, 0xF36}, {0xF38, 0xF38}, {0xFBE, 0xFC5},
+{0xFC7, 0xFCC}, {0xFCE, 0xFCF}, {0xFD5, 0xFD8}, {0x109E, 0x109F}, {0x1390, 0x1399}, {0x166D, 0x166D}, {0x17DB, 0x17DB}, {0x1940, 0x1940}, {0x19DE, 0x19FF}, {0x1B61, 0x1B6A}, {0x1B74, 0x1B7C},
+{0x1FBD, 0x1FBD}, {0x1FBF, 0x1FC1}, {0x1FCD, 0x1FCF}, {0x1FDD, 0x1FDF}, {0x1FED, 0x1FEF}, {0x1FFD, 0x1FFE}, {0x2044, 0x2044}, {0x2052, 0x2052}, {0x207A, 0x207C}, {0x208A, 0x208C}, {0x20A0, 0x20BF},
+{0x2100, 0x2101}, {0x2103, 0x2106}, {0x2108, 0x2109}, {0x2114, 0x2114}, {0x2116, 0x2118}, {0x211E, 0x2123}, {0x2125, 0x2125}, {0x2127, 0x2127}, {0x2129, 0x2129}, {0x212E, 0x212E}, {0x213A, 0x213B},
+{0x2140, 0x2144}, {0x214A, 0x214D}, {0x214F, 0x214F}, {0x218A, 0x218B}, {0x2190, 0x2307}, {0x230C, 0x2328}, {0x232B, 0x2426}, {0x2440, 0x244A}, {0x249C, 0x24E9}, {0x2500, 0x2767}, {0x2794, 0x27C4},
+{0x27C7, 0x27E5}, {0x27F0, 0x2982}, {0x2999, 0x29D7}, {0x29DC, 0x29FB}, {0x29FE, 0x2B73}, {0x2B76, 0x2B95}, {0x2B97, 0x2BFF}, {0x2CE5, 0x2CEA}, {0x2E50, 0x2E51}, {0x2E80, 0x2E99}, {0x2E9B, 0x2EF3},
+{0x2F00, 0x2FD5}, {0x2FF0, 0x2FFB}, {0x3004, 0x3004}, {0x3012, 0x3013}, {0x3020, 0x3020}, {0x3036, 0x3037}, {0x303E, 0x303F}, {0x309B, 0x309C}, {0x3190, 0x3191}, {0x3196, 0x319F}, {0x31C0, 0x31E3},
+{0x3200, 0x321E}, {0x322A, 0x3247}, {0x3250, 0x3250}, {0x3260, 0x327F}, {0x328A, 0x32B0}, {0x32C0, 0x33FF}, {0x4DC0, 0x4DFF}, {0xA490, 0xA4C6}, {0xA700, 0xA716}, {0xA720, 0xA721}, {0xA789, 0xA78A},
+{0xA828, 0xA82B}, {0xA836, 0xA839}, {0xAA77, 0xAA79}, {0xAB5B, 0xAB5B}, {0xAB6A, 0xAB6B}, {0xFB29, 0xFB29}, {0xFBB2, 0xFBC1}, {0xFDFC, 0xFDFD}, {0xFE62, 0xFE62}, {0xFE64, 0xFE66}, {0xFE69, 0xFE69},
+{0xFF04, 0xFF04}, {0xFF0B, 0xFF0B}, {0xFF1C, 0xFF1E}, {0xFF3E, 0xFF3E}, {0xFF40, 0xFF40}, {0xFF5C, 0xFF5C}, {0xFF5E, 0xFF5E}, {0xFFE0, 0xFFE6}, {0xFFE8, 0xFFEE}, {0xFFFC, 0xFFFD}, {0x10137, 0x1013F},
+{0x10179, 0x10189}, {0x1018C, 0x1018E}, {0x10190, 0x1019C}, {0x101A0, 0x101A0}, {0x101D0, 0x101FC}, {0x10877, 0x10878}, {0x10AC8, 0x10AC8}, {0x1173F, 0x1173F}, {0x11FD5, 0x11FF1}, {0x16B3C, 0x16B3F},
+{0x16B45, 0x16B45}, {0x1BC9C, 0x1BC9C}, {0x1D000, 0x1D0F5}, {0x1D100, 0x1D126}, {0x1D129, 0x1D164}, {0x1D16A, 0x1D16C}, {0x1D183, 0x1D184}, {0x1D18C, 0x1D1A9}, {0x1D1AE, 0x1D1E8}, {0x1D200, 0x1D241},
+{0x1D245, 0x1D245}, {0x1D300, 0x1D356}, {0x1D6C1, 0x1D6C1}, {0x1D6DB, 0x1D6DB}, {0x1D6FB, 0x1D6FB}, {0x1D715, 0x1D715}, {0x1D735, 0x1D735}, {0x1D74F, 0x1D74F}, {0x1D76F, 0x1D76F}, {0x1D789, 0x1D789},
+{0x1D7A9, 0x1D7A9}, {0x1D7C3, 0x1D7C3}, {0x1D800, 0x1D9FF}, {0x1DA37, 0x1DA3A}, {0x1DA6D, 0x1DA74}, {0x1DA76, 0x1DA83}, {0x1DA85, 0x1DA86}, {0x1E14F, 0x1E14F}, {0x1E2FF, 0x1E2FF}, {0x1ECAC, 0x1ECAC},
+{0x1ECB0, 0x1ECB0}, {0x1ED2E, 0x1ED2E}, {0x1EEF0, 0x1EEF1}, {0x1F000, 0x1F02B}, {0x1F030, 0x1F093}, {0x1F0A0, 0x1F0AE}, {0x1F0B1, 0x1F0BF}, {0x1F0C1, 0x1F0CF}, {0x1F0D1, 0x1F0F5}, {0x1F10D, 0x1F1AD},
+{0x1F1E6, 0x1F202}, {0x1F210, 0x1F23B}, {0x1F240, 0x1F248}, {0x1F250, 0x1F251}, {0x1F260, 0x1F265}, {0x1F300, 0x1F6D7}, {0x1F6E0, 0x1F6EC}, {0x1F6F0, 0x1F6FC}, {0x1F700, 0x1F773}, {0x1F780, 0x1F7D8},
+{0x1F7E0, 0x1F7EB}, {0x1F800, 0x1F80B}, {0x1F810, 0x1F847}, {0x1F850, 0x1F859}, {0x1F860, 0x1F887}, {0x1F890, 0x1F8AD}, {0x1F8B0, 0x1F8B1}, {0x1F900, 0x1F978}, {0x1F97A, 0x1F9CB}, {0x1F9CD, 0x1FA53},
+{0x1FA60, 0x1FA6D}, {0x1FA70, 0x1FA74}, {0x1FA78, 0x1FA7A}, {0x1FA80, 0x1FA86}, {0x1FA90, 0x1FAA8}, {0x1FAB0, 0x1FAB6}, {0x1FAC0, 0x1FAC2}, {0x1FAD0, 0x1FAD6}, {0x1FB00, 0x1FB92}, {0x1FB94, 0x1FBCA},
+};
+
+static const std::vector<std::pair<uint32_t, uint32_t>> control_ranges = {
+{0x0, 0x8}, {0xE, 0x1B}, {0x7F, 0x84}, {0x86, 0x9F}, {0xAD, 0xAD}, {0x378, 0x379}, {0x380, 0x383}, {0x38B, 0x38B}, {0x38D, 0x38D}, {0x3A2, 0x3A2}, {0x530, 0x530}, {0x557, 0x558}, {0x58B, 0x58C},
+{0x590, 0x590}, {0x5C8, 0x5CF}, {0x5EB, 0x5EE}, {0x5F5, 0x605}, {0x61C, 0x61D}, {0x6DD, 0x6DD}, {0x70E, 0x70F}, {0x74B, 0x74C}, {0x7B2, 0x7BF}, {0x7FB, 0x7FC}, {0x82E, 0x82F}, {0x83F, 0x83F},
+{0x85C, 0x85D}, {0x85F, 0x85F}, {0x86B, 0x89F}, {0x8B5, 0x8B5}, {0x8C8, 0x8D2}, {0x8E2, 0x8E2}, {0x984, 0x984}, {0x98D, 0x98E}, {0x991, 0x992}, {0x9A9, 0x9A9}, {0x9B1, 0x9B1}, {0x9B3, 0x9B5},
+{0x9BA, 0x9BB}, {0x9C5, 0x9C6}, {0x9C9, 0x9CA}, {0x9CF, 0x9D6}, {0x9D8, 0x9DB}, {0x9DE, 0x9DE}, {0x9E4, 0x9E5}, {0x9FF, 0xA00}, {0xA04, 0xA04}, {0xA0B, 0xA0E}, {0xA11, 0xA12}, {0xA29, 0xA29},
+{0xA31, 0xA31}, {0xA34, 0xA34}, {0xA37, 0xA37}, {0xA3A, 0xA3B}, {0xA3D, 0xA3D}, {0xA43, 0xA46}, {0xA49, 0xA4A}, {0xA4E, 0xA50}, {0xA52, 0xA58}, {0xA5D, 0xA5D}, {0xA5F, 0xA65}, {0xA77, 0xA80},
+{0xA84, 0xA84}, {0xA8E, 0xA8E}, {0xA92, 0xA92}, {0xAA9, 0xAA9}, {0xAB1, 0xAB1}, {0xAB4, 0xAB4}, {0xABA, 0xABB}, {0xAC6, 0xAC6}, {0xACA, 0xACA}, {0xACE, 0xACF}, {0xAD1, 0xADF}, {0xAE4, 0xAE5},
+{0xAF2, 0xAF8}, {0xB00, 0xB00}, {0xB04, 0xB04}, {0xB0D, 0xB0E}, {0xB11, 0xB12}, {0xB29, 0xB29}, {0xB31, 0xB31}, {0xB34, 0xB34}, {0xB3A, 0xB3B}, {0xB45, 0xB46}, {0xB49, 0xB4A}, {0xB4E, 0xB54},
+{0xB58, 0xB5B}, {0xB5E, 0xB5E}, {0xB64, 0xB65}, {0xB78, 0xB81}, {0xB84, 0xB84}, {0xB8B, 0xB8D}, {0xB91, 0xB91}, {0xB96, 0xB98}, {0xB9B, 0xB9B}, {0xB9D, 0xB9D}, {0xBA0, 0xBA2}, {0xBA5, 0xBA7},
+{0xBAB, 0xBAD}, {0xBBA, 0xBBD}, {0xBC3, 0xBC5}, {0xBC9, 0xBC9}, {0xBCE, 0xBCF}, {0xBD1, 0xBD6}, {0xBD8, 0xBE5}, {0xBFB, 0xBFF}, {0xC0D, 0xC0D}, {0xC11, 0xC11}, {0xC29, 0xC29}, {0xC3A, 0xC3C},
+{0xC45, 0xC45}, {0xC49, 0xC49}, {0xC4E, 0xC54}, {0xC57, 0xC57}, {0xC5B, 0xC5F}, {0xC64, 0xC65}, {0xC70, 0xC76}, {0xC8D, 0xC8D}, {0xC91, 0xC91}, {0xCA9, 0xCA9}, {0xCB4, 0xCB4}, {0xCBA, 0xCBB},
+{0xCC5, 0xCC5}, {0xCC9, 0xCC9}, {0xCCE, 0xCD4}, {0xCD7, 0xCDD}, {0xCDF, 0xCDF}, {0xCE4, 0xCE5}, {0xCF0, 0xCF0}, {0xCF3, 0xCFF}, {0xD0D, 0xD0D}, {0xD11, 0xD11}, {0xD45, 0xD45}, {0xD49, 0xD49},
+{0xD50, 0xD53}, {0xD64, 0xD65}, {0xD80, 0xD80}, {0xD84, 0xD84}, {0xD97, 0xD99}, {0xDB2, 0xDB2}, {0xDBC, 0xDBC}, {0xDBE, 0xDBF}, {0xDC7, 0xDC9}, {0xDCB, 0xDCE}, {0xDD5, 0xDD5}, {0xDD7, 0xDD7},
+{0xDE0, 0xDE5}, {0xDF0, 0xDF1}, {0xDF5, 0xE00}, {0xE3B, 0xE3E}, {0xE5C, 0xE80}, {0xE83, 0xE83}, {0xE85, 0xE85}, {0xE8B, 0xE8B}, {0xEA4, 0xEA4}, {0xEA6, 0xEA6}, {0xEBE, 0xEBF}, {0xEC5, 0xEC5},
+{0xEC7, 0xEC7}, {0xECE, 0xECF}, {0xEDA, 0xEDB}, {0xEE0, 0xEFF}, {0xF48, 0xF48}, {0xF6D, 0xF70}, {0xF98, 0xF98}, {0xFBD, 0xFBD}, {0xFCD, 0xFCD}, {0xFDB, 0xFFF}, {0x10C6, 0x10C6}, {0x10C8, 0x10CC},
+{0x10CE, 0x10CF}, {0x1249, 0x1249}, {0x124E, 0x124F}, {0x1257, 0x1257}, {0x1259, 0x1259}, {0x125E, 0x125F}, {0x1289, 0x1289}, {0x128E, 0x128F}, {0x12B1, 0x12B1}, {0x12B6, 0x12B7}, {0x12BF, 0x12BF},
+{0x12C1, 0x12C1}, {0x12C6, 0x12C7}, {0x12D7, 0x12D7}, {0x1311, 0x1311}, {0x1316, 0x1317}, {0x135B, 0x135C}, {0x137D, 0x137F}, {0x139A, 0x139F}, {0x13F6, 0x13F7}, {0x13FE, 0x13FF}, {0x169D, 0x169F},
+{0x16F9, 0x16FF}, {0x170D, 0x170D}, {0x1715, 0x171F}, {0x1737, 0x173F}, {0x1754, 0x175F}, {0x176D, 0x176D}, {0x1771, 0x1771}, {0x1774, 0x177F}, {0x17DE, 0x17DF}, {0x17EA, 0x17EF}, {0x17FA, 0x17FF},
+{0x180E, 0x180F}, {0x181A, 0x181F}, {0x1879, 0x187F}, {0x18AB, 0x18AF}, {0x18F6, 0x18FF}, {0x191F, 0x191F}, {0x192C, 0x192F}, {0x193C, 0x193F}, {0x1941, 0x1943}, {0x196E, 0x196F}, {0x1975, 0x197F},
+{0x19AC, 0x19AF}, {0x19CA, 0x19CF}, {0x19DB, 0x19DD}, {0x1A1C, 0x1A1D}, {0x1A5F, 0x1A5F}, {0x1A7D, 0x1A7E}, {0x1A8A, 0x1A8F}, {0x1A9A, 0x1A9F}, {0x1AAE, 0x1AAF}, {0x1AC1, 0x1AFF}, {0x1B4C, 0x1B4F},
+{0x1B7D, 0x1B7F}, {0x1BF4, 0x1BFB}, {0x1C38, 0x1C3A}, {0x1C4A, 0x1C4C}, {0x1C89, 0x1C8F}, {0x1CBB, 0x1CBC}, {0x1CC8, 0x1CCF}, {0x1CFB, 0x1CFF}, {0x1DFA, 0x1DFA}, {0x1F16, 0x1F17}, {0x1F1E, 0x1F1F},
+{0x1F46, 0x1F47}, {0x1F4E, 0x1F4F}, {0x1F58, 0x1F58}, {0x1F5A, 0x1F5A}, {0x1F5C, 0x1F5C}, {0x1F5E, 0x1F5E}, {0x1F7E, 0x1F7F}, {0x1FB5, 0x1FB5}, {0x1FC5, 0x1FC5}, {0x1FD4, 0x1FD5}, {0x1FDC, 0x1FDC},
+{0x1FF0, 0x1FF1}, {0x1FF5, 0x1FF5}, {0x1FFF, 0x1FFF}, {0x200B, 0x200F}, {0x202A, 0x202E}, {0x2060, 0x206F}, {0x2072, 0x2073}, {0x208F, 0x208F}, {0x209D, 0x209F}, {0x20C0, 0x20CF}, {0x20F1, 0x20FF},
+{0x218C, 0x218F}, {0x2427, 0x243F}, {0x244B, 0x245F}, {0x2B74, 0x2B75}, {0x2B96, 0x2B96}, {0x2C2F, 0x2C2F}, {0x2C5F, 0x2C5F}, {0x2CF4, 0x2CF8}, {0x2D26, 0x2D26}, {0x2D28, 0x2D2C}, {0x2D2E, 0x2D2F},
+{0x2D68, 0x2D6E}, {0x2D71, 0x2D7E}, {0x2D97, 0x2D9F}, {0x2DA7, 0x2DA7}, {0x2DAF, 0x2DAF}, {0x2DB7, 0x2DB7}, {0x2DBF, 0x2DBF}, {0x2DC7, 0x2DC7}, {0x2DCF, 0x2DCF}, {0x2DD7, 0x2DD7}, {0x2DDF, 0x2DDF},
+{0x2E53, 0x2E7F}, {0x2E9A, 0x2E9A}, {0x2EF4, 0x2EFF}, {0x2FD6, 0x2FEF}, {0x2FFC, 0x2FFF}, {0x3040, 0x3040}, {0x3097, 0x3098}, {0x3100, 0x3104}, {0x3130, 0x3130}, {0x318F, 0x318F}, {0x31E4, 0x31EF},
+{0x321F, 0x321F}, {0x9FFD, 0x9FFF}, {0xA48D, 0xA48F}, {0xA4C7, 0xA4CF}, {0xA62C, 0xA63F}, {0xA6F8, 0xA6FF}, {0xA7C0, 0xA7C1}, {0xA7CB, 0xA7F4}, {0xA82D, 0xA82F}, {0xA83A, 0xA83F}, {0xA878, 0xA87F},
+{0xA8C6, 0xA8CD}, {0xA8DA, 0xA8DF}, {0xA954, 0xA95E}, {0xA97D, 0xA97F}, {0xA9CE, 0xA9CE}, {0xA9DA, 0xA9DD}, {0xA9FF, 0xA9FF}, {0xAA37, 0xAA3F}, {0xAA4E, 0xAA4F}, {0xAA5A, 0xAA5B}, {0xAAC3, 0xAADA},
+{0xAAF7, 0xAB00}, {0xAB07, 0xAB08}, {0xAB0F, 0xAB10}, {0xAB17, 0xAB1F}, {0xAB27, 0xAB27}, {0xAB2F, 0xAB2F}, {0xAB6C, 0xAB6F}, {0xABEE, 0xABEF}, {0xABFA, 0xABFF}, {0xD7A4, 0xD7AF}, {0xD7C7, 0xD7CA},
+{0xD7FC, 0xF8FF}, {0xFA6E, 0xFA6F}, {0xFADA, 0xFAFF}, {0xFB07, 0xFB12}, {0xFB18, 0xFB1C}, {0xFB37, 0xFB37}, {0xFB3D, 0xFB3D}, {0xFB3F, 0xFB3F}, {0xFB42, 0xFB42}, {0xFB45, 0xFB45}, {0xFBC2, 0xFBD2},
+{0xFD40, 0xFD4F}, {0xFD90, 0xFD91}, {0xFDC8, 0xFDEF}, {0xFDFE, 0xFDFF}, {0xFE1A, 0xFE1F}, {0xFE53, 0xFE53}, {0xFE67, 0xFE67}, {0xFE6C, 0xFE6F}, {0xFE75, 0xFE75}, {0xFEFD, 0xFF00}, {0xFFBF, 0xFFC1},
+{0xFFC8, 0xFFC9}, {0xFFD0, 0xFFD1}, {0xFFD8, 0xFFD9}, {0xFFDD, 0xFFDF}, {0xFFE7, 0xFFE7}, {0xFFEF, 0xFFFB}, {0xFFFE, 0xFFFF}, {0x1000C, 0x1000C}, {0x10027, 0x10027}, {0x1003B, 0x1003B},
+{0x1003E, 0x1003E}, {0x1004E, 0x1004F}, {0x1005E, 0x1007F}, {0x100FB, 0x100FF}, {0x10103, 0x10106}, {0x10134, 0x10136}, {0x1018F, 0x1018F}, {0x1019D, 0x1019F}, {0x101A1, 0x101CF}, {0x101FE, 0x1027F},
+{0x1029D, 0x1029F}, {0x102D1, 0x102DF}, {0x102FC, 0x102FF}, {0x10324, 0x1032C}, {0x1034B, 0x1034F}, {0x1037B, 0x1037F}, {0x1039E, 0x1039E}, {0x103C4, 0x103C7}, {0x103D6, 0x103FF}, {0x1049E, 0x1049F},
+{0x104AA, 0x104AF}, {0x104D4, 0x104D7}, {0x104FC, 0x104FF}, {0x10528, 0x1052F}, {0x10564, 0x1056E}, {0x10570, 0x105FF}, {0x10737, 0x1073F}, {0x10756, 0x1075F}, {0x10768, 0x107FF}, {0x10806, 0x10807},
+{0x10809, 0x10809}, {0x10836, 0x10836}, {0x10839, 0x1083B}, {0x1083D, 0x1083E}, {0x10856, 0x10856}, {0x1089F, 0x108A6}, {0x108B0, 0x108DF}, {0x108F3, 0x108F3}, {0x108F6, 0x108FA}, {0x1091C, 0x1091E},
+{0x1093A, 0x1093E}, {0x10940, 0x1097F}, {0x109B8, 0x109BB}, {0x109D0, 0x109D1}, {0x10A04, 0x10A04}, {0x10A07, 0x10A0B}, {0x10A14, 0x10A14}, {0x10A18, 0x10A18}, {0x10A36, 0x10A37}, {0x10A3B, 0x10A3E},
+{0x10A49, 0x10A4F}, {0x10A59, 0x10A5F}, {0x10AA0, 0x10ABF}, {0x10AE7, 0x10AEA}, {0x10AF7, 0x10AFF}, {0x10B36, 0x10B38}, {0x10B56, 0x10B57}, {0x10B73, 0x10B77}, {0x10B92, 0x10B98}, {0x10B9D, 0x10BA8},
+{0x10BB0, 0x10BFF}, {0x10C49, 0x10C7F}, {0x10CB3, 0x10CBF}, {0x10CF3, 0x10CF9}, {0x10D28, 0x10D2F}, {0x10D3A, 0x10E5F}, {0x10E7F, 0x10E7F}, {0x10EAA, 0x10EAA}, {0x10EAE, 0x10EAF}, {0x10EB2, 0x10EFF},
+{0x10F28, 0x10F2F}, {0x10F5A, 0x10FAF}, {0x10FCC, 0x10FDF}, {0x10FF7, 0x10FFF}, {0x1104E, 0x11051}, {0x11070, 0x1107E}, {0x110BD, 0x110BD}, {0x110C2, 0x110CF}, {0x110E9, 0x110EF}, {0x110FA, 0x110FF},
+{0x11135, 0x11135}, {0x11148, 0x1114F}, {0x11177, 0x1117F}, {0x111E0, 0x111E0}, {0x111F5, 0x111FF}, {0x11212, 0x11212}, {0x1123F, 0x1127F}, {0x11287, 0x11287}, {0x11289, 0x11289}, {0x1128E, 0x1128E},
+{0x1129E, 0x1129E}, {0x112AA, 0x112AF}, {0x112EB, 0x112EF}, {0x112FA, 0x112FF}, {0x11304, 0x11304}, {0x1130D, 0x1130E}, {0x11311, 0x11312}, {0x11329, 0x11329}, {0x11331, 0x11331}, {0x11334, 0x11334},
+{0x1133A, 0x1133A}, {0x11345, 0x11346}, {0x11349, 0x1134A}, {0x1134E, 0x1134F}, {0x11351, 0x11356}, {0x11358, 0x1135C}, {0x11364, 0x11365}, {0x1136D, 0x1136F}, {0x11375, 0x113FF}, {0x1145C, 0x1145C},
+{0x11462, 0x1147F}, {0x114C8, 0x114CF}, {0x114DA, 0x1157F}, {0x115B6, 0x115B7}, {0x115DE, 0x115FF}, {0x11645, 0x1164F}, {0x1165A, 0x1165F}, {0x1166D, 0x1167F}, {0x116B9, 0x116BF}, {0x116CA, 0x116FF},
+{0x1171B, 0x1171C}, {0x1172C, 0x1172F}, {0x11740, 0x117FF}, {0x1183C, 0x1189F}, {0x118F3, 0x118FE}, {0x11907, 0x11908}, {0x1190A, 0x1190B}, {0x11914, 0x11914}, {0x11917, 0x11917}, {0x11936, 0x11936},
+{0x11939, 0x1193A}, {0x11947, 0x1194F}, {0x1195A, 0x1199F}, {0x119A8, 0x119A9}, {0x119D8, 0x119D9}, {0x119E5, 0x119FF}, {0x11A48, 0x11A4F}, {0x11AA3, 0x11ABF}, {0x11AF9, 0x11BFF}, {0x11C09, 0x11C09},
+{0x11C37, 0x11C37}, {0x11C46, 0x11C4F}, {0x11C6D, 0x11C6F}, {0x11C90, 0x11C91}, {0x11CA8, 0x11CA8}, {0x11CB7, 0x11CFF}, {0x11D07, 0x11D07}, {0x11D0A, 0x11D0A}, {0x11D37, 0x11D39}, {0x11D3B, 0x11D3B},
+{0x11D3E, 0x11D3E}, {0x11D48, 0x11D4F}, {0x11D5A, 0x11D5F}, {0x11D66, 0x11D66}, {0x11D69, 0x11D69}, {0x11D8F, 0x11D8F}, {0x11D92, 0x11D92}, {0x11D99, 0x11D9F}, {0x11DAA, 0x11EDF}, {0x11EF9, 0x11FAF},
+{0x11FB1, 0x11FBF}, {0x11FF2, 0x11FFE}, {0x1239A, 0x123FF}, {0x1246F, 0x1246F}, {0x12475, 0x1247F}, {0x12544, 0x12FFF}, {0x1342F, 0x143FF}, {0x14647, 0x167FF}, {0x16A39, 0x16A3F}, {0x16A5F, 0x16A5F},
+{0x16A6A, 0x16A6D}, {0x16A70, 0x16ACF}, {0x16AEE, 0x16AEF}, {0x16AF6, 0x16AFF}, {0x16B46, 0x16B4F}, {0x16B5A, 0x16B5A}, {0x16B62, 0x16B62}, {0x16B78, 0x16B7C}, {0x16B90, 0x16E3F}, {0x16E9B, 0x16EFF},
+{0x16F4B, 0x16F4E}, {0x16F88, 0x16F8E}, {0x16FA0, 0x16FDF}, {0x16FE5, 0x16FEF}, {0x16FF2, 0x16FFF}, {0x187F8, 0x187FF}, {0x18CD6, 0x18CFF}, {0x18D09, 0x1AFFF}, {0x1B11F, 0x1B14F}, {0x1B153, 0x1B163},
+{0x1B168, 0x1B16F}, {0x1B2FC, 0x1BBFF}, {0x1BC6B, 0x1BC6F}, {0x1BC7D, 0x1BC7F}, {0x1BC89, 0x1BC8F}, {0x1BC9A, 0x1BC9B}, {0x1BCA0, 0x1CFFF}, {0x1D0F6, 0x1D0FF}, {0x1D127, 0x1D128}, {0x1D173, 0x1D17A},
+{0x1D1E9, 0x1D1FF}, {0x1D246, 0x1D2DF}, {0x1D2F4, 0x1D2FF}, {0x1D357, 0x1D35F}, {0x1D379, 0x1D3FF}, {0x1D455, 0x1D455}, {0x1D49D, 0x1D49D}, {0x1D4A0, 0x1D4A1}, {0x1D4A3, 0x1D4A4}, {0x1D4A7, 0x1D4A8},
+{0x1D4AD, 0x1D4AD}, {0x1D4BA, 0x1D4BA}, {0x1D4BC, 0x1D4BC}, {0x1D4C4, 0x1D4C4}, {0x1D506, 0x1D506}, {0x1D50B, 0x1D50C}, {0x1D515, 0x1D515}, {0x1D51D, 0x1D51D}, {0x1D53A, 0x1D53A}, {0x1D53F, 0x1D53F},
+{0x1D545, 0x1D545}, {0x1D547, 0x1D549}, {0x1D551, 0x1D551}, {0x1D6A6, 0x1D6A7}, {0x1D7CC, 0x1D7CD}, {0x1DA8C, 0x1DA9A}, {0x1DAA0, 0x1DAA0}, {0x1DAB0, 0x1DFFF}, {0x1E007, 0x1E007}, {0x1E019, 0x1E01A},
+{0x1E022, 0x1E022}, {0x1E025, 0x1E025}, {0x1E02B, 0x1E0FF}, {0x1E12D, 0x1E12F}, {0x1E13E, 0x1E13F}, {0x1E14A, 0x1E14D}, {0x1E150, 0x1E2BF}, {0x1E2FA, 0x1E2FE}, {0x1E300, 0x1E7FF}, {0x1E8C5, 0x1E8C6},
+{0x1E8D7, 0x1E8FF}, {0x1E94C, 0x1E94F}, {0x1E95A, 0x1E95D}, {0x1E960, 0x1EC70}, {0x1ECB5, 0x1ED00}, {0x1ED3E, 0x1EDFF}, {0x1EE04, 0x1EE04}, {0x1EE20, 0x1EE20}, {0x1EE23, 0x1EE23}, {0x1EE25, 0x1EE26},
+{0x1EE28, 0x1EE28}, {0x1EE33, 0x1EE33}, {0x1EE38, 0x1EE38}, {0x1EE3A, 0x1EE3A}, {0x1EE3C, 0x1EE41}, {0x1EE43, 0x1EE46}, {0x1EE48, 0x1EE48}, {0x1EE4A, 0x1EE4A}, {0x1EE4C, 0x1EE4C}, {0x1EE50, 0x1EE50},
+{0x1EE53, 0x1EE53}, {0x1EE55, 0x1EE56}, {0x1EE58, 0x1EE58}, {0x1EE5A, 0x1EE5A}, {0x1EE5C, 0x1EE5C}, {0x1EE5E, 0x1EE5E}, {0x1EE60, 0x1EE60}, {0x1EE63, 0x1EE63}, {0x1EE65, 0x1EE66}, {0x1EE6B, 0x1EE6B},
+{0x1EE73, 0x1EE73}, {0x1EE78, 0x1EE78}, {0x1EE7D, 0x1EE7D}, {0x1EE7F, 0x1EE7F}, {0x1EE8A, 0x1EE8A}, {0x1EE9C, 0x1EEA0}, {0x1EEA4, 0x1EEA4}, {0x1EEAA, 0x1EEAA}, {0x1EEBC, 0x1EEEF}, {0x1EEF2, 0x1EFFF},
+{0x1F02C, 0x1F02F}, {0x1F094, 0x1F09F}, {0x1F0AF, 0x1F0B0}, {0x1F0C0, 0x1F0C0}, {0x1F0D0, 0x1F0D0}, {0x1F0F6, 0x1F0FF}, {0x1F1AE, 0x1F1E5}, {0x1F203, 0x1F20F}, {0x1F23C, 0x1F23F}, {0x1F249, 0x1F24F},
+{0x1F252, 0x1F25F}, {0x1F266, 0x1F2FF}, {0x1F6D8, 0x1F6DF}, {0x1F6ED, 0x1F6EF}, {0x1F6FD, 0x1F6FF}, {0x1F774, 0x1F77F}, {0x1F7D9, 0x1F7DF}, {0x1F7EC, 0x1F7FF}, {0x1F80C, 0x1F80F}, {0x1F848, 0x1F84F},
+{0x1F85A, 0x1F85F}, {0x1F888, 0x1F88F}, {0x1F8AE, 0x1F8AF}, {0x1F8B2, 0x1F8FF}, {0x1F979, 0x1F979}, {0x1F9CC, 0x1F9CC}, {0x1FA54, 0x1FA5F}, {0x1FA6E, 0x1FA6F}, {0x1FA75, 0x1FA77}, {0x1FA7B, 0x1FA7F},
+{0x1FA87, 0x1FA8F}, {0x1FAA9, 0x1FAAF}, {0x1FAB7, 0x1FABF}, {0x1FAC3, 0x1FACF}, {0x1FAD7, 0x1FAFF}, {0x1FB93, 0x1FB93}, {0x1FBCB, 0x1FBEF}, {0x1FBFA, 0x1FFFF}, {0x2A6DE, 0x2A6FF}, {0x2B735, 0x2B73F},
+{0x2B81E, 0x2B81F}, {0x2CEA2, 0x2CEAF}, {0x2EBE1, 0x2F7FF}, {0x2FA1E, 0x2FFFF}, {0x3134B, 0xE00FF}, {0xE01F0, 0x10FFFF},
+};
+
+static std::string codepoint_to_utf8(uint32_t cp) {
+    std::string result;
+    if (/* 0x00 <= cp && */ cp <= 0x7f) {
+        result.push_back(cp);
+    }
+    else if (0x80 <= cp && cp <= 0x7ff) {
+        result.push_back(0xc0 | ((cp >> 6) & 0x1f));
+        result.push_back(0x80 | (cp & 0x3f));
+    }
+    else if (0x800 <= cp && cp <= 0xffff) {
+        result.push_back(0xe0 | ((cp >> 12) & 0x0f));
+        result.push_back(0x80 | ((cp >> 6) & 0x3f));
+        result.push_back(0x80 | (cp & 0x3f));
+    }
+    else if (0x10000 <= cp && cp <= 0x10ffff) {
+        result.push_back(0xf0 | ((cp >> 18) & 0x07));
+        result.push_back(0x80 | ((cp >> 12) & 0x3f));
+        result.push_back(0x80 | ((cp >> 6) & 0x3f));
+        result.push_back(0x80 | (cp & 0x3f));
+    }
+    else {
+        throw std::invalid_argument("invalid codepoint");
+    }
+    return result;
+}
+
+static std::string codepoints_to_utf8(const std::vector<uint32_t> & cps) {
+    std::string result;
+    for (size_t i = 0; i < cps.size(); ++i) {
+        result.append(codepoint_to_utf8(cps[i]));
+    }
+    return result;
+}
+
+static uint32_t codepoint_from_utf8(const std::string & utf8, size_t & offset) {
+    assert(offset < utf8.size());
+    if (!(utf8[offset + 0] & 0x80)) {
+        auto result = utf8[offset + 0];
+        offset += 1;
+        return result;
+    }
+    else if (!(utf8[offset + 0] & 0x40)) {
+        throw std::invalid_argument("invalid character");
+    }
+    else if (!(utf8[offset + 0] & 0x20)) {
+        if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80))
+            throw std::invalid_argument("invalid character");
+        auto result = ((utf8[offset + 0] & 0x1f) << 6) | (utf8[offset + 1] & 0x3f);
+        offset += 2;
+        return result;
+    }
+    else if (!(utf8[offset + 0] & 0x10)) {
+        if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80))
+            throw std::invalid_argument("invalid character");
+        auto result = ((utf8[offset + 0] & 0x0f) << 12) | ((utf8[offset + 1] & 0x3f) << 6) | (utf8[offset + 2] & 0x3f);
+        offset += 3;
+        return result;
+    }
+    else if (!(utf8[offset + 0] & 0x08)) {
+        if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80))
+            throw std::invalid_argument("invalid character");
+        auto result = ((utf8[offset + 0] & 0x07) << 18) | ((utf8[offset + 1] & 0x3f) << 12) | ((utf8[offset + 2] & 0x3f) << 6) | (utf8[offset + 3] & 0x3f);
+        offset += 4;
+        return result;
+    }
+    throw std::invalid_argument("invalid string");
+}
+
+static std::vector<uint32_t> codepoints_from_utf8(const std::string & utf8) {
+    std::vector<uint32_t> result;
+    size_t offset = 0;
+    while (offset < utf8.size()) {
+        result.push_back(codepoint_from_utf8(utf8, offset));
+    }
+    return result;
+}
+
+static std::vector<uint16_t> codepoint_to_utf16(uint32_t cp) {
+    std::vector<uint16_t> result;
+    if (/* 0x0000 <= cp && */ cp <= 0xffff) {
+        result.emplace_back(cp);
+    }
+    else if (0x10000 <= cp && cp <= 0x10ffff) {
+        result.emplace_back(0xd800 | ((cp - 0x10000) >> 10));
+        result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff));
+    }
+    else {
+        throw std::invalid_argument("invalid codepoint");
+    }
+    return result;
+}
+
+static std::vector<uint16_t> codepoints_to_utf16(const std::vector<uint32_t> & cps) {
+    std::vector<uint16_t> result;
+    for (size_t i = 0; i < cps.size(); ++i) {
+        auto temp = codepoint_to_utf16(cps[i]);
+        result.insert(result.end(), temp.begin(), temp.end());
+    }
+    return result;
+}
+
+static uint32_t codepoint_from_utf16(const std::vector<uint16_t> & utf16, size_t & offset) {
+    assert(offset < utf16.size());
+    if (((utf16[0] >> 10) << 10) != 0xd800) {
+        auto result = utf16[offset + 0];
+        offset += 1;
+        return result;
+    }
+    else {
+        if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00))
+            throw std::invalid_argument("invalid character");
+        auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff));
+        offset += 2;
+        return result;
+    }
+    throw std::invalid_argument("invalid string");
+}
+
+static std::vector<uint32_t> codepoints_from_utf16(const std::vector<uint16_t> & utf16) {
+    std::vector<uint32_t> result;
+    size_t offset = 0;
+    while (offset < utf16.size())
+        result.push_back(codepoint_from_utf16(utf16, offset));
+    return result;
+}
+
+#define CODEPOINT_TYPE_UNIDENTIFIED 0
+#define CODEPOINT_TYPE_DIGIT 1
+#define CODEPOINT_TYPE_LETTER 2
+#define CODEPOINT_TYPE_WHITESPACE 3
+#define CODEPOINT_TYPE_ACCENT_MARK 4
+#define CODEPOINT_TYPE_PUNCTUATION 5
+#define CODEPOINT_TYPE_SYMBOL 6
+#define CODEPOINT_TYPE_CONTROL 7
+
+static std::unordered_map<uint32_t, int> codepoint_type_map() {
+    std::unordered_map<uint32_t, int> codepoint_types;
+    for (auto p : digit_ranges) {
+        for(auto i = p.first; i <= p.second; ++ i)
+            codepoint_types[i] = CODEPOINT_TYPE_DIGIT;
+    }
+    for(auto p : letter_ranges) {
+        for(auto i = p.first; i <= p.second; ++ i)
+            codepoint_types[i] = CODEPOINT_TYPE_LETTER;
+    }
+    for(auto p : whitespace_ranges) {
+        for(auto i = p.first; i <= p.second; ++ i)
+            codepoint_types[i] = CODEPOINT_TYPE_WHITESPACE;
+    }
+    for(auto p : accent_mark_ranges) {
+        for(auto i = p.first; i <= p.second; ++ i)
+            codepoint_types[i] = CODEPOINT_TYPE_ACCENT_MARK;
+    }
+    for(auto p : punctuation_ranges) {
+        for(auto i = p.first; i <= p.second; ++ i)
+            codepoint_types[i] = CODEPOINT_TYPE_PUNCTUATION;
+    }
+    for (auto p : symbol_ranges) {
+        for (auto i = p.first; i <= p.second; ++i)
+            codepoint_types[i] = CODEPOINT_TYPE_SYMBOL;
+    }
+    for(auto p : control_ranges) {
+        for(auto i = p.first; i <= p.second; ++ i)
+            codepoint_types[i] = CODEPOINT_TYPE_CONTROL;
+    }
+    return codepoint_types;
+}
+
+static int codepoint_type(uint32_t cp) {
+    static std::unordered_map<uint32_t, int> codepoint_types = codepoint_type_map();
+    return codepoint_types[cp];
+}
+
+static int codepoint_type(const std::string & utf8) {
+    if (utf8.length() == 0)
+        return CODEPOINT_TYPE_UNIDENTIFIED;
+    size_t offset = 0;
+    return codepoint_type(codepoint_from_utf8(utf8, offset));
+}
+
+static std::unordered_map<uint8_t, std::string> bytes_to_unicode_map_bpe() {
+    std::unordered_map<uint8_t, std::string> map;
+    for (int ch = u'!'; ch <= u'~'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[ch] = codepoint_to_utf8(ch);
+    }
+    for (int ch = u'¡'; ch <= u'¬'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[ch] = codepoint_to_utf8(ch);
+    }
+    for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[ch] = codepoint_to_utf8(ch);
+    }
+    auto n = 0;
+    for (int ch = 0; ch < 256; ++ch) {
+        if (map.find(ch) == map.end()) {
+            map[ch] = codepoint_to_utf8(256 + n);
+            ++n;
+        }
+    }
+    return map;
+}
+
+static std::string bytes_to_unicode_bpe(uint8_t byte) {
+    static std::unordered_map<uint8_t, std::string> map = bytes_to_unicode_map_bpe();
+    return map.at(byte);
+}
+
+static std::unordered_map<std::string, uint8_t> unicode_to_bytes_map_bpe() {
+    std::unordered_map<std::string, uint8_t> map;
+    for (int ch = u'!'; ch <= u'~'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[codepoint_to_utf8(ch)] = ch;
+    }
+    for (int ch = u'¡'; ch <= u'¬'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[codepoint_to_utf8(ch)] = ch;
+    }
+    for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[codepoint_to_utf8(ch)] = ch;
+    }
+    auto n = 0;
+    for (int ch = 0; ch < 256; ++ch) {
+        if (map.find(codepoint_to_utf8(ch)) == map.end()) {
+            map[codepoint_to_utf8(256 + n)] = ch;
+            ++n;
+        }
+    }
+    return map;
+}
+
+static uint8_t unicode_to_bytes_bpe(const std::string & utf8) {
+    static std::unordered_map<std::string, uint8_t> map = unicode_to_bytes_map_bpe();
+    return map.at(utf8);
+}
+

runner/update.sh

@@ -0,0 +1,70 @@
+#!/bin/sh
+
+set -eu
+
+
+status() { echo >&2 ">>> $*"; }
+error() { status "ERROR $*"; }
+usage() {
+    echo "usage: $(basename $0) /path/to/repo"
+    exit 1
+}
+
+OUT=$(dirname $0)
+while getopts "hC:" OPTION; do
+    case $OPTION in
+        C) OUT=$OPTARG ;;
+        *) usage ;;
+    esac
+done
+
+shift $(( $OPTIND - 1 ))
+[ $# -eq 1 ] || usage
+
+status "updating source..."
+cp -a "$1"/*.{c,h,cpp,m,metal,cu} "$OUT"
+
+status "removing incompatible files..."
+rm -f "$OUT"/build-info.h
+
+SHA1=$(git -C $1 rev-parse @)
+
+LICENSE=$(mktemp)
+cleanup() {
+    rm -f $LICENSE
+}
+trap cleanup 0
+
+cat <<EOF | sed 's/ *$//' >$LICENSE
+/**
+ * llama.cpp - git $SHA1
+ *
+$(sed 's/^/ * /' <$1/LICENSE)
+ */
+
+EOF
+
+for IN in $OUT/*.{c,h,cpp,m,metal,cu}; do
+    TMP=$(mktemp)
+    status "updating license $IN"
+    cat $LICENSE $IN >$TMP
+    mv $TMP $IN
+done
+
+touchup() {
+    local CONSTRAINT=$1 && shift
+
+    for IN in $*; do
+        status "touching up $IN..."
+        TMP=$(mktemp)
+        {
+            echo "//go:build $CONSTRAINT"
+            echo
+        } | cat - $IN >$TMP
+        mv $TMP $IN
+    done
+}
+
+touchup darwin $OUT/ggml-metal.*
+touchup mpi $OUT/ggml-mpi.*
+touchup opencl $OUT/ggml-opencl.*