ollama/llama/patches/0008-add-mllama-support.patch

From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
From: jmorganca <jmorganca@gmail.com>
Date: Thu, 17 Oct 2024 15:18:22 -0700
Subject: [PATCH] add mllama support

mllama adds cross-attention layers to the standard llama architecture
it also requires a way to input a new tensor: cross_attention_state
once per generation

cross-attention layers don't change and so they are cached in the
kv cache once per run

remaining is to implement the cross attention mask
---
 examples/llava/llava.cpp      |   5 +-
 ggml/src/ggml-backend-reg.cpp |   6 +-
 include/llama.h               |   6 +
 src/llama-arch.cpp            |  44 +++++
 src/llama-arch.h              |  10 ++
 src/llama-batch.cpp           |   3 +
 src/llama-context.cpp         |  19 ++-
 src/llama-context.h           |   2 +
 src/llama-cparams.h           |   1 +
 src/llama-hparams.cpp         |   8 +-
 src/llama-hparams.h           |   4 +
 src/llama-kv-cache.cpp        |  33 ++++
 src/llama-model-loader.cpp    |   2 +
 src/llama-model.cpp           |  59 ++-----
 src/llama-model.h             |  51 ++++++
 src/llama-quant.cpp           |   4 +-
 src/llama.cpp                 | 307 +++++++++++++++++++++++++++++++++-
 17 files changed, 508 insertions(+), 56 deletions(-)

diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index 16f30c56..0f0f3f62 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -429,7 +429,7 @@ struct llava_embd_batch {
     std::vector<llama_seq_id *> seq_ids;
     std::vector<int8_t>         logits;
     llama_batch batch;
-    llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
+    llava_embd_batch(float * embd, int32_t n_embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
         pos     .resize(n_tokens);
         n_seq_id.resize(n_tokens);
         seq_ids .resize(n_tokens + 1);
@@ -441,6 +441,7 @@ struct llava_embd_batch {
             /*n_tokens       =*/ n_tokens,
             /*tokens         =*/ nullptr,
             /*embd           =*/ embd,
+            /*n_embd         =*/ n_embd,
             /*pos            =*/ pos.data(),
             /*n_seq_id       =*/ n_seq_id.data(),
             /*seq_id         =*/ seq_ids.data(),
@@ -464,7 +465,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
             n_eval = n_batch;
         }
         float * embd = image_embed->embed+i*n_embd;
-        llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, *n_past, 0);
+        llava_embd_batch llava_batch = llava_embd_batch(embd, n_embd, n_eval, *n_past, 0);
         if (llama_decode(ctx_llama, llava_batch.batch)) {
             LOG_ERR("%s : failed to eval\n", __func__);
             return false;
diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
index 7ddd178b..899d16f2 100644
--- a/ggml/src/ggml-backend-reg.cpp
+++ b/ggml/src/ggml-backend-reg.cpp
@@ -171,9 +171,9 @@ struct ggml_backend_registry {
 #ifdef GGML_USE_CANN
         register_backend(ggml_backend_cann_reg());
 #endif
-#ifdef GGML_USE_BLAS
-        register_backend(ggml_backend_blas_reg());
-#endif
+// #ifdef GGML_USE_BLAS
+//         register_backend(ggml_backend_blas_reg());
+// #endif
 #ifdef GGML_USE_RPC
         register_backend(ggml_backend_rpc_reg());
 #endif
diff --git a/include/llama.h b/include/llama.h
index a0d5ba5d..9f411960 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -250,6 +250,7 @@ extern "C" {
 
         llama_token  *  token;
         float        *  embd;
+        int32_t         n_embd;
         llama_pos    *  pos;
         int32_t      *  n_seq_id;
         llama_seq_id ** seq_id;
@@ -347,6 +348,7 @@ extern "C" {
         bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
         bool flash_attn;  // whether to use flash attention [EXPERIMENTAL]
         bool no_perf;     // whether to measure performance timings
+        bool cross_attn;  // whether to use cross attention
 
         // Abort callback
         // if it returns true, execution of llama_decode() will be aborted
@@ -426,6 +428,10 @@ extern "C" {
                      struct llama_model * model,
             struct llama_context_params   params);
 
+    // TODO (jmorganca): this should most likely be passed in as part of a batch
+    // and not set on the context for all batches.
+    LLAMA_API void llama_set_cross_attention(struct llama_context * ctx, bool cross_attn_state);
+
     // Frees all allocated memory
     LLAMA_API void llama_free(struct llama_context * ctx);
 
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 5b376c5e..b35aeb31 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -6,6 +6,7 @@
 
 static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_LLAMA,            "llama"            },
+    { LLM_ARCH_MLLAMA,           "mllama"           },
     { LLM_ARCH_DECI,             "deci"             },
     { LLM_ARCH_FALCON,           "falcon"           },
     { LLM_ARCH_GROK,             "grok"             },
@@ -124,6 +125,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_SLIDING_WINDOW,           "%s.attention.sliding_window"           },
     { LLM_KV_ATTENTION_SCALE,                    "%s.attention.scale"                    },
     { LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION,    "%s.attention.block_skip_connection"    },
+    { LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS,   "%s.attention.cross_attention_layers"   },
 
     { LLM_KV_ROPE_DIMENSION_COUNT,      "%s.rope.dimension_count"                 },
     { LLM_KV_ROPE_DIMENSION_SECTIONS,   "%s.rope.dimension_sections"              },
@@ -220,6 +222,40 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
         },
     },
+    {
+        LLM_ARCH_MLLAMA,
+        {
+            { 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_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { 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_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
+            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
+            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_CROSS_ATTN_K_NORM,    "blk.%d.cross_attn_k_norm" },
+            { LLM_TENSOR_CROSS_ATTN_K_PROJ,    "blk.%d.cross_attn_k_proj" },
+            { LLM_TENSOR_CROSS_ATTN_O_PROJ,    "blk.%d.cross_attn_o_proj" },
+            { LLM_TENSOR_CROSS_ATTN_Q_NORM,    "blk.%d.cross_attn_q_norm" },
+            { LLM_TENSOR_CROSS_ATTN_Q_PROJ,    "blk.%d.cross_attn_q_proj" },
+            { LLM_TENSOR_CROSS_ATTN_V_PROJ,    "blk.%d.cross_attn_v_proj" },
+            { LLM_TENSOR_CROSS_ATTN_ATTN_GATE, "blk.%d.cross_attn_attn_gate" },
+            { LLM_TENSOR_CROSS_ATTN_MLP_GATE,  "blk.%d.cross_attn_mlp_gate" },
+        },
+    },
     {
         LLM_ARCH_DECI,
         {
@@ -1393,6 +1429,14 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     // this tensor is loaded for T5, but never used
     {LLM_TENSOR_DEC_CROSS_ATTN_REL_B,       {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
     {LLM_TENSOR_BSKCN_TV,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_CROSS_ATTN_K_NORM,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_CROSS_ATTN_K_PROJ,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CROSS_ATTN_O_PROJ,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CROSS_ATTN_Q_NORM,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_CROSS_ATTN_Q_PROJ,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CROSS_ATTN_V_PROJ,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CROSS_ATTN_ATTN_GATE,       {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_CROSS_ATTN_MLP_GATE,        {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_CONV1D,                     {LLM_TENSOR_LAYER_INPUT,     GGML_OP_IM2COL}},
     {LLM_TENSOR_POS_NET_NORM,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_POS_NET_NORM1,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
diff --git a/src/llama-arch.h b/src/llama-arch.h
index eac7055b..e8235ae0 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -10,6 +10,7 @@
 
 enum llm_arch {
     LLM_ARCH_LLAMA,
+    LLM_ARCH_MLLAMA,
     LLM_ARCH_DECI,
     LLM_ARCH_FALCON,
     LLM_ARCH_BAICHUAN,
@@ -128,6 +129,7 @@ enum llm_kv {
     LLM_KV_ATTENTION_SLIDING_WINDOW,
     LLM_KV_ATTENTION_SCALE,
     LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION,
+    LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS,
 
     LLM_KV_ROPE_DIMENSION_COUNT,
     LLM_KV_ROPE_DIMENSION_SECTIONS,
@@ -308,6 +310,14 @@ enum llm_tensor {
     LLM_TENSOR_CLS,
     LLM_TENSOR_CLS_OUT,
     LLM_TENSOR_BSKCN_TV,
+    LLM_TENSOR_CROSS_ATTN_K_NORM,
+    LLM_TENSOR_CROSS_ATTN_K_PROJ,
+    LLM_TENSOR_CROSS_ATTN_O_PROJ,
+    LLM_TENSOR_CROSS_ATTN_Q_NORM,
+    LLM_TENSOR_CROSS_ATTN_Q_PROJ,
+    LLM_TENSOR_CROSS_ATTN_V_PROJ,
+    LLM_TENSOR_CROSS_ATTN_ATTN_GATE,
+    LLM_TENSOR_CROSS_ATTN_MLP_GATE,
     LLM_TENSOR_CONV1D,
     LLM_TENSOR_CONVNEXT_DW,
     LLM_TENSOR_CONVNEXT_NORM,
diff --git a/src/llama-batch.cpp b/src/llama-batch.cpp
index 01d5ca57..8682b0e6 100644
--- a/src/llama-batch.cpp
+++ b/src/llama-batch.cpp
@@ -316,6 +316,7 @@ struct llama_batch llama_batch_get_one(
         /*n_tokens       =*/ n_tokens,
         /*tokens         =*/ tokens,
         /*embd           =*/ nullptr,
+        /*n_embd         =*/ 0,
         /*pos            =*/ nullptr,
         /*n_seq_id       =*/ nullptr,
         /*seq_id         =*/ nullptr,
@@ -328,6 +329,7 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_
         /*n_tokens       =*/ 0,
         /*tokens         =*/ nullptr,
         /*embd           =*/ nullptr,
+        /*n_embd         =*/ 0,
         /*pos            =*/ nullptr,
         /*n_seq_id       =*/ nullptr,
         /*seq_id         =*/ nullptr,
@@ -336,6 +338,7 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_
 
     if (embd) {
         batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd);
+        batch.n_embd = embd;
     } else {
         batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
     }
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index b9c4a5bf..9d0e7ca3 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -71,10 +71,19 @@ void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch) {
     }
 
     if (ubatch.embd) {
-        const int64_t n_embd   = hparams.n_embd;
-        const int64_t n_tokens = ubatch.n_tokens;
+        if (lctx.inp_cross_attn_state && lctx.inp_cross_attn_state->buffer) {
+            ggml_backend_tensor_set(lctx.inp_cross_attn_state, ubatch.embd, 0, ggml_nbytes(lctx.inp_cross_attn_state));
+            // zero out inp_embd since it's not used
+            float * inp_embd_data = (float *)lctx.inp_embd->data;
+            for (int i = 0; i < ggml_nelements(lctx.inp_embd); ++i) {
+                inp_embd_data[i] = 0.0f;
+            }
+        } else {
+            const int64_t n_embd   = hparams.n_embd;
+            const int64_t n_tokens = ubatch.n_tokens;
 
-        ggml_backend_tensor_set(lctx.inp_embd, ubatch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
+            ggml_backend_tensor_set(lctx.inp_embd, ubatch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
+        }
     }
 
     if (ubatch.pos && lctx.inp_pos) {
@@ -653,6 +662,10 @@ void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
     ctx->cparams.causal_attn = causal_attn;
 }
 
+void llama_set_cross_attention(struct llama_context * ctx, bool cross_attention) {
+    ctx->cparams.cross_attn = cross_attention;
+}
+
 void llama_synchronize(struct llama_context * ctx) {
     ggml_backend_sched_synchronize(ctx->sched.get());
 
diff --git a/src/llama-context.h b/src/llama-context.h
index 0d163c47..4980a60e 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -107,6 +107,8 @@ struct llama_context {
     struct ggml_tensor * inp_pos_bucket;    // I32 [n_batch|n_kv, n_batch]
     struct ggml_tensor * inp_embd_enc;      // F32 [n_embd, n_outputs_enc]
     struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
+
+    struct ggml_tensor * inp_cross_attn_state; // F32 [4, n_embd, 1061]
 };
 
 // TODO: make these methods of llama_context
diff --git a/src/llama-cparams.h b/src/llama-cparams.h
index 252012f3..9681e5a0 100644
--- a/src/llama-cparams.h
+++ b/src/llama-cparams.h
@@ -29,6 +29,7 @@ struct llama_cparams {
     bool offload_kqv;
     bool flash_attn;
     bool no_perf;
+    bool cross_attn;
 
     enum llama_pooling_type pooling_type;
 
diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp
index 450738da..42f8a58f 100644
--- a/src/llama-hparams.cpp
+++ b/src/llama-hparams.cpp
@@ -2,6 +2,8 @@
 
 #include "ggml.h"
 
+#include <algorithm>
+
 uint32_t llama_hparams::n_head(uint32_t il) const {
     if (il < n_layer) {
         return n_head_arr[il];
@@ -76,4 +78,8 @@ bool llama_hparams::n_bskcn(uint32_t n, uint32_t il) const {
     }
 
     GGML_ABORT("fatal error");
-}
\ No newline at end of file
+}
+
+bool llama_hparams::cross_attention_layers(uint32_t il) const {
+    return std::find(cross_attn_layers.begin(), cross_attn_layers.end(), il) != cross_attn_layers.end();
+}
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index fd898e27..f826cd9a 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -53,6 +53,7 @@ struct llama_hparams {
     std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
 
     std::array<std::array<uint32_t, LLAMA_MAX_LAYERS>, 4> n_bskcn_arr = {};
+    std::array<uint32_t, LLAMA_MAX_LAYERS> cross_attn_layers;
 
     uint32_t n_layer_dense_lead = 0;
     uint32_t n_lora_q           = 0;
@@ -139,6 +140,9 @@ struct llama_hparams {
 
     // Block skip connection
     bool n_bskcn(uint32_t n, uint32_t il) const;
+
+    // cross attention layers   
+    bool cross_attention_layers(uint32_t il) const;
 };
 
 static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index 53379253..cf814dbe 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -72,6 +72,39 @@ bool llama_kv_cache_init(
     cache.v_l.reserve(n_layer);
 
     for (int i = 0; i < n_layer; i++) {
+        // for cross attention layers
+        if (model.arch == LLM_ARCH_MLLAMA && hparams.cross_attention_layers(i)) {
+            const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
+            const llama_model::buft_list_t * buft_list;
+            if (offload) {
+                buft_list = model.dev_layer.at(i).buft_list;
+            } else {
+                buft_list = &model.cpu_buft_list;
+            }
+            ggml_backend_buffer_type_t buft = select_buft(*buft_list,
+                [&](ggml_context * ctx) {
+                    ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
+                    if (hparams.rope_type == LLAMA_ROPE_TYPE_NONE) {
+                        return k;
+                    }
+                    ggml_tensor * p = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1);
+                    return ggml_rope(ctx, k, p, hparams.n_rot, hparams.rope_type);
+                });
+            ggml_context * ctx = ctx_for_buft(buft);
+
+            if (!ctx) {
+                LLAMA_LOG_ERROR("%s: failed to create ggml context for kv cache\n", __func__);
+                return false;
+            }
+            ggml_tensor * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_k, 6404, hparams.n_head_kv(i));
+            ggml_tensor * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_v, 6404, hparams.n_head_kv(i));
+            ggml_format_name(k, "cache_k_l%d", i);
+            ggml_format_name(v, "cache_v_l%d", i);
+            cache.k_l.push_back(k);
+            cache.v_l.push_back(v);
+            continue;
+        }
+
         const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
         const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
 
diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
index 422524a8..b12d6566 100644
--- a/src/llama-model-loader.cpp
+++ b/src/llama-model-loader.cpp
@@ -240,6 +240,8 @@ namespace GGUFMeta {
         return true;
     }
 
+    template bool llama_model_loader::get_arr<std::array<unsigned int, 512>>(enum llm_kv kid, std::array<unsigned int, 512>& result, bool required);
+
     template<typename T, size_t N_MAX>
     bool llama_model_loader::get_arr(const std::string & key, std::array<T, N_MAX> & result, bool required) {
         const int kid = gguf_find_key(meta.get(), key.c_str());
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 306c557d..4f9bbf90 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -146,46 +146,6 @@ std::string llama_model_ftype_name(const llama_model & model) {
     return llama_model_ftype_name(model.ftype);
 }
 
-template<typename F>
-static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) {
-    ggml_init_params params = {
-        /*.mem_size   =*/ ggml_tensor_overhead()*8,
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ true,
-    };
-
-    ggml_context_ptr ctx { ggml_init(params) };
-    if (!ctx) {
-        throw std::runtime_error(format("failed to create ggml context"));
-    }
-
-    ggml_backend_buffer_ptr buf { ggml_backend_buft_alloc_buffer(buft, 0) };
-    ggml_tensor * op_tensor = fn(ctx.get());
-    for (int i = 0; i < GGML_MAX_SRC; i++) {
-        if (op_tensor->src[i] != nullptr) {
-            assert(op_tensor->src[i]->buffer == nullptr);
-            op_tensor->src[i]->buffer = buf.get();
-        }
-    }
-
-    bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
-
-    return op_supported;
-}
-
-template<typename F>
-static ggml_backend_buffer_type_t select_buft(const llama_model::buft_list_t & buft_list, const F & fn) {
-    for (const auto & cur : buft_list) {
-        ggml_backend_dev_t cur_dev = cur.first;
-        ggml_backend_buffer_type_t cur_buft = cur.second;
-        if (buft_supported(cur_buft, cur_dev, fn)) {
-            return cur_buft;
-        }
-    }
-
-    throw std::runtime_error(format("no suitable buffer type found"));
-}
-
 ggml_backend_buffer_type_t llama_model_select_buft(const llama_model & model, int il) {
     return select_buft(
             *model.dev_layer.at(il).buft_list,
@@ -312,9 +272,11 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
     std::fill(hparams.n_head_arr.begin(),    hparams.n_head_arr.end(),    0);
     std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
     std::fill(hparams.n_ff_arr.begin(),      hparams.n_ff_arr.end(),      0);
+    std::fill(hparams.cross_attn_layers.begin(), hparams.cross_attn_layers.end(), -1);
 
-    ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff_arr,   hparams.n_layer, false);
-    ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
+    ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH,       hparams.n_ff_arr,   hparams.n_layer, false);
+    ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT,      hparams.n_head_arr, hparams.n_layer, false);
+    ml.get_arr(LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS, hparams.cross_attn_layers, false);
 
     // n_head_kv is optional, default to n_head
     hparams.n_head_kv_arr = hparams.n_head_arr;
@@ -363,7 +325,7 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
 
         ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
 
-        if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_DECI || model.arch == LLM_ARCH_FALCON) {
+        if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_MLLAMA || model.arch == LLM_ARCH_DECI || model.arch == LLM_ARCH_FALCON) {
             if (hparams.n_rot != hparams.n_embd_head_k) {
                 throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
             }
@@ -405,6 +367,16 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
                     }
                 }
             } break;
+        case LLM_ARCH_MLLAMA:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 40: model.type = e_model::MODEL_11B; break;
+                    case 100: model.type = e_model::MODEL_90B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_DECI:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -2062,6 +2034,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
 
         // use what we call a normal RoPE, operating on pairs of consecutive head values
         case LLM_ARCH_LLAMA:
+        case LLM_ARCH_MLLAMA:
         case LLM_ARCH_DECI:
         case LLM_ARCH_BAICHUAN:
         case LLM_ARCH_STARCODER:
diff --git a/src/llama-model.h b/src/llama-model.h
index c1b9c0a1..5b23e2ba 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -9,6 +9,7 @@
 #include "ggml-cpp.h"
 
 #include <vector>
+#include <stdexcept>
 
 // available models
 // TODO: this enum does not follow the enum naming convention
@@ -62,6 +63,7 @@ enum llm_type {
     MODEL_40B,
     MODEL_65B,
     MODEL_70B,
+    MODEL_90B,
     MODEL_236B,
     MODEL_314B,
     MODEL_671B,
@@ -278,6 +280,16 @@ struct llama_layer {
 
     struct ggml_tensor * bskcn_tv = nullptr;
 
+     // cross attention
+    struct ggml_tensor * cross_attn_k_norm = nullptr;
+    struct ggml_tensor * cross_attn_k_proj = nullptr;
+    struct ggml_tensor * cross_attn_o_proj = nullptr;
+    struct ggml_tensor * cross_attn_q_norm = nullptr;
+    struct ggml_tensor * cross_attn_q_proj = nullptr;
+    struct ggml_tensor * cross_attn_v_proj = nullptr;
+    struct ggml_tensor * cross_attn_attn_gate = nullptr;
+    struct ggml_tensor * cross_attn_mlp_gate = nullptr;
+
     struct llama_layer_posnet posnet;
 
     struct llama_layer_convnext convnext;
@@ -376,6 +388,45 @@ std::string llama_model_arch_name (const llama_model & model);
 std::string llama_model_type_name (const llama_model & model);
 std::string llama_model_ftype_name(const llama_model & model);
 
+template<typename F>
+bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) {
+    ggml_init_params params = {
+        /*.mem_size   =*/ ggml_tensor_overhead()*8,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true,
+    };
+
+    ggml_context_ptr ctx { ggml_init(params) };
+    if (!ctx) {
+        throw std::runtime_error("failed to create ggml context");
+    }
+
+    ggml_backend_buffer_ptr buf { ggml_backend_buft_alloc_buffer(buft, 0) };
+    ggml_tensor * op_tensor = fn(ctx.get());
+    for (int i = 0; i < GGML_MAX_SRC; i++) {
+        if (op_tensor->src[i] != nullptr) {
+            op_tensor->src[i]->buffer = buf.get();
+        }
+    }
+
+    bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
+
+    return op_supported;
+}
+
+template<typename F>
+ggml_backend_buffer_type_t select_buft(const llama_model::buft_list_t & buft_list, const F & fn) {
+    for (const auto & cur : buft_list) {
+        ggml_backend_dev_t cur_dev = cur.first;
+        ggml_backend_buffer_type_t cur_buft = cur.second;
+        if (buft_supported(cur_buft, cur_dev, fn)) {
+            return cur_buft;
+        }
+    }
+
+    throw std::runtime_error("no suitable buffer type found");
+}
+
 // used by llama_adapter_cvec
 ggml_backend_buffer_type_t llama_model_select_buft(const llama_model & model, int il);
 
diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index 42974f8f..27def6fd 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -629,7 +629,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         if (llama_model_has_encoder(&model)) {
             n_attn_layer *= 3;
         }
-        GGML_ASSERT((qs.n_attention_wv == n_attn_layer) && "n_attention_wv is unexpected");
+        if (qs.n_attention_wv != n_attn_layer) {
+            LLAMA_LOG_WARN("%s: n_attention_wv is unexpected, expected: %d, found: %d\n", __func__, n_attn_layer, qs.n_attention_wv);
+        }
     }
 
     size_t total_size_org = 0;
diff --git a/src/llama.cpp b/src/llama.cpp
index 7dec50ae..bac66c24 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -563,6 +563,52 @@ static bool llm_load_tensors(
                         }
                     }
                 } break;
+            case LLM_ARCH_MLLAMA:
+                {
+                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab+8}, 0);
+
+                    // output
+                    {
+                        model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                        model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+                        }
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = model.layers[i];
+
+                        if (hparams.cross_attention_layers(i)) {
+                            layer.cross_attn_k_norm = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_K_NORM,   "weight", i), {128}, 0);
+                            layer.cross_attn_k_proj = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_K_PROJ,   "weight", i), {n_embd, 1024}, 0);
+                            layer.cross_attn_o_proj = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_O_PROJ,   "weight", i), {n_embd, n_embd}, 0);
+                            layer.cross_attn_q_norm = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_Q_NORM, "weight", i), {128}, 0);
+                            layer.cross_attn_q_proj = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_Q_PROJ, "weight", i), {n_embd, n_embd}, 0);
+                            layer.cross_attn_v_proj = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_V_PROJ, "weight", i), {n_embd, 1024}, 0);
+                            layer.cross_attn_attn_gate = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_ATTN_GATE, i), {1}, 0);
+                            layer.cross_attn_mlp_gate = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_MLP_GATE, i), {1}, 0);
+                            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                            layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        } else {
+                            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+                            layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                        }
+                    }
+                } break;
             case LLM_ARCH_DECI:
                 {
                     model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -2514,7 +2560,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
 
         if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE &&
             model.hparams.n_vocab != model.vocab.id_to_token.size()) {
-            throw std::runtime_error("vocab size mismatch");
+            LLAMA_LOG_WARN("%s: vocab mismatch %u !- %zu ...\n", __func__, model.hparams.n_vocab, model.vocab.id_to_token.size());
         }
 
         if (params.vocab_only) {
@@ -2598,6 +2644,21 @@ static struct ggml_tensor * llm_build_inp_embd(
     return inpL;
 }
 
+static struct ggml_tensor * llm_build_inp_cross_attn_state(
+        struct ggml_context * ctx,
+       struct llama_context & lctx,
+        const llama_hparams & hparams,
+         const llm_build_cb & cb) {
+    const int64_t n_embd = hparams.n_embd;
+
+    struct ggml_tensor * inpCAS = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, 1601, 4);
+    cb(inpCAS, "inp_cross_attn_state", -1);
+    ggml_set_input(inpCAS);
+    lctx.inp_cross_attn_state = inpCAS;
+
+    return inpCAS;
+}
+
 static void llm_build_kv_store(
         struct ggml_context * ctx,
         const llama_hparams & hparams,
@@ -3593,6 +3654,7 @@ struct llm_build_context {
         lctx.inp_pos_bucket    = nullptr;
         lctx.inp_embd_enc      = nullptr;
         lctx.inp_KQ_mask_cross = nullptr;
+        lctx.inp_cross_attn_state = nullptr;
     }
 
     void free() {
@@ -4074,6 +4136,240 @@ struct llm_build_context {
         return gf;
     }
 
+        struct ggml_cgraph * build_mllama() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+        struct ggml_tensor * inpCAS;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
+        inpCAS = llm_build_inp_cross_attn_state(ctx0, lctx, hparams, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            if (hparams.cross_attention_layers(il)) {
+                if (!ubatch.embd && !cparams.cross_attn) {
+                    continue;
+                }
+
+                // cross attention layer
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_q_proj, cur);
+                cb(Qcur, "Qcur", il);
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+                cb(Qcur, "Qcur", il);
+
+                Qcur = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 0, 2, 1, 3));
+                cb(Qcur, "Qcur", il);
+
+                Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].cross_attn_q_norm, NULL, LLM_NORM_RMS, cb, il);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur, * Vcur;
+                if (ubatch.embd) {
+                    Kcur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_k_proj, inpCAS);
+                    cb(Kcur, "Kcur", il);
+
+                    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, 6404);
+                    cb(Kcur, "Kcur", il);
+
+                    Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
+                    cb(Kcur, "Kcur", il);
+
+                    Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].cross_attn_k_norm, NULL, LLM_NORM_RMS, cb, il);
+                    cb(Kcur, "Kcur", il);
+
+                    ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, kv_self.k_l[il]));
+
+                    Vcur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_v_proj, inpCAS);
+                    cb(Vcur, "Vcur", il);
+
+                    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, 6404);
+                    cb(Vcur, "Vcur", il);
+
+                    Vcur = ggml_permute(ctx0, Vcur, 0, 2, 1, 3);
+                    cb(Vcur, "Vcur", il);
+
+                    ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, kv_self.v_l[il]));
+                } else {
+                    Kcur = ggml_view_tensor(ctx0, kv_self.k_l[il]);
+                    cb(Kcur, "Kcur (view)", il);
+
+                    Vcur = ggml_view_tensor(ctx0, kv_self.v_l[il]);
+                    cb(Vcur, "Vcur (view)", il);
+                }
+
+                struct ggml_tensor * kq = ggml_mul_mat(ctx0, Kcur, Qcur);
+                cb(kq, "kq", il);
+
+                // TODO: apply causal masks
+                struct ggml_tensor * kq_soft_max = ggml_soft_max_ext(ctx0, kq, nullptr, 1.f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias);
+                cb(kq_soft_max, "kq_soft_max", il);
+
+                Vcur = ggml_cont(ctx0, ggml_transpose(ctx0, Vcur));
+                cb(Vcur, "Vcur", il);
+
+                struct ggml_tensor * kqv = ggml_mul_mat(ctx0, Vcur, kq_soft_max);
+                cb(kqv, "kqv", il);
+
+                struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+                cb(kqv_merged, "kqv_merged", il);
+
+                cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_head_v*n_head, n_tokens);
+                cb(cur, "kqv_merged_cont", il);
+
+                cur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_o_proj, cur);
+                cb(cur, "cur", il);
+
+                // TODO: do this in place once?
+                cur = ggml_mul(ctx0, cur, ggml_tanh(ctx0, model.layers[il].cross_attn_attn_gate));
+
+                struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+                cb(ffn_inp, "ffn_inp", il);
+
+                // feed-forward network
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+
+                // TODO: do this inplace once?
+                cur = ggml_add_inplace(ctx0, ggml_mul_inplace(ctx0, cur, ggml_tanh(ctx0, model.layers[il].cross_attn_mlp_gate)), ffn_inp);
+                cb(cur, "ffn_out", il);
+
+                cur = lctx.cvec.apply_to(ctx0, cur, il);
+                cb(cur, "l_out", il);
+
+                // input for next layer
+                inpL = cur;
+            } else {
+                // self attention layer
+
+                // rope freq factors for llama3; may return nullptr for llama2 and other models
+                struct ggml_tensor * rope_factors = build_rope_factors(il);
+
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+
+
+                if (il == n_layer - 1) {
+                    // skip computing output for unused tokens
+                    struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                    n_tokens = n_outputs;
+                    cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                    inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+                }
+
+                struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+                cb(ffn_inp, "ffn_inp", il);
+
+                // feed-forward network
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+
+                cur = ggml_add(ctx0, cur, ffn_inp);
+                cb(cur, "ffn_out", il);
+
+                cur = lctx.cvec.apply_to(ctx0, cur, il);
+                cb(cur, "l_out", il);
+
+                // input for next layer
+                inpL = cur;
+            }
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     struct ggml_cgraph * build_deci() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
@@ -10646,6 +10942,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_llama();
             } break;
+        case LLM_ARCH_MLLAMA:
+            {
+                result = llm.build_mllama();
+            } break;
         case LLM_ARCH_DECI:
             {
                 result = llm.build_deci();
@@ -10971,7 +11271,7 @@ static int llama_decode_internal(
         n_outputs = 1;
     }
 
-    lctx.sbatch.from_batch(batch, n_embd,
+    lctx.sbatch.from_batch(batch, batch.n_embd,
         /* simple_split */ !kv_self.recurrent,
         /* logits_all   */ n_outputs == n_tokens_all);
 
@@ -11282,7 +11582,7 @@ static int llama_encode_internal(
 
     const int64_t n_embd = hparams.n_embd;
 
-    lctx.sbatch.from_batch(batch, n_embd, /* simple_split */ true, /* logits_all */ true);
+    lctx.sbatch.from_batch(batch, batch.n_embd, /* simple_split */ true, /* logits_all */ true);
 
     const llama_ubatch ubatch = lctx.sbatch.split_simple(n_tokens);
 
@@ -11775,6 +12075,7 @@ struct llama_context_params llama_context_default_params() {
         /*.offload_kqv                 =*/ true,
         /*.flash_attn                  =*/ false,
         /*.no_perf                     =*/ true,
+        /*.cross_attn                  =*/ false,
         /*.abort_callback              =*/ nullptr,
         /*.abort_callback_data         =*/ nullptr,
     };