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+/**
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+ * llama.cpp - commit a8db2a9ce64cd4417f6a312ab61858f17f0f8584 - do not edit this file
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+ *
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+ * MIT License
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+ *
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+ * Copyright (c) 2023-2024 The ggml authors
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+ *
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+ * Permission is hereby granted, free of charge, to any person obtaining a copy
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+ * of this software and associated documentation files (the "Software"), to deal
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+ * in the Software without restriction, including without limitation the rights
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+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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+ * copies of the Software, and to permit persons to whom the Software is
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+ * furnished to do so, subject to the following conditions:
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+ *
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+ * The above copyright notice and this permission notice shall be included in all
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+ * copies or substantial portions of the Software.
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+ *
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+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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+ * SOFTWARE.
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+ */
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+
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+#ifndef LLAMA_H
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+#define LLAMA_H
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+
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+#include "ggml.h"
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+#include "ggml-backend.h"
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+
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+#include <stddef.h>
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+#include <stdint.h>
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+#include <stdio.h>
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+#include <stdbool.h>
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+
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+#ifdef LLAMA_SHARED
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+# if defined(_WIN32) && !defined(__MINGW32__)
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+# ifdef LLAMA_BUILD
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+# define LLAMA_API __declspec(dllexport)
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+# else
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+# define LLAMA_API __declspec(dllimport)
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+# endif
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+# else
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+# define LLAMA_API __attribute__ ((visibility ("default")))
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+# endif
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+#else
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+# define LLAMA_API
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+#endif
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+
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+#ifdef __GNUC__
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+# define DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
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+#elif defined(_MSC_VER)
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+# define DEPRECATED(func, hint) __declspec(deprecated(hint)) func
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+#else
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+# define DEPRECATED(func, hint) func
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+#endif
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+
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+#define LLAMA_DEFAULT_SEED 0xFFFFFFFF
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+
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+#define LLAMA_MAX_RNG_STATE (64*1024)
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+
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+#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
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+#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
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+#define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq'
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+
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+#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
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+#define LLAMA_SESSION_VERSION 6
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+
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+#define LLAMA_STATE_SEQ_MAGIC LLAMA_FILE_MAGIC_GGSQ
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+#define LLAMA_STATE_SEQ_VERSION 1
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+
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+#ifdef __cplusplus
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+extern "C" {
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+#endif
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+
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+ //
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+ // C interface
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+ //
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+ // TODO: show sample usage
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+ //
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+
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+ struct llama_model;
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+ struct llama_context;
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+
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+ typedef int32_t llama_pos;
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+ typedef int32_t llama_token;
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+ typedef int32_t llama_seq_id;
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+
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+ enum llama_vocab_type {
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+ LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
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+ LLAMA_VOCAB_TYPE_SPM = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback
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+ LLAMA_VOCAB_TYPE_BPE = 2, // GPT-2 tokenizer based on byte-level BPE
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+ LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece
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+ LLAMA_VOCAB_TYPE_UGM = 4, // T5 tokenizer based on Unigram
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+ };
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+
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+ // pre-tokenization types
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+ enum llama_vocab_pre_type {
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+ LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0,
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+ LLAMA_VOCAB_PRE_TYPE_LLAMA3 = 1,
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+ LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM = 2,
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+ LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER = 3,
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+ LLAMA_VOCAB_PRE_TYPE_FALCON = 4,
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+ LLAMA_VOCAB_PRE_TYPE_MPT = 5,
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+ LLAMA_VOCAB_PRE_TYPE_STARCODER = 6,
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+ LLAMA_VOCAB_PRE_TYPE_GPT2 = 7,
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+ LLAMA_VOCAB_PRE_TYPE_REFACT = 8,
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+ LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9,
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+ LLAMA_VOCAB_PRE_TYPE_STABLELM2 = 10,
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+ LLAMA_VOCAB_PRE_TYPE_QWEN2 = 11,
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+ LLAMA_VOCAB_PRE_TYPE_OLMO = 12,
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+ LLAMA_VOCAB_PRE_TYPE_DBRX = 13,
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+ LLAMA_VOCAB_PRE_TYPE_SMAUG = 14,
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+ LLAMA_VOCAB_PRE_TYPE_PORO = 15,
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+ LLAMA_VOCAB_PRE_TYPE_CHATGLM3 = 16,
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+ LLAMA_VOCAB_PRE_TYPE_CHATGLM4 = 17,
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+ LLAMA_VOCAB_PRE_TYPE_VIKING = 18,
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+ LLAMA_VOCAB_PRE_TYPE_JAIS = 19,
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+ };
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+
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+ // note: these values should be synchronized with ggml_rope
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+ // TODO: maybe move this enum to ggml.h (ggml_rope_type)
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+ enum llama_rope_type {
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+ LLAMA_ROPE_TYPE_NONE = -1,
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+ LLAMA_ROPE_TYPE_NORM = 0,
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+ LLAMA_ROPE_TYPE_NEOX = 2,
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+ LLAMA_ROPE_TYPE_GLM = 4,
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+ };
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+
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+ enum llama_token_type { //TODO: remove, required until per token attributes are available from GGUF file
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+ LLAMA_TOKEN_TYPE_UNDEFINED = 0,
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+ LLAMA_TOKEN_TYPE_NORMAL = 1,
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+ LLAMA_TOKEN_TYPE_UNKNOWN = 2,
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+ LLAMA_TOKEN_TYPE_CONTROL = 3,
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+ LLAMA_TOKEN_TYPE_USER_DEFINED = 4,
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+ LLAMA_TOKEN_TYPE_UNUSED = 5,
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+ LLAMA_TOKEN_TYPE_BYTE = 6,
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+ };
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+
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+ enum llama_token_attr {
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+ LLAMA_TOKEN_ATTR_UNDEFINED = 0,
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+ LLAMA_TOKEN_ATTR_UNKNOWN = 1 << 0,
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+ LLAMA_TOKEN_ATTR_UNUSED = 1 << 1,
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+ LLAMA_TOKEN_ATTR_NORMAL = 1 << 2,
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+ LLAMA_TOKEN_ATTR_CONTROL = 1 << 3, // SPECIAL?
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+ LLAMA_TOKEN_ATTR_USER_DEFINED = 1 << 4,
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+ LLAMA_TOKEN_ATTR_BYTE = 1 << 5,
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+ LLAMA_TOKEN_ATTR_NORMALIZED = 1 << 6,
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+ LLAMA_TOKEN_ATTR_LSTRIP = 1 << 7,
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+ LLAMA_TOKEN_ATTR_RSTRIP = 1 << 8,
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+ LLAMA_TOKEN_ATTR_SINGLE_WORD = 1 << 9,
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+ };
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+
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+ // model file types
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+ enum llama_ftype {
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+ LLAMA_FTYPE_ALL_F32 = 0,
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+ LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
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+ // LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed
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+ // LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed
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+ LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q3_K_S = 11, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q3_K_M = 12, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q3_K_L = 13, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q4_K_S = 14, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q4_K_M = 15, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q5_K_S = 16, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q5_K_M = 17, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q6_K = 18, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_IQ3_XS = 22, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_IQ1_S = 24, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_IQ4_NL = 25, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_IQ3_S = 26, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_IQ3_M = 27, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_IQ2_S = 28, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_IQ1_M = 31, // except 1d tensors
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+ LLAMA_FTYPE_MOSTLY_BF16 = 32, // except 1d tensors
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+
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+ LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
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+ };
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+
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+ enum llama_rope_scaling_type {
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+ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1,
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+ LLAMA_ROPE_SCALING_TYPE_NONE = 0,
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+ LLAMA_ROPE_SCALING_TYPE_LINEAR = 1,
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+ LLAMA_ROPE_SCALING_TYPE_YARN = 2,
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+ LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_YARN,
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+ };
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+
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+ enum llama_pooling_type {
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+ LLAMA_POOLING_TYPE_UNSPECIFIED = -1,
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+ LLAMA_POOLING_TYPE_NONE = 0,
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+ LLAMA_POOLING_TYPE_MEAN = 1,
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+ LLAMA_POOLING_TYPE_CLS = 2,
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+ LLAMA_POOLING_TYPE_LAST = 3,
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+ };
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+
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+ enum llama_attention_type {
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+ LLAMA_ATTENTION_TYPE_UNSPECIFIED = -1,
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+ LLAMA_ATTENTION_TYPE_CAUSAL = 0,
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+ LLAMA_ATTENTION_TYPE_NON_CAUSAL = 1,
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+ };
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+
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+ enum llama_split_mode {
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+ LLAMA_SPLIT_MODE_NONE = 0, // single GPU
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+ LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs
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+ LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs
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+ };
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+
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+ typedef struct llama_token_data {
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+ llama_token id; // token id
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+ float logit; // log-odds of the token
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+ float p; // probability of the token
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+ } llama_token_data;
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+
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+ typedef struct llama_token_data_array {
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+ llama_token_data * data;
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+ size_t size;
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+ bool sorted;
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+ } llama_token_data_array;
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+
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+ typedef bool (*llama_progress_callback)(float progress, void * user_data);
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+
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+ // Input data for llama_decode
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+ // A llama_batch object can contain input about one or many sequences
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+ // The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
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+ //
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+ // - token : the token ids of the input (used when embd is NULL)
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+ // - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
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+ // - pos : the positions of the respective token in the sequence
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+ // - seq_id : the sequence to which the respective token belongs
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+ // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output
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+ //
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+ typedef struct llama_batch {
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+ int32_t n_tokens;
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+
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+ llama_token * token;
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+ float * embd;
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+ llama_pos * pos;
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+ int32_t * n_seq_id;
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+ llama_seq_id ** seq_id;
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+ int8_t * logits; // TODO: rename this to "output"
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+
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+ // NOTE: helpers for smooth API transition - can be deprecated in the future
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+ // for future-proof code, use the above fields instead and ignore everything below
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+ //
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+ // pos[i] = all_pos_0 + i*all_pos_1
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+ //
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+ llama_pos all_pos_0; // used if pos == NULL
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+ llama_pos all_pos_1; // used if pos == NULL
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+ llama_seq_id all_seq_id; // used if seq_id == NULL
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+ } llama_batch;
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+
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+ enum llama_model_kv_override_type {
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+ LLAMA_KV_OVERRIDE_TYPE_INT,
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+ LLAMA_KV_OVERRIDE_TYPE_FLOAT,
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+ LLAMA_KV_OVERRIDE_TYPE_BOOL,
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+ LLAMA_KV_OVERRIDE_TYPE_STR,
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+ };
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+
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+ struct llama_model_kv_override {
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+ enum llama_model_kv_override_type tag;
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+
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+ char key[128];
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+
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+ union {
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+ int64_t val_i64;
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+ double val_f64;
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+ bool val_bool;
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+ char val_str[128];
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+ };
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+ };
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+
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+ struct llama_model_params {
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+ int32_t n_gpu_layers; // number of layers to store in VRAM
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+ enum llama_split_mode split_mode; // how to split the model across multiple GPUs
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+
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+ // main_gpu interpretation depends on split_mode:
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+ // LLAMA_SPLIT_NONE: the GPU that is used for the entire model
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+ // LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results
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+ // LLAMA_SPLIT_LAYER: ignored
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+ int32_t main_gpu;
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+
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+ // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
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+ const float * tensor_split;
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+
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+ // comma separated list of RPC servers to use for offloading
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+ const char * rpc_servers;
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+
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+ // Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
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+ // If the provided progress_callback returns true, model loading continues.
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+ // If it returns false, model loading is immediately aborted.
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+ llama_progress_callback progress_callback;
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+
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+ // context pointer passed to the progress callback
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+ void * progress_callback_user_data;
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+
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+ // override key-value pairs of the model meta data
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+ const struct llama_model_kv_override * kv_overrides;
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+
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+ // Keep the booleans together to avoid misalignment during copy-by-value.
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+ bool vocab_only; // only load the vocabulary, no weights
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+ bool use_mmap; // use mmap if possible
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+ bool use_mlock; // force system to keep model in RAM
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+ bool check_tensors; // validate model tensor data
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+ };
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+
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+ // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
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+ // https://github.com/ggerganov/llama.cpp/pull/7544
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+ struct llama_context_params {
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+ uint32_t seed; // RNG seed, -1 for random
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+ uint32_t n_ctx; // text context, 0 = from model
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+ uint32_t n_batch; // logical maximum batch size that can be submitted to llama_decode
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+ uint32_t n_ubatch; // physical maximum batch size
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+ uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models)
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+ uint32_t n_threads; // number of threads to use for generation
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+ uint32_t n_threads_batch; // number of threads to use for batch processing
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+
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+ enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
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+ enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id
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+ enum llama_attention_type attention_type; // attention type to use for embeddings
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+
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+ // ref: https://github.com/ggerganov/llama.cpp/pull/2054
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+ float rope_freq_base; // RoPE base frequency, 0 = from model
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+ float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model
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+ float yarn_ext_factor; // YaRN extrapolation mix factor, negative = from model
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+ float yarn_attn_factor; // YaRN magnitude scaling factor
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+ float yarn_beta_fast; // YaRN low correction dim
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+ float yarn_beta_slow; // YaRN high correction dim
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+ uint32_t yarn_orig_ctx; // YaRN original context size
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+ float defrag_thold; // defragment the KV cache if holes/size > thold, < 0 disabled (default)
|
|
|
+
|
|
|
+ ggml_backend_sched_eval_callback cb_eval;
|
|
|
+ void * cb_eval_user_data;
|
|
|
+
|
|
|
+ enum ggml_type type_k; // data type for K cache [EXPERIMENTAL]
|
|
|
+ enum ggml_type type_v; // data type for V cache [EXPERIMENTAL]
|
|
|
+
|
|
|
+ // Keep the booleans together to avoid misalignment during copy-by-value.
|
|
|
+ bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
|
|
|
+ bool embeddings; // if true, extract embeddings (together with logits)
|
|
|
+ bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
|
|
|
+ bool flash_attn; // whether to use flash attention [EXPERIMENTAL]
|
|
|
+
|
|
|
+ // Abort callback
|
|
|
+ // if it returns true, execution of llama_decode() will be aborted
|
|
|
+ // currently works only with CPU execution
|
|
|
+ ggml_abort_callback abort_callback;
|
|
|
+ void * abort_callback_data;
|
|
|
+ };
|
|
|
+
|
|
|
+ // model quantization parameters
|
|
|
+ typedef struct llama_model_quantize_params {
|
|
|
+ int32_t 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
|
|
|
+ enum ggml_type output_tensor_type; // output tensor type
|
|
|
+ enum ggml_type token_embedding_type; // itoken embeddings tensor type
|
|
|
+ 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
|
|
|
+ bool pure; // quantize all tensors to the default type
|
|
|
+ bool keep_split; // quantize to the same number of shards
|
|
|
+ void * imatrix; // pointer to importance matrix data
|
|
|
+ void * kv_overrides; // pointer to vector containing overrides
|
|
|
+
|
|
|
+ llama_progress_callback quantize_callback; // callback to report quantization progress
|
|
|
+ void * quantize_callback_data; // user data for the callback
|
|
|
+ } 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,
|
|
|
+
|
|
|
+ // any character (.)
|
|
|
+ LLAMA_GRETYPE_CHAR_ANY = 7,
|
|
|
+ };
|
|
|
+
|
|
|
+ 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;
|
|
|
+ };
|
|
|
+
|
|
|
+ // used in chat template
|
|
|
+ typedef struct llama_chat_message {
|
|
|
+ const char * role;
|
|
|
+ const char * content;
|
|
|
+ } llama_chat_message;
|
|
|
+
|
|
|
+ // 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(void);
|
|
|
+
|
|
|
+ //optional:
|
|
|
+ LLAMA_API void llama_numa_init(enum ggml_numa_strategy 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 size_t llama_max_devices(void);
|
|
|
+
|
|
|
+ LLAMA_API bool llama_supports_mmap (void);
|
|
|
+ LLAMA_API bool llama_supports_mlock (void);
|
|
|
+ LLAMA_API bool llama_supports_gpu_offload(void);
|
|
|
+
|
|
|
+ LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
|
|
|
+
|
|
|
+ LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);
|
|
|
+ LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);
|
|
|
+ LLAMA_API uint32_t llama_n_ubatch (const struct llama_context * ctx);
|
|
|
+ LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx);
|
|
|
+
|
|
|
+ LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx);
|
|
|
+
|
|
|
+ LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model);
|
|
|
+ LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model);
|
|
|
+
|
|
|
+ LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);
|
|
|
+ LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
|
|
|
+ LLAMA_API int32_t llama_n_embd (const struct llama_model * model);
|
|
|
+ LLAMA_API int32_t llama_n_layer (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);
|
|
|
+
|
|
|
+ // Functions to access the model's GGUF metadata scalar values
|
|
|
+ // - The functions return the length of the string on success, or -1 on failure
|
|
|
+ // - The output string is always null-terminated and cleared on failure
|
|
|
+ // - GGUF array values are not supported by these functions
|
|
|
+
|
|
|
+ // Get metadata value as a string by key name
|
|
|
+ LLAMA_API int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);
|
|
|
+
|
|
|
+ // Get the number of metadata key/value pairs
|
|
|
+ LLAMA_API int32_t llama_model_meta_count(const struct llama_model * model);
|
|
|
+
|
|
|
+ // Get metadata key name by index
|
|
|
+ LLAMA_API int32_t llama_model_meta_key_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
|
|
|
+
|
|
|
+ // Get metadata value as a string by index
|
|
|
+ LLAMA_API int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
|
|
|
+
|
|
|
+ // Get a string describing the model type
|
|
|
+ LLAMA_API int32_t 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 true if the model contains an encoder that requires llama_encode() call
|
|
|
+ LLAMA_API bool llama_model_has_encoder(const struct llama_model * model);
|
|
|
+
|
|
|
+ // For encoder-decoder models, this function returns id of the token that must be provided
|
|
|
+ // to the decoder to start generating output sequence. For other models, it returns -1.
|
|
|
+ LLAMA_API llama_token llama_model_decoder_start_token(const struct llama_model * model);
|
|
|
+
|
|
|
+ // Returns 0 on success
|
|
|
+ LLAMA_API uint32_t 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 int32_t llama_model_apply_lora_from_file(
|
|
|
+ const struct llama_model * model,
|
|
|
+ const char * path_lora,
|
|
|
+ float scale,
|
|
|
+ const char * path_base_model,
|
|
|
+ int32_t n_threads);
|
|
|
+
|
|
|
+ // Apply a loaded control vector to a llama_context, or if data is NULL, clear
|
|
|
+ // the currently loaded vector.
|
|
|
+ // n_embd should be the size of a single layer's control, and data should point
|
|
|
+ // to an n_embd x n_layers buffer starting from layer 1.
|
|
|
+ // il_start and il_end are the layer range the vector should apply to (both inclusive)
|
|
|
+ // See llama_control_vector_load in common to load a control vector.
|
|
|
+ LLAMA_API int32_t llama_control_vector_apply(
|
|
|
+ struct llama_context * lctx,
|
|
|
+ const float * data,
|
|
|
+ size_t len,
|
|
|
+ int32_t n_embd,
|
|
|
+ int32_t il_start,
|
|
|
+ int32_t il_end);
|
|
|
+
|
|
|
+ //
|
|
|
+ // KV cache
|
|
|
+ //
|
|
|
+
|
|
|
+ // Information associated with an individual cell in the KV cache view.
|
|
|
+ struct llama_kv_cache_view_cell {
|
|
|
+ // The position for this cell. Takes KV cache shifts into account.
|
|
|
+ // May be negative if the cell is not populated.
|
|
|
+ llama_pos pos;
|
|
|
+ };
|
|
|
+
|
|
|
+ // An updateable view of the KV cache.
|
|
|
+ struct llama_kv_cache_view {
|
|
|
+ // Number of KV cache cells. This will be the same as the context size.
|
|
|
+ int32_t n_cells;
|
|
|
+
|
|
|
+ // Maximum number of sequences that can exist in a cell. It's not an error
|
|
|
+ // if there are more sequences in a cell than this value, however they will
|
|
|
+ // not be visible in the view cells_sequences.
|
|
|
+ int32_t n_seq_max;
|
|
|
+
|
|
|
+ // Number of tokens in the cache. For example, if there are two populated
|
|
|
+ // cells, the first with 1 sequence id in it and the second with 2 sequence
|
|
|
+ // ids then you'll have 3 tokens.
|
|
|
+ int32_t token_count;
|
|
|
+
|
|
|
+ // Number of populated cache cells.
|
|
|
+ int32_t used_cells;
|
|
|
+
|
|
|
+ // Maximum contiguous empty slots in the cache.
|
|
|
+ int32_t max_contiguous;
|
|
|
+
|
|
|
+ // Index to the start of the max_contiguous slot range. Can be negative
|
|
|
+ // when cache is full.
|
|
|
+ int32_t max_contiguous_idx;
|
|
|
+
|
|
|
+ // Information for an individual cell.
|
|
|
+ struct llama_kv_cache_view_cell * cells;
|
|
|
+
|
|
|
+ // The sequences for each cell. There will be n_seq_max items per cell.
|
|
|
+ llama_seq_id * cells_sequences;
|
|
|
+ };
|
|
|
+
|
|
|
+ // Create an empty KV cache view. (use only for debugging purposes)
|
|
|
+ LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max);
|
|
|
+
|
|
|
+ // Free a KV cache view. (use only for debugging purposes)
|
|
|
+ LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
|
|
|
+
|
|
|
+ // Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)
|
|
|
+ LLAMA_API void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view);
|
|
|
+
|
|
|
+ // Returns the number of tokens in the KV cache (slow, use only for debug)
|
|
|
+ // If a KV cell has multiple sequences assigned to it, it will be counted multiple times
|
|
|
+ LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx);
|
|
|
+
|
|
|
+ // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
|
|
|
+ LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx);
|
|
|
+
|
|
|
+ // Clear the KV cache - both cell info is erased and KV data is zeroed
|
|
|
+ LLAMA_API void llama_kv_cache_clear(
|
|
|
+ struct llama_context * ctx);
|
|
|
+
|
|
|
+ // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
|
|
|
+ // Returns false if a partial sequence cannot be removed. Removing a whole sequence never fails
|
|
|
+ // seq_id < 0 : match any sequence
|
|
|
+ // p0 < 0 : [0, p1]
|
|
|
+ // p1 < 0 : [p0, inf)
|
|
|
+ LLAMA_API bool 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:
|
|
|
+ // - lazily on next llama_decode()
|
|
|
+ // - explicitly with llama_kv_cache_update()
|
|
|
+ // p0 < 0 : [0, p1]
|
|
|
+ // p1 < 0 : [p0, inf)
|
|
|
+ LLAMA_API void llama_kv_cache_seq_add(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ llama_seq_id seq_id,
|
|
|
+ llama_pos p0,
|
|
|
+ llama_pos p1,
|
|
|
+ llama_pos delta);
|
|
|
+
|
|
|
+ // Integer division of the positions by factor of `d > 1`
|
|
|
+ // If the KV cache is RoPEd, the KV data is updated accordingly:
|
|
|
+ // - lazily on next llama_decode()
|
|
|
+ // - explicitly with llama_kv_cache_update()
|
|
|
+ // p0 < 0 : [0, p1]
|
|
|
+ // p1 < 0 : [p0, inf)
|
|
|
+ LLAMA_API void llama_kv_cache_seq_div(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ llama_seq_id seq_id,
|
|
|
+ llama_pos p0,
|
|
|
+ llama_pos p1,
|
|
|
+ int d);
|
|
|
+
|
|
|
+ // Returns the largest position present in the KV cache for the specified sequence
|
|
|
+ LLAMA_API llama_pos llama_kv_cache_seq_pos_max(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ llama_seq_id seq_id);
|
|
|
+
|
|
|
+ // Defragment the KV cache
|
|
|
+ // This will be applied:
|
|
|
+ // - lazily on next llama_decode()
|
|
|
+ // - explicitly with llama_kv_cache_update()
|
|
|
+ LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx);
|
|
|
+
|
|
|
+ // Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
|
|
|
+ LLAMA_API void llama_kv_cache_update(struct llama_context * ctx);
|
|
|
+
|
|
|
+ //
|
|
|
+ // 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_state_get_size(const struct llama_context * ctx);
|
|
|
+ LLAMA_API DEPRECATED(size_t llama_get_state_size(const struct llama_context * ctx),
|
|
|
+ "use llama_state_get_size instead");
|
|
|
+
|
|
|
+ // 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_state_get_data(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ uint8_t * dst);
|
|
|
+ LLAMA_API DEPRECATED(size_t llama_copy_state_data(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ uint8_t * dst),
|
|
|
+ "use llama_state_get_data instead");
|
|
|
+
|
|
|
+ // Set the state reading from the specified address
|
|
|
+ // Returns the number of bytes read
|
|
|
+ LLAMA_API size_t llama_state_set_data(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ const uint8_t * src);
|
|
|
+ LLAMA_API DEPRECATED(size_t llama_set_state_data(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ const uint8_t * src),
|
|
|
+ "use llama_state_set_data instead");
|
|
|
+
|
|
|
+ // Save/load session file
|
|
|
+ LLAMA_API bool llama_state_load_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 DEPRECATED(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),
|
|
|
+ "use llama_state_load_file instead");
|
|
|
+
|
|
|
+ LLAMA_API bool llama_state_save_file(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ const char * path_session,
|
|
|
+ const llama_token * tokens,
|
|
|
+ size_t n_token_count);
|
|
|
+ LLAMA_API DEPRECATED(bool llama_save_session_file(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ const char * path_session,
|
|
|
+ const llama_token * tokens,
|
|
|
+ size_t n_token_count),
|
|
|
+ "use llama_state_save_file instead");
|
|
|
+
|
|
|
+ // Get the exact size needed to copy the KV cache of a single sequence
|
|
|
+ LLAMA_API size_t llama_state_seq_get_size(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ llama_seq_id seq_id);
|
|
|
+
|
|
|
+ // Copy the KV cache of a single sequence into the specified buffer
|
|
|
+ LLAMA_API size_t llama_state_seq_get_data(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ uint8_t * dst,
|
|
|
+ llama_seq_id seq_id);
|
|
|
+
|
|
|
+ // Copy the sequence data (originally copied with `llama_state_seq_get_data`) into the specified sequence
|
|
|
+ // Returns:
|
|
|
+ // - Positive: Ok
|
|
|
+ // - Zero: Failed to load
|
|
|
+ LLAMA_API size_t llama_state_seq_set_data(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ const uint8_t * src,
|
|
|
+ llama_seq_id dest_seq_id);
|
|
|
+
|
|
|
+ LLAMA_API size_t llama_state_seq_save_file(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ const char * filepath,
|
|
|
+ llama_seq_id seq_id,
|
|
|
+ const llama_token * tokens,
|
|
|
+ size_t n_token_count);
|
|
|
+
|
|
|
+ LLAMA_API size_t llama_state_seq_load_file(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ const char * filepath,
|
|
|
+ llama_seq_id dest_seq_id,
|
|
|
+ llama_token * tokens_out,
|
|
|
+ size_t n_token_capacity,
|
|
|
+ size_t * n_token_count_out);
|
|
|
+
|
|
|
+ //
|
|
|
+ // Decoding
|
|
|
+ //
|
|
|
+
|
|
|
+ // 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);
|
|
|
+
|
|
|
+ // Processes a batch of tokens with the ecoder part of the encoder-decoder model.
|
|
|
+ // Stores the encoder output internally for later use by the decoder cross-attention layers.
|
|
|
+ // 0 - success
|
|
|
+ // < 0 - error
|
|
|
+ LLAMA_API int32_t llama_encode(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ 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 int32_t 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);
|
|
|
+
|
|
|
+ // Get the number of threads used for generation of a single token.
|
|
|
+ LLAMA_API uint32_t llama_n_threads(struct llama_context * ctx);
|
|
|
+
|
|
|
+ // Get the number of threads used for prompt and batch processing (multiple token).
|
|
|
+ LLAMA_API uint32_t llama_n_threads_batch(struct llama_context * ctx);
|
|
|
+
|
|
|
+ // Set whether the model is in embeddings mode or not
|
|
|
+ // If true, embeddings will be returned but logits will not
|
|
|
+ LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings);
|
|
|
+
|
|
|
+ // Set whether to use causal attention or not
|
|
|
+ // If set to true, the model will only attend to the past tokens
|
|
|
+ LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);
|
|
|
+
|
|
|
+ // Set abort callback
|
|
|
+ LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);
|
|
|
+
|
|
|
+ // Wait until all computations are finished
|
|
|
+ // This is automatically done when using one of the functions below to obtain the computation results
|
|
|
+ // and is not necessary to call it explicitly in most cases
|
|
|
+ LLAMA_API void llama_synchronize(struct llama_context * ctx);
|
|
|
+
|
|
|
+ // Token logits obtained from the last call to llama_decode()
|
|
|
+ // The logits for which llama_batch.logits[i] != 0 are stored contiguously
|
|
|
+ // in the order they have appeared in the batch.
|
|
|
+ // Rows: number of tokens for which llama_batch.logits[i] != 0
|
|
|
+ // Cols: n_vocab
|
|
|
+ LLAMA_API float * llama_get_logits(struct llama_context * ctx);
|
|
|
+
|
|
|
+ // Logits for the ith token. For positive indices, Equivalent to:
|
|
|
+ // llama_get_logits(ctx) + ctx->output_ids[i]*n_vocab
|
|
|
+ // Negative indicies can be used to access logits in reverse order, -1 is the last logit.
|
|
|
+ // returns NULL for invalid ids.
|
|
|
+ LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);
|
|
|
+
|
|
|
+ // Get all output token embeddings.
|
|
|
+ // when pooling_type == LLAMA_POOLING_TYPE_NONE or when using a generative model,
|
|
|
+ // the embeddings for which llama_batch.logits[i] != 0 are stored contiguously
|
|
|
+ // in the order they have appeared in the batch.
|
|
|
+ // shape: [n_outputs*n_embd]
|
|
|
+ // Otherwise, returns NULL.
|
|
|
+ LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
|
|
|
+
|
|
|
+ // Get the embeddings for the ith token. For positive indices, Equivalent to:
|
|
|
+ // llama_get_embeddings(ctx) + ctx->output_ids[i]*n_embd
|
|
|
+ // Negative indicies can be used to access embeddings in reverse order, -1 is the last embedding.
|
|
|
+ // shape: [n_embd] (1-dimensional)
|
|
|
+ // returns NULL for invalid ids.
|
|
|
+ LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
|
|
|
+
|
|
|
+ // Get the embeddings for a sequence id
|
|
|
+ // Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE
|
|
|
+ // shape: [n_embd] (1-dimensional)
|
|
|
+ LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id);
|
|
|
+
|
|
|
+ //
|
|
|
+ // Vocab
|
|
|
+ //
|
|
|
+
|
|
|
+ LLAMA_API const char * llama_token_get_text(const struct llama_model * model, llama_token token);
|
|
|
+
|
|
|
+ LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
|
|
|
+
|
|
|
+ LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token);
|
|
|
+
|
|
|
+ // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)
|
|
|
+ LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token);
|
|
|
+
|
|
|
+ // Identify if Token Id is a control token or a render-able token
|
|
|
+ LLAMA_API bool llama_token_is_control(const struct llama_model * model, llama_token token);
|
|
|
+
|
|
|
+ // Special tokens
|
|
|
+ LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
|
|
|
+ LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
|
|
|
+ LLAMA_API llama_token llama_token_cls(const struct llama_model * model); // classification
|
|
|
+ LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator
|
|
|
+ LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
|
|
|
+ LLAMA_API llama_token llama_token_pad(const struct llama_model * model); // padding
|
|
|
+
|
|
|
+ // Returns -1 if unknown, 1 for true or 0 for false.
|
|
|
+ LLAMA_API int32_t llama_add_bos_token(const struct llama_model * model);
|
|
|
+
|
|
|
+ // Returns -1 if unknown, 1 for true or 0 for false.
|
|
|
+ LLAMA_API int32_t llama_add_eos_token(const struct llama_model * model);
|
|
|
+
|
|
|
+ // Codellama infill tokens
|
|
|
+ LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
|
|
|
+ LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
|
|
|
+ LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
|
|
|
+ LLAMA_API llama_token llama_token_eot (const struct llama_model * model); // 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_tokens_max
|
|
|
+ /// @return Returns a negative number on failure - the number of tokens that would have been returned
|
|
|
+ /// @param add_special Allow to add BOS and EOS tokens if model is configured to do so.
|
|
|
+ /// @param parse_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 int32_t llama_tokenize(
|
|
|
+ const struct llama_model * model,
|
|
|
+ const char * text,
|
|
|
+ int32_t text_len,
|
|
|
+ llama_token * tokens,
|
|
|
+ int32_t n_tokens_max,
|
|
|
+ bool add_special,
|
|
|
+ bool parse_special);
|
|
|
+
|
|
|
+ // Token Id -> Piece.
|
|
|
+ // Uses the vocabulary in the provided context.
|
|
|
+ // Does not write null terminator to the buffer.
|
|
|
+ // User can skip up to 'lstrip' leading spaces before copying (useful when encoding/decoding multiple tokens with 'add_space_prefix')
|
|
|
+ // @param special If true, special tokens are rendered in the output.
|
|
|
+ LLAMA_API int32_t llama_token_to_piece(
|
|
|
+ const struct llama_model * model,
|
|
|
+ llama_token token,
|
|
|
+ char * buf,
|
|
|
+ int32_t length,
|
|
|
+ int32_t lstrip,
|
|
|
+ bool special);
|
|
|
+
|
|
|
+ /// @details Convert the provided tokens into text (inverse of llama_tokenize()).
|
|
|
+ /// @param text The char pointer must be large enough to hold the resulting text.
|
|
|
+ /// @return Returns the number of chars/bytes on success, no more than text_len_max.
|
|
|
+ /// @return Returns a negative number on failure - the number of chars/bytes that would have been returned.
|
|
|
+ /// @param remove_special Allow to remove BOS and EOS tokens if model is configured to do so.
|
|
|
+ /// @param unparse_special If true, special tokens are rendered in the output.
|
|
|
+ LLAMA_API int32_t llama_detokenize(
|
|
|
+ const struct llama_model * model,
|
|
|
+ const llama_token * tokens,
|
|
|
+ int32_t n_tokens,
|
|
|
+ char * text,
|
|
|
+ int32_t text_len_max,
|
|
|
+ bool remove_special,
|
|
|
+ bool unparse_special);
|
|
|
+
|
|
|
+ /// Apply chat template. Inspired by hf apply_chat_template() on python.
|
|
|
+ /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
|
|
|
+ /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
|
|
|
+ /// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead.
|
|
|
+ /// @param chat Pointer to a list of multiple llama_chat_message
|
|
|
+ /// @param n_msg Number of llama_chat_message in this chat
|
|
|
+ /// @param add_ass Whether to end the prompt with the token(s) that indicate the start of an assistant message.
|
|
|
+ /// @param buf A buffer to hold the output formatted prompt. The recommended alloc size is 2 * (total number of characters of all messages)
|
|
|
+ /// @param length The size of the allocated buffer
|
|
|
+ /// @return The total number of bytes of the formatted prompt. If is it larger than the size of buffer, you may need to re-alloc it and then re-apply the template.
|
|
|
+ LLAMA_API int32_t llama_chat_apply_template(
|
|
|
+ const struct llama_model * model,
|
|
|
+ const char * tmpl,
|
|
|
+ const struct llama_chat_message * chat,
|
|
|
+ size_t n_msg,
|
|
|
+ bool add_ass,
|
|
|
+ char * buf,
|
|
|
+ int32_t length);
|
|
|
+
|
|
|
+ //
|
|
|
+ // Grammar
|
|
|
+ //
|
|
|
+
|
|
|
+ /// Initialize a llama_grammar.
|
|
|
+ ///
|
|
|
+ /// @param rules The rule elements of the grammar to initialize.
|
|
|
+ /// @param n_rules The number of rules.
|
|
|
+ /// @param start_rule_index The index of the root rule (the starting point of the grammar).
|
|
|
+ /// @return The initialized llama_grammar or nullptr if initialization failed.
|
|
|
+ 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 logits Logits extracted from the original generation context.
|
|
|
+ /// @param logits_guidance Logits extracted from 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.
|
|
|
+ /// @param scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
|
|
|
+ LLAMA_API void llama_sample_apply_guidance(
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|
|
+ struct llama_context * ctx,
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|
|
+ float * logits,
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|
+ float * logits_guidance,
|
|
|
+ float scale);
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+
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+ /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
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|
+ LLAMA_API void llama_sample_softmax(
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|
+ struct llama_context * ctx,
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|
+ llama_token_data_array * candidates);
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+
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+ /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
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|
+ LLAMA_API void llama_sample_top_k(
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+ struct llama_context * ctx,
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|
+ llama_token_data_array * candidates,
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|
+ int32_t k,
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|
|
+ size_t min_keep);
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+
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|
|
+ /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
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|
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+ LLAMA_API void llama_sample_top_p(
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+ struct llama_context * ctx,
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|
+ llama_token_data_array * candidates,
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|
|
+ float p,
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|
|
+ size_t min_keep);
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+
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+ /// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
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+ LLAMA_API void llama_sample_min_p(
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|
+ struct llama_context * ctx,
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+ llama_token_data_array * candidates,
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|
|
+ float p,
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|
|
+ size_t min_keep);
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+
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+ /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
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+ LLAMA_API void llama_sample_tail_free(
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+ struct llama_context * ctx,
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|
+ llama_token_data_array * candidates,
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|
|
+ float z,
|
|
|
+ size_t min_keep);
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+
|
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+ /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
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+ LLAMA_API void llama_sample_typical(
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|
+ struct llama_context * ctx,
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|
+ llama_token_data_array * candidates,
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|
|
+ float p,
|
|
|
+ size_t min_keep);
|
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+
|
|
|
+ /// @details Dynamic temperature implementation described in the paper https://arxiv.org/abs/2309.02772.
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|
+ LLAMA_API void llama_sample_entropy(
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|
|
+ struct llama_context * ctx,
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|
|
+ llama_token_data_array * candidates_p,
|
|
|
+ float min_temp,
|
|
|
+ float max_temp,
|
|
|
+ float exponent_val);
|
|
|
+
|
|
|
+ LLAMA_API void llama_sample_temp(
|
|
|
+ struct llama_context * ctx,
|
|
|
+ llama_token_data_array * candidates,
|
|
|
+ float temp);
|
|
|
+
|
|
|
+ /// @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,
|
|
|
+ int32_t 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.
|
|
|
+ /// Does not compute the token probabilities. Use llama_sample_softmax() instead.
|
|
|
+ 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 using the RNG of ctx.
|
|
|
+ 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);
|
|
|
+
|
|
|
+ //
|
|
|
+ // Model split
|
|
|
+ //
|
|
|
+
|
|
|
+ /// @details Build a split GGUF final path for this chunk.
|
|
|
+ /// llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf"
|
|
|
+ // Returns the split_path length.
|
|
|
+ LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count);
|
|
|
+
|
|
|
+ /// @details Extract the path prefix from the split_path if and only if the split_no and split_count match.
|
|
|
+ /// llama_split_prefix(split_prefix, 64, "/models/ggml-model-q4_0-00002-of-00004.gguf", 2, 4) => split_prefix = "/models/ggml-model-q4_0"
|
|
|
+ // Returns the split_prefix length.
|
|
|
+ LLAMA_API int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count);
|
|
|
+
|
|
|
+ // 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 <random>
|
|
|
+#include <string>
|
|
|
+#include <vector>
|
|
|
+
|
|
|
+struct ggml_tensor;
|
|
|
+
|
|
|
+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;
|
|
|
+};
|
|
|
+
|
|
|
+const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
|
|
|
+ struct llama_context * ctx
|
|
|
+);
|
|
|
+
|
|
|
+void 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);
|
|
|
+
|
|
|
+std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
|
|
|
+ const std::string & src,
|
|
|
+ llama_partial_utf8 partial_start);
|
|
|
+
|
|
|
+// Randomly selects a token from the candidates based on their probabilities using given std::mt19937.
|
|
|
+// This is a temporary workaround in order to fix race conditions when sampling with multiple sequences.
|
|
|
+llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng);
|
|
|
+
|
|
|
+#endif // LLAMA_API_INTERNAL
|
|
|
+
|
|
|
+#endif // LLAMA_H
|
Josh Yan