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llama.cpp
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llama.cpp
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#define LLAMA_API_INTERNAL
#include "llama.h"
#include "unicode.h"
#include "ggml.h"
#include "ggml-alloc.h"
#ifdef GGML_USE_CUBLAS
# include "ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
# include "ggml-opencl.h"
#endif
#ifdef GGML_USE_METAL
# include "ggml-metal.h"
#endif
#ifdef GGML_USE_MPI
# include "ggml-mpi.h"
#endif
#ifndef QK_K
# ifdef GGML_QKK_64
# define QK_K 64
# else
# define QK_K 256
# endif
#endif
#ifdef __has_include
#if __has_include(<unistd.h>)
#include <unistd.h>
#if defined(_POSIX_MAPPED_FILES)
#include <sys/mman.h>
#endif
#if defined(_POSIX_MEMLOCK_RANGE)
#include <sys/resource.h>
#endif
#endif
#endif
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <io.h>
#include <stdio.h> // for _fseeki64
#include <direct.h>
#define F_OK 0
#else
#include <libgen.h>
#endif
#include <algorithm>
#include <array>
#include <cassert>
#include <cinttypes>
#include <climits>
#include <cmath>
#include <cstdarg>
#include <cstddef>
#include <cstdint>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <forward_list>
#include <fstream>
#include <functional>
#include <initializer_list>
#include <map>
#include <memory>
#include <mutex>
#include <numeric>
#include <queue>
#include <random>
#include <regex>
#include <set>
#include <sstream>
#include <thread>
#include <unordered_map>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#ifdef __GNUC__
#ifdef __MINGW32__
#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
#else
#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
#endif
#else
#define LLAMA_ATTRIBUTE_FORMAT(...)
#endif
#define LLAMA_MAX_NODES 4096
//
// global variables (should be removed after a better design)
//
size_t vram_budget_bytes = 0;
//
// logging
//
LLAMA_ATTRIBUTE_FORMAT(2, 3)
static void llama_log_internal (ggml_log_level level, const char* format, ...);
static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data);
#define LLAMA_LOG_INFO(...) llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
#define LLAMA_LOG_WARN(...) llama_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
#define LLAMA_LOG_ERROR(...) llama_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
//
// helpers
//
static size_t utf8_len(char src) {
const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
uint8_t highbits = static_cast<uint8_t>(src) >> 4;
return lookup[highbits];
}
static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
std::string result;
for (size_t pos = 0; ; pos += search.length()) {
auto new_pos = s.find(search, pos);
if (new_pos == std::string::npos) {
result += s.substr(pos, s.size() - pos);
break;
}
result += s.substr(pos, new_pos - pos) + replace;
pos = new_pos;
}
s = std::move(result);
}
static bool is_float_close(float a, float b, float abs_tol) {
// Check for non-negative tolerance
if (abs_tol < 0.0) {
throw std::invalid_argument("Tolerance must be non-negative");
}
// Exact equality check
if (a == b) {
return true;
}
// Check for infinities
if (std::isinf(a) || std::isinf(b)) {
return false;
}
// Regular comparison using the provided absolute tolerance
return std::fabs(b - a) <= abs_tol;
}
#ifdef GGML_USE_CPU_HBM
#include <hbwmalloc.h>
#endif
static void zeros(std::ofstream & file, size_t n) {
char zero = 0;
for (size_t i = 0; i < n; ++i) {
file.write(&zero, 1);
}
}
LLAMA_ATTRIBUTE_FORMAT(1, 2)
static std::string format(const char * fmt, ...) {
va_list ap;
va_list ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
GGML_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), size);
}
static size_t llama_set_vram_budget(double budget_gb, int gpu_device) {
#if defined(GGML_USE_CUBLAS)
if (!ggml_cublas_loaded()) {
throw std::runtime_error("CUDA is not loaded");
}
if (budget_gb < 0) {
// if the user didn't specify a budget, use all available memory
// and leave 256 MB as a safety margin
vram_budget_bytes = ggml_cuda_get_free_memory(gpu_device) - 256 * 1024 * 1024;
} else {
// otherwise, use the specified budget
vram_budget_bytes = (size_t) (budget_gb * 1024 * 1024 * 1024);
}
return vram_budget_bytes;
#else
return 0;
#endif
}
static bool llama_reduce_vram_budget(size_t budget_bytes) {
#if not defined(GGML_USE_CUBLAS)
throw std::runtime_error("CUDA is not enabled");
#endif
if (vram_budget_bytes >= budget_bytes) {
vram_budget_bytes -= budget_bytes;
return true;
}
return false;
}
//
// gguf constants (sync with gguf.py)
//
enum llm_arch {
LLM_ARCH_LLAMA,
LLM_ARCH_FALCON,
LLM_ARCH_BAICHUAN,
LLM_ARCH_GPT2,
LLM_ARCH_GPTJ,
LLM_ARCH_GPTNEOX,
LLM_ARCH_MPT,
LLM_ARCH_STARCODER,
LLM_ARCH_PERSIMMON,
LLM_ARCH_REFACT,
LLM_ARCH_BLOOM,
LLM_ARCH_STABLELM,
LLM_ARCH_BAMBOO,
LLM_ARCH_UNKNOWN,
};
static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
{ LLM_ARCH_LLAMA, "llama" },
{ LLM_ARCH_FALCON, "falcon" },
{ LLM_ARCH_GPT2, "gpt2" },
{ LLM_ARCH_GPTJ, "gptj" },
{ LLM_ARCH_GPTNEOX, "gptneox" },
{ LLM_ARCH_MPT, "mpt" },
{ LLM_ARCH_BAICHUAN, "baichuan" },
{ LLM_ARCH_STARCODER, "starcoder" },
{ LLM_ARCH_PERSIMMON, "persimmon" },
{ LLM_ARCH_REFACT, "refact" },
{ LLM_ARCH_BLOOM, "bloom" },
{ LLM_ARCH_STABLELM, "stablelm" },
{ LLM_ARCH_BAMBOO, "bamboo" },
{ LLM_ARCH_UNKNOWN, "unknown" },
};
enum llm_kv {
LLM_KV_GENERAL_ARCHITECTURE,
LLM_KV_GENERAL_QUANTIZATION_VERSION,
LLM_KV_GENERAL_ALIGNMENT,
LLM_KV_GENERAL_NAME,
LLM_KV_GENERAL_AUTHOR,
LLM_KV_GENERAL_URL,
LLM_KV_GENERAL_DESCRIPTION,
LLM_KV_GENERAL_LICENSE,
LLM_KV_GENERAL_SOURCE_URL,
LLM_KV_GENERAL_SOURCE_HF_REPO,
LLM_KV_CONTEXT_LENGTH,
LLM_KV_EMBEDDING_LENGTH,
LLM_KV_BLOCK_COUNT,
LLM_KV_FEED_FORWARD_LENGTH,
LLM_KV_USE_PARALLEL_RESIDUAL,
LLM_KV_TENSOR_DATA_LAYOUT,
LLM_KV_ATTENTION_HEAD_COUNT,
LLM_KV_ATTENTION_HEAD_COUNT_KV,
LLM_KV_ATTENTION_MAX_ALIBI_BIAS,
LLM_KV_ATTENTION_CLAMP_KQV,
LLM_KV_ATTENTION_LAYERNORM_EPS,
LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_FREQ_BASE,
LLM_KV_ROPE_SCALE_LINEAR,
LLM_KV_ROPE_SCALING_TYPE,
LLM_KV_ROPE_SCALING_FACTOR,
LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
LLM_KV_ROPE_SCALING_FINETUNED,
LLM_KV_TOKENIZER_MODEL,
LLM_KV_TOKENIZER_LIST,
LLM_KV_TOKENIZER_TOKEN_TYPE,
LLM_KV_TOKENIZER_SCORES,
LLM_KV_TOKENIZER_MERGES,
LLM_KV_TOKENIZER_BOS_ID,
LLM_KV_TOKENIZER_EOS_ID,
LLM_KV_TOKENIZER_UNK_ID,
LLM_KV_TOKENIZER_SEP_ID,
LLM_KV_TOKENIZER_PAD_ID,
LLM_KV_TOKENIZER_HF_JSON,
LLM_KV_TOKENIZER_RWKV,
LLM_KV_SPARSE_THRESHOLD,
LLM_KV_SPLIT_VRAM_CAPACITY,
};
static std::map<llm_kv, std::string> LLM_KV_NAMES = {
{ LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" },
{ LLM_KV_GENERAL_QUANTIZATION_VERSION, "general.quantization_version" },
{ LLM_KV_GENERAL_ALIGNMENT, "general.alignment" },
{ LLM_KV_GENERAL_NAME, "general.name" },
{ LLM_KV_GENERAL_AUTHOR, "general.author" },
{ LLM_KV_GENERAL_URL, "general.url" },
{ LLM_KV_GENERAL_DESCRIPTION, "general.description" },
{ LLM_KV_GENERAL_LICENSE, "general.license" },
{ LLM_KV_GENERAL_SOURCE_URL, "general.source.url" },
{ LLM_KV_GENERAL_SOURCE_HF_REPO, "general.source.huggingface.repository" },
{ LLM_KV_CONTEXT_LENGTH, "%s.context_length" },
{ LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" },
{ LLM_KV_BLOCK_COUNT, "%s.block_count" },
{ LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" },
{ LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" },
{ LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" },
{ LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
{ LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
{ LLM_KV_ATTENTION_MAX_ALIBI_BIAS, "%s.attention.max_alibi_bias" },
{ LLM_KV_ATTENTION_CLAMP_KQV, "%s.attention.clamp_kqv" },
{ LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" },
{ LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" },
{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
{ LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" },
{ LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" },
{ LLM_KV_ROPE_SCALING_FACTOR, "%s.rope.scaling.factor" },
{ LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" },
{ LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" },
{ LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" },
{ LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" },
{ LLM_KV_TOKENIZER_TOKEN_TYPE, "tokenizer.ggml.token_type" },
{ LLM_KV_TOKENIZER_SCORES, "tokenizer.ggml.scores" },
{ LLM_KV_TOKENIZER_MERGES, "tokenizer.ggml.merges" },
{ LLM_KV_TOKENIZER_BOS_ID, "tokenizer.ggml.bos_token_id" },
{ LLM_KV_TOKENIZER_EOS_ID, "tokenizer.ggml.eos_token_id" },
{ LLM_KV_TOKENIZER_UNK_ID, "tokenizer.ggml.unknown_token_id" },
{ LLM_KV_TOKENIZER_SEP_ID, "tokenizer.ggml.seperator_token_id" },
{ LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" },
{ LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" },
{ LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" },
{ LLM_KV_SPARSE_THRESHOLD, "powerinfer.sparse_threshold" },
{ LLM_KV_SPLIT_VRAM_CAPACITY, "split.vram_capacity" },
};
struct LLM_KV {
LLM_KV(llm_arch arch) : arch(arch) {}
llm_arch arch;
std::string operator()(llm_kv kv) const {
return ::format(LLM_KV_NAMES[kv].c_str(), LLM_ARCH_NAMES[arch].c_str());
}
};
enum llm_tensor {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_TOKEN_EMBD_NORM,
LLM_TENSOR_POS_EMBD,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_ROPE_FREQS,
LLM_TENSOR_ATTN_Q,
LLM_TENSOR_ATTN_K,
LLM_TENSOR_ATTN_V,
LLM_TENSOR_ATTN_QKV,
LLM_TENSOR_ATTN_OUT,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_NORM_2,
LLM_TENSOR_ATTN_ROT_EMBD,
LLM_TENSOR_FFN_GATE,
LLM_TENSOR_FFN_DOWN,
LLM_TENSOR_FFN_UP,
LLM_TENSOR_FFN_NORM,
LLM_TENSOR_ATTN_Q_NORM,
LLM_TENSOR_ATTN_K_NORM,
LLM_TENSOR_MLP_PRED_FC1,
LLM_TENSOR_MLP_PRED_FC2,
LLM_TENSOR_FFN_DOWN_T,
};
static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
{
LLM_ARCH_LLAMA,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_DOWN_T, "blk.%d.ffn_down_t" },
{ LLM_TENSOR_MLP_PRED_FC1, "blk.%d.fc1" },
{ LLM_TENSOR_MLP_PRED_FC2, "blk.%d.fc2" },
},
},
{
LLM_ARCH_BAICHUAN,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_FALCON,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_DOWN_T, "blk.%d.ffn_down_t" },
{ LLM_TENSOR_MLP_PRED_FC1, "blk.%d.fc1" },
{ LLM_TENSOR_MLP_PRED_FC2, "blk.%d.fc2" },
},
},
{
LLM_ARCH_GPT2,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
},
},
{
LLM_ARCH_GPTJ,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
},
},
{
LLM_ARCH_GPTNEOX,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_PERSIMMON,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd"},
{ LLM_TENSOR_OUTPUT_NORM, "output_norm"},
{ LLM_TENSOR_OUTPUT, "output"},
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm"},
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv"},
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output"},
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm"},
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm"},
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm"},
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down"},
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up"},
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd"},
},
},
{
LLM_ARCH_MPT,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_STARCODER,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_POS_EMBD, "position_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
},
},
{
LLM_ARCH_REFACT,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_BLOOM,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
},
},
{
LLM_ARCH_STABLELM,
{
{ 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_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_BAMBOO,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_DOWN_T, "blk.%d.ffn_down_t" },
{ LLM_TENSOR_MLP_PRED_FC1, "blk.%d.fc1" },
{ LLM_TENSOR_MLP_PRED_FC2, "blk.%d.fc2" },
},
},
{
LLM_ARCH_UNKNOWN,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
},
},
};
static llm_arch llm_arch_from_string(const std::string & name) {
for (const auto & kv : LLM_ARCH_NAMES) { // NOLINT
if (kv.second == name) {
return kv.first;
}
}
return LLM_ARCH_UNKNOWN;
}
enum tensor_offloading_levels {
TENSOR_NO_OFFLOAD,
TENSOR_OFFLOAD_FFN,
TENSOR_OFFLOAD_ATTN,
TENSOR_OFFLOAD_MLP_PRED,
TENSOR_OFFLOAD_FFN_IO,
TENSOR_OFFLOAD_OUTPUT,
TENSOR_OFFLOAD_KV_CACHE,
};
tensor_offloading_levels get_offloading_level(llm_tensor tensor) {
switch (tensor) {
case LLM_TENSOR_TOKEN_EMBD: case LLM_TENSOR_TOKEN_EMBD_NORM: case LLM_TENSOR_POS_EMBD:
case LLM_TENSOR_ROPE_FREQS:
return TENSOR_NO_OFFLOAD;
case LLM_TENSOR_OUTPUT: case LLM_TENSOR_OUTPUT_NORM:
return TENSOR_OFFLOAD_OUTPUT;
case LLM_TENSOR_ATTN_Q: case LLM_TENSOR_ATTN_K: case LLM_TENSOR_ATTN_V:
case LLM_TENSOR_ATTN_QKV: case LLM_TENSOR_ATTN_OUT: case LLM_TENSOR_ATTN_NORM:
case LLM_TENSOR_ATTN_NORM_2: case LLM_TENSOR_ATTN_ROT_EMBD:
case LLM_TENSOR_ATTN_Q_NORM: case LLM_TENSOR_ATTN_K_NORM:
return TENSOR_OFFLOAD_ATTN;
case LLM_TENSOR_FFN_GATE: case LLM_TENSOR_FFN_DOWN: case LLM_TENSOR_FFN_UP:
case LLM_TENSOR_FFN_DOWN_T:
return TENSOR_OFFLOAD_FFN;
case LLM_TENSOR_FFN_NORM:
return TENSOR_OFFLOAD_FFN_IO;
case LLM_TENSOR_MLP_PRED_FC1: case LLM_TENSOR_MLP_PRED_FC2:
return TENSOR_OFFLOAD_MLP_PRED;
default:
throw std::runtime_error("unknown tensor category");
}
}
// helper to handle gguf constants
// usage:
//
// const auto tn = LLM_TN(LLM_ARCH_LLAMA);
//
// std::string name = tn(LLM_TENSOR_OUTPUT); -> "output"
// std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias"); -> "token_embd.bias"
// std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3); -> "blk.3.attn_norm.weight"
//
struct LLM_TN {
LLM_TN(llm_arch arch) : arch(arch) {}
llm_arch arch;
std::pair<std::string, llm_tensor> operator()(llm_tensor tensor) const {
return std::make_pair(LLM_TENSOR_NAMES[arch].at(tensor), tensor);
}
std::pair<std::string, llm_tensor> operator()(llm_tensor tensor, const std::string & suffix) const {
return std::make_pair(LLM_TENSOR_NAMES[arch].at(tensor) + "." + suffix, tensor);
}
std::pair<std::string, llm_tensor> operator()(llm_tensor tensor, int bid) const {
return std::make_pair(::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid), tensor);
}
std::pair<std::string, llm_tensor> operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
return std::make_pair(::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid) + "." + suffix, tensor);
}
};
//
// gguf helpers
//
#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \
do { \
const std::string skey(key); \
const int kid = gguf_find_key(ctx, skey.c_str()); \
if (kid >= 0) { \
enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \
if (ktype != (type)) { \
throw std::runtime_error(format("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype))); \
} \
(dst) = func(ctx, kid); \
} else if (req) { \
throw std::runtime_error(format("key not found in model: %s", skey.c_str())); \
} \
} while (0)
static std::map<int8_t, std::string> LLAMA_ROPE_SCALING_TYPES = {
{ LLAMA_ROPE_SCALING_NONE, "none" },
{ LLAMA_ROPE_SCALING_LINEAR, "linear" },
{ LLAMA_ROPE_SCALING_YARN, "yarn" },
};
static int8_t llama_rope_scaling_type_from_string(const std::string & name) {
for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
if (kv.second == name) {
return kv.first;
}
}
return LLAMA_ROPE_SCALING_UNSPECIFIED;
}
//
// ggml helpers
//
static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
if (plan.work_size > 0) {
buf.resize(plan.work_size);
plan.work_data = buf.data();
}
ggml_graph_compute(graph, &plan);
}
//
// llama helpers
//
inline void * llama_host_malloc(size_t n) {
#ifdef GGML_USE_CUBLAS
if (ggml_cublas_loaded()) {
return ggml_cuda_host_malloc(n);
} else {
return malloc(n);
}
#elif GGML_USE_METAL
return ggml_metal_host_malloc(n);
#elif GGML_USE_CPU_HBM
return hbw_malloc(n);
#else
return malloc(n);
#endif
}
inline void llama_host_free(void * ptr) {
#ifdef GGML_USE_CUBLAS
if (ggml_cublas_loaded()) {
return ggml_cuda_host_free(ptr);
} else {
return free(ptr);
}
#elif GGML_USE_METAL
return ggml_metal_host_free(ptr);
#elif GGML_USE_CPU_HBM
return hbw_free(ptr);
#else
return free(ptr);
#endif
}
#if defined(_WIN32)
static std::string llama_format_win_err(DWORD err) {
LPSTR buf;
size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
if (!size) {
return "FormatMessageA failed";
}
std::string ret(buf, size);
LocalFree(buf);
return ret;
}
#endif
struct llama_buffer {
void * data = NULL;
size_t size = 0;
// fallback to malloc / free
// useful in cases where CUDA can try to allocate PINNED memory
bool fallback = false;
void resize(size_t n) {
llama_host_free(data);
data = llama_host_malloc(n);
if (!data) {
fallback = true;
data = malloc(n);
} else {
fallback = false;
}
GGML_ASSERT(data);
size = n;
}
~llama_buffer() {
if (data) {
if (fallback) { // NOLINT
free(data);
} else {
llama_host_free(data);
}
}
data = NULL;
}
};
struct llama_file {
// use FILE * so we don't have to re-open the file to mmap
FILE * fp;
std::string fname;
size_t size;
llama_file(const char * fname, const char * mode): fname(fname) {
fp = std::fopen(fname, mode);
if (fp == NULL) {
throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
}
seek(0, SEEK_END);
size = tell();
seek(0, SEEK_SET);
}
std::string get_basedir() const {
const char * model_path = fname.c_str();
#if defined(_WIN32)
size_t found = fname.find_last_of("/\\");
return fname.substr(0, found);
#else
#include <libgen.h>
const char * base_dir = dirname(const_cast<char *>(model_path));
return std::string(base_dir);
#endif
}
size_t tell() const {
#ifdef _WIN32
__int64 ret = _ftelli64(fp);
#else
long ret = std::ftell(fp);
#endif
GGML_ASSERT(ret != -1); // this really shouldn't fail
return (size_t) ret;
}
void seek(size_t offset, int whence) const {
#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset, whence);
#else
int ret = std::fseek(fp, (long) offset, whence);
#endif
GGML_ASSERT(ret == 0); // same
}
void read_raw(void * ptr, size_t len) const {
if (len == 0) {
return;
}
errno = 0;
std::size_t ret = std::fread(ptr, len, 1, fp);
if (ferror(fp)) {
throw std::runtime_error(format("read error: %s", strerror(errno)));
}
if (ret != 1) {
throw std::runtime_error(std::string("unexpectedly reached end of file"));
}
}
uint32_t read_u32() const {
uint32_t ret;
read_raw(&ret, sizeof(ret));
return ret;
}
void write_raw(const void * ptr, size_t len) const {
if (len == 0) {
return;
}
errno = 0;
size_t ret = std::fwrite(ptr, len, 1, fp);
if (ret != 1) {
throw std::runtime_error(format("write error: %s", strerror(errno)));
}
}
void write_u32(std::uint32_t val) const {
write_raw(&val, sizeof(val));
}
~llama_file() {
if (fp) {
std::fclose(fp);
}
}
};
struct llama_mmap {
void * addr;
size_t size;
llama_mmap(const llama_mmap &) = delete;
#ifdef _POSIX_MAPPED_FILES
static constexpr bool SUPPORTED = true;
llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
size = file->size;
int fd = fileno(file->fp);
int flags = MAP_SHARED;
// prefetch/readahead impairs performance on NUMA systems
if (numa) { prefetch = 0; }
#ifdef __linux__
if (prefetch) { flags |= MAP_POPULATE; }
#endif
addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
if (addr == MAP_FAILED) {
throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
}
if (prefetch > 0) {
// Advise the kernel to preload the mapped memory
if (posix_madvise(addr, std::min(file->size, prefetch), POSIX_MADV_WILLNEED)) {
fprintf(stderr, "warning: posix_madvise(.., POSIX_MADV_WILLNEED) failed: %s\n",
strerror(errno));
}
}
if (numa) {
// advise the kernel not to use readahead
// (because the next page might not belong on the same node)
if (posix_madvise(addr, file->size, POSIX_MADV_RANDOM)) {
fprintf(stderr, "warning: posix_madvise(.., POSIX_MADV_RANDOM) failed: %s\n",
strerror(errno));
}
}
}
~llama_mmap() {
munmap(addr, size);
}
#elif defined(_WIN32)
static constexpr bool SUPPORTED = true;
llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) {
(void) numa;
size = file->size;
HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
DWORD error = GetLastError();
if (hMapping == NULL) {
throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
}
addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
error = GetLastError();
CloseHandle(hMapping);
if (addr == NULL) {
throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
}
if (prefetch) {
// PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it
BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG);
HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll");
// may fail on pre-Windows 8 systems
pPrefetchVirtualMemory = reinterpret_cast<decltype(pPrefetchVirtualMemory)> (GetProcAddress(hKernel32, "PrefetchVirtualMemory"));
if (pPrefetchVirtualMemory) {
// advise the kernel to preload the mapped memory
WIN32_MEMORY_RANGE_ENTRY range;
range.VirtualAddress = addr;
range.NumberOfBytes = (SIZE_T)size;
if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
}
}
}
}