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llama.cpp/llama.cpp
Kawrakow 469e75d0a3
llama : restore intended k-quants mixes for MoE models (#4872) 
* Restore intended k-quants quantization mixes for MoE models

* Update Q2_K_S values in the quantize tool

Still using LLaMA-v1 PPL values in the quant description
today does not make much sense. But let's leave this update
for another PR.

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-11 21:43:15 +02:00

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#define LLAMA_API_INTERNAL
//#define LLAMA_GGML_BACKEND_CUDA_TEST // for testing only - enables ggml-cuda through ggml-backend, disables partial offloading
#include "llama.h"
#include "unicode.h"
#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.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>
#include <fcntl.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>
#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 <type_traits>
#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 8192
#define LLAMA_MAX_EXPERTS 8
//
// logging
//
LLAMA_ATTRIBUTE_FORMAT(2, 3)
static void llama_log_internal (ggml_log_level level, const char* format, ...);
static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data);
#define LLAMA_LOG_INFO(...) llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
#define LLAMA_LOG_WARN(...) llama_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
#define LLAMA_LOG_ERROR(...) llama_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
//
// helpers
//
static size_t utf8_len(char src) {
const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
uint8_t highbits = static_cast<uint8_t>(src) >> 4;
return lookup[highbits];
}
static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
std::string result;
for (size_t pos = 0; ; pos += search.length()) {
auto new_pos = s.find(search, pos);
if (new_pos == std::string::npos) {
result += s.substr(pos, s.size() - pos);
break;
}
result += s.substr(pos, new_pos - pos) + replace;
pos = new_pos;
}
s = std::move(result);
}
static bool is_float_close(float a, float b, float abs_tol) {
// Check for non-negative tolerance
if (abs_tol < 0.0) {
throw std::invalid_argument("Tolerance must be non-negative");
}
// Exact equality check
if (a == b) {
return true;
}
// Check for infinities
if (std::isinf(a) || std::isinf(b)) {
return false;
}
// Regular comparison using the provided absolute tolerance
return std::fabs(b - a) <= abs_tol;
}
#ifdef GGML_USE_CPU_HBM
#include <hbwmalloc.h>
#endif
static void zeros(std::ofstream & file, size_t n) {
char zero = 0;
for (size_t i = 0; i < n; ++i) {
file.write(&zero, 1);
}
}
LLAMA_ATTRIBUTE_FORMAT(1, 2)
static std::string format(const char * fmt, ...) {
va_list ap;
va_list ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
GGML_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), size);
}
//
// gguf constants (sync with gguf.py)
//
enum llm_arch {
LLM_ARCH_LLAMA,
LLM_ARCH_FALCON,
LLM_ARCH_BAICHUAN,
LLM_ARCH_GPT2,
LLM_ARCH_GPTJ,
LLM_ARCH_GPTNEOX,
LLM_ARCH_MPT,
LLM_ARCH_STARCODER,
LLM_ARCH_PERSIMMON,
LLM_ARCH_REFACT,
LLM_ARCH_BLOOM,
LLM_ARCH_STABLELM,
LLM_ARCH_QWEN,
LLM_ARCH_PHI2,
LLM_ARCH_PLAMO,
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_QWEN, "qwen" },
{ LLM_ARCH_PHI2, "phi2" },
{ LLM_ARCH_PLAMO, "plamo" },
};
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_EXPERT_COUNT,
LLM_KV_EXPERT_USED_COUNT,
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_KEY_LENGTH,
LLM_KV_ATTENTION_VALUE_LENGTH,
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_ADD_BOS,
LLM_KV_TOKENIZER_ADD_EOS,
LLM_KV_TOKENIZER_HF_JSON,
LLM_KV_TOKENIZER_RWKV,
};
static std::map<llm_kv, std::string> LLM_KV_NAMES = {
{ LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" },
{ LLM_KV_GENERAL_QUANTIZATION_VERSION, "general.quantization_version" },
{ LLM_KV_GENERAL_ALIGNMENT, "general.alignment" },
{ LLM_KV_GENERAL_NAME, "general.name" },
{ LLM_KV_GENERAL_AUTHOR, "general.author" },
{ LLM_KV_GENERAL_URL, "general.url" },
{ LLM_KV_GENERAL_DESCRIPTION, "general.description" },
{ LLM_KV_GENERAL_LICENSE, "general.license" },
{ LLM_KV_GENERAL_SOURCE_URL, "general.source.url" },
{ LLM_KV_GENERAL_SOURCE_HF_REPO, "general.source.huggingface.repository" },
{ LLM_KV_CONTEXT_LENGTH, "%s.context_length" },
{ LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" },
{ LLM_KV_BLOCK_COUNT, "%s.block_count" },
{ LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" },
{ LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" },
{ LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" },
{ LLM_KV_EXPERT_COUNT, "%s.expert_count" },
{ LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" },
{ 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_KEY_LENGTH, "%s.attention.key_length" },
{ LLM_KV_ATTENTION_VALUE_LENGTH, "%s.attention.value_length" },
{ 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_ADD_BOS, "tokenizer.ggml.add_bos_token" },
{ LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" },
{ LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" },
{ LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" },
};
struct LLM_KV {
LLM_KV(llm_arch arch) : arch(arch) {}
llm_arch arch;
std::string operator()(llm_kv kv) const {
return ::format(LLM_KV_NAMES[kv].c_str(), LLM_ARCH_NAMES[arch].c_str());
}
};
enum llm_tensor {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_TOKEN_EMBD_NORM,
LLM_TENSOR_POS_EMBD,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_ROPE_FREQS,
LLM_TENSOR_ATTN_Q,
LLM_TENSOR_ATTN_K,
LLM_TENSOR_ATTN_V,
LLM_TENSOR_ATTN_QKV,
LLM_TENSOR_ATTN_OUT,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_NORM_2,
LLM_TENSOR_ATTN_ROT_EMBD,
LLM_TENSOR_FFN_GATE_INP,
LLM_TENSOR_FFN_NORM,
LLM_TENSOR_FFN_GATE,
LLM_TENSOR_FFN_DOWN,
LLM_TENSOR_FFN_UP,
LLM_TENSOR_FFN_ACT,
LLM_TENSOR_FFN_DOWN_EXP,
LLM_TENSOR_FFN_GATE_EXP,
LLM_TENSOR_FFN_UP_EXP,
LLM_TENSOR_ATTN_Q_NORM,
LLM_TENSOR_ATTN_K_NORM,
};
static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
{
LLM_ARCH_LLAMA,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
{ LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
{ LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
},
},
{
LLM_ARCH_BAICHUAN,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_FALCON,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_GPT2,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_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_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_TENSOR_FFN_ACT, "blk.%d.ffn.act" },
},
},
{
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_QWEN,
{
{ 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_QKV, "blk.%d.attn_qkv" },
{ 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_PHI2,
{
{ 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_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_PLAMO,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_UNKNOWN,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
},
},
};
static llm_arch llm_arch_from_string(const std::string & name) {
for (const auto & kv : LLM_ARCH_NAMES) { // NOLINT
if (kv.second == name) {
return kv.first;
}
}
return LLM_ARCH_UNKNOWN;
}
// helper to handle gguf constants
// usage:
//
// const auto tn = LLM_TN(LLM_ARCH_LLAMA);
//
// std::string name = tn(LLM_TENSOR_OUTPUT); -> "output"
// std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias"); -> "token_embd.bias"
// std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3); -> "blk.3.attn_norm.weight"
//
struct LLM_TN {
LLM_TN(llm_arch arch) : arch(arch) {}
llm_arch arch;
std::string operator()(llm_tensor tensor) const {
return LLM_TENSOR_NAMES[arch].at(tensor);
}
std::string operator()(llm_tensor tensor, const std::string & suffix) const {
return LLM_TENSOR_NAMES[arch].at(tensor) + "." + suffix;
}
std::string operator()(llm_tensor tensor, int bid) const {
return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid);
}
std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid) + "." + suffix;
}
std::string operator()(llm_tensor tensor, const std::string & suffix, int bid, int xid) const {
return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid, xid) + "." + suffix;
}
};
//
// gguf helpers
//
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;
}
static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
switch (type) {
case GGUF_TYPE_UINT8: return std::to_string(((const uint8_t *)data)[i]);
case GGUF_TYPE_INT8: return std::to_string(((const int8_t *)data)[i]);
case GGUF_TYPE_UINT16: return std::to_string(((const uint16_t *)data)[i]);
case GGUF_TYPE_INT16: return std::to_string(((const int16_t *)data)[i]);
case GGUF_TYPE_UINT32: return std::to_string(((const uint32_t *)data)[i]);
case GGUF_TYPE_INT32: return std::to_string(((const int32_t *)data)[i]);
case GGUF_TYPE_UINT64: return std::to_string(((const uint64_t *)data)[i]);
case GGUF_TYPE_INT64: return std::to_string(((const int64_t *)data)[i]);
case GGUF_TYPE_FLOAT32: return std::to_string(((const float *)data)[i]);
case GGUF_TYPE_FLOAT64: return std::to_string(((const double *)data)[i]);
case GGUF_TYPE_BOOL: return ((const bool *)data)[i] ? "true" : "false";
default: return format("unknown type %d", type);
}
}
static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
switch (type) {
case GGUF_TYPE_STRING:
return gguf_get_val_str(ctx_gguf, i);
case GGUF_TYPE_ARRAY:
{
const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i);
int arr_n = gguf_get_arr_n(ctx_gguf, i);
const void * data = gguf_get_arr_data(ctx_gguf, i);
std::stringstream ss;
ss << "[";
for (int j = 0; j < arr_n; j++) {
if (arr_type == GGUF_TYPE_STRING) {
std::string val = gguf_get_arr_str(ctx_gguf, i, j);
// escape quotes
replace_all(val, "\\", "\\\\");
replace_all(val, "\"", "\\\"");
ss << '"' << val << '"';
} else if (arr_type == GGUF_TYPE_ARRAY) {
ss << "???";
} else {
ss << gguf_data_to_str(arr_type, data, j);
}
if (j < arr_n - 1) {
ss << ", ";
}
}
ss << "]";
return ss.str();
}
default:
return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0);
}
}
//
// ggml helpers
//
static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
if (plan.work_size > 0) {
buf.resize(plan.work_size);
plan.work_data = buf.data();
}
ggml_graph_compute(graph, &plan);
}
//
// llama helpers
//
#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
template <typename T>
struct no_init {
T value;
no_init() { /* do nothing */ }
};
struct llama_file {
// use FILE * so we don't have to re-open the file to mmap
FILE * fp;
size_t size;
llama_file(const char * fname, const char * mode) {
fp = std::fopen(fname, mode);
if (fp == NULL) {
throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
}
seek(0, SEEK_END);
size = tell();
seek(0, SEEK_SET);
}
size_t tell() const {
#ifdef _WIN32
__int64 ret = _ftelli64(fp);
#else
long ret = std::ftell(fp);
#endif
GGML_ASSERT(ret != -1); // this really shouldn't fail
return (size_t) ret;
}
void seek(size_t offset, int whence) const {
#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset, whence);
#else
int ret = std::fseek(fp, (long) offset, whence);
#endif
GGML_ASSERT(ret == 0); // same
}
void read_raw(void * ptr, size_t len) const {
if (len == 0) {
return;
}
errno = 0;
std::size_t ret = std::fread(ptr, len, 1, fp);
if (ferror(fp)) {
throw std::runtime_error(format("read error: %s", strerror(errno)));
}
if (ret != 1) {
throw std::runtime_error("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;
// list of mapped fragments (first_offset, last_offset)
std::vector<std::pair<size_t, size_t>> mapped_fragments;
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__
// advise the kernel to read the file sequentially (increases readahead)
if (posix_fadvise(fd, 0, 0, POSIX_FADV_SEQUENTIAL)) {
LLAMA_LOG_WARN("warning: posix_fadvise(.., POSIX_FADV_SEQUENTIAL) failed: %s\n",
strerror(errno));
}
if (prefetch) { flags |= MAP_POPULATE; }
#endif
addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
if (addr == MAP_FAILED) { // NOLINT
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)) {
LLAMA_LOG_WARN("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)) {
LLAMA_LOG_WARN("warning: posix_madvise(.., POSIX_MADV_RANDOM) failed: %s\n",
strerror(errno));
}
}
// initialize list of mapped_fragments
mapped_fragments.emplace_back(0, file->size);
}
static void align_range(size_t * first, size_t * last, size_t page_size) {
// align first to the next page
size_t offset_in_page = *first & (page_size - 1);
size_t offset_to_page = offset_in_page == 0 ? 0 : page_size - offset_in_page;
*first += offset_to_page;
// align last to the previous page
*last = *last & ~(page_size - 1);
if (*last <= *first) {
*last = *first;
}
}
// partially unmap the file in the range [first, last)
void unmap_fragment(size_t first, size_t last) {
// note: this function must not be called multiple times with overlapping ranges
// otherwise, there is a risk of invalidating addresses that have been repurposed for other mappings
int page_size = sysconf(_SC_PAGESIZE);
align_range(&first, &last, page_size);
size_t len = last - first;
if (len == 0) {
return;
}
GGML_ASSERT(first % page_size == 0);
GGML_ASSERT(last % page_size == 0);
GGML_ASSERT(last > first);
void * next_page_start = (uint8_t *) addr + first;
// unmap the range
if (munmap(next_page_start, len)) {
LLAMA_LOG_WARN("warning: munmap failed: %s\n", strerror(errno));
}
// update the list of mapped fragments to avoid unmapping the same range again in the destructor
std::vector<std::pair<size_t, size_t>> new_mapped_fragments;
for (const auto & frag : mapped_fragments) {
if (frag.first < first && frag.second > last) {
// the range is in the middle of the fragment, split it
new_mapped_fragments.emplace_back(frag.first, first);
new_mapped_fragments.emplace_back(last, frag.second);
} else if (frag.first < first && frag.second > first) {
// the range starts in the middle of the fragment
new_mapped_fragments.emplace_back(frag.first, first);
} else if (frag.first < last && frag.second > last) {
// the range ends in the middle of the fragment
new_mapped_fragments.emplace_back(last, frag.second);
} else if (frag.first >= first && frag.second <= last) {
// the range covers the entire fragment
} else {
// the range is outside the fragment
new_mapped_fragments.push_back(frag);
}
}
mapped_fragments = std::move(new_mapped_fragments);
}
~llama_mmap() {
for (const auto & frag : mapped_fragments) {
if (munmap((char *) addr + frag.first, frag.second - frag.first)) {
LLAMA_LOG_WARN("warning: munmap failed: %s\n", strerror(errno));
}
}
}
#elif defined(_WIN32)
static constexpr bool SUPPORTED = true;
llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1, bool numa = false) {
GGML_UNUSED(numa);
size = file->size;
HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
if (hMapping == NULL) {
DWORD error = GetLastError();
throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
}
addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
DWORD error = GetLastError();
CloseHandle(hMapping);
if (addr == NULL) {
throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
}
if (prefetch > 0) {
// 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) std::min(size, prefetch);
if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
LLAMA_LOG_WARN("warning: PrefetchVirtualMemory failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
}
}
}
}
void unmap_fragment(size_t first, size_t last) {
// not supported
GGML_UNUSED(first);
GGML_UNUSED(last);
}
~llama_mmap() {
if (!UnmapViewOfFile(addr)) {
LLAMA_LOG_WARN("warning: UnmapViewOfFile failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
}
}
#else
static constexpr bool SUPPORTED = false;
llama_mmap(struct llama_file * file, size_t prefetch = -1, bool numa = false) {
GGML_UNUSED(file);
GGML_UNUSED(prefetch);
GGML_UNUSED(numa);
throw std::runtime_error("mmap not supported");
}
void unmap_fragment(size_t first, size_t last) {
GGML_UNUSED(first);
GGML_UNUSED(last);
throw std::runtime_error("mmap not supported");
}
#endif
};
// Represents some region of memory being locked using mlock or VirtualLock;
// will automatically unlock on destruction.
struct llama_mlock {
void * addr = NULL;
size_t size = 0;
bool failed_already = false;
llama_mlock() {}
llama_mlock(const llama_mlock &) = delete;
~llama_mlock() {
if (size) {
raw_unlock(addr, size);
}
}
void init(void * ptr) {
GGML_ASSERT(addr == NULL && size == 0); // NOLINT
addr = ptr;
}
void grow_to(size_t target_size) {
GGML_ASSERT(addr);
if (failed_already) {
return;
}
size_t granularity = lock_granularity();
target_size = (target_size + granularity - 1) & ~(granularity - 1);
if (target_size > size) {
if (raw_lock((uint8_t *) addr + size, target_size - size)) {
size = target_size;
} else {
failed_already = true;
}
}
}
#ifdef _POSIX_MEMLOCK_RANGE
static constexpr bool SUPPORTED = true;
static size_t lock_granularity() {
return (size_t) sysconf(_SC_PAGESIZE);
}
#ifdef __APPLE__
#define MLOCK_SUGGESTION \
"Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
"decreasing 'vm.global_no_user_wire_amount'. Also try increasing RLIMIT_MLOCK (ulimit -l).\n"
#else
#define MLOCK_SUGGESTION \
"Try increasing RLIMIT_MLOCK ('ulimit -l' as root).\n"
#endif
bool raw_lock(const void * addr, size_t size) const {
if (!mlock(addr, size)) {
return true;
}
char* errmsg = std::strerror(errno);
bool suggest = (errno == ENOMEM);
// Check if the resource limit is fine after all
struct rlimit lock_limit;
if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit)) {
suggest = false;
}
if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size)) {
suggest = false;
}
fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
return false;
}
#undef MLOCK_SUGGESTION
static void raw_unlock(void * addr, size_t size) {
if (munlock(addr, size)) {
fprintf(stderr, "warning: failed to munlock buffer: %s\n", std::strerror(errno));
}
}
#elif defined(_WIN32)
static constexpr bool SUPPORTED = true;
static size_t lock_granularity() {
SYSTEM_INFO si;
GetSystemInfo(&si);
return (size_t) si.dwPageSize;
}
bool raw_lock(void * ptr, size_t len) const {
for (int tries = 1; ; tries++) {
if (VirtualLock(ptr, len)) {
return true;
}
if (tries == 2) {
fprintf(stderr, "warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
len, size, llama_format_win_err(GetLastError()).c_str());
return false;
}
// It failed but this was only the first try; increase the working
// set size and try again.
SIZE_T min_ws_size, max_ws_size;
if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
fprintf(stderr, "warning: GetProcessWorkingSetSize failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
return false;
}
// Per MSDN: "The maximum number of pages that a process can lock
// is equal to the number of pages in its minimum working set minus
// a small overhead."
// Hopefully a megabyte is enough overhead:
size_t increment = len + 1048576;
// The minimum must be <= the maximum, so we need to increase both:
min_ws_size += increment;
max_ws_size += increment;
if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
return false;
}
}
}
static void raw_unlock(void * ptr, size_t len) {
if (!VirtualUnlock(ptr, len)) {
fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n",
llama_format_win_err(GetLastError()).c_str());
}
}
#else
static constexpr bool SUPPORTED = false;
static size_t lock_granularity() {
return (size_t) 65536;
}
bool raw_lock(const void * addr, size_t len) const {
fprintf(stderr, "warning: mlock not supported on this system\n");
return false;
}
static void raw_unlock(const void * addr, size_t len) {}
#endif
};
typedef void (*offload_func_t)(struct ggml_tensor * tensor);
static void ggml_offload_nop(struct ggml_tensor * tensor) {
(void) tensor;
}
static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
std::vector<char> result(8, 0);
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
GGML_ASSERT(check == -n_tokens);
}
else {
result.resize(n_tokens);
}
return std::string(result.data(), result.size());
}
static ggml_backend_buffer_type_t llama_default_buffer_type(int n_gpu_layers) {
ggml_backend_buffer_type_t buft = nullptr;
#ifdef GGML_USE_METAL
if (n_gpu_layers > 0) {
buft = ggml_backend_metal_buffer_type();
}
#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST)
if (n_gpu_layers > 0) {
buft = ggml_backend_cuda_buffer_type(0);
}
#elif defined(GGML_USE_CUBLAS)
buft = ggml_backend_cuda_host_buffer_type();
#elif defined(GGML_USE_CPU_HBM)
buft = ggml_backend_cpu_hbm_buffer_type();
#endif
if (buft == nullptr) {
buft = ggml_backend_cpu_buffer_type();
}
return buft;
GGML_UNUSED(n_gpu_layers);
}
//
// globals
//
struct llama_state {
llama_state() {
#ifdef GGML_USE_METAL
ggml_metal_log_set_callback(log_callback, log_callback_user_data);
#endif
}
// We save the log callback globally
ggml_log_callback log_callback = llama_log_callback_default;
void * log_callback_user_data = nullptr;
};
static llama_state g_state;
// available llama models
enum e_model {
MODEL_UNKNOWN,
MODEL_1B,
MODEL_3B,
MODEL_7B,
MODEL_8B,
MODEL_13B,
MODEL_15B,
MODEL_30B,
MODEL_34B,
MODEL_40B,
MODEL_65B,
MODEL_70B,
MODEL_SMALL,
MODEL_MEDIUM,
MODEL_LARGE,
MODEL_XL,
};
static const size_t kiB = 1024;
static const size_t MiB = 1024*kiB;
static const size_t GiB = 1024*MiB;
struct llama_hparams {
bool vocab_only;
uint32_t n_vocab;
uint32_t n_ctx_train; // context size the model was trained on
uint32_t n_embd;
uint32_t n_head;
uint32_t n_head_kv;
uint32_t n_layer;
uint32_t n_rot;
uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
uint32_t n_ff;
uint32_t n_expert = 0;
uint32_t n_expert_used = 0;
float f_norm_eps;
float f_norm_rms_eps;
float rope_freq_base_train;
float rope_freq_scale_train;
uint32_t n_yarn_orig_ctx;
int8_t rope_scaling_type_train : 3;
bool rope_finetuned : 1;
float f_clamp_kqv;
float f_max_alibi_bias;
bool operator!=(const llama_hparams & other) const {
if (this->vocab_only != other.vocab_only) return true;
if (this->n_vocab != other.n_vocab) return true;
if (this->n_ctx_train != other.n_ctx_train) return true;
if (this->n_embd != other.n_embd) return true;
if (this->n_head != other.n_head) return true;
if (this->n_head_kv != other.n_head_kv) return true;
if (this->n_layer != other.n_layer) return true;
if (this->n_rot != other.n_rot) return true;
if (this->n_embd_head_k != other.n_embd_head_k) return true;
if (this->n_embd_head_v != other.n_embd_head_v) return true;
if (this->n_ff != other.n_ff) return true;
if (this->n_expert != other.n_expert) return true;
if (this->n_expert_used != other.n_expert_used) return true;
if (this->rope_finetuned != other.rope_finetuned) return true;
if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true;
const float EPSILON = 1e-9f;
if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true;
if (!is_float_close(this->f_norm_rms_eps, other.f_norm_rms_eps, EPSILON)) return true;
if (!is_float_close(this->rope_freq_base_train, other.rope_freq_base_train, EPSILON)) return true;
if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true;
return false;
}
uint32_t n_gqa() const {
return n_head/n_head_kv;
}
uint32_t n_embd_k_gqa() const { // dimension of key embeddings across all k-v heads
return n_embd_head_k * n_head_kv;
}
uint32_t n_embd_v_gqa() const { // dimension of value embeddings across all k-v heads
return n_embd_head_v * n_head_kv;
}
};
struct llama_cparams {
uint32_t n_ctx; // context size used during inference
uint32_t n_batch;
uint32_t n_threads; // number of threads to use for generation
uint32_t n_threads_batch; // number of threads to use for batch processing
float rope_freq_base;
float rope_freq_scale;
uint32_t n_yarn_orig_ctx;
// These hyperparameters are not exposed in GGUF, because all
// existing YaRN models use the same values for them.
float yarn_ext_factor;
float yarn_attn_factor;
float yarn_beta_fast;
float yarn_beta_slow;
bool mul_mat_q;
bool offload_kqv;
};
struct llama_layer {
// normalization
struct ggml_tensor * attn_norm;
struct ggml_tensor * attn_norm_b;
struct ggml_tensor * attn_norm_2;
struct ggml_tensor * attn_norm_2_b;
struct ggml_tensor * attn_q_norm;
struct ggml_tensor * attn_q_norm_b;
struct ggml_tensor * attn_k_norm;
struct ggml_tensor * attn_k_norm_b;
// attention
struct ggml_tensor * wq;
struct ggml_tensor * wk;
struct ggml_tensor * wv;
struct ggml_tensor * wo;
struct ggml_tensor * wqkv;
// attention bias
struct ggml_tensor * bq;
struct ggml_tensor * bk;
struct ggml_tensor * bv;
struct ggml_tensor * bo;
struct ggml_tensor * bqkv;
// normalization
struct ggml_tensor * ffn_norm;
struct ggml_tensor * ffn_norm_b;
// ff
struct ggml_tensor * ffn_gate; // w1
struct ggml_tensor * ffn_down; // w2
struct ggml_tensor * ffn_up; // w3
// ff MoE
struct ggml_tensor * ffn_gate_inp;
struct ggml_tensor * ffn_gate_exp[LLAMA_MAX_EXPERTS];
struct ggml_tensor * ffn_down_exp[LLAMA_MAX_EXPERTS];
struct ggml_tensor * ffn_up_exp [LLAMA_MAX_EXPERTS];
// ff bias
struct ggml_tensor * ffn_down_b; // b2
struct ggml_tensor * ffn_up_b; // b3
struct ggml_tensor * ffn_act;
};
struct llama_kv_cell {
llama_pos pos = -1;
llama_pos delta = 0;
std::set<llama_seq_id> seq_id;
bool has_seq_id(const llama_seq_id & id) const {
return seq_id.find(id) != seq_id.end();
}
};
// ring-buffer of cached KV data
struct llama_kv_cache {
bool has_shift = false;
// Note: The value of head isn't only used to optimize searching
// for a free KV slot. llama_decode_internal also uses it, so it
// cannot be freely changed after a slot has been allocated.
uint32_t head = 0;
uint32_t size = 0;
uint32_t used = 0; // used cells (i.e. at least one seq_id)
// computed before each graph build
uint32_t n = 0;
std::vector<llama_kv_cell> cells;
std::vector<struct ggml_tensor *> k_l; // per layer
std::vector<struct ggml_tensor *> v_l;
struct ggml_context * ctx = NULL;
ggml_backend_buffer_t buf = NULL;
~llama_kv_cache() {
#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
if (ggml_cublas_loaded()) {
for (size_t i = 0; i < k_l.size(); ++i) {
ggml_cuda_free_data(k_l[i]);
ggml_cuda_free_data(v_l[i]);
}
}
#endif
if (ctx) {
ggml_free(ctx);
}
ggml_backend_buffer_free(buf);
}
};
struct llama_vocab {
using id = int32_t;
using token = std::string;
using ttype = llama_token_type;
struct token_data {
token text;
float score;
ttype type;
};
enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM;
std::unordered_map<token, id> token_to_id;
std::vector<token_data> id_to_token;
std::unordered_map<token, id> special_tokens_cache;
std::map<std::pair<std::string, std::string>, int> bpe_ranks;
// default LLaMA special tokens
id special_bos_id = 1;
id special_eos_id = 2;
id special_unk_id = 0;
id special_sep_id = -1;
id special_pad_id = -1;
int special_add_bos = -1; // -1 unknown, 1 add, 0 don't add.
int special_add_eos = -1; // -1 unknown, 1 add, 0 don't add.
id linefeed_id = 13;
id special_prefix_id = 32007;
id special_middle_id = 32009;
id special_suffix_id = 32008;
id special_eot_id = 32010;
int find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
GGML_ASSERT(token_left.find(' ') == std::string::npos);
GGML_ASSERT(token_left.find('\n') == std::string::npos);
GGML_ASSERT(token_right.find(' ') == std::string::npos);
GGML_ASSERT(token_right.find('\n') == std::string::npos);
auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
if (it == bpe_ranks.end()) {
return -1;
}
return it->second;
}
};
struct llama_model {
e_model type = MODEL_UNKNOWN;
llm_arch arch = LLM_ARCH_UNKNOWN;
llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
std::string name = "n/a";
llama_hparams hparams = {};
llama_vocab vocab;
struct ggml_tensor * tok_embd;
struct ggml_tensor * pos_embd;
struct ggml_tensor * tok_norm;
struct ggml_tensor * tok_norm_b;
struct ggml_tensor * output_norm;
struct ggml_tensor * output_norm_b;
struct ggml_tensor * output;
struct ggml_tensor * output_b;
std::vector<llama_layer> layers;
int n_gpu_layers;
// gguf metadata
std::unordered_map<std::string, std::string> gguf_kv;
// context
struct ggml_context * ctx = NULL;
// the model memory buffer
ggml_backend_buffer_t buf = NULL;
// model memory mapped file
std::unique_ptr<llama_mmap> mapping;
// objects representing data potentially being locked in memory
llama_mlock mlock_buf;
llama_mlock mlock_mmap;
// for quantize-stats only
std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
int64_t t_load_us = 0;
int64_t t_start_us = 0;
~llama_model() {
#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
if (ggml_cublas_loaded()) {
for (size_t i = 0; i < tensors_by_name.size(); ++i) {
ggml_cuda_free_data(tensors_by_name[i].second);
}
ggml_cuda_free_scratch();
}
#endif
#if defined(GGML_USE_CLBLAST)
for (size_t i = 0; i < tensors_by_name.size(); ++i) {
ggml_cl_free_data(tensors_by_name[i].second);
}
#endif
if (ctx) {
ggml_free(ctx);
}
ggml_backend_buffer_free(buf);
}
};
struct llama_context {
llama_context(const llama_model & model) : model(model), t_start_us(model.t_start_us), t_load_us(model.t_load_us) {}
~llama_context() {
ggml_allocr_free(alloc);
ggml_backend_buffer_free(buf_alloc);
ggml_backend_free(backend);
}
llama_cparams cparams;
ggml_backend_t backend = nullptr;
const llama_model & model;
// key + value cache for the self attention
struct llama_kv_cache kv_self;
std::mt19937 rng;
bool has_evaluated_once = false;
int64_t t_start_us;
int64_t t_load_us;
int64_t t_sample_us = 0;
int64_t t_p_eval_us = 0;
int64_t t_eval_us = 0;
int32_t n_sample = 0; // number of tokens sampled
int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
int32_t n_eval = 0; // number of eval calls
// decode output (2-dimensional array: [n_tokens][n_vocab])
std::vector<float> logits;
#ifndef NDEBUG
// guard against access to unset logits
std::vector<bool> logits_valid;
#endif
bool logits_all = false;
// input embedding (1-dimensional array: [n_embd])
std::vector<float> embedding;
// memory buffers used to evaluate the model
std::vector<uint8_t> buf_compute_meta;
ggml_backend_buffer_t buf_alloc = NULL;
ggml_allocr * alloc = NULL;
// temporary buffer for copying data to/from the backend
std::vector<no_init<uint8_t>> buf_copy;
#ifdef GGML_USE_MPI
ggml_mpi_context * ctx_mpi = NULL;
#endif
};
//
// kv cache helpers
//
static bool llama_kv_cache_init(
const struct llama_hparams & hparams,
struct llama_kv_cache & cache,
ggml_type ktype,
ggml_type vtype,
uint32_t n_ctx,
int n_gpu_layers,
bool offload) {
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
const uint32_t n_layer = hparams.n_layer;
cache.has_shift = false;
cache.head = 0;
cache.size = n_ctx;
cache.used = 0;
cache.cells.clear();
cache.cells.resize(n_ctx);
struct ggml_init_params params;
params.mem_size = 2u*n_layer*ggml_tensor_overhead();
params.mem_buffer = NULL;
params.no_alloc = true;
cache.ctx = ggml_init(params);
size_t vram_kv_cache = 0;
if (!cache.ctx) {
LLAMA_LOG_ERROR("%s: failed to allocate memory for kv cache\n", __func__);
return false;
}
cache.k_l.reserve(n_layer);
cache.v_l.reserve(n_layer);
const int i_gpu_start = (int) n_layer - n_gpu_layers;
for (int i = 0; i < (int) n_layer; i++) {
ggml_tensor * k = ggml_new_tensor_1d(cache.ctx, ktype, n_embd_k_gqa*n_ctx);
ggml_tensor * v = ggml_new_tensor_1d(cache.ctx, vtype, n_embd_v_gqa*n_ctx);
ggml_format_name(k, "cache_k_l%d", i);
ggml_format_name(v, "cache_v_l%d", i);
cache.k_l.push_back(k);
cache.v_l.push_back(v);
#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
if (i >= i_gpu_start) {
if (offload) {
ggml_cuda_assign_buffers_no_scratch(k);
ggml_cuda_assign_buffers_no_scratch(v);
vram_kv_cache += ggml_nbytes(k);
vram_kv_cache += ggml_nbytes(v);
// HACK: mark tensor as allocated
k->data = v->data = (void *)(uintptr_t)1;
}
}
#endif // GGML_USE_CUBLAS
}
// allocate tensors
cache.buf = ggml_backend_alloc_ctx_tensors_from_buft(cache.ctx, llama_default_buffer_type(n_gpu_layers));
// buf may be NULL with full offload
if (cache.buf) {
// initialize the buffer to avoid NaNs in the padding
ggml_backend_buffer_clear(cache.buf, 0);
}
if (vram_kv_cache > 0) {
LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0);
}
GGML_UNUSED(i_gpu_start);
GGML_UNUSED(offload);
return true;
}
// find an empty slot of size "n_tokens" in the cache
// updates the cache head
// Note: On success, it's important that cache.head points
// to the first cell of the slot.
static bool llama_kv_cache_find_slot(
struct llama_kv_cache & cache,
const struct llama_batch & batch) {
const uint32_t n_ctx = cache.size;
const uint32_t n_tokens = batch.n_tokens;
if (n_tokens > n_ctx) {
LLAMA_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx);
return false;
}
uint32_t n_tested = 0;
while (true) {
if (cache.head + n_tokens > n_ctx) {
n_tested += n_ctx - cache.head;
cache.head = 0;
continue;
}
bool found = true;
for (uint32_t i = 0; i < n_tokens; i++) {
if (cache.cells[cache.head + i].pos >= 0) {
found = false;
cache.head += i + 1;
n_tested += i + 1;
break;
}
}
if (found) {
break;
}
if (n_tested >= n_ctx) {
//LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
return false;
}
}
for (uint32_t i = 0; i < n_tokens; i++) {
cache.cells[cache.head + i].pos = batch.pos[i];
for (int32_t j = 0; j < batch.n_seq_id[i]; j++) {
cache.cells[cache.head + i].seq_id.insert(batch.seq_id[i][j]);
}
}
cache.used += n_tokens;
return true;
}
// find how many cells are currently in use
static int32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
for (uint32_t i = cache.size - 1; i > 0; --i) {
if (cache.cells[i].pos >= 0 && !cache.cells[i].seq_id.empty()) {
return i + 1;
}
}
return 0;
}
static void llama_kv_cache_clear(struct llama_kv_cache & cache) {
for (int32_t i = 0; i < (int32_t) cache.size; ++i) {
cache.cells[i].pos = -1;
cache.cells[i].seq_id.clear();
}
cache.head = 0;
cache.used = 0;
}
static void llama_kv_cache_seq_rm(
struct llama_kv_cache & cache,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1) {
uint32_t new_head = cache.size;
if (p0 < 0) p0 = 0;
if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
for (uint32_t i = 0; i < cache.size; ++i) {
if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
if (seq_id < 0) {
cache.cells[i].seq_id.clear();
} else if (cache.cells[i].has_seq_id(seq_id)) {
cache.cells[i].seq_id.erase(seq_id);
} else {
continue;
}
if (cache.cells[i].seq_id.empty()) {
// keep count of the number of used cells
if (cache.cells[i].pos >= 0) cache.used--;
cache.cells[i].pos = -1;
if (new_head == cache.size) new_head = i;
}
}
}
// If we freed up a slot, set head to it so searching can start there.
if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
}
static void llama_kv_cache_seq_cp(
struct llama_kv_cache & cache,
llama_seq_id seq_id_src,
llama_seq_id seq_id_dst,
llama_pos p0,
llama_pos p1) {
if (p0 < 0) p0 = 0;
if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
cache.head = 0;
for (uint32_t i = 0; i < cache.size; ++i) {
if (cache.cells[i].has_seq_id(seq_id_src) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
cache.cells[i].seq_id.insert(seq_id_dst);
}
}
}
static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id seq_id) {
uint32_t new_head = cache.size;
for (uint32_t i = 0; i < cache.size; ++i) {
if (!cache.cells[i].has_seq_id(seq_id)) {
if (cache.cells[i].pos >= 0) cache.used--;
cache.cells[i].pos = -1;
cache.cells[i].seq_id.clear();
if (new_head == cache.size) new_head = i;
} else {
cache.cells[i].seq_id.clear();
cache.cells[i].seq_id.insert(seq_id);
}
}
// If we freed up a slot, set head to it so searching can start there.
if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
}
static void llama_kv_cache_seq_shift(
struct llama_kv_cache & cache,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1,
llama_pos delta) {
uint32_t new_head = cache.size;
if (p0 < 0) p0 = 0;
if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
for (uint32_t i = 0; i < cache.size; ++i) {
if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
cache.has_shift = true;
cache.cells[i].pos += delta;
cache.cells[i].delta += delta;
if (cache.cells[i].pos < 0) {
if (!cache.cells[i].seq_id.empty()) cache.used--;
cache.cells[i].pos = -1;
cache.cells[i].seq_id.clear();
if (new_head == cache.size) new_head = i;
}
}
}
// If we freed up a slot, set head to it so searching can start there.
// Otherwise we just start the next search from the beginning.
cache.head = new_head != cache.size ? new_head : 0;
}
static void llama_kv_cache_seq_div(
struct llama_kv_cache & cache,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1,
int d) {
if (p0 < 0) p0 = 0;
if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
for (uint32_t i = 0; i < cache.size; ++i) {
if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
cache.has_shift = true;
{
llama_pos p_old = cache.cells[i].pos;
cache.cells[i].pos /= d;
cache.cells[i].delta += cache.cells[i].pos - p_old;
}
}
}
}
//
// model loading and saving
//
enum llama_fver {
GGUF_FILE_VERSION_V1 = 1,
GGUF_FILE_VERSION_V2 = 2,
GGUF_FILE_VERSION_V3 = 3,
};
static const char * llama_file_version_name(llama_fver version) {
switch (version) {
case GGUF_FILE_VERSION_V1: return "GGUF V1 (support until nov 2023)";
case GGUF_FILE_VERSION_V2: return "GGUF V2";
case GGUF_FILE_VERSION_V3: return "GGUF V3 (latest)";
}
return "unknown";
}
static std::string llama_format_tensor_shape(const std::vector<int64_t> & ne) {
char buf[256];
snprintf(buf, sizeof(buf), "%5" PRId64, ne.at(0));
for (size_t i = 1; i < ne.size(); i++) {
snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, ne.at(i));
}
return buf;
}
static std::string llama_format_tensor_shape(const struct ggml_tensor * t) {
char buf[256];
snprintf(buf, sizeof(buf), "%5" PRId64, t->ne[0]);
for (int i = 1; i < GGML_MAX_DIMS; i++) {
snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, t->ne[i]);
}
return buf;
}
namespace GGUFMeta {
template <typename T, gguf_type gt_, T (*gfun)(const gguf_context *, const int)>
struct GKV_Base_Type {
static constexpr gguf_type gt = gt_;
static T getter(const gguf_context * ctx, const int kid) {
return gfun(ctx, kid);
}
};
template<typename T> struct GKV_Base;
template<> struct GKV_Base<bool >: GKV_Base_Type<bool, GGUF_TYPE_BOOL, gguf_get_val_bool> {};
template<> struct GKV_Base<uint8_t >: GKV_Base_Type<uint8_t, GGUF_TYPE_UINT8, gguf_get_val_u8 > {};
template<> struct GKV_Base<uint16_t >: GKV_Base_Type<uint16_t, GGUF_TYPE_UINT16, gguf_get_val_u16 > {};
template<> struct GKV_Base<uint32_t >: GKV_Base_Type<uint32_t, GGUF_TYPE_UINT32, gguf_get_val_u32 > {};
template<> struct GKV_Base<uint64_t >: GKV_Base_Type<uint64_t, GGUF_TYPE_UINT64, gguf_get_val_u64 > {};
template<> struct GKV_Base<int8_t >: GKV_Base_Type<int8_t, GGUF_TYPE_INT8, gguf_get_val_i8 > {};
template<> struct GKV_Base<int16_t >: GKV_Base_Type<int16_t, GGUF_TYPE_INT16, gguf_get_val_i16 > {};
template<> struct GKV_Base<int32_t >: GKV_Base_Type<int32_t, GGUF_TYPE_INT32, gguf_get_val_i32 > {};
template<> struct GKV_Base<int64_t >: GKV_Base_Type<int64_t, GGUF_TYPE_INT64, gguf_get_val_i64 > {};
template<> struct GKV_Base<float >: GKV_Base_Type<float, GGUF_TYPE_FLOAT32, gguf_get_val_f32 > {};
template<> struct GKV_Base<double >: GKV_Base_Type<double, GGUF_TYPE_FLOAT64, gguf_get_val_f64 > {};
template<> struct GKV_Base<const char *>: GKV_Base_Type<const char *, GGUF_TYPE_STRING, gguf_get_val_str > {};
template<> struct GKV_Base<std::string> {
static constexpr gguf_type gt = GGUF_TYPE_STRING;
static std::string getter(const gguf_context * ctx, const int kid) {
return gguf_get_val_str(ctx, kid);
}
};
struct ArrayInfo{
const gguf_type gt;
const size_t length;
const void * data;
};
template<> struct GKV_Base<ArrayInfo> {
public:
static constexpr gguf_type gt = GGUF_TYPE_ARRAY;
static ArrayInfo getter(const gguf_context *ctx, const int k) {
return ArrayInfo {
gguf_get_arr_type(ctx, k),
size_t(gguf_get_arr_n(ctx, k)),
gguf_get_arr_data(ctx, k),
};
}
};
template<typename T>
class GKV: public GKV_Base<T> {
GKV() = delete;
public:
static T get_kv(const gguf_context * ctx, const int k) {
const enum gguf_type kt = gguf_get_kv_type(ctx, k);
if (kt != GKV::gt) {
throw std::runtime_error(format("key %s has wrong type %s but expected type %s",
gguf_get_key(ctx, k), gguf_type_name(kt), gguf_type_name(GKV::gt)));
}
return GKV::getter(ctx, k);
}
static const char * override_type_to_str(const llama_model_kv_override_type ty) {
switch (ty) {
case LLAMA_KV_OVERRIDE_BOOL: return "bool";
case LLAMA_KV_OVERRIDE_INT: return "int";
case LLAMA_KV_OVERRIDE_FLOAT: return "float";
}
return "unknown";
}
static bool validate_override(const llama_model_kv_override_type expected_type, const struct llama_model_kv_override *override) {
if (!override) { return false; }
if (override->tag == expected_type) {
LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ",
__func__, override_type_to_str(override->tag), override->key);
switch (override->tag) {
case LLAMA_KV_OVERRIDE_BOOL: {
printf("%s\n", override->bool_value ? "true" : "false");
} break;
case LLAMA_KV_OVERRIDE_INT: {
printf("%" PRId64 "\n", override->int_value);
} break;
case LLAMA_KV_OVERRIDE_FLOAT: {
printf("%.6f\n", override->float_value);
} break;
default:
// Shouldn't be possible to end up here, but just in case...
throw std::runtime_error(
format("Unsupported attempt to override %s type for metadata key %s\n",
override_type_to_str(override->tag), override->key));
}
return true;
}
LLAMA_LOG_WARN("%s: Warning: Bad metadata override type for key '%s', expected %s but got %s\n",
__func__, override->key, override_type_to_str(expected_type), override_type_to_str(override->tag));
return false;
}
template<typename OT>
static typename std::enable_if<std::is_same<OT, bool>::value, bool>::type
try_override(OT & target, const struct llama_model_kv_override *override) {
if (validate_override(LLAMA_KV_OVERRIDE_BOOL, override)) {
target = override->bool_value;
return true;
}
return false;
}
template<typename OT>
static typename std::enable_if<!std::is_same<OT, bool>::value && std::is_integral<OT>::value, bool>::type
try_override(OT & target, const struct llama_model_kv_override *override) {
if (validate_override(LLAMA_KV_OVERRIDE_INT, override)) {
target = override->int_value;
return true;
}
return false;
}
template<typename OT>
static typename std::enable_if<std::is_floating_point<OT>::value, bool>::type
try_override(T & target, const struct llama_model_kv_override *override) {
if (validate_override(LLAMA_KV_OVERRIDE_FLOAT, override)) {
target = override->float_value;
return true;
}
return false;
}
template<typename OT>
static typename std::enable_if<std::is_same<OT, std::string>::value, bool>::type
try_override(T & target, const struct llama_model_kv_override *override) {
(void)target;
(void)override;
if (!override) { return false; }
// Currently, we should never end up here so it would be a bug if we do.
throw std::runtime_error(format("Unsupported attempt to override string type for metadata key %s\n",
override ? override->key : "NULL"));
}
static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override *override = nullptr) {
if (try_override<T>(target, override)) {
return true;
}
if (k < 0) { return false; }
target = get_kv(ctx, k);
return true;
}
static bool set(const gguf_context * ctx, const char * key, T & target, const struct llama_model_kv_override *override = nullptr) {
return set(ctx, gguf_find_key(ctx, key), target, override);
}
static bool set(const gguf_context * ctx, const std::string & key, T & target, const struct llama_model_kv_override *override = nullptr) {
return set(ctx, key.c_str(), target, override);
}
};
}
struct llama_model_loader {
int n_kv = 0;
int n_tensors = 0;
int n_created = 0;
int64_t n_elements = 0;
size_t n_bytes = 0;
bool use_mmap = false;
llama_file file;
llama_ftype ftype;
llama_fver fver;
std::unique_ptr<llama_mmap> mapping;
std::unordered_map<std::string, struct llama_model_kv_override> kv_overrides;
struct gguf_context * ctx_gguf = NULL;
struct ggml_context * ctx_meta = NULL;
std::string arch_name;
LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
llama_model_loader(const std::string & fname, bool use_mmap, const struct llama_model_kv_override * param_overrides_p) : file(fname.c_str(), "rb") {
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ &ctx_meta,
};
if (param_overrides_p != nullptr) {
for (const struct llama_model_kv_override *p = param_overrides_p; p->key[0] != 0; p++) {
kv_overrides.insert({std::string(p->key), *p});
}
}
ctx_gguf = gguf_init_from_file(fname.c_str(), params);
if (!ctx_gguf) {
throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str()));
}
get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
llm_kv = LLM_KV(llm_arch_from_string(arch_name));
n_kv = gguf_get_n_kv(ctx_gguf);
n_tensors = gguf_get_n_tensors(ctx_gguf);
fver = (enum llama_fver ) gguf_get_version(ctx_gguf);
for (int i = 0; i < n_tensors; i++) {
const char * name = gguf_get_tensor_name(ctx_gguf, i);
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, name);
n_elements += ggml_nelements(t);
n_bytes += ggml_nbytes(t);
}
LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
__func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver));
// determine file type based on the number of tensors for each quantization and print meta data
// TODO: make optional
{
std::map<enum ggml_type, uint32_t> n_type;
uint32_t n_type_max = 0;
enum ggml_type type_max = GGML_TYPE_F32;
for (int i = 0; i < n_tensors; i++) {
enum ggml_type type = gguf_get_tensor_type(ctx_gguf, i);
n_type[type]++;
if (n_type_max < n_type[type]) {
n_type_max = n_type[type];
type_max = type;
}
// TODO: make runtime configurable
#if 0
struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, ggml_get_name(meta), ggml_type_name(type), llama_format_tensor_shape(meta).c_str());
#endif
}
switch (type_max) {
case GGML_TYPE_F32: ftype = LLAMA_FTYPE_ALL_F32; break;
case GGML_TYPE_F16: ftype = LLAMA_FTYPE_MOSTLY_F16; break;
case GGML_TYPE_Q4_0: ftype = LLAMA_FTYPE_MOSTLY_Q4_0; break;
case GGML_TYPE_Q4_1: ftype = LLAMA_FTYPE_MOSTLY_Q4_1; break;
case GGML_TYPE_Q5_0: ftype = LLAMA_FTYPE_MOSTLY_Q5_0; break;
case GGML_TYPE_Q5_1: ftype = LLAMA_FTYPE_MOSTLY_Q5_1; break;
case GGML_TYPE_Q8_0: ftype = LLAMA_FTYPE_MOSTLY_Q8_0; break;
case GGML_TYPE_Q2_K: ftype = LLAMA_FTYPE_MOSTLY_Q2_K; break;
case GGML_TYPE_Q3_K: ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M; break;
case GGML_TYPE_Q4_K: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M; break;
case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break;
case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break;
case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break;
case GGML_TYPE_IQ2_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS; break;
default:
{
LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
ftype = LLAMA_FTYPE_ALL_F32;
} break;
}
// this is a way to mark that we have "guessed" the file type
ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);
{
const int kid = gguf_find_key(ctx_gguf, "general.file_type");
if (kid >= 0) {
ftype = (llama_ftype) gguf_get_val_u32(ctx_gguf, kid);
}
}
LLAMA_LOG_INFO("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
for (int i = 0; i < n_kv; i++) {
const char * name = gguf_get_key(ctx_gguf, i);
const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
const std::string type_name =
type == GGUF_TYPE_ARRAY
? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(ctx_gguf, i)), gguf_get_arr_n(ctx_gguf, i))
: gguf_type_name(type);
std::string value = gguf_kv_to_str(ctx_gguf, i);
const size_t MAX_VALUE_LEN = 40;
if (value.size() > MAX_VALUE_LEN) {
value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str());
}
replace_all(value, "\n", "\\n");
LLAMA_LOG_INFO("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
}
// print type counts
for (auto & kv : n_type) {
if (kv.second == 0) {
continue;
}
LLAMA_LOG_INFO("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
}
}
if (!llama_mmap::SUPPORTED) {
LLAMA_LOG_WARN("%s: mmap is not supported on this platform\n", __func__);
use_mmap = false;
}
this->use_mmap = use_mmap;
}
~llama_model_loader() {
if (ctx_gguf) {
gguf_free(ctx_gguf);
}
if (ctx_meta) {
ggml_free(ctx_meta);
}
}
template<typename T>
typename std::enable_if<std::is_integral<T>::value, bool>::type
get_arr_n(const std::string & key, T & result, const bool required = true) {
const int kid = gguf_find_key(ctx_gguf, key.c_str());
if (kid < 0) {
if (required) {
throw std::runtime_error(format("key not found in model: %s", key.c_str()));
}
return false;
}
struct GGUFMeta::ArrayInfo arr_info =
GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(ctx_gguf, kid);
result = arr_info.length;
return true;
}
template<typename T>
typename std::enable_if<std::is_integral<T>::value, bool>::type
get_arr_n(const enum llm_kv kid, T & result, const bool required = true) {
return get_arr_n(llm_kv(kid), result, required);
}
template<typename T>
bool get_key(const std::string & key, T & result, const bool required = true) {
auto it = kv_overrides.find(key);
const struct llama_model_kv_override * override =
it != kv_overrides.end() ? &it->second : nullptr;
const bool found = GGUFMeta::GKV<T>::set(ctx_gguf, key, result, override);
if (required && !found) {
throw std::runtime_error(format("key not found in model: %s", key.c_str()));
}
return found;
}
template<typename T>
bool get_key(const enum llm_kv kid, T & result, const bool required = true) {
return get_key(llm_kv(kid), result, required);
}
std::string get_arch_name() const {
return arch_name;
}
enum llm_arch get_arch() const {
return llm_kv.arch;
}
const char * get_tensor_name(int i) const {
return gguf_get_tensor_name(ctx_gguf, i);
}
struct ggml_tensor * get_tensor_meta(const char * name) const {
return ggml_get_tensor(ctx_meta, name);
}
struct ggml_tensor * get_tensor_meta(int i) const {
return get_tensor_meta(get_tensor_name(i));
}
struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta, ggml_backend_type backend) {
struct ggml_tensor * tensor = ggml_dup_tensor(ctx, meta);
tensor->backend = backend; // TODO: ggml_set_backend
ggml_set_name(tensor, ggml_get_name(meta));
n_created++;
return tensor;
}
struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, ggml_backend_type backend, bool required = true) {
struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str());
if (cur == NULL) {
if (!required) {
return NULL;
}
throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
}
if (backend == GGML_BACKEND_GPU_SPLIT) {
if (ne.size() == 1) {
throw std::runtime_error(format("%s: 1-dimensional tensor '%s' cannot be split on the GPU", __func__, name.c_str()));
}
}
{
bool is_ok = true;
for (size_t i = 0; i < ne.size(); ++i) {
if (ne[i] != cur->ne[i]) {
is_ok = false;
break;
}
}
if (!is_ok) {
throw std::runtime_error(
format("%s: tensor '%s' has wrong shape; expected %s, got %s",
__func__, name.c_str(),
llama_format_tensor_shape(ne).c_str(),
llama_format_tensor_shape(cur).c_str()));
}
}
return create_tensor_for(ctx, cur, backend);
}
void done_getting_tensors() const {
if (n_created != n_tensors) {
throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
}
}
size_t file_offset(const char * name) const {
const int idx = gguf_find_tensor(ctx_gguf, name);
if (idx < 0) {
throw std::runtime_error(format("%s: tensor '%s' not found in the file", __func__, name));
}
return gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, idx);
}
void init_mapping(bool prefetch = true) {
/*
// prefetch only CPU tensors
if (use_mmap) {
size_t size_pref = 0; // prefetch
for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
if (cur->backend == GGML_BACKEND_CPU) {
size_t tensor_end = gguf_get_tensor_offset(ctx_gguf, i) + ggml_nbytes(cur);
size_pref = std::max(size_pref, tensor_end);
}
}
mapping.reset(new llama_mmap(&file, gguf_get_data_offset(ctx_gguf) + size_pref, ggml_is_numa()));
}
*/
// prefetch the whole file - all the data is needed anyway
if (use_mmap) {
mapping.reset(new llama_mmap(&file, prefetch ? -1 : 0, ggml_is_numa()));
}
}
// for backwards compatibility, does not support ggml-backend
void load_data_for(struct ggml_tensor * cur) const {
const size_t offs = file_offset(ggml_get_name(cur));
if (use_mmap && mapping) {
GGML_ASSERT(cur->data == nullptr);
cur->data = (uint8_t *)mapping->addr + offs;
} else {
GGML_ASSERT(cur->data != nullptr);
file.seek(offs, SEEK_SET);
file.read_raw(cur->data, ggml_nbytes(cur));
}
}
// Returns false if cancelled by progress_callback
bool load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, ggml_backend_buffer_t buf_mmap, llama_mlock * lmlock) const {
size_t size_data = 0;
for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
size_data += ggml_nbytes(cur);
}
if (use_mmap && buf_mmap) {
if (lmlock) {
lmlock->init(mapping->addr);
}
}
#if (defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)) || defined(GGML_USE_CLBLAST)
const bool legacy_offload = true;
#else
const bool legacy_offload = false;
#endif
std::vector<no_init<uint8_t>> read_buf;
size_t size_done = 0;
size_t mmap_first = -1;
size_t mmap_last = 0;
for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
GGML_ASSERT(cur); // unused tensors should have been caught by load_data already
if (progress_callback) {
if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) {
return false;
}
}
const size_t offs = file_offset(ggml_get_name(cur));
if (!legacy_offload || cur->backend == GGML_BACKEND_CPU) {
if (use_mmap && mapping) {
if (buf_mmap) {
ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + offs);
if (lmlock) {
lmlock->grow_to(offs + ggml_nbytes(cur));
}
mmap_first = std::min(mmap_first, offs);
mmap_last = std::max(mmap_last, offs + ggml_nbytes(cur));
} else {
ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + offs, 0, ggml_nbytes(cur));
}
} else {
if (ggml_backend_buffer_is_host(cur->buffer)) {
file.seek(offs, SEEK_SET);
file.read_raw(cur->data, ggml_nbytes(cur));
} else {
read_buf.resize(ggml_nbytes(cur));
file.seek(offs, SEEK_SET);
file.read_raw(read_buf.data(), ggml_nbytes(cur));
ggml_backend_tensor_set(cur, read_buf.data(), 0, ggml_nbytes(cur));
}
}
} else {
// HACK: mark tensor as allocated
cur->data = (void *)(uintptr_t)1;
void * data;
if (use_mmap && mapping) {
data = (uint8_t *) mapping->addr + offs;
} else {
read_buf.resize(ggml_nbytes(cur));
file.seek(offs, SEEK_SET);
file.read_raw(read_buf.data(), ggml_nbytes(cur));
data = read_buf.data();
}
#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
ggml_cuda_transform_tensor(data, cur);
#elif defined(GGML_USE_CLBLAST)
GGML_ASSERT(cur->backend == GGML_BACKEND_GPU);
ggml_cl_transform_tensor(data, cur);
#else
GGML_ASSERT(!"GPU tensor without a GPU backend");
GGML_UNUSED(data);
#endif
}
size_done += ggml_nbytes(cur);
}
// unmap offloaded tensors and metadata
if (use_mmap && mapping) {
mapping->unmap_fragment(0, mmap_first);
mapping->unmap_fragment(mmap_last, mapping->size);
}
if (progress_callback) {
// Even though the model is done loading, we still honor
// cancellation since we need to free allocations.
return progress_callback(1.0f, progress_callback_user_data);
}
return true;
}
};
//
// load LLaMA models
//
static std::string llama_model_arch_name(llm_arch arch) {
auto it = LLM_ARCH_NAMES.find(arch);
if (it == LLM_ARCH_NAMES.end()) {
return "unknown";
}
return it->second;
}
static std::string llama_model_ftype_name(llama_ftype ftype) {
if (ftype & LLAMA_FTYPE_GUESSED) {
return llama_model_ftype_name((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) + " (guessed)";
}
switch (ftype) {
case LLAMA_FTYPE_ALL_F32: return "all F32";
case LLAMA_FTYPE_MOSTLY_F16: return "F16";
case LLAMA_FTYPE_MOSTLY_Q4_0: return "Q4_0";
case LLAMA_FTYPE_MOSTLY_Q4_1: return "Q4_1";
case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
return "Q4_1, some F16";
case LLAMA_FTYPE_MOSTLY_Q5_0: return "Q5_0";
case LLAMA_FTYPE_MOSTLY_Q5_1: return "Q5_1";
case LLAMA_FTYPE_MOSTLY_Q8_0: return "Q8_0";
// K-quants
case LLAMA_FTYPE_MOSTLY_Q2_K: return "Q2_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q2_K_S: return "Q2_K - Small";
case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "Q3_K - Small";
case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "Q3_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "Q3_K - Large";
case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "Q4_K - Small";
case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "Q4_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "Q5_K - Small";
case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q6_K: return "Q6_K";
case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XSS - 2.0625 bpw";
case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw";
default: return "unknown, may not work";
}
}
static const char * llama_model_type_name(e_model type) {
switch (type) {
case MODEL_1B: return "1B";
case MODEL_3B: return "3B";
case MODEL_7B: return "7B";
case MODEL_8B: return "8B";
case MODEL_13B: return "13B";
case MODEL_15B: return "15B";
case MODEL_30B: return "30B";
case MODEL_34B: return "34B";
case MODEL_40B: return "40B";
case MODEL_65B: return "65B";
case MODEL_70B: return "70B";
case MODEL_SMALL: return "0.1B";
case MODEL_MEDIUM: return "0.4B";
case MODEL_LARGE: return "0.8B";
case MODEL_XL: return "1.5B";
default: return "?B";
}
}
static void llm_load_arch(llama_model_loader & ml, llama_model & model) {
model.arch = ml.get_arch();
if (model.arch == LLM_ARCH_UNKNOWN) {
throw std::runtime_error("unknown model architecture: '" + ml.get_arch_name() + "'");
}
}
static void llm_load_hparams(
llama_model_loader & ml,
llama_model & model) {
auto & hparams = model.hparams;
const gguf_context * ctx = ml.ctx_gguf;
// get metadata as string
for (int i = 0; i < gguf_get_n_kv(ctx); i++) {
enum gguf_type type = gguf_get_kv_type(ctx, i);
if (type == GGUF_TYPE_ARRAY) {
continue;
}
const char * name = gguf_get_key(ctx, i);
const std::string value = gguf_kv_to_str(ctx, i);
model.gguf_kv.emplace(name, value);
}
// get general kv
ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
// get hparams kv
ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
ml.get_key (LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train);
ml.get_key (LLM_KV_EMBEDDING_LENGTH, hparams.n_embd);
ml.get_key (LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff);
ml.get_key (LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head);
ml.get_key (LLM_KV_BLOCK_COUNT, hparams.n_layer);
ml.get_key (LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
ml.get_key (LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
if (hparams.n_expert > 0) {
GGML_ASSERT(hparams.n_expert_used > 0);
} else {
GGML_ASSERT(hparams.n_expert_used == 0);
}
// n_head_kv is optional, default to n_head
hparams.n_head_kv = hparams.n_head;
ml.get_key(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv, false);
bool rope_finetuned = false;
ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
hparams.rope_finetuned = rope_finetuned;
hparams.n_yarn_orig_ctx = hparams.n_ctx_train;
ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_yarn_orig_ctx, false);
// rope_freq_base (optional)
hparams.rope_freq_base_train = 10000.0f;
ml.get_key(LLM_KV_ROPE_FREQ_BASE, hparams.rope_freq_base_train, false);
std::string rope_scaling("linear");
ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false);
hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_UNSPECIFIED);
// rope_freq_scale (inverse of the kv) is optional
float ropescale = 0.0f;
if (!ml.get_key(LLM_KV_ROPE_SCALING_FACTOR, ropescale, false)) {
// try the old key name
ml.get_key(LLM_KV_ROPE_SCALE_LINEAR, ropescale, false);
}
hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;
// sanity check for n_rot (optional)
{
hparams.n_rot = hparams.n_embd / hparams.n_head;
ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) {
if (hparams.n_rot != hparams.n_embd / hparams.n_head) {
throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head));
}
}
// gpt-neox n_rot = rotary_pct * (n_embd / n_head)
// gpt-j n_rot = rotary_dim
}
hparams.n_embd_head_k = hparams.n_embd / hparams.n_head;
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
hparams.n_embd_head_v = hparams.n_embd / hparams.n_head;
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
// arch-specific KVs
switch (model.arch) {
case LLM_ARCH_LLAMA:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 22: model.type = e_model::MODEL_1B; break;
case 26: model.type = e_model::MODEL_3B; break;
case 32: model.type = e_model::MODEL_7B; break;
case 40: model.type = e_model::MODEL_13B; break;
case 48: model.type = e_model::MODEL_34B; break;
case 60: model.type = e_model::MODEL_30B; break;
case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_FALCON:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_7B; break;
case 60: model.type = e_model::MODEL_40B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_BAICHUAN:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_7B; break;
case 40: model.type = e_model::MODEL_13B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_STARCODER:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (hparams.n_layer) {
case 24: model.type = e_model::MODEL_1B; break;
case 36: model.type = e_model::MODEL_3B; break;
case 42: model.type = e_model::MODEL_7B; break;
case 40: model.type = e_model::MODEL_15B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_PERSIMMON:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (hparams.n_layer) {
case 36: model.type = e_model::MODEL_8B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_REFACT:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_1B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_BLOOM:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (hparams.n_layer) {
case 24: model.type = e_model::MODEL_1B; break;
case 30:
switch (hparams.n_embd) {
case 2560: model.type = e_model::MODEL_3B; break;
case 4096: model.type = e_model::MODEL_7B; break;
} break;
}
} break;
case LLM_ARCH_MPT:
{
hparams.f_clamp_kqv = 0.0f;
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv, false);
ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_7B; break;
case 48: model.type = e_model::MODEL_30B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_STABLELM:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_3B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_QWEN:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_7B; break;
case 40: model.type = e_model::MODEL_13B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_PHI2:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (hparams.n_layer) {
case 24: model.type = e_model::MODEL_1B; break;
case 32: model.type = e_model::MODEL_3B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_PLAMO:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 40: model.type = e_model::MODEL_13B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_GPT2:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (hparams.n_layer) {
case 12: model.type = e_model::MODEL_SMALL; break;
case 24: model.type = e_model::MODEL_MEDIUM; break;
case 36: model.type = e_model::MODEL_LARGE; break;
case 48: model.type = e_model::MODEL_XL; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
default: (void)0;
}
model.ftype = ml.ftype;
}
// TODO: This should probably be in llama.h
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special = false);
static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch);
static void llm_load_vocab(
llama_model_loader & ml,
llama_model & model) {
auto & vocab = model.vocab;
struct gguf_context * ctx = ml.ctx_gguf;
const auto kv = LLM_KV(model.arch);
const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
if (token_idx == -1) {
throw std::runtime_error("cannot find tokenizer vocab in model file\n");
}
const float * scores = nullptr;
const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
if (score_idx != -1) {
scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
}
const int * toktypes = nullptr;
const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
if (toktype_idx != -1) {
toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
}
// determine vocab type
{
std::string tokenizer_name;
ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_name);
if (tokenizer_name == "llama") {
vocab.type = LLAMA_VOCAB_TYPE_SPM;
// default special tokens
vocab.special_bos_id = 1;
vocab.special_eos_id = 2;
vocab.special_unk_id = 0;
vocab.special_sep_id = -1;
vocab.special_pad_id = -1;
} else if (tokenizer_name == "gpt2") {
vocab.type = LLAMA_VOCAB_TYPE_BPE;
// read bpe merges and populate bpe ranks
const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
if (merges_keyidx == -1) {
throw std::runtime_error("cannot find tokenizer merges in model file\n");
}
const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
for (int i = 0; i < n_merges; i++) {
const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
GGML_ASSERT(codepoints_from_utf8(word).size() > 0);
std::string first;
std::string second;
const size_t pos = word.find(' ', 1);
if (pos != std::string::npos) {
first = word.substr(0, pos);
second = word.substr(pos + 1);
}
vocab.bpe_ranks.emplace(std::make_pair(first, second), i);
}
// default special tokens
vocab.special_bos_id = 11;
vocab.special_eos_id = 11;
vocab.special_unk_id = -1;
vocab.special_sep_id = -1;
vocab.special_pad_id = -1;
} else {
LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str());
LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__);
vocab.type = LLAMA_VOCAB_TYPE_SPM;
}
}
const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
vocab.id_to_token.resize(n_vocab);
for (uint32_t i = 0; i < n_vocab; i++) {
std::string word = gguf_get_arr_str(ctx, token_idx, i);
GGML_ASSERT(codepoints_from_utf8(word).size() > 0);
vocab.token_to_id[word] = i;
auto & token_data = vocab.id_to_token[i];
token_data.text = std::move(word);
token_data.score = scores ? scores[i] : 0.0f;
token_data.type = toktypes ? (llama_token_type) toktypes[i] : LLAMA_TOKEN_TYPE_NORMAL;
}
GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size());
// determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
} else {
const std::vector<int> ids = llama_tokenize_internal(vocab, "\u010A", false);
GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
vocab.linefeed_id = ids[0];
}
// special tokens
{
const std::vector<std::pair<enum llm_kv, int32_t &>> special_token_types = {
{ LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id },
{ LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id },
{ LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id },
{ LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id },
{ LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id },
};
for (const auto & it : special_token_types) {
const std::string & key = kv(std::get<0>(it));
int32_t & id = std::get<1>(it);
uint32_t new_id;
if (!ml.get_key(std::get<0>(it), new_id, false)) {
continue;
}
if (new_id >= vocab.id_to_token.size()) {
LLAMA_LOG_WARN("%s: bad special token: '%s' = %ud, using default id %d\n",
__func__, key.c_str(), new_id, id);
} else {
id = new_id;
}
}
// Handle add_bos_token and add_eos_token
{
bool temp = true;
if (ml.get_key(LLM_KV_TOKENIZER_ADD_BOS, temp, false)) {
vocab.special_add_bos = int(temp);
}
if (ml.get_key(LLM_KV_TOKENIZER_ADD_EOS, temp, false)) {
vocab.special_add_eos = int(temp);
}
}
}
// build special tokens cache
{
// TODO: It is unclear (to me) at this point, whether special tokes are guaranteed to be of a deterministic type,
// and will always be correctly labeled in 'added_tokens.json' etc.
// The assumption is, since special tokens aren't meant to be exposed to end user, they are designed
// to be unmatchable by the tokenizer, therefore tokens from the vocab, which are unmatchable by the tokenizer
// are special tokens.
// From testing, this appears to correlate 1:1 with special tokens.
//
// Counting special tokens and verifying in only one direction
// is sufficient to detect difference in those two sets.
//
uint32_t special_tokens_count_by_type = 0;
uint32_t special_tokens_count_from_verification = 0;
bool special_tokens_definition_mismatch = false;
for (const auto & t : vocab.token_to_id) {
const auto & token = t.first;
const auto & id = t.second;
// Count all non-normal tokens in the vocab while iterating
if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) {
special_tokens_count_by_type++;
}
// Skip single character tokens
if (token.length() > 1) {
bool is_tokenizable = false;
// Split token string representation in two, in all possible ways
// and check if both halves can be matched to a valid token
for (unsigned i = 1; i < token.length();) {
const auto left = token.substr(0, i);
const auto right = token.substr(i);
// check if we didnt partition in the middle of a utf sequence
auto utf = utf8_len(left.at(left.length() - 1));
if (utf == 1) {
if (vocab.token_to_id.find(left) != vocab.token_to_id.end() &&
vocab.token_to_id.find(right) != vocab.token_to_id.end() ) {
is_tokenizable = true;
break;
}
i++;
} else {
// skip over the rest of multibyte utf sequence
i += utf - 1;
}
}
if (!is_tokenizable) {
// Some tokens are multibyte, but they are utf sequences with equivalent text length of 1
// it's faster to re-filter them here, since there are way less candidates now
// Calculate a total "utf" length of a token string representation
size_t utf8_str_len = 0;
for (unsigned i = 0; i < token.length();) {
utf8_str_len++;
i += utf8_len(token.at(i));
}
// And skip the ones which are one character
if (utf8_str_len > 1) {
// At this point what we have left are special tokens only
vocab.special_tokens_cache[token] = id;
// Count manually found special tokens
special_tokens_count_from_verification++;
// If this manually found special token is not marked as such, flag a mismatch
if (vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL) {
special_tokens_definition_mismatch = true;
}
}
}
}
}
if (special_tokens_definition_mismatch || special_tokens_count_from_verification != special_tokens_count_by_type) {
LLAMA_LOG_WARN("%s: mismatch in special tokens definition ( %u/%zu vs %u/%zu ).\n",
__func__,
special_tokens_count_from_verification, vocab.id_to_token.size(),
special_tokens_count_by_type, vocab.id_to_token.size()
);
} else {
LLAMA_LOG_INFO("%s: special tokens definition check successful ( %u/%zu ).\n",
__func__,
special_tokens_count_from_verification, vocab.id_to_token.size()
);
}
}
}
static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
const auto & hparams = model.hparams;
const auto & vocab = model.vocab;
const auto rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
// hparams
LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver));
LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch).c_str());
LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, vocab.type == LLAMA_VOCAB_TYPE_SPM ? "SPM" : "BPE"); // TODO: fix
LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab);
LLAMA_LOG_INFO("%s: n_merges = %u\n", __func__, (int) vocab.bpe_ranks.size());
LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train);
LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd);
LLAMA_LOG_INFO("%s: n_head = %u\n", __func__, hparams.n_head);
LLAMA_LOG_INFO("%s: n_head_kv = %u\n", __func__, hparams.n_head_kv);
LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer);
LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot);
LLAMA_LOG_INFO("%s: n_embd_head_k = %u\n", __func__, hparams.n_embd_head_k);
LLAMA_LOG_INFO("%s: n_embd_head_v = %u\n", __func__, hparams.n_embd_head_v);
LLAMA_LOG_INFO("%s: n_gqa = %u\n", __func__, hparams.n_gqa());
LLAMA_LOG_INFO("%s: n_embd_k_gqa = %u\n", __func__, hparams.n_embd_k_gqa());
LLAMA_LOG_INFO("%s: n_embd_v_gqa = %u\n", __func__, hparams.n_embd_v_gqa());
LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_eps);
LLAMA_LOG_INFO("%s: f_norm_rms_eps = %.1e\n", __func__, hparams.f_norm_rms_eps);
LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv);
LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n", __func__, hparams.f_max_alibi_bias);
LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff);
LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert);
LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used);
LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type.c_str());
LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train);
LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train);
LLAMA_LOG_INFO("%s: n_yarn_orig_ctx = %u\n", __func__, hparams.n_yarn_orig_ctx);
LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown");
LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type));
LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype).c_str());
if (ml.n_elements >= 1e12) {
LLAMA_LOG_INFO("%s: model params = %.2f T\n", __func__, ml.n_elements*1e-12);
} else if (ml.n_elements >= 1e9) {
LLAMA_LOG_INFO("%s: model params = %.2f B\n", __func__, ml.n_elements*1e-9);
} else if (ml.n_elements >= 1e6) {
LLAMA_LOG_INFO("%s: model params = %.2f M\n", __func__, ml.n_elements*1e-6);
} else {
LLAMA_LOG_INFO("%s: model params = %.2f K\n", __func__, ml.n_elements*1e-3);
}
if (ml.n_bytes < GiB) {
LLAMA_LOG_INFO("%s: model size = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
} else {
LLAMA_LOG_INFO("%s: model size = %.2f GiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
}
// general kv
LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, model.name.c_str());
// special tokens
if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
}
// Returns false if cancelled by progress_callback
static bool llm_load_tensors(
llama_model_loader & ml,
llama_model & model,
int n_gpu_layers,
int main_gpu,
const float * tensor_split,
bool use_mlock,
llama_progress_callback progress_callback,
void * progress_callback_user_data) {
model.t_start_us = ggml_time_us();
auto & ctx = model.ctx;
auto & hparams = model.hparams;
model.n_gpu_layers = n_gpu_layers;
size_t ctx_size = ggml_tensor_overhead() * ml.n_tensors;
LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, ctx_size/1024.0/1024.0);
// create the ggml context
{
struct ggml_init_params params = {
/*.mem_size =*/ ctx_size,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
model.ctx = ggml_init(params);
if (!model.ctx) {
throw std::runtime_error(format("ggml_init() failed"));
}
}
(void) main_gpu;
enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU;
enum ggml_backend_type llama_backend_offload_split = GGML_BACKEND_CPU;
#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
if (ggml_cublas_loaded()) {
LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__);
ggml_cuda_set_main_device(main_gpu);
llama_backend_offload = GGML_BACKEND_GPU;
llama_backend_offload_split = GGML_BACKEND_GPU_SPLIT;
}
#elif defined(GGML_USE_CLBLAST)
LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__);
llama_backend_offload = GGML_BACKEND_GPU;
llama_backend_offload_split = GGML_BACKEND_GPU;
#endif
// create tensors for the weights
{
const int64_t n_embd = hparams.n_embd;
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
const int64_t n_layer = hparams.n_layer;
const int64_t n_vocab = hparams.n_vocab;
const auto tn = LLM_TN(model.arch);
switch (model.arch) {
case LLM_ARCH_LLAMA:
case LLM_ARCH_REFACT:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
// output
{
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
}
const uint32_t n_ff = hparams.n_ff;
const int64_t n_embd_gqa = n_embd_v_gqa;
GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split);
layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
// optional bias tensors
layer.bq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, backend, false);
layer.bk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, backend, false);
layer.bv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, backend, false);
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend, false);
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.ffn_gate_inp = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd}, backend, false);
if (layer.ffn_gate_inp == nullptr) {
GGML_ASSERT(hparams.n_expert == 0);
GGML_ASSERT(hparams.n_expert_used == 0);
layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
} else {
GGML_ASSERT(hparams.n_expert > 0);
GGML_ASSERT(hparams.n_expert_used > 0);
// MoE branch
for (uint32_t x = 0; x < hparams.n_expert; ++x) {
layer.ffn_gate_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), {n_embd, n_ff}, backend_split);
layer.ffn_down_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd}, backend_split);
layer.ffn_up_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), {n_embd, n_ff}, backend_split);
}
}
}
} break;
case LLM_ARCH_BAICHUAN:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
{
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
}
const uint32_t n_ff = hparams.n_ff;
const int64_t n_embd_gqa = n_embd_v_gqa;
GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split);
layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
}
} break;
case LLM_ARCH_FALCON:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
// output
{
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
}
const uint32_t n_ff = hparams.n_ff;
const int64_t n_embd_gqa = n_embd_v_gqa;
GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i).c_str()) >= 0) {
layer.attn_norm_2 = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, backend);
layer.attn_norm_2_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, backend);
}
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
}
} break;
case LLM_ARCH_STARCODER:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
// output
{
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
}
const uint32_t n_ff = hparams.n_ff;
const int64_t n_embd_gqa = n_embd_v_gqa;
GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend);
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend);
}
} break;
case LLM_ARCH_PERSIMMON:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
{
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
}
const uint32_t n_ff = hparams.n_ff;
const int64_t n_embd_gqa = n_embd_v_gqa;
GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload;
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split;
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend);
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
layer.attn_q_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}, backend);
layer.attn_q_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {64}, backend);
layer.attn_k_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64}, backend);
layer.attn_k_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64}, backend);
}
} break;
case LLM_ARCH_BLOOM:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
model.tok_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, GGML_BACKEND_CPU);
model.tok_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, GGML_BACKEND_CPU);
// output
{
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
}
const uint32_t n_ff = hparams.n_ff;
const int64_t n_embd_gqa = n_embd_v_gqa;
GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend);
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend);
}
} break;
case LLM_ARCH_MPT:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
// output
{
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
}
const uint32_t n_ff = hparams.n_ff;
const int64_t n_embd_gqa = n_embd_v_gqa;
GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
// AWQ ScaleActivation layer
layer.ffn_act = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, backend, false);
}
} break;
case LLM_ARCH_STABLELM:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
// output
{
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
}
const uint32_t n_ff = hparams.n_ff;
const int64_t n_embd_gqa = n_embd_v_gqa;
GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
/*
llama_model_loader: - tensor 4: blk.0.attn_output.weight f16 [ 2560, 2560, 1, 1 ]
*/
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split);
layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
}
} break;
case LLM_ARCH_QWEN:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
{
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
}
const uint32_t n_ff = hparams.n_ff / 2;
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd * 3}, backend_split);
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd * 3}, backend);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
}
} break;
case LLM_ARCH_PHI2:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
// output
{
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
model.output_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}, backend_output);
}
const uint32_t n_ff = hparams.n_ff;
const int64_t n_embd_gqa = n_embd_v_gqa;
GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend);
}
} break;
case LLM_ARCH_PLAMO:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
// output
{
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
}
const uint32_t n_ff = hparams.n_ff;
const int64_t n_embd_gqa = n_embd_v_gqa;
GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split);
layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
}
} break;
case LLM_ARCH_GPT2:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
// output
{
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
}
const uint32_t n_ff = hparams.n_ff;
const int64_t n_embd_gqa = n_embd_v_gqa;
GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend);
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend);
}
} break;
default:
throw std::runtime_error("unknown architecture");
}
}
ml.done_getting_tensors();
ml.init_mapping();
// allocate tensors
size_t vram_weights = 0;
size_t buf_size = 0;
ggml_backend_buffer_type_t buft = llama_default_buffer_type(n_gpu_layers);
for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
// GGML_BACKEND_GPU tensors are for CUDA and OpenCL only, which are handled separately without ggml-backend
if (t->backend == GGML_BACKEND_CPU) {
buf_size += GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), ggml_backend_buft_get_alignment(buft));
} else {
vram_weights += ggml_nbytes(t);
}
}
// create backend buffer
ggml_backend_buffer_t buf_mmap = nullptr;
#ifdef GGML_USE_METAL
if (n_gpu_layers > 0) {
if (ml.use_mmap) {
const size_t max_size = ggml_get_max_tensor_size(ctx);
model.buf = ggml_backend_metal_buffer_from_ptr(ml.mapping->addr, ml.mapping->size, max_size);
buf_mmap = model.buf;
} else {
model.buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_metal_buffer_type());
}
}
#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST)
// for testing only
if (n_gpu_layers > 0) {
model.buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cuda_buffer_type(0));
}
#endif
if (model.buf == nullptr) {
// CPU backend, and indirectly CUDA and OpenCL
if (ml.use_mmap) {
model.buf = ggml_backend_cpu_buffer_from_ptr(ml.mapping->addr, ml.mapping->size);
buf_mmap = model.buf;
} else {
// allocate only CPU tensors
model.buf = ggml_backend_buft_alloc_buffer(buft, buf_size);
ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(model.buf);
for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
if (t->backend == GGML_BACKEND_CPU) {
ggml_tallocr_alloc(alloc, t);
}
}
ggml_tallocr_free(alloc);
}
}
if (use_mlock && ggml_backend_buffer_is_host(model.buf)) {
model.mlock_buf.init (ggml_backend_buffer_get_base(model.buf));
model.mlock_buf.grow_to(ggml_backend_buffer_get_size(model.buf));
}
// print memory requirements
{
size_t sys_mem_required = ctx_size + buf_size;
if (sys_mem_required > 0) {
LLAMA_LOG_INFO("%s: system memory used = %7.2f MiB\n", __func__, sys_mem_required / 1024.0 / 1024.0);
}
if (vram_weights > 0) {
LLAMA_LOG_INFO("%s: VRAM used = %7.2f MiB\n", __func__, vram_weights / 1024.0 / 1024.0);
}
#if (defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)) || defined(GGML_USE_CLBLAST)
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
if (n_gpu_layers > (int) hparams.n_layer) {
LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU\n", __func__);
}
const int max_backend_supported_layers = hparams.n_layer + 1;
const int max_offloadable_layers = hparams.n_layer + 1;
LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
}
#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
ggml_cuda_set_tensor_split(tensor_split);
#else
GGML_UNUSED(tensor_split);
#endif // GGML_USE_CUBLAS
// populate tensors_by_name
for (int i = 0; i < ml.n_tensors; ++i) {
struct ggml_tensor * cur = ggml_get_tensor(ctx, ml.get_tensor_name(i));
model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
}
if (!ml.load_all_data(ctx, progress_callback, progress_callback_user_data, buf_mmap, use_mlock ? &model.mlock_mmap : NULL)) {
return false;
}
model.mapping = std::move(ml.mapping);
// loading time will be recalculate after the first eval, so
// we take page faults deferred by mmap() into consideration
model.t_load_us = ggml_time_us() - model.t_start_us;
return true;
}
// Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
static int llama_model_load(const std::string & fname, llama_model & model, const llama_model_params & params) {
try {
llama_model_loader ml(fname, params.use_mmap, params.kv_overrides);
model.hparams.vocab_only = params.vocab_only;
llm_load_arch (ml, model);
llm_load_hparams(ml, model);
llm_load_vocab (ml, model);
llm_load_print_meta(ml, model);
if (model.hparams.n_vocab != model.vocab.id_to_token.size()) {
throw std::runtime_error("vocab size mismatch");
}
if (params.vocab_only) {
LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
return 0;
}
if (!llm_load_tensors(
ml, model, params.n_gpu_layers, params.main_gpu, params.tensor_split, params.use_mlock,
params.progress_callback, params.progress_callback_user_data
)) {
return -2;
}
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("error loading model: %s\n", err.what());
return -1;
}
return 0;
}
//
// llm_build
//
using llm_build_cb = std::function<void(struct ggml_tensor * cur, const char * name, int nl)>;
enum llm_rope_type {
LLM_ROPE,
LLM_ROPE_NEOX,
LLM_ROPE_GLM,
};
enum llm_ffn_op_type {
LLM_FFN_SILU,
LLM_FFN_GELU,
LLM_FFN_RELU,
LLM_FFN_RELU_SQR,
};
enum llm_ffn_gate_type {
LLM_FFN_SEQ,
LLM_FFN_PAR, // ffn_gate is parallel to ffn_up
};
enum llm_norm_type {
LLM_NORM,
LLM_NORM_RMS,
};
static struct ggml_tensor * llm_build_inp_embd(
struct ggml_context * ctx,
const llama_hparams & hparams,
const llama_batch & batch,
struct ggml_tensor * tok_embd,
const llm_build_cb & cb) {
const int64_t n_embd = hparams.n_embd;
struct ggml_tensor * inpL;
if (batch.token) {
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
cb(inp_tokens, "inp_tokens", -1);
inpL = ggml_get_rows(ctx, tok_embd, inp_tokens);
} else {
#ifdef GGML_USE_MPI
GGML_ASSERT(false && "not implemented");
#endif
inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
}
return inpL;
}
// Persimmon: n_rot = n_embd_head_k/2
// Other: n_rot = n_embd_head_k
static void llm_build_k_shift(
struct ggml_context * ctx,
const llama_hparams & hparams,
const llama_cparams & cparams,
const llama_kv_cache & kv,
struct ggml_cgraph * graph,
llm_rope_type type,
int64_t n_ctx,
int n_rot,
float freq_base,
float freq_scale,
const llm_build_cb & cb) {
const int64_t n_layer = hparams.n_layer;
const int64_t n_head_kv = hparams.n_head_kv;
const int64_t n_embd_head_k = hparams.n_embd_head_k;
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
const int32_t n_orig_ctx = cparams.n_yarn_orig_ctx;
const float ext_factor = cparams.yarn_ext_factor;
const float attn_factor = cparams.yarn_attn_factor;
const float beta_fast = cparams.yarn_beta_fast;
const float beta_slow = cparams.yarn_beta_slow;
GGML_ASSERT(n_embd_head_k % n_rot == 0);
struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_ctx);
cb(K_shift, "K_shift", -1);
int rope_type = 0;
switch (type) {
case LLM_ROPE: rope_type = 0; break;
case LLM_ROPE_NEOX: rope_type = 2; break;
case LLM_ROPE_GLM: rope_type = 4; break;
}
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * tmp =
// we rotate only the first n_rot dimensions
ggml_rope_custom_inplace(ctx,
ggml_view_3d(ctx, kv.k_l[il],
n_embd_head_k, n_head_kv, n_ctx,
ggml_row_size(kv.k_l[il]->type, n_embd_head_k),
ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa),
0),
K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(tmp, "K_shifted", il);
ggml_build_forward_expand(graph, tmp);
}
}
static void llm_build_kv_store(
struct ggml_context * ctx,
const llama_hparams & hparams,
const llama_kv_cache & kv,
struct ggml_cgraph * graph,
struct ggml_tensor * k_cur,
struct ggml_tensor * v_cur,
int64_t n_ctx,
int32_t n_tokens,
int32_t kv_head,
const llm_build_cb & cb,
int64_t il) {
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
// compute the transposed [n_tokens, n_embd] V matrix
struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens));
//struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); // TODO: reshape above is likely not needed
cb(v_cur_t, "v_cur_t", il);
struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_k_gqa,
(ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa))*kv_head);
cb(k_cache_view, "k_cache_view", il);
struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_v_gqa,
( n_ctx)*ggml_element_size(kv.v_l[il]),
(kv_head)*ggml_element_size(kv.v_l[il]));
cb(v_cache_view, "v_cache_view", il);
// important: storing RoPE-ed version of K in the KV cache!
ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur, k_cache_view));
ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur_t, v_cache_view));
}
static struct ggml_tensor * llm_build_norm(
struct ggml_context * ctx,
struct ggml_tensor * cur,
const llama_hparams & hparams,
struct ggml_tensor * mw,
struct ggml_tensor * mb,
llm_norm_type type,
const llm_build_cb & cb,
int il) {
switch (type) {
case LLM_NORM: cur = ggml_norm (ctx, cur, hparams.f_norm_eps); break;
case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps); break;
}
if (mw || mb) {
cb(cur, "norm", il);
}
if (mw) {
cur = ggml_mul(ctx, cur, mw);
if (mb) {
cb(cur, "norm_w", il);
}
}
if (mb) {
cur = ggml_add(ctx, cur, mb);
}
return cur;
}
static struct ggml_tensor * llm_build_ffn(
struct ggml_context * ctx,
struct ggml_tensor * cur,
struct ggml_tensor * up,
struct ggml_tensor * up_b,
struct ggml_tensor * gate,
struct ggml_tensor * gate_b,
struct ggml_tensor * down,
struct ggml_tensor * down_b,
struct ggml_tensor * act_scales,
llm_ffn_op_type type_op,
llm_ffn_gate_type type_gate,
const llm_build_cb & cb,
int il) {
struct ggml_tensor * tmp = ggml_mul_mat(ctx, up, cur);
cb(tmp, "ffn_up", il);
if (up_b) {
tmp = ggml_add(ctx, tmp, up_b);
cb(tmp, "ffn_up_b", il);
}
if (gate) {
switch (type_gate) {
case LLM_FFN_SEQ:
{
cur = ggml_mul_mat(ctx, gate, tmp);
cb(cur, "ffn_gate", il);
} break;
case LLM_FFN_PAR:
{
cur = ggml_mul_mat(ctx, gate, cur);
cb(cur, "ffn_gate", il);
} break;
}
if (gate_b) {
cur = ggml_add(ctx, cur, gate_b);
cb(cur, "ffn_gate_b", il);
}
} else {
cur = tmp;
}
switch (type_op) {
case LLM_FFN_SILU:
{
cur = ggml_silu(ctx, cur);
cb(cur, "ffn_silu", il);
} break;
case LLM_FFN_GELU:
{
cur = ggml_gelu(ctx, cur);
cb(cur, "ffn_gelu", il);
if (act_scales != NULL) {
cur = ggml_div(ctx, cur, act_scales);
cb(cur, "ffn_act", il);
}
} break;
case LLM_FFN_RELU:
{
cur = ggml_relu(ctx, cur);
cb(cur, "ffn_relu", il);
} break;
case LLM_FFN_RELU_SQR:
{
cur = ggml_relu(ctx, cur);
cb(cur, "ffn_relu", il);
cur = ggml_sqr(ctx, cur);
cb(cur, "ffn_sqr(relu)", il);
} break;
}
if (type_gate == LLM_FFN_PAR) {
cur = ggml_mul(ctx, cur, tmp);
cb(cur, "ffn_gate_par", il);
}
cur = ggml_mul_mat(ctx, down, cur);
if (down_b) {
cb(cur, "ffn_down", il);
}
if (down_b) {
cur = ggml_add(ctx, cur, down_b);
}
return cur;
}
// if max_alibi_bias > 0 then apply ALiBi
static struct ggml_tensor * llm_build_kqv(
struct ggml_context * ctx,
const llama_model & model,
const llama_hparams & hparams,
const llama_kv_cache & kv,
struct ggml_tensor * wo,
struct ggml_tensor * wo_b,
struct ggml_tensor * q_cur,
struct ggml_tensor * kq_mask,
int64_t n_ctx,
int32_t n_tokens,
int32_t n_kv,
float max_alibi_bias,
float kq_scale,
const llm_build_cb & cb,
int il) {
const int64_t n_head = hparams.n_head;
const int64_t n_head_kv = hparams.n_head_kv;
const int64_t n_embd_head_k = hparams.n_embd_head_k;
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
const int64_t n_embd_head_v = hparams.n_embd_head_v;
struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
cb(q, "q", il);
struct ggml_tensor * k =
ggml_view_3d(ctx, kv.k_l[il],
n_embd_head_k, n_kv, n_head_kv,
ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa),
ggml_row_size(kv.k_l[il]->type, n_embd_head_k),
0);
cb(k, "k", il);
struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
cb(kq, "kq", il);
if (model.arch == LLM_ARCH_PHI2) {
// for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
// ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
}
if (max_alibi_bias > 0.0f) {
// temporary branch until we figure out how to handle ggml_alibi through ggml_add
kq = ggml_scale(ctx, kq, kq_scale);
cb(kq, "kq_scaled", il);
if (max_alibi_bias > 0.0f) {
// TODO: n_head or n_head_kv
// TODO: K-shift is likely not working
// TODO: change to ggml_add
kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, max_alibi_bias);
cb(kq, "kq_scaled_alibi", il);
}
kq = ggml_add(ctx, kq, kq_mask);
cb(kq, "kq_masked", il);
kq = ggml_soft_max(ctx, kq);
cb(kq, "kq_soft_max", il);
} else {
kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale);
cb(kq, "kq_soft_max_ext", il);
}
// split cached v into n_head heads
struct ggml_tensor * v =
ggml_view_3d(ctx, kv.v_l[il],
n_kv, n_embd_head_v, n_head_kv,
ggml_element_size(kv.v_l[il])*n_ctx,
ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head_v,
0);
cb(v, "v", il);
struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
cb(kqv, "kqv", il);
struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
cb(kqv_merged, "kqv_merged", il);
struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens);
cb(cur, "kqv_merged_cont", il);
cur = ggml_mul_mat(ctx, wo, cur);
if (wo_b) {
cb(cur, "kqv_wo", il);
}
if (wo_b) {
cur = ggml_add(ctx, cur, wo_b);
}
return cur;
}
struct llm_build_context {
const llama_model & model;
const llama_hparams & hparams;
const llama_cparams & cparams;
const llama_batch & batch;
const llama_kv_cache & kv_self;
const int64_t n_embd;
const int64_t n_layer;
const int64_t n_ctx; // user-specified context size (can be different from n_ctx_train)
const int64_t n_head;
const int64_t n_head_kv;
const int64_t n_embd_head_k;
const int64_t n_embd_k_gqa;
const int64_t n_embd_head_v;
const int64_t n_embd_v_gqa;
const int64_t n_expert;
const int64_t n_expert_used;
const float freq_base;
const float freq_scale;
const float ext_factor;
const float attn_factor;
const float beta_fast;
const float beta_slow;
const float norm_eps;
const float norm_rms_eps;
const int32_t n_tokens;
const int32_t n_kv; // size of KV cache to consider (n_kv <= n_ctx)
const int32_t kv_head; // index of where we store new KV data in the cache
const int32_t n_orig_ctx;
const bool do_rope_shift;
const llm_build_cb & cb;
std::vector<uint8_t> & buf_compute_meta;
struct ggml_context * ctx0 = nullptr;
// TODO: consider making the entire interface noexcept
llm_build_context(
llama_context & lctx,
const llama_batch & batch,
const llm_build_cb & cb,
bool worst_case) :
model (lctx.model),
hparams (model.hparams),
cparams (lctx.cparams),
batch (batch),
kv_self (lctx.kv_self),
n_embd (hparams.n_embd),
n_layer (hparams.n_layer),
n_ctx (cparams.n_ctx),
n_head (hparams.n_head),
n_head_kv (hparams.n_head_kv),
n_embd_head_k (hparams.n_embd_head_k),
n_embd_k_gqa (hparams.n_embd_k_gqa()),
n_embd_head_v (hparams.n_embd_head_v),
n_embd_v_gqa (hparams.n_embd_v_gqa()),
n_expert (hparams.n_expert),
n_expert_used (hparams.n_expert_used),
freq_base (cparams.rope_freq_base),
freq_scale (cparams.rope_freq_scale),
ext_factor (cparams.yarn_ext_factor),
attn_factor (cparams.yarn_attn_factor),
beta_fast (cparams.yarn_beta_fast),
beta_slow (cparams.yarn_beta_slow),
norm_eps (hparams.f_norm_eps),
norm_rms_eps (hparams.f_norm_rms_eps),
n_tokens (batch.n_tokens),
n_kv (worst_case ? n_ctx : kv_self.n),
kv_head (worst_case ? n_ctx - n_tokens : kv_self.head),
n_orig_ctx (cparams.n_yarn_orig_ctx),
do_rope_shift (worst_case || kv_self.has_shift),
cb (cb),
buf_compute_meta (lctx.buf_compute_meta) {
GGML_ASSERT(!!kv_self.ctx);
// all initializations should be done in init()
}
void init() {
struct ggml_init_params params = {
/*.mem_size =*/ buf_compute_meta.size(),
/*.mem_buffer =*/ buf_compute_meta.data(),
/*.no_alloc =*/ true,
};
ctx0 = ggml_init(params);
}
void free() {
if (ctx0) {
ggml_free(ctx0);
ctx0 = nullptr;
}
}
struct ggml_cgraph * build_llama() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_head == hparams.n_rot);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
// shift the entire K-cache if needed
if (do_rope_shift) {
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
}
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
// self-attention
{
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
if (model.layers[il].bq) {
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
cb(Qcur, "Qcur", il);
}
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
if (model.layers[il].bk) {
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
cb(Kcur, "Kcur", il);
}
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
if (model.layers[il].bv) {
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
cb(Vcur, "Vcur", il);
}
Qcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
Kcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, model.layers[il].bo,
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
if (model.layers[il].ffn_gate_inp == nullptr) {
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, NULL,
model.layers[il].ffn_gate, NULL,
model.layers[il].ffn_down, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
} else {
// MoE branch
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
cb(logits, "ffn_moe_logits", il);
ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
cb(probs, "ffn_moe_probs", il);
// select experts
ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
cb(selected_experts->src[0], "ffn_moe_argsort", il);
ggml_tensor * weights = ggml_get_rows(ctx0,
ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
cb(weights, "ffn_moe_weights", il);
weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
cb(weights_sum, "ffn_moe_weights_sum", il);
weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
cb(weights, "ffn_moe_weights_norm", il);
// compute expert outputs
ggml_tensor * moe_out = nullptr;
for (int i = 0; i < n_expert_used; ++i) {
ggml_tensor * cur_expert;
ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exp, n_expert, selected_experts, i, cur);
cb(cur_up, "ffn_moe_up", il);
ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exp, n_expert, selected_experts, i, cur);
cb(cur_gate, "ffn_moe_gate", il);
cur_gate = ggml_silu(ctx0, cur_gate);
cb(cur_gate, "ffn_moe_silu", il);
cur_expert = ggml_mul(ctx0, cur_up, cur_gate); // [n_tokens, n_embd]
cb(cur_expert, "ffn_moe_gate_par", il);
cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exp, n_expert, selected_experts, i, cur_expert); // [n_tokens, n_embd]
cb(cur_expert, "ffn_moe_down", il);
cur_expert = ggml_mul(ctx0, cur_expert,
ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
cb(cur_expert, "ffn_moe_weighted", il);
if (i == 0) {
moe_out = cur_expert;
} else {
moe_out = ggml_add(ctx0, moe_out, cur_expert);
cb(moe_out, "ffn_moe_out", il);
}
}
cur = moe_out;
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm, NULL,
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_baichuan() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
// shift the entire K-cache if needed
if (do_rope_shift) {
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
}
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
// self-attention
{
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
switch (model.type) {
case MODEL_7B:
Qcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
break;
case MODEL_13B:
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens);
break;
default:
GGML_ASSERT(false);
}
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
// apply ALiBi for 13B model
const float max_alibi_bias = model.type == MODEL_13B ? 8.0f : -1.0f;
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, NULL,
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, NULL,
model.layers[il].ffn_gate, NULL,
model.layers[il].ffn_down, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm, NULL,
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_falcon() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
// shift the entire K-cache if needed
if (do_rope_shift) {
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
}
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * attn_norm;
attn_norm = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm,
model.layers[il].attn_norm_b,
LLM_NORM, cb, il);
cb(attn_norm, "attn_norm", il);
// self-attention
{
if (model.layers[il].attn_norm_2) {
// Falcon-40B
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm_2,
model.layers[il].attn_norm_2_b,
LLM_NORM, cb, il);
cb(cur, "attn_norm_2", il);
} else {
cur = attn_norm;
}
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
// using mode = 2 for neox mode
Qcur = ggml_rope_custom(
ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
Kcur = ggml_rope_custom(
ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, NULL,
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
struct ggml_tensor * ffn_inp = cur;
// feed forward
{
cur = llm_build_ffn(ctx0, attn_norm, // !! use the attn norm, not the result
model.layers[il].ffn_up, NULL,
NULL, NULL,
model.layers[il].ffn_down, NULL,
NULL,
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "l_out", il);
cur = ggml_add(ctx0, cur, inpL);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
// norm
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm,
model.output_norm_b,
LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_starcoder() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
struct ggml_tensor * cur;
struct ggml_tensor * pos;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
cb(pos, "pos_embd", -1);
inpL = ggml_add(ctx0, inpL, pos);
cb(inpL, "inpL", -1);
for (int il = 0; il < n_layer; ++il) {
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm,
model.layers[il].attn_norm_b,
LLM_NORM, cb, il);
cb(cur, "attn_norm", il);
// self-attention
{
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
cb(cur, "bqkv", il);
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, model.layers[il].bo,
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
// add the input
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
cb(ffn_inp, "ffn_inp", il);
// FF
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm,
model.layers[il].ffn_norm_b,
LLM_NORM, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
NULL, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
NULL,
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
cb(cur, "ffn_out", il);
}
inpL = ggml_add(ctx0, cur, ffn_inp);
cb(inpL, "l_out", il);
}
cur = llm_build_norm(ctx0, inpL, hparams,
model.output_norm,
model.output_norm_b,
LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_persimmon() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
const int64_t n_rot = n_embd_head_k / 2;
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "imp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
if (do_rope_shift) {
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
}
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * residual = inpL;
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm,
model.layers[il].attn_norm_b,
LLM_NORM, cb, il);
cb(cur, "attn_norm", il);
// self attention
{
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
cb(cur, "bqkv", il);
// split qkv
GGML_ASSERT(n_head_kv == n_head);
struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens);
cb(tmpqkv, "tmpqkv", il);
struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
cb(tmpqkv_perm, "tmpqkv", il);
struct ggml_tensor * tmpq = ggml_view_3d(
ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
ggml_element_size(tmpqkv_perm) * n_embd_head,
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
0
);
cb(tmpq, "tmpq", il);
struct ggml_tensor * tmpk = ggml_view_3d(
ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
ggml_element_size(tmpqkv_perm) * n_embd_head,
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens
);
cb(tmpk, "tmpk", il);
// Q/K Layernorm
tmpq = llm_build_norm(ctx0, tmpq, hparams,
model.layers[il].attn_q_norm,
model.layers[il].attn_q_norm_b,
LLM_NORM, cb, il);
cb(tmpq, "tmpq", il);
tmpk = llm_build_norm(ctx0, tmpk, hparams,
model.layers[il].attn_k_norm,
model.layers[il].attn_k_norm_b,
LLM_NORM, cb, il);
cb(tmpk, "tmpk", il);
// RoPE the first n_rot of q/k, pass the other half, and concat.
struct ggml_tensor * qrot = ggml_view_3d(
ctx0, tmpq, n_rot, n_head, n_tokens,
ggml_element_size(tmpq) * n_embd_head,
ggml_element_size(tmpq) * n_embd_head * n_head,
0
);
cb(qrot, "qrot", il);
struct ggml_tensor * krot = ggml_view_3d(
ctx0, tmpk, n_rot, n_head, n_tokens,
ggml_element_size(tmpk) * n_embd_head,
ggml_element_size(tmpk) * n_embd_head * n_head,
0
);
cb(krot, "krot", il);
// get the second half of tmpq, e.g tmpq[n_rot:, :, :]
struct ggml_tensor * qpass = ggml_view_3d(
ctx0, tmpq, n_rot, n_head, n_tokens,
ggml_element_size(tmpq) * n_embd_head,
ggml_element_size(tmpq) * n_embd_head * n_head,
ggml_element_size(tmpq) * n_rot
);
cb(qpass, "qpass", il);
struct ggml_tensor * kpass = ggml_view_3d(
ctx0, tmpk, n_rot, n_head, n_tokens,
ggml_element_size(tmpk) * n_embd_head,
ggml_element_size(tmpk) * n_embd_head * n_head,
ggml_element_size(tmpk) * n_rot
);
cb(kpass, "kpass", il);
struct ggml_tensor * qrotated = ggml_rope_custom(
ctx0, qrot, inp_pos, n_rot, 2, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(qrotated, "qrotated", il);
struct ggml_tensor * krotated = ggml_rope_custom(
ctx0, krot, inp_pos, n_rot, 2, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(krotated, "krotated", il);
// ggml currently only supports concatenation on dim=2
// so we need to permute qrot, qpass, concat, then permute back.
qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
cb(qrotated, "qrotated", il);
krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
cb(krotated, "krotated", il);
qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
cb(qpass, "qpass", il);
kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
cb(kpass, "kpass", il);
struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
cb(Kcur, "Kcur", il);
struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3));
cb(Q, "Q", il);
Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
cb(Kcur, "Kcur", il);
struct ggml_tensor * Vcur = ggml_view_3d(
ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
ggml_element_size(tmpqkv_perm) * n_embd_head,
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2
);
cb(Vcur, "Vcur", il);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
// TODO: not tested, could be broken
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, model.layers[il].bo,
Q, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm,
model.layers[il].ffn_norm_b,
LLM_NORM, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
NULL, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
NULL,
LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "l_out", il);
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm,
model.output_norm_b,
LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_refact() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
// self-attention
{
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
cb(Kcur, "Kcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
cb(Qcur, "Qcur", il);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, NULL,
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, NULL,
model.layers[il].ffn_gate, NULL,
model.layers[il].ffn_down, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm, NULL,
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_bloom() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
inpL = llm_build_norm(ctx0, inpL, hparams,
model.tok_norm,
model.tok_norm_b,
LLM_NORM, cb, -1);
cb(inpL, "inp_norm", -1);
for (int il = 0; il < n_layer; ++il) {
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm,
model.layers[il].attn_norm_b,
LLM_NORM, cb, il);
cb(cur, "attn_norm", il);
// self-attention
{
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
cb(cur, "bqkv", il);
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, model.layers[il].bo,
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
// Add the input
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
cb(ffn_inp, "ffn_inp", il);
// FF
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm,
model.layers[il].ffn_norm_b,
LLM_NORM, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
NULL, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
NULL,
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
cb(cur, "ffn_out", il);
}
inpL = ggml_add(ctx0, cur, ffn_inp);
cb(inpL, "l_out", il);
}
cur = llm_build_norm(ctx0, inpL, hparams,
model.output_norm,
model.output_norm_b,
LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_mpt() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * attn_norm;
attn_norm = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm,
NULL,
LLM_NORM, cb, il);
cb(attn_norm, "attn_norm", il);
// self-attention
{
cur = attn_norm;
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
if (hparams.f_clamp_kqv > 0.0f) {
cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
cb(cur, "wqkv_clamped", il);
}
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, NULL,
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
// Add the input
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
cb(ffn_inp, "ffn_inp", il);
// feed forward
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm,
NULL,
LLM_NORM, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, NULL,
NULL, NULL,
model.layers[il].ffn_down, NULL,
model.layers[il].ffn_act,
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm,
NULL,
LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_stablelm() {
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
// shift the entire K-cache if needed
if (do_rope_shift) {
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, hparams.n_rot, freq_base, freq_scale, cb);
}
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm,
model.layers[il].attn_norm_b,
LLM_NORM, cb, il);
cb(cur, "attn_norm", il);
// self-attention
{
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
Qcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
Kcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, NULL,
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm,
model.layers[il].ffn_norm_b,
LLM_NORM, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, NULL,
model.layers[il].ffn_gate, NULL,
model.layers[il].ffn_down, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm,
model.output_norm_b,
LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_qwen() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
// shift the entire K-cache if needed
if (do_rope_shift) {
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
}
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
// self-attention
{
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
cb(cur, "bqkv", il);
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 2*sizeof(float)*(n_embd)));
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
// using mode = 2 for neox mode
Qcur = ggml_rope_custom(
ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
Kcur = ggml_rope_custom(
ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, NULL,
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward forward
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, NULL,
model.layers[il].ffn_gate, NULL,
model.layers[il].ffn_down, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm, NULL,
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_phi2() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
struct ggml_tensor * cur;
struct ggml_tensor * attn_norm_output;
struct ggml_tensor * ffn_output;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
// shift the entire K-cache if needed
if (do_rope_shift) {
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
}
for (int il = 0; il < n_layer; ++il) {
attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm,
model.layers[il].attn_norm_b,
LLM_NORM, cb, il);
cb(attn_norm_output, "attn_norm", il);
// self-attention
{
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
cb(cur, "wqkv", il);
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
cb(cur, "bqkv", il);
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Qcur = ggml_rope_custom(
ctx0, Qcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
// with phi2, we scale the Q to avoid precision issues
// ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66
Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head)));
cb(Qcur, "Qcur", il);
Kcur = ggml_rope_custom(
ctx0, Kcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, model.layers[il].bo,
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f, cb, il);
cb(cur, "kqv_out", il);
}
// FF
{
ffn_output = llm_build_ffn(ctx0, attn_norm_output,
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
NULL, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
NULL,
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
cb(ffn_output, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_output);
cb(cur, "l_out", il);
cur = ggml_add(ctx0, cur, inpL);
cb(cur, "l_out", il);
inpL = cur;
}
cur = llm_build_norm(ctx0, inpL, hparams,
model.output_norm,
model.output_norm_b,
LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output_no_bias", -1);
cur = ggml_add(ctx0, cur, model.output_b);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_plamo() {
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
// shift the entire K-cache if needed
if (do_rope_shift) {
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
}
for (int il = 0; il < n_layer; ++il) {
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
struct ggml_tensor * attention_norm = cur;
// self-attention
{
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
Qcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Qcur, "Qcur", il);
Kcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Kcur, "Kcur", il);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, NULL,
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
struct ggml_tensor * sa_out = cur;
cur = attention_norm;
// feed-forward network
{
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, NULL,
model.layers[il].ffn_gate, NULL,
model.layers[il].ffn_down, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, sa_out);
cb(cur, "l_out", il);
cur = ggml_add(ctx0, cur, inpL);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm, NULL,
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_gpt2() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
struct ggml_tensor * cur;
struct ggml_tensor * pos;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
cb(pos, "pos_embd", -1);
inpL = ggml_add(ctx0, inpL, pos);
cb(inpL, "inpL", -1);
for (int il = 0; il < n_layer; ++il) {
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm,
model.layers[il].attn_norm_b,
LLM_NORM, cb, il);
cb(cur, "attn_norm", il);
// self-attention
{
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
cb(cur, "bqkv", il);
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, model.layers[il].bo,
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
// add the input
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
cb(ffn_inp, "ffn_inp", il);
// FF
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm,
model.layers[il].ffn_norm_b,
LLM_NORM, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
NULL, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
NULL,
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
cb(cur, "ffn_out", il);
}
inpL = ggml_add(ctx0, cur, ffn_inp);
cb(inpL, "l_out", il);
}
cur = llm_build_norm(ctx0, inpL, hparams,
model.output_norm,
model.output_norm_b,
LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
};
//
// tensor offloading helpers
//
// TODO: will be removed with backend v2
enum llm_offload_func_e {
OFFLOAD_FUNC_NOP,
OFFLOAD_FUNC,
OFFLOAD_FUNC_FRC, // force offload
OFFLOAD_FUNC_KQV,
OFFLOAD_FUNC_NR,
OFFLOAD_FUNC_EMB, // embeddings
OFFLOAD_FUNC_OUT,
};
// TODO: will be removed with backend v2
struct llm_offload_trie {
struct node {
~node() {
for (int i = 0; i < 256; ++i) {
if (children[i]) {
delete children[i];
}
}
}
node * children[256] = { nullptr };
llm_offload_func_e func = OFFLOAD_FUNC_NOP;
};
llm_offload_trie() {
root = new node;
}
llm_offload_trie(const std::unordered_map<const char *, llm_offload_func_e> & map) {
root = new node;
for (const auto & kv : map) {
add(kv.first, kv.second);
}
}
~llm_offload_trie() {
delete root;
}
void add(const char * name, llm_offload_func_e func) {
node * cur = root;
for (int i = 0; ; ++i) {
const uint8_t c = name[i];
if (!c) {
break;
}
if (!cur->children[c]) {
cur->children[c] = new node;
}
cur = cur->children[c];
}
cur->func = func;
}
llm_offload_func_e find(const char * name) const {
const node * cur = root;
for (int i = 0; ; ++i) {
const uint8_t c = name[i];
if (!c) {
break;
}
if (!cur->children[c]) {
return OFFLOAD_FUNC_NOP;
}
cur = cur->children[c];
}
return cur->func;
}
node * root = nullptr;
};
// TODO: will be removed with backend v2
static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map = {
//{ "inp_tokens", OFFLOAD_FUNC_NR }, // TODO: missing K-quants get_rows kernel
//{ "inp_embd", OFFLOAD_FUNC_NR }, // TODO: missing K-quants get_rows kernel
{ "pos_embd", OFFLOAD_FUNC_NR },
{ "inp_pos", OFFLOAD_FUNC_FRC }, // this is often used for KQ ops (e.g. rope)
{ "KQ_mask", OFFLOAD_FUNC_FRC },
{ "K_shift", OFFLOAD_FUNC_FRC },
{ "K_shifted", OFFLOAD_FUNC },
{ "inp_norm", OFFLOAD_FUNC_NR },
{ "inp_norm_w", OFFLOAD_FUNC_NR },
{ "inp_norm_wb", OFFLOAD_FUNC_NR },
{ "norm", OFFLOAD_FUNC },
{ "norm_w", OFFLOAD_FUNC },
{ "norm_wb", OFFLOAD_FUNC },
{ "attn_norm", OFFLOAD_FUNC },
{ "attn_norm_2", OFFLOAD_FUNC },
{ "wqkv", OFFLOAD_FUNC_KQV },
{ "bqkv", OFFLOAD_FUNC_KQV },
{ "wqkv_clamped", OFFLOAD_FUNC_KQV },
{ "tmpk", OFFLOAD_FUNC_KQV },
{ "tmpq", OFFLOAD_FUNC_KQV },
{ "tmpv", OFFLOAD_FUNC_KQV },
{ "Kcur", OFFLOAD_FUNC_KQV },
{ "Qcur", OFFLOAD_FUNC_KQV },
{ "Vcur", OFFLOAD_FUNC_KQV },
{ "krot", OFFLOAD_FUNC_KQV },
{ "qrot", OFFLOAD_FUNC_KQV },
{ "kpass", OFFLOAD_FUNC_KQV },
{ "qpass", OFFLOAD_FUNC_KQV },
{ "krotated", OFFLOAD_FUNC_KQV },
{ "qrotated", OFFLOAD_FUNC_KQV },
{ "q", OFFLOAD_FUNC_KQV },
{ "k", OFFLOAD_FUNC_KQV },
{ "kq", OFFLOAD_FUNC_KQV },
{ "kq_scaled", OFFLOAD_FUNC_KQV },
{ "kq_scaled_alibi", OFFLOAD_FUNC_KQV },
{ "kq_masked", OFFLOAD_FUNC_KQV },
{ "kq_soft_max", OFFLOAD_FUNC_KQV },
{ "kq_soft_max_ext", OFFLOAD_FUNC_KQV },
{ "v", OFFLOAD_FUNC_KQV },
{ "kqv", OFFLOAD_FUNC_KQV },
{ "kqv_merged", OFFLOAD_FUNC_KQV },
{ "kqv_merged_cont", OFFLOAD_FUNC_KQV },
{ "kqv_wo", OFFLOAD_FUNC_KQV },
{ "kqv_out", OFFLOAD_FUNC_KQV },
{ "ffn_inp", OFFLOAD_FUNC },
{ "ffn_norm", OFFLOAD_FUNC },
{ "ffn_up", OFFLOAD_FUNC },
{ "ffn_up_b", OFFLOAD_FUNC },
{ "ffn_gate", OFFLOAD_FUNC },
{ "ffn_gate_b", OFFLOAD_FUNC },
{ "ffn_gate_par", OFFLOAD_FUNC },
{ "ffn_act", OFFLOAD_FUNC },
{ "ffn_down", OFFLOAD_FUNC },
{ "ffn_down_b", OFFLOAD_FUNC },
{ "ffn_out", OFFLOAD_FUNC },
{ "ffn_silu", OFFLOAD_FUNC },
{ "ffn_gelu", OFFLOAD_FUNC },
{ "ffn_relu", OFFLOAD_FUNC },
{ "ffn_sqr(relu)", OFFLOAD_FUNC },
{ "ffn_moe_logits", OFFLOAD_FUNC },
{ "ffn_moe_probs", OFFLOAD_FUNC },
{ "ffn_moe_argsort", OFFLOAD_FUNC },
{ "ffn_moe_weights", OFFLOAD_FUNC },
{ "ffn_moe_weights_sum", OFFLOAD_FUNC },
{ "ffn_moe_weights_norm", OFFLOAD_FUNC },
{ "ffn_moe_weighted", OFFLOAD_FUNC },
{ "ffn_moe_up", OFFLOAD_FUNC },
{ "ffn_moe_gate", OFFLOAD_FUNC },
{ "ffn_moe_silu", OFFLOAD_FUNC },
{ "ffn_moe_gate_par", OFFLOAD_FUNC },
{ "ffn_moe_down", OFFLOAD_FUNC },
{ "ffn_moe_out", OFFLOAD_FUNC },
{ "l_out", OFFLOAD_FUNC },
{ "result_norm", OFFLOAD_FUNC_EMB },
{ "result_output_no_bias", OFFLOAD_FUNC_EMB },
{ "result_output", OFFLOAD_FUNC_OUT },
};
static llm_offload_trie k_offload_func_trie(k_offload_map);
static struct ggml_cgraph * llama_build_graph(
llama_context & lctx,
const llama_batch & batch) {
const auto & model = lctx.model;
// check if we should build the worst-case graph (for memory measurement)
const bool worst_case = ggml_allocr_is_measure(lctx.alloc);
// keep track of the input that has already been allocated
bool alloc_inp_tokens = false;
bool alloc_inp_embd = false;
bool alloc_inp_pos = false;
bool alloc_inp_KQ_mask = false;
bool alloc_inp_K_shift = false;
#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
const bool do_offload = true;
#else
const bool do_offload = true; // TODO: set to false after finishing refactoring
#endif
int n_non_view = 0; // number of non-view tensors that have been processed by the callback
// this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.)
// TODO: will be removed with backend v2
llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) {
if (il >= 0) {
ggml_format_name(cur, "%s-%d", name, il);
} else {
ggml_set_name(cur, name);
}
//
// allocate input tensors and set input data
//
// TODO: will be removed with backend v2
if (!alloc_inp_tokens && strcmp(name, "inp_tokens") == 0) {
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc) && batch.token) {
const int64_t n_tokens = cur->ne[0];
ggml_backend_tensor_set(cur, batch.token, 0, n_tokens*ggml_element_size(cur));
}
alloc_inp_tokens = true;
}
if (!alloc_inp_embd && strcmp(name, "inp_embd") == 0) {
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc) && batch.embd) {
const int64_t n_embd = cur->ne[0];
const int64_t n_tokens = cur->ne[1];
ggml_backend_tensor_set(cur, batch.embd, 0, n_tokens*n_embd*ggml_element_size(cur));
}
alloc_inp_embd = true;
}
if (!alloc_inp_pos && strcmp(name, "inp_pos") == 0) {
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc) && batch.pos) {
const int64_t n_tokens = cur->ne[0];
static_assert(std::is_same<llama_pos, int32_t>::value, "llama_pos must be int32_t");
ggml_backend_tensor_set(cur, batch.pos, 0, n_tokens*ggml_element_size(cur));
}
alloc_inp_pos = true;
}
if (!alloc_inp_KQ_mask && strcmp(name, "KQ_mask") == 0) {
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc)) {
const int64_t n_kv = cur->ne[0];
const int64_t n_tokens = cur->ne[1];
float * data;
if (ggml_backend_buffer_is_host(cur->buffer)) {
data = (float *) cur->data;
} else {
lctx.buf_copy.resize(ggml_nbytes(cur));
data = (float *) lctx.buf_copy.data();
}
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
const llama_pos pos = batch.pos[j];
const llama_seq_id seq_id = batch.seq_id[j][0];
for (int i = 0; i < n_kv; ++i) {
float f;
if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) {
f = -INFINITY;
} else {
f = 0;
}
data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
}
}
}
if (data != cur->data) {
ggml_backend_tensor_set(cur, data, 0, ggml_nbytes(cur));
}
}
alloc_inp_KQ_mask = true;
}
if (!alloc_inp_K_shift && strcmp(name, "K_shift") == 0) {
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc)) {
const int64_t n_ctx = cur->ne[0];
int32_t * data;
if (ggml_backend_buffer_is_host(cur->buffer)) {
data = (int32_t *) cur->data;
} else {
lctx.buf_copy.resize(ggml_nbytes(cur));
data = (int32_t *) lctx.buf_copy.data();
}
for (int i = 0; i < n_ctx; ++i) {
data[i] = lctx.kv_self.cells[i].delta;
}
if (data != cur->data) {
ggml_backend_tensor_set(cur, data, 0, ggml_nbytes(cur));
}
}
alloc_inp_K_shift = true;
}
// view tensors are not processed further
if (cur->view_src != nullptr) {
return;
}
if (cur->op != GGML_OP_NONE) {
n_non_view++;
}
//
// offload layers
//
// TODO: will be removed with backend v2
//#define LLAMA_OFFLOAD_DEBUG
if (!do_offload) {
return;
}
const int n_layer = model.hparams.n_layer;
const int n_gpu_layers = model.n_gpu_layers;
const int i_gpu_start = n_layer - n_gpu_layers;
// should we offload the final norm? yes if we are not computing embeddings
const bool offload_emb = lctx.embedding.empty();
static const std::unordered_map<llm_offload_func_e, std::string, std::hash<int>> k_offload_func_name = {
{ OFFLOAD_FUNC_NOP, "CPU" },
{ OFFLOAD_FUNC_OUT, "CPU" },
#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
{ OFFLOAD_FUNC, "GPU (CUDA)" },
{ OFFLOAD_FUNC_FRC, "GPU (CUDA) FRC" },
{ OFFLOAD_FUNC_KQV, "GPU (CUDA) KQV" },
{ OFFLOAD_FUNC_NR, "GPU (CUDA) NR" },
{ OFFLOAD_FUNC_EMB, "GPU (CUDA) EMB" },
#else
{ OFFLOAD_FUNC, "CPU" },
{ OFFLOAD_FUNC_FRC, "CPU" },
{ OFFLOAD_FUNC_KQV, "CPU" },
{ OFFLOAD_FUNC_NR, "CPU" },
{ OFFLOAD_FUNC_EMB, "CPU" },
#endif // GGML_USE_CUBLAS
};
// check the global map for what offload function to use for this tensor
llm_offload_func_e func_e = k_offload_func_trie.find(name);
if (func_e == OFFLOAD_FUNC_NOP) {
#ifdef LLAMA_OFFLOAD_DEBUG
// if a tensor hasn't been offloaded, we warn the user
if (worst_case) {
LLAMA_LOG_WARN("%s: %32s: not offloaded (ref: %s)\n", __func__,
cur->name, "https://github.com/ggerganov/llama.cpp/pull/3837");
}
#endif
return;
}
// count the number of layers and respect the provided n_gpu_layers
switch (func_e) {
case OFFLOAD_FUNC_NOP:
case OFFLOAD_FUNC_OUT:
break;
case OFFLOAD_FUNC:
if (n_gpu_layers < n_layer) {
if (il < i_gpu_start) {
func_e = OFFLOAD_FUNC_NOP;
}
}
break;
case OFFLOAD_FUNC_FRC:
if (!lctx.cparams.offload_kqv) {
func_e = OFFLOAD_FUNC_NOP;
} break;
case OFFLOAD_FUNC_KQV:
if (!lctx.cparams.offload_kqv) {
func_e = OFFLOAD_FUNC_NOP;
} else {
if (n_gpu_layers < n_layer) {
if (il < i_gpu_start) {
func_e = OFFLOAD_FUNC_NOP;
}
}
}
break;
case OFFLOAD_FUNC_NR:
if (n_gpu_layers <= n_layer + 0) {
func_e = OFFLOAD_FUNC_NOP;
}
break;
case OFFLOAD_FUNC_EMB:
if (!offload_emb || n_gpu_layers < n_layer) {
func_e = OFFLOAD_FUNC_NOP;
}
break;
default: GGML_ASSERT(false);
}
offload_func_t func = ggml_offload_nop;
// this is needed for compatibility with Metal for example
#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
static offload_func_t ggml_offload_gpu = ggml_cuda_assign_buffers_no_alloc;
#else
static offload_func_t ggml_offload_gpu = ggml_offload_nop;
#endif
switch (func_e) {
case OFFLOAD_FUNC_NOP:
case OFFLOAD_FUNC_OUT: func = ggml_offload_nop; break;
case OFFLOAD_FUNC:
case OFFLOAD_FUNC_KQV:
case OFFLOAD_FUNC_FRC:
case OFFLOAD_FUNC_NR:
case OFFLOAD_FUNC_EMB: func = ggml_offload_gpu; break;
default: GGML_ASSERT(false);
}
// apply offload function to the tensor
func(cur);
#ifdef LLAMA_OFFLOAD_DEBUG
if (worst_case) {
LLAMA_LOG_INFO("%s: %32s: %s\n", __func__, cur->name, k_offload_func_name.at(func_e).c_str());
}
#endif
};
struct ggml_cgraph * result = NULL;
struct llm_build_context llm(lctx, batch, cb, worst_case);
llm.init();
switch (model.arch) {
case LLM_ARCH_LLAMA:
{
result = llm.build_llama();
} break;
case LLM_ARCH_BAICHUAN:
{
result = llm.build_baichuan();
} break;
case LLM_ARCH_FALCON:
{
result = llm.build_falcon();
} break;
case LLM_ARCH_STARCODER:
{
result = llm.build_starcoder();
} break;
case LLM_ARCH_PERSIMMON:
{
result = llm.build_persimmon();
} break;
case LLM_ARCH_REFACT:
{
result = llm.build_refact();
} break;
case LLM_ARCH_BLOOM:
{
result = llm.build_bloom();
} break;
case LLM_ARCH_MPT:
{
result = llm.build_mpt();
} break;
case LLM_ARCH_STABLELM:
{
result = llm.build_stablelm();
} break;
case LLM_ARCH_QWEN:
{
result = llm.build_qwen();
} break;
case LLM_ARCH_PHI2:
{
result = llm.build_phi2();
} break;
case LLM_ARCH_PLAMO:
{
result = llm.build_plamo();
} break;
case LLM_ARCH_GPT2:
{
result = llm.build_gpt2();
} break;
default:
GGML_ASSERT(false);
}
llm.free();
if (worst_case) {
int n_non_view_total = 0;
for (int i = 0; i < result->n_nodes; ++i) {
if (result->nodes[i]->view_src == nullptr) {
n_non_view_total++;
}
}
LLAMA_LOG_INFO("%s: non-view tensors processed: %d/%d\n", __func__, n_non_view, n_non_view_total);
if (n_non_view != n_non_view_total) {
LLAMA_LOG_WARN("%s: ****************************************************************\n", __func__);
LLAMA_LOG_WARN("%s: not all non-view tensors have been processed with a callback\n", __func__);
LLAMA_LOG_WARN("%s: this can indicate an inefficiency in the graph implementation\n", __func__);
LLAMA_LOG_WARN("%s: build with LLAMA_OFFLOAD_DEBUG for more info\n", __func__);
LLAMA_LOG_WARN("%s: ref: https://github.com/ggerganov/llama.cpp/pull/3837\n", __func__);
LLAMA_LOG_WARN("%s: ****************************************************************\n", __func__);
}
}
return result;
}
// decode a batch of tokens by evaluating the transformer
//
// - lctx: llama context
// - batch: batch to evaluate
//
// return 0 on success
// return positive int on warning
// return negative int on error
//
static int llama_decode_internal(
llama_context & lctx,
llama_batch batch) {
const uint32_t n_tokens = batch.n_tokens;
if (n_tokens == 0) {
LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__);
return -1;
}
const auto & model = lctx.model;
const auto & hparams = model.hparams;
const auto & cparams = lctx.cparams;
const auto n_batch = cparams.n_batch;
GGML_ASSERT(n_tokens <= n_batch);
int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
const int64_t t_start_us = ggml_time_us();
#ifdef GGML_USE_MPI
// TODO: needs fix after #3228
GGML_ASSERT(false && "not implemented");
//ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
#endif
GGML_ASSERT(n_threads > 0);
auto & kv_self = lctx.kv_self;
GGML_ASSERT(!!kv_self.ctx);
const int64_t n_embd = hparams.n_embd;
const int64_t n_vocab = hparams.n_vocab;
// helpers for smoother batch API transition
// after deprecating the llama_eval calls, these will be removed
std::vector<llama_pos> pos;
std::vector<int32_t> n_seq_id;
std::vector<llama_seq_id *> seq_id_arr;
std::vector<std::vector<llama_seq_id>> seq_id;
if (batch.pos == nullptr) {
pos.resize(n_tokens);
for (uint32_t i = 0; i < n_tokens; i++) {
pos[i] = batch.all_pos_0 + i*batch.all_pos_1;
}
batch.pos = pos.data();
}
if (batch.seq_id == nullptr) {
n_seq_id.resize(n_tokens);
seq_id.resize(n_tokens);
seq_id_arr.resize(n_tokens);
for (uint32_t i = 0; i < n_tokens; i++) {
n_seq_id[i] = 1;
seq_id[i].resize(1);
seq_id[i][0] = batch.all_seq_id;
seq_id_arr[i] = seq_id[i].data();
}
batch.n_seq_id = n_seq_id.data();
batch.seq_id = seq_id_arr.data();
}
// if we have enough unused cells before the current head ->
// better to start searching from the beginning of the cache, hoping to fill it
if (kv_self.head > kv_self.used + 2*n_tokens) {
kv_self.head = 0;
}
if (!llama_kv_cache_find_slot(kv_self, batch)) {
return 1;
}
// a heuristic, to avoid attending the full cache if it is not yet utilized
// after enough generations, the benefit from this heuristic disappears
// if we start defragmenting the cache, the benefit from this will be more important
kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)));
//kv_self.n = llama_kv_cache_cell_max(kv_self);
//printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);
ggml_allocr_reset(lctx.alloc);
ggml_cgraph * gf = llama_build_graph(lctx, batch);
ggml_allocr_alloc_graph(lctx.alloc, gf);
// the output is always the last tensor in the graph
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
GGML_ASSERT(strcmp(res->name, "result_output") == 0);
// the embeddings could be the second to last tensor, or the third to last tensor
struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
if (strcmp(embeddings->name, "result_norm") != 0) {
embeddings = gf->nodes[gf->n_nodes - 3];
GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0);
}
#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
char * buf_alloc_base = (char *)ggml_backend_buffer_get_base(lctx.buf_alloc);
for (int i = 0; i < gf->n_leafs; i++) {
ggml_tensor * node = gf->leafs[i];
if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) {
ggml_cuda_assign_scratch_offset(node, (char *)node->data - buf_alloc_base);
ggml_cuda_copy_to_device(node);
}
}
for (int i = 0; i < gf->n_nodes; i++) {
ggml_tensor * node = gf->nodes[i];
if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) {
ggml_cuda_assign_scratch_offset(node, (char *)node->data - buf_alloc_base);
}
}
// HACK: ggml-alloc may change the tensor backend when reusing a parent, so force output to be on the CPU here if needed
if (!lctx.embedding.empty()) {
embeddings->backend = GGML_BACKEND_CPU;
}
res->backend = GGML_BACKEND_CPU;
#endif
// LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
// for big prompts, if BLAS is enabled, it is better to use only one thread
// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
// TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well
// we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering
// with the BLAS calls. need a better solution
if (n_tokens >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
n_threads = std::min(4, n_threads);
}
const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 1;
if (ggml_cpu_has_cublas() && fully_offloaded) {
n_threads = 1;
}
#ifdef GGML_USE_MPI
const int64_t n_layer = hparams.n_layer;
ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
#endif
#ifdef GGML_USE_METAL
if (ggml_backend_is_metal(lctx.backend)) {
ggml_backend_metal_set_n_cb(lctx.backend, n_threads);
}
#endif
if (ggml_backend_is_cpu(lctx.backend)) {
ggml_backend_cpu_set_n_threads(lctx.backend, n_threads);
}
ggml_backend_graph_compute(lctx.backend, gf);
#ifdef GGML_USE_MPI
ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
#endif
// update the kv ring buffer
{
if (kv_self.has_shift) {
kv_self.has_shift = false;
for (uint32_t i = 0; i < kv_self.size; ++i) {
kv_self.cells[i].delta = 0;
}
}
kv_self.head += n_tokens;
// Ensure kv cache head points to a valid index.
if (kv_self.head >= kv_self.size) {
kv_self.head = 0;
}
}
#ifdef GGML_PERF
// print timing information per ggml operation (for debugging purposes)
// requires GGML_PERF to be defined
ggml_graph_print(gf);
#endif
// plot the computation graph in dot format (for debugging purposes)
//if (n_past%100 == 0) {
// ggml_graph_dump_dot(gf, NULL, "llama.dot");
//}
// extract logits
// TODO: do not compute and extract logits if only embeddings are needed
// need to update the graphs to skip "result_output"
{
auto & logits_out = lctx.logits;
#ifndef NDEBUG
auto & logits_valid = lctx.logits_valid;
logits_valid.clear();
logits_valid.resize(n_tokens);
logits_out.clear();
#endif
if (batch.logits) {
logits_out.resize(n_vocab * n_tokens);
for (uint32_t i = 0; i < n_tokens; i++) {
if (batch.logits[i] == 0) {
continue;
}
ggml_backend_tensor_get(res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float));
#ifndef NDEBUG
logits_valid[i] = true;
#endif
}
} else if (lctx.logits_all) {
logits_out.resize(n_vocab * n_tokens);
ggml_backend_tensor_get(res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float));
#ifndef NDEBUG
std::fill(logits_valid.begin(), logits_valid.end(), true);
#endif
} else {
logits_out.resize(n_vocab);
ggml_backend_tensor_get(res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float));
#ifndef NDEBUG
logits_valid[0] = true;
#endif
}
}
// extract embeddings
if (!lctx.embedding.empty()) {
auto & embedding_out = lctx.embedding;
embedding_out.resize(n_embd);
ggml_backend_tensor_get(embeddings, embedding_out.data(), (n_embd*(n_tokens - 1))*sizeof(float), n_embd*sizeof(float));
}
// measure the performance only for the single-token evals
if (n_tokens == 1) {
lctx.t_eval_us += ggml_time_us() - t_start_us;
lctx.n_eval++;
}
else if (n_tokens > 1) {
lctx.t_p_eval_us += ggml_time_us() - t_start_us;
lctx.n_p_eval += n_tokens;
}
// get a more accurate load time, upon first eval
// TODO: fix this
if (!lctx.has_evaluated_once) {
lctx.t_load_us = ggml_time_us() - lctx.t_start_us;
lctx.has_evaluated_once = true;
}
return 0;
}
//
// tokenizer
//
static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
return vocab.type;
}
static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL;
}
static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN;
}
static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
}
static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE;
}
static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) {
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED;
}
static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
GGML_ASSERT(llama_is_byte_token(vocab, id));
const auto& token_data = vocab.id_to_token.at(id);
switch (llama_vocab_get_type(vocab)) {
case LLAMA_VOCAB_TYPE_SPM: {
auto buf = token_data.text.substr(3, 2);
return strtol(buf.c_str(), NULL, 16);
}
case LLAMA_VOCAB_TYPE_BPE: {
GGML_ASSERT(false);
return unicode_to_bytes_bpe(token_data.text);
}
default:
GGML_ASSERT(false);
}
}
static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
static const char * hex = "0123456789ABCDEF";
switch (llama_vocab_get_type(vocab)) {
case LLAMA_VOCAB_TYPE_SPM: {
const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
return vocab.token_to_id.at(buf);
}
case LLAMA_VOCAB_TYPE_BPE: {
return vocab.token_to_id.at(bytes_to_unicode_bpe(ch));
}
default:
GGML_ASSERT(false);
}
}
static void llama_escape_whitespace(std::string & text) {
replace_all(text, " ", "\xe2\x96\x81");
}
static void llama_unescape_whitespace(std::string & word) {
replace_all(word, "\xe2\x96\x81", " ");
}
struct llm_symbol {
using index = int;
index prev;
index next;
const char * text;
size_t n;
};
static_assert(std::is_trivially_copyable<llm_symbol>::value, "llm_symbol is not trivially copyable");
// SPM tokenizer
// original implementation:
// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
struct llm_bigram_spm {
struct comparator {
bool operator()(llm_bigram_spm & l, llm_bigram_spm & r) {
return (l.score < r.score) || (l.score == r.score && l.left > r.left);
}
};
using queue_storage = std::vector<llm_bigram_spm>;
using queue = std::priority_queue<llm_bigram_spm, queue_storage, comparator>;
llm_symbol::index left;
llm_symbol::index right;
float score;
size_t size;
};
struct llm_tokenizer_spm {
llm_tokenizer_spm(const llama_vocab & vocab): vocab(vocab) {}
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
// split string into utf8 chars
int index = 0;
size_t offs = 0;
while (offs < text.size()) {
llm_symbol sym;
size_t len = utf8_len(text[offs]);
sym.text = text.c_str() + offs;
sym.n = std::min(len, text.size() - offs);
offs += sym.n;
sym.prev = index - 1;
sym.next = offs == text.size() ? -1 : index + 1;
index++;
symbols.emplace_back(sym);
}
// seed the work queue with all possible 2-character tokens.
for (size_t i = 1; i < symbols.size(); ++i) {
try_add_bigram(i - 1, i);
}
// keep substituting the highest frequency pairs for as long as we can.
while (!work_queue.empty()) {
auto bigram = work_queue.top();
work_queue.pop();
auto & left_sym = symbols[bigram.left];
auto & right_sym = symbols[bigram.right];
// if one of the symbols already got merged, skip it.
if (left_sym.n == 0 || right_sym.n == 0 ||
left_sym.n + right_sym.n != bigram.size) {
continue;
}
// merge the right sym into the left one
left_sym.n += right_sym.n;
right_sym.n = 0;
//LLAMA_LOG_INFO("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
// remove the right sym from the chain
left_sym.next = right_sym.next;
if (right_sym.next >= 0) {
symbols[right_sym.next].prev = bigram.left;
}
// find more substitutions
try_add_bigram(left_sym.prev, bigram.left);
try_add_bigram(bigram.left, left_sym.next);
}
for (int i = 0; i != -1; i = symbols[i].next) {
auto & symbol = symbols[i];
resegment(symbol, output);
}
}
private:
void resegment(llm_symbol & symbol, std::vector<llama_vocab::id> & output) {
auto text = std::string(symbol.text, symbol.n);
auto token = vocab.token_to_id.find(text);
// Do we need to support is_unused?
if (token != vocab.token_to_id.end()) {
output.push_back((*token).second);
return;
}
const auto p = rev_merge.find(text);
if (p == rev_merge.end()) {
// output any symbols that did not form tokens as bytes.
for (int j = 0; j < (int)symbol.n; ++j) {
llama_vocab::id token_id = llama_byte_to_token(vocab, symbol.text[j]);
output.push_back(token_id);
}
return;
}
resegment(symbols[p->second.first], output);
resegment(symbols[p->second.second], output);
}
void try_add_bigram(int left, int right) {
if (left == -1 || right == -1) {
return;
}
const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n);
auto token = vocab.token_to_id.find(text);
if (token == vocab.token_to_id.end()) {
return;
}
if (static_cast<size_t>((*token).second) >= vocab.id_to_token.size()) {
return;
}
const auto & tok_data = vocab.id_to_token[(*token).second];
llm_bigram_spm bigram;
bigram.left = left;
bigram.right = right;
bigram.score = tok_data.score;
bigram.size = text.size();
work_queue.push(bigram);
// Do we need to support is_unused?
rev_merge[text] = std::make_pair(left, right);
}
const llama_vocab & vocab;
std::vector<llm_symbol> symbols;
llm_bigram_spm::queue work_queue;
std::map<std::string, std::pair<int, int>> rev_merge;
};
// BPE tokenizer
// adapted from https://github.com/cmp-nct/ggllm.cpp [MIT License]
// tried to simplify unicode stuff, so most likely does not work 100% correctly!
// TODO: there are a lot of common parts between spm and bpe tokenizers, should be refactored and reused
struct llm_bigram_bpe {
struct comparator {
bool operator()(const llm_bigram_bpe & l, const llm_bigram_bpe & r) const {
return l.rank > r.rank || (l.rank == r.rank && l.left > r.left);
}
};
using queue_storage = std::vector<llm_bigram_bpe>;
using queue = std::priority_queue<llm_bigram_bpe, queue_storage, comparator>;
llm_symbol::index left;
llm_symbol::index right;
std::string text;
int rank;
size_t size;
};
struct llm_tokenizer_bpe {
llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {}
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
int final_prev_index = -1;
auto word_collection = bpe_gpt2_preprocess(text);
symbols_final.clear();
for (auto & word : word_collection) {
work_queue = llm_bigram_bpe::queue();
symbols.clear();
int index = 0;
size_t offset = 0;
while (offset < word.size()) {
llm_symbol sym;
size_t char_len = std::min(word.size() - offset, (size_t) ::utf8_len(word[offset]));
sym.text = word.c_str() + offset;
sym.n = char_len;
offset += sym.n;
sym.prev = index - 1;
sym.next = offset == word.size() ? -1 : index + 1;
index++;
symbols.emplace_back(sym);
}
for (size_t i = 1; i < symbols.size(); ++i) {
add_new_bigram(i - 1, i);
}
// build token(s)
while (!work_queue.empty()) {
auto bigram = work_queue.top();
work_queue.pop();
auto & left_symbol = symbols[bigram.left];
auto & right_symbol = symbols[bigram.right];
if (left_symbol.n == 0 || right_symbol.n == 0) {
continue;
}
std::string left_token = std::string(left_symbol.text, left_symbol.n);
std::string right_token = std::string(right_symbol.text, right_symbol.n);
if (left_token + right_token != bigram.text) {
continue; // Skip this bigram if it's outdated
}
// merge the right sym into the left one
left_symbol.n += right_symbol.n;
right_symbol.n = 0;
// remove the right sym from the chain
left_symbol.next = right_symbol.next;
if (right_symbol.next >= 0) {
symbols[right_symbol.next].prev = bigram.left;
}
add_new_bigram(left_symbol.prev, bigram.left); // left side of current symbol
add_new_bigram(bigram.left, left_symbol.next); // right side of current symbol
}
// add the fnished tokens to the final list keeping correct order for next and prev
for (auto & sym : symbols) {
if (sym.n > 0) {
sym.prev = final_prev_index;
sym.next = -1;
if (final_prev_index != -1) {
symbols_final[final_prev_index].next = symbols_final.size();
}
symbols_final.emplace_back(sym);
final_prev_index = symbols_final.size() - 1;
}
}
}
symbols = symbols_final;
if (!symbols.empty()) {
for (int i = 0; i != -1; i = symbols[i].next) {
auto & symbol = symbols[i];
if (symbol.n == 0) {
continue;
}
const std::string str = std::string(symbol.text, symbol.n);
const auto token = vocab.token_to_id.find(str);
if (token == vocab.token_to_id.end()) {
for (auto j = str.begin(); j != str.end(); ++j) {
std::string byte_str(1, *j);
auto token_multibyte = vocab.token_to_id.find(byte_str);
if (token_multibyte == vocab.token_to_id.end()) {
throw std::runtime_error("ERROR: byte not found in vocab");
}
output.push_back((*token_multibyte).second);
}
} else {
output.push_back((*token).second);
}
}
}
}
private:
void add_new_bigram(int left, int right) {
if (left == -1 || right == -1) {
return;
}
std::string left_token = std::string(symbols[left].text, symbols[left].n);
std::string right_token = std::string(symbols[right].text, symbols[right].n);
int rank_found = -1;
rank_found = vocab.find_bpe_rank(left_token, right_token);
if (rank_found < 0) {
return;
}
llm_bigram_bpe bigram;
bigram.left = left;
bigram.right = right;
bigram.text = left_token + right_token;
bigram.size = left_token.size() + right_token.size();
bigram.rank = rank_found;
work_queue.push(bigram);
}
std::vector<std::string> bpe_gpt2_preprocess(const std::string & text) {
std::vector<std::string> bpe_words;
std::vector<std::string> bpe_encoded_words;
std::string token = "";
// GPT2 system regex: 's|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+
bool collecting_numeric = false;
bool collecting_letter = false;
bool collecting_special = false;
bool collecting_whitespace_lookahead = false;
bool collecting = false;
std::vector<std::string> text_utf;
text_utf.reserve(text.size());
bpe_words.reserve(text.size());
bpe_encoded_words.reserve(text.size());
auto cps = codepoints_from_utf8(text);
for (size_t i = 0; i < cps.size(); ++i)
text_utf.emplace_back(codepoint_to_utf8(cps[i]));
for (int i = 0; i < (int)text_utf.size(); i++) {
const std::string & utf_char = text_utf[i];
bool split_condition = false;
int bytes_remain = text_utf.size() - i;
// forward backward lookups
const std::string & utf_char_next = (i + 1 < (int)text_utf.size()) ? text_utf[i + 1] : "";
const std::string & utf_char_next_next = (i + 2 < (int)text_utf.size()) ? text_utf[i + 2] : "";
// handling contractions
if (!split_condition && bytes_remain >= 2) {
// 's|'t|'m|'d
if (utf_char == "\'" && (utf_char_next == "s" || utf_char_next == "t" || utf_char_next == "m" || utf_char_next == "d")) {
split_condition = true;
}
if (split_condition) {
if (token.size()) {
bpe_words.emplace_back(token); // push previous content as token
}
token = utf_char + utf_char_next;
bpe_words.emplace_back(token);
token = "";
i++;
continue;
}
}
if (!split_condition && bytes_remain >= 3) {
// 're|'ve|'ll
if (utf_char == "\'" && (
(utf_char_next == "r" && utf_char_next_next == "e") ||
(utf_char_next == "v" && utf_char_next_next == "e") ||
(utf_char_next == "l" && utf_char_next_next == "l"))
) {
split_condition = true;
}
if (split_condition) {
// current token + next token can be defined
if (token.size()) {
bpe_words.emplace_back(token); // push previous content as token
}
token = utf_char + utf_char_next + utf_char_next_next;
bpe_words.emplace_back(token); // the contraction
token = "";
i += 2;
continue;
}
}
if (!split_condition && !collecting) {
if (codepoint_type(utf_char) == CODEPOINT_TYPE_LETTER || (!token.size() && utf_char == " " && codepoint_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) {
collecting_letter = true;
collecting = true;
}
else if (codepoint_type(utf_char) == CODEPOINT_TYPE_DIGIT || (!token.size() && utf_char == " " && codepoint_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
collecting_numeric = true;
collecting = true;
}
else if (
((codepoint_type(utf_char) != CODEPOINT_TYPE_LETTER && codepoint_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (codepoint_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) ||
(!token.size() && utf_char == " " && codepoint_type(utf_char_next) != CODEPOINT_TYPE_LETTER && codepoint_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && codepoint_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE)
) {
collecting_special = true;
collecting = true;
}
else if (codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && codepoint_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) {
collecting_whitespace_lookahead = true;
collecting = true;
}
else if (codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) {
split_condition = true;
}
}
else if (!split_condition && collecting) {
if (collecting_letter && codepoint_type(utf_char) != CODEPOINT_TYPE_LETTER) {
split_condition = true;
}
else if (collecting_numeric && codepoint_type(utf_char) != CODEPOINT_TYPE_DIGIT) {
split_condition = true;
}
else if (collecting_special && (codepoint_type(utf_char) == CODEPOINT_TYPE_LETTER || codepoint_type(utf_char) == CODEPOINT_TYPE_DIGIT || codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) {
split_condition = true;
}
else if (collecting_whitespace_lookahead && (codepoint_type(utf_char_next) == CODEPOINT_TYPE_LETTER || codepoint_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
split_condition = true;
}
}
if (utf_char_next == "") {
split_condition = true; // final
token += utf_char;
}
if (split_condition) {
if (token.size()) {
bpe_words.emplace_back(token);
}
token = utf_char;
collecting = false;
collecting_letter = false;
collecting_numeric = false;
collecting_special = false;
collecting_whitespace_lookahead = false;
}
else {
token += utf_char;
}
}
for (std::string & word : bpe_words) {
std::string encoded_token = "";
for (char & c : word) {
encoded_token += bytes_to_unicode_bpe(c);
}
bpe_encoded_words.emplace_back(encoded_token);
}
return bpe_encoded_words;
}
const llama_vocab & vocab;
std::vector<llm_symbol> symbols;
std::vector<llm_symbol> symbols_final;
llm_bigram_bpe::queue work_queue;
};
typedef enum FRAGMENT_BUFFER_VARIANT_TYPE{
FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN,
FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT
} FRAGMENT_BUFFER_VARIANT_TYPE;
struct fragment_buffer_variant{
fragment_buffer_variant(llama_vocab::id _token)
:
type(FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN),
token(_token),
raw_text(_dummy),
offset(0),
length(0){}
fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length)
:
type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT),
token((llama_vocab::id)-1),
raw_text(_raw_text),
offset(_offset),
length(_length){
GGML_ASSERT( _offset >= 0 );
GGML_ASSERT( _length >= 1 );
GGML_ASSERT( offset + length <= raw_text.length() );
}
const FRAGMENT_BUFFER_VARIANT_TYPE type;
const llama_vocab::id token;
const std::string _dummy;
const std::string & raw_text;
const uint64_t offset;
const uint64_t length;
};
// #define PRETOKENIZERDEBUG
static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer)
{
// for each special token
for (const auto & st: vocab.special_tokens_cache) {
const auto & special_token = st.first;
const auto & special_id = st.second;
// for each text fragment
std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
while (it != buffer.end()) {
auto & fragment = (*it);
// if a fragment is text ( not yet processed )
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
auto * raw_text = &(fragment.raw_text);
auto raw_text_base_offset = fragment.offset;
auto raw_text_base_length = fragment.length;
// loop over the text
while (true) {
// find the first occurrence of a given special token in this fragment
// passing offset argument only limit the "search area" but match coordinates
// are still relative to the source full raw_text
auto match = raw_text->find(special_token, raw_text_base_offset);
// no occurrences found, stop processing this fragment for a given special token
if (match == std::string::npos) break;
// check if match is within bounds of offset <-> length
if (match + special_token.length() > raw_text_base_offset + raw_text_base_length) break;
#ifdef PRETOKENIZERDEBUG
fprintf(stderr, "FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
#endif
auto source = std::distance(buffer.begin(), it);
// if match is further than base offset
// then we have some text to the left of it
if (match > raw_text_base_offset) {
// left
const int64_t left_reminder_offset = raw_text_base_offset + 0;
const int64_t left_reminder_length = match - raw_text_base_offset;
buffer.emplace_after(it, (*raw_text), left_reminder_offset, left_reminder_length);
#ifdef PRETOKENIZERDEBUG
fprintf(stderr, "FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
#endif
it++;
}
// special token
buffer.emplace_after(it, special_id);
it++;
// right
if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) {
const int64_t right_reminder_offset = match + special_token.length();
const int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
buffer.emplace_after(it, (*raw_text), right_reminder_offset, right_reminder_length);
#ifdef PRETOKENIZERDEBUG
fprintf(stderr, "FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
#endif
it++;
if (source == 0) {
buffer.erase_after(buffer.before_begin());
} else {
buffer.erase_after(std::next(buffer.begin(), (source-1)));
}
// repeat for the right side
raw_text_base_offset = right_reminder_offset;
raw_text_base_length = right_reminder_length;
#ifdef PRETOKENIZERDEBUG
fprintf(stderr, "RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
#endif
} else {
if (source == 0) {
buffer.erase_after(buffer.before_begin());
} else {
buffer.erase_after(std::next(buffer.begin(), (source-1)));
}
break;
}
}
}
it++;
}
}
}
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special) {
std::vector<llama_vocab::id> output;
// OG tokenizer behavior:
//
// tokenizer.encode('', add_bos=True) returns [1]
// tokenizer.encode('', add_bos=False) returns []
if (bos && vocab.special_bos_id != -1) {
output.push_back(vocab.special_bos_id);
}
if (raw_text.empty()) {
return output;
}
std::forward_list<fragment_buffer_variant> fragment_buffer;
fragment_buffer.emplace_front( raw_text, 0, raw_text.length() );
if (special) tokenizer_st_partition( vocab, fragment_buffer );
switch (vocab.type) {
case LLAMA_VOCAB_TYPE_SPM:
{
for (const auto & fragment: fragment_buffer)
{
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT)
{
// without adding this leading whitespace, we do not get the same results as the original tokenizer
// TODO: It's likely possible to get rid of this string copy entirely
// by modifying llm_tokenizer_x to operate with string offsets like pre-tokenizer
// and passing 'add space prefix' as bool argument
//
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
if (&fragment == &fragment_buffer.front()) {
raw_text = " " + raw_text; // prefix with space if the first token is not special
}
#ifdef PRETOKENIZERDEBUG
fprintf(stderr,"TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif
llm_tokenizer_spm tokenizer(vocab);
llama_escape_whitespace(raw_text);
tokenizer.tokenize(raw_text, output);
}
else // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
{
output.push_back(fragment.token);
}
}
} break;
case LLAMA_VOCAB_TYPE_BPE:
{
for (const auto & fragment: fragment_buffer)
{
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT)
{
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
#ifdef PRETOKENIZERDEBUG
fprintf(stderr,"TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif
llm_tokenizer_bpe tokenizer(vocab);
tokenizer.tokenize(raw_text, output);
}
else // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
{
output.push_back(fragment.token);
}
}
} break;
}
return output;
}
//
// grammar - internal
//
struct llama_partial_utf8 {
uint32_t value; // bit value so far (unshifted)
int n_remain; // num bytes remaining; -1 indicates invalid sequence
};
struct llama_grammar {
const std::vector<std::vector<llama_grammar_element>> rules;
std::vector<std::vector<const llama_grammar_element *>> stacks;
// buffer for partially generated UTF-8 sequence from accepted tokens
llama_partial_utf8 partial_utf8;
};
struct llama_grammar_candidate {
size_t index;
const uint32_t * code_points;
llama_partial_utf8 partial_utf8;
};
// Decodes a UTF-8 string which may end in an incomplete sequence. Adds a terminating 0 for use as
// pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`.
static std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
const std::string & src,
llama_partial_utf8 partial_start) {
static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
const char * pos = src.c_str();
std::vector<uint32_t> code_points;
// common english strings have the same number of codepoints and bytes. `+ 1` for the terminating 0.
code_points.reserve(src.size() + 1);
uint32_t value = partial_start.value;
int n_remain = partial_start.n_remain;
// continue previous decode, if applicable
while (*pos != 0 && n_remain > 0) {
uint8_t next_byte = static_cast<uint8_t>(*pos);
if ((next_byte >> 6) != 2) {
// invalid sequence, abort
code_points.push_back(0);
return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, -1 });
}
value = (value << 6) + (next_byte & 0x3F);
++pos;
--n_remain;
}
if (partial_start.n_remain > 0 && n_remain == 0) {
code_points.push_back(value);
}
// decode any subsequent utf-8 sequences, which may end in an incomplete one
while (*pos != 0) {
uint8_t first_byte = static_cast<uint8_t>(*pos);
uint8_t highbits = first_byte >> 4;
n_remain = lookup[highbits] - 1;
if (n_remain < 0) {
// invalid sequence, abort
code_points.clear();
code_points.push_back(0);
return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, n_remain });
}
uint8_t mask = (1 << (7 - n_remain)) - 1;
value = first_byte & mask;
++pos;
while (*pos != 0 && n_remain > 0) {
value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
++pos;
--n_remain;
}
if (n_remain == 0) {
code_points.push_back(value);
}
}
code_points.push_back(0);
return std::make_pair(std::move(code_points), llama_partial_utf8{ value, n_remain });
}
// returns true iff pos points to the end of one of the definitions of a rule
static bool llama_grammar_is_end_of_sequence(const llama_grammar_element * pos) {
switch (pos->type) {
case LLAMA_GRETYPE_END: return true; // NOLINT
case LLAMA_GRETYPE_ALT: return true; // NOLINT
default: return false;
}
}
// returns true iff chr satisfies the char range at pos (regular or inverse range)
// asserts that pos is pointing to a char range element
static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
const llama_grammar_element * pos,
const uint32_t chr) {
bool found = false;
bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT); // NOLINT
do {
if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
// inclusive range, e.g. [a-z]
found = found || (pos->value <= chr && chr <= pos[1].value);
pos += 2;
} else {
// exact char match, e.g. [a] or "a"
found = found || pos->value == chr;
pos += 1;
}
} while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
return std::make_pair(found == is_positive_char, pos);
}
// returns true iff some continuation of the given partial UTF-8 sequence could satisfy the char
// range at pos (regular or inverse range)
// asserts that pos is pointing to a char range element
static bool llama_grammar_match_partial_char(
const llama_grammar_element * pos,
const llama_partial_utf8 partial_utf8) {
bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);
uint32_t partial_value = partial_utf8.value;
int n_remain = partial_utf8.n_remain;
// invalid sequence or 7-bit char split across 2 bytes (overlong)
if (n_remain < 0 || (n_remain == 1 && partial_value < 2)) {
return false;
}
// range of possible code points this partial UTF-8 sequence could complete to
uint32_t low = partial_value << (n_remain * 6);
uint32_t high = low | ((1 << (n_remain * 6)) - 1);
if (low == 0) {
if (n_remain == 2) {
low = 1 << 11;
} else if (n_remain == 3) {
low = 1 << 16;
}
}
do {
if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
// inclusive range, e.g. [a-z]
if (pos->value <= high && low <= pos[1].value) {
return is_positive_char;
}
pos += 2;
} else {
// exact char match, e.g. [a] or "a"
if (low <= pos->value && pos->value <= high) {
return is_positive_char;
}
pos += 1;
}
} while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
return !is_positive_char;
}
// transforms a grammar pushdown stack into N possible stacks, all ending
// at a character range (terminal element)
static void llama_grammar_advance_stack(
const std::vector<std::vector<llama_grammar_element>> & rules,
const std::vector<const llama_grammar_element *> & stack,
std::vector<std::vector<const llama_grammar_element *>> & new_stacks) {
if (stack.empty()) {
new_stacks.emplace_back(stack);
return;
}
const llama_grammar_element * pos = stack.back();
switch (pos->type) {
case LLAMA_GRETYPE_RULE_REF: {
const size_t rule_id = static_cast<size_t>(pos->value);
const llama_grammar_element * subpos = rules[rule_id].data();
do {
// init new stack without the top (pos)
std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
if (!llama_grammar_is_end_of_sequence(pos + 1)) {
// if this rule ref is followed by another element, add that to stack
new_stack.push_back(pos + 1);
}
if (!llama_grammar_is_end_of_sequence(subpos)) {
// if alternate is nonempty, add to stack
new_stack.push_back(subpos);
}
llama_grammar_advance_stack(rules, new_stack, new_stacks);
while (!llama_grammar_is_end_of_sequence(subpos)) {
// scan to end of alternate def
subpos++;
}
if (subpos->type == LLAMA_GRETYPE_ALT) {
// there's another alternate def of this rule to process
subpos++;
} else {
break;
}
} while (true);
break;
}
case LLAMA_GRETYPE_CHAR:
case LLAMA_GRETYPE_CHAR_NOT:
new_stacks.emplace_back(stack);
break;
default:
// end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range
// (LLAMA_GRETYPE_CHAR_ALT, LLAMA_GRETYPE_CHAR_RNG_UPPER); stack should never be left on
// those
GGML_ASSERT(false);
}
}
// takes a set of possible pushdown stacks on a grammar, which are required to
// be positioned at a character range (see `llama_grammar_advance_stack`), and
// produces the N possible stacks if the given char is accepted at those
// positions
static std::vector<std::vector<const llama_grammar_element *>> llama_grammar_accept(
const std::vector<std::vector<llama_grammar_element>> & rules,
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
const uint32_t chr) {
std::vector<std::vector<const llama_grammar_element *>> new_stacks;
for (const auto & stack : stacks) {
if (stack.empty()) {
continue;
}
auto match = llama_grammar_match_char(stack.back(), chr);
if (match.first) {
const llama_grammar_element * pos = match.second;
// update top of stack to next element, if any
std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
if (!llama_grammar_is_end_of_sequence(pos)) {
new_stack.push_back(pos);
}
llama_grammar_advance_stack(rules, new_stack, new_stacks);
}
}
return new_stacks;
}
static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
const std::vector<std::vector<llama_grammar_element>> & rules,
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
const std::vector<llama_grammar_candidate> & candidates);
static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
const std::vector<std::vector<llama_grammar_element>> & rules,
const std::vector<const llama_grammar_element *> & stack,
const std::vector<llama_grammar_candidate> & candidates) {
std::vector<llama_grammar_candidate> rejects;
if (stack.empty()) {
for (const auto & tok : candidates) {
if (*tok.code_points != 0 || tok.partial_utf8.n_remain != 0) {
rejects.push_back(tok);
}
}
return rejects;
}
const llama_grammar_element * stack_pos = stack.back();
std::vector<llama_grammar_candidate> next_candidates;
for (const auto & tok : candidates) {
if (*tok.code_points == 0) {
// reached end of full codepoints in token, reject iff it ended in a partial sequence
// that cannot satisfy this position in grammar
if (tok.partial_utf8.n_remain != 0 &&
!llama_grammar_match_partial_char(stack_pos, tok.partial_utf8)) {
rejects.push_back(tok);
}
} else if (llama_grammar_match_char(stack_pos, *tok.code_points).first) {
next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8 });
} else {
rejects.push_back(tok);
}
}
const auto * stack_pos_after = llama_grammar_match_char(stack_pos, 0).second;
// update top of stack to next element, if any
std::vector<const llama_grammar_element *> stack_after(stack.begin(), stack.end() - 1);
if (!llama_grammar_is_end_of_sequence(stack_pos_after)) {
stack_after.push_back(stack_pos_after);
}
std::vector<std::vector<const llama_grammar_element *>> next_stacks;
llama_grammar_advance_stack(rules, stack_after, next_stacks);
auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates);
for (const auto & tok : next_rejects) {
rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 });
}
return rejects;
}
static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
const std::vector<std::vector<llama_grammar_element>> & rules,
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
const std::vector<llama_grammar_candidate> & candidates) {
GGML_ASSERT(!stacks.empty()); // REVIEW
if (candidates.empty()) {
return std::vector<llama_grammar_candidate>();
}
auto rejects = llama_grammar_reject_candidates_for_stack(rules, stacks.front(), candidates);
for (size_t i = 1, size = stacks.size(); i < size; ++i) {
rejects = llama_grammar_reject_candidates_for_stack(rules, stacks[i], rejects);
}
return rejects;
}
//
// grammar - external
//
struct llama_grammar * llama_grammar_init(
const llama_grammar_element ** rules,
size_t n_rules,
size_t start_rule_index) {
const llama_grammar_element * pos;
// copy rule definitions into vectors
std::vector<std::vector<llama_grammar_element>> vec_rules(n_rules);
for (size_t i = 0; i < n_rules; i++) {
for (pos = rules[i]; pos->type != LLAMA_GRETYPE_END; pos++) {
vec_rules[i].push_back(*pos);
}
vec_rules[i].push_back({LLAMA_GRETYPE_END, 0});
}
// loop over alternates of start rule to build initial stacks
std::vector<std::vector<const llama_grammar_element *>> stacks;
pos = rules[start_rule_index];
do {
std::vector<const llama_grammar_element *> stack;
if (!llama_grammar_is_end_of_sequence(pos)) {
// if alternate is nonempty, add to stack
stack.push_back(pos);
}
llama_grammar_advance_stack(vec_rules, stack, stacks);
while (!llama_grammar_is_end_of_sequence(pos)) {
// scan to end of alternate def
pos++;
}
if (pos->type == LLAMA_GRETYPE_ALT) {
// there's another alternate def of this rule to process
pos++;
} else {
break;
}
} while (true);
return new llama_grammar{ std::move(vec_rules), std::move(stacks), {} };
}
void llama_grammar_free(struct llama_grammar * grammar) {
delete grammar;
}
struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar) {
llama_grammar * result = new llama_grammar{ grammar->rules, grammar->stacks, grammar->partial_utf8 };
// redirect elements in stacks to point to new rules
for (size_t is = 0; is < result->stacks.size(); is++) {
for (size_t ie = 0; ie < result->stacks[is].size(); ie++) {
for (size_t ir0 = 0; ir0 < grammar->rules.size(); ir0++) {
for (size_t ir1 = 0; ir1 < grammar->rules[ir0].size(); ir1++) {
if (grammar->stacks[is][ie] == &grammar->rules[ir0][ir1]) {
result->stacks[is][ie] = &result->rules[ir0][ir1];
}
}
}
}
}
return result;
}
//
// sampling
//
void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed) {
if (seed == LLAMA_DEFAULT_SEED) {
seed = time(NULL);
}
ctx->rng.seed(seed);
}
void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates) {
GGML_ASSERT(candidates->size > 0);
const int64_t t_start_sample_us = ggml_time_us();
// Sort the logits in descending order
if (!candidates->sorted) {
std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
return a.logit > b.logit;
});
candidates->sorted = true;
}
float max_l = candidates->data[0].logit;
float cum_sum = 0.0f;
for (size_t i = 0; i < candidates->size; ++i) {
float p = expf(candidates->data[i].logit - max_l);
candidates->data[i].p = p;
cum_sum += p;
}
for (size_t i = 0; i < candidates->size; ++i) {
candidates->data[i].p /= cum_sum;
}
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
const int64_t t_start_sample_us = ggml_time_us();
k = std::max(k, (int) min_keep);
k = std::min(k, (int) candidates->size);
// Sort scores in descending order
if (!candidates->sorted) {
auto comp = [](const llama_token_data & a, const llama_token_data & b) {
return a.logit > b.logit;
};
if (k == (int) candidates->size) {
std::sort(candidates->data, candidates->data + candidates->size, comp);
} else {
std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
}
candidates->sorted = true;
}
candidates->size = k;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
if (p >= 1.0f) {
return;
}
llama_sample_softmax(ctx, candidates);
const int64_t t_start_sample_us = ggml_time_us();
// Compute the cumulative probabilities
float cum_sum = 0.0f;
size_t last_idx = candidates->size;
for (size_t i = 0; i < candidates->size; ++i) {
cum_sum += candidates->data[i].p;
// Check if the running sum is at least p or if we have kept at least min_keep tokens
// we set the last index to i+1 to indicate that the current iterate should be included in the set
if (cum_sum >= p && i + 1 >= min_keep) {
last_idx = i + 1;
break;
}
}
// Resize the output vector to keep only the top-p tokens
candidates->size = last_idx;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
if (p <= 0.0f || !candidates->size) {
return;
}
llama_sample_softmax(ctx, candidates);
const int64_t t_start_sample_us = ggml_time_us();
float scale = candidates->data[0].p; // scale by max prob
size_t i = 1; // first token always matches
for (; i < candidates->size; ++i) {
if (candidates->data[i].p < p * scale && i >= min_keep) {
break; // prob too small
}
}
// Resize the output vector to keep only the matching tokens
candidates->size = i;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
if (z >= 1.0f || candidates->size <= 2) {
return;
}
llama_sample_softmax(nullptr, candidates);
const int64_t t_start_sample_us = ggml_time_us();
// Compute the first and second derivatives
std::vector<float> first_derivatives(candidates->size - 1);
std::vector<float> second_derivatives(candidates->size - 2);
for (size_t i = 0; i < first_derivatives.size(); ++i) {
first_derivatives[i] = candidates->data[i].p - candidates->data[i + 1].p;
}
for (size_t i = 0; i < second_derivatives.size(); ++i) {
second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
}
// Calculate absolute value of second derivatives
for (size_t i = 0; i < second_derivatives.size(); ++i) {
second_derivatives[i] = std::abs(second_derivatives[i]);
}
// Normalize the second derivatives
{
const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
if (second_derivatives_sum > 1e-6f) {
for (float & value : second_derivatives) {
value /= second_derivatives_sum;
}
} else {
for (float & value : second_derivatives) {
value = 1.0f / second_derivatives.size();
}
}
}
float cum_sum = 0.0f;
size_t last_idx = candidates->size;
for (size_t i = 0; i < second_derivatives.size(); ++i) {
cum_sum += second_derivatives[i];
// Check if the running sum is greater than z or if we have kept at least min_keep tokens
if (cum_sum > z && i >= min_keep) {
last_idx = i;
break;
}
}
// Resize the output vector to keep only the tokens above the tail location
candidates->size = last_idx;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
// Reference implementation:
// https://github.com/huggingface/transformers/compare/main...cimeister:typical-sampling:typical-pr
if (p >= 1.0f) {
return;
}
// Compute the softmax of logits and calculate entropy
llama_sample_softmax(nullptr, candidates);
const int64_t t_start_sample_us = ggml_time_us();
float entropy = 0.0f;
for (size_t i = 0; i < candidates->size; ++i) {
entropy += -candidates->data[i].p * logf(candidates->data[i].p);
}
// Compute the absolute difference between negative log probability and entropy for each candidate
std::vector<float> shifted_scores;
for (size_t i = 0; i < candidates->size; ++i) {
float shifted_score = fabsf(-logf(candidates->data[i].p) - entropy);
shifted_scores.push_back(shifted_score);
}
// Sort tokens based on the shifted_scores and their corresponding indices
std::vector<size_t> indices(candidates->size);
std::iota(indices.begin(), indices.end(), 0);
std::sort(indices.begin(), indices.end(), [&](size_t a, size_t b) {
return shifted_scores[a] < shifted_scores[b];
});
// Compute the cumulative probabilities
float cum_sum = 0.0f;
size_t last_idx = indices.size();
for (size_t i = 0; i < indices.size(); ++i) {
size_t idx = indices[i];
cum_sum += candidates->data[idx].p;
// Check if the running sum is greater than typical or if we have kept at least min_keep tokens
if (cum_sum > p && i >= min_keep - 1) {
last_idx = i + 1;
break;
}
}
// Resize the output vector to keep only the locally typical tokens
std::vector<llama_token_data> new_candidates;
for (size_t i = 0; i < last_idx; ++i) {
size_t idx = indices[i];
new_candidates.push_back(candidates->data[idx]);
}
// Replace the data in candidates with the new_candidates data
std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
candidates->size = new_candidates.size();
candidates->sorted = false;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
const int64_t t_start_sample_us = ggml_time_us();
for (size_t i = 0; i < candidates_p->size; ++i) {
candidates_p->data[i].logit /= temp;
}
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
llama_sample_temp(ctx, candidates_p, temp);
}
void llama_sample_repetition_penalties(
struct llama_context * ctx,
llama_token_data_array * candidates,
const llama_token * last_tokens,
size_t penalty_last_n,
float penalty_repeat,
float penalty_freq,
float penalty_present) {
if (penalty_last_n == 0 || (penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f)) {
return;
}
const int64_t t_start_sample_us = ggml_time_us();
// Create a frequency map to count occurrences of each token in last_tokens
std::unordered_map<llama_token, int> token_count;
for (size_t i = 0; i < penalty_last_n; ++i) {
token_count[last_tokens[i]]++;
}
// Apply frequency and presence penalties to the candidates
for (size_t i = 0; i < candidates->size; ++i) {
const auto token_iter = token_count.find(candidates->data[i].id);
if (token_iter == token_count.end()) {
continue;
}
const int count = token_iter->second;
// The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
// This is common fix for this problem, which is to multiply by the penalty instead of dividing.
if (candidates->data[i].logit <= 0) {
candidates->data[i].logit *= penalty_repeat;
} else {
candidates->data[i].logit /= penalty_repeat;
}
candidates->data[i].logit -= float(count) * penalty_freq + float(count > 0) * penalty_present;
}
candidates->sorted = false;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
GGML_ASSERT(ctx);
const int64_t t_start_sample_us = ggml_time_us();
bool allow_eos = false;
for (const auto & stack : grammar->stacks) {
if (stack.empty()) {
allow_eos = true;
break;
}
}
const llama_token eos = llama_token_eos(&ctx->model);
std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
candidates_decoded.reserve(candidates->size);
std::vector<llama_grammar_candidate> candidates_grammar;
candidates_grammar.reserve(candidates->size);
for (size_t i = 0; i < candidates->size; ++i) {
const llama_token id = candidates->data[i].id;
const std::string piece = llama_token_to_piece(ctx, id);
if (id == eos) {
if (!allow_eos) {
candidates->data[i].logit = -INFINITY;
}
} else if (piece.empty() || piece[0] == 0) {
candidates->data[i].logit = -INFINITY;
} else {
candidates_decoded.push_back(decode_utf8(piece, grammar->partial_utf8));
candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
}
}
const auto rejects = llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar);
for (const auto & reject : rejects) {
candidates->data[reject.index].logit = -INFINITY;
}
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
static void llama_log_softmax(float * array, size_t size) {
float max_l = *std::max_element(array, array + size);
float sum = 0.f;
for (size_t i = 0; i < size; ++i) {
float p = expf(array[i] - max_l);
sum += p;
array[i] = p;
}
for (size_t i = 0; i < size; ++i) {
array[i] = logf(array[i] / sum);
}
}
void llama_sample_classifier_free_guidance(
struct llama_context * ctx,
llama_token_data_array * candidates,
struct llama_context * guidance_ctx,
float scale) {
int64_t t_start_sample_us = ggml_time_us();
GGML_ASSERT(ctx);
auto n_vocab = llama_n_vocab(llama_get_model(ctx));
GGML_ASSERT(n_vocab == (int)candidates->size);
GGML_ASSERT(!candidates->sorted);
std::vector<float> logits_base;
logits_base.reserve(candidates->size);
for (size_t i = 0; i < candidates->size; ++i) {
logits_base.push_back(candidates->data[i].logit);
}
llama_log_softmax(logits_base.data(), candidates->size);
float* logits_guidance = llama_get_logits(guidance_ctx);
llama_log_softmax(logits_guidance, n_vocab);
for (int i = 0; i < n_vocab; ++i) {
float logit_guidance = logits_guidance[i];
float logit_base = logits_base[i];
candidates->data[i].logit = scale * (logit_base - logit_guidance) + logit_guidance;
}
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
GGML_ASSERT(ctx);
auto N = float(llama_n_vocab(llama_get_model(ctx)));
int64_t t_start_sample_us;
t_start_sample_us = ggml_time_us();
llama_sample_softmax(nullptr, candidates);
// Estimate s_hat using the most probable m tokens
float s_hat = 0.0;
float sum_ti_bi = 0.0;
float sum_ti_sq = 0.0;
for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
float t_i = logf(float(i + 2) / float(i + 1));
float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
sum_ti_bi += t_i * b_i;
sum_ti_sq += t_i * t_i;
}
s_hat = sum_ti_bi / sum_ti_sq;
// Compute k from the estimated s_hat and target surprise value
float epsilon_hat = s_hat - 1;
float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(N, -epsilon_hat)), 1 / s_hat);
// Sample the next word X using top-k sampling
llama_sample_top_k(nullptr, candidates, int(k), 1);
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
llama_token X = llama_sample_token(ctx, candidates);
t_start_sample_us = ggml_time_us();
// Compute error as the difference between observed surprise and target surprise value
size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
return candidate.id == X;
}));
float observed_surprise = -log2f(candidates->data[X_idx].p);
float e = observed_surprise - tau;
// Update mu using the learning rate and error
*mu = *mu - eta * e;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
return X;
}
llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
int64_t t_start_sample_us;
t_start_sample_us = ggml_time_us();
llama_sample_softmax(ctx, candidates);
// Truncate the words with surprise values greater than mu
candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
return -log2f(candidate.p) > *mu;
}));
if (candidates->size == 0) {
candidates->size = 1;
}
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
// Normalize the probabilities of the remaining words
llama_sample_softmax(ctx, candidates);
// Sample the next word X from the remaining words
llama_token X = llama_sample_token(ctx, candidates);
t_start_sample_us = ggml_time_us();
// Compute error as the difference between observed surprise and target surprise value
size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
return candidate.id == X;
}));
float observed_surprise = -log2f(candidates->data[X_idx].p);
float e = observed_surprise - tau;
// Update mu using the learning rate and error
*mu = *mu - eta * e;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
return X;
}
llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates) {
const int64_t t_start_sample_us = ggml_time_us();
// Find max element
auto * max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
return a.logit < b.logit;
});
llama_token result = max_iter->id;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
ctx->n_sample++;
}
return result;
}
llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
GGML_ASSERT(ctx);
const int64_t t_start_sample_us = ggml_time_us();
llama_sample_softmax(nullptr, candidates);
std::vector<float> probs;
probs.reserve(candidates->size);
for (size_t i = 0; i < candidates->size; ++i) {
probs.push_back(candidates->data[i].p);
}
std::discrete_distribution<> dist(probs.begin(), probs.end());
auto & rng = ctx->rng;
int idx = dist(rng);
llama_token result = candidates->data[idx].id;
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
ctx->n_sample++;
return result;
}
void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
const int64_t t_start_sample_us = ggml_time_us();
if (token == llama_token_eos(&ctx->model)) {
for (const auto & stack : grammar->stacks) {
if (stack.empty()) {
return;
}
}
GGML_ASSERT(false);
}
const std::string piece = llama_token_to_piece(ctx, token);
// Note terminating 0 in decoded string
const auto decoded = decode_utf8(piece, grammar->partial_utf8);
const auto & code_points = decoded.first;
for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
}
grammar->partial_utf8 = decoded.second;
GGML_ASSERT(!grammar->stacks.empty());
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
//
// Beam search
//
struct llama_beam {
std::vector<llama_token> tokens;
float p; // Cumulative beam probability (renormalized relative to all beams)
bool eob; // Initialize end-of-beam to false. Callback sets this to true.
// Sort beams by probability. In case of ties, prefer beams at eob.
bool operator<(const llama_beam & rhs) const {
return std::make_pair(p, eob) < std::make_pair(rhs.p, rhs.eob);
}
// Shift off first n tokens and discard them.
void shift_tokens(const size_t n) {
if (n) {
std::copy(tokens.begin() + n, tokens.end(), tokens.begin());
tokens.resize(tokens.size() - n);
}
}
llama_beam_view view() const { return {tokens.data(), tokens.size(), p, eob}; }
};
// A struct for calculating logit-related info.
struct llama_logit_info {
const float * const logits;
const int n_vocab;
const float max_l;
const float normalizer;
struct sum_exp {
float max_l;
float operator()(float sum, float l) const { return sum + std::exp(l - max_l); }
};
llama_logit_info(llama_context * ctx)
: logits(llama_get_logits(ctx))
, n_vocab(llama_n_vocab(llama_get_model(ctx)))
, max_l(*std::max_element(logits, logits + n_vocab))
, normalizer(1.0f / std::accumulate(logits, logits + n_vocab, 0.0f, sum_exp{max_l}))
{ }
llama_token_data get_token_data(const llama_token token_id) const {
constexpr auto p = std::numeric_limits<float>::quiet_NaN(); // never used
return {token_id, logits[token_id], p};
}
// Return top k token_data by logit.
std::vector<llama_token_data> top_k(size_t k) {
std::vector<llama_token_data> min_heap; // min-heap by logit
const llama_token k_min = std::min(static_cast<llama_token>(k), n_vocab);
min_heap.reserve(k_min);
for (llama_token token_id = 0 ; token_id < k_min ; ++token_id) {
min_heap.push_back(get_token_data(token_id));
}
auto comp = [](const llama_token_data & a, const llama_token_data & b) { return a.logit > b.logit; };
std::make_heap(min_heap.begin(), min_heap.end(), comp);
for (llama_token token_id = k_min ; token_id < n_vocab ; ++token_id) {
if (min_heap.front().logit < logits[token_id]) {
std::pop_heap(min_heap.begin(), min_heap.end(), comp);
min_heap.back().id = token_id;
min_heap.back().logit = logits[token_id];
std::push_heap(min_heap.begin(), min_heap.end(), comp);
}
}
return min_heap;
}
float probability_from_logit(float logit) const {
return normalizer * std::exp(logit - max_l);
}
};
struct llama_beam_search_data {
llama_context * ctx;
size_t n_beams;
int n_past;
int n_predict;
std::vector<llama_beam> beams;
std::vector<llama_beam> next_beams;
// Re-calculated on each loop iteration
size_t common_prefix_length;
// Used to communicate to/from callback on beams state.
std::vector<llama_beam_view> beam_views;
llama_beam_search_data(llama_context * ctx, size_t n_beams, int n_past, int n_predict)
: ctx(ctx)
, n_beams(n_beams)
, n_past(n_past)
, n_predict(n_predict)
, beam_views(n_beams) {
beams.reserve(n_beams);
next_beams.reserve(n_beams);
}
// Collapse beams to a single beam given by index.
void collapse_beams(const size_t beam_idx) {
if (0u < beam_idx) {
std::swap(beams[0], beams[beam_idx]);
}
beams.resize(1);
}
// Min-heaps are used to efficiently collect the top-k elements (k=n_beams).
// The repetitive patterns below reflect the 2 stages of heaps:
// * Gather elements until the vector is full, then call std::make_heap() on it.
// * If the heap is full and a new element is found that should be included, pop the
// least element to the back(), replace it with the new, then push it into the heap.
void fill_next_beams_by_top_probabilities(llama_beam & beam) {
// Min-heaps use a greater-than comparator.
const auto comp = [](const llama_beam & a, const llama_beam & b) { return a.p > b.p; };
if (beam.eob) {
// beam is at end-of-sentence, so just copy it to next_beams if its probability is high enough.
if (next_beams.size() < n_beams) {
next_beams.push_back(std::move(beam));
if (next_beams.size() == n_beams) {
std::make_heap(next_beams.begin(), next_beams.end(), comp);
}
} else if (next_beams.front().p < beam.p) {
std::pop_heap(next_beams.begin(), next_beams.end(), comp);
next_beams.back() = std::move(beam);
std::push_heap(next_beams.begin(), next_beams.end(), comp);
}
} else {
// beam is not at end-of-sentence, so branch with next top_k tokens.
if (!beam.tokens.empty()) {
llama_decode(ctx, llama_batch_get_one(beam.tokens.data(), beam.tokens.size(), n_past, 0));
}
llama_logit_info logit_info(ctx);
std::vector<llama_token_data> next_tokens = logit_info.top_k(n_beams);
size_t i=0;
if (next_beams.size() < n_beams) {
for (; next_beams.size() < n_beams ; ++i) {
llama_beam next_beam = beam;
next_beam.tokens.push_back(next_tokens[i].id);
next_beam.p *= logit_info.probability_from_logit(next_tokens[i].logit);
next_beams.push_back(std::move(next_beam));
}
std::make_heap(next_beams.begin(), next_beams.end(), comp);
} else {
for (; next_beams.front().p == 0.0f ; ++i) {
std::pop_heap(next_beams.begin(), next_beams.end(), comp);
next_beams.back() = beam;
next_beams.back().tokens.push_back(next_tokens[i].id);
next_beams.back().p *= logit_info.probability_from_logit(next_tokens[i].logit);
std::push_heap(next_beams.begin(), next_beams.end(), comp);
}
}
for (; i < n_beams ; ++i) {
const float next_p = beam.p * logit_info.probability_from_logit(next_tokens[i].logit);
if (next_beams.front().p < next_p) {
std::pop_heap(next_beams.begin(), next_beams.end(), comp);
next_beams.back() = beam;
next_beams.back().tokens.push_back(next_tokens[i].id);
next_beams.back().p = next_p;
std::push_heap(next_beams.begin(), next_beams.end(), comp);
}
}
}
}
// Find common_prefix_length based on beams.
// Requires beams is not empty.
size_t find_common_prefix_length() {
size_t common_prefix_length = beams[0].tokens.size();
for (size_t i = 1 ; i < beams.size() ; ++i) {
common_prefix_length = std::min(common_prefix_length, beams[i].tokens.size());
for (size_t j = 0 ; j < common_prefix_length ; ++j) {
if (beams[0].tokens[j] != beams[i].tokens[j]) {
common_prefix_length = j;
break;
}
}
}
return common_prefix_length;
}
// Construct beams_state to send back to caller via the callback function.
// Side effect: set common_prefix_length = find_common_prefix_length();
llama_beams_state get_beams_state(const bool last_call) {
for (size_t i = 0 ; i < beams.size() ; ++i) {
beam_views[i] = beams[i].view();
}
common_prefix_length = find_common_prefix_length();
return {beam_views.data(), beams.size(), common_prefix_length, last_call};
}
// Loop:
// * while i < n_predict, AND
// * any of the beams have not yet reached end-of-beam (eob), AND
// * the highest probability beam(s) (plural in case of ties) are not at end-of-sentence
// (since all other beam probabilities can only decrease)
void loop(const llama_beam_search_callback_fn_t callback, void * const callback_data) {
beams.push_back({{}, 1.0f, false}); // Start with one empty beam w/ probability = 1.0 and !eob.
const auto not_eob = [](const llama_beam & beam) { return !beam.eob; };
for (int i = 0 ; i < n_predict && std::any_of(beams.begin(),beams.end(),not_eob) &&
!beams[top_beam_index()].eob ; ++i) {
callback(callback_data, get_beams_state(false)); // Sets common_prefix_length
update_beams_from_beam_views(); // Update values (p,eob) that callback may have changed.
if (common_prefix_length) {
llama_decode(ctx, llama_batch_get_one(beams[0].tokens.data(), common_prefix_length, n_past, 0));
n_past += common_prefix_length;
}
// Zero-out next_beam probabilities to place them last in following min-heap.
std::for_each(next_beams.begin(), next_beams.end(), [](llama_beam & beam) { beam.p = 0.0f; });
for (llama_beam & beam : beams) {
beam.shift_tokens(common_prefix_length);
fill_next_beams_by_top_probabilities(beam);
}
// next_beams become the beams of next/final iteration. Swap them to re-use memory.
beams.swap(next_beams);
renormalize_beam_probabilities(beams);
}
collapse_beams(top_beam_index());
callback(callback_data, get_beams_state(true));
}
// As beams grow, the cumulative probabilities decrease.
// Renormalize them to avoid floating point underflow.
static void renormalize_beam_probabilities(std::vector<llama_beam> & beams) {
const auto sum_p = [](float sum, llama_beam & beam) { return sum + beam.p; };
const float inv_sum = 1.0f / std::accumulate(beams.begin(), beams.end(), 0.0f, sum_p);
std::for_each(beams.begin(), beams.end(), [=](llama_beam & beam) { beam.p *= inv_sum; });
}
// Assumes beams is non-empty. Uses llama_beam::operator<() for ordering.
size_t top_beam_index() {
return std::max_element(beams.begin(), beams.end()) - beams.begin();
}
// Copy (p,eob) for each beam which may have been changed by the callback.
void update_beams_from_beam_views() {
for (size_t i = 0 ; i < beams.size() ; ++i) {
beams[i].p = beam_views[i].p;
beams[i].eob = beam_views[i].eob;
}
}
};
void llama_beam_search(llama_context * ctx,
llama_beam_search_callback_fn_t callback, void * callback_data,
size_t n_beams, int n_past, int n_predict) {
assert(ctx);
const int64_t t_start_sample_us = ggml_time_us();
llama_beam_search_data beam_search_data(ctx, n_beams, n_past, n_predict);
beam_search_data.loop(callback, callback_data);
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
ctx->n_sample++;
}
//
// quantization
//
struct quantize_state_internal {
const llama_model & model;
const llama_model_quantize_params * params;
int n_attention_wv = 0;
int n_feed_forward_w2 = 0;
int i_attention_wv = 0;
int i_feed_forward_w2 = 0;
int n_k_quantized = 0;
int n_fallback = 0;
quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
: model(model)
, params(params)
{}
};
static void llama_convert_tensor_internal(
struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
const size_t nelements, const int nthread
) {
if (output.size() < nelements) {
output.resize(nelements);
}
float * f32_output = (float *) output.data();
ggml_type_traits_t qtype;
if (ggml_is_quantized(tensor->type)) {
qtype = ggml_internal_get_type_traits(tensor->type);
if (qtype.to_float == NULL) {
throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor->type)));
}
} else if (tensor->type != GGML_TYPE_F16) {
throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(tensor->type)));
}
if (nthread < 2) {
if (tensor->type == GGML_TYPE_F16) {
ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor->data, f32_output, nelements);
} else if (ggml_is_quantized(tensor->type)) {
qtype.to_float(tensor->data, f32_output, nelements);
} else {
GGML_ASSERT(false); // unreachable
}
return;
}
size_t block_size = tensor->type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor->type);
size_t block_size_bytes = ggml_type_size(tensor->type);
GGML_ASSERT(nelements % block_size == 0);
size_t nblocks = nelements / block_size;
size_t blocks_per_thread = nblocks / nthread;
size_t spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
size_t in_buff_offs = 0;
size_t out_buff_offs = 0;
for (int tnum = 0; tnum < nthread; tnum++) {
size_t thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
size_t thr_elems = thr_blocks * block_size; // number of elements for this thread
size_t thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
if (typ == GGML_TYPE_F16) {
ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
} else {
qtype.to_float(inbuf, outbuf, nels);
}
};
workers.emplace_back(compute, tensor->type, (uint8_t *) tensor->data + in_buff_offs, f32_output + out_buff_offs, thr_elems);
in_buff_offs += thr_block_bytes;
out_buff_offs += thr_elems;
}
for (auto & w : workers) { w.join(); }
workers.clear();
}
static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
const std::string name = ggml_get_name(tensor);
// TODO: avoid hardcoded tensor names - use the TN_* constants
const llm_arch arch = qs.model.arch;
const auto tn = LLM_TN(arch);
auto use_more_bits = [](int i_layer, int num_layers) -> bool {
return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2;
};
if (name == tn(LLM_TENSOR_OUTPUT, "weight")) {
int nx = tensor->ne[0];
if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
new_type = GGML_TYPE_Q8_0;
}
else if (new_type != GGML_TYPE_Q8_0) {
new_type = GGML_TYPE_Q6_K;
}
} else if (name.find("attn_v.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
(qs.i_attention_wv < qs.n_attention_wv/8 || qs.i_attention_wv >= 7*qs.n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
if (qs.model.type == MODEL_70B) {
// In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
// 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
// nearly negligible increase in model size by quantizing this tensor with more bits:
if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
}
if (qs.model.hparams.n_expert == 8) {
// for the 8-expert model, bumping this to Q8_0 trades just ~128MB
// TODO: explore better strategies
new_type = GGML_TYPE_Q8_0;
}
++qs.i_attention_wv;
} else if (name.find("attn_k.weight") != std::string::npos) {
if (qs.model.hparams.n_expert == 8) {
// for the 8-expert model, bumping this to Q8_0 trades just ~128MB
// TODO: explore better strategies
new_type = GGML_TYPE_Q8_0;
}
} else if (name.find("ffn_down") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
if (qs.i_feed_forward_w2 < qs.n_feed_forward_w2/8) new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
new_type = qs.i_feed_forward_w2 < qs.n_feed_forward_w2/16 ? GGML_TYPE_Q5_K
: arch != LLM_ARCH_FALCON || use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q4_K
: GGML_TYPE_Q3_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
if (arch == LLM_ARCH_FALCON) {
new_type = qs.i_feed_forward_w2 < qs.n_feed_forward_w2/16 ? GGML_TYPE_Q6_K :
use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
} else {
if (use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
}
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && qs.i_feed_forward_w2 < qs.n_feed_forward_w2/8) {
new_type = GGML_TYPE_Q5_K;
}
++qs.i_feed_forward_w2;
} else if (name.find("attn_output.weight") != std::string::npos) {
if (arch != LLM_ARCH_FALCON) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
}
}
else if (name.find("attn_qkv.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
}
// IK: let's remove this, else Q2_K is almost the same as Q3_K_S
//else if (name.find("ffn_gate") != std::string::npos || name.find("ffn_up") != std::string::npos) {
// if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
//}
// This can be used to reduce the size of the Q5_K_S model.
// The associated PPL increase is fully in line with the size reduction
//else {
// if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q4_K;
//}
bool convert_incompatible_tensor = false;
if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) {
int nx = tensor->ne[0];
int ny = tensor->ne[1];
if (nx % QK_K != 0) {
LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type));
convert_incompatible_tensor = true;
} else {
++qs.n_k_quantized;
}
}
if (convert_incompatible_tensor) {
switch (new_type) {
case GGML_TYPE_Q2_K: new_type = GGML_TYPE_Q4_0; break;
case GGML_TYPE_Q3_K: new_type = GGML_TYPE_Q4_1; break;
case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break;
case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break;
default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
}
LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
++qs.n_fallback;
}
return new_type;
}
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
ggml_type quantized_type;
llama_ftype ftype = params->ftype;
switch (params->ftype) {
case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
case LLAMA_FTYPE_MOSTLY_F16: quantized_type = GGML_TYPE_F16; break;
case LLAMA_FTYPE_ALL_F32: quantized_type = GGML_TYPE_F32; break;
// K-quants
case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break;
case LLAMA_FTYPE_MOSTLY_Q2_K_S: quantized_type = GGML_TYPE_Q2_K; break;
case LLAMA_FTYPE_MOSTLY_Q3_K_S:
case LLAMA_FTYPE_MOSTLY_Q3_K_M:
case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break;
case LLAMA_FTYPE_MOSTLY_Q4_K_S:
case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break;
case LLAMA_FTYPE_MOSTLY_Q5_K_S:
case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break;
case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break;
case LLAMA_FTYPE_MOSTLY_IQ2_XXS:quantized_type = GGML_TYPE_IQ2_XXS; break;
case LLAMA_FTYPE_MOSTLY_IQ2_XS :quantized_type = GGML_TYPE_IQ2_XS; break;
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
}
int nthread = params->nthread;
if (nthread <= 0) {
nthread = std::thread::hardware_concurrency();
}
// mmap consistently increases speed Linux, and also increases speed on Windows with
// hot cache. It may cause a slowdown on macOS, possibly related to free memory.
#if defined(__linux__) || defined(_WIN32)
constexpr bool use_mmap = true;
#else
constexpr bool use_mmap = false;
#endif
llama_model_loader ml(fname_inp, use_mmap, NULL);
ml.init_mapping(false); // no prefetching?
llama_model model;
llm_load_arch(ml, model);
llm_load_hparams(ml, model);
struct quantize_state_internal qs(model, params);
if (params->only_copy) {
ftype = model.ftype;
}
const size_t align = GGUF_DEFAULT_ALIGNMENT;
struct gguf_context * ctx_out = gguf_init_empty();
// copy the KV pairs from the input file
gguf_set_kv (ctx_out, ml.ctx_gguf);
gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
gguf_set_val_u32(ctx_out, "general.file_type", ftype);
for (int i = 0; i < ml.n_tensors; ++i) {
struct ggml_tensor * meta = ml.get_tensor_meta(i);
const std::string name = ggml_get_name(meta);
// TODO: avoid hardcoded tensor names - use the TN_* constants
if (name.find("attn_v.weight") != std::string::npos || name.find("attn_qkv.weight") != std::string::npos) {
++qs.n_attention_wv;
}
else if (name.find("ffn_down") != std::string::npos) {
++qs.n_feed_forward_w2;
}
}
if (qs.n_attention_wv != qs.n_feed_forward_w2 || (uint32_t)qs.n_attention_wv != model.hparams.n_layer) {
LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n",
__func__, qs.n_attention_wv, qs.n_feed_forward_w2, model.hparams.n_layer);
}
size_t total_size_org = 0;
size_t total_size_new = 0;
std::vector<int64_t> hist_all(1 << 4, 0);
std::vector<std::thread> workers;
workers.reserve(nthread);
std::mutex mutex;
int idx = 0;
std::vector<no_init<uint8_t>> read_data;
std::vector<no_init<uint8_t>> work;
std::vector<no_init<float>> f32_conv_buf;
// populate the original tensors so we get an initial meta data
for (int i = 0; i < ml.n_tensors; ++i) {
struct ggml_tensor * meta = ml.get_tensor_meta(i);
gguf_add_tensor(ctx_out, meta);
}
std::ofstream fout(fname_out, std::ios::binary);
fout.exceptions(std::ofstream::failbit); // fail fast on write errors
const size_t meta_size = gguf_get_meta_size(ctx_out);
LLAMA_LOG_INFO("%s: meta size = %zu bytes\n", __func__, meta_size);
// placeholder for the meta data
::zeros(fout, meta_size);
for (int i = 0; i < ml.n_tensors; ++i) {
struct ggml_tensor * tensor = ml.get_tensor_meta(i);
const std::string name = ggml_get_name(tensor);
if (!ml.use_mmap) {
if (read_data.size() < ggml_nbytes(tensor)) {
read_data.resize(ggml_nbytes(tensor));
}
tensor->data = read_data.data();
}
ml.load_data_for(tensor);
LLAMA_LOG_INFO("[%4d/%4d] %36s - [%s], type = %6s, ",
++idx, ml.n_tensors,
ggml_get_name(tensor),
llama_format_tensor_shape(tensor).c_str(),
ggml_type_name(tensor->type));
// This used to be a regex, but <regex> has an extreme cost to compile times.
bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
// quantize only 2D tensors
quantize &= (ggml_n_dims(tensor) == 2);
quantize &= params->quantize_output_tensor || name != "output.weight";
quantize &= !params->only_copy;
// do not quantize expert gating tensors
quantize &= name.find("ffn_gate_inp.weight") == std::string::npos;
enum ggml_type new_type;
void * new_data;
size_t new_size;
if (quantize) {
new_type = quantized_type;
if (!params->pure) {
new_type = get_k_quant_type(qs, new_type, tensor, ftype);
}
// If we've decided to quantize to the same type the tensor is already
// in then there's nothing to do.
quantize = tensor->type != new_type;
}
if (!quantize) {
new_type = tensor->type;
new_data = tensor->data;
new_size = ggml_nbytes(tensor);
LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0);
} else {
const size_t nelements = ggml_nelements(tensor);
float * f32_data;
if (tensor->type == GGML_TYPE_F32) {
f32_data = (float *) tensor->data;
} else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
} else {
llama_convert_tensor_internal(tensor, f32_conv_buf, workers, nelements, nthread);
f32_data = (float *) f32_conv_buf.data();
}
LLAMA_LOG_INFO("quantizing to %s .. ", ggml_type_name(new_type));
fflush(stdout);
if (work.size() < nelements * 4) {
work.resize(nelements * 4); // upper bound on size
}
new_data = work.data();
std::array<int64_t, 1 << 4> hist_cur = {};
static const int chunk_size = 32 * 512;
const int nchunk = (nelements + chunk_size - 1)/chunk_size;
const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
if (nthread_use < 2) {
new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nelements, hist_cur.data());
} else {
size_t counter = 0;
new_size = 0;
auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements]() {
std::array<int64_t, 1 << 4> local_hist = {};
size_t local_size = 0;
while (true) {
std::unique_lock<std::mutex> lock(mutex);
size_t first = counter; counter += chunk_size;
if (first >= nelements) {
if (local_size > 0) {
for (int j=0; j<int(local_hist.size()); ++j) {
hist_cur[j] += local_hist[j];
}
new_size += local_size;
}
break;
}
lock.unlock();
size_t last = std::min(nelements, first + chunk_size);
local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first, last - first, local_hist.data());
}
};
for (int it = 0; it < nthread_use - 1; ++it) {
workers.emplace_back(compute);
}
compute();
for (auto & w : workers) { w.join(); }
workers.clear();
}
LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB | hist: ", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
int64_t tot_count = 0;
for (size_t i = 0; i < hist_cur.size(); i++) {
hist_all[i] += hist_cur[i];
tot_count += hist_cur[i];
}
if (tot_count > 0) {
for (size_t i = 0; i < hist_cur.size(); i++) {
LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements));
}
}
LLAMA_LOG_INFO("\n");
}
total_size_org += ggml_nbytes(tensor);
total_size_new += new_size;
// update the gguf meta data as we go
gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size);
// write tensor data + padding
fout.write((const char *) new_data, new_size);
zeros(fout, GGML_PAD(new_size, align) - new_size);
}
// go back to beginning of file and write the updated meta data
{
fout.seekp(0);
std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
gguf_get_meta_data(ctx_out, data.data());
fout.write((const char *) data.data(), data.size());
}
fout.close();
gguf_free(ctx_out);
LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
// print histogram for all tensors
{
int64_t sum_all = 0;
for (size_t i = 0; i < hist_all.size(); i++) {
sum_all += hist_all[i];
}
if (sum_all > 0) {
LLAMA_LOG_INFO("%s: hist: ", __func__);
for (size_t i = 0; i < hist_all.size(); i++) {
LLAMA_LOG_INFO("%5.3f ", hist_all[i] / float(sum_all));
}
LLAMA_LOG_INFO("\n");
}
}
if (qs.n_fallback > 0) {
LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) incompatible with k-quants and required fallback quantization\n",
__func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback);
}
}
static int llama_apply_lora_from_file_internal(
const struct llama_model & model, const char * path_lora, float scale, const char * path_base_model, int n_threads
) {
LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
const int64_t t_start_lora_us = ggml_time_us();
llama_file fin(path_lora, "rb");
// verify magic and version
{
uint32_t magic = fin.read_u32();
if (magic != LLAMA_FILE_MAGIC_GGLA) {
LLAMA_LOG_ERROR("%s: bad file magic\n", __func__);
return 1;
}
uint32_t format_version = fin.read_u32();
if (format_version != 1) {
LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
return 1;
}
}
int32_t lora_r = fin.read_u32();
int32_t lora_alpha = fin.read_u32();
float scaling = scale * (float)lora_alpha / (float)lora_r;
LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
// create a name -> tensor map of the model to accelerate lookups
// find the max tensor size to estimate the required temporary buffer size
size_t max_tensor_size = 0;
std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
for (const auto & kv : model.tensors_by_name) {
model_tensors.insert(kv);
size_t f32_size = ggml_nelements(kv.second) * sizeof(float);
max_tensor_size = std::max(max_tensor_size, f32_size);
}
// create a temporary ggml context to store the lora tensors
// TODO: use ggml-alloc
size_t lora_ctx_size = max_tensor_size * 3;
LLAMA_LOG_INFO("%s: allocating %.f MB for lora temporary buffer\n", __func__, lora_ctx_size / 1024.0 / 1024.0);
std::vector<uint8_t> lora_buf(lora_ctx_size);
struct ggml_init_params params;
params.mem_size = lora_buf.size();
params.mem_buffer = lora_buf.data();
params.no_alloc = false;
using unique_context = std::unique_ptr<ggml_context, decltype(&ggml_free)>;
unique_context lora_ctx(nullptr, ggml_free);
lora_ctx.reset(ggml_init(params));
std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
// load base model
std::unique_ptr<llama_model_loader> ml;
if (path_base_model) {
LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*kv_overrides*/ nullptr));
ml->init_mapping(false); // no prefetching
}
// read tensors and apply
bool warned = false;
int n_tensors = 0;
std::vector<uint8_t> work_buffer;
while (true) {
if (fin.tell() == fin.size) {
// eof
break;
}
int32_t n_dims;
int32_t name_len;
int32_t ftype;
fin.read_raw(&n_dims, sizeof(n_dims));
fin.read_raw(&name_len, sizeof(name_len));
fin.read_raw(&ftype, sizeof(ftype));
if (n_dims != 1 && n_dims != 2) {
LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
return 1;
}
int32_t ne[2] = { 1, 1 };
for (int i = 0; i < n_dims; ++i) {
fin.read_raw(&ne[i], sizeof(ne[i]));
}
std::string name;
{
GGML_ASSERT(name_len <= 1024);
char buf[1024];
fin.read_raw(buf, name_len);
name = std::string(buf, name_len);
}
// check for lora suffix and get the type of tensor
const std::string lora_suffix = ".lora";
size_t pos = name.rfind(lora_suffix);
if (pos == std::string::npos) {
LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
return 1;
}
std::string lora_type = name.substr(pos + lora_suffix.length());
std::string base_name = name;
base_name.erase(pos);
// LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(), base_name.c_str(), lora_type.c_str());
if (model_tensors.find(base_name) == model_tensors.end()) {
LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
return 1;
}
// create ggml tensor
ggml_type wtype;
switch (ftype) {
case 0: wtype = GGML_TYPE_F32; break;
case 1: wtype = GGML_TYPE_F16; break;
default:
{
LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n",
__func__, ftype);
return false;
}
}
ggml_tensor * lora_tensor = ggml_new_tensor_2d(lora_ctx.get(), wtype, ne[0], ne[1]);
ggml_set_name(lora_tensor, name.c_str());
// load tensor data
size_t offset = fin.tell();
size_t tensor_data_size = ggml_nbytes(lora_tensor);
offset = (offset + 31) & -32;
fin.seek(offset, SEEK_SET);
fin.read_raw(lora_tensor->data, tensor_data_size);
lora_tensors[name] = lora_tensor;
// check if we have both A and B tensors and apply
if (lora_tensors.find(base_name + ".loraA") != lora_tensors.end() &&
lora_tensors.find(base_name + ".loraB") != lora_tensors.end()) {
ggml_tensor * dest_t = model_tensors[base_name];
offload_func_t offload_func = ggml_offload_nop;
offload_func_t offload_func_force_inplace = ggml_offload_nop;
#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) {
if (dest_t->type != GGML_TYPE_F16) {
throw std::runtime_error(format(
"%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models. dest_t->type: %d", __func__, dest_t->type));
}
offload_func = ggml_cuda_assign_buffers;
offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace;
}
#endif // GGML_USE_CUBLAS
ggml_tensor * base_t;
if (ml) {
struct gguf_context * ctx_gguf = ml->ctx_gguf;
// load from base model
if (gguf_find_tensor(ctx_gguf, base_name.c_str()) < 0) {
LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
return 1;
}
base_t = ml->get_tensor_meta(base_name.c_str());
ml->load_data_for(base_t);
} else {
base_t = dest_t;
}
if (ggml_is_quantized(base_t->type)) {
if (!warned) {
LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, "
"use a f16 or f32 base model with --lora-base\n", __func__);
warned = true;
}
}
ggml_tensor * loraA = lora_tensors[base_name + ".loraA"];
GGML_ASSERT(loraA->type == GGML_TYPE_F32);
ggml_set_name(loraA, "loraA");
ggml_tensor * loraB = lora_tensors[base_name + ".loraB"];
GGML_ASSERT(loraB->type == GGML_TYPE_F32);
ggml_set_name(loraB, "loraB");
if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
" are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
return 1;
}
// w = w + BA*s
ggml_tensor * BA = ggml_mul_mat(lora_ctx.get(), loraA, loraB);
offload_func(BA);
ggml_set_name(BA, "BA");
if (scaling != 1.0f) {
BA = ggml_scale_inplace(lora_ctx.get(), BA, scaling);
offload_func(BA);
ggml_set_name(BA, "BA_scaled");
}
ggml_tensor * r;
if (base_t == dest_t) {
r = ggml_add_inplace(lora_ctx.get(), dest_t, BA);
offload_func_force_inplace(r);
ggml_set_name(r, "r_add_inplace");
}
else {
r = ggml_add(lora_ctx.get(), base_t, BA);
offload_func(r);
ggml_set_name(r, "r_add");
r = ggml_cpy(lora_ctx.get(), r, dest_t);
offload_func(r);
ggml_set_name(r, "r_cpy");
}
struct ggml_cgraph * gf = ggml_new_graph(lora_ctx.get());
ggml_build_forward_expand(gf, r);
ggml_graph_compute_helper(work_buffer, gf, n_threads);
// the tensors in the adapter must be sorted such that loraA and loraB of the same tensor are next to each other
GGML_ASSERT(lora_tensors.size() == 2);
// we won't need these tensors again, reset the context to save memory
lora_ctx.reset(ggml_init(params));
lora_tensors.clear();
n_tensors++;
if (n_tensors % 4 == 0) {
LLAMA_LOG_INFO(".");
}
}
}
const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);
return 0;
}
//
// interface implementation
//
struct llama_model_params llama_model_default_params() {
struct llama_model_params result = {
/*.n_gpu_layers =*/ 0,
/*.main_gpu =*/ 0,
/*.tensor_split =*/ nullptr,
/*.progress_callback =*/ nullptr,
/*.progress_callback_user_data =*/ nullptr,
/*.kv_overrides =*/ nullptr,
/*.vocab_only =*/ false,
/*.use_mmap =*/ true,
/*.use_mlock =*/ false,
};
#ifdef GGML_USE_METAL
result.n_gpu_layers = 1;
#endif
return result;
}
struct llama_context_params llama_context_default_params() {
struct llama_context_params result = {
/*.seed =*/ LLAMA_DEFAULT_SEED,
/*.n_ctx =*/ 512,
/*.n_batch =*/ 512,
/*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
/*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS,
/*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_UNSPECIFIED,
/*.rope_freq_base =*/ 0.0f,
/*.rope_freq_scale =*/ 0.0f,
/*.yarn_ext_factor =*/ -1.0f,
/*.yarn_attn_factor =*/ 1.0f,
/*.yarn_beta_fast =*/ 32.0f,
/*.yarn_beta_slow =*/ 1.0f,
/*.yarn_orig_ctx =*/ 0,
/*.type_k =*/ GGML_TYPE_F16,
/*.type_v =*/ GGML_TYPE_F16,
/*.mul_mat_q =*/ true,
/*.logits_all =*/ false,
/*.embedding =*/ false,
/*.offload_kqv =*/ true,
};
return result;
}
struct llama_model_quantize_params llama_model_quantize_default_params() {
struct llama_model_quantize_params result = {
/*.nthread =*/ 0,
/*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
/*.allow_requantize =*/ false,
/*.quantize_output_tensor =*/ true,
/*.only_copy =*/ false,
/*.pure =*/ false,
};
return result;
}
int32_t llama_max_devices(void) {
return LLAMA_MAX_DEVICES;
}
bool llama_mmap_supported(void) {
return llama_mmap::SUPPORTED;
}
bool llama_mlock_supported(void) {
return llama_mlock::SUPPORTED;
}
void llama_backend_init(bool numa) {
ggml_time_init();
// needed to initialize f16 tables
{
struct ggml_init_params params = { 0, NULL, false };
struct ggml_context * ctx = ggml_init(params);
ggml_free(ctx);
}
if (numa) {
ggml_numa_init();
}
#ifdef GGML_USE_MPI
ggml_mpi_backend_init();
#endif
}
void llama_backend_free(void) {
#ifdef GGML_USE_MPI
ggml_mpi_backend_free();
#endif
}
int64_t llama_time_us(void) {
return ggml_time_us();
}
struct llama_model * llama_load_model_from_file(
const char * path_model,
struct llama_model_params params) {
ggml_time_init();
llama_model * model = new llama_model;
unsigned cur_percentage = 0;
if (params.progress_callback == NULL) {
params.progress_callback_user_data = &cur_percentage;
params.progress_callback = [](float progress, void * ctx) {
unsigned * cur_percentage_p = (unsigned *) ctx;
unsigned percentage = (unsigned) (100 * progress);
while (percentage > *cur_percentage_p) {
*cur_percentage_p = percentage;
LLAMA_LOG_INFO(".");
if (percentage >= 100) {
LLAMA_LOG_INFO("\n");
}
}
return true;
};
}
int status = llama_model_load(path_model, *model, params);
GGML_ASSERT(status <= 0);
if (status < 0) {
if (status == -1) {
LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
} else if (status == -2) {
LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
}
delete model;
return nullptr;
}
return model;
}
void llama_free_model(struct llama_model * model) {
delete model;
}
struct llama_context * llama_new_context_with_model(
struct llama_model * model,
struct llama_context_params params) {
if (!model) {
return nullptr;
}
llama_context * ctx = new llama_context(*model);
const auto & hparams = model->hparams;
auto & cparams = ctx->cparams;
cparams.n_batch = params.n_batch;
cparams.n_threads = params.n_threads;
cparams.n_threads_batch = params.n_threads_batch;
cparams.yarn_ext_factor = params.yarn_ext_factor;
cparams.yarn_attn_factor = params.yarn_attn_factor;
cparams.yarn_beta_fast = params.yarn_beta_fast;
cparams.yarn_beta_slow = params.yarn_beta_slow;
cparams.mul_mat_q = params.mul_mat_q;
cparams.offload_kqv = params.offload_kqv;
cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx;
cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base;
cparams.rope_freq_scale = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;
cparams.n_yarn_orig_ctx = params.yarn_orig_ctx != 0 ? params.yarn_orig_ctx :
hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx :
hparams.n_ctx_train;
auto rope_scaling_type = params.rope_scaling_type;
if (rope_scaling_type == LLAMA_ROPE_SCALING_UNSPECIFIED) {
rope_scaling_type = hparams.rope_scaling_type_train;
}
if (rope_scaling_type == LLAMA_ROPE_SCALING_NONE) {
cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
}
if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set'
cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f;
}
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx);
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base);
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale);
ctx->rng = std::mt19937(params.seed);
ctx->logits_all = params.logits_all;
const ggml_type type_k = params.type_k;
const ggml_type type_v = params.type_v;
GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0);
GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0);
// reserve memory for context buffers
if (!hparams.vocab_only) {
// initialize backend
#ifdef GGML_USE_METAL
if (model->n_gpu_layers > 0) {
ctx->backend = ggml_backend_metal_init();
if (ctx->backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize Metal backend\n", __func__);
}
}
#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST)
// for testing only
if (model->n_gpu_layers > 0) {
ctx->backend = ggml_backend_cuda_init(0);
if (ctx->backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize CUDA backend\n", __func__);
}
}
#endif
if (ctx->backend == nullptr && ggml_backend_buffer_is_host(model->buf)) {
ctx->backend = ggml_backend_cpu_init();
if (ctx->backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__);
}
}
if (ctx->backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize a backend\n", __func__);
delete ctx;
return nullptr;
}
if (!llama_kv_cache_init(ctx->model.hparams, ctx->kv_self, type_k, type_v,
cparams.n_ctx, model->n_gpu_layers, cparams.offload_kqv)) {
LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
llama_free(ctx);
return nullptr;
}
{
size_t memory_size_k = 0;
size_t memory_size_v = 0;
for (auto & k : ctx->kv_self.k_l) {
memory_size_k += ggml_nbytes(k);
}
for (auto & v : ctx->kv_self.v_l) {
memory_size_v += ggml_nbytes(v);
}
LLAMA_LOG_INFO("%s: KV self size = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
(float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f),
ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
}
// resized during inference
if (params.logits_all) {
ctx->logits.reserve(cparams.n_ctx*hparams.n_vocab);
} else {
ctx->logits.reserve(hparams.n_vocab);
}
if (params.embedding){
ctx->embedding.resize(hparams.n_embd);
}
{
// the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data
ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead());
// create measure allocator
ctx->alloc = ggml_allocr_new_measure_from_backend(ctx->backend);
// build worst-case graph
int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_batch);
int n_past = cparams.n_ctx - n_tokens;
llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0));
// measure memory requirements for the graph
size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf);
LLAMA_LOG_INFO("%s: compute buffer total size = %.2f MiB\n", __func__, (ctx->buf_compute_meta.size() + alloc_size) / 1024.0 / 1024.0);
// create allocator again with exact memory requirements
ggml_allocr_free(ctx->alloc);
ctx->buf_alloc = ggml_backend_alloc_buffer(ctx->backend, alloc_size);
ctx->alloc = ggml_allocr_new_from_buffer(ctx->buf_alloc);
#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
if (model->n_gpu_layers > 0) {
// the CPU buffer adds this padding in case the malloc buffer is not aligned, so we need to do the same for the GPU buffer, since we use the same offsets
ggml_cuda_set_scratch_size(alloc_size + 64);
LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MiB\n", __func__, alloc_size / 1024.0 / 1024.0);
// calculate total VRAM usage
auto add_tensor = [](const ggml_tensor * t, size_t & size) {
if (t->backend == GGML_BACKEND_GPU || t->backend == GGML_BACKEND_GPU_SPLIT) {
size += ggml_nbytes(t);
}
};
size_t model_vram_size = 0;
for (const auto & kv : model->tensors_by_name) {
add_tensor(kv.second, model_vram_size);
}
size_t kv_vram_size = 0;
for (auto & k : ctx->kv_self.k_l) {
add_tensor(k, kv_vram_size);
}
for (auto & v : ctx->kv_self.v_l) {
add_tensor(v, kv_vram_size);
}
size_t ctx_vram_size = alloc_size + kv_vram_size;
size_t total_vram_size = model_vram_size + ctx_vram_size;
LLAMA_LOG_INFO("%s: total VRAM used: %.2f MiB (model: %.2f MiB, context: %.2f MiB)\n", __func__,
total_vram_size / 1024.0 / 1024.0,
model_vram_size / 1024.0 / 1024.0,
ctx_vram_size / 1024.0 / 1024.0);
}
#endif
}
}
#ifdef GGML_USE_MPI
ctx->ctx_mpi = ggml_mpi_init();
if (ggml_mpi_rank(ctx->ctx_mpi) > 0) {
// Enter a blocking eval loop with dummy input, letting rank=0 drive the process
// TODO: needs fix after #3228
GGML_ASSERT(false && "not implemented");
//const std::vector<llama_token> tmp(ctx->model.hparams.n_ctx, llama_token_bos(ctx));
//while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {};
llama_backend_free();
exit(1);
}
#endif
return ctx;
}
void llama_free(struct llama_context * ctx) {
delete ctx;
}
const llama_model * llama_get_model(const struct llama_context * ctx) {
return &ctx->model;
}
uint32_t llama_n_ctx(const struct llama_context * ctx) {
return ctx->cparams.n_ctx;
}
uint32_t llama_n_batch(const struct llama_context * ctx) {
return ctx->cparams.n_batch;
}
enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
return model->vocab.type;
}
int32_t llama_n_vocab(const struct llama_model * model) {
return model->vocab.id_to_token.size();
}
int32_t llama_n_ctx_train(const struct llama_model * model) {
return model->hparams.n_ctx_train;
}
int32_t llama_n_embd(const struct llama_model * model) {
return model->hparams.n_embd;
}
float llama_rope_freq_scale_train(const struct llama_model * model) {
return model->hparams.rope_freq_scale_train;
}
int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
const auto & it = model->gguf_kv.find(key);
if (it == model->gguf_kv.end()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
return snprintf(buf, buf_size, "%s", it->second.c_str());
}
int32_t llama_model_meta_count(const struct llama_model * model) {
return (int)model->gguf_kv.size();
}
int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
if (i < 0 || i >= (int)model->gguf_kv.size()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
auto it = model->gguf_kv.begin();
std::advance(it, i);
return snprintf(buf, buf_size, "%s", it->first.c_str());
}
int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size) {
if (i < 0 || i >= (int)model->gguf_kv.size()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
auto it = model->gguf_kv.begin();
std::advance(it, i);
return snprintf(buf, buf_size, "%s", it->second.c_str());
}
int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
return snprintf(buf, buf_size, "%s %s%s %s",
llama_model_arch_name(model->arch).c_str(),
model->hparams.n_expert > 0 ? (std::to_string(model->hparams.n_expert) + "x").c_str() : "",
llama_model_type_name(model->type),
llama_model_ftype_name(model->ftype).c_str());
}
uint64_t llama_model_size(const struct llama_model * model) {
uint64_t size = 0;
for (const auto & it : model->tensors_by_name) {
size += ggml_nbytes(it.second);
}
return size;
}
uint64_t llama_model_n_params(const struct llama_model * model) {
uint64_t nparams = 0;
for (const auto & it : model->tensors_by_name) {
nparams += ggml_nelements(it.second);
}
return nparams;
}
struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name) {
return ggml_get_tensor(model->ctx, name);
}
uint32_t llama_model_quantize(
const char * fname_inp,
const char * fname_out,
const llama_model_quantize_params * params) {
try {
llama_model_quantize_internal(fname_inp, fname_out, params);
return 0;
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
return 1;
}
}
int32_t llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
try {
return llama_apply_lora_from_file_internal(ctx->model, path_lora, scale, path_base_model, n_threads);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
return 1;
}
}
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) {
try {
return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
return 1;
}
}
struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq) {
struct llama_kv_cache_view result = {
/*.n_cells = */ 0,
/*.n_max_seq = */ n_max_seq,
/*.token_count = */ 0,
/*.used_cells = */ llama_get_kv_cache_used_cells(ctx),
/*.max_contiguous = */ 0,
/*.max_contiguous_idx = */ -1,
/*.cells = */ nullptr,
/*.cells_sequences = */ nullptr,
};
return result;
}
void llama_kv_cache_view_free(struct llama_kv_cache_view * view) {
if (view->cells != nullptr) {
free(view->cells);
view->cells = nullptr;
}
if (view->cells_sequences != nullptr) {
free(view->cells_sequences);
view->cells_sequences = nullptr;
}
}
void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view) {
if (uint32_t(view->n_cells) < ctx->kv_self.size || view->cells == nullptr) {
view->n_cells = int32_t(ctx->kv_self.size);
void * p = realloc(view->cells, sizeof(struct llama_kv_cache_view_cell) * view->n_cells);
GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells");
view->cells = (struct llama_kv_cache_view_cell *)p;
p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_max_seq * view->n_cells);
GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells sequences");
view->cells_sequences = (llama_seq_id *)p;
}
const std::vector<llama_kv_cell> & kv_cells = ctx->kv_self.cells;
llama_kv_cache_view_cell * c_curr = view->cells;
llama_seq_id * cs_curr = view->cells_sequences;
int32_t used_cells = 0;
int32_t token_count = 0;
int32_t curr_contig_idx = -1;
uint32_t max_contig = 0;
int32_t max_contig_idx = -1;
for (int32_t i = 0; i < int32_t(ctx->kv_self.size); i++, c_curr++, cs_curr += view->n_max_seq) {
const size_t curr_size = kv_cells[i].seq_id.size();
token_count += curr_size;
c_curr->pos = kv_cells[i].pos + kv_cells[i].delta;
if (curr_size > 0) {
if (curr_contig_idx >= 0 && uint32_t(i - curr_contig_idx) > max_contig) {
max_contig = i - curr_contig_idx;
max_contig_idx = curr_contig_idx;
}
curr_contig_idx = -1;
} else if (curr_contig_idx < 0) {
curr_contig_idx = i;
}
int seq_idx = 0;
for (const llama_seq_id it : kv_cells[i].seq_id) {
if (seq_idx >= view->n_max_seq) {
break;
}
cs_curr[seq_idx] = it;
seq_idx++;
}
if (seq_idx != 0) {
used_cells++;
}
for (; seq_idx < view->n_max_seq; seq_idx++) {
cs_curr[seq_idx] = -1;
}
}
if (curr_contig_idx >= 0 && kv_cells.size() - curr_contig_idx > max_contig) {
max_contig_idx = curr_contig_idx;
max_contig = kv_cells.size() - curr_contig_idx;
}
view->max_contiguous = max_contig;
view->max_contiguous_idx = max_contig_idx;
view->token_count = token_count;
view->used_cells = used_cells;
if (uint32_t(used_cells) != ctx->kv_self.used) {
LLAMA_LOG_ERROR("%s: used cells mismatch. kv_cache says %d but we calculated %d\n",
__func__, ctx->kv_self.used, used_cells);
}
}
int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx) {
int result = 0;
for (uint32_t i = 0; i < ctx->kv_self.size; i++) {
result += ctx->kv_self.cells[i].seq_id.size();
}
return result;
}
int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx) {
return ctx->kv_self.used;
}
void llama_kv_cache_clear(struct llama_context * ctx) {
llama_kv_cache_clear(ctx->kv_self);
}
void llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1);
}
void llama_kv_cache_seq_cp(struct llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
if (seq_id_src == seq_id_dst) {
return;
}
llama_kv_cache_seq_cp(ctx->kv_self, seq_id_src, seq_id_dst, p0, p1);
}
void llama_kv_cache_seq_keep(struct llama_context * ctx, llama_seq_id seq_id) {
llama_kv_cache_seq_keep(ctx->kv_self, seq_id);
}
void llama_kv_cache_seq_shift(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
if (delta == 0) {
return;
}
llama_kv_cache_seq_shift(ctx->kv_self, seq_id, p0, p1, delta);
}
void llama_kv_cache_seq_div(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
if (d == 1) {
return;
}
llama_kv_cache_seq_div(ctx->kv_self, seq_id, p0, p1, d);
}
// Returns the *maximum* size of the state
size_t llama_get_state_size(const struct llama_context * ctx) {
// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
// for reference, std::mt19937(1337) serializes to 6701 bytes.
const size_t s_rng_size = sizeof(size_t);
const size_t s_rng = LLAMA_MAX_RNG_STATE;
const size_t s_logits_capacity = sizeof(size_t);
const size_t s_logits_size = sizeof(size_t);
const size_t s_logits = ctx->logits.capacity() * sizeof(float);
const size_t s_embedding_size = sizeof(size_t);
const size_t s_embedding = ctx->embedding.size() * sizeof(float);
const size_t s_kv_size = sizeof(size_t);
const size_t s_kv_ntok = sizeof(int);
const size_t s_kv = ggml_backend_buffer_get_size(ctx->kv_self.buf);
const size_t s_total = (
+ s_rng_size
+ s_rng
+ s_logits_capacity
+ s_logits_size
+ s_logits
+ s_embedding_size
+ s_embedding
+ s_kv_size
+ s_kv_ntok
+ s_kv
);
return s_total;
}
// llama_context_data
struct llama_data_context {
virtual void write(const void * src, size_t size) = 0;
virtual size_t get_size_written() = 0;
virtual ~llama_data_context() = default;
};
struct llama_data_buffer_context : llama_data_context {
uint8_t * ptr;
size_t size_written = 0;
llama_data_buffer_context(uint8_t * p) : ptr(p) {}
void write(const void * src, size_t size) override {
memcpy(ptr, src, size);
ptr += size;
size_written += size;
}
size_t get_size_written() override {
return size_written;
}
};
struct llama_data_file_context : llama_data_context {
llama_file * file;
size_t size_written = 0;
llama_data_file_context(llama_file * f) : file(f) {}
void write(const void * src, size_t size) override {
file->write_raw(src, size);
size_written += size;
}
size_t get_size_written() override {
return size_written;
}
};
/** copy state data into either a buffer or file depending on the passed in context
*
* file context:
* llama_file file("/path", "wb");
* llama_data_file_context data_ctx(&file);
* llama_copy_state_data(ctx, &data_ctx);
*
* buffer context:
* std::vector<uint8_t> buf(max_size, 0);
* llama_data_buffer_context data_ctx(&buf.data());
* llama_copy_state_data(ctx, &data_ctx);
*
*/
static void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
// copy rng
{
std::stringstream rng_ss;
rng_ss << ctx->rng;
const size_t rng_size = rng_ss.str().size();
char rng_buf[LLAMA_MAX_RNG_STATE];
memset(&rng_buf[0], 0, LLAMA_MAX_RNG_STATE);
memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
data_ctx->write(&rng_size, sizeof(rng_size));
data_ctx->write(&rng_buf[0], LLAMA_MAX_RNG_STATE);
}
// copy logits
{
const size_t logits_cap = ctx->logits.capacity();
const size_t logits_size = ctx->logits.size();
data_ctx->write(&logits_cap, sizeof(logits_cap));
data_ctx->write(&logits_size, sizeof(logits_size));
if (logits_size) {
data_ctx->write(ctx->logits.data(), logits_size * sizeof(float));
}
// If there is a gap between the size and the capacity, write padding
size_t padding_size = (logits_cap - logits_size) * sizeof(float);
if (padding_size > 0) {
std::vector<uint8_t> padding(padding_size, 0); // Create a buffer filled with zeros
data_ctx->write(padding.data(), padding_size);
}
}
// copy embeddings
{
const size_t embedding_size = ctx->embedding.size();
data_ctx->write(&embedding_size, sizeof(embedding_size));
if (embedding_size) {
data_ctx->write(ctx->embedding.data(), embedding_size * sizeof(float));
}
}
// copy kv cache
{
const auto & kv_self = ctx->kv_self;
const auto & hparams = ctx->model.hparams;
const auto & cparams = ctx->cparams;
const auto n_layer = hparams.n_layer;
const auto n_embd_k_gqa = hparams.n_embd_k_gqa();
const auto n_embd_v_gqa = hparams.n_embd_v_gqa();
const auto n_ctx = cparams.n_ctx;
const size_t kv_buf_size = ggml_backend_buffer_get_size(kv_self.buf);
const uint32_t kv_head = kv_self.head;
const uint32_t kv_size = kv_self.size;
const uint32_t kv_used = kv_self.used;
data_ctx->write(&kv_buf_size, sizeof(kv_buf_size));
data_ctx->write(&kv_head, sizeof(kv_head));
data_ctx->write(&kv_size, sizeof(kv_size));
data_ctx->write(&kv_used, sizeof(kv_used));
if (kv_buf_size) {
const size_t elt_size = ggml_element_size(kv_self.k_l[0]);
ggml_context * cpy_ctx = ggml_init({ 6*n_layer*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true });
ggml_cgraph * gf = ggml_new_graph(cpy_ctx);
std::vector<struct ggml_tensor *> kout2d(n_layer);
std::vector<struct ggml_tensor *> vout2d(n_layer);
for (int il = 0; il < (int) n_layer; ++il) {
kout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd_k_gqa, kv_head);
vout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd_v_gqa);
ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il],
n_embd_k_gqa, kv_head,
elt_size*n_embd_k_gqa, 0);
ggml_tensor * v2d = ggml_view_2d(cpy_ctx, kv_self.v_l[il],
kv_head, n_embd_v_gqa,
elt_size*n_ctx, 0);
ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, k2d, kout2d[il]));
ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, v2d, vout2d[il]));
}
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(cpy_ctx, ctx->backend);
ggml_backend_graph_compute(ctx->backend, gf);
std::vector<uint8_t> tmp_buf;
for (int il = 0; il < (int) n_layer; ++il) {
tmp_buf.resize(ggml_nbytes(kout2d[il]));
ggml_backend_tensor_get(kout2d[il], tmp_buf.data(), 0, tmp_buf.size());
data_ctx->write(tmp_buf.data(), tmp_buf.size());
tmp_buf.resize(ggml_nbytes(vout2d[il]));
ggml_backend_tensor_get(vout2d[il], tmp_buf.data(), 0, tmp_buf.size());
data_ctx->write(tmp_buf.data(), tmp_buf.size());
}
ggml_free(cpy_ctx);
ggml_backend_buffer_free(buf);
}
for (uint32_t i = 0; i < kv_size; ++i) {
const auto & cell = kv_self.cells[i];
const llama_pos pos = cell.pos;
const size_t seq_id_size = cell.seq_id.size();
data_ctx->write(&pos, sizeof(pos));
data_ctx->write(&seq_id_size, sizeof(seq_id_size));
for (auto seq_id : cell.seq_id) {
data_ctx->write(&seq_id, sizeof(seq_id));
}
}
}
}
size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
llama_data_buffer_context data_ctx(dst);
llama_copy_state_data_internal(ctx, &data_ctx);
return data_ctx.get_size_written();
}
// Sets the state reading from the specified source address
size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
uint8_t * inp = src;
// set rng
{
size_t rng_size;
char rng_buf[LLAMA_MAX_RNG_STATE];
memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size);
memcpy(&rng_buf[0], inp, LLAMA_MAX_RNG_STATE); inp += LLAMA_MAX_RNG_STATE;
std::stringstream rng_ss;
rng_ss.str(std::string(&rng_buf[0], rng_size));
rng_ss >> ctx->rng;
GGML_ASSERT(!rng_ss.fail());
}
// set logits
{
size_t logits_cap;
size_t logits_size;
memcpy(&logits_cap, inp, sizeof(logits_cap)); inp += sizeof(logits_cap);
memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);
GGML_ASSERT(ctx->logits.capacity() == logits_cap);
if (logits_size) {
ctx->logits.resize(logits_size);
memcpy(ctx->logits.data(), inp, logits_size * sizeof(float));
}
inp += logits_cap * sizeof(float);
}
// set embeddings
{
size_t embedding_size;
memcpy(&embedding_size, inp, sizeof(embedding_size)); inp += sizeof(embedding_size);
GGML_ASSERT(ctx->embedding.capacity() == embedding_size);
if (embedding_size) {
memcpy(ctx->embedding.data(), inp, embedding_size * sizeof(float));
inp += embedding_size * sizeof(float);
}
}
// set kv cache
{
const auto & kv_self = ctx->kv_self;
const auto & hparams = ctx->model.hparams;
const auto & cparams = ctx->cparams;
const int n_layer = hparams.n_layer;
const int n_embd_k_gqa = hparams.n_embd_k_gqa();
const int n_embd_v_gqa = hparams.n_embd_v_gqa();
const int n_ctx = cparams.n_ctx;
size_t kv_buf_size;
uint32_t kv_head;
uint32_t kv_size;
uint32_t kv_used;
memcpy(&kv_buf_size, inp, sizeof(kv_buf_size)); inp += sizeof(kv_buf_size);
memcpy(&kv_head, inp, sizeof(kv_head)); inp += sizeof(kv_head);
memcpy(&kv_size, inp, sizeof(kv_size)); inp += sizeof(kv_size);
memcpy(&kv_used, inp, sizeof(kv_used)); inp += sizeof(kv_used);
if (kv_buf_size) {
GGML_ASSERT(ggml_backend_buffer_get_size(kv_self.buf) == kv_buf_size);
const size_t elt_size = ggml_element_size(kv_self.k_l[0]);
ggml_context * cpy_ctx = ggml_init({ 6*n_layer*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true });
ggml_cgraph * gf = ggml_new_graph(cpy_ctx);
std::vector<struct ggml_tensor *> kin2d(n_layer);
std::vector<struct ggml_tensor *> vin2d(n_layer);
for (int il = 0; il < n_layer; ++il) {
kin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd_k_gqa, kv_head);
vin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd_v_gqa);
ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il],
n_embd_k_gqa, kv_head,
elt_size*n_embd_k_gqa, 0);
ggml_tensor * v2d = ggml_view_2d(cpy_ctx, kv_self.v_l[il],
kv_head, n_embd_v_gqa,
elt_size*n_ctx, 0);
ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, kin2d[il], k2d));
ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, vin2d[il], v2d));
}
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(cpy_ctx, ctx->backend);
// load data into the tensors
for (int il = 0; il < n_layer; ++il) {
ggml_backend_tensor_set(kin2d[il], inp, 0, ggml_nbytes(kin2d[il]));
inp += ggml_nbytes(kin2d[il]);
ggml_backend_tensor_set(vin2d[il], inp, 0, ggml_nbytes(vin2d[il]));
inp += ggml_nbytes(vin2d[il]);
}
ggml_backend_graph_compute(ctx->backend, gf);
ggml_free(cpy_ctx);
ggml_backend_buffer_free(buf);
}
ctx->kv_self.head = kv_head;
ctx->kv_self.size = kv_size;
ctx->kv_self.used = kv_used;
ctx->kv_self.cells.resize(kv_size);
for (uint32_t i = 0; i < kv_size; ++i) {
llama_pos pos;
size_t seq_id_size;
memcpy(&pos, inp, sizeof(pos)); inp += sizeof(pos);
memcpy(&seq_id_size, inp, sizeof(seq_id_size)); inp += sizeof(seq_id_size);
ctx->kv_self.cells[i].pos = pos;
llama_seq_id seq_id;
for (size_t j = 0; j < seq_id_size; ++j) {
memcpy(&seq_id, inp, sizeof(seq_id)); inp += sizeof(seq_id);
ctx->kv_self.cells[i].seq_id.insert(seq_id);
}
}
}
const size_t nread = inp - src;
const size_t max_size = llama_get_state_size(ctx);
GGML_ASSERT(nread <= max_size);
return nread;
}
static bool llama_load_session_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
llama_file file(path_session, "rb");
// sanity checks
{
const uint32_t magic = file.read_u32();
const uint32_t version = file.read_u32();
if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
LLAMA_LOG_ERROR("%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
return false;
}
llama_hparams session_hparams;
file.read_raw(&session_hparams, sizeof(llama_hparams));
if (session_hparams != ctx->model.hparams) {
LLAMA_LOG_INFO("%s : model hparams didn't match from session file!\n", __func__);
return false;
}
}
// load the prompt
{
const uint32_t n_token_count = file.read_u32();
if (n_token_count > n_token_capacity) {
LLAMA_LOG_ERROR("%s : token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
return false;
}
file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
*n_token_count_out = n_token_count;
}
// restore the context state
{
const size_t n_state_size_cur = file.size - file.tell();
const size_t n_state_size_max = llama_get_state_size(ctx);
if (n_state_size_cur > n_state_size_max) {
LLAMA_LOG_ERROR("%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur);
return false;
}
std::vector<uint8_t> state_data(n_state_size_max);
file.read_raw(state_data.data(), n_state_size_cur);
llama_set_state_data(ctx, state_data.data());
}
return true;
}
bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
try {
return llama_load_session_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("error loading session file: %s\n", err.what());
return false;
}
}
bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
llama_file file(path_session, "wb");
file.write_u32(LLAMA_SESSION_MAGIC);
file.write_u32(LLAMA_SESSION_VERSION);
file.write_raw(&ctx->model.hparams, sizeof(llama_hparams));
// save the prompt
file.write_u32((uint32_t) n_token_count);
file.write_raw(tokens, sizeof(llama_token) * n_token_count);
// save the context state using stream saving
llama_data_file_context data_ctx(&file);
llama_copy_state_data_internal(ctx, &data_ctx);
return true;
}
int llama_eval(
struct llama_context * ctx,
llama_token * tokens,
int32_t n_tokens,
int32_t n_past) {
llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);
const int ret = llama_decode_internal(*ctx, llama_batch_get_one(tokens, n_tokens, n_past, 0));
if (ret < 0) {
LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
}
return ret;
}
int llama_eval_embd(
struct llama_context * ctx,
float * embd,
int32_t n_tokens,
int32_t n_past) {
llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);
llama_batch batch = { n_tokens, nullptr, embd, nullptr, nullptr, nullptr, nullptr, n_past, 1, 0, };
const int ret = llama_decode_internal(*ctx, batch);
if (ret < 0) {
LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
}
return ret;
}
void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch) {
ctx->cparams.n_threads = n_threads;
ctx->cparams.n_threads_batch = n_threads_batch;
}
struct llama_batch llama_batch_get_one(
llama_token * tokens,
int32_t n_tokens,
llama_pos pos_0,
llama_seq_id seq_id) {
return {
/*n_tokens =*/ n_tokens,
/*tokens =*/ tokens,
/*embd =*/ nullptr,
/*pos =*/ nullptr,
/*n_seq_id =*/ nullptr,
/*seq_id =*/ nullptr,
/*logits =*/ nullptr,
/*all_pos_0 =*/ pos_0,
/*all_pos_1 =*/ 1,
/*all_seq_id =*/ seq_id,
};
}
struct llama_batch llama_batch_init(int32_t n_tokens, int32_t embd, int32_t n_seq_max) {
llama_batch batch = { 0, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, };
if (embd) {
batch.embd = (float *) malloc(sizeof(float) * n_tokens * embd);
} else {
batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens);
}
batch.pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens);
batch.n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens);
batch.seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens);
for (int i = 0; i < n_tokens; ++i) {
batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
}
batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens);
return batch;
}
void llama_batch_free(struct llama_batch batch) {
if (batch.token) free(batch.token);
if (batch.embd) free(batch.embd);
if (batch.pos) free(batch.pos);
if (batch.n_seq_id) free(batch.n_seq_id);
if (batch.seq_id) {
for (int i = 0; i < batch.n_tokens; ++i) {
free(batch.seq_id[i]);
}
free(batch.seq_id);
}
if (batch.logits) free(batch.logits);
}
int32_t llama_decode(
struct llama_context * ctx,
struct llama_batch batch) {
const int ret = llama_decode_internal(*ctx, batch);
if (ret < 0) {
LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
}
return ret;
}
float * llama_get_logits(struct llama_context * ctx) {
return ctx->logits.data();
}
float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
assert(ctx->logits_valid.at(i));
return ctx->logits.data() + i*ctx->model.hparams.n_vocab;
}
float * llama_get_embeddings(struct llama_context * ctx) {
return ctx->embedding.data();
}
const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
return model->vocab.id_to_token[token].text.c_str();
}
float llama_token_get_score(const struct llama_model * model, llama_token token) {
return model->vocab.id_to_token[token].score;
}
llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) {
return model->vocab.id_to_token[token].type;
}
llama_token llama_token_bos(const struct llama_model * model) {
return model->vocab.special_bos_id;
}
llama_token llama_token_eos(const struct llama_model * model) {
return model->vocab.special_eos_id;
}
llama_token llama_token_nl(const struct llama_model * model) {
return model->vocab.linefeed_id;
}
int32_t llama_add_bos_token(const struct llama_model * model) {
return model->vocab.special_add_bos;
}
int32_t llama_add_eos_token(const struct llama_model * model) {
return model->vocab.special_add_eos;
}
llama_token llama_token_prefix(const struct llama_model * model) {
return model->vocab.special_prefix_id;
}
llama_token llama_token_middle(const struct llama_model * model) {
return model->vocab.special_middle_id;
}
llama_token llama_token_suffix(const struct llama_model * model) {
return model->vocab.special_suffix_id;
}
llama_token llama_token_eot(const struct llama_model * model) {
return model->vocab.special_eot_id;
}
int32_t llama_tokenize(
const struct llama_model * model,
const char * text,
int32_t text_len,
llama_token * tokens,
int32_t n_max_tokens,
bool add_bos,
bool special) {
auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special);
if (n_max_tokens < (int) res.size()) {
// LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
return -((int) res.size());
}
for (size_t i = 0; i < res.size(); i++) {
tokens[i] = res[i];
}
return res.size();
}
static std::string llama_decode_text(const std::string & text) {
std::string decoded_text;
auto unicode_sequences = codepoints_from_utf8(text);
for (auto& unicode_sequence : unicode_sequences) {
decoded_text += unicode_to_bytes_bpe(codepoint_to_utf8(unicode_sequence));
}
return decoded_text;
}
// does not write null-terminator to buf
int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length) {
if (0 <= token && token < llama_n_vocab(model)) {
switch (llama_vocab_get_type(model->vocab)) {
case LLAMA_VOCAB_TYPE_SPM: {
if (llama_is_normal_token(model->vocab, token)) {
std::string result = model->vocab.id_to_token[token].text;
llama_unescape_whitespace(result);
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
} else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
if (length < 3) {
return -3;
}
memcpy(buf, "\xe2\x96\x85", 3);
return 3;
} else if (llama_is_control_token(model->vocab, token)) {
;
} else if (llama_is_byte_token(model->vocab, token)) {
if (length < 1) {
return -1;
}
buf[0] = llama_token_to_byte(model->vocab, token);
return 1;
} else {
// TODO: for now we accept all unsupported token types,
// suppressing them like CONTROL tokens.
// GGML_ASSERT(false);
}
break;
}
case LLAMA_VOCAB_TYPE_BPE: {
if (llama_is_normal_token(model->vocab, token)) {
std::string result = model->vocab.id_to_token[token].text;
result = llama_decode_text(result);
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
} else if (llama_is_control_token(model->vocab, token)) {
;
} else {
// TODO: for now we accept all unsupported token types,
// suppressing them like CONTROL tokens.
// GGML_ASSERT(false);
}
break;
}
default:
GGML_ASSERT(false);
}
}
return 0;
}
struct llama_timings llama_get_timings(struct llama_context * ctx) {
struct llama_timings result = {
/*.t_start_ms =*/ 1e-3 * ctx->t_start_us,
/*.t_end_ms =*/ 1.00 * ggml_time_ms(),
/*.t_load_ms =*/ 1e-3 * ctx->t_load_us,
/*.t_sample_ms =*/ 1e-3 * ctx->t_sample_us,
/*.t_p_eval_ms =*/ 1e-3 * ctx->t_p_eval_us,
/*.t_eval_ms =*/ 1e-3 * ctx->t_eval_us,
/*.n_sample =*/ std::max(1, ctx->n_sample),
/*.n_p_eval =*/ std::max(1, ctx->n_p_eval),
/*.n_eval =*/ std::max(1, ctx->n_eval),
};
return result;
}
void llama_print_timings(struct llama_context * ctx) {
const llama_timings timings = llama_get_timings(ctx);
LLAMA_LOG_INFO("\n");
LLAMA_LOG_INFO("%s: load time = %10.2f ms\n", __func__, timings.t_load_ms);
LLAMA_LOG_INFO("%s: sample time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, timings.t_sample_ms, timings.n_sample, timings.t_sample_ms / timings.n_sample, 1e3 / timings.t_sample_ms * timings.n_sample);
LLAMA_LOG_INFO("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval);
LLAMA_LOG_INFO("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval);
LLAMA_LOG_INFO("%s: total time = %10.2f ms / %5d tokens\n", __func__, (timings.t_end_ms - timings.t_start_ms), (timings.n_p_eval + timings.n_eval));
}
void llama_reset_timings(struct llama_context * ctx) {
ctx->t_start_us = ggml_time_us();
ctx->t_sample_us = ctx->n_sample = 0;
ctx->t_eval_us = ctx->n_eval = 0;
ctx->t_p_eval_us = ctx->n_p_eval = 0;
}
const char * llama_print_system_info(void) {
static std::string s;
s = "";
s += "AVX = " + std::to_string(ggml_cpu_has_avx()) + " | ";
s += "AVX_VNNI = " + std::to_string(ggml_cpu_has_avx_vnni()) + " | ";
s += "AVX2 = " + std::to_string(ggml_cpu_has_avx2()) + " | ";
s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | ";
s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | ";
s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | ";
s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | ";
s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | ";
s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | ";
s += "F16C = " + std::to_string(ggml_cpu_has_f16c()) + " | ";
s += "FP16_VA = " + std::to_string(ggml_cpu_has_fp16_va()) + " | ";
s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | ";
s += "BLAS = " + std::to_string(ggml_cpu_has_blas()) + " | ";
s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | ";
s += "SSSE3 = " + std::to_string(ggml_cpu_has_ssse3()) + " | ";
s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | ";
return s.c_str();
}
void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) {
fprintf(stream, "\n");
fprintf(stream, "###########\n");
fprintf(stream, "# Timings #\n");
fprintf(stream, "###########\n");
fprintf(stream, "\n");
fprintf(stream, "mst_eval: %.2f # ms / token during generation\n",
1.0e-3 * ctx->t_eval_us / ctx->n_eval);
fprintf(stream, "mst_p_eval: %.2f # ms / token during prompt processing\n",
1.0e-3 * ctx->t_p_eval_us / ctx->n_p_eval);
fprintf(stream, "mst_sample: %.2f # ms / token during sampling\n",
1.0e-3 * ctx->t_sample_us / ctx->n_sample);
fprintf(stream, "n_eval: %d # number of tokens generated (excluding the first one)\n", ctx->n_eval);
fprintf(stream, "n_p_eval: %d # number of tokens processed in batches at the beginning\n", ctx->n_p_eval);
fprintf(stream, "n_sample: %d # number of sampled tokens\n", ctx->n_sample);
fprintf(stream, "t_eval_us: %" PRId64 " # total microseconds spent generating tokens\n", ctx->t_eval_us);
fprintf(stream, "t_load_us: %" PRId64 " # total microseconds spent loading the model\n", ctx->t_load_us);
fprintf(stream, "t_p_eval_us: %" PRId64 " # total microseconds spent prompt processing\n", ctx->t_p_eval_us);
fprintf(stream, "t_sample_us: %" PRId64 " # total microseconds spent sampling\n", ctx->t_sample_us);
fprintf(stream, "ts_eval: %.2f # tokens / second during generation\n",
1.0e6 * ctx->n_eval / ctx->t_eval_us);
fprintf(stream, "ts_p_eval: %.2f # tokens / second during prompt processing\n",
1.0e6 * ctx->n_p_eval / ctx->t_p_eval_us);
fprintf(stream, "ts_sample: %.2f # tokens / second during sampling\n",
1.0e6 * ctx->n_sample / ctx->t_sample_us);
}
// For internal test use
const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
struct llama_context * ctx
) {
return ctx->model.tensors_by_name;
}
void llama_log_set(ggml_log_callback log_callback, void * user_data) {
g_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
g_state.log_callback_user_data = user_data;
#ifdef GGML_USE_METAL
ggml_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
#endif
}
static void llama_log_internal_v(ggml_log_level level, const char * format, va_list args) {
va_list args_copy;
va_copy(args_copy, args);
char buffer[128];
int len = vsnprintf(buffer, 128, format, args);
if (len < 128) {
g_state.log_callback(level, buffer, g_state.log_callback_user_data);
} else {
char* buffer2 = new char[len+1];
vsnprintf(buffer2, len+1, format, args_copy);
buffer2[len] = 0;
g_state.log_callback(level, buffer2, g_state.log_callback_user_data);
delete[] buffer2;
}
va_end(args_copy);
}
static void llama_log_internal(ggml_log_level level, const char * format, ...) {
va_list args;
va_start(args, format);
llama_log_internal_v(level, format, args);
va_end(args);
}
static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
(void) level;
(void) user_data;
fputs(text, stderr);
fflush(stderr);
}
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