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072e0a4d3b
llama.cpp/llama.cpp
0cc4m ba0c7c70ab
Vulkan k-quant mmq and ggml-backend offload functionality (#6155) 
* Fix Vulkan no kv offload incoherence

* Add k-quant mul mat mat shaders

* Rework working buffer allocation, reduces vram use noticeably

Clean up cpu assist code, replaced with ggml-backend offload function

* Default to all dedicated GPUs

* Add fallback for integrated GPUs if no dedicated GPUs are found

* Add debug info which device is allocating memory

* Fix Intel dequant issue

Fix validation issue

* Fix Vulkan GGML_OP_GET_ROWS implementation

* Clean up merge artifacts

* Remove Vulkan warning
2024-03-29 17:29:21 +01:00

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#define LLAMA_API_INTERNAL
#include "llama.h"
#include "unicode.h"
#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#ifdef GGML_USE_CUDA
# include "ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
# include "ggml-opencl.h"
#elif defined(GGML_USE_VULKAN)
# include "ggml-vulkan.h"
#elif defined(GGML_USE_SYCL)
# include "ggml-sycl.h"
#elif defined(GGML_USE_KOMPUTE)
# include "ggml-kompute.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>
#ifndef PATH_MAX
#define PATH_MAX MAX_PATH
#endif
#include <io.h>
#endif
#include <algorithm>
#include <array>
#include <cassert>
#include <cctype>
#include <cfloat>
#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 <locale>
#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;
}
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_GROK,
LLM_ARCH_GPT2,
LLM_ARCH_GPTJ,
LLM_ARCH_GPTNEOX,
LLM_ARCH_MPT,
LLM_ARCH_STARCODER,
LLM_ARCH_PERSIMMON,
LLM_ARCH_REFACT,
LLM_ARCH_BERT,
LLM_ARCH_NOMIC_BERT,
LLM_ARCH_BLOOM,
LLM_ARCH_STABLELM,
LLM_ARCH_QWEN,
LLM_ARCH_QWEN2,
LLM_ARCH_PHI2,
LLM_ARCH_PLAMO,
LLM_ARCH_CODESHELL,
LLM_ARCH_ORION,
LLM_ARCH_INTERNLM2,
LLM_ARCH_MINICPM,
LLM_ARCH_GEMMA,
LLM_ARCH_STARCODER2,
LLM_ARCH_MAMBA,
LLM_ARCH_XVERSE,
LLM_ARCH_COMMAND_R,
LLM_ARCH_UNKNOWN,
};
static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_LLAMA, "llama" },
{ LLM_ARCH_FALCON, "falcon" },
{ LLM_ARCH_GROK, "grok" },
{ 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_BERT, "bert" },
{ LLM_ARCH_NOMIC_BERT, "nomic-bert" },
{ LLM_ARCH_BLOOM, "bloom" },
{ LLM_ARCH_STABLELM, "stablelm" },
{ LLM_ARCH_QWEN, "qwen" },
{ LLM_ARCH_QWEN2, "qwen2" },
{ LLM_ARCH_PHI2, "phi2" },
{ LLM_ARCH_PLAMO, "plamo" },
{ LLM_ARCH_CODESHELL, "codeshell" },
{ LLM_ARCH_ORION, "orion" },
{ LLM_ARCH_INTERNLM2, "internlm2" },
{ LLM_ARCH_MINICPM, "minicpm" },
{ LLM_ARCH_GEMMA, "gemma" },
{ LLM_ARCH_STARCODER2, "starcoder2" },
{ LLM_ARCH_MAMBA, "mamba" },
{ LLM_ARCH_XVERSE, "xverse" },
{ LLM_ARCH_COMMAND_R, "command-r" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
enum llm_kv {
LLM_KV_GENERAL_ARCHITECTURE,
LLM_KV_GENERAL_QUANTIZATION_VERSION,
LLM_KV_GENERAL_ALIGNMENT,
LLM_KV_GENERAL_NAME,
LLM_KV_GENERAL_AUTHOR,
LLM_KV_GENERAL_URL,
LLM_KV_GENERAL_DESCRIPTION,
LLM_KV_GENERAL_LICENSE,
LLM_KV_GENERAL_SOURCE_URL,
LLM_KV_GENERAL_SOURCE_HF_REPO,
LLM_KV_VOCAB_SIZE,
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_POOLING_TYPE,
LLM_KV_LOGIT_SCALE,
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_ATTENTION_CAUSAL,
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_SPLIT_NO,
LLM_KV_SPLIT_COUNT,
LLM_KV_SPLIT_TENSORS_COUNT,
LLM_KV_SSM_INNER_SIZE,
LLM_KV_SSM_CONV_KERNEL,
LLM_KV_SSM_STATE_SIZE,
LLM_KV_SSM_TIME_STEP_RANK,
LLM_KV_TOKENIZER_MODEL,
LLM_KV_TOKENIZER_LIST,
LLM_KV_TOKENIZER_TOKEN_TYPE,
LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,
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_ADD_PREFIX,
LLM_KV_TOKENIZER_HF_JSON,
LLM_KV_TOKENIZER_RWKV,
};
static const std::map<llm_kv, const char *> 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_VOCAB_SIZE, "%s.vocab_size" },
{ 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_POOLING_TYPE , "%s.pooling_type" },
{ LLM_KV_LOGIT_SCALE, "%s.logit_scale" },
{ 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_ATTENTION_CAUSAL, "%s.attention.causal" },
{ 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_SPLIT_NO, "split.no" },
{ LLM_KV_SPLIT_COUNT, "split.count" },
{ LLM_KV_SPLIT_TENSORS_COUNT, "split.tensors.count" },
{ LLM_KV_SSM_CONV_KERNEL, "%s.ssm.conv_kernel" },
{ LLM_KV_SSM_INNER_SIZE, "%s.ssm.inner_size" },
{ LLM_KV_SSM_STATE_SIZE, "%s.ssm.state_size" },
{ LLM_KV_SSM_TIME_STEP_RANK, "%s.ssm.time_step_rank" },
{ 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_TOKEN_TYPE_COUNT, "tokenizer.ggml.token_type_count" },
{ 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_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" },
{ 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.at(kv), LLM_ARCH_NAMES.at(arch));
}
};
enum llm_tensor {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_TOKEN_EMBD_NORM,
LLM_TENSOR_TOKEN_TYPES,
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_OUT_NORM,
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,
LLM_TENSOR_LAYER_OUT_NORM,
LLM_TENSOR_SSM_IN,
LLM_TENSOR_SSM_CONV1D,
LLM_TENSOR_SSM_X,
LLM_TENSOR_SSM_DT,
LLM_TENSOR_SSM_A,
LLM_TENSOR_SSM_D,
LLM_TENSOR_SSM_OUT,
};
static const 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_GROK,
{
{ 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_EXP, "blk.%d.ffn_gate.%d" },
{ LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
{ LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
},
},
{
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_BERT,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
{ LLM_TENSOR_TOKEN_TYPES, "token_types" },
{ LLM_TENSOR_POS_EMBD, "position_embd" },
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_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_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_NOMIC_BERT,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
{ LLM_TENSOR_TOKEN_TYPES, "token_types" },
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_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_QWEN2,
{
{ 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_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_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_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_CODESHELL,
{
{ 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_QKV, "blk.%d.attn_qkv" },
{ 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_ORION,
{
{ 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_INTERNLM2,
{
{ 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_MINICPM,
{
{ 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_GEMMA,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ 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_STARCODER2,
{
{ 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_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_MAMBA,
{
{ 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_SSM_IN, "blk.%d.ssm_in" },
{ LLM_TENSOR_SSM_CONV1D, "blk.%d.ssm_conv1d" },
{ LLM_TENSOR_SSM_X, "blk.%d.ssm_x" },
{ LLM_TENSOR_SSM_DT, "blk.%d.ssm_dt" },
{ LLM_TENSOR_SSM_A, "blk.%d.ssm_a" },
{ LLM_TENSOR_SSM_D, "blk.%d.ssm_d" },
{ LLM_TENSOR_SSM_OUT, "blk.%d.ssm_out" },
},
},
{
LLM_ARCH_XVERSE,
{
{ 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_COMMAND_R,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ 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_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 {
if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
return "__missing__";
}
return LLM_TENSOR_NAMES.at(arch).at(tensor);
}
std::string operator()(llm_tensor tensor, const std::string & suffix) const {
if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
return "__missing__";
}
return LLM_TENSOR_NAMES.at(arch).at(tensor) + "." + suffix;
}
std::string operator()(llm_tensor tensor, int bid) const {
if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
return "__missing__";
}
return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid);
}
std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
return "__missing__";
}
return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid) + "." + suffix;
}
std::string operator()(llm_tensor tensor, const std::string & suffix, int bid, int xid) const {
if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
return "__missing__";
}
return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid, xid) + "." + suffix;
}
};
//
// gguf helpers
//
static const std::map<llama_rope_scaling_type, const char *> LLAMA_ROPE_SCALING_TYPES = {
{ LLAMA_ROPE_SCALING_TYPE_NONE, "none" },
{ LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" },
{ LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" },
};
static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) {
for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
if (kv.second == name) {
return (llama_rope_scaling_type) kv.first;
}
}
return LLAMA_ROPE_SCALING_TYPE_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);
}
}
//
// 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 = ggml_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);
}
}
};
using llama_files = std::vector<std::unique_ptr<llama_file>>;
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) {
#if _WIN32_WINNT >= 0x602
// 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());
}
}
#else
throw std::runtime_error("PrefetchVirtualMemory unavailable");
#endif
}
}
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
};
using llama_mmaps = std::vector<std::unique_ptr<llama_mmap>>;
// 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_MEMLOCK (ulimit -l).\n"
#else
#define MLOCK_SUGGESTION \
"Try increasing RLIMIT_MEMLOCK ('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;
}
LLAMA_LOG_WARN("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)) {
LLAMA_LOG_WARN("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) {
LLAMA_LOG_WARN("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)) {
LLAMA_LOG_WARN("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)) {
LLAMA_LOG_WARN("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)) {
LLAMA_LOG_WARN("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 {
LLAMA_LOG_WARN("warning: mlock not supported on this system\n");
return false;
}
static void raw_unlock(const void * addr, size_t len) {}
#endif
};
using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
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_cpu(bool host_buffer) {
ggml_backend_buffer_type_t buft = nullptr;
#if defined(GGML_USE_CUDA)
// host buffers should only be used when data is expected to be copied to/from the GPU
if (host_buffer) {
buft = ggml_backend_cuda_host_buffer_type();
}
#elif defined(GGML_USE_SYCL)
if (host_buffer) {
buft = ggml_backend_sycl_host_buffer_type();
}
#elif defined(GGML_USE_CPU_HBM)
buft = ggml_backend_cpu_hbm_buffer_type();
#elif defined(GGML_USE_VULKAN)
if (host_buffer) {
buft = ggml_backend_vk_host_buffer_type();
}
#endif
if (buft == nullptr) {
buft = ggml_backend_cpu_buffer_type();
}
return buft;
GGML_UNUSED(host_buffer);
}
static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) {
ggml_backend_buffer_type_t buft = nullptr;
#ifdef GGML_USE_METAL
buft = ggml_backend_metal_buffer_type();
#elif defined(GGML_USE_CUDA)
buft = ggml_backend_cuda_buffer_type(gpu);
#elif defined(GGML_USE_VULKAN)
buft = ggml_backend_vk_buffer_type(gpu);
#elif defined(GGML_USE_SYCL)
buft = ggml_backend_sycl_buffer_type(gpu);
#elif defined(GGML_USE_CLBLAST)
buft = ggml_backend_opencl_buffer_type();
#elif defined(GGML_USE_KOMPUTE)
buft = ggml_backend_kompute_buffer_type(gpu);
if (buft == nullptr) {
LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu);
}
#endif
if (buft == nullptr) {
buft = llama_default_buffer_type_cpu(true);
}
return buft;
GGML_UNUSED(gpu);
}
static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_gpu, const float * tensor_split) {
ggml_backend_buffer_type_t buft = nullptr;
#ifdef GGML_USE_CUDA
if (ggml_backend_cuda_get_device_count() > 1) {
buft = ggml_backend_cuda_split_buffer_type(tensor_split);
}
#endif
#ifdef GGML_USE_SYCL
if (ggml_backend_sycl_get_device_count() > 1) {
buft = ggml_backend_sycl_split_buffer_type(tensor_split);
}
#endif
if (buft == nullptr) {
buft = llama_default_buffer_type_offload(fallback_gpu);
}
return buft;
GGML_UNUSED(tensor_split);
}
static size_t llama_get_device_count() {
#if defined(GGML_USE_CUDA)
return ggml_backend_cuda_get_device_count();
#elif defined(GGML_USE_SYCL)
return ggml_backend_sycl_get_device_count();
#elif defined(GGML_USE_VULKAN)
return ggml_backend_vk_get_device_count();
#else
return 1;
#endif
}
static size_t llama_get_device_memory(int device) {
#if defined(GGML_USE_CUDA)
size_t total;
size_t free;
ggml_backend_cuda_get_device_memory(device, &total, &free);
return free;
#elif defined(GGML_USE_SYCL)
size_t total;
size_t free;
ggml_backend_sycl_get_device_memory(device, &total, &free);
return free;
#elif defined(GGML_USE_VULKAN)
size_t total;
size_t free;
ggml_backend_vk_get_device_memory(device, &total, &free);
return free;
#else
return 1;
GGML_UNUSED(device);
#endif
}
//
// globals
//
struct llama_state {
llama_state() {
#ifdef GGML_USE_METAL
ggml_backend_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_17M,
MODEL_22M,
MODEL_33M,
MODEL_109M,
MODEL_137M,
MODEL_335M,
MODEL_0_5B,
MODEL_1B,
MODEL_2B,
MODEL_3B,
MODEL_4B,
MODEL_7B,
MODEL_8B,
MODEL_13B,
MODEL_14B,
MODEL_15B,
MODEL_20B,
MODEL_30B,
MODEL_34B,
MODEL_35B,
MODEL_40B,
MODEL_65B,
MODEL_70B,
MODEL_314B,
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;
bool rope_finetuned;
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;
uint32_t n_vocab_type = 0; // for BERT-style token types
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;
// for State Space Models
uint32_t ssm_d_conv = 0;
uint32_t ssm_d_inner = 0;
uint32_t ssm_d_state = 0;
uint32_t ssm_dt_rank = 0;
float f_clamp_kqv = 0.0f;
float f_max_alibi_bias = 0.0f;
float f_logit_scale = 0.0f;
bool causal_attn = true;
bool need_kq_pos = false;
enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE;
enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE;
enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
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;
if (this->ssm_d_conv != other.ssm_d_conv) return true;
if (this->ssm_d_inner != other.ssm_d_inner) return true;
if (this->ssm_d_state != other.ssm_d_state) return true;
if (this->ssm_dt_rank != other.ssm_dt_rank) 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 {
if (n_head_kv == 0) {
return 0;
}
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;
}
uint32_t n_embd_k_s() const { // dimension of the rolling state embeddings
// corresponds to Mamba's conv_states size
// TODO: maybe support other convolution strides than 1
// NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
}
uint32_t n_embd_v_s() const { // dimension of the recurrent state embeddings
// corresponds to Mamba's ssm_states size
return ssm_d_state * ssm_d_inner;
}
};
struct llama_cparams {
uint32_t n_ctx; // context size used during inference
uint32_t n_batch;
uint32_t n_ubatch;
uint32_t n_seq_max;
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;
float defrag_thold;
bool embeddings;
bool causal_attn;
bool offload_kqv;
enum llama_pooling_type pooling_type;
ggml_backend_sched_eval_callback cb_eval;
void * cb_eval_user_data;
};
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;
struct ggml_tensor * attn_out_norm;
struct ggml_tensor * attn_out_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;
struct ggml_tensor * layer_out_norm;
struct ggml_tensor * layer_out_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;
// mamba proj
struct ggml_tensor * ssm_in;
struct ggml_tensor * ssm_x;
struct ggml_tensor * ssm_dt;
struct ggml_tensor * ssm_out;
// mamba
struct ggml_tensor * ssm_conv1d;
struct ggml_tensor * ssm_a;
struct ggml_tensor * ssm_d;
// mamba bias
struct ggml_tensor * ssm_conv1d_b;
struct ggml_tensor * ssm_dt_b;
};
struct llama_kv_cell {
llama_pos pos = -1;
llama_pos delta = 0;
int32_t src = 0; // used by recurrent state models to copy states
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();
}
bool is_empty() const {
return seq_id.empty();
}
bool is_same_seq(const llama_kv_cell & other) const {
return seq_id == other.seq_id;
}
};
// ring-buffer of cached KV data
struct llama_kv_cache {
bool has_shift = false;
bool do_defrag = false;
bool do_copy = false;
// with recurrent state models, a cell can hold the state for more than one past token
bool recurrent = 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;
ggml_type type_k = GGML_TYPE_F16;
ggml_type type_v = GGML_TYPE_F16;
std::vector<llama_kv_cell> cells;
std::vector<struct ggml_tensor *> k_l; // per layer
std::vector<struct ggml_tensor *> v_l;
std::vector<struct ggml_context *> ctxs;
std::vector<ggml_backend_buffer_t> bufs;
size_t total_size() const {
size_t size = 0;
for (ggml_backend_buffer_t buf : bufs) {
size += ggml_backend_buffer_get_size(buf);
}
return size;
}
~llama_kv_cache() {
for (struct ggml_context * ctx : ctxs) {
ggml_free(ctx);
}
for (ggml_backend_buffer_t buf : bufs) {
ggml_backend_buffer_free(buf);
}
}
};
struct llama_control_vector {
std::vector<struct ggml_tensor *> tensors; // per layer
std::vector<struct ggml_context *> ctxs;
std::vector<ggml_backend_buffer_t> bufs;
int32_t layer_start = -1;
int32_t layer_end = -1;
ggml_tensor * tensor_for(int il) const {
if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
return nullptr;
}
return tensors[il];
}
~llama_control_vector() {
for (struct ggml_context * ctx : ctxs) {
ggml_free(ctx);
}
for (ggml_backend_buffer_t buf : bufs) {
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;
bool add_space_prefix = true;
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 * type_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;
llama_split_mode split_mode;
int main_gpu;
int n_gpu_layers;
// gguf metadata
std::unordered_map<std::string, std::string> gguf_kv;
// layer -> buffer type mapping
struct layer_buft {
layer_buft() : buft_matrix(nullptr), buft(nullptr) {}
layer_buft(ggml_backend_buffer_type_t matrix) : buft_matrix(matrix), buft(matrix) {}
layer_buft(ggml_backend_buffer_type_t matrix, ggml_backend_buffer_type_t other) : buft_matrix(matrix), buft(other) {}
ggml_backend_buffer_type_t buft_matrix; // matrices only - used by split buffers and backends that support only matrix multiplication
ggml_backend_buffer_type_t buft; // everything else
};
layer_buft buft_input;
layer_buft buft_output;
std::vector<layer_buft> buft_layer;
// contexts where the model tensors metadata is stored
std::vector<struct ggml_context *> ctxs;
// the model memory buffers for the tensor data
std::vector<ggml_backend_buffer_t> bufs;
// model memory mapped files
llama_mmaps mappings;
// objects representing data potentially being locked in memory
llama_mlocks mlock_bufs;
llama_mlocks mlock_mmaps;
// 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() {
for (struct ggml_context * ctx : ctxs) {
ggml_free(ctx);
}
for (ggml_backend_buffer_t buf : bufs) {
#ifdef GGML_USE_CUDA
if (ggml_backend_buffer_get_type(buf) == ggml_backend_cpu_buffer_type()) {
ggml_backend_cuda_unregister_host_buffer(ggml_backend_buffer_get_base(buf));
}
#endif
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_backend_sched_free(sched);
for (ggml_backend_t backend : backends) {
ggml_backend_free(backend);
}
ggml_backend_buffer_free(buf_output);
}
llama_cparams cparams;
std::vector<ggml_backend_t> backends;
#ifdef GGML_USE_METAL
ggml_backend_t backend_metal = nullptr;
#endif
ggml_backend_t backend_cpu = 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;
int64_t t_compute_start_us = 0;
int64_t n_queued_tokens = 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
// host buffer for the model output (logits and embeddings)
ggml_backend_buffer_t buf_output = nullptr;
// decode output (2-dimensional array: [n_outputs][n_vocab])
size_t logits_size = 0; // capacity (of floats) for logits
float * logits = nullptr;
std::vector<int32_t> output_ids; // map batch token positions to ids of the logits and embd buffers
size_t output_size = 0; // capacity (of tokens positions) for the output buffers
int32_t n_outputs = 0; // number of actually-used outputs in the current ubatch
bool logits_all = false;
// embeddings output (2-dimensional array: [n_outputs][n_embd])
// populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
size_t embd_size = 0; // capacity (of floats) for embeddings
float * embd = nullptr;
// sequence embeddings output (map of [n_embd] vectors)
// populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
std::map<llama_seq_id, std::vector<float>> embd_seq;
// memory buffers used to evaluate the model
std::vector<uint8_t> buf_compute_meta;
ggml_backend_sched_t sched = nullptr;
ggml_abort_callback abort_callback = nullptr;
void * abort_callback_data = nullptr;
// input tensors
struct ggml_tensor * inp_tokens; // I32 [n_batch]
struct ggml_tensor * inp_embd; // F32 [n_embd, n_batch]
struct ggml_tensor * inp_pos; // I32 [n_batch]
struct ggml_tensor * inp_out_ids; // I32 [n_outputs]
struct ggml_tensor * inp_KQ_mask; // F32 [kv_size, n_batch]
struct ggml_tensor * inp_KQ_pos; // F32 [n_kv]
struct ggml_tensor * inp_K_shift; // I32 [kv_size]
struct ggml_tensor * inp_mean; // F32 [n_batch, n_batch]
struct ggml_tensor * inp_cls; // I32 [n_batch]
struct ggml_tensor * inp_s_copy; // I32 [kv_size]
struct ggml_tensor * inp_s_mask; // F32 [1, n_kv]
struct ggml_tensor * inp_s_seq; // I32 [n_kv, n_batch]
// control vectors
struct llama_control_vector cvec;
#ifdef GGML_USE_MPI
ggml_mpi_context * ctx_mpi = NULL;
#endif
};
//
// kv cache helpers
//
static bool llama_kv_cache_init(
struct llama_kv_cache & cache,
const llama_model & model,
ggml_type type_k,
ggml_type type_v,
uint32_t kv_size,
bool offload) {
const struct llama_hparams & hparams = model.hparams;
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
const int64_t n_layer = hparams.n_layer;
cache.has_shift = false;
// TODO: find a nicer way to add other recurrent model architectures
cache.recurrent = model.arch == LLM_ARCH_MAMBA;
// TODO: support mixed reccurent Transformer architectues
// NOTE: (!a || b) is a logical implication (a -> b)
GGML_ASSERT(!cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_s());
GGML_ASSERT(!cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_s());
GGML_ASSERT( cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_gqa());
GGML_ASSERT( cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_gqa());
cache.head = 0;
cache.size = kv_size;
cache.used = 0;
cache.type_k = type_k;
cache.type_v = type_v;
cache.cells.clear();
cache.cells.resize(kv_size);
if (cache.recurrent) {
// init state copy sources
for (uint32_t i = 0; i < cache.size; ++i) {
cache.cells[i].src = i;
}
}
#ifdef GGML_USE_CLBLAST
offload = false;
#endif
// count used buffer types
std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
if (offload) {
for (int64_t i = 0; i < n_layer; ++i) {
buft_layer_count[model.buft_layer[i].buft]++;
}
} else {
buft_layer_count[llama_default_buffer_type_cpu(true)] = n_layer;
}
// create a context for each buffer type
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
for (auto & it : buft_layer_count) {
int n_layers = it.second;
struct ggml_init_params params = {
/*.mem_size =*/ 2u*n_layers*ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ggml_context * ctx = ggml_init(params);
if (!ctx) {
LLAMA_LOG_ERROR("%s: failed to allocate context for kv cache\n", __func__);
return false;
}
ctx_map[it.first] = ctx;
cache.ctxs.push_back(ctx);
}
cache.k_l.reserve(n_layer);
cache.v_l.reserve(n_layer);
for (int i = 0; i < (int) n_layer; i++) {
struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
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);
}
// allocate tensors and initialize the buffers to avoid NaNs in the padding
for (auto it : ctx_map) {
ggml_backend_buffer_type_t buft = it.first;
ggml_context * ctx = it.second;
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
if (!buf) {
LLAMA_LOG_ERROR("%s: failed to allocate buffer for kv cache\n", __func__);
return false;
}
ggml_backend_buffer_clear(buf, 0);
LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
cache.bufs.push_back(buf);
}
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 (cache.recurrent) {
// For recurrent state architectures (like Mamba),
// each KV cache cell can store the state for a whole sequence.
llama_seq_id min = cache.size - 1;
llama_seq_id max = 0;
for (uint32_t i = 0; i < n_tokens; ++i) {
for (int32_t j = 0; j < batch.n_seq_id[i]; ++j) {
llama_seq_id seq_id = batch.seq_id[i][j];
// make sure it's a valid seq_id
if ((uint32_t) seq_id < cache.size) {
if (seq_id > max) {
max = seq_id;
}
if (seq_id < min) {
min = seq_id;
}
// Assuming the tokens are in-order
if (batch.pos[i] != cache.cells[seq_id].pos + 1) {
// What should happen when the pos backtracks or skips a value?
// Clearing the state mid-batch would require special-casing which isn't done.
LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d\n",
__func__, batch.pos[i], cache.cells[seq_id].pos, seq_id);
}
if (cache.cells[seq_id].pos < 0 && 0 <= batch.pos[i]) {
cache.used += 1;
}
cache.cells[seq_id].pos = batch.pos[i];
// NOTE: seq_ids are not inserted here; they are handled when the input tensors are set
} else {
// too big seq_id
// TODO: would it be possible to resize the KV cache size instead?
LLAMA_LOG_ERROR("%s: seq_id=%d >= kv_size=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size);
return false;
}
}
}
// allow getting the range of used cells, from head to head + n
cache.head = min;
cache.n = max - min + 1;
// sanity check
return max >= min;
}
// otherwise, one cell per token.
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 uint32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
for (uint32_t i = cache.size; i > 0; --i) {
const llama_kv_cell & cell = cache.cells[i - 1];
if (cell.pos >= 0 && !cell.is_empty()) {
return i;
}
}
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 bool 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();
// models like Mamba can't have a state partially erased
if (cache.recurrent) {
if (seq_id >= (int64_t) cache.size) {
// could be fatal
return false;
}
if (0 <= seq_id) {
// partial intersection is invalid
if ((0 < p0 && p0 <= cache.cells[seq_id].pos) || (0 < p1 && p1 <= cache.cells[seq_id].pos)) {
return false;
}
} else {
// seq_id is negative, then the range should include everything or nothing
if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits<llama_pos>::max())) {
return false;
}
}
}
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].is_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;
return true;
}
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();
if (cache.recurrent) {
if ((uint32_t) seq_id_dst < cache.size && (uint32_t) seq_id_src < cache.size) {
seq_id_src = cache.cells[seq_id_src].src;
GGML_ASSERT((uint32_t) seq_id_src < cache.size);
// intent to "copy from"
// supports copy chains thanks to taking the source of the source
cache.cells[seq_id_dst].src = seq_id_src;
// preserve the "keep or clear" status of the copied sequence
if (cache.cells[seq_id_src].has_seq_id(seq_id_src)) {
cache.cells[seq_id_dst].seq_id.insert(seq_id_dst);
} else {
cache.cells[seq_id_dst].seq_id.erase(seq_id_dst);
}
cache.do_copy = true;
cache.cells[seq_id_dst].pos = cache.cells[seq_id_src].pos;
}
return;
}
// otherwise, this is the KV cache of a Transformer-like model
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_add(
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();
if (cache.recurrent) {
// for Mamba-like models, only the pos needs to be shifted
if (0 <= seq_id && seq_id < (int64_t) cache.size) {
llama_kv_cell & cell = cache.cells[seq_id];
if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
cell.pos += delta;
}
}
return;
}
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].is_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();
if (cache.recurrent) {
// for Mamba-like models, only the pos needs to be changed
if (0 <= seq_id && seq_id < (int64_t) cache.size) {
llama_kv_cell & cell = cache.cells[seq_id];
if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
cell.pos /= d;
}
}
return;
}
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;
}
}
}
}
static llama_pos llama_kv_cache_seq_pos_max(struct llama_kv_cache & cache, llama_seq_id seq_id) {
llama_pos result = 0;
for (uint32_t i = 0; i < cache.size; ++i) {
if (cache.cells[i].has_seq_id(seq_id)) {
result = std::max(result, cache.cells[i].pos);
}
}
return result;
}
static void llama_kv_cache_defrag(struct llama_kv_cache & cache) {
cache.do_defrag = true;
}
//
// 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_TYPE_BOOL: return "bool";
case LLAMA_KV_OVERRIDE_TYPE_INT: return "int";
case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float";
}
return "unknown";
}
static bool validate_override(const llama_model_kv_override_type expected_type, const struct llama_model_kv_override * ovrd) {
if (!ovrd) { return false; }
if (ovrd->tag == expected_type) {
LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ",
__func__, override_type_to_str(ovrd->tag), ovrd->key);
switch (ovrd->tag) {
case LLAMA_KV_OVERRIDE_TYPE_BOOL: {
LLAMA_LOG_INFO("%s\n", ovrd->bool_value ? "true" : "false");
} break;
case LLAMA_KV_OVERRIDE_TYPE_INT: {
LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->int_value);
} break;
case LLAMA_KV_OVERRIDE_TYPE_FLOAT: {
LLAMA_LOG_INFO("%.6f\n", ovrd->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(ovrd->tag), ovrd->key));
}
return true;
}
LLAMA_LOG_WARN("%s: Warning: Bad metadata override type for key '%s', expected %s but got %s\n",
__func__, ovrd->key, override_type_to_str(expected_type), override_type_to_str(ovrd->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 * ovrd) {
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, ovrd)) {
target = ovrd->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 * ovrd) {
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, ovrd)) {
target = ovrd->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 * ovrd) {
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, ovrd)) {
target = ovrd->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 * ovrd) {
(void)target;
(void)ovrd;
if (!ovrd) { 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",
ovrd ? ovrd->key : "NULL"));
}
static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
if (try_override<T>(target, ovrd)) {
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 * ovrd = nullptr) {
return set(ctx, gguf_find_key(ctx, key), target, ovrd);
}
static bool set(const gguf_context * ctx, const std::string & key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
return set(ctx, key.c_str(), target, ovrd);
}
};
}
using llama_buf_map = std::unordered_map<uint32_t, ggml_backend_buffer_t>;
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_files files;
llama_ftype ftype;
llama_fver fver;
llama_mmaps mappings;
// Holds information on a model weights
struct llama_tensor_weights {
uint16_t idx; // source file index
size_t offs; // tensor data offset in the original file
ggml_tensor * tensor;
llama_tensor_weights(uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) {
const int tensor_idx = gguf_find_tensor(gguf_ctx, name);
offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx);
}
};
std::vector<llama_tensor_weights> weights;
std::unordered_map<std::string, struct llama_model_kv_override> kv_overrides;
struct gguf_context * meta = NULL;
std::vector<ggml_context *> contexts;
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) {
int trace = 0;
if (getenv("LLAMA_TRACE")) {
trace = atoi(getenv("LLAMA_TRACE"));
}
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});
}
}
struct ggml_context * ctx = NULL;
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ &ctx,
};
meta = gguf_init_from_file(fname.c_str(), params);
if (!meta) {
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));
// Save tensors data offset of the main file.
// For subsidiary files, `meta` tensor data offset must not be used,
// so we build a unified tensors index for weights.
for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
weights.emplace_back(llama_tensor_weights(0, cur->name, meta, cur));
}
files.emplace_back(new llama_file(fname.c_str(), "rb"));
contexts.emplace_back(ctx);
uint16_t n_split = 0;
get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false);
// Load additional GGML contexts
if (n_split > 1) {
uint16_t idx = 0;
get_key(llm_kv(LLM_KV_SPLIT_NO), idx);
if (idx != 0) {
throw std::runtime_error(format("illegal split file: %d, model must be loaded with the first split", idx));
}
char split_prefix[PATH_MAX] = {0};
if (!llama_split_prefix(split_prefix, sizeof(split_prefix), fname.c_str(), idx, n_split)) {
throw std::runtime_error(format("invalid split file: %s", fname.c_str()));
}
if (trace > 0) {
LLAMA_LOG_INFO("%s: loading additional %d GGUFs\n", __func__, n_split);
}
char split_path[PATH_MAX] = {0};
for (idx = 1; idx < n_split; idx++) {
llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split);
struct gguf_init_params split_params = {
/*.no_alloc = */ true,
/*.ctx = */ &ctx,
};
struct gguf_context * ctx_gguf = gguf_init_from_file(split_path, split_params);
if (!ctx_gguf) {
throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, split_path));
}
// Save tensors data offset info of the shard.
for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
weights.emplace_back(llama_tensor_weights(idx, cur->name, ctx_gguf, cur));
}
files.emplace_back(new llama_file(split_path, "rb"));
contexts.emplace_back(ctx);
gguf_free(ctx_gguf);
}
get_key(llm_kv(LLM_KV_SPLIT_TENSORS_COUNT), n_tensors);
// sanity check
{
const int n_tensors_loaded = (int) weights.size();
if (n_tensors != n_tensors_loaded) {
throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded));
}
}
LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n", __func__, n_split - 1);
}
n_kv = gguf_get_n_kv(meta);
n_tensors = weights.size();
fver = (enum llama_fver) gguf_get_version(meta);
for (auto & w : weights) {
n_elements += ggml_nelements(w.tensor);
n_bytes += ggml_nbytes(w.tensor);
}
LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
__func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver));
// determine file type based on the number of tensors for each quantization and print meta data
// TODO: make optional
{
std::map<enum ggml_type, uint32_t> n_type;
uint32_t n_type_max = 0;
enum ggml_type type_max = GGML_TYPE_F32;
for (int i = 0; i < n_tensors; i++) {
const ggml_tensor * tensor = weights.at(i).tensor;
enum ggml_type type = tensor->type;
n_type[type]++;
if (n_type_max < n_type[type]) {
n_type_max = n_type[type];
type_max = type;
}
if (trace > 0) {
const uint16_t sid = weights.at(i).idx;
LLAMA_LOG_INFO("%s: - tensor %4d, split %2d: %32s %-8s [ %s ]\n", __func__, i, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str());
}
}
switch (type_max) {
case GGML_TYPE_F32: ftype = LLAMA_FTYPE_ALL_F32; break;
case GGML_TYPE_F16: ftype = LLAMA_FTYPE_MOSTLY_F16; break;
case GGML_TYPE_Q4_0: ftype = LLAMA_FTYPE_MOSTLY_Q4_0; break;
case GGML_TYPE_Q4_1: ftype = LLAMA_FTYPE_MOSTLY_Q4_1; break;
case GGML_TYPE_Q5_0: ftype = LLAMA_FTYPE_MOSTLY_Q5_0; break;
case GGML_TYPE_Q5_1: ftype = LLAMA_FTYPE_MOSTLY_Q5_1; break;
case GGML_TYPE_Q8_0: ftype = LLAMA_FTYPE_MOSTLY_Q8_0; break;
case GGML_TYPE_Q2_K: ftype = LLAMA_FTYPE_MOSTLY_Q2_K; break;
case GGML_TYPE_Q3_K: ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M; break;
case GGML_TYPE_Q4_K: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M; break;
case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break;
case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break;
case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break;
case GGML_TYPE_IQ2_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS; break;
case GGML_TYPE_IQ2_S: ftype = LLAMA_FTYPE_MOSTLY_IQ2_S; break;
case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break;
case GGML_TYPE_IQ1_M: ftype = LLAMA_FTYPE_MOSTLY_IQ1_M; break;
case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break;
case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; 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(meta, "general.file_type");
if (kid >= 0) {
ftype = (llama_ftype) gguf_get_val_u32(meta, 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(meta, i);
const enum gguf_type type = gguf_get_kv_type(meta, 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(meta, i)), gguf_get_arr_n(meta, i))
: gguf_type_name(type);
std::string value = gguf_kv_to_str(meta, 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 (meta) {
gguf_free(meta);
}
for (auto * ctx : contexts) {
ggml_free(ctx);
}
}
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(meta, 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(meta, 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(meta, 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 weights.at(i).tensor->name;
}
const llama_tensor_weights & get_weights(const char * name) const {
for (const auto & weight : weights) {
if (strcmp(name, weight.tensor->name) == 0) {
return weight;
}
}
throw std::runtime_error(format("tensor %s not found", name));
}
struct ggml_tensor * get_tensor_meta(const char * name) const {
try {
return get_weights(name).tensor;
} catch (const std::runtime_error & e) {
return NULL;
}
}
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, const struct ggml_tensor * cur) {
struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur);
ggml_set_name(tensor, ggml_get_name(cur));
n_created++;
return tensor;
}
struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, bool required = true) {
const struct ggml_tensor * cur = get_tensor_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()));
}
{
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);
}
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));
}
}
void init_mappings(bool prefetch = true, llama_mlocks * mlock_mmaps = nullptr) {
if (use_mmap) {
mappings.reserve(files.size());
mmaps_used.reserve(files.size());
for (const auto & file : files) {
std::unique_ptr<llama_mmap> mapping(new llama_mmap(file.get(), prefetch ? -1 : 0, ggml_is_numa()));
mmaps_used.emplace_back(std::make_pair(mapping->size, 0));
if (mlock_mmaps) {
std::unique_ptr<llama_mlock> mlock_mmap(new llama_mlock());
mlock_mmap->init(mapping->addr);
mlock_mmaps->emplace_back(std::move(mlock_mmap));
}
mappings.emplace_back(std::move(mapping));
}
}
// compute the total size of all tensors for progress reporting
for (auto & w : weights) {
size_data += ggml_nbytes(w.tensor);
}
}
void get_mapping_range(size_t * first, size_t * last, void ** addr, int idx, ggml_context * ctx) const {
GGML_ASSERT(!mappings.empty());
const auto & mapping = mappings.at(idx);
*first = mapping->size;
*last = 0;
*addr = mapping->addr;
for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) {
const auto & w = get_weights(ggml_get_name(tensor));
if (w.idx != idx) {
continue;
}
*first = std::min(*first, w.offs);
*last = std::max(*last, w.offs + ggml_nbytes(tensor));
}
}
// for backwards compatibility, does not support ggml-backend
void load_data_for(struct ggml_tensor * cur) const {
const auto & w = get_weights(ggml_get_name(cur));
if (use_mmap) {
const auto & mapping = mappings.at(w.idx);
if (cur->data == nullptr) {
cur->data = (uint8_t *)mapping->addr + w.offs;
} else {
memcpy(cur->data, (uint8_t *)mapping->addr + w.offs, ggml_nbytes(cur));
}
} else {
GGML_ASSERT(cur->data != nullptr);
GGML_ASSERT(w.idx < files.size());
const auto & file = files.at(w.idx);
file->seek(w.offs, SEEK_SET);
file->read_raw(cur->data, ggml_nbytes(cur));
}
}
size_t size_done = 0;
size_t size_data = 0;
std::vector<std::pair<size_t, size_t>> mmaps_used;
// Returns false if cancelled by progress_callback
bool load_all_data(
struct ggml_context * ctx,
llama_buf_map & bufs_mmap,
llama_mlocks * lmlocks,
llama_progress_callback progress_callback,
void * progress_callback_user_data) {
GGML_ASSERT(size_data != 0 && "call init_mappings() first");
std::vector<no_init<uint8_t>> read_buf;
for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
if (progress_callback) {
if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) {
return false;
}
}
const auto & w = get_weights(ggml_get_name(cur));
size_t n_size = ggml_nbytes(cur);
if (use_mmap) {
const auto & mapping = mappings.at(w.idx);
ggml_backend_buffer_t buf_mmap = nullptr;
if (bufs_mmap.count(w.idx)) {
buf_mmap = bufs_mmap.at(w.idx);
}
GGML_ASSERT(buf_mmap || cur->data); // either we have a buffer to allocate the tensor in, or it is already allocated
if (buf_mmap && cur->data == nullptr) {
ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + w.offs);
if (lmlocks) {
const auto & lmlock = lmlocks->at(w.idx);
lmlock->grow_to(w.offs + ggml_nbytes(cur));
}
auto & mmap_used = mmaps_used[w.idx];
mmap_used.first = std::min(mmap_used.first, w.offs);
mmap_used.second = std::max(mmap_used.second, w.offs + n_size);
} else {
ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + w.offs, 0, n_size);
}
} else {
GGML_ASSERT(w.idx < files.size());
const auto & file = files.at(w.idx);
if (ggml_backend_buffer_is_host(cur->buffer)) {
file->seek(w.offs, SEEK_SET);
file->read_raw(cur->data, ggml_nbytes(cur));
} else {
read_buf.resize(ggml_nbytes(cur));
file->seek(w.offs, SEEK_SET);
file->read_raw(read_buf.data(), ggml_nbytes(cur));
ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
}
}
size_done += n_size;
}
// check if this is the last call and do final cleanup
if (size_done >= size_data) {
// unmap offloaded tensors and metadata
if (use_mmap) {
for (uint32_t idx = 0; idx < mappings.size(); idx++) {
const auto & mmap_used = mmaps_used.at(idx);
auto & mapping = mappings.at(idx);
mapping->unmap_fragment(0, mmap_used.first);
if (mmap_used.second != 0) {
mapping->unmap_fragment(mmap_used.second, 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;
}
};
template<>
bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) {
uint32_t tmp;
const bool found = get_key(kid, tmp, required);
if (found) {
result = (enum llama_pooling_type) tmp;
} else {
result = LLAMA_POOLING_TYPE_UNSPECIFIED;
}
return found;
}
//
// load LLaMA models
//
static const char * 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_XXS - 2.0625 bpw";
case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw";
case LLAMA_FTYPE_MOSTLY_IQ2_S: return "IQ2_S - 2.5 bpw";
case LLAMA_FTYPE_MOSTLY_IQ2_M: return "IQ2_M - 2.7 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_XS: return "IQ3_XS - 3.3 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw";
case LLAMA_FTYPE_MOSTLY_IQ1_S :return "IQ1_S - 1.5625 bpw";
case LLAMA_FTYPE_MOSTLY_IQ1_M :return "IQ1_M - 1.75 bpw";
case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw";
case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_S: return "IQ3_S - 3.4375 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_M: return "IQ3_S mix - 3.66 bpw";
default: return "unknown, may not work";
}
}
static const char * llama_model_type_name(e_model type) {
switch (type) {
case MODEL_22M: return "22M";
case MODEL_33M: return "33M";
case MODEL_109M: return "109M";
case MODEL_137M: return "137M";
case MODEL_0_5B: return "0.5B";
case MODEL_1B: return "1B";
case MODEL_2B: return "2B";
case MODEL_3B: return "3B";
case MODEL_7B: return "7B";
case MODEL_8B: return "8B";
case MODEL_13B: return "13B";
case MODEL_14B: return "14B";
case MODEL_15B: return "15B";
case MODEL_20B: return "20B";
case MODEL_30B: return "30B";
case MODEL_34B: return "34B";
case MODEL_35B: return "35B";
case MODEL_40B: return "40B";
case MODEL_65B: return "65B";
case MODEL_70B: return "70B";
case MODEL_314B: return "314B";
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 const char * llama_model_vocab_type_name(enum llama_vocab_type type){
switch (type) {
case LLAMA_VOCAB_TYPE_NONE: return "no vocab";
case LLAMA_VOCAB_TYPE_SPM: return "SPM";
case LLAMA_VOCAB_TYPE_BPE: return "BPE";
case LLAMA_VOCAB_TYPE_WPM: return "WPM";
default: return "unknown";
}
}
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.meta;
// 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_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || 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_TYPE_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_head == 0) ? 0 : 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_head == 0) ? 0 : 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_head == 0) ? 0 : 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_MINICPM:
{
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_2B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_GROK:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 64: model.type = e_model::MODEL_314B; 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;
}
if (model.type == e_model::MODEL_13B) {
// TODO: become GGUF KV parameter
hparams.f_max_alibi_bias = 8.0f;
}
} 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;
}
// TODO: become GGUF KV parameter
hparams.f_max_alibi_bias = 8.0f;
} break;
case LLM_ARCH_BERT:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
switch (hparams.n_layer) {
case 3:
model.type = e_model::MODEL_17M; break; // bge-micro
case 6:
model.type = e_model::MODEL_22M; break; // MiniLM-L6
case 12:
switch (hparams.n_embd) {
case 384: model.type = e_model::MODEL_33M; break; // MiniLM-L12, bge-small
case 768: model.type = e_model::MODEL_109M; break; // bge-base
} break;
case 24:
model.type = e_model::MODEL_335M; break; // bge-large
}
} break;
case LLM_ARCH_NOMIC_BERT:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
if (hparams.n_layer == 12 && hparams.n_embd == 768) {
model.type = e_model::MODEL_137M;
}
} 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;
}
// TODO: become GGUF KV parameter
hparams.f_max_alibi_bias = 8.0f;
} break;
case LLM_ARCH_MPT:
{
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 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_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_QWEN2:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break;
case 32: model.type = e_model::MODEL_7B; break;
case 40: model.type = hparams.n_head == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break;
case 80: model.type = e_model::MODEL_70B; 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;
case LLM_ARCH_CODESHELL:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (hparams.n_layer) {
case 42: model.type = e_model::MODEL_SMALL; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_ORION:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (hparams.n_layer) {
case 40: model.type = e_model::MODEL_14B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_INTERNLM2:
{
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 48: model.type = e_model::MODEL_20B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_GEMMA:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 18: model.type = e_model::MODEL_2B; break;
case 28: model.type = e_model::MODEL_7B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_STARCODER2:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (hparams.n_layer) {
case 30: model.type = e_model::MODEL_3B; break;
case 32: 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_MAMBA:
{
ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 24:
switch (hparams.n_embd) {
case 768: model.type = e_model::MODEL_SMALL; break;
default: model.type = e_model::MODEL_UNKNOWN;
} break;
case 48:
switch (hparams.n_embd) {
case 1024: model.type = e_model::MODEL_MEDIUM; break;
case 1536: model.type = e_model::MODEL_LARGE; break;
case 2048: model.type = e_model::MODEL_XL; break;
default: model.type = e_model::MODEL_UNKNOWN;
} break;
case 64:
switch (hparams.n_embd) {
case 2560: model.type = e_model::MODEL_3B; break;
default: model.type = e_model::MODEL_UNKNOWN;
} break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_XVERSE:
{
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;
case 80: model.type = e_model::MODEL_65B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_COMMAND_R:
{
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (hparams.n_layer) {
case 40: model.type = e_model::MODEL_35B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
default: (void)0;
}
model.ftype = ml.ftype;
if (hparams.f_max_alibi_bias > 0.0f) {
hparams.need_kq_pos = true;
}
hparams.rope_type = llama_rope_type(&model);
}
// 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.meta;
const auto kv = LLM_KV(model.arch);
// determine vocab type
{
std::string tokenizer_name;
ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_name);
if (tokenizer_name == "no_vocab") {
vocab.type = LLAMA_VOCAB_TYPE_NONE;
// default special tokens
vocab.special_bos_id = -1;
vocab.special_eos_id = -1;
vocab.special_unk_id = -1;
vocab.special_sep_id = -1;
vocab.special_pad_id = -1;
vocab.linefeed_id = -1;
return;
} else 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;
const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
if (add_space_prefix_keyidx != -1) {
vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
} // The default value of add_space_prefix is true.
} 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(unicode_cpts_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 if (tokenizer_name == "bert") {
vocab.type = LLAMA_VOCAB_TYPE_WPM;
// default special tokens
vocab.special_bos_id = 101;
vocab.special_eos_id = 102;
vocab.special_unk_id = 100;
vocab.special_sep_id = -1;
vocab.special_pad_id = -1;
vocab.add_space_prefix = false;
} 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 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);
}
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(unicode_cpts_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) {
try {
vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
} catch (const std::exception & e) {
LLAMA_LOG_WARN("%s: SPM vocabulary, but newline token not found: %s! Using special_pad_id instead.", __func__, e.what());
vocab.linefeed_id = vocab.special_pad_id;
}
} else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
vocab.linefeed_id = vocab.special_pad_id;
} else {
const std::vector<int> ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A
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 char * 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));
LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, llama_model_vocab_type_name(vocab.type));
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: f_logit_scale = %.1e\n", __func__, hparams.f_logit_scale);
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: causal attn = %d\n", __func__, hparams.causal_attn);
LLAMA_LOG_INFO("%s: pooling type = %d\n", __func__, hparams.pooling_type);
LLAMA_LOG_INFO("%s: rope type = %d\n", __func__, hparams.rope_type);
LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type);
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: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv);
LLAMA_LOG_INFO("%s: ssm_d_inner = %u\n", __func__, hparams.ssm_d_inner);
LLAMA_LOG_INFO("%s: ssm_d_state = %u\n", __func__, hparams.ssm_d_state);
LLAMA_LOG_INFO("%s: ssm_dt_rank = %u\n", __func__, hparams.ssm_dt_rank);
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,
enum llama_split_mode split_mode,
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 & hparams = model.hparams;
model.split_mode = split_mode;
model.main_gpu = main_gpu;
model.n_gpu_layers = n_gpu_layers;
const int64_t n_layer = hparams.n_layer;
const int64_t i_gpu_start = std::max((int64_t) hparams.n_layer - n_gpu_layers, (int64_t) 0);
// there is very little benefit to offloading the input layer, so always keep it on the CPU
model.buft_input = llama_default_buffer_type_cpu(true);
//model.buft_input = llama_default_buffer_type_offload(main_gpu);
model.buft_layer.resize(n_layer);
// assign cpu layers
for (int64_t i = 0; i < i_gpu_start; ++i) {
model.buft_layer[i] = llama_default_buffer_type_cpu(true);
}
if (split_mode == LLAMA_SPLIT_MODE_LAYER) {
// calculate the split points
int device_count = llama_get_device_count();
bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
std::vector<float> splits(device_count);
if (all_zero) {
// default split, by free memory
for (int i = 0; i < device_count; ++i) {
splits[i] = llama_get_device_memory(i);
}
} else {
std::copy(tensor_split, tensor_split + device_count, splits.begin());
}
// sum and normalize the splits to get the split points
float split_sum = 0.0f;
for (int i = 0; i < device_count; ++i) {
split_sum += splits[i];
splits[i] = split_sum;
}
for (int i = 0; i < device_count; ++i) {
splits[i] /= split_sum;
}
// assign the repeating layers to the devices according to the splits
int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1);
for (int64_t i = i_gpu_start; i < n_layer; ++i) {
int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin();
model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu);
}
// assign the output layer
if (n_gpu_layers > n_layer) {
int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin();
model.buft_output = llama_default_buffer_type_offload(layer_gpu);
} else {
model.buft_output = llama_default_buffer_type_cpu(true);
}
} else {
ggml_backend_buffer_type_t split_buft;
if (split_mode == LLAMA_SPLIT_MODE_ROW) {
split_buft = llama_default_buffer_type_split(main_gpu, tensor_split);
} else {
// LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported
split_buft = llama_default_buffer_type_offload(main_gpu);
}
// assign the repeating layers
for (int64_t i = i_gpu_start; i < n_layer; ++i) {
model.buft_layer[i] = {
split_buft,
llama_default_buffer_type_offload(main_gpu)
};
}
// assign the output layer
if (n_gpu_layers > n_layer) {
model.buft_output = {
split_buft,
llama_default_buffer_type_offload(main_gpu)
};
} else {
model.buft_output = llama_default_buffer_type_cpu(true);
}
}
// count used buffer types
std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
buft_layer_count[model.buft_input.buft]++;
buft_layer_count[model.buft_input.buft_matrix]++;
buft_layer_count[model.buft_output.buft]++;
buft_layer_count[model.buft_output.buft_matrix]++;
for (int64_t i = 0; i < n_layer; ++i) {
buft_layer_count[model.buft_layer[i].buft]++;
buft_layer_count[model.buft_layer[i].buft_matrix]++;
}
// create one context per buffer type
size_t ctx_size = ggml_tensor_overhead()*(ml.n_tensors + 1); // +1 for models where tok_embd is duplicated as output
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
for (auto & it : buft_layer_count) {
struct ggml_init_params params = {
/*.mem_size =*/ ctx_size,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ggml_context * ctx = ggml_init(params);
if (!ctx) {
throw std::runtime_error(format("failed to create context"));
}
ctx_map[it.first] = ctx;
model.ctxs.push_back(ctx);
}
LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, model.ctxs.size()*ctx_size/1024.0/1024.0);
// 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_embd_gqa = n_embd_v_gqa;
const int64_t n_vocab = hparams.n_vocab;
const int64_t n_vocab_type = hparams.n_vocab_type;
const int64_t n_ff = hparams.n_ff;
GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
ggml_context * ctx_input = ctx_map.at(model.buft_input.buft);
ggml_context * ctx_output = ctx_map.at(model.buft_output.buft);
ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix);
auto ctx_for_layer = [&](int i) { return ctx_map.at(model.buft_layer[i].buft); };
auto ctx_for_layer_split = [&](int i) { return ctx_map.at(model.buft_layer[i].buft_matrix); };
model.layers.resize(n_layer);
const auto tn = LLM_TN(model.arch);
switch (model.arch) {
case LLM_ARCH_LLAMA:
case LLM_ARCH_REFACT:
case LLM_ARCH_MINICPM:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
if (model.arch != LLM_ARCH_MINICPM){
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
// if output is NULL, init from the input tok embed
if (model.output == NULL) {
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
ml.n_created--; // artificial tensor
ml.size_data += ggml_nbytes(model.output);
}
}
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
// optional bias tensors
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, false);
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false);
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false);
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, false);
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd}, 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_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
} 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_split, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), {n_embd, n_ff});
layer.ffn_down_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd});
layer.ffn_up_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), {n_embd, n_ff});
}
}
}
} break;
case LLM_ARCH_GROK:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
// if output is NULL, init from the input tok embed
if (model.output == NULL) {
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
ml.n_created--; // artificial tensor
ml.size_data += ggml_nbytes(model.output);
}
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd});
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_split, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), {n_embd, n_ff});
layer.ffn_down_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd});
layer.ffn_up_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), {n_embd, n_ff});
}
layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
}
} break;
case LLM_ARCH_BAICHUAN:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
case LLM_ARCH_FALCON:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
if (!model.output) {
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
ml.n_created--; // artificial tensor
ml.size_data += ggml_nbytes(model.output);
}
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, false);
layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, false);
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
case LLM_ARCH_STARCODER:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
}
} break;
case LLM_ARCH_PERSIMMON:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64});
layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {64});
layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64});
layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64});
}
} break;
case LLM_ARCH_BERT:
case LLM_ARCH_NOMIC_BERT:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type});
if (model.arch == LLM_ARCH_BERT) {
model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train});
}
model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd});
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
if (model.arch == LLM_ARCH_BERT) {
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
} else {
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
}
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
if (model.arch == LLM_ARCH_BERT) {
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
} else {
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
}
layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd});
}
} break;
case LLM_ARCH_BLOOM:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
}
} break;
case LLM_ARCH_MPT:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, false);
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
if (!model.output) {
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
ml.n_created--; // artificial tensor
ml.size_data += ggml_nbytes(model.output);
}
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, false);
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, false);
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, false);
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, false);
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, false);
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, false);
// AWQ ScaleActivation layer
layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false);
}
} break;
case LLM_ARCH_STABLELM:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
// optional bias tensors, present in Stable LM 2 1.6B
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, false);
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false);
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false);
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
case LLM_ARCH_QWEN:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3});
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd*3});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff/2});
}
} break;
case LLM_ARCH_QWEN2:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
// optional bias tensors
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
case LLM_ARCH_PHI2:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
model.output_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab});
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, false);
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, false);
if (layer.wqkv == nullptr) {
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
}
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
}
} break;
case LLM_ARCH_PLAMO:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
case LLM_ARCH_GPT2:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
}
} break;
case LLM_ARCH_CODESHELL:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
}
} break;
case LLM_ARCH_ORION:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
case LLM_ARCH_INTERNLM2:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
// layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
case LLM_ARCH_GEMMA:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // same as tok_embd, duplicated to allow offloading
ml.n_created--; // artificial tensor
ml.size_data += ggml_nbytes(model.output);
const int64_t n_ff = hparams.n_ff;
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_v_gqa = hparams.n_embd_v_gqa();
for (uint32_t i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * hparams.n_head});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head, n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
}
} break;
case LLM_ARCH_STARCODER2:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
// if output is NULL, init from the input tok embed
if (model.output == NULL) {
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
ml.n_created--; // artificial tensor
ml.size_data += ggml_nbytes(model.output);
}
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
// optional bias tensors
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
// optional bias tensors
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff});
}
} break;
case LLM_ARCH_MAMBA:
{
const int64_t d_conv = hparams.ssm_d_conv;
const int64_t d_inner = hparams.ssm_d_inner;
const int64_t d_state = hparams.ssm_d_state;
const int64_t dt_rank = hparams.ssm_dt_rank;
// only an expansion factor of 2 is supported for now
GGML_ASSERT(2 * n_embd == d_inner);
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
// if output is NULL, init from the input tok embed, duplicated to allow offloading
if (model.output == NULL) {
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
ml.n_created--; // artificial tensor
ml.size_data += ggml_nbytes(model.output);
}
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
// norm
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.ssm_in = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner});
layer.ssm_conv1d = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner});
layer.ssm_conv1d_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner});
layer.ssm_x = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state});
layer.ssm_dt = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner});
layer.ssm_dt_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner});
// no "weight" suffix for these
layer.ssm_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner});
layer.ssm_d = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_D, i), {d_inner});
// out_proj
layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd});
}
} break;
case LLM_ARCH_XVERSE:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
case LLM_ARCH_COMMAND_R:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
// init output from the input tok embed
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
ml.n_created--; // artificial tensor
ml.size_data += ggml_nbytes(model.output);
}
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
default:
throw std::runtime_error("unknown architecture");
}
}
ml.done_getting_tensors();
ml.init_mappings(true, use_mlock ? &model.mlock_mmaps : nullptr);
model.mappings.reserve(ml.mappings.size());
// create the backend buffers
std::vector<std::pair<ggml_context *, llama_buf_map>> ctx_bufs;
ctx_bufs.reserve(ctx_map.size());
// Ensure we have enough capacity for the maximum backend buffer we will potentially create
size_t n_max_backend_buffer = ctx_map.size() * ml.files.size();
model.bufs.reserve(n_max_backend_buffer);
for (auto & it : ctx_map) {
ggml_backend_buffer_type_t buft = it.first;
ggml_context * ctx = it.second;
llama_buf_map bufs;
bufs.reserve(n_max_backend_buffer);
// only the mmap region containing the tensors in the model is mapped to the backend buffer
// this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
// this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
if (ml.use_mmap && buft == llama_default_buffer_type_cpu(true)) {
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
void * addr = nullptr;
size_t first, last;
ml.get_mapping_range(&first, &last, &addr, idx, ctx);
if (first >= last) {
continue;
}
ggml_backend_buffer_t buf = ggml_backend_cpu_buffer_from_ptr((char *) addr + first, last - first);
if (buf == nullptr) {
throw std::runtime_error("unable to allocate backend CPU buffer");
}
model.bufs.push_back(buf);
bufs.emplace(idx, buf);
#ifdef GGML_USE_CUDA
if (n_layer >= n_gpu_layers) {
ggml_backend_cuda_register_host_buffer(
ggml_backend_buffer_get_base(buf),
ggml_backend_buffer_get_size(buf));
}
#endif
}
}
#ifdef GGML_USE_METAL
else if (ml.use_mmap && buft == ggml_backend_metal_buffer_type()) {
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
const size_t max_size = ggml_get_max_tensor_size(ctx);
void * addr = nullptr;
size_t first, last;
ml.get_mapping_range(&first, &last, &addr, idx, ctx);
if (first >= last) {
continue;
}
ggml_backend_buffer_t buf = ggml_backend_metal_buffer_from_ptr((char *) addr + first, last - first, max_size);
if (buf == nullptr) {
throw std::runtime_error("unable to allocate backend metal buffer");
}
model.bufs.push_back(buf);
bufs.emplace(idx, buf);
}
}
#endif
else {
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
if (buf == nullptr) {
throw std::runtime_error("unable to allocate backend buffer");
}
model.bufs.push_back(buf);
if (use_mlock && ggml_backend_buffer_is_host(buf)) {
model.mlock_bufs.emplace_back(new llama_mlock);
auto & mlock_buf = model.mlock_bufs.back();
mlock_buf->init (ggml_backend_buffer_get_base(buf));
mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
}
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
bufs.emplace(idx, buf);
}
}
if (bufs.empty()) {
throw std::runtime_error("failed to allocate buffer");
}
for (auto & buf : bufs) {
// indicate that this buffer contains weights
// this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight
ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
}
ctx_bufs.emplace_back(ctx, bufs);
}
if (llama_supports_gpu_offload()) {
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);
}
// print memory requirements
for (ggml_backend_buffer_t buf : model.bufs) {
LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0);
}
// populate tensors_by_name
for (ggml_context * ctx : model.ctxs) {
for (auto * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
}
}
// load tensor data
for (auto & it : ctx_bufs) {
ggml_context * ctx = it.first;
auto & bufs = it.second;
if (!ml.load_all_data(ctx, bufs, use_mlock ? &model.mlock_mmaps : NULL, progress_callback, progress_callback_user_data)) {
return false;
}
}
for (auto & mapping : ml.mappings) {
model.mappings.emplace_back(std::move(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, llama_model_params & params) {
try {
llama_model_loader ml(fname, params.use_mmap, params.kv_overrides);
model.hparams.vocab_only = params.vocab_only;
try {
llm_load_arch(ml, model);
} catch(const std::exception & e) {
throw std::runtime_error("error loading model architecture: " + std::string(e.what()));
}
try {
llm_load_hparams(ml, model);
} catch(const std::exception & e) {
throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what()));
}
try {
llm_load_vocab(ml, model);
} catch(const std::exception & e) {
throw std::runtime_error("error loading model vocabulary: " + std::string(e.what()));
}
llm_load_print_meta(ml, model);
if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE &&
model.hparams.n_vocab != model.vocab.id_to_token.size()) {
throw std::runtime_error("vocab size mismatch");
}
if (params.vocab_only) {
LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
return 0;
}
#ifdef GGML_USE_KOMPUTE
if (params.n_gpu_layers > 0 && (
!(model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON)
|| !(
model.ftype == LLAMA_FTYPE_ALL_F32 ||
model.ftype == LLAMA_FTYPE_MOSTLY_F16 ||
model.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ||
model.ftype == LLAMA_FTYPE_MOSTLY_Q4_1
)
)) {
// TODO(cebtenzzre): propagate this error outside of llama_load_model_from_file
LLAMA_LOG_WARN("%s: disabling Kompute due to unsupported model arch or quantization\n", __func__);
params.n_gpu_layers = 0;
}
#endif
#ifdef GGML_USE_SYCL
if (params.split_mode == LLAMA_SPLIT_MODE_NONE) {
ggml_backend_sycl_set_single_device_mode(params.main_gpu);
//SYCL use device index (0, 1, 2) directly, uer input device id, then convert to device index.
params.main_gpu = ggml_backend_sycl_get_device_index(params.main_gpu);
} else {
ggml_backend_sycl_set_mul_device_mode();
}
#endif
if (!llm_load_tensors(
ml, model, params.n_gpu_layers, params.split_mode, 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("%s: error loading model: %s\n", __func__, 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_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,
struct llama_context & lctx,
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) {
lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
cb(lctx.inp_tokens, "inp_tokens", -1);
ggml_set_input(lctx.inp_tokens);
inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
} else {
#ifdef GGML_USE_MPI
GGML_ASSERT(false && "not implemented");
#endif
lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
inpL = lctx.inp_embd;
ggml_set_input(lctx.inp_embd);
}
cb(inpL, "inp_embd", -1);
return inpL;
}
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();
GGML_ASSERT(kv.size == n_ctx);
// compute the transposed [n_tokens, n_embd] V matrix
assert(v_cur->ne[0] == n_embd_v_gqa && v_cur->ne[1] == n_tokens);
struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur);
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_cgraph * graph,
struct ggml_tensor * wo,
struct ggml_tensor * wo_b,
struct ggml_tensor * q_cur,
struct ggml_tensor * kq_mask,
struct ggml_tensor * kq_pos,
int64_t n_ctx,
int32_t n_tokens,
int32_t n_kv,
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 (model.arch == LLM_ARCH_GROK) {
// need to do the following:
// multiply by attn_output_multiplyer of 0.08838834764831845
// and then :
// kq = 30 * tanh(kq / 30)
// before the softmax below
//try from phi2
//ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f));
kq = ggml_scale(ctx, kq, 30);
}
#if defined(GGML_USE_KOMPUTE)
#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Kompute")
#pragma message(" Falling back to ggml_alibi(). Will become an error in Mar 2024")
#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5488")
if (hparams.f_max_alibi_bias > 0.0f) {
kq = ggml_scale(ctx, kq, kq_scale);
cb(kq, "kq_scaled", il);
kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, hparams.f_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
#endif
{
kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_pos, kq_scale, hparams.f_max_alibi_bias);
cb(kq, "kq_soft_max_ext", il);
}
GGML_ASSERT(kv.size == n_ctx);
// 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);
ggml_build_forward_expand(graph, cur);
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;
}
static struct ggml_tensor * llm_build_kv(
struct ggml_context * ctx,
const llama_model & model,
const llama_hparams & hparams,
const llama_kv_cache & kv,
struct ggml_cgraph * graph,
struct ggml_tensor * wo,
struct ggml_tensor * wo_b,
struct ggml_tensor * k_cur,
struct ggml_tensor * v_cur,
struct ggml_tensor * q_cur,
struct ggml_tensor * kq_mask,
struct ggml_tensor * kq_pos,
int64_t n_ctx,
int32_t n_tokens,
int32_t kv_head,
int32_t n_kv,
float kq_scale,
const llm_build_cb & cb,
int il) {
// these nodes are added to the graph together so that they are not reordered
// by doing so, the number of splits in the graph is reduced
ggml_build_forward_expand(graph, q_cur);
ggml_build_forward_expand(graph, k_cur);
ggml_build_forward_expand(graph, v_cur);
llm_build_kv_store(ctx, hparams, kv, graph, k_cur, v_cur, n_ctx, n_tokens, kv_head, cb, il);
struct ggml_tensor * cur;
cur = llm_build_kqv(ctx, model, hparams, kv, graph, wo, wo_b,
q_cur, kq_mask, kq_pos, n_ctx, n_tokens, n_kv, kq_scale, cb, il);
cb(cur, "kqv_out", il);
return cur;
}
struct llm_build_context {
const llama_model & model;
llama_context & lctx;
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_rot;
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 <= kv_self.size)
const int32_t n_outputs;
const int32_t kv_head; // index of where we store new KV data in the cache
const int32_t n_orig_ctx;
const enum llama_pooling_type pooling_type;
const enum llama_rope_type rope_type;
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),
lctx (lctx),
hparams (model.hparams),
cparams (lctx.cparams),
batch (batch),
kv_self (lctx.kv_self),
n_embd (hparams.n_embd),
n_layer (hparams.n_layer),
n_rot (hparams.n_rot),
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 ? kv_self.size : kv_self.n),
n_outputs (worst_case ? n_tokens : lctx.n_outputs),
kv_head (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head),
n_orig_ctx (cparams.n_yarn_orig_ctx),
pooling_type (cparams.pooling_type),
rope_type (hparams.rope_type),
cb (cb),
buf_compute_meta (lctx.buf_compute_meta) {
// 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);
lctx.inp_tokens = nullptr;
lctx.inp_embd = nullptr;
lctx.inp_pos = nullptr;
lctx.inp_out_ids = nullptr;
lctx.inp_KQ_mask = nullptr;
lctx.inp_KQ_pos = nullptr;
lctx.inp_K_shift = nullptr;
lctx.inp_mean = nullptr;
lctx.inp_cls = nullptr;
lctx.inp_s_copy = nullptr;
lctx.inp_s_mask = nullptr;
lctx.inp_s_seq = nullptr;
}
void free() {
if (ctx0) {
ggml_free(ctx0);
ctx0 = nullptr;
}
}
struct ggml_cgraph * build_k_shift() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
GGML_ASSERT(kv_self.size == n_ctx);
lctx.inp_K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
cb(lctx.inp_K_shift, "K_shift", -1);
ggml_set_input(lctx.inp_K_shift);
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * tmp =
// we rotate only the first n_rot dimensions
ggml_rope_custom_inplace(ctx0,
ggml_view_3d(ctx0, kv_self.k_l[il],
n_embd_head_k, n_head_kv, n_ctx,
ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
0),
lctx.inp_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(gf, tmp);
}
return gf;
}
struct ggml_cgraph * build_s_copy() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
GGML_ASSERT(kv_self.recurrent);
struct ggml_tensor * state_copy = build_inp_s_copy();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size);
struct ggml_tensor * ssm_states = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size);
conv_states = ggml_get_rows(ctx0, conv_states, state_copy);
ssm_states = ggml_get_rows(ctx0, ssm_states, state_copy);
// TODO: name the intermediate tensors with cb()
ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_states, kv_self.k_l[il]));
ggml_build_forward_expand(gf, ggml_cpy(ctx0, ssm_states, kv_self.v_l[il]));
}
return gf;
}
struct ggml_cgraph * build_defrag(const std::vector<uint32_t> & ids) {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
for (uint32_t i = 0; i < ids.size(); ++i) {
const uint32_t id = ids[i];
if (i == id || id == ids.size()) {
continue;
}
uint32_t nm = 1;
while (i + nm < ids.size() && ids[i + nm] == id + nm) {
nm++;
}
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self.k_l[il],
n_embd_k_gqa, nm,
ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*i));
ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self.k_l[il],
n_embd_k_gqa, nm,
ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*id));
ggml_tensor * view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
nm, n_embd_v_gqa,
ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
ggml_row_size(kv_self.v_l[il]->type, i));
ggml_tensor * view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
nm, n_embd_v_gqa,
ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
ggml_row_size(kv_self.v_l[il]->type, id));
ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst));
ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst));
}
i += nm - 1;
}
//LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);
return gf;
}
struct ggml_tensor * build_inp_pos() {
lctx.inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(lctx.inp_pos, "inp_pos", -1);
ggml_set_input(lctx.inp_pos);
return lctx.inp_pos;
}
struct ggml_tensor * build_inp_out_ids() {
lctx.inp_out_ids = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_outputs);
cb(lctx.inp_out_ids, "inp_out_ids", -1);
ggml_set_input(lctx.inp_out_ids);
return lctx.inp_out_ids;
}
struct ggml_tensor * build_inp_KQ_mask(bool causal = true) {
if (causal) {
lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, n_tokens);
} else {
lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
}
cb(lctx.inp_KQ_mask, "KQ_mask", -1);
ggml_set_input(lctx.inp_KQ_mask);
return lctx.inp_KQ_mask;
}
struct ggml_tensor * build_inp_KQ_pos() {
lctx.inp_KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_kv);
cb(lctx.inp_KQ_pos, "KQ_pos", -1);
ggml_set_input(lctx.inp_KQ_pos);
return lctx.inp_KQ_pos;
}
struct ggml_tensor * build_inp_mean() {
lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
cb(lctx.inp_mean, "inp_mean", -1);
ggml_set_input(lctx.inp_mean);
return lctx.inp_mean;
}
struct ggml_tensor * build_inp_cls() {
lctx.inp_cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(lctx.inp_cls, "inp_cls", -1);
ggml_set_input(lctx.inp_cls);
return lctx.inp_cls;
}
struct ggml_tensor * build_inp_s_copy() {
lctx.inp_s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, kv_self.size);
cb(lctx.inp_s_copy, "inp_s_copy", -1);
ggml_set_input(lctx.inp_s_copy);
return lctx.inp_s_copy;
}
struct ggml_tensor * build_inp_s_mask() {
lctx.inp_s_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_kv);
cb(lctx.inp_s_mask, "inp_s_mask", -1);
ggml_set_input(lctx.inp_s_mask);
return lctx.inp_s_mask;
}
struct ggml_tensor * build_inp_s_seq() {
lctx.inp_s_seq = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_tokens);
cb(lctx.inp_s_seq, "inp_s_seq", -1);
ggml_set_input(lctx.inp_s_seq);
return lctx.inp_s_seq;
}
struct ggml_cgraph * build_llama() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
// mutable variable, needed during the last layer of the computation to skip unused tokens
int32_t n_tokens = this->n_tokens;
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_head == hparams.n_rot);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
// 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_rot, rope_type, 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_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
n_tokens = n_outputs;
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
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, "ffn_out", il);
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
if (layer_dir != nullptr) {
cur = ggml_add(ctx0, cur, layer_dir);
}
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);
GGML_ASSERT(n_embd_head == hparams.n_rot);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = model.type == MODEL_7B ? build_inp_pos() : nullptr;
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
// positions of the tokens in the KV cache
struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
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_rot, rope_type, 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_rot, rope_type, 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);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, NULL,
Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, 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_xverse() {
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, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
// positions of the tokens in the KV cache
struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
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);
Qcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
n_rot, rope_type, 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_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, NULL,
Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, 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);
GGML_ASSERT(n_embd_head == hparams.n_rot);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * 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_rot, rope_type, 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_rot, rope_type, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, NULL,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
attn_norm = ggml_get_rows(ctx0, attn_norm, inp_out_ids);
}
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_grok() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
// mutable variable, needed during the last layer of the computation to skip unused tokens
int32_t n_tokens = this->n_tokens;
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_head == hparams.n_rot);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
// multiply by embedding_multiplier_scale of 78.38367176906169
inpL = ggml_scale(ctx0, inpL, 78.38367176906169f);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
// 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_rot, rope_type, 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_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
n_tokens = n_outputs;
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
// Grok
// if attn_out_norm is present then apply it before adding the input
if (model.layers[il].attn_out_norm) {
cur = llm_build_norm(ctx0, cur, hparams,
model.layers[il].attn_out_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_out_norm", il);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
// 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);
//GeLU
cur_gate = ggml_gelu(ctx0, cur_gate);
cb(cur_gate, "ffn_moe_gelu", 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;
// Grok
// if layer_out_norm is present then apply it before adding the input
// Idea: maybe ffn_out_norm is a better name
if (model.layers[il].layer_out_norm) {
cur = llm_build_norm(ctx0, cur, hparams,
model.layers[il].layer_out_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "layer_out_norm", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
if (layer_dir != nullptr) {
cur = ggml_add(ctx0, cur, layer_dir);
}
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);
// Grok
// multiply logits by output_multiplier_scale of 0.5773502691896257
cur = ggml_scale(ctx0, cur, 0.5773502691896257f);
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 * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
struct ggml_tensor * 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);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}
// 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);
GGML_ASSERT(n_embd_head/2 == hparams.n_rot);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * 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, rope_type, 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, rope_type, 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);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Q, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
residual = ggml_get_rows(ctx0, residual, inp_out_ids);
}
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, lctx, hparams, batch, model.tok_embd, cb);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
// positions of the tokens in the KV cache
struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
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);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, NULL,
Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, 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_bert() {
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;
struct ggml_tensor * inp_pos = build_inp_pos();
struct ggml_tensor * inp_mean = build_inp_mean();
struct ggml_tensor * inp_cls = build_inp_cls();
// construct input embeddings (token, type, position)
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
// token types are hardcoded to zero ("Sentence A")
struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
inpL = ggml_add(ctx0, inpL, type_row0);
if (model.arch == LLM_ARCH_BERT) {
inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL);
}
cb(inpL, "inp_embd", -1);
// embed layer norm
inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, LLM_NORM, cb, -1);
cb(inpL, "inp_norm", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask(false);
// iterate layers
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * cur = inpL;
struct ggml_tensor * Qcur;
struct ggml_tensor * Kcur;
struct ggml_tensor * Vcur;
// self-attention
if (model.arch == LLM_ARCH_BERT) {
Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
cb(Qcur, "Qcur", il);
Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk);
cb(Kcur, "Kcur", il);
Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv);
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);
} else {
// compute Q and K and RoPE them
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
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_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
n_rot, rope_type, 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_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
}
struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
cb(kq, "kq", il);
kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, nullptr, 1.0f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias);
cb(kq, "kq_soft_max_ext", il);
struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens)));
cb(v, "v", il);
struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq);
cb(kqv, "kqv", il);
struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
cb(kqv_merged, "kqv_merged", il);
cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
cb(cur, "kqv_merged_cont", il);
ggml_build_forward_expand(gf, cur);
cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
if (model.layers[il].bo) {
cb(cur, "kqv_wo", il);
}
if (model.layers[il].bo) {
cur = ggml_add(ctx0, cur, model.layers[il].bo);
}
cb(cur, "kqv_out", il);
if (il == n_layer - 1 && pooling_type == LLAMA_POOLING_TYPE_NONE) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}
// re-add the layer input
cur = ggml_add(ctx0, cur, inpL);
// attention layer norm
cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, cb, il);
struct ggml_tensor * ffn_inp = cur;
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
if (model.arch == LLM_ARCH_BERT) {
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);
} else {
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);
// attentions bypass the intermediate layer
cur = ggml_add(ctx0, cur, ffn_inp);
// output layer norm
cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].layer_out_norm, model.layers[il].layer_out_norm_b, LLM_NORM, cb, il);
// input for next layer
inpL = cur;
}
// final output
cur = inpL;
cb(cur, "result_embd", -1);
// pooling layer
switch (pooling_type) {
case LLAMA_POOLING_TYPE_NONE:
{
// nop
} break;
case LLAMA_POOLING_TYPE_MEAN:
{
cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
cb(cur, "result_embd_pooled", -1);
} break;
case LLAMA_POOLING_TYPE_CLS:
{
cur = ggml_get_rows(ctx0, cur, inp_cls);
cb(cur, "result_embd_pooled", -1);
} break;
case LLAMA_POOLING_TYPE_UNSPECIFIED:
{
GGML_ASSERT(false && "Invalid pooling type");
} break;
}
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, lctx, hparams, batch, model.tok_embd, cb);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
// positions of the tokens in the KV cache
struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
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);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}
// 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, lctx, hparams, batch, model.tok_embd, cb);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
// positions of the tokens in the KV cache
struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
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
{
cur = attn_norm;
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
if (model.layers[il].bqkv){
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
cb(cur, "bqkv", 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);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}
// 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,
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,
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,
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_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, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm,
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);
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_rot, rope_type, 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_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, NULL,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm,
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, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
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_rot, rope_type, 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_rot, rope_type, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, NULL,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward 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_qwen2() {
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, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
// 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);
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);
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);
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
cb(Vcur, "Vcur", il);
// these nodes are added to the graph together so that they are not reordered
// by doing so, the number of splits in the graph is reduced
ggml_build_forward_expand(gf, Qcur);
ggml_build_forward_expand(gf, Kcur);
ggml_build_forward_expand(gf, Vcur);
Qcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
n_rot, rope_type, 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_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, 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, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
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
{
struct ggml_tensor * Qcur = nullptr;
struct ggml_tensor * Kcur = nullptr;
struct ggml_tensor * Vcur = nullptr;
if (model.layers[il].wqkv) {
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);
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
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)));
} else {
Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
}
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, n_rot, rope_type, 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, n_rot, rope_type, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
attn_norm_output = ggml_get_rows(ctx0, attn_norm_output, inp_out_ids);
}
// 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);
GGML_ASSERT(n_embd_head == hparams.n_rot);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
// 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_rot, n_head, n_tokens), inp_pos,
n_embd_head, rope_type, 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_rot, n_head_kv, n_tokens), inp_pos,
n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, NULL,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
struct ggml_tensor * sa_out = cur;
cur = attention_norm;
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
sa_out = ggml_get_rows(ctx0, sa_out, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}
// 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, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
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);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}
// 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_codeshell() {
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);
GGML_ASSERT(n_embd_head == hparams.n_rot);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
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 * tmpq = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
struct ggml_tensor * tmpk = 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(tmpq, "tmpq", il);
cb(tmpk, "tmpk", il);
cb(Vcur, "Vcur", il);
struct ggml_tensor * Qcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), inp_pos,
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos,
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}
// 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_orion() {
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, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, 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);
// 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_rot, rope_type, 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_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, NULL,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, 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_internlm2() {
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, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
// 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_rot, rope_type, 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_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, 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;
}
// ref: https://arxiv.org/abs/2203.03466
// https://github.com/ggerganov/llama.cpp/issues/5276#issuecomment-1925774738
// based on the original build_llama() function
struct ggml_cgraph * build_minicpm() {
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);
const int64_t n_embd = hparams.n_embd;
//TODO: if the model varies, these parameters need to be read from the model
const int64_t n_embd_base = 256;
const float scale_embd = 12.0f;
const float scale_depth = 1.4f;
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
// scale the input embeddings
inpL = ggml_scale(ctx0, inpL, scale_embd);
cb(inpL, "inp_scaled", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
// 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_rot, rope_type, 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_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
// scale_res - scale the hidden states for residual connection
const float scale_res = scale_depth/sqrtf(float(n_layer));
cur = ggml_scale(ctx0, cur, scale_res);
cb(cur, "hidden_scaled", -1);
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);
}
// scale the hidden states for residual connection
cur = ggml_scale(ctx0, cur, scale_res);
cb(cur, "hidden_scaled_ffn", -1);
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 scaling
const float scale_lmhead = float(n_embd_base)/float(n_embd);
cur = ggml_scale(ctx0, cur, scale_lmhead);
cb(cur, "lmhead_scaling", -1);
// lm_head
cur = ggml_mul_mat(ctx0, model.tok_embd, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_gemma() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head_k = hparams.n_embd_head_k;
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
cb(inpL, "inp_scaled", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
// 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);
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_k, n_head, n_tokens), inp_pos,
n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Qcur, "Qcur", il);
Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));
cb(Qcur, "Qcur_scaled", il);
Kcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos,
n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, NULL,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}
struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
cb(sa_out, "sa_out", il);
cur = llm_build_norm(ctx0, sa_out, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
// 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_GELU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, sa_out);
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_starcoder2() {
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, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, 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);
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_rot, rope_type, 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_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, 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);
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_mamba() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t d_model = n_embd;
const int64_t d_conv = hparams.ssm_d_conv;
const int64_t d_inner = hparams.ssm_d_inner;
GGML_ASSERT(2 * d_model == d_inner);
const int64_t d_state = hparams.ssm_d_state;
const int64_t dt_rank = hparams.ssm_dt_rank;
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
// {n_embd, n_tokens}
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
struct ggml_tensor * state_mask = build_inp_s_mask();
struct ggml_tensor * state_seq = build_inp_s_seq();
for (int il = 0; il < n_layer; ++il) {
// (ab)using the KV cache to store the states
struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size);
struct ggml_tensor * ssm_states = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size);
// clear states of sequences which are starting at the beginning of this batch
{
conv_states = ggml_mul(ctx0,
ggml_view_2d(ctx0, conv_states, conv_states->ne[0], n_kv, conv_states->nb[1], kv_head*conv_states->nb[1]),
state_mask);
ssm_states = ggml_mul(ctx0,
ggml_view_2d(ctx0, ssm_states, ssm_states->ne[0], n_kv, ssm_states->nb[1], kv_head*ssm_states->nb[1]),
state_mask);
}
conv_states = ggml_reshape_3d(ctx0, conv_states, d_conv - 1, d_inner, n_kv);
ssm_states = ggml_reshape_3d(ctx0, ssm_states, d_state, d_inner, n_kv);
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
// {n_embd, 2*d_inner} * {n_embd, n_tokens} => {2*d_inner, n_tokens}
struct ggml_tensor * xz = ggml_mul_mat(ctx0, model.layers[il].ssm_in, cur);
// split the above in two
// => {d_inner, n_tokens}
struct ggml_tensor * x = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], 0);
struct ggml_tensor * z = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], ggml_element_size(xz)*d_inner);
// conv
{
// Custom operator which is needed only to ease simultaneous sequence processing.
// For a single sequence, the equivalent is to concatenate the columns of conv_states and x,
// then make a self-overlapping view of that over d_conv columns at each stride in the 3rd dimension,
// then element-wise multiply that with the conv1d weigth,
// then sum the elements of each row,
// (the last two steps are a dot product over rows (also doable with mul_mat))
// then permute away the ne[0] dimension,
// and then you're left with the resulting x tensor.
// The new conv_states is the last (d_conv - 1) columns
// of the last 3rd dimensional "layer" of the self-overlapping view.
// For simultaneous sequences, it's more complicated.
struct ggml_tensor * x_conv = ggml_ssm_conv(ctx0, conv_states, x, model.layers[il].ssm_conv1d, state_seq);
// store last (d_conv - 1) columns of the conv_state part of x_conv back into the KV cache
ggml_build_forward_expand(gf,
ggml_cpy(ctx0,
ggml_view_2d(ctx0, x_conv, d_conv - 1, d_inner*n_kv, d_conv*ggml_element_size(x_conv), (1+d_inner*n_tokens)*ggml_element_size(x_conv)),
ggml_view_1d(ctx0, kv_self.k_l[il], (d_conv - 1)*(d_inner)*(n_kv), kv_head*(d_conv - 1)*(d_inner)*ggml_element_size(x_conv))));
// extract x from x_conv
x = ggml_view_2d(ctx0, x_conv, d_inner, n_tokens, d_inner*ggml_element_size(x_conv), 0);
// bias
x = ggml_add(ctx0, x, model.layers[il].ssm_conv1d_b);
x = ggml_silu(ctx0, x);
}
// ssm
{
// {d_inner, dt_rank + 2*d_state} * {d_inner, n_tokens} => {dt_rank + 2*d_state, n_tokens}
struct ggml_tensor * x_db = ggml_mul_mat(ctx0, model.layers[il].ssm_x, x);
// split
struct ggml_tensor * dt = ggml_view_2d(ctx0, x_db, dt_rank, n_tokens, x_db->nb[1], 0);
struct ggml_tensor * B = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*dt_rank);
struct ggml_tensor * C = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*(dt_rank+d_state));
// {dt_rank, d_inner} * {dt_rank, n_tokens} => {d_inner, n_tokens}
dt = ggml_mul_mat(ctx0, model.layers[il].ssm_dt, dt);
dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b);
// Custom operator to optimize the parallel associative scan
// as described in the Annex D of the Mamba paper.
// => {d_inner, n_tokens} and {d_state, d_inner, n_kv} combined,
// because only a single tensor can be returned.
struct ggml_tensor * y_ssm_states = ggml_ssm_scan(ctx0, ssm_states, x, dt, model.layers[il].ssm_a, B, C, state_seq);
// store last states (the second part of y_ssm_states)
ggml_build_forward_expand(gf,
ggml_cpy(ctx0,
ggml_view_1d(ctx0, y_ssm_states, d_state*d_inner*n_kv, d_inner*n_tokens*ggml_element_size(y_ssm_states)),
ggml_view_1d(ctx0, kv_self.v_l[il], d_state*d_inner*n_kv, kv_head*d_state*d_inner*ggml_element_size(ssm_states))));
struct ggml_tensor * y = ggml_view_2d(ctx0, y_ssm_states, d_inner, n_tokens, d_inner*ggml_element_size(y_ssm_states), 0);
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
x = ggml_get_rows(ctx0, x, inp_out_ids);
y = ggml_get_rows(ctx0, y, inp_out_ids);
z = ggml_get_rows(ctx0, z, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}
// {d_inner, n_tokens} * {d_inner} => {d_inner, n_tokens}
y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d));
y = ggml_mul(ctx0, y, ggml_silu(ctx0, z));
// {d_inner, n_embd} * {d_inner, n_tokens} => {n_embd, n_tokens}
cur = ggml_mul_mat(ctx0, model.layers[il].ssm_out, y);
}
// residual
cur = ggml_add(ctx0, cur, inpL);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
// final rmsnorm
cur = llm_build_norm(ctx0, inpL, 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_command_r() {
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 float f_logit_scale = hparams.f_logit_scale;
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
for (int il = 0; il < n_layer; ++il) {
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM, cb, il);
cb(cur, "attn_norm", il);
struct ggml_tensor * ffn_inp = 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);
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_rot, rope_type, 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_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
}
struct ggml_tensor * attn_out = cur;
// feed-forward network
{
cur = llm_build_ffn(ctx0, ffn_inp,
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);
}
// add together residual + FFN + self-attention
cur = ggml_add(ctx0, cur, inpL);
cur = ggml_add(ctx0, cur, attn_out);
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);
// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);
if (f_logit_scale) {
cur = ggml_scale(ctx0, cur, f_logit_scale);
}
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
};
static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
llama_batch dummy;
dummy.n_tokens = 0;
llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
struct llm_build_context llm(lctx, dummy, cb, false);
llm.init();
struct ggml_cgraph * result = llm.build_defrag(ids);
llm.free();
return result;
}
static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) {
llama_batch dummy;
dummy.n_tokens = 0;
llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
struct llm_build_context llm(lctx, dummy, cb, false);
llm.init();
struct ggml_cgraph * result = llm.build_k_shift();
llm.free();
return result;
}
static struct ggml_cgraph * llama_build_graph_s_copy(llama_context & lctx) {
llama_batch dummy;
dummy.n_tokens = 0;
llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
struct llm_build_context llm(lctx, dummy, cb, false);
llm.init();
struct ggml_cgraph * result = llm.build_s_copy();
llm.free();
return result;
}
static struct ggml_cgraph * llama_build_graph(
llama_context & lctx,
const llama_batch & batch,
bool worst_case) {
const auto & model = lctx.model;
// this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.)
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);
}
if (!lctx.cparams.offload_kqv) {
if (strcmp(name, "kqv_merged_cont") == 0) {
// all nodes between the KV store and the attention output are run on the CPU
ggml_backend_sched_set_tensor_backend(lctx.sched, cur, lctx.backend_cpu);
}
}
// norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
// FIXME: fix in ggml_backend_sched
const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer;
if (batch.n_tokens < 32 || full_offload) {
if (il != -1 && strcmp(name, "norm") == 0) {
for (auto * backend : lctx.backends) {
if (ggml_backend_buft_supports_backend(lctx.model.buft_layer[il].buft, backend)) {
ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend);
break;
}
}
}
}
};
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_GROK:
{
result = llm.build_grok();
} 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_BERT:
case LLM_ARCH_NOMIC_BERT:
{
result = llm.build_bert();
} 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_QWEN2:
{
result = llm.build_qwen2();
} 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;
case LLM_ARCH_CODESHELL:
{
result = llm.build_codeshell();
} break;
case LLM_ARCH_ORION:
{
result = llm.build_orion();
} break;
case LLM_ARCH_INTERNLM2:
{
result = llm.build_internlm2();
} break;
case LLM_ARCH_MINICPM:
{
result = llm.build_minicpm();
} break;
case LLM_ARCH_GEMMA:
{
result = llm.build_gemma();
} break;
case LLM_ARCH_STARCODER2:
{
result = llm.build_starcoder2();
} break;
case LLM_ARCH_MAMBA:
{
result = llm.build_mamba();
} break;
case LLM_ARCH_XVERSE:
{
result = llm.build_xverse();
} break;
case LLM_ARCH_COMMAND_R:
{
result = llm.build_command_r();
} break;
default:
GGML_ASSERT(false);
}
llm.free();
return result;
}
static void llama_set_k_shift(llama_context & lctx) {
const int64_t kv_size = lctx.kv_self.size;
assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer));
int32_t * data = (int32_t *) lctx.inp_K_shift->data;
for (int i = 0; i < kv_size; ++i) {
data[i] = lctx.kv_self.cells[i].delta;
}
}
static void llama_set_s_copy(llama_context & lctx) {
const int64_t kv_size = lctx.kv_self.size;
assert(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer));
int32_t * data = (int32_t *) lctx.inp_s_copy->data;
for (int i = 0; i < kv_size; ++i) {
data[i] = lctx.kv_self.cells[i].src;
}
}
static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
//
// set input data
//
const auto & hparams = lctx.model.hparams;
const auto & cparams = lctx.cparams;
const auto & kv_self = lctx.kv_self;
if (batch.token) {
const int64_t n_tokens = batch.n_tokens;
ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens));
}
if (batch.embd) {
const int64_t n_embd = hparams.n_embd;
const int64_t n_tokens = batch.n_tokens;
ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
}
if (batch.pos && lctx.inp_pos) {
const int64_t n_tokens = batch.n_tokens;
ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
}
if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer));
int32_t * data = (int32_t *) lctx.inp_out_ids->data;
if (lctx.n_outputs == n_tokens) {
for (int i = 0; i < n_tokens; ++i) {
data[i] = i;
}
} else if (batch.logits) {
int32_t n_outputs = 0;
for (int i = 0; i < n_tokens; ++i) {
if (batch.logits[i]) {
data[n_outputs++] = i;
}
}
// the graph needs to have been passed the correct number of outputs
GGML_ASSERT(lctx.n_outputs == n_outputs);
} else if (lctx.n_outputs == 1) {
// only keep last output
data[0] = n_tokens - 1;
} else {
GGML_ASSERT(lctx.n_outputs == 0);
}
}
GGML_ASSERT(
// (!a || b) is a logical implication (a -> b)
// !hparams.causal_attn -> !cparams.causal_attn
(hparams.causal_attn || !cparams.causal_attn) &&
"causal attention with embedding models is not supported"
);
if (lctx.inp_KQ_mask) {
// NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
if (cparams.causal_attn) {
const int64_t n_kv = kv_self.n;
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
float * data = (float *) lctx.inp_KQ_mask->data;
// For causal attention, use only the previous KV cells
// of the correct sequence for each token of the batch.
// It's assumed that if a token in the batch has multiple sequences, they are equivalent.
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 = 0.0f;
}
data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
}
}
}
} else {
// when using kv cache, the mask needs to match the kv cache size
const int64_t n_tokens = batch.n_tokens;
const int64_t n_stride = hparams.causal_attn ? kv_self.n : n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
float * data = (float *) lctx.inp_KQ_mask->data;
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
const llama_seq_id seq_id = batch.seq_id[j][0];
for (int i = 0; i < n_tokens; ++i) {
float f = -INFINITY;
for (int s = 0; s < batch.n_seq_id[i]; ++s) {
if (batch.seq_id[i][s] == seq_id) {
f = 0.0f;
break;
}
}
data[h*(n_tokens*n_tokens) + j*n_stride + i] = f;
}
for (int i = n_tokens; i < n_stride; ++i) {
data[h*(n_tokens*n_tokens) + j*n_stride + i] = -INFINITY;
}
}
}
}
}
if (hparams.need_kq_pos) {
const int64_t n_kv = kv_self.n;
GGML_ASSERT(lctx.inp_KQ_pos);
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_pos->buffer));
float * data = (float *) lctx.inp_KQ_pos->data;
for (int i = 0; i < n_kv; ++i) {
data[i] = float(lctx.kv_self.cells[i].pos);
}
}
if (cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(lctx.inp_mean);
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
float * data = (float *) lctx.inp_mean->data;
memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean));
std::vector<uint64_t> sum(n_tokens, 0);
for (int i = 0; i < n_tokens; ++i) {
const llama_seq_id seq_id = batch.seq_id[i][0];
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN");
sum[seq_id] += 1;
}
std::vector<float> div(n_tokens, 0.0f);
for (int i = 0; i < n_tokens; ++i) {
const uint64_t s = sum[i];
if (s > 0) {
div[i] = 1.0f/float(s);
}
}
for (int i = 0; i < n_tokens; ++i) {
const llama_seq_id seq_id = batch.seq_id[i][0];
data[seq_id*n_tokens + i] = div[seq_id];
}
}
if (cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(lctx.inp_cls);
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
uint32_t * data = (uint32_t *) lctx.inp_cls->data;
memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
for (int i = 0; i < n_tokens; ++i) {
const llama_seq_id seq_id = batch.seq_id[i][0];
const llama_pos pos = batch.pos[i];
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS");
if (pos == 0) {
data[seq_id] = i;
}
}
}
if (kv_self.recurrent) {
const int64_t n_kv = kv_self.n;
if (lctx.inp_s_mask) {
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_mask->buffer));
float * data = (float *) lctx.inp_s_mask->data;
// states which are not affected by the current batch are left untouched
for (int i = 0; i < n_kv; ++i) {
llama_seq_id seq_id = i + lctx.kv_self.head;
llama_kv_cell & kv_cell = lctx.kv_self.cells[seq_id];
bool has_self_seq = kv_cell.has_seq_id(seq_id);
data[i] = (float) has_self_seq;
// ensure current sequences will be kept
if (!has_self_seq && kv_cell.pos >= 0) {
kv_cell.seq_id.insert(seq_id);
}
}
}
// For Mamba (and other recurrent architectures),
// update the correct state(s)/sequence(s) for each token of the batch.
// Like with the KQ_mask, if a token in the batch has multiple sequences,
// they are assumed to be equivalent (not here, but in ggml_ssm_scan and ggml_ssm_conv).
if (lctx.inp_s_seq) {
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_seq->buffer));
int32_t * data = (int32_t *) lctx.inp_s_seq->data;
for (int j = 0; j < n_tokens; ++j) {
const int32_t n_seq = batch.n_seq_id[j];
GGML_ASSERT(0 < n_seq); // a token should be part of at least 1 sequence
for (int i = 0; i < n_kv; ++i) {
if (i < n_seq) {
// for this type of model, the head is the minimum seq_id of the batch
data[j*n_kv + i] = batch.seq_id[j][i] - kv_self.head;
} else {
data[j*n_kv + i] = -1;
}
}
}
}
}
}
// Make sure enough space is available for outputs.
// Returns max number of outputs for which space was reserved.
static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
const auto & cparams = lctx.cparams;
const auto & hparams = lctx.model.hparams;
const size_t n_outputs_max = std::max(n_outputs, (size_t) cparams.n_seq_max);
const auto n_batch = cparams.n_batch;
const auto n_vocab = hparams.n_vocab;
const auto n_embd = hparams.n_embd;
// TODO: use a per-batch flag for logits presence instead
const bool has_logits = cparams.causal_attn;
const bool has_embd = cparams.embeddings && (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
const size_t embd_size = has_embd ? n_embd*n_outputs_max : 0;
if (lctx.output_ids.empty()) {
// init, never resized afterwards
lctx.output_ids.resize(n_batch);
}
const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output) : 0;
const size_t new_size = (logits_size + embd_size) * sizeof(float);
// alloc only when more than the current capacity is required
// TODO: also consider shrinking the buffer
if (!lctx.buf_output || prev_size < new_size) {
if (lctx.buf_output) {
#ifndef NDEBUG
// This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
#endif
ggml_backend_buffer_free(lctx.buf_output);
lctx.buf_output = nullptr;
lctx.logits = nullptr;
lctx.embd = nullptr;
}
lctx.buf_output = ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), new_size);
if (lctx.buf_output == nullptr) {
LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0));
return 0;
}
}
float * output_base = (float *) ggml_backend_buffer_get_base(lctx.buf_output);
lctx.logits = has_logits ? output_base : nullptr;
lctx.embd = has_embd ? output_base + logits_size : nullptr;
lctx.output_size = n_outputs_max;
lctx.logits_size = logits_size;
lctx.embd_size = embd_size;
// set all ids as invalid (negative)
std::fill(lctx.output_ids.begin(), lctx.output_ids.end(), -1);
ggml_backend_buffer_clear(lctx.buf_output, 0);
lctx.n_outputs = 0;
return n_outputs_max;
}
static void llama_graph_compute(
llama_context & lctx,
ggml_cgraph * gf,
int n_threads) {
#ifdef GGML_USE_MPI
const int64_t n_layer = lctx.model.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_metal)) {
ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads);
}
#endif
if (lctx.backend_cpu != nullptr) {
ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads);
ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data);
}
ggml_backend_sched_graph_compute_async(lctx.sched, gf);
// fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));
#ifdef GGML_USE_MPI
ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
#endif
}
// 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_all) { // TODO: rename back to batch
const uint32_t n_tokens_all = batch_all.n_tokens;
if (n_tokens_all == 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;
GGML_ASSERT((!batch_all.token && batch_all.embd) || (batch_all.token && !batch_all.embd)); // NOLINT
GGML_ASSERT(n_tokens_all <= cparams.n_batch);
GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
if (lctx.t_compute_start_us == 0) {
lctx.t_compute_start_us = ggml_time_us();
}
lctx.n_queued_tokens += n_tokens_all;
#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
auto & kv_self = lctx.kv_self;
const int64_t n_embd = hparams.n_embd;
const int64_t n_vocab = hparams.n_vocab;
uint32_t n_outputs = 0;
uint32_t n_outputs_prev = 0;
const auto n_ubatch = cparams.n_ubatch;
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;
// count outputs
if (batch_all.logits) {
for (uint32_t i = 0; i < n_tokens_all; ++i) {
n_outputs += batch_all.logits[i] != 0;
}
} else if (lctx.logits_all || (cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE)) {
n_outputs = n_tokens_all;
} else {
// keep last output only
n_outputs = 1;
}
// reserve output buffer
if (llama_output_reserve(lctx, n_outputs) < n_outputs) {
LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_outputs);
return -2;
};
// set output mappings
if (batch_all.logits) {
int32_t i_logits = 0;
for (uint32_t i = 0; i < n_tokens_all; ++i) {
if (batch_all.logits[i]) {
lctx.output_ids[i] = i_logits++;
}
}
} else {
for (uint32_t i = 0; i < n_outputs; ++i) {
lctx.output_ids[i] = i;
}
}
for (uint32_t cur_token = 0; cur_token < n_tokens_all; cur_token += n_ubatch) {
const uint32_t n_tokens = std::min(n_ubatch, n_tokens_all - cur_token);
llama_batch u_batch = {
/* .n_tokens = */ (int32_t) n_tokens,
/* .token = */ batch_all.token ? batch_all.token + cur_token : nullptr,
/* .embd = */ batch_all.embd ? batch_all.embd + cur_token*n_embd : nullptr,
/* .pos = */ batch_all.pos ? batch_all.pos + cur_token : nullptr,
/* .n_seq_id = */ batch_all.n_seq_id ? batch_all.n_seq_id + cur_token : nullptr,
/* .seq_id = */ batch_all.seq_id ? batch_all.seq_id + cur_token : nullptr,
/* .logits = */ batch_all.logits ? batch_all.logits + cur_token : nullptr,
/* .all_pos_0 = */ batch_all.all_pos_0 + (llama_pos) cur_token*batch_all.all_pos_1,
/* .all_pos_1 = */ batch_all.all_pos_1,
/* .all_seq_id = */ batch_all.all_seq_id,
};
// count the outputs in this u_batch
{
int32_t n_outputs_new = 0;
if (u_batch.logits) {
for (uint32_t i = 0; i < n_tokens; i++) {
n_outputs_new += u_batch.logits[i] != 0;
}
} else if (n_outputs == n_tokens_all) {
n_outputs_new = n_tokens;
} else {
// keep last output only
if (cur_token + n_tokens >= n_tokens_all) {
n_outputs_new = 1;
}
}
// needs to happen before the graph is built
lctx.n_outputs = n_outputs_new;
}
int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
GGML_ASSERT(n_threads > 0);
// helpers for smoother batch API transition
// after deprecating the llama_eval calls, these will be removed
if (u_batch.pos == nullptr) {
pos.resize(n_tokens);
for (uint32_t i = 0; i < n_tokens; i++) {
pos[i] = u_batch.all_pos_0 + i*u_batch.all_pos_1;
}
u_batch.pos = pos.data();
}
if (u_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] = u_batch.all_seq_id;
seq_id_arr[i] = seq_id[i].data();
}
u_batch.n_seq_id = n_seq_id.data();
u_batch.seq_id = seq_id_arr.data();
}
// non-causal masks do not use the KV cache
if (hparams.causal_attn) {
llama_kv_cache_update(&lctx);
// 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, u_batch)) {
return 1;
}
if (!kv_self.recurrent) {
// 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(kv_self.size, std::max(32u, 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_backend_sched_reset(lctx.sched);
ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);
ggml_cgraph * gf = llama_build_graph(lctx, u_batch, false);
// the output is always the last tensor in the graph
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2];
if (lctx.n_outputs == 0) {
// no output
res = nullptr;
embd = nullptr;
} else if (!hparams.causal_attn) {
res = nullptr; // do not extract logits for embedding models such as BERT
// token or sequence embeddings
embd = gf->nodes[gf->n_nodes - 1];
GGML_ASSERT(strcmp(embd->name, "result_embd") == 0 || strcmp(embd->name, "result_embd_pooled") == 0);
} else if (cparams.embeddings) {
// the embeddings could be in the second to last tensor, or any of the previous tensors
int i_embd = gf->n_nodes - 2;
for (int i = 3; strcmp(embd->name, "result_norm") != 0; ++i) {
i_embd = gf->n_nodes - i;
if (i_embd < 0) { break; }
embd = gf->nodes[i_embd];
}
GGML_ASSERT(i_embd >= 0 && "missing result_norm tensor");
// TODO: use a per-batch flag to know when to skip logits while keeping embeddings
if (!cparams.causal_attn) {
res = nullptr; // do not extract logits when not needed
// skip computing logits
// TODO: is this safe?
gf->n_nodes = i_embd + 1;
}
} else {
embd = nullptr; // do not extract embeddings when not needed
GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
}
// 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
// MoE Special Case: This logic applies when hparams.n_expert == 0, i.e. the model is NOT an MoE model. When an MoE is
// being processed then Accelerate/BLAS will not be involved, so capping would limit performance.
if (n_tokens >= 32 && hparams.n_expert == 0 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
n_threads = std::min(4, n_threads);
}
ggml_backend_sched_alloc_graph(lctx.sched, gf);
llama_set_inputs(lctx, u_batch);
llama_graph_compute(lctx, gf, n_threads);
// update the kv ring buffer
{
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
if (res) {
ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched, res);
GGML_ASSERT(backend_res != nullptr);
GGML_ASSERT(lctx.logits != nullptr);
float * logits_out = lctx.logits + n_outputs_prev*n_vocab;
const int32_t n_outputs_new = lctx.n_outputs;
if (n_outputs_new) {
GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_vocab <= (int64_t) lctx.logits_size);
ggml_backend_tensor_get_async(backend_res, res, logits_out, 0, n_outputs_new*n_vocab*sizeof(float));
}
}
// extract embeddings
if (embd) {
ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd);
GGML_ASSERT(backend_embd != nullptr);
switch (cparams.pooling_type) {
case LLAMA_POOLING_TYPE_NONE:
{
// extract token embeddings
GGML_ASSERT(lctx.embd != nullptr);
float * embd_out = lctx.embd + n_outputs_prev*n_embd;
const int32_t n_outputs_new = lctx.n_outputs;
if (n_outputs_new) {
GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_embd <= (int64_t) lctx.embd_size);
ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_outputs_new*n_embd*sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_CLS:
case LLAMA_POOLING_TYPE_MEAN:
{
GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0);
// extract sequence embeddings
auto & embd_seq_out = lctx.embd_seq;
embd_seq_out.clear();
for (uint32_t i = 0; i < n_tokens; i++) {
const llama_seq_id seq_id = u_batch.seq_id[i][0];
if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
continue;
}
embd_seq_out[seq_id].resize(n_embd);
ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_UNSPECIFIED:
{
GGML_ASSERT(false && "unknown pooling type");
} break;
}
}
n_outputs_prev += lctx.n_outputs;
}
// wait for the computation to finish (automatically done when obtaining the model output)
//llama_synchronize(&lctx);
// decide if we need to defrag the kv cache
if (cparams.causal_attn && cparams.defrag_thold >= 0.0f) {
const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used)/float(kv_self.n) : 0.0f;
// queue defragmentation for next llama_kv_cache_update
if (fragmentation > cparams.defrag_thold) {
//LLAMA_LOG_INFO("fragmentation: %.2f\n", fragmentation);
llama_kv_cache_defrag(kv_self);
}
}
return 0;
}
// find holes from the beginning of the KV cache and fill them by moving data from the end of the cache
static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
auto & kv_self = lctx.kv_self;
const auto & hparams = lctx.model.hparams;
const uint32_t n_layer = hparams.n_layer;
const uint32_t n_kv = llama_kv_cache_cell_max(kv_self);
const uint32_t n_used = kv_self.used;
assert(n_used <= n_kv);
//const int64_t t_start = ggml_time_us();
// number of cells moved
uint32_t n_moves = 0;
// each move requires 6*n_layer tensors (see build_defrag)
// - source view, destination view, copy operation
// - x2 for keys and values
const uint32_t max_moves = LLAMA_MAX_NODES/(6*n_layer);
// determine which KV cells to move where
//
// cell i moves to ids[i]
//
// if ids[i] == i || ids[i] == n_kv, then cell i is not moved
//
std::vector<uint32_t> ids(n_kv, n_kv);
for (uint32_t i0 = 0; i0 < n_used; ++i0) {
const auto & cell0 = kv_self.cells[i0];
if (!cell0.is_empty()) {
ids[i0] = i0;
continue;
}
// found a hole - fill it with data from the end of the cache
uint32_t nh = 1;
// determine the size of the hole
while (i0 + nh < n_used && kv_self.cells[i0 + nh].is_empty()) {
nh++;
}
uint32_t nf = 0;
uint32_t is = n_kv - 1;
// starting from the end, find nh non-empty cells
for (; is > i0; --is) {
const auto & cell1 = kv_self.cells[is];
if (cell1.is_empty() || ids[is] != n_kv) {
continue;
}
// non-empty cell which is not yet moved
nf++;
if (nf == nh) {
break;
}
}
// this can only happen if `n_used` is not accurate, which would be a bug
GGML_ASSERT(nf == nh && "KV defrag bug: nf != nh");
nf = 0;
uint32_t i1 = is;
// are we moving a continuous block of memory?
bool cont = false;
// should we stop searching for the next move?
bool stop = false;
// go back and move the nf cells to the hole
for (; i1 < n_kv; ++i1) {
auto & cell1 = kv_self.cells[i1];
if (cell1.is_empty() || ids[i1] != n_kv) {
if (n_moves == max_moves) {
stop = true;
break;
}
cont = false;
continue;
}
// this cell goes to (i0 + nf)
ids[i1] = i0 + nf;
// move the cell meta data
kv_self.cells[i0 + nf] = cell1;
// clear the old cell and move the head there
cell1 = llama_kv_cell();
kv_self.head = n_used;
if (!cont) {
n_moves++;
cont = true;
}
nf++;
if (nf == nh) {
break;
}
}
if (stop || n_moves == max_moves) {
break;
}
//LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh);
i0 += nh - 1;
}
if (n_moves == 0) {
return;
}
//LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves);
//LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*n_moves*n_layer);
#if 0
// CPU defrag
//
// TODO: optimizations are possible:
// - multiple threads
// - avoid copying to the host memory when already there
//
// likely not worth the effort, as we have ggml_graph based defrag
//
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 kv_size = kv_self.size;
std::vector<uint8_t> buf_k;
std::vector<uint8_t> buf_v;
for (uint32_t il = 0; il < n_layer; ++il) {
const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_size);
const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
const size_t v_size = ggml_row_size (kv_self.v_l[il]->type, n_embd_v_gqa*kv_size);
buf_k.resize(k_size);
buf_v.resize(v_size);
ggml_backend_tensor_get(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
ggml_backend_tensor_get(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
// batch move [i, i+nm) to [id, id+nm)
// note: cells can move only to a lower index
for (uint32_t i = 0; i < n_kv; ++i) {
const uint32_t id = ids[i];
if (i == id || id == n_kv) {
continue;
}
uint32_t nm = 1;
while (i + nm < n_kv && ids[i + nm] == id + nm) {
nm++;
}
// move keys
{
const int64_t os = i*k_size_row;
const int64_t od = id*k_size_row;
memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row);
}
// move values (note: they are transposed)
{
const int64_t os = i;
const int64_t od = id;
for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el);
}
}
i += nm - 1;
}
ggml_backend_tensor_set(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
ggml_backend_tensor_set(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
}
#else
// ggml_graph defrag
ggml_backend_sched_reset(lctx.sched);
ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids);
llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
#endif
//const int64_t t_end = ggml_time_us();
//LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0);
}
static void llama_kv_cache_update_internal(struct llama_context & lctx) {
bool need_reserve = false;
// apply K-shift if needed
if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE && lctx.kv_self.has_shift) {
{
ggml_backend_sched_reset(lctx.sched);
ggml_cgraph * gf = llama_build_graph_k_shift(lctx);
ggml_backend_sched_alloc_graph(lctx.sched, gf);
llama_set_k_shift(lctx);
llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
need_reserve = true;
}
{
auto & kv_self = lctx.kv_self;
kv_self.has_shift = false;
for (uint32_t i = 0; i < kv_self.size; ++i) {
kv_self.cells[i].delta = 0;
}
}
}
if (lctx.kv_self.recurrent && lctx.kv_self.do_copy) {
{
ggml_backend_sched_reset(lctx.sched);
ggml_cgraph * gf = llama_build_graph_s_copy(lctx);
ggml_backend_sched_alloc_graph(lctx.sched, gf);
llama_set_s_copy(lctx);
llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
need_reserve = true;
}
{
auto & kv_self = lctx.kv_self;
kv_self.do_copy = false;
for (uint32_t i = 0; i < kv_self.size; ++i) {
kv_self.cells[i].src = i;
}
}
}
// defragment the KV cache if needed
if (lctx.kv_self.do_defrag) {
llama_kv_cache_defrag_internal(lctx);
need_reserve = true;
lctx.kv_self.do_defrag = false;
}
// reserve a worst case graph again
if (need_reserve) {
// TODO: extract to a function
// build worst-case graph
int n_tokens = (int)std::min(lctx.cparams.n_ctx, lctx.cparams.n_ubatch);
int n_past = lctx.cparams.n_ctx - n_tokens;
llama_token token = llama_token_bos(&lctx.model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
ggml_cgraph * gf = llama_build_graph(lctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true);
// initialize scheduler with the worst-case graph
ggml_backend_sched_reset(lctx.sched);
if (!ggml_backend_sched_reserve(lctx.sched, gf)) {
LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
}
}
}
//
// 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) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL;
}
static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN;
}
static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
}
static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
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) {
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
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_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
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_utf8_to_byte(token_data.text);
}
case LLAMA_VOCAB_TYPE_WPM: {
GGML_ASSERT(false);
}
default:
GGML_ASSERT(false);
}
}
static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
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 };
auto token = vocab.token_to_id.find(buf);
if (token != vocab.token_to_id.end()) {
return (*token).second;
}
// Try to fall back to just the byte as a string
const char buf2[2] = { (char)ch, 0 };
return vocab.token_to_id.at(buf2);
}
case LLAMA_VOCAB_TYPE_WPM:
case LLAMA_VOCAB_TYPE_BPE: {
return vocab.token_to_id.at(unicode_byte_to_utf8(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.
output.reserve(output.size() + symbol.n);
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());
const auto cpts = unicode_cpts_from_utf8(text);
for (size_t i = 0; i < cpts.size(); ++i)
text_utf.emplace_back(unicode_cpt_to_utf8(cpts[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 (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) {
collecting_letter = true;
collecting = true;
}
else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
collecting_numeric = true;
collecting = true;
}
else if (
((unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (unicode_cpt_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) ||
(!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE)
) {
collecting_special = true;
collecting = true;
}
else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) {
collecting_whitespace_lookahead = true;
collecting = true;
}
else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) {
split_condition = true;
}
}
else if (!split_condition && collecting) {
if (collecting_letter && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER) {
split_condition = true;
}
else if (collecting_numeric && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) {
split_condition = true;
}
else if (collecting_special && (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) {
split_condition = true;
}
else if (collecting_whitespace_lookahead && (unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER || unicode_cpt_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 += unicode_byte_to_utf8(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;
};
struct llm_tokenizer_wpm {
llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {}
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
auto * token_map = &vocab.token_to_id;
// normalize and split by whitespace
std::vector<std::string> words = preprocess(text);
// bos token prepended already
// find the longest tokens that form the words
for (const std::string &word : words) {
// skip empty words
if (word.size() == 0) {
continue;
}
// prepend phantom space
std::string word1 = "\xe2\x96\x81" + word;
int n = word1.size();
// we're at the start of a new word
int i = 0;
bool match_any = false;
// move through character position in word
while (i < n) {
// loop through possible match length
bool match = false;
for (int j = n; j > i; j--) {
auto it = token_map->find(word1.substr(i, j - i));
if (it != token_map->end()) {
output.push_back(it->second);
match = true;
match_any = true;
i = j;
break;
}
}
// must be an unknown character
if (!match) {
i++;
}
}
// we didn't find any matches for this word
if (!match_any) {
output.push_back(vocab.special_unk_id);
}
}
// append eos token
output.push_back(vocab.special_eos_id);
}
std::vector<std::string> preprocess(const std::string & text) {
std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
// strip accents, strip control, uniformize whitespace,
// to lowercase, pad chinese characters, pad punctuation
std::string new_str = "";
for (uint32_t code : cpts_nfd) {
int type = unicode_cpt_type(code);
if (type == CODEPOINT_TYPE_ACCENT_MARK || type == CODEPOINT_TYPE_CONTROL) {
continue;
}
code = unicode_tolower(code);
if (type == CODEPOINT_TYPE_WHITESPACE) {
code = ' ';
}
std::string s = unicode_cpt_to_utf8(code);
if (type == CODEPOINT_TYPE_PUNCTUATION || is_ascii_punct(code) || is_chinese_char(code)) {
new_str += " ";
new_str += s;
new_str += " ";
} else {
new_str += s;
}
}
// split by whitespace
uint64_t l = 0;
uint64_t r = 0;
std::vector<std::string> words;
while (r < new_str.size()) {
// if is whitespace
if (isspace(new_str[r], std::locale::classic())) {
if (r > l) words.push_back(new_str.substr(l, (r - l)));
l = r + 1;
r = l;
} else {
r += 1;
}
}
if (r > l) {
words.push_back(new_str.substr(l, (r - l)));
}
return words;
}
bool is_ascii_punct(uint32_t code) {
if (code > 0xFF) {
return false;
}
auto c = char(static_cast<unsigned char>(code));
return ispunct(c, std::locale::classic());
}
bool is_chinese_char(uint32_t cpt) {
if ((cpt >= 0x4E00 && cpt <= 0x9FFF) ||
(cpt >= 0x3400 && cpt <= 0x4DBF) ||
(cpt >= 0x20000 && cpt <= 0x2A6DF) ||
(cpt >= 0x2A700 && cpt <= 0x2B73F) ||
(cpt >= 0x2B740 && cpt <= 0x2B81F) ||
(cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920
(cpt >= 0xF900 && cpt <= 0xFAFF) ||
(cpt >= 0x2F800 && cpt <= 0x2FA1F) ||
(cpt >= 0x3000 && cpt <= 0x303F) ||
(cpt >= 0xFF00 && cpt <= 0xFFEF)) {
return true; // NOLINT
}
return false;
}
const llama_vocab & vocab;
};
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
LLAMA_LOG_WARN("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
LLAMA_LOG_WARN("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
LLAMA_LOG_WARN("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
LLAMA_LOG_WARN("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()) {
if (vocab.add_space_prefix) {
raw_text = " " + raw_text; // prefix with space if the first token is not special
}
}
#ifdef PRETOKENIZERDEBUG
LLAMA_LOG_WARN("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
LLAMA_LOG_WARN("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;
case LLAMA_VOCAB_TYPE_WPM:
{
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
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif
llm_tokenizer_wpm tokenizer(vocab);
tokenizer.tokenize(raw_text, output);
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
output.push_back(fragment.token);
}
}
} break;
case LLAMA_VOCAB_TYPE_NONE:
GGML_ASSERT(false);
}
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 = vec_rules[start_rule_index].data();
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) {
// TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast
// if (k >= (int32_t)candidates->size) {
// return;
// }
const int64_t t_start_sample_us = ggml_time_us();
if (k <= 0) {
k = candidates->size;
}
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 <= 128) {
std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
} else {
constexpr int nbuckets = 128;
constexpr float bucket_low = -10.0f;
constexpr float bucket_high = 10.0f;
constexpr float bucket_scale = nbuckets/(bucket_high - bucket_low);
constexpr float bucker_inter = -bucket_low * bucket_scale;
std::vector<int> bucket_idx(candidates->size);
std::vector<int> histo(nbuckets, 0);
for (int i = 0; i < (int)candidates->size; ++i) {
const float val = candidates->data[i].logit;
int ib = int(bucket_scale * val + bucker_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low);
ib = std::max(0, std::min(nbuckets-1, ib));
bucket_idx[i] = ib;
++histo[ib];
}
int nhave = 0;
int ib = nbuckets - 1;
for ( ; ib >= 0; --ib) {
nhave += histo[ib];
if (nhave >= k) break;
}
std::vector<llama_token_data> tmp_tokens(nhave);
auto ptr = tmp_tokens.data();
std::vector<llama_token_data*> bucket_ptrs;
bucket_ptrs.reserve(nbuckets - ib);
for (int j = nbuckets - 1; j >= ib; --j) {
bucket_ptrs.push_back(ptr);
ptr += histo[j];
}
for (int i = 0; i < (int)candidates->size; ++i) {
int j = bucket_idx[i];
if (j >= ib) {
*bucket_ptrs[nbuckets-1-j]++ = candidates->data[i];
}
}
ptr = tmp_tokens.data();
int ndone = 0;
for (int j = nbuckets-1; j > ib; --j) {
std::sort(ptr, ptr + histo[j], comp);
ptr += histo[j];
ndone += histo[j];
}
std::partial_sort(ptr, ptr + k - ndone, ptr + histo[ib], comp);
std::memcpy(candidates->data, tmp_tokens.data(), k*sizeof(llama_token_data));
}
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;
}
const int64_t t_start_sample_us = ggml_time_us();
bool min_p_applied = false;
// if the candidates aren't sorted, try the unsorted implementation first
if (!candidates->sorted) {
std::vector<llama_token_data> filtered_tokens;
float max_logit = -FLT_MAX;
for (size_t i = 0; i < candidates->size; ++i) {
max_logit = std::max(max_logit, candidates->data[i].logit);
}
const float min_logit = max_logit + logf(p); // min logit for p_i >= p * p_max
for (size_t i = 0; i < candidates->size; ++i) {
if (candidates->data[i].logit >= min_logit) {
filtered_tokens.push_back(candidates->data[i]);
}
}
// if we have enough values the operation was a success
if (filtered_tokens.size() >= min_keep) {
memcpy(candidates->data, filtered_tokens.data(), filtered_tokens.size()*sizeof(llama_token_data));
candidates->size = filtered_tokens.size();
min_p_applied = true;
}
}
// if the candidates are sorted or the unsorted implementation failed, use this implementation
if (!min_p_applied) {
// 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;
}
const float min_logit = candidates->data[0].logit + logf(p); // min logit for p_i >= p * p_max
size_t i = 1; // first token always matches
for (; i < candidates->size; ++i) {
if (candidates->data[i].logit < min_logit && 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_entropy(struct llama_context * ctx, llama_token_data_array * candidates_p, float min_temp, float max_temp, float exponent_val) {
const int64_t t_start_sample_us = ggml_time_us();
// no need to do anything if there is only one (or zero) candidates
if(candidates_p->size <= 1) {
return;
}
// Calculate maximum possible entropy
float max_entropy = -logf(1.0f / candidates_p->size);
llama_sample_softmax(nullptr, candidates_p);
// Calculate entropy of the softmax probabilities
float entropy = 0.0f;
for (size_t i = 0; i < candidates_p->size; ++i) {
float prob = candidates_p->data[i].p;
if (prob > 0.0f) { // Ensure no log(0)
entropy -= prob * logf(prob);
}
}
// Normalize the entropy (max_entropy cannot be 0 here because we checked candidates_p->size != 1 above)
float normalized_entropy = entropy / max_entropy;
// Map the normalized entropy to the desired temperature range using the power function
float dyn_temp = min_temp + (max_temp - min_temp) * powf(normalized_entropy, exponent_val);
#ifdef DEBUG
LLAMA_LOG_INFO("Your text maxtemp value is: %f\n", max_temp);
LLAMA_LOG_INFO("Entropy: %f\n", entropy);
LLAMA_LOG_INFO("Max Possible Entropy: %f\n", max_entropy);
LLAMA_LOG_INFO("Normalized Entropy: %f\n", normalized_entropy);
LLAMA_LOG_INFO("Exponent: %f\n", exponent_val);
LLAMA_LOG_INFO("Dynamic Temperature (dyn_temp): %f\n", dyn_temp);
#endif
// Apply the dynamically calculated temperature scaling
for (size_t i = 0; i < candidates_p->size; ++i) {
candidates_p->data[i].logit /= dyn_temp;
}
// Re-compute softmax probabilities after scaling logits with dynamic temperature
double max_l_double = candidates_p->data[0].logit;
double cum_sum_double = 0.0;
for (size_t i = 0; i < candidates_p->size; ++i) {
double p = exp(candidates_p->data[i].logit - max_l_double);
candidates_p->data[i].p = p; // Store the scaled probability
cum_sum_double += p;
}
for (size_t i = 0; i < candidates_p->size; ++i) {
candidates_p->data[i].p /= cum_sum_double; // Re-normalize the probabilities
}
#ifdef DEBUG
// Print the updated top 25 probabilities after temperature scaling
LLAMA_LOG_INFO("\nUpdated Top 25 Probabilities After Dynamic Temperature Scaling (in percentages):\n");
for (size_t i = 0; i < 25 && i < candidates_p->size; ++i) {
LLAMA_LOG_INFO("Token %zu: %f%%\n", i + 1, candidates_p->data[i].p * 100.0f);
}
#endif
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_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_apply_guidance(
struct llama_context * ctx,
float * logits,
float * logits_guidance,
float scale) {
GGML_ASSERT(ctx);
const auto t_start_sample_us = ggml_time_us();
const auto n_vocab = llama_n_vocab(llama_get_model(ctx));
llama_log_softmax(logits, n_vocab);
llama_log_softmax(logits_guidance, n_vocab);
for (int i = 0; i < n_vocab; ++i) {
auto & l = logits[i];
const auto & g = logits_guidance[i];
l = scale * (l - g) + g;
}
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_ffn_down = 0;
int n_ffn_gate = 0;
int n_ffn_up = 0;
int i_attention_wv = 0;
int i_ffn_down = 0;
int i_ffn_gate = 0;
int i_ffn_up = 0;
int n_k_quantized = 0;
int n_fallback = 0;
bool has_imatrix = false;
// used to figure out if a model shares tok_embd with the output weight
bool has_output = false;
quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
: model(model)
, params(params)
{}
};
static void llama_tensor_dequantize_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 llama_tensor_get_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;
};
const int n_expert = std::max(1, (int)qs.model.hparams.n_expert);
auto layer_info = [n_expert] (int i_layer, int n_layer, const char * name) {
if (n_expert > 1) {
// Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but iccasionally randomly
// sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work
// for getting the current layer as I initially thought, and we need to resort to parsing the
// tensor name.
n_layer /= n_expert;
if (sscanf(name, "blk.%d.", &i_layer) != 1) {
throw std::runtime_error(format("Failed to determine layer for tensor %s", name));
}
if (i_layer < 0 || i_layer >= n_layer) {
throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name, n_layer));
}
}
return std::make_pair(i_layer, n_layer);
};
// for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings
// with the quantization of the output tensor
if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) {
if (qs.params->output_tensor_type < GGML_TYPE_COUNT) {
new_type = qs.params->output_tensor_type;
} else {
int nx = tensor->ne[0];
if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
new_type = GGML_TYPE_Q8_0;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
new_type = GGML_TYPE_Q5_K;
}
else if (new_type != GGML_TYPE_Q8_0) {
new_type = GGML_TYPE_Q6_K;
}
}
} else if (name == "token_embd.weight") {
if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
new_type = qs.params->token_embedding_type;
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
new_type = GGML_TYPE_Q2_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
new_type = GGML_TYPE_IQ3_S;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
new_type = GGML_TYPE_IQ3_S;
}
}
} else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
if (name.find("attn_v.weight") != std::string::npos) {
if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
++qs.i_attention_wv;
}
else if (qs.model.hparams.n_expert == 8 && name.find("attn_k.weight") != std::string::npos) {
new_type = GGML_TYPE_Q4_K;
}
else if (name.find("ffn_down") != std::string::npos) {
if (qs.i_ffn_down < qs.n_ffn_down/8) {
new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
}
++qs.i_ffn_down;
}
else if (name.find("attn_output.weight") != std::string::npos) {
if (qs.model.hparams.n_expert == 8) {
new_type = GGML_TYPE_Q5_K;
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_XXS;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
}
}
} else if (name.find("attn_v.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS;
}
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S) && qs.model.hparams.n_gqa() >= 4) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
new_type = GGML_TYPE_Q4_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_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && qs.model.hparams.n_gqa() >= 4) {
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 (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
new_type = GGML_TYPE_IQ3_XXS;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
new_type = GGML_TYPE_IQ2_S;
}
} else if (name.find("attn_q.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
new_type = GGML_TYPE_IQ3_XXS;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
new_type = GGML_TYPE_IQ2_S;
}
} else if (name.find("ffn_down") != std::string::npos) {
auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str());
int i_layer = info.first, n_layer = info.second;
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K
: arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
: GGML_TYPE_Q3_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M && (i_layer < n_layer/8 ||
(qs.model.hparams.n_expert == 8 && use_more_bits(i_layer, n_layer)))) {
new_type = GGML_TYPE_Q4_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 = i_layer < n_layer/16 ? GGML_TYPE_Q6_K :
use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
} else {
if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
}
}
else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && !qs.has_imatrix) {
new_type = GGML_TYPE_Q5_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) {
new_type = GGML_TYPE_Q5_K;
}
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || ftype == LLAMA_FTYPE_MOSTLY_Q5_0)
&& qs.has_imatrix && i_layer < n_layer/8) {
// Guard against craziness in the first few ffn_down layers that can happen even with imatrix for Q4_0/Q5_0.
// We only do it when an imatrix is provided because a) we want to make sure that one can always get the
// same quantization as before imatrix stuff, and b) Q4_1/Q5_1 do go crazy on ffn_down without an imatrix.
new_type = ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q5_1;
}
++qs.i_ffn_down;
} else if (name.find("attn_output.weight") != std::string::npos) {
if (arch != LLM_ARCH_FALCON) {
if (qs.model.hparams.n_expert == 8) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL ||
ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S ||
ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) {
new_type = GGML_TYPE_Q5_K;
}
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_IQ3_S;
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_IQ3_M ) new_type = GGML_TYPE_Q4_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 || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
}
else if (name.find("ffn_gate") != std::string::npos) {
auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str());
int i_layer = info.first, n_layer = info.second;
if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
new_type = GGML_TYPE_IQ3_XXS;
}
++qs.i_ffn_gate;
}
else if (name.find("ffn_up") != std::string::npos) {
auto info = layer_info(qs.i_ffn_up, qs.n_ffn_up, name.c_str());
int i_layer = info.first, n_layer = info.second;
if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
new_type = GGML_TYPE_IQ3_XXS;
}
++qs.i_ffn_up;
}
// if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_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 || new_type == GGML_TYPE_IQ4_XS ||
new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S ||
new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S || new_type == GGML_TYPE_IQ3_S ||
new_type == GGML_TYPE_IQ1_M) {
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_IQ2_XXS:
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; 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 size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int chunk_size, int nrows, int n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
std::mutex mutex;
int counter = 0;
size_t new_size = 0;
if (nthread < 2) {
// single-thread
return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
}
auto compute = [&mutex, &counter, &new_size, new_type, f32_data, new_data, chunk_size,
nrows, n_per_row, imatrix]() {
const int nrows_per_chunk = chunk_size / n_per_row;
size_t local_size = 0;
while (true) {
std::unique_lock<std::mutex> lock(mutex);
int first_row = counter; counter += nrows_per_chunk;
if (first_row >= nrows) {
if (local_size > 0) {
new_size += local_size;
}
break;
}
lock.unlock();
const int this_nrow = std::min(nrows - first_row, nrows_per_chunk);
local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
}
};
for (int it = 0; it < nthread - 1; ++it) {
workers.emplace_back(compute);
}
compute();
for (auto & w : workers) { w.join(); }
workers.clear();
return new_size;
}
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
ggml_type default_type;
llama_ftype ftype = params->ftype;
switch (params->ftype) {
case LLAMA_FTYPE_MOSTLY_Q4_0: default_type = GGML_TYPE_Q4_0; break;
case LLAMA_FTYPE_MOSTLY_Q4_1: default_type = GGML_TYPE_Q4_1; break;
case LLAMA_FTYPE_MOSTLY_Q5_0: default_type = GGML_TYPE_Q5_0; break;
case LLAMA_FTYPE_MOSTLY_Q5_1: default_type = GGML_TYPE_Q5_1; break;
case LLAMA_FTYPE_MOSTLY_Q8_0: default_type = GGML_TYPE_Q8_0; break;
case LLAMA_FTYPE_MOSTLY_F16: default_type = GGML_TYPE_F16; break;
case LLAMA_FTYPE_ALL_F32: default_type = GGML_TYPE_F32; break;
// K-quants
case LLAMA_FTYPE_MOSTLY_Q2_K_S:
case LLAMA_FTYPE_MOSTLY_Q2_K: default_type = GGML_TYPE_Q2_K; break;
case LLAMA_FTYPE_MOSTLY_IQ3_XS: default_type = GGML_TYPE_IQ3_S; break;
case LLAMA_FTYPE_MOSTLY_Q3_K_S:
case LLAMA_FTYPE_MOSTLY_Q3_K_M:
case LLAMA_FTYPE_MOSTLY_Q3_K_L: default_type = GGML_TYPE_Q3_K; break;
case LLAMA_FTYPE_MOSTLY_Q4_K_S:
case LLAMA_FTYPE_MOSTLY_Q4_K_M: default_type = GGML_TYPE_Q4_K; break;
case LLAMA_FTYPE_MOSTLY_Q5_K_S:
case LLAMA_FTYPE_MOSTLY_Q5_K_M: default_type = GGML_TYPE_Q5_K; break;
case LLAMA_FTYPE_MOSTLY_Q6_K: default_type = GGML_TYPE_Q6_K; break;
case LLAMA_FTYPE_MOSTLY_IQ2_XXS: default_type = GGML_TYPE_IQ2_XXS; break;
case LLAMA_FTYPE_MOSTLY_IQ2_XS: default_type = GGML_TYPE_IQ2_XS; break;
case LLAMA_FTYPE_MOSTLY_IQ2_S: default_type = GGML_TYPE_IQ2_XS; break;
case LLAMA_FTYPE_MOSTLY_IQ2_M: default_type = GGML_TYPE_IQ2_S; break;
case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break;
case LLAMA_FTYPE_MOSTLY_IQ1_S: default_type = GGML_TYPE_IQ1_S; break;
case LLAMA_FTYPE_MOSTLY_IQ1_M: default_type = GGML_TYPE_IQ1_M; break;
case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break;
case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break;
case LLAMA_FTYPE_MOSTLY_IQ3_S: default_type = GGML_TYPE_IQ3_S; break;
case LLAMA_FTYPE_MOSTLY_IQ3_M: default_type = GGML_TYPE_IQ3_S; 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_kv_override * kv_overrides = nullptr;
if (params->kv_overrides) {
auto v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
kv_overrides = v->data();
}
llama_model_loader ml(fname_inp, use_mmap, kv_overrides);
ml.init_mappings(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 std::unordered_map<std::string, std::vector<float>> * imatrix_data = nullptr;
if (params->imatrix) {
imatrix_data = static_cast<const std::unordered_map<std::string, std::vector<float>>*>(params->imatrix);
if (imatrix_data) {
LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size()));
qs.has_imatrix = true;
}
}
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.meta);
gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
gguf_set_val_u32(ctx_out, "general.file_type", ftype);
if (params->kv_overrides) {
const std::vector<llama_model_kv_override> & overrides = *(const std::vector<llama_model_kv_override> *)params->kv_overrides;
for (auto & o : overrides) {
if (o.key[0] == 0) break;
if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) {
gguf_set_val_f32(ctx_out, o.key, o.float_value);
} else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) {
gguf_set_val_i32(ctx_out, o.key, o.int_value);
} else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) {
gguf_set_val_bool(ctx_out, o.key, o.bool_value);
} else {
LLAMA_LOG_WARN("%s: unknown KV override type for key %s\n", __func__, o.key);
}
}
}
for (int i = 0; i < ml.n_tensors; ++i) {
const 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_ffn_down;
} else if (name.find("ffn_gate") != std::string::npos) {
++qs.n_ffn_gate;
} else if (name.find("ffn_up") != std::string::npos) {
++qs.n_ffn_up;
} else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) {
qs.has_output = true;
}
}
if (qs.n_attention_wv != qs.n_ffn_down || (uint32_t) qs.n_attention_wv != model.hparams.n_layer) {
LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_ffn_down = %d, hparams.n_layer = %d\n",
__func__, qs.n_attention_wv, qs.n_ffn_down, model.hparams.n_layer);
}
size_t total_size_org = 0;
size_t total_size_new = 0;
std::vector<std::thread> workers;
workers.reserve(nthread);
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) {
const 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
// NOTE: can't use LLM_TN here because the layer number is not known
quantize &= name.find("ffn_gate_inp.weight") == std::string::npos;
// do not quantize positional embeddings and token types (BERT)
quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD, "weight");
quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight");
// do not quantize Mamba's small yet 2D weights
// NOTE: can't use LLM_TN here because the layer number is not known
quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
quantize &= name.find("ssm_x.weight") == std::string::npos;
quantize &= name.find("ssm_dt.weight") == std::string::npos;
enum ggml_type new_type;
void * new_data;
size_t new_size;
if (quantize) {
new_type = default_type;
// get more optimal quantization type based on the tensor shape, layer, etc.
if (!params->pure && ggml_is_quantized(default_type)) {
new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
}
else if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
new_type = params->token_embedding_type;
}
else if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) {
new_type = params->output_tensor_type;
}
// 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);
const float * imatrix = nullptr;
if (imatrix_data) {
auto it = imatrix_data->find(tensor->name);
if (it == imatrix_data->end()) {
LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name);
} else {
if (it->second.size() == (size_t)tensor->ne[0]) {
imatrix = it->second.data();
} else {
LLAMA_LOG_INFO("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__,
int(it->second.size()), int(tensor->ne[0]), tensor->name);
}
}
}
if ((new_type == GGML_TYPE_IQ2_XXS ||
new_type == GGML_TYPE_IQ2_XS ||
new_type == GGML_TYPE_IQ2_S ||
new_type == GGML_TYPE_IQ1_S ||
(new_type == GGML_TYPE_IQ1_M && strcmp(tensor->name, "token_embd.weight") && strcmp(tensor->name, "output.weight")) ||
(new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) {
LLAMA_LOG_ERROR("\n\n============================================================\n");
LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);
LLAMA_LOG_ERROR("The result will be garbage, so bailing out\n");
LLAMA_LOG_ERROR("============================================================\n\n");
throw std::runtime_error(format("Missing importance matrix for tensor %s in a very low-bit quantization", tensor->name));
}
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_tensor_dequantize_internal(tensor, f32_conv_buf, workers, nelements, nthread);
f32_data = (float *) f32_conv_buf.data();
}
LLAMA_LOG_INFO("converting 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();
const int n_per_row = tensor->ne[0];
const int nrows = nelements / n_per_row;
static const int min_chunk_size = 32 * 512;
const int chunk_size = n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row);
const int nchunk = (nelements + chunk_size - 1)/chunk_size;
const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
new_size = llama_tensor_quantize_internal(new_type, f32_data, new_data, chunk_size, nrows, n_per_row, imatrix, workers, nthread_use);
LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
}
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);
if (qs.n_fallback > 0) {
LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) 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);
// 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_mappings(/*prefetch*/ false); // no prefetching
}
struct tensor_meta {
std::string name;
ggml_type type;
int32_t ne[2];
size_t offset;
};
std::map<std::string, tensor_meta> tensor_meta_map;
// load all tensor meta
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 < GGML_MAX_NAME);
char buf[GGML_MAX_NAME];
fin.read_raw(buf, name_len);
name = std::string(buf, name_len);
}
// check for lora suffix
std::string lora_suffix;
if (name.length() > 6) {
lora_suffix = name.substr(name.length() - 6);
}
if (lora_suffix != ".loraA" && lora_suffix != ".loraB") {
LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
return 1;
}
// tensor type
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 1;
}
}
// data offset
size_t offset = fin.tell();
offset = (offset + 31) & -32;
// skip tensor data
fin.seek(offset + ggml_row_size(wtype, ne[0]) * ne[1], SEEK_SET);
tensor_meta_map.emplace(name, tensor_meta{ name, wtype, { ne[0], ne[1] }, offset });
}
bool warned = false;
int n_tensors = 0;
// apply
ggml_backend_t backend_cpu = ggml_backend_cpu_init();
if (backend_cpu == nullptr) {
LLAMA_LOG_ERROR("%s: error: failed to initialize cpu backend\n", __func__);
return 1;
}
ggml_backend_cpu_set_n_threads(backend_cpu, n_threads);
std::vector<no_init<uint8_t>> read_buf;
for (const auto & it : model.tensors_by_name) {
const std::string & base_name = it.first;
ggml_tensor * model_t = it.second;
if (tensor_meta_map.find(base_name + ".loraA") == tensor_meta_map.end() ||
tensor_meta_map.find(base_name + ".loraB") == tensor_meta_map.end()) {
continue;
}
tensor_meta & metaA = tensor_meta_map.at(base_name + ".loraA");
tensor_meta & metaB = tensor_meta_map.at(base_name + ".loraB");
ggml_init_params lora_init_params = {
/* .mem_size */ ggml_tensor_overhead()*128 + ggml_graph_overhead(),
/* .mem_buffer */ nullptr,
/* .no_alloc */ true,
};
ggml_context * lora_ctx = ggml_init(lora_init_params);
if (lora_ctx == nullptr) {
LLAMA_LOG_ERROR("%s: error: failed to initialize lora context\n", __func__);
ggml_backend_free(backend_cpu);
return 1;
}
// create tensors
ggml_tensor * loraA = ggml_new_tensor_2d(lora_ctx, metaA.type, metaA.ne[0], metaA.ne[1]);
ggml_tensor * loraB = ggml_new_tensor_2d(lora_ctx, metaB.type, metaB.ne[0], metaB.ne[1]);
ggml_set_name(loraA, metaA.name.c_str());
ggml_set_name(loraB, metaB.name.c_str());
ggml_tensor * base_t;
if (ml) {
if (!ml->get_tensor_meta(base_name.c_str())) {
LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
return 1;
}
base_t = ggml_dup_tensor(lora_ctx, ml->get_tensor_meta(base_name.c_str()));
} else {
base_t = ggml_dup_tensor(lora_ctx, model_t);
}
ggml_set_name(base_t, base_name.c_str());
// allocate in backend buffer
ggml_backend_buffer_t lora_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
if (lora_buf == nullptr) {
LLAMA_LOG_ERROR("%s: error: failed to allocate lora tensors\n", __func__);
return 1;
}
// load tensor data
auto load_tensor = [&read_buf, &fin](const tensor_meta & tensor_meta, ggml_tensor * tensor) {
read_buf.resize(ggml_nbytes(tensor));
fin.seek(tensor_meta.offset, SEEK_SET);
fin.read_raw(read_buf.data(), ggml_nbytes(tensor));
ggml_backend_tensor_set(tensor, read_buf.data(), 0, read_buf.size());
};
load_tensor(metaA, loraA);
load_tensor(metaB, loraB);
// load base model tensor data
if (ml) {
ml->load_data_for(base_t);
} else {
ggml_backend_tensor_copy(model_t, base_t);
}
if (ggml_is_quantized(base_t->type) && !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;
}
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]);
ggml_free(lora_ctx);
ggml_backend_buffer_free(lora_buf);
ggml_backend_free(backend_cpu);
return 1;
}
auto build_lora_graph = [&]() {
// w = w + BA*s
ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
ggml_set_name(BA, "BA");
if (scaling != 1.0f) {
BA = ggml_scale(lora_ctx, BA, scaling);
ggml_set_name(BA, "BA_scaled");
}
ggml_tensor * r;
r = ggml_add_inplace(lora_ctx, base_t, BA);
ggml_set_name(r, "r_add");
if (base_t->type != model_t->type) {
// convert the result to the model type
r = ggml_cast(lora_ctx, r, model_t->type);
ggml_set_name(r, "r_cast");
}
return r;
};
ggml_cgraph * gf = ggml_new_graph(lora_ctx);
ggml_tensor * r = build_lora_graph();
ggml_build_forward_expand(gf, r);
ggml_backend_buffer_t graph_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
if (graph_buf == nullptr) {
LLAMA_LOG_ERROR("%s: error: failed to allocate graph tensors\n", __func__);
ggml_free(lora_ctx);
ggml_backend_buffer_free(lora_buf);
ggml_backend_free(backend_cpu);
return 1;
}
ggml_backend_graph_compute(backend_cpu, gf);
ggml_backend_tensor_set(model_t, r->data, 0, ggml_nbytes(r));
#if 0
// TODO: use scheduler with fallback to CPU for less copies between CPU and GPU
//ggml_backend_sched_t sched = ggml_backend_sched_new(backends.data(), backends.size(), GGML_DEFAULT_GRAPH_SIZE);
// sched compute
ggml_build_forward_expand(gf, build_graph());
ggml_backend_sched_init_measure(sched, gf);
// create the graph again, since the previous one was destroyed by the measure
ggml_graph_clear(gf);
ggml_build_forward_expand(gf, build_graph());
ggml_backend_sched_graph_compute(sched, gf);
ggml_backend_sched_free(sched);
#endif
ggml_backend_buffer_free(lora_buf);
ggml_backend_buffer_free(graph_buf);
ggml_free(lora_ctx);
n_tensors++;
if (n_tensors % 4 == 0) {
LLAMA_LOG_INFO(".");
}
}
ggml_backend_free(backend_cpu);
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,
/*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER,
/*.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
// note: we usually have plenty of VRAM, so by default offload all layers to the GPU
result.n_gpu_layers = 999;
#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 =*/ 2048,
/*.n_ubatch =*/ 512,
/*.n_seq_max =*/ 1,
/*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
/*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS,
/*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
/*.pooling_type =*/ LLAMA_POOLING_TYPE_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,
/*.defrag_thold =*/ -1.0f,
/*.cb_eval =*/ nullptr,
/*.cb_eval_user_data =*/ nullptr,
/*.type_k =*/ GGML_TYPE_F16,
/*.type_v =*/ GGML_TYPE_F16,
/*.logits_all =*/ false,
/*.embeddings =*/ false,
/*.offload_kqv =*/ true,
/*.abort_callback =*/ nullptr,
/*.abort_callback_data =*/ nullptr,
};
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,
/*.output_tensor_type =*/ GGML_TYPE_COUNT,
/*.token_embedding_type =*/ GGML_TYPE_COUNT,
/*.allow_requantize =*/ false,
/*.quantize_output_tensor =*/ true,
/*.only_copy =*/ false,
/*.pure =*/ false,
/*.imatrix =*/ nullptr,
/*.kv_overrides =*/ nullptr,
};
return result;
}
size_t llama_max_devices(void) {
#if defined(GGML_USE_METAL)
return 1;
#elif defined(GGML_USE_CUDA)
return GGML_CUDA_MAX_DEVICES;
#elif defined(GGML_USE_SYCL)
return GGML_SYCL_MAX_DEVICES;
#elif defined(GGML_USE_VULKAN)
return GGML_VK_MAX_DEVICES;
#else
return 1;
#endif
}
bool llama_supports_mmap(void) {
return llama_mmap::SUPPORTED;
}
bool llama_supports_mlock(void) {
return llama_mlock::SUPPORTED;
}
bool llama_supports_gpu_offload(void) {
#if defined(GGML_USE_CUDA) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \
defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE)
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
return true;
#else
return false;
#endif
}
void llama_backend_init(void) {
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);
}
#ifdef GGML_USE_MPI
ggml_mpi_backend_init();
#endif
}
void llama_numa_init(enum ggml_numa_strategy numa) {
if (numa != GGML_NUMA_STRATEGY_DISABLED) {
ggml_numa_init(numa);
}
}
void llama_backend_free(void) {
#ifdef GGML_USE_MPI
ggml_mpi_backend_free();
#endif
ggml_quantize_free();
}
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) {
LLAMA_LOG_ERROR("%s: model cannot be NULL\n", __func__);
return nullptr;
}
if (params.n_batch == 0 && params.n_ubatch == 0) {
LLAMA_LOG_ERROR("%s: n_batch and n_ubatch cannot both be zero\n", __func__);
return nullptr;
}
if (params.n_ctx == 0 && model->hparams.n_ctx_train == 0) {
LLAMA_LOG_ERROR("%s: n_ctx and model->hparams.n_ctx_train cannot both be zero\n", __func__);
return nullptr;
}
llama_context * ctx = new llama_context(*model);
const auto & hparams = model->hparams;
auto & cparams = ctx->cparams;
cparams.n_seq_max = std::max(1u, params.n_seq_max);
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.defrag_thold = params.defrag_thold;
cparams.embeddings = params.embeddings;
cparams.offload_kqv = params.offload_kqv;
cparams.pooling_type = params.pooling_type;
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;
// this is necessary due to kv_self.n being padded later during inference
cparams.n_ctx = GGML_PAD(cparams.n_ctx, 32);
// with causal attention, the batch size is limited by the context size
cparams.n_batch = hparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch;
cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
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;
cparams.cb_eval = params.cb_eval;
cparams.cb_eval_user_data = params.cb_eval_user_data;
auto rope_scaling_type = params.rope_scaling_type;
if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) {
rope_scaling_type = hparams.rope_scaling_type_train;
}
if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_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_TYPE_YARN ? 1.0f : 0.0f;
}
cparams.causal_attn = hparams.causal_attn;
if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
if (hparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
cparams.pooling_type = LLAMA_POOLING_TYPE_NONE;
} else {
cparams.pooling_type = hparams.pooling_type;
}
}
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: n_batch = %u\n", __func__, cparams.n_batch);
LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch);
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->abort_callback = params.abort_callback;
ctx->abort_callback_data = params.abort_callback_data;
ctx->rng = std::mt19937(params.seed);
ctx->logits_all = params.logits_all;
uint32_t kv_size = cparams.n_ctx;
ggml_type type_k = params.type_k;
ggml_type type_v = params.type_v;
// Mamba only needs a constant number of KV cache cells per sequence
if (model->arch == LLM_ARCH_MAMBA) {
// Mamba needs at least as many KV cells as there are sequences kept at any time
kv_size = std::max((uint32_t) 1, params.n_seq_max);
// it's probably best to keep as much precision as possible for the states
type_k = GGML_TYPE_F32; // required by ggml_ssm_conv for Mamba's conv_states
type_v = GGML_TYPE_F32; // required by ggml_ssm_scan for Mamba's ssm_states
}
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);
if (!hparams.vocab_only) {
// initialize backends
#ifdef GGML_USE_METAL
if (model->n_gpu_layers > 0) {
ctx->backend_metal = ggml_backend_metal_init();
if (ctx->backend_metal == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize Metal backend\n", __func__);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(ctx->backend_metal);
}
#elif defined(GGML_USE_CUDA)
if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
// with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
} else {
// LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) {
ggml_backend_t backend = ggml_backend_cuda_init(device);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
}
}
#elif defined(GGML_USE_VULKAN)
if (model->split_mode == LLAMA_SPLIT_MODE_ROW) {
LLAMA_LOG_ERROR("%s: Row split not supported. Failed to initialize Vulkan backend\n", __func__);
llama_free(ctx);
return nullptr;
}
if (model->split_mode == LLAMA_SPLIT_MODE_NONE) {
ggml_backend_t backend = ggml_backend_vk_init(0);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
} else {
for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) {
ggml_backend_t backend = ggml_backend_vk_init(device);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
}
}
#elif defined(GGML_USE_SYCL)
// with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu);
if (backend == nullptr) {
int main_gpu_id = ggml_backend_sycl_get_device_id(model->main_gpu);
LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, main_gpu_id, model->main_gpu);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
} else {
// LLAMA_SPLIT_LAYER requires a backend for each GPU
for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) {
ggml_backend_t backend = ggml_backend_sycl_init(i);
if (backend == nullptr) {
int id_list[GGML_SYCL_MAX_DEVICES];
ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES);
LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, id_list[i], i);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
}
}
#elif defined(GGML_USE_KOMPUTE)
if (model->n_gpu_layers > 0) {
auto * backend = ggml_backend_kompute_init(model->main_gpu);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize Kompute backend\n", __func__);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
}
#endif
ctx->backend_cpu = ggml_backend_cpu_init();
if (ctx->backend_cpu == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(ctx->backend_cpu);
if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v, kv_size, 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));
}
// graph outputs buffer
{
// resized during inference when a batch uses more outputs
if (llama_output_reserve(*ctx, params.n_seq_max) < params.n_seq_max) {
LLAMA_LOG_ERROR("%s: failed to reserve initial output buffer\n", __func__);
llama_free(ctx);
return nullptr;
}
LLAMA_LOG_INFO("%s: %10s output buffer size = %8.2f MiB\n", __func__,
ggml_backend_buffer_name(ctx->buf_output),
ggml_backend_buffer_get_size(ctx->buf_output) / 1024.0 / 1024.0);
}
// scheduler and compute buffers
{
// buffer types used for the compute buffer of each backend
std::vector<ggml_backend_buffer_type_t> backend_buft;
for (auto * backend : ctx->backends) {
if (ggml_backend_is_cpu(backend)) {
// use host buffers for the CPU backend compute buffer
backend_buft.push_back(llama_default_buffer_type_cpu(true));
} else {
backend_buft.push_back(ggml_backend_get_default_buffer_type(backend));
}
}
// buffer used to store the computation graph and the tensor meta data
ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead_custom(LLAMA_MAX_NODES, false));
// enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary
bool pipeline_parallel = llama_get_device_count() > 1 && model->n_gpu_layers > (int)model->hparams.n_layer && model->split_mode == LLAMA_SPLIT_MODE_LAYER;
#ifndef GGML_USE_CUDA
// pipeline parallelism requires support for async compute and events
// currently this is only implemented in the CUDA backend
pipeline_parallel = false;
#endif
ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), LLAMA_MAX_NODES, pipeline_parallel);
if (pipeline_parallel) {
LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(ctx->sched));
}
// build worst-case graph
int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_ubatch);
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), true);
// initialize scheduler with the worst-case graph
if (!ggml_backend_sched_reserve(ctx->sched, gf)) {
LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
llama_free(ctx);
return nullptr;
}
for (size_t i = 0; i < ctx->backends.size(); i++) {
ggml_backend_t backend = ctx->backends[i];
ggml_backend_buffer_type_t buft = backend_buft[i];
size_t size = ggml_backend_sched_get_buffer_size(ctx->sched, backend);
if (size > 1) {
LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
ggml_backend_buft_name(buft),
size / 1024.0 / 1024.0);
}
}
// note: the number of splits during measure is higher than during inference due to the kv shift
int n_splits = ggml_backend_sched_get_n_splits(ctx->sched);
LLAMA_LOG_INFO("%s: graph nodes = %d\n", __func__, gf->n_nodes);
LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits);
}
}
#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;
}
uint32_t llama_n_ubatch(const struct llama_context * ctx) {
return ctx->cparams.n_ubatch;
}
uint32_t llama_n_seq_max(const struct llama_context * ctx) {
return ctx->kv_self.size;
}
enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
return model->vocab.type;
}
enum llama_rope_type llama_rope_type(const struct llama_model * model) {
switch (model->arch) {
// these models do not use RoPE
case LLM_ARCH_GPT2:
case LLM_ARCH_GPTJ:
case LLM_ARCH_GPTNEOX:
case LLM_ARCH_MPT:
case LLM_ARCH_REFACT:
case LLM_ARCH_BLOOM:
case LLM_ARCH_MAMBA:
return LLAMA_ROPE_TYPE_NONE;
// use what we call a normal RoPE, operating on pairs of consecutive head values
case LLM_ARCH_LLAMA:
case LLM_ARCH_BAICHUAN:
case LLM_ARCH_STARCODER:
case LLM_ARCH_PLAMO:
case LLM_ARCH_CODESHELL:
case LLM_ARCH_ORION:
case LLM_ARCH_INTERNLM2:
case LLM_ARCH_MINICPM:
case LLM_ARCH_XVERSE:
case LLM_ARCH_COMMAND_R:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2
case LLM_ARCH_FALCON:
case LLM_ARCH_GROK:
case LLM_ARCH_PERSIMMON:
case LLM_ARCH_BERT:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_STABLELM:
case LLM_ARCH_QWEN:
case LLM_ARCH_QWEN2:
case LLM_ARCH_PHI2:
case LLM_ARCH_GEMMA:
case LLM_ARCH_STARCODER2:
return LLAMA_ROPE_TYPE_NEOX;
// all model arches should be listed explicitly here
case LLM_ARCH_UNKNOWN:
GGML_ASSERT(false && "unknown architecture");
break;
}
return LLAMA_ROPE_TYPE_NONE;
}
int32_t llama_n_vocab(const struct llama_model * model) {
return model->hparams.n_vocab;
}
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;
}
int32_t llama_n_layer(const struct llama_model * model) {
return model->hparams.n_layer;
}
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",
llama_model_arch_name(model->arch),
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) {
auto it = std::find_if(model->tensors_by_name.begin(), model->tensors_by_name.end(),
[name](const std::pair<std::string, struct ggml_tensor *> & it) {
return it.first == name;
});
if (it == model->tensors_by_name.end()) {
return nullptr;
}
return it->second;
}
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_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;
}
}
static bool llama_control_vector_init(struct llama_control_vector & cvec, const llama_model & model) {
GGML_ASSERT(cvec.tensors.empty());
GGML_ASSERT(cvec.ctxs.empty());
GGML_ASSERT(cvec.bufs.empty());
// count layer buffer types
std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
for (int64_t i = 0; i < model.hparams.n_layer; i++) {
buft_layer_count[model.buft_layer[i].buft]++;
}
// allocate contexts
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
for (auto & it : buft_layer_count) {
int n_layers = it.second;
struct ggml_init_params params = {
/*.mem_size =*/ n_layers * ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ggml_context * ctx = ggml_init(params);
if (!ctx) {
LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__);
return 1;
}
ctx_map[it.first] = ctx;
}
// make tensors
cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
for (size_t il = 1; il < model.hparams.n_layer; il++) {
struct ggml_context * ctx = ctx_map.at(model.buft_layer[il].buft);
ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
cvec.tensors.push_back(tensor);
}
// allocate tensors / buffers and zero
for (auto it : ctx_map) {
ggml_backend_buffer_type_t buft = it.first;
ggml_context * ctx = it.second;
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
if (!buf) {
LLAMA_LOG_ERROR("%s: failed to allocate buffer for control vector\n", __func__);
return false;
}
ggml_backend_buffer_clear(buf, 0);
cvec.ctxs.push_back(ctx);
cvec.bufs.push_back(buf);
}
return true;
}
int32_t llama_control_vector_apply(struct llama_context * lctx, const float * data, size_t len, int32_t n_embd, int32_t il_start, int32_t il_end) {
const llama_model & model = lctx->model;
llama_control_vector & cvec = lctx->cvec;
if (data == nullptr) {
// disable the current control vector (but leave allocated for later)
cvec.layer_start = -1;
cvec.layer_end = -1;
return 0;
}
if (n_embd != (int) model.hparams.n_embd) {
LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);
return 1;
}
if (cvec.tensors.empty()) {
if (!llama_control_vector_init(cvec, model)) {
return 1;
}
}
cvec.layer_start = il_start;
cvec.layer_end = il_end;
for (size_t il = 1; il < model.hparams.n_layer; il++) {
assert(cvec.tensors[il] != nullptr);
const size_t off = n_embd * (il - 1); // buffer doesn't have data for layer 0, since it's never present
if (off + n_embd <= len) {
ggml_backend_tensor_set(cvec.tensors[il], data + off, 0, n_embd * ggml_element_size(cvec.tensors[il]));
}
}
return 0;
}
struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max) {
struct llama_kv_cache_view result = {
/*.n_cells = */ 0,
/*.n_seq_max = */ n_seq_max,
/*.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_seq_max * 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_seq_max) {
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_seq_max) {
break;
}
cs_curr[seq_idx] = it;
seq_idx++;
}
if (seq_idx != 0) {
used_cells++;
}
for (; seq_idx < view->n_seq_max; 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);
}
bool llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
return 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_add(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_add(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);
}
llama_pos llama_kv_cache_seq_pos_max(struct llama_context * ctx, llama_seq_id seq_id) {
return llama_kv_cache_seq_pos_max(ctx->kv_self, seq_id);
}
void llama_kv_cache_defrag(struct llama_context * ctx) {
llama_kv_cache_defrag(ctx->kv_self);
}
void llama_kv_cache_update(struct llama_context * ctx) {
llama_kv_cache_update_internal(*ctx);
}
// Returns the *maximum* size of the state
size_t llama_get_state_size(const struct llama_context * ctx) {
const auto & cparams = ctx->cparams;
const auto & hparams = ctx->model.hparams;
// 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_n_outputs = sizeof(size_t);
// assume worst case for outputs although only currently set ones are serialized
const size_t s_output_pos = ctx->cparams.n_batch * sizeof(int32_t);
const size_t s_logits_size = sizeof(size_t);
const size_t s_logits = ctx->logits_size ? cparams.n_batch * hparams.n_vocab * sizeof(float) : 0;
const size_t s_embedding_size = sizeof(size_t);
const size_t s_embedding = ctx->embd_size ? cparams.n_batch * hparams.n_embd * sizeof(float) : 0;
const size_t s_kv_buf_size = sizeof(size_t);
const size_t s_kv_head = sizeof(uint32_t);
const size_t s_kv_size = sizeof(uint32_t);
const size_t s_kv_used = sizeof(uint32_t);
const size_t s_kv = ctx->kv_self.total_size();
const size_t s_kv_cell = sizeof(llama_pos) + sizeof(size_t) + cparams.n_seq_max*sizeof(llama_seq_id);
const size_t s_kv_cells = ctx->kv_self.size * s_kv_cell;
const size_t s_total = (
+ s_rng_size
+ s_rng
+ s_n_outputs
+ s_output_pos
+ s_logits_size
+ s_logits
+ s_embedding_size
+ s_embedding
+ s_kv_buf_size
+ s_kv_head
+ s_kv_size
+ s_kv_used
+ s_kv
+ s_kv_cells
);
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::ostringstream rng_ss;
rng_ss << ctx->rng;
const std::string & rng_str = rng_ss.str();
const size_t rng_size = rng_str.size();
GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE);
data_ctx->write(&rng_size, sizeof(rng_size));
data_ctx->write(rng_str.data(), rng_size);
}
// copy outputs
{
// Can't use ctx->n_outputs because it's not for the
// entire last batch when n_ubatch is smaller than n_batch
size_t n_outputs = 0;
// copy output ids
{
std::vector<int32_t> output_pos;
const size_t n_batch = ctx->cparams.n_batch;
const auto & output_ids = ctx->output_ids;
output_pos.resize(ctx->output_size);
// build a more compact representation of the output ids
for (size_t i = 0; i < n_batch; ++i) {
// map an output id to a position in the batch
int32_t pos = output_ids[i];
if (pos >= 0) {
if ((size_t) pos >= n_outputs) {
n_outputs = pos + 1;
}
GGML_ASSERT((size_t) pos < ctx->output_size);
output_pos[pos] = i;
}
}
data_ctx->write(&n_outputs, sizeof(n_outputs));
if (n_outputs) {
data_ctx->write(output_pos.data(), n_outputs * sizeof(int32_t));
}
}
// copy logits
{
const size_t logits_size = std::min(ctx->logits_size, n_outputs * ctx->model.hparams.n_vocab);
data_ctx->write(&logits_size, sizeof(logits_size));
if (logits_size) {
data_ctx->write(ctx->logits, logits_size * sizeof(float));
}
}
// copy embeddings
{
const size_t embeddings_size = std::min(ctx->embd_size, n_outputs * ctx->model.hparams.n_embd);
data_ctx->write(&embeddings_size, sizeof(embeddings_size));
if (embeddings_size) {
data_ctx->write(ctx->embd, embeddings_size * sizeof(float));
}
}
}
// copy kv cache
{
const auto & kv_self = ctx->kv_self;
const auto & hparams = ctx->model.hparams;
const uint32_t n_layer = hparams.n_layer;
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
// NOTE: kv_size and kv_buf_size are mostly used for sanity checks
const uint32_t kv_head = llama_kv_cache_cell_max(kv_self);
const uint32_t kv_size = kv_self.size;
const size_t kv_buf_size = kv_self.total_size() / (kv_size ? kv_size : 1) * kv_head;
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 pre_kv_buf_size = data_ctx->get_size_written();
std::vector<uint8_t> tmp_buf;
for (int il = 0; il < (int) n_layer; ++il) {
const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
tmp_buf.resize(k_size);
ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size());
data_ctx->write(tmp_buf.data(), tmp_buf.size());
if (kv_self.recurrent) {
// v is contiguous for recurrent models
// TODO: use other tensors for state models than k and v
const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);
tmp_buf.resize(v_size);
ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), 0, tmp_buf.size());
data_ctx->write(tmp_buf.data(), tmp_buf.size());
continue;
}
// v is not contiguous, copy row by row
const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head);
const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size);
tmp_buf.resize(v_row_size);
for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), ir*v_row_stride, tmp_buf.size());
data_ctx->write(tmp_buf.data(), tmp_buf.size());
}
}
GGML_ASSERT(kv_buf_size == data_ctx->get_size_written() - pre_kv_buf_size);
}
for (uint32_t i = 0; i < kv_head; ++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, const uint8_t * src) {
const uint8_t * inp = src;
// set rng
{
size_t rng_size;
memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size);
GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE);
std::string rng_str((const char *)inp, rng_size); inp += rng_size;
std::istringstream rng_ss(rng_str);
rng_ss >> ctx->rng;
GGML_ASSERT(!rng_ss.fail());
}
// set output ids
{
size_t n_outputs;
std::vector<int32_t> output_pos;
memcpy(&n_outputs, inp, sizeof(n_outputs)); inp += sizeof(n_outputs);
GGML_ASSERT(n_outputs <= llama_output_reserve(*ctx, n_outputs));
if (n_outputs) {
output_pos.resize(n_outputs);
memcpy(output_pos.data(), inp, n_outputs * sizeof(int32_t));
inp += n_outputs * sizeof(int32_t);
for (int32_t i = 0; i < (int32_t) output_pos.size(); ++i) {
int32_t id = output_pos[i];
GGML_ASSERT((uint32_t) id < ctx->cparams.n_batch);
ctx->output_ids[id] = i;
}
}
}
// set logits
{
size_t logits_size;
memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);
GGML_ASSERT(ctx->logits_size >= logits_size);
if (logits_size) {
memcpy(ctx->logits, inp, logits_size * sizeof(float));
inp += logits_size * sizeof(float);
}
}
// set embeddings
{
size_t embeddings_size;
memcpy(&embeddings_size, inp, sizeof(embeddings_size)); inp += sizeof(embeddings_size);
GGML_ASSERT(ctx->embd_size >= embeddings_size);
if (embeddings_size) {
memcpy(ctx->embd, inp, embeddings_size * sizeof(float));
inp += embeddings_size * sizeof(float);
}
}
// set kv cache
{
const auto & kv_self = ctx->kv_self;
const auto & hparams = ctx->model.hparams;
const uint32_t n_layer = hparams.n_layer;
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
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_self.size != kv_size) {
// the KV cache needs to be big enough to load all the KV cells from the saved state
GGML_ASSERT(kv_self.size >= kv_head);
LLAMA_LOG_INFO("%s: state contains %d KV cells, was saved with kv_size=%d, but is loaded with kv_size=%d (fine, but different)\n",
__func__, kv_head, kv_size, kv_self.size);
}
if (kv_buf_size) {
const size_t pre_kv_buf_size = inp - src;
GGML_ASSERT(kv_self.total_size() >= kv_buf_size);
for (int il = 0; il < (int) n_layer; ++il) {
const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size);
inp += k_size;
if (kv_self.recurrent) {
// v is contiguous for recurrent models
// TODO: use other tensors for state models than k and v
const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);
ggml_backend_tensor_set(kv_self.v_l[il], inp, 0, v_size);
inp += v_size;
continue;
}
// v is not contiguous, copy row by row
const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head);
const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_self.size);
for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*v_row_stride, v_row_size);
inp += v_row_size;
}
}
GGML_ASSERT(kv_buf_size == inp - src - pre_kv_buf_size);
}
llama_kv_cache_clear(ctx);
ctx->kv_self.head = kv_head;
ctx->kv_self.used = kv_used;
for (uint32_t i = 0; i < kv_head; ++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;
}
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;
}
void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) {
ctx->abort_callback = abort_callback;
ctx->abort_callback_data = abort_callback_data;
}
void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
ctx->cparams.causal_attn = causal_attn;
}
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_alloc, 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_alloc * embd);
} else {
batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
}
batch.pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens_alloc);
batch.n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens_alloc);
batch.seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * (n_tokens_alloc + 1));
for (int i = 0; i < n_tokens_alloc; ++i) {
batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
}
batch.seq_id[n_tokens_alloc] = nullptr;
batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens_alloc);
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; batch.seq_id[i] != nullptr; ++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;
}
void llama_synchronize(struct llama_context * ctx) {
ggml_backend_sched_synchronize(ctx->sched);
// FIXME: if multiple single tokens are evaluated without a synchronization,
// the stats will be added to the prompt evaluation stats
// this should only happen when using batch size 1 to evaluate a batch
// add the evaluation to the stats
if (ctx->n_queued_tokens == 1) {
ctx->t_eval_us += ggml_time_us() - ctx->t_compute_start_us;
ctx->n_eval++;
} else if (ctx->n_queued_tokens > 1) {
ctx->t_p_eval_us += ggml_time_us() - ctx->t_compute_start_us;
ctx->n_p_eval += ctx->n_queued_tokens;
}
// get a more accurate load time, upon first eval
if (ctx->n_queued_tokens > 0 && !ctx->has_evaluated_once) {
ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
ctx->has_evaluated_once = true;
}
ctx->n_queued_tokens = 0;
ctx->t_compute_start_us = 0;
}
float * llama_get_logits(struct llama_context * ctx) {
llama_synchronize(ctx);
return ctx->logits;
}
float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
llama_synchronize(ctx);
try {
if (ctx->logits == nullptr) {
throw std::runtime_error("no logits");
}
if ((size_t) i >= ctx->output_ids.size()) {
throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size()));
}
const int32_t j = ctx->output_ids[i];
if (j < 0) {
throw std::runtime_error(format("batch.logits[%d] != true", i));
}
if ((size_t) j >= ctx->output_size) {
// This should not happen
throw std::runtime_error(format("corrupt output buffer (j=%d, output_size=%lu)", j, ctx->output_size));
}
return ctx->logits + j*ctx->model.hparams.n_vocab;
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
#ifndef NDEBUG
GGML_ASSERT(false);
#endif
return nullptr;
}
}
float * llama_get_embeddings(struct llama_context * ctx) {
llama_synchronize(ctx);
return ctx->embd;
}
float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) {
llama_synchronize(ctx);
try {
if (ctx->embd == nullptr) {
throw std::runtime_error("no embeddings");
}
if ((size_t) i >= ctx->output_ids.size()) {
throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size()));
}
const int32_t j = ctx->output_ids[i];
if (j < 0) {
throw std::runtime_error(format("batch.logits[%d] != true", i));
}
if ((size_t) j >= ctx->output_size) {
// This should not happen
throw std::runtime_error(format("corrupt output buffer (j=%d, output_size=%lu)", j, ctx->output_size));
}
return ctx->embd + j*ctx->model.hparams.n_embd;
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid embeddings id %d, reason: %s\n", __func__, i, err.what());
#ifndef NDEBUG
GGML_ASSERT(false);
#endif
return nullptr;
}
}
float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id) {
llama_synchronize(ctx);
auto it = ctx->embd_seq.find(seq_id);
if (it == ctx->embd_seq.end()) {
return nullptr;
}
return it->second.data();
}
const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
return model->vocab.id_to_token[token].text.c_str();
}
float llama_token_get_score(const struct llama_model * model, llama_token token) {
GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
return model->vocab.id_to_token[token].score;
}
llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) {
GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
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_tokens_max,
bool add_bos,
bool special) {
auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special);
if (n_tokens_max < (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 = unicode_cpts_from_utf8(text);
for (auto & unicode_sequence : unicode_sequences) {
decoded_text += unicode_utf8_to_byte(unicode_cpt_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_WPM:
case LLAMA_VOCAB_TYPE_SPM: {
// NOTE: we accept all unsupported token types,
// suppressing them like CONTROL tokens.
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_user_defined_token(model->vocab, token)) {
std::string result = model->vocab.id_to_token[token].text;
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;
}
break;
}
case LLAMA_VOCAB_TYPE_BPE: {
// NOTE: we accept all unsupported token types,
// suppressing them like CONTROL tokens.
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_user_defined_token(model->vocab, token)) {
std::string result = model->vocab.id_to_token[token].text;
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)) {
;
}
break;
}
default:
GGML_ASSERT(false);
}
}
return 0;
}
// trim whitespace from the beginning and end of a string
static std::string trim(const std::string & str) {
size_t start = 0;
size_t end = str.size();
while (start < end && isspace(str[start])) {
start += 1;
}
while (end > start && isspace(str[end - 1])) {
end -= 1;
}
return str.substr(start, end - start);
}
// Simple version of "llama_apply_chat_template" that only works with strings
// This function uses heuristic checks to determine commonly used template. It is not a jinja parser.
static int32_t llama_chat_apply_template_internal(
const std::string & tmpl,
const std::vector<const llama_chat_message *> & chat,
std::string & dest, bool add_ass) {
// Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
std::stringstream ss;
if (tmpl == "chatml" || tmpl.find("<|im_start|>") != std::string::npos) {
// chatml template
for (auto message : chat) {
ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n";
}
if (add_ass) {
ss << "<|im_start|>assistant\n";
}
} else if (tmpl == "llama2" || tmpl.find("[INST]") != std::string::npos) {
// llama2 template and its variants
// [variant] support system message
bool support_system_message = tmpl.find("<<SYS>>") != std::string::npos;
// [variant] space before + after response
bool space_around_response = tmpl.find("' ' + eos_token") != std::string::npos;
// [variant] add BOS inside history
bool add_bos_inside_history = tmpl.find("bos_token + '[INST]") != std::string::npos;
// [variant] trim spaces from the input message
bool strip_message = tmpl.find("content.strip()") != std::string::npos;
// construct the prompt
bool is_inside_turn = true; // skip BOS at the beginning
ss << "[INST] ";
for (auto message : chat) {
std::string content = strip_message ? trim(message->content) : message->content;
std::string role(message->role);
if (!is_inside_turn) {
is_inside_turn = true;
ss << (add_bos_inside_history ? "<s>[INST] " : "[INST] ");
}
if (role == "system") {
if (support_system_message) {
ss << "<<SYS>>\n" << content << "\n<</SYS>>\n\n";
} else {
// if the model does not support system message, we still include it in the first message, but without <<SYS>>
ss << content << "\n";
}
} else if (role == "user") {
ss << content << " [/INST]";
} else {
ss << (space_around_response ? " " : "") << content << (space_around_response ? " " : "") << "</s>";
is_inside_turn = false;
}
}
// llama2 templates seem to not care about "add_generation_prompt"
} else if (tmpl == "zephyr" || tmpl.find("<|user|>") != std::string::npos) {
// zephyr template
for (auto message : chat) {
ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n";
}
if (add_ass) {
ss << "<|assistant|>\n";
}
} else if (tmpl == "monarch" || tmpl.find("bos_token + message['role']") != std::string::npos) {
// mlabonne/AlphaMonarch-7B template (the <s> is included inside history)
for (auto message : chat) {
std::string bos = (message == chat.front()) ? "" : "<s>"; // skip BOS for first message
ss << bos << message->role << "\n" << message->content << "</s>\n";
}
if (add_ass) {
ss << "<s>assistant\n";
}
} else if (tmpl == "gemma" || tmpl.find("<start_of_turn>") != std::string::npos) {
// google/gemma-7b-it
std::string system_prompt = "";
for (auto message : chat) {
std::string role(message->role);
if (role == "system") {
// there is no system message for gemma, but we will merge it with user prompt, so nothing is broken
system_prompt = trim(message->content);
continue;
}
// in gemma, "assistant" is "model"
role = role == "assistant" ? "model" : message->role;
ss << "<start_of_turn>" << role << "\n";
if (!system_prompt.empty() && role != "model") {
ss << system_prompt << "\n\n";
system_prompt = "";
}
ss << trim(message->content) << "<end_of_turn>\n";
}
if (add_ass) {
ss << "<start_of_turn>model\n";
}
} else if (tmpl == "orion" || tmpl.find("'\\n\\nAssistant: ' + eos_token") != std::string::npos) {
// OrionStarAI/Orion-14B-Chat
std::string system_prompt = "";
for (auto message : chat) {
std::string role(message->role);
if (role == "system") {
// there is no system message support, we will merge it with user prompt
system_prompt = message->content;
continue;
} else if (role == "user") {
ss << "Human: ";
if (!system_prompt.empty()) {
ss << system_prompt << "\n\n";
system_prompt = "";
}
ss << message->content << "\n\nAssistant: </s>";
} else {
ss << message->content << "</s>";
}
}
} else {
// template not supported
return -1;
}
dest = ss.str();
return dest.size();
}
LLAMA_API int32_t llama_chat_apply_template(
const struct llama_model * model,
const char * tmpl,
const struct llama_chat_message * chat,
size_t n_msg,
bool add_ass,
char * buf,
int32_t length) {
std::string curr_tmpl(tmpl == nullptr ? "" : tmpl);
if (tmpl == nullptr) {
GGML_ASSERT(model != nullptr);
// load template from model
std::vector<char> model_template(2048, 0); // longest known template is about 1200 bytes
std::string template_key = "tokenizer.chat_template";
int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
if (res < 0) {
// worst case: there is no information about template, we will use chatml by default
curr_tmpl = "chatml"; // see llama_chat_apply_template_internal
} else {
curr_tmpl = std::string(model_template.data(), model_template.size());
}
}
// format the chat to string
std::vector<const llama_chat_message *> chat_vec;
chat_vec.resize(n_msg);
for (size_t i = 0; i < n_msg; i++) {
chat_vec[i] = &chat[i];
}
std::string formatted_chat;
int32_t res = llama_chat_apply_template_internal(curr_tmpl, chat_vec, formatted_chat, add_ass);
if (res < 0) {
return res;
}
if (buf && length > 0) {
strncpy(buf, formatted_chat.c_str(), length);
}
return res;
}
LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count) {
static const char * const SPLIT_PATH_FORMAT = "%s-%05d-of-%05d.gguf";
if (snprintf(split_path, maxlen, SPLIT_PATH_FORMAT, path_prefix, split_no + 1, split_count)) {
return strlen(split_path);
}
return 0;
}
int llama_split_prefix(char * dest, size_t maxlen, const char * split_path, int split_no, int split_count) {
std::string str_split_path(split_path);
char postfix[32];
snprintf(postfix, 32, "-%05d-of-%05d.gguf", split_no + 1, split_count);
std::string str_postfix(postfix);
// check if dest ends with postfix
int size_prefix = str_split_path.size() - str_postfix.size();
if (size_prefix > 0 && str_split_path.find(str_postfix, size_prefix) != std::string::npos) {
snprintf(dest, std::min((size_t) size_prefix + 1, maxlen), "%s", split_path);
return size_prefix;
}
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()) + " | ";
s += "MATMUL_INT8 = " + std::to_string(ggml_cpu_has_matmul_int8()) + " | ";
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_backend_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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