haraldwolff/llama.cpp
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llama.cpp/llama.cpp

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// Defines fileno on msys:
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#include <cstddef>
#include <cstdint>
#include <cstdio>
#endif
#include "llama-util.h"
#include "llama.h"
#include "ggml.h"
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
#include "ggml-opencl.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#ifdef GGML_USE_MPI
#include "ggml-mpi.h"
#endif
#ifdef GGML_USE_K_QUANTS
#ifndef QK_K
#ifdef GGML_QKK_64
#define QK_K 64
#else
#define QK_K 256
#endif
#endif
#endif
#include <array>
#include <ctime>
#include <cinttypes>
#include <fstream>
#include <random>
#include <map>
#include <unordered_map>
#include <queue>
#include <cassert>
#include <cstring>
#include <climits>
#include <memory>
#include <algorithm>
#include <initializer_list>
#include <thread>
#include <atomic>
#include <mutex>
#include <sstream>
#include <numeric>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#define LLAMA_USE_SCRATCH
#define LLAMA_MAX_SCRATCH_BUFFERS 16
// available llama models
enum e_model {
MODEL_UNKNOWN,
MODEL_3B,
MODEL_7B,
MODEL_13B,
MODEL_30B,
MODEL_65B,
};
static const size_t kB = 1024;
static const size_t MB = 1024*1024;
// computed for n_ctx == 2048
// TODO: dynamically determine these sizes
// needs modifications in ggml
typedef void (*offload_func_t)(struct ggml_tensor * tensor);
void llama_nop(struct ggml_tensor * tensor) { // don't offload by default
(void) tensor;
}
//
// ggml helpers
//
static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
if (plan.work_size > 0) {
buf.resize(plan.work_size);
plan.work_data = buf.data();
}
ggml_graph_compute(graph, &plan);
}
//
// memory sizes
//
static const std::map<e_model, size_t> & MEM_REQ_SCRATCH0(int n_ctx)
{
static std::map<e_model, size_t> k_sizes = {
/* empirical scaling, still a guess */
{ MODEL_3B, ((size_t) n_ctx / 16ull + 128ull) * MB },
{ MODEL_7B, ((size_t) n_ctx / 16ull + 256ull) * MB },
{ MODEL_13B, ((size_t) n_ctx / 12ull + 256ull) * MB },
{ MODEL_30B, ((size_t) n_ctx / 10ull + 256ull) * MB },
{ MODEL_65B, ((size_t) n_ctx / 8ull + 512ull) * MB },
};
return k_sizes;
}
static const std::map<e_model, size_t> & MEM_REQ_SCRATCH1()
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_3B, 256ull * MB },
{ MODEL_7B, 512ull * MB },
{ MODEL_13B, 512ull * MB },
{ MODEL_30B, 512ull * MB },
{ MODEL_65B, 1024ull * MB },
};
return k_sizes;
}
// 2*n_embd*n_ctx*n_layer*sizeof(float16)
static const std::map<e_model, size_t> & MEM_REQ_KV_SELF()
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_3B, 682ull * MB },
{ MODEL_7B, 1026ull * MB },
{ MODEL_13B, 1608ull * MB },
{ MODEL_30B, 3124ull * MB },
{ MODEL_65B, 5120ull * MB },
};
return k_sizes;
}
// this is mostly needed for temporary mul_mat buffers to dequantize the data
// not actually needed if BLAS is disabled
static const std::map<e_model, size_t> & MEM_REQ_EVAL(int n_ctx)
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_3B, ((size_t) n_ctx / 256ull + 512ull) * MB },
{ MODEL_7B, ((size_t) n_ctx / 256ull + 768ull) * MB },
{ MODEL_13B, ((size_t) n_ctx / 256ull + 1024ull) * MB },
{ MODEL_30B, ((size_t) n_ctx / 256ull + 1280ull) * MB },
{ MODEL_65B, ((size_t) n_ctx / 256ull + 1536ull) * MB },
};
return k_sizes;
}
// amount of VRAM needed per batch size to hold temporary results
// the values for 3b and 65b are not derived from testing but instead chosen conservatively
static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_BASE()
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_3B, 512ull * kB },
{ MODEL_7B, 512ull * kB },
{ MODEL_13B, 640ull * kB },
{ MODEL_30B, 768ull * kB },
{ MODEL_65B, 1536ull * kB },
};
return k_sizes;
}
// amount of VRAM needed per batch size and context to hold temporary results
// the values for 3b and 65b are not derived from testing but instead chosen conservatively
static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_PER_CONTEXT()
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_3B, 128ull },
{ MODEL_7B, 128ull },
{ MODEL_13B, 160ull },
{ MODEL_30B, 208ull },
{ MODEL_65B, 416ull },
};
return k_sizes;
}
// default hparams (LLaMA 7B)
struct llama_hparams {
uint32_t n_vocab = 32000;
uint32_t n_ctx = 512; // this is provided as user input?
uint32_t n_embd = 4096;
uint32_t n_mult = 256;
uint32_t n_head = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
float rope_freq_base = 10000.0f;
float rope_freq_scale = 1.0f;
enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
bool operator!=(const llama_hparams & other) const {
return static_cast<bool>(memcmp(this, &other, sizeof(llama_hparams)));
}
};
struct llama_layer {
// normalization
struct ggml_tensor * attention_norm;
// attention
struct ggml_tensor * wq;
struct ggml_tensor * wk;
struct ggml_tensor * wv;
struct ggml_tensor * wo;
// normalization
struct ggml_tensor * ffn_norm;
// ff
struct ggml_tensor * w1;
struct ggml_tensor * w2;
struct ggml_tensor * w3;
};
struct llama_kv_cache {
struct ggml_tensor * k = NULL;
struct ggml_tensor * v = NULL;
struct ggml_context * ctx = NULL;
llama_ctx_buffer buf;
int n; // number of tokens currently in the cache
~llama_kv_cache() {
if (ctx) {
ggml_free(ctx);
}
#ifdef GGML_USE_CUBLAS
ggml_cuda_free_data(k);
ggml_cuda_free_data(v);
#endif // GGML_USE_CUBLAS
}
};
struct llama_vocab {
using id = int32_t;
using token = std::string;
struct token_score {
token tok;
float score;
};
std::unordered_map<token, id> token_to_id;
std::vector<token_score> id_to_token;
};
struct llama_model {
e_model type = MODEL_UNKNOWN;
llama_hparams hparams;
struct ggml_tensor * tok_embeddings;
struct ggml_tensor * norm;
struct ggml_tensor * output;
std::vector<llama_layer> layers;
int n_gpu_layers;
// context
struct ggml_context * ctx = NULL;
// the model memory buffer
llama_ctx_buffer buf;
// model memory mapped file
std::unique_ptr<llama_mmap> mapping;
// objects representing data potentially being locked in memory
llama_mlock mlock_buf;
llama_mlock mlock_mmap;
// for quantize-stats only
std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
int64_t t_load_us = 0;
int64_t t_start_us = 0;
llama_vocab vocab;
~llama_model() {
if (ctx) {
ggml_free(ctx);
}
#ifdef GGML_USE_CUBLAS
for (size_t i = 0; i < tensors_by_name.size(); ++i) {
ggml_cuda_free_data(tensors_by_name[i].second);
}
ggml_cuda_free_scratch();
#elif defined(GGML_USE_CLBLAST)
for (size_t i = 0; i < tensors_by_name.size(); ++i) {
ggml_cl_free_data(tensors_by_name[i].second);
}
#endif
}
};
struct llama_context {
llama_context(const llama_model & model) : model(model), t_load_us(model.t_load_us), t_start_us(model.t_start_us) {}
#ifdef GGML_USE_METAL
~llama_context() {
if (ctx_metal) {
ggml_metal_free(ctx_metal);
}
}
#endif
std::mt19937 rng;
bool has_evaluated_once = false;
int64_t t_sample_us = 0;
int64_t t_eval_us = 0;
int64_t t_p_eval_us = 0;
int32_t n_sample = 0; // number of tokens sampled
int32_t n_eval = 0; // number of eval calls
int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
const llama_model & model;
bool model_owner = false;
int64_t t_load_us;
int64_t t_start_us;
// key + value cache for the self attention
struct llama_kv_cache kv_self;
size_t mem_per_token = 0;
// decode output (2-dimensional array: [n_tokens][n_vocab])
std::vector<float> logits;
bool logits_all = false;
// input embedding (1-dimensional array: [n_embd])
std::vector<float> embedding;
// reusable buffer for `struct ggml_graph_plan.work_data`
std::vector<uint8_t> work_buffer;
// memory buffers used to evaluate the model
// TODO: move in llama_state
llama_ctx_buffer buf_compute;
llama_ctx_buffer buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
#ifdef GGML_USE_METAL
ggml_metal_context * ctx_metal = NULL;
#endif
#ifdef GGML_USE_MPI
ggml_mpi_context * ctx_mpi = NULL;
#endif
int buf_last = 0;
size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
void use_buf(struct ggml_context * ctx, int i) {
#if defined(LLAMA_USE_SCRATCH)
size_t last_size = 0;
if (i == -1) {
last_size = ggml_set_scratch(ctx, { 0, 0, nullptr, });
} else {
auto & buf = buf_scratch[i];
last_size = ggml_set_scratch(ctx, { 0, buf.size, buf.addr, });
}
if (buf_last >= 0) {
buf_max_size[buf_last] = std::max(buf_max_size[buf_last], last_size);
}
buf_last = i;
#else
(void) i;
(void) ctx;
#endif
}
size_t get_buf_max_mem(int i) const {
#if defined(LLAMA_USE_SCRATCH)
return buf_max_size[i];
#else
(void) i;
return 0;
#endif
}
};
template <typename T>
static T checked_mul(T a, T b) {
T ret = a * b;
if (a != 0 && ret / a != b) {
throw std::runtime_error(format("overflow multiplying %llu * %llu",
(unsigned long long) a, (unsigned long long) b));
}
return ret;
}
static size_t checked_div(size_t a, size_t b) {
if (b == 0 || a % b != 0) {
throw std::runtime_error(format("error dividing %zu / %zu", a, b));
}
return a / b;
}
static std::string llama_format_tensor_shape(const std::vector<uint32_t> & ne) {
char buf[256];
snprintf(buf, sizeof(buf), "%5u", ne.at(0));
for (size_t i = 1; i < ne.size(); i++) {
snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), " x %5u", ne.at(i));
}
return buf;
}
static size_t llama_calc_tensor_size(const std::vector<uint32_t> & ne, enum ggml_type type) {
size_t size = ggml_type_size(type);
for (uint32_t dim : ne) {
size = checked_mul<size_t>(size, dim);
}
return size / ggml_blck_size(type);
}
struct llama_load_tensor {
std::string name;
enum ggml_type type = GGML_TYPE_F32;
std::vector<uint32_t> ne;
size_t file_off;
size_t size;
struct ggml_tensor * ggml_tensor = NULL;
uint8_t * data;
};
struct llama_load_tensors_map {
// tensors is kept in a separate vector to preserve file order
std::vector<llama_load_tensor> tensors;
std::unordered_map<std::string, size_t> name_to_idx;
};
enum llama_file_version {
LLAMA_FILE_VERSION_GGML,
LLAMA_FILE_VERSION_GGMF_V1, // added version field and scores in vocab
LLAMA_FILE_VERSION_GGJT_V1, // added padding
LLAMA_FILE_VERSION_GGJT_V2, // changed quantization format
LLAMA_FILE_VERSION_GGJT_V3, // changed Q4 and Q8 quantization format
};
struct llama_file_loader {
llama_file file;
llama_file_version file_version;
llama_hparams hparams;
llama_vocab vocab;
llama_file_loader(const char * fname, llama_load_tensors_map & tensors_map)
: file(fname, "rb") {
fprintf(stderr, "llama.cpp: loading model from %s\n", fname);
read_magic();
read_hparams();
read_vocab();
read_tensor_metadata(tensors_map);
}
void read_magic() {
uint32_t magic = file.read_u32();
if (magic == LLAMA_FILE_MAGIC_GGML) {
file_version = LLAMA_FILE_VERSION_GGML;
return;
}
uint32_t version = file.read_u32();
switch (magic) {
case LLAMA_FILE_MAGIC_GGMF:
switch (version) {
case 1: file_version = LLAMA_FILE_VERSION_GGMF_V1; return;
}
break;
case LLAMA_FILE_MAGIC_GGJT:
switch (version) {
case 1: file_version = LLAMA_FILE_VERSION_GGJT_V1; return;
case 2: file_version = LLAMA_FILE_VERSION_GGJT_V2; return;
case 3: file_version = LLAMA_FILE_VERSION_GGJT_V3; return;
}
}
throw std::runtime_error(format("unknown (magic, version) combination: %08x, %08x; is this really a GGML file?",
magic, version));
}
void read_hparams() {
hparams.n_vocab = file.read_u32();
hparams.n_embd = file.read_u32();
hparams.n_mult = file.read_u32();
hparams.n_head = file.read_u32();
hparams.n_layer = file.read_u32();
hparams.n_rot = file.read_u32();
hparams.ftype = (enum llama_ftype) file.read_u32();
}
void read_vocab() {
vocab.id_to_token.resize(hparams.n_vocab);
for (uint32_t i = 0; i < hparams.n_vocab; i++) {
uint32_t len = file.read_u32();
std::string word = file.read_string(len);
float score = 0.0f;
file.read_raw(&score, sizeof(score));
vocab.token_to_id[word] = i;
auto & tok_score = vocab.id_to_token[i];
tok_score.tok = std::move(word);
tok_score.score = score;
}
}
void read_tensor_metadata(llama_load_tensors_map & tensors_map) {
while (file.tell() < file.size) {
llama_load_tensor tensor;
uint32_t n_dims = file.read_u32();
uint32_t name_len = file.read_u32();
tensor.type = (enum ggml_type) file.read_u32();
tensor.ne.resize(n_dims);
file.read_raw(tensor.ne.data(), sizeof(tensor.ne[0]) * n_dims);
std::string name = file.read_string(name_len);
if (n_dims < 1 || n_dims > 2) {
throw std::runtime_error(format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims));
}
switch (tensor.type) {
case GGML_TYPE_F32:
case GGML_TYPE_F16:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K:
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:
break;
default: {
throw std::runtime_error(format("unrecognized tensor type %u\n", tensor.type));
}
}
// skip to the next multiple of 32 bytes
if (file_version >= LLAMA_FILE_VERSION_GGJT_V1) {
file.seek(-static_cast<ptrdiff_t>(file.tell()) & 31, SEEK_CUR);
}
tensor.file_off = file.tell();
tensor.name = name;
tensor.size = llama_calc_tensor_size(tensor.ne, tensor.type);
file.seek(tensor.size, SEEK_CUR);
tensors_map.tensors.push_back(tensor);
tensors_map.name_to_idx[name] = tensors_map.tensors.size() - 1;
}
}
};
struct llama_file_saver {
llama_file file;
llama_file_loader * any_file_loader;
llama_file_saver(const char * fname, llama_file_loader * any_file_loader, enum llama_ftype new_ftype)
: file(fname, "wb"), any_file_loader(any_file_loader) {
fprintf(stderr, "llama.cpp: saving model to %s\n", fname);
write_magic();
write_hparams(new_ftype);
write_vocab();
}
void write_magic() {
file.write_u32(LLAMA_FILE_MAGIC); // magic
file.write_u32(LLAMA_FILE_VERSION); // version
}
void write_hparams(enum llama_ftype new_ftype) {
const llama_hparams & hparams = any_file_loader->hparams;
file.write_u32(hparams.n_vocab);
file.write_u32(hparams.n_embd);
file.write_u32(hparams.n_mult);
file.write_u32(hparams.n_head);
file.write_u32(hparams.n_layer);
file.write_u32(hparams.n_rot);
file.write_u32(new_ftype);
}
void write_vocab() {
if (any_file_loader->file_version == LLAMA_FILE_VERSION_GGML) {
fprintf(stderr, "llama.cpp: WARNING: input is an old file that doesn't have scores; will add dummy scores\n");
}
uint32_t n_vocab = any_file_loader->hparams.n_vocab;
for (uint32_t i = 0; i < n_vocab; i++) {
const auto & token_score = any_file_loader->vocab.id_to_token.at(i);
file.write_u32((uint32_t) token_score.tok.size());
file.write_raw(token_score.tok.data(), token_score.tok.size());
file.write_raw(&token_score.score, sizeof(token_score.score));
}
}
void write_tensor(llama_load_tensor & tensor, enum ggml_type new_type, const void * new_data, size_t new_size) {
switch (new_type) {
case GGML_TYPE_F32:
case GGML_TYPE_F16:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K:
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:
break;
default: LLAMA_ASSERT(false);
}
file.write_u32((uint32_t) tensor.ne.size());
file.write_u32((uint32_t) tensor.name.size());
file.write_u32(new_type);
file.write_raw(tensor.ne.data(), sizeof(tensor.ne[0]) * tensor.ne.size());
file.write_raw(tensor.name.data(), tensor.name.size());
file.seek(-static_cast<ptrdiff_t>(file.tell()) & 31, SEEK_CUR);
LLAMA_ASSERT(new_size == llama_calc_tensor_size(tensor.ne, new_type));
file.write_raw(new_data, new_size);
}
};
struct llama_model_loader {
std::unique_ptr<llama_file_loader> file_loader;
llama_load_tensors_map tensors_map;
bool use_mmap;
size_t num_ggml_tensors_created = 0;
struct ggml_context * ggml_ctx = NULL;
std::unique_ptr<llama_mmap> mapping;
llama_model_loader(const std::string & fname_base, bool use_mmap) {
file_loader = std::unique_ptr<llama_file_loader>(new llama_file_loader(fname_base.c_str(), tensors_map));
if (!llama_mmap::SUPPORTED) {
use_mmap = false;
}
this->use_mmap = use_mmap;
}
void calc_sizes(size_t * ctx_size_p, size_t * mmapped_size_p) const {
*ctx_size_p = *mmapped_size_p = 0;
for (const llama_load_tensor & lt : tensors_map.tensors) {
*ctx_size_p += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE;
*(use_mmap ? mmapped_size_p : ctx_size_p) += lt.size + 16;
}
}
struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne, ggml_backend backend) {
auto it = tensors_map.name_to_idx.find(name);
if (it == tensors_map.name_to_idx.end()) {
throw std::runtime_error(std::runtime_error(format("llama.cpp: tensor '%s' is missing from model", name.c_str())));
}
llama_load_tensor & lt = tensors_map.tensors.at(it->second);
if (lt.ne != ne) {
throw std::runtime_error(format("llama.cpp: tensor '%s' has wrong shape; expected %s, got %s",
name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str()));
}
return get_tensor_for(lt, backend);
}
struct ggml_tensor * get_tensor_for(llama_load_tensor & lt, ggml_backend backend) {
struct ggml_tensor * tensor;
if (backend != GGML_BACKEND_CPU) {
ggml_set_no_alloc(ggml_ctx, true);
}
if (lt.ne.size() == 2) {
tensor = ggml_new_tensor_2d(ggml_ctx, lt.type, lt.ne.at(0), lt.ne.at(1));
} else {
LLAMA_ASSERT(lt.ne.size() == 1);
tensor = ggml_new_tensor_1d(ggml_ctx, lt.type, lt.ne.at(0));
}
ggml_set_name(tensor, lt.name.c_str());
LLAMA_ASSERT(lt.ggml_tensor == NULL); // if this fails, we called get_tensor twice on the same tensor
if (backend != GGML_BACKEND_CPU) {
ggml_set_no_alloc(ggml_ctx, use_mmap);
}
tensor->backend = backend;
lt.ggml_tensor = tensor;
num_ggml_tensors_created++;
return tensor;
}
void done_getting_tensors() const {
if (num_ggml_tensors_created != tensors_map.tensors.size()) {
throw std::runtime_error(std::string("llama.cpp: file contained more tensors than expected"));
}
}
void load_all_data(llama_progress_callback progress_callback, void * progress_callback_user_data, llama_mlock * lmlock) {
size_t data_size = 0;
size_t prefetch_size = 0;
size_t lock_size = 0;
for (const llama_load_tensor & lt : tensors_map.tensors) {
data_size += lt.size;
if (lt.ggml_tensor->backend == GGML_BACKEND_CPU) {
prefetch_size += lt.size;
}
}
if (use_mmap) {
mapping.reset(new llama_mmap(&file_loader->file, prefetch_size, ggml_is_numa()));
if (lmlock) {
lmlock->init(mapping->addr);
}
}
size_t done_size = 0;
for (llama_load_tensor & lt : tensors_map.tensors) {
if (progress_callback) {
progress_callback((float) done_size / data_size, progress_callback_user_data);
}
LLAMA_ASSERT(lt.ggml_tensor); // unused tensors should have been caught by load_data already
lt.data = (uint8_t *) lt.ggml_tensor->data;
// allocate temp buffer if not using mmap
if (!use_mmap && lt.data == NULL) {
GGML_ASSERT(lt.ggml_tensor->backend != GGML_BACKEND_CPU);
lt.data = (uint8_t*)malloc(ggml_nbytes(lt.ggml_tensor));
}
load_data_for(lt);
switch(lt.ggml_tensor->backend) {
case GGML_BACKEND_CPU:
lt.ggml_tensor->data = lt.data;
if (use_mmap && lmlock) {
lock_size += lt.size;
lmlock->grow_to(lock_size);
}
break;
#if defined(GGML_USE_CUBLAS)
case GGML_BACKEND_GPU:
case GGML_BACKEND_GPU_SPLIT:
ggml_cuda_transform_tensor(lt.data, lt.ggml_tensor);
if (!use_mmap) {
free(lt.data);
}
break;
#elif defined(GGML_USE_CLBLAST)
case GGML_BACKEND_GPU:
ggml_cl_transform_tensor(lt.data, lt.ggml_tensor);
if (!use_mmap) {
free(lt.data);
}
break;
#endif
default:
continue;
}
done_size += lt.size;
}
}
void load_data_for(llama_load_tensor & lt) {
if (use_mmap) {
lt.data = (uint8_t *) mapping->addr + lt.file_off;
} else {
llama_file & file = file_loader->file;
file.seek(lt.file_off, SEEK_SET);
file.read_raw(lt.data, lt.size);
}
if (0) {
print_checksum(lt);
}
}
static void print_checksum(llama_load_tensor & lt) {
uint32_t sum = 0;
for (size_t i = 0; i < lt.size; i++) {
uint8_t byte = lt.data[i];
sum = byte + (sum << 6) + (sum << 16) - sum; // sdbm hash
}
fprintf(stderr, "%s checksum: %#08x (%s, size %zu)\n", lt.name.c_str(), sum,
llama_format_tensor_shape(lt.ne).c_str(), lt.size);
}
};
//
// kv cache
//
static bool kv_cache_init(
const struct llama_hparams & hparams,
struct llama_kv_cache & cache,
ggml_type wtype,
int n_ctx,
int n_gpu_layers) {
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int64_t n_mem = n_layer*n_ctx;
const int64_t n_elements = n_embd*n_mem;
cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB);
cache.n = 0;
struct ggml_init_params params;
params.mem_size = cache.buf.size;
params.mem_buffer = cache.buf.addr;
params.no_alloc = false;
cache.ctx = ggml_init(params);
if (!cache.ctx) {
fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
return false;
}
cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
ggml_set_name(cache.k, "cache_k");
ggml_set_name(cache.v, "cache_v");
(void) n_gpu_layers;
#ifdef GGML_USE_CUBLAS
if (n_gpu_layers > n_layer + 1) {
ggml_cuda_assign_buffers_no_scratch(cache.v);
}
if (n_gpu_layers > n_layer + 2) {
ggml_cuda_assign_buffers_no_scratch(cache.k);
}
#endif // GGML_USE_CUBLAS
return true;
}
struct llama_context_params llama_context_default_params() {
struct llama_context_params result = {
/*.seed =*/ LLAMA_DEFAULT_SEED,
/*.n_ctx =*/ 512,
/*.n_batch =*/ 512,
/*.gpu_layers =*/ 0,
/*.main_gpu =*/ 0,
/*.tensor_split =*/ {0},
/*.rope_freq_base =*/ 10000.0f,
/*.rope_freq_scale =*/ 1.0f,
/*.progress_callback =*/ nullptr,
/*.progress_callback_user_data =*/ nullptr,
/*.low_vram =*/ false,
/*.f16_kv =*/ true,
/*.logits_all =*/ false,
/*.vocab_only =*/ false,
/*.use_mmap =*/ true,
/*.use_mlock =*/ false,
/*.embedding =*/ false,
};
return result;
}
struct llama_model_quantize_params llama_model_quantize_default_params() {
struct llama_model_quantize_params result = {
/*.nthread =*/ 0,
/*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
/*.allow_requantize =*/ false,
/*.quantize_output_tensor =*/ true,
};
return result;
}
int llama_max_devices() {
return LLAMA_MAX_DEVICES;
}
bool llama_mmap_supported() {
return llama_mmap::SUPPORTED;
}
bool llama_mlock_supported() {
return llama_mlock::SUPPORTED;
}
void llama_backend_init(bool numa) {
ggml_time_init();
// needed to initialize f16 tables
{
struct ggml_init_params params = { 0, NULL, false };
struct ggml_context * ctx = ggml_init(params);
ggml_free(ctx);
}
if (numa) {
ggml_numa_init();
}
#ifdef GGML_USE_MPI
ggml_mpi_backend_init();
#endif
}
void llama_backend_free() {
#ifdef GGML_USE_MPI
ggml_mpi_backend_free();
#endif
}
int64_t llama_time_us() {
return ggml_time_us();
}
//
// model loading
//
static const char *llama_file_version_name(llama_file_version version) {
switch (version) {
case LLAMA_FILE_VERSION_GGML: return "'ggml' (old version with low tokenizer quality and no mmap support)";
case LLAMA_FILE_VERSION_GGMF_V1: return "ggmf v1 (old version with no mmap support)";
case LLAMA_FILE_VERSION_GGJT_V1: return "ggjt v1 (pre #1405)";
case LLAMA_FILE_VERSION_GGJT_V2: return "ggjt v2 (pre #1508)";
case LLAMA_FILE_VERSION_GGJT_V3: return "ggjt v3 (latest)";
}
return "unknown";
}
static const char *llama_ftype_name(enum llama_ftype ftype) {
switch (ftype) {
case LLAMA_FTYPE_ALL_F32: return "all F32";
case LLAMA_FTYPE_MOSTLY_F16: return "mostly F16";
case LLAMA_FTYPE_MOSTLY_Q4_0: return "mostly Q4_0";
case LLAMA_FTYPE_MOSTLY_Q4_1: return "mostly Q4_1";
case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
return "mostly Q4_1, some F16";
case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0";
case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1";
case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0";
// K-quants
case LLAMA_FTYPE_MOSTLY_Q2_K: return "mostly Q2_K";
case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "mostly Q3_K - Small";
case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "mostly Q3_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "mostly Q3_K - Large";
case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "mostly Q4_K - Small";
case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "mostly Q4_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "mostly Q5_K - Small";
case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "mostly Q5_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q6_K: return "mostly Q6_K";
default: return "unknown, may not work";
}
}
static const char *llama_model_type_name(e_model type) {
switch (type) {
case MODEL_3B: return "3B";
case MODEL_7B: return "7B";
case MODEL_13B: return "13B";
case MODEL_30B: return "30B";
case MODEL_65B: return "65B";
default: LLAMA_ASSERT(false);
}
}
static void llama_model_load_internal(
const std::string & fname,
llama_model & model,
llama_vocab & vocab,
int n_ctx,
int n_batch,
int n_gpu_layers,
int main_gpu,
const float * tensor_split,
float rope_freq_base,
float rope_freq_scale,
bool low_vram,
ggml_type memory_type,
bool use_mmap,
bool use_mlock,
bool vocab_only,
llama_progress_callback progress_callback,
void * progress_callback_user_data) {
model.t_start_us = ggml_time_us();
std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname, use_mmap));
vocab = std::move(ml->file_loader->vocab);
model.hparams = ml->file_loader->hparams;
model.n_gpu_layers = n_gpu_layers;
llama_file_version file_version = ml->file_loader->file_version;
auto & hparams = model.hparams;
{
switch (hparams.n_layer) {
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 60: model.type = e_model::MODEL_30B; break;
case 80: model.type = e_model::MODEL_65B; break;
default:
{
if (hparams.n_layer < 32) {
model.type = e_model::MODEL_7B;
}
} break;
}
hparams.n_ctx = n_ctx;
hparams.rope_freq_base = rope_freq_base;
hparams.rope_freq_scale = rope_freq_scale;
}
const uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
{
fprintf(stderr, "%s: format = %s\n", __func__, llama_file_version_name(file_version));
fprintf(stderr, "%s: n_vocab = %u\n", __func__, hparams.n_vocab);
fprintf(stderr, "%s: n_ctx = %u\n", __func__, hparams.n_ctx);
fprintf(stderr, "%s: n_embd = %u\n", __func__, hparams.n_embd);
fprintf(stderr, "%s: n_mult = %u\n", __func__, hparams.n_mult);
fprintf(stderr, "%s: n_head = %u\n", __func__, hparams.n_head);
fprintf(stderr, "%s: n_layer = %u\n", __func__, hparams.n_layer);
fprintf(stderr, "%s: n_rot = %u\n", __func__, hparams.n_rot);
fprintf(stderr, "%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base);
fprintf(stderr, "%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale);
fprintf(stderr, "%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
fprintf(stderr, "%s: n_ff = %u\n", __func__, n_ff);
fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type));
}
if (file_version < LLAMA_FILE_VERSION_GGJT_V2) {
if (hparams.ftype != LLAMA_FTYPE_ALL_F32 &&
hparams.ftype != LLAMA_FTYPE_MOSTLY_F16 &&
hparams.ftype != LLAMA_FTYPE_MOSTLY_Q8_0) {
throw std::runtime_error(format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1405)"));
}
}
if (file_version < LLAMA_FILE_VERSION_GGJT_V3) {
if (hparams.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ||
hparams.ftype == LLAMA_FTYPE_MOSTLY_Q4_1 ||
hparams.ftype == LLAMA_FTYPE_MOSTLY_Q8_0) {
throw std::runtime_error(format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1508)"));
}
}
if (vocab_only) {
return;
}
auto & ctx = model.ctx;
size_t ctx_size;
size_t mmapped_size;
ml->calc_sizes(&ctx_size, &mmapped_size);
fprintf(stderr, "%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/1024.0/1024.0);
// create the ggml context
{
model.buf.resize(ctx_size);
if (use_mlock) {
model.mlock_buf.init(model.buf.addr);
model.mlock_buf.grow_to(model.buf.size);
}
struct ggml_init_params params = {
/*.mem_size =*/ model.buf.size,
/*.mem_buffer =*/ model.buf.addr,
/*.no_alloc =*/ ml->use_mmap,
};
model.ctx = ggml_init(params);
if (!model.ctx) {
throw std::runtime_error(format("ggml_init() failed"));
}
}
(void) main_gpu;
#if defined(GGML_USE_CUBLAS)
fprintf(stderr, "%s: using CUDA for GPU acceleration\n", __func__);
ggml_cuda_set_main_device(main_gpu);
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT
#elif defined(GGML_USE_CLBLAST)
fprintf(stderr, "%s: using OpenCL for GPU acceleration\n", __func__);
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU
#else
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CPU
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_CPU
#endif
// prepare memory for the weights
size_t vram_weights = 0;
size_t vram_scratch = 0;
{
const uint32_t n_embd = hparams.n_embd;
const uint32_t n_layer = hparams.n_layer;
const uint32_t n_vocab = hparams.n_vocab;
ml->ggml_ctx = ctx;
model.tok_embeddings = ml->get_tensor("tok_embeddings.weight", {n_embd, n_vocab}, GGML_BACKEND_CPU);
// "output" tensor
{
ggml_backend backend_norm;
ggml_backend backend_output;
if (n_gpu_layers > int(n_layer)) { // NOLINT
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = low_vram ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
#else
backend_norm = low_vram || n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
#endif // _WIN32
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.norm = ml->get_tensor("norm.weight", {n_embd}, backend_norm);
model.output = ml->get_tensor("output.weight", {n_embd, n_vocab}, backend_output);
if (backend_norm == GGML_BACKEND_GPU) {
vram_weights += ggml_nbytes(model.norm);
}
if (backend_output == GGML_BACKEND_GPU_SPLIT) {
vram_weights += ggml_nbytes(model.output);
}
}
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
auto & layer = model.layers[i];
std::string layers_i = "layers." + std::to_string(i);
layer.attention_norm = ml->get_tensor(layers_i + ".attention_norm.weight", {n_embd}, backend);
layer.wq = ml->get_tensor(layers_i + ".attention.wq.weight", {n_embd, n_embd}, backend_split);
layer.wk = ml->get_tensor(layers_i + ".attention.wk.weight", {n_embd, n_embd}, backend_split);
layer.wv = ml->get_tensor(layers_i + ".attention.wv.weight", {n_embd, n_embd}, backend_split);
layer.wo = ml->get_tensor(layers_i + ".attention.wo.weight", {n_embd, n_embd}, backend_split);
layer.ffn_norm = ml->get_tensor(layers_i + ".ffn_norm.weight", {n_embd}, backend);
layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}, backend_split);
layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}, backend_split);
layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}, backend_split);
if (backend == GGML_BACKEND_GPU) {
vram_weights +=
ggml_nbytes(layer.attention_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
}
}
}
ml->done_getting_tensors();
// print memory requirements
{
const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
// this is the total memory required to run the inference
const size_t mem_required =
ctx_size +
mmapped_size - vram_weights + // weights in VRAM not in memory
MEM_REQ_SCRATCH0(hparams.n_ctx).at(model.type) +
MEM_REQ_SCRATCH1().at(model.type) +
MEM_REQ_EVAL(hparams.n_ctx).at(model.type);
// this is the memory required by one llama_state
const size_t mem_required_state =
scale*MEM_REQ_KV_SELF().at(model.type);
fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__,
mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
(void) vram_scratch;
(void) n_batch;
#ifdef GGML_USE_CUBLAS
if (low_vram) {
fprintf(stderr, "%s: not allocating a VRAM scratch buffer due to low VRAM option\n", __func__);
ggml_cuda_set_scratch_size(0); // disable scratch
} else {
const size_t vram_scratch_base = VRAM_REQ_SCRATCH_BASE().at(model.type);
const size_t vram_scratch_per_context = VRAM_REQ_SCRATCH_PER_CONTEXT().at(model.type);
vram_scratch = n_batch * (vram_scratch_base + n_ctx * vram_scratch_per_context);
ggml_cuda_set_scratch_size(vram_scratch);
if (n_gpu_layers > 0) {
fprintf(stderr, "%s: allocating batch_size x (%zd kB + n_ctx x %zd B) = %zd MB VRAM for the scratch buffer\n",
__func__, vram_scratch_base / kB, vram_scratch_per_context,
(vram_scratch + MB - 1) / MB); // round up
}
}
#endif // GGML_USE_CUBLAS
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
fprintf(stderr, "%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
if (n_gpu_layers > (int) hparams.n_layer) {
fprintf(stderr, "%s: offloading non-repeating layers to GPU\n", __func__);
}
size_t vram_kv_cache = 0;
#ifdef GGML_USE_CUBLAS
const int max_backend_supported_layers = hparams.n_layer + 3;
const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3;
if (n_gpu_layers > (int) hparams.n_layer + 1) {
if (low_vram) {
fprintf(stderr, "%s: cannot offload v cache to GPU due to low VRAM option\n", __func__);
} else {
fprintf(stderr, "%s: offloading v cache to GPU\n", __func__);
vram_kv_cache += MEM_REQ_KV_SELF().at(model.type) / 2;
}
}
if (n_gpu_layers > (int) hparams.n_layer + 2) {
if (low_vram) {
fprintf(stderr, "%s: cannot offload k cache to GPU due to low VRAM option\n", __func__);
} else {
fprintf(stderr, "%s: offloading k cache to GPU\n", __func__);
vram_kv_cache += MEM_REQ_KV_SELF().at(model.type) / 2;
}
}
#elif defined(GGML_USE_CLBLAST)
const int max_backend_supported_layers = hparams.n_layer + 1;
const int max_offloadable_layers = hparams.n_layer + 1;
#endif // GGML_USE_CUBLAS
fprintf(stderr, "%s: offloaded %d/%d layers to GPU\n",
__func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
fprintf(stderr, "%s: total VRAM used: %zu MB\n",
__func__, (vram_weights + vram_scratch + vram_kv_cache + MB - 1) / MB); // round up
#else
(void) n_gpu_layers;
#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
}
// populate `tensors_by_name`
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
model.tensors_by_name.emplace_back(lt.name, lt.ggml_tensor);
}
(void) tensor_split;
#if defined(GGML_USE_CUBLAS)
{
ggml_cuda_set_tensor_split(tensor_split);
}
#endif
ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &model.mlock_mmap : NULL);
if (progress_callback) {
progress_callback(1.0f, progress_callback_user_data);
}
model.mapping = std::move(ml->mapping);
// loading time will be recalculate after the first eval, so
// we take page faults deferred by mmap() into consideration
model.t_load_us = ggml_time_us() - model.t_start_us;
}
static bool llama_model_load(
const std::string & fname,
llama_model & model,
llama_vocab & vocab,
int n_ctx,
int n_batch,
int n_gpu_layers,
int main_gpu,
float * tensor_split,
float rope_freq_base,
float rope_freq_scale,
bool low_vram,
ggml_type memory_type,
bool use_mmap,
bool use_mlock,
bool vocab_only,
llama_progress_callback progress_callback,
void *progress_callback_user_data) {
try {
llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, rope_freq_base, rope_freq_scale, low_vram, memory_type,
use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
return true;
} catch (const std::exception & err) {
fprintf(stderr, "error loading model: %s\n", err.what());
return false;
}
}
// evaluate the transformer
//
// - lctx: llama context
// - tokens: new batch of tokens to process
// - embd embeddings input
// - n_tokens number of tokens
// - n_past: the context size so far
// - n_threads: number of threads to use
//
static bool llama_eval_internal(
llama_context & lctx,
const llama_token * tokens,
const float * embd,
int n_tokens,
int n_past,
int n_threads,
const char * cgraph_fname) {
LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
#ifdef GGML_USE_MPI
ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
#endif
const int64_t t_start_us = ggml_time_us();
const int N = n_tokens;
const auto & model = lctx.model;
const auto & hparams = model.hparams;
const auto & kv_self = lctx.kv_self;
LLAMA_ASSERT(!!kv_self.ctx);
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int n_ctx = hparams.n_ctx;
const int n_head = hparams.n_head;
const int n_vocab = hparams.n_vocab;
const int n_rot = hparams.n_embd/hparams.n_head;
const int n_gpu_layers = model.n_gpu_layers;
const float freq_base = hparams.rope_freq_base;
const float freq_scale = hparams.rope_freq_scale;
auto & mem_per_token = lctx.mem_per_token;
auto & buf_compute = lctx.buf_compute;
struct ggml_init_params params = {
/*.mem_size =*/ buf_compute.size,
/*.mem_buffer =*/ buf_compute.addr,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx0 = ggml_init(params);
ggml_cgraph gf = {};
// 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
n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
if (tokens) {
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
memcpy(inp_tokens->data, tokens, N*ggml_element_size(inp_tokens));
ggml_set_name(inp_tokens, "inp_tokens");
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
} else {
#ifdef GGML_USE_MPI
GGML_ASSERT(false && "not implemented");
#endif
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N);
memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
}
const int i_gpu_start = n_layer - n_gpu_layers;
(void) i_gpu_start;
// offload functions set the tensor output backend to GPU
// tensors are GPU-accelerated if any input or the output has been offloaded
//
// with the low VRAM option VRAM scratch is disabled in llama_load_model_internal
// in that case ggml_cuda_assign_buffers has no effect
offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
offload_func_t offload_func_kq = llama_nop;
offload_func_t offload_func_v = llama_nop;
#ifdef GGML_USE_CUBLAS
if (n_gpu_layers > n_layer) {
offload_func_nr = ggml_cuda_assign_buffers;
}
if (n_gpu_layers > n_layer + 1) {
offload_func_v = ggml_cuda_assign_buffers;
}
if (n_gpu_layers > n_layer + 2) {
offload_func_kq = ggml_cuda_assign_buffers;
}
#endif // GGML_USE_CUBLAS
for (int il = 0; il < n_layer; ++il) {
ggml_format_name(inpL, "layer_inp_%d", il);
offload_func_t offload_func = llama_nop;
#ifdef GGML_USE_CUBLAS
if (il >= i_gpu_start) {
offload_func = ggml_cuda_assign_buffers;
}
#endif // GGML_USE_CUBLAS
struct ggml_tensor * inpSA = inpL;
lctx.use_buf(ctx0, 0);
// norm
{
cur = ggml_rms_norm(ctx0, inpL);
offload_func(cur);
ggml_set_name(cur, "rms_norm_0");
// cur = cur*attention_norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.layers[il].attention_norm);
offload_func(cur);
ggml_set_name(cur, "attention_norm_0");
}
// self-attention
{
// compute Q and K and RoPE them
struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
offload_func_kq(tmpk);
ggml_set_name(tmpk, "tmpk");
struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
offload_func_kq(tmpq);
ggml_set_name(tmpq, "tmpq");
struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, freq_base, freq_scale, 0);
offload_func_kq(Kcur);
ggml_set_name(Kcur, "Kcur");
struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, freq_base, freq_scale, 0);
offload_func_kq(Qcur);
ggml_set_name(Qcur, "Qcur");
// store key and value to memory
{
// compute the transposed [N, n_embd] V matrix
struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
offload_func_v(tmpv);
ggml_set_name(tmpv, "tmpv");
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd, N));
offload_func_v(Vcur);
ggml_set_name(Vcur, "Vcur");
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
offload_func_kq(k);
ggml_set_name(k, "k");
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
( n_ctx)*ggml_element_size(kv_self.v),
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
offload_func_v(v);
ggml_set_name(v, "v");
// important: storing RoPE-ed version of K in the KV cache!
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
}
struct ggml_tensor * Q =
ggml_permute(ctx0,
Qcur,
0, 2, 1, 3);
offload_func_kq(Q);
ggml_set_name(Q, "Q");
struct ggml_tensor * K =
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
ggml_view_1d(ctx0, kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.k)*n_embd),
n_embd/n_head, n_head, n_past + N),
0, 2, 1, 3);
offload_func_kq(K);
ggml_set_name(K, "K");
// K * Q
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
offload_func_kq(KQ);
ggml_set_name(KQ, "KQ");
// KQ_scaled = KQ / sqrt(n_embd/n_head)
struct ggml_tensor * KQ_scale = ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head));
ggml_set_name(KQ_scale, "1/sqrt(n_embd/n_head)");
// KQ_scaled shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
offload_func_kq(KQ_scaled);
ggml_set_name(KQ_scaled, "KQ_scaled");
// KQ_masked = mask_past(KQ_scaled)
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
offload_func_kq(KQ_masked);
ggml_set_name(KQ_masked, "KQ_masked");
// KQ = soft_max(KQ_masked)
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
offload_func_v(KQ_soft_max);
ggml_set_name(KQ_soft_max, "KQ_soft_max");
// split cached V into n_head heads
struct ggml_tensor * V =
ggml_view_3d(ctx0, kv_self.v,
n_past + N, n_embd/n_head, n_head,
n_ctx*ggml_element_size(kv_self.v),
n_ctx*ggml_element_size(kv_self.v)*n_embd/n_head,
il*n_ctx*ggml_element_size(kv_self.v)*n_embd);
offload_func_v(V);
ggml_set_name(V, "V");
#if 1
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
offload_func_v(KQV);
ggml_set_name(KQV, "KQV");
#else
// make V contiguous in memory to speed up the matmul, however we waste time on the copy
// on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
// is there a better way?
struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_past + N, n_embd/n_head, n_head));
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max);
#endif
// KQV_merged = KQV.permute(0, 2, 1, 3)
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
offload_func_v(KQV_merged);
ggml_set_name(KQV_merged, "KQV_merged");
// cur = KQV_merged.contiguous().view(n_embd, N)
cur = ggml_cpy(ctx0,
KQV_merged,
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
offload_func_v(cur);
ggml_set_name(cur, "KQV_merged_contiguous");
// projection (no bias)
cur = ggml_mul_mat(ctx0,
model.layers[il].wo,
cur);
offload_func(cur);
ggml_set_name(cur, "result_wo");
}
lctx.use_buf(ctx0, 1);
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
offload_func(inpFF);
ggml_set_name(inpFF, "inpFF");
// feed-forward network
{
// norm
{
cur = ggml_rms_norm(ctx0, inpFF);
offload_func(cur);
ggml_set_name(cur, "rms_norm_1");
// cur = cur*ffn_norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
offload_func(cur);
ggml_set_name(cur, "ffn_norm");
}
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
model.layers[il].w3,
cur);
offload_func(tmp);
ggml_set_name(tmp, "result_w3");
cur = ggml_mul_mat(ctx0,
model.layers[il].w1,
cur);
offload_func(cur);
ggml_set_name(cur, "result_w1");
// SILU activation
cur = ggml_silu(ctx0, cur);
offload_func(cur);
ggml_set_name(cur, "silu");
cur = ggml_mul(ctx0, cur, tmp);
offload_func(cur);
ggml_set_name(cur, "silu_x_result_w3");
cur = ggml_mul_mat(ctx0,
model.layers[il].w2,
cur);
offload_func(cur);
ggml_set_name(cur, "result_w2");
}
cur = ggml_add(ctx0, cur, inpFF);
offload_func(cur);
ggml_set_name(cur, "inpFF_+_result_w2");
// input for next layer
inpL = cur;
}
lctx.use_buf(ctx0, 0);
// used at the end to optionally extract the embeddings
struct ggml_tensor * embeddings = NULL;
// norm
{
cur = ggml_rms_norm(ctx0, inpL);
offload_func_nr(cur);
ggml_set_name(cur, "rms_norm_2");
// cur = cur*norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.norm);
// offload_func_nr(cur); // TODO CPU + GPU mirrored backend
ggml_set_name(cur, "result_norm");
embeddings = cur;
}
// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);
ggml_set_name(cur, "result_output");
lctx.use_buf(ctx0, -1);
// logits -> probs
//cur = ggml_soft_max_inplace(ctx0, cur);
// run the computation
ggml_build_forward_expand(&gf, cur);
#if GGML_USE_MPI
ggml_mpi_graph_compute_pre(lctx.ctx_mpi, &gf, n_layer);
#endif
#ifdef GGML_USE_METAL
if (lctx.ctx_metal && N == 1) {
ggml_metal_set_n_cb (lctx.ctx_metal, n_threads);
ggml_metal_graph_compute(lctx.ctx_metal, &gf);
ggml_metal_get_tensor (lctx.ctx_metal, cur);
} else {
// IMPORTANT:
// Since we don't have efficient Matrix x Matrix Metal multiplication yet, we fallback to vanilla
// ggml_graph_compute(). It uses Apple's Accelerate CBLAS API which takes advantage of the ANE or the AMX
// coprocessor.
//
// When we implement Matrix x Matrix Metal multiplication, we can avoid this branch.
// But for now, we have focused only on Matrix x Vector Metal multiplication.
//
// TODO: avoid these syncs via shared memory (ref #1696)
//
if (lctx.ctx_metal) {
// We need to sync the GPU KV cache with the CPU KV cache
ggml_metal_get_tensor(lctx.ctx_metal, kv_self.k);
ggml_metal_get_tensor(lctx.ctx_metal, kv_self.v);
}
ggml_graph_compute_helper(lctx.work_buffer, &gf, n_threads);
}
#else
ggml_graph_compute_helper(lctx.work_buffer, &gf, n_threads);
#endif
#if GGML_USE_MPI
ggml_mpi_graph_compute_post(lctx.ctx_mpi, &gf, n_layer);
#endif
// update kv token count
lctx.kv_self.n = n_past + N;
struct ggml_tensor * res = gf.nodes[gf.n_nodes - 1];
if (cgraph_fname) {
ggml_graph_export(&gf, cgraph_fname);
}
#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
{
auto & logits_out = lctx.logits;
if (lctx.logits_all) {
logits_out.resize(n_vocab * N);
memcpy(logits_out.data(), (float *) ggml_get_data(res), sizeof(float)*n_vocab*N);
} else {
// return result for just the last token
logits_out.resize(n_vocab);
memcpy(logits_out.data(), (float *) ggml_get_data(res) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
}
}
// extract embeddings
if (!lctx.embedding.empty()) {
auto & embedding_out = lctx.embedding;
embedding_out.resize(n_embd);
memcpy(embedding_out.data(), (float *) ggml_get_data(embeddings) + (n_embd*(N - 1)), sizeof(float)*n_embd);
}
if (mem_per_token == 0) {
mem_per_token = ggml_used_mem(ctx0)/N;
}
#if 0
printf("\n%s: used_mem = %.3f MB, scratch -- %.3f MB %.3f MB\n", __func__,
ggml_used_mem(ctx0)/1024.0/1024.0,
lctx.get_buf_max_mem(0)/1024.0/1024.0,
lctx.get_buf_max_mem(1)/1024.0/1024.0);
#endif
ggml_free(ctx0);
// measure the performance only for the single-token evals
if (N == 1) {
lctx.t_eval_us += ggml_time_us() - t_start_us;
lctx.n_eval++;
}
else if (N > 1) {
lctx.t_p_eval_us += ggml_time_us() - t_start_us;
lctx.n_p_eval += N;
}
return true;
}
//
// tokenizer
//
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];
}
struct llama_sp_symbol {
using index = int;
index prev;
index next;
const char * text;
size_t n;
};
static_assert(std::is_trivially_copyable<llama_sp_symbol>::value, "llama_sp_symbol is not trivially copyable");
struct llama_sp_bigram {
struct comparator {
bool operator()(llama_sp_bigram & l, llama_sp_bigram & r) {
return (l.score < r.score) || (l.score == r.score && l.left > r.left);
}
};
using queue_storage = std::vector<llama_sp_bigram>;
using queue = std::priority_queue<llama_sp_bigram, queue_storage, comparator>;
llama_sp_symbol::index left;
llama_sp_symbol::index right;
float score;
size_t size;
};
// original implementation:
// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
struct llama_tokenizer {
llama_tokenizer(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()) {
llama_sp_symbol sym;
size_t char_len = std::min(text.size() - offs, utf8_len(text[offs]));
sym.text = text.c_str() + offs;
sym.n = char_len;
offs += char_len;
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;
//printf("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];
auto token = vocab_.token_to_id.find(std::string(symbol.text, symbol.n));
if (token == vocab_.token_to_id.end()) {
// output any symbols that did not form tokens as bytes.
for (int j = 0; j < (int) symbol.n; ++j) {
llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
output.push_back(token_id);
}
} else {
output.push_back((*token).second);
}
}
}
private:
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_score = vocab_.id_to_token[(*token).second];
llama_sp_bigram bigram;
bigram.left = left;
bigram.right = right;
bigram.score = tok_score.score;
bigram.size = text.size();
work_queue_.push(bigram);
}
const llama_vocab & vocab_;
std::vector<llama_sp_symbol> symbols_;
llama_sp_bigram::queue work_queue_;
};
static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos) {
llama_tokenizer tokenizer(vocab);
std::vector<llama_vocab::id> output;
if (text.empty()) {
return output;
}
if (bos) {
output.push_back(llama_token_bos());
}
tokenizer.tokenize(text, output);
return output;
}
//
// sampling
//
void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates) {
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, int k, size_t min_keep) {
const int64_t t_start_sample_us = ggml_time_us();
k = std::max(k, (int) min_keep);
k = std::min(k, (int) candidates->size);
// Sort scores in descending order
if (!candidates->sorted) {
auto comp = [](const llama_token_data & a, const llama_token_data & b) {
return a.logit > b.logit;
};
if (k == (int) candidates->size) {
std::sort(candidates->data, candidates->data + candidates->size, comp);
} else {
std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
}
candidates->sorted = true;
}
candidates->size = k;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
if (p >= 1.0f) {
return;
}
llama_sample_softmax(ctx, candidates);
const int64_t t_start_sample_us = ggml_time_us();
// Compute the cumulative probabilities
float cum_sum = 0.0f;
size_t last_idx = candidates->size;
for (size_t i = 0; i < candidates->size; ++i) {
cum_sum += candidates->data[i].p;
// Check if the running sum is at least p or if we have kept at least min_keep tokens
// we set the last index to i+1 to indicate that the current iterate should be included in the set
if (cum_sum >= p && i + 1 >= min_keep) {
last_idx = i + 1;
break;
}
}
// Resize the output vector to keep only the top-p tokens
candidates->size = last_idx;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_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] = 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();
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
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_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty) {
if (last_tokens_size == 0 || penalty == 1.0f) {
return;
}
const int64_t t_start_sample_us = ggml_time_us();
for (size_t i = 0; i < candidates->size; ++i) {
const auto * token_iter = std::find(last_tokens, last_tokens + last_tokens_size, candidates->data[i].id);
if (token_iter == last_tokens + last_tokens_size) {
continue;
}
// 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;
} else {
candidates->data[i].logit /= penalty;
}
}
candidates->sorted = false;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens_p, size_t last_tokens_size, float alpha_frequency, float alpha_presence) {
if (last_tokens_size == 0 || (alpha_frequency == 0.0f && alpha_presence == 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 < last_tokens_size; ++i) {
token_count[last_tokens_p[i]]++;
}
// Apply frequency and presence penalties to the candidates
for (size_t i = 0; i < candidates->size; ++i) {
auto token_iter = token_count.find(candidates->data[i].id);
if (token_iter == token_count.end()) {
continue;
}
int count = token_iter->second;
candidates->data[i].logit -= float(count) * alpha_frequency + float(count > 0) * alpha_presence;
}
candidates->sorted = false;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
static void llama_log_softmax(float * array, size_t size) {
float max_l = *std::max_element(array, array + size);
float sum = 0.f;
for (size_t i = 0; i < size; ++i) {
float p = expf(array[i] - max_l);
sum += p;
array[i] = p;
}
for (size_t i = 0; i < size; ++i) {
array[i] = logf(array[i] / sum);
}
}
void llama_sample_classifier_free_guidance(
struct llama_context * ctx,
llama_token_data_array * candidates,
struct llama_context * guidance_ctx,
float scale,
float smooth_factor) {
int64_t t_start_sample_us = ggml_time_us();
assert(ctx);
auto n_vocab = llama_n_vocab(ctx);
assert(n_vocab == (int)candidates->size);
assert(!candidates->sorted);
std::vector<float> logits_base;
logits_base.reserve(candidates->size);
for (size_t i = 0; i < candidates->size; ++i) {
logits_base.push_back(candidates->data[i].logit);
}
llama_log_softmax(logits_base.data(), candidates->size);
float* logits_guidance = llama_get_logits(guidance_ctx);
llama_log_softmax(logits_guidance, n_vocab);
for (int i = 0; i < n_vocab; ++i) {
float logit_guidance = logits_guidance[i];
float logit_base = logits_base[i];
logits_guidance[i] = scale * (logit_base - logit_guidance) + logit_guidance;
}
llama_log_softmax(logits_guidance, n_vocab);
for (int i = 0; i < n_vocab; ++i) {
float logit_base = logits_base[i];
float logit_guidance = logits_guidance[i];
candidates->data[i].logit = smooth_factor * logit_guidance + (1.f - smooth_factor) * logit_base;
}
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu) {
assert(ctx);
auto N = float(llama_n_vocab(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) {
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;
}
//
// quantization
//
static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llama_buffer & output, const int nelements, const int nthread) {
if (output.size < nelements * sizeof(float)) {
output.resize(nelements * sizeof(float));
}
float * f32_output = (float *) output.addr;
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 {
LLAMA_ASSERT(false); // unreachable
}
return;
}
auto block_size = tensor.type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor.type);
auto block_size_bytes = ggml_type_size(tensor.type);
LLAMA_ASSERT(nelements % block_size == 0);
auto nblocks = nelements / block_size;
auto blocks_per_thread = nblocks / nthread;
auto spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
std::vector<std::thread> workers;
for (auto tnum = 0, in_buff_offs = 0, out_buff_offs = 0; tnum < nthread; tnum++) {
auto thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
auto thr_elems = thr_blocks * block_size; // number of elements for this thread
auto 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.push_back(std::thread(compute, tensor.type, 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 & worker : workers) {
worker.join();
}
}
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
ggml_type quantized_type;
llama_ftype ftype = params->ftype;
int nthread = params->nthread;
switch (params->ftype) {
case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
case LLAMA_FTYPE_MOSTLY_F16: quantized_type = GGML_TYPE_F16; break;
case LLAMA_FTYPE_ALL_F32: quantized_type = GGML_TYPE_F32; break;
#ifdef GGML_USE_K_QUANTS
// K-quants
case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break;
case LLAMA_FTYPE_MOSTLY_Q3_K_S:
case LLAMA_FTYPE_MOSTLY_Q3_K_M:
case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break;
case LLAMA_FTYPE_MOSTLY_Q4_K_S:
case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break;
case LLAMA_FTYPE_MOSTLY_Q5_K_S:
case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break;
case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break;
#endif
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
}
if (nthread <= 0) {
nthread = std::thread::hardware_concurrency();
}
std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp, /*use_mmap*/ false));
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loader.get(), params->ftype);
#ifdef GGML_USE_K_QUANTS
int n_attention_wv = 0;
int n_feed_forward_w2 = 0;
for (auto& tensor : model_loader->tensors_map.tensors) {
if (tensor.name.find("attention.wv.weight") != std::string::npos) {
++n_attention_wv;
}
else if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
++n_feed_forward_w2;
}
}
int i_attention_wv = 0;
int i_feed_forward_w2 = 0;
#endif
size_t total_size_org = 0;
size_t total_size_new = 0;
std::vector<int64_t> hist_all(1 << 4, 0);
std::vector<std::thread> workers;
std::mutex mutex;
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;
};
size_t idx = 0;
for (llama_load_tensor & tensor : model_loader->tensors_map.tensors) {
llama_buffer read_data;
read_data.resize(tensor.size);
tensor.data = read_data.addr;
model_loader->load_data_for(tensor);
printf("[%4zu/%4zu] %36s - %16s, type = %6s, ",
++idx, model_loader->tensors_map.tensors.size(),
tensor.name.c_str(), llama_format_tensor_shape(tensor.ne).c_str(),
ggml_type_name(tensor.type));
// This used to be a regex, but <regex> has an extreme cost to compile times.
bool quantize = tensor.name.rfind("weight") == tensor.name.size() - 6; // ends with 'weight'?
// quantize only 2D tensors
quantize &= (tensor.ne.size() == 2);
quantize &= params->quantize_output_tensor || tensor.name != "output.weight";
quantize &= quantized_type != tensor.type;
enum ggml_type new_type;
void * new_data;
size_t new_size;
llama_buffer work;
if (!quantize) {
new_type = tensor.type;
new_data = tensor.data;
new_size = tensor.size;
printf("size = %8.3f MB\n", tensor.size/1024.0/1024.0);
} else {
new_type = quantized_type;
#ifdef GGML_USE_K_QUANTS
bool convert_incompatible_tensor = false;
if (quantized_type == GGML_TYPE_Q2_K || quantized_type == GGML_TYPE_Q3_K || quantized_type == GGML_TYPE_Q4_K ||
quantized_type == GGML_TYPE_Q5_K || quantized_type == GGML_TYPE_Q6_K) {
int nx = tensor.ne.at(0);
int ny = tensor.ne.at(1);
if (nx % QK_K != 0 || ny % QK_K != 0) {
fprintf(stderr, "\n\nTensor sizes %d x %d are not divisible by %d, required for k-quants.\n",nx,ny,QK_K);
convert_incompatible_tensor = true;
}
}
if (tensor.name == "output.weight") {
int nx = tensor.ne.at(0);
int ny = tensor.ne.at(1);
if (nx % QK_K == 0 && ny % QK_K == 0) {
new_type = GGML_TYPE_Q6_K;
}
} else if (tensor.name.find("attention.wv.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) 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_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
use_more_bits(i_attention_wv, n_attention_wv)) new_type = GGML_TYPE_Q6_K;
else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
(i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
++i_attention_wv;
} else if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) 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_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
//else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_feed_forward_w2 < n_feed_forward_w2/8) new_type = GGML_TYPE_Q6_K;
++i_feed_forward_w2;
} else if (tensor.name.find("attention.wo.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
}
if (convert_incompatible_tensor) {
if (tensor.name == "output.weight") {
new_type = GGML_TYPE_F16; //fall back to F16 instead of just failing.
fprintf(stderr, "F16 will be used for this tensor instead.\n");
} else if (tensor.name == "tok_embeddings.weight") {
new_type = GGML_TYPE_Q4_0; //fall back to Q4_0 instead of just failing.
fprintf(stderr, "Q4_0 will be used for this tensor instead.\n");
} else {
throw std::runtime_error("Unsupported tensor size encountered\n");
}
}
#endif
float * f32_data;
size_t nelements = tensor.ne.at(0) * tensor.ne.at(1);
llama_buffer f32_conv_buf;
if (tensor.type == GGML_TYPE_F32) {
f32_data = (float *) tensor.data;
} else if (ggml_is_quantized(tensor.type) && !params->allow_requantize) {
throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor.type)));
} else {
llama_convert_tensor_internal(tensor, f32_conv_buf, nelements, nthread);
f32_data = (float *) f32_conv_buf.addr;
}
printf("quantizing .. ");
fflush(stdout);
work.resize(nelements * 4); // upper bound on size
new_data = work.addr;
std::vector<int64_t> hist_cur(1 << 4, 0);
int chunk_size = 32 * 512;
const int nchunk = (nelements + chunk_size - 1)/chunk_size;
const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
if (nthread_use < 2) {
new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nelements, hist_cur.data());
} else {
size_t counter = 0;
new_size = 0;
auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements, chunk_size] () {
std::vector<int64_t> local_hist;
size_t local_size = 0;
while (true) {
std::unique_lock<std::mutex> lock(mutex);
size_t first = counter; counter += chunk_size;
if (first >= nelements) {
if (!local_hist.empty()) {
for (int j=0; j<int(local_hist.size()); ++j) {
hist_cur[j] += local_hist[j];
}
new_size += local_size;
}
break;
}
lock.unlock();
size_t last = std::min(nelements, first + chunk_size);
if (local_hist.empty()) {
local_hist.resize(hist_cur.size(), 0);
}
local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first, last - first, local_hist.data());
}
};
if ((int) workers.size() < nthread_use - 1) {
workers.resize(nthread_use - 1);
}
for (int it = 0; it < nthread_use - 1; ++it) {
workers[it] = std::thread(compute);
}
compute();
for (int it = 0; it < nthread_use - 1; ++it) {
workers[it].join();
}
}
printf("size = %8.2f MB -> %8.2f MB | hist: ", tensor.size/1024.0/1024.0, new_size/1024.0/1024.0);
int64_t tot_count = 0;
for (size_t i = 0; i < hist_cur.size(); i++) {
hist_all[i] += hist_cur[i];
tot_count += hist_cur[i];
}
if (tot_count > 0) {
for (size_t i = 0; i < hist_cur.size(); i++) {
printf("%5.3f ", hist_cur[i] / float(nelements));
}
}
printf("\n");
}
total_size_org += tensor.size;
total_size_new += new_size;
file_saver.write_tensor(tensor, new_type, new_data, new_size);
}
printf("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
printf("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
{
int64_t sum_all = 0;
for (size_t i = 0; i < hist_all.size(); i++) {
sum_all += hist_all[i];
}
if (sum_all > 0) {
printf("%s: hist: ", __func__);
for (size_t i = 0; i < hist_all.size(); i++) {
printf("%5.3f ", hist_all[i] / float(sum_all));
}
printf("\n");
}
}
}
//
// interface implementation
//
struct llama_model * llama_load_model_from_file(
const char * path_model,
struct llama_context_params params) {
ggml_time_init();
llama_model * model = new llama_model;
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gpu_layers,
params.main_gpu, params.tensor_split, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
memory_type, params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback,
params.progress_callback_user_data)) {
delete model;
fprintf(stderr, "%s: failed to load model\n", __func__);
return nullptr;
}
return model;
}
void llama_free_model(struct llama_model * model) {
delete model;
}
struct llama_context * llama_new_context_with_model(
struct llama_model * model,
struct llama_context_params params) {
if (!model) {
return nullptr;
}
llama_context * ctx = new llama_context(*model);
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
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;
fprintf(stderr, ".");
fflush(stderr);
if (percentage >= 100) {
fprintf(stderr, "\n");
}
}
};
}
ctx->rng = std::mt19937(params.seed);
ctx->logits_all = params.logits_all;
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
// reserve memory for context buffers
if (!params.vocab_only) {
if (!kv_cache_init(ctx->model.hparams, ctx->kv_self, memory_type, ctx->model.hparams.n_ctx, params.n_gpu_layers)) {
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
llama_free(ctx);
return nullptr;
}
{
const size_t memory_size = ggml_nbytes(ctx->kv_self.k) + ggml_nbytes(ctx->kv_self.v);
fprintf(stderr, "%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
}
const auto & hparams = ctx->model.hparams;
// resized during inference
if (params.logits_all) {
ctx->logits.reserve(hparams.n_ctx*hparams.n_vocab);
} else {
ctx->logits.reserve(hparams.n_vocab);
}
if (params.embedding){
ctx->embedding.resize(hparams.n_embd);
}
ctx->buf_compute.resize(MEM_REQ_EVAL(hparams.n_ctx).at(ctx->model.type));
ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type));
ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type));
}
#ifdef GGML_USE_METAL
if (params.n_gpu_layers > 0) {
// this allocates all Metal resources and memory buffers
ctx->ctx_metal = ggml_metal_init(1);
void * data_ptr = NULL;
size_t data_size = 0;
if (params.use_mmap) {
data_ptr = ctx->model.mapping->addr;
data_size = ctx->model.mapping->size;
} else {
data_ptr = ggml_get_mem_buffer(ctx->model.ctx);
data_size = ggml_get_mem_size (ctx->model.ctx);
}
const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx);
printf("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0);
#define LLAMA_METAL_CHECK_BUF(result) \
if (!(result)) { \
fprintf(stderr, "%s: failed to add buffer\n", __func__); \
llama_free(ctx); \
return NULL; \
}
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size, max_size));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size, 0));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->kv_self.buf.addr, ctx->kv_self.buf.size, 0));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].addr, ctx->buf_scratch[0].size, 0));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].addr, ctx->buf_scratch[1].size, 0));
#undef LLAMA_METAL_CHECK_BUF
}
#endif
#ifdef GGML_USE_MPI
ctx->ctx_mpi = ggml_mpi_init();
if (ggml_mpi_rank(ctx->ctx_mpi) > 0) {
// Enter a blocking eval loop with dummy input, letting rank=0 drive the process
const std::vector<llama_token> tmp(ctx->model.hparams.n_ctx, llama_token_bos());
while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {};
llama_backend_free();
exit(1);
}
#endif
return ctx;
}
struct llama_context * llama_init_from_file(
const char * path_model,
struct llama_context_params params) {
struct llama_model * model = llama_load_model_from_file(path_model, params);
if (!model) {
return nullptr;
}
struct llama_context * ctx = llama_new_context_with_model(model, params);
ctx->model_owner = true;
return ctx;
}
void llama_free(struct llama_context * ctx) {
if (ctx->model_owner) {
delete &ctx->model;
}
delete ctx;
}
int 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) {
fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.what());
return 1;
}
}
int llama_apply_lora_from_file_internal(const struct llama_model & model, const char * path_lora, const char * path_base_model, int n_threads) {
fprintf(stderr, "%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
const int64_t t_start_lora_us = ggml_time_us();
auto fin = std::ifstream(path_lora, std::ios::binary);
if (!fin) {
fprintf(stderr, "%s: failed to open '%s'\n", __func__, path_lora);
return 1;
}
// verify magic and version
{
uint32_t magic;
fin.read((char *) &magic, sizeof(magic));
if (magic != LLAMA_FILE_MAGIC_GGLA) {
fprintf(stderr, "%s: bad file magic\n", __func__);
return 1;
}
uint32_t format_version;
fin.read((char *) &format_version, sizeof(format_version));
if (format_version != 1) {
fprintf(stderr, "%s: unsupported file version\n", __func__ );
return 1;
}
}
int32_t lora_r;
int32_t lora_alpha;
fin.read((char *) &lora_r, sizeof(lora_r));
fin.read((char *) &lora_alpha, sizeof(lora_alpha));
float scaling = (float)lora_alpha / (float)lora_r;
fprintf(stderr, "%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
// create a temporary ggml context to store the lora tensors
// todo: calculate size from biggest possible tensor
std::vector<uint8_t> lora_buf(1024ull * 1024ull * 1024ull);
struct ggml_init_params params;
params.mem_size = lora_buf.size();
params.mem_buffer = lora_buf.data();
params.no_alloc = false;
ggml_context * lora_ctx = ggml_init(params);
std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
// create a name -> tensor map of the model to accelerate lookups
std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
for (const auto & kv: model.tensors_by_name) {
model_tensors.insert(kv);
}
// load base model
std::unique_ptr<llama_model_loader> model_loader;
ggml_context * base_ctx = NULL;
llama_buffer base_buf;
if (path_base_model) {
fprintf(stderr, "%s: loading base model from '%s'\n", __func__, path_base_model);
model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true));
size_t ctx_size;
size_t mmapped_size;
model_loader->calc_sizes(&ctx_size, &mmapped_size);
base_buf.resize(ctx_size);
ggml_init_params base_params;
base_params.mem_size = base_buf.size;
base_params.mem_buffer = base_buf.addr;
base_params.no_alloc = model_loader->use_mmap;
base_ctx = ggml_init(base_params);
model_loader->ggml_ctx = base_ctx;
// maybe this should in llama_model_loader
if (model_loader->use_mmap) {
model_loader->mapping.reset(new llama_mmap(&model_loader->file_loader->file, /* prefetch */ 0, ggml_is_numa()));
}
}
// read tensors and apply
bool warned = false;
int n_tensors = 0;
std::vector<uint8_t> work_buffer;
while (true) {
int32_t n_dims;
int32_t length;
int32_t ftype;
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
if (fin.eof()) {
break;
}
int32_t ne[2] = { 1, 1 };
for (int i = 0; i < n_dims; ++i) {
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
}
std::string name;
{
char buf[1024];
fin.read(buf, length);
name = std::string(buf, length);
}
// check for lora suffix and get the type of tensor
const std::string lora_suffix = ".lora";
size_t pos = name.rfind(lora_suffix);
if (pos == std::string::npos) {
fprintf(stderr, "%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
return 1;
}
std::string lora_type = name.substr(pos + lora_suffix.length());
std::string base_name = name;
base_name.erase(pos);
// fprintf(stderr, "%s: %s => %s (lora type %s) ", __func__, name.c_str(),base_name.c_str(), lora_type.c_str());
if (model_tensors.find(base_name) == model_tensors.end()) {
fprintf(stderr, "%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
return 1;
}
// create ggml tensor
ggml_type wtype;
switch (ftype) {
case 0: wtype = GGML_TYPE_F32; break;
case 1: wtype = GGML_TYPE_F16; break;
default:
{
fprintf(stderr, "%s: invalid tensor data type '%d'\n",
__func__, ftype);
return false;
}
}
ggml_tensor * lora_tensor;
if (n_dims == 2) {
lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]);
}
else {
fprintf(stderr, "%s: unsupported tensor dimension %d\n", __func__, n_dims);
return 1;
}
ggml_set_name(lora_tensor, "lora_tensor");
// load tensor data
size_t offset = fin.tellg();
size_t tensor_data_size = ggml_nbytes(lora_tensor);
offset = (offset + 31) & -32;
fin.seekg(offset);
fin.read((char*)lora_tensor->data, tensor_data_size);
lora_tensors[name] = lora_tensor;
// check if we have both A and B tensors and apply
if (lora_tensors.find(base_name + ".loraA") != lora_tensors.end() &&
lora_tensors.find(base_name + ".loraB") != lora_tensors.end()) {
ggml_tensor * dest_t = model_tensors[base_name];
offload_func_t offload_func = llama_nop;
offload_func_t offload_func_force_inplace = llama_nop;
#ifdef GGML_USE_CUBLAS
if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) {
if (dest_t->type != GGML_TYPE_F16) {
throw std::runtime_error(format(
"%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models", __func__));
}
offload_func = ggml_cuda_assign_buffers;
offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace;
}
#endif // GGML_USE_CUBLAS
ggml_tensor * base_t;
if (model_loader) {
// load from base model
if (model_loader->tensors_map.name_to_idx.find(base_name) == model_loader->tensors_map.name_to_idx.end()) {
fprintf(stderr, "%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
return 1;
}
size_t idx = model_loader->tensors_map.name_to_idx[base_name];
llama_load_tensor & lt = model_loader->tensors_map.tensors[idx];
base_t = model_loader->get_tensor(base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
lt.data = (uint8_t *) lt.ggml_tensor->data;
model_loader->load_data_for(lt);
lt.ggml_tensor->data = lt.data;
}
else {
base_t = dest_t;
}
if (ggml_is_quantized(base_t->type)) {
if (!warned) {
fprintf(stderr, "%s: warning: using a lora adapter with a quantized model may result in poor quality, "
"use a f16 or f32 base model with --lora-base\n", __func__);
warned = true;
}
}
ggml_tensor * loraA = lora_tensors[base_name + ".loraA"];
GGML_ASSERT(loraA->type == GGML_TYPE_F32);
ggml_set_name(loraA, "loraA");
ggml_tensor * loraB = lora_tensors[base_name + ".loraB"];
GGML_ASSERT(loraB->type == GGML_TYPE_F32);
ggml_set_name(loraB, "loraB");
if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
fprintf(stderr, "%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
" are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
return 1;
}
// w = w + BA*s
ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
offload_func(BA);
ggml_set_name(BA, "BA");
if (scaling != 1.0f) {
ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
ggml_set_name(scale_tensor, "scale_tensor");
BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor);
offload_func(BA);
ggml_set_name(BA, "BA_scaled");
}
ggml_tensor * r;
if (base_t == dest_t) {
r = ggml_add_inplace(lora_ctx, dest_t, BA);
offload_func_force_inplace(r);
ggml_set_name(r, "r_add_inplace");
}
else {
r = ggml_add(lora_ctx, base_t, BA);
offload_func(r);
ggml_set_name(r, "r_add");
r = ggml_cpy(lora_ctx, r, dest_t);
offload_func(r);
ggml_set_name(r, "r_cpy");
}
struct ggml_cgraph gf = ggml_build_forward(r);
ggml_graph_compute_helper(work_buffer, &gf, n_threads);
// we won't need these tensors again, reset the context to save memory
ggml_free(lora_ctx);
lora_ctx = ggml_init(params);
lora_tensors.clear();
n_tensors++;
if (n_tensors % 4 == 0) {
fprintf(stderr, ".");
}
}
}
// TODO: this should be in a destructor, it will leak on failure
ggml_free(lora_ctx);
if (base_ctx) {
ggml_free(base_ctx);
}
const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
fprintf(stderr, " done (%.2f ms)\n", t_lora_us / 1000.0);
return 0;
}
int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads) {
try {
return llama_apply_lora_from_file_internal(ctx->model, path_lora, path_base_model, n_threads);
} catch (const std::exception & err) {
fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.what());
return 1;
}
}
int llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, const char * path_base_model, int n_threads) {
try {
return llama_apply_lora_from_file_internal(*model, path_lora, path_base_model, n_threads);
} catch (const std::exception & err) {
fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.what());
return 1;
}
}
int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
return ctx->kv_self.n;
}
#define LLAMA_MAX_RNG_STATE (64*1024)
void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed) {
if (seed == LLAMA_DEFAULT_SEED) {
seed = time(NULL);
}
ctx->rng.seed(seed);
}
// Returns the *maximum* size of the state
size_t llama_get_state_size(const struct llama_context * ctx) {
// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
// for reference, std::mt19937(1337) serializes to 6701 bytes.
const size_t s_rng_size = sizeof(size_t);
const size_t s_rng = LLAMA_MAX_RNG_STATE;
const size_t s_logits_capacity = sizeof(size_t);
const size_t s_logits_size = sizeof(size_t);
const size_t s_logits = ctx->logits.capacity() * sizeof(float);
const size_t s_embedding_size = sizeof(size_t);
const size_t s_embedding = ctx->embedding.size() * sizeof(float);
const size_t s_kv_size = sizeof(size_t);
const size_t s_kv_ntok = sizeof(int);
const size_t s_kv = ctx->kv_self.buf.size;
const size_t s_total = (
+ s_rng_size
+ s_rng
+ s_logits_capacity
+ s_logits_size
+ s_logits
+ s_embedding_size
+ s_embedding
+ s_kv_size
+ s_kv_ntok
+ s_kv
);
return s_total;
}
// Copies the state to the specified destination address
size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
uint8_t * out = dst;
// copy rng
{
std::stringstream rng_ss;
rng_ss << ctx->rng;
const size_t rng_size = rng_ss.str().size();
char rng_buf[LLAMA_MAX_RNG_STATE];
memset(&rng_buf[0], 0, LLAMA_MAX_RNG_STATE);
memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
memcpy(out, &rng_size, sizeof(rng_size)); out += sizeof(rng_size);
memcpy(out, &rng_buf[0], LLAMA_MAX_RNG_STATE); out += LLAMA_MAX_RNG_STATE;
}
// copy logits
{
const size_t logits_cap = ctx->logits.capacity();
const size_t logits_size = ctx->logits.size();
memcpy(out, &logits_cap, sizeof(logits_cap)); out += sizeof(logits_cap);
memcpy(out, &logits_size, sizeof(logits_size)); out += sizeof(logits_size);
if (logits_size) {
memcpy(out, ctx->logits.data(), logits_size * sizeof(float));
}
out += logits_cap * sizeof(float);
}
// copy embeddings
{
const size_t embedding_size = ctx->embedding.size();
memcpy(out, &embedding_size, sizeof(embedding_size)); out += sizeof(embedding_size);
if (embedding_size) {
memcpy(out, ctx->embedding.data(), embedding_size * sizeof(float));
out += embedding_size * sizeof(float);
}
}
// copy kv cache
{
const auto & kv_self = ctx->kv_self;
const auto & hparams = ctx->model.hparams;
const int n_layer = hparams.n_layer;
const int n_embd = hparams.n_embd;
const int n_ctx = hparams.n_ctx;
const size_t kv_size = kv_self.buf.size;
const int kv_ntok = llama_get_kv_cache_token_count(ctx);
memcpy(out, &kv_size, sizeof(kv_size)); out += sizeof(kv_size);
memcpy(out, &kv_ntok, sizeof(kv_ntok)); out += sizeof(kv_ntok);
if (kv_size) {
const size_t elt_size = ggml_element_size(kv_self.k);
ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true });
ggml_cgraph gf{};
ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
kout3d->data = out;
out += ggml_nbytes(kout3d);
ggml_tensor * vout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_ntok, n_embd, n_layer);
vout3d->data = out;
out += ggml_nbytes(vout3d);
ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
n_embd, kv_ntok, n_layer,
elt_size*n_embd, elt_size*n_embd*n_ctx, 0);
ggml_tensor * v3d = ggml_view_3d(cpy_ctx, kv_self.v,
kv_ntok, n_embd, n_layer,
elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d));
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, v3d, vout3d));
ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1);
ggml_free(cpy_ctx);
}
}
const size_t written = out - dst;
const size_t max_size = llama_get_state_size(ctx);
LLAMA_ASSERT(written <= max_size);
return written;
}
// Sets the state reading from the specified source address
size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
uint8_t * inp = src;
// set rng
{
size_t rng_size;
char rng_buf[LLAMA_MAX_RNG_STATE];
memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size);
memcpy(&rng_buf[0], inp, LLAMA_MAX_RNG_STATE); inp += LLAMA_MAX_RNG_STATE;
std::stringstream rng_ss;
rng_ss.str(std::string(&rng_buf[0], rng_size));
rng_ss >> ctx->rng;
LLAMA_ASSERT(rng_ss.fail() == false);
}
// set logits
{
size_t logits_cap;
size_t logits_size;
memcpy(&logits_cap, inp, sizeof(logits_cap)); inp += sizeof(logits_cap);
memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);
LLAMA_ASSERT(ctx->logits.capacity() == logits_cap);
if (logits_size) {
ctx->logits.resize(logits_size);
memcpy(ctx->logits.data(), inp, logits_size * sizeof(float));
}
inp += logits_cap * sizeof(float);
}
// set embeddings
{
size_t embedding_size;
memcpy(&embedding_size, inp, sizeof(embedding_size)); inp += sizeof(embedding_size);
LLAMA_ASSERT(ctx->embedding.capacity() == embedding_size);
if (embedding_size) {
memcpy(ctx->embedding.data(), inp, embedding_size * sizeof(float));
inp += embedding_size * sizeof(float);
}
}
// set kv cache
{
const auto & kv_self = ctx->kv_self;
const auto & hparams = ctx->model.hparams;
const int n_layer = hparams.n_layer;
const int n_embd = hparams.n_embd;
const int n_ctx = hparams.n_ctx;
size_t kv_size;
int kv_ntok;
memcpy(&kv_size, inp, sizeof(kv_size)); inp += sizeof(kv_size);
memcpy(&kv_ntok, inp, sizeof(kv_ntok)); inp += sizeof(kv_ntok);
if (kv_size) {
LLAMA_ASSERT(kv_self.buf.size == kv_size);
const size_t elt_size = ggml_element_size(kv_self.k);
ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true });
ggml_cgraph gf{};
ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
kin3d->data = (void *) inp;
inp += ggml_nbytes(kin3d);
ggml_tensor * vin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_ntok, n_embd, n_layer);
vin3d->data = (void *) inp;
inp += ggml_nbytes(vin3d);
ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
n_embd, kv_ntok, n_layer,
elt_size*n_embd, elt_size*n_embd*n_ctx, 0);
ggml_tensor * v3d = ggml_view_3d(cpy_ctx, kv_self.v,
kv_ntok, n_embd, n_layer,
elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, kin3d, k3d));
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, vin3d, v3d));
ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1);
ggml_free(cpy_ctx);
}
ctx->kv_self.n = kv_ntok;
}
const size_t nread = inp - src;
const size_t max_size = llama_get_state_size(ctx);
LLAMA_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) {
fprintf(stderr, "%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) {
fprintf(stderr, "%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) {
fprintf(stderr, "%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) {
fprintf(stderr, "%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) {
fprintf(stderr, "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
{
const size_t n_state_size_max = llama_get_state_size(ctx);
std::vector<uint8_t> state_data(n_state_size_max);
const size_t n_state_size_cur = llama_copy_state_data(ctx, state_data.data());
file.write_raw(state_data.data(), n_state_size_cur);
}
return true;
}
int llama_eval(
struct llama_context * ctx,
const llama_token * tokens,
int n_tokens,
int n_past,
int n_threads) {
if (!llama_eval_internal(*ctx, tokens, nullptr, n_tokens, n_past, n_threads, nullptr)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}
// get a more accurate load time, upon first eval
// TODO: fix this
if (!ctx->has_evaluated_once) {
ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
ctx->has_evaluated_once = true;
}
return 0;
}
int llama_eval_embd(
struct llama_context * ctx,
const float * embd,
int n_tokens,
int n_past,
int n_threads) {
if (!llama_eval_internal(*ctx, nullptr, embd, n_tokens, n_past, n_threads, nullptr)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}
// get a more accurate load time, upon first eval
// TODO: fix this
if (!ctx->has_evaluated_once) {
ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
ctx->has_evaluated_once = true;
}
return 0;
}
int llama_eval_export(struct llama_context * ctx, const char * fname) {
const int n_batch = 1;
const int n_ctx = 512 - n_batch;
const std::vector<llama_token> tmp(n_batch, llama_token_bos());
if (!llama_eval_internal(*ctx, tmp.data(), nullptr, tmp.size(), n_ctx, 1, fname)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}
return 0;
}
int llama_tokenize_with_model(
const struct llama_model * model,
const char * text,
llama_token * tokens,
int n_max_tokens,
bool add_bos) {
auto res = llama_tokenize(model->vocab, text, add_bos);
if (n_max_tokens < (int) res.size()) {
fprintf(stderr, "%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();
}
int llama_tokenize(
struct llama_context * ctx,
const char * text,
llama_token * tokens,
int n_max_tokens,
bool add_bos) {
return llama_tokenize_with_model(&ctx->model, text, tokens, n_max_tokens, add_bos);
}
int llama_n_vocab_from_model(const struct llama_model * model) {
return model->vocab.id_to_token.size();
}
int llama_n_ctx_from_model(const struct llama_model * model) {
return model->hparams.n_ctx;
}
int llama_n_embd_from_model(const struct llama_model * model) {
return model->hparams.n_embd;
}
int llama_n_vocab(const struct llama_context * ctx) {
return ctx->model.vocab.id_to_token.size();
}
int llama_n_ctx(const struct llama_context * ctx) {
return ctx->model.hparams.n_ctx;
}
int llama_n_embd(const struct llama_context * ctx) {
return ctx->model.hparams.n_embd;
}
int llama_get_vocab_from_model(
const struct llama_model * model,
const char * * strings,
float * scores,
int capacity) {
int n = std::min(capacity, (int) model->vocab.id_to_token.size());
for (int i = 0; i<n; ++i) {
strings[i] = model->vocab.id_to_token[i].tok.c_str();
scores[i] = model->vocab.id_to_token[i].score;
}
return n;
}
int llama_get_vocab(
const struct llama_context * ctx,
const char * * strings,
float * scores,
int capacity) {
return llama_get_vocab_from_model(&ctx->model, strings, scores, capacity);
}
float * llama_get_logits(struct llama_context * ctx) {
return ctx->logits.data();
}
float * llama_get_embeddings(struct llama_context * ctx) {
return ctx->embedding.data();
}
const char * llama_token_to_str_with_model(const struct llama_model * model, llama_token token) {
if (token >= llama_n_vocab_from_model(model)) {
return nullptr;
}
return model->vocab.id_to_token[token].tok.c_str();
}
const char * llama_token_to_str(const struct llama_context * ctx, llama_token token) {
return llama_token_to_str_with_model(&ctx->model, token);
}
llama_token llama_token_bos() {
return 1;
}
llama_token llama_token_eos() {
return 2;
}
llama_token llama_token_nl() {
return 13;
}
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);
fprintf(stderr, "\n");
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, timings.t_load_ms);
fprintf(stderr, "%s: sample time = %8.2f 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);
fprintf(stderr, "%s: prompt eval time = %8.2f 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);
fprintf(stderr, "%s: eval time = %8.2f 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);
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (timings.t_end_ms - timings.t_start_ms));
}
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 += "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 += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | ";
return s.c_str();
}
// 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;
}
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