Mirrors/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-25 05:48:47 +01:00
Code Issues Packages Projects Releases Wiki Activity

Introduce C-style API (#370)

Browse Source 
* Major refactoring - introduce C-style API

* Clean up

* Add <cassert>

* Add <iterator>

* Add <algorithm> ....

* Fix timing reporting and accumulation

* Measure eval time only for single-token calls

* Change llama_tokenize return meaning
This commit is contained in:
Georgi Gerganov 2023-03-22 07:32:36 +02:00 committed by GitHub
parent da0e9fe90c
commit f5a77a629b
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
14 changed files with 1954 additions and 1752 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

31
CMakeLists.txt
View File

@ -207,15 +207,10 @@ else()
message(STATUS "Unknown architecture")
endif()
#
# Build library
# Build libraries
#
add_executable(llama main.cpp)
add_executable(quantize quantize.cpp)
add_library(utils OBJECT
utils.cpp
utils.h)
@ -229,14 +224,24 @@ add_library(ggml OBJECT
target_include_directories(ggml PUBLIC .)
target_compile_features(ggml PUBLIC c_std_11) # don't bump
#
# Linking
#
target_link_libraries(ggml PRIVATE Threads::Threads ${LLAMA_EXTRA_LIBS})
target_link_libraries(llama PRIVATE ggml utils)
target_link_libraries(quantize PRIVATE ggml utils)
add_library(llama OBJECT
llama.cpp
llama.h)
target_include_directories(llama PUBLIC .)
target_compile_features(llama PUBLIC cxx_std_11) # don't bump
#
# Executables
#
add_executable(main main.cpp)
target_link_libraries(main PRIVATE llama ggml utils)
add_executable(quantize quantize.cpp)
target_link_libraries(quantize PRIVATE llama ggml utils)
#
# programs, examples and tests

11
Makefile
View File

@ -220,18 +220,21 @@ default: main quantize
ggml.o: ggml.c ggml.h
$(CC) $(CFLAGS) -c ggml.c -o ggml.o
llama.o: llama.cpp llama.h
$(CXX) $(CXXFLAGS) -c llama.cpp -o llama.o
utils.o: utils.cpp utils.h
$(CXX) $(CXXFLAGS) -c utils.cpp -o utils.o
clean:
rm -f *.o main quantize
main: main.cpp ggml.o utils.o
$(CXX) $(CXXFLAGS) main.cpp ggml.o utils.o -o main $(LDFLAGS)
main: main.cpp ggml.o llama.o utils.o
$(CXX) $(CXXFLAGS) main.cpp ggml.o llama.o utils.o -o main $(LDFLAGS)
@echo "\x1b[36mrun ./main -h for help\x1b[0m"
quantize: quantize.cpp ggml.o utils.o
$(CXX) $(CXXFLAGS) quantize.cpp ggml.o utils.o -o quantize $(LDFLAGS)
quantize: quantize.cpp ggml.o llama.o utils.o
$(CXX) $(CXXFLAGS) quantize.cpp ggml.o llama.o utils.o -o quantize $(LDFLAGS)
#
# Tests

2
convert-pth-to-ggml.py
View File

@ -148,7 +148,7 @@ def main():
model = torch.load(fname_model, map_location="cpu")
with open(fname_out, "wb") as fout:
fout.write(struct.pack("i", hparams["vocab_size"]))
write_header(fout, hparams, ftype)
write_tokens(fout, tokenizer)
del model

121
ggml.c
View File

@ -10702,6 +10702,127 @@ enum ggml_opt_result ggml_opt(
////////////////////////////////////////////////////////////////////////////////
size_t ggml_quantize_q4_0(float * src, void * dst, int n, int k, int qk, int64_t * hist) {
const int nb = k / qk;
const size_t bs = (sizeof(float) + sizeof(uint8_t)*qk/2);
const size_t row_size = nb*bs;
assert(k % qk == 0);
const size_t pp_size = qk / 2;
uint8_t * pp = (uint8_t *) alloca(pp_size);
char * pdst = (char *) dst;
for (int j = 0; j < n; j += k) {
uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
for (int i = 0; i < nb; i++) {
float amax = 0.0f; // absolute max
{
for (int l = 0; l < qk; l++) {
const float v = src[j + i*qk + l];
amax = MAX(amax, fabsf(v));
}
const float d = amax / ((1 << 3) - 1);
const float id = d ? 1.0f/d : 0.0f;
*(float *) pd = d;
pd += bs;
for (int l = 0; l < qk; l += 2) {
const float v0 = (src[j + i*qk + l + 0])*id;
const float v1 = (src[j + i*qk + l + 1])*id;
const uint8_t vi0 = ((int8_t) (round(v0))) + 8;
const uint8_t vi1 = ((int8_t) (round(v1))) + 8;
assert(vi0 >= 0 && vi0 < 16);
assert(vi1 >= 0 && vi1 < 16);
hist[vi0]++;
hist[vi1]++;
pp[l/2] = vi0 | (vi1 << 4);
}
memcpy(pb, pp, pp_size);
pb += bs;
}
}
}
return (n/k)*row_size;
}
size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t * hist) {
const int nb = k / qk;
const size_t bs = (2*sizeof(float) + sizeof(uint8_t)*qk/2);
const size_t row_size = nb*bs;
assert(k % qk == 0);
const size_t pp_size = qk / 2;
uint8_t * pp = (uint8_t *) alloca(pp_size);
char * pdst = (char *) dst;
for (int j = 0; j < n; j += k) {
uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
uint8_t * pm = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + 2*sizeof(float));
//printf("n = %d, k = %d, nb = %d, row_size = %d, j = %d, pm = %p, pd = %p, pb = %p\n", n, k, nb, row_size, j, pm, pd, pb);
for (int i = 0; i < nb; i++) {
float min = FLT_MAX;
float max = -FLT_MAX;
{
for (int l = 0; l < qk; l++) {
const float v = src[j + i*qk + l];
if (v < min) min = v;
if (v > max) max = v;
}
const float d = (max - min) / ((1 << 4) - 1);
const float id = d ? 1.0f/d : 0.0f;
*(float *) pd = d;
*(float *) pm = min;
pd += bs;
pm += bs;
for (int l = 0; l < qk; l += 2) {
const float v0 = (src[j + i*qk + l + 0] - min)*id;
const float v1 = (src[j + i*qk + l + 1] - min)*id;
const uint8_t vi0 = round(v0);
const uint8_t vi1 = round(v1);
assert(vi0 >= 0 && vi0 < 16);
assert(vi1 >= 0 && vi1 < 16);
hist[vi0]++;
hist[vi1]++;
pp[l/2] = vi0 | (vi1 << 4);
}
memcpy(pb, pp, pp_size);
pb += bs;
}
}
}
return (n/k)*row_size;
}
////////////////////////////////////////////////////////////////////////////////
int ggml_cpu_has_avx(void) {
#if defined(__AVX__)
return 1;

7
ggml.h
View File

@ -741,6 +741,13 @@ enum ggml_opt_result ggml_opt(
struct ggml_opt_params params,
struct ggml_tensor * f);
//
// quantization
//
size_t ggml_quantize_q4_0(float * src, void * dst, int n, int k, int qk, int64_t * hist);
size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t * hist);
//
// system info
//

1565
llama.cpp Normal file
View File

File diff suppressed because it is too large Load Diff

139
llama.h Normal file
View File

@ -0,0 +1,139 @@
#ifndef LLAMA_H
#define LLAMA_H
#include <stddef.h>
#include <stdint.h>
#include <stdbool.h>
#ifdef LLAMA_SHARED
# ifdef _WIN32
# ifdef LLAMA_BUILD
# define LLAMA_API __declspec(dllexport)
# else
# define LLAMA_API __declspec(dllimport)
# endif
# else
# define LLAMA_API __attribute__ ((visibility ("default")))
# endif
#else
# define LLAMA_API
#endif
#define LLAMA_FILE_VERSION 1
#define LLAMA_FILE_MAGIC 0x67676d66 // 'ggmf' in hex
#define LLAMA_FILE_MAGIC_UNVERSIONED 0x67676d6c // pre-versioned files
#ifdef __cplusplus
extern "C" {
#endif
//
// C interface
//
// TODO: show sample usage
//
struct llama_context;
typedef int llama_token;
typedef struct llama_token_data {
llama_token id; // token id
float p; // probability of the token
float plog; // log probability of the token
} llama_token_data;
struct llama_context_params {
int n_ctx; // text context
int n_parts; // -1 for default
int seed; // RNG seed, 0 for random
bool f16_kv; // use fp16 for KV cache
bool logits_all; // the llama_eval() call computes all logits, not just the last one
bool vocab_only; // only load the vocabulary, no weights
};
LLAMA_API struct llama_context_params llama_context_default_params();
// Various functions for loading a ggml llama model.
// Allocate (almost) all memory needed for the model.
// Return NULL on failure
LLAMA_API struct llama_context * llama_init_from_file(
const char * path_model,
struct llama_context_params params);
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);
// TODO: not great API - very likely to change
// Returns 0 on success
LLAMA_API int llama_model_quantize(
const char * fname_inp,
const char * fname_out,
int itype,
int qk);
// Run the llama inference to obtain the logits and probabilities for the next token.
// tokens + n_tokens is the provided batch of new tokens to process
// n_past is the number of tokens to use from previous eval calls
// Returns 0 on success
LLAMA_API int llama_eval(
struct llama_context * ctx,
const llama_token * tokens,
int n_tokens,
int n_past,
int n_threads);
// Convert the provided text into tokens.
// The tokens pointer must be large enough to hold the resulting tokens.
// Returns the number of tokens on success, no more than n_max_tokens
// Returns a negative number on failure - the number of tokens that would have been returned
// TODO: not sure if correct
LLAMA_API int llama_tokenize(
struct llama_context * ctx,
const char * text,
llama_token * tokens,
int n_max_tokens,
bool add_bos);
LLAMA_API int llama_n_vocab(struct llama_context * ctx);
LLAMA_API int llama_n_ctx (struct llama_context * ctx);
// Token logits obtained from the last call to llama_eval()
// The logits for the last token are stored in the last row
// Can be mutated in order to change the probabilities of the next token
// Rows: n_tokens
// Cols: n_vocab
LLAMA_API float * llama_get_logits(struct llama_context * ctx);
// Token Id -> String. Uses the vocabulary in the provided context
LLAMA_API const char * llama_token_to_str(struct llama_context * ctx, llama_token token);
// Special tokens
LLAMA_API llama_token llama_token_bos();
LLAMA_API llama_token llama_token_eos();
// TODO: improve the last_n_tokens interface ?
LLAMA_API llama_token llama_sample_top_p_top_k(
llama_context * ctx,
const llama_token * last_n_tokens_data,
int last_n_tokens_size,
int top_k,
double top_p,
double temp,
double repeat_penalty);
// Performance information
LLAMA_API void llama_print_timings(struct llama_context * ctx);
LLAMA_API void llama_reset_timings(struct llama_context * ctx);
// Print system information
LLAMA_API const char * llama_print_system_info(void);
#ifdef __cplusplus
}
#endif
#endif

914
main.cpp
View File

File diff suppressed because it is too large Load Diff

BIN
models/ggml-vocab.bin
View File

Binary file not shown.

310
quantize.cpp
View File

@ -1,319 +1,17 @@
#include "ggml.h"
#include "llama.h"
#include "utils.h"
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <string>
#include <vector>
#include <regex>
// TODO: move somewhere else
#define QK 32
// default hparams (LLaMA76B)
struct llama_hparams {
int32_t n_vocab = 32000;
int32_t n_ctx = 512; // this is provided as user input?
int32_t n_embd = 4096;
int32_t n_mult = 256;
int32_t n_head = 32;
int32_t n_layer = 32;
int32_t n_rot = 64;
int32_t f16 = 1;
};
// quantize a model
bool llama_model_quantize(const std::string & fname_inp, const std::string & fname_out, int itype) {
ggml_type type = GGML_TYPE_Q4_1;
switch (itype) {
case 2: type = GGML_TYPE_Q4_0; break;
case 3: type = GGML_TYPE_Q4_1; break;
default: fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype); return 1;
};
if (type != GGML_TYPE_Q4_0 && type != GGML_TYPE_Q4_1) {
fprintf(stderr, "%s: invalid quantization type %d\n", __func__, type);
return false;
}
llama_vocab vocab;
printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str());
auto finp = std::ifstream(fname_inp, std::ios::binary);
if (!finp) {
fprintf(stderr, "%s: failed to open '%s' for reading\n", __func__, fname_inp.c_str());
return false;
}
auto fout = std::ofstream(fname_out, std::ios::binary);
if (!fout) {
fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname_out.c_str());
return false;
}
// verify magic
{
uint32_t magic;
finp.read((char *) &magic, sizeof(magic));
if (magic == FILE_MAGIC_UNVERSIONED) {
fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
__func__, fname_inp.c_str());
return false;
}
if (magic != FILE_MAGIC) {
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname_inp.c_str());
return false;
}
fout.write((char *) &magic, sizeof(magic));
uint32_t format_version;
finp.read((char *) &format_version, sizeof(format_version));
if (format_version != FILE_VERSION) {
fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n",
__func__, fname_inp.c_str(), format_version, FILE_VERSION);
return false;
}
fout.write((char *) &format_version, sizeof(format_version));
}
llama_hparams hparams;
// load hparams
{
finp.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
//finp.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
finp.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
finp.read((char *) &hparams.n_mult, sizeof(hparams.n_mult));
finp.read((char *) &hparams.n_head, sizeof(hparams.n_head));
finp.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
finp.read((char *) &hparams.n_rot, sizeof(hparams.n_rot));
finp.read((char *) &hparams.f16, sizeof(hparams.f16));
printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
printf("%s: n_mult = %d\n", __func__, hparams.n_mult);
printf("%s: n_head = %d\n", __func__, hparams.n_head);
printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
printf("%s: f16 = %d\n", __func__, hparams.f16);
fout.write((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
//fout.write((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
fout.write((char *) &hparams.n_embd, sizeof(hparams.n_embd));
fout.write((char *) &hparams.n_mult, sizeof(hparams.n_mult));
fout.write((char *) &hparams.n_head, sizeof(hparams.n_head));
fout.write((char *) &hparams.n_layer, sizeof(hparams.n_layer));
fout.write((char *) &hparams.n_rot, sizeof(hparams.n_rot));
fout.write((char *) &itype, sizeof(hparams.f16));
}
// load vocab
{
const int32_t n_vocab = hparams.n_vocab;
if (n_vocab != hparams.n_vocab) {
fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
__func__, fname_inp.c_str(), n_vocab, hparams.n_vocab);
return false;
}
std::string word;
vocab.id_to_token.resize(n_vocab);
for (int i = 0; i < n_vocab; i++) {
uint32_t len;
finp.read ((char *) &len, sizeof(len));
fout.write((char *) &len, sizeof(len));
word.resize(len);
finp.read ((char *) word.data(), len);
fout.write((char *) word.data(), len);
float score;
finp.read ((char *) &score, sizeof(score));
fout.write((char *) &score, sizeof(score));
vocab.token_to_id[word] = i;
auto &tok_score = vocab.id_to_token[i];
tok_score.tok = word;
tok_score.score = score;
}
}
// load weights
{
size_t total_size_org = 0;
size_t total_size_new = 0;
std::vector<float> work;
std::vector<uint8_t> data_u8;
std::vector<ggml_fp16_t> data_f16;
std::vector<float> data_f32;
std::vector<int64_t> hist_all(1 << 4, 0);
while (true) {
int32_t n_dims;
int32_t length;
int32_t ftype;
finp.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
finp.read(reinterpret_cast<char *>(&length), sizeof(length));
finp.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
if (finp.eof()) {
break;
}
int32_t nelements = 1;
int32_t ne[2] = { 1, 1 };
for (int i = 0; i < n_dims; ++i) {
finp.read (reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
nelements *= ne[i];
}
std::string name(length, 0);
finp.read (&name[0], length);
{
static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
printf("%48s - [%5d, %5d], type = %6s ", name.data(), ne[0], ne[1], ftype_str[ftype]);
}
// regexes of tensor names to be quantized
const std::vector<std::string> k_names = {
".*weight",
};
bool quantize = false;
for (const auto & s : k_names) {
if (std::regex_match(name, std::regex(s))) {
quantize = true;
break;
}
}
// quantize only 2D tensors
quantize &= (n_dims == 2);
if (quantize) {
if (ftype != 0 && ftype != 1) {
fprintf(stderr, "%s: unsupported ftype %d for integer quantization\n", __func__, ftype);
return false;
}
if (ftype == 1) {
data_f16.resize(nelements);
finp.read(reinterpret_cast<char *>(data_f16.data()), nelements * sizeof(ggml_fp16_t));
data_f32.resize(nelements);
for (int i = 0; i < nelements; ++i) {
data_f32[i] = ggml_fp16_to_fp32(data_f16[i]);
}
} else {
data_f32.resize(nelements);
finp.read(reinterpret_cast<char *>(data_f32.data()), nelements * sizeof(float));
}
ftype = itype;
} else {
const int bpe = (ftype == 0) ? sizeof(float) : sizeof(uint16_t);
data_u8.resize(nelements*bpe);
finp.read(reinterpret_cast<char *>(data_u8.data()), nelements * bpe);
}
fout.write(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
fout.write(reinterpret_cast<char *>(&length), sizeof(length));
fout.write(reinterpret_cast<char *>(&ftype), sizeof(ftype));
for (int i = 0; i < n_dims; ++i) {
fout.write(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
}
fout.write(&name[0], length);
if (quantize) {
printf("quantizing .. ");
work.resize(nelements); // for quantization
size_t cur_size = 0;
std::vector<int64_t> hist_cur(1 << 4, 0);
switch (type) {
case GGML_TYPE_Q4_0:
{
cur_size = ggml_quantize_q4_0(data_f32.data(), work.data(), nelements, ne[0], QK, hist_cur.data());
} break;
case GGML_TYPE_Q4_1:
{
cur_size = ggml_quantize_q4_1(data_f32.data(), work.data(), nelements, ne[0], QK, hist_cur.data());
} break;
default:
{
fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, type);
return false;
}
}
fout.write(reinterpret_cast<char *>(work.data()), cur_size);
total_size_new += cur_size;
printf("size = %8.2f MB -> %8.2f MB | hist: ", nelements * sizeof(float)/1024.0/1024.0, cur_size/1024.0/1024.0);
for (int i = 0; i < hist_cur.size(); ++i) {
hist_all[i] += hist_cur[i];
}
for (int i = 0; i < hist_cur.size(); ++i) {
printf("%5.3f ", hist_cur[i] / (float)nelements);
}
printf("\n");
} else {
printf("size = %8.3f MB\n", data_u8.size()/1024.0/1024.0);
fout.write(reinterpret_cast<char *>(data_u8.data()), data_u8.size());
total_size_new += data_u8.size();
}
total_size_org += nelements * sizeof(float);
}
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 (int i = 0; i < hist_all.size(); ++i) {
sum_all += hist_all[i];
}
printf("%s: hist: ", __func__);
for (int i = 0; i < hist_all.size(); ++i) {
printf("%5.3f ", hist_all[i] / (float)sum_all);
}
printf("\n");
}
}
finp.close();
fout.close();
return true;
}
const int QK = 32;
// usage:
// ./llama-quantize models/llama/ggml-model.bin models/llama/ggml-model-quant.bin type
//
int main(int argc, char ** argv) {
ggml_time_init();
if (argc != 4) {
fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n", argv[0]);
fprintf(stderr, " type = 2 - q4_0\n");
@ -341,7 +39,7 @@ int main(int argc, char ** argv) {
{
const int64_t t_start_us = ggml_time_us();
if (!llama_model_quantize(fname_inp, fname_out, itype)) {
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), itype, QK)) {
fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
return 1;
}

2
tests/CMakeLists.txt
View File

@ -1,4 +1,4 @@
set(TEST_TARGET test-tokenizer-0)
add_executable(${TEST_TARGET} ${TEST_TARGET}.cpp)
target_link_libraries(${TEST_TARGET} PRIVATE utils)
target_link_libraries(${TEST_TARGET} PRIVATE llama ggml utils)
add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab.bin)

24
tests/test-tokenizer-0.cpp
View File

@ -1,10 +1,11 @@
#include "utils.h"
#include "llama.h"
#include <cstdio>
#include <string>
#include <map>
static const std::map<std::string, std::vector<llama_vocab::id>> k_tests = {
static const std::map<std::string, std::vector<llama_token>> k_tests = {
{ "Hello World", { 1, 10994, 2787, }, },
{ " Hello World", { 1, 15043, 2787, }, },
{ " Hello World!", { 1, 15043, 2787, 29991, }, },
@ -23,14 +24,23 @@ int main(int argc, char **argv) {
fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
llama_vocab vocab;
llama_context * ctx;
if (!llama_vocab_load(fname, vocab)) {
fprintf(stderr, "%s : failed to load vocab from: '%s'\n", __func__, fname.c_str());
return 1;
// load the vocab
{
auto lparams = llama_context_default_params();
lparams.vocab_only = true;
ctx = llama_init_from_file(fname.c_str(), lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
return 1;
}
}
const int n_vocab = vocab.id_to_token.size();
const int n_vocab = llama_n_vocab(ctx);
if (n_vocab != 32000) {
fprintf(stderr, "%s : expected 32000 tokens, got %d\n", __func__, n_vocab);
@ -38,7 +48,7 @@ int main(int argc, char **argv) {
}
for (const auto & test_kv : k_tests) {
const auto res = llama_tokenize(vocab, test_kv.first, true);
const auto res = ::llama_tokenize(ctx, test_kv.first, true);
bool correct = res.size() == test_kv.second.size();

519
utils.cpp
View File

@ -3,12 +3,9 @@
#include <cassert>
#include <cstring>
#include <fstream>
#include <regex>
#include <iostream>
#include <iterator>
#include <queue>
#include <string>
#include <math.h>
#include <iterator>
#include <algorithm>
#if defined(_MSC_VER) || defined(__MINGW32__)
#include <malloc.h> // using malloc.h with MSC/MINGW
@ -147,509 +144,11 @@ std::string gpt_random_prompt(std::mt19937 & rng) {
return "The";
}
void replace(std::string & str, const std::string & needle, const std::string & replacement) {
size_t pos = 0;
while ((pos = str.find(needle, pos)) != std::string::npos) {
str.replace(pos, needle.length(), replacement);
pos += replacement.length();
}
}
std::unordered_map<std::string, int32_t> json_parse(const std::string & fname) {
std::unordered_map<std::string, int32_t> result;
// read file into string
std::string json;
{
std::ifstream ifs(fname);
if (!ifs) {
fprintf(stderr, "Failed to open %s\n", fname.c_str());
exit(1);
}
json = std::string((std::istreambuf_iterator<char>(ifs)),
(std::istreambuf_iterator<char>()));
}
if (json[0] != '{') {
return result;
}
// parse json
{
bool has_key = false;
bool in_token = false;
std::string str_key = "";
std::string str_val = "";
int n = json.size();
for (int i = 1; i < n; ++i) {
if (!in_token) {
if (json[i] == ' ') continue;
if (json[i] == '"') {
in_token = true;
continue;
}
} else {
if (json[i] == '\\' && i+1 < n) {
if (has_key == false) {
str_key += json[i];
} else {
str_val += json[i];
}
++i;
} else if (json[i] == '"') {
if (has_key == false) {
has_key = true;
++i;
while (json[i] == ' ') ++i;
++i; // :
while (json[i] == ' ') ++i;
if (json[i] != '\"') {
while (json[i] != ',' && json[i] != '}') {
str_val += json[i++];
}
has_key = false;
} else {
in_token = true;
continue;
}
} else {
has_key = false;
}
::replace(str_key, "\\u0120", " " ); // \u0120 -> space
::replace(str_key, "\\u010a", "\n"); // \u010a -> new line
::replace(str_key, "\\\"", "\""); // \\\" -> "
try {
result[str_key] = std::stoi(str_val);
} catch (...) {
//fprintf(stderr, "%s: ignoring key '%s' with value '%s'\n", fname.c_str(), str_key.c_str(), str_val.c_str());
}
str_key = "";
str_val = "";
in_token = false;
continue;
}
if (has_key == false) {
str_key += json[i];
} else {
str_val += json[i];
}
}
}
}
return result;
}
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;
};
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(std::move(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_;
};
// TODO: temporary code duplication with llama.cpp
// will resolve after #77 is merged
bool llama_vocab_load(const std::string & fname, llama_vocab & vocab) {
std::ifstream fin(fname, std::ios::binary);
if (!fin.is_open()) {
return false;
}
int n_vocab = 0;
fin.read((char *) &n_vocab, sizeof(n_vocab));
std::string word;
std::vector<char> tmp(64);
vocab.id_to_token.resize(n_vocab);
for (int i = 0; i < n_vocab; i++) {
uint32_t len;
fin.read((char *) &len, sizeof(len));
word.resize(len);
if (len > 0) {
tmp.resize(len);
fin.read(tmp.data(), len);
word.assign(tmp.data(), len);
} else {
word.clear();
}
float score;
fin.read((char *) &score, sizeof(score));
vocab.token_to_id[word] = i;
auto &tok_score = vocab.id_to_token[i];
tok_score.tok = word;
tok_score.score = score;
}
return true;
}
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.size() == 0) {
return output;
}
if (bos) {
output.push_back(1);
}
tokenizer.tokenize(text, output);
return output;
}
void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k) {
// find the top K tokens
std::partial_sort(
logits_id.begin(),
logits_id.begin() + top_k, logits_id.end(),
[](const std::pair<double, llama_vocab::id> & a, const std::pair<double, llama_vocab::id> & b) {
return a.first > b.first;
});
logits_id.resize(top_k);
}
llama_vocab::id llama_sample_top_p_top_k(
const llama_vocab & vocab,
const float * logits,
std::vector<llama_vocab::id> & last_n_tokens,
double repeat_penalty,
int top_k,
double top_p,
double temp,
std::mt19937 & rng) {
int n_logits = vocab.id_to_token.size();
std::vector<std::pair<double, llama_vocab::id>> logits_id;
logits_id.reserve(n_logits);
{
const double scale = 1.0/temp;
for (int i = 0; i < n_logits; ++i) {
// repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858)
// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
if (logits[i] < 0.0) {
logits_id.push_back(std::make_pair(logits[i]*scale*repeat_penalty, i));
} else {
logits_id.push_back(std::make_pair(logits[i]*scale/repeat_penalty, i));
}
} else {
logits_id.push_back(std::make_pair(logits[i]*scale, i));
}
}
}
sample_top_k(logits_id, top_k);
double maxl = -INFINITY;
for (const auto & kv : logits_id) {
maxl = std::max(maxl, kv.first);
}
// compute probs for the top K tokens
std::vector<double> probs;
probs.reserve(logits_id.size());
double sum = 0.0;
for (const auto & kv : logits_id) {
double p = exp(kv.first - maxl);
probs.push_back(p);
sum += p;
}
// normalize the probs
for (auto & p : probs) {
p /= sum;
}
if (top_p < 1.0f) {
double cumsum = 0.0f;
for (int i = 0; i < (int) probs.size(); i++) {
cumsum += probs[i];
if (cumsum >= top_p) {
probs.resize(i + 1);
logits_id.resize(i + 1);
break;
}
}
cumsum = 1.0/cumsum;
for (int i = 0; i < (int) probs.size(); i++) {
probs[i] *= cumsum;
}
}
//printf("\n");
//for (int i = 0; i < (int) 10; i++) {
// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
//}
//printf("\n\n");
//exit(0);
std::discrete_distribution<> dist(probs.begin(), probs.end());
int idx = dist(rng);
return logits_id[idx].second;
}
size_t ggml_quantize_q4_0(float * src, void * dst, int n, int k, int qk, int64_t * hist) {
const int nb = k / qk;
const size_t bs = (sizeof(float) + sizeof(uint8_t)*qk/2);
const size_t row_size = nb*bs;
assert(k % qk == 0);
const size_t pp_size = qk / 2;
uint8_t *pp = static_cast<uint8_t*>(alloca(pp_size));
char * pdst = (char *) dst;
for (int j = 0; j < n; j += k) {
uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
for (int i = 0; i < nb; i++) {
float amax = 0.0f; // absolute max
{
for (int l = 0; l < qk; l++) {
const float v = src[j + i*qk + l];
amax = std::max(amax, fabsf(v));
}
const float d = amax / ((1 << 3) - 1);
const float id = d ? 1.0f/d : 0.0f;
*(float *) pd = d;
pd += bs;
for (int l = 0; l < qk; l += 2) {
const float v0 = (src[j + i*qk + l + 0])*id;
const float v1 = (src[j + i*qk + l + 1])*id;
const uint8_t vi0 = ((int8_t) (round(v0))) + 8;
const uint8_t vi1 = ((int8_t) (round(v1))) + 8;
assert(vi0 >= 0 && vi0 < 16);
assert(vi1 >= 0 && vi1 < 16);
hist[vi0]++;
hist[vi1]++;
pp[l/2] = vi0 | (vi1 << 4);
}
memcpy(pb, pp, pp_size);
pb += bs;
}
}
}
return (n/k)*row_size;
}
size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t * hist) {
const int nb = k / qk;
const size_t bs = (2*sizeof(float) + sizeof(uint8_t)*qk/2);
const size_t row_size = nb*bs;
assert(k % qk == 0);
const size_t pp_size = qk / 2;
uint8_t *pp = static_cast<uint8_t*>(alloca(pp_size));
char * pdst = (char *) dst;
for (int j = 0; j < n; j += k) {
uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
uint8_t * pm = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + 2*sizeof(float));
//printf("n = %d, k = %d, nb = %d, row_size = %d, j = %d, pm = %p, pd = %p, pb = %p\n", n, k, nb, row_size, j, pm, pd, pb);
for (int i = 0; i < nb; i++) {
float min = std::numeric_limits<float>::max();
float max = std::numeric_limits<float>::min();
{
for (int l = 0; l < qk; l++) {
const float v = src[j + i*qk + l];
if (v < min) min = v;
if (v > max) max = v;
}
const float d = (max - min) / ((1 << 4) - 1);
const float id = d ? 1.0f/d : 0.0f;
*(float *) pd = d;
*(float *) pm = min;
pd += bs;
pm += bs;
for (int l = 0; l < qk; l += 2) {
const float v0 = (src[j + i*qk + l + 0] - min)*id;
const float v1 = (src[j + i*qk + l + 1] - min)*id;
const uint8_t vi0 = round(v0);
const uint8_t vi1 = round(v1);
assert(vi0 >= 0 && vi0 < 16);
assert(vi1 >= 0 && vi1 < 16);
hist[vi0]++;
hist[vi1]++;
pp[l/2] = vi0 | (vi1 << 4);
}
memcpy(pb, pp, pp_size);
pb += bs;
}
}
}
return (n/k)*row_size;
// TODO: not great allocating this every time
std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) {
std::vector<llama_token> res(8096);
int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
res.resize(n);
return res;
}

61
utils.h
View File

@ -2,8 +2,9 @@
#pragma once
#include "llama.h"
#include <string>
#include <unordered_map>
#include <vector>
#include <random>
#include <thread>
@ -49,64 +50,8 @@ void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
std::string gpt_random_prompt(std::mt19937 & rng);
//
// Model file parsing
//
#define FILE_MAGIC_UNVERSIONED 0x67676d6c // pre-versioned files
#define FILE_MAGIC 0x67676d66 // 'ggmf' in hex
#define FILE_VERSION 1
//
// Vocab utils
//
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;
};
void replace(std::string & str, const std::string & needle, const std::string & replacement);
// poor-man's JSON parsing
std::unordered_map<std::string, int32_t> json_parse(const std::string & fname);
// TODO: temporary until #77 is merged, need this now for some tokenizer tests
bool llama_vocab_load(const std::string & fname, llama_vocab & vocab);
// TODO: this is probably wrong, but I cannot figure out how this tokenizer works ..
// ref: https://github.com/google/sentencepiece
std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos);
// sample next token given probabilities for each embedding
//
// - consider only the top K tokens
// - from them, consider only the top tokens with cumulative probability > P
//
llama_vocab::id llama_sample_top_p_top_k(
const llama_vocab & vocab,
const float * logits,
std::vector<llama_vocab::id> & last_n_tokens,
double repeat_penalty,
int top_k,
double top_p,
double temp,
std::mt19937 & rng);
// filer to top K tokens from list of logits
void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k);
//
// Quantization
//
size_t ggml_quantize_q4_0(float * src, void * dst, int n, int k, int qk, int64_t * hist);
size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t * hist);
std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos);