cd build
cmake ..
cmake --build . --config Release
+ ctest --output-on-failure
macOS-latest-make:
runs-on: macos-latest
cd build
cmake ..
cmake --build . --config Release
+ ctest --output-on-failure
windows-latest-cmake:
runs-on: windows-latest
cd build
cmake ..
cmake --build . --config Release
+ ctest --output-on-failure
- name: Get commit hash
id: commit
-cmake_minimum_required(VERSION 3.12)
+cmake_minimum_required(VERSION 3.12) # Don't bump this version for no reason
project("llama.cpp" C CXX)
+set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
+
if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
endif()
+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
+
+if(CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
+ set(LLAMA_STANDALONE ON)
+
+ # configure project version
+ # TODO
+else()
+ set(LLAMA_STANDALONE OFF)
+endif()
+
+if (EMSCRIPTEN)
+ set(BUILD_SHARED_LIBS_DEFAULT OFF)
+
+ option(LLAMA_WASM_SINGLE_FILE "llama: embed WASM inside the generated llama.js" ON)
+else()
+ if (MINGW)
+ set(BUILD_SHARED_LIBS_DEFAULT OFF)
+ else()
+ set(BUILD_SHARED_LIBS_DEFAULT ON)
+ endif()
+endif()
+
+
#
# Option list
#
option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON)
option(LLAMA_OPENBLAS "llama: use OpenBLAS" OFF)
+option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
+
#
# Compile flags
#
add_executable(quantize quantize.cpp)
-add_library(ggml OBJECT
- ggml.c
- ggml.h)
-
add_library(utils OBJECT
utils.cpp
utils.h)
+target_include_directories(utils PUBLIC .)
+target_compile_features(utils PUBLIC cxx_std_11) # don't bump
+
+add_library(ggml OBJECT
+ ggml.c
+ ggml.h)
+
target_include_directories(ggml PUBLIC .)
-target_compile_features(ggml PUBLIC c_std_11)
-target_compile_features(utils PUBLIC cxx_std_17)
+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)
+
+#
+# programs, examples and tests
+#
+
+if (LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
+ enable_testing()
+ add_subdirectory(tests)
+endif ()
+
+#if (LLAMA_BUILD_EXAMPLES)
+# add_subdirectory(examples)
+#endif()
# Compile flags
#
+# keep standard at C11 and C++11
CFLAGS = -I. -O3 -DNDEBUG -std=c11 -fPIC
-CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++17 -fPIC
+CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC
LDFLAGS =
# OS specific
# - Name (char[name_length])
# - Data (float[n_dims])
#
-# By default, the bigger matrices are converted to 16-bit floats.
-# This can be disabled by adding the "use-f32" CLI argument.
-#
# At the start of the ggml file we write the model parameters
# and vocabulary.
#
+
import argparse
import os
import sys
import struct
import numpy as np
import torch
+
from sentencepiece import SentencePieceProcessor
def parse_args():
parser = argparse.ArgumentParser(description='Convert a LLaMA model checkpoint to a ggml compatible file')
parser.add_argument('dir_model', help='directory containing the model checkpoint')
parser.add_argument('ftype', type=int, choices=[0, 1], default=1, help='file type (0: float32, 1: float16)')
+ parser.add_argument('vocab_only', type=bool, default=False, help='only write vocab to file')
return parser.parse_args()
def get_n_parts(dim):
ftype_str = ["f32", "f16"]
hparams, tokenizer = load_hparams_and_tokenizer(dir_model)
+
+ # if only writing vocab to file
+ if args.vocab_only:
+
+ fname_model = f"{dir_model}/consolidated.00.pth"
+ fname_out = f"{dir_model}/ggml-vocab.bin"
+
+ print(f"Extracting only the vocab from '{fname_model}'\n")
+
+ model = torch.load(fname_model, map_location="cpu")
+
+ with open(fname_out, "wb") as fout:
+ fout.write(struct.pack("i", hparams["vocab_size"]))
+ write_tokens(fout, tokenizer)
+
+ del model
+
+ print(f"Done. Output file: {fname_out}\n")
+
+ return
+
n_parts = get_n_parts(hparams["dim"])
for p in range(n_parts):
process_and_write_variables(fout, model, ftype)
del model
+
print(f"Done. Output file: {fname_out}, (part {p})\n")
if __name__ == "__main__":
};
// load the model's weights from a file
-bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx, ggml_type memory_type = GGML_TYPE_F32) {
+bool llama_model_load(const std::string & fname, llama_model & model, llama_vocab & vocab, int n_ctx, ggml_type memory_type = GGML_TYPE_F32) {
fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
std::vector<char> f_buf(1024*1024);
const llama_model & model,
const int n_threads,
const int n_past,
- const std::vector<gpt_vocab::id> & embd_inp,
- std::vector<float> & embd_w,
- size_t & mem_per_token) {
+ const std::vector<llama_vocab::id> & embd_inp,
+ std::vector<float> & embd_w,
+ size_t & mem_per_token) {
const int N = embd_inp.size();
const auto & hparams = model.hparams;
int64_t t_load_us = 0;
- gpt_vocab vocab;
+ llama_vocab vocab;
llama_model model;
// load the model
// Add a space in front of the first character to match OG llama tokenizer behavior
params.prompt.insert(0, 1, ' ');
// tokenize the prompt
- std::vector<gpt_vocab::id> embd_inp = ::llama_tokenize(vocab, params.prompt, true);
+ std::vector<llama_vocab::id> embd_inp = ::llama_tokenize(vocab, params.prompt, true);
params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
// prefix & suffix for instruct mode
- const std::vector<gpt_vocab::id> inp_pfx = ::llama_tokenize(vocab, "\n\n### Instruction:\n\n", true);
- const std::vector<gpt_vocab::id> inp_sfx = ::llama_tokenize(vocab, "\n\n### Response:\n\n", false);
+ const std::vector<llama_vocab::id> inp_pfx = ::llama_tokenize(vocab, "\n\n### Instruction:\n\n", true);
+ const std::vector<llama_vocab::id> inp_sfx = ::llama_tokenize(vocab, "\n\n### Response:\n\n", false);
// in instruct mode, we inject a prefix and a suffix to each input by the user
if (params.instruct) {
}
// tokenize the reverse prompt
- std::vector<std::vector<gpt_vocab::id>> antipromptv_inp;
-
+ std::vector<std::vector<llama_vocab::id>> antipromptv_inp;
+
for (auto antiprompt : params.antiprompt) {
antipromptv_inp.push_back(::llama_tokenize(vocab, antiprompt, false));
}
fprintf(stderr, "sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
fprintf(stderr, "\n\n");
- std::vector<gpt_vocab::id> embd;
+ std::vector<llama_vocab::id> embd;
// determine the required inference memory per token:
size_t mem_per_token = 0;
llama_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
int last_n_size = params.repeat_last_n;
- std::vector<gpt_vocab::id> last_n_tokens(last_n_size);
+ std::vector<llama_vocab::id> last_n_tokens(last_n_size);
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
if (params.interactive) {
const int n_vocab = model.hparams.n_vocab;
- gpt_vocab::id id = 0;
+ llama_vocab::id id = 0;
{
const int64_t t_start_sample_us = ggml_time_us();
} while (another_line);
if (params.use_color) printf(ANSI_COLOR_RESET);
- std::vector<gpt_vocab::id> line_inp = ::llama_tokenize(vocab, buffer, false);
+ std::vector<llama_vocab::id> line_inp = ::llama_tokenize(vocab, buffer, false);
embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
if (params.instruct) {
return false;
}
- gpt_vocab vocab;
+ llama_vocab vocab;
printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str());
--- /dev/null
+set(TEST_TARGET test-tokenizer-0)
+add_executable(${TEST_TARGET} ${TEST_TARGET}.cpp)
+target_link_libraries(${TEST_TARGET} PRIVATE utils)
+add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab.bin)
--- /dev/null
+#include "utils.h"
+
+#include <cstdio>
+#include <string>
+#include <map>
+
+static const std::map<std::string, std::vector<llama_vocab::id>> k_tests = {
+ { "Hello World", { 1, 10994, 2787, }, },
+ { " Hello World", { 1, 15043, 2787, }, },
+ { " Hello World!", { 1, 15043, 2787, 29991, }, },
+ { " this is 🦙.cpp", { 1, 445, 338, 29871, 243, 162, 169, 156, 29889, 8223, }, },
+ { "w048 7tuijk dsdfhu", { 1, 29893, 29900, 29946, 29947, 29871, 29955, 9161, 13535, 18031, 2176, 6905, }, },
+ { "нещо на Български", { 1, 821, 4851, 665, 1386, 29713, 1305, }, },
+};
+
+int main(int argc, char **argv) {
+ if (argc < 2) {
+ fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
+ return 1;
+ }
+
+ const std::string fname = argv[1];
+
+ fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
+
+ llama_vocab vocab;
+
+ if (!llama_vocab_load(fname, vocab)) {
+ fprintf(stderr, "%s : failed to load vocab from: '%s'\n", __func__, fname.c_str());
+ return 1;
+ }
+
+ const int n_vocab = vocab.id_to_token.size();
+
+ if (n_vocab != 32000) {
+ fprintf(stderr, "%s : expected 32000 tokens, got %d\n", __func__, n_vocab);
+ return 2;
+ }
+
+ for (const auto & test_kv : k_tests) {
+ const auto res = llama_tokenize(vocab, test_kv.first, true);
+
+ bool correct = res.size() == test_kv.second.size();
+
+ for (int i = 0; i < (int) res.size() && correct; ++i) {
+ if (res[i] != test_kv.second[i]) {
+ correct = false;
+ }
+ }
+
+ if (!correct) {
+ fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
+ fprintf(stderr, "%s : expected tokens: ", __func__);
+ for (const auto & t : test_kv.second) {
+ fprintf(stderr, "%6d, ", t);
+ }
+ fprintf(stderr, "\n");
+ fprintf(stderr, "%s : got tokens: ", __func__);
+ for (const auto & t : res) {
+ fprintf(stderr, "%6d, ", t);
+ }
+ fprintf(stderr, "\n");
+
+ return 3;
+ }
+ }
+
+ return 0;
+}
return result;
}
-std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
- std::vector<std::string> words;
-
- // first split the text into words
- {
- std::string str = text;
- std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
-
- std::regex re(pat);
- std::smatch m;
-
- while (std::regex_search(str, m, re)) {
- for (auto x : m) {
- words.push_back(x);
- }
- str = m.suffix();
- }
- }
-
- // find the longest tokens that form the words:
- std::vector<gpt_vocab::id> tokens;
- for (const auto & word : words) {
- if (word.size() == 0) continue;
-
- int i = 0;
- int n = word.size();
- while (i < n) {
- int j = n;
- while (j > i) {
- auto it = vocab.token_to_id.find(word.substr(i, j-i));
- if (it != vocab.token_to_id.end()) {
- tokens.push_back(it->second);
- i = j;
- break;
- }
- --j;
- }
- if (i == n) {
- break;
- }
- if (j == i) {
- auto sub = word.substr(i, 1);
- if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
- tokens.push_back(vocab.token_to_id.at(sub));
- } else {
- fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
- }
- ++i;
- }
- }
- }
-
- return tokens;
-}
-
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;
using index = int;
index prev;
index next;
- std::string_view text;
+ const char * text;
+ size_t n;
};
struct llama_sp_bigram {
size_t size;
};
+// original implementation:
+// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
struct llama_tokenizer {
- llama_tokenizer(const gpt_vocab & vocab): vocab_(vocab) {}
+ llama_tokenizer(const llama_vocab & vocab): vocab_(vocab) {}
- void tokenize(std::string_view text, std::vector<gpt_vocab::id> & output) {
+ void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
// split string into utf8 chars
int index = 0;
- while (!text.empty()) {
+ size_t offs = 0;
+ while (offs < text.size()) {
llama_sp_symbol sym;
- size_t char_len = std::min(text.size(), utf8_len(text.data()[0]));
- sym.text = std::string_view(text.data(), char_len);
+ 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;
- text.remove_prefix(char_len);
- sym.next = text.empty() ? -1 : index + 1;
+ sym.next = offs == text.size() ? -1 : index + 1;
index++;
symbols_.emplace_back(std::move(sym));
}
auto & right_sym = symbols_[bigram.right];
// if one of the symbols already got merged, skip it.
- if (left_sym.text.empty() || right_sym.text.empty() ||
- left_sym.text.size() + right_sym.text.size() != bigram.size) {
+ 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.text = std::string_view(left_sym.text.data(), left_sym.text.size() + right_sym.text.size());
- right_sym.text = std::string_view("");
+ 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;
}
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));
+ 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 < symbol.text.size(); ++j) {
- gpt_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
+ 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 {
return;
}
- std::string_view text(symbols_[left].text.data(), symbols_[left].text.size() + symbols_[right].text.size());
- auto token = vocab_.token_to_id.find(std::string(text));
+ 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;
work_queue_.push(bigram);
}
- const gpt_vocab & vocab_;
+ const llama_vocab & vocab_;
std::vector<llama_sp_symbol> symbols_;
llama_sp_bigram::queue work_queue_;
};
-std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, std::string_view text, bool bos) {
+// 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);
+
+ 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;
+ vocab.id_to_token[i] = word;
+ vocab.score[i] = 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<gpt_vocab::id> output;
+ std::vector<llama_vocab::id> output;
if (text.size() == 0) {
return output;
return output;
}
-bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
- printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());
-
- vocab.token_to_id = ::json_parse(fname);
-
- for (const auto & kv : vocab.token_to_id) {
- vocab.id_to_token[kv.second] = kv.first;
- }
-
- printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());
-
- // print the vocabulary
- //for (auto kv : vocab.token_to_id) {
- // printf("'%s' -> %d\n", kv.first.data(), kv.second);
- //}
-
- return true;
-}
-
-
-void sample_top_k(std::vector<std::pair<double, gpt_vocab::id>> & logits_id, int top_k) {
+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, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
+ [](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);
}
-gpt_vocab::id llama_sample_top_p_top_k(
- const gpt_vocab & vocab,
+llama_vocab::id llama_sample_top_p_top_k(
+ const llama_vocab & vocab,
const float * logits,
- std::vector<gpt_vocab::id> & last_n_tokens,
+ std::vector<llama_vocab::id> & last_n_tokens,
double repeat_penalty,
int top_k,
double top_p,
std::mt19937 & rng) {
int n_logits = vocab.id_to_token.size();
- std::vector<std::pair<double, gpt_vocab::id>> logits_id;
+ std::vector<std::pair<double, llama_vocab::id>> logits_id;
logits_id.reserve(n_logits);
{
// Vocab utils
//
-struct gpt_vocab {
+struct llama_vocab {
using id = int32_t;
using token = std::string;
// poor-man's JSON parsing
std::map<std::string, int32_t> json_parse(const std::string & fname);
-// split text into tokens
-//
-// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
-//
-// Regex (Python):
-// r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
-//
-// Regex (C++):
-// R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"
-//
-std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text);
+// 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<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, std::string_view text, bool bos);
-
-// load the tokens from encoder.json
-bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);
+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
//
-gpt_vocab::id llama_sample_top_p_top_k(
- const gpt_vocab & vocab,
+llama_vocab::id llama_sample_top_p_top_k(
+ const llama_vocab & vocab,
const float * logits,
- std::vector<gpt_vocab::id> & last_n_tokens,
+ std::vector<llama_vocab::id> & last_n_tokens,
double repeat_penalty,
int top_k,
double top_p,
std::mt19937 & rng);
// filer to top K tokens from list of logits
-void sample_top_k(std::vector<std::pair<double, gpt_vocab::id>> & logits_id, int top_k);
+void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k);
//
// Quantization
overview / pkg / ggml / sources / llama.cpp / commitdiff