mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 13:30:35 +00:00
remove unused files
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zhenweijin
parent
8be5d11c0d
commit
25d4599e19
@ -84,25 +84,6 @@ llama_test(test-tokenizer-0 NAME test-tokenizer-0-qwen2 ARGS ${CMAKE
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llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
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llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
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# build test-tokenizer-parallel target once and add many tests
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add_executable(test-tokenizer-parallel test-tokenizer-parallel.cpp)
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target_link_libraries(test-tokenizer-parallel PRIVATE common)
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install(TARGETS test-tokenizer-parallel RUNTIME)
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llama_test(test-tokenizer-parallel NAME test-tokenizer-parallel-bert-bge ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bert-bge.gguf)
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llama_test(test-tokenizer-parallel NAME test-tokenizer-parallel-command-r ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-command-r.gguf)
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llama_test(test-tokenizer-parallel NAME test-tokenizer-parallel-deepseek-coder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-coder.gguf)
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llama_test(test-tokenizer-parallel NAME test-tokenizer-parallel-deepseek-llm ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-llm.gguf)
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llama_test(test-tokenizer-parallel NAME test-tokenizer-parallel-falcon ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
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llama_test(test-tokenizer-parallel NAME test-tokenizer-parallel-gpt-2 ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf)
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llama_test(test-tokenizer-parallel NAME test-tokenizer-parallel-llama-bpe ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf)
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llama_test(test-tokenizer-parallel NAME test-tokenizer-parallel-llama-spm ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-spm.gguf)
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llama_test(test-tokenizer-parallel NAME test-tokenizer-parallel-mpt ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
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llama_test(test-tokenizer-parallel NAME test-tokenizer-parallel-phi-3 ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-phi-3.gguf)
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llama_test(test-tokenizer-parallel NAME test-tokenizer-parallel-qwen2 ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-qwen2.gguf)
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llama_test(test-tokenizer-parallel NAME test-tokenizer-parallel-refact ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
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llama_test(test-tokenizer-parallel NAME test-tokenizer-parallel-starcoder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
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# build test-tokenizer-1-bpe target once and add many tests
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add_executable(test-tokenizer-1-bpe test-tokenizer-1-bpe.cpp)
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target_link_libraries(test-tokenizer-1-bpe PRIVATE common)
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@ -1,180 +0,0 @@
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#include "llama.h"
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#include "common.h"
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#include "console.h"
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#include <cstdio>
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#include <string>
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#include <map>
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#include <vector>
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#include <fstream>
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#include <thread>
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using llama_tests = std::map<std::string, std::vector<llama_token>>;
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static llama_tests read_tests(const std::string & fname_inp, const std::string & fname_out) {
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llama_tests tests;
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std::ifstream ifs_inp(fname_inp);
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if (!ifs_inp) {
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fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_inp.c_str());
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return tests;
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}
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std::string sraw((std::istreambuf_iterator<char>(ifs_inp)), std::istreambuf_iterator<char>());
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std::ifstream ifs_out(fname_out);
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if (!ifs_out) {
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fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
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return tests;
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}
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std::vector<std::string> sout;
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for (std::string line; std::getline(ifs_out, line);) {
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sout.push_back(line);
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}
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const std::string sep = "\n__ggml_vocab_test__\n";
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std::vector<std::string> sinp;
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size_t pos = 0;
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while (pos < sraw.size()) {
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const size_t next = sraw.find(sep, pos);
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if (next == std::string::npos) {
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sinp.push_back(sraw.substr(pos));
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break;
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}
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sinp.push_back(sraw.substr(pos, next - pos));
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pos = next + sep.size();
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}
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if (sinp.size() != sout.size()) {
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fprintf(stderr, "%s : error: input and output files have different number of tests\n", __func__);
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return tests;
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}
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for (size_t i = 0; i < sinp.size(); ++i) {
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const std::string & s = sinp[i];
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const std::string & o = string_strip(sout[i]);
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std::vector<llama_token> toks;
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size_t pos = 0;
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while (pos < o.size()) {
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size_t next = o.find(' ', pos);
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if (next == std::string::npos) {
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next = o.size();
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}
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const std::string stok = o.substr(pos, next - pos);
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toks.push_back(std::stoi(stok));
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pos = next + 1;
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}
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tests[s] = toks;
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}
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return tests;
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}
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int main(int argc, char const *argv[]) {
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if (argc < 2) {
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fprintf(stderr, "Usage: %s vocab-file \n", argv[0]);
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return 1;
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}
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const std::string fname = argv[1];
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const std::string fname_inp = fname + ".inp";
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const std::string fname_out = fname + ".out";
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fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
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llama_model * model;
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llama_context * ctx;
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llama_backend_init();
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// load the vocab
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{
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auto mparams = llama_model_default_params();
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mparams.vocab_only = true;
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model = llama_load_model_from_file(fname.c_str(), mparams);
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if (model == NULL) {
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fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
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return 1;
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}
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auto cparams = llama_context_default_params();
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ctx = llama_new_context_with_model(model, cparams);
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if (ctx == NULL) {
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fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
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llama_free_model(model);
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return 1;
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}
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}
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#ifdef _WIN32
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// We need this for unicode console support
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console::init(false, false);
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atexit([]() { console::cleanup(); });
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#endif
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const int nthread = std::thread::hardware_concurrency();
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std::vector<std::thread> threads(nthread);
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bool success = true;
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const auto k_tests = [&]() -> llama_tests {
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const auto res = read_tests(fname_inp, fname_out);
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if (res.empty()) {
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fprintf(stderr, "%s : error: no tests found\n", __func__);
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exit(1);
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}
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return res;
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}();
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const bool add_special = false;
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for (int i = 0; i < nthread; i++) {
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threads[i] = std::thread([&]() {
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for (const auto & test_kv : k_tests) {
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const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, false);
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bool correct = res.size() == test_kv.second.size();
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for (int i = 0; i < (int) res.size() && correct; ++i) {
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if (test_kv.second[i] != res[i]) {
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correct = false;
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}
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}
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if (!correct) {
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success = false;
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}
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}
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});
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}
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for (int i = 0; i < nthread; i++) {
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threads[i].join();
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}
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llama_free_model(model);
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llama_free(ctx);
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llama_backend_free();
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printf("\n");
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printf("Tests %s\n", success ? "passed" : "failed");
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return success ? 0 : 3;
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}
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