mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-09-22 21:16:20 +00:00
fa79495bb4
* llama : fix mpt and olmo pre-tokenizer * llama : pre-tokenize non-special user-defined tokens first * llama : fix detection of control-like user-defined tokens * convert_hf : identify which user-defined tokens are control tokens Only used in _set_vocab_gpt2() for now. * convert_hf : identify more added control tokens for SPM tokenziers This makes Gemma and Gemma-2 tokenize pretty much EVERYTHING correctly, including HTML tags and consecutive spaces, but it unfortunately requires model re-conversion. There seems to be a weird behavior of the HF tokenizer for Gemma, which prefers to use the 16-space token over more lengthy space tokens, while using the SentencePiece tokenizer does not do this. (the implementation in llama.cpp has the same behavior as SentencePiece) * llama : fix wrong pre-tokenization of byte tokens * llama : fix Viking pre-tokenizer regex The order was previously wrong, which caused errors in some tests. * llama : fix command-r detokenization * convert_hf : reduce usages of the UNKNOWN token type * llama : add UNKNOWN tokens in the special tokens cache * convert_hf : reduce usages of UNKNOWN for InternLM2 This makes the changes from #8321 more consistent with the other changes made here. * test-tokenizer-random : reduce potential confilcts with #8379 * test-tokenizer-random : add a failing edge case for falcon
293 lines
10 KiB
C++
293 lines
10 KiB
C++
#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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//static const std::map<std::string, std::vector<llama_token>> & k_tests() {
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// static std::map<std::string, std::vector<llama_token>> _k_tests = {
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// { "" , { }, },
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// { " " , { 220, }, },
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// { " " , { 256, }, },
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// { " " , { 262, }, },
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// { "\t" , { 197, }, },
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// { "\n" , { 198, }, },
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// { "\n\n" , { 271, }, },
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// { "\n\n\n" , { 1432, }, },
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// { "\t\n" , { 1602, }, },
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// { "Hello world" , { 9906, 1917, }, },
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// { " Hello world" , { 22691, 1917, }, },
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// { "Hello World" , { 9906, 4435, }, },
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// { " Hello World" , { 22691, 4435, }, },
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// { " Hello World!" , { 22691, 4435, 0, }, },
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// { "Hello, world!" , { 9906, 11, 1917, 0, }, },
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// { " Hello, world!" , { 22691, 11, 1917, 0, }, },
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// { " this is 🦙.cpp" , { 420, 374, 11410, 99, 247, 13, 11055, }, },
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// { "w048 7tuijk dsdfhu" , { 86, 23904, 220, 22, 83, 2005, 42908, 11729, 3013, 17156, }, },
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// { "нещо на Български" , { 79862, 102118, 13373, 64571, 34694, 3114, 112203, 80112, }, },
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// { "កាន់តែពិសេសអាចខលចេញ" , { 21549, 222, 98629, 241, 45358, 233, 21549, 237, 45358, 224, 21549, 244, 21549, 115, 21549, 253, 45358, 223, 21549, 253, 21549, 95, 98629, 227, 21549, 223, 21549, 249, 21549, 227, 45358, 223, 21549, 231, }, },
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// { "🚀 (normal) 😶🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", { 9468, 248, 222, 320, 8416, 8, 27623, 114, 102470, 9468, 234, 104, 31643, 320, 36773, 100166, 98634, 8, 26602, 227, 320, 3323, 43465, 430, 706, 1202, 1866, 4037, 8, }, },
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// { "Hello" , { 9906, }, },
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// { " Hello" , { 22691, }, },
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// { " Hello" , { 220, 22691, }, },
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// { " Hello" , { 256, 22691, }, },
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// { " Hello" , { 262, 22691, }, },
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// { " Hello\n Hello" , { 262, 22691, 198, 262, 22691, }, },
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// { " (" , { 320, }, },
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// { "\n =" , { 198, 284, }, },
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// { "' era" , { 6, 11639, }, },
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// { "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～", { 9906, 11, 379, 65948, 0, 2650, 527, 499, 27623, 223, 949, 37046, 101067, 19000, 23182, 102301, 9263, 18136, 16, 36827, 21909, }, },
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// { "3" , { 18, }, },
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// { "33" , { 1644, }, },
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// { "333" , { 8765, }, },
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// { "3333" , { 8765, 18, }, },
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// { "33333" , { 8765, 1644, }, },
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// { "333333" , { 8765, 8765, }, },
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// { "3333333" , { 8765, 8765, 18, }, },
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// { "33333333" , { 8765, 8765, 1644, }, },
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// { "333333333" , { 8765, 8765, 8765, }, },
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// };
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//
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// return _k_tests;
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//}
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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 **argv) {
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if (argc < 2) {
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fprintf(stderr, "Usage: %s vocab-file [text-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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std::string fname_text;
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if (argc > 2) {
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fname_text = argv[2];
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}
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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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bool success = true;
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const auto k_tests = [&]() -> llama_tests {
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if (!fname_text.empty()) {
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return {};
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}
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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 (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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printf("\n");
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printf("src: '%s'\n", test_kv.first.c_str());
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printf("res: '%s'\n", llama_detokenize(ctx, res).c_str());
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printf("tok: ");
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for (const auto & tok : res) {
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printf("%d ", tok);
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}
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printf("\n");
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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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fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
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fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
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llama_detokenize(ctx, res).c_str(),
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llama_detokenize(ctx, test_kv.second).c_str());
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fprintf(stderr, "%s : expected tokens: ", __func__);
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for (const auto & t : test_kv.second) {
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fprintf(stderr, "%6d '%s', ", t, llama_token_to_piece(ctx, t).c_str());
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}
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fprintf(stderr, "\n");
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fprintf(stderr, "%s : got tokens: ", __func__);
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for (const auto & t : res) {
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fprintf(stderr, "%6d '%s', ", t, llama_token_to_piece(ctx, t).c_str());
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}
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fprintf(stderr, "\n");
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success = false;
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}
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}
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if (!fname_text.empty()) {
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fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());
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std::string text;
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{
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std::ifstream ifs(fname_text);
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if (!ifs) {
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fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str());
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return 1;
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}
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text = std::string(std::istreambuf_iterator<char>(ifs), std::istreambuf_iterator<char>());
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}
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fprintf(stderr, "%s : text size: %zu\n", __func__, text.size());
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std::vector<llama_token> res;
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{
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const auto t_start = ggml_time_us();
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res = llama_tokenize(ctx, text, add_special, false);
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const auto t_end = ggml_time_us();
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fprintf(stderr, "%s : tokenized in %.3f ms (cpp)\n", __func__, (t_end - t_start) / 1000.0);
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}
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fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size());
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{
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const std::string fname_out = fname_text + ".tokcpp";
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std::ofstream ofs(fname_out);
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if (!ofs) {
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fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
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return 1;
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}
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for (const auto & tok : res) {
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//ofs << tok << " '" << string_strip(llama_detokenize(ctx, std::vector<int>{tok})) << "'" << std::endl;
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ofs << tok << "\n";
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}
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}
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fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());
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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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