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int main(int argc, char **argv) { | |
if (argc < 2 || argc > 3) { | |
fprintf(stderr, "Usage: %s <vocab-file> [--ignore-merges]\n", argv[0]); | |
return 1; | |
} | |
const std::string fname = argv[1]; | |
bool ignore_merges = false; | |
if (argc == 3) { | |
if (std::strcmp(argv[2], "--ignore-merges") != 0) { | |
fprintf(stderr, "Usage: %s <vocab-file> [--ignore-merges]\n", argv[0]); | |
return 1; | |
} | |
ignore_merges = true; | |
} | |
fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str()); | |
if (ignore_merges) { | |
fprintf(stderr, "%s : ignoring merges for tokens inside vocab\n", __func__); | |
} | |
llama_model * model; | |
llama_context * ctx; | |
llama_backend_init(); | |
// load the vocab | |
{ | |
auto mparams = llama_model_default_params(); | |
mparams.vocab_only = true; | |
model = llama_load_model_from_file(fname.c_str(), mparams); | |
if (model == NULL) { | |
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); | |
return 1; | |
} | |
auto cparams = llama_context_default_params(); | |
ctx = llama_new_context_with_model(model, cparams); | |
if (ctx == NULL) { | |
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); | |
llama_free_model(model); | |
return 1; | |
} | |
} | |
//GGML_ASSERT(llama_vocab_type(model) == LLAMA_VOCAB_TYPE_BPE); | |
if (llama_vocab_type(model) != LLAMA_VOCAB_TYPE_BPE) { | |
return 99; | |
} | |
// We need this for unicode console support | |
console::init(false, false); | |
atexit([]() { console::cleanup(); }); | |
const int n_vocab = llama_n_vocab(model); | |
for (int i = 0; i < n_vocab; ++i) { | |
std::string str = common_detokenize(ctx, std::vector<int>(1, i)); | |
try { | |
auto cps = unicode_cpts_from_utf8(str); | |
std::vector<llama_token> tokens = common_tokenize(ctx, str, false, true); | |
if (ignore_merges && tokens.size() > 1) { | |
fprintf(stderr, | |
"%s : error: token %d detokenizes to '%s'(%zu) but " | |
"tokenization of this to multiple tokens: [", | |
__func__, i, str.c_str(), str.length()); | |
fprintf(stderr, "%d", tokens[0]); | |
for (size_t i = 1; i < tokens.size(); i++) { | |
fprintf(stderr, ", %d", tokens[i]); | |
} | |
fprintf(stderr, "]\n"); | |
return 2; | |
} | |
std::string check = common_detokenize(ctx, tokens); | |
if (check != str) { | |
fprintf(stderr, "%s : error: token %d detokenizes to '%s'(%zu) but tokenization of this detokenizes to '%s'(%zu)\n", | |
__func__, i, str.c_str(), str.length(), check.c_str(), check.length()); | |
return 2; | |
} | |
} | |
catch (const std::invalid_argument &) { | |
//fprintf(stderr, "%s : info: utf8 conversion %d '%s'\n", __func__, i, str.c_str()); | |
} | |
} | |
// unicode | |
{ | |
const int nthread = std::thread::hardware_concurrency(); | |
std::vector<std::thread> threads(nthread); | |
std::atomic_int errcode = {}; | |
for (int i = 0; i < nthread; ++i) { | |
threads[i] = std::thread([i, nthread, ctx, &errcode]() { | |
for (uint32_t cp = i; !errcode && cp < 0x00110000; cp += nthread) { | |
if ((0x0000D800 <= cp && cp <= 0x0000DFFF) || // surrogates \p{Cs} | |
(0x00040000 <= cp && cp <= 0x000E0000)) { // undefined \p{Cn} | |
continue; | |
} | |
std::string str = unicode_cpt_to_utf8(cp); | |
std::vector<llama_token> tokens = common_tokenize(ctx, str, false); | |
std::string check = common_detokenize(ctx, tokens); | |
if (cp != 9601 && str != check) { | |
fprintf(stderr, "error: codepoint 0x%x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n", | |
cp, check.c_str(), check.length(), str.c_str(), str.length()); | |
errcode = 3; | |
} | |
} | |
}); | |
} | |
for (auto & t : threads) { | |
t.join(); | |
} | |
if (errcode) { | |
return errcode; | |
} | |
} | |
llama_free_model(model); | |
llama_free(ctx); | |
llama_backend_free(); | |
return 0; | |
} | |