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llama : more tokenizer fixes (#2810)

Browse Source 
* tests : write a Python tokenizer test (wip)

* llama : prefix input text for tokenization with whitespace

* llama : distinguish pieces from decoded text + fix detokenization

* common : add comments

* examples : no longer manually add leading space when tokenizing

* tests : use Python to generate tokenizer tests for C++

* tests : add option to tokenize text files

ggml-ci

* tests : add test-tokenizer-1.py

* llama.cpp : fix LF token

* hellaswag : move the concat space for clarity

* tests : add falcon tests (py + cpp, currently do not pass Unicode)

ggml-ci

* common : temporary separate llama_detokenize calls for SPM and BPE

---------

Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
This commit is contained in:
Georgi Gerganov 2023-08-27 14:19:19 +03:00 committed by GitHub
parent 1591e2e590
commit edd4c14817
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GPG Key ID: 4AEE18F83AFDEB23
20 changed files with 671 additions and 224 deletions
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39
common/common.cpp
View File

@ -733,12 +733,12 @@ std::vector<llama_token> llama_tokenize(
return result;
}
std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
std::vector<char> result(8, 0);
const int n_tokens = llama_token_to_str(ctx, token, result.data(), result.size());
const int n_tokens = llama_token_to_piece(ctx, token, result.data(), result.size());
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_token_to_str(ctx, token, result.data(), result.size());
int check = llama_token_to_piece(ctx, token, result.data(), result.size());
GGML_ASSERT(check == -n_tokens);
} else {
result.resize(n_tokens);
@ -746,3 +746,36 @@ std::string llama_token_to_str(const struct llama_context * ctx, llama_token tok
return std::string(result.data(), result.size());
}
std::string llama_detokenize_spm(llama_context * ctx, const std::vector<llama_token> & tokens) {
const llama_token bos_id = llama_token_bos(ctx);
std::string piece;
std::string result;
for (size_t i = 0; i < tokens.size(); ++i) {
piece = llama_token_to_piece(ctx, tokens[i]);
// remove the leading space of the first non-BOS token
if (((tokens[0] == bos_id && i == 1) || (tokens[0] != bos_id && i == 0)) && piece[0] == ' ') {
piece = piece.substr(1);
}
result += piece;
}
return result;
}
std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_token> & tokens) {
std::string piece;
std::string result;
for (size_t i = 0; i < tokens.size(); ++i) {
piece = llama_token_to_piece(ctx, tokens[i]);
result += piece;
}
return result;
}

22
common/common.h
View File

@ -116,11 +116,31 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
// Vocab utils
//
// tokenizes a string into a vector of tokens
// should work similar to Python's `tokenizer.encode`
std::vector<llama_token> llama_tokenize(
struct llama_context * ctx,
const std::string & text,
bool add_bos);
std::string llama_token_to_str(
// tokenizes a token into a piece
// should work similar to Python's `tokenizer.id_to_piece`
std::string llama_token_to_piece(
const struct llama_context * ctx,
llama_token token);
// TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function
// that takes into account the tokenizer type and decides how to handle the leading space
//
// detokenizes a vector of tokens into a string
// should work similar to Python's `tokenizer.decode`
// removes the leading space from the first non-BOS token
std::string llama_detokenize_spm(
llama_context * ctx,
const std::vector<llama_token> & tokens);
// detokenizes a vector of tokens into a string
// should work similar to Python's `tokenizer.decode`
std::string llama_detokenize_bpe(
llama_context * ctx,
const std::vector<llama_token> & tokens);

6
examples/beam_search/beam_search.cpp
View File

@ -35,7 +35,7 @@ struct ostream_beam_view {
std::ostream& operator<<(std::ostream& os, const ostream_beam_view & obv) {
os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens(";
for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) {
os << llama_token_to_str(obv.ctx, obv.beam_view.tokens[i]);
os << llama_token_to_piece(obv.ctx, obv.beam_view.tokens[i]);
}
return os << ')';
}
@ -156,7 +156,7 @@ int main(int argc, char ** argv)
for( auto id : tokens_list )
{
std::cout << llama_token_to_str(ctx, id);
std::cout << llama_token_to_piece(ctx, id);
}
std::cout << std::flush;
@ -175,7 +175,7 @@ int main(int argc, char ** argv)
std::cout << "\n\n";
for (llama_token const token_id : callback_data.response) {
std::cout << llama_token_to_str(ctx,token_id);
std::cout << llama_token_to_piece(ctx,token_id);
}
std::cout << std::endl;

2
examples/embd-input/embd-input-lib.cpp
View File

@ -214,7 +214,7 @@ const char * sampling(struct MyModel * mymodel) {
if (id == llama_token_eos(ctx)) {
ret = "</s>";
} else {
ret = llama_token_to_str(ctx, id);
ret = llama_token_to_piece(ctx, id);
}
eval_id(mymodel, id);
return ret.c_str();

5
examples/embedding/embedding.cpp
View File

@ -56,9 +56,6 @@ int main(int argc, char ** argv) {
int n_past = 0;
// Add a space in front of the first character to match OG llama tokenizer behavior
params.prompt.insert(0, 1, ' ');
// tokenize the prompt
auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
@ -67,7 +64,7 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
for (int i = 0; i < (int) embd_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]).c_str());
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
}
fprintf(stderr, "\n");
}

20
examples/main/main.cpp
View File

@ -195,11 +195,6 @@ int main(int argc, char ** argv) {
// tokenize the prompt
std::vector<llama_token> embd_inp;
if (llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM) {
// Add a space in front of the first character to match OG llama tokenizer behavior
params.prompt.insert(0, 1, ' ');
}
if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) {
embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
} else {
@ -216,7 +211,6 @@ int main(int argc, char ** argv) {
int guidance_offset = 0;
int original_prompt_len = 0;
if (ctx_guidance) {
params.cfg_negative_prompt.insert(0, 1, ' ');
guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, add_bos);
std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
@ -285,7 +279,7 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
for (int i = 0; i < (int) embd_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]).c_str());
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
}
if (ctx_guidance) {
@ -293,14 +287,14 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str());
fprintf(stderr, "%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
for (int i = 0; i < (int) guidance_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_str(ctx, guidance_inp[i]).c_str());
fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
}
}
if (params.n_keep > 0) {
fprintf(stderr, "%s: static prompt based on n_keep: '", __func__);
for (int i = 0; i < params.n_keep; i++) {
fprintf(stderr, "%s", llama_token_to_str(ctx, embd_inp[i]).c_str());
fprintf(stderr, "%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
}
fprintf(stderr, "'\n");
}
@ -456,7 +450,7 @@ int main(int argc, char ** argv) {
//printf("\n---\n");
//printf("resetting: '");
//for (int i = 0; i < (int) embd.size(); i++) {
// printf("%s", llama_token_to_str(ctx, embd[i]));
// printf("%s", llama_token_to_piece(ctx, embd[i]));
//}
//printf("'\n");
//printf("\n---\n");
@ -509,7 +503,7 @@ int main(int argc, char ** argv) {
input_size = embd_guidance.size();
//fprintf(stderr, "\n---------------------\n");
//for (int i = 0; i < (int) embd_guidance.size(); i++) {
//fprintf(stderr, "%s", llama_token_to_str(ctx, embd_guidance[i]));
//fprintf(stderr, "%s", llama_token_to_piece(ctx, embd_guidance[i]));
//}
//fprintf(stderr, "\n---------------------\n");
} else {
@ -673,7 +667,7 @@ int main(int argc, char ** argv) {
// display text
if (input_echo) {
for (auto id : embd) {
printf("%s", llama_token_to_str(ctx, id).c_str());
printf("%s", llama_token_to_piece(ctx, id).c_str());
}
fflush(stdout);
}
@ -689,7 +683,7 @@ int main(int argc, char ** argv) {
if (params.antiprompt.size()) {
std::string last_output;
for (auto id : last_n_tokens) {
last_output += llama_token_to_str(ctx, id);
last_output += llama_token_to_piece(ctx, id);
}
is_antiprompt = false;

4
examples/perplexity/perplexity.cpp
View File

@ -392,7 +392,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
hs_data[i].context = prompt_lines[idx*6];
hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
for (size_t j=0; j < 4; j++) {
hs_data[i].ending[j] = " " + prompt_lines[idx*6+2+j];
hs_data[i].ending[j] = prompt_lines[idx*6+2+j];
}
// Delete the selected random example from the prompt
@ -417,7 +417,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
size_t context_size = context_embd.size();
for (int i = 0; i < 4; ++i) {
ending_tokens[i] = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[i], add_bos);
ending_tokens[i] = ::llama_tokenize(ctx, hs_data[task_idx].context + " " + hs_data[task_idx].ending[i], add_bos);
for (int k = 0; k < int(context_size); ++k) {
if (ending_tokens[i][k] != context_embd[k]) {
fprintf(stderr, "Oops: ending %d of task %d differs from context at position %d\n",i,int(task_idx),k);

4
examples/save-load-state/save-load-state.cpp
View File

@ -87,7 +87,7 @@ int main(int argc, char ** argv) {
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
auto next_token = llama_sample_token(ctx, &candidates_p);
auto next_token_str = llama_token_to_str(ctx, next_token);
auto next_token_str = llama_token_to_piece(ctx, next_token);
last_n_tokens_data.push_back(next_token);
printf("%s", next_token_str.c_str());
@ -147,7 +147,7 @@ int main(int argc, char ** argv) {
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
auto next_token = llama_sample_token(ctx2, &candidates_p);
auto next_token_str = llama_token_to_str(ctx2, next_token);
auto next_token_str = llama_token_to_piece(ctx2, next_token);
last_n_tokens_data.push_back(next_token);
printf("%s", next_token_str.c_str());

16
examples/server/server.cpp
View File

@ -94,7 +94,7 @@ static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end)
std::string ret;
for (; begin != end; ++begin)
{
ret += llama_token_to_str(ctx, *begin);
ret += llama_token_to_piece(ctx, *begin);
}
return ret;
}
@ -123,7 +123,7 @@ static void server_log(const char *level, const char *function, int line,
// format incomplete utf-8 multibyte character for output
static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token)
{
std::string out = token == -1 ? "" : llama_token_to_str(ctx, token);
std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
// if the size is 1 and first bit is 1, meaning it's a partial character
// (size > 1 meaning it's already a known token)
if (out.size() == 1 && (out[0] & 0x80) == 0x80)
@ -286,7 +286,6 @@ struct llama_server_context
std::vector<llama_token> p;
if (first)
{
s.insert(0, 1, ' '); // add a space if it's the first
p = ::llama_tokenize(ctx, s, add_bos);
first = false;
}
@ -309,7 +308,6 @@ struct llama_server_context
else
{
auto s = json_prompt.template get<std::string>();
s.insert(0, 1, ' '); // always add a first space
prompt_tokens = ::llama_tokenize(ctx, s, add_bos);
}
@ -566,7 +564,7 @@ struct llama_server_context
if (!embd.empty() && embd.back() == llama_token_eos(ctx))
{
// stopping_word = llama_token_to_str(ctx, embd.back());
// stopping_word = llama_token_to_piece(ctx, embd.back());
has_next_token = false;
stopped_eos = true;
LOG_VERBOSE("eos token found", {});
@ -613,7 +611,7 @@ struct llama_server_context
{
const completion_token_output token_with_probs = nextToken();
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(ctx, token_with_probs.tok);
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok);
generated_text += token_text;
if (params.n_probs > 0)
@ -1254,7 +1252,7 @@ void beam_search_callback(void * callback_data, llama_beams_state beams_state) {
struct token_translator {
llama_context * ctx;
std::string operator()(llama_token tok) const { return llama_token_to_str(ctx, tok); }
std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); }
std::string operator()(completion_token_output cto) const { return (*this)(cto.tok); }
};
@ -1364,7 +1362,7 @@ int main(int argc, char **argv)
while (llama.has_next_token) {
const completion_token_output token_with_probs = llama.doCompletion();
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok);
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(llama.ctx, token_with_probs.tok);
stop_pos = llama.findStoppingStrings(llama.generated_text,
token_text.size(), STOP_FULL);
@ -1395,7 +1393,7 @@ int main(int argc, char **argv)
if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) {
continue;
}
const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok);
const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok);
size_t pos = std::min(sent_count, llama.generated_text.size());

4
examples/simple/simple.cpp
View File

@ -63,7 +63,7 @@ int main(int argc, char ** argv) {
fprintf(stderr, "\n\n");
for (auto id : tokens_list) {
fprintf(stderr, "%s", llama_token_to_str(ctx, id).c_str());
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
}
fflush(stderr);
@ -112,7 +112,7 @@ int main(int argc, char ** argv) {
}
// print the new token :
printf("%s", llama_token_to_str(ctx, new_token_id).c_str());
printf("%s", llama_token_to_piece(ctx, new_token_id).c_str());
fflush(stdout);
// push this new token for next evaluation

4
examples/train-text-from-scratch/train-text-from-scratch.cpp
View File

@ -1964,7 +1964,7 @@ void print_matrix(struct ggml_tensor * probs) {
void print_token(struct llama_context * ctx, llama_token token) {
printf("%s", llama_token_to_str(ctx, token).c_str());
printf("%s", llama_token_to_piece(ctx, token).c_str());
}
void print_tokens(struct llama_context* ctx, struct ggml_tensor * tokens) {
@ -2202,7 +2202,7 @@ int tokenize_file(struct llama_context * lctx, const char * filename, std::vecto
const char * in = buf.data();
const char * end = buf.data() + buf.size();
for (int i = 0; i < (int) out.size(); ++i) {
std::string s = llama_token_to_str(lctx, out[i]);
std::string s = llama_token_to_piece(lctx, out[i]);
int len = s.length();
if (in >= end) {
printf("%s: unexpected end of original text.\n", __func__);

60
llama.cpp
View File

@ -796,12 +796,12 @@ static void llama_nop(struct ggml_tensor * tensor) { // don't offload by default
(void) tensor;
}
static std::string llama_token_to_text(const struct llama_context * ctx, llama_token token) {
static std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
std::vector<char> result(8, 0);
const int n_tokens = llama_token_to_str(ctx, token, result.data(), result.size());
const int n_tokens = llama_token_to_piece(ctx, token, result.data(), result.size());
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_token_to_str(ctx, token, result.data(), result.size());
int check = llama_token_to_piece(ctx, token, result.data(), result.size());
GGML_ASSERT(check == -n_tokens);
} else {
result.resize(n_tokens);
@ -1635,7 +1635,8 @@ static void llm_load_hparams(
}
// TODO: This should probably be in llama.h
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos);
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos);
static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch);
static void llm_load_vocab(
llama_model_loader & ml,
@ -1737,7 +1738,11 @@ static void llm_load_vocab(
}
// determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
vocab.linefeed_id = llama_tokenize_internal(vocab, "\n", false)[0];
if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
} else {
vocab.linefeed_id = llama_tokenize_internal(vocab, "\n", false)[0];
}
// special tokens
GGUF_GET_KEY(ctx, vocab.special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID));
@ -3026,10 +3031,8 @@ static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
return vocab.token_to_id.at(buf);
}
static std::string llama_escape_whitespace(const std::string& text) {
std::string result = text;
replace_all(result, " ", "\xe2\x96\x81");
return result;
static void llama_escape_whitespace(std::string & text) {
replace_all(text, " ", "\xe2\x96\x81");
}
static void llama_unescape_whitespace(std::string & word) {
@ -3373,22 +3376,31 @@ private:
llm_bigram_bpe::queue work_queue;
};
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos) {
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos) {
std::vector<llama_vocab::id> output;
if (raw_text.empty()) {
return output;
}
// OG tokenizer behavior:
//
// tokenizer.encode('', add_bos=True) returns [1]
// tokenizer.encode('', add_bos=False) returns []
if (bos && vocab.special_bos_id != -1) {
output.push_back(vocab.special_bos_id);
}
if (raw_text.empty()) {
return output;
}
switch (vocab.type) {
case LLAMA_VOCAB_TYPE_SPM:
{
// without adding this leading whitespace, we do not get the same results as the original tokenizer
raw_text = " " + raw_text;
llm_tokenizer_spm tokenizer(vocab);
tokenizer.tokenize(llama_escape_whitespace(raw_text), output);
llama_escape_whitespace(raw_text);
tokenizer.tokenize(raw_text, output);
} break;
case LLAMA_VOCAB_TYPE_BPE:
{
@ -4078,16 +4090,16 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
std::vector<llama_grammar_candidate> candidates_grammar;
for (size_t i = 0; i < candidates->size; ++i) {
const llama_token id = candidates->data[i].id;
const std::string text = llama_token_to_text(ctx, id);
const llama_token id = candidates->data[i].id;
const std::string piece = llama_token_to_str(ctx, id);
if (id == eos) {
if (!allow_eos) {
candidates->data[i].logit = -INFINITY;
}
} else if (text.empty() || text[0] == 0) {
} else if (piece.empty() || piece[0] == 0) {
candidates->data[i].logit = -INFINITY;
} else {
candidates_decoded.push_back(decode_utf8(text.c_str(), grammar->partial_utf8));
candidates_decoded.push_back(decode_utf8(piece.c_str(), grammar->partial_utf8));
candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
}
}
@ -4291,10 +4303,10 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
GGML_ASSERT(false);
}
const std::string text = llama_token_to_text(ctx, token);
const std::string piece = llama_token_to_str(ctx, token);
// Note terminating 0 in decoded string
const auto decoded = decode_utf8(text.c_str(), grammar->partial_utf8);
const auto decoded = decode_utf8(piece.c_str(), grammar->partial_utf8);
const auto & code_points = decoded.first;
for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
@ -6101,12 +6113,12 @@ int llama_tokenize_with_model(
return res.size();
}
int llama_token_to_str(const struct llama_context * ctx, llama_token token, char * buf, int length) {
return llama_token_to_str_with_model(&ctx->model, token, buf, length);
int llama_token_to_piece(const struct llama_context * ctx, llama_token token, char * buf, int length) {
return llama_token_to_piece_with_model(&ctx->model, token, buf, length);
}
// does not write null-terminator to str
int llama_token_to_str_with_model(const struct llama_model * model, llama_token token, char * buf, int length) {
// does not write null-terminator to buf
int llama_token_to_piece_with_model(const struct llama_model * model, llama_token token, char * buf, int length) {
if (0 <= token && token < llama_model_n_vocab(model)) {
if (llama_is_normal_token(model->vocab, token)) {
std::string result = model->vocab.id_to_token[token].text;

10
llama.h
View File

@ -381,15 +381,17 @@ extern "C" {
int n_max_tokens,
bool add_bos);
// Token Id -> String. Uses the vocabulary in the provided context
// Does not write null terminator to the buffer
LLAMA_API int llama_token_to_str(
// Token Id -> Piece.
// Uses the vocabulary in the provided context.
// Does not write null terminator to the buffer.
// User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
LLAMA_API int llama_token_to_piece(
const struct llama_context * ctx,
llama_token token,
char * buf,
int length);
LLAMA_API int llama_token_to_str_with_model(
LLAMA_API int llama_token_to_piece_with_model(
const struct llama_model * model,
llama_token token,
char * buf,

6
tests/CMakeLists.txt
View File

@ -25,8 +25,10 @@ endfunction()
llama_build_and_test_executable(test-quantize-fns.cpp)
llama_build_and_test_executable(test-quantize-perf.cpp)
llama_build_and_test_executable(test-sampling.cpp)
llama_build_executable(test-tokenizer-0.cpp)
llama_test_executable (test-tokenizer-0.llama test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
llama_build_executable(test-tokenizer-0-llama.cpp)
llama_test_executable (test-tokenizer-0-llama test-tokenizer-0-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
llama_build_executable(test-tokenizer-0-falcon.cpp)
#llama_test_executable (test-tokenizer-0-falcon test-tokenizer-0-falcon.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
llama_build_executable(test-tokenizer-1.cpp)
# test-tokenizer-1 requires a BPE vocab. re-enable when we have one.
#llama_test_executable (test-tokenizer-1.llama test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)

178
tests/test-tokenizer-0-falcon.cpp Normal file
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@ -0,0 +1,178 @@
#include "llama.h"
#include "common.h"
#include <cstdio>
#include <string>
#include <map>
#include <vector>
#include <fstream>
// generate using test-tokenizer-0-falcon.py
static const std::map<std::string, std::vector<llama_token>> & k_tests() {
static std::map<std::string, std::vector<llama_token>> _k_tests = {
{ "" , { }, },
{ " " , { 204, }, },
{ " " , { 258, }, },
{ " " , { 466, }, },
{ "\t" , { 192, }, },
{ "\n" , { 193, }, },
{ "\t\n" , { 19125, }, },
{ "Hello world" , { 9856, 1079, }, },
{ " Hello world" , { 23090, 1079, }, },
{ "Hello World" , { 9856, 2889, }, },
{ " Hello World" , { 23090, 2889, }, },
{ " Hello World!" , { 23090, 2889, 12, }, },
{ "Hello, world!" , { 9856, 23, 1079, 12, }, },
{ " Hello, world!" , { 23090, 23, 1079, 12, }, },
{ " this is 🦙.cpp" , { 414, 304, 3346, 111, 231, 25, 29247, }, },
{ "w048 7tuijk dsdfhu" , { 98, 55866, 204, 34, 16682, 7149, 36190, 6869, 11481, }, },
{ "нещо на Български" , { 150, 133, 6207, 151, 215, 150, 134, 5052, 133, 6279, 5052, 223, 151, 216, 49679, 123, 53110, 47043, 7795, }, },
{ "កាន់តែពិសេសអាចខលចេញ" , { 38154, 206, 38154, 126, 38154, 225, 167, 237, 217, 38154, 221, 167, 237, 208, 38154, 228, 38154, 127, 38154, 237, 167, 237, 207, 38154, 237, 38154, 107, 38154, 126, 38154, 211, 38154, 207, 38154, 233, 38154, 211, 167, 237, 207, 38154, 215, }, },
{ "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", { 2571, 232, 206, 204, 19, 11003, 20, 8196, 126, 283, 219, 48778, 116, 13392, 204, 19, 51831, 732, 63209, 1741, 7955, 522, 20, 22438, 211, 204, 19, 7927, 53360, 325, 504, 701, 946, 10930, 20, }, },
{ "Hello" , { 9856, }, },
{ " Hello" , { 23090, }, },
{ " Hello" , { 204, 23090, }, },
{ " Hello" , { 258, 23090, }, },
{ " Hello" , { 466, 23090, }, },
{ " Hello\n Hello" , { 466, 23090, 742, 23090, }, },
};
return _k_tests;
}
int main(int argc, char **argv) {
if (argc < 2) {
fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]);
return 1;
}
const std::string fname = argv[1];
std::string fname_text;
if (argc > 2) {
fname_text = argv[2];
}
fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
llama_model * model;
llama_context * ctx;
llama_backend_init(false);
// load the vocab
{
auto lparams = llama_context_default_params();
lparams.vocab_only = true;
model = llama_load_model_from_file(fname.c_str(), lparams);
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
return 1;
}
ctx = llama_new_context_with_model(model, lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
llama_free_model(model);
return 1;
}
}
if (llama_vocab_type(ctx) != LLAMA_VOCAB_TYPE_BPE) {
fprintf(stderr, "%s : error: vocab type is not SPM\n", __func__);
llama_free_model(model);
llama_free(ctx);
return 2;
}
bool success = true;
for (const auto & test_kv : k_tests()) {
const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, false);
printf("\n");
printf("src: '%s'\n", test_kv.first.c_str());
printf("res: '%s'\n", llama_detokenize_bpe(ctx, res).c_str());
printf("tok: ");
for (const auto & tok : res) {
printf("%d ", tok);
}
printf("\n");
bool correct = res.size() == test_kv.second.size();
for (int i = 0; i < (int) res.size() && correct; ++i) {
if (test_kv.second[i] != res[i]) {
correct = false;
}
}
if (!correct) {
fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
llama_detokenize_bpe(ctx, res).c_str(),
llama_detokenize_bpe(ctx, test_kv.second).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");
success = false;
}
}
if (!fname_text.empty()) {
fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());
std::string text;
{
std::ifstream ifs(fname_text);
if (!ifs) {
fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str());
return 1;
}
text = std::string(std::istreambuf_iterator<char>(ifs), std::istreambuf_iterator<char>());
}
fprintf(stderr, "%s : text size: %zu\n", __func__, text.size());
const std::vector<llama_token> res = llama_tokenize(ctx, text, true);
fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size());
{
const std::string fname_out = fname_text + ".tokcpp";
std::ofstream ofs(fname_out);
if (!ofs) {
fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
return 1;
}
for (const auto & tok : res) {
ofs << tok << " ";
}
ofs << "\n";
}
fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());
}
llama_free_model(model);
llama_free(ctx);
llama_backend_free();
return success ? 0 : 3;
}

83
tests/test-tokenizer-0-falcon.py Normal file
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@ -0,0 +1,83 @@
# tests with BPE tokenizer
import os
import sys
import argparse
from transformers import AutoTokenizer
parser = argparse.ArgumentParser()
parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
parser.add_argument("--fname-tok", help="path to a text file to tokenize")
args = parser.parse_args()
dir_tokenizer = args.dir_tokenizer
tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer)
tests = [
"",
" ",
" ",
" ",
"\t",
"\n",
"\t\n",
"Hello world",
" Hello world",
"Hello World",
" Hello World",
" Hello World!",
"Hello, world!",
" Hello, world!",
" this is 🦙.cpp",
"w048 7tuijk dsdfhu",
"нещо на Български",
"កាន់តែពិសេសអាចខលចេញ",
"🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
"Hello",
" Hello",
" Hello",
" Hello",
" Hello",
" Hello\n Hello",
]
for text in tests:
print('text: ', text)
print(tokenizer.encode(text))
print(tokenizer.decode(tokenizer.encode(text)))
print("\n\ntests for C++:\n")
for text in tests:
res = tokenizer.encode(text)
k = text.replace('\n', '\\n')
k = k.replace('\t', '\\t')
k = '"' + k + '"'
print("{ %-24s, { " % k, end='')
for x in res:
print("%7d," % x, end='')
print(" }, },")
print(tokenizer.encode('hello'))
print(tokenizer.encode('world'))
print(tokenizer.encode(' world'))
print(tokenizer.encode('hello world'))
fname_tok = args.fname_tok
if fname_tok:
print('tokenizing file: ', fname_tok)
fname_out = fname_tok + '.tok'
with open(fname_tok, 'r') as f:
lines = f.readlines()
s = ''.join(lines)
res = tokenizer.encode(s)
# write to file
with open(fname_out, 'w') as f:
for x in res:
f.write(str(x) + ' ')
f.write('\n')
print('len(res): ', len(res))
print('len(lines): ', len(lines))
print('results written to: ', fname_out)

182
tests/test-tokenizer-0-llama.cpp Normal file
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@ -0,0 +1,182 @@
#include "llama.h"
#include "common.h"
#include <cstdio>
#include <string>
#include <map>
#include <vector>
#include <fstream>
// generate using test-tokenizer-0-llama.py
static const std::map<std::string, std::vector<llama_token>> & k_tests() {
static std::map<std::string, std::vector<llama_token>> _k_tests = {
{ "" , { }, },
{ " " , { 259, }, },
{ " " , { 1678, }, },
{ " " , { 268, }, },
{ "\t" , { 29871, 12, }, },
{ "\n" , { 29871, 13, }, },
{ "\t\n" , { 29871, 12, 13, }, },
{ "Hello world" , { 15043, 3186, }, },
{ " Hello world" , { 29871, 15043, 3186, }, },
{ "Hello World" , { 15043, 2787, }, },
{ " Hello World" , { 29871, 15043, 2787, }, },
{ " Hello World!" , { 29871, 15043, 2787, 29991, }, },
{ "Hello, world!" , { 15043, 29892, 3186, 29991, }, },
{ " Hello, world!" , { 29871, 15043, 29892, 3186, 29991, }, },
{ " this is 🦙.cpp" , { 29871, 445, 338, 29871, 243, 162, 169, 156, 29889, 8223, }, },
{ "w048 7tuijk dsdfhu" , { 281, 29900, 29946, 29947, 29871, 29955, 9161, 13535, 18031, 2176, 6905, }, },
{ "нещо на Български" , { 1538, 4851, 665, 1386, 29713, 1305, }, },
{ "កាន់តែពិសេសអាចខលចេញ" , { 29871, 31849, 31324, 31934, 228, 162, 142, 228, 161, 146, 228, 162, 133, 228, 161, 153, 228, 161, 186, 31708, 228, 162, 132, 31708, 228, 161, 165, 31324, 228, 161, 136, 228, 161, 132, 228, 161, 158, 228, 161, 136, 228, 162, 132, 228, 161, 140, }, },
{ "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", { 29871, 243, 162, 157, 131, 313, 8945, 29897, 29871, 243, 162, 155, 185, 30722, 243, 162, 143, 174, 30598, 313, 20787, 953, 3848, 275, 16125, 630, 29897, 29871, 31681, 313, 6194, 953, 29877, 2397, 393, 756, 967, 1914, 5993, 29897, }, },
{ "Hello" , { 15043, }, },
{ " Hello" , { 29871, 15043, }, },
{ " Hello" , { 259, 15043, }, },
{ " Hello" , { 1678, 15043, }, },
{ " Hello" , { 268, 15043, }, },
{ " Hello\n Hello" , { 268, 15043, 13, 1678, 15043, }, },
};
return _k_tests;
}
int main(int argc, char **argv) {
if (argc < 2) {
fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]);
return 1;
}
const std::string fname = argv[1];
std::string fname_text;
if (argc > 2) {
fname_text = argv[2];
}
fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
llama_model * model;
llama_context * ctx;
llama_backend_init(false);
// load the vocab
{
auto lparams = llama_context_default_params();
lparams.vocab_only = true;
model = llama_load_model_from_file(fname.c_str(), lparams);
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
return 1;
}
ctx = llama_new_context_with_model(model, lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
llama_free_model(model);
return 1;
}
}
if (llama_vocab_type(ctx) != LLAMA_VOCAB_TYPE_SPM) {
fprintf(stderr, "%s : error: vocab type is not SPM\n", __func__);
llama_free_model(model);
llama_free(ctx);
return 2;
}
bool success = true;
for (const auto & test_kv : k_tests()) {
const std::vector<llama_token> res_bos = llama_tokenize(ctx, test_kv.first, true);
const std::vector<llama_token> res_nobos = llama_tokenize(ctx, test_kv.first, false);
printf("\n");
printf("src: '%s'\n", test_kv.first.c_str());
printf("res: '%s'\n", llama_detokenize_spm(ctx, res_bos).c_str());
printf("tok: ");
for (const auto & tok : res_bos) {
printf("%d ", tok);
}
printf("\n");
bool correct = res_nobos.size() == test_kv.second.size() && res_bos.size() == res_nobos.size() + 1 && res_bos[0] == 1;
for (int i = 0; i < (int) res_nobos.size() && correct; ++i) {
if (test_kv.second[i] != res_bos[i + 1]) {
correct = false;
}
if (test_kv.second[i] != res_nobos[i]) {
correct = false;
}
}
if (!correct) {
fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
llama_detokenize_spm(ctx, res_nobos).c_str(),
llama_detokenize_spm(ctx, test_kv.second).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_nobos) {
fprintf(stderr, "%6d, ", t);
}
fprintf(stderr, "\n");
success = false;
}
}
if (!fname_text.empty()) {
fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());
std::string text;
{
std::ifstream ifs(fname_text);
if (!ifs) {
fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str());
return 1;
}
text = std::string(std::istreambuf_iterator<char>(ifs), std::istreambuf_iterator<char>());
}
fprintf(stderr, "%s : text size: %zu\n", __func__, text.size());
const std::vector<llama_token> res = llama_tokenize(ctx, text, true);
fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size());
{
const std::string fname_out = fname_text + ".tokcpp";
std::ofstream ofs(fname_out);
if (!ofs) {
fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
return 1;
}
for (const auto & tok : res) {
ofs << tok << " ";
}
ofs << "\n";
}
fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());
}
llama_free_model(model);
llama_free(ctx);
llama_backend_free();
return success ? 0 : 3;
}

95
tests/test-tokenizer-0-llama.py Normal file
View File

@ -0,0 +1,95 @@
# tests with SPM tokenizer
import os
import sys
import argparse
from sentencepiece import SentencePieceProcessor
parser = argparse.ArgumentParser()
parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
parser.add_argument("--fname-tok", help="path to a text file to tokenize")
args = parser.parse_args()
dir_tokenizer = args.dir_tokenizer
tokenizer = SentencePieceProcessor(dir_tokenizer + '/tokenizer.model')
tests = [
"",
" ",
" ",
" ",
"\t",
"\n",
"\t\n",
"Hello world",
" Hello world",
"Hello World",
" Hello World",
" Hello World!",
"Hello, world!",
" Hello, world!",
" this is 🦙.cpp",
"w048 7tuijk dsdfhu",
"нещо на Български",
"កាន់តែពិសេសអាចខលចេញ",
"🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
"Hello",
" Hello",
" Hello",
" Hello",
" Hello",
" Hello\n Hello",
]
for text in tests:
print('text: ', text)
print('\nwith bos:')
print(tokenizer.encode(text, add_bos=True))
print(tokenizer.decode(tokenizer.encode(text, add_bos=True)))
print('\nwithout bos:')
print(tokenizer.encode(text, add_bos=False))
print(tokenizer.decode(tokenizer.encode(text, add_bos=False)))
print("'" + tokenizer.id_to_piece(15043) + "'") # '_Hello'
print("'" + tokenizer.id_to_piece(29871) + "'") # '_'
print("'" + tokenizer.decode([15043]) + "'") # 'Hello'
print("'" + tokenizer.decode([15043, 15043]) + "'") # 'Hello Hello'
print("'" + tokenizer.decode([29871, 15043]) + "'") # ' Hello'
print("'" + tokenizer.decode([29871, 15043, 29871, 15043]) + "'") # ' Hello Hello'
print("\n\ntests for C++:\n")
for text in tests:
res = tokenizer.encode(text, add_bos=False)
k = text.replace('\n', '\\n')
k = k.replace('\t', '\\t')
k = '"' + k + '"'
print("{ %-24s, { " % k, end='')
for x in res:
print("%7d," % x, end='')
print(" }, },")
print(tokenizer.encode('hello'))
print(tokenizer.encode('world'))
print(tokenizer.encode(' world'))
print(tokenizer.encode('hello world'))
fname_tok = args.fname_tok
if fname_tok:
print('tokenizing file: ', fname_tok)
fname_out = fname_tok + '.tok'
with open(fname_tok, 'r') as f:
lines = f.readlines()
s = ''.join(lines)
res = tokenizer.encode(s, add_bos=True)
# write to file
with open(fname_out, 'w') as f:
for x in res:
f.write(str(x) + ' ')
f.write('\n')
print('len(res): ', len(res))
print('len(lines): ', len(lines))
print('results written to: ', fname_out)

141
tests/test-tokenizer-0.cpp
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@ -1,141 +0,0 @@
#include "llama.h"
#include "common.h"
#include <cstdio>
#include <string>
#include <map>
#include <vector>
static std::string unescape_whitespace(llama_context* ctx, const std::vector<llama_token>& tokens) {
std::string result;
for (size_t i = 0; i < tokens.size(); ++i) {
result += llama_token_to_str(ctx, tokens[i]);
}
return result;
}
static const std::map<std::string, std::vector<llama_token>> & k_tests() {
static std::map<std::string, std::vector<llama_token>> _k_tests = {
{ " ", {1, 259, }, },
{ " ", { 1, 1678, }, },
{ " ", { 1, 268, }, },
{ "\t", { 1, 29871, 12, }, },
{ "\n", { 1, 29871, 13, }, },
{ "\t\n", { 1, 29871, 12, 13, }, },
{ "Hello world", { 1, 15043, 3186, }, },
{ " Hello world", { 1, 29871, 15043, 3186, }, },
{ "Hello World", { 1, 15043, 2787, }, },
{ " Hello World", { 1, 29871, 15043, 2787, }, },
{ " Hello World!", { 1, 29871, 15043, 2787, 29991, }, },
{ " this is 🦙.cpp", { 1, 29871, 445, 338, 29871, 243, 162, 169, 156, 29889, 8223, }, },
{ "w048 7tuijk dsdfhu", { 1, 281, 29900, 29946, 29947, 29871, 29955, 9161, 13535, 18031, 2176, 6905, }, },
{ "нещо на Български", { 1, 1538, 4851, 665, 1386, 29713, 1305, }, },
{ "កាន់តែពិសេសអាចខលចេញ", { 1, 29871, 31849, 31324, 31934, 228, 162, 142, 228, 161,
146, 228, 162, 133, 228, 161, 153, 228, 161, 186,
31708, 228, 162, 132, 31708, 228, 161, 165, 31324, 228,
161, 136, 228, 161, 132, 228, 161, 158, 228, 161,
136, 228, 162, 132, 228, 161, 140, }, },
{ "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
{ 1, 29871, 243, 162, 157, 131, 313, 8945, 29897, 29871,
243, 162, 155, 185, 30722, 243, 162, 143, 174, 30598,
313, 20787, 953, 3848, 275, 16125, 630, 29897, 29871, 31681,
313, 6194, 953, 29877, 2397, 393, 756, 967, 1914, 5993, 29897, }, },
{ "Hello", { 1, 15043 }, },
{ " Hello", { 1, 29871, 15043 }, },
{ " Hello", { 1, 259, 15043 }, },
{ " Hello", { 1, 1678, 15043 }, },
{ " Hello", { 1, 268, 15043 }, },
{ " Hello\n Hello", { 1, 268, 15043, 13, 1678, 15043 }, },
};
return _k_tests;
}
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_model * model;
llama_context * ctx;
llama_backend_init(false);
// load the vocab
{
auto lparams = llama_context_default_params();
lparams.vocab_only = true;
model = llama_load_model_from_file(fname.c_str(), lparams);
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
return 1;
}
ctx = llama_new_context_with_model(model, lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
llama_free_model(model);
return 1;
}
}
const int n_vocab = llama_n_vocab(ctx);
if (n_vocab != 32000) {
fprintf(stderr, "%s : expected 32000 tokens, got %d\n", __func__, n_vocab);
llama_free_model(model);
llama_free(ctx);
return 2;
}
bool success = true;
for (const auto & test_kv : k_tests()) {
// Add a space in front of the first character to match OG llama tokenizer behavior
std::vector<llama_token> res = llama_tokenize(ctx, " " + test_kv.first, true);
fprintf(stderr, "%s : '%s' tokenized to '%s'\n",
__func__, test_kv.first.c_str(), unescape_whitespace(ctx, res).c_str());
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 : detokenized to: '%s' instead of '%s'\n", __func__,
unescape_whitespace(ctx, res).c_str(), unescape_whitespace(ctx, test_kv.second).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");
success = false;
}
}
llama_free_model(model);
llama_free(ctx);
llama_backend_free();
return success ? 0 : 3;
}

14
tests/test-tokenizer-1.cpp
View File

@ -22,14 +22,6 @@ static std::string escape_whitespace(const std::string& text) {
return result;
}
static std::string unescape_whitespace(llama_context * ctx, const std::vector<llama_token> & tokens) {
std::string result;
for (size_t i = 0; i < tokens.size(); ++i) {
result += llama_token_to_str(ctx, tokens[i]);
}
return result;
}
int main(int argc, char **argv) {
if (argc < 2) {
fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
@ -72,13 +64,13 @@ int main(int argc, char **argv) {
const int n_vocab = llama_n_vocab(ctx);
for (int i = 0; i < n_vocab; ++i) {
std::string forward = llama_token_to_str(ctx, i);
std::string forward = llama_token_to_piece(ctx, i);
std::vector<llama_token> tokens = llama_tokenize(ctx, forward, false);
if (tokens.size() == 1) {
if (i != tokens[0]) {
std::string backward = llama_token_to_str(ctx, tokens[0]);
std::string backward = llama_token_to_piece(ctx, tokens[0]);
fprintf(stderr, "%s : error: token %d is string %s but bpe returns token %d %s\n",
__func__, i, llama_token_to_str(ctx, i).c_str(), tokens[0], backward.c_str());
__func__, i, llama_token_to_piece(ctx, i).c_str(), tokens[0], backward.c_str());
return 2;
}
}