vbatts/llama.cpp
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llama : new sampling algorithms (#1126)

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* Sample interface, new samplers.

New samplers:
- locally typical sampling
- tail free sampling
- frequency and presence penalty
- mirostat

Ignore EOS fix: -inf should be used.

* mirostat

* Added --logit-bias and --no-penalize-nl, removed std::span

* Use C++11, clarify llama API documentation, rename Mirostat parameters to --mirostat_lr and --mirostat_ent, add temperature sampling for Mirostat, simplify Mirostat sampling API parameters (removed N and *k)

Use C++11, clarify llama API documentation, rename Mirostat parameters to --mirostat_lr and --mirostat_ent, add temperature sampling for Mirostat, simplify Mirostat sampling API parameters (removed N and *k)

* Save and load example adjust

* Tests

* Windows build fix

* Windows test fix
This commit is contained in:
Ivan Stepanov 2023-04-29 08:34:41 +03:00 • committed by GitHub
parent 7fc50c051a
commit dd7eff57d8
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8 changed files with 812 additions and 160 deletions
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34
examples/save-load-state/save-load-state.cpp
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@ -64,14 +64,15 @@ int main(int argc, char ** argv) {
// first run
printf("\n%s", params.prompt.c_str());
for (auto i = 0; i < params.n_predict; i++) {
auto next_token = llama_sample_top_p_top_k(
ctx,
&last_n_tokens_data.back() - params.repeat_last_n,
params.repeat_last_n,
40,
1.0,
1.0,
1.1);
auto logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(ctx);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
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);
last_n_tokens_data.push_back(next_token);
printf("%s", next_token_str);
@ -106,14 +107,15 @@ int main(int argc, char ** argv) {
// second run
for (auto i = 0; i < params.n_predict; i++) {
auto next_token = llama_sample_top_p_top_k(
ctx2,
&last_n_tokens_data.back() - params.repeat_last_n,
params.repeat_last_n,
40,
1.0,
1.0,
1.1);
auto logits = llama_get_logits(ctx2);
auto n_vocab = llama_n_vocab(ctx2);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
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);
last_n_tokens_data.push_back(next_token);
printf("%s", next_token_str);