sampling : refactor init to use llama_sampling_params (#3696)
* sampling : refactor init to use llama_sampling_params * llama : combine repetition, frequency and presence penalties in 1 call * examples : remove embd-input and gptneox-wip * sampling : rename penalty params + reduce size of "prev" vector * sampling : add llama_sampling_print helper * sampling : hide prev behind API and apply #3661 ggml-ci
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30 changed files with 365 additions and 4502 deletions
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@ -112,16 +112,16 @@ int main(int argc, char ** argv) {
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bool has_eos = false;
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// target model sampling context
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struct llama_sampling_context * ctx_sampling = llama_sampling_init(params);
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struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
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// draft sequence data
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std::vector<seq_draft> drafts(n_seq_dft);
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params.grammar.clear(); // the draft samplers will copy the target sampler's grammar
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params.sampling_params.temp = std::max(0.01f, params.sampling_params.temp);
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params.sparams.grammar.clear(); // the draft samplers will copy the target sampler's grammar
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params.sparams.temp = std::max(0.01f, params.sparams.temp);
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for (int s = 0; s < n_seq_dft; ++s) {
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drafts[s].ctx_sampling = llama_sampling_init(params);
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drafts[s].ctx_sampling = llama_sampling_init(params.sparams);
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}
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llama_batch batch_dft = llama_batch_init(params.n_ctx, 0, 1);
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@ -154,7 +154,7 @@ int main(int argc, char ** argv) {
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// sample from the target model
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llama_token id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]);
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llama_sampling_accept(ctx_sampling, ctx_tgt, id);
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llama_sampling_accept(ctx_sampling, ctx_tgt, id, true);
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//LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str());
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@ -328,7 +328,7 @@ int main(int argc, char ** argv) {
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const int s = sa[is];
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llama_sampling_accept(drafts[s].ctx_sampling, ctx_dft, id);
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llama_sampling_accept(drafts[s].ctx_sampling, ctx_dft, id, true);
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drafts[s].tokens.push_back(id);
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