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llama : add option for greedy sampling with probs (#3813)
* llama : add option for greedy sampling with probs * llama : add comment about llama_sample_token_greedy() missing probs * sampling : temp == 0.0 -> no probs, temp < 0.0 -> probs
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@ -224,6 +224,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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break;
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}
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sparams.temp = std::stof(argv[i]);
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sparams.temp = std::max(sparams.temp, 0.0f);
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} else if (arg == "--tfs") {
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if (++i >= argc) {
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invalid_param = true;
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@ -167,8 +167,12 @@ llama_token llama_sampling_sample(
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llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
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}
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if (temp <= 0) {
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// greedy sampling
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if (temp < 0.0) {
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// greedy sampling, with probs
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llama_sample_softmax(ctx_main, &cur_p);
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id = cur_p.data[0].id;
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} else if (temp == 0.0) {
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// greedy sampling, no probs
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id = llama_sample_token_greedy(ctx_main, &cur_p);
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} else {
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if (mirostat == 1) {
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@ -148,7 +148,7 @@ int main(int argc, char ** argv) {
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std::vector<seq_draft> drafts(n_seq_dft);
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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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params.sparams.temp = -1.0f; // force greedy sampling with probs for the draft model
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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.sparams);
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1
llama.h
1
llama.h
@ -658,6 +658,7 @@ extern "C" {
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float * mu);
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/// @details Selects the token with the highest probability.
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/// Does not compute the token probabilities. Use llama_sample_softmax() instead.
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LLAMA_API llama_token llama_sample_token_greedy(
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struct llama_context * ctx,
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llama_token_data_array * candidates);
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