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gpt4all-backend: Add temperature sampling with repetition penalty #541

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gpt4all-backend: Add temperature sampling with repetition penalty #541

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24 changes: 21 additions & 3 deletions gpt4all-backend/llamamodel.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -170,10 +170,28 @@ void LLamaModel::prompt(const std::string &prompt,
int32_t totalPredictions = 0;
for (int i = 0; i < promptCtx.n_predict; i++) {
// sample next token
llama_token id = llama_sample_top_p_top_k(d_ptr->ctx,
promptCtx.tokens.data() + promptCtx.n_ctx - promptCtx.repeat_last_n,
promptCtx.repeat_last_n, promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
float* logits = llama_get_logits(d_ptr->ctx);
std::vector<llama_token_data> candidates;
candidates.resize(llama_n_vocab(d_ptr->ctx));

for (llama_token i = 0; i < candidates.size(); i++) {
candidates[i] = llama_token_data{
i, logits[i], 0.0f,
};
}
llama_token_data_array candidates_data = {
candidates.data(), candidates.size(), false,
};

// Temperature sampling with repetition penalty
llama_sample_repetition_penalty(
d_ptr->ctx, &candidates_data,
promptCtx.tokens.data() + promptCtx.n_ctx - promptCtx.repeat_last_n, promptCtx.repeat_last_n,
promptCtx.repeat_penalty);
llama_sample_top_k(d_ptr->ctx, &candidates_data, promptCtx.top_k, 1);
llama_sample_top_p(d_ptr->ctx, &candidates_data, promptCtx.top_p, 1);
llama_sample_temperature(d_ptr->ctx, &candidates_data, promptCtx.temp);
llama_token id = llama_sample_token(d_ptr->ctx, &candidates_data);

// Check if the context has run out...
if (promptCtx.n_past + 1 > promptCtx.n_ctx) {
Expand Down