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talk-llama : sync llama.cpp
ggml-ci
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@ -1750,23 +1750,35 @@ static const char * llama_sampler_top_n_sigma_name(const struct llama_sampler *
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static void llama_sampler_top_n_sigma_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
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const auto * ctx = (llama_sampler_top_n_sigma *) smpl->ctx;
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if (ctx->n <= 0.0f || cur_p->size <= 1) {
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return;
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}
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// find max logit and calculate mean
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float max = cur_p->data[0].logit;
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float logits_sum = 0;
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size_t valid_count = 0;
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for (size_t i = 0; i < cur_p->size; ++i) {
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if (cur_p->data[i].logit > max) {
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max = cur_p->data[i].logit;
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// Only count non-negative infinity values
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if (cur_p->data[i].logit != -INFINITY) {
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if (cur_p->data[i].logit > max) {
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max = cur_p->data[i].logit;
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}
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logits_sum += cur_p->data[i].logit;
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valid_count++;
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}
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logits_sum += cur_p->data[i].logit;
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}
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float mean = logits_sum/cur_p->size;
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float mean = valid_count > 0 ? logits_sum/valid_count : 0;
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// calculate standard deviation
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float acc = 0;
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for (size_t i = 0; i < cur_p->size; ++i) {
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acc += pow(cur_p->data[i].logit - mean, 2);
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// Skip -infinity in std calculation
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if (cur_p->data[i].logit != -INFINITY) {
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acc += pow(cur_p->data[i].logit - mean, 2);
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}
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}
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float std = sqrt(acc/cur_p->size);
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float std = valid_count > 0 ? sqrt(acc/valid_count) : 0;
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//apply mask
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for (size_t i = 0; i < cur_p->size; ++i) {
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