vbatts/llama.cpp
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Debugging print statements removed

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kalomaze 2023-10-28 21:14:55 -05:00
parent 838d58dc32
commit 69ef4ca885
1 changed files with 2 additions and 26 deletions
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llama.cpp
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@ -7364,22 +7364,13 @@ void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * can
float base_min_p = p; // This will hold the base minimum probability value float base_min_p = p; // This will hold the base minimum probability value
float multiplied_min_p; // This will hold the adjusted minimum probability threshold float multiplied_min_p; // This will hold the adjusted minimum probability threshold
printf("\nUSING MIN P SAMPLING MODE\n\n");
// Ensure the probabilities are calculated. // Ensure the probabilities are calculated.
llama_sample_softmax(ctx, candidates); llama_sample_softmax(ctx, candidates);
// Print the top tokens before filtering
printf("Top tokens before filtering:\n");
for (size_t i = 0; i < candidates->size && i < 10; ++i) {
printf("Token %zu: %.6f%%\n", i + 1, candidates->data[i].p * 100); // Multiplying by 100 to convert to percentage
}
// Calculate the multiplication factor based on the highest scoring token. // Calculate the multiplication factor based on the highest scoring token.
float multiplication_factor = candidates->data[0].p; // Assuming the probabilities are sorted float multiplication_factor = candidates->data[0].p;
printf("Highest scoring token probability (multiplication factor): %f\n", multiplication_factor);
// Calculate the dynamic threshold. // Calculate the minimum percentage requirement.
multiplied_min_p = base_min_p * multiplication_factor; multiplied_min_p = base_min_p * multiplication_factor;
printf("Base min_p value: %f\n", base_min_p); printf("Base min_p value: %f\n", base_min_p);
printf("Calculated multiplied_min_p (threshold) value: %f\n", multiplied_min_p); printf("Calculated multiplied_min_p (threshold) value: %f\n", multiplied_min_p);
@ -7388,31 +7379,16 @@ void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * can
std::vector<llama_token_data> filtered_candidates; std::vector<llama_token_data> filtered_candidates;
filtered_candidates.reserve(candidates->size); // Reserve to avoid multiple reallocations filtered_candidates.reserve(candidates->size); // Reserve to avoid multiple reallocations
// Variable to count how many tokens meet the condition
int count_qualifying_tokens = 0;
for (size_t i = 0; i < candidates->size; ++i) { for (size_t i = 0; i < candidates->size; ++i) {
// If a token's probability is above the threshold, we keep it. // If a token's probability is above the threshold, we keep it.
if (candidates->data[i].p >= multiplied_min_p) { if (candidates->data[i].p >= multiplied_min_p) {
filtered_candidates.push_back(candidates->data[i]); filtered_candidates.push_back(candidates->data[i]);
++count_qualifying_tokens; // Increase count
} }
} }
// Debug information about how many tokens were retained
printf("Number of tokens that met the multiplied_min_p condition: %d\n", count_qualifying_tokens);
// Print the top tokens after filtering
printf("Tokens after filtering:\n\n");
for (size_t i = 0; i < filtered_candidates.size() && i < 10; ++i) { // Adjust 10 to however many top tokens you want to display
printf("Token %zu: %.6f%%\n", i + 1, filtered_candidates[i].p * 100); // Multiplying by 100 to convert to percentage
}
// Now we replace the original candidates with the filtered list. // Now we replace the original candidates with the filtered list.
std::copy(filtered_candidates.begin(), filtered_candidates.end(), candidates->data); std::copy(filtered_candidates.begin(), filtered_candidates.end(), candidates->data);
candidates->size = filtered_candidates.size(); candidates->size = filtered_candidates.size();
return;
} }
void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) { void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {