all : be more strict about converting float to double (#458)
* Be more strict about converting float to double * Test equivalence of round, SILU implementations Test module is commented out in CMakeLists.txt because the tests may take a long time, depending on how much the compiler optimizes. * Fix softmax in perplexity.cpp * all : prefer float over double where appropriate * perplexity : add <cmath> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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Stephan Walter
GitHub
parent
20e1e84884
commit
436e561931
11 changed files with 185 additions and 117 deletions
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@ -215,13 +215,13 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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fprintf(stderr, " prompt file to start generation.\n");
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fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d, -1 = infinity)\n", params.n_predict);
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fprintf(stderr, " --top_k N top-k sampling (default: %d)\n", params.top_k);
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fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p);
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fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", (double)params.top_p);
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fprintf(stderr, " --repeat_last_n N last n tokens to consider for penalize (default: %d)\n", params.repeat_last_n);
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fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty);
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fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", (double)params.repeat_penalty);
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fprintf(stderr, " -c N, --ctx_size N size of the prompt context (default: %d)\n", params.n_ctx);
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fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating\n");
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fprintf(stderr, " --memory_f32 use f32 instead of f16 for memory key+value\n");
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fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
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fprintf(stderr, " --temp N temperature (default: %.1f)\n", (double)params.temp);
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fprintf(stderr, " --n_parts N number of model parts (default: -1 = determine from dimensions)\n");
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fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
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fprintf(stderr, " --perplexity compute perplexity over the prompt\n");
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@ -209,7 +209,8 @@ int main(int argc, char ** argv) {
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fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str());
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}
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}
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fprintf(stderr, "sampling: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
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fprintf(stderr, "sampling: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n",
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params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
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fprintf(stderr, "generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
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fprintf(stderr, "\n\n");
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@ -274,10 +275,10 @@ int main(int argc, char ** argv) {
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if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
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// out of user input, sample next token
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const float top_k = params.top_k;
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const float top_p = params.top_p;
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const float temp = params.temp;
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const float repeat_penalty = params.repeat_penalty;
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const int32_t top_k = params.top_k;
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const float top_p = params.top_p;
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const float temp = params.temp;
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const float repeat_penalty = params.repeat_penalty;
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llama_token id = 0;
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@ -1,15 +1,17 @@
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#include "common.h"
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#include "llama.h"
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std::vector<double> softmax(const std::vector<float>& logits) {
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std::vector<double> probs(logits.size());
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#include <cmath>
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std::vector<float> softmax(const std::vector<float>& logits) {
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std::vector<float> probs(logits.size());
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float max_logit = logits[0];
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for (float v : logits) max_logit = std::max(max_logit, v);
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double sum_exp = 0.0;
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for (size_t i = 0; i < logits.size(); i++) {
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// Subtract the maximum logit value from the current logit value for numerical stability
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float logit = logits[i] - max_logit;
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double exp_logit = std::exp(logit);
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const float logit = logits[i] - max_logit;
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const float exp_logit = expf(logit);
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sum_exp += exp_logit;
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probs[i] = exp_logit;
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}
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@ -24,14 +26,16 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
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auto tokens = ::llama_tokenize(ctx, params.prompt, true);
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int count = 0;
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double nll = 0.0;
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int seq_count = tokens.size() / params.n_ctx;
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double nll = 0.0;
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fprintf(stderr, "%s : calculating perplexity over %d chunks\n", __func__, seq_count);
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for (int i = 0; i < seq_count; ++i) {
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int start = i * params.n_ctx;
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int end = start + params.n_ctx - 1;
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int end = start + params.n_ctx - 1; // TODO: this is not optimal, e.g. it makes the batch 511 instead of 512
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// it is better to always be power of 2 for better performance
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std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end);
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auto start_t = std::chrono::high_resolution_clock::now();
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if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads)) {
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@ -40,7 +44,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
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}
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auto end_t = std::chrono::high_resolution_clock::now();
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if (i == 0) {
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double seconds = std::chrono::duration<double>(end_t - start_t).count();
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const float seconds = std::chrono::duration<float>(end_t - start_t).count();
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printf("%.2f seconds per pass - ETA %.2f hours\n", seconds, (seconds * seq_count) / (60.0*60.0));
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}
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// We get the logits for all the tokens in the context window (params.n_ctx)
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@ -63,7 +67,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
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std::vector<float> tok_logits(
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logits + j * n_vocab,
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logits + (j + 1) * n_vocab);
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double prob = softmax(tok_logits)[tokens[start + j + 1]];
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const float prob = softmax(tok_logits)[tokens[start + j + 1]];
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nll += -std::log(prob);
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++count;
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}
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@ -50,8 +50,8 @@ int main(int argc, char ** argv) {
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const int64_t t_main_end_us = ggml_time_us();
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printf("\n");
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printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0f);
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printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
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printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0);
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printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0);
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}
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return 0;
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