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
52
llama.cpp
52
llama.cpp
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@ -779,8 +779,8 @@ static bool llama_model_load(
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// progress
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if (progress_callback) {
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double current_file_progress = double(size_t(fin.tellg()) - file_offset) / double(file_size - file_offset);
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double current_progress = (double(i) + current_file_progress) / double(n_parts);
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float current_file_progress = float(size_t(fin.tellg()) - file_offset) / float(file_size - file_offset);
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float current_progress = (float(i) + current_file_progress) / float(n_parts);
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progress_callback(current_progress, progress_callback_user_data);
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}
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if (model.n_loaded % 8 == 0) {
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@ -922,7 +922,7 @@ static bool llama_eval_internal(
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struct ggml_tensor * KQ_scaled =
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ggml_scale(ctx0,
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KQ,
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ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head)));
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ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
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// KQ_masked = mask_past(KQ_scaled)
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struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
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@ -1240,12 +1240,12 @@ static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, co
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// sampling
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//
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static void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k) {
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static void sample_top_k(std::vector<std::pair<float, llama_vocab::id>> & logits_id, int top_k) {
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// find the top k tokens
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std::partial_sort(
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logits_id.begin(),
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logits_id.begin() + top_k, logits_id.end(),
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[](const std::pair<double, llama_vocab::id> & a, const std::pair<double, llama_vocab::id> & b) {
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[](const std::pair<float, llama_vocab::id> & a, const std::pair<float, llama_vocab::id> & b) {
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return a.first > b.first;
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});
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@ -1256,9 +1256,9 @@ static llama_vocab::id llama_sample_top_p_top_k(
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llama_context & lctx,
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const std::vector<llama_vocab::id> & last_n_tokens,
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int top_k,
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double top_p,
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double temp,
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double repeat_penalty) {
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float top_p,
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float temp,
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float repeat_penalty) {
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auto & rng = lctx.rng;
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const int n_logits = lctx.model.hparams.n_vocab;
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@ -1266,17 +1266,17 @@ static llama_vocab::id llama_sample_top_p_top_k(
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const auto & logits = lctx.logits;
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const auto * plogits = logits.data() + logits.size() - n_logits;
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std::vector<std::pair<double, llama_vocab::id>> logits_id;
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std::vector<std::pair<float, llama_vocab::id>> logits_id;
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logits_id.reserve(n_logits);
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{
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const double scale = 1.0/temp;
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const float scale = 1.0f/temp;
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for (int i = 0; i < n_logits; ++i) {
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// repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
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// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
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if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
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// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
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if (plogits[i] < 0.0) {
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if (plogits[i] < 0.0f) {
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logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
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} else {
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logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));
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@ -1289,18 +1289,18 @@ static llama_vocab::id llama_sample_top_p_top_k(
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sample_top_k(logits_id, top_k);
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double maxl = -std::numeric_limits<double>::infinity();
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float maxl = -std::numeric_limits<float>::infinity();
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for (const auto & kv : logits_id) {
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maxl = std::max(maxl, kv.first);
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}
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// compute probs for the top k tokens
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std::vector<double> probs;
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std::vector<float> probs;
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probs.reserve(logits_id.size());
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double sum = 0.0;
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for (const auto & kv : logits_id) {
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double p = exp(kv.first - maxl);
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const float p = expf(kv.first - maxl);
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probs.push_back(p);
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sum += p;
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}
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@ -1310,8 +1310,8 @@ static llama_vocab::id llama_sample_top_p_top_k(
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p /= sum;
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}
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if (top_p < 1.0f) {
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double cumsum = 0.0f;
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if (top_p < 1.0) {
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double cumsum = 0.0;
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for (int i = 0; i < (int) probs.size(); i++) {
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cumsum += probs[i];
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if (cumsum >= top_p) {
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@ -1590,7 +1590,7 @@ static bool llama_model_quantize_internal(const std::string & fname_inp, const s
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}
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for (int i = 0; i < (int) hist_cur.size(); ++i) {
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printf("%5.3f ", hist_cur[i] / (float)nelements);
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printf("%5.3f ", hist_cur[i] / float(nelements));
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}
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printf("\n");
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} else {
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@ -1613,7 +1613,7 @@ static bool llama_model_quantize_internal(const std::string & fname_inp, const s
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printf("%s: hist: ", __func__);
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for (int i = 0; i < (int) hist_all.size(); ++i) {
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printf("%5.3f ", hist_all[i] / (float)sum_all);
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printf("%5.3f ", hist_all[i] / float(sum_all));
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}
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printf("\n");
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}
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@ -1795,9 +1795,9 @@ llama_token llama_sample_top_p_top_k(
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const llama_token * last_n_tokens_data,
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int last_n_tokens_size,
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int top_k,
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double top_p,
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double temp,
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double repeat_penalty) {
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float top_p,
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float temp,
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float repeat_penalty) {
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const int64_t t_start_sample_us = ggml_time_us();
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llama_token result = 0;
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@ -1828,11 +1828,11 @@ void llama_print_timings(struct llama_context * ctx) {
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const int32_t n_p_eval = std::max(1, ctx->n_p_eval);
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fprintf(stderr, "\n");
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fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0f);
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fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->t_sample_us, n_sample, 1e-3f * ctx->t_sample_us / n_sample);
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fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token)\n", __func__, 1e-3f * ctx->t_p_eval_us, n_p_eval, 1e-3f * ctx->t_p_eval_us / n_p_eval);
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fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->t_eval_us, n_eval, 1e-3f * ctx->t_eval_us / n_eval);
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fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f);
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fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0);
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fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3 * ctx->t_sample_us, n_sample, 1e-3 * ctx->t_sample_us / n_sample);
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fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token)\n", __func__, 1e-3 * ctx->t_p_eval_us, n_p_eval, 1e-3 * ctx->t_p_eval_us / n_p_eval);
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fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3 * ctx->t_eval_us, n_eval, 1e-3 * ctx->t_eval_us / n_eval);
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fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0);
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}
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void llama_reset_timings(struct llama_context * ctx) {
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