diff options
Diffstat (limited to 'examples/perplexity')
| -rw-r--r-- | examples/perplexity/perplexity.cpp | 51 |
1 files changed, 36 insertions, 15 deletions
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 2b375e34..de08bd4a 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -80,7 +80,9 @@ static void write_logfile( static std::vector<float> softmax(const std::vector<float>& logits) { std::vector<float> probs(logits.size()); float max_logit = logits[0]; - for (float v : logits) max_logit = std::max(max_logit, v); + for (float v : logits) { + max_logit = std::max(max_logit, v); + } double sum_exp = 0.0; for (size_t i = 0; i < logits.size(); i++) { // Subtract the maximum logit value from the current logit value for numerical stability @@ -89,15 +91,21 @@ static std::vector<float> softmax(const std::vector<float>& logits) { sum_exp += exp_logit; probs[i] = exp_logit; } - for (size_t i = 0; i < probs.size(); i++) probs[i] /= sum_exp; + for (size_t i = 0; i < probs.size(); i++) { + probs[i] /= sum_exp; + } return probs; } static results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) { float max_logit = logits[0]; - for (int i = 1; i < n_vocab; ++i) max_logit = std::max(max_logit, logits[i]); + for (int i = 1; i < n_vocab; ++i) { + max_logit = std::max(max_logit, logits[i]); + } double sum_exp = 0.0; - for (int i = 0; i < n_vocab; ++i) sum_exp += expf(logits[i] - max_logit); + for (int i = 0; i < n_vocab; ++i) { + sum_exp += expf(logits[i] - max_logit); + } return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp}; } @@ -108,7 +116,8 @@ static void process_logits( std::mutex mutex; int counter = 0; auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () { - double local_nll = 0, local_nll2 = 0; + double local_nll = 0; + double local_nll2 = 0; while (true) { std::unique_lock<std::mutex> lock(mutex); int i = counter++; @@ -126,10 +135,13 @@ static void process_logits( prob_history[i] = results.prob; } }; - for (auto & w : workers) w = std::thread(compute); + for (auto & w : workers) { + w = std::thread(compute); + } compute(); - for (auto & w : workers) w.join(); - + for (auto & w : workers) { + w.join(); + } } static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) { @@ -152,8 +164,8 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & return {std::move(tokens), 0., {}, {}}; } - std::vector<float> logit_history; - std::vector<float> prob_history; + std::vector<float> logit_history; + std::vector<float> prob_history; logit_history.resize(tokens.size()); prob_history.resize(tokens.size()); @@ -195,12 +207,15 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & const auto t_start = std::chrono::high_resolution_clock::now(); + // clear the KV cache + llama_kv_cache_tokens_rm(ctx, -1, -1); + for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); //fprintf(stderr, " Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch); - if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) { + if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0), params.n_threads)) { //fprintf(stderr, "%s : failed to eval\n", __func__); return {tokens, -1, logit_history, prob_history}; } @@ -320,6 +335,9 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par const auto t_start = std::chrono::high_resolution_clock::now(); + // clear the KV cache + llama_kv_cache_tokens_rm(ctx, -1, -1); + for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); @@ -332,7 +350,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par tokens[batch_start] = llama_token_bos(ctx); } - if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) { + if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0), params.n_threads)) { fprintf(stderr, "%s : failed to eval\n", __func__); return {tokens, -1, logit_history, prob_history}; } @@ -402,7 +420,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par } static std::vector<float> hellaswag_evaluate_tokens( - llama_context * ctx, const std::vector<int>& tokens, int n_past, int n_batch, int n_vocab, int n_thread + llama_context * ctx, std::vector<int> & tokens, int n_past, int n_batch, int n_vocab, int n_thread ) { std::vector<float> result; result.reserve(tokens.size() * n_vocab); @@ -410,7 +428,7 @@ static std::vector<float> hellaswag_evaluate_tokens( for (size_t i_chunk = 0; i_chunk < n_chunk; ++i_chunk) { size_t n_tokens = tokens.size() - i_chunk * n_batch; n_tokens = std::min(n_tokens, size_t(n_batch)); - if (llama_eval(ctx, tokens.data() + i_chunk * n_batch, n_tokens, n_past, n_thread)) { + if (llama_decode(ctx, llama_batch_get_one(tokens.data() + i_chunk * n_batch, n_tokens, n_past, 0), n_thread)) { fprintf(stderr, "%s : failed to eval\n", __func__); return {}; } @@ -550,6 +568,9 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { query_embd.resize(32); } + // clear the KV cache + llama_kv_cache_tokens_rm(ctx, -1, -1); + auto logits = hellaswag_evaluate_tokens(ctx, query_embd, 0, params.n_batch, n_vocab, params.n_threads); if (logits.empty()) { fprintf(stderr, "%s : failed to eval\n", __func__); @@ -661,7 +682,7 @@ int main(int argc, char ** argv) { return 1; } - params.perplexity = true; + params.logits_all = true; params.n_batch = std::min(params.n_batch, params.n_ctx); if (params.ppl_stride > 0) { |