winogrande: evaluate log-probs in parallel (#5036)

This is a relatively minor performance tweak resulting in
~10% speedup on my system.

Co-authored-by: Iwan Kawrakow <redacted>

diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index 292502f878c962381d1a6a59d0712575e7e0d315..b0732019082497631331385906f21dfae70b7aba 100644 (file)
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -458,7 +458,7 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<
     return true;
 }
 
-static void hellaswag_compute_logprobs(const float * batch_logits, int n_vocab, std::vector<std::thread>& workers,
+static void compute_logprobs(const float * batch_logits, int n_vocab, std::vector<std::thread>& workers,
         const std::vector<std::pair<size_t, llama_token>>& eval_pairs, std::vector<float>& eval_results) {
     constexpr int k_token_chunk = 4;
     if (eval_results.size() != eval_pairs.size()) {
@@ -700,7 +700,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
             }
         }
         // Then we do the actual calculation
-        hellaswag_compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
+        compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
 
         size_t ir = 0;
 
@@ -906,6 +906,10 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
     std::vector<float> tok_logits(n_vocab);
     std::vector<float> batch_logits(n_vocab*n_ctx);
 
+    std::vector<std::pair<size_t, llama_token>> eval_pairs;
+    std::vector<float> eval_results;
+    std::vector<std::thread> workers(std::thread::hardware_concurrency());
+
     int n_correct = 0;
     int n_done    = 0;
 
@@ -956,6 +960,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
             return;
         }
 
+        eval_pairs.clear();
         for (size_t i = i0; i < i1; ++i) {
             auto & task = data[i];
 
@@ -963,54 +968,44 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
                 task.seq_tokens[0].size() - task.common_prefix > k_min_trailing_ctx &&
                 task.seq_tokens[1].size() - task.common_prefix > k_min_trailing_ctx;
 
-            float score_1st = 0;
-            bool is_nan_1st = false;
             const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix;
             const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0;
             size_t li = n_base1 - 1;
             for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) {
-                std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(task.i_batch + li++), n_vocab*sizeof(float));
-                const float prob = softmax(tok_logits)[task.seq_tokens[0][j+1]];
-                if (std::isnan(prob) || !prob) {
-                    fprintf(stderr, "%s: %g probability for token %zu when evaluating <%s>. Base context has %zu tokens\n", __func__,
-                            prob, j, (task.first + task.choices[0] + task.second).c_str(), n_base1);
-                    is_nan_1st = true;
-                    break;
-                }
-                score_1st += std::log(prob);
+                eval_pairs.push_back(std::make_pair(task.i_batch + li++, task.seq_tokens[0][j+1]));
             }
-            score_1st /= (task.seq_tokens[0].size() - n_base1 - last_1st);
-
-            float score_2nd = 0;
-            bool is_nan_2nd = false;
             const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix;
             const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0;
             li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - 1;
             for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) {
-                std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(task.i_batch + li++), n_vocab*sizeof(float));
-                const float prob = softmax(tok_logits)[task.seq_tokens[1][j+1]];
-                if (std::isnan(prob) || !prob) {
-                    fprintf(stderr, "%s: %g probability for token %zu when evaluating <%s>. Base context has %zu tokens\n", __func__,
-                            prob, j, (task.first + task.choices[1] + task.second).c_str(), n_base2);
-                    is_nan_2nd = true;
-                    break;
-                }
-                score_2nd += std::log(prob);
+                eval_pairs.push_back(std::make_pair(task.i_batch + li++, task.seq_tokens[1][j+1]));
             }
-            score_2nd /= (task.seq_tokens[1].size() - n_base2 - last_2nd);
+        }
+        compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
+
+        size_t ir = 0;
+        for (size_t i = i0; i < i1; ++i) {
+            auto & task = data[i];
+
+            const bool skip_choice =
+                task.seq_tokens[0].size() - task.common_prefix > k_min_trailing_ctx &&
+                task.seq_tokens[1].size() - task.common_prefix > k_min_trailing_ctx;
 
-            if (is_nan_1st || is_nan_2nd) {
-                continue;
+            float score_1st = 0;
+            const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix;
+            const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0;
+            for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) {
+                score_1st += eval_results[ir++];
             }
+            score_1st /= (task.seq_tokens[0].size() - n_base1 - last_1st);
 
-            if (std::isnan(score_1st) || std::isnan(score_2nd)) {
-                printf("================== NaN score %g, %g) for:\n", score_1st, score_2nd);
-                printf("Q1: <%s> - %zu tokens\n", (task.first + task.choices[0] + task.second).c_str(), task.seq_tokens[0].size());
-                printf("Q2: <%s> - %zu tokens\n", (task.first + task.choices[1] + task.second).c_str(), task.seq_tokens[1].size());
-                printf("B : <%s> - %zu tokens\n", task.first.c_str(), task.common_prefix);
-                printf("base_1 has %zu tokens, base_2 has %zu tokens, skip_choice = %d\n", n_base1, n_base2, skip_choice);
-                continue;
+            float score_2nd = 0;
+            const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix;
+            const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0;
+            for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) {
+                score_2nd += eval_results[ir++];
             }
+            score_2nd /= (task.seq_tokens[1].size() - n_base2 - last_2nd);
 
             int result = score_1st > score_2nd ? 1 : 2;
 
@@ -1019,7 +1014,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
             }
             ++n_done;
 
-            // Print the accumulated accuracy mean x 100
+            // print the accumulated accuracy mean x 100
             printf("%zu\t%.4lf\t%10.6f  %10.6f  %d  %d\n", i+1, 100.0 * n_correct/n_done, score_1st, score_2nd, result, task.answer);
             fflush(stdout);
         }