Batch bench: 4 runs to give autotuning more time

Also print auto-tune info for verbosity 3.

PiperOrigin-RevId: 823555008

evals/benchmark_helper.cc

@@ -47,6 +47,9 @@ GemmaEnv::GemmaEnv(const LoaderArgs& loader, const ThreadingArgs& threading,
     ShowConfig(loader, threading, inference, config, gemma_.WeightReadMode(),
                ctx_);
   }
+  if (inference.verbosity >= 3) {
+    env_.print_config = env_.print_best = true;
+  }
 
   runtime_config_ = {
       .max_generated_tokens = inference.max_generated_tokens,

evals/gemma_batch_bench.cc

@@ -48,7 +48,7 @@ class GemmaBatchBench : public ::testing::Test {
   }
 };
 
-TEST_F(GemmaBatchBench, RandomQuestionsBatched) {
+std::vector<std::string> GenerateInputs() {
   std::vector<std::string> prompts = {
       {"Describe dynamic programming."},
       {"Explain how electric cars work."},
@@ -122,22 +122,25 @@ TEST_F(GemmaBatchBench, RandomQuestionsBatched) {
     inputs.push_back(prompts[qpos++]);
     if (qpos == prompts.size()) qpos = 0;
   }
-  s_env->SetMaxGeneratedTokens(24);
+  return inputs;
+}
+
+TEST_F(GemmaBatchBench, RandomQuestionsBatched) {
+  s_env->SetMaxGeneratedTokens(12);
+  const std::vector<std::string> inputs = GenerateInputs();
+
+  // Run multiple times so that auto-tuning is closer to complete.
+  for (size_t rep = 0; rep < 4; ++rep) {
     std::vector<std::string> responses = BatchGemmaReply(inputs);
     for (size_t i = 0; i < HWY_MIN(hwy::Unpredictable1() * 3, responses.size());
          ++i) {
-    fprintf(stderr, "Batch answer %zu '%s'\n\n", i, responses[i].c_str());
+      fprintf(stderr, "Rep %zu batch answer %zu '%s'\n\n", rep, i,
+              responses[i].c_str());
     }
-
-  PROFILER_PRINT_RESULTS();
-
-  // Run again: prefill will be faster due to autotuning. Fewer decode steps
-  // because those are already fast.
-  s_env->SetMaxGeneratedTokens(2);
-  responses = BatchGemmaReply(inputs);
-
     PROFILER_PRINT_RESULTS();
+  }
 }
+
 }  // namespace
 }  // namespace gcpp