Move MatMulEnv out of Gemma to enable concurrent calls

jan-wassenberg · copybara-github · commit a04cc287b23f · 2025-06-23T01:20:09.000-07:00
Also update benchmark_helper config print: add profiler, remove free mem

PiperOrigin-RevId: 774662974
diff --git a/BUILD.bazel b/BUILD.bazel
@@ -551,6 +551,7 @@ cc_library(
         "//compression:compress",
         "@highway//:hwy",
         "@highway//:nanobenchmark",
+        "@highway//:profiler",
     ],
 )
 
diff --git a/evals/benchmark.cc b/evals/benchmark.cc
@@ -75,8 +75,9 @@ int BenchmarkCrossEntropy(GemmaEnv& env, const Path& text,
     std::vector<int> prompt_slice(prompt.begin() + pos,
                                   prompt.begin() + pos + num_tokens);
     KVCache kv_cache(gemma.GetModelConfig(), gemma.Inference());
-    float entropy = ComputeCrossEntropy(
-        *env.GetGemma(), num_tokens, prompt_slice, kv_cache, env.Verbosity());
+    float entropy =
+        ComputeCrossEntropy(*env.GetGemma(), num_tokens, prompt_slice, kv_cache,
+                            env.MutableEnv(), env.Verbosity());
     total_entropy += entropy;
     LogSpeedStats(time_start, pos + num_tokens);
     std::string text_slice = env.StringFromTokens(prompt_slice);
diff --git a/evals/benchmark_helper.cc b/evals/benchmark_helper.cc
@@ -32,6 +32,7 @@
 #include "util/threading_context.h"
 #include "hwy/highway.h"
 #include "hwy/per_target.h"  // DispatchedTarget
+#include "hwy/profiler.h"    // PROFILER_ENABLED
 #include "hwy/timer.h"
 
 namespace gcpp {
@@ -50,7 +51,7 @@ void InitGenerator(const InferenceArgs& inference, std::mt19937& gen) {
 GemmaEnv::GemmaEnv(const LoaderArgs& loader, const ThreadingArgs& threading,
                    const InferenceArgs& inference)
     : env_(MakeMatMulEnv(threading, inference)),
-      gemma_(loader, inference, env_) {
+      gemma_(loader, inference, env_.ctx.pools) {
   const ModelConfig& config = gemma_.GetModelConfig();
   // Only allocate one for starters because GenerateBatch might not be called.
   kv_caches_.push_back(KVCache(config, inference));
@@ -94,7 +95,7 @@ QueryResult GemmaEnv::QueryModel(const std::vector<int>& tokens) {
   }
   gcpp::TimingInfo timing_info { .verbosity = runtime_config_.verbosity };
   runtime_config_.batch_stream_token = batch_stream_token;
-  gemma_.Generate(runtime_config_, tokens, /*start_pos=*/0, kv_caches_[0],
+  gemma_.Generate(runtime_config_, tokens, /*start_pos=*/0, kv_caches_[0], env_,
                   timing_info);
   return result;
 }
@@ -104,7 +105,7 @@ void GemmaEnv::QueryModel(
   gcpp::TimingInfo timing_info { .verbosity = runtime_config_.verbosity };
   const StreamFunc previous_stream_token = runtime_config_.stream_token;
   runtime_config_.stream_token = stream_token;
-  gemma_.Generate(runtime_config_, tokens, /*start_pos=*/0, kv_caches_[0],
+  gemma_.Generate(runtime_config_, tokens, /*start_pos=*/0, kv_caches_[0], env_,
                   timing_info);
   runtime_config_.stream_token = previous_stream_token;
 }
@@ -146,7 +147,7 @@ std::vector<QueryResult> GemmaEnv::BatchQueryModel(
 
   gcpp::AllQueries all_queries(queries_prompt, kv_caches, prefix_end);
   gcpp::TimingInfo timing_info = {.verbosity = runtime_config_.verbosity};
-  gemma_.GenerateBatch(runtime_config_, all_queries, timing_info);
+  gemma_.GenerateBatch(runtime_config_, all_queries, env_, timing_info);
   return res;
 }
 
@@ -176,7 +177,7 @@ float GemmaEnv::CrossEntropy(const std::string& input) {
   std::vector<int> prompt = Tokenize(input);
   prompt.insert(prompt.begin(), BOS_ID);
   return ComputeCrossEntropy(*GetGemma(), /*max_generated_tokens=*/3072, prompt,
-                             MutableKVCache(),
+                             MutableKVCache(), env_,
                              /*verbosity=*/0) /
          static_cast<int>(input.size());
 }
@@ -247,13 +248,13 @@ void ShowConfig(const LoaderArgs& loader, const ThreadingArgs& threading,
             "CPU                           : %s, bind %d\n"
             "CPU topology                  : %s, %s, %s\n"
             "Instruction set               : %s (%zu bits)\n"
-            "Compiled config               : %s\n"
-            "Memory MiB                    : %4zu, %4zu free\n",
+            "Compiled config               : %s, profiler %d\n"
+            "Memory MiB                    : %4zu\n",
             dt, cpu100, static_cast<int>(threading.bind),
             ctx.topology.TopologyString(), ctx.pools.PinString(),
             CacheString().c_str(), hwy::TargetName(hwy::DispatchedTarget()),
-            ctx.allocator.VectorBytes() * 8, CompiledConfig(),
-            ctx.allocator.TotalMiB(), ctx.allocator.FreeMiB());
+            ctx.allocator.VectorBytes() * 8, CompiledConfig(), PROFILER_ENABLED,
+            ctx.allocator.TotalMiB());
   }
 }
 
diff --git a/evals/benchmark_helper.h b/evals/benchmark_helper.h
@@ -112,6 +112,7 @@ class GemmaEnv {
   RuntimeConfig& MutableConfig() { return runtime_config_; }
   std::mt19937& MutableGen() { return gen_; }
   KVCache& MutableKVCache() { return kv_caches_[0]; }
+  MatMulEnv& MutableEnv() { return env_; }
 
  private:
   MatMulEnv env_;
diff --git a/evals/cross_entropy.cc b/evals/cross_entropy.cc
@@ -99,7 +99,7 @@ HWY_EXPORT(CallSoftmax);
 
 float ComputeCrossEntropy(const Gemma& gemma, size_t max_generated_tokens,
                           const std::vector<int>& prompt, KVCache& kv_cache,
-                          int verbosity) {
+                          MatMulEnv& env, int verbosity) {
   const StreamFunc stream_token = [](int, float) { return true; };
 
   const int vocab_size = gemma.GetModelConfig().vocab_size;
@@ -145,7 +145,7 @@ float ComputeCrossEntropy(const Gemma& gemma, size_t max_generated_tokens,
   };
   TimingInfo timing_info;
 
-  gemma.Generate(runtime, prompt0, 0, kv_cache, timing_info);
+  gemma.Generate(runtime, prompt0, 0, kv_cache, env, timing_info);
 
   const float scale = 1.0f / std::log(2.0f);
   return cross_entropy * scale;
diff --git a/evals/cross_entropy.h b/evals/cross_entropy.h
@@ -26,7 +26,7 @@ namespace gcpp {
 
 float ComputeCrossEntropy(const Gemma& gemma, size_t max_generated_tokens,
                           const std::vector<int>& prompt, KVCache& kv_cache,
-                          int verbosity);
+                          MatMulEnv& env, int verbosity);
 
 }  // namespace gcpp
 
diff --git a/evals/gemma_test.cc b/evals/gemma_test.cc
@@ -127,7 +127,7 @@ TEST_F(GemmaTest, Multiturn) {
                       config.wrapping, abs_pos, mutable_prompt);
 
   model->Generate(runtime_config, tokens, abs_pos, s_env->MutableKVCache(),
-                  timing_info);
+                  s_env->MutableEnv(), timing_info);
   // Note: we do not rewind any <end_of_turn> tokens here. If the model
   // produced one and WrapAndTokenize() inserts another one, it will just be
   // duplicated.
@@ -139,7 +139,7 @@ TEST_F(GemmaTest, Multiturn) {
   // access to the previous turn by asking to reproduce.
   response.clear();
   model->Generate(runtime_config, tokens, abs_pos, s_env->MutableKVCache(),
-                  timing_info);
+                  s_env->MutableEnv(), timing_info);
   fprintf(stderr, "decoded: '%s'\n", response.c_str());
   bool remembered_turquoise =
       response.find("turquoise") != std::string::npos;              // NOLINT
diff --git a/evals/run_mmlu.cc b/evals/run_mmlu.cc
@@ -131,7 +131,8 @@ void Run(GemmaEnv& env, JsonArgs& json) {
         .stream_token = stream_token,
     };
     env.GetGemma()->Generate(runtime_config, prompt, /*pos=*/0,
-                             env.MutableKVCache(), timing_info);
+                             env.MutableKVCache(), env.MutableEnv(),
+                             timing_info);
 
     std::string output_string = env.StringFromTokens(predicted_token_ids);
     fprintf(stderr, "Correct %s, model '%s'\n", correct_answer.c_str(),
diff --git a/examples/hello_world/run.cc b/examples/hello_world/run.cc
@@ -52,7 +52,7 @@ int main(int argc, char** argv) {
 
   // Instantiate model and KV Cache
   gcpp::MatMulEnv env(MakeMatMulEnv(threading, inference));
-  gcpp::Gemma gemma(loader, inference, env);
+  gcpp::Gemma gemma(loader, inference, env.ctx.pools);
   gcpp::KVCache kv_cache(gemma.GetModelConfig(), inference);
   size_t generated = 0;
 
@@ -93,5 +93,5 @@ int main(int argc, char** argv) {
             return !reject_tokens.contains(token);
           },
   };
-  gemma.Generate(runtime_config, tokens, 0, kv_cache, timing_info);
+  gemma.Generate(runtime_config, tokens, 0, kv_cache, env, timing_info);
 }
diff --git a/examples/simplified_gemma/gemma.hpp b/examples/simplified_gemma/gemma.hpp
@@ -36,7 +36,7 @@ class SimplifiedGemma {
                   const gcpp::ThreadingArgs& threading = gcpp::ThreadingArgs(),
                   const gcpp::InferenceArgs& inference = gcpp::InferenceArgs())
       : env_(MakeMatMulEnv(threading, inference)),
-        gemma_(loader, inference, env_),
+        gemma_(loader, inference, env_.ctx.pools),
         kv_cache_(gemma_.GetModelConfig(), inference) {
     // Initialize random number generator
     std::random_device rd;
@@ -83,7 +83,7 @@ class SimplifiedGemma {
               return !reject_tokens.contains(token);
             },
     };
-    gemma_.Generate(runtime_config, tokens, 0, kv_cache_, timing_info);
+    gemma_.Generate(runtime_config, tokens, 0, kv_cache_, env_, timing_info);
   }
   ~SimplifiedGemma() = default;
 
diff --git a/gemma/bindings/context.cc b/gemma/bindings/context.cc
@@ -103,7 +103,7 @@ GemmaContext::GemmaContext(const LoaderArgs& loader,
       threading_args(threading_args),
       matmul_env(MakeMatMulEnv(threading_args, inference_args)),
       active_conversation_name("default"),
-      model(loader, inference_args, matmul_env) {
+      model(loader, inference_args, matmul_env.ctx.pools) {
   std::stringstream ss;
 
   LogDebug("Creating initial ConversationData");
@@ -207,7 +207,7 @@ int GemmaContext::GenerateInternal(const char* prompt_string,
     // Pass the populated image object to GenerateImageTokens
     model.GenerateImageTokens(runtime_config,
                               active_conversation->kv_cache->SeqLen(), image,
-                              image_tokens);
+                              image_tokens, matmul_env);
     double image_tokens_duration = hwy::platform::Now() - image_tokens_start;
 
     ss.str("");
@@ -244,7 +244,8 @@ int GemmaContext::GenerateInternal(const char* prompt_string,
 
   // Pass the KVCache object by reference from the active conversation
   model.Generate(runtime_config, prompt_span, active_conversation->abs_pos,
-                 prefix_end, *(active_conversation->kv_cache), timing_info);
+                 prefix_end, *active_conversation->kv_cache, matmul_env,
+                 timing_info);
 
   // prepare for next turn
   if (!inference_args.multiturn ||
diff --git a/gemma/gemma.cc b/gemma/gemma.cc
@@ -610,62 +610,62 @@ MatMulEnv MakeMatMulEnv(const ThreadingArgs& threading_args,
 }
 
 Gemma::Gemma(const LoaderArgs& loader, const InferenceArgs& inference,
-             MatMulEnv& env)
-    : env_(env),
-      reader_(loader.weights),
+             NestedPools& pools)
+    : reader_(loader.weights),
       model_(reader_, loader.tokenizer, loader.wrapping),
       weights_(model_.Config()),
       chat_template_(model_.Tokenizer(), model_.Config().model),
       inference_(inference) {
   weights_.ReadFromBlobs(model_, reader_, loader, inference, mat_owners_,
-                         env.ctx.pools.Pool());
+                         pools.Pool());
   reader_.CloseFile();
 }
 
 Gemma::~Gemma() = default;
 
-void Gemma::Save(const Path& weights_path, hwy::ThreadPool& pool) const {
+void Gemma::Save(const Path& weights_path, NestedPools& pools) const {
   BlobWriter writer;
   const std::vector<uint32_t> serialized_mat_ptrs =
       weights_.AddTensorDataToWriter(writer);
   WriteSingleFile(model_.Config(), model_.Tokenizer(), serialized_mat_ptrs,
-                  writer, env_.ctx.pools.Pool(), weights_path);
+                  writer, pools.Pool(), weights_path);
 }
 
 void Gemma::Generate(const RuntimeConfig& runtime_config,
                      const PromptTokens& prompt, size_t pos, size_t prefix_end,
-                     KVCache& kv_cache, TimingInfo& timing_info) const {
-  env_.ctx.pools.MaybeStartSpinning(runtime_config.use_spinning);
+                     KVCache& kv_cache, MatMulEnv& env,
+                     TimingInfo& timing_info) const {
+  env.ctx.pools.MaybeStartSpinning(runtime_config.use_spinning);
 
   HWY_DYNAMIC_DISPATCH(GenerateSingleT)(prompt, pos, prefix_end,
                                         model_.Config(), runtime_config,
-                                        weights_, kv_cache, env_, timing_info);
+                                        weights_, kv_cache, env, timing_info);
 
-  env_.ctx.pools.MaybeStopSpinning(runtime_config.use_spinning);
+  env.ctx.pools.MaybeStopSpinning(runtime_config.use_spinning);
 }
 
 void Gemma::GenerateBatch(const RuntimeConfig& runtime_config,
-                          AllQueries& all_queries,
+                          AllQueries& all_queries, MatMulEnv& env,
                           TimingInfo& timing_info) const {
-  env_.ctx.pools.MaybeStartSpinning(runtime_config.use_spinning);
+  env.ctx.pools.MaybeStartSpinning(runtime_config.use_spinning);
 
   HWY_DYNAMIC_DISPATCH(GenerateBatchT)(model_.Config(), runtime_config,
-                                       weights_, all_queries, env_,
-                                       timing_info);
+                                       weights_, all_queries, env, timing_info);
 
-  env_.ctx.pools.MaybeStopSpinning(runtime_config.use_spinning);
+  env.ctx.pools.MaybeStopSpinning(runtime_config.use_spinning);
 }
 
 void Gemma::GenerateImageTokens(const RuntimeConfig& runtime_config,
                                 size_t seq_len, const Image& image,
-                                ImageTokens& image_tokens) const {
-  env_.ctx.pools.MaybeStartSpinning(runtime_config.use_spinning);
+                                ImageTokens& image_tokens,
+                                MatMulEnv& env) const {
+  env.ctx.pools.MaybeStartSpinning(runtime_config.use_spinning);
 
   HWY_DYNAMIC_DISPATCH(GenerateImageTokensT)(model_.Config(), runtime_config,
                                              seq_len, weights_, image,
-                                             image_tokens, env_);
+                                             image_tokens, env);
 
-  env_.ctx.pools.MaybeStopSpinning(runtime_config.use_spinning);
+  env.ctx.pools.MaybeStopSpinning(runtime_config.use_spinning);
 }
 
 }  // namespace gcpp
diff --git a/gemma/gemma.h b/gemma/gemma.h
@@ -229,46 +229,48 @@ struct TimingInfo {
 MatMulEnv MakeMatMulEnv(const ThreadingArgs& threading_args,
                         const InferenceArgs& inference_args);
 
+// After construction, all methods are const and thread-compatible if using
+// separate MatMulEnv for each thread.
 class Gemma {
  public:
   // Reads weights/config/tokenizer from the `BlobStore` at `loader.weights`.
-  // `env` must remain valid for the lifetime of this Gemma.
+  // `pools` are used to parallelize loading.
   Gemma(const LoaderArgs& loader, const InferenceArgs& inference,
-        MatMulEnv& env);
-
+        NestedPools& pools);
   ~Gemma();
 
-  MatMulEnv& Env() const { return env_; }
   // TODO: rename to Config()
   const ModelConfig& GetModelConfig() const { return model_.Config(); }
   const GemmaTokenizer& Tokenizer() const { return model_.Tokenizer(); }
   const ModelWeightsPtrs& Weights() const { return weights_; }
   const GemmaChatTemplate& ChatTemplate() const { return chat_template_; }
   const InferenceArgs& Inference() const { return inference_; }
 
-  void Save(const Path& weights_path, hwy::ThreadPool& pool) const;
+  void Save(const Path& weights_path, NestedPools& pools) const;
 
   // `pos` is the position in the KV cache. Users are responsible for
   // incrementing it in the `*StreamFunc`, or setting to zero for single-turn.
   void Generate(const RuntimeConfig& runtime_config, const PromptTokens& prompt,
-                size_t pos, KVCache& kv_cache, TimingInfo& timing_info) const {
-    Generate(runtime_config, prompt, pos, /*prefix_end=*/0, kv_cache,
+                size_t pos, KVCache& kv_cache, MatMulEnv& env,
+                TimingInfo& timing_info) const {
+    Generate(runtime_config, prompt, pos, /*prefix_end=*/0, kv_cache, env,
              timing_info);
   }
   // For prefix-LM style attention, we can pass the end of the prefix.
   void Generate(const RuntimeConfig& runtime_config, const PromptTokens& prompt,
                 size_t pos, size_t prefix_end, KVCache& kv_cache,
-                TimingInfo& timing_info) const;
+                MatMulEnv& env, TimingInfo& timing_info) const;
 
   void GenerateBatch(const RuntimeConfig& runtime_config,
-                     AllQueries& all_queries, TimingInfo& timing_info) const;
+                     AllQueries& all_queries, MatMulEnv& env,
+                     TimingInfo& timing_info) const;
 
   // Generates the image tokens by running the image encoder ViT.
   void GenerateImageTokens(const RuntimeConfig& runtime_config, size_t seq_len,
-                           const Image& image, ImageTokens& image_tokens) const;
+                           const Image& image, ImageTokens& image_tokens,
+                           MatMulEnv& env) const;
 
  private:
-  MatMulEnv& env_;
   BlobReader reader_;
   ModelStore model_;
   std::vector<MatOwner> mat_owners_;
diff --git a/gemma/run.cc b/gemma/run.cc
diff --git a/io/migrate_weights.cc b/io/migrate_weights.cc
diff --git a/paligemma/paligemma_helper.cc b/paligemma/paligemma_helper.cc
diff --git a/python/gemma_py.cc b/python/gemma_py.cc

Original file line number	Diff line number	Diff line change
`@@ -551,6 +551,7 @@ cc_library(`
`551`	`551`	`"//compression:compress",`
`552`	`552`	`"@highway//:hwy",`
`553`	`553`	`"@highway//:nanobenchmark",`
	`554`	`+ "@highway//:profiler",`
`554`	`555`	`],`
`555`	`556`	`)`
`556`	`557`