From b842effa296814c5b74cd43ad577f4b5c13ecf24 Mon Sep 17 00:00:00 2001 From: Scott McKay Date: Fri, 26 Apr 2024 20:29:21 +1000 Subject: [PATCH] Fix some x86 build warnings in training code (#20451) ### Description Fix some misc build warnings from x86 Windows build ### Motivation and Context --- .../test/training_api/core/checkpoint_test.cc | 18 +++++++++--------- .../training_api/core/training_api_tests.cc | 10 +++++----- .../training_ops/cpu/nn/dropout_op_test.cc | 4 ++-- orttraining/orttraining/training_api/module.cc | 4 ++-- 4 files changed, 18 insertions(+), 18 deletions(-) diff --git a/orttraining/orttraining/test/training_api/core/checkpoint_test.cc b/orttraining/orttraining/test/training_api/core/checkpoint_test.cc index af11921ff2..3a2c158a37 100644 --- a/orttraining/orttraining/test/training_api/core/checkpoint_test.cc +++ b/orttraining/orttraining/test/training_api/core/checkpoint_test.cc @@ -117,8 +117,8 @@ TEST(CheckpointApiTest, SaveOnnxModelAsCheckpoint_ThenLoad_CPU) { // Check loaded parameter's values are same with original ones. ASSERT_EQ(expected_trainable_param_name_to_ort_value.size(), restored_trainable_param_names.size()); - ASSERT_EQ(expected_trainable_param_name_to_ort_value.size(), 7); - ASSERT_EQ(restored_param_name_to_ort_values.size(), 9); + ASSERT_EQ(expected_trainable_param_name_to_ort_value.size(), size_t{7}); + ASSERT_EQ(restored_param_name_to_ort_values.size(), size_t{9}); std::sort(expected_trainable_param_names.begin(), expected_trainable_param_names.end()); std::sort(restored_trainable_param_names.begin(), restored_trainable_param_names.end()); @@ -225,8 +225,8 @@ TEST(CheckpointApiTest, SaveOnnxModelAsCheckpointThenLoadFromBufferCPU) { // Check loaded parameter's values are same with original ones. ASSERT_EQ(expected_trainable_param_name_to_ort_value.size(), restored_trainable_param_names.size()); - ASSERT_EQ(expected_trainable_param_name_to_ort_value.size(), 7); - ASSERT_EQ(restored_param_name_to_ort_values.size(), 9); + ASSERT_EQ(expected_trainable_param_name_to_ort_value.size(), size_t{7}); + ASSERT_EQ(restored_param_name_to_ort_values.size(), size_t{9}); std::sort(expected_trainable_param_names.begin(), expected_trainable_param_names.end()); std::sort(restored_trainable_param_names.begin(), restored_trainable_param_names.end()); @@ -308,7 +308,7 @@ TEST(CheckpointApiTest, SaveOptimizerStateAsCheckpoint_ThenLoad) { std::vector all_weights_values; data_loader.GetNextSampleBatch(all_weights_values); - ASSERT_EQ(all_weights_values.size(), 4); + ASSERT_EQ(all_weights_values.size(), size_t{4}); NameMLValMap name_to_ort_value{ {"fc1.weight", *all_weights_values[0]}, {"fc1.bias", *all_weights_values[1]}, @@ -360,7 +360,7 @@ TEST(CheckpointApiTest, SaveOptimizerStateAsCheckpoint_ThenLoad) { InlinedHashMap>& group_optimizer_states = optimizer_state.group_named_optimizer_states; - ASSERT_EQ(group_optimizer_states.size(), 1); + ASSERT_EQ(group_optimizer_states.size(), size_t{1}); ASSERT_EQ(group_optimizer_states.begin()->first, "group0"); InlinedHashMap& @@ -429,7 +429,7 @@ TEST(CheckpointApiTest, SaveCustomPropertyAsCheckpoint_ThenLoad_CPU) { CheckpointState checkpoint_state_to_load; ASSERT_STATUS_OK(LoadCheckpoint(checkpoint_path, checkpoint_state_to_load)); PropertyBag& restored_property_bag = checkpoint_state_to_load.property_bag; - ASSERT_EQ(restored_property_bag.size(), 3); + ASSERT_EQ(restored_property_bag.size(), size_t{3}); float restored_f_data = restored_property_bag.GetProperty(f_property_name); ASSERT_FLOAT_EQ(f_data, restored_f_data); int64_t restored_i_data = restored_property_bag.GetProperty(i_property_name); @@ -559,8 +559,8 @@ TEST(CheckpointApiTest, SaveOnnxModelAsCheckpoint_ThenLoad_WithExternalData) { // Check loaded parameter's values are same with original ones. ASSERT_EQ(expected_trainable_param_name_to_ort_value.size(), restored_trainable_param_names.size()); - ASSERT_EQ(expected_trainable_param_name_to_ort_value.size(), 7); - ASSERT_EQ(restored_param_name_to_ort_values.size(), 9); + ASSERT_EQ(expected_trainable_param_name_to_ort_value.size(), size_t{7}); + ASSERT_EQ(restored_param_name_to_ort_values.size(), size_t{9}); std::sort(expected_trainable_param_names.begin(), expected_trainable_param_names.end()); std::sort(restored_trainable_param_names.begin(), restored_trainable_param_names.end()); diff --git a/orttraining/orttraining/test/training_api/core/training_api_tests.cc b/orttraining/orttraining/test/training_api/core/training_api_tests.cc index 3cbb05cced..90c97eed0c 100644 --- a/orttraining/orttraining/test/training_api/core/training_api_tests.cc +++ b/orttraining/orttraining/test/training_api/core/training_api_tests.cc @@ -208,8 +208,8 @@ TEST(TrainingApiTest, ModuleParametersSize) { } // ((500*784) + 500 + (10*500) + 10) = 397510 - ASSERT_EQ(params_size, 397510); - ASSERT_EQ(model->GetParametersSize(), 397510); + ASSERT_EQ(params_size, size_t{397510}); + ASSERT_EQ(model->GetParametersSize(), size_t{397510}); } TEST(TrainingApiTest, ModuleCopyBufferToParameters) { @@ -269,7 +269,7 @@ TEST(TrainingApiTest, ModuleTrainStep) { auto model = std::make_unique(model_identifier, &state, session_option, *env, std::vector>()); - ASSERT_EQ(model->GetTrainingModelOutputCount(), 1); + ASSERT_EQ(model->GetTrainingModelOutputCount(), size_t{1}); OrtValue input, target; GenerateRandomInput(std::array{2, 784}, input); target = onnxruntime::test::CreateInputOrtValueOnCPU( @@ -659,7 +659,7 @@ TEST(TrainingApiTest, ModuleAndOptimizerWithNominalState) { ASSERT_STATUS_OK(model_with_complete_state->TrainStep(inputs, complete_fetches)); ASSERT_STATUS_OK(model_with_nominal_state->TrainStep(inputs, nominal_fetches)); - ASSERT_GT(complete_fetches.size(), 0); + ASSERT_GT(complete_fetches.size(), size_t{0}); for (size_t i = 0; i < complete_fetches.size(); ++i) { ASSERT_TRUE(complete_fetches[i].IsTensor()); ASSERT_TRUE(nominal_fetches[i].IsTensor()); @@ -730,7 +730,7 @@ TEST(TrainingApiTest, ModuleAndOptimizerWithNominalState) { ASSERT_STATUS_OK(model_with_complete_state->EvalStep(inputs, complete_eval_fetches)); ASSERT_STATUS_OK(model_with_nominal_state->EvalStep(inputs, nominal_eval_fetches)); - ASSERT_GT(complete_eval_fetches.size(), 0); + ASSERT_GT(complete_eval_fetches.size(), size_t{0}); for (size_t i = 0; i < complete_eval_fetches.size(); ++i) { ASSERT_TRUE(complete_eval_fetches[i].IsTensor()); ASSERT_TRUE(nominal_eval_fetches[i].IsTensor()); diff --git a/orttraining/orttraining/test/training_ops/cpu/nn/dropout_op_test.cc b/orttraining/orttraining/test/training_ops/cpu/nn/dropout_op_test.cc index 1b4fdbd6ac..521d4aa52b 100644 --- a/orttraining/orttraining/test/training_ops/cpu/nn/dropout_op_test.cc +++ b/orttraining/orttraining/test/training_ops/cpu/nn/dropout_op_test.cc @@ -78,7 +78,7 @@ void RunDropoutTest(const bool use_mask, const std::vector& input_shape } auto output_verifier = [&](const std::vector& fetches, const std::string& provider_type) { - ASSERT_GE(fetches.size(), 1); + ASSERT_GE(fetches.size(), size_t{1}); const auto& output_tensor = fetches[0].Get(); auto output_span = output_tensor.DataAsSpan(); @@ -99,7 +99,7 @@ void RunDropoutTest(const bool use_mask, const std::vector& input_shape } if (use_mask) { - ASSERT_GE(fetches.size(), 2); + ASSERT_GE(fetches.size(), size_t{2}); const auto& mask_tensor = fetches[1].Get(); auto mask_span = mask_tensor.DataAsSpan(); ASSERT_EQ(mask_span.size(), output_span.size()) << "provider: " << provider_type; diff --git a/orttraining/orttraining/training_api/module.cc b/orttraining/orttraining/training_api/module.cc index 562b5da1b2..347673628e 100644 --- a/orttraining/orttraining/training_api/module.cc +++ b/orttraining/orttraining/training_api/module.cc @@ -517,7 +517,7 @@ Status Module::CopyParametersToBuffer(OrtValue& parameters_buffer, const bool tr "Only float is supported."); } ORT_RETURN_IF_ERROR(sess_data_transfer_manager.CopyTensor(*weight_tensor, *p_tensor.get())); - offset += shape.Size(); + offset += narrow(shape.Size()); } return Status::OK(); } @@ -601,7 +601,7 @@ Status Module::CopyBufferToParameters(OrtValue& parameters_buffer, const bool tr ORT_THROW_IF_ERROR(sess_data_transfer_manager.CopyTensor(*src_tensor.get(), *weight_tensor)); } - offset += shape.Size(); + offset += narrow(shape.Size()); } if (state_->module_checkpoint_state.is_nominal_state) {