Enable exporting for inference when loading from buffer without behav…

…ior changes (#21601)

### Description
Added eval model buffer as optional field in Module so that you can
export for inference using the eval model stored as a buffer.

### Motivation and Context
- Resolves #21152 
- Previous solution (PR #21422) produced an eval model that was specific
to the EP's used to train because of unavoidable runtime optimizations
that changed the graph stored with the eval session.

onnxruntime/core/graph/model.cc

@@ -646,7 +646,7 @@ Status Model::SaveWithExternalInitializers(Model& model, const std::filesystem::
   return SaveModelWithExternalInitializers(model, file_path, external_file_name, initializer_size_threshold);
 }
 
-Status Model::LoadFromBytes(int count, void* p_bytes, /*out*/ ONNX_NAMESPACE::ModelProto& model_proto) {
+Status Model::LoadFromBytes(int count, const void* p_bytes, /*out*/ ONNX_NAMESPACE::ModelProto& model_proto) {
   const bool result = model_proto.ParseFromArray(p_bytes, count);
   if (!result) {
     return Status(ONNXRUNTIME, INVALID_PROTOBUF, "Protobuf parsing failed.");

onnxruntime/core/graph/model.h

@@ -234,7 +234,7 @@ class Model {
                              const ModelOptions& options = {});
 
   // 'int' rather than 'size_t' because of a protobuf design choice; let callers handle type checks
-  static common::Status LoadFromBytes(int count, void* pBytes,
+  static common::Status LoadFromBytes(int count, const void* pBytes,
                                       /*out*/ ONNX_NAMESPACE::ModelProto& model_proto);
 
   // 'int' rather than 'size_t' because of a protobuf design choice; let callers handle type checks

orttraining/orttraining/test/training_api/core/training_capi_tests.cc

@@ -265,6 +265,41 @@ TEST(TrainingCApiTest, LoadONNXModelsFromBuffer) {
                                                                train_model_data);
 }
 
+TEST(TrainingCApiTest, LoadONNXModelsFromBufferThenExport) {
+  auto model_path = MODEL_FOLDER "training_model.onnx";
+  size_t model_data_len = 0;
+  ASSERT_STATUS_OK(Env::Default().GetFileLength(model_path, model_data_len));
+  std::vector<uint8_t> train_model_data(model_data_len);
+  std::ifstream bytes_stream(model_path, std::ifstream::in | std::ifstream::binary);
+  bytes_stream.read(reinterpret_cast<char*>(train_model_data.data()), model_data_len);
+  ASSERT_TRUE(train_model_data.size() == model_data_len);
+
+  auto eval_model_path = MODEL_FOLDER "eval_model.onnx";
+  size_t eval_model_data_len = 0;
+  ASSERT_STATUS_OK(Env::Default().GetFileLength(eval_model_path, eval_model_data_len));
+  std::vector<uint8_t> eval_model_data(eval_model_data_len);
+  std::ifstream eval_bytes_stream(eval_model_path, std::ifstream::in | std::ifstream::binary);
+  eval_bytes_stream.read(reinterpret_cast<char*>(eval_model_data.data()), eval_model_data_len);
+  ASSERT_TRUE(eval_model_data.size() == eval_model_data_len);
+
+  Ort::Env env;
+  Ort::CheckpointState checkpoint_state = Ort::CheckpointState::LoadCheckpoint(MODEL_FOLDER "checkpoint.ckpt");
+  Ort::TrainingSession training_session = Ort::TrainingSession(env,
+                                                               Ort::SessionOptions(),
+                                                               checkpoint_state,
+                                                               train_model_data,
+                                                               eval_model_data);
+
+  // randomly selected output name
+  std::vector<std::string> graph_output_names({"onnx::loss::21273"});
+  training_session.ExportModelForInferencing(MODEL_FOLDER "inference_model.onnx", graph_output_names);
+
+  // Check that the model is a valid inference model by loading into an InferenceSession
+  std::unique_ptr<Environment> environment;
+  ASSERT_STATUS_OK(Environment::Create(nullptr, environment));
+  InferenceSession inference_session = InferenceSession(SessionOptions(), *environment, MODEL_FOLDER "inference_model.onnx");
+}
+
 TEST(TrainingCApiTest, LoadORTFormatModelsFromBuffer) {
   auto train_model_path = ORT_FORMAT_MODEL_FOLDER "training_model.ort";
   auto eval_model_path = ORT_FORMAT_MODEL_FOLDER "eval_model.ort";

orttraining/orttraining/training_api/module.cc

@@ -412,11 +412,12 @@ Module::Module(const ModelIdentifiers& model_identifiers,
   eval_user_input_count_ = eval_user_input_names.size();
   eval_input_names_.insert(eval_input_names_.end(), eval_param_input_names.begin(), eval_param_input_names.end());
 
-  // Keep a copy of the eval model path to be able to later export the model for inferencing.
+  // Keep a copy of the eval model path or buffer to be able to later export the model for inferencing.
   // The inference model will be reconstructed from the eval model.
-  // TODO(askhade): Find a fix to export model for inference when the eval model is loaded from a buffer.
   if (std::holds_alternative<std::optional<std::string>>(model_identifiers.eval_model)) {
     eval_model_path_ = std::get<std::optional<std::string>>(model_identifiers.eval_model);
+  } else if (std::holds_alternative<gsl::span<const uint8_t>>(model_identifiers.eval_model)) {
+    eval_model_buffer_ = std::get<gsl::span<const uint8_t>>(model_identifiers.eval_model);
   }
 }
 
@@ -658,11 +659,17 @@ Status Module::ExportModelForInferencing(const std::string& inference_model_path
                                          gsl::span<const std::string> graph_output_names) const {
   ORT_RETURN_IF(state_->module_checkpoint_state.is_nominal_state,
                 "Cannot export the model with a nominal state. Please load the model parameters first.");
-  ORT_RETURN_IF(!eval_sess_ || !eval_model_path_.has_value(),
+  ORT_RETURN_IF(!eval_sess_ || (!eval_model_path_.has_value() && !eval_model_buffer_.has_value()),
                 "Eval model was not provided. Cannot export a model for inferencing.");
 
   ONNX_NAMESPACE::ModelProto eval_model;
-  ORT_THROW_IF_ERROR(Model::Load(ToPathString(eval_model_path_.value()), eval_model));
+  if (eval_model_path_.has_value()) {
+    ORT_THROW_IF_ERROR(Model::Load(ToPathString(eval_model_path_.value()), eval_model));
+  } else if (eval_model_buffer_.has_value()) {
+    int eval_model_buffer_size = static_cast<int>(eval_model_buffer_.value().size());
+    const void* eval_model_buffer_ptr = static_cast<const void*>(eval_model_buffer_.value().data());
+    ORT_THROW_IF_ERROR(Model::LoadFromBytes(eval_model_buffer_size, eval_model_buffer_ptr, eval_model));
+  }
 
   // Clone the eval mode into an inference onnxruntime::Model.
   std::shared_ptr<Model> inference_model;

orttraining/orttraining/training_api/module.h

@@ -198,6 +198,7 @@ struct Module {
 
   bool accumulate_gradient_ = false;
   std::optional<std::string> eval_model_path_;
+  std::optional<gsl::span<const uint8_t>> eval_model_buffer_;
   size_t eval_user_input_count_{0U};
 };