Batchnorm training mode support in a minimal build (#17103)

Author: baijumeswani

Diff for: onnxruntime/core/providers/cpu/nn/batch_norm.h

@@ -29,10 +29,6 @@
 
 namespace onnxruntime {
 
-#if !defined(ORT_MINIMAL_BUILD)
-#define BATCHNORM_INCLUDE_TRAINING_SUPPORT
-#endif
-
 template <typename T>
 class BatchNorm : public OpKernel {
  public:
@@ -51,7 +47,7 @@ class BatchNorm : public OpKernel {
     }
 
     if (is_train_) {
-#if defined(BATCHNORM_INCLUDE_TRAINING_SUPPORT)
+#ifdef ENABLE_TRAINING_OPS
       momentum_ = op_kernel_info.GetAttrOrDefault<float>("momentum", 0.9f);
       ORT_ENFORCE(is_spatial_, "Training mode only supports spatial BN");
 #else
@@ -88,7 +84,7 @@ class BatchNorm : public OpKernel {
     // calculate sample_size (including all channels)
     size_t sample_size_incl_all_channels = sample_size * C;
 
-#if defined(BATCHNORM_INCLUDE_TRAINING_SUPPORT)
+#ifdef ENABLE_TRAINING_OPS
     AllocatorPtr alloc;
     ORT_RETURN_IF_ERROR(p_op_kernel_context->GetTempSpaceAllocator(&alloc));
 
@@ -115,7 +111,7 @@ class BatchNorm : public OpKernel {
     ConstEigenVectorArrayMap<T> scale_arr(scale->Data<T>(), is_spatial_ ? C : sample_size_incl_all_channels);
     ConstEigenVectorArrayMap<T> bias_arr(B->Data<T>(), is_spatial_ ? C : sample_size_incl_all_channels);
 
-#if defined(BATCHNORM_INCLUDE_TRAINING_SUPPORT)
+#ifdef ENABLE_TRAINING_OPS
     // Note that we only support spatial BN for training
     if (is_train_) {
       EigenVectorArrayMap<T> saved_mean_arr(saved_mean->MutableData<T>(), C);
@@ -166,7 +162,7 @@ class BatchNorm : public OpKernel {
       ConstEigenVectorArrayMap<T> var_arr(var->Data<T>(), is_spatial_ ? C : sample_size_incl_all_channels);
       inv_std = (var_arr + epsilon_).sqrt().inverse();
     } else {
-#if defined(BATCHNORM_INCLUDE_TRAINING_SUPPORT)
+#ifdef ENABLE_TRAINING_OPS
       EigenVectorArrayMap<T> saved_inv_std_arr(saved_inv_std->MutableData<T>(), C);
       saved_inv_std_arr = (saved_inv_std_arr + epsilon_).inverse().sqrt();
       inv_std = saved_inv_std_arr;
@@ -175,7 +171,7 @@ class BatchNorm : public OpKernel {
 
     // If we're training, do batch normalization based on computation from this batch
     ConstEigenVectorArrayMap<T> mean_arr(
-#if defined(BATCHNORM_INCLUDE_TRAINING_SUPPORT)
+#ifdef ENABLE_TRAINING_OPS
         !is_train_ ? mean->Data<T>() : saved_mean->Data<T>(),
 #else
         mean->Data<T>(),

Diff for: onnxruntime/test/providers/cpu/nn/batch_norm_op_test.cc

@@ -2,7 +2,6 @@
 // Licensed under the MIT License.
 
 #include "core/framework/tensor.h"
-#include "core/providers/cpu/nn/batch_norm.h"  // for BATCHNORM_INCLUDE_TRAINING_SUPPORT
 #include "core/session/inference_session.h"
 #include "test/common/dnnl_op_test_utils.h"
 #include "test/providers/provider_test_utils.h"
@@ -847,7 +846,7 @@ TEST(BatchNormTest, BatchNorm2d_bfloat16) {
 #endif  //  USE_DNNL
 
 // TODO fix flaky test for CUDA
-#ifdef BATCHNORM_INCLUDE_TRAINING_SUPPORT
+#ifdef ENABLE_TRAINING_OPS
 TEST(BatchNormTest, ForwardTrainingTestWithSavedOutputsOpset9) {
   // TODO: Unskip when fixed #41968513
   if (DefaultDmlExecutionProvider().get() != nullptr) {
@@ -937,7 +936,7 @@ TEST(BatchNormTest, ForwardTrainingTestOpset15) {
            {kCudaExecutionProvider, kRocmExecutionProvider,
             kTensorrtExecutionProvider, kOpenVINOExecutionProvider, kDnnlExecutionProvider});
 }
-#endif  // BATCHNORM_INCLUDE_TRAINING_SUPPORT
+#endif  // ENABLE_TRAINING_OPS
 
 }  // namespace test
 }  // namespace onnxruntime