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merged 2 commits into from
Aug 11, 2023
Merged

Batchnorm training mode support in a minimal build #17103

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20ff81f
Batchnorm training mode support in a minimal build
baijumeswani Aug 10, 2023
84b97e4
Use enable_training_ops instead of enable_training_core
baijumeswani Aug 10, 2023
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Use enable_training_ops instead of enable_training_core
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@baijumeswani
baijumeswani committed Aug 10, 2023
commit 84b97e49cb00787a70d9430569b83147157e2812
10 changes: 5 additions & 5 deletions onnxruntime/core/providers/cpu/nn/batch_norm.h
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Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,7 @@ class BatchNorm : public OpKernel {
}

if (is_train_) {
#ifdef ENABLE_TRAINING_CORE
#ifdef ENABLE_TRAINING_OPS
momentum_ = op_kernel_info.GetAttrOrDefault<float>("momentum", 0.9f);
ORT_ENFORCE(is_spatial_, "Training mode only supports spatial BN");
#else
Expand Down Expand Up @@ -84,7 +84,7 @@ class BatchNorm : public OpKernel {
// calculate sample_size (including all channels)
size_t sample_size_incl_all_channels = sample_size * C;

#ifdef ENABLE_TRAINING_CORE
#ifdef ENABLE_TRAINING_OPS
AllocatorPtr alloc;
ORT_RETURN_IF_ERROR(p_op_kernel_context->GetTempSpaceAllocator(&alloc));

Expand All @@ -111,7 +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);

#ifdef ENABLE_TRAINING_CORE
#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);
Expand Down Expand Up @@ -162,7 +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 {
#ifdef ENABLE_TRAINING_CORE
#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;
Expand All @@ -171,7 +171,7 @@ class BatchNorm : public OpKernel {

// If we're training, do batch normalization based on computation from this batch
ConstEigenVectorArrayMap<T> mean_arr(
#ifdef ENABLE_TRAINING_CORE
#ifdef ENABLE_TRAINING_OPS
!is_train_ ? mean->Data<T>() : saved_mean->Data<T>(),
#else
mean->Data<T>(),
Expand Down
4 changes: 2 additions & 2 deletions onnxruntime/test/providers/cpu/nn/batch_norm_op_test.cc
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Expand Up @@ -846,7 +846,7 @@ TEST(BatchNormTest, BatchNorm2d_bfloat16) {
#endif // USE_DNNL

// TODO fix flaky test for CUDA
#ifdef ENABLE_TRAINING_CORE
#ifdef ENABLE_TRAINING_OPS
TEST(BatchNormTest, ForwardTrainingTestWithSavedOutputsOpset9) {
// TODO: Unskip when fixed #41968513
if (DefaultDmlExecutionProvider().get() != nullptr) {
Expand Down Expand Up @@ -936,7 +936,7 @@ TEST(BatchNormTest, ForwardTrainingTestOpset15) {
{kCudaExecutionProvider, kRocmExecutionProvider,
kTensorrtExecutionProvider, kOpenVINOExecutionProvider, kDnnlExecutionProvider});
}
#endif // ENABLE_TRAINING_CORE
#endif // ENABLE_TRAINING_OPS

} // namespace test
} // namespace onnxruntime