experiments/STT-tensorflow
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Merge pull request #28672 from ROCmSoftwarePlatform:google_upstream_rocm_platform_fix_190513

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PiperOrigin-RevId: 248009969
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TensorFlower Gardener 2019-05-13 16:21:36 -07:00
commit 43343ce22a
2 changed files with 7 additions and 10 deletions
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12
tensorflow/core/kernels/avgpooling_op.cc
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@ -36,10 +36,10 @@ limitations under the License.
#include "tensorflow/core/util/padding.h" #include "tensorflow/core/util/padding.h"
#include "tensorflow/core/util/tensor_format.h" #include "tensorflow/core/util/tensor_format.h"
#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM #if GOOGLE_CUDA
#include "tensorflow/core/kernels/maxpooling_op_gpu.h" #include "tensorflow/core/kernels/maxpooling_op_gpu.h"
#include "tensorflow/core/kernels/pooling_ops_common_gpu.h" #include "tensorflow/core/kernels/pooling_ops_common_gpu.h"
#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM #endif // GOOGLE_CUDA
namespace tensorflow { namespace tensorflow {
@ -112,7 +112,7 @@ REGISTER_KERNEL_BUILDER(
Name("AvgPool").Device(DEVICE_CPU).TypeConstraint<Eigen::half>("T"), Name("AvgPool").Device(DEVICE_CPU).TypeConstraint<Eigen::half>("T"),
AvgPoolingOp<CPUDevice, Eigen::half>); AvgPoolingOp<CPUDevice, Eigen::half>);
#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM #if GOOGLE_CUDA
template <typename T> template <typename T>
class AvgPoolingOp<GPUDevice, T> : public UnaryOp<T> { class AvgPoolingOp<GPUDevice, T> : public UnaryOp<T> {
public: public:
@ -205,7 +205,7 @@ REGISTER_KERNEL_BUILDER(
REGISTER_KERNEL_BUILDER( REGISTER_KERNEL_BUILDER(
Name("AvgPool").Device(DEVICE_GPU).TypeConstraint<double>("T"), Name("AvgPool").Device(DEVICE_GPU).TypeConstraint<double>("T"),
AvgPoolingOp<GPUDevice, double>); AvgPoolingOp<GPUDevice, double>);
#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM #endif // GOOGLE_CUDA
// The operation to compute AvgPool gradients. // The operation to compute AvgPool gradients.
// It takes two inputs: // It takes two inputs:
@ -368,7 +368,7 @@ TF_CALL_float(REGISTER_CPU_KERNEL);
TF_CALL_double(REGISTER_CPU_KERNEL); TF_CALL_double(REGISTER_CPU_KERNEL);
TF_CALL_half(REGISTER_CPU_KERNEL); TF_CALL_half(REGISTER_CPU_KERNEL);
#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM #if GOOGLE_CUDA
// A CUDNN based AvgPoolingGrad implementation. It includes the padding as the // A CUDNN based AvgPoolingGrad implementation. It includes the padding as the
// candidates for the pooling operation. // candidates for the pooling operation.
@ -577,6 +577,6 @@ REGISTER_KERNEL_BUILDER(Name("AvgPoolGrad")
.HostMemory("orig_input_shape"), .HostMemory("orig_input_shape"),
AvgPoolingGradOpCustomGPUKernel<Eigen::half>); AvgPoolingGradOpCustomGPUKernel<Eigen::half>);
#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM #endif // GOOGLE_CUDA
} // namespace tensorflow } // namespace tensorflow

5
tensorflow/stream_executor/gpu/gpu_driver.h
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@ -19,16 +19,13 @@ limitations under the License.
#define TENSORFLOW_STREAM_EXECUTOR_GPU_GPU_DRIVER_H_ #define TENSORFLOW_STREAM_EXECUTOR_GPU_GPU_DRIVER_H_
#include <stddef.h> #include <stddef.h>
#include "tensorflow/stream_executor/platform/port.h"
#include "third_party/gpus/cuda/include/cuda.h"
#include "tensorflow/stream_executor/device_options.h" #include "tensorflow/stream_executor/device_options.h"
#include "tensorflow/stream_executor/gpu/gpu_types.h"
#include "tensorflow/stream_executor/lib/status.h" #include "tensorflow/stream_executor/lib/status.h"
#include "tensorflow/stream_executor/lib/statusor.h" #include "tensorflow/stream_executor/lib/statusor.h"
#include "tensorflow/stream_executor/platform/port.h" #include "tensorflow/stream_executor/platform/port.h"
#include "tensorflow/stream_executor/gpu/gpu_types.h"
namespace stream_executor { namespace stream_executor {
namespace gpu { namespace gpu {