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// The CUDA-specific DNN library support, implementing the general DnnSupport
// interface.
#ifndef TENSORFLOW_STREAM_EXECUTOR_CUDA_CUDA_DNN_H_
#define TENSORFLOW_STREAM_EXECUTOR_CUDA_CUDA_DNN_H_
#include "tensorflow/stream_executor/dnn.h"
#include "tensorflow/stream_executor/lib/status.h"
#include "tensorflow/stream_executor/platform/mutex.h"
#include "tensorflow/stream_executor/platform/thread_annotations.h"
#include "tensorflow/stream_executor/plugin_registry.h"
#include "tensorflow/stream_executor/temporary_device_memory.h"
namespace perftools {
namespace gputools {
namespace cuda {
class CUDAExecutor;
// Opaque and unique identifer for the cuDNN plugin.
extern const PluginId kCuDnnPlugin;
// cudnn-library based DNN support. For details on overridden interface
// functions, see dnn.h.
class CudnnSupport : public dnn::DnnSupport {
public:
explicit CudnnSupport(CUDAExecutor* parent);
~CudnnSupport() override;
port::Status Init() override;
bool DoConvolve(Stream* stream, const dnn::BatchDescriptor& input_descriptor,
const DeviceMemory<float>& input_data,
const dnn::FilterDescriptor& filter_descriptor,
const DeviceMemory<float>& filter_data,
const dnn::ConvolutionDescriptor& convolution_descriptor,
const dnn::BatchDescriptor& output_descriptor,
DeviceMemory<float>* output_data) override;
bool DoConvolve(Stream* stream, const dnn::BatchDescriptor& batch_descriptor,
const DeviceMemory<double>& input_data,
const dnn::FilterDescriptor& filter_descriptor,
const DeviceMemory<double>& filter_data,
const dnn::ConvolutionDescriptor& convolution_descriptor,
const dnn::BatchDescriptor& output_descriptor,
DeviceMemory<double>* output_data) override;
bool DoSeparableConvolve(
Stream* stream, const dnn::BatchDescriptor& batch_descriptor,
const DeviceMemory<float>& input_data,
const dnn::FilterDescriptor& filter_descriptor, int depth_multiplier,
const DeviceMemory<float>& first_weights,
const DeviceMemory<float>& second_weights,
const dnn::ConvolutionDescriptor& convolution_descriptor,
const dnn::BatchDescriptor& output_descriptor,
DeviceMemory<float>* output_data) override {
LOG(ERROR) << "separable convolution not supported by CUDNN";
return false;
}
bool DoConvolveBackwardData(
Stream* stream, const dnn::FilterDescriptor& filter_descriptor,
const DeviceMemory<float>& filter_data,
const dnn::BatchDescriptor& output_descriptor,
DeviceMemory<float> backward_output_data,
const dnn::ConvolutionDescriptor& convolution_descriptor,
const dnn::BatchDescriptor& input_descriptor,
DeviceMemory<float>* backward_input_data) override;
bool DoConvolveBackwardFilter(
Stream* stream, const dnn::BatchDescriptor& input_descriptor,
const DeviceMemory<float>& input_data,
const dnn::BatchDescriptor& output_descriptor,
DeviceMemory<float> backward_output_data,
const dnn::ConvolutionDescriptor& convolution_descriptor,
const dnn::FilterDescriptor& filter_descriptor,
DeviceMemory<float>* backward_filter_data) override;
bool DoMatMul(Stream* stream, const DeviceMemory<float>& input_data,
const DeviceMemory<float>& weights,
const dnn::BatchDescriptor& input_dimensions,
const dnn::BatchDescriptor& output_dimensions,
DeviceMemory<float>* output_data) override;
bool DoMatMulQuantized(Stream* stream, const DeviceMemory<float>& input_data,
const DeviceMemory<int8>& quantized_weights,
const DeviceMemory<float>& weight_scales,
const dnn::BatchDescriptor& input_dimensions,
const dnn::BatchDescriptor& output_dimensions,
DeviceMemory<float>* output_data) override {
LOG(ERROR) << "DNN MatMulQuantized not supported by CUDNN";
return false;
}
bool DoMatMulQuantized(Stream* stream, const DeviceMemory<float>& input_data,
const DeviceMemory<int16>& quantized_weights,
const DeviceMemory<float>& weight_scales,
const dnn::BatchDescriptor& input_dimensions,
const dnn::BatchDescriptor& output_dimensions,
DeviceMemory<float>* output_data) override {
LOG(ERROR) << "DNN MatMulQuantized not supported by CUDNN";
return false;
}
bool DoBiasAdd(Stream* stream, const DeviceMemory<float>& input_data,
const DeviceMemory<float>& biases,
const dnn::BatchDescriptor& dimensions,
DeviceMemory<float>* output_data) override;
bool DoActivate(Stream* stream, dnn::ActivationMode activation_mode,
const dnn::BatchDescriptor& dimensions,
const DeviceMemory<float>& input_data,
DeviceMemory<float>* output_data) override;
bool DoPoolForward(Stream* stream,
const dnn::PoolingDescriptor& pooling_dimensions,
const dnn::BatchDescriptor& input_dimensions,
const DeviceMemory<float>& input_data,
const dnn::BatchDescriptor& output_dimensions,
DeviceMemory<float>* output_data) override;
bool DoPoolBackward(Stream* stream,
const dnn::PoolingDescriptor& pooling_dimensions,
const dnn::BatchDescriptor& input_dimensions,
const DeviceMemory<float>& input_data,
const dnn::BatchDescriptor& output_dimensions,
const DeviceMemory<float>& output_data,
const DeviceMemory<float>& input_diff_data,
DeviceMemory<float>* output_diff_data) override;
bool DoNormalize(Stream* stream,
const dnn::NormalizeDescriptor& normalize_descriptor,
const DeviceMemory<float>& input_data,
DeviceMemory<float>* output_data) override;
bool DoDepthConcatenate(
Stream* stream, port::ArraySlice<dnn::BatchDescriptor> input_dimensions,
port::ArraySlice<const DeviceMemory<float>*> input_data,
DeviceMemory<float>* output_data) override;
bool DoElementwiseOperate(
Stream* stream, dnn::ElementwiseOperation operation,
port::ArraySlice<dnn::BatchDescriptor> input_dimensions,
port::ArraySlice<const DeviceMemory<float>*> input_data,
const dnn::BatchDescriptor& output_dimensions,
DeviceMemory<float>* output_data) override;
bool DoMemcpyD2HQuantized(Stream* stream,
const DeviceMemory<float>& device_unquantized_src,
port::MutableArraySlice<uint8> host_dst) override;
bool DoMemcpyD2HQuantized(Stream* stream,
const DeviceMemory<float>& device_unquantized_src,
port::MutableArraySlice<uint16> host_dst) override;
bool DoMemcpyD2HQuantized(Stream* stream,
const DeviceMemory<float>& device_unquantized_src,
port::MutableArraySlice<int32> host_dst) override;
bool DoMemcpyH2DQuantized(
Stream* stream, port::ArraySlice<uint8> host_src,
DeviceMemory<float>* device_unquantized_dst) override;
// Derives an output batch descriptor from an input batch and convolution
// descriptors.
bool DeriveOutputBatchDescriptor(
const dnn::BatchDescriptor& batch_descriptor,
const dnn::FilterDescriptor& filter_descriptor,
const dnn::ConvolutionDescriptor& convolution_descriptor,
dnn::BatchDescriptor* output_batch_descriptor);
private:
// Guards the enqueueing of DNN operations via the dnn_handle_ below.
mutex dnn_handle_mutex_;
CUDAExecutor* parent_; // Parent executor object. Not owned.
// cudnn library handle. cudnnHandle_t type is not present in this header to
// prevent third-party library header inclusions from leaking outside the
// single cuda_dnn translation unit.
void* dnn_handle_ GUARDED_BY(dnn_handle_mutex_);
// NOTE(keveman): Temporary data layout transformation until cuDNN supports
// kBatchYXDepth for backward pass. This function allocates temporary memory,
// lays out the source data into the temporary but in the kBatchDepthXY
// layout, and returns the temporary memory. The caller is responsible for
// deallocating the temporary. Since the allocation is done using Stream's
// AllocateTemporaryMemory, a later BlockHostUntilDone could be used for
// deallocation.
//
// transform_scratch is populated with a legitimate temporary allocation iff
// the original output data needs to be transformed.
DeviceMemory<float> MaybeTransformLayout(
Stream* stream, dnn::BatchDescriptor* output_descriptor,
DeviceMemory<float> backward_output_data,
std::unique_ptr<TemporaryDeviceMemory<float>>* transform_scratch)
EXCLUSIVE_LOCKS_REQUIRED(dnn_handle_mutex_);
SE_DISALLOW_COPY_AND_ASSIGN(CudnnSupport);
};
} // namespace cuda
} // namespace gputools
} // namespace perftools
#endif // TENSORFLOW_STREAM_EXECUTOR_CUDA_CUDA_DNN_H_
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