Home | History | Annotate | Download | only in gpu
      1 /* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
      2 
      3 Licensed under the Apache License, Version 2.0 (the "License");
      4 you may not use this file except in compliance with the License.
      5 You may obtain a copy of the License at
      6 
      7     http://www.apache.org/licenses/LICENSE-2.0
      8 
      9 Unless required by applicable law or agreed to in writing, software
     10 distributed under the License is distributed on an "AS IS" BASIS,
     11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     12 See the License for the specific language governing permissions and
     13 limitations under the License.
     14 ==============================================================================*/
     15 
     16 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_CONVOLUTION_THUNK_H_
     17 #define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_CONVOLUTION_THUNK_H_
     18 
     19 #include "tensorflow/compiler/xla/service/buffer_assignment.h"
     20 #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h"
     21 #include "tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.h"
     22 #include "tensorflow/compiler/xla/service/gpu/gpu_executable.h"
     23 #include "tensorflow/compiler/xla/service/gpu/thunk.h"
     24 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
     25 #include "tensorflow/compiler/xla/types.h"
     26 #include "tensorflow/compiler/xla/xla_data.pb.h"
     27 #include "tensorflow/core/lib/core/status.h"
     28 #include "tensorflow/core/lib/gtl/optional.h"
     29 #include "tensorflow/core/platform/stream_executor_no_cuda.h"
     30 
     31 namespace xla {
     32 namespace gpu {
     33 
     34 // This class stores everything that StreamExecutor needs to launch a BNN
     35 // convolution. It is generated by IrEmitter.
     36 //
     37 // This is thread-compatible.
     38 class ConvolutionThunk : public Thunk {
     39  public:
     40   // Constructs a thunk for launching a DNN convolution.  When run, it will
     41   // write a tuple (result, scratch_memory) into `tuple_result_buffer`.
     42   //
     43   // `algorithm` is a cudnn algorithm number.  `algorithm == -1` indicates that
     44   // we should use the default (i.e. baseline) cudnn algorithm.
     45   //
     46   // Note that "output" here doesn't refer to the output from running this
     47   // thunk, but rather to the "output" of a hypothetical forward convolution
     48   // that corresponds to this input+filter+output triple.  That is, the result
     49   // generated by this thunk is "output" for forward convs, "input" for
     50   // backward-input convs, and "filter" for backward-filter convs.
     51   //
     52   // Semantics of null hlo_instruction argument are as in Thunk.
     53   ConvolutionThunk(CudnnConvKind convolution_kind,
     54                    const BufferAllocation::Slice& input_buffer,
     55                    const BufferAllocation::Slice& filter_buffer,
     56                    const BufferAllocation::Slice& output_buffer,
     57                    const BufferAllocation::Slice& tuple_result_buffer,
     58                    const BufferAllocation::Slice& scratch_buffer,
     59                    const Shape& input_shape, const Shape& filter_shape,
     60                    const Shape& output_shape, const Window& window,
     61                    const ConvolutionDimensionNumbers& dim_nums, int64 algorithm,
     62                    bool tensor_ops_enabled, const HloInstruction* hlo);
     63 
     64   ConvolutionThunk(const ConvolutionThunk&) = delete;
     65   ConvolutionThunk& operator=(const ConvolutionThunk&) = delete;
     66 
     67   // Does the convolution for the thunk on "stream".
     68   Status ExecuteOnStream(const BufferAllocations& buffer_allocations,
     69                          perftools::gputools::Stream* stream) override;
     70 
     71  private:
     72   class ScratchAllocator;
     73 
     74   Status Convolve(
     75       const perftools::gputools::dnn::BatchDescriptor& input_descriptor,
     76       perftools::gputools::DeviceMemory<float> input_data,
     77       const perftools::gputools::dnn::FilterDescriptor& filter_descriptor,
     78       perftools::gputools::DeviceMemory<float> filter_data,
     79       const perftools::gputools::dnn::BatchDescriptor& output_descriptor,
     80       perftools::gputools::DeviceMemory<float> output_data,
     81       const perftools::gputools::dnn::ConvolutionDescriptor&
     82           convolution_descriptor,
     83       const perftools::gputools::dnn::AlgorithmConfig& algorithm_config,
     84       perftools::gputools::Stream* stream, ScratchAllocator* scratch_allocator,
     85       perftools::gputools::dnn::ProfileResult* profile_result);
     86 
     87   const CudnnConvKind convolution_kind_;
     88 
     89   const BufferAllocation::Slice input_buffer_;
     90   const BufferAllocation::Slice filter_buffer_;
     91   const BufferAllocation::Slice output_buffer_;
     92   const BufferAllocation::Slice tuple_result_buffer_;
     93   const BufferAllocation::Slice scratch_buffer_;
     94 
     95   const Shape input_shape_;
     96   const Shape filter_shape_;
     97   const Shape output_shape_;
     98 
     99   const Window window_;
    100   const ConvolutionDimensionNumbers dim_nums_;
    101   int64 algorithm_;
    102   bool tensor_ops_enabled_;
    103 };
    104 
    105 }  // namespace gpu
    106 }  // namespace xla
    107 
    108 #endif  // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_CONVOLUTION_THUNK_H_
    109