tensorflow/lite/delegates/gpu/cl/kernels/depthwise_conv_3d.h

/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_LITE_DELEGATES_GPU_CL_KERNELS_DEPTHWISE_CONV_3D_H_
#define TENSORFLOW_LITE_DELEGATES_GPU_CL_KERNELS_DEPTHWISE_CONV_3D_H_

#include <vector>

#include "tensorflow/lite/delegates/gpu/cl/buffer.h"
#include "tensorflow/lite/delegates/gpu/cl/kernels/gpu_operation.h"
#include "tensorflow/lite/delegates/gpu/cl/linear_storage.h"
#include "tensorflow/lite/delegates/gpu/cl/tensor.h"
#include "tensorflow/lite/delegates/gpu/cl/texture2d.h"
#include "tensorflow/lite/delegates/gpu/cl/util.h"
#include "tensorflow/lite/delegates/gpu/common/data_type.h"
#include "tensorflow/lite/delegates/gpu/common/operations.h"
#include "tensorflow/lite/delegates/gpu/common/shape.h"
#include "tensorflow/lite/delegates/gpu/common/status.h"
#include "tensorflow/lite/delegates/gpu/common/tensor.h"
#include "tensorflow/lite/delegates/gpu/common/types.h"

namespace tflite {
namespace gpu {
namespace cl {

class DepthwiseConvolution3D : public GPUOperation {
 public:
  DepthwiseConvolution3D() = default;
  absl::Status AddToQueue(CLCommandQueue* queue) override;
  absl::Status Tune(const TuningParameters& params) override;

  absl::Status Compile(const CreationContext& creation_context) override;

  // Move only
  DepthwiseConvolution3D(DepthwiseConvolution3D&& operation);
  DepthwiseConvolution3D& operator=(DepthwiseConvolution3D&& operation);
  DepthwiseConvolution3D(const DepthwiseConvolution3D&) = delete;
  DepthwiseConvolution3D& operator=(const DepthwiseConvolution3D&) = delete;

 private:
  friend absl::Status CreateDepthwiseConvolution3D(
      const CreationContext& creation_context, const OperationDef& definition,
      const DepthwiseConvolution3DAttributes& attr,
      DepthwiseConvolution3D* result);
  DepthwiseConvolution3D(const OperationDef& definition,
                         const DepthwiseConvolution3DAttributes& attr,
                         const CLDevice& device);
  template <DataType T>
  absl::Status UploadWeights(const tflite::gpu::Tensor<OHWDI, T>& weights,
                             CLContext* context);

  template <DataType S, typename T>
  void RearrangeWeightsData(const tflite::gpu::Tensor<OHWDI, S>& weights,
                            absl::Span<T> dst);

  absl::Status BindArguments();
  int3 GetGridSize() const;

  Texture2D weights_tex2d_;
  Buffer weights_buf_;
  bool weights_are_buffer_;

  LinearStorage biases_;

  int3 kernel_size_;
  int3 stride_;
  int3 padding_;
  int3 dilation_;
  int channel_multiplier_;

  CLKernel kernel_;
  int3 work_group_size_;
};

template <DataType T>
absl::Status DepthwiseConvolution3D::UploadWeights(
    const tflite::gpu::Tensor<OHWDI, T>& weights, CLContext* context) {
  const int dst_channels = weights.shape.i * weights.shape.o;
  const int dst_slices = IntegralDivideRoundUp(dst_channels, 4);
  const int kernel_x = weights.shape.w;
  const int kernel_y = weights.shape.h;
  const int kernel_z = weights.shape.d;

  const int elements_count = kernel_x * kernel_y * kernel_z * dst_slices;
  const bool f32_weights = definition_.precision == CalculationsPrecision::F32;

  const int float4_size = f32_weights ? 16 : 8;

  if (f32_weights) {
    std::vector<float4> gpu_data(elements_count);
    RearrangeWeightsData(weights, absl::MakeSpan(gpu_data));
    if (weights_are_buffer_) {
      RETURN_IF_ERROR(CreateReadOnlyBuffer(float4_size * elements_count,
                                           gpu_data.data(), context,
                                           &weights_buf_));
    } else {
      RETURN_IF_ERROR(CreateTexture2DRGBA(
          definition_.GetDataType(), kernel_x * kernel_y * kernel_z, dst_slices,
          gpu_data.data(), context, &weights_tex2d_));
    }
  } else {
    std::vector<half4> gpu_data(elements_count);
    RearrangeWeightsData(weights, absl::MakeSpan(gpu_data));
    if (weights_are_buffer_) {
      RETURN_IF_ERROR(CreateReadOnlyBuffer(float4_size * elements_count,
                                           gpu_data.data(), context,
                                           &weights_buf_));
    } else {
      RETURN_IF_ERROR(CreateTexture2DRGBA(
          definition_.GetDataType(), kernel_x * kernel_y * kernel_z, dst_slices,
          gpu_data.data(), context, &weights_tex2d_));
    }
  }
  return absl::OkStatus();
}

template <DataType S, typename T>
void DepthwiseConvolution3D::RearrangeWeightsData(
    const tflite::gpu::Tensor<OHWDI, S>& weights, absl::Span<T> dst) {
  const int dst_channels = weights.shape.i * weights.shape.o;
  const int dst_slices = IntegralDivideRoundUp(dst_channels, 4);
  const int kernel_x = weights.shape.w;
  const int kernel_y = weights.shape.h;
  const int kernel_z = weights.shape.d;

  int counter = 0;
  for (int d = 0; d < dst_slices; ++d) {
    for (int z = 0; z < kernel_z; ++z) {
      for (int y = 0; y < kernel_y; ++y) {
        for (int x = 0; x < kernel_x; ++x) {
          T filter_val;
          for (int i = 0; i < 4; ++i) {
            const int d_ch = d * 4 + i;
            if (d_ch < dst_channels) {
              const int f_index = weights.shape.LinearIndex(
                  {d_ch % weights.shape.o, y, x, z, d_ch / weights.shape.o});
              filter_val[i] = weights.data[f_index];
            } else {
              filter_val[i] = 0.0f;
            }
          }
          dst[counter++] = filter_val;
        }
      }
    }
  }
}

absl::Status CreateDepthwiseConvolution3D(
    const CreationContext& creation_context, const OperationDef& definition,
    const DepthwiseConvolution3DAttributes& attr,
    DepthwiseConvolution3D* result);

}  // namespace cl
}  // namespace gpu
}  // namespace tflite

#endif  // TENSORFLOW_LITE_DELEGATES_GPU_CL_KERNELS_DEPTHWISE_CONV_3D_H_