| /* 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_CONVOLUTION_TRANSPOSED_H_ |
| #define TENSORFLOW_LITE_DELEGATES_GPU_CL_KERNELS_CONVOLUTION_TRANSPOSED_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 ConvolutionTransposed : public GPUOperation { |
| public: |
| ConvolutionTransposed() = default; |
| absl::Status Tune(const TuningParameters& params) override; |
| absl::Status Compile(const CreationContext& creation_context) override; |
| absl::Status BindArguments() override; |
| int3 GetGridSize() const override; |
| |
| // Move only |
| ConvolutionTransposed(ConvolutionTransposed&& operation); |
| ConvolutionTransposed& operator=(ConvolutionTransposed&& operation); |
| ConvolutionTransposed(const ConvolutionTransposed&) = delete; |
| ConvolutionTransposed& operator=(const ConvolutionTransposed&) = delete; |
| |
| private: |
| friend absl::Status CreateConvolutionTransposed( |
| const CreationContext& creation_context, const OperationDef& definition, |
| const ConvolutionTransposedAttributes& attr, |
| ConvolutionTransposed* result); |
| explicit ConvolutionTransposed(const OperationDef& definition, |
| const ConvolutionTransposedAttributes& attr, |
| const CLDevice& device); |
| template <DataType T> |
| absl::Status UploadWeights(const tflite::gpu::Tensor<OHWI, T>& weights, |
| CLContext* context); |
| |
| template <DataType S, typename T> |
| void RearrangeWeightsData(const tflite::gpu::Tensor<OHWI, S>& weights, |
| absl::Span<T> dst); |
| |
| bool weights_are_buffer_; |
| |
| int2 kernel_size_; |
| int2 stride_; |
| int2 padding_; |
| |
| int3 block_size_ = int3(1, 1, 1); |
| }; |
| |
| template <DataType T> |
| absl::Status ConvolutionTransposed::UploadWeights( |
| const tflite::gpu::Tensor<OHWI, T>& weights, CLContext* context) { |
| const int dst_depth = |
| AlignByN(DivideRoundUp(weights.shape.o, 4), block_size_.z); |
| const int src_depth = DivideRoundUp(weights.shape.i, 4); |
| const int kernel_x = kernel_size_.x; |
| const int kernel_y = kernel_size_.y; |
| int texture_width = dst_depth; |
| int texture_height = src_depth * kernel_x * kernel_y; |
| |
| const int elements_count = kernel_x * kernel_y * src_depth * dst_depth * 4; |
| const bool f32_weights = definition_.precision == CalculationsPrecision::F32; |
| |
| const int float4_size = f32_weights ? 16 : 8; |
| |
| Texture2D weights_0; |
| Texture2D weights_1; |
| Texture2D weights_2; |
| Texture2D weights_3; |
| Buffer weights_buf; |
| 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(), dst_depth, src_depth * kernel_x * kernel_y, |
| gpu_data.data(), context, &weights_0)); |
| RETURN_IF_ERROR(CreateTexture2DRGBA( |
| definition_.GetDataType(), dst_depth, src_depth * kernel_x * kernel_y, |
| gpu_data.data() + texture_width * texture_height, context, |
| &weights_1)); |
| RETURN_IF_ERROR(CreateTexture2DRGBA( |
| definition_.GetDataType(), dst_depth, src_depth * kernel_x * kernel_y, |
| gpu_data.data() + texture_width * texture_height * 2, context, |
| &weights_2)); |
| RETURN_IF_ERROR(CreateTexture2DRGBA( |
| definition_.GetDataType(), dst_depth, src_depth * kernel_x * kernel_y, |
| gpu_data.data() + texture_width * texture_height * 3, context, |
| &weights_3)); |
| } |
| } 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(), dst_depth, src_depth * kernel_x * kernel_y, |
| gpu_data.data(), context, &weights_0)); |
| RETURN_IF_ERROR(CreateTexture2DRGBA( |
| definition_.GetDataType(), dst_depth, src_depth * kernel_x * kernel_y, |
| gpu_data.data() + texture_width * texture_height, context, |
| &weights_1)); |
| RETURN_IF_ERROR(CreateTexture2DRGBA( |
| definition_.GetDataType(), dst_depth, src_depth * kernel_x * kernel_y, |
| gpu_data.data() + texture_width * texture_height * 2, context, |
| &weights_2)); |
| RETURN_IF_ERROR(CreateTexture2DRGBA( |
| definition_.GetDataType(), dst_depth, src_depth * kernel_x * kernel_y, |
| gpu_data.data() + texture_width * texture_height * 3, context, |
| &weights_3)); |
| } |
| } |
| |
| if (weights_are_buffer_) { |
| BufferDescriptor desc; |
| desc.element_type = f32_weights ? DataType::FLOAT32 : DataType::FLOAT16; |
| desc.element_size = 16; |
| args_.AddObject("weights", AccessType::READ, |
| absl::make_unique<Buffer>(std::move(weights_buf)), |
| absl::make_unique<BufferDescriptor>(desc)); |
| } else { |
| Texture2DDescriptor desc; |
| desc.element_type = f32_weights ? DataType::FLOAT32 : DataType::FLOAT16; |
| args_.AddObject("weights0", AccessType::READ, |
| absl::make_unique<Texture2D>(std::move(weights_0)), |
| absl::make_unique<Texture2DDescriptor>(desc)); |
| args_.AddObject("weights1", AccessType::READ, |
| absl::make_unique<Texture2D>(std::move(weights_1)), |
| absl::make_unique<Texture2DDescriptor>(desc)); |
| args_.AddObject("weights2", AccessType::READ, |
| absl::make_unique<Texture2D>(std::move(weights_2)), |
| absl::make_unique<Texture2DDescriptor>(desc)); |
| args_.AddObject("weights3", AccessType::READ, |
| absl::make_unique<Texture2D>(std::move(weights_3)), |
| absl::make_unique<Texture2DDescriptor>(desc)); |
| } |
| |
| return absl::OkStatus(); |
| } |
| |
| template <DataType S, typename T> |
| void ConvolutionTransposed::RearrangeWeightsData( |
| const tflite::gpu::Tensor<OHWI, S>& weights, absl::Span<T> dst) { |
| const int dst_depth = |
| AlignByN(DivideRoundUp(weights.shape.o, 4), block_size_.z); |
| const int src_depth = DivideRoundUp(weights.shape.i, 4); |
| const int kernel_x = kernel_size_.x; |
| const int kernel_y = kernel_size_.y; |
| int texture_width = dst_depth; |
| int texture_height = src_depth * kernel_x * kernel_y; |
| |
| int counter = 0; |
| for (int d = 0; d < dst_depth / block_size_.z; ++d) { |
| for (int y = 0; y < kernel_y; ++y) { |
| for (int x = 0; x < kernel_x; ++x) { |
| for (int s = 0; s < src_depth; ++s) { |
| for (int sub_d = 0; sub_d < block_size_.z; ++sub_d) { |
| T filters[4]; |
| for (int i = 0; i < 4; ++i) { |
| for (int j = 0; j < 4; ++j) { |
| const int s_ch = s * 4 + j; |
| const int d_ch = (d * block_size_.z + sub_d) * 4 + i; |
| if (s_ch < weights.shape.i && d_ch < weights.shape.o) { |
| const int f_index = |
| weights.shape.LinearIndex({d_ch, y, x, s_ch}); |
| filters[j][i] = weights.data[f_index]; |
| } else { |
| filters[j][i] = 0.0f; |
| } |
| } |
| } |
| if (weights_are_buffer_) { |
| dst[counter++] = filters[0]; |
| dst[counter++] = filters[1]; |
| dst[counter++] = filters[2]; |
| dst[counter++] = filters[3]; |
| } else { |
| int x_coord = d * block_size_.z + sub_d; |
| int y_coord = (y * kernel_x + x) * src_depth + s; |
| int offset = y_coord * dst_depth + x_coord; |
| dst[offset + texture_width * texture_height * 0] = filters[0]; |
| dst[offset + texture_width * texture_height * 1] = filters[1]; |
| dst[offset + texture_width * texture_height * 2] = filters[2]; |
| dst[offset + texture_width * texture_height * 3] = filters[3]; |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| absl::Status CreateConvolutionTransposed( |
| const CreationContext& creation_context, const OperationDef& definition, |
| const ConvolutionTransposedAttributes& attr, ConvolutionTransposed* result); |
| |
| } // namespace cl |
| } // namespace gpu |
| } // namespace tflite |
| |
| #endif // TENSORFLOW_LITE_DELEGATES_GPU_CL_KERNELS_CONVOLUTION_TRANSPOSED_H_ |
