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193 lines
8.4 KiB
193 lines
8.4 KiB
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/index_sample_op.h"
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#include "paddle/fluid/operators/math/math_function.h"
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#include "paddle/fluid/platform/cuda_device_function.h"
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#include "paddle/fluid/platform/cuda_primitives.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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template <typename T, typename IndexT = int>
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__global__ void IndexSampleForward(const IndexT* index, const T* in_data,
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T* out_data, size_t index_length,
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size_t input_length, size_t batch_size) {
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int index_i = blockDim.x * blockIdx.x + threadIdx.x;
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int index_j = blockDim.y * blockIdx.y + threadIdx.y;
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int index_idx = index_j * index_length + index_i;
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int in_idx = index_j * input_length + index_i;
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if (index_i < index_length & index_j < batch_size) {
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IndexT sample_idx = index[index_idx];
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out_data[index_idx] = in_data[in_idx - index_i + sample_idx];
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}
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}
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template <typename T, typename IndexT = int>
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__global__ void IndexSampleGrad(const IndexT* index, T* in_grad,
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const T* out_grad, size_t index_length,
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size_t input_length, size_t batch_size,
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bool same_data_in_row = true) {
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int index_i = blockDim.x * blockIdx.x + threadIdx.x;
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int index_j = blockDim.y * blockIdx.y + threadIdx.y;
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int index_idx = index_j * index_length + index_i;
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int in_idx = index_j * input_length + index_i;
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if (index_i < index_length & index_j < batch_size) {
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IndexT sample_idx = index[index_idx];
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if (same_data_in_row) {
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platform::CudaAtomicAdd(&(in_grad[in_idx - index_i + sample_idx]),
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out_grad[sample_idx]);
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} else {
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in_grad[in_idx - index_i + sample_idx] = out_grad[index_idx];
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}
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}
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}
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template <typename T>
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class IndexSampleKernel<platform::CUDADeviceContext, T>
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: public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* input = ctx.Input<LoDTensor>("X");
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auto* index = ctx.Input<LoDTensor>("Index");
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auto* output = ctx.Output<LoDTensor>("Out");
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const auto& index_type = index->type();
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bool index_type_match = index_type == framework::proto::VarType::INT64 ||
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index_type == framework::proto::VarType::INT32;
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PADDLE_ENFORCE_EQ(index_type_match, true,
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platform::errors::InvalidArgument(
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"Input(Index) holds the wrong type, it holds %s, but "
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"desires to be %s or %s",
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paddle::framework::DataTypeToString(index_type),
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paddle::framework::DataTypeToString(
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framework::proto::VarType::INT32),
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paddle::framework::DataTypeToString(
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framework::proto::VarType::INT64)));
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const auto* in_data = input->data<T>();
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auto* out_data = output->mutable_data<T>(ctx.GetPlace());
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auto stream =
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ctx.template device_context<platform::CUDADeviceContext>().stream();
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auto input_dim = input->dims();
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auto index_dim = index->dims();
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size_t batch_size = input_dim[0];
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size_t input_length = input_dim[1];
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size_t index_length = index_dim[1];
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auto block_width = platform::RoundToPowerOfTwo(index_length);
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int block_height =
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platform::RoundToPowerOfTwo(index_length * batch_size) / block_width;
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dim3 block_dim(block_width, block_height);
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dim3 grid_dim((index_length + block_dim.x - 1) / block_dim.x,
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(batch_size + block_dim.y - 1) / block_dim.y);
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if (index_type == framework::proto::VarType::INT64) {
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const int64_t* index_data = index->data<int64_t>();
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IndexSampleForward<T, int64_t><<<grid_dim, block_dim, 0, stream>>>(
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index_data, in_data, out_data, index_length, input_length,
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batch_size);
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} else if (index_type == framework::proto::VarType::INT32) {
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const int* index_data = index->data<int>();
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IndexSampleForward<T, int><<<grid_dim, block_dim, 0, stream>>>(
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index_data, in_data, out_data, index_length, input_length,
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batch_size);
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}
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}
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};
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template <typename T>
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class IndexSampleGradKernel<platform::CUDADeviceContext, T>
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: public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* output_grad = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
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auto* input_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
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auto* index = ctx.Input<LoDTensor>("Index");
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const auto* output_grad_data = output_grad->data<T>();
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auto* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
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const auto& index_type = index->type();
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bool index_type_match = index_type == framework::proto::VarType::INT64 ||
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index_type == framework::proto::VarType::INT32;
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PADDLE_ENFORCE_EQ(index_type_match, true,
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platform::errors::InvalidArgument(
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"Input(Index) holds the wrong type, it holds %s, but "
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"desires to be %s or %s",
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paddle::framework::DataTypeToString(index_type),
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paddle::framework::DataTypeToString(
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framework::proto::VarType::INT32),
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paddle::framework::DataTypeToString(
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framework::proto::VarType::INT64)));
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auto stream =
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ctx.template device_context<platform::CUDADeviceContext>().stream();
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auto input_num = input_grad->numel();
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auto input_dim = input_grad->dims();
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auto index_dim = index->dims();
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size_t batch_size = index_dim[0];
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size_t input_length = input_dim[1];
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size_t index_length = index_dim[1];
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bool same_data_in_index_row = index_length == 1 ? false : true;
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auto block_width = platform::RoundToPowerOfTwo(index_length);
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auto block_height =
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platform::RoundToPowerOfTwo(index_length * batch_size) / block_width;
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dim3 block_dim(block_width, block_height);
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dim3 grid_dim((index_length + block_dim.x - 1) / block_dim.x,
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(batch_size + block_dim.y - 1) / block_dim.y);
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math::SetConstant<platform::CUDADeviceContext, T> set_zero;
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auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
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set_zero(dev_ctx, input_grad, static_cast<T>(0));
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if (index_type == framework::proto::VarType::INT64) {
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const int64_t* index_data = index->data<int64_t>();
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IndexSampleGrad<T, int64_t><<<grid_dim, block_dim, 0, stream>>>(
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index_data, input_grad_data, output_grad_data, index_length,
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input_length, batch_size, same_data_in_index_row);
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} else if (index_type == framework::proto::VarType::INT32) {
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const int* index_data = index->data<int>();
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IndexSampleGrad<T, int><<<grid_dim, block_dim, 0, stream>>>(
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index_data, input_grad_data, output_grad_data, index_length,
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input_length, batch_size, same_data_in_index_row);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(
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index_sample,
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ops::IndexSampleKernel<paddle::platform::CUDADeviceContext, float>,
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ops::IndexSampleKernel<paddle::platform::CUDADeviceContext, double>,
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ops::IndexSampleKernel<paddle::platform::CUDADeviceContext, int>,
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ops::IndexSampleKernel<paddle::platform::CUDADeviceContext, int64_t>);
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REGISTER_OP_CUDA_KERNEL(
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index_sample_grad,
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ops::IndexSampleGradKernel<paddle::platform::CUDADeviceContext, float>,
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ops::IndexSampleGradKernel<paddle::platform::CUDADeviceContext, double>,
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ops::IndexSampleGradKernel<paddle::platform::CUDADeviceContext, int>,
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ops::IndexSampleGradKernel<paddle::platform::CUDADeviceContext, int64_t>);
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