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98 lines
3.9 KiB
98 lines
3.9 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/operators/elementwise/elementwise_mul_op.h"
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#include "paddle/fluid/platform/float16.h"
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#define TILE_SIZE 512
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namespace ops = paddle::operators;
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namespace plat = paddle::platform;
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namespace paddle {
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namespace operators {
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template <typename T>
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static __global__ void SimpleElemwiseMulGradCUDAKernel(const T* x, const T* y,
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const T* out,
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const T* dout,
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int64_t size, T* dx,
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T* dy) {
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int col = blockIdx.x * blockDim.x + threadIdx.x;
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while (col < size) {
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T o = dout[col];
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dx[col] = y[col] * o;
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dy[col] = x[col] * o;
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col += blockDim.x * gridDim.x;
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}
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}
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template <typename T>
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class ElementwiseMulGradKernel<plat::CUDADeviceContext, T>
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: public ElemwiseGradKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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ElemwiseGradKernel<T>::Compute(ctx);
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using Tensor = framework::Tensor;
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auto* x = ctx.Input<Tensor>("X");
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auto* y = ctx.Input<Tensor>("Y");
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auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
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auto* out = dout; // out is not necessary
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auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
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int axis = ctx.Attr<int>("axis");
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if (x->dims() == y->dims() && dx && dy) {
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dim3 block_size = dim3(TILE_SIZE, 1);
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auto size = x->numel();
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dim3 gird_size = dim3((size + TILE_SIZE - 1) / TILE_SIZE, 1);
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SimpleElemwiseMulGradCUDAKernel<T><<<
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gird_size, block_size, 0,
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ctx.template device_context<plat::CUDADeviceContext>().stream()>>>(
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x->data<T>(), y->data<T>(), out->data<T>(), dout->data<T>(), size,
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dx->mutable_data<T>(ctx.GetPlace()),
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dy->mutable_data<T>(ctx.GetPlace()));
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return;
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} else {
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ElemwiseGradCompute<plat::CUDADeviceContext, T, MulGradDX<T>,
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MulGradDY<T>>(ctx, *x, *y, *out, *dout, axis, dx, dy,
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MulGradDX<T>(), MulGradDY<T>());
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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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REGISTER_OP_CUDA_KERNEL(
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elementwise_mul, ops::ElementwiseMulKernel<plat::CUDADeviceContext, float>,
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ops::ElementwiseMulKernel<plat::CUDADeviceContext, double>,
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ops::ElementwiseMulKernel<plat::CUDADeviceContext, int>,
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ops::ElementwiseMulKernel<plat::CUDADeviceContext, int64_t>,
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ops::ElementwiseMulKernel<plat::CUDADeviceContext, plat::float16>);
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REGISTER_OP_CUDA_KERNEL(
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elementwise_mul_grad,
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ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, float>,
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ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, double>,
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ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, int>,
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ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, int64_t>,
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ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, plat::float16>);
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REGISTER_OP_CUDA_KERNEL(
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elementwise_mul_grad_grad,
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ops::ElementwiseMulDoubleGradKernel<plat::CUDADeviceContext, float>,
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ops::ElementwiseMulDoubleGradKernel<plat::CUDADeviceContext, double>,
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ops::ElementwiseMulDoubleGradKernel<plat::CUDADeviceContext, int>,
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ops::ElementwiseMulDoubleGradKernel<plat::CUDADeviceContext, int64_t>);
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