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88 lines
3.3 KiB
88 lines
3.3 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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#define EIGEN_USE_GPU
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#include "paddle/fluid/operators/elementwise_add_op.h"
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#include "paddle/fluid/platform/float16.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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__global__ void ElementwiseAddCUDAKernel(const T *x, const T *y, T *z, int n,
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int post, int size) {
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int idx_x = threadIdx.x + blockIdx.x * blockDim.x;
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if (idx_x < size) {
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int idx_y = idx_x / post - (idx_x / (n * post)) * n;
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z[idx_x] = x[idx_x] + y[idx_y];
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}
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}
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template <typename T>
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class ElementwiseAddKernel<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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using Tensor = framework::Tensor;
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const auto x = ctx.Input<Tensor>("X");
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const auto y = ctx.Input<Tensor>("Y");
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auto z = ctx.Output<Tensor>("Out");
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auto *z_data = z->mutable_data<T>(ctx.GetPlace());
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auto &device = *(ctx.cuda_device_context().eigen_device());
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const framework::DDim &x_dim = x->dims();
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framework::DDim y_dim = y->dims();
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int size = x->numel();
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if (x_dim == y_dim) {
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auto dim = framework::make_ddim({size});
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auto z_eigen = framework::EigenTensor<T, 1>::From(*z, dim);
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auto x_eigen = framework::EigenTensor<T, 1>::From(*x, dim);
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auto y_eigen = framework::EigenTensor<T, 1>::From(*y, dim);
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z_eigen.device(device) = x_eigen + y_eigen;
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} else {
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int axis = ctx.Attr<int>("axis");
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axis = (axis == -1 ? x_dim.size() - y_dim.size() : axis);
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y_dim = trim_trailing_singular_dims(y_dim);
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axis = (y_dim.size() == 0) ? x_dim.size() : axis;
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int pre, n, post;
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get_mid_dims(x_dim, y_dim, axis, &pre, &n, &post);
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int threads = 512;
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int grids = (size + threads - 1) / threads;
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auto stream = ctx.cuda_device_context().stream();
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ElementwiseAddCUDAKernel<T><<<grids, threads, 0, stream>>>(
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x->data<T>(), y->data<T>(), z_data, n, post, size);
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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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namespace plat = paddle::platform;
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REGISTER_OP_CUDA_KERNEL(
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elementwise_add, ops::ElementwiseAddKernel<plat::CUDADeviceContext, float>,
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ops::ElementwiseAddKernel<plat::CUDADeviceContext, double>,
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ops::ElementwiseAddKernel<plat::CUDADeviceContext, int>,
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ops::ElementwiseAddKernel<plat::CUDADeviceContext, int64_t>,
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ops::ElementwiseAddKernel<plat::CUDADeviceContext, plat::float16>);
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REGISTER_OP_CUDA_KERNEL(
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elementwise_add_grad,
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ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, float>,
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ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, double>,
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ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, int>,
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ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, int64_t>);
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