wehub-resource-sync
269 lines
9.3 KiB
C++
269 lines
9.3 KiB
C++
// Copyright (c) 2021 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 <iostream>
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#include <vector>
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#include "paddle/extension.h"
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#define CHECK_CPU_INPUT(x) PD_CHECK(x.is_cpu(), #x " must be a CPU Tensor.")
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template <typename data_t>
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void relu_cpu_forward_kernel(const data_t* x_data,
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data_t* out_data,
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int64_t x_numel) {
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PD_CHECK(x_data != nullptr, "x_data is nullptr.");
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PD_CHECK(out_data != nullptr, "out_data is nullptr.");
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for (int64_t i = 0; i < x_numel; ++i) {
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out_data[i] = std::max(static_cast<data_t>(0.), x_data[i]);
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}
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}
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template <typename data_t>
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void relu_cpu_backward_kernel(const data_t* grad_out_data,
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const data_t* out_data,
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data_t* grad_x_data,
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int64_t out_numel) {
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for (int64_t i = 0; i < out_numel; ++i) {
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grad_x_data[i] =
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grad_out_data[i] * (out_data[i] > static_cast<data_t>(0) ? 1. : 0.);
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}
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}
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template <typename data_t>
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void relu_cpu_double_backward_kernel(const data_t* out_data,
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const data_t* ddx_data,
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data_t* ddout_data,
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int64_t ddout_numel) {
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for (int64_t i = 0; i < ddout_numel; ++i) {
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ddout_data[i] =
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ddx_data[i] * (out_data[i] > static_cast<data_t>(0) ? 1. : 0.);
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}
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}
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std::vector<paddle::Tensor> relu_cpu_forward(const paddle::Tensor& x) {
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auto out = paddle::empty_like(x);
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PD_DISPATCH_FLOATING_TYPES(
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x.type(), "relu_cpu_forward", ([&] {
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relu_cpu_forward_kernel<data_t>(
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x.data<data_t>(), out.data<data_t>(), x.numel());
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}));
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return {out};
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}
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std::vector<paddle::Tensor> relu_cpu_backward(const paddle::Tensor& x,
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const paddle::Tensor& out,
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const paddle::Tensor& grad_out) {
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auto grad_x = paddle::empty_like(x);
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PD_DISPATCH_FLOATING_TYPES(out.type(), "relu_cpu_backward", ([&] {
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relu_cpu_backward_kernel<data_t>(
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grad_out.data<data_t>(),
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out.data<data_t>(),
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grad_x.data<data_t>(),
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out.size());
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}));
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return {grad_x};
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}
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std::vector<paddle::Tensor> relu_cpu_double_backward(
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const paddle::Tensor& out, const paddle::Tensor& ddx) {
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CHECK_CPU_INPUT(out);
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CHECK_CPU_INPUT(ddx);
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auto ddout = paddle::empty(out.shape(), out.dtype(), out.place());
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PD_DISPATCH_FLOATING_TYPES(out.type(), "relu_cpu_double_backward", ([&] {
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relu_cpu_double_backward_kernel<data_t>(
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out.data<data_t>(),
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ddx.data<data_t>(),
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ddout.mutable_data<data_t>(out.place()),
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ddout.size());
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}));
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return {ddout};
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}
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std::vector<paddle::Tensor> relu_cuda_forward(const paddle::Tensor& x);
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std::vector<paddle::Tensor> relu_cuda_backward(const paddle::Tensor& x,
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const paddle::Tensor& out,
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const paddle::Tensor& grad_out);
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std::vector<paddle::Tensor> relu_cuda_double_backward(
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const paddle::Tensor& out, const paddle::Tensor& ddx);
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std::vector<paddle::Tensor> ReluForward(const paddle::Tensor& x) {
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if (x.is_cpu()) {
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return relu_cpu_forward(x);
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} else if (x.is_gpu()) {
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return relu_cuda_forward(x);
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} else {
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PD_THROW("Not implemented.");
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}
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}
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std::vector<paddle::Tensor> ReluBackward(const paddle::Tensor& x,
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const paddle::Tensor& out,
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const paddle::Tensor& grad_out) {
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if (x.is_cpu()) {
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return relu_cpu_backward(x, out, grad_out);
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} else if (x.is_gpu()) {
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return relu_cuda_backward(x, out, grad_out);
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} else {
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PD_THROW("Not implemented.");
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}
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}
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std::vector<paddle::Tensor> ReluDoubleBackward(const paddle::Tensor& out,
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const paddle::Tensor& ddx) {
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if (out.is_cpu()) {
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return relu_cpu_double_backward(out, ddx);
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} else if (out.is_gpu()) {
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return relu_cuda_double_backward(out, ddx);
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} else {
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PD_THROW("Not implemented.");
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}
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}
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std::vector<std::vector<int64_t>> ReluDoubleBackwardInferShape(
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const std::vector<int64_t>& out_shape,
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const std::vector<int64_t>& ddx_shape) {
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return {out_shape};
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}
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PD_BUILD_OP(custom_relu)
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.Inputs({"X"})
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.Outputs({"Out"})
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.SetKernelFn(PD_KERNEL(ReluForward));
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PD_BUILD_GRAD_OP(custom_relu)
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.Inputs({"X", "Out", paddle::Grad("Out")})
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.Outputs({paddle::Grad("X")})
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.SetKernelFn(PD_KERNEL(ReluBackward));
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PD_BUILD_DOUBLE_GRAD_OP(custom_relu)
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.Inputs({"Out", paddle::Grad(paddle::Grad("X"))})
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.Outputs({paddle::Grad(paddle::Grad("Out"))})
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.SetKernelFn(PD_KERNEL(ReluDoubleBackward))
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.SetInferShapeFn(PD_INFER_SHAPE(ReluDoubleBackwardInferShape));
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std::vector<paddle::Tensor> relu_cpu_backward_without_x(
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const paddle::Tensor& out, const paddle::Tensor& grad_out) {
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auto grad_x = paddle::empty(out.shape(), out.dtype(), out.place());
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PD_DISPATCH_FLOATING_TYPES(out.type(), "relu_cpu_backward", ([&] {
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relu_cpu_backward_kernel<data_t>(
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grad_out.data<data_t>(),
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out.data<data_t>(),
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grad_x.mutable_data<data_t>(out.place()),
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out.size());
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}));
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return {grad_x};
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}
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std::vector<paddle::Tensor> relu_cuda_backward_without_x(
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const paddle::Tensor& out, const paddle::Tensor& grad_out);
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std::vector<paddle::Tensor> ReluBackwardWithoutX(
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const paddle::Tensor& out, const paddle::Tensor& grad_out) {
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if (out.is_cpu()) {
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return relu_cpu_backward_without_x(out, grad_out);
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} else if (out.is_gpu()) {
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return relu_cuda_backward_without_x(out, grad_out);
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} else {
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PD_THROW("Not implemented.");
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}
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}
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std::vector<std::vector<int64_t>> ReluBackwardWithoutXInferShape(
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const std::vector<int64_t>& out_shape,
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const std::vector<int64_t>& grad_out_shape) {
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return {out_shape};
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}
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PD_BUILD_OP(custom_relu_no_x_in_backward)
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.Inputs({"X"})
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.Outputs({"Out"})
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.SetKernelFn(PD_KERNEL(ReluForward));
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PD_BUILD_GRAD_OP(custom_relu_no_x_in_backward)
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.Inputs({"Out", paddle::Grad("Out")})
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.Outputs({paddle::Grad("X")})
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.SetKernelFn(PD_KERNEL(ReluBackwardWithoutX))
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.SetInferShapeFn(PD_INFER_SHAPE(ReluBackwardWithoutXInferShape));
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void relu_cpu_forward_out(const paddle::Tensor& x, paddle::Tensor* out) {
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out->reshape(x.shape());
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PD_DISPATCH_FLOATING_TYPES(
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x.type(), "relu_cpu_forward", ([&] {
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relu_cpu_forward_kernel<data_t>(
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x.data<data_t>(), out->mutable_data<data_t>(x.place()), x.numel());
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}));
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}
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void relu_cpu_backward_out(const paddle::Tensor& x,
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const paddle::Tensor& out,
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const paddle::Tensor& grad_out,
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paddle::Tensor* grad_x) {
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grad_x->reshape(x.shape());
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PD_DISPATCH_FLOATING_TYPES(out.type(), "relu_cpu_backward", ([&] {
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relu_cpu_backward_kernel<data_t>(
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grad_out.data<data_t>(),
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out.data<data_t>(),
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grad_x->mutable_data<data_t>(x.place()),
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out.size());
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}));
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}
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void relu_cuda_forward_out(const paddle::Tensor& x, paddle::Tensor* out);
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void relu_cuda_backward_out(const paddle::Tensor& x,
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const paddle::Tensor& out,
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const paddle::Tensor& grad_out,
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paddle::Tensor* grad_x);
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void ReluForwardOut(const paddle::Tensor& x, paddle::Tensor* out) {
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if (x.is_cpu()) {
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return relu_cpu_forward_out(x, out);
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} else if (x.is_gpu()) {
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return relu_cuda_forward_out(x, out);
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} else {
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PD_THROW("Not implemented.");
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}
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}
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void ReluBackwardOut(const paddle::Tensor& x,
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const paddle::Tensor& out,
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const paddle::Tensor& grad_out,
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paddle::Tensor* grad_x) {
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if (x.is_cpu()) {
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return relu_cpu_backward_out(x, out, grad_out, grad_x);
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} else if (x.is_gpu()) {
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return relu_cuda_backward_out(x, out, grad_out, grad_x);
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} else {
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PD_THROW("Not implemented.");
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}
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}
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PD_BUILD_OP(custom_relu_out)
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.Inputs({"X"})
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.Outputs({"Out"})
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.SetKernelFn(PD_KERNEL(ReluForwardOut));
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PD_BUILD_GRAD_OP(custom_relu_out)
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.Inputs({"X", "Out", paddle::Grad("Out")})
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.Outputs({paddle::Grad("X")})
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.SetKernelFn(PD_KERNEL(ReluBackwardOut));
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