wehub-resource-sync
113 lines
3.9 KiB
C++
113 lines
3.9 KiB
C++
// Copyright (c) 2022 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/extension.h"
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#define CHECK_INPUT(x) \
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PADDLE_ENFORCE_EQ( \
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x.is_cpu(), true, common::errors::Fatal(#x " must be a CPU Tensor."))
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template <typename data_t>
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void leaky_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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float alpha) {
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// x < 0.0f ? alpha * x : x
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for (int i = 0; i < x_numel; ++i) {
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if (x_data[i] > static_cast<data_t>(0.)) {
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out_data[i] = x_data[i];
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} else {
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out_data[i] = static_cast<data_t>(alpha) * x_data[i];
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}
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}
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}
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template <typename data_t>
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void leaky_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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float alpha) {
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// (grad * (x < 0.0f ? alpha : 1))
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for (int i = 0; i < out_numel; ++i) {
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if (out_data[i]<out_data[i]> static_cast<data_t>(0)) {
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grad_x_data[i] = static_cast<data_t>(alpha);
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} else {
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grad_x_data[i] = static_cast<data_t>(1.);
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}
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}
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}
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std::vector<paddle::Tensor> LeakyReluCPUForward(const paddle::Tensor& x,
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double alpha) {
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CHECK_INPUT(x);
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auto out = paddle::Tensor(x);
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PD_DISPATCH_FLOATING_TYPES(x.type(), "relu_cpu_forward_kernel", ([&] {
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leaky_relu_cpu_forward_kernel<data_t>(
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x.data<data_t>(),
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out.mutable_data<data_t>(x.place()),
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x.size(),
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alpha);
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}));
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return {out};
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}
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std::vector<paddle::Tensor> LeakyReluCPUBackward(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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double alpha) {
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CHECK_INPUT(x);
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CHECK_INPUT(out);
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CHECK_INPUT(grad_out);
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auto grad_x = paddle::Tensor(x);
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PD_DISPATCH_FLOATING_TYPES(out.type(), "relu_cpu_backward_kernel", ([&] {
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leaky_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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alpha);
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}));
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return {grad_x};
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}
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std::vector<std::vector<int64_t>> LeakyReluInferShape(
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std::vector<int64_t> x_shape) {
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return {x_shape};
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}
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std::vector<paddle::DataType> LeakyReluInferDtype(paddle::DataType x_dtype) {
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return {x_dtype};
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}
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PD_BUILD_OP(custom_leaky_relu)
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.Inputs({"X"})
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.Outputs({"Out"})
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.Attrs({"alpha: float"})
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.SetKernelFn(PD_KERNEL(LeakyReluCPUForward))
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.SetInferShapeFn(PD_INFER_SHAPE(LeakyReluInferShape))
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.SetInferDtypeFn(PD_INFER_DTYPE(LeakyReluInferDtype));
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PD_BUILD_GRAD_OP(custom_leaky_relu)
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.Inputs({"X", "Out", paddle::Grad("Out")})
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.Outputs({paddle::Grad("X")})
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.Attrs({"alpha: float"})
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.SetKernelFn(PD_KERNEL(LeakyReluCPUBackward));
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