wehub-resource-sync
139 lines
4.7 KiB
C++
139 lines
4.7 KiB
C++
// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
#include <iostream>
|
|
#include <vector>
|
|
|
|
#include "paddle/extension.h"
|
|
#include "paddle/phi/api/ext/spmd_infer.h"
|
|
#include "paddle/phi/infermeta/spmd_rules/rules.h"
|
|
|
|
#define CHECK_CPU_INPUT(x) \
|
|
PADDLE_ENFORCE_EQ( \
|
|
x.is_cpu(), true, common::errors::Fatal(#x " must be a CPU Tensor."))
|
|
|
|
template <typename data_t>
|
|
void relu_cpu_forward_kernel(const data_t* x_data,
|
|
data_t* out_data,
|
|
int64_t x_numel) {
|
|
PADDLE_ENFORCE_NE(
|
|
x_data, nullptr, common::errors::Fatal("x_data is nullptr."));
|
|
PADDLE_ENFORCE_NE(
|
|
out_data, nullptr, common::errors::Fatal("out_data is nullptr."));
|
|
for (int64_t i = 0; i < x_numel; ++i) {
|
|
out_data[i] = std::max(static_cast<data_t>(0.), x_data[i]);
|
|
}
|
|
}
|
|
|
|
template <typename data_t>
|
|
void relu_cpu_backward_kernel(const data_t* grad_out_data,
|
|
const data_t* out_data,
|
|
data_t* grad_x_data,
|
|
int64_t out_numel) {
|
|
for (int64_t i = 0; i < out_numel; ++i) {
|
|
grad_x_data[i] =
|
|
grad_out_data[i] * (out_data[i] > static_cast<data_t>(0) ? 1. : 0.);
|
|
}
|
|
}
|
|
|
|
std::vector<paddle::Tensor> relu_cpu_forward(const paddle::Tensor& x) {
|
|
auto out = paddle::empty_like(x);
|
|
|
|
PD_DISPATCH_FLOATING_TYPES(
|
|
x.type(), "relu_cpu_forward", ([&] {
|
|
relu_cpu_forward_kernel<data_t>(
|
|
x.data<data_t>(), out.data<data_t>(), x.numel());
|
|
}));
|
|
|
|
return {out};
|
|
}
|
|
|
|
std::vector<paddle::Tensor> relu_cpu_backward(const paddle::Tensor& x,
|
|
const paddle::Tensor& out,
|
|
const paddle::Tensor& grad_out) {
|
|
auto grad_x = paddle::empty_like(x);
|
|
|
|
PD_DISPATCH_FLOATING_TYPES(out.type(), "relu_cpu_backward", ([&] {
|
|
relu_cpu_backward_kernel<data_t>(
|
|
grad_out.data<data_t>(),
|
|
out.data<data_t>(),
|
|
grad_x.data<data_t>(),
|
|
out.size());
|
|
}));
|
|
|
|
return {grad_x};
|
|
}
|
|
|
|
std::vector<paddle::Tensor> relu_cuda_forward(const paddle::Tensor& x);
|
|
std::vector<paddle::Tensor> relu_cuda_backward(const paddle::Tensor& x,
|
|
const paddle::Tensor& out,
|
|
const paddle::Tensor& grad_out);
|
|
|
|
std::vector<paddle::Tensor> ReluForward(const paddle::Tensor& x) {
|
|
if (x.is_cpu()) {
|
|
return relu_cpu_forward(x);
|
|
} else if (x.is_gpu()) {
|
|
return relu_cuda_forward(x);
|
|
} else {
|
|
PD_THROW("Not implemented.");
|
|
}
|
|
}
|
|
|
|
std::vector<paddle::Tensor> ReluBackward(const paddle::Tensor& x,
|
|
const paddle::Tensor& out,
|
|
const paddle::Tensor& grad_out) {
|
|
if (x.is_cpu()) {
|
|
return relu_cpu_backward(x, out, grad_out);
|
|
} else if (x.is_gpu()) {
|
|
return relu_cuda_backward(x, out, grad_out);
|
|
} else {
|
|
PD_THROW("Not implemented.");
|
|
}
|
|
}
|
|
|
|
phi::distributed::SpmdInfo ReluGradInferSpmd(
|
|
const phi::distributed::DistMetaTensor& x,
|
|
const phi::distributed::DistMetaTensor& out,
|
|
const phi::distributed::DistMetaTensor& out_grad) {
|
|
return phi::distributed::ElementwiseUnaryGradInferSpmd(x, out, out_grad);
|
|
}
|
|
|
|
PD_BUILD_OP(custom_relu)
|
|
.Inputs({"X"})
|
|
.Outputs({"Out"})
|
|
.SetKernelFn(PD_KERNEL(ReluForward))
|
|
.SetInferSpmdFn(
|
|
PD_INFER_SPMD_RULE(phi::distributed::ElementwiseUnaryInferSpmd));
|
|
|
|
PD_BUILD_GRAD_OP(custom_relu)
|
|
.Inputs({"X", "Out", paddle::Grad("Out")})
|
|
.Outputs({paddle::Grad("X")})
|
|
.SetKernelFn(PD_KERNEL(ReluBackward))
|
|
.SetInferSpmdFn(PD_INFER_SPMD_RULE(ReluGradInferSpmd));
|
|
|
|
PD_BUILD_OP(custom_relu_no_spmd)
|
|
.Inputs({"X"})
|
|
.Outputs({"Out"})
|
|
.SetKernelFn(PD_KERNEL(ReluForward));
|
|
|
|
PD_BUILD_GRAD_OP(custom_relu_no_spmd)
|
|
.Inputs({"X", "Out", paddle::Grad("Out")})
|
|
.Outputs({paddle::Grad("X")})
|
|
.SetKernelFn(PD_KERNEL(ReluBackward));
|
|
|
|
PD_REGISTER_SPMD_RULE(
|
|
custom_relu,
|
|
PD_INFER_SPMD(phi::distributed::ElementwiseUnaryInferSpmd),
|
|
PD_INFER_SPMD(phi::distributed::ElementwiseUnaryInferSpmdReverse));
|