custom_relu_op.cc

// Copyright (c) 2022 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"

#define CHECK_CPU_INPUT(x) PD_CHECK(x.is_cpu(), #x " must be a CPU Tensor.")
#define CHECK_CUSTOM_INPUT(x) \
  PD_CHECK(x.is_custom_device(), #x " must be a custom Tensor.")

template <typename data_t>
void relu_cpu_forward_kernel(const data_t* x_data,
                             data_t* out_data,
                             int64_t x_numel) {
  PD_CHECK(x_data != nullptr, "x_data is nullptr.");
  PD_CHECK(out_data != nullptr, "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.);
  }
}

template <typename data_t>
void relu_cpu_double_backward_kernel(const data_t* out_data,
                                     const data_t* ddx_data,
                                     data_t* ddout_data,
                                     int64_t ddout_numel) {
  for (int64_t i = 0; i < ddout_numel; ++i) {
    ddout_data[i] =
        ddx_data[i] * (out_data[i] > static_cast<data_t>(0) ? 1. : 0.);
  }
}

std::vector<paddle::Tensor> relu_cpu_forward(const paddle::Tensor& x) {
  CHECK_CPU_INPUT(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_cpu_double_backward(
    const paddle::Tensor& out, const paddle::Tensor& ddx) {
  CHECK_CPU_INPUT(out);
  CHECK_CPU_INPUT(ddx);
  auto ddout = paddle::empty(out.shape(), out.dtype(), out.place());

  PD_DISPATCH_FLOATING_TYPES(out.type(), "relu_cpu_double_backward", ([&] {
                               relu_cpu_double_backward_kernel<data_t>(
                                   out.data<data_t>(),
                                   ddx.data<data_t>(),
                                   ddout.mutable_data<data_t>(out.place()),
                                   ddout.size());
                             }));

  return {ddout};
}

std::vector<paddle::Tensor> relu_custom_forward(const paddle::Tensor& x) {
  CHECK_CUSTOM_INPUT(x);
  auto out = paddle::relu(x);
  return {out};
}

std::vector<paddle::Tensor> relu_custom_backward(
    const paddle::Tensor& x,
    const paddle::Tensor& out,
    const paddle::Tensor& grad_out) {
  CHECK_CUSTOM_INPUT(x);
  CHECK_CUSTOM_INPUT(out);
  auto grad_x = paddle::empty_like(x, x.dtype(), x.place());
  auto ones = paddle::experimental::full_like(x, 1.0, x.dtype(), x.place());
  auto zeros = paddle::experimental::full_like(x, 0.0, x.dtype(), x.place());
  auto condition = paddle::experimental::greater_than(x, zeros);

  grad_x = paddle::multiply(grad_out, paddle::where(condition, ones, zeros));

  return {grad_x};
}

std::vector<paddle::Tensor> relu_custom_double_backward(
    const paddle::Tensor& out, const paddle::Tensor& ddx) {
  CHECK_CUSTOM_INPUT(out);
  auto ddout = paddle::empty(out.shape(), out.dtype(), out.place());
  auto ones =
      paddle::experimental::full_like(out, 1.0, out.dtype(), out.place());
  auto zeros =
      paddle::experimental::full_like(out, 0.0, out.dtype(), out.place());
  auto condition = paddle::experimental::greater_than(out, zeros);

  ddout = paddle::multiply(ddx, paddle::where(condition, ones, zeros));

  return {ddout};
}

std::vector<paddle::Tensor> ReluForward(const paddle::Tensor& x) {
  if (x.is_cpu()) {
    return relu_cpu_forward(x);
  } else if (x.is_custom_device()) {
    return relu_custom_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_custom_device()) {
    return relu_custom_backward(x, out, grad_out);
  } else {
    PD_THROW("Not implemented.");
  }
}

std::vector<paddle::Tensor> ReluDoubleBackward(const paddle::Tensor& out,
                                               const paddle::Tensor& ddx) {
  if (out.is_cpu()) {
    return relu_cpu_double_backward(out, ddx);
  } else if (out.is_custom_device()) {
    return relu_custom_double_backward(out, ddx);
  } else {
    PD_THROW("Not implemented.");
  }
}

std::vector<std::vector<int64_t>> ReluDoubleBackwardInferShape(
    const std::vector<int64_t>& out_shape,
    const std::vector<int64_t>& ddx_shape) {
  return {out_shape};
}

PD_BUILD_OP(custom_relu)
    .Inputs({"X"})
    .Outputs({"Out"})
    .SetKernelFn(PD_KERNEL(ReluForward));

PD_BUILD_GRAD_OP(custom_relu)
    .Inputs({"X", "Out", paddle::Grad("Out")})
    .Outputs({paddle::Grad("X")})
    .SetKernelFn(PD_KERNEL(ReluBackward));

PD_BUILD_DOUBLE_GRAD_OP(custom_relu)
    .Inputs({"Out", paddle::Grad(paddle::Grad("X"))})
    .Outputs({paddle::Grad(paddle::Grad("Out"))})
    .SetKernelFn(PD_KERNEL(ReluDoubleBackward))
    .SetInferShapeFn(PD_INFER_SHAPE(ReluDoubleBackwardInferShape));