wehub-resource-sync
123 lines
3.9 KiB
C++
123 lines
3.9 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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template <typename data_t>
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void assign_cpu_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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for (int i = 0; i < x_numel; ++i) {
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out_data[i] = x_data[i];
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}
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}
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template <typename data_t>
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void fill_constant_cpu_kernel(data_t* out_data, int64_t x_numel, data_t value) {
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for (int i = 0; i < x_numel; ++i) {
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out_data[i] = value;
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}
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}
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std::vector<paddle::Tensor> MultiOutCPU(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(), "assign_cpu_kernel", ([&] {
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assign_cpu_kernel<data_t>(
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x.data<data_t>(), out.mutable_data<data_t>(x.place()), x.size());
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}));
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// fake multi output: Fake_float64 with float64 dtype
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auto fake_float64 = paddle::empty_like(x);
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fill_constant_cpu_kernel<double>(
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fake_float64.mutable_data<double>(x.place()), x.size(), 0.);
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// fake multi output: ZFake_int32 with int32 dtype
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auto zfake_int32 = paddle::empty_like(x);
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fill_constant_cpu_kernel<int32_t>(
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zfake_int32.mutable_data<int32_t>(x.place()), x.size(), 1);
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return {out, fake_float64, zfake_int32};
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}
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std::vector<std::vector<int64_t>> InferShape(std::vector<int64_t> x_shape) {
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return {x_shape, x_shape, x_shape};
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}
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std::vector<paddle::DataType> InferDtype(paddle::DataType x_dtype) {
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return {x_dtype, paddle::DataType::FLOAT64, paddle::DataType::INT32};
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}
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// out = w * 1 + x * 2 + y * 3 + z * 4
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std::vector<paddle::Tensor> DiscreteOutForward(const paddle::Tensor& w,
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const paddle::Tensor& x,
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const paddle::Tensor& y,
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const paddle::Tensor& z) {
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paddle::Tensor out = w * 1 + x * 2 + y * 3 + z * 4;
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return {out};
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}
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std::vector<std::vector<int64_t>> DiscreteOutInferShape(
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const std::vector<int64_t>& w_shape,
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const std::vector<int64_t>& x_shape,
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const std::vector<int64_t>& y_shape,
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const std::vector<int64_t>& z_shape) {
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return {w_shape};
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}
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std::vector<paddle::DataType> DiscreteOutInferDtype(
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const paddle::DataType& w_dtype,
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const paddle::DataType& x_dtype,
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const paddle::DataType& y_dtype,
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const paddle::DataType& z_dtype) {
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return {w_dtype};
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}
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// w_grad = out_grad
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// y_grad = out_grad * 3
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std::vector<paddle::Tensor> DiscreteOutBackward(
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const paddle::Tensor& w,
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const paddle::Tensor& x,
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const paddle::Tensor& y,
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const paddle::Tensor& z,
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const paddle::Tensor& out_grad) {
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return {out_grad, out_grad * 3};
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}
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PD_BUILD_OP(multi_out)
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.Inputs({"X"})
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.Outputs({"Out", "Fake_float64", "ZFake_int32"})
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.SetKernelFn(PD_KERNEL(MultiOutCPU))
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.SetInferShapeFn(PD_INFER_SHAPE(InferShape))
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.SetInferDtypeFn(PD_INFER_DTYPE(InferDtype));
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PD_BUILD_OP(discrete_out)
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.Inputs({"w", "x", "y", "z"})
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.Outputs({"output"})
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.SetKernelFn(PD_KERNEL(DiscreteOutForward))
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.SetInferShapeFn(PD_INFER_SHAPE(DiscreteOutInferShape))
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.SetInferDtypeFn(PD_INFER_DTYPE(DiscreteOutInferDtype));
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// Test gradient operator whose output order is discrete.
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PD_BUILD_GRAD_OP(discrete_out)
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.Inputs({"w", "x", "y", "z", paddle::Grad("output")})
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.Outputs({paddle::Grad("w"), paddle::Grad("y")})
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.SetKernelFn(PD_KERNEL(DiscreteOutBackward));
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