add conj op for complex types (#29527)
* add conj op for complex types * add conj for complex types * add more test case * add conj_op test * modify conj api and impl * add complex type for fill_constant_op xpu * add setConstant for complex type * remove complex conj test file * user define grad for test_conj_op * add test case for static mode of conj api * modify conj doc * change input args name to x * remove useless codes * conj support real types * add conj test case for real numberrevert-31562-mean
chentianyu03
4 years ago
committed by
GitHub
parent
b593d588aa
commit
71063b8137
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// Copyright (c) 2020 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/fluid/operators/conj_op.h"
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#include <memory>
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#include <string>
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#include <unordered_map>
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#include <vector>
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#ifdef PADDLE_WITH_MKLDNN
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#include "paddle/fluid/platform/mkldnn_helper.h"
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#endif
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namespace paddle {
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namespace operators {
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class ConjOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext *ctx) const override {
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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "conj");
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OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "conj");
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auto in_dims = ctx->GetInputDim("X");
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ctx->SetOutputDim("Out", in_dims);
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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};
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class ConjOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "(Tensor), The input tensor of conj op.");
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AddOutput("Out", "(Tensor), The output tensor of conj op.");
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AddComment(R"DOC(
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Conj Operator.
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This operator is used to perform elementwise conjugate for input $X$.
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)DOC");
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}
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};
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template <typename T>
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class ConjGradMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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void Apply(GradOpPtr<T> retv) const override {
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retv->SetType("conj");
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retv->SetInput("X", this->OutputGrad("Out"));
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retv->SetAttrMap(this->Attrs());
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retv->SetOutput("Out", this->InputGrad("X"));
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(conj, ops::ConjOp, ops::ConjOpMaker,
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ops::ConjGradMaker<paddle::framework::OpDesc>,
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ops::ConjGradMaker<paddle::imperative::OpBase>);
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REGISTER_OP_CPU_KERNEL(
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conj, ops::ConjKernel<paddle::platform::CPUDeviceContext,
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paddle::platform::complex64>,
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ops::ConjKernel<paddle::platform::CPUDeviceContext,
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paddle::platform::complex128>,
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ops::ConjKernel<paddle::platform::CPUDeviceContext, float>,
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ops::ConjKernel<paddle::platform::CPUDeviceContext, double>,
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ops::ConjKernel<paddle::platform::CPUDeviceContext, int>,
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ops::ConjKernel<paddle::platform::CPUDeviceContext, int64_t>);
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@ -0,0 +1,28 @@
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// Copyright (c) 2020 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/fluid/operators/conj_op.h"
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#include "paddle/fluid/platform/complex128.h"
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#include "paddle/fluid/platform/complex64.h"
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(
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conj, ops::ConjKernel<paddle::platform::CUDADeviceContext,
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paddle::platform::complex64>,
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ops::ConjKernel<paddle::platform::CUDADeviceContext,
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paddle::platform::complex128>,
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ops::ConjKernel<paddle::platform::CUDADeviceContext, float>,
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ops::ConjKernel<paddle::platform::CUDADeviceContext, double>,
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ops::ConjKernel<paddle::platform::CUDADeviceContext, int>,
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ops::ConjKernel<paddle::platform::CUDADeviceContext, int64_t>);
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@ -0,0 +1,85 @@
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// Copyright (c) 2020 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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#pragma once
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/fluid/platform/for_range.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T>
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using EnableComplex =
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typename std::enable_if<std::is_same<T, platform::complex64>::value ||
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std::is_same<T, platform::complex128>::value>::type;
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template <typename T>
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using DisableComplex = typename std::enable_if<
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!std::is_same<T, platform::complex64>::value &&
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!std::is_same<T, platform::complex128>::value>::type;
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template <typename T, typename Enable = void>
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struct ConjFunctor;
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template <typename T>
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struct ConjFunctor<T, EnableComplex<T>> {
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ConjFunctor(const T* input, int64_t numel, T* output)
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: input_(input), numel_(numel), output_(output) {}
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HOSTDEVICE void operator()(size_t idx) const {
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output_[idx] = T(input_[idx].real, -input_[idx].imag);
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}
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const T* input_;
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int64_t numel_;
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T* output_;
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};
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template <typename T>
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struct ConjFunctor<T, DisableComplex<T>> {
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ConjFunctor(const T* input, int64_t numel, T* output)
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: input_(input), numel_(numel), output_(output) {}
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HOSTDEVICE void operator()(size_t idx) const { output_[idx] = input_[idx]; }
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const T* input_;
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int64_t numel_;
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T* output_;
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};
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template <typename DeviceContext, typename T>
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class ConjKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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const Tensor* x = context.Input<Tensor>("X");
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Tensor* out = context.Output<Tensor>("Out");
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auto numel = x->numel();
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auto* x_data = x->data<T>();
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auto* out_data = out->mutable_data<T>(context.GetPlace(),
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size_t(x->numel() * sizeof(T)));
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auto& dev_ctx = context.template device_context<DeviceContext>();
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platform::ForRange<DeviceContext> for_range(dev_ctx, numel);
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ConjFunctor<T> functor(x_data, numel, out_data);
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for_range(functor);
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}
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};
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DECLARE_INPLACE_OP_INFERER(ConjOpInplaceInferer, {"X", "Out"});
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,126 @@
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# Copyright (c) 2020 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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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle
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import paddle.fluid.core as core
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import sys
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sys.path.append("..")
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from op_test import OpTest
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from paddle.fluid import Program, program_guard
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import paddle.fluid.dygraph as dg
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import paddle.static as static
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from numpy.random import random as rand
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paddle.enable_static()
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class TestConjOp(OpTest):
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def setUp(self):
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self.op_type = "conj"
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self.init_dtype_type()
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self.init_input_output()
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self.init_grad_input_output()
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def init_dtype_type(self):
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self.dtype = np.complex64
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def init_input_output(self):
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x = (np.random.random((12, 14)) + 1j * np.random.random(
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(12, 14))).astype(self.dtype)
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out = np.conj(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def init_grad_input_output(self):
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self.grad_out = (np.ones((12, 14)) + 1j * np.ones(
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(12, 14))).astype(self.dtype)
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self.grad_in = np.conj(self.grad_out)
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(
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['X'],
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'Out',
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user_defined_grads=[self.grad_in],
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user_defined_grad_outputs=[self.grad_out])
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class TestComplexConjOp(unittest.TestCase):
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def setUp(self):
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self._dtypes = ["float32", "float64"]
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self._places = [paddle.CPUPlace()]
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if paddle.is_compiled_with_cuda():
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self._places.append(paddle.CUDAPlace(0))
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def test_conj_api(self):
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for dtype in self._dtypes:
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input = rand([2, 20, 2, 3]).astype(dtype) + 1j * rand(
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[2, 20, 2, 3]).astype(dtype)
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for place in self._places:
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with dg.guard(place):
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var_x = paddle.to_tensor(input)
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result = paddle.conj(var_x).numpy()
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target = np.conj(input)
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self.assertTrue(np.array_equal(result, target))
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def test_conj_operator(self):
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for dtype in self._dtypes:
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input = rand([2, 20, 2, 3]).astype(dtype) + 1j * rand(
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[2, 20, 2, 3]).astype(dtype)
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for place in self._places:
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with dg.guard(place):
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var_x = paddle.to_tensor(input)
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result = var_x.conj().numpy()
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target = np.conj(input)
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self.assertTrue(np.array_equal(result, target))
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def test_conj_static_mode(self):
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def init_input_output(dtype):
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input = rand([2, 20, 2, 3]).astype(dtype) + 1j * rand(
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[2, 20, 2, 3]).astype(dtype)
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return {'x': input}, np.conj(input)
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for dtype in self._dtypes:
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input_dict, np_res = init_input_output(dtype)
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for place in self._places:
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with static.program_guard(static.Program()):
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x_dtype = np.complex64 if dtype == "float32" else np.complex128
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x = static.data(
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name="x", shape=[2, 20, 2, 3], dtype=x_dtype)
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out = paddle.conj(x)
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exe = static.Executor(place)
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out_value = exe.run(feed=input_dict, fetch_list=[out.name])
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self.assertTrue(np.array_equal(np_res, out_value[0]))
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def test_conj_api_real_number(self):
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for dtype in self._dtypes:
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input = rand([2, 20, 2, 3]).astype(dtype)
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for place in self._places:
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with dg.guard(place):
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var_x = paddle.to_tensor(input)
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result = paddle.conj(var_x).numpy()
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target = np.conj(input)
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self.assertTrue(np.array_equal(result, target))
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if __name__ == "__main__":
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unittest.main()
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