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123 lines
4.8 KiB
123 lines
4.8 KiB
// Copyright (c) 2018 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/allclose_op.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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namespace paddle {
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namespace operators {
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class AllcloseOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("Input", "The first input tensor to compare.");
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AddInput("Other", "The second input tensor to compare.");
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AddOutput("Out", "The output tensor of allclose op.");
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AddAttr<float>("rtol", "The relative tolerance. Default: :math:`1e-5` .")
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.SetDefault(1e-5);
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AddAttr<float>("atol", "The absolute tolerance. Default: :math:`1e-8` .")
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.SetDefault(1e-8);
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AddAttr<bool>("equal_nan",
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"If :math:`True` , then two :math:`NaNs` will be "
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"compared as equal. Default: :math:`False` .")
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.SetDefault(false);
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AddComment(R"DOC(
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This operator checks if all :math:`input` and :math:`other` satisfy the condition:
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:math:`\left| input - other \right| \leq atol + rtol \times \left| other \right|`
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elementwise, for all elements of :math:`input` and :math:`other`. The behaviour of this
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operator is analogous to :math:`numpy.allclose`, namely that it returns :math:`True` if
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two tensors are elementwise equal within a tolerance.
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)DOC");
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}
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};
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class AllcloseOp : 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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PADDLE_ENFORCE_EQ(ctx->HasInput("Input"), true,
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platform::errors::NotFound(
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"Input(Input) of allclose op should not be null."));
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PADDLE_ENFORCE_EQ(ctx->HasInput("Other"), true,
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platform::errors::NotFound(
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"Input(Other) of allclose op should not be null."));
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PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
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platform::errors::NotFound(
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"The output(Out) of allclose op must not be null."));
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auto input_dim = ctx->GetInputDim("Input");
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auto other_dim = ctx->GetInputDim("Other");
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PADDLE_ENFORCE_EQ(input_dim.size(), other_dim.size(),
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platform::errors::PreconditionNotMet(
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"Input(Input) and Input(Other) must have the same "
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"dimension size."));
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int n = input_dim.size();
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bool is_runtime = ctx->IsRuntime();
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for (int i = 0; i < n; i++) {
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if (is_runtime) {
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PADDLE_ENFORCE_EQ(input_dim[i], other_dim[i],
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platform::errors::PreconditionNotMet(
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"The value at dim %d of Input(Input) is not "
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"equal to the Input(Other): %ld != %ld.",
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i, input_dim[i], other_dim[i]));
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} else {
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if (!(input_dim[i] < 0 || other_dim[i] < 0)) {
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PADDLE_ENFORCE_EQ(input_dim[i], other_dim[i],
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platform::errors::PreconditionNotMet(
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"The value at dim %d of Input(Input) is not "
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"equal to the Input(Other): %ld != %ld.",
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i, input_dim[i], other_dim[i]));
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}
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}
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}
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ctx->SetOutputDim("Out", framework::make_ddim({1}));
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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return framework::OpKernelType(
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OperatorWithKernel::IndicateVarDataType(ctx, "Input"),
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ctx.device_context());
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}
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};
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class AllcloseOpVarTypeInference : public framework::VarTypeInference {
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public:
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void operator()(framework::InferVarTypeContext *ctx) const override {
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ctx->SetOutputDataType("Out", framework::proto::VarType::BOOL);
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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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using CPU = paddle::platform::CPUDeviceContext;
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REGISTER_OPERATOR(
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allclose, ops::AllcloseOp, ops::AllcloseOpMaker,
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paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
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paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
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ops::AllcloseOpVarTypeInference);
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REGISTER_OP_CPU_KERNEL(allclose, ops::AllcloseKernel<CPU, float>,
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ops::AllcloseKernel<CPU, double>);
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