add expand_as op, test=develop (#20565)
* add expand_as op, test=develop * add expand_as op,test=develop * add expand_as op,test=develop * add nn.py, test=develop * delele paddle_enforce, test=developrevert-20712-fix_depthwise_conv
JesseyXujin
6 years ago
committed by
GitHub
parent
5c41805dc9
commit
2ff18e537f
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/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/expand_as_op.h"
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#include <memory>
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#include <vector>
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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class ExpandAsOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true);
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PADDLE_ENFORCE_EQ(ctx->HasInput("target_tensor"), true);
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PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true);
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auto x_dims = ctx->GetInputDim("X");
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auto target_tensor_dims = ctx->GetInputDim("target_tensor");
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PADDLE_ENFORCE_EQ(static_cast<size_t>(x_dims.size()),
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target_tensor_dims.size(),
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"The rank of input(target_tensor) must be equal "
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"to the rank of Input(X).");
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PADDLE_ENFORCE_LE(x_dims.size(), 6,
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"The rank of Input(X) must not be greater than 6.");
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std::vector<int64_t> out_shape(x_dims.size());
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ctx->SetOutputDim("Out", framework::make_ddim(out_shape));
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}
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};
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class ExpandAsOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"(Tensor, default Tensor<float>). A tensor with rank in [1, 6]."
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"X is the input to be expanded.");
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AddOutput("Out",
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"(Tensor, default Tensor<float>). A tensor with rank in [1, 6]."
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"The rank of Output(Out) have the same with Input(X). "
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"After expanding, size of each dimension of Output(Out) is equal "
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"to size of the corresponding dimension of Input(X) multiplying "
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"the corresponding value given by Attr(expand_times).");
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AddInput("target_tensor", "Expand tensor's shape for each dimension.");
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AddComment(R"DOC(
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Expand as operator tiles the input by given times number. You should set times
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number for each dimension by providing tensor 'expend_tensor'. The rank of X
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should be in [1, 6]. Please note that size of 'expend_tensor' must be the same
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with X's rank. Following is a using case:
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Input(X) is a 3-D tensor with shape [2, 3, 1]:
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[
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[[1], [2], [3]],
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[[4], [5], [6]]
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]
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target_tensors'shape: [2, 6, 2]
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Output(Out) is a 3-D tensor with shape [2, 6, 2]:
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[
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[[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],
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[[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]
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]
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)DOC");
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}
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};
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class ExpandAsGradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true);
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PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true);
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auto x_dims = ctx->GetInputDim("X");
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auto x_grad_name = framework::GradVarName("X");
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if (ctx->HasOutput(x_grad_name)) {
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ctx->SetOutputDim(x_grad_name, x_dims);
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}
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}
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};
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class ExpandAsGradOpDescMaker : public framework::SingleGradOpDescMaker {
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public:
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using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
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protected:
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std::unique_ptr<framework::OpDesc> Apply() const override {
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std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
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op->SetType("expand_as_grad");
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op->SetInput("X", Input("X"));
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op->SetInput("target_tensor", Input("target_tensor"));
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op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
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op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
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op->SetAttrMap(Attrs());
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return op;
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}
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};
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// DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(ExpandGradNoNeedBufVarsInferer, "X");
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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(expand_as, ops::ExpandAsOp, ops::ExpandAsOpMaker,
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ops::ExpandAsGradOpDescMaker);
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REGISTER_OPERATOR(expand_as_grad, ops::ExpandAsGradOp);
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REGISTER_OP_CPU_KERNEL(
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expand_as, ops::ExpandAsKernel<paddle::platform::CPUDeviceContext, float>,
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ops::ExpandAsKernel<paddle::platform::CPUDeviceContext, double>,
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ops::ExpandAsKernel<paddle::platform::CPUDeviceContext, int>,
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ops::ExpandAsKernel<paddle::platform::CPUDeviceContext, bool>);
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REGISTER_OP_CPU_KERNEL(
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expand_as_grad,
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ops::ExpandAsGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::ExpandAsGradKernel<paddle::platform::CPUDeviceContext, double>);
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@ -0,0 +1,22 @@
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/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/expand_as_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(
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expand_as, ops::ExpandAsKernel<paddle::platform::CUDADeviceContext, float>,
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ops::ExpandAsKernel<paddle::platform::CUDADeviceContext, double>,
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ops::ExpandAsKernel<paddle::platform::CUDADeviceContext, int>,
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ops::ExpandAsKernel<paddle::platform::CUDADeviceContext, bool>);
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REGISTER_OP_CUDA_KERNEL(
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expand_as_grad,
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ops::ExpandAsGradKernel<paddle::platform::CUDADeviceContext, float>,
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ops::ExpandAsGradKernel<paddle::platform::CUDADeviceContext, double>);
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@ -0,0 +1,185 @@
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/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 <vector>
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#include <boost/preprocessor/arithmetic/div.hpp>
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#include <boost/preprocessor/arithmetic/mod.hpp>
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#include <boost/preprocessor/comparison/greater.hpp>
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#include <boost/preprocessor/comparison/greater_equal.hpp>
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#include <boost/preprocessor/control/if.hpp>
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#include <boost/preprocessor/repetition/repeat.hpp>
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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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#define MAX_RANK_SUPPORTED 6
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#define EXPAND_AS_TEMPLATE(z, n, data) \
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case n + 1: { \
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ExpandAs<n + 1>(context); \
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break; \
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}
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#define REP_EXPAND_AS_TEMPLATE(n) BOOST_PP_REPEAT(n, EXPAND_AS_TEMPLATE, ~)
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#define COND(n) \
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BOOST_PP_GREATER_EQUAL(BOOST_PP_DIV(n, MAX_RANK_SUPPORTED), \
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BOOST_PP_MOD(n, MAX_RANK_SUPPORTED))
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#define EXPAND_AS_GRAD_CASE(n) \
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case n: { \
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ExpandAsBackward<n>(context, reshape_dims_vec, reduce_dims_vec); \
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break; \
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}
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#define EXPAND_AS_GRAD_TEMPLATE(z, n, data) \
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BOOST_PP_IF(COND(n), EXPAND_AS_GRAD_CASE(n), )
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#define REP_EXPAND_AS_GRAD_TEMPLATE(n) \
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BOOST_PP_REPEAT(n, EXPAND_AS_GRAD_TEMPLATE, ~)
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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, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
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template <typename T, size_t D, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
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template <typename DeviceContext, typename T>
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class ExpandAsKernel : 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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auto rank = context.Input<Tensor>("X")->dims().size();
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switch (rank) {
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REP_EXPAND_AS_TEMPLATE(MAX_RANK_SUPPORTED)
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default:
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PADDLE_THROW("Only support tensor with rank being between 1 and 6.");
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}
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}
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protected:
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template <int Rank>
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void ExpandAs(const framework::ExecutionContext& context) const {
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auto* in0 = context.Input<Tensor>("X");
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auto in_dims = in0->dims();
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auto* target_tensor = context.Input<Tensor>("target_tensor");
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auto* out0 = context.Output<Tensor>("Out");
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Eigen::DSizes<int, Rank> bcast_dims;
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int bcast_dims_remainder = 0;
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auto x_dims = in0->dims();
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auto y_dims = target_tensor->dims();
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for (int i = 0; i < y_dims.size(); ++i) {
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PADDLE_ENFORCE_NE(x_dims[i], 0, "X(input) should not have 0 dim");
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bcast_dims[i] = y_dims[i] / x_dims[i];
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bcast_dims_remainder += y_dims[i] % x_dims[i];
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}
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PADDLE_ENFORCE_EQ(bcast_dims_remainder, 0,
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"X(input) could not be broadcast together with remapped "
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"shape(expand tensor's shape)");
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framework::DDim out_dims(in_dims);
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for (size_t i = 0; i < bcast_dims.size(); ++i) {
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out_dims[i] *= bcast_dims[i];
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}
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out0->Resize(out_dims);
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auto x = EigenTensor<T, Rank>::From(*in0);
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out0->mutable_data<T>(context.GetPlace());
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auto y = EigenTensor<T, Rank>::From(*out0);
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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y.device(place) = x.broadcast(bcast_dims);
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}
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};
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template <typename DeviceContext, typename T>
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class ExpandAsGradKernel : 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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auto* in0 = context.Input<Tensor>("X");
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auto* target_tensor = context.Input<Tensor>("target_tensor");
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auto x_dims = in0->dims();
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auto y_dims = target_tensor->dims();
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std::vector<int> bcast_dims;
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for (int i = 0; i < y_dims.size(); ++i) {
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bcast_dims.push_back(y_dims[i] / x_dims[i]);
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}
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std::vector<int> reshape_dims_vec;
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std::vector<int> reduce_dims_vec;
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for (size_t i = 0; i < bcast_dims.size(); ++i) {
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if (bcast_dims[i] == 1) {
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reshape_dims_vec.push_back(x_dims[i]);
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} else {
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if (x_dims[i] == 1) {
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reduce_dims_vec.push_back(reshape_dims_vec.size());
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reshape_dims_vec.push_back(bcast_dims[i]);
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} else {
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reduce_dims_vec.push_back(reshape_dims_vec.size());
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reshape_dims_vec.push_back(bcast_dims[i]);
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reshape_dims_vec.push_back(x_dims[i]);
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}
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}
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}
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int dims = reshape_dims_vec.size() * MAX_RANK_SUPPORTED +
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reduce_dims_vec.size() - MAX_RANK_SUPPORTED - 1;
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// no need reduce, just copy
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if (reduce_dims_vec.size() == 0) {
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auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
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auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
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out0->mutable_data<T>(context.GetPlace());
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framework::TensorCopy(*in0, context.GetPlace(), context.device_context(),
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out0);
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} else {
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switch (dims) {
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REP_EXPAND_AS_GRAD_TEMPLATE(72)
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default:
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PADDLE_THROW("Only support tensor with rank being between 1 and 6.");
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}
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}
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}
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protected:
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template <int Dims>
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void ExpandAsBackward(const framework::ExecutionContext& context,
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const std::vector<int>& reshape_dims_vec,
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const std::vector<int>& reduce_dims_vec) const {
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size_t reshape_size = Dims / MAX_RANK_SUPPORTED + 1;
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size_t reduce_size = Dims % MAX_RANK_SUPPORTED + 1;
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PADDLE_ENFORCE_EQ(reshape_size, reshape_dims_vec.size(),
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"Inconsistent size between template Dims and "
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"reshape dimensions.");
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PADDLE_ENFORCE_EQ(reduce_size, reduce_dims_vec.size(),
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"Inconsistent size between template Dims and "
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"reduce dimensions.");
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auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
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auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
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out0->mutable_data<T>(context.GetPlace());
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auto x_grad = EigenVector<T>::Flatten(*out0);
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Eigen::DSizes<int, Dims / MAX_RANK_SUPPORTED + 1> reshape_dims;
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for (size_t i = 0; i < reshape_size; ++i) {
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reshape_dims[i] = reshape_dims_vec[i];
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}
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Eigen::DSizes<int, Dims % MAX_RANK_SUPPORTED + 1> reduce_dims;
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for (size_t i = 0; i < reduce_size; ++i) {
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reduce_dims[i] = reduce_dims_vec[i];
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}
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auto out_grad = EigenVector<T>::Flatten(*in0);
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x_grad.device(
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*context.template device_context<DeviceContext>().eigen_device()) =
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out_grad.reshape(reshape_dims)
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.sum(reduce_dims)
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.reshape(x_grad.dimensions());
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}
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};
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,130 @@
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# Copyright (c) 2019 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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from op_test import OpTest
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import paddle.fluid as fluid
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def bcast(x, target_tensor):
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x_dims = x.shape
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y_dims = target_tensor.shape
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bcast_dims = []
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for i in range(len(x_dims)):
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bcast_dims.append(int(y_dims[i] / x_dims[i]))
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bcast_dims = np.array(bcast_dims).astype("int64")
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return bcast_dims
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class TestExpandAsOpRank1(OpTest):
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def setUp(self):
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self.op_type = "expand_as"
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x = np.random.rand(12).astype("float64")
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target_tensor = np.random.rand(24).astype("float64")
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self.inputs = {'X': x, 'target_tensor': target_tensor}
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self.attrs = {}
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bcast_dims = bcast(x, target_tensor)
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output = np.tile(self.inputs['X'], bcast_dims)
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self.outputs = {'Out': output}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestExpandAsOpRank2(OpTest):
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def setUp(self):
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self.op_type = "expand_as"
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x = np.random.rand(2, 3).astype("float64")
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target_tensor = np.random.rand(4, 6).astype("float64")
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self.inputs = {'X': x, 'target_tensor': target_tensor}
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self.attrs = {}
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bcast_dims = bcast(x, target_tensor)
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output = np.tile(self.inputs['X'], bcast_dims)
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self.outputs = {'Out': output}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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|
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class TestExpandAsOpRank3(OpTest):
|
||||
def setUp(self):
|
||||
self.op_type = "expand_as"
|
||||
x = np.random.rand(2, 3, 3).astype("float64")
|
||||
target_tensor = np.random.rand(4, 6, 6).astype("float64")
|
||||
self.inputs = {'X': x, 'target_tensor': target_tensor}
|
||||
self.attrs = {}
|
||||
bcast_dims = bcast(x, target_tensor)
|
||||
output = np.tile(self.inputs['X'], bcast_dims)
|
||||
self.outputs = {'Out': output}
|
||||
|
||||
def test_check_output(self):
|
||||
self.check_output()
|
||||
|
||||
def test_check_grad(self):
|
||||
self.check_grad(['X'], 'Out')
|
||||
|
||||
|
||||
class TestExpandAsOpRank4(OpTest):
|
||||
def setUp(self):
|
||||
self.op_type = "expand_as"
|
||||
x = np.random.rand(1, 1, 3, 16).astype("float64")
|
||||
target_tensor = np.random.rand(4, 6, 6, 32).astype("float64")
|
||||
self.inputs = {'X': x, 'target_tensor': target_tensor}
|
||||
self.attrs = {}
|
||||
bcast_dims = bcast(x, target_tensor)
|
||||
output = np.tile(self.inputs['X'], bcast_dims)
|
||||
self.outputs = {'Out': output}
|
||||
|
||||
def test_check_output(self):
|
||||
self.check_output()
|
||||
|
||||
def test_check_grad(self):
|
||||
self.check_grad(['X'], 'Out')
|
||||
|
||||
|
||||
# Test python API
|
||||
class TestExpandAPI(OpTest):
|
||||
def test_api(self):
|
||||
input1 = np.random.random([12, 14]).astype("float32")
|
||||
input2 = np.random.random([48, 14]).astype("float32")
|
||||
x = fluid.layers.data(
|
||||
name='x', shape=[12, 14], append_batch_size=False, dtype="float32")
|
||||
|
||||
y = fluid.layers.data(
|
||||
name='target_tensor',
|
||||
shape=[48, 14],
|
||||
append_batch_size=False,
|
||||
dtype="float32")
|
||||
|
||||
out_1 = fluid.layers.expand_as(x, target_tensor=y)
|
||||
|
||||
exe = fluid.Executor(place=fluid.CPUPlace())
|
||||
res_1 = exe.run(fluid.default_main_program(),
|
||||
feed={"x": input1,
|
||||
"target_tensor": input2},
|
||||
fetch_list=[out_1])
|
||||
assert np.array_equal(res_1[0], np.tile(input1, (4, 1)))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue