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182 lines
7.2 KiB
182 lines
7.2 KiB
/* Copyright (c) 2016 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/reshape_op.h"
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#include <string>
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#include <vector>
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namespace paddle {
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namespace operators {
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class ReshapeOpMaker : 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 reshape operator.");
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AddInput("Shape",
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"(Tensor<int32>, optional). If provided, reshape according to "
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"this given shape. That is to say it has a higher priority than "
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"the shape attribute, while the shape attribute still should be "
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"set correctly to gurantee shape inference in compile time.")
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.AsDispensable();
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AddOutput("Out", "(Tensor). The output tensor of reshape operator.");
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AddAttr<std::vector<int>>(
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"shape", "(std::vector<int>) Target shape of reshape operator.");
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AddAttr<bool>("inplace",
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"(default: false) Change the source tensor's shape without "
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"memory copy. When Attr(inplace) is set true, the output "
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"tensor shares memory with Input(X), otherwise, a new output "
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"tensor is created, and its data are copied from Input(x).")
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.SetDefault(false);
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AddComment(R"DOC(
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Reshape Operator.
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Reshape Input(X) into the shape specified by Attr(shape) or Input(Shape). The
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data in Input(X) are unchanged.
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Examples:
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1. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
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specified by Attr(shape) is [6, 8], the reshape operator will transform Input(X)
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into a 2-D tensor with shape [6, 8] and leaving Input(X)'s data unchanged.
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2. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
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specified by Attr(shape) is [2, 3, -1, 2], the reshape operator will transform
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Input(X) into a 4-D tensor with shape [2, 3, 4, 2] and leaving Input(X)'s data
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unchanged. In this case, one and only dimension of Attr(shape) can be set to -1,
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the value of this dimension is inferred from the total element number of
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Input(X) and remaining dimensions.
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3. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
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specified by Attr(shape) is [-1, 0, 3, 2], the reshape operator will transform
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Input(X) into a 4-D tensor with shape [2, 4, 3, 2] and leaving Input(X)'s data
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unchanged. In this case, besides -1, 0 means the actual dimension value is going
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to be copied from the corresponding dimension of Input(X).
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Note:
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1. One and only one dimension in Attr(shape) can be set -1. In this case,
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the actual dimension value will be infered from the total element number of
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Input(X) and remaining dimensions.
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2. More than one dimensions in Attr(shape) can be set to 0, which means the real
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dimension value will be copied from Input(X) at runtime. Note that the index of
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0 can not exceed Rank(X). For example, Input(X) is a 3-D tensor with shape
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[2, 3, 4], Attr(shape) = [2, 3, 2, 0] is an invalid input.
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3. Input(Shape) has a higher priority than Attr(shape) if it is provided, while
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Attr(shape) still should be set correctly to gurantee shape inference in
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compile-time.
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)DOC");
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}
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};
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class ReshapeGradOp : public framework::OperatorWithKernel {
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public:
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ReshapeGradOp(const std::string &type,
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const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorWithKernel(type, inputs, outputs, attrs) {}
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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) shouldn't be null.");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) shouldn't be null.");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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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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framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
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ctx.device_context());
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}
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};
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void ReshapeKernel::Compute(const framework::ExecutionContext &ctx) const {
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auto *out = ctx.Output<framework::LoDTensor>("Out");
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auto *in = ctx.Input<framework::LoDTensor>("X");
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auto *shape_tensor = ctx.HasInput("Shape")
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? ctx.Input<framework::LoDTensor>("Shape")
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: nullptr;
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framework::DDim out_dims = out->dims();
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if (shape_tensor) {
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auto *shape_data = shape_tensor->data<int>();
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framework::Tensor cpu_shape_tensor;
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if (platform::is_gpu_place(ctx.GetPlace())) {
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TensorCopySync(*shape_tensor, platform::CPUPlace(), &cpu_shape_tensor);
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shape_data = cpu_shape_tensor.data<int>();
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}
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auto shape =
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std::vector<int>(shape_data, shape_data + shape_tensor->numel());
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out_dims = ReshapeOp::ValidateShape(shape, in->dims());
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}
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if (!in->lod().empty()) {
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PADDLE_ENFORCE_EQ(out_dims[0], in->dims()[0],
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"Reshape operator cannot reshape an input sequence batch "
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"into an output sequence batch that has a different "
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"number of time steps. Please consider using "
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"sequence_reshape op.");
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}
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bool inplace = ctx.Attr<bool>("inplace");
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out->Resize(out_dims);
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if (!inplace) {
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out->mutable_data(ctx.GetPlace(), in->type());
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framework::TensorCopySync(*in, ctx.GetPlace(), out);
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out->Resize(out_dims);
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} else {
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out->ShareDataWith(*in);
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out->Resize(out_dims);
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}
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}
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void ReshapeGradKernelBase::Compute(
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const framework::ExecutionContext &ctx) const {
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auto *d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto *d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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d_x->mutable_data(ctx.GetPlace(), d_out->type());
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bool inplace = ctx.Attr<bool>("inplace");
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auto in_dims = d_x->dims();
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if (!inplace) {
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framework::TensorCopy(*d_out, ctx.GetPlace(), ctx.device_context(), d_x);
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ctx.device_context().Wait();
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d_x->Resize(in_dims);
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} else {
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d_x->ShareDataWith(*d_out);
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d_x->Resize(in_dims);
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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(reshape, ops::ReshapeOp, ops::ReshapeOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(reshape_grad, ops::ReshapeGradOp);
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REGISTER_OP_CPU_KERNEL_EX(reshape, float, ops::ReshapeKernel, double,
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ops::ReshapeKernel, int, ops::ReshapeKernel, int64_t,
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ops::ReshapeKernel);
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REGISTER_OP_CPU_KERNEL(reshape_grad, ops::ReshapeGradKernel<float>,
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ops::ReshapeGradKernel<double>,
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ops::ReshapeGradKernel<int>,
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ops::ReshapeGradKernel<int64_t>);
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