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108 lines
3.7 KiB
108 lines
3.7 KiB
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/multiplex_op.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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class MultiplexOp : public framework::OperatorWithKernel {
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public:
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MultiplexOp(const std::string &type, 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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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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auto ins = ctx.MultiInput<Tensor>("X");
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auto *out = ctx.Output<Tensor>("Out");
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auto num_ins = ins.size();
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PADDLE_ENFORCE(num_ins > 2,
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"multiplex operator should have more than 2 inputs.");
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PADDLE_ENFORCE_EQ(ins[0]->dims().size(), 1,
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"The first input must be a index vector.");
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auto in_dim = ins[1]->dims();
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for (size_t i = 2; i < num_ins; i++) {
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auto dim = ins[i]->dims();
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PADDLE_ENFORCE(
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in_dim == dim,
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"All the input tensors except the first one must have the same size");
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}
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out->Resize(in_dim);
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}
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};
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class MultiplexOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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MultiplexOpMaker(framework::OpProto *proto,
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framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "The input tensor of multiplex operator.").AsDuplicable();
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AddOutput("Out", "The output tensor of multiplex operator.");
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AddComment(R"DOC(Multiplex operator
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Multiplex multiple tensors according to the index provided by the first
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input tensor.
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ins[0]: the index of the tensor to output of size batchSize.
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ins[1:N]: the candidate output tensor.
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For each index i from 0 to batchSize - 1, the output is the i-th row of the
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the (index[i] + 1)-th tensor.
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For each i-th row of output:
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y[i][j] = x_{k}[i][j], j = 0,1, ... , (x_{1}.width - 1)
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where y is the output tensor. `x_{k}` is the k-th input layer
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and `k = x{0}[i] + 1`.
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)DOC");
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}
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};
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class MultiplexGradOp : public framework::OperatorWithKernel {
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public:
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MultiplexGradOp(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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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
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"Input(Out@GRAD) shouldn't be null.");
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auto d_ins = ctx.MultiOutput<Tensor>(framework::GradVarName("X"));
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auto ins = ctx.MultiInput<Tensor>("X");
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for (size_t i = 0; i < ins.size(); i++) {
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auto dims = ins[i]->dims();
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d_ins[i]->Resize(dims);
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}
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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_OP(multiplex, ops::MultiplexOp, ops::MultiplexOpMaker, multiplex_grad,
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ops::MultiplexGradOp);
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REGISTER_OP_CPU_KERNEL(multiplex, ops::MultiplexCPUKernel<float>);
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REGISTER_OP_CPU_KERNEL(multiplex_grad, ops::MultiplexGradCPUKernel<float>);
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