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105 lines
4.0 KiB
105 lines
4.0 KiB
/* 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/rowwise_add_op.h"
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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class RowwiseAddOp : 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(const framework::InferShapeContext &ctx) const override {
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
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"Input(X) of RowwiseAddOp should not be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("b"),
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"Input(b) of RowwiseAddOp should not be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
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"Output(Out) of RowwiseAddOp should not be null.");
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auto x_dims = ctx.Input<Tensor>("X")->dims();
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auto b_dims = ctx.Input<Tensor>("b")->dims();
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PADDLE_ENFORCE_GT(
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x_dims.size(), b_dims.size(),
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"The rank of input `X` must be larger than the one of input `b`.");
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int num_col_dims = x_dims.size() - b_dims.size();
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PADDLE_ENFORCE_EQ(
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framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims,
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"The width of two operands must be same");
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PADDLE_ENFORCE_EQ(ctx.OutputSize("Out"), 1, "The output size must be 1");
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ctx.Output<framework::Tensor>("Out")->Resize(x_dims);
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ctx.ShareLoD("X", /*->*/ "Out");
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}
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};
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class RowwiseAddOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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RowwiseAddOpMaker(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 left input of row-wise add op, must be matrix");
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AddInput("b", "The right input of row-wise add op, must be vector");
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AddOutput("Out", "The output of row-wise add op");
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AddComment(R"DOC(Row-wise Add operator
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for i in xrange(X.shape[0]):
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Out = X[i] + b
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)DOC");
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}
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};
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class RowwiseAddGradOp : 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(const framework::InferShapeContext &ctx) const override {
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X should not be null");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("b"), "b should not be null");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
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"Input(Out@GRAD) should not be null");
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auto x_dims = ctx.Input<Tensor>("X")->dims();
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auto b_dims = ctx.Input<Tensor>("b")->dims();
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PADDLE_ENFORCE_GT(
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x_dims.size(), b_dims.size(),
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"The rank of input `X` must be larger than the one of input `b`.");
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int num_col_dims = x_dims.size() - b_dims.size();
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PADDLE_ENFORCE_EQ(
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framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims,
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"The width of two operands must be same");
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auto *dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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auto *db = ctx.Output<framework::Tensor>(framework::GradVarName("b"));
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if (dx) dx->Resize(x_dims);
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if (db) db->Resize(b_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_OP(rowwise_add, ops::RowwiseAddOp, ops::RowwiseAddOpMaker,
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rowwise_add_grad, ops::RowwiseAddGradOp);
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REGISTER_OP_CPU_KERNEL(
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rowwise_add, ops::RowwiseAddKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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rowwise_add_grad,
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ops::RowwiseAddGradKernel<paddle::platform::CPUPlace, float>);
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