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121 lines
5.1 KiB
121 lines
5.1 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/squared_l2_distance_op.h"
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namespace paddle {
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namespace operators {
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class SquaredL2DistanceOp : 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(ctx->HasInput("X"),
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"Input(X) of SquaredL2DistanceOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Y"),
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"Input(Y) of SquaredL2DistanceOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("sub_result"),
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"Output(sub_result) of SquaredL2DistanceOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of SquaredL2DistanceOp should not be null.");
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auto x_dims = ctx->GetInputDim("X");
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auto y_dims = ctx->GetInputDim("Y");
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PADDLE_ENFORCE_EQ(framework::arity(x_dims), framework::arity(y_dims),
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"Tensor rank of both SquaredL2DistanceOp's "
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"inputs must be same.");
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int rank = framework::arity(x_dims);
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PADDLE_ENFORCE_GE(rank, 2, "Tensor rank should be at least equal to 2.");
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PADDLE_ENFORCE_EQ(product(x_dims) / x_dims[0], product(y_dims) / y_dims[0],
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"Product of dimensions expcet the first dimension of "
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"input and target must be equal.");
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PADDLE_ENFORCE(y_dims[0] == 1 || y_dims[0] == x_dims[0],
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"First dimension of target must be equal to input "
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"or to 1.");
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ctx->SetOutputDim("sub_result", {x_dims[0], product(x_dims) / x_dims[0]});
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ctx->SetOutputDim("Out", {x_dims[0], 1});
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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};
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class SquaredL2DistanceOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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SquaredL2DistanceOpMaker(OpProto* proto, OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "(Tensor) Input of SquaredL2DistanceOp.");
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AddInput("Y", "(Tensor) Target of SquaredL2DistanceOp.");
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AddOutput("sub_result",
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"(Tensor) Buffering subtraction result which "
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"will be reused in backward.")
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.AsIntermediate();
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AddOutput("Out", "(Tensor) Squared l2 distance between input and target.");
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AddComment(R"DOC(
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SquaredL2Distance operator
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This operator will cacluate the squared L2 distance for the input and
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the target. Number of distance value will be equal to the first dimension
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of input. First dimension of the target could be equal to the input or to 1.
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If the first dimension of target is 1, the operator will broadcast target's
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first dimension to input's first dimension. During backward propagation,
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the user can decide whether to calculate the gradient of the input or
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the target or both.
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Both the input X and Y can carry the LoD (Level of Details) information.
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However, the output only shares the LoD information with input X.
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)DOC");
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}
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};
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class SquaredL2DistanceGradOp : 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(ctx->HasInput(framework::GradVarName("Out")),
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"Gradient of Out should not be null");
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auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
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auto x_dims = ctx->GetInputDim("X");
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auto y_dims = ctx->GetInputDim("Y");
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PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0],
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"First dimension of output gradient and "
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"input value must be equal.");
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PADDLE_ENFORCE_EQ(out_dims[1], 1,
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"Second dimension of output gradient "
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"must be 1.");
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auto x_grad_name = framework::GradVarName("X");
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auto y_grad_name = framework::GradVarName("Y");
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if (ctx->HasOutput(x_grad_name)) ctx->SetOutputDim(x_grad_name, x_dims);
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if (ctx->HasOutput(y_grad_name)) ctx->SetOutputDim(y_grad_name, y_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(squared_l2_distance, ops::SquaredL2DistanceOp,
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ops::SquaredL2DistanceOpMaker, squared_l2_distance_grad,
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ops::SquaredL2DistanceGradOp);
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REGISTER_OP_CPU_KERNEL(
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squared_l2_distance,
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ops::SquaredL2DistanceKernel<paddle::platform::CPUDeviceContext, float>);
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REGISTER_OP_CPU_KERNEL(squared_l2_distance_grad,
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ops::SquaredL2DistanceGradKernel<
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paddle::platform::CPUDeviceContext, float>);
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