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199 lines
8.0 KiB
199 lines
8.0 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/squared_l2_distance_op.h"
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#include <memory>
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#include "paddle/fluid/framework/no_need_buffer_vars_inference.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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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SquaredL2DistanceOp");
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OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "SquaredL2DistanceOp");
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OP_INOUT_CHECK(ctx->HasOutput("sub_result"), "Output", "sub_result",
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"SquaredL2DistanceOp");
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OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out",
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"SquaredL2DistanceOp");
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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(
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framework::arity(x_dims), framework::arity(y_dims),
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platform::errors::InvalidArgument(
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"Input(X) and Input(X) of SquaredL2DistanceOp should ",
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"have same dimensions.",
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"But received X's shape = [%s] and Y's shape = [%s],",
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"the dimensions are %d and %d respectively", x_dims, y_dims,
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framework::arity(x_dims), framework::arity(y_dims)));
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int rank = framework::arity(x_dims);
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PADDLE_ENFORCE_GE(
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rank, 2,
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platform::errors::InvalidArgument(
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"Input dimensions of SquaredL2DistanceOp should be ", "at least 2.",
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"But received shape = [%s] and dimension is %d.", x_dims, rank));
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bool check = true;
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if ((!ctx->IsRuntime()) &&
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(framework::product(x_dims) <= 0 || framework::product(y_dims) <= 0)) {
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check = false;
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}
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if (check) {
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PADDLE_ENFORCE_EQ(
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product(x_dims) / x_dims[0], product(y_dims) / y_dims[0],
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platform::errors::InvalidArgument(
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"Input(X) and Input(Y) of SquaredL2DistanceOp should ",
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"have same dimensions.",
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"But received X's shape = [%s] and Y's shape = [%s]",
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", the products are %d and %d respectively", x_dims, y_dims,
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product(x_dims) / x_dims[0], product(y_dims) / y_dims[0]));
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}
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check = true;
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if ((!ctx->IsRuntime()) && (y_dims[0] <= 0 || x_dims[0] <= 0)) {
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check = false;
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}
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if (check) {
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PADDLE_ENFORCE_EQ(
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y_dims[0] == 1 || y_dims[0] == x_dims[0], true,
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platform::errors::InvalidArgument(
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"First dimension of Input(Y) of SquaredL2DistanceOp ",
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"must be equal to 1", "or to first dimension of Input(X).",
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"But received X's shape = [%s] and Y's shape = [%s],",
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"the first dimensions are %d and %d respectively", x_dims, y_dims,
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x_dims[0], y_dims[0]));
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}
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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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DECLARE_NO_NEED_BUFFER_VARS_INFERER(SquaredL2DistanceGradOpNoBuffer, "X", "Y");
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template <typename T>
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class SquaredL2DistanceGradOpMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> op) const override {
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op->SetType("squared_l2_distance_grad");
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op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
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op->SetInput("sub_result", this->Output("sub_result"));
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op->SetInput("X", this->Input("X"));
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op->SetInput("Y", this->Input("Y"));
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op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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op->SetOutput(framework::GradVarName("Y"), this->InputGrad("Y"));
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op->SetAttrMap(this->Attrs());
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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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void Make() override {
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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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OP_INOUT_CHECK(ctx->HasInput("sub_result"), "Input", "sub_result",
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"SquaredL2DistanceGradOp");
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OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
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"Out@GRAD", "SquaredL2DistanceGradOp");
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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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if (ctx->IsRuntime()) {
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PADDLE_ENFORCE_EQ(
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out_dims[0], x_dims[0],
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platform::errors::InvalidArgument(
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"First dimension of output gradient and Input(X) ",
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"of SquaredL2DistanceGradOp must be equal",
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"But received X's shape = [%s] and grad's shape = [%s],",
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"the first dimensions are %d and %d respectively", x_dims,
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out_dims, x_dims[0], out_dims[0]));
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PADDLE_ENFORCE_EQ(out_dims[1], 1,
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platform::errors::InvalidArgument(
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"Second dimension of output gradient of ",
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"SquaredL2DistanceGradOp must be 1."
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"But received grad's shape = [%s],",
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"with first dimensions %d", out_dims, out_dims[1]));
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}
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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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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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OperatorWithKernel::IndicateVarDataType(ctx, "sub_result"),
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ctx.GetPlace());
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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(
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squared_l2_distance, ops::SquaredL2DistanceOp,
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ops::SquaredL2DistanceOpMaker,
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ops::SquaredL2DistanceGradOpMaker<paddle::framework::OpDesc>,
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ops::SquaredL2DistanceGradOpMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(squared_l2_distance_grad, ops::SquaredL2DistanceGradOp,
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ops::SquaredL2DistanceGradOpNoBuffer);
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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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