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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/cos_sim_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 CosSimOp : 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"), "Input(X) must not be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
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PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
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ctx.Input<Tensor>("Y")->dims(),
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"Dimensions of Input(X) and Input(Y) must be the same.");
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auto dims = ctx.Input<Tensor>("X")->dims();
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ctx.Output<Tensor>("Out")->Resize({dims[0], 1});
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ctx.Output<Tensor>("XNorm")->Resize({dims[0], 1});
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ctx.Output<Tensor>("YNorm")->Resize({dims[0], 1});
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}
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};
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class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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CosSimOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "The first input of cos_sim op.");
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AddInput("Y", "The second input of cos_sim op.");
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AddOutput("Out", "The output of cos_sim op.");
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AddOutput("XNorm", "Row norm of the first input.").AsIntermediate();
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AddOutput("YNorm", "Row norm of the second input.").AsIntermediate();
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AddComment(R"DOC(
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Cosine Similarity Operator.
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The equation is: Out = X^T * Y / (sqrt(X^T * X) * sqrt(Y^T * Y))
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)DOC");
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}
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};
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class CosSimOpGrad : 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"), "Input(X) must not be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("XNorm"),
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"Input(XNorm) must not be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("YNorm"),
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"Input(YNorm) must not be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
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"Input(Out@GRAD) must not be null.");
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auto x_dims = ctx.Input<Tensor>("X")->dims();
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auto y_dims = ctx.Input<Tensor>("Y")->dims();
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auto xnorm_dims = ctx.Input<Tensor>("XNorm")->dims();
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auto ynorm_dims = ctx.Input<Tensor>("YNorm")->dims();
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auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
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PADDLE_ENFORCE_EQ(x_dims, y_dims,
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"Dimensions of Input(X) and Input(Y) must be the same.");
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PADDLE_ENFORCE_EQ(xnorm_dims[0], x_dims[0],
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"1st dimension of XNorm must equal that of Input(X).");
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PADDLE_ENFORCE_EQ(xnorm_dims[1], 1, "2st dimension of XNorm must be one.");
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PADDLE_ENFORCE_EQ(ynorm_dims[0], y_dims[0],
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"1st dimension of YNorm must equal that of Input(Y).");
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PADDLE_ENFORCE_EQ(ynorm_dims[1], 1, "2st dimension of YNorm must be one.");
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PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0],
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"1st dimension of Out@GRAD must equal that of Input(X)");
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PADDLE_ENFORCE_EQ(out_dims[1], 1, "1st dimension of Out@GRAD must be one.");
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auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
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if (x_grad) x_grad->Resize(x_dims);
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if (y_grad) y_grad->Resize(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(cos_sim, ops::CosSimOp, ops::CosSimOpMaker, cos_sim_grad,
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ops::CosSimOpGrad);
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REGISTER_OP_CPU_KERNEL(cos_sim,
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ops::CosSimKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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cos_sim_grad, ops::CosSimGradKernel<paddle::platform::CPUPlace, float>);
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@ -0,0 +1,107 @@
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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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#pragma once
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.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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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
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template <typename Place, typename T>
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class CosSimKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* input_x = context.Input<Tensor>("X");
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auto* input_y = context.Input<Tensor>("Y");
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auto* output_z = context.Output<Tensor>("Out");
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auto* output_x_norm = context.Output<Tensor>("XNorm");
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auto* output_y_norm = context.Output<Tensor>("YNorm");
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output_z->mutable_data<T>(context.GetPlace());
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output_x_norm->mutable_data<T>(context.GetPlace());
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output_y_norm->mutable_data<T>(context.GetPlace());
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auto dims = input_x->dims();
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int size = static_cast<int>(framework::product(dims));
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auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
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auto x = EigenMatrix<T>::From(*input_x, new_dims);
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auto y = EigenMatrix<T>::From(*input_y, new_dims);
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auto z = EigenVector<T>::Flatten(*output_z);
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auto x_norm = EigenVector<T>::Flatten(*output_x_norm);
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auto y_norm = EigenVector<T>::Flatten(*output_y_norm);
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auto place = context.GetEigenDevice<Place>();
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auto xy = (x * y).sum(Eigen::array<int, 1>({{1}}));
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x_norm.device(place) = x.square().sum(Eigen::array<int, 1>({{1}})).sqrt();
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y_norm.device(place) = y.square().sum(Eigen::array<int, 1>({{1}})).sqrt();
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z.device(place) = xy / x_norm / y_norm;
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}
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};
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template <typename Place, typename T>
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class CosSimGradKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* input_x = context.Input<Tensor>("X");
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auto* input_y = context.Input<Tensor>("Y");
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auto* input_z = context.Input<Tensor>("Out");
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auto* input_x_norm = context.Input<Tensor>("XNorm");
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auto* input_y_norm = context.Input<Tensor>("YNorm");
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auto* output_grad_x = context.Output<Tensor>(framework::GradVarName("X"));
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auto* output_grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
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auto* input_grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
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auto dims = input_x->dims();
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int size = static_cast<int>(framework::product(dims));
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auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
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auto x = EigenMatrix<T>::From(*input_x, new_dims);
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auto y = EigenMatrix<T>::From(*input_y, new_dims);
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auto z = EigenMatrix<T>::From(*input_z);
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auto x_norm = EigenMatrix<T>::From(*input_x_norm);
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auto y_norm = EigenMatrix<T>::From(*input_y_norm);
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auto dz = EigenMatrix<T>::From(*input_grad_z);
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Eigen::DSizes<int, 2> bcast(1, new_dims[1]);
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auto z_bcast = z.broadcast(bcast);
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auto dz_bcast = dz.broadcast(bcast);
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auto place = context.GetEigenDevice<Place>();
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auto x_snorm_bcast = x_norm.square().eval().broadcast(bcast);
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auto y_snorm_bcast = y_norm.square().eval().broadcast(bcast);
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auto norm_prod_bcast = (x_norm * y_norm).eval().broadcast(bcast);
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if (output_grad_x) {
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output_grad_x->mutable_data<T>(context.GetPlace());
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auto dx = EigenMatrix<T>::From(*output_grad_x, new_dims);
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dx.device(place) =
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dz_bcast * (y / norm_prod_bcast - z_bcast * x / x_snorm_bcast);
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}
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if (output_grad_y) {
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output_grad_y->mutable_data<T>(context.GetPlace());
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auto dy = EigenMatrix<T>::From(*output_grad_y, new_dims);
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dy.device(place) =
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dz_bcast * (x / norm_prod_bcast - z_bcast * y / y_snorm_bcast);
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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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@ -0,0 +1,57 @@
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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/net_op.h"
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#include "paddle/operators/scale_op.h"
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namespace paddle {
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namespace operators {
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// The identity operator is an alias of the scale operator. This is also an
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// example for creating the alias for an existing operator.
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template <typename AttrType>
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class IdentityOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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IdentityOpMaker(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 identity operator.");
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AddOutput("Out", "The output tensor of identity operator.");
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AddComment(R"DOC(
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The identity operator is an alias of the scale operator
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with the attribute scale fixed to 1.0
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)DOC");
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}
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};
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template <typename AttrType>
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class IdentityOp : public NetOp {
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public:
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IdentityOp(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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: NetOp(type, inputs, outputs, attrs) {
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AppendOp(framework::OpRegistry::CreateOp(
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"scale", {{"X", {Input("X")}}}, {{"Out", {Output("Out")}}},
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{{"scale", static_cast<AttrType>(1)}}));
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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_WITHOUT_GRADIENT(identity, ops::IdentityOp<float>,
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ops::IdentityOpMaker<float>);
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@ -1,20 +0,0 @@
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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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|
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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
|
||||
limitations under the License. */
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#define EIGEN_USE_GPU
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#include "paddle/operators/scatter_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_GPU_KERNEL(scatter,
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ops::ScatterOpKernel<paddle::platform::GPUPlace, float>);
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