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180 lines
7.2 KiB
180 lines
7.2 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/bpr_loss_op.h"
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#include <memory>
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namespace paddle {
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namespace operators {
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class BprLossOp : 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", "BprLoss");
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OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", "BprLoss");
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OP_INOUT_CHECK(ctx->HasOutput("Y"), "Output", "Y", "BprLoss");
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auto x_dims = ctx->GetInputDim("X");
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auto label_dims = ctx->GetInputDim("Label");
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int rank = x_dims.size();
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PADDLE_ENFORCE_EQ(
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rank, label_dims.size(),
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platform::errors::InvalidArgument(
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"Input(X) and Input(Label) shall have the same rank."));
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if (ctx->IsRuntime() || (framework::product(x_dims) > 0 &&
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framework::product(label_dims) > 0)) {
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PADDLE_ENFORCE_EQ(
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framework::slice_ddim(x_dims, 0, rank - 1),
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framework::slice_ddim(label_dims, 0, rank - 1),
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platform::errors::InvalidArgument(
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"Input(X) and Input(Label) shall have the same shape "
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"except the last dimension."));
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}
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auto y_dims = x_dims;
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y_dims[rank - 1] = 1;
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ctx->SetOutputDim("Y", y_dims);
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ctx->ShareLoD("X", /*->*/ "Y");
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}
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protected:
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// Explicitly set that the data type of computation kernel of Seq-bpr
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// is determined by its input "X".
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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, "X"),
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platform::CPUPlace());
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}
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};
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class BprLossGradientOp : 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", "BprLossGradient");
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OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", "BprLossGradient");
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OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Y")), "Input",
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framework::GradVarName("Y"), "BprLossGradient");
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OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
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framework::GradVarName("X"), "BprLossGradient");
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auto x_dims = ctx->GetInputDim("X");
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auto label_dims = ctx->GetInputDim("Label");
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auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
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int rank = x_dims.size();
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PADDLE_ENFORCE_EQ(
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dy_dims.size(), rank,
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platform::errors::InvalidArgument(
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"Input(Y@Grad) and Input(X) should have the same rank."));
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PADDLE_ENFORCE_EQ(
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label_dims.size(), rank,
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platform::errors::InvalidArgument(
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"Input(Label) and Input(X) should have the same rank."));
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PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
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framework::slice_ddim(label_dims, 0, rank - 1),
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platform::errors::InvalidArgument(
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"The Input(X) and Input(Label) should have the same "
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"shape except the last dimension."));
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PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
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framework::slice_ddim(dy_dims, 0, rank - 1),
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platform::errors::InvalidArgument(
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"The Input(X) and Input(Y@Grad) should have the same "
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"shape except the last dimension."));
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PADDLE_ENFORCE_EQ(dy_dims[rank - 1], 1,
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platform::errors::InvalidArgument(
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"The last dimension of Input(Y@Grad) should be 1."));
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PADDLE_ENFORCE_EQ(label_dims[rank - 1], 1,
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platform::errors::InvalidArgument(
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" the last dimension of Input(Label) should be 1."));
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ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
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ctx->ShareLoD("X", framework::GradVarName("X"));
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}
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protected:
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// Explicitly set that the data type of computation kernel of cross_entropy
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// is determined by its input "X".
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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, "X"),
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platform::CPUPlace());
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}
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};
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class BprLossOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"(Tensor, default Tensor<float>), a tensor whose last dimension "
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"size is equal to the number of classes. This input is a "
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"real number.");
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AddInput(
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"Label",
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"(Tensor), the tensor which represents the ground truth. It has the "
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"same shape with 'X' except the last dimension. the last dimension "
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"size is 1.");
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AddOutput("Y",
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"(Tensor, default Tensor<float>), a tensor whose shape is same "
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"with 'X' except that the last dimension size is 1. It "
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"represents the sequence bpr loss.");
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AddComment(R"DOC(
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Bayesian Personalized Ranking Loss Operator.
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This operator belongs to pairwise ranking loss. Label is the desired item.
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The loss at a given point in one session is defined as:
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$Y[i] = -\frac{1}{N_{i}} * \sum_{j=0}^{N_{i}}\log(\sigma(X[i, Label[i]]-X[i, j]))$
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Learn more details by reading paper <session-based recommendations with recurrent
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neural networks>(https://arxiv.org/abs/1511.06939)
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)DOC");
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}
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};
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template <typename T>
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class BprLossGradMaker : 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("bpr_loss_grad");
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op->SetInput("X", this->Input("X"));
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op->SetInput("Label", this->Input("Label"));
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op->SetInput(framework::GradVarName("Y"), this->OutputGrad("Y"));
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op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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op->SetAttrMap(this->Attrs());
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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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using CPUCtx = paddle::platform::CPUDeviceContext;
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REGISTER_OPERATOR(bpr_loss, ops::BprLossOp, ops::BprLossOpMaker,
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ops::BprLossGradMaker<paddle::framework::OpDesc>,
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ops::BprLossGradMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(bpr_loss_grad, ops::BprLossGradientOp);
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REGISTER_OP_CPU_KERNEL(bpr_loss, ops::BprLossOpKernel<CPUCtx, float>,
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ops::BprLossOpKernel<CPUCtx, double>);
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REGISTER_OP_CPU_KERNEL(bpr_loss_grad,
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ops::BprLossGradientOpKernel<CPUCtx, float>,
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ops::BprLossGradientOpKernel<CPUCtx, double>);
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