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179 lines
8.0 KiB
179 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/positive_negative_pair_op.h"
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namespace paddle {
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namespace operators {
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class PositiveNegativePairOp : 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(
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ctx->HasInput("Score"),
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"Input(Score) of PositiveNegativePairOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasInput("Label"),
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"Input(Label) of PositiveNegativePairOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasInput("QueryID"),
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"Input(QueryID) of PositiveNegativePairOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("PositivePair"),
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"Output(PositivePair) of PositiveNegativePairOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("NegativePair"),
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"Output(NegativePair) of PositiveNegativePairOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("NeutralPair"),
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"Output(NeutralPair) of PositiveNegativePairOp should not be null.");
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auto scalar_dim = framework::make_ddim({1});
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if (ctx->HasInput("AccumulatePositivePair") ||
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ctx->HasInput("AccumulateNegativePair") ||
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ctx->HasInput("AccumulateNeutralPair")) {
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PADDLE_ENFORCE(ctx->HasInput("AccumulatePositivePair") &&
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ctx->HasInput("AccumulateNegativePair") &&
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ctx->HasInput("AccumulateNeutralPair"),
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"All optional inputs(AccumulatePositivePair, "
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"AccumulateNegativePair, AccumulateNeutralPair) of "
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"PositiveNegativePairOp are required if one of them is "
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"specified.");
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PADDLE_ENFORCE_EQ(ctx->GetInputDim("AccumulatePositivePair"), scalar_dim,
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"Shape of AccumulatePositivePair should be {1}.");
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PADDLE_ENFORCE_EQ(ctx->GetInputDim("AccumulateNegativePair"), scalar_dim,
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"Shape of AccumulateNegativePair should be {1}.");
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PADDLE_ENFORCE_EQ(ctx->GetInputDim("AccumulateNeutralPair"), scalar_dim,
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"Shape of AccumulateNeutralPair should be {1}.");
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}
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auto score_dim = ctx->GetInputDim("Score");
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auto label_dim = ctx->GetInputDim("Label");
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auto query_dim = ctx->GetInputDim("QueryID");
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PADDLE_ENFORCE_EQ(score_dim.size(), 2, "Score should be a 2-D tensor.");
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PADDLE_ENFORCE_EQ(label_dim.size(), 2, "Label should be a 2-D tensor.");
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PADDLE_ENFORCE_EQ(
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label_dim[0], score_dim[0],
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"Tensor Score and Label should have the same height (batch size).");
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PADDLE_ENFORCE_EQ(label_dim[1], 1,
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"The width of Label should be 1, i.e. each item should "
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"have a scalar label.");
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PADDLE_ENFORCE(query_dim == label_dim,
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"QueryID should have the same shape as Label.");
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if (ctx->HasInput("Weight")) {
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PADDLE_ENFORCE(ctx->GetInputDim("Weight") == label_dim,
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"Weight should have the same shape as Label.");
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}
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int column = ctx->Attrs().Get<int>("column");
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auto depth = score_dim[1];
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PADDLE_ENFORCE(column < depth && column >= -depth,
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"Attribute column should be in the range of [-%l, %l)",
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depth, depth);
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ctx->SetOutputDim("PositivePair", scalar_dim);
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ctx->SetOutputDim("NegativePair", scalar_dim);
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ctx->SetOutputDim("NeutralPair", scalar_dim);
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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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framework::ToDataType(ctx.Input<Tensor>("Score")->type()),
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ctx.device_context());
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}
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};
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class PositiveNegativePairOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("Score",
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"(Tensor, float) Model Score on an item (with "
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"respect to QueryID). It's a 2-D tensor with shape [batch_size, "
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"depth], where the column specified by the attribute \"column\" "
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"is used as item score.");
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AddInput("Label",
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"(Tensor, float) Label of an item (with repsect to "
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"QueryId). It's a 2-D tensor with shape [batch_size, 1].");
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AddInput("QueryID",
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"(Tensor, int64) Query ID that indicates the context. Its shape "
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"should be the same as Label.");
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AddInput(
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"AccumulatePositivePair",
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"(float) Optional. The accumulated number of positive pairs over a "
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"stream of data. If provided, the output PositivePair will be "
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"initialized with this number rather than 0. it won't be modified "
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"in place.")
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.AsDispensable();
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AddInput(
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"AccumulateNegativePair",
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"(float) Optional. The accumulated number of negative pairs over a "
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"stream of data. If provided, the output NegativePair will be "
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"initialized with this number rather than 0. it won't be modified "
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"in place.")
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.AsDispensable();
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AddInput("AccumulateNeutralPair",
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"(float) Optional. The accumulated number of neutral pairs over a "
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"stream of data. If provided, the output NeutralPair will be "
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"initialized with this number rather than 0. it won't be modified "
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"in place.")
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.AsDispensable();
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AddInput("Weight",
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"(float) Optional. Weight of current item. If specified, its "
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"shape should be the same as Label, and the meaning of the output "
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"changes from numbers of pairs to the total sum of pairs' "
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"weights. Weight of a pair of items is the average of their "
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"weights.")
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.AsDispensable();
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AddOutput("PositivePair",
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"(float) Number of positive pairs, i.e. the pairs of "
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"items that are ranked correctly.");
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AddOutput("NegativePair",
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"(float) Number of negative pairs, i.e. the pairs of "
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"items that are ranked incorrectly.");
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AddOutput("NeutralPair",
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"(float) Number of neutral pairs, i.e. the pairs of items "
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"that have the same score.")
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.AsDispensable();
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AddAttr<int>(
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"column",
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"(int, default -1) The column position of Score used to rank items in "
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"descending order. It must be in the range of [-rank(Score), "
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"rank(Score)). "
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"If `dim < 0`, the dim to reduce is `rank + dim`. "
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"Noting that reducing on the first dim will make the LoD info lost.")
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.SetDefault(0);
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AddComment(R"DOC(
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PositiveNegativePairOp can be used to evaluate Learning To Rank(LTR) model's
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performance.
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Within some context, e.g. the "query", a LTR model generates scores for a list
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of items, which gives a partial order of the items. PositiveNegativePairOp
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takes a list of reference rank order (Input("Label")) and the model generated
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scores (Input(Score)) as inputs and counts the pairs that ranked correctly
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and incorrectly.
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)DOC");
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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(positive_negative_pair,
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ops::PositiveNegativePairOp,
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ops::PositiveNegativePairOpMaker);
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REGISTER_OP_CPU_KERNEL(
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positive_negative_pair,
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ops::PositiveNegativePairKernel<paddle::platform::CPUPlace, float>,
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ops::PositiveNegativePairKernel<paddle::platform::CPUPlace, double>);
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