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Merge pull request #4285 from kuke/margin_rank_loss_op_dev

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Add margin rank loss operator
revert-4814-Add_sequence_project_op
Yibing Liu 7 years ago committed by GitHub
parent 2002c78e63 989e19caf9
commit b56cbd307d
4 changed files with 285 additions and 0 deletions
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124
paddle/operators/margin_rank_loss_op.cc
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/margin_rank_loss_op.h"
namespace paddle {
namespace operators {
class MarginRankLossOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext *ctx) const override {
// input check
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("X1"), "Input(X1) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("X2"), "Input(X2) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) shouldn't be null.");
auto label_dims = ctx->GetInputDim("Label");
auto x1_dims = ctx->GetInputDim("X1");
auto x2_dims = ctx->GetInputDim("X2");
PADDLE_ENFORCE(
(label_dims == x1_dims) && (x1_dims == x2_dims) &&
(label_dims.size() == 2) && (label_dims[1] == 1),
"All inputs must be 2-D tensor with shape [batch_size x 1].");
ctx->SetOutputDim("Activated", label_dims);
ctx->SetOutputDim("Out", label_dims);
}
};
template <typename T>
class MarginRankLossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MarginRankLossOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X1",
"(2-D tensor with shape [batch_size x 1]) The score for "
"one item X1 to be ranked, from pairwise ranking model.");
AddInput("X2",
"(2-D tensor with shape [batch_size x 1]) The score for "
"another item X2 to be ranked, from pairwise ranking model.");
AddInput("Label",
"(2-D tensor with shape [batch_size x 1]) "
"The label indicating X1 ranked higher than X2 or not, "
"can only be +1 or -1.");
AddAttr<T>("margin", "(scalar, default 0) Margin for MarginRankLossOp.")
.SetDefault(static_cast<T>(0));
AddOutput("Activated",
"(2-D tensor with shape [batch_size x 1]) Intermediate tensor "
"to indicate whether each element of Output(Out) is activated.")
.AsIntermediate();
AddOutput("Out",
"(2-D tensor with shape [batch_size x 1]) "
"The output loss of MarginRankLoss operator.");
AddComment(R"DOC(
MarginRankLoss operator measures the loss given a pair of training sample
{`X1`, `X2`} and the `Label` with attribute `margin`, where `Label = +1`
indicating X1 is ranked higher than `X2`, otherwise `Label = -1`. The loss
turns out
loss(X1, X2, Label) = max(0, -Label * (X1 - X2) + margin).
The attribute `margin` involved here helps make the predictions more robust.
Denote the item ranked higher as the positive sample, otherwise the negative
sample. If the score of the two samples satisfies
positive sample - negative sample < margin,
the pair of samples will contribute to the final loss, which will backpropogate
and train the ranking model to enlarge the difference of the two score.
For batch input with size `batch_size`, `X1`, `X2` and `Label`
all have the same shape [batch_size x 1].
)DOC");
}
};
class MarginRankLossGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("X1"), "Input(X1) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("X2"), "Input(X2) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("Activated"),
"Intermediate(Activated) shouldn't be null.");
auto dims = ctx->GetInputDim("Label");
ctx->SetOutputDim(framework::GradVarName("X1"), dims);
ctx->SetOutputDim(framework::GradVarName("X2"), dims);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(margin_rank_loss, ops::MarginRankLossOp,
ops::MarginRankLossOpMaker<float>, margin_rank_loss_grad,
ops::MarginRankLossGradOp);
REGISTER_OP_CPU_KERNEL(
margin_rank_loss,
ops::MarginRankLossKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
margin_rank_loss_grad,
ops::MarginRankLossGradKernel<paddle::platform::CPUPlace, float>);

24
paddle/operators/margin_rank_loss_op.cu
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/margin_rank_loss_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
margin_rank_loss,
ops::MarginRankLossKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
margin_rank_loss_grad,
ops::MarginRankLossGradKernel<paddle::platform::GPUPlace, float>);

98
paddle/operators/margin_rank_loss_op.h
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename T>
struct ReLU {
HOSTDEVICE T operator()(const T& val) const {
return val > 0 ? val : static_cast<T>(0);
}
};
template <typename T>
struct Heaviside {
HOSTDEVICE T operator()(const T& val) const {
return static_cast<T>(val > 0 ? 1 : 0);
}
};
template <typename Place, typename T>
class MarginRankLossKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* out_t = ctx.Output<framework::Tensor>("Out");
auto* act_t = ctx.Output<framework::Tensor>("Activated");
auto* label_t = ctx.Input<framework::Tensor>("Label");
auto* x1_t = ctx.Input<framework::Tensor>("X1");
auto* x2_t = ctx.Input<framework::Tensor>("X2");
out_t->mutable_data<T>(ctx.GetPlace());
act_t->mutable_data<T>(ctx.GetPlace());
auto margin = static_cast<T>(ctx.Attr<T>("margin"));
auto out = framework::EigenVector<T>::Flatten(*out_t);
auto act = framework::EigenVector<T>::Flatten(*act_t);
auto label = framework::EigenVector<T>::Flatten(*label_t);
auto x1 = framework::EigenVector<T>::Flatten(*x1_t);
auto x2 = framework::EigenVector<T>::Flatten(*x2_t);
auto& dev = ctx.GetEigenDevice<Place>();
out.device(dev) = (-label * (x1 - x2) + margin).unaryExpr(ReLU<T>());
act.device(dev) = out.unaryExpr(Heaviside<T>());
}
};
template <typename Place, typename T>
class MarginRankLossGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* d_x1_t =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X1"));
auto* d_x2_t =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X2"));
auto* act_t = ctx.Input<framework::Tensor>("Activated");
auto* d_out_t = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* label_t = ctx.Input<framework::Tensor>("Label");
auto d_out = framework::EigenVector<T>::Flatten(*d_out_t);
auto act = framework::EigenVector<T>::Flatten(*act_t);
auto label = framework::EigenVector<T>::Flatten(*label_t);
auto& dev = ctx.GetEigenDevice<Place>();
// compute d_x1
if (d_x1_t) {
d_x1_t->mutable_data<T>(ctx.GetPlace());
auto d_x1 = framework::EigenVector<T>::Flatten(*d_x1_t);
d_x1.device(dev) = -d_out * act * label;
}
// compute d_x2
if (d_x2_t) {
d_x2_t->mutable_data<T>(ctx.GetPlace());
auto d_x2 = framework::EigenVector<T>::Flatten(*d_x2_t);
d_x2.device(dev) = d_out * act * label;
}
}
};
} // namespace operators
} // namespace paddle

39
python/paddle/v2/framework/tests/test_margin_rank_loss_op.py
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import unittest
import numpy as np
from op_test import OpTest
class TestMarginRankLossOp(OpTest):
def setUp(self):
self.op_type = "margin_rank_loss"
batch_size = 5
margin = 0.5
# labels_{i} = {-1, 1}
label = 2 * np.random.randint(
0, 2, size=(batch_size, 1)).astype("float32") - 1
x1 = np.random.random((batch_size, 1)).astype("float32")
x2 = np.random.random((batch_size, 1)).astype("float32")
# loss = max(0, -label * (x1 - x2) + margin)
loss = -label * (x1 - x2) + margin
loss = np.where(loss > 0, loss, 0)
act = np.where(loss > 0, 1., 0.)
self.attrs = {'margin': margin}
self.inputs = {'Label': label, 'X1': x1, 'X2': x2}
self.outputs = {'Activated': act, 'Out': loss}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X1", "X2"], "Out")
def test_check_grad_ignore_x1(self):
self.check_grad(["X2"], "Out", no_grad_set=set('X1'))
def test_check_grad_ignore_x2(self):
self.check_grad(["X1"], "Out", no_grad_set=set('X2'))
if __name__ == '__main__':
unittest.main()
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