Add rank loss operator

paddle/operators/rank_loss_op.cc

@@ -0,0 +1,126 @@
+/* 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/rank_loss_op.h"
+
+namespace paddle {
+namespace operators {
+
+class RankLossOp : public framework::OperatorWithKernel {
+ public:
+  RankLossOp(const std::string &type, const framework::VariableNameMap &inputs,
+             const framework::VariableNameMap &outputs,
+             const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    // input check
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
+                            "Input(Label) shouldn't be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Left"),
+                            "Input(Left) shouldn't be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Right"),
+                            "Input(Right) shouldn't be null");
+    auto label_dims = ctx.Input<framework::Tensor>("Label")->dims();
+    auto left_dims = ctx.Input<framework::Tensor>("Left")->dims();
+    auto right_dims = ctx.Input<framework::Tensor>("Right")->dims();
+    PADDLE_ENFORCE((label_dims == left_dims) && (left_dims == right_dims),
+                   "All inputs must have the same size");
+    PADDLE_ENFORCE((label_dims.size() == 2) && (label_dims[1] == 1),
+                   "All inputs must be row vector with size batch_size x 1.");
+    ctx.Output<framework::LoDTensor>("Out")->Resize(label_dims);
+  }
+};
+
+class RankLossOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  RankLossOpMaker(framework::OpProto *proto,
+                  framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Label",
+             "The label indicating A ranked higher than B or not, row vector.");
+    AddInput("Left", "The output of RankNet for doc A, vector.");
+    AddInput("Right", "The output of RankNet for doc B, vetor");
+    AddOutput("Out", "The output loss of RankLoss operator, vector.");
+    AddComment(R"DOC(RankLoss operator
+
+Rank loss operator for RankNet[1]. RankNet is a pairwise ranking model with
+one training sample consisting of a pair of doc A and B, and the label P
+indicating that A is ranked higher than B or not:
+
+P = {0, 1} or {0, 0.5, 1}, where 0.5 means no information about the rank of
+the input pair.
+
+The RankLoss operator contains three inputs: Left (o_i), Right (o_j) and Label
+(P_{i,j}), which represent the output of RankNet for two docs and the label
+respectively, and yields the rank loss C_{i,j} by following the expression
+
+\f[
+  C_{i,j} = -\tilde{P_{ij}} * o_{i,j} + log(1 + e^{o_{i,j}}) \\
+  o_{i,j} =  o_i - o_j  \\
+  \tilde{P_{i,j}} = \left \{0, 0.5, 1 \right \} \ or \ \left \{0, 1 \right \}
+\f]
+
+The operator can take inputs of one sample or in batch.
+
+[1]. Chris Burges, Tal Shaked, Erin Renshaw, et al. Learning to
+     Rank using Gradient Descent.
+     http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf
+)DOC");
+  }
+};
+
+class RankLossGradOp : public framework::OperatorWithKernel {
+ public:
+  RankLossGradOp(const std::string &type,
+                 const framework::VariableNameMap &inputs,
+                 const framework::VariableNameMap &outputs,
+                 const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
+                            "Input(Label) shouldn't be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Left"),
+                            "Input(Left) shouldn't be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Right"),
+                            "Input(Right) shouldn't be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) shouldn't be null.");
+    auto dims = ctx.Input<framework::Tensor>("Left")->dims();
+    auto *left_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Left"));
+    auto *right_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Right"));
+    if (left_grad) {
+      left_grad->Resize(dims);
+    }
+    if (right_grad) {
+      right_grad->Resize(dims);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+namespace ops = paddle::operators;
+
+REGISTER_OP(rank_loss, ops::RankLossOp, ops::RankLossOpMaker, rank_loss_grad,
+            ops::RankLossGradOp);
+REGISTER_OP_CPU_KERNEL(rank_loss,
+                       ops::RankLossKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    rank_loss_grad, ops::RankLossGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/rank_loss_op.cu

@@ -0,0 +1,22 @@
+/* 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/rank_loss_op.h"
+
+REGISTER_OP_GPU_KERNEL(
+    rank_loss,
+    paddle::operators::RankLossKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    rank_loss_grad,
+    paddle::operators::RankLossGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/rank_loss_op.h

@@ -0,0 +1,80 @@
+/* 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 Place, typename T>
+class RankLossKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto* out_t = ctx.Output<framework::LoDTensor>("Out");
+    auto* label_t = ctx.Input<framework::Tensor>("Label");
+    auto* left_t = ctx.Input<framework::Tensor>("Left");
+    auto* right_t = ctx.Input<framework::Tensor>("Right");
+    out_t->mutable_data<T>(ctx.GetPlace());
+
+    auto out = framework::EigenVector<T>::Flatten(*out_t);
+    auto label = framework::EigenVector<T>::Flatten(*label_t);
+    auto left = framework::EigenVector<T>::Flatten(*left_t);
+    auto right = framework::EigenVector<T>::Flatten(*right_t);
+
+    auto& dev = ctx.GetEigenDevice<Place>();
+    out.device(dev) =
+        (1. + (left - right).exp()).log() - label * (left - right);
+  }
+};
+
+template <typename Place, typename T>
+class RankLossGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto* d_left_t =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Left"));
+    auto* d_right_t =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Right"));
+
+    auto* d_out_t = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* label_t = ctx.Input<framework::Tensor>("Label");
+    auto* left_t = ctx.Input<framework::Tensor>("Left");
+    auto* right_t = ctx.Input<framework::Tensor>("Right");
+
+    auto& dev = ctx.GetEigenDevice<Place>();
+    auto d_out = framework::EigenVector<T>::Flatten(*d_out_t);
+    auto label = framework::EigenVector<T>::Flatten(*label_t);
+    auto left = framework::EigenVector<T>::Flatten(*left_t);
+    auto right = framework::EigenVector<T>::Flatten(*right_t);
+
+    // compute d_left
+    if (d_left_t) {
+      d_left_t->mutable_data<T>(ctx.GetPlace());
+      auto d_left = framework::EigenVector<T>::Flatten(*d_left_t);
+      d_left.device(dev) = d_out * (1. / (1. + (right - left).exp()) - label);
+    }
+    // compute d_right
+    if (d_right_t) {
+      d_right_t->mutable_data<T>(ctx.GetPlace());
+      auto d_right = framework::EigenVector<T>::Flatten(*d_right_t);
+      d_right.device(dev) =
+          -d_out * (1.0 / (1. + (right - left).exp()) - label);
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle

python/paddle/v2/framework/tests/test_rank_loss_op.py

@@ -0,0 +1,32 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestRankLossOp(OpTest):
+    def setUp(self):
+        self.op_type = "rank_loss"
+        batch_size = 5
+        # labels_{i} = {0, 1.0} or {0, 0.5, 1.0}
+        label = np.random.randint(0, 2, size=(batch_size, 1)).astype("float32")
+        left = np.random.random((batch_size, 1)).astype("float32")
+        right = np.random.random((batch_size, 1)).astype("float32")
+        loss = np.log(1.0 + np.exp(left - right)) - label * (left - right)
+        self.inputs = {'Label': label, 'Left': left, 'Right': right}
+        self.outputs = {'Out': loss}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["Left", "Right"], "Out")
+
+    def test_check_grad_ignore_left(self):
+        self.check_grad(["Right"], "Out", no_grad_set=set('Left'))
+
+    def test_check_grad_ignore_right(self):
+        self.check_grad(["Left"], "Out", no_grad_set=set('Right'))
+
+
+if __name__ == '__main__':
+    unittest.main()