Merge branch 'develop' of github.com:baidu/Paddle into stable_elemwise_mul

paddle/operators/sigmoid_cross_entropy_with_logits_op.cc

+/* 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/sigmoid_cross_entropy_with_logits_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContextBase* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("Labels"),
+                   "Input(Labels) should be not null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should be not null.");
+
+    auto x_dims = ctx->GetInputDim("X");
+    auto labels_dims = ctx->GetInputDim("Labels");
+    PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(labels_dims.size(), 2,
+                      "Input(Labels)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(x_dims[0], labels_dims[0],
+                      "The 1st dimension of Input(X) and Input(Labels) should "
+                      "be equal.");
+    PADDLE_ENFORCE_EQ(x_dims[1], labels_dims[1],
+                      "The 2nd dimension of Input(X) and Input(Labels) should "
+                      "be equal.");
+
+    ctx->SetOutputDim("Out", x_dims);
+    ctx->ShareLoD("X", /*->*/ "Out");
+  }
+};
+
+class SigmoidCrossEntropyWithLogitsGradOp
+    : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContextBase* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("Labels"),
+                   "Input(Labels) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                   "Input(Out@GRAD) shoudl be not null.");
+    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
+                   "Output(X@GRAD) should be not null.");
+
+    auto x_dims = ctx->GetInputDim("X");
+    auto labels_dims = ctx->GetInputDim("Labels");
+    auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
+    PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(labels_dims.size(), 2,
+                      "Input(Labels)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(dout_dims.size(), 2,
+                      "Input(Out@Grad)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(x_dims[0], labels_dims[0],
+                      "The 1st dimension of Input(X) and Input(Labels) should "
+                      "be equal.");
+    PADDLE_ENFORCE_EQ(x_dims[1], labels_dims[1],
+                      "The 2nd dimension of Input(X) and Input(Labels) should "
+                      "be equal.");
+    PADDLE_ENFORCE_EQ(x_dims[0], dout_dims[0],
+                      "The 1st dimension of Input(X) and Input(Out@Grad) "
+                      "should be equal.");
+    PADDLE_ENFORCE_EQ(x_dims[1], dout_dims[1],
+                      "The 2nd dimension of Input(X) and Input(Out@Grad) "
+                      "should be equal.");
+
+    ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
+  }
+};
+
+class SigmoidCrossEntropyWithLogitsOpMaker
+    : public framework::OpProtoAndCheckerMaker {
+ public:
+  SigmoidCrossEntropyWithLogitsOpMaker(framework::OpProto* proto,
+                                       framework::OpAttrChecker* op_checker)
+      : framework::OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, "
+             "where N is the batch size and D is the number of classes. "
+             "This input is a tensor of logits computed by the previous "
+             " operator. Logits are unscaled log probabilities given as "
+             "log(p/(1-p)).");
+    AddInput("Labels",
+             "(Tensor, default Tensor<float>), a 2-D tensor of the same type "
+             "and shape as X. This input is a tensor of probabalistic labels "
+             "for each logit");
+    AddOutput("Out",
+              "(Tensor, default Tensor<float>), a 2-D tensor with shape N x D "
+              " of elementwise logistic losses.");
+    AddComment(R"DOC(
+SigmoidCrossEntropyWithLogits Operator.
+
+This measures the elementwise probability error in discrete classification tasks
+in which each class is independent. This can be thought of as predicting labels
+for a data-point that are not mutually exclusive. For example, a news article
+can be about politics, technology or sports at the same time or none of these.
+
+The logistic loss is given as follows:
+
+       loss = -Labels * log(sigmoid(X)) - (1 - Labels) * log(1 - sigmoid(X))
+
+We know that sigmoid(X) = (1 / (1 + exp(-X))). By substituting this we get
+
+       loss = X - X * Labels + log(1 + exp(-X))
+
+For stability and to prevent overflow of exp(-X) when X < 0,
+we can reformulate the loss as follows:
+
+       loss = max(X, 0) - X * Labels + log(1 + exp(-abs(X)))
+
+Both the input `X` and `Labels` can carry the LoD (Level of Details) information.
+However the output only shares the LoD with input `X`.
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(sigmoid_cross_entropy_with_logits,
+            ops::SigmoidCrossEntropyWithLogitsOp,
+            ops::SigmoidCrossEntropyWithLogitsOpMaker,
+            sigmoid_cross_entropy_with_logits_grad,
+            ops::SigmoidCrossEntropyWithLogitsGradOp);
+REGISTER_OP_CPU_KERNEL(sigmoid_cross_entropy_with_logits,
+                       ops::SigmoidCrossEntropyWithLogitsKernel<
+                           paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(sigmoid_cross_entropy_with_logits_grad,
+                       ops::SigmoidCrossEntropyWithLogitsGradKernel<
+                           paddle::platform::CPUPlace, float>);

paddle/operators/sigmoid_cross_entropy_with_logits_op.cu

+/* 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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/sigmoid_cross_entropy_with_logits_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(sigmoid_cross_entropy_with_logits,
+                       ops::SigmoidCrossEntropyWithLogitsKernel<
+                           paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(sigmoid_cross_entropy_with_logits_grad,
+                       ops::SigmoidCrossEntropyWithLogitsGradKernel<
+                           paddle::platform::GPUPlace, float>);

paddle/operators/sigmoid_cross_entropy_with_logits_op.h

+/* 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 {
+
+// Out = max(X, 0) - X * Labels + log(1 + exp(-abs(X)))
+template <typename Place, typename T>
+class SigmoidCrossEntropyWithLogitsKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext &context) const override {
+    const framework::Tensor *X = context.Input<framework::Tensor>("X");
+    const framework::Tensor *Labels =
+        context.Input<framework::Tensor>("Labels");
+    framework::Tensor *Out = context.Output<framework::Tensor>("Out");
+    Out->mutable_data<T>(context.GetPlace());
+
+    auto x = framework::EigenVector<T>::Flatten(*X);
+    auto labels = framework::EigenVector<T>::Flatten(*Labels);
+    auto out = framework::EigenVector<T>::Flatten(*Out);
+    auto place = context.GetEigenDevice<Place>();
+
+    // term1 = max(x, 0)
+    auto term1 = x.cwiseMax(static_cast<T>(0));
+    // term2 = x * labels
+    auto term2 = x * labels;
+    // term3 = log(1 + exp(-abs(x)))
+    auto term3 = (static_cast<T>(1) + (-(x.abs())).exp()).log();
+
+    out.device(place) = term1 - term2 + term3;
+  }
+};
+
+// dX = sigmoid(X) - labels
+template <typename Place, typename T>
+class SigmoidCrossEntropyWithLogitsGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext &context) const override {
+    const framework::Tensor *X = context.Input<framework::Tensor>("X");
+    const framework::Tensor *Labels =
+        context.Input<framework::Tensor>("Labels");
+    const framework::Tensor *dOut =
+        context.Input<framework::Tensor>(framework::GradVarName("Out"));
+    framework::Tensor *dX =
+        context.Output<framework::Tensor>(framework::GradVarName("X"));
+    dX->mutable_data<T>(context.GetPlace());
+
+    auto x = framework::EigenVector<T>::Flatten(*X);
+    auto labels = framework::EigenVector<T>::Flatten(*Labels);
+    auto dout = framework::EigenVector<T>::Flatten(*dOut);
+    auto dx = framework::EigenVector<T>::Flatten(*dX);
+    auto place = context.GetEigenDevice<Place>();
+
+    auto sigmoid_x = static_cast<T>(1) / (static_cast<T>(1) + (-x).exp());
+    dx.device(place) = dout * (sigmoid_x - labels);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

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

+import numpy as np
+from op_test import OpTest
+from scipy.special import logit
+from scipy.special import expit
+
+
+class TestSigmoidCrossEntropyWithLogitsOp1(OpTest):
+    '''Test sigmoid_cross_entropy_with_logit_op with binary labels
+    '''
+
+    def setUp(self):
+        self.op_type = "sigmoid_cross_entropy_with_logits"
+        batch_size = 64
+        num_classes = 20
+        self.inputs = {
+            'X': logit(
+                np.random.uniform(0, 1, (batch_size, num_classes))
+                .astype("float32")),
+            'Labels': np.random.randint(0, 2, (batch_size, num_classes))
+            .astype("float32")
+        }
+
+        # Fw Pass is implemented as elementwise sigmoid followed by
+        # elementwise logistic loss
+        # Labels * -log(sigmoid(X)) + (1 - labels) * -log(1 - sigmoid(X))
+        sigmoid_X = expit(self.inputs['X'])
+        term1 = self.inputs['Labels'] * np.log(sigmoid_X)
+        term2 = (1 - self.inputs['Labels']) * np.log(1 - sigmoid_X)
+        self.outputs = {'Out': -term1 - term2}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestSigmoidCrossEntropyWithLogitsOp2(OpTest):
+    '''Test sigmoid_cross_entropy_with_logit_op with probabalistic labels
+    '''
+
+    def setUp(self):
+        self.op_type = "sigmoid_cross_entropy_with_logits"
+        batch_size = 64
+        num_classes = 20
+        self.inputs = {
+            'X': logit(
+                np.random.uniform(0, 1, (batch_size, num_classes))
+                .astype("float32")),
+            'Labels': np.random.uniform(0, 1, (batch_size, num_classes))
+            .astype("float32")
+        }
+
+        # Fw Pass is implemented as elementwise sigmoid followed by
+        # elementwise logistic loss
+        # Labels * -log(sigmoid(X)) + (1 - labels) * -log(1 - sigmoid(X))
+        sigmoid_X = expit(self.inputs['X'])
+        term1 = self.inputs['Labels'] * np.log(sigmoid_X)
+        term2 = (1 - self.inputs['Labels']) * np.log(1 - sigmoid_X)
+        self.outputs = {'Out': -term1 - term2}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')