remove some comments & refine doc & put template class in .h

test=develop

paddle/fluid/operators/teacher_student_sigmoid_loss_op.cc

@@ -115,18 +115,22 @@ class TeacherStudentSigmoidLossOpMaker
     AddOutput("Y",
               "(Tensor, default Tensor<float>), a 2-D tensor with shape "
               "[N x 1]. The teacher student sigmoid loss.");
-    AddAttr<float>("soft_max_up_bound", "fp32, default 15.0").SetDefault(15.0);
-    AddAttr<float>("soft_max_lower_bound", "fp32, default -15.0")
+    AddAttr<float>(
+        "soft_max_up_bound",
+        "fp32, if input > soft_max_up_bound, will be bound, default 15.0")
+        .SetDefault(15.0);
+    AddAttr<float>(
+        "soft_max_lower_bound",
+        "fp32, if input < soft_max_lower_bound, will be bound, default -15.0")
         .SetDefault(-15.0);
     AddComment(R"DOC(
 TeacherStudentSigmoidLoss Operator.
-TeacherStudentSigmoidLoss Operator.
 
 It's similarity to SigmoidCrossEntropyWithLogits Operator. The difference is that
 we add another label(z') to original.
         loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' + log(1 + exp(-abs(x)))
         z is click or not
-        z' is value q of feed_fine
+        z' is teacher value 
         label = {-2, -1, [0, 2]}
         when z' is not exist, clk = 0 : label = -2;
         when z' is not exist, clk = 1 : label = -1;
@@ -137,104 +141,6 @@ we add another label(z') to original.
   }
 };
 
-// template <typename DeviceContext, typename T>
-template <typename T>
-class TeacherStudentSigmoidLossOpKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    PADDLE_ENFORCE(platform::is_cpu_place(context.GetPlace()),
-                   "This kernel only runs on CPU.");
-
-    Tensor* y = context.Output<Tensor>("Y");
-    const Tensor* x = context.Input<Tensor>("X");
-    const Tensor* labels = context.Input<Tensor>("Label");
-    T* y_data = y->mutable_data<T>(context.GetPlace());
-    const T* x_data = x->data<T>();
-    const T* label_data = labels->data<T>();
-    int64_t batch_size = x->dims()[0];
-    // loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' +
-    // log(1 + exp(-abs(x)))
-    // z is click or not
-    // z' is value q of feed_fine
-    // label = {-2, -1, [0, 2]}
-    // when z' is not exist, clk = 0 : label = -2;
-    // when z' is not exist, clk = 1 : label = -1;
-    // when z' is exist    , clk = 0 : label = 0 + z';
-    // when z' is exist    , clk = 1 : label = 1 + z';
-    for (int i = 0; i < batch_size; ++i) {
-      if (label_data[i] < -1.0) {
-        y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) +
-                    log(1.0 + exp(-fabs(x_data[i])));
-      } else if (label_data[i] < 0.0) {
-        y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) - x_data[i] +
-                    log(1.0 + exp(-fabs(x_data[i])));
-      } else if (label_data[i] < 1.0) {
-        y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) +
-                    log(1.0 + exp(-fabs(x_data[i]))) +
-                    (x_data[i] > 0 ? x_data[i] : 0.0) -
-                    x_data[i] * label_data[i] +
-                    log(1.0 + exp(-fabs(x_data[i])));
-      } else {
-        y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) - x_data[i] +
-                    log(1.0 + exp(-fabs(x_data[i]))) +
-                    (x_data[i] > 0 ? x_data[i] : 0.0) -
-                    x_data[i] * (label_data[i] - 1.0) +
-                    log(1.0 + exp(-fabs(x_data[i])));
-      }
-    }
-  }
-};
-
-template <typename T>
-class TeacherStudentSigmoidLossGradOpKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    const Tensor* x = context.Input<Tensor>("X");
-    const T* x_data = x->data<T>();
-
-    Tensor* dx = context.Output<Tensor>(framework::GradVarName("X"));
-    T* dx_data = dx->mutable_data<T>(context.GetPlace());
-
-    const Tensor* labels = context.Input<Tensor>("Label");
-    const T* label_data = labels->data<T>();
-
-    T soft_max_up_bound =
-        static_cast<T>(context.Attr<float>("soft_max_up_bound"));
-    T soft_max_lower_bound =
-        static_cast<T>(context.Attr<float>("soft_max_lower_bound"));
-
-    int64_t batch_size = x->dims()[0];
-
-    const framework::Tensor* dOut =
-        context.Input<framework::Tensor>(framework::GradVarName("Y"));
-
-    const T* dout_data = dOut->data<T>();
-
-    for (int i = 0; i < batch_size; ++i) {
-      T sum_val = x_data[i];
-      if (sum_val > soft_max_up_bound) {
-        sum_val = soft_max_up_bound;
-      } else {
-        if (sum_val < soft_max_lower_bound) {
-          sum_val = soft_max_lower_bound;
-        }
-      }
-
-      T pred = 1.0 / (1.0 + exp(-sum_val));
-      if (label_data[i] < -1.0) {
-        dx_data[i] = 0.0 - pred;
-      } else if (label_data[i] < 0.0) {
-        dx_data[i] = 1.0 - pred;
-      } else {
-        dx_data[i] = label_data[i] - 2.0 * pred;
-      }
-      if (sum_val >= soft_max_up_bound || sum_val <= soft_max_lower_bound) {
-        dx_data[i] = 0;
-      }
-      dx_data[i] *= dout_data[i] * -1;
-    }
-  }
-};
 }  // namespace operators
 }  // namespace paddle
 

paddle/fluid/operators/teacher_student_sigmoid_loss_op.h

@@ -20,6 +20,99 @@ namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
+template <typename T>
+class TeacherStudentSigmoidLossOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    Tensor* y = context.Output<Tensor>("Y");
+    const Tensor* x = context.Input<Tensor>("X");
+    const Tensor* labels = context.Input<Tensor>("Label");
+    T* y_data = y->mutable_data<T>(context.GetPlace());
+    const T* x_data = x->data<T>();
+    const T* label_data = labels->data<T>();
+    int64_t batch_size = x->dims()[0];
+    // loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' +
+    // log(1 + exp(-abs(x)))
+    // z is click or not
+    // z' is value q of feed_fine
+    // label = {-2, -1, [0, 2]}
+    // when z' is not exist, clk = 0 : label = -2;
+    // when z' is not exist, clk = 1 : label = -1;
+    // when z' is exist    , clk = 0 : label = 0 + z';
+    // when z' is exist    , clk = 1 : label = 1 + z';
+    for (int i = 0; i < batch_size; ++i) {
+      if (label_data[i] < -1.0) {
+        y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) +
+                    log(1.0 + exp(-fabs(x_data[i])));
+      } else if (label_data[i] < 0.0) {
+        y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) - x_data[i] +
+                    log(1.0 + exp(-fabs(x_data[i])));
+      } else if (label_data[i] < 1.0) {
+        y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) +
+                    log(1.0 + exp(-fabs(x_data[i]))) +
+                    (x_data[i] > 0 ? x_data[i] : 0.0) -
+                    x_data[i] * label_data[i] +
+                    log(1.0 + exp(-fabs(x_data[i])));
+      } else {
+        y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) - x_data[i] +
+                    log(1.0 + exp(-fabs(x_data[i]))) +
+                    (x_data[i] > 0 ? x_data[i] : 0.0) -
+                    x_data[i] * (label_data[i] - 1.0) +
+                    log(1.0 + exp(-fabs(x_data[i])));
+      }
+    }
+  }
+};
 
+template <typename T>
+class TeacherStudentSigmoidLossGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const Tensor* x = context.Input<Tensor>("X");
+    const T* x_data = x->data<T>();
+
+    Tensor* dx = context.Output<Tensor>(framework::GradVarName("X"));
+    T* dx_data = dx->mutable_data<T>(context.GetPlace());
+
+    const Tensor* labels = context.Input<Tensor>("Label");
+    const T* label_data = labels->data<T>();
+
+    T soft_max_up_bound =
+        static_cast<T>(context.Attr<float>("soft_max_up_bound"));
+    T soft_max_lower_bound =
+        static_cast<T>(context.Attr<float>("soft_max_lower_bound"));
+
+    int64_t batch_size = x->dims()[0];
+
+    const framework::Tensor* dOut =
+        context.Input<framework::Tensor>(framework::GradVarName("Y"));
+
+    const T* dout_data = dOut->data<T>();
+
+    for (int i = 0; i < batch_size; ++i) {
+      T sum_val = x_data[i];
+      if (sum_val > soft_max_up_bound) {
+        sum_val = soft_max_up_bound;
+      } else {
+        if (sum_val < soft_max_lower_bound) {
+          sum_val = soft_max_lower_bound;
+        }
+      }
+
+      T pred = 1.0 / (1.0 + exp(-sum_val));
+      if (label_data[i] < -1.0) {
+        dx_data[i] = 0.0 - pred;
+      } else if (label_data[i] < 0.0) {
+        dx_data[i] = 1.0 - pred;
+      } else {
+        dx_data[i] = label_data[i] - 2.0 * pred;
+      }
+      if (sum_val >= soft_max_up_bound || sum_val <= soft_max_lower_bound) {
+        dx_data[i] = 0;
+      }
+      dx_data[i] *= dout_data[i] * -1;
+    }
+  }
+};
 }  // namespace operators
 }  // namespace paddle

python/paddle/fluid/tests/unittests/test_teacher_student_sigmoid_loss_op.py

@@ -27,9 +27,6 @@ class TestTeacherStudentSigmoidLossOp(OpTest):
     """
 
     def setUp(self):
-        """
-            ut
-        """
         self.op_type = "teacher_student_sigmoid_loss"
         batch_size = 16
         num_classes = 1
@@ -50,21 +47,13 @@ class TestTeacherStudentSigmoidLossOp(OpTest):
             elif label < 1.0:
                 outs.append(max(x, 0.0) + log(1.0 + exp(-abs(x))) + \
                             max(x, 0.0) - x * label + log(1.0 + exp(-abs(x))))
-                #print "33 python x:", x, "python label:", label, "term1:", max(x, 0.0) + log(1.0 + exp(-abs(x))), "term2:", max(x, 0.0) - x * label + log(1.0 + exp(-abs(x)))
             else:
                 outs.append(max(x, 0.0) - x + log(1.0 + exp(-abs(x))) + \
                             max(x, 0.0) - x * (label - 1.0) + log(1.0 + exp(-abs(x))))
-                #print "44 python x:", x, "python label:", label, "term1:", max(x, 0.0) - x + log(1.0 + exp(-abs(x))), "term2:", max(x, 0.0) - x * (label - 1.0) + log(1.0 + exp(-abs(x)))
         self.outputs = {'Y': np.array(outs)}
 
     def test_check_output(self):
-        """
-            ut
-        """
         self.check_output()
 
     def test_check_grad(self):
-        """
-            ut
-        """
         self.check_grad(["X"], "Y", numeric_grad_delta=0.005)