Implemented the hardShrink activation (#4653)

* Implemented the hardShrink activation

* Fixing the unit test

paddle/operators/activation_op.cc

@@ -137,6 +137,24 @@ class TanhShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
   }
 };
 
+template <typename AttrType>
+class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  HardShrinkOpMaker(framework::OpProto *proto,
+                    framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of HardShrink operator");
+    AddOutput("Y", "Output of HardShrink operator");
+    AddComment(
+        "HardShrink activation operator, "
+        "hard_shrink(x) = x if x > lambda"
+        "hard_shrink(x) = x if x < -lambda"
+        "hard_shrink(x) = 0 otherwise");
+    AddAttr<AttrType>("threshold", "The value of threshold for HardShrink")
+        .SetDefault(static_cast<AttrType>(0.5));
+  }
+};
+
 class SqrtOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   SqrtOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
@@ -357,6 +375,9 @@ REGISTER_OP(pow, ops::ActivationOp, ops::PowOpMaker<float>, pow_grad,
 REGISTER_OP(stanh, ops::ActivationOp, ops::STanhOpMaker<float>, stanh_grad,
             ops::ActivationOpGrad);
 
+REGISTER_OP(hard_shrink, ops::ActivationOp, ops::HardShrinkOpMaker<float>,
+            hard_shrink_grad, ops::ActivationOpGrad);
+
 #define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor)        \
   REGISTER_OP_CPU_KERNEL(                                                      \
       act_type,                                                                \

paddle/operators/activation_op.h

@@ -199,6 +199,39 @@ struct TanhShrinkGradFunctor : public BaseActivationFunctor<T> {
   }
 };
 
+// tanhshrink(x) = x - tanh(x)
+// where tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
+template <typename T>
+struct HardShrinkFunctor : public BaseActivationFunctor<T> {
+  float threshold;
+
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"threshold", &threshold}};
+  }
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) const {
+    auto temp1 = (x < (threshold * -1)).template cast<T>().eval();
+    auto temp2 = (x > threshold).template cast<T>().eval();
+    y.device(d) = x * (temp1 + temp2);
+  }
+};
+
+template <typename T>
+struct HardShrinkGradFunctor : public BaseActivationFunctor<T> {
+  float threshold;
+
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"threshold", &threshold}};
+  }
+
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
+    auto temp1 = (x < (threshold * -1)).template cast<T>().eval();
+    auto temp2 = (x > threshold).template cast<T>().eval();
+    dx.device(d) = dy * (temp1 + temp2).template cast<T>();
+  }
+};
+
 // softshrink(x) = x - lambda, if x > lambda; x + lambda, if x < lambda; 0
 // otherwise
 template <typename T>
@@ -351,8 +384,6 @@ template <typename T>
 struct Relu6Functor : public BaseActivationFunctor<T> {
   float threshold;
 
-  // NOTE: Explicit hides the `BaseActivationFunctor<T>::GetAttrs`
-  // not polymorphism for speed.
   typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
     return {{"threshold", &threshold}};
   }
@@ -555,4 +586,5 @@ struct STanhGradFunctor : public BaseActivationFunctor<T> {
   __macro(relu6, Relu6Functor, Relu6GradFunctor);                 \
   __macro(leaky_relu, LeakyReluFunctor, LeakyReluGradFunctor);    \
   __macro(tanh_shrink, TanhShrinkFunctor, TanhShrinkGradFunctor); \
-  __macro(elu, ELUFunctor, ELUGradFunctor)
+  __macro(elu, ELUFunctor, ELUGradFunctor);                       \
+  __macro(hard_shrink, HardShrinkFunctor, HardShrinkGradFunctor)

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

@@ -78,6 +78,26 @@ class TestTanhShrink(OpTest):
         self.check_grad(['X'], 'Y', max_relative_error=0.008)
 
 
+class TestHardShrink(OpTest):
+    def setUp(self):
+        self.op_type = "hard_shrink"
+        x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
+        threshold = 0.5
+
+        self.inputs = {'X': x}
+        self.attrs = {'lambda': threshold}
+
+        t = np.copy(x)
+        t[(t >= -threshold) & (t <= threshold)] = 0
+        self.outputs = {'Y': t}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.005)
+
+
 class TestSoftShrink(OpTest):
     def setUp(self):
         self.op_type = "softshrink"