Add clip double grad (#29590)

paddle/fluid/operators/clip_op.cc

@@ -109,6 +109,29 @@ DECLARE_INPLACE_OP_INFERER(ClipGradInplaceInferer,
                            {framework::GradVarName("Out"),
                             framework::GradVarName("X")});
 
+template <typename T>
+class ClipDoubleGradOpMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("clip_grad");
+    op->SetInput("X", this->Input("X"));
+    if (this->HasInput("Min")) {
+      op->SetInput("Min", this->Input("Min"));
+    }
+    if (this->HasInput("Max")) {
+      op->SetInput("Max", this->Input("Max"));
+    }
+    op->SetInput(framework::GradVarName("Out"),
+                 this->OutputGrad(framework::GradVarName("X")));
+    op->SetOutput(framework::GradVarName("X"),
+                  this->InputGrad(framework::GradVarName("Out")));
+    op->SetAttrMap(this->Attrs());
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -117,7 +140,9 @@ REGISTER_OPERATOR(clip, ops::ClipOp, ops::ClipOpMaker<float>,
                   ops::ClipGradOpMaker<paddle::framework::OpDesc>,
                   ops::ClipGradOpMaker<paddle::imperative::OpBase>,
                   ops::ClipInplaceInferer);
-REGISTER_OPERATOR(clip_grad, ops::ClipOpGrad, ops::ClipGradInplaceInferer);
+REGISTER_OPERATOR(clip_grad, ops::ClipOpGrad, ops::ClipGradInplaceInferer,
+                  ops::ClipDoubleGradOpMaker<paddle::framework::OpDesc>,
+                  ops::ClipDoubleGradOpMaker<paddle::imperative::OpBase>);
 REGISTER_OP_CPU_KERNEL(
     clip, ops::ClipKernel<paddle::platform::CPUDeviceContext, float>,
     ops::ClipKernel<paddle::platform::CPUDeviceContext, double>);

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

@@ -329,5 +329,26 @@ class TestUnsqueezeDoubleGradCheck(unittest.TestCase):
             self.func(p)
 
 
+class TestClipDoubleGradCheck(unittest.TestCase):
+    @prog_scope()
+    def func(self, place):
+        x_shape = [2, 4, 10]
+        dtype = np.float64
+
+        x = layers.data('x', x_shape, False, dtype)
+        x.persistable = True
+        out = paddle.clip(x, min=-1., max=1.)
+        x_arr = np.random.uniform(-5., 5., x_shape).astype(dtype)
+
+        gradient_checker.double_grad_check([x], out, x_init=x_arr, place=place)
+
+    def test_grad(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+        for p in places:
+            self.func(p)
+
+
 if __name__ == "__main__":
     unittest.main()