Add some error messages for the op without double grads. (#25951)

* Add some error messages for the op without double grads.

* fix the test_imperative_double_grad UT.

paddle/fluid/imperative/partial_grad_engine.cc

@@ -885,12 +885,14 @@ void PartialGradTask::RunEachOp(OpBase *op) {
   if (create_graph_) {
     auto double_grad_node = CreateGradOpNode(op->InnerOp(), tmp_ins, tmp_outs,
                                              op->Attrs(), op->place());
-    if (double_grad_node) {
-      VLOG(10) << "Create " << double_grad_node->size()
-               << " double grad op(s) for " << op->Type()
-               << ", pending ops: " << GradPendingOpTypes(*double_grad_node);
-      double_grad_nodes_.emplace_back(std::move(double_grad_node));
-    }
+    PADDLE_ENFORCE_NOT_NULL(
+        double_grad_node,
+        platform::errors::NotFound("The Op %s doesn't have any grad op.",
+                                   op->Type()));
+    VLOG(10) << "Create " << double_grad_node->size()
+             << " double grad op(s) for " << op->Type()
+             << ", pending ops: " << GradPendingOpTypes(*double_grad_node);
+    double_grad_nodes_.emplace_back(std::move(double_grad_node));
   }
 
   VLOG(10) << "There are " << grads_to_accumulate_.size() << " to sum gradient";

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

@@ -298,16 +298,15 @@ class TestDygraphDoubleGradSortGradient(TestDygraphDoubleGrad):
 
 class TestDygraphDoubleGradVisitedUniq(TestCase):
     def test_compare(self):
-        value = np.random.uniform(-0.5, 0.5, 100).reshape(10, 2,
-                                                          5).astype("float32")
+        value = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32')
 
         def model_f(input):
-            linear = fluid.dygraph.Linear(5, 3, bias_attr=False)
+            conv2d = fluid.dygraph.Conv2D(3, 2, 3)
             for i in range(10):
                 if i == 0:
-                    out = linear(input)
+                    out = conv2d(input)
                 else:
-                    out = out + linear(input)
+                    out = out + conv2d(input)
             return out
 
         backward_strategy = fluid.dygraph.BackwardStrategy()
@@ -319,8 +318,14 @@ class TestDygraphDoubleGradVisitedUniq(TestCase):
 
             out = model_f(a)
 
-            dx=fluid.dygraph.grad(outputs=[out],inputs=[a],create_graph=True,retain_graph=True,  \
-                        only_inputs=True,allow_unused=False, backward_strategy=backward_strategy)
+            dx = fluid.dygraph.grad(
+                outputs=[out],
+                inputs=[a],
+                create_graph=True,
+                retain_graph=True,
+                only_inputs=True,
+                allow_unused=False,
+                backward_strategy=backward_strategy)
 
             grad_1 = dx[0].numpy()
 
@@ -334,7 +339,9 @@ class TestDygraphDoubleGradVisitedUniq(TestCase):
 
             grad_2 = a.gradient()
 
-        self.assertTrue(np.array_equal(grad_1, grad_2))
+        self.assertTrue(
+            np.allclose(
+                grad_1, grad_2, rtol=1.e-5, atol=1.e-8, equal_nan=True))
 
 
 if __name__ == '__main__':