Add ConditionalBlockGradInferVarType

paddle/fluid/operators/controlflow/conditional_block_op.cc

@@ -265,6 +265,18 @@ class ConditionalBlockGradInferShape : public framework::InferShapeBase {
   }
 };
 
+class ConditionalBlockGradInferVarType : public framework::VarTypeInference {
+ public:
+  void operator()(framework::InferVarTypeContext *ctx) const override {
+    // NOTE(Aurelius84): VarType of Output is LoDTensor by default. In case of
+    // Input is {Tensor, LoDTensorArray}, we need synchronous the Input's
+    // VarType into Input@GRAD to avoid generating {Tensor, Tensor} as
+    // Input@GRAD.
+    ctx->SyncTypeAndDataType(ConditionalOp::kInputs,
+                             framework::GradVarName(ConditionalOp::kInputs));
+  }
+};
+
 template <typename T>
 class ConditionalBlockGradMaker : public framework::SingleGradOpMaker<T> {
  public:
@@ -300,4 +312,5 @@ REGISTER_OPERATOR(conditional_block, ops::ConditionalBlockOp,
                   ops::ConditionalBlockOpProtoMaker,
                   ops::ConditionalBlockGradMaker<paddle::framework::OpDesc>);
 REGISTER_OPERATOR(conditional_block_grad, ops::ConditionalBlockGradOp,
-                  ops::ConditionalBlockGradInferShape);
+                  ops::ConditionalBlockGradInferShape,
+                  ops::ConditionalBlockGradInferVarType);

python/paddle/fluid/tests/unittests/dygraph_to_static/test_list.py

@@ -306,5 +306,35 @@ def init_data(self):
         self.input = np.random.random((3, 4)).astype('float32')
 
 
+class ListWithCondNet(paddle.nn.Layer):
+    def __init__(self):
+        super(ListWithCondNet, self).__init__()
+
+    @paddle.jit.to_static
+    def forward(self, x, index):
+        y = paddle.nn.functional.relu(x)
+        a = []
+
+        for i in y:
+            a.append(i)
+
+        if index > 0:
+            res = a[0] * a[0]
+        else:
+            res = a[-1] * a[-1]
+
+        z = a[-1] * res
+        return z
+
+
+class TestListWithCondGradInferVarType(unittest.TestCase):
+    def test_to_static(self):
+        net = ListWithCondNet()
+        x = paddle.to_tensor([2, 3, 4], dtype='float32')
+        index = paddle.to_tensor([1])
+        res = net(x, index)
+        self.assertEqual(res[0], 16.)
+
+
 if __name__ == '__main__':
     unittest.main()