[Dy2St]Fix cond grad error when handle tensor array (#39689)

* fix cond grad error when handle tensor array

* add UT

paddle/fluid/framework/var_type_inference.h

@@ -69,6 +69,12 @@ class InferVarTypeContext {
     return op_->Inputs().at(name).size();
   }
 
+  virtual size_t OutputSize(const std::string& name) const {
+    PADDLE_ENFORCE_NOT_NULL(
+        op_, platform::errors::PreconditionNotMet("op_ should not be null"));
+    return op_->Outputs().at(name).size();
+  }
+
   virtual const std::string& InputVarName(const std::string& name,
                                           const int index = 0) const {
     PADDLE_ENFORCE_NOT_NULL(

paddle/fluid/operators/controlflow/conditional_block_op.cc

@@ -272,8 +272,18 @@ class ConditionalBlockGradInferVarType : public framework::VarTypeInference {
     // 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));
+    auto input_size = ctx->InputSize(ConditionalOp::kInputs);
+    auto output_size =
+        ctx->OutputSize(framework::GradVarName(ConditionalOp::kInputs));
+    PADDLE_ENFORCE_EQ(input_size, output_size,
+                      platform::errors::InvalidArgument(
+                          "input_size and output_size should be equal for "
+                          "conditional_block_grad_op."));
+    for (size_t i = 0; i < output_size; ++i) {
+      ctx->SyncTypeAndDataType(ConditionalOp::kInputs,
+                               framework::GradVarName(ConditionalOp::kInputs),
+                               i);
+    }
   }
 };
 

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

@@ -320,10 +320,12 @@ class ListWithCondNet(paddle.nn.Layer):
 
         if index > 0:
             res = a[0] * a[0]
+            y = y + 1
         else:
             res = a[-1] * a[-1]
+            y = y - 1
 
-        z = a[-1] * res
+        z = a[-1] * res * y[0]
         return z
 
 
@@ -333,7 +335,7 @@ class TestListWithCondGradInferVarType(unittest.TestCase):
         x = paddle.to_tensor([2, 3, 4], dtype='float32')
         index = paddle.to_tensor([1])
         res = net(x, index)
-        self.assertEqual(res[0], 16.)
+        self.assertEqual(res[0], 48.)
 
 
 if __name__ == '__main__':