map output from composite rule to origin op

python/paddle/incubate/autograd/composite_rules.py

@@ -98,6 +98,6 @@ def composite_batchnorm(
     run_mean_ = assign(run_mean)
     run_var_ = assign(run_var)
     if trainable_statistics or not is_test:
-        return run_mean_, None, batch_mean_, batch_var_, run_var_, y
+        return y, run_mean_, run_var_, batch_mean_, batch_var_, None
     else:
-        return run_mean_, batch_mean_, batch_var_, run_var_, y
+        return y, run_mean_, run_var_, batch_mean_, batch_var_

python/paddle/incubate/autograd/primx.py

@@ -36,6 +36,7 @@ from .utils import (
     flatten_and_remove_none,
     get_input_var_list,
     get_output_var_list,
+    get_output_vars_from_comosite,
     prepare_python_api_arguments,
 )
 
@@ -605,14 +606,15 @@ def _lower_composite(block, blacklist=[]):
                 bind(input_args, to_bind, value_table)
 
                 for orig_out, new_out in zip(
-                    expand_nested_list(get_output_var_list(op)),
+                    expand_nested_list(get_output_vars_from_comosite(op)),
                     expand_nested_list(as_tensors(lower_fn(op, *input_args))),
                 ):
                     if new_out is not None:
-                        assert orig_out.shape == new_out.shape, (
-                            f'when replace origin op with composite rule, origin out shape should be equal to new out shape, '
-                            f'but orig_out.shape={orig_out.shape} and new_out.shape={new_out.shape}'
-                        )
+                        if orig_out.shape and new_out.shape:
+                            assert orig_out.shape == new_out.shape, (
+                                f'when replace origin op with composite rule, origin out shape should be equal to new out shape, '
+                                f'but orig_out.shape={orig_out.shape} and new_out.shape={new_out.shape}'
+                            )
                         assert not (orig_out is None) ^ (
                             new_out is None
                         ), "orig_out and new_out should match."

python/paddle/incubate/autograd/utils.py

@@ -219,6 +219,30 @@ def get_output_var_list(op):
         ]
 
 
+def get_output_vars_from_comosite(op):
+    """origin op outputs must be mapped into outputs of composite rule."""
+    origin_output_names = op.output_names
+    if origin_output_names is None:
+        return []
+    else:
+        name = op.type
+        res = []
+        if op_map[name].get("outputs"):
+            for item in op_map[name]["outputs"].keys():
+                origin_output_name = op_map[name]["outputs"][item]
+                if origin_output_name not in origin_output_names:
+                    continue
+                origin_output_var = get_var_block(op.block, op.output(origin_output_name))
+                res.append(origin_output_var)
+        elif len(origin_output_names) == 1:
+            # When origin output num is 1, map info is not needed.
+            origin_output_var = get_var_block(op.block, op.output(origin_output_names[0]))
+            res.append(origin_output_var)
+        else:
+            raise ValueError("When replace op with composite rule, there must exist output map info from origin op to composite rule.")
+        return res
+
+
 def flatten(inp):
     if inp is None or isinstance(inp, paddle.fluid.framework.Variable):
         return [inp]