fix(mge/module): fix named_tensors

GitOrigin-RevId: bb5aa1f41d3577c6b346c913337839d12f8c1559

imperative/python/megengine/module/module.py

@@ -67,6 +67,10 @@ def _is_parameter(obj):
     return isinstance(obj, Parameter)
 
 
+def _is_tensor(obj):
+    return isinstance(obj, Tensor)
+
+
 def _is_buffer(obj):
     return isinstance(obj, Tensor) and not isinstance(obj, Parameter)
 
@@ -309,8 +313,9 @@ class Module(metaclass=ABCMeta):
             module, else only returns :class:`~.Tensor` that are direct attributes
             of this module.
         """
-
-        yield from self._flatten(with_key=False, recursive=recursive, **kwargs)
+        yield from self._flatten(
+            with_key=False, predicate=_is_tensor, recursive=recursive, **kwargs
+        )
 
     def named_tensors(
         self, prefix: Optional[str] = None, recursive: bool = True, **kwargs
@@ -325,7 +330,11 @@ class Module(metaclass=ABCMeta):
             of this module.
         """
         yield from self._flatten(
-            with_key=True, prefix=prefix, recursive=recursive, **kwargs,
+            with_key=True,
+            prefix=prefix,
+            predicate=_is_tensor,
+            recursive=recursive,
+            **kwargs,
         )
 
     def children(self, **kwargs) -> "Iterable[Module]":

imperative/python/test/unit/module/test_module.py

@@ -124,6 +124,30 @@ def test_module_api(test_traced_module):
         ("i.bn.weight", m.i.bn.weight),
         ("param", m.param),
     ]
+    assert list(m.tensors()) == [
+        m.bn.bias,
+        m.bn.running_mean,
+        m.bn.running_var,
+        m.bn.weight,
+        m.buff,
+        m.i.bn.bias,
+        m.i.bn.running_mean,
+        m.i.bn.running_var,
+        m.i.bn.weight,
+        m.param,
+    ]
+    assert list(m.named_tensors()) == [
+        ("bn.bias", m.bn.bias),
+        ("bn.running_mean", m.bn.running_mean),
+        ("bn.running_var", m.bn.running_var),
+        ("bn.weight", m.bn.weight),
+        ("buff", m.buff),
+        ("i.bn.bias", m.i.bn.bias),
+        ("i.bn.running_mean", m.i.bn.running_mean),
+        ("i.bn.running_var", m.i.bn.running_var),
+        ("i.bn.weight", m.i.bn.weight),
+        ("param", m.param),
+    ]
     m.eval()
     assert (
         m.training == False