make utils support empty dim order

exir/dim_order_utils.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import List
+from typing import List, Optional
 
 import torch
 
@@ -27,11 +27,13 @@ def _get_channels_last_dim_order(ndim: int) -> List[int]:
     raise AssertionError(f"Unsupported rank: {ndim}")
 
 
-def get_memory_format(dim_order: List[int]) -> torch.memory_format:
+def get_memory_format(dim_order: Optional[List[int]]) -> torch.memory_format:
     """
     Given a dim_order try to map it to torch.memory_format
     """
-    if dim_order == _get_contiguous_dim_order(len(dim_order)):
+    if dim_order is None:
+        return torch.preserve_format
+    elif dim_order == _get_contiguous_dim_order(len(dim_order)):
         return torch.contiguous_format
     elif len(dim_order) == 4 and dim_order == _get_channels_last_dim_order(
         len(dim_order)
@@ -43,11 +45,15 @@ def get_memory_format(dim_order: List[int]) -> torch.memory_format:
     )
 
 
-def get_dim_order(memory_format: torch.memory_format, ndim: int) -> List[int]:
+def get_dim_order(
+    memory_format: Optional[torch.memory_format], ndim: int
+) -> Optional[List[int]]:
     """
     Given a memory_format and a tensor rank, generate a dim_order
     """
-    if memory_format == torch.contiguous_format:
+    if memory_format in [None, torch.preserve_format]:
+        return None
+    elif memory_format == torch.contiguous_format:
         return _get_contiguous_dim_order(ndim)
     elif memory_format == torch.channels_last:
         return _get_channels_last_dim_order(ndim)