Fix lint issues.

pytorch · Oct 11, 2023 · 8b00209 · 8b00209
1 parent c4c701b
commit 8b00209
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Showing 2 changed files with 13 additions and 9 deletions.
diff --git a/test/dynamo/test_bridge.py b/test/dynamo/test_bridge.py
@@ -208,11 +208,14 @@ class TorchXLAReuseGraphTest(torch._dynamo.test_case.TestCase):
   test_training_maxpool = make_training_test(MaxPoolModule)
 
   def test_non_tensor_args_for_partition(self):
+
     class Emb(torch.nn.Embedding):
+
       def __init__(self):
         super().__init__(num_embeddings=10, embedding_dim=10, padding_idx=0)
 
     class Main(torch.nn.Module):
+
       def __init__(self):
         super().__init__()
         self.embedding = Emb()
@@ -231,6 +234,7 @@ def foo(x):
     x = torch.randint(0, 10, (10,), device=device)
     foo(x)
 
+
 if __name__ == "__main__":
   from torch._dynamo.test_case import run_tests
 

diff --git a/torch_xla/core/dynamo_bridge.py b/torch_xla/core/dynamo_bridge.py
@@ -230,13 +230,12 @@ def extract_graph_helper(xla_model: torch.fx.GraphModule):
       for xla_arg in xla_args
   ]
 
-  xla_tensor_args = [
-      (i, xla_arg) for i, xla_arg in enumerate(xla_args) if isinstance(xla_arg, torch.Tensor)
-  ]
+  xla_tensor_args = [(i, xla_arg)
+                     for i, xla_arg in enumerate(xla_args)
+                     if isinstance(xla_arg, torch.Tensor)]
 
-  args_tensor_ids = [
-      (index, torch_xla._XLAC._xla_get_tensor_id(xla_arg)) for index, xla_arg in xla_tensor_args
-  ]
+  args_tensor_ids = [(index, torch_xla._XLAC._xla_get_tensor_id(xla_arg))
+                     for index, xla_arg in xla_tensor_args]
 
   if dynamo_debug:
     print(f"Graph module:\n{xla_model.code}")
@@ -262,8 +261,7 @@ def extract_graph_helper(xla_model: torch.fx.GraphModule):
 
   # If a arg is being in place updated by model, we need to include arg as part of the graph result.
   xla_args_need_update_bool = torch_xla._XLAC._check_tensor_need_materialization(
-      [tensor for _, tensor in xla_tensor_args]
-  )
+      [tensor for _, tensor in xla_tensor_args])
   xla_args_need_update = []
   arg_index_to_need_update_index = {}
   for i, need_update in enumerate(xla_args_need_update_bool):
@@ -354,7 +352,9 @@ def optimized_mod(*args):
 
     # mark_step needs to be blocking since we want to access args's XLADatas
     # and they can't be placeholder.
-    if any(torch_xla._XLAC._check_tensor_need_materialization([a for a in args if isinstance(a, torch.Tensor)])):
+    if any(
+        torch_xla._XLAC._check_tensor_need_materialization(
+            [a for a in args if isinstance(a, torch.Tensor)])):
       xm.mark_step(wait=True)
 
     # If input sharding has changed from the previous program, dynamo current can