fix lints

Author: mcr229

backends/xnnpack/test/ops/test_cat.py

@@ -186,9 +186,14 @@ def test_qs8_cat_gt_5(self):
             for _ in range(num_inputs):
                 inputs.append(torch.randn(1, 2, 3))
             self._test_cat(self.Cat(), tuple(inputs), cat_num=num_inputs, quant=True)
-    
+
     def test_qs8_cat_with_empty_tensor(self):
-        inputs = (torch.randn(0, 2, 3), torch.randn(1, 2, 3), torch.randn(3, 2, 3), torch.randn(0, 2, 3))
+        inputs = (
+            torch.randn(0, 2, 3),
+            torch.randn(1, 2, 3),
+            torch.randn(3, 2, 3),
+            torch.randn(0, 2, 3),
+        )
         self._test_cat(self.Cat(), inputs, cat_num=4, quant=True)
 
     class CatNegativeDim(torch.nn.Module):

exir/passes/__init__.py

@@ -42,9 +42,9 @@
 from executorch.exir.passes.memory_format_ops_pass import MemoryFormatOpsPass
 from executorch.exir.passes.memory_planning_pass import MemoryPlanningPass
 from executorch.exir.passes.normalize_transpose_pass import NormalizeTransposePass
+from executorch.exir.passes.prune_empty_tensors_pass import PruneEmptyTensorsPass
 from executorch.exir.passes.quant_fusion_pass import QuantFusionPass
 from executorch.exir.passes.remove_noop_pass import RemoveNoopPass, RemoveToCopyPass
-from executorch.exir.passes.prune_empty_tensors_pass import PruneEmptyTensorsPass
 from executorch.exir.passes.replace_aten_with_edge_pass import OpReplacePass
 from executorch.exir.passes.replace_broken_ops_with_function_ops_pass import (
     ReplaceBrokenOpsWithFunctionalOpsPass,

exir/passes/prune_empty_tensors_pass.py

@@ -17,6 +17,7 @@
 # Which means that if we remove the empty tensor as input to this op,
 # The result of the operation will stay the same
 
+
 class PruneEmptyTensorsPass(ExportPass):
     """
     Removes Any empty tensors from the graph that can safely be removed
@@ -25,7 +26,9 @@ class PruneEmptyTensorsPass(ExportPass):
     - aten.cat.default
     """
 
-    def remove_empty_tensors_from_cat(self, graph_module: GraphModule, cat_node: Node) -> None:
+    def remove_empty_tensors_from_cat(
+        self, graph_module: GraphModule, cat_node: Node
+    ) -> None:
         """
         Removes empty tensors from the graph that are inputs to aten.cat.default
         """
@@ -35,7 +38,7 @@ def remove_empty_tensors_from_cat(self, graph_module: GraphModule, cat_node: Nod
             input_arg_tensor = input_arg.meta["val"]
             if input_arg_tensor.numel() != 0:
                 pruned_concat_list.append(input_arg)
-            
+
         cat_node.args = (pruned_concat_list,) + cat_node.args[1:]
         if len(pruned_concat_list) == 0:
             # if all the inputs to the cat are empty tensors, then we can replace
@@ -50,13 +53,12 @@ def remove_empty_tensors_from_cat(self, graph_module: GraphModule, cat_node: Nod
                 )
                 full_like.meta = cat_node.meta
                 cat_node.replace_all_uses_with(full_like)
-                    
 
     def call(self, graph_module: GraphModule) -> PassResult:
         for node in graph_module.graph.nodes:
             if node.op != "call_function":
                 continue
-            
+
             if node.target == torch.ops.aten.cat.default:
                 self.remove_empty_tensors_from_cat(graph_module, node)
 

exir/tests/test_prune_empty_tensors_pass.py

@@ -11,8 +11,8 @@
 import torch.nn as nn
 from executorch.exir import to_edge
 from executorch.exir.capture._config import ExecutorchBackendConfig
-from executorch.exir.passes import MemoryPlanningPass
 from executorch.exir.dialects._ops import ops as exir_ops
+from executorch.exir.passes import MemoryPlanningPass
 
 
 class TestCat(nn.Module):
@@ -21,7 +21,7 @@ def forward(self, x, y, z):
         return torch.cat([empty, x, empty, y, z, empty])
 
     def get_example_inputs(self):
-        return (torch.rand(5, 6),torch.rand(5, 6),torch.rand(5, 6))
+        return (torch.rand(5, 6), torch.rand(5, 6), torch.rand(5, 6))
 
 
 class TestPruneEmptyTensors(unittest.TestCase):
@@ -46,10 +46,8 @@ def test_empty_tensor_removed_from_cat(self) -> None:
                 for input_arg in node.all_input_nodes:
                     tensor_val = input_arg.meta["val"]
                     self.assertTrue(tensor_val.numel() != 0)
-        
-        actual = etpm.exported_program().module()(
-            *example_inputs
-        )
+
+        actual = etpm.exported_program().module()(*example_inputs)
 
         reference = model(*example_inputs)
 
@@ -58,7 +56,7 @@ def test_empty_tensor_removed_from_cat(self) -> None:
     def test_cat_removed_all_empty(self) -> None:
         model = TestCat()
         model.eval()
-        example_inputs = (torch.empty((0, 6)),torch.empty((0, 6)),torch.empty((0, 6)))
+        example_inputs = (torch.empty((0, 6)), torch.empty((0, 6)), torch.empty((0, 6)))
         ep = torch.export.export(model, example_inputs, strict=True)
         etpm = to_edge(ep).to_executorch(
             config=ExecutorchBackendConfig(
@@ -69,15 +67,11 @@ def test_cat_removed_all_empty(self) -> None:
 
         for node in etpm.exported_program().graph_module.graph.nodes:
             self.assertFalse(
-                node.target in [
-                    exir_ops.edge.aten.cat.default,
-                    torch.ops.aten.cat.default
-                ]
+                node.target
+                in [exir_ops.edge.aten.cat.default, torch.ops.aten.cat.default]
             )
-        
-        actual = etpm.exported_program().module()(
-            *example_inputs
-        )
+
+        actual = etpm.exported_program().module()(*example_inputs)
 
         reference = model(*example_inputs)