Fix lint warning

Summary: Was working on D48006877 but run into some unrelated lint warning. Seperate out the changes just to fix lint warning

Reviewed By: angelayi

Differential Revision: D48042818

fbshipit-source-id: 76e51477d58d4b57949c2f52e1f0d0dcbe3875e8

Author: cccclai

backends/backend_api.py

@@ -23,7 +23,6 @@
 
 from executorch.exir.graph_module import get_control_flow_submodules
 from executorch.exir.lowered_backend_module import (
-    arrange_graph_placeholders,
     create_exported_program_from_submodule,
     create_submodule_from_nodes,
     LoweredBackendModule,

backends/test/test_backends.py

@@ -650,7 +650,7 @@ def forward(self, x_raw, h, c):
         )
 
         new_res = program_with_delegates.dump_graph_module()(*inputs)
-        for t1, t2 in zip(new_res, orig_res):
+        for t1, t2 in zip(new_res, orig_res, strict=True):
             self.assertTrue(torch.allclose(t1, t2, atol=1e-03, rtol=1e-03))
 
         # Check the backend delegate
@@ -764,7 +764,7 @@ def forward(self, x_raw, h, c):
         )
 
         new_res = traced_with_delegate(*inputs)
-        for t1, t2 in zip(new_res, orig_res):
+        for t1, t2 in zip(new_res, orig_res, strict=True):
             self.assertTrue(torch.allclose(t1, t2, atol=1e-03, rtol=1e-03))
 
         program_with_delegates = traced_with_delegate.to_executorch(
@@ -785,7 +785,7 @@ def forward(self, x_raw, h, c):
         # )
 
         new_res = program_with_delegates.dump_graph_module()(*inputs)
-        for t1, t2 in zip(new_res, orig_res):
+        for t1, t2 in zip(new_res, orig_res, strict=True):
             self.assertTrue(torch.allclose(t1, t2, atol=1e-03, rtol=1e-03))
 
         # Check the backend delegate

backends/test/test_backends_lifted.py

@@ -647,7 +647,7 @@ def forward(self, x_raw, h, c):
         )
 
         new_res = program_with_delegates.dump_graph_module()(*inputs)
-        for t1, t2 in zip(new_res, orig_res):
+        for t1, t2 in zip(new_res, orig_res, strict=True):
             self.assertTrue(torch.allclose(t1, t2, atol=1e-03, rtol=1e-03))
 
         # Check the backend delegate
@@ -759,7 +759,7 @@ def forward(self, x_raw, h, c):
         )
 
         new_res = traced_with_delegate(*inputs)
-        for t1, t2 in zip(new_res, orig_res):
+        for t1, t2 in zip(new_res, orig_res, strict=True):
             self.assertTrue(torch.allclose(t1, t2, atol=1e-03, rtol=1e-03))
 
         program_with_delegates = traced_with_delegate.to_executorch(
@@ -780,7 +780,7 @@ def forward(self, x_raw, h, c):
         # )
 
         new_res = program_with_delegates.dump_graph_module()(*inputs)
-        for t1, t2 in zip(new_res, orig_res):
+        for t1, t2 in zip(new_res, orig_res, strict=True):
             self.assertTrue(torch.allclose(t1, t2, atol=1e-03, rtol=1e-03))
 
         # Check the backend delegate

backends/test/test_utils.py

@@ -280,6 +280,7 @@ def partition(
 
     test_lib = Library("test_lib", "DEF")
 
+    @staticmethod
     @bind_pattern_to_op(
         test_lib, "test_q_linear(Tensor x, Tensor weight, Tensor bias) -> Tensor"
     )

backends/utils.py

@@ -28,19 +28,17 @@ def is_same_node(
             and (node_left.op == node_right.op)
             and (len(node_left.all_input_nodes) == len(node_right.all_input_nodes))
             and all(
-                [
-                    is_same_node(arg_left, arg_right)
-                    for arg_left, arg_right in zip(
-                        node_left.all_input_nodes, node_right.all_input_nodes
-                    )
-                ]
+                is_same_node(arg_left, arg_right)
+                for arg_left, arg_right in zip(
+                    node_left.all_input_nodes, node_right.all_input_nodes, strict=True
+                )
             )
         ):
             return False
     else:
         if len(list(node_left)) != len(list(node_right)):
             return False
-        for n_left, n_right in zip(node_left, node_right):
+        for n_left, n_right in zip(node_left, node_right, strict=True):
             # pyre-fixme[6]: For 1st argument expected `Iterable[Node]` but got `Node`.
             # pyre-fixme[6]: For 2nd argument expected `Iterable[Node]` but got `Node`.
             if not is_same_node(n_left, n_right):
@@ -57,7 +55,9 @@ def is_identical_graph(
     # is not the same.
     if len(list(graph_left.graph.nodes)) != len(list(graph_right.graph.nodes)):
         return False
-    for node_left, node_right in zip(graph_left.graph.nodes, graph_right.graph.nodes):
+    for node_left, node_right in zip(
+        graph_left.graph.nodes, graph_right.graph.nodes, strict=True
+    ):
         if not (is_same_node(node_left, node_right)):
             return False
     return True

exir/tests/test_serde.py

@@ -13,9 +13,8 @@
 
 import torch
 from executorch.backends.backend_api import CompileSpec, to_backend
-from executorch.backends.test.backend_with_compiler_demo import (  # noqa
-    BackendWithCompilerDemo,
-)
+from executorch.backends.test.backend_with_compiler_demo import BackendWithCompilerDemo
+
 from executorch.backends.test.op_partitioner_demo import AddMulPartitionerDemo
 from executorch.exir import EdgeCompileConfig
 from executorch.exir.serde.serialize import deserialize, serialize
@@ -40,7 +39,7 @@ def check_ep(
         flat_orig_outputs, _ = pytree.tree_flatten(orig_outputs)
         flat_loaded_outputs, _ = pytree.tree_flatten(loaded_outputs)
 
-        for orig, loaded in zip(flat_orig_outputs, flat_loaded_outputs):
+        for orig, loaded in zip(flat_orig_outputs, flat_loaded_outputs, strict=True):
             self.assertTrue(torch.allclose(orig, loaded))
 
     # pyre-ignore
@@ -118,7 +117,7 @@ def forward(self, x):
         max_value = model_inputs[0].shape[0]
         compile_specs = [CompileSpec("max_value", bytes([max_value]))]
         lowered_sin_module = to_backend(
-            "BackendWithCompilerDemo", edgeir_m.exported_program, compile_specs
+            BackendWithCompilerDemo.__name__, edgeir_m.exported_program, compile_specs
         )
 
         class CompositeModule(torch.nn.Module):