format

.pre-commit-config.yaml

@@ -15,6 +15,7 @@ repos:
     hooks:
     - id: ruff
       args: [--fix, --exit-non-zero-on-fix, --no-cache]
+      exclude: .cmake-format.py
   - repo: https://github.com/asottile/yesqa
     rev: v1.4.0
     hooks:

pyproject.toml

@@ -18,7 +18,8 @@ select = [
 ]
 ignore = [
     "UP015",
-    "Q000"
+    "Q000",
+    "F405"
 ]
 target-version = "py38"
 

src/paddlefx/legacy_module/translator.py

@@ -128,7 +128,7 @@ def call_function(self, fn, args, kwargs):
             res = self.output.create_node('call_function', fn, args, kwargs)
             self.stack.push(res)
         elif is_custom_call:
-            raise NotImplementedError(f"custom_call is not supported")
+            raise NotImplementedError("custom_call is not supported")
         else:
             raise NotImplementedError(f"call function {fn} is not supported")
 
@@ -237,7 +237,7 @@ def STORE_SUBSCR(self, inst):
         self.output.create_node('call_method', "__setitem__", [root, idx, value], {})
 
     def POP_TOP(self, inst: Instruction):
-        value = self.stack.pop()
+        self.stack.pop()
 
     def STORE_FAST(self, inst: Instruction):
         self.f_locals[inst.argval] = self.stack.pop()

src/paddlefx/pyeval.py

@@ -62,10 +62,10 @@ def wrapper(self: PyEval, inst: Instruction):
             state = self.get_state()
             try:
                 return inner_fn(self, inst)
-            except (BreakGraphError, NotImplementedError) as e:
+            except (BreakGraphError, NotImplementedError):
                 # TODO: remove NotImplementedError
                 logger.debug(
-                    f"break_graph_if_unsupported triggered compile", exc_info=True
+                    "break_graph_if_unsupported triggered compile", exc_info=True
                 )
 
                 if not isinstance(self, PyEval):

src/paddlefx/utils.py

@@ -61,7 +61,7 @@ def hashable(obj) -> bool:
     try:
         hash(obj)
         return True
-    except TypeError as e:
+    except TypeError:
         return False
 
 

src/paddlefx/variables/callable.py

@@ -67,7 +67,7 @@ def __call__(self, tx: PyEvalBase, *args: VariableBase, **kwargs) -> VariableBas
                 return result
             else:  # basic layer
                 ot = type(args[0].var)
-                obj_cls = type(args[0])
+
                 target = ''
                 model = (
                     tx.f_locals['self'] if 'self' in tx.f_locals else globals()['self']
@@ -82,7 +82,7 @@ def __call__(self, tx: PyEvalBase, *args: VariableBase, **kwargs) -> VariableBas
         elif fn.__module__.startswith("paddle"):
             # TODO: support multiple ouputs and containers
             ot = type(args[0].var)
-            obj_cls = type(args[0])
+
             output = graph.call_function(fn, args, kwargs, ot)
             return TensorVariable(None, node=output)
         elif inspect.isbuiltin(fn):
@@ -95,7 +95,7 @@ def __call__(self, tx: PyEvalBase, *args: VariableBase, **kwargs) -> VariableBas
                     if isinstance(attr, types.MethodType):
                         # For method variables
                         ot = type(args[0].var)
-                        obj_cls = type(args[0])
+
                         return CallableVariable(fn, tx=tx)
                     else:
                         # the attr could be callable function
@@ -110,17 +110,17 @@ def __call__(self, tx: PyEvalBase, *args: VariableBase, **kwargs) -> VariableBas
                 operator.iadd,
             ]:
                 ot = type(args[0].var)
-                obj_cls = type(args[0])
+
                 output = graph.call_function(fn, args, kwargs, ot)
                 return TensorVariable(None, node=output)
             elif fn in [operator.gt, operator.lt, operator.ge, operator.le]:
                 ot = type(args[0].var)
-                obj_cls = type(args[0])
+
                 output = graph.call_function(fn, args, kwargs, ot)
                 return TensorVariable(None, node=output)
             elif fn in [operator.is_, operator.is_not]:
                 ot = type(args[0].var)
-                obj_cls = type(args[0])
+
                 output = graph.call_function(fn, args, kwargs, ot)
                 return TensorVariable(None, node=output)
             else: