[BE] Enable ruff's UP rules and autoformat torchgen/ (pytorch#105423)

Pull Request resolved: pytorch#105423
Approved by: https://github.com/Skylion007

torchgen/api/python.py

@@ -315,7 +315,7 @@ def from_outputs(
                 outputs=outputs,
             )
         elif size > 1:
-            if any((not a.type.is_tensor_like() for a in outputs)):
+            if any(not a.type.is_tensor_like() for a in outputs):
                 raise RuntimeError(f"Unsupported output type: {outputs}")
             return PythonOutArgument(
                 name="out",
@@ -882,10 +882,10 @@ def topt_default_init(name: str) -> Optional[str]:
 
 
 def namedtuple_fieldnames(returns: Tuple[Return, ...]) -> List[str]:
-    if len(returns) <= 1 or all((r.name is None for r in returns)):
+    if len(returns) <= 1 or all(r.name is None for r in returns):
         return []
     else:
-        if any((r.name is None for r in returns)):
+        if any(r.name is None for r in returns):
             # When building on Windows, `PyStructSequence_UnnamedField` could not be
             # resolved by the linker for some reason, which cause error in building:
             #
@@ -1163,7 +1163,7 @@ def dispatch_lambda_return_str(f: NativeFunction) -> str:
     # mutable reference to temporary.  Maybe we could assign it to a
     # variable itself.)
     returns_without_annotation = tuple(
-        (Return(r.name, r.type, None) for r in f.func.returns)
+        Return(r.name, r.type, None) for r in f.func.returns
     )
     return_str = cpp.returns_type(returns_without_annotation, symint=True).cpp_type()
     if return_str not in SUPPORTED_RETURN_TYPES:
@@ -1195,7 +1195,7 @@ def cpp_dispatch_exprs(
     exprs: Tuple[str, ...] = tuple()
     if not isinstance(python_signature, PythonSignatureDeprecated):
         # By default the exprs are consistent with the C++ signature.
-        exprs = tuple((a.name for a in cpp_args))
+        exprs = tuple(a.name for a in cpp_args)
     else:
         # For deprecated python signature we may need fill in some constants.
         exprs = tuple(
@@ -1426,7 +1426,7 @@ def dispatch_lambda_exprs(
                     f"{f.func}: unrecognized type '{str(a.type)}' for tensor options field '{a.name}'"
                 )
         if not all(
-            (a in tensor_options_args_names for a in TENSOR_OPTIONS_FIELDS.keys())
+            a in tensor_options_args_names for a in TENSOR_OPTIONS_FIELDS.keys()
         ):
             raise RuntimeError(
                 f"{f.func}: incomplete tensor options args: {tensor_options_args_names}"
@@ -1454,7 +1454,7 @@ def dispatch_lambda_exprs(
                 raise RuntimeError(
                     f"{f.func}: dtype in tensor_options_args without output arg"
                 )
-            if not all((a in tensor_options_args_names for a in ("layout", "device"))):
+            if not all(a in tensor_options_args_names for a in ("layout", "device")):
                 raise RuntimeError(
                     f"{f.func}: incomplete tensor options for output check"
                 )
@@ -1473,6 +1473,6 @@ def dispatch_lambda_exprs(
             )
 
     return DispatchLambdaArgumentExprs(
-        exprs=tuple((lambda_args_exprs[a.name] for a in lambda_args)),
+        exprs=tuple(lambda_args_exprs[a.name] for a in lambda_args),
         inits=inits,
     )

torchgen/code_template.py

@@ -20,7 +20,7 @@ class CodeTemplate:
 
     @staticmethod
     def from_file(filename: str) -> "CodeTemplate":
-        with open(filename, "r") as f:
+        with open(filename) as f:
             return CodeTemplate(f.read(), filename)
 
     def __init__(self, pattern: str, filename: str = "") -> None:

torchgen/executorch/parse.py

@@ -124,7 +124,7 @@ def parse_et_yaml(
     """Parse native_functions.yaml into NativeFunctions and an Operator Indexed Dict
     of fields to persist from native_functions.yaml to functions.yaml
     """
-    with open(path, "r") as f:
+    with open(path) as f:
         es = yaml.load(f, Loader=LineLoader)
 
     et_kernel = extract_kernel_fields(es)

torchgen/gen.py

@@ -212,7 +212,7 @@ def parse_tags_yaml_struct(es: object, path: str = "<stdin>") -> Set[str]:
 def parse_tags_yaml(path: str) -> Set[str]:
     global _GLOBAL_PARSE_TAGS_YAML_CACHE
     if path not in _GLOBAL_PARSE_TAGS_YAML_CACHE:
-        with open(path, "r") as f:
+        with open(path) as f:
             es = yaml.load(f, Loader=LineLoader)
             _GLOBAL_PARSE_TAGS_YAML_CACHE[path] = parse_tags_yaml_struct(es, path=path)
 
@@ -233,7 +233,7 @@ def parse_native_yaml(
 
         # if a loaded yaml is provided, use that instead of reading from path
         if loaded_yaml is None:
-            with open(path, "r") as f:
+            with open(path) as f:
                 es = yaml.load(f, Loader=LineLoader)
         else:
             es = loaded_yaml

torchgen/gen_backend_stubs.py

@@ -47,7 +47,7 @@ def parse_backend_yaml(
         )
     }
 
-    with open(backend_yaml_path, "r") as f:
+    with open(backend_yaml_path) as f:
         yaml_values = yaml.load(f, Loader=YamlLoader)
     assert isinstance(yaml_values, dict)
 
@@ -253,9 +253,9 @@ def error_on_missing_kernels(
     full_codegen: Optional[List[OperatorName]] = None,
 ) -> None:
     try:
-        with open(kernel_defn_file_path, "r") as f:
+        with open(kernel_defn_file_path) as f:
             backend_defns = f.read()
-    except IOError as e:
+    except OSError as e:
         raise AssertionError(
             f"Unable to read from the specified impl_path file: {kernel_defn_file_path}"
         ) from e

torchgen/gen_executorch.py

@@ -575,7 +575,7 @@ def translate_native_yaml(
         None
     """
     if use_aten_lib:
-        with open(aten_yaml_path, "r") as aten_yaml:
+        with open(aten_yaml_path) as aten_yaml:
             out_file.writelines(aten_yaml.readlines())
         return
 
@@ -604,7 +604,7 @@ def translate_native_yaml(
         or os.stat(native_yaml_path).st_size == 0
     ):
         return
-    with open(native_yaml_path, "r") as native_yaml:
+    with open(native_yaml_path) as native_yaml:
         native_es = yaml.load(native_yaml, Loader=LineLoader)
         if not native_es:
             return
@@ -641,7 +641,7 @@ def parse_yaml(
     Union[Dict[DispatchKey, Dict[OperatorName, BackendMetadata]], ETKernelIndex],
 ]:
     if path and os.path.exists(path) and os.stat(path).st_size > 0:
-        with open(path, "r") as f:
+        with open(path) as f:
             es = yaml.load(f, Loader=LineLoader)
 
         # Check for kernel index structure

torchgen/gen_lazy_tensor.py

@@ -115,7 +115,7 @@ def parse_native_functions_keys(
         )
     }
 
-    with open(backend_yaml_path, "r") as f:
+    with open(backend_yaml_path) as f:
         yaml_values = yaml.load(f, Loader=YamlLoader)
     assert isinstance(yaml_values, dict)
 
@@ -134,10 +134,10 @@ def validate_shape_inference_header(
     shape_inference_hdr: str, expected_shape_infr_decls: List[str]
 ) -> None:
     try:
-        with open(shape_inference_hdr, "r") as f:
+        with open(shape_inference_hdr) as f:
             shape_infr_decls = f.read()
             shape_infr_decl_lines = set(shape_infr_decls.split("\n"))
-    except IOError as e:
+    except OSError as e:
         raise AssertionError(
             f"Unable to read from the specified shape_inference_hdr file: {shape_inference_hdr}"
         ) from e

torchgen/model.py

@@ -40,7 +40,7 @@ class Location:
     line: int
 
     def __str__(self) -> str:
-        return "{}:{}".format(self.file, self.line)
+        return f"{self.file}:{self.line}"
 
 
 # Valid values of the 'variants' field in native_functions.yaml

torchgen/selective_build/operator.py

@@ -83,7 +83,7 @@ def from_yaml_dict(
         if "debug_info" in op_info:
             di_list = op_info["debug_info"]
             assert isinstance(di_list, list)
-            debug_info = tuple((str(x) for x in di_list))
+            debug_info = tuple(str(x) for x in di_list)
 
         return SelectiveBuildOperator(
             name=op_name,

torchgen/selective_build/selector.py

@@ -93,7 +93,7 @@ def from_yaml_dict(data: Dict[str, object]) -> "SelectiveBuilder":
             di_list = data["debug_info"]
             assert isinstance(di_list, list)
 
-            debug_info = tuple((str(x) for x in di_list))
+            debug_info = tuple(str(x) for x in di_list)
 
         operators = {}
         operators_dict = data.get("operators", {})
@@ -141,7 +141,7 @@ def from_yaml_str(config_contents: str) -> "SelectiveBuilder":
 
     @staticmethod
     def from_yaml_path(config_path: str) -> "SelectiveBuilder":
-        with open(config_path, "r") as f:
+        with open(config_path) as f:
             contents = yaml.safe_load(f)
             return SelectiveBuilder.from_yaml_dict(contents)
 

torchgen/utils.py

@@ -105,7 +105,7 @@ def context(msg_fn: Callable[[], str]) -> Iterator[None]:
 # for getting mypy to do exhaustiveness checking
 # TODO: put this somewhere else, maybe
 def assert_never(x: NoReturn) -> NoReturn:
-    raise AssertionError("Unhandled type: {}".format(type(x).__name__))
+    raise AssertionError(f"Unhandled type: {type(x).__name__}")
 
 
 @functools.lru_cache(maxsize=None)
@@ -137,9 +137,9 @@ def __init__(self, install_dir: str, template_dir: str, dry_run: bool) -> None:
     def _write_if_changed(self, filename: str, contents: str) -> None:
         old_contents: Optional[str]
         try:
-            with open(filename, "r") as f:
+            with open(filename) as f:
                 old_contents = f.read()
-        except IOError:
+        except OSError:
             old_contents = None
         if contents != old_contents:
             # Create output directory if it doesn't exist
@@ -157,7 +157,7 @@ def substitute_with_template(
             # TODO: Update the comment reference to the correct location
             if "generated_comment" not in env:
                 comment = "@" + "generated by torchgen/gen.py"
-                comment += " from {}".format(os.path.basename(template_path))
+                comment += f" from {os.path.basename(template_path)}"
                 env["generated_comment"] = comment
             template = _read_template(template_path)
             return template.substitute(env)
@@ -172,7 +172,7 @@ def write_with_template(
         template_fn: str,
         env_callable: Callable[[], Union[str, Dict[str, Any]]],
     ) -> None:
-        filename = "{}/{}".format(self.install_dir, filename)
+        filename = f"{self.install_dir}/{filename}"
         assert filename not in self.filenames, "duplicate file write {filename}"
         self.filenames.add(filename)
         if not self.dry_run: