update flake8 to stock pytorch (#3547)

* update flake8 version.
* backup lint fix.
* fix C417
* fix b020

Author: xuhancn

Diff for: .flake8

@@ -12,6 +12,16 @@ ignore =
     B007,B008,
     # these ignores are from flake8-comprehensions; please fix!
     C400,C401,C402,C403,C404,C405,C407,C411,C413,C414,C415
+    # Ignore due to that we need decision,
+    # B907: https://peps.python.org/pep-3101/#explicit-conversion-flag
+    # B023: https://docs.astral.sh/ruff/rules/function-uses-loop-variable/
+    # B905: https://docs.astral.sh/ruff/rules/zip-without-explicit-strict/
+    B907, B023, B905
+    # B028 is for stack level of warning. We don't want it because we only
+    # want 1 level of stack.
+    B028,
+    # Mostly on CPU side, need CPU team to fix it.
+    B031, C419
 per-file-ignores = __init__.py: F401,F403
 optional-ascii-coding = True
 exclude =
@@ -24,4 +34,5 @@ exclude =
     ./tests,
     ./scripts,
     ./third_party,
+    ./examples,
     *.pyi

Diff for: .lintrunner.toml

@@ -18,14 +18,14 @@ init_command = [
     'run',
     'pip_init',
     '--dry-run={{DRYRUN}}',
-    'flake8==3.8.2',
-    'flake8-bugbear==20.1.4',
-    'flake8-comprehensions==3.3.0',
-    'flake8-executable==2.0.4',
+    'flake8==6.1.0',
+    'flake8-bugbear==23.3.23',
+    'flake8-comprehensions==3.15.0',
+    'flake8-executable==2.1.3',
     # 'git+https://github.com/malfet/flake8-coding.git',
-    'flake8-pyi==20.5.0',
-    'mccabe==0.6.1',
-    'pycodestyle==2.6.0',
-    'pyflakes==2.2.0',
+    'flake8-pyi==23.3.1',
+    'mccabe==0.7.0',
+    'pycodestyle==2.11.1',
+    'pyflakes==3.1.0',
     'black==24.3.0',
 ]

Diff for: cmake/ClangFormat.cmake

@@ -3,6 +3,11 @@ if(BUILD_NO_CLANGFORMAT)
   return()
 endif()
 
+# Skip for WIN32 to avoid potential issue 
+if(WIN32)
+  return()
+endif()
+
 if(CLANGFORMAT_enabled)
   return()
 endif()

Diff for: intel_extension_for_pytorch/cpu/hypertune/conf/config.py

@@ -191,10 +191,10 @@ def _read_conf(self, conf_fpath):
                 validated_conf = schema.validate(conf)
             return validated_conf
 
-        except BaseException:
+        except BaseException as e:
             raise RuntimeError(
                 "The yaml file format is not correct. Please refer to document."
-            )
+            ) from e
 
     def _convert_conf(self, src, dst):
         hyperparam_default_val = {"launcher": launcher_hyperparam_default_val}
@@ -246,10 +246,10 @@ def _parse_hypertune_token(line):
                 line = lineseg.group(1)
                 objective = ast.literal_eval(line)
                 objective = objective_schema.validate(objective)
-            except BaseException:
+            except BaseException as e:
                 raise RuntimeError(
                     f"Parsing @hypertune failed for line {line} of {program_fpath} file"
-                )
+                ) from e
             return objective
 
         with Path(program_fpath).open("r") as f:

Diff for: intel_extension_for_pytorch/cpu/hypertune/objective.py

@@ -108,10 +108,10 @@ def extract_usr_objectives(self, output):
             if HYPERTUNE_TOKEN in s:
                 try:
                     objectives.append(float(output[i + 1]))
-                except BaseException:
+                except BaseException as e:
                     raise RuntimeError(
                         f"Extracting objective {output[i]} failed for {self.program} file. \
                             Make sure to print an int/float value after the @hypertune token as \
                             the objective value to be minimized or maximized."
-                    )
+                    ) from e
         return objectives

Diff for: intel_extension_for_pytorch/distributed/dist.py

@@ -106,7 +106,7 @@ def init_process_group(
         try:
             import oneccl_bindings_for_pytorch  # noqa
         except ImportError as e:
-            raise RuntimeError("oneccl_bindings_for_pytorch is not installed!")
+            raise RuntimeError("oneccl_bindings_for_pytorch is not installed!") from e
     return dist.init_process_group(
         backend,
         init_method,

Diff for: intel_extension_for_pytorch/optim/_optimizer_utils.py

@@ -267,13 +267,12 @@ def cast(param, value, key=None):
             )
 
         # Update the state
-        id_map = {
-            old_id: p
-            for old_id, p in zip(
+        id_map = dict(
+            zip(
                 chain.from_iterable((g["params"] for g in saved_groups)),
                 chain.from_iterable((g["params"] for g in groups)),
             )
-        }
+        )
 
         # Copy state assigned to params (and cast tensors to appropriate types).
         # State that is not assigned to params is copied as is (needed for

Diff for: intel_extension_for_pytorch/xpu/cpp_extension.py

@@ -433,7 +433,7 @@ def unix_wrap_ninja_compile(
             return objects
 
         if self.compiler.compiler_type == "msvc":
-            raise "Not implemented"
+            raise NotImplementedError("Not implemented for ninja build!")
         else:
             if self.use_ninja:
                 self.compiler.compile = unix_wrap_ninja_compile
@@ -1370,11 +1370,15 @@ def __init__(self):
 
     def check_onemkl_cfg(self):
         if self.__onemkl_root is None:
-            raise "Didn't detect mkl root. Please source <oneapi_dir>/mkl/<version>/env/vars.sh "
+            raise RuntimeError(
+                "Didn't detect mkl root. Please source <oneapi_dir>/mkl/<version>/env/vars.sh "
+            )
 
     def check_onednn_cfg(self):
         if self.__onednn_root is None:
-            raise "Didn't detect dnnl root. Please source <oneapi_dir>/dnnl/<version>/env/vars.sh "
+            raise RuntimeError(
+                "Didn't detect dnnl root. Please source <oneapi_dir>/dnnl/<version>/env/vars.sh "
+            )
         else:
             logger.warning(
                 "This extension has static linked onednn library. Please attaction to \
@@ -1383,7 +1387,9 @@ def check_onednn_cfg(self):
 
     def check_dpcpp_cfg(self):
         if self.__dpcpp_root is None:
-            raise "Didn't detect dpcpp root. Please source <oneapi_dir>/compiler/<version>/env/vars.sh "
+            raise RuntimeError(
+                "Didn't detect dpcpp root. Please source <oneapi_dir>/compiler/<version>/env/vars.sh "
+            )
 
     def get_default_include_dir(self):
         return [os.path.join(self.__default_root, "include")]

Diff for: scripts/tools/setup/flake8.py

@@ -3,6 +3,8 @@
 import subprocess
 import sys
 
+_IS_WINDOWS = sys.platform == "win32"
+
 
 def check_flake8_errors(base_dir, filepath):
     if shutil.which("flake8") is None:
@@ -51,6 +53,10 @@ def check_flake8_errors(base_dir, filepath):
 
 
 if __name__ == "__main__":
+    if _IS_WINDOWS:
+        print("skip flake8 check for Windows")
+        sys.exit(0)
+
     base_dir = os.path.abspath(
         os.path.dirname(os.path.join(os.path.abspath(__file__), "../../../../"))
     )

Diff for: setup.py

@@ -138,7 +138,7 @@ def _get_build_target():
         import torch
         from torch.utils.cpp_extension import BuildExtension, CppExtension
     except ImportError as e:
-        raise RuntimeError("Fail to import torch!")
+        raise RuntimeError("Fail to import torch!") from e
 
 
 def _check_env_flag(name, default=""):
@@ -159,7 +159,7 @@ def create_if_not_exist(path_dir):
             Path(path_dir).mkdir(parents=True, exist_ok=True)
         except OSError as exc:  # Guard against race condition
             if exc.errno != errno.EEXIST:
-                raise RuntimeError("Fail to create path {}".format(path_dir))
+                raise RuntimeError("Fail to create path {}".format(path_dir)) from e
 
 
 def get_version_num():

Diff for: tests/cpu/common_nn.py

@@ -5023,12 +5023,12 @@ def test_cuda(self, test_case):
                 # are unreachable (which can happen if you differentiate
                 # only on the gradient.
                 cpu_gg = torch.autograd.grad(
-                    cpu_output.sum() + sum(map(lambda x: x.sum(), cpu_gradInputs)),
+                    cpu_output.sum() + sum(x.sum() for x in cpu_gradInputs),
                     (cpu_input, cpu_gradOutput) + tuple(cpu_module.parameters()),
                     retain_graph=True,
                 )
                 gpu_gg = torch.autograd.grad(
-                    gpu_output.sum() + sum(map(lambda x: x.sum(), gpu_gradInputs)),
+                    gpu_output.sum() + sum(x.sum() for x in gpu_gradInputs),
                     (gpu_input, gpu_gradOutput) + tuple(gpu_module.parameters()),
                     retain_graph=True,
                 )

Diff for: tests/cpu/common_utils.py

@@ -1142,7 +1142,7 @@ def accept_output(update_type):
                         "No expect file exists; to accept the current output, run:\n"
                         "python {} {} --accept"
                     ).format(munged_id, subname_output, s, __main__.__file__, munged_id)
-                )
+                ) from e
 
         # a hack for JIT tests
         if IS_WINDOWS:
@@ -1314,10 +1314,10 @@ def download_file(url, binary=True):
         with open(path, "wb" if binary else "w") as f:
             f.write(data)
         return path
-    except error.URLError:
+    except error.URLError as e:
         msg = "could not download test file '{}'".format(url)
         warnings.warn(msg, RuntimeWarning)
-        raise unittest.SkipTest(msg)
+        raise unittest.SkipTest(msg) from e
 
 
 def find_free_port():

Diff for: tests/cpu/test_ipex_optimize_transformers.py

@@ -169,12 +169,12 @@ def test_model_replacement(self):
         enable_torchcompile = [False, True]
         deployment_mode = [True, False]
         return_dict = [False, True]
-        for m, torchcompile, dtype, jit, return_dict in itertools.product(
+        for m, torchcompile, dtype, jit, ret_dict in itertools.product(
             supported_models, enable_torchcompile, dtypes, deployment_mode, return_dict
         ):
             if torchcompile and deployment_mode:
                 continue
-            self.model_replacement_check(m, dtype, jit, torchcompile, return_dict)
+            self.model_replacement_check(m, dtype, jit, torchcompile, ret_dict)
         _disable_tpp()
 
     def _model_replacement_check_woq(self, model):

Diff for: tests/cpu/test_ipex_optimize_transformers_nightly.py

@@ -396,12 +396,12 @@ def test_model_replacement(self):
         enable_torchcompile = [False, True]
         deployment_mode = [True, False]
         return_dict = [False, True]
-        for m, torchcompile, dtype, jit, return_dict in itertools.product(
+        for m, torchcompile, dtype, jit, ret_dict in itertools.product(
             supported_models, enable_torchcompile, dtypes, deployment_mode, return_dict
         ):
             if torchcompile and deployment_mode:
                 continue
-            self.model_replacement_check(m, dtype, jit, torchcompile, return_dict)
+            self.model_replacement_check(m, dtype, jit, torchcompile, ret_dict)
         _disable_tpp()
 
     def test_load_low_precision_checkpoint(self):

Diff for: tests/cpu/test_quantization_default_recipe.py

@@ -134,8 +134,8 @@ def forward(self, x):
                 ]
             ],
         ]
-        for quantized_modules, pattern in zip(quantized_modules, patterns):
-            m = M(quantized_modules).eval()
+        for quantized_module, pattern in zip(quantized_modules, patterns):
+            m = M(quantized_module).eval()
 
             x = torch.rand(1, 2, 14, 14)