Enable more Ruff lints & fix issues

docs/source/usage_guides/deepspeed.md

@@ -656,7 +656,7 @@ ZeRO Stage-3 has 2 options:
  Below is the snippet from `examples/by_feature/deepspeed_with_config_support.py` showing this:
  ```python
  success = model.save_checkpoint(PATH, ckpt_id, checkpoint_state_dict)
- status_msg = "checkpointing: PATH={}, ckpt_id={}".format(PATH, ckpt_id)
+ status_msg = f"checkpointing: PATH={PATH}, ckpt_id={ckpt_id}"
  if success:
  logging.info(f"Success {status_msg}")
  else:

examples/by_feature/checkpointing.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/by_feature/cross_validation.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2022 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/by_feature/deepspeed_with_config_support.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2022 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/by_feature/early_stopping.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/by_feature/fsdp_with_peak_mem_tracking.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -334,13 +333,11 @@ def collate_fn(examples):
  accelerator.save_state(output_dir)
  # New Code #
  # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
- accelerator.print("Memory before entering the train : {}".format(b2mb(tracemalloc.begin)))
- accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used))
- accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked))
+ accelerator.print(f"Memory before entering the train : {b2mb(tracemalloc.begin)}")
+ accelerator.print(f"Memory consumed at the end of the train (end-begin): {tracemalloc.used}")
+ accelerator.print(f"Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}")
  accelerator.print(
- "Total Peak Memory consumed during the train (max): {}".format(
- tracemalloc.peaked + b2mb(tracemalloc.begin)
- )
+ f"Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
  )
  # Logging the peak memory usage of the GPU to the tracker
  if args.with_tracking:
@@ -387,11 +384,11 @@ def collate_fn(examples):
  accelerator.save_state(output_dir)
  # New Code #
  # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
- accelerator.print("Memory before entering the eval : {}".format(b2mb(tracemalloc.begin)))
- accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used))
- accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked))
+ accelerator.print(f"Memory before entering the eval : {b2mb(tracemalloc.begin)}")
+ accelerator.print(f"Memory consumed at the end of the eval (end-begin): {tracemalloc.used}")
+ accelerator.print(f"Peak Memory consumed during the eval (max-begin): {tracemalloc.peaked}")
  accelerator.print(
- "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + b2mb(tracemalloc.begin))
+ f"Total Peak Memory consumed during the eval (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
  )
  # Logging the peak memory usage of the GPU to the tracker
  if args.with_tracking:

examples/by_feature/gradient_accumulation.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/by_feature/local_sgd.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/by_feature/megatron_lm_gpt_pretraining.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/by_feature/multi_process_metrics.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2022 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/by_feature/tracking.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/complete_cv_example.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/complete_nlp_example.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/cv_example.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/inference/bert.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/inference/gpt2.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/inference/llama.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/inference/t5.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/nlp_example.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

pyproject.toml

@@ -1,15 +1,32 @@
 [tool.ruff]
-# Never enforce `E501` (line length violations).
-ignore = ["E501", "E741", "W605"]
-select = ["E", "F", "I", "W"]
 line-length = 119
+target-version = "py38"
 
-# Ignore import violations in all `__init__.py` files.
-[tool.ruff.per-file-ignores]
-"__init__.py" = ["E402", "F401", "F403", "F811"]
+[tool.ruff.lint]
+ignore-init-module-imports = true
+extend-select = [
+ "B009", # static getattr
+ "B010", # static setattr
+ "E", # PEP8 errors
+ "F", # PEP8 formatting
+ "I", # Import sorting
+ "W", # PEP8 warnings
+ "UP", # Pyupgrade
+]
+ignore = [
+ "E501", # Line length (handled by ruff-format)
+ "E741", # Ambiguous variable name
+ "W605", # Invalid escape sequence
+ "UP007", # X | Y type annotations
+]
+
+[tool.ruff.lint.per-file-ignores]
+"__init__.py" = [
+ "F401", # Ignore seemingly unused imports (they're meant for re-export)
+]
 "manim_animations/*" = ["ALL"]
 
-[tool.ruff.isort]
+[tool.ruff.lint.isort]
 lines-after-imports = 2
 known-first-party = ["accelerate"]
 

setup.py

@@ -19,7 +19,7 @@
 extras["quality"] = [
  "black ~= 23.1", # hf-doc-builder has a hidden dependency on `black`
  "hf-doc-builder >= 0.3.0",
- "ruff ~= 0.1.15",
+ "ruff ~= 0.2.1",
 ]
 extras["docs"] = []
 extras["test_prod"] = ["pytest", "pytest-xdist", "pytest-subtests", "parameterized"]
@@ -49,7 +49,7 @@
  name="accelerate",
  version="0.28.0.dev",
  description="Accelerate",
- long_description=open("README.md", "r", encoding="utf-8").read(),
+ long_description=open("README.md", encoding="utf-8").read(),
  long_description_content_type="text/markdown",
  keywords="deep learning",
  license="Apache",

src/accelerate/accelerator.py

@@ -430,15 +430,14 @@ def __init__(
  # Mixed precision attributes
  self.scaler = None
  self.native_amp = False
- err = "{mode} mixed precision requires {requirement}"
  if (
  self.state.mixed_precision == "fp16"
  and self.device.type != "cpu"
  and self.distributed_type not in (DistributedType.DEEPSPEED, DistributedType.MEGATRON_LM)
  ):
  self.native_amp = True
  if self.device.type not in ("xpu", "cuda", "mps", "npu"):
- raise ValueError(err.format(mode="fp16", requirement="a GPU"))
+ raise ValueError(f"fp16 mixed precision requires a GPU (not {self.device.type!r}).")
  kwargs = self.scaler_handler.to_kwargs() if self.scaler_handler is not None else {}
  if self.distributed_type == DistributedType.FSDP:
  from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
@@ -458,7 +457,7 @@ def __init__(
  else:
  self.native_amp = is_bf16_available(True)
  if mixed_precision == "bf16" and not self.native_amp and not is_tpu_available():
- raise ValueError(err.format(mode="bf16", requirement="PyTorch >= 1.10 and a supported device."))
+ raise ValueError("bf16 mixed precision requires PyTorch >= 1.10 and a supported device.")
 
  # Start of internal step tracking
  self.step = 0
@@ -1245,7 +1244,7 @@ def prepare(self, *args, device_placement=None):
  item in container
  for container in (self._dataloaders, self._models, self._optimizers, self._schedulers)
  ):
- setattr(item, "_is_accelerate_prepared", True)
+ item._is_accelerate_prepared = True
 
  return result if len(result) > 1 else result[0]
 
@@ -2386,7 +2385,7 @@ def init_trackers(self, project_name: str, config: dict | None = None, init_kwar
  self.trackers.append(tracker)
  else:
  tracker_init = LOGGER_TYPE_TO_CLASS[str(tracker)]
- if getattr(tracker_init, "requires_logging_directory"):
+ if tracker_init.requires_logging_directory:
  # We can skip this check since it was done in `__init__`
  self.trackers.append(
  tracker_init(project_name, self.logging_dir, **init_kwargs.get(str(tracker), {}))

src/accelerate/commands/config/config_args.py

@@ -51,7 +51,7 @@ def load_config_from_file(config_file):
  )
  else:
  config_file = default_config_file
- with open(config_file, "r", encoding="utf-8") as f:
+ with open(config_file, encoding="utf-8") as f:
  if config_file.endswith(".json"):
  if (
  json.load(f).get("compute_environment", ComputeEnvironment.LOCAL_MACHINE)
@@ -94,7 +94,7 @@ def to_dict(self):
  @classmethod
  def from_json_file(cls, json_file=None):
  json_file = default_json_config_file if json_file is None else json_file
- with open(json_file, "r", encoding="utf-8") as f:
+ with open(json_file, encoding="utf-8") as f:
  config_dict = json.load(f)
  if "compute_environment" not in config_dict:
  config_dict["compute_environment"] = ComputeEnvironment.LOCAL_MACHINE
@@ -126,7 +126,7 @@ def to_json_file(self, json_file):
  @classmethod
  def from_yaml_file(cls, yaml_file=None):
  yaml_file = default_yaml_config_file if yaml_file is None else yaml_file
- with open(yaml_file, "r", encoding="utf-8") as f:
+ with open(yaml_file, encoding="utf-8") as f:
  config_dict = yaml.safe_load(f)
  if "compute_environment" not in config_dict:
  config_dict["compute_environment"] = ComputeEnvironment.LOCAL_MACHINE

src/accelerate/commands/launch.py

@@ -105,19 +105,19 @@ def __call__(self, parser, namespace, values, option_string=None):
  for i, arg in enumerate(opts):
  # If the argument's container is outside of the used titles, hide it
  if arg.container.title not in titles + used_titles:
- setattr(opts[i], "help", argparse.SUPPRESS)
+ opts[i].help = argparse.SUPPRESS
  # If the argument is hardware selection, but not being passed, hide it
  elif arg.container.title == "Hardware Selection Arguments":
  if set(arg.option_strings).isdisjoint(set(args)):
- setattr(opts[i], "help", argparse.SUPPRESS)
+ opts[i].help = argparse.SUPPRESS
  else:
- setattr(opts[i], "help", arg.help + " (currently selected)")
+ opts[i].help = arg.help + " (currently selected)"
  # If the argument is a training paradigm, but not being passed, hide it
  elif arg.container.title == "Training Paradigm Arguments":
  if set(arg.option_strings).isdisjoint(set(used_platforms)):
- setattr(opts[i], "help", argparse.SUPPRESS)
+ opts[i].help = argparse.SUPPRESS
  else:
- setattr(opts[i], "help", arg.help + " (currently selected)")
+ opts[i].help = arg.help + " (currently selected)"
  for i, group in enumerate(list(parser._action_groups)):
  # If all arguments in the group are hidden, hide the group
  if all([arg.help == argparse.SUPPRESS for arg in group._group_actions]):
@@ -924,14 +924,12 @@ def _validate_launch_command(args):
  args.mixed_precision = defaults.mixed_precision
  mp_from_config_flag = True
  else:
- native_amp = False
- err = "{mode} mixed precision requires {requirement}"
  if args.use_cpu or (args.use_xpu and torch.xpu.is_available()):
  native_amp = is_torch_version(">=", "1.10")
  else:
  native_amp = is_bf16_available(True)
  if args.mixed_precision == "bf16" and not native_amp and not (args.tpu and is_tpu_available()):
- raise ValueError(err.format(mode="bf16", requirement="PyTorch >= 1.10 and a supported device."))
+ raise ValueError("bf16 mixed precision requires PyTorch >= 1.10 and a supported device.")
 
  # Silently set the default here
  if args.dynamo_backend is None:

src/accelerate/commands/menu/input.py

@@ -30,7 +30,7 @@ def mark(key: str):
  def decorator(func):
  handle = getattr(func, "handle_key", [])
  handle += [key]
- setattr(func, "handle_key", handle)
+ func.handle_key = handle
  return func
 
  return decorator
@@ -44,7 +44,7 @@ def mark_multiple(*keys: List[str]):
  def decorator(func):
  handle = getattr(func, "handle_key", [])
  handle += keys
- setattr(func, "handle_key", handle)
+ func.handle_key = handle
  return func
 
  return decorator
@@ -58,8 +58,8 @@ class KeyHandler(type):
  def __new__(cls, name, bases, attrs):
  new_cls = super().__new__(cls, name, bases, attrs)
  if not hasattr(new_cls, "key_handler"):
- setattr(new_cls, "key_handler", {})
- setattr(new_cls, "handle_input", KeyHandler.handle_input)
+ new_cls.key_handler = {}
+ new_cls.handle_input = KeyHandler.handle_input
 
  for value in attrs.values():
  handled_keys = getattr(value, "handle_key", [])

src/accelerate/commands/tpu.py

@@ -112,7 +112,7 @@ def tpu_command_launcher(args):
  raise ValueError("You must specify either a command file or a command to run on the pod.")
 
  if args.command_file:
- with open(args.command_file, "r") as f:
+ with open(args.command_file) as f:
  args.command = [f.read().splitlines()]
 
  # To turn list of lists into list of strings

src/accelerate/test_utils/examples.py

@@ -84,14 +84,14 @@ def compare_against_test(base_filename: str, feature_filename: str, parser_only:
  functionalities off of "examples/nlp_example.py", so if `base_filename` is a script other than
  `complete_nlp_example.py`, the template script should be included here. Such as `examples/cv_example.py`
  """
- with open(base_filename, "r") as f:
+ with open(base_filename) as f:
  base_file_contents = f.readlines()
- with open(os.path.abspath(os.path.join("examples", "nlp_example.py")), "r") as f:
+ with open(os.path.abspath(os.path.join("examples", "nlp_example.py"))) as f:
  full_file_contents = f.readlines()
- with open(feature_filename, "r") as f:
+ with open(feature_filename) as f:
  feature_file_contents = f.readlines()
  if secondary_filename is not None:
- with open(secondary_filename, "r") as f:
+ with open(secondary_filename) as f:
  secondary_file_contents = f.readlines()
 
  # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content

src/accelerate/test_utils/scripts/external_deps/test_checkpointing.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2022 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -182,7 +181,7 @@ def training_function(config, args):
  accelerator.print("resumed checkpoint performance:", accuracy)
  accelerator.print("resumed checkpoint's scheduler's lr:", lr_scheduler.get_lr()[0])
  accelerator.print("resumed optimizers's lr:", optimizer.param_groups[0]["lr"])
- with open(os.path.join(args.output_dir, f"state_{starting_epoch-1}.json"), "r") as f:
+ with open(os.path.join(args.output_dir, f"state_{starting_epoch-1}.json")) as f:
  resumed_state = json.load(f)
  assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
  assert (