MNT Move code quality fully to ruff (#1421)

Makefile

@@ -1,19 +1,19 @@
 .PHONY: quality style test docs
 
-check_dirs := src tests examples docs
+check_dirs := .
 
 # Check that source code meets quality standards
 
 # this target runs checks on all files
 quality:
- black --check $(check_dirs)
  ruff $(check_dirs)
+ ruff format --check $(check_dirs)
  doc-builder style src/peft tests docs/source --max_len 119 --check_only
 
 # Format source code automatically and check is there are any problems left that need manual fixing
 style:
- black $(check_dirs)
  ruff $(check_dirs) --fix
+ ruff format $(check_dirs)
  doc-builder style src/peft tests docs/source --max_len 119
 
 test:

examples/causal_language_modeling/peft_lora_clm_accelerate_ds_zero3_offload.py

@@ -250,18 +250,16 @@ def test_preprocess_function(examples):
  lr_scheduler.step()
  optimizer.zero_grad()
  # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
- accelerator.print("GPU Memory before entering the train : {}".format(b2mb(tracemalloc.begin)))
- accelerator.print("GPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used))
- accelerator.print("GPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked))
+ accelerator.print(f"GPU Memory before entering the train : {b2mb(tracemalloc.begin)}")
+ accelerator.print(f"GPU Memory consumed at the end of the train (end-begin): {tracemalloc.used}")
+ accelerator.print(f"GPU Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}")
  accelerator.print(
- "GPU Total Peak Memory consumed during the train (max): {}".format(
- tracemalloc.peaked + b2mb(tracemalloc.begin)
- )
+ f"GPU Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
  )
 
- accelerator.print("CPU Memory before entering the train : {}".format(b2mb(tracemalloc.cpu_begin)))
- accelerator.print("CPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.cpu_used))
- accelerator.print("CPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.cpu_peaked))
+ accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
+ accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
+ accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
  accelerator.print(
  "CPU Total Peak Memory consumed during the train (max): {}".format(
  tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
@@ -286,18 +284,16 @@ def test_preprocess_function(examples):
  eval_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True))
 
  # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
- accelerator.print("GPU Memory before entering the eval : {}".format(b2mb(tracemalloc.begin)))
- accelerator.print("GPU Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used))
- accelerator.print("GPU Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked))
+ accelerator.print(f"GPU Memory before entering the eval : {b2mb(tracemalloc.begin)}")
+ accelerator.print(f"GPU Memory consumed at the end of the eval (end-begin): {tracemalloc.used}")
+ accelerator.print(f"GPU Peak Memory consumed during the eval (max-begin): {tracemalloc.peaked}")
  accelerator.print(
- "GPU Total Peak Memory consumed during the eval (max): {}".format(
- tracemalloc.peaked + b2mb(tracemalloc.begin)
- )
+ f"GPU Total Peak Memory consumed during the eval (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
  )
 
- accelerator.print("CPU Memory before entering the eval : {}".format(b2mb(tracemalloc.cpu_begin)))
- accelerator.print("CPU Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.cpu_used))
- accelerator.print("CPU Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.cpu_peaked))
+ accelerator.print(f"CPU Memory before entering the eval : {b2mb(tracemalloc.cpu_begin)}")
+ accelerator.print(f"CPU Memory consumed at the end of the eval (end-begin): {tracemalloc.cpu_used}")
+ accelerator.print(f"CPU Peak Memory consumed during the eval (max-begin): {tracemalloc.cpu_peaked}")
  accelerator.print(
  "CPU Total Peak Memory consumed during the eval (max): {}".format(
  tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)

examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py

@@ -201,18 +201,16 @@ def collate_fn(examples):
  lr_scheduler.step()
  optimizer.zero_grad()
  # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
- accelerator.print("GPU Memory before entering the train : {}".format(b2mb(tracemalloc.begin)))
- accelerator.print("GPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used))
- accelerator.print("GPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked))
+ accelerator.print(f"GPU Memory before entering the train : {b2mb(tracemalloc.begin)}")
+ accelerator.print(f"GPU Memory consumed at the end of the train (end-begin): {tracemalloc.used}")
+ accelerator.print(f"GPU Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}")
  accelerator.print(
- "GPU Total Peak Memory consumed during the train (max): {}".format(
- tracemalloc.peaked + b2mb(tracemalloc.begin)
- )
+ f"GPU Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
  )
 
- accelerator.print("CPU Memory before entering the train : {}".format(b2mb(tracemalloc.cpu_begin)))
- accelerator.print("CPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.cpu_used))
- accelerator.print("CPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.cpu_peaked))
+ accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
+ accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
+ accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
  accelerator.print(
  "CPU Total Peak Memory consumed during the train (max): {}".format(
  tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
@@ -236,18 +234,16 @@ def collate_fn(examples):
  eval_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True))
 
  # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
- accelerator.print("GPU Memory before entering the eval : {}".format(b2mb(tracemalloc.begin)))
- accelerator.print("GPU Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used))
- accelerator.print("GPU Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked))
+ accelerator.print(f"GPU Memory before entering the eval : {b2mb(tracemalloc.begin)}")
+ accelerator.print(f"GPU Memory consumed at the end of the eval (end-begin): {tracemalloc.used}")
+ accelerator.print(f"GPU Peak Memory consumed during the eval (max-begin): {tracemalloc.peaked}")
  accelerator.print(
- "GPU Total Peak Memory consumed during the eval (max): {}".format(
- tracemalloc.peaked + b2mb(tracemalloc.begin)
- )
+ f"GPU Total Peak Memory consumed during the eval (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
  )
 
- accelerator.print("CPU Memory before entering the eval : {}".format(b2mb(tracemalloc.cpu_begin)))
- accelerator.print("CPU Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.cpu_used))
- accelerator.print("CPU Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.cpu_peaked))
+ accelerator.print(f"CPU Memory before entering the eval : {b2mb(tracemalloc.cpu_begin)}")
+ accelerator.print(f"CPU Memory consumed at the end of the eval (end-begin): {tracemalloc.cpu_used}")
+ accelerator.print(f"CPU Peak Memory consumed during the eval (max-begin): {tracemalloc.cpu_peaked}")
  accelerator.print(
  "CPU Total Peak Memory consumed during the eval (max): {}".format(
  tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)

examples/feature_extraction/peft_lora_embedding_semantic_search.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2023-present the HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -170,7 +169,7 @@ def load_model_hook(models, input_dir):
 
 class AutoModelForSentenceEmbedding(nn.Module):
  def __init__(self, model_name, tokenizer, normalize=True):
- super(AutoModelForSentenceEmbedding, self).__init__()
+ super().__init__()
 
  self.model = AutoModel.from_pretrained(model_name) # , load_in_8bit=True, device_map={"":0})
  self.normalize = normalize

examples/int8_training/fine_tune_blip2_int8.py

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

examples/loftq_finetuning/quantize_save_load.py

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

examples/loftq_finetuning/train_gsm8k_llama.py

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

examples/lora_dreambooth/train_dreambooth.py

@@ -1048,22 +1048,18 @@ def main(args):
  # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
 
  if not args.no_tracemalloc:
- accelerator.print("GPU Memory before entering the train : {}".format(b2mb(tracemalloc.begin)))
- accelerator.print("GPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used))
- accelerator.print("GPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked))
+ accelerator.print(f"GPU Memory before entering the train : {b2mb(tracemalloc.begin)}")
+ accelerator.print(f"GPU Memory consumed at the end of the train (end-begin): {tracemalloc.used}")
+ accelerator.print(f"GPU Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}")
  accelerator.print(
  "GPU Total Peak Memory consumed during the train (max): {}".format(
  tracemalloc.peaked + b2mb(tracemalloc.begin)
  )
  )
 
- accelerator.print("CPU Memory before entering the train : {}".format(b2mb(tracemalloc.cpu_begin)))
- accelerator.print(
- "CPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.cpu_used)
- )
- accelerator.print(
- "CPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.cpu_peaked)
- )
+ accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
+ accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
+ accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
  accelerator.print(
  "CPU Total Peak Memory consumed during the train (max): {}".format(
  tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)

examples/oft_dreambooth/train_dreambooth.py

@@ -1056,22 +1056,18 @@ def main(args):
  # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
 
  if not args.no_tracemalloc:
- accelerator.print("GPU Memory before entering the train : {}".format(b2mb(tracemalloc.begin)))
- accelerator.print("GPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used))
- accelerator.print("GPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked))
+ accelerator.print(f"GPU Memory before entering the train : {b2mb(tracemalloc.begin)}")
+ accelerator.print(f"GPU Memory consumed at the end of the train (end-begin): {tracemalloc.used}")
+ accelerator.print(f"GPU Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}")
  accelerator.print(
  "GPU Total Peak Memory consumed during the train (max): {}".format(
  tracemalloc.peaked + b2mb(tracemalloc.begin)
  )
  )
 
- accelerator.print("CPU Memory before entering the train : {}".format(b2mb(tracemalloc.cpu_begin)))
- accelerator.print(
- "CPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.cpu_used)
- )
- accelerator.print(
- "CPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.cpu_peaked)
- )
+ accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
+ accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
+ accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
  accelerator.print(
  "CPU Total Peak Memory consumed during the train (max): {}".format(
  tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)

examples/stable_diffusion/convert_sd_adapter_to_peft.py

@@ -184,7 +184,7 @@ def construct_peft_lohaconfig(info: Dict[str, LoHaInfo], **kwargs) -> LoHaConfig
  alpha_pattern = dict(sorted(filter(lambda x: x[1] != alpha, alphas.items()), key=lambda x: x[0]))
 
  # Determine whether any of modules have effective conv2d decomposition
- use_effective_conv2d = any(((val.hada_t1 is not None) or (val.hada_t2 is not None) for val in info.values()))
+ use_effective_conv2d = any((val.hada_t1 is not None) or (val.hada_t2 is not None) for val in info.values())
 
  config = LoHaConfig(
  r=r,
@@ -227,7 +227,7 @@ def construct_peft_lokrconfig(info: Dict[str, LoKrInfo], decompose_factor: int =
  alpha_pattern = dict(sorted(filter(lambda x: x[1] != alpha, alphas.items()), key=lambda x: x[0]))
 
  # Determine whether any of modules have effective conv2d decomposition
- use_effective_conv2d = any(((val.lokr_t2 is not None) for val in info.values()))
+ use_effective_conv2d = any((val.lokr_t2 is not None) for val in info.values())
 
  # decompose_both should be enabled if any w1 matrix in any layer is decomposed into 2
  decompose_both = any((val.lokr_w1_a is not None and val.lokr_w1_b is not None) for val in info.values())

examples/stable_diffusion/train_dreambooth.py

@@ -1214,18 +1214,16 @@ def main(args):
  if global_step >= args.max_train_steps:
  break
  # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
- accelerator.print("GPU Memory before entering the train : {}".format(b2mb(tracemalloc.begin)))
- accelerator.print("GPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used))
- accelerator.print("GPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked))
+ accelerator.print(f"GPU Memory before entering the train : {b2mb(tracemalloc.begin)}")
+ accelerator.print(f"GPU Memory consumed at the end of the train (end-begin): {tracemalloc.used}")
+ accelerator.print(f"GPU Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}")
  accelerator.print(
- "GPU Total Peak Memory consumed during the train (max): {}".format(
- tracemalloc.peaked + b2mb(tracemalloc.begin)
- )
+ f"GPU Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
  )
 
- accelerator.print("CPU Memory before entering the train : {}".format(b2mb(tracemalloc.cpu_begin)))
- accelerator.print("CPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.cpu_used))
- accelerator.print("CPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.cpu_peaked))
+ accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
+ accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
+ accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
  accelerator.print(
  "CPU Total Peak Memory consumed during the train (max): {}".format(
  tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)

pyproject.toml

@@ -1,33 +1,31 @@
 [tool.black]
+# Only used by `hf-doc-builder´.
 line-length = 119
-target-version = ['py36']
+target-version = ['py38']
 
 [tool.ruff]
-ignore = ["C901", "E501", "E741", "W605"]
-select = ["C", "E", "F", "I", "W"]
+target-version = "py38"
 line-length = 119
 
-[tool.ruff.isort]
+[tool.ruff.lint]
+extend-select = [
+ "C", # Complexity
+ "E", # PEP8 errors
+ "F", # PEP8 formatting
+ "I", # Import sorting
+ "UP", # Pyupgrade upgrades
+ "W", # PEP8 warnings
+]
+ignore = [
+ "C901", # Function too complex
+ "E501", # Line length (handled by ruff-format)
+ "UP007", # X | Y style Unions
+]
+
+[tool.ruff.lint.isort]
 lines-after-imports = 2
 known-first-party = ["peft"]
 
-[isort]
-default_section = "FIRSTPARTY"
-known_first_party = "peft"
-known_third_party = [
- "numpy",
- "torch",
- "accelerate",
- "transformers",
-]
-line_length = 119
-lines_after_imports = 2
-multi_line_output = 3
-include_trailing_comma = true
-force_grid_wrap = 0
-use_parentheses = true
-ensure_newline_before_comments = true
-
 [tool.pytest]
 doctest_optionflags = [
  "NORMALIZE_WHITESPACE",

scripts/launch_notebook_mp.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2023-present the HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -20,9 +19,10 @@
 # This script exists to ensure that this issue does not reoccur.
 
 import torch
-import peft
 from accelerate import notebook_launcher
 
+import peft
+
 
 def init():
  class MyModule(torch.nn.Module):