MNT Move code quality fully to ruff (huggingface#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):