pre-commit: replace linters + formatters with Ruff; fix some issues (h…

…uggingface#1300)

* pre-commit: replace linters + formatters with Ruff

* Don't use bare except

* Clean up `noqa`s

* Enable Ruff UP; apply auto-fixes

* Enable Ruff B; apply fixes

* Enable Ruff T with exceptions

* Enable Ruff C (complexity); autofix

* Upgrade Ruff to 0.2.0

.pre-commit-config.yaml

@@ -1,37 +1,10 @@
 repos:
-  - repo: https://github.com/PyCQA/isort
-    rev: 5.12.0
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.2.0
     hooks:
-      - id: isort
-        args:
-          - --profile=black
-          - --skip-glob=wandb/**/*
-          - --thirdparty=wandb
-  - repo: https://github.com/myint/autoflake
-    rev: v1.4
-    hooks:
-      - id: autoflake
-        args:
-          - -r
-          - --exclude=wandb,__init__.py
-          - --in-place
-          - --remove-unused-variables
-          - --remove-all-unused-imports
-  - repo: https://github.com/python/black
-    rev: 22.3.0
-    hooks:
-      - id: black
-        args:
-          - --line-length=119
-          - --target-version=py38
-          - --exclude=wandb
-  - repo: https://github.com/pycqa/flake8
-    rev: 6.0.0
-    hooks:
-      - id: flake8
-        args:
-          - --ignore=E203,E501,W503,E128
-          - --max-line-length=119
+      - id: ruff
+        args: [ --fix ]
+      - id: ruff-format
 
   # - repo: https://github.com/codespell-project/codespell
   #   rev: v2.1.0

examples/hello_world.py

@@ -29,7 +29,7 @@
     "pad_token_id": tokenizer.eos_token_id,
     "max_new_tokens": 20,
 }
-response_tensor = ppo_trainer.generate([item for item in query_tensor], return_prompt=False, **generation_kwargs)
+response_tensor = ppo_trainer.generate(list(query_tensor), return_prompt=False, **generation_kwargs)
 response_txt = tokenizer.decode(response_tensor[0])
 
 # 5. define a reward for response

examples/research_projects/stack_llama/scripts/rl_training.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");
@@ -163,7 +162,7 @@ def preprocess_function(examples):
 
 
 def collator(data):
-    return dict((key, [d[key] for d in data]) for key in data[0])
+    return {key: [d[key] for d in data] for key in data[0]}
 
 
 # set seed before initializing value head for deterministic eval

examples/research_projects/tools/calculator.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");
@@ -107,7 +106,7 @@ def exact_match_reward(responses, answers=None):
 )
 
 # main training loop
-for step in range(100):
+for _step in range(100):
     tasks, answers = generate_data(ppo_config.batch_size)
     queries, responses, masks, rewards, histories = text_env.run(tasks, answers=answers)
     train_stats = ppo_trainer.step(queries, responses, rewards, masks)

examples/research_projects/tools/python_interpreter.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");
@@ -61,9 +60,9 @@ def exact_match_reward(responses, answers=None):
             if match_pattern:
                 predicted_number = float(match_pattern[0])
             if predicted_number is not None:
-                if np.abs((predicted_number - float(answer))) < 0.1:
+                if np.abs(predicted_number - float(answer)) < 0.1:
                     reward += 1.0
-        except:  # noqa
+        except Exception:
             pass
         rewards.append(torch.tensor(reward))
     return rewards

examples/research_projects/tools/triviaqa.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");
@@ -114,7 +113,7 @@ class ScriptArguments:
 
 def data_generator():
     for i in range(len(dataset)):
-        yield dataset[i]["question"], [item for item in dataset[i]["answer"]["normalized_aliases"]]
+        yield dataset[i]["question"], list(dataset[i]["answer"]["normalized_aliases"])
 
 
 gen = data_generator()
@@ -123,7 +122,7 @@ def data_generator():
 
 def generate_data(n):
     tasks, answers = [], []
-    for i in range(n):
+    for _i in range(n):
         q, a = next(gen)
         tasks.append(q)
         answers.append(a)
@@ -143,10 +142,14 @@ def exact_match_reward(responses, answers=None):
     return rewards
 
 
+def tool_fn(x):
+    # limit the amount of tokens
+    return tool(x).split("\n")[1][:600]
+
+
 # text env
 tool = load_tool("vwxyzjn/pyserini-wikipedia-kilt-doc")
-# limit the amount if tokens
-tool_fn = lambda x: tool(x).split("\n")[1][:600]  # noqa
+
 text_env = TextEnvironment(
     model,
     tokenizer,
@@ -184,8 +187,6 @@ def print_trainable_parameters(model):
         "answer": [", ".join(item) for item in answers],
     }
     all_rewards = ppo_trainer.accelerator.gather(torch.tensor(rewards, device=ppo_trainer.accelerator.device))
-    ppo_trainer.log_stats(
-        train_stats, texts, [item for item in all_rewards], columns_to_log=["query", "response", "answer"]
-    )
+    ppo_trainer.log_stats(train_stats, texts, list(all_rewards), columns_to_log=["query", "response", "answer"])
     if i % 100 == 0:
         ppo_trainer.save_pretrained(f"models/{args.model_name}_{args.seed}_{i}_triviaqa")

examples/research_projects/toxicity/scripts/gpt-j-6b-toxicity.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");
@@ -146,7 +145,7 @@ def tokenize(sample):
 
 
 def collator(data):
-    return dict((key, [d[key] for d in data]) for key in data[0])
+    return {key: [d[key] for d in data] for key in data[0]}
 
 
 # set seed before initializing value head for deterministic eval
@@ -218,7 +217,7 @@ def collator(data):
         response_tensors.append(response.squeeze()[-gen_len:])
     batch["response"] = [tokenizer.decode(r.squeeze()) for r in response_tensors]
 
-    # Compute sentiment score # noqa
+    # Compute sentiment score
     texts = batch["response"]
     toxicity_inputs = toxicity_tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(
         ppo_trainer.accelerator.device

examples/scripts/dpo.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/scripts/ppo.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");
@@ -95,7 +94,7 @@ def tokenize(sample):
 
 
 def collator(data):
-    return dict((key, [d[key] for d in data]) for key in data[0])
+    return {key: [d[key] for d in data] for key in data[0]}
 
 
 # set seed before initializing value head for deterministic eval
@@ -171,7 +170,7 @@ def collator(data):
     "max_new_tokens": 32,
 }
 
-for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
+for _epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
     query_tensors = batch["input_ids"]
 
     # Get response from gpt2

examples/scripts/ppo_multi_adapter.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");
@@ -97,7 +96,7 @@ def tokenize(example):
 
 
 def collator(data):
-    return dict((key, [d[key] for d in data]) for key in data[0])
+    return {key: [d[key] for d in data] for key in data[0]}
 
 
 config = PPOConfig(
@@ -131,7 +130,7 @@ def collator(data):
     "max_new_tokens": 32,
 }
 
-for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
+for _epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
     question_tensors = batch["input_ids"]
 
     response_tensors = ppo_trainer.generate(

examples/scripts/reward_modeling.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/scripts/sft.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");

pyproject.toml

@@ -1,16 +1,22 @@
-[tool.black]
-line-length = 119
-target-version = ['py38']
-
 [tool.ruff]
-ignore = ["E501", "E741", "W605"]
-select = ["E", "F", "I", "W"]
+target-version = "py37"
 line-length = 119
 
-# Ignore import violations in all `__init__.py` files.
-[tool.ruff.per-file-ignores]
-"__init__.py" = ["E402", "F401", "F403", "F811"]
+[tool.ruff.lint]
+ignore = [
+    "B028", # warning without explicit stacklevel
+    "C408", # dict() calls (stylistic)
+    "C901", # function complexity
+    "E501",
+]
+extend-select = ["E", "F", "I", "W", "UP", "B", "T", "C"]
+
+[tool.ruff.lint.per-file-ignores]
+# Allow prints in auxiliary scripts
+"benchmark/**.py" = ["T201"]
+"examples/**.py" = ["T201"]
+"scripts/**.py" = ["T201"]
 
-[tool.ruff.isort]
+[tool.ruff.lint.isort]
 lines-after-imports = 2
 known-first-party = ["trl"]

scripts/log_example_reports.py

@@ -31,7 +31,7 @@ def main(text_file_name, slack_channel_name=None):
     if os.path.isfile(text_file_name):
         final_results = {}
 
-        file = open(text_file_name, "r")
+        file = open(text_file_name)
         lines = file.readlines()
         for line in lines:
             result, config_name = line.split(",")

scripts/log_reports.py

@@ -40,7 +40,7 @@ def main(slack_channel_name=None):
     for log in Path().glob("*.log"):
         section_num_failed = 0
         i = 0
-        with open(log, "r") as f:
+        with open(log) as f:
             for line in f:
                 line = json.loads(line)
                 i += 1

scripts/stale.py

@@ -35,7 +35,7 @@ def main():
     open_issues = repo.get_issues(state="open")
 
     for issue in open_issues:
-        comments = sorted([comment for comment in issue.get_comments()], key=lambda i: i.created_at, reverse=True)
+        comments = sorted(issue.get_comments(), key=lambda i: i.created_at, reverse=True)
         last_comment = comments[0] if len(comments) > 0 else None
         if (
             last_comment is not None

setup.cfg

@@ -1,11 +1,2 @@
 [metadata]
 license_file = LICENSE
-
-[isort]
-ensure_newline_before_comments = True
-force_grid_wrap = 0
-include_trailing_comma = True
-line_length = 119
-lines_after_imports = 2
-multi_line_output = 3
-use_parentheses = True

tests/test_no_peft.py

@@ -95,14 +95,14 @@ def test_no_peft(self):
 
             # Check that loading a model with `peft` will raise an error
             with pytest.raises(ModuleNotFoundError):
-                import peft  # noqa
+                import peft  # noqa: F401
 
-            trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(self.causal_lm_model_id)  # noqa
-            trl_seq2seq_model = AutoModelForSeq2SeqLMWithValueHead.from_pretrained(self.seq_to_seq_model_id)  # noqa
+            _trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(self.causal_lm_model_id)
+            _trl_seq2seq_model = AutoModelForSeq2SeqLMWithValueHead.from_pretrained(self.seq_to_seq_model_id)
 
     def test_imports_no_peft(self):
         with patch.dict(sys.modules, {"peft": None}):
-            from trl import (  # noqa
+            from trl import (  # noqa: F401
                 AutoModelForCausalLMWithValueHead,
                 AutoModelForSeq2SeqLMWithValueHead,
                 PPOConfig,
@@ -141,7 +141,7 @@ def test_ppo_trainer_no_peft(self):
                 # (this could be any reward such as human feedback or output from another model)
                 reward = [torch.tensor(1.0), torch.tensor(0.0)]
                 # train model
-                train_stats = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
+                train_stats = ppo_trainer.step(list(query_tensor), list(response_tensor), reward)
                 break
 
             # check gradients are not None