[Enhancement] Test linting in CI and fix existing linting errors (#69)

* [Enhancement] Test linting in CI

* fix linting

.codespellrc

@@ -2,4 +2,4 @@
 skip = *.ipynb
 count =
 quiet-level = 3
-ignore-words-list = nd, ans, ques
+ignore-words-list = nd, ans, ques, rouge

.github/workflows/lint.yml

+name: lint
+
+on: [push, pull_request]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.10
+      - name: Install pre-commit hook
+        run: |
+          pip install pre-commit
+          pre-commit install
+      - name: Linting
+        run: pre-commit run --all-files

docs/en/_static/image/logo_icon.svg

@@ -28,4 +28,4 @@
       </g>
     </g>
   </g>
-</svg>
+</svg>
\ No newline at end of file

docs/zh_cn/_static/image/logo_icon.svg

@@ -28,4 +28,4 @@
       </g>
     </g>
   </g>
-</svg>
+</svg>
\ No newline at end of file

docs/zh_cn/advanced_guides/new_dataset.md

@@ -3,56 +3,56 @@
 尽管 OpenCompass 已经包含了大多数常用数据集，用户在支持新数据集的时候需要完成以下几个步骤：
 
 1. 在 `opencompass/datasets` 文件夹新增数据集脚本 `mydataset.py`, 该脚本需要包含：
-    
-    - 数据集及其加载方式，需要定义一个 `MyDataset` 类，实现数据集加载方法 `load`，该方法为静态方法，需要返回 `datasets.Dataset` 类型的数据。这里我们使用 huggingface dataset 作为数据集的统一接口，避免引入额外的逻辑。具体示例如下：
 
-    ```python
-    import datasets
-    from .base import BaseDataset
+   - 数据集及其加载方式，需要定义一个 `MyDataset` 类，实现数据集加载方法 `load`，该方法为静态方法，需要返回 `datasets.Dataset` 类型的数据。这里我们使用 huggingface dataset 作为数据集的统一接口，避免引入额外的逻辑。具体示例如下：
 
-    class MyDataset(BaseDataset):
+   ```python
+   import datasets
+   from .base import BaseDataset
 
-        @staticmethod
-        def load(**kwargs) -> datasets.Dataset:
-            pass
-    ```
+   class MyDataset(BaseDataset):
 
-    - （可选）如果 OpenCompass 已有的评测器不能满足需要，需要用户定义 `MyDatasetlEvaluator` 类，实现评分方法 `score`，需要根据输入的 `predictions` 和 `references` 列表，得到需要的字典。由于一个数据集可能存在多种 metric，需要返回一个 metrics 以及对应 scores 的相关字典。具体示例如下：
+       @staticmethod
+       def load(**kwargs) -> datasets.Dataset:
+           pass
+   ```
 
-    ```python
-    from opencompass.openicl.icl_evaluator import BaseEvaluator
+   - （可选）如果 OpenCompass 已有的评测器不能满足需要，需要用户定义 `MyDatasetlEvaluator` 类，实现评分方法 `score`，需要根据输入的 `predictions` 和 `references` 列表，得到需要的字典。由于一个数据集可能存在多种 metric，需要返回一个 metrics 以及对应 scores 的相关字典。具体示例如下：
 
-    class MyDatasetlEvaluator(BaseEvaluator):
+   ```python
+   from opencompass.openicl.icl_evaluator import BaseEvaluator
 
-        def score(self, predictions: List, references: List) -> dict:
-            pass
+   class MyDatasetlEvaluator(BaseEvaluator):
 
-    ```
+       def score(self, predictions: List, references: List) -> dict:
+           pass
 
-    - （可选）如果 OpenCompass 已有的后处理方法不能满足需要，需要用户定义 `mydataset_postprocess` 方法，根据输入的字符串得到相应后处理的结果。具体示例如下：
+   ```
 
-    ```python
-    def mydataset_postprocess(text: str) -> str:
-        pass
-    ```
+   - （可选）如果 OpenCompass 已有的后处理方法不能满足需要，需要用户定义 `mydataset_postprocess` 方法，根据输入的字符串得到相应后处理的结果。具体示例如下：
+
+   ```python
+   def mydataset_postprocess(text: str) -> str:
+       pass
+   ```
 
 2. 在定义好数据集加载、评测以及数据后处理等方法之后，需要在配置文件中新增以下配置：
 
-    ```python
-    from opencompass.datasets import MyDataset, MyDatasetlEvaluator, mydataset_postprocess
-    
-    mydataset_eval_cfg = dict(
-        evaluator=dict(type=MyDatasetlEvaluator),
-        pred_postprocessor=dict(type=mydataset_postprocess))
-
-    mydataset_datasets = [
-        dict(
-            type=MyDataset,
-            ...,
-            reader_cfg=...,
-            infer_cfg=...,
-            eval_cfg=mydataset_eval_cfg)
-    ]
-    ```
-
-    配置好数据集之后，其他需要的配置文件直接参考[快速上手](../get_started.md)教程即可。
+   ```python
+   from opencompass.datasets import MyDataset, MyDatasetlEvaluator, mydataset_postprocess
+
+   mydataset_eval_cfg = dict(
+       evaluator=dict(type=MyDatasetlEvaluator),
+       pred_postprocessor=dict(type=mydataset_postprocess))
+
+   mydataset_datasets = [
+       dict(
+           type=MyDataset,
+           ...,
+           reader_cfg=...,
+           infer_cfg=...,
+           eval_cfg=mydataset_eval_cfg)
+   ]
+   ```
+
+   配置好数据集之后，其他需要的配置文件直接参考[快速上手](../get_started.md)教程即可。

docs/zh_cn/conf.py

@@ -29,14 +29,12 @@ author = 'OpenCompass Authors'
 # The full version, including alpha/beta/rc tags
 # version_file = '../../opencompass/version.py'
 
-
 # def get_version():
 #     with open(version_file, 'r') as f:
 #         exec(compile(f.read(), version_file, 'exec'))
 #     return locals()['__version__']
 
-
-release = "0.5.0"
+release = '0.5.0'
 
 # -- General configuration ---------------------------------------------------
 
@@ -141,14 +139,16 @@ latex_elements = {
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (root_doc, 'opencompass.tex', 'OpenCompass Documentation', author, 'manual'),
+    (root_doc, 'opencompass.tex', 'OpenCompass Documentation', author,
+     'manual'),
 ]
 
 # -- Options for manual page output ------------------------------------------
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [(root_doc, 'opencompass', 'OpenCompass Documentation', [author], 1)]
+man_pages = [(root_doc, 'opencompass', 'OpenCompass Documentation', [author],
+              1)]
 
 # -- Options for Texinfo output ----------------------------------------------
 
@@ -156,8 +156,9 @@ man_pages = [(root_doc, 'opencompass', 'OpenCompass Documentation', [author], 1)
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (root_doc, 'opencompass', 'OpenCompass Documentation', author, 'OpenCompass Authors',
-     'AGI evaluation toolbox and benchmark.', 'Miscellaneous'),
+    (root_doc, 'opencompass', 'OpenCompass Documentation', author,
+     'OpenCompass Authors', 'AGI evaluation toolbox and benchmark.',
+     'Miscellaneous'),
 ]
 
 # -- Options for Epub output -------------------------------------------------

docs/zh_cn/prompt/overview.md

-# Prompt 概括
+# Prompt 概括
\ No newline at end of file

opencompass/datasets/ceval.py

@@ -12,20 +12,20 @@ class CEvalDataset(BaseDataset):
 
     @staticmethod
     def load(path: str, name: str):
-        dev_dataset = load_dataset(
-            'csv',
-            data_files=osp.join(path, 'dev', f'{name}_dev.csv'),
-            split='train')
-        val_dataset = load_dataset(
-            'csv',
-            data_files=osp.join(path, 'val', f'{name}_val.csv'),
-            split='train')
+        dev_dataset = load_dataset('csv',
+                                   data_files=osp.join(path, 'dev',
+                                                       f'{name}_dev.csv'),
+                                   split='train')
+        val_dataset = load_dataset('csv',
+                                   data_files=osp.join(path, 'val',
+                                                       f'{name}_val.csv'),
+                                   split='train')
         val_dataset = val_dataset.add_column('explanation',
                                              [''] * len(val_dataset))
-        test_dataset = load_dataset(
-            'csv',
-            data_files=osp.join(path, 'test', f'{name}_test.csv'),
-            split='train')
+        test_dataset = load_dataset('csv',
+                                    data_files=osp.join(
+                                        path, 'test', f'{name}_test.csv'),
+                                    split='train')
         test_dataset = test_dataset.add_column(
             'answer',
             [''] * len(test_dataset)).add_column('explanation',

opencompass/datasets/flores.py

@@ -16,8 +16,9 @@ class FloresFirst100Dataset(BaseDataset):
             'dev':
             load_dataset(path='facebook/flores', name=name, split='dev'),
             'devtest':
-            load_dataset(
-                path='facebook/flores', name=name, split='devtest[:100]')
+            load_dataset(path='facebook/flores',
+                         name=name,
+                         split='devtest[:100]')
         })
 
 

opencompass/datasets/summedits.py

@@ -3,6 +3,7 @@ import json
 from datasets import Dataset
 
 from opencompass.registry import LOAD_DATASET
+
 from .base import BaseDataset
 
 
@@ -17,4 +18,4 @@ class SummeditsDataset_V2(BaseDataset):
                 line = json.loads(line)
                 line['label'] = 'BA'[line['label']]
                 dataset.append(line)
-        return Dataset.from_list(dataset)
+        return Dataset.from_list(dataset)
\ No newline at end of file

opencompass/models/huggingface.py

@@ -142,7 +142,8 @@ class HuggingFace(BaseModel):
         tokens = self.tokenizer.batch_encode_plus(inputs,
                                                   padding=True,
                                                   truncation=True,
-                                                  max_length=self.max_seq_len - max_out_len)
+                                                  max_length=self.max_seq_len -
+                                                  max_out_len)
         tokens = {
             k: torch.tensor(np.array(tokens[k]), device=self.model.device)
             for k in tokens if k in ['input_ids', 'attention_mask']
@@ -180,10 +181,10 @@ class HuggingFace(BaseModel):
 
         input_ids = self.tokenizer(inputs,
                                    truncation=True,
-                                   max_length=self.max_seq_len - max_out_len)['input_ids']
+                                   max_length=self.max_seq_len -
+                                   max_out_len)['input_ids']
         input_ids = torch.tensor(input_ids, device=self.model.device)
-        outputs = self.model.generate(input_ids,
-                                      max_new_tokens=max_out_len)
+        outputs = self.model.generate(input_ids, max_new_tokens=max_out_len)
 
         if not self.extract_pred_after_decode:
             outputs = outputs[:, input_ids.shape[1]:]

opencompass/openicl/__init__.py

-from .icl_dataset_reader import DatasetReader
-from .icl_evaluator import *
-from .icl_prompt_template import PromptTemplate
-from .icl_retriever import *
-from .icl_inferencer import *
+from .icl_dataset_reader import DatasetReader  # noqa
+from .icl_evaluator import *  # noqa
+from .icl_inferencer import *  # noqa
+from .icl_prompt_template import PromptTemplate  # noqa
+from .icl_retriever import *  # noqa

opencompass/openicl/icl_evaluator/__init__.py

-from .icl_aucroc_evaluator import AUCROCEvaluator
-from .icl_base_evaluator import BaseEvaluator
-from .icl_em_evaluator import EMEvaluator
+from .icl_aucroc_evaluator import AUCROCEvaluator  # noqa
+from .icl_base_evaluator import BaseEvaluator  # noqa
+from .icl_em_evaluator import EMEvaluator  # noqa
 from .icl_hf_evaluator import *  # noqa
-from .icl_toxic_evaluator import ToxicEvaluator
+from .icl_toxic_evaluator import ToxicEvaluator  # noqa

opencompass/openicl/icl_evaluator/icl_aucroc_evaluator.py

 from typing import List
+
 import numpy as np
 from sklearn.metrics import roc_auc_score
 
@@ -10,7 +11,7 @@ from .icl_base_evaluator import BaseEvaluator
 @ICL_EVALUATORS.register_module()
 class AUCROCEvaluator(BaseEvaluator):
     """Calculate AUC-ROC scores and accuracy according the prediction.
-    
+
     For some dataset, the accuracy cannot reveal the difference between
     models because of the saturation. AUC-ROC scores can further exam
     model abilities to distinguish different labels. More details can refer to

opencompass/openicl/icl_evaluator/icl_base_evaluator.py

 """Base Evaluator."""
-from typing import List
 
 
 class BaseEvaluator:
+
     def __init__(self) -> None:
         pass
 

opencompass/openicl/icl_evaluator/icl_hf_evaluator.py

 from typing import List
+
 import evaluate
 
 from opencompass.registry import ICL_EVALUATORS
@@ -54,9 +55,12 @@ class HuggingfaceEvaluator(BaseEvaluator):
             dict: calculated scores.
         """
         if len(predictions) != len(references):
-            return {'error': 'predictions and references have different '
+            return {
+                'error':
+                'predictions and references have different '
                 f'length. len(predictions): {len(predictions)}, '
-                f'len(references): {len(references)}'}
+                f'len(references): {len(references)}'
+            }
         metric = evaluate.load(self.metric)
         scores = metric.compute(**self._preprocess(predictions, references))
         return self._postprocess(scores)
@@ -103,7 +107,7 @@ class AccEvaluator(HuggingfaceEvaluator):
         Returns:
             dict: postprocessed scores.
         """
-        scores["accuracy"] *= 100
+        scores['accuracy'] *= 100
         return scores
 
 
@@ -150,7 +154,7 @@ class MccEvaluator(AccEvaluator):
         Returns:
             dict: postprocessed scores.
         """
-        scores["matthews_correlation"] *= 100
+        scores['matthews_correlation'] *= 100
         return scores
 
 

opencompass/openicl/icl_inferencer/__init__.py

-from .icl_base_inferencer import BaseInferencer
-from .icl_gen_inferencer import GenInferencer
-from .icl_ppl_inferencer import PPLInferencer
-from .icl_clp_inferencer import CLPInferencer
+from .icl_base_inferencer import BaseInferencer  # noqa
+from .icl_clp_inferencer import CLPInferencer  # noqa
+from .icl_gen_inferencer import GenInferencer  # noqa
+from .icl_ppl_inferencer import PPLInferencer  # noqa

opencompass/openicl/icl_inferencer/icl_clp_inferencer.py

@@ -98,8 +98,8 @@ class CLPInferencer(BaseInferencer):
         # 3. Generate in-context examples for testing inputs
         for idx in range(len(ice_idx_list)):
             ice.append(
-                retriever.generate_ice(
-                    ice_idx_list[idx], ice_template=ice_template))
+                retriever.generate_ice(ice_idx_list[idx],
+                                       ice_template=ice_template))
         output_handler.save_ice(ice)
 
         # 4. Collect prompts and calculate conditional log probs
@@ -165,11 +165,10 @@ class CLPInferencer(BaseInferencer):
                 choice_target_ids.append(prompt_token_num - 1)
 
             logger.info('Calculating conditional log probability for prompts.')
-            for idx in trange(
-                    0,
-                    len(prompt_list),
-                    self.batch_size,
-                    disable=not self.is_main_process):
+            for idx in trange(0,
+                              len(prompt_list),
+                              self.batch_size,
+                              disable=not self.is_main_process):
                 sub_prompt_list = prompt_list[idx:idx + self.batch_size]
                 sub_choice_target_ids = choice_target_ids[idx:idx +
                                                           self.batch_size]

opencompass/openicl/icl_inferencer/icl_ppl_inferencer.py

@@ -25,7 +25,7 @@ class PPLInferencer(BaseInferencer):
         model (:obj:`BaseModel`, optional): The module to inference.
         max_seq_len (:obj:`int`): Maximum number of tokenized words allowed by
             the LM.
-        batch_size (:obj:`int`, optional): Batch size for the :obj:`DataLoader`.
+        batch_size (:obj:`int`, optional): Batch size for the :obj:`DataLoader`
         output_json_filepath (:obj:`str`, optional): File path for output
             `JSON` file.
         output_json_filename (:obj:`str`, optional): File name for output
@@ -126,7 +126,7 @@ class PPLInferencer(BaseInferencer):
                             label,
                             ice_template=ice_template,
                             prompt_template=prompt_template)
-                        prompt_token_num = self.model.get_token_len_from_template(
+                        prompt_token_num = self.model.get_token_len_from_template(  # noqa
                             prompt, mode='ppl')  # noqa
 
                 if normalizing_str is not None:

opencompass/openicl/icl_retriever/__init__.py

-from .icl_base_retriever import BaseRetriever
-from .icl_bm25_retriever import BM25Retriever
-from .icl_dpp_retriever import DPPRetriever
-from .icl_fix_k_retriever import FixKRetriever
-from .icl_mdl_retriever import MDLRetriever
-from .icl_random_retriever import RandomRetriever
-from .icl_topk_retriever import TopkRetriever
-from .icl_votek_retriever import VotekRetriever
-from .icl_zero_retriever import ZeroRetriever
+from .icl_base_retriever import BaseRetriever  # noqa
+from .icl_bm25_retriever import BM25Retriever  # noqa
+from .icl_dpp_retriever import DPPRetriever  # noqa
+from .icl_fix_k_retriever import FixKRetriever  # noqa
+from .icl_mdl_retriever import MDLRetriever  # noqa
+from .icl_random_retriever import RandomRetriever  # noqa
+from .icl_topk_retriever import TopkRetriever  # noqa
+from .icl_votek_retriever import VotekRetriever  # noqa
+from .icl_zero_retriever import ZeroRetriever  # noqa