Apply some best practices and guideline recommendations to code (Eleu…

…therAI#1363)

* raise Exception, not a string

Additional info https://peps.python.org/pep-0352/#exception-hierarchy-changes
https://docs.python.org/3.8/tutorial/errors.html#raising-exceptions

* Apply PEP8 recommendation to prefer isinstance

"Object type comparisons should always use isinstance() instead of comparing types directly"
https://peps.python.org/pep-0008/

* Remove dangerous default mutable values in arguments

https://pylint.readthedocs.io/en/stable/user_guide/messages/warning/dangerous-default-value.html

* Format logging messages with fstring (not with format)

Additional info
https://pylint.readthedocs.io/en/stable/user_guide/messages/warning/logging-format-interpolation.html
There are also discussions about the speed of formatting while logging or some unintended code executions
pylint-dev/pylint#2395
https://stackoverflow.com/a/54368109
but at least one format (fstring one) will be used throughout the project

* Specify utf-8 encoding for `open` explicitly

If not specified, it may be supposed differently in different environments, OSes, and Python versions. See
https://peps.python.org/pep-0597/
https://docs.python.org/3.11/library/locale.html#locale.getencoding
https://docs.python.org/3.10/library/os.html#utf8-mode
https://pylint.readthedocs.io/en/stable/user_guide/messages/warning/unspecified-encoding.html

Helps also if some code from English language tasks is taken as inspiration for tasks in non-English languages.

* Use inline-ignoring comments to pass pre-commit instead of identity process

https://flake8.pycqa.org/en/3.0.1/user/ignoring-errors.html#in-line-ignoring-errors
https://www.flake8rules.com/rules/F841.html

flake8 comments are supported by ruff: https://docs.astral.sh/ruff/linter/#error-suppression

lm_eval/__main__.py

@@ -171,7 +171,7 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None:
         task_names = ALL_TASKS
     elif args.tasks == "list":
         eval_logger.info(
-            "Available Tasks:\n - {}".format("\n - ".join(sorted(ALL_TASKS)))
+            f"Available Tasks:\n - {(os.linesep + ' - ').join(sorted(ALL_TASKS))}"
         )
         sys.exit()
     else:
@@ -257,7 +257,7 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None:
         batch_sizes = ",".join(map(str, results["config"]["batch_sizes"]))
 
         if args.output_path:
-            output_path_file.open("w").write(dumped)
+            output_path_file.open("w", encoding="utf-8").write(dumped)
 
             if args.log_samples:
                 for task_name, config in results["configs"].items():

lm_eval/api/registry.py

@@ -152,18 +152,14 @@ def get_aggregation(name):
     try:
         return AGGREGATION_REGISTRY[name]
     except KeyError:
-        eval_logger.warning(
-            "{} not a registered aggregation metric!".format(name),
-        )
+        eval_logger.warning(f"{name} not a registered aggregation metric!")
 
 
 def get_metric_aggregation(name):
     try:
         return METRIC_AGGREGATION_REGISTRY[name]
     except KeyError:
-        eval_logger.warning(
-            "{} metric is not assigned a default aggregation!".format(name),
-        )
+        eval_logger.warning(f"{name} metric is not assigned a default aggregation!")
 
 
 def is_higher_better(metric_name):

lm_eval/decontamination/archiver.py

@@ -30,7 +30,9 @@ def __init__(self, file_path: str, compression_level: int = 3) -> None:
         self.cctx = zstandard.ZstdCompressor(level=compression_level)
         self.compressor = self.cctx.stream_writer(self.fh)
 
-    def add_data(self, data, meta={}) -> None:
+    def add_data(self, data, meta=None) -> None:
+        if meta is None:
+            meta = {}
         self.compressor.write(
             json.dumps({"text": data, "meta": meta}, default=json_serial).encode(
                 "UTF-8"
@@ -108,7 +110,7 @@ def __init__(self, file_path) -> None:
     def read_tqdm(self, update_frequency: int = 10000):
         current_file_position = 0
         line_counter = 0
-        with open(self.file_path, "r") as fh, tqdm.tqdm(
+        with open(self.file_path, "r", encoding="utf-8") as fh, tqdm.tqdm(
             total=os.path.getsize(self.file_path),
             dynamic_ncols=True,
             unit="byte",

lm_eval/decontamination/decontaminate.py

@@ -38,7 +38,7 @@ def get_train_overlap(docs_by_task_set: dict, ngrams_path: str, limit: int) -> d
     # return get_train_overlap_stub(docs, ngrams_path, ngrams_n_size)
 
     info_dict_path = os.path.join(ngrams_path, "info.json")
-    info_dict = json.load(open(info_dict_path, "r"))
+    info_dict = json.load(open(info_dict_path, "r", encoding="utf-8"))
     ngrams_n_size = info_dict["ngram_size"]
 
     janitor = Janitor()

lm_eval/evaluator.py

@@ -25,7 +25,7 @@
 def simple_evaluate(
     model,
     model_args=None,
-    tasks=[],
+    tasks=None,
     num_fewshot=None,
     batch_size=None,
     max_batch_size=None,
@@ -80,6 +80,8 @@ def simple_evaluate(
         1234
     )  # TODO: this may affect training runs that are run with evaluation mid-run.
 
+    if tasks is None:
+        tasks = []
     assert (
         tasks != []
     ), "No tasks specified, or no tasks found. Please verify the task names."
@@ -122,7 +124,7 @@ def simple_evaluate(
     task_dict = lm_eval.tasks.get_task_dict(tasks)
     for task_name in task_dict.keys():
         task_obj = task_dict[task_name]
-        if type(task_obj) == tuple:
+        if isinstance(task_obj, tuple):
             group, task_obj = task_obj
             if task_obj is None:
                 continue
@@ -242,7 +244,7 @@ def evaluate(
 
     # get lists of each type of request
     for task_name, task in task_dict.items():
-        if type(task) == tuple:
+        if isinstance(task, tuple):
             group_name, task = task
             task_hierarchy[group_name].append(task_name)
             versions[group_name] = "N/A"
@@ -316,7 +318,7 @@ def evaluate(
     ### Run LM on inputs, get all outputs ###
     # execute each type of request
     for reqtype, reqs in requests.items():
-        eval_logger.info("Running {} requests".format(reqtype))
+        eval_logger.info(f"Running {reqtype} requests")
         # create `K` copies of each request `req` based off `K = req.repeats`
         cloned_reqs = []
         for req in reqs:
@@ -339,7 +341,7 @@ def evaluate(
     ### Postprocess outputs ###
     # TODO: del model here, maybe (idea: allow user to specify device of e.g. reward model separately)
     for task_name, task in task_dict.items():
-        if type(task) == tuple:
+        if isinstance(task, tuple):
             group, task = task
             if task is None:
                 continue
@@ -350,7 +352,7 @@ def evaluate(
 
     # unpack results and sort back in order and return control to Task
     for task_name, task in task_dict.items():
-        if type(task) == tuple:
+        if isinstance(task, tuple):
             group, task = task
             if task is None:
                 continue
@@ -401,7 +403,7 @@ def evaluate(
         vals_torch = collections.defaultdict(list)
         for (task_name, key, metric), items in vals.items():
             numitem = 0
-            if type(items[0]) == tuple:
+            if isinstance(items[0], tuple):
                 numitem = len(items[0])
 
             if isinstance(items[0], (str, list, tuple)):
@@ -447,7 +449,7 @@ def evaluate(
             task = task_dict[task_name]
             metric_key = metric + "," + key
 
-            if type(task) == tuple:
+            if isinstance(task, tuple):
                 group_name, task = task
             else:
                 group_name = None

lm_eval/filters/transformation.py

@@ -24,7 +24,7 @@ def filter_set(inst):
 
 
 class MapFilter(Filter):
-    def __init__(self, mapping_dict: dict = {}, default_value=None) -> None:
+    def __init__(self, mapping_dict: dict = None, default_value=None) -> None:
         """
         Initializes the MapFilter with a given mapping dictionary and default value.
 
@@ -37,6 +37,8 @@ def __init__(self, mapping_dict: dict = {}, default_value=None) -> None:
         Example:
         mapper = MapFilter({'A': 1, 'B': 2}, default_value=0)
         """
+        if mapping_dict is None:
+            mapping_dict = {}
         assert isinstance(
             mapping_dict, dict
         ), "Provided mapping_dict is not a dictionary"

lm_eval/models/huggingface.py

@@ -603,8 +603,7 @@ def forward_batch(batch_size):
                     (batch_size, max_length), device=self.device
                 ).long()
             for _ in range(5):
-                out = F.log_softmax(self._model_call(test_batch, **call_kwargs), dim=-1)
-                out = out  # Identity process so that it passes pre-commit
+                out = F.log_softmax(self._model_call(test_batch, **call_kwargs), dim=-1)  # noqa: F841
 
             return batch_size
 

lm_eval/prompts/__init__.py

@@ -117,7 +117,7 @@ def apply(self, doc):
 
         # TODO need a way to process doc_to_choice
         if "doc_to_choice" in self.prompt_string:
-            raise "Not yet implemented to accept doc_to_choice"
+            raise Exception("Not yet implemented to accept doc_to_choice")
 
         text_string = utils.apply_template(doc_to_text, doc)
         target_string = utils.apply_template(doc_to_target, doc)

lm_eval/tasks/__init__.py

@@ -43,7 +43,7 @@ def register_configurable_task(config: Dict[str, str]) -> int:
     if "group" in config:
         if config["group"] == config["task"]:
             raise ValueError("task and group name cannot be the same")
-        elif type(config["group"]) == str:
+        elif isinstance(config["group"], str):
             group_name = [config["group"]]
         else:
             group_name = config["group"]
@@ -57,8 +57,8 @@ def register_configurable_task(config: Dict[str, str]) -> int:
 def register_configurable_group(config: Dict[str, str], yaml_path: str = None) -> int:
     group = config["group"]
     all_task_list = config["task"]
-    config_list = [task for task in all_task_list if type(task) != str]
-    task_list = [task for task in all_task_list if type(task) == str]
+    config_list = [task for task in all_task_list if not isinstance(task, str)]
+    task_list = [task for task in all_task_list if isinstance(task, str)]
 
     for task_config in config_list:
 
@@ -68,7 +68,7 @@ def register_configurable_group(config: Dict[str, str], yaml_path: str = None) -
             task_name = task_config["task"]
             if task_name in ALL_TASKS:
                 task_obj = TASK_REGISTRY[task_name]
-                if type(task_obj) == tuple:
+                if isinstance(task_obj, tuple):
                     _, task_obj = task_obj
 
                 if task_obj is not None:
@@ -166,10 +166,10 @@ def include_task_folder(task_dir: str, register_task: bool = True) -> None:
                     )
                     for config in all_configs:
                         if register_task:
-                            if type(config["task"]) == str:
+                            if isinstance(config["task"], str):
                                 register_configurable_task(config)
                         else:
-                            if type(config["task"]) == list:
+                            if isinstance(config["task"], list):
                                 register_configurable_group(config, yaml_path)
 
                 # Log this silently and show it only when
@@ -243,7 +243,7 @@ def get_task_dict(task_name_list: List[Union[str, Dict, Task]], **kwargs):
     task_name_from_config_dict = {}
     task_name_from_object_dict = {}
 
-    if type(task_name_list) != list:
+    if not isinstance(task_name_list, list):
         task_name_list = [task_name_list]
 
     for task_element in task_name_list:

lm_eval/tasks/bbh/_generate_configs.py

@@ -28,7 +28,7 @@ def parse_args():
 
     # get filename of base_yaml so we can `"include": ` it in our other YAMLs.
     base_yaml_name = os.path.split(args.base_yaml_path)[-1]
-    with open(args.base_yaml_path) as f:
+    with open(args.base_yaml_path, encoding="utf-8") as f:
         base_yaml = yaml.full_load(f)
 
     base_doc_to_text = "Q: {{input}}\nA:"
@@ -70,7 +70,7 @@ def parse_args():
 
         file_save_path = args.save_prefix_path + f"/{task}.yaml"
         utils.eval_logger.info(f"Saving yaml for subset {task} to {file_save_path}")
-        with open(file_save_path, "w") as yaml_file:
+        with open(file_save_path, "w", encoding="utf-8") as yaml_file:
             yaml.dump(
                 yaml_dict,
                 yaml_file,

lm_eval/tasks/belebele/_generate_configs.py

@@ -27,13 +27,13 @@ def parse_args():
 
     # get filename of base_yaml so we can `"include": ` it in our other YAMLs.
     base_yaml_name = os.path.split(args.base_yaml_path)[-1]
-    with open(args.base_yaml_path) as f:
+    with open(args.base_yaml_path, encoding="utf-8") as f:
         base_yaml = yaml.full_load(f)
 
     if args.cot_prompt_path is not None:
         import json
 
-        with open(args.cot_prompt_path) as f:
+        with open(args.cot_prompt_path, encoding="utf-8") as f:
             cot_file = json.load(f)
 
     def query():
@@ -54,7 +54,7 @@ def query():
 
         file_save_path = args.save_prefix_path + f"_{lang}.yaml"
         logging.info(f"Saving yaml for subset {lang} to {file_save_path}")
-        with open(file_save_path, "w") as yaml_file:
+        with open(file_save_path, "w", encoding="utf-8") as yaml_file:
             yaml.dump(
                 yaml_dict,
                 yaml_file,

lm_eval/tasks/bigbench/generate_tasks.py

@@ -181,7 +181,7 @@ def main() -> None:
         for task in all_subtasks:
             file_name = f"{task}.yaml"
             try:
-                with open(f"{path}/{file_name}", "w") as f:
+                with open(f"{path}/{file_name}", "w", encoding="utf-8") as f:
                     f.write("# Generated by utils.py\n")
                     yaml.dump(
                         {

lm_eval/tasks/blimp/generate_configs.py

@@ -75,7 +75,7 @@ def main() -> None:
     for task in all_subtasks:
         file_name = f"{task}.yaml"
         try:
-            with open(f"{file_name}", "w") as f:
+            with open(f"{file_name}", "w", encoding="utf-8") as f:
                 f.write("# Generated by utils.py\n")
                 yaml.dump(
                     {

lm_eval/tasks/ceval/_generate_configs.py

@@ -79,13 +79,13 @@ def parse_args():
 
     # get filename of base_yaml so we can `"include": ` it in our other YAMLs.
     base_yaml_name = os.path.split(args.base_yaml_path)[-1]
-    with open(args.base_yaml_path) as f:
+    with open(args.base_yaml_path, encoding="utf-8") as f:
         base_yaml = yaml.full_load(f)
 
     if args.cot_prompt_path is not None:
         import json
 
-        with open(args.cot_prompt_path) as f:
+        with open(args.cot_prompt_path, encoding="utf-8") as f:
             cot_file = json.load(f)
 
     for subject_eng, subject_zh in tqdm(SUBJECTS.items()):
@@ -107,7 +107,7 @@ def parse_args():
 
         file_save_path = args.save_prefix_path + f"_{subject_eng}.yaml"
         eval_logger.info(f"Saving yaml for subset {subject_eng} to {file_save_path}")
-        with open(file_save_path, "w") as yaml_file:
+        with open(file_save_path, "w", encoding="utf-8") as yaml_file:
             yaml.dump(
                 yaml_dict,
                 yaml_file,

lm_eval/tasks/cmmlu/_generate_configs.py

@@ -94,13 +94,13 @@ def parse_args():
 
     # get filename of base_yaml so we can `"include": ` it in our other YAMLs.
     base_yaml_name = os.path.split(args.base_yaml_path)[-1]
-    with open(args.base_yaml_path) as f:
+    with open(args.base_yaml_path, encoding="utf-8") as f:
         base_yaml = yaml.full_load(f)
 
     if args.cot_prompt_path is not None:
         import json
 
-        with open(args.cot_prompt_path) as f:
+        with open(args.cot_prompt_path, encoding="utf-8") as f:
             cot_file = json.load(f)
 
     for subject_eng, subject_zh in tqdm(SUBJECTS.items()):
@@ -122,7 +122,7 @@ def parse_args():
 
         file_save_path = args.save_prefix_path + f"_{subject_eng}.yaml"
         eval_logger.info(f"Saving yaml for subset {subject_eng} to {file_save_path}")
-        with open(file_save_path, "w") as yaml_file:
+        with open(file_save_path, "w", encoding="utf-8") as yaml_file:
             yaml.dump(
                 yaml_dict,
                 yaml_file,

lm_eval/tasks/code_x_glue/code-text/bleu.py

@@ -184,7 +184,7 @@ def splitPuncts(line):
 def computeMaps(predictions, goldfile):
     predictionMap: Dict[str, list] = {}
     goldMap: Dict[str, list] = {}
-    gf = open(goldfile, "r")
+    gf = open(goldfile, "r", encoding="utf-8")
 
     for row in predictions:
         cols = row.strip().split("\t")

lm_eval/tasks/csatqa/_generate_configs.py

@@ -25,7 +25,7 @@ def parse_args():
 
     # get filename of base_yaml so we can `"include": ` it in our other YAMLs.
     base_yaml_name = os.path.split(args.base_yaml_path)[-1]
-    with open(args.base_yaml_path) as f:
+    with open(args.base_yaml_path, encoding="utf-8") as f:
         base_yaml = yaml.full_load(f)
 
     for name in tqdm(SUBSETS):
@@ -39,7 +39,7 @@ def parse_args():
 
         file_save_path = args.save_prefix_path + f"_{name.lower()}.yaml"
         eval_logger.info(f"Saving yaml for subset {name} to {file_save_path}")
-        with open(file_save_path, "w") as yaml_file:
+        with open(file_save_path, "w", encoding="utf-8") as yaml_file:
             yaml.dump(
                 yaml_dict,
                 yaml_file,