batch commit

docs/interface.md

@@ -102,10 +102,12 @@ results = lm_eval.simple_evaluate( # call simple_evaluate
 )
 ```
 
-See the `simple_evaluate()` and `evaluate()` functions in [lm_eval/evaluator.py](../lm_eval/evaluator.py#:~:text=simple_evaluate) for a full description of all arguments available. All keyword arguments to simple_evaluate share the same role as the command-line flags described previously.
+See https://github.com/EleutherAI/lm-evaluation-harness/blob/365fcda9b85bbb6e0572d91976b8daf409164500/lm_eval/evaluator.py#L35 for a full description of all arguments available. All keyword arguments to simple_evaluate share the same role as the command-line flags described previously.
 
 Additionally, the `evaluate()` function offers the core evaluation functionality provided by the library, but without some of the special handling and simplification + abstraction provided by `simple_evaluate()`.
 
+See https://github.com/EleutherAI/lm-evaluation-harness/blob/365fcda9b85bbb6e0572d91976b8daf409164500/lm_eval/evaluator.py#L173 for more details.
+
 As a brief example usage of `evaluate()`:
 
 ```python
@@ -145,7 +147,7 @@ task_dict = lm_eval.tasks.get_task_dict(
     task_manager # A task manager that allows lm_eval to
                  # load the task during evaluation.
                  # If none is provided, `get_task_dict`
-                 # will instantiate one itself, but this
+                 # will instantiated one itself, but this
                  # only includes the stock tasks so users
                  # will need to set this if including
                  # custom paths is required.

lm_eval/__main__.py

@@ -5,6 +5,7 @@ import os
 import sys
 from functools import partial
 from typing import Union
+from accelerate import Accelerator
 
 from lm_eval import evaluator, utils
 from lm_eval.evaluator import request_caching_arg_to_dict
@@ -292,13 +293,6 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None:
             "If fewshot_as_multiturn is set, apply_chat_template must be set to True."
         )
 
-    if (
-        args.num_fewshot is None or args.num_fewshot == 0
-    ) and args.fewshot_as_multiturn:
-        raise ValueError(
-            "If fewshot_as_multiturn is set, num_fewshot must be greater than 0."
-        )
-
     if args.include_path is not None:
         eval_logger.info(f"Including path: {args.include_path}")
     task_manager = TaskManager(args.verbosity, include_path=args.include_path)
@@ -354,17 +348,11 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None:
 
     # Respect user's value passed in via CLI, otherwise default to True and add to comma-separated model args
     if args.trust_remote_code:
-        eval_logger.info(
-            "Passed `--trust_remote_code`, setting environment variable `HF_DATASETS_TRUST_REMOTE_CODE=true`"
+        os.environ["HF_DATASETS_TRUST_REMOTE_CODE"] = str(args.trust_remote_code)
+        args.model_args = (
+            args.model_args
+            + f",trust_remote_code={os.environ['HF_DATASETS_TRUST_REMOTE_CODE']}"
         )
-        # HACK: import datasets and override its HF_DATASETS_TRUST_REMOTE_CODE value internally,
-        # because it's already been determined based on the prior env var before launching our
-        # script--`datasets` gets imported by lm_eval internally before these lines can update the env.
-        import datasets
-
-        datasets.config.HF_DATASETS_TRUST_REMOTE_CODE = True
-
-        args.model_args = args.model_args + ",trust_remote_code=True"
 
     eval_logger.info(f"Selected Tasks: {task_names}")
 
@@ -400,7 +388,9 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None:
         **request_caching_args,
     )
 
-    if results is not None:
+    accelerator = Accelerator()
+
+    if results is not None and accelerator.is_main_process:
         if args.log_samples:
             samples = results.pop("samples")
         dumped = json.dumps(

lm_eval/api/task.py

@@ -67,9 +67,9 @@ class TaskConfig(dict):
     training_split: Optional[str] = None
     validation_split: Optional[str] = None
     test_split: Optional[str] = None
-    fewshot_split: Optional[str] = (
-        None  # TODO: assert that this not None if num_fewshot > 0. (?) assert if this is same split as one evaling (?)
-    )
+    fewshot_split: Optional[
+        str
+    ] = None  # TODO: assert that this not None if num_fewshot > 0. (?) assert if this is same split as one evaling (?)
     # formatting / prompting options.
     # see docs/advanced_task_guide.md for more info
     process_docs: Optional[Callable] = None
@@ -92,9 +92,9 @@ class TaskConfig(dict):
     filter_list: Optional[Union[str, list]] = None
     should_decontaminate: bool = False
     doc_to_decontamination_query: Optional[str] = None
-    metadata: Optional[dict] = (
-        None  # by default, not used in the code. allows for users to pass arbitrary info to tasks
-    )
+    metadata: Optional[
+        dict
+    ] = None  # by default, not used in the code. allows for users to pass arbitrary info to tasks
 
     def __post_init__(self) -> None:
         if self.generation_kwargs is not None:
@@ -229,9 +229,9 @@ class Task(abc.ABC):
         self._config: TaskConfig = TaskConfig({**config}) if config else TaskConfig()
 
         self._filters = [build_filter_ensemble("none", [["take_first", None]])]
-        self.fewshot_rnd: Optional[random.Random] = (
-            None  # purposely induce errors in case of improper usage
-        )
+        self.fewshot_rnd: Optional[
+            random.Random
+        ] = None  # purposely induce errors in case of improper usage
 
     def download(
         self,
@@ -368,16 +368,15 @@ class Task(abc.ABC):
     def build_all_requests(
         self,
         *,
-        limit: Union[int, None] = None,
-        rank: int = 0,
-        world_size: int = 1,
-        cache_requests: bool = False,
-        rewrite_requests_cache: bool = False,
-        system_instruction: Optional[str] = None,
-        apply_chat_template: bool = False,
-        fewshot_as_multiturn: bool = False,
-        chat_template: Optional[Callable] = None,
-        tokenizer_name: str = "",
+        limit=None,
+        rank=None,
+        world_size=None,
+        cache_requests=False,
+        rewrite_requests_cache=False,
+        system_instruction=None,
+        apply_chat_template=False,
+        fewshot_as_multiturn=False,
+        lm=None,
     ) -> None:
         """Build a set of Instances for a task, and store them in task.instances"""
 
@@ -392,7 +391,7 @@ class Task(abc.ABC):
             if system_instruction is not None
             else ""
         )
-        cache_key += f"-tokenizer{tokenizer_name}"
+        cache_key += f"-tokenizer{lm.tokenizer_name}" if apply_chat_template else ""
 
         cached_instances = load_from_cache(file_name=cache_key)
 
@@ -437,7 +436,7 @@ class Task(abc.ABC):
                 system_instruction,
                 apply_chat_template,
                 fewshot_as_multiturn,
-                chat_template,
+                lm,
             )
 
             # TODO: we should override self.config.repeats if doing greedy gen so users don't waste time+compute
@@ -445,6 +444,7 @@ class Task(abc.ABC):
                 doc=doc,
                 ctx=fewshot_ctx,
                 metadata=(self.config["task"], doc_id, self.config.repeats),
+                apply_chat_template=apply_chat_template
             )
 
             if not isinstance(inst, list):
@@ -987,6 +987,28 @@ class ConfigurableTask(Task):
             return super().fewshot_docs()
 
     @staticmethod
+    def append_target_question(
+        labeled_examples: List[Dict[str, str]],
+        question: str,
+        fewshot_as_multiturn: bool = False,
+    ) -> None:
+        """Adds a target question to the labeled examples list.
+        If fewshot_as_multiturn is True, or labeled_examples is empty, or the last entry is a system turn, appends the question as a new user entry.
+        Otherwise, it is appended to the last user entry, ensuring that the conversation alternates between the user and the assistant.
+        """
+        if not fewshot_as_multiturn:
+            # if no messages or last message is system, append as new user entry
+            if len(labeled_examples) == 0 or labeled_examples[-1]["role"] == "system":
+                labeled_examples.append({"role": "user", "content": question})
+            # if last message is user, append to it to avoid two user messages in a row
+            else:
+                labeled_examples[-1]["content"] += question
+        else:
+            return self.sampler.fewshot_delimiter + "".join(
+                f"{s['role']}: {s['content']}" + self.sampler.fewshot_delimiter
+                for s in chat_history
+            )
+    @staticmethod
     def append_target_question(
         labeled_examples: List[Dict[str, str]],
         question: str,
@@ -1015,7 +1037,7 @@ class ConfigurableTask(Task):
         system_instruction: Optional[str] = None,
         apply_chat_template: bool = False,
         fewshot_as_multiturn: bool = False,
-        chat_template: Optional[Callable] = None,
+        lm=None,
     ) -> str:
         """Returns a fewshot context string that is made up of a prepended description
         (if provided), the `num_fewshot` number of examples, and an appended prompt example.
@@ -1028,10 +1050,12 @@ class ConfigurableTask(Task):
             System instruction to be applied to the prompt.
         :param apply_chat_template: bool
             Whether to apply the chat template to the fewshot context.
+        :param tokenizer:
+            The tokenizer to use for applying the chat template.
         :param fewshot_as_multiturn: bool
             Whether to provide the fewshot examples as a multiturn conversation or a single user turn.
-        :param chat_template: Callable
-            Chat template to be applied to the fewshot context.
+        :param lm:
+            Language model with definition of the tokenizer/function to use for applying the chat template.
         :returns: str
             The fewshot context.
         """
@@ -1078,7 +1102,7 @@ class ConfigurableTask(Task):
         example = self.doc_to_text(doc)
         if apply_chat_template:
             if self.multiple_input:
-                return chat_template(labeled_examples)
+                return lm.apply_chat_template(labeled_examples)
             if isinstance(example, str):
                 self.append_target_question(
                     labeled_examples, example, fewshot_as_multiturn
@@ -1090,7 +1114,7 @@ class ConfigurableTask(Task):
                 for ex in example:
                     chat = deepcopy(labeled_examples)
                     self.append_target_question(chat, ex, fewshot_as_multiturn)
-                    labeled_examples_list.append(chat_template(chat))
+                    labeled_examples_list.append(lm.apply_chat_template(chat))
                 return labeled_examples_list
             # if example is an integer, append the choice or convert to string
             elif isinstance(example, int):
@@ -1104,7 +1128,7 @@ class ConfigurableTask(Task):
                         labeled_examples, str(example), fewshot_as_multiturn
                     )
                 # return lm.apply_chat_template(labeled_examples)
-            return chat_template(labeled_examples)
+            return lm.apply_chat_template(labeled_examples)
         else:
             if self.multiple_input:
                 return labeled_examples
@@ -1271,6 +1295,8 @@ class ConfigurableTask(Task):
         elif self.OUTPUT_TYPE == "multiple_choice":
             choices = self.doc_to_choice(doc)
             target_delimiter = self.config.target_delimiter
+            if kwargs.get("apply_chat_template", False) is True:
+                target_delimiter = ""
             if self.multiple_input:
                 # If there are multiple inputs, choices are placed in the ctx
                 cont = self.doc_to_target(doc)
@@ -1280,6 +1306,7 @@ class ConfigurableTask(Task):
             else:
                 # Otherwise they are placed in the continuation
                 arguments = [(ctx, f"{target_delimiter}{cont}") for cont in choices]
+            kwargs.pop("apply_chat_template")
 
             request_list = [
                 Instance(
@@ -1316,6 +1343,7 @@ class ConfigurableTask(Task):
         elif self.OUTPUT_TYPE == "generate_until":
             arguments = (ctx, deepcopy(self.config.generation_kwargs))
 
+        kwargs.pop("apply_chat_template")
         return Instance(
             request_type=self.OUTPUT_TYPE, doc=doc, arguments=arguments, idx=0, **kwargs
         )

lm_eval/evaluator.py

@@ -22,7 +22,7 @@ from lm_eval.evaluator_utils import (
     run_task_tests,
 )
 from lm_eval.loggers import EvaluationTracker
-from lm_eval.loggers.utils import add_env_info, add_tokenizer_info, get_git_commit_hash
+from lm_eval.loggers.utils import add_env_info, get_git_commit_hash
 from lm_eval.tasks import TaskManager, get_task_dict
 from lm_eval.utils import (
     eval_logger,
@@ -271,7 +271,6 @@ def simple_evaluate(
             model_args=model_args,
             system_instruction=system_instruction,
             chat_template=lm.chat_template if apply_chat_template else None,
-            fewshot_as_multiturn=fewshot_as_multiturn,
         )
 
     results = evaluate(
@@ -326,7 +325,6 @@ def simple_evaluate(
         results["git_hash"] = get_git_commit_hash()
         results["date"] = start_date
         add_env_info(results)  # additional environment info to results
-        add_tokenizer_info(results, lm)  # additional info about tokenizer
         return results
     else:
         return None
@@ -399,12 +397,7 @@ def evaluate(
             system_instruction=system_instruction,
             apply_chat_template=apply_chat_template,
             fewshot_as_multiturn=fewshot_as_multiturn,
-            chat_template=getattr(lm, "apply_chat_template")
-            if apply_chat_template
-            else None,
-            tokenizer_name=getattr(lm, "tokenizer_name", "")
-            if apply_chat_template
-            else "",
+            lm=lm,
         )
         eval_logger.debug(
             f"Task: {task_output.task_name}; number of requests on this rank: {len(task.instances)}"
@@ -614,16 +607,16 @@ def evaluate(
                     ]
 
                     # compute group's pooled metric and stderr
-                    results[group][metric] = (
-                        lm_eval.api.metrics.aggregate_subtask_metrics(metrics, sizes)
-                    )
+                    results[group][
+                        metric
+                    ] = lm_eval.api.metrics.aggregate_subtask_metrics(metrics, sizes)
                     # TODO: calculate grouped metric using aggregation fn
                     if "N/A" in stderrs:
                         results[group][stderr] = "N/A"
                     else:
-                        results[group][stderr] = (
-                            lm_eval.api.metrics.pooled_sample_stderr(stderrs, sizes)
-                        )
+                        results[group][
+                            stderr
+                        ] = lm_eval.api.metrics.pooled_sample_stderr(stderrs, sizes)
                         # TODO: allow GroupConfigs to choose which variance formula is used, for back-compatibility
                         # To use the old (likely incorrect) variance formula, comment out the above and uncomment this line:
                         # results[group][stderr] = lm_eval.api.metrics.combined_sample_stderr(stderrs, sizes, metrics=metrics)

lm_eval/evaluator_utils.py

@@ -275,9 +275,9 @@ def consolidate_results(
                 metric_key
             ]
             results[task_output.task_name]["samples"] = task_output.sample_len
-            results[task_output.task_name][f"{metric}_stderr,{filter_key}"] = (
-                task_output.agg_metrics[f"{metric}_stderr,{filter_key}"]
-            )
+            results[task_output.task_name][
+                f"{metric}_stderr,{filter_key}"
+            ] = task_output.agg_metrics[f"{metric}_stderr,{filter_key}"]
     return results, samples, configs, versions, num_fewshot, higher_is_better
 
 

lm_eval/filters/decontamination.py

@@ -4,6 +4,7 @@ from lm_eval.api.registry import register_filter
 
 @register_filter("decontaminate")
 class DecontaminationFilter(Filter):
+
     """
     A filter which evaluates
     """

lm_eval/filters/extraction.py

@@ -62,11 +62,8 @@ class WhitespaceFilter(Filter):
         def filter_set(inst):
             filtered_resp = []
             for resp in inst:
-                if resp.startswith(" "):
-                    resp = resp[1:]
-
+                resp = resp.lstrip()
                 filtered_resp.append(resp)
-
             return filtered_resp
 
         filtered_resps = [filter_set(resp) for resp in resps]

lm_eval/loggers/evaluation_tracker.py

 import json
+import os
 import re
 import time
 from collections import defaultdict
 from dataclasses import asdict, dataclass
 from datetime import datetime
 from pathlib import Path
+from huggingface_hub.utils import build_hf_headers, get_session, hf_raise_for_status
 
 from datasets import load_dataset
 from datasets.utils.metadata import MetadataConfigs
@@ -17,15 +19,9 @@ from huggingface_hub import (
 
 from lm_eval.utils import (
     eval_logger,
-    get_file_datetime,
-    get_file_task_name,
-    get_results_filenames,
-    get_sample_results_filenames,
     handle_non_serializable,
     hash_string,
     sanitize_list,
-    sanitize_model_name,
-    sanitize_task_name,
 )
 
 
@@ -48,7 +44,6 @@ class GeneralConfigTracker:
     model_name_sanitized: str = None
     system_instruction: str = None
     system_instruction_sha: str = None
-    fewshot_as_multiturn: bool = None
     chat_template: str = None
     chat_template_sha: str = None
     start_time: float = None
@@ -81,19 +76,24 @@ class GeneralConfigTracker:
         model_args: str,
         system_instruction: str,
         chat_template: str,
-        fewshot_as_multiturn: bool,
     ) -> None:
         """Logs model parameters and job ID."""
         self.model_source = model_source
         self.model_name = GeneralConfigTracker._get_model_name(model_args)
-        self.model_name_sanitized = sanitize_model_name(self.model_name)
+        self.model_name_sanitized = re.sub(
+            r"[\"<>:/\|\\?\*\[\]]+", "__", self.model_name
+        )
         self.system_instruction = system_instruction
         self.system_instruction_sha = (
             hash_string(system_instruction) if system_instruction else None
         )
         self.chat_template = chat_template
-        self.chat_template_sha = hash_string(chat_template) if chat_template else None
-        self.fewshot_as_multiturn = fewshot_as_multiturn
+        self.chat_template_sha = None
+        if chat_template:
+            if not isinstance(chat_template, str):
+                self.chat_template_sha = hash_string(str(chat_template))
+            else:
+                self.chat_template_sha = hash_string(chat_template)
 
     def log_end_time(self) -> None:
         """Logs the end time of the evaluation and calculates the total evaluation time."""
@@ -210,21 +210,17 @@ class EvaluationTracker:
                 file_results_aggregated.open("w", encoding="utf-8").write(dumped)
 
                 if self.api and self.push_results_to_hub:
-                    repo_id = (
-                        self.hub_results_repo
-                        if self.public_repo
-                        else self.hub_results_repo_private
-                    )
+                    repo_id = "open-llm-leaderboard/results_v2"
                     self.api.create_repo(
                         repo_id=repo_id,
                         repo_type="dataset",
                         private=not self.public_repo,
                         exist_ok=True,
                     )
-                    self.api.upload_folder(
+                    self.api.upload_file(
                         repo_id=repo_id,
-                        folder_path=str(path),
-                        path_in_repo=self.general_config_tracker.model_name_sanitized,
+                        path_or_fileobj=str(path.joinpath(f"results_{self.date_id}.json")),
+                        path_in_repo=os.path.join(self.general_config_tracker.model_name, f"results_{self.date_id}.json"),
                         repo_type="dataset",
                         commit_message=f"Adding aggregated results for {self.general_config_tracker.model_name}",
                     )
@@ -262,7 +258,7 @@ class EvaluationTracker:
                 path.mkdir(parents=True, exist_ok=True)
 
                 file_results_samples = path.joinpath(
-                    f"samples_{task_name}_{self.date_id}.jsonl"
+                    f"samples_{task_name}_{self.date_id}.json"
                 )
 
                 for sample in samples:
@@ -278,6 +274,7 @@ class EvaluationTracker:
                     sample["resps"] = sanitize_list(sample["resps"])
                     sample["filtered_resps"] = sanitize_list(sample["filtered_resps"])
                     sample["arguments"] = arguments
+                    sample["target"] = str(sample["target"])
 
                     sample_dump = (
                         json.dumps(
@@ -303,6 +300,13 @@ class EvaluationTracker:
                         private=not self.public_repo,
                         exist_ok=True,
                     )
+                    headers = build_hf_headers()
+                    r = get_session().put(
+                        url=f"https://huggingface.co/api/datasets/{repo_id}/settings",
+                        headers=headers,
+                        json={"gated": "auto"},
+                    )
+                    hf_raise_for_status(r)
                     self.api.upload_folder(
                         repo_id=repo_id,
                         folder_path=str(path),
@@ -326,14 +330,23 @@ class EvaluationTracker:
         Creates a metadata card for the evaluation results dataset and pushes it to the Hugging Face hub.
         """
 
+        def get_file_task_name(filename: str) -> str:
+            return filename[filename.find("_") + 1 : filename.rfind("_")]
+
+        def get_file_datetime(filename: str) -> str:
+            return filename[filename.rfind("_") + 1 :].replace(".json", "")
+
+        def sanitize_task_name(task_name: str) -> str:
+            return re.sub(r"\W", "_", task_name)
+
         eval_logger.info("Recreating metadata card")
         repo_id = (
             self.hub_results_repo if self.public_repo else self.hub_results_repo_private
         )
 
         files_in_repo = self.api.list_repo_files(repo_id=repo_id, repo_type="dataset")
-        results_files = get_results_filenames(files_in_repo)
-        sample_files = get_sample_results_filenames(files_in_repo)
+        results_files = [f for f in files_in_repo if "/results_" in f and ".json" in f]
+        sample_files = [f for f in files_in_repo if "/samples_" in f and ".json" in f]
 
         # Build a dictionary to store the latest evaluation datetime for:
         # - Each tested model and its aggregated results
@@ -360,7 +373,10 @@ class EvaluationTracker:
                 results_datetime,
             )
             latest_task_results_datetime[samples_key] = latest_datetime
-            latest_task_results_datetime[results_key] = latest_datetime
+            latest_task_results_datetime[results_key] = max(
+                latest_task_results_datetime[results_key],
+                latest_datetime,
+            )
 
         # Create metadata card
         card_metadata = MetadataConfigs()
@@ -377,14 +393,15 @@ class EvaluationTracker:
             sanitized_last_eval_date_results = re.sub(
                 r"[^\w\.]", "_", latest_task_results_datetime[config_name]
             )
-            # Ensure that all results files are listed in the metadata card
-            current_results = card_metadata.get(config_name, {"data_files": []})
-            current_results["data_files"].append(
-                {"split": eval_date_sanitized, "path": [str(results_filename)]}
-            )
-            card_metadata[config_name] = current_results
-            # If the results file is the newest, update the "latest" field in the metadata card
+
             if eval_date_sanitized == sanitized_last_eval_date_results:
+                # Ensure that all results files are listed in the metadata card
+                current_results = card_metadata.get(config_name, {"data_files": []})
+                current_results["data_files"].append(
+                    {"split": eval_date_sanitized, "path": [str(results_filename)]}
+                )
+                card_metadata[config_name] = current_results
+                # If the results file is the newest, update the "latest" field in the metadata card
                 card_metadata[config_name]["data_files"].append(
                     {"split": "latest", "path": [str(results_filename)]}
                 )
@@ -403,64 +420,65 @@ class EvaluationTracker:
             sanitized_last_eval_date_results = re.sub(
                 r"[^\w\.]", "_", latest_task_results_datetime[config_name]
             )
-            # Ensure that all sample results files are listed in the metadata card
-            current_details_for_task = card_metadata.get(
-                config_name, {"data_files": []}
-            )
-            current_details_for_task["data_files"].append(
-                {"split": eval_date_sanitized, "path": [str(results_filename)]}
-            )
-            card_metadata[config_name] = current_details_for_task
-            # If the samples results file is the newest, update the "latest" field in the metadata card
             if eval_date_sanitized == sanitized_last_eval_date_results:
+                print(f"adding {config_name} for {eval_date_sanitized}")
+                # Ensure that all sample results files are listed in the metadata card
+                current_details_for_task = card_metadata.get(
+                    config_name, {"data_files": []}
+                )
+                current_details_for_task["data_files"].append(
+                    {"split": eval_date_sanitized, "path": [str(results_filename)]}
+                )
+                card_metadata[config_name] = current_details_for_task
+                # If the samples results file is the newest, update the "latest" field in the metadata card
                 card_metadata[config_name]["data_files"].append(
                     {"split": "latest", "path": [str(results_filename)]}
                 )
 
-            # Special case for MMLU with a single split covering it all
-            # We add another config with all MMLU splits results together for easy inspection
-            SPECIAL_TASKS = ["mmlu", "gpqa", "minerva_math"]
-            for special_task in SPECIAL_TASKS:
-                if special_task in config_name:
-                    special_task = f"{model_name}__{special_task}"
-                    former_entry = card_metadata.get(special_task, {"data_files": []})
-
-                    former_split = [
-                        (i, entry)
-                        for i, entry in enumerate(former_entry["data_files"])
-                        if entry.get("split", None) == eval_date_sanitized
-                    ]
-
-                    if len(former_split) == 0:
-                        former_entry["data_files"].append(
-                            {
-                                "split": eval_date_sanitized,
-                                "path": [str(results_filename)],
-                            }
-                        )
-                    else:
-                        split_index, _ = former_split[0]
-                        former_entry["data_files"][split_index]["path"].append(
-                            str(results_filename)
-                        )
+                # Special case for MMLU with a single split covering it all
+                # We add another config with all MMLU splits results together for easy inspection
+                SPECIAL_TASKS = ["leaderboard_gpqa", "leaderboard_math", "leaderboard_bbh", "leaderboard_musr"]
+                for special_task in SPECIAL_TASKS:
+                    if special_task in config_name:
+                        special_task = f"{model_name}__{special_task}"
+                        former_entry = card_metadata.get(special_task, {"data_files": []})
 
-                    if eval_date_sanitized == sanitized_last_eval_date_results:
-                        latest_split = [
+                        former_split = [
                             (i, entry)
                             for i, entry in enumerate(former_entry["data_files"])
-                            if entry.get("split", None) == "latest"
+                            if entry.get("split", None) == eval_date_sanitized
                         ]
-                        if len(latest_split) == 0:
+
+                        if len(former_split) == 0:
                             former_entry["data_files"].append(
-                                {"split": "latest", "path": [str(results_filename)]}
+                                {
+                                    "split": eval_date_sanitized,
+                                    "path": [str(results_filename)],
+                                }
                             )
                         else:
-                            latest_index, _ = latest_split[0]
-                            former_entry["data_files"][latest_index]["path"].append(
+                            split_index, _ = former_split[0]
+                            former_entry["data_files"][split_index]["path"].append(
                                 str(results_filename)
                             )
 
-                    card_metadata[special_task] = former_entry
+                        if eval_date_sanitized == sanitized_last_eval_date_results:
+                            latest_split = [
+                                (i, entry)
+                                for i, entry in enumerate(former_entry["data_files"])
+                                if entry.get("split", None) == "latest"
+                            ]
+                            if len(latest_split) == 0:
+                                former_entry["data_files"].append(
+                                    {"split": "latest", "path": [str(results_filename)]}
+                                )
+                            else:
+                                latest_index, _ = latest_split[0]
+                                former_entry["data_files"][latest_index]["path"].append(
+                                    str(results_filename)
+                                )
+
+                        card_metadata[special_task] = former_entry
 
         # Get latest results and extract info to update metadata card examples
         latest_datetime = max(latest_task_results_datetime.values())

lm_eval/loggers/utils.py

@@ -110,20 +110,3 @@ def add_env_info(storage: Dict[str, Any]):
         "upper_git_hash": upper_dir_commit,  # in case this repo is submodule
     }
     storage.update(added_info)
-
-
-def add_tokenizer_info(storage: Dict[str, Any], lm):
-    if getattr(lm, "tokenizer", False):
-        tokenizer_info = {
-            "tokenizer_pad_token": [lm.tokenizer.pad_token, lm.tokenizer.pad_token_id],
-            "tokenizer_eos_token": [lm.tokenizer.eos_token, lm.tokenizer.eos_token_id],
-            "tokenizer_bos_token": [lm.tokenizer.bos_token, lm.tokenizer.bos_token_id],
-            "eot_token_id": getattr(lm, "eot_token_id", None),
-            "max_length": getattr(lm, "max_length", None),
-        }
-        storage.update(tokenizer_info)
-    # seems gguf and textsynth do not have tokenizer
-    else:
-        logger.debug(
-            "LM does not have a 'tokenizer' attribute, not logging tokenizer metadata to results."
-        )

lm_eval/models/anthropic_llms.py

@@ -307,7 +307,7 @@ please install anthropic via `pip install 'lm-eval[anthropic]'` or `pip install
         # defaults to os.environ.get("ANTHROPIC_API_KEY")
         self.client = anthropic.Anthropic()
         self.temperature = temperature
-        self.max_tokens = max_tokens
+        self.max_token = max_tokens
         self.tokenizer = self.client.get_tokenizer()
         self.kwargs = kwargs
 

lm_eval/models/neuron_optimum.py

@@ -288,7 +288,7 @@ class NEURON_HF(TemplateLM):
 
         self.vocab_size = self.tokenizer.vocab_size
         self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
-        self.add_bos_token = add_bos_token
+        self.add_bos_token = self.add_bos_token
 
         self._max_length = max_length
 

lm_eval/models/textsynth.py

-"""TextSynth API
+""" TextSynth API
 Implementation provided by Fabrice Bellard:
     https://github.com/EleutherAI/lm-evaluation-harness/issues/295
 
@@ -11,7 +11,6 @@ Example usage:
 
 Homepage: https://textsynth.com/index.html
 """
-
 import logging
 import os
 

lm_eval/models/utils.py

@@ -389,7 +389,7 @@ class Collator:
             self._arr_with_indices, fn=self._group_fn, group_by="contexts"
         )
 
-    def get_batched(self, n: int = 1, batch_fn: Optional[Callable] = None) -> Iterator:
+    def get_batched(self, n: int = 1, batch_fn: Optional[Callable] = None, reset_batch_fn: Optional[Callable] = None, accelerator=None) -> Iterator:
         """
         Generates and yields batches from the reordered array. The method of grouping and batching
         depends on the parameter `group_by`.
@@ -402,6 +402,8 @@ class Collator:
         - n (int): The size of each batch. Defaults to 1.
         - batch_fn ([Callable[[int, Iterable], int]] | None): A function to determine the size of
           each batch. Optional, defaults to None.
+        - reset_batch_fn ([Callable[[int, Iterable], int]] | None): A function to reset the scheduler of 
+          the batch_fn, if present, when we change group in generative mode.
 
         Returns:
         Iterator: An iterator over batches of reordered elements grouped as per the `group_by`
@@ -411,10 +413,9 @@ class Collator:
         List of batched elements according to the `group_by` attribute.
         """
         if self._group_by == "gen_kwargs":
-            for (
-                key,
-                values,
-            ) in self._arr_with_indices.items():  # type: ignore
+            for key, values in self._arr_with_indices.items():  # type: ignore
+                if reset_batch_fn is not None: # with each group change, we must recompute the batch size, so we restart the scheduler
+                    reset_batch_fn()
                 values = self._reorder(values)
                 batch = self.get_chunks(values, n=n, fn=batch_fn)
                 yield from batch

lm_eval/models/vllm_causallms.py

@@ -119,12 +119,6 @@ class VLLM(TemplateLM):
             tokenizer_revision=tokenizer_revision,
         )
         self.add_bos_token = add_bos_token
-        if "gemma" in pretrained.lower():
-            self.add_bos_token = True
-            eval_logger.info(
-                "Found 'gemma' in model name, a BOS token will be used as Gemma underperforms without it."
-            )
-
         self.custom_prefix_token_id = prefix_token_id
         if prefix_token_id is not None:
             eval_logger.info(
@@ -499,10 +493,7 @@ class VLLM(TemplateLM):
     def modify_gen_kwargs(kwargs: dict) -> dict:
         # sampling_params
         do_sample = kwargs.pop("do_sample", None)
-        if do_sample is False and "temperature" not in kwargs:
-            eval_logger.debug(
-                "Got `do_sample=False` and no temperature value, setting VLLM temperature to 0.0 ..."
-            )
+        if do_sample is False or "temperature" not in kwargs:
             kwargs["temperature"] = 0.0
         # hf defaults
         kwargs["skip_special_tokens"] = kwargs.get("skip_special_tokens", False)

lm_eval/tasks/README.md

@@ -10,8 +10,8 @@
 | [aclue](aclue/README.md) | Tasks focusing on ancient Chinese language understanding and cultural aspects. | Ancient Chinese |
 | [aexams](aexams/README.md) | Tasks in Arabic related to various academic exams covering a range of subjects. | Arabic |
 | [agieval](agieval/README.md) | Tasks involving historical data or questions related to history and historical texts. | English, Chinese |
+| [ammlu](ammlu/README.md) | Arabic version of MMLU. | Arabic |
 | [anli](anli/README.md) | Adversarial natural language inference tasks designed to test model robustness. | English |
-| [arabicmmlu](arabicmmlu/README.md) | Localized Arabic version of MMLU with multiple-choice questions from 40 subjects. | Arabic |
 | [arc](arc/README.md) | Tasks involving complex reasoning over a diverse set of questions.  | English |
 | [arithmetic](arithmetic/README.md) | Tasks involving numerical computations and arithmetic reasoning. | English |
 | [asdiv](asdiv/README.md) | Tasks involving arithmetic and mathematical reasoning challenges. | English |
@@ -20,13 +20,11 @@
 | [bbh](bbh/README.md) | Tasks focused on deep semantic understanding through hypothesization and reasoning. | English, German |
 | [belebele](belebele/README.md) | Language understanding tasks in a variety of languages and scripts. | Multiple (122 languages) |
 | benchmarks | General benchmarking tasks that test a wide range of language understanding capabilities. | |
-| [bertaqa](bertaqa/README.md) | Local Basque cultural trivia QA tests in English and Basque languages. | English, Basque, Basque (MT) |
 | [bigbench](bigbench/README.md) | Broad tasks from the BIG-bench benchmark designed to push the boundaries of large models. | Multiple |
 | [blimp](blimp/README.md) | Tasks testing grammatical phenomena to evaluate language model's linguistic capabilities. | English |
 | [ceval](ceval/README.md) | Tasks that evaluate language understanding and reasoning in an educational context. | Chinese |
 | [cmmlu](cmmlu/README.md) | Multi-subject multiple choice question tasks for comprehensive academic assessment. | Chinese |
 | code_x_glue | Tasks that involve understanding and generating code across multiple programming languages. | Go, Java, JS, PHP, Python, Ruby |
-| [commonsense_qa](commmonsense_qa/README.md) | CommonsenseQA, a multiple-choice QA dataset for measuring commonsense knowledge. | English |
 | [copal_id](copal_id/README.md) | Indonesian causal commonsense reasoning dataset that captures local nuances. | Indonesian |
 | [coqa](coqa/README.md) | Conversational question answering tasks to test dialog understanding. | English |
 | [crows_pairs](crows_pairs/README.md) | Tasks designed to test model biases in various sociodemographic groups. | English, French |
@@ -73,7 +71,6 @@
 | okapi/mmlu_multilingual | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (34 languages) |
 | [okapi/truthfulqa_multilingual](okapi/truthfulqa_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (31 languages) |
 | [openbookqa](openbookqa/README.md) | Open-book question answering tasks that require external knowledge and reasoning. | English |
-| [paloma](paloma/README.md) | Paloma is a comprehensive benchmark designed to evaluate open language models across a wide range of domains, ranging from niche artist communities to mental health forums on Reddit. | English |
 | [paws-x](paws-x/README.md) | Paraphrase Adversaries from Word Scrambling, focusing on cross-lingual capabilities. | English, French, Spanish, German, Chinese, Japanese, Korean |
 | [pile](pile/README.md) | Open source language modelling data set that consists of 22 smaller, high-quality datasets. | English |
 | [pile_10k](pile_10k/README.md) | The first 10K elements of The Pile, useful for debugging models trained on it. | English |

lm_eval/tasks/__init__.py

@@ -14,43 +14,27 @@ class TaskManager:
 
     """
 
-    def __init__(
-        self,
-        verbosity="INFO",
-        include_path: Optional[Union[str, List]] = None,
-        include_defaults: bool = True,
-    ) -> None:
+    def __init__(self, verbosity="INFO", include_path: Optional[str] = None) -> None:
         self.verbosity = verbosity
         self.include_path = include_path
         self.logger = utils.eval_logger
         self.logger.setLevel(getattr(logging, f"{verbosity}"))
 
-        self._task_index = self.initialize_tasks(
-            include_path=include_path, include_defaults=include_defaults
-        )
+        self._task_index = self.initialize_tasks(include_path=include_path)
         self._all_tasks = sorted(list(self._task_index.keys()))
 
         self.task_group_map = collections.defaultdict(list)
 
-    def initialize_tasks(
-        self,
-        include_path: Optional[Union[str, List]] = None,
-        include_defaults: bool = True,
-    ):
+    def initialize_tasks(self, include_path: Optional[str] = None):
         """Creates a dictionary of tasks index.
 
-        :param include_path: Union[str, List] = None
-            An additional path to be searched for tasks recursively.
-            Can provide more than one such path as a list.
-        :param include_defaults: bool = True
-            If set to false, default tasks (those in lm_eval/tasks/) are not indexed.
+        :param include_path: str = None
+            An additional path to be searched for tasks
+
         :return
             Dictionary of task names as key and task metadata
         """
-        if include_defaults:
-            all_paths = [os.path.dirname(os.path.abspath(__file__)) + "/"]
-        else:
-            all_paths = []
+        all_paths = [os.path.dirname(os.path.abspath(__file__)) + "/"]
         if include_path is not None:
             if isinstance(include_path, str):
                 include_path = [include_path]
@@ -312,13 +296,8 @@ class TaskManager:
         :return
             Dictionary of task names as key and task metadata
         """
-        ignore_dirs = [
-            "__pycache__",
-            ".ipynb_checkpoints",
-        ]
         tasks_and_groups = collections.defaultdict()
-        for root, dirs, file_list in os.walk(task_dir):
-            dirs[:] = [d for d in dirs if d not in ignore_dirs]
+        for root, _, file_list in os.walk(task_dir):
             for f in file_list:
                 if f.endswith(".yaml"):
                     yaml_path = os.path.join(root, f)

lm_eval/tasks/aclue/_generate_configs.py

 """
 Take in a YAML, and output all other splits with this YAML
 """
-
 import argparse
 import os
 

lm_eval/tasks/bbh/_generate_configs.py

 """
 Take in a YAML, and output all other splits with this YAML
 """
-
 import argparse
 import os
 import re

lm_eval/tasks/belebele/_generate_configs.py

 """
 Take in a YAML, and output all other splits with this YAML
 """
-
 import argparse
 import os