Merge branch 'main' into group-agg-rework

.github/workflows/new_tasks.yml

@@ -20,13 +20,13 @@ jobs:
         with:
           fetch-depth: 2  # OR "2" -> To retrieve the preceding commit.
 
-      # Uses the tj-actions/changed-files@v37 action to check for changes.
+      # Uses the tj-actions/changed-files action to check for changes.
       # Outputs provided here: https://github.com/tj-actions/changed-files#outputs
       # The `files_yaml` input optionally takes a yaml string to specify filters,
       # and prepends the filter name to the standard output names.
       - name: Check task folders
         id: changed-tasks
-        uses: tj-actions/changed-files@v37.1.2
+        uses: tj-actions/changed-files@v44.5.2
         with:
           # tasks checks the tasks folder and api checks the api folder for changes
           files_yaml: |
@@ -56,7 +56,7 @@ jobs:
         if: steps.changed-tasks.outputs.tasks_any_modified == 'true' || steps.changed-tasks.outputs.api_any_modified == 'true'
         run: |
             python -m pip install --upgrade pip
-            pip install -e '.[dev]' --extra-index-url https://download.pytorch.org/whl/cpu
+            pip install -e '.[dev,ifeval]' --extra-index-url https://download.pytorch.org/whl/cpu
     #   Install optional git dependencies
     #       pip install bleurt@https://github.com/google-research/bleurt/archive/b610120347ef22b494b6d69b4316e303f5932516.zip#egg=bleurt
     #       if [ -f requirements.txt ]; then pip install -r requirements.txt; fi

.github/workflows/unit_tests.yml

@@ -32,7 +32,7 @@ jobs:
       env:
         SKIP: "no-commit-to-branch,mypy"
 
-      uses: pre-commit/action@v3.0.0
+      uses: pre-commit/action@v3.0.1
 #       # mypy turned off for now
 #    - name: Lint with mypy
 #      run: mypy . --ignore-missing-imports --check-untyped-defs --explicit-package-bases --warn-unreachable
@@ -56,12 +56,37 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install -e '.[dev,anthropic,sentencepiece,optimum,deepsparse,sparseml]' --extra-index-url https://download.pytorch.org/whl/cpu
+        pip install -e '.[dev,anthropic,sentencepiece]' --extra-index-url https://download.pytorch.org/whl/cpu
 #         Install optional git dependencies
 #                pip install bleurt@https://github.com/google-research/bleurt/archive/b610120347ef22b494b6d69b4316e303f5932516.zip#egg=bleurt
 #        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
     - name: Test with pytest
-      run: python -m pytest --showlocals -s -vv -n=auto
+      run: python -m pytest --showlocals -s -vv -n=auto --ignore=tests/models/test_neuralmagic.py --ignore=tests/models/test_openvino.py
+    - name: Archive artifacts
+      uses: actions/upload-artifact@v3
+      with:
+        name: output_results
+        path: |
+          test_logs/*
+  testmodels:
+    name: External LM Tests
+    runs-on: ubuntu-latest
+    timeout-minutes: 30
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v4
+    - name: Set up Python 3.8
+      uses: actions/setup-python@v5
+      with:
+        python-version: 3.8
+        cache: pip
+        cache-dependency-path: pyproject.toml
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -e '.[dev,optimum,deepsparse,sparseml]' --extra-index-url https://download.pytorch.org/whl/cpu
+    - name: Test with pytest
+      run: python -m pytest tests/models --showlocals -s -vv
     - name: Archive artifacts
       uses: actions/upload-artifact@v3
       with:

.pre-commit-config.yaml

@@ -10,6 +10,7 @@ repos:
       - id: check-case-conflict
       - id: check-json
       - id: check-merge-conflict
+        args: [--assume-in-merge]
       - id: check-symlinks
       - id: check-yaml
         args: ["--unsafe"]
@@ -28,8 +29,7 @@ repos:
       - id: mixed-line-ending
         args: [--fix=lf]
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    # Ruff version.
-    rev: v0.2.2
+    rev: v0.4.8
     hooks:
       # Run the linter.
       - id: ruff
@@ -38,7 +38,7 @@ repos:
         # Run the formatter.
       - id: ruff-format
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.6
+    rev: v2.3.0
     hooks:
       - id: codespell
         exclude: >
@@ -46,9 +46,9 @@ repos:
               .*\.json|ignore.txt|lm_eval/tasks/.*|.*yaml|.*\.ipynb
           )$
         args: [--check-filenames, --check-hidden, --ignore-words=ignore.txt]
-  - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.5.1
-    hooks:
-    - id: mypy
-      additional_dependencies: [".[sentencepiece,multilingual,promptsource,gptq]", "types-PyYAML", "types-requests"]
-      exclude: ^tests/.*$
+#  - repo: https://github.com/pre-commit/mirrors-mypy
+#    rev: v1.5.1
+#    hooks:
+#    - id: mypy
+#      additional_dependencies: [".[sentencepiece,multilingual,promptsource,gptq]", "types-PyYAML", "types-requests"]
+#      exclude: ^tests/.*$

README.md

@@ -7,6 +7,7 @@
 
 New updates and features include:
 
+- **New Open LLM Leaderboard tasks have been added ! You can find them under the [leaderboard](lm_eval/tasks/leaderboard/README.md) task group.**
 - Internal refactoring
 - Config-based task creation and configuration
 - Easier import and sharing of externally-defined task config YAMLs

docs/interface.md

@@ -102,12 +102,10 @@ results = lm_eval.simple_evaluate( # call simple_evaluate
 )
 ```
 
-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.
+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.
 
 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
@@ -147,7 +145,7 @@ 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 instantiated one itself, but this
+                 # will instantiate one itself, but this
                  # only includes the stock tasks so users
                  # will need to set this if including
                  # custom paths is required.

examples/visualize-wandb.ipynb

@@ -110,13 +110,15 @@
    "cell_type": "markdown",
    "id": "e974cabdbe70b667",
    "metadata": {},
-   "source": ""
+   "source": []
   },
   {
    "cell_type": "markdown",
    "id": "5178ca9445b844e4",
    "metadata": {},
-   "source": "W&B can also be initialized programmatically for use outside the CLI to parse and log the results."
+   "source": [
+    "W&B can also be initialized programmatically for use outside the CLI to parse and log the results."
+   ]
   },
   {
    "cell_type": "code",
@@ -126,7 +128,7 @@
    "outputs": [],
    "source": [
     "import lm_eval\n",
-    "from lm_eval.logging_utils import WandbLogger\n",
+    "from lm_eval.loggers import WandbLogger\n",
     "\n",
     "results = lm_eval.simple_evaluate(\n",
     "    model=\"hf\",\n",

lm_eval/__main__.py

@@ -237,7 +237,7 @@ def setup_parser() -> argparse.ArgumentParser:
         help=(
             "Set seed for python's random, numpy, torch, and fewshot sampling.\n"
             "Accepts a comma-separated list of 4 values for python's random, numpy, torch, and fewshot sampling seeds, "
-            "respectively, or a single integer to set the same seed for all three.\n"
+            "respectively, or a single integer to set the same seed for all four.\n"
             f"The values are either an integer or 'None' to not set the seed. Default is `{default_seed_string}` "
             "(for backward compatibility).\n"
             "E.g. `--seed 0,None,8,52` sets `random.seed(0)`, `torch.manual_seed(8)`, and fewshot sampling seed to 52. "
@@ -354,11 +354,17 @@ 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:
-        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']}"
+        eval_logger.info(
+            "Passed `--trust_remote_code`, setting environment variable `HF_DATASETS_TRUST_REMOTE_CODE=true`"
         )
+        # 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}")
 

lm_eval/api/metrics.py

 import logging
 import math
 import random
+import re
+import string
 from collections.abc import Iterable
 from typing import List
 
-import evaluate as hf_evaluate
 import numpy as np
 import sacrebleu
 import sklearn.metrics
@@ -166,7 +167,60 @@ def acc_mutual_info_fn(items):  # This is a passthrough function
     return items
 
 
-exact_match = hf_evaluate.load("exact_match")
+### the code used in the `exact_match_hf_evaluate` function is ported from
+### https://github.com/huggingface/evaluate/blob/main/metrics/exact_match/exact_match.py
+### which is under the apache license.
+
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+def exact_match_hf_evaluate(
+    predictions,
+    references,
+    regexes_to_ignore=None,
+    ignore_case=False,
+    ignore_punctuation=False,
+    ignore_numbers=False,
+):
+    if regexes_to_ignore is not None:
+        for s in regexes_to_ignore:
+            predictions = np.array([re.sub(s, "", x) for x in predictions])
+            references = np.array([re.sub(s, "", x) for x in references])
+    else:
+        predictions = np.asarray(predictions)
+        references = np.asarray(references)
+
+    if ignore_case:
+        predictions = np.char.lower(predictions)
+        references = np.char.lower(references)
+
+    if ignore_punctuation:
+        repl_table = string.punctuation.maketrans("", "", string.punctuation)
+        predictions = np.char.translate(predictions, table=repl_table)
+        references = np.char.translate(references, table=repl_table)
+
+    if ignore_numbers:
+        repl_table = string.digits.maketrans("", "", string.digits)
+        predictions = np.char.translate(predictions, table=repl_table)
+        references = np.char.translate(references, table=repl_table)
+
+    score_list = predictions == references
+
+    return {"exact_match": np.mean(score_list)}
+
+
+###
 
 
 @register_metric(
@@ -176,7 +230,7 @@ exact_match = hf_evaluate.load("exact_match")
     aggregation="mean",
 )
 def exact_match_fn(**kwargs):
-    return exact_match.compute(**kwargs)
+    return exact_match_hf_evaluate(**kwargs)
 
 
 @register_metric(

lm_eval/api/model.py

@@ -246,9 +246,10 @@ class CachingLM:
         # add hook to lm
         lm.set_cache_hook(self.get_cache_hook())
 
-    def __getattr__(self, attr):
+    def __getattr__(self, attr: str):
         lm_attr = getattr(self.lm, attr)
-        if not callable(lm_attr):
+        if attr not in ["loglikelihood", "loglikelihood_rolling", "generate_until"]:
+            eval_logger.debug(f"Passing through attribute '{attr}' to underlying LM")
             return lm_attr
 
         def fn(requests):

lm_eval/api/task.py

@@ -187,9 +187,9 @@ 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
@@ -212,9 +212,9 @@ 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:
@@ -351,9 +351,9 @@ 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,
@@ -490,15 +490,16 @@ class Task(abc.ABC):
     def build_all_requests(
         self,
         *,
-        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,
+        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 = "",
     ) -> None:
         """Build a set of Instances for a task, and store them in task.instances"""
 
@@ -513,7 +514,7 @@ class Task(abc.ABC):
             if system_instruction is not None
             else ""
         )
-        cache_key += f"-tokenizer{lm.tokenizer_name}" if apply_chat_template else ""
+        cache_key += f"-tokenizer{tokenizer_name}"
 
         cached_instances = load_from_cache(file_name=cache_key)
 
@@ -558,7 +559,7 @@ class Task(abc.ABC):
                 system_instruction,
                 apply_chat_template,
                 fewshot_as_multiturn,
-                lm,
+                chat_template,
             )
 
             # TODO: we should override self.config.repeats if doing greedy gen so users don't waste time+compute
@@ -1147,7 +1148,7 @@ class ConfigurableTask(Task):
         system_instruction: Optional[str] = None,
         apply_chat_template: bool = False,
         fewshot_as_multiturn: bool = False,
-        lm=None,
+        chat_template: Optional[Callable] = 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.
@@ -1162,8 +1163,8 @@ class ConfigurableTask(Task):
             Whether to apply the chat template to the fewshot context.
         :param fewshot_as_multiturn: bool
             Whether to provide the fewshot examples as a multiturn conversation or a single user turn.
-        :param lm:
-            Language model with definition of the tokenizer/function to use for applying the chat template.
+        :param chat_template: Callable
+            Chat template to be applied to the fewshot context.
         :returns: str
             The fewshot context.
         """
@@ -1210,7 +1211,7 @@ class ConfigurableTask(Task):
         example = self.doc_to_text(doc)
         if apply_chat_template:
             if self.multiple_input:
-                return lm.apply_chat_template(labeled_examples)
+                return chat_template(labeled_examples)
             if isinstance(example, str):
                 self.append_target_question(
                     labeled_examples, example, fewshot_as_multiturn
@@ -1222,7 +1223,7 @@ class ConfigurableTask(Task):
                 for ex in example:
                     chat = deepcopy(labeled_examples)
                     self.append_target_question(chat, ex, fewshot_as_multiturn)
-                    labeled_examples_list.append(lm.apply_chat_template(chat))
+                    labeled_examples_list.append(chat_template(chat))
                 return labeled_examples_list
             # if example is an integer, append the choice or convert to string
             elif isinstance(example, int):
@@ -1236,7 +1237,7 @@ class ConfigurableTask(Task):
                         labeled_examples, str(example), fewshot_as_multiturn
                     )
                 # return lm.apply_chat_template(labeled_examples)
-            return lm.apply_chat_template(labeled_examples)
+            return chat_template(labeled_examples)
         else:
             if self.multiple_input:
                 return labeled_examples

lm_eval/evaluator.py

@@ -24,7 +24,7 @@ from lm_eval.evaluator_utils import (
     run_task_tests,
 )
 from lm_eval.loggers import EvaluationTracker
-from lm_eval.loggers.utils import add_env_info, get_git_commit_hash
+from lm_eval.loggers.utils import add_env_info, add_tokenizer_info, get_git_commit_hash
 from lm_eval.tasks import (
     ConfigurableGroup,
     ConfigurableTask,
@@ -289,6 +289,7 @@ 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(
@@ -343,6 +344,7 @@ 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
@@ -415,7 +417,12 @@ def evaluate(
             system_instruction=system_instruction,
             apply_chat_template=apply_chat_template,
             fewshot_as_multiturn=fewshot_as_multiturn,
-            lm=lm,
+            chat_template=getattr(lm, "apply_chat_template")
+            if apply_chat_template
+            else None,
+            tokenizer_name=getattr(lm, "tokenizer_name", "")
+            if apply_chat_template
+            else "",
         )
         eval_logger.debug(
             f"Task: {task_output.task_name}; number of requests on this rank: {len(task.instances)}"

lm_eval/evaluator_utils.py

@@ -289,7 +289,7 @@ def prepare_print_tasks(
 
 def consolidate_results(
     eval_tasks: List[TaskOutput],
-) -> Tuple[dict, dict, dict, dict, dict]:
+) -> Tuple[dict, dict, dict, dict, dict, dict]:
     """
     @param eval_tasks: list(TaskOutput).
     @return: A tuple containing the consolidated results, samples, configs, versions, and num_fewshot.
@@ -306,6 +306,8 @@ def consolidate_results(
     - configs: A defaultdict with task names as keys and task configurations as values.
     - versions: A defaultdict with task names as keys and task versions as values.
     - num_fewshot: A defaultdict with task names as keys and number of few-shot samples as values.
+    - higher_is_better: A defaultdict with task names as keys and indicators of whether higher values are better
+    for each metric as values.
 
     The method then returns the consolidated results, samples, configs, versions, and num_fewshot as a tuple.
     """

lm_eval/filters/decontamination.py

@@ -4,7 +4,6 @@ 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

@@ -17,9 +17,15 @@ 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,
 )
 
 
@@ -42,6 +48,7 @@ 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
@@ -74,19 +81,19 @@ 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 = re.sub(
-            r"[\"<>:/\|\\?\*\[\]]+", "__", self.model_name
-        )
+        self.model_name_sanitized = sanitize_model_name(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
 
     def log_end_time(self) -> None:
         """Logs the end time of the evaluation and calculates the total evaluation time."""
@@ -255,7 +262,7 @@ class EvaluationTracker:
                 path.mkdir(parents=True, exist_ok=True)
 
                 file_results_samples = path.joinpath(
-                    f"samples_{task_name}_{self.date_id}.json"
+                    f"samples_{task_name}_{self.date_id}.jsonl"
                 )
 
                 for sample in samples:
@@ -319,23 +326,14 @@ 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 = [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]
+        results_files = get_results_filenames(files_in_repo)
+        sample_files = get_sample_results_filenames(files_in_repo)
 
         # Build a dictionary to store the latest evaluation datetime for:
         # - Each tested model and its aggregated results

lm_eval/loggers/utils.py

@@ -110,3 +110,34 @@ 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):
+        try:
+            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)
+        except Exception as err:
+            logger.debug(
+                f"Logging detailed tokenizer info failed with {err}, skipping..."
+            )
+        # 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_token = max_tokens
+        self.max_tokens = max_tokens
         self.tokenizer = self.client.get_tokenizer()
         self.kwargs = kwargs
 

lm_eval/models/huggingface.py

@@ -30,6 +30,7 @@ from lm_eval.api.registry import register_model
 from lm_eval.models.utils import (
     Collator,
     clear_torch_cache,
+    configure_pad_token,
     get_dtype,
     pad_and_concat,
     stop_sequences_criteria,
@@ -253,32 +254,10 @@ class HFLM(TemplateLM):
         self.logits_cache = logits_cache
         self.vocab_size = self.tokenizer.vocab_size
         # select (or create) a pad token to use
-        if self.tokenizer.pad_token:
-            pass
-        elif self.tokenizer.unk_token:
-            self.tokenizer.pad_token_id = self.tokenizer.unk_token_id
-        elif self.tokenizer.eos_token:
-            self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
-        else:
-            if getattr(self.config, "model_type", None) == "qwen":
-                # Qwen's trust_remote_code tokenizer does not allow for adding special tokens
-                self.tokenizer.pad_token = "<|endoftext|>"
-            elif (
-                self.tokenizer.__class__.__name__ == "RWKVWorldTokenizer"
-                or self.tokenizer.__class__.__name__ == "Rwkv5Tokenizer"
-            ):
-                # The RWKV world tokenizer, does not allow for adding special tokens / setting the pad token (which is set as 0)
-                # The additional tokenizer name check is needed, as there exists rwkv4 models with neox tokenizer
-                # ---
-                # Note that the world tokenizer class name, might change in the future for the final huggingface merge
-                # https://github.com/huggingface/transformers/pull/26963
-                assert self.tokenizer.pad_token_id == 0
-            else:
-                self.tokenizer.add_special_tokens({"pad_token": "<|pad|>"})
+        self.tokenizer = configure_pad_token(self.tokenizer, model_config=self.config)
 
-        # TODO: override this for Gemma
         self.add_bos_token = add_bos_token
-        if getattr(self.config, "model_type", None) == "gemma":
+        if getattr(self.config, "model_type", None) in ["gemma", "gemma2"]:
             self.add_bos_token = True
             eval_logger.info(
                 f"Model type is '{self.config.model_type}', a BOS token will be used as Gemma underperforms without it."

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 = self.add_bos_token
+        self.add_bos_token = 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
 
@@ -11,6 +11,7 @@ Example usage:
 
 Homepage: https://textsynth.com/index.html
 """
+
 import logging
 import os