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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 datasets import load_dataset
from datasets.utils.metadata import MetadataConfigs
from huggingface_hub import (
DatasetCard,
DatasetCardData,
HfApi,
hf_hub_url,
)
from huggingface_hub.utils import build_hf_headers, get_session, hf_raise_for_status
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,
)
@dataclass(init=False)
class GeneralConfigTracker:
"""
Tracker for the evaluation parameters.
Attributes:
model_source (str): Source of the model (e.g. Hugging Face, GGUF, etc.)
model_name (str): Name of the model.
model_name_sanitized (str): Sanitized model name for directory creation.
start_time (float): Start time of the experiment. Logged at class init.
end_time (float): Start time of the experiment. Logged when calling [`GeneralConfigTracker.log_end_time`]
total_evaluation_time_seconds (str): Inferred total evaluation time in seconds (from the start and end times).
"""
model_source: str = None
model_name: str = None
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
end_time: float = None
total_evaluation_time_seconds: str = None
def __init__(self) -> None:
"""Starts the evaluation timer."""
self.start_time = time.perf_counter()
@staticmethod
def _get_model_name(model_args: str) -> str:
"""Extracts the model name from the model arguments."""
def extract_model_name(model_args: str, key: str) -> str:
"""Extracts the model name from the model arguments using a key."""
args_after_key = model_args.split(key)[1]
return args_after_key.split(",")[0]
# order does matter, e.g. peft and delta are provided together with pretrained
prefixes = ["peft=", "delta=", "pretrained=", "model=", "path=", "engine="]
for prefix in prefixes:
if prefix in model_args:
return extract_model_name(model_args, prefix)
return ""
def log_experiment_args(
self,
model_source: str,
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.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."""
self.end_time = time.perf_counter()
self.total_evaluation_time_seconds = str(self.end_time - self.start_time)
class EvaluationTracker:
"""
Keeps track and saves relevant information of the evaluation process.
Compiles the data from trackers and writes it to files, which can be published to the Hugging Face hub if requested.
"""
def __init__(
self,
output_path: str = None,
hub_results_org: str = "",
hub_repo_name: str = "",
details_repo_name: str = "",
results_repo_name: str = "",
push_results_to_hub: bool = False,
push_samples_to_hub: bool = False,
public_repo: bool = False,
token: str = "",
leaderboard_url: str = "",
point_of_contact: str = "",
gated: bool = False,
) -> None:
"""
Creates all the necessary loggers for evaluation tracking.
Args:
output_path (str): Path to save the results. If not provided, the results won't be saved.
hub_results_org (str): The Hugging Face organization to push the results to. If not provided, the results will be pushed to the owner of the Hugging Face token.
hub_repo_name (str): The name of the Hugging Face repository to push the results to. If not provided, the results will be pushed to `lm-eval-results`.
details_repo_name (str): The name of the Hugging Face repository to push the details to. If not provided, the results will be pushed to `lm-eval-results`.
result_repo_name (str): The name of the Hugging Face repository to push the results to. If not provided, the results will not be pushed and will be found in the details_hub_repo.
push_results_to_hub (bool): Whether to push the results to the Hugging Face hub.
push_samples_to_hub (bool): Whether to push the samples to the Hugging Face hub.
public_repo (bool): Whether to push the results to a public or private repository.
token (str): Token to use when pushing to the Hugging Face hub. This token should have write access to `hub_results_org`.
leaderboard_url (str): URL to the leaderboard on the Hugging Face hub on the dataset card.
point_of_contact (str): Contact information on the Hugging Face hub dataset card.
gated (bool): Whether to gate the repository.
"""
self.general_config_tracker = GeneralConfigTracker()
self.output_path = output_path
self.push_results_to_hub = push_results_to_hub
self.push_samples_to_hub = push_samples_to_hub
self.public_repo = public_repo
self.leaderboard_url = leaderboard_url
self.point_of_contact = point_of_contact
self.api = HfApi(token=token) if token else None
self.gated_repo = gated
if not self.api and (push_results_to_hub or push_samples_to_hub):
raise ValueError(
"Hugging Face token is not defined, but 'push_results_to_hub' or 'push_samples_to_hub' is set to True. "
"Please provide a valid Hugging Face token by setting the HF_TOKEN environment variable."
)
if (
self.api
and hub_results_org == ""
and (push_results_to_hub or push_samples_to_hub)
):
hub_results_org = self.api.whoami()["name"]
eval_logger.warning(
f"hub_results_org was not specified. Results will be pushed to '{hub_results_org}'."
)
if hub_repo_name == "":
details_repo_name = (
details_repo_name if details_repo_name != "" else "lm-eval-results"
)
results_repo_name = (
results_repo_name if results_repo_name != "" else details_repo_name
)
else:
details_repo_name = hub_repo_name
results_repo_name = hub_repo_name
eval_logger.warning(
"hub_repo_name was specified. Both details and results will be pushed to the same repository. Using hub_repo_name is no longer recommended, details_repo_name and results_repo_name should be used instead."
)
self.details_repo = f"{hub_results_org}/{details_repo_name}"
self.details_repo_private = f"{hub_results_org}/{details_repo_name}-private"
self.results_repo = f"{hub_results_org}/{results_repo_name}"
self.results_repo_private = f"{hub_results_org}/{results_repo_name}-private"
def save_results_aggregated(
self,
results: dict,
samples: dict,
) -> None:
"""
Saves the aggregated results and samples to the output path and pushes them to the Hugging Face hub if requested.
Args:
results (dict): The aggregated results to save.
samples (dict): The samples results to save.
"""
self.general_config_tracker.log_end_time()
if self.output_path:
try:
eval_logger.info("Saving results aggregated")
# calculate cumulative hash for each task - only if samples are provided
task_hashes = {}
if samples:
for task_name, task_samples in samples.items():
sample_hashes = [
s["doc_hash"] + s["prompt_hash"] + s["target_hash"]
for s in task_samples
]
task_hashes[task_name] = hash_string("".join(sample_hashes))
# update initial results dict
results.update({"task_hashes": task_hashes})
results.update(asdict(self.general_config_tracker))
dumped = json.dumps(
results,
indent=2,
default=handle_non_serializable,
ensure_ascii=False,
)
path = Path(self.output_path if self.output_path else Path.cwd())
path = path.joinpath(self.general_config_tracker.model_name_sanitized)
path.mkdir(parents=True, exist_ok=True)
self.date_id = datetime.now().isoformat().replace(":", "-")
file_results_aggregated = path.joinpath(f"results_{self.date_id}.json")
file_results_aggregated.open("w", encoding="utf-8").write(dumped)
if self.api and self.push_results_to_hub:
repo_id = (
self.results_repo
if self.public_repo
else self.results_repo_private
)
self.api.create_repo(
repo_id=repo_id,
repo_type="dataset",
private=not self.public_repo,
exist_ok=True,
)
self.api.upload_file(
repo_id=repo_id,
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}",
)
eval_logger.info(
"Successfully pushed aggregated results to the Hugging Face Hub. "
f"You can find them at: {repo_id}"
)
except Exception as e:
eval_logger.warning("Could not save results aggregated")
eval_logger.info(repr(e))
else:
eval_logger.info(
"Output path not provided, skipping saving results aggregated"
)
def save_results_samples(
self,
task_name: str,
samples: dict,
) -> None:
"""
Saves the samples results to the output path and pushes them to the Hugging Face hub if requested.
Args:
task_name (str): The task name to save the samples for.
samples (dict): The samples results to save.
"""
if self.output_path:
try:
eval_logger.info(f"Saving per-sample results for: {task_name}")
path = Path(self.output_path if self.output_path else Path.cwd())
path = path.joinpath(self.general_config_tracker.model_name_sanitized)
path.mkdir(parents=True, exist_ok=True)
file_results_samples = path.joinpath(
f"samples_{task_name}_{self.date_id}.jsonl"
)
for sample in samples:
# we first need to sanitize arguments and resps
# otherwise we won't be able to load the dataset
# using the datasets library
arguments = {}
for i, arg in enumerate(sample["arguments"]):
arguments[f"gen_args_{i}"] = {}
for j, tmp in enumerate(arg):
arguments[f"gen_args_{i}"][f"arg_{j}"] = tmp
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(
sample,
default=handle_non_serializable,
ensure_ascii=False,
)
+ "\n"
)
with open(file_results_samples, "a", encoding="utf-8") as f:
f.write(sample_dump)
if self.api and self.push_samples_to_hub:
repo_id = (
self.details_repo
if self.public_repo
else self.details_repo_private
)
self.api.create_repo(
repo_id=repo_id,
repo_type="dataset",
private=not self.public_repo,
exist_ok=True,
)
try:
if self.gated_repo:
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)
except Exception as e:
eval_logger.warning("Could not gate the repository")
eval_logger.info(repr(e))
self.api.upload_folder(
repo_id=repo_id,
folder_path=str(path),
path_in_repo=self.general_config_tracker.model_name_sanitized,
repo_type="dataset",
commit_message=f"Adding samples results for {task_name} to {self.general_config_tracker.model_name}",
)
eval_logger.info(
f"Successfully pushed sample results for task: {task_name} to the Hugging Face Hub. "
f"You can find them at: {repo_id}"
)
except Exception as e:
eval_logger.warning("Could not save sample results")
eval_logger.info(repr(e))
else:
eval_logger.info("Output path not provided, skipping saving sample results")
def recreate_metadata_card(self) -> None:
"""
Creates a metadata card for the evaluation results dataset and pushes it to the Hugging Face hub.
"""
eval_logger.info("Recreating metadata card")
repo_id = self.details_repo if self.public_repo else self.details_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)
# Build a dictionary to store the latest evaluation datetime for:
# - Each tested model and its aggregated results
# - Each task and sample results, if existing
# i.e. {
# "org__model_name__gsm8k": "2021-09-01T12:00:00",
# "org__model_name__ifeval": "2021-09-01T12:00:00",
# "org__model_name__results": "2021-09-01T12:00:00"
# }
latest_task_results_datetime = defaultdict(lambda: datetime.min.isoformat())
for file_path in sample_files:
file_path = Path(file_path)
filename = file_path.name
model_name = file_path.parent
task_name = get_file_task_name(filename)
results_datetime = get_file_datetime(filename)
task_name_sanitized = sanitize_task_name(task_name)
# Results and sample results for the same model and task will have the same datetime
samples_key = f"{model_name}__{task_name_sanitized}"
results_key = f"{model_name}__results"
latest_datetime = max(
latest_task_results_datetime[samples_key],
results_datetime,
)
latest_task_results_datetime[samples_key] = latest_datetime
latest_task_results_datetime[results_key] = max(
latest_task_results_datetime[results_key],
latest_datetime,
)
# Create metadata card
card_metadata = MetadataConfigs()
# Add the latest aggregated results to the metadata card for easy access
for file_path in results_files:
file_path = Path(file_path)
results_filename = file_path.name
model_name = file_path.parent
eval_date = get_file_datetime(results_filename)
eval_date_sanitized = re.sub(r"[^\w\.]", "_", eval_date)
results_filename = Path("**") / Path(results_filename).name
config_name = f"{model_name}__results"
sanitized_last_eval_date_results = re.sub(
r"[^\w\.]", "_", latest_task_results_datetime[config_name]
)
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)]}
)
# Add the tasks details configs
for file_path in sample_files:
file_path = Path(file_path)
filename = file_path.name
model_name = file_path.parent
task_name = get_file_task_name(filename)
eval_date = get_file_datetime(filename)
task_name_sanitized = sanitize_task_name(task_name)
eval_date_sanitized = re.sub(r"[^\w\.]", "_", eval_date)
results_filename = Path("**") / Path(filename).name
config_name = f"{model_name}__{task_name_sanitized}"
sanitized_last_eval_date_results = re.sub(
r"[^\w\.]", "_", latest_task_results_datetime[config_name]
)
if eval_date_sanitized == sanitized_last_eval_date_results:
# 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)]}
)
# Get latest results and extract info to update metadata card examples
latest_datetime = max(latest_task_results_datetime.values())
latest_model_name = max(
latest_task_results_datetime, key=lambda k: latest_task_results_datetime[k]
)
last_results_file = [
f for f in results_files if latest_datetime.replace(":", "-") in f
][0]
last_results_file_path = hf_hub_url(
repo_id=repo_id, filename=last_results_file, repo_type="dataset"
)
latest_results_file = load_dataset(
"json", data_files=last_results_file_path, split="train"
)
results_dict = latest_results_file["results"][0]
new_dictionary = {"all": results_dict}
new_dictionary.update(results_dict)
results_string = json.dumps(new_dictionary, indent=4)
dataset_summary = (
"Dataset automatically created during the evaluation run of model "
)
if self.general_config_tracker.model_source == "hf":
dataset_summary += f"[{self.general_config_tracker.model_name}](https://huggingface.co/{self.general_config_tracker.model_name})\n"
else:
dataset_summary += f"{self.general_config_tracker.model_name}\n"
dataset_summary += (
f"The dataset is composed of {len(card_metadata) - 1} configuration(s), each one corresponding to one of the evaluated task.\n\n"
f"The dataset has been created from {len(results_files)} run(s). Each run can be found as a specific split in each "
'configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.\n\n'
'An additional configuration "results" store all the aggregated results of the run.\n\n'
"To load the details from a run, you can for instance do the following:\n"
)
if self.general_config_tracker.model_source == "hf":
dataset_summary += (
"```python\nfrom datasets import load_dataset\n"
f'data = load_dataset(\n\t"{repo_id}",\n\tname="{latest_model_name}",\n\tsplit="latest"\n)\n```\n\n'
)
dataset_summary += (
"## Latest results\n\n"
f"These are the [latest results from run {latest_datetime}]({last_results_file_path.replace('/resolve/', '/blob/')}) "
"(note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. "
'You find each in the results and the "latest" split for each eval):\n\n'
f"```python\n{results_string}\n```"
)
card_data = DatasetCardData(
dataset_summary=dataset_summary,
repo_url=f"https://huggingface.co/{self.general_config_tracker.model_name}",
pretty_name=f"Evaluation run of {self.general_config_tracker.model_name}",
leaderboard_url=self.leaderboard_url,
point_of_contact=self.point_of_contact,
)
card_metadata.to_dataset_card_data(card_data)
card = DatasetCard.from_template(
card_data,
pretty_name=card_data.pretty_name,
)
card.push_to_hub(repo_id, repo_type="dataset")
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