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import glob | |
import json | |
import math | |
import os | |
from dataclasses import dataclass | |
import dateutil | |
import numpy as np | |
from huggingface_hub import ModelCard | |
from src.display.formatting import make_clickable_model | |
from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType | |
from src.submission.check_validity import is_model_on_hub, check_model_card | |
class EvalResult: | |
# Also see src.display.utils.AutoEvalColumn for what will be displayed. | |
eval_name: str # org_model_precision (uid) | |
full_model: str # org/model (path on hub) | |
org: str | |
model: str | |
revision: str # commit hash, "" if main | |
results: dict | |
precision: Precision = Precision.Unknown | |
model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ... | |
weight_type: WeightType = WeightType.Original # Original or Adapter | |
architecture: str = "Unknown" # From config file | |
license: str = "?" | |
likes: int = 0 | |
num_params: int = 0 | |
date: str = "" # submission date of request file | |
still_on_hub: bool = False | |
is_merge: bool = False | |
flagged: bool = False | |
def init_from_json_file(self, json_filepath): | |
"""Inits the result from the specific model result file""" | |
with open(json_filepath) as fp: | |
data = json.load(fp) | |
# We manage the legacy config format | |
config = data.get("config", data.get("config_general", None)) | |
# Precision | |
precision = Precision.from_str(config.get("model_dtype")) | |
# Get model and org | |
org_and_model = config.get("model_name", config.get("model_args", None)) | |
org_and_model = org_and_model.split("/", 1) | |
if len(org_and_model) == 1: | |
org = None | |
model = org_and_model[0] | |
result_key = f"{model}_{precision.value.name}" | |
else: | |
org = org_and_model[0] | |
model = org_and_model[1] | |
result_key = f"{org}_{model}_{precision.value.name}" | |
full_model = "/".join(org_and_model) | |
still_on_hub, error, model_config = is_model_on_hub( | |
full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False | |
) | |
architecture = "?" | |
if model_config is not None: | |
architectures = getattr(model_config, "architectures", None) | |
if architectures: | |
architecture = ";".join(architectures) | |
# If the model doesn't have a model card or a license, we consider it's deleted | |
if still_on_hub: | |
try: | |
if check_model_card(full_model)[0] is False: | |
still_on_hub = False | |
except Exception: | |
still_on_hub = False | |
# Check if the model is a merge | |
is_merge_from_metadata = False | |
flagged = False | |
if still_on_hub: | |
model_card = ModelCard.load(full_model) | |
if model_card.data.tags: | |
is_merge_from_metadata = "merge" in model_card.data.tags | |
merge_keywords = ["mergekit", "merged model", "merge model", "merging", "merge", "merged", "Carbon"] | |
# If the model is a merge but not saying it in the metadata, we flag it | |
is_merge_from_model_card = any(keyword in model_card.text.lower() for keyword in merge_keywords) | |
flagged = is_merge_from_model_card and not is_merge_from_metadata | |
# Extract results available in this file (some results are split in several files) | |
results = {} | |
for task in Tasks: | |
task = task.value | |
# Some truthfulQA values are NaNs | |
if task.benchmark == "truthfulqa:mc" and "harness|truthfulqa:mc|0" in data["results"]: | |
if math.isnan(float(data["results"]["harness|truthfulqa:mc|0"][task.metric])): | |
results[task.benchmark] = 0.0 | |
continue | |
# We average all scores of a given metric (mostly for mmlu) | |
accs = np.array([v.get(task.metric, None) for k, v in data["results"].items() if task.benchmark in k]) | |
if accs.size == 0 or any([acc is None for acc in accs]): | |
continue | |
mean_acc = np.mean(accs) * 100.0 | |
results[task.benchmark] = mean_acc | |
return self( | |
eval_name=result_key, | |
full_model=full_model, | |
org=org, | |
model=model, | |
results=results, | |
precision=precision, | |
revision= config.get("model_sha", ""), | |
still_on_hub=still_on_hub, | |
architecture=architecture, | |
is_merge=is_merge_from_metadata, | |
flagged=flagged, | |
) | |
def update_with_request_file(self, requests_path): | |
"""Finds the relevant request file for the current model and updates info with it""" | |
request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name) | |
try: | |
with open(request_file, "r") as f: | |
request = json.load(f) | |
self.model_type = ModelType.from_str(request.get("model_type", "")) | |
self.weight_type = WeightType[request.get("weight_type", "Original")] | |
self.license = request.get("license", "?") | |
self.likes = request.get("likes", 0) | |
self.num_params = request.get("params", 0) | |
self.date = request.get("submitted_time", "") | |
except Exception: | |
print(f"Could not find request file for {self.org}/{self.model}") | |
def to_dict(self): | |
"""Converts the Eval Result to a dict compatible with our dataframe display""" | |
average = sum([v for v in self.results.values() if v is not None]) / len(Tasks) | |
data_dict = { | |
"eval_name": self.eval_name, # not a column, just a save name, | |
AutoEvalColumn.precision.name: self.precision.value.name, | |
AutoEvalColumn.model_type.name: self.model_type.value.name, | |
AutoEvalColumn.merged.name: self.is_merge, | |
AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol, # + "🥦" if self.is_merge, | |
AutoEvalColumn.weight_type.name: self.weight_type.value.name, | |
AutoEvalColumn.architecture.name: self.architecture, | |
AutoEvalColumn.model.name: make_clickable_model(self.full_model), | |
AutoEvalColumn.dummy.name: self.full_model, | |
AutoEvalColumn.revision.name: self.revision, | |
AutoEvalColumn.average.name: average, | |
AutoEvalColumn.license.name: self.license, | |
AutoEvalColumn.likes.name: self.likes, | |
AutoEvalColumn.params.name: self.num_params, | |
AutoEvalColumn.still_on_hub.name: self.still_on_hub, | |
AutoEvalColumn.flagged.name: self.flagged | |
} | |
for task in Tasks: | |
data_dict[task.value.col_name] = self.results[task.value.benchmark] | |
return data_dict | |
def get_request_file_for_model(requests_path, model_name, precision): | |
"""Selects the correct request file for a given model. Only keeps runs tagged as FINISHED""" | |
request_files = os.path.join( | |
requests_path, | |
f"{model_name}_eval_request_*.json", | |
) | |
request_files = glob.glob(request_files) | |
# Select correct request file (precision) | |
request_file = "" | |
request_files = sorted(request_files, reverse=True) | |
for tmp_request_file in request_files: | |
with open(tmp_request_file, "r") as f: | |
req_content = json.load(f) | |
if ( | |
req_content["status"] in ["FINISHED"] | |
and req_content["precision"] == precision.split(".")[-1] | |
): | |
request_file = tmp_request_file | |
return request_file | |
def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResult]: | |
"""From the path of the results folder root, extract all needed info for results""" | |
model_result_filepaths = [] | |
for root, _, files in os.walk(results_path): | |
# We should only have json files in model results | |
if len(files) == 0 or any([not f.endswith(".json") for f in files]): | |
continue | |
# Sort the files by date | |
try: | |
files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7]) | |
except dateutil.parser._parser.ParserError: | |
files = [files[-1]] | |
for file in files: | |
model_result_filepaths.append(os.path.join(root, file)) | |
eval_results = {} | |
for model_result_filepath in model_result_filepaths: | |
# Creation of result | |
eval_result = EvalResult.init_from_json_file(model_result_filepath) | |
eval_result.update_with_request_file(requests_path) | |
# Store results of same eval together | |
eval_name = eval_result.eval_name | |
if eval_name in eval_results.keys(): | |
eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None}) | |
else: | |
eval_results[eval_name] = eval_result | |
results = [] | |
for v in eval_results.values(): | |
try: | |
v.to_dict() # we test if the dict version is complete | |
results.append(v) | |
except KeyError: # not all eval values present | |
continue | |
return results | |