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import glob
import json
import math
import os
from dataclasses import dataclass
import dateutil
import numpy as np
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
def report_hyperlink(link):
return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">🔗 Report</a>' if link else "N/A"
@dataclass
class EvalResult:
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"
license: str = "?"
likes: int = 0
num_params: int = 0
date: str = "" # submission date of request file
still_on_hub: bool = False
model_report: str = "",
@classmethod
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)
config = data.get("config")
# print(json_filepath)
# 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}"
else:
org = org_and_model[0]
model = org_and_model[1]
result_key = f"{org}_{model}"
full_model = "/".join(org_and_model)
still_on_hub, _, 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)
# Extract results available in this file (some results are split in several files)
results = {}
for task in Tasks:
task = task.value
# We average all scores of a given metric (not all metrics are present in all files)
accs = np.array([v.get(task.metric, None) for k, v in data["results"].items() if task.benchmark == k])
if accs.size == 0 or any([acc is None for acc in accs]):
# print('skip', full_model)
results[task.benchmark] = None
continue
# print(task)
# print(accs)
mean_acc = np.mean(accs) # * 100.0
results[task.benchmark] = round(mean_acc, 2)
return self(
eval_name=result_key,
full_model=full_model,
org=org,
model=model,
results=results,
model_report=config.get("model_report", ""),
# precision=precision,
revision=config.get("model_sha", ""),
still_on_hub=still_on_hub,
architecture=architecture
)
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.revision
)
try:
with open(request_file, "r") as f:
request = json.load(f)
# print(f"Read Request from {request_file}")
# print(request)
# self.model_type = ModelType.from_str("open" if "/" in self.full_model and "openai" not in self.full_model else "closed")
# self.model_type = ModelType.from_str("open" if self.still_on_hub else "closed")
self.model_type = ModelType.from_str("open" if "/" in self.full_model and "openai" not in self.full_model else "closed")
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", None)
self.date = request.get("submitted_time", "")
except Exception as e:
# print(e)
self.model_type = ModelType.from_str("open" if "/" in self.full_model and "openai" not in self.full_model else "closed")
print(f"Could not find request file ({requests_path}) 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.model_report.name: report_hyperlink(self.model_report),
AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
# AutoEvalColumn.weight_type.name: self.weight_type.value.name,
# AutoEvalColumn.architecture.name: self.architecture,
AutoEvalColumn.model.name: make_clickable_model(self.full_model, self.model_type.value.name),
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,
}
for task in Tasks:
data_dict[task.value.col_name] = self.results[task.value.benchmark] if self.results[task.value.benchmark] is not None else "N/A"
return data_dict
def get_request_file_for_model(requests_path, model_name, revision=""):
"""Selects the correct request file for a given model. Only keeps runs tagged as FINISHED"""
request_files = os.path.join(
requests_path,
f"**/request_{model_name}*_eval_request*.json"
)
# print(f"Looking up request file(s) with pattern {request_files}")
request_files = glob.glob(request_files, recursive=True)
# print(f"Found request file(s) {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)
# print("Precision", req_content["precision"])
if (
req_content["status"] in ["FINISHED"]
# and req_content["precision"] == precision.split(".")[-1]
):
request_file = tmp_request_file
# print(f"Selected {request_file} for model metadata")
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)
# print()
# print('eval result')
# print(eval_result)
# print()
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:
# print()
# print(v)
# print()
v.to_dict() # we test if the dict version is complete
results.append(v)
except KeyError: # not all eval values present
continue
return results
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