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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, Format
@dataclass
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
weight_precision: Precision = Precision.Unknown
activation_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 = True
is_merge: bool = False
flagged: bool = False
status: str = "FINISHED"
tags: list = None
format: Format = Format.Unknown
@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)
# We manage the legacy config format
config = data.get("config_general")
try:
model_type = ModelType.from_str(config.get("model_type", "Unknown"))
weight_type = WeightType[config.get("weight_type", "Original")]
num_params = config.get("params", 0)
date = os.path.basename(json_filepath).removesuffix(".json").removeprefix("result_")
architecture = config.get("architectures", "Unknown")
tags = config.get("model_tag", None)
except Exception as e:
self.status = "FAILED"
print(f"Could not find request file for {self.org}/{self.model}")
# Precision
weight_precision = Precision.from_str(config.get("weight_precision"))
activation_precision = Precision.from_str(config.get("activation_precision"))
format = Format.from_str(config.get("format"))
# Get model and org
org_and_model = config.get("model")
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}_W{weight_precision.value.name}A{activation_precision.value.name}"
else:
org = org_and_model[0]
model = org_and_model[1]
result_key = f"{org}_{model}_W{weight_precision.value.name}A{activation_precision.value.name}"
full_model = "/".join(org_and_model)
# Extract results available in this file (some results are split in several files)
results = {}
for task in Tasks:
try:
task = task.value
# We skip old mmlu entries
# Some truthfulQA values are NaNs
if task.benchmark == "truthfulqa_mc2" and "truthfulqa_mc2|0" in data["results"]:
if math.isnan(float(data["results"]["truthfulqa_mc2|0"][task.metric])):
results[task.benchmark] = 0.0
continue
# We average all scores of a given metric (mostly for mmlu)
if task.benchmark == "mmlu":
accs = np.array([data["results"].get(task.benchmark, {}).get(task.metric, None)])
else:
accs = np.array([Tasks.get_metric(task, v) 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
except Exception as e:
print(e)
continue
return self(
eval_name=result_key,
full_model=full_model,
org=org,
model=model,
results=results,
weight_precision=weight_precision,
activation_precision=activation_precision,
revision=config.get("model_sha", ""),
model_type=model_type,
weight_type=weight_type,
num_params=num_params,
date=date,
architecture=architecture,
tags=tags,
format=format,
)
# 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", "Unknown"))
# self.weight_type = WeightType[request.get("weight_type", "Original")]
# self.num_params = request.get("params", 0)
# self.date = request.get("submitted_time", "")
# self.architecture = request.get("architectures", "Unknown")
# self.status = request.get("status", "FAILED")
# except Exception as e:
# self.status = "FAILED"
# print(f"Could not find request file for {self.org}/{self.model}")
# def update_with_dynamic_file_dict(self, file_dict):
# self.license = file_dict.get("license", "?")
# self.likes = file_dict.get("likes", 0)
# self.still_on_hub = file_dict["still_on_hub"]
# self.flagged = any("flagged" in tag for tag in file_dict["tags"])
# self.tags = file_dict["tags"]
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.weight_precision.name: self.weight_precision.value.name,
AutoEvalColumn.activation_precision.name: self.activation_precision.value.name,
AutoEvalColumn.model_type.name: self.model_type.value.name,
# 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),
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.merged.name: "merge" in self.tags if self.tags else False,
AutoEvalColumn.moe.name: ("moe" in self.tags if self.tags else False) or "moe" in self.full_model.lower(),
AutoEvalColumn.flagged.name: self.flagged,
AutoEvalColumn.format.name: self.format.value.name,
}
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) -> 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("result_")[:-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:
print(f"Read {model_result_filepath}")
# Creation of result
eval_result = EvalResult.init_from_json_file(model_result_filepath)
# 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:
if v.status == "FINISHED":
v.to_dict() # we test if the dict version is complete
results.append(v)
except KeyError as e: # not all eval values present
print(f"Fail to get results from {v.eval_name} with the error {e}")
print(v)
continue
return results
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