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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
@dataclass
class EvalResult:
"""Represents one full evaluation. Built from a combination of the result and request file for a given run.
"""
eval_name: str # org_model_precision (uid)
org: str
generative_model: str
retrieval_model: str
# revision: str # commit hash, "" if main
results: dict
generative_model_link: str = "" # link to the model on the hub
generative_model_args: dict = None
retrieval_model_link: str = "" # link to the model on the hub
retrieval_model_args: dict = None
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
date: str = "" # submission date of request file
still_on_hub: bool = False
@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")
# Precision
# precision = Precision.from_str(config.get("model_dtype"))
# Get model and org
eval_name= config.get("eval_name", "")
generative_model = config.get("generative_model", "")
retrieval_model = config.get("retrieval_model", "")
org= config.get("org", "")
# 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, _, model_config = is_model_on_hub(
# full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
# )
# 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 and task.col_name != "hallucination"])
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
results[task.metric] = data["results"][task.benchmark].get(task.metric, None)
if results[task.metric] is not None:
results[task.metric] = results[task.metric] * 100.0
generative_model_args = config.get("generative_model_args", None)
retrieval_model_args = config.get("retrieval_model_args", None)
open_source= True
if not generative_model_args or not generative_model_args.get("open_source", False):
open_source = False
if not retrieval_model_args or not retrieval_model_args.get("open_source", False):
open_source = False
return self(
eval_name=eval_name,
# full_model=full_model,
org=org,
generative_model=generative_model,
retrieval_model=retrieval_model,
results=results,
generative_model_args=generative_model_args,
retrieval_model_args=retrieval_model_args,
model_type=ModelType.OpenSource if open_source else ModelType.ClosedSource,
# 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.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} with precision {self.precision.value.name}")
def to_dict(self):
"""Converts the Eval Result to a dict compatible with our dataframe display"""
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_type_symbol.name: self.model_type.value.symbol,
# AutoEvalColumn.weight_type.name: self.weight_type.value.name,
# AutoEvalColumn.architecture.name: self.architecture,
AutoEvalColumn.generative_model.name: self.generative_model,
AutoEvalColumn.retrieval_model.name: self.retrieval_model,
AutoEvalColumn.generative_model_link.name: make_clickable_model(self.generative_model, self.generative_model_link),
AutoEvalColumn.retrieval_model_link.name: make_clickable_model(self.retrieval_model, self.retrieval_model_link),
# AutoEvalColumn.revision.name: self.revision,
AutoEvalColumn.ret_average.name: self.results["retrieval"],
AutoEvalColumn.gen_average.name: self.results["generation"],
# AutoEvalColumn.license.name: self.license,
# AutoEvalColumn.likes.name: self.likes,
# AutoEvalColumn.generative_model_params.name: self.num_params,
# AutoEvalColumn.still_on_hub.name: self.still_on_hub,
"Gen#Params (B)": self.generative_model_args.get("num_params", "Unknown"),
"Ret#Params (B)": self.retrieval_model_args.get("num_params", "Unknown"),
}
for task in Tasks:
data_dict[task.value.col_name] = self.results[task.value.metric]
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 = []
print(f"Reading results from {results_path}")
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))
print(f"Adding {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
import traceback
traceback.print_exc()
continue
return results
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