Clémentine
fixing display bug when importing missing tags
26bbde7
raw
history blame
9.09 kB
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
@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
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
@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")
# Precision
precision = Precision.from_str(config.get("model_dtype"))
# Get model and org
org_and_model = config.get("model_name")
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)
# Extract results available in this file (some results are split in several files)
results = {}
for task in Tasks:
task = task.value
# We skip old mmlu entries
wrong_mmlu_version = False
if task.benchmark == "hendrycksTest":
for mmlu_k in ["harness|hendrycksTest-abstract_algebra|5", "hendrycksTest-abstract_algebra"]:
if mmlu_k in data["versions"] and data["versions"][mmlu_k] == 0:
wrong_mmlu_version = True
if wrong_mmlu_version:
continue
# 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", ""),
)
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.tags = file_dict.get("tags", [])
self.flagged = any("flagged" in tag for tag in self.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.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.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
}
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, dynamic_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))
with open(dynamic_path) as f:
dynamic_data = json.load(f)
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)
if eval_result.full_model in dynamic_data:
eval_result.update_with_dynamic_file_dict(dynamic_data[eval_result.full_model])
# Hardcoding because of gating problem
if "meta-llama" in eval_result.full_model:
eval_result.still_on_hub = True
# 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: # not all eval values present
continue
return results