Spaces:
Restarting
Restarting
File size: 8,407 Bytes
df66f6e 2a5f9fb df66f6e 2a5f9fb b1a1395 3dfaf22 2a5f9fb df66f6e 2a5f9fb 3dfaf22 2a5f9fb 3dfaf22 2a5f9fb 3dfaf22 2a5f9fb 3dfaf22 2a5f9fb 3dfaf22 2a5f9fb 002172c 2a5f9fb 3dfaf22 002172c 3dfaf22 2a5f9fb df66f6e 2a5f9fb 002172c 2a5f9fb 3dfaf22 2a5f9fb 3dfaf22 2a5f9fb 3dfaf22 2a5f9fb 3dfaf22 2a5f9fb b1a1395 2a5f9fb 3dfaf22 2a5f9fb 3dfaf22 2a5f9fb 3dfaf22 2a5f9fb 3dfaf22 2a5f9fb 3dfaf22 2a5f9fb 3dfaf22 2a5f9fb 3dfaf22 2a5f9fb 3dfaf22 2a5f9fb b1a1395 df66f6e 2a5f9fb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 |
import glob
import json
import math
import os
from dataclasses import dataclass
import dateutil
from datetime import datetime
from transformers import AutoConfig
import numpy as np
from src.display.formatting import make_clickable_model
from src.display.utils import AutoEvalColumn, ModelType, Tasks
from src.submission.check_validity import is_model_on_hub
@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: str = ""
model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ...
weight_type: str = "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
@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", data.get("config_general", None))
# Precision
precision = config.get("model_dtype")
if precision == "None":
precision = "GPTQ"
# 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}"
else:
org = org_and_model[0]
model = org_and_model[1]
result_key = f"{org}_{model}_{precision}"
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
)
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 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", ""),
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)
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 = request.get("weight_type", "?")
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,
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,
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,
}
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", "PENDING_NEW_EVAL"]
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
|