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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# flake8: noqa E501
import glob
import json
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, Precision, Tasks, WeightType
from src.submission.check_validity import is_model_on_hub
from src.utils import get_model_name_from_filepath, get_org_and_model_names_from_filepath, get_request_hash
@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)
model_name: 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 = "Unknown"
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(cls, json_filepath):
"""Inits the result from the specific model result file"""
with open(json_filepath) as fp:
data = json.load(fp)
org, model = get_org_and_model_names_from_filepath(json_filepath)
config = data.get("config")
# Precision
precision = Precision.from_str(config.get("model_dtype"))
result_key = f"{org}_{model}_{precision.value.name}"
model_name = get_model_name_from_filepath(json_filepath)
still_on_hub, _, model_config = is_model_on_hub(
model_name,
config.get("model_sha", "main"),
trust_remote_code=True,
test_tokenizer=False,
)
architecture = "Unknown"
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]):
continue
mean_acc = np.mean(accs) * 100.0
results[task.benchmark] = mean_acc
return cls(
eval_name=result_key,
model_name=model_name,
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.model_name,
self.revision,
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", "Unknown")
self.likes = request.get("likes", 0)
self.num_params = request.get("params", 0)
self.date = request.get("submitted_time", "")
except Exception as error:
print(f"Could not find request file for {self.org}/{self.model} with precision {self.precision.value.name}")
print(f"Error: {error}")
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)
solbench = sum([
self.results.get('naive_judge', 0),
self.results.get('human_eval_solidity', 0)
]) / 2
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.model_name),
AutoEvalColumn.revision.name: self.revision,
# AutoEvalColumn.average.name: average,
AutoEvalColumn.solbench.name: solbench,
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: str,
model_name: str,
revision: str,
precision: str,
):
request_hash = get_request_hash(model_name, revision, precision)
filepath = os.path.join(requests_path, model_name, '{}.json'.format(request_hash))
print(f'Loading {filepath}...')
filepath = glob.glob(filepath)[0]
return filepath
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
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