import json
from pathlib import Path
from json import JSONDecodeError
import logging
import math
from dataclasses import dataclass, field
from typing import Optional, Dict, List
from tqdm import tqdm
from tqdm.contrib.logging import logging_redirect_tqdm
import numpy as np
from src.display.formatting import make_clickable_model
from src.display.utils import AutoEvalColumn, ModelType, Precision, Tasks, WeightType, parse_datetime
# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
@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: Optional[str]
model: str
revision: str # commit hash, "" if main
results: Dict[str, float]
precision: Precision = Precision.Unknown
model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ...
weight_type: WeightType = WeightType.Original
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
not_flagged: bool = False
status: str = "FINISHED"
# List of tags, initialized to a new empty list for each instance to avoid the pitfalls of mutable default arguments.
tags: List[str] = field(default_factory=list)
@classmethod
def init_from_json_file(cls, json_filepath: str) -> "EvalResult":
with open(json_filepath, "r") as fp:
data = json.load(fp)
config = data.get("config_general", {})
precision = Precision.from_str(config.get("model_dtype", "unknown"))
org_and_model = config.get("model_name", "").split("/", 1)
org = org_and_model[0] if len(org_and_model) > 1 else None
model = org_and_model[-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)
results = cls.extract_results(data) # Properly call the method to extract results
return cls(
eval_name=result_key,
full_model=full_model,
org=org,
model=model,
results=results,
precision=precision,
revision=config.get("model_sha", ""),
)
@staticmethod
def extract_results(data: Dict) -> Dict[str, float]:
"""
Extract and process benchmark results from a given dict.
Parameters:
- data (Dict): A dictionary containing benchmark data. This dictionary must
include 'versions' and 'results' keys with respective sub-data.
Returns:
- Dict[str, float]: A dictionary where keys are benchmark names and values
are the processed average scores as percentages.
Notes:
- The method specifically checks for certain benchmark names to skip outdated entries.
- Handles NaN values by setting the corresponding benchmark result to 0.0.
- Averages scores across metrics for benchmarks found in the data, in a percentage format.
"""
results = {}
for task in Tasks:
task = task.value
# We skip old mmlu entries
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:
continue
# Some benchamrk values are NaNs, mostly truthfulQA
# Would be more optimal (without the whole dict itertion) if benchmark name was same as key in results
# e.g. not harness|truthfulqa:mc|0 but truthfulqa:mc
for k, v in data["results"].items():
if task.benchmark in k:
if math.isnan(float(v[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 results
def update_with_request_file(self, requests_path):
"""Finds the relevant request file for the current model and updates info with it."""
try:
request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name)
if request_file is None:
logging.warning(f"No request file for {self.org}/{self.model}")
self.status = "FAILED"
return
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 = int(request.get("params", 0)) # Ensuring type safety
self.date = request.get("submitted_time", "")
self.architecture = request.get("architectures", "Unknown")
self.status = request.get("status", "FAILED")
except FileNotFoundError:
self.status = "FAILED"
logging.error(f"Request file: {request_file} not found for {self.org}/{self.model}")
except JSONDecodeError:
self.status = "FAILED"
logging.error(f"Error decoding JSON from the request file for {self.org}/{self.model}")
except KeyError as e:
self.status = "FAILED"
logging.error(f"Key error {e} in processing request file for {self.org}/{self.model}")
except Exception as e: # Catch-all for any other unexpected exceptions
self.status = "FAILED"
logging.error(f"Unexpected error {e} for {self.org}/{self.model}")
def update_with_dynamic_file_dict(self, file_dict):
"""Update object attributes based on the provided dictionary, with error handling for missing keys and type validation."""
# Default values set for optional or potentially missing keys.
self.license = file_dict.get("license", "?")
self.likes = int(file_dict.get("likes", 0)) # Ensure likes is treated as an integer
self.still_on_hub = file_dict.get("still_on_hub", False) # Default to False if key is missing
self.tags = file_dict.get("tags", [])
# Calculate `flagged` only if 'tags' is not empty and avoid calculating each time
self.not_flagged = not (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.fullname.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: not ("merge" in self.tags if self.tags else False),
AutoEvalColumn.moe.name: not (
("moe" in self.tags if self.tags else False) or "moe" in self.full_model.lower()
),
AutoEvalColumn.not_flagged.name: self.not_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"""
requests_path = Path(requests_path)
pattern = f"{model_name}_eval_request_*.json"
# Using pathlib to find files matching the pattern
request_files = list(requests_path.glob(pattern))
# Sort the files by name in descending order to mimic 'reverse=True'
request_files.sort(reverse=True)
# Select the correct request file based on 'status' and 'precision'
request_file = None
for request_file in request_files:
with request_file.open("r") as f:
req_content = json.load(f)
if req_content["status"] == "FINISHED" and req_content["precision"] == precision.split(".")[-1]:
request_file = str(request_file)
# Return empty string if no file found that matches criteria
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"""
with open(dynamic_path) as f:
dynamic_data = json.load(f)
results_path = Path(results_path)
model_files = list(results_path.rglob("results_*.json"))
model_files.sort(key=lambda file: parse_datetime(file.stem.removeprefix("results_")))
eval_results = {}
# Wrap model_files iteration with tqdm for progress display
for model_result_filepath in tqdm(model_files, desc="Processing model files"):
# Creation of result
eval_result = EvalResult.init_from_json_file(model_result_filepath)
with logging_redirect_tqdm():
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 any([org in eval_result.full_model for org in ["meta-llama/", "google/", "tiiuae/"]]):
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 k, v in eval_results.items():
try:
if v.status == "FINISHED":
v.to_dict() # we test if the dict version is complete
results.append(v)
except KeyError as e:
logging.error(f"Error while checking model {k} {v.date} json, no key: {e}") # not all eval values present
continue
return results