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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, QuantType, WeightDtype, ComputeDtype
@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
quant_type: QuantType = QuantType.Unknown
precision: Precision = Precision.Unknown
weight_dtype: WeightDtype = WeightDtype.Unknown
compute_dtype: ComputeDtype = ComputeDtype.Unknown
double_quant: bool = False
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
model_size: int = 0
group_size: int = -1
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
result_file: str = ""
@classmethod
def init_from_json_file(self, json_filepath):
"""Inits the result from the specific model result file"""
result_file = "/".join(json_filepath.split("/")[2:])
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("precision", "4bit"))
quant_type = QuantType.from_str(str(config.get("quant_type", "GPTQ")))
weight_dtype = WeightDtype.from_str(data["task_info"].get("weight_dtype", "int4"))
compute_dtype = ComputeDtype.from_str(data["task_info"].get("compute_dtype", "bfloat16"))
# double_quant = data["quantization_config"].get("bnb_4bit_use_double_quant", False)
model_params = config["model_params"]
model_size = config["model_size"]
# group_size = data["quantization_config"].get("group_size", -1)
if data.get("quantization_config", None):
double_quant = data["quantization_config"].get("bnb_4bit_use_double_quant", False)
group_size = data["quantization_config"].get("group_size", -1)
else:
double_quant = False
group_size = -1
local = config.get("local", False)
if not local:
local = data["task_info"].get("local", False)
# Get model and org
org_and_model = config.get("model_name")
org_and_model = org_and_model.split("/", 1)
if local and org_and_model[0] != "Intel":
org_and_model = config.get("model_name").split("/")
# temporary "local"
org_and_model = ["local", org_and_model[-1]]
quant_type = QuantType.autoround
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
if task.benchmark == "mmlu":
accs = np.array([data["results"]["harness|mmlu|0"][task.metric]])
else:
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
mean_acc = round(mean_acc, 2)
results[task.benchmark] = mean_acc
return self(
eval_name=result_key,
full_model=full_model,
org=org,
model=model,
results=results,
precision=precision,
quant_type=quant_type,
weight_dtype=weight_dtype,
compute_dtype=compute_dtype,
double_quant=double_quant,
revision=config.get("model_sha", "main"),
num_params=model_params,
model_size=model_size,
group_size=group_size,
result_file=result_file
)
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.quant_type.value.name, self.precision.value.name,
self.weight_dtype.value.name, self.compute_dtype.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.precision = WeightType[request.get("weight_type", "Original")]
# self.num_params = request.get("model_size", 0) / 2 # need fix
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.quant_type.name: self.quant_type.value.name,
AutoEvalColumn.model_type_symbol.name: self.quant_type.value.symbol,
AutoEvalColumn.weight_dtype.name: self.weight_dtype.value.name,
AutoEvalColumn.compute_dtype.name: self.compute_dtype.value.name,
AutoEvalColumn.double_quant.name: self.double_quant,
AutoEvalColumn.model_type.name: self.model_type.value.name,
AutoEvalColumn.weight_type.name: self.weight_type.value.name,
AutoEvalColumn.architecture.name: self.architecture,
AutoEvalColumn.model.name: make_clickable_model(self.full_model, self.result_file),
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.model_size.name: self.model_size,
AutoEvalColumn.group_size.name: self.group_size,
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,
quant_type, precision, weight_dtype, compute_dtype):
"""Selects the correct request file for a given model. Only keeps runs tagged as FINISHED"""
# {model_path}_eval_request_{private}_{quant_type}_{precision}_{weight_dtype}_{compute_dtype}.json
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]
and str(req_content["quant_type"]) == quant_type
and req_content["weight_dtype"] == weight_dtype.split(".")[-1]
and req_content["compute_dtype"] == compute_dtype.split(".")[-1]
):
request_file = tmp_request_file
elif (
req_content["status"] in ["Finished"]
and req_content["precision"] == precision.split(".")[-1]
and quant_type == "AutoRound"
and req_content["weight_dtype"] == weight_dtype.split(".")[-1]
and req_content["compute_dtype"] == compute_dtype.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
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