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import glob |
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import json |
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import math |
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import os |
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from dataclasses import dataclass |
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import dateutil |
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import numpy as np |
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from huggingface_hub import ModelCard |
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from src.display.formatting import make_clickable_model |
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from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType, QuantType, WeightDtype, ComputeDtype |
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@dataclass |
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class EvalResult: |
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eval_name: str |
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full_model: str |
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org: str |
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model: str |
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revision: str |
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results: dict |
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quant_type: QuantType = QuantType.Unknown |
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precision: Precision = Precision.Unknown |
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weight_dtype: WeightDtype = WeightDtype.Unknown |
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compute_dtype: ComputeDtype = ComputeDtype.Unknown |
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double_quant: bool = False |
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model_type: ModelType = ModelType.Unknown |
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weight_type: WeightType = WeightType.Original |
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architecture: str = "Unknown" |
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license: str = "?" |
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likes: int = 0 |
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num_params: int = 0 |
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model_size: int = 0 |
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group_size: int = -1 |
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date: str = "" |
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still_on_hub: bool = True |
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is_merge: bool = False |
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flagged: bool = False |
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status: str = "Finished" |
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tags: list = None |
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result_file: str = "" |
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@classmethod |
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def init_from_json_file(self, json_filepath): |
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"""Inits the result from the specific model result file""" |
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result_file = "/".join(json_filepath.split("/")[2:]) |
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with open(json_filepath) as fp: |
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data = json.load(fp) |
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config = data.get("config_general") |
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precision = Precision.from_str(config.get("precision", "4bit")) |
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quant_type = QuantType.from_str(str(config.get("quant_type", "GPTQ"))) |
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weight_dtype = WeightDtype.from_str(data["task_info"].get("weight_dtype", "int4")) |
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compute_dtype = ComputeDtype.from_str(data["task_info"].get("compute_dtype", "bfloat16")) |
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model_params = round(float(config["model_params"]), 2) |
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model_size = round(float(config["model_size"]), 2) |
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if data.get("quantization_config", None): |
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double_quant = data["quantization_config"].get("bnb_4bit_use_double_quant", False) |
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group_size = data["quantization_config"].get("group_size", -1) |
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else: |
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double_quant = False |
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group_size = -1 |
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local = config.get("local", False) |
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if not local: |
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local = data["task_info"].get("local", False) |
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org_and_model = config.get("model_name") |
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org_and_model = org_and_model.split("/", 1) |
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if local and org_and_model[0] != "Intel": |
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org_and_model = config.get("model_name").split("/") |
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org_and_model = ["local", org_and_model[-1]] |
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quant_type = QuantType.autoround |
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if len(org_and_model) == 1: |
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org = None |
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model = org_and_model[0] |
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result_key = f"{model}_{precision.value.name}" |
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else: |
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org = org_and_model[0] |
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model = org_and_model[1] |
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result_key = f"{org}_{model}_{precision.value.name}" |
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full_model = "/".join(org_and_model) |
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results = {} |
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for task in Tasks: |
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task = task.value |
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if task.benchmark == "mmlu": |
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accs = np.array([data["results"]["harness|mmlu|0"][task.metric]]) |
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else: |
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accs = np.array([v.get(task.metric, None) for k, v in data["results"].items() if task.benchmark in k]) |
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if accs.size == 0 or any([acc is None for acc in accs]): |
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continue |
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mean_acc = np.mean(accs) * 100.0 |
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mean_acc = round(mean_acc, 2) |
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results[task.benchmark] = mean_acc |
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return self( |
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eval_name=result_key, |
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full_model=full_model, |
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org=org, |
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model=model, |
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results=results, |
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precision=precision, |
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quant_type=quant_type, |
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weight_dtype=weight_dtype, |
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compute_dtype=compute_dtype, |
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double_quant=double_quant, |
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revision=config.get("model_sha", "main"), |
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num_params=model_params, |
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model_size=model_size, |
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group_size=group_size, |
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result_file=result_file |
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) |
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def update_with_request_file(self, requests_path): |
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"""Finds the relevant request file for the current model and updates info with it""" |
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request_file = get_request_file_for_model(requests_path, self.full_model, |
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self.quant_type.value.name, self.precision.value.name, |
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self.weight_dtype.value.name, self.compute_dtype.value.name) |
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try: |
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with open(request_file, "r") as f: |
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request = json.load(f) |
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self.date = request.get("submitted_time", "") |
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self.architecture = request.get("architectures", "Unknown") |
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self.status = request.get("status", "Failed") |
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except Exception as e: |
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self.status = "Failed" |
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print(f"Could not find request file for {self.org}/{self.model}") |
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def update_with_dynamic_file_dict(self, file_dict): |
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self.license = file_dict.get("license", "?") |
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self.likes = file_dict.get("likes", 0) |
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self.still_on_hub = file_dict["still_on_hub"] |
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self.tags = file_dict.get("tags", []) |
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self.flagged = any("flagged" in tag for tag in self.tags) |
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def to_dict(self): |
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"""Converts the Eval Result to a dict compatible with our dataframe display""" |
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average = sum([v for v in self.results.values() if v is not None]) / len(Tasks) |
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data_dict = { |
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"eval_name": self.eval_name, |
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AutoEvalColumn.precision.name: self.precision.value.name, |
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AutoEvalColumn.quant_type.name: self.quant_type.value.name, |
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AutoEvalColumn.model_type_symbol.name: self.quant_type.value.symbol, |
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AutoEvalColumn.weight_dtype.name: self.weight_dtype.value.name, |
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AutoEvalColumn.compute_dtype.name: self.compute_dtype.value.name, |
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AutoEvalColumn.double_quant.name: self.double_quant, |
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AutoEvalColumn.model_type.name: self.model_type.value.name, |
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AutoEvalColumn.weight_type.name: self.weight_type.value.name, |
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AutoEvalColumn.architecture.name: self.architecture, |
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AutoEvalColumn.model.name: make_clickable_model(self.full_model, self.result_file), |
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AutoEvalColumn.dummy.name: self.full_model, |
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AutoEvalColumn.revision.name: self.revision, |
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AutoEvalColumn.average.name: average, |
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AutoEvalColumn.license.name: self.license, |
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AutoEvalColumn.likes.name: self.likes, |
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AutoEvalColumn.params.name: self.num_params, |
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AutoEvalColumn.model_size.name: self.model_size, |
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AutoEvalColumn.group_size.name: self.group_size, |
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AutoEvalColumn.still_on_hub.name: self.still_on_hub, |
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AutoEvalColumn.merged.name: "merge" in self.tags if self.tags else False, |
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AutoEvalColumn.moe.name: ("moe" in self.tags if self.tags else False) or "moe" in self.full_model.lower(), |
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AutoEvalColumn.flagged.name: self.flagged |
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} |
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for task in Tasks: |
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data_dict[task.value.col_name] = self.results[task.value.benchmark] |
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return data_dict |
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def get_request_file_for_model(requests_path, model_name, |
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quant_type, precision, weight_dtype, compute_dtype): |
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"""Selects the correct request file for a given model. Only keeps runs tagged as FINISHED""" |
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request_files = os.path.join( |
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requests_path, |
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f"{model_name}_eval_request_*.json", |
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) |
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request_files = glob.glob(request_files) |
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request_file = "" |
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request_files = sorted(request_files, reverse=True) |
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for tmp_request_file in request_files: |
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with open(tmp_request_file, "r") as f: |
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req_content = json.load(f) |
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if ( |
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req_content["status"] in ["Finished"] |
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and req_content["precision"] == precision.split(".")[-1] |
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and str(req_content["quant_type"]) == quant_type |
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and req_content["weight_dtype"] == weight_dtype.split(".")[-1] |
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and req_content["compute_dtype"] == compute_dtype.split(".")[-1] |
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): |
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request_file = tmp_request_file |
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elif ( |
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req_content["status"] in ["Finished"] |
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and req_content["precision"] == precision.split(".")[-1] |
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and quant_type == "AutoRound" |
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and req_content["weight_dtype"] == weight_dtype.split(".")[-1] |
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and req_content["compute_dtype"] == compute_dtype.split(".")[-1] |
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): |
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request_file = tmp_request_file |
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return request_file |
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def get_raw_eval_results(results_path: str, requests_path: str, dynamic_path: str) -> list[EvalResult]: |
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"""From the path of the results folder root, extract all needed info for results""" |
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model_result_filepaths = [] |
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for root, _, files in os.walk(results_path): |
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if len(files) == 0 or any([not f.endswith(".json") for f in files]): |
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continue |
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try: |
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files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7]) |
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except dateutil.parser._parser.ParserError: |
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files = [files[-1]] |
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for file in files: |
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model_result_filepaths.append(os.path.join(root, file)) |
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with open(dynamic_path) as f: |
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dynamic_data = json.load(f) |
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eval_results = {} |
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for model_result_filepath in model_result_filepaths: |
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eval_result = EvalResult.init_from_json_file(model_result_filepath) |
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eval_result.update_with_request_file(requests_path) |
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if eval_result.full_model in dynamic_data: |
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if "meta-llama" in eval_result.full_model: |
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eval_result.still_on_hub = True |
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eval_name = eval_result.eval_name |
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if eval_name in eval_results.keys(): |
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eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None}) |
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else: |
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eval_results[eval_name] = eval_result |
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results = [] |
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for v in eval_results.values(): |
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try: |
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if v.status == "Finished": |
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v.to_dict() |
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results.append(v) |
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except KeyError: |
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continue |
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return results |
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