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import glob | |
import json | |
import math | |
import os | |
import re | |
from dataclasses import dataclass | |
import dateutil | |
import numpy as np | |
from src.about import all_tasks, g_tasks, mc_tasks, rag_tasks | |
from src.display.formatting import make_clickable_model | |
from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType, NShotType | |
from src.submission.check_validity import is_model_on_hub | |
NUM_FEWSHOT = 0 | |
class EvalResult: | |
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: Precision = Precision.Unknown | |
model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ... | |
weight_type: WeightType = WeightType.Original # Original or Adapter | |
architecture: str = "Unknown" | |
license: str = "?" | |
lang: str = "?" | |
likes: int = 0 | |
num_params: int = 0 | |
date: str = "" # submission date of request file | |
still_on_hub: bool = False | |
n_shot: NShotType = NShotType.n0 | |
org_and_model: str = "" | |
start_date: float = 0 | |
def init_from_json_file(self, json_filepath, n_shot_num): | |
"""Inits the result from the specific model result file""" | |
with open(json_filepath) as fp: | |
data = json.load(fp) | |
config = data.get("config") | |
n_shot = data.get("n-shot") | |
start_date = data.get("date", 0) | |
chat_template = data.get("chat_template", None) | |
fewshot_as_multiturn = data.get("fewshot_as_multiturn", False) | |
# Precision | |
precision = Precision.from_str(config.get("model_dtype")) | |
# Get model and org | |
org_and_model = config.get("model_name", config.get("model_args", None)) | |
orig_org_and_model = org_and_model | |
SPICHLERZ_ORG = "speakleash/" | |
if re.match(r"^pretrained=(.*/(plgkwrobel|plggspkl)/)(models/)?", org_and_model): | |
org_and_model = re.sub(r"^pretrained=(.*/(plgkwrobel|plggspkl)/)(models/)?", SPICHLERZ_ORG, org_and_model) | |
org_and_model = org_and_model.replace(",dtype=bfloat16", "") | |
org_and_model = org_and_model.replace(",dtype=float16", "") | |
org_and_model = org_and_model.replace("models/hf_v7_e1", "APT3-1B-Instruct-e1") | |
org_and_model = org_and_model.replace("models/hf_v7_e2", "APT3-1B-Instruct-e2") | |
org_and_model = re.sub(r"^pretrained=", "", org_and_model) | |
org_and_model = re.sub(r"^model=", "", org_and_model) | |
org_and_model = org_and_model.replace(",trust_remote_code=True", "") | |
org_and_model = org_and_model.replace(",parallelize=True", "") | |
org_and_model = org_and_model.replace(",tokenizer_backend=huggingface", "") | |
org_and_model = re.sub(",base_url=[^,]+", ",API", org_and_model) | |
org_and_model = re.sub(",prefix_token_id=\d+", "", org_and_model) | |
org_and_model = re.sub("/$", "", org_and_model) | |
model_mapping={ | |
'speakleash/mistral_7B-v2/spkl-only-e1_333887a5':'speakleash/Bielik-7B-v0.1', | |
'speakleash/mistral_7B-v2/spkl-only_sft_v2/e1_base/spkl-only_v10wa_7e6-e2_bbc67e89':'speakleash/Bielik-7B-Instruct-v0.1', | |
'meta-llama/Meta-Llama-3.1-405B-Instruct-FP8,API': 'meta-llama/Meta-Llama-3.1-405B-Instruct-FP8,API' | |
} | |
#map org_and_model using model_mapping | |
if org_and_model in model_mapping: | |
org_and_model=model_mapping[org_and_model] | |
# if org_and_model=='speakleash/mistral_7B-v2/spkl-only-e1_333887a5': | |
# org_and_model='speakleash/Bielik-7B-v0.1' | |
# elif org_and_model=='speakleash/mistral_7B-v2/spkl-only_sft_v2/e1_base/spkl-only_v10wa_7e6-e2_bbc67e89': | |
# org_and_model='speakleash/Bielik-7B-Instruct-v0.1' | |
if chat_template: | |
org_and_model += ",chat" | |
if fewshot_as_multiturn: | |
org_and_model += ",multiturn" | |
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.value.name} | |
else: | |
org = org_and_model[0] | |
model = org_and_model[1] | |
result_key = f"{org}_{model}" # _{precision.value.name} | |
# if chat_template: | |
# result_key = f"{result_key}_chat" | |
# model = f"{model},chat" | |
# org_and_model= f"{org_and_model[1]},chat" | |
full_model = "/".join(org_and_model) | |
still_on_hub, err, model_config = is_model_on_hub( | |
full_model.split(',')[0], config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False | |
) | |
if err: | |
print(full_model, err) | |
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 | |
task_n_shot_num = n_shot_num | |
if 'perplexity' in task.metric or task.benchmark=='polish_eq_bench': # perplexity is the same for 0-shot and 5-shot and is calculated only with 0-shot | |
task_n_shot_num = 0 | |
# 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 and n_shot.get(k, -1) == task_n_shot_num]) | |
if accs.size == 0 or any([acc is None for acc in accs]): | |
continue | |
if 'perplexity' in task.metric or 'eqbench' in task.metric: | |
mean_acc = np.mean(accs) | |
else: | |
mean_acc = np.mean(accs) * 100.0 | |
results[task.benchmark] = (mean_acc, start_date) | |
# 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, | |
n_shot=NShotType.from_str(n_shot_num), | |
org_and_model=orig_org_and_model, | |
start_date=start_date | |
) | |
def update_with_metadata(self, metadata): | |
# print('UPDATE', self.full_model, self.model, self.eval_name) | |
try: | |
k = self.full_model.replace(',chat', '').replace(',multiturn', '') | |
meta = metadata[k] | |
self.model_type = ModelType.from_str(meta.get("type", "?")) | |
self.num_params = meta.get("params", 0) | |
self.license = meta.get("license", "?") | |
self.lang = meta.get("lang", "?") | |
# TODO desc name | |
except KeyError: | |
print(f"Could not find metadata for {self.full_model}") | |
def update_with_request_file(self, requests_path): | |
"""Finds the relevant request file for the current model and updates info with it""" | |
return | |
request_file = get_request_file_for_model(requests_path, self.full_model, 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", "?") | |
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} with precision {self.precision.value.name}") | |
def to_dict(self): | |
"""Converts the Eval Result to a dict compatible with our dataframe display""" | |
# g_tasks = [task.value.benchmark for task in Tasks if task.value.type == "generate_until"] | |
# mc_tasks = [task.value.benchmark for task in Tasks if task.value.type == "multiple_choice"] | |
# rag_tasks = ['polish_polqa_reranking_multiple_choice', 'polish_polqa_open_book', 'polish_poquad_open_book'] | |
# all_tasks = g_tasks + mc_tasks | |
all_tasks_wo_polqa = [task for task in all_tasks if 'polqa' not in task] | |
baselines = {task.value.benchmark: task.value.baseline*100 for task in Tasks} | |
average_old = sum([v for task, v in self.results.items() if v is not None and task in all_tasks_wo_polqa]) / len(all_tasks_wo_polqa) | |
average = sum([(self.results.get(task,0) - baselines.get(task, 0)) / (100 - baselines.get(task, 0)) * 100 for task in all_tasks]) / len(all_tasks) | |
average_g = sum([(self.results.get(task,0) - baselines.get(task, 0)) / (100 - baselines.get(task, 0)) * 100 for task in g_tasks]) / len(g_tasks) | |
average_mc = sum([(self.results.get(task,0) - baselines.get(task, 0)) / (100 - baselines.get(task, 0)) * 100 for task in mc_tasks]) / len(mc_tasks) | |
average_rag = sum([(self.results.get(task,0) - baselines.get(task, 0)) / (100 - baselines.get(task, 0)) * 100 for task in rag_tasks]) / len(rag_tasks) | |
data_dict = {} | |
# 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.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, | |
# } | |
try: | |
data_dict["eval_name"] = self.eval_name | |
except KeyError: | |
print(f"Could not find eval name") | |
try: | |
data_dict[AutoEvalColumn.precision.name] = self.precision.value.name | |
except KeyError: | |
print(f"Could not find precision") | |
except AttributeError: | |
print(f"AttributeError precision") | |
try: | |
data_dict[AutoEvalColumn.model_type.name] = self.model_type.value.name | |
except KeyError: | |
print(f"Could not find model type") | |
try: | |
data_dict[AutoEvalColumn.model_type_symbol.name] = self.model_type.value.symbol | |
except KeyError: | |
print(f"Could not find model type symbol") | |
except AttributeError: | |
print(f"AttributeError model_type") | |
try: | |
data_dict[AutoEvalColumn.weight_type.name] = self.weight_type.value.name | |
except KeyError: | |
print(f"Could not find weight type") | |
try: | |
data_dict[AutoEvalColumn.architecture.name] = self.architecture | |
except KeyError: | |
print(f"Could not find architecture") | |
except AttributeError: | |
print(f"AttributeError architecture") | |
try: | |
data_dict[AutoEvalColumn.model.name] = make_clickable_model( | |
self.full_model, self.model) if self.still_on_hub else self.model #TODO or full_model | |
except KeyError: | |
print(f"Could not find model") | |
try: | |
data_dict[AutoEvalColumn.dummy.name] = self.full_model | |
except KeyError: | |
print(f"Could not find dummy") | |
try: | |
data_dict[AutoEvalColumn.revision.name] = self.revision | |
except KeyError: | |
print(f"Could not find revision") | |
except AttributeError: | |
print(f"AttributeError revision") | |
try: | |
data_dict[AutoEvalColumn.average_old.name] = average_old | |
except KeyError: | |
print(f"Could not find average_old") | |
try: | |
data_dict[AutoEvalColumn.average.name] = average | |
except KeyError: | |
print(f"Could not find average") | |
try: | |
data_dict[AutoEvalColumn.average_g.name] = average_g | |
except KeyError: | |
print(f"Could not find average_g") | |
try: | |
data_dict[AutoEvalColumn.average_mc.name] = average_mc | |
except KeyError: | |
print(f"Could not find average_mc") | |
try: | |
data_dict[AutoEvalColumn.average_rag.name] = average_rag | |
except KeyError: | |
print(f"Could not find average_rag") | |
try: | |
data_dict[AutoEvalColumn.license.name] = self.license | |
except KeyError: | |
print(f"Could not find license") | |
except AttributeError: | |
print(f"AttributeError license") | |
try: | |
data_dict[AutoEvalColumn.lang.name] = self.lang | |
except KeyError: | |
print(f"Could not find lang") | |
except AttributeError: | |
print(f"AttributeError lang") | |
try: | |
data_dict[AutoEvalColumn.likes.name] = self.likes | |
except KeyError: | |
print(f"Could not find likes") | |
except AttributeError: | |
print(f"AttributeError likes") | |
try: | |
data_dict[AutoEvalColumn.params.name] = self.num_params | |
except KeyError: | |
print(f"Could not find params") | |
except AttributeError: | |
print(f"AttributeError params") | |
try: | |
data_dict[AutoEvalColumn.still_on_hub.name] = self.still_on_hub | |
except KeyError: | |
print(f"Could not find still on hub") | |
except AttributeError: | |
print(f"AttributeError stillonhub") | |
try: | |
data_dict[AutoEvalColumn.n_shot.name] = self.n_shot.value.name | |
except KeyError: | |
print(f"Could not find still on hub") | |
for task in Tasks: | |
try: | |
data_dict[task.value.col_name] = self.results[task.value.benchmark] | |
except KeyError: | |
print(f"Could not find {task.value.col_name}") | |
data_dict[task.value.col_name] = None | |
data_dict[AutoEvalColumn.rank.name] = 0 | |
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"] | |
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, metadata) -> 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)) | |
# print('PATHS:', model_result_filepaths) | |
eval_results = {} | |
for n_shot in [0, 5]: | |
for model_result_filepath in model_result_filepaths: | |
# Creation of result | |
eval_result = EvalResult.init_from_json_file(model_result_filepath, n_shot_num=n_shot) | |
eval_result.update_with_request_file(requests_path) | |
# update with metadata | |
eval_result.update_with_metadata(metadata) | |
# Store results of same eval together | |
eval_name = f"{eval_result.eval_name}_{n_shot}-shot" | |
if eval_name in eval_results.keys(): | |
for k, (v, start_date) in eval_result.results.items(): | |
if v is not None: | |
if k in eval_results[eval_name].results: | |
if start_date > eval_results[eval_name].results[k][1]: | |
print( | |
f"Overwriting {eval_name}.results {k} {eval_results[eval_name].results[k]} with {v}: {model_result_filepath} {n_shot} {eval_result.start_date} {eval_results[eval_name].start_date}") | |
eval_results[eval_name].results[k] = (v, start_date) | |
else: | |
print( | |
f"Skipping {eval_name} {eval_result.start_date} {eval_results[eval_name].start_date}: {model_result_filepath} {n_shot}") | |
else: | |
eval_results[eval_name].results[k] = (v, start_date) | |
# eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None}) | |
# TODO: log updated | |
else: | |
eval_results[eval_name] = eval_result | |
for k,v in eval_results.items(): | |
v.results = {k: v for k, (v, start_date) in v.results.items()} | |
all_models = [] | |
missing_results_for_task = {} | |
missing_metadata = [] | |
for_run=[] | |
for v in eval_results.values(): | |
r = v.to_dict() | |
in_progress=False | |
for task in Tasks: | |
if r[task.value.col_name] is None: | |
task_name = f"{r['n_shot']}|{task.value.benchmark}" | |
if task_name in missing_results_for_task: | |
missing_results_for_task[task_name].append(f"{v.full_model}|{v.org_and_model}") | |
if v.still_on_hub and task.value.benchmark in all_tasks: | |
for_run.append([r["n_shot"], task.value.benchmark, v.full_model]) | |
in_progress=True | |
# print(f'sbatch start.sh "bash eval_model_task_bs1.sh {r["n_shot"]} {task.value.benchmark} {v.full_model}"') | |
else: | |
missing_results_for_task[task_name] = [f"{v.full_model}|{v.org_and_model}"] | |
if v.still_on_hub and task.value.benchmark in all_tasks: | |
for_run.append([r["n_shot"], task.value.benchmark, v.full_model]) | |
in_progress=True | |
# print(f'sbatch start.sh "bash eval_model_task_bs1.sh {r["n_shot"]} {task.value.benchmark} {v.full_model}"') | |
if in_progress: | |
v.model = '🚧' + v.model | |
if r[AutoEvalColumn.lang.name] is None or r[AutoEvalColumn.lang.name] == "?": | |
missing_metadata.append(f"{v.full_model}") | |
all_models.append((v.full_model, v.num_params, v.still_on_hub)) | |
results = [] | |
for v in eval_results.values(): | |
try: | |
print(v) | |
v.to_dict() # we test if the dict version is complete | |
# if v.results: | |
results.append(v) | |
except KeyError: # not all eval values present | |
print(f"not all eval values present {v.eval_name} {v.full_model}") | |
continue | |
print(f"Missing sbatch results:") | |
for r in for_run: | |
if r[0]==5 and r[1] in ['polish_eq_bench']: continue | |
fm=r[2] | |
script='eval_model_task_bs1.sh' | |
if ',chat' in fm: | |
script='eval_model_task_bs1_chat.sh' | |
fm=fm.replace(',chat','') | |
if ',multiturn' in fm: | |
script='eval_model_task_bs1_chat_few.sh' | |
fm=fm.replace(',multiturn','') | |
print(f'sbatch start.sh "bash {script} {r[0]} {r[1]} {fm}"') | |
# print('missing_results_for_task', missing_results_for_task) | |
for task, models in missing_results_for_task.items(): | |
print(f"Missing results for {task} for {len(models)} models") | |
# print(" ".join(models)) | |
for model in models: | |
print(f'"{model}"') | |
print() | |
print(f"Missing metadata for {len(missing_metadata)} models") | |
for model in missing_metadata: | |
print(model) | |
print() | |
print(f"All models:") | |
for model in all_models: | |
print(model) | |
print() | |
return results | |