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eval.py
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eval.py
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import json
import shutil
import torch
import time
import os
from hydra.utils import instantiate
import omegaconf
import yaml
import gc
import pandas as pd
pd.set_option("display.precision", 4)
class Evaluate:
@staticmethod
def eval(experiment_folder="experiments/", split="dev", bem: bool=False, llm: list[str]=None, llm_ollama: list[str]=None, vllm: list[str]=None, gpt: bool=None, bem_batch_size: int=1, lid: bool=None, lid_advanced: bool=None, llm_batch_size: int=None, llm_prompt: str = "default_qa", ollama_url: str=None, folder: str=None, force: bool=False, samples: int=-1):
def eval_single(experiment_folder, folder, split: str, model, metric_name: str, nb_samples: int =-1):
if folder != None:
folders = [folder]
else:
folders = [ f.path for f in os.scandir(experiment_folder) if f.is_dir() and 'tmp_' not in f.path]
for experiment_folder in folders:
print('evaluating', experiment_folder)
def load_data(input_file):
result_dict = json.load(open(input_file))
return pd.DataFrame(result_dict)
input_file = f'{experiment_folder}/eval_{split}_out.json'
if os.path.exists(input_file):
data = load_data(input_file)
if nb_samples >0 and nb_samples < len(data):
data = data[:nb_samples]
metrics_file = f'{experiment_folder}/eval_{split}_metrics.json'
try:
metrics_dict = json.load(open(metrics_file))
except: continue
if metric_name in metrics_dict and not force:
print (f"{experiment_folder}\t{metric_name}\talready done")
continue
predictions = data['response'].values
references = data['label'].values
questions = data['question'].values
if gpt is not None:
# openai costs
model_score, scores, cost = model(predictions, references, questions)
costs_out_file = f'{experiment_folder}/eval_{split}_cost_{metric_name}_out.json'
with open(costs_out_file, 'w') as fout: fout.write(json.dumps(cost))
else:
model_score, scores = model(predictions, references, questions)
data[metric_name] = scores
metrics_out_file = f'{experiment_folder}/eval_{split}_out.json'
if nb_samples >0:
metrics_out_file = f'{experiment_folder}/eval_{split}_out_{nb_samples}.json'
# temporary print eval_out results with updated metric (to avoid loosing eval_dev_out.json if smth goes wrong)
data.to_json(metrics_out_file+"_", orient='records')
#move temprorary result into final name
shutil.move(metrics_out_file + '_', metrics_out_file)
if nb_samples >0:
metric_name = f"{metric_name}_{nb_samples}"
metrics_dict.update({metric_name: model_score})
print(metric_name,model_score)
# save to _ tmp file
with open(metrics_file + '_', 'w') as fp:
json.dump(metrics_dict, fp, indent=2)
# when writing successful remove tmp file
shutil.move(metrics_file + '_', metrics_file)
if bem:
from models.evaluators.bem import BEM
model = BEM(batch_size=bem_batch_size)
eval_single(experiment_folder, folder, split, model, 'BEM', nb_samples = samples)
if gpt is not None:
from models.evaluators.openai import OpenAI
model = OpenAI(gpt)
eval_single(experiment_folder, folder, split, model, gpt, nb_samples = samples)
if llm is not None:
if len(llm) == 0:
model_config, short_name = "SOLAR-107B", "LLMeval"
elif len(llm)==1:
model_config = llm[0]
short_name = model_config
short_name = f"LLMeval_{short_name}"
elif len(llm)==2:
model_config = llm[0]
short_name = llm[1]
short_name = f"LLMeval_{short_name}"
model_config = omegaconf.OmegaConf.load(f"config/generator/{model_config}.yaml")
if model_config['init_args']['_target_']=='models.generators.vllm.VLLM':
from models.evaluators.vllm import VLLMeval
model = VLLMeval(model_config, batch_size=llm_batch_size, config=llm_prompt)
else:
from models.evaluators.llm import LLMeval
model = LLMeval(model_config, batch_size=llm_batch_size, config=llm_prompt)
if model.use_logits :
short_name = f"{short_name}_logits"
eval_single(experiment_folder, folder, split, model, short_name, nb_samples = samples)
del model
torch.cuda.empty_cache()
gc.collect()
if llm_ollama is not None:
from models.evaluators.llm_ollama import OllamaEval
if len(llm_ollama)==1:
model_config = llm_ollama[0]
short_name = model_config
short_name = f"LLMeval_{short_name}"
elif len(llm_ollama)==2:
model_config = llm_ollama[0]
short_name = llm_ollama[1]
short_name = f"LLMeval_{short_name}"
if llm_batch_size == None:
llm_batch_size = 1
model = OllamaEval(model_config, batch_size=llm_batch_size, config=llm_prompt, basic_url=ollama_url)
eval_single(experiment_folder, folder, split, model, short_name, nb_samples = samples)
if lid is not None or lid_advanced is not None:
from models.evaluators.lid import LID
from models.evaluators.lid_advanced import LID_advanced
if folder == None:
folders = [ f.path for f in os.scandir(experiment_folder) if f.is_dir() and 'tmp_' not in f.path]
else:
folders = [folder]
for folder in folders:
# we need to get language from each folder config separately
config = yaml.safe_load(open(f"{folder}/config.yaml"))
if 'lng' in config['dataset'][split]['query']['init_args']:
tgt_lng = config['dataset'][split]['query']['init_args']['lng']
elif 'lang' in config['dataset'][split]['query']['init_args']:
tgt_lng = config['dataset'][split]['query']['init_args']['lang']
else:
#if language is not specified we set it to English by default
tgt_lng = 'en'
print(f"{folder}: didn't find lng in the config.yaml, set it to English by default")
if lid is not None:
model=LID(tgt_lng)
eval_single(experiment_folder, folder, split, model, "lid", nb_samples = samples)
if lid_advanced is not None:
model = LID_advanced(tgt_lng)
eval_single(experiment_folder, folder, split, model, "lid_advanced", nb_samples = samples)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--experiments_folder', type=str, default="experiments/")
parser.add_argument('--folder', type=str, default=None)
parser.add_argument('--split', type=str, default='dev')
parser.add_argument('--sample', type=int, default=-1, help="Use only subsample of the experiment folder for evaluation, useful for debug purposes (default -1: use full dataset)")
parser.add_argument('--bem', action='store_true')
parser.add_argument('--lid', action='store_true', default=None)
parser.add_argument('--lid_advanced', action='store_true', default=None)
parser.add_argument('--llm', type=str, nargs='*', default=None,
help="""
- full model name (corresponding to generator config name) and short name (used for naming output files and metrics):
eg. -llm SOLAR-107B solar
- if short name is missing: use full name in naming,
- if no arguments specified: falls back to default arguments: uses default values (SOLAR-107B LLMeval).
""")
parser.add_argument('--llm_ollama', type=str, nargs='*', default=None,
help="""
Calls ollama server to run evaluation. Requires 1 or 2 arguments:
- full model name and short name (used for naming output files and metrics): eg. -llm_ollama llama3:default llama3
- if short name is missing: use full name in naming
""" )
parser.add_argument('--gpt', type=str,default=None)
parser.add_argument('--bem_batch_size', type=int, default=1024)
parser.add_argument('--llm_batch_size', type=int, default=None)
parser.add_argument('--force', action='store_true')
parser.add_argument('--llm_prompt', type=str, default="default_qa", help="Provide yaml config file with updated prompt. Default prompt: config/evaluator/default_prompt.yaml")
parser.add_argument('--ollama_url', type=str, default="http://localhost:11434", help="")
args = parser.parse_args()
e = Evaluate.eval(
folder=args.folder,
experiment_folder=args.experiments_folder,
split=args.split,
bem=args.bem,
llm=args.llm,
llm_ollama=args.llm_ollama,
gpt=args.gpt,
lid=args.lid,
lid_advanced=args.lid_advanced,
bem_batch_size=args.bem_batch_size,
llm_batch_size=args.llm_batch_size,
llm_prompt=args.llm_prompt,
ollama_url=args.ollama_url,
force=args.force,
samples=args.sample
)