import argparse import json import logging import os import shutil import urllib.request import multiprocessing from os.path import join as pj import torch import numpy as np from huggingface_hub import create_repo from datasets import load_dataset, load_metric from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer from ray import tune logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S') PARALLEL = bool(int(os.getenv("PARALLEL", 1))) RAY_RESULTS = os.getenv("RAY_RESULTS", "ray_results") def internet_connection(host='http://google.com'): try: urllib.request.urlopen(host) return True except: return False def get_metrics(): metric_accuracy = load_metric("accuracy", "multilabel") metric_f1 = load_metric("f1", "multilabel") # metric_f1.compute(predictions=[[0, 1, 1], [1, 1, 0]], references=[[0, 1, 1], [0, 1, 0]], average='micro') # metric_accuracy.compute(predictions=[[0, 1, 1], [1, 1, 0]], references=[[0, 1, 1], [0, 1, 0]]) def compute_metric_search(eval_pred): logits, labels = eval_pred predictions = np.argmax(logits, axis=-1) return metric_f1.compute(predictions=predictions, references=labels, average='micro') def compute_metric_all(eval_pred): logits, labels = eval_pred predictions = np.argmax(logits, axis=-1) return { 'f1': metric_f1.compute(predictions=predictions, references=labels, average='micro')['f1'], 'f1_macro': metric_f1.compute(predictions=predictions, references=labels, average='macro')['f1'], 'accuracy': metric_accuracy.compute(predictions=predictions, references=labels)['accuracy'] } return compute_metric_search, compute_metric_all def main(): parser = argparse.ArgumentParser(description='Fine-tuning language model.') parser.add_argument('-m', '--model', help='transformer LM', default='roberta-base', type=str) parser.add_argument('-d', '--dataset', help='', default='cardiffnlp/tweet_topic_multi', type=str) parser.add_argument('--dataset-name', help='huggingface dataset name', default='citation_intent', type=str) parser.add_argument('-l', '--seq-length', help='', default=128, type=int) parser.add_argument('--random-seed', help='', default=42, type=int) parser.add_argument('--eval-step', help='', default=50, type=int) parser.add_argument('-o', '--output-dir', help='Directory to output', default='ckpt_tmp', type=str) parser.add_argument('-t', '--n-trials', default=10, type=int) parser.add_argument('--push-to-hub', action='store_true') parser.add_argument('--use-auth-token', action='store_true') parser.add_argument('--hf-organization', default=None, type=str) parser.add_argument('-a', '--model-alias', help='', default=None, type=str) parser.add_argument('--summary-file', default='metric_summary.json', type=str) parser.add_argument('--skip-train', action='store_true') parser.add_argument('--skip-eval', action='store_true') opt = parser.parse_args() assert opt.summary_file.endswith('.json'), f'`--summary-file` should be a json file {opt.summary_file}' # setup data dataset = load_dataset(opt.dataset, opt.dataset_name) network = internet_connection() # setup model tokenizer = AutoTokenizer.from_pretrained(opt.model, local_files_only=not network) model = AutoModelForSequenceClassification.from_pretrained( opt.model, num_labels=len(dataset['train'][0]['label']), local_files_only=not network, problem_type="multi_label_classification" ) tokenized_datasets = dataset.map( lambda x: tokenizer(x["text"], padding="max_length", truncation=True, max_length=opt.seq_length), batched=True) # setup metrics compute_metric_search, compute_metric_all = get_metrics() if not opt.skip_train: # setup trainer trainer = Trainer( model=model, args=TrainingArguments( output_dir=opt.output_dir, evaluation_strategy="steps", eval_steps=opt.eval_step, seed=opt.random_seed ), train_dataset=tokenized_datasets["train"], eval_dataset=tokenized_datasets["validation"], compute_metrics=compute_metric_search, model_init=lambda x: AutoModelForSequenceClassification.from_pretrained( opt.model, return_dict=True, num_labels=dataset['train'].features['label'].num_classes) ) # parameter search if PARALLEL: best_run = trainer.hyperparameter_search( hp_space=lambda x: { "learning_rate": tune.loguniform(1e-6, 1e-4), "num_train_epochs": tune.choice(list(range(1, 6))), "per_device_train_batch_size": tune.choice([4, 8, 16, 32, 64]), }, local_dir=RAY_RESULTS, direction="maximize", backend="ray", n_trials=opt.n_trials, resources_per_trial={'cpu': multiprocessing.cpu_count(), "gpu": torch.cuda.device_count()}, ) else: best_run = trainer.hyperparameter_search( hp_space=lambda x: { "learning_rate": tune.loguniform(1e-6, 1e-4), "num_train_epochs": tune.choice(list(range(1, 6))), "per_device_train_batch_size": tune.choice([4, 8, 16, 32, 64]), }, local_dir=RAY_RESULTS, direction="maximize", backend="ray", n_trials=opt.n_trials ) # finetuning for n, v in best_run.hyperparameters.items(): setattr(trainer.args, n, v) trainer.train() trainer.save_model(pj(opt.output_dir, 'best_model')) best_model_path = pj(opt.output_dir, 'best_model') else: best_model_path = opt.output_dir # evaluation model = AutoModelForSequenceClassification.from_pretrained( best_model_path, num_labels=dataset['train'].features['label'].num_classes, local_files_only=not network) trainer = Trainer( model=model, args=TrainingArguments( output_dir=opt.output_dir, evaluation_strategy="no", seed=opt.random_seed ), train_dataset=tokenized_datasets["train"], eval_dataset=tokenized_datasets["test"], compute_metrics=compute_metric_all, model_init=lambda x: AutoModelForSequenceClassification.from_pretrained( opt.model, return_dict=True, num_labels=dataset['train'].features['label'].num_classes) ) summary_file = pj(opt.output_dir, opt.summary_file) if not opt.skip_eval: result = {f'test/{k}': v for k, v in trainer.evaluate().items()} logging.info(json.dumps(result, indent=4)) with open(summary_file, 'w') as f: json.dump(result, f) if opt.push_to_hub: assert opt.hf_organization is not None, f'specify hf organization `--hf-organization`' assert opt.model_alias is not None, f'specify hf organization `--model-alias`' url = create_repo(opt.model_alias, organization=opt.hf_organization, exist_ok=True) # if not opt.skip_train: args = {"use_auth_token": opt.use_auth_token, "repo_url": url, "organization": opt.hf_organization} trainer.model.push_to_hub(opt.model_alias, **args) tokenizer.push_to_hub(opt.model_alias, **args) if os.path.exists(summary_file): shutil.copy2(summary_file, opt.model_alias) os.system( f"cd {opt.model_alias} && git lfs install && git add . && git commit -m 'model update' && git push && cd ../") shutil.rmtree(f"{opt.model_alias}") # clean up the cloned repo if __name__ == '__main__': main()