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""" Fine-tune T5 on topic classification (multi-label multi-class classification)
```
python finetune_t5.py --dataset-name ja --model-alias mt5-small-tweet-topic-ja --model-organization cardiffnlp --low-cpu-mem-usage
```
"""
import json
import logging
import os
import argparse
import gc
from glob import glob
from typing import List, Set
from shutil import copyfile
from statistics import mean
from distutils.dir_util import copy_tree
import torch
import transformers
from datasets import load_dataset
from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments, pipeline
from huggingface_hub import Repository
os.environ['TOKENIZERS_PARALLELISM'] = 'false' # turn-off the warning message
os.environ['WANDB_DISABLED'] = 'true' # disable wandb
_LR = [1e-6, 1e-5, 1e-4]
_BATCH = 32
_EPOCH = 5
def load_model(
model_name: str,
use_auth_token: bool = False,
low_cpu_mem_usage: bool = False) -> transformers.PreTrainedModel:
"""Load language model from huggingface model hub."""
# config & tokenizer
config = transformers.AutoConfig.from_pretrained(model_name, use_auth_token=use_auth_token)
if config.model_type == 't5': # T5 model requires T5ForConditionalGeneration class
model_class = transformers.T5ForConditionalGeneration.from_pretrained
elif config.model_type == 'mt5':
model_class = transformers.MT5ForConditionalGeneration.from_pretrained
elif config.model_type == 'bart':
model_class = transformers.BartForConditionalGeneration.from_pretrained
elif config.model_type == 'mbart':
model_class = transformers.MBartForConditionalGeneration.from_pretrained
else:
raise ValueError(f'unsupported model type: {config.model_type}')
param = {'config': config, 'use_auth_token': use_auth_token, 'low_cpu_mem_usage': low_cpu_mem_usage}
return model_class(model_name, **param)
def train(
model_name: str,
model_low_cpu_mem_usage: bool,
dataset: str,
dataset_name: str,
dataset_column_label: str,
dataset_column_text: str,
random_seed: int,
use_auth_token: bool):
"""Fine-tune seq2seq model."""
logging.info(f'[TRAIN]\n\t *LM: {model_name}, \n\t *Data: {dataset} ({dataset_name})')
output_dir = f'ckpt/{os.path.basename(model_name)}.{os.path.basename(dataset)}.{dataset_name}'
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name, use_auth_token=use_auth_token)
dataset_instance = load_dataset(dataset, dataset_name, split="train", use_auth_token=use_auth_token)
tokenized_dataset = []
for d in dataset_instance:
model_inputs = tokenizer(d[dataset_column_text], truncation=True)
model_inputs['labels'] = tokenizer(text_target=d[dataset_column_label], truncation=True)['input_ids']
tokenized_dataset.append(model_inputs)
for n, lr_tmp in enumerate(_LR):
logging.info(f"[TRAIN {n}/{len(_LR)}] lr: {lr_tmp}")
output_dir_tmp = f"{output_dir}/model_lr_{lr_tmp}"
if os.path.exists(f"{output_dir_tmp}/pytorch_model.bin"):
continue
model = load_model(
model_name=model_name, use_auth_token=use_auth_token, low_cpu_mem_usage=model_low_cpu_mem_usage
)
trainer = Seq2SeqTrainer(
model=model,
args=Seq2SeqTrainingArguments(
num_train_epochs=_EPOCH,
learning_rate=lr_tmp,
output_dir=output_dir_tmp,
save_strategy="epoch",
evaluation_strategy="no",
seed=random_seed,
per_device_train_batch_size=_BATCH,
),
data_collator=transformers.DataCollatorForSeq2Seq(tokenizer, model=model),
train_dataset=tokenized_dataset.copy(),
)
# train
trainer.train()
# trainer.log_metrics("train", result.metrics)
# trainer.save_metrics("train", result.metrics)
# clean up memory
# trainer.save_model()
# trainer.save_state()
del trainer
del model
gc.collect()
torch.cuda.empty_cache()
for model_path in glob(f"{output_dir}/*/*"):
tokenizer.save_pretrained(model_path)
def get_f1_score(references: List[Set[str]], predictions: List[Set[str]]) -> float:
scores = []
for g, r in zip(references, predictions):
tp = len(set(g).intersection(set(r)))
fp = len([_g for _g in g if _g not in r])
fn = len([_r for _r in r if _r not in g])
f1 = 0 if tp == 0 else 2 * tp / (2 * tp + fp + fn)
scores.append(f1)
return mean(scores)
def get_metric(
prediction_file: str,
metric_file: str,
model_path: str,
data: List[str],
label: List[str]) -> float:
if os.path.exists(metric_file):
with open(metric_file) as f:
eval_metric = json.load(f)
return eval_metric['f1']
if not os.path.exists(prediction_file):
pipe = pipeline(
'text2text-generation',
model=model_path,
device='cuda:0' if torch.cuda.is_available() else 'cpu',
)
output = pipe(data, batch_size=_BATCH)
output = [i['generated_text'] for i in output]
with open(prediction_file, 'w') as f:
f.write('\n'.join(output))
with open(prediction_file) as f:
output = [set(i.split(',')) for i in f.read().split('\n')]
label = [set(i.split(',')) for i in label]
eval_metric = {'f1': get_f1_score(label, output)}
logging.info(json.dumps(eval_metric, indent=4))
with open(metric_file, 'w') as f:
json.dump(eval_metric, f)
return eval_metric['f1']
def validate(
model_name: str,
dataset: str,
dataset_name: str,
dataset_column_text: str,
use_auth_token: bool,
dataset_column_label: str):
logging.info(f'[VALIDATE]\n\t *LM: {model_name}, \n\t *Data: {dataset} ({dataset_name})')
output_dir = f'ckpt/{os.path.basename(model_name)}.{os.path.basename(dataset)}.{dataset_name}'
dataset_instance = load_dataset(dataset, dataset_name, split='validation', use_auth_token=use_auth_token)
label = [i[dataset_column_label] for i in dataset_instance]
data = [i[dataset_column_text] for i in dataset_instance]
model_score = []
for model_path in glob(f"{output_dir}/*/*/pytorch_model.bin"):
model_path = os.path.dirname(model_path)
prediction_file = f"{model_path}/prediction.validate.{os.path.basename(dataset)}.{dataset_name}.txt"
metric_file = f"{model_path}/metric.validate.{os.path.basename(dataset)}.{dataset_name}.json"
metric = get_metric(
prediction_file=prediction_file,
metric_file=metric_file,
model_path=model_path,
label=label,
data=data
)
model_score.append([model_path, metric])
model_score = sorted(model_score, key=lambda x: x[1])
logging.info('Validation Result')
for k, v in model_score:
logging.info(f'{k}: {v}')
best_model = model_score[-1][0]
best_model_path = f'{output_dir}/best_model'
copy_tree(best_model, best_model_path)
def test(
model_name: str,
dataset: str,
dataset_name: str,
dataset_column_text: str,
use_auth_token: bool,
dataset_column_label: str):
logging.info(f'[TEST]\n\t *LM: {model_name}, \n\t *Data: {dataset} ({dataset_name})')
output_dir = f'ckpt/{os.path.basename(model_name)}.{os.path.basename(dataset)}.{dataset_name}'
dataset_instance = load_dataset(dataset, dataset_name, split='test', use_auth_token=use_auth_token)
label = [i[dataset_column_label] for i in dataset_instance]
data = [i[dataset_column_text] for i in dataset_instance]
model_path = f'{output_dir}/best_model'
prediction_file = f"{model_path}/prediction.{os.path.basename(dataset)}.{dataset_name}.txt"
metric_file = f"{model_path}/metric.{os.path.basename(dataset)}.{dataset_name}.json"
metric = get_metric(
prediction_file=prediction_file,
metric_file=metric_file,
model_path=model_path,
label=label,
data=data
)
logging.info(f'Test Result: {metric}')
def upload(
model_name: str,
dataset: str,
dataset_name: str,
dataset_column_text: str,
use_auth_token: bool,
model_alias: str,
model_organization: str):
assert model_alias is not None and model_organization is not None,\
'model_organization must be specified when model_alias is specified'
logging.info('uploading to huggingface')
output_dir = f'ckpt/{os.path.basename(model_name)}.{os.path.basename(dataset)}.{dataset_name}'
args = {'use_auth_token': use_auth_token, 'organization': model_organization}
model = load_model(model_name=f'{output_dir}/best_model')
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name, use_auth_token=use_auth_token)
model.push_to_hub(model_alias, **args)
tokenizer.push_to_hub(model_alias, **args)
repo = Repository(model_alias, f'{model_organization}/{model_alias}')
if os.path.exists(f'{output_dir}/best_model/prediction_test.txt'):
copyfile(f'{output_dir}/best_model/prediction_test.txt', f'{model_alias}/prediction_test.txt')
if os.path.exists(f'{output_dir}/best_model/evaluation_metrics.json'):
copyfile(f'{output_dir}/best_model/evaluation_metrics.json', f'{model_alias}/evaluation_metrics.json')
dataset_instance = load_dataset(dataset, dataset_name, split='test', use_auth_token=use_auth_token)
sample = [i[dataset_column_text] for i in dataset_instance]
sample = [i for i in sample if ''' not in i and ''' not in i][:3]
widget = '\n'.join([f"- text: '{t}'\n example_title: example {_n + 1}" for _n, t in enumerate(sample)])
with open(f'{model_alias}/README.md', 'w') as f:
f.write(f"""
---
widget:
{widget}
---
# {model_organization}/{model_alias}
This is [{model_name}](https://huggingface.co/{model_name}) fine-tuned on [{dataset} ({dataset_name})](https://huggingface.co/datasets/{dataset}).
### Usage
```python
from transformers import pipeline
pipe = pipeline('text2text-generation', model='{model_organization}/{model_alias}')
output = pipe('{sample[0]}')
```
""")
repo.push_to_hub()
if __name__ == '__main__':
# arguments
logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S')
parser = argparse.ArgumentParser(description='Seq2Seq LM Fine-tuning on topic classification.')
parser.add_argument('-m', '--model-name', default='google/mt5-small', type=str)
parser.add_argument('--low-cpu-mem-usage', action='store_true')
parser.add_argument('-d', '--dataset', default='cardiffnlp/tweet_topic_multilingual', type=str)
parser.add_argument('--dataset-name', default='ja', type=str)
parser.add_argument('--dataset-column-label', default='label_name_flatten', type=str)
parser.add_argument('--dataset-column-text', default='text', type=str)
parser.add_argument('--random-seed', default=42, type=int)
parser.add_argument('--use-auth-token', action='store_true')
parser.add_argument('--model-alias', default=None, type=str)
parser.add_argument('--model-organization', default=None, type=str)
parser.add_argument('--skip-train', action='store_true')
parser.add_argument('--skip-validate', action='store_true')
parser.add_argument('--skip-test', action='store_true')
parser.add_argument('--skip-upload', action='store_true')
opt = parser.parse_args()
if not opt.skip_train:
train(
model_name=opt.model_name,
model_low_cpu_mem_usage=opt.low_cpu_mem_usage,
dataset=opt.dataset,
dataset_name=opt.dataset_name,
dataset_column_label=opt.dataset_column_label,
dataset_column_text=opt.dataset_column_text,
random_seed=opt.random_seed,
use_auth_token=opt.use_auth_token,
)
if not opt.skip_validate:
validate(
model_name=opt.model_name,
dataset=opt.dataset,
dataset_name=opt.dataset_name,
dataset_column_label=opt.dataset_column_label,
dataset_column_text=opt.dataset_column_text,
use_auth_token=opt.use_auth_token
)
if not opt.skip_test:
test(
model_name=opt.model_name,
dataset=opt.dataset,
dataset_name=opt.dataset_name,
dataset_column_label=opt.dataset_column_label,
dataset_column_text=opt.dataset_column_text,
use_auth_token=opt.use_auth_token
)
if not opt.skip_upload:
upload(
model_name=opt.model_name,
dataset=opt.dataset,
dataset_name=opt.dataset_name,
dataset_column_text=opt.dataset_column_text,
use_auth_token=opt.use_auth_token,
model_alias=opt.model_alias,
model_organization=opt.model_organization
)
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