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train_eval_multilabel_model.py
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train_eval_multilabel_model.py
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import argparse
import json
from transformers.modelcard import TrainingSummary
from config.datasets import DataFactory, DATASETS_CONFIG_INFO
from const import *
from metrics.multiclasseval import Multiclasseval
from metrics.utils import compute_multilabel_metrics
from utils import set_seed, dotdict
import logging
import torch
from functools import partial
from modeling.bert_multilabel_classification import BertForMultiLabelSequenceClassification
from modeling.bert_cnn_classification import BertForClassificationCNN
from transformers import (AutoTokenizer,
Trainer,
TrainerCallback,
AdamW,
get_linear_schedule_with_warmup,
TrainingArguments,
PrinterCallback, BertPreTrainedModel)
logging.basicConfig(format='%(asctime)s\t%(levelname)s\t%(name)s\t%(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
AVAILABLE_CLASS_MODELS = {
"BertForClassificationCNN": BertForClassificationCNN,
"BertForMultiLabelSequenceClassification": BertForMultiLabelSequenceClassification
}
def eval_metrics(trainer,
device=device,
dict_args=None):
"""
customized version of trainer.save_metrics
"""
if device == "cpu":
trainer.place_model_on_device = True
trainer.model = trainer.model.to(device)
dict_args["no_cuda"] = True
trainer.args = TrainingArguments(**dict_args)
metrics = trainer.evaluate()
return metrics
def save_metrics(trainer,
filename,
metrics,
combined=True):
if not trainer.is_world_process_zero():
return
with open(filename, "w") as f:
json.dump(metrics, f, indent=4, sort_keys=True)
if combined:
if os.path.exists(filename):
with open(filename, "r") as f:
all_metrics = json.load(f)
else:
all_metrics = {}
all_metrics.update(metrics)
with open(filename, "w") as f:
json.dump(all_metrics, f, indent=4, sort_keys=True)
def main(args):
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, do_lower_case=args.do_lower_case)
ds_with_info = DataFactory.create_from_config(args.dataset_config,
tokenizer=tokenizer,
max_length=args.max_seq_length,
train_size=args.train_size,
val_size=args.val_size)
dataset_info = ds_with_info.config
ds = ds_with_info.dataset
id2label = dataset_info.id2label
label2id = dataset_info.label2id
label_list = dataset_info.labels
if 'n_threads' in args:
torch.set_num_threads(args['n_threads'])
logger.info(f"Setting #threads to {args['n_threads']}")
logger.info(f"device: {device} \t #number of gpu: {args.n_gpu}")
model_class: BertPreTrainedModel = AVAILABLE_CLASS_MODELS.get(args.model_class,
BertForMultiLabelSequenceClassification)
logger.info(f'Using model class: {str(model_class)}')
model = model_class.from_pretrained(args.model_name,
num_labels=len(label_list)).to(device)
# model.resize_token_embeddings(len(tokenizer))
model.config.label2id = label2id
model.config.id2label = id2label
logger.info(f'Using model: {str(args.model_name)}')
train_features = ds["train"]
eval_features = ds["test"]
metric = Multiclasseval()
metric.threshold = args.threshold
metric.num_classes = len(label_list)
metric.labels = label_list
metric.calculate_per_class = args.calculate_per_class
compute_metrics = partial(compute_multilabel_metrics, metric=metric)
optimizer = AdamW(model.parameters(),
lr=args.learning_rate,
eps=1e-8)
total_steps = len(train_features) * args.num_train_epochs
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=0, # Default value in run_glue.py
num_training_steps=total_steps)
report_to = None
if args.use_wandb:
import wandb
wandb.login(key=os.environ.get("WANDB_API_TOKEN", args.wandb_api_token))
wandb_env_vars = ["WANDB_NOTES", "WANDB_NAME", "WANDB_ENTITY", "WANDB_PROJECT", "WANDB_TAGS"]
for v in wandb_env_vars:
if v.lower() in args and args[v.lower()]:
os.environ[v] = args[v.lower()]
report_to = "wandb"
else:
os.environ["WANDB_DISABLED"] = "true"
dict_training_args = dict(
run_name= args.wandb_name if args.wandb_name else f"training",
evaluation_strategy="epoch",
save_strategy="epoch",
learning_rate=args.learning_rate,
per_device_train_batch_size=args.train_batch_size,
per_device_eval_batch_size=args.val_batch_size,
num_train_epochs=args.num_train_epochs,
weight_decay=args.weight_decay,
load_best_model_at_end=True,
save_total_limit=2,
report_to=report_to,
gradient_accumulation_steps=args.gradient_accumulation_steps,
output_dir=args.output_dir
)
if args.do_train:
logger.info('Training')
if args.one_cycle_train:
import copy
dict_training_args_cycle = copy.deepcopy(dict_training_args)
dict_training_args_cycle["num_train_epochs"] = 1
dict_training_args_cycle["run_name"] = args.wandb_name if args.wandb_name else "one_cycle_training"
model.unfreeze_bert_encoder(['pooler'])
training_args = TrainingArguments(**dict_training_args_cycle)
trainer = Trainer(
model,
training_args,
train_dataset=train_features,
eval_dataset=eval_features,
# data_collator=DataCollatorWithPadding(tokenizer),
tokenizer=tokenizer,
compute_metrics=compute_metrics,
callbacks=[PrinterCallback()],
optimizers=(optimizer, scheduler)
)
trainer.train()
dict_training_args_cycle["num_train_epochs"] -= 1
model.unfreeze_bert_encoder(['pooler', '11', '10', '9', '8', '7', '6', '5']) # , '9', '8', '7', '6'])
trainer = Trainer(
model,
TrainingArguments(**dict_training_args),
train_dataset=train_features,
eval_dataset=eval_features,
# data_collator=DataCollatorWithPadding(tokenizer),
tokenizer=tokenizer,
compute_metrics=compute_metrics,
callbacks=[PrinterCallback()],
optimizers=(optimizer, scheduler)
)
if args.do_train:
trainer.train()
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
trainer.save_model(args.output_dir)
if args.do_eval:
logger.info('evaluation')
metric_results = trainer.evaluate()
save_metrics(trainer=trainer,
filename=os.path.join(args.output_dir, "eval_results.json"),
metrics=metric_results)
training_summary = TrainingSummary.from_trainer(
trainer,
language="en",
license=license,
model_name=args.model_name,
finetuned_from="",
tasks="multilabels classification",
dataset=args.dataset_config,
)
model_card = training_summary.to_model_card()
with open(os.path.join(args.output_dir, "README.md"), "w") as f:
f.write(model_card)
metric_results = eval_metrics(trainer=trainer,
device="cpu",
dict_args=dict_training_args)
key_metric = ["eval_runtime", "eval_samples_per_second", "eval_steps_per_second"]
metric_results = {k if k not in key_metric else f"{k}_cpu": v
for k, v in metric_results.items()}
save_metrics(trainer=trainer,
filename=os.path.join(args.output_dir, "eval_results_cpu.json"),
metrics=metric_results)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='training and evaluation bert model')
parser.add_argument("--model_name", default="", type=str,
required=True)
parser.add_argument("--dataset_config",
default="",
type=str,
required=True,
choices=list(DATASETS_CONFIG_INFO.keys()),
help="need to choose a dataset config")
parser.add_argument("--model_class",
default="BertForMultiLabelSequenceClassification",
type=str,
required=False,
choices=list(AVAILABLE_CLASS_MODELS.keys()),
help="need to choose a model class. by default it's BertForMultiLabelSequenceClassification")
parser.add_argument("--max_seq_length",
default=512,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--calculate_per_class",
action='store_true',
help="Calculate metrics per class")
parser.add_argument("--do_lower_case",
action='store_true',
default=True,
help="Set this flag if you are using an uncased model.")
parser.add_argument("--one_cycle_train",
default=True,
action='store_true',
required=False)
parser.add_argument("--train_size", default=-1, type=int, required=False)
parser.add_argument("--val_size", default=-1, type=int, required=False)
parser.add_argument("--tokenizer_name",
default="bert-base-uncased",
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
required=False)
parser.add_argument("--output_dir", default=None, type=str, required=False,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--train_batch_size", default=24, type=int, required=False)
parser.add_argument("--val_batch_size", default=12, type=int, required=False)
parser.add_argument("--n_threads", default=4, type=int, required=False)
parser.add_argument("--warmup_linear", default=0.1, type=float, required=False)
parser.add_argument("--optimize_on_cpu", default=True, type=bool, required=False)
parser.add_argument("--loss_scale", default=128, type=int, required=False)
parser.add_argument("--use_wandb", action='store_true', required=False)
parser.add_argument("--wandb_api_token", default='', type=str, required=False)
parser.add_argument("--wandb_notes", default='', type=str, required=False)
parser.add_argument("--wandb_project", default='', type=str, required=False)
parser.add_argument("--wandb_entity", default='', type=str, required=False)
parser.add_argument("--wandb_group", default='', type=str, required=False)
parser.add_argument("--wandb_name", default='', type=str, required=False)
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument("--learning_rate", default=3e-5, type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay", default=0.01, type=float,
help="Weight deay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs", default=5, type=int,
help="Total number of training epochs to perform.")
parser.add_argument("--max_steps", default=-1, type=int,
help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
parser.add_argument("--warmup_steps", default=0, type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument("--warmup_proportion", default=0.1, type=float,
help="warmup_proportion")
parser.add_argument('--logging_steps', type=int, default=50,
help="Log every X updates steps.")
parser.add_argument('--save_steps', type=int, default=50,
help="Save checkpoint every X updates steps.")
parser.add_argument("--eval_all_checkpoints", action='store_true',
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
parser.add_argument("--no_cuda", action='store_true',
help="Avoid using CUDA when available")
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
parser.add_argument('--fp16', action='store_true',
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
parser.add_argument('--fp16_opt_level', type=str, default='O1',
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument("--local_rank", type=int, default=-1,
help="For distributed training: local_rank")
parser.add_argument("--threshold", default=0.5, type=float, required=False)
args = parser.parse_args()
set_seed(args.seed)
args = vars(args)
n_gpu = torch.cuda.device_count()
args['n_gpu'] = n_gpu
args = dotdict(args)
main(args)