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import logging
import os
import random
import torch
from transformers import (
AutoConfig,
AutoTokenizer,
)
from model.utils import get_model, TaskType
from tasks.superglue.dataset import SuperGlueDataset
from training import BaseTrainer
from training.trainer_exp import ExponentialTrainer
from tasks import utils
from .utils import load_from_cache
logger = logging.getLogger(__name__)
def get_trainer(args):
model_args, data_args, training_args, _ = args
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
model_args.model_name_or_path = load_from_cache(model_args.model_name_or_path)
if "llama" in model_args.model_name_or_path:
from transformers import LlamaTokenizer
model_path = f'openlm-research/{model_args.model_name_or_path}'
tokenizer = LlamaTokenizer.from_pretrained(model_path)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.mask_token = tokenizer.unk_token
tokenizer.mask_token_id = tokenizer.unk_token_id
elif 'gpt' in model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
)
tokenizer.pad_token_id = '<|endoftext|>'
tokenizer.pad_token = '<|endoftext|>'
else:
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
)
tokenizer = utils.add_task_specific_tokens(tokenizer)
dataset = SuperGlueDataset(tokenizer, data_args, training_args)
if training_args.do_train:
for index in random.sample(range(len(dataset.train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {dataset.train_dataset[index]}.")
if not dataset.multiple_choice:
if "llama" in model_args.model_name_or_path:
model_path = f'openlm-research/{model_args.model_name_or_path}'
config = AutoConfig.from_pretrained(
model_path,
num_labels=dataset.num_labels,
label2id=dataset.label2id,
id2label=dataset.id2label,
finetuning_task=data_args.dataset_name,
revision=model_args.model_revision,
trust_remote_code=True
)
else:
config = AutoConfig.from_pretrained(
model_args.model_name_or_path,
num_labels=dataset.num_labels,
label2id=dataset.label2id,
id2label=dataset.id2label,
finetuning_task=data_args.dataset_name,
revision=model_args.model_revision,
trust_remote_code=True
)
else:
config = AutoConfig.from_pretrained(
model_args.model_name_or_path,
num_labels=dataset.num_labels,
finetuning_task=data_args.dataset_name,
revision=model_args.model_revision,
)
config.trigger = training_args.trigger
config.clean_labels = training_args.clean_labels
config.target_labels = training_args.target_labels
if not dataset.multiple_choice:
model = get_model(model_args, TaskType.SEQUENCE_CLASSIFICATION, config)
else:
model = get_model(model_args, TaskType.MULTIPLE_CHOICE, config, fix_bert=True)
# Initialize our Trainer
trainer = BaseTrainer(
model=model,
args=training_args,
train_dataset=dataset.train_dataset if training_args.do_train else None,
eval_dataset=dataset.eval_dataset if training_args.do_eval else None,
compute_metrics=dataset.compute_metrics,
tokenizer=tokenizer,
data_collator=dataset.data_collator,
test_key=dataset.test_key
)
return trainer, None
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