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import logging
from dataclasses import dataclass, field
from typing import Optional
import torch
from tqdm.auto import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, HfArgumentParser
from data_collator import T2TDataCollator
device = 'cuda' if torch.cuda.is_available else 'cpu'
logger = logging.getLogger(__name__)
@dataclass
class EvalArguments:
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
valid_file_path: str = field(
metadata={"help": "Path for cached valid dataset"}
)
model_type: str = field(metadata={"help": "One of 't5', 'bart'"})
tokenizer_name_or_path: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
num_beams: Optional[int] = field(
default=4,
metadata={"help": "num_beams to use for decoding"}
)
max_decoding_length: Optional[int] = field(
default=32,
metadata={"help": "maximum length for decoding"}
)
output_path: Optional[str] = field(
default="hypothesis.txt",
metadata={"help": "path to save the generated questions."}
)
def get_predictions(model, tokenizer, data_loader, num_beams=4, max_length=32, length_penalty=1):
model.to(device)
predictions = []
model.eval()
with torch.no_grad():
for batch in tqdm(data_loader):
outs = model.generate(
input_ids=batch['input_ids'].to(device),
attention_mask=batch['attention_mask'].to(device),
num_beams=num_beams,
max_length=max_length,
length_penalty=length_penalty,
)
prediction = [tokenizer.decode(ids, skip_special_tokens=True) for ids in outs]
predictions.extend(prediction)
return predictions
def main():
parser = HfArgumentParser((EvalArguments,))
args = parser.parse_args_into_dataclasses()[0]
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer_name_or_path if args.tokenizer_name_or_path else args.model_name_or_path,
)
model = AutoModelForSeq2SeqLM.from_pretrained(args.model_name_or_path)
valid_dataset = torch.load(args.valid_file_path)
collator = T2TDataCollator(
tokenizer=tokenizer,
model_type=args.model_type,
mode="inference"
)
loader = torch.utils.data.DataLoader(valid_dataset, batch_size=32, collate_fn=collator)
predictions = get_predictions(
model=model,
tokenizer=tokenizer,
data_loader=loader,
num_beams=args.num_beams,
max_length=args.max_decoding_length
)
with open(args.output_path, 'w') as f:
f.write("\n".join(predictions))
logging.info(f"Output saved at {args.output_path}")
if __name__ == "__main__":
main()
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