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Add batch prediction #42

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78 changes: 56 additions & 22 deletions simplet5/simplet5.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks.progress import TQDMProgressBar
from typing import List, Union

torch.cuda.empty_cache()
pl.seed_everything(42)
Expand Down Expand Up @@ -426,7 +427,8 @@ def load_model(

def predict(
self,
source_text: str,
source_text: Union[str, List[str]],
source_max_token_len: int = 512,
max_length: int = 512,
num_return_sequences: int = 1,
num_beams: int = 2,
Expand Down Expand Up @@ -457,27 +459,59 @@ def predict(
Returns:
list[str]: returns predictions
"""
input_ids = self.tokenizer.encode(
source_text, return_tensors="pt", add_special_tokens=True
)
input_ids = input_ids.to(self.device)
generated_ids = self.model.generate(
input_ids=input_ids,
num_beams=num_beams,
max_length=max_length,
repetition_penalty=repetition_penalty,
length_penalty=length_penalty,
early_stopping=early_stopping,
top_p=top_p,
top_k=top_k,
num_return_sequences=num_return_sequences,
)
preds = [
self.tokenizer.decode(
g,
if isinstance(source_text, str):
input_ids = self.tokenizer.encode(
source_text, return_tensors="pt", add_special_tokens=True
)
input_ids = input_ids.to(self.device)
generated_ids = self.model.generate(
input_ids=input_ids,
num_beams=num_beams,
max_length=max_length,
repetition_penalty=repetition_penalty,
length_penalty=length_penalty,
early_stopping=early_stopping,
top_p=top_p,
top_k=top_k,
num_return_sequences=num_return_sequences,
do_sample=do_sample,
)
preds = [
self.tokenizer.decode(
g,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
) for g in generated_ids
]
return preds
elif isinstance(source_text, list):
source_text_encoding = self.tokenizer(
source_text,
max_length=source_max_token_len,
padding="max_length",
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
input_ids = source_text_encoding["input_ids"].to(self.device)
attention_mask = source_text_encoding["attention_mask"].to(self.device)
generated_ids = self.model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
num_beams=num_beams,
max_length=max_length,
repetition_penalty=repetition_penalty,
length_penalty=length_penalty,
early_stopping=early_stopping,
top_p=top_p,
top_k=top_k,
num_return_sequences=num_return_sequences,
do_sample=do_sample,
)
preds = self.tokenizer.batch_decode(
generated_ids,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
)
for g in generated_ids
]
return preds
return preds