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import logging
import torch
from tqdm.auto import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
def load_model_and_tokenizer(model_name):
"""
load_model_and_tokenizer - a function that loads a model and tokenizer from huggingface
Args:
model_name (str): the name of the model to load
Returns:
AutoModelForSeq2SeqLM: the model
AutoTokenizer: the tokenizer
"""
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForSeq2SeqLM.from_pretrained(
model_name,
# low_cpu_mem_usage=True,
# use_cache=False,
).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name)
logging.info(f"Loaded model {model_name} to {device}")
return model, tokenizer
def summarize_and_score(
ids, mask, model, tokenizer, is_general_attention_model=True, **kwargs
):
"""
summarize_and_score - given a batch of ids and a mask, return a summary and a score for the summary
Args:
ids (): the batch of ids
mask (): the attention mask for the batch
model (): the model to use for summarization
tokenizer (): the tokenizer to use for summarization
is_general_attention_model (bool, optional): whether the model is a general attention model. Defaults to True.
Returns:
str: the summary of the batch
"""
ids = ids[None, :]
mask = mask[None, :]
input_ids = ids.to("cuda") if torch.cuda.is_available() else ids
attention_mask = mask.to("cuda") if torch.cuda.is_available() else mask
global_attention_mask = torch.zeros_like(attention_mask)
# put global attention on <s> token
global_attention_mask[:, 0] = 1
if is_general_attention_model:
summary_pred_ids = model.generate(
input_ids,
attention_mask=attention_mask,
output_scores=True,
return_dict_in_generate=True,
**kwargs,
)
else:
summary_pred_ids = model.generate(
input_ids,
attention_mask=attention_mask,
global_attention_mask=global_attention_mask,
output_scores=True,
return_dict_in_generate=True,
**kwargs,
)
summary = tokenizer.batch_decode(
summary_pred_ids.sequences,
skip_special_tokens=True,
remove_invalid_values=True,
)
score = round(summary_pred_ids.sequences_scores.cpu().numpy()[0], 4)
return summary, score
def summarize_via_tokenbatches(
input_text: str,
model,
tokenizer,
batch_length=2048,
batch_stride=16,
**kwargs,
):
"""
summarize_via_tokenbatches - a function that takes a string and returns a summary
Args:
input_text (str): the text to summarize
model (): the model to use for summarizationz
tokenizer (): the tokenizer to use for summarization
batch_length (int, optional): the length of each batch. Defaults to 2048.
batch_stride (int, optional): the stride of each batch. Defaults to 16. The stride is the number of tokens that overlap between batches.
Returns:
str: the summary
"""
# log all input parameters
if batch_length < 512:
batch_length = 512
print("WARNING: batch_length was set to 512")
print(
f"input parameters: {kwargs}, batch_length={batch_length}, batch_stride={batch_stride}"
)
encoded_input = tokenizer(
input_text,
padding="max_length",
truncation=True,
max_length=batch_length,
stride=batch_stride,
return_overflowing_tokens=True,
add_special_tokens=False,
return_tensors="pt",
)
in_id_arr, att_arr = encoded_input.input_ids, encoded_input.attention_mask
gen_summaries = []
pbar = tqdm(total=len(in_id_arr))
for _id, _mask in zip(in_id_arr, att_arr):
result, score = summarize_and_score(
ids=_id,
mask=_mask,
model=model,
tokenizer=tokenizer,
**kwargs,
)
score = round(float(score), 4)
_sum = {
"input_tokens": _id,
"summary": result,
"summary_score": score,
}
gen_summaries.append(_sum)
print(f"\t{result[0]}\nScore:\t{score}")
pbar.update()
pbar.close()
return gen_summaries
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