File size: 1,218 Bytes
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from transformers import AutoModelForMaskedLM, AutoTokenizer
import torch
class EndpointHandler():
def __init__(self, path=""):
tokenizer = AutoTokenizer.from_pretrained(path)
model = AutoModelForMaskedLM.from_pretrained(path)
self.tokenizer = tokenizer
self.model = model
def __call__(self, data: Dict[str, Any]) -> List[Dict[Any, Any]]:
"""
data args:
inputs (:obj: `str`)
date (:obj: `str`)
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# get inputs
text = data.pop("text", data)
tokens = self.tokenizer(text, return_tensors='pt')
output = self.model(**tokens)
vec = torch.max(
torch.log(
1 + torch.relu(output.logits)
) * tokens.attention_mask.unsqueeze(-1),
dim=1)[0].squeeze()
cols = vec.nonzero().squeeze().cpu().tolist()
# extract the non-zero values
weights = vec[cols].cpu().tolist()
# use to create a dictionary of token ID to weight
sparse_dict = dict(zip(map(str, cols), weights))
return sparse_dict
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