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from typing import Dict, List, Any, Tuple
from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch
from subprocess import run
# set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# set path
query_emb_model_path = "/splade_query"
doc_emb_model_path = "/splade_doc"
class EndpointHandler():
def __init__(self, path=""):
self.query_model = AutoModelForMaskedLM.from_pretrained(path+query_emb_model_path).to(device)
self.query_tokenizer = AutoTokenizer.from_pretrained(path+query_emb_model_path)
self.doc_model = AutoModelForMaskedLM.from_pretrained(path+doc_emb_model_path).to(device)
self.doc_tokenizer = AutoTokenizer.from_pretrained(path+doc_emb_model_path)
def __call__(self, data: Dict[str, Any]) -> Tuple[List[List[int]], List[List[float]]]:
"""
data args:
inputs (:obj: `List[str]`)
task (:obj: `str`)
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# get inputs
texts = data.pop("inputs", data)
task = data.pop("task", data)
emb_model = None
tokenizer = None
if task == "query_emb":
emb_model = self.query_model
tokenizer = self.query_tokenizer
elif task == "doc_emb":
emb_model = self.doc_model
tokenizer = self.doc_tokenizer
else:
raise ValueError("task must be either 'query_emb' or 'doc_emb'")
tokens = tokenizer(
texts, truncation=True, padding=True, return_tensors="pt"
)
if torch.cuda.is_available():
tokens = tokens.to("cuda")
output = emb_model(**tokens)
logits, attention_mask = output.logits, tokens.attention_mask
relu_log = torch.log(1 + torch.relu(logits))
weighted_log = relu_log * attention_mask.unsqueeze(-1)
tvecs, _ = torch.max(weighted_log, dim=1)
# extract the vectors that are non-zero and their indices
indices = []
vecs = []
for batch in tvecs:
indices.append(batch.nonzero(as_tuple=True)[0].tolist())
vecs.append(batch[indices[-1]].tolist())
# release all the GPU memory cache that can be freed
if torch.cuda.is_available():
torch.cuda.empty_cache()
return [indices, vecs]
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