File size: 2,425 Bytes
4853fdc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModel, T5Tokenizer, T5EncoderModel
from transformers.modeling_outputs import BaseModelOutput
try:
import torch_npu
from torch_npu.contrib import transfer_to_npu
DEVICE_TYPE = "npu"
except ModuleNotFoundError:
DEVICE_TYPE = "cuda"
class TransformersTextEncoderBase(nn.Module):
def __init__(self, model_name: str, embed_dim: int):
super().__init__()
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModel.from_pretrained(model_name)
self.proj = nn.Linear(self.model.config.hidden_size, embed_dim)
def forward(
self,
text: list[str],
):
output, mask = self.encode(text)
output = self.projection(output)
return {"output": output, "mask": mask}
def encode(self, text: list[str]):
device = self.model.device
batch = self.tokenizer(
text,
max_length=self.tokenizer.model_max_length,
padding=True,
truncation=True,
return_tensors="pt",
)
input_ids = batch.input_ids.to(device)
attention_mask = batch.attention_mask.to(device)
output: BaseModelOutput = self.model(
input_ids=input_ids, attention_mask=attention_mask
)
output = output.last_hidden_state
mask = (attention_mask == 1).to(device)
return output, mask
def projection(self, x):
return self.proj(x)
class T5TextEncoder(TransformersTextEncoderBase):
def __init__(
self, embed_dim: int, model_name: str = "google/flan-t5-large"
):
nn.Module.__init__(self)
self.tokenizer = T5Tokenizer.from_pretrained(model_name)
self.model = T5EncoderModel.from_pretrained(model_name)
for param in self.model.parameters():
param.requires_grad = False
self.model.eval()
self.proj = nn.Linear(self.model.config.hidden_size, embed_dim)
def encode(
self,
text: list[str],
):
with torch.no_grad(), torch.amp.autocast(
device_type=DEVICE_TYPE, enabled=False
):
return super().encode(text)
if __name__ == "__main__":
text_encoder = T5TextEncoder(embed_dim=512)
text = ["a man is speaking", "a woman is singing while a dog is barking"]
output = text_encoder(text)
|