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Running on Zero
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a8a9bce | 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 | from torch import Tensor, nn
from transformers import (
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
T5Config,
T5EncoderModel,
T5Tokenizer,
)
class HFEmbedder(nn.Module):
def __init__(self, version: str, max_length: int, is_clip: bool, **hf_kwargs):
super().__init__()
self.is_clip = is_clip
self.max_length = max_length
self.output_key = "pooler_output" if self.is_clip else "last_hidden_state"
if self.is_clip:
self.tokenizer: CLIPTokenizer = CLIPTokenizer.from_pretrained(version, max_length=max_length)
config = CLIPTextConfig.from_pretrained(version, **hf_kwargs)
self.hf_module: CLIPTextModel = CLIPTextModel._from_config(config)
else:
self.tokenizer: T5Tokenizer = T5Tokenizer.from_pretrained(version, max_length=max_length, legacy=True)
config = T5Config.from_pretrained(version, **hf_kwargs)
self.hf_module: T5EncoderModel = T5EncoderModel._from_config(config)
def forward(self, text: list[str]) -> Tensor:
batch_encoding = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
return_length=False,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="pt",
)
outputs = self.hf_module(
input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
attention_mask=None,
output_hidden_states=False,
)
return outputs[self.output_key]
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