112 lines
4.0 KiB
Python
112 lines
4.0 KiB
Python
import torch
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from transformers import AutoTokenizer, T5EncoderModel
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class T5Embedder:
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# available_models = ["google/t5-v1_1-xxl"]
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def __init__(
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self,
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device,
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from_pretrained=None,
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*,
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cache_dir=None,
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hf_token=None,
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use_text_preprocessing=True,
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t5_model_kwargs=None,
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torch_dtype=None,
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use_offload_folder=None,
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model_max_length=120,
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local_files_only=False,
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):
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# from_pretrained="google/t5-v1_1-xxl" # zijian
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self.device = torch.device(device)
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self.torch_dtype = torch_dtype or torch.bfloat16
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self.cache_dir = cache_dir
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if t5_model_kwargs is None:
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t5_model_kwargs = {
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"low_cpu_mem_usage": True,
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"torch_dtype": self.torch_dtype,
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}
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if use_offload_folder is not None:
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t5_model_kwargs["offload_folder"] = use_offload_folder
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t5_model_kwargs["device_map"] = {
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"shared": self.device,
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"encoder.embed_tokens": self.device,
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"encoder.block.0": self.device,
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"encoder.block.1": self.device,
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"encoder.block.2": self.device,
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"encoder.block.3": self.device,
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"encoder.block.4": self.device,
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"encoder.block.5": self.device,
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"encoder.block.6": self.device,
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"encoder.block.7": self.device,
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"encoder.block.8": self.device,
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"encoder.block.9": self.device,
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"encoder.block.10": self.device,
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"encoder.block.11": self.device,
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"encoder.block.12": "disk",
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"encoder.block.13": "disk",
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"encoder.block.14": "disk",
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"encoder.block.15": "disk",
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"encoder.block.16": "disk",
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"encoder.block.17": "disk",
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"encoder.block.18": "disk",
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"encoder.block.19": "disk",
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"encoder.block.20": "disk",
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"encoder.block.21": "disk",
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"encoder.block.22": "disk",
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"encoder.block.23": "disk",
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"encoder.final_layer_norm": "disk",
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"encoder.dropout": "disk",
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}
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else:
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t5_model_kwargs["device_map"] = {
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"shared": self.device,
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"encoder": self.device,
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}
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self.use_text_preprocessing = use_text_preprocessing
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self.hf_token = hf_token
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# assert from_pretrained in self.available_models
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self.tokenizer = AutoTokenizer.from_pretrained(
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from_pretrained,
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model_max_length=model_max_length,
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cache_dir=cache_dir,
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local_files_only=local_files_only,
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)
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self.model = T5EncoderModel.from_pretrained(
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from_pretrained,
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cache_dir=cache_dir,
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local_files_only=local_files_only,
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**t5_model_kwargs,
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).eval()
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self.model_max_length = model_max_length
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def get_text_embeddings(self, texts):
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text_tokens_and_mask = self.tokenizer(
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texts,
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max_length=self.model_max_length,
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padding="longest",
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truncation=True,
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return_attention_mask=True,
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add_special_tokens=True,
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return_tensors="pt",
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)
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input_ids = text_tokens_and_mask["input_ids"].to(self.device)
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attention_mask = text_tokens_and_mask["attention_mask"].to(self.device)
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with torch.no_grad():
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text_encoder_embs = self.model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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)["last_hidden_state"].detach()
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return text_encoder_embs, attention_mask
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if __name__ == "__main__":
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T5Embedder(from_pretrained="google/t5-v1_1-xxl", device='cuda:7')
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