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