172 lines
6.0 KiB
Python
172 lines
6.0 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import torch
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import transformers
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from scepter.modules.model.embedder.base_embedder import BaseEmbedder
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from scepter.modules.model.registry import EMBEDDERS
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from scepter.modules.model.tokenizer.tokenizer_component import (
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basic_clean, canonicalize, whitespace_clean)
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from scepter.modules.utils.config import dict_to_yaml
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from scepter.modules.utils.file_system import FS
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@EMBEDDERS.register_class()
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class HFEmbedder(BaseEmbedder):
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para_dict = {
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'HF_MODEL_CLS': {
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'value': None,
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'description': 'huggingface cls in transfomer'
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},
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'MODEL_PATH': {
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'value': None,
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'description': 'model folder path'
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},
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'HF_TOKENIZER_CLS': {
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'value': None,
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'description': 'huggingface cls in transfomer'
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},
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'TOKENIZER_PATH': {
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'value': None,
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'description': 'tokenizer folder path'
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},
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'MAX_LENGTH': {
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'value': 77,
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'description': 'max length of input'
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},
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'OUTPUT_KEY': {
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'value': 'last_hidden_state',
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'description': 'output key'
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},
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'D_TYPE': {
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'value': 'float',
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'description': 'dtype'
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},
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'BATCH_INFER': {
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'value': False,
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'description': 'batch infer'
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}
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}
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para_dict.update(BaseEmbedder.para_dict)
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def __init__(self, cfg, logger=None):
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super().__init__(cfg, logger=logger)
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hf_model_cls = cfg.get('HF_MODEL_CLS', None)
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model_path = cfg.get('MODEL_PATH', None)
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hf_tokenizer_cls = cfg.get('HF_TOKENIZER_CLS', None)
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tokenizer_path = cfg.get('TOKENIZER_PATH', None)
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self.max_length = cfg.get('MAX_LENGTH', 77)
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self.output_key = cfg.get('OUTPUT_KEY', 'last_hidden_state')
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self.d_type = cfg.get('D_TYPE', 'float')
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self.clean = cfg.get('CLEAN', 'whitespace')
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self.batch_infer = cfg.get('BATCH_INFER', False)
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torch_dtype = getattr(torch, self.d_type)
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assert hf_model_cls is not None and hf_tokenizer_cls is not None
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assert model_path is not None and tokenizer_path is not None
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with FS.get_dir_to_local_dir(tokenizer_path,
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wait_finish=True) as local_path:
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self.tokenizer = getattr(transformers,
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hf_tokenizer_cls).from_pretrained(
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local_path,
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max_length=self.max_length,
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torch_dtype=torch_dtype)
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with FS.get_dir_to_local_dir(model_path,
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wait_finish=True) as local_path:
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self.hf_module = getattr(transformers,
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hf_model_cls).from_pretrained(
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local_path, torch_dtype=torch_dtype)
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self.hf_module = self.hf_module.eval().requires_grad_(False)
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def forward(self, text: list[str], return_mask=False):
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batch_encoding = self.tokenizer(
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text,
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truncation=True,
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max_length=self.max_length,
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return_length=False,
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return_overflowing_tokens=False,
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padding='max_length',
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return_tensors='pt',
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)
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outputs = self.hf_module(
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input_ids=batch_encoding['input_ids'].to(self.hf_module.device),
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attention_mask=None,
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output_hidden_states=False,
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)
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if return_mask:
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return outputs[
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self.output_key], batch_encoding['attention_mask'].to(
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self.hf_module.device)
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else:
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return outputs[self.output_key], None
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def encode(self, text, return_mask=False):
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if isinstance(text, str):
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text = [text]
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if self.clean:
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text = [self._clean(u) for u in text]
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if not self.batch_infer:
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cont, mask = [], []
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for tt in text:
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one_cont, one_mask = self([tt], return_mask=return_mask)
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cont.append(one_cont)
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mask.append(one_mask)
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if return_mask:
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return torch.cat(cont, dim=0), torch.cat(mask, dim=0)
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else:
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return torch.cat(cont, dim=0)
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else:
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ret_data = self(text, return_mask=return_mask)
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if return_mask:
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return ret_data
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else:
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return ret_data[0]
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def _clean(self, text):
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if self.clean == 'whitespace':
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text = whitespace_clean(basic_clean(text))
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elif self.clean == 'lower':
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text = whitespace_clean(basic_clean(text)).lower()
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elif self.clean == 'canonicalize':
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text = canonicalize(basic_clean(text))
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return text
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@staticmethod
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def get_config_template():
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return dict_to_yaml('EMBEDDER',
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__class__.__name__,
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HFEmbedder.para_dict,
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set_name=True)
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@EMBEDDERS.register_class()
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class T5PlusClipFluxEmbedder(BaseEmbedder):
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"""
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Uses the OpenCLIP transformer encoder for text
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"""
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para_dict = {'T5_MODEL': {}, 'CLIP_MODEL': {}}
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def __init__(self, cfg, logger=None):
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super().__init__(cfg, logger=logger)
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self.t5_model = EMBEDDERS.build(cfg.T5_MODEL, logger=logger)
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self.clip_model = EMBEDDERS.build(cfg.CLIP_MODEL, logger=logger)
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def encode(self, text):
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t5_embeds = self.t5_model.encode(text, return_mask=False)
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clip_embeds = self.clip_model.encode(text, return_mask=False)
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# change embedding strategy here
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return {
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'context': t5_embeds,
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'y': clip_embeds,
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}
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@staticmethod
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def get_config_template():
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return dict_to_yaml('EMBEDDER',
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__class__.__name__,
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T5PlusClipFluxEmbedder.para_dict,
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set_name=True)
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