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Update t5.py #93

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70 changes: 32 additions & 38 deletions language/t5.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,49 +12,43 @@
from huggingface_hub import hf_hub_download


import os
import re
import torch
from transformers import AutoTokenizer, T5EncoderModel

import os
import re
import torch
from transformers import AutoTokenizer, T5EncoderModel

class T5Embedder:
available_models = ['t5-v1_1-xxl', 't5-v1_1-xl', 'flan-t5-xl']
bad_punct_regex = re.compile(r'['+'#®•©™&@·º½¾¿¡§~'+'\)'+'\('+'\]'+'\['+'\}'+'\{'+'\|'+'\\'+'\/'+'\*' + r']{1,}') # noqa

def __init__(self, device, dir_or_name='t5-v1_1-xxl', *, local_cache=False, 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):
bad_punct_regex = re.compile(r'['+'#®•©™&@·º½¾¿¡§~'+'\)'+'\('+'\]'+'\['+'\}'+'\{'+'\|'+'\\'+'\/'+'\*' + r']{1,}')

def __init__(
self,
device,
model_path,
*,
torch_dtype=None,
model_max_length=120
):
self.device = torch.device(device)
self.torch_dtype = torch_dtype or torch.bfloat16
if t5_model_kwargs is None:
t5_model_kwargs = {'low_cpu_mem_usage': True, 'torch_dtype': self.torch_dtype}
t5_model_kwargs['device_map'] = {'shared': self.device, 'encoder': self.device}

self.use_text_preprocessing = use_text_preprocessing
self.hf_token = hf_token
self.cache_dir = cache_dir or os.path.expanduser('~/.cache/IF_')
self.dir_or_name = dir_or_name
tokenizer_path, path = dir_or_name, dir_or_name
if local_cache:
cache_dir = os.path.join(self.cache_dir, dir_or_name)
tokenizer_path, path = cache_dir, cache_dir
elif dir_or_name in self.available_models:
cache_dir = os.path.join(self.cache_dir, dir_or_name)
for filename in [
'config.json', 'special_tokens_map.json', 'spiece.model', 'tokenizer_config.json',
'pytorch_model.bin.index.json', 'pytorch_model-00001-of-00002.bin', 'pytorch_model-00002-of-00002.bin'
]:
hf_hub_download(repo_id=f'DeepFloyd/{dir_or_name}', filename=filename, cache_dir=cache_dir,
force_filename=filename, token=self.hf_token)
tokenizer_path, path = cache_dir, cache_dir
else:
cache_dir = os.path.join(self.cache_dir, 't5-v1_1-xxl')
for filename in [
'config.json', 'special_tokens_map.json', 'spiece.model', 'tokenizer_config.json',
]:
hf_hub_download(repo_id='DeepFloyd/t5-v1_1-xxl', filename=filename, cache_dir=cache_dir,
force_filename=filename, token=self.hf_token)
tokenizer_path = cache_dir

print(tokenizer_path)
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
self.model = T5EncoderModel.from_pretrained(path, **t5_model_kwargs).eval()
self.model_max_length = model_max_length

tokenizer_path = os.path.abspath(model_path)
model_path = tokenizer_path

self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, local_files_only=True)
self.model = T5EncoderModel.from_pretrained(model_path, local_files_only=True,
low_cpu_mem_usage=True,
torch_dtype=self.torch_dtype,
device_map={'shared': self.device, 'encoder': self.device}).eval()



def get_text_embeddings(self, texts):
texts = [self.text_preprocessing(text) for text in texts]

Expand Down Expand Up @@ -198,4 +192,4 @@ def clean_caption(self, caption):
caption = re.sub(r'[\'\_,\-\:\-\+]$', r'', caption)
caption = re.sub(r'^\.\S+$', '', caption)

return caption.strip()
return caption.strip()