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#!/bin/env python
"""
Purpose:
Read in "dictionary" for list of works and token
Generate "proper" embedding for each token, and store in tensor file
Generate a tensor array of distance to every other token/embedding
Save it out to "[email protected]"
Warning:
Some models require more VRAM than others.
Some require more RAM than others.
"""
import sys
import torch
import open_clip
from safetensors.torch import save_file
"""
REMEMBER!!!
You MUST use the same settings when you READ from the output file as well!!
"""
# See "list_models.txt" for full combination sets
#mtype='ViT-L-14-336'
mtype='ViT-L-14'
mname='openai'
import argparse
parser = argparse.ArgumentParser(
prog='generate-embeddings',
epilog=f"defaults: mtype={mtype}, mname={mname}",
description='Read in "dictionary" wordlist and generate calculated embeddings')
parser.add_argument('--mtype',default=mtype)
parser.add_argument('--mname',default=mname)
args = parser.parse_args()
mtype=args.mtype
mname=args.mname
#### Warning, this requires more than 4GB vram
#mtype='ViT-H-14-quickgelu'
#mname='dfn5b'
# May also be able to use syntax of
# hf-hub:hf-internal-testing/tiny-open-clip-model'
# for mname
outfile=f"{mtype}@{mname}.safetensors"
print("Will save to:")
print(" ",outfile)
print("Loading",mtype,mname)
cmodel, _, preprocess = open_clip.create_model_and_transforms(
mtype,
pretrained=mname)
tokenizer = open_clip.get_tokenizer(mtype)
device=torch.device("cuda")
try:
cmodel.to(device)
except torch.cuda.OutOfMemoryError as e:
print(f"FALLING BACK TO CPU!! \n {e}")
device=torch.device("cpu")
cmodel.to(device)
# This is very rare... unless you are trying to load the quickgelu sets
# on a 4GB card. Or maybe have 2 things running
def standard_embed_calc(text):
with torch.no_grad():
ttext = tokenizer(text).to(device)
text_features = cmodel.encode_text(ttext)
text_features.to(device)
#print("shape of text is",ttext.shape)
embedding = text_features[0]
#print("shape of embedding is",embedding.shape)
# For VIT-B, expected is [512]
return embedding
with open("dictionary","r") as f:
tokendict = f.readlines()
tokendict = [token.strip() for token in tokendict] # Remove trailing newlines
print("generate embeddings for each now",file=sys.stderr)
count=1
all_embeddings = []
for word in tokendict:
emb = standard_embed_calc(word)
emb=emb.unsqueeze(0) # stupid matrix magic to make the cat work
all_embeddings.append(emb)
count+=1
if (count %100) ==0:
print(count)
embs = torch.cat(all_embeddings,dim=0)
print("Shape of result = ",embs.shape)
print("Saving to ",outfile)
save_file({"embeddings": embs}, outfile)
print("calculate distances now")
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