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#!/bin/env python
"""
Plan:
Read in "dictionary" for list of words
Read in pre-calculated "proper" embedding for each word from safetensor file
Prompt user for a word from the list
Generate a tensor array of distance to all the other known words
Print out the 20 closest ones
"""
import sys
import torch
import open_clip
from safetensors import safe_open
#from transformers import CLIPProcessor,CLIPModel
device=torch.device("cuda")
mtype='ViT-g-14'
mname='laion2b_s12b_b42k'
print("Loading",mtype,mname)
cmodel, _, preprocess = open_clip.create_model_and_transforms(mtype,
pretrained=mname)
tokenizer = open_clip.get_tokenizer(mtype)
## model = model.to(device)
#embed_file="embeddings.safetensors"
embed_file=sys.argv[1]
dictionary=sys.argv[2]
print(f"read in words from {dictionary} now",file=sys.stderr)
with open(dictionary,"r") as f:
tokendict = f.readlines()
wordlist = [token.strip() for token in tokendict] # Remove trailing newlines
print(len(wordlist),"lines read")
print(f"read in {embed_file} now",file=sys.stderr)
emodel = safe_open(embed_file,framework="pt",device="cuda")
embs=emodel.get_tensor("embeddings")
embs.to(device)
print("Shape of loaded embeds =",embs.shape)
def standard_embed_calc(text):
with torch.no_grad():
ttext = tokenizer(text)
text_features = cmodel.encode_text(ttext)
embedding = text_features[0]
#print("shape of text is",ttext.shape)
return embedding
def print_distances(targetemb):
targetdistances = torch.cdist( targetemb.unsqueeze(0), embs, p=2)
print("shape of distances...",targetdistances.shape)
smallest_distances, smallest_indices = torch.topk(targetdistances[0], 20, largest=False)
smallest_distances=smallest_distances.tolist()
smallest_indices=smallest_indices.tolist()
for d,i in zip(smallest_distances,smallest_indices):
print(wordlist[i],"(",d,")")
# Find 10 closest tokens to targetword.
# Will include the word itself
def find_closest(targetword):
try:
targetindex=wordlist.index(targetword)
targetemb=embs[targetindex]
print_distances(targetemb)
return
except ValueError:
print(targetword,"not found in cache")
print("Now doing with full calc embed")
targetemb=standard_embed_calc(targetword)
print_distances(targetemb)
while True:
input_text=input("Input a word now:")
find_closest(input_text)
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