Upload calculate-2distance.py

calculate-2distance.py

@@ -0,0 +1,101 @@
+#!/bin/env python
+
+""" 
+Plan:
+   Read in "dictionary" for list of words
+   Read in pre-calculated "proper" embedding for each word from
+     safetensor file named "embeddings.safetensors"
+   Prompt user for two words from the list
+   (but may also be off the list, or a phrase)
+   Print out Euclidean distance between the two
+ 
+  (the point of the dictionary is that it can make loading super fast for known words)
+  
+"""
+
+
+import sys
+import json
+import torch
+from safetensors import safe_open
+
+import numpy
+
+from transformers import CLIPProcessor,CLIPModel
+
+clipsrc="openai/clip-vit-large-patch14"
+processor=None
+model=None
+
+device=torch.device("cuda")
+
+
+def init():
+    global processor
+    global model
+    # Load the processor and model
+    print("loading processor from "+clipsrc,file=sys.stderr)
+    processor = CLIPProcessor.from_pretrained(clipsrc)
+    print("done",file=sys.stderr)
+    print("loading model from "+clipsrc,file=sys.stderr)
+    model = CLIPModel.from_pretrained(clipsrc)
+    print("done",file=sys.stderr)
+
+    model = model.to(device)
+
+
+
+embed_file="embeddings.safetensors"
+
+device=torch.device("cuda")
+
+print("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("read in embeddings now",file=sys.stderr)
+model = safe_open(embed_file,framework="pt",device="cuda")
+embs=model.get_tensor("embeddings")
+embs.to(device)
+print("Shape of loaded embeds =",embs.shape)
+
+def standard_embed_calc(text):
+    if processor == None:
+        init()
+
+    inputs = processor(text=text, return_tensors="pt")
+    inputs.to(device)
+    with torch.no_grad():
+        text_features = model.get_text_features(**inputs)
+    embedding = text_features[0]
+    return embedding
+
+
+def print_distance(emb1,emb2):
+    targetdistance = torch.norm( emb1 - emb2)
+    print("DISTANCE:",targetdistance)
+
+
+# return embed of target word.
+# pull from dictionary, or do full calc
+def find_word(targetword):
+    try:
+        targetindex=wordlist.index(targetword)
+        targetemb=embs[targetindex]
+        return targetemb
+        return
+    except ValueError:
+        print(targetword,"not found in cache")
+    print("Now doing lookup with full calc embed")
+    targetemb=standard_embed_calc(targetword)
+    return targetemb
+
+
+while True:
+    input_text1=input("Input a word1(or phrase) now:")
+    input_text2=input("Input word2 now:")
+    emb1=find_word(input_text1)
+    emb2=find_word(input_text2)
+    print_distance(emb1,emb2)