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calculate-distances.py

@@ -21,6 +21,12 @@ clipsrc="openai/clip-vit-large-patch14"
 processor=None
 model=None
 
+if len(sys.argv) == 2:
+    embed_file=sys.argv[1]
+else:
+    print("You have to give name of embeddings file")
+    sys.exit(1)
+
 device=torch.device("cuda")
 
 
@@ -39,10 +45,6 @@ def init():
 
 
 
-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()

calculate-distancesXL.py

@@ -0,0 +1,102 @@
+#!/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
+from safetensors import safe_open
+
+from transformers import CLIPProcessor,CLIPModel, CLIPTextModelWithProjection
+
+processor=None
+tmodel2=None
+model_path2=None
+model_config2=None
+
+if len(sys.argv) == 4:
+    model_path2=sys.argv[1]
+    model_config2=sys.argv[2]
+    embed_file=sys.argv[3]
+else:
+    print("You have to give name of textencoder modelfile,config file, and embeddings file")
+    sys.exit(1)
+
+device=torch.device("cuda")
+
+
+def init():
+    global tmodel2,processor
+    # yes, oddly they all use the same tokenizer, basically
+    processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
+
+    print("loading",model_path)
+    tmodel2 = CLIPTextModelWithProjection.from_pretrained(model_path,config=model_config,local_files_only=True,use_safetensors=True)
+    tmodel2.to(device)
+
+
+
+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):
+    global processor,tmodel2
+    inputs = processor(text=text, return_tensors="pt")
+    inputs.to(device)
+
+    with torch.no_grad():
+        outputs = tmodel2(**inputs)
+        embeddings = outputs.text_embeds
+    return embeddings[0]
+
+
+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)