Upload subtracted-distances.py

subtracted-distances.py

@@ -0,0 +1,90 @@
+#!/usr/bin/python3
+
+""" Work in progress
+Plan:
+   Kinda a "temp" hack (maybe)
+   Prompt for TWO things. Subtract the second embed from the first.
+   Then see what is near
+"""
+
+
+import sys
+import json
+import torch
+from safetensors import safe_open
+
+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("reading 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("reading 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_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,")")
+
+
+
+
+text1=input("First text? ")
+text2=input("Second text? ")
+emb1=standard_embed_calc(text1)
+emb2=standard_embed_calc(text2)
+
+result=torch.sub(emb1,emb2)
+print_distances(result)