Util to find top token in each dimention

generate-allid-toptokens.py

@@ -0,0 +1,55 @@
+#!/bin/env python
+
+"""
+    CONCEPT:
+    Load in a precalculated embeddings file of all the tokenids (0-49405)
+    (see "generate-allid-embeddings[XL].py")
+
+     For each dimension, calculate which tokenid has the highest value.
+     Print out list, keyed by dimension.
+
+     In theory, this should auto-adjust, whether the embeddings file 
+     is SD, or SDXL (clip_l or clip_g)
+
+"""
+
+
+import sys
+import json
+import torch
+from safetensors import safe_open
+
+file1=sys.argv[1]
+file2=sys.argv[2]
+
+print(f"reading in json from {file2} now",file=sys.stderr)
+with open(file2, "r") as file:
+    json_data = json.load(file)
+
+token_names = {v: k for k, v in json_data.items()}
+
+#print(token_names)
+
+device=torch.device("cuda")
+print(f"reading {file1} embeddings now",file=sys.stderr)
+model = safe_open(file1,framework="pt",device="cuda")
+embs1=model.get_tensor("embeddings")
+embs1.to(device)
+print("Shape of loaded embeds =",embs1.shape)
+
+
+print(f"calculating distances...",file=sys.stderr)
+
+indices = torch.argmax(embs1, dim=0)
+
+print("Shape of results=",indices.shape,file=sys.stderr)
+
+indices=indices.tolist()
+
+counter=0
+for token_num in indices:
+    #print("num:",token_num)
+    print(counter,token_names.get(token_num))
+    counter+=1
+
+