Upload graph-allids.py

Util to visualize the "allembeddings" file

graph-allids.py

@@ -0,0 +1,72 @@
+#!/bin/env python
+
+""" Work in progress
+Plan:
+    Take a pre-calculated embeddings file.
+    calculate an average distance-from-origin across ALL IDs, and graph that.
+    Typically, you would use
+   "embeddings.allids.safetensors"
+   This covers the full official range of tokenids, 0-49405
+   But, you could use a partial file
+
+"""
+
+
+#embed_file="embeddings.allids.safetensors"
+embed_file="cliptextmodel.embeddings.allids.safetensors"
+
+
+import sys
+if len(sys.argv) !=2:
+    print("ERROR: Expect an embeddings.safetensors file as argument")
+    sys.exit(1)
+embed_file=sys.argv[1]
+
+import torch
+from safetensors import safe_open
+
+import PyQt5
+import matplotlib
+matplotlib.use('QT5Agg') 
+
+import matplotlib.pyplot as plt
+
+device=torch.device("cuda")
+print(f"reading {embed_file} 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 embed_from_tokenid(num: int):
+    embed = embs[num]
+    return embed
+
+
+
+fig, ax = plt.subplots()
+
+
+#type="variance"
+type="mean"
+print(f"calculating {type}...")
+
+#emb1 = torch.var(embs,dim=0)
+
+emb1 = torch.mean(embs,dim=0)
+
+print("shape of emb1:",emb1.shape)
+
+graph1=emb1.tolist()
+ax.plot(graph1, label=f"{type} of each all embedding")
+
+
+# Add labels, title, and legend
+#ax.set_xlabel('Index')
+ax.set_ylabel('Values')
+ax.set_title(f'Graph of {embed_file}')
+ax.legend()
+
+# Display the graph
+print("Pulling up the graph")
+plt.show()