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embeddings.allids.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c0c899126f5f2ab05d0714a5b07461c087c4f43450317ae07b2bcb1fd237512b
+size 151772248

generate-id-embeddings.py

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+#!/usr/bin/python3
+
+""" Work in progress
+Plan:
+   Similar to generate-embeddings.py
+   However, instead of reading from a dictionary, just generate by pure
+   numeric tokenID
+   Save it out
+"""
+
+
+import sys
+import json
+import torch
+from safetensors.torch import save_file
+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)
+
+
+
+def embed_from_inputs(inputs):
+    with torch.no_grad():
+        text_features = model.get_text_features(**inputs)
+    embedding = text_features[0]
+
+    return embedding
+
+
+init()
+inputs = processor(text="dummy", return_tensors="pt")
+inputs.to(device)
+
+all_embeddings = []
+
+for id in range(49405):
+    inputs.input_ids[0][1]=id
+
+    emb=embed_from_inputs(inputs)
+    emb=emb.unsqueeze(0) # stupid matrix magic to make the cat work
+    all_embeddings.append(emb)
+    if (id %100) ==0:
+        print(id)
+
+embs = torch.cat(all_embeddings,dim=0)
+print("Shape of result = ",embs.shape)
+print("Saving all the things...")
+save_file({"embeddings": embs}, "embeddings.safetensors")
+
+

graph-byid.py

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+#!/bin/env python
+
+""" Work in progress
+Plan:
+   Unlike the other versions of graph-*.py, this one
+   requires "embeddings.allids.safetensors"
+   (created by generate-id-embeddings.py)
+   This covers the full official range of tokenids,
+   0-49405
+
+   It will then ask for 1-2 numeric token IDs.
+   It will pull the embedding matching those ids, and
+   graph them
+"""
+
+
+import sys
+import json
+import torch
+from safetensors import safe_open
+
+import PyQt5
+import matplotlib
+matplotlib.use('QT5Agg')  # Set the backend to TkAgg
+
+import matplotlib.pyplot as plt
+
+embed_file="embeddings.allids.safetensors"
+device=torch.device("cuda")
+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 embed_from_tokenid(num: int):
+    embed = embs[num]
+    return embed
+
+
+
+fig, ax = plt.subplots()
+
+
+text1 = input("First tokenid: ")
+text2 = input("Second tokenid(or leave blank): ")
+
+
+emb1 = embed_from_tokenid(int(text1))
+print("shape of emb1:",emb1.shape)
+
+graph1=emb1.tolist()
+ax.plot(graph1, label=text1[:20])
+
+if len(text2) >0:
+    emb2 = embed_from_tokenid(int(text2))
+    graph2=emb2.tolist()
+    ax.plot(graph2, label=text2[:20])
+
+# Add labels, title, and legend
+#ax.set_xlabel('Index')
+ax.set_ylabel('Values')
+ax.set_title('Comparative Graph of Two Embeddings')
+ax.legend()
+
+# Display the graph
+print("Pulling up the graph")
+plt.show()