| |
|
|
| """ 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="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="mean" |
| print(f"calculating {type}...") |
|
|
| |
|
|
| 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") |
|
|
|
|
| |
| |
| ax.set_ylabel('Values') |
| ax.set_title(f'Graph of {embed_file}') |
| ax.legend() |
|
|
| |
| print("Pulling up the graph") |
| plt.show() |
|
|
