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""" Work in progress |
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Plan: |
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Read in fullword.json for list of words and token |
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Read in pre-calculated "proper" embedding for each token from safetensor file |
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Generate a tensor array of distance for each token, to every other token/embedding |
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Save it out |
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""" |
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import sys |
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import json |
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import torch |
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from safetensors import safe_open |
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embed_file="embeddings.safetensors" |
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device=torch.device("cuda") |
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print("read in words from json now",file=sys.stderr) |
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with open("fullword.json","r") as f: |
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tokendict = json.load(f) |
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wordlist = list(tokendict.keys()) |
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print("read in embeddingsnow",file=sys.stderr) |
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model = safe_open(embed_file,framework="pt",device="cuda") |
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embs=model.get_tensor("embeddings") |
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embs.to(device) |
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print("Shape of loaded embeds =",embs.shape) |
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print("calculate distances now") |
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distances = torch.cdist(embs, embs, p=2) |
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print("distances shape is",distances.shape) |
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targetword="cat" |
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targetindex=wordlist.index(targetword) |
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print("index of cat is",targetindex) |
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targetdistances=distances[targetindex] |
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smallest_distances, smallest_indices = torch.topk(targetdistances, 5, largest=False) |
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smallest_distances=smallest_distances.tolist() |
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smallest_indices=smallest_indices.tolist() |
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print("The smallest distance values are",smallest_distances) |
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print("The smallest index values are",smallest_indices) |
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for t in smallest_indices: |
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print(wordlist[t]) |
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""" |
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import torch.nn.functional as F |
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pos=0 |
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for word in tokendict.keys(): |
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print("Calculating distances from",word) |
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home=embs[pos] |
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#distances = torch.cdist(embs, home.unsqueeze(0), p=2) |
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#distance = F.pairwise_distance(home, embs[,p=2).item() |
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""" |
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