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| # This code references https://huggingface.co/JosephusCheung/ASimilarityCalculatior/blob/main/qwerty.py | |
| # Fill in the path of the model to be queried and the root directory of the reference models, and this script will return the similarity between the model to be queried and all reference models. | |
| import os | |
| import logging | |
| logger = logging.getLogger(__name__) | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| def cal_cross_attn(to_q, to_k, to_v, rand_input): | |
| hidden_dim, embed_dim = to_q.shape | |
| attn_to_q = nn.Linear(hidden_dim, embed_dim, bias=False) | |
| attn_to_k = nn.Linear(hidden_dim, embed_dim, bias=False) | |
| attn_to_v = nn.Linear(hidden_dim, embed_dim, bias=False) | |
| attn_to_q.load_state_dict({"weight": to_q}) | |
| attn_to_k.load_state_dict({"weight": to_k}) | |
| attn_to_v.load_state_dict({"weight": to_v}) | |
| return torch.einsum( | |
| "ik, jk -> ik", | |
| F.softmax( | |
| torch.einsum("ij, kj -> ik", attn_to_q(rand_input), attn_to_k(rand_input)), | |
| dim=-1, | |
| ), | |
| attn_to_v(rand_input), | |
| ) | |
| def model_hash(filename): | |
| try: | |
| with open(filename, "rb") as file: | |
| import hashlib | |
| m = hashlib.sha256() | |
| file.seek(0x100000) | |
| m.update(file.read(0x10000)) | |
| return m.hexdigest()[0:8] | |
| except FileNotFoundError: | |
| return "NOFILE" | |
| def eval(model, n, input): | |
| qk = f"enc_p.encoder.attn_layers.{n}.conv_q.weight" | |
| uk = f"enc_p.encoder.attn_layers.{n}.conv_k.weight" | |
| vk = f"enc_p.encoder.attn_layers.{n}.conv_v.weight" | |
| atoq, atok, atov = model[qk][:, :, 0], model[uk][:, :, 0], model[vk][:, :, 0] | |
| attn = cal_cross_attn(atoq, atok, atov, input) | |
| return attn | |
| def main(path, root): | |
| torch.manual_seed(114514) | |
| model_a = torch.load(path, map_location="cpu")["weight"] | |
| logger.info("Query:\t\t%s\t%s" % (path, model_hash(path))) | |
| map_attn_a = {} | |
| map_rand_input = {} | |
| for n in range(6): | |
| hidden_dim, embed_dim, _ = model_a[ | |
| f"enc_p.encoder.attn_layers.{n}.conv_v.weight" | |
| ].shape | |
| rand_input = torch.randn([embed_dim, hidden_dim]) | |
| map_attn_a[n] = eval(model_a, n, rand_input) | |
| map_rand_input[n] = rand_input | |
| del model_a | |
| for name in sorted(list(os.listdir(root))): | |
| path = "%s/%s" % (root, name) | |
| model_b = torch.load(path, map_location="cpu")["weight"] | |
| sims = [] | |
| for n in range(6): | |
| attn_a = map_attn_a[n] | |
| attn_b = eval(model_b, n, map_rand_input[n]) | |
| sim = torch.mean(torch.cosine_similarity(attn_a, attn_b)) | |
| sims.append(sim) | |
| logger.info( | |
| "Reference:\t%s\t%s\t%s" | |
| % (path, model_hash(path), f"{torch.mean(torch.stack(sims)) * 1e2:.2f}%") | |
| ) | |
| if __name__ == "__main__": | |
| query_path = r"assets\weights\mi v3.pth" | |
| reference_root = r"assets\weights" | |
| main(query_path, reference_root) | |