JosephusCheung commited on
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9c56881
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Upload qwerty.py

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  1. qwerty.py +91 -0
qwerty.py ADDED
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+ from safetensors.torch import load_file
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+ import sys
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+ import torch
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+ from pathlib import Path
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+ import torch.nn as nn
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+ import torch.nn.functional as F
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+
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+ def cal_cross_attn(to_q, to_k, to_v, rand_input):
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+ hidden_dim, embed_dim = to_q.shape
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+ attn_to_q = nn.Linear(hidden_dim, embed_dim, bias=False)
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+ attn_to_k = nn.Linear(hidden_dim, embed_dim, bias=False)
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+ attn_to_v = nn.Linear(hidden_dim, embed_dim, bias=False)
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+ attn_to_q.load_state_dict({"weight": to_q})
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+ attn_to_k.load_state_dict({"weight": to_k})
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+ attn_to_v.load_state_dict({"weight": to_v})
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+
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+ return torch.einsum(
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+ "ik, jk -> ik",
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+ F.softmax(torch.einsum("ij, kj -> ik", attn_to_q(rand_input), attn_to_k(rand_input)), dim=-1),
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+ attn_to_v(rand_input)
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+ )
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+
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+ def model_hash(filename):
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+ try:
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+ with open(filename, "rb") as file:
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+ import hashlib
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+ m = hashlib.sha256()
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+
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+ file.seek(0x100000)
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+ m.update(file.read(0x10000))
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+ return m.hexdigest()[0:8]
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+ except FileNotFoundError:
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+ return 'NOFILE'
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+
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+ def load_model(path):
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+ if path.suffix == ".safetensors":
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+ return load_file(path, device="cpu")
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+ else:
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+ ckpt = torch.load(path, map_location="cpu")
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+ return ckpt["state_dict"] if "state_dict" in ckpt else ckpt
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+
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+ def eval(model, n, input):
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+ qk = f"model.diffusion_model.output_blocks.{n}.1.transformer_blocks.0.attn1.to_q.weight"
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+ uk = f"model.diffusion_model.output_blocks.{n}.1.transformer_blocks.0.attn1.to_k.weight"
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+ vk = f"model.diffusion_model.output_blocks.{n}.1.transformer_blocks.0.attn1.to_v.weight"
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+ atoq, atok, atov = model[qk], model[uk], model[vk]
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+
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+ attn = cal_cross_attn(atoq, atok, atov, input)
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+ return attn
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+
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+ def main():
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+ file1 = Path(sys.argv[1])
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+ files = sys.argv[2:]
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+
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+ seed = 114514
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+ torch.manual_seed(seed)
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+ print(f"seed: {seed}")
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+
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+ model_a = load_model(file1)
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+
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+ print()
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+ print(f"base: {file1.name} [{model_hash(file1)}]")
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+ print()
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+
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+ map_attn_a = {}
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+ map_rand_input = {}
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+ for n in range(3, 11):
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+ hidden_dim, embed_dim = model_a[f"model.diffusion_model.output_blocks.{n}.1.transformer_blocks.0.attn1.to_q.weight"].shape
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+ rand_input = torch.randn([embed_dim, hidden_dim])
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+
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+ map_attn_a[n] = eval(model_a, n, rand_input)
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+ map_rand_input[n] = rand_input
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+
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+ del model_a
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+
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+ for file2 in files:
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+ file2 = Path(file2)
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+ model_b = load_model(file2)
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+
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+ sims = []
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+ for n in range(3, 11):
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+ attn_a = map_attn_a[n]
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+ attn_b = eval(model_b, n, map_rand_input[n])
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+
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+ sim = torch.mean(torch.cosine_similarity(attn_a, attn_b))
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+ sims.append(sim)
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+
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+ print(f"{file2} [{model_hash(file2)}] - {torch.mean(torch.stack(sims)) * 1e2:.2f}%")
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+
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+ if __name__ == "__main__":
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+ main()