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85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
import torch
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from safetensors.torch import load_file, save_file
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from collections import OrderedDict
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meta = OrderedDict()
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meta['format'] = "pt"
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def reduce_weight(weight, target_size):
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weight = weight.to(device, torch.float32)
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original_shape = weight.shape
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flattened = weight.view(-1, original_shape[-1])
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if flattened.shape[1] <= target_size:
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return weight
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U, S, V = torch.svd(flattened)
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reduced = torch.mm(U[:, :target_size], torch.diag(S[:target_size]))
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if reduced.shape[1] < target_size:
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padding = torch.zeros(reduced.shape[0], target_size - reduced.shape[1], device=device)
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reduced = torch.cat((reduced, padding), dim=1)
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return reduced.view(original_shape[:-1] + (target_size,))
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def reduce_bias(bias, target_size):
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bias = bias.to(device, torch.float32)
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original_size = bias.shape[0]
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if original_size <= target_size:
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return torch.nn.functional.pad(bias, (0, target_size - original_size))
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else:
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return bias.view(-1, original_size // target_size).mean(dim=1)[:target_size]
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# Load your original state dict
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state_dict = load_file(
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"/home/jaret/Dev/models/hf/PixArt-Sigma-XL-2-512_MS_t5large_raw/transformer/diffusion_pytorch_model.orig.safetensors")
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# Create a new state dict for the reduced model
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new_state_dict = {}
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source_hidden_size = 1152
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target_hidden_size = 1024
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for key, value in state_dict.items():
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value = value.to(device, torch.float32)
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if 'weight' in key or 'scale_shift_table' in key:
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if value.shape[0] == source_hidden_size:
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value = value[:target_hidden_size]
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elif value.shape[0] == source_hidden_size * 4:
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value = value[:target_hidden_size * 4]
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elif value.shape[0] == source_hidden_size * 6:
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value = value[:target_hidden_size * 6]
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if len(value.shape) > 1 and value.shape[
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1] == source_hidden_size and 'attn2.to_k.weight' not in key and 'attn2.to_v.weight' not in key:
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value = value[:, :target_hidden_size]
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elif len(value.shape) > 1 and value.shape[1] == source_hidden_size * 4:
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value = value[:, :target_hidden_size * 4]
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elif 'bias' in key:
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if value.shape[0] == source_hidden_size:
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value = value[:target_hidden_size]
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elif value.shape[0] == source_hidden_size * 4:
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value = value[:target_hidden_size * 4]
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elif value.shape[0] == source_hidden_size * 6:
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value = value[:target_hidden_size * 6]
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new_state_dict[key] = value
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# Move all to CPU and convert to float16
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for key, value in new_state_dict.items():
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new_state_dict[key] = value.cpu().to(torch.float16)
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# Save the new state dict
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save_file(new_state_dict,
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"/home/jaret/Dev/models/hf/PixArt-Sigma-XL-2-512_MS_t5large_raw/transformer/diffusion_pytorch_model.safetensors",
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metadata=meta)
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print("Done!")
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