54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
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from collections import OrderedDict
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import os
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import sys
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from typing import Dict
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import typing
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import torch
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if '-h' in sys.argv or '--help' in sys.argv:
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print(f'Usage: python3 {sys.argv[0]} [--use-gpu] <lora_alpha> <base_model.pth> <lora_checkpoint.pth> <output.pth>')
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if sys.argv[1] == '--use-gpu':
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device = 'cuda'
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lora_alpha, base_model, lora, output = float(sys.argv[2]), sys.argv[3], sys.argv[4], sys.argv[5]
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else:
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device = 'cpu'
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lora_alpha, base_model, lora, output = float(sys.argv[1]), sys.argv[2], sys.argv[3], sys.argv[4]
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with torch.no_grad():
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w: Dict[str, torch.Tensor] = torch.load(base_model, map_location='cpu')
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# merge LoRA-only slim checkpoint into the main weights
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w_lora: Dict[str, torch.Tensor] = torch.load(lora, map_location='cpu')
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for k in w_lora.keys():
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w[k] = w_lora[k]
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output_w: typing.OrderedDict[str, torch.Tensor] = OrderedDict()
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# merge LoRA weights
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keys = list(w.keys())
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for k in keys:
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if k.endswith('.weight'):
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prefix = k[:-len('.weight')]
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lora_A = prefix + '.lora_A'
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lora_B = prefix + '.lora_B'
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if lora_A in keys:
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assert lora_B in keys
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print(f'merging {lora_A} and {lora_B} into {k}')
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assert w[lora_B].shape[1] == w[lora_A].shape[0]
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lora_r = w[lora_B].shape[1]
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w[k] = w[k].to(device=device)
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w[lora_A] = w[lora_A].to(device=device)
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w[lora_B] = w[lora_B].to(device=device)
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w[k] += w[lora_B] @ w[lora_A] * (lora_alpha / lora_r)
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output_w[k] = w[k].to(device='cpu', copy=True)
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del w[k]
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del w[lora_A]
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del w[lora_B]
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continue
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if 'lora' not in k:
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print(f'retaining {k}')
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output_w[k] = w[k].clone()
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del w[k]
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torch.save(output_w, output)
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