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