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| import os | |
| from collections import OrderedDict | |
| from safetensors.torch import save_file | |
| from jobs.process.BaseProcess import BaseProcess | |
| from toolkit.metadata import get_meta_for_safetensors | |
| from toolkit.train_tools import get_torch_dtype | |
| class BaseMergeProcess(BaseProcess): | |
| def __init__( | |
| self, | |
| process_id: int, | |
| job, | |
| config: OrderedDict | |
| ): | |
| super().__init__(process_id, job, config) | |
| self.process_id: int | |
| self.config: OrderedDict | |
| self.output_path = self.get_conf('output_path', required=True) | |
| self.dtype = self.get_conf('dtype', self.job.dtype) | |
| self.torch_dtype = get_torch_dtype(self.dtype) | |
| def run(self): | |
| # implement in child class | |
| # be sure to call super().run() first | |
| pass | |
| def save(self, state_dict): | |
| # prepare meta | |
| save_meta = get_meta_for_safetensors(self.meta, self.job.name) | |
| # save | |
| os.makedirs(os.path.dirname(self.output_path), exist_ok=True) | |
| for key in list(state_dict.keys()): | |
| v = state_dict[key] | |
| v = v.detach().clone().to("cpu").to(self.torch_dtype) | |
| state_dict[key] = v | |
| # having issues with meta | |
| save_file(state_dict, self.output_path, save_meta) | |
| print(f"Saved to {self.output_path}") | |