| |
| import copy |
|
|
| from transformers import PretrainedConfig |
|
|
| from lmdeploy.turbomind.deploy.target_model.base import TurbomindModelConfig |
| from lmdeploy.version import __version__ as lm_version |
|
|
|
|
| class LMDeployConfig(PretrainedConfig): |
| """Lmdeploy config.""" |
|
|
| def __init__(self, turbomind: dict = None, **kwargs): |
| default_tm_cfg = copy.deepcopy( |
| TurbomindModelConfig.from_dict({}, allow_none=True).__dict__) |
| if turbomind is not None: |
| default_tm_cfg.update(turbomind) |
| self.turbomind = default_tm_cfg |
| self.lmdeploy_version = lm_version |
| super().__init__(**kwargs) |
|
|
| @classmethod |
| def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): |
| return_unused_kwargs = kwargs.pop('return_unused_kwargs', False) |
| config, kwargs = super().from_pretrained(pretrained_model_name_or_path, |
| return_unused_kwargs=True, |
| **kwargs) |
| for k, v in kwargs.items(): |
| if k in config.turbomind.keys(): |
| config.turbomind[k] = v |
| if 'tp' in kwargs: |
| config.turbomind['tensor_para_size'] = kwargs['tp'] |
| if return_unused_kwargs: |
| return config, kwargs |
| else: |
| return config |
|
|
|
|
