If you input a real model loaded by transformers, the init will take quite a long time Currently we don't support model(nn.Module) format as the param.
(
self,
model_or_path: Union[nn.Module, str],
tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
inference_config: InferenceConfig,
verbose: bool = False,
model_policy: Policy = None,
)
| 49 | """ |
| 50 | |
| 51 | def __init__( |
| 52 | self, |
| 53 | model_or_path: Union[nn.Module, str], |
| 54 | tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast], |
| 55 | inference_config: InferenceConfig, |
| 56 | verbose: bool = False, |
| 57 | model_policy: Policy = None, |
| 58 | ) -> None: |
| 59 | """ |
| 60 | If you input a real model loaded by transformers, the init will take quite a long time |
| 61 | Currently we don't support model(nn.Module) format as the param. |
| 62 | """ |
| 63 | |
| 64 | torch.multiprocessing.set_start_method("spawn", force=True) |
| 65 | |
| 66 | self.inference_config = inference_config |
| 67 | self.tokenizer = tokenizer |
| 68 | self.tokenizer.pad_token = self.tokenizer.eos_token |
| 69 | |
| 70 | self.verbose = verbose |
| 71 | self.logger = get_dist_logger(__name__) |
| 72 | |
| 73 | try: |
| 74 | if isinstance(model_or_path, str): |
| 75 | self.model_config = AutoConfig.from_pretrained( |
| 76 | model_or_path, trust_remote_code=True, torch_dtype=self.dtype |
| 77 | ) |
| 78 | elif isinstance(model_or_path, nn.Module): |
| 79 | self.logger.error( |
| 80 | f"An exception occurred during loading model Config: For {__class__.__name__}, we don't support param like nn.Module currently\n" |
| 81 | ) |
| 82 | # self.model_config = model_or_path.config |
| 83 | else: |
| 84 | self.logger.error( |
| 85 | f"An exception occurred during loading model Config: Please pass right param for {__class__.__name__}\n" |
| 86 | ) |
| 87 | except Exception as e: |
| 88 | self.logger.error( |
| 89 | f"An exception occurred during loading model Config: {e}, The path should be transformers-like\n" |
| 90 | ) |
| 91 | self.generation_config = inference_config.to_generation_config(self.model_config) |
| 92 | |
| 93 | self.tp_size = inference_config.tp_size |
| 94 | self.events = [mp.Event() for _ in range(self.tp_size)] |
| 95 | |
| 96 | # This operation will init the dist env and models |
| 97 | self.workers: List[rpcWorkerService] = [] |
| 98 | self.init_workers() |
| 99 | |
| 100 | asyncio.run(self.init_model(model_or_path, model_policy)) |
| 101 | |
| 102 | # init the scheduler and logic block manager |
| 103 | self.request_handler = self.init_scheduler(self.inference_config, self.model_config) |
| 104 | |
| 105 | # init the physical cache |
| 106 | alloc_shape = self.request_handler.cache_manager.get_physical_cache_shape() |
| 107 | self.init_device_cache(alloc_shape) |
| 108 |
nothing calls this directly
no test coverage detected