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import dataclasses |
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import os |
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from contextlib import contextmanager |
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from dataclasses import dataclass, field |
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from itertools import count |
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from queue import Queue |
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from typing import List, Optional, Tuple, Union |
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from huggingface_hub import snapshot_download |
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from transformers import PretrainedConfig |
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from transformers.modeling_utils import PreTrainedModel |
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from transformers.utils import logging |
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from lmdeploy.turbomind import TurboMind |
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from lmdeploy.turbomind.utils import get_gen_param |
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from .configuration_lmdeploy import LMDeployConfig |
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logger = logging.get_logger(__name__) |
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@dataclass |
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class Session: |
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_count = count() |
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_session_id: int = None |
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_message: List[Tuple[str, str]] = field(default_factory=list) |
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_step: int = 0 |
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_nth_round: int = 0 |
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_error: int = 0 |
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def __init__(self): |
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self._session_id = next(Session._count) |
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self._message = [] |
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self._step = 0 |
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self._nth_round = 0 |
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@property |
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def session_id(self): |
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return self._session_id |
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@property |
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def message(self): |
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return self._message |
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@property |
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def step(self): |
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return self._step |
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@property |
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def nth_round(self): |
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return self._nth_round |
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@property |
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def error(self): |
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return self._error |
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class LMDeployForCausalLM(PreTrainedModel): |
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config_class = LMDeployConfig |
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def __init__(self, |
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config: LMDeployConfig, |
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*inputs, |
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model_path: str = None, |
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**kwargs): |
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super().__init__(config) |
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self.tm_model = TurboMind.from_pretrained(model_path, **kwargs) |
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que = Queue() |
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for _ in range(config.turbomind['max_batch_size']): |
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que.put(self.tm_model.create_instance()) |
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self.que = que |
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@classmethod |
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def from_pretrained(cls, |
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pretrained_model_name_or_path, |
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*model_args, |
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config: Optional[Union[PretrainedConfig, str, |
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os.PathLike]] = None, |
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cache_dir: Optional[Union[str, os.PathLike]] = None, |
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force_download: bool = False, |
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local_files_only: bool = False, |
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token: Optional[Union[str, bool]] = None, |
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revision: str = 'main', |
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**kwargs): |
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"""Instantiate a LM model with turbomind backend.""" |
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resume_download = kwargs.pop('resume_download', True) |
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proxies = kwargs.pop('proxies', None) |
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if os.path.isdir(pretrained_model_name_or_path): |
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local_folder = pretrained_model_name_or_path |
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else: |
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local_folder = snapshot_download( |
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pretrained_model_name_or_path, |
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revision=revision, |
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cache_dir=cache_dir, |
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proxies=proxies, |
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resume_download=resume_download, |
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force_download=force_download, |
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token=token, |
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local_files_only=local_files_only, |
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) |
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if not isinstance(config, PretrainedConfig): |
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config_path = config if config is not None else local_folder |
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kwargs.pop('return_unused_kwargs') |
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config, model_kwargs = cls.config_class.from_pretrained( |
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config_path, return_unused_kwargs=True, **kwargs) |
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else: |
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model_kwargs = kwargs |
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model = cls(config, |
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*model_args, |
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model_path=local_folder, |
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**model_kwargs) |
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generation_config = model.tm_model.model.sampling_param |
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for k, v in dataclasses.asdict(generation_config).items(): |
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if hasattr(model.generation_config, k): |
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base_value = getattr(model.generation_config, k) |
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setattr(generation_config, k, base_value) |
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if k in kwargs: |
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setattr(generation_config, k, v) |
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model.generation_config = generation_config |
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return model |
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@contextmanager |
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def managed_generator(self, session: Session): |
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generator = self.que.get() |
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try: |
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yield generator |
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except: |
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for _ in generator.stream_infer(session.session_id, [0], |
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request_output_len=0, |
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sequence_start=False, |
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sequence_end=False, |
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stop=True): |
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pass |
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session._error = 1 |
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finally: |
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self.que.put(generator) |
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def generate( |
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self, |
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input_ids: List[int], |
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session: Session, |
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**kwargs, |
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): |
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"""Generates sequences of token ids for models with a language modeling |
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head. |
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Args: |
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input_ids (List(int)): list of input token ids |
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session (Session) session information |
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kwargs (dict): hoc parametrization of generation |
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""" |
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with self.managed_generator(session) as generator: |
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for outputs in generator.stream_infer( |
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session_id=session.session_id, |
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input_ids=[input_ids], |
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**kwargs, |
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): |
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res, tokens = outputs[0] |
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yield res, tokens |
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def chat( |
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self, |
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query: str, |
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session: Optional[Session] = None, |
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cap: str = 'chat', |
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request_output_len: int = 512, |
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stream_output: bool = False, |
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ignore_eos=False, |
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random_seed: Optional[int] = None, |
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**kwargs, |
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) -> Tuple[str, Session]: |
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"""chat.""" |
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if session is None: |
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session = Session() |
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assert session._error == 0, 'An error occurred before, ' \ |
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'please start a new session.' |
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session._message.append([query, '']) |
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prompt = self.tm_model.model.get_prompt(query, session.nth_round == 0) |
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input_ids = self.tm_model.tokenizer.encode(prompt) |
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if len( |
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input_ids |
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) + session.step + request_output_len >= self.tm_model.session_len: |
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logger.error( |
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f'session_length exceeded {self.tm_model.session_len}') |
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session._error = 1 |
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yield '', session |
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else: |
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gen_param = get_gen_param(cap, self.generation_config, |
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session.nth_round + 1, session.step, |
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request_output_len, **kwargs) |
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gen_kwargs = dataclasses.asdict(gen_param) |
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gen_kwargs.update( |
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random_seed=random_seed if session.nth_round == 0 else None, |
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stream_output=stream_output, |
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ignore_eos=ignore_eos, |
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**kwargs) |
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_step = session._step |
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_nth_round = session._nth_round |
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response_size = 0 |
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for res, tokens in self.generate(input_ids, |
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session=session, |
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**gen_kwargs): |
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response = self.tm_model.tokenizer.decode(res.tolist(), |
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offset=response_size) |
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if response.endswith('�'): |
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continue |
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response_size = tokens |
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session._message[-1][-1] += response |
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session._nth_round = _nth_round + 1 |
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session._step = _step + response_size |
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yield response, session |
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