import os import shutil import tempfile from pathlib import Path from typing import Any, Iterable, List, Optional, Union from transformers import PreTrainedTokenizerBase from .. import bindings as tllm from ..bindings import executor as tllm from ..executor import GenerationExecutor, GenerationResult from ..logger import logger from .llm_utils import (CachedModelLoader, LlmArgs, LlmBuildStats, ModelLoader, _ModelRuntimeContext) from .mpi_session import (MpiCommSession, MpiPoolSession, MpiSession, external_mpi_comm_available) from .tokenizer import TokenizerBase # TODO[chunweiy]: move the following symbols back to utils scope, and remove the following import from .utils import SamplingParams, exception_handler, get_device_count class RequestOutput(GenerationResult): """The output data of a completion request to the LLM. Fields: request_id (int): The unique ID of the request. prompt (str): The prompt string of the request. prompt_token_ids (List[int]): The token ids of the prompt. outputs (List[CompletionOutput]): The output sequences of the request. context_logits (torch.Tensor): The logits on the prompt token ids. finished (bool): Whether the whole request is finished. """ def __init__(self, generation_result: GenerationResult, prompt: Optional[str] = None, tokenizer: Optional[TokenizerBase] = None) -> None: self.__dict__.update(generation_result.__dict__) self.prompt = prompt self.tokenizer = tokenizer def handle_generation_msg(self, tensors: tuple, error: str): super().handle_generation_msg(tensors, error) if self.tokenizer is not None: for beam_output in self.outputs: beam_output.text = self.tokenizer.decode(beam_output.token_ids) def _repr_fields(self): return [ 'request_id', 'prompt', 'prompt_token_ids', 'outputs', 'finished' ] class LLM: def __init__(self, model: str, tokenizer: Optional[Union[str, Path, TokenizerBase, PreTrainedTokenizerBase]] = None, skip_tokenizer_init: bool = False, tensor_parallel_size: int = 1, dtype: str = "auto", revision: Optional[str] = None, tokenizer_revision: Optional[str] = None, **kwargs: Any): ''' Args: model(str): The model name or a local path to the model directory. It could be a HuggingFace(HF) model name, a local path to the HF model, or a local path to the TRT-LLM engine or checkpoint. tokenizer(Optional[Union[str, Path, TokenizerBase, PreTrainedTokenizerBase]]): The tokenizer name or a local path to the tokenizer directory. skip_tokenizer_init: If true, skip initialization of tokenizer and detokenizer. generate and generate_async will accept prompt token ids as input only. tensor_parallel_size(int): The number of processes for tensor parallelism. dtype(str): The data type for the model weights and activations. revision(Optional[str]): The revision of the model. tokenzier_revision(Optional[str]): The revision of the tokenizer. kwargs: Contains the optional arguments for expert users, please refer to `llm_utils.LlmArgs` for more details. ''' try: self.args = LlmArgs.from_kwargs( model=model, tokenizer=tokenizer, skip_tokenizer_init=skip_tokenizer_init, tensor_parallel_size=tensor_parallel_size, dtype=dtype, revision=revision, tokenizer_revision=tokenizer_revision, **kwargs) except Exception as e: logger.error( f"Failed to parse the arguments for the LLM constructor: {e}") raise e self.mpi_session: Optional[MpiSession] = None if self.args.parallel_config.is_multi_gpu: if get_device_count() < self.args.parallel_config.world_size: raise RuntimeError( f"Only {get_device_count()} GPUs are available, but {self.args.parallel_config.world_size} are required." ) logger.info( f'start MpiSession with {self.args.parallel_config.world_size} workers' ) if not external_mpi_comm_available( self.args.parallel_config.world_size): self.mpi_session = MpiPoolSession( n_workers=self.args.parallel_config.world_size) else: self.mpi_session = MpiCommSession( n_workers=self.args.parallel_config.world_size) # Due to the Executor can only accept a engine path, we need to save the engine to a directory self._engine_dir: Optional[Path] = None self._executor: Optional[GenerationExecutor] = None self._workspace = tempfile.TemporaryDirectory("llm-workspace") self.runtime_context: Optional[_ModelRuntimeContext] = None self.llm_build_stats = LlmBuildStats() self._build_model() exception_handler.register(self) @property def workspace(self) -> Path: return Path(self._workspace.name) def generate( self, prompts: Union[str, Iterable[str], List[int], Iterable[List[int]]], sampling_params: Optional[Union[SamplingParams, List[SamplingParams]]] = None ) -> Union[RequestOutput, List[RequestOutput]]: ''' Generate output for the given prompts in the synchronous mode. Synchronous generation accepts either single prompt or batched prompts. Args: prompts: The prompt text or token ids; could be either single prompt or batched prompts. sampling_params: The sampling params for the generation, a default one will be used if not provided. ''' if isinstance(prompts, str) or isinstance(prompts[0], str): unbatched = isinstance(prompts, str) else: unbatched = isinstance(prompts[0], int) if unbatched: prompts = [prompts] futures = [] for i, prompt in enumerate(prompts): if isinstance(sampling_params, list): sp = sampling_params[i] else: sp = sampling_params future = self.generate_async(prompt, sampling_params=sp, streaming=False) futures.append(future) for future in futures: future.result() if unbatched: futures = futures[0] return futures def generate_async( self, prompt: Union[str, List[int]], sampling_params: Optional[SamplingParams] = None, streaming: bool = False, ) -> RequestOutput: ''' Generate output for the given prompt in the asynchronous mode. Asynchronous generation accepts single prompt only. Args: prompt: The prompt text or token ids; must be single prompt. sampling_params: The sampling params for the generation, a default one will be used if not provided. streaming: Whether to use the streaming mode for the generation. ''' if isinstance(prompt, str): prompt_token_ids = self._prepare_prompt_token_ids(prompt) elif isinstance(prompt, list) and isinstance(prompt[0], int): prompt_token_ids = prompt prompt = None else: raise TypeError( f"The prompt must be type str or list of int, but got {type(prompt)}" ) sampling_params = self._prepare_sampling_params(sampling_params) self._check_arguments(prompt_token_ids, sampling_params) result = self._executor.generate_async( prompt_token_ids, sampling_params=sampling_params, streaming=streaming, ) return RequestOutput(result, prompt, self.tokenizer) def _prepare_prompt_token_ids(self, prompt: str) -> List[int]: if self.tokenizer is None: raise ValueError("tokenizer is required to tokenize string prompt") return self.tokenizer.encode(prompt) def _prepare_sampling_params( self, sampling_params: Optional[SamplingParams] = None) -> SamplingParams: if sampling_params is None: if self.tokenizer is None: raise ValueError( "tokenizer is required to initialize a default sampling_params, or you can explicitly specify a sampling_params" ) return SamplingParams(end_id=self.tokenizer.eos_token_id, pad_id=self.tokenizer.pad_token_id) elif isinstance(sampling_params, SamplingParams): if sampling_params.end_id is None: if self.tokenizer is None: raise ValueError( "tokenizer is required to reset end_id if it is None, or you can explicitly specify the end_id for sampling_params" ) return sampling_params.setup(self.tokenizer) else: raise TypeError( f"The sampling_params must be type SamplingParams or None, but got {type(sampling_params)}" ) def _check_arguments(self, prompt_token_ids: List[int], sampling_params: SamplingParams) -> None: build_config = self.args.build_config prompt_len = len(prompt_token_ids) if prompt_len + sampling_params.max_new_tokens > build_config.max_seq_len: raise ValueError( f"The sum of prompt length ({prompt_len}) and max_new_tokens ({sampling_params.max_new_tokens}) should not exceed " f"max_seq_len ({build_config.max_seq_len})") if sampling_params.beam_width > build_config.max_beam_width: raise ValueError( f"sampling_params's beam_width ({sampling_params.beam_width}) should not exceed max_beam_width ({build_config.max_beam_width})" ) def _build_model(self): model_loader = CachedModelLoader(self.args, mpi_session=self.mpi_session, workspace=self.workspace, llm_build_stats=self.llm_build_stats) self._engine_dir = model_loader() executor_config = tllm.ExecutorConfig( max_beam_width=self.args.build_config.max_beam_width, scheduler_config=self.args.scheduler_config, batching_type=tllm.BatchingType.INFLIGHT) if self.args.kv_cache_config is not None: executor_config.kv_cache_config = self.args.kv_cache_config if self.args.peft_cache_config is not None: executor_config.peft_cache_config = self.args.peft_cache_config if self.args.decoding_config is not None: executor_config.decoding_config = self.args.decoding_config if self.args.logits_post_processor_map: executor_config.logits_post_processor_map = self.args.logits_post_processor_map executor_config.normalize_log_probs = self.args.normalize_log_probs executor_config.enable_chunked_context = self.args.enable_chunked_context executor_config.max_beam_width = self.args.build_config.max_beam_width self._executor = GenerationExecutor.create( self._engine_dir, executor_config=executor_config, model_world_size=self.args.parallel_config.world_size, mpi_session=self.mpi_session, reuse_mpi_comm=external_mpi_comm_available( self.args.parallel_config.world_size)) @property def tokenizer(self) -> TokenizerBase: if self.args.skip_tokenizer_init: return None if self.args.tokenizer is not None: return self.args.tokenizer if self.runtime_context is not None: return self.runtime_context.tokenizer try: self.args.tokenizer = ModelLoader.load_hf_tokenizer( self.args.model_dir) except: pass return self.args.tokenizer def save(self, engine_dir: str): ''' Save the built engine to the given path. ''' logger.info(f"Save model to {engine_dir}") if self._engine_dir is None: raise RuntimeError("The engine is not built yet.") if self._engine_dir.absolute() != os.path.abspath(engine_dir): shutil.copytree(self._engine_dir, engine_dir, dirs_exist_ok=True) def shutdown(self): if hasattr(self, "_executor") and self._executor is not None: self._executor.shutdown() if self.mpi_session is not None: self.mpi_session.shutdown() self.mpi_session = None def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback) -> bool: del exc_value, traceback self.shutdown() return exc_type is not None def __getstate__(self): raise RuntimeError("LLM object can not be pickled.") def __del__(self): self.shutdown()