import atexit
import json
import os
import shutil
import tempfile
import weakref
from pathlib import Path
from typing import Any, List, Literal, Optional, Sequence, Union
from tqdm import tqdm
from transformers import PreTrainedTokenizerBase
from tensorrt_llm.inputs.data import TextPrompt
from tensorrt_llm.inputs.registry import DefaultInputProcessor
from .. import bindings as tllm
from .._utils import nvtx_range_debug
from ..bindings import executor as tllm
from ..builder import EngineConfig
from ..disaggregated_params import DisaggregatedParams
from ..executor import (DetokenizedGenerationResultBase, GenerationExecutor,
GenerationResult, IterationResult, LoRARequest,
PostprocWorkerConfig, PromptAdapterRequest)
from ..executor.postproc_worker import PostprocParams
from ..executor.utils import (create_mpi_comm_session,
get_spawn_proxy_process_env)
from ..inputs import PromptInputs, create_input_processor, prompt_inputs
from ..logger import logger
from ..sampling_params import SamplingParams
from .llm_args import LLMARGS_EXPLICIT_DOCSTRING, PybindMirror
from .llm_utils import (CachedModelLoader, KvCacheRetentionConfig, LlmArgs,
LlmBuildStats, ModelLoader, _ModelRuntimeContext)
from .mpi_session import MpiPoolSession, external_mpi_comm_available
from .tokenizer import TokenizerBase, _xgrammar_tokenizer_info
# TODO[chunweiy]: move the following symbols back to utils scope, and remove the following import
from .utils import (append_docstring, exception_handler, get_device_count,
print_colored_debug)
[docs]
class RequestOutput(DetokenizedGenerationResultBase, GenerationResult):
"""The output data of a completion request to the LLM.
Attributes:
request_id (int): The unique ID of the request.
prompt (str, optional): 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, optional): The logits on the prompt token ids.
finished (bool): Whether the whole request is finished.
"""
[docs]
def __init__(self) -> None:
raise RuntimeError(
f"{self.__class__.__name__} is designed to be instantiated using {self.__class__.__name__}._from_generation_result by GenerationExecutor. "
f"Users are not expected to create {self.__class__.__name__} directly."
)
@classmethod
def _from_generation_result(
cls,
generation_result: GenerationResult,
prompt: Optional[str] = None,
tokenizer: Optional[TokenizerBase] = None) -> 'RequestOutput':
inst = cls.__new__(cls)
inst.__dict__.update(generation_result.__dict__)
inst.tokenizer = tokenizer
inst._streaming = generation_result._streaming
inst._prompt = prompt
return inst
@property
def prompt(self) -> Optional[str]:
return self._prompt
def _repr_fields(self):
return [
"request_id", "prompt", "prompt_token_ids", "outputs", "finished"
]
LLM_DOCSTRING = LLMARGS_EXPLICIT_DOCSTRING + """
kwargs (Any): Advanced arguments passed to `LlmArgs`.
Attributes:
tokenizer (tensorrt_llm.llmapi.tokenizer.TokenizerBase, optional): The tokenizer loaded by LLM instance, if any.
workspace (pathlib.Path): The directory to store intermediate files.
"""
[docs]
@append_docstring(LLM_DOCSTRING)
class LLM:
"""LLM class is the main class for running a LLM model.
Parameters:
"""
[docs]
def __init__(self,
model: Union[str, Path],
tokenizer: Optional[Union[str, Path, TokenizerBase,
PreTrainedTokenizerBase]] = None,
tokenizer_mode: Literal['auto', 'slow'] = 'auto',
skip_tokenizer_init: bool = False,
trust_remote_code: bool = False,
tensor_parallel_size: int = 1,
dtype: str = "auto",
revision: Optional[str] = None,
tokenizer_revision: Optional[str] = None,
**kwargs: Any) -> None:
self._executor_cls = kwargs.pop("executor_cls", GenerationExecutor)
try:
self.pytorch_backend_config = kwargs.pop('pytorch_backend_config',
None)
self.args = LlmArgs.from_kwargs(
model=model,
tokenizer=tokenizer,
tokenizer_mode=tokenizer_mode,
skip_tokenizer_init=skip_tokenizer_init,
trust_remote_code=trust_remote_code,
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
print_colored_debug(f"LLM.args.mpi_session: {self.args.mpi_session}\n",
"yellow")
self.mpi_session = self.args.mpi_session
if self.args.parallel_config.is_multi_gpu:
if get_device_count(
) < self.args.parallel_config.world_size_per_node:
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 self.mpi_session:
mpi_process_pre_spawned: bool = get_spawn_proxy_process_env()
if not mpi_process_pre_spawned:
print_colored_debug(f"LLM create MpiPoolSession\n",
"yellow")
self.mpi_session = MpiPoolSession(
n_workers=self.args.parallel_config.world_size)
else:
print_colored_debug(f"LLM create MpiCommSession\n",
"yellow")
self.mpi_session = create_mpi_comm_session(
self.args.parallel_config.world_size)
try:
# 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(
suffix="-llm-workspace", dir=self.args.workspace)
self._hf_model_dir: Optional[Path] = None
self.runtime_context: Optional[_ModelRuntimeContext] = None
self.llm_build_stats = LlmBuildStats()
self._build_model()
except Exception as e:
if self.mpi_session is not None:
self.mpi_session.shutdown()
raise e
exception_handler.register(self, 'shutdown')
atexit.register(LLM._shutdown_wrapper, weakref.ref(self))
@property
def workspace(self) -> Path:
return Path(self._workspace.name)
[docs]
def generate(
self,
inputs: Union[PromptInputs, Sequence[PromptInputs]],
sampling_params: Optional[Union[SamplingParams,
List[SamplingParams]]] = None,
use_tqdm: bool = True,
lora_request: Optional[Union[LoRARequest,
Sequence[LoRARequest]]] = None,
prompt_adapter_request: Optional[Union[
PromptAdapterRequest, Sequence[PromptAdapterRequest]]] = None,
kv_cache_retention_config: Optional[Union[
KvCacheRetentionConfig, Sequence[KvCacheRetentionConfig]]] = None,
disaggregated_params: Optional[Union[
DisaggregatedParams, Sequence[DisaggregatedParams]]] = 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:
inputs (tensorrt_llm.inputs.data.PromptInputs, Sequence[tensorrt_llm.inputs.data.PromptInputs]): The prompt text or token ids.
It can be single prompt or batched prompts.
sampling_params (tensorrt_llm.sampling_params.SamplingParams, List[tensorrt_llm.sampling_params.SamplingParams], optional): The sampling params for the generation. Defaults to None.
A default one will be used if not provided.
use_tqdm (bool): Whether to use tqdm to display the progress bar. Defaults to True.
lora_request (tensorrt_llm.executor.request.LoRARequest, Sequence[tensorrt_llm.executor.request.LoRARequest], optional):
LoRA request to use for generation, if any. Defaults to None.
prompt_adapter_request (tensorrt_llm.executor.request.PromptAdapterRequest, Sequence[tensorrt_llm.executor.request.PromptAdapterRequest], optional):
Prompt Adapter request to use for generation, if any. Defaults to None.
kv_cache_retention_config (tensorrt_llm.bindings.executor.KvCacheRetentionConfig, Sequence[tensorrt_llm.bindings.executor.KvCacheRetentionConfig], optional):
Configuration for the request's retention in the KV Cache. Defaults to None.
disaggregated_params (tensorrt_llm.disaggregated_params.DisaggregatedParams, Sequence[tensorrt_llm.disaggregated_params.DisaggregatedParams], optional):
Disaggregated parameters. Defaults to None.
Returns:
Union[tensorrt_llm.llmapi.RequestOutput, List[tensorrt_llm.llmapi.RequestOutput]]: The output data of the completion request to the LLM.
"""
unbatched = not isinstance(inputs, list)
if not unbatched:
if isinstance(inputs[0], int):
unbatched = True
if unbatched:
inputs = [inputs]
inputs = [prompt_inputs(i) for i in inputs]
def _item_at(maybe_batched: Union[Any, Sequence[Any]], pos: int) -> Any:
if isinstance(maybe_batched, list):
return maybe_batched[pos]
else:
return maybe_batched
futures = []
for i, request_inputs in enumerate(inputs):
future = self.generate_async(
request_inputs,
sampling_params=_item_at(sampling_params, i),
lora_request=_item_at(lora_request, i),
prompt_adapter_request=_item_at(prompt_adapter_request, i),
kv_cache_retention_config=_item_at(kv_cache_retention_config,
i),
disaggregated_params=_item_at(disaggregated_params, i),
streaming=False)
futures.append(future)
for future in tqdm(futures,
desc="Processed requests",
dynamic_ncols=True,
disable=not use_tqdm):
future.result()
if unbatched:
futures = futures[0]
return futures
[docs]
@nvtx_range_debug("LLM.generate_async", color="green", category="LLM")
def generate_async(
self,
inputs: PromptInputs,
sampling_params: Optional[SamplingParams] = None,
lora_request: Optional[LoRARequest] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
streaming: bool = False,
kv_cache_retention_config: Optional[KvCacheRetentionConfig] = None,
disaggregated_params: Optional[DisaggregatedParams] = None,
_postproc_params: Optional[PostprocParams] = None,
) -> RequestOutput:
"""Generate output for the given prompt in the asynchronous mode.
Asynchronous generation accepts single prompt only.
Args:
inputs (tensorrt_llm.inputs.data.PromptInputs): The prompt text or token ids; it must be single prompt.
sampling_params (tensorrt_llm.sampling_params.SamplingParams, optional): The sampling params for the generation. Defaults to None.
A default one will be used if not provided.
lora_request (tensorrt_llm.executor.request.LoRARequest, optional): LoRA request to use for generation, if any. Defaults to None.
prompt_adapter_request (tensorrt_llm.executor.request.PromptAdapterRequest, optional): Prompt Adapter request to use for generation, if any. Defaults to None.
streaming (bool): Whether to use the streaming mode for the generation. Defaults to False.
kv_cache_retention_config (tensorrt_llm.bindings.executor.KvCacheRetentionConfig, optional): Configuration for the request's retention in the KV Cache. Defaults to None.
disaggregated_params (tensorrt_llm.disaggregated_params.DisaggregatedParams, optional): Disaggregated parameters. Defaults to None.
Returns:
tensorrt_llm.llmapi.RequestOutput: The output data of the completion request to the LLM.
"""
sampling_params = self._prepare_sampling_params(sampling_params)
if sampling_params.n > self.args.build_config.max_batch_size:
raise ValueError(
f"SamplingParams.n ({sampling_params.n}) should not exceed max_batch_size ({self.args.build_config.max_batch_size})"
)
inputs = prompt_inputs(inputs)
if not inputs.get("prompt") and inputs.get(
"prompt_token_ids") and inputs.get(
"multi_modal_data") and not isinstance(
self.input_processor, DefaultInputProcessor):
# VLMs need to process/tokenize the prompt in their own way
prompt = self.tokenizer.decode(inputs['prompt_token_ids'])
inputs = TextPrompt(
prompt=prompt,
multi_modal_data=inputs.get("multi_modal_data"),
mm_processor_kwargs=inputs.get("mm_processor_kwargs"))
if sampling_params.add_special_tokens:
logger.debug(
"Setting add_special_tokens to False because prompt_token_ids were provided to generate. VLMs will re-encode the prompt."
)
sampling_params.add_special_tokens = False
query_token_ids = None
multimodal_embedding = None
mrope_config = None
if "prompt_token_ids" in inputs:
prompt_token_ids = inputs['prompt_token_ids']
prompt = None
query_token_ids = inputs.get("query_token_ids", None)
elif "prompt" in inputs:
prompt_token_ids, extra_processed_inputs = self.input_processor(
inputs, sampling_params)
prompt = inputs['prompt']
if extra_processed_inputs is not None:
query_token_ids = extra_processed_inputs.get('query_token_ids')
multimodal_embedding = extra_processed_inputs.get(
'mm_embedding')
mrope_config = extra_processed_inputs.get('mrope_config')
else:
raise TypeError(
f"The inputs must be type str or list of int, but got {type(inputs)}"
)
self._check_arguments(
len(prompt_token_ids),
len(query_token_ids) if query_token_ids is not None else 0,
sampling_params)
if _postproc_params:
_postproc_params.postproc_args.num_prompt_tokens = len(
prompt_token_ids)
result = self._executor.generate_async(
prompt_token_ids,
query_token_ids=query_token_ids,
sampling_params=sampling_params,
lora_request=lora_request,
prompt_adapter_request=prompt_adapter_request,
streaming=streaming,
multimodal_embedding=multimodal_embedding,
mrope_config=mrope_config,
kv_cache_retention_config=kv_cache_retention_config,
disaggregated_params=disaggregated_params,
postproc_params=_postproc_params,
)
return RequestOutput._from_generation_result(result, prompt,
self.tokenizer)
[docs]
def get_stats(self, timeout: Optional[float] = 2) -> List[dict]:
'''Get iteration statistics from the runtime.
To collect statistics, call this function after prompts have been submitted with LLM().generate().
Args:
timeout (float, optional): Max wait time in seconds when retrieving stats from queue. Defaults to 2.
Returns:
List[dict]: A list of runtime stats as dict.
e.g., ['{"cpuMemUsage": ..., "iter": 0, ...}', '{"cpuMemUsage": ..., "iter": 1, ...}']
'''
return self._executor.get_stats(timeout=timeout)
[docs]
def get_stats_async(self, timeout: Optional[float] = 2) -> IterationResult:
'''Get iteration statistics from the runtime.
To collect statistics, you can call this function in an async coroutine or the /metrics endpoint (if you're using trtllm-serve)
after prompts have been submitted.
Args:
timeout (float, optional): Max wait time in seconds when retrieving stats from queue. Defaults to 2.
Returns:
tensorrt_llm.executor.result.IterationResult: An async iterable object containing runtime stats.
'''
return self._executor.aget_stats(timeout=timeout)
[docs]
def get_kv_cache_events(self, timeout: Optional[float] = 2) -> List[dict]:
'''Get iteration KV events from the runtime.
KV events are used to track changes and operations within the KV Cache. Types of events:
- KVCacheCreatedData: Indicates the creation of cache blocks.
- KVCacheStoredData: Represents a sequence of stored blocks.
- KVCacheRemovedData: Contains the hashes of blocks that are being removed from the cache.
- KVCacheUpdatedData: Captures updates to existing cache blocks.
To enable KV events:
- set `event_buffer_max_size` to a positive integer in the `KvCacheConfig`.
- set `enable_block_reuse` to True in the `KvCacheConfig`.
Args:
timeout (float, optional): Max wait time in seconds when retrieving events from queue. Defaults to 2.
Returns:
List[dict]: A list of runtime events as dict.
'''
return self._executor.get_kv_events(timeout=timeout)
[docs]
def get_kv_cache_events_async(self,
timeout: Optional[float] = 2
) -> IterationResult:
'''Get iteration KV events from the runtime.
KV events are used to track changes and operations within the KV Cache. Types of events:
- KVCacheCreatedData: Indicates the creation of cache blocks.
- KVCacheStoredData: Represents a sequence of stored blocks.
- KVCacheRemovedData: Contains the hashes of blocks that are being removed from the cache.
- KVCacheUpdatedData: Captures updates to existing cache blocks.
To enable KV events:
- set `event_buffer_max_size` to a positive integer in the `KvCacheConfig`.
- set `enable_block_reuse` to True in the `KvCacheConfig`.
Args:
timeout (float, optional): Max wait time in seconds when retrieving events from queue. . Defaults to 2.
Returns:
tensorrt_llm.executor.result.IterationResult: An async iterable object containing runtime events.
'''
return self._executor.aget_kv_events(timeout=timeout)
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"
)
sampling_params._setup(self.tokenizer)
# auto enabled context and/or generation logits flags, as they are required by logprob computation for TRT backend.
if self.args.backend not in ["pytorch", "autodeploy"]:
if sampling_params.prompt_logprobs and not sampling_params.return_context_logits:
sampling_params.return_context_logits = True
sampling_params._context_logits_auto_enabled = True
if sampling_params.logprobs and not sampling_params.return_generation_logits:
sampling_params.return_generation_logits = True
sampling_params._generation_logits_auto_enabled = True
return sampling_params
else:
raise TypeError(
f"The sampling_params must be type SamplingParams or None, but got {type(sampling_params)}"
)
def _check_arguments(self, prompt_len: int, query_len: int,
sampling_params: SamplingParams) -> None:
if self.args.backend == "pytorch":
# TODO: remove these checks after PyTorch backend
# fully support TopK prompt and generation logprobs.
if sampling_params.prompt_logprobs:
raise ValueError(
f"`prompt_logprobs` in sampling_params is not supported in the PyTorch backend yet. Received `prompt_logprobs={sampling_params.prompt_logprobs}`. Please unset this field."
)
if sampling_params.logprobs and sampling_params.logprobs > 1:
raise ValueError(
f"PyTorch backend currently only supports `logprobs=1`. Received `logprobs={sampling_params.logprobs}` (Top{sampling_params.logprobs} logprobs). Please set `logprobs=1` in `sampling_params` instead."
)
return
elif self.args.backend == "autodeploy":
return
build_config = self.args.build_config
built_enging_cfg_file = Path(self.args.model) / 'config.json'
with open(built_enging_cfg_file) as f:
built_enging_cfg = json.load(f)
max_seq_len = built_enging_cfg['build_config'][
'max_seq_len'] if 'build_config' in built_enging_cfg else build_config.max_seq_len
# TODO: Remove this check and left the request verification to cpp runtime
if (not self.args.enable_chunked_prefill) and (
prompt_len / self.args.parallel_config.cp_size + query_len +
(sampling_params.max_tokens or 0) > max_seq_len):
raise ValueError(
f"The sum of prompt length ({prompt_len/self.args.parallel_config.cp_size}) and query length ({query_len}) max_tokens ({sampling_params.max_tokens}) should not exceed "
f"max_seq_len ({build_config.max_seq_len})")
if sampling_params.use_beam_search and sampling_params.n > build_config.max_beam_width:
raise ValueError(
f"sampling_params's n ({sampling_params.n}) should not exceed max_beam_width ({build_config.max_beam_width}) when use_beam_search is True"
)
if sampling_params.prompt_logprobs and not build_config.gather_context_logits:
raise ValueError(
f"`sampling_params's prompt_logprobs={sampling_params.prompt_logprobs}` requires `gather_context_logits=True` "
f"in the `BuildConfig` when constructing the LLM. "
f"Example: LLM(..., build_config=BuildConfig(gather_context_logits=True))."
)
if sampling_params.logprobs and not self.args.gather_generation_logits:
raise ValueError(
f"`sampling_params.logprobs={sampling_params.logprobs}` requires `gather_generation_logits=True` "
f"to be passed explicitly to the `LLM()` constructor.")
def _build_model(self):
model_loader = CachedModelLoader(self.args,
mpi_session=self.mpi_session,
workspace=self.workspace,
llm_build_stats=weakref.proxy(
self.llm_build_stats))
self._engine_dir, self._hf_model_dir = model_loader()
# update the model_dir to a local dir for the runtime, such as tokenizer loading.
if self._engine_dir is not None:
self.args.model = self._engine_dir
# Tokenizer loading should be after calling model_loader(), since model_loader() may download the model from HF hub.
# It should also be before bindings ExecutorConfig, which may depend on tokenizer info.
self._tokenizer = self._try_load_tokenizer()
# Multimodal special handling:
# 1. Default load_tokenizer may fail because MM has different tokenizer configuration. Hence we initialize it inside input processor
# 2. May need to modify model weights for MM (e.g., resize vocab embedding). We must do such operation via input processor's __init__
self.input_processor = create_input_processor(self._hf_model_dir,
self.tokenizer)
self.tokenizer = self.input_processor.tokenizer
max_batch_size = self.args.max_batch_size or self.args.build_config.max_batch_size
max_num_tokens = self.args.max_num_tokens or self.args.build_config.max_num_tokens
max_seq_len = self.args.max_seq_len or self.args.build_config.max_seq_len
executor_config = tllm.ExecutorConfig(
max_beam_width=self.args.build_config.max_beam_width,
scheduler_config=PybindMirror.maybe_to_pybind(
self.args.scheduler_config),
batching_type=PybindMirror.maybe_to_pybind(self.args.batching_type)
or tllm.BatchingType.INFLIGHT,
max_batch_size=max_batch_size,
max_num_tokens=max_num_tokens,
gather_generation_logits=self.args.gather_generation_logits)
if self.args.backend is None:
# also set executor_config.max_seq_len in TRT workflow, to deduce default max_tokens
if max_seq_len is not None:
executor_config.max_seq_len = max_seq_len
else:
engine_config = EngineConfig.from_json_file(self._engine_dir /
"config.json")
executor_config.max_seq_len = engine_config.build_config.max_seq_len
if self.args.kv_cache_config is not None:
executor_config.kv_cache_config = PybindMirror.maybe_to_pybind(
self.args.kv_cache_config)
if os.getenv("FORCE_DETERMINISTIC", "0") == "1":
# Disable KV cache reuse for deterministic mode
executor_config.kv_cache_config.enable_block_reuse = False
executor_config.kv_cache_config.enable_partial_reuse = False
if self.args.peft_cache_config is not None:
executor_config.peft_cache_config = PybindMirror.maybe_to_pybind(
self.args.peft_cache_config)
elif self.args.build_config.plugin_config.lora_plugin:
engine_config = EngineConfig.from_json_file(self._engine_dir /
"config.json")
lora_config = engine_config.build_config.lora_config
max_lora_rank = lora_config.max_lora_rank
num_lora_modules = engine_config.pretrained_config.num_hidden_layers * \
len(lora_config.lora_target_modules + lora_config.missing_qkv_modules)
executor_config.peft_cache_config = tllm.PeftCacheConfig(
num_device_module_layer=max_lora_rank * num_lora_modules *
self.args.max_loras,
num_host_module_layer=max_lora_rank * num_lora_modules *
self.args.max_cpu_loras,
)
if self.args.decoding_config is not None:
executor_config.decoding_config = self.args.decoding_config
if self.args.guided_decoding_backend == 'xgrammar':
executor_config.guided_decoding_config = tllm.GuidedDecodingConfig(
backend=tllm.GuidedDecodingConfig.GuidedDecodingBackend.
XGRAMMAR,
**_xgrammar_tokenizer_info(self.tokenizer))
elif self.args.guided_decoding_backend is not None:
raise ValueError(
f"Unrecognized guided decoding backend {self.args.guided_decoding_backend}"
)
executor_config.normalize_log_probs = self.args.normalize_log_probs
executor_config.enable_chunked_context = self.args.enable_chunked_prefill
executor_config.max_beam_width = self.args.max_beam_width or self.args.build_config.max_beam_width
if self.args.extended_runtime_perf_knob_config is not None:
executor_config.extended_runtime_perf_knob_config = PybindMirror.maybe_to_pybind(
self.args.extended_runtime_perf_knob_config)
if self.args.cache_transceiver_config is not None:
executor_config.cache_transceiver_config = PybindMirror.maybe_to_pybind(
self.args.cache_transceiver_config)
from tensorrt_llm._torch.pyexecutor.config import update_executor_config
update_executor_config(
executor_config,
backend=self.args.backend,
pytorch_backend_config=self.pytorch_backend_config,
mapping=self.args.parallel_config.to_mapping(),
build_config=self.args.build_config,
speculative_config=self.args.speculative_config,
hf_model_dir=self._hf_model_dir,
trt_engine_dir=self._engine_dir,
max_input_len=self.args.max_input_len,
max_seq_len=max_seq_len)
executor_config.llm_parallel_config = self.args.parallel_config
return_logits = self.args.gather_generation_logits or (
self.args.build_config
and self.args.build_config.gather_context_logits)
self._executor = self._executor_cls.create(
self._engine_dir,
executor_config=executor_config,
batched_logits_processor=self.args.batched_logits_processor,
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),
return_logits=return_logits,
postproc_worker_config=PostprocWorkerConfig(
num_postprocess_workers=self.args.num_postprocess_workers,
postprocess_tokenizer_dir=self.args.postprocess_tokenizer_dir,
),
is_llm_executor=True,
lora_config=self.args.lora_config)
def _try_load_tokenizer(self) -> Optional[TokenizerBase]:
if self.args.skip_tokenizer_init:
return None
if self.args.tokenizer is not None:
assert isinstance(self.args.tokenizer, TokenizerBase)
return self.args.tokenizer
if self.runtime_context is not None:
return self.runtime_context.tokenizer
# TODO smor- need to refine what is the desired behavior if lora is enabled
# in terms of the tokenizer initialization process
if hasattr(
self.args, "backend"
) and self.args.backend == "pytorch" and self.args.lora_config is not None:
num_lora_dirs = len(self.args.lora_config.lora_dir)
if num_lora_dirs == 1:
tokenizer_path = self.args.lora_config.lora_dir[0]
try:
tokenizer = ModelLoader.load_hf_tokenizer(
tokenizer_path,
trust_remote_code=self.args.trust_remote_code,
use_fast=self.args.tokenizer_mode != 'slow')
if tokenizer is None:
tokenizer_path = self.args.model
else:
return tokenizer
except Exception:
tokenizer_path = self.args.model
else:
tokenizer_path = self.args.model
else:
tokenizer_path = self.args.model
return ModelLoader.load_hf_tokenizer(
tokenizer_path,
trust_remote_code=self.args.trust_remote_code,
use_fast=self.args.tokenizer_mode != 'slow')
@property
def tokenizer(self) -> Optional[TokenizerBase]:
if hasattr(self, "input_processor"):
if hasattr(self.input_processor, "tokenizer"):
return self.input_processor.tokenizer
return self._tokenizer
@tokenizer.setter
def tokenizer(self, tokenizer: TokenizerBase):
self._tokenizer = tokenizer
[docs]
def save(self, engine_dir: str) -> None:
"""Save the built engine to the given path.
Args:
engine_dir (str): The path to save the engine.
"""
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):
return
if not self.mpi_session or not self.mpi_session.is_comm_session():
shutil.copytree(self._engine_dir, engine_dir, dirs_exist_ok=True)
else:
# NFS is fragile, so we copy files one by one
target_engine_dir = Path(engine_dir)
target_engine_dir.mkdir(parents=True, exist_ok=True)
# copy files one by one
for file in self._engine_dir.iterdir():
print_colored_debug(
f"Copying {file} to {target_engine_dir / file.name}\n")
shutil.copy(file, target_engine_dir / file.name)
[docs]
def shutdown(self) -> None:
if hasattr(self, "_executor") and self._executor is not None:
self._executor.shutdown()
self._executor = None
if hasattr(self, 'mpi_session') and self.mpi_session is not None:
self.mpi_session.shutdown()
self.mpi_session = None
@staticmethod
def _shutdown_wrapper(self_ref):
# Retrieve the instance if it still exists
instance = self_ref()
if instance is not None:
instance.shutdown()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback) -> bool:
del exc_value, traceback
self.shutdown()
return False # propagate exceptions
def __getstate__(self):
raise RuntimeError("LLM object can not be pickled.")
def __del__(self):
self.shutdown()
