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feat: continuous batching with vLLM #349

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merged 5 commits into from
Sep 14, 2023
Merged

feat: continuous batching with vLLM #349

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aa2c440
chore: make sure to call generation within no grad
aarnphm Sep 13, 2023
2e31d16
chore: wip
aarnphm Sep 13, 2023
c9cf103
feat: continuous batching
aarnphm Sep 14, 2023
7e59e1e
chore: add changeloe
aarnphm Sep 14, 2023
9cb6d23
chore: add one shot generation
aarnphm Sep 14, 2023
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chore: wip 
Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>
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@aarnphm
aarnphm committed Sep 13, 2023
commit 2e31d16cb4764e3797705b0a5846529ee29cd927
25 changes: 12 additions & 13 deletions openllm-python/src/openllm/_assign.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@
from openllm_core.utils import codegen
from openllm_core.utils import device_count
from openllm_core.utils import first_not_none
from openllm_core.utils import get_debug_mode
from openllm_core.utils import is_torch_available

if t.TYPE_CHECKING:
Expand All @@ -38,11 +39,8 @@
def import_model(fn: import_model_protocol[bentoml.Model, M, T]) -> t.Callable[[LLM[M, T]], bentoml.Model]:
@functools.wraps(fn)
def inner(self: LLM[M, T], *decls: t.Any, trust_remote_code: bool | None = None, **attrs: t.Any) -> bentoml.Model:
trust_remote_code = first_not_none(trust_remote_code, default=self.trust_remote_code)
(model_decls, model_attrs), _ = self.llm_parameters
decls = (*model_decls, *decls)
attrs = {**model_attrs, **attrs}
return fn(self, *decls, trust_remote_code=trust_remote_code, **attrs)
return fn(self, *model_decls, *decls, trust_remote_code=first_not_none(trust_remote_code, default=self.trust_remote_code), **model_attrs, **attrs)

return inner

Expand All @@ -52,21 +50,22 @@ def inner(self: LLM[M, T], *decls: t.Any, **attrs: t.Any) -> M | vllm.LLMEngine:
if self.__llm_backend__ == 'vllm':
num_gpus, dev = 1, device_count()
if dev >= 2: num_gpus = min(dev // 2 * 2, dev)
# TODO: Do some more processing with token_id once we support token streaming
try:
return vllm.LLMEngine.from_engine_args(
vllm.EngineArgs(model=self._bentomodel.path,
tokenizer=self._bentomodel.path if self.tokenizer_id == 'local' else self.tokenizer_id,
tokenizer_mode='auto',
tensor_parallel_size=num_gpus,
dtype='auto',
worker_use_ray=False))
return vllm.AsyncLLMEngine.from_engine_args(
vllm.AsyncEngineArgs(model=self._bentomodel.path,
tokenizer=self._bentomodel.path if self.tokenizer_id == 'local' else self.tokenizer_id,
tokenizer_mode='auto',
tensor_parallel_size=num_gpus,
dtype='auto',
disable_log_requests=not get_debug_mode(),
worker_use_ray=False,
engine_use_ray=False))
except Exception as err:
traceback.print_exc()
raise OpenLLMException(f'Failed to initialise vLLMEngine due to the following error:\n{err}') from None
else:
(model_decls, model_attrs), _ = self.llm_parameters
return fn(self, *(*model_decls, *decls), **{**model_attrs, **attrs})
return fn(self, *model_decls, *decls, **model_attrs, **attrs)

return inner

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