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[BugFix] Fix multi-modal async scheduling race condition (#28706)

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Signed-off-by: Nick Hill <nhill@redhat.com>
This commit is contained in:
Nick Hill
2025-11-14 01:11:13 -08:00
committed by GitHub
parent c36bcfe6b3
commit bc3e43069a
3 changed files with 43 additions and 31 deletions
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6
vllm/distributed/device_communicators/shm_object_storage.py
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@@ -342,8 +342,8 @@ class MsgpackSerde(ObjectSerde):
from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder
self.encoder = MsgpackEncoder()
self.tensor_decoder = MsgpackDecoder(torch.Tensor)
self.mm_decoder = MsgpackDecoder(MultiModalKwargsItem)
self.tensor_decoder = MsgpackDecoder(torch.Tensor, share_mem=False)
self.mm_decoder = MsgpackDecoder(MultiModalKwargsItem, share_mem=False)
self._mm_kwargs_item_cls = MultiModalKwargsItem
def serialize(self, value: Any) -> tuple[bytes | list[bytes], int, bytes, int]:
@@ -368,7 +368,7 @@ class MsgpackSerde(ObjectSerde):
# pickle.loads do not read past the end of a pickled object
# within a large buffer, so we can skip storing the metadata size
type_name, nbytes, len_arr = pickle.loads(data_view)
serialized_data = bytearray(data_view[-nbytes:])
serialized_data = data_view[-nbytes:]
if type_name == torch.Tensor.__name__:
obj = []