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[VLM] Reduce GPU memory footprint of CUDA IPC MM feature transport (#22662)

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This commit is contained in:
Yuhao Yang
2026-04-17 10:38:36 +08:00
committed by GitHub
parent 5a0eea8ac5
commit a77abbe005
3 changed files with 66 additions and 19 deletions
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python/sglang/srt/environ.py
+1 -1
View File
@@ -469,7 +469,7 @@ class Envs:
# VLM Item CUDA IPC Transport
SGLANG_USE_CUDA_IPC_TRANSPORT = EnvBool(False)
SGLANG_USE_IPC_POOL_HANDLE_CACHE = EnvBool(False)
SGLANG_MM_FEATURE_CACHE_MB = EnvInt(4 * 1024)
SGLANG_MM_FEATURE_CACHE_MB = EnvInt(1 * 1024)
SGLANG_MM_ITEM_MEM_POOL_RECYCLE_INTERVAL_SEC = EnvFloat(0.05)
# Mamba
python/sglang/srt/multimodal/processors/base_processor.py
+16 -1
View File
@@ -254,8 +254,23 @@ class BaseMultimodalProcessor(ABC):
skip_mm_pool = kwargs.get("skip_mm_pool", False)
if SGL_USE_CUDA_IPC and not skip_mm_pool:
# SGLANG_MM_FEATURE_CACHE_MB is the total pool budget across all
# tokenizer workers. Each worker gets an equal share so that adding
# workers doesn't multiply the GPU-side footprint.
worker_num = self.server_args.tokenizer_worker_num
per_worker_pool_size = max(
MM_FEATURE_CACHE_SIZE // worker_num,
128 * 1024 * 1024,
)
logger.info(
"MmItemMemoryPool size per tokenizer worker: %.0f MiB "
"(budget %.0f MiB / %d worker(s))",
per_worker_pool_size / (1024 * 1024),
MM_FEATURE_CACHE_SIZE / (1024 * 1024),
worker_num,
)
self.cudaipc_mmfeature_pool = MmItemMemoryPool(
MM_FEATURE_CACHE_SIZE,
per_worker_pool_size,
MM_ITEM_MEMORY_POOL_RECYCLE_INTERVAL,
)
python/sglang/srt/utils/cuda_ipc_transport_utils.py
+49 -17
View File
@@ -134,6 +134,7 @@ class MmItemMemoryPool:
self.occupied_chunks = []
self._lock = threading.Lock()
self._pool_full_warned = False
self._recycle_interval = recycle_interval
self._stop_recycler = False
@@ -229,8 +230,25 @@ class MmItemMemoryPool:
self.memory_pool[available_chunk.start : available_chunk.end],
available_chunk.start,
)
self._warn_pool_full_once(src_tensor)
return None, None, None
def _warn_pool_full_once(self, src_tensor: torch.Tensor):
if self._pool_full_warned:
return
self._pool_full_warned = True
pool_mb = (
self.memory_pool.numel() * self.memory_pool.element_size() / (1024 * 1024)
)
need_mb = src_tensor.numel() * src_tensor.element_size() / (1024 * 1024)
logger.warning(
"MmItemMemoryPool has no free chunk large enough for a %.2f MiB tensor "
"(pool size: %.2f MiB); falling back to non-IPC transport. "
"Consider increasing SGLANG_MM_FEATURE_CACHE_MB.",
need_mb,
pool_mb,
)
def recycle_chunks(self):
new_occupied_chunks = []
@@ -350,23 +368,25 @@ class CudaIpcTensorTransportProxy:
return state
def _reconstruct_from_ipc_extra(self, ipc_extra, *, use_cache: bool):
def _reconstruct_from_ipc_extra(
self, ipc_extra, *, use_cache: bool, rebuild_device_idx: int
):
shape = ipc_extra["shape"]
dtype = ipc_extra["dtype"]
stride = ipc_extra["stride"]
target_device = torch.device(f"cuda:{ipc_extra['pool_device_index']}")
cache_key = _normalize_pool_cache_key(
ipc_extra["pool_handle"], ipc_extra["pool_device_index"]
)
# Redirect handle[0] to the consumer's device so _new_shared_cuda's
# CUDAGuard stays there; peer access handles the cross-GPU open.
pool_handle = ipc_extra["pool_handle"]
redirected_handle = (rebuild_device_idx,) + tuple(pool_handle)[1:]
target_device = torch.device(f"cuda:{rebuild_device_idx}")
cache_key = _normalize_pool_cache_key(pool_handle, rebuild_device_idx)
with torch.cuda.device(target_device):
if use_cache:
storage = _pool_handle_cache_get_or_open(
cache_key, ipc_extra["pool_handle"]
)
storage = _pool_handle_cache_get_or_open(cache_key, redirected_handle)
storage_to_cache = None
else:
storage = _open_pooled_storage_uncached(ipc_extra["pool_handle"])
storage = _open_pooled_storage_uncached(redirected_handle)
storage_to_cache = storage
slice_storage = storage[
ipc_extra["pool_byte_offset"] : ipc_extra["pool_byte_offset"]
@@ -426,10 +446,14 @@ class CudaIpcTensorTransportProxy:
_target_device,
cache_key,
storage_to_cache,
) = self._reconstruct_from_ipc_extra(ipc_extra, use_cache=True)
) = self._reconstruct_from_ipc_extra(
ipc_extra,
use_cache=True,
rebuild_device_idx=rebuild_device_idx,
)
except Exception as e:
cache_key = _normalize_pool_cache_key(
ipc_extra["pool_handle"], ipc_extra["pool_device_index"]
ipc_extra["pool_handle"], rebuild_device_idx
)
logger.info(
"Failed to deserialize from cached pooled CUDA IPC handle (%s). "
@@ -442,17 +466,25 @@ class CudaIpcTensorTransportProxy:
_target_device,
_cache_key,
storage_to_cache,
) = self._reconstruct_from_ipc_extra(ipc_extra, use_cache=False)
) = self._reconstruct_from_ipc_extra(
ipc_extra,
use_cache=False,
rebuild_device_idx=rebuild_device_idx,
)
if storage_to_cache is not None:
_pool_handle_cache_set(cache_key, storage_to_cache)
else:
# Non-pooled path: open handle directly (original behavior)
# Non-pooled path: redirect handle[0] the same way as the pooled path.
try:
storage = torch.UntypedStorage._new_shared_cuda(
*ipc_extra["handle"]
)
target_device = torch.device(f"cuda:{ipc_extra['device_index']}")
original_handle = ipc_extra["handle"]
redirected_handle = (rebuild_device_idx,) + tuple(original_handle)[
1:
]
target_device = torch.device(f"cuda:{rebuild_device_idx}")
with torch.cuda.device(target_device):
storage = torch.UntypedStorage._new_shared_cuda(
*redirected_handle
)
slice_tensor = torch.empty(
0, dtype=ipc_extra["dtype"], device=target_device
).set_(