perf: Route prompt_embeds through shared memory across both IPC hops.#39
perf: Route prompt_embeds through shared memory across both IPC hops.#39LuisRobaina wants to merge 5 commits into
prompt_embeds through shared memory across both IPC hops.#39Conversation
Signed-off-by: Luis Robaina <luis@protopia.ai>
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LuisRobaina
requested review from
CandiedCode,
KyleMylonakisProtopia,
Nan2018,
Copilot and
qthequartermasterman
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⚠️ Not ready to approve
There are correctness issues in multi-node and multi-engine (DP>1/remote) configurations plus a few codebase-convention fixes that should be addressed before merging.
Pull request overview
This PR reduces host-side overhead for prompt_embeds by avoiding full tensor serialization across vLLM’s two V1 IPC hops, routing large embed tensors through shared memory and broadcasting only lightweight handles (gated by VLLM_SHM_EMBEDS=1, and requiring VLLM_WORKER_MULTIPROC_METHOD=spawn).
Changes:
- Enable tensor-IPC (torch.multiprocessing queue) for
prompt_embedson the client → EngineCore hop whenenable_prompt_embedsis set. - Replace
prompt_embedstensor broadcast in EngineCore → TP workers with a filename-based shared-memory handle (SharedTensorHandle) plus worker-side rebuild. - Add a standalone profiling script to compare text vs
prompt_embedsprefill latency.
File summaries
| File | Description |
|---|---|
vllm/v1/executor/multiproc_executor.py |
Externalizes prompt_embeds before MQ broadcast and internalizes on worker receive. |
vllm/v1/engine/utils.py |
Creates tensor IPC queue when enable_prompt_embeds is enabled. |
vllm/v1/engine/core_client.py |
Uses TensorIpcSender whenever a tensor queue is provided (not just multimodal torch_shm). |
vllm/_shm_embeds.py |
New shared-memory handle implementation for broadcasting prompt_embeds without pickling tensor bytes. |
prof_embeds.py |
New script to profile prefill latency for token vs embed inputs. |
Copilot's findings
- Files reviewed: 5/5 changed files
- Comments generated: 7
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internalize_prompt_embeds runs on every worker message, but it doesn't short-circuit when the feature is disabled. Since externalize_prompt_embeds never emits handles unless VLLM_SHM_EMBEDS=1, adding an _ENABLED guard here avoids scanning scheduled_new_reqs on the default path. Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
…r remote readers.
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⚠️ Not ready to approve
A correctness bug in the CPU-tensor guard plus missing SPDX headers and lack of targeted regression tests should be addressed before approval.
Copilot's findings
- Files reviewed: 5/5 changed files
- Comments generated: 4
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| # Swap large `prompt_embeds` tensors for filename-based shm handles so the | ||
| # broadcast does not ship the full tensor. shm_keepalive holds the shared | ||
| # storages alive until all workers have rebuilt. | ||
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| # Only correct when every reader is local: the handle is a /dev/shm path valid | ||
| # only on this host, but the broadcast also fans out to remote (cross-node) | ||
| # readers over TCP, which cannot mmap it. Fall back to the default path | ||
| # whenever any reader is remote. | ||
| from vllm import _shm_embeds | ||
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| shm_local_only = self.rpc_broadcast_mq.n_remote_reader == 0 | ||
| shm_keepalive = ( | ||
| _shm_embeds.externalize_prompt_embeds(args) | ||
| if send_method == "execute_model" and shm_local_only | ||
| else [] | ||
| ) | ||
| self.rpc_broadcast_mq.enqueue((send_method, args, kwargs, output_rank)) |
LuisRobaina
changed the title
prompt_embeds through shared memory across both IPC hops.
Problem
prompt_embedsprefill stalls the GPU at the start of each request while the LARGEembedstensor is copied/serialized across vLLM's two V1 IPC hops. GPU compute is identical totext-only prefill. The overhead (~2.6x at 24k, TP=4) is pure host-side starvation.Solution
Route the large
embedstensor through shared memory on both hops so only a smallhandlecrosses each process boundary.client -> EngineCore): Create the tensor-IPC queue and wireTensorIpcSenderinto the request encoder wheneverenable_prompt_embedsis set, not only for multimodal torch_shm as currently done. The embeds tensor is shared viatorch.mpinstead of serialized inline over ZMQ.EngineCore -> (N) TP workers): theSchedulerOutputbroadcast picklesprompt_embedsviatorch.save(full serialize on the sender side + memcpy into the shm ring +torch.loadon every TP worker). Replace the tensor with a small picklableSharedTensorHandle:reduce_storagemoves the storage to a/dev/shmfile once (file_system strategy, required for the 1->N fan-out) and every workermmapsthe same file. externalize/internalize hook collective_rpc and the worker loop.These optimizations are currently gated by
VLLM_SHM_EMBEDS=1and requiresVLLM_WORKER_MULTIPROC_METHOD=spawn.Results with
NemotronH-120B, TP=4, 24k, eager:text-only prefill: 2.56x -> 1.5x.Comparison of Prefill latency for
textonly LLM request vsprompt_embedsLLL request.See profiling script at ...
Before Optimizations
After Optimizations