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…ive for captured graphs Growing the MNNVL all-reduce workspace replaced it and freed the previous buffers, pointer tables and flags while CUDA graphs captured before the growth still launched on them. The replaced workspaces now stay alive with the process, and creating or growing a workspace during graph capture raises instead of failing inside the driver. test_mnnvl_workspace_growth_keeps_captured_graphs captures a one-token all-reduce, grows the workspace with an eager two-shot call, replays the graph and checks growth under capture is refused. Signed-off-by: Vasanth Sabavat <vsabavat@nvidia.com>
…lti-GPU list test_mnnvl_workspace_growth_keeps_captured_graphs needs fabric-backed MNNVL on two GB200 GPUs, like the checkpoint graph tests next to it. Signed-off-by: Vasanth Sabavat <vsabavat@nvidia.com>
The workspace-growth test came from a branch whose yapf settings differ from main's; format it as main's pre-commit does. No behaviour change. Signed-off-by: Vasanth Sabavat <vsabavat@nvidia.com>
…re guard Creating or growing the MNNVL all-reduce workspace under CUDA-graph capture raises before the collective allocation. Its only test was the last block of the 2-rank growth test, which runs on GB200 multi-GPU stages only. test_mnnvl_workspace_creation_refuses_graph_capture creates the workspace for a one-rank group inside a capture and expects the guard's RuntimeError. The guard runs before any MNNVL call, so one GPU suffices; without it the call enters the workspace construction under capture and fails inside it with another error. The test joins l0_b200's single-GPU PyTorch block. Signed-off-by: Vasanth Sabavat <vsabavat@nvidia.com>
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Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 11 remain after this review. WalkthroughMNNVL workspace creation or growth now raises ChangesMNNVL workspace lifecycle
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~20 minutes Change: Bug fix Merge Risk: 🔵 Low · up to If an explicit MNNVL AllReduce is constructed during CUDA graph capture, the new warmup guidance may be swallowed and the code may fall back to regular AllReduce. The impact is narrow and the fallback is safe, so this is mergeable with the owner aware of it. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
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Review comments at @tensorrt_llm/_torch/distributed/ops.py:
- Line 290: Preserve the CUDA graph capture workspace-creation failure as a
distinct error in _get_or_scale_allreduce_mnnvl_workspace, and in the AllReduce
constructor, re-raise that error when self.strategy is AllReduceStrategy.MNNVL
instead of silently falling back; retain fallback behavior for other strategies.
Add a constructor-path regression test in the specified MNNVL all-reduce test
module.
- Around line 373-374: Update the workspace-retirement logic in MNNVLAllReduce
so growth does not unconditionally add previous_workspace to the process-wide
list; retain it only while a captured graph can replay against it, and release
it once no graph references it.
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…ture used it Growth retired every replaced workspace for the life of the process, including ones no CUDA graph had captured. Growth rounds to 8 MiB, so a run of rising eager sizes could keep many dead workspaces. The getter now marks a workspace it hands out during capture, and a growth keeps the previous workspace only when it is marked. An unmarked one is released at the replacement, after every rank's device synchronize in the protocol reset. The growth test checks that a second growth, past a workspace no capture used, leaves only the captured one in the retired list and that the graph still replays. Signed-off-by: Vasanth Sabavat <vsabavat@nvidia.com>
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Description
When a call needs more than the current MNNVL all-reduce workspace,
_get_or_scale_allreduce_mnnvl_workspace(
tensorrt_llm/_torch/distributed/ops.py) builds a newMcastGPUBufferandbuffer_flagsand replaces theper-
Mappingentry. Nothing else keeps the old workspace alive:at::for_blob,runtime/mcastGPUBuffer.h);~McastDeviceMemoryunmaps the buffers and frees the pointer tables (runtime/mcastDeviceMemory.cpp).CUDA graphs captured before the growth keep the old pointers (
thop/allreduceOp.cpp) and launch on freed memory.Every MNNVL all-reduce of a
Mappingshares the workspace, so one larger eager call after capture invalidates everycaptured graph. A long prefill after the decode graphs are captured is one example, when warmup did not reach that
size.
get_or_scale_allreduce_mnnvl_workspacemarks a workspace it hands outduring CUDA-graph capture. When a growth replaces a marked workspace, it moves to
MNNVLAllReduce.allreduce_mnnvl_retired_workspacesand stays alive with the process, so graphs captured on it keepvalid buffers and flags. A workspace no capture used is released at the replacement, as before: every rank has
synchronized its device in the protocol reset by then. The cost is the memory of the workspaces graphs were
captured on.
("run each shape once outside capture first") before the collective allocation. Before, it entered the allocation
and failed inside it, on every rank.
Repro before the fix (4 GB200 ranks, MNNVL): capture a one-token all-reduce, grow the workspace from 16 to 112 MiB per
buffer with an eager call, then replay. Every rank gets
cudaErrorIllegalAddress. After the fix both replays matcheager bit for bit.
Test Coverage
tests/unittest/_torch/multi_gpu/test_mnnvl_allreduce.py:test_mnnvl_workspace_growth_keeps_captured_graphs(2 GB200 ranks with fabric MNNVL; added tol0_gb200_multi_gpus.yml):captured workspace and that the graph still replays;
It reads the retired list this PR adds, so it does not run before the fix; the repro above is its fail-before.
Results on GB200:
ipcNvlsAllocateunder capture and assertsgroup.size() >= 2. With this PR it passes.retired list also holds the uncaptured workspace).
this PR.
test_mnnvl_workspace_creation_refuses_graph_capture(one GPU; added tol0_b200.yml's single-GPU PyTorchblock): creating the workspace for a one-rank group inside a capture raises the guard's error. The guard runs
before any MNNVL call, so one GPU suffices. Without the guard the call enters the workspace construction under
capture and fails with another error.
PR Checklist
Please review the following before submitting your PR:
PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.
PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
Test cases are provided for new code paths (see test instructions)
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api-compatibleorapi-breaking. Forapi-breaking, includeBREAKINGin the PR title. (No API change.)Any new dependencies have been scanned for license and vulnerabilities (No new dependencies.)
CODEOWNERS updated if ownership changes (No ownership change.)
Documentation updated as needed (No user-facing change.)
Update tava architecture diagram if there is a significant design change in PR. (No design change.)
The reviewers assigned automatically/manually are appropriate for the PR. (To check once the PR is open.)
Please check this after reviewing the above items as appropriate for this PR.
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/bot help.Dev Engineer Review
MNNVLAllReduceretains replaced workspaces so captured CUDA graphs can continue using their buffers and flags. Workspace creation or growth during graph capture raisesRuntimeErrorand instructs callers to create each shape outside capture first.QA Engineer Review
The test module covers workspace growth after graph capture, graph replay, and refusal to create a workspace during capture. The tests are listed in the GB200 multi-GPU and B200/B100 CI lists. The author reports that both tests pass in their stated configurations; those results were not independently verified. Coverage verdict: sufficient for the reported regression and capture guard.
Per-File QA Perspective
tensorrt_llm/_torch/distributed/ops.py: Verify that capture-time creation and growth raise the expected error, and that graphs captured before workspace growth remain valid.tests/unittest/_torch/multi_gpu/test_mnnvl_allreduce.py: Tests workspace retention and graph replay after growth, plus the capture-time creation guard. The tests are listed in the GB200 multi-GPU and B200/B100 CI lists, respectively.tests/integration/test_lists/test-db/l0_gb200_multi_gpus.yml: Adds the workspace-growth graph test to the GB200 multi-GPU CI list.tests/integration/test_lists/test-db/l0_b200.yml: Adds the workspace-creation capture-guard test to the B200/B100 CI list.