[None][infra] Reuse rank processes across visual_gen multi-GPU tests by ishovkun · Pull Request #20239 · NVIDIA/TensorRT-LLM

ishovkun · 2026-10-13T19:09:36Z

Description

The visual_gen multi-GPU tests run their logic as fn(rank, world_size) on
world_size processes joined by dist.init_process_group. Each test spawns
those processes fresh through mp.spawn and every rank imports tensorrt_llm
from scratch (about 1m30 per rank on CI, about 40 s on a warm B300 node) for a
test body that runs in seconds. The cosmos3 file alone has 12 such tests.

This PR adds tests/unittest/_torch/visual_gen/multi_gpu/_worker_pool.py
(next to the tests that use it, like _visual_gen_dist_utils.py): one pool of
rank processes per (world_size, backend), kept alive for the pytest session
and handed each test function by pickled reference, the same pickling rule
mp.spawn already imposes. A conftest.py in the same package shuts the
pools down after any failed test and at session end. Test logic functions are unchanged;
run_test_in_distributed in the cosmos3 file becomes a skip check plus one
call into the pool.

Failure contract, which is what makes this more than a work queue:

A rank whose test raises reports its traceback. The parent does not wait for
the other ranks, which may be blocked in a collective the failed rank never
joined; after one grace period (10 s) for stragglers' reports it kills the whole
pool and raises with every traceback that arrived. Ranks that never reported
are listed as killed, not as failures of their own.
A rank that dies without reporting, or a test that exceeds its deadline,
likewise kills the pool. The deadline is the test's remaining time: the
conftest records it at test setup from the effective pytest-timeout minus
the kill path's budget (CI runs pytest with --timeout=2400 --timeout-method=thread, which kills the whole process on expiry), and
every pool call in the test, spawn included, is bounded by what is left;
timeout= per call can only shorten it. Without pytest-timeout there is no
deadline. The pool has one timing constant, the 10 s grace period, shared by
all ranks of a pool for peers' reports, graceful exit, reaping and GPU
release; a GPU check that cannot run (no nvidia-smi) is reported as
unverified, never as success; ranks the kernel will not reap are re-checked
at session end.
Every failure retires the pool and the next test spawns a fresh one, so a
failure can cost time, never a cascade of false failures in later tests. If
every test in a stage fails, the cost equals today's spawn-per-test.
After a kill the parent verifies the ranks were reaped and, for NCCL pools,
that nvidia-smi no longer lists their pids; anything still there is part of
the raised error instead of an unexplained OOM in the next test.
Ranks die with the parent: PR_SET_PDEATHSIG at rank start, plus the
library's native coordinator watchdog when preload brought tensorrt_llm
in. The mp.spawn harness had neither.
Each rank forces TLLM_DISABLE_MPI=1 for itself and imports the modules the
harness names in preload before reporting ready (cosmos3 preloads
tensorrt_llm), so the library import is paid under the spawn deadline and
shows in the pool's ready-time log line instead of in the first test's time.
The env part matters: the cosmos3 file's module-scope fixture pops
TLLM_DISABLE_MPI at teardown, and ranks spawned after that imported the
library in MPI mode and failed every Ulysses test in a forward-twice audit.
Between tests each rank restores os.environ, re-seeds the RNGs, clears the
logger's log-once memory and Dynamo caches, resets the visual_gen mesh
singletons, destroys every non-default process group, clears torch's
current-mesh context stack and empties the CUDA allocator cache. Ranks do
not wait for each other in the reset (tests may let some ranks return early
while rank 0 runs a single-GPU reference); the parent's wait for every
rank's report is the fence, so a collective leaked by one test fails that
test at its deadline rather than the next one.
A pool is also retired when the parent's env / sys.path differ from what
the ranks froze at spawn.

How long a pool otherwise lives is TRTLLM_TEST_VISUALGEN_POOL_LIFETIME:
session (default, the whole pytest run), module (shut down at each test
file's teardown), or test (fresh ranks for every run_distributed call,
which is what mp.spawn did; for state-bleed debugging and A/B timing in CI).
run_distributed(..., reuse=False) gives one test fresh ranks. Under
pytest-xdist workers the lifetime is forced to test.

Parent/rank plumbing is mp.Pipe, not mp.Queue: queues keep a feeder thread
alive in the parent for the life of the queue, which pytest-threadleak charges
to whichever test left a pool cached.

Every test file in tests/unittest/_torch/visual_gen/multi_gpu runs on the
pool; test_visual_gen_multinode.py stays on mp.spawn because it mocks
destroy_process_group and tests the bootstrap itself. No production code
changes.

Forking ranks from a process that has already imported the library
(forkserver with set_forkserver_preload) would make a respawn nearly free,
but import tensorrt_llm initializes CUDA (torch.cuda.is_initialized() is
True right after it, measured on B300), so a fork after the import is unsafe
as the code stands. Out of scope.

Test Coverage

tests/unittest/_torch/visual_gen/multi_gpu/test_worker_pool.py (new,
cpu_only, gloo backend, added to l0_cpu.yml): reuse keeps the same rank
pids; a rank raising while its peer is blocked in a collective returns in
one grace period against a 120 s deadline with the peer reported as killed; a dead rank
is detected with its exit code; a hung test hits an explicit deadline; with
@pytest.mark.timeout(60) a hung test fails through the pool at 30 s;
lifetime test, an env change and reuse=False each yield fresh pids;
lifetime module keeps the pool within a file; a bad lifetime value is
rejected; lifetime module drains at module teardown and session does
not; env, RNG state (first run included) and extra process groups are reset
between runs;
ranks die within seconds of their parent being SIGKILLed while busy in a
test body; an unpicklable function fails before any rank is touched; a per-
call timeout= is capped by the test's remaining time. All pass on B300.
Order audit of the 12 cosmos3 tests on one pool:
reversed order 12/12; forward twice (--keep-duplicates) 24/24 after the
rank-side TLLM_DISABLE_MPI fix.
The whole multi_gpu/ directory in one pytest session on 4xB300
(umb-b300-020, Tue 14 Oct 2026, same node, same build, branch rebased on
main 4e564a0acc):

Variant pytest time wall

spawn per call (TRTLLM_TEST_VISUALGEN_POOL_LIFETIME=test) 2h26m11 2h27m15

pool (session, default) 24m11 25m08

The two failures in the spawn-per-call run were both in the pool's own
self-test file, test_worker_pool.py, not in any visual_gen test:
test_ranks_die_with_parent still passed backend="gloo" in its embedded
owner script after the parameter was renamed to dist_backend, so the
keyword reached the rank-side function and the ranks failed; and
test_reuses_ranks_across_runs asserts that consecutive calls see the same
rank pids, which cannot hold when the whole suite runs with lifetime
test. Both are fixed (42852bdc38, 864201b005); the self-test file
passes 16/16 and the pool run above is on the fixed tree. The
spawn-per-call run has not been repeated on the fixed tree.

14 GPU pools were spawned over the pool session (1/2/3/4 ranks, NCCL and
gloo, plus env-change respawns at file boundaries), each paying one library
import; after a pool's first test, every test pays only its own work.

tests/unittest/_torch/visual_gen/multi_gpu/test_cosmos3_transformer_parallel.py
(l0_dgx_b200.yml): the 12 TestCosmos3TransformerParallel tests on
4xB300, same node, same tree, warm page cache:

Variant	pytest time	wall (incl. parent import)	per test
spawn per test (`TRTLLM_TEST_VISUALGEN_POOL_LIFETIME=test`)	9m13	10m14	34 to 56 s each
pool	1m40	2m38	first test per pool 47 s (2-rank) / 33 s (4-rank), rest 0.1 to 6 s

Per test, same two runs:

Test	spawn per test	pool
test_tp2_vs_single_gpu	49.6 s	47.2 s (first on the 2-rank pool: test-module import + model build)
test_ulysses2_vs_single_gpu	38.6 s	0.33 s
test_ulysses2_unequal_text_lengths_vs_single_gpu	46.5 s	0.43 s
test_edge_tp2_vs_single_gpu	49.6 s	0.13 s
test_edge_ulysses2_vs_single_gpu	34.2 s	0.08 s
test_ulysses2_audio_vs_single_gpu	46.1 s	0.20 s
test_ulysses2_action_vs_single_gpu	49.4 s	0.13 s
test_tp2_ulysses2_vs_single_gpu (gpu4)	38.1 s	33.4 s (first on the 4-rank pool)
test_cfg2_ulysses2_vs_single_gpu (gpu4)	45.7 s	1.37 s
test_attn2d_2x1_vs_single_gpu	56.0 s	5.13 s
test_attn2d_1x2_unequal_text_lengths_vs_single_gpu	40.5 s	0.69 s
test_attn2d_2x1_ulysses2_vs_single_gpu (gpu4)	52.5 s	6.39 s

With preload=("tensorrt_llm",) (final code, measured on a different and
slower node the next day, so only the shape is comparable): pool ready in
65 s (2-rank) and 62 s (4-rank), first test per pool about 80 s of model
build and JIT, the other ten tests 0.1 to 7 s; 4m05 pytest time.

The pool's remaining cost is one library import per pool size plus the GPU
work. The per-rank import on these nodes is about 40 s; CI's is about 1m30,
so the CI gap should be wider. The DGX_B200 stage with and without the env
var is the number that matters and goes here once the PR has run there.

Same file on 1xB300: 7 single-GPU tests pass, 12 multi-GPU tests skip, so
the file still collects and runs where the pool is not used.

Rollout

Measure the DGX_B200 stage with and without
TRTLLM_TEST_VISUALGEN_POOL_LIFETIME=test once CI runs; both numbers go here.

PR Checklist

PR description clearly explains what and why.
Follows CODING_GUIDELINES.md.
Test cases provided for new code paths (pool failure-contract tests).
No API changes, no new dependencies, no CODEOWNERS change.
Documentation: none needed beyond the module docstring.
Tava architecture diagram: unaffected (test infrastructure only).
Reviewed the above items.

The visual_gen multi-GPU tests spawned fresh ranks per test through mp.spawn, and every rank imported tensorrt_llm from scratch (about 1m30 per rank on CI) for a test body that runs in seconds. Add tests/unittest/_torch/visual_gen/multi_gpu/_worker_pool.py: one pool of rank processes per (world_size, backend), alive for the pytest session, handed each test function by pickled reference. Test logic functions are unchanged; the cosmos3 file's run_test_in_distributed becomes a skip check plus one call. Failure contract: - A rank that raises reports its traceback; after a 5 s grace period for the other ranks the whole pool is killed and the failure is raised with every traceback that arrived. Ranks blocked in a collective the failed rank never joined are killed, not left to the NCCL timeout. - A rank that dies without reporting, or a test that exceeds its deadline, likewise kills the pool. Every failure retires the pool; the next test spawns a fresh one, so a failure can cost time but never a cascade of false failures. - After a kill the parent verifies the ranks were reaped and, for NCCL pools, that nvidia-smi no longer lists their pids; anything still held is part of the raised error. - Ranks die with the parent: PR_SET_PDEATHSIG at rank start, plus the library's native coordinator watchdog once tensorrt_llm is loaded. - Between tests each rank restores os.environ, re-seeds the RNGs, clears the logger's log-once memory and Dynamo caches, resets the visual_gen mesh singletons, destroys every non-default process group and torch's mesh cache, empties the CUDA allocator cache and runs a barrier. - A pool is retired when the parent's env/sys.path differ from what the ranks froze at spawn, and after TRTLLM_TEST_VISUALGEN_POOL_MAX_USES tests (16). TRTLLM_TEST_REUSE_VISUALGEN_WORKERS=0 gives fresh ranks per test; run_distributed(..., reuse=False) does so for one test. Reuse is off under pytest-xdist workers. - Each rank forces TLLM_DISABLE_MPI=1 and imports the modules named in preload before reporting ready, so the library import is paid under the spawn deadline and shows in the pool's ready-time log line. A conftest.py in the package shuts the pools down after any failed test and at session end. Parent/rank plumbing is mp.Pipe, not mp.Queue, whose feeder thread pytest-threadleak would charge to the test that left a pool cached. Measured on a warm 4xB300, the 12 cosmos3 multi-GPU tests: spawn per test 9m13, pool 1m40. Other files in the package are converted separately. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>

Replace the pool's own knobs with what the test session already defines. - Lifetime: TRTLLM_TEST_VISUALGEN_POOL_LIFETIME is session (default), module (the package conftest shuts pools down at each file's teardown) or test (fresh ranks per test, the old spawn-per-test behaviour). The use-count cap and the separate reuse switch are gone: the between-test reset covers the state a test can leave behind, and a cap only inserted unexplained respawns mid-file. - Deadline: the conftest hands the pool each test's effective pytest-timeout (marker, --timeout, PYTEST_TIMEOUT, ini); the pool fails the test three grace periods before it so pytest-timeout, which kills the whole pytest process, never fires first. Spawning draws from the same deadline. Without pytest-timeout there is no deadline, as before. The two timeout environment variables are gone. - One timing constant, the 10 s grace period: the other ranks' reports after a failure, exit after the shutdown sentinel, reaping after SIGKILL, GPU memory release after a kill. - The parent sleeps on the ranks' result pipes and death sentinels at once instead of polling. - Port-collision retries go through spawn_with_retry, which gains a `retryable` parameter and allocates a plain ephemeral port when the CI-aware allocator is not importable (parents outside pytest). - Module-level state is one block after the imports; timing names spell out what they wait for. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>

Review follow-ups on the visual_gen rank pool. - Nothing touches a rank before everything that can fail without one has run: pickling, lifetime validation and the deadline computation come first in run_distributed. start() and run() kill the ranks on any exception including interrupts, so no rank is ever owned by nobody. - The deadline is the test's remaining time: the conftest records an absolute deadline at test setup from the effective pytest-timeout and every pool call, spawn included, is capped by it; an explicit timeout can only shorten it. - Cleanup fits its budget: all ranks share one grace period to be reaped and one more for GPU release, with nvidia-smi given the remaining time. A release check that cannot run is logged as unverified, never counted as success; ranks that survive SIGKILL stay on record and are re-checked at every later shutdown. - A dead rank's result pipe leaves the wait set instead of keeping the parent spinning on a readable EOF for the whole grace period. - Ranks are seeded before their first task as well as after every test. - The three parallel per-rank lists became one record; docstrings state the observable guarantees (process state is what a failed test cannot leak; ranks live as long as the thread that spawned them; lifetime "test" means one run_distributed call) and the contract lives once, in the module docstring. - Tests: an unpicklable function touches no rank; per-call timeouts are capped by the test deadline; the module-lifetime teardown action is exercised directly; the parent-death readiness read is unbuffered. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>

- One cleanup owner per attempt: _Pool._owning runs the action, collects the reports, and is the only place that kills the ranks (appending the kill report on a reported failure, propagating anything else). The collector only reports; the formatting helper that used to kill is gone, and so is the second kill on the same failure. - The collector keeps one pending map (result pipe -> rank) and drops entries on report or EOF; ranks no longer repeat their own number in every message. - The task pipe carries the pre-pickled bytes as bytes, with an empty message as the shutdown sentinel, instead of pickling them a second time. - The between-test reset clears only torch's current-mesh context stack; the registry's other fields are caches keyed by the finished test's own mesh objects or user-set options, and are left alone. - The native coordinator watchdog starts in one place, before the rank reports ready, when preload brought tensorrt_llm in. - Deadline computation in guard form; the xdist notice is printed once from the conftest; the spawn context is local to launch. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>

Convert the remaining files in tests/unittest/_torch/visual_gen/multi_gpu to the rank pool: each launcher keeps its skip checks and signature (*fn_args, **kwargs, use_cuda) and calls run_distributed with the library preloaded; the per-file init/cleanup/worker helpers and the mp.spawn calls are gone. Test logic functions are unchanged. Three files had their own worker bodies: test_minimax_h3_parallel.py (the two worker bodies become plain logic functions, with the GPU-count skips the mp.spawn path lacked), test_ulysses_async.py (the tests call their logic functions directly) and test_visual_gen_mapping.py. test_ulysses_attention.py keeps its _disable_mpi context manager because it also guards module-level imports. test_visual_gen_multinode.py stays on mp.spawn: it mocks destroy_process_group and tests the bootstrap itself. The pool import moves to module level in every file: the function-level import was a constraint of the old helper, whose module-level defs.common import only resolved inside the pytest parent; the pool module has no such import, and the ranks have already imported it before they touch a test module. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>

Drop the barrier at the end of the between-test reset. Tests may let some ranks return early while others keep computing: the Wan pipeline parity test returns on ranks 1..3 while rank 0 runs a single-GPU reference, and with the barrier the early ranks waited on rank 0 until NCCL's watchdog aborted them. The parent's wait for every rank's report already fences one test from the next: a rank stuck in a collective a test leaked never reports, and that test fails at its deadline rather than the next one. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>

Two fixes from the first whole-directory run on the pool. - Destroying a NCCL sub-group is a finalize that waits for the group's peers. The Wan pipeline parity test lets ranks 1..3 return while rank 0 runs a single-GPU reference for minutes, so an early rank destroying the test's sub-groups in the reset waited on rank 0 until NCCL's watchdog aborted it. The previous test's sub-groups (and torch's current-mesh stack) are now destroyed at the start of the next task, when the parent has seen every rank finish; the post-test reset has nothing collective left in it. - The pool's torch.distributed backend parameter is dist_backend: the test functions in this package take an attention backend kwarg that travels through **kwargs, and the Qwen-Image topology tests collided with it. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>

In a whole-directory session sys.path can contain tests/integration, which makes defs.common importable in the test's owner subprocess and pulls the full library import in before it reports ready, past the 120 s bound. The owner now gets exactly the two entries it needs. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>

…er script too The owner script embedded in test_ranks_die_with_parent still passed backend="gloo", which after the dist_backend rename reached the rank-side test function as an unexpected keyword; the ranks failed and the owner never reported ready. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>

test_reuses_ranks_across_runs asserts that consecutive calls see the same rank pids, which only holds under lifetime session; running the suite with TRTLLM_TEST_VISUALGEN_POOL_LIFETIME=test made it fail by construction. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>

coderabbitai · 2026-10-14T09:28:28Z

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Walkthrough

Visual-generation multi-GPU tests now run through a shared rank pool. The pool manages worker reuse, task deadlines, rank state, and cleanup. Pytest hooks manage pool lifetimes, and CPU-only tests cover pool behavior and failure paths.

Changes

Visual-generation distributed worker pool

Layer / File(s)	Summary
Port allocation and spawn retry `tests/unittest/_torch/visual_gen/multi_gpu/_visual_gen_dist_utils.py`	The spawn helper uses a CI port allocator when available and falls back to an ephemeral localhost port. It retries configured exception types only for address-in-use errors.
Rank startup and per-task state `tests/unittest/_torch/visual_gen/multi_gpu/_worker_pool.py`	The pool configures lifetimes and deadlines, starts distributed ranks, and resets rank state after successful tasks. Linux ranks also bind their lifetime to the parent process.
Task dispatch, failure handling, and pool reuse `tests/unittest/_torch/visual_gen/multi_gpu/_worker_pool.py`	The parent dispatches tasks, collects rank reports, enforces deadlines, and manages pool reuse and shutdown. NCCL cleanup checks whether ranks still hold GPU memory.
Multi-GPU tests use the shared runner `tests/unittest/_torch/visual_gen/multi_gpu/test_*.py`	Visual-generation multi-GPU tests delegate worker execution to `run_distributed`. Existing GPU checks remain, and wrappers pass the backend and `tensorrt_llm` preload.
Pytest lifecycle and pool tests `tests/unittest/_torch/visual_gen/multi_gpu/conftest.py`, `tests/unittest/_torch/visual_gen/multi_gpu/test_worker_pool.py`, `tests/integration/test_lists/test-db/l0_cpu.yml`	Pytest hooks set test deadlines and shut down pools at configured lifecycle points. CPU-only tests cover reuse, failure handling, deadlines, cleanup, and parent-death behavior; the integration list includes the test module.

Priority: ➖ Normal

Estimated code review effort: 4 (Complex) | ~60 minutes

Change: Refactor

Sequence Diagram(s)

sequenceDiagram
  participant Test
  participant run_distributed
  participant WorkerPool
  participant Rank
  Test->>run_distributed: Submit pickled function and arguments
  run_distributed->>WorkerPool: Acquire pool for world size, backend, and preload
  WorkerPool->>Rank: Dispatch task to each rank
  Rank->>WorkerPool: Report result or failure
  WorkerPool->>run_distributed: Return reports or raise WorkerPoolError
  run_distributed->>Test: Return or propagate error

Suggested reviewers: juney-nvidia

Merge Risk: ⚪ Minimal · up to e09b2

The selected test changes address the identified reuse and timing concerns. No actionable merge blocker remains after normal checks.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 25.20% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 123 functions across 29 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Title check		The title clearly identifies the infrastructure change: reusing rank processes across visual_gen multi-GPU tests. It follows the repository format with [None][infra].
Description check		The description is complete and on topic. It explains the problem and solution, documents failure handling and configuration, lists extensive test coverage and performance results, and includes the re…
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Inline comments:
Review comments at
@tests/unittest/_torch/visual_gen/multi_gpu/test_worker_pool.py:
- Around line 161-166: Warm the worker pool in `test_dead_rank_is_detected` by
calling `run_distributed` with `_allreduce_ok` before starting the timer, so
process startup and rendezvous happen outside the measured window; keep the
timer around the `_rank1_dies` call and preserve the existing assertions.
- Around line 33-37: Update the _fresh_pool_cache fixture to set
TRTLLM_TEST_VISUALGEN_POOL_LIFETIME to session so reuse-dependent tests exercise
rank reuse, and skip this test module under pytest-xdist, where pool lifetime is
forced to test.

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tests/integration/test_lists/test-db/l0_cpu.yml
tests/unittest/_torch/visual_gen/multi_gpu/_visual_gen_dist_utils.py
tests/unittest/_torch/visual_gen/multi_gpu/_worker_pool.py
tests/unittest/_torch/visual_gen/multi_gpu/conftest.py
tests/unittest/_torch/visual_gen/multi_gpu/test_attn2d_attention.py
tests/unittest/_torch/visual_gen/multi_gpu/test_cosmos3_transformer_parallel.py
tests/unittest/_torch/visual_gen/multi_gpu/test_flux2_transformer_parallel.py
tests/unittest/_torch/visual_gen/multi_gpu/test_flux_tp.py
tests/unittest/_torch/visual_gen/multi_gpu/test_flux_ulysses.py
tests/unittest/_torch/visual_gen/multi_gpu/test_ltx2_async_ulysses.py
tests/unittest/_torch/visual_gen/multi_gpu/test_ltx2_parallel_vae.py
tests/unittest/_torch/visual_gen/multi_gpu/test_ltx2_ulysses.py
tests/unittest/_torch/visual_gen/multi_gpu/test_minimax_h3_parallel.py
tests/unittest/_torch/visual_gen/multi_gpu/test_parallel_attention.py
tests/unittest/_torch/visual_gen/multi_gpu/test_parallel_conv.py
tests/unittest/_torch/visual_gen/multi_gpu/test_parallel_group_norm.py
tests/unittest/_torch/visual_gen/multi_gpu/test_parallel_vae.py
tests/unittest/_torch/visual_gen/multi_gpu/test_qwen_image_attention_parallel.py
tests/unittest/_torch/visual_gen/multi_gpu/test_ring_attention.py
tests/unittest/_torch/visual_gen/multi_gpu/test_tp_attention.py
tests/unittest/_torch/visual_gen/multi_gpu/test_ulysses_async.py
tests/unittest/_torch/visual_gen/multi_gpu/test_ulysses_attention.py
tests/unittest/_torch/visual_gen/multi_gpu/test_ulysses_sage_attention.py
tests/unittest/_torch/visual_gen/multi_gpu/test_visual_gen_mapping.py
tests/unittest/_torch/visual_gen/multi_gpu/test_wan_async_ulysses.py
tests/unittest/_torch/visual_gen/multi_gpu/test_wan_pipeline_parallel.py
tests/unittest/_torch/visual_gen/multi_gpu/test_wan_tp.py
tests/unittest/_torch/visual_gen/multi_gpu/test_wan_transformer_parallel.py
tests/unittest/_torch/visual_gen/multi_gpu/test_wan_vsa_ulysses.py
tests/unittest/_torch/visual_gen/multi_gpu/test_worker_pool.py

Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 11 remain after this review.

…efore timing The autouse fixture now pins lifetime session (what the reuse and reset tests assert) and skips the module under pytest-xdist, where the lifetime is forced to test; test_dead_rank_is_detected spawns its pool before the timed window, as the peer-failure test already did. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>

chzblych · 2026-10-15T04:57:34Z

  - test_kimi_k3_specdec.py::test_kimi_k3_disagg_parity_selftest
  - examples/visual_gen/test_media_deps_install.py::test_install_ffmpeg_via_apt_installs_ffmpeg
  - unittest/test_s3_output.py
+  - unittest/_torch/visual_gen/multi_gpu/test_worker_pool.py


Is it really necessary to have this CPU only test? It looks like it calls torch but apparently which is torch CPU path, which may be different from the GPU execution.

chang-l · 2026-10-15T05:59:13Z

+) -> _Pool:
+    key = (world_size, backend, preload)
+    pool = _POOLS.pop(key, None)
+    if pool is not None and pool.snapshot != _spawn_snapshot():


A rank that dies while the pool is idle (OOM killer, NCCL watchdog) is handed to the next test here, which then fails with a bare BrokenPipeError unrelated to its own code. Suggest retiring the pool when any rank is dead:

if pool is not None and not all(r.proc.is_alive() for r in pool._ranks): _retire(pool, "rank died while idle") pool = None

Side note: CI runs each test-db entry as its own pytest process, so reuse there is per file (about none for the per-test-id minimax entries). The 2h26m to 24m figure is one session, so the DGX_B200 before/after is the number to watch.

github-actions · 2026-10-15T06:01:09Z

Automatically added "ci: full pre-merge approved" because this PR has satisfied the required GitHub review approvals. Unresolved review conversations and other required checks remain independent merge requirements.

github-actions Bot assigned ishovkun Oct 13, 2026

github-actions Bot added the VisualGen label Oct 13, 2026

ishovkun added 5 commits October 13, 2026 15:00

ishovkun force-pushed the test-worker-pool branch from be569be to 593799a Compare October 13, 2026 22:03

ishovkun added 5 commits October 13, 2026 15:08

ishovkun marked this pull request as ready for review October 14, 2026 09:20

ishovkun requested review from a team as code owners October 14, 2026 09:20

ishovkun requested review from BowenFu, hanjingtian, schetlur-nv, taianz-nv, weiminwang-nv and yibinl-nvidia October 14, 2026 09:20

coderabbitai Bot reviewed Oct 14, 2026

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Comment thread tests/unittest/_torch/visual_gen/multi_gpu/test_worker_pool.py

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[None][infra] Reuse rank processes across visual_gen multi-GPU tests - #20239

[None][infra] Reuse rank processes across visual_gen multi-GPU tests#20239
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Variant	pytest time	wall
spawn per call (`TRTLLM_TEST_VISUALGEN_POOL_LIFETIME=test`)	2h26m11	2h27m15
pool (`session`, default)	24m11	25m08

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