[None][perf] Overlap TRTLLM-Gen FMHA JIT warmup compilation with engine warmup (opt-in) by aaronmeoded-b10 · Pull Request #18542 · NVIDIA/TensorRT-LLM

aaronmeoded-b10 · 2026-09-01T15:47:11Z

Description

At engine startup, TRTLLM-Gen FMHA generation kernels are NVRTC-compiled synchronously inside the first warmup forward — ~5–11s per kernel variant depending on host CPU, 80–160s total on MLA fp8-KV configs (DeepSeek-family), ~2min for sparse gen kernels. This sits directly on the engine cold-start critical path.

This PR overlaps that compilation with the rest of engine warmup, opt-in via TRTLLM_GEN_FMHA_ASYNC_WARMUP=1. Default off; flag-off behavior is unchanged apart from deduplicating identical per-layer warmup sweeps (every layer of a warmup forward requests the same grid — only the first compiles anything).

How it works (flag on)

The warmup grid compiles on a single background thread while the general warmup and autotuning proceed. Every sweep is queued during the attention warmup; identical sweeps are deduplicated by a fingerprint of the kernel object, device and every runner param kernel selection reads.
Pre-capture barrier. The scheduled warmup (DecoderWarmup) calls torch.ops.trtllm.trtllm_gen_fmha_jit_warmup_drain_and_verify() after every other warmup phase and before the graph-shape warmup and capture passes (DecoderCudaGraphs.warmup_and_capture, whose capture pass runs under the serving MoE communication timeouts), outside the graph-enabled and general-warmup conditions, so no forward that runs all-to-alls starts while a compile may still be in flight. The op waits for every queued sweep, then re-runs each finished sweep once on the calling thread through the kernel object and device that scheduled it: a cache-hit loop when the background pass compiled everything, and the place where anything it missed — or a sweep whose background task failed — is compiled instead of on the first real request. So every kernel the warmup derived is compiled before any launch is captured and before readiness, whether or not CUDA graphs are enabled.
Serialization. All calls into the FMHA export lib go through a global interface mutex: the lib's kernelCache is an unsynchronized global LRU (cache hits mutate it too). Kernel selection and computeNumCtas take a second lock inside selectKernelAutotuned() / computeNumCtasSerialized(), because both mutate and then query the autotuner's shared dummy kernel (occupancy probe).
Ownership. The sweep registry records, per sweep, the owning TllmGenFmhaKernel object and the device whose context the modules were loaded into; a verify pass only claims its own sweeps, so two kernel objects on one device (e.g. fp8 and bf16 configs) can never verify each other's work.
Shutdown ordering. The worker thread is joinable and is shut down (drained, then joined) through a std::atexit handler registered when the worker is first created. That handler's order against other statics is not relied upon: everything a sweep reads is either leaked (the registry, the interface/selection mutexes, the install-path string) or constructed at library load (the export library's globals), and each kernel object drains the worker in its destructor before a sweep that captured it could outlive it. Worker creation is gated so the barrier, the capture-time drain and kernel destruction never create a worker just to find it idle.
Fallbacks. Worker-thread start failure, a shut-down worker, and background compile exceptions all degrade to inline compilation; run() also drains the worker if invoked on a capturing stream and keeps an in-run() verify pass for callers that never reach the barrier.
torch.ops.trtllm.trtllm_gen_fmha_jit_num_cache_misses() / ..._num_unknown_cache_results() expose the kernel-cache results the export library reports for every generateAndCompileKernel() call. A miss is a compile (the same key compiled again after an LRU eviction counts again), so a miss count that is stable across a request proves the request compiled nothing; a non-zero unknown count means the library did not report and the measure is incomplete. The GPU test uses these instead of a latency heuristic.

Files

cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaJitWarmup.h (new): the worker, the sweep registry, the register-then-queue-or-run-inline scheduling path (scheduleJITWarmupSweep, shared by the kernel and the tests) and the compile-stats tally, CUDA-free so they are host-unit-testable.
cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h: the sweep fingerprint, verification, the barrier, cache-result recording, locking.
cpp/tensorrt_llm/thop/trtllmGenFmhaJitWarmupOp.cpp (new) + thop/CMakeLists.txt: the three ops above.
tensorrt_llm/_torch/pyexecutor/engine/runners/decoder/warmup.py: the barrier (DecoderWarmup._drain_and_verify_trtllm_gen_fmha_jit_warmup) ahead of warmup_and_capture, followed by a rank sync on the warmup agreement group, so a peer still compiling cannot be waited on inside an all-to-all either.

Context: discussed with @PerkzZheng in the NVIDIA↔Baseten channel; he asked us to share the change so the code owners can integrate and validate. Measured on B200 deployments (Kimi-K2.x-class MLA fp8-KV, eagle3 one-model spec decode): critical-path warmup 163s → ~26–30s, spec-decode acceptance and steady-state throughput unchanged, no first-traffic compile misses. The win scales with host CPU slowness (compile time dominates) and with the widened kDefaultWarmupSeqLenQCandidates, which grows the warmup grid this PR takes off the critical path.

Test Coverage

cpp/tests/unit_tests/kernels/fmhaJitWarmupTest.cpp (host-only, registered in CMake): worker ordering and drain, failing tasks, shutdown with pending work, enqueue after shutdown; registry dedup, same-device multi-configuration isolation, multi-device isolation, claim-once semantics; compile stats (repeated misses for one key, unknown tallied separately); and the scheduling path itself (scheduleJITWarmupSweep, driven with the real worker and registry): every sweep verified exactly once, a background failure retried on the foreground, the inline fallback when the worker is shut down, sync mode running inline and still deduplicating, and the sweep receiving the device it was registered on.
cpp/tests/unit_tests/kernels/fmhaJitWarmupFingerprintTest.cpp (registered in CMake): the fingerprint is deterministic, ignores per-call pointers and the stream, and separates devices, kernel-object dtypes, the spec-decoding and grid params, and the DSv4 epilogue fusion flag.
cpp/tests/unit_tests/kernels/fmhaJitWarmupProcessExitTest.cpp (death test, registered in CMake): empty barrier, worker created before the install path exists, work queued and still pending at exit(); the child exits 0 only if that work completed during exit with the install path intact.
tests/unittest/_torch/pyexecutor/test_fmha_jit_warmup_barrier.py (cpu_only, collected through l0_cpu.yml's unittest/_torch/pyexecutor entry): drives DecoderWarmup.run with the graph passes, the serving-timeout switch and the ops recorded: the barrier and the rank sync run before the graph-shape warmup pass and before the capture pass enters the serving timeouts; the drain phase is not nested in the capture phase of the warmup timer; foreground recovery of failed background sweeps is reported; and the barrier is skipped for non-TRTLLM attention backends and when the flag is off.
tests/unittest/_torch/attention/test_trtllm_gen_fmha_async_jit_warmup.py (Blackwell, collected through l0_b200.yml's unittest/_torch/attention entry): boots Qwen3-0.6B with CUDA graphs in a fresh process per arm (async and sync warmup) and asserts capture succeeds, the warmup exercised the NVRTC path, the library reported a cache result for every compile request, and the first requests after readiness produce no cache miss, i.e. compile nothing. This one needs the B200 CI stage; it has not been run on our side.
Flag off (default): existing FMHA tests cover this path — behavior is identical apart from the sweep dedupe.
Flag on, end to end: internal B200 deployments (TP4 MLA fp8-KV + eagle3 one-model spec decode, and GLM-class sparse-attention configs), A/B vs flag-off: identical spec-decode acceptance and steady-state performance; burst traffic immediately after readiness shows no compile-triggered latency spikes.

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)
If PR introduces API changes, an appropriate PR label is added - either api-compatible or api-breaking. For api-breaking, include BREAKING in the PR title.
Any new dependencies have been scanned for license and vulnerabilities
CODEOWNERS updated if ownership changes
Documentation updated as needed
Update tava architecture diagram if there is a significant design change in PR.
The reviewers assigned automatically/manually are appropriate for the PR.
Please check this after reviewing the above items as appropriate for this PR.

🤖 Generated with Claude Code

…ne warmup (opt-in) At engine startup, TRTLLM-Gen FMHA generation kernels are NVRTC-compiled synchronously inside the first warmup forward (~5-11s per kernel variant, 80-160s total for MLA fp8-KV configs, ~2min for sparse gen kernels; scales with node CPU speed and now with the widened seqLenQ warmup candidates). With TRTLLM_GEN_FMHA_ASYNC_WARMUP=1 the warmup grid compiles on a single background thread, overlapping autotuning, CUDA-graph capture, and memory profiling. Default off; flag-off behavior is unchanged apart from deduplicating identical per-layer warmup sweeps. Safety: - All calls into the FMHA export lib are serialized behind a global interface mutex: the lib's kernelCache is an unsynchronized global LRU (hits mutate it too), and kernel selection mutates shared state via the FmhaAutoTuner occupancy probe (attribute set->query on a shared dummy kernel). - The background queue is drained before CUDA-graph capture; run() drains if invoked on a capturing stream. - A foreground verify pass re-walks each sweep before readiness, so any gap degrades to inline compilation - never a post-ready miss. - Worker-thread start failure and background compile exceptions degrade to inline compilation. Validated on B200 deployments (DeepSeek-family MLA fp8-KV, eagle3 one-model spec decode): critical-path warmup 163s -> ~26-30s, spec-decode acceptance and steady-state throughput unchanged, no first-traffic compile misses. Signed-off-by: Aaron Meoded <aaron.meoded@baseten.co>

coderabbitai · 2026-09-01T16:32:23Z

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Walkthrough

The FMHA kernel path adds optional asynchronous JIT warmup with deduplicated sweeps and foreground verification. The model engine drains and verifies warmup between graph warmup and capture for TRTLLM attention. Torch operators expose warmup status and counts.

Changes

FMHA asynchronous JIT warmup

Layer / File(s)	Summary
Warmup worker and sweep registry `cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaJitWarmup.h`, `cpp/tests/unit_tests/kernels/fmhaJitWarmupTest.cpp`, `cpp/tests/unit_tests/kernels/CMakeLists.txt`	A host-only worker queues tasks, supports draining and shutdown, and continues processing after task exceptions. A registry deduplicates sweeps and tracks scheduled, background-done, and verified states. Unit tests cover worker, registry, and scheduling behavior.
Kernel warmup scheduling and verification `cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h`	The FMHA kernel path supports environment-controlled asynchronous warmup and deduplicates requests by configuration and device. It serializes shared interface and selection operations, tracks requested kernel keys, and verifies completed sweeps before capture or execution.
Capture barrier and runtime exposure `tensorrt_llm/_torch/pyexecutor/model_engine.py`, `cpp/tensorrt_llm/thop/trtllmGenFmhaJitWarmupOp.cpp`, `cpp/tensorrt_llm/thop/CMakeLists.txt`, `tests/unittest/_torch/attention/test_trtllm_gen_fmha_async_jit_warmup.py`, `tests/unittest/_torch/executor/test_fmha_jit_warmup_barrier.py`	The engine drains and verifies warmup between graph warmup and capture for TRTLLM attention. Torch operators expose warmup controls and counts. Tests cover barrier ordering and async and synchronous warmup runs.

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

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant ModelEngine
  participant FMHAWarmupOps
  participant FMHAWarmupWorker
  participant FMHAKernel
  ModelEngine->>FMHAWarmupOps: drain and verify before capture
  FMHAWarmupOps->>FMHAWarmupWorker: drain queued sweeps
  FMHAWarmupOps->>FMHAKernel: verify completed sweeps
  FMHAKernel-->>ModelEngine: return verified sweep count
  ModelEngine->>ModelEngine: run graph capture

Suggested reviewers: juney-nvidia, bowenfu

Merge Risk: Moderate · previous review ea045

The change adds opt-in asynchronous FMHA kernel compilation. It does not yet pass CI: pre-commit fails, and one new unit test inspects the wrong method. In multi-GPU runs with the flag enabled, ranks can leave the compile drain at different times. This skew can make CUDA-graph capture time out under the MoE all-to-all budget. Fix these issues before merging.

Pre-merge checks: 4

Check name	Status	Explanation
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.
Title check		The title clearly and concisely identifies the main change: opt-in overlap of TRTLLM-Gen FMHA JIT warmup compilation with engine warmup.
Description check		The author description covers the problem (synchronous NVRTC compilation on the engine cold-start path), the opt-in flag, the design (background worker, dedup, pre-capture barrier, serialization, owne…

Full details: Description check

Explanation

The author description covers the problem (synchronous NVRTC compilation on the engine cold-start path), the opt-in flag, the design (background worker, dedup, pre-capture barrier, serialization, ownership, shutdown, fallbacks), the affected files, and a detailed test coverage section with CPU-only and Blackwell tests. It also includes measured B200 results. It does not follow the repository's PR title format, but the template's required sections (Description, Test Coverage, PR Checklist) are all present and substantively filled. The description is mostly complete. The remaining gaps are minor: the GPU end-to-end test has not been run by the author, and the A/B results are internal and not reproducible from the PR. The checklist is marked as checked.

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Inline comments:
In `@cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h`:
- Around line 793-794: Ensure TllmFmhaKernelFactory drains the asynchronous
warmup worker before destroying its mKernels-owned TllmGenFmhaKernel instances.
Add an explicit factory destruction path that invokes drain() before unique_ptr
teardown, preserving the queued callback’s use of this in
runJITWarmupGridIfRequested().
- Around line 709-718: Update the warmup registry key used by
getJITWarmupRegistry and the surrounding registration, lookup, and verification
logic to include the device identity along with the existing
jitWarmupFingerprint. Ensure TllmFmhaKernelFactory::Get instances on different
devices cannot consume or verify each other’s kBackgroundDone entries, while
preserving matching behavior for the same device and kernel configuration.

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…el destruction - Include the current device in the JIT warmup fingerprint and store it in the registry entry; the verify pass now only consumes sweeps registered for the current device. Compiled modules load into the calling device's CUDA context, so an identical configuration on another device still needs its own sweep (single-process multi-GPU flows). - Drain the async warmup worker in ~TllmGenFmhaKernel(): a queued background task captures 'this', and nothing forced a drain before static teardown of the owning factory if a process exits mid-startup. The worker singleton is intentionally leaked, so draining during static destruction is safe. Also delete copy operations (the factory owns the kernel via unique_ptr; nothing copies it). Signed-off-by: Aaron Meoded <aaron.meoded@baseten.co>

aaronmeoded-b10 · 2026-09-01T21:39:24Z

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coderabbitai · 2026-09-01T21:39:29Z

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Signed-off-by: Aaron Meoded <aaron.meoded@baseten.co>

chienchunhung

Existing tests run with the feature disabled. Please add deterministic C++ tests for same-device multi-configuration isolation, multi-device deduplication, background failure and retry, drain/destruction, and verification ordering, plus a focused GPU test showing that capture succeeds and the first post-ready request does not JIT-compile.

…try singletons Review feedback (chienchunhung): 1. The warmup registry is shared across all TllmGenFmhaKernel objects on the same device (different dtypes / attention configs). The verify pass filtered only by device, so kernel A could consume kernel B's kBackgroundDone entry, mark it verified, and leave B to JIT-compile on its first real request. Now each registry entry stores the registering kernel instance (mKernelInstance = this) and the verify pass filters on both device and instance. 2. The registry mutex, registry map, and unverified counter were function-local statics, destroyed before the factory's kernels during static teardown. A destructor drain() could then wait on a worker task using already-destroyed state. All three are now leaked heap singletons (same pattern as the worker itself), guaranteeing they outlive every kernel object. Signed-off-by: Aaron Meoded <aaron.meoded@baseten.co>

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🟠 Major · Include the kernel instance in warmup deduplication. · fmhaKernels.h:698-700

cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h:698-700
🎯 Functional Correctness | 🟠 Major | ⚡ Quick win

Include the kernel instance in warmup deduplication.

tryRegisterJITWarmupConfig() returns for every matching fingerprint without checking mKernelInstance. TllmFmhaKernelFactory::hashID() creates separate fused and unfused kernel objects, but jitWarmupFingerprint() does not hash the fusion setting. The second object can therefore skip scheduling. The later verify pass filters by instance and finds no entry, so that object can JIT-compile on its first runtime request.

Use a registry key that includes both the fingerprint and the kernel instance, or keep separate entries per instance for each fingerprint.
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In `@cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h` around lines
698 - 700, The warmup registry deduplication in tryRegisterJITWarmupConfig must
distinguish kernel instances in addition to fingerprint. Include mKernelInstance
in the registry key or maintain separate per-instance entries so fused and
unfused objects each schedule and pass the later instance-specific verification.

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Outside diff comments:
In `@cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h`:
- Around line 698-700: The warmup registry deduplication in
tryRegisterJITWarmupConfig must distinguish kernel instances in addition to
fingerprint. Include mKernelInstance in the registry key or maintain separate
per-instance entries so fused and unfused objects each schedule and pass the
later instance-specific verification.

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chienchunhung

The reported reduction in startup time is promising. Keeping this feature disabled by default is a reasonable approach. Please address the remaining inline comments before approval. Update the PR description to match the final implementation.

Resolve fmhaKernels.h conflicts against the selectKernelAutotuned refactor: the kernel-selection lock now lives inside selectKernelAutotuned(), and computeNumCtas() calls go through computeNumCtasSerialized(), so all three call sites (checkIfKernelExist, warmupOneKernel, run) keep upstream's shape. Signed-off-by: Aaron Meoded <aaron.meoded@baseten.co>

…down, per-sweep verification Review feedback (chienchunhung): - Pre-capture barrier: ModelEngine.warmup now runs torch.ops.trtllm.trtllm_gen_fmha_jit_warmup_drain_and_verify() between the graph-shape warmup pass and graph capture, outside the graph-enabled and general-warmup conditions. The op waits for every queued background sweep and re-runs each finished-but-unverified sweep on the calling thread, through the kernel object and device that scheduled it, so every kernel the warmup derived is compiled before any launch is captured and before readiness. The in-run() verify pass stays as the fallback for callers that never reach the barrier. - Ordered shutdown: the worker thread is now joinable and shut down through a std::atexit handler registered when the worker is first created (from the first warmup forward). atexit handlers run before the destruction of any static whose initialization completed earlier, so an in-flight sweep finishes while everything it reads is alive. getExecPath()'s static string, which the sweep would otherwise construct lazily on the worker thread, is touched on the registering thread first. The interface and selection mutexes are leaked like the registry. - The worker and the sweep registry move to fmhaJitWarmup.h, with no CUDA dependency, so the state machine is covered by host unit tests. Each registry entry records the owning kernel object and device; a verify pass only claims its own sweeps. - torch.ops.trtllm.trtllm_gen_fmha_jit_num_requested_kernel_keys() exposes the number of distinct NVRTC kernel configurations requested from the export library (hashed as its cache keys them), so a test can check for request-time compilation directly instead of inferring it from latency. - Merged with main: kernel selection and computeNumCtas now take the selection lock inside selectKernelAutotuned()/computeNumCtasSerialized() rather than at each call site. Signed-off-by: Aaron Meoded <aaron.meoded@baseten.co>

…e barrier, no post-ready JIT - cpp/tests/unit_tests/kernels/fmhaJitWarmupTest.cpp (host-only, registered in CMake): worker ordering, drain, failing tasks, shutdown with pending work, enqueue after shutdown; registry dedup, same-device multi-config isolation, multi-device isolation, claim-once semantics; and the scheduling protocol end to end including background failure retried on the foreground and inline fallback when the worker is shut down. - tests/unittest/_torch/executor/test_fmha_jit_warmup_barrier.py (cpu_only, picked up by l0_cpu.yml's unittest/_torch/executor entry): the barrier runs after the graph-shape warmup pass and before capture, with CUDA graphs enabled and disabled, independently of the general-warmup gate, reports foreground recovery of failed background sweeps, is skipped for non-TRTLLM attention backends, and warmup() only reaches capture through the helper that carries it. - tests/unittest/_torch/attention/test_trtllm_gen_fmha_async_jit_warmup.py (Blackwell, picked up by l0_b200.yml's unittest/_torch/attention entry): boots Qwen3-0.6B with CUDA graphs in a fresh process per arm (async and sync warmup) and asserts capture succeeds, the warmup exercised the NVRTC path, and the first requests after readiness request no new kernel configuration, i.e. compile nothing. Signed-off-by: Aaron Meoded <aaron.meoded@baseten.co>

Signed-off-by: Aaron Meoded <aaron.meoded@baseten.co>

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Inline comments:
Review comments at @tensorrt_llm/_torch/pyexecutor/model_engine.py:
- Around line 2650-2658: In the CUDA graph warmup flow, synchronize ranks
immediately after _drain_and_verify_trtllm_gen_fmha_jit_warmup() and before the
next _run_cuda_graph_warmup(). Use self.dist.tp_allgather(1) for standard TP,
and the DWDP subgroup communicator used by the distributed forward for DWDP
rather than self.dist.

Review comments at
@tests/unittest/_torch/attention/test_trtllm_gen_fmha_async_jit_warmup.py:
- Around line 102-106: Add
test_first_requests_after_readiness_do_not_jit_compile from
test_trtllm_gen_fmha_async_jit_warmup.py to the single-GPU Blackwell CI list,
such as l0_b200.yml, so the @skip_pre_blackwell test runs on Blackwell; do not
leave it covered only by the H100 list.
- Line 92: Rename the ambiguous comprehension variable in the RESULT-line
filtering expression to a descriptive name, and format the comprehension to
match Ruff’s formatting for the surrounding code.

Review comments at
@tests/unittest/_torch/executor/test_fmha_jit_warmup_barrier.py:
- Around line 143-149: Update test_warmup_routes_capture_through_the_helper to
inspect PyTorchModelEngine._warmup_scheduled rather than
PyTorchModelEngine.warmup, since the scheduled method contains the
capture-helper call being checked.
- Around line 71-84: Reformat the test containing the `_run_cuda_graph_warmup`
and `trtllm_gen_fmha_jit_warmup_drain_and_verify` patches to match `ruff-format`
output, keeping the existing test behavior unchanged.

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cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaJitWarmup.h
cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h
cpp/tensorrt_llm/thop/CMakeLists.txt
cpp/tensorrt_llm/thop/trtllmGenFmhaJitWarmupOp.cpp
cpp/tests/unit_tests/kernels/CMakeLists.txt
cpp/tests/unit_tests/kernels/fmhaJitWarmupTest.cpp
tensorrt_llm/_torch/pyexecutor/model_engine.py
tests/unittest/_torch/attention/test_trtllm_gen_fmha_async_jit_warmup.py
tests/unittest/_torch/executor/test_fmha_jit_warmup_barrier.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.

aaronmeoded-b10 · 2026-10-20T05:14:46Z

Thanks. Agreed on the broader CI run; the bot commands need a maintainer, so could you or @yuxianq trigger the wider stage set now that the rebase onto main is pushed? Good to know the disk cache sits on the library side.

aaronmeoded-b10 · 2026-10-20T05:14:47Z

Rebased onto main after #20134 and #20246: the barrier moved with the warmup into DecoderWarmup and now runs right before warmup_and_capture; the barrier test drives DecoderWarmup.run. No change to the C++ side.

aaronmeoded-b10 · 2026-10-28T19:59:43Z

Merged main again after #20270 (runner surface), #20242 and #20517 (both touch selectKernelAutotuned next to the selection lock). The barrier test follows the new DecoderWarmup constructor; no change to the C++ side beyond the conflict resolution.

aaronmeoded-b10 · 2026-10-28T19:59:45Z

@lori-ren all five of your threads from 10/15 are answered and the branch is merged with main after the DecoderWarmup split and your runner refactor, so the barrier now lives in warmup.py. Could you take another look when you have a moment? The remaining code-owner approval is the runtime one.

chienchunhung · 2026-10-28T21:57:40Z

/bot run --disable-fail-fast

tensorrt-cicd · 2026-10-28T22:03:58Z

PR_Github #81262 [ run ] triggered by Bot. Commit: a17aa79 Link to invocation

tensorrt-cicd · 2026-10-28T22:40:20Z

PR_Github #81262 [ run ] completed with state FAILURE. Commit: a17aa79
/LLM/main/L0_MergeRequest_PR pipeline #66952 completed with status: 'FAILURE'

CI Report

⚠️ Action Required:

Please check the failed tests and fix your PR
If you cannot view the failures, ask the CI triggerer to share details
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chienchunhung · 2026-11-03T03:01:18Z

@aaronmeoded-b10 Please rebase and resolve the conflicts.

Resolves fmhaKernels.h against NVIDIA#20242 and NVIDIA#20517: both include lists are kept, and selectKernelAutotuned() takes the selection lock before the new configurePaddedMlaTile() call. The barrier test follows NVIDIA#20270: DecoderWarmup no longer carries kv_cache_manager_key, run() warms the named all-reduce topologies on the model, and the general warmup adds the MiniMax-M3 MSA indexer prewarm. Signed-off-by: Aaron Meoded <aaron.meoded@baseten.co> Signed-off-by: Chien-Chun Hung <2679986+chienchunhung@users.noreply.github.com>

chienchunhung · 2026-11-03T17:35:39Z

/bot run --disable-fail-fast

tensorrt-cicd · 2026-11-03T17:51:16Z

PR_Github #82328 [ run ] triggered by Bot. Commit: 91f3057 Link to invocation

aaronmeoded-b10 · 2026-11-03T17:52:52Z

Thanks for carrying the rebase. One leftover from #20669: test_fmha_jit_warmup_barrier.py is still under tests/unittest/_torch/executor, which no test list collects any more (l0_cpu.yml now lists unittest/_torch/pyexecutor), and that path also picked up the catch-all code-owner request. Moving it to tests/unittest/_torch/pyexecutor/ fixes both. Happy to push that once this CI run finishes, or go ahead if you prefer.

Place the test under the directory selected by l0_cpu.yml and the runtime CODEOWNERS rule. Signed-off-by: Chien-Chun Hung <2679986+chienchunhung@users.noreply.github.com>

chienchunhung · 2026-11-03T18:38:43Z

/bot run --disable-fail-fast

tensorrt-cicd · 2026-11-03T18:55:26Z

PR_Github #82336 [ run ] triggered by Bot. Commit: 80ac500 Link to invocation

tensorrt-cicd · 2026-11-03T18:59:03Z

PR_Github #82328 [ run ] completed with state ABORTED. Commit: 91f3057

Link to invocation

chienchunhung · 2026-11-03T19:03:08Z

Thanks for carrying the rebase. One leftover from #20669: test_fmha_jit_warmup_barrier.py is still under tests/unittest/_torch/executor, which no test list collects any more (l0_cpu.yml now lists unittest/_torch/pyexecutor), and that path also picked up the catch-all code-owner request. Moving it to tests/unittest/_torch/pyexecutor/ fixes both. Happy to push that once this CI run finishes, or go ahead if you prefer.

Thanks for the notice. Triggered the new CI run with the updated changes.

Please also make sure all the open comments are addressed; resolve the threads once addressed.

tensorrt-cicd · 2026-11-03T23:15:21Z

PR_Github #82336 [ run ] completed with state FAILURE. Commit: 80ac500
/LLM/main/L0_MergeRequest_PR pipeline #67958 completed with status: 'FAILURE'

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Once fixed, request an NVIDIA team member to trigger CI again

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chienchunhung · 2026-11-04T04:07:51Z

Hi @aaronmeoded-b10 , please take a look at the CI agent report and address the test failures. Thanks!

aaronmeoded-b10 · 2026-11-04T04:11:30Z

Thanks @chienchunhung . The report and CI dashboard links are NVIDIA-internal, so I can't open them from outside. Could you paste the failing test names and the relevant log excerpt? I re-checked test_fmha_jit_warmup_barrier.py against the current warmup.py and nothing on its path changed with the move, so I'd like to see what actually failed before pushing anything. I'll fix it as soon as I have the details.

Signed-off-by: Aaron Meoded <aaron.meoded@baseten.co>

aaronmeoded-b10 · 2026-11-04T16:27:12Z

Merged main (3d5ab2e): fmhaKernels.h conflicted with #20623. Both sides are kept: this PR's computeNumCtasSerialized and the warmup registry/verify code alongside #20623's nvrtcCompileOptions and the SM107 context-kernel warmup grid. The context grid stays on the foreground path as in main; only the generation-kernel grid goes through the background worker. warmup.py auto-merged. CI will need a re-trigger, and I still need the failing test names from the last run to address them.

chienchunhung · 2026-11-10T17:44:15Z

/bot run --disable-fail-fast

tensorrt-cicd · 2026-11-10T17:51:02Z

PR_Github #83854 [ run ] triggered by Bot. Commit: 3d5ab2e Link to invocation

tensorrt-cicd · 2026-11-11T01:03:42Z

PR_Github #83854 [ run ] completed with state SUCCESS. Commit: 3d5ab2e
/LLM/main/L0_MergeRequest_PR pipeline #69349 completed with status: 'FAILURE'

CI Report

⚠️ Resource Maintenance — Test Coverage Reduced:

Resource maintenance in LLM/main/L0_Test-x86_64-Single-GPU #10746: H100_PCIe-PyTorch-Perf-1 was retired from main; skip the legacy stage in older PR revisions.
Affected stages:
H100_PCIe-PyTorch-Perf-1

⚠️ Action Required:

Please check the failed tests and fix your PR
If you cannot view the failures, ask the CI triggerer to share details
Once fixed, request an NVIDIA team member to trigger CI again

CI Agent Failure Analysis

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Comment thread cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h Resolved

Comment thread cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h Resolved

[None][chore] Apply clang-format

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Comment thread cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h Resolved

Comment thread cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h Resolved

Comment thread cpp/tensorrt_llm/kernels/trtllmGenKernels/fmha/fmhaKernels.h Resolved

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aaronmeoded-b10 added 3 commits October 7, 2026 14:56

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[None][chore] Apply clang-format

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lori-ren approved these changes Nov 3, 2026

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chienchunhung force-pushed the fmha-async-jit-warmup branch from a17aa79 to 91f3057 Compare November 3, 2026 17:35

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chienchunhung requested review from brnguyen2 and schetlur-nv November 3, 2026 17:35

[None][test] Move FMHA warmup barrier test into pyexecutor

80ac500

Place the test under the directory selected by l0_cpu.yml and the runtime CODEOWNERS rule. Signed-off-by: Chien-Chun Hung <2679986+chienchunhung@users.noreply.github.com>

Merge branch 'main' into fmha-async-jit-warmup

3d5ab2e

Signed-off-by: Aaron Meoded <aaron.meoded@baseten.co>

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