[None][fix] [fix] Make NCCL resource manager destructor exception-safe

[nv-lschneider]

Summary by CodeRabbit

• Bug Fixes
  • Improved stability during application shutdown by strengthening exception handling in resource cleanup sequences.
  • Enhanced robustness of cleanup operations to prevent crashes in static destruction scenarios through safer locking patterns and guarded exception handling.
  • Made logging operations during teardown more resilient to prevent failure propagation during shutdown procedures.

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Description

It was reported that the clean up of NCCL resources is not always clean and could end in segfaults.

I am struggling to reproduce this exact error.
But upon inspection of the code, I can see why they is a potential problem when tearing down the static ResourceManager.

In particular the unprotected access of the Logger is potentially the issue here.

I updated the code, to be a lot more conservative in the tear-down process to prevent problem in the future.

Test Coverage

No new test needed.

PR Checklist

• Please check this after reviewing the above items as appropriate for this PR.

[coderabbitai]

📝 Walkthrough

Walkthrough

Hardens the NCCL resource manager's destructor and cleanup path to be exception-safe during static destruction. Introduces a destruction-state flag, moves resource cleanup outside the mutex, wraps cleanup and logging calls in try-catch blocks, and prevents re-entrant cleanup.

Changes

Cohort / File(s)	Change Summary
NCCL Resource Manager Header cpp/tensorrt_llm/common/ncclUtils.h	Changed destructor from defaulted to explicit declaration; added private atomic flag mIsDestroying to track destruction state and prevent re-entrant cleanup.
NCCL Resource Manager Implementation cpp/tensorrt_llm/common/ncclUtils.cpp	Added custom destructor that safely cleans up registered resources outside the mutex; extended cleanupResources with destruction checks and exception handling; wrapped resource cleanup and logging in try-catch to prevent exceptions during static destruction.
Operation Utilities cpp/tensorrt_llm/common/opUtils.cpp	Protected logging call in NCCL comm destructor path with try-catch to prevent logging failures from propagating during static destruction.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

• Exception-handling patterns — Multiple similar try-catch blocks around logging and cleanup; verify exception swallowing is intentional and complete
• Mutex and atomic interaction — Confirm proper lock acquire/release semantics and that the atomic flag prevents data races
• Resource iteration during cleanup — Verify moving resources outside the lock preserves expected cleanup order and doesn't introduce use-after-free

Pre-merge checks and finishing touches

❌ Failed checks (2 warnings)

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✅ Passed checks (1 passed)

Check name	Status	Explanation
Description check	✅ Passed	The description covers the core issue, root cause analysis, and approach, with the PR checklist box checked. However, the description lacks detail on what 'more conservative' changes entail and test coverage explanation is minimal.

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📥 Commits

Reviewing files that changed from the base of the PR and between 20b69a9 and f3000d8.

📒 Files selected for processing (3)

• cpp/tensorrt_llm/common/ncclUtils.cpp (3 hunks)
• cpp/tensorrt_llm/common/ncclUtils.h (2 hunks)
• cpp/tensorrt_llm/common/opUtils.cpp (1 hunks)

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📓 Path-based instructions (3)

**/*.{cpp,h,cu,cuh}

📄 CodeRabbit inference engine (CODING_GUIDELINES.md)

**/*.{cpp,h,cu,cuh}: Closing braces of namespaces should have a comment saying the namespace it closes: } // namespace foo
Prefer const or constexpr variables over #define whenever possible, as the latter are not visible to the compiler
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For naming of constants in C++, follow the naming section conventions
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Files:

• cpp/tensorrt_llm/common/ncclUtils.h
• cpp/tensorrt_llm/common/ncclUtils.cpp
• cpp/tensorrt_llm/common/opUtils.cpp

**/*.h

📄 CodeRabbit inference engine (CODING_GUIDELINES.md)

**/*.h: Use a preprocessor guard in C++ header files with the format TRTLLM_<FILENAME>_H derived from the filename in all caps
The preprocessor guard name in C++ must have prefix TRTLLM_ followed by the filename, all in caps. Only use the file name, not directory names
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Files:

• cpp/tensorrt_llm/common/ncclUtils.h

**/*.{cpp,h,cu,cuh,py}

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Files:

• cpp/tensorrt_llm/common/ncclUtils.h
• cpp/tensorrt_llm/common/ncclUtils.cpp
• cpp/tensorrt_llm/common/opUtils.cpp

🧠 Learnings (11)

📚 Learning: 2025-09-23T15:12:38.312Z

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device implementation, NCCL version 2.28+ requirements are handled at runtime in the nccl_device/config layer rather than with compile-time guards. This allows the allreduceOp to remain version-agnostic and delegates version compatibility validation to the appropriate lower-level components that can gracefully handle unsupported configurations.

Applied to files:

• cpp/tensorrt_llm/common/ncclUtils.h
• cpp/tensorrt_llm/common/ncclUtils.cpp
• cpp/tensorrt_llm/common/opUtils.cpp

📚 Learning: 2025-09-23T15:01:00.070Z

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/kernels/nccl_device/config.cu:15-17
Timestamp: 2025-09-23T15:01:00.070Z
Learning: In TensorRT-LLM NCCL device kernels, the <sstream> header is not needed as an explicit include in config.cu because it's provided transitively through other headers. Local compilation testing confirms this works without the explicit include.

Applied to files:

• cpp/tensorrt_llm/common/ncclUtils.h
• cpp/tensorrt_llm/common/ncclUtils.cpp
• cpp/tensorrt_llm/common/opUtils.cpp

📚 Learning: 2025-09-23T15:01:00.070Z

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/kernels/nccl_device/config.cu:15-17
Timestamp: 2025-09-23T15:01:00.070Z
Learning: In TensorRT-LLM NCCL device kernels (cpp/tensorrt_llm/kernels/nccl_device/config.cu), std::ostringstream is used but <sstream> doesn't need to be explicitly included because it's provided transitively through other headers like tensorrt_llm/common/cudaUtils.h or config.h. Local compilation testing confirms this works without the explicit include.

Applied to files:

• cpp/tensorrt_llm/common/ncclUtils.h
• cpp/tensorrt_llm/common/ncclUtils.cpp
• cpp/tensorrt_llm/common/opUtils.cpp

📚 Learning: 2025-09-16T09:30:09.716Z

Learnt from: tongyuantongyu
Repo: NVIDIA/TensorRT-LLM PR: 7763
File: cpp/tensorrt_llm/CMakeLists.txt:297-301
Timestamp: 2025-09-16T09:30:09.716Z
Learning: In the TensorRT-LLM project, NCCL libraries are loaded earlier by PyTorch libraries or the bindings library, so the main shared library doesn't need NCCL paths in its RPATH - the libraries will already be available in the process address space when needed.

Applied to files:

• cpp/tensorrt_llm/common/ncclUtils.h
• cpp/tensorrt_llm/common/opUtils.cpp

📚 Learning: 2025-10-13T19:45:03.518Z

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: tests/unittest/_torch/multi_gpu/test_nccl_device.py:138-149
Timestamp: 2025-10-13T19:45:03.518Z
Learning: In test_nccl_device.py, the NCCL device AllReduce implementation compares the entire residual tensor on each rank, unlike the UB implementation which compares per-rank chunks. The residual chunking calculations in the test are intentionally overridden to reflect this design difference.

Applied to files:

• cpp/tensorrt_llm/common/ncclUtils.h
• cpp/tensorrt_llm/common/ncclUtils.cpp

📚 Learning: 2025-09-23T15:12:38.312Z

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device allreduce implementation (cpp/tensorrt_llm/thop/allreduceOp.cpp), the goto pattern in runNCCLAllReduceDeviceFusion is intentionally used for future extensibility, allowing multiple switch cases to fallback to the default handler. While not aesthetically ideal, this pattern supports adding more fusion cases later that can reuse the same fallback logic.

Applied to files:

• cpp/tensorrt_llm/common/ncclUtils.h
• cpp/tensorrt_llm/common/ncclUtils.cpp
• cpp/tensorrt_llm/common/opUtils.cpp

📚 Learning: 2025-09-23T14:58:05.372Z

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/kernels/nccl_device/config.cu:42-49
Timestamp: 2025-09-23T14:58:05.372Z
Learning: In TensorRT-LLM NCCL device kernels (cpp/tensorrt_llm/kernels/nccl_device/), the token partitioning intentionally uses ceil-like distribution (same token_per_rank for all ranks) to ensure all ranks launch the same number of blocks. This is required for optimal NCCL device API barrier performance, even though it may launch extra blocks for non-existent tokens on later ranks. Runtime bounds checking in the kernel (blockID validation) handles the overshoot cases.

Applied to files:

• cpp/tensorrt_llm/common/ncclUtils.h
• cpp/tensorrt_llm/common/opUtils.cpp

📚 Learning: 2025-08-20T06:56:02.889Z

Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 6768
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:577-579
Timestamp: 2025-08-20T06:56:02.889Z
Learning: In cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, maxSequenceLength is now enforced as a non-optional argument in the BlockManager constructor, so concerns about std::nullopt defaulting to 0 are not applicable. When windowSize > maxSequenceLength, a warning should be added instead of handling optional parameter cases.

Applied to files:

• cpp/tensorrt_llm/common/ncclUtils.h

📚 Learning: 2025-09-02T13:42:44.885Z

Learnt from: pcastonguay
Repo: NVIDIA/TensorRT-LLM PR: 7455
File: tensorrt_llm/_torch/pyexecutor/py_executor.py:1852-1860
Timestamp: 2025-09-02T13:42:44.885Z
Learning: In MPI communication within TensorRT-LLM pipeline parallelism, different communication types (tokens, logits, termination sync) must use disjoint tag namespaces to avoid message routing collisions when using the same source/destination patterns.

Applied to files:

• cpp/tensorrt_llm/common/ncclUtils.h
• cpp/tensorrt_llm/common/opUtils.cpp

📚 Learning: 2025-08-21T09:41:49.347Z

Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 6768
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:2010-2045
Timestamp: 2025-08-21T09:41:49.347Z
Learning: In cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, updateSequenceCacheBlockOffsets is specifically for updating bookkeeping when blocks are added during the context phase, not for refreshing offsets after detach operations. During detach operations, GenerationRequest::removeFrontBlock handles the necessary cache block bookkeeping internally.

Applied to files:

• cpp/tensorrt_llm/common/ncclUtils.h

📚 Learning: 2025-09-22T19:25:45.607Z

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/kernels/userbuffers/ub_allocator.cpp:170-179
Timestamp: 2025-09-22T19:25:45.607Z
Learning: In NCCLUserBufferAllocator::getNCCLDevComm(), multimem support is hard-coded to true because multimem is required for this function. The caller is responsible for ensuring multimem is available before calling this function - it should not be called if multimem is not supported.

Applied to files:

• cpp/tensorrt_llm/common/opUtils.cpp

🧬 Code graph analysis (2)

cpp/tensorrt_llm/common/ncclUtils.h (1)

cpp/tensorrt_llm/common/ncclUtils.cpp (1)

• NcclCommResourceManager (40-78)

cpp/tensorrt_llm/common/opUtils.cpp (1)

cpp/tensorrt_llm/common/ncclUtils.cpp (6)

• getInstance (34-38)
• getInstance (34-34)
• getInstance (217-221)
• getInstance (217-217)
• getInstance (345-349)
• getInstance (345-345)

🪛 Cppcheck (2.18.0)

cpp/tensorrt_llm/common/ncclUtils.cpp

[error] 51-51: There is an unknown macro here somewhere. Configuration is required. If TRTLLM_NAMESPACE_BEGIN is a macro then please configure it.

(unknownMacro)

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)

• GitHub Check: Pre-commit Check

🔇 Additional comments (8)

cpp/tensorrt_llm/common/ncclUtils.h (2)

29-29: LGTM!

The <atomic> include is correctly added to support the new mIsDestroying atomic member variable.

---

143-149: Well-designed destruction safety mechanism.

The custom destructor declaration and mIsDestroying atomic flag properly enable the destruction-safe cleanup pattern. The atomic ensures visibility across threads during the static destruction phase, and the implementation (in ncclUtils.cpp) correctly uses memory_order_release when setting and memory_order_acquire when loading for proper synchronization.

cpp/tensorrt_llm/common/opUtils.cpp (2)

126-129: LGTM!

The comment clarifies the destruction guard mechanism - cleanupResources will return early if the singleton is being destroyed, allowing the destructor to handle cleanup proactively.

---

135-143: Appropriate destruction-safe logging.

Wrapping the logging call in try-catch prevents potential segfaults if the Logger singleton is destroyed before this deleter runs during static destruction. This is consistent with the pattern applied in ncclUtils.cpp.

cpp/tensorrt_llm/common/ncclUtils.cpp (4)

40-78: Solid destruction-safe cleanup implementation.

The destructor correctly:

1. Sets mIsDestroying with memory_order_release before any cleanup
2. Moves resources out of the map under the lock
3. Clears the map while holding the lock
4. Performs cleanup outside the lock to avoid deadlocks
5. Wraps each cleanup call in try-catch to prevent exceptions from escaping the destructor

This ensures resources are cleaned up in a controlled manner during static destruction, avoiding races with cleanupResources calls from shared_ptr deleters.

---

103-108: Correct early-return guard for destruction phase.

The memory_order_acquire load correctly synchronizes with the memory_order_release store in the destructor, ensuring that if we see mIsDestroying == true, we also see all prior writes made by the destructor. Returning early prevents double-cleanup and potential use-after-free.

---

113-152: Conservative but appropriate defensive coding for static destruction.

The try-catch around mutex acquisition handles the edge case where the mutex itself may be in an undefined state during static destruction. The double-check of mIsDestroying after acquiring the lock (lines 119-123) correctly handles the race where destruction starts between the first check and lock acquisition.

Moving resources out under the lock, then processing cleanup outside the lock, is the right pattern to avoid deadlocks if cleanup callbacks try to access the manager.

---

157-196: Maximally defensive cleanup loop.

Each logging call is individually wrapped in try-catch, ensuring that:

1. Logging failures don't prevent the actual cleanup() call
2. Exceptions from one resource's cleanup don't prevent cleanup of subsequent resources
3. Error logging failures during exception handling don't mask the original error

While verbose, this level of defensiveness is appropriate for static destruction code paths where the Logger may be destroyed at any point.

---

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[nv-lschneider]

/bot run

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PR_Github #29190 [ run ] triggered by Bot. Commit: 2209ce0

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PR_Github #29190 [ run ] completed with state SUCCESS. Commit: 2209ce0
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[nv-lschneider]

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PR_Github #29427 [ run ] triggered by Bot. Commit: 2209ce0

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PR_Github #29427 [ run ] completed with state SUCCESS. Commit: 2209ce0
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[nv-lschneider]

/bot run --disable-fail-fast

[taylor-yb-lee]

Reproduce steps:

• The original symptom happend only with CI's prebuilt wheel. With wheel built from source locally, the symptom was not observed
• CMD: for i in {1..10}; do mpirun -n 1 trtllm-bench --model /lustre/fsw/coreai_comparch_trtllm/yeonbokl/dev/models/Llama-3.3-70B-FP8 --model_path /lustre/fsw/coreai_comparch_trtllm/yeonbokl/dev/models/Llama-3.3-70B-FP8 throughput --max_batch_size 1 --max_num_tokens 1024 --kv_cache_free_gpu_mem_fraction 0.8 --tp 4 --concurrency 1 --num_requests 3 --extra_llm_api_options extra_llm_api_config.yml --dataset outputs_req3_trace/isl1024osl20_dataset.txt --warmup 0 --streaming --backend pytorch --max_seq_len 1044; done
• With prebuilt wheel from CI with main branch :
  !!!!!!! Segfault encountered !!!!!!! File "<unknown>", line 0, in tensorrt_llm::_v1::common::nccl_util::NcclCommResourceManager::cleanupResources(ncclComm*) File "<unknown>", line 0, in std::_Sp_counted_deleter<ncclComm**, tensorrt_llm::_v1::getComm(std::set<int, std::less<int>, std::allocator<int> > const&)::{lambda(ncclComm**)#1}, std::allocator<void>, (__gnu_cxx::_Lock_policy)2>::_M_dispose() File "<unknown>", line 0, in std::map<std::set<int, std::less<int>, std::allocator<int> >, std::shared_ptr<ncclComm*>, std::less<std::set<int, std::less<int>, std::allocator<int> > >, std::allocator<std::pair<std::set<int, std::less<int>, std::allocator<int> > const, std::shared_ptr<ncclComm*> > > >::~map() File "<unknown>", line 0, in exit File "<unknown>", line 0, in _start File "<unknown>", line 0, in 0xffffffffffffffff
• With prebuilt wheel from CI with this PR
  - No segfault observed

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PR_Github #29464 [ run ] triggered by Bot. Commit: 2209ce0

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PR_Github #29464 [ run ] completed with state SUCCESS. Commit: 2209ce0
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[nv-lschneider]

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PR_Github #29466 [ run ] triggered by Bot. Commit: 591739e

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[nv-lschneider]

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PR_Github #29489 [ run ] triggered by Bot. Commit: 591739e

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PR_Github #29489 [ run ] completed with state SUCCESS. Commit: 591739e
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/bot run --disable-fail-fast

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PR_Github #30040 [ run ] triggered by Bot. Commit: b80c07c

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PR_Github #30040 [ run ] completed with state SUCCESS. Commit: b80c07c
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[hyukn]

Maybe we should add multi-gpu stages for more coverage?

[nv-lschneider]

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PR_Github #30387 [ run ] triggered by Bot. Commit: b80c07c

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PR_Github #30387 [ run ] completed with state SUCCESS. Commit: b80c07c
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Pipeline passed with automatic retried tests. Check the rerun report for details.

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PR_Github #30402 [ run ] triggered by Bot. Commit: ffe6e8c

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PR_Github #30402 [ run ] completed with state SUCCESS. Commit: ffe6e8c
/LLM/main/L0_MergeRequest_PR pipeline #23431 completed with status: 'SUCCESS'

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/bot run --only-multi-gpu-test

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PR_Github #30413 [ run ] triggered by Bot. Commit: ffe6e8c

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PR_Github #30413 [ run ] completed with state SUCCESS. Commit: ffe6e8c
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/bot run --reuse-pipeline

[tensorrt-cicd]

PR_Github #30450 Bot args parsing error: usage: /bot [-h]
{run,kill,skip,submit,reviewers,reuse-pipeline,reuse-review} ...
/bot: error: unrecognized arguments: --reuse-pipeline

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PR_Github #30451 [ reuse-pipeline ] triggered by Bot. Commit: ffe6e8c

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PR_Github #30451 [ reuse-pipeline ] completed with state SUCCESS. Commit: ffe6e8c
Reusing PR_Github #30413 (Partly Tested) for commit ffe6e8c