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Root Cause The trtllm_allreduce() function was creating a new AllReduce module instance on every call. During CUDA graph warmup (which involves multiple forward passes), this caused repeated initialization of MNNVL workspace allocation. When strategy=AUTO, the AllReduce.init() attempts to initialize MNNVLAllReduce, which calls get_allreduce_mnnvl_workspace(). This function performs CPU synchronization operations (torch.cuda.synchronize() and comm.Barrier()) that are incompatible with CUDA graph capture. The code even includes a comment acknowledging this: "CPU barrier since we assume this should not be called in cuda graph". Repeated module initialization during warmup → repeated barrier calls → hang. Solution Implemented module-level caching for AllReduce instances in trtllm.py. The cache key is (rank, world_size, dtype) to handle different tensor configurations. Key changes: Added _allreduce_cache dictionary to store AllReduce modules Modified trtllm_allreduce() to check cache before creating new instances Each unique configuration creates module only once, before CUDA graph capture Subsequent calls during warmup reuse the cached module without re-initialization With caching, the MNNVL workspace allocation (with its CPU synchronization) happens once before CUDA graph capture begins. During warmup and capture, only the cached module's forward pass executes, which contains no blocking CPU operations.
Why this works This fix benefits all AllReduce strategies (AUTO, NCCL, MIN_LATENCY, MNNVL) as they all allocate workspace during initialization, though NCCL was less affected as it skips MNNVL initialization entirely.
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Introduces module-level caching for AllReduce operators intrtllm_allreduce, replacing per-call creation with lazy initialization and reuse based on configuration key (rank, world_size, dtype), improving CUDA graph compatibility.
Added module-level_allreduce_cache dictionary for storing AllReduce operator instances. Refactoredtrtllm_allreduce to lazily construct and cache AllReduce operations per configuration key instead of creating new instances per call. Switched toAllReduceStrategy.AUTO and included tensor dtype in cache key. Behavior oftrtllm_allgather remains unchanged.
Sequence Diagram(s)
sequenceDiagram participant Caller participant trtllm_allreduce participant _allreduce_cache participant AllReduce rect rgb(200, 240, 255) note over trtllm_allreduce: Old Behavior (Per-call) Caller->>trtllm_allreduce: call with (x, rank, world_size, dtype) trtllm_allreduce->>AllReduce: create new instance AllReduce-->>trtllm_allreduce: instance trtllm_allreduce->>AllReduce: execute AllReduce-->>trtllm_allreduce: result trtllm_allreduce-->>Caller: return result end rect rgb(200, 255, 220) note over trtllm_allreduce: New Behavior (Cached) Caller->>trtllm_allreduce: call with (x, rank, world_size, dtype) trtllm_allreduce->>_allreduce_cache: lookup key=(rank, world_size, dtype) alt Cache Hit _allreduce_cache-->>trtllm_allreduce: cached instance else Cache Miss trtllm_allreduce->>AllReduce: create new instance AllReduce-->>trtllm_allreduce: instance trtllm_allreduce->>_allreduce_cache: store instance end trtllm_allreduce->>AllReduce: execute AllReduce-->>trtllm_allreduce: result trtllm_allreduce-->>Caller: return result end
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Estimated code review effort
🎯 2 (Simple) | ⏱️ ~10 minutes
Areas requiring attention:
Cache key construction and correctness—verify that (rank, world_size, dtype) tuple uniquely identifies configuration
Thread safety of cache access, if applicable in multi-threaded contexts
Cache initialization and potential memory implications with persistent AllReduce instances
Verification thatAllReduceStrategy.AUTO is the appropriate replacement for prior strategy choice
Pre-merge checks and finishing touches
❌ Failed checks (2 warnings)
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Description Check
⚠️ Warning
The PR description provides excellent and detailed explanation of the root cause, the specific problem with CUDA graph compatibility, and a comprehensive solution with key changes. However, the description does not follow the required template structure: the detailed content is placed before the template rather than within the designated "Description" section, and more importantly, the "Test Coverage" section is completely empty with no tests listed. While the Root Cause and Solution sections are thorough and the PR Checklist is marked complete, an explicit required template section is missing, which represents a structural incompleteness of the description.
The Test Coverage section must be filled out with relevant tests that safeguard the AllReduce caching changes. Additionally, the description content should be moved into the proper template sections (Description and Test Coverage) rather than placed outside the template structure. The author should document which test suites validate that the caching mechanism works correctly and doesn't introduce regressions in AllReduce functionality.
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Title Check
✅ Passed
The PR title "[#8781][fix] Cache the AllReduce wrapper to avoid re-allocating workspace which caused a hang" directly and specifically describes the main change in the pull request. It follows the repository's required format with the GitHub issue number, type indicator, and a clear summary. The title accurately reflects the core objective: implementing caching for AllReduce instances to fix a hang caused by repeated workspace re-allocation during CUDA graph warmup. The phrasing is concise and sufficiently specific that developers scanning the history would immediately understand the primary change without ambiguity.
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**/*.py: Python code must target Python 3.8+. Indent Python code with 4 spaces; do not use tabs. Maintain module namespace when importing; prefer 'from package.subpackage import foo' then 'foo.SomeClass()' instead of importing the class directly. Python filenames should be snake_case (e.g., some_file.py). Python classes use PascalCase names. Functions and methods use snake_case names. Local variables use snake_case; prefix 'k' for variables that start with a number (e.g., k_99th_percentile). Global variables use upper SNAKE_CASE prefixed with 'G' (e.g., G_MY_GLOBAL). Constants use upper SNAKE_CASE (e.g., MY_CONSTANT). Avoid shadowing variables from an outer scope. Initialize all externally visible members of a class in the constructor. Prefer docstrings for interfaces that may be used outside a file; comments for in-function or file-local interfaces. Use Google-style docstrings for classes and functions (Sphinx-parsable). Document attributes and variables inline so they render under the class/function docstring. Avoid reflection when a simpler, explicit approach suffices (e.g., avoid dict(**locals()) patterns). In try/except, catch the most specific exceptions possible. For duck-typing try/except, keep the try body minimal and use else for the main logic.
Learnt from: timlee0212PR: NVIDIA/TensorRT-LLM#6886File: tensorrt_llm/_torch/models/modeling_deepseekv3.py:0-0Timestamp: 2025-08-14T06:36:40.701ZLearning: In DeepSeek V3 model (tensorrt_llm/_torch/models/modeling_deepseekv3.py), the disagreement between AllReduce.__init__ guard and _compute_mlp_tp_size logic for MNNVL usage is expected by design. The AllReduce component and MLP TP-size computation intentionally use different criteria for MNNVL availability decisions.
Learnt from: amitz-nvPR: NVIDIA/TensorRT-LLM#5616File: tensorrt_llm/executor/worker.py:375-384Timestamp: 2025-07-17T09:01:27.402ZLearning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks `is_adapter_in_cpu_cache()` and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
Learnt from: nv-lschneiderPR: NVIDIA/TensorRT-LLM#7910File: tests/unittest/_torch/multi_gpu/test_nccl_device.py:138-149Timestamp: 2025-10-13T19:45:03.518ZLearning: 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.
Learnt from: nv-lschneiderPR: NVIDIA/TensorRT-LLM#7910File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446Timestamp: 2025-09-23T15:12:38.312ZLearning: 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.
📚 Learning: 2025-09-23T15:12:38.312Z
Learnt from: nv-lschneiderPR: NVIDIA/TensorRT-LLM#7910File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446Timestamp: 2025-09-23T15:12:38.312ZLearning: 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.
Learnt from: timlee0212PR: NVIDIA/TensorRT-LLM#6886File: tensorrt_llm/_torch/models/modeling_deepseekv3.py:0-0Timestamp: 2025-08-14T06:36:40.701ZLearning: In DeepSeek V3 model (tensorrt_llm/_torch/models/modeling_deepseekv3.py), the disagreement between AllReduce.__init__ guard and _compute_mlp_tp_size logic for MNNVL usage is expected by design. The AllReduce component and MLP TP-size computation intentionally use different criteria for MNNVL availability decisions.
Learnt from: nv-lschneiderPR: NVIDIA/TensorRT-LLM#7910File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446Timestamp: 2025-09-23T15:12:38.312ZLearning: 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.
Learnt from: nv-lschneiderPR: NVIDIA/TensorRT-LLM#7910File: tests/unittest/_torch/multi_gpu/test_nccl_device.py:138-149Timestamp: 2025-10-13T19:45:03.518ZLearning: 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.
Learnt from: tongyuantongyuPR: NVIDIA/TensorRT-LLM#7520File: tensorrt_llm/_torch/pyexecutor/resource_manager.py:605-613Timestamp: 2025-09-24T03:31:28.908ZLearning: In TensorRT-LLM Ray orchestrator mode, ProcessGroups are initialized with both Gloo and NCCL backends (e.g., "cuda:nccl,cpu:gloo"), allowing PyTorch distributed to automatically route CPU tensors through Gloo and GPU tensors through NCCL. This eliminates the need for manual device placement when performing allreduce operations on base types.
Learnt from: nv-lschneiderPR: NVIDIA/TensorRT-LLM#7910File: cpp/tensorrt_llm/kernels/nccl_device/config.cu:42-49Timestamp: 2025-09-23T14:58:05.372ZLearning: 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.
Learnt from: tongyuantongyuPR: NVIDIA/TensorRT-LLM#7763File: cpp/tensorrt_llm/CMakeLists.txt:297-301Timestamp: 2025-09-16T09:30:09.716ZLearning: 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.
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The module-level cache with clear explanatory comments effectively communicates the purpose and criticality for CUDA graph compatibility. The naming convention and placement within the import try-block are appropriate.
1-1:Verify copyright header includes current year.
Ensure the NVIDIA Apache-2.0 copyright header at the top of this file includes 2025 as per coding guidelines.
As per coding guidelines
21-35:Caching logic is correct and verified as safe.
The cache key(rank, world_size, tensor.dtype) appropriately captures the configuration needed for AllReduce initialization. Verification confirms thatAllReduce.forward() is stateless with respect to input tensors—it does not modify any instance attributes or accumulate state across calls. The method safely accepts different tensor inputs andall_reduce_params on each invocation, making instance reuse appropriate for avoiding CUDA graph hangs during capture.
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… workspace which caused a hang (NVIDIA#8803)Signed-off-by: Eran Geva <19514940+MrGeva@users.noreply.github.com>Signed-off-by: FredricZ-2007 <226039983+fredricz-20070104@users.noreply.github.com>
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Root Cause
The trtllm_allreduce() function was creating a new AllReduce module instance on every call. During CUDA graph warmup (which involves multiple forward passes), this caused repeated initialization of MNNVL workspace allocation.
When strategy=AUTO, the AllReduce.init() attempts to initialize MNNVLAllReduce, which calls get_allreduce_mnnvl_workspace(). This function performs CPU synchronization operations (torch.cuda.synchronize() and comm.Barrier()) that are incompatible with CUDA graph capture. The code even includes a comment acknowledging this: "CPU barrier since we assume this should not be called in cuda graph".
Repeated module initialization during warmup → repeated barrier calls → hang.
Solution
Implemented module-level caching for AllReduce instances in trtllm.py. The cache key is (rank, world_size, dtype) to handle different tensor configurations.
Key changes:
Added _allreduce_cache dictionary to store AllReduce modules
Modified trtllm_allreduce() to check cache before creating new instances
Each unique configuration creates module only once, before CUDA graph capture
Subsequent calls during warmup reuse the cached module without re-initialization
With caching, the MNNVL workspace allocation (with its CPU synchronization) happens once before CUDA graph capture begins. During warmup and capture, only the cached module's forward pass executes, which contains no blocking CPU operations.
Why this works
This fix benefits all AllReduce strategies (AUTO, NCCL, MIN_LATENCY, MNNVL) as they all allocate workspace during initialization, though NCCL was less affected as it skips MNNVL initialization entirely.
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