[TRTLLM-9676][fix] Fix mamba_cache_manager when enabling cuda_graph_padding and let test cover this case #9873
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Signed-off-by: jiant <[email protected]>
JadoTu
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nv-guomingz and
yuantailing
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📝 WalkthroughWalkthroughThree files are modified to support Mamba state reordering during request padding: cuda_graph_runner.py adds conditional calls to new reordering logic, mamba_cache_manager.py introduces state index reordering handling with request masking, and test configuration is updated with dynamic batch sizes and sampling parameter adjustments. Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes
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🧹 Nitpick comments (2)
tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py (1)
113-120: Reordering logic for padded Mamba state indices looks sound; consider clearing new buffers on shutdownThe new
request_mask/state_indices_arangehelpers andreorder_state_indices_when_padding_requests()correctly ensure that padded slots instate_indicesare filled with distinct, currently unused indices, avoiding repetitions that can upset the CUDA kernels. Under the existing invariants (padded batch size ≤max_batch_size, one block per active request), you will always have at leastpadding_sizefree indices, so the boolean‑mask / advanced indexing pattern is safe.One small lifecycle nit:
shutdown()currently only clearsconv_states,ssm_states, andstate_indices; for completeness and to keep GPU memory hygiene consistent, it would be good to also clearrequest_maskandstate_indices_arangethere.For example:
def shutdown(self): # release tensor memory, keeping python references as tensors self.conv_states = torch.tensor([]) self.ssm_states = torch.tensor([]) self.state_indices = torch.tensor([]) + self.request_mask = torch.tensor([]) + self.state_indices_arange = torch.tensor([]) torch.cuda.empty_cache()Also applies to: 137-147, 179-184
tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py (1)
393-397: Verify call order: Mamba state reordering must happen afterprepare_resourcesfor the current batchThe idea to special‑case Mamba by calling
reorder_state_indices_when_padding_requests(batch_size, padding_size)when padding is enabled is exactly what’s needed to avoid duplicate SSM state indices for the padded dummy requests.However, this relies on
self.state_indicesalready reflecting the current batch (including the dummy request ID) as populated byMambaCacheManager._prepare_mamba_cache_blocks().From the visible code,
_get_padded_batch()is invoked frompad_batch()before yielding control back to the caller, andMambaCacheManager.prepare_resources()is (in most architectures) called later as part of the main engine flow. If that’s the case,_prepare_mamba_cache_blocks()will overwritestate_indicesafter your reorder call, so duplicates for this step’s padding would remain.You might want to double‑check the actual call order in the engine, and, if
prepare_resources()does indeed run afterpad_batch, consider moving the reordering to a point that is guaranteed to run post‑_prepare_mamba_cache_blocks(for example, a small post‑processing hook insideMambaCacheManager.prepare_resources, guarded by the samepadding_sizelogic passed in via the caller).Please verify the engine’s call sequence to ensure this fix is actually taking effect for the padded CUDA‑graph batches.
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tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py(2 hunks)tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py(2 hunks)tests/integration/defs/accuracy/test_llm_api_pytorch.py(2 hunks)
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**/*.py
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Files:
tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.pytests/integration/defs/accuracy/test_llm_api_pytorch.pytensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py
**/*.{cpp,h,cu,py}
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Files:
tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.pytests/integration/defs/accuracy/test_llm_api_pytorch.pytensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py
🧠 Learnings (10)
📚 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:
tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.pytensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py
📚 Learning: 2025-08-08T04:10:19.038Z
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 6728
File: cpp/tensorrt_llm/plugins/mixtureOfExperts/mixtureOfExpertsPlugin.cpp:966-966
Timestamp: 2025-08-08T04:10:19.038Z
Learning: TensorRT plugins currently don't support padding functionality, and TensorRT is not getting new features (in maintenance mode). This means that duplicating parameters like mExpertHiddenSize in function calls, even with TODO comments, can be acceptable as pragmatic solutions within these constraints.
Applied to files:
tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py
📚 Learning: 2025-07-28T17:06:08.621Z
Learnt from: moraxu
Repo: NVIDIA/TensorRT-LLM PR: 6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.
Applied to files:
tests/integration/defs/accuracy/test_llm_api_pytorch.py
📚 Learning: 2025-08-26T09:49:04.956Z
Learnt from: pengbowang-nv
Repo: NVIDIA/TensorRT-LLM PR: 7192
File: tests/integration/test_lists/test-db/l0_dgx_b200.yml:56-72
Timestamp: 2025-08-26T09:49:04.956Z
Learning: In TensorRT-LLM test configuration files, the test scheduling system handles wildcard matching with special rules that prevent duplicate test execution even when the same tests appear in multiple yaml files with overlapping GPU wildcards (e.g., "*b200*" and "*gb200*").
Applied to files:
tests/integration/defs/accuracy/test_llm_api_pytorch.py
📚 Learning: 2025-09-04T07:33:10.618Z
Learnt from: MrGeva
Repo: NVIDIA/TensorRT-LLM PR: 7219
File: tensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py:162-168
Timestamp: 2025-09-04T07:33:10.618Z
Learning: When users explicitly provide cuda_graph_batch_sizes in TorchCudagraphCompiler, respect their choices and only sanitize the values (clamp, dedupe, sort) without forcing additional batch sizes like 1 or max_batch_size. Only add commonly-used batch sizes when falling back to the heuristic.
Applied to files:
tests/integration/defs/accuracy/test_llm_api_pytorch.py
📚 Learning: 2025-09-09T09:40:45.658Z
Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 7645
File: tests/integration/test_lists/qa/llm_function_core.txt:648-648
Timestamp: 2025-09-09T09:40:45.658Z
Learning: In TensorRT-LLM test lists, it's common and intentional for the same test to appear in multiple test list files when they serve different purposes (e.g., llm_function_core.txt for comprehensive core functionality testing and llm_function_core_sanity.txt for quick sanity checks). This duplication allows tests to be run in different testing contexts.
Applied to files:
tests/integration/defs/accuracy/test_llm_api_pytorch.py
📚 Learning: 2025-08-26T09:37:10.463Z
Learnt from: jiaganc
Repo: NVIDIA/TensorRT-LLM PR: 7031
File: tensorrt_llm/bench/dataclasses/configuration.py:90-104
Timestamp: 2025-08-26T09:37:10.463Z
Learning: In TensorRT-LLM's bench configuration, the `get_pytorch_perf_config()` method returns `self.pytorch_config` which is a Dict[str, Any] that can contain default values including `cuda_graph_config`, making the fallback `llm_args["cuda_graph_config"]` safe to use.
Applied to files:
tests/integration/defs/accuracy/test_llm_api_pytorch.py
📚 Learning: 2025-08-26T09:37:10.463Z
Learnt from: jiaganc
Repo: NVIDIA/TensorRT-LLM PR: 7031
File: tensorrt_llm/bench/dataclasses/configuration.py:90-104
Timestamp: 2025-08-26T09:37:10.463Z
Learning: In TensorRT-LLM, the `get_pytorch_perf_config()` method returns `self.pytorch_config` which can contain default `cuda_graph_config` values, so `llm_args` may already have this config before the extra options processing.
Applied to files:
tests/integration/defs/accuracy/test_llm_api_pytorch.py
📚 Learning: 2025-08-14T21:04:50.248Z
Learnt from: thorjohnsen
Repo: NVIDIA/TensorRT-LLM PR: 6910
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-14T21:04:50.248Z
Learning: In KV cache onboarding logic during prefill in cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, when calculating which blocks fall within the attention window, use getTokensPerBlock() to advance token indices rather than block->getUniqueTokens().size(), because the calculation needs to consider the post-prefill state where blocks will be filled to capacity, not their current token count.
Applied to files:
tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py
📚 Learning: 2025-08-15T06:46:54.897Z
Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 6767
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-15T06:46:54.897Z
Learning: In cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp addToken function, newly allocated blocks are unshared by design. The beam search path in addToken (when sequence.getNumTokens() > windowSize) is currently broken/non-functional with SWA, so the block allocation doesn't follow a shared-then-unshared pattern.
Applied to files:
tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py
🧬 Code graph analysis (2)
tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py (1)
tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py (3)
MambaCacheManager(28-184)MambaHybridCacheManager(187-268)reorder_state_indices_when_padding_requests(138-147)
tests/integration/defs/accuracy/test_llm_api_pytorch.py (3)
tensorrt_llm/llmapi/llm_args.py (1)
CudaGraphConfig(102-159)tests/integration/defs/accuracy/accuracy_core.py (1)
GSM8K(334-349)tensorrt_llm/evaluate/lm_eval.py (1)
GSM8K(455-510)
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🔇 Additional comments (3)
tests/integration/defs/accuracy/test_llm_api_pytorch.py (2)
4539-4544: Explicit CUDA graphbatch_sizeslist looks appropriateUsing an explicit, sorted
batch_sizeslist withenable_padding=Truewhencuda_graphis enabled is consistent with howCudaGraphConfigis intended to be used and should exercise the padding path over a wide range of batch sizes. No issues from my side here.
4559-4559: GSM8KNUM_SAMPLESpatch is safe and test‑localizes behaviorPatching
GSM8K.NUM_SAMPLESto 1319 inside the test keeps this test independent from any global modifications of that attribute elsewhere and aligns with the full‑dataset default. Looks good.tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py (1)
18-18: Mamba cache manager import is appropriate and localizedImporting
MambaCacheManager/MambaHybridCacheManagerhere for type checks in_get_padded_batchis reasonable and doesn’t introduce obvious cyclic dependencies.
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| self.request_mask[:] = True | ||
| self.request_mask[self.state_indices[:request_size]] = False |
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There are implicit CUDA synchronizations which negate the benefits of the overlap scheduler.
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I checked whether there is CUDA synchronizations here and there is none.
But anyway, there is a new method in the newer patch with lower overhead.
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The nsys results of newer patch. |
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