[None][fix] Recover TRTLLM MoE Perf for DEP #9562
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📝 WalkthroughWalkthroughThe autotuner context manager is refactored to accept a Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes
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⚠️ Outside diff range comments (1)
tensorrt_llm/_torch/autotuner.py (1)
228-271: Guardmappingbeing None and gate MPI sync on actual tuningThe
autotunesignature allowsmappingto be omitted (defaults toNone), but thefinallyblock unconditionally dereferences it withmapping.world_sizeandmapping.rank, which will raiseAttributeErrorfor any caller that doesn't providemapping. Additionally, the MPI barrier and broadcast operations run whenevermappingis notNone, even when this context didn't actually enable tuning (whenautotune_enabledisFalse).Type hints should also use
Optional[...]instead of bare type names with= None:-@contextlib.contextmanager -def autotune(tune_mode: bool = True, - cache_path: str = None, - mapping: "tensorrt_llm.Mapping" = None): +@contextlib.contextmanager +def autotune( + tune_mode: bool = True, + cache_path: Optional[str] = None, + mapping: Optional["tensorrt_llm.Mapping"] = None, +): @@ - AutoTuner.get().is_tuning_mode = old_mode - if autotune_enabled: - logger.info("[Autotuner] Autotuning process ends") - - # Broadcast the cache from rank 0 to all other ranks - if mapping.world_size > 1: - # Synchronize all ranks before broadcasting - mpi_barrier() - cache_data = AutoTuner.get().profiling_cache.cache - cache_data = mpi_broadcast(cache_data, root=0) - AutoTuner.get().profiling_cache.cache = cache_data - - # save cache - if cache_path is not None and mapping.rank == 0: - logger.info(f"[Autotuner] Saving cache to {cache_path}") - AutoTuner.get().profiling_cache.save_cache(cache_path) + AutoTuner.get().is_tuning_mode = old_mode + if autotune_enabled: + logger.info("[Autotuner] Autotuning process ends") + + # Broadcast the cache from rank 0 to all other ranks + if mapping is not None and mapping.world_size > 1: + # Synchronize all ranks before broadcasting + mpi_barrier() + cache_data = AutoTuner.get().profiling_cache.cache + cache_data = mpi_broadcast(cache_data, root=0) + AutoTuner.get().profiling_cache.cache = cache_data + + # Save cache from rank 0 (or the only rank if mapping is None) + is_rank0 = mapping is None or mapping.rank == 0 + if cache_path is not None and is_rank0: + logger.info(f"[Autotuner] Saving cache to {cache_path}") + AutoTuner.get().profiling_cache.save_cache(cache_path)
🧹 Nitpick comments (2)
tensorrt_llm/_torch/custom_ops/trtllm_gen_custom_ops.py (2)
18-85: Tightencreate_dummy_topk_idsfor robustness and minor cleanupThe new
TopkIdsGenMethodandcreate_dummy_topk_idscover the required generation strategies and produce tensors with the right shape/dtype. A couple of small improvements would make this more robust and tidy:
- Explicitly assert the basic invariant once, to fail fast in misconfigured setups instead of letting
multinomialraise cryptic errors:def create_dummy_topk_ids( @@ - method: TopkIdsGenMethod, + method: TopkIdsGenMethod, ) -> torch.Tensor: @@ - # Note: RANDINT is uniform distribution with replacement which can cause duplicates. However we + # Note: RANDINT is uniform distribution with replacement which can cause duplicates. However we @@ - if method == TopkIdsGenMethod.UNIFORM: + assert 0 < top_k <= num_experts, "top_k must satisfy 0 < top_k <= num_experts" + + if method == TopkIdsGenMethod.UNIFORM: @@ - return topk_ids.to(torch.int32).to(device) + return topk_ids.to(dtype=torch.int32, device=device)This keeps the API the same, documents expectations, and removes a redundant device transfer.
87-198: prepare_dummy_topk_and_hook integration looks correct; consider exposing method selectionThe refactor to have
prepare_dummy_topk_and_hookcallcreate_dummy_topk_idsfor both initial and shape-change paths is consistent:
- Dummy tensors in the attention-DP path still have shapes
(num_tokens, top_k)and uselocal_num_expertsas the index range.- The
recreate_dummy_topk_if_neededhook correctly updatesinputs[-2]andinputs[-1]when the token count changes, now delegating dummy id creation tocreate_dummy_topk_idswith the same parameters.- Normal routing (non-attention-DP) behavior for
routing_logitsis unchanged.Right now, all callers rely on the default
topk_ids_gen_method=TopkIdsGenMethod.RANDINT. If you later want to experiment with UNIFORM/GAUSSIAN globally, consider threading a configuration flag (e.g., from env or MoE config) into the various*_moe_runnerwrappers instead of hard-coding RANDINT at the call sites.
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tensorrt_llm/_torch/autotuner.py(4 hunks)tensorrt_llm/_torch/custom_ops/trtllm_gen_custom_ops.py(6 hunks)tensorrt_llm/_torch/pyexecutor/model_engine.py(1 hunks)
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**/*.py
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**/*.py: The code developed for TensorRT-LLM should conform to Python 3.8+
Indent Python code with 4 spaces; do not use tabs
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🧠 Learnings (12)
📚 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:
tensorrt_llm/_torch/autotuner.py
📚 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:
tensorrt_llm/_torch/autotuner.py
📚 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:
tensorrt_llm/_torch/autotuner.py
📚 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:
tensorrt_llm/_torch/autotuner.py
📚 Learning: 2025-08-06T13:58:07.506Z
Learnt from: galagam
Repo: NVIDIA/TensorRT-LLM PR: 6487
File: tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.py:1-12
Timestamp: 2025-08-06T13:58:07.506Z
Learning: In TensorRT-LLM, test files (files under tests/ directories) do not require NVIDIA copyright headers, unlike production source code files. Test files typically start directly with imports, docstrings, or code.
Applied to files:
tensorrt_llm/_torch/autotuner.py
📚 Learning: 2025-08-14T15:38:01.771Z
Learnt from: MatthiasKohl
Repo: NVIDIA/TensorRT-LLM PR: 6904
File: cpp/tensorrt_llm/pybind/thop/bindings.cpp:55-57
Timestamp: 2025-08-14T15:38:01.771Z
Learning: In TensorRT-LLM Python bindings, tensor parameter collections like mla_tensor_params and spec_decoding_tensor_params are kept as required parameters without defaults to maintain API consistency, even when it might affect backward compatibility.
Applied to files:
tensorrt_llm/_torch/autotuner.py
📚 Learning: 2025-08-01T15:14:45.673Z
Learnt from: yibinl-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 6506
File: examples/models/core/mixtral/requirements.txt:3-3
Timestamp: 2025-08-01T15:14:45.673Z
Learning: In TensorRT-LLM, examples directory can have different dependency versions than the root requirements.txt file. Version conflicts between root and examples dependencies are acceptable because examples are designed to be standalone and self-contained.
Applied to files:
tensorrt_llm/_torch/autotuner.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:
tensorrt_llm/_torch/autotuner.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:
tensorrt_llm/_torch/autotuner.py
📚 Learning: 2025-10-20T17:07:18.745Z
Learnt from: nvchenghaoz
Repo: NVIDIA/TensorRT-LLM PR: 8469
File: tensorrt_llm/_torch/auto_deploy/models/patches/nemotron_h.py:98-116
Timestamp: 2025-10-20T17:07:18.745Z
Learning: In NemotronH models (tensorrt_llm/_torch/auto_deploy/models/patches/nemotron_h.py), the gate (self.gate) returns topk_indices and topk_weights that are already in the correct shape to be passed directly to torch_ops.auto_deploy.torch_moe without needing to reshape them when hidden_states is flattened.
Applied to files:
tensorrt_llm/_torch/custom_ops/trtllm_gen_custom_ops.py
📚 Learning: 2025-08-19T12:45:11.997Z
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 7033
File: tensorrt_llm/_torch/pyexecutor/model_engine.py:0-0
Timestamp: 2025-08-19T12:45:11.997Z
Learning: In tensorrt_llm/_torch/pyexecutor/model_engine.py, DoRA (Delta Orthogonal Rank Adaptation) functionality was removed from the PyTorch flow to eliminate issues with inverted DoRA detection logic. The original is_dora condition was checking if scaling_vec_pointer == 0, which was potentially incorrect.
Applied to files:
tensorrt_llm/_torch/pyexecutor/model_engine.py
📚 Learning: 2025-08-26T06:07:02.166Z
Learnt from: shaharmor98
Repo: NVIDIA/TensorRT-LLM PR: 7231
File: tensorrt_llm/_torch/pyexecutor/_util.py:504-509
Timestamp: 2025-08-26T06:07:02.166Z
Learning: In tensorrt_llm/_torch/pyexecutor/_util.py, when calling model_engine.set_lora_model_config(), pass model_binding_config.mlp_hidden_size directly without multiplying by mapping.tp_size, as the mlp_hidden_size from get_bindings_model_config() is already the per-TP rank value needed for LoRA weight packaging.
Applied to files:
tensorrt_llm/_torch/pyexecutor/model_engine.py
🧬 Code graph analysis (2)
tensorrt_llm/_torch/autotuner.py (1)
tensorrt_llm/_utils.py (2)
mpi_barrier(577-579)mpi_broadcast(587-588)
tensorrt_llm/_torch/pyexecutor/model_engine.py (1)
tensorrt_llm/_torch/models/checkpoints/base_weight_mapper.py (1)
mapping(162-163)
🪛 Ruff (0.14.6)
tensorrt_llm/_torch/autotuner.py
230-230: PEP 484 prohibits implicit Optional
Convert to T | None
(RUF013)
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🔇 Additional comments (3)
tensorrt_llm/_torch/pyexecutor/model_engine.py (1)
635-637: Wiring mapping into autotune context looks correctPassing
mapping=self.mappinghere aligns with the new autotuner API and enables rank-aware cache broadcast without changing warmup control flow. Givenself.mappingis already assumed non-Noneelsewhere (e.g.,mapping.cp_config,mapping.pp_size), this doesn’t introduce new nullability risks in this class.tensorrt_llm/_torch/autotuner.py (1)
863-871: Profiling failure logging change is reasonableThe updated warning message for profiling failures (including runner, tactic, shapes, and error) improves diagnosability without changing behavior. No issues from a correctness or performance perspective.
tensorrt_llm/_torch/custom_ops/trtllm_gen_custom_ops.py (1)
452-463: Callers ofprepare_dummy_topk_and_hookare consistent with the new contractAll the MoE custom ops (
fp4_block_scale_moe_runner,fp8_block_scale_moe_runner,mxe4m3_mxe2m1_block_scale_moe_runner,e4m3_mxe2m1_block_scale_moe_runner,bf16_mxe2m1_block_scale_moe_runner,fp8_fp4_block_scale_moe_runner) now:
- Pass
hidden_statesandhidden_states_index=2, matching the layout expected byrecreate_dummy_topk_if_needed.- Build
input_tensors_for_tunerwithtopk_weights_for_tunerandtopk_ids_for_tuneras the last two entries, consistent withinputs[-2]/inputs[-1]usage in the hook.- Replace dummy
{routing_logits, topk_weights, topk_ids}with the actual tensors only after tuning, so the tuning-time distribution is controlled by the factory while the final run uses real data.This wiring looks coherent and should preserve existing behavior while enabling improved dummy top-k distributions.
Also applies to: 781-792, 1089-1099, 1358-1368, 1621-1631, 1872-1882
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…back; last minute fix expert weight dtype Signed-off-by: Anthony Chang <[email protected]>
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Description
Rank0 broadcasts autotuner cache in an attempt to align the best kernelssuperceded by [TRTLLM-9615][feat] Implement a distributed tuning system. #9621#9087 introduces autotune with
topk_id/topk_weightsinstead ofrouting_logits. But the uniform distribution of topk_id during autotune brings down perf in some test cases. This PR attempt to recover the performance.Test Coverage
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