[https://nvbugs/5355219][fix] Fix trtllm moe backend test config and Qwen3 MoE multi node #7724
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📝 WalkthroughWalkthroughAdds TRTLLM MOE parameter combinations (with eagle3 and attention_dp variants) to LLM PyTorch latency tests, updates test method signatures to accept Changes
Sequence Diagram(s)sequenceDiagram
participant Test as Test/Runner
participant ModelConfig as ModelConfig
participant MoE as Qwen3MoE
participant Layer as Qwen3MoEDecoderLayer
note right of ModelConfig #D3E4CD: runtime mapping info
Test->>ModelConfig: provide mapping
ModelConfig->>MoE: init with mapping
MoE->>ModelConfig: call can_access_peer(mapping)
ModelConfig-->MoE: is_p2p_supported (true/false)
MoE->>Layer: init layer with is_p2p_supported
Test->>MoE: forward(input)
MoE->>Layer: call forward
alt is_p2p_supported == true
Layer->>Layer: perform Post-MOE fusion finalization (do_finalize allowed)
else is_p2p_supported == false
Layer->>Layer: skip/fallback finalization path (do_finalize gated)
end
Layer-->MoE: output
MoE-->Test: final output
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes Possibly related PRs
Suggested reviewers
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🧹 Nitpick comments (1)
tests/integration/defs/accuracy/test_llm_api_pytorch.py (1)
2294-2310: Add sanity asserts for backend and quant algoGuard against misconfig by asserting expected MOE backend and NVFP4 quantization in this test.
@@ def test_nvfp4(...): - with LLM( + with LLM( f"{llm_models_root()}/Qwen3/saved_models_Qwen3-235B-A22B_nvfp4_hf", tensor_parallel_size=tp_size, pipeline_parallel_size=pp_size, moe_expert_parallel_size=ep_size, **pytorch_config, enable_attention_dp=attention_dp, kv_cache_config=kv_cache_config, speculative_config=spec_config) as llm: + # Sanity checks to catch mismatched artifacts/configs + assert llm.args.moe_config.backend == moe_backend + assert llm.args.quant_config.quant_algo == QuantAlgo.NVFP4
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🧠 Learnings (4)
📓 Common learnings
Learnt from: fredricz-20070104
PR: NVIDIA/TensorRT-LLM#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.
Learnt from: moraxu
PR: NVIDIA/TensorRT-LLM#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.
Learnt from: Funatiq
PR: NVIDIA/TensorRT-LLM#6754
File: tests/integration/test_lists/test-db/l0_a30.yml:41-47
Timestamp: 2025-08-13T11:07:11.772Z
Learning: In TensorRT-LLM test configuration files like tests/integration/test_lists/test-db/l0_a30.yml, TIMEOUT values are specified in minutes, not seconds.
Learnt from: pengbowang-nv
PR: NVIDIA/TensorRT-LLM#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*").
📚 Learning: 2025-08-13T11:07:11.772Z
Learnt from: Funatiq
PR: NVIDIA/TensorRT-LLM#6754
File: tests/integration/test_lists/test-db/l0_a30.yml:41-47
Timestamp: 2025-08-13T11:07:11.772Z
Learning: In TensorRT-LLM test configuration files like tests/integration/test_lists/test-db/l0_a30.yml, TIMEOUT values are specified in minutes, not seconds.
Applied to files:
tests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml
📚 Learning: 2025-09-09T09:40:45.658Z
Learnt from: fredricz-20070104
PR: NVIDIA/TensorRT-LLM#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/test_lists/test-db/l0_gb200_multi_nodes.ymltests/integration/defs/accuracy/test_llm_api_pytorch.py
📚 Learning: 2025-07-28T17:06:08.621Z
Learnt from: moraxu
PR: NVIDIA/TensorRT-LLM#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
⏰ 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)
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🔇 Additional comments (3)
tests/integration/defs/accuracy/test_llm_api_pytorch.py (1)
2294-2310: TRTLLM MOE NVFP4 variants added correctlyParam tuples and ids are consistent; gating (skip on SM120/121) and EAGLE3 spec config wiring look sound. KV block‑reuse disabled only for EAGLE3 is reasonable.
tests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml (2)
21-21: New multi‑node entry matches test id and timeout policyThe id aligns with the added parametrization and TIMEOUT is in minutes (90), consistent with test‑db conventions.
21-21: Verified — test id wiring OKParameter 'latency_moe_trtllm_attention_dp' found in defs at tests/integration/defs/accuracy/test_llm_api_pytorch.py:2305 and referenced once in test-db at tests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml:21.
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⚠️ Outside diff range comments (1)
tensorrt_llm/_torch/models/modeling_qwen3_moe.py (1)
1-1: Missing NVIDIA Apache-2.0 header (compliance).Add the 2025 NVIDIA Apache-2.0 header at the file top.
Apply:
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License.
🧹 Nitpick comments (1)
tensorrt_llm/_torch/models/modeling_qwen3_moe.py (1)
191-191: Avoid repeated cudaDeviceCanAccessPeer scans per layer (cache once).Minor perf/log noise nit: this runs once per layer; cache per process.
Apply within this line:
- self.is_p2p_supported = can_access_peer(model_config.mapping) + self.is_p2p_supported = _get_p2p_supported(model_config.mapping)Add the helper near imports (outside this hunk):
_P2P_SUPPORTED_CACHE = None def _get_p2p_supported(mapping): global _P2P_SUPPORTED_CACHE if _P2P_SUPPORTED_CACHE is None: _P2P_SUPPORTED_CACHE = can_access_peer(mapping) return _P2P_SUPPORTED_CACHE
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📒 Files selected for processing (6)
jenkins/L0_Test.groovy(1 hunks)tensorrt_llm/_torch/models/modeling_qwen3_moe.py(3 hunks)tests/integration/defs/accuracy/test_llm_api_pytorch.py(1 hunks)tests/integration/test_lists/qa/llm_function_full.txt(1 hunks)tests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml(1 hunks)tests/integration/test_lists/waives.txt(1 hunks)
✅ Files skipped from review due to trivial changes (1)
- tests/integration/test_lists/waives.txt
🚧 Files skipped from review as they are similar to previous changes (3)
- tests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml
- jenkins/L0_Test.groovy
- tests/integration/defs/accuracy/test_llm_api_pytorch.py
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Files:
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**/*.py
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
**/*.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.
Files:
tensorrt_llm/_torch/models/modeling_qwen3_moe.py
**/*.{cpp,cxx,cc,h,hpp,hh,hxx,cu,cuh,py}
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
Prepend the NVIDIA Apache-2.0 copyright header with current year to the top of all source files (e.g., .cpp, .h, .cu, .py).
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tensorrt_llm/_torch/models/modeling_qwen3_moe.py
🧠 Learnings (3)
📓 Common learnings
Learnt from: djns99
PR: NVIDIA/TensorRT-LLM#7104
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:1475-1480
Timestamp: 2025-08-21T02:39:12.009Z
Learning: The min latency mode functionality in TensorRT-LLM MOE kernels (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu) is deprecated and no longer being maintained/updated, as confirmed by djns99. Bug reports and optimization suggestions for the computeStridesTmaWarpSpecializedLowLatencyKernel and related min latency code paths should be deprioritized.
📚 Learning: 2025-09-09T09:40:45.658Z
Learnt from: fredricz-20070104
PR: NVIDIA/TensorRT-LLM#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/test_lists/qa/llm_function_full.txt
📚 Learning: 2025-07-28T17:06:08.621Z
Learnt from: moraxu
PR: NVIDIA/TensorRT-LLM#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/test_lists/qa/llm_function_full.txt
🧬 Code graph analysis (1)
tensorrt_llm/_torch/models/modeling_qwen3_moe.py (3)
tensorrt_llm/_ipc_utils.py (1)
can_access_peer(37-61)tensorrt_llm/_torch/models/checkpoints/base_weight_mapper.py (1)
mapping(152-153)tensorrt_llm/_torch/modules/fused_moe/interface.py (1)
has_nvfp4(121-124)
⏰ 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)
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🔇 Additional comments (3)
tensorrt_llm/_torch/models/modeling_qwen3_moe.py (2)
8-8: P2P capability import — OK.Localizes the decision to runtime feasibility checks; import path is correct.
248-252: POST‑MoE fusion gating now checks P2P support — good safeguard.Short‑circuit order keeps the has_nvfp4 assertion cold when earlier conditions fail.
tests/integration/test_lists/qa/llm_function_full.txt (1)
541-541: Approve: add Qwen3-235B TRTLLM-MoE attention_dp latency testVerified — test is present in tests/integration/test_lists/qa/llm_function_full.txt, TIMEOUT mapping exists in tests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml, and the id is defined in tests/integration/defs/accuracy/test_llm_api_pytorch.py. Placement and formatting align with adjacent nvfp4 TRTLLM variants.
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PR_Github #18617 [ run ] completed with state |
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…Qwen3 MoE multi node (NVIDIA#7724) Signed-off-by: Yi Zhang <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
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skip --comment COMMENTSkip testing for latest commit on pull request.
--comment "Reason for skipping build/test"is required. IMPORTANT NOTE: This is dangerous since lack of user care and validation can cause top of tree to break.reuse-pipeline
reuse-pipelineReuse a previous pipeline to validate current commit. This action will also kill all currently running builds associated with the pull request. IMPORTANT NOTE: This is dangerous since lack of user care and validation can cause top of tree to break.