[None][feat] Add Qwen3-Next to layer-wise benchmarks #9065
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Signed-off-by: Tailing Yuan <yuantailing@gmail.com>
Signed-off-by: Tailing Yuan <yuantailing@gmail.com>
Signed-off-by: Tailing Yuan <yuantailing@gmail.com>
Signed-off-by: Tailing Yuan <yuantailing@gmail.com>
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📝 WalkthroughWalkthroughThis pull request introduces a modular runner framework for layer-wise benchmarking that replaces direct dependencies on DeepSeekV3Runner with a pluggable factory pattern. New base classes (RunnerBase, RunnerMixin) and utility functions define a standard interface. DeepSeekV3Runner is refactored to inherit from this new base, Qwen3NextRunner is added as a new implementation, and a factory function ( Changes
Sequence DiagramsequenceDiagram
participant user as run_single.py
participant factory as runner_factory
participant runner as Runner (DeepSeekV3/Qwen3)
participant base as RunnerBase
participant mixin as RunnerMixin
user->>factory: get_runner_cls(model_path)
factory->>factory: load_pretrained_config(model_path)
factory-->>user: Runner class (DeepSeekV3Runner/Qwen3NextRunner)
user->>user: Instantiate Runner with args
user->>runner: __init__(mapping, moe_backend, layer_indices, ...)
runner->>mixin: scaled_from_ctx(scaled_from, mapping, config)
mixin->>mixin: Build layers, apply quant config, init weights
mixin-->>runner: Context exit, layers ready
runner-->>user: Runner instance initialized
user->>runner: create_kv_cache_manager(...)
runner->>mixin: create_kv_cache_manager(...)
mixin-->>runner: KVCacheManager instance
runner-->>user: KVCacheManager ready
user->>runner: create_run_pack(run_type, batch_size, ...)
runner->>mixin: create_run_pack(...)
mixin->>mixin: Build attention metadata, assemble run_pack
mixin-->>runner: run_pack callable
runner-->>user: Executable run_pack function
user->>user: Execute benchmark loop with run_pack
Estimated code review effort🎯 4 (Complex) | ⏱️ ~60 minutes
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Pre-merge checks and finishing touches❌ Failed checks (2 warnings)
✅ Passed checks (1 passed)
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⚠️ Outside diff range comments (2)
examples/layer_wise_benchmarks/run_single.py (1)
1-7: Restore the NVIDIA Apache-2.0 header.This Python entry point is missing the required 2025 NVIDIA Apache-2.0 banner at the top. Please restore it to keep the file compliant.
Apply this diff:
+# 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. + import argparseAs per coding guidelines
tensorrt_llm/tools/layer_wise_benchmarks/deepseekv3_runner.py (1)
1-24: Restore the NVIDIA Apache-2.0 header.This file also needs the standard NVIDIA Apache-2.0 header at the top to satisfy the repository’s compliance requirements. As per coding guidelines.
+# 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)
examples/layer_wise_benchmarks/README.md (1)
101-105: Fix the section heading spelling.Please collapse “Trouble shooting” into the standard “Troubleshooting” to match common usage.
Apply this diff:
-## Trouble shooting +## Troubleshooting
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.pre-commit-config.yaml(0 hunks)examples/layer_wise_benchmarks/README.md(5 hunks)examples/layer_wise_benchmarks/config_ctx.yaml(0 hunks)examples/layer_wise_benchmarks/config_gen.yaml(0 hunks)examples/layer_wise_benchmarks/run_single.py(6 hunks)jenkins/L0_MergeRequest.groovy(3 hunks)pyproject.toml(0 hunks)tensorrt_llm/tools/layer_wise_benchmarks/deepseekv3_runner.py(4 hunks)tensorrt_llm/tools/layer_wise_benchmarks/qwen3_next_runner.py(1 hunks)tensorrt_llm/tools/layer_wise_benchmarks/runner_base.py(1 hunks)tensorrt_llm/tools/layer_wise_benchmarks/runner_factory.py(1 hunks)tensorrt_llm/tools/layer_wise_benchmarks/runner_utils.py(1 hunks)tests/integration/test_lists/test-db/l0_b200.yml(1 hunks)tests/unittest/tools/test_layer_wise_benchmarks.py(1 hunks)
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🧠 Learnings (13)
📓 Common learnings
Learnt from: venkywonka
Repo: NVIDIA/TensorRT-LLM PR: 6029
File: .github/pull_request_template.md:45-53
Timestamp: 2025-08-27T17:50:13.264Z
Learning: For PR templates in TensorRT-LLM, avoid suggesting changes that would increase developer overhead, such as converting plain bullets to mandatory checkboxes. The team prefers guidance-style bullets that don't require explicit interaction to reduce friction in the PR creation process.
📚 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/test_lists/test-db/l0_b200.ymljenkins/L0_MergeRequest.groovytests/unittest/tools/test_layer_wise_benchmarks.py
📚 Learning: 2025-09-17T02:48:52.732Z
Learnt from: tongyuantongyu
Repo: NVIDIA/TensorRT-LLM PR: 7781
File: tests/integration/test_lists/waives.txt:313-313
Timestamp: 2025-09-17T02:48:52.732Z
Learning: In TensorRT-LLM, `tests/integration/test_lists/waives.txt` is specifically for waiving/skipping tests, while other test list files like those in `test-db/` and `qa/` directories are for different test execution contexts (pre-merge, post-merge, QA tests). The same test appearing in both waives.txt and execution list files is intentional - the test is part of test suites but will be skipped due to the waiver.
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📚 Learning: 2025-07-28T17:06:08.621Z
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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.
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📚 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*").
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📚 Learning: 2025-08-06T13:58:07.506Z
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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
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📚 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.
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jenkins/L0_MergeRequest.groovy
📚 Learning: 2025-08-11T20:09:24.389Z
Learnt from: achartier
Repo: NVIDIA/TensorRT-LLM PR: 6763
File: tests/integration/defs/triton_server/conftest.py:16-22
Timestamp: 2025-08-11T20:09:24.389Z
Learning: In the TensorRT-LLM test infrastructure, the team prefers simple, direct solutions (like hard-coding directory traversal counts) over more complex but robust approaches when dealing with stable directory structures. They accept the maintenance cost of updating tests if the layout changes.
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📚 Learning: 2025-08-20T07:43:36.447Z
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Repo: NVIDIA/TensorRT-LLM PR: 7068
File: cpp/tensorrt_llm/kernels/moeTopKFuncs.cuh:169-172
Timestamp: 2025-08-20T07:43:36.447Z
Learning: In TensorRT-LLM MOE kernels, when processing up to 128 experts across 32 threads, each thread handles at most 4 experts (N < 5 constraint), where N represents candidates per thread rather than total system capacity.
Applied to files:
examples/layer_wise_benchmarks/README.md
📚 Learning: 2025-09-29T15:14:28.503Z
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Repo: NVIDIA/TensorRT-LLM PR: 8063
File: tensorrt_llm/lora_manager.py:1080-1112
Timestamp: 2025-09-29T15:14:28.503Z
Learning: In tensorrt_llm/lora_manager.py, when calculating part_sizes for attn_qkv fused LoRA modules, the sizes are correctly multiplied by tp_size because model_config.num_heads and model_config.num_kv_heads are already divided by tp_size (per-TP-rank values), so multiplication is needed to get the original full concatenated dimension size. The interleave_fused_lora_weights_for_tp function provides proper validation.
Applied to files:
tests/unittest/tools/test_layer_wise_benchmarks.py
📚 Learning: 2025-09-29T15:14:28.503Z
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 8063
File: tensorrt_llm/lora_manager.py:1080-1112
Timestamp: 2025-09-29T15:14:28.503Z
Learning: In tensorrt_llm/lora_manager.py, when calculating part_sizes for attn_qkv fused LoRA modules, the sizes are correctly multiplied by tp_size because model_config.num_heads and model_config.num_kv_heads are already divided by tp_size (per-TP-rank values), so multiplication is needed to get the original full concatenated dimension size. The interleave_fused_lora_weights_for_tp function provides proper validation with asserts for total size and TP divisibility.
Applied to files:
tests/unittest/tools/test_layer_wise_benchmarks.py
📚 Learning: 2025-09-19T21:28:13.751Z
Learnt from: jhaotingc
Repo: NVIDIA/TensorRT-LLM PR: 7856
File: cpp/tensorrt_llm/thop/fp8BlockScaleMoe.cpp:159-166
Timestamp: 2025-09-19T21:28:13.751Z
Learning: In TensorRT-LLM blockScaleMoe routing (cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/runner.cu), the DeepSeek routing method performs reinterpret_cast<float*>(routingLogits) at line 89, which could cause issues if routing_logits are BF16. However, Qwen3-FP8 models use RenormalizeNaive routing method and are not affected by this dtype casting issue.
Applied to files:
tensorrt_llm/tools/layer_wise_benchmarks/deepseekv3_runner.py
📚 Learning: 2025-08-14T06:36:40.701Z
Learnt from: timlee0212
Repo: NVIDIA/TensorRT-LLM PR: 6886
File: tensorrt_llm/_torch/models/modeling_deepseekv3.py:0-0
Timestamp: 2025-08-14T06:36:40.701Z
Learning: 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.
Applied to files:
tensorrt_llm/tools/layer_wise_benchmarks/deepseekv3_runner.py
🧬 Code graph analysis (7)
tests/unittest/tools/test_layer_wise_benchmarks.py (2)
tests/integration/defs/conftest.py (2)
llm_root(192-193)llm_models_root(80-94)tests/integration/defs/trt_test_alternative.py (1)
check_call(250-258)
tensorrt_llm/tools/layer_wise_benchmarks/qwen3_next_runner.py (4)
tensorrt_llm/functional.py (1)
AllReduceStrategy(3876-3885)tensorrt_llm/mapping.py (1)
Mapping(336-493)tensorrt_llm/tools/layer_wise_benchmarks/runner_base.py (1)
RunnerBase(17-49)tensorrt_llm/tools/layer_wise_benchmarks/runner_utils.py (4)
RunnerMixin(36-303)has_mamba_metadata(39-40)scaled_from_ctx(44-82)apply_quant_config_exclude_modules(85-113)
tensorrt_llm/tools/layer_wise_benchmarks/runner_base.py (2)
tensorrt_llm/tools/layer_wise_benchmarks/runner_utils.py (4)
create_run_pack(115-187)replace_routing_method(189-191)create_kv_cache_manager(194-283)create_mapping(286-303)tensorrt_llm/tools/layer_wise_benchmarks/deepseekv3_runner.py (1)
replace_routing_method(211-219)
tensorrt_llm/tools/layer_wise_benchmarks/runner_factory.py (3)
tensorrt_llm/_torch/pyexecutor/config_utils.py (1)
load_pretrained_config(39-52)tensorrt_llm/tools/layer_wise_benchmarks/deepseekv3_runner.py (1)
DeepSeekV3Runner(124-219)tensorrt_llm/tools/layer_wise_benchmarks/qwen3_next_runner.py (1)
Qwen3NextRunner(15-90)
tensorrt_llm/tools/layer_wise_benchmarks/runner_utils.py (13)
tensorrt_llm/_torch/attention_backend/utils.py (1)
get_attention_backend(15-37)tensorrt_llm/_torch/metadata.py (2)
KVCacheParams(9-31)CacheType(34-40)tensorrt_llm/_torch/modules/linear.py (2)
Linear(1877-2102)WeightMode(35-41)tensorrt_llm/_torch/modules/mamba/mamba2_metadata.py (1)
Mamba2Metadata(88-137)tensorrt_llm/_torch/pyexecutor/_util.py (1)
get_kv_cache_manager_cls(47-55)tensorrt_llm/_torch/pyexecutor/config_utils.py (2)
is_mla(12-16)is_qwen3_next(19-23)tensorrt_llm/_torch/utils.py (2)
get_model_extra_attrs(64-65)model_extra_attrs(69-75)tensorrt_llm/_utils.py (3)
local_mpi_size(557-558)mpi_world_size(549-550)torch_dtype_to_binding(417-420)tensorrt_llm/mapping.py (1)
Mapping(336-493)tensorrt_llm/models/modeling_utils.py (2)
QuantConfig(131-271)is_module_excluded_from_quantization(237-250)tensorrt_llm/tools/layer_wise_benchmarks/runner_base.py (5)
BalanceMethod(10-14)create_run_pack(19-28)replace_routing_method(31-32)create_kv_cache_manager(36-44)create_mapping(48-49)tensorrt_llm/tools/layer_wise_benchmarks/deepseekv3_runner.py (2)
has_mamba_metadata(126-127)replace_routing_method(211-219)tensorrt_llm/tools/layer_wise_benchmarks/qwen3_next_runner.py (1)
has_mamba_metadata(17-18)
examples/layer_wise_benchmarks/run_single.py (4)
tensorrt_llm/tools/layer_wise_benchmarks/runner_base.py (5)
BalanceMethod(10-14)create_mapping(48-49)create_kv_cache_manager(36-44)create_run_pack(19-28)replace_routing_method(31-32)tensorrt_llm/tools/layer_wise_benchmarks/runner_factory.py (1)
get_runner_cls(7-13)tensorrt_llm/tools/layer_wise_benchmarks/runner_utils.py (5)
create_mapping(286-303)create_kv_cache_manager(194-283)run_pack(179-185)create_run_pack(115-187)replace_routing_method(189-191)tensorrt_llm/tools/layer_wise_benchmarks/deepseekv3_runner.py (1)
replace_routing_method(211-219)
tensorrt_llm/tools/layer_wise_benchmarks/deepseekv3_runner.py (3)
tensorrt_llm/_torch/models/modeling_deepseekv3.py (2)
DeepseekV3DecoderLayer(1004-1350)DeepseekV3Gate(725-791)tensorrt_llm/tools/layer_wise_benchmarks/runner_base.py (3)
BalanceMethod(10-14)RunnerBase(17-49)replace_routing_method(31-32)tensorrt_llm/tools/layer_wise_benchmarks/runner_utils.py (6)
RunnerMixin(36-303)ceil_div(28-29)has_mamba_metadata(39-40)scaled_from_ctx(44-82)apply_quant_config_exclude_modules(85-113)replace_routing_method(189-191)
🪛 LanguageTool
examples/layer_wise_benchmarks/README.md
[misspelling] ~101-~101: This word is normally spelled as one.
Context: ...`` ## Parse profiles Coming soon. ## Trouble shooting 1. Error `fp8 blockscale gemm only sup...
(EN_COMPOUNDS_TROUBLE_SHOOTING)
🪛 Ruff (0.14.4)
tests/unittest/tools/test_layer_wise_benchmarks.py
73-73: Redefinition of unused llm_root from line 6
(F811)
77-77: subprocess call: check for execution of untrusted input
(S603)
tensorrt_llm/tools/layer_wise_benchmarks/qwen3_next_runner.py
86-86: zip() without an explicit strict= parameter
Add explicit value for parameter strict=
(B905)
86-86: Prefer itertools.pairwise() over zip() when iterating over successive pairs
Replace zip() with itertools.pairwise()
(RUF007)
tensorrt_llm/tools/layer_wise_benchmarks/runner_utils.py
68-68: Unused function argument: cls
(ARG001)
125-125: Avoid specifying long messages outside the exception class
(TRY003)
189-189: Unused method argument: balance_ratio
(ARG002)
examples/layer_wise_benchmarks/run_single.py
146-146: zip() without an explicit strict= parameter
Add explicit value for parameter strict=
(B905)
146-146: Prefer itertools.pairwise() over zip() when iterating over successive pairs
Replace zip() with itertools.pairwise()
(RUF007)
tensorrt_llm/tools/layer_wise_benchmarks/deepseekv3_runner.py
204-204: zip() without an explicit strict= parameter
Add explicit value for parameter strict=
(B905)
204-204: Prefer itertools.pairwise() over zip() when iterating over successive pairs
Replace zip() with itertools.pairwise()
(RUF007)
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chzblych
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lucaslie
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zheyuf
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Nov 19, 2025
Signed-off-by: Tailing Yuan <yuantailing@gmail.com>
greg-kwasniewski1
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Signed-off-by: Tailing Yuan <yuantailing@gmail.com>
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Summary by CodeRabbit
Documentation
New Features
Description
Test Coverage
PR Checklist
Please review the following before submitting your PR:
PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.
PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
Test cases are provided for new code paths (see test instructions)
Any new dependencies have been scanned for license and vulnerabilities
CODEOWNERS updated if ownership changes
Documentation updated as needed
Update tava architecture diagram if there is a significant design change in PR.
The reviewers assigned automatically/manually are appropriate for the PR.
Please check this after reviewing the above items as appropriate for this PR.
GitHub Bot Help
/bot [-h] ['run', 'kill', 'skip', 'reuse-pipeline'] ...Provide a user friendly way for developers to interact with a Jenkins server.
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run [--reuse-test (optional)pipeline-id --disable-fail-fast --skip-test --stage-list "A10-PyTorch-1, xxx" --gpu-type "A30, H100_PCIe" --test-backend "pytorch, cpp" --add-multi-gpu-test --only-multi-gpu-test --disable-multi-gpu-test --post-merge --extra-stage "H100_PCIe-TensorRT-Post-Merge-1, xxx" --detailed-log --debug(experimental)]Launch build/test pipelines. All previously running jobs will be killed.
--reuse-test (optional)pipeline-id(OPTIONAL) : Allow the new pipeline to reuse build artifacts and skip successful test stages from a specified pipeline or the last pipeline if no pipeline-id is indicated. If the Git commit ID has changed, this option will be always ignored. The DEFAULT behavior of the bot is to reuse build artifacts and successful test results from the last pipeline.--disable-reuse-test(OPTIONAL) : Explicitly prevent the pipeline from reusing build artifacts and skipping successful test stages from a previous pipeline. Ensure that all builds and tests are run regardless of previous successes.--disable-fail-fast(OPTIONAL) : Disable fail fast on build/tests/infra failures.--skip-test(OPTIONAL) : Skip all test stages, but still run build stages, package stages and sanity check stages. Note: Does NOT update GitHub check status.--stage-list "A10-PyTorch-1, xxx"(OPTIONAL) : Only run the specified test stages. Examples: "A10-PyTorch-1, xxx". Note: Does NOT update GitHub check status.--gpu-type "A30, H100_PCIe"(OPTIONAL) : Only run the test stages on the specified GPU types. Examples: "A30, H100_PCIe". Note: Does NOT update GitHub check status.--test-backend "pytorch, cpp"(OPTIONAL) : Skip test stages which don't match the specified backends. Only support [pytorch, cpp, tensorrt, triton]. Examples: "pytorch, cpp" (does not run test stages with tensorrt or triton backend). Note: Does NOT update GitHub pipeline status.--only-multi-gpu-test(OPTIONAL) : Only run the multi-GPU tests. Note: Does NOT update GitHub check status.--disable-multi-gpu-test(OPTIONAL) : Disable the multi-GPU tests. Note: Does NOT update GitHub check status.--add-multi-gpu-test(OPTIONAL) : Force run the multi-GPU tests in addition to running L0 pre-merge pipeline.--post-merge(OPTIONAL) : Run the L0 post-merge pipeline instead of the ordinary L0 pre-merge pipeline.--extra-stage "H100_PCIe-TensorRT-Post-Merge-1, xxx"(OPTIONAL) : Run the ordinary L0 pre-merge pipeline and specified test stages. Examples: --extra-stage "H100_PCIe-TensorRT-Post-Merge-1, xxx".--detailed-log(OPTIONAL) : Enable flushing out all logs to the Jenkins console. This will significantly increase the log volume and may slow down the job.--debug(OPTIONAL) : Experimental feature. Enable access to the CI container for debugging purpose. Note: Specify exactly one stage in thestage-listparameter to access the appropriate container environment. Note: Does NOT update GitHub check status.For guidance on mapping tests to stage names, see
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