[None][chore] NVLinkOneSided AlltoAll Support zero local_num_tokens.#9822
[None][chore] NVLinkOneSided AlltoAll Support zero local_num_tokens.#9822bobboli merged 2 commits intoNVIDIA:mainfrom
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📝 WalkthroughWalkthroughThese changes enable the MoE all-to-all communication kernels to handle zero local tokens gracefully. The dispatch and combine kernels are updated with conditional guards, synchronized shared-memory allocations, and relaxed validation checks to safely process cases where some ranks have no tokens. Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20–30 minutes
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✅ Passed checks (1 passed)
✨ Finishing touches
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🧹 Nitpick comments (1)
tests/unittest/_torch/multi_gpu/test_moe_a2a.py (1)
569-569: Consider adding the zero-token configuration totest_dispatchas well.The zero-token test case
(4, [32, 0, 16, 0], 2)is added totest_combinebut not totest_dispatch(lines 477-496). Since the dispatch kernel also includes zero-token handling logic, adding this configuration totest_dispatchwould provide symmetric test coverage and ensure both dispatch and combine operations are validated for zero-token scenarios.Apply this diff to add the zero-token test case to
test_dispatch:(4, [32, 32, 32, 32], 8), # Four ranks with top_k = 8 # Edge cases (4, [1, 1, 1, 1], 2), # Four ranks with single token per rank + (4, [32, 0, 16, 0], 2), # Four ranks with zero tokens on some ranks ], indirect=["mpi_pool_executor"]) def test_dispatch(self, mpi_pool_executor, all_num_tokens, top_k):
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📒 Files selected for processing (3)
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu(8 hunks)cpp/tensorrt_llm/thop/moeAlltoAllOp.cpp(0 hunks)tests/unittest/_torch/multi_gpu/test_moe_a2a.py(1 hunks)
💤 Files with no reviewable changes (1)
- cpp/tensorrt_llm/thop/moeAlltoAllOp.cpp
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📓 Path-based instructions (4)
**/*.py
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tests/unittest/_torch/multi_gpu/test_moe_a2a.py
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All TensorRT-LLM Open Source Software code files should contain an NVIDIA copyright header that includes the current year at the top
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cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu
🧠 Learnings (14)
📓 Common learnings
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.
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4616-4626
Timestamp: 2025-08-19T03:35:20.866Z
Learning: In the MOE profiler TMA workspace preparation (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu), the overlapping of TMA WS regions for NONE and FINALIZE variants is deliberate design to save memory space, as confirmed by djns99. The comment "reuse the same pointers to save space" reflects this intentional behavior.
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.
Learnt from: sklevtsov-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 3294
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:1198-1209
Timestamp: 2025-08-08T22:03:40.707Z
Learning: In the CUTLASS MoE kernels (cpp/tensorrt_llm/cutlass_extensions), when `layout_info.fusion` is set to `TmaWarpSpecializedGroupedGemmInput::EpilogueFusion::FINALIZE`, the `router_scales` parameter must be non-null by design. The fused finalize kernel epilogue does not perform nullptr checks and requires valid router scales to function correctly. This is an implicit contract that callers must satisfy when enabling the FINALIZE fusion mode.
📚 Learning: 2025-08-19T03:35:20.866Z
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4616-4626
Timestamp: 2025-08-19T03:35:20.866Z
Learning: In the MOE profiler TMA workspace preparation (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu), the overlapping of TMA WS regions for NONE and FINALIZE variants is deliberate design to save memory space, as confirmed by djns99. The comment "reuse the same pointers to save space" reflects this intentional behavior.
Applied to files:
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu
📚 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:
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu
📚 Learning: 2025-08-09T20:57:04.084Z
Learnt from: sklevtsov-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 3294
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_gemm_tma_warp_specialized_input.cu:118-127
Timestamp: 2025-08-09T20:57:04.084Z
Learning: In the CUTLASS MoE finalize fusion implementation (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_gemm_tma_warp_specialized_input.cu), when setting `fused_finalize_epilogue.stride_final_output` with shape `(hidden_size, num_output_tokens, 1)`, the `num_rows_in_final_output` should be set to `num_output_tokens` (not `hidden_size`) because of a swap+transpose operation that maps rows of the output tensor to `hidden_size` and columns to `num_output_tokens`.
Applied to files:
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu
📚 Learning: 2025-08-08T22:03:40.707Z
Learnt from: sklevtsov-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 3294
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:1198-1209
Timestamp: 2025-08-08T22:03:40.707Z
Learning: In the CUTLASS MoE kernels (cpp/tensorrt_llm/cutlass_extensions), when `layout_info.fusion` is set to `TmaWarpSpecializedGroupedGemmInput::EpilogueFusion::FINALIZE`, the `router_scales` parameter must be non-null by design. The fused finalize kernel epilogue does not perform nullptr checks and requires valid router scales to function correctly. This is an implicit contract that callers must satisfy when enabling the FINALIZE fusion mode.
Applied to files:
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu
📚 Learning: 2025-08-21T02:39:12.009Z
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 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.
Applied to files:
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu
📚 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:
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu
📚 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:
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu
📚 Learning: 2025-08-14T23:23:27.449Z
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4010-4012
Timestamp: 2025-08-14T23:23:27.449Z
Learning: For MOE (Mixture of Experts) code reviews in TensorRT-LLM, avoid repeatedly suggesting finalize fusion validation checks and safety assertions. The user djns99 has indicated these suggestions are repetitive and unwanted across multiple MOE-related changes.
Applied to files:
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu
📚 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:
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu
📚 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:
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu
📚 Learning: 2025-08-14T15:36:37.610Z
Learnt from: MatthiasKohl
Repo: NVIDIA/TensorRT-LLM PR: 6904
File: cpp/tensorrt_llm/kernels/mlaKernels.cu:436-439
Timestamp: 2025-08-14T15:36:37.610Z
Learning: CUDA kernels prioritize performance and should avoid runtime bounds checking or conditional operations that cause branching/warp divergence. Input validation should be done at the host level before kernel launch, not per-thread in the kernel.
Applied to files:
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu
📚 Learning: 2025-08-20T07:43:36.447Z
Learnt from: ChristinaZ
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:
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu
📚 Learning: 2025-08-14T15:43:23.107Z
Learnt from: MatthiasKohl
Repo: NVIDIA/TensorRT-LLM PR: 6904
File: tensorrt_llm/_torch/attention_backend/trtllm.py:259-262
Timestamp: 2025-08-14T15:43:23.107Z
Learning: In TensorRT-LLM's attention backend, tensor parameters in the plan() method are assigned directly without validation (dtype, device, contiguity checks). This maintains consistency across all tensor inputs and follows the pattern of trusting callers to provide correctly formatted tensors.
Applied to files:
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu
🧬 Code graph analysis (1)
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu (1)
tests/unittest/_torch/multi_gpu/test_moe_a2a.py (1)
compute_target_rank_id(44-56)
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🔇 Additional comments (8)
cpp/tensorrt_llm/kernels/communicationKernels/moeAlltoAllKernels.cu (8)
350-353: LGTM! Boundary check correctly enables zero-token synchronization.The condition
local_token_idx >= local_num_tokens && local_num_tokens != 0ensures that whenlocal_num_tokens == 0, threads withlocal_token_idx == 0proceed past this check to participate in synchronization barriers (lines 435-503), while skipping token processing (guarded at line 355).
355-433: LGTM! Token processing correctly guarded for zero-token case.The token processing logic (shared memory setup, routing computation, and payload dispatch) is properly guarded by
if (local_num_tokens != 0). This ensures ranks with zero tokens skip all token-related work while still participating in synchronization (lines 435-503).Minor note: Shared memory is still allocated in the launch configuration (lines 571, 585) even when
local_num_tokens == 0, causing unused allocation. This is a negligible inefficiency and doesn't affect correctness.
443-451: LGTM! Last-token detection correctly handles zero-token case.When
local_num_tokens == 0, settingis_last_token = trueimmediately (without atomicAdd) is correct. This allows all threads to proceed directly to synchronization barriers without incrementing a meaningless counter.
521-521: LGTM! Validation correctly relaxed to allow zero tokens.Changing the check from
> 0to>= 0enables the zero-token support that this PR implements. Other validations remain appropriately strict.
566-584: LGTM! Minimal grid launch ensures synchronization participation.Setting
grid_size = 1whenlocal_num_tokens == 0ensures ranks with no tokens still launch a minimal kernel to participate in synchronization barriers. This approach is consistent with existing patterns in TensorRT-LLM NCCL device kernels where all ranks launch uniform grids for optimal barrier performance.Based on learnings, this design aligns with established practices in the codebase.
799-802: LGTM! Combine kernel boundary check mirrors dispatch pattern.The condition
local_token_idx >= local_num_tokens && local_num_tokens != 0follows the same pattern as the dispatch kernel (line 350), ensuring consistent zero-token handling across both operations.
853-854: LGTM! Early return correctly placed after synchronization.Returning early when
local_num_tokens == 0is placed after the synchronization barriers (lines 804-851), ensuring ranks with no tokens participate in cross-rank coordination before exiting. This ordering is critical for correctness.
908-924: LGTM! Combine launch logic consistently handles zero tokens.The validation relaxation (line 908) and minimal grid launch (lines 917-924) mirror the dispatch implementation, providing symmetric zero-token handling across both dispatch and combine operations.
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Signed-off-by: Bo Li <22713281+bobboli@users.noreply.github.com>
Signed-off-by: Bo Li <22713281+bobboli@users.noreply.github.com>
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…VIDIA#9822) Signed-off-by: Bo Li <22713281+bobboli@users.noreply.github.com>
<!-- .github/pull_request_template.md --> ## 📌 Description This is a port of NVIDIA/TensorRT-LLM#9822 which was done by @bobboli This feature is necessary for SGlang integration because some DP workers may have 0 tokens. The workaround to use a dummy token is quite messy and brittle. ## 🔍 Related Issues Follow up to #2102 ## 🚀 Pull Request Checklist Thank you for contributing to FlashInfer! Before we review your pull request, please make sure the following items are complete. ### ✅ Pre-commit Checks - [ ] I have installed `pre-commit` by running `pip install pre-commit` (or used your preferred method). - [ ] I have installed the hooks with `pre-commit install`. - [ ] I have run the hooks manually with `pre-commit run --all-files` and fixed any reported issues. > If you are unsure about how to set up `pre-commit`, see [the pre-commit documentation](https://pre-commit.com/). ## 🧪 Tests - [ ] Tests have been added or updated as needed. - [ ] All tests are passing (`unittest`, etc.). ## Reviewer Notes <!-- Optional: anything you'd like reviewers to focus on, concerns, etc. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Bug Fixes** * Improved robustness of mixture-of-experts all-to-all communication to gracefully handle scenarios with zero local tokens, preventing synchronization failures and ensuring stable operation in edge cases. <sub>✏️ Tip: You can customize this high-level summary in your review settings.</sub> <!-- end of auto-generated comment: release notes by coderabbit.ai -->
…VIDIA#9822) Signed-off-by: Bo Li <22713281+bobboli@users.noreply.github.com>
<!-- .github/pull_request_template.md --> ## 📌 Description This is a port of NVIDIA/TensorRT-LLM#9822 which was done by @bobboli This feature is necessary for SGlang integration because some DP workers may have 0 tokens. The workaround to use a dummy token is quite messy and brittle. ## 🔍 Related Issues Follow up to flashinfer-ai/flashinfer#2102 ## 🚀 Pull Request Checklist Thank you for contributing to FlashInfer! Before we review your pull request, please make sure the following items are complete. ### ✅ Pre-commit Checks - [ ] I have installed `pre-commit` by running `pip install pre-commit` (or used your preferred method). - [ ] I have installed the hooks with `pre-commit install`. - [ ] I have run the hooks manually with `pre-commit run --all-files` and fixed any reported issues. > If you are unsure about how to set up `pre-commit`, see [the pre-commit documentation](https://pre-commit.com/). ## 🧪 Tests - [ ] Tests have been added or updated as needed. - [ ] All tests are passing (`unittest`, etc.). ## Reviewer Notes <!-- Optional: anything you'd like reviewers to focus on, concerns, etc. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Bug Fixes** * Improved robustness of mixture-of-experts all-to-all communication to gracefully handle scenarios with zero local tokens, preventing synchronization failures and ensuring stable operation in edge cases. <sub>✏️ Tip: You can customize this high-level summary in your review settings.</sub> <!-- end of auto-generated comment: release notes by coderabbit.ai -->
…VIDIA#9822) Signed-off-by: Bo Li <22713281+bobboli@users.noreply.github.com> Signed-off-by: Daniil Kulko <kulkodaniil@gmail.com>
<!-- .github/pull_request_template.md --> ## 📌 Description This is a port of NVIDIA/TensorRT-LLM#9822 which was done by @bobboli This feature is necessary for SGlang integration because some DP workers may have 0 tokens. The workaround to use a dummy token is quite messy and brittle. ## 🔍 Related Issues Follow up to flashinfer-ai/flashinfer#2102 ## 🚀 Pull Request Checklist Thank you for contributing to FlashInfer! Before we review your pull request, please make sure the following items are complete. ### ✅ Pre-commit Checks - [ ] I have installed `pre-commit` by running `pip install pre-commit` (or used your preferred method). - [ ] I have installed the hooks with `pre-commit install`. - [ ] I have run the hooks manually with `pre-commit run --all-files` and fixed any reported issues. > If you are unsure about how to set up `pre-commit`, see [the pre-commit documentation](https://pre-commit.com/). ## 🧪 Tests - [ ] Tests have been added or updated as needed. - [ ] All tests are passing (`unittest`, etc.). ## Reviewer Notes <!-- Optional: anything you'd like reviewers to focus on, concerns, etc. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Bug Fixes** * Improved robustness of mixture-of-experts all-to-all communication to gracefully handle scenarios with zero local tokens, preventing synchronization failures and ensuring stable operation in edge cases. <sub>✏️ Tip: You can customize this high-level summary in your review settings.</sub> <!-- end of auto-generated comment: release notes by coderabbit.ai -->
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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.
Run
/bot [-h|--help]to print this help message.See details below for each supported subcommand.
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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
docs/source/reference/ci-overview.mdand the
scripts/test_to_stage_mapping.pyhelper.kill
killKill all running builds associated with pull request.
skip
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.