[AMD] Clear pre-built AITER kernels and warmup to prevent segfaults and test timeouts#15318
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HaiShaw merged 7 commits intosgl-project:mainfrom Dec 18, 2025
Merged
[AMD] Clear pre-built AITER kernels and warmup to prevent segfaults and test timeouts#15318HaiShaw merged 7 commits intosgl-project:mainfrom
HaiShaw merged 7 commits intosgl-project:mainfrom
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CI Status UpdateAMD CI ✅ ALL PASSINGAll AMD CI tests are now passing:
NVIDIA CI ❌ Infrastructure Issue (Unrelated)The NVIDIA CI tests are failing with This PR is ready for review/merge as all AMD CI tests pass. |
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…nd test timeouts The Docker image contains pre-compiled AITER kernels that may be incompatible with the current environment, causing segfaults when imported. After clearing these kernels, they need to be rebuilt at runtime (~156s for module_rmsnorm alone), which causes test timeouts. This fix: 1. Clears pre-built AITER kernels from the Docker image 2. Pre-builds commonly used AITER kernels (RMSNorm, rotary embedding, activation) during dependency installation, before any tests run The warmup is done once in amd_ci_install_dependency.sh, which is shared across all CI jobs, rather than in each individual workflow step. Changes: - Add scripts/ci/amd_ci_warmup_aiter.py to trigger JIT compilation - Update scripts/ci/amd_ci_install_dependency.sh to clear and warmup AITER kernels
The previous commit cleared pre-built AITER kernels to avoid segfaults from incompatible kernels in the Docker image. However, this caused test timeouts because AITER kernels needed to be rebuilt at runtime (~156s for module_rmsnorm alone). This fix adds a warmup step that pre-builds commonly used AITER kernels after clearing, so tests don't timeout waiting for JIT compilation. Changes: - Add scripts/ci/amd_ci_warmup_aiter.py to trigger JIT compilation - Update workflow to run warmup after clearing AITER kernels - Add timeout-minutes: 10 for the warmup step
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Remove the temporary disable flag to allow the 8-GPU test job to run.
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Remove the temporary disable flag to allow the 8-GPU test job to run.
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@sunxxuns skip torchao one seems appropriate. |
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Merging this now, but to root cause and provide update @yctseng0211 |
HaiShaw
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Dec 18, 2025
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* 'main' of https://github.com/sgl-project/sglang: (136 commits) fix: unreachable error check in retraction (sgl-project#15433) [sgl-kernel] chore: update deepgemm version (sgl-project#13402) [diffusion] multi-platform: support diffusion on amd and fix encoder loading on MI325 (sgl-project#13760) [amd] Add deterministic all-reduce kernel for AMD (ROCm) (sgl-project#15340) [diffusion] refactor: refactor _build_req_from_sampling to use shallow_asdict (sgl-project#13782) Add customized sampler registration (sgl-project#15423) Update readme (sgl-project#15425) Fix Mindspore model import warning (sgl-project#15287) [Feature] Xiaomi `MiMo-V2-Flash` day0 support (sgl-project#15207) [diffusion] profiling: add bench_serving.py and VBench (sgl-project#15410) [DLLM] Fix dLLM regression (sgl-project#15371) [Deepseek V3.2] Fix Deepseek MTP in V1 mode (sgl-project#15429) chore: update CI_PERMISSIONS (sgl-project#15431) [DLLM] Add CI for diffusion LLMs (sgl-project#14723) Support using different attention backend for draft decoding. (sgl-project#14843) feat(dsv32): better error handling for DeepSeek-v3.2 encoder (sgl-project#14353) tiny fix lint on main (sgl-project#15424) multimodal: precompute hash for MultimodalDataItem (sgl-project#14354) [AMD] Clear pre-built AITER kernels and warmup to prevent segfaults and test timeouts (sgl-project#15318) [Performance] optimize NSA backend metadata computation for multi-step speculative decoding (sgl-project#14781) ...
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Summary
This PR fixes multiple AMD CI issues related to AITER kernel compatibility:
1. AITER Kernel Segfault Fix
2. AITER Kernel Timeout Fix
scripts/ci/amd_ci_warmup_aiter.py) to pre-build commonly used AITER JIT kernels (RMSNorm, rotary embedding, activation)amd_ci_install_dependency.shbefore tests to avoid timeout waiting for JIT compilation (~156s for module_rmsnorm alone)3. TorchAO Test Skip
TestTransformersFallbackTorchAOtest on AMD GPUs_convert_weight_to_int4pack_cudarequires CDNA2+ and is not supported on current AMD hardware4. test_rope_rocm.py Tolerance Fix
Test Plan