fix sparge on torch2.13 - #2130
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triton 3.7.x enables has_predicated_io for non-LTS Intel GPU drivers and emits the SPV_INTEL_predicated_io SPIR-V extension. Some level-zero loaders (e.g. 1.13.35563) reject that extension at kernel load time, aborting every triton kernel launch with 'InvalidModule: ... unknown extension'. Since it is not a catchable Python exception, the torch preprocess fallback never ran and the whole benchmark died. Add auto_round_kernel/_xpu_triton_compat.py which patches the intel triton backend to force has_predicated_io off (standard predicated loads instead), and apply it when the triton preprocess module loads. The triton-xpu preprocess backend now works and is faster than torch (seq 32768/50% e2e: 327ms -> 206ms).
tools/run_sparse_sagev1_bench.sh documents and runs the sparse SAGE v1 (fp16 / INT8) benchmark on main: sources oneAPI, pins a GPU, uses the rebuilt extension, and saves timestamped CSV + log under benchmarks/results/. Includes env overrides and a note on the triton preprocess backend.
for more information, see https://pre-commit.ci
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/azp run Unit-Test-CUDA-AutoRound |
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Aug 8, 2026
Signed-off-by: yiliu30 <yi4.liu@intel.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Description
Please briefly describe your main changes, the motivation.
Type of Change
Bug fix
Related Issues
Fixes or relates to #
Checklist Before Submitting
/azp run Unit-Test-CUDA-AutoRound.