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Fix triton_kernels import error on some hardwares #11831
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Summary of ChangesHello @fzyzcjy, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request implements a fix for an import error related to Highlights
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Code Review
This pull request correctly fixes a potential import error for triton_kernels
on certain hardware. The change makes the import conditional on the use_triton_kernels
flag, which is the intended behavior. I have one minor suggestion to improve code style by consolidating two import statements into one, located near the modified line.
if torch.cuda.is_available() and has_triton_kernels: | ||
if torch.cuda.is_available() and use_triton_kernels: | ||
from sglang.srt.layers.moe.fused_moe_triton.triton_kernels_moe import ( | ||
triton_kernel_moe_forward as _tk_forward, |
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While you are modifying this block, you can consolidate the two separate imports from sglang.srt.layers.moe.fused_moe_triton.triton_kernels_moe
on lines 147-152 into a single statement. This improves readability and follows standard Python style (PEP 8).
from sglang.srt.layers.moe.fused_moe_triton.triton_kernels_moe import (
triton_kernel_moe_forward as _tk_forward,
triton_kernel_moe_with_bias_forward as _tk_with_bias_forward,
)
Motivation
Modifications
Accuracy Tests
Benchmarking and Profiling
Checklist