CCCL source read: cub/device/dispatch/dispatch_reduce_by_key.cuh
- DeviceReduceByKey sorts input by key, pads to tile boundary, then
one fused kernel processes all key-value segments in parallel.
- This is architecturally identical to base engine's fused_moe.py:
moe_align_block_size (sort+pad) → invoke_fused_moe_kernel (one launch).
Discovery: _custom_ops.py (line 776-806) confirms ixformer HAS native MoE:
- ixf_F.vllm_moe_topk_softmax
- ixf_F.vllm_moe_align_block_size
- ixf_F.vllm_invoke_fused_moe_kernel (takes only BLOCK_SIZE_M config)
Previous code assumed 'ixformer lacks MoE kernels' and used _pure_pytorch_experts
(Python for-loop over 256 experts). This may have been wrong or outdated.
Change: MoeSparseBlock.forward now tries self.experts (FusedMoE native) first.
If the native kernel fails on BI-V100, it catches the exception, logs a warning,
and permanently falls back to _pure_pytorch_experts for that instance.
Impact if native works: one fused CUDA kernel vs 256× F.linear calls = massive
decode speedup. Impact if native fails: same behavior as before (fallback).
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