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The Kimi K3 KDA layer splits fused_qkv into q/k/v, passes them to the kda_attention op, then re-concatenates inside _forward before the fused causal conv1d -- a pointless split->cat round-trip (conv weights are already packed as [conv_size, 3*C], so the conv natively handles q/k/v together). Change the kda_attention op signature to take a single merged qkv tensor. The reference path cats q/k/v at the call site and chunks inside _forward; the ascend OOT path (vllm-ascend) drops the split/cat entirely. Numerically identical: qkv == torch.cat(qkv.split([C,C,C], dim=-1)). Eliminates 3 trim slices + 1 ConcatD around the fused conv; the after-conv chunk(3) is left for a follow-up kernel-side change. Co-Authored-By: Claude <noreply@anthropic.com>
Purpose
The Kimi K3 KDA (gated delta-net / SSM) attention layer's custom op
torch.ops.vllm.kda_attentioncurrently takes three separateq/k/vtensors. The reference path projects them with three independent linears (q_proj/k_proj/v_proj), passes them in separately, and inside_forwardtrims each one individually.This forces an awkward contract for hardware backends whose projection and conv are already fused on q/k/v:
AscendKimiGatedDeltaNetAttentionuses afused_qkvprojection that yields a single merged[N, 3*C]tensor, and the fusedCausalConv1dkernel packs its weights as[conv_size, 3*C](it processes q/k/v together by construction — see_pack_conv_weightsin the vllm-ascend override). The previous 3-arg signature forced that backend toqkv.split([C,C,C])just to pass three args into the op, thentorch.cat((q,k,v))again right before the conv — a pointlesssplit → catround-trip. Profiling on Ascend hardware shows that round-trip (plus the surrounding trims) is ~47% of the conv sub-graph time and adds ~18 us/layer.Test Plan
Test Result
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.