[Perf][GLM-5.3-Flash] Decode hot-path cleanups: strided KDA recurrent inputs, NoPE MQA query without concat, no duplicate router GEMM - #55736
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…ent kernel The decode path feeds fused_recurrent_kda column slices of the merged q|k|v conv output and of the fused qkvbfg_a projection (beta). The wrapper made each of them contiguous, i.e. four copy kernels per KDA layer per step (~11 us/layer at 64 requests, ~0.4 ms/step across 34 layers). Give the Triton kernel explicit token strides for q/k/v/beta so those slices are consumed in place; contiguous callers are unchanged (stride == H*K). Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: Jared Wen <jaredwen@inferact.ai>
…ty RoPE concat Without head padding, run the W_UK bmm straight into a (B, N, L) buffer (transposed out view; cuBLAS handles the strides, verified: one GEMM kernel, no copy) so the MQA query is already token-major contiguous. For NoPE models (qk_rope_head_dim == 0, e.g. GLM-5.3-Flash) the FlashInfer sparse backend then uses ql_nope directly instead of torch.cat with a zero-width q_pe, which went through the slow CatArrayBatchedCopy path (13.7 us/layer at 256 decode tokens, ~0.75 ms/layer per 16k prefill chunk). Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: Jared Wen <jaredwen@inferact.ai>
MoERunner holds the gate module (passed via FusedMoEFactory) and computes the router logits itself, so the extra gate GEMM in Glm5NextMoE.forward was wasted work (two bf16->fp32 GEMMs + reductions per MoE layer, ~0.5 ms per decode step at 256 requests). Follow DeepseekV2MoE and pass a placeholder. Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: Jared Wen <jaredwen@inferact.ai>
WalkthroughThe change adds token-strided KDA recurrent support with CUDA coverage, changes MQA decode buffer layouts for NoPE handling, and moves GLM5Next MoE gate execution into ChangesStrided KDA recurrent decode
MQA query layout handling
GLM5Next MoE routing
Estimated code review effort: 3 (Moderate) | ~25 minutes Merge Risk: 🟡 Moderate · up to The KDA decode optimization can produce incorrect recurrent outputs for fixed-batch inputs backed by non-densely strided batch storage. The input contract must reject this layout or the kernel must support batch strides before merge. Suggested reviewers: Sequence Diagram(s)sequenceDiagram
participant fused_recurrent_kda
participant KDAWrapper
participant KDAKernel
fused_recurrent_kda->>KDAWrapper: Pass q, k, v, beta, and state
KDAWrapper->>KDAWrapper: Validate token strides
KDAWrapper->>KDAKernel: Launch with explicit token strides
KDAKernel->>KDAWrapper: Write output and updated state
🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
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In `@vllm/models/glm5next/nvidia/ops/third_party/kda/kernels.py`:
- Line 42: Update _token_strided to reject non-dense batch strides for inputs
with B > 1 by requiring stride(0) == T * stride(1) before preserving the input;
keep the existing B=1 varlen behavior unchanged. Add a fixed-batch B=2
regression test using a sliced wider buffer to verify q/k/v/beta addresses
remain correct.
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📒 Files selected for processing (6)
tests/kernels/test_glm5next_kda_recurrent_strided.pyvllm/model_executor/layers/attention/mla_attention.pyvllm/models/glm5next/nvidia/model.pyvllm/models/glm5next/nvidia/ops/third_party/kda/fused_recurrent.pyvllm/models/glm5next/nvidia/ops/third_party/kda/kernels.pyvllm/v1/attention/backends/mla/flashinfer_mla_sparse.py
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| """ | ||
| st = x.stride() | ||
| if x.dim() == 4: | ||
| return st[3] == 1 and st[2] == x.shape[3] |
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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win
Reject non-dense batch strides before preserving the input.
_token_strided accepts [B, T, H, D] tensors when stride(0) != T * stride(1). With cu_seqlens is None and B > 1, the kernel calculates input addresses from bos * stride_*_t and does not apply stride(0). A slice that selects alternate batches from a wider buffer then reads q/k/v/beta data from the wrong physical batch.
Require stride(0) == T * stride(1) when B > 1, or pass and use batch strides in the kernel. Add a fixed-batch B=2 regression case; the new test covers only the B=1 varlen path.
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only still-valid issues, skip the rest with a brief reason, keep changes
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In `@vllm/models/glm5next/nvidia/ops/third_party/kda/kernels.py` at line 42,
Update _token_strided to reject non-dense batch strides for inputs with B > 1 by
requiring stride(0) == T * stride(1) before preserving the input; keep the
existing B=1 varlen behavior unchanged. Add a fixed-batch B=2 regression test
using a sliced wider buffer to verify q/k/v/beta addresses remain correct.
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🟡 Changes recommended
The new token-stride helper currently ignores its inner parameter, so it doesn’t guard against invalid/overlapping strided layouts; adding a minimal stride_t >= inner assertion would harden correctness.
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Pull request overview
This PR targets GLM-5.3-Flash decode hot paths, reducing avoidable copies/concats and removing redundant MoE router GEMMs while keeping behavior identical.
Changes:
- KDA recurrent Triton kernel now supports token-strided
q/k/v/beta(so decode can consume column slices in-place instead of materializing contiguous copies). - MLA decode writes the absorbed MQA query into a token-major buffer and, for NoPE (
qk_rope_head_dim == 0), skips the zero-widthtorch.cat. - Glm5NextMoE stops precomputing router logits in the model and relies on
MoERunner’s internal gate compute (matching existing patterns like DeepSeek).
File summaries
| File | Description |
|---|---|
| vllm/v1/attention/backends/mla/flashinfer_mla_sparse.py | Skip torch.cat when RoPE part is empty and ql_nope is already contiguous. |
| vllm/models/glm5next/nvidia/ops/third_party/kda/kernels.py | Add token-stride detection and plumb token stride scalars into the recurrent kernel; avoid allocating output with non-contiguous strides. |
| vllm/models/glm5next/nvidia/ops/third_party/kda/fused_recurrent.py | Update Triton kernel pointer math to use explicit per-token strides for q/k/v/beta. |
| vllm/models/glm5next/nvidia/model.py | Remove redundant router-logits GEMM; pass placeholder since MoERunner computes logits internally. |
| vllm/model_executor/layers/attention/mla_attention.py | Write bmm output into (B, N, L) layout (via transposed out) to keep NoPE query contiguous and enable concat elision. |
| tests/kernels/test_glm5next_kda_recurrent_strided.py | New CUDA test validating strided vs contiguous KDA inputs are bit-identical. |
Review details
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I think we should stash this under tests/models/glm5next? similar to the recent models
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great! create a dir and buildkite test against glm5next
| @pytest.mark.parametrize("num_seqs", [1, 7]) | ||
| @pytest.mark.parametrize("num_heads", [16]) | ||
| @pytest.mark.parametrize("head_dim", [128]) | ||
| def test_fused_recurrent_kda_strided_inputs_match_contiguous( |
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It's probably better to test against a reference implementation (e.g. pure PyTorch) instead of testing against itself
| stride_q_t=H * K, | ||
| stride_k_t=H * K, | ||
| stride_v_t=HV * V, | ||
| stride_beta_t=HV * (V if beta.ndim == v.ndim else 1), |
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Since you are adding strided support, might as well just pass q.stride(something) here?
| return x.dim() == 3 and st[2] == 1 | ||
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| def _token_stride(x: torch.Tensor, inner: int) -> int: |
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inner doesn't do anything? Btw, I think you can do something like assert x[0].is_contiguous() as well to be more concise
| q=q if _token_strided(q) else q.contiguous(), | ||
| k=k if _token_strided(k) else k.contiguous(), | ||
| v=v if _token_strided(v) else v.contiguous(), |
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(non-blocking) I think if you are already tackling layout issue, it might be better to assert certain contiguity instead of falling back to .contiguous() silently -> loud failure, so you know it's working / something else needs fixing.
…ence-based test - `token_stride` (fused_recurrent.py) asserts the layout the recurrent kernel can address instead of silently falling back to `.contiguous()`: dense per-token block, non-overlapping tokens and, for B > 1, a dense batch (`stride(0) == T * stride(1)`), mirroring the Kimi-K3 KDA wrapper. - The generic gated-delta-rule wrapper passes the tensors' own token strides instead of recomputed constants. - Test moved to tests/models/glm5next and now compares against an fp32 PyTorch recurrence (in-kernel gate, beta sigmoid, q/k l2norm, per-token state slots) for plain decode and spec-decode (T > 1 per sequence) shapes, keeps the strided-vs-contiguous bit-identity check, and asserts the unaddressable layouts are rejected. New CI block for tests/models/glm5next. Signed-off-by: Jared Wen <jaredwen@inferact.ai> Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
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@gau-nernst PTAL, thanks! |
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/ci run |
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❌ @JaredforReal, A reviewer with write access must run |
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/ci run |
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✅ Triggered Buildkite CI #87910 for commit |
… inputs, NoPE MQA query without concat, no duplicate router GEMM (vllm-project#55736) Signed-off-by: Jared Wen <jaredwen@inferact.ai> Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: Jyotirmoy Roy <jyotirmoyroy649@gmail.com>
… inputs, NoPE MQA query without concat, no duplicate router GEMM (vllm-project#55736) Signed-off-by: Jared Wen <jaredwen@inferact.ai> Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
…yout GLM-5.3-Flash has MLA dims (qk_nope 256, qk_rope 0, v 256), which is not in FlashAttnPrefillBackend.supports_mla_dimensions, so get_mla_prefill_backend raises and every sparse-MLA layer logs No MLA prefill backend supports this model; sparse MLA will use the top-k MQA path only (no dense-MHA prefill). and prefills through the per-token top-k MQA kernel even when the whole sequence fits in index_topk. Register the layout: it runs the same kernels as the (192, 64, 256) one, and sequences up to index_topk (2048 here) then take the dense-MHA path, where selecting the top 2048 of at most 2048 tokens is the identity. Checked on sm_80 before enabling it: flash_attn_varlen_func with head_dim 256, bf16, 64 heads, varlen causal matches an fp32 reference to 2e-3 relative on FA2. The masked-MHA half of the upstream PR is deliberately not ported -- _is_masked_mha_available requires the SM100 family, FA4 and an unquantized KV cache, and this fleet has none of the three. Also drop the K-side concat when there is no RoPE half, the counterpart of the query-side skip in the vllm-project#55736 port: _concat_k_nope_k_pe allocated and copied a same-sized tensor to append nothing.
Purpose
One of three independent GLM-5.3-Flash perf PRs
Three small, independent decode hot-path cleanups found while profiling GLM-5.3-Flash (
zai-org/GLM-5.3-Flash, FP8, TP4 on 4x GB300,FLASHINFER_MLA_SPARSE):fused_recurrent_gated_delta_rule_fwd_kernel. The decode path hands the recurrent kernel column slices of the mergedq|k|vconv output and of the fusedqkvbfg_aprojection (beta);fused_recurrent_kdamade each contiguous, i.e. 4 copy kernels per KDA layer per step (34 layers; ~11 us/layer at 64 requests, ~0.4 ms/step). The Triton kernel now takes explicit token strides; contiguous callers are unchanged. The stride check is pure integer arithmetic (no views) because the KDA forward is a CUDA-graph break and runs eagerly.(B, N, L)buffer through a transposedoutview (verified: one GEMM kernel, no copy, identical result). For NoPE models (qk_rope_head_dim == 0) the FlashInfer sparse backend then usesql_nopedirectly instead oftorch.catwith a zero-widthq_pe, which took the slowCatArrayBatchedCopypath (13.7 us/layer at 256 decode tokens, ~0.75 ms/layer per 16k-token prefill chunk).MoERunnerholds the gate (passed viaFusedMoEFactory) and recomputes the logits (moe_runner.py:897), so the gate GEMM inGlm5NextMoE.forwardwas wasted (two bf16->fp32 GEMMs + reductions per MoE layer). FollowsDeepseekV2MoEand passes a placeholder.Test Plan
lm_eval --model local-completions --tasks gsm8k --num_fewshot 5 --gen_kwargs temperature=0Test Result
Performance of this PR alone
main
156050598vs main + this PR, 4x GB300, TP4,--attention-backend FLASHINFER_MLA_SPARSE --max-model-len 69632 --max-num-seqs 256 --max-num-batched-tokens 16384, prefix caching disabled (hit rate 0.0% checked in the server log),vllm bench serverandom dataset with warmups, both servers run back-to-back in the same session. Decode tok/s is the steady-state window value (full concurrency); TPOT is the per-request median.Decode: +3-4% tok/s (−3-4% TPOT) at c=64, c=256 and in the 32k-context point, −1.7% TPOT at c=1 (host-bound). Prefill: a consistent −2.5-2.8% TTFT on all four points
Accuracy (per build, same session)
Each rung of the ablation was also evaluated on its own: gsm8k (1319 questions, 5-shot, greedy, lm_eval
local-completions)Duplicate-work check
gh pr list --repo vllm-project/vllm --state open --search "fused_recurrent_kda stride","MoERunner gate router_logits Glm5Next","GLM-5.3-Flash prefill": no open PR covers these (the open GLM-5.3-Flash PRs #54951 / #55222 / #55385 / #55543 touch the indexer prefill sharding, SM90 sparse-MLA dtypes and the SM90 FA/FlashMLA wiring).Tests
pytest tests/models/glm5next/test_kda_recurrent.py(new; fp32 PyTorch reference for plain and spec-decode shapes, strided-vs-contiguous bit-identity, unaddressable layouts rejected): 6 passed.out: max diff 0.0 vsbmm().transpose(), profiler shows a singlenvjetkernel.pre-commit run ruff-check / ruff-formaton the changed files: passed.Notes for review
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.