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[model, ops, perf] refactor: preserve grouped Q/K heads in packed FlashQLA GDN - #1303
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Check name Status Explanation Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Title check The title clearly and concisely describes the main change: preserving grouped Q/K heads in packed FlashQLA GDN paths, with relevant model, ops, and performance scope. Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request.
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Heads-up on ordering: #1142 ( What changes in #1142:
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Review comments at @tests/ops/benchmark_gdn_grouped_heads.py:
- Around line 146-147: Update the benchmark flows in bench_kernel and
bench_layer so run_expanded and run_grouped execute in alternating or
interleaved order rather than fixed sequential order. Record the order used
alongside each timing result, while preserving the existing _time_cuda
measurements and comparison outputs.
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📒 Files selected for processing: 7
docs/examples/qwen3_5.mdtests/ops/benchmark_gdn_grouped_heads.pytests/ops/gated_delta_rule/test_gated_delta_rule.pytests/ops/test_gdn_grouped_heads_contract.pyveomni/models/transformers/qwen3_5/qwen3_5_gpu_patch_gen_config.pyveomni/models/transformers/qwen4_exp/README.mdveomni/models/transformers/qwen4_exp/qwen4_exp_gpu_patch_gen_config.py
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| exp_fwd, exp_bwd, exp_tot, exp_mem = _time_cuda(run_expanded, warmup=warmup, iters=iters) | ||
| grp_fwd, grp_bwd, grp_tot, grp_mem = _time_cuda(run_grouped, warmup=warmup, iters=iters) |
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🚀 Performance & Scalability | 🟡 Minor | ⚡ Quick win
Alternate or interleave the expanded and grouped runs. The fixed order biases the comparison.
run_expanded always runs all of its samples before run_grouped, in both bench_kernel and bench_layer. GPU clock and thermal state drift in one direction during a run, and so does allocator cache state. With a fixed order, that drift always favors the same implementation. The PR reports layer gains of 0.99–4.42%. Drift of that size can account for a large part of those gains.
Interleave the two variants per iteration, or alternate the order per config. Record the executed order next to the timings.
♻️ Proposed fix: alternate order per call
--- "a/tests/ops/benchmark_gdn_grouped_heads.py"
+++ "b/tests/ops/benchmark_gdn_grouped_heads.py"
@@ -143,8 +143,12 @@
)
return out, grad_out
- exp_fwd, exp_bwd, exp_tot, exp_mem = _time_cuda(run_expanded, warmup=warmup, iters=iters)
- grp_fwd, grp_bwd, grp_tot, grp_mem = _time_cuda(run_grouped, warmup=warmup, iters=iters)
+ # Run grouped first, then expanded, then grouped again; average the two grouped runs.
+ grp_a = _time_cuda(run_grouped, warmup=warmup, iters=iters)
+ exp_fwd, exp_bwd, exp_tot, exp_mem = _time_cuda(run_expanded, warmup=warmup, iters=iters)
+ grp_b = _time_cuda(run_grouped, warmup=warmup, iters=iters)
+ grp_fwd, grp_bwd, grp_tot = ((a + b) / 2 for a, b in zip(grp_a[:3], grp_b[:3]))
+ grp_mem = max(grp_a[3], grp_b[3])
return (
BenchResult(
"kernel_expanded_qk", total_tokens, k_heads, v_heads, head_dim, exp_fwd, exp_bwd, exp_tot, exp_mem, 0.0Based on learnings: "When a benchmark suite always executes competing implementations in a fixed sequential order ... flag this because cache state, thermal throttling, and CPU/GPU clock frequency scaling drift systematically".
Also applies to: 223-224
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Review comment at @tests/ops/benchmark_gdn_grouped_heads.py around lines 146 -
147:
Update the benchmark flows in bench_kernel and bench_layer so run_expanded and
run_grouped execute in alternating or interleaved order rather than fixed
sequential order. Record the order used alongside each timing result, while
preserving the existing _time_cuda measurements and comparison outputs.
After applying the fix, consider running `coderabbit review --agent` for local
review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
Source: Learnings
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| out_e, _ = eager(q_e, k_e, v_e, g_e, b_e, use_qk_l2norm_in_kernel=True) | ||
| out_o, _ = other(q_o, k_o, v_o, g_o, b_o, use_qk_l2norm_in_kernel=True) | ||
| assert torch.allclose(out_e, out_o, atol=GDN_CHUNK_ATOL, rtol=GDN_CHUNK_RTOL) | ||
| ref_out, ref_state, ref_grads = run_case(eager, expand_qk=True) |
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[P2] The eager wrapper rejects nonempty cu_seqlens, so this reference call raises ValueError in every enabled case before FlashQLA runs. Build the eager reference per packed segment with the corresponding initial state, then concatenate outputs and states while preserving gradients.
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[P2] The eager reference now evaluates each packed segment with cu_seqlens=None and its corresponding h0 slice, then concatenates outputs and states before backward. This fixes the unsupported-metadata exception.
| cu_seqlens = torch.tensor([0, 19, 64], device="cuda", dtype=torch.int32) | ||
| num_k_heads = 2 | ||
| num_v_heads = num_k_heads * group_size | ||
| q_base = torch.randn(1, 64, num_k_heads, 64, device="cuda", dtype=torch.bfloat16) |
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[P2] FlashQLA 0.1.2 requires 128-wide key and value heads on SM90/SM100, but these tensors use 64. Once the eager-reference failure is fixed, every case will still hit a vendor assertion. Use 128 consistently for Q/K/V, h0_base, grad_out, and grad_state.
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[P2] head_dim is now 128 for Q/K/V, h0_base, grad_out, and grad_state, matching FlashQLA 0.1.2’s requirements. This fixes the vendor dimension-assertion failure.
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What does this PR do?
This PR removes unnecessary Q/K head expansion from the existing packed FlashQLA GDN path for Qwen3.5 dense, Qwen3.5 MoE, and Qwen3.8 Next's GDN path (named
qwen4_expin this source revision). When several value heads share one query/key head, FlashQLA already accepts the smaller grouped Q/K layout, so the model no longer copies Q and K withrepeat_interleavebefore the kernel call.The change does not alter weights, token counts, value-head counts, packed boundaries, the training objective, or public configuration. Each layer caches whether grouped heads are safe at construction time together with its bound kernel, so later module-global dispatch rebinding cannot change an existing layer's Q/K layout.
Single-H100 validation passed 80 layout checks and 12 output/gradient comparisons, and all 72 benchmark cases completed. For value/key head ratios 2 and 4, measured GDN kernel forward+backward latency fell by 6.93–9.82%; corresponding GDN layer latency fell by 0.99–4.42%, depending on model and shape. These are single-kernel/layer results, not whole-model training speedups.
Checklist Before Starting
[{modules}] {type}: {description}format.Test
tests/ops/test_kernel_registry_numerical.pywith Hopper-only FlashQLA output, final-state, input-gradient, and parameter-gradient comparisons. The repository GPU unit fleet is L20/SM89, so these SM90 cases skip in current CI and are manual H100 validation rather than continuous regression coverage.tests/ops/test_gdn_grouped_heads_contract.py;tests/ops/is collected as a directory by GPU unit CI, so no workflow entry is required. This continuous CI coverage uses mock/meta tensors to check ratios 1/2/4, packed and unpacked calls, eager/FLA/FlashQLA/NPU selections, per-instance dispatch caching, Qwen3.5 Ulysses local-head shapes, recurrent-cache rejection, and Qwen4-Exp fallback behavior. It does not validate real FlashQLA numerics or multi-rank Ulysses communication.tests/ops/benchmark_gdn_grouped_heads.pyas the reproducible single-H100 benchmark driver. It is not collected as a pytest test.make quality: passed (ruff checkandruff format --check, 737 files).patchgen --check: passed for all 31 patch configurations; generated GPU/NPU files match their source configs.56 passed, 24 skippedon macOS; the 24 Qwen4-Exp cases require a CUDA-capable import environment.Numerical comparisons used BF16 inputs, finite-value checks, and
atol=rtol=5e-2. Correctness inputs used 129 tokens split at[0, 31, 64, 129], key-head dimension 128, and two key heads. The tests do not establish bitwise identity, full-model loss/convergence, cached decode, gradients with respect to the returned final state, multi-GPU numerical parity, or real multi-rank grouped-head Ulysses behavior. The Ulysses check in continuous CI verifies local tensor layouts with mocked collectives only; a real multi-rank Hopper run remains follow-up validation.API and Usage Example
No new flag or dependency is introduced. Select the existing FlashQLA backend and provide packed metadata through the normal model builder:
The grouped layout is enabled only when FlashQLA is selected and its kernel is actually bound. Qwen3.5 additionally requires packed
cu_seq_lens_qmetadata and no precomputed recurrent state. Other backends, unbound-kernel fallbacks, unpacked Qwen3.5 calls, and cached decode preserve their existing behavior.Design & Code Changes
OpsConfigSlotforchunk_gated_delta_rule_implementationto the Qwen3.5 dense/MoE GPU and NPU generated-model configs and Qwen4-Exp.gdn_grouped_headsper GDN layer from both the selected implementation and the resolved kernel, matching the existing instance-local kernel cache.repeat_interleaveonly on the supported packed FlashQLA path; preserve all fallback and cache guards.Simplified before/after for a shared-head packed call:
The actual Qwen3.5 guard is narrower:
Scope and compatibility
Performance method
[0, sequence // 4, sequence].Representative forward+backward result at 8192 tokens and head ratio 4:
Across ratios 2 and 4, kernel latency reductions were 4.48–12.46% forward and 6.93–9.82% forward+backward. Layer reductions were 1.89–3.31% forward and 0.99–4.42% forward+backward. All 48 shared-head rows had lower candidate medians, but this is one GPU session and not a fleet-wide stability claim. The 24 ratio-1 controls ranged from a 1.97% slowdown to a 1.90% reduction despite identical layouts, so small layer differences near that range should be treated cautiously.
For BF16 Q/K inputs at 8192 tokens, four key heads, head dimension 128, and ratio 4, avoiding the two expanded inputs saves 48 MiB of forward allocations. The layer forward peak allocation fell by exactly 48 MiB in those cases; forward+backward saves more because gradients and backend intermediates also depend on the layout.
Reproduction and evidence
8e5f8891ff5a731d7a91d12f7a12af637e97e1a0.3cdc5eaa5d31fd7c51490099454795297fb8f13cc6253e1ffbbfb1d82c428a13.2.11.0+cu128, Transformers5.16.1, FlashQLA0.1.2, TileLang0.1.9, FLA/FLA-core0.4.1, Triton3.6.0, pytest7.4.4.6d96611f2fd02347c1919253587b4f45f3a1aa406909bdfc4360c1a593bf7263.b245ed92acb8738c, trial/run303689762, completed October 14, 2026 at 18:36:46 China time with exit code 0 and markerGDN_VALIDATION_COMPLETE trial=303689762 run=303689762.Reproduce from this branch on one H100:
Checklist Before Submitting
tasks/training scripts were moved or renamed: not applicabletests/ops/directorySummary by CodeRabbit
Performance
Documentation
Tests