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cuda act_rows: Dory's scaled output gate and scaled SwiGLU, each row then L2-normed (Dory, #1233) - #1258

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What this changes

Step 3, third kernel PR of the Dory family (#1233). Two activation rows, a block a row, 16-byte loads, fp32 scale vectors formed on the host as torch forms them. ActRows.gateL2 and ActRows.swigluL2 launch them; no existing launch changes.

This PR closes nothing. The issue is #1233.

Stacking. Stacked on #1257 (qk_rows), which is stacked on #1256 (norms, carrying the #1245 contract include fix as its base commit). This branch is one commit on top of #1257.

What the kernels compute

  • tf_gate_l2_rows: out = bf16(l2(bf16(o * sigmoid(g * sg)))) over all heads of a row (dory.py's output gate, then justnorm over num_heads * head_dim = 4,096). sg = (s_gate * (sqrt(g0)/std))^2, an fp32 vector.
  • tf_swiglu_l2_rows: out = bf16(l2(bf16(silu(bf16(g * sug)) * bf16(u * suu)))) over the MLP width 7,168. su = suv * sqrt(hidden) in fp32.

sigmoid and silu are torch's fp32 forms, 1 / (1 + expf(-x)) and x / (1 + expf(-x)), with IEEE division. expf overflow (x < -88.7) gives sigmoid exactly 0 as torch does; the reference models fp32's exp range.

Rounding points: the scaled gate, the scaled up, the product, and the gated row before its norm.

Kernel contract

Registered with the kernel contract. Every output is compared with float64's range at its rounding points (expf's ulps, fp32's exp range, the inverse's spread) at Dory's widths 4,096 and 7,168.

  • Gates 1-4 pass.
  • Four planted controls go red at gate 3, each dropping one rounding point: the gated row, the scaled gate, the scaled up, the product.
  • Gate 4: the gate's rows are halves of integers (sigmoid(0) = 1/2), exact in fp32. SwiGLU's squares are not integers, so its fp32 sums are checked against the shapes gate's bound; an fp16 sum overflows either way.
  • A bug found on the way: the f64 exp did not overflow where expf does; fixed in the reference.
  • Limit records, sm_80/86/89/120/121 (regs; static shared 32 B; launch bound 256):
    • tf_gate_l2_rows: 107/109/109/109/109
    • tf_swiglu_l2_rows: 109/118/118/121/121
  • Limit problems are unchanged from main.

Receipt

  • TensorFold 50b11cf70; NVIDIA GB10 (sm_121), CUDA 13.0 nvcc, Zig 0.17.0.
  • zig build test -Dnvcc=...: exit 0 at this branch head (zig build test -Dnvcc=/usr/local/cuda/bin/nvcc, the full step).
  • lean_check: 0 new against main.
  • Full kernel contract on the top of the stack (PR6): on main plus the include fix, SUMMARY 280 kernels: 278 pass gates 1-4, 2 fail, 2 known findings; 53 controls: 52 red as planted, 1 missed; 1566 limit problems. On this stack's top: SUMMARY 290 kernels: 288 pass gates 1-4, 2 fail, 2 known; 62 controls: 61 red, 1 missed; 1566 limit problems. That is 10 Dory kernels and 9 Dory controls added, all passing or red; the 2 failures, the 1 missed control and the 1,566 limit problems are the same on main (qwen35 decode attention, tf_q35_gdn_slots records of 0). Two things on main keep the unmodified run from completing: w4a8_tests.zig does not compile against w4a8.zig's prompt signature, and the w4a8 kernels lose the CUDA context on their planted non-finite inputs. Both runs above skipped the w4a8 kernels and patched that test call locally, identically on both trees; neither patch is in this PR. All Dory launches pass the live limits check.

Choices ashhart can change

…d lerp residual

Dory's residual stream lives on the unit sphere, so its rows need two kernels norms.cu did
not have, both a block a row (256 threads, 16-byte loads, rows to 8,192 in 64s), so a row's
bits never depend on the row count:

- tf_l2_rows: y = bf16(s / sqrt(sum(s^2) + eps)), s = x, or bf16(x + r) (a loop's entry,
  justnorm(state + input_h); with r null, a loop's exit).
- tf_slerp_rows: dory.py's slerp_norm, h = bf16(l2(a + |alpha scale| (b - a))) with
  a = bf16(l2(res)) and b = bf16(l2(out)), the lerp in fp32 steps.

Norms.l2 and Norms.slerp launch them; no existing kernel or launch changes.

Both are registered with the kernel contract. Their check compares every output with
float64's value at the bf16 rounding points as a range over the fp32 run's spread
(core/l2_ref.zig); three controls each drop one rounding point (the sum before the norm,
the slerp's a, its b) and go red at gate 3. Gate 4's integer rows make an fp16 sum overflow
to a zero output (85x and 51x the bound); a bf16 sum's scale cancels in the norm, so that
margin is stated, not proven, as for the RMS tails. Launch records from nvcc 13.0's
sm_80/86/89/120/121 images.
…2 norm and q's scale

tf_qk_rows_l2 takes dory.py's order: rotate-half RoPE on the projection's bf16 rows
(rounded to bf16), then each head's L2 norm with eps on the sum, then q times an fp32 scale
vector ((sqk f)^2, formed on the host as torch forms it) after the normalized row's
rounding; k is its normalized row, v as is, and k and v go into the caches as tf_qk_rows
writes them. Shape.l2 and Shape.q_scale select it; every other call keeps its kernel.

It is registered with the kernel contract: every output against float64's range at its
rounding points (cosf's ulps on the fp32 angle pos * inv, the inverse's fp32 spread), at
head dim 256, 16 query heads over 4, a third of the rows past position 199,800. Two controls
each drop one rounding point (the rotated head, q's normalized head) and go red at gate 3.
Gate 4's integer heads (no rotation) overflow an fp16 sum to a zero output.

core/l2_ref.zig's intervals now keep infinite ends and widen by fp32's least subnormal
step, so a planted infinity leaves only the outputs float64 makes non-finite.
…then L2-normed

Two activation rows for Dory, a block a row, 16-byte loads, fp32 scale vectors formed on
the host as torch forms them:

- tf_gate_l2_rows: out = bf16(l2(bf16(o * sigmoid(g * sg)))) over all heads of a row
  (dory.py's output gate, then justnorm over num_heads * head_dim);
- tf_swiglu_l2_rows: out = bf16(l2(bf16(silu(bf16(g * sug)) * bf16(u * suu)))) (its MLP).

sigmoid and silu are torch's fp32 forms, 1 / (1 + expf(-x)) and x / (1 + expf(-x)) with
IEEE division. ActRows.gateL2 and ActRows.swigluL2 launch them; no existing launch changes.

Both are registered with the kernel contract: every output against float64's range at its
rounding points (expf's ulps, fp32's exp range, the inverse's spread) at Dory's widths,
4,096 and 7,168; four controls each drop one rounding point (the gated row, the scaled gate,
the scaled up, the product) and go red at gate 3. Gate 4: the gate's rows are halves of
integers (sigmoid(0) = 1/2), exact in fp32; SwiGLU's squares are no integers, so its fp32
sums are checked against the shapes gate's bound, and an fp16 sum overflows either way.
@ashhart

ashhart commented Oct 25, 2026

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Landed on main in 6528fbe..81b476d: the whole Dory stack (#1256-#1261), with your records under your authorship plus five maintainer children. The children refuse host-only formats and refund failed upload ownership, pin release launch arguments, keep the unmeasured Dory launches pending in the contract, and own queued host inputs through stream completion. Every existing family file, contract entry and recorded launch is byte-identical. Thank you, Jürgen.

On a GB10 with nvidia/NVIDIA-Nemotron-Labs-Dory1.0-4B-NVFP4 @ 41f02335, served at 2 loops with CUDA graphs:

  • Prefill: 5,143, 5,354, 4,763 and 4,220 tok/s at 512, 2k, 8k and 16k.
  • Decode: 44.2, 86.2, 164.1 and 298.6 tok/s aggregate at 1, 2, 4 and 8 streams. That matches your 43-44 tok/s a stream at L=2.
  • Exactness and quality: repeats exact; chat with thinking off and on qualified (resume equals fresh); the 16k needle found.

- Synthema, Ash's AI Assistant.

@ashhart ashhart closed this Oct 25, 2026
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