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drama: the tower's one-row projections on drama_tower_gemv, in cuBLASLt's summation order - #926

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ashhart:mainfrom
plotarmordev:perf/drama3-tower-gemv

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What this changes

The tower's one-row projections run on drama_tower_gemv, which reproduces the summation order of cuBLASLt's gemv
kernel (part of #914; stacked on #925, its own commit is the last one). It is enabled per projection
only after that projection's plan matched it bit for bit at init.

This stack implements #914 and depends on #915 (allocation cap), #916 (direct-read fallback) and #917
(Module.holds), inherited through #920.

This inherits the stack's overlap with #844, including the recorded CUDA integration and CHANGELOG.md conflicts;
land #844 first, then rebase the stack.

Receipt

  • Environment, with the TensorFold commit, MLX or PyTorch version, machine and GPU, checkpoint and revision:
    • Code. Head 2757724d5dc6, on main c7137bb3. No MLX or PyTorch.
    • Builds and host tests. An NVIDIA GB10 build host on Ubuntu 24.04.4 LTS aarch64 (Linux 6.17), with Zig
      0.17.0, nvcc 13.0.88 and CUDA_VISIBLE_DEVICES empty.
    • GPU measurements. A GB10 (sm_121) host with CUDA 13 libraries (cuBLASLt 13.1.1). Checkpoint
      fishaudio/drama-3 at e0a9218a; reference sglang-omni ab095ef9.
    • Which build was measured. These commits built on TensorFold 1e76a70e. Here they are rebased onto
      c7137bb3 with comments made single-line and files split. Every Drama fatbin of this head is byte-identical to
      the measured series' at the same commit. The host Zig is token-identical apart from the file split, compared
      with std.zig.Ast.
  • Exactness, from tools/bench_concurrent.py --alone --serial or token_sha against "draft": false: n/a for
    tokens. Each speed stage was gated on all 46 normal replies of a 48-request corpus capture: byte-identical to the
    build before any speed change. The two capped long-05 replies were identical to the
    reviewed clipped capture, and bench/stream_check.py passed 9/9 (speed-stages.json). Fidelity against the reference is not qualified
    (qualification.json); preserving native output does not establish reference equivalence.
    • The kernel is enabled per projection only after the initialization probe matches that projection's cuBLASLt plan
      bit for bit; this finite probe is not an exhaustive proof over all inputs.
  • Decode speed before and after, from tools/bench_openai.py: n/a, because it measures tokens. Warm real-time factor
    0.741 → 0.678 for stage 04, this commit (speed-stages.json).
  • Prompt speed before and after: the cold first request's ttft_s from tools/bench_cache.py --require-cold on a long prompt, or "n/a" with the reason when the change cannot reach prompt processing: Not measured. Multi-row prompt calls retain cuBLASLt plans; the optimized
    projection dispatch is based on row count, so single-row prompt calls can use the qualified GEMV path.
  • Tests run, with pass, fail and skip counts:
    • GPU audio gate on the stack's tip bef432bbb25e (one GB10, under the recipe's guard): all 46 normal-request
      audio bodies byte-identical to the promoted pre-upstream build, the long-05 clipped body identical in both
      passes, bench/stream_check.py 9/9, warm RTF 0.654 against the pre-speed 1.087, and all six tower one-row
      projections on drama_tower_gemv.
    • CUDA_VISIBLE_DEVICES= zig build test-cuda-host --summary all from an empty local cache: 29/29 steps, 498 passed, 0 failed, 0 skipped. This includes the two new tests: the probe separates the plans' order from five neighbouring orders, and the qualification commits only when every projection was probed.
    • zig build native -Dnvcc=/usr/local/cuda/bin/nvcc --summary all: 47/47 steps.
    • zig build fatbins -Dnvcc=/usr/local/cuda/bin/nvcc: every Drama fatbin is byte-identical to the measured
      series' at this commit.
    • python3 tools/zig/lean_check.py: 527 problems at every commit, main's 527 (lean_new.py: 0 new).
    • zig fmt --check: clean at every commit.
  • Not run, and why:
    • Other GPUs and cuBLASLt versions: the qualification is limited to sm 121 with cuBLASLt 130101 and keeps
      cuBLASLt elsewhere.
    • A GPU run on this exact head: the GPU audio gate ran once, on the stack's tip bef432bbb25e; this head is
      host-tested only.
    • Metal: the family is CUDA-only.
    • tools/bench_openai.py, bench_concurrent.py and bench_cache.py: they drive text tokens, so speech used the
      recipe's harness.

Checklist

  • Every token still goes through the lane rounds. No serial path, and nothing that needs drafts off. (Drama 3
    generates audio frames in its own host loop, as in drama: Drama 3 (fishaudio/drama-3) text-to-speech on CUDA #920. Text families are unchanged.)
  • Drafted output equals "draft": false, a resumed prompt equals a fresh one, and concurrent equals solo. (No
    drafts or resume. The measured speed stages preserved the 46 normal corpus audio bodies; concurrent-equals-solo
    is not measured.)
  • No precision traded for speed. If the bits change, the description says which sums change and why. (The kernel targets cuBLASLt's summation order and is enabled only after its per-projection initialization probe matches; the measured speed-stage gate preserved the 46 normal corpus audio bodies.)
  • Prompt processing is no slower than the last release. (Not measured. Text prompt paths are not touched.)
  • The description says which platforms I ran, Metal M1 to M5 and CUDA, and which I could not.
  • New tests fail before the change, pass after it, and skip cleanly without their dependency. (The new tests pass and need no GPU. They test new code, so there is no before.)
  • Comments and docstrings are one line. No measurements or history in the source.
  • No personal data, machine names, internal hosts or local paths. No AI attribution lines.

setAllocationLimit bounds the bytes of device buffers plus pinned host buffers. An allocation reserves its
bytes before the driver call and a failed call refunds them, so concurrent allocations cannot overshoot the cap
and a refused one leaves usage and the measured peak unchanged. Host counts now take the same lock as device
counts. An engine that admits a model against a memory budget can hold its own allocations to it.
The tower turns a backbone row into a frame's ten codebook values: four shared and nine residual layers whose
cache keeps every preceding text and audio row, a semantic head and the reference sampler (temperature, top-p over
the whole vocabulary, then top-k). Its projections run on cuBLASLt; drama_tower.cu holds the mixer, norms, RoPE,
attention and sampling kernels.
The codec decodes ten codebooks a frame to mono 44.1 kHz PCM, 2048 samples a frame, as the reference's FP16
vocoder does: RVQ, the windowed post-module transformer, two upsample stages and the DAC decoder. codec_pth.zig
reads torch.save's zip archive and runs its state-dict pickle on a VM that knows only the opcodes and globals a
tensor state dict uses, so nothing in the file runs. Weight norm is folded in FP32 and tensors are cast to FP16
with round-to-nearest-even, as the reference does.

codec.zig holds the decode; codec_load.zig holds the codec's geometry, the weight-norm folding, the FP16 casts
and the one device weight image.
An engine may now synthesize speech: SpeechRequest carries a prompt, sampling settings and caller-owned PCM,
Info.speech describes the audio it produces, and Info.text_generation lets a speech-only engine refuse the text
routes. Text submit gains error.Unsupported, which the chat route reports as a request error.
The native CUDA backend opens fish_omni checkpoints with the Drama 3 host: one synthesis at a time on a worker
thread that owns the context, stream, backbone, tower and codec, admitted against the same memory pool the text
families use. The pool reading moves to cuda_memory.readPool so both paths share it, and the host caps its own
allocations at what it was admitted for. Options the host cannot honor are refused at open.
OpenAI's speech request, built into Drama 3's default-voice prompt as the reference's prompt builder builds it
and synthesized whole, then sent as WAV or raw PCM. Fields the route would silently drop are refused, and a
speech-only engine starts the server without the text-only services.
Drivers that check the native backbone and tower against captured reference tensors, replay single operators
against them, and trace each stage for attribution, plus tf-cuda-test drama-conv and drama-qk on synthetic rows.
None of them runs as part of the host tests.
Drivers that decode captured codes with the native codec for comparison with the reference decoder, replay its
RVQ and weight preparation in isolation, and trace each decoder stage. None of them runs as part of the host tests.
…ve host and their tool steps

Embeds the backbone and tower fatbins, adds the drama module to the native server and its host tests, and a build
step per backbone and tower oracle, replay and trace driver. The codec's kernels and tools follow on their own.
Embeds the codec fatbin and adds a build step per codec oracle, replay and trace driver. With this the series
builds.
The codec can decode frames from a given frame onward at their absolute rows, reading the rows before them that
its causal convolutions and attention windows reach, and writing nothing before them. Every chunk boundary gives
the same samples as one decode of all frames, so audio can be decoded while it is generated.
… generates

A streamed speech request now receives audio events: each one hands the caller a final, rising prefix of the PCM
before the finished event. The Drama host decodes the first frame alone, for the earliest audio, then frames in
groups, and publishes each decoded prefix.
… are decoded

With stream: true and response_format pcm, the route sends HTTP/1.1 chunks as the engine publishes audio. A stream
that fails after its first chunk ends without a terminator, so a client cannot mistake it for complete audio.
Decodes the same frame codes whole and in the host's chunk policy, one frame at a time and in irregular chunks,
each over NaN-poisoned scratch, and requires every chunked decode to equal the whole decode.
Some file systems accept an O_DIRECT open and then fail the aligned read with EINVAL. The reader now clears
O_DIRECT on that descriptor and reads again through the page cache instead of failing the load; a test injects
the refusal.
A marker test: an image can carry a device global that promises something about its kernels, and the host can
check for it before relying on the promise. Any driver failure, a missing name included, answers false rather
than an error. A test drives it through a fake driver table.
Drama 3's backbone is a Qwen3.5-35B-A3B-style hybrid: 30 Gated DeltaNet and 10 gated full-attention layers,
each with a 256-expert top-8 block-FP8 MoE and a gated shared expert. Model.run appends rows and returns every
row's final-norm hidden state and the last row's semantic logits.

The kernels follow the arithmetic of the sglang-omni reference: the canonical 64-token chunked GDN prefill
(drama_gdn_chunk.cuh, after flash-linear-attention's kernels as SGLang carries them), the packed recurrent decode,
gated attention with Q/K norm and partial NeoX RoPE, dynamic 1 x 128 FP8 activation quantization, and the BF16
tensor-core GEMMs in drama_backbone_gemm.cu. Weights load as stored; nothing is requantized.

THIRD_PARTY_NOTICES.md credits flash-linear-attention (MIT), whose chunked kernels the GDN prefill is adapted from
and whose packed decode the recurrence follows.
…udio

A synthesis that reaches max_frames without an end of speech keeps the audio of those frames, as the reference
returns its capped replies, instead of failing without audio. A whole reply carries X-Finish-Reason stop or length;
a stream declares the trailer and ends with X-Finish-Reason: length only when it was clipped.
…memory

A single-row projection copies each warp's weight chunks into a shared ring with cp.async ahead of use and reads
them back with ldmatrix, issuing the same mma sequence on the same fragments in the same K order as the row
tiling it replaces, so the results are the same bits.
…ync ring

The one-row path keeps the same mma sequence and fragments as the tilings it replaces, now for the semantic
head's grouping too, while its weights stream through an unpadded shared ring whose 16-byte pieces are swizzled
so each read still hits distinct banks.
…ation

The residual add and RMS norm before the MoE also write the normed rows' FP8 groups, in the same order and
rounding as the separate quantization launch they replace.
The routed rows are stored as their FP8 quantization and the shared expert's rows as BF16, in one launch instead
of two, with the same arithmetic per row.
Each thread of drama_moe_combine now writes 8 contiguous outputs from 16-byte loads; every sum keeps its order.
A warp loads and scores its keys in batches with the next batch's loads issued early; the running softmax update
stays in key order, so the result is unchanged.
…ame launch

For a single row, the attention launch also does drama_attn_prep's norm, RoPE and cache append, taking the new
key and value from registers, with the same roundings as the separate launch.
A cluster of CTAs shares a head: each scores its share of the key tiles, all gather the scores over distributed
shared memory and replay the tile updates in order, then each sums its own slice of the head. Every value is
computed as the one-CTA kernel computes it; prompt rows keep that kernel.
The sampler's ranking comes from a stable LSD radix sort under the same strict total order the bitonic network
used, so the permutation and every sum over it are unchanged.
… order

stager.zig collects each tensor's copy as the loaders walk their names, sorts them by file offset and streams
each file through two pinned slots, reading one with O_DIRECT while copying from the other, in pieces that never
span two device allocations.
…one loads

The codec's host-side weight image is CPU work that the backbone and tower reads leave room for; it now runs on
its own thread with the context current there, and the host joins it before it finishes opening.
…Lt's summation order

drama_tower_gemv sums in the order of the gemv kernel cuBLASLt picks for the tower's one-row plans. At init each
projection is checked against its cuBLASLt plan bit for bit and uses the kernel only if they agree.

The qualification and its probe weights live in tower_gemv.zig.
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