TurboQuant KV cache (2/4): CUDA kernels

[TimPietrusky]

Description

CUDA kernels for the TurboQuant 4-bit KV cache path defined by #28560. When the graph transformer in #28560 has rewritten a GroupQueryAttention node's KV-cache schema to the packed uint8 layout, the CUDA GQA kernel now dispatches into the launchers added here.

Real-model end-to-end speedups (RTX A40)

Model	ctx	fp16 reply	TQ reply	speedup
LFM2.5-1.2B	4 K	6.2 s	6.0 s	tied
LFM2.5-1.2B	32 K	26.0 s	24.1 s	7%
LFM2.5-1.2B	64 K	63.0 s	41.1 s	53%
LFM2.5-1.2B	128 K	fp16 OOM	65 s	TQ-only
Qwen3-0.6B	4 K	26.6 s	17.6 s	51%
Qwen3-0.6B	16 K	93 s	58 s	60%
Qwen3-0.6B	32 K	187 s	96 s	94%

KV cache 3.56× smaller (LFM2.5, head_dim=64) / 3.76× (Qwen3-0.6B, head_dim=128). Per-layer cosine sim vs fp16 = 0.99526 across-context. Top-1 token match 7-of-9 on a 9-step decode chain.

What this PR contains

• contrib_ops/cuda/bert/group_query_attention_turboquant.cuh — templated kernels:
  • TQEncodeKernel packs fresh K/V (Walsh-Hadamard + Lloyd-Max → 4-bit indices).
  • TQFlashAttentionKernel (v4-lite) fused-FA on packed K with online softmax for the decode step.
  • TQFlashAttentionQTiledKernel (v5) for continuation-prefill — ~2× prompt speedup over v4-lite at 4K.
  • TQFlashAttentionWmmaKernel (v6) tensor-core Q*K^T at head_dim=64.
• contrib_ops/cuda/bert/group_query_attention_turboquant_impl.{cu,h} — launch shims, vectorised uint4 K/V smem loads (8 fp16 per HBM transaction), Option-ε dispatch that routes the prompt step to the stock FlashAttention kernel when past_seq == 0 so model-load output is byte-stable vs fp16.
• contrib_ops/cuda/bert/group_query_attention.cc/.h — TQ-aware dispatch. When kv_quant_method != KVQuantMethod::None we read the new attributes from TurboQuant KV cache (1/4): graph rewrite + schema (foundation) #28560 and route to the kernels above; otherwise the op is byte-identical to today.
• contrib_ops/cuda/bert/attention_data.h — three new fields in GroupQueryAttentionData for the codebook / Hadamard pointers and the quant-method enum.

Things I'd appreciate a sharp eye on

1. WMMA tile schedule in v6 — kBlockK is coupled to kHeadDim today. Decoupling unlocks hd=128 (Qwen3-0.6B uses this) at v6 speed. Follow-up patch ready, kept out to minimise diff size.
2. cp.async — tried double-buffered loads, reverted because per-tile commit_group/wait_group overhead exceeded overlap savings at our tile size (74 ms → 95 ms decode at 32K). Worth revisiting if tile sizing changes.
3. Option ε prompt delegation in group_query_attention.cc — by design, prompt step (past_sequence_length == 0) routes to the stock FA kernel for bit-equivalent output. Cost: v6 wmma prefill never fires on the first call. Step 2+ still uses it.

Validation

Host-side bit-layout tests are in #28560. CUDA kernel correctness is validated via real-model e2e runs because onnxruntime_provider_test doesn't link libonnxruntime_providers_cuda.so directly — the EP is loaded via dlopen at test time and our launcher symbols aren't exposed there today. Per-layer cosine-sim numbers above come from a Python harness running both sessions on the same prompt and comparing logits. That harness ships in the Python tooling PR.

Depends on

• TurboQuant KV cache (1/4): graph rewrite + schema (foundation) #28560 (foundation — graph rewrite + schema).

Does not intersect with

• WebGPU kernel PR (#TBD).
• Python tooling PR (#TBD).

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