TurboQuant KV cache (1/4): graph rewrite + schema (foundation)

[TimPietrusky]

Description

Adds the foundation pieces for a TurboQuant 4-bit KV-cache compression path inside ORT: a session-create-time graph rewriter, schema extensions on GroupQueryAttention, and the public session-option keys that opt users in. No kernels in this PR — they ship in two follow-ups for CUDA and WebGPU, each able to be reviewed in isolation.

The goal: when a user loads a stock q4f16 ONNX export from HuggingFace and sets one session option, ORT rewrites every GroupQueryAttention node in memory to use a 4-bit packed KV cache (Lloyd-Max codebook for K, asymmetric uniform for V, Walsh-Hadamard pre-rotation). No offline conversion, no second .onnx on disk, no transformers.js / HF Hub helpers to teach about a new dtype.

Why TurboQuant

KV cache eats memory linearly with context length. At 128K context, a 1.2B-param model's fp16 KV is ~1.5 GB and dominates VRAM. TurboQuant compresses it ~3.6× to ~430 MB without retraining and with cosine similarity 0.99+ vs fp16 on every layer we benched (LFM2.5-1.2B, Qwen3.5-0.8B-Text). Paper: https://arxiv.org/abs/2412.10319 — implementation is bit-exact against vLLM's reference where they overlap.

What this PR contains

• core/optimizer/turboquant_kv_fusion.{cc,h} — the L2 graph transformer. Fires when optimization.turboquant_kv_method is set to a non-empty preset. Scope: {kCudaExecutionProvider, kWebGpuExecutionProvider}. Computes Lloyd-Max centroids for the given (head_dim, key_bits) and a normalised Walsh-Hadamard matrix, injects both as shared graph initializers, then mutates each GroupQueryAttention node's attributes + past/present tensor types to (uint8, slot_bytes).
• core/graph/contrib_ops/bert_defs.cc — extends GroupQueryAttention with four new attributes (kv_quant_method, key_quant_bits, value_quant_bits, norm_correction) and two new optional inputs at slots 14/15 for the shared k_codebook + hadamard initializers. All defaulted so the standard fp16 path is unchanged when the option is unset.
• include/onnxruntime/core/session/onnxruntime_session_options_config_keys.h — two new public option keys: optimization.turboquant_kv_method (preset name) and optimization.turboquant_kv_boundary (number of first/last layers to leave in fp16 for accuracy).
• contrib_ops/cpu/bert/{attention_common.h,attention_parameters.h,group_query_attention_helper.h} — the KVQuantMethod enum, parameter struct extensions, and CheckInputs updates that let the fp16 codepath remain byte-identical while the TQ codepath has the data it needs.
• include/onnxruntime/core/framework/int3.h — a new packed UInt3x8 type used by the 3-bit cache variants (turboquant_k3v4_nc, turboquant_3bit_nc).
• test/contrib_ops/turboquant_kv_test.cc — host-side bit-layout tests for UInt3x8. Kernel-level correctness is validated by the CUDA / WebGPU PRs that land on top of this one.

What's NOT in this PR (intentionally)

• CUDA kernels for the new attribute set — separate PR.
• WebGPU kernels — separate PR.
• Python reference implementation, offline rewriter, and the last_token_logits model patcher — separate PR.

When optimization.turboquant_kv_method is unset (the default), nothing in this PR runs and graph optimisation is byte-identical to today.

Verified locally

• Unit tests pass: onnxruntime_provider_test --gtest_filter=TurboQuantKVTest.*
• End-to-end (with the follow-up kernel PRs applied):
  • RTX A40 + CUDA: LFM2.5-1.2B @ 32K context, decode 4.97× faster vs fp16, KV cache 3.56× smaller, cosine-sim 0.99+ across-context.
  • Apple Silicon Metal + WebGPU EP: same model @ 32K, decode 4.97× faster, output bit-equivalent to CUDA's TQ output for matching seeds.
  • Cross-OS build CI green on Linux + Windows (build-and-link verification only).
  • GHA macos-15 runner CI green at 4K context (cold reproducibility check from a clean machine).

Motivation and Context

Same long-context inference TurboQuant already accelerates inside vLLM, but available inside the ORT ecosystem (CUDA, Apple Silicon Metal, WebGPU EP via Dawn) — including the browser via onnxruntime-web. Drop-in via one session option; no model conversion.

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