TurboQuant KV cache (4/4): Python reference impl + last_token_logits patcher

[TimPietrusky]

Description

Reference NumPy implementation, offline graph rewriter, paper-validation tests, and a one-time model patcher that unlocks long-context inference on stock HuggingFace q4f16 ONNX exports. Standalone — depends on the schema in #28560 but not on any kernel PR.

What this PR contains

Under onnxruntime/python/tools/quantization/turboquant_kv/:

• centroids.py — Lloyd-Max solver for the K codebook. Computes the optimal scalar quantiser for N(0, 1/d) (the distribution of components of (k / ||k||) @ H_norm where k is fp16 and H_norm is the normalised Walsh-Hadamard). Deterministic given (d, bits); identical to what the C++ graph transformer in TurboQuant KV cache (1/4): graph rewrite + schema (foundation) #28560 injects.
• hadamard.py — Sylvester-construction Walsh-Hadamard, scaled to H @ H^T = I. Same scaling as the kernels.
• packing.py — uint8 / uint4 / uint3 bit-pack and unpack. Bit layouts match the C++ kernels in TurboQuant KV cache (2/4): CUDA kernels #28561 (CUDA) and TurboQuant KV cache (3/4): WebGPU kernels + Safari/Firefox fallback #28562 (WebGPU).
• quantizer.py — encode_keys / decode_keys / encode_values / decode_values. Pure NumPy reference; used by both the offline rewriter and the validation harness.
• onnx_rewriter.py — Python equivalent of the C++ graph transformer in TurboQuant KV cache (1/4): graph rewrite + schema (foundation) #28560. Useful when users want to ship a pre-rewritten .onnx instead of relying on session-create-time rewriting (e.g. so a model registry can stamp a hash).
• validate.py — paper-replication tests. 23 / 23 pass against the TurboQuant paper's published numbers. Tests are cross-validated bit-exact against vLLM's reference implementation where overlap exists.
• benchmark.py — standalone perf bench. Used to generate the numbers in TurboQuant KV cache (2/4): CUDA kernels #28561 and TurboQuant KV cache (3/4): WebGPU kernels + Safari/Firefox fallback #28562.
• last_token_logits.py — standalone model patcher. HuggingFace causal-LM ONNX exports compute logits for every prompt position by default. At long contexts (S × vocab > 2³¹) this trips an int32 overflow in ORT's CUDA Cast kernel — see #28385. This patcher inserts a Slice op before the LM-head MatMul so logits are computed only for the last position (the standard logits_to_keep=1 pattern in HF transformers). One-time, ~30s, idempotent.

Why ship this even though #28560 makes online rewriting work

Even with the C++ graph transformer landed:

• onnx_rewriter.py produces pre-rewritten models for environments where session options can't be set, so a model registry can stamp a hash.
• packing.py reproduces the bit-exact bit layout the kernels rely on — validate.py uses it to compare against the published TurboQuant paper numbers without spinning up a full e2e benchmark.
• validate.py is the cheapest way to validate future kernel changes — both TurboQuant KV cache (2/4): CUDA kernels #28561 (CUDA) and TurboQuant KV cache (3/4): WebGPU kernels + Safari/Firefox fallback #28562 (WebGPU) read the same packed cache layout, and validate.py enforces it bit-exact.
• last_token_logits.py patches the long-context cliff on stock HF exports today, before CUDA Cast kernel crashes with illegal memory access on tensors with >2^31 elements (int32 overflow) — same family as #28107 #28385 is fixed upstream.

Depends on

• TurboQuant KV cache (1/4): graph rewrite + schema (foundation) #28560 (schema the rewriter writes against).

Does not intersect with

• TurboQuant KV cache (2/4): CUDA kernels #28561 (CUDA kernels — they read the schema; this writes it).
• TurboQuant KV cache (3/4): WebGPU kernels + Safari/Firefox fallback #28562 (WebGPU kernels — same).

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