Add an unrun native CUDA dequant kernel for the TurboQuant PoC

[lgoyal6]

What this is

TurboQuantMSE.dequantize in turboquant_poc.py does four passes: promote the uint8 codes to int64 to index the codebook, materialise an fp32 gather result, run the GEMM, then scale the rows. On the decode path that runs for every layer on every token, and turboquant-poc/README.md already names the PyTorch dequantize path as the reason token rate sits below fp16.

Row scaling commutes with the rotation, because the rotation is a row-wise linear map:

(y_hat @ Pi) * norms[:, None]  ==  (y_hat * norms[:, None]) @ Pi

so the scale folds into the gather, leaving exactly one GEMM and no int64 index copy. tq_dequant_gather_scale does the fused half and deliberately leaves the GEMM to cuBLAS rather than reimplementing it. The codebook is at most 256 entries, so it is staged in shared memory once per block.

STATUS: UNRUN

The file says so in its own first paragraph, and this PR says it again so nobody has to open the file to find out.

This kernel has never been compiled or executed. The host it was written on has no GPU: nvidia-smi and nvcc are both absent. There are no performance numbers for it anywhere in this repository, and none may be quoted until it has been built and benchmarked on a CUDA device. bench_tq_dequant.py is that harness, and it refuses to run without CUDA rather than estimating.

What is verified is the algebra the fusion rests on. --check-math-only compares the fused result against the existing implementation on CPU and needs no GPU.

Known issue, reported rather than fixed

The kernel uses three symbols whose headers it does not include:

line	symbol	header it needs
117	at::cuda::getCurrentCUDAStream()	<ATen/cuda/CUDAContext.h>
130	C10_CUDA_KERNEL_LAUNCH_CHECK()	<c10/cuda/CUDAException.h>
115	std::min<long>	<algorithm>

It includes only <torch/extension.h>, <cuda.h> and <cuda_runtime.h>. This is left unfixed on purpose: the compile failure cannot be reproduced on a host with no nvcc, and a fix nobody can run is a guess. Whoever takes this to a CUDA box should expect to add them.

Tests

No Python that the suite covers changed. .agent-work/ is excluded from discovery: worktrees under it hold duplicate copies of these same test files.

file	result
test_token_merge.py	14/14
test_model_guard.py	21/21
attentionrag/test_core.py	10/10
experiments/test_data.py	rc=0, 0 tests (fixture module)
total	45 passing, 0 failing

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