[Attention][TurboQuant] Pre-bake Lloyd-Max centroids for common (d, bits) shapes

[TheTom]

Purpose

solve_lloyd_max(d, bits) runs a 200-iteration trapezoidal-integration loop over 2^bits levels at each unique (d, bits) pair the engine encounters on first use. The output is fully deterministic given d and bits (Gaussian density, Lloyd-Max iteration, no seed), so it can be pre-computed at commit time and short-circuited on the hot path.

Summary

• Embed pre-baked centroid tables in centroids.py for the nine (d, bits) pairs the TQ presets actually exercise: d ∈ {64, 128, 256} × bits ∈ {3, 4, 8}.
• get_centroids() short-circuits to the table when (d, bits) matches; any other shape falls back to solve_lloyd_max() with no behavior change.
• Tables are 3.4 KB total, embedded inline as Python tuples — no asset to ship and trivial to regenerate (solve_lloyd_max(d, bits)[0].tolist() and paste).
• New unit test asserts every embedded table equals the runtime solver to bit precision (max_abs_diff = 0.0).

Duplicate-work check

Searched open and closed PRs / issues with combinations of turboquant, centroid, lloyd-max, pre-bake, startup. No existing PR proposes this. Tracking issue #40069 (TurboQuant follow-ups) does not list centroid pre-baking. The closest adjacent perf work is #40941 (shared dequant buffers) — orthogonal: that touches the workspace, this touches the centroid table.

Test Plan / Results

Tested on AMD MI300X (gfx942), ROCm 7.2, vLLM ROCm 7.2.1 wheels.

Direct centroid-call timing — get_centroids(d, bits) in a fresh process:

python3 -c "
import time
from vllm.model_executor.layers.quantization.turboquant.centroids import get_centroids
for d in (64, 128, 256):
    for b in (3, 4, 8):
        t0 = time.perf_counter()
        get_centroids(d, b)
        print(d, b, f'{time.perf_counter()-t0:.6f}s')
"

(d, bits)	upstream main	this PR	speedup
(64, 3)	71.5 ms	0.13 ms	~550×
(64, 4)	198.8 ms	0.006 ms	~33,000×
(64, 8)	3,222.6 ms	0.013 ms	~250,000×
(128, 3)	73.4 ms	0.003 ms	~24,000×
(128, 4) — most-used	205.7 ms	0.004 ms	~52,000×
(128, 8)	3,290.3 ms	0.013 ms	~253,000×
(256, 3)	67.6 ms	0.002 ms	~34,000×
(256, 4)	201.2 ms	0.004 ms	~50,000×
(256, 8)	3,276.1 ms	0.013 ms	~252,000×

Bit-identical output — tests/quantization/test_turboquant.py::TestLloydMax::test_prebaked_matches_solver:

python3 -m pytest tests/quantization/test_turboquant.py::TestLloydMax::test_prebaked_matches_solver -v

→ 9/9 passed, max_abs_diff = 0.0 for every (d, bits) pair.

End-to-end LLM cold-start — LLM(Qwen/Qwen3-8B, kv_cache_dtype="turboquant_4bit_nc"), 3 trials per branch (fresh process each):

Trial	upstream main	this PR	Δ
1	39.30 s	38.96 s	-0.34 s
2	39.00 s	38.45 s	-0.55 s
3	38.55 s	38.98 s	+0.43 s
mean	38.95 s	38.80 s	-0.15 s

Mean delta -150 ms, within the per-branch noise spread (~750 ms). The 200 ms direct savings is real but lost in single-shot cold-start variance from torch.compile, CUDA graph capture, and weight loading. The savings amortize across long-running services and cold-start storms (autoscaling, multi-process spawn) where a single solver run is paid per process; it is not a top-line single-process win.

Full TQ unit-test suite — tests/quantization/test_turboquant.py on this PR:

python3 -m pytest tests/quantization/test_turboquant.py -v

→ 127/127 passed in 36.16 s. 10 new tests in TestLloydMax cover:

• test_prebaked_matches_solver[(d, bits)] for every (d, bits) in the embedded table (9 cases)
• test_get_centroids_falls_back_for_unbaked_shape — confirms the fallback path runs solver for d=192 (not in table)

AI assistance

This PR was prepared with AI assistance (Anthropic Claude). Each line of the diff was reviewed by the human submitter, the byte-equality assertion was run on the human's hardware (AMD MI300X dev cloud), and the timing numbers come from runs the human supervised. Commits carry a Co-authored-by: Claude trailer per AGENTS.md.

cc @vibhavagarwal5

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[claude]

Claude Code Review

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[gemini-code-assist]

Code Review

This pull request introduces pre-baked centroid tables for common (d, bits) configurations in TurboQuant to optimize performance by avoiding runtime Lloyd-Max iterations. It also adds corresponding unit tests to ensure the pre-baked values match the solver output. I have identified a critical issue where the _PREBAKED_CENTROIDS dictionary contains duplicate keys, which will cause data overwriting; this must be corrected.

[TheTom]

@gemini-code-assist Thanks for the look — those aren't duplicate keys, they're three distinct head-dimension blocks. The dictionary has 9 unique entries:

• Lines 89-91: `(64, 3)`, `(64, 4)`, `(64, 8)`
• Lines 125-127: `(128, 3)`, `(128, 4)`, `(128, 8)`
• Lines 161-163: `(256, 3)`, `(256, 4)`, `(256, 8)`

The visual confusion comes from the `bits=8` entries, each of which spans ~32 source lines (256 centroids per table), pushing the next block's keys far down the file. Verified at runtime:

```python

from vllm.model_executor.layers.quantization.turboquant.centroids import _PREBAKED_CENTROIDS
sorted(_PREBAKED_CENTROIDS.keys())
[(64, 3), (64, 4), (64, 8), (128, 3), (128, 4), (128, 8), (256, 3), (256, 4), (256, 8)]
len(_PREBAKED_CENTROIDS)
9
```

Each `(d, bits)` pair is unique and the `test_prebaked_matches_solver` parametrized test (9/9 PASSED) verifies each entry is bit-identical to `solve_lloyd_max(d, bits)`. If there were silent overwrites we'd be failing on `max_abs_diff = 0.0` for either `(128, *)` or `(256, *)`.

[gemini-code-assist]

Thanks for the clarification, @TheTom. I appreciate you walking through the structure and providing the verification steps. Given that the keys are indeed distinct (d, bits) pairs and the unit tests confirm bit-identical output to the solver, this approach is sound and addresses the performance concerns effectively. The visual density of the file is understandable given the nature of the pre-baked data.