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README.md
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---
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license: apache-2.0
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tags:
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- kernel
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- audio
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- codec
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- quantization
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- cpu
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---
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# rvq-codec
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Residual vector quantization encode and decode, for EnCodec/DAC/SNAC-class audio
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codecs and the audio language models built on them.
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```python
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import kernels
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rv = kernels.get_kernel("phanerozoic/rvq-codec", version=1, trust_remote_code=True)
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q = rv.ResidualVQ(codebooks) # [Q, K, D], or a list of [K, D]
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codes = q.encode(x) # [..., D] -> [..., Q] int32
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recon = q.decode(codes) # [..., Q] -> [..., D]
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recon = q.decode(codes, n_stages=4) # truncate for a lower bitrate
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```
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## Where the win is, and where it isn't
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The two directions have opposite structure, and the measurements say so.
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**Decode is 43-349x and bitwise exact.** Written in framework ops it is Q
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separate advanced-index gathers, each materializing an `[N, D]` tensor, followed
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by Q-1 adds. Every one of those intermediates exists only to be summed away.
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Fused, it is a single traversal.
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**Encode is at parity, and should be.** Its cost is a dense `[N, D] x [D, K]`
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product per stage, which BLAS already runs at hardware peak. A hand-blocked
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search measured 0.03-0.09x against it; transposing the codebook to remove the
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horizontal reductions did not help, because the real constraint is codebook
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reuse across vectors, which is exactly what a blocked GEMM provides. So the
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product stays in the framework and only what follows it is fused: argmin, the
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gather, and the residual subtract, which a framework implementation
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materializes twice. That took the worst case from 4641 ms to 272 ms.
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Distances use `||r - c||² = ||r||² - 2 r·c + ||c||²`, dropping the term constant
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in k, with the codebook norms precomputed once by the plan.
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## Measured
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Sized after real codecs, 30 s of audio, x86-64 (i9-12900H, 14 threads).
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| config | decode | framework | speedup |
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|---|---:|---:|---:|
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| EnCodec Q=8 K=1024 D=128 | 0.26 ms | 92.00 ms | 349x |
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| SNAC Q=4 K=4096 D=768 | 1.10 ms | 47.96 ms | 43x |
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| Mimi Q=32 K=2048 D=256 | 4.23 ms | 374.02 ms | 88x |
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Decode output is identical to the framework result, not merely close.
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Encode runs at 0.78-0.93x of the framework matmul formulation while avoiding the
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`[N, K]` distance materialization, which is 74-393 MB across the stage loop at
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these sizes.
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## Notes on codebooks
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Reconstruction error is non-increasing in the number of stages only when each
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stage's codebook scale tracks the residual it quantizes, which is what training
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produces. With every stage at the same scale, stage 2 subtracts a code far
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larger than stage 1's residual and error goes up. That is a property of the
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codebooks, not of the algorithm, and the test suite pins both behaviors.
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## Backends
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Published binaries are `x86_64-linux`, for PyTorch 2.11, 2.12, and 2.13.
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Correctness and the aarch64 timings above were measured by building this same
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source on aarch64 (Cortex-A76) rather than from a prebuilt variant; the kernel
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builder only emits variants for the host it runs on, and the build host here is
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x86-64. `load_local.load()` in the repo JIT-builds the source, which is how
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aarch64 was validated and how it can be used there today.
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