perf: accelerate packed INT4 CUDA inference with CUTLASS (#2278)

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leejet authored and GitHub committed 2026-11-06 01:59:05 +08:00
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@@ -123,13 +123,18 @@ Both formats imply convrot rotation even without a `convrot` flag; missing
Both formats reuse the convrot activation cache when the input scale is `1` and
produce F32 outputs with optional bias. CPU provides a portable implementation.
CUDA/HIP/MUSA use shared packed-weight kernels without requiring INT8 BLAS;
CUDA and supported AMD targets use packed byte-dot instructions, with portable
byte arithmetic for other targets. Vulkan requires integer dot product support
and at least 13312 bytes of shared memory for W4A4 or 16452 bytes for W4A8.
GPU W4 kernels require the logical input width to be divisible by 64; H64 and
H256 are supported. Other shapes use CPU fallback. HIP W4A8 selects wave64 WMMA
by the active compilation target's
CUDA/HIP/MUSA use shared packed-weight kernels without requiring INT8 BLAS.
With CUTLASS enabled, NVIDIA
SM80 and newer devices use mixed-input S8 x S4 GEMM for eligible W4A4 shapes,
reusing the INT8 scale epilogue without expanding the full weight tensor. W4A8
uses CUTLASS integer MMA per 16-value group with FP32 scale restoration.
Other CUDA shapes and builds retain the packed byte-dot path. Set
`GGML_CUDA_W4_FORCE_DP4A=1` to compare against that path. Supported AMD targets
use packed byte-dot instructions, with portable byte arithmetic for other targets.
Vulkan requires integer dot product support and at least 13312 bytes of shared
memory for W4A4 or 16452 bytes for W4A8. GPU W4 kernels require the logical input
width to be divisible by 64; H64 and H256 are supported. Other shapes use CPU
fallback. HIP W4A8 selects wave64 WMMA by the active compilation target's
intrinsic support, preferring the GFX12 fragment layout over the GFX11 layout.
There is no GPU architecture allowlist: newer targets supporting either
intrinsic automatically use its matching layout. Selection happens per device