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marcsun13/
ggml-quantization

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ggml-quantization

GGUF quantization kernels from llama.cpp, computing directly on the packed blocks of a quantized checkpoint rather than on a dense copy of its weights.

mul_mat_vec — fused dequantize + gemv, for up to MAX_GEMV_ROWS rows
dequantize — blocks to values
get_rows — gathers rows, unpacking as it goes
mul_mat_id — one dispatch for a bank of routed experts, given the router's choices

GEMV_TYPES lists the quantization types this build has a gemv for.

Usage

python

import torch
from kernels import get_kernel

k = get_kernel("marcsun13/ggml-quantization", version=1)

Q4_K = 12                          # ggml type id; `k.GEMV_TYPES` lists what this build covers
out_features = in_features = 4096
# a GGUF weight as stored: one row per output feature, 144 bytes per 256-element Q4_K block
blocks = torch.randint(0, 256, (out_features, in_features // 256 * 144), dtype=torch.uint8, device="mps")
x = torch.randn(1, in_features, device="mps")

y = k.mul_mat_vec(blocks, x, Q4_K, out_features)                          # (1, 4096) f32
w = k.dequantize(blocks, Q4_K, out_features, in_features, torch.bfloat16)  # (4096, 4096)
rows = k.get_rows(blocks, torch.tensor([3, 7], device="mps"), Q4_K, in_features, torch.bfloat16)