Kernels:

---

marcsun13

/

ggml-attn

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ggml-attn

Flash attention from llama.cpp, as a torch op (flash_attn) and as a transformers attention implementation (flash_attn_forward). Grouped-query attention is native, so k and v are passed unexpanded.

Both of upstream's paths are ported — the vector kernel for n_q < 20, the tiled one above it — so decode and prefill both run on ggml's kernels. A head-dim pair neither has a template for raises rather than quietly falling back to torch; ask supports_flash_attn.

Usage

import torch
from kernels import get_kernel

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

q = torch.randn(1, 16, 1, 128, device="mps")    # (n_seqs, n_heads, n_q, head_dim)
k = torch.randn(1, 4, 512, 128, device="mps")   # 4 kv heads, left unexpanded
v = torch.randn(1, 4, 512, 128, device="mps")

out = attn.flash_attn(q, k, v)                  # (1, 1, 16, 128) — tokens before heads

Or as a model's attention implementation:

model = AutoModelForCausalLM.from_pretrained(
    ..., attn_implementation="marcsun13/ggml-attn"
)