# /// script
# requires-python = ">=3.10"
# dependencies = [
# "numpy",
# "torch==2.8.0",
# "kernels-benchmark-tools",
# ]
#
# [tool.uv.sources]
# kernels-benchmark-tools = { path = "../../../../../tools", editable = true }
# ///
import torch
import sys
import os
import kernels_benchmark_tools as kbt
def torch_flash(q, k, v):
qt, kt, vt = (x.transpose(1, 2).contiguous() for x in (q, k, v))
with torch.nn.attention.sdpa_kernel(torch.nn.attention.SDPBackend.FLASH_ATTENTION):
o = torch.nn.functional.scaled_dot_product_attention(qt, kt, vt)
return o.transpose(1, 2).contiguous()
kbt.add(
"torch_flash_ma",
torch_flash,
tags={"family": "torch-sdpa", "backend": "FLASH", "compile": "max-autotune"},
)
if __name__ == "__main__":
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = "float32" if device == "cpu" else "bfloat16"
# Flux-like workloads scaled down for CPU testing
base = 1024 if device == "cuda" else 512
flux_sizes = (
[128, 256, 320, 384, 448, 512] if device == "cuda" else [64, 128, 192, 256]
)
heads = 24 if device == "cuda" else 8
head_dim = 128 if device == "cuda" else 64
wl = []
for L in flux_sizes:
wl.append(
{
"name": f"flux_L{L}",
"batch": 1,
"seq_len": base + L,
"heads": heads,
"head_dim": head_dim,
"dtype": dtype,
"device": device,
"seed": 0,
}
)
kbt.run(
wl,
jsonl="attn.jsonl",
reps=5,
warmup=2,
gen=kbt.attn.gen_qkv,
ref=kbt.attn.ref_math,
cmp=kbt.attn.cmp_allclose,
profile_trace=True
)
kbt.summarize(["attn.jsonl"])