#!/usr/bin/env python3 # SPDX-License-Identifier: AGPL-3.0-only # Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0 """Which SDPA backends tolerate a dense bool attn_mask, and at what cost, at Hunyuan's real joint shape (B=1, H=16, N=50345, D=128, bf16)? Decides whether nulling the all-True mask is the real win on the PRODUCTION cuDNN path (not just the native math fallback).""" import time import torch import torch.nn.functional as F from torch.nn.attention import SDPBackend, sdpa_kernel B, H, N, D = 1, 16, 50345, 128 dev, dt = "cuda:0", torch.bfloat16 def mk(): return torch.randn(B, H, N, D, device = dev, dtype = dt) def timed(fn, iters = 20): torch.cuda.synchronize() for _ in range(3): try: fn() except Exception as e: # noqa: BLE001 return f"UNSUPPORTED ({type(e).__name__})" torch.cuda.synchronize() t0 = time.perf_counter() for _ in range(iters): fn() torch.cuda.synchronize() return (time.perf_counter() - t0) / iters * 1e3 q, k, v = mk(), mk(), mk() dense = torch.ones(B, 1, N, N, dtype = torch.bool, device = dev) backends = { "default(dispatch)": None, "MATH": [SDPBackend.MATH], "FLASH": [SDPBackend.FLASH_ATTENTION], "EFFICIENT": [SDPBackend.EFFICIENT_ATTENTION], "CUDNN": [SDPBackend.CUDNN_ATTENTION], } print(f"shape B={B} H={H} N={N} D={D} {dt}\n") print(f"{'backend':<20}{'mask=dense(ms)':>18}{'mask=None(ms)':>18}") for name, bk in backends.items(): def run_dense(): if bk is None: return F.scaled_dot_product_attention(q, k, v, attn_mask = dense) with sdpa_kernel(bk): return F.scaled_dot_product_attention(q, k, v, attn_mask = dense) def run_none(): if bk is None: return F.scaled_dot_product_attention(q, k, v, attn_mask = None) with sdpa_kernel(bk): return F.scaled_dot_product_attention(q, k, v, attn_mask = None) dms = timed(run_dense) nms = timed(run_none) d_s = f"{dms:.2f}" if isinstance(dms, float) else dms n_s = f"{nms:.2f}" if isinstance(nms, float) else nms print(f"{name:<20}{d_s:>18}{n_s:>18}")