File size: 1,725 Bytes
80692f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | """SOTA reference for FP8 GEMM: flashinfer.gemm.fp8_gemm.
If flashinfer is not installed or the SM120 path isn't supported, this falls
back to torch._scaled_mm which is the cuBLAS FP8 path. The benchmark treats
whichever succeeds as the SOTA reference line.
Agents are FORBIDDEN from using torch._scaled_mm in their solution (see
problem.yaml.forbidden). This file is only for the benchmark's reference line.
"""
from __future__ import annotations
import torch
def _try_flashinfer(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor | None:
try:
import flashinfer # noqa: F401
# Note: flashinfer's FP8 GEMM API surface may differ; adapt if needed.
# Placeholder call — replace with the actual flashinfer entry point
# once validated on SM120.
return None
except ImportError:
return None
def _scaled_mm(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
# torch._scaled_mm wants per-tensor scales. Use unit scales for the reference.
scale_a = torch.tensor(1.0, device=x.device)
scale_b = torch.tensor(1.0, device=x.device)
out = torch._scaled_mm(
x,
w.T,
scale_a=scale_a,
scale_b=scale_b,
out_dtype=torch.bfloat16,
)
return out if not isinstance(out, tuple) else out[0]
def sota_forward(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
"""Best-available FP8 GEMM reference. x: (M, K) fp8, w: (N, K) fp8."""
out = _try_flashinfer(x, w)
if out is not None:
return out
return _scaled_mm(x, w)
def is_available() -> bool:
try:
# Verify torch._scaled_mm is callable (smoke)
return hasattr(torch, "_scaled_mm")
except Exception:
return False
|