Files
ROCm/scripts/amd/benchmark_flash_attention.py
2023-11-08 12:42:54 -06:00

72 lines
2.0 KiB
Python

import argparse
import sys
import git
git_repo = git.Repo('.', search_parent_directories=True)
git_root = git_repo.git.rev_parse("--show-toplevel")
sys.path.insert(0, git_root+'/python/perf-kernels')
FA = __import__('06-fused-attention-fwd-transV')
attention = FA._attention.apply
import torch
def benchmark_FA(BATCH, H, N_CTX, D_HEAD, causal, rep, mode, dtype=torch.float16, device="cuda"):
q = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
k = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
v = torch.randn((BATCH, H, D_HEAD, N_CTX), dtype=dtype, device="cuda", requires_grad=True)
sm_scale = 1.3
split_kernel = True
if mode == "bwd":
causal=True
fn = lambda: attention(q, k, v, sm_scale)
o = fn()
if mode == "bwd":
do = torch.randn_like(o)
o.backward(do, retain_graph=True)
for i in range(rep):
if mode == "bwd":
o = fn()
o.backward(do, retain_graph=True)
if mode == "fwd":
fn()
torch.cuda.synchronize()
def main(args=None):
if args is None:
args = sys.argv[1:]
parser = argparse.ArgumentParser(
prog="FA benchmarking",
description="benchmark FA fwd and bwd with 2 GPUs",
allow_abbrev=False,
)
parser.add_argument("-bs", type=int, default=argparse.SUPPRESS)
parser.add_argument("-nheads", type=int, default=argparse.SUPPRESS)
parser.add_argument("-d", type=int, default=argparse.SUPPRESS)
parser.add_argument("-seqlen", type=int, default=argparse.SUPPRESS)
parser.add_argument("-rep", type=int, default=argparse.SUPPRESS)
parser.add_argument("-mode", type=str, default=argparse.SUPPRESS)
parsed_args = parser.parse_args(args)
bs = parsed_args.bs
nheads = parsed_args.nheads
d = parsed_args.d
seqlen = parsed_args.seqlen
rep = parsed_args.rep
mode = parsed_args.mode
benchmark_FA(bs, nheads, seqlen, d, False, rep, mode)
if __name__ == '__main__':
sys.exit(main())