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4754707 | 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 55 56 | """Benchmark FastBitLMv57 vs BitLMv57 end-to-end train-step wall time."""
import time
import torch
torch.set_float32_matmul_precision('high')
import model_v16 as _v16
from model_v57 import BitLMv57
from model_v57_fast import BitLMv57Fast
def bench(m, bs=64, T=256, iters=20, warmup=5):
m = m.cuda()
opt = torch.optim.AdamW(m.parameters(), lr=3e-4, betas=(0.9, 0.95))
mm = torch.compile(m)
x = torch.randint(0, 128, (bs, T), device='cuda')
y = torch.randint(0, 128, (bs, T), device='cuda')
_v16.set_gumbel_tau(0.5)
for _ in range(warmup):
_, loss = mm(x, y)
opt.zero_grad(set_to_none=True)
loss.backward()
torch.nn.utils.clip_grad_norm_(m.parameters(), 1.0)
opt.step()
torch.cuda.synchronize()
t0 = time.time()
for _ in range(iters):
_, loss = mm(x, y)
opt.zero_grad(set_to_none=True)
loss.backward()
torch.nn.utils.clip_grad_norm_(m.parameters(), 1.0)
opt.step()
torch.cuda.synchronize()
return (time.time() - t0) / iters * 1000
def main():
# Match v73 config
kw = dict(d_model=1024, n_layers=8, n_heads=32, d_ff=512)
m_ref = BitLMv57(**kw)
t_ref = bench(m_ref)
del m_ref
torch.cuda.empty_cache()
m_fast = BitLMv57Fast(**kw)
t_fast = bench(m_fast)
print(f'reference BitLMv57 : {t_ref:.2f} ms / step')
print(f'triton BitLMv57Fast : {t_fast:.2f} ms / step')
print(f'speedup: {t_ref/t_fast:.2f}x')
if __name__ == '__main__':
main()
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