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import torch
import triton
from flash_attn import flash_attn_func
from fla.ops.nsa import parallel_nsa
@triton.testing.perf_report(
triton.testing.Benchmark(
# argument names to use as an x-axis for the plot
x_names=['T'],
# different possible values for `x_name`
x_vals=[128 * 2 ** i for i in range(0, 8)],
# argument name whose value corresponds to a different line in the plot
line_arg='provider',
# possible values for `line_arg``
line_vals=['nsa', 'nsa_bwd', 'flash', 'flash_bwd'],
# label name for the lines
line_names=['nsa', 'nsa_bwd', 'flash', 'flash_bwd'],
# line styles
styles=[('green', '-'), ('blue', '-'), ('red', '-'), ('green', 'dotted'),
('blue', 'dotted'), ('red', 'dotted'), ('cyan', '-'), ('cyan', 'dotted')],
ylabel="Execution Time (ms)", # label name for the y-axis
# name for the plot. Used also as a file name for saving the plot.
plot_name="Performance",
args={},
),
)
def benchmark(T, provider):
from fla.utils import device
dtype = torch.bfloat16
requires_grad = True
B, H, HQ, D, S = 4, 4, 64, 128, 16
block_size = 64
q = torch.randn(B, T, HQ, D, device=device, requires_grad=requires_grad, dtype=dtype)
k = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype)
v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype)
do = torch.ones_like(q, dtype=dtype)
indices = torch.full((B, T, H, S), T, dtype=torch.long, device=device)
for b in range(B):
for t in range(T):
for h in range(H):
i_i = torch.randperm(max(1, triton.cdiv(t, block_size)))[:S]
indices[b, t, h, :len(i_i)] = i_i
indices = indices.sort(-1)[0]
quantiles = [0.5, 0.2, 0.8]
results = 0, 0, 0
if provider == 'nsa':
results = triton.testing.do_bench(
lambda: parallel_nsa(q, k, v, block_indices=indices, block_size=block_size),
quantiles=quantiles,
)
elif provider == 'nsa_bwd':
results = triton.testing.do_bench(
lambda: parallel_nsa(q, k, v, block_indices=indices, block_size=block_size).backward(do),
quantiles=quantiles,
)
elif provider == 'flash':
results = triton.testing.do_bench(
lambda: flash_attn_func(q, k, v, causal=True),
quantiles=quantiles,
)
elif provider == 'flash_bwd':
results = triton.testing.do_bench(
lambda: flash_attn_func(q, k, v, causal=True).backward(do),
quantiles=quantiles,
)
return results
if __name__ == '__main__':
benchmark.run(print_data=True, save_path='.')
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