File size: 2,236 Bytes
eafbe80 | 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 57 58 59 60 | import torch
import torch.nn as nn
import triton
from fla.modules.token_shift import token_shift
def token_shift_ref(x):
shifted = nn.functional.pad(x, (0, 0, 1, -1))
delta = shifted - x
return delta
@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, 9)],
# argument name whose value corresponds to a different line in the plot
line_arg='provider',
# possible values for `line_arg``
line_vals=['naive_token_shift', 'fused_token_shift', 'naive_token_shift_bwd', 'fused_token_shift_bwd'],
# label name for the lines
line_names=['naive_token_shift', 'fused_token_shift', 'naive_token_shift_bwd', 'fused_token_shift_bwd'],
# line styles
styles=[('green', '-'), ('blue', '--'), ('red', '-.'),
('cyan', ':')],
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, D = 8, 4096
x = torch.randn(B, T, D, device=device, requires_grad=requires_grad, dtype=dtype)
quantiles = [0.5, 0.2, 0.8]
results = 0, 0, 0
if provider.startswith('naive_token_shift'):
results = triton.testing.do_bench(lambda: token_shift_ref(x), quantiles=quantiles)
if provider.startswith('fused_token_shift'):
results = triton.testing.do_bench(lambda: token_shift(x), quantiles=quantiles)
if provider.startswith('naive_token_shift_bwd'):
grad_output = torch.randn_like(x)
results = triton.testing.do_bench(lambda: token_shift_ref(x).backward(grad_output), quantiles=quantiles)
if provider.startswith('fused_token_shift_bwd'):
grad_output = torch.randn_like(x)
results = triton.testing.do_bench(lambda: token_shift(x).backward(grad_output), quantiles=quantiles)
return results
if __name__ == '__main__':
benchmark.run(print_data=True)
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