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import torch
import triton
from fla.ops.hgrn import chunk_hgrn, fused_recurrent_hgrn
@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=['chunk', 'recurrent', 'chunk_bwd', 'recurrent_bwd'],
# label name for the lines
line_names=['chunk', 'recurrent', 'chunk_bwd', 'recurrent_bwd'],
# line styles
styles=[('green', '-'), ('blue', '--'), ('red', '-.'), ('cyan', ':'), ('yellow', 'dotted'), ('black', 'dashed')],
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
B, D = 16, 512
x = torch.randn((B, T, D), dtype=dtype, device=device)
g = torch.randn((B, T, D), dtype=dtype, device=device).sigmoid()
x = (1 - g) * x
x, g = (i.detach().clone().to(dtype).requires_grad_() for i in (x, g))
do = torch.randn_like(x, dtype=dtype)
quantiles = [0.5, 0.2, 0.8]
results = 0, 0, 0
if provider == 'chunk':
results = triton.testing.do_bench(lambda: chunk_hgrn(x, g), quantiles=quantiles)
if provider == 'recurrent':
results = triton.testing.do_bench(lambda: fused_recurrent_hgrn(x, g), quantiles=quantiles)
if provider == 'chunk_bwd':
results = triton.testing.do_bench(lambda: chunk_hgrn(x, g)[0].backward(do), quantiles=quantiles)
if provider == 'recurrent_bwd':
results = triton.testing.do_bench(lambda: fused_recurrent_hgrn(x, g)[0].backward(do), quantiles=quantiles)
return results
if __name__ == '__main__':
benchmark.run(print_data=True)
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