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import torch
import triton
from fla.ops.based import parallel_based
from fla.ops.gla import fused_chunk_gla
from fla.ops.retention import fused_chunk_retention, parallel_retention
try:
from flash_attn import flash_attn_func
HAS_FLASH = True
except ImportError:
HAS_FLASH = False
@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=['retention_parallel', 'retention_fused_chunk',
'gla_fused_chunk', 'based_parallel'] + (['flash'] if HAS_FLASH else []),
# label name for the lines
line_names=['retention_parallel_fwdbwd', 'retention_fused_chunk_fwdbwd',
'gla_fused_chunk_fwdbwd', 'based_parallel_fwdbwd'] + (['flash_fwdbwd'] if HAS_FLASH else []),
# line styles
styles=[('green', '-'), ('blue', '--'), ('red', '-.'), ('cyan', ':')] + \
([('yellow', 'dotted')] if HAS_FLASH else []),
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, D = 16, 8, 128
if "based" in provider:
q = torch.randn(B, T, H, 16, device=device, requires_grad=requires_grad, dtype=dtype)
k = torch.randn(B, T, H, 16, device=device, requires_grad=requires_grad, dtype=dtype)
v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype)
elif "gla" in provider:
q = torch.randn(B, T, H, 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)
g = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype)
else:
q = torch.randn(B, T, H, 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.rand_like(v, dtype=dtype)
quantiles = [0.5, 0.2, 0.8]
results = 0, 0, 0
if provider == 'flash':
results = triton.testing.do_bench(lambda: flash_attn_func(q, k, v).backward(do), quantiles=quantiles)
elif provider == 'retention_parallel':
results = triton.testing.do_bench(lambda: parallel_retention(q, k, v)[0].backward(do), quantiles=quantiles)
elif provider == 'retention_fused_chunk':
results = triton.testing.do_bench(lambda: fused_chunk_retention(q, k, v)[0].backward(do), quantiles=quantiles)
elif provider == 'based_parallel':
results = triton.testing.do_bench(lambda: parallel_based(q, k, v).backward(do), quantiles=quantiles)
elif provider == 'gla_fused_chunk':
results = triton.testing.do_bench(lambda: fused_chunk_gla(q, k, v, g)[0].backward(do), quantiles=quantiles)
return results
if __name__ == '__main__':
benchmark.run(print_data=True, show_plots=True)
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