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import torch
import triton
from torch.nn import functional as F
from fla.ops.gla import chunk_gla, fused_chunk_gla, fused_recurrent_gla
from fla.ops.retention import chunk_retention, parallel_retention
from fla.ops.retention.naive import naive_retention
@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=['fused_chunk_gla', 'recurrent_gla', 'chunk_gla', 'chunk_retention',
'fused_chunk_gla_bwd', 'recurrent_gla_bwd', 'chunk_gla_bwd', 'chunk_retention_bwd'],
# label name for the lines
line_names=['fused_chunk_gla', 'recurrent_gla', 'chunk_gla', 'chunk_retention',
'fused_chunk_gla_bwd', 'recurrent_gla_bwd', 'chunk_gla_bwd', 'chunk_retention_bwd'],
# line styles
styles=[('green', '-'), ('blue', '--'), ('red', '-.'),
('cyan', ':'), ('yellow', 'dotted'), ('cyan', '--'), ('cyan', '-'), ('black', ':')],
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
# dtype = torch.float32
requires_grad = True
B, H, D = 16, 8, 128
if provider in ("fused_chunk_gla", "fused_chunk_gla_bwd"):
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)
g = F.logsigmoid(torch.randn(B, T, H, D, device=device, dtype=dtype)).clamp_min(-5).requires_grad_(requires_grad)
v = torch.randn(B, T, H, D, device=device, requires_grad=requires_grad, dtype=dtype)
do = torch.ones_like(q, dtype=dtype)
quantiles = [0.5, 0.2, 0.8]
results = 0, 0, 0
if provider == 'torch':
if T > 2048:
return results
results = triton.testing.do_bench(lambda: naive_retention(q, k, v), quantiles=quantiles)
elif provider == 'recurrent_gla':
results = triton.testing.do_bench(lambda: fused_recurrent_gla(q, k, v, g), quantiles=quantiles)
elif provider == 'fused_chunk_gla':
results = triton.testing.do_bench(lambda: fused_chunk_gla(q, k, v, g), quantiles=quantiles)
elif provider == 'chunk_retention':
results = triton.testing.do_bench(lambda: chunk_retention(q, k, v), quantiles=quantiles)
elif provider == 'chunk_gla':
results = triton.testing.do_bench(lambda: chunk_gla(q, k, v, g), quantiles=quantiles)
elif provider == 'parallel':
results = triton.testing.do_bench(lambda: parallel_retention(q, k, v), quantiles=quantiles)
elif provider == 'torch_bwd':
if T > 2048:
return results
elif provider == 'chunk_retention_bwd':
results = triton.testing.do_bench(lambda: chunk_retention(q, k, v)[0].backward(do), quantiles=quantiles)
elif provider == 'recurrent_gla_bwd':
results = triton.testing.do_bench(lambda: fused_recurrent_gla(q, k, v, gk=g)[0].backward(do), quantiles=quantiles)
elif provider == 'fused_chunk_gla_bwd':
results = triton.testing.do_bench(lambda: fused_chunk_gla(q, k, v, g)[0].backward(do), quantiles=quantiles)
elif provider == 'chunk_gla_bwd':
results = triton.testing.do_bench(lambda: chunk_gla(q, k, v, g)[0].backward(do), quantiles=quantiles)
elif provider == 'parallel_bwd':
results = triton.testing.do_bench(lambda: parallel_retention(q, k, v)[0].backward(do), quantiles=quantiles)
return results
if __name__ == '__main__':
benchmark.run(print_data=True)
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