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import torch
import torch.nn as nn
import triton
from fla.modules import GroupNorm, LayerNorm
@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=['naive_ln', 'fused_ln', 'naive_gn', 'fused_gn',
'naive_ln_bwd', 'fused_ln_bwd', 'naive_gn_bwd', 'fused_gn_bwd'],
# label name for the lines
line_names=['naive_ln', 'fused_ln', 'naive_gn', 'fused_gn',
'naive_ln_bwd', 'fused_ln_bwd', 'naive_gn_bwd', 'fused_gn_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
requires_grad = True
B, D = 16, 1024
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_ln'):
norm = nn.LayerNorm(D, elementwise_affine=True, bias=True).to(device=device, dtype=dtype)
results = triton.testing.do_bench(lambda: norm(x), quantiles=quantiles)
if provider.startswith('fused_ln'):
norm = LayerNorm(D, elementwise_affine=True, bias=True).to(device=device, dtype=dtype)
results = triton.testing.do_bench(lambda: norm(x), quantiles=quantiles)
if provider.startswith('naive_gn'):
norm = nn.GroupNorm(4, D).to(device=device, dtype=dtype)
results = triton.testing.do_bench(lambda: norm(x), quantiles=quantiles)
if provider.startswith('fused_gn'):
norm = GroupNorm(4, D, elementwise_affine=True, bias=True).to(device=device, dtype=dtype)
results = triton.testing.do_bench(lambda: norm(x), quantiles=quantiles)
if provider.startswith('naive_ln_bwd'):
norm = nn.LayerNorm(D, elementwise_affine=True, bias=True).to(device=device, dtype=dtype)
results = triton.testing.do_bench(lambda: norm(x).backward(x), quantiles=quantiles)
if provider.startswith('fused_ln_bwd'):
norm = LayerNorm(D, elementwise_affine=True, bias=True).to(device=device, dtype=dtype)
results = triton.testing.do_bench(lambda: norm(x).backward(x), quantiles=quantiles)
if provider.startswith('naive_gn_bwd'):
norm = nn.GroupNorm(4, D).to(device=device, dtype=dtype)
results = triton.testing.do_bench(lambda: norm(x).backward(x), quantiles=quantiles)
if provider.startswith('fused_gn_bwd'):
norm = GroupNorm(4, D, elementwise_affine=True, bias=True).to(device=device, dtype=dtype)
results = triton.testing.do_bench(lambda: norm(x).backward(x), quantiles=quantiles)
return results
if __name__ == '__main__':
benchmark.run(print_data=True)
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