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import torch
import torch.nn as nn
import torch.nn.functional as F
import triton
from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss
@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', 'fused', 'fused_linear', 'naive_bwd', 'fused_bwd', 'fused_linear_bwd'],
# label name for the lines
line_names=['naive', 'fused', 'fused_linear', 'naive_bwd', 'fused_bwd', 'fused_linear_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, H, V = 4, 4096, 120000
x = torch.randn(B * T, H, device=device, requires_grad=requires_grad, dtype=dtype)
target = torch.randint(0, V, (B * T,), device=device, dtype=torch.int64)
w = torch.randn(V, H, device=device, requires_grad=requires_grad, dtype=dtype)
b = torch.randn(V, device=device, requires_grad=requires_grad, dtype=dtype)
quantiles = [0.5, 0.2, 0.8]
results = 0, 0, 0
if provider == 'naive':
criterion = nn.CrossEntropyLoss()
results = triton.testing.do_bench(lambda: criterion(F.linear(x, w, b), target), quantiles=quantiles)
elif provider == 'naive_bwd':
criterion = nn.CrossEntropyLoss()
results = triton.testing.do_bench(lambda: criterion(F.linear(x, w, b), target).backward(), quantiles=quantiles)
elif provider == 'fused':
criterion = FusedCrossEntropyLoss()
results = triton.testing.do_bench(lambda: criterion(F.linear(x, w, b), target), quantiles=quantiles)
elif provider == 'fused_bwd':
criterion = FusedCrossEntropyLoss()
results = triton.testing.do_bench(lambda: criterion(F.linear(x, w, b), target).backward(), quantiles=quantiles)
elif provider == 'fused_linear':
criterion = FusedLinearCrossEntropyLoss()
results = triton.testing.do_bench(lambda: criterion(x, target, w, b), quantiles=quantiles)
elif provider == 'fused_linear_bwd':
criterion = FusedLinearCrossEntropyLoss()
results = triton.testing.do_bench(lambda: criterion(x, target, w, b).backward(), quantiles=quantiles)
return results
if __name__ == '__main__':
benchmark.run(print_data=True)
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