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import os
import torch
import triton
from flash_attn import flash_attn_func
from fla.ops.retention import chunk_retention, parallel_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=['chunk', 'parallel', 'flash', 'chunk_bwd', 'parallel_bwd', 'flash_bwd'],
# label name for the lines
line_names=['chunk_fwd', 'parallel_fwd', 'flash_fwd', 'chunk_fwdbwd', 'parallel_fwdbwd', 'flash_fwdbwd'],
# line styles
styles=[('green', '-'), ('blue', '-'), ('red', '-'), ('green', 'dotted'), ('blue', 'dotted'), ('red', 'dotted')],
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 = 4, 8, 256
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
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.ones_like(q, dtype=dtype)
quantiles = [0.5, 0.2, 0.8]
results = 0, 0, 0
if provider == 'chunk':
results = triton.testing.do_bench(lambda: chunk_retention(q, k, v), quantiles=quantiles)
elif provider == 'parallel':
results = triton.testing.do_bench(lambda: parallel_retention(q, k, v), quantiles=quantiles)
elif provider == 'flash':
results = triton.testing.do_bench(lambda: flash_attn_func(q, k, v, causal=True), quantiles=quantiles)
elif provider == 'chunk_bwd':
results = triton.testing.do_bench(lambda: chunk_retention(q, k, v)[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)
elif provider == 'flash_bwd':
results = triton.testing.do_bench(lambda: flash_attn_func(q, k, v, causal=True).backward(do), quantiles=quantiles)
return results
if __name__ == '__main__':
benchmark.run(print_data=True, save_path='.')
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