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import random
from typing import List
import operator_benchmark as op_bench
import torch
"""Microbenchmarks for Cat operator"""
cross_product_configs = {
"device": ["cpu", "cuda"],
}
# Configs for PT Cat operator
cat_configs_short = op_bench.config_list(
attr_names=["sizes", "N", "dim"],
attrs=[
[(1, 1, 1), 2, 0], # noqa: E241
[(512, 512, 2), 2, 1], # noqa: E241
[(128, 1024, 2), 2, 1], # noqa: E241
],
cross_product_configs=cross_product_configs,
tags=["short"],
)
# Configs specific to static runtime feature - a fast path runtime for pared down models
cat_configs_static_runtime = op_bench.config_list(
attr_names=["sizes", "N", "dim"],
attrs=[
[[(1, 160), (1, 14)], -1, 1],
[[(1, 20, 40), (1, 4, 40), (1, 5, 40)], -1, 1],
[[(1, 580), (1, 174)], -1, 1],
[[(20, 160), (20, 14)], -1, 1],
[[(20, 20, 40), (20, 4, 40), (20, 5, 40)], -1, 1],
[[(20, 580), (20, 174)], -1, 1],
],
cross_product_configs=cross_product_configs,
tags=["static_runtime"],
)
cat_configs_long = op_bench.config_list(
attr_names=["sizes", "N", "dim"],
attrs=[
[(2**10, 2**10, 2), 2, 0], # noqa: E241
[(2**10 + 1, 2**10 - 1, 2), 2, 1], # noqa: E226,E241
[(2**10, 2**10, 2), 2, 2], # noqa: E241
[
[
lambda: random.randint(2**6, 2**7),
2**7 - 17,
2**6 + 1,
], # noqa: E201,E226,E241
5,
0,
],
[
[
2**6 + 2**5,
lambda: random.randint(2**6, 2**7),
2**6,
], # noqa: E201,E226,E241,E272
5,
1,
],
[
[
2**7,
2**6,
lambda: random.randint(2**6, 2**7),
], # noqa: E201,E241,E272
5,
2,
],
[[lambda: random.randint(2**5, 2**6), 2**5, 2**6], 50, 0], # noqa: E241
[
[2**5, lambda: random.randint(2**5, 2**6), 2**6], # noqa: E241,E272
50,
1,
],
[
[
2**5 + 1,
2**6 + 1,
lambda: random.randint(2**5, 2**6),
], # noqa: E226,E241,E272
50,
2,
],
],
cross_product_configs=cross_product_configs,
tags=["long"],
)
# There is a different codepath on CUDA for >4 dimensions
cat_configs_multidim = op_bench.config_list(
attr_names=["sizes", "N", "dim"],
attrs=[
[(2**6, 2**5, 2**2, 2**4, 2**5), 2, 2], # noqa: E241
[(2**4, 2**5, 2**2, 2**4, 2**5), 8, 2], # noqa: E241
[
(2**3 + 1, 2**5 - 1, 2**2 + 1, 2**4 - 1, 2**5 + 1),
17,
4,
], # noqa: E226,E241
],
cross_product_configs=cross_product_configs,
tags=["multidim"],
)
cat_configs_manyinputs = op_bench.config_list(
attr_names=["sizes", "N", "dim"],
attrs=[
[[lambda: random.randint(1, 10000)], 100, 0],
[[lambda: random.randint(1, 1000)], 1000, 0],
[[lambda: random.randint(1, 500)], 2000, 0],
[[lambda: random.randint(1, 300)], 3000, 0],
],
cross_product_configs=cross_product_configs,
tags=["manyinputs"],
)
class CatBenchmark(op_bench.TorchBenchmarkBase):
def init(self, sizes, N, dim, device):
random.seed(42)
inputs = []
gen_sizes = []
if type(sizes) == list and N == -1:
gen_sizes = sizes
else:
for i in range(N):
gen_sizes.append(
[
old_size() if callable(old_size) else old_size
for old_size in sizes
]
)
for s in gen_sizes:
inputs.append(torch.rand(s, device=device))
result = torch.empty(0, device=device)
self.inputs = {"result": result, "inputs": inputs, "dim": dim}
self.set_module_name("cat")
def forward(self, result: torch.Tensor, inputs: List[torch.Tensor], dim: int):
return torch.cat(inputs, dim=dim, out=result)
op_bench.generate_pt_test(
cat_configs_short
+ cat_configs_long
+ cat_configs_multidim
+ cat_configs_manyinputs
+ cat_configs_static_runtime,
CatBenchmark,
)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
|
