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import benchmark_caffe2 as op_bench_c2
import operator_benchmark as op_bench
from benchmark_caffe2 import Caffe2BenchmarkBase # noqa: F401
from caffe2.python import core
import numpy
"""Microbenchmarks for element-wise BatchGather operator."""
# Configs for C2 BatherGather operator
batch_gather_configs_short = op_bench.config_list(
attr_names=["M", "N", "K"],
attrs=[
[8, 8, 1],
[256, 512, 1],
[512, 512, 1],
[8, 8, 2],
[256, 512, 2],
[512, 512, 2],
],
cross_product_configs={
'device': ['cpu', 'cuda'],
},
tags=["short"]
)
batch_gather_configs_long = op_bench.cross_product_configs(
M=[128, 1024],
N=[128, 1024],
K=[1, 2],
device=['cpu', 'cuda'],
tags=["long"]
)
class BatchGatherBenchmark(op_bench_c2.Caffe2BenchmarkBase):
def init(self, M, N, K, device):
self.input_one = self.tensor([M, N, K], device=device)
max_val = N
numpy.random.seed((1 << 32) - 1)
index_dim = numpy.random.randint(0, N)
self.index = self.feed_tensor(numpy.random.randint(0, max_val, index_dim), device=device)
self.output = self.tensor([M, index_dim, K], device=device)
self.set_module_name("batch_gather")
def forward(self):
op = core.CreateOperator("BatchGather", [self.input_one, self.index], self.output)
return op
op_bench_c2.generate_c2_test(
batch_gather_configs_long + batch_gather_configs_short, BatchGatherBenchmark
)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
|
