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c37baa9177
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/46457 Wanted to see if using CopyMatrix specialized for float that uses mkl_somatcopy can be faster but it wasn't. Still want to check in benchmark that can be used later. Test Plan: . Reviewed By: dskhudia Differential Revision: D24345901 fbshipit-source-id: d3e68dbb560e3138fda11c55789cd41bc0715c6d
32 lines
1.2 KiB
Python
32 lines
1.2 KiB
Python
import argparse
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import numpy as np
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from caffe2.python import core, workspace
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def benchmark_concat(num_inputs, input_dim, axis, add_axis, iterations):
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input_names = [f"input{i}" for i in range(num_inputs)]
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for n in input_names:
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workspace.FeedBlob(n, np.random.randn(*input_dim).astype(np.float32))
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net = core.Net("benchmark_net")
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net.Concat(input_names, ["output", "split_info"], axis=axis, add_axis=add_axis)
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workspace.CreateNet(net)
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runtimes = workspace.BenchmarkNet(net.Name(), 1, iterations, True)
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print(f"{num_inputs * np.prod(input_dim) * 4 / runtimes[1] / 1e6} GB/s")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="minimal benchmark for concat.")
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parser.add_argument("--num_inputs", type=int, default=2)
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parser.add_argument("--input_dim", nargs="+", type=int, required=True)
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parser.add_argument("--axis", type=int, default=-1)
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parser.add_argument("--add_axis", type=int, default=0)
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parser.add_argument("--iterations", type=int, default=64)
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args, extra_args = parser.parse_known_args()
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core.GlobalInit(["python"] + extra_args)
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benchmark_concat(
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args.num_inputs, args.input_dim, args.axis, args.add_axis, args.iterations
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)
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