pytorch

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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_mul_gradient(args):
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    workspace.FeedBlob("dC", np.random.rand(args.m, args.n).astype(np.float32))
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    workspace.FeedBlob("A", np.random.rand(args.m, args.n).astype(np.float32))
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    workspace.FeedBlob("B", np.random.rand(args.n).astype(np.float32))
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    net = core.Net("mynet")
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    net.MulGradient(
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        ["dC", "A", "B"],
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        ["dC" if args.inplace else "dA", "dB"],
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        broadcast=True,
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        axis=1,
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        allow_broadcast_fastpath=args.allow_broadcast_fastpath,
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    )
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    workspace.CreateNet(net)
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    workspace.BenchmarkNet(net.Name(), 1, args.iteration, True)
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if __name__ == "__main__":
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    parser = argparse.ArgumentParser(
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        description="benchmark for MulGradient.")
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    parser.add_argument(
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        '-m', type=int, default=9508,
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        help="The number of rows of A")
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    parser.add_argument(
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        "-n", type=int, default=80,
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        help="The number of columns of A")
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    parser.add_argument(
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        '-i', "--iteration", type=int, default=100,
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        help="The number of iterations.")
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    parser.add_argument(
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        "--inplace",
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        action='store_true', help="Whether to perform the op in-place.")
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    parser.add_argument(
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        "--allow-broadcast-fastpath",
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        action='store_true', help="Whether the broadcast fastpath is enabled.")
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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_mul_gradient(args)
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