pytorch

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import argparse
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import sys
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import timeit
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import torch
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from torch.utils.benchmark import Timer
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PARALLEL_TASKS_NUM = 4
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INTERNAL_ITER = None
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def loop_workload(x):
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    for i in range(INTERNAL_ITER):
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        x = torch.mm(x, x)
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    return x
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def parallel_workload(x):
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    def parallel_task(x):
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        for i in range(int(INTERNAL_ITER / PARALLEL_TASKS_NUM)):
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            x = torch.mm(x, x)
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        return x
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    futs = []
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    for i in range(PARALLEL_TASKS_NUM):
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        futs.append(torch.jit._fork(parallel_task, x))
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    for i in range(PARALLEL_TASKS_NUM):
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        torch.jit._wait(futs[i])
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    return x
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if __name__ == "__main__":
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    torch._C._set_graph_executor_optimize(False)
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    parser = argparse.ArgumentParser(description="Profiler benchmark")
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    parser.add_argument("--with-cuda", "--with_cuda", action="store_true")
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    parser.add_argument("--with-stack", "--with_stack", action="store_true")
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    parser.add_argument("--use-script", "--use_script", action="store_true")
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    parser.add_argument("--use-kineto", "--use_kineto", action="store_true")
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    parser.add_argument(
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        "--profiling-tensor-size", "--profiling_tensor_size", default=1, type=int
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    )
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    parser.add_argument("--workload", "--workload", default="loop", type=str)
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    parser.add_argument("--internal-iter", "--internal_iter", default=256, type=int)
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    parser.add_argument(
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        "--timer-min-run-time", "--timer_min_run_time", default=10, type=int
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    )
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    parser.add_argument("--cuda-only", "--cuda_only", action="store_true")
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    args = parser.parse_args()
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    if args.with_cuda and not torch.cuda.is_available():
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        print("No CUDA available")
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        sys.exit()
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    print(
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        f"Payload: {args.workload}, {args.internal_iter} iterations; timer min. runtime = {args.timer_min_run_time}\n"
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    )
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    INTERNAL_ITER = args.internal_iter
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    for profiling_enabled in [False, True]:
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        print(
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            "Profiling {}, tensor size {}x{}, use cuda: {}, use kineto: {}, with stacks: {}, use script: {}".format(
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                "enabled" if profiling_enabled else "disabled",
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                args.profiling_tensor_size,
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                args.profiling_tensor_size,
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                args.with_cuda,
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                args.use_kineto,
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                args.with_stack,
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                args.use_script,
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            )
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        )
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        input_x = torch.rand(args.profiling_tensor_size, args.profiling_tensor_size)
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        if args.with_cuda:
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            input_x = input_x.cuda()
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        workload = None
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        assert args.workload in ["loop", "parallel"]
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        if args.workload == "loop":
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            workload = loop_workload
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        else:
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            workload = parallel_workload
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        if args.use_script:
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            traced_workload = torch.jit.trace(workload, (input_x,))
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            workload = traced_workload
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        if profiling_enabled:
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            def payload():
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                x = None
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                with torch.autograd.profiler.profile(
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                    use_cuda=args.with_cuda,
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                    with_stack=args.with_stack,
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                    use_kineto=args.use_kineto,
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                    use_cpu=not args.cuda_only,
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                ) as prof:
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                    x = workload(input_x)
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                return x
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        else:
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            def payload():
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                return workload(input_x)
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        t = Timer(
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            "payload()",
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            globals={"payload": payload},
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            timer=timeit.default_timer,
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        ).blocked_autorange(min_run_time=args.timer_min_run_time)
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        print(t)
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