mirror of
https://github.com/pytorch/pytorch.git
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498a7808ff
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136359 Approved by: https://github.com/albanD
115 lines
3.4 KiB
Python
115 lines
3.4 KiB
Python
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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):
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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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