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5339d534a3
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/54652 This PR adds a fairly robust runner for the instruction count microbenchmarks. Key features are: * Timeout and retry. (In rare cases, Callgrind will hang under heavy load.) * Robust error handling and keyboard interrupt support. * Benchmarks are pinned to cores. (Wall times still won't be great, but it's something.) * Progress printouts, including a rough ETA. Test Plan: Imported from OSS Reviewed By: pbelevich Differential Revision: D27537823 Pulled By: robieta fbshipit-source-id: 699ac907281d28bf7ffa08594253716ca40204ba
28 lines
937 B
Python
28 lines
937 B
Python
"""Basic runner for the instruction count microbenchmarks.
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The contents of this file are placeholders, and will be replaced by more
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expressive and robust components (e.g. better runner and result display
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components) in future iterations. However this allows us to excercise the
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underlying benchmark generation infrastructure in the mean time.
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"""
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from core.expand import materialize
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from definitions.standard import BENCHMARKS
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from execution.runner import Runner
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from execution.work import WorkOrder
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def main() -> None:
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work_orders = tuple(
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WorkOrder(label, autolabels, timer_args, timeout=600, retries=2)
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for label, autolabels, timer_args in materialize(BENCHMARKS)
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)
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results = Runner(work_orders).run()
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for work_order in work_orders:
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print(work_order.label, work_order.autolabels, work_order.timer_args.num_threads, results[work_order].instructions)
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if __name__ == "__main__":
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main()
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