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758d7dea9c
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/68074 This is the first step of many PRs towards implementing the `torch.monitor` RFC https://github.com/pytorch/rfcs/pull/30 This defines the aggregation types, the `Stat` class and provides some simple collection of the stats. This doesn't match the RFC exactly as it incorporates some of the comments on the RFC as well as a few changes for performance. Changes: * added window_size to the stats. If specified it will always compute the stat using the `window_size` number of values. If there aren't enough values within that window it reports the previous stats. * This doesn't include the push metrics yet (will be coming). After more discussion it looks like the best way to handle this is to support a hybrid where the metric can set how frequently it'll be logged. For fixed window_size metrics it'll be logged each time it hits the window size. This will allow performant counters as well as lower frequency push counters (window_size=1). Performance considerations: * Updating the stats acquires a lock on that Stat object. This should be performant unless there's many-many threads writing to the same stat. Single thread will typically use futex so should be quite fast. * Adding/removing/fetching all stats sets a global lock on the stat list -- this shouldn't be an issue since these events happen infrequently. * Fetching stats accesses one stat at a time instead of a global lock. This means the exported values are linearizable but not serializable across multiple stats but I don't expect this to be an issue. Next steps: 1. Add StatCollector interface for push style metrics 1. Add pybind interfaces to expose to Python 1. Add default metric providers 1. Integrate into Kineto trace view Test Plan: buck test //caffe2/test/cpp/monitor:monitor CI Reviewed By: kiukchung Differential Revision: D32266032 fbshipit-source-id: dab8747b4712f5dba5644387817a3a0fda18b66a |
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