Create RFC for a logging system and list some required features

RFC-0026-logging-system.md

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+# PyTorch Logging System
+
+## **Summary**
+Create a new message logging system for PyTorch with the following requirements:
+
+* All errors, warnings, and other messages generated by PyTorch should do so
+  using the the logging system API
+
+* The APIs for emitting messages and changing settings should all be consistent
+  between C++ and Python
+
+* Offer different log levels (info, warning, error, ...)
+
+* Offer different classes of messages
+
+  - TODO: What are some of the message classes that we'll need?
+
+* Creating new message classes and log levels should be easy
+
+* Settings to turn warnings for a specific message class into errors
+
+* Settings to disable specific message classes and log levels
+
+  - TODO: However, I assume most errors should not be disableable
+
+* Settings to avoid emitting duplicate messages generated by multiple
+  `torch.distribted` ranks (related to issue
+  [#68768](https://github.com/pytorch/pytorch/issues/68768))
+
+* Ability to make a particular warning class or log level only warn once
+
+  - NOTE: Currently `TORCH_WARN_ONCE` does this in C++, but there is no Python
+    equivalent
+
+  - TODO: Should there be a setting to turn a warn-always into a warn-once for
+    a given message class?
+
+* Settings can be changed from Python, C++, or environment variables
+
+* Should integrate with Meta's internal logging system, which is
+  [glog](https://github.com/google/glog)
+
+  - TODO: What are all the requirements that definine "integrating with glog"
+
+* Must be OSS-friendly, so it shouldn't require libraries (like glog) which may
+  cause incompatibility issues for projects that use PyTorch
+
+
+## **Motivation**
+Original issue: [link](https://github.com/pytorch/pytorch/issues/72948)
+
+Currently, it is challenging for PyTorch developers to provide messages that
+act consistently between Python and C++.
+
+It is also challenging for PyTorch users to manage the messages that PyTorch
+emits. For instance, if a PyTorch user happens to be calling PyTorch functions
+that emit lots of warnings, it can be difficult for them to filter out those
+warnings so that their project's users don't get bombarded with warnings that
+they don't need to see.