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 message logging system for PyTorch with the following requirements:
+
+* All errors, warnings, and other messages generated by PyTorch should be
+  emitted using the the logging system API
+
+* The APIs for emitting messages and changing settings should all be consistent
+  between C++ and Python
+
+* Offer different message severity levels, including at least the following:
+
+  - **Info**: Emits a message without creating a warning or error. By default,
+    this gets printed to stdout
+
+  - **Warning**: Emits a message as a warning. By default, this will turn into
+    a Python warning
+
+  - **Error**: Emits a message as an error. By default, this will turn into
+    a Python error
+
+  - TODO: Should we also have a **Fatal** severity for integration with
+    Meta's internal logging system? A fatal message terminates the program
+
+* Offer different classes of messages, including at least the following:
+
+  - **Default**: A catch-all message class
+
+  - **Nondeterministic**: Emitted when `torch.use_deterministic_algorithms(True)`
+    is set and a nondeterministic operation is called
+
+  - **Deprecated**: Emitted when a deprecated function is called
+
+  - **Beta**: Emitted when a beta feature is called. See
+    [PyTorch feature classifications](https://pytorch.org/blog/pytorch-feature-classification-changes/)
+
+  - **Prototype**: Emitted when a prototype feature is called. See
+    [PyTorch feature classifications](https://pytorch.org/blog/pytorch-feature-classification-changes/)
+
+* Continue using warning/error APIs that currently exist in PyTorch wherever
+  possible. For instance, `TORCH_CHECK`, `TORCH_WARN`, and `TORCH_WARN_ONCE`
+  should continue to be used in C++
+
+  - NOTE: These existing APIs don't currently have a concept of message classes,
+    so that will need to be added
+
+* Creating new message classes and severity levels should be easy
+
+* Settings to turn warnings for a specific message class into errors
+
+* Settings to disable specific message classes and severity levels
+
+  - TODO: However, most errors should not be disableable, right? Perhaps only
+    some message classes should allow disabling or downgrading errors. For
+    instance, currently in PyTorch, we can downgrade a nondeterministic error
+    to a warning, but we wouldn't want to downgrade an error from invalid
+    arguments given to an operation.
+
+* 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 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 and vice versa?
+
+  - TODO: Should warn-once be its own separate severity level?
+
+* Settings can be changed from Python, C++, or environment variables
+
+  - Filtering warnings with Python command line arguments should
+    remain possible. For instance, the following turns a `DeprecationWarning`
+    into an error: `python -W error::DeprecationWarning your_script.py`
+
+* Should integrate with Meta's internal logging system, which is
+  [glog](https://github.com/google/glog)
+
+  - TODO: What are all the requirements that define "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
+
+* TODO: Determine the requirements for the following concepts:
+
+  - Log files (default behavior and any settings)
+
+
+## **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.