Adds a bool is_available() method to the backend contract (#53068)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53068
Adds a ```bool is_available()``` method to the backend contract: it returns ```true``` if ```compile()``` and ```execute()``` can be called; ```false``` otherwise.
It is used to implement the following changes in the ```LoweredModule```:
* ```compile()``` in ```__setstate__``` will run if ```is_available()```, else ```__setstate__``` throws an exception (“Backend not available.”).
* ```compile()``` at ```LoweredModule``` creation will run if ```is_available()```, else a WARNING will be thrown.
* ```execute()``` will only be executed if ```is_available()``` returns true; else throws an exception (“Backend not available.”).
The goal of these changes is to ensure we have a well defined behaviour for the different combinations of backend availability on-host and on-target.
More specifically, backends may have different capabilities to compile and/or execute the Module, depending whether this happens on-host (i.e. where the program is being written) or on-target (where the program is being executed).
First of all, we know that "preprocess" always takes place, and that only happens on-host at creation time. So, we can assume that any compilation is needed/possible on-host then all of it could be pushed here.
Overall, we want to ensure the following:
**On host**
| compile | execute | Outcome |
| -- | -- | -- |
| No | No | On module creation, LoweredModule is generated, with a warning (since compilation and execution can still take place on-target). On module load, throws an exception (since execution is not possible). |
| No | Yes | This configuration should not be possible. This assumes the full compiler is not available, even if some work was done in preprocess the program cannot be finalized for execution. |
| Yes | No | In this case, the expectation would be for is_available() to return false, and compilation logic to move into preprocess. |
| Yes | Yes | All good. This is the only case that is_available() should return true. |
**On target**
| compile | execute | Outcome |
| -- | -- | -- |
| No | No | Loading the LoweredModule throws an exception. Since execution is not possible. |
| No | Yes | Basically this is another instance of Yes/Yes: compilation per se may not be possible on device, which means compile() can be called without issue but it is a no-op, and thus is_available should return true. Consequently, loading the LoweredModule: Succeeds, if the preprocessed module is ready for execution. Fails with exception otherwise. |
| Yes | No | This configuration should not be possible. Just putting here for completeness. |
| Yes | Yes | All good. This, along with No/Yes case (because compilation is assumed to have happened on-host, so it's just another instance of Yes/Yes), are the cases where is_available() should return true. |
**Refactoring existing code**
This change also updates other backends (Glow) code, to implement the is_available() method to have the same behaviour as before this change (i.e. always available).
This should not cause backward incompatibilities with already saved models since we're adding a new method to the PyTorchBackendInterface.
Models saved with the old interface that didn't have is_available() will still find the other 2 methods in the bound object (i.e. compile and execute), and the saved LoweredModule logic will be the old one.
**Future**
We plan to use is_available() to implement support for fallback to the PyTorch interpreter.
ghstack-source-id: 123498571
Test Plan: Added C++ (test_backend.cpp) and Python (test_backends.py) tests to validate the exceptions.
Reviewed By: jackm321, spaugh, iseeyuan
Differential Revision: D26615833
fbshipit-source-id: 562e8b11db25784348b5f86bbc4179aedf15e0d3