This will be the last disruptive functorch internals change.
Why are we moving these files?
- As a part of rationalizing functorch we are moving the code in
functorch/_src to torch/_functorch
- This is so that we can offer the functorch APIs as native PyTorch APIs
(coming soon) and resolve some internal build issues.
Why are we moving all of these files at once?
- It's better to break developers all at once rather than many times
Test Plan:
- wait for tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90091
Approved by: https://github.com/anijain2305, https://github.com/ezyang
- Add graph index to the profile information of the Inductor kernel for better debugability.
The generated code for different graphs could produce kernels with the same name. The side effect is that it is hard to identify the portion of E2E performance for these kernels because the profiler will aggregate the performance with the same kernel name regardless of different graphs. Hence, this PR added the graph index to the profile information to address this limitation.
- Label arbitrary code ranges for `eager` and `opt` modes for better debugability
The profile information of dynamo benchmarks mixes the eager mode and opt mode. It is hard to separate the range for different modes. This PR added eager and opt marks to the profile information to address this limitation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90008
Approved by: https://github.com/jgong5, https://github.com/jansel
This will be the last disruptive functorch internals change.
Why are we moving these files?
- As a part of rationalizing functorch we are moving the code in
functorch/_src to torch/_functorch
- This is so that we can offer the functorch APIs as native PyTorch APIs
(coming soon) and resolve some internal build issues.
Why are we moving all of these files at once?
- It's better to break developers all at once rather than many times
Test Plan:
- wait for tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88756
Approved by: https://github.com/ezyang
There was a lot of strangeness in how AOTAutograd backends were previously defined. This refactor replaces the strangeness with something simple and straightforward. The improvements:
- There is no longer a footgun aot_autograd "backend" which doesn't actually work. No more mistyping `torch._dynamo.optimize("aot_autograd")` when you meant "aot_eager"
- Deleted aot_print because it's annoying and anyway there's no uses of it
- Instead of having BOTH the backend Subgraph and AotAutogradStrategy, there is now only an aot_autograd function which takes the kwargs to configure AOTAutograd, and then gives you a compiler function that does AOTAutograd given those kwargs. Easy.
- The primary downside is that we are now eagerly populating all of the kwargs, and that can get us into import cycle shenanigans. Some cycles I resolved directly (e.g., we now no longer manually disable the forward function before passing it to aot_autograd; aot_autograd it does it for us), but for getting inductor decompositions I had to make it take a lambda so I could lazily populate the decomps later.
New code is 130 lines shorter!
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89736
Approved by: https://github.com/anjali411, https://github.com/albanD
