Wrapper for users to insert constraints into model code.
The constraints will not be maintained in the graph after tracing through make_fx so retracing with dynamo/make_fx will not work. This will be supported after torch._assert supported is implemented. Then we can convert the constrain_range calls to torch._asserts.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98433
Approved by: https://github.com/avikchaudhuri, https://github.com/tugsbayasgalan
Wrapper for users to insert constraints into model code.
The constraints will not be maintained in the graph after tracing through make_fx so retracing with dynamo/make_fx will not work. This will be supported after torch._assert supported is implemented. Then we can convert the constrain_range calls to torch._asserts.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98433
Approved by: https://github.com/avikchaudhuri, https://github.com/tugsbayasgalan
Summary:
Replace _dynamo.config with an object instead of module
Current usage patterns of setting and reading fields on config will work
unchanged.
Only changes needed going forward:
1. import torch._dynamo.config will not work. However, just doing
import torch._dynamo is sufficient to access dynamo config
as torch._dynamo.config.
2. Files inside of _dynamo folder need to access config via
from torch._dynamo.config_util import config instead of
from torch._dynamo import config. Because _dynamo/__init__.py
imports some of the files so it would be circular import.
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96455
Approved by: https://github.com/williamwen42
Fix Meta internal use case:
* We are going to skip tracing ```torchrec.distributed```, however, in fbcode, the structure is a bit different from OSS torchrec.
* Meta internally uses ```torch.package```, so we should support skip tracing files like ```<torch_package_0>.torchrec/distributed/...```.
* We put the logic behind a flag ```is_fbcode``` to avoid misuse.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98192
Approved by: https://github.com/yf225
This was used to unblock Meta internal use cases, where ```torchrec.distributed``` was used, however, it can't be traced by dynamo properly right now.
We were sending the same fix(#90087) several months ago, but was reverted due to ```fbgemm``` conflicts. This PR catches ```Exception``` rather than ```ImportError``` which can handle the conflicts.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97824
Approved by: https://github.com/wconstab
This was used to unblock Meta internal use cases, where ```torchrec.distributed``` was used, however, it can't be traced by dynamo properly right now.
We were sending the same fix(#90087) several months ago, but was reverted due to ```fbgemm``` conflicts. This PR catches ```Exception``` rather than ```ImportError``` which can handle the conflicts.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97824
Approved by: https://github.com/wconstab
I applied some flake8 fixes and enabled checking for them in the linter. I also enabled some checks for my previous comprehensions PR.
This is a follow up to #94323 where I enable the flake8 checkers for the fixes I made and fix a few more of them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94601
Approved by: https://github.com/ezyang
This PR adds `@comptime`, a decorator that causes a given function to be executed at compile time when Dynamo is symbolically evaluating their program. To query the Dynamo state, we offer a public ComptimeContext API which provides a limited set of APIs for querying Dynamo's internal state. We intend for users to use this API and plan to keep it stable. Here are some things you can do with it:
* You want to breakpoint Dynamo compilation when it starts processing a particular line of user code: give comptime a function that calls breakpoint
* You want to manually induce a graph break for testing purposes; give comptime a function that calls unimplemented
* You want to perform a debug print, but you don't want to induce a graph break; give comptime a function that prints.
* You can print what the symbolic locals at a given point in time are.
* You can print out the partial graph the Dynamo had traced at this point.
* (My original motivating use case.) You want to add some facts to the shape env, so that a guard evaluation on an unbacked SymInt doesn't error with data-dependent. Even if you don't know what the final user API for this should be, with comptime you can hack out something quick and dirty. (This is not in this PR, as it depends on some other in flight PRs.)
Check out the tests to see examples of comptime in action.
In short, comptime is a very powerful debugging tool that lets you drop into Dynamo from user code, without having to manually jerry-rig pdb inside Dynamo to trigger after N calls.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90983
Approved by: https://github.com/jansel
Why we want to graph-break FSDP
- FSDP has communication ops during forward and backward which we currently can't trace into the graph but also want to ensure are overlapped with compute
- dynamo has issues tracing into or capturing a call to fsdp module without a break (see below)
How we graph-break on FSDP
- marking FSDP.forward code as skip means the code frames will graph-break; but in this case all of torch.* is listed in skipfiles.py anyway, so this is taken care of
- disallowing the FSDP module prevents dynamo trying to record a 'call_module(FSDPmodule)' node into a graph, which happens earlier than the graphbreak that would be caused by skip, and causes additional issues: dynamo deepcopies modules before call-module handling, and FSDP module isn't trivially deep-copyable
cc @jansel @lezcano @fdrocha @mlazos @soumith @voznesenskym @yanboliang @penguinwu @anijain2305
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87420
Approved by: https://github.com/aazzolini
