Summary -
`disallow_in_graph` is mostly useful for backends. Suppose, your backend does not support `torch.abs()`. So, you can use `disallow_in_graph` to do a graph break.
The assumption in the above statement is that `disallow_in_graph` is called on an `allowed` callable. `allowed` in Dynamo language refers to a callable that is put as-is in the Dynamo graph.
Therefore, if one uses `disallow_in_graph` on some non-torch non-allowed function, we want to raise an exception to tell user that they probably want something else.
* If they want to disable Dynamo - they should use torch._dynamo.disable
* If they wanted to stop inlining - they should use torch._dynamo.graph_break. However this is not a decorator. So, we need to provide another API. But, the question - who would want to do this?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98892
Approved by: https://github.com/jansel
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
Summary:
Adds NNC-like logging that is configured through an env var `TORCH_COMPILE_LOGS`
Examples:
`TORCH_LOGS="dynamo,guards" python script.py` - prints dynamo logs at level INFO with guards of all functions that are compiled
`TORCH_LOGS="+dynamo,guards,graph" python script.py` - prints dynamo logs at level DEBUG with guards and graphs (in tabular) format of all graphs that are compiled
[More examples with full output](https://gist.github.com/mlazos/b17f474457308ce15e88c91721ac1cce)
Implementation:
The implementation parses the log settings from the environment, finds any components (aot, dynamo, inductor) or other loggable objects (guards, graph, etc.) and generates a log_state object. This object contains all of the enabled artifacts, and a qualified log name -> level mapping. _init_logs then adds handlers to the highest level logs (the registered logs), and sets any artifact loggers to level DEBUG if the artifact is enabled.
Note: set_logs is an alternative for manipulating the log_state, but if the environment contains TORCH_LOGS, the environment settings will be prioritized.
Adding a new log:
To add a new log, a dev should add their log name to torch._logging._registrations (there are examples there already).
Adding a new artifact:
To add a new artifact, a dev should add their artifact name to torch._logging._registrations as well.
Additionally, wherever the artifact is logged, `torch._logging.getArtifactLogger(__name__, <artifact_name>)` should be used instead of the standard logging implementation.
[design doc](https://docs.google.com/document/d/1ZRfTWKa8eaPq1AxaiHrq4ASTPouzzlPiuquSBEJYwS8/edit#)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94858
Approved by: https://github.com/ezyang
Summary: Makes the debug dir location configurable with TORCH_COMPILE_DEBUG_DIR env var
Test Plan: TORCH_COMPILE_DEBUG_DIR=”.” buck2 run mode/dev-nosan //caffe2/test/inductor:minifier_smoke
Reviewed By: bertmaher
Differential Revision: D43639955
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96089
Approved by: https://github.com/bertmaher
I noticed that a lot of bugs are being suppressed by torchdynamo's default
error suppression, and worse yet, there's no way to unsuppress them. After
discussion with voz and soumith, we decided that we will unify error suppression
into a single option (suppress_errors) and default suppression to False.
If your model used to work and no longer works, try TORCHDYNAMO_SUPPRESS_ERRORS=1
to bring back the old suppression behavior.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
cc @jansel @lezcano @fdrocha @mlazos @soumith @voznesenskym @yanboliang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87440
Approved by: https://github.com/voznesenskym, https://github.com/albanD
