Commit Graph

4 Commits

Author SHA1 Message Date
Edward Z. Yang
76163a56c0 Refactor stack handling to always use TracingContext to populate real stack on exception (#106277)
The basic gist of the PR is simple, but it's accompanied with some careful modifications and unit tests to make sure I got it right. Check inline comments for more details.

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/106277
Approved by: https://github.com/albanD, https://github.com/voznesenskym
2023-08-02 00:09:16 +00:00
Yanbo Liang
4c73016ff2 [Dynamo] Enable torch._dynamo.config.suppress_errors by default (#105307)
Summary:
We are working toward full model compilation, where when compilation error happens, we just fall back to eager mode rather than error out.
But at the same time, we should fix these issues if they are bugs. We will:
* 1/ log warnings in OSS;
* 2/ log warnings and write them into Scuba in fbcode;

to prevent us from ignoring these issues.

Test Plan: Manual test

Differential Revision: D47506314

Pull Request resolved: https://github.com/pytorch/pytorch/pull/105307
Approved by: https://github.com/jansel
2023-07-21 19:17:46 +00:00
Michael Lazos
3a400a5adc Enable passing a dict of module names: log level to set_logs python api (#98989)
Adds "module" kwarg to set_logs to allow a user to pass a dict of module qualified names to log level to the API.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/98989
Approved by: https://github.com/ezyang
2023-04-13 09:42:32 +00:00
Michael Lazos
a1c46e5f8f component-level configurable logging for dynamo, inductor, aot (#94858)
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
2023-03-18 04:17:31 +00:00