https://github.com/pytorch/pytorch/issues/162858 The issue described the feature implemented.
This adds to the existing graph break log with the latest 20 (or viable user frame) bytecode instructions. The scenario is when the graph_break happens without errors. It happens during the case when user calling torch._dynamo.graph_break().
Meanwhile, in the testing, one can find that the generated frame based on step() is not deterministic as sometimes it reached the maximum amount, sometimes it generated the less than that. The bytecode generation is python version dependent. Thus, the testing plan excludes the bytecode output but generated the total bytecode line count.
This is a helpful process to understand bytecode transformation, symbolic convert, and convert frame. It is a helpful task to provide hands-on experience with dynamo workflow.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164422
Approved by: https://github.com/williamwen42, https://github.com/mlazos
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Before:
```
.Observed exception
Explanation: Dynamo found no exception handler at the top-level compiled function when encountering an exception. Exception will propagate outside the compiled region.
Hint: Dynamo has detected that tracing the code will result in an error when running in eager. Please double check that your code doesn't contain a similar error when actually running eager/uncompiled.
Hint: It may be possible to write Dynamo tracing rules for this code. Please report an issue to PyTorch if you encounter this graph break often and it is causing performance issues.
Developer debug context:
```
After:
```
Observed exception
Explanation: Dynamo found no exception handler at the top-level compiled function when encountering an exception. Exception will propagate outside the compiled region.
Hint: Dynamo has detected that tracing the code will result in an error when running in eager. Please double check that your code doesn't contain a similar error when actually running eager/uncompiled.
Hint: It may be possible to write Dynamo tracing rules for this code. Please report an issue to PyTorch if you encounter this graph break often and it is causing performance issues.
Developer debug context: raised exception TypeError([ConstantVariable(str: "unhashable type: <class 'torch._dynamo.variables.dicts.SetVariable'>")])
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158924
Approved by: https://github.com/williamwen42, https://github.com/zou3519
This should prevent bad resume function prologues from slipping by. In particular, graph breaks in resume function prologues will now hard error.
Implementation details:
- The resume function prologue is surrounded by `LOAD_CONST arg, STORE_FAST __is_tracing_resume_prologue` instructions. The first sequence has `arg=True` and the second sequence has `arg=False`.
- InstructionTranslator will know when it is tracing a resume function prologue when it detects `STORE_FAST __is_tracing_resume_prologue`. The top of stack will be True to mark the start of the prologue, False to mark the end.
- When `convert_frame.py` detects that an error occurred while the InstructionTranslator was tracing a resume function prologue, we will wrap the exception and hard error
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154564
Approved by: https://github.com/jansel
ghstack dependencies: #154283, #154289, #154782, #156762, #155166
This PR runs an automatic check as part of dynamo_wrapped to make sure that all unimplemented_v2() callsites are mapped to the JSON file. It also fixes the issue of the CI not able to expand the hints, which was the root cause of the previous workflow failure. If not, the dev gets a message giving them instructions on how to update the JSON file. I also updated a dynamic gb_type to static and updated its test_error_message to include the GBID link for the graph break (before the link would not be produced).
Testing:
I ran the file with the argument to ensure all cases were covered, and also tested the test in CI.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156237
Approved by: https://github.com/williamwen42
I had to create a new PR for this because of @atalman request of temporary reverting the previous PR to restore diff train sync. Nothing has changed from this PR and the original one.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156639
Approved by: https://github.com/atalman
This PR refers to the issue: https://github.com/pytorch/pytorch/issues/155352
This PR uses torch._dynamo.utils.warn_once so that this warning only emits once, clarifies in the warning that silent incorrectness is potential, not observed, Doesn't warn for functions that come from torch.*
As of right now with this code change the terminal outputs:
if the code came from torch.* :
Nothing, as we shouldn't warn for functions that come from torch.*
else:
/data/users/ssubbarao8/pytorch/torch/_dynamo/variables/functions.py:1565: UserWarning: Dynamo detected a call to a `functools.lru_cache`-wrapped function. Dynamo ignores the cache wrapper and directly traces the wrapped function. Silent incorrectness is only a *potential* risk, not something we have observed. Enable TORCH_LOGS="+dynamo" for a DEBUG stack trace.
torch._dynamo.utils.warn_once(msg)
If the user runs the command 'TORCH_LOGS="+dynamo" python foo4.py', in the debug logs it shows(this log below is based on chillee's repro:
/data/users/ssubbarao8/pytorch/torch/_dynamo/variables/functions.py:1565: UserWarning: Dynamo detected a call to a `functools.lru_cache`-wrapped function. Dynamo ignores the cache wrapper and directly traces the wrapped function. Silent incorrectness is only a *potential* risk, not something we have observed. Enable TORCH_LOGS="+dynamo" for a DEBUG stack trace.
torch._dynamo.utils.warn_once(msg)
V0619 21:00:16.504000 956424 torch/_dynamo/variables/functions.py:1575] [0/0] call to a lru_cache` wrapped function from user code at: /data/users/ssubbarao8/pytorch/foo4.py:9
V0619 21:00:16.504000 956424 torch/_dynamo/variables/functions.py:1575] [0/0] File "/data/users/ssubbarao8/pytorch/foo4.py", line 9, in <module>
V0619 21:00:16.504000 956424 torch/_dynamo/variables/functions.py:1575] [0/0] torch.compile(foo, backend="eager")(torch.randn(4))
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156463
Approved by: https://github.com/williamwen42
This PR includes the GBID weblink whenever a user encounters a graph break. I also had to include the JSON file in setup.py, so it can be part of the files that are packaged in during CI. It also fixes the issue of the hardcoded error messages stripping away one of the '/' in 'https'.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156033
Approved by: https://github.com/williamwen42
This PR includes the GBID weblink whenever a user encounters a graph break. I also had to include the JSON file in setup.py, so it can be part of the files that are packaged in during CI. It also fixes the issue of the hardcoded error messages stripping away one of the '/' in 'https'.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156033
Approved by: https://github.com/williamwen42
This should prevent bad resume function prologues from slipping by. In particular, graph breaks in resume function prologues will now hard error.
Implementation details:
- The resume function prologue is surrounded by `LOAD_CONST arg, STORE_FAST __is_tracing_resume_prologue` instructions. The first sequence has `arg=True` and the second sequence has `arg=False`.
- InstructionTranslator will know when it is tracing a resume function prologue when it detects `STORE_FAST __is_tracing_resume_prologue`. The top of stack will be True to mark the start of the prologue, False to mark the end.
- When `convert_frame.py` detects that an error occurred while the InstructionTranslator was tracing a resume function prologue, we will wrap the exception and hard error
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154564
Approved by: https://github.com/jansel
ghstack dependencies: #154283, #154289, #154782, #155166
This should prevent bad resume function prologues from slipping by. In particular, graph breaks in resume function prologues will now hard error.
Implementation details:
- The resume function prologue is surrounded by `LOAD_CONST arg, STORE_FAST __is_tracing_resume_prologue` instructions. The first sequence has `arg=True` and the second sequence has `arg=False`.
- InstructionTranslator will know when it is tracing a resume function prologue when it detects `STORE_FAST __is_tracing_resume_prologue`. The top of stack will be True to mark the start of the prologue, False to mark the end.
- When `convert_frame.py` detects that an error occurred while the InstructionTranslator was tracing a resume function prologue, we will wrap the exception and hard error
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154564
Approved by: https://github.com/jansel
ghstack dependencies: #154283, #154289, #154782, #155166
Implement traceable config patching for Dynamo: enables restricted patching of Dynamo config where user can use a context manager/decorator to change tracing behavior for parts of the code.
The new `dont_skip_tracing` decorator/context manager for ignoring most trace rules is easily implemented with this more generic traceable config patching feature.
Implementation:
- Create a new specialized context manager class representing a wrapper around torch._dynamo.config.patch
- Dynamo doesn't trace into the context manager but updates config at compile time
- Correctness is based on our correctness for handling supported context managers
- Implementation is inspired by how `GradModeVariable` is implemented.
Previous attempts: https://github.com/pytorch/pytorch/pull/148736 (decorator-only global approach) and https://github.com/pytorch/pytorch/pull/149439 (decorator-only traceback approach)
See https://docs.google.com/document/d/1vWNwKL_jpg-PLopifcaSa338wks3GqSVF4GHRguybGg/edit?tab=t.0 for more details on implementation - including previous approaches.
NOTE: this PR fixes a bug where skipped code objects were not tracked by convert_frame.py, leading to cases where code objects would be automatically skipped even after `torch._dynamo.reset()`. This exposed some latent dynamo-wrapped test failures in CI that previously passed in CI but not locally.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150586
Approved by: https://github.com/jansel, https://github.com/zou3519, https://github.com/anijain2305
Implement traceable config patching for Dynamo: enables restricted patching of Dynamo config where user can use a context manager/decorator to change tracing behavior for parts of the code.
The new `dont_skip_tracing` decorator/context manager for ignoring most trace rules is easily implemented with this more generic traceable config patching feature.
Implementation:
- Create a new specialized context manager class representing a wrapper around torch._dynamo.config.patch
- Dynamo doesn't trace into the context manager but updates config at compile time
- Correctness is based on our correctness for handling supported context managers
- Implementation is inspired by how `GradModeVariable` is implemented.
Previous attempts: https://github.com/pytorch/pytorch/pull/148736 (decorator-only global approach) and https://github.com/pytorch/pytorch/pull/149439 (decorator-only traceback approach)
See https://docs.google.com/document/d/1vWNwKL_jpg-PLopifcaSa338wks3GqSVF4GHRguybGg/edit?tab=t.0 for more details on implementation - including previous approaches.
NOTE: this PR fixes a bug where skipped code objects were not tracked by convert_frame.py, leading to cases where code objects would be automatically skipped even after `torch._dynamo.reset()`. This exposed some latent dynamo-wrapped test failures in CI that previously passed in CI but not locally.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150586
Approved by: https://github.com/jansel, https://github.com/zou3519, https://github.com/anijain2305
