Fixes#165911
- Add message to Attribute error so we see ` Developer debug context: raised exception AttributeError(["'Linear' object has no attribute 'w'"])` instead of just `Developer debug context: raised exception AttributeError([])`
- Add stack trace in `ObservedException` so we display the inner most error stack trace back to user code
Output:
```
/data/users/shangdiy/pytorch/torch/__init__.py:2641: UserWarning: You are calling torch.compile inside torch.export region. To capture an useful graph, we will implicitly switch to torch.compile(backend=eager)
warnings.warn(
Traceback (most recent call last):
File "/data/users/shangdiy/pytorch/torch/_dynamo/variables/user_defined.py", line 1385, in var_getattr
subobj = self._getattr_static(name)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/data/users/shangdiy/pytorch/torch/_dynamo/variables/user_defined.py", line 1256, in _getattr_static
subobj = type(self.value).__getattribute__(self.value, name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Linear' object has no attribute 'w'
During handling of the above exception, another exception occurred:
torch._dynamo.exc.ObservedAttributeError: 'Linear' object has no attribute 'w'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/users/shangdiy/pytorch/test.py", line 34, in <module>
mod = torch._dynamo.functional_export._dynamo_graph_capture_for_export(Model())(x)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/data/users/shangdiy/pytorch/torch/_dynamo/functional_export.py", line 481, in inner
out = fullgraph_capture(
^^^^^^^^^^^^^^^^^^
File "/data/users/shangdiy/pytorch/torch/_dynamo/convert_frame.py", line 1053, in fullgraph_capture
return _fullgraph_capture_frame(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/data/users/shangdiy/pytorch/torch/_dynamo/convert_frame.py", line 1115, in _fullgraph_capture_frame
raise e.with_traceback(None) from e.__cause__ # User compiler error
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
torch._dynamo.exc.Unsupported: 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 AttributeError(["'Linear' object has no attribute 'w'"])
For more details about this graph break, please visit: https://meta-pytorch.github.io/compile-graph-break-site/gb/gb0088.html
from user code:
File "/data/users/shangdiy/pytorch/torch/_dynamo/functional_export.py", line 171, in forward
res = self._export_root(*args, **kwargs)
File "/data/users/shangdiy/pytorch/test.py", line 31, in forward
weight = self.linear.w
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165930
Approved by: https://github.com/anijain2305
Reapply of https://github.com/pytorch/pytorch/pull/163260
AOTI utils expect free function sometimes so adjust export API to handle that, haven't seen any methods getting exported. Some AOTI flows also require we populate dynamo_flat_name_to_original_fqn so i just copy how it is done in eval_frame.py. I also cleaned up how we get rid of export_root and fixed some overcomplicated nn_module_stack handling in export code. The logic is simpler now thanks to @anijain2305 .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165582
Approved by: https://github.com/anijain2305
Three fixes:
1. When doing t[u0] +=1 if u0 is unbacked we could allocate a new unbacked symbol during the the indexing of t[u0] (when we fake trace setitem), namely because meta_select does allocate a new unbacked symbol for the storage offset when we do not know if u0>=0 or u0<0. but the output size/stride of setitem(), does not depend on that new symbol. it's self consumed in setitem so we shall ignore it.
2. Also when we trace through generalized_scatter the applications of the views could allocate unbacked symints
but those do not effect final output, we also shall ignore them.
3.Before accessing strides in lowering we shall materialize.
Address https://github.com/pytorch/pytorch/issues/114293 and https://github.com/pytorch/pytorch/issues/131911
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164341
Approved by: https://github.com/bobrenjc93
Eager AC/SAC reapplies the mutations (like global dict mutations) in the backward during the recomputation of forward. torch.compile has no easy way to reapply python mutations in the backward. But many users might be ok to skip reapplication of side effects in the backward. They can set this config flag to accept this eager and compile divergence.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165775
Approved by: https://github.com/zou3519
ghstack dependencies: #165734
Creates the fork/join stream ops. These ops are passthrough ops which mutate all of their args (without actually performing any computation on them) so that during functionalization, implicit dependencies are added on all of their args. This allows us to prevent reordering during our pre/post grad graph passes.
Make custom ops inplace
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162900
Approved by: https://github.com/anijain2305
ghstack dependencies: #163027, #162899, #163028
Stores streams in a global object look table that maps a dynamo selected index to objects. This index is generated during tracing, and at runtime, a helper function is called from the bytecode to populate this map.
This differs from the previous implementation that simply mapped IDs to the associated objects. This required specialization on the IDs of the specific objects, while this new approach does not.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162899
Approved by: https://github.com/anijain2305
ghstack dependencies: #163027
This is follow-up of #165037. It generally recommended to use `is/is not` to compare types. Therefore this series of changes apply this suggestion in the code base, and it aims to finally enabling related linter checks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165142
Approved by: https://github.com/albanD
It generally recommended to use `is/is not` to compare types. Therefore this series of changes apply this suggestion in the code base, and it aims to finally enabling related linter checks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165037
Approved by: https://github.com/mlazos
Reviewed GPT5 summary:
**Summary / Goal**
Improve error reporting when local_map subgraph input/output counts mismatch placement info.
**Details**
- Adds descriptive runtime error messages.
**Motivation**
Helps debug local_map misalignments.
```python
AssertionError: Expecting 2 inputs to local_map function based on placements, but found 1. If the count matches for eager, Dynamo may have flattened inputs to the function or found additional tensors used via closures. Please adjust the input placements to match what the traced graph sees:
class GraphModule(torch.nn.Module):
def forward(self, l_args_0_: "f32[8, 8, 16]"):
# File: /home/xmfan/core/a/pytorch/test/higher_order_ops/test_local_map.py:523 in mismatch_input, code: return x + scalar, scalar
child: "f32[8, 8, 16]" = l_args_0_ + 10; l_args_0_ = None
return (child,)
.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164321
Approved by: https://github.com/ezyang, https://github.com/mlazos
ghstack dependencies: #164296
In https://github.com/pytorch/pytorch/pull/106824, export decided to slow-path for MultiHeadAttention module (look into the PR description as to why). But that PR eventually caused a divergence between Dynamo and export.
Today, strict-export does not inline into builtin modules (like MultiHeadAttention), and therefore make_fx sees the original nn.Module and takes the slow path. But compile inlines into the nn module, and at this time the condition `_is_make_fx_tracing` is False. As a result, Dynamo takes a fast path, resulting in a different op being called.
This divergence is undesirable. There are 2 ways to fix it
1) Make export take the fast path - As explained in the https://github.com/pytorch/pytorch/pull/106824 , this might be difficult. So, we go to (2)
2) Make compile as well take the slow path - This is easy to implement. The con here is that Pytorch eager and compile will use different operators, which can cause numerics issues etc.
Since (2) is easy to do, we will follow this path. We are tracking the issue in https://github.com/pytorch/pytorch/issues/164062
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164721
Approved by: https://github.com/avikchaudhuri, https://github.com/tugsbayasgalan
Builds on top of https://github.com/pytorch/pytorch/pull/163673 and https://github.com/pytorch/pytorch/pull/164174. This will be used in the followup PRs to apply regional inductor compilation.
The existing implementation let Dynamo trace into the `torch.fx.traceback.annotate`, but thats not what we want. We want Dynamo to essentially run the torch.fx.traceback.annotate function in eager, so that every Fx node created in Dynamo Fx graph has the custom meta node.
What does not work?
* We still have to set the context manager `torch.fx.traceback.preserve_node_meta()` in the user code because CI was unhappy. This can be fixed but with some perseverance.
* This does not work with graph breaks yet. But we can solve that problem, if needed, in a separate PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164678
Approved by: https://github.com/SherlockNoMad, https://github.com/jansel, https://github.com/xmfan
Turns out codegen'ing a nested step graph break is significantly more complicated than first thought. The optimized function should actually do:
- call graph/load values/do side effects etc.
- call into the leaf's resume function, but skipped (this essentially step graph break function for just the leaf function)
- call into all the other resume functions, traced.
This PR also adds `torch._dynamo.step_unsupported()`, which can be used for internal testing purposes to better test step graph break handling.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162737
Approved by: https://github.com/Lucaskabela
ghstack dependencies: #160601
