# Why?
I want the following code to work.
minimal repro:
```
class M(torch.nn.Module):
def forward(self, dilate_flag):
return dilate_flag.item()
input1 = (torch.tensor([1], dtype=torch.bool, device="cuda"),)
model = M().cuda()
ep = torch.export.export(model, input1, strict=True)
path = torch._inductor.aot_compile(ep.module(), input1)
aot_model = torch._export.aot_load(path, device="cuda")
actual_output = aot_model(*input1)
```
error: AssertionError: Encountered an unsupported object of type <class 'torch.SymBool'> while writing the metadata for exported program
second error will be handled by https://github.com/pytorch/pytorch/pull/138760
# Motivation
I could technically bypass it with a torch.int tensor. However, it doesn't work with torch.cond. I want the following to work. It would also require https://github.com/pytorch/pytorch/pull/138760 for aot compile to work.
```
class M(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.dilate_flag = 0
def forward(self, dilate_flag):
self.dilate_flag = dilate_flag.item()
def true_fn(dilate_flag):
return dilate_flag.clone()
def false_fn(dilate_flag):
return dilate_flag.clone()
torch.cond(
self.dilate_flag,
true_fn,
false_fn,
(dilate_flag,),
)
return self.dilate_flag
input1 = (torch.tensor([1], dtype=torch.bool, device="cuda"),)
input2 = (torch.tensor([0], dtype=torch.bool, device="cuda"),)
inputs = (input1, input2)
model = M().cuda()
for input in inputs:
expected_output = model(*input)
ep = torch.export.export(model, input, strict=False)
path = torch._inductor.aot_compile(ep.module(), input)
aot_model = torch._export.aot_load(path, device="cuda")
actual_output = aot_model(*input)
assert (
expected_output == actual_output
), f"henry they are not equal {expected_output} != {actual_output}"
```
Differential Revision: D64867504
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138765
Approved by: https://github.com/ydwu4
Refactors construction of ExportGraphSignature object for export & training IR, explicitly creating AOTAutograd signature for training IR. This will be helpful for upcoming refactors for placeholder naming & runtime asserts prettifying.
Changes:
- dedups `make_argument_spec` call, moved to export/graph_signature.py
- `_sig_to_specs` wrapped into new function `_convert_to_export_graph_signature`, directly converts GraphSignature -> ExportGraphSignature
- `_make_fx_helper` explicitly creates AOTAutograd GraphSignature object
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134059
Approved by: https://github.com/angelayi, https://github.com/ydwu4
Summary:
Dynamo doesn't track whether buffers are `persistent`. This led to some ugly code where we would mark buffers as always persistent when creating signatures, then later check whether the buffers were not in the state dict to infer whether they were non-persistent, and use this to fix up the signature.
This PR instead defines a utility to look up all the non-persistent buffers registered inside a module (this information is recorded in a private `_non_persistent_buffers_set` module attribute), and uses it to (a) correctly set the persistent flag on buffers when creating signatures (b) transfer this information to a Dynamo-traced graph module, which then causes non-persistent buffers to (correctly) not show up in the state dict.
Test Plan: existing tests + new case with non-persistent buffer in nested module
Differential Revision: D60224656
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131756
Approved by: https://github.com/zhxchen17, https://github.com/ydwu4
This PR does two things:
1. it duplicates the fake script object because aot_export trace the program twice. The result of tracing in the first time would cause the tracing result of second time be wrong.
2. Also add a new test for methods that return constant outputs. Before the PR, there's is no meta["val"] for these nodes because fx won't track these constants. We still need to preserve these constant return operators in the graph because torchbind objects are stateful and deleting it would remove the implicit state mutation inside of the object.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128844
Approved by: https://github.com/angelayi
Summary: When I was debugging an issue, this silent error makes the debugging harder. It is better to error earlier with more descriptive error message.
Test Plan: None
Differential Revision: D56312433
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124411
Approved by: https://github.com/zhxchen17
Summary:
note: breaking the original diff D55225818 into 3 parts (top-level renaming, higher-order-op subgraphs, constant input de/serialization) because of its size.
Stacked PR to restore original names to placeholder nodes, replacing the default names arg0_1, arg1_1, ...
This PR supports constant argument placeholder (e.g. forward(self, x, y=1)) names and de/serialization, by adding a name field for ConstantArguments in the graph signature, and ConstantInputSpec in the input specs for serialization.
Test Plan: verification checks on placeholder names for all export() calls, unit test in test/export/test_export.py
Differential Revision: D55506949
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123590
Approved by: https://github.com/angelayi, https://github.com/zhxchen17
Summary:
X-link: https://github.com/pytorch/executorch/pull/1817
Basic support for non-persistent buffers, which are buffers that do not show up in the state dict.
One weird twist is that most of our other systems (FX, aot_export, dynamo) have completely buggy handling of non-persistent buffers. I tried to go on a wild goose chase to fix them all, but it got to be too much. So I introduced some sad rewrite passes in `_export` make the final state dict correctly align with the original module's state dict.
This exposed some bugs/ambiguous handling of parameters/buffers in existing test code. For example, `TestSaveLoad.test_save_buffer` traced over a module that was not in the root module hierarchy and caused some weird behavior. I think we should error explicitly on use cases like this: https://github.com/pytorch/pytorch/issues/118410. For now I just rewrote the tests or skipped them.
As a side effect, this diff tightened up quite a few sloppy behaviors around state dict handling:
- Tensor attributes were getting promoted to be buffers—bad!
- Tracing through a module not in the children of the root module would add its parameters/buffers to the state dict—bad!
This behavior is unlikely to show up in user code since the model would be totally broken, but did show up in a bunch of tests.
#buildmore
Test Plan:
unit tests
sandcastle
Differential Revision: D53340041
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118969
Approved by: https://github.com/guangy10, https://github.com/huydhn, https://github.com/titaiwangms
Summary:
X-link: https://github.com/pytorch/executorch/pull/1769
Basic support for non-persistent buffers, which are buffers that do not show up in the state dict.
One weird twist is that most of our other systems (FX, aot_export, dynamo) have completely buggy handling of non-persistent buffers. I tried to go on a wild goose chase to fix them all, but it got to be too much. So I introduced some sad rewrite passes in `_export` make the final state dict correctly align with the original module's state dict.
This exposed some bugs/ambiguous handling of parameters/buffers in existing test code. For example, `TestSaveLoad.test_save_buffer` traced over a module that was not in the root module hierarchy and caused some weird behavior. I think we should error explicitly on use cases like this: https://github.com/pytorch/pytorch/issues/118410. For now I just rewrote the tests or skipped them.
Test Plan: added a unit test
Differential Revision: D53253905
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118722
Approved by: https://github.com/SherlockNoMad, https://github.com/angelayi
Summary:
Class FQN is needed when unpacking CustomObj instance.
For all other Arguments, e.g. Tensor, TensorList, SymInt, we always know their exact type. However, CustomObjArgument had an opaque type.
Adding this field also helps unveiling the type of this opaque object.
Test Plan: CI
Differential Revision: D53029847
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118158
Approved by: https://github.com/zhxchen17
Summary: Adding an experimental API to FX graph module to place "hooks" every time when we are changing or replacing nodes in a graph, so that we can properly update the new name in graph signature and potentially other places.
Test Plan:
buck test mode/opt -c fbcode.enable_gpu_sections=true caffe2/test/distributed/_tensor/experimental:tp_transform
buck test mode/opt caffe2/test:test_export -- -r test_replace_hook
Differential Revision: D52896531
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117825
Approved by: https://github.com/avikchaudhuri
This PR adds the bare minimum functionality to get torchbind working in an e2e testable way on PT2.
It implements:
* ProxyTensor support
* Simple torch.export support (proxytensor-only path, e.g. non-strict).
* add some tests exercising the path.
Because all this is not fully baked, I hide the functionality behind a feature flag (`enable_torchbind_tracing()`) so it does not affect regular users for now.
Still on the agenda:
* Dynamo support
* Actual FakeMode support
* Mutability support
Hoping to get this first bit in as a standalone, as it will unblock some more extensive experimentation/testing going on internally.
Differential Revision: [D51825372](https://our.internmc.facebook.com/intern/diff/D51825372/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117697
Approved by: https://github.com/SherlockNoMad
