Summary:
Follow-up of https://github.com/pytorch/pytorch/issues/46461 with a similar goal
Makes them more readable and possibly faster. Care has to be taken because `map` applies the function immediately while `(x for x in xs)` is a generator expression which gets evaluated later. This is a benefit in some cases where it is not required to actually create the list of values in memory (e.g. when passing to `tuple` or `extend` or `join`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46462
Reviewed By: zou3519
Differential Revision: D24422343
Pulled By: ezyang
fbshipit-source-id: 252e33499c92ac0b15238f2df32681dbbda2b237
Summary:
Retake on https://github.com/pytorch/pytorch/issues/40493 after all the feedback from albanD
This PR implements the generic Lazy mechanism and a sample `LazyLinear` layer with the `UninitializedParameter`.
The main differences with the previous PR are two;
Now `torch.nn.Module` remains untouched.
We don't require an explicit initialization or a dummy forward pass before starting the training or inference of the actual module. Making this much simpler to use from the user side.
As we discussed offline, there was the suggestion of not using a mixin, but changing the `__class__` attribute of `LazyLinear` to become `Linear` once it's completely initialized. While this can be useful, by the time being we need `LazyLinear` to be a `torch.nn.Module` subclass since there are many checks that rely on the modules being instances of `torch.nn.Module`.
This can cause problems when we create complex modules such as
```
class MyNetwork(torch.nn.Module):
def __init__(self):
super(MyNetwork, self).__init__()
self.conv = torch.nn.Conv2d(20, 4, 2)
self.linear = torch.nn.LazyLinear(10)
def forward(self, x):
y = self.conv(x).clamp(min=0)
return self.linear(y)
```
Here, when the __setattr__ function is called at the time LazyLinear is registered, it won't be added to the child modules of `MyNetwork`, so we have to manually do it later, but currently there is no way to do such thing as we can't access the parent module from LazyLinear once it becomes the Linear module. (We can add a workaround to this if needed).
TODO:
Add convolutions once the design is OK
Fix docstrings
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44538
Reviewed By: ngimel
Differential Revision: D24162854
Pulled By: albanD
fbshipit-source-id: 6d58dfe5d43bfb05b6ee506e266db3cf4b885f0c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45940
**Summary**
In `try_ann_to_type`, if an annotation has an attribute named
`__torch_script_class__`, it is assumed to be a TorchScript class that
has already been scripted. However, if it is a class that extends
another class, this code path causes a crash because it looks up the
JIT type for the class by name in the compilation unit. This JIT type
obviously cannot exist because inheritance is not supported.
This commit fixes this by looking up the qualified name of a class
in torch.jit._state._script_class in order to ascertain whether it has
already been scripted (instead of looking for a `__torch_script_class__`
attribute on the class object.
**Test Plan**
This commit adds a unit test consisting of the code sample from the
issue that reported this problem.
**Fixes**
This commit fixes#45860.
Test Plan: Imported from OSS
Reviewed By: anjali411
Differential Revision: D24310027
Pulled By: SplitInfinity
fbshipit-source-id: 9f8225f3316fd50738d98e3544bf5562b16425b6
Summary:
The record_stream method was hard coded for CUDA device. Define the record_stream in the native_functions.yaml to enable the dynamic dispatch to different end device.
Fixes https://github.com/pytorch/pytorch/issues/36556
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44301
Reviewed By: glaringlee
Differential Revision: D23763954
Pulled By: ezyang
fbshipit-source-id: e6d24f5e7892b56101fa858a6cad2abc5cdc4293
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45908
As per subj, existing logging does not explain the cause of the error
Test Plan: unit tests pass.
Reviewed By: SplitInfinity
Differential Revision: D23609965
fbshipit-source-id: 818965176f7193c62035e3d2f0547bb525fea0fb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45262
**Summary**
This commit adds an API for ignoring arbitrary module attributes during
scripting. A class attribute named `ignored_attributes` containing names
of attributes to ignore can be added to the class of the instance being
scripted. Attributes ignored in this fashion cannot be used in
`forward`, methods used by `forward` or by `exported` methods. They
are, however, copied to the `RecursiveScriptModule` wrapper and can be
used by `ignored` methods and regular Python code.
**Test Plan**
This commit adds unit tests to `TestScriptPy3` to test this new API.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D23971882
Pulled By: SplitInfinity
fbshipit-source-id: 8c81fb415fde7b78aa2f87e5d83a477e876a7cc3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45261
**Summary**
This commit enables `unused` syntax for ignoring
properties. Inoring properties is more intuitive with this feature enabled.
`ignore` is not supported because class type properties cannot be
executed in Python (because they exist only as TorchScript types) like
an `ignored` function and module properties that cannot be scripted
are not added to the `ScriptModule` wrapper so that they
may execute in Python.
**Test Plan**
This commit updates the existing unit tests for class type and module
properties to test properties ignored using `unused`.
Test Plan: Imported from OSS
Reviewed By: navahgar, Krovatkin, mannatsingh
Differential Revision: D23971881
Pulled By: SplitInfinity
fbshipit-source-id: 8d3cc1bbede7753d6b6f416619e4660c56311d33
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43872
This PR allows the recursive scripting to have a separate
submodule_stubs_fn to create its submodule with specific user provided
rules.
Fixes https://github.com/pytorch/pytorch/issues/43729
Test Plan: Imported from OSS
Reviewed By: suo
Differential Revision: D23430176
Pulled By: wanchaol
fbshipit-source-id: 20530d7891ac3345b36f1ed813dc9c650b28d27a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45098
**Summary**
This commit adds support for default arguments in methods of class
types. Similar to how default arguments are supported for regular
script functions and methods on scripted modules, default values are
retrieved from the definition of a TorchScript class in Python as Python
objects, converted to IValues, and then attached to the schemas of
already compiled class methods.
**Test Plan**
This commit adds a set of new tests to TestClassType to test default
arguments.
**Fixes**
This commit fixes#42562.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D23844769
Pulled By: SplitInfinity
fbshipit-source-id: ceedff7703bf9ede8bd07b3abcb44a0f654936bd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42390
**Summary**
This commit extends support for properties to include
ScriptModules.
**Test Plan**
This commit adds a unit test that has a ScriptModule with
a user-defined property.
`python test/test_jit_py3.py TestScriptPy3.test_module_properties`
Test Plan: Imported from OSS
Reviewed By: eellison, mannatsingh
Differential Revision: D22880298
Pulled By: SplitInfinity
fbshipit-source-id: 74f6cb80f716084339e2151ca25092b6341a1560
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44226
**Summary**
At present, the `share_types` argument to `create_script_module` is used
to decide whether to reuse a previously created type for a top-level
module that has not yet been compiled. However, that setting does not apply
to the compilation of submodules of the top-level module; types are
still reused if possible.
This commit modifies `create_script_module` so that the `share_types`
flag is honoured during submodule compilation as well.
**Test Plan**
This commit adds a unit test to `TestTypeSharing` that checks that
submodule types are not shared or reused when `share_types` is set to
`False`.
**Fixes**
This commit fixes#43605.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D23602371
Pulled By: SplitInfinity
fbshipit-source-id: b909b8b6abbe3b4cb9be8319ac263ade90e83bd3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44233
**Summary**
By default, scripting tries to share concrete and JIT types across
compilations. However, this can lead to incorrect results if a module
extends `torch.jit.ScriptModule`, and injects instance variables into
methods defined using `define`.
This commit detects when this has happened and disables type sharing
for the compilation of the module that uses `define` in `__init__`.
**Test Plan**
This commit adds a test to TestTypeSharing that tests this scenario.
**Fixes**
This commit fixes#43580.
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D23553870
Pulled By: SplitInfinity
fbshipit-source-id: d756e87fcf239befa0012998ce29eeb25728d3e1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44054
**Summary**
This commit improves the error message that is printed when an
`Optional` type annotation with an unsupported contained type is
encountered. At present, the `Optional` is printed as-is, and
`Optional[T]` is syntatic sugar for `Union[T, None]`, so that is what
shows up in the error message and can be confusing. This commit modifies
the error message so that it prints `T` instead of `Union[T, None]`.
**Test Plan**
Continuous integration.
Example of old message:
```
AssertionError: Unsupported annotation typing.Union[typing.List, NoneType] could not be resolved.
```
Example of new message:
```
AssertionError: Unsupported annotation typing.Union[typing.List, NoneType] could not be resolved because typing.List could not be resolved.
```
**Fixes**
This commit fixes#42859.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D23490365
Pulled By: SplitInfinity
fbshipit-source-id: 2aa9233718e78cf1ba3501ae11f5c6f0089e29cd
Summary:
In case we want to store binary files using `ScriptModule.save(..., _extra_files=...)` functionality. With python3 we can just use bytes only and not bother about it.
I had to do a copy-pasta from pybind sources, maybe we should upstream it, but it'd mean adding a bunch of template arguments to `bind_map` which is a bind untidy.
Let me know if there's a better place to park this function (it seems to be the only invocation of `bind_map` so I put it in the same file)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43241
Reviewed By: zdevito
Differential Revision: D23205244
Pulled By: dzhulgakov
fbshipit-source-id: 8f291eb4294945fe1c581c620d48ba2e81b3dd9c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41371
**Summary**
This commit enables the use of `torch.no_grad()` in a with item of a
with statement within JIT. Note that the use of this context manager as
a decorator is not supported.
**Test Plan**
This commit adds a test case to the existing with statements tests for
`torch.no_grad()`.
**Fixes**
This commit fixes#40259.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D22649519
Pulled By: SplitInfinity
fbshipit-source-id: 7fa675d04835377666dfd0ca4e6bc393dc541ab9
Summary:
- `torch._VF` is a hack to work around the lack of support for `torch.functional` in the JIT
- that hack hides `torch._VF` functions from Mypy
- could be worked around by re-introducing a stub file for `torch.functional`, but that's undesirable
- so instead try to make both happy at the same time: the type ignore comments are needed for Mypy, and don't seem to affect the JIT after excluding them from the `get_type_line()` logic
Encountered this issue while trying to make `mypy` run on `torch/functional.py` in gh-43446.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43454
Reviewed By: glaringlee
Differential Revision: D23305579
Pulled By: malfet
fbshipit-source-id: 50e490693c1e53054927b57fd9acc7dca57e88ca
Summary:
[Re-review tips: nothing changed other than a type in python_ir.cpp to fix a windows build failure]
Adds code printing for enum type
Enhance enum type to include all contained enum names and values
Adds code parsing for enum type in deserialization
Enabled serialization/deserialization test in most TestCases. (With a few dangling issues to be addressed in later PRs to avoid this PR grows too large)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43460
Reviewed By: albanD
Differential Revision: D23284929
Pulled By: gmagogsfm
fbshipit-source-id: e3e81d6106f18b7337ac3ff5cd1eeaff854904f3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42963
* Adds code printing for enum type
* Enhance enum type to include all contained enum names and values
* Adds code parsing for enum type in deserialization
* Enabled serialization/deserialization test in most TestCases. (With a few dangling issues to be addressed in later PRs to avoid this PR grows too large)
Test Plan: Imported from OSS
Reviewed By: SplitInfinity
Differential Revision: D23223281
Pulled By: gmagogsfm
fbshipit-source-id: 716d1866b7770dfb7bd8515548cfe7dc4c4585f7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43093
without this it's hard to tell which module is going wrong
Test Plan:
```
> TypeError:
> 'numpy.int64' object in attribute 'Linear.in_features' is not a valid constant.
> Valid constants are:
> 1. a nn.ModuleList
> 2. a value of type {bool, float, int, str, NoneType, torch.device, torch.layout, torch.dtype}
> 3. a list or tuple of (2)
```
Reviewed By: eellison
Differential Revision: D23148516
fbshipit-source-id: b86296cdeb7b47c9fd69b5cfa479914c58ef02e6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42389
**Summary**
This commit adds support for properties to TorchScript classes,
specifically for getters and setters. They are implemented essentially
as pointers to the methods that the corresponding decorators decorate,
which are treated like regular class methods. Deleters for properties
are considered to be out of scope (and probably useless for TorchScript
anyway).
**Test Plan**
This commit adds a unit test for a class with a property that has both
getter and setter and one that has only a getter.
`python test/test_jit.py TestClassType.test_properties`
Test Plan: Imported from OSS
Reviewed By: eellison, ppwwyyxx
Differential Revision: D22880232
Pulled By: SplitInfinity
fbshipit-source-id: 4828640f4234cb3b0d4f3da4872a75fbf519e5b0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42611
**Summary**
This commit modifies the Python frontend to ignore static functions on
Torchscript classes when compiling them. They are currently included
along with methods, which causes the first argument of the
staticfunction to be unconditionally inferred to be of the type of the
class it belongs to (regardless of how it is annotated or whether it is
annotated at all). This can lead to compilation errors depending on
how that argument is used in the body of the function.
Static functions are instead imported and scripted as if they were
standalone functions.
**Test Plan**
This commit augments the unit test for static methods in `test_class_types.py`
to test that static functions can call each other and the class
constructor.
**Fixes**
This commit fixes#39308.
Test Plan: Imported from OSS
Reviewed By: ZolotukhinM
Differential Revision: D22958163
Pulled By: SplitInfinity
fbshipit-source-id: 45c3c372792299e6e5288e1dbb727291e977a2af
Summary:
This PR adds the `torch.linalg` namespace as part of our continued effort to be more compatible with NumPy. The namespace is tested by adding a single function, `torch.linalg.outer`, and testing it in a new test suite, test_linalg.py. It follows the same pattern that https://github.com/pytorch/pytorch/pull/41911, which added the `torch.fft` namespace, did.
Future PRs will likely:
- add more functions to torch.linalg
- expand the testing done in test_linalg.py, including legacy functions, like torch.ger
- deprecate existing linalg functions outside of `torch.linalg` in preference to the new namespace
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42664
Reviewed By: ngimel
Differential Revision: D22991019
Pulled By: mruberry
fbshipit-source-id: 39258d9b116a916817b3588f160b141f956e5d0b
Summary:
This PR creates a new namespace, torch.fft (torch::fft) and puts a single function, fft, in it. This function is analogous to is a simplified version of NumPy's [numpy.fft.fft](https://numpy.org/doc/1.18/reference/generated/numpy.fft.fft.html?highlight=fft#numpy.fft.fft) that accepts no optional arguments. It is intended to demonstrate how to add and document functions in the namespace, and is not intended to deprecate the existing torch.fft function.
Adding this namespace was complicated by the existence of the torch.fft function in Python. Creating a torch.fft Python module makes this name ambiguous: does it refer to a function or module? If the JIT didn't exist, a solution to this problem would have been to make torch.fft refer to a callable class that mimicked both the function and module. The JIT, however, cannot understand this pattern. As a workaround it's required to explicitly `import torch.fft` to access the torch.fft.fft function in Python:
```
import torch.fft
t = torch.randn(128, dtype=torch.cdouble)
torch.fft.fft(t)
```
See https://github.com/pytorch/pytorch/issues/42175 for future work. Another possible future PR is to get the JIT to understand torch.fft as a callable class so it need not be imported explicitly to be used.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41911
Reviewed By: glaringlee
Differential Revision: D22941894
Pulled By: mruberry
fbshipit-source-id: c8e0b44cbe90d21e998ca3832cf3a533f28dbe8d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42486
**Summary**
This commit fixes a small bug in which `torch.jit.is_tracing()` returns
`torch._C.is_tracing`, the function object, instead of calling the
function and returning the result.
**Test Plan**
Continuous integration?
**Fixes**
This commit fixes#42448.
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D22911062
Pulled By: SplitInfinity
fbshipit-source-id: b94eca0c1c65ca6f22acc6c5542af397f2dc37f0
Summary:
Raise and assert used to have a hard-coded error message "Exception". User provided error message was ignored. This PR adds support to represent user's error message in TorchScript.
This breaks backward compatibility because now we actually need to script the user's error message, which can potentially contain unscriptable expressions. Such programs can break when scripting, but saved models can still continue to work.
Increased an op count in test_mobile_optimizer.py because now we need aten::format to form the actual exception message.
This is built upon an WIP PR: https://github.com/pytorch/pytorch/pull/34112 by driazati
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41907
Reviewed By: ngimel
Differential Revision: D22778301
Pulled By: gmagogsfm
fbshipit-source-id: 2b94f0db4ae9fe70c4cd03f4048e519ea96323ad
Summary:
xref gh-38010 and gh-38011.
After this PR, there should be only two warnings:
```
pytorch/docs/source/index.rst:65: WARNING: toctree contains reference to nonexisting \
document 'torchvision/index'
WARNING: autodoc: failed to import class 'tensorboard.writer.SummaryWriter' from module \
'torch.utils'; the following exception was raised:
No module named 'tensorboard'
```
If tensorboard and torchvision are prerequisites to building docs, they should be added to the `requirements.txt`.
As for breaking up quantization into smaller pieces: I split out the list of supported operations and the list of modules to separate documents. I think this makes the page flow better, makes it much "lighter" in terms of page cost, and also removes some warnings since the same class names appear in multiple sub-modules.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41321
Reviewed By: ngimel
Differential Revision: D22753099
Pulled By: mruberry
fbshipit-source-id: d504787fcf1104a0b6e3d1c12747ec53450841da
Summary:
* Add EnumType and AnyEnumType as first-class jit type
* Add Enum-typed IValue
* Enhanced aten::eq to support Enum
Supported:
Enum-typed function targuments
using Enum type and comparing them
TODO:
Add PyThon sugared value for Enum
Support getting name/value attrs of enums
Support Enum-typed return values
Support enum values of different types in same Enum class
Support serialization and deserialization
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41390
Reviewed By: eellison
Differential Revision: D22524388
Pulled By: gmagogsfm
fbshipit-source-id: 1627154a64e752d8457cd53270f3d14aea4b1150
