Summary:
As per title. This in particular allows to more easily override backward function for which the underlying backend returns `None`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67793
Reviewed By: zou3519
Differential Revision: D32242962
Pulled By: albanD
fbshipit-source-id: 6e114def90ee9499161e1303d301ba7fd003ff89
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65345
FooType::get() can return a const reference. Inconveniently, converting shared_ptr<FooType> to shared_ptr<Type> requires a copy & refcount bump, so to properly take advantage of this in unshapedType() we need to take a const Type& in isSubtypeOf(), which is good practice anyway -- don't require a shared_ptr if you don't need to take ownership.
ghstack-source-id: 140044165
Test Plan:
CI
perf says c10::unshapedType time decreased from 2.8% to 2.2% during static runtime startup, though I expect this to be generally beneficial.
Reviewed By: hlu1
Differential Revision: D31027361
fbshipit-source-id: 676feb81db9f74ad7b8651d8774f4ecb4cfa6ab8
Summary:
This PR is created to replace https://github.com/pytorch/pytorch/pull/53180 PR stack, which has all the review discussions. Reason for needing a replacement is due to a messy Sandcastle issue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64234
Reviewed By: gmagogsfm
Differential Revision: D30656444
Pulled By: ansley
fbshipit-source-id: 77536c8bcc88162e2c72636026ca3c16891d669a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61520
This commit widens the exception caught by the try-catch block that checks if
an object passed to a scripted function is a `ScriptList`. It turns out that
there are internal tests that do not throw a `py::cast_error` so catching only
that is not sufficient.
Test Plan: Ran the failing tests in T94889011.
Reviewed By: Chillee
Differential Revision: D29560815
fbshipit-source-id: 442258f8997146d833a9d5db923e1f6359f2bfdd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52832
**Summary**
This commit adds `torch._C.ScriptList`, a list type that has reference
semantics across the Python/TorchScript boundary. That is, modifications
made in TorchScript to instances of `torch._C.ScriptList`
are visible in Python even when it is not returned from the function.
`torch._C.ScriptList` is implemented using a modified version of pybind's
`stl_bind.h`-style bindings attached to `ScriptList` and `ScriptListIterator`,
wrapper classes around `c10::impl::GenericList` and
`c10::impl::GenericList::iterator`. These bindings allow instances of
`torch._C.ScriptList` to be used as if it were a
regular `list` in Python. Reference semantics are achieved by simply
retrieving the `IValue` contained in `ScriptList` in `toIValue` (invoked
when converting Python arguments to `IValues` before calling TorchScript
code).
**Test Plan**
This commit adds `TestScriptList` to `test_list_dict.py`, a set of tests
that check that all of the common list operations are supported
and that instances have reference semantics across the
Python/TorchScript boundary.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D29478121
Pulled By: SplitInfinity
fbshipit-source-id: 652cc25cfa37debe28db9527504846f22abd8b54
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44324
**Summary**
This commit adds reference semantics to TorchScript class types;
modifications made to them within TorchScript will be visible in Python.
**Test Plan**
This commit adds a unit test to `TestClassType` that checks that
modifications made to a class type instance passed into TorchScript are
visible in Python after executing the scripted function or module.
**Fixes**
This commit closes#41421.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D24912807
Pulled By: SplitInfinity
fbshipit-source-id: d64ac6211012425b040b987e3358253016e84ca0
Summary:
This commit removes the warning that suggests that users script their
dictionaries before passing them into TorchScript code. The ScriptDict feature
is not fully ready, so it does not make sense to recommend this yet.
Test Plan:
Sandcastle.
In addition, the PyPER test broken by the original diff passes:
```
buck test mode/opt //caffe2/torch/fb/training_toolkit/backend/tests:test_model_materializer_full_sync_lwt -- --exact 'caffe2/torch/fb/training_toolkit/backend/tests:test_model_materializer_full_sync_lwt - caffe2.torch.fb.training_toolkit.backend.tests.test_model_materializer_full_sync_lwt.ModelMaterializerFullSyncLwtTest: test_materialization_determinism_cpu' --run-disabled
```
Differential Revision: D28891351
fbshipit-source-id: 2a3a00cde935d670fb1dc7fd8c709ae9c2ad8cdc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59287
D27211605 added a warning in `toIValue` that warns users to script their
dictionaries before passing them to TorchScript functions in order to get some
performance benefits and reference semantics. However, this warning is emitted
every time `toIValue` is called (e.g. when a dictionary is passed to
TorchScript function), which can lead to noisy log output. This diff changes
this changes to use `TORCH_WARN_ONCE` instead.
Test Plan: Sandcastle, OSS CI.
Reviewed By: hyuen
Differential Revision: D28824468
fbshipit-source-id: e651eade4380abaf77c6c8a81ec4e565b0c2c714
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52659
**Summary**
This commit adds `torch._C.ScriptDict`, a dictionary type that has reference
semantics across the Python/TorchScript boundary. That is, modifications
made to instances of `torch._C.ScriptDict` in TorchScript are visible in
Python even when it is not returned from the function. Instances can be
constructed by passing an instance of a Python dictionary to
`torch.jit.script`. In the case of an empty dictionary, its type is
assumed to be `Dict[str, Tensor]` to be consistent with the handling of
empty dictionaries in TorchScript source code.
`torch._C.ScriptDict` is implemented using a modified version of pybind's `stl_bind.h`-style bindings attached to `ScriptDict`, `ScriptDictIterator` and `ScriptDictKeyIterator`, wrapper classes around `c10::impl::GenericDict` and `c10::impl::GenericDict::iterator`. These bindings allow instances of `torch._C.ScriptDict` to be used as if it were a regular `dict` Python. Reference semantics are achieved by simply retrieving the `IValue` contained in `ScriptDict` in `toIValue` (invoked when converting Python arguments to `IValues` before calling TorchScript code).
**Test Plan**
This commit adds `TestScriptDict` to `test_list_dict.py`, a set of tests
that check that all of the common dictionary operations are supported
and that instances have reference semantics across the
Python/TorchScript boundary.
Differential Revision:
D27211605
D27211605
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Pulled By: SplitInfinity
fbshipit-source-id: 446d4e5328375791aa73eb9e8b04dfe3465af960
Summary:
Previously we might have gotten segfaults and all, now it raises an exception.
Thread safety hasn't been an objective.
I have a followup to expand the Python interface for the API.
Fixes https://github.com/pytorch/pytorch/issues/49969.
wanchaol
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50326
Reviewed By: pbelevich
Differential Revision: D26096234
Pulled By: gmagogsfm
fbshipit-source-id: 5425772002eb4deb3830ed51eaa3964f22505840
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51340
**Summary**
`toIValue` assumes that any value passed for an argument of type
`torch.device` is a valid device object, even when it is not. This can
lead to device type arguments of functions being assigned incorrect
values (see #51098).
This commit adds an explicit check that the passed in object is indeed a
`torch.device` using `THPDevice_Check` and only then does is it
converted to an `IValue`. Since implicit conversion from strings to
devices is generally allowed, if `THPDevice_Check` fails, it is assumed
that the object is a string and an `IValue` containing a `c10::Device`
containing the passed in string is returned.
**Test Plan**
This commit adds a unit test to `test_jit.py` to test that invalid
strings passed as devices are not longer silently accepted.
**Fixes**
This commit fixes#51098.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26187190
Pulled By: SplitInfinity
fbshipit-source-id: 48c990203431da30f9f09381cbec8218d763325b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50228
`fastmod -m 'expect(<((at|c10)::)?\w+Type>\(\)\s*)->'
'expectRef${1}.'`
Presuming it builds, this is a safe change: the result of `expect()`
wasn't being saved anywhere, so we didn't need it, so we can take a
reference instead of a new `shared_ptr`.
ghstack-source-id: 119782961
Test Plan: CI
Reviewed By: SplitInfinity
Differential Revision: D25837374
fbshipit-source-id: 86757b70b1520e3dbaa141001e7976400cdd3b08
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50255
**Summary**
TorchScript classes are copied attribute-by-attribute from a py::object into
a `jit::Object` in `toIValue`, which is called when copying objects from
Python into TorchScript. However, if an attribute of the class cannot be
converted, the error thrown is a standard pybind error that is hard to
act on.
This commit adds code to `toIValue` to convert each attribute to an
`IValue` inside a try-catch block, throwing a `cast_error` containing
the name of the attribute and the target type if the conversion fails.
**Test Plan**
This commit adds a unit test to `test_class_type.py`
based on the code in the issue that commit fixes.
**Fixes**
This commit fixes#46341.
Test Plan: Imported from OSS
Reviewed By: pbelevich, tugsbayasgalan
Differential Revision: D25854183
Pulled By: SplitInfinity
fbshipit-source-id: 69d6e49cce9144af4236b8639d8010a20b7030c0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48840
The CUDAFuture class needs to inspect the values it contains in order to extract its tensors (in fact, the DataPtrs backing those). These are needed first to determine what CUDA devices back those tensors, so that an event for each such device can be recorded; and later to record these DataPtrs with the CUDA caching allocator if they are used in other streams.
This became complicated when Python was added to the mix, because to inspect a Python object we need to acquire the GIL, but we couldn't do so from code that was supposed to also work in C++-only mode. The solution was for users to provide a custom way to extract DataPtrs, so that the PythonFutureWrapper could install such a custom Python-aware one. This was the DataPtr extractor.
In https://github.com/pytorch/pytorch/pull/48502 a different suggestion was proposed. At its root, it consists in adding support for IValues of type PyObject to the visit() and getSubValues() methods. In order to deal with the GIL, we do this through a virtual method: PyObjectHolder, which is the base class, is available also in C++-only mode, and thus defines this method but leaves it unimplemented; ConcretePyObjectHolder, which is the subclass, is only included in Python mode, and thus it can implement that method, acquire the GIL, and do what it's supposed to.
In my opinion, this approach is just brilliant! Thank wanchaol for proposing it! It hides the complexity of dealing with Python inside getSubValues(), where it can be done properly, thus simplifying enormously the CUDAFuture and the PythonFutureWrapper classes.
ghstack-source-id: 118704935
Test Plan: Unit tests
Reviewed By: wanchaol
Differential Revision: D25334355
fbshipit-source-id: 3f1d3bf6e6e8505a114c877fb9a6fcc3f68d91d3
