Summary:
There's an annoying O(N^2) in module export logic that makes saving some of the models (if they have many classes) take eternity.
I'm not super familiar with this code to properly untangle the deps and make it a pure hash lookup. So I just added a side lookup table for raw pointers. It's still quadratic, but it's O(num_classes^2) instead of O(num_classes * num_references) which already gives huge savings.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44589
Test Plan:
Tested with one of the offending models - just loading a saving a Torchscript file:
```
Before:
load 1.9239683151245117
save 165.74712467193604
After:
load 1.9409027099609375
save 1.4711427688598633
```
Reviewed By: suo
Differential Revision: D23675278
Pulled By: dzhulgakov
fbshipit-source-id: 8f3fa7730941085ea20d9255b49a149ac1bf64fe
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/44202
In preparation for changing mobile run_method() to be variadic, this diff:
* Implements get_method() for mobile Module, which is similar to find_method but expects the method to exist.
* Replaces calls to the current nonvariadic implementation of run_method() by calling get_method() and then invoking the operator() overload on Method objects.
ghstack-source-id: 111848222
Test Plan: CI, and all the unit tests which currently contain run_method that are being changed.
Reviewed By: iseeyuan
Differential Revision: D23436351
fbshipit-source-id: 4655ed7182d8b6f111645d69798465879b67a577
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43298
IR emitter uses `ModuleValue` to represent ScriptModules and emit IR for
attribute access, submodule access, etc.
`ModuleValue` relies on two pieces of information, the JIT type of the
module, and the `ConcreteModuleType`, which encapsulates Python-only
information about the module.
ScriptModules loaded from a package used to create a dummy
ConcreteModuleType without any info in it. This led to divergences in
behavior during compilation.
This PR makes the two ways of constructing a ConcreteModuleType equivalent,
modulo any py-only information (which, by definition, is never present in
packaged files anyway).
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D23228738
Pulled By: suo
fbshipit-source-id: f6a660f42272640ca1a1bb8c4ee7edfa2d1b07cc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43965
As part of a larger effort to unify the API between the lite interpreter and full JIT:
- implement torch::jit::mobile::Method, a proxy for torch::jit::mobile::Function
- add support for overloaded operator() to mobile Method and Function
- mobile find_method now returns a c10::optional<Method> (so signature matches full jit)
- moves some implementation of Function from module.cpp to function.cpp
ghstack-source-id: 111161942
Test Plan: CI
Reviewed By: iseeyuan
Differential Revision: D23330762
fbshipit-source-id: bf0ba0d711d9566c92af31772057ecd35983ee6d
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/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/42133
Test Plan:
We save a module with module debugging information as follows.
```
import torch
m = torch.jit.load('./detect.pt')
# Save module without debug info
m._save_for_lite_interpreter('./detect.bc')
# Save module with debug info
m._save_for_lite_interpreter('./detect.bc', _save_debug_info_in_bytecode=True)
```
Size of the file without module debugging information: 4.508 MB
Size of the file with module debugging information: 4.512 MB
Reviewed By: kimishpatel
Differential Revision: D22803740
Pulled By: taivu1998
fbshipit-source-id: c82ea62498fde36a1cfc5b073e2cea510d3b7edb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41146
**Summary**
This commit adds support for using `Modules` that have been lowered as
submodules in `ScriptModules`.
**Test Plan**
This commit adds execution and save/load tests to test_backends.py for
backend-lowered submodules.
**Fixes**
This commit fixes#40069.
Test Plan: Imported from OSS
Reviewed By: ailzhang
Differential Revision: D22459543
Pulled By: SplitInfinity
fbshipit-source-id: 02e0c0ccdce26c671ade30a34aca3e99bcdc5ba7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40718
Currently only constant except tensor must be inlined during serialization.
Tensor are stored in the contant table. This patch generalizes this capability
to any IValue. This is particularly useful for non ASCII string literal that
cannot be inlined.
Test Plan: Imported from OSS
Differential Revision: D22298169
Pulled By: bzinodev
fbshipit-source-id: 88cc59af9cc45e426ca8002175593b9e431f4bac
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40902
See the bottom of this stack for context.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D22360210
Pulled By: suo
fbshipit-source-id: 4275127173a36982ce9ad357aa344435b98e1faf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40841
**Summary**
This commit adds support for using `Modules` that have been lowered as
submodules in `ScriptModules`.
**Test Plan**
This commit adds execution and save/load tests to test_backends.py for
backend-lowered submodules.
**Fixes**
This commit fixes#40069.
Test Plan: Imported from OSS
Differential Revision: D22418716
Pulled By: SplitInfinity
fbshipit-source-id: d2b2c6d5d2cf3042a620b3bde7d494f1abe28dc1
Summary:
**Summary**
This commit adds an instance method `_reconstruct` that permits users
to reconstruct a `ScriptModule` from a given C++ `Module` instance.
**Testing**
This commit adds a unit test for `_reconstruct`.
**Fixes**
This pull request fixes https://github.com/pytorch/pytorch/issues/33912.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39979
Differential Revision: D22172323
Pulled By: SplitInfinity
fbshipit-source-id: 9aa6551c422a5a324b822a09cd8d7c660f99ca5c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40270
Original commit changeset: 1227e243ab94
D22082806 (1e03d603c6) broke the model generation of pyper models. We trace the namedtuple as input. To unblock the development of PyPer project, let's revert the diff first.
Sorry about the inconvenience, SplitInfinity
ghstack-source-id: 106217609
Test Plan: buck run dper3/dper3_models/experimental/pytorch/feed:feed_generation_script -- --model_files_dir=/tmp/
Reviewed By: alyssawangqq
Differential Revision: D22132960
fbshipit-source-id: ce9278c8462602a341e231ea890e46f74e743ddf
Summary:
**Summary**
This commit modifies type inference for `nn.Module` instance attributes
such that the type of a `NamedTuple` attribute is inferred correctly and
such that the field names of this `NamedTuple` instance can be used in
scripted methods. At present, the type of this attribute is inferred to be
`Tuple[T, U, ..., V]`, so the field must be referred to by index and
cannot be referred to by name.
**Test Plan**
This commit adds a unit test to test that a field of a `NamedTuple`
attribute can be referred to by name in a scripted method.
**Fixes**
This commit fixes https://github.com/pytorch/pytorch/issues/37668.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39116
Differential Revision: D22082806
Pulled By: SplitInfinity
fbshipit-source-id: 1227e243ab941376cd5e382fb093751e88dc8846
Summary: Add 'find_method' into 'LiteScriptModule' python binding method, so that we use it to find existence of methods, e.g. "get_all_bundled_inputs".
Reviewed By: linbinyu, houseroad
Differential Revision: D22029002
fbshipit-source-id: 9acf76880fc989e825dc3a9186dab6928caee75e
Summary:
Enhance FileCheck util to check for highlighted source ranges. This is useful when writing tests regarding generated error messages that require source code highlighting.
Here is how the error looks like in different cases:
- In case of needed source code token not found at all in input string:
```
RuntimeError: Expected to find "invalid_token" but did not find it
Searched string:
... <--- HERE
def to_list_missing_type_annotation(x):
# type: (torch.Tensor) -> List[float]
From CHECK-SOURCE-HIGHLIGHTED: invalid_token
```
- In case of source code token not highlighted:
```
Traceback (most recent call last):
File "test_range.py", line 11, in <module>
FileCheck().check_source_highlighted("x.tolist()").run(s)
RuntimeError: Expected to find "~~~~~~~~~~" but did not find it
Searched string:
# type: (torch.Tensor) -> List[float]
li = x.tolist()
~~~~~~~~~ <--- HERE
~~~~~~~~~~~~~~~~~~~... <--- HERE
return li
```
It is a bit confusing since both input text (usually an error message) and generated error messages have their highlighted portions, but this is consistent of previous behavior. Another option is to generate plain error messages without additional range highlighting on input text.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39692
Test Plan:
Added unit test.
Closes https://github.com/pytorch/pytorch/issues/38698
Differential Revision: D22001765
Pulled By: gmagogsfm
fbshipit-source-id: 6681441eee5853ab061d198ccfe55ebffddca202
Summary:
Clearly expressing a type is inferred by PyTorch instead of explicitly annotated by user makes many error messages more user-friendly
Currently Type has two string conversion methods. str() for IR printing and python_str() for serialization and error message generation. If we want to include more information in type printing while maintaining serialization/deserialization correctness, we need to split python_str() into annotation_str() and repr_str().
annotation_str is solely responsible for serialization, it strictly matches format of python type annotation. repr_str() is responsible for generating a human-readable error message that includes information like "this type is inferred, not explicitly annotated"
Closes https://github.com/pytorch/pytorch/issues/39449
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39544
Differential Revision: D21978759
Pulled By: gmagogsfm
fbshipit-source-id: 733566f5a62e748b5ca4bb3c5943ebb6d5b664d0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39181
Create a python binding classes torch._C. LiteScriptModule for mobile::module, a python class called LiteScriptModule is created which wrap torch._C. LiteScriptModule.
Python class LiteScriptModule contains preliminary functions including forward, run_method and __call__.
Create a python api "load_for_lite_interpreter" under torch.jit.mobile where takes pre-saved mobile module in a file-like object as input and returns python class LiteScriptModule.
Add a python binding method "_save_to_buffer_for_mobile" under ScriptModule, and python method "_save_to_buffer_for_lite_interpreter" under RecursiveScriptModule which saves mobile module into buffer instead of file.
ghstack-source-id: 105215736
Test Plan: buck test caffe2/test:mobile
Differential Revision: D21757474
fbshipit-source-id: 758b87497d65c4686459a567d41887c7a577aa4c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38735
Follow up to my comment https://github.com/pytorch/pytorch/pull/36597/#issuecomment-613674329
This adds a pass to convert op aliases into a normalized form. Having two ops generated in our IR that do the same thing makes the IR harder for downstream consumers of the IR, such as TorchScript passes but also ONNX, glow, etc.
Another solution would have been to fix our code generation to only emit `aten::abs` from the start. This seems trickier, and doesn't really buy us much if we still have to expose `aten::absolute` in C++, as glaringlee of the C++ API thinks we should.
Bike shedding: maybe this should be `CanonicalizeOps` instead
Test Plan: Imported from OSS
Differential Revision: D21673108
Pulled By: eellison
fbshipit-source-id: c328618907de1af22e07f57fd27fa619978c2817
Summary:
Make it so that non-nn Module classes do not need to be annotated with `torch.jit.script`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38050
Differential Revision: D21482654
Pulled By: eellison
fbshipit-source-id: 22689e4d7a33f6e1574b9495cff29a1fe6abb910
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37655
Add override name for aten::tensor and aten::as_tensor.
These two ops are used in NLU model, and they will included them in lite interpreter
Test Plan: verified model can be loaded correctly
Reviewed By: iseeyuan
Differential Revision: D21346142
fbshipit-source-id: 05ff4d9e0bcf7f4f9a30d95ca81aef9c3f6b0990
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32684
Previously we have `clone` and `clone_instance`, where `clone` will clone both type
and value, and `clone_instance` only clone the value, both of them are shallow copies.
We need to re-evaluate whether we should expose them as a user facing API.
I think we should hide `clone`, but `clone_instance` might be useful as well, especially
when we are copying a model with very large weights, people might just want to do shallow copy.
This PR adds a `deepcopy` that might be useful as a user API, which deep copies the values, including
Tensor, but we didn't deepcopy `Blob`, `Capsule`, `Future` or `PyObject`.
For more discussions please see the following issue.
fixes: https://github.com/pytorch/pytorch/issues/32519
Test Plan: Imported from OSS
Differential Revision: D21220756
fbshipit-source-id: 476bf11fe82c08fac36e7457879a09f545ffdc5e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36277
This PR introduce a flag to the tracer that guard the risky behaviors
like adding list/dict as output of the tracer. Currently to ensure not
BC breaking user, we throw warning if the tracer output is list, and
will throw error when the tracer output is dict to enforce using this
flag (next PR)
Test Plan: Imported from OSS
Differential Revision: D20998157
Pulled By: wanchaol
fbshipit-source-id: 0d2c55f1a263a48b1b92dd6ad54407815e0a6f72
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35630
Prefix underscored for now because the semantics of this method can be
confusing. It adds a new attribute to the *type*, which can be shared
by several objects.
Test Plan:
Next diff in stack uses it, and has unit tests.
Imported from OSS
Differential Revision: D20904253
fbshipit-source-id: dcbf60eacf0e0e075c19238165aa33954aa73b5f
Summary:
Someone messaged me abt this when a better error msg would have solved their problem
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35888
Differential Revision: D20819538
Pulled By: eellison
fbshipit-source-id: 95d124bfd162e1747dcdf7a981703a279a5dfaa6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35720
When modules are saved, all relevant types are serialized according to
their qualified name with a compilation unit. Since qualified names are
guaranteed to be unique within a compilation unit, this normally works
fine.
On load, all types are registered in a compilation unit owned by the
script::Module. Type names are not unique across compilation units, so
if you load two modules with colliding type names, make them submodules
of yet another module, and save that module, there is the potential of a
name collision. See the added tests for examples if that description is
confusing.
The solution is to unique type names when serializing code by mangling
them if we detect a name collision.
Test Plan: Imported from OSS
Differential Revision: D20749423
Pulled By: suo
fbshipit-source-id: a8827ff1d4a89f3e7964dbbb49b4381863da3e6a
Summary:
Fixes#29035
Previously we were missing a case for namedtuples in our Python value resolution logic, so they were just getting resolved as regular Python values, hence the `OSError`s in the linked issue
](https://our.intern.facebook.com/intern/diff/20653496/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35409
Pulled By: driazati
Differential Revision: D20653496
fbshipit-source-id: b5db1a11e918175aa02fda92993d233695417c56
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35115
This commit runs the newly added tools/clang_format.py on the JIT
codebase and includes all of the formatting changes thus produced.
Testing:
Ran the script, CI.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D20568523
Pulled By: SplitInfinity
fbshipit-source-id: e09bdb982ccf090eecfb7c7b461b8d0681eef82b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34515
Once upon a time we thought this was necessary. In reality it is not, so
removing it.
For backcompat, our public interface (defined in `api/`) still has
typedefs to the old `script::` names.
There was only one collision: `Pass` as a `Stmt` and `Pass` as a graph
transform. I renamed one of them.
Test Plan: Imported from OSS
Differential Revision: D20353503
Pulled By: suo
fbshipit-source-id: 48bb911ce75120a8c9e0c6fb65262ef775dfba93
Summary:
This adds some machinery so that we use Python to resolve types to a value and the corresponding resolution logic in `annotations.py` instead of using the string.
This PR also `slowTests` a random test since it was taking > 1 min whereas all the other tests take < 10 seconds.
Fixes#31864Fixes#31950
](https://our.intern.facebook.com/intern/diff/20144407/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29623
Pulled By: driazati
Differential Revision: D20144407
fbshipit-source-id: ef3699f6b86039d8b4646ffc42c21bd1132d1681
