Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49138
See for details: https://fb.quip.com/QRtJAin66lPN
We need to model optional types explicitly, mostly for schema inference. So we cannot pass a `Tensor?[]` as `ArrayRef<Tensor>`, instead we need to pass it as an optional type. This PR changes it to `torch::List<c10::optional<Tensor>>`. It also makes the ops c10-full that were blocked by this.
## Backwards Compatibility
- This should not break the Python API because the representation in Python is the same and python_arg_parser just transforms the python list into a `List<optional<Tensor>>` instead of into a `List<Tensor>`.
- This should not break serialized models because there's some logic that allows loading a serialized `List<Tensor>` as `List<optional<Tensor>>`, see https://github.com/pytorch/pytorch/pull/49138/files#diff-9315f5dd045f47114c677174dcaa2f982721233eee1aa19068a42ff3ef775315R57
- This will break backwards compatibility for the C++ API. There is no implicit conversion from `ArrayRef<Tensor>` (which was the old argument type) to `List<optional<Tensor>>`. One common call pattern is `tensor.index({indices_tensor})`, where indices_tensor is another `Tensor`, and that will continue working because the `{}` initializer_list constructor for `List<optional<Tensor>>` can take `Tensor` elements that are implicitly converted to `optional<Tensor>`, but another common call pattern was `tensor.index(indices_tensor)`, where previously, the `Tensor` got implicitly converted to an `ArrayRef<Tensor>`, and to implicitly convert `Tensor -> optional<Tensor> -> List<optional<Tensor>>` would be two implicit conversions. C++ doesn't allow chaining. two implicit conversions. So those call sites have to be rewritten to `tensor.index({indices_tensor})`.
ghstack-source-id: 119269131
Test Plan:
## Benchmarks (C++ instruction counts):
### Forward
#### Script
```py
from torch.utils.benchmark import Timer
counts = Timer(
stmt="""
auto t = {{op call to measure}};
""",
setup="""
using namespace torch::indexing;
auto x = torch::ones({4, 4, 4});
""",
language="cpp",
).collect_callgrind(number=1_000)
print(counts)
```
#### Results
| Op call |before |after |delta | |
|------------------------------------------------------------------------|---------|--------|-------|------|
|x[0] = 1 |11566015 |11566015|0 |0.00% |
|x.index({0}) |6807019 |6801019 |-6000 |-0.09%|
|x.index({0, 0}) |13529019 |13557019|28000 |0.21% |
|x.index({0, 0, 0}) |10677004 |10692004|15000 |0.14% |
|x.index({"..."}) |5512015 |5506015 |-6000 |-0.11%|
|x.index({Slice(None, None, None)}) |6866016 |6936016 |70000 |1.02% |
|x.index({None}) |8554015 |8548015 |-6000 |-0.07%|
|x.index({false}) |22400000 |22744000|344000 |1.54% |
|x.index({true}) |27624088 |27264393|-359695|-1.30%|
|x.index({"...", 0, true, Slice(1, None, 2), torch::tensor({1, 2})})|123472000|123463306|-8694|-0.01%|
### Autograd
#### Script
```py
from torch.utils.benchmark import Timer
counts = Timer(
stmt="""
auto t = {{op call to measure}};
""",
setup="""
using namespace torch::indexing;
auto x = torch::ones({4, 4, 4}, torch::requires_grad());
""",
language="cpp",
).collect_callgrind(number=1_000)
print(counts)
```
Note: the script measures the **forward** path of an op call with autograd enabled (i.e. calls into VariableType). It does not measure the backward path.
#### Results
| Op call |before |after |delta | |
|------------------------------------------------------------------------|---------|--------|-------|------|
|x.index({0}) |14839019|14833019|-6000| 0.00% |
|x.index({0, 0}) |28342019|28370019|28000| 0.00% |
|x.index({0, 0, 0}) |24434004|24449004|15000| 0.00% |
|x.index({"..."}) |12773015|12767015|-6000| 0.00% |
|x.index({Slice(None, None, None)}) |14837016|14907016|70000| 0.47% |
|x.index({None}) |15926015|15920015|-6000| 0.00% |
|x.index({false}) |36958000|37477000|519000| 1.40% |
|x.index({true}) |41971408|42426094|454686| 1.08% |
|x.index({"...", 0, true, Slice(1, None, 2), torch::tensor({1, 2})}) |168184392|164545682|-3638710| -2.16% |
Reviewed By: bhosmer
Differential Revision: D25454632
fbshipit-source-id: 28ab0cffbbdbdff1c40b4130ca62ee72f981b76d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43613
**Summary**
This commit adds a helper/utility to faciliate the selective lowering of
specific submodules within a module hierarchy to a JIT backend. The reason
that this is needed is that lowering a submodule of a scripted
module to a backend after the module has been scripted requires
adjusting its JIT type.
**Test Plan**
This commit refactors `NestedModuleTest` in `jit/test_backends.py` to
use this new selective lowering API.
**Fixes**
This commit fixes ##41432.
Test Plan: Imported from OSS
Reviewed By: mortzur
Differential Revision: D23339855
Pulled By: SplitInfinity
fbshipit-source-id: d9e69aa502febbe04fd41558c70d219729252be9
Summary:
Pull Request resolved: https://github.com/pytorch/glow/pull/5029
Support single element tuples in to_backend
Test Plan: new unit test for to_glow
Reviewed By: andrewmillspaugh
Differential Revision: D24539869
fbshipit-source-id: fb385a7448167b2b948e70f6af081bcf78f338dc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43612
**Summary**
This commit modifies the `torch._C._jit_to_backend` function so that it
accepts `ScriptModules` as inputs. It already returns `ScriptModules`
(as opposed to C++ modules), so this makes sense and makes the API more
intuitive.
**Test Plan**
Continuous integration, which includes unit tests and out-of-tree tests
for custom backends.
**Fixes**
This commit fixes#41432.
Test Plan: Imported from OSS
Reviewed By: suo, jamesr66a
Differential Revision: D23339854
Pulled By: SplitInfinity
fbshipit-source-id: 08ecef729c4e1e6bddf3f483276947fc3559ea88
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/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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40840
**Summary**
This commit moves the TestBackend used for the JIT backend
extension to the tests directory. It was temporarily placed
in the source directory while figuring out some details of
the user experience for this feature.
**Test Plan**
`python test/test_jit.py TestBackends`
**Fixes**
This commit fixes#40067.
Test Plan: Imported from OSS
Differential Revision: D22418682
Pulled By: SplitInfinity
fbshipit-source-id: 9356af1341ec4d552a41c2a8929b327bc8b56057
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40839
**Summary**
This commit splits the to_backend API properly into
`libtorch` and `libtorch_python`. The backend interface and all
of the code needed to run a graph on a backend is in
libtorch, and all of the code related to creating a Python binding
for the lowering process is in `libtorch_python`.
**Test Plan**
`python test/test_jit.py TestBackends`
**Fixes**
This commit fixes#40072.
Test Plan: Imported from OSS
Differential Revision: D22418664
Pulled By: SplitInfinity
fbshipit-source-id: b96e0c34ab84e45dff0df68b8409ded57a55ab25
Summary:
**Summary**
This commit adds a registry for storing lowering functions for backends.
Instead of backends registering these lowering functions in separate C
extension modules, these will be registered in the Torch extension.
Backends are registered statically, so a registry is needed to hold
these lowering functions until Python bindings are created.
**Test Plan**
`python test/test_jit.py TestBackends`
```
Couldn't download test skip set, leaving all tests enabled...
..
----------------------------------------------------------------------
Ran 2 tests in 0.104s
OK
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39552
Reviewed By: mortzur
Differential Revision: D22033855
Pulled By: SplitInfinity
fbshipit-source-id: 05abf152274e5e51c37b6004886ea25bd4d33b80
Summary:
**Summary**
This commit adds support for seralization and deserialization of
`ScriptModules` that have been lowered to a specific backend. Nothing
special was required to accomplish this, other than removing some code
in `unpickler.cpp` that guarded against the deserialization of `Any`
type objects. Now that lists and dicts are tagged with their types
during serialization, this check is no longer necessary.
**Test Plan**
This commit adds a unit test for testing that a lowered module still
produces the same results as Python and regular JIT after saving and
loading.
**Fixes**
This pull request fixes part of https://github.com/pytorch/pytorch/issues/37841.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38893
Differential Revision: D21825813
Pulled By: SplitInfinity
fbshipit-source-id: 77a7b84504e0dddf14c89b3ed5dd6b438c086f66
Summary:
**Summary**
This commit gets rid of the separate compilation unit that is currently
being created for every backend-specific module generated by
`jit::backend::generateToBackendFn` and mangles the name properly to
allow multiple backend-specific modules to coexist in the same
compilation unit.
**Test Plan**
`python test/test_jit.py TestBackends`
**Fixes**
This pull request fixes part of https://github.com/pytorch/pytorch/issues/37841.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38679
Differential Revision: D21744620
Pulled By: SplitInfinity
fbshipit-source-id: ac85b8ce0d179c057991e9299fd53a4e13ba02a9
Summary:
**Summary**
This commit adjusts the `pybind` includes in `backend.h` so
that we can avoid exporting some unrelated headers during install (which
probably shouldn't be exposed anyway). In addition, the headers that this commit
removes are not used.
**Test Plan**
Continuous integration (includes tests for JIT backends).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38562
Differential Revision: D21601694
Pulled By: SplitInfinity
fbshipit-source-id: c8f8103d24cb4f10d9eb6b3657eed75878078945
Summary:
**Summary**
This commit adds `torch::jit::RegisterBackend`, an API that allows
external backends to be registered for the execution of JIT subgraphs
outside the JIT interpreter. In order to register an external backend,
one must extend the provided abstract class `PyTorchBackendInterface` and provide
two additional functions: one that creates an instance of the aforementioned subclass
of `PyTorchBackendInterface`, and another that preprocesses a `ScriptModule` so that
it can run on the backend. Then, a `ScriptModule` that can compile and execute a given
JIT subgraph using the functions provided at registration time is generated
for each registered backend.
**Testing**
This commit adds a unit test that uses a minimal test backend
to make sure that the registration endpoint and generated
`ScriptModule` work.
```
$ python test/test_jit.py TestBackends
Fail to import hypothesis in common_utils, tests are not derandomized
.
----------------------------------------------------------------------
Ran 1 test in 0.183s
OK
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35833
Differential Revision: D21231955
Pulled By: SplitInfinity
fbshipit-source-id: 452db1123d0e5d83f97fe5da8a00fdfdb50dbef9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33806
as title
Test Plan: Imported from OSS
Differential Revision: D20122117
Pulled By: suo
fbshipit-source-id: 209d29ed2c873181140c9fb5cdc305c200ce4008
