Summary:
Tested locally this fix#19039, did not add a test since there's no way to create a script module in the cpp world.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19700
Differential Revision: D15094195
Pulled By: wanchaol
fbshipit-source-id: fcc2c1e5efbc160d976ae485ba2457442f62f065
Summary:
Currently, the Python API doesn't serialize layers that don't have weights (such as `nn.ReLU` and `nn.MaxPool2d`e.g. in https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py#L80-L81). If one saves a model that contains weight-less layers in Python and tries to load it into C++, the C++ module loading code (`torch::load(...)`) will throw an error complaining that the expected layers are not found in the serialized file (e.g. https://github.com/pytorch/vision/pull/728#issuecomment-480974175). This PR solves the problem by ignoring layers that are not serializable (which currently only include `nn::Functional`) in the C++ module serialization code (`torch::save(...)` and `torch::load(...)`), and the user is expected to use `nn::Functional` to wrap the weight-less layers so that they can be ignored when serializing / deserializing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19740
Differential Revision: D15100575
Pulled By: yf225
fbshipit-source-id: 956481a2355d1de45341585abedda05e35d2ee8b
Summary:
* adds TORCH_API and AT_CUDA_API in places
* refactor code generation Python logic to separate
caffe2/torch outputs
* fix hip and asan
* remove profiler_cuda from hip
* fix gcc warnings for enums
* Fix PythonOp::Kind
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19554
Differential Revision: D15082727
Pulled By: kostmo
fbshipit-source-id: 83a8a99717f025ab44b29608848928d76b3147a4
Summary:
In the distributed training development work, we need to be able to serialize a `std::vector` of `torch::Tensor`s. This PR adds support for serializing `std::vector<torch::Tensor>`.
cc. mrshenli
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19677
Differential Revision: D15069860
Pulled By: yf225
fbshipit-source-id: 505147e5f5fea78be1bf60fb8418bc187dbc2a98
Summary:
We can't introduce aliasing to a graph output, since they may be mutated after.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19576
Differential Revision: D15057734
Pulled By: eellison
fbshipit-source-id: 33594c05d985a0c58edebd6252e1ee2c0efb6f0e
Summary:
Add setting requires_grad = True within torchscript to torch.Tensor
Within constant propagation, we can't insert any constants that require grad.
Also added shape analysis and requires grad analysis to torch.tensor
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19445
Differential Revision: D15046211
Pulled By: eellison
fbshipit-source-id: b4ef7a6b4b6b8dc03e1fa49f87dc415874cd1998
Summary:
Add setting requires_grad = True within torchscript to torch.Tensor
Within constant propagation, we can't insert any constants that require grad.
Also added shape analysis and requires grad analysis to torch.tensor
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19445
Differential Revision: D15039713
Pulled By: eellison
fbshipit-source-id: 47f1931b6fc4a1137c13d80110cc404465bfdf06
Summary:
First step at allowing container types within alias analysis.
Since the current implementation hides the concept of Wildcards within alias analysis and does not expose it to memory dag, we cannot represent whether a container type holds a wildcard. As a result, only handle TupleConstruct, where we can directly inspect if any input values are wildcards, and don't handle nested containers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18710
Differential Revision: D15017068
Pulled By: eellison
fbshipit-source-id: 3ee76a5482cef1cc4a10f034593ca21019161c18
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18092
Previously, tracing required all inputs to be either tensors,
or tuples of tensor. Now, we allow users to pass dicts as well.
Differential Revision: D14491795
fbshipit-source-id: 7a2df218e5d00f898d01fa5b9669f9d674280be3
Summary:
Add input information into generated RecordFunction calls in
VariableType wrappers, JIT operators and a few more locations
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18717
Differential Revision: D14729156
Pulled By: ilia-cher
fbshipit-source-id: 811ac4cbfd85af5c389ef030a7e82ef454afadec
Summary:
This PR propagates where we use first-class modules objects into the compiler. This creates a transitionary state where:
* compiler.cpp creates Graphs where `self` is a Module class and attributes/parameters/buffers/submodules are looked up with `prim::GetAttr`
* GraphExecutor still runs "lowered graphs" where the self object has been removed by a compiler pass `lower_first_class_method`.
* Tracing still creates "lowered graphs", and a pass "lift_lowered_method" creates a first-class method graph for things.
* This PR separates out Method and Function. A script::Function is a pure Graph with no `self` bound. Similar to Python, a script::Method is just a bound `self` and its underlying `script::Function`.
* This PR also separates CompilationUnit from Module. A CompilationUnit is just a list of named script::Functions. Class's have a CompilationUnit holding the class methods, and Modules also have a CompilationUnit holding their Methods. This avoids the weird circular case Module --has a-> Class -> has a -> Module ...
Details:
* In this transitionary state, we maintain two copies of a Graph, first-class module and lowered. Th first-class one has a self argument that is the module's class type. The lowered one is the lowered graph that uses the initial_ivalues inputs.
* When defining lowered methods using `_defined_lowered` we immediately create the first-class equivalent. The reverse is done lazily, creating lowered_methods on demand from the class.
* The two way conversions will be deleted in a future PR when the executor itself runs first-class objects. However this requires more changes to (1) the traces, (2) the python bindings, and (3) the onnx export pass and would make this PR way to large.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19167
Differential Revision: D14891966
Pulled By: zdevito
fbshipit-source-id: 0b5f03118aa65448a15c7a7818e64089ec93d7ea
Summary:
Fixes#18518
I changed the C++ API torch::nn::init::orthogonal_ implementation to match the Python implementation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18915
Differential Revision: D14851833
Pulled By: ezyang
fbshipit-source-id: 45b5e9741582777c203e9ebed564ab3ac1f94baf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18711
ghimport-source-id: c9caedc0660b2b7ba3730cd0e1a2e0e9c3cf422b
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18711 [jit] fix side-effects and aliasing for custom ops**
Previously we didn't track aliasing, mutation, or side effects for
custom ops. This PR adds in guards with the most conservative
assumptions possible: the op will
1) have side effects,
2) write to everything
3) produce a wildcard.
In order to tell whether a given operator is a custom op, this PR introduces
the concept of a "reserved" namespace (basically all our builtin namespaces).
Custom ops live in non-reserved namespaces, so a check on the namespace
is sufficient to tell whether a schema/node is "custom" or not.
This is just to get things correct for now. Follow-ups to this:
- Users should be able to specify aliasing/mutability without having to learn
the whole alias annotation schema.
- Relax assumptions a bit. In particular outputs can only alias input tensors,
they don't have to be wildcards.
Fixes#18490
Differential Revision: D14730978
fbshipit-source-id: 540b47a24ccf24145051609bdcc99c97e46e0fe0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18826
ghimport-source-id: 7ffa3bc7ef7402a6d6eb6ba5849e197019d77bf8
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18826 [jit] run cpp tests for non-cuda builds in test_jit.py**
We did all the work of nicely separating our cpp tests that don't require
CUDA, but they aren't run from test_jit.py if CUDA is missing.
Reviewed By: ZolotukhinM
Differential Revision: D14766287
fbshipit-source-id: 9326b3a5c90f6c20fc8cfaf1a1885a363b91f30a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18314
ghimport-source-id: 8cecb768d476ab19c9460f39c8f94a764e4cb052
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18314 Add ability to specialize class types to ArgumentSpec**
* #18226 Add Slot type to abstract the raw pointers being used for slots.
Differential Revision: D14574395
fbshipit-source-id: cc3af6e56e9ae52990f4a1ad56ecceaa2d493577
Summary:
The last time I tried to land it there was a merge race with the docs coverage test lol. Re-landing with the fix.
Re-land of https://github.com/pytorch/pytorch/pull/18304
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18570
Reviewed By: driazati
Differential Revision: D14707285
Pulled By: eellison
fbshipit-source-id: 3a0265928aa8cad78961723d8bf0fbf871fdb71d
Summary:
The last time I tried to land it there was a merge race with the docs coverage test lol. Re-landing with the fix.
Re-land of https://github.com/pytorch/pytorch/pull/18304
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18570
Differential Revision: D14668859
Pulled By: eellison
fbshipit-source-id: 3825a35ddc6179a0d433d70d22b5c1a96c20b21a
Summary:
Previously, we were not able to assign names to `nn::Sequential`'s submodules. This PR adds this feature to match the Python API. Example use:
```cpp
Sequential sequential(named_submodule({
{"linear", Linear(10, 3)},
{"conv2d", Conv2d(1, 2, 3)},
{"dropout", Dropout(0.5)},
{"batchnorm", BatchNorm(5)},
{"embedding", Embedding(4, 10)},
{"lstm", LSTM(4, 5)}
}));
```
It also enables loading parameters of Python `nn.Sequential` module with custom submodules names into C++ frontend, unblocking https://github.com/pytorch/vision/pull/728#issuecomment-466661344.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17552
Differential Revision: D14246834
Pulled By: yf225
fbshipit-source-id: 3030b5c5d68f6dd5d3e37ac4b4f98dc6d6d9ba72
Summary:
This allows you to embed checks in IR, making the test more readable.
E.g.
```
graph_str = 'graph(%0 : Double(5, 5)):
# CHECK: aten::relu
%1 : Double(5, 5) = aten::relu(%0)
return (%1)'
FileCheck().run(graph_str, parseIR(graph_str))
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18304
Differential Revision: D14652372
Pulled By: eellison
fbshipit-source-id: 7430b9d1dc2b7584704375aac02d7392ecec76a0
Summary:
Simplify or eliminate boolean and/or expressions, optimize unwrapping a value that cannot be None, and optimize using `is` with a None and a non-None value
Since peephole optimize is now introducing constants, i added another constant propagation pass after running it.
Previously i had a PR that did this & optimized shape ops - i will add the shape optimizations in a separate PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18259
Differential Revision: D14602749
Pulled By: eellison
fbshipit-source-id: 1c3f5a67067d8dfdf55d7b78dcb616472ea8a267
Summary:
Further breakup test_misc.h. The remaining tests don't directly map to a jit file so I left them in test_misc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18191
Differential Revision: D14533442
Pulled By: eellison
fbshipit-source-id: 7f538ce0aea208b6b55a4716dfcf039548305041
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18146
ghimport-source-id: 4b061c27c5c44ef0d06066490ed16cab3d0c7a64
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18146 [jit] fix bug in alias analysis**
We handled hasWriters() incorrectly in the case of wildcards. There's
even a comment describing the correct behavior. Sad!
Much thanks to t-vi for tracking this down and suggesting the fix!
Differential Revision: D14524208
fbshipit-source-id: 8010b54257241bd64013a0d0a8b6e7d22d8c70af
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18121
ghimport-source-id: 70c273bfbcb68f7b25cf87f5614c662960864758
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18121 [jit] fix double free in test_jit**
These definitions used to be in anonymous namespace so they weren't exported from the translation unit. #18071 put those in a `test` namespace so I guess they were getting their destructors called twice on exit somehow. Making them static again fixes the problem.
Reviewed By: ezyang
Differential Revision: D14498349
fbshipit-source-id: f969781695dcbebdfcfce667fce5b986222a373e
Summary:
Breakup test_misc so that a test for a file is in test_filename. I think we might want to wait on moving test files into the source directory, since that would involve moving some tests over to the C10 folder, and this goes 99% of the way for test discoverability IMO anyway.
I added a file test_utils for common functions invoked in the tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18071
Differential Revision: D14485787
Pulled By: eellison
fbshipit-source-id: dcb20d1978d490999d435ea20c1d0503413a5c80
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17947
Instead of having a gtest and a no-gtest file that you have to remember to register tests in, add a single registration point and use some macro magic to make it work for both gtest and non-gtest builds
Reviewed By: eellison
Differential Revision: D14431302
fbshipit-source-id: e1abac135992577a943eaa7abcc81a6ed31fa6e5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17931
When converting from NetDef to IR and back, the prefix string should be removed so the operator types are preserved in caffe2.
Reviewed By: ZolotukhinM
Differential Revision: D14425954
fbshipit-source-id: 2807e7337b0f804f126970768b1250a4a8c5f35c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17628
This is not hooked up anywhere yet, just adding support.
This shares the same restrictions as the python frontend—namely, that the only exprs allowed right now are method defs.
Reviewed By: shannonzhu
Differential Revision: D14291654
fbshipit-source-id: 7798e5ff412a52ef8803c7bae8f439e50968a73a
