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
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17585
Create a sugared value that represents a class during initialization. This is
so that assignments to attributes correctly define attributes in __init__ but
raise an error elsewhere.
Reviewed By: shannonzhu
Differential Revision: D14263403
fbshipit-source-id: 09b2feeb272302f00a79c2a0302fbdf5483aed6a
Summary:
Last batch of IR expect files removed. Includes some removal of expect files that are no longer used.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17886
Differential Revision: D14414435
Pulled By: eellison
fbshipit-source-id: 0bfd7ce66ac2f72a57f15f45ebd60b95e80b6c16
Summary:
Check for Tuple Matching in isSubvalueOf, since they may contain container types that need to be recursed within isSubvalueOf
Fix for https://github.com/pytorch/pytorch/issues/17650
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17687
Differential Revision: D14324642
Pulled By: eellison
fbshipit-source-id: 7f1e019875286b2640a3b9c003d1635dda8cf543
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17594
The original version of this broke things because a concurrent change raced with it in CI.
Reviewed By: ezyang
Differential Revision: D14266663
fbshipit-source-id: e8ac5dfcb7349b4f2c425d9f0eabbfc964314063
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17511
AliasTracker was doing bookkeeping for three concepts: the points-to graph,
writes, and wildcards.
This PR makes AliasTracker's job clearer: it keeps track of the points-to
graph. Thus it has been renamed MemoryDAG. Write and wildcard information were
pulled back into AliasDb as part of this—I may decide to pull them into their
own little modules since I don't want the alias analysis stuff to get too
bloated.
This refactor is necessary because we want to start tracking information for
aliasing elements that _aren't_ first-class IR Values (e.g. the "stuff" inside
a list). So MemoryDAG can't know too much about Values
Reviewed By: houseroad
Differential Revision: D14231251
fbshipit-source-id: 6cd98ae6fced8d6c1522c2454da77c3c1b2b0504
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17480
This was always part of our "spec" but not implemented
Reviewed By: houseroad
Differential Revision: D14214301
fbshipit-source-id: 118db320b43ec099dc3e730c67d39487474c23ea
Summary:
The chunk buffer had a possibility to hang when no data is read and the buffer size is lower than chunk size. We detected this while running with larger dataset and hence the fix. I added a test to mimic the situation and validated that the fix is working. Thank you Xueyun for finding this issue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17409
Differential Revision: D14198546
Pulled By: soumith
fbshipit-source-id: b8ca43b0400deaae2ebb6601fdc65b47f32b0554
Summary:
This PR removes a few size of `self` that passed from forward pass to backward pass when `self` is already required in backward pass. This could be reason that cause the potential slow down in #16689 . I will attach a few perf numbers (still a bit volatile among runs tho) I got in the comment.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17187
Differential Revision: D14179512
Pulled By: ailzhang
fbshipit-source-id: 5f3b1f6f26a3fef6dec15623b940380cc13656fa
Summary:
Creates a new shared type parser to be shared between the IR parser and the Schema Parser.
Also adds parsing of CompleteTensorType and DimensionedTensorType, and feature-gates that for the IRParser.
Renames the existing type_parser for python annotations, python_type_parser, and names the new one jit_type_parser.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17383
Differential Revision: D14186438
Pulled By: eellison
fbshipit-source-id: bbd5e337917d8862c7c6fa0a0006efa101c76afe
Summary:
Currently there is a mismatch in naming between Python BatchNorm `running_var` and C++ BatchNorm `running_variance`, which causes JIT model parameters loading to fail (https://github.com/pytorch/vision/pull/728#issuecomment-466067138):
```
terminate called after throwing an instance of 'c10::Error'
what(): No such serialized tensor 'running_variance' (read at /home/shahriar/Build/pytorch/torch/csrc/api/src/serialize/input-archive.cpp:27)
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x85 (0x7f2d92d32f95 in /usr/local/lib/libc10.so)
frame #1: torch::serialize::InputArchive::read(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, at::Tensor&, bool) + 0xdeb (0x7f2d938551ab in /usr/local/lib/libtorch.so.1)
frame #2: torch::nn::Module::load(torch::serialize::InputArchive&) + 0x98 (0x7f2d9381cd08 in /usr/local/lib/libtorch.so.1)
frame #3: torch::nn::Module::load(torch::serialize::InputArchive&) + 0xf9 (0x7f2d9381cd69 in /usr/local/lib/libtorch.so.1)
frame #4: torch::nn::Module::load(torch::serialize::InputArchive&) + 0xf9 (0x7f2d9381cd69 in /usr/local/lib/libtorch.so.1)
frame #5: torch::nn::operator>>(torch::serialize::InputArchive&, std::shared_ptr<torch::nn::Module> const&) + 0x32 (0x7f2d9381c7b2 in /usr/local/lib/libtorch.so.1)
frame #6: <unknown function> + 0x2b16c (0x5645f4d1916c in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest)
frame #7: <unknown function> + 0x27a3c (0x5645f4d15a3c in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest)
frame #8: <unknown function> + 0x2165c (0x5645f4d0f65c in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest)
frame #9: <unknown function> + 0x1540b (0x5645f4d0340b in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest)
frame #10: __libc_start_main + 0xf3 (0x7f2d051dd223 in /usr/lib/libc.so.6)
frame #11: <unknown function> + 0x1381e (0x5645f4d0181e in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest)
```
Renaming C++ BatchNorm `running_variance` to `running_var` should fix this problem.
This is a BC-breaking change, but it should be easy for end user to rename `running_variance` to `running_var` in their call sites.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17371
Reviewed By: goldsborough
Differential Revision: D14172775
Pulled By: yf225
fbshipit-source-id: b9d3729ec79272a8084269756f28a8f7c4dd16b6
Summary:
Trying to land again, make prim::None into a case of prim::Constant. Reverted the previous landing because it broke an important onnx export test.
https://github.com/pytorch/pytorch/pull/16160
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17186
Differential Revision: D14115304
Pulled By: eellison
fbshipit-source-id: 161435fc30460b4e116cdd62c7b2e5b94581dcb7
Summary:
Adding two distrbuted samplers, Random and Sequential to the mix. Similar to python counterpart, DistributedSampler introduces a new method `set_epoch(size_t epoch)` which can be use to shuffle data determinstically between distributed processes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16910
Differential Revision: D14130980
Pulled By: soumith
fbshipit-source-id: ec08b7130c01e2fc6dc3693f7ac622a0a6d60f10
Summary:
It might need some cleaning up and might be missing some features, but it should be already working for most cases.
This PR is based on top of PR16986 (so please review only the last commit here).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16987
Differential Revision: D14074577
Pulled By: ZolotukhinM
fbshipit-source-id: 712b598f423265655f574bb9903e2066628eaad3
Summary:
Currently the converters are very straightforward, i.e. there is no code for trying to
preserve semantics, we're purely perform conversion from one format to another.
Two things that we might want to add/change:
1. Add semantic conversion as well (but probably it would be a good idea to keep
it separate as a temporary thing).
2. Make sure we don't mess with value names, as they are crucial for current
uses of NetDefs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17123
Differential Revision: D14090244
Pulled By: ZolotukhinM
fbshipit-source-id: 07175fa9235582e1d1da5f10a42a5c1280b1b394
Summary:
This change simplifies analysis done on constants since prim::None does not need to be handled separately now. To check if a constant node is None, use node->isNone().
Next step will be to remove prim::Undefined.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16160
Differential Revision: D14109636
Pulled By: eellison
fbshipit-source-id: d26fd383976163a2ddd4c24984bd672a541cc876
Summary:
/cc goldsborough
Working on #14582
The corresponding python implementations are at: [pytorch/torch/nn/init.py](6302e4001a/torch/nn/init.py (L261-L327))
Here is my initial implementation of Kaiming Initialization. I have not been able to figure out how to successfully run tests locally so I haven't added any yet.
A couple questions:
- Are the enums defined in the right place? I copied their names from Python, but do you prefer different naming conventions for C++?
- To run tests locally do I use `python setup.py test`? Can I run just a subset of the tests somehow?
- Should I add my tests at [test/cpp/api/misc.cpp](https://github.com/pytorch/pytorch/blob/master/test/cpp/api/misc.cpp#L47-L54)?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14718
Differential Revision: D14049159
Pulled By: goldsborough
fbshipit-source-id: 966ac5126875936e69b185b5041f16476ed4cf70
Summary:
- Moved a few functions from `autograd` namespace to `aten` namespace to be visible from JIT nativeResolver.
- Added a hack to loop up keyword only argument. Will add proper support for kw only later
- Simulate function overload in aten using `_<number>` as function name suffix.
- Even `forward` returns multiple outputs like in `kthvalue`, there's at most one requires grad that we currently support.
- Removed the `TensorList` related ops here since partial `TensorList` support is prone to bugs. Our symbolic diff for `cat` was never tested with autodiff, and it seems broken. Need to find another proper way to support these ops(either by properly supporting `TensorList` or sth like `prim::ConstantChunk` and leave them for next PR.
Ops supported in this PR:
```
erf
expand_as
index
kthvalue
mean
permute
pow
rsub
select
sqrt
squeeze
t
to
topk
transpose
view
var
embedding
logsumexp
// grad is None
_dim_arange
contiguous
nonzero
ones_like
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16689
Differential Revision: D14020806
Pulled By: ailzhang
fbshipit-source-id: a5e2c144a7be5a0d39d7ac5f93cb402ec12503a5
Summary:
libshm_manager doesn't need to depend on all of libtorch. It only uses tiny tempfile.h which can be moved to c10. I could just duplicate the file too, but it's not worth it as c10 is small enough.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17019
Differential Revision: D14052688
Pulled By: dzhulgakov
fbshipit-source-id: 8797d15f8c7c49c49d40b7ab2f43aa3bf6becb0c
Summary:
Currently the converters are very straightforward, i.e. there is no code for trying to
preserve semantics, we're purely perform conversion from one format to another.
Two things that we might want to add/change:
1. Add semantic conversion as well (but probably it would be a good idea to keep
it separate as a temporary thing).
2. Make sure we don't mess with value names, as they are crucial for current
uses of NetDefs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16967
Differential Revision: D14062537
Pulled By: ZolotukhinM
fbshipit-source-id: 88b184ee7276779e5e9152b149d69857515ad98a
Summary:
Previously, the ChunkBuffer depends on the remaining chunk count to signal end of dataloading. This does not work with distributed samplers where each sampler only loads a subset of chunks. This refactor remove the dependency on the remaining chunk count at the ChunkBuffer.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16868
Differential Revision: D14066517
Pulled By: goldsborough
fbshipit-source-id: 293dfe282ceff326dff0876c2f75c2ee4f4463e2
Summary:
(review top commit only).
As expected, fork/wait introduces some corner cases into the alias analysis. The comments inline should describe the changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16671
Differential Revision: D13963219
Pulled By: suo
fbshipit-source-id: 2bec6fc03a4989cf309fbb9473f3f2ffe2c31431
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16751
This was made more complicated by the fact that ivalue::IntList
is a thing. So I had to fix all of the sites where we referring
to IValue post facto.
The following codemods were run, in this order:
```
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in IntList IntArrayRef
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in IntArrayRef::create IntList::create
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in ivalue::IntArrayRef ivalue::IntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in Tag::IntArrayRef Tag::IntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in isIntArrayRef isIntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in toIntArrayRef toIntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in 'Shared<IntArrayRef>' 'Shared<IntList>'
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in 'intrusive_ptr<IntArrayRef>' 'intrusive_ptr<IntList>'
```
Some manual fixups were done afterwards; they can be reviewed separately
at https://github.com/pytorch/pytorch/pull/16752
Reviewed By: dzhulgakov
Differential Revision: D13954363
fbshipit-source-id: b5c40aacba042402155a2f5a229fa6db7992ac64
Summary:
This PR reworks the mutability API to be simpler (updates passes to use "mayAlias" calls) and improves the caching logic.
The difference is that we now directly express the idea of a "memory location." Leaves in the alias trackers points-to graph are considered unique memory locations, and mayAlias questions can be boiled down whether two values share a leaf.
To speed up queries, some basic path compression has been added.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16605
Differential Revision: D13952738
Pulled By: suo
fbshipit-source-id: cfc7fb2b23369f1dc425d1d8ca2c753c193d95dd
Summary:
I went through my build log and did what I thought were reasonable fixes to all the C++ compilation warnings that came up
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16411
Differential Revision: D13901006
Pulled By: jamesr66a
fbshipit-source-id: 02df4e3e5a5c8dd9e69ac9f065cd3f2a80645033
Summary:
Start splitting up these tests so we don't have a massive test file. Doesn't change how you run them, since `gtest.cpp` and `no-gtest.cpp` will still collect everything.
Renamed `tests.h` to `test_misc.h` to vaguely discourage people from adding yet more stuff to it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16536
Reviewed By: zdevito, eellison
Differential Revision: D13882215
Pulled By: suo
fbshipit-source-id: 61cf97f3c2c50703dcf6a3a34da01415ecb7e7d6
Summary:
Fixes https://github.com/pytorch/pytorch/issues/16326
Previously we didn't handle module inputs which included Generic Lists. When checking whether a generic list if a subvalue of the input arg type, I currently recurse on every element of the list. This shouldn't be too slow since the innermost list will be specialized and we won't have to check it's elements.
E.g. Tensor[][] -> GenericList [TensorList ].
The error message could be improved, but extracting the complete type of nested lists would have to deal with unifying types across lists / empty lists & typevars so I'm going to save that for a follow up PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16482
Differential Revision: D13882582
Pulled By: eellison
fbshipit-source-id: 3609bc572f0ee9ebf20a77ea5ebc8fa3b165e24b
Summary:
This PR changes the way we store aliasing information from a "set" approach to a "points-to" analysis. Set-based approaches lose information in ways that make it difficult to do "live" updates to the alias DB as one as mutating the graph.
The tradeoff is that simple queries get more expensive, since they require traversing the points-to graph to answer most questions. In practice, this is unlikely to be that costly since we don't have massive aliasing chains, but we could create an approximation/caching layer if this becomes a problem.
My rough plan is:
1. This PR, switching to a points-to graph
2. Make it "live": analyzing a node should record all the edges the node added, so that we can rollback when the node is destroyed.
3. Reduce wildcard scope: we can make the wildcard a special vertex that points to anything that we're not "sure" about; namely, things that have been put inside lists, or graph inputs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16386
Differential Revision: D13855117
Pulled By: suo
fbshipit-source-id: f009f58143173c275501624eb105d07ab60fe5e1
Summary:
This PR contains the implementation of chunk dataset, with the API proposed in PR https://github.com/pytorch/pytorch/pull/15562
A chunk dataset is derived from StatefulDataset. It utilizes worker threads to prefetches chunk data, splits it into batches and caches them into a queue. When get_batch is called from dataloader, batch data is retrieved from the queue, and data in new chunks will be pushed for later following batches.
Chunk dataset uses two samplers (chunk_sampler and example_sampler) to perform sampling. The chunk_sampler decides which chunk to load, and example_sampler shuffles the examples inside a specific chunk. More detail of this sampling approach can be found here: http://martin.zinkevich.org/publications/nips2010.pdf
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15932
Differential Revision: D13868688
Pulled By: soumith
fbshipit-source-id: a43000c478ca2a3c64cc84b3626d6b8b1ad9a07e
Summary:
This PR inlines `Attributes` into `Node`. It helps to cleanup the code a little as everything is one place (some of the cleanups are included in the PR).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16098
Differential Revision: D13717637
Pulled By: ZolotukhinM
fbshipit-source-id: c54ae65178a95a01354688921a9ccb1ca699f8eb
Summary:
In Python, you can use the call operator to invoke the `forward()` method of a module. In C++ this was currently not possible, because I couldn't figure out how to deduce the return type of a module's `forward()` method under the constraint that `forward()` may not exist at all (since the base module class in C++ does not mandate a `forward()` method). I now figured it out, so the call operator can be used.
ezyang ebetica
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15831
Differential Revision: D13652676
Pulled By: goldsborough
fbshipit-source-id: ccab45a15215dda56460e560f0038781b539135f
