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2019-04-02Bool Tensor for CUDA (#18166)Iurii Zdebskyi1-118/+122
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18166 ghimport-source-id: a8e2ba2d966e49747a55701c4f6863c5e24d6f14 Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18166 Bool Tensor for CUDA** * #18165 Resolved comments from Bool Tensor for CPU PR ------ This PR enables bool tensor creation and some basic operations for the CPU backend. This is a part of Bool Tensor feature implementation work. The whole plan looks like this: 1. Storage Implementation [Done] 2. Tensor Creation. a) CPU [Done] b) CUDA [This PR] 3. Tensor Conversions. 4. Tensor Indexing. 5. Tensor Operations. 6. Back compatibility related changes. Change: Enable bool tensor in CUDA with the following operations: torch.zeros torch.tensor torch.ones torch.rand/rand_like/randint/randint_like torch.full torch.full_like torch.empty torch.empty_like Tested via unit tests and local scripts. Differential Revision: D14605104 fbshipit-source-id: b7d7340a7d70edd03a109222d271e68becba762c
2019-04-02torch.cross' dim default changed to c10::optional instead of int=-1 (#17582)Igor Fedan1-0/+29
Summary: Argument dim=-1 doesn't work for torch.cross. The signature of the torch.cross has been changed to c10::optional<int64_t> dim instead of int64_t. So based on document "If dim is not given, it defaults to the first dimension found with the size 3." and if dim is specified (even negative) it will use the correspondent dim. Fixes #17229 Pull Request resolved: https://github.com/pytorch/pytorch/pull/17582 Differential Revision: D14483063 Pulled By: ifedan fbshipit-source-id: f9699093ec401cb185fd33ca4563c8a46cdcd746
2019-04-02Fix flake8 issues in gragrad testIgor Fedan1-1/+2
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18727 Differential Revision: D14724887 Pulled By: ifedan fbshipit-source-id: 8c1db6460303e746e4aea0142302b8d61277c067
2019-04-02Adding pin_memory kwarg to zeros, ones, empty, ... tensor constructors. (#18455)Vitaly Fedyunin1-0/+34
Summary: Make it possible to construct a pinned memory tensor without creating a storage first and without calling pin_memory() function. It is also faster, as copy operation is unnecessary. Supported functions: ```python torch.rand_like(t, pin_memory=True) torch.randn_like(t, pin_memory=True) torch.empty_like(t, pin_memory=True) torch.full_like(t, 4, pin_memory=True) torch.zeros_like(t, pin_memory=True) torch.ones_like(t, pin_memory=True) torch.tensor([10,11], pin_memory=True) torch.randn(3, 5, pin_memory=True) torch.rand(3, pin_memory=True) torch.zeros(3, pin_memory=True) torch.randperm(3, pin_memory=True) torch.empty(6, pin_memory=True) torch.ones(6, pin_memory=True) torch.eye(6, pin_memory=True) torch.arange(3, 5, pin_memory=True) ``` Part of the bigger: `Remove Storage` plan. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18455 Reviewed By: ezyang Differential Revision: D14672084 Pulled By: VitalyFedyunin fbshipit-source-id: 9d0997ec00f59500ee018f8b851934d334012124
2019-04-02Expose alias multinomial methods to ATen (#17904)vishwakftw2-0/+37
Summary: This PR exposes the multinomialAliasSetup and multinomialAliasDraw methods. cc: neerajprad Pull Request resolved: https://github.com/pytorch/pytorch/pull/17904 Differential Revision: D14700205 Pulled By: ezyang fbshipit-source-id: 16462fb1f1ef1d560fd586632ea356b23e966ee3
2019-04-01fixes multiprocessing serialization for integer nn.Parameter (#18639)Soumith Chintala1-0/+14
Summary: Fixes https://github.com/pytorch/pytorch/issues/17345 Pull Request resolved: https://github.com/pytorch/pytorch/pull/18639 Differential Revision: D14711565 Pulled By: soumith fbshipit-source-id: 0063ed138a215b95d6571dcd68b18569714abe19
2019-04-01Peephole Optimize Shape Ops (#18549)Elias Ellison1-0/+56
Summary: Peephole optimize ops that just require Dimensioned Tensor Type, which is what we specialize graphs on. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18549 Differential Revision: D14690827 Pulled By: eellison fbshipit-source-id: 9d7439eb584f0a5b877f5aa53cf80150f00e7e5f
2019-04-01Fix for double backwards tests (#18190)Igor Fedan1-0/+9
Summary: If none of the outputs require_grad, we don't actually check gradgrad, instead we will check that their numerical gradients are 0. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18190 Differential Revision: D14563388 Pulled By: ifedan fbshipit-source-id: a4eb94c9eb60f14dbe6986cd8cef1fe78a7bc839
2019-04-01Add string index/slice operations (#18247)David Riazati1-1/+38
Summary: Adds support for string indexing (`"a"[0]`) and slicing (`"abc"[1:3]`) to script. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18247 Differential Revision: D14574486 Pulled By: driazati fbshipit-source-id: 4b42aa0881e5398ea7f112be46c0335e6e19dced
2019-04-01Re-land Parsing file check (#18570)eellison3-1/+77
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
2019-03-31Fix test on windows (#18667)David Riazati1-0/+1
Summary: Breakage in #18188 Pull Request resolved: https://github.com/pytorch/pytorch/pull/18667 Differential Revision: D14700133 Pulled By: driazati fbshipit-source-id: 4cc26bd579fc1b074b3bef6046cc1030facee130
2019-03-31Enforce check ad in test_jit (#18509)Ailing Zhang3-457/+523
Summary: If a test triggers autodiff, it must have a `DifferentiableGraph` in its differentiated forward graph, and this subgraph must have either the original aten node, or the corresponding nodes used in AD formula. Typically a forward differentiable graph looks like this: ``` graph(%i0 : Float(), %i1 : Float()): %3 : Float() = prim::DifferentiableGraph_0(%i0, %i1) return (%3) with prim::DifferentiableGraph_0 = graph(%0 : Float(), %1 : Float()): %2 : Float() = aten::max(%0, %1) return (%2) ``` which tells us `aten::max(Tensor self, Tensor other) -> Tensor` is symbolically differentiable. Update: there're a lot of cases (fusions/ConstantChunk/python implementations) that breaks it so I decided to make the check optionally take node names if different from function name. ~~[OLD]Theoretically I could also check if `aten::max` is in the differentiable block or not to be more precise, but there're also cases like `chunk` where in a differentiable block it's replaced with a prim node (ConstantChunk) and we will have to special case them. Any suggestions here (to be more precise or no) is very welcome!~~ We used to have a list containing nn tests should be run against AD, I moved it to an field when constructing our test(either torch or nn). I think it's cleaner this way, and it matches the fact that for the same op we support one schema of it but not all, in this way we could just turn on the corresponding test which triggers that supported schema. cc: apaszke zdevito wanchaol ngimel for a review [Done] : - Going through a manual second pass of all tests to check if they should enable AD test or not.... - Add a readme about how to add AD for an op and how to add/enable its test in test_jit. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18509 Differential Revision: D14696811 Pulled By: ailzhang fbshipit-source-id: c5e693277baac585cd3aed5ab2c0e7faa5e6f29f
2019-03-30Turn on F401: Unused import warning. (#18598)Edward Yang34-102/+33
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598 ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18598 Turn on F401: Unused import warning.** This was requested by someone at Facebook; this lint is turned on for Facebook by default. "Sure, why not." I had to noqa a number of imports in __init__. Hypothetically we're supposed to use __all__ in this case, but I was too lazy to fix it. Left for future work. Be careful! flake8-2 and flake8-3 behave differently with respect to import resolution for # type: comments. flake8-3 will report an import unused; flake8-2 will not. For now, I just noqa'd all these sites. All the changes were done by hand. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14687478 fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
2019-03-29Attribute serialization improvements (#18188)David Riazati1-3/+67
Summary: * adds attributes to `ScriptModule.__getattr__` so they can be accessed in Python after re-importing * full support for all the possible values for an `int64_t` * this necessitated a bunch more `pushWhatever` functions, so re-introduced a templated version to cut down on duplicate code * tests to validate references / value sharing works * adds `torch.jit.Unpickler` which people can use to de-serialize the pickle files into Python / have a quick reference on how to do this without PyTorch Pull Request resolved: https://github.com/pytorch/pytorch/pull/18188 Differential Revision: D14527490 Pulled By: driazati fbshipit-source-id: efd15579cc04aa2e28c4b2c9490d82d849dee559
2019-03-29Add hash() global (#18258)David Riazati1-0/+28
Summary: This adds `hash()` which supports `int`, `str`, and `float`. It relies on `std::hash` which is implementation defined, so the result of `hash()` in TorchScript is not the same as in Python, but should satisfy the same properties. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18258 Differential Revision: D14692317 Pulled By: driazati fbshipit-source-id: 909df5d024bb3feea157d5a203b7de53c72261c9
2019-03-29Move fuser to test_jit_fuser (#18590)Elias Ellison3-864/+884
Summary: Start of breaking up test_jit.py New files will have the format test_jit_* so they are easily grepable but remain in the same directory so we don't have to go through multiple sources for imports. I am adding a test that's expected to fail to be sure it's running. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18590 Reviewed By: wanchaol Differential Revision: D14677094 Pulled By: eellison fbshipit-source-id: 9782c6aa9525bb6f332fc75cfff004c83a417522
2019-03-29Experimental logging/counters API (#18235)James Reed1-0/+101
Summary: This defines a generic counters API that users can utilize to provide monitoring functionality in e.g. a production service. We expose both counters for runtime internals as well as a TorchScript API to create user-defined counters. Synopsis of the API: - `torch/csrc/jit/script/logging.h` specifies the externally-facing API in C++ - `torch/jit/_logging.py` specifies the Python API We use an interface, `LoggerBase`, to define the interactions between users and a logging backend. Implementing a subclass of `LoggerBase` allows the user to handle these events in a custom way, such as logging into a DB or calling into an infra-specific counters API. From the frontend perspective, we can create log events in two ways: 1. We provide an `add_stat_value(name, val)` function. This calls into the Logger backend with a key/value pair. For example, we might call `add_stat_value('foo', 1)` to bump an event counter. 2. We provide a `time_point()` function to record a timestamp in nanoseconds. This can be used in conjunction with `add_stat_value` to record runtime wall clock durations. Examples of frontend usage can be found in `test_jit.py TestLogging`. We provide a trivial `LockingLogger` implementation as an example and for testing purposes. It is likely not ready for production usage. It demonstrates that a backend implementing the API can do things like specify aggregation types and report these aggregate stats via the `get_counters()` API. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18235 Differential Revision: D14545060 Pulled By: jamesr66a fbshipit-source-id: 04099543a1898cfdd411511e46e03d5dce9b4881
2019-03-29Revert D14668859: [pytorch][PR] Re-land Parsing file checkDavid Riazati3-77/+1
Differential Revision: D14668859 Original commit changeset: 3825a35ddc61 fbshipit-source-id: f3343ec6b63fe8f1f04959adfac4331865990047
2019-03-29Re-land Parsing file check (#18570)eellison3-1/+77
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
2019-03-29Refactoring serialization of ONNX initializers to be name-based ↵Spandan Tiwari6-35/+87
(Resubmission) (#17830) Summary: houseroad - this is the resubmission of https://github.com/pytorch/pytorch/pull/17420, as suggested. Pull Request resolved: https://github.com/pytorch/pytorch/pull/17830 Reviewed By: zrphercule Differential Revision: D14398714 Pulled By: houseroad fbshipit-source-id: bda475f1ae8a5273ebdb0f6883fc66036c29d326
2019-03-29Initial implementation of InsertObserverNodes pass. (#18152)Mikhail Zolotukhin1-2/+41
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18152 ghimport-source-id: 1dd5e62c4d93394dcd8d8af2871554575c8d3d1a Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18152 Initial implementation of InsertObserverNodes pass.** * #18151 Add quant-passes stubs. gh-metadata: pytorch pytorch 18150 gh/zolotukhinm@gmail.com/2/head Differential Revision: D14584223 fbshipit-source-id: 30896acc1a8901d22c6a167eb87d2fbaafbbeb6f
2019-03-29Add named submodule support to nn::Sequential (#17552)Will Feng1-0/+82
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
2019-03-29Rename `btriunpack` to `lu_unpack` (#18529)Vishwak Srinivasan2-9/+9
Summary: Changelog: - Renames `btriunpack` to `lu_unpack` to remain consistent with the `lu` function interface. - Rename all relevant tests, fix callsites - Create a tentative alias for `lu_unpack` under the name `btriunpack` and add a deprecation warning to not promote usage. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18529 Differential Revision: D14683161 Pulled By: soumith fbshipit-source-id: 994287eaa15c50fd74c2f1c7646edfc61e8099b1
2019-03-29fix lint (#18623)Elias Ellison1-4/+0
Summary: Fix lint Pull Request resolved: https://github.com/pytorch/pytorch/pull/18623 Differential Revision: D14686265 Pulled By: eellison fbshipit-source-id: 4bbe0f5bc58f508cbf4bc1baef2029ce1eaa42d8
2019-03-29Rename `btrifact*` to `lu` (#18435)Vishwak Srinivasan2-24/+25
Summary: Changelog: - Renames `btrifact` and `btrifact_with_info` to `lu`to remain consistent with other factorization methods (`qr` and `svd`). - Now, we will only have one function and methods named `lu`, which performs `lu` decomposition. This function takes a get_infos kwarg, which when set to True includes a infos tensor in the tuple. - Rename all tests, fix callsites - Create a tentative alias for `lu` under the name `btrifact` and `btrifact_with_info`, and add a deprecation warning to not promote usage. - Add the single batch version for `lu` so that users don't have to unsqueeze and squeeze for a single square matrix (see changes in determinant computation in `LinearAlgebra.cpp`) Pull Request resolved: https://github.com/pytorch/pytorch/pull/18435 Differential Revision: D14680352 Pulled By: soumith fbshipit-source-id: af58dfc11fa53d9e8e0318c720beaf5502978cd8
2019-03-28Delete batch tensor (#18575)Elias Ellison2-545/+1
Summary: Deleting batch tensor since we are no longer maintaining the project and keeping it functional is blocking other improvements. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18575 Differential Revision: D14671126 Pulled By: eellison fbshipit-source-id: b42d5b699c4d12171ed95e6d3a977532167f0d2c
2019-03-28Add external callbacks into RecordFunction (#17844)Ilia Cherniavskii2-1/+68
Summary: Add a way to insert external callbacks into PT's RecordFunction Pull Request resolved: https://github.com/pytorch/pytorch/pull/17844 Differential Revision: D14399664 Pulled By: ilia-cher fbshipit-source-id: 76654799811fefd3ffed4abfb46ed95b492cebab
2019-03-28Revert D14652372: [pytorch][PR] Add parsing to file checkElias Ellison2-76/+1
Differential Revision: D14652372 Original commit changeset: 7430b9d1dc2b fbshipit-source-id: fa3d0f68515fe53447746469844d2db20c1292e0
2019-03-27Adds Cyclical Learning Rate and Momentum (#18001)Sam Pepose1-1/+177
Summary: This implements a cyclical learning rate (CLR) schedule with an optional inverse cyclical momentum. More info about CLR: https://github.com/bckenstler/CLR This is finishing what #2016 started. Resolves #1909. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18001 Differential Revision: D14451845 Pulled By: sampepose fbshipit-source-id: 8f682e0c3dee3a73bd2b14cc93fcf5f0e836b8c9
2019-03-27add slow tests annotation to some jit tests (#18545)Elias Ellison1-1/+5
Summary: Adds slow test annotation to the following very slow tests - 70.33s test/test_jit.py::TestScript::test_script_module_script_resnet 32.33s test/test_jit.py::TestBatched::test_beam_search 17.70s test/test_jit.py::TestBatched::test_greedy_search 15.58s test/test_jit.py::TestScript::test_script_module_trace_resnet18 The list of remaining slow tests is below. Let me know if you think any of the others should be added to slow tests as well. Slow tests will only run on master. 15.28s call test/test_jit.py::TestJit::test_export_batchnorm 12.96s call test/test_jit.py::TestEndToEndHybridFrontendModels::test_snli 11.65s call test/test_jit.py::TestEndToEndHybridFrontendModels::test_neural_style 6.38s call test/test_jit.py::TestJitGeneratedModule::test_nn_LocalResponseNorm_1d 5.96s call test/test_jit.py::TestJitGeneratedModule::test_nn_LocalResponseNorm_2d_uneven_pad 5.91s call test/test_jit.py::TestJitGeneratedModule::test_nn_LocalResponseNorm_3d_custom_params 4.76s call test/test_jit.py::TestJit::test_alexnet 3.82s call test/test_jit.py::TestScript::test_number_math 3.81s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_no_bias 3.76s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_groups_thnn 3.65s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_stride_pad1circular 3.49s call test/test_jit.py::TestBatched::test_lstm 3.33s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_pad2circular 3.19s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_stride1_pad2circular 3.11s call test/test_jit.py::TestEndToEndHybridFrontendModels::test_dcgan_models 3.11s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_stride_padding 3.11s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_stride 3.08s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_no_bias 3.08s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_stride1_pad1circular 3.07s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_groups 3.05s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_dilated 3.05s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_depthwise_with_multiplier 3.04s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_groups 3.03s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_dilated 3.02s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_depthwise_dilated 3.02s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_dilated_strided Pull Request resolved: https://github.com/pytorch/pytorch/pull/18545 Differential Revision: D14656064 Pulled By: eellison fbshipit-source-id: d17ee23c3b3679276cee983555d43e83ce099356
2019-03-27Add parsing to file check (#18304)Elias Ellison2-1/+76
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
2019-03-27bug fix for node with writers in create autodiff subgraph (#18491)Elias Ellison1-0/+22
Summary: Previously we were moving nodes with writers into differentiable subgraphs, without necessarily preserving whether or not they were written to. This can lead to bugs with CSE, which needs that context. I'm not completely sure if there's anything else we can do to be more aggresive here - inline these nodes and not run CSE and just run constant pooling, or possibly something else, but I think we should land this correctness condition first and then possibly think further. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18491 Differential Revision: D14648562 Pulled By: eellison fbshipit-source-id: bc1e444774ccdb708e22f0e06a477a221a231f9e
2019-03-27Const trace error v2 (#18535)Elias Ellison1-0/+15
Summary: Trying to reland https://github.com/pytorch/pytorch/pull/18298 Pull Request resolved: https://github.com/pytorch/pytorch/pull/18535 Differential Revision: D14652391 Pulled By: eellison fbshipit-source-id: 699e30045dd5f14f0a2b98378272045a292e1e2a
2019-03-27enable more unit tests (#18537)jithunnair-amd3-22/+0
Summary: Enable unit tests working with ROCm 2.3. In particular, these are unit tests where we skipped for double data types previously and some tests for multi-GPU setups. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18537 Differential Revision: D14651822 Pulled By: ezyang fbshipit-source-id: 7dd575504ebe235a91489866c91000e9754b1235
2019-03-27Upgrade flake8-bugbear to master, fix the new lints. (#18507)Edward Yang1-5/+5
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18507 ghimport-source-id: 1c3642befad2da78a7e5f39d6d58732b85c76267 Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18507 Upgrade flake8-bugbear to master, fix the new lints.** It turns out Facebobok is internally using the unreleased master flake8-bugbear, so upgrading it grabs a few more lints that Phabricator was complaining about but we didn't get in open source. A few of the getattr sites that I fixed look very suspicious (they're written as if Python were a lazy language), but I didn't look more closely into the matter. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14633682 fbshipit-source-id: fc3f97c87dca40bbda943a1d1061953490dbacf8
2019-03-26fix #16448 (#18479)Tongzhou Wang1-6/+1
Summary: Fixes #16448 bddppq Pull Request resolved: https://github.com/pytorch/pytorch/pull/18479 Differential Revision: D14635360 Pulled By: ezyang fbshipit-source-id: 4010319fbce050dd0bdf4da3cd1171b9737f3c4c
2019-03-26Add some missing docs for tensor methods and attributes, new unittest to ↵Xiang Gao2-43/+57
enforce tensors.rst no longer miss anything (#16057) Summary: This depend on https://github.com/pytorch/pytorch/pull/16039 This prevent people (reviewer, PR author) from forgetting adding things to `tensors.rst`. When something new is added to `_tensor_doc.py` or `tensor.py` but intentionally not in `tensors.rst`, people should manually whitelist it in `test_docs_coverage.py`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/16057 Differential Revision: D14619550 Pulled By: ezyang fbshipit-source-id: e1c6dd6761142e2e48ec499e118df399e3949fcc
2019-03-26Revert D14605905: [pytorch][PR] Add return_counts to torch.uniqueSoumith Chintala1-98/+7
Differential Revision: D14605905 Original commit changeset: 555f5a12a8e2 fbshipit-source-id: c7874f5987893e956c022180a37763d88bba38db
2019-03-26Improve numerical precision of (s)logdet (#18449)Tongzhou Wang2-53/+92
Summary: Fixes https://github.com/pytorch/pytorch/issues/18448 and https://github.com/pytorch/pytorch/issues/18450 Pull Request resolved: https://github.com/pytorch/pytorch/pull/18449 Differential Revision: D14611638 Pulled By: soumith fbshipit-source-id: 4f1f27ab5316a92d2783e734169f599afed743cf
2019-03-26fix arange shape issue inconsistency across cpu and cuda (#18462)Soumith Chintala1-0/+4
Summary: Fixes https://github.com/pytorch/pytorch/issues/18363 Pull Request resolved: https://github.com/pytorch/pytorch/pull/18462 Differential Revision: D14620263 Pulled By: soumith fbshipit-source-id: 223524cdda2f5d55c2ca8d4cdcf6f7a05a6c15eb
2019-03-26Minor fix for onnx ConstantOfShape export (#18199)BowenBao4-0/+4
Summary: Set value as tensor of 1 element instead of scalar, according to ONNX spec. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18199 Reviewed By: dzhulgakov Differential Revision: D14542588 Pulled By: houseroad fbshipit-source-id: 70dc978d870ebe6ef37c519ba4a20061c3f07372
2019-03-26Namedtuple return for solve, slogdet, sort, topk (#17093)Xiang Gao2-1/+36
Summary: More ops for https://github.com/pytorch/pytorch/issues/394. ~~Also need to rebase after landing #16186, because we need to update the whitelist of the new unit test added in #16186.~~ cc: ezyang Pull Request resolved: https://github.com/pytorch/pytorch/pull/17093 Differential Revision: D14620068 Pulled By: ezyang fbshipit-source-id: deec5ffc9bf7624e0350c85392ee59789bad4237
2019-03-26Resolving comments from Bool Tensor for CPU PR (#18165)Iurii Zdebskyi2-6/+29
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18165 ghimport-source-id: 55cb3fb63a25c2faab1725b4ec14c688bf45bd38 Stack from [ghstack](https://github.com/ezyang/ghstack): * #18166 Bool Tensor for CUDA * **#18165 Resolved comments from Bool Tensor for CPU PR** ------- ------------ This is a follow up PR that resolves some additional feedback on one the of previous Bool Tensor PRs. gchanan, here is a list of almost all the comments from the original PR with respective fixes and replies: **[utils/python_scalars.h]** why is this converting from uint8_t and not bool? (comment?) When i was adding this, i was testing by creating a tensor and then calling its .tolist(). it worked for bool and uint8_t equally good so i left uint8_t as thought it makes more sense as we are calling PyBool_FromLong. �Changing it to bool. **[ATen/Dispatch.h]**better name?. fixed. **[test/test_torch.py]** what about other factories, such as full? (and more). There is a test that goes through the factory methods - test_tensor_factories_empty. i added some bool cases above it and added a comment that once CUDA will be done, i will unite them and it will iterate not just between CUDA and CPU but also all types. ��Adding all bool cases now. Will unite in CUDA PR. **[generic/THTensorMath.h]** any changes in this file actually needed? Bad merge. Fixed. **[TH/THTensor.h]** this generates code for random, clampedRandom, and cappedRandom -- do we have tests for all of these with bool? Added **[c10/core/ScalarType.h]** I'm not very confident about the lack of Bool here -- can you look at the call sites and see what makes sense to do here? Added bool to the macro and created a similar one without for a single case which fails the build with errors: _./torch/csrc/jit/symbolic_variable.h:79:20: error: ambiguous overload for ‘operator*’ (operand types are ‘const torch::jit::SymbolicVariable’ and ‘torch::jit::Value*’) return (*this) * insertConstant(rhs);_ Differential Revision: D14605105 fbshipit-source-id: abf82d50e8f8c50b386545ac068268651b28496d
2019-03-26Unify cudaGetDeviceCount implementations. (#18445)Edward Yang1-1/+1
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18445 ghimport-source-id: 30d018737bf6989bc68b7e3676f44e0ca6141fde Stack from [ghstack](https://github.com/ezyang/ghstack): * #18242 Test running a CUDA build on CPU machine. * **#18445 Unify cudaGetDeviceCount implementations.** I went about doing this by searching for calls to cudaGetDeviceCount, and then methodically replacing them with references to c10::cuda::device_count() or at::cuda::device_count(). There is a point to doing this: the various implementations wildly differed in their handling of what to do when cudaGetDeviceCount returns an error. The final standardized behavior is that **all errors are swallowed** and we return device count of zero. This indirectly fixes running CUDA builds on CPU, which was broken in #17847. I added 'noexcept' to the 'deviceCount' virtual method on DeviceGuardImpl. This is a BC-breaking change for anyone inheriting from DeviceGuardImpl but all you need to do is put 'noexcept' on your method and it is backwards compatible with older libtorch. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14612189 fbshipit-source-id: 3c8d186e3dd623c0e27625212c7ce30f75d943cb
2019-03-26Introduce SobolEngine (#10505)vishwakftw1-0/+81
Summary: `SobolEngine` is a quasi-random sampler used to sample points evenly between [0,1]. Here we use direction numbers to generate these samples. The maximum supported dimension for the sampler is 1111. Documentation has been added, tests have been added based on Balandat 's references. The implementation is an optimized / tensor-ized implementation of Balandat 's implementation in Cython as provided in #9332. This closes #9332 . cc: soumith Balandat Pull Request resolved: https://github.com/pytorch/pytorch/pull/10505 Reviewed By: zou3519 Differential Revision: D9330179 Pulled By: ezyang fbshipit-source-id: 01d5588e765b33b06febe99348f14d1e7fe8e55d
2019-03-25Optimize boolean expressions & unwraps (#18259)eellison3-1/+127
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
2019-03-25Support dim=None for argmax and argmin (#18264)Xiang Gao1-0/+16
Summary: Fixes: https://github.com/pytorch/pytorch/issues/18263 cc: houseroad Pull Request resolved: https://github.com/pytorch/pytorch/pull/18264 Reviewed By: ezyang Differential Revision: D14559234 Pulled By: houseroad fbshipit-source-id: c5b8623752d6c6af41c6d715fd9585a65294868d
2019-03-25Add return_counts to torch.unique (#18391)Xiang Gao1-7/+98
Summary: Fixes: https://github.com/pytorch/pytorch/issues/12598 This PR was originally authorized by ptrblck at https://github.com/pytorch/pytorch/pull/15495, but since there was no update for months after the request change, I clone that branch and resolve the code reviews here. Hope everything is good now. Especially, the implementation of count is changed from ptrblck's original algorithm to the one ngimel suggest, i.e. using `unique_by_key` and `adjacent_difference`. The currently implementation of `_unique_dim` is VERY slow for computing inverse index and counts, see https://github.com/pytorch/pytorch/issues/18405. I will refactor `_unique_dim` in a later PR. For this PR, please allow me to keep the implementation as is. cc: ptrblck ezyang ngimel colesbury Pull Request resolved: https://github.com/pytorch/pytorch/pull/18391 Reviewed By: soumith Differential Revision: D14605905 Pulled By: VitalyFedyunin fbshipit-source-id: 555f5a12a8e28c38b10dfccf1b6bb16c030bfdce
2019-03-25change dropout lowering in symbolic_script (#18375)Natalia Gimelshein1-0/+2
Summary: Dropout is now eligible for fusion, and generated fused kernels are just as fast as dropout in ATen. Change its lowering in symbolic script so that it can actually be fused. Still special-cased for cuda, because without fusion this lowering is less efficient than current (bernoulli_ * input). Testing is covered by the test case that ailzhang added (test_dropout_cuda). Pull Request resolved: https://github.com/pytorch/pytorch/pull/18375 Differential Revision: D14611938 Pulled By: soumith fbshipit-source-id: 11b18f4784e6c9265e382a8f8deca7add8df3b37
2019-03-25Enable printing to stderr for test_proper_exit for better debugging (#18458)Tongzhou Wang1-5/+12
Summary: related to https://github.com/pytorch/pytorch/issues/16608 Pull Request resolved: https://github.com/pytorch/pytorch/pull/18458 Differential Revision: D14611718 Pulled By: soumith fbshipit-source-id: 6dc903ff2d32b9c3b76470869d1f4e9a67f706df
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