{"push_id":2883769614,"size":41,"distinct_size":0,"ref":"refs/heads/export-D9763422","head":"850ad0b7295b8c2033e54aaa87ae0d5400e1d576","before":"a0968b05dcc645c2bdf23272b987578c00bb19ea","commits":[{"sha":"2631da0822149c19214dba94510e29abb5b25f75","author":{"name":"Gregory Chanan","email":"405a4c10dc10d9753e4d637a51ebfa857b6fcbb3@fb.com"},"message":"Move some Tensor method definitions from Type.h to TensorMethods.h. (#11650)\n\nSummary:\nThere's no reason they need to be in Type.h and this moves us along the path of not having circular dependencies (so we can get rid of TensorMethods.h).\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11650\n\nReviewed By: ezyang\n\nDifferential Revision: D9812271\n\nPulled By: gchanan\n\nfbshipit-source-id: 8b70db9a5eb0a332398ab2e8998eeaf7d2eea6d7","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/2631da0822149c19214dba94510e29abb5b25f75"},{"sha":"72822ee6b251118aefbe9af75ac0fcb4ee56a0ee","author":{"name":"Edward Yang","email":"dbd597f5635f432486c5d365e9bb585b3eaa1853@fb.com"},"message":"Fix #11430 (CPU only builds raise opaque error message when calling .… (#11533)\n\nSummary:\n…cuda())\n\nWhile I was at it, I audited all other ways I know how we might get a CUDA\ntype from PyTorch and fixed more constructors which don't work.\n\nSigned-off-by: Edward Z. Yang <ezyang@fb.com>\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11533\n\nDifferential Revision: D9775786\n\nPulled By: ezyang\n\nfbshipit-source-id: cd07cdd375fdf74945539ec475a48bf08cbc0c17","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/72822ee6b251118aefbe9af75ac0fcb4ee56a0ee"},{"sha":"0d9b9100f977f677a0734af9096e9121ed85b337","author":{"name":"Vishwak Srinivasan","email":"7d9dbaf6fdbe0bc4465fbe69a9c17ceb70dc8fb9@iith.ac.in"},"message":"Fix gesv and gels docs (#11699)\n\nSummary: Closes #9935 and closes #5431 .\n\nDifferential Revision: D9830448\n\nPulled By: soumith\n\nfbshipit-source-id: 4e5320a1d0c1d4c8253a5b26f4842cea76530514","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/0d9b9100f977f677a0734af9096e9121ed85b337"},{"sha":"eb039dc92c2801f40415d4e366251d506accf726","author":{"name":"Alexander Sidorov","email":"82fc4eea1c6deb731a16542c2da8737f41fe0696@fb.com"},"message":"Add CHECKs into GetTensorInfo and ExtractDeviceOption (#11597)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11597\n\nWe should always CHECK pointers which we plan to dereference\nif they are inputs to the function. Nobody knows how the function will\nbe called in the future.\n\nReviewed By: yinghai\n\nDifferential Revision: D9800002\n\nfbshipit-source-id: 7fd05f4717f2256d1b09a9e75475b12de6685b03","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/eb039dc92c2801f40415d4e366251d506accf726"},{"sha":"115b13ffabec5d7ac3441aa1e2c7a792619bbd39","author":{"name":"Roy Li","email":"afbcf2dc3e50c9069bb6f45eae26434caf156b9c@fb.com"},"message":"clean up some old Half stuff\n\nSummary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/11687\n\nDifferential Revision: D9829027\n\nPulled By: li-roy\n\nfbshipit-source-id: f35dcdf93ea57ba4fa775e36e9d6378bed46a710","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/115b13ffabec5d7ac3441aa1e2c7a792619bbd39"},{"sha":"278e304c18b0da89cbc58ac43b2d5eabdd3fd6f3","author":{"name":"James Reed","email":"f6111144b37277d4771706e14416c5fc33d0fdf4@fb.com"},"message":"Implement elif in string frontend (#11667)\n\nSummary:\nCloses #11625\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11667\n\nDifferential Revision: D9828145\n\nPulled By: jamesr66a\n\nfbshipit-source-id: c72dc41cb310a4211b4e4c6b33f7e2c1fb3581a0","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/278e304c18b0da89cbc58ac43b2d5eabdd3fd6f3"},{"sha":"3258fc11a73a5b6a90c2ad416c4fbaa0d667bc24","author":{"name":"Peter Goldsborough","email":"887f44f8e6d2ec27368b0275bb31f402fb8ca659@fb.com"},"message":"Delete torch/csrc/api/README.md (#11703)\n\nSummary:\nWe'll have separate docs for the C++ frontend, right now this file is just misleading\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11703\n\nDifferential Revision: D9832847\n\nPulled By: goldsborough\n\nfbshipit-source-id: 2e8b30ccf6b5cba9d0526e6261160f7c6211a35c","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/3258fc11a73a5b6a90c2ad416c4fbaa0d667bc24"},{"sha":"7535d98ec436cab718d12d83bd412223cbfc55c8","author":{"name":"Pieter Noordhuis","email":"177aca405d8f85334d5f348395ebc138d0a69b62@gmail.com"},"message":"Add message tag parameter to send/recv\n\nSummary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/11490\n\nReviewed By: teng-li\n\nDifferential Revision: D9828116\n\nPulled By: pietern\n\nfbshipit-source-id: 98be1ae84b6763ffb329e63c030c5e3ec0e748b7","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/7535d98ec436cab718d12d83bd412223cbfc55c8"},{"sha":"b90872c00ee159bc7e0fc936de8d58dcf1dc803b","author":{"name":"Gregory Chanan","email":"405a4c10dc10d9753e4d637a51ebfa857b6fcbb3@fb.com"},"message":"Get rid of default arguments for TH/THC factory functions. (#11673)\n\nSummary:\nThis is causing codegen problems in caffe2, when we try to remove the circular Tensor/Type declarations.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11673\n\nDifferential Revision: D9819341\n\nPulled By: gchanan\n\nfbshipit-source-id: f2c2cd96e8a16f6de6aa4889e71b8a78e12e9256","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/b90872c00ee159bc7e0fc936de8d58dcf1dc803b"},{"sha":"0c2648830fbb48f1d600b18e1e4d1a6be071336d","author":{"name":"Michael Carilli","email":"5598654a278f0d7fc5fab046d4b818491d625f6a@nvidia.com"},"message":"Augment emit_nvtx to help connect backward-pass Function apply calls with their corresponding forward pass ops (#10881)\n\nSummary:\nOften, we find ourselves looking at some long-running kernel or emit_nvtx range on an nvvp profile and trying to connect it to the offending line in a training script.  If the op is in the forward pass that's easy:  ops are enqueued explicitly from the Python side, so tracking it down with manual nvtx ranges supplemented by the built-in emit_nvtx ranges is straightforward.  If the op is in the backward pass, it's much more difficult.  From the Python side, all you can do is wrap loss.backward() in an nvtx range, and if you also use emit_nvtx, the automatic ranges provide only local information.  Right now, the only consistent way to connect backward-pass kernels to their associated forward-pass lines of Python is to understand your script line by line, and know exactly where in the backward pass you are.\n\nThis PR augments the existing nvtx machinery to bridge the gap between forward and backward, allowing connection of backward-pass Function apply calls to the forward-pass operations that required/created those Functions.\n\nThe method is simple and surgical.  During the forward pass, when running with emit_nvtx, the nvtx range for each function in VariableType is tagged with the current sequence number.  During the backward pass, the nvtx range associated with each Function's operator() is tagged with that Function's stashed sequence number, which can be compared to \"current sequence numbers\" from the forward pass to locate the associated op.\n\nDouble-backward is not a problem.  If a backward pass with create_graph = True is underway, the relationship between backward and double-backward is conceptually the same as the relationship between forward and backward:  The functions in VariableType still spit out current-sequence-number-tagged ranges, the Function objects they create still stash those sequence numbers, and in the eventual double-backward execution, their operator() ranges are still tagged with the stashed numbers, which can be compared to \"current sequence numbers\" from the backward pass.\n\nMinor caveats:\n\n- The sequence number is thread-local, and many VariableType functions (specifically, those without a derivative explicitly defined in derivatives.yaml) don't create an associated function object (instead delegating that to sub-functions further down the call chain, perhaps called from within at::native functions that route back through VariableType by calling at::function_name).  So the correspondence of stashed sequence numbers in Function operator() ranges with numbers in forward-pass ranges is not guaranteed to be 1 to 1.  However, it's still a vast improvement over the current situation, and I don't think this issue should be a blocker.\n- Feel free to litigate my use of stringstream in profiler.cpp.  I did it because it was easy and clean.  If that's too big a hammer, let's figure out something more lightweight.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/10881\n\nDifferential Revision: D9833371\n\nPulled By: apaszke\n\nfbshipit-source-id: 1844f2e697117880ef5e31394e36e801d1de6088","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/0c2648830fbb48f1d600b18e1e4d1a6be071336d"},{"sha":"224e62bbec2b00719f38a2a2fb2191e40120bc4a","author":{"name":"Anders Papitto","email":"4d7c9f8e345f48661e9217b9935ef6fb676bed91@gmail.com"},"message":"respect USE_CUDA_STATIC_LINK in build_libtorch.py\n\nSummary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/11713\n\nDifferential Revision: D9835972\n\nPulled By: anderspapitto\n\nfbshipit-source-id: 046363b132e5487c05ef7e6e6d88b508196386a1","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/224e62bbec2b00719f38a2a2fb2191e40120bc4a"},{"sha":"70e68e755a8fdd94218e9b8e0abfb6b805354a08","author":{"name":"David Riazati","email":"ff36620a3ee63bd6d8ae92408cbddaf6f4dc951a@fb.com"},"message":"Casting for binary ops (#11708)\n\nSummary:\nFixes #11663\n\n`TensorIterator` was replacing the op tensors with type casted tensors\nwhich ended up producing side effects in binary ops like `a.float() * b`\nwhere `a` and `b` are `LongTensor`s.\n\ncolesbury ezyang apaszke\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11708\n\nDifferential Revision: D9834016\n\nPulled By: driazati\n\nfbshipit-source-id: 4082eb9710b31dfc741161a0fbdb9a8eba8fe39d","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/70e68e755a8fdd94218e9b8e0abfb6b805354a08"},{"sha":"96d3f968ebcbbcff5a7d3adf1ceb357e78ca672a","author":{"name":"Mike Ruberry","email":"3b6c81a5bb1dcc1d5f19bc15b1d7e79960d88113@users.noreply.github.com"},"message":"Splits CPU and CUDA fusion compilers (#10981)\n\nSummary:\nThis PR splits the CPU and CUDA fusion compilers, putting them into a new jit/fusers/ directory with jit/fusers/common for common components. In particular:\n\n- A fusion interface is created that allows \"fusion handles\" to be requested\n- The CPU and CUDA fusers implement this interface, with dispatch determined by device\n- The fusion compilers, fusion function specializations and resource strings are split\n- CPU-specific classes like TempFile and DynamicLibrary are in the CPU fuser\n- Common classes likes TensorDesc and the base fusion function class are in jit/fusers/common\n- There is still some specialization in jit/fusers/common, but these specializations are small(-ish)\n- Updates the build system to remove the dummy interface on Windows and minimize the use of macros\n\nThis structure should allow in-flight PRs to easily rebase while providing a clear interface to the fusers.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/10981\n\nReviewed By: soumith\n\nDifferential Revision: D9701999\n\nPulled By: apaszke\n\nfbshipit-source-id: 3b6bec7b97e0444b2a93caa38d9b897f2e68c1b3","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/96d3f968ebcbbcff5a7d3adf1ceb357e78ca672a"},{"sha":"8e3f8c52e85ee91995f8a9137a4649f5c39dfc3a","author":{"name":"Peter Goldsborough","email":"887f44f8e6d2ec27368b0275bb31f402fb8ca659@fb.com"},"message":"Document the Sequential module (#11648)\n\nSummary:\n1. Document the Sequential module in the C++ API at a high, why-does-this-exist, and low, how-to-use, level\n2. Change the Sequential tests to be in a style that makes them easier to convert to gtest. No code changes.\n\nebetica ezyang apaszke\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11648\n\nDifferential Revision: D9834526\n\nPulled By: goldsborough\n\nfbshipit-source-id: 39f2f5c6cbbf8ed5a1b69986978c8ef127036de1","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/8e3f8c52e85ee91995f8a9137a4649f5c39dfc3a"},{"sha":"d24bcfd9304f0e38c4024620de4da8f2379f6956","author":{"name":"Junjie Bai","email":"7c824f2e1c4d8e5c9445dd7ded4e96febed020f7@in.tum.de"},"message":"Suppress hiprand \"duplicate-decl-specifier\" warning (#11698)\n\nSummary:\nOtherwise each build produces 65MB of warnings log, which makes the CI hard to debug.\n\niotamudelta Jorghi12\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11698\n\nDifferential Revision: D9840356\n\nPulled By: bddppq\n\nfbshipit-source-id: b69bf6a5c38a97b188221f9c084c608ffc9b37c8","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/d24bcfd9304f0e38c4024620de4da8f2379f6956"},{"sha":"8e76dcf173f7fb28c2736b86b38a829531496bab","author":{"name":"Tongzhou Wang","email":"96b5c3d5a7a36c8be47478427b2e4d4231204fe0@gmail.com"},"message":"Prevent raising KeyboardInterrupt in worker (#11718)\n\nSummary:\nCurrent behavior is that each process (main and workers) will print trace from `KeyboardInterrupt`. And the main process will also print\n```\nRuntimeError: DataLoader worker (pid 46045) exited unexpectedly with exit code 1. Details are lost due to multiprocessing. Rerunning with nm_workers=0 may give better error trace.\n```\ndue to our SIGCLD handler.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11718\n\nDifferential Revision: D9840844\n\nPulled By: SsnL\n\nfbshipit-source-id: 1a05060bb02907fef5aac3f274d2c84f9f42d187","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/8e76dcf173f7fb28c2736b86b38a829531496bab"},{"sha":"2c8a1b957ea19c25d672ccc5e51047279e68d85d","author":{"name":"Edward Yang","email":"dbd597f5635f432486c5d365e9bb585b3eaa1853@fb.com"},"message":"Back out \"Refactor Tensor/TensorImpl constructors.\"\n\nSummary: Original commit changeset: 7501b54fe5f3\n\nReviewed By: gchanan\n\nDifferential Revision: D9838097\n\nfbshipit-source-id: 093e4c47d5574ce99f706b0683ef369a89b62b38","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/2c8a1b957ea19c25d672ccc5e51047279e68d85d"},{"sha":"f4d9fe395d59f140d1a52c6becf89f6ca59f303b","author":{"name":"Sebastian Messmer","email":"ef83834e7ce69695dd3376888da48b8a95d5228b@fb.com"},"message":"Remove intrusive_ptr::reclaim() in Storage (#11352)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11352\n\nPushing manual refcounting further back, making things safer.\n\nReviewed By: ezyang\n\nDifferential Revision: D9694327\n\nfbshipit-source-id: befdbcac199225383a93520472ee7c6511a0e9cd","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/f4d9fe395d59f140d1a52c6becf89f6ca59f303b"},{"sha":"270fb22bd84e915d76fd6316bf4f22a8c7882da6","author":{"name":"Sebastian Messmer","email":"ef83834e7ce69695dd3376888da48b8a95d5228b@fb.com"},"message":"Remove intrusive_ptr::reclaim() in Storage (2/2) (#11547)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11547\n\nPushing manual refcounting further back, making things safer.\n\nReviewed By: ezyang\n\nDifferential Revision: D9778042\n\nfbshipit-source-id: c9572edc440c5ce5ea1b2355b5c54f87078ea28e","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/270fb22bd84e915d76fd6316bf4f22a8c7882da6"},{"sha":"690c999bba2da3bb25370fe49181e7d0eb3e4b69","author":{"name":"Sebastian Messmer","email":"ef83834e7ce69695dd3376888da48b8a95d5228b@fb.com"},"message":"Simplify union payload copying (#11353)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11353\n\nBefore, there was one extra member in the union that had to be at least as large as the largest other member, because it was used for copying.\n\nNow, this isn't needed anymore and we copy the union directly.\n\nReviewed By: ezyang\n\nDifferential Revision: D9694326\n\nfbshipit-source-id: 42b2f7d51ac5d4ea5ebafea3a598b018e10fed68","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/690c999bba2da3bb25370fe49181e7d0eb3e4b69"}]}
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