{"push_id":2741091192,"size":28,"distinct_size":27,"ref":"refs/heads/dynamic","head":"b4f6ff9801183072d7c7d7fc37db658fd2305134","before":"3425f662d0e4645d15956ad2259371e6d7886f3e","commits":[{"sha":"7b9d8916e5dde83bb982e0950617738cbfc25f21","author":{"name":"Vishwak Srinivasan","email":"7d9dbaf6fdbe0bc4465fbe69a9c17ceb70dc8fb9@iith.ac.in"},"message":"Fix integral type dispatch error message (#9625)\n\nSummary:\nThis fix will prevent errors like (found in `bincount`)\n```\nRuntimeError: %s not implemented for '%s'bincounttorch.FloatTensor\n```\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9625\n\nDifferential Revision: D8932945\n\nPulled By: soumith\n\nfbshipit-source-id: 794e3b58d662779402ab318e274661826a5db8b2","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/7b9d8916e5dde83bb982e0950617738cbfc25f21"},{"sha":"f84fdc7866a566d2a1c31d5006b24c6d6e841846","author":{"name":"Christian Puhrsch","email":"b7a905850ca55c064cd1088c2e252f92e10708de@fb.com"},"message":"Remove unnecessary functions from StorageDerived.h\n\nSummary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9553\n\nReviewed By: ezyang\n\nDifferential Revision: D8915526\n\nPulled By: cpuhrsch\n\nfbshipit-source-id: 32013d3aa58a1a68637f99ee619d06e27fadaad6","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/f84fdc7866a566d2a1c31d5006b24c6d6e841846"},{"sha":"bbb30ad4ab86f06f90408fdba2d311088be7d55d","author":{"name":"Christian Puhrsch","email":"b7a905850ca55c064cd1088c2e252f92e10708de@fb.com"},"message":"Use THTensor/Storage for THVoidTensor/Storage (#9588)\n\nSummary:\nChange akin to change for THVoidStorage.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9588\n\nReviewed By: gchanan\n\nDifferential Revision: D8915559\n\nPulled By: cpuhrsch\n\nfbshipit-source-id: 6cc69df0e29942c62750f990903dfd8e4d344581","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/bbb30ad4ab86f06f90408fdba2d311088be7d55d"},{"sha":"b9e89cf9fdf134affc184d36296bb6ac321aec91","author":{"name":"Sam Gross","email":"48245c485a3f0765c7face6e57b9f7e40c8624a4@fb.com"},"message":"Revert \"Extend DispatchStub to support CUDA dispatch (#9579)\" (#9614)\n\nSummary:\nThis reverts commit bcf0bf42a1727c8ee788f733c28579d0e36a387c.\n\nThe commit was causing issues for some internal FB projects.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9614\n\nReviewed By: Yangqing\n\nDifferential Revision: D8929552\n\nPulled By: colesbury\n\nfbshipit-source-id: ae9026ad8762a4c5de401273694b4c878fc241a6","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/b9e89cf9fdf134affc184d36296bb6ac321aec91"},{"sha":"1d4d9fc7da4c09c64fa276a47032291e3d0f77b6","author":{"name":"Adam Paszke","email":"71faef99c0ae9d81958d0035c1ce72f9c3888cfe@gmail.com"},"message":"Prepare to stop using attributes in the JIT (#9505)\n\nSummary:\nThis PR adds machinery to cache the schema in an IR node, and allows lookups of (possibly) constant inputs by their names (instead of position). The new methods are:\n\n- `at::optional<T> get<T>(Symbol name)` - if the argument called name is a constant, then casts it to type `T` and returns it. If it's not constant returns `nullopt`. Raises an error if there's no argument with that name.\n- `at::optional<IValue> get<T>(Symbol name)` - like above, but packs the result in an IValue\n- `Value* getValue(Symbol name)` - retrieves a `Value*` for an argument (no need to know its position).\n\nAll above functions currently inspect the attributes as well, but that's only so that I could start using them in other places in the JIT without disrupting our current functionality. I wanted this diff to be a preparation that doesn't change the semantics too much, and so both the tracer and script create nodes with attributes. The next PR will put that to a stop, and hopefully the changes we need to make to other components will be simpler thanks to what I did here.\n\nOne more thing I'd like to do before actually stopping creating the non-attributed nodes is to have a convenient way of creating a schema programmatically, matching nodes against it, and creating them without having to pack inputs into flat argument lists (which is quite error prone).\n\nzdevito\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9505\n\nReviewed By: ezyang\n\nDifferential Revision: D8915496\n\nPulled By: apaszke\n\nfbshipit-source-id: 39d14fc9a9d73d8494f128367bf70357dbba83f5","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/1d4d9fc7da4c09c64fa276a47032291e3d0f77b6"},{"sha":"a3a6ab60cdb17845a4f46d56451638277559cc6a","author":{"name":"Zhaoheng Ni","email":"25d508652c8799722bd3413ffcb42f11fee3f49f@fb.com"},"message":"Fix the error in UnpackSegmentsOp when calculating the gradient with \"max_length\" argument (#9598)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9598\n\nThe \"max_length\" should be passed to UnPackSegmentsOp if \"max_length\" is given when calling PackSegmentsOp.\n\nReviewed By: jerryzh168\n\nDifferential Revision: D8919799\n\nfbshipit-source-id: 8c97aa717b69177b8a5d5d56892817d488853840","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/a3a6ab60cdb17845a4f46d56451638277559cc6a"},{"sha":"d3688861ec8607af0e7bf4119911053f13aee500","author":{"name":"vmirly","email":"6ee289d1b503c5f201eec54e1426c6b261827cef@msu.edu"},"message":"Fixed a missing '=' in LPPoolNd repr function (#9629)\n\nSummary:\nIn the repr funciton of LPPoolNd(..) class, there was a missing '='. (`kernel_size{kernel_size}`)\n\nLink to line in the code: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/pooling.py#L694\n\nOriginal:\n\n       return 'norm_type={norm_type}, kernel_size{kernel_size}, stride={stride}, ' \\\n              'ceil_mode={ceil_mode}'.format(**self.__dict__)\n\nFixed:\n\n       return 'norm_type={norm_type}, kernel_size={kernel_size}, stride={stride}, ' \\\n              'ceil_mode={ceil_mode}'.format(**self.__dict__)\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9629\n\nDifferential Revision: D8932913\n\nPulled By: soumith\n\nfbshipit-source-id: 9030dff6b14659b5c7b6992d87ef53ec8891f674","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/d3688861ec8607af0e7bf4119911053f13aee500"},{"sha":"bae156a481351a7f2d0f5f2dbced5dfab2c58f19","author":{"name":"Gregory Chanan","email":"405a4c10dc10d9753e4d637a51ebfa857b6fcbb3@fb.com"},"message":"Support (some) CUDA Lapack on n-dimensional empty tensors.\n\nSummary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9631\n\nReviewed By: ezyang\n\nDifferential Revision: D8933202\n\nPulled By: gchanan\n\nfbshipit-source-id: 1ade4ca439bf26aa921df1da83a827d860f8f48f","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/bae156a481351a7f2d0f5f2dbced5dfab2c58f19"},{"sha":"8a0fe0a5889bd8c7404102dfd007bc4c2ffc3512","author":{"name":"Kittipat Virochsiri","email":"9d87b89a774fdaaca76108747c3471f1c21ddfed@fb.com"},"message":"set_input_record() should always add external input (#9636)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9636\n\nMake sure that the blobs are registered to the net\n\nReviewed By: pjh5\n\nDifferential Revision: D8924883\n\nfbshipit-source-id: f09422a2d4d5ba8bf6cfbfd00172097b5ab1fcd6","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/8a0fe0a5889bd8c7404102dfd007bc4c2ffc3512"},{"sha":"1003ccfa15e944251a65ba2289f25e8f1ed14a46","author":{"name":"Mike Ruberry","email":"42fe9a903ac79e22ebdbdd651d8a00cb91c679a8@nvidia.com"},"message":"Creates CUDAContext (#9435)\n\nSummary:\nezyang noticed that the CUDAStream files lived under ATen/ despite being CUDA-specific, and suggested porting them to ATen/cuda and exposing them with a new CUDAContext. This PR does that. It also:\n\n- Moves ATen's CUDA-specific exceptions for ATen/cudnn to ATen/cuda for consistency\n- Moves getDeviceProperties() and getCurrentCUDASparseHandle() to CUDAContext from CUDAHooks\n\nThe separation between CUDAContext and CUDAHooks is straightforward. Files that are in CUDA-only builds should rely on CUDAContext, while CUDAHooks is for runtime dispatch in files that can be included in CPU-only builds. A comment in CUDAContext.h explains this pattern. Acquiring device properties and CUDA-specific handles is something only done in builds with CUDA, for example, so I moved them from CUDAHooks to CUDAContext.\n\nThis PR will conflict with #9277 and I will merge with master after #9277 goes in.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9435\n\nReviewed By: soumith\n\nDifferential Revision: D8917236\n\nPulled By: ezyang\n\nfbshipit-source-id: 219718864234fdd21a2baff1dd3932ff289b5751","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/1003ccfa15e944251a65ba2289f25e8f1ed14a46"},{"sha":"01581037dca1176508f4544b377a4c0f2d0dd165","author":{"name":"Kittipat Virochsiri","email":"9d87b89a774fdaaca76108747c3471f1c21ddfed@fb.com"},"message":"Add workspace.RunPlanInBackground (#9637)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9637\n\nAdding a method to run plan in background. The intended use is to run BlueWhale's data reading & preprocessing net in background while the GPU is training.\n\nReviewed By: MisterTea\n\nDifferential Revision: D8906439\n\nfbshipit-source-id: b1c73ca7327e2d87a8f873924e05ab3d161a3f1e","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/01581037dca1176508f4544b377a4c0f2d0dd165"},{"sha":"a61c34be59568babf7bc9cc45a2b67bd41a81ca6","author":{"name":"Jorg Doku","email":"7f83c7ca16238b0321fe9c484bcaf62de5737b89@cornell.edu"},"message":"fix nvcc","distinct":false,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/a61c34be59568babf7bc9cc45a2b67bd41a81ca6"},{"sha":"45e5c17ecfbe00708e2c61488d688b3d651ab8ca","author":{"name":"Yinghai Lu","email":"0674548f4d596393408a51d6287a76ebba2f42aa@fb.com"},"message":"ONNXIFI transform (#9569)\n\nSummary:\nCut-off runnable subgraph and off-load to ONNXIFI backend\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9569\n\nReviewed By: Maratyszcza\n\nDifferential Revision: D8930408\n\nPulled By: yinghai\n\nfbshipit-source-id: 2b494f7f8dc10c00e58cf0fed5c4a9434be6155b","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/45e5c17ecfbe00708e2c61488d688b3d651ab8ca"},{"sha":"3efdece9daade24630c72ebb7b17502134995196","author":{"name":"Gregory Chanan","email":"405a4c10dc10d9753e4d637a51ebfa857b6fcbb3@fb.com"},"message":"Support n-dimensional empty tensors in take/put.\n\nSummary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9635\n\nDifferential Revision: D8935119\n\nPulled By: gchanan\n\nfbshipit-source-id: 5035583e7322b1a1720d961945dd0eefb4cb28ef","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/3efdece9daade24630c72ebb7b17502134995196"},{"sha":"5e84403d5fc92ad2ed594b86a436c571dac55d30","author":{"name":"Ashish","email":"cbdc4993661fda90e570c8241eea3099d01fdfdf@amd.com"},"message":"Fix for half conversion for ROCm 1.8.2 (#9663)\n\nSummary:\nThis PR contains the change for explicit conversion between ushort and __half required for ROCm 1.8.2 support\nbddppq\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9663\n\nDifferential Revision: D8943937\n\nPulled By: bddppq\n\nfbshipit-source-id: 16102f9dbc68ed4ece2e8fc244825c3992c24901","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/5e84403d5fc92ad2ed594b86a436c571dac55d30"},{"sha":"23ed26a0c399c121fc1bcb5824dff33d8eb463b4","author":{"name":"Edward Yang","email":"dbd597f5635f432486c5d365e9bb585b3eaa1853@fb.com"},"message":"Guard include of cuda-only header comm.h (#9656)\n\nSummary:\nSigned-off-by: Edward Z. Yang <ezyang@fb.com>\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9656\n\nReviewed By: colesbury\n\nDifferential Revision: D8941361\n\nPulled By: ezyang\n\nfbshipit-source-id: c18cb0e606ae0608e5892040192b8792ae542b74","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/23ed26a0c399c121fc1bcb5824dff33d8eb463b4"},{"sha":"b5c8d59451be8ae2e892afb28d0dc39661c666f1","author":{"name":"Edward Yang","email":"dbd597f5635f432486c5d365e9bb585b3eaa1853@fb.com"},"message":"Add a CUDAContext header include\n\nSummary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9662\n\nDifferential Revision: D8945581\n\nPulled By: ezyang\n\nfbshipit-source-id: 2fe0adc96456788579f7d6f1c4513fe45360c030","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/b5c8d59451be8ae2e892afb28d0dc39661c666f1"},{"sha":"3bb8c5eab1029a7de971b7368e43be06ef97182d","author":{"name":"Owen Anderson","email":"24a74c757816385a523b6755e302643f8b781853@oculus.com"},"message":"Allow MKLDNN on macOS, and any other OS where CMake is able to detect it.\n\nSummary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9638\n\nReviewed By: soumith\n\nDifferential Revision: D8946130\n\nPulled By: resistor\n\nfbshipit-source-id: 87bd9cb12608467b05bd4998fdb00bfdbd038ca2","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/3bb8c5eab1029a7de971b7368e43be06ef97182d"},{"sha":"a01d6f01b5547a396d6e28f95538cd061e14d29a","author":{"name":"Xiaomeng Yang","email":"fa3cdf6a720cb5ffb1e60f18b20b9cd8e7c08285@fb.com"},"message":"Update channel_shuffle_op and transpose 2d to speed up ShuffleNet (#9525)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9525\n\nUpdate channel_shuffle_op and transpose 2d to speed up ShuffleNet\n\nReviewed By: houseroad\n\nDifferential Revision: D8889361\n\nfbshipit-source-id: 60196e819b6842becc53b4859b62d4419a0e2c6e","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/a01d6f01b5547a396d6e28f95538cd061e14d29a"},{"sha":"769cb5a6405b39a0678e6bc4f2d6fea62e0d3f12","author":{"name":"Adam Paszke","email":"71faef99c0ae9d81958d0035c1ce72f9c3888cfe@gmail.com"},"message":"Add new ways of matching nodes with schemas in the JIT (#9567)\n\nSummary:\n**REVIEW LAST COMMIT ONLY**\n\nAs discussed in our yesterday's meeting. Nodes can be now matched to particular overloads using the `matches(...)` function:\n```cpp\nn->matches(\"aten::type_as(Tensor self, Tensor other) -> Tensor\")\n```\n\nThis also changes the shape prop and peephole passes to use those functions for matching. This fixes a few bugs, makes them much more robust, and prepares us for removal of attributes.\n\nzdevito\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9567\n\nReviewed By: zdevito\n\nDifferential Revision: D8938482\n\nPulled By: apaszke\n\nfbshipit-source-id: eb2382eeeae99692aada2d78d5d0c87c8ef1545e","distinct":true,"url":"https://api.github.com/repos/Jorghi12/pytorch/commits/769cb5a6405b39a0678e6bc4f2d6fea62e0d3f12"}]}
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