{"push_id":2869336994,"size":15,"distinct_size":15,"ref":"refs/heads/master","head":"35d52dbb0e026f7f0b24f6b81bc1d10c0cb0983a","before":"a566bc2f1140f9a51343656f8b1b47a6b72a5fc9","commits":[{"sha":"781737f84c5f6e067e8e02648c8bfc3a2df2dbaa","author":{"name":"Adam Paszke","email":"71faef99c0ae9d81958d0035c1ce72f9c3888cfe@gmail.com"},"message":"Remove time prefix from rsync (#11525)\n\nSummary:\nThis fails with zsh saying \"time: command not found\".\n\ncc soumith\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11525\n\nDifferential Revision: D9772522\n\nPulled By: apaszke\n\nfbshipit-source-id: b80d108fa6b174d68ada08a9fdbf7260ee37e08f","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/781737f84c5f6e067e8e02648c8bfc3a2df2dbaa"},{"sha":"fbc17321fdd96ce4dcc275ff61264d04f2fbd6e2","author":{"name":"James Reed","email":"f6111144b37277d4771706e14416c5fc33d0fdf4@fb.com"},"message":"Update pybind11 to fix Python 3.7 support for script (#11473)\n\nSummary:\nFixes https://github.com/pytorch/pytorch/issues/11419\n\nIn particular pulling in https://github.com/pybind/pybind11/pull/1454\nas well as pending bugfix in https://github.com/pybind/pybind11/pull/1517 (documenting in comment)\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11473\n\nDifferential Revision: D9776003\n\nPulled By: jamesr66a\n\nfbshipit-source-id: a225dcfb66c06bcae98fd2508d9e690c24be551a","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/fbc17321fdd96ce4dcc275ff61264d04f2fbd6e2"},{"sha":"5952acc041db96b99dc0d463a28d74da9d3b28e5","author":{"name":"Will Feng","email":"1afd6515b09d0b3eb7e8e4008edd2316b575fbe3@fb.com"},"message":"Add \"merge to master\" step before build in CircleCI (#11443)\n\nSummary:\nThis PR adds the \"merge to master\" step before the build step in CircleCI, so that all PR commits are built against master instead of against the PR's branch. Note that all PRs still need to rebase to master to pick up this new config, so it won't apply to old PR branches retroactively.\n\nTo check in CI: make sure it's performing the git merge to master appropriately in \"Merge Onto Master\" step.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11443\n\nDifferential Revision: D9775628\n\nPulled By: yf225\n\nfbshipit-source-id: 8083db6b098d234a44ae4481f40a486e9906f6f8","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/5952acc041db96b99dc0d463a28d74da9d3b28e5"},{"sha":"c56a7cfc37aae8b5e1515da37b314ed03bff8641","author":{"name":"vishwakftw","email":"7d9dbaf6fdbe0bc4465fbe69a9c17ceb70dc8fb9@iith.ac.in"},"message":"More use of AT_CHECK and AT_ERROR (#11457)\n\nSummary: Considering these increase the size of the message stack, I didn't touch the code outside `ATen/native`\n\nDifferential Revision: D9754283\n\nPulled By: soumith\n\nfbshipit-source-id: 04198ec4fd0c4abae09eeba92c493a783408537a","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/c56a7cfc37aae8b5e1515da37b314ed03bff8641"},{"sha":"c1dce21fd5505085ef148aa9d70a312d8d14257f","author":{"name":"Thomas Viehmann","email":"36ac19d4ad6fe01b06633a4b23ed512831899735@beamnet.de"},"message":"Cuda TensorAccessor (#11373)\n\nSummary:\nProvide a TensorAccessor-Like interface for CUDA as discussed in #8366.\n\nCompared to TensorAccessor\n- the CUDATensorAccessor copies the sizes and strides while on the host (I didn't implement a host indexing function, though) to enable transfer to the device, on the device, `[]` works like for TensorAccessors,\n- instantiation is from TensorAccessors in order to allow using `.accessor<..>`. The drawback is that it you cannot use `auto` for the variable declaration, but the alternative would be a cuda-specific `.accessor`-like function,\n- there is a PtrTraits argument to enable `__restrict__`,\n\nExample for the intended use:\n```\n...\ntemplate <typename scalar_t>\n__global__ void\napply_homography_2d_kernel(cuda::CUDATensorAccessor<scalar_t, 4> dest_a,\n\t\t\t   cuda::CUDATensorAccessor<scalar_t, 4> src_a,\n\t\t\t   cuda::CUDATensorAccessor<float, 2> transform) {\n...\n}\n\ntemplate <typename scalar_t>\nTensor apply_homography_2d_template(Tensor& res, const Tensor& image, const Tensor& transform) {\n  ...\n  cuda::CUDATensorAccessor<scalar_t, 4> image_a(image.accessor<scalar_t, 4>());\n  cuda::CUDATensorAccessor<scalar_t, 4> res_a(res.accessor<scalar_t, 4>());\n  cuda::CUDATensorAccessor<float, 2> transform_a(transform.accessor<float, 2>());\n  auto stream = at::cuda::getCurrentCUDAStream();\n\n  apply_homography_2d_kernel<scalar_t>\n    <<<grid, block, 0, stream>>>(res_a, image_a, transform_a);\n  return res;\n}\n\n...\n```\n\nI could use a hint where to put a test for this (e.g. doing a plain vanilla matrix multiplication with a custom kernel) and comparing with the aten mm.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11373\n\nDifferential Revision: D9735573\n\nPulled By: ezyang\n\nfbshipit-source-id: 482b218a0d514e19a8b692bbc77c0e37082cfded","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/c1dce21fd5505085ef148aa9d70a312d8d14257f"},{"sha":"4db21a1d8e97dd087293988e46c7d43a7ac59ebe","author":{"name":"Lukasz Wesolowski","email":"6811fe934908901e60ccbd65b5d00cfe0627c3de@fb.com"},"message":"Optimize LengthsTileOp on GPU to run a kernel instead of a sequence of memcopies (#11413)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11413\n\nLengthsTileOp was implemented using a sequence of device memcopies initiated on the CPU. This was very slow. I changed it to use a kernel. TUM benchmark QPS improved from 13k QPS to 20k QPS as a result.\n\nReviewed By: manojkris, xianjiec\n\nDifferential Revision: D9724988\n\nfbshipit-source-id: 2f98c697730982734d7c6a26d0b6967310d49900","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/4db21a1d8e97dd087293988e46c7d43a7ac59ebe"},{"sha":"17776db2ee8cb9248233c22f33810345859352b8","author":{"name":"Yangqing Jia","email":"d468ff703b23435fce3e870638a3446bcdbc607b@gmail.com"},"message":"Add gtest dependency on aten tests. (#11429)\n\nSummary:\nezyang delivering my promise to you :)\n\nBasically, now aten tests can use gtest as part of our test harness unification effort. I also converted one test (atest.cpp) to show how one can do this.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11429\n\nReviewed By: ezyang\n\nDifferential Revision: D9762934\n\nPulled By: Yangqing\n\nfbshipit-source-id: 68ec3a748403c6bd88399b1e756200985a4e07e3","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/17776db2ee8cb9248233c22f33810345859352b8"},{"sha":"289a8c9b7def723b64b77dc32786252a9d35865b","author":{"name":"Zachary DeVito","email":"ffbacb804f60e1eea39c96029c00fe9a377f84d7@fb.com"},"message":"Allow train/eval, and non-Tensor arguments to python functions (#11505)\n\nSummary:\nThis whitelists train/eval functions in script modules, and tests that nested nn.Modules still work.\n\nThis also changes the code for calling python functions from script to allow non-tensor inputs/outputs.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11505\n\nDifferential Revision: D9765466\n\nPulled By: zdevito\n\nfbshipit-source-id: 1177bff931324422b69e18fa0bbaa82e3c98ec69","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/289a8c9b7def723b64b77dc32786252a9d35865b"},{"sha":"3a8e39b21506752f77c24229008a48a0e33a66c9","author":{"name":"Roger-luo","email":"8aa6a28c4ead652138b000ac0aee3f61020e0f1d@qq.com"},"message":"Support load and store between Py_complex and std::complex (#11493)\n\nSummary: Printing for complex numbers requires loading and storing between `Py_complex` and `std::complex`. This patch aims to support this for the plugin.\n\nDifferential Revision: D9771808\n\nPulled By: ezyang\n\nfbshipit-source-id: 024865f1945d63ddb5efc775a35438c8ea06408e","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/3a8e39b21506752f77c24229008a48a0e33a66c9"},{"sha":"3a39006d38e797f0a9aea2edb1b2846ec81c7c30","author":{"name":"Tongzhou Wang","email":"96b5c3d5a7a36c8be47478427b2e4d4231204fe0@gmail.com"},"message":"Fix some more doc\n\nSummary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/11531\n\nDifferential Revision: D9776541\n\nPulled By: SsnL\n\nfbshipit-source-id: 8725485639ea6e9479b6ea95a49f5b75a9457db7","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/3a39006d38e797f0a9aea2edb1b2846ec81c7c30"},{"sha":"b6b0b5222db91309fbdb4e4b2ff40548216f06b6","author":{"name":"Soumith Chintala","email":"88de463b5797707cf3425f85a415c3d869db732b@gmail.com"},"message":"fix missing libnccl.so.1 error (#11553)\n\nSummary:\nwhat it says on the tin.\n\nI broke the build in https://github.com/pytorch/pytorch/pull/11487 but contbuild didn't end up catching it.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11553\n\nDifferential Revision: D9781557\n\nPulled By: soumith\n\nfbshipit-source-id: 2a1fa314af4b85b5491d74110bfee3d80599aa95","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/b6b0b5222db91309fbdb4e4b2ff40548216f06b6"},{"sha":"8b196d671b1a2be26fd6fe73f55aa45fb8a91e1e","author":{"name":"Adam Paszke","email":"71faef99c0ae9d81958d0035c1ce72f9c3888cfe@gmail.com"},"message":"Allow tracing random functions (only when using default generators) (#11539)\n\nSummary:\nFixes #11504.\n\nzdevito, neerajprad, fritzo\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11539\n\nDifferential Revision: D9777897\n\nPulled By: apaszke\n\nfbshipit-source-id: 56983260f5b93da7d5540a6242769ea7bd50eb06","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/8b196d671b1a2be26fd6fe73f55aa45fb8a91e1e"},{"sha":"cda74ac476471e753ca06bd3491c84dc0bcaa813","author":{"name":"Wei Yang","email":"acbca9fda1bd78fcd284ddae8b872fb12de49b82@fb.com"},"message":"fix nested no_grad decorator and with-statement (#11479)\n\nSummary:\n- fixes https://github.com/pytorch/pytorch/issues/10858\n- allow `no_grad` decorator to apply `with torch.no_grad()` at the correct context\n- current behavior:\n```\nimport torch\n\ntorch.no_grad()\ndef nothing(x):\n    return x\n\ntestin = torch.Tensor([0])\nwith torch.no_grad():\n    print(torch.is_grad_enabled()) # False\n    testout = nothing(testin)\n    print(torch.is_grad_enabled()) # False\n```\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11479\n\nDifferential Revision: D9758691\n\nPulled By: weiyangfb\n\nfbshipit-source-id: 87de2219c6c45f65a2c0406ae152c3ad760be8f2","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/cda74ac476471e753ca06bd3491c84dc0bcaa813"},{"sha":"bbf54ea37cc063503d6e30ffe2124031f3c87f59","author":{"name":"Fritz Obermeyer","email":"e4b12f906cbb8dae64628fedce2ae208e639a6ae@uber.com"},"message":"Ensure .enumerate_support() methods are jittable (#11542)\n\nSummary:\nThis works around #11535 by avoiding `arange(n, out=x)` and `eye(n, out=x)` in `torch.distributions`. I've confirmed that the `.enumerate_support()` methods are now jittable.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11542\n\nDifferential Revision: D9777805\n\nPulled By: apaszke\n\nfbshipit-source-id: fa38f2f1acfc0a289f725fd8c92478573cfdbefb","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/bbf54ea37cc063503d6e30ffe2124031f3c87f59"},{"sha":"35d52dbb0e026f7f0b24f6b81bc1d10c0cb0983a","author":{"name":"Yangqing Jia","email":"d468ff703b23435fce3e870638a3446bcdbc607b@gmail.com"},"message":"re-enable USE_MPI (#11416)\n\nSummary:\nThe previous error was caused by mpi_test not depending on MPI_CXX_LIBRARIES. This might solve the problem.\n\nNot tested locally - waiting for CI test.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11416\n\nReviewed By: mingzhe09088\n\nDifferential Revision: D9771694\n\nPulled By: Yangqing\n\nfbshipit-source-id: 53e7b4f64eadc88313bc4dd9b8e3f7931cda6e91","distinct":true,"url":"https://api.github.com/repos/vishwakftw/pytorch/commits/35d52dbb0e026f7f0b24f6b81bc1d10c0cb0983a"}]}
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