{"push_id":3516639677,"size":18,"distinct_size":0,"ref":"refs/heads/export-D14756833","head":"ab209595e4be33d15c7cb772b0b571e887326f8a","before":"b39852e730670cbda59ff42f047e06bf113ce335","commits":[{"sha":"6ed57e052dbf603cc8c5cdb31f5404b67ab7c768","author":{"name":"Gemfield","email":"936a7c0076ce7b5f990ae4769ac951077824d482@civilnet.cn"},"message":"Fix the return value of ParseFromString (#19262)\n\nSummary:\nFix the return value of ParseFromString.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/19262\n\nDifferential Revision: D14937605\n\nPulled By: ezyang\n\nfbshipit-source-id: 3f441086517186a075efb3d74f09160463b696b3","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/6ed57e052dbf603cc8c5cdb31f5404b67ab7c768"},{"sha":"a0263ec04765fb9b20decc178241d2668ea58cee","author":{"name":"Pieter Noordhuis","email":"f2de9eb0f834bcce60d9834a48c4365c3f6ec82c@fb.com"},"message":"Make DistributedDataParallel use new reducer (#18953)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/18953\n\nThis removes Python side bucketing code from DistributedDataParallel\nand replaces it with calls to the new C++ based bucketing and reducing\ncode. To confirm this is working well, we ran a test with both the\nprevious implementation and the new implementation, and confirmed they\nare numerically equivalent.\n\nPerformance is improved by a couple percent or more, including the\nsingle machine multiple GPU runs.\n\nCloses #13273.\n\nReviewed By: mrshenli\n\nDifferential Revision: D14580911\n\nfbshipit-source-id: 44e76f8b0b7e58dd6c91644e3df4660ca2ee4ae2","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/a0263ec04765fb9b20decc178241d2668ea58cee"},{"sha":"00148825fc35ddada2cfc59a30d904e0d8cace7c","author":{"name":"Karl Ostmo","email":"76d6cbc6501b31d6c53b4fbb0f37608f5fd4996b@gmail.com"},"message":"Run shellcheck on Jenkins scripts (#18874)\n\nSummary:\ncloses #18873\n\nDoesn't fail the build on warnings yet.\nAlso fix most severe shellcheck warnings\nLimited to `.jenkins/pytorch/` at this time\nPull Request resolved: https://github.com/pytorch/pytorch/pull/18874\n\nDifferential Revision: D14936165\n\nPulled By: kostmo\n\nfbshipit-source-id: 1ee335695e54fe6c387ef0f6606ea7011dad0fd4","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/00148825fc35ddada2cfc59a30d904e0d8cace7c"},{"sha":"ffc9e29844207f4befe548d79fcf18b46f2142e9","author":{"name":"Jongsoo Park","email":"9a3e35cef3c98d8190c15db076afab6069cc7549@fb.com"},"message":"unit test with multiple op invocations (#19118)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/19118\n\nA bug introduced by D14700576 reported by Yufei (fixed by D14778810 and D14785256) was not detected by our units tests.\nThis diff improves unit tests to catch such errors (with this diff and without D14778810, we can reproduce the bug Yufei reported).\nThis improvement also revealed a bug that affects the accuracy when we pre-pack weight and bias together and the pre-packed weight/bias are used by multiple nets. We were modifying the pre-packed bias in-place which was supposed to be constants.\n\nReviewed By: csummersea\n\nDifferential Revision: D14806077\n\nfbshipit-source-id: aa9049c74b6ea98d21fbd097de306447a662a46d","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/ffc9e29844207f4befe548d79fcf18b46f2142e9"},{"sha":"84b264b17dd59d9db8a1afc88f2509af74cbfdfe","author":{"name":"Summer Deng","email":"601255e35563852a4b111fcd0d33280e92d488e9@fb.com"},"message":"Add NHWC order support in the cost inference function of 3d conv (#19170)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/19170\n\nAs title\nThe quantized resnext3d model in production got the following failures without the fix:\n\n```\n Caffe2 operator Int8ConvRelu logging error: [enforce fail at conv_pool_op_base.h:463] order == StorageOrder::NCHW. 1 vs 2. Conv3D only supports NCHW on the production quantized model\n```\n\nReviewed By: jspark1105\n\nDifferential Revision: D14894276\n\nfbshipit-source-id: ef97772277f322ed45215e382c3b4a3702e47e59","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/84b264b17dd59d9db8a1afc88f2509af74cbfdfe"},{"sha":"f1c8e01524067f6eb0976ade32139e591b907f24","author":{"name":"Ilia Cherniavskii","email":"5704210cc64fe7da6bbbfb00a46e3b2985c634cb@fb.com"},"message":"Add input information in RecordFunction calls (#18717)\n\nSummary:\nAdd input information into generated RecordFunction calls in\nVariableType wrappers, JIT operators and a few more locations\nPull Request resolved: https://github.com/pytorch/pytorch/pull/18717\n\nDifferential Revision: D14729156\n\nPulled By: ilia-cher\n\nfbshipit-source-id: 811ac4cbfd85af5c389ef030a7e82ef454afadec","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/f1c8e01524067f6eb0976ade32139e591b907f24"},{"sha":"ada10ad416f91602d46797b50b3faebcacc6e767","author":{"name":"Nikolay Korovaiko","email":"528632496459efd07ba3c391cb8ec30c03b8f97f@gmail.com"},"message":"Ellipsis in subscript\n\nSummary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17763\n\nDifferential Revision: D14893533\n\nPulled By: Krovatkin\n\nfbshipit-source-id: c46b4e386d3aa30e6dc03e3052d2e5ff097fa74b","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/ada10ad416f91602d46797b50b3faebcacc6e767"},{"sha":"20fc7b6ec7cb7b43f66526abf9d5b4fa44ffefb7","author":{"name":"Mark Santaniello","email":"a5c32efa0f73bab965c155306e25a6c53848289c@fb.com"},"message":"Avoid undefined symbol error when building AdIndexer LTO (#19009)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/19009\n\nMove the definition of `MulFunctor<>::Backward()` into a header file.\n\nReviewed By: BIT-silence\n\nDifferential Revision: D14823230\n\nfbshipit-source-id: 1efaec01863fcc02dcbe7e788d376e72f8564501","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/20fc7b6ec7cb7b43f66526abf9d5b4fa44ffefb7"},{"sha":"646cb6157df948643ec98b8947026ccaee5c7415","author":{"name":"Ilia Cherniavskii","email":"5704210cc64fe7da6bbbfb00a46e3b2985c634cb@fb.com"},"message":"Move OMP/MKL thread initialization into ATen/Parallel (#19011)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/19011\nghimport-source-id: 432e31eccfd0e59fa21a790f861e6b2ff4fdbac6\n\nDifferential Revision: D14846034\n\nPulled By: ilia-cher\n\nfbshipit-source-id: d9d03c761d34bac80e09ce776e41c20fd3b04389","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/646cb6157df948643ec98b8947026ccaee5c7415"},{"sha":"35015762307a68654b8c72d690b7e3e58c3ad137","author":{"name":"Mingzhe Li","email":"87b4aaa1dc912a8334eaabde375e4f886aa3e900@fb.com"},"message":"calculate execution time based on final iterations (#19299)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/19299\n\nI saw larger than 5% performance variation with small operators, this diff aims to reduce the variation by avoiding python overhead. Previously, in the benchmark, we run the main loop for 100 iterations then look at the time. If it's not significant, we will double the number of iterations to rerun and look at the result. We continue this process until it becomes significant. We calculate the time by total_time / number of iterations. The issue is that we are including multiple python trigger overhead.\n\nNow, I change the logic to calculate execution time based on the last run instead of all runs, the equation is time_in_last_run/number of iterations.\n\nReviewed By: hl475\n\nDifferential Revision: D14925287\n\nfbshipit-source-id: cb646298c08a651e27b99a5547350da367ffff47","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/35015762307a68654b8c72d690b7e3e58c3ad137"},{"sha":"3b29cbaf861d5daaff7c7506b62a252b9c7d5614","author":{"name":"Richard Zou","email":"32cb0331e7af743fc44515966c14887fb6c0bc3c@gmail.com"},"message":"Enable half for CUDA dense EmbeddingBag backward. (#19293)\n\nSummary:\nI audited the relevant kernel and saw it accumulates a good deal into float\nso it should be fine.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/19293\n\nDifferential Revision: D14942274\n\nPulled By: zou3519\n\nfbshipit-source-id: 36996ba0fbb29fbfb12b27bfe9c0ad1eb012ba3c","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/3b29cbaf861d5daaff7c7506b62a252b9c7d5614"},{"sha":"b9c20d5224caea1f360285f765d351014fd29df5","author":{"name":"Zachary DeVito","email":"ffbacb804f60e1eea39c96029c00fe9a377f84d7@fb.com"},"message":"graph_for based on last_optimized_executed_graph (#19142)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/19142\nghimport-source-id: 822013fb7e93032c74867fc77c6774c680aef6d1\n\nDifferential Revision: D14888703\n\nPulled By: zdevito\n\nfbshipit-source-id: a2ad65a042d08b1adef965c2cceef37bb5d26ba9","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/b9c20d5224caea1f360285f765d351014fd29df5"},{"sha":"344acaa0caa91d3acb90b2e1f89be53d153dadc1","author":{"name":"Shen Li","email":"e44e257c8fad777ef6693177f50378ef399f0886@fb.com"},"message":"Revert replicate.py to disallow replicating multi-device modules (#19278)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/19278\n\nBased on discussion in https://github.com/pytorch/pytorch/pull/19278 and https://github.com/pytorch/pytorch/pull/18687, changes to replicate.py will be reverted to disallow replicating multi-device modules.\n\nReviewed By: pietern\n\nDifferential Revision: D14940018\n\nfbshipit-source-id: 7504c0f4325c2639264c52dcbb499e61c9ad2c26","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/344acaa0caa91d3acb90b2e1f89be53d153dadc1"},{"sha":"da4ff17eee9f27bb5582418fb168c57c7e6b3e67","author":{"name":"Alexandr Morev","email":"9765c120f3afe882e5c90ca42b81cb0cdf1a9199@yandex.ru"},"message":"math module support (#19115)\n\nSummary:\nThis PR refer to issue [#19026](https://github.com/pytorch/pytorch/issues/19026)\nPull Request resolved: https://github.com/pytorch/pytorch/pull/19115\n\nDifferential Revision: D14936053\n\nPulled By: driazati\n\nfbshipit-source-id: 68d5f33ced085fcb8c10ff953bc7e99df055eccc","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/da4ff17eee9f27bb5582418fb168c57c7e6b3e67"},{"sha":"e1f38a847df7f2dd124528fea64f75f841fd4940","author":{"name":"Jerry Zhang","email":"210765c83ad755402ff54425bf352bae661dc900@fb.com"},"message":"Fix type conversion in dequant and add a test (#19226)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/19226\n\nType conversoin was wrong previously. Thanks zafartahirov for finding it!\n\nDifferential Revision: D14926610\n\nfbshipit-source-id: 6824f9813137a3d171694d743fbb437a663b1f88","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/e1f38a847df7f2dd124528fea64f75f841fd4940"},{"sha":"31686805f2c31073168a7710da04d7cbe542fe13","author":{"name":"J M Dieterich","email":"983328b4d8548eec6654ae4a0bae98b677b3cb2d@ogolem.org"},"message":"Enable unit tests for ROCm 2.3 (#19307)\n\nSummary:\nUnit tests that hang on clock64() calls are now fixed.\n\ntest_gamma_gpu_sample is now fixed.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/19307\n\nDifferential Revision: D14953420\n\nPulled By: bddppq\n\nfbshipit-source-id: efe807b54e047578415eb1b1e03f8ad44ea27c13","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/31686805f2c31073168a7710da04d7cbe542fe13"},{"sha":"1c5073fb4bb7fdb73b4137e4fcf9bc7345ab4b9a","author":{"name":"Vitaly Fedyunin","email":"e7052996e8a2eee74b138ced1948100bd74f64b2@fb.com"},"message":"Adding pin_memory kwarg to zeros, ones, empty, ... tensor constructors (#18952)\n\nSummary:\nMake 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.\n\nSupported functions:\n```python\ntorch.rand_like(t, pin_memory=True)\ntorch.randn_like(t, pin_memory=True)\ntorch.empty_like(t, pin_memory=True)\ntorch.full_like(t, 4, pin_memory=True)\ntorch.zeros_like(t, pin_memory=True)\ntorch.ones_like(t, pin_memory=True)\ntorch.tensor([10,11], pin_memory=True)\ntorch.randn(3, 5, pin_memory=True)\ntorch.rand(3, pin_memory=True)\ntorch.zeros(3, pin_memory=True)\ntorch.randperm(3, pin_memory=True)\ntorch.empty(6, pin_memory=True)\ntorch.ones(6, pin_memory=True)\ntorch.eye(6, pin_memory=True)\ntorch.arange(3, 5, pin_memory=True)\n```\n\nPart of the bigger: `Remove Storage` plan.\n\nNow compatible with both torch scripts:\n `  _1 = torch.zeros([10], dtype=6, layout=0, device=torch.device(\"cpu\"), pin_memory=False)`\nand\n`  _1 = torch.zeros([10], dtype=6, layout=0, device=torch.device(\"cpu\"))`\n\nSame checked for all similar functions `rand_like`, `empty_like` and others\n\nIt is fixed version of #18455\nPull Request resolved: https://github.com/pytorch/pytorch/pull/18952\n\nDifferential Revision: D14801792\n\nPulled By: VitalyFedyunin\n\nfbshipit-source-id: 8dbc61078ff7a637d0ecdb95d4e98f704d5450ba","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/1c5073fb4bb7fdb73b4137e4fcf9bc7345ab4b9a"},{"sha":"ab209595e4be33d15c7cb772b0b571e887326f8a","author":{"name":"Jerry Zhang","email":"fc6a68f0c6006725452d1e587d085214e8b43e31@gmail.com"},"message":"V13: Creating diff from temporary pull request metadata.\n\nDifferential Revision: D14756833\nDifferential Version: 79734657","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/ab209595e4be33d15c7cb772b0b571e887326f8a"}]}
{"actor":{"display_login":"jerryzh168"}}