{"push_id":2932859613,"size":6,"distinct_size":3,"ref":"refs/heads/export-D9874140","head":"2c0792005da2e51e1da5b2bcea36e3467ec4d75a","before":"854731efaa43c58ec7a710e97d7bb51528b5a60f","commits":[{"sha":"ae7a7fb398cb859732a5f6f0bfdfb87b803a2ade","author":{"name":"Teng Li","email":"36698d3fcf047847a1e178044ef9ae81f841cd43@fb.com"},"message":"Use open-source NCCL2 in PyTorch (#12312)\n\nSummary:\n- Removed the old nccl file\n- Make open-source NCCL a submodule\n- CMake to make NCCL itself\n\nNCCL2 now is in the default build.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/12312\n\nDifferential Revision: D10190845\n\nPulled By: teng-li\n\nfbshipit-source-id: 08d42253b774149a66919d194f88b34628c39bae","distinct":false,"url":"https://api.github.com/repos/yinghai/pytorch/commits/ae7a7fb398cb859732a5f6f0bfdfb87b803a2ade"},{"sha":"c064f8a89d2fd666a06ee9a9b0e635ce453b8efc","author":{"name":"Gu, Jinghui","email":"87e9f383c80b541ce865cadee2cc85ad05fa7602@intel.com"},"message":"Fix build error mkldnn due to corruptted CMAKE_REQUIRED_LIBRARIES (#12195)\n\nSummary:\nThis is to fix cmake-time compilation error.\n\n When we change script to build Caffe2 with mkldnn, we run into some cmake-time compilation support check (like in libsleef) failed due to incorrect setting of CMAKE_REQUIRED_LIBRARIES.  It is a global setting which can interfere camke compilation if it is not clean up properly.  FindBLAS.cmake and FindLAPACK.cmake didn't clean this flag, and causes incorrect building of libsleef.so.\n\nyinghai gujinghui\nPull Request resolved: https://github.com/pytorch/pytorch/pull/12195\n\nDifferential Revision: D10159314\n\nPulled By: yinghai\n\nfbshipit-source-id: 04908738f7d005579605b9c2a58d54f035d3baf4","distinct":false,"url":"https://api.github.com/repos/yinghai/pytorch/commits/c064f8a89d2fd666a06ee9a9b0e635ce453b8efc"},{"sha":"39bd73ae513358e1fe5881e58b1ab6aea14bed41","author":{"name":"vishwakftw","email":"7d9dbaf6fdbe0bc4465fbe69a9c17ceb70dc8fb9@iith.ac.in"},"message":"Guard NumPy usage using USE_NUMPY (#11798)\n\nSummary:\nAll usages of the `ndarray` construct have now been guarded with `USE_NUMPY`. This eliminates the requirement of NumPy while building PyTorch from source.\n\nFixes #11757\n\nReviewed By: Yangqing\n\nDifferential Revision: D10031862\n\nPulled By: SsnL\n\nfbshipit-source-id: 32d84fd770a7714d544e2ca1895a3d7c75b3d712","distinct":false,"url":"https://api.github.com/repos/yinghai/pytorch/commits/39bd73ae513358e1fe5881e58b1ab6aea14bed41"},{"sha":"a98489747ddcd8793d79230c9a50b79c849b43e3","author":{"name":"iotamudelta","email":"983328b4d8548eec6654ae4a0bae98b677b3cb2d@ogolem.org"},"message":"Enable sparse functionality and tests (#12323)\n\nSummary:\n* Enable sparse functions for ROCm\n\n* Reenable test_sparse unit tests that are now passing in ROCm\n\nezyang bddppq\nPull Request resolved: https://github.com/pytorch/pytorch/pull/12323\n\nDifferential Revision: D10203540\n\nPulled By: bddppq\n\nfbshipit-source-id: 33ffcfbda32875676c27b33ad1e7cd96fbadc790","distinct":true,"url":"https://api.github.com/repos/yinghai/pytorch/commits/a98489747ddcd8793d79230c9a50b79c849b43e3"},{"sha":"895994a7c30ca9696e57444caf2a74b1aa1d59e7","author":{"name":"Orion Reblitz-Richardson","email":"e68e608cba1189ad338a33f5a502b06d361894c6@fb.com"},"message":"Back out \"[pytorch][PR] [Build] Use open-source NCCL2 in PyTorch\"\n\nReviewed By: The controller you requested could not be found.\n\nfbshipit-source-id: a13075339d3a7b970e81be0b1a32a7c4c3a6c68d","distinct":true,"url":"https://api.github.com/repos/yinghai/pytorch/commits/895994a7c30ca9696e57444caf2a74b1aa1d59e7"},{"sha":"2c0792005da2e51e1da5b2bcea36e3467ec4d75a","author":{"name":"Yinghai Lu","email":"0674548f4d596393408a51d6287a76ebba2f42aa@fb.com"},"message":"New chaining/partitioning algorithm for async_scheduling for inference (#11957)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/11957\n\nFor distributed inference, we want to use async_scheduling net to run the net as we need its async part. However, according to the profiling, async_net has big overhead of dispatching tasks onto worker threads. This diff improves the issue by generating a smaller number of chains/tasks by grouping the sync ops that can be run in one shot. Note that it also schedule individual async ops as a single chain because unlike gpu ops, rpc ops are not guaranteed to be linearized at the remote site. For example, if you have two rps ops `op1->op2`, op2 won't implicitly block until op1 finishes. Therefore we need to put each of the async op as one chain as async_scheduling net will only sync the tail of the chain.\n\nFor the all sync op nets, this change give us `1.5X` slower than simple_net, while without the change, it is `7X` slower.\n\nNext step is to work on the executor to make the task scheduling faster. And add a fallback path to be able to run ops inline if it's a all-sync net.\n\nDifferential Revision: D9874140\n\nfbshipit-source-id: 060cd78a64af77219d59b3a3890c45cdaf1ed854","distinct":true,"url":"https://api.github.com/repos/yinghai/pytorch/commits/2c0792005da2e51e1da5b2bcea36e3467ec4d75a"}]}
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