{"push_id":4203716358,"size":4,"distinct_size":4,"ref":"refs/heads/master","head":"7ed9a3ec4895f0f501cf435fec88ff974d93f3da","before":"5835ad07cbe9cfb961e6e6c7c00001447502c21a","commits":[{"sha":"0e86c99bfb9b3dd4f66c8bfc762f7c38336a7046","author":{"name":"svcscm","email":"319d4a8b107b971d56786637202f7ee82b269e79@fb.com"},"message":"Updating submodules\n\nSummary:\nGitHub commits:\n\nhttps://github.com/facebook/litho/commit/a3277b4e50c351ca2938109245018a284ad4d7dc\nhttps://github.com/facebook/mcrouter/commit/9ac4d710723d82cec310f6eaa82eba005858513c\nhttps://github.com/pytorch/fbgemm/commit/98141ffe1b1657459512b621d622c2cf3a1537a3\n\nTest Plan: n/a\n\nReviewed By: cdelahousse\n\nfbshipit-source-id: e1d9ec467e9d72774fda12ca1e8ca2e740fbe5c7","distinct":true,"url":"https://api.github.com/repos/KabirKwatra/pytorch/commits/0e86c99bfb9b3dd4f66c8bfc762f7c38336a7046"},{"sha":"4230132baf259bf10336b27755a1e0d6ae6927e7","author":{"name":"Prasun Anand","email":"d9e04cede6f753dc2df4dbdde2dcf255f084ef02@gmail.com"},"message":"Added docs for context method mixins. Fixes issue #27365 (#28643)\n\nSummary:\nFixes https://github.com/pytorch/pytorch/issues/27365 .\n\nThis PR:\n1. Makes Context method docs available.\n2. Links [Extending torch autograd](https://pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd) notes to Context method docs.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/28643\n\nDifferential Revision: D18170089\n\nPulled By: albanD\n\nfbshipit-source-id: a1119ea8e2f8a71f0d1aadf416f2f98343aa9b7b","distinct":true,"url":"https://api.github.com/repos/KabirKwatra/pytorch/commits/4230132baf259bf10336b27755a1e0d6ae6927e7"},{"sha":"82f31e02a396fe1aae1a0140f20feb2e0f6aeffe","author":{"name":"titaneric","email":"177964d8b5d638d79e940294b8e5e645d85de8d7@gmail.com"},"message":"Remove the redundant calculation of derivative of power function (#28651)\n\nSummary:\nHi, I notice that the pytorch faced the the issue as HIPS/autograd#541 .\nI try to solve it, hope it can help.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/28651\n\nReviewed By: gchanan\n\nDifferential Revision: D18137163\n\nPulled By: albanD\n\nfbshipit-source-id: 888bef65c72c4c15c2acdd4b13d5041008b1354e","distinct":true,"url":"https://api.github.com/repos/KabirKwatra/pytorch/commits/82f31e02a396fe1aae1a0140f20feb2e0f6aeffe"},{"sha":"7ed9a3ec4895f0f501cf435fec88ff974d93f3da","author":{"name":"Xinyu Li","email":"d6a78b7127ce1fc9ef8c05891f83a10495512ad4@fb.com"},"message":"Change reorder_dimensions behavior to favor output writting sequence (#28615)\n\nSummary:\nreorder_dimensions() currently iterate all the operands when determining the dimension order in the TensorIterator. It tries to move a dimension to front if any operand has a dimension whose stride is bigger than this dimension.\n\nreorder_dimensions() do respect the case that stride has zero value. I did not see a reason why reorder_dimensions() need to keep probing each operand under regular cases.\n\nChanged behavior a little bit.\nSince operands is ordered by outputs tensor first followed by input tensor.  I would favor the writing of outputs is as sequential as possible. This could make the copy between tensors with different memory format faster.\n\nPls correct me if this change is wrong, thanks.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/28615\n\nReviewed By: VitalyFedyunin\n\nDifferential Revision: D18122474\n\nPulled By: glaringlee\n\nfbshipit-source-id: f36467489fe6c6514b14ce9dcc439628d5d5ad0e","distinct":true,"url":"https://api.github.com/repos/KabirKwatra/pytorch/commits/7ed9a3ec4895f0f501cf435fec88ff974d93f3da"}]}
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