{"push_id":2758349041,"size":35,"distinct_size":35,"ref":"refs/heads/master","head":"384d9c3f5667466d932f07a8660802bde219c283","before":"e9e31016661bd645668dcc35959c81f21c0f4d32","commits":[{"sha":"bc66d982482e6d6c586d12de5176d9d4bf38eec5","author":{"name":"Gregory Chanan","email":"405a4c10dc10d9753e4d637a51ebfa857b6fcbb3@fb.com"},"message":"Fix narrow on empty tensors after negative size support.\n\nSummary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9838\n\nDifferential Revision: D9002345\n\nPulled By: gchanan\n\nfbshipit-source-id: 13f4bacff94d9d0ea31a3b73a75b9b3e774eabf5","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/bc66d982482e6d6c586d12de5176d9d4bf38eec5"},{"sha":"1af1b0c2a5d1dc708f5ff1d2acf69ff186a67a1a","author":{"name":"Gregory Chanan","email":"405a4c10dc10d9753e4d637a51ebfa857b6fcbb3@fb.com"},"message":"Remove THTensor::_dim, temporarily remove THTensor_nDimension. (#9895)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9895\n\nThe primary goal here was to remove THTensor::_dim, which isn't part of the API moving forward.\nInstead, we provide 3 options for getting the dimensionality (this is temporary although non-trivial to remove!):\n```\nnDimension                 corresponds to the \"true\" ATen dimension. TODO: implement.\nnDimensionLegacyNoScalars  correpsonds to the ATen dimension, except scalars are viewed as 1-dimensional tensors.\nnDimensionLegacyAll        corresponds to the ATen dimension, except scalars are viewed as 1-dimensional tensors\n                           and tensors with a dimension of size zero are collapsed to 0-dimensional tensors.\n```\nSo in this patch, nDimension -> nDimensionLegacyNoScalars and _dim/_nDimension goes to nDimensionLegacyAll.\nThese are just codemods.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9835\n\nReviewed By: ezyang\n\nDifferential Revision: D8999338\n\nPulled By: gchanan\n\nfbshipit-source-id: a4d676ac728f6f36ca09604a41e888d545ae9311","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/1af1b0c2a5d1dc708f5ff1d2acf69ff186a67a1a"},{"sha":"d3ba9a173e9b97e3b8a7a5007ef231a7aea95ed8","author":{"name":"Gregory Chanan","email":"405a4c10dc10d9753e4d637a51ebfa857b6fcbb3@fb.com"},"message":"Handle case where THC btrifact doesn't zero info. (#9907)\n\nSummary:\nThis was showing up in the n-dimensional empty tests as flaky because it's reading uninitialized cuda memory.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9907\n\nDifferential Revision: D9021413\n\nPulled By: gchanan\n\nfbshipit-source-id: 31542b7597919df9afd6e528bb108a4a3e8eaf60","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/d3ba9a173e9b97e3b8a7a5007ef231a7aea95ed8"},{"sha":"bf32ea80942ce720b105efcd517fd11182edeb08","author":{"name":"Eugene Vorontsov","email":"8b33ba6761b4b48fd1a6aa9076c97bc75ed2afa0@gmail.com"},"message":"Fix dimension check in 1D instance norm, allowing 2D tensors alongside 3D. (#9924)\n\nSummary:\nFixes #9776.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9924\n\nDifferential Revision: D9028328\n\nPulled By: soumith\n\nfbshipit-source-id: d5f22abb2be83b34aee95ebe144c97519a6854f8","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/bf32ea80942ce720b105efcd517fd11182edeb08"},{"sha":"c0bacc628415cfd8f7040ece39b54867790e025c","author":{"name":"Gregory Chanan","email":"405a4c10dc10d9753e4d637a51ebfa857b6fcbb3@fb.com"},"message":"Guard test_lapack_empty with has_magma. (#9936)\n\nSummary:\nCUDA lapack functions generally don't work unless has_magma is true.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9936\n\nDifferential Revision: D9028579\n\nPulled By: gchanan\n\nfbshipit-source-id: 9b77e3b05253fd49bcabf604d0924ffa0e116055","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/c0bacc628415cfd8f7040ece39b54867790e025c"},{"sha":"94439d7df4d158023ea964db9afacaa2e1370074","author":{"name":"Lu Fang","email":"6e6bc3b7f607f5d674fb8a406040e26b26ffec27@fb.com"},"message":"Suppress the vptr warning in ubsan (#9909)\n\nSummary:\nUnblock https://github.com/pytorch/pytorch/pull/8469\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9909\n\nDifferential Revision: D9023650\n\nPulled By: houseroad\n\nfbshipit-source-id: 7682a9cd7905e98c802b820ad59745672b32970d","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/94439d7df4d158023ea964db9afacaa2e1370074"},{"sha":"aebf3b47aef7c7bbdfe30f74dc35ed0ba727bdc1","author":{"name":"Jerry Zhang","email":"210765c83ad755402ff54425bf352bae661dc900@fb.com"},"message":"Remove template parameter from Tensor (#9939)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9939\n\nPull Request resolved: https://github.com/facebookresearch/weakly-supervised-action-detection/pull/13\n\nPull Request resolved: https://github.com/pytorch/translate/pull/166\n\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9125\n\nCloses https://github.com/pytorch/pytorch/pull/9125\n\nUse inheritance for polymorphism, and remove template parameter\nThis is to change the templating in call sites, the core implementations will change later\n\nBefore Caffe2 Tensor class was compile-time fixed to bind to a particular device/context. With this change, we're making it a runtime property (stored inside the tensor), but preserve the same semantics. For example, one has to specify device type in order to create a Tensor - there are no uninitialized tensors. More specifically the changes are:\n\n1. We added an extra argument *DeviceType* to most of the constructors of the tensor, e.g. (Tensor(DeviceType type)),\n2. Semantics of constructor Tensor(const Tensor<SrcContext>& src, ContextForCopy* context); is changed, in this constructor, the second context is passed in to enable us to call the templated Copy function, it could be in a different context as source and target previously, now we'll enforce that the context should have same device type as src, if it is provided.\n3. To preserve 'get-or-construct' semantics of Blob, we added specialized getter Blob::GetMutableTensor that verifies both that Blob contains a Tensor and that it's of a correct type\n4. Specifically, Tensor type is not default-constructible any more (as we don't have unknown device tensors) and thus some of the code handling STL containers needs to change\n\nNote: Some changes are postponed just to keep this diff a bit smaller. Please see `TODO`s.\n\nReviewed By: ezyang, houseroad\n\nDifferential Revision: D9024330\n\nfbshipit-source-id: e0b8295d2dc6ebe2963383ded5af799ad17164ba","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/aebf3b47aef7c7bbdfe30f74dc35ed0ba727bdc1"},{"sha":"c77e4bc4d5f0efc784b0173bdcdd3249dcb8c2ca","author":{"name":"Thomas Viehmann","email":"36ac19d4ad6fe01b06633a4b23ed512831899735@beamnet.de"},"message":"export tensor(ArrayRef, options) on Windows (#9904)\n\nSummary:\nI hope this helps me for the windows build failure in #9628 .\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9904\n\nDifferential Revision: D9026715\n\nPulled By: soumith\n\nfbshipit-source-id: bb97d41d060823f5a37bfc9a1659815b8b9f4eab","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/c77e4bc4d5f0efc784b0173bdcdd3249dcb8c2ca"},{"sha":"ce0b895a0c6c691be7b7424361ec65370271145e","author":{"name":"Adam Paszke","email":"b8189466466bc06fbb8080d5586ffcbc857edaf5@fb.com"},"message":"Fix UBSAN error in ONNX peephole pass, make it more robust.\n\nSummary: Minor fix for a bug introduced by D9004285\n\nReviewed By: anderspapitto\n\nDifferential Revision: D9028762\n\nfbshipit-source-id: 9b9c5eef30e61d7ae19784e0418fa29bad2b5564","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/ce0b895a0c6c691be7b7424361ec65370271145e"},{"sha":"8c0355c90d794f0dd9c9deaa4227ec23ef8074bd","author":{"name":"Wei Yang","email":"acbca9fda1bd78fcd284ddae8b872fb12de49b82@fb.com"},"message":"convert lambd directly to scalar_t at hardshrink (#9919)\n\nSummary:\n- convert lambd directly to scalar_t instead of creating a tensor\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9919\n\nDifferential Revision: D9026708\n\nPulled By: weiyangfb\n\nfbshipit-source-id: d20ab06ecc12aa972ee9d1323ee2f84abf8d5ffd","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/8c0355c90d794f0dd9c9deaa4227ec23ef8074bd"},{"sha":"6ed41adb04bbb3bf33451e2bbccecbf08bd2a178","author":{"name":"Owen Anderson","email":"24a74c757816385a523b6755e302643f8b781853@oculus.com"},"message":"Use round-to-negative division when computing output sizes for convolutions involving striding and dilation.\n\nSummary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9640\n\nDifferential Revision: D8948081\n\nPulled By: resistor\n\nfbshipit-source-id: 06f2e3ad1bdb448be6f36577cb9bd27c884df595","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/6ed41adb04bbb3bf33451e2bbccecbf08bd2a178"},{"sha":"ef9801f32cb945c4771310f8407116886b10cae3","author":{"name":"Christian Puhrsch","email":"b7a905850ca55c064cd1088c2e252f92e10708de@fb.com"},"message":"Merge THStorage into at::Storage\n\nSummary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9772\n\nReviewed By: ezyang\n\nDifferential Revision: D9019375\n\nPulled By: cpuhrsch\n\nfbshipit-source-id: d5185e29747929d648e4260db4967452cd40f563","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/ef9801f32cb945c4771310f8407116886b10cae3"},{"sha":"e3c4057b6c7f07fc0dbf3453f19f6730cd7ab02d","author":{"name":"Owen Anderson","email":"24a74c757816385a523b6755e302643f8b781853@oculus.com"},"message":"Eliminate an extra lookup in the hashtable during CSE. (#9668)\n\nSummary:\nzdevito\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9668\n\nDifferential Revision: D8955185\n\nPulled By: resistor\n\nfbshipit-source-id: f3f929efc11be63850bd863679cc7b297c98d679","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/e3c4057b6c7f07fc0dbf3453f19f6730cd7ab02d"},{"sha":"829d763c6992924401c32828e3f05ae5715eb4fb","author":{"name":"Sam Gross","email":"48245c485a3f0765c7face6e57b9f7e40c8624a4@fb.com"},"message":"Implement add, sub, mul, div using TensorIterator (#8919)\n\nSummary:\n```\nThis adds TensorIterator, a helper class for computing element-wise\noperations that's intended to replace the CPU and CUDA apply utils\nfunctions.\n\nCPU kernels are implemented as functions that operate on strided 1-d\ntensors compared to CPUApplyUtils which operated individual elements. This\nallows the kernels to handle vectorization, while TensorIterator handles\nparallelization and non-coalesced dimensions.\n\nGPU kernels continue to operate on elements, but the number of\nspecializations is reduced. The contiguous case remains the same. The\nnon-contiguous case uses a single (reduced) shape for all operands and\nthe fast integer division from THCIntegerDivider. To avoid extra\nspecializations for indexing with 64-bits, large operations are split\ninto smaller operations that can be indexed with 32-bits.\n\nMajor semantic changes:\n\n - No more s_add, s_mul, s_div, or s_sub. Broadcasting is handled by\n   TensorIterator. The autograd engine performs the reduction assuming\n   standard broadcasting if the gradient shape does not match the\n   expected shape. Functions that do not use standard broadcasting rules\n   should either continue to trace the expand calls or handle the\n   reduction in their derivative formula.\n\n - Use ONNX v7, which supports broadcasting ops.\n\nPerformance impact:\n\n - Small increased fixed overhead (~0.5 us)\n - Larger overhead for wrapped numbers (~2.5 us)\n - No significant change for ops on contiguous tensors\n - Much faster worst-case performance for non-contiguous GPU tensors\n - Faster CPU bias addition (~2x)\n - Faster GPU bias addition (~30% faster)\n\nFuture work:\n\n - Decrease overhead, especially for wrapping numbers in Tensors\n - Handle general inter-type operations\n - Extend to unary ops and reductions\n - Use buffering for compute-bound operations on non-contiguous tensors\n   (pull in from CPUApplyUtils)\n```\nPull Request resolved: https://github.com/pytorch/pytorch/pull/8919\n\nDifferential Revision: D8677600\n\nPulled By: colesbury\n\nfbshipit-source-id: 61bc9cc2a36931dfd00eb7153501003fe0584afd","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/829d763c6992924401c32828e3f05ae5715eb4fb"},{"sha":"eb9bb1f09abd04f5c95f9425e05e55e206c8ac61","author":{"name":"cclauss","email":"585dd401264c1e46d17ebec1862e5d2a9b6a6c5d@bluewin.ch"},"message":"Travis CI: Run flake on Python 2.7 and 3.7 (#9953)\n\nSummary:\nFlake8 will produce different results on Python 2 and 3.  Python 3.7 has __async__ as a reserved word https://github.com/pytorch/pytorch/pull/4999.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9953\n\nDifferential Revision: D9035415\n\nPulled By: soumith\n\nfbshipit-source-id: 8a46e028a2e20a7e3f6d90137020268d65a7cc64","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/eb9bb1f09abd04f5c95f9425e05e55e206c8ac61"},{"sha":"a532c1a48ccdc7adcb29f320c4de3a0a2b306228","author":{"name":"Norman Mu","email":"63e37a0d1d51b8efd953de2fcdb71ed70eedafcd@fb.com"},"message":"Fix default argument value for CTCGreedyDecoder op (#9747)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9747\n\nCurrently the ctc_greedy_decoder op initializes the `merge_repeated` argument only if it has been provided by the user. Change to initialize in all cases.\n\nReviewed By: houseroad\n\nDifferential Revision: D8963635\n\nfbshipit-source-id: 18955c7c26a77d9d7f5137e4dec085252ffabfeb","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/a532c1a48ccdc7adcb29f320c4de3a0a2b306228"},{"sha":"c3fe071483457a84d892b1060ee7b08caa84f156","author":{"name":"root","email":"8e7b07a372f9f2a17893f061240ac03d98916d75@gmail.com"},"message":"Update hip files (#9826)\n\nSummary:\nThe goal of this PR is to update the hip files to reflect relevant changes in cuda source files.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9826\n\nDifferential Revision: D9032840\n\nPulled By: bddppq\n\nfbshipit-source-id: 504e55c46308eebfee3c9a7beea1f294fe03470f","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/c3fe071483457a84d892b1060ee7b08caa84f156"},{"sha":"9eeb4e17afdfab948d3e2362af7a558562f83988","author":{"name":"Jerry Zhang","email":"210765c83ad755402ff54425bf352bae661dc900@fb.com"},"message":"Split gather op for easier smaller code size (#9916)\n\nSummary:\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9916\n\natt\n\nDifferential Revision: D8961085\n\nfbshipit-source-id: 39a9838647dc97611e77beb0607c4655de727ada","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/9eeb4e17afdfab948d3e2362af7a558562f83988"},{"sha":"12a1af37316315784a0413a9e44e43c4915a7e28","author":{"name":"Igor Milyakov","email":"3a0e4882069780c5ef0d8e261f98a283e8316481@fb.com"},"message":"Adding conv tests with explicit algo definition\n\nSummary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9798\n\nDifferential Revision: D9034663\n\nPulled By: virtan\n\nfbshipit-source-id: d722f25f1dd00231ccc3ad5960bbbef63af02c2d","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/12a1af37316315784a0413a9e44e43c4915a7e28"},{"sha":"ee827f6ba3179e51fce41659c50a437889cf67ce","author":{"name":"Lu Fang","email":"6e6bc3b7f607f5d674fb8a406040e26b26ffec27@fb.com"},"message":"Fix a testcase in logsoftmax onnx export (#9660)\n\nSummary:\nWe only support special case. The original dim is not supported by ONNX.\nPull Request resolved: https://github.com/pytorch/pytorch/pull/9660\n\nReviewed By: bddppq\n\nDifferential Revision: D8965507\n\nPulled By: houseroad\n\nfbshipit-source-id: 021dffdf0489c2d3a50bfd1e0c4cfd00d4a3d776","distinct":true,"url":"https://api.github.com/repos/iotamudelta/pytorch/commits/ee827f6ba3179e51fce41659c50a437889cf67ce"}]}
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