Migrate erfinv and erfinv_ from the TH to Aten (CUDA) #24943
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module: cpu
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| static void erfinv_kernel(TensorIterator& iter) { |
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I had to implement this method to be able to use dispatch. It will implemented when merging with correspondent CPU PR: #24908
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| void erfinv_kernel_cuda(TensorIterator& iter) { | ||
| AT_DISPATCH_FLOATING_TYPES_AND_HALF(iter.dtype(), "erfinv_cuda", [&]() { |
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Currently it's generated for FLOAT, DOUBLE and HALF
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@pytorchbot retest this please |
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| /*check_mem_overlap=*/true); \ | ||
| op##_stub(iter.device_type(), iter); \ | ||
| return result; \ | ||
| #define IMPLEMENT_UNARY_OP_CORE(op) \ |
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There are many reasons not to use macro (terrible debug experience). Please check #24879 if solution is suitable for you.
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Overall good,, but I rather wait #24879 to land and use helpers from it.
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Summary: pytorch/pytorch#24560 Pull Request resolved: pytorch/pytorch#24943 Differential Revision: D16996434 Pulled By: ifedan fbshipit-source-id: 77111a4e47bb2b20f65225d48e7213cd77ddae19
…ble (#25337) Summary: This best preserves accuracy, while erfinvf() should be used for half and float. This is also consistent with the implementation before the migration: #24943 Pull Request resolved: #25337 Differential Revision: D17102333 Pulled By: zou3519 fbshipit-source-id: 5178cff534cf5f10d86ab04d4b6c1779ffedf49e
…ble (#25337) Summary: This best preserves accuracy, while erfinvf() should be used for half and float. This is also consistent with the implementation before the migration: pytorch/pytorch#24943 Pull Request resolved: pytorch/pytorch#25337 Differential Revision: D17102333 Pulled By: zou3519 fbshipit-source-id: 5178cff534cf5f10d86ab04d4b6c1779ffedf49e
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#24560