pytorch

b7129050 - Makes floor_divide a method, adds sparse floor division (#34552)

Makes floor_divide a method, adds sparse floor division (#34552) Summary: (Updated per review feedback) `torch.floor_divide` is currently a function that can operate on two tensors or a tensor and a scalar (scalar x scalar floor division is handled natively by Python and the JIT has a builtin function for it). This PR updates it to: - have an out variant: `floor_divide(x, y, out=z)` - be a method on a tensor: `x.floor_divide(y)` - have an in-place variant: `x.floor_divide_(y)` - work with sparse tensors Tests are added to test_sparse.py and test_torch.py for these new behaviors. In addition, this PR: - cleans up the existing sparse division and true_division code and improves their error message - adds testing of sparse true_division to test_sparse.py - extends existing floor_divide testing in test_torch to run on CUDA, too, not just the CPU Unfortunately, making floor_divide a method requires breaking backwards compatibility, and floor_divide has been added to the BC whitelist since this is international. The BC issue is that the first parameter name to torch.floor_divide is changing from input to self. If you previously called torch.floor_divide with keyword arguments, e.g. torch.floor_divide(input=x, other=y), you will need to update to torch.floor_divide(self=x, other=y), or the more common torch.floor_divide(x, y). The intent of this PR is to allow floor_divide to be substituted for division (torch.div, /) wherever division was previously used. In 1.6 we expect torch.div to perform true_division, and floor_divide is how users can continue to perform integer division with tensors. There are two potential follow-up issues suggested by this PR: - the test framework might benefit from additional tensor construction classes, like one to create dividends and divisors for multiple dtypes - the test framework might benefit from a universal function test class. while methods have reasonable coverage as part of test_torch.py's TestTensorOp tests, function coverage is spotty. Universal functions are similar enough it should be possible to generate tests for them. Pull Request resolved: https://github.com/pytorch/pytorch/pull/34552 Differential Revision: D20497453 Pulled By: mruberry fbshipit-source-id: ac326f2007d8894f730d1278fef84d63bcb07b5d