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3b7e1cd2cc
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: D20509850 Pulled By: mruberry fbshipit-source-id: 2cd3c828aad67191c77f2ed8470411e246f604f8 |
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| community | ||
| notes | ||
| org/pytorch | ||
| scripts | ||
| __config__.rst | ||
| amp.rst | ||
| autograd.rst | ||
| bottleneck.rst | ||
| checkpoint.rst | ||
| conf.py | ||
| cpp_extension.rst | ||
| cuda.rst | ||
| cuda_deterministic.rst | ||
| cuda_deterministic_backward.rst | ||
| cudnn_deterministic.rst | ||
| cudnn_persistent_rnn.rst | ||
| data.rst | ||
| distributed.rst | ||
| distributions.rst | ||
| dlpack.rst | ||
| hub.rst | ||
| index.rst | ||
| jit.rst | ||
| jit_builtin_functions.rst | ||
| jit_language_reference.rst | ||
| jit_python_reference.rst | ||
| jit_unsupported.rst | ||
| math-quantizer-equation.png | ||
| model_zoo.rst | ||
| multiprocessing.rst | ||
| name_inference.rst | ||
| named_tensor.rst | ||
| nn.functional.rst | ||
| nn.init.rst | ||
| nn.rst | ||
| onnx.rst | ||
| optim.rst | ||
| packages.rst | ||
| quantization.rst | ||
| random.rst | ||
| rpc.rst | ||
| sparse.rst | ||
| storage.rst | ||
| tensor_attributes.rst | ||
| tensor_view.rst | ||
| tensorboard.rst | ||
| tensors.rst | ||
| torch.rst | ||
| type_info.rst | ||
