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4c81282c33
* Introduce torch.layout and split layout from dtypes. Tensors (and tensor types) now have a 'layout' attribute that returns either 'torch.strided' or 'torch.sparse_coo'. Previously, dtypes were 1-to-1 with ATen types/PyTensorTypes; the impetus behind this decision was to make things easy in the common case (i.e. specifying a type in a factory function). But this doesn't really follow for sparity, which isn't a common case. It also doesn't properly represent the concept or a dtype, which in numpy are proper scalar types (i.e. roughly the type returned from indexing the last dimension of an n-d array). But this should be the same whether or not the tensor is represented via strides, sparsity, etc. This is accomplished by: 1) having the dtype of tensor return the (device-type, scalar-type) combination, i.e. torch.cuda.float32, so both torch.cuda.FloatTensor and torch.cuda.sparse.FloatTensor have the same dtype 2) Adding a layout parameter to python functions, where the combination of (dtype, layout) maps to an ATen type that is used for dispatch. * Formatting, make init throw python_error. * Fix cuda not enabled error message. * Fix test.
8 lines
107 B
C++
8 lines
107 B
C++
#pragma once
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namespace torch { namespace utils {
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void initializeLayouts();
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}} // namespace torch::utils
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