Make sparse tensor types extend the existing tensor types #303
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brandonwillard
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@@ Coverage Diff @@
## master #303 +/- ##
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+ Coverage 71.89% 71.91% +0.01%
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Files 166 166
Lines 54567 54632 +65
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+ Hits 39232 39288 +56
- Misses 15335 15344 +9
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twiecki
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twiecki
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twiecki
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Feb 15, 2021
brandonwillard
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| @@ -355,8 +360,7 @@ def __getitem__(self, args): | |||
| return ret | |||
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| class SparseVariable(_sparse_py_operators, Variable): | |||
| dtype = property(lambda self: self.type.dtype) | |||
| class SparseVariable(_sparse_py_operators, TensorVariable): | |||
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Why are the supertypes in a different order compared to the SparseConstant below?
| @@ -442,7 +451,7 @@ def bsr_matrix(name=None, dtype=None): | |||
| csr_fmatrix = SparseType(format="csr", dtype="float32") | |||
| bsr_fmatrix = SparseType(format="bsr", dtype="float32") | |||
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| all_dtypes = SparseType.dtype_set | |||
| all_dtypes = list(SparseType.dtype_specs_map.keys()) | |||
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Did you make it a list so it can be appended at runtime?
| else: | ||
| raise NotImplementedError( | ||
| f'unsupported dtype "{dtype}" not in list', list(self.dtype_set) | ||
| "You can't make SparseType object when SciPy is not available." |
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Is it even a supported use case to NOT have scipy?
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That's how this project was originally designed, I believe, but there's no point in supporting that optionality now.
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This sparse implementation only supports matrices, but we could cover more ground with |
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This PR refactors the sparse tensor type structure so that sparse types extend the existing tensor types. A new meta type is used to construct a
DenseTensorTypethat can be used to distinguish dense from sparseTensorTypes.Closes #142.
DenseTensorVariable(using the same approach asDenseTensorType)SparseTensorsOps (e.g.Elemwise,CAReduce)