Make NumpyOps CPU kernels generic #627
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This PR makes most CPU kernels generic, so that they can take both float32 and float64 arrays (and hopefully in the future float16). I experimented with kernels in Cython + fused types and kernels as C++ with templates, I found the C++ template route more promising: - More compact/ergonomic implementations with fewer compile-time conditionals. - Opens up the possibility to easily use SIMD intrinsics in the future. To allow genericity in the NumpyOps method arguments, we use: - Fused types when we require a specific dimensionality; - np.ndarray otherwise. Some of the kernels are not made generic: - cpu_scatter_add: needs tests to verify that the op still works correctly. - cpu_position_encode: the position_encode op doesn't take float array(s). - lstm kernels: I need to look more deeply into them.
svlandeg
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shadeMe
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Apr 5, 2022
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svlandeg
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Apr 6, 2022
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Ugh - do we have a flaky test? |
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Oh no, again the dreadful |
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Rerunning it 😐 |
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This PR makes most CPU kernels generic, so that they can take both
float32andfloat64arrays (and hopefully in the futurefloat16). I experimented with kernels in Cython + fused types and kernels as C++ with templates, I found the C++ template route more promising:To allow genericity in the
NumpyOpsmethod arguments, we use:np.ndarrayotherwise.Some of the kernels are not (yet) made generic:
cpu_scatter_add: needs tests to verify that the op still workscorrectly.
cpu_position_encode: the position_encode op doesn't take floatarray(s).