einops
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arogozhnikov
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
In pytorch, we have 'expand_as' which check dim before expand. I'm aware of 'repeat' layer as replace for 'expand' but could you add 'repeat_as' as expand for 'expand as' ?...
My sense in that in many cases the size of new axis should match the size of an existing axis on a different tensor. I wonder if a helper function...
Hello, I'm just throwing an idea, I'm not sure it fits in the scope of Einops, and it will probably require a lot of work, but I think it would...
opt_einsum: https://optimized-einsum.readthedocs.io/en/latest/ Not sure how integration would look like. Maybe with a module flag for "einsum optimizer" (`EINSUM_OPT in ['opt_einsum', None]`). Since the einsum part should work the same for...
need to investigate if backend packages make strides available for analysis (or at least as_contiguous). This may help with optimizations
It would be nice to have it, but there are problems with backends - numpy.logaddexp.reduce is available (scipy.special.logsumexp is better, but I can't use it) - tf.reduce_logsumexp is available -...
- normally, it is an `mxnet` issue - seems it was like for ages (see code around `MXNET_SPECIAL_MAX_NDIM`) --- After digging into mxnet: - neighboring reduced axes (and non-reduced axes)...
Thanks for making einops, it's a really helpful package! **Describe the bug** When I try to use `reduce()` on a [`torch.bfloat16` tensor](https://pytorch.org/docs/stable/tensor_attributes.html#torch-dtype) , I get an error: NotImplementedError: reduce_mean is...
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Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)