Comments (5)
I am planning to add a different macro, which really just creates explicit for loops around such an index expression. That would allow for much more flexible operations (e.g. custom functions etc). It would be less efficient for large tensors for the operations the @tensor
macro can do. Something along the lines of einsum
(see also #10 ).
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@shashi - I believe what I have in Einsum.jl will work for you. The code is still a bit preliminary so be cautious and don't hesitate to open an issue if it fails.
See - https://github.com/ahwillia/Einsum.jl/blob/master/test/runtests.jl#L54
from tensoroperations.jl.
So @ahwillia , do you plan to register this as an independent package (or did you do so already)? If not, I would certainly be interested to a include it in TensorOperations.jl . I've had this is a planned feature in the Readme for a while already (see the very last paragraph).
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Hmm. I think there are still changes/improvements I want to fiddle with.
What if I register it as a package and then you use reexport
to add that functionality here? That might give us the most flexibility for now.
Does that agree with you? I can register it today.
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@ahwillia thanks! that's useful.
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Related Issues (20)
- Question: @cutensor not defined HOT 5
- possible memory leak with metaprogramming
- Why drop caching Tensors? HOT 3
- Is TensorOperations able to take advantage of symmetry in the output? HOT 8
- Manual allocation strategy HOT 2
- Floating Point Accuracy of @tensor results with CUDA HOT 3
- Enable multithreads when doing the permutedims in the TTGT algorithms HOT 2
- Unexpected `DimensionMismatch` (v4.0.2 -> v4.0.3) HOT 3
- Wrong result with subnetworks with equal labels HOT 2
- Bug in CUDA backend HOT 6
- Unintuitive `ncon` result when scalar HOT 2
- Taking gradients of traces HOT 6
- np.einsum_path vs TensorOperations HOT 3
- `ncon` fails with AD HOT 2
- `tensortrace` not working on Arrays of Symbolic Expressions from Symbolics.jl. HOT 2
- Combining LinearAlgebra.Diagonal with a CuArray inside @tensor HOT 2
- Compability with CUDA 5.2 HOT 3
- Confusion when using cuTENSOR HOT 4
- cuTENSOR not working with automatic differentiation HOT 5
- Freed reference problem when combining cuTENSOR and Zygote HOT 3
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