Comments (2)
lkdvos
commented on August 28, 2024
I think it depends a bit on what you would like. The current implementation uses cuTENSOR to implement arbitrary contractions between CuArrays, and as far as I know this does not support Diagonal. Of course, in the specific case of matrix multiplication, we could use CUDA to compute the result. I would think it is possible to play some stride tricks to get cuTENSOR to work with diagonal matrices in the cases where the contractions are not just matrix multiplication but I can't say I currently have the time to play around with it. However, if you want to give it a go, I could point you to some examples of what I have in mind
from tensoroperations.jl.
OliverDudgeon
commented on August 28, 2024
Thanks for the explanation. In my case, I was able to do the scaling due to the Diagonal matrix separately to the @tensor macro, so this isn't too important to me now.
from tensoroperations.jl.
Related Issues (20)
- 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 3
- Taking gradients of traces HOT 6
- np.einsum_path vs TensorOperations HOT 3
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- `tensortrace` not working on Arrays of Symbolic Expressions from Symbolics.jl. HOT 2
- Compability with CUDA 5.2 HOT 4
- Confusion when using cuTENSOR HOT 5
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- Freed reference problem when combining cuTENSOR and Zygote HOT 8
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- Costchecks are not using `GlobalRef` HOT 2
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- install newest version? HOT 8
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from tensoroperations.jl.