Comments (3)
lungd
commented on July 28, 2024
It would be nice if one could solve this issue to get type stable containers.
What was missing in that PR besides applybranching and applypairwisefusion? Maybe I will have some spare time to give it a try the next few days.
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avik-pal
commented on July 28, 2024
In terms of the functionality, nothing was missing except applybranching, applyparallel, and applypairwisefusion.
I am curious as to what you mean by type stable containers. Currently all the container layers in lux should be type stable
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lungd
commented on July 28, 2024
I tried to recreate the MWE I played with some weeks ago. I don't see the performance drop any longer (~20 additional allocations + exec time). Most likely, I actually benchmarked the construction of the chain. That would explain the additional allocations because I see a Body::Any for the construction with the current version vs no red line and 0 allocations with a NamedTuple.
Sorry for the false alarm. Finishing that PR to get rid of the red lines one sees using @code_warntype would be a good idea, though.
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Related Issues (20)
- The MNIST Neural ODE example does not work with `ReverseDiffAdjoint` HOT 6
- Update Documentation to mention loading AD Packages for Training HOT 4
- `ComponentArrays` makes coupling layers type-unstable unexpectedly HOT 2
- ComponentArrays makes Custom Layers containing Chains type-unstable HOT 4
- Custom Layer, Differential Equation as Activation Function. HOT 4
- Gradients of shared parameters do not behave as expected HOT 1
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- Broadcast Layer
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- Custom Model for Neural ODE HOT 1
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- Generating Parameters with CUDA HOT 2
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- Support for MultiRNNCell HOT 5
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