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feat: use LoopVectorization for faster operations #111
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main LuxDL/LuxLib.jl#111 +/- ##
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- Coverage 80.58% 73.86% -6.73%
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Files 29 30 +1
Lines 1329 1580 +251
==========================================
+ Hits 1071 1167 +96
- Misses 258 413 +155 ☔ View full report in Codecov by Sentry. |
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I'm getting significant speedups from this. Zygote pretty much works OOTB because we already have the rrules defined. SciML/NeuralOperators.jl#17 should be pretty much handled once this is merged.
fixes #94
TODOs
@tturbo
.replace some of the reduction operations withVectorizedReductions.jl
fast_activation
that is completely OOP but still allows for LoopVectorization optimizationsmatmul
andmatmuladd
internal_operation_mode
correctlymatmul
activation
affine_normalize
groupnorm
batchnorm
bias_activation
dropout
normalization
-- update statistics. Can we mark it non-diff for enzyme?fast_mean_var
matmul
andmatmuladd
Current Shortcomings
@turbo
the activation gradient function. We might want to hardcode some of the common cases for gradient computation --tanh
,gelu
,sigmoid
andrelu
would be my top contenders here