fix: minor reactant stuff + docs build #1548
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Summary of ChangesHello @avik-pal, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request focuses on enhancing the integration of Highlights
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ext/LuxReactantExt/training.jl
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| ) | ||
| else | ||
| cache = TrainingBackendCache( | ||
| backend, False(), dparameters, (; compiled_grad_and_step_function, is_sharded) |
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| backend, False(), dparameters, (; compiled_grad_and_step_function, is_sharded) | |
| backend, | |
| False(), | |
| dparameters, | |
| (; compiled_grad_and_step_function, is_sharded), |
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Code Review
This pull request updates the documentation to use Reactant and Enzyme and refactors the Reactant training extension. The documentation changes look good but introduce a small bug in the quickstart example. The refactoring of the training extension is a nice improvement, but it introduces a critical bug in the apply_gradients implementation that could lead to a crash. My review includes suggestions to fix these issues.
| ## First construct a TrainState | ||
| train_state = Lux.Training.TrainState(model, ps, st, Adam(0.0001f0)) | ||
| train_state = Training.TrainState(model, ps, st, Adam(0.0001f0)) | ||
| ## We can compute the gradients using Training.compute_gradients | ||
| ## TrainState handles compilation internally | ||
| gs, loss, stats, train_state = Lux.Training.compute_gradients( | ||
| AutoZygote(), MSELoss(), | ||
| (x, dev(rand(rng, Float32, 10, 2))), train_state | ||
| AutoEnzyme(), | ||
| MSELoss(), | ||
| (x, dev(rand(rng, Float32, 10, 2))), | ||
| train_state | ||
| ) | ||
| ## Optimization | ||
| train_state = Training.apply_gradients!(train_state, gs) # or Training.apply_gradients (no `!` at the end) | ||
| # Both these steps can be combined into a single call | ||
| # Both these steps can be combined into a single call (preferred approach) | ||
| gs, loss, stats, train_state = Training.single_train_step!( | ||
| AutoZygote(), MSELoss(), | ||
| (x, dev(rand(rng, Float32, 10, 2))), train_state | ||
| AutoEnzyme(), | ||
| MSELoss(), | ||
| (x, dev(rand(rng, Float32, 10, 2))), | ||
| train_state | ||
| ) |
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The Training module is not imported directly, so calls to Training.TrainState, Training.apply_gradients!, and Training.single_train_step! will fail. You should prefix them with Lux. to match the style in the rest of the file and ensure the example code is runnable.
## First construct a TrainState
train_state = Lux.Training.TrainState(model, ps, st, Adam(0.0001f0))
## We can compute the gradients using Training.compute_gradients
## TrainState handles compilation internally
gs, loss, stats, train_state = Lux.Training.compute_gradients(
AutoEnzyme(),
MSELoss(),
(x, dev(rand(rng, Float32, 10, 2))),
train_state
)
## Optimization
train_state = Lux.Training.apply_gradients!(train_state, gs) # or Training.apply_gradients (no `!` at the end)
# Both these steps can be combined into a single call (preferred approach)
gs, loss, stats, train_state = Lux.Training.single_train_step!(
AutoEnzyme(),
MSELoss(),
(x, dev(rand(rng, Float32, 10, 2))),
train_state
)
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Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #1548 +/- ##
==========================================
- Coverage 82.47% 73.87% -8.61%
==========================================
Files 168 168
Lines 6957 6954 -3
==========================================
- Hits 5738 5137 -601
- Misses 1219 1817 +598 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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Benchmark Results (Julia v1.11)Time benchmarks
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