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Train through GeometricOptimizers' TrainingOptimizer (G4), one method per leaf - #310
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A symplectic autoencoder has PSD layers that carry only Stiefel weights beside SympNet layers that carry only Euclidean ones, and `ScalarMomentAdam` accepts exactly one `StiefelManifold` solution. A caller wanting that method on the former and ordinary `Adam` on the latter had to add methods to `_make_optimizer_cache`, `_make_optimizer_state` and `_leaf_optim_step!` from outside this package -- type piracy on four underscore-prefixed internals, which breaks silently at run time on any refactor here, in the middle of whatever it was training. `GeometricOptimizers.CompositeMethod` is now accepted wherever a method is. The three functions resolve the layer's method with `leafmethod` and then behave exactly as they do for that method, so a layer stepped under a composite is bit-for-bit a layer stepped under the method selected for it. Two lists of methods maintained in the package that does not define them go with it. `_leaf_optim_step!`'s `isa AdamState / isa MomentumState` chain becomes one `sync_state!` call -- that chain had the failure mode such a list has, and `ScalarMomentAdam` was simply missing from it -- and `_go_update_leaf!` dispatches on `FirstOrderMethodWithState` rather than naming `Adam` and `MomentumMethod`. `ScalarMomentAdam` is GO-native here as a result; it had been falling through to `GMLEuclideanState`, which is coordinate-wise Adam on the ambient array rather than the method, and off the manifold. Requires GeometricOptimizers 0.8. Co-Authored-By: Claude Opus 5 <[email protected]>
The per-layer CompositeMethod seam of this branch is replaced in the next commit, so main's versions of the files it touched are taken. Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
…neration GeometricOptimizers 0.9 requires AbstractNeuralNetworks 0.9, NeuralNetworkParameters 0.4.2 and GeometricBase 0.15, and no registered release of either GeometricOptimizers or SymbolicNeuralNetworks allows those, so the five bounds move together (JuliaGNI/SymbolicNeuralNetworks.jl#80). - AbstractNeuralNetworks 0.9 defines the three-axis methods of Dense and Linear and save/load for a NeuralNetwork itself: delete the three pirated functors in src/layers/resnet.jl and ext/HDF5Ext.jl. Aqua's piracies and ambiguities checks run again and report none. - LowerTriangular/UpperTriangular are StrictlyLowerTriangular/ StrictlyUpperTriangular; the legacy-file test covers a network with them (needs JuliaGNI/GeometricOptimizers.jl#146). - PoissonTensor answers KernelAbstractions.get_backend, which GeometricOptimizers 0.9 asks of both factors of a product. - Under NeuralNetworkParameters 0.4 a gradient through a NetworkParameters is the storage gradient: the gradient-structure tests assert that, and #319's structure loss is gone. The optimizer is moved to GeometricOptimizers 0.9 in the next commit; this one does not build on its own against GeometricOptimizers 0.9. Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
…leaf GML's half of G4. `Optimizer` holds one GeometricOptimizers.TrainingOptimizer over the whole parameter set, and `optimization_step!(ps, opt, dp)` is GeometricOptimizers' function extended for it. The per-layer walk, the Euclidean update rules (GMLEuclideanState), _GMLGradient and the λY argument of the step and of optimize_for_one_epoch! are gone. A CompositeMethod (JuliaGNI/GeometricOptimizers.jl#121) now passes straight through: every leaf gets the cache and state of the method selected for it, so ScalarMomentAdam can train the Stiefel weights of a network beside Adam on the rest. test/optimizers/composite_method.jl checks the per-leaf states and that a composite of Adam on every leaf reproduces Adam exactly. The GeometricOptimizers 0.9 API throughout: methods without an element type, Cayley()/Geodesic() as retractions. KNOWN_ISSUES C2 is closed and C1 narrowed to the shared name `Optimizer`. Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
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📝 WalkthroughWalkthroughThe optimizer now delegates parameter updates to GeometricOptimizers and uses revised training signatures. The changes add per-leaf optimizer methods, adopt strict triangular matrix types, remove selected APIs and the package HDF5 extension, and update tests, dependencies, and documentation. ChangesOptimizer and gradient updates
Strict triangular matrix types
Package integration and removed APIs
Priority: ⬇️ Low Estimated code review effort: 4 (Complex) | ~45 minutes Change: Feature · Severity of issue fixed: Low Sequence Diagram(s)sequenceDiagram
participant optimize_for_one_epoch
participant optimization_step
participant TrainingOptimizer
optimize_for_one_epoch->>optimization_step: pass parameters, optimizer, and gradient
optimization_step->>TrainingOptimizer: delegate the optimization step
Merge Risk: 🔵 Low · up to The optimizer, tensor, and API changes show no established defects in the reviewed evidence. One tutorial snippet uses a variable that is only defined later, so a reader who copies it hits an error. It is a one-line documentation fix and does not block merging. Security Architecture ReviewSecurity architecture risk: 🔵 Low · up to The inspected backend change is narrowly scoped and does not introduce a privileged operation. The optimizer ownership and API migration warrant design review, although dependency behavior and some affected paths remain incompletely verified. Retained concerns Security review detailsSecurity Blast Radius
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Review comments at @docs/src/tutorials/symplectic_autoencoder.md:
- Line 148: Update the optimizer setup to pass the already-defined n_epochs to
AdamOptimizerWithDecay instead of integrator_train_epochs, which is declared
later in the tutorial.
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CHANGELOG.mdKNOWN_ISSUES.mdProject.tomldocs/check_references.jldocs/make.jldocs/src/architectures/abstract_neural_networks.mddocs/src/arrays/tensors.mddocs/src/index.mddocs/src/introduction.mddocs/src/layers/volume_preserving_feedforward.mddocs/src/optimizers/optimizer.mddocs/src/tutorials/grassmann_layer.mddocs/src/tutorials/hamiltonian_neural_network.mddocs/src/tutorials/linear_symplectic_transformer.mddocs/src/tutorials/symplectic_autoencoder.mddocs/src/tutorials/symplectic_transformer.mddocs/src/tutorials/sympnet_tutorial.mddocs/src/tutorials/volume_preserving_attention.mddocs/src/tutorials/volume_preserving_transformer_rigid_body.mdext/HDF5Ext.jlscripts/reproduction/symplectic_autoencoders/training.jlscripts/reproduction/sympnets/sympnet_pendulum.jlscripts/reproduction/volume_preserving_feedforward/double_pendulum.jlscripts/reproduction/volume_preserving_transformer/rigid_body.jlsrc/GeometricMachineLearning.jlsrc/arrays/poisson_tensor.jlsrc/data_loader/optimize.jlsrc/kernels/kernel_ad_routines/mat_tensor_mul.jlsrc/kernels/mat_tensor_mul.jlsrc/layers/resnet.jlsrc/layers/symplectic_attention.jlsrc/layers/volume_preserving_feedforward.jlsrc/optimizers/optimizer.jltest/arrays/triangular.jltest/data_loader/data_loader_optimization_step.jltest/data_loader/optimizer_functor_with_adam.jltest/helpers/network_parameters_gradient_structure.jltest/integration/changebackend_tests.jltest/integration/hdf5_support.jltest/layers/double_multiplication_network_parameters_gradient.jltest/layers/gradient_layer_tests.jltest/layers/manifold_layers.jltest/layers/multi_head_attention_stiefel_optim_cache.jltest/layers/multi_head_attention_stiefel_retraction.jltest/layers/multi_head_attention_stiefel_setup.jltest/layers/symplectic_attention_network_parameters_gradient.jltest/layers/transformer_optimizer.jltest/layers/volume_preserving_feedforward.jltest/map_to_cpu_tests.jltest/optimizers/adam_with_learning_rate_decay.jltest/optimizers/composite_method.jltest/optimizers/gml_gradient_dispatch.jltest/optimizers/gradient_optimizer.jltest/optimizers/momentum_optimizer.jltest/optimizers/optimization_step.jltest/optimizers/psd_optim.jltest/optimizers/step_size_element_type.jltest/optimizers/structured_array_parameters.jltest/optimizers/svd_optim.jltest/quality/aqua.jltest/runtests.jl
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| sae_nn_gpu = NeuralNetwork(sae_arch, CUDADevice(), Float32) | ||
| o = Optimizer(sae_nn_gpu; AdamOptimizerWithDecay(integrator_train_epochs, Float32)...) | ||
| o = Optimizer(sae_nn_gpu; AdamOptimizerWithDecay(integrator_train_epochs)...) |
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
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Length of output: 2216
Pass n_epochs to the optimizer.
This block defines n_epochs, but integrator_train_epochs is not defined until later in the tutorial. Copying the block before that later declaration can raise UndefVarError.
Suggested fix
-o = Optimizer(sae_nn_gpu; AdamOptimizerWithDecay(integrator_train_epochs)...)
+o = Optimizer(sae_nn_gpu; AdamOptimizerWithDecay(n_epochs)...)📝 Committable suggestion
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Suggested change
| o = Optimizer(sae_nn_gpu; AdamOptimizerWithDecay(integrator_train_epochs)...) | |
| o = Optimizer(sae_nn_gpu; AdamOptimizerWithDecay(n_epochs)...) |
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Review comment at @docs/src/tutorials/symplectic_autoencoder.md at line 148:
Update the optimizer setup to pass the already-defined n_epochs to
AdamOptimizerWithDecay instead of integrator_train_epochs, which is declared
later in the tutorial.
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Note
Rewritten after JuliaGNI/GeometricOptimizers.jl#121 was redesigned. GO now steps a
CompositeMethodwith oneTrainingOptimizerthat keeps a cache and a state per leaf. So this PR no longer adds a per-layer seam to GML's own optimizer loop. It deletes that loop: this is GML's half of G4. The earlier design (leafmethod/accepts_parameter_set/sync_state!) is gone from both PRs.Summary
Optimizerholds oneGeometricOptimizers.TrainingOptimizerover the whole parameter set of a network, andoptimization_step!(ps, opt, dp)is GO's own function extended for it. A composite then needs nothing of its own here:Every leaf gets the cache and state of the method chosen for it. That is how
ScalarMomentAdam, which steps a singleStiefelManifold, trains the Stiefel weights of a symplectic autoencoder besideAdamon the SympNet layers.Two commits
1.
8470206e, move to the GeometricOptimizers 0.9 dependency generation. GO 0.9 requiresAbstractNeuralNetworks0.9,NeuralNetworkParameters0.4.2 andGeometricBase0.15. No registered GO release before 0.9 allows those (GO 0.8.0 pins NNP 0.3, GB 0.14 and ANN 0.8), and every registeredSymbolicNeuralNetworks0.8.x pins ANN 0.8 and NNP 0.3, so all five bounds move together. I had planned this as a separate PR, but it can't pass without GO 0.9.Dense/Linear(src/layers/resnet.jl) andext/HDF5Ext.jl(save/loadforNeuralNetwork). Aqua'spiraciesandambiguitieschecks are switched back on and report 0, down from 3 and 1, which closes B7.LowerTriangular/UpperTriangularare nowStrictlyLowerTriangular/StrictlyUpperTriangular, as in GO 0.9. Files GML 0.8 wrote with these leaves load again with Register the pre-0.9 names of the triangular types for loading GeometricOptimizers.jl#146; the legacy-file test now covers aVolumePreservingFeedForwardnetwork.KernelAbstractions.get_backend(::PoissonTensor), because GO 0.9's*asks both factors for their backend.NetworkParametersis now the gradient with respect to the leaf's storage. ASymmetricMatrixleaf keeps its type however often the loss reads it, which resolves Zygote gradient w.r.t. NetworkParameters loses the SymmetricMatrix leaf on a second access #319, and its off-diagonal entries are doubled. The gradient-structure tests assert that instead of the old split.2.
84d0c4ef,Optimizeris aTrainingOptimizer._make_optimizer_cache,_make_optimizer_state,_tree_optim_step!,_leaf_optim_step!,GMLEuclideanState,_GMLGradient,_as_go_solution, theisachain over the state types, and theλYargument ofoptimization_step!andoptimize_for_one_epoch!.TrainingOptimizerreproduces GML 0.8's iterates: exactly forAdam, and to4eps(T)for gradient and momentum on manifold models.optimization_step!(ps, opt, dp)instead ofoptimization_step!(opt, λY, ps, dp);retraction = Cayley()/Geodesic()instead of the functions; methods without an element type (Adam(),MomentumMethod(; α),AdamOptimizerWithDecay(n)). Every call site insrc,test,docsandscriptsis updated.CompositeMethod,CompositeState,ScalarMomentAdamandScalarMomentAdamState.optimization_step!is now one function across both packages.Optimizeris still exported by both, and C1 in KNOWN_ISSUES is narrowed to that.test/optimizers/composite_method.jlchecks that each leaf gets the state of its method, and that a composite ofAdamon every leaf equalsAdamexactly.Blocked on
GeometricSolutionsandForwardDiff. #146, then a GO 0.9 release. GOmainalready holds 0.9's breaking changes but still saysversion = "0.8.0", so[compat] GeometricOptimizers = "0.9"won't resolve before the release.CI here can't go green until both are released.
Testing
Run locally on Julia 1.13 (Apple M4 Max). The test environment had GO from the local clone (#121 with #146 on top) and SNN from the #80 branch, both developed into it. Because those still carry 0.8.x version numbers, the GO and SNN compat bounds were relaxed to
"0.8"for the run only and not committed.core,slow,metal)@test_brokenfor #325), 0 fail/errorslow)docs/check_references.jl.githooks/test-layout.jl --checkThe full docs build was not run: the docs environment failed to precompile
LibCURL_jll(a missingZstd_jllpackage image), which has nothing to do with this change, and the tutorials train networks. The scripts underscripts/were updated but not run.— Posted by Claude Opus 5.5 (Claude Code)
🤖 Generated with Claude Code
Summary by CodeRabbit
CompositeMethodsupport for choosing an optimizer method for each parameter group, including mixed manifold and dense parameters.StrictlyLowerTriangularandStrictlyUpperTriangular.DenseandLinearmethods.