Migrate mge_gradients.py JAX profiling to pytree inputs#13
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Matches the pattern shipped in mge.py (#11) and pixelization.py (#12): pass a ModelInstance pytree through jit/value_and_grad instead of a flat 1D vector + Fitness/instance_from_vector inside the trace. - register_model(model) once after eager instance build - params_tree = jax.tree_util.tree_map(jnp.asarray, instance) - per-step closures take params_tree and build Tracer from params_tree.galaxies (dropping inner instance_from_vector calls) - test_grad tree-flattens gradient output for statistics - PART C full pipeline uses AnalysisImaging(use_jax=True) .log_likelihood_function(instance=params_tree) in place of Fitness - NNLS kappa diagnostic (_diagnose_kappa) uses pytree params Verified: all 9 gradient tests PASS, all 4 NNLS kappa values report FULLY FINITE GRADIENTS.
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Summary
Migrates
jax_profiling/imaging/mge_gradients.pyto pass aModelInstancepytree throughjit/value_and_gradinstead of a flat 1D parameter vector. Matches the pattern shipped in #11 (mge.py) and #12 (pixelization.py), now thatautofit.jax.register_modelis onmain(PyAutoFit#1220, #1221).Removes
Fitness+Model.instance_from_vectorfrom JIT traces so gradient profiling reflects how a real pytree-native sampler would run.Scripts Changed
jax_profiling/imaging/mge_gradients.py— per-step JIT closures takeparams_tree(built once viajax.tree_util.tree_map(jnp.asarray, instance)) instead of a flatjnp_paramsvector;test_gradhelper tree-flattens the gradient output; PART C full-pipeline gradient now callsAnalysisImaging(use_jax=True).log_likelihood_function(instance=params_tree)in place ofFitness.call; NNLS_diagnose_kappainner loss updated the same way.Test Plan
AnalysisImagingRelated: follow-up to #10.
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