[NNX] Delete Linen (12.1c): collapse dispatch in quantization and model creation - #4685
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[NNX] Delete Linen (12.1c): collapse dispatch in quantization and model creation#4685ecnal-cienet wants to merge 3 commits into
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…ate setup and train loop The pure_nnx defaults are true, so the Linen branches in the pre-train state and train-loop path are dead. Collapse them: - train_utils.setup_train_loop: always build the abstract NNX model and a TrainStateNNX init_state_fn; drop the Linen model/TrainState branch and the Linen arms of the DiLoCo sharding and debug_sharding blocks. - maxtext_utils: get_functional_train_with_signature and get_functional_eval_with_signature drop the trailing rng in_sharding; load_compiled drops the example rng; get_abstract_state delegates to get_abstract_state_nnx. setup_initial_state is deliberately left alone. Its Linen branch is entangled with the checkpoint restore overlay, and the orbax v1 migration is touching that code; it is collapsed in a later change. Tests follow the same narrowing: the Linen-only cases in maxtext_utils_test, state_dtypes_test and the sharding_compare_test Linen-golden driver go away. test_deepseek4 is skipped rather than pinned to Linen, since nnx_decoders.py has no deepseek4 decoder_block branch yet.
… muon utils - sharding.maybe_update_params_sharding_with_opt delegates to the _nnx variant; build_zero1_input_state_mesh_shardings drops its Linen early return. - muon_utils.get_muon_weight_dimension_numbers drops the isinstance(nnx.Module) test and the Linen get_abstract_param path; get_model_mdn loses its pure_nnx parameter and always builds the abstract NNX model. - run_sharding_dump loses its --pure_nnx flag. Tests follow: the Linen branch test in muon_utils_test goes away, optimizers_test drops the pure_nnx argument and the dual-shape comparison, sharding_nnx_test drops the flag from its fake config.
… and model creation - quantizations.maybe_quantize_model always runs the qwix forward pass with the dummy tokens/positions/segment ids (and the MTP decoder targets when mtp_num_layers > 0), then pops the transient nnx.Intermediate variables the traced forward sows. - model_creation_utils.from_pretrained always builds the sharded model through maxtext_utils_nnx.create_nnx_sharded_model. Tests follow: quantizations_test and nnx_quant_guard_test drop their flag arguments and Linen expectations, correctness_tests_nnx_dispatch_test keeps only the NNX case, and forward_pass_logit_checker always loads via from_pretrained.
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feat/nnx-del-linen-pretrain-quant (base: feat/nnx-del-linen-pretrain-sharding-opt
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