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- pure_nnx: a flag to to choose pure NNX logic when NNX and linen models co-exist. - init_state_fn: a function to initialize the model state for the training. It will be set to different function for NNX and Linen.
- Add utils to manipulate the NNX shardings with abstract state of a
model
- also add unit tests for the utils
- Extract mesh creation function to maxtext_utils.get_mesh_from_config()
- also add unit tests for this func
Note:
flax v0.12 has DeprecationWarning in multiple places:
- DeprecationWarning: '.value' access is now deprecated. Use
variable.get_value() or variable[...] (for [Array]).
- DeprecationWarning: 'VariableState' was removed, this is just
an alias to 'Variable'. Plase use 'Variable' directly instead.
But since the code needs to work with post-training, which currently
requires flax v0.11, we didn't change code for these warnings.
A TrainState for NNX, which includes model and optimizer Unit tests include checkpoint tests: - restore a saved state - convert linen TrainState to NNX TrainState - Parameter only restore (no opt_state)
…ion_utils Also added unit tests. Refactored model_creation_utils to provide common create_nnx_abstract_model() func. b/src/maxtext/utils/model_creation_utils.py
1. A new func get_abstract_state_nnx() is added to maxtext_utils.py The it will be called during training to create NNX training state. Same as the linen version, it handles shard_optimizer_over_data, optimizer_memory_host_offload, and parameter_memory_host_offload Unit tests are added to this NNX func. 2. Add nnx train_state handling in train_utils.py DPO handling will be supported (or removed) later in train_utils.py
Also added unit tests
Also added unit tests for NNX model.
- get_functional_train_with_signature: use (state, batch) shardings when pure_nnx=True - get_functional_eval_with_signature: use (state, batch) shardings when pure_nnx=True
- Convert nnx.State to pure dict for checkpoint saving - Restore pure dict back to nnx.State after loading
Add a bidirectional Linen <-> NNX checkpoint converter tool that handles: - Auto-detection of checkpoint format - Conversion of params structure (double nesting vs flat) - Stacking/unstacking per-layer parameters - Value wrapper handling for NNX format
Add a tool to compare checkpoint tree structures, shapes, and values across Linen and NNX formats.Supports cross-format and same-format comparisons with auto-detection, layer axis transposition, and RNG filtering.
- Add --pure_nnx CLI flag to run_sharding_dump.py - Propagate pure_nnx=true to the sharding_dump subprocess when flag is set - Refactor run_single_dump() to build the command as a list for conditional flag appending
- Replace nn.Dropout with linears.Dropout in gpt_oss and olmo3 decoder layers - Add num_activations logical axis rule to base.yml - Fix integration and unit tests for NNX compatibility I will relocate these files accordingly once the work is done.
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Description
Contains all the commits to make the NNX migrated MaxText work.
Tests
Current focus is on:
Example:
More features and models will be tested and compared with Linen.
Test track doc
Checklist
Before submitting this PR, please make sure (put X in square brackets):
gemini-reviewlabel.