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NNX: fix sharding#4215

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hsuan-lun-chiang wants to merge 3 commits into
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fix/nn-sharding-fix
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NNX: fix sharding#4215
hsuan-lun-chiang wants to merge 3 commits into
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fix/nn-sharding-fix

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PR6-PR10 promoted every routed-to-Linen feature to NNX-native; PR#2885 lands NNX-native pipeline parallelism. This PR flips the three defaults in base.yml so NNX is the production path, and bundles the NNX-only fixes that surface once pure_nnx=True (DiLoCo merge/checkpoint, Zero-1 input shardings on flat nnx.State, MTP sown-Variable handling, generate_param_only_checkpoint NNX flow, maxengine Linen-parity removal).

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xibinliu and others added 3 commits June 19, 2026 02:06
In pure NNX training runs, model variables retrieve physical PartitionSpecs
via `get_nnx_named_sharding_with_scan_axis` in `maxtext_utils.py`. Previously,
this helper used Flax core SPMD's `from_sharding_rules` to map logical names
to physical axes. However, `from_sharding_rules` resolves rules by converting the
rules list into a dictionary (last-write-wins). This caused fallback rules
sharing the same logical name (e.g. 'embed') to overwrite preceding specific
rules, dropping essential axes like `fsdp_transpose` and leading to unsharded
parameter percentage assertion errors.

Additionally, resolving specifications independently for each dimension without
tracking assigned axes could bind a single physical axis (like `fsdp_transpose`)
to multiple positional dimensions of a tensor, causing `DuplicateSpecError`.

To fix this:
1. Replaced `from_sharding_rules` with a Rules-first resolution loop that matches
   rules sequentially (first-match-wins), matching Flax Linen's mapping behavior.
2. Implemented an `assigned_axes` tracker within the loop to ensure physical
   mesh axes are bound to at most one dimension per tensor.
3. Added unit tests covering sequential matching (first-match-wins) and
   duplicate physical axis prevention during resolution.
PR6-PR10 promoted every routed-to-Linen feature to NNX-native; PR#2885 lands NNX-native pipeline parallelism. This PR flips the three defaults in base.yml so NNX is the production path, and bundles the NNX-only fixes that surface once pure_nnx=True (DiLoCo merge/checkpoint, Zero-1 input shardings on flat nnx.State, MTP sown-Variable handling, generate_param_only_checkpoint NNX flow, maxengine Linen-parity removal).
@hsuan-lun-chiang hsuan-lun-chiang changed the title NNX: flip pure_nnx/enable_nnx/pure_nnx_decoder defaults to True NNX: fix sharding Jun 22, 2026
@hsuan-lun-chiang hsuan-lun-chiang deleted the fix/nn-sharding-fix branch June 22, 2026 07:31
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3 participants