Reshard loss under explicit mesh on the NNX path

loss_fn's NNX branch constrained xent/z_loss with raw
nn.with_logical_constraint, which keeps the size-1 context axis, while
the model shards activations via create_sharding (remove_size_one_mesh_axis
drops context). Under an explicit mesh the mismatch is a hard assert.

Use sharding.maybe_shard_with_logical, as the Linen branch already does:
it builds the sharding via create_sharding (dropping size-1 context, so it
matches the array) and reshards instead of asserting under explicit mode.

Author: ecnal-cienet

src/maxtext/trainers/pre_train/train.py

@@ -221,8 +221,20 @@ def loss_fn(model, config, data, dropout_rng, params, sparsity_state=None, is_tr
       one_hot_targets = jax.nn.one_hot(data["targets"], config.vocab_size)
       xent, z_loss = max_utils.cross_entropy_with_logits(logits, one_hot_targets, z_loss=config.z_loss_multiplier)
 
-      xent = nn.with_logical_constraint(xent, ("activation_embed_and_logits_batch", "activation_length"))
-      z_loss = nn.with_logical_constraint(z_loss, ("activation_embed_and_logits_batch", "activation_length"))
+      xent = sharding.maybe_shard_with_logical(
+          xent,
+          ("activation_embed_and_logits_batch", "activation_length"),
+          model.mesh,
+          config.shard_mode,
+          debug_sharding=config.debug_sharding,
+      )
+      z_loss = sharding.maybe_shard_with_logical(
+          z_loss,
+          ("activation_embed_and_logits_batch", "activation_length"),
+          model.mesh,
+          config.shard_mode,
+          debug_sharding=config.debug_sharding,
+      )
 
       # Mask out paddings at the end of each example.
       xent = xent * (data["targets_segmentation"] != 0)

tests/unit/train_nnx_test.py

@@ -23,6 +23,7 @@
 import unittest
 
 from flax import nnx
+import jax
 import jax.numpy as jnp
 from maxtext.common import train_state_nnx
 from maxtext.trainers.pre_train import train as pre_train
@@ -59,6 +60,7 @@ class _Cfg:
   record_internal_nn_metrics: bool = False
   skip_step_on_spikes: bool = False
   shard_mode: int = 0  # ShardMode.AUTO
+  debug_sharding: bool = False
   weight_sparsity_n: int = 0
   weight_sparsity_m: int = 0
 
@@ -73,6 +75,8 @@ class _TinyDecoder(nnx.Module):
   def __init__(self, vocab_size: int, hidden: int, rngs: nnx.Rngs):
     self.embed = nnx.Embed(vocab_size, hidden, rngs=rngs)
     self.proj = nnx.Linear(hidden, vocab_size, rngs=rngs)
+    # loss_fn shards activations against model.mesh, so the stub needs one.
+    self.mesh = jax.make_mesh((1, 1, 1, 1), ("data", "fsdp", "expert", "context"))
 
   def __call__(
       self,