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Extend LoRA for Gemma4 #3969

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copybara-service merged 1 commit into from
Jun 25, 2026
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Extend LoRA for Gemma4 #3969

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1 change: 1 addition & 0 deletions src/maxtext/configs/post_train/lora_module_path.yml
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@gagika gagika Jun 2, 2026

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Are there any tests for Gemma4 Lora?

@RexBearIU RexBearIU Jun 3, 2026

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We ran the end-to-end LoRA training loop for Gemma 4 successfully without any issues.4 successfully without any issues. log

Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@ mistral: "decoder/layers/.*(attention/(query|key|value|out)|mlp/(wi_0|wi_1|wo))"
deepseek2: "decoder/(dense_layers|moe_stack)/self_attention/(query|out|wkv_a|wkv_b)|decoder/(dense_layers|moe_stack)/(mlp|shared_experts)/(wi_0|wi_1|wo)"
gemma2: "decoder/layers/(self_attention_local|self_attention_global)/(query|key|value|out)|decoder/layers/(mlp_local|mlp_global)/(wi_0|wi_1|wo)"
gemma3: "decoder/layers/.*(self_attention/(query|key|value|out)|mlp/(wi_0|wi_1|wo|gate|up|down))"
gemma4: "decoder/(scanned_blocks|layers_remainder)/layers.*/.*(self_attention/(query|key|value|out)|mlp/.*(wi_0|wi_1|wo|shared_experts/(wi_0|wi_1|wo)))"

@gagika gagika Jun 24, 2026

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is the scope of the project both scan_layers=true and false, Gemma4 dense and moe?

Suggest adding a CPU unit test that asserts the regex matches the expected module paths for both scan_layers values and dense and moe.

olmo3: "decoder/layers/.*(attention/(query|key|value|out)|mlp/(wi_0|wi_1|wo))"
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gpt3: "decoder/layers/(self_attention/(qkv_proj|out)|mlp/(wi|wo))"

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5 changes: 5 additions & 0 deletions src/maxtext/trainers/post_train/sft/train_sft.py
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Original file line number Diff line number Diff line change
Expand Up @@ -274,9 +274,14 @@ def setup_trainer_state(mt_config, goodput_recorder=None):
def train_model(mt_config, trainer, mesh):
"""Runs the SFT training loop in Tunix."""
with jax.set_mesh(mesh), nn_partitioning.axis_rules(mt_config.logical_axis_rules):
# Disable NNX graph caching for MoE models (where experts > 1) to allow
# necessary dynamic metadata synchronization during forward passes (e.g., in jax.lax.scan).
enable_nnx_cache = mt_config.num_experts <= 1

trainer.train(
trainer.data_hooks.train_data_iterator,
trainer.data_hooks.eval_data_iterator,
cache_nnx_graph=enable_nnx_cache,
)
return trainer

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