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Fix MoE target_parameters module_count alignment (#3405, #3701) #499

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Fix MoE target_parameters module_count alignment (#3405, #3701) #499

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42133ed
Fix MoE target_parameters module_count alignment (#3405, #3701)
GoldenGrapeGentleman Feb 14, 2026
8754834
Refine: use stats.module is None instead of string matching
GoldenGrapeGentleman Feb 14, 2026
2d7e4a1
Add prefix inference fallback for models without _checkpoint_conversi…
GoldenGrapeGentleman Feb 24, 2026
643f3d1
Refactor: extract _infer_prefix_and_remap as standalone function
GoldenGrapeGentleman Feb 25, 2026
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15 changes: 12 additions & 3 deletions unsloth_zoo/saving_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -390,6 +390,18 @@ def create_lora_statistics(model, merge_into_original = False, return_state_dict
remove_keys.add(name)
pass
pass
# MoE target_parameters (ParamWrapper) entries have lora_A/B/scaling but
# may lack a corresponding .base_layer module, leaving module_count short.
# Count these so the diagnostic check below stays accurate (#3405, #3701).
for key, stats in lora_weights.items():
if (
stats.lora_A is not None
and stats.lora_B is not None
and stats.module is None
and ".mlp.experts" in key
):
module_count += 1
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@Datta0 Datta0 Feb 24, 2026

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So this pretty much fixes only the count part. I think my previous changes (in the #450 perhaps) would automatically handle the right tensor and file placement things I presume.

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@GoldenGrapeGentleman GoldenGrapeGentleman Feb 25, 2026

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Exactly right — this fix only addresses the module_count alignment so the mismatch warning no longer fires for nn.Parameter targets. The actual tensor placement and file writing is handled by your work in #450.

The two fixes are complementary: #450 handles the merge mechanics, this PR ensures the diagnostic counts are correct so users don't see a misleading warning during an otherwise successful merge.


if not (module_count == lora_A_count == lora_B_count == scaling_count):
print(
f"[Unsloth merge debug] LoRA count mismatch: modules={module_count}, "
Expand All @@ -405,9 +417,6 @@ def create_lora_statistics(model, merge_into_original = False, return_state_dict
print(f" key={k} param={param_name} A={a_shape} B={b_shape}")
except Exception:
pass
# Allow merge to continue; downstream checks will still fail loudly if tensors are missing
# but this avoids silent assertion without context.
# TODO: handle MoE target_parameters to align counts.

# Also return state_dict if needed
if return_state_dict:
Expand Down