fix: auto-untie word embeddings on merge_and_unload when both are adapted

[www-spam]

Summary

This PR fixes the issue where merge_and_unload() produces broken models when adapters are applied to both embed_tokens and lm_head on models with tie_word_embeddings=True.

Resolves #2777

Problem

When a base model has tie_word_embeddings=True (e.g., Gemma, Llama):

1. embed_tokens and lm_head share the same weight tensor
2. Adapters can be applied to both layers (via modules_to_save or target_modules)
3. After training, each layer has different adapter deltas
4. merge_and_unload() merges both layers with their respective deltas
5. Bug: The config still has tie_word_embeddings=True
6. When the merged model is loaded with AutoModel.from_pretrained(), the lm_head weights are overwritten with embed_tokens weights due to weight tying
7. Result: The merged lm_head weights are lost, causing degraded or garbage output

Solution

This PR modifies _unload_and_optionally_merge() in BaseTuner to:

1. Detect if both embedding-like and lm_head-like modules have adapters
2. Untie the weights by cloning lm_head.weight before merge
3. Update config.tie_word_embeddings = False in all relevant config locations

This ensures that:

• Merged weights are preserved for both layers
• The saved model can be loaded correctly
• Backward compatibility is maintained (no change when embeddings aren't both targeted)

Changes

• src/peft/tuners/tuners_utils.py:

  • Added _untie_embedding_weights() helper method
  • Added _update_tie_word_embeddings_config() helper method
  • Added _has_adapters_on_both_embeddings() helper method
  • Modified _unload_and_optionally_merge() to auto-handle tied embeddings

• tests/test_tie_word_embeddings_merge.py:

  • Added tests for tie_word_embeddings merge behavior

Test Plan

• Tested with Gemma 3 4B model (tie_word_embeddings=True)
• Verified merged model produces coherent output
• Verified config.tie_word_embeddings is correctly set to False
• Verified embed_tokens and lm_head have independent weights after merge
• Unit tests added

Example

Before this fix:

# Model with tie_word_embeddings=True + adapters on embed_tokens and lm_head
merged = peft_model.merge_and_unload()
merged.save_pretrained("merged_model")

# Loading produces broken model
loaded = AutoModel.from_pretrained("merged_model")  # lm_head weights lost!

After this fix:

# Same setup
merged = peft_model.merge_and_unload()  # Auto-unties and updates config
merged.save_pretrained("merged_model")

# Loading works correctly
loaded = AutoModel.from_pretrained("merged_model")  # Works as expected