Summary

Enhanced DistillationTrainer with advanced distillation strategies for more effective knowledge transfer from teacher to student models.

New Features

1. Attention Transfer Distillation

• Matches attention patterns between teacher and student models
• Uses cosine distance to align attention maps across layers
• Supports layer-wise selection for targeted matching
• Properly handles padding with correct normalization

2. Feature Matching Distillation

• Matches intermediate hidden representations between models
• Uses MSE loss to align feature spaces
• Supports layer-wise selection
• Properly handles padding with correct normalization

3. Automatic Dimension Matching

• Pooling-based shape alignment for different architectures
• Handles mismatches in:
  • Number of layers (e.g., 80 → 24)
  • Hidden dimensions (e.g., 8192 → 2048)
  • Attention heads (e.g., 64 → 32)
• Zero trainable parameters (pooling-only approach)

Files Changed

Total: ~1,180 lines added

Technical Details

Masking Implementation

Both new loss functions properly handle variable-length sequences:

• Attention Transfer: Zeros padded keys/queries, normalizes by valid_tokens × num_heads
• Feature Matching: Normalizes by valid_tokens × hidden_dim for parity with unmasked branch
• Invariance: Losses are invariant to padding amount, batch size, and sequence length

Design Choices

Usage Example

import easydel as ed

# Basic logit distillation (unchanged)
config = ed.DistillationConfig(
    temperature=2.0,
    alpha=0.9,
)

# With attention transfer
config = ed.DistillationConfig(
    temperature=2.0,
    alpha=0.8,
    use_attention_transfer=True,
    attention_loss_weight=0.1,
    attention_match_layers=(6, 12, 18),  # Match specific layers
)

# With feature matching
config = ed.DistillationConfig(
    temperature=2.0,
    alpha=0.7,
    use_feature_matching=True,
    feature_loss_weight=0.2,
    feature_match_layers=(6, 12, 18),
)

# Combined strategies
config = ed.DistillationConfig(
    temperature=2.0,
    alpha=0.7,
    use_attention_transfer=True,
    attention_loss_weight=0.1,
    use_feature_matching=True,
    feature_loss_weight=0.2,
)

trainer = ed.DistillationTrainer(
    arguments=config,
    student_model=student,
    teacher_model=teacher,
    train_dataset=dataset,
    processing_class=tokenizer,
)
trainer.train()

Backward Compatibility

✅ Fully backward compatible

All new parameters default to False or None, so existing code continues to work without modification.

Testing Recommendations

1. Backward compatibility: Verify existing distillation code works unchanged
2. Attention transfer: Test with different model architectures
3. Feature matching: Test with different hidden dimensions
4. Dimension mismatch: Test teacher/student with very different architectures
5. Padding invariance: Test with variable-length sequences
6. Masking correctness: Verify loss denominators match expected formulas

See DISTILLATION_UPDATES.md for detailed testing procedures.

References

• Hinton et al., "Distilling the Knowledge in a Neural Network" (2015)
• Zagoruyko & Komodakis, "Paying More Attention to Attention" (2017)
• Romero et al., "FitNets: Hints for Thin Deep Nets" (2014)

Documentation

Full technical documentation available in DISTILLATION_UPDATES.md including:

• Implementation details
• Code comparisons (before/after)
• Masking behavior analysis
• Performance characteristics
• Future enhancement ideas