Performance Optimization: Enable Dataset Caching

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Performance Optimization: Enable Dataset Caching

Summary

This PR implements a high-impact performance optimization by enabling dataset caching in the C3PO pipeline. The change removes load_from_cache_file=False parameters from all dataset.map() calls, allowing HuggingFace datasets to use their built-in caching mechanism.

Changes Made

• Fixed dataset caching: Removed load_from_cache_file=False from 13 dataset transformation calls in src/dataset/format.py
• Added comprehensive report: Created PERFORMANCE_OPTIMIZATION_REPORT.md documenting 7+ performance inefficiencies found in the codebase

Performance Impact

• High Impact: Affects all data processing operations (sampling, training, evaluation)
• Significant speedup: Eliminates redundant dataset transformations on repeated runs
• Memory efficient: Leverages HuggingFace's optimized caching system
• Zero risk: Only enables existing functionality without changing logic

Files Modified

• src/dataset/format.py: Enabled caching for to_dpo(), to_lcdpo(), to_sft(), and to_sft_weighted() functions
• PERFORMANCE_OPTIMIZATION_REPORT.md: Comprehensive analysis of optimization opportunities

Testing

• ✅ Verified all dataset operations produce identical results
• ✅ No functional changes to existing logic
• ✅ Maintains backward compatibility

Additional Optimizations Identified

The performance report documents 6 additional optimization opportunities:

1. API call batching improvements (Medium Impact)
2. Redundant dataset operations (Medium Impact)
3. String processing inefficiencies (Medium Impact)
4. File I/O optimizations (Low-Medium Impact)
5. Model loading optimizations (Low Impact)
6. Linear layer discovery optimization (Low Impact)

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Link to Devin run: https://staging.itsdev.in/sessions/4be7382ca8424ed8b29365650eb98ca6
Requested by: moritz.stephan01+0622@gmail.com

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