Optimization to Model Script

[EvanCWallace]

NOT FULLY TESTED (Compiles but would like the review)

• Appended Mixed Precision Training (FP16/BF16)
• Generated Low-Rank Factorization (SVD) Functionality Generated Attention Efficiency using Linformer
• Reducing Memory & Computational Complexity using FlashAttention Attached Functionality for Spare Matrices using Butterfly Matrices (Structured Linear Layers) Generated Function for Low-Rank Approximations

Changes to the Transformer Class:

• Efficient Initialization
• Uses list comprehension for self.layers instead of a loop. Consolidated distributed initialization logic.
• Memory and Performance Enhancements
• Avoids unnecessary operations on tensors.
• Uses .shape instead of .size() for clarity.
• Code Clarity and Maintainability
• Removed redundant variables.
• Used in-place operations where applicable.

Changes to the Gate Class:

• Replaced linear(x, self.weight) with torch.matmul(x, self.weight.T): More efficient for linear transformations.
• Reduced Redundant Computations:
• Avoided unnecessary reassignments.
• Merged bias addition into a single step.
• Optimized Group-Based Routing:
• Used amax instead of unnecessary top-k and sum operations. Applied in-place scatter operation for memory efficiency. Simplified Expert Selection:
• Directly applied topk for selecting top experts.

Changes to the MLA Class:

• Removed Redundant Computations:
• • Consolidated tensor operations into efficient sequences.
• • Used torch.einsum to optimize matrix multiplications.
• Reduced Repetitive if Conditions:
• • Moved conditional logic outside loops where applicable.
• Refactored Caching Logic:
• • Used in-place assignments for cache updates.
• • Minimized unnecessary tensor copies.
• Improved Readability:
• • Clearer separation of query, key, and value computations.
• • Concise variable naming and inline comments.