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Enhancements in test_llama_attention.py / floating-point comparison, variable naming, and code cleanup #6051

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Enhancements in test_llama_attention.py / floating-point comparison, variable naming, and code cleanup #6051

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Commits on Aug 25, 2024

  1. ai.llamapy 

    This commit introduces significant updates to the `test_llama_attention.py` script, enhancing its functionality with new AI features and addressing various issues identified in the code. Below is a detailed explanation of the changes made:

1. AI-Driven Accuracy Improvements:
   - Introduced a mechanism to handle floating-point precision issues. The script now avoids direct equality checks with floating-point values to prevent errors due to precision limitations. This ensures more robust and reliable comparisons by using tolerance-based assertions.

2. Refactored Variable Naming:
   - Renamed a variable that was shadowing a built-in function. This change improves code readability and prevents potential conflicts with Python’s built-in functions, making the script clearer and less prone to errors.

3. Removed Redundant Code:
   - Eliminated an unused assignment to a local variable, which previously served no functional purpose. This cleanup reduces unnecessary code and enhances script efficiency by focusing on relevant computations.

4. Enhanced Test Function:
   - The `test_llama_context_attention` function was updated to better integrate with new AI features and improvements. It now includes advanced checks and balances for evaluating model outputs, ensuring that the attention mechanism’s performance is accurately assessed.

5. Updated Floating-Point Handling:
   - Implemented tolerance-based comparison for floating-point operations to account for minor numerical differences that occur due to the nature of floating-point arithmetic. This adjustment makes the tests more reliable and reflects real-world performance more accurately.

6. Optimized AI Feature Integration:
   - Refined the integration of AI-driven features, including adjustments to the `LLamaSmoothquantAttention` model and the `int8_rotary_embedding_fwd` function. These changes enhance the model’s performance and ensure compatibility with the latest AI advancements.

Overall, these updates refine the script’s accuracy, readability, and functionality, aligning it with best practices and making it more robust for AI-related tasks and tests.
    @RahulVadisetty91
    RahulVadisetty91 authored Aug 25, 2024
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  2. Merge pull request #1 from RahulVadisetty91/RahulVadisetty91-patch-1 

    Enhance Floating-Point Accuracy, Refactor Variable Naming, and Optimize AI Integration in llama Test Script
    @RahulVadisetty91
    RahulVadisetty91 authored Aug 25, 2024
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    5547447 View commit details
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