Inference optimization with HPC

Research
1

Hello guys.
I have a task where i need to optimize inference on a LLama model.
The task involves creating a inference framework but not allowing to use a existing one such TensorRT-LLM and vLLM.

Task description = "Enhance the baseline code by crafting a specialized, high-performance inference engine that aligns with the architectural traits of your HPC cluster. In the early stages, employ either FP16 or BF16 precision, depending on your computing devices, to steer away from exclusive focus on low-precision optimization. Strictly avoid using 8-bit or lower numerical precision. Your proposal should offer in-depth insights into the optimization strategies employed and the attained results.’

The dataset is given too. And this is what is needed to send

LLM_inference Root directory.
LLM_inference/Log Inference log file.
LLM_inference/*.py Language model inference script or code files used in the
inference process.
LLM_inference/proposal Doc of pdf file including the results and the comprehensive
optimization methods.
LLM_inference/other_items

I really not sure how to start and even tough i tried to think in something I just dont be able to move forward.

I really appreciate your help guys, any hint is going to help me tons

1 Like
2

Creating a specialized inference engine for an LLama model involves several steps and considerations. Here’s a high-level guide to help you get started:

  1. Understand LLama Model and Architecture:
  • Familiarize yourself with the LLama model architecture, its components, and its computational requirements.
  • Understand how the model is structured, its layers, and the operations it performs during inference.
  1. Hardware Profiling:
  • Profile your HPC cluster to understand its hardware specifications and capabilities. Identify the computational resources available.
  1. Data Preparation:
  • Prepare the dataset for inference. Ensure it’s formatted correctly and ready for use in your code.
  1. Programming Language and Frameworks:
  • Choose a programming language (Python, C++, etc.) and frameworks/libraries that align with the hardware and model requirements. You mentioned not using existing inference engines, so you may have to work with low-level libraries for optimizations.
  1. Precision and Optimization Techniques:
  • Decide on the precision level (FP16 or BF16) based on the capabilities of your computing devices. Implement these precisions in your code.
  • Explore optimization strategies like:
    • Parallelism: Utilize multi-threading or distributed computing if your hardware supports it.
    • Memory Optimization: Optimize memory access patterns and minimize unnecessary data movement.
    • Kernel Fusion: Combine multiple operations into a single kernel to reduce overhead.
    • Cache Optimization: Ensure efficient utilization of CPU/GPU caches.
    • Algorithmic optimizations: Modify algorithms or use approximation techniques where possible to reduce computational complexity.
  1. Inference Engine Development:
  • Develop the inference engine according to the chosen language and optimization strategies.
  • Implement the LLama model inference logic in your code, ensuring compatibility with the chosen precision and optimization techniques.
  1. Benchmarking and Testing:
  • Benchmark your inference engine using the provided dataset. Measure its performance in terms of speed, accuracy, and resource utilization.
  • Perform rigorous testing to ensure the correctness and efficiency of your inference engine.
  1. Documentation and Reporting:
  • Create a comprehensive proposal or report documenting your optimization methods, strategies employed, results obtained, and insights gained during the process.
  • Include the inference script or code files used, along with any necessary logs or additional items requested.

Remember, this is a complex task that requires a deep understanding of both the LLama model and the optimization techniques suitable for your hardware. It might involve iterative improvements and fine-tuning to achieve the desired performance.

Break down the task into smaller steps, tackle each step methodically, and keep experimenting and optimizing until you achieve the best possible results within the constraints provided.

3

Thanks a lot, Is it possible if i dm you?. Would love to discuss it further. Please lmk