Optimized Transformer Inference with CUDA

High-performance transformer inference with custom CUDA kernels and optimization

Project Overview

This project demonstrates advanced GPU optimization techniques for transformer inference, implementing custom CUDA kernels and achieving significant performance improvements through architectural enhancements and memory management optimizations.

Key Performance Achievements

  • 73% Throughput Improvement: Multi-head Latent Attention (MLA) optimization with PyTorch and Triton
  • 90% Memory Reduction: Advanced paged KV cache management implementation
  • 6.5x Softmax Speedup: Custom CUDA kernel implementation for core operations
  • 3.7x LayerNorm Speedup: Optimized normalization operations with custom kernels
  • Distributed Training: Multi-GPU pipeline and data parallelism with efficient gradient synchronization

Technical Implementation

CUDA Kernel Development

  • Custom Softmax Kernels: Implemented memory-efficient softmax operations achieving 6.5x speedup
  • LayerNorm Optimization: Custom kernels for layer normalization with 3.7x performance improvement
  • Memory Management: Advanced GPU memory optimization strategies
  • Warp-level Primitives: Utilized CUDA cooperative groups for efficient parallel reductions

Attention Mechanism Optimization

  • Multi-head Latent Attention (MLA): Redesigned attention mechanism for better memory efficiency
  • Paged KV Cache: Implemented dynamic memory allocation for key-value caching
  • Memory-efficient Attention: Reduced memory footprint while maintaining accuracy
  • Flash Attention Integration: Incorporated state-of-the-art attention optimization techniques

Distributed Training Pipeline

  • Data Parallelism: Efficient data distribution across multiple GPUs
  • Pipeline Parallelism: Model partitioning for large transformer architectures
  • Gradient Synchronization: Optimized all-reduce operations for distributed training
  • Dynamic Load Balancing: Adaptive scheduling across heterogeneous GPU clusters

Framework Integration

PyTorch & Triton

  • PyTorch Extensions: Custom C++/CUDA extensions for seamless integration
  • Triton Kernels: High-level GPU kernel programming for complex operations
  • Autograd Support: Custom backward passes for optimized forward operations
  • Just-in-Time Compilation: Runtime kernel optimization based on input characteristics

Deep Learning Framework Support

  • Model Export: ONNX compatibility for production deployment
  • Quantization: INT8 and FP16 inference optimization
  • Dynamic Batching: Adaptive batch size optimization for varying workloads
  • Benchmark Suite: Comprehensive performance evaluation framework

Research Impact

This project contributes to the advancement of efficient transformer inference, demonstrating practical applications of GPU optimization techniques in production AI systems. The optimizations achieved have direct implications for reducing computational costs and enabling larger model deployments.

Technical Skills Demonstrated

  • CUDA Programming: Advanced GPU kernel development and optimization
  • Deep Learning Frameworks: PyTorch, Triton, and custom extension development
  • Distributed Systems: Multi-GPU training and inference optimization
  • Performance Engineering: Systematic optimization and benchmarking methodologies