Model Inference Optimization: A Comprehensive Study of Modern Acceleration Techniques

Elevator pitch

This project aims to demonstrate and benchmark various modern techniques for accelerating neural network model inference. Through hands-on implementation and rigorous profiling, the project will showcase how combining multiple optimization strategies—from low-level GPU kernel optimization to high-level model restructuring—can achieve significant speedups in real-world inference scenarios. The work will include detailed performance analysis using PyTorch profilers and NVIDIA's NCU (NVIDIA Compute Utility).

Project Objectives

• Understand Performance Bottlenecks: Use profiling tools to identify where computation time is actually spent in model inference
• Implement Multiple Optimization Techniques: Apply a range of techniques from compilation to kernel-level optimization
• Quantify Improvements: Measure and compare performance gains from each technique independently and in combination
• Create Educational Documentation: Provide clear explanations of each technique and practical guidance for implementation
• Demonstrate Trade-offs: Show how different optimizations affect accuracy, memory usage, and latency

Scope and Techniques

Baseline and Profiling

• Select a target model (e.g., a transformer-based language model or vision model)
• Establish baseline inference metrics (latency, throughput, memory usage)
• Use PyTorch Profiler and NVIDIA NCU to identify performance bottlenecks
• Document which operations consume the most compute time

Inference-Specific Optimizations

• KV Caching - Implement efficient key-value cache management for autoregressive generation
• Batch inference
• Speculative decoding - draft token generation using a smaller model

Compiler-Level Optimizations

• torch.compile(): Leverage PyTorch's built-in compilation features to optimize the computational graph
• Experiment with different backends (inductor, cudagraph)
• Measure speedup from graph optimization and kernel fusion
• Compare compiled vs. non-compiled inference

Custom Kernel Development

• CUDA Kernels: Implement custom CUDA kernels and use them in python code
• Triton Kernels: Use Triton's higher-level abstractions to write performant kernels without low-level CUDA details
• Attention Optimization - Implement or integrate FlashAttention or similar memory-efficient attention mechanisms
• Benchmark custom kernels against PyTorch's built-in implementations
• Use NCU to analyze achieved compute utilization

Model-Level Quantization

• Dynamic Quantization: Convert weights and activations to lower precision (INT8, FP8) at runtime
• Post-Training Quantization: Apply quantization without retraining, with calibration on representative data
• Quantization-Aware Training: Fine-tune models with simulated quantization for better accuracy preservation
• Benchmark different quantization schemes (symmetric, asymmetric, per-channel, per-tensor)
• Measure accuracy degradation on validation datasets alongside inference speedup
• Combine quantization with other optimizations (e.g., quantized + compiled models)