ML Inference Optimizer

A high-performance optimization framework for transformer-based inference with a focus on generative media models.

Overview

ML Inference Optimizer provides tools and implementations to improve inference performance for large language models and diffusion models.

Key Features

• Advanced Attention Mechanisms

  • Flash Attention 3 implementation with tiled block-based computation
  • Ring Attention for distributed processing of extremely long sequences
  • Memory-efficient attention patterns that reduce complexity from O(N²) to O(N)

• Fusion Optimizations

  • FusedMLP implementation that keeps intermediates in fast memory
  • Kernel fusion for reduced memory bandwidth requirements
  • Support for various activation functions (GELU, SwiGLU, etc.)

• Parallelism Strategies

  • Tensor parallelism for distributed model weights
  • Sequence parallelism for handling long contexts
  • Multi-dimensional parallelism orchestration

• Performance Analysis

  • Profiling system for identifying bottlenecks
  • Memory usage tracking and optimization
  • Visualization dashboard for performance metrics

Installation

# Clone the repository
git clone https://github.com/aslitaser/ml-inference-optimizer.git
cd ml-inference-optimizer

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Requirements

• Python 3.8+
• PyTorch 2.0+
• CUDA 11.8+ and compatible GPU
• Triton 2.0+ (for optimized kernels)

Quick Start

from ml_inference_optimizer import Optimizer
from ml_inference_optimizer.baseline import load_model

# Load a transformer model
model = load_model("gpt2")

# Create an optimizer instance
optimizer = Optimizer(model)

# Profile the model to identify bottlenecks
bottlenecks = optimizer.profile()
print(f"Main bottlenecks: {bottlenecks}")

# Apply optimizations
optimized_model = optimizer.optimize(
    use_flash_attention=True,
    use_fused_mlp=True,
    tensor_parallel_size=1  # Set to >1 for multi-GPU
)

# Run optimized inference
output = optimized_model.generate(
    input_text="Hello, world!",
    max_new_tokens=100
)

Optimization Details

Flash Attention 3

Our FA3 implementation uses tiled block-based computation to reduce memory complexity while maintaining computational efficiency:

• Processes attention in manageable blocks to fit in GPU shared memory
• Implements interleaved matmul and softmax operations
• Supports causal masking for autoregressive generation
• Compatible with both single-GPU and multi-GPU setups

Ring Attention

Ring Attention enables processing of extremely long sequences by distributing workload in a ring communication pattern:

• Scales memory requirements linearly with number of GPUs
• Enables context lengths of 65K+ tokens with standard hardware
• Optimizes communication patterns to overlap with computation
• Integrates with other optimizations like Flash Attention

FusedMLP

FusedMLP implementation significantly improves performance for feed-forward networks:

• Combines multiple operations into single kernels
• Keeps intermediate activations in shared memory
• Supports various activation functions including SwiGLU
• Reduces memory bandwidth requirements by 40-60%

Benchmarks

Model	Optimization	Throughput Improvement	Latency Reduction	Memory Reduction
GPT-2	Baseline	1.0x	1.0x	1.0x
GPT-2	FA3	2.3x	1.8x	3.5x
GPT-2	FusedMLP	1.8x	1.6x	1.7x
GPT-2	Combined	3.7x	2.9x	4.2x

For long sequences (4096 tokens), improvements are even more

Project Structure

ml-inference-optimizer/
├── baseline/               # Baseline model implementations
├── benchmarks/             # Performance benchmarking tools
├── config/                 # Configuration system
├── dashboard/              # Performance visualization
├── kernels/                # Optimized kernel implementations
│   ├── attention/          # Attention optimizations
│   ├── mlp/                # MLP optimizations
│   └── triton/             # Triton kernels
├── parallelism/            # Parallel execution strategies
├── profiling/              # Performance analysis tools
└── utils/                  # Common utilities

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• This project builds on research from multiple papers about FlashAttention, Ring Attention, and others