Course Goal: To provide learners with an in-depth understanding of Mamba models, their theoretical underpinnings, their relationship to Transformers and other architectures, and their practical applications in various domains, including natural language processing and computer vision.
Prerequisites:
- Successful completion of "Modern AI Development: From Transformers to Generative Models" or equivalent knowledge.
- Strong proficiency in Python programming.
- Solid understanding of deep learning concepts (neural networks, backpropagation, optimization, etc.).
- Familiarity with sequence models (RNNs, Transformers).
- Experience with PyTorch or a similar deep learning framework.
Course Duration: 8 weeks (flexible, could be adjusted to 6 or 10 weeks)
Tools:
- Python 3.8+
- PyTorch (or another DL framework, but examples will focus on PyTorch)
- Hugging Face Libraries (Transformers, Datasets, etc.) - if applicable for demonstrations
- Jupyter Notebooks/Google Colab
- Relevant Mamba implementation libraries (official implementations, community forks)
- Standard scientific computing libraries (NumPy, Pandas, etc.)
Curriculum Draft:
Module 1: Foundations: SSMs and the Rise of Mamba (Week 1)
- Topic 1.1: Review of Sequence Models and Limitations of Transformers:
- Recap of RNNs and their challenges (vanishing/exploding gradients).
- Refresher on Transformers and Attention: benefits and limitations (quadratic complexity, memory usage).
- Motivation for exploring beyond Transformers.
- Topic 1.2: Introduction to State Space Models (SSMs):
- Classical SSMs and their connection to continuous-time systems.
- Discretization of SSMs.
- SSMs as linear recurrences and global convolutions.
- The concept of Linear Time Invariance (LTI) and its limitations.
- Structured State Space Sequence models (S4) and its variants.
- Topic 1.3: The Need for Selectivity - Introducing the Mamba Architecture (Paper: "Mamba: Linear-Time Sequence Modeling with Selective State Spaces"):
- Limitations of previous SSMs (LTI models) in handling complex sequences.
- The concept of selectivity: content-aware routing and information filtering.
- Introducing input-dependent SSM parameters.
- The core Mamba block architecture.
- Efficiency considerations: why Mamba scales linearly.
- Topic 1.4: Implementing a Simplified SSM (Hands-on):
- Building a basic SSM from scratch in PyTorch.
- Implementing a simplified version of the selective scan mechanism.
- Experimenting with different input sequences and visualizing hidden states.
- Paper Discussion: "Mamba: Linear-Time Sequence Modeling with Selective State Spaces" (key ideas, strengths, initial results).
Module 2: Theoretical Underpinnings: Structured Matrices and Duality (Week 2)
- Topic 2.1: Structured Matrices and Semiseparable Matrices (Paper: "Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality"):
- Introduction to structured matrices and their properties.
- Semiseparable matrices: definition, properties, and representations (e.g., SSS).
- Connecting SSMs to semiseparable matrices.
- Topic 2.2: State Space Duality (SSD):
- The concept of duality in sequence models.
- Quadratic vs. linear formulations of sequence transformations.
- SSD as a framework for connecting SSMs and attention variants.
- Topic 2.3: Mamba-2 and SSD Optimization (Paper: "Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality"):
- Introducing Mamba-2: refinements to the Mamba architecture.
- The SSD algorithm: block decomposition, parallel scan, and recomputation.
- Efficiency analysis of SSD: comparisons to attention and convolutions.
- Topic 2.4: Implementing SSD (Hands-on):
- Implementing a basic version of the SSD algorithm.
- Comparing its performance to a naive SSM implementation.
- Experimenting with different block sizes and sequence lengths.
- Paper Discussion: "Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality" (core contributions, theoretical framework, connections to other models).
Module 3: Mamba in Language Modeling (Week 3)
- Topic 3.1: Scaling Mamba for Language Modeling:
- Challenges of applying Mamba to large-scale language tasks.
- Strategies for scaling Mamba: model parallelism, data parallelism.
- Training considerations: optimizers, learning rate schedules, regularization.
- Topic 3.2: Pre-training and Fine-tuning Mamba Models:
- Pre-training objectives for Mamba language models.
- Fine-tuning strategies for downstream NLP tasks.
- Exploring different pre-training datasets.
- Topic 3.3: Analyzing Mamba's Performance on Language Tasks (Paper: "Falcon Mamba: The First Competitive Attention-free 7B Language Model"):
- Evaluating Mamba on standard language modeling benchmarks (perplexity, downstream tasks).
- Comparing Mamba's performance to Transformers and other sequence models.
- Analyzing the impact of model size and training data on performance.
- Topic 3.4: Falcon Mamba and Hybrid Architectures (Paper: "Falcon Mamba: The First Competitive Attention-free 7B Language Model"):
- Introducing Falcon Mamba: a hybrid Mamba-Transformer model.
- Design choices in Falcon Mamba: attention/SSM layer ratios, positional encodings.
- Performance analysis of Falcon Mamba: comparisons to pure Mamba and Transformer models.
- Hands-on Exercises:
- Loading and using pre-trained Mamba language models.
- Fine-tuning a Mamba model for a specific NLP task (e.g., sentiment analysis).
- Experimenting with different configurations of Falcon Mamba.
- Paper Discussion: "Falcon Mamba: The First Competitive Attention-free 7B Language Model" (key contributions, architectural choices, performance comparisons).
Module 4: Hybrid Architectures: Jamba and Beyond (Week 4)
- Topic 4.1: Introduction to Jamba (Paper: "Jamba: A Hybrid Transformer-Mamba Language Model"):
- Motivation for combining Transformers and Mamba.
- The Jamba architecture: interleaving Transformer and Mamba blocks.
- Mixture-of-Experts (MoE) in Jamba: increasing model capacity efficiently.
- Topic 4.2: Design Choices in Jamba:
- Choosing the ratio of Transformer to Mamba blocks.
- Optimizing the placement of MoE layers.
- Balancing model capacity, throughput, and memory usage.
- Topic 4.3: Performance Analysis of Jamba:
- Comparing Jamba to pure Transformer and Mamba models.
- Evaluating Jamba on long-context tasks.
- Analyzing the impact of MoE on Jamba's performance.
- Topic 4.4: Other Hybrid Architectures:
- Exploring alternative ways of combining Mamba with other architectures.
- Discussing the potential benefits and drawbacks of hybrid approaches.
- Researching further into hybrid architectures and future developments.
- Hands-on Exercises:
- Loading and using pre-trained Jamba models (if available).
- Experimenting with different configurations of Jamba (e.g., varying the Transformer/Mamba ratio).
- Implementing a simplified version of a hybrid Mamba-Transformer block.
- Paper Discussion: "Jamba: A Hybrid Transformer-Mamba Language Model" (key contributions, architectural choices, performance analysis).
Module 5: Mamba for Computer Vision (Week 5)
- Topic 5.1: Adapting Mamba to Images (Paper: "Vision Mamba: Efficient Visual Representation Learning with Bidirectional") :
- Challenges of applying Mamba to images: non-sequential nature of visual data.
- Introducing scanning strategies for images: 2D scanning, multi-directional scanning.
- Visual Mamba (Vim) and its bidirectional scanning.
- Integrating positional information in visual Mamba: spatial embeddings.
- Topic 5.2: Visual Mamba Architectures:
- Overview of different visual Mamba backbone architectures.
- Analyzing the design choices in various visual Mamba models.
- Comparing visual Mamba to other vision backbones (CNNs, ViTs).
- Topic 5.3: Applications of Visual Mamba:
- Image classification with visual Mamba.
- Object detection and segmentation with visual Mamba.
- Other computer vision tasks: image restoration, generation, etc.
- Topic 5.4: Lightweight Visual Mamba and Efficiency Considerations (Paper: "MobileMamba: Lightweight Multi-Receptive Visual Mamba Network"):
- Designing efficient visual Mamba models for mobile devices.
- Introducing techniques for reducing computational complexity and memory usage.
- MobileMamba and its performance-efficiency trade-offs.
- Hands-on Exercises:
- Loading and using pre-trained visual Mamba models.
- Fine-tuning a visual Mamba model for a specific vision task (e.g., image classification).
- Experimenting with different scanning strategies and visualizing their effects.
- Paper Discussion: "Vision Mamba: Efficient Visual Representation Learning with Bidirectional", "MobileMamba: Lightweight Multi-Receptive Visual Mamba Network", "A Survey of Mamba", "Visual Mamba: A Survey and New Outlooks" (key ideas, adaptation techniques, applications, efficiency considerations).
Module 6: Advanced Topics in Mamba-based Vision (Week 6)
- Topic 6.1: Exploring Hybrid Vision Models (Paper: "BlackMamba: Mixture of Experts for State-Space Models"):
- Rationale behind combining Mamba with other architectures in vision.
- BlackMamba architecture: integrating Mamba with Mixture-of-Experts (MoE).
- Performance and efficiency analysis of BlackMamba on vision tasks.
- Topic 6.2: In-Context Learning in Mamba (Paper: "Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks"):
- Investigating the in-context learning capabilities of Mamba models.
- Comparing Mamba's in-context learning performance to Transformers.
- Analyzing Mamba's strengths and weaknesses on different ICL tasks.
- Discussing the potential of hybrid models for in-context learning.
- Topic 6.3: Diffusion Mamba (DiM) for Image Generation (Paper: "DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis"):
- Extending Mamba for generative tasks.
- The DiM architecture: combining Mamba with diffusion models.
- Strategies for training and fine-tuning DiM on high-resolution images.
- Evaluating DiM's performance on image generation benchmarks.
- Topic 6.4: Future Directions in Mamba-based Vision:
- Scaling up visual Mamba models: challenges and opportunities.
- Exploring novel applications of Mamba in computer vision.
- Improving the interpretability and explainability of visual Mamba models.
- Addressing the limitations of Mamba in specific vision tasks.
- Hands-on Exercises:
- Experimenting with different configurations of BlackMamba (if available).
- Training a small DiM model for image generation.
- Analyzing the in-context learning capabilities of a pre-trained Mamba model.
- Paper Discussion: "BlackMamba: Mixture of Experts for State-Space Models", "Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks", "DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis" (key contributions, architectural designs, performance analysis, future directions).
Module 7: Advanced Mamba Concepts & Research Directions (Week 7)
- Topic 7.1: Beyond Standard Mamba Architectures:
- Exploring variations of the Mamba block design.
- Investigating alternative scanning strategies and their impact on performance.
- Researching novel methods for incorporating positional information.
- Topic 7.2: Mamba in Other Modalities:
- Applying Mamba to time-series data, audio, and video.
- Exploring the potential of Mamba in multimodal learning.
- Discussing the challenges and opportunities of adapting Mamba to different data types.
- Topic 7.3: Theoretical Analysis of Mamba:
- Deeper dive into the mathematical foundations of SSMs and Mamba.
- Analyzing the expressivity and representational power of Mamba.
- Investigating the connections between Mamba and other sequence models.
- Topic 7.4: Efficiency and Scalability of Mamba:
- Optimizing Mamba for different hardware platforms.
- Exploring techniques for model compression and quantization.
- Addressing the challenges of scaling Mamba to very large models and datasets.
- Topic 7.5: Open Problems and Future Research Directions:
- Discussing the limitations of current Mamba models.
- Identifying promising research avenues for improving Mamba.
- Exploring the potential impact of Mamba on the future of AI.
- Paper Discussion: "An Empirical Study of Mamba-based Language Models"
Module 8: Project Presentations and Conclusion (Week 8)
- Topic 8.1: Project Work and Consultations:
- Students work on their final projects, applying the knowledge and skills gained throughout the course.
- Instructor provides guidance, feedback, and consultations to support project development.
- Topic 8.2: Project Presentations:
- Students present their final projects to the class, showcasing their understanding of Mamba architectures and their ability to apply them to real-world problems.
- Peer feedback and discussions on project outcomes and potential improvements.
- Topic 8.3: Course Review and Future Outlook:
- Recap of key concepts and techniques covered in the course.
- Discussion of the current state and future directions of Mamba research.
- Exploration of potential career paths and opportunities related to Mamba and sequence modeling.
Assessment:
- Weekly Quizzes/Assignments: Short quizzes or coding assignments to assess understanding of the weekly topics.
- Midterm Project/Exam: A more substantial project or exam covering the first half of the course, focusing on the theoretical foundations of SSMs, Mamba, and SSD.
- Final Project: A significant project involving the implementation, training, and evaluation of a Mamba-based model for a specific task or application. This could involve:
- Fine-tuning a pre-trained Mamba model for a new domain.
- Designing a novel Mamba architecture for a specific task.
- Conducting a thorough experimental evaluation of different Mamba variants.
- Exploring the theoretical properties of Mamba models.
- Class Participation: Active engagement in discussions and Q&A sessions.