Analysis of Core Technical Architecture in Mainstream AI Frameworks

[baoshi.rao]

According to their functional positioning, the core technologies of current mainstream AI frameworks can be categorized into foundational layer, component layer, and ecosystem layer.

1. Foundational Layer

The foundational layer implements the most fundamental functions of AI frameworks, specifically including three sub-layers: programming development, compilation optimization, and hardware enablement.

The programming development layer serves as the interface between developers and AI frameworks, providing APIs for building AI models. The compilation optimization layer is a critical component of AI frameworks, responsible for compiling and optimizing AI models while scheduling hardware resources for computation. The hardware enablement layer acts as the bridge between AI frameworks and computing hardware, shielding developers from underlying hardware complexities.

Programming Development - API Interfaces: Developers describe algorithmic processes by calling programming interfaces. The usability and expressiveness of these interfaces are crucial, as algorithmic descriptions are mapped to computational graphs. Programming interfaces fall into three main categories: dataflow-based (e.g., TensorFlow, MXNet, Theano, Torch7), layer-based (e.g., Caffe), and algorithm-based (e.g., Scikit-Learn for traditional machine learning).

Programming Development - Languages: Given diverse AI application scenarios, frameworks should support multiple languages (e.g., Python, Julia) with equivalent functionality and performance across languages.

Compilation Optimization - Distributed Parallelism: Includes strategies like data parallelism, model parallelism, pipeline parallelism, and optimizer parallelism. As models grow, automatic parallelization becomes essential for splitting computations across devices while optimizing communication overhead and computational efficiency.

Compilation Optimization - Automatic Differentiation: Decomposes complex math operations into basic steps via forward mode (computing derivatives during forward pass) or reverse mode (requiring forward pass storage for backpropagation). Reverse mode has higher memory demands.

Compilation Optimization - Static/Dynamic Graph Conversion: Static graphs (predefined operations, better performance) trade flexibility for efficiency, while dynamic graphs (immediate execution) prioritize debuggability. Frameworks like TensorFlow 2.0 and MindSpore enable hybrid approaches.

Compilation Optimization - Model Lightweighting: Techniques to reduce model size (compression) and computational complexity (acceleration), including matrix factorization, quantization, pruning, knowledge distillation, and hardware-aware optimizations like NEON instructions.

Compilation Optimization - Graph Operator Fusion: Automatically optimizes computational graphs through simplification, operator fusion/splitting, and hardware-specific compilation to improve resource utilization. This cross-layer optimization requires minimal developer intervention.

Compilation Optimization - Memory Optimization

Due to limited memory resources in hardware systems, especially in AI chips, efficient memory optimization strategies are required to reduce AI network's memory consumption. Common techniques include:

• Static Memory Reuse Optimization: Analyzes computational graph data flow relationships, memory footprint sizes, and lifecycle overlaps to plan memory reuse strategies for minimal memory usage.
• Dynamic Memory Allocation: Creates large memory blocks during runtime and provides memory slices as needed by operators. Memory slices are released after operator execution completes, enabling effective memory reuse.

Compilation Optimization - Operator Generation

While AI frameworks provide basic operators, these often can't meet evolving algorithmic needs. Frameworks need unified operator generation/optimization capabilities across different hardware, allowing developers to:

• Write high-level programming languages (e.g., DSL)
• Generate high-quality low-level operators through framework compilation
• Significantly reduce development/maintenance costs
• Expand application scope

Compilation Optimization - Intermediate Representation

Intermediate Representation (IR) defines computational graphs and operator formats. A complete IR should:

• Support hardware-specific operator definitions
• Enable computational graph performance optimization
• Flexibly express different AI model architectures
• Facilitate model transfer between devices

Hardware Integration - Computational Operators

In deep learning, computational operators are function nodes that:

• Accept zero/more tensors as input
• Produce zero/more tensors as output
• Perform calculations using gradient, divergence, or curl expressions

Hardware Integration - Communication Operators

Function nodes for distributed node communication.

2. Component Layer

Provides configurable high-level components for AI model lifecycle optimization, including:

• Compilation optimization components
• Scientific computing components
• Security/trust components
• Tool components
  Visible to AI model developers.

Parallelism & Optimization - Automatic Parallelism

Supports diverse combinations of parallel techniques:

• Data parallelism + model parallelism
• Data parallelism + pipeline parallelism
  Enables customized parallel strategies for flexible model training/application adaptation.

Parallelism & Optimization - Advanced Optimizers

Supports various first/second-order optimizers with flexible interfaces:

• SGD, SGDM, NAG
• AdaGrad, AdaDelta
• Adam, Nadam

Scientific Computing - Numerical Methods

As scientific computing becomes crucial for AI, frameworks should:

• Provide scientific computing functionality
• Offer functional programming paradigms
• Enable math-like programming approaches
• Address current limitations in handling mathematical expressions (e.g., differential equations)

Scientific Computing - AI Methods

For AI-assisted scientific computing, frameworks need:

• Unified data infrastructure
• Conversion of traditional scientific data to tensors
• Support for numerical methods (e.g., high-order differentials, linear algebra)
• Computational graph optimization for hybrid AI/numerical methods
• End-to-end acceleration of "AI + Scientific Computing"

Security/Trust - AI Interpretability

Frameworks need three-layer interpretability support:

1. Pre-modeling Data Interpretability: Analyze data distributions and select representative features
2. Interpretable AI Models: Combine with traditional ML (e.g., Bayesian programming) to balance effectiveness and interpretability
3. Post-model Interpretation: Analyze input/output dependencies (e.g., TB-Net) and verify model logic

Secure and Trusted Components - Data Security: In the field of artificial intelligence, data security issues not only involve the protection of raw data but also preventing the inference of private information from model outputs. Therefore, AI frameworks must not only provide data asset protection capabilities but also safeguard model data privacy through methods like differential privacy. Additionally, to ensure data security at the source, AI frameworks employ techniques such as federated learning for model training, enabling updates without data leaving the local environment.

Secure and Trusted Components - Model Safety: Insufficient training samples can lead to poor model generalization, resulting in incorrect judgments when faced with malicious inputs. To address this, AI frameworks must offer robust testing tools, including black-box, white-box, and gray-box adversarial testing techniques (e.g., static structure analysis, dynamic path analysis). Furthermore, frameworks can enhance model robustness by supporting methods like network distillation and adversarial training.

Tool Components - Training Visualization: Supports visualization of the training process, allowing users to directly view key metrics such as training scalars, parameter distributions, computation graphs, data graphs, and data sampling.

Tool Components - Debugger: Neural network training often encounters numerical errors (e.g., infinite values), making it difficult for developers to diagnose convergence issues. Debuggers enable inspection of graph structures, node inputs/outputs (e.g., tensor values, corresponding Python code), and allow setting conditional breakpoints to monitor node computations in real-time.

3. Ecosystem Layer: This layer focuses on application services, supporting the development, maintenance, and improvement of AI models. It is accessible to both developers and end-users.

Suites/Model Libraries: AI frameworks should provide pre-trained models or predefined architectures (e.g., for CV, NLP) to facilitate model training and inference.

AI Domain-Specific Libraries: Frameworks should offer extensive support for specialized tasks (e.g., GNN, reinforcement learning, transfer learning) with practical examples to enhance application services.

AI + Scientific Computing: Unlike traditional fields like CV or NLP, scientific computing requires domain expertise. To accelerate AI integration, frameworks should provide user-friendly toolkits for fields such as electromagnetic simulation, pharmaceutical research, energy, meteorology, biology, and materials science, including high-quality datasets, accurate base models, and pre/post-processing tools.

Documentation: Comprehensive documentation is essential, covering framework descriptions, APIs, version changes, FAQs, and feature details.

Community: A thriving community is vital for AI service development. Good frameworks should maintain an active community with long-term support for projects and applications built on them.