STARK Core Language Specification Overview

Introduction

This document provides an overview of the complete STARK core language specification. The core language focuses on AI/ML deployment and inference optimization, with tensor operations, model loading, and memory-safe execution as first-class features.

Design Philosophy

Core Principles

  1. AI-Native Design: Tensor operations and ML workflows as primary abstractions
  2. Memory Safety: Prevent common memory errors through ownership and borrowing
  3. Inference Performance: Zero-cost abstractions optimized for model serving
  4. Python Interoperability: Seamless loading of existing PyTorch/TensorFlow models
  5. Production Readiness: Predictable performance for real-time AI applications

Language Goals

  • AI/ML deployment capabilities with production-grade performance
  • Compile-time guarantees for memory and type safety in tensor operations
  • 2-10x faster inference than Python-based solutions
  • Seamless integration with existing ML model formats
  • Clear and maintainable AI workflow code

Specification Structure

1. Lexical Grammar (01-Lexical-Grammar.md)

Defines how source code is tokenized:

  • Keywords: Control flow, declarations, types, operators
  • Identifiers: Variables, functions, types (snake_case/PascalCase)
  • Literals: Integers, floats, strings, characters, booleans
  • Operators: Arithmetic, comparison, logical, bitwise, assignment
  • Comments: Single-line (//) and multi-line (/* */)
  • Whitespace: Space, tab, newline handling

2. Syntax Grammar (02-Syntax-Grammar.md)

Defines the concrete syntax using EBNF:

  • Program Structure: Items (functions, structs, enums, traits)
  • Expressions: Precedence, associativity, type inference
  • Statements: Variable declarations, control flow, returns
  • Type Syntax: Primitives, composites, references, functions
  • Pattern Matching: Destructuring and exhaustiveness

3. Type System (03-Type-System.md)

Comprehensive type system with safety guarantees:

  • Primitive Types: Integers, floats, booleans, characters, strings
  • Composite Types: Arrays, tuples, structs, enums
  • Reference Types: Immutable and mutable references
  • Ownership Model: Move semantics, borrowing rules
  • Type Inference: Local and function return type inference
  • Trait System: Interfaces and generic constraints

4. Semantic Analysis (04-Semantic-Analysis.md)

Rules for meaningful program validation:

  • Symbol Resolution: Scoping, shadowing, name lookup
  • Type Checking: Assignment compatibility, function calls
  • Ownership Analysis: Move tracking, borrow checking
  • Control Flow: Reachability, return path analysis
  • Pattern Exhaustiveness: Match completeness checking
  • Error Reporting: Comprehensive diagnostics with suggestions

5. Memory Model (05-Memory-Model.md)

Memory safety through compile-time analysis:

  • Ownership Rules: Single ownership, automatic cleanup
  • Move Semantics: Explicit ownership transfer
  • Borrowing System: Immutable and mutable references
  • Lifetime Tracking: Reference validity guarantees
  • Stack vs Heap: Allocation strategy and layout
  • Drop System: Automatic and manual resource cleanup

6. Standard Library (06-Standard-Library.md)

Essential types and functions for practical programming:

  • Core Types: Option, Result, Box, Vec, HashMap
  • String Handling: Unicode-aware string operations
  • Collections: Dynamic arrays, hash tables, sets
  • IO Operations: File handling, console output
  • Math Functions: Arithmetic, trigonometric, random numbers
  • Error Handling: Structured error types and propagation

Core Language Features

Variables and Mutability

let x = 42              // Immutable by default
let mut y = 10          // Explicitly mutable
const MAX_SIZE: Int32 = 1000  // Compile-time constant

Functions

fn add(a: Int32, b: Int32) -> Int32 {
    a + b
}

fn greet(name: &str) {
    println("Hello, " + name)
}

AI/ML Data Types

// Tensor types (core AI primitive)
let features: Tensor<Float32>[batch, 128] = load_data("input.json")
let weights: Tensor<Float32>[128, 10] = tensor_zeros([128, 10])

// Model loading
let model: Model = load_pytorch_model("classifier.pt")
let llm: LLMClient = LLMClient(provider="openai", model="gpt-4")

// Dataset handling
let dataset: Dataset<Tuple<Tensor<Float32>[128], Int32>> = 
    load_dataset("train.csv").batch(32).shuffle(42)

// Traditional types still available
struct Point {
    x: Float64,
    y: Float64
}

AI Workflow Control Flow

// Batch processing
for batch in dataset {
    let predictions = model.predict(batch.features)
    let accuracy = evaluate(predictions, batch.labels)
    println("Batch accuracy: " + accuracy.to_string())
}

// LLM integration with pattern matching
@llm as classifier:
    system: "You are a text classifier"
    user: "Classify this text: {{input}}"

match classifier.call({input: text}) {
    "positive" => handle_positive(),
    "negative" => handle_negative(),
    "neutral" => handle_neutral()
}

// Tensor operations
let logits = model.forward(features)
let probabilities = softmax(logits)
let prediction = argmax(probabilities)

AI Error Handling

fn load_model(path: &str) -> Result<Model, ModelError> {
    if !file_exists(path) {
        Err(ModelError::FileNotFound(path.to_string()))
    } else {
        load_pytorch_model(path)
    }
}

fn run_inference(model: &Model, input: Tensor<Float32>) -> Result<Tensor<Float32>, InferenceError> {
    if input.shape()[0] != model.input_shape()[0] {
        Err(InferenceError::ShapeMismatch)
    } else {
        Ok(model.predict(input)?)
    }
}

Memory-Safe Tensor Operations

fn process_tensor(tensor: Tensor<Float32>) -> Tensor<Float32> {
    // tensor is owned by this function
    tensor.map(|x| x * 2.0)
}

fn compute_statistics(tensor: &Tensor<Float32>) -> (Float32, Float32) {
    (tensor.mean(), tensor.std())  // Can read but not modify
}

fn normalize_in_place(tensor: &mut Tensor<Float32>) {
    let mean = tensor.mean();
    let std = tensor.std();
    tensor.map_mut(|x| (x - mean) / std);  // Can read and modify
}

Implementation Phases

Phase 1: AI Core MVP

Goal: Basic AI deployment capability

  • Lexer and parser for tensor-aware syntax
  • Type checker with tensor shape validation
  • Tensor operations and memory management
  • Python model loading (PyTorch/TensorFlow)
  • Basic inference runtime

Phase 2: Production AI Runtime

Goal: Production-ready AI deployment

  • Optimizing compiler for tensor operations
  • Full tensor library implementation
  • LLM integration (@llm blocks)
  • Batch processing and streaming inference
  • Performance profiling and optimization

Phase 3: Advanced AI Features

Goal: Complete AI deployment ecosystem

  • Multi-model serving and routing
  • Dynamic batching and caching
  • Edge deployment optimization
  • Model quantization and compression
  • Integration with popular ML frameworks

Comparison with AI/ML Languages

vs Python

  • Similarities: Readable syntax, good for ML workflows
  • Differences: Compiled performance, memory safety, tensor types
  • AI Focus: 2-10x faster inference, seamless model loading

vs Mojo

  • Similarities: Python interop, performance focus
  • Differences: Memory safety, production deployment focus
  • Trade-offs: Research flexibility vs production reliability

vs Julia

  • Similarities: Performance, mathematical computing
  • Differences: Better Python ecosystem integration, AI-native design
  • Trade-offs: General computing vs AI specialization

vs JAX/PyTorch JIT

  • Similarities: Compilation, optimization
  • Differences: Full language vs library, deployment focus
  • Trade-offs: Python flexibility vs standalone performance

Success Criteria

AI Performance

  • [ ] 2-10x faster inference than Python equivalents
  • [ ] 50-80% memory reduction vs Python runtime
  • [ ] Zero-copy tensor operations
  • [ ] Sub-millisecond model loading

Correctness

  • [ ] Memory safety verified for tensor operations
  • [ ] Shape checking prevents runtime tensor errors
  • [ ] Comprehensive AI workflow test suite
  • [ ] Formal verification of ownership in tensor sharing

Ecosystem Integration

  • [ ] Seamless PyTorch/TensorFlow model loading
  • [ ] Python library interoperability for preprocessing
  • [ ] ONNX export/import support
  • [ ] Integration with popular ML serving frameworks

Developer Experience

  • [ ] Clear tensor shape error messages
  • [ ] AI-focused IDE support and debugging
  • [ ] Model performance profiling tools
  • [ ] Migration guides from Python ML codebases

Next Steps

  1. Implement Tensor-Aware Lexer: Start with tokenization including tensor syntax
  2. Build AI-Focused Parser: Parse tensor operations, model loading, and @llm blocks
  3. Tensor Type Checker: Develop shape inference and tensor type validation
  4. Memory-Safe Tensor Operations: Implement ownership tracking for tensor sharing
  5. Python Model Loader: Create PyTorch/TensorFlow model import functionality
  6. AI Standard Library: Implement Tensor, Dataset, Model, and LLMClient types
  7. Inference Runtime: Develop optimized execution engine for tensor operations
  8. Performance Benchmarking: Compare against Python equivalents

This specification provides a focused foundation for implementing a memory-safe, high-performance AI deployment language that bridges the gap between Python research and production inference systems.