Technical Architecture - Julia AOT + LLM Development Pipeline

┌─────────────────────────────────────────────────────────────┐
│                    AMD Ryzen AI Max+ 395                    │
├─────────────────────────────────────────────────────────────┤
│  CPU Cores: 16/32 cores + Zen 4 architecture                │
│  Memory: 128GB DDR5 Unified Memory                          │
│  GPU: Integrated Radeon Graphics                            │
│  NPU: Neural Processing Unit acceleration                   │
│  ROCm Support: Full AMD GPU compute stack                   │
└─────────────────────────────────────────────────────────────┘

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Local LLM     │    │  Reactant.jl    │    │   JuliaC AOT    │
│  Generator      │    │  MLIR Backend   │    │  Compiler       │
│                 │    │                 │    │                 │
│ • Qwen3-Coder-  │    │ • GPU Optimiza- │    │ • Standalone    │
│   30B           │    │   tion          │    │   Executables   │
│ • Fine-tuned    │    │ • MLIR Compile- │    │ • Instant       │
│   Julia Expert  │    │   tion          │    │   Execution     │
│ • Zero API Cost │    │ • Multi-target  │    │ • Self-contained│
│                 │    │   Output        │    │   Deployment    │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         └───────────┬───────────┴───────────┬───────────┘
                     │                       │
            Julia Code Generation          Optimized
                     │                       │
            ┌───────────────────────────────────────┐
            │        Deployment Pipeline           │
            │         Production Ready              │
            └───────────────────────────────────────┘

# Device Detection and Selection
function get_optimal_device()
    if MLDataDevices.functional(CUDADevice)
        return CUDADevice()
    elseif MLDataDevices.functional(AMDGPUDevice)
        return AMDGPUDevice()
    elseif MLDataDevices.functional(MetalDevice)
        return MetalDevice()
    elseif MLDataDevices.functional(oneAPIDevice)
        return oneAPIDevice()
    else
        @info "No GPU available. Using CPU."
        return cpu_device()
    end
end

# Data Movement and GPU Acceleration
function accelerate_computation(data)
    gdev = get_optimal_device()
    
    # Move data to GPU if available
    if gdev isa CPUDevice
        @debug "Using CPU for computation"
        return data
    else
        @debug "Using GPU: $(typeof(gdev)) for computation"
        return data |> gdev
    end
end

# Reactant.jl MLIR Compilation
function compile_with_mlir(julia_code)
    # Parse Julia AST
    ast = parse_julia_code(julia_code)
    
    # Convert to MLIR IR
    mlir_ir = julia_to_mlir(ast)
    
    # Optimize using MLIR passes
    optimized_mlir = run_mlir_optimization_passes(mlir_ir)
    
    # Compile to target architecture
    binary = mlir_to_native(optimized_mlir)
    
    return binary
end

# Standard Julia package structure for AOT compilation
module AgentProject

function @main(ARGS)
    println(Core.stdout, "AOT Julia Agent Starting...")
    
    # Agent initialization
    println(Core.stdout, "Agent initialized successfully!")
    println(Core.stdout, "Ready for directed evolution workflows...")
    
    # Computation example
    result = sum(1:100)
    println(Core.stdout, "Test computation: sum(1:100) = $result")
    
    println(Core.stdout, "Agent execution complete!")
    return 0
end

end

# JuliaC compilation command
juliac \
  --output-exe julia_agent \
  --bundle build \
  --trim=safe \
  --experimental \
  agent_project

# Executable structure
build/
├── bin/
│   └── julia_agent          # Compiled executable (1.75MB)
└── lib/
    ├── libjulia.so.1.12.1   # Julia runtime
    ├── libLLVM.so.18.1jl    # LLVM backend
    └── stdlibs/             # Standard libraries

# Qwen3-Coder-30B Fine-tuning Pipeline
class JuliaCodeFineTuner:
    def __init__(self):
        self.model = Qwen3Coder30B()
        self.tokenizer = QwenTokenizer()
        self.dataset = JuliaCodeCorpus()
        
    def train_model(self):
        # Generate diverse Julia code examples
        training_data = self.generate_training_corpus()
        
        # Fine-tuning configuration
        training_config = TrainingConfig(
            batch_size=32,
            learning_rate=2e-5,
            num_epochs=3,
            gradient_accumulation_steps=4
        )
        
        # Start training
        self.model.train(
            training_data,
            **training_config
        )
        
    def generate_training_corpus(self):
        """Generate diverse working Julia code examples"""
        corpus = [
            # Performance-critical code
            "function optimized_matrix_multiply(A, B)\n    # Optimized BLAS operations\nend",
            
            # Machine learning code
            "function neural_network_forward(X, weights, bias)\n    # GPU-accelerated NN\nend",
            
            # Scientific computing
            "function differential_equation_solver(f, y0, t_span)\n    # High-precision DE solver\nend",
        ]
        
        return corpus

┌─────────────────────────────────────────────────────────────┐
│                   LLM Fine-tuning Infrastructure            │
├─────────────────────────────────────────────────────────────┤
│  Model: Qwen3-Coder-30B (30B parameters)                    │
│  Memory: 128GB RAM + ROCm GPU offloading                   │
│  Framework: PyTorch + Julia integration                     │
│  Dataset: Generated working Julia code corpus                │
└─────────────────────────────────────────────────────────────┘

# Efficient memory usage with unified memory architecture
struct UnifiedMemoryManager
    total_memory::UInt64  # 128GB available
    gpu_memory::UInt64    # ROCm GPU memory
    cpu_memory::UInt64    # System RAM
    
    function UnifiedMemoryManager()
        new(
            128 * GiB,  # 128GB total
            get_gpu_memory(),
            get_system_memory()
        )
    end
end

# Intelligent memory allocation
function allocate_memory(manager, size, ::Type{GPU})
    if size <= manager.gpu_memory
        return allocate_gpu_memory(size)
    else
        # Use CPU memory with GPU offloading
        return allocate_cpu_memory_with_offloading(size)
    end
end

# Local LLM KV cache optimization
struct KVCacheManager
    max_cache_size::UInt64
    current_cache::Dict{String, Any}
    
    function KVCacheManager(max_size=64*GiB)
        new(max_size, Dict{String, Any}())
    end
    
    function get_kv_cache(self, key)
        if haskey(self.current_cache, key)
            return self.current_cache[key]
        else
            # Generate new cache entry
            cache_entry = generate_kv_cache(key)
            self.current_cache[key] = cache_entry
            return cache_entry
        end
    end
end

# Reactant.jl performance optimization
function optimize_reactant_compilation()
    # MLIR optimization passes
    optimization_passes = [
        "gpu-bufferize",
        "gpu-lower-to-nvvm", 
        "gpu-map-symbols-to-llvm-values",
        "gpu-parallel-loop-mapper",
        "gpu-vectorize"
    ]
    
    # Apply optimization passes
    for pass in optimization_passes
        apply_mlir_pass(compilation_ir, pass)
    end
    
    return compilation_ir
end

# JuliaC AOT optimization
function optimize_juliac_compilation()
    # Compilation flags optimization
    juliac_flags = [
        "--experimental",    # Enable experimental features
        "--trim=safe",       # Remove unreachable code
        "--optimize=3",      # Maximum optimization
        "--inline=yes",      # Aggressive inlining
        "--math-mode=fast"   # Fast floating point
    ]
    
    return juliac_flags
end

# Multi-target compilation support
function compile_for_target(target::String)
    case target
        "linux-x86_64"
            compile_with_flags("--cpu-target=x86-64-v3")
        "linux-aarch64"
            compile_with_flags("--cpu-target=aarch64")
        "windows-x86_64"
            compile_with_flags("--sysimage=windows_sysimage.so")
        "darwin-x86_64"
            compile_with_flags("--sysimage=darwin_sysimage.dylib")
    end
end

# Containerized deployment
function create_deployment_container()
    container = DockerContainer()
    
    # Add compiled binaries
    container.add_binary("julia_agent")
    container.add_library("libjulia.so.1.12.1")
    container.add_library("libLLVM.so.18.1jl")
    
    # Set environment variables
    container.set_env("JULIA_DEPOT_PATH", "/opt/julia")
    container.set_env("LD_LIBRARY_PATH", "/opt/julia/lib")
    
    return container
end

# Create self-contained deployment package
create_deployment_package() {
    # Create package directory
    mkdir -p deployment_package/{bin,lib,share}
    
    # Copy compiled executable
    cp build/bin/julia_agent deployment_package/bin/
    
    # Copy libraries
    cp build/lib/* deployment_package/lib/
    
    # Create deployment script
    cat > deployment_package/deploy.sh << 'EOF'
#!/bin/bash
# Self-contained Julia agent deployment
export LD_LIBRARY_PATH="$(dirname $0)/lib:$LD_LIBRARY_PATH"
exec "$(dirname $0)/bin/julia_agent" "$@"
EOF
    
    chmod +x deployment_package/deploy.sh
    
    # Create compressed distribution
    tar -czf julia-agent-v1.0.tar.gz deployment_package/
}

# IDE plugin for AOT compilation
struct IDEPlugin
    compiler::JuliaCCompiler
    llm::LocalLLMInterface
    
    function IDEPlugin()
        new(JuliaCCompiler(), LocalLLMInterface())
    end
    
    function compile_current_file(plugin, file_path)
        # Read Julia source file
        source_code = read(file_path)
        
        # Generate compilation commands
        compile_cmd = plugin.compiler.generate_command(source_code)
        
        # Execute compilation
        result = run(compile_cmd)
        
        # Return compiled binary
        return result.binary_path
    end
    
    function suggest_code_improvements(plugin, code)
        # Use local LLM to suggest improvements
        suggestions = plugin.llm.analyze_code(code)
        return suggestions
    end
end

# GitHub Actions workflow for AOT compilation
name: Julia AOT Compilation

on: [push, pull_request]

jobs:
  compile:
    runs-on: ubuntu-latest
    
    steps:
    - uses: actions/checkout@v3
    
    - name: Setup Julia
      uses: julia-actions/setup-julia@v1
      with:
        julia-version: '1.12'
        
    - name: Install Dependencies
      run: |
        julia -e 'using Pkg; Pkg.add(["JuliaC", "Reactant"])'
        
    - name: AOT Compilation
      run: |
        juliac --output-exe app --bundle build --trim=safe --experimental .
        
    - name: Test Compiled Binary
      run: |
        ./build/bin/app
        
    - name: Upload Artifacts
      uses: actions/upload-artifact@v3
      with:
        name: compiled-binary
        path: build/bin/

# Plugin system for extending functionality
abstract type CompilerPlugin end

struct GPUOptimizationPlugin <: CompilerPlugin
    optimization_level::Int
    
    function GPUOptimizationPlugin(level=3)
        new(level)
    end
end

struct MemoryManagementPlugin <: CompilerPlugin
    max_memory::UInt64
    
    function MemoryManagementPlugin(max_mem=64*GiB)
        new(max_mem)
    end
end

# Plugin registry
const PLUGIN_REGISTRY = Dict{String, CompilerPlugin}()

function register_plugin(name::String, plugin::CompilerPlugin)
    PLUGIN_REGISTRY[name] = plugin
end

function apply_plugin_compilation(source_code::String, plugin_name::String)
    plugin = PLUGIN_REGISTRY[plugin_name]
    return plugin.apply_optimization(source_code)
end

# Performance monitoring for compilation
struct CompilationMetrics
    start_time::DateTime
    end_time::DateTime
    memory_usage::UInt64
    cpu_usage::Float64
    gpu_usage::Float64
    binary_size::UInt64
    compilation_flags::String
    
    function CompilationMetrics(flags::String)
        new(
            now(),
            now(),
            get_memory_usage(),
            get_cpu_usage(),
            get_gpu_usage(),
            0,  # Will be set after compilation
            flags
        )
    end
    
    function record_metrics(self)
        self.end_time = now()
        self.memory_usage = get_memory_usage()
        self.cpu_usage = get_cpu_usage()
        self.gpu_usage = get_gpu_usage()
    end
    
    function get_compilation_time(self)
        return Millisecond(self.end_time - self.start_time)
    end
end

# Runtime performance monitoring
struct RuntimeProfiler
    execution_times::Dict{String, Float64}
    memory_allocations::Dict{String, UInt64}
    gpu_utilization::Dict{String, Float64}
    
    function RuntimeProfiler()
        new(Dict{String, Float64}(), 
            Dict{String, UInt64}(), 
            Dict{String, Float64}())
    end
    
    function profile_execution(self, func::Function, name::String)
        start_time = time_ns()
        
        # Execute function
        result = func()
        
        end_time = time_ns()
        
        # Record metrics
        self.execution_times[name] = (end_time - start_time) / 1e9
        self.memory_allocations[name] = get_current_memory_usage()
        self.gpu_utilization[name] = get_gpu_utilization()
        
        return result
    end
end

# Security analysis of compiled binaries
function analyze_binary_security(binary_path::String)
    security_report = SecurityReport()
    
    # Check for vulnerabilities
    vulnerabilities = scan_for_vulnerabilities(binary_path)
    
    # Analyze memory safety
    memory_issues = analyze_memory_safety(binary_path)
    
    # Check dependency security
    dependency_issues = check_dependency_security(binary_path)
    
    return security_report
end

# Sandbox execution environment
function create_sandbox_execution()
    sandbox = SandboxEnvironment()
    
    # Restrict file system access
    sandbox.restrict_filesystem("/tmp/julia_agent")
    
    # Limit memory usage
    sandbox.set_memory_limit(4*GiB)
    
    # Set timeout limits
    sandbox.set_timeout_limit(300)  # 5 minutes
    
    return sandbox
end

# Fault-tolerant compilation system
struct FaultTolerantCompiler
    max_retries::Int
    fallback_compiler::Compiler
    health_checker::HealthChecker
    
    function FaultTolerantCompiler(max_retries=3)
        new(max_retries, FallbackCompiler(), HealthChecker())
    end
    
    function compile_with_fallback(self, source_code::String)
        for attempt in 1:self.max_retries
            if self.health_checker.system_healthy()
                try
                    result = self.compile(source_code)
                    return result
                catch e
                    @warn "Compilation attempt $attempt failed: $e"
                    continue
                end
            else
                @warn "System not healthy, using fallback compiler"
                return self.fallback_compiler.compile(source_code)
            end
        end
        
        error("All compilation attempts failed")
    end
end