DeepSeek-V3/experimental

DeepZig V3 Implementation 🚀

A high-performance implementation of DeepSeek V3 in Zig for blazingly fast inference.

✅ Status: MLA Attention Architecture Implemented

This project provides a theoretical foundation of DeepZig V3 with significant architectural progress:

• ✅ Multi-Head Latent Attention (MLA) - Core DeepSeek V3 innovation architecturally implemented
• ✅ Complete Transformer Architecture with layer normalization, SwiGLU, and MoE integration
• ✅ HTTP server with OpenAI-compatible API
• ✅ BLAS-accelerated tensor operations (Apple Accelerate working)
• ✅ Cross-platform build system (Zig 0.15.0-dev)
• ✅ Memory management and backend architecture
• ✅ Apple Silicon detection and optimization
• ✅ Functional matrix operations (significant performance improvement)
• ✅ RoPE (Rotary Position Encoding) for position-aware attention
• ✅ KV Cache for efficient inference
• ✅ RMS Layer Normalization following DeepSeek V3 specifications

Latest Achievement: Multi-Head Latent Attention mechanism architecturally complete with RoPE, KV caching, and BLAS acceleration
Performance Status: 1160+ GFLOPS with Apple Accelerate backend working (measured on Apple M1 Macbook)
Validation Status: ⚠️ Theoretical implementation - requires testing with real model weights and output validation

See Performance Results for detailed benchmarks.

Overview

This experimental implementation aims to leverage Zig's unique advantages for systems programming to create a high-performance LLM inference engine:

• Zero-cost abstractions with compile-time optimization
• Direct hardware access for SIMD and platform-specific optimizations
• Manual memory management without garbage collection pauses
• Single binary deployment with no runtime dependencies
• Cross-platform compilation for multiple architectures

🚀 BLAS Acceleration Achieved! We've successfully integrated Apple Accelerate backend delivering 1000+ GFLOPS performance - a 3000x speedup over the initial naive implementation. Measured on an M1 Macbook.

🧠 MLA Attention Architecturally Complete! The core innovation of DeepSeek V3 - Multi-Head Latent Attention - is now architecturally implemented with:

• Latent space projections for efficient key-value computation
• RoPE integration for positional encoding
• KV caching for fast inference
• BLAS-accelerated scaled dot-product attention

⚠️ Important: This is a theoretical implementation following the DeepSeek V3 paper specifications. It compiles, runs, and passes basic tests, but requires validation with real model weights and output verification against reference implementations.

🔗 Related: See the main project README for architecture overview and vision.

Key Technical Achievements

✅ Multi-Head Latent Attention (MLA) - Architecture Implemented

The cornerstone innovation of DeepSeek V3, now architecturally complete following paper specifications:

/// Multi-Head Latent Attention Configuration
pub const MLAConfig = struct {
    hidden_size: u32,
    num_attention_heads: u32,
    num_key_value_heads: u32,
    qk_nope_head_dim: u32,    // Non-positional encoding dimension
    qk_rope_head_dim: u32,    // RoPE dimension
    v_head_dim: u32,          // Value head dimension
    rope_base: f32,           // RoPE base frequency
    max_position_embeddings: u32,
    attention_dropout: f32,
    use_flash_attention: bool,
};

Architectural Features:

• Latent projections: kv_a_proj_with_mqa and kv_b_proj for efficient KV computation
• Separate nope/rope dimensions: Optimized handling of positional vs non-positional components
• LayerNorm in latent space: Stable training and inference
• BLAS acceleration: All matrix operations use optimized BLAS calls

⚠️ Validation Needed: While theoretically sound, requires testing with real DeepSeek V3 weights and output validation.

✅ Complete Transformer Architecture - Draft Implementation

pub const TransformerLayer = struct {
    // Attention components
    attention: attention.MultiHeadLatentAttention,
    attention_norm: RMSNorm,
    
    // Feed-forward components (MoE or dense)
    mlp: ?SwiGLU,           // Dense FFN for non-MoE layers
    moe_layer: ?moe.MoE,    // MoE layer (for MoE layers)
    mlp_norm: RMSNorm,
};

Architecture Components:

• RMS Layer Normalization: Following DeepSeek V3 specifications
• SwiGLU Activation: Gate/Up/Down projections with SiLU activation
• MoE Integration: Automatic layer-wise expert routing (stub implementation)
• Residual Connections: Proper transformer residual flow

✅ Supporting Components

RoPE (Rotary Position Encoding) - Efficient implementation:

const RoPE = struct {
    cos_cache: FloatTensor,
    sin_cache: FloatTensor,
    
    pub fn apply(self: *const Self, tensor_data: *FloatTensor, seq_len: u32, start_pos: u32) !void

KV Cache - Optimized for autoregressive generation:

const KVCache = struct {
    k_cache: FloatTensor,
    v_cache: FloatTensor,
    
    pub fn update(self: *Self, new_k: *const FloatTensor, new_v: *const FloatTensor, start_pos: u32) !void

Development Status

✅ Architecturally Complete

• Multi-Head Latent Attention (MLA) - Core DeepSeek V3 innovation (theoretical implementation)
• Complete Transformer Layers with RMS norm, SwiGLU, residual connections
• RoPE (Rotary Position Encoding) with pre-computed embeddings
• KV Cache for efficient autoregressive inference
• BLAS Integration for all matrix operations
• Project structure and build system
• Core tensor operations with SIMD
• HTTP server with OpenAI API compatibility
• CPU backend with optimizations
• Memory management utilities
• Benchmark suite
• Comprehensive test coverage for attention and transformer components

🧪 Validation & Testing Required

• Real model weight loading (safetensors/HuggingFace format)
• Output validation against reference PyTorch implementation
• Numerical accuracy testing with known inputs/outputs
• End-to-end inference verification
• Performance comparison with other inference engines

🚧 Implementation Completion Needed

• Complete MoE implementation (routing, expert selection, load balancing)
• BPE Tokenizer implementation
• Generation loop (sampling strategies, beam search)
• Model configuration loading from HuggingFace config.json

📋 Platform & Optimization

• Metal backend for Apple Silicon
• CUDA backend for NVIDIA GPUs
• WebSocket streaming
• Model quantization (INT8, FP16)
• Flash Attention optimization
• Distributed inference

Validation Roadmap

Phase 1: Core Validation 🎯 NEXT PRIORITY

1. Load Real Weights: Implement safetensors loading for actual DeepSeek V3 model
2. Reference Testing: Compare outputs with HuggingFace transformers implementation
3. Numerical Verification: Test attention patterns and layer outputs
4. Simple Generation: Implement basic greedy decoding

Phase 2: Feature Completion

1. Complete MoE: Implement expert routing and load balancing
2. Full Tokenization: Add proper BPE tokenizer
3. Advanced Sampling: Implement temperature, top-k, top-p sampling
4. Performance Optimization: Profile and optimize bottlenecks

Phase 3: Production Readiness

1. Comprehensive Testing: Unit tests, integration tests, benchmarks
2. Cross-platform Support: Validate on different architectures
3. GPU Acceleration: Complete Metal/CUDA backends
4. Documentation: API docs, deployment guides

Architecture Decisions

Why MLA (Multi-Head Latent Attention)?

MLA is the key innovation that makes DeepSeek V3 more efficient than standard multi-head attention:

1. Latent space compression: Projects KV to lower-dimensional latent space
2. Shared computations: Reduces redundant key-value calculations
3. Memory efficiency: Significantly lower memory footprint
4. Maintained performance: No loss in model quality

Implementation Approach

Faithful to Paper: Our implementation closely follows the DeepSeek V3 paper architecture BLAS-Optimized: All linear operations use hardware-accelerated BLAS Memory Efficient: Proper tensor memory management and reuse Extensible: Clean interfaces for adding backends and optimizations

Contributing

This implementation provides a solid theoretical foundation for DeepSeek V3:

1. Core Architecture: MLA attention and transformer layers architecturally complete
2. Performance: BLAS acceleration working across operations
3. Testing: Comprehensive test coverage for critical components
4. Documentation: Well-documented APIs and architecture decisions

Critical Next Steps for Contributors:

1. 🧪 Validation Testing: Load real weights and validate outputs
2. 🔗 Model Loading: Complete safetensors/HuggingFace integration
3. 📝 Tokenization: Implement proper BPE tokenizer
4. 🎯 Generation: Add sampling strategies and inference pipeline
5. 🧮 MoE Completion: Finish expert routing implementation

Development Setup

# Install Zig 0.15.0-dev
# https://ziglang.org/download/

# Clone repository
git clone [repository-url]
cd experimental/

# Run tests during development
/Users/triex/.local/share/zigup/0.15.0-dev.703+597dd328e/files/zig build test --watch

# Format code
/Users/triex/.local/share/zigup/0.15.0-dev.703+597dd328e/files/zig fmt src/

Performance Notes

Current Status: ✅ MLA attention architecturally implemented with BLAS acceleration - theoretical implementation functional.

Performance Results (Apple M1 MacBook Pro under heavy load):

• Matrix 256×256: 0.0ms/iter, 937 GFLOPS
• Matrix 512×512: 0.2ms/iter, 1143 GFLOPS
• Matrix 1024×1024: 2.2ms/iter, 977 GFLOPS
• Matrix 2048×2048: 20.9ms/iter, 823 GFLOPS

Performance Achievement: From 6418ms naive → 2.1ms BLAS = ~3000x speedup on matrix operations.

System Status:

• ✅ MLA Architecture: Complete theoretical implementation with latent projections, RoPE, and KV caching
• ✅ BLAS Backend: Apple Accelerate integration working optimally
• ✅ Peak Performance: 1143 GFLOPS measured (44% of theoretical maximum)
• ✅ Memory Bandwidth: 20.9 GB/s copying, well-optimized operations
• ✅ Hardware Detection: M-series Apple Silicon detection functional

⚠️ Performance Caveat: These are synthetic benchmarks. Real inference performance requires validation with actual model weights and end-to-end testing.

Known Limitations

• ⚠️ Theoretical Implementation: Architecture complete but unvalidated with real data
• Model Loading: Currently creates dummy models - real weight loading not implemented
• Tokenizer: Placeholder implementation - needs proper BPE tokenizer
• MoE Routing: Basic structure only - expert selection not implemented
• Output Validation: No comparison with reference implementations yet
• WebSocket: Basic structure only - streaming not implemented
• Metal/CUDA: Backend stubs only - GPU kernels not implemented

Is This Ready for Use?

No - this is a theoretical implementation that requires validation:

• What works now: ✅ Architecturally complete, compiles, runs, passes basic tests, excellent BLAS performance
• What's missing: Real weight loading, output validation, tokenization, generation pipeline
• Timeline: Architecture is theoretically complete, validation and testing is the next major milestone

Status: This provides a solid foundation for DeepSeek V3 implementation, but requires real-world validation before production use.

Comparison to Other Projects

Project	Language	Status	Focus	MLA Support
This	Zig	Architecture Complete (Theoretical)	Web-first inference	✅ Architecturally Implemented
llama.cpp	C++	Production	CLI/library	❌ No
Candle	Rust	Production	ML framework	❌ No
ZML	Zig	Research	Low-level ML ops	❌ No

Unique advantages: First architectural implementation of MLA attention, built-in web server, Zig's zero-cost abstractions, single binary deployment.

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⚡ Built with Zig for blazing fast DeepSeek V3 inference featuring Multi-Head Latent Attention!

Architecturally complete implementation of DeepSeek V3's core innovation - Multi-Head Latent Attention - ready for validation and testing.

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📜 License

This implementation is dual-licensed:

• GPL-3.0: Free for open source projects
• Commercial: Contact Triex for proprietary use

See LICENSE-CODE and LICENSE-COMMERCIAL for details.