# DeepZig V3 Implementation 🚀

A high-performance implementation of DeepSeek V3 in [Zig](https://ziglang.org/) 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<br/>
> **Performance Status**: 1160+ GFLOPS with Apple Accelerate backend working (measured on Apple M1 Macbook)<br/>
> **Validation Status**: ⚠️ **Theoretical implementation - requires testing with real model weights and output validation**<br/>
> 
> See [Performance Results](#performance-notes) 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](../README.md) 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:

```zig
/// 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

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

```bash
# 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.

---

**⚡ 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.* 

---

## 📜 License

This implementation is dual-licensed:
- **GPL-3.0**: Free for open source projects
- **Commercial**: Contact Triex for proprietary use

See [LICENSE-CODE](../LICENSE-CODE) and [LICENSE-COMMERCIAL](../LICENSE-COMMERCIAL) for details.