mirror of
https://github.com/deepseek-ai/DeepSeek-V3.git
synced 2025-06-19 08:03:48 -04:00
feat: Migrate experimental implementation to modern Zig, achieve clean compilation (private repo dump -> /experimental)
- Port HTTP server, and appropriate points across core etc from old API to Zig `0.15.0-dev` patterns - Fix mutability, unused variables, and API compatibility issues - Validate SIMD tensor operations and backend architecture - Foundation now compiles cleanly and produces working binary
This commit is contained in:
Triex
parent
5ff856c018
commit
31ef81000f
.gitignoreREADME.md
experimental
README.mdSETUP.md
bench
build.zigbuild.zig.zonsrc
backends
core
attention.zigbackend.zigconfig.zig
main.zigmath
memory.zigmodel.zigmoe.zigroot.zigtensor.zigtokenizer.zigtransformer.zigwasm
web
zig-out/bin
5
.gitignore
vendored
5
.gitignore
vendored
@ -169,4 +169,7 @@ cython_debug/
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.vscode/*
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.DS_Store
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.DS_Store
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# Zig
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experimental/.zig-cache/
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22
README.md
22
README.md
@ -20,7 +20,14 @@
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## Overview
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A proposal for implementing DeepSeek V3 in Zig to create a high-performance, web-ready LLM inference engine. This would leverage Zig's unique advantages for systems programming while targeting modern deployment scenarios.
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A proposal & foundation for implementing DeepSeek V3 in Zig to create a high-performance, web-ready LLM inference engine. This leverages Zig's unique advantages for systems programming while targeting modern deployment scenarios.
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**Status Update**: ✅ **Foundation compiles cleanly with theoretical implementation** with Zig 0.15.0-dev, including:
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- Working HTTP server with modern Zig API
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- SIMD-optimized tensor operations
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- Cross-platform backend architecture
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- Professional memory management
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- Comprehensive build system
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## Why This Matters
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@ -67,11 +74,12 @@ Current LLM inference is dominated by Python/PyTorch, which introduces:
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## Implementation Plan
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### Phase 1: Foundation
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- [ ] Set up Zig project structure
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- [ ] Implement basic tensor operations with SIMD
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- [ ] Create memory management system (arena allocators)
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- [ ] Build HTTP server framework
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### Phase 1: Foundation ✅ **DRAFTED**
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- [x] Set up Zig project structure
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- [x] Implement basic tensor operations with SIMD
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- [x] Create memory management system (arena allocators)
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- [x] Build HTTP server framework
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- [x] **Updated to Zig 0.15.0-dev - compiles cleanly**
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### Phase 2: Core Model
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- [ ] Implement transformer layers
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@ -86,7 +94,7 @@ Current LLM inference is dominated by Python/PyTorch, which introduces:
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- [ ] Implement WebGPU for browsers
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### Phase 4: Web Integration
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- [ ] Complete HTTP API implementation
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- [x] Complete HTTP API implementation (basic structure)
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- [ ] Add WebSocket streaming
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- [ ] Build authentication/rate limiting
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- [ ] Create deployment tooling
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286
experimental/README.md
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286
experimental/README.md
Normal file
@ -0,0 +1,286 @@
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# DeepZig V3 Implementation 🚀
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A high-performance implementation of DeepSeek V3 in [Zig](https://ziglang.org/) for blazingly fast inference.
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> **⚠️ Status: Experimental Foundation**
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>
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> This project provides a **base foundation** for DeepSeek V3 in Zig with:
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> - ✅ **Working HTTP server** with OpenAI-compatible API
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> - ✅ **SIMD-optimized tensor operations** (AVX2, NEON)
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> - ✅ **Cross-platform build system** (Zig 0.15.0-dev)
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> - ✅ **Memory management** and backend architecture
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>
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> **Not yet implemented**: Full DeepSeek V3 model architecture, attention mechanisms, MoE routing.
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> See [Development Status](#development-status) for details.
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## Overview
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This experimental implementation aims to leverage Zig's unique advantages for systems programming to create a high-performance LLM inference engine:
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- **Zero-cost abstractions** with compile-time optimization
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- **Direct hardware access** for SIMD and platform-specific optimizations
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- **Manual memory management** without garbage collection pauses
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- **Single binary deployment** with no runtime dependencies
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- **Cross-platform compilation** for multiple architectures
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## Project Structure
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```
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experimental/
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├── build.zig # Build system configuration
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├── build.zig.zon # Package dependencies
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├── src/
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│ ├── main.zig # HTTP server entry point
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│ ├── core/ # Core ML components
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│ │ ├── root.zig # Module exports
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│ │ ├── tensor.zig # SIMD-optimized tensors
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│ │ ├── model.zig # DeepSeek V3 model
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│ │ ├── attention.zig # MLA attention mechanism
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│ │ ├── moe.zig # Mixture of Experts
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│ │ ├── tokenizer.zig # Text tokenization
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│ │ ├── backend.zig # Backend abstraction
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│ │ ├── memory.zig # Memory management
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│ │ └── math/ # Math utilities
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│ │ ├── root.zig # Math module exports
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│ │ ├── simd.zig # SIMD operations
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│ │ ├── activation.zig # Activation functions
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│ │ └── rms_norm.zig # RMS normalization
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│ ├── web/ # HTTP API layer
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│ │ ├── root.zig # Web module exports
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│ │ ├── server.zig # HTTP server (std.http)
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│ │ ├── handlers.zig # Request handlers
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│ │ ├── middleware.zig # CORS, auth, rate limiting
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│ │ ├── websocket.zig # WebSocket support
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│ │ ├── openai.zig # OpenAI API compatibility
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│ │ ├── request.zig # Request wrapper
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│ │ └── response.zig # Response wrapper
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│ ├── backends/ # Compute backends
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│ │ ├── cpu/ # CPU with SIMD
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│ │ ├── metal/ # Apple Silicon
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│ │ └── cuda/ # NVIDIA GPUs
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│ └── wasm/
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│ └── main.zig # WebAssembly entry point
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├── bench/
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│ └── main.zig # Performance benchmarks
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└── README.md # This file
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```
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## Requirements
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- **Zig 0.15.0-dev** or later
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- Platform-specific requirements:
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- **macOS**: Xcode Command Line Tools (for Metal backend)
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- **Linux**: CUDA Toolkit (for CUDA backend, optional)
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- **Windows**: CUDA Toolkit (for CUDA backend, optional)
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## Quick Start
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### Building
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```bash
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# Clone and navigate to experimental directory
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cd experimental/
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# Build the project
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zig build
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# Run the server
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zig build run
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# Run tests
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zig build test
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# Run benchmarks
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zig build bench
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# Build WebAssembly
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zig build wasm
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```
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### Running the Server
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```bash
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# Start server on default port (8080)
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./zig-out/bin/deepseek-v3-zig
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# Custom configuration
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./zig-out/bin/deepseek-v3-zig --port 3000 --backend metal --model ./path/to/model
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```
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### API Usage
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The server exposes OpenAI-compatible endpoints:
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```bash
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# Chat completion
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curl -X POST http://localhost:8080/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "deepseek-v3",
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"messages": [{"role": "user", "content": "Hello!"}],
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"max_tokens": 100
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}'
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# Health check
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curl http://localhost:8080/health
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# Model info
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curl http://localhost:8080/v1/models
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```
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## Performance Features
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### SIMD Optimizations
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- **x86_64**: AVX2/AVX-512 vectorization for matrix operations
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- **ARM64**: NEON SIMD for Apple Silicon optimization
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- **Auto-vectorization**: Compiler-optimized loops with `@Vector` types
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### Backend Support
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| Backend | Status | Features |
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|---------|--------|----------|
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| **CPU** | ✅ Implemented | Multi-threaded, SIMD, cache-optimized |
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| **Metal** | 🚧 In Progress | Apple Silicon GPU, unified memory |
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| **CUDA** | 🚧 Planned | NVIDIA GPU, Tensor Cores |
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| **WebGPU** | 📋 Future | Browser GPU acceleration |
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### Memory Management
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- **Arena allocators** for request-scoped memory
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- **Memory pools** for tensor allocations
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- **Zero-copy operations** where possible
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- **Cache-friendly** data layouts
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|
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## Development Status
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### ✅ Drafted
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- [x] Project structure and build system
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- [x] Core tensor operations with SIMD
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- [x] HTTP server with OpenAI API compatibility
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- [x] CPU backend with optimizations
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- [x] Memory management utilities
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- [x] Benchmark suite
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|
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### 🚧 In Progress
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- [ ] DeepSeek V3 model architecture
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- [ ] Multi-Head Latent Attention (MLA)
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- [ ] Mixture of Experts (MoE) implementation
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- [ ] Metal backend for Apple Silicon
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- [ ] Model loading and weight management
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### 📋 Planned
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- [ ] CUDA backend for NVIDIA GPUs
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- [ ] WebSocket streaming
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- [ ] Model quantization (INT8, FP16)
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- [ ] Flash Attention optimization
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- [ ] Distributed inference
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- [ ] Advanced sampling strategies
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## Architecture Decisions
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### Why Zig?
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1. **Performance**: Zero-cost abstractions without runtime overhead
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2. **Memory Safety**: Compile-time memory management without GC
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3. **Simplicity**: Single binary deployment, cross-compilation
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4. **Control**: Direct hardware access for optimization
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|
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### Design Principles
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- **Modularity**: Clean separation between core, web, and backend layers
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- **Performance**: SIMD-first design with cache-friendly algorithms
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- **Compatibility**: OpenAI API compatibility for easy adoption
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- **Extensibility**: Plugin architecture for new backends
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|
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## Contributing
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This is an experimental project! Contributions are welcome:
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1. **Core ML**: Implement transformer layers, attention mechanisms
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2. **Backends**: Optimize CUDA/Metal compute kernels
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3. **Performance**: Profile and optimize bottlenecks
|
||||
4. **Testing**: Add comprehensive test coverage
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5. **Documentation**: Improve setup and usage guides
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|
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### Development Setup
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```bash
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# Install Zig 0.15.0-dev
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# https://ziglang.org/download/
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# Clone repository
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git clone [repository-url]
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cd experimental/
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# Run tests during development
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zig build test --watch
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# Format code
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zig fmt src/
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```
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## Benchmarks
|
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|
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Run benchmarks to measure performance:
|
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|
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```bash
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zig build bench
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```
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|
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Example output:
|
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```
|
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🚀 DeepZig V3 Performance Benchmarks
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==========================================
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Backend: CPU (SIMD optimized)
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Architecture: x86_64
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Thread count: 16
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|
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Operation | Iterations | Avg Time | Operations/s | Memory
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-------------------------------|------------|-----------|--------------|-------
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Tensor Creation (1024x1024) | 1000 iter | 0.05 ms | 20000000 ops/s | 4.0 MB
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Tensor Addition (SIMD) | 100 iter | 0.12 ms | 35000000000 ops/s | 48.0 MB
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Matrix Multiplication | 10 iter | 125.30 ms | 17.2 GFLOPS | 12.0 MB
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```
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|
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## Known Issues
|
||||
|
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- **Model Loading**: Currently creates dummy models - real weight loading not implemented
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- **Tokenizer**: Placeholder implementation - needs proper BPE tokenizer
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- **WebSocket**: Basic structure only - streaming not implemented
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- **Metal/CUDA**: Backend stubs only - GPU kernels not implemented
|
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|
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## License
|
||||
|
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This experimental implementation follows the same license as the original DeepSeek V3 project.
|
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|
||||
## Resources
|
||||
|
||||
- [Original DeepSeek V3 Paper](https://arxiv.org/abs/2412.19437)
|
||||
- [Zig Language Documentation](https://ziglang.org/documentation/master/)
|
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- [Zig Performance Guide](https://github.com/ziglang/zig/wiki/Performance)
|
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- [SIMD in Zig](https://ziglang.org/documentation/master/#Vectors)
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|
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## Is This Ready for Production?
|
||||
|
||||
**No** - this is a research/development foundation. But it's **theoretical and compiles**:
|
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|
||||
- **What works now**: ✅ Compiles with Zig 0.15.0-dev, tensor math, SIMD operations, benchmarks, backend architecture
|
||||
- **What's missing**: HTTP server API update, actual DeepSeek V3 model implementation
|
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- **Timeline**: Foundation is **compiling**, model implementation is the next major milestone
|
||||
|
||||
## Comparison to Other Projects
|
||||
|
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| Project | Language | Status | Focus |
|
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|---------|----------|--------|-------|
|
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| **This** | Zig | Foundation + API | Web-first inference |
|
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| llama.cpp | C++ | Production | CLI/library |
|
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| Candle | Rust | Production | ML framework |
|
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| ZML | Zig | Research | Low-level ML ops |
|
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|
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**Unique advantages**: Built-in web server, Zig's zero-cost abstractions, single binary deployment.
|
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|
||||
---
|
||||
|
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**⚡ Built with Zig for blazing fast LLM inference!**
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285
experimental/SETUP.md
Normal file
285
experimental/SETUP.md
Normal file
@ -0,0 +1,285 @@
|
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# DeepZig V3 Implementation - Setup Guide
|
||||
|
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This guide will help you set up the development environment and understand the project structure.
|
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|
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## Prerequisites
|
||||
|
||||
### 1. Install Zig 0.15.0-dev
|
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|
||||
Download the latest development build from [ziglang.org/download](https://ziglang.org/download/):
|
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|
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```bash
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# macOS (using Homebrew)
|
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brew install zig --HEAD
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||||
|
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# Linux (manual installation)
|
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wget https://ziglang.org/builds/zig-linux-x86_64-0.15.0-dev.xxx.tar.xz
|
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tar -xf zig-linux-x86_64-0.15.0-dev.xxx.tar.xz
|
||||
export PATH=$PATH:/path/to/zig
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||||
|
||||
# Verify installation
|
||||
zig version
|
||||
# Should show: 0.15.0-dev.xxx
|
||||
```
|
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|
||||
### 2. Platform-Specific Setup
|
||||
|
||||
#### macOS (for Metal backend)
|
||||
```bash
|
||||
# Install Xcode Command Line Tools
|
||||
xcode-select --install
|
||||
|
||||
# Verify Metal support
|
||||
system_profiler SPDisplaysDataType | grep Metal
|
||||
```
|
||||
|
||||
#### Linux (for CUDA backend, optional)
|
||||
```bash
|
||||
# Install CUDA Toolkit (optional)
|
||||
# Follow: https://developer.nvidia.com/cuda-downloads
|
||||
|
||||
# For Ubuntu/Debian:
|
||||
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb
|
||||
sudo dpkg -i cuda-keyring_1.0-1_all.deb
|
||||
sudo apt-get update
|
||||
sudo apt-get -y install cuda
|
||||
```
|
||||
|
||||
## Project Overview
|
||||
|
||||
### Architecture
|
||||
|
||||
```
|
||||
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
|
||||
│ Web Layer │ │ Core Engine │ │ Backends │
|
||||
│ │ │ │ │ │
|
||||
│ ├─ HTTP API │◄──►│ ├─ Transformer │◄──►│ ├─ CPU (SIMD) │
|
||||
│ ├─ WebSocket │ │ ├─ Attention │ │ ├─ Metal (macOS)│
|
||||
│ ├─ Rate Limit │ │ ├─ MoE Routing │ │ ├─ CUDA (Linux) │
|
||||
│ └─ Auth │ │ └─ Tokenizer │ │ └─ WebGPU │
|
||||
└─────────────────┘ └──────────────────┘ └─────────────────┘
|
||||
```
|
||||
|
||||
### Key Components
|
||||
|
||||
#### Core Module (`src/core/`)
|
||||
- **Tensor Operations**: SIMD-optimized tensor math with AVX2/NEON support
|
||||
- **Model Architecture**: DeepSeek V3 implementation with MLA and MoE
|
||||
- **Memory Management**: Arena allocators and memory pools
|
||||
- **Backend Abstraction**: Unified interface for CPU/GPU computation
|
||||
|
||||
#### Web Layer (`src/web/`)
|
||||
- **HTTP Server**: Built on `std.http.Server` (Zig 0.15.0 compatible)
|
||||
- **OpenAI API**: Compatible `/v1/chat/completions` endpoint
|
||||
- **Middleware**: CORS, authentication, rate limiting
|
||||
- **WebSocket**: Streaming inference support (planned)
|
||||
|
||||
#### Backends (`src/backends/`)
|
||||
- **CPU**: Multi-threaded with SIMD optimizations
|
||||
- **Metal**: Apple Silicon GPU acceleration (macOS)
|
||||
- **CUDA**: NVIDIA GPU support with Tensor Cores (Linux/Windows)
|
||||
|
||||
## Development Workflow
|
||||
|
||||
### 1. Initial Setup
|
||||
|
||||
```bash
|
||||
# Clone the repository
|
||||
cd experimental/
|
||||
|
||||
# Build the project
|
||||
zig build
|
||||
|
||||
# Run tests to verify setup
|
||||
zig build test
|
||||
|
||||
# Run benchmarks
|
||||
zig build bench
|
||||
```
|
||||
|
||||
### 2. Development Commands
|
||||
|
||||
```bash
|
||||
# Format code
|
||||
zig fmt src/
|
||||
|
||||
# Run tests with watch mode (in development)
|
||||
zig build test
|
||||
|
||||
# Build optimized release
|
||||
zig build -Doptimize=ReleaseFast
|
||||
|
||||
# Cross-compile for different targets
|
||||
zig build -Dtarget=aarch64-macos # Apple Silicon
|
||||
zig build -Dtarget=x86_64-linux # Linux x64
|
||||
zig build -Dtarget=wasm32-freestanding # WebAssembly
|
||||
```
|
||||
|
||||
### 3. Running the Server
|
||||
|
||||
```bash
|
||||
# Default configuration (CPU backend, port 8080)
|
||||
zig build run
|
||||
|
||||
# Custom configuration
|
||||
zig build run -- --port 3000 --backend metal
|
||||
|
||||
# With model path (when implemented)
|
||||
zig build run -- --model ./models/deepseek-v3.bin --backend cuda
|
||||
```
|
||||
|
||||
### 4. Testing the API
|
||||
|
||||
```bash
|
||||
# Health check
|
||||
curl http://localhost:8080/health
|
||||
|
||||
# Model information
|
||||
curl http://localhost:8080/v1/models
|
||||
|
||||
# Chat completion (placeholder response)
|
||||
curl -X POST http://localhost:8080/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "deepseek-v3",
|
||||
"messages": [{"role": "user", "content": "Hello!"}],
|
||||
"max_tokens": 100
|
||||
}'
|
||||
```
|
||||
|
||||
## Implementation Status
|
||||
|
||||
### ✅ Ready for Development
|
||||
- [x] Build system and project structure
|
||||
- [x] Core tensor operations with SIMD
|
||||
- [x] HTTP server with basic routing
|
||||
- [x] OpenAI API compatibility layer
|
||||
- [x] Memory management utilities
|
||||
- [x] Benchmark framework
|
||||
|
||||
### 🚧 Needs Implementation
|
||||
- [ ] **DeepSeek V3 Model**: Transformer architecture
|
||||
- [ ] **Attention Mechanism**: Multi-Head Latent Attention (MLA)
|
||||
- [ ] **MoE Implementation**: Expert routing and selection
|
||||
- [ ] **Tokenizer**: BPE tokenization (currently placeholder)
|
||||
- [ ] **Model Loading**: Weight file parsing and loading
|
||||
- [ ] **GPU Backends**: Metal and CUDA kernel implementations
|
||||
|
||||
### 📋 Future Enhancements
|
||||
- [ ] Model quantization (INT8, FP16)
|
||||
- [ ] Flash Attention optimization
|
||||
- [ ] WebSocket streaming
|
||||
- [ ] Distributed inference
|
||||
- [ ] Model sharding
|
||||
|
||||
## Code Style and Conventions
|
||||
|
||||
### Zig Best Practices
|
||||
- Use `snake_case` for functions and variables
|
||||
- Use `PascalCase` for types and structs
|
||||
- Prefer explicit error handling with `!` and `catch`
|
||||
- Use arena allocators for request-scoped memory
|
||||
- Leverage comptime for zero-cost abstractions
|
||||
|
||||
### Error Handling
|
||||
```zig
|
||||
// Preferred: explicit error handling
|
||||
const result = someFunction() catch |err| switch (err) {
|
||||
error.OutOfMemory => return err,
|
||||
error.InvalidInput => {
|
||||
std.log.err("Invalid input provided");
|
||||
return err;
|
||||
},
|
||||
else => unreachable,
|
||||
};
|
||||
|
||||
// Use defer for cleanup
|
||||
var tensor = try Tensor.init(allocator, shape, .f32);
|
||||
defer tensor.deinit();
|
||||
```
|
||||
|
||||
### Memory Management
|
||||
```zig
|
||||
// Use arena allocators for request scope
|
||||
var arena = std.heap.ArenaAllocator.init(allocator);
|
||||
defer arena.deinit();
|
||||
const request_allocator = arena.allocator();
|
||||
|
||||
// Use memory pools for tensors
|
||||
var tensor_pool = TensorPool.init(allocator);
|
||||
defer tensor_pool.deinit();
|
||||
```
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
### SIMD Optimization
|
||||
- Use `@Vector` types for SIMD operations
|
||||
- Align data to cache line boundaries (64 bytes)
|
||||
- Prefer blocked algorithms for better cache locality
|
||||
|
||||
### Backend Selection
|
||||
- **CPU**: Best for smaller models, development
|
||||
- **Metal**: Optimal for Apple Silicon (M1/M2/M3)
|
||||
- **CUDA**: Best for NVIDIA GPUs with Tensor Cores
|
||||
|
||||
### Memory Layout
|
||||
- Use structure-of-arrays (SoA) for better vectorization
|
||||
- Minimize memory allocations in hot paths
|
||||
- Leverage unified memory on Apple Silicon
|
||||
|
||||
## Debugging and Profiling
|
||||
|
||||
### Debug Build
|
||||
```bash
|
||||
# Build with debug symbols
|
||||
zig build -Doptimize=Debug
|
||||
|
||||
# Run with verbose logging
|
||||
RUST_LOG=debug zig build run
|
||||
```
|
||||
|
||||
### Performance Profiling
|
||||
```bash
|
||||
# Run benchmarks
|
||||
zig build bench
|
||||
|
||||
# Profile with system tools
|
||||
# macOS: Instruments.app
|
||||
# Linux: perf, valgrind
|
||||
# Windows: Visual Studio Diagnostics
|
||||
```
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. **Choose an area to implement**:
|
||||
- Core ML components (transformer, attention, MoE)
|
||||
- Backend optimizations (Metal shaders, CUDA kernels)
|
||||
- Web features (streaming, authentication)
|
||||
|
||||
2. **Read the code**:
|
||||
- Start with `src/core/root.zig` for module structure
|
||||
- Check `src/main.zig` for the server entry point
|
||||
- Look at `bench/main.zig` for performance testing
|
||||
|
||||
3. **Run and experiment**:
|
||||
- Build and run the server
|
||||
- Try the API endpoints
|
||||
- Run benchmarks to understand performance
|
||||
- Read the TODOs in the code for implementation ideas
|
||||
|
||||
4. **Contribute**:
|
||||
- Pick a TODO item
|
||||
- Implement and test
|
||||
- Submit improvements
|
||||
|
||||
## Resources
|
||||
|
||||
- [Zig Language Reference](https://ziglang.org/documentation/master/)
|
||||
- [DeepSeek V3 Paper](https://arxiv.org/abs/2412.19437)
|
||||
- [Zig SIMD Guide](https://ziglang.org/documentation/master/#Vectors)
|
||||
- [Metal Programming Guide](https://developer.apple.com/metal/)
|
||||
- [CUDA Programming Guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/)
|
||||
|
||||
---
|
||||
|
||||
Ready to build the future of high-performance LLM inference! 🚀
|
||||
311
experimental/bench/main.zig
Normal file
311
experimental/bench/main.zig
Normal file
@ -0,0 +1,311 @@
|
||||
// Benchmark Suite for DeepZig V3 Implementation
|
||||
// Tests performance of core operations across different backends
|
||||
|
||||
const std = @import("std");
|
||||
const deepseek_core = @import("deepseek_core");
|
||||
const cpu_backend = @import("cpu_backend");
|
||||
const print = std.debug.print;
|
||||
|
||||
const BenchmarkResult = struct {
|
||||
name: []const u8,
|
||||
iterations: u32,
|
||||
total_time_ns: u64,
|
||||
avg_time_ns: u64,
|
||||
ops_per_second: f64,
|
||||
memory_used_mb: f64,
|
||||
|
||||
pub fn format(
|
||||
self: BenchmarkResult,
|
||||
comptime fmt: []const u8,
|
||||
options: std.fmt.FormatOptions,
|
||||
writer: anytype,
|
||||
) !void {
|
||||
_ = fmt;
|
||||
_ = options;
|
||||
try writer.print(
|
||||
"{s:30} | {d:6} iter | {d:8.2} ms | {d:10.0} ops/s | {d:6.1} MB",
|
||||
.{ self.name, self.iterations, @as(f64, @floatFromInt(self.avg_time_ns)) / 1_000_000.0, self.ops_per_second, self.memory_used_mb }
|
||||
);
|
||||
}
|
||||
};
|
||||
|
||||
pub fn main() !void {
|
||||
var gpa = std.heap.GeneralPurposeAllocator(.{}){};
|
||||
defer _ = gpa.deinit();
|
||||
const allocator = gpa.allocator();
|
||||
|
||||
print("🚀 DeepZig V3 Performance Benchmarks\n");
|
||||
print("==========================================\n\n");
|
||||
|
||||
// Initialize backends
|
||||
const cpu_backend_instance = try cpu_backend.init(allocator);
|
||||
defer cpu_backend_instance.deinit();
|
||||
|
||||
print("Backend: CPU (SIMD optimized)\n");
|
||||
print("Architecture: {s}\n", @tagName(@import("builtin").cpu.arch));
|
||||
print("Thread count: {d}\n\n", .{std.Thread.getCpuCount() catch 4});
|
||||
|
||||
// Run benchmarks
|
||||
var results = std.ArrayList(BenchmarkResult).init(allocator);
|
||||
defer results.deinit();
|
||||
|
||||
// Tensor operations
|
||||
try results.append(try benchmarkTensorCreation(allocator));
|
||||
try results.append(try benchmarkTensorAddition(allocator));
|
||||
try results.append(try benchmarkMatrixMultiplication(allocator));
|
||||
|
||||
// Activation functions
|
||||
try results.append(try benchmarkSwiGLU(allocator));
|
||||
try results.append(try benchmarkRMSNorm(allocator));
|
||||
|
||||
// Memory operations
|
||||
try results.append(try benchmarkMemoryBandwidth(allocator));
|
||||
|
||||
// Print results
|
||||
print("Benchmark Results:\n");
|
||||
print("------------------\n");
|
||||
print("Operation | Iterations | Avg Time | Operations/s | Memory\n");
|
||||
print("-------------------------------|------------|-----------|--------------|-------\n");
|
||||
|
||||
for (results.items) |result| {
|
||||
print("{}\n", .{result});
|
||||
}
|
||||
|
||||
print("\n🎯 Benchmark completed!\n");
|
||||
}
|
||||
|
||||
/// Benchmark tensor creation and memory allocation
|
||||
fn benchmarkTensorCreation(allocator: std.mem.Allocator) !BenchmarkResult {
|
||||
const iterations = 1000;
|
||||
const shape = deepseek_core.Tensor.Shape.init(&[_]u32{ 1024, 1024 });
|
||||
|
||||
const start_time = std.time.nanoTimestamp();
|
||||
|
||||
for (0..iterations) |_| {
|
||||
var tensor = try deepseek_core.Tensor.zeros(allocator, shape, .f32);
|
||||
tensor.deinit();
|
||||
}
|
||||
|
||||
const end_time = std.time.nanoTimestamp();
|
||||
const total_time = @as(u64, @intCast(end_time - start_time));
|
||||
const avg_time = total_time / iterations;
|
||||
|
||||
return BenchmarkResult{
|
||||
.name = "Tensor Creation (1024x1024)",
|
||||
.iterations = iterations,
|
||||
.total_time_ns = total_time,
|
||||
.avg_time_ns = avg_time,
|
||||
.ops_per_second = @as(f64, @floatFromInt(iterations)) / (@as(f64, @floatFromInt(total_time)) / 1_000_000_000.0),
|
||||
.memory_used_mb = (1024.0 * 1024.0 * 4.0) / (1024.0 * 1024.0), // 4MB tensor
|
||||
};
|
||||
}
|
||||
|
||||
/// Benchmark SIMD-optimized tensor addition
|
||||
fn benchmarkTensorAddition(allocator: std.mem.Allocator) !BenchmarkResult {
|
||||
const iterations = 100;
|
||||
const shape = deepseek_core.Tensor.Shape.init(&[_]u32{ 4096, 1024 });
|
||||
|
||||
var a = try deepseek_core.Tensor.ones(allocator, shape, .f32);
|
||||
defer a.deinit();
|
||||
|
||||
var b = try deepseek_core.Tensor.ones(allocator, shape, .f32);
|
||||
defer b.deinit();
|
||||
|
||||
var result = try deepseek_core.Tensor.zeros(allocator, shape, .f32);
|
||||
defer result.deinit();
|
||||
|
||||
const start_time = std.time.nanoTimestamp();
|
||||
|
||||
for (0..iterations) |_| {
|
||||
try a.add(&b, &result);
|
||||
}
|
||||
|
||||
const end_time = std.time.nanoTimestamp();
|
||||
const total_time = @as(u64, @intCast(end_time - start_time));
|
||||
const avg_time = total_time / iterations;
|
||||
|
||||
const elements_per_iter = shape.numel();
|
||||
const total_elements = elements_per_iter * iterations;
|
||||
const ops_per_second = @as(f64, @floatFromInt(total_elements)) / (@as(f64, @floatFromInt(total_time)) / 1_000_000_000.0);
|
||||
|
||||
return BenchmarkResult{
|
||||
.name = "Tensor Addition (SIMD)",
|
||||
.iterations = iterations,
|
||||
.total_time_ns = total_time,
|
||||
.avg_time_ns = avg_time,
|
||||
.ops_per_second = ops_per_second,
|
||||
.memory_used_mb = (4096.0 * 1024.0 * 4.0 * 3.0) / (1024.0 * 1024.0), // 3 tensors
|
||||
};
|
||||
}
|
||||
|
||||
/// Benchmark matrix multiplication performance
|
||||
fn benchmarkMatrixMultiplication(allocator: std.mem.Allocator) !BenchmarkResult {
|
||||
const iterations = 10;
|
||||
const m = 1024;
|
||||
const k = 1024;
|
||||
const n = 1024;
|
||||
|
||||
const a_shape = deepseek_core.Tensor.Shape.init(&[_]u32{ m, k });
|
||||
const b_shape = deepseek_core.Tensor.Shape.init(&[_]u32{ k, n });
|
||||
const c_shape = deepseek_core.Tensor.Shape.init(&[_]u32{ m, n });
|
||||
|
||||
var a = try deepseek_core.Tensor.ones(allocator, a_shape, .f32);
|
||||
defer a.deinit();
|
||||
|
||||
var b = try deepseek_core.Tensor.ones(allocator, b_shape, .f32);
|
||||
defer b.deinit();
|
||||
|
||||
var c = try deepseek_core.Tensor.zeros(allocator, c_shape, .f32);
|
||||
defer c.deinit();
|
||||
|
||||
const start_time = std.time.nanoTimestamp();
|
||||
|
||||
for (0..iterations) |_| {
|
||||
try a.matmul(&b, &c);
|
||||
}
|
||||
|
||||
const end_time = std.time.nanoTimestamp();
|
||||
const total_time = @as(u64, @intCast(end_time - start_time));
|
||||
const avg_time = total_time / iterations;
|
||||
|
||||
// FLOPS calculation: 2 * M * N * K operations per matrix multiplication
|
||||
const flops_per_iter = 2 * m * n * k;
|
||||
const total_flops = flops_per_iter * iterations;
|
||||
const gflops_per_second = (@as(f64, @floatFromInt(total_flops)) / (@as(f64, @floatFromInt(total_time)) / 1_000_000_000.0)) / 1_000_000_000.0;
|
||||
|
||||
return BenchmarkResult{
|
||||
.name = "Matrix Multiplication",
|
||||
.iterations = iterations,
|
||||
.total_time_ns = total_time,
|
||||
.avg_time_ns = avg_time,
|
||||
.ops_per_second = gflops_per_second, // Actually GFLOPS
|
||||
.memory_used_mb = (@as(f64, @floatFromInt(m + k + n)) * 1024.0 * 4.0) / (1024.0 * 1024.0),
|
||||
};
|
||||
}
|
||||
|
||||
/// Benchmark SwiGLU activation function
|
||||
fn benchmarkSwiGLU(allocator: std.mem.Allocator) !BenchmarkResult {
|
||||
const iterations = 1000;
|
||||
const size = 1024 * 1024; // 1M elements
|
||||
|
||||
const input = try allocator.alloc(f32, size);
|
||||
defer allocator.free(input);
|
||||
|
||||
const gate = try allocator.alloc(f32, size);
|
||||
defer allocator.free(gate);
|
||||
|
||||
const output = try allocator.alloc(f32, size);
|
||||
defer allocator.free(output);
|
||||
|
||||
// Fill with random data
|
||||
for (input, gate) |*i, *g| {
|
||||
i.* = 0.5;
|
||||
g.* = 0.3;
|
||||
}
|
||||
|
||||
const start_time = std.time.nanoTimestamp();
|
||||
|
||||
for (0..iterations) |_| {
|
||||
// SwiGLU: input * swish(gate)
|
||||
for (0..size) |i| {
|
||||
const g = gate[i];
|
||||
const swish_g = g / (1.0 + @exp(-g));
|
||||
output[i] = input[i] * swish_g;
|
||||
}
|
||||
}
|
||||
|
||||
const end_time = std.time.nanoTimestamp();
|
||||
const total_time = @as(u64, @intCast(end_time - start_time));
|
||||
const avg_time = total_time / iterations;
|
||||
|
||||
const total_elements = size * iterations;
|
||||
const ops_per_second = @as(f64, @floatFromInt(total_elements)) / (@as(f64, @floatFromInt(total_time)) / 1_000_000_000.0);
|
||||
|
||||
return BenchmarkResult{
|
||||
.name = "SwiGLU Activation",
|
||||
.iterations = iterations,
|
||||
.total_time_ns = total_time,
|
||||
.avg_time_ns = avg_time,
|
||||
.ops_per_second = ops_per_second,
|
||||
.memory_used_mb = (@as(f64, @floatFromInt(size)) * 3.0 * 4.0) / (1024.0 * 1024.0),
|
||||
};
|
||||
}
|
||||
|
||||
/// Benchmark RMS normalization
|
||||
fn benchmarkRMSNorm(allocator: std.mem.Allocator) !BenchmarkResult {
|
||||
const iterations = 1000;
|
||||
const size = 4096; // Typical hidden dimension
|
||||
|
||||
const input = try allocator.alloc(f32, size);
|
||||
defer allocator.free(input);
|
||||
|
||||
const weight = try allocator.alloc(f32, size);
|
||||
defer allocator.free(weight);
|
||||
|
||||
const output = try allocator.alloc(f32, size);
|
||||
defer allocator.free(output);
|
||||
|
||||
// Initialize data
|
||||
for (input, weight) |*i, *w| {
|
||||
i.* = 0.1;
|
||||
w.* = 1.0;
|
||||
}
|
||||
|
||||
const start_time = std.time.nanoTimestamp();
|
||||
|
||||
for (0..iterations) |_| {
|
||||
deepseek_core.math.rms_norm.rmsNormVec(input, weight, output, 1e-6);
|
||||
}
|
||||
|
||||
const end_time = std.time.nanoTimestamp();
|
||||
const total_time = @as(u64, @intCast(end_time - start_time));
|
||||
const avg_time = total_time / iterations;
|
||||
|
||||
const ops_per_second = @as(f64, @floatFromInt(iterations)) / (@as(f64, @floatFromInt(total_time)) / 1_000_000_000.0);
|
||||
|
||||
return BenchmarkResult{
|
||||
.name = "RMS Normalization (SIMD)",
|
||||
.iterations = iterations,
|
||||
.total_time_ns = total_time,
|
||||
.avg_time_ns = avg_time,
|
||||
.ops_per_second = ops_per_second,
|
||||
.memory_used_mb = (@as(f64, @floatFromInt(size)) * 3.0 * 4.0) / (1024.0 * 1024.0),
|
||||
};
|
||||
}
|
||||
|
||||
/// Benchmark memory bandwidth
|
||||
fn benchmarkMemoryBandwidth(allocator: std.mem.Allocator) !BenchmarkResult {
|
||||
const iterations = 100;
|
||||
const size = 64 * 1024 * 1024; // 64MB
|
||||
|
||||
const source = try allocator.alloc(u8, size);
|
||||
defer allocator.free(source);
|
||||
|
||||
const dest = try allocator.alloc(u8, size);
|
||||
defer allocator.free(dest);
|
||||
|
||||
// Fill source with data
|
||||
@memset(source, 0x42);
|
||||
|
||||
const start_time = std.time.nanoTimestamp();
|
||||
|
||||
for (0..iterations) |_| {
|
||||
@memcpy(dest, source);
|
||||
}
|
||||
|
||||
const end_time = std.time.nanoTimestamp();
|
||||
const total_time = @as(u64, @intCast(end_time - start_time));
|
||||
const avg_time = total_time / iterations;
|
||||
|
||||
const total_bytes = size * iterations;
|
||||
const gb_per_second = (@as(f64, @floatFromInt(total_bytes)) / (@as(f64, @floatFromInt(total_time)) / 1_000_000_000.0)) / (1024.0 * 1024.0 * 1024.0);
|
||||
|
||||
return BenchmarkResult{
|
||||
.name = "Memory Bandwidth",
|
||||
.iterations = iterations,
|
||||
.total_time_ns = total_time,
|
||||
.avg_time_ns = avg_time,
|
||||
.ops_per_second = gb_per_second, // Actually GB/s
|
||||
.memory_used_mb = (@as(f64, @floatFromInt(size)) * 2.0) / (1024.0 * 1024.0),
|
||||
};
|
||||
}
|
||||
151
experimental/build.zig
Normal file
151
experimental/build.zig
Normal file
@ -0,0 +1,151 @@
|
||||
const std = @import("std");
|
||||
|
||||
pub fn build(b: *std.Build) void {
|
||||
// Standard optimization options
|
||||
const target = b.standardTargetOptions(.{});
|
||||
const optimize = b.standardOptimizeOption(.{});
|
||||
|
||||
// === CORE LIBRARY MODULE ===
|
||||
const deepseek_core = b.addModule("deepseek_core", .{
|
||||
.root_source_file = b.path("src/core/root.zig"),
|
||||
.target = target,
|
||||
.optimize = optimize,
|
||||
});
|
||||
|
||||
// === WEB LAYER MODULE ===
|
||||
const web_layer = b.addModule("web_layer", .{
|
||||
.root_source_file = b.path("src/web/root.zig"),
|
||||
.target = target,
|
||||
.optimize = optimize,
|
||||
});
|
||||
web_layer.addImport("deepseek_core", deepseek_core);
|
||||
|
||||
// === BACKEND MODULES ===
|
||||
const cpu_backend = b.addModule("cpu_backend", .{
|
||||
.root_source_file = b.path("src/backends/cpu/root.zig"),
|
||||
.target = target,
|
||||
.optimize = optimize,
|
||||
});
|
||||
cpu_backend.addImport("deepseek_core", deepseek_core);
|
||||
|
||||
const metal_backend = b.addModule("metal_backend", .{
|
||||
.root_source_file = b.path("src/backends/metal/root.zig"),
|
||||
.target = target,
|
||||
.optimize = optimize,
|
||||
});
|
||||
metal_backend.addImport("deepseek_core", deepseek_core);
|
||||
|
||||
const cuda_backend = b.addModule("cuda_backend", .{
|
||||
.root_source_file = b.path("src/backends/cuda/root.zig"),
|
||||
.target = target,
|
||||
.optimize = optimize,
|
||||
});
|
||||
cuda_backend.addImport("deepseek_core", deepseek_core);
|
||||
|
||||
// === MAIN EXECUTABLE ===
|
||||
const exe = b.addExecutable(.{
|
||||
.name = "deepseek-v3-zig",
|
||||
.root_source_file = b.path("src/main.zig"),
|
||||
.target = target,
|
||||
.optimize = optimize,
|
||||
});
|
||||
|
||||
// Add imports to main executable
|
||||
exe.root_module.addImport("deepseek_core", deepseek_core);
|
||||
exe.root_module.addImport("web_layer", web_layer);
|
||||
exe.root_module.addImport("cpu_backend", cpu_backend);
|
||||
exe.root_module.addImport("metal_backend", metal_backend);
|
||||
exe.root_module.addImport("cuda_backend", cuda_backend);
|
||||
|
||||
// Platform-specific backend linking
|
||||
if (target.result.os.tag == .macos) {
|
||||
exe.linkFramework("Metal");
|
||||
exe.linkFramework("MetalKit");
|
||||
exe.linkFramework("Foundation");
|
||||
}
|
||||
|
||||
// CUDA linking for Linux/Windows
|
||||
if (target.result.os.tag == .linux or target.result.os.tag == .windows) {
|
||||
// TODO: Add CUDA library paths when available
|
||||
// exe.addLibraryPath(b.path("cuda/lib"));
|
||||
// exe.linkSystemLibrary("cuda");
|
||||
// exe.linkSystemLibrary("cublas");
|
||||
}
|
||||
|
||||
b.installArtifact(exe);
|
||||
|
||||
// === RUN COMMAND ===
|
||||
const run_cmd = b.addRunArtifact(exe);
|
||||
run_cmd.step.dependOn(b.getInstallStep());
|
||||
|
||||
if (b.args) |args| {
|
||||
run_cmd.addArgs(args);
|
||||
}
|
||||
|
||||
const run_step = b.step("run", "Run the DeepSeek V3 server");
|
||||
run_step.dependOn(&run_cmd.step);
|
||||
|
||||
// === TESTING ===
|
||||
const test_step = b.step("test", "Run unit tests");
|
||||
|
||||
// Core tests
|
||||
const core_tests = b.addTest(.{
|
||||
.root_source_file = b.path("src/core/root.zig"),
|
||||
.target = target,
|
||||
.optimize = optimize,
|
||||
});
|
||||
test_step.dependOn(&b.addRunArtifact(core_tests).step);
|
||||
|
||||
// Web tests
|
||||
const web_tests = b.addTest(.{
|
||||
.root_source_file = b.path("src/web/root.zig"),
|
||||
.target = target,
|
||||
.optimize = optimize,
|
||||
});
|
||||
web_tests.root_module.addImport("deepseek_core", deepseek_core);
|
||||
test_step.dependOn(&b.addRunArtifact(web_tests).step);
|
||||
|
||||
// Backend tests
|
||||
const cpu_tests = b.addTest(.{
|
||||
.root_source_file = b.path("src/backends/cpu/root.zig"),
|
||||
.target = target,
|
||||
.optimize = optimize,
|
||||
});
|
||||
cpu_tests.root_module.addImport("deepseek_core", deepseek_core);
|
||||
test_step.dependOn(&b.addRunArtifact(cpu_tests).step);
|
||||
|
||||
// === BENCHMARKS ===
|
||||
const bench_step = b.step("bench", "Run benchmarks");
|
||||
|
||||
const bench_exe = b.addExecutable(.{
|
||||
.name = "bench",
|
||||
.root_source_file = b.path("bench/main.zig"),
|
||||
.target = target,
|
||||
.optimize = .ReleaseFast,
|
||||
});
|
||||
bench_exe.root_module.addImport("deepseek_core", deepseek_core);
|
||||
bench_exe.root_module.addImport("cpu_backend", cpu_backend);
|
||||
|
||||
const bench_run = b.addRunArtifact(bench_exe);
|
||||
bench_step.dependOn(&bench_run.step);
|
||||
|
||||
// === WASM TARGET ===
|
||||
const wasm_step = b.step("wasm", "Build WebAssembly target");
|
||||
const wasm_target = b.resolveTargetQuery(.{
|
||||
.cpu_arch = .wasm32,
|
||||
.os_tag = .freestanding,
|
||||
});
|
||||
|
||||
const wasm_exe = b.addExecutable(.{
|
||||
.name = "deepseek-v3-wasm",
|
||||
.root_source_file = b.path("src/wasm/main.zig"),
|
||||
.target = wasm_target,
|
||||
.optimize = .ReleaseSmall,
|
||||
});
|
||||
wasm_exe.root_module.addImport("deepseek_core", deepseek_core);
|
||||
wasm_exe.entry = .disabled;
|
||||
wasm_exe.rdynamic = true;
|
||||
|
||||
const wasm_install = b.addInstallArtifact(wasm_exe, .{});
|
||||
wasm_step.dependOn(&wasm_install.step);
|
||||
}
|
||||
32
experimental/build.zig.zon
Normal file
32
experimental/build.zig.zon
Normal file
@ -0,0 +1,32 @@
|
||||
.{
|
||||
.name = .deepzig_v3,
|
||||
.version = "0.1.0",
|
||||
.fingerprint = 0x602e54a97e1751eb,
|
||||
.minimum_zig_version = "0.15.0-dev.703",
|
||||
|
||||
.dependencies = .{
|
||||
// HTTP/Web dependencies
|
||||
// TODO: Add when available for 0.15.0-dev
|
||||
// .httpz = .{
|
||||
// .url = "https://github.com/karlseguin/http.zig/archive/refs/heads/master.tar.gz",
|
||||
// .hash = "1220...",
|
||||
// },
|
||||
|
||||
// JSON parsing
|
||||
// TODO: Add structured JSON library if needed beyond std.json
|
||||
|
||||
// SIMD/Math libraries
|
||||
// TODO: Add optimized math libraries if available
|
||||
|
||||
// Tokenizer dependencies
|
||||
// TODO: Add tokenizer libraries or implement from scratch
|
||||
},
|
||||
|
||||
.paths = .{
|
||||
"build.zig",
|
||||
"build.zig.zon",
|
||||
"src",
|
||||
"bench",
|
||||
"README.md",
|
||||
},
|
||||
}
|
||||
245
experimental/src/backends/cpu/root.zig
Normal file
245
experimental/src/backends/cpu/root.zig
Normal file
@ -0,0 +1,245 @@
|
||||
// CPU Backend for DeepSeek V3
|
||||
// Optimized for x86_64 (AVX2) and ARM64 (NEON) SIMD instructions
|
||||
|
||||
const std = @import("std");
|
||||
const deepseek_core = @import("deepseek_core");
|
||||
const Allocator = std.mem.Allocator;
|
||||
|
||||
/// CPU-specific backend implementation
|
||||
pub const CpuBackend = struct {
|
||||
allocator: Allocator,
|
||||
thread_pool: std.Thread.Pool,
|
||||
capabilities: deepseek_core.Backend.Capabilities,
|
||||
|
||||
const Self = @This();
|
||||
|
||||
/// Initialize CPU backend with optimal thread count
|
||||
pub fn init(allocator: Allocator) !Self {
|
||||
const thread_count = @max(1, std.Thread.getCpuCount() catch 4);
|
||||
var thread_pool: std.Thread.Pool = undefined;
|
||||
try thread_pool.init(.{ .allocator = allocator, .n_jobs = thread_count });
|
||||
|
||||
std.log.info("CPU Backend initialized with {} threads", .{thread_count});
|
||||
|
||||
return Self{
|
||||
.allocator = allocator,
|
||||
.thread_pool = thread_pool,
|
||||
.capabilities = detectCapabilities(),
|
||||
};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *Self) void {
|
||||
self.thread_pool.deinit();
|
||||
}
|
||||
|
||||
/// Matrix multiplication optimized for CPU
|
||||
pub fn matmul(
|
||||
self: *Self,
|
||||
a: *deepseek_core.Tensor,
|
||||
b: *const deepseek_core.Tensor,
|
||||
c: *deepseek_core.Tensor,
|
||||
) !void {
|
||||
if (a.dtype != .f32 or b.dtype != .f32 or c.dtype != .f32) {
|
||||
return error.UnsupportedDataType;
|
||||
}
|
||||
|
||||
const a_data = try a.asSliceF32();
|
||||
const b_data = @as([]const f32, @alignCast(std.mem.bytesAsSlice(f32, b.data)));
|
||||
const c_data = try c.asSliceF32();
|
||||
|
||||
const m = a.shape.dims[0];
|
||||
const k = a.shape.dims[1];
|
||||
const n = b.shape.dims[1];
|
||||
|
||||
// Use blocking algorithm for better cache performance
|
||||
const block_size = 64; // Optimized for L1 cache
|
||||
|
||||
var i: usize = 0;
|
||||
while (i < m) : (i += block_size) {
|
||||
var j: usize = 0;
|
||||
while (j < n) : (j += block_size) {
|
||||
var l: usize = 0;
|
||||
while (l < k) : (l += block_size) {
|
||||
const i_end = @min(i + block_size, m);
|
||||
const j_end = @min(j + block_size, n);
|
||||
const l_end = @min(l + block_size, k);
|
||||
|
||||
try self.matmulBlock(
|
||||
a_data, b_data, c_data,
|
||||
i, i_end, j, j_end, l, l_end,
|
||||
k, n
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Blocked matrix multiplication with SIMD
|
||||
fn matmulBlock(
|
||||
self: *Self,
|
||||
a: []const f32,
|
||||
b: []const f32,
|
||||
c: []f32,
|
||||
i_start: usize, i_end: usize,
|
||||
j_start: usize, j_end: usize,
|
||||
l_start: usize, l_end: usize,
|
||||
k: usize, n: usize,
|
||||
) !void {
|
||||
_ = self;
|
||||
|
||||
const VecSize = if (@import("builtin").cpu.arch == .x86_64) 8 else 4;
|
||||
|
||||
var i = i_start;
|
||||
while (i < i_end) : (i += 1) {
|
||||
var j = j_start;
|
||||
|
||||
// Vectorized inner loop
|
||||
while (j + VecSize <= j_end) : (j += VecSize) {
|
||||
var sum_vec: @Vector(VecSize, f32) = @splat(0.0);
|
||||
|
||||
var l = l_start;
|
||||
while (l < l_end) : (l += 1) {
|
||||
const a_val: @Vector(VecSize, f32) = @splat(a[i * k + l]);
|
||||
const b_vals: @Vector(VecSize, f32) = b[l * n + j..l * n + j + VecSize][0..VecSize].*;
|
||||
sum_vec = @mulAdd(@Vector(VecSize, f32), a_val, b_vals, sum_vec);
|
||||
}
|
||||
|
||||
c[i * n + j..i * n + j + VecSize][0..VecSize].* = sum_vec;
|
||||
}
|
||||
|
||||
// Handle remainder
|
||||
while (j < j_end) : (j += 1) {
|
||||
var sum: f32 = 0.0;
|
||||
var l = l_start;
|
||||
while (l < l_end) : (l += 1) {
|
||||
sum += a[i * k + l] * b[l * n + j];
|
||||
}
|
||||
c[i * n + j] = sum;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Optimized RMS normalization
|
||||
pub fn rmsNorm(
|
||||
self: *Self,
|
||||
input: []const f32,
|
||||
weight: []const f32,
|
||||
output: []f32,
|
||||
eps: f32,
|
||||
) !void {
|
||||
_ = self;
|
||||
|
||||
const VecSize = if (@import("builtin").cpu.arch == .x86_64) 8 else 4;
|
||||
const vec_len = input.len / VecSize * VecSize;
|
||||
|
||||
// Compute mean square using SIMD
|
||||
var sum_squares: @Vector(VecSize, f32) = @splat(0.0);
|
||||
var i: usize = 0;
|
||||
while (i < vec_len) : (i += VecSize) {
|
||||
const x: @Vector(VecSize, f32) = input[i..i+VecSize][0..VecSize].*;
|
||||
sum_squares = @mulAdd(@Vector(VecSize, f32), x, x, sum_squares);
|
||||
}
|
||||
|
||||
// Sum vector elements
|
||||
var mean_square: f32 = 0.0;
|
||||
for (0..VecSize) |j| {
|
||||
mean_square += sum_squares[j];
|
||||
}
|
||||
|
||||
// Handle remainder
|
||||
while (i < input.len) : (i += 1) {
|
||||
mean_square += input[i] * input[i];
|
||||
}
|
||||
|
||||
mean_square /= @floatFromInt(input.len);
|
||||
|
||||
// Normalize
|
||||
const rms = @sqrt(mean_square + eps);
|
||||
const rms_vec: @Vector(VecSize, f32) = @splat(rms);
|
||||
|
||||
i = 0;
|
||||
while (i < vec_len) : (i += VecSize) {
|
||||
const x: @Vector(VecSize, f32) = input[i..i+VecSize][0..VecSize].*;
|
||||
const w: @Vector(VecSize, f32) = weight[i..i+VecSize][0..VecSize].*;
|
||||
const normalized = (x / rms_vec) * w;
|
||||
output[i..i+VecSize][0..VecSize].* = normalized;
|
||||
}
|
||||
|
||||
// Handle remainder
|
||||
while (i < input.len) : (i += 1) {
|
||||
output[i] = (input[i] / rms) * weight[i];
|
||||
}
|
||||
}
|
||||
|
||||
/// SwiGLU activation function with SIMD
|
||||
pub fn swiglu(
|
||||
self: *Self,
|
||||
input: []const f32,
|
||||
gate: []const f32,
|
||||
output: []f32,
|
||||
) !void {
|
||||
_ = self;
|
||||
|
||||
const VecSize = if (@import("builtin").cpu.arch == .x86_64) 8 else 4;
|
||||
const vec_len = input.len / VecSize * VecSize;
|
||||
|
||||
var i: usize = 0;
|
||||
while (i < vec_len) : (i += VecSize) {
|
||||
const x: @Vector(VecSize, f32) = input[i..i+VecSize][0..VecSize].*;
|
||||
const g: @Vector(VecSize, f32) = gate[i..i+VecSize][0..VecSize].*;
|
||||
|
||||
// SwiGLU: x * (g / (1 + exp(-g)))
|
||||
const ones: @Vector(VecSize, f32) = @splat(1.0);
|
||||
const swish_g = g / (ones + @exp(-g));
|
||||
const result = x * swish_g;
|
||||
|
||||
output[i..i+VecSize][0..VecSize].* = result;
|
||||
}
|
||||
|
||||
// Handle remainder
|
||||
while (i < input.len) : (i += 1) {
|
||||
const g_val = gate[i];
|
||||
const swish_val = g_val / (1.0 + @exp(-g_val));
|
||||
output[i] = input[i] * swish_val;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
/// Create the backend interface
|
||||
pub fn init(allocator: Allocator) !deepseek_core.Backend {
|
||||
// For now, return a simple backend struct
|
||||
// In a full implementation, this would create a CpuBackend and wrap it
|
||||
return deepseek_core.Backend.init(allocator, .cpu, 0);
|
||||
}
|
||||
|
||||
/// Detect CPU capabilities at runtime
|
||||
fn detectCapabilities() deepseek_core.Backend.Capabilities {
|
||||
const arch = @import("builtin").cpu.arch;
|
||||
|
||||
return switch (arch) {
|
||||
.x86_64 => .{
|
||||
.supports_fp16 = true,
|
||||
.supports_bf16 = true, // Check for AVX-512 BF16 in real implementation
|
||||
.supports_int8 = true,
|
||||
.max_memory_gb = 128,
|
||||
.compute_capability = null,
|
||||
.simd_width = 8, // AVX2
|
||||
},
|
||||
.aarch64 => .{
|
||||
.supports_fp16 = true,
|
||||
.supports_bf16 = true, // ARM64 has native BF16 support
|
||||
.supports_int8 = true,
|
||||
.max_memory_gb = 96,
|
||||
.compute_capability = null,
|
||||
.simd_width = 4, // NEON 128-bit
|
||||
},
|
||||
else => .{
|
||||
.supports_fp16 = false,
|
||||
.supports_bf16 = false,
|
||||
.supports_int8 = true,
|
||||
.max_memory_gb = 16,
|
||||
.compute_capability = null,
|
||||
.simd_width = 1,
|
||||
},
|
||||
};
|
||||
}
|
||||
297
experimental/src/backends/cuda/root.zig
Normal file
297
experimental/src/backends/cuda/root.zig
Normal file
@ -0,0 +1,297 @@
|
||||
// CUDA Backend for DeepSeek V3
|
||||
// Optimized for NVIDIA GPUs with Tensor Cores and high-bandwidth memory
|
||||
|
||||
const std = @import("std");
|
||||
const deepseek_core = @import("deepseek_core");
|
||||
const Allocator = std.mem.Allocator;
|
||||
|
||||
/// CUDA backend implementation
|
||||
pub const CudaBackend = struct {
|
||||
allocator: Allocator,
|
||||
device_id: u32,
|
||||
device_available: bool,
|
||||
compute_capability: []const u8,
|
||||
memory_gb: u32,
|
||||
|
||||
const Self = @This();
|
||||
|
||||
pub fn init(allocator: Allocator, device_id: u32) !Self {
|
||||
// Check if CUDA is available at runtime
|
||||
const cuda_available = detectCudaRuntime();
|
||||
|
||||
if (cuda_available) {
|
||||
std.log.info("CUDA Backend initialized on device {d}", .{device_id});
|
||||
// TODO: Initialize CUDA context and device
|
||||
// TODO: Query device properties
|
||||
} else {
|
||||
std.log.warn("CUDA Backend not available - no CUDA runtime detected");
|
||||
}
|
||||
|
||||
return Self{
|
||||
.allocator = allocator,
|
||||
.device_id = device_id,
|
||||
.device_available = cuda_available,
|
||||
.compute_capability = if (cuda_available) "8.0" else "0.0", // H100 default
|
||||
.memory_gb = if (cuda_available) 80 else 0, // H100 80GB
|
||||
};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *Self) void {
|
||||
if (self.device_available) {
|
||||
// TODO: Cleanup CUDA context and memory
|
||||
std.log.debug("Cleaning up CUDA device {d}", .{self.device_id});
|
||||
}
|
||||
}
|
||||
|
||||
/// Matrix multiplication using cuBLAS/Tensor Cores
|
||||
pub fn matmul(
|
||||
self: *Self,
|
||||
a: *deepseek_core.Tensor,
|
||||
b: *const deepseek_core.Tensor,
|
||||
c: *deepseek_core.Tensor,
|
||||
) !void {
|
||||
if (!self.device_available) {
|
||||
return error.CudaNotAvailable;
|
||||
}
|
||||
|
||||
std.log.debug("CUDA matmul on device {d}: {}x{} * {}x{} -> {}x{}", .{
|
||||
self.device_id,
|
||||
a.shape.dims[0], a.shape.dims[1],
|
||||
b.shape.dims[0], b.shape.dims[1],
|
||||
c.shape.dims[0], c.shape.dims[1]
|
||||
});
|
||||
|
||||
// TODO: Implement CUDA matrix multiplication
|
||||
// This would involve:
|
||||
// 1. Allocate GPU memory with cudaMalloc
|
||||
// 2. Copy data to GPU with cudaMemcpy
|
||||
// 3. Call cuBLAS gemm or custom Tensor Core kernel
|
||||
// 4. Copy results back to host
|
||||
// 5. Free GPU memory
|
||||
|
||||
return error.NotImplemented;
|
||||
}
|
||||
|
||||
/// RMS normalization using custom CUDA kernel
|
||||
pub fn rmsNorm(
|
||||
self: *Self,
|
||||
input: []const f32,
|
||||
weight: []const f32,
|
||||
output: []f32,
|
||||
eps: f32,
|
||||
) !void {
|
||||
if (!self.device_available) {
|
||||
return error.CudaNotAvailable;
|
||||
}
|
||||
|
||||
_ = input;
|
||||
_ = weight;
|
||||
_ = output;
|
||||
_ = eps;
|
||||
|
||||
std.log.debug("CUDA RMS normalization on device {d}", .{self.device_id});
|
||||
|
||||
// TODO: Launch CUDA kernel for RMS normalization
|
||||
// GPU excels at parallel reduction and normalization
|
||||
|
||||
return error.NotImplemented;
|
||||
}
|
||||
|
||||
/// SwiGLU activation using CUDA
|
||||
pub fn swiglu(
|
||||
self: *Self,
|
||||
input: []const f32,
|
||||
gate: []const f32,
|
||||
output: []f32,
|
||||
) !void {
|
||||
if (!self.device_available) {
|
||||
return error.CudaNotAvailable;
|
||||
}
|
||||
|
||||
_ = input;
|
||||
_ = gate;
|
||||
_ = output;
|
||||
|
||||
std.log.debug("CUDA SwiGLU activation on device {d}", .{self.device_id});
|
||||
|
||||
// TODO: Launch CUDA kernel for SwiGLU
|
||||
// Element-wise operations are perfect for GPU parallelization
|
||||
|
||||
return error.NotImplemented;
|
||||
}
|
||||
|
||||
/// Optimized attention with flash attention
|
||||
pub fn flashAttention(
|
||||
self: *Self,
|
||||
query: *deepseek_core.Tensor,
|
||||
key: *const deepseek_core.Tensor,
|
||||
value: *const deepseek_core.Tensor,
|
||||
output: *deepseek_core.Tensor,
|
||||
) !void {
|
||||
if (!self.device_available) {
|
||||
return error.CudaNotAvailable;
|
||||
}
|
||||
|
||||
_ = query;
|
||||
_ = key;
|
||||
_ = value;
|
||||
_ = output;
|
||||
|
||||
std.log.debug("CUDA Flash Attention on device {d}", .{self.device_id});
|
||||
|
||||
// TODO: Implement Flash Attention algorithm
|
||||
// This provides memory-efficient attention for long sequences
|
||||
// Critical for DeepSeek V3's 32K context window
|
||||
|
||||
return error.NotImplemented;
|
||||
}
|
||||
|
||||
/// Check GPU memory usage
|
||||
pub fn getMemoryInfo(self: *Self) struct { free: u64, total: u64, used: u64 } {
|
||||
if (!self.device_available) {
|
||||
return .{ .free = 0, .total = 0, .used = 0 };
|
||||
}
|
||||
|
||||
// TODO: Call cudaMemGetInfo to get actual memory usage
|
||||
const total = @as(u64, self.memory_gb) * 1024 * 1024 * 1024;
|
||||
return .{
|
||||
.free = total, // TODO: Get actual free memory
|
||||
.total = total,
|
||||
.used = 0, // TODO: Calculate used memory
|
||||
};
|
||||
}
|
||||
|
||||
/// Synchronize device (wait for all operations to complete)
|
||||
pub fn synchronize(self: *Self) !void {
|
||||
if (!self.device_available) {
|
||||
return;
|
||||
}
|
||||
|
||||
// TODO: Call cudaDeviceSynchronize()
|
||||
std.log.debug("Synchronizing CUDA device {d}", .{self.device_id});
|
||||
}
|
||||
};
|
||||
|
||||
/// Create the CUDA backend interface
|
||||
pub fn init(allocator: Allocator) !deepseek_core.Backend {
|
||||
// For now, return a simple backend struct
|
||||
// In a full implementation, this would create a CudaBackend and wrap it
|
||||
return deepseek_core.Backend.init(allocator, .cuda, 0);
|
||||
}
|
||||
|
||||
/// Detect CUDA runtime availability
|
||||
fn detectCudaRuntime() bool {
|
||||
// TODO: Check for CUDA library availability
|
||||
// This would involve trying to load libcuda.so/cuda.dll
|
||||
// and checking for basic CUDA functions
|
||||
return false; // Disabled for now
|
||||
}
|
||||
|
||||
/// CUDA kernel templates (would be compiled with nvcc)
|
||||
const cuda_kernels = struct {
|
||||
// Matrix multiplication kernel using Tensor Cores
|
||||
const matmul_kernel =
|
||||
\\__global__ void matmul_kernel(
|
||||
\\ const float* __restrict__ a,
|
||||
\\ const float* __restrict__ b,
|
||||
\\ float* __restrict__ c,
|
||||
\\ int M, int N, int K
|
||||
\\) {
|
||||
\\ // Use Tensor Cores for mixed precision
|
||||
\\ // This would use wmma API for Tensor Core acceleration
|
||||
\\ int row = blockIdx.y * blockDim.y + threadIdx.y;
|
||||
\\ int col = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
\\
|
||||
\\ if (row < M && col < N) {
|
||||
\\ float sum = 0.0f;
|
||||
\\ for (int k = 0; k < K; k++) {
|
||||
\\ sum += a[row * K + k] * b[k * N + col];
|
||||
\\ }
|
||||
\\ c[row * N + col] = sum;
|
||||
\\ }
|
||||
\\}
|
||||
;
|
||||
|
||||
// RMS normalization kernel with warp-level reduction
|
||||
const rms_norm_kernel =
|
||||
\\__global__ void rms_norm_kernel(
|
||||
\\ const float* __restrict__ input,
|
||||
\\ const float* __restrict__ weight,
|
||||
\\ float* __restrict__ output,
|
||||
\\ int size,
|
||||
\\ float eps
|
||||
\\) {
|
||||
\\ int tid = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
\\
|
||||
\\ // Compute mean square using cooperative groups
|
||||
\\ __shared__ float shared_sum[32]; // For warp reduction
|
||||
\\
|
||||
\\ float thread_sum = 0.0f;
|
||||
\\ for (int i = tid; i < size; i += gridDim.x * blockDim.x) {
|
||||
\\ thread_sum += input[i] * input[i];
|
||||
\\ }
|
||||
\\
|
||||
\\ // Warp-level reduction
|
||||
\\ for (int mask = 16; mask > 0; mask /= 2) {
|
||||
\\ thread_sum += __shfl_down_sync(0xffffffff, thread_sum, mask);
|
||||
\\ }
|
||||
\\
|
||||
\\ if (threadIdx.x % 32 == 0) {
|
||||
\\ shared_sum[threadIdx.x / 32] = thread_sum;
|
||||
\\ }
|
||||
\\ __syncthreads();
|
||||
\\
|
||||
\\ // Final reduction and normalization
|
||||
\\ if (threadIdx.x == 0) {
|
||||
\\ float mean_square = 0.0f;
|
||||
\\ for (int i = 0; i < blockDim.x / 32; i++) {
|
||||
\\ mean_square += shared_sum[i];
|
||||
\\ }
|
||||
\\ mean_square /= size;
|
||||
\\ float rms = sqrtf(mean_square + eps);
|
||||
\\
|
||||
\\ // Store in shared memory for other threads
|
||||
\\ shared_sum[0] = rms;
|
||||
\\ }
|
||||
\\ __syncthreads();
|
||||
\\
|
||||
\\ float rms = shared_sum[0];
|
||||
\\ if (tid < size) {
|
||||
\\ output[tid] = (input[tid] / rms) * weight[tid];
|
||||
\\ }
|
||||
\\}
|
||||
;
|
||||
|
||||
// SwiGLU activation kernel
|
||||
const swiglu_kernel =
|
||||
\\__global__ void swiglu_kernel(
|
||||
\\ const float* __restrict__ input,
|
||||
\\ const float* __restrict__ gate,
|
||||
\\ float* __restrict__ output,
|
||||
\\ int size
|
||||
\\) {
|
||||
\\ int tid = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
\\
|
||||
\\ if (tid < size) {
|
||||
\\ float g = gate[tid];
|
||||
\\ float swish_g = g / (1.0f + expf(-g));
|
||||
\\ output[tid] = input[tid] * swish_g;
|
||||
\\ }
|
||||
\\}
|
||||
;
|
||||
};
|
||||
|
||||
/// CUDA device capabilities
|
||||
fn getCudaCapabilities(compute_capability: []const u8) deepseek_core.Backend.Capabilities {
|
||||
// Parse compute capability (e.g., "8.0" for H100)
|
||||
const major = std.fmt.parseInt(u8, compute_capability[0..1], 10) catch 0;
|
||||
|
||||
return .{
|
||||
.supports_fp16 = major >= 6, // Pascal and newer
|
||||
.supports_bf16 = major >= 8, // Ampere and newer
|
||||
.supports_int8 = major >= 6, // Pascal and newer
|
||||
.max_memory_gb = if (major >= 8) 80 else 24, // H100 vs V100
|
||||
.compute_capability = compute_capability,
|
||||
.simd_width = 32, // CUDA warp size
|
||||
};
|
||||
}
|
||||
230
experimental/src/backends/metal/root.zig
Normal file
230
experimental/src/backends/metal/root.zig
Normal file
@ -0,0 +1,230 @@
|
||||
// Metal Backend for DeepSeek V3 on Apple Silicon
|
||||
// Leverages Metal Performance Shaders and unified memory architecture
|
||||
|
||||
const std = @import("std");
|
||||
const deepseek_core = @import("deepseek_core");
|
||||
const Allocator = std.mem.Allocator;
|
||||
|
||||
/// Metal backend implementation for Apple Silicon
|
||||
pub const MetalBackend = struct {
|
||||
allocator: Allocator,
|
||||
device_available: bool,
|
||||
unified_memory_size: u64,
|
||||
|
||||
const Self = @This();
|
||||
|
||||
pub fn init(allocator: Allocator) !Self {
|
||||
// Check if Metal is available (compile-time check for macOS)
|
||||
const metal_available = @import("builtin").os.tag == .macos;
|
||||
|
||||
if (metal_available) {
|
||||
std.log.info("Metal Backend initialized on Apple Silicon");
|
||||
// TODO: Initialize MTLDevice and command queue
|
||||
// TODO: Query unified memory size
|
||||
} else {
|
||||
std.log.warn("Metal Backend not available on this platform");
|
||||
}
|
||||
|
||||
return Self{
|
||||
.allocator = allocator,
|
||||
.device_available = metal_available,
|
||||
.unified_memory_size = if (metal_available) 16 * 1024 * 1024 * 1024 else 0, // 16GB default
|
||||
};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *Self) void {
|
||||
// TODO: Release Metal resources
|
||||
_ = self;
|
||||
}
|
||||
|
||||
/// Matrix multiplication using Metal Performance Shaders
|
||||
pub fn matmul(
|
||||
self: *Self,
|
||||
a: *deepseek_core.Tensor,
|
||||
b: *const deepseek_core.Tensor,
|
||||
c: *deepseek_core.Tensor,
|
||||
) !void {
|
||||
if (!self.device_available) {
|
||||
return error.MetalNotAvailable;
|
||||
}
|
||||
|
||||
std.log.debug("Metal matmul: {}x{} * {}x{} -> {}x{}", .{
|
||||
a.shape.dims[0], a.shape.dims[1],
|
||||
b.shape.dims[0], b.shape.dims[1],
|
||||
c.shape.dims[0], c.shape.dims[1]
|
||||
});
|
||||
|
||||
// TODO: Implement actual Metal compute shader
|
||||
// This would involve:
|
||||
// 1. Create MTLBuffer from tensor data
|
||||
// 2. Set up compute pipeline with matmul shader
|
||||
// 3. Dispatch compute commands
|
||||
// 4. Copy results back to tensor
|
||||
|
||||
// For now, fallback to CPU implementation
|
||||
return error.NotImplemented;
|
||||
}
|
||||
|
||||
/// RMS normalization using Metal compute shader
|
||||
pub fn rmsNorm(
|
||||
self: *Self,
|
||||
input: []const f32,
|
||||
weight: []const f32,
|
||||
output: []f32,
|
||||
eps: f32,
|
||||
) !void {
|
||||
if (!self.device_available) {
|
||||
return error.MetalNotAvailable;
|
||||
}
|
||||
|
||||
_ = input;
|
||||
_ = weight;
|
||||
_ = output;
|
||||
_ = eps;
|
||||
|
||||
std.log.debug("Metal RMS normalization");
|
||||
|
||||
// TODO: Implement Metal compute shader for RMS norm
|
||||
// Metal excels at parallel operations like normalization
|
||||
|
||||
return error.NotImplemented;
|
||||
}
|
||||
|
||||
/// SwiGLU activation using Metal
|
||||
pub fn swiglu(
|
||||
self: *Self,
|
||||
input: []const f32,
|
||||
gate: []const f32,
|
||||
output: []f32,
|
||||
) !void {
|
||||
if (!self.device_available) {
|
||||
return error.MetalNotAvailable;
|
||||
}
|
||||
|
||||
_ = input;
|
||||
_ = gate;
|
||||
_ = output;
|
||||
|
||||
std.log.debug("Metal SwiGLU activation");
|
||||
|
||||
// TODO: Implement Metal compute shader for SwiGLU
|
||||
// GPU is perfect for element-wise operations like activations
|
||||
|
||||
return error.NotImplemented;
|
||||
}
|
||||
|
||||
/// Attention mechanism optimized for Apple Silicon
|
||||
pub fn attention(
|
||||
self: *Self,
|
||||
query: *deepseek_core.Tensor,
|
||||
key: *const deepseek_core.Tensor,
|
||||
value: *const deepseek_core.Tensor,
|
||||
output: *deepseek_core.Tensor,
|
||||
) !void {
|
||||
if (!self.device_available) {
|
||||
return error.MetalNotAvailable;
|
||||
}
|
||||
|
||||
_ = query;
|
||||
_ = key;
|
||||
_ = value;
|
||||
_ = output;
|
||||
|
||||
std.log.debug("Metal attention mechanism");
|
||||
|
||||
// TODO: Implement optimized attention for Apple Silicon
|
||||
// This would leverage:
|
||||
// - Unified memory for zero-copy operations
|
||||
// - Metal Performance Shaders for optimized GEMM
|
||||
// - Custom shaders for attention-specific operations
|
||||
|
||||
return error.NotImplemented;
|
||||
}
|
||||
|
||||
/// Check GPU memory usage
|
||||
pub fn getMemoryInfo(self: *Self) struct { used: u64, total: u64 } {
|
||||
if (!self.device_available) {
|
||||
return .{ .used = 0, .total = 0 };
|
||||
}
|
||||
|
||||
// TODO: Query actual Metal device memory usage
|
||||
return .{
|
||||
.used = 0, // TODO: Get current usage
|
||||
.total = self.unified_memory_size,
|
||||
};
|
||||
}
|
||||
};
|
||||
|
||||
/// Create the Metal backend interface
|
||||
pub fn init(allocator: Allocator) !deepseek_core.Backend {
|
||||
// For now, return a simple backend struct
|
||||
// In a full implementation, this would create a MetalBackend and wrap it
|
||||
return deepseek_core.Backend.init(allocator, .metal, 0);
|
||||
}
|
||||
|
||||
/// Metal compute shader templates (would be loaded from .metal files)
|
||||
const metal_shaders = struct {
|
||||
// Matrix multiplication shader (simplified)
|
||||
const matmul_shader =
|
||||
\\#include <metal_stdlib>
|
||||
\\using namespace metal;
|
||||
\\
|
||||
\\kernel void matmul_kernel(
|
||||
\\ device const float* a [[buffer(0)]],
|
||||
\\ device const float* b [[buffer(1)]],
|
||||
\\ device float* c [[buffer(2)]],
|
||||
\\ constant uint& M [[buffer(3)]],
|
||||
\\ constant uint& N [[buffer(4)]],
|
||||
\\ constant uint& K [[buffer(5)]],
|
||||
\\ uint2 gid [[thread_position_in_grid]]
|
||||
\\) {
|
||||
\\ if (gid.x >= N || gid.y >= M) return;
|
||||
\\
|
||||
\\ float sum = 0.0;
|
||||
\\ for (uint k = 0; k < K; k++) {
|
||||
\\ sum += a[gid.y * K + k] * b[k * N + gid.x];
|
||||
\\ }
|
||||
\\ c[gid.y * N + gid.x] = sum;
|
||||
\\}
|
||||
;
|
||||
|
||||
// RMS normalization shader
|
||||
const rms_norm_shader =
|
||||
\\#include <metal_stdlib>
|
||||
\\using namespace metal;
|
||||
\\
|
||||
\\kernel void rms_norm_kernel(
|
||||
\\ device const float* input [[buffer(0)]],
|
||||
\\ device const float* weight [[buffer(1)]],
|
||||
\\ device float* output [[buffer(2)]],
|
||||
\\ constant uint& size [[buffer(3)]],
|
||||
\\ constant float& eps [[buffer(4)]],
|
||||
\\ uint gid [[thread_position_in_grid]]
|
||||
\\) {
|
||||
\\ // Simplified RMS norm - would need proper reduction
|
||||
\\ if (gid >= size) return;
|
||||
\\
|
||||
\\ // TODO: Implement proper parallel reduction for mean square
|
||||
\\ float mean_square = 0.0;
|
||||
\\ for (uint i = 0; i < size; i++) {
|
||||
\\ mean_square += input[i] * input[i];
|
||||
\\ }
|
||||
\\ mean_square /= size;
|
||||
\\
|
||||
\\ float rms = sqrt(mean_square + eps);
|
||||
\\ output[gid] = (input[gid] / rms) * weight[gid];
|
||||
\\}
|
||||
;
|
||||
};
|
||||
|
||||
/// Capabilities for Apple Silicon
|
||||
fn getAppleSiliconCapabilities() deepseek_core.Backend.Capabilities {
|
||||
return .{
|
||||
.supports_fp16 = true, // Native fp16 support
|
||||
.supports_bf16 = true, // M3+ supports bf16
|
||||
.supports_int8 = true, // Efficient int8 operations
|
||||
.max_memory_gb = 128, // Up to 128GB unified memory on Mac Studio
|
||||
.compute_capability = null,
|
||||
.simd_width = 32, // Metal SIMD-group size
|
||||
};
|
||||
}
|
||||
14
experimental/src/core/attention.zig
Normal file
14
experimental/src/core/attention.zig
Normal file
@ -0,0 +1,14 @@
|
||||
const std = @import("std");
|
||||
|
||||
/// Multi-Head Latent Attention (MLA) for DeepSeek V3
|
||||
pub const Attention = struct {
|
||||
// TODO: Implement MLA attention mechanism
|
||||
|
||||
pub fn init() Attention {
|
||||
return Attention{};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *Attention) void {
|
||||
_ = self;
|
||||
}
|
||||
};
|
||||
88
experimental/src/core/backend.zig
Normal file
88
experimental/src/core/backend.zig
Normal file
@ -0,0 +1,88 @@
|
||||
const std = @import("std");
|
||||
const Allocator = std.mem.Allocator;
|
||||
|
||||
/// Backend types supported by DeepSeek V3
|
||||
pub const BackendType = enum {
|
||||
cpu,
|
||||
metal,
|
||||
cuda,
|
||||
webgpu,
|
||||
};
|
||||
|
||||
/// Backend capabilities
|
||||
pub const Capabilities = struct {
|
||||
supports_fp16: bool,
|
||||
supports_bf16: bool,
|
||||
supports_int8: bool,
|
||||
max_memory_gb: u32,
|
||||
compute_capability: ?[]const u8, // For CUDA
|
||||
simd_width: u32,
|
||||
};
|
||||
|
||||
/// Backend interface for different compute backends
|
||||
pub const Backend = struct {
|
||||
type: BackendType,
|
||||
device_id: u32,
|
||||
allocator: Allocator,
|
||||
|
||||
const Self = @This();
|
||||
|
||||
pub fn init(allocator: Allocator, backend_type: BackendType, device_id: u32) Self {
|
||||
return Self{
|
||||
.type = backend_type,
|
||||
.device_id = device_id,
|
||||
.allocator = allocator,
|
||||
};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *Self) void {
|
||||
// TODO: Backend-specific cleanup
|
||||
_ = self;
|
||||
}
|
||||
|
||||
pub fn capabilities(self: *const Self) Capabilities {
|
||||
return switch (self.type) {
|
||||
.cpu => Capabilities{
|
||||
.supports_fp16 = true,
|
||||
.supports_bf16 = true,
|
||||
.supports_int8 = true,
|
||||
.max_memory_gb = 128, // Typical system RAM
|
||||
.compute_capability = null,
|
||||
.simd_width = if (@import("builtin").cpu.arch == .x86_64) 8 else 4,
|
||||
},
|
||||
.metal => Capabilities{
|
||||
.supports_fp16 = true,
|
||||
.supports_bf16 = true,
|
||||
.supports_int8 = true,
|
||||
.max_memory_gb = 96, // Apple Silicon unified memory
|
||||
.compute_capability = null,
|
||||
.simd_width = 16, // Metal SIMD groups
|
||||
},
|
||||
.cuda => Capabilities{
|
||||
.supports_fp16 = true,
|
||||
.supports_bf16 = true,
|
||||
.supports_int8 = true,
|
||||
.max_memory_gb = 80, // H100 VRAM
|
||||
.compute_capability = "8.0", // TODO: Detect actual capability
|
||||
.simd_width = 32, // CUDA warp size
|
||||
},
|
||||
.webgpu => Capabilities{
|
||||
.supports_fp16 = false, // Limited support
|
||||
.supports_bf16 = false,
|
||||
.supports_int8 = false,
|
||||
.max_memory_gb = 4, // Browser limitations
|
||||
.compute_capability = null,
|
||||
.simd_width = 1,
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
pub fn name(self: *const Self) []const u8 {
|
||||
return switch (self.type) {
|
||||
.cpu => "CPU",
|
||||
.metal => "Metal",
|
||||
.cuda => "CUDA",
|
||||
.webgpu => "WebGPU",
|
||||
};
|
||||
}
|
||||
};
|
||||
13
experimental/src/core/config.zig
Normal file
13
experimental/src/core/config.zig
Normal file
@ -0,0 +1,13 @@
|
||||
const std = @import("std");
|
||||
|
||||
/// Global configuration for DeepSeek V3
|
||||
pub const Config = struct {
|
||||
log_level: std.log.Level = .info,
|
||||
enable_telemetry: bool = false,
|
||||
cache_dir: ?[]const u8 = null,
|
||||
|
||||
pub fn loadFromEnv() Config {
|
||||
// TODO: Load configuration from environment variables
|
||||
return Config{};
|
||||
}
|
||||
};
|
||||
33
experimental/src/core/math/activation.zig
Normal file
33
experimental/src/core/math/activation.zig
Normal file
@ -0,0 +1,33 @@
|
||||
const std = @import("std");
|
||||
|
||||
/// SwiGLU activation function used in DeepSeek V3
|
||||
pub fn swiglu(x: f32, gate: f32) f32 {
|
||||
return x * swish(gate);
|
||||
}
|
||||
|
||||
/// Swish activation (SiLU)
|
||||
pub fn swish(x: f32) f32 {
|
||||
return x / (1.0 + @exp(-x));
|
||||
}
|
||||
|
||||
/// GELU activation
|
||||
pub fn gelu(x: f32) f32 {
|
||||
const tanh_arg = 0.7978845608 * (x + 0.044715 * x * x * x);
|
||||
return 0.5 * x * (1.0 + std.math.tanh(tanh_arg));
|
||||
}
|
||||
|
||||
/// ReLU activation
|
||||
pub fn relu(x: f32) f32 {
|
||||
return @max(0.0, x);
|
||||
}
|
||||
|
||||
/// Vectorized SwiGLU for SIMD
|
||||
pub fn swigluVec(comptime size: comptime_int, x: @Vector(size, f32), gate: @Vector(size, f32)) @Vector(size, f32) {
|
||||
return x * swishVec(size, gate);
|
||||
}
|
||||
|
||||
/// Vectorized Swish for SIMD
|
||||
pub fn swishVec(comptime size: comptime_int, x: @Vector(size, f32)) @Vector(size, f32) {
|
||||
const ones: @Vector(size, f32) = @splat(1.0);
|
||||
return x / (ones + @exp(-x));
|
||||
}
|
||||
64
experimental/src/core/math/rms_norm.zig
Normal file
64
experimental/src/core/math/rms_norm.zig
Normal file
@ -0,0 +1,64 @@
|
||||
const std = @import("std");
|
||||
|
||||
/// RMS Normalization used in DeepSeek V3
|
||||
pub fn rmsNorm(input: []const f32, weight: []const f32, output: []f32, eps: f32) void {
|
||||
std.debug.assert(input.len == weight.len);
|
||||
std.debug.assert(input.len == output.len);
|
||||
|
||||
// Compute mean square
|
||||
var mean_square: f32 = 0.0;
|
||||
for (input) |x| {
|
||||
mean_square += x * x;
|
||||
}
|
||||
mean_square /= @floatFromInt(input.len);
|
||||
|
||||
// Compute RMS and normalize
|
||||
const rms = @sqrt(mean_square + eps);
|
||||
for (0..input.len) |i| {
|
||||
output[i] = (input[i] / rms) * weight[i];
|
||||
}
|
||||
}
|
||||
|
||||
/// Vectorized RMS normalization for better performance
|
||||
pub fn rmsNormVec(input: []const f32, weight: []const f32, output: []f32, eps: f32) void {
|
||||
const VecSize = 8;
|
||||
const vec_len = input.len / VecSize * VecSize;
|
||||
|
||||
// Compute mean square using SIMD
|
||||
var sum_squares: @Vector(VecSize, f32) = @splat(0.0);
|
||||
var i: usize = 0;
|
||||
while (i < vec_len) : (i += VecSize) {
|
||||
const x: @Vector(VecSize, f32) = input[i..i+VecSize][0..VecSize].*;
|
||||
sum_squares += x * x;
|
||||
}
|
||||
|
||||
// Sum the vector elements
|
||||
var mean_square: f32 = 0.0;
|
||||
for (0..VecSize) |j| {
|
||||
mean_square += sum_squares[j];
|
||||
}
|
||||
|
||||
// Handle remainder
|
||||
while (i < input.len) : (i += 1) {
|
||||
mean_square += input[i] * input[i];
|
||||
}
|
||||
|
||||
mean_square /= @floatFromInt(input.len);
|
||||
|
||||
// Normalize using SIMD
|
||||
const rms = @sqrt(mean_square + eps);
|
||||
const rms_vec: @Vector(VecSize, f32) = @splat(rms);
|
||||
|
||||
i = 0;
|
||||
while (i < vec_len) : (i += VecSize) {
|
||||
const x: @Vector(VecSize, f32) = input[i..i+VecSize][0..VecSize].*;
|
||||
const w: @Vector(VecSize, f32) = weight[i..i+VecSize][0..VecSize].*;
|
||||
const normalized = (x / rms_vec) * w;
|
||||
output[i..i+VecSize][0..VecSize].* = normalized;
|
||||
}
|
||||
|
||||
// Handle remainder
|
||||
while (i < input.len) : (i += 1) {
|
||||
output[i] = (input[i] / rms) * weight[i];
|
||||
}
|
||||
}
|
||||
13
experimental/src/core/math/root.zig
Normal file
13
experimental/src/core/math/root.zig
Normal file
@ -0,0 +1,13 @@
|
||||
const std = @import("std");
|
||||
|
||||
// Math utilities for DeepSeek V3
|
||||
pub const simd = @import("simd.zig");
|
||||
pub const activation = @import("activation.zig");
|
||||
pub const rms_norm = @import("rms_norm.zig");
|
||||
|
||||
// Re-export common math functions
|
||||
pub const sqrt = std.math.sqrt;
|
||||
pub const exp = std.math.exp;
|
||||
pub const tanh = std.math.tanh;
|
||||
pub const sin = std.math.sin;
|
||||
pub const cos = std.math.cos;
|
||||
25
experimental/src/core/math/simd.zig
Normal file
25
experimental/src/core/math/simd.zig
Normal file
@ -0,0 +1,25 @@
|
||||
const std = @import("std");
|
||||
|
||||
/// SIMD utilities for high-performance computation
|
||||
pub fn vectorAdd(comptime T: type, comptime size: comptime_int, a: @Vector(size, T), b: @Vector(size, T)) @Vector(size, T) {
|
||||
return a + b;
|
||||
}
|
||||
|
||||
pub fn vectorMul(comptime T: type, comptime size: comptime_int, a: @Vector(size, T), b: @Vector(size, T)) @Vector(size, T) {
|
||||
return a * b;
|
||||
}
|
||||
|
||||
pub fn vectorFma(comptime T: type, comptime size: comptime_int, a: @Vector(size, T), b: @Vector(size, T), c: @Vector(size, T)) @Vector(size, T) {
|
||||
return @mulAdd(@Vector(size, T), a, b, c);
|
||||
}
|
||||
|
||||
/// Horizontal sum of vector elements
|
||||
pub fn horizontalSum(comptime T: type, comptime size: comptime_int, vec: @Vector(size, T)) T {
|
||||
if (size == 1) return vec[0];
|
||||
|
||||
var result: T = 0;
|
||||
for (0..size) |i| {
|
||||
result += vec[i];
|
||||
}
|
||||
return result;
|
||||
}
|
||||
35
experimental/src/core/memory.zig
Normal file
35
experimental/src/core/memory.zig
Normal file
@ -0,0 +1,35 @@
|
||||
const std = @import("std");
|
||||
const Allocator = std.mem.Allocator;
|
||||
|
||||
/// Arena allocator for request-scoped memory
|
||||
pub const ArenaAllocator = std.heap.ArenaAllocator;
|
||||
|
||||
/// Memory pool for tensor allocations
|
||||
pub const TensorPool = struct {
|
||||
allocator: Allocator,
|
||||
pool: std.ArrayList([]u8),
|
||||
|
||||
pub fn init(allocator: Allocator) TensorPool {
|
||||
return TensorPool{
|
||||
.allocator = allocator,
|
||||
.pool = std.ArrayList([]u8).init(allocator),
|
||||
};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *TensorPool) void {
|
||||
for (self.pool.items) |buf| {
|
||||
self.allocator.free(buf);
|
||||
}
|
||||
self.pool.deinit();
|
||||
}
|
||||
|
||||
pub fn alloc(self: *TensorPool, size: usize) ![]u8 {
|
||||
// TODO: Implement memory pooling
|
||||
return try self.allocator.alloc(u8, size);
|
||||
}
|
||||
|
||||
pub fn free(self: *TensorPool, buf: []u8) void {
|
||||
// TODO: Return to pool instead of freeing
|
||||
self.allocator.free(buf);
|
||||
}
|
||||
};
|
||||
296
experimental/src/core/model.zig
Normal file
296
experimental/src/core/model.zig
Normal file
@ -0,0 +1,296 @@
|
||||
const std = @import("std");
|
||||
const Allocator = std.mem.Allocator;
|
||||
const Tensor = @import("tensor.zig").Tensor;
|
||||
const Shape = @import("tensor.zig").Shape;
|
||||
const Transformer = @import("transformer.zig").Transformer;
|
||||
const Tokenizer = @import("tokenizer.zig").Tokenizer;
|
||||
const Backend = @import("backend.zig").Backend;
|
||||
const CoreError = @import("root.zig").CoreError;
|
||||
|
||||
pub const ModelError = CoreError || error{
|
||||
InvalidModelFile,
|
||||
UnsupportedModelVersion,
|
||||
CorruptedWeights,
|
||||
MissingTokenizer,
|
||||
};
|
||||
|
||||
/// Model configuration matching DeepSeek V3 architecture
|
||||
pub const ModelConfig = struct {
|
||||
// Model dimensions
|
||||
vocab_size: u32,
|
||||
hidden_size: u32,
|
||||
intermediate_size: u32,
|
||||
num_hidden_layers: u32,
|
||||
num_attention_heads: u32,
|
||||
num_key_value_heads: u32,
|
||||
max_position_embeddings: u32,
|
||||
|
||||
// MoE configuration
|
||||
num_experts: u32,
|
||||
num_experts_per_token: u32,
|
||||
expert_capacity: u32,
|
||||
|
||||
// Multi-head Latent Attention (MLA) config
|
||||
qk_nope_head_dim: u32,
|
||||
qk_rope_head_dim: u32,
|
||||
v_head_dim: u32,
|
||||
qk_rope_base: f32,
|
||||
|
||||
// Activation function
|
||||
hidden_act: []const u8, // "swiglu" for DeepSeek V3
|
||||
|
||||
// Normalization
|
||||
rms_norm_eps: f32,
|
||||
|
||||
// Quantization settings
|
||||
use_fp16: bool,
|
||||
use_bf16: bool,
|
||||
|
||||
pub fn deepseekV3Default() ModelConfig {
|
||||
return ModelConfig{
|
||||
.vocab_size = 129280,
|
||||
.hidden_size = 7168,
|
||||
.intermediate_size = 18432,
|
||||
.num_hidden_layers = 61,
|
||||
.num_attention_heads = 128,
|
||||
.num_key_value_heads = 128,
|
||||
.max_position_embeddings = 32768,
|
||||
.num_experts = 256,
|
||||
.num_experts_per_token = 8,
|
||||
.expert_capacity = 64,
|
||||
.qk_nope_head_dim = 128,
|
||||
.qk_rope_head_dim = 64,
|
||||
.v_head_dim = 128,
|
||||
.qk_rope_base = 10000.0,
|
||||
.hidden_act = "swiglu",
|
||||
.rms_norm_eps = 1e-6,
|
||||
.use_fp16 = false,
|
||||
.use_bf16 = true,
|
||||
};
|
||||
}
|
||||
};
|
||||
|
||||
/// Model information
|
||||
pub const ModelInfo = struct {
|
||||
name: []const u8,
|
||||
version: []const u8,
|
||||
config: ModelConfig,
|
||||
num_parameters: u64,
|
||||
memory_usage: u64,
|
||||
};
|
||||
|
||||
/// DeepSeek V3 Model
|
||||
pub const Model = struct {
|
||||
config: ModelConfig,
|
||||
transformer: Transformer,
|
||||
tokenizer: Tokenizer,
|
||||
backend: Backend,
|
||||
allocator: Allocator,
|
||||
|
||||
// Embedding layers
|
||||
embed_tokens: Tensor,
|
||||
embed_positions: ?Tensor,
|
||||
|
||||
// Output layers
|
||||
lm_head: Tensor,
|
||||
norm: Tensor,
|
||||
|
||||
const Self = @This();
|
||||
|
||||
/// Load model from file path
|
||||
pub fn loadFromPath(allocator: Allocator, path: []const u8, backend: Backend) !Self {
|
||||
std.log.info("Loading DeepSeek V3 model from: {s}", .{path});
|
||||
|
||||
// TODO: Implement model loading from file
|
||||
// For now, create a default model
|
||||
return loadDefault(allocator, backend);
|
||||
}
|
||||
|
||||
/// Load default/demo model
|
||||
pub fn loadDefault(allocator: Allocator, backend: Backend) !Self {
|
||||
const config = ModelConfig.deepseekV3Default();
|
||||
|
||||
std.log.info("Creating default DeepSeek V3 model...", .{});
|
||||
std.log.info(" Hidden size: {}", .{config.hidden_size});
|
||||
std.log.info(" Layers: {}", .{config.num_hidden_layers});
|
||||
std.log.info(" Experts: {}", .{config.num_experts});
|
||||
std.log.info(" Vocab size: {}", .{config.vocab_size});
|
||||
|
||||
// Initialize transformer
|
||||
const transformer = try Transformer.init(allocator, config, backend);
|
||||
|
||||
// Initialize tokenizer
|
||||
const tokenizer = try Tokenizer.init(allocator, config.vocab_size);
|
||||
|
||||
// Initialize embedding layers
|
||||
const embed_shape = Shape.init(&[_]u32{ config.vocab_size, config.hidden_size });
|
||||
var embed_tokens = try Tensor.init(allocator, embed_shape, .f32);
|
||||
|
||||
// Initialize with random values (in real implementation, load from weights)
|
||||
try initializeEmbedding(&embed_tokens);
|
||||
|
||||
// Output projection
|
||||
const lm_head_shape = Shape.init(&[_]u32{ config.hidden_size, config.vocab_size });
|
||||
var lm_head = try Tensor.init(allocator, lm_head_shape, .f32);
|
||||
try initializeLinear(&lm_head);
|
||||
|
||||
// Final layer norm
|
||||
const norm_shape = Shape.init(&[_]u32{config.hidden_size});
|
||||
const norm = try Tensor.ones(allocator, norm_shape, .f32);
|
||||
|
||||
return Self{
|
||||
.config = config,
|
||||
.transformer = transformer,
|
||||
.tokenizer = tokenizer,
|
||||
.backend = backend,
|
||||
.allocator = allocator,
|
||||
.embed_tokens = embed_tokens,
|
||||
.embed_positions = null,
|
||||
.lm_head = lm_head,
|
||||
.norm = norm,
|
||||
};
|
||||
}
|
||||
|
||||
/// Free model memory
|
||||
pub fn deinit(self: *Self) void {
|
||||
self.transformer.deinit();
|
||||
self.tokenizer.deinit();
|
||||
self.embed_tokens.deinit();
|
||||
if (self.embed_positions) |*pos| pos.deinit();
|
||||
self.lm_head.deinit();
|
||||
self.norm.deinit();
|
||||
}
|
||||
|
||||
/// Get model information
|
||||
pub fn info(self: *const Self) ModelInfo {
|
||||
const num_params = self.estimateParameters();
|
||||
const memory_usage = self.estimateMemoryUsage();
|
||||
|
||||
return ModelInfo{
|
||||
.name = "DeepSeek V3",
|
||||
.version = "0.1.0",
|
||||
.config = self.config,
|
||||
.num_parameters = num_params,
|
||||
.memory_usage = memory_usage,
|
||||
};
|
||||
}
|
||||
|
||||
/// Generate text completion
|
||||
pub fn generate(self: *Self, input_tokens: []const u32, max_tokens: u32) ![]u32 {
|
||||
_ = self;
|
||||
_ = input_tokens;
|
||||
_ = max_tokens;
|
||||
|
||||
// TODO: Implement actual generation
|
||||
// This would involve:
|
||||
// 1. Run forward pass through transformer layers
|
||||
// 2. Apply final layer norm and output projection
|
||||
// 3. Sample next token from logits
|
||||
// 4. Repeat until max_tokens or EOS
|
||||
|
||||
std.log.debug("Generation not yet implemented");
|
||||
return error.NotImplemented;
|
||||
}
|
||||
|
||||
/// Forward pass through the model
|
||||
pub fn forward(
|
||||
self: *Self,
|
||||
input_ids: []const u32,
|
||||
output: *Tensor,
|
||||
) !void {
|
||||
// TODO: Implement forward pass
|
||||
// 1. Embedding lookup
|
||||
// 2. Transformer forward pass
|
||||
// 3. Final layer norm
|
||||
// 4. Language model head
|
||||
|
||||
_ = self;
|
||||
_ = input_ids;
|
||||
_ = output;
|
||||
|
||||
std.log.debug("Model forward pass (placeholder)");
|
||||
}
|
||||
|
||||
/// Estimate model parameters
|
||||
fn estimateParameters(self: *const Self) u64 {
|
||||
var params: u64 = 0;
|
||||
|
||||
// Embedding parameters
|
||||
params += @as(u64, self.config.vocab_size) * self.config.hidden_size;
|
||||
|
||||
// Transformer parameters (rough estimate)
|
||||
const layer_params = @as(u64, self.config.hidden_size) * self.config.hidden_size * 4; // Attention + FFN
|
||||
params += layer_params * self.config.num_hidden_layers;
|
||||
|
||||
// MoE parameters
|
||||
const expert_params = @as(u64, self.config.hidden_size) * self.config.intermediate_size * 2;
|
||||
params += expert_params * self.config.num_experts;
|
||||
|
||||
// Output head
|
||||
params += @as(u64, self.config.hidden_size) * self.config.vocab_size;
|
||||
|
||||
return params;
|
||||
}
|
||||
|
||||
/// Estimate memory usage in bytes
|
||||
fn estimateMemoryUsage(self: *const Self) u64 {
|
||||
const params = self.estimateParameters();
|
||||
const dtype_size: u64 = if (self.config.use_fp16 or self.config.use_bf16) 2 else 4;
|
||||
|
||||
// Model weights + activation memory + KV cache
|
||||
return params * dtype_size * 2; // Rough estimate
|
||||
}
|
||||
};
|
||||
|
||||
// Initialize embedding with small random values
|
||||
fn initializeEmbedding(tensor: *Tensor) !void {
|
||||
const data = try tensor.asSliceF32();
|
||||
var rng = std.Random.DefaultPrng.init(42);
|
||||
const random = rng.random();
|
||||
|
||||
for (data) |*val| {
|
||||
val.* = (random.float(f32) - 0.5) * 0.02; // Small random values
|
||||
}
|
||||
}
|
||||
|
||||
// Initialize linear layer with Xavier initialization
|
||||
fn initializeLinear(tensor: *Tensor) !void {
|
||||
const data = try tensor.asSliceF32();
|
||||
var rng = std.Random.DefaultPrng.init(123);
|
||||
const random = rng.random();
|
||||
|
||||
const fan_in = tensor.shape.dims[0];
|
||||
const fan_out = tensor.shape.dims[1];
|
||||
const limit = std.math.sqrt(6.0 / @as(f32, @floatFromInt(fan_in + fan_out)));
|
||||
|
||||
for (data) |*val| {
|
||||
val.* = (random.float(f32) - 0.5) * 2.0 * limit;
|
||||
}
|
||||
}
|
||||
|
||||
// Tests
|
||||
test "model creation" {
|
||||
const testing = std.testing;
|
||||
const allocator = testing.allocator;
|
||||
|
||||
// Create a dummy backend for testing
|
||||
const backend = Backend{
|
||||
.type = .cpu,
|
||||
.device_id = 0,
|
||||
.allocator = allocator,
|
||||
};
|
||||
|
||||
var model = try Model.loadDefault(allocator, backend);
|
||||
defer model.deinit();
|
||||
|
||||
const model_info = model.info();
|
||||
try testing.expect(model_info.num_parameters > 0);
|
||||
try testing.expect(std.mem.eql(u8, model_info.name, "DeepSeek V3"));
|
||||
}
|
||||
|
||||
test "model config" {
|
||||
const config = ModelConfig.deepseekV3Default();
|
||||
std.testing.expect(config.vocab_size == 129280) catch unreachable;
|
||||
std.testing.expect(config.num_experts == 256) catch unreachable;
|
||||
std.testing.expect(config.num_experts_per_token == 8) catch unreachable;
|
||||
}
|
||||
14
experimental/src/core/moe.zig
Normal file
14
experimental/src/core/moe.zig
Normal file
@ -0,0 +1,14 @@
|
||||
const std = @import("std");
|
||||
|
||||
/// Mixture of Experts implementation for DeepSeek V3
|
||||
pub const MoE = struct {
|
||||
// TODO: Implement MoE routing and expert selection
|
||||
|
||||
pub fn init() MoE {
|
||||
return MoE{};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *MoE) void {
|
||||
_ = self;
|
||||
}
|
||||
};
|
||||
61
experimental/src/core/root.zig
Normal file
61
experimental/src/core/root.zig
Normal file
@ -0,0 +1,61 @@
|
||||
// DeepSeek V3 Core Module
|
||||
// This module contains the fundamental components for LLM inference
|
||||
|
||||
const std = @import("std");
|
||||
|
||||
// Core components
|
||||
pub const Tensor = @import("tensor.zig").Tensor;
|
||||
pub const Model = @import("model.zig").Model;
|
||||
pub const Transformer = @import("transformer.zig").Transformer;
|
||||
pub const Attention = @import("attention.zig").Attention;
|
||||
pub const MoE = @import("moe.zig").MoE;
|
||||
pub const Tokenizer = @import("tokenizer.zig").Tokenizer;
|
||||
pub const Backend = @import("backend.zig").Backend;
|
||||
|
||||
// Math utilities
|
||||
pub const math = @import("math/root.zig");
|
||||
|
||||
// Memory management
|
||||
pub const memory = @import("memory.zig");
|
||||
|
||||
// Configuration
|
||||
pub const Config = @import("config.zig").Config;
|
||||
|
||||
// Error types
|
||||
pub const CoreError = error{
|
||||
InvalidTensorShape,
|
||||
UnsupportedOperation,
|
||||
ModelLoadError,
|
||||
TokenizerError,
|
||||
BackendError,
|
||||
OutOfMemory,
|
||||
InvalidConfiguration,
|
||||
};
|
||||
|
||||
// Version information
|
||||
pub const version = struct {
|
||||
pub const major = 0;
|
||||
pub const minor = 1;
|
||||
pub const patch = 0;
|
||||
pub const string = "0.1.0";
|
||||
};
|
||||
|
||||
// Core test suite
|
||||
test "core module" {
|
||||
const testing = std.testing;
|
||||
|
||||
// Basic smoke tests
|
||||
try testing.expect(version.major == 0);
|
||||
try testing.expect(version.minor == 1);
|
||||
}
|
||||
|
||||
// Utility functions
|
||||
pub fn init() void {
|
||||
// TODO: Initialize any global state if needed
|
||||
std.log.info("DeepSeek V3 Core initialized (v{s})", .{version.string});
|
||||
}
|
||||
|
||||
pub fn deinit() void {
|
||||
// TODO: Cleanup any global state
|
||||
std.log.info("DeepSeek V3 Core deinitialized");
|
||||
}
|
||||
312
experimental/src/core/tensor.zig
Normal file
312
experimental/src/core/tensor.zig
Normal file
@ -0,0 +1,312 @@
|
||||
const std = @import("std");
|
||||
const Allocator = std.mem.Allocator;
|
||||
const CoreError = @import("root.zig").CoreError;
|
||||
|
||||
pub const TensorError = CoreError || error{
|
||||
ShapeMismatch,
|
||||
InvalidDimension,
|
||||
BufferTooSmall,
|
||||
};
|
||||
|
||||
/// Shape of a tensor - maximum 8 dimensions for DeepSeek V3
|
||||
pub const Shape = struct {
|
||||
dims: [8]u32,
|
||||
ndim: u8,
|
||||
|
||||
pub fn init(dimensions: []const u32) Shape {
|
||||
var shape = Shape{
|
||||
.dims = [_]u32{0} ** 8,
|
||||
.ndim = @intCast(dimensions.len),
|
||||
};
|
||||
for (dimensions, 0..) |dim, i| {
|
||||
shape.dims[i] = dim;
|
||||
}
|
||||
return shape;
|
||||
}
|
||||
|
||||
pub fn numel(self: Shape) u64 {
|
||||
var total: u64 = 1;
|
||||
for (0..self.ndim) |i| {
|
||||
total *= self.dims[i];
|
||||
}
|
||||
return total;
|
||||
}
|
||||
|
||||
pub fn equals(self: Shape, other: Shape) bool {
|
||||
if (self.ndim != other.ndim) return false;
|
||||
for (0..self.ndim) |i| {
|
||||
if (self.dims[i] != other.dims[i]) return false;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
pub fn format(
|
||||
self: Shape,
|
||||
comptime fmt: []const u8,
|
||||
options: std.fmt.FormatOptions,
|
||||
writer: anytype,
|
||||
) !void {
|
||||
_ = fmt;
|
||||
_ = options;
|
||||
try writer.print("Shape([");
|
||||
for (0..self.ndim) |i| {
|
||||
if (i > 0) try writer.print(", ");
|
||||
try writer.print("{}", .{self.dims[i]});
|
||||
}
|
||||
try writer.print("])");
|
||||
}
|
||||
};
|
||||
|
||||
/// Tensor data type
|
||||
pub const DType = enum {
|
||||
f32,
|
||||
f16,
|
||||
bf16,
|
||||
i32,
|
||||
u32,
|
||||
i8,
|
||||
u8,
|
||||
|
||||
pub fn size(self: DType) u8 {
|
||||
return switch (self) {
|
||||
.f32, .i32, .u32 => 4,
|
||||
.f16, .bf16 => 2,
|
||||
.i8, .u8 => 1,
|
||||
};
|
||||
}
|
||||
};
|
||||
|
||||
/// Multi-dimensional tensor with SIMD optimizations
|
||||
pub const Tensor = struct {
|
||||
data: []u8,
|
||||
shape: Shape,
|
||||
dtype: DType,
|
||||
allocator: Allocator,
|
||||
|
||||
const Self = @This();
|
||||
|
||||
/// Create a new tensor with given shape and data type
|
||||
pub fn init(allocator: Allocator, shape: Shape, dtype: DType) !Self {
|
||||
const size = shape.numel() * dtype.size();
|
||||
const data = try allocator.alloc(u8, size);
|
||||
@memset(data, 0);
|
||||
|
||||
return Self{
|
||||
.data = data,
|
||||
.shape = shape,
|
||||
.dtype = dtype,
|
||||
.allocator = allocator,
|
||||
};
|
||||
}
|
||||
|
||||
/// Create tensor from existing data (takes ownership)
|
||||
pub fn fromData(allocator: Allocator, data: []u8, shape: Shape, dtype: DType) !Self {
|
||||
const expected_size = shape.numel() * dtype.size();
|
||||
if (data.len != expected_size) {
|
||||
return TensorError.BufferTooSmall;
|
||||
}
|
||||
|
||||
return Self{
|
||||
.data = data,
|
||||
.shape = shape,
|
||||
.dtype = dtype,
|
||||
.allocator = allocator,
|
||||
};
|
||||
}
|
||||
|
||||
/// Create tensor filled with zeros
|
||||
pub fn zeros(allocator: Allocator, shape: Shape, dtype: DType) !Self {
|
||||
return init(allocator, shape, dtype);
|
||||
}
|
||||
|
||||
/// Create tensor filled with ones
|
||||
pub fn ones(allocator: Allocator, shape: Shape, dtype: DType) !Self {
|
||||
var tensor = try init(allocator, shape, dtype);
|
||||
try tensor.fill(1.0);
|
||||
return tensor;
|
||||
}
|
||||
|
||||
/// Free tensor memory
|
||||
pub fn deinit(self: *Self) void {
|
||||
self.allocator.free(self.data);
|
||||
}
|
||||
|
||||
/// Fill tensor with a scalar value
|
||||
pub fn fill(self: *Self, value: f32) !void {
|
||||
switch (self.dtype) {
|
||||
.f32 => {
|
||||
const data_f32 = @as([]f32, @alignCast(std.mem.bytesAsSlice(f32, self.data)));
|
||||
@memset(data_f32, value);
|
||||
},
|
||||
.f16 => {
|
||||
const data_f16 = @as([]f16, @alignCast(std.mem.bytesAsSlice(f16, self.data)));
|
||||
@memset(data_f16, @floatCast(value));
|
||||
},
|
||||
.i32 => {
|
||||
const data_i32 = @as([]i32, @alignCast(std.mem.bytesAsSlice(i32, self.data)));
|
||||
@memset(data_i32, @intFromFloat(value));
|
||||
},
|
||||
else => return TensorError.UnsupportedOperation,
|
||||
}
|
||||
}
|
||||
|
||||
/// Get tensor as typed slice (f32)
|
||||
pub fn asSliceF32(self: *Self) ![]f32 {
|
||||
if (self.dtype != .f32) return TensorError.UnsupportedOperation;
|
||||
return @as([]f32, @alignCast(std.mem.bytesAsSlice(f32, self.data)));
|
||||
}
|
||||
|
||||
/// Get tensor as typed slice (f16)
|
||||
pub fn asSliceF16(self: *Self) ![]f16 {
|
||||
if (self.dtype != .f16) return TensorError.UnsupportedOperation;
|
||||
return @as([]f16, @alignCast(std.mem.bytesAsSlice(f16, self.data)));
|
||||
}
|
||||
|
||||
/// Element-wise addition (SIMD optimized)
|
||||
pub fn add(self: *Self, other: *const Self, result: *Self) !void {
|
||||
if (!self.shape.equals(other.shape) or !self.shape.equals(result.shape)) {
|
||||
return TensorError.ShapeMismatch;
|
||||
}
|
||||
if (self.dtype != other.dtype or self.dtype != result.dtype) {
|
||||
return TensorError.UnsupportedOperation;
|
||||
}
|
||||
|
||||
switch (self.dtype) {
|
||||
.f32 => try addF32SIMD(self.data, other.data, result.data),
|
||||
.f16 => try addF16(self.data, other.data, result.data),
|
||||
else => return TensorError.UnsupportedOperation,
|
||||
}
|
||||
}
|
||||
|
||||
/// Matrix multiplication (optimized for transformers)
|
||||
pub fn matmul(self: *Self, other: *const Self, result: *Self) !void {
|
||||
if (self.shape.ndim != 2 or other.shape.ndim != 2 or result.shape.ndim != 2) {
|
||||
return TensorError.InvalidDimension;
|
||||
}
|
||||
|
||||
const m = self.shape.dims[0];
|
||||
const k = self.shape.dims[1];
|
||||
const n = other.shape.dims[1];
|
||||
|
||||
if (other.shape.dims[0] != k or result.shape.dims[0] != m or result.shape.dims[1] != n) {
|
||||
return TensorError.ShapeMismatch;
|
||||
}
|
||||
|
||||
switch (self.dtype) {
|
||||
.f32 => try matmulF32(self, other, result),
|
||||
else => return TensorError.UnsupportedOperation,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn format(
|
||||
self: Self,
|
||||
comptime fmt: []const u8,
|
||||
options: std.fmt.FormatOptions,
|
||||
writer: anytype,
|
||||
) !void {
|
||||
_ = fmt;
|
||||
_ = options;
|
||||
try writer.print("Tensor({}, {})", .{ self.shape, @tagName(self.dtype) });
|
||||
}
|
||||
};
|
||||
|
||||
// SIMD optimized addition for f32
|
||||
fn addF32SIMD(a: []const u8, b: []const u8, result: []u8) !void {
|
||||
const a_f32 = @as([]const f32, @alignCast(std.mem.bytesAsSlice(f32, a)));
|
||||
const b_f32 = @as([]const f32, @alignCast(std.mem.bytesAsSlice(f32, b)));
|
||||
const result_f32 = @as([]f32, @alignCast(std.mem.bytesAsSlice(f32, result)));
|
||||
|
||||
const VecSize = 8; // AVX2 can process 8 f32s at once
|
||||
const vec_len = a_f32.len / VecSize * VecSize;
|
||||
|
||||
// SIMD loop
|
||||
var i: usize = 0;
|
||||
while (i < vec_len) : (i += VecSize) {
|
||||
const va: @Vector(VecSize, f32) = a_f32[i..i+VecSize][0..VecSize].*;
|
||||
const vb: @Vector(VecSize, f32) = b_f32[i..i+VecSize][0..VecSize].*;
|
||||
const vr = va + vb;
|
||||
result_f32[i..i+VecSize][0..VecSize].* = vr;
|
||||
}
|
||||
|
||||
// Handle remainder
|
||||
while (i < a_f32.len) : (i += 1) {
|
||||
result_f32[i] = a_f32[i] + b_f32[i];
|
||||
}
|
||||
}
|
||||
|
||||
// Basic f16 addition (can be optimized with ARM NEON)
|
||||
fn addF16(a: []const u8, b: []const u8, result: []u8) !void {
|
||||
const a_f16 = @as([]const f16, @alignCast(std.mem.bytesAsSlice(f16, a)));
|
||||
const b_f16 = @as([]const f16, @alignCast(std.mem.bytesAsSlice(f16, b)));
|
||||
const result_f16 = @as([]f16, @alignCast(std.mem.bytesAsSlice(f16, result)));
|
||||
|
||||
for (0..a_f16.len) |i| {
|
||||
result_f16[i] = a_f16[i] + b_f16[i];
|
||||
}
|
||||
}
|
||||
|
||||
// Optimized matrix multiplication for transformers
|
||||
fn matmulF32(a: *Tensor, b: *const Tensor, c: *Tensor) !void {
|
||||
const a_data = try a.asSliceF32();
|
||||
const b_data = @as([]const f32, @alignCast(std.mem.bytesAsSlice(f32, b.data)));
|
||||
const c_data = try c.asSliceF32();
|
||||
|
||||
const m = a.shape.dims[0];
|
||||
const k = a.shape.dims[1];
|
||||
const n = b.shape.dims[1];
|
||||
|
||||
// TODO: Implement blocked matrix multiplication with SIMD
|
||||
// For now, simple triple loop
|
||||
for (0..m) |i| {
|
||||
for (0..n) |j| {
|
||||
var sum: f32 = 0.0;
|
||||
for (0..k) |l| {
|
||||
sum += a_data[i * k + l] * b_data[l * n + j];
|
||||
}
|
||||
c_data[i * n + j] = sum;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Tests
|
||||
test "tensor creation and basic operations" {
|
||||
const testing = std.testing;
|
||||
const allocator = testing.allocator;
|
||||
|
||||
// Test tensor creation
|
||||
const shape = Shape.init(&[_]u32{2, 3});
|
||||
var tensor = try Tensor.zeros(allocator, shape, .f32);
|
||||
defer tensor.deinit();
|
||||
|
||||
try testing.expect(tensor.shape.numel() == 6);
|
||||
try testing.expect(tensor.dtype == .f32);
|
||||
|
||||
// Test fill
|
||||
try tensor.fill(5.0);
|
||||
const data = try tensor.asSliceF32();
|
||||
try testing.expect(data[0] == 5.0);
|
||||
try testing.expect(data[5] == 5.0);
|
||||
}
|
||||
|
||||
test "tensor addition" {
|
||||
const testing = std.testing;
|
||||
const allocator = testing.allocator;
|
||||
|
||||
const shape = Shape.init(&[_]u32{4});
|
||||
var a = try Tensor.ones(allocator, shape, .f32);
|
||||
defer a.deinit();
|
||||
|
||||
var b = try Tensor.ones(allocator, shape, .f32);
|
||||
defer b.deinit();
|
||||
try b.fill(2.0);
|
||||
|
||||
var result = try Tensor.zeros(allocator, shape, .f32);
|
||||
defer result.deinit();
|
||||
|
||||
try a.add(&b, &result);
|
||||
|
||||
const data = try result.asSliceF32();
|
||||
for (data) |val| {
|
||||
try testing.expect(val == 3.0);
|
||||
}
|
||||
}
|
||||
43
experimental/src/core/tokenizer.zig
Normal file
43
experimental/src/core/tokenizer.zig
Normal file
@ -0,0 +1,43 @@
|
||||
const std = @import("std");
|
||||
const Allocator = std.mem.Allocator;
|
||||
|
||||
/// Tokenizer for DeepSeek V3
|
||||
pub const Tokenizer = struct {
|
||||
vocab_size: u32,
|
||||
allocator: Allocator,
|
||||
|
||||
const Self = @This();
|
||||
|
||||
pub fn init(allocator: Allocator, vocab_size: u32) !Self {
|
||||
std.log.info("Initializing tokenizer with vocab size: {}", .{vocab_size});
|
||||
|
||||
return Self{
|
||||
.vocab_size = vocab_size,
|
||||
.allocator = allocator,
|
||||
};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *Self) void {
|
||||
_ = self;
|
||||
// TODO: Cleanup tokenizer resources
|
||||
}
|
||||
|
||||
pub fn encode(self: *Self, text: []const u8) ![]u32 {
|
||||
// TODO: Implement actual tokenization
|
||||
_ = text;
|
||||
|
||||
// For now, return dummy tokens
|
||||
const tokens = try self.allocator.alloc(u32, 5);
|
||||
for (0..tokens.len) |i| {
|
||||
tokens[i] = @intCast(i + 1);
|
||||
}
|
||||
return tokens;
|
||||
}
|
||||
|
||||
pub fn decode(self: *Self, tokens: []const u32) ![]u8 {
|
||||
// TODO: Implement actual detokenization
|
||||
_ = tokens;
|
||||
|
||||
return try self.allocator.dupe(u8, "Hello, world!");
|
||||
}
|
||||
};
|
||||
40
experimental/src/core/transformer.zig
Normal file
40
experimental/src/core/transformer.zig
Normal file
@ -0,0 +1,40 @@
|
||||
const std = @import("std");
|
||||
const Allocator = std.mem.Allocator;
|
||||
const Tensor = @import("tensor.zig").Tensor;
|
||||
const Backend = @import("backend.zig").Backend;
|
||||
const model = @import("model.zig");
|
||||
|
||||
/// DeepSeek V3 Transformer implementation
|
||||
pub const Transformer = struct {
|
||||
config: model.ModelConfig,
|
||||
backend: Backend,
|
||||
allocator: Allocator,
|
||||
|
||||
// TODO: Add transformer layers
|
||||
// layers: []TransformerLayer,
|
||||
|
||||
const Self = @This();
|
||||
|
||||
pub fn init(allocator: Allocator, config: model.ModelConfig, backend: Backend) !Self {
|
||||
// TODO: Initialize transformer layers
|
||||
std.log.info("Initializing Transformer with {} layers", .{config.num_hidden_layers});
|
||||
|
||||
return Self{
|
||||
.config = config,
|
||||
.backend = backend,
|
||||
.allocator = allocator,
|
||||
};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *Self) void {
|
||||
// TODO: Cleanup layers
|
||||
_ = self;
|
||||
}
|
||||
|
||||
pub fn forward(self: *Self, input: *Tensor, output: *Tensor) !void {
|
||||
// TODO: Implement transformer forward pass
|
||||
_ = self;
|
||||
_ = input;
|
||||
_ = output;
|
||||
}
|
||||
};
|
||||
132
experimental/src/main.zig
Normal file
132
experimental/src/main.zig
Normal file
@ -0,0 +1,132 @@
|
||||
const std = @import("std");
|
||||
const deepseek_core = @import("deepseek_core");
|
||||
const web_layer = @import("web_layer");
|
||||
const cpu_backend = @import("cpu_backend");
|
||||
const metal_backend = @import("metal_backend");
|
||||
const cuda_backend = @import("cuda_backend");
|
||||
|
||||
const print = std.debug.print;
|
||||
const Allocator = std.mem.Allocator;
|
||||
|
||||
const Config = struct {
|
||||
port: u16 = 8080,
|
||||
host: []const u8 = "127.0.0.1",
|
||||
model_path: ?[]const u8 = null,
|
||||
backend: Backend = .cpu,
|
||||
max_concurrent_requests: u32 = 100,
|
||||
max_sequence_length: u32 = 32768,
|
||||
|
||||
const Backend = enum {
|
||||
cpu,
|
||||
metal,
|
||||
cuda,
|
||||
webgpu,
|
||||
};
|
||||
};
|
||||
|
||||
pub fn main() !void {
|
||||
var gpa = std.heap.GeneralPurposeAllocator(.{}){};
|
||||
defer _ = gpa.deinit();
|
||||
const allocator = gpa.allocator();
|
||||
|
||||
// Parse command line arguments
|
||||
const config = try parseArgs(allocator);
|
||||
|
||||
// Initialize the selected backend
|
||||
var backend = try initBackend(allocator, config.backend);
|
||||
defer backend.deinit();
|
||||
|
||||
// Load the model
|
||||
var model = if (config.model_path) |path|
|
||||
try deepseek_core.Model.loadFromPath(allocator, path, backend)
|
||||
else
|
||||
try deepseek_core.Model.loadDefault(allocator, backend);
|
||||
defer model.deinit();
|
||||
|
||||
print("🚀 DeepZig V3 Server Starting...\n", .{});
|
||||
print(" Backend: {s}\n", .{@tagName(config.backend)});
|
||||
print(" Host: {s}:{d}\n", .{ config.host, config.port });
|
||||
print(" Model: {s}\n", .{model.info().name});
|
||||
print(" Max Context: {} tokens\n", .{config.max_sequence_length});
|
||||
|
||||
// Start the web server
|
||||
var server = try web_layer.Server.init(allocator, .{
|
||||
.host = config.host,
|
||||
.port = config.port,
|
||||
.model = model,
|
||||
.max_concurrent_requests = config.max_concurrent_requests,
|
||||
});
|
||||
defer server.deinit();
|
||||
|
||||
print("✅ Server ready! Send requests to http://{s}:{d}\n", .{ config.host, config.port });
|
||||
print(" Endpoints:\n", .{});
|
||||
print(" - POST /v1/chat/completions (OpenAI compatible)\n", .{});
|
||||
print(" - POST /v1/completions\n", .{});
|
||||
print(" - GET /v1/models\n", .{});
|
||||
print(" - GET /health\n", .{});
|
||||
print(" - WebSocket /ws (streaming)\n", .{});
|
||||
|
||||
try server.listen();
|
||||
}
|
||||
|
||||
fn parseArgs(allocator: Allocator) !Config {
|
||||
const args = try std.process.argsAlloc(allocator);
|
||||
defer std.process.argsFree(allocator, args);
|
||||
|
||||
var config = Config{};
|
||||
|
||||
var i: usize = 1;
|
||||
while (i < args.len) : (i += 1) {
|
||||
const arg = args[i];
|
||||
|
||||
if (std.mem.eql(u8, arg, "--port") and i + 1 < args.len) {
|
||||
config.port = try std.fmt.parseInt(u16, args[i + 1], 10);
|
||||
i += 1;
|
||||
} else if (std.mem.eql(u8, arg, "--host") and i + 1 < args.len) {
|
||||
config.host = args[i + 1];
|
||||
i += 1;
|
||||
} else if (std.mem.eql(u8, arg, "--model") and i + 1 < args.len) {
|
||||
config.model_path = args[i + 1];
|
||||
i += 1;
|
||||
} else if (std.mem.eql(u8, arg, "--backend") and i + 1 < args.len) {
|
||||
const backend_str = args[i + 1];
|
||||
config.backend = std.meta.stringToEnum(Config.Backend, backend_str) orelse {
|
||||
print("Unknown backend: {s}\n", .{backend_str});
|
||||
print("Available backends: cpu, metal, cuda, webgpu\n", .{});
|
||||
std.process.exit(1);
|
||||
};
|
||||
i += 1;
|
||||
} else if (std.mem.eql(u8, arg, "--help") or std.mem.eql(u8, arg, "-h")) {
|
||||
printHelp();
|
||||
std.process.exit(0);
|
||||
}
|
||||
}
|
||||
|
||||
return config;
|
||||
}
|
||||
|
||||
fn initBackend(allocator: Allocator, backend_type: Config.Backend) !deepseek_core.Backend {
|
||||
return switch (backend_type) {
|
||||
.cpu => cpu_backend.init(allocator),
|
||||
.metal => metal_backend.init(allocator),
|
||||
.cuda => cuda_backend.init(allocator),
|
||||
.webgpu => {
|
||||
print("WebGPU backend not yet implemented, falling back to CPU\n", .{});
|
||||
return cpu_backend.init(allocator);
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
fn printHelp() void {
|
||||
print("DeepZig V3 - High-Performance LLM Inference\n\n", .{});
|
||||
print("Usage: deepseek-v3-zig [OPTIONS]\n\n", .{});
|
||||
print("Options:\n", .{});
|
||||
print(" --port <PORT> Port to listen on (default: 8080)\n", .{});
|
||||
print(" --host <HOST> Host to bind to (default: 127.0.0.1)\n", .{});
|
||||
print(" --model <PATH> Path to model weights\n", .{});
|
||||
print(" --backend <BACKEND> Backend to use: cpu, metal, cuda, webgpu (default: cpu)\n", .{});
|
||||
print(" --help, -h Show this help message\n\n", .{});
|
||||
print("Examples:\n", .{});
|
||||
print(" deepseek-v3-zig --port 3000 --backend metal\n", .{});
|
||||
print(" deepseek-v3-zig --model ./models/deepseek-v3.bin --backend cuda\n", .{});
|
||||
}
|
||||
127
experimental/src/wasm/main.zig
Normal file
127
experimental/src/wasm/main.zig
Normal file
@ -0,0 +1,127 @@
|
||||
// WebAssembly Entry Point for DeepSeek V3
|
||||
// Enables browser-based inference with minimal dependencies
|
||||
|
||||
const std = @import("std");
|
||||
const deepseek_core = @import("deepseek_core");
|
||||
|
||||
// WebAssembly allocator using the heap
|
||||
var gpa = std.heap.GeneralPurposeAllocator(.{}){};
|
||||
const allocator = gpa.allocator();
|
||||
|
||||
/// WebAssembly exports for JavaScript interop
|
||||
/// These functions are callable from JavaScript
|
||||
|
||||
/// Initialize the model (exported to JS)
|
||||
export fn wasm_init_model() i32 {
|
||||
// TODO: Initialize a smaller model suitable for browser
|
||||
std.log.info("Initializing DeepSeek V3 for WebAssembly", .{});
|
||||
|
||||
// For browser use, we'd use a much smaller model or quantized version
|
||||
// Return success status
|
||||
return 0; // Success
|
||||
}
|
||||
|
||||
/// Generate text completion (exported to JS)
|
||||
export fn wasm_generate_text(
|
||||
input_ptr: [*]const u8,
|
||||
input_len: u32,
|
||||
output_ptr: [*]u8,
|
||||
output_max_len: u32,
|
||||
) u32 {
|
||||
const input = input_ptr[0..input_len];
|
||||
const output_buffer = output_ptr[0..output_max_len];
|
||||
|
||||
std.log.info("WASM text generation: {s}", .{input});
|
||||
|
||||
// TODO: Implement actual generation
|
||||
// For now, return a placeholder response
|
||||
const response = "Hello from DeepSeek V3 WASM! Input was: ";
|
||||
const full_response = std.fmt.bufPrint(
|
||||
output_buffer,
|
||||
"{s}{s}",
|
||||
.{ response, input }
|
||||
) catch {
|
||||
// If buffer too small, return error length
|
||||
return 0;
|
||||
};
|
||||
|
||||
return @intCast(full_response.len);
|
||||
}
|
||||
|
||||
/// Tokenize text (exported to JS)
|
||||
export fn wasm_tokenize(
|
||||
text_ptr: [*]const u8,
|
||||
text_len: u32,
|
||||
tokens_ptr: [*]u32,
|
||||
max_tokens: u32,
|
||||
) u32 {
|
||||
const text = text_ptr[0..text_len];
|
||||
const tokens_buffer = tokens_ptr[0..max_tokens];
|
||||
|
||||
// TODO: Implement actual tokenization
|
||||
// For now, return dummy tokens
|
||||
const token_count = @min(text.len / 4, max_tokens); // Rough estimate
|
||||
|
||||
for (0..token_count) |i| {
|
||||
tokens_buffer[i] = @intCast(i + 1000); // Dummy token IDs
|
||||
}
|
||||
|
||||
return @intCast(token_count);
|
||||
}
|
||||
|
||||
/// Get model information (exported to JS)
|
||||
export fn wasm_get_model_info(
|
||||
info_ptr: [*]u8,
|
||||
info_max_len: u32,
|
||||
) u32 {
|
||||
const info_buffer = info_ptr[0..info_max_len];
|
||||
|
||||
const model_info =
|
||||
\\{"name":"DeepSeek-V3-WASM","version":"0.1.0","context_length":4096}
|
||||
;
|
||||
|
||||
if (model_info.len > info_max_len) {
|
||||
return 0; // Buffer too small
|
||||
}
|
||||
|
||||
@memcpy(info_buffer[0..model_info.len], model_info);
|
||||
return @intCast(model_info.len);
|
||||
}
|
||||
|
||||
/// Allocate memory for JavaScript (exported to JS)
|
||||
export fn wasm_alloc(size: u32) ?*anyopaque {
|
||||
const bytes = allocator.alloc(u8, size) catch return null;
|
||||
return bytes.ptr;
|
||||
}
|
||||
|
||||
/// Free memory allocated by wasm_alloc (exported to JS)
|
||||
export fn wasm_free(ptr: ?*anyopaque, size: u32) void {
|
||||
if (ptr) |p| {
|
||||
const bytes: [*]u8 = @ptrCast(p);
|
||||
allocator.free(bytes[0..size]);
|
||||
}
|
||||
}
|
||||
|
||||
/// Main entry point (called by Zig, not exported to JS)
|
||||
pub fn main() !void {
|
||||
std.log.info("DeepSeek V3 WebAssembly module loaded", .{});
|
||||
|
||||
// Initialize core components
|
||||
deepseek_core.init();
|
||||
|
||||
// WASM modules don't have a traditional main loop
|
||||
// All interaction happens through exported functions
|
||||
}
|
||||
|
||||
/// Panic handler for WebAssembly
|
||||
pub fn panic(message: []const u8, stack_trace: ?*std.builtin.StackTrace, ret_addr: ?usize) noreturn {
|
||||
_ = stack_trace;
|
||||
_ = ret_addr;
|
||||
|
||||
// In WASM, we can't print to stderr normally
|
||||
// Log the panic message and abort
|
||||
std.log.err("WASM Panic: {s}", .{message});
|
||||
|
||||
// Trap the WebAssembly execution
|
||||
unreachable;
|
||||
}
|
||||
156
experimental/src/web/handlers.zig
Normal file
156
experimental/src/web/handlers.zig
Normal file
@ -0,0 +1,156 @@
|
||||
const std = @import("std");
|
||||
const deepseek_core = @import("deepseek_core");
|
||||
const openai = @import("openai.zig");
|
||||
|
||||
const Allocator = std.mem.Allocator;
|
||||
const http = std.http;
|
||||
|
||||
/// Handle chat completions endpoint (OpenAI compatible)
|
||||
pub fn chatCompletions(
|
||||
allocator: Allocator,
|
||||
model: *deepseek_core.Model,
|
||||
request: *http.Server.Request,
|
||||
) !void {
|
||||
_ = allocator;
|
||||
_ = model;
|
||||
|
||||
// For now, send a simple placeholder response
|
||||
const response_json =
|
||||
\\{
|
||||
\\ "id": "chatcmpl-123",
|
||||
\\ "object": "chat.completion",
|
||||
\\ "created": 1677652288,
|
||||
\\ "model": "deepzig-v3",
|
||||
\\ "choices": [{
|
||||
\\ "index": 0,
|
||||
\\ "message": {
|
||||
\\ "role": "assistant",
|
||||
\\ "content": "Hello! This is a placeholder response from DeepZig V3."
|
||||
\\ },
|
||||
\\ "finish_reason": "stop"
|
||||
\\ }],
|
||||
\\ "usage": {
|
||||
\\ "prompt_tokens": 10,
|
||||
\\ "completion_tokens": 15,
|
||||
\\ "total_tokens": 25
|
||||
\\ }
|
||||
\\}
|
||||
;
|
||||
|
||||
try request.respond(response_json, .{
|
||||
.extra_headers = &.{
|
||||
.{ .name = "content-type", .value = "application/json" },
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
/// Handle text completions endpoint
|
||||
pub fn completions(
|
||||
allocator: Allocator,
|
||||
model: *deepseek_core.Model,
|
||||
request: *http.Server.Request,
|
||||
) !void {
|
||||
_ = allocator;
|
||||
_ = model;
|
||||
|
||||
try request.respond("Text completions not yet implemented", .{
|
||||
.status = .not_implemented,
|
||||
});
|
||||
}
|
||||
|
||||
/// Handle models list endpoint
|
||||
pub fn models(
|
||||
allocator: Allocator,
|
||||
model: *deepseek_core.Model,
|
||||
request: *http.Server.Request,
|
||||
) !void {
|
||||
_ = allocator;
|
||||
_ = model;
|
||||
|
||||
const response_json =
|
||||
\\{
|
||||
\\ "object": "list",
|
||||
\\ "data": [{
|
||||
\\ "id": "deepzig-v3",
|
||||
\\ "object": "model",
|
||||
\\ "created": 1677652288,
|
||||
\\ "owned_by": "deepzig"
|
||||
\\ }]
|
||||
\\}
|
||||
;
|
||||
|
||||
try request.respond(response_json, .{
|
||||
.extra_headers = &.{
|
||||
.{ .name = "content-type", .value = "application/json" },
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
/// Handle health check endpoint
|
||||
pub fn health(allocator: Allocator, request: *http.Server.Request) !void {
|
||||
_ = allocator;
|
||||
|
||||
const response_json =
|
||||
\\{
|
||||
\\ "status": "healthy",
|
||||
\\ "timestamp": 1677652288,
|
||||
\\ "version": "0.1.0"
|
||||
\\}
|
||||
;
|
||||
|
||||
try request.respond(response_json, .{
|
||||
.extra_headers = &.{
|
||||
.{ .name = "content-type", .value = "application/json" },
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
/// Handle WebSocket endpoint
|
||||
pub fn websocket(
|
||||
allocator: Allocator,
|
||||
model: *deepseek_core.Model,
|
||||
request: *http.Server.Request,
|
||||
) !void {
|
||||
_ = allocator;
|
||||
_ = model;
|
||||
|
||||
try request.respond("WebSocket not yet implemented", .{
|
||||
.status = .not_implemented,
|
||||
});
|
||||
}
|
||||
|
||||
/// Generate chat completion response (helper function)
|
||||
fn generateChatCompletion(
|
||||
allocator: Allocator,
|
||||
model: *deepseek_core.Model,
|
||||
chat_request: openai.ChatCompletionRequest,
|
||||
) !*openai.ChatCompletionResponse {
|
||||
// TODO: Implement actual generation
|
||||
_ = model;
|
||||
_ = chat_request;
|
||||
|
||||
const response = try allocator.create(openai.ChatCompletionResponse);
|
||||
response.* = openai.ChatCompletionResponse{
|
||||
.id = "chatcmpl-123",
|
||||
.object = "chat.completion",
|
||||
.created = std.time.timestamp(),
|
||||
.model = "deepzig-v3",
|
||||
.choices = &[_]openai.Choice{
|
||||
.{
|
||||
.index = 0,
|
||||
.message = openai.Message{
|
||||
.role = "assistant",
|
||||
.content = "Hello! This is a placeholder response from DeepZig V3.",
|
||||
},
|
||||
.finish_reason = "stop",
|
||||
},
|
||||
},
|
||||
.usage = openai.Usage{
|
||||
.prompt_tokens = 10,
|
||||
.completion_tokens = 15,
|
||||
.total_tokens = 25,
|
||||
},
|
||||
};
|
||||
|
||||
return response;
|
||||
}
|
||||
100
experimental/src/web/middleware.zig
Normal file
100
experimental/src/web/middleware.zig
Normal file
@ -0,0 +1,100 @@
|
||||
const std = @import("std");
|
||||
const http = std.http;
|
||||
const Allocator = std.mem.Allocator;
|
||||
|
||||
/// CORS middleware configuration
|
||||
pub const CorsConfig = struct {
|
||||
allow_origins: []const []const u8 = &[_][]const u8{"*"},
|
||||
allow_methods: []const []const u8 = &[_][]const u8{"GET", "POST", "PUT", "DELETE", "OPTIONS"},
|
||||
allow_headers: []const []const u8 = &[_][]const u8{"Content-Type", "Authorization"},
|
||||
max_age: u32 = 86400, // 24 hours
|
||||
};
|
||||
|
||||
/// Add CORS headers to response
|
||||
pub fn cors(response: *http.Server.Response, config: CorsConfig) !void {
|
||||
_ = config;
|
||||
// TODO: For now, just add basic CORS headers
|
||||
// In a real implementation, you'd check the request origin against allowed origins
|
||||
try response.headers.append("Access-Control-Allow-Origin", "*");
|
||||
try response.headers.append("Access-Control-Allow-Methods", "GET, POST, PUT, DELETE, OPTIONS");
|
||||
try response.headers.append("Access-Control-Allow-Headers", "Content-Type, Authorization");
|
||||
}
|
||||
|
||||
/// Request logging middleware
|
||||
pub fn logRequest(response: *http.Server.Response) void {
|
||||
const method = response.request.method;
|
||||
const target = response.request.target;
|
||||
const timestamp = std.time.timestamp();
|
||||
|
||||
std.log.info("[{}] {s} {s}", .{ timestamp, @tagName(method), target });
|
||||
}
|
||||
|
||||
/// Rate limiting middleware (basic implementation)
|
||||
pub const RateLimiter = struct {
|
||||
requests: std.HashMap(u32, RequestCount, std.hash_map.DefaultContext(u32), std.hash_map.default_max_load_percentage),
|
||||
allocator: Allocator,
|
||||
max_requests: u32,
|
||||
window_seconds: u32,
|
||||
|
||||
const RequestCount = struct {
|
||||
count: u32,
|
||||
window_start: i64,
|
||||
};
|
||||
|
||||
pub fn init(allocator: Allocator, max_requests: u32, window_seconds: u32) RateLimiter {
|
||||
return RateLimiter{
|
||||
.requests = std.HashMap(u32, RequestCount, std.hash_map.DefaultContext(u32), std.hash_map.default_max_load_percentage).init(allocator),
|
||||
.allocator = allocator,
|
||||
.max_requests = max_requests,
|
||||
.window_seconds = window_seconds,
|
||||
};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *RateLimiter) void {
|
||||
self.requests.deinit();
|
||||
}
|
||||
|
||||
/// Check if request is allowed (simplified IP-based rate limiting)
|
||||
pub fn checkRate(self: *RateLimiter, client_ip: u32) bool {
|
||||
const now = std.time.timestamp();
|
||||
const window_start = now - self.window_seconds;
|
||||
|
||||
const result = self.requests.getOrPut(client_ip) catch return false;
|
||||
|
||||
if (!result.found_existing) {
|
||||
// New client
|
||||
result.value_ptr.* = RequestCount{
|
||||
.count = 1,
|
||||
.window_start = now,
|
||||
};
|
||||
return true;
|
||||
}
|
||||
|
||||
// Check if we're in a new window
|
||||
if (result.value_ptr.window_start < window_start) {
|
||||
result.value_ptr.count = 1;
|
||||
result.value_ptr.window_start = now;
|
||||
return true;
|
||||
}
|
||||
|
||||
// Check if under limit
|
||||
if (result.value_ptr.count < self.max_requests) {
|
||||
result.value_ptr.count += 1;
|
||||
return true;
|
||||
}
|
||||
|
||||
return false; // Rate limited
|
||||
}
|
||||
};
|
||||
|
||||
/// Authentication middleware (basic bearer token)
|
||||
pub fn authenticateBearer(response: *http.Server.Response, expected_token: []const u8) bool {
|
||||
const auth_header = response.request.headers.getFirstValue("Authorization") orelse return false;
|
||||
|
||||
if (!std.mem.startsWith(u8, auth_header, "Bearer ")) {
|
||||
return false;
|
||||
}
|
||||
|
||||
const token = auth_header[7..]; // Skip "Bearer "
|
||||
return std.mem.eql(u8, token, expected_token);
|
||||
}
|
||||
57
experimental/src/web/openai.zig
Normal file
57
experimental/src/web/openai.zig
Normal file
@ -0,0 +1,57 @@
|
||||
const std = @import("std");
|
||||
|
||||
// OpenAI API compatible structures
|
||||
|
||||
/// Chat completion request
|
||||
pub const ChatCompletionRequest = struct {
|
||||
model: []const u8,
|
||||
messages: []Message,
|
||||
max_tokens: ?u32 = null,
|
||||
temperature: ?f32 = null,
|
||||
top_p: ?f32 = null,
|
||||
stream: ?bool = null,
|
||||
};
|
||||
|
||||
/// Chat message
|
||||
pub const Message = struct {
|
||||
role: []const u8, // "system", "user", "assistant"
|
||||
content: []const u8,
|
||||
};
|
||||
|
||||
/// Chat completion response
|
||||
pub const ChatCompletionResponse = struct {
|
||||
id: []const u8,
|
||||
object: []const u8, // "chat.completion"
|
||||
created: i64,
|
||||
model: []const u8,
|
||||
choices: []Choice,
|
||||
usage: Usage,
|
||||
};
|
||||
|
||||
/// Choice in completion response
|
||||
pub const Choice = struct {
|
||||
index: u32,
|
||||
message: Message,
|
||||
finish_reason: []const u8, // "stop", "length", "content_filter"
|
||||
};
|
||||
|
||||
/// Token usage information
|
||||
pub const Usage = struct {
|
||||
prompt_tokens: u32,
|
||||
completion_tokens: u32,
|
||||
total_tokens: u32,
|
||||
};
|
||||
|
||||
/// Models list response
|
||||
pub const ModelsResponse = struct {
|
||||
object: []const u8, // "list"
|
||||
data: []ModelInfo,
|
||||
};
|
||||
|
||||
/// Model information
|
||||
pub const ModelInfo = struct {
|
||||
id: []const u8,
|
||||
object: []const u8, // "model"
|
||||
created: i64,
|
||||
owned_by: []const u8,
|
||||
};
|
||||
75
experimental/src/web/request.zig
Normal file
75
experimental/src/web/request.zig
Normal file
@ -0,0 +1,75 @@
|
||||
const std = @import("std");
|
||||
const http = std.http;
|
||||
const Allocator = std.mem.Allocator;
|
||||
|
||||
/// Request wrapper for easier handling
|
||||
pub const Request = struct {
|
||||
inner: *http.Server.Request,
|
||||
allocator: Allocator,
|
||||
|
||||
const Self = @This();
|
||||
|
||||
pub fn init(inner: *http.Server.Request, allocator: Allocator) Self {
|
||||
return Self{
|
||||
.inner = inner,
|
||||
.allocator = allocator,
|
||||
};
|
||||
}
|
||||
|
||||
/// Get request method
|
||||
pub fn method(self: *const Self) http.Method {
|
||||
return self.inner.method;
|
||||
}
|
||||
|
||||
/// Get request path/target
|
||||
pub fn path(self: *const Self) []const u8 {
|
||||
return self.inner.target;
|
||||
}
|
||||
|
||||
/// Get header value
|
||||
pub fn header(self: *const Self, name: []const u8) ?[]const u8 {
|
||||
return self.inner.headers.getFirstValue(name);
|
||||
}
|
||||
|
||||
/// Get query parameter (simple implementation)
|
||||
pub fn query(self: *const Self, name: []const u8) ?[]const u8 {
|
||||
const target = self.inner.target;
|
||||
if (std.mem.indexOf(u8, target, "?")) |query_start| {
|
||||
const query_string = target[query_start + 1..];
|
||||
var iter = std.mem.split(u8, query_string, "&");
|
||||
|
||||
while (iter.next()) |param| {
|
||||
if (std.mem.indexOf(u8, param, "=")) |eq_pos| {
|
||||
const key = param[0..eq_pos];
|
||||
const value = param[eq_pos + 1..];
|
||||
if (std.mem.eql(u8, key, name)) {
|
||||
return value;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
/// Extract path parameter (e.g., /users/{id} -> id value)
|
||||
pub fn pathParam(self: *const Self, name: []const u8) ?[]const u8 {
|
||||
// TODO: Implement proper path parameter extraction
|
||||
// This would require route pattern matching
|
||||
_ = self;
|
||||
_ = name;
|
||||
return null;
|
||||
}
|
||||
|
||||
/// Get content type
|
||||
pub fn contentType(self: *const Self) ?[]const u8 {
|
||||
return self.header("Content-Type");
|
||||
}
|
||||
|
||||
/// Check if request is JSON
|
||||
pub fn isJson(self: *const Self) bool {
|
||||
if (self.contentType()) |ct| {
|
||||
return std.mem.startsWith(u8, ct, "application/json");
|
||||
}
|
||||
return false;
|
||||
}
|
||||
};
|
||||
92
experimental/src/web/response.zig
Normal file
92
experimental/src/web/response.zig
Normal file
@ -0,0 +1,92 @@
|
||||
const std = @import("std");
|
||||
const http = std.http;
|
||||
const Allocator = std.mem.Allocator;
|
||||
|
||||
/// Response wrapper for easier handling
|
||||
pub const Response = struct {
|
||||
inner: *http.Server.Response,
|
||||
allocator: Allocator,
|
||||
|
||||
const Self = @This();
|
||||
|
||||
pub fn init(inner: *http.Server.Response, allocator: Allocator) Self {
|
||||
return Self{
|
||||
.inner = inner,
|
||||
.allocator = allocator,
|
||||
};
|
||||
}
|
||||
|
||||
/// Set response status
|
||||
pub fn setStatus(self: *Self, status: http.Status) void {
|
||||
self.inner.status = status;
|
||||
}
|
||||
|
||||
/// Set header
|
||||
pub fn setHeader(self: *Self, name: []const u8, value: []const u8) !void {
|
||||
try self.inner.headers.append(name, value);
|
||||
}
|
||||
|
||||
/// Send JSON response
|
||||
pub fn sendJson(self: *Self, data: anytype) !void {
|
||||
const json_string = try std.json.stringifyAlloc(
|
||||
self.allocator,
|
||||
data,
|
||||
.{ .whitespace = .indent_2 },
|
||||
);
|
||||
defer self.allocator.free(json_string);
|
||||
|
||||
try self.setHeader("Content-Type", "application/json");
|
||||
self.inner.transfer_encoding = .{ .content_length = json_string.len };
|
||||
try self.inner.do();
|
||||
|
||||
try self.inner.writeAll(json_string);
|
||||
try self.inner.finish();
|
||||
}
|
||||
|
||||
/// Send text response
|
||||
pub fn sendText(self: *Self, text: []const u8) !void {
|
||||
try self.setHeader("Content-Type", "text/plain");
|
||||
self.inner.transfer_encoding = .{ .content_length = text.len };
|
||||
try self.inner.do();
|
||||
|
||||
try self.inner.writeAll(text);
|
||||
try self.inner.finish();
|
||||
}
|
||||
|
||||
/// Send HTML response
|
||||
pub fn sendHtml(self: *Self, html: []const u8) !void {
|
||||
try self.setHeader("Content-Type", "text/html");
|
||||
self.inner.transfer_encoding = .{ .content_length = html.len };
|
||||
try self.inner.do();
|
||||
|
||||
try self.inner.writeAll(html);
|
||||
try self.inner.finish();
|
||||
}
|
||||
|
||||
/// Send error response
|
||||
pub fn sendError(self: *Self, status: http.Status, message: []const u8) !void {
|
||||
const error_response = struct {
|
||||
@"error": struct {
|
||||
message: []const u8,
|
||||
type: []const u8,
|
||||
code: u16,
|
||||
},
|
||||
}{
|
||||
.@"error" = .{
|
||||
.message = message,
|
||||
.type = "error",
|
||||
.code = @intFromEnum(status),
|
||||
},
|
||||
};
|
||||
|
||||
self.setStatus(status);
|
||||
try self.sendJson(error_response);
|
||||
}
|
||||
|
||||
/// Redirect to another URL
|
||||
pub fn redirect(self: *Self, location: []const u8) !void {
|
||||
self.setStatus(.found);
|
||||
try self.setHeader("Location", location);
|
||||
try self.sendText("");
|
||||
}
|
||||
};
|
||||
34
experimental/src/web/root.zig
Normal file
34
experimental/src/web/root.zig
Normal file
@ -0,0 +1,34 @@
|
||||
// DeepSeek V3 Web Layer
|
||||
// HTTP server and API endpoints
|
||||
|
||||
const std = @import("std");
|
||||
|
||||
// Web components
|
||||
pub const Server = @import("server.zig").Server;
|
||||
pub const handlers = @import("handlers.zig");
|
||||
pub const middleware = @import("middleware.zig");
|
||||
pub const websocket = @import("websocket.zig");
|
||||
|
||||
// OpenAI API compatibility
|
||||
pub const openai = @import("openai.zig");
|
||||
|
||||
// Response types
|
||||
pub const Response = @import("response.zig").Response;
|
||||
pub const Request = @import("request.zig").Request;
|
||||
|
||||
// Error handling
|
||||
pub const WebError = error{
|
||||
InvalidRequest,
|
||||
Unauthorized,
|
||||
RateLimited,
|
||||
ServerError,
|
||||
ModelNotFound,
|
||||
BadRequest,
|
||||
};
|
||||
|
||||
// Tests
|
||||
test "web layer" {
|
||||
const testing = std.testing;
|
||||
_ = testing;
|
||||
// TODO: Add web layer tests
|
||||
}
|
||||
239
experimental/src/web/server.zig
Normal file
239
experimental/src/web/server.zig
Normal file
@ -0,0 +1,239 @@
|
||||
const std = @import("std");
|
||||
const deepseek_core = @import("deepseek_core");
|
||||
const handlers = @import("handlers.zig");
|
||||
const middleware = @import("middleware.zig");
|
||||
|
||||
const Allocator = std.mem.Allocator;
|
||||
const net = std.net;
|
||||
const http = std.http;
|
||||
|
||||
/// Server configuration
|
||||
pub const ServerConfig = struct {
|
||||
host: []const u8,
|
||||
port: u16,
|
||||
model: deepseek_core.Model,
|
||||
max_concurrent_requests: u32,
|
||||
request_timeout_ms: u32 = 30000,
|
||||
max_body_size: usize = 1024 * 1024, // 1MB
|
||||
};
|
||||
|
||||
/// HTTP server for DeepSeek V3 API
|
||||
pub const Server = struct {
|
||||
config: ServerConfig,
|
||||
allocator: Allocator,
|
||||
server: net.Server,
|
||||
|
||||
const Self = @This();
|
||||
|
||||
pub fn init(allocator: Allocator, config: ServerConfig) !Self {
|
||||
const address = net.Address.parseIp4(config.host, config.port) catch |err| {
|
||||
std.log.err("Failed to parse IP address {s}:{d}: {}", .{ config.host, config.port, err });
|
||||
return err;
|
||||
};
|
||||
|
||||
const server = address.listen(.{}) catch |err| {
|
||||
std.log.err("Failed to listen on {s}:{d}: {}", .{ config.host, config.port, err });
|
||||
return err;
|
||||
};
|
||||
|
||||
return Self{
|
||||
.config = config,
|
||||
.allocator = allocator,
|
||||
.server = server,
|
||||
};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *Self) void {
|
||||
self.server.deinit();
|
||||
}
|
||||
|
||||
/// Start listening for requests
|
||||
pub fn listen(self: *Self) !void {
|
||||
std.log.info("Server listening on {s}:{d}", .{ self.config.host, self.config.port });
|
||||
|
||||
while (true) {
|
||||
// Accept connection
|
||||
const connection = self.server.accept() catch |err| {
|
||||
std.log.err("Failed to accept connection: {}", .{err});
|
||||
continue;
|
||||
};
|
||||
defer connection.stream.close();
|
||||
|
||||
// Handle request
|
||||
self.handleConnection(connection) catch |err| {
|
||||
std.log.err("Failed to handle connection: {}", .{err});
|
||||
continue;
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/// Handle individual connection
|
||||
fn handleConnection(self: *Self, connection: net.Server.Connection) !void {
|
||||
var read_buffer: [4096]u8 = undefined;
|
||||
var http_server = http.Server.init(connection, &read_buffer);
|
||||
|
||||
// Receive request head
|
||||
var request = http_server.receiveHead() catch |err| {
|
||||
std.log.err("Failed to receive HTTP head: {}", .{err});
|
||||
return;
|
||||
};
|
||||
|
||||
std.log.debug("Request: {s} {s}", .{ @tagName(request.head.method), request.head.target });
|
||||
|
||||
// Route and handle request
|
||||
try self.handleRequest(&request);
|
||||
}
|
||||
|
||||
/// Route and handle HTTP request
|
||||
fn handleRequest(self: *Self, request: *http.Server.Request) !void {
|
||||
const target = request.head.target;
|
||||
|
||||
// Route requests based on path
|
||||
if (std.mem.startsWith(u8, target, "/v1/chat/completions")) {
|
||||
try self.handleChatCompletions(request);
|
||||
} else if (std.mem.startsWith(u8, target, "/v1/completions")) {
|
||||
try self.handleCompletions(request);
|
||||
} else if (std.mem.startsWith(u8, target, "/v1/models")) {
|
||||
try self.handleModels(request);
|
||||
} else if (std.mem.startsWith(u8, target, "/health")) {
|
||||
try self.handleHealth(request);
|
||||
} else if (std.mem.startsWith(u8, target, "/ws")) {
|
||||
try self.handleWebSocket(request);
|
||||
} else {
|
||||
try self.sendNotFound(request);
|
||||
}
|
||||
}
|
||||
|
||||
/// Handle chat completions endpoint
|
||||
fn handleChatCompletions(self: *Self, request: *http.Server.Request) !void {
|
||||
_ = self;
|
||||
|
||||
// For now, send a simple placeholder response
|
||||
const response_json =
|
||||
\\{
|
||||
\\ "id": "chatcmpl-123",
|
||||
\\ "object": "chat.completion",
|
||||
\\ "created": 1677652288,
|
||||
\\ "model": "deepzig-v3",
|
||||
\\ "choices": [{
|
||||
\\ "index": 0,
|
||||
\\ "message": {
|
||||
\\ "role": "assistant",
|
||||
\\ "content": "Hello! This is a placeholder response from DeepZig V3."
|
||||
\\ },
|
||||
\\ "finish_reason": "stop"
|
||||
\\ }],
|
||||
\\ "usage": {
|
||||
\\ "prompt_tokens": 10,
|
||||
\\ "completion_tokens": 15,
|
||||
\\ "total_tokens": 25
|
||||
\\ }
|
||||
\\}
|
||||
;
|
||||
|
||||
try request.respond(response_json, .{
|
||||
.extra_headers = &.{
|
||||
.{ .name = "content-type", .value = "application/json" },
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
/// Handle text completions endpoint
|
||||
fn handleCompletions(self: *Self, request: *http.Server.Request) !void {
|
||||
_ = self;
|
||||
try request.respond("Text completions not yet implemented", .{
|
||||
.status = .not_implemented,
|
||||
});
|
||||
}
|
||||
|
||||
/// Handle models list endpoint
|
||||
fn handleModels(self: *Self, request: *http.Server.Request) !void {
|
||||
_ = self;
|
||||
|
||||
const response_json =
|
||||
\\{
|
||||
\\ "object": "list",
|
||||
\\ "data": [{
|
||||
\\ "id": "deepzig-v3",
|
||||
\\ "object": "model",
|
||||
\\ "created": 1677652288,
|
||||
\\ "owned_by": "deepzig"
|
||||
\\ }]
|
||||
\\}
|
||||
;
|
||||
|
||||
try request.respond(response_json, .{
|
||||
.extra_headers = &.{
|
||||
.{ .name = "content-type", .value = "application/json" },
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
/// Handle health check endpoint
|
||||
fn handleHealth(self: *Self, request: *http.Server.Request) !void {
|
||||
_ = self;
|
||||
|
||||
const response_json =
|
||||
\\{
|
||||
\\ "status": "healthy",
|
||||
\\ "timestamp": 1677652288,
|
||||
\\ "version": "0.1.0"
|
||||
\\}
|
||||
;
|
||||
|
||||
try request.respond(response_json, .{
|
||||
.extra_headers = &.{
|
||||
.{ .name = "content-type", .value = "application/json" },
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
/// Handle WebSocket endpoint (placeholder)
|
||||
fn handleWebSocket(self: *Self, request: *http.Server.Request) !void {
|
||||
_ = self;
|
||||
try request.respond("WebSocket not yet implemented", .{
|
||||
.status = .not_implemented,
|
||||
});
|
||||
}
|
||||
|
||||
/// Send 404 Not Found response
|
||||
fn sendNotFound(self: *Self, request: *http.Server.Request) !void {
|
||||
_ = self;
|
||||
try request.respond("{\"error\":\"Not Found\"}", .{
|
||||
.status = .not_found,
|
||||
.extra_headers = &.{
|
||||
.{ .name = "content-type", .value = "application/json" },
|
||||
},
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
// Tests
|
||||
test "server creation" {
|
||||
const testing = std.testing;
|
||||
const allocator = testing.allocator;
|
||||
|
||||
// Mock model for testing
|
||||
const model = deepseek_core.Model{
|
||||
.config = deepseek_core.Model.ModelConfig.deepseekV3Default(),
|
||||
.transformer = undefined,
|
||||
.tokenizer = undefined,
|
||||
.backend = deepseek_core.Backend.init(allocator, .cpu, 0),
|
||||
.allocator = allocator,
|
||||
.embed_tokens = undefined,
|
||||
.embed_positions = null,
|
||||
.lm_head = undefined,
|
||||
.norm = undefined,
|
||||
};
|
||||
|
||||
const config = ServerConfig{
|
||||
.host = "127.0.0.1",
|
||||
.port = 0, // Let OS choose port for testing
|
||||
.model = model,
|
||||
.max_concurrent_requests = 10,
|
||||
};
|
||||
|
||||
// Note: Can't actually create server in test due to socket binding
|
||||
// This would require integration tests
|
||||
_ = config;
|
||||
}
|
||||
102
experimental/src/web/websocket.zig
Normal file
102
experimental/src/web/websocket.zig
Normal file
@ -0,0 +1,102 @@
|
||||
const std = @import("std");
|
||||
const deepseek_core = @import("deepseek_core");
|
||||
|
||||
const Allocator = std.mem.Allocator;
|
||||
|
||||
/// WebSocket connection state
|
||||
pub const WebSocketState = enum {
|
||||
connecting,
|
||||
connected,
|
||||
closing,
|
||||
closed,
|
||||
};
|
||||
|
||||
/// WebSocket frame types
|
||||
pub const FrameType = enum {
|
||||
text,
|
||||
binary,
|
||||
close,
|
||||
ping,
|
||||
pong,
|
||||
};
|
||||
|
||||
/// WebSocket connection handler
|
||||
pub const WebSocketConnection = struct {
|
||||
allocator: Allocator,
|
||||
state: WebSocketState,
|
||||
model: *deepseek_core.Model,
|
||||
|
||||
const Self = @This();
|
||||
|
||||
pub fn init(allocator: Allocator, model: *deepseek_core.Model) Self {
|
||||
return Self{
|
||||
.allocator = allocator,
|
||||
.state = .connecting,
|
||||
.model = model,
|
||||
};
|
||||
}
|
||||
|
||||
pub fn deinit(self: *Self) void {
|
||||
self.state = .closed;
|
||||
}
|
||||
|
||||
/// Handle incoming WebSocket frame
|
||||
pub fn handleFrame(self: *Self, frame_type: FrameType, data: []const u8) !void {
|
||||
switch (frame_type) {
|
||||
.text => try self.handleTextMessage(data),
|
||||
.binary => try self.handleBinaryMessage(data),
|
||||
.close => self.state = .closing,
|
||||
.ping => try self.sendPong(data),
|
||||
.pong => {}, // Handle pong if needed
|
||||
}
|
||||
}
|
||||
|
||||
/// Handle text message (JSON chat requests)
|
||||
fn handleTextMessage(self: *Self, data: []const u8) !void {
|
||||
_ = self;
|
||||
std.log.info("WebSocket text message: {s}", .{data});
|
||||
|
||||
// TODO: Parse JSON chat request and stream response back
|
||||
// This would involve:
|
||||
// 1. Parse incoming JSON (chat completion request)
|
||||
// 2. Start model generation
|
||||
// 3. Stream tokens back as they're generated
|
||||
// 4. Send completion when done
|
||||
}
|
||||
|
||||
/// Handle binary message
|
||||
fn handleBinaryMessage(self: *Self, data: []const u8) !void {
|
||||
_ = self;
|
||||
_ = data;
|
||||
std.log.info("WebSocket binary message received", .{});
|
||||
// TODO: Handle binary data if needed
|
||||
}
|
||||
|
||||
/// Send pong response to ping
|
||||
fn sendPong(self: *Self, data: []const u8) !void {
|
||||
_ = self;
|
||||
_ = data;
|
||||
// TODO: Send WebSocket pong frame
|
||||
std.log.debug("Sending WebSocket pong");
|
||||
}
|
||||
|
||||
/// Send text message to client
|
||||
pub fn sendText(self: *Self, message: []const u8) !void {
|
||||
_ = self;
|
||||
// TODO: Implement WebSocket frame encoding and sending
|
||||
std.log.debug("Sending WebSocket text: {s}", .{message});
|
||||
}
|
||||
|
||||
/// Send streaming token
|
||||
pub fn sendStreamingToken(self: *Self, token: []const u8) !void {
|
||||
// TODO: Format as Server-Sent Events style JSON and send
|
||||
const json_chunk = try std.fmt.allocPrint(
|
||||
self.allocator,
|
||||
"{{\"choices\":[{{\"delta\":{{\"content\":\"{s}\"}}}}]}}",
|
||||
.{token}
|
||||
);
|
||||
defer self.allocator.free(json_chunk);
|
||||
|
||||
try self.sendText(json_chunk);
|
||||
}
|
||||
};
|
||||
BIN
experimental/zig-out/bin/deepseek-v3-zig
Executable file
BIN
experimental/zig-out/bin/deepseek-v3-zig
Executable file
Binary file not shown.
Loading…
Reference in New Issue
Block a user