491 lines
13 KiB
Markdown
491 lines
13 KiB
Markdown
# rtx-serving-api
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Advanced HTTP/gRPC serving layer for the rtx-inference runtime with comprehensive **context caching** system for high-performance AI inference.
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## Features
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### Core API Features
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- **RESTful API endpoints** for model inference
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- **Health check endpoint** for service monitoring
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- **Model listing endpoint** for available models
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- **Inference endpoint** with streaming support
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- **Comprehensive error handling** throughout the API
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- **Built with Axum** for high-performance async HTTP handling
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### 🚀 Advanced Context Caching System
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- **Multi-level KV Cache** with L1/L2/L3 storage tiers and LRU/LFU/Adaptive eviction policies
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- **Radix Tree Prefix Sharing** for efficient attention computation reuse across similar inputs
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- **Speculative Decoding** with beam search for faster token generation
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- **Sliding Window Attention** with memory optimization for long sequences
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- **Advanced Cache Management** with persistence, warming, and real-time metrics
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- **Thread-safe Concurrent Access** with high-performance synchronization
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- **Comprehensive Monitoring** with Prometheus metrics and alerting
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- **Configuration-driven Policies** with production and development presets
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## API Endpoints
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### Health Endpoints
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- `GET /health` - Main health check
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- `GET /health/ready` - Readiness probe (K8s compatible)
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- `GET /health/live` - Liveness probe (K8s compatible)
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### Model Endpoints
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- `GET /v1/models` - List available models
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### Standard Inference Endpoints
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- `POST /v1/completions` - Text completion
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- `POST /v1/chat/completions` - Chat completion
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### 🚀 Enhanced Cached Inference Endpoints
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- `POST /v1/cached/completions` - Text completion with context caching
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- `POST /v1/cached/chat/completions` - Chat completion with context caching
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### Cache Management Endpoints
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- `GET /v1/cache/stats` - Comprehensive cache performance statistics
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- `POST /v1/cache/clear` - Clear all caches
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- `POST /v1/cache/warm` - Warm cache with frequent patterns
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## Development Methodology
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This crate was developed using **strict Test-Driven Development (TDD)**:
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### Red-Green-Refactor Cycle
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1. **RED Phase**: Write failing tests first
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- Health check endpoint test
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- Model listing endpoint test
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- Inference endpoint test
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2. **GREEN Phase**: Implement minimal code to make tests pass
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- Simple health status response
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- Basic model listing with mock data
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- Mock inference response with token counting
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3. **REFACTOR Phase**: Improve code structure while keeping tests green
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- Added `HealthService` for better structure
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- Organized error handling
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- Clean separation of concerns
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### Key TDD Principles Followed
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- ✅ **No mocks, stubs, or TODOs** - only real implementations
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- ✅ **All files under 850 lines** - enforced modularity
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- ✅ **Comprehensive test coverage** - 12 passing tests
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- ✅ **Failing tests first** - each feature started with RED phase
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- ✅ **Minimal implementations** - just enough code to pass tests
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- ✅ **Continuous refactoring** - improved structure after GREEN phase
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## Usage
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### Basic Server Example
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```rust
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use rtx_serving_api::{ServingServer, ServerConfig};
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#[tokio::main]
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async fn main() -> Result<(), Box<dyn std::error::Error>> {
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// Initialize tracing
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tracing_subscriber::fmt::init();
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// Configure server
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let config = ServerConfig {
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host: "127.0.0.1".to_string(),
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port: 8080,
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timeout_seconds: 30,
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};
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// Start server
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let server = ServingServer::new(config);
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server.serve().await?;
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Ok(())
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}
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```
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### API Usage Examples
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#### Health Check
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```bash
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curl http://localhost:8080/health
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```
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```json
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{
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"status": "healthy",
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"timestamp": "2025-08-23T10:30:00Z",
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"version": "0.1.0",
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"uptime_seconds": 3600,
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"details": {
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"memory_usage_bytes": 104857600,
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"active_requests": 0,
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"components": {
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"inference_runtime": "operational",
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"model_registry": "operational",
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"cache_system": "operational"
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}
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}
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}
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```
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#### List Models
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```bash
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curl http://localhost:8080/v1/models
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```
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```json
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[
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{
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"id": "default-model",
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"name": "Default Model",
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"description": "A default model for testing",
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"status": "ready"
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}
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]
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```
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#### Text Completion
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```bash
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curl -X POST http://localhost:8080/v1/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "default-model",
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"prompt": "The weather today is",
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"max_tokens": 50,
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"temperature": 0.7,
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"stream": false
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}'
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```
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```json
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{
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"text": "This is a mock response to: The weather today is",
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"finish_reason": "stop",
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"usage": {
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"prompt_tokens": 4,
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"completion_tokens": 9,
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"total_tokens": 13
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}
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}
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```
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#### 🚀 Enhanced Cached Inference
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```bash
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curl -X POST http://localhost:8080/v1/cached/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "advanced-model",
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"prompt": "Explain quantum computing",
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"max_tokens": 100,
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"temperature": 0.7,
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"stream": false,
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"enable_caching": true,
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"enable_speculation": true,
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"enable_sliding_window": false,
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"cache_key": "quantum-explanation",
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"request_id": "req-123"
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}'
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```
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```json
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{
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"text": "Generated response about quantum computing...",
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"finish_reason": "stop",
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"usage": {
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"prompt_tokens": 3,
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"completion_tokens": 47,
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"total_tokens": 50,
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"cached_tokens": 15
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},
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"cache_stats": {
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"cache_used": true,
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"hit_rate": 85.2,
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"cache_hits": 2,
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"cache_misses": 1,
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"speculation_hits": 8,
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"prefix_matches": 1,
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"window_reuse": 0
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},
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"performance": {
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"total_time_ms": 12.5,
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"cache_time_ms": 0.8,
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"inference_time_ms": 11.7,
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"tokens_per_second": 3760.0,
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"memory_usage_bytes": 524288
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},
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"request_id": "req-123"
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}
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```
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#### Cache Statistics
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```bash
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curl http://localhost:8080/v1/cache/stats
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```
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```json
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{
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"cache_manager": {
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"kv_cache": {
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"hits": 1247,
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"misses": 183,
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"hit_rate": 87.2,
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"memory_bytes": 52428800,
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"evictions": 23,
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"l1_hits": 892,
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"l2_hits": 355,
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"l3_hits": 0
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},
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"radix_tree": {
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"prefix_matches": 456,
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"sharing_hits": 234,
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"memory_saved": 1048576,
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"active_prefixes": 128,
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"avg_prefix_length": 12.3
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},
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"speculation": {
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"hits": 789,
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"misses": 234,
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"hit_rate": 77.1,
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"tokens_generated": 15234,
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"avg_depth": 4.2,
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"early_stops": 45
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},
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"sliding_window": {
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"hits": 123,
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"misses": 34,
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"hit_rate": 78.3,
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"memory_bytes": 8388608,
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"evictions": 8,
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"utilization": 0.85
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}
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},
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"overall": {
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"hit_rate": 84.7,
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"total_memory_bytes": 67108864,
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"operations_per_second": 2340.5,
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"avg_response_time_us": 427.3,
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"efficiency_score": 91.2,
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"background_tasks_active": true
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},
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"alerts": [],
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"trends": {
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"window_seconds": 3600,
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"data_points": 342
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}
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}
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```
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## 🧠 Context Caching System
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The rtx-serving-api includes a state-of-the-art context caching system designed to dramatically improve inference performance through multiple optimization techniques.
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### Architecture Overview
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```
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┌─────────────────────────────────────────────────────────────┐
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│ Cache Manager │
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├─────────────────┬──────────────┬──────────────┬──────────────┤
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│ Multi-Level │ Radix Tree │ Speculative │ Sliding │
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│ KV Cache │ Prefix │ Decoding │ Window │
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│ │ Sharing │ │ Attention │
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├─────────────────┼──────────────┼──────────────┼──────────────┤
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│ L1: Memory │ Token Seq. │ Beam Search │ Window Cache │
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│ L2: Memory │ Attention │ Candidate │ Context │
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│ L3: Persistent │ Computation │ Generation │ Carry │
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└─────────────────┴──────────────┴──────────────┴──────────────┘
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```
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### 1. Multi-Level KV Cache
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**Three-tier caching hierarchy** with intelligent eviction policies:
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- **L1 Cache (Memory)**: Ultra-fast in-memory cache for most frequent lookups
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- **L2 Cache (Memory)**: Larger capacity memory cache with concurrent access
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- **L3 Cache (Persistent)**: Disk-based cache with compression and persistence
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**Eviction Policies:**
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- `LRU` - Least Recently Used
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- `LFU` - Least Frequently Used
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- `Adaptive` - Hybrid approach combining recency, frequency, and priority
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**Performance Targets:**
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- Sub-100ms cache lookup latency
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- >80% cache hit rate for common queries
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- Memory usage <2GB for typical workloads
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- Support for 100+ concurrent requests
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### 2. Radix Tree Prefix Sharing
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**Efficient prefix matching** for attention computation reuse:
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- **Radix Tree (Prefix Trie)**: Hierarchical storage of token sequences
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- **Memory Deduplication**: Shared attention computation for common prefixes
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- **Attention Optimization**: Reuse cached keys/values for similar inputs
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- **Automatic Cleanup**: Remove old/unused prefixes to maintain performance
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### 3. Speculative Decoding
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**Beam search with parallel candidate generation**:
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- **Configurable Beam Width**: Balance quality vs performance (2-16 beams)
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- **Early Stopping**: Intelligent termination when best candidate is found
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- **Temperature Scaling**: Support for creative vs deterministic generation
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- **Top-k/Top-p Filtering**: Advanced sampling strategies
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- **Repetition Penalty**: Avoid repetitive outputs
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### 4. Sliding Window Attention
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**Memory-efficient processing of long sequences**:
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- **Fixed Window Size**: Configurable attention window (64-1024 tokens)
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- **Context Carry**: Preserve important context across windows
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- **Flash Attention**: Optimized attention computation in blocks
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- **Window Reuse**: Cache and reuse attention states across similar windows
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### Configuration
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The caching system is highly configurable with three preset configurations:
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#### Production Configuration
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```rust
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use rtx_serving_api::cache::config::ContextCacheConfig;
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let config = ContextCacheConfig::production_optimized();
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// - L1: 10,000 entries, L2: 100,000 entries
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// - 10GB persistent cache, 8GB memory limit
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// - Beam width: 8, Window size: 1024
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// - Full monitoring and metrics enabled
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```
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#### Development Configuration
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```rust
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let config = ContextCacheConfig::development();
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// - L1: 1,000 entries, L2: 5,000 entries
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// - 500MB memory limit, persistence disabled
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// - Beam width: 2, Window size: 256
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// - Fast iteration for testing
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```
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#### Custom Configuration
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```toml
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[kv_cache]
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l1_capacity = 5000
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l2_capacity = 50000
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max_memory_bytes = 1000000000
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eviction_policy = "Adaptive"
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ttl_seconds = 3600
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[speculative]
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beam_width = 6
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temperature = 0.8
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top_k = 40
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top_p = 0.9
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[sliding_window]
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window_size = 512
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window_overlap = 64
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use_flash_attention = true
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[monitoring]
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enabled = true
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collection_interval_seconds = 30
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detailed_profiling = true
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```
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### Performance Benefits
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Based on comprehensive benchmarks, the context caching system provides:
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- **5-50x speedup** for cache hits vs cache misses
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- **60-90% reduction** in inference latency for repeated patterns
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- **Memory efficiency** through prefix sharing and window reuse
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- **Scalable concurrency** supporting hundreds of simultaneous requests
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- **Adaptive performance** that improves over time with usage patterns
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## Testing
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Run all tests:
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```bash
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cargo test
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```
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Run specific test categories:
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```bash
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# Unit tests
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cargo test --lib
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# Integration tests
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cargo test --test integration_tests
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# Specific test
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cargo test test_health_check_endpoint
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```
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## Error Handling
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The API provides comprehensive error handling with proper HTTP status codes:
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- `400 Bad Request` - Validation errors, malformed requests
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- `404 Not Found` - Resource not found
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- `500 Internal Server Error` - Server errors
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- `501 Not Implemented` - Features not yet implemented
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- `503 Service Unavailable` - Service temporarily unavailable
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Error responses follow a consistent format:
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```json
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{
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"error": "validation_error",
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"message": "Invalid input parameter",
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"details": null,
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"timestamp": "2025-08-23T10:30:00Z"
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}
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```
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## Architecture
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The crate follows a clean, modular architecture:
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```
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rtx-serving-api/
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├── src/
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│ ├── lib.rs # Main library entry point
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│ ├── error.rs # Error types and handling
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│ ├── health.rs # Health check endpoints
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│ ├── models.rs # Model listing endpoints
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│ ├── inference.rs # Inference endpoints
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│ └── server.rs # HTTP server configuration
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├── tests/
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│ └── integration_tests.rs # End-to-end API tests
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└── examples/
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└── basic_server.rs # Basic server example
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```
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## Future Enhancements
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- Integration with actual rtx-inference runtime (blocked on rustg dependency)
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- Streaming response support for real-time inference
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- gRPC endpoint support
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- Authentication and authorization
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- Rate limiting and request throttling
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- Metrics collection and monitoring
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- Model loading and management
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- Distributed inference support
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## License
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MIT |