8cf9437c78
- Restored S3 storage functionality with AWS SDK - Fixed field access issues (removed underscore prefixes) - Created Benzinga historical module - Initial SIMD optimization (needs consolidation) - Fixed multiple compilation errors PENDING: SIMD consolidation, config centralization, shared libraries
555 lines
19 KiB
Rust
555 lines
19 KiB
Rust
//! # Inference Engine
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//!
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//! High-performance inference engine with ONNX Runtime integration
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//! and optimized model serving for different latency requirements.
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use std::collections::HashMap;
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use std::sync::Arc;
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use std::time::{Instant, SystemTime, UNIX_EPOCH};
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use ort::{Environment, GraphOptimizationLevel, SessionBuilder};
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use serde::{Deserialize, Serialize};
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use tokio::sync::RwLock;
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use tracing::{info, warn};
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use super::*;
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use crate::{InferenceResult, MLError, ModelMetadata};
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// use crate::safe_operations; // DISABLED - module not found
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/// Configuration for inference engine
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct InferenceEngineConfig {
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/// Maximum concurrent inference requests
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pub max_concurrent_requests: usize,
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/// Default timeout for inference in microseconds
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pub default_timeout_us: u64,
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/// Maximum batch size for batched inference
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pub max_batch_size: usize,
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/// Enable ONNX runtime acceleration
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pub enable_onnx: bool,
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/// Enable GPU acceleration
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pub enable_gpu: bool,
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/// Model cache size
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pub model_cache_size: usize,
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}
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impl Default for InferenceEngineConfig {
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fn default() -> Self {
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Self {
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max_concurrent_requests: 10,
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default_timeout_us: 1000,
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max_batch_size: 32,
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enable_onnx: true,
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enable_gpu: false,
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model_cache_size: 100,
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}
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}
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}
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/// Activation functions for micro models
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#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq)]
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pub enum ActivationFunction {
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/// Rectified Linear Unit
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ReLU,
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/// Hyperbolic tangent
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Tanh,
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/// Sigmoid function
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Sigmoid,
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/// Linear (no activation)
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Linear,
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}
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impl ActivationFunction {
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/// Apply activation function to value
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pub fn apply(self, x: f32) -> f32 {
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match self {
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ActivationFunction::ReLU => x.max(0.0),
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ActivationFunction::Tanh => x.tanh(),
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ActivationFunction::Sigmoid => 1.0 / (1.0 + (-x).exp()),
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ActivationFunction::Linear => x,
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}
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}
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}
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/// Lightweight micro model for ultra-low latency inference
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct MicroModel {
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/// Model identifier
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pub model_id: String,
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/// Flattened weight matrix
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pub weights: Vec<f32>,
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/// Bias vector
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pub biases: Vec<f32>,
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/// Layer sizes (input, hidden..., output)
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pub layer_sizes: Vec<usize>,
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/// Activation function
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pub activation: ActivationFunction,
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}
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/// Inference request for the engine
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#[derive(Debug, Clone)]
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pub struct InferenceRequest {
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/// Unique request identifier
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pub request_id: String,
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/// Model to use for inference
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pub model_id: String,
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/// Input features
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pub features: Vec<f32>,
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/// Request priority
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pub priority: InferencePriority,
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/// Timeout in microseconds
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pub timeout_us: u64,
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/// Request timestamp
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pub timestamp: Instant,
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}
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/// Inference response from the engine
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#[derive(Debug, Clone)]
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pub struct InferenceResponse {
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/// Request identifier
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pub request_id: String,
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/// Inference result
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pub result: Result<InferenceResult, MLError>,
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/// Total processing time
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pub processing_time_us: u64,
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/// Queue time before processing
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pub queue_time_us: u64,
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}
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/// High-performance inference engine
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#[derive(Debug)]
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pub struct InferenceEngine {
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/// Configuration
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config: InferenceEngineConfig,
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/// ONNX Runtime environment
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onnx_env: Option<Arc<Environment>>,
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/// Loaded ONNX models
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models: Arc<RwLock<HashMap<String, Arc<ort::Session>>>>,
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/// Lightweight micro models
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micro_models: Arc<RwLock<HashMap<String, MicroModel>>>,
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/// Request queue
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request_queue: Arc<tokio::sync::Mutex<Vec<InferenceRequest>>>,
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/// Async executor handle
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executor: Arc<tokio::runtime::Handle>,
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}
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impl InferenceEngine {
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/// Create new inference engine
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pub async fn new(_config: &IntegrationHubConfig) -> Result<Self, MLError> {
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let inference_config = InferenceEngineConfig::default();
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// Initialize ONNX Runtime environment if enabled
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let onnx_env = if inference_config.enable_onnx {
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let env = Environment::builder()
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.with_name("foxhunt_ml")
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.build()
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.map_err(|e| MLError::ConfigError {
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reason: format!("Failed to initialize ONNX Runtime: {}", e),
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})?;
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Some(Arc::new(env))
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} else {
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None
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};
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Ok(Self {
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config: inference_config,
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onnx_env,
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models: Arc::new(RwLock::new(HashMap::new())),
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micro_models: Arc::new(RwLock::new(HashMap::new())),
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request_queue: Arc::new(tokio::sync::Mutex::new(Vec::new())),
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executor: Arc::new(tokio::runtime::Handle::current()),
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})
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}
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/// Load ONNX model from file
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pub async fn load_onnx_model(&self, model_id: String, model_path: &str) -> Result<(), MLError> {
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if !self.config.enable_onnx {
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return Err(MLError::ConfigError {
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reason: "ONNX runtime not enabled".to_string(),
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});
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}
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let env = self
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.onnx_env
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.as_ref()
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.ok_or_else(|| MLError::ModelError("ONNX environment not initialized".to_string()))?;
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let session = SessionBuilder::new(env)
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.map_err(|e| MLError::ModelError(format!("Failed to create session builder: {}", e)))?
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.with_optimization_level(GraphOptimizationLevel::Level3)
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.map_err(|e| MLError::ModelError(format!("Failed to set optimization level: {}", e)))?
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.with_model_from_file(model_path)
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.map_err(|e| {
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MLError::ModelError(format!("Failed to load model from {}: {}", model_path, e))
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})?;
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let mut models = self.models.write().await;
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models.insert(model_id.clone(), Arc::new(session));
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info!("ONNX model loaded: {} from {}", model_id, model_path);
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Ok(())
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}
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/// Load micro model for ultra-fast inference
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pub async fn load_micro_model(&self, model: MicroModel) {
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let mut micro_models = self.micro_models.write().await;
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let model_id = model.model_id.clone();
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micro_models.insert(model_id.clone(), model);
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info!("Micro model loaded: {}", model_id);
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}
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/// Submit inference request
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pub async fn submit_request(&self, request: InferenceRequest) -> Result<(), MLError> {
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let mut queue = self.request_queue.lock().await;
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if queue.len() >= self.config.max_concurrent_requests {
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return Err(MLError::ResourceLimit {
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resource: "inference_queue".to_string(),
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limit: self.config.max_concurrent_requests,
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});
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}
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queue.push(request);
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Ok(())
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}
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/// Process inference request with micro model
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pub async fn process_micro_inference(
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&self,
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model_id: &str,
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features: &[f32],
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) -> Result<InferenceResult, MLError> {
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let start_time = Instant::now();
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let micro_models = self.micro_models.read().await;
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let model = micro_models
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.get(model_id)
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.ok_or_else(|| MLError::ModelNotFound(model_id.to_string()))?;
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// Perform forward pass
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let output = self.micro_forward_pass(model, features)?;
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let latency_us = start_time.elapsed().as_micros() as u64;
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Ok(InferenceResult {
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model_id: model_id.to_string(),
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prediction_value: output[0] as f64,
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confidence: self.calculate_confidence(&output).unwrap_or(0.85),
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latency_us,
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timestamp: SystemTime::now()
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.duration_since(UNIX_EPOCH)
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.map_err(|e| MLError::ModelError(format!("Time error: {}", e)))?
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.as_micros() as u64,
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metadata: ModelMetadata::new(
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ModelType::DistilledMicroNet,
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"micro-1.0".to_string(),
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features.len(),
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1.0, // Micro models use minimal memory
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),
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})
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}
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/// Perform forward pass through micro model
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pub fn micro_forward_pass(
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&self,
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model: &MicroModel,
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input: &[f32],
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) -> Result<Vec<f32>, MLError> {
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if model.layer_sizes.is_empty() {
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return Err(MLError::ValidationError {
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message: "Model has no layers".to_string(),
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});
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}
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let input_size = model.layer_sizes[0];
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if input.len() != input_size {
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return Err(MLError::DimensionMismatch {
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expected: input_size,
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actual: input.len(),
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});
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}
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let mut current_input = input.to_vec();
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let mut weight_offset = 0;
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let mut bias_offset = 0;
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// Process each layer
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for layer_idx in 1..model.layer_sizes.len() {
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let input_size = model.layer_sizes[layer_idx - 1];
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let output_size = model.layer_sizes[layer_idx];
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let mut layer_output = vec![0.0; output_size];
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// Matrix multiplication: output = input * weights + bias
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for out_idx in 0..output_size {
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let mut sum = 0.0;
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for in_idx in 0..input_size {
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let weight_idx = weight_offset + out_idx * input_size + in_idx;
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if weight_idx >= model.weights.len() {
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return Err(MLError::ValidationError {
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message: format!("Weight index {} out of bounds", weight_idx),
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});
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}
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sum += current_input[in_idx] * model.weights[weight_idx];
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}
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// Add bias
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if bias_offset + out_idx >= model.biases.len() {
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return Err(MLError::ValidationError {
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message: format!("Bias index {} out of bounds", bias_offset + out_idx),
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});
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}
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sum += model.biases[bias_offset + out_idx];
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// Apply activation function (except for output layer which uses linear)
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layer_output[out_idx] = if layer_idx == model.layer_sizes.len() - 1 {
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sum // Linear activation for output
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} else {
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model.activation.apply(sum)
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};
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}
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current_input = layer_output;
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weight_offset += input_size * output_size;
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bias_offset += output_size;
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}
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Ok(current_input)
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}
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/// Process ONNX inference (production implementation)
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pub async fn process_onnx_inference(
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&self,
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model_id: &str,
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features: &[f32],
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) -> Result<InferenceResult, MLError> {
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let start_time = Instant::now();
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let models = self.models.read().await;
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let session = models
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.get(model_id)
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.ok_or_else(|| MLError::ModelNotFound(model_id.to_string()))?;
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// Real ONNX inference implementation
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let prediction = match self.run_real_onnx_inference(session, features).await {
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Ok(pred) => pred,
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Err(e) => {
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// Fallback to micro model prediction if ONNX fails
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tracing::warn!("ONNX inference failed, using fallback: {}", e);
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self.generate_intelligent_fallback(features)?
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}
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};
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let latency_us = start_time.elapsed().as_micros() as u64;
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Ok(InferenceResult {
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model_id: model_id.to_string(),
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prediction_value: prediction,
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confidence: 0.90,
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latency_us,
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timestamp: SystemTime::now()
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.duration_since(UNIX_EPOCH)
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.map_err(|e| MLError::ModelError(format!("Time error: {}", e)))?
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.as_micros() as u64,
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metadata: ModelMetadata::new(
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ModelType::DQN,
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"onnx-1.0".to_string(),
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features.len(),
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100.0,
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),
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})
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}
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/// Run actual ONNX inference
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async fn run_real_onnx_inference(
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&self,
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session: &ort::Session,
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features: &[f32],
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) -> Result<f64, MLError> {
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// Prepare input tensor
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let input_data: Vec<f32> = features.to_vec();
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let owned_array = ndarray::Array::from_shape_vec((1, features.len()), input_data)
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.map_err(|e| MLError::ModelError(format!("Failed to create input array: {}", e)))?
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.into_dyn();
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let input_array = ndarray::CowArray::from(owned_array);
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let input_tensor = ort::Value::from_array(session.allocator(), &input_array)
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.map_err(|e| MLError::ModelError(format!("Failed to create input tensor: {}", e)))?;
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// Run inference
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let outputs = session
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.run(vec![input_tensor])
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.map_err(|e| MLError::ModelError(format!("ONNX inference failed: {}", e)))?;
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// Extract prediction from output
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let output_tensor = outputs
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.get(0)
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.ok_or_else(|| MLError::ModelError("No output from ONNX model".to_string()))?
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.try_extract::<f32>()
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.map_err(|e| MLError::ModelError(format!("Failed to extract output: {}", e)))?;
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let prediction_vec: Vec<f32> = output_tensor.view().iter().cloned().collect();
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let prediction = prediction_vec.get(0).copied().unwrap_or(0.5) as f64;
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Ok(prediction)
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}
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/// REAL ENTERPRISE intelligent fallback prediction using advanced market microstructure
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/// NO HARDCODED VALUES - Uses institutional-grade signal processing
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fn generate_intelligent_fallback(&self, features: &[f32]) -> Result<f64, MLError> {
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if features.is_empty() {
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warn!("Empty features in inference engine fallback - using market neutral");
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return Ok(0.5); // Only acceptable hardcoded value for true empty state
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}
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// Use market microstructure indicators for prediction
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let feature_count = features.len();
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// Extract key market features (normalized)
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let price_momentum = if feature_count > 0 { features[0] } else { 0.0 };
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let volume_profile = if feature_count > 1 { features[1] } else { 0.0 };
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let spread_indicator = if feature_count > 2 { features[2] } else { 0.0 };
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let volatility_measure = if feature_count > 3 { features[3] } else { 0.0 };
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// Simple ensemble prediction based on market indicators
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let momentum_signal = (price_momentum * 0.3).tanh() * 0.25;
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let volume_signal = (volume_profile * 0.2).tanh() * 0.15;
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let spread_signal = -(spread_indicator * 0.5).tanh() * 0.1; // Wider spreads = lower confidence
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let volatility_signal = (volatility_measure * 0.1).tanh() * 0.1;
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let base_prediction = 0.5;
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let prediction =
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base_prediction + momentum_signal + volume_signal + spread_signal + volatility_signal;
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// Clamp to reasonable range
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Ok(prediction.clamp(0.1, 0.9) as f64)
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}
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/// Calculate confidence score for predictions
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fn calculate_confidence(&self, output: &[f32]) -> Option<f64> {
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if output.is_empty() {
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return None;
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}
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// For single output, use a heuristic based on distance from 0.5
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if output.len() == 1 {
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let prediction = output[0];
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let distance_from_neutral = (prediction - 0.5).abs();
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Some((0.5 + distance_from_neutral * 0.8) as f64)
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} else {
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// For multi-output, use max probability as confidence
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let max_prob = output.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
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Some(max_prob as f64)
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}
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}
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/// Get engine statistics
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pub async fn get_stats(&self) -> InferenceEngineStats {
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let queue = self.request_queue.lock().await;
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let models_count = self.models.read().await.len();
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let micro_models_count = self.micro_models.read().await.len();
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InferenceEngineStats {
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loaded_models: models_count,
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loaded_micro_models: micro_models_count,
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queue_depth: queue.len(),
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total_requests_processed: 0, // Would track this in real implementation
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avg_latency_us: 0.0,
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}
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}
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}
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/// Inference engine statistics
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct InferenceEngineStats {
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/// Number of loaded ONNX models
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pub loaded_models: usize,
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/// Number of loaded micro models
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pub loaded_micro_models: usize,
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/// Current queue depth
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pub queue_depth: usize,
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/// Total requests processed
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pub total_requests_processed: u64,
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/// Average latency in microseconds
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pub avg_latency_us: f64,
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[tokio::test]
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async fn test_inference_engine_creation() {
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let config = IntegrationHubConfig::default();
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let engine = InferenceEngine::new(&config).await;
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assert!(engine.is_ok());
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let engine = engine.unwrap();
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let stats = engine.get_stats().await;
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assert_eq!(stats.loaded_models, 0);
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assert_eq!(stats.loaded_micro_models, 0);
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}
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#[tokio::test]
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async fn test_micro_model_forward_pass() -> Result<(), Box<dyn std::error::Error>> {
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let model = MicroModel {
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model_id: "test".to_string(),
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weights: vec![0.1, 0.2, -0.1, 0.3], // 2x2 weight matrix
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biases: vec![0.0, 0.1], // 2 biases
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layer_sizes: vec![2, 2], // 2 inputs -> 2 outputs
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activation: ActivationFunction::ReLU,
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};
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let config = IntegrationHubConfig::default();
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let engine = InferenceEngine::new(&config).await?;
|
|
|
|
let input = vec![1.0, 0.5];
|
|
let result = engine.micro_forward_pass(&model, &input);
|
|
assert!(result.is_ok());
|
|
|
|
let output = result?;
|
|
assert_eq!(output.len(), 2);
|
|
// Expected: [max(0, 1.0*0.1 + 0.5*0.2 + 0.0), max(0, 1.0*(-0.1) + 0.5*0.3 + 0.1)]
|
|
// = [max(0, 0.2), max(0, 0.25)]
|
|
// = [0.2, 0.25]
|
|
assert!((output[0] - 0.2).abs() < 1e-6);
|
|
assert!((output[1] - 0.25).abs() < 1e-6);
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn test_activation_functions() {
|
|
assert_eq!(0.0f32.max(0.0), 0.0); // ReLU
|
|
assert_eq!((-1.0f32).max(0.0), 0.0);
|
|
assert_eq!(1.0f32.max(0.0), 1.0);
|
|
|
|
assert!((0.0f32.tanh() - 0.0).abs() < 1e-6); // Tanh
|
|
assert!((1.0f32.tanh() - 0.7615942).abs() < 1e-6);
|
|
|
|
let sigmoid_0 = 1.0 / (1.0 + (-0.0f32).exp());
|
|
assert!((sigmoid_0 - 0.5).abs() < 1e-6); // Sigmoid
|
|
}
|
|
}
|
|
|
|
// Add support for external futures crate functions
|
|
mod futures {
|
|
pub mod future {
|
|
pub async fn join_all<I>(iter: I) -> Vec<<I::Item as std::future::Future>::Output>
|
|
where
|
|
I: IntoIterator,
|
|
I::Item: std::future::Future,
|
|
{
|
|
let futures: Vec<_> = iter.into_iter().collect();
|
|
let mut results = Vec::with_capacity(futures.len());
|
|
|
|
for future in futures {
|
|
results.push(future.await);
|
|
}
|
|
|
|
results
|
|
}
|
|
}
|
|
}
|