models.rsā¢17.6 kB
use std::sync::Arc;
use std::time::{Duration, Instant};
use anyhow::{Context, Result};
use futures::FutureExt;
use parking_lot::RwLock;
use prometheus::{
register_gauge, register_histogram, register_int_counter, Gauge, Histogram, IntCounter,
};
use serde::{Deserialize, Serialize};
use thiserror::Error;
use tokio::sync::{mpsc, oneshot};
use tokio::time::sleep;
use tracing::{debug, error, info, warn};
#[derive(Debug, Error)]
pub enum AiOptimizeError {
#[error("backend not available: {0}")]
BackendUnavailable(&'static str),
#[error("operation not implemented for backend: {0}")]
NotImplemented(&'static str),
#[error(transparent)]
Other(#[from] anyhow::Error),
}
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum QuantizationLevel {
FP32,
FP16,
INT8,
}
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum GpuAcceleration {
None,
Cuda,
TensorRt,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PruningConfig {
pub target_sparsity: f32, // 0.0 .. 1.0
pub structured: bool,
}
impl Default for PruningConfig {
fn default() -> Self {
Self {
target_sparsity: 0.5,
structured: true,
}
}
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DistillationConfig {
pub teacher_model_path: String,
pub temperature: f32,
pub alpha: f32, // loss mix factor
}
impl Default for DistillationConfig {
fn default() -> Self {
Self {
teacher_model_path: String::new(),
temperature: 2.0,
alpha: 0.9,
}
}
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationSummary {
pub original_size_bytes: u64,
pub optimized_size_bytes: u64,
pub quantization: Option<QuantizationLevel>,
pub pruning_sparsity: Option<f32>,
pub distilled: bool,
pub gpu: GpuAcceleration,
pub estimated_accuracy_drop: Option<f32>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BatchingConfig {
pub max_batch_size: usize,
pub max_delay_ms: u64,
}
impl Default for BatchingConfig {
fn default() -> Self {
Self {
max_batch_size: 32,
max_delay_ms: 8,
}
}
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MonitoringThresholds {
pub target_size_reduction_ratio: f32, // e.g., 0.60 for 60%
pub max_accuracy_drop: f32, // e.g., 0.02 for 2%
pub gpu_utilization_target: f64, // 0..100
}
impl Default for MonitoringThresholds {
fn default() -> Self {
Self {
target_size_reduction_ratio: 0.60,
max_accuracy_drop: 0.02,
gpu_utilization_target: 85.0,
}
}
}
// Inputs/Outputs ā keep language-agnostic
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorInput(pub serde_json::Value);
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorOutput(pub serde_json::Value);
// Trait abstractions for backend models
pub trait InferenceModel: Send + Sync {
fn predict(&self, input: TensorInput) -> Result<TensorOutput>;
fn predict_batch(&self, inputs: &[TensorInput]) -> Result<Vec<TensorOutput>>;
fn size_bytes(&self) -> Result<u64>;
}
// Simple no-op backend for development/testing
#[derive(Debug)]
pub struct NoopModel {
pub size: u64,
}
impl InferenceModel for NoopModel {
fn predict(&self, input: TensorInput) -> Result<TensorOutput> {
Ok(TensorOutput(input.0))
}
fn predict_batch(&self, inputs: &[TensorInput]) -> Result<Vec<TensorOutput>> {
Ok(inputs.iter().cloned().map(|i| TensorOutput(i.0)).collect())
}
fn size_bytes(&self) -> Result<u64> {
Ok(self.size)
}
}
// Metrics registry
#[derive(Clone)]
pub struct OptimizerMetrics {
pub inference_requests_total: IntCounter,
pub inference_latency_seconds: Histogram,
pub batch_size: Histogram,
pub model_size_bytes: Gauge,
pub gpu_utilization: Gauge,
}
impl OptimizerMetrics {
pub fn new() -> Self {
Self {
inference_requests_total: register_int_counter!(
"cg_ai_inference_requests_total",
"Total inference requests"
)
.expect("register metric"),
inference_latency_seconds: register_histogram!(
"cg_ai_inference_latency_seconds",
"Inference latency (seconds)",
vec![0.001, 0.002, 0.005, 0.01, 0.05, 0.1, 0.25, 0.5, 1.0]
)
.expect("register metric"),
batch_size: register_histogram!(
"cg_ai_batch_size",
"Batch sizes for dynamic batching",
vec![1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0]
)
.expect("register metric"),
model_size_bytes: register_gauge!(
"cg_ai_model_size_bytes",
"Current optimized model size in bytes"
)
.expect("register metric"),
gpu_utilization: register_gauge!(
"cg_ai_gpu_utilization_percent",
"GPU utilization percentage"
)
.expect("register metric"),
}
}
}
// Dynamic batching implementation
struct BatchRequest<I, O> {
input: I,
tx: oneshot::Sender<Result<O>>,
}
pub struct DynamicBatcher<I: Send + 'static, O: Send + 'static> {
tx: mpsc::Sender<BatchRequest<I, O>>,
}
impl<I: Send + 'static, O: Send + 'static> DynamicBatcher<I, O> {
pub fn spawn<F, Fut>(cfg: BatchingConfig, metrics: OptimizerMetrics, mut run_batch: F) -> Self
where
F: FnMut(Vec<I>) -> Fut + Send + 'static,
Fut: std::future::Future<Output = Result<Vec<O>>> + Send + 'static,
{
let (tx, mut rx) = mpsc::channel::<BatchRequest<I, O>>(1024);
tokio::spawn(async move {
let mut buffer: Vec<BatchRequest<I, O>> = Vec::with_capacity(cfg.max_batch_size);
let max_delay = Duration::from_millis(cfg.max_delay_ms);
let mut last_flush = Instant::now();
loop {
let delay = async {
let wait = max_delay
.checked_sub(last_flush.elapsed())
.unwrap_or_default();
sleep(wait).await;
};
tokio::select! {
maybe_req = rx.recv() => {
match maybe_req {
Some(req) => {
buffer.push(req);
if buffer.len() >= cfg.max_batch_size {
Self::flush(&mut buffer, &mut run_batch, &metrics).await;
last_flush = Instant::now();
}
},
None => {
if !buffer.is_empty() {
Self::flush(&mut buffer, &mut run_batch, &metrics).await;
}
break;
}
}
}
_ = delay => {
if !buffer.is_empty() {
Self::flush(&mut buffer, &mut run_batch, &metrics).await;
last_flush = Instant::now();
}
}
}
}
});
Self { tx }
}
async fn flush<F, Fut>(
buffer: &mut Vec<BatchRequest<I, O>>,
run_batch: &mut F,
metrics: &OptimizerMetrics,
) where
F: FnMut(Vec<I>) -> Fut + Send + 'static,
Fut: std::future::Future<Output = Result<Vec<O>>> + Send + 'static,
{
if buffer.is_empty() {
return;
}
let inputs: Vec<I> = buffer
.iter_mut()
.map(|r| {
std::mem::replace(&mut r.input, unsafe {
std::mem::MaybeUninit::zeroed().assume_init()
})
})
.collect();
let size = inputs.len();
metrics.batch_size.observe(size as f64);
let start = Instant::now();
match run_batch(inputs).await {
Ok(outputs) => {
if outputs.len() != size {
let err = anyhow::anyhow!(
"batch output size mismatch: got {} expected {}",
outputs.len(),
size
);
for req in buffer.drain(..) {
let _ = req.tx.send(Err(anyhow::anyhow!("{}", err)));
}
} else {
for (req, out) in buffer.drain(..).zip(outputs.into_iter()) {
let _ = req.tx.send(Ok(out));
}
}
metrics
.inference_latency_seconds
.observe(start.elapsed().as_secs_f64());
}
Err(e) => {
error!(error=?e, "batch inference failed");
for req in buffer.drain(..) {
let _ = req.tx.send(Err(anyhow::anyhow!("{}", e)));
}
}
}
}
pub async fn infer(&self, input: I) -> Result<O> {
let (tx, rx) = oneshot::channel();
let req = BatchRequest { input, tx };
self.tx
.send(req)
.await
.map_err(|_| anyhow::anyhow!("dynamic batch queue full or closed"))?;
rx.await
.map_err(|_| anyhow::anyhow!("inference canceled"))?
}
}
// Model optimizer facade
#[derive(Clone)]
pub struct ModelOptimizer {
pub model: Arc<dyn InferenceModel>,
pub metrics: OptimizerMetrics,
pub thresholds: MonitoringThresholds,
pub state: Arc<RwLock<OptimizationSummary>>, // track applied optimizations
}
impl ModelOptimizer {
pub fn new(model: Arc<dyn InferenceModel>, thresholds: MonitoringThresholds) -> Result<Self> {
let metrics = OptimizerMetrics::new();
let size = model.size_bytes().unwrap_or(0);
metrics.model_size_bytes.set(size as f64);
Ok(Self {
model,
metrics,
thresholds,
state: Arc::new(RwLock::new(OptimizationSummary {
original_size_bytes: size,
optimized_size_bytes: size,
quantization: None,
pruning_sparsity: None,
distilled: false,
gpu: GpuAcceleration::None,
estimated_accuracy_drop: None,
})),
})
}
pub fn summary(&self) -> OptimizationSummary {
self.state.read().clone()
}
// Quantization APIs ā backend-specific implementations behind feature flags.
pub fn quantize_fp16(&self) -> Result<()> {
#[cfg(feature = "tch")]
{
// With tch, a full-graph conversion requires model-specific access.
// For now, expose as not implemented until integrated with a model holder.
return Err(AiOptimizeError::NotImplemented("tch fp16 quantization").into());
}
#[cfg(feature = "onnx")]
{
// ONNX Runtime quantization should be handled offline or through tooling.
return Err(AiOptimizeError::NotImplemented("onnx fp16 quantization").into());
}
#[cfg(feature = "candle")]
{
return Err(AiOptimizeError::NotImplemented("candle fp16 quantization").into());
}
#[allow(unreachable_code)]
{
warn!("fp16 quantization requested but no backend enabled");
Err(AiOptimizeError::BackendUnavailable("no quantization backend").into())
}
}
pub fn quantize_int8(&self) -> Result<()> {
#[cfg(feature = "onnx")]
{
return Err(AiOptimizeError::NotImplemented("onnx int8 quantization").into());
}
#[cfg(feature = "tch")]
{
return Err(AiOptimizeError::NotImplemented("tch int8 quantization").into());
}
#[cfg(feature = "candle")]
{
return Err(AiOptimizeError::NotImplemented("candle int8 quantization").into());
}
#[allow(unreachable_code)]
{
warn!("int8 quantization requested but no backend enabled");
Err(AiOptimizeError::BackendUnavailable("no quantization backend").into())
}
}
pub fn apply_pruning(&self, _cfg: PruningConfig) -> Result<()> {
// Placeholder; pruning typically requires weight access
Err(AiOptimizeError::NotImplemented("pruning").into())
}
pub fn apply_distillation(&self, _cfg: DistillationConfig) -> Result<()> {
// Placeholder; distillation is a training-time process.
Err(AiOptimizeError::NotImplemented("distillation").into())
}
pub fn enable_gpu(&self, accel: GpuAcceleration) -> Result<()> {
match accel {
GpuAcceleration::None => {
self.state.write().gpu = GpuAcceleration::None;
Ok(())
}
GpuAcceleration::Cuda | GpuAcceleration::TensorRt => {
// Actual enabling is backend/model-specific; mark in state for monitoring intents.
self.state.write().gpu = accel;
Ok(())
}
}
}
pub fn dynamic_batcher(
&self,
cfg: BatchingConfig,
) -> DynamicBatcher<TensorInput, TensorOutput> {
let model = self.model.clone();
let metrics = self.metrics.clone();
DynamicBatcher::spawn(cfg, metrics.clone(), move |inputs| {
let model = model.clone();
async move {
let outputs = model.predict_batch(&inputs)?;
Ok(outputs)
}
})
}
pub fn start_monitoring(&self) {
// GPU utilization polling via NVML if available
#[cfg(feature = "nvml")]
{
use nvml_wrapper::Nvml;
let nvml = Nvml::init().ok();
let metrics = self.metrics.clone();
tokio::spawn(async move {
if let Some(nvml) = nvml {
loop {
if let Ok(device) = nvml.device_by_index(0) {
if let Ok(util) = device.utilization_rates() {
metrics.gpu_utilization.set(util.gpu as f64);
}
}
sleep(Duration::from_millis(1000)).await;
}
} else {
warn!("NVML init failed; GPU utilization metrics disabled");
}
});
}
// Alerting loop to check thresholds; emits logs (integrate with Alertmanager externally)
let thresholds = self.thresholds.clone();
let metrics = self.metrics.clone();
let state = self.state.clone();
tokio::spawn(async move {
loop {
let s = state.read().clone();
let original = s.original_size_bytes as f64;
let optimized = s.optimized_size_bytes as f64;
if original > 0.0 {
let ratio = (original - optimized) / original;
if ratio + f64::EPSILON < thresholds.target_size_reduction_ratio as f64 {
warn!(target=?thresholds.target_size_reduction_ratio, actual=?ratio, "Size reduction below target");
}
}
if let Some(drop) = s.estimated_accuracy_drop {
if (drop as f64) - f64::EPSILON > thresholds.max_accuracy_drop as f64 {
warn!(max=?thresholds.max_accuracy_drop, actual=?drop, "Accuracy drop above allowed threshold");
}
}
let gpu = metrics.gpu_utilization.get();
if gpu + f64::EPSILON < thresholds.gpu_utilization_target {
debug!(target=?thresholds.gpu_utilization_target, actual=?gpu, "GPU utilization below target");
}
sleep(Duration::from_secs(5)).await;
}
});
}
pub fn update_model_size(&self, new_size_bytes: u64) {
self.metrics.model_size_bytes.set(new_size_bytes as f64);
self.state.write().optimized_size_bytes = new_size_bytes;
}
}
// Convenience helper to construct a Noop optimizer for testing / integration
pub fn noop_optimizer(initial_size: u64) -> Result<ModelOptimizer> {
let model = Arc::new(NoopModel { size: initial_size });
let opt = ModelOptimizer::new(model, MonitoringThresholds::default())?;
Ok(opt)
}
#[cfg(test)]
mod tests {
use super::*;
#[tokio::test]
async fn test_dynamic_batcher_basic() {
let model = Arc::new(NoopModel { size: 10 });
let opt = ModelOptimizer::new(model, MonitoringThresholds::default()).unwrap();
let batcher = opt.dynamic_batcher(BatchingConfig {
max_batch_size: 8,
max_delay_ms: 5,
});
let futs: Vec<_> = (0..10)
.map(|i| {
let inp = TensorInput(serde_json::json!({ "x": i }));
batcher.infer(inp)
})
.collect();
for (i, f) in futs.into_iter().enumerate() {
let out = f.await.unwrap();
assert_eq!(out.0, serde_json::json!({"x": i as i32 }));
}
}
}