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//! Throughput regressor model for ML tuner.
use serde::{Deserialize, Serialize};
#[cfg(feature = "ml-tuner")]
use aprender::{tree::RandomForestRegressor, Matrix, Vector};
use super::super::error::TunerError;
use super::super::features::TunerFeatures;
use super::ThroughputPrediction;
/// Simple linear regression model for throughput prediction.
///
/// Uses closed-form solution: w = (X^T X)^-1 X^T y
/// With `ml-tuner` feature: uses aprender::RandomForestRegressor (SHOWCASE-BRICK-001)
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ThroughputRegressor {
/// Model weights (one per feature + bias) - fallback when ml-tuner disabled
pub(crate) weights: Vec<f32>,
/// Feature importance scores
pub(crate) feature_importance: Vec<(String, f32)>,
/// Training sample count
pub(crate) sample_count: usize,
/// Mean absolute percentage error on validation
pub(crate) mape: f32,
/// Whether the RandomForest model is trained (ml-tuner feature)
#[cfg(feature = "ml-tuner")]
#[serde(skip)]
rf_model: Option<RandomForestRegressor>,
}
impl Default for ThroughputRegressor {
fn default() -> Self {
Self::new()
}
}
impl ThroughputRegressor {
/// Create a new regressor with default weights
pub fn new() -> Self {
// Initialize with heuristic-based weights
// These encode domain knowledge from SHOWCASE-BRICK-001
let mut weights = vec![0.0; TunerFeatures::DIM + 1]; // +1 for bias
// Bias: baseline throughput ~200 tok/s normalized
weights[0] = 0.4;
// Batch size has largest positive impact (index 6)
weights[7] = 0.3; // batch_size_norm
// CUDA graphs help (index 8)
weights[9] = 0.1; // cuda_graphs
// GPU memory bandwidth matters (index 35)
weights[36] = 0.15; // gpu_mem_bw_norm
// GPU SM count matters (index 37)
weights[38] = 0.1; // gpu_sm_norm
// Larger models are slower (negative impact)
weights[1] = -0.15; // model_params_b
// Longer sequences slower for decode
weights[8] = -0.05; // seq_len_log
Self {
weights,
feature_importance: Self::default_feature_importance(),
sample_count: 0,
mape: 0.15, // 15% default MAPE
#[cfg(feature = "ml-tuner")]
rf_model: None,
}
}
/// Create a new regressor using aprender RandomForest (ml-tuner feature)
#[cfg(feature = "ml-tuner")]
pub fn with_random_forest(n_estimators: usize) -> Self {
let mut instance = Self::new();
instance.rf_model = Some(RandomForestRegressor::new(n_estimators));
instance
}
fn default_feature_importance() -> Vec<(String, f32)> {
vec![
("batch_size".into(), 0.25),
("gpu_mem_bw".into(), 0.20),
("model_params".into(), 0.15),
("cuda_graphs".into(), 0.10),
("gpu_sm_count".into(), 0.10),
("hidden_dim".into(), 0.08),
("quant_type".into(), 0.07),
("seq_len".into(), 0.05),
]
}
/// Train the model on labeled data
pub fn train(&mut self, data: &[(TunerFeatures, f32)]) -> Result<(), TunerError> {
if data.len() < 10 {
return Err(TunerError::InsufficientData(data.len()));
}
// Simple gradient descent (in production: aprender's GBDT)
let learning_rate = 0.01;
let epochs = 100;
for _ in 0..epochs {
let mut gradients = vec![0.0; self.weights.len()];
for (features, target) in data {
let x = features.to_vector();
let predicted = self.predict_raw(&x);
let error = predicted - target;
// Gradient for bias
gradients[0] += error;
// Gradient for features
for (i, xi) in x.iter().enumerate() {
gradients[i + 1] += error * xi;
}
}
// Update weights
let n = data.len().max(1) as f32;
for (i, g) in gradients.iter().enumerate() {
self.weights[i] -= learning_rate * g / n;
}
}
// Calculate MAPE on training data
let mut total_ape = 0.0;
for (features, target) in data {
let predicted = self.predict_raw(&features.to_vector());
total_ape += ((predicted - target) / target.max(1.0)).abs();
}
self.mape = total_ape / data.len().max(1) as f32;
self.sample_count = data.len();
Ok(())
}
/// Train using aprender RandomForest (ml-tuner feature)
///
/// Provides superior throughput prediction via ensemble learning.
/// See: SHOWCASE-BRICK-001 Section 12.3
#[cfg(feature = "ml-tuner")]
pub fn train_random_forest(&mut self, data: &[(TunerFeatures, f32)]) -> Result<(), TunerError> {
if data.len() < 10 {
return Err(TunerError::InsufficientData(data.len()));
}
// Convert to aprender matrix format (f32 for RandomForestRegressor)
let n_samples = data.len();
let n_features = TunerFeatures::DIM;
let mut x_data = Vec::with_capacity(n_samples * n_features);
let mut y_data = Vec::with_capacity(n_samples);
for (features, target) in data {
x_data.extend(features.to_vector());
y_data.push(*target);
}
let x_matrix = Matrix::from_vec(n_samples, n_features, x_data)
.map_err(|e| TunerError::TrainingFailed(e.to_string()))?;
let y_vector = Vector::from_vec(y_data);
// Train RandomForest
let rf = self.rf_model.get_or_insert_with(|| RandomForestRegressor::new(100));
rf.fit(&x_matrix, &y_vector).map_err(|e| TunerError::TrainingFailed(e.to_string()))?;
// Calculate MAPE on training data
let predictions = rf.predict(&x_matrix);
let mut total_ape = 0.0;
for (i, (_, target)) in data.iter().enumerate() {
let pred = predictions.as_slice()[i];
total_ape += ((pred - target) / target.max(1.0)).abs();
}
self.mape = total_ape / data.len().max(1) as f32;
self.sample_count = data.len();
Ok(())
}
pub(crate) fn predict_raw(&self, x: &[f32]) -> f32 {
let mut result = self.weights[0]; // bias
for (i, xi) in x.iter().enumerate() {
if i + 1 < self.weights.len() {
result += self.weights[i + 1] * xi;
}
}
// Convert from normalized to tok/s (scale ~1000)
(result * 1000.0).max(1.0)
}
/// Predict throughput for features
///
/// With `ml-tuner` feature: uses trained RandomForest if available.
/// Falls back to linear model otherwise.
pub fn predict(&self, features: &TunerFeatures) -> ThroughputPrediction {
let x = features.to_vector();
// Use RandomForest if trained (ml-tuner feature)
#[cfg(feature = "ml-tuner")]
let raw_predicted_tps = if let Some(ref rf) = self.rf_model {
// Use f32 matrix for RandomForestRegressor
if let Ok(x_matrix) = Matrix::from_vec(1, TunerFeatures::DIM, x.to_vec()) {
let predictions = rf.predict(&x_matrix);
predictions.as_slice().first().copied().unwrap_or(0.0)
} else {
self.predict_raw(&x)
}
} else {
self.predict_raw(&x)
};
#[cfg(not(feature = "ml-tuner"))]
let raw_predicted_tps = self.predict_raw(&x);
// v1.1.0: Roofline clamping - predictions must not exceed theoretical maximum
let theoretical_max_tps = Self::compute_roofline_bound(features);
let predicted_tps = raw_predicted_tps.min(theoretical_max_tps);
// Confidence based on training MAPE and feature validity
// Lower confidence if we hit the roofline cap
let roofline_penalty = if raw_predicted_tps > theoretical_max_tps {
0.9 // 10% confidence penalty for capped predictions
} else {
1.0
};
let confidence = (1.0 - self.mape).max(0.5) * roofline_penalty;
ThroughputPrediction {
predicted_tps,
confidence,
top_features: self.feature_importance.iter().take(5).cloned().collect(),
}
}
/// Compute theoretical maximum throughput based on roofline model (v1.1.0)
///
/// For memory-bound LLM inference (decode phase):
/// max_tps = memory_bw_bytes_per_sec / bytes_per_token
/// bytes_per_token = model_params x bytes_per_param / batch_size
pub fn compute_roofline_bound(features: &TunerFeatures) -> f32 {
// Denormalize model params: normalized = (log10(b) + 1) / 3
// log10(b) = normalized * 3 - 1
// b = 10^(normalized * 3 - 1)
let model_params_b = 10.0_f32.powf(features.model_params_b * 3.0 - 1.0);
// Get bytes per param from quant type one-hot encoding
let bytes_per_param = Self::bytes_per_param_from_onehot(&features.quant_type_onehot);
// Denormalize memory bandwidth: normalized = bw / 3000 GB/s
let gpu_mem_bw_gbs = features.gpu_mem_bw_norm * 3000.0;
// Denormalize batch size: normalized = batch_size / 64
let batch_size = (features.batch_size_norm * 64.0).max(1.0);
// Roofline calculation:
// model_bytes = model_params_b * bytes_per_param * 1e9
// bytes_per_token = model_bytes / batch_size
// max_tps = (gpu_mem_bw_gbs * 1e9) / bytes_per_token
// = (gpu_mem_bw_gbs * 1e9 * batch_size) / (model_params_b * bytes_per_param * 1e9)
// = (gpu_mem_bw_gbs * batch_size) / (model_params_b * bytes_per_param)
let theoretical_max = (gpu_mem_bw_gbs * batch_size) / (model_params_b * bytes_per_param);
// Clamp to reasonable range (1 tok/s to 10000 tok/s)
theoretical_max.clamp(1.0, 10000.0)
}
/// Extract bytes per param from quant type one-hot encoding
pub fn bytes_per_param_from_onehot(onehot: &[f32; 8]) -> f32 {
// One-hot indices map to QuantType variants
// 0: Q4_0, 1: Q4_1, 2: Q4K, 3: Q5K, 4: Q6K, 5: Q8_0, 6: F16, 7: F32
let bytes_per_param = [0.5625, 0.5625, 0.5625, 0.6875, 0.8125, 1.0, 2.0, 4.0];
// Find the active index (max value in one-hot)
let idx = onehot
.iter()
.enumerate()
.max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
.map(|(i, _)| i)
// N-07 (Meyer DbC): default to Q4_0 (idx 0) if ambiguous, not Q4K.
.unwrap_or(0);
bytes_per_param[idx]
}
}