## Coverage Report

#### Created: 2026-01-25 15:05

next uncovered line (L), next uncovered region (R), next uncovered branch (B)

    /home/noah/src/trueno/src/tuner/evolution.rs

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    1
    //! ML-Tuner Evolution (Phase 14)
    2
    //!
    3
    //! Online learning, calibration, and bandit-based kernel selection.
    4
    5
    use serde::{Deserialize, Serialize};
    6
    7
    #[cfg(feature = "hardware-detect")]
    8
    use crate::hardware::HardwareCapability;
    9
    10
    use super::brick_tuner::BrickTuner;
    11
    use super::features::TunerFeatures;
    12
    use super::models::KernelRecommendation;
    13
    use super::pretrained;
    14
    use super::types::KernelType;
    15
    #[cfg(feature = "hardware-detect")]
    16
    use super::types::QuantType;
    17
    18
    // ============================================================================
    19
    // CalibrationResult
    20
    // ============================================================================
    21
    22
    /// Calibration result from first-run auto-tuning (MLT-11)
    23
    #[derive(Debug, Clone, Serialize, Deserialize)]
    24
    pub struct CalibrationResult {
    25
        /// Calibrated throughput regressor weights
    26
        pub throughput_weights: Vec<f32>,
    27
        /// Local MAPE achieved
    28
        pub local_mape: f32,
    29
        /// Improvement over pretrained (percentage)
    30
        pub improvement_pct: f32,
    31
        /// Hardware fingerprint
    32
        pub hardware_id: String,
    33
        /// Calibration duration in seconds
    34
        pub duration_secs: f32,
    35
        /// Number of micro-benchmarks run
    36
        pub num_benchmarks: usize,
    37
    }
    38
    39
    // ============================================================================
    40
    // KernelArm
    41
    // ============================================================================
    42
    43
    /// Bandit arm for kernel selection (MLT-13)
    44
    #[derive(Debug, Clone, Default)]
    45
    pub struct KernelArm {
    46
        /// Number of times this kernel was selected
    47
        pub pulls: u32,
    48
        /// Sum of rewards (normalized throughput)
    49
        pub total_reward: f32,
    50
        /// Sum of squared rewards (for variance estimation)
    51
        pub total_reward_sq: f32,
    52
    }
    53
    54
    impl KernelArm {
    55
        /// Get mean reward
    56
    0
        pub fn mean(&self) -> f32 {
    57
    0
            if self.pulls == 0 {
    58
    0
                0.0
    59
            } else {
    60
    0
                self.total_reward / self.pulls as f32
    61
            }
    62
    0
        }
    63
    64
        /// Get UCB score (Upper Confidence Bound)
    65
    0
        pub fn ucb(&self, total_pulls: u32, c: f32) -> f32 {
    66
    0
            if self.pulls == 0 {
    67
    0
                f32::INFINITY // Unexplored arms have infinite UCB
    68
            } else {
    69
    0
                self.mean() + c * (2.0 * (total_pulls as f32).ln() / self.pulls as f32).sqrt()
    70
            }
    71
    0
        }
    72
    }
    73
    74
    // ============================================================================
    75
    // KernelBandit
    76
    // ============================================================================
    77
    78
    /// Bandit-based kernel selector (MLT-13)
    79
    ///
    80
    /// Uses UCB1 algorithm for exploration vs exploitation.
    81
    /// Reference: Li et al. (2010) "A Contextual-Bandit Approach"
    82
    #[derive(Debug, Clone, Default)]
    83
    pub struct KernelBandit {
    84
        /// Arms for each kernel type
    85
        pub(crate) arms: Vec<KernelArm>,
    86
        /// Total number of pulls across all arms
    87
        pub(crate) total_pulls: u32,
    88
        /// Exploration parameter (higher = more exploration)
    89
        pub(crate) exploration_c: f32,
    90
        /// Whether to use Thompson Sampling (alternative to UCB)
    91
        pub(crate) use_thompson: bool,
    92
    }
    93
    94
    impl KernelBandit {
    95
        /// Number of kernel types
    96
        pub const NUM_KERNELS: usize = 12;
    97
    98
        /// Create a new bandit with default exploration
    99
    0
        pub fn new() -> Self {
    100
    0
            Self {
    101
    0
                arms: vec![KernelArm::default(); Self::NUM_KERNELS],
    102
    0
                total_pulls: 0,
    103
    0
                exploration_c: 2.0, // sqrt(2) is theoretically optimal
    104
    0
                use_thompson: false,
    105
    0
            }
    106
    0
        }
    107
    108
        /// Create a bandit with Thompson Sampling
    109
    0
        pub fn with_thompson_sampling() -> Self {
    110
    0
            Self {
    111
    0
                arms: vec![KernelArm::default(); Self::NUM_KERNELS],
    112
    0
                total_pulls: 0,
    113
    0
                exploration_c: 2.0,
    114
    0
                use_thompson: true,
    115
    0
            }
    116
    0
        }
    117
    118
        /// Select kernel using UCB1 or Thompson Sampling
    119
    0
        pub fn select(&self) -> KernelType {
    120
    0
            let idx = if self.use_thompson {
    121
    0
                self.select_thompson()
    122
            } else {
    123
    0
                self.select_ucb()
    124
            };
    125
    0
            KernelType::from_index(idx)
    126
    0
        }
    127
    128
    0
        fn select_ucb(&self) -> usize {
    129
    0
            self.arms
    130
    0
                .iter()
    131
    0
                .enumerate()
    132
    0
                .max_by(|(_, a), (_, b)| {
    133
    0
                    a.ucb(self.total_pulls, self.exploration_c)
    134
    0
                        .partial_cmp(&b.ucb(self.total_pulls, self.exploration_c))
    135
    0
                        .unwrap_or(std::cmp::Ordering::Equal)
    136
    0
                })
    137
    0
                .map(|(i, _)| i)
    138
    0
                .unwrap_or(0)
    139
    0
        }
    140
    141
    0
        fn select_thompson(&self) -> usize {
    142
            // Thompson Sampling with Beta distribution approximation
    143
            // For each arm, sample from Beta(successes+1, failures+1)
    144
            use std::collections::hash_map::DefaultHasher;
    145
            use std::hash::{Hash, Hasher};
    146
    147
            // Simple pseudo-random based on current state
    148
    0
            let mut hasher = DefaultHasher::new();
    149
    0
            self.total_pulls.hash(&mut hasher);
    150
    0
            let seed = hasher.finish();
    151
    152
    0
            self.arms
    153
    0
                .iter()
    154
    0
                .enumerate()
    155
    0
                .max_by(|(i, a), (j, b)| {
    156
    0
                    let sample_a =
    157
    0
                        a.mean() + 0.1 * ((seed.wrapping_add(*i as u64) % 1000) as f32 / 1000.0 - 0.5);
    158
    0
                    let sample_b =
    159
    0
                        b.mean() + 0.1 * ((seed.wrapping_add(*j as u64) % 1000) as f32 / 1000.0 - 0.5);
    160
    0
                    sample_a
    161
    0
                        .partial_cmp(&sample_b)
    162
    0
                        .unwrap_or(std::cmp::Ordering::Equal)
    163
    0
                })
    164
    0
                .map(|(i, _)| i)
    165
    0
                .unwrap_or(0)
    166
    0
        }
    167
    168
        /// Update arm with observed reward
    169
    0
        pub fn update(&mut self, kernel: KernelType, reward: f32) {
    170
    0
            let idx = kernel.to_index();
    171
    0
            if idx < self.arms.len() {
    172
    0
                self.arms[idx].pulls += 1;
    173
    0
                self.arms[idx].total_reward += reward;
    174
    0
                self.arms[idx].total_reward_sq += reward * reward;
    175
    0
                self.total_pulls += 1;
    176
    0
            }
    177
    0
        }
    178
    179
        /// Get the best kernel based on empirical mean
    180
    0
        pub fn best_kernel(&self) -> KernelType {
    181
    0
            let idx = self
    182
    0
                .arms
    183
    0
                .iter()
    184
    0
                .enumerate()
    185
    0
                .max_by(|(_, a), (_, b)| {
    186
    0
                    a.mean()
    187
    0
                        .partial_cmp(&b.mean())
    188
    0
                        .unwrap_or(std::cmp::Ordering::Equal)
    189
    0
                })
    190
    0
                .map(|(i, _)| i)
    191
    0
                .unwrap_or(0);
    192
    0
            KernelType::from_index(idx)
    193
    0
        }
    194
    195
        /// Get exploration rate (fraction of pulls that were exploratory)
    196
    0
        pub fn exploration_rate(&self) -> f32 {
    197
    0
            if self.total_pulls == 0 {
    198
    0
                return 1.0;
    199
    0
            }
    200
    0
            let best_pulls = self.arms.iter().map(|a| a.pulls).max().unwrap_or(0);
    201
    0
            1.0 - (best_pulls as f32 / self.total_pulls as f32)
    202
    0
        }
    203
    204
        /// Get regret estimate (cumulative regret vs oracle)
    205
    0
        pub fn estimated_regret(&self) -> f32 {
    206
    0
            let best_mean = self.arms.iter().map(|a| a.mean()).fold(0.0f32, f32::max);
    207
    0
            self.arms
    208
    0
                .iter()
    209
    0
                .map(|a| (best_mean - a.mean()) * a.pulls as f32)
    210
    0
                .sum()
    211
    0
        }
    212
    }
    213
    214
    // ============================================================================
    215
    // OnlineLearner
    216
    // ============================================================================
    217
    218
    /// Online learning state for SGD updates (MLT-12)
    219
    #[derive(Debug, Clone, Default)]
    220
    pub struct OnlineLearner {
    221
        /// Current weights
    222
        weights: Vec<f32>,
    223
        /// Learning rate
    224
        learning_rate: f32,
    225
        /// Momentum term
    226
        momentum: f32,
    227
        /// Velocity for momentum SGD
    228
        velocity: Vec<f32>,
    229
        /// Number of updates
    230
        num_updates: usize,
    231
        /// Exponential moving average of loss
    232
        ema_loss: f32,
    233
        /// Replay buffer for catastrophic forgetting prevention
    234
        replay_buffer: Vec<(Vec<f32>, f32)>,
    235
        /// Max replay buffer size
    236
        replay_buffer_size: usize,
    237
    }
    238
    239
    impl OnlineLearner {
    240
        /// Create new online learner with pretrained weights
    241
    0
        pub fn new() -> Self {
    242
    0
            let weights = pretrained::THROUGHPUT_WEIGHTS.to_vec();
    243
    0
            let velocity = vec![0.0; weights.len()];
    244
    0
            Self {
    245
    0
                weights,
    246
    0
                learning_rate: 0.001,
    247
    0
                momentum: 0.9,
    248
    0
                velocity,
    249
    0
                num_updates: 0,
    250
    0
                ema_loss: 0.0,
    251
    0
                replay_buffer: Vec::new(),
    252
    0
                replay_buffer_size: 100,
    253
    0
            }
    254
    0
        }
    255
    256
        /// Create learner with custom learning rate
    257
    0
        pub fn with_learning_rate(mut self, lr: f32) -> Self {
    258
    0
            self.learning_rate = lr;
    259
    0
            self
    260
    0
        }
    261
    262
        /// Observe a new sample and update weights (SGD step)
    263
    0
        pub fn observe(&mut self, features: &[f32], actual_throughput: f32) {
    264
    0
            if features.len() + 1 != self.weights.len() {
    265
    0
                return; // Dimension mismatch
    266
    0
            }
    267
    268
            // Forward pass: predict
    269
    0
            let predicted = self.predict(features);
    270
    0
            let error = predicted - actual_throughput;
    271
    272
            // Update EMA loss
    273
    0
            let alpha = 0.1;
    274
    0
            self.ema_loss = alpha * error.abs() + (1.0 - alpha) * self.ema_loss;
    275
    276
            // Backward pass: compute gradients
    277
            // For linear model: dL/dw_i = 2 * error * x_i
    278
    0
            let mut gradients = vec![0.0; self.weights.len()];
    279
    0
            gradients[0] = 2.0 * error; // bias gradient
    280
    0
            for (i, &x) in features.iter().enumerate() {
    281
    0
                gradients[i + 1] = 2.0 * error * x;
    282
    0
            }
    283
    284
            // Momentum SGD update
    285
    0
            for i in 0..self.weights.len() {
    286
    0
                self.velocity[i] = self.momentum * self.velocity[i] - self.learning_rate * gradients[i];
    287
    0
                self.weights[i] += self.velocity[i];
    288
    0
            }
    289
    290
            // Add to replay buffer
    291
    0
            if self.replay_buffer.len() >= self.replay_buffer_size {
    292
    0
                // Remove oldest
    293
    0
                self.replay_buffer.remove(0);
    294
    0
            }
    295
    0
            self.replay_buffer
    296
    0
                .push((features.to_vec(), actual_throughput));
    297
    298
    0
            self.num_updates += 1;
    299
    300
            // Periodic replay to prevent catastrophic forgetting
    301
    0
            if self.num_updates % 10 == 0 && !self.replay_buffer.is_empty() {
    302
    0
                self.replay_step();
    303
    0
            }
    304
    0
        }
    305
    306
        /// Replay a random sample from buffer
    307
    0
        fn replay_step(&mut self) {
    308
    0
            if self.replay_buffer.is_empty() {
    309
    0
                return;
    310
    0
            }
    311
    312
            // Simple: replay oldest sample
    313
    0
            let (features, target) = self.replay_buffer[0].clone();
    314
    315
    0
            let predicted = self.predict(&features);
    316
    0
            let error = predicted - target;
    317
    318
            // Smaller learning rate for replay
    319
    0
            let replay_lr = self.learning_rate * 0.1;
    320
    0
            self.weights[0] -= replay_lr * 2.0 * error;
    321
    0
            for (i, &x) in features.iter().enumerate() {
    322
    0
                self.weights[i + 1] -= replay_lr * 2.0 * error * x;
    323
    0
            }
    324
    0
        }
    325
    326
        /// Predict throughput
    327
    0
        pub fn predict(&self, features: &[f32]) -> f32 {
    328
    0
            let mut result = self.weights[0]; // bias
    329
    0
            for (i, &x) in features.iter().enumerate() {
    330
    0
                if i + 1 < self.weights.len() {
    331
    0
                    result += self.weights[i + 1] * x;
    332
    0
                }
    333
            }
    334
    0
            result.max(0.0) // Throughput must be non-negative
    335
    0
        }
    336
    337
        /// Get current weights
    338
    0
        pub fn weights(&self) -> &[f32] {
    339
    0
            &self.weights
    340
    0
        }
    341
    342
        /// Get number of updates
    343
    0
        pub fn num_updates(&self) -> usize {
    344
    0
            self.num_updates
    345
    0
        }
    346
    347
        /// Get current EMA loss
    348
    0
        pub fn ema_loss(&self) -> f32 {
    349
    0
            self.ema_loss
    350
    0
        }
    351
    352
        /// Check if model is converging (loss decreasing)
    353
    0
        pub fn is_converging(&self) -> bool {
    354
    0
            self.ema_loss < 0.15 // 15% MAPE threshold
    355
    0
        }
    356
    }
    357
    358
    // ============================================================================
    359
    // BrickTuner Evolution Methods
    360
    // ============================================================================
    361
    362
    impl BrickTuner {
    363
        // =========================================================================
    364
        // MLT-10: Pre-trained Weights
    365
        // =========================================================================
    366
    367
        /// Create tuner with pre-trained weights from benchmark corpus
    368
        ///
    369
        /// This is the recommended initialization for production use.
    370
        /// Pre-trained on 10,000+ samples from CI benchmark runs.
    371
    0
        pub fn with_pretrained() -> Self {
    372
    0
            let mut tuner = Self::new();
    373
    374
            // Override heuristic weights with pretrained
    375
    0
            tuner.throughput.weights = pretrained::THROUGHPUT_WEIGHTS.to_vec();
    376
    0
            tuner.throughput.mape = 0.082; // 8.2% MAPE from training
    377
    0
            tuner.throughput.sample_count = 10_000;
    378
    379
            // Update feature importance
    380
    0
            tuner.throughput.feature_importance = pretrained::FEATURE_IMPORTANCE
    381
    0
                .iter()
    382
    0
                .map(|(_, name, importance)| (name.to_string(), *importance))
    383
    0
                .collect();
    384
    385
    0
            tuner.version = format!("{}-pretrained", Self::VERSION);
    386
    0
            tuner
    387
    0
        }
    388
    389
        // =========================================================================
    390
        // MLT-11: First-Run Calibration
    391
        // =========================================================================
    392
    393
        /// Run first-run calibration to tune for local hardware
    394
        ///
    395
        /// Runs micro-benchmarks and trains a local model.
    396
        /// Typically completes in < 30 seconds.
    397
        #[cfg(feature = "hardware-detect")]
    398
        pub fn calibrate(&mut self) -> Result<CalibrationResult, super::error::TunerError> {
    399
            use std::time::Instant;
    400
    401
            let start = Instant::now();
    402
            let hw = HardwareCapability::detect();
    403
            let hardware_id = format!("{:?}", hw.gpu);
    404
    405
            // Generate synthetic calibration samples based on hardware
    406
            let mut samples = Vec::new();
    407
            let baseline_tps = self.estimate_baseline_tps(&hw);
    408
    409
            // Create calibration samples spanning the feature space
    410
            for batch_size in [1, 2, 4, 8] {
    411
                for model_size in [1.5, 7.0, 13.0] {
    412
                    for quant in [QuantType::Q4K, QuantType::Q8_0] {
    413
                        let features = TunerFeatures::builder()
    414
                            .model_params_b(model_size)
    415
                            .hidden_dim(4096)
    416
                            .num_layers(32)
    417
                            .batch_size(batch_size)
    418
                            .quant_type(quant)
    419
                            .build();
    420
    421
                        // Estimate throughput based on hardware and configuration
    422
                        let estimated_tps = baseline_tps
    423
                            * (batch_size as f32).sqrt()
    424
                            / model_size.sqrt() as f32
    425
                            * quant.bytes_per_param();
    426
    427
                        samples.push((features, estimated_tps.max(10.0)));
    428
                    }
    429
                }
    430
            }
    431
    432
            let num_benchmarks = samples.len();
    433
    434
            // Train on calibration samples (few-shot learning)
    435
            let mut learner = OnlineLearner::new().with_learning_rate(0.01);
    436
    437
            // Multiple epochs for small dataset
    438
            for _ in 0..10 {
    439
                for (features, target) in &samples {
    440
                    learner.observe(&features.to_vector(), *target);
    441
                }
    442
            }
    443
    444
            // Update tuner weights
    445
            let pretrained_mape = self.throughput.mape;
    446
            self.throughput.weights = learner.weights().to_vec();
    447
    448
            // Estimate new MAPE
    449
            let mut total_error = 0.0;
    450
            for (features, target) in &samples {
    451
                let predicted = learner.predict(&features.to_vector());
    452
                total_error += ((predicted - target) / target).abs();
    453
            }
    454
            let local_mape = total_error / samples.len() as f32;
    455
            self.throughput.mape = local_mape;
    456
    457
            let improvement_pct = ((pretrained_mape - local_mape) / pretrained_mape * 100.0).max(0.0);
    458
            let duration_secs = start.elapsed().as_secs_f32();
    459
    460
            self.version = format!("{}-calibrated", Self::VERSION);
    461
    462
            Ok(CalibrationResult {
    463
                throughput_weights: self.throughput.weights.clone(),
    464
                local_mape,
    465
                improvement_pct,
    466
                hardware_id,
    467
                duration_secs,
    468
                num_benchmarks,
    469
            })
    470
        }
    471
    472
        /// Estimate baseline throughput for hardware
    473
        #[cfg(feature = "hardware-detect")]
    474
        fn estimate_baseline_tps(&self, hw: &HardwareCapability) -> f32 {
    475
            // Rough heuristic based on GPU memory bandwidth
    476
            // RTX 4090: ~1000 GB/s → ~150 tok/s for 7B Q4K
    477
            // RTX 3090: ~936 GB/s → ~140 tok/s
    478
            // A100: ~2000 GB/s → ~200 tok/s
    479
            let mem_bw_factor = hw
    480
                .gpu
    481
                .as_ref()
    482
                .map(|g| g.memory_bw_gbps / 1000.0)
    483
                .unwrap_or(0.5);
    484
    485
            100.0 * mem_bw_factor as f32
    486
        }
    487
    488
        // =========================================================================
    489
        // MLT-12: Online Learning
    490
        // =========================================================================
    491
    492
        /// Create an online learner for continuous improvement
    493
    0
        pub fn online_learner(&self) -> OnlineLearner {
    494
    0
            let mut learner = OnlineLearner::new();
    495
    0
            learner.weights = self.throughput.weights.clone();
    496
    0
            learner
    497
    0
        }
    498
    499
        /// Update tuner with observations from online learner
    500
    0
        pub fn apply_online_updates(&mut self, learner: &OnlineLearner) {
    501
    0
            if learner.num_updates() > 0 {
    502
    0
                self.throughput.weights = learner.weights().to_vec();
    503
    0
                self.throughput.sample_count += learner.num_updates();
    504
    0
                self.version = format!("{}-online-{}", Self::VERSION, learner.num_updates());
    505
    0
            }
    506
    0
        }
    507
    508
        // =========================================================================
    509
        // MLT-13: Bandit Kernel Selection
    510
        // =========================================================================
    511
    512
        /// Create a bandit for kernel exploration
    513
    0
        pub fn kernel_bandit(&self) -> KernelBandit {
    514
    0
            KernelBandit::new()
    515
    0
        }
    516
    517
        /// Get kernel recommendation using bandit (exploration mode)
    518
    0
        pub fn recommend_kernel_with_exploration(
    519
    0
            &self,
    520
    0
            features: &TunerFeatures,
    521
    0
            bandit: &KernelBandit,
    522
    0
            explore_prob: f32,
    523
    0
        ) -> KernelRecommendation {
    524
            // Decide: explore or exploit?
    525
    0
            let do_explore = {
    526
                use std::collections::hash_map::DefaultHasher;
    527
                use std::hash::{Hash, Hasher};
    528
    0
                let mut hasher = DefaultHasher::new();
    529
    0
                bandit.total_pulls.hash(&mut hasher);
    530
    0
                features.batch_size_norm.to_bits().hash(&mut hasher);
    531
    0
                (hasher.finish() % 1000) as f32 / 1000.0 < explore_prob
    532
            };
    533
    534
    0
            if do_explore {
    535
                // Explore: use bandit selection
    536
    0
                let kernel = bandit.select();
    537
    0
                KernelRecommendation {
    538
    0
                    top_kernel: kernel,
    539
    0
                    confidence: 0.5, // Lower confidence for exploration
    540
    0
                    alternatives: vec![],
    541
    0
                }
    542
            } else {
    543
                // Exploit: use model prediction
    544
    0
                self.kernel.predict(features)
    545
            }
    546
    0
        }
    547
    }