core/scheduler.py

"""
Learning Rate Schedulers for v6.2.0
Advanced scheduling with warmup and phase-based adjustments
"""

import torch
import math
from typing import Optional, Dict, List, Any
import numpy as np


class WarmupCosineScheduler:
    """
    Cosine annealing with linear warmup
    GPT-5 suggested: Essential for stable progressive splitting training
    """

    def __init__(self,
                 optimizer: torch.optim.Optimizer,
                 warmup_steps: int,
                 total_steps: int,
                 min_lr: float = 1e-6,
                 max_lr: Optional[float] = None):
        self.optimizer = optimizer
        self.warmup_steps = warmup_steps
        self.total_steps = total_steps
        self.min_lr = min_lr
        self.max_lr = max_lr or optimizer.param_groups[0]['lr']
        self.current_step = 0

    def step(self):
        """Update learning rate"""
        self.current_step += 1

        if self.current_step <= self.warmup_steps:
            # Linear warmup
            lr = self.max_lr * (self.current_step / self.warmup_steps)
        else:
            # Cosine annealing (GPT fix: guard against division by zero)
            if self.total_steps <= self.warmup_steps:
                lr = self.min_lr
            else:
                progress = (self.current_step - self.warmup_steps) / max(1, self.total_steps - self.warmup_steps)
                progress = min(1.0, max(0.0, progress))  # Clamp to [0, 1]
                lr = self.min_lr + (self.max_lr - self.min_lr) * 0.5 * (1 + math.cos(math.pi * progress))

        for param_group in self.optimizer.param_groups:
            param_group['lr'] = lr

        return lr

    def get_lr(self):
        """Get current learning rate"""
        return self.optimizer.param_groups[0]['lr']


class PhaseBasedScheduler:
    """
    Curriculum learning scheduler with phase transitions
    Adjusts learning rate based on training phases
    """

    def __init__(self,
                 optimizer: torch.optim.Optimizer,
                 phase_configs: List[Dict],
                 current_epoch: int = 0):
        """
        Args:
            optimizer: PyTorch optimizer
            phase_configs: List of phase configurations
                [{
                    'epochs': (start, end),
                    'lr': learning_rate,
                    'warmup_epochs': warmup_duration
                }, ...]
        """
        self.optimizer = optimizer
        self.phase_configs = phase_configs
        self.current_epoch = current_epoch
        self.current_phase = 0
        self.base_lr = optimizer.param_groups[0]['lr']

    def step(self, epoch: Optional[int] = None):
        """Update learning rate based on current phase"""
        if epoch is not None:
            self.current_epoch = epoch

        # Find current phase
        for i, phase in enumerate(self.phase_configs):
            start_epoch, end_epoch = phase['epochs']
            if start_epoch <= self.current_epoch <= end_epoch:
                self.current_phase = i
                break

        phase = self.phase_configs[self.current_phase]
        target_lr = phase['lr']
        warmup_epochs = phase.get('warmup_epochs', 0)
        start_epoch = phase['epochs'][0]

        # Apply warmup if in warmup period
        if self.current_epoch - start_epoch < warmup_epochs:
            warmup_progress = (self.current_epoch - start_epoch + 1) / warmup_epochs
            lr = target_lr * warmup_progress
        else:
            lr = target_lr

        # Update optimizer
        for param_group in self.optimizer.param_groups:
            param_group['lr'] = lr

        return lr


class AdaptiveScheduler:
    """
    Adaptive learning rate based on validation metrics
    Reduces LR when metrics plateau
    """

    def __init__(self,
                 optimizer: torch.optim.Optimizer,
                 mode: str = 'min',
                 factor: float = 0.5,
                 patience: int = 10,
                 threshold: float = 1e-4,
                 min_lr: float = 1e-7):
        """
        Args:
            optimizer: PyTorch optimizer
            mode: 'min' or 'max' - whether to reduce LR when metric stops decreasing or increasing
            factor: Factor to reduce LR by
            patience: Number of epochs with no improvement to wait
            threshold: Minimum change to qualify as improvement
            min_lr: Minimum learning rate
        """
        self.optimizer = optimizer
        self.mode = mode
        self.factor = factor
        self.patience = patience
        self.threshold = threshold
        self.min_lr = min_lr

        self.best_score = None
        self.num_bad_epochs = 0
        self.last_reduction = 0

    def step(self, metric: float, epoch: int = 0):
        """Update learning rate based on metric"""
        current_lr = self.optimizer.param_groups[0]['lr']

        if self.best_score is None:
            self.best_score = metric
        else:
            if self.mode == 'min':
                improved = metric < self.best_score - self.threshold
            else:
                improved = metric > self.best_score + self.threshold

            if improved:
                self.best_score = metric
                self.num_bad_epochs = 0
            else:
                self.num_bad_epochs += 1

            # Reduce LR if patience exceeded
            if self.num_bad_epochs >= self.patience:
                new_lr = max(current_lr * self.factor, self.min_lr)

                if new_lr < current_lr:
                    print(f"Reducing learning rate from {current_lr:.2e} to {new_lr:.2e}")

                    for param_group in self.optimizer.param_groups:
                        param_group['lr'] = new_lr

                    self.num_bad_epochs = 0
                    self.last_reduction = epoch

        return current_lr


class ProgressiveSplittingScheduler:
    """
    Adaptive scheduler for progressive splitting
    No fixed targets - adjusts based on quality feedback
    """

    def __init__(self,
                 optimizer: torch.optim.Optimizer,
                 initial_lr: float = 1e-4,
                 min_reconstruction: float = 0.85,
                 ema: float = 0.98,
                 min_lr: float = 1e-7):
        self.optimizer = optimizer
        self.initial_lr = initial_lr
        self.min_reconstruction = min_reconstruction  # Quality threshold
        self.ema = ema
        self.min_lr = min_lr

        # Adaptive multipliers based on performance
        self.quality_multiplier = 1.0  # Adjusts with reconstruction quality

        # No phases - continuous adaptation
        self.current_state = 'learning'

        # EMA tracking for smooth transitions
        self._ema_comp = None
        self._ema_recon = None

    def step(self, metrics: Dict[str, float]):
        """
        Update learning rate based on current metrics
        GPT fix: EMA smoothing and minimum floor

        Args:
            metrics: Dictionary containing:
                - compression_ratio: Current compression ratio
                - reconstruction_acc: Reconstruction accuracy
        """
        compression_ratio = float(metrics.get('compression_ratio', 0.0))
        reconstruction_acc = float(metrics.get('reconstruction_acc', 0.0))

        # Update EMA (GPT fix: smooth transitions)
        if self._ema_comp is None:
            self._ema_comp = compression_ratio
            self._ema_recon = reconstruction_acc
        else:
            self._ema_comp = self.ema * self._ema_comp + (1 - self.ema) * compression_ratio
            self._ema_recon = self.ema * self._ema_recon + (1 - self.ema) * reconstruction_acc

        # Adaptive adjustment based on reconstruction quality only
        # No fixed compression target - emerges from quality
        if self._ema_recon < self.min_reconstruction:
            # Poor reconstruction - reduce LR for careful learning
            self.quality_multiplier = max(0.5, self._ema_recon)
        else:
            # Good reconstruction - normal learning
            self.quality_multiplier = 1.0

        # Smooth LR changes
        reconstruction_factor = max(0.1, self._ema_recon)

        # Combined learning rate (adaptive, no phase multiplier)
        lr = self.initial_lr * self.quality_multiplier * reconstruction_factor
        lr = max(lr, self.min_lr)  # Ensure minimum LR

        # Update optimizer
        for param_group in self.optimizer.param_groups:
            param_group['lr'] = lr

        return lr


class GumbelTemperatureScheduler:
    """
    Temperature annealing for Gumbel-Softmax
    GPT-5 suggestion: Critical for progressive splitting
    """

    def __init__(self,
                 initial_temp: float = 1.0,
                 final_temp: float = 0.1,
                 anneal_rate: float = 0.99995,
                 anneal_steps: Optional[int] = None):
        self.initial_temp = initial_temp
        self.final_temp = final_temp
        self.anneal_rate = anneal_rate
        self.anneal_steps = anneal_steps
        self.current_step = 0
        self.current_temp = initial_temp

    def step(self):
        """Update temperature"""
        self.current_step += 1

        if self.anneal_steps:
            # Linear annealing
            progress = min(1.0, self.current_step / self.anneal_steps)
            self.current_temp = self.initial_temp + (self.final_temp - self.initial_temp) * progress
        else:
            # Exponential annealing
            self.current_temp = max(
                self.final_temp,
                self.initial_temp * (self.anneal_rate ** self.current_step)
            )

        return self.current_temp

    def get_temperature(self):
        """Get current temperature"""
        return self.current_temp


class CompressionRatioScheduler:
    """
    Schedule target compression ratio during training
    Gradually increase compression requirements
    """

    def __init__(self,
                 initial_ratio: float = 8.0,
                 target_ratio: float = 24.0,
                 warmup_epochs: int = 10,
                 total_epochs: int = 100):
        self.initial_ratio = initial_ratio
        self.target_ratio = target_ratio
        self.warmup_epochs = warmup_epochs
        self.total_epochs = total_epochs
        self.current_epoch = 0

    def step(self, epoch: Optional[int] = None):
        """Update target compression ratio"""
        if epoch is not None:
            self.current_epoch = epoch
        else:
            self.current_epoch += 1

        if self.current_epoch < self.warmup_epochs:
            # Start with lower compression requirement
            ratio = self.initial_ratio
        else:
            # Gradually increase to target
            progress = (self.current_epoch - self.warmup_epochs) / (self.total_epochs - self.warmup_epochs)
            progress = min(1.0, progress)
            ratio = self.initial_ratio + (self.target_ratio - self.initial_ratio) * progress

        return ratio


class MultiScheduler:
    """
    Combine multiple schedulers for comprehensive training control
    """

    def __init__(self, schedulers: Dict):
        """
        Args:
            schedulers: Dictionary of schedulers
                {
                    'lr': learning_rate_scheduler,
                    'gumbel': gumbel_temperature_scheduler,
                    'compression': compression_ratio_scheduler,
                    ...
                }
        """
        self.schedulers = schedulers

    def step(self, **kwargs):
        """
        Step all schedulers
        GPT fix: unified input convention

        Returns:
            Dictionary with all scheduler outputs
        """
        results = {}

        for name, scheduler in self.schedulers.items():
            try:
                # Check scheduler type and pass appropriate arguments
                if hasattr(scheduler, '__class__'):
                    class_name = scheduler.__class__.__name__

                    if class_name == 'AdaptiveScheduler' and 'metric' in kwargs:
                        results[name] = scheduler.step(kwargs['metric'], kwargs.get('epoch', 0))
                    elif class_name == 'PhaseBasedScheduler' and 'epoch' in kwargs:
                        results[name] = scheduler.step(kwargs['epoch'])
                    elif class_name == 'CompressionRatioScheduler' and 'epoch' in kwargs:
                        results[name] = scheduler.step(kwargs['epoch'])
                    elif class_name == 'ProgressiveSplittingScheduler' and 'metrics' in kwargs:
                        results[name] = scheduler.step(kwargs['metrics'])
                    elif hasattr(scheduler, 'step'):
                        # Generic step (no arguments)
                        results[name] = scheduler.step()
                else:
                    if hasattr(scheduler, 'step'):
                        results[name] = scheduler.step()
            except Exception as e:
                print(f"Warning: Scheduler '{name}' step failed: {e}")
                results[name] = None

        return results

    def get_current_values(self):
        """Get current values from all schedulers"""
        values = {}

        for name, scheduler in self.schedulers.items():
            if hasattr(scheduler, 'get_lr'):
                values[name] = scheduler.get_lr()
            elif hasattr(scheduler, 'get_temperature'):
                values[name] = scheduler.get_temperature()
            elif hasattr(scheduler, 'current_temp'):
                values[name] = scheduler.current_temp
            elif hasattr(scheduler, 'current_epoch'):
                values[name] = scheduler.current_epoch

        return values


class GateWarmupScheduler:
    """게이트 파라미터 웜업 스케줄러
    
    초기: 모든 레이어 동등 사용 (gate=1.0)
    웜업: 점진적 게이트 학습 시작
    후기: 최적 게이트 값으로 수렴
    """
    
    def __init__(
        self, 
        optimizer: torch.optim.Optimizer,
        warmup_steps: int = 1000,
        gate_param_group_name: str = 'gates',
        importance_param_group_name: str = 'importance'
    ):
        """
        Args:
            optimizer: 옵티마이저
            warmup_steps: 웜업 스텝 수
            gate_param_group_name: 게이트 파라미터 그룹 이름
            importance_param_group_name: 중요도 파라미터 그룹 이름
        """
        self.optimizer = optimizer
        self.warmup_steps = warmup_steps
        self.gate_group_name = gate_param_group_name
        self.importance_group_name = importance_param_group_name
        
        # 초기 학습률 저장
        self.base_lrs = {}
        for group in optimizer.param_groups:
            if 'name' in group:
                self.base_lrs[group['name']] = group['lr']
    
    def get_gate_factor(self, step: int) -> float:
        """게이트 학습률 계수 계산
        
        웜업 기간 동안은 낮은 학습률,
        이후 정상 학습률로 전환
        """
        if step < self.warmup_steps:
            # 웜업 기간: 선형 증가
            return step / self.warmup_steps
        else:
            # 정상 학습
            return 1.0
    
    def get_importance_factor(self, step: int) -> float:
        """중요도 학습률 계수 계산
        
        게이트보다 느리게 학습 시작
        """
        delayed_warmup = self.warmup_steps * 1.5
        if step < delayed_warmup:
            return step / delayed_warmup * 0.5
        else:
            return 1.0
    
    def step(self, current_step: int):
        """스케줄러 스텝
        
        Args:
            current_step: 현재 글로벌 스텝
        """
        # 게이트 파라미터 그룹 학습률 조정
        gate_factor = self.get_gate_factor(current_step)
        importance_factor = self.get_importance_factor(current_step)
        
        for group in self.optimizer.param_groups:
            if 'name' not in group:
                continue
                
            if group['name'] == self.gate_group_name:
                # 게이트 학습률 조정
                group['lr'] = self.base_lrs[self.gate_group_name] * gate_factor
                
            elif group['name'] == self.importance_group_name:
                # 중요도 학습률 조정
                group['lr'] = self.base_lrs[self.importance_group_name] * importance_factor
    
    def get_lr(self) -> Dict[str, float]:
        """현재 학습률 반환"""
        lrs = {}
        for group in self.optimizer.param_groups:
            if 'name' in group:
                lrs[group['name']] = group['lr']
        return lrs


class UniversalCosineScheduler:
    """Universal Cosine Annealing 스케줄러
    
    모든 언어에 대해 동일한 스케줄 적용
    """
    
    def __init__(
        self,
        optimizer: torch.optim.Optimizer,
        warmup_steps: int = 1000,
        total_steps: int = 10000,
        min_lr_ratio: float = 0.1
    ):
        self.optimizer = optimizer
        self.warmup_steps = warmup_steps
        self.total_steps = total_steps
        self.min_lr_ratio = min_lr_ratio
        self.current_step = 0
        
        # 초기 학습률 저장
        self.base_lrs = [group['lr'] for group in optimizer.param_groups]
    
    def step(self):
        """스케줄러 스텝"""
        self.current_step += 1
        
        for idx, param_group in enumerate(self.optimizer.param_groups):
            if self.current_step < self.warmup_steps:
                # Warmup 단계
                lr = self.base_lrs[idx] * (self.current_step / self.warmup_steps)
            else:
                # Cosine annealing
                if self.total_steps <= self.warmup_steps:
                    # warmup_steps가 total_steps보다 크거나 같은 경우
                    lr = self.base_lrs[idx] * self.min_lr_ratio
                else:
                    progress = min(1.0, (self.current_step - self.warmup_steps) / max(1, self.total_steps - self.warmup_steps))
                    lr = self.base_lrs[idx] * (
                        self.min_lr_ratio + (1 - self.min_lr_ratio) * 0.5 * (1 + math.cos(math.pi * progress))
                    )
            
            param_group['lr'] = lr
    
    def get_last_lr(self) -> List[float]:
        """마지막 학습률 반환"""
        return [group['lr'] for group in self.optimizer.param_groups]
    
    def state_dict(self) -> Dict[str, Any]:
        """스케줄러 상태 딕셔너리 반환 (체크포인트 저장용)"""
        return {
            'current_step': self.current_step,
            'warmup_steps': self.warmup_steps,
            'total_steps': self.total_steps,
            'min_lr_ratio': self.min_lr_ratio,
            'base_lrs': self.base_lrs
        }
    
    def load_state_dict(self, state_dict: Dict[str, Any]):
        """스케줄러 상태 로드 (체크포인트 재시작용)"""
        self.current_step = state_dict['current_step']
        self.warmup_steps = state_dict['warmup_steps']
        self.total_steps = state_dict['total_steps'] 
        self.min_lr_ratio = state_dict['min_lr_ratio']
        self.base_lrs = state_dict['base_lrs']


class AdaptiveLayerScheduler:
    """레이어별 적응적 스케줄러
    
    각 레이어의 학습 진행도에 따라 동적으로 조정
    """
    
    def __init__(
        self,
        layer_builder,
        threshold_active: float = 0.7,
        threshold_skip: float = 0.3
    ):
        """
        Args:
            layer_builder: LayerBuilder 인스턴스
            threshold_active: 활성 레이어 임계값
            threshold_skip: 스킵 레이어 임계값
        """
        self.layer_builder = layer_builder
        self.threshold_active = threshold_active
        self.threshold_skip = threshold_skip
        
        # 레이어별 통계
        self.layer_stats = {
            'usage_count': torch.zeros(5),
            'contribution': torch.zeros(5)
        }
    
    def update_stats(self, batch_output):
        """배치 출력으로 통계 업데이트"""
        with torch.no_grad():
            gates = torch.sigmoid(self.layer_builder.layer_gates)
            
            # 사용 횟수 업데이트
            self.layer_stats['usage_count'] += (gates > self.threshold_skip).float()
            
            # 기여도 추정 (간단한 버전)
            importance = torch.nn.functional.softmax(
                self.layer_builder.layer_importance, dim=0
            )
            self.layer_stats['contribution'] += importance.detach()
    
    def get_layer_status(self) -> Dict[int, str]:
        """각 레이어의 상태 반환"""
        gates = torch.sigmoid(self.layer_builder.layer_gates)
        status = {}
        
        for i in range(5):
            if gates[i] > self.threshold_active:
                status[i] = "ACTIVE"
            elif gates[i] > self.threshold_skip:
                status[i] = "PARTIAL"
            else:
                status[i] = "SKIP"
        
        return status
    
    def suggest_pruning(self) -> List[int]:
        """프루닝 가능한 레이어 제안"""
        gates = torch.sigmoid(self.layer_builder.layer_gates)
        prunable = []
        
        for i in range(5):
            if gates[i] < self.threshold_skip:
                # 낮은 게이트 값 + 낮은 기여도
                if self.layer_stats['contribution'][i] < 0.1:
                    prunable.append(i)
        
        return prunable


if __name__ == "__main__":
    # Test schedulers
    print("Testing Schedulers")

    # Create dummy optimizer
    model = torch.nn.Linear(10, 10)
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

    # Test WarmupCosineScheduler
    print("\n1. WarmupCosineScheduler:")
    scheduler = WarmupCosineScheduler(optimizer, warmup_steps=100, total_steps=1000)
    lrs = []
    for step in range(200):
        lr = scheduler.step()
        if step % 20 == 0:
            print(f"  Step {step}: LR = {lr:.6f}")
        lrs.append(lr)

    # Test PhaseBasedScheduler
    print("\n2. PhaseBasedScheduler:")
    phase_configs = [
        {'epochs': (0, 30), 'lr': 1e-4, 'warmup_epochs': 5},
        {'epochs': (31, 60), 'lr': 5e-5, 'warmup_epochs': 2},
        {'epochs': (61, 100), 'lr': 1e-5, 'warmup_epochs': 0}
    ]
    scheduler = PhaseBasedScheduler(optimizer, phase_configs)
    for epoch in [0, 5, 31, 35, 61, 80]:
        lr = scheduler.step(epoch)
        print(f"  Epoch {epoch}: LR = {lr:.6f}")

    # Test GumbelTemperatureScheduler
    print("\n3. GumbelTemperatureScheduler:")
    scheduler = GumbelTemperatureScheduler()
    for step in [0, 100, 500, 1000, 5000]:
        for _ in range(step - scheduler.current_step):
            scheduler.step()
        temp = scheduler.get_temperature()
        print(f"  Step {step}: Temperature = {temp:.4f}")

    # Test CompressionRatioScheduler
    print("\n4. CompressionRatioScheduler:")
    scheduler = CompressionRatioScheduler()
    for epoch in [0, 5, 10, 30, 50, 80, 100]:
        ratio = scheduler.step(epoch)
        print(f"  Epoch {epoch}: Target ratio = {ratio:.1f}:1")