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OmniCoreX Trainer Module
Provides the most super advanced, highest level training routines for OmniCoreX including:
- Efficient training loops with mixed precision support
- Advanced optimizer and scheduler setup
- Checkpoint saving/restoring with state dict management
- Gradient accumulation and clipping for large batch training
- Multi-device and distributed training ready
- Extensive logging and real-time progress tracking
"""
import os
import time
import torch
import torch.nn as nn
from torch.cuda.amp import GradScaler, autocast
from torch.utils.data import DataLoader
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from typing import Optional, Dict, Any
class Trainer:
def __init__(self,
model: nn.Module,
train_loader: DataLoader,
valid_loader: Optional[DataLoader],
save_dir: str,
lr: float = 5e-5,
weight_decay: float = 0.01,
max_grad_norm: float = 1.0,
accumulation_steps: int = 1,
total_steps: int = 100000,
warmup_steps: int = 1000,
device: Optional[torch.device] = None,
mixed_precision: bool = True):
"""
Initialize the training module.
Args:
model: OmniCoreX neural network model.
train_loader: DataLoader for training data.
valid_loader: Optional DataLoader for validation data.
save_dir: Directory path to save checkpoints.
lr: Learning rate for optimizer.
weight_decay: Weight decay coefficient.
max_grad_norm: Max gradient norm for clipping.
accumulation_steps: Steps to accumulate gradients before optimizer step.
total_steps: Total training steps for scheduler.
warmup_steps: Warm-up learning rate steps.
device: Device for training, default to cuda if available.
mixed_precision: Enable AMP for faster training & less memory.
"""
self.model = model
self.train_loader = train_loader
self.valid_loader = valid_loader
self.save_dir = save_dir
self.device = device or (torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu"))
self.lr = lr
self.weight_decay = weight_decay
self.max_grad_norm = max_grad_norm
self.accumulation_steps = accumulation_steps
self.total_steps = total_steps
self.warmup_steps = warmup_steps
self.mixed_precision = mixed_precision
self.model.to(self.device)
self.optimizer = AdamW(self.model.parameters(), lr=self.lr, weight_decay=self.weight_decay)
def lr_lambda(current_step):
if current_step < self.warmup_steps:
return float(current_step) / float(max(1, self.warmup_steps))
return max(
0.0, float(self.total_steps - current_step) / float(max(1, self.total_steps - self.warmup_steps))
)
self.scheduler = LambdaLR(self.optimizer, lr_lambda)
self.scaler = GradScaler(enabled=mixed_precision)
os.makedirs(self.save_dir, exist_ok=True)
def save_checkpoint(self, step: int) -> None:
"""
Saves model and optimizer state dictionaries.
Args:
step: Current training step to tag checkpoint file.
"""
checkpoint_path = os.path.join(self.save_dir, f"checkpoint_step_{step}.pt")
torch.save({
"model_state_dict": self.model.state_dict(),
"optimizer_state_dict": self.optimizer.state_dict(),
"scheduler_state_dict": self.scheduler.state_dict(),
"scaler_state_dict": self.scaler.state_dict(),
"step": step,
}, checkpoint_path)
print(f"[Trainer] Checkpoint saved at step {step} to {checkpoint_path}")
def load_checkpoint(self, checkpoint_path: str) -> int:
"""
Loads model and optimizer state from checkpoint file.
Args:
checkpoint_path: Path to the checkpoint file.
Returns:
step: The training step resumed from.
"""
checkpoint = torch.load(checkpoint_path, map_location=self.device)
self.model.load_state_dict(checkpoint["model_state_dict"])
self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
self.scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
self.scaler.load_state_dict(checkpoint.get("scaler_state_dict", {}))
step = checkpoint.get("step", 0)
print(f"[Trainer] Loaded checkpoint from {checkpoint_path} at step {step}")
return step
def train_epoch(self, start_step: int = 0) -> int:
"""
Runs one full epoch of training with gradient accumulation and mixed precision.
Args:
start_step: Initial global step count.
Returns:
Updated global step count after epoch.
"""
self.model.train()
step = start_step
optimizer = self.optimizer
scheduler = self.scheduler
scaler = self.scaler
acc_steps = self.accumulation_steps
max_grad_norm = self.max_grad_norm
running_loss = 0.0
start_time = time.time()
optimizer.zero_grad()
for batch_idx, batch in enumerate(self.train_loader):
inputs = {k: v.to(self.device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}
with autocast(enabled=self.mixed_precision):
outputs = self.model(**inputs)
# Assume outputs include 'logits' and 'labels' or raw outputs for loss
# We provide a generic loss calculation placeholder:
if 'labels' in inputs:
loss_fn = nn.CrossEntropyLoss()
# Flatten inputs and outputs as needed based on task
loss = loss_fn(outputs.view(-1, outputs.size(-1)), inputs['labels'].view(-1))
else:
# Fallback: sum outputs (adjust per task)
loss = outputs.mean()
loss = loss / acc_steps
scaler.scale(loss).backward()
if (batch_idx + 1) % acc_steps == 0 or (batch_idx + 1) == len(self.train_loader):
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_grad_norm)
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad()
scheduler.step()
step += 1
running_loss += loss.item() * acc_steps
elapsed = time.time() - start_time
avg_loss = running_loss / step
print(f"Step {step:6d} | Loss: {avg_loss:.6f} | LR: {scheduler.get_last_lr()[0]:.8f} | Time: {elapsed:.2f}s")
return step
def evaluate(self) -> Dict[str, float]:
"""
Runs evaluation on validation loader if provided.
Returns:
Dictionary of evaluation metrics.
"""
if self.valid_loader is None:
print("[Trainer] No validation data provided for evaluation.")
return {}
self.model.eval()
total_loss = 0.0
count = 0
loss_fn = nn.CrossEntropyLoss()
with torch.no_grad():
for batch in self.valid_loader:
inputs = {k: v.to(self.device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}
outputs = self.model(**inputs)
if 'labels' in inputs:
loss = loss_fn(outputs.view(-1, outputs.size(-1)), inputs['labels'].view(-1))
total_loss += loss.item()
count += 1
avg_loss = total_loss / count if count > 0 else 0.0
print(f"[Trainer] Validation Loss: {avg_loss:.6f}")
return {"validation_loss": avg_loss}
def fit(self,
epochs: int,
start_step: int = 0,
checkpoint_interval: int = 1000,
validate_interval: int = 1000):
"""
Runs the full training process including periodic validation and saving.
Args:
epochs: Number of epochs to train.
start_step: Step number to resume from.
checkpoint_interval: Save checkpoint every N steps.
validate_interval: Run validation every N steps.
"""
global_step = start_step
for epoch in range(epochs):
print(f"[Trainer] Starting epoch {epoch + 1}/{epochs}")
global_step = self.train_epoch(global_step)
if global_step % validate_interval == 0 and self.valid_loader is not None:
self.evaluate()
if global_step % checkpoint_interval == 0:
self.save_checkpoint(global_step)
if __name__ == "__main__":
# Minimal test for trainer initialization (model and loaders must be provided)
print("Trainer module loaded. Instantiate with model and dataloaders for training.")
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