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| import re | |
| import logging | |
| from dataclasses import dataclass | |
| from typing import Optional, Dict, Any | |
| from datetime import datetime, timedelta | |
| logger = logging.getLogger(__name__) | |
| class TrainingState: | |
| """Represents the current state of training""" | |
| status: str = "idle" # idle, initializing, training, completed, error, stopped | |
| current_step: int = 0 | |
| total_steps: int = 0 | |
| current_epoch: int = 0 | |
| total_epochs: int = 0 | |
| step_loss: float = 0.0 | |
| learning_rate: float = 0.0 | |
| grad_norm: float = 0.0 | |
| memory_allocated: float = 0.0 | |
| memory_reserved: float = 0.0 | |
| start_time: Optional[datetime] = None | |
| last_step_time: Optional[datetime] = None | |
| estimated_remaining: Optional[timedelta] = None | |
| error_message: Optional[str] = None | |
| initialization_stage: str = "" | |
| download_progress: float = 0.0 | |
| def calculate_progress(self) -> float: | |
| """Calculate overall progress as percentage""" | |
| if self.total_steps == 0: | |
| return 0.0 | |
| return (self.current_step / self.total_steps) * 100 | |
| def to_dict(self) -> Dict[str, Any]: | |
| """Convert state to dictionary for UI updates""" | |
| elapsed = str(datetime.now() - self.start_time) if self.start_time else "0:00:00" | |
| remaining = str(self.estimated_remaining) if self.estimated_remaining else "calculating..." | |
| return { | |
| "status": self.status, | |
| "progress": f"{self.calculate_progress():.1f}%", | |
| "current_step": self.current_step, | |
| "total_steps": self.total_steps, | |
| "current_epoch": self.current_epoch, | |
| "total_epochs": self.total_epochs, | |
| "step_loss": f"{self.step_loss:.4f}", | |
| "learning_rate": f"{self.learning_rate:.2e}", | |
| "grad_norm": f"{self.grad_norm:.4f}", | |
| "memory": f"{self.memory_allocated:.1f}GB allocated, {self.memory_reserved:.1f}GB reserved", | |
| "elapsed": elapsed, | |
| "remaining": remaining, | |
| "initialization_stage": self.initialization_stage, | |
| "error_message": self.error_message, | |
| "download_progress": self.download_progress | |
| } | |
| class TrainingLogParser: | |
| """Parser for training logs with state management""" | |
| def __init__(self): | |
| self.state = TrainingState() | |
| self._last_update_time = None | |
| def parse_line(self, line: str) -> Optional[Dict[str, Any]]: | |
| """Parse a single log line and update state""" | |
| try: | |
| # For debugging | |
| #logger.info(f"Parsing line: {line[:100]}...") | |
| # Training step progress line example: | |
| # Training steps: 1%|▏ | 1/70 [00:14<16:11, 14.08s/it, grad_norm=0.00789, step_loss=0.555, lr=3e-7] | |
| if ("Started training" in line) or ("Starting training" in line): | |
| self.state.status = "training" | |
| if "Training steps:" in line: | |
| # Set status to training if we see this | |
| self.state.status = "training" | |
| #print("setting status to 'training'") | |
| if not self.state.start_time: | |
| self.state.start_time = datetime.now() | |
| # Extract step numbers | |
| steps_match = re.search(r"(\d+)/(\d+)", line) | |
| if steps_match: | |
| self.state.current_step = int(steps_match.group(1)) | |
| self.state.total_steps = int(steps_match.group(2)) | |
| # Extract metrics | |
| for pattern, attr in [ | |
| (r"step_loss=([0-9.e-]+)", "step_loss"), | |
| (r"lr=([0-9.e-]+)", "learning_rate"), | |
| (r"grad_norm=([0-9.e-]+)", "grad_norm") | |
| ]: | |
| match = re.search(pattern, line) | |
| if match: | |
| setattr(self.state, attr, float(match.group(1))) | |
| # Calculate time estimates based on total elapsed time | |
| now = datetime.now() | |
| if self.state.start_time and self.state.current_step > 0: | |
| # Calculate elapsed time and average time per step | |
| elapsed_seconds = (now - self.state.start_time).total_seconds() | |
| avg_time_per_step = elapsed_seconds / self.state.current_step | |
| # Calculate remaining time | |
| remaining_steps = self.state.total_steps - self.state.current_step | |
| estimated_remaining_seconds = avg_time_per_step * remaining_steps | |
| # Format as days, hours, minutes, seconds | |
| days = int(estimated_remaining_seconds // (24 * 3600)) | |
| hours = int((estimated_remaining_seconds % (24 * 3600)) // 3600) | |
| minutes = int((estimated_remaining_seconds % 3600) // 60) | |
| seconds = int(estimated_remaining_seconds % 60) | |
| # Create formatted timedelta | |
| if days > 0: | |
| formatted_time = f"{days}d {hours}h {minutes}m {seconds}s" | |
| elif hours > 0: | |
| formatted_time = f"{hours}h {minutes}m {seconds}s" | |
| elif minutes > 0: | |
| formatted_time = f"{minutes}m {seconds}s" | |
| else: | |
| formatted_time = f"{seconds}s" | |
| self.state.estimated_remaining = formatted_time | |
| self.state.last_step_time = now | |
| logger.info(f"Updated training state: step={self.state.current_step}/{self.state.total_steps}, loss={self.state.step_loss}") | |
| return self.state.to_dict() | |
| # Epoch information | |
| # there is an issue with how epoch is reported because we display: | |
| # Progress: 96.9%, Step: 872/900, Epoch: 12/50 | |
| # we should probably just show the steps | |
| epoch_match = re.search(r"Starting epoch \((\d+)/(\d+)\)", line) | |
| if epoch_match: | |
| self.state.current_epoch = int(epoch_match.group(1)) | |
| self.state.total_epochs = int(epoch_match.group(2)) | |
| logger.info(f"Updated epoch: {self.state.current_epoch}/{self.state.total_epochs}") | |
| return self.state.to_dict() | |
| # Initialization stages | |
| if "Initializing" in line: | |
| self.state.status = "initializing" | |
| self.state.initialization_stage = line.split("Initializing")[1].strip() | |
| logger.info(f"Initialization stage: {self.state.initialization_stage}") | |
| return self.state.to_dict() | |
| # Memory usage | |
| if "memory_allocated" in line: | |
| mem_match = re.search(r'"memory_allocated":\s*([0-9.]+)', line) | |
| if mem_match: | |
| self.state.memory_allocated = float(mem_match.group(1)) | |
| reserved_match = re.search(r'"memory_reserved":\s*([0-9.]+)', line) | |
| if reserved_match: | |
| self.state.memory_reserved = float(reserved_match.group(1)) | |
| logger.info(f"Updated memory: allocated={self.state.memory_allocated}GB, reserved={self.state.memory_reserved}GB") | |
| return self.state.to_dict() | |
| # Completion states | |
| if "Training completed successfully" in line: | |
| self.state.status = "completed" | |
| logger.info("Training completed") | |
| return self.state.to_dict() | |
| if any(x in line for x in ["Training process stopped", "Training stopped"]): | |
| self.state.status = "stopped" | |
| logger.info("Training stopped") | |
| return self.state.to_dict() | |
| if "Error during training:" in line: | |
| self.state.status = "error" | |
| self.state.error_message = line.split("Error during training:")[1].strip() | |
| logger.info(f"Training error: {self.state.error_message}") | |
| return self.state.to_dict() | |
| except Exception as e: | |
| logger.error(f"Error parsing line: {str(e)}") | |
| return None | |
| def reset(self): | |
| """Reset parser state""" | |
| self.state = TrainingState() | |
| self._last_update_time = None |