f8fb4d7133
- Deleted `setup_tensorflow_tpu.sh` which was responsible for setting up the TensorFlow environment on TPU v5e-8. - Removed `tpu_memory_monitor.py`, a tool for monitoring TPU memory usage during training. - Eliminated `train_model.py`, the script for training the Brain-to-Text RNN model.
265 lines
8.2 KiB
Python
265 lines
8.2 KiB
Python
#!/usr/bin/env python3
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"""
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TensorFlow Training Script for Brain-to-Text RNN Model
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Optimized for TPU v5e-8
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This script trains the TripleGRUDecoder model using TensorFlow and TPU hardware.
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It provides the same functionality as the PyTorch version but with TensorFlow
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operations optimized for TPU performance.
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Usage:
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python train_model_tf.py -config_path rnn_args.yaml
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Requirements:
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- TensorFlow >= 2.15.0
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- TPU v5e-8 environment
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- Access to brain-to-text HDF5 dataset
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"""
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import argparse
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import os
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import sys
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import logging
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from omegaconf import OmegaConf
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# Add the current directory to Python path for imports
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from trainer_tf import BrainToTextDecoderTrainerTF
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def setup_tpu_environment():
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"""Setup TPU environment variables for optimal performance"""
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# TPU v5e-8 optimizations
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os.environ.setdefault('TPU_ML_PLATFORM', 'PyTorch/XLA') # Enable XLA optimizations
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os.environ.setdefault('XLA_USE_BF16', '1') # Enable bfloat16 for memory efficiency
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os.environ.setdefault('TF_XLA_FLAGS', '--tf_xla_auto_jit=2') # Enable XLA JIT compilation
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# TPU memory optimizations
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os.environ.setdefault('TPU_MEGACORE', '1') # Enable megacore mode for larger models
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os.environ.setdefault('LIBTPU_INIT_ARGS', '--xla_tpu_spmd_threshold_for_allgather_cse=10000')
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# Disable warnings for cleaner output
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os.environ.setdefault('TF_CPP_MIN_LOG_LEVEL', '2')
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print("TPU environment configured for v5e-8 optimizations")
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def validate_config(config):
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"""Validate configuration for TensorFlow TPU training"""
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required_fields = [
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'model.n_input_features',
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'model.n_units',
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'dataset.sessions',
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'dataset.n_classes',
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'num_training_batches',
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'output_dir',
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'checkpoint_dir'
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]
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for field in required_fields:
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keys = field.split('.')
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value = config
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try:
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for key in keys:
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value = value[key]
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except KeyError:
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raise ValueError(f"Missing required configuration field: {field}")
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# TPU-specific validations
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if config.get('use_tpu', True):
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if config['dataset']['batch_size'] < 8:
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logging.warning("Small batch size may not utilize TPU efficiently. Consider batch_size >= 32")
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if not config.get('use_amp', True):
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logging.warning("Mixed precision disabled. Consider enabling for better TPU performance")
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# Dataset validation
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dataset_dir = config['dataset']['dataset_dir']
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if not os.path.exists(dataset_dir):
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raise FileNotFoundError(f"Dataset directory not found: {dataset_dir}")
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# Check if at least one session file exists
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session_found = False
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for session in config['dataset']['sessions']:
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train_path = os.path.join(dataset_dir, session, 'data_train.hdf5')
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if os.path.exists(train_path):
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session_found = True
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break
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if not session_found:
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raise FileNotFoundError("No valid session data files found in dataset directory")
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print("Configuration validation passed")
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def main():
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"""Main training function"""
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parser = argparse.ArgumentParser(
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description='Train Brain-to-Text RNN Model with TensorFlow on TPU v5e-8',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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'--config_path',
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default='rnn_args.yaml',
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help='Path to configuration YAML file'
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)
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parser.add_argument(
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'--output_dir',
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default=None,
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help='Override output directory from config'
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)
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parser.add_argument(
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'--checkpoint_dir',
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default=None,
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help='Override checkpoint directory from config'
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)
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parser.add_argument(
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'--resume_from',
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default=None,
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help='Path to checkpoint to resume training from'
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)
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parser.add_argument(
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'--num_batches',
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type=int,
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default=None,
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help='Override number of training batches'
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)
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parser.add_argument(
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'--batch_size',
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type=int,
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default=None,
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help='Override batch size'
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)
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parser.add_argument(
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'--mixed_precision',
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action='store_true',
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default=None,
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help='Enable mixed precision training (bfloat16)'
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)
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parser.add_argument(
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'--disable_mixed_precision',
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action='store_true',
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help='Disable mixed precision training'
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)
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parser.add_argument(
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'--validate_only',
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action='store_true',
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help='Only run validation, do not train'
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)
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parser.add_argument(
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'--debug',
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action='store_true',
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help='Enable debug logging'
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)
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args = parser.parse_args()
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# Setup logging
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log_level = logging.DEBUG if args.debug else logging.INFO
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logging.basicConfig(
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level=log_level,
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format='%(asctime)s - %(levelname)s - %(message)s'
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)
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# Setup TPU environment
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setup_tpu_environment()
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# Load configuration
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if not os.path.exists(args.config_path):
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raise FileNotFoundError(f"Configuration file not found: {args.config_path}")
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config = OmegaConf.load(args.config_path)
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print(f"Loaded configuration from: {args.config_path}")
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# Apply command line overrides
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if args.output_dir:
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config.output_dir = args.output_dir
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if args.checkpoint_dir:
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config.checkpoint_dir = args.checkpoint_dir
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if args.num_batches:
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config.num_training_batches = args.num_batches
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if args.batch_size:
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config.dataset.batch_size = args.batch_size
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if args.mixed_precision:
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config.use_amp = True
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if args.disable_mixed_precision:
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config.use_amp = False
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# Validate configuration
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validate_config(config)
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try:
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# Initialize trainer
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print("Initializing TensorFlow Brain-to-Text trainer...")
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trainer = BrainToTextDecoderTrainerTF(config)
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# Load checkpoint if specified
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if args.resume_from:
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if os.path.exists(args.resume_from + '.weights.h5'):
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trainer.load_checkpoint(args.resume_from)
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print(f"Resumed training from checkpoint: {args.resume_from}")
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else:
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raise FileNotFoundError(f"Checkpoint not found: {args.resume_from}")
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if args.validate_only:
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print("Running validation only...")
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# Create validation dataset
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from dataset_tf import create_input_fn
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val_dataset = create_input_fn(
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trainer.val_dataset_tf,
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trainer.args['dataset']['data_transforms'],
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training=False
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)
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val_dist_dataset = trainer.strategy.experimental_distribute_dataset(val_dataset)
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# Run validation
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val_metrics = trainer._validate(val_dist_dataset)
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print(f"Validation Results:")
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print(f" Average Loss: {val_metrics['avg_loss']:.4f}")
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print(f" Average PER: {val_metrics['avg_per']:.4f}")
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print(f" Total Edit Distance: {val_metrics['total_edit_distance']}")
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print(f" Total Sequence Length: {val_metrics['total_seq_length']}")
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else:
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# Start training
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print("Starting training...")
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train_stats = trainer.train()
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print("\nTraining completed successfully!")
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print(f"Best validation PER: {trainer.best_val_per:.5f}")
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print(f"Final training loss: {train_stats['train_losses'][-1]:.4f}")
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print(f"Final validation loss: {train_stats['val_losses'][-1]:.4f}")
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print(f"Total training batches: {len(train_stats['train_losses'])}")
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# Save final training statistics
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import pickle
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stats_path = os.path.join(config.output_dir, 'training_stats.pkl')
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with open(stats_path, 'wb') as f:
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pickle.dump(train_stats, f)
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print(f"Training statistics saved to: {stats_path}")
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except KeyboardInterrupt:
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print("\nTraining interrupted by user")
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sys.exit(1)
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except Exception as e:
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print(f"\nTraining failed with error: {e}")
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if args.debug:
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import traceback
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traceback.print_exc()
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sys.exit(1)
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if __name__ == "__main__":
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main() |