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import os
import torch
import wandb
import shutil
from config import SmolLM2Config
from model import SmolLM2Lightning
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint, Callback
from pytorch_lightning.loggers import WandbLogger
from env_setup import setup_environment, cleanup_environment
# Set CUDA environment variables before any other CUDA operations
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
os.environ['TORCH_USE_CUDA_DSA'] = '1'
def setup_training():
"""Setup training environment"""
try:
if torch.cuda.is_available():
# Configure CUDA settings
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.backends.cudnn.benchmark = True
torch.set_float32_matmul_precision('high')
# Set default device
device = torch.device('cuda:0')
torch.cuda.set_device(device)
# Print GPU info
print(f"Using GPU: {torch.cuda.get_device_name()}")
print(f"GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB")
return device
except Exception as e:
print(f"CUDA setup error: {str(e)}")
print("Using CPU")
return torch.device('cpu')
def cleanup_training():
"""Cleanup training resources"""
try:
# Move model to CPU before cleanup
if torch.cuda.is_available():
torch.cuda.empty_cache()
# Clean up wandb
try:
wandb.finish()
except:
pass
except Exception as e:
print(f"Cleanup error: {str(e)}")
# Setup CUDA at module level
device = setup_training()
class GenerationMonitorCallback(Callback):
def __init__(self, prompt="Explain what machine learning is:", sample_every_n_steps=500):
super().__init__()
self.prompt = prompt
self.sample_every_n_steps = sample_every_n_steps
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
try:
if (trainer.global_step + 1) % self.sample_every_n_steps == 0:
# Switch to eval mode
pl_module.eval()
with torch.no_grad():
# Tokenize prompt
inputs = pl_module.tokenizer(
self.prompt,
return_tensors="pt",
truncation=True,
max_length=pl_module.config.model.max_position_embeddings,
padding=True
).to(pl_module.device)
try:
# Generate text with error handling
outputs = pl_module.generate(
input_ids=inputs.input_ids,
attention_mask=inputs.attention_mask,
max_length=100,
temperature=0.7,
top_p=0.9,
top_k=50,
do_sample=True,
pad_token_id=pl_module.tokenizer.pad_token_id,
bos_token_id=pl_module.tokenizer.bos_token_id,
eos_token_id=pl_module.tokenizer.eos_token_id
)
# Decode generated text
generated_text = pl_module.tokenizer.decode(outputs[0], skip_special_tokens=True)
# Print results
print(f"\n=== Generation at step {trainer.global_step + 1} ===")
print(f"Prompt: {self.prompt}")
print(f"Generated: {generated_text}\n")
except RuntimeError as e:
print(f"\nError during generation at step {trainer.global_step + 1}: {str(e)}")
print(f"Input shape: {inputs.input_ids.shape}")
print(f"Input device: {inputs.input_ids.device}")
# Switch back to train mode
pl_module.train()
except Exception as e:
print(f"\nCallback error at step {trainer.global_step + 1}: {str(e)}")
def init_wandb(project_name, run_name):
"""Initialize WandB with error handling and cleanup"""
try:
# Try to clean up any existing wandb directory
wandb_dir = os.path.join(os.getcwd(), "wandb")
if os.path.exists(wandb_dir):
try:
shutil.rmtree(wandb_dir)
print("Cleaned up existing wandb directory")
except Exception as e:
print(f"Warning: Could not clean up wandb directory: {str(e)}")
# Create fresh wandb directory with proper permissions
os.makedirs(wandb_dir, exist_ok=True)
# Initialize WandB logger
logger = WandbLogger(
project=project_name,
name=run_name,
save_dir=os.getcwd(),
settings=wandb.Settings(start_method="thread")
)
return logger
except Exception as e:
print(f"Error initializing WandB: {str(e)}")
print("Continuing without WandB logging...")
return None
def main():
device = setup_training()
try:
# Load configuration
config = SmolLM2Config("config.yaml")
# Initialize model
model = SmolLM2Lightning(config)
# Phase 1: Initial Training
print("\n=== Starting Phase 1 Training ===")
# Initialize wandb logger for phase 1 with error handling
wandb_logger = init_wandb("smol-lm2", "training_run_phase1")
# Setup checkpoint callback for phase 1
checkpoint_callback = ModelCheckpoint(
dirpath=config.training.checkpoint_dir,
filename="smol-lm2-phase1-{epoch:02d}-{train_loss:.2f}",
save_top_k=3,
monitor="train_loss",
mode="min",
every_n_train_steps=config.training.save_steps
)
# Setup generation monitoring callback for phase 1
generation_callback = GenerationMonitorCallback(
prompt=config.training.sample_prompt,
sample_every_n_steps=config.training.sample_frequency
)
# Initialize trainer for phase 1
trainer_phase1 = pl.Trainer(
max_steps=config.training.first_phase_steps,
accelerator=config.hardware.accelerator,
devices=config.hardware.devices,
precision=config.hardware.precision,
logger=wandb_logger,
callbacks=[checkpoint_callback, generation_callback],
gradient_clip_val=config.hardware.gradient_clip,
accumulate_grad_batches=config.training.gradient_accumulation_steps,
log_every_n_steps=config.training.logging_steps,
deterministic=False,
benchmark=True,
strategy='auto', # Let PyTorch Lightning handle device strategy
)
# Train phase 1 with error handling
try:
trainer_phase1.fit(model)
except Exception as e:
print(f"Error during phase 1 training: {str(e)}")
raise
# Save phase 1 checkpoint
phase1_checkpoint_path = os.path.join(config.training.checkpoint_dir, "smol-lm2-phase1-final.ckpt")
trainer_phase1.save_checkpoint(phase1_checkpoint_path)
print(f"Phase 1 completed. Model saved to {phase1_checkpoint_path}")
# Clear GPU memory between phases
if torch.cuda.is_available():
torch.cuda.empty_cache()
# Phase 2: Fine-tuning
print("\n=== Starting Phase 2 Training ===")
# Load the model from phase 1 checkpoint with error handling
try:
model = SmolLM2Lightning.load_from_checkpoint(phase1_checkpoint_path, config=config)
except Exception as e:
print(f"Error loading checkpoint for phase 2: {str(e)}")
raise
# Initialize wandb logger for phase 2 with error handling
wandb_logger = init_wandb("smol-lm2", "training_run_phase2")
# Setup generation monitoring callback with higher frequency for phase 2
generation_callback = GenerationMonitorCallback(
prompt=config.training.sample_prompt,
sample_every_n_steps=config.training.second_phase_sample_frequency
)
# Initialize trainer for phase 2
trainer_phase2 = pl.Trainer(
max_steps=config.training.second_phase_steps,
accelerator=config.hardware.accelerator,
devices=config.hardware.devices,
precision=config.hardware.precision,
logger=wandb_logger,
callbacks=[generation_callback],
gradient_clip_val=config.hardware.gradient_clip,
accumulate_grad_batches=config.training.gradient_accumulation_steps,
log_every_n_steps=config.training.logging_steps,
deterministic=False,
benchmark=True,
)
# Train phase 2 with error handling
try:
trainer_phase2.fit(model)
except Exception as e:
print(f"Error during phase 2 training: {str(e)}")
raise
# Save final model
final_checkpoint_path = os.path.join(config.training.checkpoint_dir, "smol-lm2-final.ckpt")
trainer_phase2.save_checkpoint(final_checkpoint_path)
print(f"Phase 2 completed. Final model saved to {final_checkpoint_path}")
except Exception as e:
print(f"\nTraining failed with error: {str(e)}")
if torch.cuda.is_available():
print(f"CUDA memory allocated: {torch.cuda.memory_allocated() / 1e9:.2f} GB")
print(f"CUDA memory cached: {torch.cuda.memory_reserved() / 1e9:.2f} GB")
raise
finally:
cleanup_training()
if __name__ == "__main__":
main() |