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Copy pathtrain_tokenizer.py
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281 lines (234 loc) · 11.2 KB
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import os
import sys
import json
import time
from time import gmtime, strftime
import argparse
import datetime
import torch.distributed as dist
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
import comet_ml
# Ensure project root is in path
sys.path.append("../")
from config import Config
from dataset import QlibDataset
from model.kronos import KronosTokenizer
# Import shared utilities
from utils.training_utils import (
setup_ddp,
cleanup_ddp,
set_seed,
get_model_size,
format_time,
)
def create_dataloaders(config: dict, rank: int, world_size: int):
"""
Creates and returns distributed dataloaders for training and validation.
Args:
config (dict): A dictionary of configuration parameters.
rank (int): The global rank of the current process.
world_size (int): The total number of processes.
Returns:
tuple: A tuple containing (train_loader, val_loader, train_dataset, valid_dataset).
"""
print(f"[Rank {rank}] Creating distributed dataloaders...")
train_dataset = QlibDataset('train')
valid_dataset = QlibDataset('val')
print(f"[Rank {rank}] Train dataset size: {len(train_dataset)}, Validation dataset size: {len(valid_dataset)}")
train_sampler = DistributedSampler(train_dataset, num_replicas=world_size, rank=rank, shuffle=True)
val_sampler = DistributedSampler(valid_dataset, num_replicas=world_size, rank=rank, shuffle=False)
train_loader = DataLoader(
train_dataset,
batch_size=config['batch_size'],
sampler=train_sampler,
shuffle=False, # Shuffle is handled by the sampler
num_workers=config.get('num_workers', 2),
pin_memory=True,
drop_last=True
)
val_loader = DataLoader(
valid_dataset,
batch_size=config['batch_size'],
sampler=val_sampler,
shuffle=False,
num_workers=config.get('num_workers', 2),
pin_memory=True,
drop_last=False
)
print(f"[Rank {rank}] Dataloaders created. Train steps/epoch: {len(train_loader)}, Val steps: {len(val_loader)}")
return train_loader, val_loader, train_dataset, valid_dataset
def train_model(model, device, config, save_dir, logger, rank, world_size):
"""
The main training and validation loop for the tokenizer.
Args:
model (DDP): The DDP-wrapped model to train.
device (torch.device): The device for the current process.
config (dict): Configuration dictionary.
save_dir (str): Directory to save checkpoints.
logger (comet_ml.Experiment): Comet logger instance.
rank (int): Global rank of the process.
world_size (int): Total number of processes.
Returns:
tuple: A tuple containing the trained model and a dictionary of results.
"""
start_time = time.time()
if rank == 0:
effective_bs = config['batch_size'] * world_size * config['accumulation_steps']
print(f"[Rank {rank}] BATCHSIZE (per GPU): {config['batch_size']}")
print(f"[Rank {rank}] Effective total batch size: {effective_bs}")
train_loader, val_loader, train_dataset, valid_dataset = create_dataloaders(config, rank, world_size)
optimizer = torch.optim.AdamW(
model.parameters(),
lr=config['tokenizer_learning_rate'],
weight_decay=config['adam_weight_decay']
)
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer=optimizer,
max_lr=config['tokenizer_learning_rate'],
steps_per_epoch=len(train_loader),
epochs=config['epochs'],
pct_start=0.03,
div_factor=10
)
best_val_loss = float('inf')
dt_result = {}
batch_idx_global_train = 0
for epoch_idx in range(config['epochs']):
epoch_start_time = time.time()
model.train()
train_loader.sampler.set_epoch(epoch_idx)
# Set dataset seeds for reproducible sampling
train_dataset.set_epoch_seed(epoch_idx * 10000 + rank)
valid_dataset.set_epoch_seed(0) # Keep validation sampling consistent
for i, (ori_batch_x, _) in enumerate(train_loader):
ori_batch_x = ori_batch_x.squeeze(0).to(device, non_blocking=True)
# --- Gradient Accumulation Loop ---
current_batch_total_loss = 0.0
for j in range(config['accumulation_steps']):
start_idx = j * (ori_batch_x.shape[0] // config['accumulation_steps'])
end_idx = (j + 1) * (ori_batch_x.shape[0] // config['accumulation_steps'])
batch_x = ori_batch_x[start_idx:end_idx]
# Forward pass
zs, bsq_loss, _, _ = model(batch_x)
z_pre, z = zs
# Loss calculation
recon_loss_pre = F.mse_loss(z_pre, batch_x)
recon_loss_all = F.mse_loss(z, batch_x)
recon_loss = recon_loss_pre + recon_loss_all
loss = (recon_loss + bsq_loss) / 2 # Assuming w_1=w_2=1
loss_scaled = loss / config['accumulation_steps']
current_batch_total_loss += loss.item()
loss_scaled.backward()
# --- Optimizer Step after Accumulation ---
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=2.0)
optimizer.step()
scheduler.step()
optimizer.zero_grad()
# --- Logging (Master Process Only) ---
if rank == 0 and (batch_idx_global_train + 1) % config['log_interval'] == 0:
avg_loss = current_batch_total_loss / config['accumulation_steps']
print(
f"[Rank {rank}, Epoch {epoch_idx + 1}/{config['epochs']}, Step {i + 1}/{len(train_loader)}] "
f"LR {optimizer.param_groups[0]['lr']:.6f}, Loss: {avg_loss:.4f}"
)
if rank == 0 and logger:
avg_loss = current_batch_total_loss / config['accumulation_steps']
logger.log_metric('train_tokenizer_loss_batch', avg_loss, step=batch_idx_global_train)
logger.log_metric(f'train_vqvae_vq_loss_each_batch', bsq_loss.item(), step=batch_idx_global_train)
logger.log_metric(f'train_recon_loss_pre_each_batch', recon_loss_pre.item(), step=batch_idx_global_train)
logger.log_metric(f'train_recon_loss_each_batch', recon_loss_all.item(), step=batch_idx_global_train)
logger.log_metric('tokenizer_learning_rate', optimizer.param_groups[0]["lr"], step=batch_idx_global_train)
batch_idx_global_train += 1
# --- Validation Loop ---
model.eval()
tot_val_loss_sum_rank = 0.0
val_sample_count_rank = 0
with torch.no_grad():
for ori_batch_x, _ in val_loader:
ori_batch_x = ori_batch_x.squeeze(0).to(device, non_blocking=True)
zs, _, _, _ = model(ori_batch_x)
_, z = zs
val_loss_item = F.mse_loss(z, ori_batch_x)
tot_val_loss_sum_rank += val_loss_item.item() * ori_batch_x.size(0)
val_sample_count_rank += ori_batch_x.size(0)
# Reduce validation losses from all processes
val_loss_sum_tensor = torch.tensor(tot_val_loss_sum_rank, device=device)
val_count_tensor = torch.tensor(val_sample_count_rank, device=device)
dist.all_reduce(val_loss_sum_tensor, op=dist.ReduceOp.SUM)
dist.all_reduce(val_count_tensor, op=dist.ReduceOp.SUM)
avg_val_loss = val_loss_sum_tensor.item() / val_count_tensor.item() if val_count_tensor.item() > 0 else 0
# --- End of Epoch Summary & Checkpointing (Master Process Only) ---
if rank == 0:
print(f"\n--- Epoch {epoch_idx + 1}/{config['epochs']} Summary ---")
print(f"Validation Loss: {avg_val_loss:.4f}")
print(f"Time This Epoch: {format_time(time.time() - epoch_start_time)}")
print(f"Total Time Elapsed: {format_time(time.time() - start_time)}\n")
if logger:
logger.log_metric('val_tokenizer_loss_epoch', avg_val_loss, epoch=epoch_idx)
if avg_val_loss < best_val_loss:
best_val_loss = avg_val_loss
save_path = f"{save_dir}/checkpoints/best_model"
model.module.save_pretrained(save_path)
print(f"Best model saved to {save_path} (Val Loss: {best_val_loss:.4f})")
if logger:
logger.log_model("best_model", save_path)
dist.barrier() # Ensure all processes finish the epoch before starting the next one.
dt_result['best_val_loss'] = best_val_loss
return model, dt_result
def main(config: dict):
"""
Main function to orchestrate the DDP training process.
"""
rank, world_size, local_rank = setup_ddp()
device = torch.device(f"cuda:{local_rank}")
set_seed(config['seed'], rank)
save_dir = os.path.join(config['save_path'], config['tokenizer_save_folder_name'])
# Logger and summary setup (master process only)
comet_logger, master_summary = None, {}
if rank == 0:
os.makedirs(os.path.join(save_dir, 'checkpoints'), exist_ok=True)
master_summary = {
'start_time': strftime("%Y-%m-%dT%H-%M-%S", gmtime()),
'save_directory': save_dir,
'world_size': world_size,
}
if config['use_comet']:
comet_logger = comet_ml.Experiment(
api_key=config['comet_config']['api_key'],
project_name=config['comet_config']['project_name'],
workspace=config['comet_config']['workspace'],
)
comet_logger.add_tag(config['comet_tag'])
comet_logger.set_name(config['comet_name'])
comet_logger.log_parameters(config)
print("Comet Logger Initialized.")
dist.barrier() # Ensure save directory is created before proceeding
# Model Initialization
model = KronosTokenizer.from_pretrained(config['pretrained_tokenizer_path'])
model.to(device)
model = DDP(model, device_ids=[local_rank], find_unused_parameters=False)
if rank == 0:
print(f"Model Size: {get_model_size(model.module)}")
# Start Training
_, dt_result = train_model(
model, device, config, save_dir, comet_logger, rank, world_size
)
# Finalize and save summary (master process only)
if rank == 0:
master_summary['final_result'] = dt_result
with open(os.path.join(save_dir, 'summary.json'), 'w') as f:
json.dump(master_summary, f, indent=4)
print('Training finished. Summary file saved.')
if comet_logger:
comet_logger.end()
cleanup_ddp()
if __name__ == '__main__':
# Usage: torchrun --standalone --nproc_per_node=NUM_GPUS train_tokenizer.py
if "WORLD_SIZE" not in os.environ:
raise RuntimeError("This script must be launched with `torchrun`.")
config_instance = Config()
main(config_instance.__dict__)
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