File size: 10,077 Bytes
85653bc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 |
import os
import sys
import json
import time
from time import gmtime, strftime
import torch.distributed as dist
import torch
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, Kronos
# 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: (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,
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,
num_workers=config.get('num_workers', 2), pin_memory=True, drop_last=False
)
return train_loader, val_loader, train_dataset, valid_dataset
def train_model(model, tokenizer, device, config, save_dir, logger, rank, world_size):
"""
The main training and validation loop for the predictor.
"""
start_time = time.time()
if rank == 0:
effective_bs = config['batch_size'] * world_size
print(f"Effective BATCHSIZE per GPU: {config['batch_size']}, Total: {effective_bs}")
train_loader, val_loader, train_dataset, valid_dataset = create_dataloaders(config, rank, world_size)
optimizer = torch.optim.AdamW(
model.parameters(),
lr=config['predictor_learning_rate'],
betas=(config['adam_beta1'], config['adam_beta2']),
weight_decay=config['adam_weight_decay']
)
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer, max_lr=config['predictor_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 = 0
for epoch_idx in range(config['epochs']):
epoch_start_time = time.time()
model.train()
train_loader.sampler.set_epoch(epoch_idx)
train_dataset.set_epoch_seed(epoch_idx * 10000 + rank)
valid_dataset.set_epoch_seed(0)
for i, (batch_x, batch_x_stamp) in enumerate(train_loader):
batch_x = batch_x.squeeze(0).to(device, non_blocking=True)
batch_x_stamp = batch_x_stamp.squeeze(0).to(device, non_blocking=True)
# Tokenize input data on-the-fly
with torch.no_grad():
token_seq_0, token_seq_1 = tokenizer.encode(batch_x, half=True)
# Prepare inputs and targets for the language model
token_in = [token_seq_0[:, :-1], token_seq_1[:, :-1]]
token_out = [token_seq_0[:, 1:], token_seq_1[:, 1:]]
# Forward pass and loss calculation
logits = model(token_in[0], token_in[1], batch_x_stamp[:, :-1, :])
loss, s1_loss, s2_loss = model.module.head.compute_loss(logits[0], logits[1], token_out[0], token_out[1])
# Backward pass and optimization
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=3.0)
optimizer.step()
scheduler.step()
# Logging (Master Process Only)
if rank == 0 and (batch_idx_global + 1) % config['log_interval'] == 0:
lr = optimizer.param_groups[0]['lr']
print(
f"[Rank {rank}, Epoch {epoch_idx + 1}/{config['epochs']}, Step {i + 1}/{len(train_loader)}] "
f"LR {lr:.6f}, Loss: {loss.item():.4f}"
)
if rank == 0 and logger:
lr = optimizer.param_groups[0]['lr']
logger.log_metric('train_predictor_loss_batch', loss.item(), step=batch_idx_global)
logger.log_metric('train_S1_loss_each_batch', s1_loss.item(), step=batch_idx_global)
logger.log_metric('train_S2_loss_each_batch', s2_loss.item(), step=batch_idx_global)
logger.log_metric('predictor_learning_rate', lr, step=batch_idx_global)
batch_idx_global += 1
# --- Validation Loop ---
model.eval()
tot_val_loss_sum_rank = 0.0
val_batches_processed_rank = 0
with torch.no_grad():
for batch_x, batch_x_stamp in val_loader:
batch_x = batch_x.squeeze(0).to(device, non_blocking=True)
batch_x_stamp = batch_x_stamp.squeeze(0).to(device, non_blocking=True)
token_seq_0, token_seq_1 = tokenizer.encode(batch_x, half=True)
token_in = [token_seq_0[:, :-1], token_seq_1[:, :-1]]
token_out = [token_seq_0[:, 1:], token_seq_1[:, 1:]]
logits = model(token_in[0], token_in[1], batch_x_stamp[:, :-1, :])
val_loss, _, _ = model.module.head.compute_loss(logits[0], logits[1], token_out[0], token_out[1])
tot_val_loss_sum_rank += val_loss.item()
val_batches_processed_rank += 1
# Reduce validation metrics
val_loss_sum_tensor = torch.tensor(tot_val_loss_sum_rank, device=device)
val_batches_tensor = torch.tensor(val_batches_processed_rank, device=device)
dist.all_reduce(val_loss_sum_tensor, op=dist.ReduceOp.SUM)
dist.all_reduce(val_batches_tensor, op=dist.ReduceOp.SUM)
avg_val_loss = val_loss_sum_tensor.item() / val_batches_tensor.item() if val_batches_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_predictor_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})")
dist.barrier()
dt_result['best_val_loss'] = best_val_loss
return 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['predictor_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()
# Model Initialization
tokenizer = KronosTokenizer.from_pretrained(config['finetuned_tokenizer_path'])
tokenizer.eval().to(device)
model = Kronos.from_pretrained(config['pretrained_predictor_path'])
model.to(device)
model = DDP(model, device_ids=[local_rank], find_unused_parameters=False)
if rank == 0:
print(f"Predictor Model Size: {get_model_size(model.module)}")
# Start Training
dt_result = train_model(
model, tokenizer, device, config, save_dir, comet_logger, rank, world_size
)
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_predictor.py
if "WORLD_SIZE" not in os.environ:
raise RuntimeError("This script must be launched with `torchrun`.")
config_instance = Config()
main(config_instance.__dict__)
|