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torchrun --nproc_per_node 4 train.py --dp_size 4 --micro_batch_size 1 --gradient_accumulation_steps 8 --seq_len 128 --max_tokens 40960 --num_proc 16 --run_name dp_naive --use_wandb
torchrun --nproc_per_node 4 train.py --dp_size 4 --micro_batch_size 4 --gradient_accumulation_steps 8 --seq_len 128 --max_tokens 40960 --num_proc 16
"""
import os
import time
import wandb
import datetime
import torch
import torch.nn.functional as F
import torch.distributed as dist
import argparse
from torch.optim import AdamW
from transformers import AutoConfig
# import lovely_tensors as lt
# lt.monkey_patch()
from model import Llama
from dataloader import MicroBatchDataLoader
import process_group_manager as pgm
from process_group_manager import setup_process_group_manager
from utils import set_all_seed, print, to_readable_format
from tensor_parallel import apply_tensor_parallel
from data_parallel import DataParallelNaive
def train_step(model, dataloader, device):
acc_loss = 0.0
requires_grad_sync = pgm.process_group_manager.dp_world_size > 1
for i in range(dataloader.grad_acc_steps):
# get the next batch
batch = next(dataloader)
input_ids = batch["input_ids"].to(device)
target_ids = batch["target_ids"].to(device)
# enable gradient synchronization for the last micro-batch only
if requires_grad_sync:
model.require_backward_grad_sync = (i == dataloader.grad_acc_steps - 1)
outputs = model(input_ids=input_ids)
# compute the loss
batch_size, seq_len = input_ids.shape
target_ids = target_ids.reshape(-1)
outputs = outputs.view(seq_len*batch_size, -1)
loss = F.cross_entropy(outputs, target_ids, reduction='mean') / dataloader.grad_acc_steps
loss.backward()
acc_loss += loss.item()
return acc_loss
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Training script for LLaMA model")
# Environment arguments
parser.add_argument("--omp_num_threads", type=str, default="1")
parser.add_argument("--tokenizers_parallelism", type=str, default="false")
# Model arguments
parser.add_argument("--model_name", type=str, default="HuggingFaceTB/SmolLM-360M-Instruct")
parser.add_argument("--num_hidden_layers", type=int, default=32)
parser.add_argument("--num_attention_heads", type=int, default=16)
parser.add_argument("--num_key_value_heads", type=int, default=4)
# Dataset arguments
parser.add_argument("--dataset_name", type=str, default="roneneldan/TinyStories")
parser.add_argument("--num_workers", type=int, default=1)
parser.add_argument("--num_proc", type=int, default=4)
# Training arguments
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--learning_rate", type=float, default=3e-4)
parser.add_argument("--seq_len", type=int, default=32)
parser.add_argument("--micro_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
parser.add_argument("--max_tokens", type=int, default=1e6)
# Distributed training arguments
parser.add_argument("--tp_size", type=int, default=1, help="Tensor Parallel size")
parser.add_argument("--dp_size", type=int, default=1, help="Data Parallel size")
parser.add_argument("--pp_size", type=int, default=1, help="Pipeline Parallel size")
parser.add_argument("--pp_engine", type=str, default="afab", choices=["1f1b", "afab"])
# Logging arguments
parser.add_argument("--run_name", type=str, default="default_run")
parser.add_argument("--use_wandb", action="store_true")
args = parser.parse_args()
# Set environment variables
os.environ["OMP_NUM_THREADS"] = args.omp_num_threads
os.environ["TOKENIZERS_PARALLELISM"] = args.tokenizers_parallelism
os.environ["DEVICE"] = "cpu"
local_rank = int(os.environ["LOCAL_RANK"])
global_rank = int(os.environ["RANK"])
world_size = int(os.environ["WORLD_SIZE"])
backend = "nccl" if torch.cuda.is_available() else "gloo"
# torch.cuda.set_device(local_rank)
device = torch.device("cpu", local_rank)
dtype = torch.bfloat16
dist.init_process_group(rank=global_rank, world_size=world_size, backend=backend, init_method=f"env://", timeout=datetime.timedelta(minutes=2))
setup_process_group_manager(dp_size=args.dp_size, pp_size=args.pp_size, tp_size=args.tp_size)
is_wandb_rank = pgm.process_group_manager.tp_rank == 0 and pgm.process_group_manager.dp_rank == 0 and pgm.process_group_manager.pp_is_last_stage
set_all_seed(args.seed)
if is_wandb_rank and args.use_wandb:
wandb.init(
project="picotron_tutorial",
name=f"{args.run_name}_{pgm.process_group_manager}",
config={
"tensor_parallel_size": pgm.process_group_manager.tp_world_size,
"pipeline_parallel_size": pgm.process_group_manager.pp_world_size,
"data_parallel_size": pgm.process_group_manager.dp_world_size,
"model": args.model_name,
"learning_rate": args.learning_rate,
"seed": args.seed,
},
)
model_config = AutoConfig.from_pretrained(args.model_name)
model_config.num_hidden_layers = args.num_hidden_layers
model_config.num_attention_heads = args.num_attention_heads
model_config.num_key_value_heads = args.num_key_value_heads
model_config.max_position_embeddings = args.seq_len
model = Llama(config=model_config)
if pgm.process_group_manager.tp_world_size > 1:
model = apply_tensor_parallel(model)
model.to(dtype).to(device)
if pgm.process_group_manager.dp_world_size > 1:
model = DataParallelNaive(model)
model.train()
dist.barrier()
optimizer = AdamW(model.parameters(), lr=args.learning_rate)
dist.barrier()
# Create dataloader
dataloader = MicroBatchDataLoader(
seq_len=args.seq_len,
micro_batch_size=args.micro_batch_size,
grad_acc_steps=args.gradient_accumulation_steps,
dataset_name=args.dataset_name,
tokenizer_name=args.model_name,
max_tokens=args.max_tokens,
num_workers=args.num_workers,
num_proc=args.num_proc,
seed=args.seed
)
tokens_per_step = dataloader.global_batch_size * args.seq_len
if pgm.process_group_manager.global_rank == 0:
print("Tokens per step:", to_readable_format(tokens_per_step), is_print_rank=is_wandb_rank)
trained_token, step = 0, 0
dist.barrier()
# Training loop
while trained_token < args.max_tokens:
step_start_time = time.time()
optimizer.zero_grad()
loss = train_step(model, dataloader, device)
optimizer.step()
step_duration = time.time() - step_start_time
trained_token += tokens_per_step
step += 1
if hasattr(model, 'reset'):
model.reset()
print(f"[rank {pgm.process_group_manager.global_rank}] Step: {step}, Loss: {loss:.4f}, "
f"Global batch size (with seq_len): {to_readable_format(tokens_per_step)}, "
f"Tokens/s: {to_readable_format(tokens_per_step / step_duration)}, "
f"Tokens/s/GPU: {to_readable_format(tokens_per_step / step_duration / world_size)}, "
f"Tokens: {to_readable_format(trained_token)}{('/' + to_readable_format(args.max_tokens))}, "
f"Memory usage: {torch.cuda.memory_reserved() / 1e9:.2f}GB"
, is_print_rank=is_wandb_rank
)
if is_wandb_rank and args.use_wandb:
wandb.log({"loss": loss, "tokens_per_step": tokens_per_step, "tokens_per_second": tokens_per_step / step_duration,\
"memory_usage": torch.cuda.memory_reserved() / 1e9, "trained_tokens": tokens_per_step})
if is_wandb_rank and args.use_wandb:
wandb.finish()
dist.destroy_process_group() |