234 lines
11 KiB
Python
234 lines
11 KiB
Python
"""Training script for LLaMA model.
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torchrun --nproc_per_node 1 --master_addr localhost --master_port 25500 train.py --use_wandb
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torchrun --nproc_per_node 2 --master_addr localhost --master_port 25500 train.py --dp_size 2 --use_wandb
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torchrun --nproc_per_node 4 --master_addr localhost --master_port 25500 train.py --tp_size 2 --pp_size 2 --use_wandb
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torchrun --nproc_per_node 4 --master_addr localhost --master_port 25500 train.py --tp_size 2 --pp_size 2 --load_path ckpt/150
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torchrun --nproc_per_node 8 --master_addr localhost --master_port 25500 train.py --tp_size 2 --dp_size 2 --pp_size 2 --use_wandb
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CUDA_DEVICE_MAX_CONNECTIONS=1 debugpy-run -p 5678 -m torch.distributed.run -- --nproc_per_node=2 --nnodes=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:29400 train.py --dp_size 2
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CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun --nproc_per_node=4 --nnodes=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:29400 --max_restarts=0 --tee=3 train.py
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#VERBOSE=0 torchrun --nproc_per_node 4 --master_addr localhost --master_port 25500 train.py --pp_size 2 --dp_size 2
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"""
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import os
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import time
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import argparse
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from src.parallel.context_parallel import parallel_input
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import torch.nn.functional as F
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import torch, torch.distributed as dist
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from torch.optim import AdamW
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from transformers import AutoConfig
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import numpy as np
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from src.parallel.tensor_parallel.tensor_parallel import TensorParallel
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import src.distributed.process_group_manager as pgm
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from utils import MicroBatchDataLoader, set_all_seed, print, to_readable_format, save_checkpoint, load_checkpoint
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from src.distributed.process_group_manager import setup_process_group_manager
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from src.parallel.pipeline_parallel import train_step_pipeline_1f1b, train_step_pipeline_afab, PipelineParallel
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from src.parallel.data_parallel.data_parallel_bucket import DataParallel
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from model import Llama
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import wandb
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from src.distributed.distributed_primtives import all_reduce_loss_across_dp_cp_ranks
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def train_step(model, data_loader, device):
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acc_loss = 0.0
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requires_grad_sync = pgm.process_group_manager.cp_dp_world_size > 1
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for i in range(data_loader.grad_acc):
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# get the next batch
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batch = next(data_loader)
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input_ids = batch["input_ids"].to(device)
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target_ids = batch["target_ids"].to(device)
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input_ids, target_ids = parallel_input(input_ids, target_ids) # for context parallel, we need to split the input
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# disable gradient synchronization for all but the last micro-batch
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if requires_grad_sync:
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model.require_backward_grad_sync = (i == data_loader.grad_acc - 1)
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outputs = model(input_ids=input_ids)
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# compute the loss
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batch_size, seq_len = input_ids.shape
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target_ids = target_ids.reshape(-1)
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outputs = outputs.view(seq_len*batch_size, -1)
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loss = F.cross_entropy(outputs, target_ids, reduction='mean') / data_loader.grad_acc
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loss.backward()
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acc_loss += loss.item()
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return acc_loss
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--tp_size", type=int, default=1)
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parser.add_argument("--cp_size", type=int, default=1)
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parser.add_argument("--pp_size", type=int, default=1)
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parser.add_argument("--dp_size", type=int, default=1)
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parser.add_argument("--use_wandb", action="store_true", default=False)
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parser.add_argument("--use_cpu", action="store_true", default=False)
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parser.add_argument("--master_addr", type=str, default="localhost")
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parser.add_argument("--master_port", type=int, default=29500)
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parser.add_argument("--load_path", type=str, default="", help="Path to load the model from")
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parser.add_argument("--ckpt_dir", type=str, default="ckpt", help="Directory to save checkpoints")
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parser.add_argument("--ckpt_freq", type=int, default=300, help="Frequency to save checkpoints")
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args = parser.parse_args()
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os.environ["OMP_NUM_THREADS"] = "1"
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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os.environ["FLASH_ATTEN"] = "1" # Use cuda kernels from flash attention repo to accelerate the training. Model dtype should be torch.float16!
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os.environ["DEVICE"] = "cuda" if not args.use_cpu else "cpu"
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dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() and not args.use_cpu else torch.float32 # if GPU is not available or not supported, use torch.float32
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assert (dtype == torch.bfloat16 and os.getenv("FLASH_ATTEN") == "1") or os.getenv("FLASH_ATTEN") != "1", "Kernel operations requires dtype=torch.bfloat16"
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local_rank = int(os.environ["LOCAL_RANK"])
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world_size = int(os.environ["WORLD_SIZE"])
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host = os.environ["MASTER_ADDR"]
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port = int(os.environ["MASTER_PORT"])
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## hyperparameters
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SEQ_LEN, LOCAL_BATCH_SIZE, MICRO_BATCH_SIZE, LEARNING_RATE, NUM_SAMPLES, MAX_TOKENS, SEED = 1024, 64, 32, 3e-4, 400000, None, 42
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total_train_steps = 200
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grad_acc = 1
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assert SEQ_LEN % args.cp_size == 0, "SEQ_LEN must be divisible by cp_size for Context Parallelism"
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backend = "gloo" if args.use_cpu else "nccl"
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if backend == "nccl":
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torch.cuda.set_device(local_rank)
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device = torch.device("cuda", local_rank)
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else:
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device = torch.device("cpu")
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dist.init_process_group(rank=local_rank, world_size=world_size, backend=backend, init_method=f"tcp://{host}:{port}")
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setup_process_group_manager(tp_size=args.tp_size, cp_size=args.cp_size, pp_size=args.pp_size, dp_size=args.dp_size)
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is_wandb_rank = pgm.process_group_manager.tp_rank == 0 and pgm.process_group_manager.dp_rank == 0 and pgm.process_group_manager.cp_rank == 0 and pgm.process_group_manager.pp_is_last_stage
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# if pgm.process_group_manager.global_rank == 0:
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# display_4D_parallelism_grid()
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tokens_per_step = LOCAL_BATCH_SIZE * SEQ_LEN * grad_acc * args.dp_size
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if pgm.process_group_manager.global_rank == 0:
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print("Tokens per step:", to_readable_format(tokens_per_step), is_print_rank=is_wandb_rank)
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set_all_seed(SEED)
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dataset_name = "roneneldan/TinyStories"
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model_name = "HuggingFaceTB/SmolLM-360M-Instruct"
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# model_name = "meta-llama/Llama-2-7b-hf"
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config = AutoConfig.from_pretrained(model_name)
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config.num_hidden_layers = 16
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config.num_attention_heads = 16
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config.num_key_value_heads = 4
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start_time = time.time()
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model = Llama(config=config)
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print("init model time:", time.time()-start_time, is_print_rank=is_wandb_rank)
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if is_wandb_rank and args.use_wandb:
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wandb.init(
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project="picotron",
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name=f"test_convergence_GBS_{tokens_per_step}_{pgm.process_group_manager}",
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config={
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"tensor_parallel_size": pgm.process_group_manager.tp_size,
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"pipeline_parallel_size": pgm.process_group_manager.pp_size,
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"data_parallel_size": pgm.process_group_manager.dp_size,
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"model": model_name,
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"dataset": dataset_name,
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"max_tokens": MAX_TOKENS,
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"learning_rate": LEARNING_RATE,
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"seed": SEED,
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"micro_batch_size": MICRO_BATCH_SIZE,
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"global_batch_size": LOCAL_BATCH_SIZE * args.dp_size * grad_acc,
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"gradient_accumulation": grad_acc,
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},
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)
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start_time = time.time()
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if pgm.process_group_manager.tp_world_size > 1:
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TensorParallel(model)
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if pgm.process_group_manager.pp_world_size > 1:
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model = PipelineParallel(model, config)
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model.to(dtype).to(device)
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# Context parallel and Data parallel both need gradient synchronization
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if pgm.process_group_manager.cp_dp_world_size > 1:
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model = DataParallel(model)
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print("init parallel time:", time.time()-start_time, is_print_rank=is_wandb_rank)
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start_time = time.time()
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model.train()
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print("model to device time:", time.time()-start_time, is_print_rank=is_wandb_rank)
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start_time = time.time()
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data_loader = MicroBatchDataLoader(local_batch_size=LOCAL_BATCH_SIZE, micro_batch_size=MICRO_BATCH_SIZE, seq_length=SEQ_LEN, dataset_name=dataset_name, tokenizer_name=model_name, grad_acc = grad_acc,num_workers=4, num_proc=4, num_samples=NUM_SAMPLES)
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print("init dataloader time:", time.time()-start_time, is_print_rank=is_wandb_rank)
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tensor_shapes = (data_loader.micro_batch_size, data_loader.seq_length_per_gpu, config.hidden_size)
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optimizer = AdamW(model.parameters(), lr=LEARNING_RATE)
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trained_tokens, step = 0, 0
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if args.load_path:
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step, trained_tokens = load_checkpoint(model, optimizer, args.load_path)
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checkpoint_dir = args.ckpt_dir
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checkpoint_freq = args.ckpt_freq
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dist.barrier()
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#TODO: Double-check consumed tokens after each steps (for example, MICRO_BATCH_SIZE=2 and using only dp_size=4, num_local_micro_batches=0 => division by 0)
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#TODO: Check convergence
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#TODO: Try multi-nodes
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#TODO: Add activation checkpointing
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#TODO: add gradient accumulation
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while trained_tokens < MAX_TOKENS:
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#TODO: Add epoch support
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# data_loader.set_epoch(step)
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step_start_time = time.time()
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optimizer.zero_grad()
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if pgm.process_group_manager.pp_world_size > 1:
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loss = train_step_pipeline_afab(model, data_loader, tensor_shapes, device, dtype)
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else:
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loss = train_step(model, data_loader, device)
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loss = all_reduce_loss_across_dp_cp_ranks(loss, device)
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optimizer.step()
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trained_tokens += tokens_per_step
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step += 1
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# In DDP implementation I need to reset the gradient buffers
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if hasattr(model, 'reset'):
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model.reset()
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step_duration = time.time() - step_start_time
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if is_wandb_rank:
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print(f"[rank {pgm.process_group_manager.global_rank}] Step: {step}, Loss: {loss:.4f}, "
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f"Global batch size: {to_readable_format(tokens_per_step)}, "
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f"Tokens/s: {to_readable_format(tokens_per_step / step_duration)}, "
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f"Tokens/s/GPU: {to_readable_format(tokens_per_step / step_duration / world_size)}, "
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f"Tokens: {to_readable_format(trained_tokens)}{('/' + to_readable_format(MAX_TOKENS)) if MAX_TOKENS else ''}, "
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f"Memory usage: {torch.cuda.memory_reserved() / 1e9:.2f}GB"
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, is_print_rank=is_wandb_rank)
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if args.use_wandb:
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wandb.log({"loss": loss, "tokens_per_step": tokens_per_step, "tokens_per_second": tokens_per_step / step_duration,\
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"memory_usage": torch.cuda.memory_reserved() / 1e9, "trained_tokens": trained_tokens})
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if step % checkpoint_freq == 0:
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save_checkpoint(model, optimizer, step, trained_tokens, checkpoint_dir+f"/{step}")
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if step >= total_train_steps:
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break
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if is_wandb_rank and args.use_wandb:
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wandb.finish()
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dist.destroy_process_group()
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