156 lines
5.8 KiB
Python
156 lines
5.8 KiB
Python
#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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import os
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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, AutoModelForCausalLM
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import argparse
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import process_group_manager as pgm
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from utils import set_all_seed, display_parallelism_grid, print
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from process_group_manager import setup_process_group_manager
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from pipeline_parallel import train_step_pipeline_1f1b, train_step_pipeline_afab, PipelineParallel
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from data_parallel import DataParallel
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from dataset import MicroBatchDataLoader
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import wandb
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def train_step(model, data_loader, device):
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total_loss = 0.0
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for _ in range(data_loader.num_local_micro_batches):
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batch = next(iter(data_loader))
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input_ids = batch["input_ids"].to(device)
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position_ids = batch["position_index"].to(device)
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target_ids = batch["target_ids"].to(device)
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outputs = model(input_ids=input_ids, position_ids=position_ids)
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logits = outputs.logits
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# Use your suggested cross_entropy calculation
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loss = F.cross_entropy(logits.transpose(1, 2), target_ids, reduction='mean')
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loss.backward()
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total_loss += loss.item()
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avg_loss = total_loss / data_loader.num_local_micro_batches
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return avg_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("--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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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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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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SEQ_LEN, GLOBAL_BATCH_SIZE, MICRO_BATCH_SIZE, LEARNING_RATE, NUM_SAMPLES, MAX_TOKENS, SEED = 10, 6, 2, 1e-4, 20, 1800, 42
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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, pp_size=args.pp_size, dp_size=args.dp_size)
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if pgm.process_group_manager.global_rank == local_rank:
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display_parallelism_grid()
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set_all_seed(SEED)
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model_name = "HuggingFaceTB/SmolLM-360M-Instruct"
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dataset_name = "roneneldan/TinyStories"
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config = AutoConfig.from_pretrained(model_name)
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if pgm.process_group_manager.global_rank == 0 and args.use_wandb:
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wandb.init(
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project="picotron",
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name=f"test_convergence_{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": GLOBAL_BATCH_SIZE,
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},
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)
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model = AutoModelForCausalLM.from_pretrained(model_name, config=config).to(device)
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if pgm.process_group_manager.pp_world_size > 1:
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model = PipelineParallel(model, config).to(device)
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if pgm.process_group_manager.dp_world_size > 1:
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model = DataParallel(model, config).to(device)
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model.train()
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data_loader = MicroBatchDataLoader(GLOBAL_BATCH_SIZE, MICRO_BATCH_SIZE, SEQ_LEN, dataset_name, model_name, num_samples=NUM_SAMPLES)
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tensor_shapes = (SEQ_LEN, data_loader.micro_batch_size, 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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tokens_per_step = data_loader.num_global_micro_batches * data_loader.micro_batch_size * SEQ_LEN
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dist.barrier()
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#TODO: Add Context Parallelism
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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: Add activation checkpointing
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#TODO: add gradient accumulation
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while trained_tokens < MAX_TOKENS:
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data_loader.set_epoch(step)
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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)
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else:
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loss = train_step(model, data_loader, device)
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if pgm.process_group_manager.dp_world_size > 1:
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# Average gradient across DP ranks
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model.all_reduce_gradients()
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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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if pgm.process_group_manager.global_rank == 0:
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print(f"[rank {pgm.process_group_manager.global_rank}] Step: {step}, Loss: {loss:.4f}, Tokens: {trained_tokens}/{MAX_TOKENS}")
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if pgm.process_group_manager.global_rank == 0 and args.use_wandb:
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wandb.log({"loss": loss, "trained_tokens": trained_tokens})
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if pgm.process_group_manager.global_rank == 0 and args.use_wandb:
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wandb.finish()
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dist.destroy_process_group()
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