76 lines
4.7 KiB
Python
76 lines
4.7 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, torch.distributed as dist
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from torch.optim import AdamW
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from torch.utils.data import DataLoader, DistributedSampler
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from datasets import load_dataset
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from transformers import AutoTokenizer
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import argparse
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import parallel_context as pc
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from utils import set_all_seed, display_parallelism_grid
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from parallel_context import setup_parallel_context
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from pipeline_parallel import pipeline_parallel_1f1b, pipeline_parallel_afab, PipelineParallel
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class MicroBatchDataLoader(DataLoader):
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def __init__(self, global_batch_size, micro_batch_size, data_parallel_size, seq_length, dataset_name, tokenizer_name, split="train", num_samples=None):
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self.global_batch_size, self.micro_batch_size, self.data_parallel_size, self.seq_length = global_batch_size, micro_batch_size, data_parallel_size, seq_length
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self.local_batch_size = self.global_batch_size // self.data_parallel_size
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self.num_local_micro_batches = self.local_batch_size // self.micro_batch_size
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self.num_global_micro_batches = self.global_batch_size // self.micro_batch_size
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self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
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self.dataset = load_dataset(dataset_name, split=split)
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if num_samples: self.dataset = self.dataset.select(range(min(num_samples, len(self.dataset))))
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dist.barrier()
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self.dataset = self.dataset.map(lambda examples: self.tokenizer(examples["text"], padding="max_length", truncation=True, max_length=self.seq_length + 1, return_special_tokens_mask=False), batched=True, remove_columns=self.dataset.column_names).with_format("torch", columns=["input_ids"])
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super().__init__(self.dataset, batch_size=micro_batch_size, collate_fn=self.collate_batch, pin_memory=True, num_workers=3, sampler=DistributedSampler(self.dataset, num_replicas=data_parallel_size, rank=0, shuffle=False), shuffle=False)
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def collate_batch(self, batch_data):
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batch_input_ids = torch.stack([item['input_ids'] for item in batch_data])
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batch_size, seq_len = batch_input_ids.shape
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return {"input_ids": batch_input_ids[:, :-1].T.contiguous(), "target_ids": batch_input_ids[:, 1:].T.contiguous(), "position_index": torch.arange(seq_len-1, dtype=torch.long).unsqueeze(1).expand(-1, batch_size).contiguous(), "attn_mask": torch.tril(torch.ones((seq_len-1, seq_len-1), dtype=torch.bool)).unsqueeze(0).expand(batch_size, -1, -1).contiguous(), "hidden_states": None}
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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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args = parser.parse_args()
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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local_rank, world_size = int(os.environ["LOCAL_RANK"]), int(os.environ["WORLD_SIZE"])
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host, port = os.environ["MASTER_ADDR"], int(os.environ["MASTER_PORT"])
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SEQ_LEN, GLOBAL_BATCH_SIZE, MICRO_BATCH_SIZE, LEARNING_RATE, NUM_SAMPLES, MAX_TOKENS = 10, 6, 2, 1e-4, 20, 1800
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dist.init_process_group(rank=local_rank, world_size=world_size, backend="nccl", init_method=f"tcp://{host}:{port}")
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torch.cuda.set_device(local_rank)
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device = torch.device("cuda", local_rank)
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setup_parallel_context(tp_size=args.tp_size, pp_size=args.pp_size, dp_size=args.dp_size)
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if pc.parallel_context.global_rank == local_rank:
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display_parallelism_grid()
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set_all_seed(seed=42)
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model = PipelineParallel("HuggingFaceTB/SmolLM-360M-Instruct").to(device)
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data_loader = MicroBatchDataLoader(GLOBAL_BATCH_SIZE, MICRO_BATCH_SIZE, 1, SEQ_LEN, "roneneldan/TinyStories", "HuggingFaceTB/SmolLM-360M-Instruct", num_samples=NUM_SAMPLES)
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tensor_shapes = (SEQ_LEN, data_loader.micro_batch_size, model.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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while trained_tokens < MAX_TOKENS:
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optimizer.zero_grad()
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loss = pipeline_parallel_afab(model, data_loader, tensor_shapes, 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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#NOTE(fmom): change later to log on rank 0 (g00) everytime ?
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if pc.parallel_context.pp_is_last_stage and pc.parallel_context.global_rank == pc.parallel_context.tp_first_rank and pc.parallel_context.global_rank == pc.parallel_context.dp_first_rank:
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print(f"[rank {pc.parallel_context.global_rank}] Step: {step}, Loss: {loss:.4f}, Tokens: {trained_tokens}/{MAX_TOKENS}")
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dist.destroy_process_group()
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