mirror of
https://github.com/hpcaitech/ColossalAI.git
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[Colossal-LLaMA] Refactor latest APIs (#6030)
* refactor latest code * update api * add dummy dataset * update Readme * add setup * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update files * add PP support * update arguments * update argument * reorg folder * update version * remove IB infor * update utils * update readme * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update save for zero * update save * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add apex * update --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Tong Li
GitHub
@@ -11,24 +11,24 @@ import resource
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from contextlib import nullcontext
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import torch
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import torch.distributed as dist
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from colossal_llama.dataset.dummy_dataset import RandomDataset
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from colossal_llama.dataset.loader import (
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DataCollatorForSupervisedDataset,
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StatefulDistributedSampler,
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load_tokenized_dataset,
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)
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from colossal_llama.utils.ckpt_io import load_checkpoint, save_checkpoint
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from colossal_llama.utils.flash_attention_patch import replace_with_flash_attention
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from colossal_llama.utils.froze import freeze_non_embeds_parameters
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from colossal_llama.utils.neftune_patch import activate_neftune, deactivate_neftune
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from colossal_llama.utils.utils import all_reduce_mean, format_numel_str, get_model_numel
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from torch.utils.tensorboard import SummaryWriter
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from tqdm import tqdm
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from transformers import AutoTokenizer, LlamaForCausalLM
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import colossalai
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from colossalai.accelerator import get_accelerator
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from colossalai.booster import Booster
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from colossalai.booster.plugin import GeminiPlugin, HybridParallelPlugin, LowLevelZeroPlugin
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from colossalai.booster.plugin import GeminiPlugin, HybridParallelPlugin, LowLevelZeroPlugin, TorchDDPPlugin
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from colossalai.cluster import DistCoordinator
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from colossalai.lazy import LazyInitContext
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from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
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@@ -36,109 +36,7 @@ from colossalai.nn.optimizer import HybridAdam
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from colossalai.utils import get_current_device
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def get_model_numel(model: torch.nn.Module) -> int:
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return sum(p.numel() for p in model.parameters())
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def format_numel_str(numel: int) -> str:
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B = 1024**3
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M = 1024**2
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K = 1024
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if numel >= B:
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return f"{numel / B:.2f} B"
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elif numel >= M:
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return f"{numel / M:.2f} M"
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elif numel >= K:
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return f"{numel / K:.2f} K"
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else:
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return f"{numel}"
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def all_reduce_mean(tensor: torch.Tensor) -> torch.Tensor:
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dist.all_reduce(tensor=tensor, op=dist.ReduceOp.SUM)
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tensor = tensor.data
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tensor.div_(dist.get_world_size())
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return tensor
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def main() -> None:
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# ==============================
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# Parse Arguments
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# ==============================
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--pretrained",
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type=str,
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default=None,
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help="Address of the pre-trained modeling",
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)
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parser.add_argument("--dataset", nargs="+", default=[])
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parser.add_argument(
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"--plugin",
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type=str,
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default="gemini",
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choices=["gemini", "gemini_auto", "zero2", "zero2_cpu", "3d"],
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help="Choose which plugin to use",
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)
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parser.add_argument("--load_checkpoint", type=str, default=None, help="Load checkpoint")
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parser.add_argument("--save_interval", type=int, default=1000, help="Save interval")
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parser.add_argument("--save_dir", type=str, default="checkpoint_dir", help="Checkpoint directory")
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parser.add_argument("--tensorboard_dir", type=str, default="logs_dir", help="Tensorboard directory")
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parser.add_argument("--config_file", type=str, default="config_file", help="Config file")
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parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
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parser.add_argument("--accumulation_steps", type=int, default=1, help="Number of accumulation steps")
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parser.add_argument("--micro_batch_size", type=int, default=2, help="Batch size of each process")
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parser.add_argument("--lr", type=float, default=3e-4, help="Learning rate")
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parser.add_argument("--max_length", type=int, default=8192, help="Model max length")
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parser.add_argument(
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"--mixed_precision",
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type=str,
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default="fp16",
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choices=["fp16", "bf16"],
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help="Mixed precision",
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)
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parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping value")
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parser.add_argument("--weight_decay", type=float, default=0.1, help="Weight decay")
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parser.add_argument("--warmup_steps", type=int, default=None, help="Warmup steps")
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parser.add_argument(
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"--use_grad_checkpoint",
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action="store_true",
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default=False,
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help="Use gradient checkpointing",
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)
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parser.add_argument(
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"--use_flash_attn",
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action="store_true",
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default=False,
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help="Use flash-attention",
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)
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parser.add_argument(
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"--use_neft",
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action="store_true",
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default=False,
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help="Use NEFTune",
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)
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parser.add_argument(
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"--freeze_non_embeds_params",
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action="store_true",
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default=False,
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help="Freeze non embeddings parameters",
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)
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parser.add_argument("--tp", type=int, default=1)
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parser.add_argument("--zero", type=int, default=1)
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parser.add_argument("--pad_token", choices=["eos", "unk"], default="eos")
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parser.add_argument("--padding_mode", choices=["max_length", "longest"], default="max_length")
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parser.add_argument(
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"--skip_save_each_epoch",
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action="store_true",
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default=False,
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help="skip saving the model checkpoint after each epoch is completed.",
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)
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args = parser.parse_args()
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with open(args.config_file, "w") as f:
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json.dump(args.__dict__, f, indent=4)
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def train(args) -> None:
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# ==============================
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# Initialize Distributed Training
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# ==============================
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@@ -147,21 +45,27 @@ def main() -> None:
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coordinator = DistCoordinator()
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# ==============================
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# Initialize Tensorboard
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# Initialize Tensorboard and Save Config
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# ==============================
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if coordinator.is_master():
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os.makedirs(args.tensorboard_dir, exist_ok=True)
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writer = SummaryWriter(args.tensorboard_dir)
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with open(args.config_file, "w") as f:
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json.dump(args.__dict__, f, indent=4)
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# ==============================
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# Initialize Booster
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# ==============================
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if args.plugin == "gemini":
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if args.plugin == "ddp":
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plugin = TorchDDPPlugin(find_unused_parameters=True if args.use_grad_checkpoint is False else False)
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elif args.plugin == "gemini":
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plugin = GeminiPlugin(
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precision=args.mixed_precision,
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initial_scale=2**16,
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max_norm=args.grad_clip,
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enable_gradient_accumulation=(args.accumulation_steps > 1),
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enable_flash_attention=args.use_flash_attn,
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)
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elif args.plugin == "gemini_auto":
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plugin = GeminiPlugin(
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@@ -170,6 +74,7 @@ def main() -> None:
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initial_scale=2**16,
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max_norm=args.grad_clip,
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enable_gradient_accumulation=(args.accumulation_steps > 1),
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enable_flash_attention=args.use_flash_attn,
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)
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elif args.plugin == "zero2":
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plugin = LowLevelZeroPlugin(
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@@ -189,10 +94,17 @@ def main() -> None:
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elif args.plugin == "3d":
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plugin = HybridParallelPlugin(
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tp_size=args.tp,
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pp_size=1,
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zero_stage=args.zero,
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pp_size=args.pp,
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sp_size=args.sp,
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sequence_parallelism_mode=args.sp_mode,
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zero_stage=args.zero_stage,
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enable_flash_attention=args.use_flash_attn,
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enable_sequence_parallelism=args.enable_sequence_parallelism,
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cpu_offload=True if args.zero_stage >= 1 and args.zero_cpu_offload else False,
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parallel_output=False,
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max_norm=args.grad_clip,
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precision=args.mixed_precision,
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microbatch_size=args.microbatch_size,
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)
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else:
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raise ValueError(f"Unknown plugin {args.plugin}")
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@@ -210,24 +122,38 @@ def main() -> None:
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tokenizer.add_bos_token = False
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tokenizer.add_eos_token = False
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coordinator.print_on_master(f"Configuration file will be saved at: {args.config_file}")
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coordinator.print_on_master(f"Tensorboard logs will be saved at: {args.tensorboard_dir}")
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coordinator.print_on_master(f"Model checkpoint will be saved at: {args.save_dir}")
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coordinator.print_on_master(f"Load dataset: {args.dataset}")
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dataset = load_tokenized_dataset(dataset_paths=args.dataset, mode="train")
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data_collator = DataCollatorForSupervisedDataset(
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tokenizer=tokenizer, max_length=args.max_length, padding=args.padding_mode
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)
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dataloader = plugin.prepare_dataloader(
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dataset=dataset,
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batch_size=args.micro_batch_size,
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shuffle=True,
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drop_last=True,
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collate_fn=data_collator,
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distributed_sampler_cls=StatefulDistributedSampler,
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coordinator.print_on_master(
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f"Training Info:\nConfig file: {args.config_file} \nTensorboard logs: {args.tensorboard_dir} \nModel checkpoint: {args.save_dir}"
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)
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if args.benchmark:
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coordinator.print_on_master(f"Run benchmark with {args.num_samples} random samples.")
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dataset = RandomDataset(
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num_samples=args.num_samples, max_length=args.max_length, vocab_size=tokenizer.vocab_size
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)
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dataloader = plugin.prepare_dataloader(
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dataset,
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batch_size=args.batch_size,
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shuffle=True,
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drop_last=True,
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seed=42,
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distributed_sampler_cls=StatefulDistributedSampler,
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)
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else:
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coordinator.print_on_master(f"Load dataset: {args.dataset}")
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dataset = load_tokenized_dataset(dataset_paths=args.dataset, mode="train")
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data_collator = DataCollatorForSupervisedDataset(
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tokenizer=tokenizer, max_length=args.max_length, padding=args.padding_mode
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)
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dataloader = plugin.prepare_dataloader(
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dataset=dataset,
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batch_size=args.batch_size,
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shuffle=True,
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drop_last=True,
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collate_fn=data_collator,
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distributed_sampler_cls=StatefulDistributedSampler,
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)
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coordinator.print_on_master(
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f"Max device memory after data loader: {accelerator.max_memory_allocated() / 1024 ** 2:.2f} MB"
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)
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@@ -241,7 +167,19 @@ def main() -> None:
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else nullcontext()
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)
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with init_ctx:
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model = LlamaForCausalLM.from_pretrained(args.pretrained)
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if args.use_flash_attn:
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model = AutoModelForCausalLM.from_pretrained(
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args.pretrained,
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attn_implementation="flash_attention_2",
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torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16,
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trust_remote_code=True,
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)
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else:
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model = AutoModelForCausalLM.from_pretrained(
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args.pretrained,
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torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16,
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trust_remote_code=True,
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)
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# Freeze part of parameters.
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if args.freeze_non_embeds_params:
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freeze_non_embeds_parameters(model=model)
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@@ -251,9 +189,6 @@ def main() -> None:
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if args.use_grad_checkpoint:
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model.gradient_checkpointing_enable()
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coordinator.print_on_master(msg="Gradient checkpointing enabled successfully")
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if args.use_flash_attn:
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replace_with_flash_attention(model=model)
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coordinator.print_on_master(msg="Flash-attention enabled successfully")
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model_numel = get_model_numel(model)
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coordinator.print_on_master(f"Model params: {format_numel_str(model_numel)}")
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@@ -342,43 +277,98 @@ def main() -> None:
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for epoch in range(start_epoch, args.num_epochs):
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dataloader.sampler.set_epoch(epoch=epoch)
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pbar = tqdm(
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desc=f"Epoch {epoch}",
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disable=not coordinator.is_master(),
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total=num_steps_per_epoch,
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initial=start_step // args.accumulation_steps,
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)
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total_loss = torch.tensor(0.0, device=get_current_device())
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for step, batch in enumerate(dataloader, start=start_step):
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batch = {k: v.to(get_current_device()) for k, v in batch.items() if isinstance(v, torch.Tensor)}
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batch_output = model(**batch)
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loss = batch_output.loss / args.accumulation_steps
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total_loss.add_(loss.data)
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booster.backward(loss=loss, optimizer=optimizer)
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if (step + 1) % args.accumulation_steps == 0:
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if isinstance(plugin, HybridParallelPlugin) and plugin.pp_size > 1:
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data_iter = iter(dataloader)
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step_bar = tqdm(
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range(len(dataloader)),
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desc="Step",
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disable=not (coordinator._local_rank == coordinator._world_size - 1),
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)
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for step in step_bar:
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outputs = booster.execute_pipeline(
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data_iter,
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model,
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criterion=lambda outputs, inputs: outputs[0],
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optimizer=optimizer,
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return_loss=True,
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)
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loss = outputs["loss"]
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if booster.plugin.stage_manager.is_last_stage():
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global_loss = all_reduce_mean(loss, plugin)
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if coordinator._local_rank == coordinator._world_size - 1:
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step_bar.set_postfix({"train/loss": global_loss.item()})
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optimizer.step()
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lr_scheduler.step()
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optimizer.zero_grad()
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all_reduce_mean(tensor=total_loss)
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pbar.set_postfix({"Loss": f"{total_loss.item():.4f}"})
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if coordinator.is_master():
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global_step = (epoch * num_steps_per_epoch) + (step + 1) // args.accumulation_steps
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writer.add_scalar(tag="Loss", scalar_value=total_loss.item(), global_step=global_step)
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writer.add_scalar(
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tag="Learning Rate",
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scalar_value=lr_scheduler.get_last_lr()[0],
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global_step=global_step,
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# Save modeling.
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save_model_condition = args.save_interval > 0 and (step + 1) % args.save_interval == 0
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if not args.skip_save_each_epoch:
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save_model_condition = save_model_condition or (step + 1) == len(dataloader)
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if save_model_condition and not args.benchmark:
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coordinator.print_on_master("\nStart saving model checkpoint with running states")
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if args.use_neft:
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coordinator.print_on_master("Deactivate NEFTune before saving model.")
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deactivate_neftune(model, handle)
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accelerator.empty_cache()
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save_checkpoint(
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save_dir=args.save_dir,
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booster=booster,
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model=model,
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optimizer=optimizer,
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lr_scheduler=lr_scheduler,
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epoch=epoch,
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step=step + 1,
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batch_size=args.batch_size,
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coordinator=coordinator,
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)
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total_loss.fill_(0.0)
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pbar.update()
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coordinator.print_on_master(
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f"Saved checkpoint at epoch {epoch} step {step + 1} at folder {args.save_dir}"
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)
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|
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if args.use_neft:
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coordinator.print_on_master("Activate NEFTune.")
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model, handle = activate_neftune(model)
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else:
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pbar = tqdm(
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desc=f"Epoch {epoch}",
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disable=not coordinator.is_master(),
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total=num_steps_per_epoch,
|
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initial=start_step // args.accumulation_steps,
|
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)
|
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total_loss = torch.tensor(0.0, device=get_current_device())
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for step, batch in enumerate(dataloader, start=start_step):
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batch = {k: v.to(get_current_device()) for k, v in batch.items() if isinstance(v, torch.Tensor)}
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|
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batch_output = model(**batch)
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|
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loss = batch_output.loss / args.accumulation_steps
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total_loss.add_(loss.data)
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booster.backward(loss=loss, optimizer=optimizer)
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if (step + 1) % args.accumulation_steps == 0:
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optimizer.step()
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lr_scheduler.step()
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optimizer.zero_grad()
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|
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all_reduce_mean(tensor=total_loss)
|
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pbar.set_postfix({"Loss": f"{total_loss.item():.4f}"})
|
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if coordinator.is_master():
|
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global_step = (epoch * num_steps_per_epoch) + (step + 1) // args.accumulation_steps
|
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writer.add_scalar(tag="Loss", scalar_value=total_loss.item(), global_step=global_step)
|
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writer.add_scalar(
|
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tag="Learning Rate",
|
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scalar_value=lr_scheduler.get_last_lr()[0],
|
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global_step=global_step,
|
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)
|
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total_loss.fill_(0.0)
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pbar.update()
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# Save modeling.
|
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|
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save_model_condition = (
|
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args.save_interval > 0 and (step + 1) % (args.save_interval * args.accumulation_steps) == 0
|
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)
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@@ -386,7 +376,7 @@ def main() -> None:
|
||||
if not args.skip_save_each_epoch:
|
||||
save_model_condition = save_model_condition or (step + 1) == len(dataloader)
|
||||
|
||||
if save_model_condition:
|
||||
if save_model_condition and not args.benchmark:
|
||||
coordinator.print_on_master("\nStart saving model checkpoint with running states")
|
||||
|
||||
if args.use_neft:
|
||||
@@ -402,7 +392,7 @@ def main() -> None:
|
||||
lr_scheduler=lr_scheduler,
|
||||
epoch=epoch,
|
||||
step=step + 1,
|
||||
batch_size=args.micro_batch_size,
|
||||
batch_size=args.batch_size,
|
||||
coordinator=coordinator,
|
||||
)
|
||||
coordinator.print_on_master(
|
||||
@@ -426,12 +416,114 @@ def main() -> None:
|
||||
deactivate_neftune(model, handle)
|
||||
|
||||
# Final save.
|
||||
coordinator.print_on_master("Start saving final model checkpoint")
|
||||
booster.save_model(model, os.path.join(args.save_dir, "modeling"), shard=True)
|
||||
coordinator.print_on_master(f"Saved final model checkpoint at epoch {epoch} at folder {args.save_dir}")
|
||||
if not args.benchmark:
|
||||
coordinator.print_on_master("Start saving final model checkpoint")
|
||||
booster.save_model(model, os.path.join(args.save_dir, "modeling"), shard=True)
|
||||
coordinator.print_on_master(f"Saved final model checkpoint at epoch {epoch} at folder {args.save_dir}")
|
||||
|
||||
coordinator.print_on_master(f"Max device memory usage: {accelerator.max_memory_allocated()/1024**2:.2f} MB")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
parser = argparse.ArgumentParser()
|
||||
# Basic training information.
|
||||
parser.add_argument(
|
||||
"--pretrained",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Address of the pre-trained model",
|
||||
)
|
||||
parser.add_argument("--load_checkpoint", type=str, default=None, help="Load checkpoint for continuous training.")
|
||||
parser.add_argument("--dataset", nargs="+", default=[])
|
||||
parser.add_argument(
|
||||
"--plugin",
|
||||
type=str,
|
||||
default="gemini",
|
||||
choices=["gemini", "gemini_auto", "zero2", "zero2_cpu", "3d", "ddp"],
|
||||
help="Choose which plugin to use",
|
||||
)
|
||||
parser.add_argument("--save_interval", type=int, default=1000, help="Save interval")
|
||||
parser.add_argument("--save_dir", type=str, default="checkpoint_dir", help="Checkpoint directory")
|
||||
parser.add_argument("--tensorboard_dir", type=str, default="logs_dir", help="Tensorboard directory")
|
||||
parser.add_argument("--config_file", type=str, default="config_file", help="Config file")
|
||||
# Training parameters
|
||||
parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
|
||||
parser.add_argument("--accumulation_steps", type=int, default=1, help="Number of accumulation steps")
|
||||
parser.add_argument("--batch_size", type=int, default=2, help="Global Batch size of each process")
|
||||
parser.add_argument("--lr", type=float, default=3e-4, help="Learning rate")
|
||||
parser.add_argument("--max_length", type=int, default=8192, help="Model max length")
|
||||
parser.add_argument(
|
||||
"--mixed_precision",
|
||||
type=str,
|
||||
default="fp16",
|
||||
choices=["fp16", "bf16"],
|
||||
help="Mixed precision",
|
||||
)
|
||||
parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping value")
|
||||
parser.add_argument("--weight_decay", type=float, default=0.1, help="Weight decay")
|
||||
parser.add_argument("--warmup_steps", type=int, default=None, help="Warmup steps")
|
||||
parser.add_argument(
|
||||
"--use_grad_checkpoint",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Use gradient checkpointing",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--use_flash_attn",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Use flash-attention",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--use_neft",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Use NEFTune",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--freeze_non_embeds_params",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Freeze non embeddings parameters",
|
||||
)
|
||||
parser.add_argument("--pad_token", choices=["eos", "unk"], default="eos")
|
||||
parser.add_argument("--padding_mode", choices=["max_length", "longest"], default="max_length")
|
||||
parser.add_argument(
|
||||
"--skip_save_each_epoch",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Skip saving the model checkpoint after each epoch is completed.",
|
||||
)
|
||||
|
||||
# Additional arguments for 3d plugin.
|
||||
parser.add_argument("--tp", type=int, default=1, help="TP size, used for 3d plugin.")
|
||||
parser.add_argument("--pp", type=int, default=1, help="PP size, used for 3d plugin.")
|
||||
parser.add_argument("--sp", type=int, default=1, help="SP size, used for 3d plugin.")
|
||||
parser.add_argument("--zero_stage", type=int, default=0, help="Zero stage, used for 3d plugin.", choices=[0, 1, 2])
|
||||
parser.add_argument(
|
||||
"--sp_mode",
|
||||
type=str,
|
||||
default="split_gather",
|
||||
choices=["split_gather", "ring", "all_to_all"],
|
||||
help="SP mode, used for 3d plugin.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--enable_sequence_parallelism",
|
||||
default=False,
|
||||
action="store_true",
|
||||
help="Whether to enable SP, used for 3d plugin.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--zero_cpu_offload", default=False, action="store_true", help="Whether to use offloading, used for 3d plugin."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--microbatch_size", type=int, default=1, help="Batch size for each process in PP, used for 3d plugin."
|
||||
)
|
||||
|
||||
# Additional arguments for benchmark.
|
||||
parser.add_argument("--num_samples", type=int, default=500, help="Number of samples for benchmarking.")
|
||||
parser.add_argument(
|
||||
"--benchmark", action="store_true", default=False, help="Benchmark performance using random dataset."
|
||||
)
|
||||
args = parser.parse_args()
|
||||
train(args)
|
||||
|
||||
Reference in New Issue
Block a user