大模型训练框架DeepSpeed使用入门(1): 训练设置

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官方文档直接抄过来，留个笔记。
https://deepspeed.readthedocs.io/en/latest/initialize.html

使用案例来自：
https://github.com/OvJat/DeepSpeedTutorial

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大模型训练的痛点是模型参数过大，动辄上百亿，如果单靠单个GPU来完成训练基本不可能。所以需要多卡或者分布式训练来完成这项工作。

DeepSpeed是由Microsoft提供的分布式训练工具，旨在支持更大规模的模型和提供更多的优化策略和工具。对于更大模型的训练来说，DeepSpeed提供了更多策略，例如：Zero、Offload等。

本文简单介绍下如何使用DeepSpeed。

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一、安装

pip install deepspeed

二、训练设置

Step1 第一步参数解析

DeepSpeed 使用 argparse 来应用控制台的设置，使用

deepspeed.add_config_arguments()

可以将DeepSpeed内置的参数增加到我们自己的应用参数解析中。

parser = argparse.ArgumentParser(description='My training script.')
parser.add_argument('--local_rank', type=int, default=-1,help='local rank passed from distributed launcher')
# Include DeepSpeed configuration arguments
parser = deepspeed.add_config_arguments(parser)
cmd_args = parser.parse_args()

Step2 初始化后端

与Step3中的 deepspeed.initialize() 不同，
直接调用即可。
一般发生在以下场景

when using model parallelism, pipeline parallelism, or certain data loader scenarios.

在Step3的initialize前，进行调用

deepspeed.init_distributed()

Step3 训练初始化

首先调用 deepspeed.initialize() 进行初始化，是整个调用DeepSpeed训练的入口。
调用后，如果分布式后端没有被初始化后，此时会初始化分布式后端。
使用案例：

model_engine, optimizer, _, _ = deepspeed.initialize(args=cmd_args,model=net,model_parameters=net.parameters(),training_data=ds)

API如下：

def initialize(args=None,model: torch.nn.Module = None,optimizer: Optional[Union[Optimizer, DeepSpeedOptimizerCallable]] = None,model_parameters: Optional[torch.nn.Module] = None,training_data: Optional[torch.utils.data.Dataset] = None,lr_scheduler: Optional[Union[_LRScheduler, DeepSpeedSchedulerCallable]] = None,distributed_port: int = TORCH_DISTRIBUTED_DEFAULT_PORT,mpu=None,dist_init_required: Optional[bool] = None,collate_fn=None,config=None,config_params=None):"""Initialize the DeepSpeed Engine.Arguments:args: an object containing local_rank and deepspeed_config fields.This is optional if `config` is passed.model: Required: nn.module class before apply any wrappersoptimizer: Optional: a user defined Optimizer or Callable that returns an Optimizer object.This overrides any optimizer definition in the DeepSpeed json config.model_parameters: Optional: An iterable of torch.Tensors or dicts.Specifies what Tensors should be optimized.training_data: Optional: Dataset of type torch.utils.data.Datasetlr_scheduler: Optional: Learning Rate Scheduler Object or a Callable that takes an Optimizer and returns a Scheduler object.The scheduler object should define a get_lr(), step(), state_dict(), and load_state_dict() methodsdistributed_port: Optional: Master node (rank 0)'s free port that needs to be used for communication during distributed trainingmpu: Optional: A model parallelism unit object that implementsget_{model,data}_parallel_{rank,group,world_size}()dist_init_required: Optional: None will auto-initialize torch distributed if needed,otherwise the user can force it to be initialized or not via boolean.collate_fn: Optional: Merges a list of samples to form amini-batch of Tensor(s).  Used when using batched loading from amap-style dataset.config: Optional: Instead of requiring args.deepspeed_config you can pass your deepspeed configas an argument instead, as a path or a dictionary.config_params: Optional: Same as `config`, kept for backwards compatibility.Returns:A tuple of ``engine``, ``optimizer``, ``training_dataloader``, ``lr_scheduler``* ``engine``: DeepSpeed runtime engine which wraps the client model for distributed training.* ``optimizer``: Wrapped optimizer if a user defined ``optimizer`` is supplied, or ifoptimizer is specified in json config else ``None``.* ``training_dataloader``: DeepSpeed dataloader if ``training_data`` was supplied,otherwise ``None``.* ``lr_scheduler``: Wrapped lr scheduler if user ``lr_scheduler`` is passed, orif ``lr_scheduler`` specified in JSON configuration. Otherwise ``None``."""

三、训练代码展示

def parse_arguments():import argparseparser = argparse.ArgumentParser(description='deepspeed training script.')parser.add_argument('--local_rank', type=int, default=-1,help='local rank passed from distributed launcher')# Include DeepSpeed configuration argumentsparser = deepspeed.add_config_arguments(parser)args = parser.parse_args()return argsdef train():args = parse_arguments()# init distributeddeepspeed.init_distributed()# init modelmodel = MyClassifier(3, 100, ch_multi=128)# init datasetds = MyDataset((3, 512, 512), 100, sample_count=int(1e6))# init engineengine, optimizer, training_dataloader, lr_scheduler = deepspeed.initialize(args=args,model=model,model_parameters=model.parameters(),training_data=ds,# config=deepspeed_config,)# load checkpointengine.load_checkpoint("./data/checkpoints/MyClassifier/")# trainlast_time = time.time()loss_list = []echo_interval = 10engine.train()for step, (xx, yy) in enumerate(training_dataloader):step += 1xx = xx.to(device=engine.device, dtype=torch.float16)yy = yy.to(device=engine.device, dtype=torch.long).reshape(-1)outputs = engine(xx)loss = tnf.cross_entropy(outputs, yy)engine.backward(loss)engine.step()loss_list.append(loss.detach().cpu().numpy())if step % echo_interval == 0:loss_avg = np.mean(loss_list[-echo_interval:])used_time = time.time() - last_timetime_p_step = used_time / echo_intervalif args.local_rank == 0:logging.info("[Train Step] Step:{:10d}  Loss:{:8.4f} | Time/Batch: {:6.4f}s",step, loss_avg, time_p_step,)last_time = time.time()# save checkpointengine.save_checkpoint("./data/checkpoints/MyClassifier/")

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