文章目录
- 一、安装
- 二、训练设置
-
- [Step1 第一步参数解析](#Step1 第一步参数解析)
- [Step2 初始化后端](#Step2 初始化后端)
- [Step3 训练初始化](#Step3 训练初始化)
- 三、训练代码展示
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
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。
一、安装
python
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
python
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。
python
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
python
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：
python
model_engine, optimizer, _, _ = deepspeed.initialize(args=cmd_args,
model=net,
model_parameters=net.parameters(),
training_data=ds)API如下：
python
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 wrappers
optimizer: 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.Dataset
lr_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() methods
distributed_port: Optional: Master node (rank 0)'s free port that needs to be used for communication during distributed training
mpu: Optional: A model parallelism unit object that implements
get_{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 a
mini-batch of Tensor(s). Used when using batched loading from a
map-style dataset.
config: Optional: Instead of requiring args.deepspeed_config you can pass your deepspeed config
as 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 if
optimizer 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, or
if ``lr_scheduler`` specified in JSON configuration. Otherwise ``None``.
"""三、训练代码展示
python
def parse_arguments():
import argparse
parser = 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 arguments
parser = deepspeed.add_config_arguments(parser)
args = parser.parse_args()
return args
def train():
args = parse_arguments()
# init distributed
deepspeed.init_distributed()
# init model
model = MyClassifier(3, 100, ch_multi=128)
# init dataset
ds = MyDataset((3, 512, 512), 100, sample_count=int(1e6))
# init engine
engine, optimizer, training_dataloader, lr_scheduler = deepspeed.initialize(
args=args,
model=model,
model_parameters=model.parameters(),
training_data=ds,
# config=deepspeed_config,
)
# load checkpoint
engine.load_checkpoint("./data/checkpoints/MyClassifier/")
# train
last_time = time.time()
loss_list = []
echo_interval = 10
engine.train()
for step, (xx, yy) in enumerate(training_dataloader):
step += 1
xx = 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_time
time_p_step = used_time / echo_interval
if 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 checkpoint
engine.save_checkpoint("./data/checkpoints/MyClassifier/")最后~
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