eat_accelerate_in_30_minites.md

30 分钟吃掉 Accelerate 模型训练加速工具

🤗 Accelerate 是 Hugging Face 开源的一个方便将 PyTorch 模型迁移到 GPU/multi-GPUs/TPU/fp16/bf16 模式下训练的小巧工具。

和标准的 PyTorch 方法相比，使用 Accelerate 进行多 GPU DDP 模式/TPU/fp16/bf16 训练你的模型变得非常简单，而且训练速度非常快。

官方范例：https://github.com/huggingface/accelerate/tree/main/examples

本文将以一个图片分类模型为例，演示在 Accelerate 的帮助下使用 PyTorch 编写一套可以在 CPU、单 GPU、多 GPU (DDP) 模式、TPU 下通用的训练代码。

在我们的演示范例中，在 Kaggle 的双 GPU 环境下，双 GPU (DDP) 模式是单 GPU 训练速度的 1.6 倍，加速效果非常明显。

DP 和 DDP 的区别

• DP (DataParallel)：实现简单但更慢。只能单机多卡使用。GPU 分成 server 节点和 worker 节点，有负载不均衡。
• DDP (DistributedDataParallel)：更快但实现麻烦。可单机多卡也可多机多卡。各个 GPU 是平等的，无负载不均衡。

参考文章：《PyTorch 中的分布式训练之 DP vs. DDP》https://zhuanlan.zhihu.com/p/356967195

#从 git 安装最新的 accelerate 仓库
!pip install git+https://github.com/huggingface/accelerate

Kaggle 源码：https://www.kaggle.com/code/lyhue1991/kaggle-ddp-tpu-examples

一、使用 CPU / 单 GPU 训练你的 PyTorch 模型

当系统存在 GPU 时，Accelerate 会自动使用 GPU 训练你的 PyTorch 模型，否则会使用 CPU 训练模型。

import os,PIL 
import numpy as np
from torch.utils.data import DataLoader, Dataset
import torch 
from torch import nn 

import torchvision 
from torchvision import transforms
import datetime

#======================================================================
# import accelerate
from accelerate import Accelerator
from accelerate.utils import set_seed
#======================================================================


def create_dataloaders(batch_size=64):
    transform = transforms.Compose([transforms.ToTensor()])

    ds_train = torchvision.datasets.MNIST(root="./mnist/",train=True,download=True,transform=transform)
    ds_val = torchvision.datasets.MNIST(root="./mnist/",train=False,download=True,transform=transform)

    dl_train =  torch.utils.data.DataLoader(ds_train, batch_size=batch_size, shuffle=True,
                                            num_workers=2,drop_last=True)
    dl_val =  torch.utils.data.DataLoader(ds_val, batch_size=batch_size, shuffle=False, 
                                          num_workers=2,drop_last=True)
    return dl_train,dl_val


def create_net():
    net = nn.Sequential()
    net.add_module("conv1",nn.Conv2d(in_channels=1,out_channels=512,kernel_size = 3))
    net.add_module("pool1",nn.MaxPool2d(kernel_size = 2,stride = 2)) 
    net.add_module("conv2",nn.Conv2d(in_channels=512,out_channels=256,kernel_size = 5))
    net.add_module("pool2",nn.MaxPool2d(kernel_size = 2,stride = 2))
    net.add_module("dropout",nn.Dropout2d(p = 0.1))
    net.add_module("adaptive_pool",nn.AdaptiveMaxPool2d((1,1)))
    net.add_module("flatten",nn.Flatten())
    net.add_module("linear1",nn.Linear(256,128))
    net.add_module("relu",nn.ReLU())
    net.add_module("linear2",nn.Linear(128,10))
    return net 



def training_loop(epochs = 5,
                  lr = 1e-3,
                  batch_size= 1024,
                  ckpt_path = "checkpoint.pt",
                  mixed_precision="no", #'fp16' or 'bf16'
                 ):
    
    train_dataloader, eval_dataloader = create_dataloaders(batch_size)
    model = create_net()
    

    optimizer = torch.optim.AdamW(params=model.parameters(), lr=lr)
    lr_scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer=optimizer, max_lr=25*lr, 
                              epochs=epochs, steps_per_epoch=len(train_dataloader))
    
    #======================================================================
    # initialize accelerator and auto move data/model to accelerator.device
    set_seed(42)
    accelerator = Accelerator(mixed_precision=mixed_precision)
    accelerator.print(f'device {str(accelerator.device)} is used!')
    model, optimizer,lr_scheduler, train_dataloader, eval_dataloader = accelerator.prepare(
        model, optimizer,lr_scheduler, train_dataloader, eval_dataloader)
    #======================================================================
    

    for epoch in range(epochs):
        model.train()
        for step, batch in enumerate(train_dataloader):
            features,labels = batch
            preds = model(features)
            loss = nn.CrossEntropyLoss()(preds,labels)
            
            #======================================================================
            #attention here! 
            accelerator.backward(loss) #loss.backward()
            #======================================================================
            
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
            
        
        model.eval()
        accurate = 0
        num_elems = 0
        
        for _, batch in enumerate(eval_dataloader):
            features,labels = batch
            with torch.no_grad():
                preds = model(features)
            predictions = preds.argmax(dim=-1)
            
            #======================================================================
            #gather data from multi-gpus (used when in ddp mode)
            predictions = accelerator.gather_for_metrics(predictions)
            labels = accelerator.gather_for_metrics(labels)
            #======================================================================
            
            accurate_preds =  (predictions==labels)
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        eval_metric = accurate.item() / num_elems
        
        #======================================================================
        #print logs and save ckpt  
        accelerator.wait_for_everyone()
        nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
        accelerator.print(f"epoch【{epoch}】@{nowtime} --> eval_metric= {100 * eval_metric:.2f}%")
        net_dict = accelerator.get_state_dict(model)
        accelerator.save(net_dict,ckpt_path+"_"+str(epoch))
        #======================================================================
        
#training_loop(epochs = 5,lr = 1e-3,batch_size= 1024,ckpt_path = "checkpoint.pt",
#             mixed_precision="no") 

training_loop(epochs = 5,lr = 1e-4,batch_size= 1024,
              ckpt_path = "checkpoint.pt",
              mixed_precision="no") #mixed_precision='fp16' or  'bf16'

device cuda is used!
epoch【0】@2023-01-15 12:06:45 --> eval_metric= 95.20%
epoch【1】@2023-01-15 12:07:01 --> eval_metric= 96.79%
epoch【2】@2023-01-15 12:07:17 --> eval_metric= 98.47%
epoch【3】@2023-01-15 12:07:34 --> eval_metric= 98.78%
epoch【4】@2023-01-15 12:07:51 --> eval_metric= 98.87%

二、使用多 GPU DDP 模式训练你的 PyTorch 模型

Kaggle 中右边 settings 中的 ACCELERATOR 选择 GPU T4x2。

1. 设置 config

import os
from accelerate.utils import write_basic_config
write_basic_config() # Write a config file
os._exit(0) # Restart the notebook to reload info from the latest config file 

# or answer some question to create a config
#!accelerate config  

# %load /root/.cache/huggingface/accelerate/default_config.yaml
{
  "compute_environment": "LOCAL_MACHINE",
  "deepspeed_config": {},
  "distributed_type": "MULTI_GPU",
  "downcast_bf16": false,
  "dynamo_backend": "NO",
  "fsdp_config": {},
  "machine_rank": 0,
  "main_training_function": "main",
  "megatron_lm_config": {},
  "mixed_precision": "no",
  "num_machines": 1,
  "num_processes": 2,
  "rdzv_backend": "static",
  "same_network": false,
  "use_cpu": false
}

2. 训练代码

与之前代码完全一致。

如果是脚本方式启动，需要将训练代码写入到脚本文件中，如 cv_example.py

%%writefile cv_example.py 
import os,PIL 
import numpy as np
from torch.utils.data import DataLoader, Dataset
import torch 
from torch import nn 

import torchvision 
from torchvision import transforms
import datetime

#======================================================================
# import accelerate
from accelerate import Accelerator
from accelerate.utils import set_seed
#======================================================================


def create_dataloaders(batch_size=64):
    transform = transforms.Compose([transforms.ToTensor()])

    ds_train = torchvision.datasets.MNIST(root="./mnist/",train=True,download=True,transform=transform)
    ds_val = torchvision.datasets.MNIST(root="./mnist/",train=False,download=True,transform=transform)

    dl_train =  torch.utils.data.DataLoader(ds_train, batch_size=batch_size, shuffle=True,
                                            num_workers=2,drop_last=True)
    dl_val =  torch.utils.data.DataLoader(ds_val, batch_size=batch_size, shuffle=False, 
                                          num_workers=2,drop_last=True)
    return dl_train,dl_val


def create_net():
    net = nn.Sequential()
    net.add_module("conv1",nn.Conv2d(in_channels=1,out_channels=512,kernel_size = 3))
    net.add_module("pool1",nn.MaxPool2d(kernel_size = 2,stride = 2)) 
    net.add_module("conv2",nn.Conv2d(in_channels=512,out_channels=256,kernel_size = 5))
    net.add_module("pool2",nn.MaxPool2d(kernel_size = 2,stride = 2))
    net.add_module("dropout",nn.Dropout2d(p = 0.1))
    net.add_module("adaptive_pool",nn.AdaptiveMaxPool2d((1,1)))
    net.add_module("flatten",nn.Flatten())
    net.add_module("linear1",nn.Linear(256,128))
    net.add_module("relu",nn.ReLU())
    net.add_module("linear2",nn.Linear(128,10))
    return net 



def training_loop(epochs = 5,
                  lr = 1e-3,
                  batch_size= 1024,
                  ckpt_path = "checkpoint.pt",
                  mixed_precision="no", #'fp16' or  'bf16'
                 ):
    
    train_dataloader, eval_dataloader = create_dataloaders(batch_size)
    model = create_net()
    

    optimizer = torch.optim.AdamW(params=model.parameters(), lr=lr)
    lr_scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer=optimizer, max_lr=25*lr, 
                              epochs=epochs, steps_per_epoch=len(train_dataloader))
    
    #======================================================================
    # initialize accelerator and auto move data/model to accelerator.device
    set_seed(42)
    accelerator = Accelerator(mixed_precision=mixed_precision)
    accelerator.print(f'device {str(accelerator.device)} is used!')
    model, optimizer,lr_scheduler, train_dataloader, eval_dataloader = accelerator.prepare(
        model, optimizer,lr_scheduler, train_dataloader, eval_dataloader)
    #======================================================================
    

    for epoch in range(epochs):
        model.train()
        for step, batch in enumerate(train_dataloader):
            features,labels = batch
            preds = model(features)
            loss = nn.CrossEntropyLoss()(preds,labels)
            
            #======================================================================
            #attention here! 
            accelerator.backward(loss) #loss.backward()
            #======================================================================
            
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
            
        
        model.eval()
        accurate = 0
        num_elems = 0
        
        for _, batch in enumerate(eval_dataloader):
            features,labels = batch
            with torch.no_grad():
                preds = model(features)
            predictions = preds.argmax(dim=-1)
            
            #======================================================================
            #gather data from multi-gpus (used when in ddp mode)
            predictions = accelerator.gather_for_metrics(predictions)
            labels = accelerator.gather_for_metrics(labels)
            #======================================================================
            
            accurate_preds =  (predictions==labels)
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        eval_metric = accurate.item() / num_elems
        
        #======================================================================
        #print logs and save ckpt  
        accelerator.wait_for_everyone()
        nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
        accelerator.print(f"epoch【{epoch}】@{nowtime} --> eval_metric= {100 * eval_metric:.2f}%")
        net_dict = accelerator.get_state_dict(model)
        accelerator.save(net_dict,ckpt_path+"_"+str(epoch))
        #======================================================================
        
training_loop(epochs = 5,lr = 1e-4,batch_size= 1024,ckpt_path = "checkpoint.pt",
            mixed_precision="no") #mixed_precision='fp16' or  'bf16'

3. 执行代码

方式 1: 在 Notebook 中启动

from accelerate import notebook_launcher
#args = (5,1e-4,1024,'checkpoint.pt','no')
args = dict(epochs = 5,
        lr = 1e-4,
        batch_size= 1024,
        ckpt_path = "checkpoint.pt",
        mixed_precision="no").values()
notebook_launcher(training_loop, args, num_processes=2)

Launching training on 2 GPUs.
device cuda:0 is used!
epoch【0】@2023-01-15 12:10:48 --> eval_metric= 89.18%
epoch【1】@2023-01-15 12:10:58 --> eval_metric= 97.20%
epoch【2】@2023-01-15 12:11:08 --> eval_metric= 98.03%
epoch【3】@2023-01-15 12:11:19 --> eval_metric= 98.16%
epoch【4】@2023-01-15 12:11:30 --> eval_metric= 98.32%

方式 2: Accelerate 方式执行脚本

!accelerate launch ./cv_example.py  

device cuda:0 is used!
epoch【0】@2023-02-03 11:38:02 --> eval_metric= 91.79%
epoch【1】@2023-02-03 11:38:13 --> eval_metric= 97.22%
epoch【2】@2023-02-03 11:38:22 --> eval_metric= 98.18%
epoch【3】@2023-02-03 11:38:32 --> eval_metric= 98.33%
epoch【4】@2023-02-03 11:38:43 --> eval_metric= 98.38%

方式 3: PyTorch 方式执行脚本

# or traditional PyTorch style
!python -m torch.distributed.launch --nproc_per_node 2 --use_env ./cv_example.py

device cuda:0 is used!
epoch【0】@2023-01-15 12:18:26 --> eval_metric= 94.79%
epoch【1】@2023-01-15 12:18:37 --> eval_metric= 96.44%
epoch【2】@2023-01-15 12:18:48 --> eval_metric= 98.34%
epoch【3】@2023-01-15 12:18:59 --> eval_metric= 98.41%
epoch【4】@2023-01-15 12:19:10 --> eval_metric= 98.51%

三、使用 TPU 加速你的 PyTorch 模型

Kaggle 中右边 Settings 中的 ACCELERATOR 选择 TPU v3-8。

1. 安装 torch_xla

#安装torch_xla支持
!pip uninstall -y torch torch_xla 
!pip install torch==1.8.2+cpu -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
!pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.8-cp37-cp37m-linux_x86_64.whl

#从git安装最新的accelerate仓库
!pip install git+https://github.com/huggingface/accelerate

#检查是否成功安装 torch_xla 
import torch_xla 

2. 训练代码

和之前代码完全一样。

import os,PIL 
import numpy as np
from torch.utils.data import DataLoader, Dataset
import torch 
from torch import nn 

import torchvision 
from torchvision import transforms
import datetime

#======================================================================
# import accelerate
from accelerate import Accelerator
from accelerate.utils import set_seed
#======================================================================


def create_dataloaders(batch_size=64):
    transform = transforms.Compose([transforms.ToTensor()])

    ds_train = torchvision.datasets.MNIST(root="./mnist/",train=True,download=True,transform=transform)
    ds_val = torchvision.datasets.MNIST(root="./mnist/",train=False,download=True,transform=transform)

    dl_train =  torch.utils.data.DataLoader(ds_train, batch_size=batch_size, shuffle=True,
                                            num_workers=2,drop_last=True)
    dl_val =  torch.utils.data.DataLoader(ds_val, batch_size=batch_size, shuffle=False, 
                                          num_workers=2,drop_last=True)
    return dl_train,dl_val


def create_net():
    net = nn.Sequential()
    net.add_module("conv1",nn.Conv2d(in_channels=1,out_channels=512,kernel_size = 3))
    net.add_module("pool1",nn.MaxPool2d(kernel_size = 2,stride = 2)) 
    net.add_module("conv2",nn.Conv2d(in_channels=512,out_channels=256,kernel_size = 5))
    net.add_module("pool2",nn.MaxPool2d(kernel_size = 2,stride = 2))
    net.add_module("dropout",nn.Dropout2d(p = 0.1))
    net.add_module("adaptive_pool",nn.AdaptiveMaxPool2d((1,1)))
    net.add_module("flatten",nn.Flatten())
    net.add_module("linear1",nn.Linear(256,128))
    net.add_module("relu",nn.ReLU())
    net.add_module("linear2",nn.Linear(128,10))
    return net 



def training_loop(epochs = 5,
                  lr = 1e-3,
                  batch_size= 1024,
                  ckpt_path = "checkpoint.pt",
                  mixed_precision="no", #fp16' or  'bf16'
                 ):
    
    train_dataloader, eval_dataloader = create_dataloaders(batch_size)
    model = create_net()
    

    optimizer = torch.optim.AdamW(params=model.parameters(), lr=lr)
    lr_scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer=optimizer, max_lr=25*lr, 
                              epochs=epochs, steps_per_epoch=len(train_dataloader))
    
    #======================================================================
    # initialize accelerator and auto move data/model to accelerator.device
    set_seed(42)
    accelerator = Accelerator(mixed_precision=mixed_precision)
    accelerator.print(f'device {str(accelerator.device)} is used!')
    model, optimizer,lr_scheduler, train_dataloader, eval_dataloader = accelerator.prepare(
        model, optimizer,lr_scheduler, train_dataloader, eval_dataloader)
    #======================================================================
    

    for epoch in range(epochs):
        model.train()
        for step, batch in enumerate(train_dataloader):
            features,labels = batch
            preds = model(features)
            loss = nn.CrossEntropyLoss()(preds,labels)
            
            #======================================================================
            #attention here! 
            accelerator.backward(loss) #loss.backward()
            #======================================================================
            
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
            
        
        model.eval()
        accurate = 0
        num_elems = 0
        
        for _, batch in enumerate(eval_dataloader):
            features,labels = batch
            with torch.no_grad():
                preds = model(features)
            predictions = preds.argmax(dim=-1)
            
            #======================================================================
            #gather data from multi-gpus (used when in ddp mode)
            predictions = accelerator.gather_for_metrics(predictions)
            labels = accelerator.gather_for_metrics(labels)
            #======================================================================
            
            accurate_preds =  (predictions==labels)
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        eval_metric = accurate.item() / num_elems
        
        #======================================================================
        #print logs and save ckpt  
        accelerator.wait_for_everyone()
        nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
        accelerator.print(f"epoch【{epoch}】@{nowtime} --> eval_metric= {100 * eval_metric:.2f}%")
        net_dict = accelerator.get_state_dict(model)
        accelerator.save(net_dict,ckpt_path+"_"+str(epoch))
        #======================================================================
        
#training_loop(epochs = 5,lr = 1e-3,batch_size= 1024,ckpt_path = "checkpoint.pt",
#             mixed_precision="no") #mixed_precision='fp16' or  'bf16'

3. 启动训练

from accelerate import notebook_launcher
#args = (5,1e-4,1024,'checkpoint.pt','no')
args = dict(epochs = 5,
        lr = 1e-4,
        batch_size= 1024,
        ckpt_path = "checkpoint.pt",
        mixed_precision="no").values()
notebook_launcher(training_loop, args, num_processes=8)

作者介绍：吃货本货。算法工程师，擅长数据挖掘和计算机视觉算法。eat pytorch/tensorflow/pyspark 系列 GitHub 开源教程的作者。