eat_accelerate_in_30_minites.md

30分钟吃掉accelerate模型训练加速工具

accelerate 是huggingface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，torch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。