# 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 ```python #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。 ```python 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") ``` ```python 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 ```python 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 ``` ```python # or answer some question to create a config #!accelerate config ``` ```python # %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 ```python %%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** ```python 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** ```python !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** ```python # 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 ```python #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 ``` ```python #从git安装最新的accelerate仓库 !pip install git+https://github.com/huggingface/accelerate ``` ```python #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。 ```python 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 ```python 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) ```