| # 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) | |
| ``` | |