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eat_accelerate_in_30_minites.md

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+
+# 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)
+
+```
+