15 KiB
Use Engine and Trainer in Training
Author: Shenggui Li, Siqi Mai
⚠️ The information on this page is outdated and will be deprecated. Please check Booster API for more information.
Prerequisite:
Introduction
In this tutorial, you will learn how to use the engine and trainer provided in Colossal-AI to train your model. Before we delve into the details, we would like to first explain the concept of engine and trainer.
Engine
Engine is essentially a wrapper class for model, optimizer and loss function.
When we call colossalai.initialize, an engine object will be returned, and it has already been equipped with
functionalities such as gradient clipping, gradient accumulation and zero optimizer as specified in your configuration file.
An engine object will use similar APIs to those of PyTorch training components such that the user has minimum change
to their code.
Below is a table which shows the commonly used APIs for the engine object.
| Component | Function | PyTorch | Colossal-AI |
|---|---|---|---|
| optimizer | Set all gradients to zero before an iteration | optimizer.zero_grad() | engine.zero_grad() |
| optimizer | Update the parameters | optimizer.step() | engine.step() |
| model | Run a forward pass | outputs = model(inputs) | outputs = engine(inputs) |
| criterion | Calculate the loss value | loss = criterion(output, label) | loss = engine.criterion(output, label) |
| criterion | Execute back-propagation on the model | loss.backward() | engine.backward(loss) |
The reason why we need such an engine class is that we can add more functionalities while hiding the implementations in
the colossalai.initialize function.
Imaging we are gonna add a new feature, we can manipulate the model, optimizer, dataloader and loss function in the
colossalai.initialize function and only expose an engine object to the user.
The user only needs to modify their code to the minimum extent by adapting the normal PyTorch APIs to the Colossal-AI
engine APIs. In this way, they can enjoy more features for efficient training.
A normal training iteration using engine can be:
import colossalai
# build your model, optimizer, criterion, dataloaders
...
engine, train_dataloader, test_dataloader, _ = colossalai.initialize(model,
optimizer,
criterion,
train_dataloader,
test_dataloader)
for img, label in train_dataloader:
engine.zero_grad()
output = engine(img)
loss = engine.criterion(output, label)
engine.backward(loss)
engine.step()
Trainer
Trainer is a more high-level wrapper for the user to execute training with fewer lines of code. However, in pursuit of more abstraction, it loses some flexibility compared to engine. The trainer is designed to execute a forward and backward step to perform model weight update. It is easy to create a trainer object by passing the engine object. The trainer has a default value None for the argument schedule. In most cases, we leave this value to None unless we want to use pipeline parallelism. If you wish to explore more about this parameter, you can go to the tutorial on pipeline parallelism.
from colossalai.logging import get_dist_logger
from colossalai.trainer import Trainer, hooks
# build components and initialize with colossalai.initialize
...
# create a logger so that trainer can log on the console
logger = get_dist_logger()
# create a trainer object
trainer = Trainer(
engine=engine,
logger=logger
)
In trainer, the user can customize some hooks and attach these hooks to the trainer object. A hook object will execute life-cycle methods periodically based on the training scheme. For example, The LRSchedulerHook will execute lr_scheduler.step() to update the learning rate of the model during either after_train_iter or after_train_epoch stages depending on whether the user wants to update the learning rate after each training iteration or only after the entire training epoch. You can store the hook objects in a list and pass it to trainer.fit method. trainer.fit method will execute training and testing based on your parameters. If display_process is True, a progress bar will be displayed on your console to show the training process.
# define the hooks to attach to the trainer
hook_list = [
hooks.LossHook(),
hooks.LRSchedulerHook(lr_scheduler=lr_scheduler, by_epoch=True),
hooks.AccuracyHook(accuracy_func=Accuracy()),
hooks.LogMetricByEpochHook(logger),
]
# start training
trainer.fit(
train_dataloader=train_dataloader,
epochs=NUM_EPOCHS,
test_dataloader=test_dataloader,
test_interval=1,
hooks=hook_list,
display_progress=True
)
If you want to customize your own hook class, you can inherit hooks.BaseHook and override the life-cycle methods of your interest. A dummy example to demonstrate how to create a simple log message hook is provided below for your reference.
from colossalai.logging import get_dist_logger
from colossalai.trainer import hooks
class LogMessageHook(hooks.BaseHook):
def __init__(self, priority=10):
self._logger = get_dist_logger()
def before_train(self, trainer):
self._logger.info('training starts')
def after_train(self, trainer):
self._logger.info('training finished')
...
# then in your training script
hook_list.append(LogMessageHook())
In the sections below, I will guide you through the steps required to train a ResNet model with both engine and trainer.
Explain with ResNet
Overview
In this section we will cover:
- Use an engine object to train a ResNet34 model on CIFAR10 dataset
- Use a trainer object to train a ResNet34 model on CIFAR10 dataset
The project structure will be like:
-- config.py
-- run_resnet_cifar10_with_engine.py
-- run_resnet_cifar10_with_trainer.py
Steps 1-4 below are commonly used regardless of using engine or trainer. Thus, steps 1-4 + step 5 will be your run_resnet_cifar10_with_engine.py and steps 1-4 + step 6 will form run_resnet_cifar10_with_trainer.py.
Hands-on Practice
Step 1. Create a Config File
In your project folder, create a config.py. This file is to specify some features you may want to use to train your model. A sample config file is as below:
from colossalai.amp import AMP_TYPE
BATCH_SIZE = 128
NUM_EPOCHS = 200
fp16=dict(
mode=AMP_TYPE.TORCH
)
In this config file, we specify that we want to use batch size 128 per GPU and run for 200 epochs. These two parameters are exposed by gpc.config. For example, you can use gpc.config.BATCH_SIZE to access the value you store in your config file. The fp16 configuration tells colossalai.initialize to use mixed precision training provided by PyTorch to train the model with better speed and lower memory consumption.
Step 2. Initialize Distributed Environment
We need to initialize the distributed training environment. This has been introduced in the tutorial on how to
launch Colossal-AI. For this demonstration, we use launch_from_torch and PyTorch launch utility.
import colossalai
# ./config.py refers to the config file we just created in step 1
colossalai.launch_from_torch(config='./config.py')
Step 3. Create all the training components
In this step, we can create all the components used for training. These components include:
- Model
- Optimizer
- Criterion/loss function
- Training/Testing dataloaders
- Learning rate Scheduler
- Logger
To build these components, you need to import the following modules:
from pathlib import Path
from colossalai.logging import get_dist_logger
import torch
import os
from colossalai.core import global_context as gpc
from colossalai.utils import get_dataloader
from torchvision import transforms
from colossalai.nn.lr_scheduler import CosineAnnealingLR
from torchvision.datasets import CIFAR10
from torchvision.models import resnet34
Then build your components in the same way as how to normally build them in your PyTorch scripts. In the script below, we set the root path for CIFAR10 dataset as an environment variable DATA. You can change it to any path you like, for example, you can change root=Path(os.environ['DATA']) to root='./data' so that there is no need to set the environment variable.
# build logger
logger = get_dist_logger()
# build resnet
model = resnet34(num_classes=10)
# build datasets
train_dataset = CIFAR10(
root='./data',
download=True,
transform=transforms.Compose(
[
transforms.RandomCrop(size=32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[
0.2023, 0.1994, 0.2010]),
]
)
)
test_dataset = CIFAR10(
root='./data',
train=False,
transform=transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[
0.2023, 0.1994, 0.2010]),
]
)
)
# build dataloaders
train_dataloader = get_dataloader(dataset=train_dataset,
shuffle=True,
batch_size=gpc.config.BATCH_SIZE,
num_workers=1,
pin_memory=True,
)
test_dataloader = get_dataloader(dataset=test_dataset,
add_sampler=False,
batch_size=gpc.config.BATCH_SIZE,
num_workers=1,
pin_memory=True,
)
# build criterion
criterion = torch.nn.CrossEntropyLoss()
# optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
# lr_scheduler
lr_scheduler = CosineAnnealingLR(optimizer, total_steps=gpc.config.NUM_EPOCHS)
Step 4. Initialize with Colossal-AI
Next, the essential step is to obtain the engine class by calling colossalai.initialize. As stated in config.py, we will be using mixed precision training for training ResNet34 model. colossalai.initialize will automatically check your config file and assign relevant features to your training components. In this way, our engine object has already been able to train with mixed precision, but you do not have to explicitly take care of it.
engine, train_dataloader, test_dataloader, _ = colossalai.initialize(model,
optimizer,
criterion,
train_dataloader,
test_dataloader,
)
Step 5. Train with engine
With all the training components ready, we can train ResNet34 just like how to normally deal with PyTorch training.
for epoch in range(gpc.config.NUM_EPOCHS):
# execute a training iteration
engine.train()
for img, label in train_dataloader:
img = img.cuda()
label = label.cuda()
# set gradients to zero
engine.zero_grad()
# run forward pass
output = engine(img)
# compute loss value and run backward pass
train_loss = engine.criterion(output, label)
engine.backward(train_loss)
# update parameters
engine.step()
# update learning rate
lr_scheduler.step()
# execute a testing iteration
engine.eval()
correct = 0
total = 0
for img, label in test_dataloader:
img = img.cuda()
label = label.cuda()
# run prediction without back-propagation
with torch.no_grad():
output = engine(img)
test_loss = engine.criterion(output, label)
# compute the number of correct prediction
pred = torch.argmax(output, dim=-1)
correct += torch.sum(pred == label)
total += img.size(0)
logger.info(
f"Epoch {epoch} - train loss: {train_loss:.5}, test loss: {test_loss:.5}, acc: {correct / total:.5}, lr: {lr_scheduler.get_last_lr()[0]:.5g}", ranks=[0])
Step 6. Train with trainer
If you wish to train with a trainer object, you can follow the code snippet below:
from colossalai.nn.metric import Accuracy
from colossalai.trainer import Trainer, hooks
# create a trainer object
trainer = Trainer(
engine=engine,
logger=logger
)
# define the hooks to attach to the trainer
hook_list = [
hooks.LossHook(),
hooks.LRSchedulerHook(lr_scheduler=lr_scheduler, by_epoch=True),
hooks.AccuracyHook(accuracy_func=Accuracy()),
hooks.LogMetricByEpochHook(logger),
hooks.LogMemoryByEpochHook(logger)
]
# start training
# run testing every 1 epoch
trainer.fit(
train_dataloader=train_dataloader,
epochs=gpc.config.NUM_EPOCHS,
test_dataloader=test_dataloader,
test_interval=1,
hooks=hook_list,
display_progress=True
)
Step 7. Start Distributed Training
Lastly, we can invoke the scripts using the distributed launcher provided by PyTorch as we used launch_from_torch in Step 2. You need to replace <num_gpus> with the number of GPUs available on your machine. This number can be 1 if you only want to use 1 GPU. If you wish to use other launchers, you can refer to the tutorial on How to Launch Colossal-AI.
# with engine
python -m torch.distributed.launch --nproc_per_node <num_gpus> --master_addr localhost --master_port 29500 run_resnet_cifar10_with_engine.py
# with trainer
python -m torch.distributed.launch --nproc_per_node <num_gpus> --master_addr localhost --master_port 29500 run_resnet_cifar10_with_trainer.py