General Overview
This tutorial assumes you have a basic understanding of PyTorch and how to train a simple model. It will showcase training on multiple GPUs through a process called Distributed Data Parallelism (DDP) through three different levels of increasing abstraction:
- Native PyTorch DDP through the
pytorch.distributedmodule - Utilizing 🤗 Accelerate’s light wrapper around
pytorch.distributedthat also helps ensure the code can be run on a single GPU and TPUs with zero code changes and miminimal code changes to the original code - Utilizing 🤗 Transformer’s high-level Trainer API which abstracts all the boilerplate code and supports various devices and distributed scenarios
What is “Distributed” training and why does it matter?
Take some very basic PyTorch training code below, which sets up and trains a model on MNIST based on the official MNIST example
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
class BasicNet(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 = nn.Dropout(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
self.act = F.relu
def forward(self, x):
x = self.act(self.conv1(x))
x = self.act(self.conv2(x))
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.act(self.fc1(x))
x = self.dropout2(x)
x = self.fc2(x)
output = F.log_softmax(x, dim=1)
return output
We define the training device (cuda):
device = "cuda"
Build some PyTorch DataLoaders:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307), (0.3081))
])
train_dset = datasets.MNIST('data', train=True, download=True, transform=transform)
test_dset = datasets.MNIST('data', train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dset, shuffle=True, batch_size=64)
test_loader = torch.utils.data.DataLoader(test_dset, shuffle=False, batch_size=64)
Move the model to the CUDA device:
model = BasicNet().to(device)
Build a PyTorch optimizer:
optimizer = optim.AdamW(model.parameters(), lr=1e-3)
Before finally creating a simplistic training and evaluation loop that performs one full iteration over the dataset and calculates the test accuracy:
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
optimizer.zero_grad()
model.eval()
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
print(f'Accuracy: {100. * correct / len(test_loader.dataset)}')
Typically from here, one could either throw all of this into a python script or run it on a Jupyter Notebook.
However, how would you then get this script to run on say two GPUs or on multiple machines if these resources are available, which could improve training speed through distributed training? Just doing python myscript.py will only ever run the script using a single GPU. This is where torch.distributed comes into play
PyTorch Distributed Data Parallelism
As the name implies, torch.distributed is meant to work on distributed setups. This can include multi-node, where you have a number of machines each with a single GPU, or multi-gpu where a single system has multiple GPUs, or some combination of both.
To convert our above code to work within a distributed setup, a few setup configurations must first be defined, detailed in the Getting Started with DDP Tutorial
First a setup and a cleanup function must be declared. This will open up a processing group that all of the compute processes can communicate through
Note: for this section of the tutorial it should be assumed these are sent in python script files. Later on a launcher using Accelerate will be discussed that removes this necessity
import os
import torch.distributed as dist
def setup(rank, world_size):
"Sets up the process group and configuration for PyTorch Distributed Data Parallelism"
os.environ["MASTER_ADDR"] = 'localhost'
os.environ["MASTER_PORT"] = "12355"
dist.init_process_group("gloo", rank=rank, world_size=world_size)
def cleanup():
"Cleans up the distributed environment"
dist.destroy_process_group()
The last piece of the puzzle is how do I send my data and model to another GPU?
This is where the DistributedDataParallel module comes into play. It will copy your model onto each GPU, and when loss.backward() is called the backpropagation is performed and the resulting gradients across all these copies of the model will be averaged/reduced. This ensures each device has the same weights post the optimizer step.
Below is an example of our training setup, refactored as a function, with this capability:
Note: Here rank is the overall rank of the current GPU compared to all the other GPUs available, meaning they have a rank of
0 -> n-1
from torch.nn.parallel import DistributedDataParallel as DDP
def train(model, rank, world_size):
setup(rank, world_size)
model = model.to(rank)
ddp_model = DDP(model, device_ids=[rank])
optimizer = optim.AdamW(ddp_model.parameters(), lr=1e-3)
ddp_model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(rank), target.to(rank)
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
optimizer.zero_grad()
cleanup()
The optimizer needs to be declared based on the model on the specific device (so ddp_model and not model) for all of the gradients to properly be calculated.
Lastly, to run the script PyTorch has a convenient torchrun command line module that can help. Just pass in the number of nodes it should use as well as the script to run and you are set:
torchrun --nproc_per_node=2 --nnodes=1 example_script.py
The above will run the training script on two GPUs that live on a single machine and this is the barebones for performing only distributed training with PyTorch.
Now let’s talk about Accelerate, a library aimed to make this process more seameless and also help with a few best practices
🤗 Accelerate
Accelerate is a library designed to allow you to perform what we just did above, without needing to modify your code greatly. On top of this, the data pipeline innate to Accelerate can also improve performance to your code as well.
First, let’s wrap all of the above code we just performed into a single function, to help us visualize the difference:
def train_ddp(rank, world_size):
setup(rank, world_size)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307), (0.3081))
])
train_dset = datasets.MNIST('data', train=True, download=True, transform=transform)
test_dset = datasets.MNIST('data', train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dset, shuffle=True, batch_size=64)
test_loader = torch.utils.data.DataLoader(test_dset, shuffle=False, batch_size=64)
model = model.to(rank)
ddp_model = DDP(model, device_ids=[rank])
optimizer = optim.AdamW(ddp_model.parameters(), lr=1e-3)
ddp_model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(rank), target.to(rank)
output = ddp_model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
optimizer.zero_grad()
model.eval()
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(rank), target.to(rank)
output = ddp_model(data)
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
print(f'Accuracy: {100. * correct / len(test_loader.dataset)}')
Next let’s talk about how Accelerate can help. There’s a few issues with the above code:
- This is slightly inefficient, given that
ndataloaders are made based on each device and pushed. - This code will only work for multi-GPU, so special care would need to be made for it to be ran on a single node again, or on TPU.
Accelerate helps this through the Accelerator class. Through it, the code remains much the same except for three lines of code when comparing a single node to multinode, as shown below:
def train_ddp_accelerate():
accelerator = Accelerator()
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307), (0.3081))
])
train_dset = datasets.MNIST('data', train=True, download=True, transform=transform)
test_dset = datasets.MNIST('data', train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dset, shuffle=True, batch_size=64)
test_loader = torch.utils.data.DataLoader(test_dset, shuffle=False, batch_size=64)
model = BasicNet()
optimizer = optim.AdamW(model.parameters(), lr=1e-3)
train_loader, test_loader, model, optimizer = accelerator.prepare(
train_loader, test_loader, model, optimizer
)
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
output = model(data)
loss = F.nll_loss(output, target)
accelerator.backward(loss)
optimizer.step()
optimizer.zero_grad()
model.eval()
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
print(f'Accuracy: {100. * correct / len(test_loader.dataset)}')
With this your PyTorch training loop is now setup to be ran on any distributed setup thanks to the Accelerator object. This code can then still be launched through the torchrun CLI or through Accelerate’s own CLI interface, accelerate launch.
As a result its now trivialized to perform distributed training with Accelerate and keeping as much of the barebones PyTorch code the same as possible.
Earlier it was mentioned that Accelerate also makes the DataLoaders more efficient. This is through custom Samplers that can send parts of the batches automatically to different devices during training allowing for a single copy of the data to be known at one time, rather than four at once into memory depending on the configuration. Along with this, there is only a single full copy of the original dataset in memory total. Subsets of this dataset are split between all of the nodes that are utilized for training, allowing for much larger datasets to be trained on a single instance without an explosion in memory utilized.
Using the notebook_launcher
Earlier it was mentioned you can start distributed code directly out of your Jupyter Notebook. This comes from Accelerate’s notebook_launcher utility, which allows for starting multi-gpu training based on code inside of a Jupyter Notebook.
To use it is as trivial as importing the launcher:
from accelerate import notebook_launcher
And passing the training function we declared earlier, any arguments to be passed, and the number of processes to use (such as 8 on a TPU, or 2 for two GPUs). Both of the above training functions can be ran, but do note that after you start a single launch, the instance needs to be restarted before spawning another
notebook_launcher(train_ddp, args=(), num_processes=2)
Or:
notebook_launcher(train_ddp_accelerate, args=(), num_processes=2)
Using 🤗 Trainer
Finally, we arrive at the highest level of API — the Hugging Face Trainer.
This wraps as much training as possible while still being able to train on distributed systems without the user needing to do anything at all.
First we need to import the Trainer:
from transformers import Trainer
Then we define some TrainingArguments to control all the usual hyper-parameters. The trainer also works through dictionaries, so a custom collate function needs to be made.
Finally, we subclass the trainer and write our own compute_loss.
Afterwards, this code will also work on a distributed setup without any training code needing to be written whatsoever!
from transformers import Trainer, TrainingArguments
model = BasicNet()
training_args = TrainingArguments(
"basic-trainer",
per_device_train_batch_size=64,
per_device_eval_batch_size=64,
num_train_epochs=1,
evaluation_strategy="epoch",
remove_unused_columns=False
)
def collate_fn(examples):
pixel_values = torch.stack([example[0] for example in examples])
labels = torch.tensor([example[1] for example in examples])
return {"x":pixel_values, "labels":labels}
class MyTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
outputs = model(inputs["x"])
target = inputs["labels"]
loss = F.nll_loss(outputs, target)
return (loss, outputs) if return_outputs else loss
trainer = MyTrainer(
model,
training_args,
train_dataset=train_dset,
eval_dataset=test_dset,
data_collator=collate_fn,
)
trainer.train()
***** Running training *****
Num examples = 60000
Num Epochs = 1
Instantaneous batch size per device = 64
Total train batch size (w. parallel, distributed & accumulation) = 64
Gradient Accumulation steps = 1
Total optimization steps = 938
| Epoch | Training Loss | Validation Loss |
|---|---|---|
| 1 | 0.875700 | 0.282633 |
Similarly to the above examples with the notebook_launcher, this can be done again here by throwing it all into a training function:
def train_trainer_ddp():
model = BasicNet()
training_args = TrainingArguments(
"basic-trainer",
per_device_train_batch_size=64,
per_device_eval_batch_size=64,
num_train_epochs=1,
evaluation_strategy="epoch",
remove_unused_columns=False
)
def collate_fn(examples):
pixel_values = torch.stack([example[0] for example in examples])
labels = torch.tensor([example[1] for example in examples])
return {"x":pixel_values, "labels":labels}
class MyTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
outputs = model(inputs["x"])
target = inputs["labels"]
loss = F.nll_loss(outputs, target)
return (loss, outputs) if return_outputs else loss
trainer = MyTrainer(
model,
training_args,
train_dataset=train_dset,
eval_dataset=test_dset,
data_collator=collate_fn,
)
trainer.train()
notebook_launcher(train_trainer_ddp, args=(), num_processes=2)
Resources
To learn more about PyTorch Distributed Data Parallelism, check out the documentation here
To learn more about 🤗 Accelerate, check out the documentation here
To learn more about 🤗 Transformers, check out the documentation here
