Getting Started with PyTorch and Gaudi

This guide provides simple steps for preparing a PyTorch model to run on Gaudi. Make sure to install the PyTorch packages provided by Habana. Installing public PyTorch packages is not supported.

To set up the PyTorch environment, refer to the Installation Guide.The supported PyTorch versions are listed in the Support Matrix.

Note

Please refer to the PyTorch Known Issues and Limitations section for a list of current limitations.

Creating a Simple PyTorch Example

The below example contains the highlighted Habana-specific modifications that have been added to the PyTorch Hello World example.

Create a file named example.py with the code below.

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import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import os

# Import Habana Torch Library
import habana_frameworks.torch.core as htcore

class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()

        self.fc1   = nn.Linear(784, 256)
        self.fc2   = nn.Linear(256, 64)
        self.fc3   = nn.Linear(64, 10)

    def forward(self, x):

        out = x.view(-1,28*28)
        out = F.relu(self.fc1(out))
        out = F.relu(self.fc2(out))
        out = self.fc3(out)

        return out

def train(net,criterion,optimizer,trainloader,device):

    net.train()
    train_loss = 0.0
    correct = 0
    total = 0

    for batch_idx, (data, targets) in enumerate(trainloader):

        data, targets = data.to(device), targets.to(device)

        optimizer.zero_grad()
        outputs = net(data)
        loss = criterion(outputs, targets)

        loss.backward()
        
        # API call to trigger execution
        htcore.mark_step()
        
        optimizer.step()

        # API call to trigger execution
        htcore.mark_step()

        train_loss += loss.item()
        _, predicted = outputs.max(1)
        total += targets.size(0)
        correct += predicted.eq(targets).sum().item()

    train_loss = train_loss/(batch_idx+1)
    train_acc = 100.0*(correct/total)
    print("Training loss is {} and training accuracy is {}".format(train_loss,train_acc))

def test(net,criterion,testloader,device):

    net.eval()
    test_loss = 0
    correct = 0
    total = 0

    with torch.no_grad():

        for batch_idx, (data, targets) in enumerate(testloader):

            data, targets = data.to(device), targets.to(device)

            outputs = net(data)
            loss = criterion(outputs, targets)

            # API call to trigger execution
            htcore.mark_step()

            test_loss += loss.item()
            _, predicted = outputs.max(1)
            total += targets.size(0)
            correct += predicted.eq(targets).sum().item()

    test_loss = test_loss/(batch_idx+1)
    test_acc = 100.0*(correct/total)
    print("Testing loss is {} and testing accuracy is {}".format(test_loss,test_acc))

def main():

    epochs = 20
    batch_size = 128
    lr = 0.01
    milestones = [10,15]
    load_path = './data'
    save_path = './checkpoints'

    if(not os.path.exists(save_path)):
        os.makedirs(save_path)
    
    # Target the Gaudi HPU device
    device = torch.device("hpu")
    
    # Data
    transform = transforms.Compose([
        transforms.ToTensor(),
    ])

    trainset = torchvision.datasets.MNIST(root=load_path, train=True,
                                            download=True, transform=transform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                            shuffle=True, num_workers=2)
    testset = torchvision.datasets.MNIST(root=load_path, train=False,
                                        download=True, transform=transform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                            shuffle=False, num_workers=2)

    net = SimpleModel()
    net.to(device)

    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=lr,
                        momentum=0.9, weight_decay=5e-4)
    scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=milestones, gamma=0.1)

    for epoch in range(1, epochs+1):
        print("=====================================================================")
        print("Epoch : {}".format(epoch))
        train(net,criterion,optimizer,trainloader,device)
        test(net,criterion,testloader,device)

        torch.save(net.state_dict(), os.path.join(save_path,'epoch_{}.pth'.format(epoch)))

        scheduler.step()

if __name__ == '__main__':
    main()

The example.py presents a basic PyTorch code example. The Habana-specific lines are explained below.

  • Line 10 - Import Habana Torch Library:

import habana_frameworks.torch.core as htcore

  • Line 104 - Target the Gaudi HPU device:

device = torch.device("hpu")

  • Lines 47, 52 - In Lazy mode, mark_step() must be added in all training scripts right after loss.backward() and optimizer.step().

htcore.mark_step()

Executing the Example

After creating the example.py, perform the following:

  1. Set PYTHON to python executable:

export PYTHON=/usr/bin/python3.8

  1. Execute the example.py by running:

$PYTHON example.py

The following should appear as part of the output:

Epoch 1/5
469/469 [==============================] - 1s 3ms/step - loss: 1.2647 - accuracy: 0.7208
Epoch 2/5
469/469 [==============================] - 1s 2ms/step - loss: 0.7113 - accuracy: 0.8433
Epoch 3/5
469/469 [==============================] - 1s 2ms/step - loss: 0.5845 - accuracy: 0.8606
Epoch 4/5
469/469 [==============================] - 1s 2ms/step - loss: 0.5237 - accuracy: 0.8688
Epoch 5/5
469/469 [==============================] - 1s 2ms/step - loss: 0.4865 - accuracy: 0.8749
313/313 [==============================] - 1s 2ms/step - loss: 0.4482 - accuracy: 0.8869

Since the first iteration includes graph compilation time, you can see the first iteration takes longer to run than later iterations. The software stack compiles the graph and saves the recipe to cache. Unless the graph changes or a new graph comes in, no recompilation is needed during the training. Typically, the graph compilation happens at the beginning of the training and at the beginning of the evaluation.

Torch Multiprocessing for DataLoaders

If training scripts use multiprocessing with multiple workers for PyTorch dataloader, change the start method to spawn or forkserver using the PyTorch API multiprocessing.set_start_method(...). For example:

torch.multiprocessing.set_start_method('spawn')

Default start method is fork which may result in undefined behavior.