torch_musa is an extended Python package based on PyTorch. Developing torch_musa in a plug-in way allows torch_musa to be decoupled from PyTorch, which is convenient for code maintenance. Combined with PyTorch, users can take advantage of the strong power of Moore Threads graphics cards through torch_musa. In addition, torch_musa has two significant advantages:
- CUDA compatibility could be achieved in torch_musa, which greatly reduces the workload of adapting new operators.
- torch_musa API is consistent with PyTorch in format, which allows users accustomed to PyTorch to migrate smoothly to torch_musa.
# for python3.8
pip install torch-2.0.0_xxxxxx-cp38-cp38-linux_x86_64.whl
pip install torch_musa_xxxxxx-cp38-cp38-linux_x86_64.whl
# for python3.9
pip install torch-2.0.0_xxxxxx-cp39-cp39-linux_x86_64.whl
pip install torch_musa_xxxxxx-cp39-cp39-linux_x86_64.whl- MUSA ToolKit
- MUDNN
- Other Libs (including muThrust, muSparse, muAlg, muRand)
- PyTorch Source Code
- Docker Container Toolkits
NOTE: Since some of the dependent libraries are in beta and have not yet been officially released, we recommend using the development docker provided below to compile torch_musa. If you really want to compile torch_musa in your own environment, then please contact us for additional dependencies.
apt-get install ccache
pip install -r requirements.txtexport MUSA_HOME=path/to/musa_libraries(including mudnn and musa_toolkits) # defalut value is /usr/local/musa/
export LD_LIBRARY_PATH=$MUSA_HOME/lib:$LD_LIBRARY_PATH
# if PYTORCH_REPO_PATH is not set, PyTorch-v2.0.0 will be downloaded outside this directory when building with build.sh
export PYTORCH_REPO_PATH=path/to/PyTorch source codebash build.sh # build original PyTorch and torch_musa from scratch
# Some important parameters are as follows:
bash build.sh --torch # build original PyTorch only
bash build.sh --musa # build torch_musa only
bash build.sh --fp64 # compile fp64 in kernels using mcc in torch_musa
bash build.sh --debug # build in debug mode
bash build.sh --asan # build in asan mode
bash build.sh --clean # clean everything built- Apply PyTorch patches
bash build.sh --patch- Building PyTorch
cd pytorch
pip install -r requirements.txt
python setup.py install
# debug mode: DEBUG=1 python setup.py install
# asan mode: USE_ASAN=1 python setup.py install- Building torch_musa
cd torch_musa
pip install -r requirements.txt
python setup.py install
# debug mode: DEBUG=1 python setup.py install
# asan mode: USE_ASAN=1 python setup.py installNOTE: If you want to use torch_musa in docker container, please install mt-container-toolkit first and use '--env MTHREADS_VISIBLE_DEVICES=all' when starting a container.
docker run -it --privileged --name=torch_musa_dev --env MTHREADS_VISIBLE_DEVICES=all --shm-size=80g torch_musa_develop_image /bin/bashDocker Image List
| Docker Tag | Description |
|---|---|
| latest/v1.0.0 | musatoolkits rc1.4.0 (requires musa driver musa_2.1.1) mudnn rtm_2.1.1; mccl 20230627 libomp-11-dev muAlg _dev-0.1.1 muRAND_dev1.0.0 muSPARSE_dev0.1.0 muThrust_dev-0.1.1 |
docker run -it --privileged --name=torch_musa_release --env MTHREADS_VISIBLE_DEVICES=all --shm-size=80g torch_musa_release_image /bin/bashDocker Image List
| Docker Tag | Description |
|---|---|
| latest/v1.0.0 | musatoolkits rc1.4.0 (requires musa driver musa_2.1.1) mudnn rtm_2.1.1; mccl 20230627 libomp-11-dev muAlg _dev-0.1.1 muRAND_dev1.0.0 muSPARSE_dev0.1.0 muThrust_dev-0.1.1 |
The following two key changes are required when using torch_musa:
-
Import torch_musa package
import torch import torch_musa
-
Change the device to musa
import torch import torch_musa a = torch.tensor([1.2, 2.3], dtype=torch.float32, device='musa') b = torch.tensor([1.2, 2.3], dtype=torch.float32, device='cpu').to('musa')
code
import torch
import torch_musa
torch.musa.is_available()
torch.musa.device_count()
torch.musa.synchronize()
with torch.musa.device(0):
assert torch.musa.current_device() == 0
if torch.musa.device_count() > 1:
torch.musa.set_device(1)
assert torch.musa.current_device() == 1
torch.musa.synchronize("musa:1")
a = torch.tensor([1.2, 2.3], dtype=torch.float32, device='musa')
b = torch.tensor([1.8, 1.2], dtype=torch.float32, device='musa')
c = a + bcode
import torch
import torch_musa
import torchvision.models as models
model = models.resnet50().eval()
x = torch.rand((1, 3, 224, 224), device="musa")
model = model.to("musa")
# Perform the inference
y = model(x)code
import torch
import torch_musa
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# 1. prepare dataset
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
batch_size = 4
train_set = torchvision.datasets.CIFAR10(root='./data',
train=True,
download=True,
transform=transform)
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=batch_size,
shuffle=True,
num_workers=2)
test_set = torchvision.datasets.CIFAR10(root='./data',
train=False,
download=True,
transform=transform)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=batch_size,
shuffle=False,
num_workers=2)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
device = torch.device("musa")
# 2. build network
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = torch.flatten(x, 1) # flatten all dimensions except batch
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net().to(device)
# 3. define loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# 4. train
for epoch in range(2):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs, labels = data
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs.to(device))
loss = criterion(outputs, labels.to(device))
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')
running_loss = 0.0
print('Finished Training')
PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH)
net.load_state_dict(torch.load(PATH))
# 5. test
correct = 0
total = 0
with torch.no_grad():
for data in test_loader:
images, labels = data
outputs = net(images.to(device))
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels.to(device)).sum().item()
print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')For more detailed information, please refer to the files in the docs folder. Please let us know by email [email protected] if you have any questions.