MPS 后端 ¶
译者:片刻小哥哥
“mps”设备支持使用 Metal 编程框架在 MacOS 设备上进行 GPU 高性能训练。它引入了一种新设备,可将机器学习计算图和基元分别映射到高效的 Metal Performance Shaders Graph 框架和 Metal Performance Shaders 框架提供的调整内核上。
新的 MPS 后端扩展了 PyTorch 生态系统,并提供现有脚本功能来在 GPU 上设置和运行操作。
首先,只需将tensor和模块移动到“mps”设备:
# Check that MPS is available
if not torch.backends.mps.is_available():
if not torch.backends.mps.is_built():
print("MPS not available because the current PyTorch install was not "
"built with MPS enabled.")
else:
print("MPS not available because the current MacOS version is not 12.3+ "
"and/or you do not have an MPS-enabled device on this machine.")
else:
mps_device = torch.device("mps")
# Create a Tensor directly on the mps device
x = torch.ones(5, device=mps_device)
# Or
x = torch.ones(5, device="mps")
# Any operation happens on the GPU
y = x * 2
# Move your model to mps just like any other device
model = YourFavoriteNet()
model.to(mps_device)
# Now every call runs on the GPU
pred = model(x)