MPS 后端

mps设备实现高性能 使用 Metal 编程框架对 MacOS 设备进行 GPU 培训。它 推出一种新设备来映射机器学习计算图和 高效的 Metal Performance Shaders Graph 框架上的基元和 分别由 Metal Performance Shaders 框架提供的 tuned 内核。

新的 MPS 后端扩展了 PyTorch 生态系统并提供现有脚本 在 GPU 上设置和运行操作的功能。

要开始使用，只需将 Tensor 和 Module 移动到设备即可：mps

# 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)