* Remove use of `torch.set_default_tensor_type`

This PR removes use of `torch.set_default_tensor_type`. There are
various reasons why we should probably move away from using this
function:

- Upstream will deprecate and remove it:
  pytorch/pytorch#53124
- We cannot use this mechanism for other devices than CPU/CUDA, such as
  Metal Performance Shaders.
- It offers little flexibility in allocating Torch models on different
  devices.

This PR makes `PyTorchWrapper`/`PyTorchShim` flexible in terms of the
devices it can use. Both classes add a `device` argument to their
constructors that takes a `torch.device` instance. The shim ensures that
the model is on the given device. The wrapper ensures that input tensors
are on the correct device, by calling `xp2torch` with the new `device`
keyword argument.

Even though this approach offers more flexibility, as a default we want
to use the `cpu` device when `NumpyOps` is used and `cuda:N` when
CupyOps is used. In order to do so, this PR also adds a new function
`get_torch_default_device` that returns the correct device for the
currently active Ops. `PyTorchWrapper`/`PyTorchShim`/`xp2torch` use this
function when `None` is given as the device to fall back on this
default, mimicking the behavior from before this PR.

* Add some typing fixes

* Remove spurious cupy import

* Small fixes

- Use `torch.cuda.current_device()` to get the current PyTorch CUDA
  device.
- Do not use `torch_set_default_tensor_type` in `set_active_gpu`.

…device

…evice-20220527

Merge `master` into `features/pytorch-device`

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* Add `test_slow_gpu` explosion-bot command

* Auto-format code with black (#682)

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* Add support for PyTorch Metal Performance Shaders

Nightly PyTorch versions add support for Metal Performance Shaders
(MPS). Metal is a low-level graphics API for Apple platforms that also
supports compute kernels (shaders). MPS is a framework of
highly-optimized compute and graphics kernels, including kernels for
neural networks. MPS is supported on both Apple Silicon, such as the M1
family of SoC, as well as a range of AMD GPUs used in Macs.

Since devices are handled in Thinc through a specific `Ops`
implementation (e.g. `CupyOps` == CUDA GPUs), this change introduces the
`MPSOps` class. This class is a subclass of `NumpyOps` or
`AppleOps` (when available). `MPSOps` does not override any methods, but
is used to signal to relevant code paths (e.g. `xp2torch`) that Torch
tensors should be placed on the MPS device.

The mapping in the previously introduced `get_torch_default_device`
function is updated to:

- `NumpyOps` -> `cpu`
- `CupyOps` -> `cuda:N`, where N is the selected CUDA device.
- `MPSOps` -> `mps`

to ensure placement of Torch tensors on the `mps` device when `MPSOps`
is active.

Finally, the following booleans have been added to or changed in
`compat`:

- `has_torch_mps` (new): PyTorch has MPS support
- `has_torch_mps_gpu` (new): PyTorch has MPS support and an
  MPS-capable GPU is available.
- `has_torch_cuda_gpu` (new): PyTorch has CUDA support and a
  CUDA-capable GPU is available.
- `has_torch_gpu` (changed): PyTorch has a GPU available (CUDA
  or MPS).

* Test PyTorch wrapper with all xp ops

* Azure: pin protobuf to fix Tensorflow

* Extend typing_extensions to <4.2.0 (#689)

* Fix type checking error

* Only back-off to NumpyOps on import error

We do not want to hide other issues while importing thinc_apple_ops.

* Remove unneeded `has_torch_mps` bool

* Add `has_gpu` bool and use it in `util`

* Replace another expression by has_gpu

* Set `has_torch_gpu` to `has_torch_cuda_gpu`

We need to decide whether we want to make the potentially breaking
change from `has_torch_cuda_gpu` to `has_torch_cuda_gpu or
has_torch_mps_gpu`. But since the latter is not needed for this PR,
remove the change.

* Update thinc/util.py

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…device