Mac MPS support

## 🐛 Bug

I installed `pytorch` with GPU support following this [tutorial](https://towardsdatascience.com/installing-pytorch-on-apple-m1-chip-with-gpu-acceleration-3351dc44d67c)
And I can confirm that GPU is working:
```python
import torch
import math

dtype = torch.float
device = torch.device("mps") # --> Apple Metal framework integration for GPU

# Create random input and output data
x = torch.linspace(-math.pi, math.pi, 2000, device=device, dtype=dtype)
y = torch.sin(x)

# Randomly initialize weights
a = torch.randn((), device=device, dtype=dtype)
b = torch.randn((), device=device, dtype=dtype)
c = torch.randn((), device=device, dtype=dtype)
d = torch.randn((), device=device, dtype=dtype)

learning_rate = 1e-6
for t in range(2000):
# Forward pass: compute predicted y
y_pred = a + b * x + c * x**2 + d * x**3

# Compute and print loss
loss = (y_pred - y).pow(2).sum().item()
if t % 100 == 99:
print(t, loss)

# Backprop to compute gradients of a, b, c, d with respect to loss
grad_y_pred = 2.0 * (y_pred - y)
grad_a = grad_y_pred.sum()
grad_b = (grad_y_pred * x).sum()
grad_c = (grad_y_pred * x**2).sum()
grad_d = (grad_y_pred * x**3).sum()

# Update weights using gradient descent
a -= learning_rate * grad_a
b -= learning_rate * grad_b
c -= learning_rate * grad_c
d -= learning_rate * grad_d

print(f"Result: y = {a.item()} + {b.item()} x + {c.item()} x^2 + {d.item()} x^3")
```
Outputs:
```
Result: y = 0.050219081342220306 + 0.8358809351921082 x + -0.008663627319037914 x^2 + -0.0903632640838623 x^3
```
Then I try to use this GPU on `dgl`:
```python
graph = ....
gpu = torch.device("mps")
graph.to(gpu)
```
Output:
```python
Traceback (most recent call last):
File "/Users/diogosilva/code/ubiwhere/models-hub/graph.py", line 60, in
graph.to(gpu)
File "/Users/diogosilva/.pyenv/versions/models-hub/lib/python3.9/site-packages/dgl/heterograph.py", line 5448, in to
ret._graph = self._graph.copy_to(utils.to_dgl_context(device))
File "/Users/diogosilva/.pyenv/versions/models-hub/lib/python3.9/site-packages/dgl/utils/internal.py", line 534, in to_dgl_context
device_type = nd.DGLContext.STR2MASK[F.device_type(ctx)]
KeyError: 'mps'
```

## Expected behavior
It should behave like any other GPU device.
## Environment

- DGL Version: 0.9.1
- Backend Library & Version: Python Version 1.14.0.dev20221017
- OS (e.g., Linux): MacOS
- How you installed DGL (`conda`, `pip`, source): pip
- Python version: 3.9.1

## Additional context