Stannum

Some optimizations

Fix a bug.

Details:
When some of input fields or internal fields do not need gradient (i.e. `needs_grad==False`), incorrect numbers of backward gradients will be passed to PyTorch Autograd, crashing back propagation.

New feature:
* Add complex tensor support: need to specify that a field expects a complex tensor as data source
python
tin_layer = Tin(data_oriented_vector_field, device) \
.register_kernel(data_oriented_vector_field.forward_kernel, 1.0) \
.register_input_field(data_oriented_vector_field.input_field, complex_dtype=True) \
.register_output_field(data_oriented_vector_field.output_field, complex_dtype=True) \
.register_internal_field(data_oriented_vector_field.multiplier) \
.finish()


Engineering:
* Refactored code a bit
* Add type hints to enhance code readability

Now you can register multiple kernels. These kernels will be called sequentially with the same order of registration. Please be noted that all fields needed to store intermediate results must be register.

API changes:
* Tin.register_weight_field() -> Tin.register_internal_field()

Added code documentation

Now you don't need to specify `needs_grad` when registering a field via `.register_*_field()`, as long as you use Taichi > 0.7.26. If you use a legacy version of Taichi, you must still specify `needs_grad` yourself, though.