stannum 0.4.0a2

PyTorch wrapper for Taichi data-oriented class

Stannum

Fusing Taichi into PyTorch

PRs are welcomed, please see TODOs and issues.

Usage

from stannum import Tin
import torch

data_oriented = TiClass()  # some Taichi data-oriented class 
device = torch.device("cpu")
kernel_args = (1.0,)
tin_layer = Tin(data_oriented, device=device)
    .register_kernel(data_oriented.forward_kernel, *kernel_args, kernel_name="forward")  # on old Taichi
    # .register_kernel(data_oriented.forward_kernel, *kernel_args)  # on new Taichi
    .register_input_field(data_oriented.input_field)
    .register_output_field(data_oriented.output_field)
    .register_internal_field(data_oriented.weight_field, name="field name")
    .finish() # finish() is required to finish construction
output = tin_layer(input_tensor)

Complex Tensor Support

When registering input fields and output fields, you can pass complex_dtype=True to enable simple complex tensor input and output support. For instance, Tin(..).register_input_field(input_field, complex_dtype=True).

Now the complex tensor support is limited in that the representation of complex numbers is a barebone 2D vector, since Taichi has no official support on complex numbers.

This means although stannum provides some facilities to deal with complex tensor input and output, you have to define and do the operations on the proxy 2D vectors yourself.

In practice, we now have these limitations:

• The registered field with complex_dtype=True must be an appropriate VectorField or ScalarField
  • If it's VectorField, n should be 2, like v_field = ti.Vector.field(n=2, dtype=ti.f32, shape=(2, 3, 4, 5))
  • If it's a ScalarField, the last dimension of it should be 2, like field = ti.field(ti.f32, shape=(2,3,4,5,2))
  • The above examples accept tensors of dtype=torch.cfloat, shape=(2,3,4,5)
• The semantic of complex numbers is not preserved in kernels, so you are manipulating regular fields, and as a consequence, you need to implement complex number operators yourself
  • Example:

  @ti.kernel
  def element_wise_complex_mul(self):
    for i in self.complex_array0:
        # this is not complex number multiplication, but only a 2D vector element-wise multiplication
        self.complex_output_array[i] = self.complex_array0[i] * self.complex_array1[i] 
  

Note:

It is NOT necessary to have a @ti.data_oriented class as long as you correctly register the fields that your kernel needs for forward and backward calculation. Please use EmptyTin in this case.

For input and output:

• We can register multiple input_field, output_field, weight_field.
• At least one input_field and one output_field should be registered.
• The order of input tensors must match the registration order of input_fields.
• The output order will align with the registration order of output_fields.

Installation & Dependencies

Install stannum with pip by

python -m pip install stannum

Make sure you have the following installed:

• PyTorch
• Taichi

TODOs

Documentation

• Documentation for users

Features

• PyTorch-related:
  • PyTorch checkpoint and save model
  • Proxy torch.nn.parameter.Parameter for weight fields for optimizers
• Taichi related:
  • Wait for Taichi to have native PyTorch tensor view to optimize performance(i.e., no need to copy data back and forth)
  • Automatic Batching - waiting for upstream Taichi improvement
    • workaround for now: do static manual batching, that is to extend fields with one more dimension for batching

Misc

• A nice logo