stannum 0.4.0

PyTorch wrapper for Taichi data-oriented class

Stannum

Fusing Taichi into PyTorch

PRs are welcomed, please see TODOs and issues.

Usage

Tin and EmptyTin

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)

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. Example:

from stannum import EmptyTin
import torch
import taichi as ti

input_field = ti.field(ti.f32)
output_field = ti.field(ti.f32)
internal_field = ti.field(ti.f32)


@ti.kernel
def some_kernel():
    output_field[None] = input_field[None] + internal_field[None]


device = torch.device("cpu")
kernel_args = (1.0,)
tin_layer = EmptyTin(device)\
    .register_kernel(some_kernel, *kernel_args)\
    .register_input_field(input_field)\
    .register_output_field(output_field)\
    .register_internal_field(internal_field, name="field name")\
    .finish()  # finish() is required to finish construction
output = tin_layer(input_tensor)

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.
• Kernel args must be acceptable by Taichi kernels and they will not get gradients.

Tube

Tube is more flexible than Tin and slower in that it helps you create necessary fields and do automatic batching.

Registrations

All you need to do is to register:

• Input/intermediate/output tensor shapes instead of fields
• At least one kernel that takes the following as arguments
  • Taichi fields: correspond to tensors (may or may not require gradients)
  • (Optional) Extra arguments: will NOT receive gradients

Acceptable dimensions of tensors to be registered:

• None: means the flexible batch dimension, must be the first dimension e.g. (None, 2, 3, 4)
• Positive integers: fixed dimensions with the indicated dimensionality
• Negative integers:
  • -1: means any number [1, +inf), only usable in the registration of input tensors.
  • Negative integers < -1: indices of some dimensions that must be of the same dimensionality
    • Restriction: negative indices must be "declared" in the registration of input tensors first, then used in the registration of intermediate and output tensors.
    • Example 1: tensor a and b of shapes a: (2, -2, 3) and b: (-2, 5, 6) mean the dimensions of -2 must match.
    • Example 2: tensor a and b of shapes a: (-1, 2, 3) and b: (-1, 5, 6) mean no restrictions on the first dimensions.

Registration order: Input tensors/intermediate fields/output tensors must be registered first, and then kernel.

@ti.kernel
def ti_add(arr_a: ti.template(), arr_b: ti.template(), output_arr: ti.template()):
    for i in arr_a:
        output_arr[i] = arr_a[i] + arr_b[i]

ti.init(ti.cpu)
cpu = torch.device("cpu")
a = torch.ones(10)
b = torch.ones(10)
tube = Tube(cpu) \
    .register_input_tensor((10,), torch.float32, "arr_a", False) \
    .register_input_tensor((10,), torch.float32, "arr_b", False) \
    .register_output_tensor((10,), torch.float32, "output_arr", False) \
    .register_kernel(ti_add, ["arr_a", "arr_b", "output_arr"]) \
    .finish()
out = tube(a, b)

When registering a kernel, a list of field/tensor names is required, for example, the above ["arr_a", "arr_b", "output_arr"]. This list should correspond to the fields in the arguments of a kernel (e.g. above ti_add()).

The order of input tensors should match the input fields of a kernel.

Automatic batching

Automatic batching is done simply by running kernels batch times. The batch number is determined by the leading dimension of tensors of registered shape (None, ...).

It's required that if any input tensors or intermediate fields are batched (which means they have registered the first dimension to be None), all output tensors must be registered as batched.

Examples

Simple one without negative indices or batch dimension:

@ti.kernel
def ti_add(arr_a: ti.template(), arr_b: ti.template(), output_arr: ti.template()):
    for i in arr_a:
        output_arr[i] = arr_a[i] + arr_b[i]

ti.init(ti.cpu)
cpu = torch.device("cpu")
a = torch.ones(10)
b = torch.ones(10)
tube = Tube(cpu) \
    .register_input_tensor((10,), torch.float32, "arr_a", False) \
    .register_input_tensor((10,), torch.float32, "arr_b", False) \
    .register_output_tensor((10,), torch.float32, "output_arr", False) \
    .register_kernel(ti_add, ["arr_a", "arr_b", "output_arr"]) \
    .finish()
out = tube(a, b)

With negative dimension index:

ti.init(ti.cpu)
cpu = torch.device("cpu")
tube = Tube(cpu) \
    .register_input_tensor((-2,), torch.float32, "arr_a", False) \
    .register_input_tensor((-2,), torch.float32, "arr_b", False) \
    .register_output_tensor((-2,), torch.float32, "output_arr", False) \
    .register_kernel(ti_add, ["arr_a", "arr_b", "output_arr"]) \
    .finish()
dim = 10
a = torch.ones(dim)
b = torch.ones(dim)
out = tube(a, b)
assert torch.allclose(out, torch.full((dim,), 2.))
dim = 100
a = torch.ones(dim)
b = torch.ones(dim)
out = tube(a, b)
assert torch.allclose(out, torch.full((dim,), 2.))

With batch dimension:

@ti.kernel
def int_add(a: ti.template(), b: ti.template(), out: ti.template()):
    out[None] = a[None] + b[None]

ti.init(ti.cpu)
b = torch.tensor(1., requires_grad=True)
batched_a = torch.ones(10, requires_grad=True)
tube = Tube() \
    .register_input_tensor((None,), torch.float32, "a") \
    .register_input_tensor((), torch.float32, "b") \
    .register_output_tensor((None,), torch.float32, "out", True) \
    .register_kernel(int_add, ["a", "b", "out"]) \
    .finish()
out = tube(batched_a, b)
loss = out.sum()
loss.backward()
assert torch.allclose(torch.ones_like(batched_a) + 1, out)
assert b.grad == 10.
assert torch.allclose(torch.ones_like(batched_a), batched_a.grad)

For more invalid use examples, please see tests in tests/test_tube.

Advanced field construction with FieldManager

There is a way to tweak how fields are constructed in order to gain performance improvement in kernel calculations.

By supplying a customized FieldManager when registering a field, you can construct a field however you want.

Please refer to the code FieldManger in src/stannum/auxiliary.py for more information.

If you don't know why constructing fields differently can improve performance, don't use this feature.

If you don't know how to construct fields differently, please refer to Taichi field documentation.

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] 
  

Installation & Dependencies

Install stannum with pip by

python -m pip install stannum

Make sure you have the following installed:

• PyTorch
• latest 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 for Tin - 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