statictorch 0.0.5

Provides type annotations for torch tensors.

Basic usages (only specify the count of dimensions):

import torch
from statictorch import *

def f(x: Tensor2d):
    pass

f(torch.zeros([2, 3]))  # No runtime error, but static type checkers might say: "Tensor" is not assignable to "Tensor2d"
f(Tensor2d(torch.zeros([2, 3])))  # It's ok.

Note that TensorNd() directly return the given tensor. So at runtime you can't distinguish x and TensorNd(x), and isinstance(TensorNd(x), TensorNd) will return False.

TensorNd is defined as generic classes, so that you could add a dimension descriptor for them:

from statictorch import *

class Batch(TensorDimensionDescriptor):
    pass

class Channel(TensorDimensionDescriptor):
    pass

class Sample(TensorDimensionDescriptor):
    pass

def train_on(x: Tensor3d[Batch, Channel, Sample], y: Tensor3d[Channel, Batch, Sample]):
    ...

def load_data() -> tuple[Tensor3d[Batch, Channel, Sample], Tensor3d[Batch, Channel, Sample]]:
    ...

data_x, data_y = load_data()
train_on(data_x, data_y)  # Pylance: Argument of type "Tensor3d[Batch, Channel, Sample]" cannot be assigned to parameter "y" of type "Tensor3d[Channel, Batch, Sample]" in function "train_on"

# To solve the problem:
data_y_transposed = data_y.transpose(1, 0)
train_on(data_x, Tensor3d(data_y_transposed))

# Of course in some cases you want to force passing data_y, just simply cheating the type checker with:
train_on(data_x, Tensor3d(data_y))

typing.cast is also a good idea, especially when you want to call functions like torch.stack on a list[TensorNd]:

import statictorch
import typing
from torch import Tensor

def work_on(tensors: list[Tensor]):
    ...

my_tensors: list[statictorch.Tensor0d] = []
work_on(my_tensors)  # Pylance: Argument of type "list[Tensor0d]" cannot be assigned to parameter "tensors" of type "list[Tensor]" in function "work_on"

# To solve the problem:
work_on(typing.cast(list[Tensor], my_tensors))

# If you find typing.cast too long:
work_on(statictorch.anify(my_tensors))