The torch.sub() method performs element-wise subtraction on two tensors or subtracts a scalar from a tensor, with optional scaling. It supports Pytorch’s broadcasting to a standard shape, type promotion, and integer, float, and complex inputs.
The above figure shows how the torch.sub() method works internally.
Here is the formula for subtraction:
output = input - (alpha * other)
We will discuss each variable in the upcoming parameter section.
Syntax
torch.sub(input, other, alpha=1, out=None)
Parameters
| Argument | Description |
| input (Tensor) | It is the first input tensor. |
| other (Tensor or Number) | It is either a Tensor or a scalar value to subtract from the input tensor. |
| alpha (int, optional) | It is a scalar multiplier to scale the other tensor. By default, its value is 1, but you can set it to any value you prefer. |
| out (Tensor, optional) | It is an output tensor to store the result of subtraction. By default, it is None. If you have a pre-allocated tensor, you can use this tensor. |
Element-wise subtraction
Let’s define two tensors with the same shape and dtype and perform element-wise subtraction without scaling anything.
import torch first_tensor = torch.tensor([11.0, 21.0, 18.0]) other_tensor = torch.tensor([9.0, 19.0, 10.0]) subtract_tensor = torch.sub(first_tensor, other_tensor) print(subtract_tensor) # Output: tensor([2., 2., 8.])
In the above code, we are subtracting the first element of the other_tensor from the first element of the first_tensor, which is 11.0 – 9.0 = 2.0. Therefore, the first element of the subtract_tensor is 2.0.
The same for the second elements for both tensor and their subtraction is 2, and the third element is 8.
Scalar subtraction
If you subtract a single value or scalar from a tensor, it will broadcast the scalar to all elements.
import torch tensor = torch.tensor([12.0, 13.0, 14.0]) scalar = 4 scalar_subtract_tensor = torch.subtract(tensor, scalar) print(scalar_subtract_tensor) # Output: tensor([ 8., 9., 10.])
Alpha scaling
Using the alpha argument, we can scale the other tensor and then subtract it from the first tensor.
import torch first_tensor = torch.tensor([10.0, 20.0, 30.0]) other_tensor = torch.tensor([1.0, 3.0, 5.0]) alpha_scaling_tensor = torch.subtract(first_tensor, other_tensor, alpha=3) # (first_tensor - other_tensor x 3) print(alpha_scaling_tensor) # Output: tensor([ 7., 11., 15.])
In the above code, we calculated first_tensor – 3 * other_tensor, where other_tensor is scaled by alpha = 3 before the subtraction.
That means for the first element, 1.0 x 3 = 3.0 and 10.0 – 3.0 = 7.0. So, the first element of the output tensor is 7.
The same logic works for the second element of the output tensor. 3.0 x 3 = 9.0 and 20.0 – 9.0 = 11.
For the third element: 5.0 x 3 = 15.0, and 30.0 – 15.0 = 15.
Broadcasting
With compatible shapes, you can subtract tensors effortlessly.
import torch a = torch.tensor([[5.0, 15.0], [4.0, 14.0]]) b = torch.tensor([3.0, 4.0]) broadcasting_subtraction = torch.subtract(a, b) print(broadcasting_subtraction) # Output: # tensor([[ 2., 11.], # [ 1., 10.]])
In this code example, b is broadcast to match a’s shape by adding once more [3.0, 4.0] to the tensor b. And then we will subtract [[3.0, 4.0]. [3.0, 4.0]] from tensor a.
That’s it!
