# Torch.sqrt() and other math functions requiring tensor inputs

**URL:** <https://discuss.pytorch.org/t/torch-sqrt-and-other-math-functions-requiring-tensor-inputs/74623>\
**Category:** Uncategorized\
**Created:** [March 28, 2020, 5:02pm UTC](https://discuss.pytorch.org/t/torch-sqrt-and-other-math-functions-requiring-tensor-inputs/74623 "2020-03-28T17:02:22Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![marcman411](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/m/7cd45c/32.png) [@marcman411](https://discuss.pytorch.org/u/marcman411)\
**Post date:** [March 28, 2020, 5:02pm UTC](https://discuss.pytorch.org/t/torch-sqrt-and-other-math-functions-requiring-tensor-inputs/74623/1 "2020-03-28T17:02:22Z")

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I run into additional type checking frequently when I try to write a function that uses these math functions. For example: let’s say I want to have one utility function that computes Euclidean distance. Across my whole project, I might use it on tensors or Python scalars.

For Python scalars, I have to use something like:

```auto
import math
def euclidean_distance(a, b):
    """a and b are either Python scalars or tensors with size 1"""
    return math.sqrt(a **2 + b** 2)

```

This will work for all Python scalars and all torch tensors of size 1. But for PyTorch tensors, generally, I have to use:

```auto
import torch
def euclidean_distance(a, b):
    """a and b are arbitrary tensors"""
    return torch.sqrt(a **2 + b** 2)

```

Is there some way to allow for:

```auto
import torch
def euclidean_distance(a, b):
    """a and b are arbitrary tensors or Python scalars"""
    return torch.sqrt(a **2 + b** 2)

```

without uses having to check like:

```auto
import torch
def euclidean_distance(a, b):
    """a and b are arbitrary tensors or Python scalars"""
    # Handle tensor vs not tensor inputs
    a_not_tensor = False
    if not torch.is_tensor(a):
        a_not_tensor = True
        a = torch.tensor([a])
    b_not_tensor = False
    if not torch.is_tensor(b):
        b_not_tensor = True
        b = torch.tensor([b])

    # Compute the distance
    dist = torch.sqrt(a **2 + b** 2)

    # If both inputs are Python scalars, return a Python scalar
    if a_not_tensor and b_not_tensor:
        return dist.item()

    # If either input was a tensor, return a tensor
    return dist

```

Is there a reason this functionality doesn’t exist? It’s not limited to `torch.sqrt()`, but all PyTorch math functions. I imagine it’s easier to have `torch.sqrt()` accept Python scalars then to automatically vectorize `math.sqrt()` (a la Python build-ins like `abs()`), but I suppose either would solve the problem.

[An aside: this is kind of related to the lack of a `torch.pi` constant ([1](https://discuss.pytorch.org/t/np-pi-equivalent-in-pytorch/67157), [2](https://discuss.pytorch.org/t/is-there-the-pi-greek-3-1415-defined-somewhere/30283), [3](https://github.com/pytorch/pytorch/issues/6510)). Requiring `import math` for certain things is fine to remove redundant constants, etc, but it would also be useful to provide the cross-functionality when needed.]

---

<div class="post-metadata">

**Author:** ![mishanya](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/mishanya/32/30384_2.png) [@mishanya](https://discuss.pytorch.org/u/mishanya)\
**Post date:** [January 4, 2021, 9:42am UTC](https://discuss.pytorch.org/t/torch-sqrt-and-other-math-functions-requiring-tensor-inputs/74623/2 "2021-01-04T09:42:20Z")

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I’m severely missing every single point you mention. Are there any issues on github related to them? Didn’t find any.