From 5f504d1de74a5189f93e65aa9283fc4607b8631c Mon Sep 17 00:00:00 2001
From: Pedro Caldeira <pdrocaldeira@gmail.com>
Date: Wed, 22 Nov 2023 22:57:32 +0000
Subject: [PATCH] Check for boolean values as argument on pow function. 
 (#114133)
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Hello everyone! 😄
Also @lezcano , nice to meet you! :)

Sorry if I miss anything, this is my first time around here. 🙃

This PR basically makes the same behaviour for cuda when using `torch.pow`. Basically Python considers True as 1 and False as 0. I just added this check into `pow` function. From what I understood, when I do `.equal` for `Scalar` that is boolean, I'm sure that types match so that won't cause more trouble.

I know that the issue suggest to disable this case but that could be a little more complicated, in my humble opinion. And that can create some compability problems too, I guess.

My argument is that code below is correct for native language, so I guess it does makes sense sending booleans as Scalar.

```
$ x = True
$ x + x
2
```

This was my first test:
```
Python 3.12.0 | packaged by Anaconda, Inc. | (main, Oct  2 2023, 17:29:18) [GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.pow(torch.tensor([1, 2], device='cuda'), True)
tensor([1, 2], device='cuda:0')
>>> torch.pow(torch.tensor([1, 2]), True)
tensor([1, 2])
>>> torch.pow(torch.tensor([1, 2]), False)
tensor([1, 1])
>>> torch.pow(torch.tensor([1, 2], device='cuda'), False)
tensor([1, 1], device='cuda:0')
```

I've run `test_torch.py` and got following results, so my guess is that I didn't break anything. I was just looking for a test that uses linear regression, as suggested.

```
Ran 1619 tests in 52.363s

OK (skipped=111)
[TORCH_VITAL] Dataloader.enabled		 True
[TORCH_VITAL] Dataloader.basic_unit_test		 TEST_VALUE_STRING
[TORCH_VITAL] CUDA.used		 true

```
(I can paste whole log, if necessary)

If this is a bad idea overall, dont worry about it. It's not a big deal, it's actually a two line change 😅  so can we talk of how do things in a different strategy.

For the record I've signed the agreement already. And I didn't run linter because it's not working 😞 . Looks like PyYaml 6.0 is broken and there's a 6.0.1 fix already but I have no idea how to update that 😅

Fixes #113198

Pull Request resolved: https://github.com/pytorch/pytorch/pull/114133
Approved by: https://github.com/lezcano
---
 aten/src/ATen/native/Pow.cpp | 4 ++--
 test/test_binary_ufuncs.py   | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/aten/src/ATen/native/Pow.cpp b/aten/src/ATen/native/Pow.cpp
index 0fa0fceab6ca37..5c8147d7ced38f 100644
--- a/aten/src/ATen/native/Pow.cpp
+++ b/aten/src/ATen/native/Pow.cpp
@@ -50,9 +50,9 @@ TORCH_IMPL_FUNC(pow_Tensor_Tensor_out) (const Tensor& base, const Tensor& exp, c
 }
 
 TORCH_IMPL_FUNC(pow_Tensor_Scalar_out) (const Tensor& base, const Scalar& exp, const Tensor& out) {
-  if (exp.equal(0.0)) {
+  if (exp.equal(0.0) || exp.equal(false)) {
     out.fill_(1);
-  } else if (exp.equal(1.0)) {
+  } else if (exp.equal(1.0) || exp.equal(true) ) {
     out.copy_(base);
   } else {
     pow_tensor_scalar_stub(device_type(), *this, exp);
diff --git a/test/test_binary_ufuncs.py b/test/test_binary_ufuncs.py
index 2f22569f9cf1a8..9fcc8b445eb1cd 100644
--- a/test/test_binary_ufuncs.py
+++ b/test/test_binary_ufuncs.py
@@ -1345,7 +1345,7 @@ def test_pow(self, device, dtype):
                 (100, 100), low=1, high=range_high, dtype=dtype, device=device
             )
 
-        exponents = [-2.8, -2, -1, -0.5, 0, 0.5, 1, 2, 3, 4, 3.3]
+        exponents = [-2.8, -2, -1, -0.5, 0, 0.5, 1, 2, 3, 4, 3.3, True, False]
         complex_exponents = [
             -2.5j,
             -1.0j,