Peak GPU-memory usage extremely huge when sorting with torch.sort
Labels: module: cuda, module: memory usage, triaged, module: sorting and selection

### 🐛 Describe the bug

As mentioned in the title, when sorting huge tensors on a GPU-device the peak GPU-memory usage is extremely huge.
For example:
```python
tensor = torch.rand(400_000_000, device='cuda:0')
print(torch.cuda.memory_summary())
torch.sort(tensor)
print(torch.cuda.memory_summary())
```
The first summary (i.e. after allocating this huge tensor):
```
|===========================================================================|
|                  PyTorch CUDA memory summary, device ID 0                 |
|---------------------------------------------------------------------------|
|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |
|===========================================================================|
|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |
|---------------------------------------------------------------------------|
| Allocated memory      |    1526 MB |    1526 MB |    1526 MB |       0 B  |
|       from large pool |    1526 MB |    1526 MB |    1526 MB |       0 B  |
|       from small pool |       0 MB |       0 MB |       0 MB |       0 B  |
|---------------------------------------------------------------------------|
| Active memory         |    1526 MB |    1526 MB |    1526 MB |       0 B  |
|       from large pool |    1526 MB |    1526 MB |    1526 MB |       0 B  |
|       from small pool |       0 MB |       0 MB |       0 MB |       0 B  |
|---------------------------------------------------------------------------|
| GPU reserved memory   |    1526 MB |    1526 MB |    1526 MB |       0 B  |
|       from large pool |    1526 MB |    1526 MB |    1526 MB |       0 B  |
|       from small pool |       0 MB |       0 MB |       0 MB |       0 B  |
|---------------------------------------------------------------------------|
| Non-releasable memory |       0 B  |       0 B  |       0 B  |       0 B  |
|       from large pool |       0 B  |       0 B  |       0 B  |       0 B  |
|       from small pool |       0 B  |       0 B  |       0 B  |       0 B  |
|---------------------------------------------------------------------------|
| Allocations           |       1    |       1    |       1    |       0    |
|       from large pool |       1    |       1    |       1    |       0    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Active allocs         |       1    |       1    |       1    |       0    |
|       from large pool |       1    |       1    |       1    |       0    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| GPU reserved segments |       1    |       1    |       1    |       0    |
|       from large pool |       1    |       1    |       1    |       0    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Non-releasable allocs |       0    |       0    |       0    |       0    |
|       from large pool |       0    |       0    |       0    |       0    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Oversize allocations  |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Oversize GPU segments |       0    |       0    |       0    |       0    |
|===========================================================================|
```
and then the second summary after sorting:
```
|===========================================================================|
|                  PyTorch CUDA memory summary, device ID 0                 |
|---------------------------------------------------------------------------|
|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |
|===========================================================================|
|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |
|---------------------------------------------------------------------------|
| Allocated memory      |    1526 MB |   18380 MB |   18448 MB |   16922 MB |
|       from large pool |    1526 MB |   18380 MB |   18448 MB |   16922 MB |
|       from small pool |       0 MB |       0 MB |       0 MB |       0 MB |
|---------------------------------------------------------------------------|
| Active memory         |    1526 MB |   18380 MB |   18448 MB |   16922 MB |
|       from large pool |    1526 MB |   18380 MB |   18448 MB |   16922 MB |
|       from small pool |       0 MB |       0 MB |       0 MB |       0 MB |
|---------------------------------------------------------------------------|
| GPU reserved memory   |   18380 MB |   18380 MB |   18380 MB |       0 B  |
|       from large pool |   18380 MB |   18380 MB |   18380 MB |       0 B  |
|       from small pool |       0 MB |       0 MB |       0 MB |       0 B  |
|---------------------------------------------------------------------------|
| Non-releasable memory |       0 B  |    1457 MB |    1457 MB |    1457 MB |
|       from large pool |       0 B  |    1457 MB |    1457 MB |    1457 MB |
|       from small pool |       0 B  |       0 MB |       0 MB |       0 MB |
|---------------------------------------------------------------------------|
| Allocations           |       1    |       7    |       8    |       7    |
|       from large pool |       1    |       7    |       8    |       7    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Active allocs         |       1    |       7    |       8    |       7    |
|       from large pool |       1    |       7    |       8    |       7    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| GPU reserved segments |       7    |       7    |       7    |       0    |
|       from large pool |       7    |       7    |       7    |       0    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Non-releasable allocs |       0    |       1    |       1    |       1    |
|       from large pool |       0    |       1    |       1    |       1    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Oversize allocations  |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Oversize GPU segments |       0    |       0    |       0    |       0    |
|===========================================================================|
```
Notice the ~18GB peak memory usage in the second summary for sorting a tensor that occupies ~1.5GB.

### Versions

Collecting environment information...
PyTorch version: 1.11.0
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 10 (buster) (x86_64)
GCC version: (Debian 8.3.0-6) 8.3.0
Clang version: Could not collect
CMake version: version 3.13.4
Libc version: glibc-2.28

Python version: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-4.19.0-19-amd64-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: GeForce RTX 3090

Nvidia driver version: 460.73.01
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[conda] blas 1.0 mkl
[conda] cudatoolkit 11.3.1 h2bc3f7f_2
[conda] ffmpeg 4.3 hf484d3e_0 pytorch
[conda] mkl 2021.4.0 h06a4308_640
[conda] mkl-service 2.4.0 py38h7f8727e_0
[conda] mkl_fft 1.3.1 py38hd3c417c_0
[conda] mkl_random 1.2.2 py38h51133e4_0
[conda] mypy-extensions 0.4.3 pypi_0 pypi
[conda] numpy 1.22.3 pypi_0 pypi
[conda] numpy-base 1.21.2 py38h79a1101_0
[conda] pytorch 1.11.0 py3.8_cuda11.3_cudnn8.2.0_0 pytorch
[conda] pytorch-mutex 1.0 cuda pytorch
[conda] torchvision 0.12.0 py38_cu113 pytorch

cc @ngimel