`sparse.mm` triggers INTERNAL ASSERT FAILED when backwarding
Labels: module: sparse, module: autograd, triaged

### 🐛 Describe the bug

`sparse.mm` triggers INTERNAL ASSERT FAILED when backwarding

```py
import torch

a = torch.eye(3, 4).to_sparse_coo()
b = torch.eye(4, 5).to_sparse_coo()

def func(a, b):
    result = torch.sparse.mm(a, b)
    return result


ans = func(a, b)
# tensor(indices=tensor([[0, 1, 2],
#                        [0, 1, 2]]),
#        values=tensor([1., 1., 1.]),
#        size=(3, 5), nnz=3, layout=torch.sparse_coo)

ans_grad = func(a.clone().requires_grad_(), b.clone().requires_grad_())
# tensor(indices=tensor([[0, 1, 2],
#                        [0, 1, 2]]),
#        values=tensor([1., 1., 1.]),
#        size=(3, 5), nnz=3, layout=torch.sparse_coo,
#        grad_fn=<SparseSparseMatmulBackward0>)

ans_grad.sum().backward()
# RuntimeError: mat2_.is_sparse() INTERNAL ASSERT FAILED at 
# "../aten/src/ATen/native/sparse/SparseMatMul.cpp":255, 
# please report a bug to PyTorch
```

### Versions

```
PyTorch version: 2.0.0.dev20230105
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.9.15 (main, Nov 24 2022, 14:31:59)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-56-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.7.99
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.86.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] torch==2.0.0.dev20230105
[pip3] torchaudio==2.0.0.dev20230105
[pip3] torchvision==0.15.0.dev20230105
[conda] blas                      1.0                         mkl
[conda] mkl                       2021.4.0           h06a4308_640
[conda] mkl-service               2.4.0            py39h7f8727e_0
[conda] mkl_fft                   1.3.1            py39hd3c417c_0
[conda] mkl_random                1.2.2            py39h51133e4_0
[conda] numpy                     1.23.5           py39h14f4228_0
[conda] numpy-base                1.23.5           py39h31eccc5_0
[conda] pytorch                   2.0.0.dev20230105 py3.9_cuda11.7_cudnn8.5.0_0    pytorch-nightly
[conda] pytorch-cuda              11.7                 h67b0de4_2    pytorch-nightly
[conda] pytorch-mutex             1.0                        cuda    pytorch-nightly
[conda] torchaudio                2.0.0.dev20230105      py39_cu117    pytorch-nightly
[conda] torchtriton               2.0.0+0d7e753227            py39    pytorch-nightly
[conda] torchvision               0.15.0.dev20230105      py39_cu117    pytorch-nightly
```

cc @alexsamardzic @nikitaved @pearu @cpuhrsch @amjames @bhosmer @ezyang @albanD @zou3519 @gqchen @soulitzer @Lezcano @Varal7