Repository: pytorch
Issue: ```torch.Tensor.matmul```: Internal assert failed
Labels: module: sparse,triaged

### 🐛 Describe the bug

Test code:
```python
import torch

self = torch.randn([1,1], dtype=torch.float32)

temp_other = torch.randn([1,2], dtype=torch.float16)
other = temp_other.to_sparse(layout=torch.sparse_csc)

self.matmul(other)
```
Error log:
```
/home/yonghyeon/pytorch_err_case_py/matmul.py:6: UserWarning: Sparse CSC tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /home/yonghyeon/pytorch/pytorch-asan/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
  other = temp_other.to_sparse(layout=torch.sparse_csc)
Traceback (most recent call last):
  File "/home/yonghyeon/pytorch_err_case_py/matmul.py", line 8, in <module>
    self.matmul(other)
RuntimeError: !(is_C_row_major ^ is_B_row_major) INTERNAL ASSERT FAILED at "/home/yonghyeon/pytorch/pytorch-asan/aten/src/ATen/native/mkl/SparseBlasImpl.cpp":193, please report a bug to PyTorch.
```

Test code (c++):
```cpp
#include <torch/torch.h>

int main() {
  torch::TensorOptions toptions = torch::TensorOptions();
  
  toptions = toptions.dtype(torch::kFloat32);
  torch::Tensor self = torch::randn({1,1}, toptions);

  toptions = toptions.dtype(torch::kBFloat16);
  torch::Tensor temp_other = torch::randn({1,2}, toptions);
  torch::Tensor other = temp_other.to_sparse(c10::Layout::SparseCsc);

  self.matmul(other);

  return 0;
}
```

Error log:
```
[W SparseCsrTensorImpl.cpp:55] Warning: Sparse CSC tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (function operator())
terminate called after throwing an instance of 'c10::Error'
  what():  !(is_C_row_major ^ is_B_row_major) INTERNAL ASSERT FAILED at "/home/yonghyeon/pytorch/pytorch-asan/aten/src/ATen/native/mkl/SparseBlasImpl.cpp":193, please report a bug to PyTorch. 
Exception raised from addmm_dense_result at /home/yonghyeon/pytorch/pytorch-asan/aten/src/ATen/native/mkl/SparseBlasImpl.cpp:193 (most recent call first):
frame #0: <unknown function> + 0x1e7a6e (0x7f3dc0d10a6e in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libc10.so)
frame #1: <unknown function> + 0x1e7a20 (0x7f3dc0d10a20 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libc10.so)
frame #2: <unknown function> + 0x1e79b0 (0x7f3dc0d109b0 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libc10.so)
frame #3: <unknown function> + 0x1e7860 (0x7f3dc0d10860 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libc10.so)
frame #4: <unknown function> + 0x1e9286 (0x7f3dc0d12286 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libc10.so)
frame #5: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x1b7 (0x7f3dc0d0e4d7 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libc10.so)
frame #6: c10::detail::torchCheckFail(char const*, char const*, unsigned int, char const*) + 0x259 (0x7f3dc0d07ca9 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libc10.so)
frame #7: ./reproduce_matmul() [0x40ce9e]
frame #8: <unknown function> + 0x5ee4248 (0x7f3dc6df3248 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #9: <unknown function> + 0x5ee297a (0x7f3dc6df197a in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #10: at::native::addmm_out_sparse_compressed_cpu(at::Tensor const&, at::Tensor const&, at::Tensor const&, c10::Scalar const&, c10::Scalar const&, at::Tensor&) + 0x1745 (0x7f3dc6486da5 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #11: <unknown function> + 0xd0607ad (0x7f3dcdf6f7ad in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #12: <unknown function> + 0xd06115b (0x7f3dcdf7015b in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #13: <unknown function> + 0x55048e9 (0x7f3dc64138e9 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #14: at::_ops::addmm_out::call(at::Tensor const&, at::Tensor const&, at::Tensor const&, c10::Scalar const&, c10::Scalar const&, at::Tensor&) + 0xdfd (0x7f3dc710fcdd in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #15: <unknown function> + 0x448dc1d (0x7f3dc539cc1d in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #16: at::native::addmm_sparse_compressed_dense(at::Tensor const&, at::Tensor const&, at::Tensor const&, c10::Scalar const&, c10::Scalar const&) + 0x52c (0x7f3dc648764c in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #17: <unknown function> + 0xd05f333 (0x7f3dcdf6e333 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #18: <unknown function> + 0xd05fcc1 (0x7f3dcdf6ecc1 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #19: <unknown function> + 0x6778bfd (0x7f3dc7687bfd in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #20: at::_ops::addmm::call(at::Tensor const&, at::Tensor const&, at::Tensor const&, c10::Scalar const&, c10::Scalar const&) + 0xd8c (0x7f3dc711110c in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #21: <unknown function> + 0x3833543 (0x7f3dc4742543 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #22: at::native::_sparse_csr_mm(at::Tensor const&, at::Tensor const&) + 0x11bf (0x7f3dc647301f in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #23: <unknown function> + 0xd0214fb (0x7f3dcdf304fb in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #24: <unknown function> + 0xd021d82 (0x7f3dcdf30d82 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #25: <unknown function> + 0x6664583 (0x7f3dc7573583 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #26: <unknown function> + 0x6664135 (0x7f3dc7573135 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #27: at::_ops::mm::redispatch(c10::DispatchKeySet, at::Tensor const&, at::Tensor const&) + 0x462 (0x7f3dc973c3c2 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #28: <unknown function> + 0x12bb0047 (0x7f3dd3abf047 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #29: <unknown function> + 0x1292ca7f (0x7f3dd383ba7f in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #30: <unknown function> + 0x1292ad95 (0x7f3dd3839d95 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #31: <unknown function> + 0x1292d2f3 (0x7f3dd383c2f3 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #32: <unknown function> + 0x6664583 (0x7f3dc7573583 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #33: at::_ops::mm::call(at::Tensor const&, at::Tensor const&) + 0xb7c (0x7f3dc940c66c in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #34: <unknown function> + 0x457d03b (0x7f3dc548c03b in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #35: <unknown function> + 0x44fd58c (0x7f3dc540c58c in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #36: at::native::matmul(at::Tensor const&, at::Tensor const&) + 0x1b4 (0x7f3dc540b154 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #37: <unknown function> + 0xb1d2b9b (0x7f3dcc0e1b9b in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #38: <unknown function> + 0xb1d3422 (0x7f3dcc0e2422 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #39: <unknown function> + 0x6664583 (0x7f3dc7573583 in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #40: at::_ops::matmul::call(at::Tensor const&, at::Tensor const&) + 0xb7c (0x7f3dc9c7a70c in /home/yonghyeon/pytorch/pytorch-asan/build/lib/libtorch_cpu.so)
frame #41: ./reproduce_matmul() [0x40776b]
frame #42: main + 0xce2 (0x4051f2 in ./reproduce_matmul)
frame #43: <unknown function> + 0x29d90 (0x7f3db7165d90 in /lib/x86_64-linux-gnu/libc.so.6)
frame #44: __libc_start_main + 0x80 (0x7f3db7165e40 in /lib/x86_64-linux-gnu/libc.so.6)
frame #45: _start + 0x25 (0x404445 in ./reproduce_matmul)

Aborted (core dumped)
```

### Versions

PyTorch version: 2.3.0a0+git03969cb
Is debug build: True
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 12.0.1
CMake version: version 3.26.4
Libc version: glibc-2.35

Python version: 3.12.0 | packaged by Anaconda, Inc. | (main, Oct  2 2023, 17:29:18) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-89-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             96
On-line CPU(s) list:                0-95
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Gold 6248R CPU @ 3.00GHz
CPU family:                         6
Model:                              85
Thread(s) per core:                 2
Core(s) per socket:                 24
Socket(s):                          2
Stepping:                           7
CPU max MHz:                        4000.0000
CPU min MHz:                        1200.0000
BogoMIPS:                           6000.00
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          1.5 MiB (48 instances)
L1i cache:                          1.5 MiB (48 instances)
L2 cache:                           48 MiB (48 instances)
L3 cache:                           71.5 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-23,48-71
NUMA node1 CPU(s):                  24-47,72-95
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] numpy==1.26.2
[pip3] optree==0.10.0
[pip3] torch==2.3.0a0+git03969cb
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-include               2023.1.0         h06a4308_46344  
[conda] numpy                     1.26.2                   pypi_0    pypi
[conda] optree                    0.10.0                   pypi_0    pypi
[conda] torch                     2.3.0a0+git03969cb           dev_0    <develop>

cc @alexsamardzic @nikitaved @pearu @cpuhrsch @amjames @bhosmer @jcaip