RuntimeError: CUDAPluggableAllocator does not yet support cacheInfo
Labels: feature, triaged, module: CUDACachingAllocator

### 🐛 Describe the bug

Here, I ref a third party CUDA Memory Allocator into my code and use the cudnn to be the backend.
cudnn seems required to call `cacheInfo`, but the PyTorch didn't support this interface to be called by third party lib.

cudnn's action happens at here: https://github.com/pytorch/pytorch/blob/9af82fa2b86fb71df503082b1960c9392f9dc66d/aten/src/ATen/native/cudnn/Conv_v7.cpp#L212

```python
import torch
import rmm
from rmm.allocators.torch import rmm_torch_allocator

torch.backends.cudnn.benchmark = True
rmm.reinitialize(pool_allocator=True)
torch.cuda.memory.change_current_allocator(rmm_torch_allocator)

m = torch.nn.Conv1d(16, 33, 3, stride=2).cuda()
input = torch.randn(20, 16, 50).cuda()
output = m(input)
```

```text
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
/__project__/benchmark.ipynb Cell 1 line 1
      9 m = torch.nn.Conv1d(16, 33, 3, stride=2).cuda()
     10 input = torch.randn(20, 16, 50).cuda()
---> 11 output = m(input)

File /__env__/site-packages/torch/nn/modules/module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
   1516     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517 else:
-> 1518     return self._call_impl(*args, **kwargs)

File /__env__/site-packages/torch/nn/modules/module.py:1527, in Module._call_impl(self, *args, **kwargs)
   1522 # If we don't have any hooks, we want to skip the rest of the logic in
   1523 # this function, and just call forward.
   1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1525         or _global_backward_pre_hooks or _global_backward_hooks
   1526         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527     return forward_call(*args, **kwargs)
   1529 try:
   1530     result = None

File /__env__/site-packages/torch/nn/modules/conv.py:310, in Conv1d.forward(self, input)
    309 def forward(self, input: Tensor) -> Tensor:
--> 310     return self._conv_forward(input, self.weight, self.bias)

File /__env__/site-packages/torch/nn/modules/conv.py:306, in Conv1d._conv_forward(self, input, weight, bias)
    302 if self.padding_mode != 'zeros':
    303     return F.conv1d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
    304                     weight, bias, self.stride,
    305                     _single(0), self.dilation, self.groups)
--> 306 return F.conv1d(input, weight, bias, self.stride,
    307                 self.padding, self.dilation, self.groups)

RuntimeError: CUDAPluggableAllocator does not yet support cacheInfo. If you need it, please file an issue describing your use case.
```

**For detailed error trace, could check this issue: https://github.com/rapidsai/rmm/issues/1362#issuecomment-1763996144**

### Versions

Collecting environment information...
PyTorch version: 2.1.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 9.5.0-1ubuntu1~22.04) 9.5.0
Clang version: Could not collect
CMake version: version 3.26.4
Libc version: glibc-2.35

Python version: 3.9.16 (main, May 15 2023, 23:46:34)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.19.0-40-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.128
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A100 80GB PCIe
GPU 1: NVIDIA A100 80GB PCIe

Nvidia driver version: 525.105.17
cuDNN version: Could not collect
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, 57 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 6336Y CPU @ 2.40GHz
CPU family:                      6
Model:                           106
Thread(s) per core:              2
Core(s) per socket:              24
Socket(s):                       2
Stepping:                        6
CPU max MHz:                     3600.0000
CPU min MHz:                     800.0000
BogoMIPS:                        4800.00
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 invpcid_single 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 rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualisation:                  VT-x
L1d cache:                       2.3 MiB (48 instances)
L1i cache:                       1.5 MiB (48 instances)
L2 cache:                        60 MiB (48 instances)
L3 cache:                        72 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-23,48-71
NUMA node1 CPU(s):               24-47,72-95
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:          Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
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:   Not affected

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.24.4
[pip3] pytorch-forecasting==1.0.0
[pip3] pytorch-lightning==2.0.9.post0
[pip3] pytorch_optimizer==2.12.0
[pip3] pytorch-sphinx-theme==0.0.19
[pip3] torch==2.1.0
[pip3] torch-tb-profiler==0.4.1
[pip3] torchaudio==2.1.0
[pip3] torchinfo==1.8.0
[pip3] torchmetrics==1.2.0
[pip3] torchvision==0.16.0
[pip3] triton==2.1.0
[conda] numpy                     1.24.4                   pypi_0    pypi
[conda] pytorch-forecasting       1.0.0                    pypi_0    pypi
[conda] pytorch-lightning         2.0.9.post0              pypi_0    pypi
[conda] pytorch-optimizer         2.12.0                   pypi_0    pypi
[conda] pytorch-sphinx-theme      0.0.19                   pypi_0    pypi
[conda] torch                     2.1.0                    pypi_0    pypi
[conda] torch-tb-profiler         0.4.1                    pypi_0    pypi
[conda] torchaudio                2.1.0                    pypi_0    pypi
[conda] torchinfo                 1.8.0                    pypi_0    pypi
[conda] torchmetrics              1.2.0                    pypi_0    pypi
[conda] torchvision               0.16.0                   pypi_0    pypi
[conda] triton                    2.1.0                    pypi_0    pypi