Repository: pytorch
Issue: “Can't swap an already initialized allocator” when calling torch.cuda.memory.change_current_allocator
Labels: module: cuda,triaged,module: CUDACachingAllocator

### 🐛 Describe the bug

I write an example based on the one from doc: https://pytorch.org/docs/stable/notes/cuda.html#cuda-memory-management

The reason I found out is when lazy init is already done, for example "torch.cuda.get_device_capability()", change_current_allocator will raise such error.

```
import torch
from torch.utils.cpp_extension import load_inline

dummy_allocator_source = """
#include <sys/types.h>
#include <cuda.h>
#include <cuda_runtime_api.h>
#include <stdio.h>
#include <iostream>

extern "C" {
void* my_malloc(ssize_t size, int device, cudaStream_t stream) {
   void *ptr;
   cudaMalloc(&ptr, size);
   std::cout<<"alloc "<<ptr<<size<<std::endl;
   return ptr;
}

void my_free(void* ptr, ssize_t size, int device, cudaStream_t stream) {
   std::cout<<"free "<<ptr<< " "<<stream<<std::endl;
   cudaFree(ptr);
}
}
"""

# Add with_cuda=True to explicitly enable CUDA support
dummy_allocator = load_inline(
    name="dummy_allocator",
    cpp_sources=[dummy_allocator_source],
    extra_ldflags=["-lcudart"],  # Link with the CUDA runtime library
    extra_cflags=['-O3'],
    is_python_module=False,
    keep_intermediates=False,
    verbose=True,
    with_cuda=True,  # Explicitly enable CUDA support
)

allocator = torch.cuda.memory.CUDAPluggableAllocator(
    dummy_allocator,
    "my_malloc",
    "my_free",
)

torch.cuda.memory.change_current_allocator(allocator)
```

```
Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...
No modifications detected for re-loaded extension module dummy_allocator_v4, skipping build step...
Loading extension module dummy_allocator_v4...
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
[<ipython-input-20-a80e91a588c3>](https://localhost:8080/#) in <cell line: 43>()
     41 )
     42 
---> 43 torch.cuda.memory.change_current_allocator(allocator)

[/usr/local/lib/python3.10/dist-packages/torch/cuda/memory.py](https://localhost:8080/#) in change_current_allocator(allocator)
    960         See :ref:`cuda-memory-management` for details on creating and using a custom allocator
    961     """
--> 962     torch._C._cuda_changeCurrentAllocator(allocator.allocator())
    963 
    964 

RuntimeError: Can't swap an already initialized allocator
```


### Versions

PyTorch version: 2.5.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.30.5
Libc version: glibc-2.35

Python version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.1.85+-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 535.104.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6
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):                               2
On-line CPU(s) list:                  0,1
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) CPU @ 2.00GHz
CPU family:                           6
Model:                                85
Thread(s) per core:                   2
Core(s) per socket:                   1
Socket(s):                            1
Stepping:                             3
BogoMIPS:                             4000.36
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            32 KiB (1 instance)
L1i cache:                            32 KiB (1 instance)
L2 cache:                             1 MiB (1 instance)
L3 cache:                             38.5 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0,1
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Mitigation; PTE Inversion
Vulnerability Mds:                    Vulnerable; SMT Host state unknown
Vulnerability Meltdown:               Vulnerable
Vulnerability Mmio stale data:        Vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Vulnerable
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Vulnerable
Vulnerability Spectre v1:             Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:             Vulnerable; IBPB: disabled; STIBP: disabled; PBRSB-eIBRS: Not affected; BHI: Vulnerable (Syscall hardening enabled)
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Vulnerable

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.6.3.3
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-nccl-cu12==2.23.4
[pip3] nvidia-nvjitlink-cu12==12.6.77
[pip3] nvtx==0.2.10
[pip3] optree==0.13.0
[pip3] pynvjitlink-cu12==0.4.0
[pip3] torch==2.5.0+cu121
[pip3] torchaudio==2.5.0+cu121
[pip3] torchsummary==1.5.1
[pip3] torchvision==0.20.0+cu121
[conda] Could not collect

cc @ptrblck @msaroufim