Repository: pytorch
Issue: [Bug] Memory leak in C++ libtorch
Labels: module: cpp,triaged

### 🐛 Describe the bug

It seems there is a bug leading to a memory leak in the (C++) libtorch. 
```
#include <torch/extension.h>
#include <thread>


class FrameVector {

private:
    std::vector<int> data;

public:
    FrameVector() {
        std::vector<int> data(7056);
        this->data = data;
    }
};

class FrameTensor {

private:
    torch::Tensor data;

public:
    FrameTensor() {
        this->data = torch::zeros({1, 84, 84});
    }
};

template<class T>
void f() {

    int capacity = 1000000;
    std::vector<std::vector<T>> frames(capacity);

    for (auto i = 0; i < capacity + 1000000; i++) {
        if (i == capacity) {
            std::cout << "buffer is full!" << std::endl;
            std::this_thread::sleep_for(std::chrono::seconds(2));
            std::cout << "restart!" << std::endl;
        }
        frames[i % capacity].push_back(T());
        if (i >= capacity) {
            frames[i % capacity].erase(frames[i % capacity].begin());
        }
    }

}

int main(int argc, char *argv[])
{
    f<FrameTensor>();  // needs 34G to fill the replay buffer, then memory increases to around 60G
    f<FrameVector>();  // needs 34G to fill the replay buffer, then memory stay constant (as it should)
}
```

The bug only seems to occur when the `torch::Tensor` is stored in nested containers for examples:
- `std::vector<std::vector<T>>`
- `std::vector<std::deque<T>>`

I believe the internal counter that keep track of the number of references to the `torch::Tensor` fail to count the correct number of references. This leads the tensors memory to never be released.

### Versions

Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.1 LTS (x86_64)
GCC version: (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version: Could not collect
CMake version: version 3.28.3
Libc version: glibc-2.39

Python version: 3.12.3 (main, Nov  6 2024, 18:32:19) [GCC 13.2.0] (64-bit runtime)
Python platform: Linux-6.8.0-51-generic-x86_64-with-glibc2.39
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090 Ti
Nvidia driver version: 560.35.05
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, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               36
On-line CPU(s) list:                  0-35
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz
CPU family:                           6
Model:                                85
Thread(s) per core:                   2
Core(s) per socket:                   18
Socket(s):                            1
Stepping:                             7
CPU(s) scaling MHz:                   27%
CPU max MHz:                          4800.0000
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 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 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow 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 hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512_vnni md_clear flush_l1d arch_capabilities
Virtualisation:                       VT-x
L1d cache:                            576 KiB (18 instances)
L1i cache:                            576 KiB (18 instances)
L2 cache:                             18 MiB (18 instances)
L3 cache:                             24.8 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-35
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 Reg file data sampling: Not affected
Vulnerability Retbleed:               Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow:   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 / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] numpy==2.2.0
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] torch==2.5.1
[pip3] torchrl==0.6.0
[pip3] torchvision==0.20.1
[pip3] triton==3.1.0
[conda] numpy                     1.26.4          py311h24aa872_0  
[conda] numpy-base                1.26.4          py311hbfb1bba_0  
[conda] nvidia-cublas-cu12        12.1.3.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.1.105                 pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.1.105                 pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.1.105                 pypi_0    pypi
[conda] nvidia-cudnn-cu12         8.9.2.26                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.0.2.54                pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.2.106               pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.4.5.107               pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.1.0.106               pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.4.127                 pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.1.105                 pypi_0    pypi
[conda] torch                     2.3.0                    pypi_0    pypi
[conda] torchaudio                2.3.0                    pypi_0    pypi
[conda] torchvision               0.18.0                   pypi_0    pypi
[conda] triton                    2.3.0                    pypi_0    pypi

cc @jbschlosser