Putting a tensor into a list or a dictionary row-by-row leads to high memory usage

We would like to ask a question about python and pytorch. We found that putting a tensor into a container like list or dict row by row will cause a much larger memory overhead (double to quadruple the size of the tensor itself). But we confirmed by comparing data_ptr that the container contains reference of storage instead of copy of storage. And we also confirmed that the size of container itself is not too big. So we are wondering where the memory is going. Is there any method to identity?

Here is the reproducible script.

import sys

import psutil
import torch

MB = 10 ** 6
N = 10 ** 6

def get_memory_usage():
    return psutil.virtual_memory().used / MB

def main():
    m1 = get_memory_usage()
    print("Memory usage before: {:.1f} MB".format(m1))
    x = torch.randn(N, 128)
    m2 = get_memory_usage()
    print("Memory usage after creating x: {:.1f} MB".format(m2))
    print("Memory usage diff: {:.1f} MB".format(m2 - m1))
    print("Size of x: {:.1f} MB".format(x.element_size() * x.nelement() / MB))

    d = {}
    for i in range(len(x)):
        d[i] = x[i]

    for i in range(len(x)):
        assert d[i].data_ptr() == x[i].data_ptr(), "Data pointer mismatch"

    m3 = get_memory_usage()
    print("Memory usage after creating dict: {:.1f} MB".format(m3))
    print("Memory usage diff: {:.1f} MB".format(m3 - m2))

    # size of dict
    print("Size of dict: {:.1f} MB, {:.1f} MB".format(
        sys.getsizeof(d) / MB, len(d) * sys.getsizeof(d[0]) / MB))

if __name__ == "__main__":

I run into the same issue when saving a torch tensor to a dict row-by-row. The size of dict itself is very small. Did you figure out how this can be solved?