I am trying to compute second order derivatives using the following two methods inside a custom class:
def grad_log_target(self, log_target_val):
    log_target_val.backward(create_graph=True)
    return torch.cat([p.grad.view(-1) for p in self.parameters()])

def hess_log_target(self, grad_log_target_val):
    n_params = self.num_params()

    hess_log_target_val = []
    for i in range(n_params):
        deriv_i_wrt_grad = grad(grad_log_target_val[i], self.parameters(), retain_graph=True)
        hess_log_target_val.append(torch.cat([h.view(-1) for h in deriv_i_wrt_grad]))
        hess_log_target_val = torch.cat(hess_log_target_val, 0).reshape(n_params, n_params)

        return hess_log_target_val

The code runs without error, but the computational cost increases for each iteration of my algorithm (I am trying to run a custom Monte Carlo algorithm, not optimization). Here are some reported times that demonstrate the increase in runtime per iteration:
Iteration   1, duration 0:00:00.255764
Iteration   2, duration 0:00:00.245305
Iteration   3, duration 0:00:00.245335
Iteration   4, duration 0:00:00.254027
Iteration   5, duration 0:00:00.256714
Iteration   6, duration 0:00:00.252428
Iteration   7, duration 0:00:00.255005
Iteration   8, duration 0:00:00.257299
Iteration   9, duration 0:00:00.267525
Iteration  10, duration 0:00:00.268863
Iteration  11, duration 0:00:00.280390
Iteration  12, duration 0:00:00.276839
Iteration  13, duration 0:00:00.282090
Iteration  14, duration 0:00:00.279811
Iteration  15, duration 0:00:00.279818
Iteration  16, duration 0:00:00.288001
Iteration  17, duration 0:00:00.285519
Iteration  18, duration 0:00:00.319292
Iteration  19, duration 0:00:00.303236
Iteration  20, duration 0:00:00.314423
Iteration  21, duration 0:00:00.290186
Iteration  22, duration 0:00:00.290931
Iteration  23, duration 0:00:00.296420
Iteration  24, duration 0:00:00.296681
Iteration  25, duration 0:00:00.303668
Iteration  26, duration 0:00:00.312694
Iteration  27, duration 0:00:00.311441
Iteration  28, duration 0:00:00.310313
Iteration  29, duration 0:00:00.314935
Iteration  30, duration 0:00:00.314377
Iteration  31, duration 0:00:00.317583
Iteration  32, duration 0:00:00.335503
Iteration  33, duration 0:00:00.322630
Iteration  34, duration 0:00:00.319622
Iteration  35, duration 0:00:00.343187
Iteration  36, duration 0:00:00.326392
Iteration  37, duration 0:00:00.340833
Iteration  38, duration 0:00:00.330154
Iteration  39, duration 0:00:00.336486
Iteration  40, duration 0:00:00.360137
Iteration  41, duration 0:00:00.347079
Iteration  42, duration 0:00:00.339313
Iteration  43, duration 0:00:00.342601
Iteration  44, duration 0:00:00.352304
Iteration  45, duration 0:00:00.349789
Iteration  46, duration 0:00:00.353537
Iteration  47, duration 0:00:00.367225
Iteration  48, duration 0:00:00.377087
Iteration  49, duration 0:00:00.362028
Iteration  50, duration 0:00:00.372563
Iteration  51, duration 0:00:00.363326
Iteration  52, duration 0:00:00.374069
Iteration  53, duration 0:00:00.371866
Iteration  54, duration 0:00:00.384171
Iteration  55, duration 0:00:00.380464
Iteration  56, duration 0:00:00.384370
Iteration  57, duration 0:00:00.384062
Iteration  58, duration 0:00:00.384333
Iteration  59, duration 0:00:00.392266
Iteration  60, duration 0:00:00.389869
Iteration  61, duration 0:00:00.398985
Iteration  62, duration 0:00:00.398490
Iteration  63, duration 0:00:00.414814
Iteration  64, duration 0:00:00.400672
Iteration  65, duration 0:00:00.396808
Iteration  66, duration 0:00:00.393161
Iteration  67, duration 0:00:00.409639
Iteration  68, duration 0:00:00.433340
Iteration  69, duration 0:00:00.414874
Iteration  70, duration 0:00:00.428305
Iteration  71, duration 0:00:00.446901
Iteration  72, duration 0:00:00.410948
Iteration  73, duration 0:00:00.419323
Iteration  74, duration 0:00:00.422695
Iteration  75, duration 0:00:00.434619
Iteration  76, duration 0:00:00.435668
Iteration  77, duration 0:00:00.452214
Iteration  78, duration 0:00:00.425546
Iteration  79, duration 0:00:00.443100
Iteration  80, duration 0:00:00.463992
Iteration  81, duration 0:00:00.439153
Iteration  82, duration 0:00:00.445664
Iteration  83, duration 0:00:00.447337
Iteration  84, duration 0:00:00.458129
Iteration  85, duration 0:00:00.452122
Iteration  86, duration 0:00:00.471280
Iteration  87, duration 0:00:00.451846
Iteration  88, duration 0:00:00.456373
Iteration  89, duration 0:00:00.458176
Iteration  90, duration 0:00:00.470319
Iteration  91, duration 0:00:00.463827
Iteration  92, duration 0:00:00.467097
Iteration  93, duration 0:00:00.480898
Iteration  94, duration 0:00:00.457989
Iteration  95, duration 0:00:00.497614
Iteration  96, duration 0:00:00.518428
Iteration  97, duration 0:00:00.476043
Iteration  98, duration 0:00:00.469286
Iteration  99, duration 0:00:00.499041
Iteration 100, duration 0:00:00.502108

Is this caused by memory leak because the graph is not freed and therefore tensors are carried over between iterations?