BUG: <can't running multiple chains in SMC-ABC>

[Fxinyu]

Describe the issue:

I have encountered a problem of running multiple chains. I tried the example in Sequential Monte Carlo - Approximate Bayesian Computation. The model could work under only one chain, but when it comes to two or above number of chains, the sampling process cannot proceed further.

Reproduceable code example:

import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pymc as pm

print(f"Running on PyMC v{pm.__version__}")
from scipy.integrate import odeint

# Definition of parameters
a = 1.0
b = 0.1
c = 1.5
d = 0.75

# initial population of rabbits and foxes
X0 = [10.0, 5.0]
# size of data
size = 100
# time lapse
time = 15
t = np.linspace(0, time, size)


# Lotka - Volterra equation
def dX_dt(X, t, a, b, c, d):
    """Return the growth rate of fox and rabbit populations."""

    return np.array([a * X[0] - b * X[0] * X[1], -c * X[1] + d * b * X[0] * X[1]])


# simulator function
def competition_model(rng, a, b, size=None):
    return odeint(dX_dt, y0=X0, t=t, rtol=0.01, args=(a, b, c, d))
# function for generating noisy data to be used as observed data.
def add_noise(a, b):
    noise = np.random.normal(size=(size, 2))
    simulated = competition_model(None, a, b) + noise
    return simulated
# plotting observed data.
observed = add_noise(a, b)
_, ax = plt.subplots(figsize=(12, 4))
ax.plot(t, observed[:, 0], "x", label="prey")
ax.plot(t, observed[:, 1], "x", label="predator")
ax.set_xlabel("time")
ax.set_ylabel("population")
ax.set_title("Observed data")
ax.legend();
with pm.Model() as model_lv:
    a = pm.HalfNormal("a", 1.0)
    b = pm.HalfNormal("b", 1.0)

    sim = pm.Simulator("sim", competition_model, params=(a, b), epsilon=10, observed=observed)

    idata_lv = pm.sample_smc(cores =2 ,chains=2)

Error message:

the sampling process cannot proceed further.

PyMC version information:

Running on PyMC v5.9.1

Context for the issue:

No response

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