How to fix 'recursion error' when pickling Pyomo Expressions

I haven't had trouble with pickling a Pyomo model before, but now a recursion error is being raised when trying to pickle Expressions.

Interestingly, the error can be avoided in the example below via a couple of ways:

by removing the "mutable" flag from the parameter
by reducing the size of the set to a very small value, to e.g. range(0, 10)

...but I don't know why these would fix the error, nor are they workable solutions for the actual optimization model I am trying to pickle.

The following example generates the error from the pickling of just a single Expression. (I am using pyomo=5.6.2, cloudpickle=0.6.1, and python=3.7.4)

import cloudpickle
import pyomo.environ as pyo

test_model = pyo.ConcreteModel()

# A set is added.
set_elements = list(range(0, 500))
test_model.my_set = pyo.Set(initialize=set_elements)

# A parameter is added.
param_values = dict()
for e in set_elements:
    param_values[e] = 1
test_model.my_param = pyo.Param(test_model.my_set, initialize=param_values, mutable=True)

# An expression is added.
def calculation_rule(mdl):
    return sum(mdl.my_param[e] for e in mdl.my_set)
test_model.calculation_expr = pyo.Expression(rule=calculation_rule)

# We attempt to pickle the expression.
pickle_str = cloudpickle.dumps(test_model.calculation_expr)

The last line of the above code raises the following exception:

PicklingError: Could not pickle object as excessively deep recursion required.

QUESTION: Do I need to modify the way the Expression is written in order to pickle the model, or should I save the model using something other than Cloudpickle?

Thanks in advance for any help!

Solution

One fix is to use Pyomo's quicksum instead of Python's sum. It leads to more compact expression trees and seems to fix the recursion issue you're seeing:

# An expression is added.
def calculation_rule(mdl):
    return pyo.quicksum(mdl.my_param[e] for e in mdl.my_set)
test_model.calculation_expr = pyo.Expression(rule=calculation_rule)

Documentation here.