Reduce the use of SymPy lambdify

[antalszava]

Context: Some optimization tasks using Strawberry Fields were showing increased memory allocation, although no state was being left (i.e., the engine was reset, state objects recreated, etc.).

Parameters in Strawberry Fields might be numeric or symbolic. To represent both, SymPy is used. When using gate parameters that are internally created, Strawberry Fields evaluates the parameter to get the numeric value using SymPy.

Evaluating the parameters depends on the sympy.lambdify function, which internally caches data using linecache. The memory increase was found to be due to this caching.

Description of the Change: Changes the par_evaluate function which does the parameter evaluation such that:

• If the SymPy object can be converted to a numeric Python type (float or complex), then we perform the conversion without using sympy.lambdify
• Otherwise, when using sympy.lambdify we also clear the cache using linecache.

Benefits:

• Significantly faster parameter evaluation in cases when the use of sympy.lambdify can be avoided
• No memory increase over time

Possible Drawbacks: N/A

Related GitHub Issues: N/A

See profiling data here.

[codecov[bot]]

Codecov Report

Merging #579 (c60dc89) into master (127085e) will increase coverage by 0.00%. The diff coverage is 100.00%.

@@           Coverage Diff           @@
##           master     #579   +/-   ##
=======================================
  Coverage   98.38%   98.38%           
=======================================
  Files          75       75           
  Lines        8410     8414    +4     
=======================================
+ Hits         8274     8278    +4     
  Misses        136      136           

Impacted Files	Coverage Δ
strawberryfields/parameters.py	99.10% <100.00%> (+0.03%)	:arrow_up:

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[antalszava]

Profiling

The following script was profiled for both memory and time:

import strawberryfields as sf
from strawberryfields.ops import *
from strawberryfields.parameters import par_funcs as pf
import numpy as np

eng = sf.Engine("fock", backend_options={"cutoff_dim": 2})

def evx(param, n):
    prog = sf.Program(2)    
    with prog.context as q:
        ntrot = 10
        for i in range(n): 
            step = param/ntrot
            for itrot in range(ntrot):

                s = step
                r = pf.asinh(-s / 2)
                theta = 0.5 * pf.atan2(-1.0 / pf.cosh(r), -pf.tanh(r))

                for _ in range(20):
                    # Decomposition of CXgate
                    BSgate(theta, 0) |  (q[0], q[1]),
                    Sgate(r, 0) | q[0],
                    Sgate(-r, 0) | q[1],
                    BSgate(theta + np.pi / 2, 0) | (q[0], q[1]),

        state = eng.run(prog).state
        return state.number_expectation([0])

parm = 0.05
for i in range(10):

    if eng.run_progs:
        eng.reset()

    res = evx(parm, 40)
    print(i, flush=True)

master branch:

memory:

time: 88.01 secs

calling linecache.clearcache():

time: 87.23 secs

fewer_lambdify branch (calling linecache.clearcache() & no call to lambdify if not symbolic):

time: 34.86 secs

[nquesada]

@antalszava : don't forget to update the changelog. Also you might want to make the CodeFactor bot happy.