def test_parameter_wrapping_parameters():
"""Tests that ensure wrapping works with other parameters"""
var = 5
var = Parameter(var)
res = Parameter._wrap(var)
assert isinstance(res, Parameter)
assert res.x == var
def test_parameter_wrapping(self):
"""Tests that ensure wrapping occurs as expected"""
self.logTestName()
var = 5
res = Parameter._wrap(var)
self.assertTrue(isinstance(res, Parameter))
self.assertEqual(res.x, var)
var = Parameter(var)
res = Parameter._wrap(var)
self.assertTrue(isinstance(res, Parameter))
self.assertEqual(res.x, var)
def test_parameter_shape(self):
"""Tests that ensure wrapping occurs as expected"""
self.logTestName()
var = Parameter(5)
res = var.shape
self.assertTrue(res is None)
var = np.array([[1, 2, 3], [4, 5, 6]])
p = Parameter(var)
res = p.shape
self.assertEqual(res, (2, 3))
p = Parameter(tf.convert_to_tensor(var))
res = p.shape
self.assertEqual(res, (2, 3))
def f(q):
if np.any(q.x == np.inf): # detect the HACK
if f.v is None: # only construct one instance, only when needed
f.v = Parameter(
tf.Variable(0.8, name="sf_parameter"))
return f.v
return q
def test_init(self):
"Parameter initialization and arithmetic."
self.logTestName()
# at the moment RR-inputs cannot be arithmetically combined with the others
# immediate Parameter class inputs:
# ints, floats and complex numbers
# numpy arrays
# TensorFlow objects of various types
par_inputs = [
3, 0.14, 4.2 + 0.5j,
random(3),
tf.Variable(2),
tf.Variable(0.4),
tf.Variable(0.8 + 1.1j)
]
pars = [Parameter(k) for k in par_inputs] # wrapped versions
def check(p, q):
"Check all arithmetic operations on a two-Parameter combination."
#print(p, q)
self.assertTrue(isinstance(p + q, Parameter))
self.assertTrue(isinstance(p - q, Parameter))
self.assertTrue(isinstance(p * q, Parameter))
self.assertTrue(isinstance(p / q, Parameter))
self.assertTrue(isinstance(p**q, Parameter))
## binary methods
# all combinations of two Parameter types
for p in itertools.product(pars, repeat=2):
check(*p)
# all combinations a Parameter and an unwrapped input
for p in itertools.product(pars, par_inputs):
check(*p)
for p in itertools.product(par_inputs, pars):
check(*p)
## unary methods
for p in pars:
self.assertTrue(isinstance(-p, Parameter))
def test_parameters_with_operations(batch_size, setup_eng, backend, hbar):
"""Test using different types of Parameters with different classes
of ParOperations."""
# TODO: this test is very hard to understand. Should be split up,
# and separated into many individual tests that do one thing only.
# TODO: In the original test suite, this test appears to 'work' on the
# Gaussian backend, even though it never explicitly ignores non-Gaussian
# operations. It even worked on the Fock backends, even though the Fock
# backend doesn't support the ThermalLoss channel?!
# TODO: This test doesn't assert anything!!!!!
eng, prog = setup_eng(2)
r = prog.register
@sf.convert
def func1(x):
return abs(2 * x**2 - 3 * x + 1)
@sf.convert
def func2(x, y):
return abs(2 * x * y - y**2 + 0.5)
kwargs = {}
# RegRefTransforms for deferred measurements (note that some operations
# expect nonnegative parameter values)
rr_inputs = [ops.RR(r[0], lambda x: x**2), func1(r[0]), func2(*r)]
rr_pars = tuple(Parameter(k) for k in rr_inputs)
# other types of parameters
other_inputs = [0.14]
if isinstance(eng.backend, TFBackend):
# HACK: add placeholders for tf.Variables
other_inputs.append(np.inf)
if batch_size is not None:
# test batched input
other_inputs.append(np.random.uniform(size=(batch_size, )))
other_pars = tuple(Parameter(k) for k in other_inputs)
def check(G, par, measure=False):
"""Check a ParOperation/Parameters combination"""
# construct the op using the given tuple of Parameters as args
G = G(*par)
with prog:
if measure:
r[0].val = 0.1
r[1].val = 0.2
if G.ns == 1:
G | r[0]
else:
G | (r[0], r[1])
prog.optimize()
try:
eng.run(prog, **kwargs)
except NotApplicableError as err:
# catch unapplicable op/backend combinations here
logging.debug(err)
# unsuccessful run means the queue was not emptied.
eng.reset_queue()
except NotImplementedError as err:
# catch not yet implemented op/backend combinations here
logging.debug(err)
# unsuccessful run means the queue was not emptied.
eng.reset_queue()
prog.locked = False
scalar_arg_preparations = (
ops.Coherent,
ops.Squeezed,
ops.DisplacedSqueezed,
ops.Thermal,
ops.Catstate,
)
# Fock requires an integer parameter
testset = set(ops.one_args_gates + ops.two_args_gates + ops.channels +
scalar_arg_preparations) - {ops.ThermalLossChannel}
print(testset)
for G in testset:
sig = inspect.signature(G.__init__)
n_args = (len(sig.parameters) - 1
) # number of parameters __init__ takes, minus self
if n_args < 1:
logging.debug(
"Unexpected number of args ({}) for {}, check the testset.".
format(n_args, G))
# shortcut, only test cartesian products up to two parameter types
# n_args = min(n_args, 2)
# check all combinations of Parameter types
for p in itertools.product(rr_pars + other_pars, repeat=n_args):
# TODO: For now decompose() works on unevaluated Parameters.
# This causes an error if a :class:`.RegRefTransform`-based Parameter is used, and
# decompose() tries to do arithmetic on it.
if isinstance(p[0].x, ops.RR):
continue
if isinstance(eng.backend, TFBackend):
# declare tensorflow Variables here (otherwise they will be on a different graph when we do backend.reset below)
# replace placeholders with tf.Variable instances
def f(q):
if np.any(q.x == np.inf): # detect the HACK
if f.v is None: # only construct one instance, only when needed
f.v = Parameter(
tf.Variable(0.8, name="sf_parameter"))
return f.v
return q
f.v = None # kind of like a static variable in a function in C
p = tuple(map(f, p))
# create new tf.Session (which will be associated with the default graph) and initialize variables
session = tf.Session()
kwargs["session"] = session
session.run(tf.global_variables_initializer())
check(G, p, measure=True)
session.close()
else:
check(G, p, measure=True)
eng.backend.reset()
def test_parameter_shape_tf():
"""Tests that ensure wrapping occurs as expected"""
var = np.array([[1, 2, 3], [4, 5, 6]])
p = Parameter(tf.convert_to_tensor(var))
res = p.shape
assert res == (2, 3)
def test_parameter_no_shape():
"""Tests that ensure wrapping occurs as expected"""
var = Parameter(5)
res = var.shape
assert res is None
def test_parameter_wrapping_integer():
"""Tests that ensure wrapping works with integers"""
var = 5
res = Parameter._wrap(var)
assert isinstance(res, Parameter)
assert res.x == var
np.random.random(32)
TEST_VALUES = [3, 0.14, 4.2 + 0.5j, np.random.random(3)]
try:
import tensorflow as tf
except (ImportError, ModuleNotFoundError) as e:
tf_available = False
else:
tf_available = True
TEST_VALUES.extend(
[tf.Variable(2),
tf.Variable(0.4),
tf.Variable(0.8 + 1.1j)])
TEST_PARAMETERS = [Parameter(i) for i in TEST_VALUES]
def test_parameter_wrapping_integer():
"""Tests that ensure wrapping works with integers"""
var = 5
res = Parameter._wrap(var)
assert isinstance(res, Parameter)
assert res.x == var
def test_parameter_wrapping_parameters():
"""Tests that ensure wrapping works with other parameters"""
var = 5
var = Parameter(var)
res = Parameter._wrap(var)
