def test_stochastic_differentiator_call_analytic(self, coordinate,
generator, cost, uniform):
"""Test if SGDifferentiator.differentiate_analytical doesn't crash
before running."""
programs, names, values, ops, _, true_f, true_g = \
_simple_op_inputs()
diff = stochastic_differentiator.SGDifferentiator(
coordinate, generator, cost, uniform)
op = diff.generate_differentiable_op(
analytic_op=circuit_execution_ops.get_expectation_op())
with tf.GradientTape() as g:
g.watch(values)
expectations = op(programs, names, values, ops)
grads = g.gradient(expectations, values)
self.assertAllClose(expectations, true_f, atol=1e-2, rtol=1e-2)
self.assertAllClose(grads, true_g, atol=1e-2, rtol=1e-2)
class GradientBenchmarksTest(tf.test.TestCase, parameterized.TestCase):
"""Test the Gradient benchmarking class."""
@parameterized.parameters(
list(
util.kwargs_cartesian_product(
**{
'diff': [
linear_combination.ForwardDifference(),
linear_combination.CentralDifference(),
parameter_shift.ParameterShift(),
stochastic_differentiator.SGDifferentiator(),
],
'params': [TEST_PARAMS_1, TEST_PARAMS_2]
})))
def testBenchmarkGradient(self, diff, params):
"""Test that op constructs and runs correctly."""
bench_name = "GradientBenchmarks.{}_{}_{}_{}_{}".format(
diff.__class__.__name__, params.n_qubits, params.n_moments,
params.batch_size, params.n_symbols)
proto_file_path = os.path.join(SRC, "reports/",
"{}".format(bench_name))
self.addCleanup(os.remove, proto_file_path)
bench = GradientBenchmarks(params=params)
bench.setup()
bench._benchmark_tfq_differentiator(diff, params)
res = benchmark_util.read_benchmark_entry(proto_file_path)
self.assertEqual(res.name, bench_name)
self.assertEqual(
res.extras.get("n_qubits").double_value, params.n_qubits)
self.assertEqual(
res.extras.get("n_moments").double_value, params.n_moments)
self.assertEqual(
res.extras.get("op_density").double_value, params.op_density)
assert hasattr(res, 'iters')
assert hasattr(res, 'wall_time')
from tensorflow_quantum.python.differentiators import linear_combination
from tensorflow_quantum.python.differentiators import parameter_shift
from tensorflow_quantum.python.differentiators import stochastic_differentiator
from tensorflow_quantum.core.ops import circuit_execution_ops, batch_util
DIFFS = [
linear_combination.ForwardDifference(grid_spacing=0.0001),
linear_combination.ForwardDifference(error_order=2, grid_spacing=0.0001),
linear_combination.CentralDifference(grid_spacing=0.0001),
linear_combination.CentralDifference(error_order=4, grid_spacing=0.0001),
parameter_shift.ParameterShift(),
]
STOCHASTIC_DIFFS = [
stochastic_differentiator.SGDifferentiator(stochastic_coordinate=False,
stochastic_generator=False,
stochastic_cost=False),
stochastic_differentiator.SGDifferentiator(stochastic_coordinate=True,
stochastic_generator=False,
stochastic_cost=False),
stochastic_differentiator.SGDifferentiator(stochastic_coordinate=False,
stochastic_generator=True,
stochastic_cost=False),
stochastic_differentiator.SGDifferentiator(stochastic_coordinate=True,
stochastic_generator=True,
stochastic_cost=False),
]
OPS = [
circuit_execution_ops.get_expectation_op(cirq.sim.Simulator()), # WF
circuit_execution_ops.get_expectation_op(
def test_stochastic_differentiator_instantiate(self):
"""Test SGDifferentiator type checking."""
stochastic_differentiator.SGDifferentiator()
with self.assertRaisesRegex(
TypeError, expected_regex="stochastic_coordinate must be"):
stochastic_differentiator.SGDifferentiator(stochastic_coordinate=1)
stochastic_differentiator.SGDifferentiator(
stochastic_coordinate=0.1)
stochastic_differentiator.SGDifferentiator(
stochastic_coordinate=[1])
stochastic_differentiator.SGDifferentiator(
stochastic_coordinate="junk")
with self.assertRaisesRegex(
TypeError, expected_regex="stochastic_generator must be"):
stochastic_differentiator.SGDifferentiator(stochastic_generator=1)
stochastic_differentiator.SGDifferentiator(stochastic_generator=0.1)
stochastic_differentiator.SGDifferentiator(stochastic_generator=[1])
stochastic_differentiator.SGDifferentiator(
stochastic_generator="junk")
with self.assertRaisesRegex(TypeError,
expected_regex="stochastic_cost must be"):
stochastic_differentiator.SGDifferentiator(stochastic_cost=1)
stochastic_differentiator.SGDifferentiator(stochastic_cost=0.1)
stochastic_differentiator.SGDifferentiator(stochastic_cost=[1])
stochastic_differentiator.SGDifferentiator(stochastic_cost="junk")
def benchmark_stochastic_differentiator(self):
"""Benchmark the default stochastic differentiator."""
diff = stochastic_differentiator.SGDifferentiator()
self._benchmark_tfq_differentiator(diff, self.params)
import numpy as np
import tensorflow as tf
from absl.testing import parameterized
import cirq
from tensorflow_quantum.python import util
from tensorflow_quantum.python.differentiators import stochastic_differentiator
from tensorflow_quantum.core.ops import tfq_simulate_ops, batch_util
# DISCLAIMER: Environment : Intel(R) Xeon(R) W-2135 CPU @ 3.70GHz, 12 cores.
# The overall tests take around 1 hours.
DIFFS_NUM_RUNS = [
# The tests without sampling cost Hamiltonian take 1.5 hours.
# Case 1 : ParameterShift ~ 0.04 sec/shot
(stochastic_differentiator.SGDifferentiator(stochastic_coordinate=False,
stochastic_generator=False,
stochastic_cost=False), 1),
# Case 2 : coordinate ~ 42 sec (0.04 sec/shot)
(stochastic_differentiator.SGDifferentiator(stochastic_coordinate=True,
stochastic_generator=False,
stochastic_cost=False), 1100),
# Case 3 : generator ~ 350 sec (0.023 sec/shot)
(stochastic_differentiator.SGDifferentiator(stochastic_coordinate=False,
stochastic_generator=True,
stochastic_cost=False), 15000),
# Case 4 : coordinate + generator ~ 400 sec ~ (0.020 sec/shot)
(stochastic_differentiator.SGDifferentiator(stochastic_coordinate=True,
stochastic_generator=True,
stochastic_cost=False), 20000),
# The tests with sampling cost Hamiltonian takes around 3 hours
# Case 5 : cost ~ 35 sec (0.15 sec/shot)