def test_unitary_to_rot_jax(self, U, expected_gate, expected_params):
"""Test that the transform works in the JAX interface."""
jax = pytest.importorskip("jax")
# Enable float64 support
from jax.config import config
remember = config.read("jax_enable_x64")
config.update("jax_enable_x64", True)
U = jax.numpy.array(U, dtype=jax.numpy.complex64)
transformed_qfunc = unitary_to_rot(qfunc)
ops = qml.transforms.make_tape(transformed_qfunc)(U).operations
assert len(ops) == 3
assert isinstance(ops[0], qml.Hadamard)
assert ops[0].wires == Wires("a")
assert isinstance(ops[1], expected_gate)
assert ops[1].wires == Wires("a")
assert qml.math.allclose([jax.numpy.asarray(x) for x in ops[1].parameters], expected_params)
assert isinstance(ops[2], qml.CNOT)
assert ops[2].wires == Wires(["b", "a"])
def test_commute_controlled_jax(self):
"""Test QNode and gradient in JAX interface."""
jax = pytest.importorskip("jax")
from jax import numpy as jnp
from jax.config import config
remember = config.read("jax_enable_x64")
config.update("jax_enable_x64", True)
original_qnode = qml.QNode(qfunc, dev, interface="jax")
transformed_qnode = qml.QNode(transformed_qfunc, dev, interface="jax")
input = jnp.array([0.3, 0.4], dtype=jnp.float64)
# Check that the numerical output is the same
assert qml.math.allclose(original_qnode(input),
transformed_qnode(input))
# Check that the gradient is the same
assert qml.math.allclose(
jax.grad(original_qnode)(input),
jax.grad(transformed_qnode)(input))
# Check operation list
ops = transformed_qnode.qtape.operations
compare_operation_lists(ops, expected_op_list, expected_wires_list)
def test_compile_jax(self, diff_method):
"""Test QNode and gradient in JAX interface."""
jax = pytest.importorskip("jax")
from jax import numpy as jnp
from jax.config import config
remember = config.read("jax_enable_x64")
config.update("jax_enable_x64", True)
original_qnode = qml.QNode(qfunc, dev, interface="jax", diff_method=diff_method)
transformed_qnode = qml.QNode(
transformed_qfunc, dev, interface="jax", diff_method=diff_method
)
x = jnp.array([0.1, 0.2, 0.3], dtype=jnp.float64)
params = jnp.ones((2, 3), dtype=jnp.float64)
# Check that the numerical output is the same
assert qml.math.allclose(original_qnode(x, params), transformed_qnode(x, params))
# Check that the gradient is the same
assert qml.math.allclose(
jax.grad(original_qnode, argnums=(1))(x, params),
jax.grad(transformed_qnode, argnums=(1))(x, params),
atol=1e-7,
)
# Check operation list
ops = transformed_qnode.qtape.operations
compare_operation_lists(ops, expected_op_list, expected_wires_list)
def test_zyz_decomposition_jax(self, U, expected_gate, expected_params):
"""Test that a one-qubit operation in JAX is correctly decomposed."""
jax = pytest.importorskip("jax")
# Enable float64 support
from jax.config import config
remember = config.read("jax_enable_x64")
config.update("jax_enable_x64", True)
U = jax.numpy.array(U, dtype=jax.numpy.complex128)
obtained_gates = zyz_decomposition(U, wire="a")
assert len(obtained_gates) == 1
assert isinstance(obtained_gates[0], expected_gate)
assert obtained_gates[0].wires == Wires("a")
assert qml.math.allclose(qml.math.unwrap(obtained_gates[0].parameters),
expected_params)
if obtained_gates[0].num_params == 1:
obtained_mat = qml.RZ(obtained_gates[0].parameters[0],
wires=0).matrix
else:
obtained_mat = qml.Rot(*obtained_gates[0].parameters,
wires=0).matrix
assert check_matrix_equivalence(obtained_mat, U)
def __init__(self, wires, *, shots=None, prng_key=None, analytic=None):
if jax_config.read("jax_enable_x64"):
self.C_DTYPE = jnp.complex128
self.R_DTYPE = jnp.float64
else:
self.C_DTYPE = jnp.complex64
self.R_DTYPE = jnp.float32
super().__init__(wires, shots=shots, cache=0, analytic=analytic)
# prevent using special apply methods for these gates due to slowdown in jax
# implementation
del self._apply_ops["PauliY"]
del self._apply_ops["Hadamard"]
del self._apply_ops["CZ"]
self._prng_key = prng_key
def testStaticShapeErrors(self):
if config.read("jax_disable_jit"):
raise SkipTest("test only relevant when jit enabled")
@api.jit
def feature_map(n, d, sigma=1.0, seed=123):
key = random.PRNGKey(seed)
W = random.normal(key, (d, n)) / sigma
w = random.normal(key, (d, )) / sigma
b = 2 * jnp.pi * random.uniform(key, (d, ))
phi = lambda x, t: jnp.sqrt(2.0 / d) * jnp.cos(jnp.matmul(W, x) + w*t + b)
return phi
self.assertRaisesRegex(TypeError, 'Shapes must be 1D.*',
lambda: feature_map(5, 3))
def test_gradient_unitary_to_rot_jax(self, rot_angles, diff_method):
"""Tests differentiability in jax interface."""
jax = pytest.importorskip("jax")
from jax import numpy as jnp
# Enable float64 support
from jax.config import config
remember = config.read("jax_enable_x64")
config.update("jax_enable_x64", True)
def qfunc_with_qubit_unitary(angles):
z = angles[0]
x = angles[1]
Z_mat = jnp.array([[jnp.exp(-1j * z / 2), 0.0], [0.0, jnp.exp(1j * z / 2)]])
c = jnp.cos(x / 2)
s = jnp.sin(x / 2) * 1j
X_mat = jnp.array([[c, -s], [-s, c]])
qml.Hadamard(wires="a")
qml.QubitUnitary(Z_mat, wires="a")
qml.QubitUnitary(X_mat, wires="b")
qml.CNOT(wires=["b", "a"])
return qml.expval(qml.PauliX(wires="a"))
# Setting the dtype to complex64 causes the gradients to be complex...
input = jnp.array(rot_angles, dtype=jnp.float64)
dev = qml.device("default.qubit", wires=["a", "b"])
original_qnode = qml.QNode(
original_qfunc_for_grad, dev, interface="jax", diff_method=diff_method
)
original_result = original_qnode(input)
transformed_qfunc = unitary_to_rot(qfunc_with_qubit_unitary)
transformed_qnode = qml.QNode(
transformed_qfunc, dev, interface="jax", diff_method=diff_method
)
transformed_result = transformed_qnode(input)
assert qml.math.allclose(original_result, transformed_result)
original_grad = jax.grad(original_qnode)(input)
transformed_grad = jax.grad(transformed_qnode)(input)
assert qml.math.allclose(original_grad, transformed_grad, atol=1e-7)
def test_correctly_capture_default(self, jit, enable_or_disable):
if jit == "cpp" and not config.omnistaging_enabled:
self.skipTest("cpp_jit requires omnistaging")
# The fact we defined a jitted function with a block with a different value
# of `config.enable_x64` has no impact on the output.
with enable_or_disable():
func = _maybe_jit(jit, lambda: jnp.arange(10.0))
func()
expected_dtype = "float64" if config.read("jax_enable_x64") else "float32"
self.assertEqual(func().dtype, expected_dtype)
with enable_x64():
self.assertEqual(func().dtype, "float64")
with disable_x64():
self.assertEqual(func().dtype, "float32")
def test_two_qubit_decomposition_jax(self, U, wires):
"""Test that a two-qubit operation in JAX is correctly decomposed."""
jax = pytest.importorskip("jax")
from jax.config import config
remember = config.read("jax_enable_x64")
config.update("jax_enable_x64", True)
U = jax.numpy.array(U, dtype=jax.numpy.complex128)
obtained_decomposition = two_qubit_decomposition(U, wires=wires)
with qml.tape.QuantumTape() as tape:
for op in obtained_decomposition:
qml.apply(op)
obtained_matrix = get_unitary_matrix(tape, wire_order=wires)()
assert check_matrix_equivalence(U, obtained_matrix, atol=1e-7)
def test_gradient_unitary_to_rot_two_qubit_jax(self):
"""Tests differentiability in jax interface."""
jax = pytest.importorskip("jax")
from jax import numpy as jnp
from jax.config import config
remember = config.read("jax_enable_x64")
config.update("jax_enable_x64", True)
U0 = jnp.array(test_two_qubit_unitaries[0], dtype=jnp.complex128)
U1 = jnp.array(test_two_qubit_unitaries[1], dtype=jnp.complex128)
def two_qubit_decomp_qnode(x):
qml.RX(x, wires=0)
qml.QubitUnitary(U0, wires=[0, 1])
qml.QubitUnitary(U1, wires=[1, 2])
return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1) @ qml.PauliZ(2))
x = jnp.array(0.1, dtype=jnp.float64)
dev = qml.device("default.qubit", wires=3)
original_qnode = qml.QNode(
two_qubit_decomp_qnode, device=dev, interface="jax", diff_method="backprop"
)
transformed_qfunc = unitary_to_rot(two_qubit_decomp_qnode)
transformed_qnode = qml.QNode(
transformed_qfunc, dev, interface="jax", diff_method="backprop"
)
assert qml.math.allclose(original_qnode(x), transformed_qnode(x))
# 3 normal operations + 10 for the first decomp and 2 for the second
assert len(transformed_qnode.qtape.operations) == 13
original_grad = jax.grad(original_qnode, argnums=(0))(x)
transformed_grad = jax.grad(transformed_qnode, argnums=(0))(x)
assert qml.math.allclose(original_grad, transformed_grad, atol=1e-6)
def test_coefficients_jax_interface(self):
"""Test that coefficients are correctly computed when using the JAX interface."""
jax = pytest.importorskip("jax")
# Need to enable float64 support
from jax.config import config
remember = config.read("jax_enable_x64")
config.update("jax_enable_x64", True)
qnode = qml.QNode(self.circuit,
self.dev,
interface="jax",
diff_method="parameter-shift")
weights = jax.numpy.array([0.5, 0.2])
obtained_result = coefficients(partial(qnode, weights), 2, 1)
assert np.allclose(obtained_result, self.expected_result)
config.update("jax_enable_x64", remember)
def test_merge_amplitude_embedding_jax(self):
"""Test QNode in JAX interface."""
jax = pytest.importorskip("jax")
from jax import numpy as jnp
from jax.config import config
remember = config.read("jax_enable_x64")
config.update("jax_enable_x64", True)
def qfunc(amplitude):
qml.AmplitudeEmbedding(amplitude, wires=0)
qml.AmplitudeEmbedding(amplitude, wires=1)
return qml.state()
dev = qml.device("default.qubit", wires=2)
optimized_qfunc = qml.transforms.merge_amplitude_embedding(qfunc)
optimized_qnode = qml.QNode(optimized_qfunc, dev, interface="jax")
amplitude = jnp.array([0.0, 1.0], dtype=jnp.float64)
# Check the state |11> is being generated.
assert optimized_qnode(amplitude)[-1] == 1
def test_get_unitary_matrix_interface_jax():
"""Test with JAX interface"""
jax = pytest.importorskip("jax")
from jax import numpy as jnp
from jax.config import config
remember = config.read("jax_enable_x64")
config.update("jax_enable_x64", True)
dev = qml.device("default.qubit", wires=3)
def circuit(theta):
qml.RZ(theta[0], wires=0)
qml.RZ(theta[1], wires=1)
qml.CRY(theta[2], wires=[1, 2])
return qml.expval(qml.PauliZ(1))
# set qnode interface
qnode = qml.QNode(circuit, dev, interface="jax")
get_matrix = get_unitary_matrix(qnode)
# input jax parameters
theta = jnp.array([0.1, 0.2, 0.3], dtype=jnp.float64)
matrix = get_matrix(theta)
# expected matrix
matrix1 = np.kron(
qml.RZ(theta[0], wires=0).matrix, np.kron(qml.RZ(theta[1], wires=1).matrix, I)
)
matrix2 = np.kron(I, qml.CRY(theta[2], wires=[1, 2]).matrix)
expected_matrix = matrix2 @ matrix1
assert np.allclose(matrix, expected_matrix)
from neural_tangents import predict
from neural_tangents import stax
from neural_tangents.utils import batch
from neural_tangents.utils import empirical
config.parse_flags_with_absl()
MATRIX_SHAPES = [(3, 3), (4, 4)]
OUTPUT_LOGITS = [1, 2, 3]
GETS = ('ntk', 'nngp', ('ntk', 'nngp'))
RTOL = 0.1
ATOL = 0.1
if not config.read('jax_enable_x64'):
RTOL = 0.2
ATOL = 0.2
FLAT = 'FLAT'
POOLING = 'POOLING'
# TODO(schsam): Add a pooling test when multiple inputs are supported in
# Conv + Pooling.
TRAIN_SHAPES = [(4, 4), (4, 8), (8, 8), (6, 4, 4, 3)]
TEST_SHAPES = [(2, 4), (6, 8), (16, 8), (2, 4, 4, 3)]
NETWORK = [FLAT, FLAT, FLAT, FLAT]
OUTPUT_LOGITS = [1, 2, 3]
CONVOLUTION_CHANNELS = 256
def setUp(self):
self.cfg = config.read("jax_debug_nans")
config.update("jax_debug_nans", True)
def testTrainedEnsemblePredCov(self, train_shape, test_shape, network,
out_logits):
if xla_bridge.get_backend().platform == 'gpu' and config.read(
'jax_enable_x64'):
raise jtu.SkipTest('Not running GPU x64 to save time.')
training_steps = 5000
learning_rate = 1.0
ensemble_size = 50
init_fn, apply_fn, ker_fn = stax.serial(
stax.Dense(1024, W_std=1.2, b_std=0.05), stax.Erf(),
stax.Dense(out_logits, W_std=1.2, b_std=0.05))
opt_init, opt_update, get_params = optimizers.sgd(learning_rate)
opt_update = jit(opt_update)
key = random.PRNGKey(0)
key, = random.split(key, 1)
key, split = random.split(key)
x_train = np.cos(random.normal(split, train_shape))
key, split = random.split(key)
y_train = np.array(
random.bernoulli(split, shape=(train_shape[0], out_logits)),
np.float32)
train = (x_train, y_train)
key, split = random.split(key)
x_test = np.cos(random.normal(split, test_shape))
ensemble_key = random.split(key, ensemble_size)
loss = jit(lambda params, x, y: 0.5 * np.mean(
(apply_fn(params, x) - y)**2))
grad_loss = jit(lambda state, x, y: grad(loss)
(get_params(state), x, y))
def train_network(key):
_, params = init_fn(key, (-1, ) + train_shape[1:])
opt_state = opt_init(params)
for i in range(training_steps):
opt_state = opt_update(i, grad_loss(opt_state, *train),
opt_state)
return get_params(opt_state)
params = vmap(train_network)(ensemble_key)
ensemble_fx = vmap(apply_fn, (0, None))(params, x_test)
ensemble_loss = vmap(loss, (0, None, None))(params, x_train, y_train)
ensemble_loss = np.mean(ensemble_loss)
self.assertLess(ensemble_loss, 1e-5, True)
mean_emp = np.mean(ensemble_fx, axis=0)
mean_subtracted = ensemble_fx - mean_emp
cov_emp = np.einsum(
'ijk,ilk->jl', mean_subtracted, mean_subtracted, optimize=True) / (
mean_subtracted.shape[0] * mean_subtracted.shape[-1])
reg = 1e-7
ntk_predictions = predict.gp_inference(ker_fn,
x_train,
y_train,
x_test,
'ntk',
reg,
compute_cov=True)
self.assertAllClose(mean_emp, ntk_predictions.mean, True, RTOL, ATOL)
self.assertAllClose(cov_emp, ntk_predictions.covariance, True, RTOL,
ATOL)
def test_float64(self, module_fn: ModuleFn, shape, dtype):
self.assertTrue(config.read("jax_enable_x64"))
self.assert_dtype(jnp.float64, module_fn, shape, dtype)
# -*- coding: utf-8 -*-
import logging
from jax.config import config
logger = logging.getLogger(__name__)
if not config.read("jax_enable_x64"):
logger.warning(
"exoplanet_core.jax only works with dtype float64. "
"We're enabling x64 now, but you might run into issues if you've "
"already run some jax code.\n"
"You can squash this warning by setting the environment variable "
"'JAX_ENABLE_X64=True' or by running:\n"
">>> from jax.config import config\n"
">>> config.update('jax_enable_x64', True)")
config.update("jax_enable_x64", True)
__all__ = ["ops"]
from . import ops # noqa isort:skip
