def test_ferminet_size(self, size):
atoms = [
system.Atom(symbol='H', coords=(0, 0, -1.0)),
system.Atom(symbol='H', coords=(0, 0, 1.0)),
]
ne = (size, size)
nout = 1
batch_size = 3
ndet = 4
hidden_units = [[8, 8], [8, 8]]
after_det = [8, 8, nout]
x = tf.random_normal([batch_size, 3 * sum(ne)])
ferminet = networks.FermiNet(
atoms=atoms,
nelectrons=ne,
slater_dets=ndet,
hidden_units=hidden_units,
after_det=after_det)
y = ferminet(x)
for i in range(len(ne)):
self.assertEqual(ferminet._dets[i].shape.as_list(), [batch_size, ndet])
self.assertEqual(ferminet._orbitals[i].shape.as_list(),
[batch_size, ndet, size, size])
self.assertEqual(y.shape.as_list(), [batch_size, nout])
def test_ferminet(self, hidden_units, after_det):
"""Check that FermiNet is actually antisymmetric."""
atoms = [
system.Atom(symbol='H', coords=(0, 0, -1.0)),
system.Atom(symbol='H', coords=(0, 0, 1.0)),
]
ne = (3, 2)
x1 = tf.random_normal([3, 3 * sum(ne)])
xs = tf.split(x1, sum(ne), axis=1)
# swap indices to test antisymmetry
x2 = tf.concat([xs[1], xs[0]] + xs[2:], axis=1)
x3 = tf.concat(
xs[:ne[0]] + [xs[ne[0] + 1], xs[ne[0]]] + xs[ne[0] + 2:], axis=1)
ferminet = networks.FermiNet(
atoms=atoms,
nelectrons=ne,
slater_dets=4,
hidden_units=hidden_units,
after_det=after_det)
y1 = ferminet(x1)
y2 = ferminet(x2)
y3 = ferminet(x3)
with tf.train.MonitoredSession() as session:
out1, out2, out3 = session.run([y1, y2, y3])
np.testing.assert_allclose(out1, -out2, rtol=4.e-5, atol=1.e-6)
np.testing.assert_allclose(out1, -out3, rtol=4.e-5, atol=1.e-6)
def test_ferminet_pretrain(self, hidden_units, after_det):
"""Check that FermiNet pretraining runs."""
atoms = [
system.Atom(symbol='Li', coords=(0, 0, -1.0)),
system.Atom(symbol='Li', coords=(0, 0, 1.0)),
]
ne = (4, 2)
x = tf.random_normal([1, 10, 3 * sum(ne)])
strategy = tf.distribute.get_strategy()
with strategy.scope():
ferminet = networks.FermiNet(
atoms=atoms,
nelectrons=ne,
slater_dets=4,
hidden_units=hidden_units,
after_det=after_det,
pretrain_iterations=10)
# Test Hartree fock pretraining - no change of position.
hf_approx = scf.Scf(atoms, nelectrons=ne)
hf_approx.run()
pretrain_op_hf = networks.pretrain_hartree_fock(ferminet, x, strategy,
hf_approx)
self.assertEqual(ferminet.pretrain_iterations, 10)
with tf.train.MonitoredSession() as session:
for _ in range(ferminet.pretrain_iterations):
session.run(pretrain_op_hf)
def test_ferminet_mask(self):
"""Check that FermiNet with a decaying mask on the output works."""
atoms = [
system.Atom(symbol='H', coords=(0, 0, -1.0)),
system.Atom(symbol='H', coords=(0, 0, 1.0)),
]
ne = (3, 2)
hidden_units = [[8, 8], [8, 8]]
after_det = [8, 8, 2]
x = tf.random_normal([3, 3 * sum(ne)])
ferminet = networks.FermiNet(
atoms=atoms,
nelectrons=ne,
slater_dets=4,
hidden_units=hidden_units,
after_det=after_det,
envelope=True)
y = ferminet(x)
with tf.train.MonitoredSession() as session:
session.run(y)
def train(molecule: Sequence[system.Atom],
spins: Tuple[int, int],
batch_size: int,
network_config: Optional[NetworkConfig] = None,
pretrain_config: Optional[PretrainConfig] = None,
optim_config: Optional[OptimConfig] = None,
kfac_config: Optional[KfacConfig] = None,
mcmc_config: Optional[MCMCConfig] = None,
logging_config: Optional[LoggingConfig] = None,
multi_gpu: bool = False,
double_precision: bool = False,
graph_path: Optional[str] = None):
"""Configures and runs training loop.
Args:
molecule: molecule description.
spins: pair of ints specifying number of spin-up and spin-down electrons
respectively.
batch_size: batch size. Also referred to as the number of Markov Chain Monte
Carlo configurations/walkers.
network_config: network configuration. Default settings in NetworkConfig are
used if not specified.
pretrain_config: pretraining configuration. Default settings in
PretrainConfig are used if not specified.
optim_config: optimization configuration. Default settings in OptimConfig
are used if not specified.
kfac_config: K-FAC configuration. Default settings in KfacConfig are used if
not specified.
mcmc_config: Markov Chain Monte Carlo configuration. Default settings in
MCMCConfig are used if not specified.
logging_config: logging and checkpoint configuration. Default settings in
LoggingConfig are used if not specified.
multi_gpu: Use all available GPUs. Default: use only a single GPU.
double_precision: use tf.float64 instead of tf.float32 for all operations.
Warning - double precision is not currently functional with K-FAC.
graph_path: directory to save a representation of the TF graph to. Not saved
Raises:
RuntimeError: if mcmc_config.init_means is supplied but is of the incorrect
length.
"""
if not mcmc_config:
mcmc_config = MCMCConfig()
if not logging_config:
logging_config = LoggingConfig()
if not pretrain_config:
pretrain_config = PretrainConfig()
if not optim_config:
optim_config = OptimConfig()
if not kfac_config:
kfac_config = KfacConfig()
if not network_config:
network_config = NetworkConfig()
nelectrons = sum(spins)
precision = tf.float64 if double_precision else tf.float32
if multi_gpu:
strategy = tf.distribute.MirroredStrategy()
else:
# Get the default (single-device) strategy.
strategy = tf.distribute.get_strategy()
if multi_gpu:
batch_size = batch_size // strategy.num_replicas_in_sync
logging.info('Setting per-GPU batch size to %s.', batch_size)
logging_config.replicas = strategy.num_replicas_in_sync
logging.info('Running on %s replicas.', strategy.num_replicas_in_sync)
# Create a re-entrant variable scope for network.
with tf.variable_scope('model') as model:
pass
with strategy.scope():
with tf.variable_scope(model, auxiliary_name_scope=False) as model1:
with tf.name_scope(model1.original_name_scope):
fermi_net = networks.FermiNet(
atoms=molecule,
nelectrons=spins,
slater_dets=network_config.determinants,
hidden_units=network_config.hidden_units,
after_det=network_config.after_det,
architecture=network_config.architecture,
r12_ee_features=network_config.r12_ee_features,
r12_en_features=network_config.r12_en_features,
pos_ee_features=network_config.pos_ee_features,
build_backflow=network_config.build_backflow,
use_backflow=network_config.backflow,
jastrow_en=network_config.jastrow_en,
jastrow_ee=network_config.jastrow_ee,
jastrow_een=network_config.jastrow_een,
logdet=True,
envelope=network_config.use_envelope,
residual=network_config.residual,
pretrain_iterations=pretrain_config.iterations)
scf_approx = scf.Scf(
molecule,
nelectrons=spins,
restricted=False,
basis=pretrain_config.basis)
if pretrain_config.iterations > 0:
scf_approx.run()
hamiltonian_ops = hamiltonian.operators(molecule, nelectrons)
if mcmc_config.init_means:
if len(mcmc_config.init_means) != 3 * nelectrons:
raise RuntimeError('Initial electron positions of incorrect shape. '
'({} not {})'.format(
len(mcmc_config.init_means), 3 * nelectrons))
init_means = [float(x) for x in mcmc_config.init_means]
else:
init_means = assign_electrons(molecule, spins)
# Build the MCMC state inside the same variable scope as the network.
with tf.variable_scope(model, auxiliary_name_scope=False) as model1:
with tf.name_scope(model1.original_name_scope):
data_gen = mcmc.MCMC(
fermi_net,
batch_size,
init_mu=init_means,
init_sigma=mcmc_config.init_width,
move_sigma=mcmc_config.move_width,
dtype=precision)
with tf.variable_scope('HF_data_gen'):
hf_data_gen = mcmc.MCMC(
scf_approx.tf_eval_slog_hartree_product,
batch_size,
init_mu=init_means,
init_sigma=mcmc_config.init_width,
move_sigma=mcmc_config.move_width,
dtype=precision)
with tf.name_scope('learning_rate_schedule'):
global_step = tf.train.get_or_create_global_step()
lr = optim_config.learning_rate * tf.pow(
(1.0 / (1.0 + (tf.cast(global_step, tf.float32) /
optim_config.learning_rate_delay))),
optim_config.learning_rate_decay)
if optim_config.learning_rate < 1.e-10:
logging.warning('Learning rate less than 10^-10. Not using an optimiser.')
optim_fn = lambda _: None
update_cached_data = None
elif optim_config.use_kfac:
cached_data = tf.get_variable(
'MCMC_cache',
initializer=tf.zeros(shape=data_gen.walkers.shape, dtype=precision),
use_resource=True,
trainable=False,
dtype=precision,
)
if kfac_config.adapt_damping:
update_cached_data = tf.assign(cached_data, data_gen.walkers)
else:
update_cached_data = None
optim_fn = lambda layer_collection: mean_corrected_kfac_opt.MeanCorrectedKfacOpt( # pylint: disable=g-long-lambda
invert_every=kfac_config.invert_every,
cov_update_every=kfac_config.cov_update_every,
learning_rate=lr,
norm_constraint=kfac_config.norm_constraint,
damping=kfac_config.damping,
cov_ema_decay=kfac_config.cov_ema_decay,
momentum=kfac_config.momentum,
momentum_type=kfac_config.momentum_type,
loss_fn=lambda x: tf.nn.l2_loss(fermi_net(x)[0]),
train_batch=data_gen.walkers,
prev_train_batch=cached_data,
layer_collection=layer_collection,
batch_size=batch_size,
adapt_damping=kfac_config.adapt_damping,
is_chief=True,
damping_adaptation_decay=kfac_config.damping_adaptation_decay,
damping_adaptation_interval=kfac_config.damping_adaptation_interval,
min_damping=kfac_config.min_damping,
use_passed_loss=False,
estimation_mode='exact',
)
else:
adam = tf.train.AdamOptimizer(lr)
optim_fn = lambda _: adam
update_cached_data = None
qmc_net = qmc.QMC(
hamiltonian_ops,
fermi_net,
data_gen,
hf_data_gen,
clip_el=optim_config.clip_el,
check_loss=optim_config.check_loss,
)
qmc_net.train(
optim_fn,
optim_config.iterations,
logging_config,
using_kfac=optim_config.use_kfac,
strategy=strategy,
scf_approx=scf_approx,
global_step=global_step,
determinism_mode=optim_config.deterministic,
cached_data_op=update_cached_data,
write_graph=os.path.abspath(graph_path) if graph_path else None,
burn_in=mcmc_config.burn_in,
mcmc_steps=mcmc_config.steps,
)
def main(fixed_coords_up, fixed_coords_down, mcmc_step_std, init_sigma,
batch_size, iterations):
checkpoint_path = "Neon/checkpoints"
latest = tf.train.latest_checkpoint(checkpoint_path)
ferminet = networks.FermiNet(atoms=molecule,
nelectrons=spins,
slater_dets=16,
hidden_units=((256, 32), ) * 4,
after_det=(1, ),
pretrain_iterations=0,
logdet=True,
envelope=True,
residual=True,
name="model/det_net")
init_means = train.assign_electrons(molecule, spins)
batch = np.concatenate([
np.random.normal(
size=(batch_size, 1),
loc=mu,
scale=init_sigma,
) for mu in init_means
],
axis=-1)
batch, mask = fix_coords(batch, fixed_coords_up, fixed_coords_down, spins)
print("mask: ", mask)
psi = ferminet(batch)[0]
saver = tf.train.Saver(max_to_keep=10, save_relative_paths=True)
with tf.Session() as sess:
saver.restore(sess, latest)
sess.run(psi)
psi = sess.run(psi)
# for itr in range(iterations):
#
# new_batch = batch + tf.random.normal(shape = batch.shape, stddev = mcmc_step_std)*mask
# update_psi = ferminet(new_batch)[0]
# new_psi = sess.run(update_psi)
# pmove = tf.squeeze(2*(new_psi-psi))
# pacc = tf.log(tf.random_uniform(shape = batch.shape.as_list()[:1]))
# decision = tf.less(pacc, pmove)
# with tf.control_dependencies([decision]):
# new_batch = tf.where(decision, new_batch, batch)
# new_psi = tf.where(decision, new_psi, psi)
# move_acc = tf.reduce_mean(tf.cast(decision, tf.float32))
# batch = new_batch
# psi = new_psi
# print (sess.run(psi), sess.run(move_acc))
print(type(batch.shape))
print("Burn in")
for itr in range(20):
new_batch, new_psi, move_acc = sess.run(
mcmc_step(batch, psi, ferminet, mask))
batch = tf.constant(new_batch, (tf.float32).base_dtype)
psi = new_psi
print(itr, " pacc: ", move_acc)
print("Burn in done")
h5_schema = {"walkers": batch.shape.as_list()}
with H5Writer(name="neon_data_z.h5", schema=h5_schema,
directory="./") as h5_writer:
for itr in range(iterations):
for _ in range(2):
new_batch, new_psi, move_acc = sess.run(
mcmc_step(batch, psi, ferminet, mask))
batch = tf.constant(new_batch, (tf.float32).base_dtype)
psi = new_psi
print(itr, " pacc: ", move_acc)
out = {"walkers": new_batch}
h5_writer.write(itr, out)
