def build_opt_ops(
self,
wavefunction: wavefunctions.Wavefunction,
hamiltonian: operators.Operator,
hparams: tf.contrib.training.HParams,
shared_resources: Dict[graph_builders.ResourceName, tf.Tensor],
) -> NamedTuple:
"""Adds wavefunction optimization ops to the graph.
Args:
wavefunction: Wavefunction ansatz to optimize.
hamiltonian: Hamiltonian whose ground state we are solving for.
hparams: Hyperparameters of the optimization procedure.
shared_resources: Resources sharable among different modules.
Returns:
NamedTuple holding tensors needed to run a training epoch.
"""
batch_size = hparams.batch_size
n_sites = hparams.num_sites
configs = graph_builders.get_configs(shared_resources, batch_size, n_sites)
mc_step, acc_rate = graph_builders.get_monte_carlo_sampling(
shared_resources, configs, wavefunction)
opt_v = wavefunction.get_trainable_variables()
wf_omega = copy.deepcopy(wavefunction) # building supervisor wavefunction.
beta = tf.constant(hparams.time_evolution_beta, dtype=tf.float32)
beta2 = tf.constant(hparams.time_evolution_befta ** 2, dtype=tf.float32)
psi_omega = wf_omega(configs)
h_psi_omega = hamiltonian.apply_in_place(wf_omega, configs, psi_omega)
h_psi_omega_beta = h_psi_omega * beta
ite_psi_omega = psi_omega - h_psi_omega_beta
local_energy = h_psi_omega / psi_omega
energy_expectation = tf.reduce_mean(local_energy)
squared_energy_expectation = tf.reduce_mean(tf.square(local_energy))
ite_normalization = tf.sqrt(
1. - 2 * beta * energy_expectation + squared_energy_expectation * beta2
)
ite_normalization_var = tf.get_variable(
name='ite_normalization',
initializer=tf.ones(shape=[]),
dtype=tf.float32,
trainable=False
)
num_epochs = graph_builders.get_or_create_num_epochs()
exp_moving_average = tf.train.ExponentialMovingAverage(0.999, num_epochs)
accumulate_norm = exp_moving_average.apply([ite_normalization])
normalization_value = exp_moving_average.average(ite_normalization)
accumulate_energy = exp_moving_average.apply([energy_expectation])
energy_value = exp_moving_average.average(energy_expectation)
update_normalization = tf.assign(ite_normalization_var, normalization_value)
psi = wavefunction(configs)
loss = tf.reduce_mean(
tf.squared_difference(psi, ite_psi_omega / ite_normalization_var)
/ tf.square(tf.stop_gradient(psi))
)
optimizer = create_sgd_optimizer(hparams)
train_step = optimizer.minimize(loss, var_list=opt_v)
train_step = tf.group([train_step, accumulate_norm, accumulate_energy])
update_supervisor = wavefunctions.module_transfer_ops(
wavefunction, wf_omega)
epoch_increment = tf.assign_add(num_epochs, 1)
update_wf_norm = wavefunction.update_norm(psi)
train_ops = TrainOpsSWO(
train_step=train_step,
accumulate_gradients=None,
apply_gradients=None,
reset_gradients=None,
mc_step=mc_step,
acc_rate=acc_rate,
metrics=loss,
energy=energy_value,
update_supervisor=update_supervisor,
update_normalization=update_normalization,
epoch_increment=epoch_increment,
update_wf_norm=update_wf_norm,
)
return train_ops
def build_opt_ops(
self,
wavefunction: wavefunctions.Wavefunction,
hamiltonian: operators.Operator,
hparams: tf.contrib.training.HParams,
shared_resources: Dict[graph_builders.ResourceName, tf.Tensor],
) -> NamedTuple:
"""Adds wavefunction optimization ops to the graph.
Args:
wavefunction: Wavefunction ansatz to optimize.
hamiltonian: Hamiltonian whose ground state we are solving for.
hparams: Hyperparameters of the optimization procedure.
shared_resources: Resources sharable among different modules.
Returns:
NamedTuple holding tensors needed to run a training epoch.
"""
batch_size = hparams.batch_size
n_sites = hparams.num_sites
configs = graph_builders.get_configs(shared_resources, batch_size, n_sites)
mc_step, acc_rate = graph_builders.get_monte_carlo_sampling(
shared_resources, configs, wavefunction)
opt_v = wavefunction.get_trainable_variables()
wf_omega = copy.deepcopy(wavefunction) # building supervisor wavefunction.
psi = wavefunction(configs)
psi_omega = wf_omega(configs)
beta = tf.constant(hparams.time_evolution_beta, dtype=tf.float32)
h_psi_omega = hamiltonian.apply_in_place(wf_omega, configs, psi_omega)
h_psi_omega_beta = beta * h_psi_omega
ite_psi_omega = psi_omega - h_psi_omega_beta
local_energy = h_psi_omega / psi_omega
update_wf_norm = wavefunction.update_norm(psi)
psi_no_grad = tf.stop_gradient(psi)
ratio = tf.stop_gradient(ite_psi_omega / psi)
log_psi_raw_grads = tf.gradients(psi / psi_no_grad, opt_v)
log_psi_grads = [tf.convert_to_tensor(grad) for grad in log_psi_raw_grads]
ratio_log_psi_raw_grads = tf.gradients(ratio * psi / psi_no_grad, opt_v)
ratio_log_psi_grads = [
tf.convert_to_tensor(grad) for grad in ratio_log_psi_raw_grads
]
log_grads = [
tf.metrics.mean_tensor(log_psi_grad) for log_psi_grad in log_psi_grads
]
ratio_grads = [
tf.metrics.mean_tensor(grad) for grad in ratio_log_psi_grads
]
mean_energy, accumulate_energy = tf.metrics.mean(local_energy)
mean_ratio, accumulate_ratio = tf.metrics.mean(ratio)
mean_log_grads, accumulate_log_grads = list(map(list, zip(*log_grads)))
mean_ratio_grads, accumulate_ratio_grads = list(
map(list, zip(*ratio_grads)))
grad_pairs = zip(mean_log_grads, mean_ratio_grads)
overlap_gradients = [
grad - scaled_grad / mean_ratio for grad, scaled_grad in grad_pairs
]
grads_and_vars = list(zip(overlap_gradients, opt_v))
optimizer = create_sgd_optimizer(hparams)
apply_gradients = optimizer.apply_gradients(grads_and_vars)
all_updates = [accumulate_ratio, accumulate_energy]
all_updates += accumulate_log_grads + accumulate_ratio_grads
accumulate_gradients = tf.group(all_updates)
update_network = wavefunctions.module_transfer_ops(wavefunction, wf_omega)
clear_gradients = tf.variables_initializer(tf.local_variables())
num_epochs = graph_builders.get_or_create_num_epochs()
epoch_increment = tf.assign_add(num_epochs, 1)
train_ops = TrainOpsSWO(
train_step=None,
accumulate_gradients=accumulate_gradients,
apply_gradients=apply_gradients,
reset_gradients=clear_gradients,
mc_step=mc_step,
acc_rate=acc_rate,
metrics=None,
energy=mean_energy,
update_supervisor=update_network,
update_normalization=None,
epoch_increment=epoch_increment,
update_wf_norm=update_wf_norm,
)
return train_ops
