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optim.py
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optim.py
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import tensorflow as tf
import tfops as Z
import horovod.tensorflow as hvd
# Optimizers
'''
Polyak averaging op
'''
def polyak(params, beta):
#params = tf.trainable_variables()
ema = tf.train.ExponentialMovingAverage(decay=beta, zero_debias=True)
avg_op = tf.group(ema.apply(params))
# Swapping op
updates = []
for i in range(len(params)):
p = params[i]
avg = ema.average(p)
tmp = 0. + avg * 1.
with tf.control_dependencies([tmp]):
update1 = avg.assign(p)
with tf.control_dependencies([update1]):
update2 = p.assign(tmp)
updates += [update1, update2]
swap_op = tf.group(*updates)
return avg_op, swap_op, ema
def adam(params, cost_or_grads, alpha=3e-4, hps=None, epsilon=1e-8):
updates = []
if type(cost_or_grads) is not list:
gs = tf.gradients(cost_or_grads, params)
else:
gs = cost_or_grads
beta2 = 1-1./(hps.train_its*hps.polyak_epochs)
# all-reduce
grads = [Z.allreduce_mean(g) for g in gs]
t = tf.Variable(1., 'adam_t')
alpha_t = alpha * tf.sqrt((1. - tf.pow(beta2, t))) / \
(1. - tf.pow(hps.beta1, t))
updates.append(t.assign_add(1))
for w, g in zip(params, grads):
mom2 = tf.Variable(tf.zeros(w.get_shape()), w.name + '_adam_m2')
if hps.beta1 > 0:
mom1 = tf.Variable(tf.zeros(w.get_shape()), w.name + '_adam_m1')
mom1_new = hps.beta1 * mom1 + (1. - hps.beta1) * g
updates.append(mom1.assign(mom1_new))
else:
mom1_new = g
m2_new = beta2 * mom2 + (1. - beta2) * tf.square(g)
delta_t = mom1_new / (tf.sqrt(m2_new) + epsilon)
w_new = hps.weight_decay * w - alpha_t * delta_t
updates.append(mom2.assign(m2_new))
updates.append(w.assign(w_new))
# Polyak averaging
polyak_avg_op, polyak_swap_op, ema = polyak(params, beta2)
train_op = tf.group(polyak_avg_op, *updates)
return train_op, polyak_swap_op, ema
'''
Adam optimizer
Version whose learning rate could, in theory, be scaled linearly (like SGD+momentum).
(It doesn't seem to work yet, though.)
'''
def adam2(params, cost_or_grads, alpha=3e-4, hps=None, epsilon=1e-8):
updates = []
if type(cost_or_grads) is not list:
gs = tf.gradients(cost_or_grads, params)
else:
gs = cost_or_grads
beta2 = 1-1./(hps.train_its*hps.polyak_epochs)
# all-reduce
grads1 = [Z.allreduce_mean(g) for g in gs]
grads2 = [Z.allreduce_mean(g**2) for g in gs]
t = tf.Variable(1., 'adam_t')
alpha_t = alpha * tf.sqrt((1. - tf.pow(beta2, t))) / \
(1. - tf.pow(hps.beta1, t))
updates.append(t.assign_add(1))
for w, g1, g2 in zip(params, grads1, grads2):
mom2 = tf.Variable(tf.zeros(w.get_shape()), w.name + '_adam_m2')
if hps.beta1 > 0:
mom1 = tf.Variable(tf.zeros(w.get_shape()), w.name + '_adam_m1')
mom1_new = hps.beta1 * mom1 + (1. - hps.beta1) * g1
updates.append(mom1.assign(mom1_new))
else:
mom1_new = g1
m2_new = beta2 * mom2 + (1. - beta2) * g2
delta_t = mom1_new / (tf.sqrt(m2_new) + epsilon)
w_new = hps.weight_decay * w - alpha_t * delta_t
updates.append(mom2.assign(m2_new))
updates.append(w.assign(w_new))
# Polyak averaging
polyak_avg_op, polyak_swap_op, ema = polyak(params, beta2)
train_op = tf.group(polyak_avg_op, *updates)
return train_op, polyak_swap_op, ema
'''
Adam optimizer
Version whose learning rate could, in theory, be scaled linearly (like SGD+momentum).
It doesn't seem to work though.
'''
def adam2_old(params, cost_or_grads, lr=3e-4, mom1=0.9, mom2=0.999, epsilon=1e-8):
updates = []
if type(cost_or_grads) is not list:
gs = tf.gradients(cost_or_grads, params)
else:
gs = cost_or_grads
# all-reduce
grads1 = [Z.allreduce_mean(g) for g in gs]
grads2 = [Z.allreduce_mean(tf.square(g)) for g in gs]
mom2 = tf.maximum(0., 1. - (hvd.size() * (1 - mom2)))
t = tf.Variable(1., 'adam_t')
lr_t = lr * tf.sqrt((1. - tf.pow(mom2, t))) / (1. - tf.pow(mom1, t))
updates.append(t.assign_add(1))
for p, g1, g2 in zip(params, grads1, grads2):
mg = tf.Variable(tf.zeros(p.get_shape()), p.name + '_adam_mg')
if mom1 > 0:
v = tf.Variable(tf.zeros(p.get_shape()), p.name + '_adam_v')
v_t = mom1 * v + (1. - mom1) * g1
updates.append(v.assign(v_t))
else:
v_t = g1
mg_t = mom2 * mg + (1. - mom2) * g2
delta_t = v_t / (tf.sqrt(mg_t) + epsilon)
p_t = p - lr_t * delta_t
updates.append(mg.assign(mg_t))
updates.append(p.assign(p_t))
return tf.group(*updates)
def adamax(params, cost_or_grads, alpha=3e-4, hps=None, epsilon=1e-8):
updates = []
if type(cost_or_grads) is not list:
gs = tf.gradients(cost_or_grads, params)
else:
gs = cost_or_grads
beta2 = 1-1./(hps.train_its*hps.polyak_epochs)
# all-reduce
grads = [Z.allreduce_mean(g) for g in gs]
t = tf.Variable(1., 'adam_t')
alpha_t = alpha * tf.sqrt((1. - tf.pow(beta2, t))) / \
(1. - tf.pow(hps.beta1, t))
updates.append(t.assign_add(1))
for w, g in zip(params, grads):
mom2 = tf.Variable(tf.zeros(w.get_shape()), w.name + '_adam_m2')
if hps.beta1 > 0:
mom1 = tf.Variable(tf.zeros(w.get_shape()), w.name + '_adam_m1')
mom1_new = hps.beta1 * mom1 + (1. - hps.beta1) * g
updates.append(mom1.assign(mom1_new))
else:
mom1_new = g
m2_new = tf.maximum(beta2 * mom2, abs(g))
delta_t = mom1_new / (m2_new + epsilon)
w_new = hps.weight_decay * w - alpha_t * delta_t
updates.append(mom2.assign(m2_new))
updates.append(w.assign(w_new))
# Polyak averaging
polyak_avg_op, polyak_swap_op, ema = polyak(params, beta2)
train_op = tf.group(polyak_avg_op, *updates)
return train_op, polyak_swap_op, ema
def adam(params, cost_or_grads, alpha=3e-4, hps=None, epsilon=1e-8):
updates = []
if type(cost_or_grads) is not list:
gs = tf.gradients(cost_or_grads, params)
else:
gs = cost_or_grads
beta2 = 1-1./(hps.train_its*hps.polyak_epochs)
# all-reduce
grads = [Z.allreduce_mean(g) for g in gs]
t = tf.Variable(1., 'adam_t')
alpha_t = alpha * tf.sqrt((1. - tf.pow(beta2, t))) / \
(1. - tf.pow(hps.beta1, t))
updates.append(t.assign_add(1))
for w, g in zip(params, grads):
mom2 = tf.Variable(tf.zeros(w.get_shape()), w.name + '_adam_m2')
if hps.beta1 > 0:
mom1 = tf.Variable(tf.zeros(w.get_shape()), w.name + '_adam_m1')
mom1_new = hps.beta1 * mom1 + (1. - hps.beta1) * g
updates.append(mom1.assign(mom1_new))
else:
mom1_new = g
m2_new = beta2 * mom2 + (1. - beta2) * tf.square(g)
delta_t = mom1_new / (tf.sqrt(m2_new) + epsilon)
w_new = hps.weight_decay * w - alpha_t * delta_t
updates.append(mom2.assign(m2_new))
updates.append(w.assign(w_new))
# Polyak averaging
polyak_avg_op, polyak_swap_op, ema = polyak(params, beta2)
train_op = tf.group(polyak_avg_op, *updates)
return train_op, polyak_swap_op, ema