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update lion_tf_2 #1184

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update lion_tf_2 #1184

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dffd9b6
update lion_tf_2
innat Feb 22, 2023
91d0201
support tf 2.11 or less
innat Feb 25, 2023
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190 changes: 106 additions & 84 deletions lion/lion_tf2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,91 +14,113 @@
# ==============================================================================
"""TF2 implementation of the Lion optimizer."""

import tensorflow.compat.v2 as tf
import tensorflow as tf
from packaging.version import parse

if parse(tf.__version__) > parse('2.11.0'):
from tensorflow.keras.optimizers.legacy import Optimizer as keras_opt
else:
from tensorflow.keras.optimizers import Optimizer as keras_opt


class Lion(tf.keras.optimizers.legacy.Optimizer):
r"""Optimizer that implements the Lion algorithm."""
class Lion(keras_opt):
def __init__(
self,
learning_rate=0.0001,
beta_1=0.9,
beta_2=0.99,
wd=0,
name='lion',
**kwargs
):
super().__init__(name, **kwargs)
self._set_hyper('learning_rate', kwargs.get('lr', learning_rate))
self._set_hyper('beta_1', beta_1)
self._set_hyper('beta_2', beta_2)
self._set_hyper('wd', wd)

def _create_slots(self, var_list):
# Create slots for the first and second moments.
# Separate for-loops to respect the ordering of slot variables from v1.
for var in var_list:
self.add_slot(var, 'm')

def _prepare_local(self, var_device, var_dtype, apply_state):
super(Lion, self)._prepare_local(var_device, var_dtype, apply_state)
beta_1_t = tf.identity(self._get_hyper('beta_1', var_dtype))
beta_2_t = tf.identity(self._get_hyper('beta_2', var_dtype))
wd_t = tf.identity(self._get_hyper('wd', var_dtype))
lr = apply_state[(var_device, var_dtype)]['lr_t']
apply_state[(var_device, var_dtype)].update(
dict(
lr=lr,
beta_1_t=beta_1_t,
one_minus_beta_1_t=1 - beta_1_t,
beta_2_t=beta_2_t,
one_minus_beta_2_t=1 - beta_2_t,
wd_t=wd_t
)
)

@tf.function(jit_compile=True)
def _resource_apply_dense(self, grad, var, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = (
(apply_state or {}).get(
(
var_device, var_dtype
)
) or self._fallback_apply_state(var_device, var_dtype)
)

m = self.get_slot(var, 'm')
var_t = var.assign_sub(
coefficients['lr_t'] * (
tf.math.sign(
m * coefficients['beta_1_t'] +
grad * coefficients['one_minus_beta_1_t']
) + var * coefficients['wd_t'])
)

with tf.control_dependencies([var_t]):
m.assign(
m * coefficients['beta_2_t'] + grad * coefficients['one_minus_beta_2_t']
)

@tf.function(jit_compile=True)
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = (
(apply_state or {}).get(
(
var_device, var_dtype
)
) or self._fallback_apply_state(var_device, var_dtype)
)

def __init__(self,
learning_rate=0.0001,
beta_1=0.9,
beta_2=0.99,
wd=0,
name='lion',
**kwargs):
"""Construct a new Lion optimizer."""
m = self.get_slot(var, 'm')
m_t = m.assign(m * coefficients['beta_1_t'])
m_scaled_g_values = grad * coefficients['one_minus_beta_1_t']
m_t = m_t.scatter_add(tf.IndexedSlices(m_scaled_g_values, indices))
var_t = var.assign_sub(
coefficients['lr'] * (
tf.math.sign(m_t) + var * coefficients['wd_t'])
)

super(Lion, self).__init__(name, **kwargs)
self._set_hyper('learning_rate', kwargs.get('lr', learning_rate))
self._set_hyper('beta_1', beta_1)
self._set_hyper('beta_2', beta_2)
self._set_hyper('wd', wd)

def _create_slots(self, var_list):
# Create slots for the first and second moments.
# Separate for-loops to respect the ordering of slot variables from v1.
for var in var_list:
self.add_slot(var, 'm')

def _prepare_local(self, var_device, var_dtype, apply_state):
super(Lion, self)._prepare_local(var_device, var_dtype, apply_state)

beta_1_t = tf.identity(self._get_hyper('beta_1', var_dtype))
beta_2_t = tf.identity(self._get_hyper('beta_2', var_dtype))
wd_t = tf.identity(self._get_hyper('wd', var_dtype))
lr = apply_state[(var_device, var_dtype)]['lr_t']
apply_state[(var_device, var_dtype)].update(
dict(
lr=lr,
beta_1_t=beta_1_t,
one_minus_beta_1_t=1 - beta_1_t,
beta_2_t=beta_2_t,
one_minus_beta_2_t=1 - beta_2_t,
wd_t=wd_t))

@tf.function(jit_compile=True)
def _resource_apply_dense(self, grad, var, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = ((apply_state or {}).get((var_device, var_dtype)) or
self._fallback_apply_state(var_device, var_dtype))

m = self.get_slot(var, 'm')
var_t = var.assign_sub(
coefficients['lr_t'] *
(tf.math.sign(m * coefficients['beta_1_t'] +
grad * coefficients['one_minus_beta_1_t']) +
var * coefficients['wd_t']))
with tf.control_dependencies([var_t]):
m.assign(m * coefficients['beta_2_t'] +
grad * coefficients['one_minus_beta_2_t'])

@tf.function(jit_compile=True)
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = ((apply_state or {}).get((var_device, var_dtype)) or
self._fallback_apply_state(var_device, var_dtype))

m = self.get_slot(var, 'm')
m_t = m.assign(m * coefficients['beta_1_t'])
m_scaled_g_values = grad * coefficients['one_minus_beta_1_t']
m_t = m_t.scatter_add(tf.IndexedSlices(m_scaled_g_values, indices))
var_t = var.assign_sub(coefficients['lr'] *
(tf.math.sign(m_t) + var * coefficients['wd_t']))

with tf.control_dependencies([var_t]):
m_t = m_t.scatter_add(tf.IndexedSlices(-m_scaled_g_values, indices))
m_t = m_t.assign(m_t * coefficients['beta_2_t'] /
coefficients['beta_1_t'])
m_scaled_g_values = grad * coefficients['one_minus_beta_2_t']
m_t.scatter_add(tf.IndexedSlices(m_scaled_g_values, indices))

def get_config(self):
config = super(Lion, self).get_config()
config.update({
'learning_rate': self._serialize_hyperparameter('learning_rate'),
'beta_1': self._serialize_hyperparameter('beta_1'),
'beta_2': self._serialize_hyperparameter('beta_2'),
'wd': self._serialize_hyperparameter('wd'),
})
return config
with tf.control_dependencies([var_t]):
m_t = m_t.scatter_add(tf.IndexedSlices(-m_scaled_g_values, indices))
m_t = m_t.assign(
m_t * coefficients['beta_2_t'] / coefficients['beta_1_t']
)
m_scaled_g_values = grad * coefficients['one_minus_beta_2_t']
m_t.scatter_add(tf.IndexedSlices(m_scaled_g_values, indices))

def get_config(self):
config = super(Lion, self).get_config()
config.update({
'learning_rate': self._serialize_hyperparameter('learning_rate'),
'beta_1': self._serialize_hyperparameter('beta_1'),
'beta_2': self._serialize_hyperparameter('beta_2'),
'wd': self._serialize_hyperparameter('wd'),
})
return config