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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from bitsandbytes.optim.optimizer import Optimizer1State
class Lion(Optimizer1State):
def __init__(
self,
params,
lr=1e-4,
betas=(0.9, 0.99),
weight_decay=0,
optim_bits=32,
args=None,
min_8bit_size=4096,
percentile_clipping=100,
block_wise=True,
is_paged=False,
):
"""
Base Lion optimizer.
Arguments:
params (`torch.tensor`):
The input parameters to optimize.
lr (`float`, defaults to 1e-4):
The learning rate.
betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
The beta values are the decay rates of the first and second-order moment of the optimizer.
weight_decay (`float`, defaults to 0):
The weight decay value for the optimizer.
optim_bits (`int`, defaults to 32):
The number of bits of the optimizer state.
args (`object`, defaults to `None`):
An object with additional arguments.
min_8bit_size (`int`, defaults to 4096):
The minimum number of elements of the parameter tensors for 8-bit optimization.
percentile_clipping (`int`, defaults to 100):
Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
block_wise (`bool`, defaults to `True`):
Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
is_paged (`bool`, defaults to `False`):
Whether the optimizer is a paged optimizer or not.
"""
super().__init__(
"lion",
params,
lr,
betas,
0.0,
weight_decay,
optim_bits,
args,
min_8bit_size,
percentile_clipping,
block_wise,
is_paged=is_paged,
)
class Lion8bit(Optimizer1State):
def __init__(
self,
params,
lr=1e-4,
betas=(0.9, 0.99),
weight_decay=0,
args=None,
min_8bit_size=4096,
percentile_clipping=100,
block_wise=True,
is_paged=False,
):
"""
8-bit Lion optimizer.
Arguments:
params (`torch.tensor`):
The input parameters to optimize.
lr (`float`, defaults to 1e-4):
The learning rate.
betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
The beta values are the decay rates of the first and second-order moment of the optimizer.
weight_decay (`float`, defaults to 0):
The weight decay value for the optimizer.
args (`object`, defaults to `None`):
An object with additional arguments.
min_8bit_size (`int`, defaults to 4096):
The minimum number of elements of the parameter tensors for 8-bit optimization.
percentile_clipping (`int`, defaults to 100):
Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
block_wise (`bool`, defaults to `True`):
Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
is_paged (`bool`, defaults to `False`):
Whether the optimizer is a paged optimizer or not.
"""
super().__init__(
"lion",
params,
lr,
betas,
0.0,
weight_decay,
8,
args,
min_8bit_size,
percentile_clipping,
block_wise,
is_paged=is_paged,
)
class Lion32bit(Optimizer1State):
def __init__(
self,
params,
lr=1e-4,
betas=(0.9, 0.99),
weight_decay=0,
args=None,
min_8bit_size=4096,
percentile_clipping=100,
block_wise=True,
is_paged=False,
):
"""
32-bit Lion optimizer.
Arguments:
params (`torch.tensor`):
The input parameters to optimize.
lr (`float`, defaults to 1e-4):
The learning rate.
betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
The beta values are the decay rates of the first and second-order moment of the optimizer.
weight_decay (`float`, defaults to 0):
The weight decay value for the optimizer.
args (`object`, defaults to `None`):
An object with additional arguments.
min_8bit_size (`int`, defaults to 4096):
The minimum number of elements of the parameter tensors for 8-bit optimization.
percentile_clipping (`int`, defaults to 100):
Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
block_wise (`bool`, defaults to `True`):
Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
is_paged (`bool`, defaults to `False`):
Whether the optimizer is a paged optimizer or not.
"""
super().__init__(
"lion",
params,
lr,
betas,
0.0,
weight_decay,
32,
args,
min_8bit_size,
percentile_clipping,
block_wise,
is_paged=is_paged,
)
class PagedLion(Optimizer1State):
def __init__(
self,
params,
lr=1e-4,
betas=(0.9, 0.99),
weight_decay=0,
optim_bits=32,
args=None,
min_8bit_size=4096,
percentile_clipping=100,
block_wise=True,
):
"""
Paged Lion optimizer.
Arguments:
params (`torch.tensor`):
The input parameters to optimize.
lr (`float`, defaults to 1e-4):
The learning rate.
betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
The beta values are the decay rates of the first and second-order moment of the optimizer.
weight_decay (`float`, defaults to 0):
The weight decay value for the optimizer.
optim_bits (`int`, defaults to 32):
The number of bits of the optimizer state.
args (`object`, defaults to `None`):
An object with additional arguments.
min_8bit_size (`int`, defaults to 4096):
The minimum number of elements of the parameter tensors for 8-bit optimization.
percentile_clipping (`int`, defaults to 100):
Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
block_wise (`bool`, defaults to `True`):
Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
"""
super().__init__(
"lion",
params,
lr,
betas,
0.0,
weight_decay,
optim_bits,
args,
min_8bit_size,
percentile_clipping,
block_wise,
is_paged=True,
)
class PagedLion8bit(Optimizer1State):
def __init__(
self,
params,
lr=1e-4,
betas=(0.9, 0.99),
weight_decay=0,
args=None,
min_8bit_size=4096,
percentile_clipping=100,
block_wise=True,
):
"""
Paged 8-bit Lion optimizer.
Arguments:
params (`torch.tensor`):
The input parameters to optimize.
lr (`float`, defaults to 1e-4):
The learning rate.
betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
The beta values are the decay rates of the first and second-order moment of the optimizer.
weight_decay (`float`, defaults to 0):
The weight decay value for the optimizer.
optim_bits (`int`, defaults to 32):
The number of bits of the optimizer state.
args (`object`, defaults to `None`):
An object with additional arguments.
min_8bit_size (`int`, defaults to 4096):
The minimum number of elements of the parameter tensors for 8-bit optimization.
percentile_clipping (`int`, defaults to 100):
Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
block_wise (`bool`, defaults to `True`):
Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
"""
super().__init__(
"lion",
params,
lr,
betas,
0.0,
weight_decay,
8,
args,
min_8bit_size,
percentile_clipping,
block_wise,
is_paged=True,
)
class PagedLion32bit(Optimizer1State):
def __init__(
self,
params,
lr=1e-4,
betas=(0.9, 0.99),
weight_decay=0,
args=None,
min_8bit_size=4096,
percentile_clipping=100,
block_wise=True,
):
"""
Paged 32-bit Lion optimizer.
Arguments:
params (`torch.tensor`):
The input parameters to optimize.
lr (`float`, defaults to 1e-4):
The learning rate.
betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
The beta values are the decay rates of the first and second-order moment of the optimizer.
weight_decay (`float`, defaults to 0):
The weight decay value for the optimizer.
optim_bits (`int`, defaults to 32):
The number of bits of the optimizer state.
args (`object`, defaults to `None`):
An object with additional arguments.
min_8bit_size (`int`, defaults to 4096):
The minimum number of elements of the parameter tensors for 8-bit optimization.
percentile_clipping (`int`, defaults to 100):
Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
block_wise (`bool`, defaults to `True`):
Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
"""
super().__init__(
"lion",
params,
lr,
betas,
0.0,
weight_decay,
32,
args,
min_8bit_size,
percentile_clipping,
block_wise,
is_paged=True,
)
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