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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 Optimizer2State
class LAMB(Optimizer2State):
def __init__(
self,
params,
lr=1e-3,
bias_correction=True,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0,
amsgrad=False,
adam_w_mode=True,
optim_bits=32,
args=None,
min_8bit_size=4096,
percentile_clipping=100,
block_wise=False,
max_unorm=1.0,
):
"""
Base LAMB optimizer.
Arguments:
params (`torch.tensor`):
The input parameters to optimize.
lr (`float`, defaults to 1e-3):
The learning rate.
bias_correction (`bool`, defaults to `True`):
Whether to apply bias correction to the first and second-order moments.
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.
eps (`float`, defaults to 1e-8):
The epsilon value prevents division by zero in the optimizer.
weight_decay (`float`, defaults to 1e-2):
The weight decay value for the optimizer.
amsgrad (`bool`, defaults to `False`):
Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
adam_w_mode (`bool`, defaults to `True`):
Whether to use the AdamW variant.
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.
max_unorm (`float`, defaults to 1.0):
The maximum gradient norm.
"""
super().__init__(
"lamb",
params,
lr,
betas,
eps,
weight_decay,
optim_bits,
args,
min_8bit_size,
percentile_clipping,
block_wise,
max_unorm=1.0,
)
class LAMB8bit(Optimizer2State):
def __init__(
self,
params,
lr=1e-3,
bias_correction=True,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0,
amsgrad=False,
adam_w_mode=True,
args=None,
min_8bit_size=4096,
percentile_clipping=100,
block_wise=False,
max_unorm=1.0,
):
"""
8-bit LAMB optimizer.
Arguments:
params (`torch.tensor`):
The input parameters to optimize.
lr (`float`, defaults to 1e-3):
The learning rate.
bias_correction (`bool`, defaults to `True`):
Whether to apply bias correction to the first and second-order moments.
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.
eps (`float`, defaults to 1e-8):
The epsilon value prevents division by zero in the optimizer.
weight_decay (`float`, defaults to 1e-2):
The weight decay value for the optimizer.
amsgrad (`bool`, defaults to `False`):
Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
adam_w_mode (`bool`, defaults to `True`):
Whether to use the AdamW variant.
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.
max_unorm (`float`, defaults to 1.0):
The maximum gradient norm.
"""
super().__init__(
"lamb",
params,
lr,
betas,
eps,
weight_decay,
8,
args,
min_8bit_size,
percentile_clipping,
block_wise,
max_unorm=1.0,
)
class LAMB32bit(Optimizer2State):
def __init__(
self,
params,
lr=1e-3,
bias_correction=True,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0,
amsgrad=False,
adam_w_mode=True,
args=None,
min_8bit_size=4096,
percentile_clipping=100,
block_wise=False,
max_unorm=1.0,
):
"""
32-bit LAMB optimizer.
Arguments:
params (`torch.tensor`):
The input parameters to optimize.
lr (`float`, defaults to 1e-3):
The learning rate.
bias_correction (`bool`, defaults to `True`):
Whether to apply bias correction to the first and second-order moments.
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.
eps (`float`, defaults to 1e-8):
The epsilon value prevents division by zero in the optimizer.
weight_decay (`float`, defaults to 1e-2):
The weight decay value for the optimizer.
amsgrad (`bool`, defaults to `False`):
Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
adam_w_mode (`bool`, defaults to `True`):
Whether to use the AdamW variant.
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.
max_unorm (`float`, defaults to 1.0):
The maximum gradient norm.
"""
super().__init__(
"lamb",
params,
lr,
betas,
eps,
weight_decay,
32,
args,
min_8bit_size,
percentile_clipping,
block_wise,
max_unorm=1.0,
)
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