# Copyright 2016-2023 Cerebras Systems
# SPDX-License-Identifier: BSD-3-Clause
"""contains the Cerebras Adam and AdamW implementation."""
from typing import Callable, Tuple
import torch
import cerebras.pytorch as cstorch
from .optimizer import Optimizer, ParamsT
class AdamBase(Optimizer):
"""
Base for Adam and AdamW optimizer implemented to conform to execution within
the constraints of the Cerebras WSE, including pre-initilizing optimizer
state and performing a gradual reduction of bias correction using
exponential decay of `beta1_power` and `beta2_power` rather than recomputing
`beta1^step` each step.
"""
def __init__(
self,
params: ParamsT,
lr: float = 1e-3,
betas: Tuple[float, float] = (0.9, 0.999),
eps: float = 1e-6,
weight_decay: float = 0.0,
l2_regularization_rate: float = 0.0,
correct_bias: bool = True,
amsgrad: bool = False,
):
if lr < 0.0:
raise ValueError(f"Invalid learning rate: {lr} - should be >= 0.0")
if not 0.0 <= betas[0] < 1.0:
raise ValueError(
f"Invalid beta parameter: {betas[0]} - should be in [0.0, 1.0]"
)
if not 0.0 <= betas[1] < 1.0:
raise ValueError(
f"Invalid beta parameter: {betas[1]} - should be in [0.0, 1.0]"
)
if eps < 0.0:
raise ValueError(f"Invalid epsilon value: {eps} - should be >= 0.0")
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
l2_regularization_rate=l2_regularization_rate,
correct_bias=correct_bias,
amsgrad=amsgrad,
)
super().__init__(params, defaults)
def preinitialize(self):
"""
Allocates tensors for the optimizer state to allow direct compilation
of the model before the first step.
"""
for group in self.param_groups:
for p in group["params"]:
state = self.state[p]
# State initialization
# Exponential moving average of gradient values
state["exp_avg"] = cstorch.zeros_like(p)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = cstorch.zeros_like(p)
if group["amsgrad"]:
state["max_exp_avg_sq"] = cstorch.zeros_like(p)
if group["correct_bias"]: # No bias correction for Bert
beta1, beta2 = group["betas"]
# beta1 ^ step, initialized for used on step 1
state["beta1_power"] = torch.tensor(beta1).to(p.device)
state["beta2_power"] = torch.tensor(beta2).to(p.device)
@torch.no_grad()
def step(self, closure: Callable = None):
"""
Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad
# This is equivalent to Algorithm 2 i.e Adam with L2 regularization
# (https://arxiv.org/pdf/1711.05101.pdf)
if group["l2_regularization_rate"] > 0.0:
grad = grad.add(p, alpha=group["l2_regularization_rate"])
state = self.state[p]
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
beta1, beta2 = group["betas"]
# Decay the first and second moment running average coefficient
# In-place operations to update the averages at the same time.
exp_avg.mul_(beta1).add_(grad, alpha=1.0 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)
if group["amsgrad"]:
max_exp_avg_sq = state["max_exp_avg_sq"]
torch.maximum(
max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq
)
state["max_exp_avg_sq"] = max_exp_avg_sq
denom = max_exp_avg_sq.sqrt().add_(group["eps"])
else:
denom = exp_avg_sq.sqrt().add_(group["eps"])
update = exp_avg / denom
if group["correct_bias"]: # No bias correction for Bert.
one = torch.tensor(
1.0, dtype=torch.float32, device=p.device
)
bias_correction1 = one - state["beta1_power"]
bias_correction2 = one - state["beta2_power"]
step_size = torch.sqrt(bias_correction2) / bias_correction1
update *= step_size
# Update `beta1^step` for the next step.
state["beta1_power"] *= beta1
state["beta2_power"] *= beta2
# Applying weight decay here is equivalent to Algorithm 2
# (https://arxiv.org/pdf/1711.05101.pdf)
# Decoupled Weight Decay regularization i.e AdamW
update.add_(p * group["weight_decay"])
# Scale the update by the learning rate.
update *= group["lr"]
# Finally, update the weight data.
p.sub_(update)
return loss
def load_state_dict(self, state_dict):
"""
Loads the optimizer state.
Arguments:
state_dict (dict): optimizer state. Should be an object returned
from a call to :meth:`state_dict`.
This overrides torch.optim.Optimizer to add checkpoint compatibility
with the AdamW from huggingface_common, which is otherwise API
compatible.
"""
# huggingface AdamW and PyTorch Adam stores a `step`
# Cerebras (this) AdamW/Adam stores `beta1^step` as `beta1_power`.
for param, state in state_dict["state"].items():
if "step" in state and "beta1_power" not in state:
step = state.pop("step")
# go find betas for this parameter
correct_bias = False
beta1 = None
beta2 = None
for param_group in state_dict["param_groups"]:
if param in param_group["params"]:
correct_bias = param_group["correct_bias"]
beta1, beta2 = param_group["betas"]
break
if correct_bias:
state["beta1_power"] = torch.tensor(
beta1**step, dtype=torch.float32
)
state["beta2_power"] = torch.tensor(
beta2**step, dtype=torch.float32
)
super().load_state_dict(state_dict)
[docs]class AdamW(AdamBase):
"""AdamW specific overrides to AdamBase."""
def __init__(
self,
params: ParamsT,
lr: float = 1e-3,
betas: Tuple[float, float] = (0.9, 0.999),
eps: float = 1e-6,
weight_decay: float = 0.0,
correct_bias: bool = True,
amsgrad: bool = False,
):
super().__init__(
params=params,
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
l2_regularization_rate=0.0,
correct_bias=correct_bias,
amsgrad=amsgrad,
)
for group in self.param_groups:
group["l2_regularization_rate"] = 0.0
[docs] def load_state_dict(self, state_dict):
"""
Loads the optimizer state.
Arguments:
state_dict (dict): optimizer state. Should be an object returned
from a call to :meth:`state_dict`.
Adds checkpoint compatibility with the AdamW from HuggingFace
"""
for group in state_dict["param_groups"]:
group["l2_regularization_rate"] = 0.0
super().load_state_dict(state_dict)
[docs]class Adam(AdamBase):
"""Adam specific overrides to AdamBase."""
def __init__(
self,
params: ParamsT,
lr: float = 1e-3,
betas: Tuple[float, float] = (0.9, 0.999),
eps: float = 1e-6,
weight_decay: float = 0.0,
amsgrad: bool = False,
):
# This init uses `weight_decay` to be in sync with PyTorch API
super().__init__(
params=params,
lr=lr,
betas=betas,
eps=eps,
weight_decay=0.0,
l2_regularization_rate=weight_decay,
correct_bias=True,
amsgrad=amsgrad,
)
self.handle_weight_decay(self.param_groups)
[docs] def handle_weight_decay(self, param_groups):
for group in param_groups:
group.setdefault(
"l2_regularization_rate", group.pop("weight_decay", 0.0)
)
group["weight_decay"] = 0.0
group["correct_bias"] = True
[docs] def load_state_dict(self, state_dict):
"""
Loads the optimizer state.
Arguments:
state_dict (dict): optimizer state. Should be an object returned
from a call to :meth:`state_dict`.
Adds checkpoint compatibility with the Adam from PyTorch
"""
self.handle_weight_decay(state_dict["param_groups"])
super().load_state_dict(state_dict)