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# Copyright 2025-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module contains the implementation of the LoRA-FA optimizer.
"""
from __future__ import annotations
import math
from collections.abc import Iterable
from typing import Callable
import torch
import torch.nn as nn
from accelerate.utils.imports import is_bf16_available
from torch import autocast
from torch.optim import Optimizer
from ..peft_model import PeftModel
from ..utils.other import infer_device
class LoraFAOptimizer(Optimizer):
"""
Implements the LoRA-FA optimizer designed specifically for training Low-Rank Adaptation (LoRA) parameters
efficiently. Note that LoraFAOptimizer is based on adamw-hf in transformers, with only LoRA part modified. Without
LoRA it will fall back to adamw-hf.
Args:
params (Iterable[nn.parameter.Parameter]): Parameters to optimize.
lr (float, optional): Learning rate (default: 1e-3).
betas (Tuple[float, float], optional):
Coefficients for computing running averages of gradient and squared gradient (default: (0.9, 0.999)).
eps (float, optional): Term added to denominator to improve numerical stability (default: 1e-6).
weight_decay (float, optional): Weight decay (L2 penalty) (default: 0.0).
correct_bias (bool, optional): Whether to apply bias correction as in original Adam (default: True).
Args in sub-function step:
closure (Callable, optional): A closure that reevaluates the model and returns the loss.
Reference:
- LoRA-FA: https://huggingface.co/papers/2308.03303
"""
def __init__(
self,
params: Iterable[nn.parameter.Parameter],
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,
):
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 not 0.0 <= eps:
raise ValueError(f"Invalid epsilon value: {eps} - should be >= 0.0")
defaults = {
"lr": lr,
"betas": betas,
"eps": eps,
"weight_decay": weight_decay,
"correct_bias": correct_bias,
}
super().__init__(params, defaults)
@torch.no_grad()
def step(self, closure: Callable = None):
"""
Performs a single optimization step.
Arguments:
closure (`Callable`, *optional*): 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:
scaling_factor = group["scaling_factor"]
param_list = []
name_list = []
for p, n in zip(group["params"], group["names"]):
# Skip non-lora no-grad module, since we need lora_A which is no-grad.
if "lora" not in n and p.grad is None:
continue
grad = p.grad
if "lora" in n:
param_list.append(p)
name_list.append(n)
if len(param_list) == 2:
name = n[: n.find("lora")] + "lora"
elif len(param_list) == 1:
continue
else:
name = n
# param_list contains a pair of A and B adapters
# i.e., param_list -> [A,B]
state = self.state[name]
# State initialization
if len(state) == 0:
if len(param_list) == 2:
state["step"] = 0
# Exponential moving average of gradient values
state["exp_avg_B"] = torch.zeros_like(param_list[1])
# Exponential moving average of squared gradient values
state["exp_avg_sq_B"] = torch.zeros_like(param_list[1])
else:
state["step"] = 0
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(p)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(p)
# Below is the LoRA-FA part
# 1. In this part, we optimize the gradient of B as:
# g^B = \left(\frac{r}{\alpha}\right)^2 (A^\top A)^{-1} g_{\text{LoRA-FA}}^B
# to min the func as described below:
# \min_{g^B} \|\hat{g}_\text{LoRA-FA} - g\|_F^2
# 2. After the gradient of B is ready, update the optimizer state
if len(param_list) == 2:
A = param_list[0]
B = param_list[1]
grad_B_orin = B.grad
# projection
delta = 1e-8
# computing the inverse matrix
AA_T = A @ A.T
AA_T_inv = torch.linalg.pinv(AA_T + delta * torch.eye(A.shape[0]).to(A.device))
device_type = infer_device()
if is_bf16_available():
with autocast(device_type=device_type, dtype=torch.bfloat16):
grad_B = (1 / scaling_factor**2) * (grad_B_orin @ AA_T_inv)
else:
grad_B = (1 / scaling_factor**2) * (grad_B_orin @ AA_T_inv)
if grad_B.dtype != B.grad.dtype:
grad_B = grad_B.to(B.grad.dtype)
exp_avg_B, exp_avg_sq_B = state["exp_avg_B"], state["exp_avg_sq_B"]
beta1, beta2 = group["betas"]
state["step"] += 1
exp_avg_B.mul_(beta1).add_(grad_B, alpha=(1.0 - beta1))
exp_avg_sq_B.mul_(beta2).addcmul_(grad_B, grad_B, value=1.0 - beta2)
denom_B = exp_avg_sq_B.sqrt().add_(group["eps"])
step_size = group["lr"]
if group["correct_bias"]: # No bias correction for Bert
bias_correction1 = 1.0 - beta1 ** state["step"]
bias_correction2 = 1.0 - beta2 ** state["step"]
step_size = step_size * math.sqrt(bias_correction2) / bias_correction1
B.addcdiv_(exp_avg_B, denom_B, value=-step_size)
if group["weight_decay"] > 0.0:
B.add_(B, alpha=(-group["lr"] * group["weight_decay"]))
param_list = []
name_list = []
# Below is the original AdamW
else:
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
beta1, beta2 = group["betas"]
state["step"] += 1
# 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)
denom = exp_avg_sq.sqrt().add_(group["eps"])
step_size = group["lr"]
if group["correct_bias"]: # No bias correction for Bert
bias_correction1 = 1.0 - beta1 ** state["step"]
bias_correction2 = 1.0 - beta2 ** state["step"]
step_size = step_size * math.sqrt(bias_correction2) / bias_correction1
p.addcdiv_(exp_avg, denom, value=-step_size)
# Just adding the square of the weights to the loss function is *not*
# the correct way of using L2 regularization/weight decay with Adam,
# since that will interact with the m and v parameters in strange ways.
#
# Instead we want to decay the weights in a manner that doesn't interact
# with the m/v parameters. This is equivalent to adding the square
# of the weights to the loss with plain (non-momentum) SGD.
# Add weight decay at the end (fixed version)
if group["weight_decay"] > 0.0:
p.add_(p, alpha=(-group["lr"] * group["weight_decay"]))
return loss
def create_lorafa_optimizer(
model: PeftModel, r: int, lora_alpha: int, lr: float, weight_decay: float = 0.0, use_rslora: bool = False
) -> Optimizer:
"""
Helper function to instantiate a lorafa optimizer specifically configured for a given model using the LoRA method.
This function will:
- Disable gradient updates for the "lora_A" parameters (these are typically frozen during LoRA training).
- Compute the scaling factor based on provided `lora_alpha` and rank `r` for proper gradient projection.
- Create and configure parameter groups for the optimizer including specified learning rate, weight decay, and
additional optimizer options.
For hyper-params, LoRA-FA uses the same hyper-params as AdamW, except for the LoRA hyper-params (r, lora_alpha,
use_rslora). One can always use the same hyper-params such as lr and weight_decay, as AdamW in LoRA tuning.
Args:
model (PeftModel): The model containing LoRA-adapted parameters.
r (int): Rank of the LoRA decomposition.
lora_alpha (int): Scaling factor for LoRA parameterization.
lr (float): Learning rate for optimizer updates.
weight_decay (float): Weight decay for AdamW.
use_rslora (bool):
whether to use rslora. In rslora, the lora scaling factor becomes to lora_alpha / math.sqrt(r) instead of
lora_alpha / r.
Returns:
Optimizer: Configured lorafa optimizer instance ready for training.
"""
for name, param in model.named_parameters():
if "lora_A" in name:
param.requires_grad_(False)
lora_scaling = lora_alpha / math.sqrt(r) if use_rslora else lora_alpha / r
param_groups = [
{
"params": model.parameters(),
"lr": lr,
"names": [name for name, _ in model.named_parameters()],
"scaling_factor": lora_scaling,
"betas": (0.9, 0.999),
"weight_decay": weight_decay,
}
]
return LoraFAOptimizer(param_groups)
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