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import inspect
import torch
from accelerate.logging import get_logger
logger = get_logger("finetrainers")
def get_optimizer(
params_to_optimize,
optimizer_name: str = "adam",
learning_rate: float = 1e-3,
beta1: float = 0.9,
beta2: float = 0.95,
beta3: float = 0.98,
epsilon: float = 1e-8,
weight_decay: float = 1e-4,
prodigy_decouple: bool = False,
prodigy_use_bias_correction: bool = False,
prodigy_safeguard_warmup: bool = False,
use_8bit: bool = False,
use_4bit: bool = False,
use_torchao: bool = False,
use_deepspeed: bool = False,
use_cpu_offload_optimizer: bool = False,
offload_gradients: bool = False,
) -> torch.optim.Optimizer:
optimizer_name = optimizer_name.lower()
# Use DeepSpeed optimzer
if use_deepspeed:
from accelerate.utils import DummyOptim
return DummyOptim(
params_to_optimize,
lr=learning_rate,
betas=(beta1, beta2),
eps=epsilon,
weight_decay=weight_decay,
)
# TODO: consider moving the validation logic to `args.py` when we have torchao.
if use_8bit and use_4bit:
raise ValueError("Cannot set both `use_8bit` and `use_4bit` to True.")
if (use_torchao and (use_8bit or use_4bit)) or use_cpu_offload_optimizer:
try:
import torchao # noqa
except ImportError:
raise ImportError(
"To use optimizers from torchao, please install the torchao library: `USE_CPP=0 pip install torchao`."
)
if not use_torchao and use_4bit:
raise ValueError("4-bit Optimizers are only supported with torchao.")
# Optimizer creation
supported_optimizers = ["adam", "adamw", "prodigy", "came"]
if optimizer_name not in supported_optimizers:
logger.warning(
f"Unsupported choice of optimizer: {optimizer_name}. Supported optimizers include {supported_optimizers}. Defaulting to `AdamW`."
)
optimizer_name = "adamw"
if (use_8bit or use_4bit) and optimizer_name not in ["adam", "adamw"]:
raise ValueError("`use_8bit` and `use_4bit` can only be used with the Adam and AdamW optimizers.")
if use_8bit:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
if optimizer_name == "adamw":
if use_torchao:
from torchao.prototype.low_bit_optim import AdamW4bit, AdamW8bit
optimizer_class = AdamW8bit if use_8bit else AdamW4bit if use_4bit else torch.optim.AdamW
else:
optimizer_class = bnb.optim.AdamW8bit if use_8bit else torch.optim.AdamW
init_kwargs = {
"betas": (beta1, beta2),
"eps": epsilon,
"weight_decay": weight_decay,
}
elif optimizer_name == "adam":
if use_torchao:
from torchao.prototype.low_bit_optim import Adam4bit, Adam8bit
optimizer_class = Adam8bit if use_8bit else Adam4bit if use_4bit else torch.optim.Adam
else:
optimizer_class = bnb.optim.Adam8bit if use_8bit else torch.optim.Adam
init_kwargs = {
"betas": (beta1, beta2),
"eps": epsilon,
"weight_decay": weight_decay,
}
elif optimizer_name == "prodigy":
try:
import prodigyopt
except ImportError:
raise ImportError("To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`")
optimizer_class = prodigyopt.Prodigy
if learning_rate <= 0.1:
logger.warning(
"Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0"
)
init_kwargs = {
"lr": learning_rate,
"betas": (beta1, beta2),
"beta3": beta3,
"eps": epsilon,
"weight_decay": weight_decay,
"decouple": prodigy_decouple,
"use_bias_correction": prodigy_use_bias_correction,
"safeguard_warmup": prodigy_safeguard_warmup,
}
elif optimizer_name == "came":
try:
import came_pytorch
except ImportError:
raise ImportError("To use CAME, please install the came-pytorch library: `pip install came-pytorch`")
optimizer_class = came_pytorch.CAME
init_kwargs = {
"lr": learning_rate,
"eps": (1e-30, 1e-16),
"betas": (beta1, beta2, beta3),
"weight_decay": weight_decay,
}
if use_cpu_offload_optimizer:
from torchao.prototype.low_bit_optim import CPUOffloadOptimizer
if "fused" in inspect.signature(optimizer_class.__init__).parameters:
init_kwargs.update({"fused": True})
optimizer = CPUOffloadOptimizer(
params_to_optimize, optimizer_class=optimizer_class, offload_gradients=offload_gradients, **init_kwargs
)
else:
optimizer = optimizer_class(params_to_optimize, **init_kwargs)
return optimizer
def gradient_norm(parameters):
norm = 0
for param in parameters:
if param.grad is None:
continue
local_norm = param.grad.detach().data.norm(2)
norm += local_norm.item() ** 2
norm = norm**0.5
return norm
def max_gradient(parameters):
max_grad_value = float("-inf")
for param in parameters:
if param.grad is None:
continue
local_max_grad = param.grad.detach().data.abs().max()
max_grad_value = max(max_grad_value, local_max_grad.item())
return max_grad_value
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