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"""
Lion Optimizer for BitTransformerLM Extensions
==============================================
Implementation of the Lion optimizer (EvoLved Sign Momentum).
Based on "Symbolic Discovery of Optimization Algorithms" research.
Key features:
- Sign-based momentum updates
- Extremely memory efficient (only stores momentum)
- Often outperforms Adam/AdamW with larger learning rates
- Compatible with BitTransformerLM's training infrastructure
"""
import torch
from torch.optim.optimizer import Optimizer
from typing import Any, Dict, List, Optional, Tuple, Union
class Lion(Optimizer):
"""
Lion optimizer implementation.
Lion uses the sign of the interpolated momentum for parameter updates,
making it very memory efficient while maintaining competitive performance.
Args:
params: Iterable of parameters to optimize
lr: Learning rate (default: 1e-4, typically needs to be smaller than Adam)
betas: Coefficients for computing momentum (default: (0.9, 0.99))
weight_decay: Weight decay coefficient (default: 0.0)
eps: Small constant for numerical stability (default: 1e-8)
maximize: Whether to maximize the objective (default: False)
"""
def __init__(
self,
params,
lr: float = 1e-4,
betas: Tuple[float, float] = (0.9, 0.99),
weight_decay: float = 0.0,
eps: float = 1e-8,
maximize: bool = False,
):
if not 0.0 <= lr:
raise ValueError(f"Invalid learning rate: {lr}")
if not 0.0 <= betas[0] < 1.0:
raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
if not 0.0 <= betas[1] < 1.0:
raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
if not 0.0 <= weight_decay:
raise ValueError(f"Invalid weight_decay value: {weight_decay}")
if not 0.0 <= eps:
raise ValueError(f"Invalid epsilon value: {eps}")
defaults = dict(
lr=lr,
betas=betas,
weight_decay=weight_decay,
eps=eps,
maximize=maximize,
)
super().__init__(params, defaults)
@torch.no_grad()
def step(self, closure=None):
"""Perform a single optimization step."""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad
if group["maximize"]:
grad = -grad
if grad.dtype in {torch.float16, torch.bfloat16}:
grad = grad.float()
state = self.state[p]
# State initialization
if len(state) == 0:
state["momentum"] = torch.zeros_like(p, memory_format=torch.preserve_format)
momentum = state["momentum"]
beta1, beta2 = group["betas"]
# Weight decay (applied to parameters, not gradients)
if group["weight_decay"] != 0:
p.mul_(1 - group["lr"] * group["weight_decay"])
# Interpolate between momentum and gradient
# c_t = beta1 * m_{t-1} + (1 - beta1) * g_t
interpolated = momentum.mul(beta1).add_(grad, alpha=1 - beta1)
# Update parameters using sign of interpolated momentum
# theta_t = theta_{t-1} - lr * sign(c_t)
p.add_(torch.sign(interpolated), alpha=-group["lr"])
# Update momentum
# m_t = beta2 * m_{t-1} + (1 - beta2) * g_t
momentum.mul_(beta2).add_(grad, alpha=1 - beta2)
return loss
def configure_lion_optimizer(
model: torch.nn.Module,
lr: float = 1e-4,
betas: Tuple[float, float] = (0.9, 0.99),
weight_decay: float = 0.01,
total_steps: Optional[int] = None,
warmup_ratio: float = 0.1,
**lion_kwargs
) -> Tuple[Lion, Optional[torch.optim.lr_scheduler._LRScheduler]]:
"""
Configure Lion optimizer with OneCycle learning rate schedule.
This function provides a drop-in replacement for BitTransformerLM's
configure_optimizer function, using Lion instead of AdamW.
Note: Lion typically works well with learning rates about 3-10x smaller
than Adam/AdamW, but higher weight decay (0.01-0.1).
Args:
model: PyTorch model to optimize
lr: Peak learning rate (typically smaller than Adam)
betas: Beta coefficients for momentum computation
weight_decay: Weight decay coefficient (can be higher than Adam)
total_steps: Total training steps for OneCycle schedule
warmup_ratio: Fraction of steps for warmup
**lion_kwargs: Additional arguments for Lion optimizer
Returns:
Tuple of (optimizer, scheduler)
"""
# Filter parameters that need weight decay
decay_params = []
no_decay_params = []
for name, param in model.named_parameters():
if not param.requires_grad:
continue
# Apply weight decay to weights but not biases/norms
if param.dim() >= 2:
decay_params.append(param)
else:
no_decay_params.append(param)
param_groups = [
{"params": decay_params, "weight_decay": weight_decay},
{"params": no_decay_params, "weight_decay": 0.0},
]
optimizer = Lion(
param_groups,
lr=lr,
betas=betas,
**lion_kwargs
)
scheduler = None
if total_steps is not None and total_steps > 0:
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=lr,
total_steps=total_steps,
pct_start=warmup_ratio,
anneal_strategy='cos',
cycle_momentum=False, # Lion doesn't use cycling momentum
div_factor=25.0,
final_div_factor=1e4,
)
return optimizer, scheduler
def create_lion_training_config(
lr: float = 1e-4,
betas: Tuple[float, float] = (0.9, 0.99),
weight_decay: float = 0.01,
**kwargs
) -> Dict[str, Any]:
"""
Create a training configuration dictionary for Lion optimizer.
This can be used with BitTransformerLM's training scripts by passing
the config to the training loop.
Args:
lr: Learning rate
betas: Beta coefficients for momentum
weight_decay: Weight decay coefficient
**kwargs: Additional configuration options
Returns:
Dictionary containing training configuration
"""
config = {
"optimizer_type": "lion",
"optimizer_config": {
"lr": lr,
"betas": betas,
"weight_decay": weight_decay,
**kwargs
},
"scheduler_type": "onecycle",
}
return config
class AdaptiveLion(Lion):
"""
Enhanced Lion optimizer with adaptive learning rate scaling.
This variant automatically adjusts the learning rate based on the
magnitude of gradients and momentum, potentially improving stability.
"""
def __init__(
self,
params,
lr: float = 1e-4,
betas: Tuple[float, float] = (0.9, 0.99),
weight_decay: float = 0.0,
eps: float = 1e-8,
maximize: bool = False,
adaptive_scale: float = 0.1,
min_scale: float = 0.01,
max_scale: float = 10.0,
):
"""
Args:
adaptive_scale: Scaling factor for adaptive adjustment
min_scale: Minimum learning rate scale
max_scale: Maximum learning rate scale
"""
self.adaptive_scale = adaptive_scale
self.min_scale = min_scale
self.max_scale = max_scale
super().__init__(params, lr, betas, weight_decay, eps, maximize)
@torch.no_grad()
def step(self, closure=None):
"""Perform optimization step with adaptive scaling."""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad
if group["maximize"]:
grad = -grad
if grad.dtype in {torch.float16, torch.bfloat16}:
grad = grad.float()
state = self.state[p]
if len(state) == 0:
state["momentum"] = torch.zeros_like(p, memory_format=torch.preserve_format)
state["step"] = 0
momentum = state["momentum"]
state["step"] += 1
beta1, beta2 = group["betas"]
# Adaptive learning rate based on gradient magnitude
grad_norm = grad.norm().item()
momentum_norm = momentum.norm().item()
# Scale learning rate based on gradient/momentum ratio
if momentum_norm > 1e-8:
scale = 1.0 + self.adaptive_scale * (grad_norm / momentum_norm - 1.0)
scale = torch.clamp(torch.tensor(scale), self.min_scale, self.max_scale).item()
else:
scale = 1.0
adaptive_lr = group["lr"] * scale
# Weight decay
if group["weight_decay"] != 0:
p.mul_(1 - adaptive_lr * group["weight_decay"])
# Lion update with adaptive learning rate
interpolated = momentum.mul(beta1).add_(grad, alpha=1 - beta1)
p.add_(torch.sign(interpolated), alpha=-adaptive_lr)
momentum.mul_(beta2).add_(grad, alpha=1 - beta2)
return loss
def configure_adaptive_lion_optimizer(
model: torch.nn.Module,
lr: float = 1e-4,
adaptive_scale: float = 0.1,
**kwargs
) -> Tuple[AdaptiveLion, Optional[torch.optim.lr_scheduler._LRScheduler]]:
"""Configure AdaptiveLion optimizer with learning rate scheduling."""
# Similar to configure_lion_optimizer but with AdaptiveLion
decay_params = []
no_decay_params = []
for name, param in model.named_parameters():
if not param.requires_grad:
continue
if param.dim() >= 2:
decay_params.append(param)
else:
no_decay_params.append(param)
param_groups = [
{"params": decay_params, "weight_decay": kwargs.get("weight_decay", 0.01)},
{"params": no_decay_params, "weight_decay": 0.0},
]
optimizer = AdaptiveLion(
param_groups,
lr=lr,
adaptive_scale=adaptive_scale,
**{k: v for k, v in kwargs.items() if k != "weight_decay"}
)
scheduler = None
total_steps = kwargs.get("total_steps")
if total_steps is not None and total_steps > 0:
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=lr,
total_steps=total_steps,
pct_start=kwargs.get("warmup_ratio", 0.1),
anneal_strategy='cos',
cycle_momentum=False,
div_factor=25.0,
final_div_factor=1e4,
)
return optimizer, scheduler
# Example usage and integration helpers
def integrate_with_bittransformerlm():
"""
Example of how to integrate Lion optimizer with BitTransformerLM training.
Usage:
from BTLM_Extensions.lion_optimizer import configure_lion_optimizer
# Replace the standard optimizer configuration
# Note: Lion typically needs smaller learning rates than Adam
optimizer, scheduler = configure_lion_optimizer(
model, lr=1e-4, weight_decay=0.01, total_steps=1000
)
# Use in training loop
train_loop(model, data, optimizer=optimizer, scheduler=scheduler)
# For adaptive version:
from BTLM_Extensions.lion_optimizer import configure_adaptive_lion_optimizer
optimizer, scheduler = configure_adaptive_lion_optimizer(
model, lr=1e-4, adaptive_scale=0.1, total_steps=1000
)
"""
pass
if __name__ == "__main__":
# Simple test of the optimizer
import torch.nn as nn
model = nn.Sequential(
nn.Linear(10, 20),
nn.ReLU(),
nn.Linear(20, 1)
)
print("Testing standard Lion optimizer...")
optimizer, scheduler = configure_lion_optimizer(model, lr=1e-4, total_steps=100)
# Simple training step
x = torch.randn(32, 10)
y = torch.randn(32, 1)
pred = model(x)
loss = nn.functional.mse_loss(pred, y)
initial_loss = loss.item()
loss.backward()
optimizer.step()
if scheduler:
scheduler.step()
print(f"Initial loss: {initial_loss:.4f}")
# Test adaptive version
print("Testing Adaptive Lion optimizer...")
model2 = nn.Sequential(
nn.Linear(10, 20),
nn.ReLU(),
nn.Linear(20, 1)
)
optimizer2, scheduler2 = configure_adaptive_lion_optimizer(
model2, lr=1e-4, adaptive_scale=0.1, total_steps=100
)
pred2 = model2(x)
loss2 = nn.functional.mse_loss(pred2, y)
loss2.backward()
optimizer2.step()
if scheduler2:
scheduler2.step()
print("Lion optimizers test completed successfully!")
print(f"Standard Lion loss: {initial_loss:.4f}")
print(f"Adaptive Lion loss: {loss2.item():.4f}") |