498 lines
17 KiB
Python
498 lines
17 KiB
Python
""" ADOPT PyTorch Optimizer
|
|
|
|
ADOPT: Modified Adam Can Converge with Any β2 with the Optimal Rate: https://arxiv.org/abs/2411.02853
|
|
|
|
Modified for reduced dependencies on PyTorch internals from original at: https://github.com/iShohei220/adopt
|
|
|
|
@inproceedings{taniguchi2024adopt,
|
|
author={Taniguchi, Shohei and Harada, Keno and Minegishi, Gouki and Oshima, Yuta and Jeong, Seong Cheol and Nagahara, Go and Iiyama, Tomoshi and Suzuki, Masahiro and Iwasawa, Yusuke and Matsuo, Yutaka},
|
|
booktitle = {Advances in Neural Information Processing Systems},
|
|
title = {ADOPT: Modified Adam Can Converge with Any β2 with the Optimal Rate},
|
|
year = {2024}
|
|
}
|
|
|
|
"""
|
|
|
|
from typing import cast, List, Optional, Tuple, Union
|
|
|
|
import torch
|
|
from torch import Tensor
|
|
|
|
from torch.optim.optimizer import Optimizer
|
|
|
|
__all__ = ["Adopt", "adopt"]
|
|
|
|
def _view_as_real(params, *state_and_grads):
|
|
for i, p in enumerate(params):
|
|
if torch.is_complex(p):
|
|
params[i] = torch.view_as_real(params[i])
|
|
for s in state_and_grads:
|
|
s[i] = torch.view_as_real(s[i])
|
|
|
|
|
|
def _get_scalar_dtype(is_fused=None):
|
|
if is_fused:
|
|
return torch.float32
|
|
return (
|
|
torch.float64 if torch.get_default_dtype() == torch.float64 else torch.float32
|
|
)
|
|
|
|
|
|
def _is_compiling():
|
|
return torch.compiler.is_compiling() if hasattr(torch, 'compiler') else False
|
|
|
|
|
|
def _get_value(x):
|
|
# item is significantly faster than a cpu tensor in eager mode
|
|
if not torch.jit.is_scripting() and _is_compiling():
|
|
return x
|
|
else:
|
|
return x.item() if isinstance(x, torch.Tensor) else x
|
|
|
|
|
|
class Adopt(Optimizer):
|
|
"""
|
|
ADOPT: Modified Adam Can Converge with Any β2 with the Optimal Rate: https://arxiv.org/abs/2411.02853
|
|
|
|
"""
|
|
def __init__(
|
|
self,
|
|
params,
|
|
lr: Union[float, Tensor] = 1e-3,
|
|
betas: Tuple[float, float] = (0.9, 0.9999),
|
|
eps: float = 1e-6,
|
|
weight_decay: float = 0.0,
|
|
decoupled: bool = False,
|
|
*,
|
|
foreach: Optional[bool] = None,
|
|
maximize: bool = False,
|
|
capturable: bool = False,
|
|
differentiable: bool = False,
|
|
):
|
|
if isinstance(lr, Tensor):
|
|
if foreach and not capturable:
|
|
raise ValueError(
|
|
"lr as a Tensor is not supported for capturable=False and foreach=True"
|
|
)
|
|
if lr.numel() != 1:
|
|
raise ValueError("Tensor lr must be 1-element")
|
|
if not 0.0 <= lr:
|
|
raise ValueError(f"Invalid learning rate: {lr}")
|
|
if not 0.0 <= eps:
|
|
raise ValueError(f"Invalid epsilon value: {eps}")
|
|
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}")
|
|
|
|
defaults = dict(
|
|
lr=lr,
|
|
betas=betas,
|
|
eps=eps,
|
|
weight_decay=weight_decay,
|
|
decoupled=decoupled,
|
|
maximize=maximize,
|
|
foreach=foreach,
|
|
capturable=capturable,
|
|
differentiable=differentiable,
|
|
)
|
|
super().__init__(params, defaults)
|
|
|
|
|
|
def __setstate__(self, state):
|
|
super().__setstate__(state)
|
|
for group in self.param_groups:
|
|
group.setdefault("maximize", False)
|
|
group.setdefault("foreach", None)
|
|
group.setdefault("capturable", False)
|
|
group.setdefault("differentiable", False)
|
|
for p in group["params"]:
|
|
p_state = self.state.get(p, [])
|
|
if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
|
|
step_val = float(p_state["step"])
|
|
p_state["step"] = (
|
|
torch.tensor(
|
|
step_val,
|
|
dtype=_get_scalar_dtype(),
|
|
device=p.device,
|
|
)
|
|
if group["capturable"]
|
|
else torch.tensor(step_val, dtype=_get_scalar_dtype())
|
|
)
|
|
|
|
def _init_group(
|
|
self,
|
|
group,
|
|
params_with_grad,
|
|
grads,
|
|
exp_avgs,
|
|
exp_avg_sqs,
|
|
state_steps,
|
|
):
|
|
has_complex = False
|
|
for p in group["params"]:
|
|
if p.grad is None:
|
|
continue
|
|
has_complex |= torch.is_complex(p)
|
|
params_with_grad.append(p)
|
|
if p.grad.is_sparse:
|
|
raise RuntimeError(
|
|
"ADOPT does not support sparse gradients"
|
|
)
|
|
grads.append(p.grad)
|
|
|
|
state = self.state[p]
|
|
# Lazy state initialization
|
|
if len(state) == 0:
|
|
# note(crcrpar): [special device hosting for step]
|
|
# Deliberately host `step` on CPU if both capturable and fused are off.
|
|
# This is because kernel launches are costly on CUDA and XLA.
|
|
state["step"] = (
|
|
torch.zeros(
|
|
(),
|
|
dtype=_get_scalar_dtype(),
|
|
device=p.grad.device,
|
|
)
|
|
if group["capturable"]
|
|
else torch.tensor(0.0, dtype=_get_scalar_dtype())
|
|
)
|
|
# Exponential moving average of gradient values
|
|
state["exp_avg"] = torch.zeros_like(
|
|
p.grad, memory_format=torch.preserve_format
|
|
)
|
|
# Exponential moving average of squared gradient values
|
|
state["exp_avg_sq"] = torch.zeros_like(
|
|
p.grad, memory_format=torch.preserve_format
|
|
)
|
|
|
|
exp_avgs.append(state["exp_avg"])
|
|
exp_avg_sqs.append(state["exp_avg_sq"])
|
|
|
|
if group["differentiable"] and state["step"].requires_grad:
|
|
raise RuntimeError(
|
|
"`requires_grad` is not supported for `step` in differentiable mode"
|
|
)
|
|
|
|
# Foreach without capturable does not support a tensor lr
|
|
if group["foreach"] and torch.is_tensor(group["lr"]) and not group["capturable"]:
|
|
raise RuntimeError(
|
|
"lr as a Tensor is not supported for capturable=False and foreach=True"
|
|
)
|
|
|
|
state_steps.append(state["step"])
|
|
return has_complex
|
|
|
|
#@_use_grad_for_differentiable # FIXME internal context mgr, can't use
|
|
@torch.no_grad()
|
|
def step(self, closure=None):
|
|
"""Perform a single optimization step.
|
|
|
|
Args:
|
|
closure (Callable, optional): A closure that reevaluates the model
|
|
and returns the loss.
|
|
"""
|
|
self._cuda_graph_capture_health_check()
|
|
|
|
loss = None
|
|
if closure is not None:
|
|
with torch.enable_grad():
|
|
loss = closure()
|
|
|
|
for group in self.param_groups:
|
|
params_with_grad: List[Tensor] = []
|
|
grads: List[Tensor] = []
|
|
exp_avgs: List[Tensor] = []
|
|
exp_avg_sqs: List[Tensor] = []
|
|
state_steps: List[Tensor] = []
|
|
beta1, beta2 = group["betas"]
|
|
|
|
has_complex = self._init_group(
|
|
group,
|
|
params_with_grad,
|
|
grads,
|
|
exp_avgs,
|
|
exp_avg_sqs,
|
|
state_steps,
|
|
)
|
|
|
|
adopt(
|
|
params_with_grad,
|
|
grads,
|
|
exp_avgs,
|
|
exp_avg_sqs,
|
|
state_steps,
|
|
has_complex=has_complex,
|
|
beta1=beta1,
|
|
beta2=beta2,
|
|
lr=group["lr"],
|
|
weight_decay=group["weight_decay"],
|
|
decoupled=group["decoupled"],
|
|
eps=group["eps"],
|
|
maximize=group["maximize"],
|
|
foreach=group["foreach"],
|
|
capturable=group["capturable"],
|
|
differentiable=group["differentiable"],
|
|
grad_scale=getattr(self, "grad_scale", None),
|
|
found_inf=getattr(self, "found_inf", None),
|
|
)
|
|
|
|
return loss
|
|
|
|
|
|
def _single_tensor_adopt(
|
|
params: List[Tensor],
|
|
grads: List[Tensor],
|
|
exp_avgs: List[Tensor],
|
|
exp_avg_sqs: List[Tensor],
|
|
state_steps: List[Tensor],
|
|
grad_scale: Optional[Tensor],
|
|
found_inf: Optional[Tensor],
|
|
*,
|
|
has_complex: bool,
|
|
beta1: float,
|
|
beta2: float,
|
|
lr: Union[float, Tensor],
|
|
weight_decay: float,
|
|
decoupled: bool,
|
|
eps: float,
|
|
maximize: bool,
|
|
capturable: bool,
|
|
differentiable: bool,
|
|
):
|
|
assert grad_scale is None and found_inf is None
|
|
|
|
if torch.jit.is_scripting():
|
|
# this assert is due to JIT being dumb and not realizing that the ops below
|
|
# have overloads to handle both float and Tensor lrs, so we just assert it's
|
|
# a float since most people using JIT are using floats
|
|
assert isinstance(lr, float)
|
|
|
|
for i, param in enumerate(params):
|
|
grad = grads[i] if not maximize else -grads[i]
|
|
exp_avg = exp_avgs[i]
|
|
exp_avg_sq = exp_avg_sqs[i]
|
|
step_t = state_steps[i]
|
|
|
|
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
|
|
if capturable and not _is_compiling():
|
|
from torch.optim.optimizer import _get_capturable_supported_devices
|
|
capturable_supported_devices = _get_capturable_supported_devices()
|
|
assert (
|
|
param.device.type == step_t.device.type
|
|
and param.device.type in capturable_supported_devices
|
|
), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
|
|
|
|
# update step
|
|
step_t += 1
|
|
|
|
if weight_decay != 0:
|
|
if decoupled:
|
|
param.add_(param, alpha=-lr * weight_decay)
|
|
else:
|
|
grad = grad.add(param, alpha=weight_decay)
|
|
|
|
if torch.is_complex(param):
|
|
grad = torch.view_as_real(grad)
|
|
if exp_avg is not None:
|
|
exp_avg = torch.view_as_real(exp_avg)
|
|
if exp_avg_sq is not None:
|
|
exp_avg_sq = torch.view_as_real(exp_avg_sq)
|
|
param = torch.view_as_real(param)
|
|
|
|
step = step_t if capturable or differentiable else _get_value(step_t)
|
|
if step == 1:
|
|
exp_avg_sq.addcmul_(grad, grad.conj())
|
|
continue
|
|
|
|
denom = torch.clamp(exp_avg_sq.sqrt(), eps)
|
|
if step == 2:
|
|
exp_avg.addcdiv_(grad, denom)
|
|
else:
|
|
exp_avg.mul_(beta1).addcdiv_(grad, denom, value=1 - beta1)
|
|
|
|
param.add_(exp_avg, alpha=-lr)
|
|
|
|
exp_avg_sq.mul_(beta2).addcmul_(grad, grad.conj(), value=1 - beta2)
|
|
|
|
|
|
def _multi_tensor_adopt(
|
|
params: List[Tensor],
|
|
grads: List[Tensor],
|
|
exp_avgs: List[Tensor],
|
|
exp_avg_sqs: List[Tensor],
|
|
state_steps: List[Tensor],
|
|
grad_scale: Optional[Tensor],
|
|
found_inf: Optional[Tensor],
|
|
*,
|
|
has_complex: bool,
|
|
beta1: float,
|
|
beta2: float,
|
|
lr: Union[float, Tensor],
|
|
weight_decay: float,
|
|
decoupled: bool,
|
|
eps: float,
|
|
maximize: bool,
|
|
capturable: bool,
|
|
differentiable: bool,
|
|
):
|
|
if len(params) == 0:
|
|
return
|
|
|
|
if isinstance(lr, Tensor) and not capturable:
|
|
raise RuntimeError(
|
|
"lr as a Tensor is not supported for capturable=False and foreach=True"
|
|
)
|
|
|
|
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
|
|
if capturable and not _is_compiling():
|
|
from torch.optim.optimizer import _get_capturable_supported_devices
|
|
capturable_supported_devices = _get_capturable_supported_devices(
|
|
supports_xla=False
|
|
)
|
|
assert all(
|
|
p.device.type == step.device.type
|
|
and p.device.type in capturable_supported_devices
|
|
for p, step in zip(params, state_steps)
|
|
), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
|
|
|
|
assert grad_scale is None and found_inf is None
|
|
|
|
assert not differentiable, "_foreach ops don't support autograd"
|
|
|
|
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
|
|
[params, grads, exp_avgs, exp_avg_sqs, state_steps] # type: ignore[list-item]
|
|
)
|
|
for (
|
|
device_params_,
|
|
device_grads_,
|
|
device_exp_avgs_,
|
|
device_exp_avg_sqs_,
|
|
device_state_steps_,
|
|
), _ in grouped_tensors.values():
|
|
device_params = cast(List[Tensor], device_params_)
|
|
device_grads = cast(List[Tensor], device_grads_)
|
|
device_exp_avgs = cast(List[Tensor], device_exp_avgs_)
|
|
device_exp_avg_sqs = cast(List[Tensor], device_exp_avg_sqs_)
|
|
device_state_steps = cast(List[Tensor], device_state_steps_)
|
|
|
|
# Handle complex parameters
|
|
if has_complex:
|
|
_view_as_real(
|
|
device_params, device_grads, device_exp_avgs, device_exp_avg_sqs
|
|
)
|
|
|
|
if maximize:
|
|
device_grads = torch._foreach_neg(device_grads) # type: ignore[assignment]
|
|
|
|
# Update steps
|
|
# If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
|
|
# and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
|
|
# wrapped it once now. The alpha is required to assure we go to the right overload.
|
|
if not _is_compiling() and device_state_steps[0].is_cpu:
|
|
torch._foreach_add_(
|
|
device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
|
|
)
|
|
else:
|
|
torch._foreach_add_(device_state_steps, 1)
|
|
|
|
if weight_decay != 0:
|
|
if decoupled:
|
|
torch._foreach_add_(device_params, device_params, alpha=-lr * weight_decay)
|
|
else:
|
|
# Re-use the intermediate memory (device_grads) already allocated for maximize
|
|
if maximize:
|
|
torch._foreach_add_(device_grads, device_params, alpha=weight_decay)
|
|
else:
|
|
device_grads = torch._foreach_add( # type: ignore[assignment]
|
|
device_grads, device_params, alpha=weight_decay
|
|
)
|
|
|
|
if device_state_steps[0] == 1:
|
|
torch._foreach_addcmul_(device_exp_avg_sqs, device_grads, device_grads)
|
|
continue
|
|
|
|
exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
|
|
exp_avg_sq_sqrt = torch._foreach_maximum(exp_avg_sq_sqrt, eps)
|
|
|
|
if device_state_steps[0] == 2:
|
|
torch._foreach_addcdiv_(device_exp_avgs, device_grads, exp_avg_sq_sqrt)
|
|
else:
|
|
torch._foreach_mul_(device_exp_avgs, beta1)
|
|
torch._foreach_addcdiv_(
|
|
device_exp_avgs, device_grads, exp_avg_sq_sqrt, value=1 - beta1
|
|
)
|
|
|
|
torch._foreach_add_(device_params, device_exp_avgs, alpha=-lr)
|
|
torch._foreach_mul_(device_exp_avg_sqs, beta2)
|
|
torch._foreach_addcmul_(
|
|
device_exp_avg_sqs, device_grads, device_grads, value=1 - beta2
|
|
)
|
|
|
|
|
|
#@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adopt) # FIXME internal context mgr, can't use
|
|
def adopt(
|
|
params: List[Tensor],
|
|
grads: List[Tensor],
|
|
exp_avgs: List[Tensor],
|
|
exp_avg_sqs: List[Tensor],
|
|
state_steps: List[Tensor],
|
|
# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
|
|
# setting this as kwarg for now as functional API is compiled by torch/distributed/optim
|
|
foreach: Optional[bool] = None,
|
|
capturable: bool = False,
|
|
differentiable: bool = False,
|
|
grad_scale: Optional[Tensor] = None,
|
|
found_inf: Optional[Tensor] = None,
|
|
has_complex: bool = False,
|
|
*,
|
|
beta1: float,
|
|
beta2: float,
|
|
lr: Union[float, Tensor],
|
|
weight_decay: float,
|
|
decoupled: bool,
|
|
eps: float,
|
|
maximize: bool,
|
|
):
|
|
r"""Functional API that performs ADOPT algorithm computation.
|
|
|
|
"""
|
|
if foreach is None:
|
|
foreach = False
|
|
|
|
# this check is slow during compilation, so we skip it
|
|
# if it's strictly needed we can add this check back in dynamo
|
|
if not _is_compiling() and not all(isinstance(t, torch.Tensor) for t in state_steps):
|
|
raise RuntimeError(
|
|
"API has changed, `state_steps` argument must contain a list of singleton tensors"
|
|
)
|
|
|
|
if foreach and torch.jit.is_scripting():
|
|
raise RuntimeError("torch.jit.script not supported with foreach optimizers")
|
|
|
|
if foreach and not torch.jit.is_scripting():
|
|
func = _multi_tensor_adopt
|
|
else:
|
|
func = _single_tensor_adopt
|
|
|
|
func(
|
|
params,
|
|
grads,
|
|
exp_avgs,
|
|
exp_avg_sqs,
|
|
state_steps,
|
|
has_complex=has_complex,
|
|
beta1=beta1,
|
|
beta2=beta2,
|
|
lr=lr,
|
|
weight_decay=weight_decay,
|
|
decoupled=decoupled,
|
|
eps=eps,
|
|
maximize=maximize,
|
|
capturable=capturable,
|
|
differentiable=differentiable,
|
|
grad_scale=grad_scale,
|
|
found_inf=found_inf,
|
|
)
|