Source code for torch.optim.adadelta
# mypy: allow-untyped-decorators
# mypy: allow-untyped-defs
from typing import Any, cast, Dict, List, Optional, Union
import torch
from torch import Tensor
from .optimizer import (
_capturable_doc,
_default_to_fused_or_foreach,
_differentiable_doc,
_disable_dynamo_if_unsupported,
_foreach_doc,
_get_capturable_supported_devices,
_get_scalar_dtype,
_maximize_doc,
_params_doc,
_use_grad_for_differentiable,
_view_as_real,
Optimizer,
ParamsT,
)
__all__ = ["Adadelta", "adadelta"]
[docs]class Adadelta(Optimizer):
def __init__(
self,
params: ParamsT,
lr: Union[float, Tensor] = 1.0,
rho: float = 0.9,
eps: float = 1e-6,
weight_decay: float = 0,
foreach: Optional[bool] = None,
*,
capturable: bool = False,
maximize: bool = False,
differentiable: bool = False,
):
if isinstance(lr, Tensor) and 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 <= rho <= 1.0:
raise ValueError(f"Invalid rho value: {rho}")
if not 0.0 <= eps:
raise ValueError(f"Invalid epsilon value: {eps}")
if not 0.0 <= weight_decay:
raise ValueError(f"Invalid weight_decay value: {weight_decay}")
defaults = dict(
lr=lr,
rho=rho,
eps=eps,
weight_decay=weight_decay,
maximize=maximize,
capturable=capturable,
foreach=foreach,
differentiable=differentiable,
)
super().__init__(params, defaults)
def __setstate__(self, state):
super().__setstate__(state)
for group in self.param_groups:
group.setdefault("foreach", None)
group.setdefault("maximize", False)
group.setdefault("differentiable", False)
group.setdefault("capturable", 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: Dict[str, Any],
params_with_grad: List[Tensor],
grads: List[Tensor],
square_avgs: List[Tensor],
acc_deltas: List[Tensor],
state_steps: List[Tensor],
):
has_complex = False
p: Tensor
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("Adadelta does not support sparse gradients")
grads.append(p.grad)
state = self.state[p]
# Lazy state initialization
if len(state) == 0:
state["step"] = (
torch.zeros((), dtype=_get_scalar_dtype(), device=p.device)
if group["capturable"]
else torch.zeros((), dtype=_get_scalar_dtype())
)
state["square_avg"] = torch.zeros_like(
p, memory_format=torch.preserve_format
)
state["acc_delta"] = torch.zeros_like(
p, memory_format=torch.preserve_format
)
square_avgs.append(state["square_avg"])
acc_deltas.append(state["acc_delta"])
state_steps.append(state["step"])
return has_complex
[docs] @_use_grad_for_differentiable
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] = []
square_avgs: List[Tensor] = []
acc_deltas: List[Tensor] = []
state_steps: List[Tensor] = []
(
lr,
rho,
eps,
weight_decay,
foreach,
maximize,
differentiable,
capturable,
) = (
group["lr"],
group["rho"],
group["eps"],
group["weight_decay"],
group["foreach"],
group["maximize"],
group["differentiable"],
group["capturable"],
)
has_complex = self._init_group(
group, params_with_grad, grads, square_avgs, acc_deltas, state_steps
)
adadelta(
params_with_grad,
grads,
square_avgs,
acc_deltas,
state_steps,
lr=lr,
rho=rho,
eps=eps,
weight_decay=weight_decay,
foreach=foreach,
maximize=maximize,
differentiable=differentiable,
capturable=capturable,
has_complex=has_complex,
)
return loss
Adadelta.__doc__ = (
r"""Implements Adadelta algorithm.
.. math::
\begin{aligned}
&\rule{110mm}{0.4pt} \\
&\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)},
\: f(\theta) \text{ (objective)}, \: \rho \text{ (decay)},
\: \lambda \text{ (weight decay)} \\
&\textbf{initialize} : v_0 \leftarrow 0 \: \text{ (square avg)},
\: u_0 \leftarrow 0 \: \text{ (accumulate variables)} \\[-1.ex]
&\rule{110mm}{0.4pt} \\
&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
&\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
&\hspace{5mm}if \: \lambda \neq 0 \\
&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
&\hspace{5mm} v_t \leftarrow v_{t-1} \rho + g^2_t (1 - \rho) \\
&\hspace{5mm}\Delta x_t \leftarrow \frac{\sqrt{u_{t-1} +
\epsilon }}{ \sqrt{v_t + \epsilon} }g_t \hspace{21mm} \\
&\hspace{5mm} u_t \leftarrow u_{t-1} \rho +
\Delta x^2_t (1 - \rho) \\
&\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \gamma \Delta x_t \\
&\rule{110mm}{0.4pt} \\[-1.ex]
&\bf{return} \: \theta_t \\[-1.ex]
&\rule{110mm}{0.4pt} \\[-1.ex]
\end{aligned}
For further details regarding the algorithm we refer to `ADADELTA: An Adaptive Learning Rate Method`_.
"""
+ rf"""
Args:
{_params_doc}
lr (float, Tensor, optional): coefficient that scale delta before it is applied
to the parameters (default: 1.0)
rho (float, optional): coefficient used for computing a running average
of squared gradients (default: 0.9). A higher value of `rho` will
result in a slower average, which can be helpful for preventing
oscillations in the learning process.
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-6).
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
{_foreach_doc}
{_capturable_doc}
{_maximize_doc}
{_differentiable_doc}
.. _ADADELTA\: An Adaptive Learning Rate Method:
https://arxiv.org/abs/1212.5701
"""
)
def _single_tensor_adadelta(
params: List[Tensor],
grads: List[Tensor],
square_avgs: List[Tensor],
acc_deltas: List[Tensor],
state_steps: List[Tensor],
*,
lr: float,
rho: float,
eps: float,
weight_decay: float,
maximize: bool,
differentiable: bool,
capturable: bool,
has_complex: bool,
):
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
if not torch._utils.is_compiling() and capturable:
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}."
for param, grad, square_avg, acc_delta, step in zip(
params, grads, square_avgs, acc_deltas, state_steps
):
step += 1
grad = grad if not maximize else -grad
if weight_decay != 0:
grad = grad.add(param, alpha=weight_decay)
if torch.is_complex(param):
square_avg = torch.view_as_real(square_avg)
acc_delta = torch.view_as_real(acc_delta)
grad = torch.view_as_real(grad)
square_avg.mul_(rho).addcmul_(grad, grad, value=1 - rho)
std = square_avg.add(eps).sqrt_()
delta = acc_delta.add(eps).sqrt_()
if differentiable:
delta = delta.clone()
delta.div_(std).mul_(grad)
acc_delta.mul_(rho).addcmul_(delta, delta, value=1 - rho)
if torch.is_complex(param):
delta = torch.view_as_complex(delta)
param.add_(delta, alpha=-lr)
def _multi_tensor_adadelta(
params: List[Tensor],
grads: List[Tensor],
square_avgs: List[Tensor],
acc_deltas: List[Tensor],
state_steps: List[Tensor],
*,
lr: float,
rho: float,
eps: float,
weight_decay: float,
maximize: bool,
differentiable: bool,
capturable: bool,
has_complex: bool,
):
assert not differentiable, "_foreach ops don't support autograd"
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
if not torch._utils.is_compiling() and capturable:
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}."
if len(params) == 0:
return
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
[params, grads, square_avgs, acc_deltas, state_steps] # type: ignore[list-item]
)
for (
device_params_,
device_grads_,
device_square_avgs_,
device_acc_deltas_,
device_state_steps_,
), _ in grouped_tensors.values():
device_params = cast(List[Tensor], device_params_)
device_grads = cast(List[Tensor], device_grads_)
device_square_avgs = cast(List[Tensor], device_square_avgs_)
device_acc_deltas = cast(List[Tensor], device_acc_deltas_)
device_state_steps = cast(List[Tensor], device_state_steps_)
if has_complex:
_view_as_real(
device_params, device_grads, device_square_avgs, device_acc_deltas
)
# 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 torch._utils.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 maximize:
device_grads = torch._foreach_neg(device_grads) # type: ignore[assignment]
if weight_decay != 0:
# 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
)
torch._foreach_mul_(device_square_avgs, rho)
torch._foreach_addcmul_(
device_square_avgs, device_grads, device_grads, value=1 - rho
)
std = torch._foreach_add(device_square_avgs, eps)
torch._foreach_sqrt_(std)
deltas = torch._foreach_add(device_acc_deltas, eps)
torch._foreach_sqrt_(deltas)
torch._foreach_div_(deltas, std)
torch._foreach_mul_(deltas, device_grads)
torch._foreach_mul_(device_acc_deltas, rho)
torch._foreach_addcmul_(device_acc_deltas, deltas, deltas, value=1 - rho)
# If LR is a tensor, the else branch will internally call item()
# which will cause silent incorrectness if we are capturing
if capturable and isinstance(lr, torch.Tensor):
torch._foreach_mul_(deltas, -lr)
torch._foreach_add_(device_params, deltas)
else:
torch._foreach_add_(device_params, deltas, alpha=-lr)
@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adadelta)
def adadelta(
params: List[Tensor],
grads: List[Tensor],
square_avgs: List[Tensor],
acc_deltas: 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
capturable: bool = False,
foreach: Optional[bool] = None,
differentiable: bool = False,
has_complex: bool = False,
*,
lr: float,
rho: float,
eps: float,
weight_decay: float,
maximize: bool,
):
r"""Functional API that performs Adadelta algorithm computation.
See :class:`~torch.optim.Adadelta` for details.
"""
# 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 torch._utils.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"
)
# We still respect when the user inputs False for foreach.
if foreach is None:
_, foreach = _default_to_fused_or_foreach(
params, differentiable, use_fused=False
)
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_adadelta
else:
func = _single_tensor_adadelta
func(
params,
grads,
square_avgs,
acc_deltas,
state_steps,
lr=lr,
rho=rho,
eps=eps,
weight_decay=weight_decay,
maximize=maximize,
differentiable=differentiable,
capturable=capturable,
has_complex=has_complex,
)
