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# mypy: allow-untyped-defs
from typing import cast, Optional, Union
import torch
from torch import Tensor
from .optimizer import (
_default_to_fused_or_foreach,
_device_dtype_check_for_fused,
_differentiable_doc,
_foreach_doc,
_get_scalar_dtype,
_get_value,
_maximize_doc,
_params_doc,
_use_grad_for_differentiable,
_view_as_real,
Optimizer,
ParamsT,
)
__all__ = ["Adagrad", "adagrad"]
class Adagrad(Optimizer):
def __init__(
self,
params: ParamsT,
lr: Union[float, Tensor] = 1e-2,
lr_decay: float = 0,
weight_decay: float = 0,
initial_accumulator_value: float = 0,
eps: float = 1e-10,
foreach: Optional[bool] = None,
*,
maximize: bool = False,
differentiable: bool = False,
fused: Optional[bool] = None,
):
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 <= lr_decay:
raise ValueError(f"Invalid lr_decay value: {lr_decay}")
if not 0.0 <= weight_decay:
raise ValueError(f"Invalid weight_decay value: {weight_decay}")
if not 0.0 <= initial_accumulator_value:
raise ValueError(
f"Invalid initial_accumulator_value value: {initial_accumulator_value}"
)
if not 0.0 <= eps:
raise ValueError(f"Invalid epsilon value: {eps}")
defaults = dict(
lr=lr,
lr_decay=lr_decay,
eps=eps,
weight_decay=weight_decay,
initial_accumulator_value=initial_accumulator_value,
foreach=foreach,
maximize=maximize,
differentiable=differentiable,
fused=fused,
)
super().__init__(params, defaults)
if fused:
if differentiable:
raise RuntimeError("`fused` does not support `differentiable`")
if foreach:
raise RuntimeError("`fused` and `foreach` cannot be `True` together.")
self._need_device_dtype_check_for_fused = True
for group in self.param_groups:
for p in group["params"]:
state = self.state[p]
state["step"] = (
torch.zeros(
(),
dtype=_get_scalar_dtype(is_fused=group["fused"]),
device=p.device,
)
if group["fused"]
else torch.tensor(0.0, dtype=_get_scalar_dtype())
)
init_value = (
complex(initial_accumulator_value, initial_accumulator_value)
if torch.is_complex(p)
else initial_accumulator_value
)
state["sum"] = torch.full_like(
p, init_value, memory_format=torch.preserve_format
)
def __setstate__(self, state):
super().__setstate__(state)
# define "fused" for
# MYPY error: Name "fused" may be undefined
fused = None
for group in self.param_groups:
group.setdefault("foreach", None)
group.setdefault("maximize", False)
group.setdefault("differentiable", False)
fused = group.setdefault("fused", None)
state_values = list(self.state.values())
step_is_tensor = (len(state_values) != 0) and torch.is_tensor(
state_values[0]["step"]
)
if not step_is_tensor:
for s in state_values:
s["step"] = torch.tensor(
float(s["step"]), dtype=_get_scalar_dtype(is_fused=fused)
)
def share_memory(self):
for group in self.param_groups:
for p in group["params"]:
state = self.state[p]
state["sum"].share_memory_()
def _init_group(self, group, params_with_grad, grads, state_sums, state_steps):
has_sparse_grad, has_complex = False, False
for p in group["params"]:
if p.grad is not None:
if group["fused"] and getattr(
self,
"_need_device_dtype_check_for_fused",
True,
):
_device_dtype_check_for_fused(p, cuda_unsupported=True)
self._need_device_dtype_check_for_fused = False
has_sparse_grad |= p.grad.is_sparse
has_complex |= torch.is_complex(p)
params_with_grad.append(p)
grads.append(p.grad)
state = self.state[p]
state_sums.append(state["sum"])
state_steps.append(state["step"])
return has_sparse_grad, has_complex
@_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.
"""
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] = []
state_sums: list[Tensor] = []
state_steps: list[Tensor] = []
has_sparse_grad, has_complex = self._init_group(
group, params_with_grad, grads, state_sums, state_steps
)
adagrad(
params_with_grad,
grads,
state_sums,
state_steps,
lr=group["lr"],
weight_decay=group["weight_decay"],
lr_decay=group["lr_decay"],
eps=group["eps"],
has_sparse_grad=has_sparse_grad,
foreach=group["foreach"],
maximize=group["maximize"],
differentiable=group["differentiable"],
has_complex=has_complex,
fused=group["fused"],
grad_scale=getattr(self, "grad_scale", None),
found_inf=getattr(self, "found_inf", None),
)
return loss
Adagrad.__doc__ = (
r"""Implements Adagrad algorithm.
.. math::
\begin{aligned}
&\rule{110mm}{0.4pt} \\
&\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta)
\text{ (objective)}, \: \lambda \text{ (weight decay)}, \\
&\hspace{12mm} \tau \text{ (initial accumulator value)}, \: \eta\text{ (lr decay)}\\
&\textbf{initialize} : state\_sum_0 \leftarrow \tau \\[-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} \tilde{\gamma} \leftarrow \gamma / (1 +(t-1) \eta) \\
&\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\
&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
&\hspace{5mm}state\_sum_t \leftarrow state\_sum_{t-1} + g^2_t \\
&\hspace{5mm}\theta_t \leftarrow
\theta_{t-1}- \tilde{\gamma} \frac{g_t}{\sqrt{state\_sum_t}+\epsilon} \\
&\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 `Adaptive Subgradient Methods for Online Learning
and Stochastic Optimization`_.
"""
+ rf"""
Args:
{_params_doc}
lr (float, Tensor, optional): learning rate (default: 1e-2)
lr_decay (float, optional): learning rate decay (default: 0)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
initial_accumulator_value (float, optional): initial value of the
sum of squares of gradients (default: 0)
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-10)
{_foreach_doc}
{_maximize_doc}
{_differentiable_doc}
fused (bool, optional): whether the fused implementation (CPU only) is used.
Currently, `torch.float64`, `torch.float32`, `torch.float16`, and `torch.bfloat16`
are supported. (default: None). Please note that the fused implementations does not
support sparse or complex gradients.
.. _Adaptive Subgradient Methods for Online Learning and Stochastic
Optimization: http://jmlr.org/papers/v12/duchi11a.html
"""
)
def adagrad(
params: list[Tensor],
grads: list[Tensor],
state_sums: list[Tensor],
state_steps: list[Tensor],
fused: Optional[bool] = None,
grad_scale: Optional[Tensor] = None,
found_inf: Optional[Tensor] = None,
# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
# setting these as kwargs for now as functional API is compiled by torch/distributed/optim
has_sparse_grad: bool = False,
foreach: Optional[bool] = None,
differentiable: bool = False,
has_complex: bool = False,
*,
lr: float,
weight_decay: float,
lr_decay: float,
eps: float,
maximize: bool,
):
r"""Functional API that performs Adagrad algorithm computation.
See :class:`~torch.optim.Adagrad` for details.
"""
if 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"
)
# Respect when the user inputs False/True for foreach or fused. We only want to change
# the default when neither have been user-specified. Note that we default to foreach
# and pass False to use_fused. This is not a mistake--we want to give the fused impl
# bake-in time before making it the default, even if it is typically faster.
if fused is None and foreach is None:
_, foreach = _default_to_fused_or_foreach(
params, differentiable, use_fused=False
)
if fused is None:
fused = False
if foreach is None:
foreach = False
if foreach and torch.jit.is_scripting():
raise RuntimeError("torch.jit.script not supported with foreach optimizers")
if fused and torch.jit.is_scripting():
raise RuntimeError("torch.jit.script not supported with fused optimizers")
if fused and not torch.jit.is_scripting():
func = _fused_adagrad
elif foreach and not torch.jit.is_scripting():
func = _multi_tensor_adagrad
else:
func = _single_tensor_adagrad
func(
params,
grads,
state_sums,
state_steps,
lr=lr,
weight_decay=weight_decay,
lr_decay=lr_decay,
eps=eps,
has_sparse_grad=has_sparse_grad,
maximize=maximize,
differentiable=differentiable,
has_complex=has_complex,
grad_scale=grad_scale,
found_inf=found_inf,
)
def _make_sparse(grad, grad_indices, values):
size = grad.size()
return torch.sparse_coo_tensor(grad_indices, values, size)
def _single_tensor_adagrad(
params: list[Tensor],
grads: list[Tensor],
state_sums: list[Tensor],
state_steps: list[Tensor],
grad_scale: Optional[Tensor],
found_inf: Optional[Tensor],
*,
lr: float,
weight_decay: float,
lr_decay: float,
eps: float,
has_sparse_grad: bool,
maximize: bool,
differentiable: bool,
has_complex: bool,
):
assert grad_scale is None and found_inf is None
for param, grad, state_sum, step_t in zip(params, grads, state_sums, state_steps):
# update step
step_t += 1
step = _get_value(step_t)
grad = grad if not maximize else -grad
if weight_decay != 0:
if grad.is_sparse:
raise RuntimeError(
"weight_decay option is not compatible with sparse gradients"
)
grad = grad.add(param, alpha=weight_decay)
clr = lr / (1 + (step - 1) * lr_decay)
if grad.is_sparse:
grad = grad.coalesce() # the update is non-linear so indices must be unique
grad_indices = grad._indices()
grad_values = grad._values()
state_sum.add_(_make_sparse(grad, grad_indices, grad_values.pow(2)))
std = state_sum.sparse_mask(grad)
std_values = std._values().sqrt_().add_(eps)
param.add_(
_make_sparse(grad, grad_indices, grad_values / std_values), alpha=-clr
)
else:
is_complex = torch.is_complex(param)
if is_complex:
grad = torch.view_as_real(grad)
state_sum = torch.view_as_real(state_sum)
param = torch.view_as_real(param)
state_sum.addcmul_(grad, grad, value=1)
if differentiable:
std = state_sum.sqrt() + eps
else:
std = state_sum.sqrt().add_(eps)
param.addcdiv_(grad, std, value=-clr)
if is_complex:
param = torch.view_as_complex(param)
state_sum = torch.view_as_complex(state_sum)
def _multi_tensor_adagrad(
params: list[Tensor],
grads: list[Tensor],
state_sums: list[Tensor],
state_steps: list[Tensor],
grad_scale: Optional[Tensor],
found_inf: Optional[Tensor],
*,
lr: float,
weight_decay: float,
lr_decay: float,
eps: float,
has_sparse_grad: bool,
maximize: bool,
differentiable: bool,
has_complex: bool,
):
assert not differentiable, "_foreach ops don't support autograd"
assert grad_scale is None and found_inf is None
# Foreach functions will throw errors if given empty lists
if len(params) == 0:
return
grouped_tensorlists = Optimizer._group_tensors_by_device_and_dtype(
[params, grads, state_sums, state_steps] # type: ignore[list-item]
)
for (
device_params_,
device_grads_,
device_state_sums_,
device_state_steps_,
), _ in grouped_tensorlists.values():
device_params = cast(list[Tensor], device_params_)
device_grads = cast(list[Tensor], device_grads_)
device_state_sums = cast(list[Tensor], device_state_sums_)
device_state_steps = cast(list[Tensor], device_state_steps_)
device_has_sparse_grad = has_sparse_grad and any(
grad.is_sparse for grad in device_grads
)
if device_has_sparse_grad:
_single_tensor_adagrad(
device_params,
device_grads,
device_state_sums,
device_state_steps,
lr=lr,
weight_decay=weight_decay,
lr_decay=lr_decay,
eps=eps,
has_sparse_grad=True,
maximize=maximize,
differentiable=differentiable,
has_complex=has_complex,
grad_scale=grad_scale,
found_inf=found_inf,
)
continue
# Handle complex parameters
if has_complex:
_view_as_real(device_params, device_grads, device_state_sums)
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 torch.compiler.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:
# 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
)
minus_clr = [
-lr / (1 + (_get_value(step) - 1) * lr_decay) for step in device_state_steps
]
torch._foreach_addcmul_(device_state_sums, device_grads, device_grads, value=1)
std = torch._foreach_sqrt(device_state_sums)
torch._foreach_add_(std, eps)
if weight_decay != 0 or maximize:
# Again, re-use the intermediate memory (device_grads) already allocated
torch._foreach_mul_(device_grads, minus_clr)
numerator = device_grads
else:
numerator = torch._foreach_mul(device_grads, minus_clr) # type: ignore[assignment]
torch._foreach_addcdiv_(device_params, numerator, std)
def _fused_adagrad(
params: list[Tensor],
grads: list[Tensor],
state_sums: list[Tensor],
state_steps: list[Tensor],
grad_scale: Optional[Tensor],
found_inf: Optional[Tensor],
*,
lr: float,
weight_decay: float,
lr_decay: float,
eps: float,
has_sparse_grad: bool,
maximize: bool,
differentiable: bool,
has_complex: bool,
) -> None:
if not params:
return
if has_sparse_grad or has_complex:
raise RuntimeError("`fused` does not support sparse grad or complex param")
if differentiable:
raise RuntimeError(
"adagrad with fused=True does not support differentiable=True"
)
grad_scale_dict = (
{grad_scale.device: grad_scale} if grad_scale is not None else None
)
found_inf_dict = {found_inf.device: found_inf} if found_inf is not None else None
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
[params, grads, state_sums, state_steps] # type: ignore[list-item]
)
for (device, _), (
(
device_params_,
device_grads_,
device_state_sums_,
device_state_steps_,
),
_,
) in grouped_tensors.items():
device_params = cast(list[Tensor], device_params_)
device_grads = cast(list[Tensor], device_grads_)
device_state_sums = cast(list[Tensor], device_state_sums_)
device_state_steps = cast(list[Tensor], device_state_steps_)
device_grad_scale, device_found_inf = None, None
if grad_scale is not None and grad_scale_dict is not None:
if device not in grad_scale_dict:
grad_scale_dict[device] = grad_scale.to(device, non_blocking=True) # type: ignore[index]
device_grad_scale = grad_scale_dict[device] # type: ignore[index]
if found_inf is not None and found_inf_dict is not None:
if found_inf not in found_inf_dict:
found_inf_dict[device] = found_inf.to(device, non_blocking=True) # type: ignore[index]
device_found_inf = found_inf_dict[device] # type: ignore[index]
torch._foreach_add_(device_state_steps, 1)
torch._fused_adagrad_(
device_params,
device_grads,
device_state_sums,
device_state_steps,
lr=lr,
lr_decay=lr_decay,
weight_decay=weight_decay,
eps=eps,
maximize=maximize,
grad_scale=device_grad_scale,
found_inf=device_found_inf,
)
if device_found_inf is not None:
torch._foreach_sub_(
device_state_steps, [device_found_inf] * len(device_state_steps)
)
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