565 lines
20 KiB
Python
565 lines
20 KiB
Python
# mypy: allow-untyped-defs
|
|
from typing import cast, List, 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,
|
|
_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 (iterable): iterable of parameters to optimize or dicts defining
|
|
parameter groups
|
|
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._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 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)
|
|
)
|