Source code for torch.optim.sgd
import torch
from torch import Tensor
from .optimizer import (Optimizer, _use_grad_for_differentiable, _default_to_fused_or_foreach,
_differentiable_doc, _foreach_doc, _maximize_doc, _fused_doc)
from typing import List, Optional
__all__ = ['SGD', 'sgd']
[docs]class SGD(Optimizer):
def __init__(self, params, lr=1e-3, momentum=0, dampening=0,
weight_decay=0, nesterov=False, *, maximize: bool = False, foreach: Optional[bool] = None,
differentiable: bool = False, fused: Optional[bool] = None):
if lr < 0.0:
raise ValueError(f"Invalid learning rate: {lr}")
if momentum < 0.0:
raise ValueError(f"Invalid momentum value: {momentum}")
if weight_decay < 0.0:
raise ValueError(f"Invalid weight_decay value: {weight_decay}")
defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
weight_decay=weight_decay, nesterov=nesterov,
maximize=maximize, foreach=foreach,
differentiable=differentiable, fused=fused)
if nesterov and (momentum <= 0 or dampening != 0):
raise ValueError("Nesterov momentum requires a momentum and zero dampening")
super().__init__(params, defaults)
if fused:
self._step_supports_amp_scaling = True
if differentiable:
raise RuntimeError("`fused` does not support `differentiable`")
if foreach:
raise RuntimeError("`fused` and `foreach` cannot be `True` together.")
def __setstate__(self, state):
super().__setstate__(state)
for group in self.param_groups:
group.setdefault('nesterov', False)
group.setdefault('maximize', False)
group.setdefault('foreach', None)
group.setdefault('differentiable', False)
group.setdefault('fused', False)
def _init_group(self, group, params_with_grad, d_p_list, momentum_buffer_list):
has_sparse_grad = False
for p in group['params']:
if p.grad is not None:
params_with_grad.append(p)
d_p_list.append(p.grad)
if p.grad.is_sparse:
has_sparse_grad = True
state = self.state[p]
momentum_buffer_list.append(state.get('momentum_buffer'))
return has_sparse_grad
[docs] @_use_grad_for_differentiable
def step(self, closure=None):
"""Performs 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 = []
d_p_list = []
momentum_buffer_list = []
has_sparse_grad = self._init_group(group, params_with_grad, d_p_list, momentum_buffer_list)
sgd(params_with_grad,
d_p_list,
momentum_buffer_list,
weight_decay=group['weight_decay'],
momentum=group['momentum'],
lr=group['lr'],
dampening=group['dampening'],
nesterov=group['nesterov'],
maximize=group['maximize'],
has_sparse_grad=has_sparse_grad,
foreach=group['foreach'],
fused=group['fused'],
grad_scale=getattr(self, "grad_scale", None),
found_inf=getattr(self, "found_inf", None))
# update momentum_buffers in state
for p, momentum_buffer in zip(params_with_grad, momentum_buffer_list):
state = self.state[p]
state['momentum_buffer'] = momentum_buffer
return loss
SGD.__doc__ = r"""Implements stochastic gradient descent (optionally with momentum).
.. 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{13mm} \:\mu \text{ (momentum)}, \:\tau \text{ (dampening)},
\:\textit{ nesterov,}\:\textit{ maximize} \\[-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}\textbf{if} \: \lambda \neq 0 \\
&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
&\hspace{5mm}\textbf{if} \: \mu \neq 0 \\
&\hspace{10mm}\textbf{if} \: t > 1 \\
&\hspace{15mm} \textbf{b}_t \leftarrow \mu \textbf{b}_{t-1} + (1-\tau) g_t \\
&\hspace{10mm}\textbf{else} \\
&\hspace{15mm} \textbf{b}_t \leftarrow g_t \\
&\hspace{10mm}\textbf{if} \: \textit{nesterov} \\
&\hspace{15mm} g_t \leftarrow g_{t} + \mu \textbf{b}_t \\
&\hspace{10mm}\textbf{else} \\[-1.ex]
&\hspace{15mm} g_t \leftarrow \textbf{b}_t \\
&\hspace{5mm}\textbf{if} \: \textit{maximize} \\
&\hspace{10mm}\theta_t \leftarrow \theta_{t-1} + \gamma g_t \\[-1.ex]
&\hspace{5mm}\textbf{else} \\[-1.ex]
&\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma g_t \\[-1.ex]
&\rule{110mm}{0.4pt} \\[-1.ex]
&\bf{return} \: \theta_t \\[-1.ex]
&\rule{110mm}{0.4pt} \\[-1.ex]
\end{aligned}
Nesterov momentum is based on the formula from
`On the importance of initialization and momentum in deep learning`__.
""" + fr"""
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
momentum (float, optional): momentum factor (default: 0)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
dampening (float, optional): dampening for momentum (default: 0)
nesterov (bool, optional): enables Nesterov momentum (default: False)
{_maximize_doc}
{_foreach_doc}
{_differentiable_doc}
{_fused_doc}
""" + r"""
Example:
>>> # xdoctest: +SKIP
>>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> optimizer.step()
__ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf
.. note::
The implementation of SGD with Momentum/Nesterov subtly differs from
Sutskever et. al. and implementations in some other frameworks.
Considering the specific case of Momentum, the update can be written as
.. math::
\begin{aligned}
v_{t+1} & = \mu * v_{t} + g_{t+1}, \\
p_{t+1} & = p_{t} - \text{lr} * v_{t+1},
\end{aligned}
where :math:`p`, :math:`g`, :math:`v` and :math:`\mu` denote the
parameters, gradient, velocity, and momentum respectively.
This is in contrast to Sutskever et. al. and
other frameworks which employ an update of the form
.. math::
\begin{aligned}
v_{t+1} & = \mu * v_{t} + \text{lr} * g_{t+1}, \\
p_{t+1} & = p_{t} - v_{t+1}.
\end{aligned}
The Nesterov version is analogously modified.
Moreover, the initial value of the momentum buffer is set to the
gradient value at the first step. This is in contrast to some other
frameworks that initialize it to all zeros.
"""
def sgd(params: List[Tensor],
d_p_list: List[Tensor],
momentum_buffer_list: List[Optional[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
has_sparse_grad: bool = None,
foreach: Optional[bool] = None,
fused: Optional[bool] = None,
grad_scale: Optional[Tensor] = None,
found_inf: Optional[Tensor] = None,
*,
weight_decay: float,
momentum: float,
lr: float,
dampening: float,
nesterov: bool,
maximize: bool):
r"""Functional API that performs SGD algorithm computation.
See :class:`~torch.optim.SGD` for details.
"""
# 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 foreach is None and fused is None:
# why must we be explicit about an if statement for torch.jit.is_scripting here?
# because JIT can't handle Optionals nor fancy conditionals when scripting
if not torch.jit.is_scripting():
fused, foreach = _default_to_fused_or_foreach(params, differentiable=False, use_fused=False)
else:
foreach = False
fused = False
if foreach is None:
foreach = False
if fused is None:
fused = 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 foreach and not torch.jit.is_scripting():
func = _multi_tensor_sgd
elif fused and not torch.jit.is_scripting():
func = _fused_sgd
else:
func = _single_tensor_sgd
func(params,
d_p_list,
momentum_buffer_list,
weight_decay=weight_decay,
momentum=momentum,
lr=lr,
dampening=dampening,
nesterov=nesterov,
has_sparse_grad=has_sparse_grad,
maximize=maximize,
grad_scale=grad_scale,
found_inf=found_inf)
def _single_tensor_sgd(params: List[Tensor],
d_p_list: List[Tensor],
momentum_buffer_list: List[Optional[Tensor]],
grad_scale: Optional[Tensor],
found_inf: Optional[Tensor],
*,
weight_decay: float,
momentum: float,
lr: float,
dampening: float,
nesterov: bool,
maximize: bool,
has_sparse_grad: bool):
assert grad_scale is None and found_inf is None
for i, param in enumerate(params):
d_p = d_p_list[i] if not maximize else -d_p_list[i]
if weight_decay != 0:
d_p = d_p.add(param, alpha=weight_decay)
if momentum != 0:
buf = momentum_buffer_list[i]
if buf is None:
buf = torch.clone(d_p).detach()
momentum_buffer_list[i] = buf
else:
buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
if nesterov:
d_p = d_p.add(buf, alpha=momentum)
else:
d_p = buf
param.add_(d_p, alpha=-lr)
def _multi_tensor_sgd(params: List[Tensor],
grads: List[Tensor],
momentum_buffer_list: List[Optional[Tensor]],
grad_scale: Optional[Tensor],
found_inf: Optional[Tensor],
*,
weight_decay: float,
momentum: float,
lr: float,
dampening: float,
nesterov: bool,
maximize: bool,
has_sparse_grad: bool):
assert grad_scale is None and found_inf is None
if len(params) == 0:
return
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype([params, grads, momentum_buffer_list], with_indices=True)
for ((device_params, device_grads, device_momentum_buffer_list), indices) in grouped_tensors.values():
device_has_sparse_grad = has_sparse_grad and any(grad.is_sparse for grad in device_grads)
if maximize:
device_grads = torch._foreach_neg(device_grads)
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(device_grads, device_params, alpha=weight_decay)
if momentum != 0:
bufs = []
all_states_with_momentum_buffer = True
for i in range(len(device_momentum_buffer_list)):
if device_momentum_buffer_list[i] is None:
all_states_with_momentum_buffer = False
break
else:
bufs.append(device_momentum_buffer_list[i])
if all_states_with_momentum_buffer:
torch._foreach_mul_(bufs, momentum)
torch._foreach_add_(bufs, device_grads, alpha=1 - dampening)
else:
bufs = []
for i in range(len(device_momentum_buffer_list)):
if device_momentum_buffer_list[i] is None:
buf = device_momentum_buffer_list[i] = momentum_buffer_list[indices[i]] = \
torch.clone(device_grads[i]).detach()
else:
buf = device_momentum_buffer_list[i]
buf.mul_(momentum).add_(device_grads[i], alpha=1 - dampening)
bufs.append(buf)
if nesterov:
torch._foreach_add_(device_grads, bufs, alpha=momentum)
else:
device_grads = bufs
if not device_has_sparse_grad:
torch._foreach_add_(device_params, device_grads, alpha=-lr)
else:
# foreach APIs don't support sparse
for i in range(len(device_params)):
device_params[i].add_(device_grads[i], alpha=-lr)
def _fused_sgd(
params: List[Tensor],
grads: List[Tensor],
momentum_buffer_list: List[Optional[Tensor]],
grad_scale: Optional[Tensor],
found_inf: Optional[Tensor],
*,
weight_decay: float,
momentum: float,
lr: float,
dampening: float,
nesterov: bool,
maximize: bool,
has_sparse_grad: bool,
) -> None:
if not params:
return
if has_sparse_grad:
raise RuntimeError("`_fused_sgd` does not support sparse gradients")
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
no_momentum_buffer = momentum == 0
is_first_step = all(t is None for t in momentum_buffer_list) and not no_momentum_buffer
if is_first_step:
for i, g in enumerate(grads):
momentum_buffer_list[i] = torch.empty_like(g)
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
[params, grads, momentum_buffer_list], with_indices=False)
for (device, dtype), ((device_params, device_grads, device_momentum_buffer_list), _) in grouped_tensors.items():
device_grad_scale, device_found_inf = None, None
if grad_scale is not None:
if device not in grad_scale_dict:
grad_scale_dict[device] = grad_scale.to(device)
device_grad_scale = grad_scale_dict[device]
if found_inf is not None:
if device not in found_inf_dict:
found_inf_dict[device] = found_inf.to(device)
device_found_inf = found_inf_dict[device]
torch._fused_sgd_(
device_params,
device_grads,
[] if no_momentum_buffer else device_momentum_buffer_list,
weight_decay=weight_decay,
momentum=momentum,
lr=lr,
dampening=dampening,
nesterov=nesterov,
maximize=maximize,
is_first_step=is_first_step,
grad_scale=device_grad_scale,
found_inf=device_found_inf,
)
