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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'] | |
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 | |
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, | |
) | |