| import torch |
| from torch import Tensor |
| from .optimizer import Optimizer, required, _use_grad_for_differentiable |
| from typing import List, Optional |
| |
| __all__ = ['SGD', 'sgd'] |
| |
| class SGD(Optimizer): |
| 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`__. |
| |
| Args: |
| params (iterable): iterable of parameters to optimize or dicts defining |
| parameter groups |
| lr (float): learning rate |
| 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 (bool, optional): maximize the params based on the objective, instead of |
| minimizing (default: False) |
| foreach (bool, optional): whether foreach implementation of optimizer |
| is used (default: None) |
| |
| 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. |
| """ |
| |
| def __init__(self, params, lr=required, momentum=0, dampening=0, |
| weight_decay=0, nesterov=False, *, maximize: bool = False, foreach: Optional[bool] = None, |
| differentiable: bool = False): |
| if lr is not required and lr < 0.0: |
| raise ValueError("Invalid learning rate: {}".format(lr)) |
| if momentum < 0.0: |
| raise ValueError("Invalid momentum value: {}".format(momentum)) |
| if weight_decay < 0.0: |
| raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) |
| |
| defaults = dict(lr=lr, momentum=momentum, dampening=dampening, |
| weight_decay=weight_decay, nesterov=nesterov, |
| maximize=maximize, foreach=foreach, |
| differentiable=differentiable) |
| if nesterov and (momentum <= 0 or dampening != 0): |
| raise ValueError("Nesterov momentum requires a momentum and zero dampening") |
| super(SGD, self).__init__(params, defaults) |
| |
| 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) |
| |
| 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] |
| if 'momentum_buffer' not in state: |
| momentum_buffer_list.append(None) |
| else: |
| momentum_buffer_list.append(state['momentum_buffer']) |
| |
| return has_sparse_grad |
| |
| |
| @_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']) |
| |
| # 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 |
| |
| |
| 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: bool = 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. |
| """ |
| |
| if foreach is None: |
| # Placeholder for more complex foreach logic to be added when value is not set |
| foreach = 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_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) |
| |
| def _single_tensor_sgd(params: List[Tensor], |
| d_p_list: List[Tensor], |
| momentum_buffer_list: List[Optional[Tensor]], |
| *, |
| weight_decay: float, |
| momentum: float, |
| lr: float, |
| dampening: float, |
| nesterov: bool, |
| maximize: bool, |
| has_sparse_grad: bool): |
| |
| 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]], |
| *, |
| weight_decay: float, |
| momentum: float, |
| lr: float, |
| dampening: float, |
| nesterov: bool, |
| maximize: bool, |
| has_sparse_grad: bool): |
| |
| if len(params) == 0: |
| return |
| |
| if has_sparse_grad is None: |
| has_sparse_grad = any(grad.is_sparse for grad in grads) |
| |
| if maximize: |
| grads = torch._foreach_neg(tuple(grads)) # type: ignore[assignment] |
| |
| if weight_decay != 0: |
| grads = torch._foreach_add(grads, params, alpha=weight_decay) |
| |
| if momentum != 0: |
| bufs = [] |
| |
| all_states_with_momentum_buffer = True |
| for i in range(len(momentum_buffer_list)): |
| if momentum_buffer_list[i] is None: |
| all_states_with_momentum_buffer = False |
| break |
| else: |
| bufs.append(momentum_buffer_list[i]) |
| |
| if all_states_with_momentum_buffer: |
| torch._foreach_mul_(bufs, momentum) |
| torch._foreach_add_(bufs, grads, alpha=1 - dampening) |
| else: |
| bufs = [] |
| for i in range(len(momentum_buffer_list)): |
| if momentum_buffer_list[i] is None: |
| buf = momentum_buffer_list[i] = torch.clone(grads[i]).detach() |
| else: |
| buf = momentum_buffer_list[i] |
| buf.mul_(momentum).add_(grads[i], alpha=1 - dampening) |
| |
| bufs.append(buf) |
| |
| if nesterov: |
| torch._foreach_add_(grads, bufs, alpha=momentum) |
| else: |
| grads = bufs |
| |
| if not has_sparse_grad: |
| torch._foreach_add_(params, grads, alpha=-lr) |
| else: |
| # foreach APIs dont support sparse |
| for i in range(len(params)): |
| params[i].add_(grads[i], alpha=-lr) |
