Source code for torch.optim.sgd

import torch
from torch import Tensor
from .optimizer import (Optimizer, required, _use_grad_for_differentiable, _default_to_fused_or_foreach,
                        _differentiable_doc, _foreach_doc, _maximize_doc)
from typing import List, Optional
from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype

__all__ = ['SGD', 'sgd']

[docs]class SGD(Optimizer): 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().__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
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`__. """ + r""" 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} {foreach} {differentiable} """.format(maximize=_maximize_doc, foreach=_foreach_doc, differentiable=_differentiable_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() __ .. 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, *, 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: # 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(): _, foreach = _default_to_fused_or_foreach(params, differentiable=False, use_fused=False) else: 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 grouped_tensors = _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 = any(grad.is_sparse for grad in device_grads) if maximize: device_grads = torch._foreach_neg(tuple(device_grads)) # type: ignore[assignment] if weight_decay != 0: 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)


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