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Source code for torch.optim.sgd

# mypy: allow-untyped-defs
r"""Implementation for Stochastic Gradient Descent optimizer."""
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,
    _fused_doc,
    _maximize_doc,
    _use_grad_for_differentiable,
    DeviceDict,
    Optimizer,
)


__all__ = ["SGD", "sgd"]


[docs]class SGD(Optimizer): # noqa: D101 def __init__( self, params, lr: Union[float, Tensor] = 1e-3, momentum: float = 0, dampening: float = 0, weight_decay: float = 0, nesterov=False, *, maximize: bool = False, foreach: Optional[bool] = None, differentiable: bool = False, fused: Optional[bool] = None, ): # noqa: D107 if isinstance(lr, Tensor) and lr.numel() != 1: raise ValueError("Tensor lr must be 1-element") 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 self._need_device_dtype_check_for_fused = 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): # noqa: D105 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, grads, momentum_buffer_list): has_sparse_grad = 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) self._need_device_dtype_check_for_fused = False params.append(p) grads.append(p.grad) if p.grad.is_sparse: has_sparse_grad = True if group["momentum"] != 0: 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): """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: List[Tensor] = [] grads: List[Tensor] = [] momentum_buffer_list: List[Optional[Tensor]] = [] has_sparse_grad = self._init_group( group, params, grads, momentum_buffer_list ) sgd( params, grads, 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), ) if group["momentum"] != 0: # update momentum_buffers in state for p, momentum_buffer in zip(params, 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`__. """ + rf""" Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, Tensor, 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 = False, 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], 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 for i, param in enumerate(params): grad = grads[i] if not maximize else -grads[i] if weight_decay != 0: grad = grad.add(param, alpha=weight_decay) if momentum != 0: buf = momentum_buffer_list[i] if buf is None: buf = torch.clone(grad).detach() momentum_buffer_list[i] = buf else: buf.mul_(momentum).add_(grad, alpha=1 - dampening) if nesterov: grad = grad.add(buf, alpha=momentum) else: grad = buf param.add_(grad, 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 # type: ignore[list-item] ) for ( device_params_, device_grads_, device_momentum_buffer_list, ), indices in grouped_tensors.values(): device_params: List[Tensor] = cast(List[Tensor], device_params_) device_grads: List[Tensor] = cast(List[Tensor], device_grads_) 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) # type: ignore[assignment] 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 ) if momentum != 0: bufs: List[Tensor] = [] 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(cast(Tensor, 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 = cast(Tensor, 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: # handle internal item() call if lr is a tensor if isinstance(lr, torch.Tensor) and torch._utils.is_compiling(): grads_x_lr = torch._foreach_mul(device_grads, -lr) torch._foreach_add_(device_params, grads_x_lr) else: 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: DeviceDict = ( {grad_scale.device: grad_scale} if grad_scale is not None else {} ) found_inf_dict: DeviceDict = ( {found_inf.device: found_inf} if found_inf is not None else {} ) 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 # type: ignore[list-item] ) for (device, _), ( (device_params_, device_grads_, device_momentum_buffer_list), _, ) in grouped_tensors.items(): device_params: List[Tensor] = cast(List[Tensor], device_params_) device_grads: List[Tensor] = cast(List[Tensor], device_grads_) device_grad_scale, device_found_inf = None, None if grad_scale is not None: device_grad_scale = grad_scale_dict.setdefault( device, grad_scale.to(device) ) if found_inf_dict is not None and found_inf is not None: device_found_inf = found_inf_dict.setdefault(device, found_inf.to(device)) torch._fused_sgd_( device_params, device_grads, [] if no_momentum_buffer else cast(List[Tensor], 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, )

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