MLPY/Lib/site-packages/torch/optim/sgd.py

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

    @_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,
        )