AutoAndroidController/py/venv/Lib/site-packages/torch/optim/sgd.py

# mypy: allow-untyped-defs
r"""Implementation for Stochastic Gradient Descent optimizer."""

from typing import cast, 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,
    _params_doc,
    _to_scalar,
    _use_grad_for_differentiable,
    DeviceDict,
    Optimizer,
    ParamsT,
)


__all__ = ["SGD", "sgd"]


class SGD(Optimizer):  # noqa: D101
    def __init__(
        self,
        params: ParamsT,
        lr: Union[float, Tensor] = 1e-3,
        momentum: float = 0,
        dampening: float = 0,
        weight_decay: Union[float, Tensor] = 0,
        nesterov: bool = 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 = {
            "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

    @_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}\textbf{if} \: \textit{maximize}:                                       \\
            &\hspace{10mm}g_t           \leftarrow   -\nabla_{\theta} f_t (\theta_{t-1})         \\
            &\hspace{5mm}\textbf{else}                                                           \\
            &\hspace{10mm}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}\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_doc}
        lr (float, Tensor, optional): learning rate (default: 1e-3)
        momentum (float, optional): momentum factor (default: 0)
        dampening (float, optional): dampening for momentum (default: 0)
        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
        nesterov (bool, optional): enables Nesterov momentum. Only applicable
            when momentum is non-zero. (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. One notable side effect
        of this decision is that the first momentum value will not be scaled
        by dampening. Dampening will be applied starting at the second step.

    """
)


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

    if not torch.jit.is_scripting():
        lr = _to_scalar(lr)

    for i, param in enumerate(params):
        grad = grads[i] if not maximize else -grads[i]

        if weight_decay != 0:
            # Nested if is necessary to bypass jitscript rules
            if isinstance(weight_decay, Tensor):
                if weight_decay.requires_grad:
                    # usually this is the differentiable path, which is why the param.clone() is needed
                    grad = grad.addcmul_(param.clone(), weight_decay)
                else:
                    grad = grad.add(param, alpha=weight_decay)
            else:
                grad = grad.add(param, alpha=weight_decay)

        if momentum != 0:
            buf = momentum_buffer_list[i]

            if buf is None:
                buf = grad.detach().clone()
                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

        # Nested if is necessary to bypass jitscript rules
        if isinstance(lr, Tensor):
            if lr.requires_grad:
                param.addcmul_(grad, lr, value=-1)
            else:
                param.add_(grad, alpha=-lr)
        else:
            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

    lr = _to_scalar(lr)

    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
        [params, grads, momentum_buffer_list],  # type: ignore[list-item]
        with_indices=True,
    )
    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:
            # Reuse 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]
                        ] = device_grads[i].detach().clone()
                    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.compiler.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],  # type: ignore[list-item]
        with_indices=False,
    )
    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,
        )