Source code for flash.core.optimizers.lars

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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#
# Implemented by @ananyahjha93
# also found at: https://github.com/gridai-labs/aavae/tree/main/src/optimizers
# References:
#     - https://arxiv.org/pdf/1708.03888.pdf
#     - https://github.com/pytorch/pytorch/blob/master/torch/optim/sgd.py
import torch
from torch.optim.optimizer import Optimizer, required

from flash.core.utilities.imports import _TOPIC_CORE_AVAILABLE

# Skip doctests if requirements aren't available
if not _TOPIC_CORE_AVAILABLE:
    __doctest_skip__ = ["LARS"]


class LARS(Optimizer):
    r"""Extends SGD in PyTorch with LARS scaling.

    See the paper `Large batch training of Convolutional Networks <https://arxiv.org/pdf/1708.03888.pdf>`_

    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)
        trust_coefficient (float, optional): trust coefficient for computing LR (default: 0.001)
        eps (float, optional): eps for division denominator (default: 1e-8)

    Example:
        >>> from torch import nn
        >>> model = nn.Linear(10, 1)
        >>> optimizer = LARS(model.parameters(), lr=0.1, momentum=0.9)
        >>> optimizer.zero_grad()
        >>> # loss_fn(model(input), target).backward()
        >>> optimizer.step()

    .. note::
        The application of momentum in the SGD part is modified according to
        the PyTorch standards. LARS scaling fits into the equation in the
        following fashion.

        .. math::
            \begin{aligned}
                g_{t+1} & = \text{lars\_lr} * (\beta * p_{t} + g_{t+1}), \\
                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`, :math:`\mu` and :math:`\beta` denote the
        parameters, gradient, velocity, momentum, and weight decay respectively.
        The :math:`lars_lr` is defined by Eq. 6 in the paper.
        The Nesterov version is analogously modified.

    .. warning::
        Parameters with weight decay set to 0 will automatically be excluded from
        layer-wise LR scaling. This is to ensure consistency with papers like SimCLR
        and BYOL.
    """

    def __init__(
        self,
        params,
        lr=required,
        momentum: float = 0,
        dampening: float = 0,
        weight_decay: float = 0,
        nesterov: bool = False,
        trust_coefficient: float = 0.001,
        eps: float = 1e-8,
    ):
        if lr is not required and 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,
        }
        if nesterov and (momentum <= 0 or dampening != 0):
            raise ValueError("Nesterov momentum requires a momentum and zero dampening")

        self.eps = eps
        self.trust_coefficient = trust_coefficient

        super().__init__(params, defaults)

    def __setstate__(self, state):
        super().__setstate__(state)

        for group in self.param_groups:
            group.setdefault("nesterov", False)

    @torch.no_grad()
    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()

        # exclude scaling for params with 0 weight decay
        for group in self.param_groups:
            weight_decay = group["weight_decay"]
            momentum = group["momentum"]
            dampening = group["dampening"]
            nesterov = group["nesterov"]

            for p in group["params"]:
                if p.grad is None:
                    continue

                d_p = p.grad
                p_norm = torch.norm(p.data)
                g_norm = torch.norm(p.grad.data)

                # lars scaling + weight decay part
                if weight_decay != 0 and p_norm != 0 and g_norm != 0:
                    lars_lr = p_norm / (g_norm + p_norm * weight_decay + self.eps)
                    lars_lr *= self.trust_coefficient

                    d_p = d_p.add(p, alpha=weight_decay)
                    d_p *= lars_lr

                # sgd part
                if momentum != 0:
                    param_state = self.state[p]
                    if "momentum_buffer" not in param_state:
                        buf = param_state["momentum_buffer"] = torch.clone(d_p).detach()
                    else:
                        buf = param_state["momentum_buffer"]
                        buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
                    d_p = d_p.add(buf, alpha=momentum) if nesterov else buf

                p.add_(d_p, alpha=-group["lr"])

        return loss