Meet DiffGrad - new optimizer that solves Adams overshoot issue

Hi all,
I’m happy to introduce you to a new optimizer called DiffGrad. DiffGrad is Adam, but with an adaptive ‘friction clamp’ built in that helps the optimizer lock down into global optima better than Adam (or any momentum based optimizer really).

It does this by monitoring the local gradient changes and not just the exponential moving average like Adam does. When the gradient change falls within a certain range, it clamps down on the learning rate (step size) in order to ensure that if it’s a global minima it won’t overshoot the way Adam and other optimizers can do.

Here’s some results:

I tested it on ImageWoof and ImageNette and for 20 epoch ImageWoof got within 1% of the leaderboard (set with Ranger) without any tuning. Note that I used a version 1 that is in the paper but not in their github.

I’ve written a summary with a lot more info here:

an excerpt:

And here’s the full paper:

And my github with a FastAI 1 setup and the version1 of diffGrad and param to toggle between v0 and v1:

I still want to do more testing and tuning with diffGrad, but overall I’m impressed and really like the theory of it in terms of being able to rapidly decelerate and latch onto deeper minima instead of rocketing over it as can frequently happen with an adaptive only optimizer like Adam.


Wow this is amazing! You are now the community expert on optimizers! :wink:


I integrated Rectified Adam with DiffGrad to make DiffRGrad…the better start with RAdam seems to boost it nicely.
Also, I continue to get better results with version 1 instead of the default version 0:


I’ve got a fancy new microphone for a podcast interview I did, so thought I’d make a quick video of diffGrad to try that out as a different medium than medium(.com lol):


Critical Review of DiffGrad:

I have been playing with diffgrad and subsequently trying to build improved optimizer based on the fundamental intuition on top of which diffgrad is built. First concern, diffgrad is always deaccelerating the learning rate because ξ is always less than 1, additionally, it runs into a risk of getting stuck in a local minima because of severely decreasing the ξ parameter because of the sigmoid operation.

Follow up on the official code implementation of diffgrad which can be found here, I observed some strong issues. During plotting the current step’s gradient g(t,i) and previous step’s gradient g(t-1,i), I observed that after the first iteration both the plots overlap on each other, thus resulting in the ΔG = g(t,i) - g(t-1,i) to be 0 which is incorrect because the graph of g(t-1,i) should be the replica of g(t,i) but just right shifted by 1 unit where 1 unit is the length of group[‘params’] because for the first iteration all the parameters (p1, p2, p3,…, pn) have the previous gradient g(t-1,i) to be initialized with a tensor of zeros of the same shape as that of the current gradient g(t,i). The plot obtained from the original implementation of diffgrad is shown below:

Investigating this I realize that this issue is caused by Call by reference which makes both prev_grad g(t-1,i) and current grad g(t,i) to overlap and thus the difference becomes zero.

Fixing this was easy where one has to use the .clone() function wherever previous_grad is updated/ initialized. It should like this:
state['previous_grad'] = grad.clone()

Now, using this fixed code, one can observe the correct trend in the graph as per the expected behavior as shown below:

I did a test run with the original implementation and the fixed implementation for CIFAR-10 using SEResNet-18 for 10 epochs using a Batch Size of 32 and lr = 0.001.

Original Implementation: Test Accuracy - 84.38%
Fixed Implementation: Test Accuracy - 83.42%
Adam: Test Accuracy - 84.27%

Interesting Observation:

Since, for the 1st iteration spanning for the length of group['params'], the previous grad is 0 so finding ΔG = g(t,i) - g(t-1,i) makes no sense because ΔG will always be equal to g(t,i). For a simple network like SEResNet-18, this unnecessary subtraction is computed for 114 times per optimization complete cycle.


If the paper used the original implementation to obtain the results showcased, then all of them are incorrect. Additionally, all custom optimizers @LessW2020 has built using the base of diffgrad like DiffMod (Diffgrad + AdaMod) which can be found here and Diff_RGrad (Diffgrad + RAdam) which can be found here are also incorrect.

My colleague at Landskape (Manjunath Bhat) raised the issue in the original source repository of Diffgrad which can be found here. The author in reply stated that with the updated (fixed) implementation, they are reporting better scores than what mentioned in the paper. However, this should be further tested and validated.

For reference, the fixed code for Diffgrad is:

import math
import torch
from torch.optim.optimizer import Optimizer
import numpy as np
import torch.nn as nn

class diffgrad(Optimizer):
    r"""Implements diffGrad algorithm. It is modified from the pytorch implementation of Adam.
    It has been proposed in `diffGrad: An Optimization Method for Convolutional Neural Networks`_.
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups
        lr (float, optional): learning rate (default: 1e-3)
        betas (Tuple[float, float], optional): coefficients used for computing
            running averages of gradient and its square (default: (0.9, 0.999))
        eps (float, optional): term added to the denominator to improve
            numerical stability (default: 1e-8)
        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
            algorithm from the paper `On the Convergence of Adam and Beyond`_
            (default: False)
    .. _diffGrad: An Optimization Method for Convolutional Neural Networks:
    .. _Adam\: A Method for Stochastic Optimization:
    .. _On the Convergence of Adam and Beyond:

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        super(diffgrad, self).__init__(params, defaults)

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

    def step(self, closure=None):
        """Performs a single optimization step.
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                grad =
                if grad.is_sparse:
                    raise RuntimeError('diffGrad does not support sparse gradients, please consider SparseAdam instead')

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    # Exponential moving average of gradient values
                    state['exp_avg'] = torch.zeros_like(
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(
                    # Previous gradient
                    state['previous_grad'] = torch.zeros_like(

                exp_avg, exp_avg_sq, previous_grad = state['exp_avg'], state['exp_avg_sq'], state['previous_grad'].clone()
                beta1, beta2 = group['betas']

                state['step'] += 1

                if group['weight_decay'] != 0:

                # Decay the first and second moment running average coefficient
                exp_avg.mul_(beta1).add_(1 - beta1, grad)
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                denom = exp_avg_sq.sqrt().add_(group['eps'])

                bias_correction1 = 1 - beta1 ** state['step']
                bias_correction2 = 1 - beta2 ** state['step']

                # compute diffgrad coefficient (dfc)
                diff = abs(previous_grad - grad)
                dfc = 1. / (1. + torch.exp(-diff))
                state['previous_grad'] = grad.clone()
				# update momentum with dfc
                exp_avg1 = exp_avg * dfc

                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1

      , exp_avg1, denom)

        return loss

Great work @Diganta - I’ll update my repos shortly!


Happy to help!


Thanks for pointing it out.

I have updated the code as at
Using updated code, I am getting even better performance over CIFAR10 using ResNet50.
The performance increases after fixing it as follows:
Using ResNet50 on CIFAR10 dataset with batch size 128: earlier - 94.08%, now - 94.27%
Using ResNet50 on CIFAR10 dataset with batch size 64: earlier - 94.05%, now - 94.24%
Using ResNet50 on CIFAR10 dataset with batch size 32: earlier - 93.9%, now - 94.24%

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