Why do we need to call zero_grad() in PyTorch?

In PyTorch, for every mini-batch during the training phase, we typically want to explicitly set the gradients to zero before starting to do backpropragation (i.e., updating the Weights and biases) because PyTorch accumulates the gradients on subsequent backward passes. This accumulating behaviour is convenient while training RNNs or when we want to compute the gradient of the loss summed over multiple mini-batches. So, the default action has been set to accumulate (i.e. sum) the gradients on every loss.backward() call.

Because of this, when you start your training loop, ideally you should zero out the gradients so that you do the parameter update correctly. Otherwise, the gradient would be a combination of the old gradient, which you have already used to update your model parameters, and the newly-computed gradient. It would therefore point in some other direction than the intended direction towards the minimum (or maximum, in case of maximization objectives).

Here is a simple example:

import torch
from torch.autograd import Variable
import torch.optim as optim

def linear_model(x, W, b):
    return torch.matmul(x, W) + b

data, targets = ...

W = Variable(torch.randn(4, 3), requires_grad=True)
b = Variable(torch.randn(3), requires_grad=True)

optimizer = optim.Adam([W, b])

for sample, target in zip(data, targets):
    # clear out the gradients of all Variables 
    # in this optimizer (i.e. W, b)
    optimizer.zero_grad()
    output = linear_model(sample, W, b)
    loss = (output - target) ** 2
    loss.backward()
    optimizer.step()

Alternatively, if you're doing a vanilla gradient descent, then:

W = Variable(torch.randn(4, 3), requires_grad=True)
b = Variable(torch.randn(3), requires_grad=True)

for sample, target in zip(data, targets):
    # clear out the gradients of Variables 
    # (i.e. W, b)
    W.grad.data.zero_()
    b.grad.data.zero_()

    output = linear_model(sample, W, b)
    loss = (output - target) ** 2
    loss.backward()

    W -= learning_rate * W.grad.data
    b -= learning_rate * b.grad.data

Note:

The accumulation (i.e., sum) of gradients happens when .backward() is called on the loss tensor.

As of v1.7.0, Pytorch offers the option to reset the gradients to None optimizer.zero_grad(set_to_none=True) instead of filling them with a tensor of zeroes. The docs claim that this setting reduces memory requirements and slightly improves performance, but might be error-prone if not handled carefully.

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Although the idea can be derived from the chosen answer, but I feel like I want to write that explicitly.

Being able to decide when to call optimizer.zero_grad() and optimizer.step() provides more freedom on how gradient is accumulated and applied by the optimizer in the training loop. This is crucial when the model or input data is big and one actual training batch do not fit in to the gpu card.

Here in this example from google-research, there are two arguments, named train_batch_size and gradient_accumulation_steps.

train_batch_size is the batch size for the forward pass, following the loss.backward(). This is limited by the gpu memory.

gradient_accumulation_steps is the actual training batch size, where loss from multiple forward pass is accumulated. This is NOT limited by the gpu memory.

From this example, you can see how optimizer.zero_grad() may followed by optimizer.step() but NOT loss.backward(). loss.backward() is invoked in every single iteration (line 216) but optimizer.zero_grad() and optimizer.step() is only invoked when the number of accumulated train batch equals the gradient_accumulation_steps (line 227 inside the if block in line 219)

https://github.com/google-research/xtreme/blob/master/third_party/run_classify.py

Also someone is asking about equivalent method in TensorFlow. I guess tf.GradientTape serve the same purpose.

(I am still new to AI library, please correct me if anything I said is wrong)

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zero_grad() restarts looping without losses from the last step if you use the gradient method for decreasing the error (or losses).

If you do not use zero_grad() the loss will increase not decrease as required.

For example:

If you use zero_grad() you will get the following output:

model training loss is 1.5
model training loss is 1.4
model training loss is 1.3
model training loss is 1.2

If you do not use zero_grad() you will get the following output:

model training loss is 1.4
model training loss is 1.9
model training loss is 2
model training loss is 2.8
model training loss is 3.5

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You don't have to call grad_zero() alternatively one can decay the gradients for example:

optimizer = some_pytorch_optimizer
# decay the grads :
for group in optimizer.param_groups:
    for p in group['params']:
        if p.grad is not None:
            ''' original code from git:
            if set_to_none:
                p.grad = None
            else:
                if p.grad.grad_fn is not None:
                    p.grad.detach_()
                else:
                    p.grad.requires_grad_(False)
                p.grad.zero_()
                
            '''
            p.grad = p.grad / 2

this way the learning is much more continues

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During the feed forward propagation the weights are assigned to inputs and after the 1st iteration the weights are initialized what the model has learnt seeing the samples(inputs). And when we start back propagation we want to update weights in order to get minimum loss of our cost function. So we clear off our previous weights in order to obtained more better weights. This we keep doing in training and we do not perform this in testing because we have got the weights in training time which is best fitted in our data. Hope this would clear more!

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