Gradient accumulation and zeroing in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of gradient accumulation with zeroing

Consider the following PyTorch code snippet that performs gradient accumulation over two mini-batches before updating the model parameters. What will be the value of model.linear.weight.grad after the second backward call and before optimizer.step()?

PyTorch

import torch
import torch.nn as nn
import torch.optim as optim

class SimpleModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(1, 1, bias=False)

model = SimpleModel()
optimizer = optim.SGD(model.parameters(), lr=0.1)

# Input and target batches
inputs1 = torch.tensor([[1.0]])
targets1 = torch.tensor([[2.0]])
inputs2 = torch.tensor([[2.0]])
targets2 = torch.tensor([[4.0]])

criterion = nn.MSELoss()

# First mini-batch
outputs1 = model(inputs1)
loss1 = criterion(outputs1, targets1)
loss1.backward()  # Gradients computed and accumulated

# Second mini-batch
outputs2 = model(inputs2)
loss2 = criterion(outputs2, targets2)
loss2.backward()  # Gradients accumulated again

# What is model.linear.weight.grad here?

A A tensor approximately equal to [[-15.0]] representing triple the gradient from one batch

B A tensor approximately equal to [[-10.0]] representing the sum of gradients from both batches

C None, because gradients are zeroed automatically after each backward call

D A tensor approximately equal to [[-5.0]] representing only the gradient from the second batch

💡 Hint

Remember that calling backward accumulates gradients unless you zero them explicitly.

In PyTorch, gradients accumulate by default. After the first backward call, the gradient is computed for the first batch. The second backward call adds the gradient from the second batch to the existing gradient. Since the model is a simple linear layer and the loss is MSE, the gradients add up. The value is approximately the sum of gradients from both batches.

🧠 Conceptual

intermediate

Why zero gradients before backward?

Why do we usually call optimizer.zero_grad() before calling loss.backward() in a training loop?

A To clear old gradients so that new gradients are not accumulated on top of them

B To reset model weights to zero before each update

C To initialize the optimizer's learning rate

D To save memory by deleting the computation graph

💡 Hint

Think about what happens if you don't clear gradients before backward.

Calling optimizer.zero_grad() clears the gradients from the previous backward pass. Without this, gradients would accumulate, causing incorrect updates. This is important to ensure each update step uses only the current batch's gradients.

❓ Hyperparameter

advanced

Choosing accumulation steps for gradient accumulation

You want to simulate a batch size of 64 but your GPU can only handle batch size 16. You decide to use gradient accumulation with accumulation_steps. Which value of accumulation_steps correctly simulates the larger batch size?

A 8, because 16 / 8 = 2

B 2, because 16 + 2 = 18

C 4, because 16 * 4 = 64

D 1, because no accumulation is needed

💡 Hint

Think about how many small batches you need to accumulate to reach the target batch size.

Gradient accumulation steps multiply the effective batch size. If your GPU can handle 16 samples at once, accumulating gradients over 4 steps simulates a batch size of 64 (16 * 4).

🔧 Debug

advanced

Why does the model not train when zero_grad is missing?

You wrote this training loop but the model's loss does not decrease over epochs. What is the most likely cause?

PyTorch

for epoch in range(5):
    for inputs, targets in dataloader:
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()
        # Missing optimizer.zero_grad()

A The optimizer.step() is called before loss.backward(), causing errors

B The model weights are reset to zero every batch, so no learning happens

C The loss.backward() is not called, so no gradients are computed

D Gradients accumulate every batch causing optimizer.step() to apply huge updates, destabilizing training

💡 Hint

What happens if gradients are never cleared?

Without zeroing gradients, they accumulate every batch. This causes optimizer.step() to apply very large updates, which can prevent the model from converging and cause loss to stay high or fluctuate.

❓ Metrics

expert

Effect of gradient accumulation on training metrics

You train a model with gradient accumulation over 4 steps and batch size 16, simulating batch size 64. Which of the following statements about training loss and accuracy metrics logged per optimizer step is true?

A Loss and accuracy logged per optimizer step correspond to the combined effect of 64 samples, so they are smoother and more stable

B Loss and accuracy logged per optimizer step correspond to only 16 samples, so they are noisier

C Loss and accuracy do not change because accumulation does not affect metrics

D Loss and accuracy are invalid because gradients are accumulated

💡 Hint

Think about what batch size the optimizer step represents when using accumulation.

When accumulating gradients over 4 steps of batch size 16, the optimizer step updates parameters as if using batch size 64. Metrics logged per optimizer step reflect this larger effective batch size, resulting in smoother and more stable values.