Zeroing gradients in PyTorch - Deep Dive

Overview - Zeroing gradients

What is it?

Zeroing gradients means setting all the gradient values of a model's parameters to zero before starting a new round of learning. Gradients are numbers that tell the model how to change its parameters to improve. Without zeroing, gradients from previous steps would mix with new ones, causing wrong updates. This step is essential in training models using methods like gradient descent.

Why it matters

Without zeroing gradients, the model would keep adding up old gradient values, leading to incorrect learning directions. This would make the model confused and slow to learn or even fail to learn. Zeroing gradients ensures each learning step starts fresh, allowing the model to improve correctly and efficiently. It is a small but critical step that keeps training stable and reliable.

Where it fits

Before zeroing gradients, you should understand what gradients are and how backpropagation works to compute them. After zeroing gradients, the next step is to update the model's parameters using the optimizer. This concept fits early in the training loop process and is foundational before learning advanced optimization techniques.

Mental Model

Core Idea

Zeroing gradients resets the learning signals so each training step updates the model based only on fresh information.

Think of it like...

Imagine you are filling a glass with water each day. If you never empty the glass, the water from previous days mixes and overflows, making it hard to measure how much you added today. Zeroing gradients is like emptying the glass before pouring new water, so you know exactly how much you added today.

Training Step Flow:
┌───────────────┐
│ Compute Loss  │
└──────┬────────┘
       │
┌──────▼────────┐
│ Backpropagate │
│ (Compute     │
│ Gradients)   │
└──────┬────────┘
       │
┌──────▼────────┐
│ Zero Gradients│
│ (Reset to 0) │
└──────┬────────┘
       │
┌──────▼────────┐
│ Update Params │
│ (Optimizer)  │
└───────────────┘

Build-Up - 7 Steps

1

Foundation

What are gradients in training

Concept: Gradients show how much each model parameter should change to reduce error.

When a model makes a prediction, it compares it to the correct answer and calculates an error. Gradients are numbers that tell us how to change each parameter to reduce this error. They are found using a process called backpropagation.

Result

You understand that gradients are the directions for improving the model.

Understanding gradients is key because zeroing them only makes sense if you know they carry learning signals.

2

Foundation

Why gradients accumulate by default

Concept: Gradients add up each time backpropagation runs unless reset.

In PyTorch, when you call backward(), gradients are added to existing gradients stored in each parameter. This means if you don't reset them, gradients from multiple steps pile up.

Result

You realize that gradients keep growing if not cleared.

Knowing that gradients accumulate explains why zeroing is necessary to avoid mixing old and new learning signals.

3

Intermediate

How to zero gradients in PyTorch

Concept: Use optimizer.zero_grad() or param.grad.zero_() to reset gradients.

In PyTorch, the common way to zero gradients is calling optimizer.zero_grad() before backpropagation. Alternatively, you can loop over model parameters and call param.grad.zero_() if gradients exist.

Result

You can clear gradients correctly before each training step.

Knowing the exact commands prevents bugs where gradients accumulate silently.

4

Intermediate

Where zeroing fits in the training loop

Concept: Zero gradients before computing new gradients each step.

A typical training loop looks like this: 1. Zero gradients 2. Forward pass to compute predictions 3. Compute loss 4. Backward pass to compute gradients 5. Update parameters Zeroing must happen before backward() to clear old gradients.

Result

You understand the correct order to keep training stable.

Placing zeroing in the right spot avoids mixing gradient information across steps.

5

Intermediate

What happens if you skip zeroing gradients

🤔Before reading on: do you think skipping zeroing gradients will make training faster or cause errors? Commit to your answer.

Concept: Skipping zeroing causes gradients to accumulate, leading to wrong parameter updates.

If you don't zero gradients, each backward() adds to the previous gradients. This makes the model update parameters with a sum of many steps' gradients, which is not what standard training expects.

Result

Training becomes unstable or diverges, and loss may not decrease properly.

Understanding this prevents a common silent bug that can waste hours of training time.

6

Advanced

Zeroing gradients in complex training setups

🤔Before reading on: do you think zeroing gradients is always done the same way in multi-GPU or mixed precision training? Commit to your answer.

Concept: In advanced setups, zeroing gradients may require special handling to sync or scale gradients correctly.

In multi-GPU training, gradients are computed on each device and then combined. Zeroing gradients must happen on each device separately. In mixed precision training, zeroing gradients also involves managing gradient scaling to avoid underflow.

Result

You can correctly manage gradients in complex training environments.

Knowing these details helps avoid subtle bugs in large-scale or optimized training.

7

Expert

Why PyTorch accumulates gradients by design

🤔Before reading on: do you think PyTorch accumulates gradients to save memory or to support advanced training techniques? Commit to your answer.

Concept: PyTorch accumulates gradients to allow flexible gradient manipulation and advanced optimization strategies.

By accumulating gradients, PyTorch lets users implement gradient accumulation over multiple batches, gradient clipping, or custom backward passes. This design choice trades off the need to manually zero gradients for greater flexibility.

Result

You understand the design tradeoffs behind zeroing gradients.

Recognizing this design helps you appreciate why zeroing is manual and how to leverage gradient accumulation intentionally.

Under the Hood

When backward() is called, PyTorch computes gradients for each parameter and adds them to the .grad attribute. This attribute holds the sum of gradients from all backward calls since last zeroing. Zeroing sets these .grad values to zero tensors, clearing old gradient data. This happens in-place to avoid creating new memory. The optimizer then uses these gradients to update parameters.

Why designed this way?

PyTorch accumulates gradients by default to support advanced training techniques like gradient accumulation over multiple mini-batches, gradient clipping, and custom backward passes. This design gives users control but requires explicit zeroing to avoid unintended accumulation. Alternatives like automatic zeroing would limit flexibility and complicate custom training loops.

┌───────────────┐
│ Backward Call │
└──────┬────────┘
       │
┌──────▼────────┐
│ Compute Grad  │
│ for each param│
└──────┬────────┘
       │
┌──────▼────────┐
│ Add to param. │
│ .grad attr    │
└──────┬────────┘
       │
┌──────▼────────┐
│ Optimizer uses│
│ .grad to update│
│ parameters    │
└───────────────┘

Zeroing gradients:

┌───────────────┐
│ optimizer.zero_grad() │
└──────┬────────┘
       │
┌──────▼────────┐
│ Set all param.│
│ .grad to zero │
└───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Do you think PyTorch automatically clears gradients after each backward call? Commit yes or no.

Common Belief: PyTorch automatically resets gradients after each backward call, so manual zeroing is unnecessary.

Reality: PyTorch accumulates gradients by default and requires explicit zeroing to clear them before new backward calls.

Why it matters: Believing this causes silent bugs where gradients add up, leading to incorrect model updates and wasted training time.

Quick: Do you think zeroing gradients after optimizer.step() is the same as before backward()? Commit your answer.

Common Belief: Zeroing gradients can be done anytime in the training loop without affecting results.

Reality: Zeroing must happen before backward() to clear old gradients; zeroing after optimizer.step() but before backward() is correct, but zeroing after backward() is too late.

Why it matters: Wrong timing causes gradients to accumulate unexpectedly, breaking training correctness.

Quick: Do you think zeroing gradients is only needed for SGD optimizer? Commit yes or no.

Common Belief: Only some optimizers require zeroing gradients; others handle it internally.

Reality: All PyTorch optimizers expect gradients to be zeroed manually; none reset gradients automatically.

Why it matters: Assuming otherwise leads to bugs regardless of optimizer choice.

Quick: Do you think zeroing gradients removes the model's learned knowledge? Commit yes or no.

Common Belief: Zeroing gradients resets the model's knowledge or parameters.

Reality: Zeroing gradients only clears temporary gradient values; it does not change model parameters or learned knowledge.

Why it matters: Misunderstanding this may cause hesitation or skipping zeroing, harming training.

Expert Zone

1

Zeroing gradients in-place avoids memory overhead and keeps training efficient.

2

In gradient accumulation strategies, zeroing is done less frequently to simulate larger batch sizes.

3

When using hooks or custom backward passes, manual gradient management including zeroing is critical to avoid subtle bugs.

When NOT to use

Zeroing gradients is not needed when using frameworks or wrappers that handle gradient management automatically, such as some high-level training libraries. In gradient accumulation setups, zeroing is delayed intentionally to accumulate gradients over multiple batches. Alternatives include using autograd engines that manage gradients differently, but these are rare in PyTorch.

Production Patterns

In production, zeroing gradients is always done at the start of each training iteration to ensure clean updates. For large-scale training, gradient accumulation delays zeroing to reduce communication overhead. Mixed precision training requires zeroing gradients along with managing gradient scaling factors. Debugging training often involves checking if gradients were zeroed correctly to diagnose convergence issues.

Connections

Gradient Descent Optimization

Zeroing gradients is a prerequisite step before applying gradient descent updates.

Understanding zeroing clarifies how gradient descent uses fresh gradient information each step to improve models.

Memory Management in Computing

Zeroing gradients is similar to clearing memory buffers before reuse to avoid data corruption.

Knowing this connection helps appreciate zeroing as a memory hygiene practice, preventing stale data from causing errors.

Accounting and Bookkeeping

Zeroing gradients is like closing the books at the end of a day before starting fresh accounting entries.

This cross-domain link shows zeroing as resetting state to avoid mixing old and new information, a universal principle.

Common Pitfalls

#1 Forgetting to zero gradients before backward pass

Wrong approach:

for data, target in dataloader:
output = model(data)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()

Correct approach:

for data, target in dataloader:
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()

Root cause:

Not knowing that gradients accumulate by default leads to skipping zeroing, causing incorrect parameter updates.

#2 Zeroing gradients after backward instead of before

Wrong approach:

for data, target in dataloader:
output = model(data)
loss = loss_fn(output, target)
loss.backward()
optimizer.zero_grad()
optimizer.step()

Correct approach:

for data, target in dataloader:
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()

Root cause:

Misunderstanding the order of operations causes gradients to accumulate before zeroing, breaking training logic.

#3 Calling zero_grad on model parameters without checking if gradients exist

Wrong approach:

for param in model.parameters():
param.grad.zero_()

Correct approach:

for param in model.parameters():
if param.grad is not None:
param.grad.zero_()

Root cause:

Attempting to zero gradients when they are None causes errors; checking prevents runtime exceptions.

Key Takeaways

Zeroing gradients resets the learning signals so each training step updates the model based only on fresh information.

PyTorch accumulates gradients by default, so zeroing them manually before backward() is essential to avoid mixing old and new gradients.

Zeroing gradients does not affect the model's learned parameters; it only clears temporary gradient values.

The correct place to zero gradients is before computing new gradients in each training iteration.

Understanding zeroing gradients helps prevent common silent bugs that cause training instability or failure.