PyTorch gradient accumulation training loop

gradient_accumulation.py

model.zero_grad()                                   # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss = loss_function(predictions, labels)       # Compute loss function
    loss = loss / accumulation_steps                # Normalize our loss (if averaged)
    loss.backward()                                 # Backward pass
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()                           # Reset gradients tensors
        if (i+1) % evaluation_steps == 0:           # Evaluate the model when we...
            evaluate_model()                        # ...have no gradients accumulated

@arquolo - okay thankyou for the clarification.

@thomwolf Thanks for the code. Just a little fix in the condition at line 7:

if (i+1) % accumulation_steps == 0:

This assumes that the number of batches is perfectly divisble by the accumulation steps. However, if there are, say, 10, batches, and the accumulation steps are 4, the last two batches would not make to the optimizer.step().

And even if you add an extra condition, you will still need to adjust the normalization denominator because there will be only 2 not 4 accumulation steps.

So the updated code would be:

n = len(training_set)
remainder_batches = n % accumulation_steps # calculate number of remainder batches

for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)
    loss = loss_function(predictions, labels)
    remaining = n - i

    # update the denominator if the remaining batches are leq number of remainder batches 
    denominator = remainder_batches if remaining <= remainder_batches else accumulation_steps

    loss = loss / denominator
    loss.backward()
    if (i+1) % accumulation_steps == 0 or i == n - 1: # add condition for last iteration
        optimizer.step()
        model.zero_grad()

You can emulate the logic in a standalone script as follows:

def get_value():
    return 5


n = 10
steps = 4
values = []
val = 0
remainder = n % steps
for i in range(n):
    a = get_value()
    remaining = n - i
    if remaining <= remainder:
        denom = n % steps
    else:
        denom = steps
    val += a / denom
    print(i, denom)
    if (i + 1) % steps == 0 or i == n - 1:
        values.append(val)
        val = 0
        print("update")