Gradient accumulation in PyTorch - Model Pipeline Trace

Model Pipeline - Gradient accumulation

This pipeline shows how gradient accumulation helps train a model with small batches by adding gradients over multiple steps before updating the model weights.

Data Flow - 5 Stages

1Data loading

1000 rows x 10 features→Load dataset and prepare batches of size 4→1000 rows x 10 features (batched as 250 batches of 4 rows each)

[[0.5, 1.2, ..., 0.3], [0.1, 0.4, ..., 0.7], ...] (4 samples per batch)

↓

2Model input

4 rows x 10 features→Feed batch to model→4 rows x 1 output (logits)

[[0.7], [0.2], [0.9], [0.1]]

↓

3Loss calculation

4 rows x 1 output→Calculate loss for batch→Scalar loss value

Loss = 0.45

↓

4Gradient accumulation

Gradients from batch of 4→Accumulate gradients over 4 batches (total 16 samples)→Accumulated gradients for 16 samples

Sum of gradients from 4 batches

↓

5Model update

Accumulated gradients→Update model weights once after 4 batches→Updated model weights

Weights adjusted after accumulated gradients

Training Trace - Epoch by Epoch

Loss
1.0 |*       
0.8 | **     
0.6 |  ***   
0.4 |    ****
0.2 |     ***
    +--------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.60	Loss starts high, accuracy moderate
2	0.65	0.72	Loss decreases, accuracy improves
3	0.50	0.80	Loss continues to decrease, accuracy rises
4	0.40	0.85	Model converging well
5	0.35	0.88	Loss low, accuracy high

Prediction Trace - 6 Layers

Layer 1: Input batch

Layer 2: Forward pass

Layer 3: Loss calculation

Layer 4: Backward pass

Layer 5: Gradient accumulation

Layer 6: Optimizer step

Model Quiz - 3 Questions

Why do we accumulate gradients over multiple batches before updating weights?

A To simulate a larger batch size and reduce memory usage

B To increase the learning rate automatically

C To avoid calculating gradients

D To skip the backward pass

Key Insight

Gradient accumulation allows training with effectively larger batch sizes without needing more memory. It sums gradients over several small batches before updating weights, helping models train efficiently on limited hardware.