Transformer encoder in PyTorch - Model Pipeline Trace

Model Pipeline - Transformer encoder

This pipeline uses a Transformer encoder to process sequences of data. It converts input tokens into meaningful representations by attending to all parts of the sequence at once, helping the model understand context better.

Data Flow - 5 Stages

1Input tokens

32 sequences x 10 tokens→Raw input token indices representing words→32 sequences x 10 tokens

[[12, 45, 78, 34, 9, 0, 0, 0, 0, 0], ...]

↓

2Embedding layer

32 sequences x 10 tokens→Convert tokens to vectors of size 64→32 sequences x 10 tokens x 64 features

[[[0.1, -0.2, ...], ...], ...]

↓

3Positional encoding

32 sequences x 10 tokens x 64 features→Add position info to embeddings→32 sequences x 10 tokens x 64 features

[[[0.15, -0.18, ...], ...], ...]

↓

4Transformer encoder layers

32 sequences x 10 tokens x 64 features→Apply multi-head self-attention and feed-forward layers→32 sequences x 10 tokens x 64 features

[[[0.3, 0.1, ...], ...], ...]

↓

5Output representations

32 sequences x 10 tokens x 64 features→Final encoded token vectors→32 sequences x 10 tokens x 64 features

[[[0.35, 0.12, ...], ...], ...]

Training Trace - Epoch by Epoch

Loss
1.2 |*****
1.0 |**** 
0.8 |***  
0.6 |**   
0.4 |*    
    +-----
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning, loss high, accuracy low
2	0.9	0.60	Loss decreases, accuracy improves
3	0.7	0.72	Model learns better context, accuracy rises
4	0.55	0.80	Loss continues to drop, accuracy nearing good performance
5	0.45	0.85	Training converges, model performs well

Prediction Trace - 5 Layers

Layer 1: Embedding layer

Layer 2: Positional encoding

Layer 3: Multi-head self-attention

Layer 4: Feed-forward network

Layer 5: Output representations

Model Quiz - 3 Questions

What is the main purpose of positional encoding in the Transformer encoder?

A To add information about the order of tokens

B To reduce the size of input data

C To increase the number of tokens

D To convert tokens into numbers

Key Insight

The Transformer encoder uses self-attention to understand relationships between all tokens in a sequence simultaneously. Positional encoding helps the model know token order, and training shows steady improvement in loss and accuracy, indicating effective learning.