Sequential Processing

Sequential processing means handling data one element at a time in a specific order. In the context of text, this means processing one word after another, following the sequence they appear in. Traditional models like Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) process input text sequentially. Here’s how it works:

1. Word by Word: The model takes the first word of the sentence, processes it, and updates its internal state.
2. State Propagation: This internal state, which holds the memory of previous words, is passed along with the next word to process the second word, and so on.
3. Dependency on Previous State: Each word’s processing depends on the previously processed words and their states, which can make the process slow, especially for long sentences.

Drawbacks of Sequential Processing

• Time-Consuming: Since each word is processed one after the other, the overall processing time can be significant, especially with long sentences or large datasets.
• Difficulty with Long-Range Dependencies: Maintaining contextual understanding over long distances (i.e., when relevant words are far apart in the sentence) can be challenging because the model has to carry and update the state through many steps, which can lead to the loss of important information.

Parallel Processing

Parallel processing, on the other hand, allows handling multiple elements simultaneously. In the context of the Transformer model, this means processing all words in a sentence at the same time. Here’s how the Transformer achieves this:

1. Self-Attention Mechanism: Each word in the sentence attends to every other word, computing a set of attention weights that indicate the importance of each word relative to the others. This allows the model to understand the context of each word in relation to the entire sentence in one go.
2. Positional Encoding: Since processing happens in parallel, Transformers add positional encoding to each word to maintain the order of the words. Positional encoding is a set of vectors added to the input embeddings that carry information about the position of each word in the sequence.

Benefits of Parallel Processing

• Efficiency: By processing all words simultaneously, the Transformer model can leverage the power of parallel computing, significantly speeding up the training and inference processes.
• Better Handling of Long-Range Dependencies: The self-attention mechanism allows the model to directly access any word in the sentence, making it easier to capture relationships between distant words.

Example Comparison

Let’s consider the sentence: “The quick brown fox jumps over the lazy dog.”

• Sequential Processing (RNN/LSTM):
  • Step 1: Process “The” -> Update state.
  • Step 2: Process “quick” -> Update state considering “The”.
  • Step 3: Process “brown” -> Update state considering “The quick”.
  • …and so on until the end of the sentence.
• Parallel Processing (Transformer):
  • All words (“The”, “quick”, “brown”, “fox”, “jumps”, “over”, “the”, “lazy”, “dog”) are processed simultaneously.
  • Each word’s representation is adjusted based on its relationship with every other word in the sentence using self-attention.

Visual Representation

Sequential Processing:

The -> quick -> brown -> fox -> jumps -> over -> the -> lazy -> dog

Parallel Processing:

(The, quick, brown, fox, jumps, over, the, lazy, dog)

All words are processed at once, with their relationships being evaluated simultaneously.

In summary, parallel processing allows the Transformer model to efficiently handle long-range dependencies and leverage the computational advantages of modern hardware, resulting in faster and more effective training and inference compared to sequential models.