How to Build and Train a PyTorch Transformer Encoder | Built In

A transformer encoder is a deep learning architecture designed to process input sequences efficiently. It consists of multiple layers, each containing:

Unlike traditional RNNs, transformers process all tokens in parallel, making them more efficient for large data sets.

A transformer encoder is a deep learning architecture that can process input sequences. It can process all token in parallel, making it more efficient for large data sets. A transformer encoder can be built with PyTorch, an open-source deep learning framework that provides an intuitive interface for building machine learning models.

They can be built using PyTorch, an open-source deep learning framework that provides a flexible and intuitive interface for building and training machine learning models, especially neural networks.

PyTorch is widely used for its dynamic computation graph, eager execution, which allows developers to define and modify models on the fly, making debugging and experimentation easier. It supports GPU acceleration, making it highly efficient for large-scale deep learning tasks in fields like natural language processing, computer vision, and reinforcement learning.

PyTorch is structured around a few core components:

This modular design makes PyTorch both flexible for research and efficient for production use.

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Transformer encoders are fundamental to models like BERT and vision transformers. In this guide, we’ll build a basic transformer encoder from scratch in PyTorch, covering key components such as positional encoding, embedding layers, masking and training.

We’ll construct a basic transformer encoder from scratch. First, let’s import the necessary libraries:

Since transformers process tokens in parallel rather than sequentially, positional encoding helps the model understand the order of tokens within a sequence. It injects information about each token’s position by adding a computed positional vector to its embedding. The most common method uses sinusoidal functions for this purpose.

Let’s explain what this `PositionalEncoding` class is doing:

The embedding size (or d_model) represents the dimensionality of token embeddings. It determines how much information each token can carry. For positional encoding, this size should match the token embeddings so that the two can be added directly.

The positional encoding doesn’t change if we scale the embedding size; it only affects how the position is represented across dimensions.

The formula for calculating positional encodings is:

Where:

This results in positional patterns that remain consistent across sequence lengths and provide a notion of relative positioning.

Once computed, positional encodings are added to the token embeddings before being passed into the encoder. This step ensures that each token not only carries semantic information from embeddings but also positional information.

Mechanics:

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Here’s the implementation of positional encoding using torch.nn.Module:

This class inherits from nn.Module, PyTorch’s base class for neural network components, enabling efficient model management and compatibility with GPU computations.

The __init__ method initializes the positional encoding matrix. It runs automatically when an instance of PositionalEncoding is created.

This creates a (max_len, d_model) zero matrix, where each row corresponds to a position.

This creates a column vector with positions [0, 1, 2, ..., max_len - 1].

This term scales the positions across dimensions, ensuring unique encoding patterns across embeddings.

Even-indexed dimensions use sine, odd-indexed dimensions use cosine.

This adds a batch dimension and stores the positional encodings as a non-trainable buffer.

The forward method applies the precomputed positional encodings to the input embeddings.

Conclusion:

With positional encoding integrated, transformers can effectively process and learn from sequential data without relying on recurrence or convolution mechanisms.

The embedding layer maps input tokens (e.g., words or subwords) to dense vector representations that the transformer model can process. In PyTorch, this is typically implemented using nn.Embedding.

How it fits in the transformer:

Masking is a crucial technique in transformer models. It is used to prevent the model from paying attention to certain tokens during the self-attention mechanism. Common scenarios include:

This will produce a mask that looks like:

Conclusion:

This completes the overview of positional encoding, embeddings, and masking in PyTorch transformers.

The transformer encoder block consists of two essential components: Multi-head self-attention and a feedforward neural network.

Each encoder block consists of multi-head self-attention and a feedforward network.

Multi-head self-attention captures token dependencies regardless of their distance. It enables parallel processing and better context understanding.

Feedforward network refines token representations by applying transformations independently.

Together, these components form the backbone of the transformer encoder, facilitating efficient learning from sequential data.

The TransformerEncoder class stacks multiple TransformerEncoderLayer instances to build a complete transformer encoder. It combines token embeddings, positional encodings, and self-attention mechanisms to process sequential input data.

This class inherits from nn.Module, making it compatible with PyTorch’s model management and optimization features.

The constructor takes several parameters to configure the encoder:

The forward method defines the encoder’s forward pass.

Conclusion

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The DataLoader efficiently manages batches, shuffling, and parallel processing for the training loop.

Proper loss handling (ignoring padding) and efficient optimization ensure stable training.

Runs the training process for 10 full passes through the dataset.

Ensures layers like dropout are active.

Each batch contains src (input) and target (labels).

Clears old gradients before computing new ones.

Passes the input through the transformer encoder to get predictions.

Compares predictions with the target while ignoring padding tokens.

Calculates gradients for all trainable parameters.

Adjusts model weights based on gradients.

Accumulates the total loss for reporting.

Shows the average loss per epoch.

PyTorch is an open-source machine learning framework widely used for deep learning applications such as computer vision, natural language processing (NLP) and reinforcement learning. It provides a flexible, Pythonic interface with dynamic computation graphs, making experimentation and model development intuitive. PyTorch supports GPU acceleration, making it efficient for training large-scale models. It is commonly used in research and production for tasks like image classification, object detection, sentiment analysis and generative AI.

Training a transformer in PyTorch involves defining the model architecture, preparing the dataset, and implementing the training loop. First, create embeddings for tokens and positional encodings. Next, stack multiple encoder layers, each with multi-head self-attention and feedforward layers. Prepare the dataset using TensorDataset and DataLoader, define a loss function like CrossEntropyLoss, and use an optimizer such as Adam. During training, feed batches through the model, compute the loss, backpropagate gradients and update weights. Monitor the loss to track the model's performance over epochs.