DataLoader

As explained in Build a Large Language Model (From Scratch)

A component that produces batches of data for model training or experimentation, typically by sampling and collating examples.

A DataLoader turns a Dataset into iterable batches for training, validation, or testing. It controls batch size, shuffling, worker processes, incomplete batches, and optional collation. section 6.3 section 7.4

The Dataset prepares individual examples; the DataLoader assembles them into tensors consumed by the model. For classification, padding produces uniform sequences, while instruction tuning uses a custom collate function to batch variable-length inputs and targets. section 6.3 section 7.4

Configure separate loaders for each data split:

• Shuffle training data and drop the final incomplete batch. section 6.3
• Keep validation and test ordering stable and retain incomplete final batches. section 6.3
• Set batch size and worker count, then inspect the resulting batch shapes. section 6.3
• Attach a custom collate function when batching requires padding, masking, truncation, or device transfer. section 7.4

Classification batch

With batch_size=8, a training loader can yield an input tensor shaped [8, 120] and a label tensor shaped [8]. Instruction loaders may instead yield matching variable sequence shapes such as [8, 61] and [8, 61]. section 6.3 section 7.4

Figure 6.7 A single training batch consisting of eight text messages represented as token IDs. Each text message consists of 120 token IDs. A class label array stores the eight class labels corresponding to the text messages, which can be either 0 (“not spam”) or 1 (“spam”).

Inspect the eight-example classification batch, including its token-ID matrix and binary label array. section 6.3

How Build a Large Language Model (From Scratch) covers it

How deep it goes

Demonstrates configuration, iteration, device-aware collation, and output shapes without implementing Dataset internals.

Examples

Uses spam-message loaders with padded token IDs and instruction loaders with custom-collated input-target tensors.

What it assumes

Assumes prepared Dataset objects and, when needed, a custom collate function; demonstrates both components before loader construction.

What you can do after

Operate PyTorch DataLoader instances for shuffled training and deterministic validation or testing batches.

How it connects

Connects Dataset, tokenizer, collate function, padding, truncation, batching, and GPT-2 fine-tuning.

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Also explained in

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  Deep Learning with PyTorch, Second EditionA PyTorch abstraction that batches and loads samples from datasets.
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  Inside Deep Learning: Math, Algorithms, ModelsA PyTorch utility that prepares batches of examples from training or testing data for iteration during model execution.
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  Learn Generative AI with PyTorchA PyTorch utility that wraps a dataset or iterable to provide batched access, optional shuffling, and iteration during training.

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Related concepts

• batch sizeconfigured by · Batch size is the number of items grouped together for processing or computation as a single batch.
• training loopused by · A training loop is an iterative procedure that processes training data, evaluates a model objective, and updates model parameters to improve performance.
• PyTorchuses · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.
• training datasetuses · A collection of examples or observations supplied to a learning procedure to fit or adapt a model or learn its patterns or parameters.

This explainer was generated by Manning's knowledge graph from the text of Build a Large Language Model (From Scratch). The section links point to the parts of the book it draws on.