Beam Search tutorial

Overview

What is Beam Search?

Beam Search is a decoding technique. Keeps several high-scoring partial sequences instead of committing to one token at a time. Its central idea is summarized by Bₜ = TopKₖ({(y₁:ₜ, log P(y₁:ₜ))}).

This guide connects theory to practice. You will trace the input, intermediate process, and output; decode the notation; run a dependency-light implementation; then repeat the workflow with Hugging Face Transformers and evaluate the result.

Preparation

Prerequisites and tools

You do not need an advanced software stack. Start with a recent Python environment and the fundamentals below, then install only the packages used by the practical example.

• Comfort with Python, tensor shapes, matrix multiplication, and probability distributions.
• A working understanding of token sequences, embeddings, batches, and attention masks.
• Enough memory to run the small tensor example; pretrained-model tutorials may also download model weights.

Process

How Beam Search works

1. 1

   Input

   Candidate next-token probabilities for each partial sequence.

2. 2

   Prepare and configure

   Check shapes, value ranges, ordering assumptions, missing values, and the parameters that control the algorithm’s behavior.

3. 3

   Algorithm process

   Expand every active sequence, score the results, and prune back to the best beams.

4. 4

   Output

   A high-probability completed sequence.

5. 5

   Validate

   Compare beam widths using task metrics, human review, latency, and repetition rate.

Core concept

Formula and intuition

The notation captures the main operation or complexity statement behind Beam Search. Read it together with the symbol key above and the step-by-step process in this guide.

Applications

When to use Beam Search

Structured generation, translation, and sequence-to-sequence decoding.

Implementation

Beam Search from scratch in Python

This dependency-light example emphasizes the algorithm’s mechanics so each important step remains visible.

beams = [([], 0.0)]
for _ in range(max_length):
    candidates = []
    for tokens, score in beams:
        for token, log_probability in next_token_scores(tokens):
            candidates.append((tokens + [token], score + log_probability))
    beams = sorted(candidates, key=lambda item: item[1], reverse=True)[:beam_width]

best_sequence = beams[0][0]

Practical tutorial

Build Beam Search with Hugging Face Transformers

The generate API manages beam expansion, cumulative scores, length penalties, stopping, and returned alternatives.

1. Prepare the data.Candidate next-token probabilities for each partial sequence. Validate its shape, type, range, and ordering before training or execution.
2. Configure the algorithm.Begin with explicit, conservative parameters and a fixed random seed whenever the library supports one.
3. Fit or execute.Expand every active sequence, score the results, and prune back to the best beams.
4. Inspect the result.A high-probability completed sequence. Then apply the evaluation checks in the next section.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "distilbert/distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).eval()
inputs = tokenizer("Machine learning helps us", return_tensors="pt")

with torch.no_grad():
    output = model.generate(
        **inputs, max_new_tokens=30,
        num_beams=4, num_return_sequences=2,
        no_repeat_ngram_size=3, early_stopping=True,
        pad_token_id=tokenizer.eos_token_id,
    )
for sequence in output:
    print(tokenizer.decode(sequence, skip_special_tokens=True))

API details and version-specific options: official Hugging Face Transformers reference →

Evaluation

How to evaluate the result

A successful run is not enough. Evaluate the output against the intended use, compare it with a simple baseline, and preserve a genuinely unseen test case whenever the task involves learned parameters.

• Compare beam widths using task metrics, human review, latency, and repetition rate.
• Inspect several returned sequences instead of assuming the highest likelihood is always best.

Common mistakes

Pitfalls and how to avoid them

These failure modes are common in tutorials and production systems. Treat them as review questions, not just after-the-fact debugging advice.

• Larger beams increase compute and can favor generic or repetitive text.
• Length penalty materially changes which sequence wins.
• Beam search is deterministic search, not a guarantee of factual or optimal output.

Project checklist

Before using Beam Search in a project

• ✓Write down every tensor shape and mask convention before composing layers.
• ✓Test a tiny deterministic case against a simple reference implementation.
• ✓Separate training behavior from autoregressive inference and disable dropout for evaluation.
• ✓Measure quality, latency, peak memory, and sequence-length scaling together.
• ✓Pin model, tokenizer, framework, and generation-configuration versions.

Further reading

Official documentation and next steps

This guide is an educational introduction, not a substitute for domain validation. For consequential applications, review the source documentation, test against representative data, and involve a subject-matter expert.

Continue learning

Related llm algorithms guides

• Byte Pair EncodingBuilds a compact vocabulary by repeatedly merging frequent neighboring symbols.
• Token EmbeddingsMaps discrete token IDs to dense vectors whose geometry can encode meaning.
• Positional EncodingAdds a position-dependent signal so identical tokens at different places remain distinguishable.