Salesforce CodeGen Tutorial: Generate, Validate, and Rerank Python Functions With Unit Tests and Safety Checks

In this tutorial, we implement an end-to-end workflow for Salesforce CodeGen . We load a CodeGen model from Hugging Face, prepare it for code generation, and use it to generate Python functions from natural-language prompts. We then move beyond basic inference by adding function extraction, syntax checking, static safety checks, unit-test-based validation, best-of-N candidate reranking, multi-step program synthesis, prompt-style experimentation, benchmark visualization, and artifact export. Through this workflow, we learn how CodeGen can be used not only as a code completion model but also as part of a structured code-generation pipeline that evaluates, filters, and organizes generated solutions.

We install all required libraries and prepare the environment for running Salesforce CodeGen. We check the runtime, detect GPU availability, select the CodeGen model, and load both the tokenizer and model from Hugging Face. We also define helper functions for text generation and for displaying formatted code so that the rest of the tutorial is easier to follow.

We build the utility layer that extracts generated Python functions from raw model outputs. We add syntax validation, static safety checks, restricted execution, unit-test execution, and timeout handling to make generated code easier to evaluate. We also calculate code complexity and create a scoring function to rank generated candidates by correctness, safety, and simplicity.

We start with a simple natural-language-to-code generation example using a circle area function. We generate raw CodeGen output, extract the function, and inspect its syntax, safety, and complexity. We then define multiple programming tasks that later help us benchmark CodeGen across different function-generation problems.

We create structured prompts for each task and generate multiple candidate solutions using CodeGen. We evaluate each candidate with unit tests, syntax checks, safety checks, complexity analysis, and a scoring system. We then summarize the results in a DataFrame and display the best-generated solution for each task.

We demonstrate multi-turn program synthesis by generating smaller functions that work together as a pipeline. We create functions for word normalization, word counting, and top-word selection, then compose them into a complete most-common-word workflow. We also test different prompt styles such as docstring-to-code, partial completion, test generation, and refactoring.

We aggregate benchmark results and visualize the best candidate pass rates across all tasks. We export generated candidates, benchmark summaries, best solutions, and the composed pipeline as reusable files. We finish by adding an interactive helper function that lets us generate new CodeGen solutions from custom user-defined programming tasks.