C# metaprogramming with source generator.pdf
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The document discusses C# metaprogramming using source generators as a means to facilitate code generation and performance optimization during the compilation process. It outlines the design goals, implementation strategies, and best practices for creating efficient source generators that can add new code without modifying existing user code. Additionally, the text emphasizes the importance of pipeline-based execution and offers tips for optimizing data handling and caching during the transformation process.
![Implementing a source generator
[Generator]
public class MyGenerator : IIncrementalGenerator
{
public void Initialize(IncrementalGeneratorInitializationContext context)
{
// define the execution pipeline here via a series of transformations
}
}](https://image.slidesharecdn.com/cmetaprogrammingsourcegenerator-250128081018-acea2187/85/C-metaprogramming-with-source-generator-pdf-16-320.jpg)
C# metaprogramming with source generator.pdf
- 1. C# metaprogramming con i Source Generator Alessandro Melchiori - @amelchiori CodicePlastico
- 2. Roslyn History 2010 Hiring for Rolsyn
- 3. Roslyn History 2010 Hiring for Rolsyn 2011 V1 released
- 4. Roslyn History 2010 Hiring for Rolsyn 2011 V1 released 2020 C# Source Gen
- 5. Roslyn History 2010 Hiring for Rolsyn 2011 V1 released 2012 Un compilatore per amico 2020 C# Source Gen
- 6. What exactly are source generator? ● New metaprogramming technology ● Part of compilation process ● Can access a model of our code ● Generated output are new files
- 7. What problems are solved by source generator? ● Generating boilerplate code ○ Standard implementation of Equals, GetHashCode, ==, ToString ○ Type generation from schemas such as xsd ○ Object mapping ○ Code for object materialization ● Performance optimization ○ Serialization without reflection ○ Register types for DI without reflection
- 8. How does it work?
- 9. High level design goals of source generators Generators produce one or more strings that represent C# source code to be added to the compilation.
- 10. High level design goals of source generators Generators produce one or more strings that represent C# source code to be added to the compilation. Explicitly additive only. Generators can add new source code to a compilation but may not modify existing user code.
- 11. High level design goals of source generators Generators produce one or more strings that represent C# source code to be added to the compilation. Explicitly additive only. Generators can add new source code to a compilation but may not modify existing user code. May access additional files, that is, non-C# source texts.
- 12. High level design goals of source generators Run un-ordered, each generator will see the same input compilation, with no access to files created by other source generators.
- 13. High level design goals of source generators Run un-ordered, each generator will see the same input compilation, with no access to files created by other source generators. A user specifies the generators to run via list of assemblies, much like analyzers.
- 14. High level design goals of source generators Run un-ordered, each generator will see the same input compilation, with no access to files created by other source generators. A user specifies the generators to run via list of assemblies, much like analyzers. Generators create a pipeline, starting from base input sources and mapping them to the output they wish to produce.
- 15. Implementing a source generator namespace Microsoft.CodeAnalysis { public interface IIncrementalGenerator { void Initialize(IncrementalGeneratorInitializationContext context); } }
- 16. Implementing a source generator [Generator] public class MyGenerator : IIncrementalGenerator { public void Initialize(IncrementalGeneratorInitializationContext context) { // define the execution pipeline here via a series of transformations } }
- 17. DEMO
- 18. Pipeline based execution The defined transformations are not executed directly at initialization, and instead are deferred until the data they are using changes.
- 19. Pipeline based execution The defined transformations are not executed directly at initialization, and instead are deferred until the data they are using changes. These transformations are used to form a directed graph of actions that can be executed on demand later, as the input data changes.
- 20. Pipeline based execution The defined transformations are not executed directly at initialization, and instead are deferred until the data they are using changes. These transformations are used to form a directed graph of actions that can be executed on demand later, as the input data changes. Between each transformation, the data produced is cached, allowing previously calculated values to be re-used where applicable.
- 21. Outputting values RegisterSourceOutput Allows a generator author to produce source files and diagnostics that will be included in the users compilation. RegisterImplementationSourceOutput Works in the same way as RegisterSourceOutput but declares that the source produced has no semantic impact on user code from the point of view of code analysis. RegisterPostInitializationOutput Allows a generator author to provide source code immediately after initialization has run.
- 23. Caching
- 24. DEMO
- 25. Pipeline best practices Extract out information early It is best to get the information out of the inputs as early as possible in the pipeline. This ensures the host is not caching large, expensive object such as symbols.
- 26. Pipeline best practices Extract out information early It is best to get the information out of the inputs as early as possible in the pipeline. This ensures the host is not caching large, expensive object such as symbols. Use value types where possible Value types are more amenable to caching and usually have well defined and easy to understand comparison semantics.
- 27. Pipeline best practices Extract out information early It is best to get the information out of the inputs as early as possible in the pipeline. This ensures the host is not caching large, expensive object such as symbols. Use value types where possible Value types are more amenable to caching and usually have well defined and easy to understand comparison semantics. Use multiple Transformations: The more transformations you break the operations into, the more opportunities there are to cache. Think of transformations as being 'check points' in the execution graph. The more check points the more chances there are to match a cached value and skip any remaining work.
- 28. Pipeline best practices Build a data model Rather than trying to pass each input item into a Register...Output method, consider building a data model to be the final item passed to the output. Use the transformations to manipulate the data model, and have well defined equality that allows you to correctly compare between revisions of the model. This also makes testing the final Register...Outputs significantly simpler: you can just call the method with a dummy data model and check the generated code, rather than trying to emulate the incremental transformations.
- 29. Pipeline best practices Build a data model Rather than trying to pass each input item into a Register...Output method, consider building a data model to be the final item passed to the output. Use the transformations to manipulate the data model, and have well defined equality that allows you to correctly compare between revisions of the model. This also makes testing the final Register...Outputs significantly simpler: you can just call the method with a dummy data model and check the generated code, rather than trying to emulate the incremental transformations. Consider the order of combines Ensure that you are only combining the minimal amount of information needed (this comes back to 'Extract out information early').
- 30. ALESSANDRO MELCHIORI Founder & Software developer @ CodicePlastico alessandro@codiceplastico.com @amelchiori