osobhandClaude Opus 4.6
02d382d5f6
Consistent formatting pass: line wrapping, import sorting, trailing whitespace removal, let-chain indentation, merged derive attributes, and unsafe block reformatting. Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
119 lines
3.4 KiB
Rust
119 lines
3.4 KiB
Rust
//! # RustyTorch Derive Macros
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//!
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//! This crate provides procedural macros for the RustyTorch deep learning framework.
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//!
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//! ## Available Macros
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//!
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//! - `#[derive(Module)]` - Automatically implements the `Module` trait for neural network layers
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//! - `#[derive(Config)]` - Generates builder pattern configuration structs
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//!
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//! ## Module Derive
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//!
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//! The `Module` derive macro automatically generates implementations for:
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//! - `parameters()` - Returns all trainable parameters
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//! - `parameters_mut()` - Returns mutable references to parameters
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//! - `to_device()` - Moves the module to a device
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//! - `device()` - Returns the module's device
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//! - `train()` / `training()` - Training mode control
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//!
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//! ### Field Attributes
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//!
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//! - `#[param]` - Marks a field as a trainable parameter (Tensor)
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//! - `#[module]` - Marks a field as a nested module (recursive traversal)
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//! - `#[constant]` - Marks a field as a non-trainable constant
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//! - `#[device_field]` - Marks the field that stores the Device
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//! - `#[training_field]` - Marks the field that stores training mode
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//!
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//! ### Example
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//!
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//! ```ignore
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//! use rtx_macros::Module;
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//!
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//! #[derive(Module, Debug)]
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//! pub struct MyLayer {
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//! #[param]
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//! weight: Tensor,
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//!
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//! #[param]
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//! bias: Option<Tensor>,
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//!
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//! #[module]
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//! sublayer: Linear,
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//!
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//! #[constant]
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//! dropout_rate: f32,
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//!
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//! #[device_field]
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//! device: Device,
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//!
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//! #[training_field]
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//! training: bool,
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//! }
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//! ```
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//!
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//! ## Config Derive
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//!
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//! The `Config` derive macro generates:
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//! - `new()` constructor with required fields
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//! - `with_*()` builder methods for optional fields
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//! - `init()` method to create the module
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//!
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//! ### Field Attributes
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//!
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//! - `#[config(default = "value")]` - Sets a default value for the field
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//!
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//! ### Example
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//!
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//! ```ignore
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//! use rtx_macros::Config;
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//!
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//! #[derive(Config)]
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//! pub struct LinearConfig {
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//! pub in_features: usize,
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//! pub out_features: usize,
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//!
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//! #[config(default = "true")]
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//! pub bias: bool,
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//!
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//! #[config(default = "0.0")]
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//! pub dropout: f64,
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//! }
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//! ```
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use proc_macro::TokenStream;
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mod config;
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mod module;
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mod shared;
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/// Derive macro for implementing the `Module` trait.
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///
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/// This macro automatically generates the boilerplate code needed for neural network modules,
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/// including parameter management, device handling, and training mode control.
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///
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/// See the [crate-level documentation](crate) for detailed usage examples.
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#[proc_macro_derive(
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Module,
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attributes(param, module, constant, device_field, training_field)
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)]
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pub fn module_derive(input: TokenStream) -> TokenStream {
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let input = syn::parse_macro_input!(input as syn::DeriveInput);
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module::derive_impl(&input)
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.unwrap_or_else(|err| err.to_compile_error())
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.into()
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}
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/// Derive macro for creating configuration structs with builder pattern.
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///
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/// This macro generates constructors and builder methods for configuration structs,
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/// making it easy to create module configurations with sensible defaults.
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///
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/// See the [crate-level documentation](crate) for detailed usage examples.
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#[proc_macro_derive(Config, attributes(config))]
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pub fn config_derive(input: TokenStream) -> TokenStream {
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let input = syn::parse_macro_input!(input as syn::DeriveInput);
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config::derive_impl(&input)
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.unwrap_or_else(|err| err.to_compile_error())
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.into()
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}
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