Print module part3 - Update book (#1940)

* Update book example guide

* Update Module book section on module display

burn-book/src/basic-workflow/model.md

@@ -192,7 +192,7 @@ Next, we need to instantiate the model for training.
 #     linear2: Linear<B>,
 #     activation: Relu,
 # }
-# 
+#
 #[derive(Config, Debug)]
 pub struct ModelConfig {
     num_classes: usize,
@@ -217,6 +217,40 @@ impl ModelConfig {
 }
 ```
 
+
+At a glance, you can view the model configuration by printing the model instance:
+
+```rust , ignore
+use burn::backend::Wgpu;
+use guide::model::ModelConfig;
+
+fn main() {
+    type MyBackend = Wgpu<f32, i32>;
+
+    let device = Default::default();
+    let model = ModelConfig::new(10, 512).init::<MyBackend>(&device);
+
+    println!("{}", model);
+}
+```
+
+Output:
+
+```rust , ignore
+Model {
+  conv1: Conv2d {stride: [1, 1], kernel_size: [3, 3], dilation: [1, 1], groups: 1, padding: Valid, params: 80}
+  conv2: Conv2d {stride: [1, 1], kernel_size: [3, 3], dilation: [1, 1], groups: 1, padding: Valid, params: 1168}
+  pool: AdaptiveAvgPool2d {output_size: [8, 8]}
+  dropout: Dropout {prob: 0.5}
+  linear1: Linear {d_input: 1024, d_output: 512, bias: true, params: 524800}
+  linear2: Linear {d_input: 512, d_output: 10, bias: true, params: 5130}
+  activation: Relu
+  params: 531178
+}
+```
+
+
+
 <details>
 <summary><strong>🦀 References</strong></summary>
 

burn-book/src/building-blocks/module.md

@@ -52,7 +52,7 @@ the `Module` derive, you need to be careful to achieve the behavior you want.
 These methods are available for all modules.
 
 | Burn API                                | PyTorch Equivalent                       |
-|-----------------------------------------|------------------------------------------|
+| --------------------------------------- | ---------------------------------------- |
 | `module.devices()`                      | N/A                                      |
 | `module.fork(device)`                   | Similar to `module.to(device).detach()`  |
 | `module.to_device(device)`              | `module.to(device)`                      |
@@ -69,7 +69,7 @@ Similar to the backend trait, there is also the `AutodiffModule` trait to signif
 autodiff support.
 
 | Burn API         | PyTorch Equivalent |
-|------------------|--------------------|
+| ---------------- | ------------------ |
 | `module.valid()` | `module.eval()`    |
 
 ## Visitor & Mapper
@@ -96,7 +96,62 @@ pub trait ModuleVisitor<B: Backend> {
 /// Module mapper trait.
 pub trait ModuleMapper<B: Backend> {
     /// Map a tensor in the module.
-    fn map<const D: usize>(&mut self, id: &ParamId, tensor: Tensor<B, D>) -> Tensor<B, D>;
+    fn map<const D: usize>(&mut self, id: &ParamId, tensor: Tensor<B, D>) ->
+      Tensor<B, D>;
+}
+```
+
+## Module Display
+
+Burn provides a simple way to display the structure of a module and its configuration at a glance.
+You can print the module to see its structure, which is useful for debugging and tracking changes
+across different versions of a module. (See the print output of the
+[Basic Workflow Model](../basic-workflow/model.md) example.)
+
+To customize the display of a module, you can implement the `ModuleDisplay` trait for your module.
+This will change the default display settings for the module and its children. Note that
+`ModuleDisplay` is automatically implemented for all modules, but you can override it to customize
+the display by annotating the module with `#[module(custom_display)]`.
+
+```rust
+#[derive(Module, Debug)]
+#[module(custom_display)]
+pub struct PositionWiseFeedForward<B: Backend> {
+    linear_inner: Linear<B>,
+    linear_outer: Linear<B>,
+    dropout: Dropout,
+    gelu: Gelu,
+}
+
+impl<B: Backend> ModuleDisplay for PositionWiseFeedForward<B> {
+    /// Custom settings for the display of the module.
+    /// If `None` is returned, the default settings will be used.
+    fn custom_settings(&self) -> Option<burn::module::DisplaySettings> {
+        DisplaySettings::new()
+            // Will show all attributes (default is false)
+            .with_show_all_attributes(false)
+            // Will show each attribute on a new line (default is true)
+            .with_new_line_after_attribute(true)
+            // Will show the number of parameters (default is true)
+            .with_show_num_parameters(true)
+            // Will indent by 2 spaces (default is 2)
+            .with_indentation_size(2)
+            // Will show the parameter ID (default is false)
+            .with_show_param_id(false)
+            // Convenience method to wrap settings in Some()
+            .optional()
+    }
+
+    /// Custom content to be displayed.
+    /// If `None` is returned, the default content will be used
+    /// (all attributes of the module)
+    fn custom_content(&self, content: Content) -> Option<Content> {
+        content
+            .add("linear_inner", &self.linear_inner)
+            .add("linear_outer", &self.linear_outer)
+            .add("anything", "anything_else")
+            .optional()
+    }
 }
 ```
 
@@ -107,7 +162,7 @@ Burn comes with built-in modules that you can use to build your own modules.
 ### General
 
 | Burn API       | PyTorch Equivalent                            |
-|----------------|-----------------------------------------------|
+| -------------- | --------------------------------------------- |
 | `BatchNorm`    | `nn.BatchNorm1d`, `nn.BatchNorm2d` etc.       |
 | `Dropout`      | `nn.Dropout`                                  |
 | `Embedding`    | `nn.Embedding`                                |
@@ -125,7 +180,7 @@ Burn comes with built-in modules that you can use to build your own modules.
 ### Convolutions
 
 | Burn API          | PyTorch Equivalent   |
-|-------------------|----------------------|
+| ----------------- | -------------------- |
 | `Conv1d`          | `nn.Conv1d`          |
 | `Conv2d`          | `nn.Conv2d`          |
 | `ConvTranspose1d` | `nn.ConvTranspose1d` |
@@ -134,7 +189,7 @@ Burn comes with built-in modules that you can use to build your own modules.
 ### Pooling
 
 | Burn API            | PyTorch Equivalent     |
-|---------------------|------------------------|
+| ------------------- | ---------------------- |
 | `AdaptiveAvgPool1d` | `nn.AdaptiveAvgPool1d` |
 | `AdaptiveAvgPool2d` | `nn.AdaptiveAvgPool2d` |
 | `AvgPool1d`         | `nn.AvgPool1d`         |
@@ -145,7 +200,7 @@ Burn comes with built-in modules that you can use to build your own modules.
 ### RNNs
 
 | Burn API         | PyTorch Equivalent     |
-|------------------|------------------------|
+| ---------------- | ---------------------- |
 | `Gru`            | `nn.GRU`               |
 | `Lstm`/`BiLstm`  | `nn.LSTM`              |
 | `GateController` | _No direct equivalent_ |
@@ -153,7 +208,7 @@ Burn comes with built-in modules that you can use to build your own modules.
 ### Transformer
 
 | Burn API             | PyTorch Equivalent      |
-|----------------------|-------------------------|
+| -------------------- | ----------------------- |
 | `MultiHeadAttention` | `nn.MultiheadAttention` |
 | `TransformerDecoder` | `nn.TransformerDecoder` |
 | `TransformerEncoder` | `nn.TransformerEncoder` |
@@ -163,7 +218,7 @@ Burn comes with built-in modules that you can use to build your own modules.
 ### Loss
 
 | Burn API           | PyTorch Equivalent    |
-|--------------------|-----------------------|
+| ------------------ | --------------------- |
 | `CrossEntropyLoss` | `nn.CrossEntropyLoss` |
 | `MseLoss`          | `nn.MSELoss`          |
 | `HuberLoss`        | `nn.HuberLoss`        |

examples/guide/README.md

@@ -4,6 +4,21 @@ This example corresponds to the [book's guide](https://burn.dev/book/basic-workf
 
 ## Example Usage
 
+
+### Training
+
 ```sh
-cargo run --example guide
-```
+cargo run --bin train --release
+```
+
+### Inference
+
+```sh
+cargo run --bin infer --release
+```
+
+### Print the model
+
+```sh
+cargo run --bin print --release
+```

examples/guide/examples/guide.rs

@@ -10,7 +10,7 @@ use std::process::Command;
 
 fn main() {
     Command::new("cargo")
-        .args(["run", "--bin", "guide"])
+        .args(["run", "--bin", "train", "--release"])
         .status()
         .expect("guide example should run");
 }

examples/guide/src/bin/infer.rs

@@ -0,0 +1,20 @@
+use burn::{backend::Wgpu, data::dataset::Dataset};
+use guide::inference;
+
+fn main() {
+    type MyBackend = Wgpu<f32, i32>;
+
+    let device = burn::backend::wgpu::WgpuDevice::default();
+
+    // All the training artifacts are saved in this directory
+    let artifact_dir = "/tmp/guide";
+
+    // Infer the model
+    inference::infer::<MyBackend>(
+        artifact_dir,
+        device,
+        burn::data::dataset::vision::MnistDataset::test()
+            .get(42)
+            .unwrap(),
+    );
+}

examples/guide/src/bin/print.rs

@@ -0,0 +1,11 @@
+use burn::backend::Wgpu;
+use guide::model::ModelConfig;
+
+fn main() {
+    type MyBackend = Wgpu<f32, i32>;
+
+    let device = Default::default();
+    let model = ModelConfig::new(10, 512).init::<MyBackend>(&device);
+
+    println!("{}", model);
+}

examples/guide/src/bin/train.rs

@@ -1,27 +1,33 @@
-mod data;
-mod inference;
-mod model;
-mod training;
-
-use crate::{model::ModelConfig, training::TrainingConfig};
 use burn::{
     backend::{Autodiff, Wgpu},
     data::dataset::Dataset,
     optim::AdamConfig,
 };
+use guide::{
+    inference,
+    model::ModelConfig,
+    training::{self, TrainingConfig},
+};
 
 fn main() {
     type MyBackend = Wgpu<f32, i32>;
     type MyAutodiffBackend = Autodiff<MyBackend>;
 
+    // Create a default Wgpu device
     let device = burn::backend::wgpu::WgpuDevice::default();
+
+    // All the training artifacts will be saved in this directory
     let artifact_dir = "/tmp/guide";
-    crate::training::train::<MyAutodiffBackend>(
+
+    // Train the model
+    training::train::<MyAutodiffBackend>(
         artifact_dir,
         TrainingConfig::new(ModelConfig::new(10, 512), AdamConfig::new()),
         device.clone(),
     );
-    crate::inference::infer::<MyBackend>(
+
+    // Infer the model
+    inference::infer::<MyBackend>(
         artifact_dir,
         device,
         burn::data::dataset::vision::MnistDataset::test()

examples/guide/src/inference.rs

@@ -7,10 +7,10 @@ use burn::{
 
 pub fn infer<B: Backend>(artifact_dir: &str, device: B::Device, item: MnistItem) {
     let config = TrainingConfig::load(format!("{artifact_dir}/config.json"))
-        .expect("Config should exist for the model");
+        .expect("Config should exist for the model; run train first");
     let record = CompactRecorder::new()
         .load(format!("{artifact_dir}/model").into(), &device)
-        .expect("Trained model should exist");
+        .expect("Trained model should exist; run train first");
 
     let model: Model<B> = config.model.init(&device).load_record(record);