burn/README.md

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> This library aims to be a complete deep learning framework with extreme flexibility written in Rust.
> The goal would be to satisfy researchers as well as practitioners making it easier to experiment, train and deploy your models.

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*__Disclamer__* _Burn is currently in active development, and there will be breaking changes. While any resulting issues are likely to be easy to fix, there are no guarantees at this stage._

__Sections__

* [Features](#features)
* [Get Started](#get-started)
    * [Examples](#examples)
    * [Components](#components)
        * [Backend](#backend)
        * [Tensor](#tensor)
        * [Autodiff](#autodiff)
        * [Module](#module)
        * [Config](#config)
        * [Learner](#learner)
* [no_std support](#no_std-support)
* [Sponsors](#sponsors)
* [License](#license)

## Features

 * Flexible and intuitive custom neural network [module](#module) 🔥
 * [Training](#learner) with full support for `metric`, `logging` and `checkpointing` 📈
 * [Tensor](#tensor) crate with backends as pluging 🔧
   * [Tch](https://github.com/burn-rs/burn/tree/main/burn-tch) backend with CPU/GPU support 🚀
   * [NdArray](https://github.com/burn-rs/burn/tree/main/burn-ndarray) backend with [`no_std`](#no_std-support) support, running on any platform 👌
   * [Autodiff](https://github.com/burn-rs/burn/tree/main/burn-autodiff) backend making any backend differentiable 🌟
 * [Dataset](https://github.com/burn-rs/burn/tree/main/burn-dataset) crate with multiple utilities and sources 📚

## Get Started

The best way to get started with `burn` is to clone the repo and play with the [examples](#examples).
This may also be a good idea to take a look the main [components](#components) of `burn` to get a quick overview of the fundamental building blocks.

### Examples

* [MNIST](https://github.com/burn-rs/burn/tree/main/examples/mnist) train a model on CPU/GPU using different backends.
* [MNIST Inference Web](https://github.com/burn-rs/burn/tree/main/examples/mnist-inference-web) run trained model in the browser for inference.
* [Text Classification](https://github.com/burn-rs/burn/tree/main/examples/text-classification) train a transformer encoder from scratch on GPU.
* [Text Generation](https://github.com/burn-rs/burn/tree/main/examples/text-generation) train an autoregressive transformer from scratch on GPU.

### Components

Understanding the key components and philosophy of `burn` can greatly help when beginning to work with the framework.

#### Backend

Nearly everything in `burn` is based on the `Backend` trait, which enables you to run tensor operations using different implementations without having to modify your code.
While a backend may not necessarily have autodiff capabilities, the `ADBackend` trait specifies when autodiff is needed.
This trait not only abstracts operations but also tensor, device and element types, providing each backend the flexibility they need.
It's worth noting that the trait assumes eager mode since `burn` fully supports dynamic graphs.
However, we may create another API to assist with integrating graph-based backends, without requiring any changes to the user's code.

#### Tensor

At the core of burn lies the `Tensor` struct, which encompasses multiple types of tensors, including `Float`, `Int`, and `Bool`.
The element types of these tensors are specified by the backend and are usually designated as a generic argument (e.g., `NdArrayBackend<f32>`).
Although the same struct is used for all tensors, the available methods differ depending on the tensor kind.
You can specify the desired tensor kind by setting the third generic argument, which defaults to `Float`.
The first generic argument specifies the backend, while the second specifies the number of dimensions.

```rust
use burn::tensor::backend::Backend;
use burn::tensor::{Tensor, Int};

fn function<B: Backend>(tensor_float: Tensor<B, 2>) {
    let _tensor_bool = tensor_float.clone().equal_elem(2.0); // Tensor<B, 2, Bool>
    let _tensor_int = tensor_float.argmax(1) // Tensor<B, 2, Int>
}
```

As demonstrated in the previous example, nearly all operations require owned tensors as parameters, which means that calling `Clone` explicitly is necessary when reusing the same tensor multiple times.
However, there's no need to worry since the tensor's data won't be copied, it will be flagged as readonly when multiple tensors use the same allocated memory.
This enables backends to reuse tensor data when possible, similar to a copy-on-write pattern, while remaining completely transparent to the user.

#### Autodiff

The 'Backend' trait is highly flexible, enabling backpropagation to be implemented using a simple backend decorator, which makes any backend differentiable.

```rust
use burn::tensor::backend::{ADBackend, Backend};
use burn::tensor::{Distribution, Tensor};
use burn_autodiff::ADBackendDecorator;
use burn_ndarray::NdArrayBackend;

fn linear<B: Backend>(x: Tensor<B, 2>, weight: Tensor<B, 2>, bias: Tensor<B, 2>) -> Tensor<B, 2> {
    x.matmul(weight) + bias
}

fn main() {
    type Backend = NdArrayBackend<f32>;

    let weight = Tensor::random([3, 3], Distribution::Standard);
    let bias = Tensor::zeros([1, 3]);
    let x = Tensor::random([3, 3], Distribution::Standard);

    let y = linear::<Backend>(x.clone(), weight.clone(), bias.clone());
    // y.backward() // Method backward doesn't exist

    let y = linear::<ADBackendDecorator<Backend>>(
        Tensor::from_inner(x),
        Tensor::from_inner(weight).require_grad(),
        Tensor::from_inner(bias).require_grad(),
    );
    let grads = y.backward(); // Method exists
}

```

#### Module

The `Module` derive allows you to create your own neural network modules, similar to PyTorch.
The derive function only generates the necessary methods to essentially act as a parameter container for your type, it makes no assumptions about how the forward pass is declared.

```rust
use burn::nn;
use burn::module::Module;
use burn::tensor::backend::Backend;

#[derive(Module, Debug)]
pub struct PositionWiseFeedForward<B: Backend> {
    linear_inner: Linear<B>,
    linear_outer: Linear<B>,
    dropout: Dropout,
    gelu: GELU,
}

impl<B: Backend> PositionWiseFeedForward<B> {
    pub fn forward<const D: usize>(&self, input: Tensor<B, D>) -> Tensor<B, D> {
        let x = self.linear_inner.forward(input);
        let x = self.gelu.forward(x);
        let x = self.dropout.forward(x);

        self.linear_outer.forward(x)
    }
}
```

Note that all fields declared in the struct must also implement the `Module` trait.
The `Tensor` struct doesn't implement `Module`, but `Param<Tensor<B, D>>` does.

#### Config

The `Config` derive lets you define serializable and deserializable configurations or hyper-parameters for your [modules](#module) or any components.

```rust
use burn::config::Config;

#[derive(Config)]
pub struct PositionWiseFeedForwardConfig {
    pub d_model: usize,
    pub d_ff: usize,
    #[config(default = 0.1)]
    pub dropout: f64,
}
```

The derive also adds useful methods to your config, similar to a builder pattern.

```rust
fn main() {
    let config = PositionWiseFeedForwardConfig::new(512, 2048);
    println!("{}", config.d_model); // 512
    println!("{}", config.d_ff); // 2048
    println!("{}", config.dropout); // 0.1
    let config =  config.with_dropout(0.2);
    println!("{}", config.dropout); // 0.2
}
```

#### Learner

The `Learner` is the main `struct` that let you train a neural network with support for `logging`, `metric`, `checkpointing` and more.
In order to create a learner, you must use the `LearnerBuilder`.

```rust
use burn::train::LearnerBuilder;
use burn::train::metric::{AccuracyMetric, LossMetric};
use burn::record::DefaultRecordSettings;

fn main() {
    let dataloader_train = ...;
    let dataloader_valid = ...;

    let model = ...;
    let optim = ...;

    let learner = LearnerBuilder::new("/tmp/artifact_dir")
        .metric_train_plot(AccuracyMetric::new())
        .metric_valid_plot(AccuracyMetric::new())
        .metric_train(LossMetric::new())
        .metric_valid(LossMetric::new())
        .with_file_checkpointer::<DefaultRecordSettings>(2)
        .num_epochs(10)
        .build(model, optim);

    let _model_trained = learner.fit(dataloader_train, dataloader_valid);
}
```

See this [example](https://github.com/burn-rs/burn/tree/main/examples/mnist) for a real usage.

## no_std support

Burn supports `no_std` with `alloc` for the inference mode with the NDArray backend.
Simply disable the default features of the `burn` and `burn-ndarray` crates (minimum required to run the inference mode).
See the [burn-no-std-tests](https://github.com/burn-rs/burn/tree/main/examples/burn-no-std-tests) example as a reference implementation.

Additionally `burn-core` and `burn-tensor` crates support `no_std` with `alloc` if needed to direclty include them as dependencies (the `burn` crates reexports `burn-core` and `burn-tensor`).
Note, under the `no_std` mode, a random seed is generated during the build time if the seed is not initialized by `Backend::seed` method.
Additionally, [spin::mutex::Mutex](https://docs.rs/spin/latest/spin/mutex/struct.Mutex.html) is used in place of [std::sync::Mutex](https://doc.rust-lang.org/std/sync/struct.Mutex.html) under the `no_std` mode.

## Sponsors

You can sponsor the founder of Burn from his [GitHub Sponsors profile](https://github.com/sponsors/nathanielsimard).
The Burn-rs organization doesn't yet have a fiscal entity, but other sponsor methods might become available as the project grows.

Thanks to all current sponsors 🙏.

<a href="https://github.com/smallstepman"><img src="https://github.com/smallstepman.png" width="60px" style="border-radius: 50%;" alt="nathanielsimard" /></a>

## License

Burn is distributed under the terms of both the MIT license and the Apache License (Version 2.0).
See [LICENSE-APACHE](./LICENSE-APACHE) and [LICENSE-MIT](./LICENSE-MIT) for details.
Opening a pull request is assumed to signal agreement with these licensing terms.