{"header":{"message":"feat: get to burn training","description":null,"timestamp":"2024-03-21T12:03:17.922837289Z","authors":[{}]},"hash":"T4JMISYCRZ3BODA2PEOXKJ2RSDIYK37FH5B6DHGRKVT47ABPIJUQC","authors":[{"login":"Jengamon","key":"47a75hR6eVaawZfVGBs2CqZUKZbcNz5Ago5CubrCkWvZ"}],"deps":{"hashes":[["T4JMISYCRZ3BODA2PEOXKJ2RSDIYK37FH5B6DHGRKVT47ABPIJUQC","feat: get to burn training"],["AA",null],["IX7A7EAPFCO4A6UZCFKPT7GC2RGP2VE4EVXEN3TUN2QFKKETBO4AC","feat: init repo"]],"deps":["IX7A7EAPFCO4A6UZCFKPT7GC2RGP2VE4EVXEN3TUN2QFKKETBO4AC"],"known":[]},"hunks":[{"FileAdd":{"meta":{"basename":"training.rs","metadata":0},"context":[{"change":2,"pos":15}],"contents":[{"Add":"use burn::{"},{"Add":" config::Config,"},{"Add":" data::{dataloader::DataLoaderBuilder, dataset::vision::MNISTDataset},"},{"Add":" module::Module,"},{"Add":" nn::loss::CrossEntropyLossConfig,"},{"Add":" optim::AdamConfig,"},{"Add":" record::CompactRecorder,"},{"Add":" tensor::{"},{"Add":" backend::{AutodiffBackend, Backend},"},{"Add":" Int, Tensor,"},{"Add":" },"},{"Add":" train::{"},{"Add":" metric::{AccuracyMetric, LossMetric},"},{"Add":" ClassificationOutput, LearnerBuilder, TrainOutput, TrainStep, ValidStep,"},{"Add":" },"},{"Add":"};"},{"Add":""},{"Add":"use crate::{"},{"Add":" data::{MNISTBatch, MNISTBatcher},"},{"Add":" model::{Model, ModelConfig},"},{"Add":"};"},{"Add":""},{"Add":"impl Model {"},{"Add":" pub fn forward_classification("},{"Add":" &self,"},{"Add":" images: Tensor,"},{"Add":" targets: Tensor,"},{"Add":" ) -> ClassificationOutput {"},{"Add":" let output = self.forward(images);"},{"Add":" let loss = CrossEntropyLossConfig::new()"},{"Add":" .init(&output.device())"},{"Add":" .forward(output.clone(), targets.clone());"},{"Add":""},{"Add":" ClassificationOutput::new(loss, output, targets)"},{"Add":" }"},{"Add":"}"},{"Add":""},{"Add":"impl TrainStep, ClassificationOutput> for Model {"},{"Add":" fn step(&self, batch: MNISTBatch) -> TrainOutput> {"},{"Add":" let item = self.forward_classification(batch.images, batch.targets);"},{"Add":""},{"Add":" TrainOutput::new(self, item.loss.backward(), item)"},{"Add":" }"},{"Add":"}"},{"Add":""},{"Add":"impl ValidStep, ClassificationOutput> for Model {"},{"Add":" fn step(&self, batch: MNISTBatch) -> ClassificationOutput {"},{"Add":" self.forward_classification(batch.images, batch.targets)"},{"Add":" }"},{"Add":"}"},{"Add":""},{"Add":"#[derive(Config)]"},{"Add":"pub struct TrainingConfig {"},{"Add":" pub model: ModelConfig,"},{"Add":" pub optimizer: AdamConfig,"},{"Add":" #[config(default = 10)]"},{"Add":" pub num_epochs: usize,"},{"Add":" #[config(default = 64)]"},{"Add":" pub batch_size: usize,"},{"Add":" #[config(default = 4)]"},{"Add":" pub num_workers: usize,"},{"Add":" #[config(default = 42)]"},{"Add":" pub seed: u64,"},{"Add":" #[config(default = 1.0e-4)]"},{"Add":" pub learning_rate: f64,"},{"Add":"}"},{"Add":""},{"Add":"pub fn train(artifact_dir: &str, config: TrainingConfig, device: B::Device) {"},{"Add":" std::fs::create_dir_all(artifact_dir).ok();"},{"Add":" config"},{"Add":" .save(format!(\"{artifact_dir}/config.json\"))"},{"Add":" .expect(\"config not saved correctly\");"},{"Add":""},{"Add":" B::seed(config.seed);"},{"Add":""},{"Add":" let batcher_train = MNISTBatcher::::new(device.clone());"},{"Add":" let batcher_valid = MNISTBatcher::::new(device.clone());"},{"Add":""},{"Add":" let dataloader_train = DataLoaderBuilder::new(batcher_train)"},{"Add":" .batch_size(config.batch_size)"},{"Add":" .shuffle(config.seed)"},{"Add":" .num_workers(config.num_workers)"},{"Add":" .build(MNISTDataset::train());"},{"Add":""},{"Add":" let dataloader_test = DataLoaderBuilder::new(batcher_valid)"},{"Add":" .batch_size(config.batch_size)"},{"Add":" .shuffle(config.seed)"},{"Add":" .num_workers(config.num_workers)"},{"Add":" .build(MNISTDataset::test());"},{"Add":""},{"Add":" let model = config.model.init::(&device);"},{"Add":""},{"Add":" let learner = LearnerBuilder::new(artifact_dir)"},{"Add":" .metric_train_numeric(AccuracyMetric::new())"},{"Add":" .metric_valid_numeric(AccuracyMetric::new())"},{"Add":" .metric_train_numeric(LossMetric::new())"},{"Add":" .metric_valid_numeric(LossMetric::new())"},{"Add":" .with_file_checkpointer(CompactRecorder::new())"},{"Add":" .devices(vec![device])"},{"Add":" .num_epochs(config.num_epochs)"},{"Add":" .build(model, config.optimizer.init(), config.learning_rate);"},{"Add":""},{"Add":" let model_trained = learner.fit(dataloader_train, dataloader_test);"},{"Add":""},{"Add":" model_trained"},{"Add":" .save_file(format!(\"{artifact_dir}/model\"), &CompactRecorder::new())"},{"Add":" .expect(\"Trained model should be saved successfully\");"},{"Add":"}"}]}},{"FileAdd":{"meta":{"basename":"model.rs","metadata":0},"context":[{"change":2,"pos":15}],"contents":[{"Add":"use burn::{"},{"Add":" config::Config,"},{"Add":" module::Module,"},{"Add":" nn::{"},{"Add":" conv::{Conv2d, Conv2dConfig},"},{"Add":" pool::{AdaptiveAvgPool2d, AdaptiveAvgPool2dConfig},"},{"Add":" Dropout, DropoutConfig, Linear, LinearConfig, ReLU,"},{"Add":" },"},{"Add":" tensor::{backend::Backend, Tensor},"},{"Add":"};"},{"Add":""},{"Add":"#[derive(Module, Debug)]"},{"Add":"pub struct Model {"},{"Add":" conv1: Conv2d,"},{"Add":" conv2: Conv2d,"},{"Add":" pool: AdaptiveAvgPool2d,"},{"Add":" dropout: Dropout,"},{"Add":" linear1: Linear,"},{"Add":" linear2: Linear,"},{"Add":" activation: ReLU,"},{"Add":"}"},{"Add":""},{"Add":"impl Model {"},{"Add":" /// # Shapes"},{"Add":" /// - Images [batch_size, height, width]"},{"Add":" /// - Output [batch_size, num_classes]"},{"Add":" pub fn forward(&self, images: Tensor) -> Tensor {"},{"Add":" let [batch_size, height, width] = images.dims();"},{"Add":""},{"Add":" // Create channel at second dim"},{"Add":" let x = images.reshape([batch_size, 1, height, width]);"},{"Add":""},{"Add":" let x = self.conv1.forward(x); // [batch_size, 8, _, _]"},{"Add":" let x = self.dropout.forward(x);"},{"Add":" let x = self.conv2.forward(x); // [batch_size, 16, _, _]"},{"Add":" let x = self.dropout.forward(x);"},{"Add":" let x = self.activation.forward(x);"},{"Add":""},{"Add":" let x = self.pool.forward(x); // [batch_size, 16, 8, 8]"},{"Add":" let x = x.reshape([batch_size, 16 * 8 * 8]);"},{"Add":" let x = self.linear1.forward(x);"},{"Add":" let x = self.dropout.forward(x);"},{"Add":" let x = self.activation.forward(x);"},{"Add":""},{"Add":" self.linear2.forward(x)"},{"Add":" }"},{"Add":"}"},{"Add":""},{"Add":"#[derive(Config, Debug)]"},{"Add":"pub struct ModelConfig {"},{"Add":" num_classes: usize,"},{"Add":" hidden_size: usize,"},{"Add":" #[config(default = \"0.5\")]"},{"Add":" dropout: f64,"},{"Add":"}"},{"Add":""},{"Add":"impl ModelConfig {"},{"Add":" pub fn init(&self, device: &B::Device) -> Model {"},{"Add":" Model {"},{"Add":" conv1: Conv2dConfig::new([1, 8], [3, 3]).init(device),"},{"Add":" conv2: Conv2dConfig::new([8, 16], [3, 3]).init(device),"},{"Add":" pool: AdaptiveAvgPool2dConfig::new([8, 8]).init(),"},{"Add":" activation: ReLU::new(),"},{"Add":" linear1: LinearConfig::new(16 * 8 * 8, self.hidden_size).init(device),"},{"Add":" linear2: LinearConfig::new(self.hidden_size, self.num_classes).init(device),"},{"Add":" dropout: DropoutConfig::new(self.dropout).init(),"},{"Add":" }"},{"Add":" }"},{"Add":"}"}]}},{"Edit":{"inode":{"change":2,"pos":49},"path":"src/main.rs","line":1,"atom":{"NewVertex":{"up_context":[{"change":2,"pos":49}],"down_context":[{"change":2,"pos":50}],"flag":"BLOCK","start":5700,"end":5898,"inode":{"change":2,"pos":49}}},"contents":[{"Add":"mod data;"},{"Add":"mod model;"},{"Add":"mod training;"},{"Add":""},{"Add":"use burn::{"},{"Add":" backend::{"},{"Add":" wgpu::{AutoGraphicsApi, WgpuDevice},"},{"Add":" Autodiff, Wgpu,"},{"Add":" },"},{"Add":" optim::AdamConfig,"},{"Add":"};"},{"Add":""},{"Add":"use crate::model::ModelConfig;"},{"Add":""}]}},{"Replacement":{"inode":{"change":2,"pos":49},"path":"src/main.rs","line":16,"ins":{"up_context":[{"change":2,"pos":62}],"down_context":[{"change":2,"pos":93}],"flag":"BLOCK","start":5899,"end":6209,"inode":{"change":2,"pos":49}},"del":{"edges":[{"previous":"BLOCK","flag":"BLOCK | DELETED","from":{"change":2,"pos":62},"to":{"change":2,"start":62,"end":93},"introduced_by":2}],"inode":{"change":2,"pos":49}},"ins_contents":[{"Add":" type Backend = Wgpu;"},{"Add":" type AutodiffBackend = Autodiff;"},{"Add":""},{"Add":" let device = WgpuDevice::default();"},{"Add":""},{"Add":" training::train::("},{"Add":" \"/tmp/guide\","},{"Add":" training::TrainingConfig::new(ModelConfig::new(10, 512), AdamConfig::new()),"},{"Add":" device,"},{"Add":" );"}],"del_contents":[{"Del":" println!(\"Hello, world!\");\n"}]}},{"FileAdd":{"meta":{"basename":"data.rs","metadata":0},"context":[{"change":2,"pos":15}],"contents":[{"Add":"use burn::{"},{"Add":" data::{dataloader::batcher::Batcher, dataset::vision::MNISTItem},"},{"Add":" tensor::{backend::Backend, Data, ElementConversion, Int, Tensor},"},{"Add":"};"},{"Add":""},{"Add":"pub struct MNISTBatcher {"},{"Add":" device: B::Device,"},{"Add":"}"},{"Add":""},{"Add":"impl MNISTBatcher {"},{"Add":" pub fn new(device: B::Device) -> Self {"},{"Add":" Self { device }"},{"Add":" }"},{"Add":"}"},{"Add":""},{"Add":"#[derive(Clone, Debug)]"},{"Add":"pub struct MNISTBatch {"},{"Add":" pub images: Tensor,"},{"Add":" pub targets: Tensor,"},{"Add":"}"},{"Add":""},{"Add":"impl Batcher> for MNISTBatcher {"},{"Add":" fn batch(&self, items: Vec) -> MNISTBatch {"},{"Add":" let images = items"},{"Add":" .iter()"},{"Add":" .map(|item| Data::::from(item.image))"},{"Add":" .map(|data| Tensor::::from_data(data.convert(), &self.device))"},{"Add":" .map(|tensor| tensor.reshape([1, 28, 28]))"},{"Add":" // Normalize: make between [0,1] and make the mean=0 and std=1"},{"Add":" // values mean=0.1307,std=0.3081 are from the PyTorch MNIST example"},{"Add":" // https://github.com/pytorch/examples/blob/54f4572509891883a947411fd7239237dd2a39c3/mnist/main.py#L122"},{"Add":" .map(|tensor| ((tensor / 255) - 0.1307) / 0.3081)"},{"Add":" .collect();"},{"Add":""},{"Add":" let targets = items"},{"Add":" .iter()"},{"Add":" .map(|item| {"},{"Add":" Tensor::::from_data("},{"Add":" Data::from([(item.label as i64).elem()]),"},{"Add":" &self.device,"},{"Add":" )"},{"Add":" })"},{"Add":" .collect();"},{"Add":""},{"Add":" let images = Tensor::cat(images, 0).to_device(&self.device);"},{"Add":" let targets = Tensor::cat(targets, 0).to_device(&self.device);"},{"Add":""},{"Add":" MNISTBatch { images, targets }"},{"Add":" }"},{"Add":"}"}]}},{"Replacement":{"inode":{"change":2,"pos":129},"path":"justfile","line":2,"ins":{"up_context":[{"change":2,"pos":135}],"down_context":[{"change":2,"pos":151}],"flag":"BLOCK","start":7903,"end":7917,"inode":{"change":2,"pos":129}},"del":{"edges":[{"previous":"BLOCK","flag":"BLOCK | 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