More mnist training.

candle-examples/examples/simple-training/main.rs

@@ -1,16 +1,130 @@
-// This should rearch 91.5% accuracy.
+// This should reach 91.5% accuracy.
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
 
-use anyhow::Result;
-use candle::{DType, Var, D};
-use candle_nn::{loss, ops};
+use candle::{DType, Device, Result, Shape, Tensor, Var, D};
+use candle_nn::{loss, ops, Linear};
+use std::sync::{Arc, Mutex};
 
 const IMAGE_DIM: usize = 784;
 const LABELS: usize = 10;
 
-pub fn main() -> Result<()> {
+struct TensorData {
+    tensors: std::collections::HashMap<String, Var>,
+    pub dtype: DType,
+    pub device: Device,
+}
+
+// A variant of candle_nn::VarBuilder for initializing variables before training.
+#[derive(Clone)]
+struct VarStore {
+    data: Arc<Mutex<TensorData>>,
+    path: Vec<String>,
+}
+
+impl VarStore {
+    fn new(dtype: DType, device: Device) -> Self {
+        let data = TensorData {
+            tensors: std::collections::HashMap::new(),
+            dtype,
+            device,
+        };
+        Self {
+            data: Arc::new(Mutex::new(data)),
+            path: vec![],
+        }
+    }
+
+    fn pp(&self, s: &str) -> Self {
+        let mut path = self.path.clone();
+        path.push(s.to_string());
+        Self {
+            data: self.data.clone(),
+            path,
+        }
+    }
+
+    fn get<S: Into<Shape>>(&self, shape: S, tensor_name: &str) -> Result<Tensor> {
+        let shape = shape.into();
+        let path = if self.path.is_empty() {
+            tensor_name.to_string()
+        } else {
+            [&self.path.join("."), tensor_name].join(".")
+        };
+        let mut tensor_data = self.data.lock().unwrap();
+        if let Some(tensor) = tensor_data.tensors.get(&path) {
+            let tensor_shape = tensor.shape();
+            if &shape != tensor_shape {
+                candle::bail!("shape mismatch on {path}: {shape:?} <> {tensor_shape:?}")
+            }
+            return Ok(tensor.as_tensor().clone());
+        }
+        // TODO: Proper initialization using the `Init` enum.
+        let var = Var::zeros(shape, tensor_data.dtype, &tensor_data.device)?;
+        let tensor = var.as_tensor().clone();
+        tensor_data.tensors.insert(path, var);
+        Ok(tensor)
+    }
+
+    fn all_vars(&self) -> Vec<Var> {
+        let tensor_data = self.data.lock().unwrap();
+        #[allow(clippy::map_clone)]
+        tensor_data
+            .tensors
+            .values()
+            .map(|c| c.clone())
+            .collect::<Vec<_>>()
+    }
+}
+
+fn linear(dim1: usize, dim2: usize, vs: VarStore) -> Result<Linear> {
+    let ws = vs.get((dim2, dim1), "weight")?;
+    let bs = vs.get(dim2, "bias")?;
+    Ok(Linear::new(ws, Some(bs)))
+}
+
+#[allow(unused)]
+struct LinearModel {
+    linear: Linear,
+}
+
+#[allow(unused)]
+impl LinearModel {
+    fn new(vs: VarStore) -> Result<Self> {
+        let linear = linear(IMAGE_DIM, LABELS, vs)?;
+        Ok(Self { linear })
+    }
+
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        self.linear.forward(xs)
+    }
+}
+
+#[allow(unused)]
+struct Mlp {
+    ln1: Linear,
+    ln2: Linear,
+}
+
+#[allow(unused)]
+impl Mlp {
+    fn new(vs: VarStore) -> Result<Self> {
+        let ln1 = linear(IMAGE_DIM, 100, vs.pp("ln1"))?;
+        let ln2 = linear(100, LABELS, vs.pp("ln2"))?;
+        Ok(Self { ln1, ln2 })
+    }
+
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let xs = self.ln1.forward(xs)?;
+        let xs = xs.relu()?;
+        self.ln2.forward(&xs)
+    }
+}
+
+pub fn main() -> anyhow::Result<()> {
     let dev = candle::Device::cuda_if_available(0)?;
+
+    // Load the dataset
     let m = candle_nn::vision::mnist::load_dir("data")?;
     println!("train-images: {:?}", m.train_images.shape());
     println!("train-labels: {:?}", m.train_labels.shape());
@@ -19,18 +133,23 @@ pub fn main() -> Result<()> {
     let train_labels = m.train_labels;
     let train_images = m.train_images;
     let train_labels = train_labels.to_dtype(DType::U32)?.unsqueeze(1)?;
-    let ws = Var::zeros((IMAGE_DIM, LABELS), DType::F32, &dev)?;
-    let bs = Var::zeros(LABELS, DType::F32, &dev)?;
-    let sgd = candle_nn::SGD::new(&[&ws, &bs], 1.0);
+
+    let vs = VarStore::new(DType::F32, dev);
+    let model = LinearModel::new(vs.clone())?;
+    // let model = Mlp::new(vs)?;
+
+    let all_vars = vs.all_vars();
+    let all_vars = all_vars.iter().collect::<Vec<_>>();
+    let sgd = candle_nn::SGD::new(&all_vars, 1.0);
     let test_images = m.test_images;
     let test_labels = m.test_labels.to_dtype(DType::U32)?;
     for epoch in 1..200 {
-        let logits = train_images.matmul(&ws)?.broadcast_add(&bs)?;
+        let logits = model.forward(&train_images)?;
         let log_sm = ops::log_softmax(&logits, D::Minus1)?;
         let loss = loss::nll(&log_sm, &train_labels)?;
         sgd.backward_step(&loss)?;
 
-        let test_logits = test_images.matmul(&ws)?.broadcast_add(&bs)?;
+        let test_logits = model.forward(&test_images)?;
         let sum_ok = test_logits
             .argmax(D::Minus1)?
             .eq(&test_labels)?

candle-nn/src/var_builder.rs

@@ -209,6 +209,7 @@ impl<'a> VarBuilder<'a> {
         };
         Ok(tensor)
     }
+
     pub fn get<S: Into<Shape>>(&self, s: S, tensor_name: &str) -> Result<Tensor> {
         let data = self.data.as_ref();
         let s: Shape = s.into();