Add some more developed training examples. (#199)

* Use contiguous tensors for variables.

* Sketch the mnist example.

* Start adding the reduce ops.

* Renaming.

* Refactor the reduce operations.

* Bugfix for the broadcasting vectorization.

.gitignore

@@ -1,6 +1,7 @@
 # Generated by Cargo
 # will have compiled files and executables
 debug/
+data/
 dist/
 target/
 

candle-core/src/backend.rs

@@ -16,7 +16,7 @@ pub(crate) trait BackendStorage: Sized {
 
     fn elu(&self, _: &Layout, _: f64) -> Result<Self>;
 
-    fn sum(&self, _: &Layout, _: &[usize]) -> Result<Self>;
+    fn reduce_op(&self, _: crate::op::ReduceOp, _: &Layout, _: &[usize]) -> Result<Self>;
 
     fn divide_by_sum_over_dim(&mut self, _: &Shape, _: usize) -> Result<()>;
 

candle-core/src/backprop.rs

@@ -67,6 +67,8 @@ impl Tensor {
                     Op::Reshape(node)
                     | Op::Broadcast(node)
                     | Op::Sum(node, _)
+                    | Op::Max(node, _)
+                    | Op::Min(node, _)
                     | Op::ToDType(node)
                     | Op::ToDevice(node)
                     | Op::Transpose(node, _, _)
@@ -203,6 +205,12 @@ impl Tensor {
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.broadcast_add(&grad)?
                     }
+                    Op::Max(_args, _sum_dims) => {
+                        return Err(Error::BackwardNotSupported { op: "max" })
+                    }
+                    Op::Min(_args, _sum_dims) => {
+                        return Err(Error::BackwardNotSupported { op: "min" })
+                    }
                     Op::ToDType(arg) => {
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.add(&grad.to_dtype(node.dtype())?)?

candle-core/src/cpu_backend.rs

@@ -93,47 +93,52 @@ impl<'a> Map2 for WCond<'a> {
     }
 }
 
-struct Sum<'a> {
+struct Reduce<'a> {
     dst_shape: &'a Shape,
-    sum_dims: &'a [usize],
-    sum_dims_and_stride: Vec<(usize, usize)>,
+    reduce_dims: &'a [usize],
+    reduce_dims_and_stride: Vec<(usize, usize)>,
+    op: crate::op::ReduceOp,
 }
 
-impl<'a> Map1 for Sum<'a> {
+impl<'a> Map1 for Reduce<'a> {
     #[inline(always)]
     fn f<T: WithDType>(&self, src: &[T], src_l: &Layout) -> Result<Vec<T>> {
+        match self.op {
+            crate::op::ReduceOp::Min | crate::op::ReduceOp::Max => todo!(),
+            crate::op::ReduceOp::Sum => (),
+        }
         let mut dst = vec![T::zero(); self.dst_shape.elem_count()];
         match src_l.contiguous_offsets() {
             Some((o1, o2)) => {
                 let src = &src[o1..o2];
-                // Handle the case where we sum over the last dimensions separately as it is
+                // Handle the case where we reduce over the last dimensions separately as it is
                 // fairly common and easy to optimize. This rely on the layout being contiguous!
-                // sum_dims is sorted, check if it is ranging from a to n-1.
-                let sum_over_last_dims = self
-                    .sum_dims
+                // reduce_dims is sorted, check if it is ranging from a to n-1.
+                let reduce_over_last_dims = self
+                    .reduce_dims
                     .iter()
                     .rev()
                     .enumerate()
                     .all(|(i, &v)| v == src_l.shape().rank() - 1 - i);
-                if sum_over_last_dims {
-                    let sum_sz = self
-                        .sum_dims_and_stride
+                if reduce_over_last_dims {
+                    let reduce_sz = self
+                        .reduce_dims_and_stride
                         .iter()
                         .map(|(u, _)| u)
                         .product::<usize>();
                     let mut src_i = 0;
                     for dst_v in dst.iter_mut() {
-                        for &s in src[src_i..src_i + sum_sz].iter() {
+                        for &s in src[src_i..src_i + reduce_sz].iter() {
                             *dst_v += s
                         }
-                        src_i += sum_sz
+                        src_i += reduce_sz
                     }
                     return Ok(dst);
                 };
                 for (unstr_index, &src) in src.iter().enumerate() {
                     let mut dst_index = unstr_index;
-                    // Set the sum_dims indexes to 0.
-                    for &(dim, stride) in self.sum_dims_and_stride.iter() {
+                    // Set the reduce_dims indexes to 0.
+                    for &(dim, stride) in self.reduce_dims_and_stride.iter() {
                         // The compiler is able to optimize the following in a single divmod op.
                         let (pre, post) = (dst_index / stride, dst_index % stride);
                         dst_index = (pre / dim) * stride + post;
@@ -144,8 +149,8 @@ impl<'a> Map1 for Sum<'a> {
             None => {
                 for (unstr_index, src_index) in src_l.strided_index().enumerate() {
                     let mut dst_index = unstr_index;
-                    // Set the sum_dims indexes to 0.
-                    for &(dim, stride) in self.sum_dims_and_stride.iter() {
+                    // Set the reduce_dims indexes to 0.
+                    for &(dim, stride) in self.reduce_dims_and_stride.iter() {
                         // The compiler is able to optimize the following in a single divmod op.
                         let (pre, post) = (dst_index / stride, dst_index % stride);
                         dst_index = (pre / dim) * stride + post;
@@ -340,7 +345,7 @@ fn binary_map_vec<T: Copy, F: FnMut(T, T) -> T, FV: FnMut(&[T], &[T], &mut [T])>
         }
         (Some((o_l1, o_l2)), None) => match rhs_l.offsets_b() {
             Some(ob) if ob.right_broadcast == 1 => {
-                let rhs = &rhs[ob.start..];
+                let rhs = &rhs[ob.start..ob.start + ob.len];
                 let mut ys: Vec<T> = Vec::with_capacity(el_count);
                 let ys_to_set = ys.spare_capacity_mut();
                 let ys_to_set = unsafe { std::mem::transmute::<_, &mut [T]>(ys_to_set) };
@@ -358,7 +363,7 @@ fn binary_map_vec<T: Copy, F: FnMut(T, T) -> T, FV: FnMut(&[T], &[T], &mut [T])>
                 ys
             }
             Some(ob) => {
-                let rhs = &rhs[ob.start..];
+                let rhs = &rhs[ob.start..ob.start + ob.len];
                 let mut ys = lhs[o_l1..o_l2].to_vec();
                 for idx_l in 0..ob.left_broadcast {
                     let start = idx_l * ob.len * ob.right_broadcast;
@@ -379,7 +384,7 @@ fn binary_map_vec<T: Copy, F: FnMut(T, T) -> T, FV: FnMut(&[T], &[T], &mut [T])>
         },
         (None, Some((o_r1, o_r2))) => match lhs_l.offsets_b() {
             Some(ob) if ob.right_broadcast == 1 => {
-                let lhs = &lhs[ob.start..];
+                let lhs = &lhs[ob.start..ob.start + ob.len];
                 let mut ys: Vec<T> = Vec::with_capacity(el_count);
                 let ys_to_set = ys.spare_capacity_mut();
                 let ys_to_set = unsafe { std::mem::transmute::<_, &mut [T]>(ys_to_set) };
@@ -397,7 +402,7 @@ fn binary_map_vec<T: Copy, F: FnMut(T, T) -> T, FV: FnMut(&[T], &[T], &mut [T])>
                 ys
             }
             Some(ob) => {
-                let lhs = &lhs[ob.start..];
+                let lhs = &lhs[ob.start..ob.start + ob.len];
                 let mut ys = rhs[o_r1..o_r2].to_vec();
                 for idx_l in 0..ob.left_broadcast {
                     let start = idx_l * ob.len * ob.right_broadcast;
@@ -1010,25 +1015,31 @@ impl BackendStorage for CpuStorage {
         }
     }
 
-    fn sum(&self, layout: &Layout, sum_dims: &[usize]) -> Result<Self> {
+    fn reduce_op(
+        &self,
+        op: crate::op::ReduceOp,
+        layout: &Layout,
+        reduce_dims: &[usize],
+    ) -> Result<Self> {
         let src_dims = layout.dims();
         let mut dst_dims = src_dims.to_vec();
-        for &sum_dim in sum_dims.iter() {
-            dst_dims[sum_dim] = 1;
+        for &dim in reduce_dims.iter() {
+            dst_dims[dim] = 1;
         }
         let dst_shape = Shape::from(dst_dims);
-        let mut sum_dims = sum_dims.to_vec();
-        // Sort the sum_dims as they have to be processed from left to right when converting the
+        let mut reduce_dims = reduce_dims.to_vec();
+        // Sort the reduce_dims as they have to be processed from left to right when converting the
         // indexes.
-        sum_dims.sort();
-        let sum_dims_and_stride: Vec<_> = sum_dims
+        reduce_dims.sort();
+        let reduce_dims_and_stride: Vec<_> = reduce_dims
             .iter()
             .map(|&d| (src_dims[d], src_dims[d + 1..].iter().product::<usize>()))
             .collect();
-        Sum {
+        Reduce {
             dst_shape: &dst_shape,
-            sum_dims: &sum_dims,
-            sum_dims_and_stride,
+            reduce_dims: &reduce_dims,
+            reduce_dims_and_stride,
+            op,
         }
         .map(self, layout)
     }

candle-core/src/cuda_backend.rs

@@ -955,10 +955,21 @@ impl BackendStorage for CudaStorage {
         Ok(Self { slice, device })
     }
 
-    fn sum(&self, layout: &Layout, sum_dims: &[usize]) -> Result<Self> {
-        let device = self.device().clone();
-        let slice = FastSum(sum_dims).map(&self.slice, &device, layout)?;
-        Ok(Self { slice, device })
+    fn reduce_op(
+        &self,
+        op: crate::op::ReduceOp,
+        layout: &Layout,
+        sum_dims: &[usize],
+    ) -> Result<Self> {
+        match op {
+            crate::op::ReduceOp::Sum => {
+                let device = self.device().clone();
+                let slice = FastSum(sum_dims).map(&self.slice, &device, layout)?;
+                Ok(Self { slice, device })
+            }
+            crate::op::ReduceOp::Min => Err(CudaError::InternalError("TODO: implement min").into()),
+            crate::op::ReduceOp::Max => Err(CudaError::InternalError("TODO: implement max").into()),
+        }
     }
 
     fn divide_by_sum_over_dim(&mut self, _: &Shape, _: usize) -> Result<()> {

candle-core/src/dummy_cuda_backend.rs

@@ -40,7 +40,7 @@ impl crate::backend::BackendStorage for CudaStorage {
         Err(Error::NotCompiledWithCudaSupport)
     }
 
-    fn sum(&self, _: &Layout, _: &[usize]) -> Result<Self> {
+    fn reduce_op(&self, _: crate::op::ReduceOp, _: &Layout, _: &[usize]) -> Result<Self> {
         Err(Error::NotCompiledWithCudaSupport)
     }
 

candle-core/src/op.rs

@@ -29,6 +29,8 @@ pub(crate) enum Op {
         add: f64,
     },
     Sum(Tensor, Vec<usize>),
+    Max(Tensor, Vec<usize>),
+    Min(Tensor, Vec<usize>),
     ToDType(Tensor),
     Broadcast(Tensor),
     Exp(Tensor),
@@ -354,3 +356,10 @@ impl UnaryOp for Relu {
         v
     }
 }
+
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum ReduceOp {
+    Sum,
+    Min,
+    Max,
+}

candle-core/src/storage.rs

@@ -80,14 +80,19 @@ impl Storage {
         }
     }
 
-    pub(crate) fn sum(&self, layout: &Layout, s: &[usize]) -> Result<Self> {
+    pub(crate) fn reduce_op(
+        &self,
+        op: crate::op::ReduceOp,
+        layout: &Layout,
+        s: &[usize],
+    ) -> Result<Self> {
         match self {
             Storage::Cpu(storage) => {
-                let storage = storage.sum(layout, s)?;
+                let storage = storage.reduce_op(op, layout, s)?;
                 Ok(Self::Cpu(storage))
             }
             Self::Cuda(storage) => {
-                let storage = storage.sum(layout, s)?;
+                let storage = storage.reduce_op(op, layout, s)?;
                 Ok(Self::Cuda(storage))
             }
         }

candle-core/src/tensor.rs

@@ -154,8 +154,14 @@ impl Tensor {
         device: &Device,
         is_variable: bool,
     ) -> Result<Self> {
-        let storage = device.ones(&crate::shape::SCALAR, dtype)?;
-        from_storage(storage, crate::shape::SCALAR, None, is_variable).broadcast_as(shape)
+        if is_variable {
+            let shape = shape.into();
+            let storage = device.ones(&shape, dtype)?;
+            Ok(from_storage(storage, shape, None, is_variable))
+        } else {
+            let storage = device.ones(&crate::shape::SCALAR, dtype)?;
+            from_storage(storage, crate::shape::SCALAR, None, is_variable).broadcast_as(shape)
+        }
     }
 
     /// Creates a new tensor filled with ones.
@@ -192,8 +198,14 @@ impl Tensor {
         device: &Device,
         is_variable: bool,
     ) -> Result<Self> {
-        let storage = device.zeros(&crate::shape::SCALAR, dtype)?;
-        from_storage(storage, crate::shape::SCALAR, None, is_variable).broadcast_as(shape)
+        if is_variable {
+            let shape = shape.into();
+            let storage = device.zeros(&shape, dtype)?;
+            Ok(from_storage(storage, shape, None, is_variable))
+        } else {
+            let storage = device.zeros(&crate::shape::SCALAR, dtype)?;
+            from_storage(storage, crate::shape::SCALAR, None, is_variable).broadcast_as(shape)
+        }
     }
 
     /// Creates a new tensor filled with zeros.
@@ -593,9 +605,77 @@ impl Tensor {
         }
     }
 
-    pub fn sum_impl<D: Dims>(&self, sum_dims: D, keepdim: bool) -> Result<Self> {
+    fn squeeze_dims(self, dims: &[usize]) -> Result<Self> {
+        match dims {
+            [] => Ok(self),
+            [i] => self.squeeze(*i),
+            dims => {
+                let dims = self
+                    .dims()
+                    .iter()
+                    .enumerate()
+                    .filter_map(|(dim_idx, &v)| {
+                        if dims.contains(&dim_idx) {
+                            None
+                        } else {
+                            Some(v)
+                        }
+                    })
+                    .collect::<Vec<_>>();
+                self.reshape(dims)
+            }
+        }
+    }
+
+    fn max_impl<D: Dims>(&self, max_dims: D, keepdim: bool) -> Result<Self> {
+        let max_dims = max_dims.to_indexes(self.shape(), "max")?;
+        let storage =
+            self.storage()
+                .reduce_op(crate::op::ReduceOp::Max, self.layout(), &max_dims)?;
+        let op = if self.track_op() {
+            Some(Op::Max(self.clone(), max_dims.to_vec()))
+        } else {
+            None
+        };
+        let mut dims = self.dims().to_vec();
+        for &max_dim in max_dims.iter() {
+            dims[max_dim] = 1
+        }
+        let max = from_storage(storage, dims, op, false);
+        if keepdim {
+            Ok(max)
+        } else {
+            max.squeeze_dims(&max_dims)
+        }
+    }
+
+    fn min_impl<D: Dims>(&self, min_dims: D, keepdim: bool) -> Result<Self> {
+        let min_dims = min_dims.to_indexes(self.shape(), "min")?;
+        let storage =
+            self.storage()
+                .reduce_op(crate::op::ReduceOp::Min, self.layout(), &min_dims)?;
+        let op = if self.track_op() {
+            Some(Op::Min(self.clone(), min_dims.to_vec()))
+        } else {
+            None
+        };
+        let mut dims = self.dims().to_vec();
+        for &min_dim in min_dims.iter() {
+            dims[min_dim] = 1
+        }
+        let min = from_storage(storage, dims, op, false);
+        if keepdim {
+            Ok(min)
+        } else {
+            min.squeeze_dims(&min_dims)
+        }
+    }
+
+    fn sum_impl<D: Dims>(&self, sum_dims: D, keepdim: bool) -> Result<Self> {
         let sum_dims = sum_dims.to_indexes(self.shape(), "sum")?;
-        let storage = self.storage().sum(self.layout(), &sum_dims)?;
+        let storage =
+            self.storage()
+                .reduce_op(crate::op::ReduceOp::Sum, self.layout(), &sum_dims)?;
         let op = if self.track_op() {
             Some(Op::Sum(self.clone(), sum_dims.to_vec()))
         } else {
@@ -609,25 +689,7 @@ impl Tensor {
         if keepdim {
             Ok(sum)
         } else {
-            match sum_dims.as_slice() {
-                [] => Ok(sum),
-                [i] => sum.squeeze(*i),
-                sum_dims => {
-                    let dims = sum
-                        .dims()
-                        .iter()
-                        .enumerate()
-                        .filter_map(|(dim_idx, &v)| {
-                            if sum_dims.contains(&dim_idx) {
-                                None
-                            } else {
-                                Some(v)
-                            }
-                        })
-                        .collect::<Vec<_>>();
-                    sum.reshape(dims)
-                }
-            }
+            sum.squeeze_dims(&sum_dims)
         }
     }
 
@@ -659,6 +721,22 @@ impl Tensor {
         self.sum_impl(sum_dims, false)
     }
 
+    pub fn max_keepdim<D: Dims>(&self, max_dims: D) -> Result<Self> {
+        self.max_impl(max_dims, true)
+    }
+
+    pub fn max<D: Dims>(&self, max_dims: D) -> Result<Self> {
+        self.max_impl(max_dims, false)
+    }
+
+    pub fn min_keepdim<D: Dims>(&self, min_dims: D) -> Result<Self> {
+        self.min_impl(min_dims, true)
+    }
+
+    pub fn min<D: Dims>(&self, min_dims: D) -> Result<Self> {
+        self.min_impl(min_dims, false)
+    }
+
     /// Applies a 1D convolution over the input tensor.
     pub fn conv1d(&self, kernel: &Self, padding: usize, stride: usize) -> Result<Self> {
         let (c_out, c_in_k, k_size) = kernel.shape().r3()?;

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

@@ -0,0 +1,44 @@
+// This should rearch 91.5% accuracy.
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+use anyhow::Result;
+use candle::{DType, Var, D};
+
+const IMAGE_DIM: usize = 784;
+const LABELS: usize = 10;
+
+pub fn main() -> Result<()> {
+    let dev = candle::Device::cuda_if_available(0)?;
+    let m = candle_nn::vision::mnist::load_dir("data")?;
+    println!("train-images: {:?}", m.train_images.shape());
+    println!("train-labels: {:?}", m.train_labels.shape());
+    println!("test-images: {:?}", m.test_images.shape());
+    println!("test-labels: {:?}", m.test_labels.shape());
+    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], 0.1);
+    for epoch in 1..200 {
+        let logits = m.train_images.matmul(&ws)?.broadcast_add(&bs)?;
+        let loss = logits.softmax(D::Minus1)?;
+        // TODO: log_softmax + let loss = loss.nll_loss(&m.train_labels);
+        sgd.backward_step(&loss)?;
+
+        let _test_logits = m.test_images.matmul(&ws)?.broadcast_add(&bs)?;
+        /* TODO
+        let test_accuracy = test_logits
+            .argmax(Some(-1), false)
+            .eq_tensor(&m.test_labels)
+            .to_kind(Kind::Float)
+            .mean(Kind::Float)
+            .double_value(&[]);
+        */
+        let test_accuracy = 0.;
+        println!(
+            "{epoch:4} train loss: {:8.5} test acc: {:5.2}%",
+            loss.to_scalar::<f32>()?,
+            100. * test_accuracy
+        )
+    }
+    Ok(())
+}