Add an easy way to create tensor objects.

src/device.rs

@@ -1,6 +1,6 @@
 use crate::{
     storage::{CpuStorage, Storage},
-    DType,
+    DType, Result, Shape,
 };
 
 #[derive(Debug, Copy, Clone, PartialEq, Eq, Hash)]
@@ -8,11 +8,38 @@ pub enum Device {
     Cpu,
 }
 
+// TODO: Should we back the cpu implementation using the NdArray crate or similar?
+pub trait NdArray {
+    fn shape(&self) -> Result<Shape>;
+
+    fn to_cpu_storage(&self) -> CpuStorage;
+}
+
+impl<S: crate::WithDType> NdArray for S {
+    fn shape(&self) -> Result<Shape> {
+        Ok(Shape::from(()))
+    }
+
+    fn to_cpu_storage(&self) -> CpuStorage {
+        S::to_cpu_storage(&[*self])
+    }
+}
+
+impl<S: crate::WithDType> NdArray for &[S] {
+    fn shape(&self) -> Result<Shape> {
+        Ok(Shape::from(self.len()))
+    }
+
+    fn to_cpu_storage(&self) -> CpuStorage {
+        S::to_cpu_storage(self)
+    }
+}
+
 impl Device {
-    pub(crate) fn zeros(&self, shape: &[usize], dtype: DType) -> Storage {
+    pub(crate) fn zeros(&self, shape: &Shape, dtype: DType) -> Storage {
         match self {
             Device::Cpu => {
-                let elem_count: usize = shape.iter().product();
+                let elem_count = shape.elem_count();
                 let storage = match dtype {
                     DType::F32 => {
                         let data = vec![0f32; elem_count];
@@ -27,4 +54,10 @@ impl Device {
             }
         }
     }
+
+    pub(crate) fn tensor<A: NdArray>(&self, array: A) -> Storage {
+        match self {
+            Device::Cpu => Storage::Cpu(array.to_cpu_storage()),
+        }
+    }
 }

src/dtype.rs

@@ -1,3 +1,5 @@
+use crate::CpuStorage;
+
 #[derive(Debug, Copy, Clone, PartialEq, Eq, Hash)]
 pub enum DType {
     F32,
@@ -13,14 +15,24 @@ impl DType {
     }
 }
 
-pub trait WithDType {
+pub trait WithDType: Sized + Copy {
     const DTYPE: DType;
+
+    fn to_cpu_storage(data: &[Self]) -> CpuStorage;
 }
 
 impl WithDType for f32 {
     const DTYPE: DType = DType::F32;
+
+    fn to_cpu_storage(data: &[Self]) -> CpuStorage {
+        CpuStorage::F32(data.to_vec())
+    }
 }
 
 impl WithDType for f64 {
     const DTYPE: DType = DType::F64;
+
+    fn to_cpu_storage(data: &[Self]) -> CpuStorage {
+        CpuStorage::F64(data.to_vec())
+    }
 }

src/lib.rs

@@ -9,4 +9,6 @@ mod tensor;
 pub use device::Device;
 pub use dtype::{DType, WithDType};
 pub use error::{Error, Result};
+pub use shape::Shape;
+pub use storage::{CpuStorage, Storage};
 pub use tensor::Tensor;

src/storage.rs

@@ -2,7 +2,7 @@ use crate::{DType, Device};
 
 // TODO: Think about whether we would be better off with a dtype and
 // a buffer as an owned slice of bytes.
-pub(crate) enum CpuStorage {
+pub enum CpuStorage {
     F32(Vec<f32>),
     F64(Vec<f64>),
 }
@@ -17,18 +17,18 @@ impl CpuStorage {
 }
 
 #[allow(dead_code)]
-pub(crate) enum Storage {
+pub enum Storage {
     Cpu(CpuStorage),
 }
 
 impl Storage {
-    pub(crate) fn device(&self) -> Device {
+    pub fn device(&self) -> Device {
         match self {
             Self::Cpu(_) => Device::Cpu,
         }
     }
 
-    pub(crate) fn dtype(&self) -> DType {
+    pub fn dtype(&self) -> DType {
         match self {
             Self::Cpu(storage) => storage.dtype(),
         }

src/tensor.rs

@@ -1,4 +1,4 @@
-use crate::{op::Op, shape, storage::Storage, DType, Device};
+use crate::{op::Op, shape, storage::Storage, DType, Device, Result};
 use std::sync::Arc;
 
 #[allow(dead_code)]
@@ -14,15 +14,28 @@ pub struct Tensor(Arc<Tensor_>);
 impl Tensor {
     pub fn zeros<S: Into<shape::Shape>>(shape: S, dtype: DType, device: Device) -> Self {
         let shape = shape.into();
-        let storage = device.zeros(&shape.0, dtype);
-        let rank = shape.0.len();
+        let storage = device.zeros(&shape, dtype);
+        let rank = shape.rank();
         let tensor_ = Tensor_ {
             storage,
             shape,
             stride: vec![1; rank],
             op: None,
         };
-        Tensor(Arc::new(tensor_))
+        Self(Arc::new(tensor_))
+    }
+
+    pub fn new<A: crate::device::NdArray>(array: A, device: Device) -> Result<Self> {
+        let shape = array.shape()?;
+        let storage = device.tensor(array);
+        let rank = shape.rank();
+        let tensor_ = Tensor_ {
+            storage,
+            shape,
+            stride: vec![1; rank],
+            op: None,
+        };
+        Ok(Self(Arc::new(tensor_)))
     }
 
     pub fn dtype(&self) -> DType {
@@ -38,7 +51,7 @@ impl Tensor {
     }
 
     pub fn dims(&self) -> &[usize] {
-        &self.shape().dims()
+        self.shape().dims()
     }
 
     pub fn stride(&self) -> &[usize] {

tests/tensor_tests.rs

@@ -2,9 +2,12 @@ use candle::{DType, Device, Result, Tensor};
 
 #[test]
 fn add() -> Result<()> {
-    let tensor = Tensor::zeros(&[5, 2], DType::F32, Device::Cpu);
+    let tensor = Tensor::zeros((5, 2), DType::F32, Device::Cpu);
     let (dim1, dim2) = tensor.shape().r2()?;
     assert_eq!(dim1, 5);
     assert_eq!(dim2, 2);
+    let tensor = Tensor::new([3., 1., 4.].as_slice(), Device::Cpu)?;
+    let dim1 = tensor.shape().r1()?;
+    assert_eq!(dim1, 3);
     Ok(())
 }