Fixing doctests

dfdx-core/src/data/arange.rs

@@ -11,15 +11,15 @@ pub trait Arange<E: Dtype>: Storage<E> + ZerosTensor<E> + TensorFromVec<E> {
     ///
     /// Const sized tensor:
     /// ```rust
-    /// # use dfdx::{prelude::*, data::Arange};
+    /// # use dfdx_core::{prelude::*, data::Arange};
     /// # let dev: Cpu = Default::default();
     /// let t: Tensor<Rank1<5>, f32, _> = dev.arange(Const::<5>);
     /// assert_eq!(t.array(), [0.0, 1.0, 2.0, 3.0, 4.0]);
     /// ```
     ///
     /// Runtime sized tensor:
     /// ```rust
-    /// # use dfdx::{prelude::*, data::Arange};
+    /// # use dfdx_core::{prelude::*, data::Arange};
     /// # let dev: Cpu = Default::default();
     /// let t: Tensor<(usize, ), f32, _> = dev.arange(5);
     /// assert_eq!(t.as_vec(), [0.0, 1.0, 2.0, 3.0, 4.0]);

dfdx-core/src/data/batch.rs

@@ -80,14 +80,14 @@ pub trait IteratorBatchExt: Iterator {
     ///
     /// Const batches:
     /// ```rust
-    /// # use dfdx::{prelude::*, data::IteratorBatchExt};
+    /// # use dfdx_core::{prelude::*, data::IteratorBatchExt};
     /// let items: Vec<[usize; 5]> = (0..12).batch_exact(Const::<5>).collect();
     /// assert_eq!(&items, &[[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]);
     /// ```
     ///
     /// Runtime batches:
     /// ```rust
-    /// # use dfdx::{prelude::*, data::IteratorBatchExt};
+    /// # use dfdx_core::{prelude::*, data::IteratorBatchExt};
     /// let items: Vec<Vec<usize>> = (0..12).batch_exact(5).collect();
     /// assert_eq!(&items, &[[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]);
     /// ```
@@ -104,7 +104,7 @@ pub trait IteratorBatchExt: Iterator {
     ///
     /// Example:
     /// ```rust
-    /// # use dfdx::{prelude::*, data::IteratorBatchExt};
+    /// # use dfdx_core::{prelude::*, data::IteratorBatchExt};
     /// let items: Vec<Vec<usize>> = (0..12).batch_with_last(5).collect();
     /// assert_eq!(&items, &[vec![0, 1, 2, 3, 4], vec![5, 6, 7, 8, 9], vec![10, 11]]);
     /// ```

dfdx-core/src/data/collate.rs

@@ -93,7 +93,7 @@ pub trait IteratorCollateExt: Iterator {
     ///
     /// Example implementations:
     /// ```rust
-    /// # use dfdx::data::IteratorCollateExt;
+    /// # use dfdx_core::data::IteratorCollateExt;
     /// let data = [[('a', 'b'); 10], [('c', 'd'); 10], [('e', 'f'); 10]];
     /// // we use collate to transform each batch:
     /// let mut iter = data.into_iter().collate();

dfdx-core/src/data/one_hot_encode.rs

@@ -16,7 +16,7 @@ pub trait OneHotEncode<E: Dtype>: Storage<E> + ZerosTensor<E> + TensorFromVec<E>
     ///
     /// Const class labels and const n:
     /// ```rust
-    /// # use dfdx::{prelude::*, data::OneHotEncode};
+    /// # use dfdx_core::{prelude::*, data::OneHotEncode};
     /// # let dev: Cpu = Default::default();
     /// let class_labels = [0, 1, 2, 1, 1];
     /// let probs: Tensor<Rank2<5, 3>, f32, _> = dev.one_hot_encode(Const::<3>, class_labels);
@@ -31,7 +31,7 @@ pub trait OneHotEncode<E: Dtype>: Storage<E> + ZerosTensor<E> + TensorFromVec<E>
     ///
     /// Runtime class labels and const n:
     /// ```rust
-    /// # use dfdx::{prelude::*, data::OneHotEncode};
+    /// # use dfdx_core::{prelude::*, data::OneHotEncode};
     /// # let dev: Cpu = Default::default();
     /// let class_labels = [0, 1, 2, 1, 1];
     /// let probs: Tensor<(Const<5>, usize), f32, _> = dev.one_hot_encode(3, class_labels);
@@ -46,7 +46,7 @@ pub trait OneHotEncode<E: Dtype>: Storage<E> + ZerosTensor<E> + TensorFromVec<E>
     ///
     /// Const class labels and runtime n:
     /// ```rust
-    /// # use dfdx::{prelude::*, data::OneHotEncode};
+    /// # use dfdx_core::{prelude::*, data::OneHotEncode};
     /// # let dev: Cpu = Default::default();
     /// let class_labels = std::vec![0, 1, 2, 1, 1];
     /// let probs: Tensor<(usize, Const<3>), f32, _> = dev.one_hot_encode(Const, class_labels);
@@ -61,7 +61,7 @@ pub trait OneHotEncode<E: Dtype>: Storage<E> + ZerosTensor<E> + TensorFromVec<E>
     ///
     /// Runtime both:
     /// ```rust
-    /// # use dfdx::{prelude::*, data::OneHotEncode};
+    /// # use dfdx_core::{prelude::*, data::OneHotEncode};
     /// # let dev: Cpu = Default::default();
     /// let class_labels = std::vec![0, 1, 2, 1, 1];
     /// let probs: Tensor<(usize, usize), f32, _> = dev.one_hot_encode(3, class_labels);

dfdx-core/src/data/stack.rs

@@ -29,7 +29,7 @@ pub trait IteratorStackExt: Iterator {
     ///
     /// Example implementations:
     /// ```rust
-    /// # use dfdx::{data::IteratorStackExt, prelude::*};
+    /// # use dfdx_core::{data::IteratorStackExt, prelude::*};
     /// # let dev: Cpu = Default::default();
     /// let a: Tensor<Rank1<3>, f32, _> = dev.zeros();
     /// let data = [[a.clone(), a.clone(), a]];

dfdx-core/src/lib.rs

@@ -67,7 +67,7 @@
 //! Here's how you might use a device:
 //!
 //! ```rust
-//! # use dfdx::prelude::*;
+//! # use dfdx_core::prelude::*;
 //! let dev: Cpu = Default::default();
 //! let t: Tensor<Rank2<2, 3>, f32, _> = dev.zeros();
 //! ```

dfdx-core/src/shapes/mod.rs

@@ -2,7 +2,7 @@
 //!
 //! Example shapes:
 //! ```rust
-//! # use dfdx::shapes::*;
+//! # use dfdx_core::shapes::*;
 //! let _: Rank3<2, 3, 4> = Default::default();
 //! let _: (Const<2>, Const<3>) = Default::default();
 //! let _: (usize, Const<4>) = (3, Const);

dfdx-core/src/tensor/mod.rs

@@ -15,13 +15,13 @@
 //! In order to do anything with tensors, you first need to construct the device that they will be stored on:
 //!
 //! ```rust
-//! # use dfdx::prelude::*;
+//! # use dfdx_core::prelude::*;
 //! let dev: Cpu = Default::default();
 //! let dev: Cpu = Cpu::seed_from_u64(0);
 //! ```
 //!
 //! ```ignore
-//! # use dfdx::prelude::*;
+//! # use dfdx_core::prelude::*;
 //! let dev: Cuda = Default::default();
 //! let dev: Cuda = Cuda::seed_from_u64(1234);
 //! let dev: Cuda = Cuda::try_build(0, 1234).unwrap();
@@ -34,7 +34,7 @@
 //! See [TensorFrom] & [TensorFromVec].
 //!
 //! ```rust
-//! # use dfdx::prelude::*;
+//! # use dfdx_core::prelude::*;
 //! # let dev: Cpu = Default::default();
 //! let _ = dev.tensor([1.0, 2.0, 3.0]);
 //! let _ = dev.tensor_from_vec(vec![1.0, 2.0, 3.0], (3, ));
@@ -45,7 +45,7 @@
 //! See [ZerosTensor] and [OnesTensor].
 //!
 //! ```rust
-//! # use dfdx::prelude::*;
+//! # use dfdx_core::prelude::*;
 //! # let dev: Cpu = Default::default();
 //! let _: Tensor<Rank1<5>,f32 , _> = dev.zeros();
 //! let _: Tensor<Rank2<3, 2>, f32, _> = dev.ones();
@@ -56,7 +56,7 @@
 //! See [SampleTensor]
 //!
 //! ```rust
-//! # use dfdx::prelude::*;
+//! # use dfdx_core::prelude::*;
 //! # let dev: Cpu = Default::default();
 //! let _: Tensor<Rank1<5>, f32, _> = dev.sample_uniform();
 //! let _: Tensor<Rank2<3, 5>, f32, _> = dev.sample_normal();
@@ -70,7 +70,7 @@
 //! You can use [Tensor::copy_from] and [Tensor::copy_into] to copy data into a tensor:
 //!
 //! ```rust
-//! # use dfdx::prelude::*;
+//! # use dfdx_core::prelude::*;
 //! # let dev: Cpu = Default::default();
 //! let mut a: Tensor<Rank1<1000>, f32, _> = dev.zeros();
 //! let buf: Vec<f32> = vec![1.0; 1000];
@@ -90,7 +90,7 @@
 //! with them directly.
 //!
 //! ```rust
-//! # use dfdx::prelude::*;
+//! # use dfdx_core::prelude::*;
 //! # let dev: Cpu = Default::default();
 //! let t: Tensor<Rank2<2, 3>, f32, _> = dev.zeros();
 //! let t: [[f32; 3]; 2] = t.array();
@@ -105,7 +105,7 @@
 //! Note that these two methods are only present for tensors without a tape already.
 //!
 //! ```rust
-//! # use dfdx::prelude::*;
+//! # use dfdx_core::prelude::*;
 //! # let dev: Cpu = Default::default();
 //! let t: Tensor<Rank1<5>,f32, _> = dev.zeros();
 //! let mut grads = Gradients::leaky();

dfdx-core/src/tensor/storage_traits.rs

@@ -104,7 +104,7 @@ impl<S: Shape, E, D: CopySlice<E>, T> Tensor<S, E, D, T> {
     /// Copy *physical* data from a slice - **panics** if there are not enough elements in the slice.
     ///
     /// ```rust
-    /// # use dfdx::prelude::*;
+    /// # use dfdx_core::prelude::*;
     /// # let dev: Cpu = Default::default();
     /// let data = [1.0, 2.0, 3.0, 4.0];
     /// let mut t: Tensor<Rank2<2, 2>, f32, _> = dev.zeros();
@@ -118,7 +118,7 @@ impl<S: Shape, E, D: CopySlice<E>, T> Tensor<S, E, D, T> {
     /// Copy *physical* data into a slice - **panics** if there are not enough elements in the tensor.
     ///
     /// ```rust
-    /// # use dfdx::prelude::*;
+    /// # use dfdx_core::prelude::*;
     /// # let dev: Cpu = Default::default();
     /// let t: Tensor<Rank2<2, 2>, f32, _> = dev.tensor([[1.0, 2.0], [3.0, 4.0]]);
     /// let mut data = [0.0; 4];
@@ -134,7 +134,7 @@ impl<S: Shape, E, D: CopySlice<E>, T> Tensor<S, E, D, T> {
 pub trait ZerosTensor<E>: Storage<E> {
     /// Creates a tensor filled with zeros.
     /// ```rust
-    /// # use dfdx::prelude::*;
+    /// # use dfdx_core::prelude::*;
     /// # let dev: Cpu = Default::default();
     /// let a: Tensor<Rank2<2, 3>, f32, _> = dev.zeros();
     /// ```
@@ -151,14 +151,14 @@ pub trait ZerosTensor<E>: Storage<E> {
     ///
     /// Given a shape directly:
     /// ```rust
-    /// # use dfdx::prelude::*;
+    /// # use dfdx_core::prelude::*;
     /// # let dev: Cpu = Default::default();
     /// let a: Tensor<(usize, Const<3>), f32, _> = dev.zeros_like(&(5, Const));
     /// ```
     ///
     /// Given another tensor:
     /// ```rust
-    /// # use dfdx::prelude::*;
+    /// # use dfdx_core::prelude::*;
     /// # let dev: Cpu = Default::default();
     /// let a: Tensor<Rank2<2, 3>, f32, _> = dev.zeros();
     /// let b: Tensor<Rank2<2, 3>, f32, _> = dev.zeros_like(&a);
@@ -179,7 +179,7 @@ pub trait ZeroFillStorage<E>: Storage<E> {
 pub trait OnesTensor<E>: Storage<E> {
     /// Creates a tensor filled with ones.
     /// ```rust
-    /// # use dfdx::prelude::*;
+    /// # use dfdx_core::prelude::*;
     /// # let dev: Cpu = Default::default();
     /// let a: Tensor<Rank2<2, 3>, f32, _> = dev.ones();
     /// ```
@@ -196,14 +196,14 @@ pub trait OnesTensor<E>: Storage<E> {
     ///
     /// Given a shape directly:
     /// ```rust
-    /// # use dfdx::prelude::*;
+    /// # use dfdx_core::prelude::*;
     /// # let dev: Cpu = Default::default();
     /// let a: Tensor<(usize, Const<3>), f32, _> = dev.ones_like(&(5, Const));
     /// ```
     ///
     /// Given another tensor:
     /// ```rust
-    /// # use dfdx::prelude::*;
+    /// # use dfdx_core::prelude::*;
     /// # let dev: Cpu = Default::default();
     /// let a: Tensor<Rank2<2, 3>, f32, _> = dev.ones();
     /// let b: Tensor<_, f32, _> = dev.ones_like(&a);
@@ -231,7 +231,7 @@ pub trait TriangleTensor<E>: Storage<E> {
     /// ## Examples
     ///
     /// ```rust
-    /// # use dfdx::prelude::*;
+    /// # use dfdx_core::prelude::*;
     /// # let dev: Cpu = Default::default();
     /// let a: Tensor<Rank2<3, 3>, f32, _> = dev.upper_tri(1.0, None);
     /// assert_eq!(a.array(),
@@ -297,7 +297,7 @@ pub trait TriangleTensor<E>: Storage<E> {
     /// ## Examples
     ///
     /// ```rust
-    /// # use dfdx::prelude::*;
+    /// # use dfdx_core::prelude::*;
     /// # let dev: Cpu = Default::default();
     /// let a: Tensor<Rank2<3, 3>, f32, _> = dev.lower_tri(1.0, None);
     /// assert_eq!(a.array(),
@@ -480,7 +480,7 @@ impl<S: Shape, E, D: Storage<E>, T> Tensor<S, E, D, T> {
 pub trait TensorFrom<Src, S: Shape, E>: Storage<E> {
     /// Create a tensor from rust data
     /// ```rust
-    /// # use dfdx::prelude::*;
+    /// # use dfdx_core::prelude::*;
     /// # let dev: Cpu = Default::default();
     /// let _: Tensor<Rank2<2, 3>, f32, Cpu> = dev.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]);
     /// let _: Tensor<Rank2<2, 3>, f32, Cpu> = dev.tensor(vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);

dfdx-core/src/tensor/tensor_impls.rs

@@ -17,7 +17,7 @@ use std::sync::Arc;
 ///
 /// Examples:
 /// ```rust
-/// # use dfdx::prelude::*;
+/// # use dfdx_core::prelude::*;
 /// # let dev: Cpu = Default::default();
 /// // A 1d tensor with 1000 f32 elements, stored on the Cpu
 /// type A = Tensor<Rank1<1000>, f32, Cpu>;
@@ -116,7 +116,7 @@ impl<S: Shape, E, D: Storage<E>, T> Tensor<S, E, D, T> {
 pub trait PutTape<T> {
     type Output;
     /// ```rust
-    /// # use dfdx::prelude::*;
+    /// # use dfdx_core::prelude::*;
     /// # let dev: Cpu = Default::default();
     /// let a: Tensor<Rank2<2, 3>, f32, _, NoneTape> = dev.zeros();
     /// let a: Tensor<Rank2<2, 3>, f32, _, OwnedTape<f32, Cpu>> = a.put_tape(Default::default());
@@ -146,7 +146,7 @@ pub trait SplitTape {
     type NoTape: Clone + PutTape<Self::Tape, Output = Self>;
     /// Splits tape off of self
     /// ```rust
-    /// # use dfdx::prelude::*;
+    /// # use dfdx_core::prelude::*;
     /// # let dev: Cpu = Default::default();
     /// # let grads = Gradients::leaky();
     /// let a: Tensor<Rank1<5>, f32, _, OwnedTape<f32, _>> = dev.zeros().traced(grads);

dfdx-core/src/tensor_ops/abs/mod.rs

@@ -16,7 +16,7 @@ pub struct AbsKernelOp;
 ///
 /// Examples:
 /// ```rust
-/// # use dfdx::prelude::*;
+/// # use dfdx_core::prelude::*;
 /// # let dev: Cpu = Default::default();
 /// let t = dev.tensor([-1.0, 0.0, 1.0, 2.0]);
 /// let r = t.abs();

dfdx-core/src/tensor_ops/accurate_gelu/mod.rs

@@ -28,7 +28,7 @@ pub struct AccurateGeLUKernelOp;
 ///
 /// Examples:
 /// ```rust
-/// # use dfdx::prelude::*;
+/// # use dfdx_core::prelude::*;
 /// # let dev: Cpu = Default::default();
 /// let t = dev.tensor([-1.0, 0.0, 1.0, 2.0]);
 /// let r = t.accurate_gelu();

dfdx-core/src/tensor_ops/adam/mod.rs

@@ -14,7 +14,7 @@ use super::WeightDecay;
 ///
 /// Changing all default parameters:
 /// ```rust
-/// # use dfdx::prelude::*;
+/// # use dfdx_core::prelude::*;
 /// AdamConfig {
 ///     lr: 1e-2,
 ///     betas: [0.1, 0.2],

dfdx-core/src/tensor_ops/add/mod.rs

@@ -23,7 +23,7 @@ pub struct ScalarAddKernelOp<E> {
 ///
 /// Example:
 /// ```rust
-/// # use dfdx::prelude::*;
+/// # use dfdx_core::prelude::*;
 /// # let dev: Cpu = Default::default();
 /// let a = dev.tensor([[1.0, 2.0, 3.0], [-1.0, -2.0, -3.0]]);
 /// let r = a + dev.ones();
@@ -32,7 +32,7 @@ pub struct ScalarAddKernelOp<E> {
 ///
 /// Adding a scalar:
 /// ```rust
-/// # use dfdx::prelude::*;
+/// # use dfdx_core::prelude::*;
 /// # let dev: Cpu = Default::default();
 /// let a = dev.tensor([[1.0, 2.0, 3.0], [-1.0, -2.0, -3.0]]);
 /// let r = a + 1.0;