update

Signed-off-by: Joe McCain III <jo3mccain@icloud.com>

Cargo.toml

@@ -8,8 +8,8 @@ keywords = ["acme", "autodiff", "mathematics", "tensor"]
 license = "Apache-2.0"
 repository = "https://github.com/FL03/acme"
 readme = "README.md"
-version = "0.3.0"
-# version = "0.3.0-nightly.4"
+# version = "0.3.0"
+version = "0.3.0-nightly.4"
 
 [workspace]
 default-members = [

acme/Cargo.toml

@@ -87,16 +87,16 @@ required-features = ["macros"]
 [build-dependencies]
 
 [dependencies]
-acme-core = { path = "../core", version = "0.3.0" }
-acme-derive = { optional = true, path = "../derive", version = "0.3.0" }
-acme-graphs = { optional = true, path = "../graphs", version = "0.3.0" }
-acme-macros = { optional = true, path = "../macros", version = "0.3.0" }
-acme-tensor = { optional = true, path = "../tensor", version = "0.3.0" }
-# acme-core = { path = "../core", version = "0.3.0-nightly.4" }
-# acme-derive = { optional = true, path = "../derive", version = "0.3.0-nightly.4" }
-# acme-graphs = { optional = true, path = "../graphs", version = "0.3.0-nightly.4" }
-# acme-macros = { optional = true, path = "../macros", version = "0.3.0-nightly.4" }
-# acme-tensor = { optional = true, path = "../tensor", version = "0.3.0-nightly.4" }
+# acme-core = { path = "../core", version = "0.3.0" }
+# acme-derive = { optional = true, path = "../derive", version = "0.3.0" }
+# acme-graphs = { optional = true, path = "../graphs", version = "0.3.0" }
+# acme-macros = { optional = true, path = "../macros", version = "0.3.0" }
+# acme-tensor = { optional = true, path = "../tensor", version = "0.3.0" }
+acme-core = { path = "../core", version = "0.3.0-nightly.4" }
+acme-derive = { optional = true, path = "../derive", version = "0.3.0-nightly.4" }
+acme-graphs = { optional = true, path = "../graphs", version = "0.3.0-nightly.4" }
+acme-macros = { optional = true, path = "../macros", version = "0.3.0-nightly.4" }
+acme-tensor = { optional = true, path = "../tensor", version = "0.3.0-nightly.4" }
 
 [dev-dependencies]
 approx = "0.5"

acme/benches/tensor.rs

@@ -31,4 +31,3 @@ fn bench_iter_rev(b: &mut Bencher) {
     let tensor = Tensor::linspace(0f64, n as f64, n);
     b.iter(|| tensor.strided().rev().take(n))
 }
-

core/src/ops/binary/kinds.rs

@@ -56,5 +56,3 @@ impl BinaryOp {
         }
     }
 }
-
-

graphs/Cargo.toml

@@ -44,8 +44,8 @@ strum.workspace = true
 
 [dependencies.acme-core]
 path = "../core"
-version = "0.3.0"
-# version = "0.3.0-nightly.4"
+# version = "0.3.0"
+version = "0.3.0-nightly.4"
 
 [package.metadata.docs.rs]
 all-features = true

graphs/src/ops/arithmetic.rs

@@ -92,7 +92,7 @@ macro_rules! impl_binary_op {
         $(
             impl_binary_op!($op, $bound, $operator);
         )*
-        
+
     };
     ($op:ident, $bound:ident, $operator:tt) => {
         operator!($op);
@@ -128,7 +128,6 @@ operators!(Arithmetic; {Add: Addition => add, Div: Division => div, Mul: Multipl
 
 impl_binary_op!((Addition, Add, +), (Division, Div, /), (Multiplication, Mul, *), (Remainder, Rem, %), (Subtraction, Sub, -));
 
-
 impl Arithmetic {
     pub fn new(op: Arithmetic) -> Self {
         op

graphs/src/ops/mod.rs

@@ -46,7 +46,6 @@ pub trait Operator {
 }
 
 impl Operator for Box<dyn Operator> {
-
     fn name(&self) -> String {
         self.as_ref().name()
     }

tensor/Cargo.toml

@@ -43,8 +43,8 @@ strum = { features = ["derive"], version = "0.26" }
 
 [dependencies.acme-core]
 path = "../core"
-version = "0.3.0"
-# version = "0.3.0-nightly.4"
+# version = "0.3.0"
+version = "0.3.0-nightly.4"
 
 [dev-dependencies]
 approx = "0.5"

tensor/src/actions/iter/iterator.rs

@@ -23,4 +23,3 @@ pub struct BaseIter<'a, T> {
     data: &'a [T],
     index: usize,
 }
-

tensor/src/actions/iter/strides.rs

@@ -87,7 +87,12 @@ impl<'a> DoubleEndedIterator for Strided<'a> {
         } else {
             return None;
         };
-        let position = self.shape.iter().zip(pos.iter()).map(|(s, p)| s - p).collect();
+        let position = self
+            .shape
+            .iter()
+            .zip(pos.iter())
+            .map(|(s, p)| s - p)
+            .collect();
         let scope = self.index(&position);
         println!("{:?}", &position);
         Some((position, scope))

tensor/src/backend/mod.rs

@@ -19,11 +19,9 @@ pub trait BackendStorage {
 
 #[allow(unused_imports)]
 pub(crate) mod prelude {
-    pub use super::{Backend, BackendStorage};
     pub use super::devices::Device;
+    pub use super::{Backend, BackendStorage};
 }
 
 #[cfg(test)]
-mod tests {
-
-}
+mod tests {}

tensor/src/data/container.rs

@@ -90,7 +90,6 @@ where
         dbg!("Implement a custom iter for ContainerBase");
         self.as_slice_memory_order().unwrap().iter()
     }
-
 
     pub fn layout(&self) -> &Layout {
         &self.layout

tensor/src/impls/iter.rs

@@ -5,7 +5,10 @@
 use crate::prelude::Scalar;
 use crate::tensor::TensorBase;
 
-impl<T> TensorBase<T> where T: Scalar {
+impl<T> TensorBase<T>
+where
+    T: Scalar,
+{
     pub fn sum(&self) -> T {
         self.data().iter().copied().sum()
     }

tensor/src/impls/ops/binary.rs

@@ -8,8 +8,16 @@ use acme::ops::binary::BinaryOp;
 use core::ops;
 use num::traits::Pow;
 
-
-pub(crate) fn broadcast_scalar_op<F, T>(lhs: &TensorBase<T>, rhs: &TensorBase<T>, op: BinaryOp, f: F) -> TensorBase<T> where F: Fn(T, T) -> T, T: Copy + Default {
+pub(crate) fn broadcast_scalar_op<F, T>(
+    lhs: &TensorBase<T>,
+    rhs: &TensorBase<T>,
+    op: BinaryOp,
+    f: F,
+) -> TensorBase<T>
+where
+    F: Fn(T, T) -> T,
+    T: Copy + Default,
+{
     let mut lhs = lhs.clone();
     let mut rhs = rhs.clone();
     if lhs.is_scalar() {
@@ -19,16 +27,27 @@ pub(crate) fn broadcast_scalar_op<F, T>(lhs: &TensorBase<T>, rhs: &TensorBase<T>
         rhs = rhs.broadcast(lhs.shape());
     }
     let shape = lhs.shape().clone();
-    let store = lhs.data().iter().zip(rhs.data().iter()).map(|(a, b)| f(*a, *b)).collect();
+    let store = lhs
+        .data()
+        .iter()
+        .zip(rhs.data().iter())
+        .map(|(a, b)| f(*a, *b))
+        .collect();
     let op = TensorExpr::binary(lhs, rhs, op);
     from_vec_with_op(false, op, shape, store)
 }
 
-fn check_shapes_or_scalar<T>(lhs: &TensorBase<T>, rhs: &TensorBase<T>) where T: Clone + Default {
+fn check_shapes_or_scalar<T>(lhs: &TensorBase<T>, rhs: &TensorBase<T>)
+where
+    T: Clone + Default,
+{
     let is_scalar = lhs.is_scalar() || rhs.is_scalar();
-    debug_assert!(is_scalar || lhs.shape() == rhs.shape(), "Shape Mismatch: {:?} != {:?}", lhs.shape(), rhs.shape());
-
-
+    debug_assert!(
+        is_scalar || lhs.shape() == rhs.shape(),
+        "Shape Mismatch: {:?} != {:?}",
+        lhs.shape(),
+        rhs.shape()
+    );
 }
 
 macro_rules! check {
@@ -39,32 +58,50 @@ macro_rules! check {
     };
 }
 
-impl<T> TensorBase<T> where T: Scalar {
+impl<T> TensorBase<T>
+where
+    T: Scalar,
+{
     pub fn apply_binary(&self, other: &Self, op: BinaryOp) -> Self {
         check_shapes_or_scalar(self, other);
         let shape = self.shape();
-        let store = self.data().iter().zip(other.data().iter()).map(|(a, b)| *a + *b).collect();
+        let store = self
+            .data()
+            .iter()
+            .zip(other.data().iter())
+            .map(|(a, b)| *a + *b)
+            .collect();
         let op = TensorExpr::binary(self.clone(), other.clone(), op);
         from_vec_with_op(false, op, shape, store)
     }
 
-    pub fn apply_binaryf<F>(&self, other: &Self, op: BinaryOp, f: F) -> Self where F: Fn(T, T) -> T {
+    pub fn apply_binaryf<F>(&self, other: &Self, op: BinaryOp, f: F) -> Self
+    where
+        F: Fn(T, T) -> T,
+    {
         check_shapes_or_scalar(self, other);
         let shape = self.shape();
-        let store = self.data().iter().zip(other.data().iter()).map(|(a, b)| f(*a, *b)).collect();
+        let store = self
+            .data()
+            .iter()
+            .zip(other.data().iter())
+            .map(|(a, b)| f(*a, *b))
+            .collect();
         let op = TensorExpr::binary(self.clone(), other.clone(), op);
         from_vec_with_op(false, op, shape, store)
     }
 }
 
-impl<T> TensorBase<T> where T: Scalar {
+impl<T> TensorBase<T>
+where
+    T: Scalar,
+{
     pub fn pow(&self, exp: T) -> Self {
         let shape = self.shape();
         let store = self.data().iter().copied().map(|a| a.pow(exp)).collect();
         let op = TensorExpr::binary_scalar(self.clone(), exp, BinaryOp::Pow);
         from_vec_with_op(false, op, shape, store)
     }
-
 }
 
 impl<T> Pow<T> for TensorBase<T>
@@ -199,7 +236,7 @@ macro_rules! impl_binary_op {
             }
         }
     };
-    
+
 }
 
 macro_rules! impl_assign_op {
@@ -243,7 +280,7 @@ macro_rules! impl_binary_method {
             let op = TensorExpr::binary_scalar(self.clone(), other.clone(), BinaryOp::$variant);
             from_vec_with_op(false, op, shape, store)
         }
-        
+
     };
     (tensor: $variant:ident, $method:ident, $op:tt) => {
         pub fn $method(&self, other: &Self) -> Self {
@@ -253,7 +290,7 @@ macro_rules! impl_binary_method {
             let op = TensorExpr::binary(self.clone(), other.clone(), BinaryOp::$variant);
             from_vec_with_op(false, op, shape, store)
         }
-        
+
     };
 }
 
@@ -265,7 +302,10 @@ impl_assign_op!(MulAssign, mul_assign, Mul, *);
 impl_assign_op!(RemAssign, rem_assign, Rem, %);
 impl_assign_op!(SubAssign, sub_assign, Sub, -);
 
-impl<T> TensorBase<T> where T: Scalar {
+impl<T> TensorBase<T>
+where
+    T: Scalar,
+{
     impl_binary_method!(tensor: Add, add, +);
     impl_binary_method!(scalar: Add, add_scalar, +);
-}
+}

tensor/src/ops/op.rs

@@ -9,7 +9,7 @@ use acme::prelude::{BinaryOp, UnaryOp};
 
 pub type BoxTensor<T = f64> = Box<TensorBase<T>>;
 
-#[derive(Clone, Debug, Eq, Hash, Ord, PartialEq, PartialOrd,)]
+#[derive(Clone, Debug, Eq, Hash, Ord, PartialEq, PartialOrd)]
 #[non_exhaustive]
 pub enum TensorExpr<T> {
     Binary(BoxTensor<T>, BoxTensor<T>, BinaryOp),

tensor/src/shape/shape.rs

@@ -354,7 +354,6 @@ unsafe impl Send for Shape {}
 
 unsafe impl Sync for Shape {}
 
-
 impl From<()> for Shape {
     fn from(_: ()) -> Self {
         Self::default()
@@ -415,7 +414,6 @@ impl From<(usize, usize, usize, usize, usize, usize)> for Shape {
     }
 }
 
-
 // macro_rules! tuple_vec {
 //     ($($n:tt),*) => {
 //         vec![$($n,)*]

tensor/src/stats/mod.rs

@@ -23,7 +23,6 @@ pub trait SummaryStatistics<T> {
 }
 
 pub trait TensorStats<T>: SummaryStatistics<T> {
-
     /// Compute the mean along the specified axis.
     fn mean_axis(&self, axis: Axis) -> T;
 }

tensor/src/types/mod.rs

@@ -16,4 +16,4 @@ pub(crate) mod prelude {
     pub use super::kinds::*;
     pub use super::order::*;
     pub use super::tensors::*;
-}
+}

tensor/src/types/tensors.rs

@@ -9,7 +9,17 @@ use serde::{Deserialize, Serialize};
 use strum::{Display, EnumCount, EnumDiscriminants, EnumIs, EnumIter, EnumString, VariantNames};
 
 #[derive(Clone, Debug, EnumDiscriminants, Eq, PartialEq)]
-#[strum_discriminants(derive(Display, EnumCount, EnumIs, EnumIter, EnumString, Hash, Ord, PartialOrd, VariantNames))]
+#[strum_discriminants(derive(
+    Display,
+    EnumCount,
+    EnumIs,
+    EnumIter,
+    EnumString,
+    Hash,
+    Ord,
+    PartialOrd,
+    VariantNames
+))]
 #[strum_discriminants(name(TensorType))]
 #[cfg_attr(feature = "serde", strum_discriminants(derive(Deserialize, Serialize)))]
 pub enum Tensors<T> {

tensor/tests/iter.rs

@@ -19,4 +19,4 @@ fn test_product() {
     let shape = (2, 2).into_shape();
     let a = Tensor::fill(shape, 2f64);
     assert_eq!(a.product(), 16.0);
-}
+}

tensor/tests/tensor.rs

@@ -81,4 +81,4 @@ fn test_product() {
     let shape = (2, 2).into_shape();
     let a = Tensor::fill(shape, 2f64);
     assert_eq!(a.product(), 16.0);
-}
+}