Feature/cpu rms and layer normalization

crates/ratchet-core/src/cpu/binary.rs

@@ -1,9 +1,64 @@
-use crate::{
-    binary_apply_inplace, Binary, BinaryOp, CPUOperation, DType, OpGuards, Operation,
-    OperationError, RVec, StorageView, Tensor, TensorDType,
-};
+use crate::cpu::cpu_store_result;
+use crate::{Binary, BinaryOp, CPUOperation, DType, OperationError, Tensor, TensorDType};
 use core::marker::PhantomData;
 use half::{bf16, f16};
+use num_traits::NumOps;
+
+#[inline]
+pub(crate) fn binary_map<T: TensorDType, U: TensorDType>(
+    lhs: &[T],
+    rhs: &[T],
+    dst: &mut [U],
+    f: fn(T, T) -> U,
+) {
+    assert_eq!(lhs.len(), dst.len());
+    assert_eq!(rhs.len(), dst.len());
+    for ((l, r), d) in lhs
+        .iter()
+        .copied()
+        .zip(rhs.iter().copied())
+        .zip(dst.iter_mut())
+    {
+        *d = f(l, r);
+    }
+}
+
+#[inline]
+pub(crate) fn binary_map_inplace<T: TensorDType>(lhs: &mut [T], rhs: &[T], f: fn(T, T) -> T) {
+    assert_eq!(lhs.len(), rhs.len());
+    lhs.iter_mut().zip(rhs.iter()).for_each(|(l, r)| {
+        *l = f(*l, *r);
+    });
+}
+
+#[inline]
+pub(crate) fn binary_apply<T: TensorDType, U: TensorDType>(
+    lhs: &Tensor,
+    rhs: &Tensor,
+    dst: &Tensor,
+    f: fn(T, T) -> U,
+) -> Result<(), OperationError> {
+    let lhs = lhs.to_vec::<T>()?;
+    let rhs = rhs.to_vec::<T>()?;
+    let mut result = vec![U::zero(); dst.shape().numel()];
+    binary_map(&lhs, &rhs, &mut result, f);
+    cpu_store_result(dst, &result);
+    Ok(())
+}
+
+#[inline]
+pub(crate) fn binary_apply_inplace<T: TensorDType>(
+    lhs: &Tensor,
+    rhs: &Tensor,
+    dst: &Tensor,
+    f: fn(T, T) -> T,
+) -> Result<(), OperationError> {
+    let mut lhs = lhs.to_vec::<T>()?;
+    let rhs = rhs.to_vec::<T>()?;
+    binary_map_inplace(&mut lhs, &rhs, f);
+    cpu_store_result(dst, &lhs);
+    Ok(())
+}
 
 pub struct BinaryOps<T: TensorDType> {
     dtype: PhantomData<T>,
@@ -18,6 +73,20 @@ macro_rules! impl_cpu_binary_op {
     };
 }
 
+macro_rules! cpu_binary_op_fn {
+    ($method_name:ident, $op:expr) => {
+        #[inline]
+        pub(crate) fn $method_name<T: TensorDType + NumOps>(lhs: &mut [T], rhs: &[T]) {
+            binary_map_inplace::<T>(lhs, rhs, $op);
+        }
+    };
+}
+
+cpu_binary_op_fn!(add, |lhs, rhs| lhs + rhs);
+cpu_binary_op_fn!(sub, |lhs, rhs| lhs - rhs);
+cpu_binary_op_fn!(mul, |lhs, rhs| lhs * rhs);
+cpu_binary_op_fn!(div, |lhs, rhs| lhs / rhs);
+
 macro_rules! impl_cpu_binary {
     ($dtype:ident) => {
         impl BinaryOps<$dtype> {

crates/ratchet-core/src/cpu/mod.rs

@@ -1,20 +1,19 @@
 mod binary;
 pub mod gemm;
+mod norm;
 pub mod reindex;
 pub mod rope;
 mod unary;
 mod utils;
 
 use crate::{
-    dequantize, Binary, BinaryOp, CPUBuffer, Cast, Concat, DType, IndexSelect, InvariantError,
-    LazyOp, OpGuards, Operation, OperationError, RVec, Shape, Storage, StorageView, Strides,
-    Tensor, TensorDType, Unary, UnaryOp,
+    dequantize, Cast, Concat, DType, IndexSelect, InvariantError, LazyOp, Operation,
+    OperationError, RVec, Shape, Tensor, TensorDType,
 };
 use anyhow::anyhow;
-use core::marker::PhantomData;
 use half::{bf16, f16};
-use num_traits::Float;
 use rope::cpu_rope;
+use unary::unary_apply_fn;
 use utils::cpu_store_result;
 
 pub fn apply_operation(op: LazyOp, dst: Tensor) -> Result<Tensor, OperationError> {
@@ -27,7 +26,7 @@ pub fn apply_operation(op: LazyOp, dst: Tensor) -> Result<Tensor, OperationError
         LazyOp::Unary(u) => u.apply_cpu(dst),
         LazyOp::Reindex(r) => r.apply_cpu(dst),
         LazyOp::Concat(c) => cpu_concat(c, dst),
-        LazyOp::Norm(_n) => todo!(),
+        LazyOp::Norm(n) => n.apply_cpu(dst),
         LazyOp::Conv(_c) => todo!(),
         LazyOp::Select(i) => cpu_index_select(i, dst),
         LazyOp::IndexWrite(_i) => todo!(),
@@ -209,80 +208,3 @@ pub fn cpu_concat(Concat { inputs, dim }: Concat, dst: Tensor) -> Result<Tensor,
         dtype => Err(InvariantError::UnsupportedDType(dtype).into()),
     }
 }
-
-#[inline]
-fn unary_apply_fn_helper<T: TensorDType, U: TensorDType>(src: &[T], dst: &mut [U], f: fn(T) -> U) {
-    assert_eq!(src.len(), dst.len());
-    for (s, d) in src.iter().copied().zip(dst.iter_mut()) {
-        *d = f(s);
-    }
-}
-
-#[inline]
-pub fn unary_apply_fn<T: TensorDType, U: TensorDType>(
-    input: &Tensor,
-    dst: &Tensor,
-    f: fn(T) -> U,
-) -> Result<(), OperationError> {
-    let input = input.to_vec::<T>()?;
-    let mut result = vec![U::zero(); dst.shape().numel()];
-    unary_apply_fn_helper(&input, &mut result, f);
-    cpu_store_result(dst, &result);
-    Ok(())
-}
-
-#[inline]
-fn binary_apply_fn_helper<T: TensorDType, U: TensorDType>(
-    lhs: &[T],
-    rhs: &[T],
-    dst: &mut [U],
-    f: fn(T, T) -> U,
-) {
-    assert_eq!(lhs.len(), dst.len());
-    assert_eq!(rhs.len(), dst.len());
-    for ((l, r), d) in lhs
-        .iter()
-        .copied()
-        .zip(rhs.iter().copied())
-        .zip(dst.iter_mut())
-    {
-        *d = f(l, r);
-    }
-}
-
-#[inline]
-fn binary_apply_inplace_helper<T: TensorDType>(lhs: &mut [T], rhs: &[T], f: fn(T, T) -> T) {
-    assert_eq!(lhs.len(), rhs.len());
-    lhs.iter_mut().zip(rhs.iter()).for_each(|(l, r)| {
-        *l = f(*l, *r);
-    });
-}
-
-#[inline]
-pub fn binary_apply_fn<T: TensorDType, U: TensorDType>(
-    lhs: &Tensor,
-    rhs: &Tensor,
-    dst: &Tensor,
-    f: fn(T, T) -> U,
-) -> Result<(), OperationError> {
-    let lhs = lhs.to_vec::<T>()?;
-    let rhs = rhs.to_vec::<T>()?;
-    let mut result = vec![U::zero(); dst.shape().numel()];
-    binary_apply_fn_helper(&lhs, &rhs, &mut result, f);
-    cpu_store_result(dst, &result);
-    Ok(())
-}
-
-#[inline]
-pub fn binary_apply_inplace<T: TensorDType>(
-    lhs: &Tensor,
-    rhs: &Tensor,
-    dst: &Tensor,
-    f: fn(T, T) -> T,
-) -> Result<(), OperationError> {
-    let mut lhs = lhs.to_vec::<T>()?;
-    let rhs = rhs.to_vec::<T>()?;
-    binary_apply_inplace_helper(&mut lhs, &rhs, f);
-    cpu_store_result(dst, &lhs);
-    Ok(())
-}