core/ndstrides: checkpoint 2

nac3core/src/codegen/irrt/mod.rs

@@ -953,9 +953,9 @@ pub fn call_ndarray_calc_broadcast_index<
     )
 }
 
-pub fn call_nac3_throw_dummy_error<'ctx, G: CodeGenerator + ?Sized>(
+pub fn call_nac3_throw_dummy_error<G: CodeGenerator + ?Sized>(
     generator: &mut G,
-    ctx: &CodeGenContext<'ctx, '_>,
+    ctx: &CodeGenContext<'_, '_>,
 ) {
     let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_throw_dummy_error");
     CallFunction::begin(generator, ctx, &name).returning_void();

nac3core/src/codegen/irrt/util.rs

@@ -3,9 +3,9 @@ use crate::codegen::{CodeGenContext, CodeGenerator};
 // When [`TypeContext::size_type`] is 32-bits, the function name is "{fn_name}".
 // When [`TypeContext::size_type`] is 64-bits, the function name is "{fn_name}64".
 #[must_use]
-pub fn get_sizet_dependent_function_name<'ctx, G: CodeGenerator + ?Sized>(
+pub fn get_sizet_dependent_function_name<G: CodeGenerator + ?Sized>(
     generator: &mut G,
-    ctx: &CodeGenContext<'ctx, '_>,
+    ctx: &CodeGenContext<'_, '_>,
     name: &str,
 ) -> String {
     let mut name = name.to_owned();

nac3core/src/codegen/model/structure.rs

@@ -48,9 +48,7 @@ struct TypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
     field_types: Vec<BasicTypeEnum<'ctx>>,
 }
 
-impl<'ctx, 'a, 'b, G: CodeGenerator + ?Sized> FieldTraversal<'ctx>
-    for TypeFieldTraversal<'ctx, 'a, G>
-{
+impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx> for TypeFieldTraversal<'ctx, 'a, G> {
     type Out<M> = ();
 
     fn add<M: Model<'ctx>>(&mut self, _name: &'static str, model: M) -> Self::Out<M> {
@@ -204,6 +202,6 @@ impl<'ctx, S: StructKind<'ctx>> Ptr<'ctx, StructModel<S>> {
         M: Model<'ctx>,
         GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
     {
-        self.gep(ctx, get_field).store(ctx, value)
+        self.gep(ctx, get_field).store(ctx, value);
     }
 }

nac3core/src/codegen/model/util.rs

@@ -27,14 +27,14 @@ pub fn call_memcpy_model<'ctx, Item: Model<'ctx> + Default, G: CodeGenerator + ?
 
 /// Like [`gen_for_callback_incrementing`] with [`Model`] abstractions.
 /// The [`IntKind`] is automatically inferred.
-pub fn gen_for_model_auto<'ctx, 'a, G, F, I, R>(
+pub fn gen_for_model_auto<'ctx, 'a, G, F, I>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, 'a>,
     start: Int<'ctx, I>,
     stop: Int<'ctx, I>,
     step: Int<'ctx, I>,
     body: F,
-) -> Result<R, String>
+) -> Result<(), String>
 where
     G: CodeGenerator + ?Sized,
     F: FnOnce(
@@ -42,7 +42,7 @@ where
         &mut CodeGenContext<'ctx, 'a>,
         BreakContinueHooks<'ctx>,
         Int<'ctx, I>,
-    ) -> Result<R, String>,
+    ) -> Result<(), String>,
     I: IntKind<'ctx> + Default,
 {
     let int_model = IntModel(I::default());
@@ -60,3 +60,32 @@ where
         step.value,
     )
 }
+
+/// Like [`gen_if_callback`] with [`Model`] abstractions and without the `else` block.
+pub fn gen_if_model<'ctx, 'a, G, ThenFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, 'a>,
+    cond: Int<'ctx, Bool>,
+    then: ThenFn,
+) -> Result<(), String>
+where
+    G: CodeGenerator + ?Sized,
+    ThenFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<(), String>,
+{
+    let current_bb = ctx.builder.get_insert_block().unwrap();
+    let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "if.then");
+    let end_bb = ctx.ctx.insert_basic_block_after(then_bb, "if.end");
+
+    // Inserting into `current_bb`.
+    ctx.builder.build_conditional_branch(cond.value, then_bb, end_bb).unwrap();
+
+    // Inserting into `then_bb`
+    ctx.builder.position_at_end(then_bb);
+    then(generator, ctx)?;
+    ctx.builder.build_unconditional_branch(end_bb).unwrap();
+
+    // Reposition to `end_bb` for continuation.
+    ctx.builder.position_at_end(end_bb);
+
+    Ok(())
+}

nac3core/src/codegen/numpy_new.rs

@@ -99,8 +99,7 @@ fn create_empty_ndarray<'ctx, G>(
 where
     G: CodeGenerator + ?Sized,
 {
-    let shape = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
-    let shape = shape.value.get(generator, ctx, |f| f.items, "shape");
+    let (_, shape) = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
 
     let ndarray =
         NDArrayObject::alloca_uninitialized_of_type(generator, ctx, ndarray_ty, "ndarray");
@@ -248,8 +247,7 @@ pub fn gen_ndarray_broadcast_to<'ctx>(
         split_scalar_or_ndarray(generator, ctx, input, input_ty).as_ndarray(generator, ctx);
 
     // Process `shape`
-    let broadcast_shape = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
-    let broadcast_shape = broadcast_shape.value.get(generator, ctx, |f| f.items, "shape");
+    let (_, broadcast_shape) = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
     // NOTE: shape.size should equal to `broadcasted_ndims`.
     let broadcast_ndims_llvm = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
     call_nac3_ndarray_util_assert_shape_no_negative(
@@ -300,8 +298,7 @@ pub fn gen_ndarray_reshape<'ctx>(
     // Process the shape input from user and resolve negative indices.
     // The resulting `new_shape`'s size should be equal to reshaped_ndims.
     // This is ensured by the typechecker.
-    let new_shape = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
-    let new_shape = new_shape.value.get(generator, ctx, |f| f.items, "new_shape");
+    let (_, new_shape) = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
 
     // Resolve unknown dimensions & validate `new_shape`.
     let new_ndims = sizet_model.constant(generator, ctx.ctx, reshaped_ndims);
@@ -353,7 +350,7 @@ pub fn gen_ndarray_arange<'ctx>(
 
     // Create data and set elements
     ndarray.create_data(generator, ctx);
-    ndarray.foreach(generator, ctx, |_generator, ctx, _hooks, i, pelement| {
+    ndarray.foreach_pointer(generator, ctx, |_generator, ctx, _hooks, i, pelement| {
         let val =
             ctx.builder.build_unsigned_int_to_float(i.value, ctx.ctx.f64_type(), "val").unwrap();
         ctx.builder.build_store(pelement, val).unwrap();
@@ -495,10 +492,9 @@ pub fn gen_ndarray_transpose<'ctx>(
         // Parse argument #2 axes
         let in_axes_ty = fun.0.args[1].ty;
         let in_axes = args[1].1.clone().to_basic_value_enum(ctx, generator, in_axes_ty)?;
-        let in_axes = parse_numpy_int_sequence(generator, ctx, in_axes, in_axes_ty);
 
+        let (_, axes) = parse_numpy_int_sequence(generator, ctx, in_axes, in_axes_ty);
         let num_axes = ndarray.get_ndims(generator, ctx.ctx);
-        let axes = in_axes.value.get(generator, ctx, |f| f.items, "axes");
 
         call_nac3_ndarray_transpose(
             generator,

nac3core/src/codegen/stmt.rs

@@ -1,6 +1,5 @@
 use super::model::*;
 use super::structure::cslice::CSlice;
-use super::structure::ndarray::broadcast::broadcast_all_ndarrays;
 use super::{
     super::symbol_resolver::ValueEnum,
     expr::destructure_range,
@@ -438,7 +437,7 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
             let value =
                 split_scalar_or_ndarray(generator, ctx, value, value_ty).as_ndarray(generator, ctx);
 
-            let broadcast_result = broadcast_all_ndarrays(generator, ctx, &vec![target, value]);
+            let broadcast_result = NDArrayObject::broadcast_all(generator, ctx, &[target, value]);
 
             let target = broadcast_result.ndarrays[0];
             let value = broadcast_result.ndarrays[1];

nac3core/src/codegen/structure/list.rs

@@ -33,44 +33,38 @@ impl<'ctx, Item: Model<'ctx>, Size: IntKind<'ctx>> StructKind<'ctx> for List<Ite
     }
 }
 
-pub struct ListObject<'ctx, Item: Model<'ctx>, Size: IntKind<'ctx>> {
+/// A NAC3 Python List object.
+pub struct ListObject<'ctx> {
     /// Typechecker type of the list items
     pub item_type: Type,
-    pub value: Ptr<'ctx, StructModel<List<Item, Size>>>,
+    pub value: Ptr<'ctx, StructModel<List<AnyModel<'ctx>, SizeT>>>,
 }
 
-impl<'ctx, Item: Model<'ctx>, Len: IntKind<'ctx>> ListObject<'ctx, Item, Len> {
+impl<'ctx> ListObject<'ctx> {
     /// Create a [`ListObject`] from an LLVM value and its typechecker [`Type`].
-    ///
-    /// - The `Item` model has to be manually provided, and should match the
-    /// `get_llvm_type()` of `ty` and the `get_type()`. You may want to use
-    /// [`AnyModel`] if `ty`'s type is not knowable statically.
     pub fn from_value_and_type<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
-        val: V,
-        ty: Type,
-        item_model: Item,
-        len_model: Len,
+        list_val: V,
+        list_type: Type,
     ) -> Self {
-        let plist_model = PtrModel(StructModel(List { item: item_model, len: len_model }));
-
         // Check typechecker type and extract `item_type`
-        let item_type = match &*ctx.unifier.get_ty(ty) {
+        let item_type = match &*ctx.unifier.get_ty(list_type) {
             TypeEnum::TObj { obj_id, params, .. }
                 if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
             {
                 iter_type_vars(params).next().unwrap().ty // Extract `item_type`
             }
-            _ => panic!("Expecting type to be a list, but got {}", ctx.unifier.stringify(ty)),
+            _ => {
+                panic!("Expecting type to be a list, but got {}", ctx.unifier.stringify(list_type))
+            }
         };
 
-        // LLVM types of `item_model` and `ty` should match
-        let llvm_ty = ctx.get_llvm_type(generator, ty);
-        item_model.check_type(generator, ctx.ctx, llvm_ty).unwrap();
+        let item_model = AnyModel(ctx.get_llvm_type(generator, item_type));
+        let plist_model = PtrModel(StructModel(List { item: item_model, len: SizeT }));
 
         // Create object
-        let val = plist_model.check_value(generator, ctx.ctx, val).unwrap();
-        ListObject { item_type: item_type, value: val }
+        let value = plist_model.check_value(generator, ctx.ctx, list_val).unwrap();
+        ListObject { item_type, value }
     }
 }

nac3core/src/codegen/structure/ndarray/broadcast.rs

@@ -71,12 +71,13 @@ pub struct BroadcastAllResult<'ctx> {
     pub ndarrays: Vec<NDArrayObject<'ctx>>,
 }
 
-// TODO: DOCUMENT: Behaves like `np.broadcast()`, except returns results differently.
-pub fn broadcast_all_ndarrays<'ctx, G: CodeGenerator + ?Sized>(
+impl<'ctx> NDArrayObject<'ctx> {
+    // TODO: DOCUMENT: Behaves like `np.broadcast()`, except returns results differently.
+    pub fn broadcast_all<G: CodeGenerator + ?Sized>(
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
-    ndarrays: &Vec<NDArrayObject<'ctx>>,
-) -> BroadcastAllResult<'ctx> {
+        ndarrays: &[Self],
+    ) -> BroadcastAllResult<'ctx> {
         assert!(!ndarrays.is_empty());
 
         let sizet_model = IntModel(SizeT);
@@ -120,7 +121,7 @@ pub fn broadcast_all_ndarrays<'ctx, G: CodeGenerator + ?Sized>(
 
         // Broadcast all the inputs to shape `dst_shape`.
         let broadcast_ndarrays: Vec<_> = ndarrays
-        .into_iter()
+            .iter()
             .map(|ndarray| ndarray.broadcast_to(generator, ctx, broadcast_ndims, broadcast_shape))
             .collect_vec();
 
@@ -129,4 +130,5 @@ pub fn broadcast_all_ndarrays<'ctx, G: CodeGenerator + ?Sized>(
             shape: broadcast_shape,
             ndarrays: broadcast_ndarrays,
         }
+    }
 }

nac3core/src/codegen/structure/ndarray/functions.rs

@@ -0,0 +1,477 @@
+use inkwell::{
+    values::{BasicValue, FloatValue, IntValue},
+    FloatPredicate, IntPredicate,
+};
+use itertools::Itertools;
+
+use crate::{
+    codegen::{
+        llvm_intrinsics,
+        model::{
+            util::{gen_for_model_auto, gen_if_model},
+            *,
+        },
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::Type,
+};
+
+use super::{scalar::ScalarObject, NDArrayObject};
+
+/// Convenience function to crash the program when types of arguments are not supported.
+/// Used to be debugged with a stacktrace.
+fn unsupported_type<I>(ctx: &CodeGenContext<'_, '_>, tys: I) -> !
+where
+    I: IntoIterator<Item = Type>,
+{
+    unreachable!(
+        "unsupported types found '{}'",
+        tys.into_iter().map(|ty| format!("'{}'", ctx.unifier.stringify(ty))).join(", "),
+    )
+}
+
+#[derive(Debug, Clone, Copy)]
+pub enum FloorOrCeil {
+    Floor,
+    Ceil,
+}
+
+#[derive(Debug, Clone, Copy)]
+pub enum MinOrMax {
+    Min,
+    Max,
+}
+
+fn signed_ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
+    vec![ctx.primitives.int32, ctx.primitives.int64]
+}
+
+fn unsigned_ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
+    vec![ctx.primitives.uint32, ctx.primitives.uint64]
+}
+
+fn ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
+    vec![ctx.primitives.int32, ctx.primitives.int64, ctx.primitives.uint32, ctx.primitives.uint64]
+}
+
+fn int_like(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
+    vec![
+        ctx.primitives.bool,
+        ctx.primitives.int32,
+        ctx.primitives.int64,
+        ctx.primitives.uint32,
+        ctx.primitives.uint64,
+    ]
+}
+
+fn cast_to_int_conversion<'ctx, 'a, G, HandleFloatFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, 'a>,
+    scalar: ScalarObject<'ctx>,
+    target_int_dtype: Type,
+    handle_float: HandleFloatFn,
+) -> ScalarObject<'ctx>
+where
+    G: CodeGenerator + ?Sized,
+    HandleFloatFn:
+        FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, FloatValue<'ctx>) -> IntValue<'ctx>,
+{
+    let target_int_dtype_llvm = ctx.get_llvm_type(generator, target_int_dtype).into_int_type();
+
+    let result = if ctx.unifier.unioned(scalar.dtype, ctx.primitives.float) {
+        // Special handling for floats
+        let n = scalar.value.into_float_value();
+        handle_float(generator, ctx, n)
+    } else if ctx.unifier.unioned_any(scalar.dtype, int_like(ctx)) {
+        let n = scalar.value.into_int_value();
+
+        if n.get_type().get_bit_width() <= target_int_dtype_llvm.get_bit_width() {
+            ctx.builder.build_int_z_extend(n, target_int_dtype_llvm, "zext").unwrap()
+        } else {
+            ctx.builder.build_int_truncate(n, target_int_dtype_llvm, "trunc").unwrap()
+        }
+    } else {
+        unsupported_type(ctx, [scalar.dtype]);
+    };
+
+    assert_eq!(target_int_dtype_llvm.get_bit_width(), result.get_type().get_bit_width()); // Sanity check
+    ScalarObject { value: result.into(), dtype: target_int_dtype }
+}
+
+impl<'ctx> ScalarObject<'ctx> {
+    /// Compare two scalars. Only int-to-int and float-to-float comparisons are allowed.
+    /// Panic otherwise.
+    pub fn compare<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        lhs: ScalarObject<'ctx>,
+        rhs: ScalarObject<'ctx>,
+        int_predicate: IntPredicate,
+        float_predicate: FloatPredicate,
+        name: &str,
+    ) -> Int<'ctx, Bool> {
+        if !ctx.unifier.unioned(lhs.dtype, rhs.dtype) {
+            unsupported_type(ctx, [lhs.dtype, rhs.dtype]);
+        }
+
+        let bool_model = IntModel(Bool);
+
+        let common_ty = lhs.dtype;
+        let result = if ctx.unifier.unioned(common_ty, ctx.primitives.float) {
+            let lhs = lhs.value.into_float_value();
+            let rhs = rhs.value.into_float_value();
+            ctx.builder.build_float_compare(float_predicate, lhs, rhs, name).unwrap()
+        } else if ctx.unifier.unioned_any(common_ty, int_like(ctx)) {
+            let lhs = lhs.value.into_int_value();
+            let rhs = rhs.value.into_int_value();
+            ctx.builder.build_int_compare(int_predicate, lhs, rhs, name).unwrap()
+        } else {
+            unsupported_type(ctx, [lhs.dtype, rhs.dtype]);
+        };
+
+        bool_model.check_value(generator, ctx.ctx, result).unwrap()
+    }
+
+    /// Invoke NAC3's builtin `int32()`.
+    #[must_use]
+    pub fn cast_to_int32<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Self {
+        cast_to_int_conversion(
+            generator,
+            ctx,
+            *self,
+            ctx.primitives.int32,
+            |_generator, ctx, input| {
+                let n =
+                    ctx.builder.build_float_to_signed_int(input, ctx.ctx.i64_type(), "").unwrap();
+                ctx.builder.build_int_truncate(n, ctx.ctx.i32_type(), "conv").unwrap()
+            },
+        )
+    }
+
+    /// Invoke NAC3's builtin `int64()`.
+    #[must_use]
+    pub fn cast_to_int64<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Self {
+        cast_to_int_conversion(
+            generator,
+            ctx,
+            *self,
+            ctx.primitives.int64,
+            |_generator, ctx, input| {
+                ctx.builder.build_float_to_signed_int(input, ctx.ctx.i64_type(), "").unwrap()
+            },
+        )
+    }
+
+    /// Invoke NAC3's builtin `uint32()`.
+    #[must_use]
+    pub fn cast_to_uint32<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Self {
+        cast_to_int_conversion(
+            generator,
+            ctx,
+            *self,
+            ctx.primitives.uint32,
+            |_generator, ctx, n| {
+                let n_gez = ctx
+                    .builder
+                    .build_float_compare(FloatPredicate::OGE, n, n.get_type().const_zero(), "")
+                    .unwrap();
+
+                let to_int32 =
+                    ctx.builder.build_float_to_signed_int(n, ctx.ctx.i32_type(), "").unwrap();
+                let to_uint64 =
+                    ctx.builder.build_float_to_unsigned_int(n, ctx.ctx.i64_type(), "").unwrap();
+
+                ctx.builder
+                    .build_select(
+                        n_gez,
+                        ctx.builder.build_int_truncate(to_uint64, ctx.ctx.i32_type(), "").unwrap(),
+                        to_int32,
+                        "conv",
+                    )
+                    .unwrap()
+                    .into_int_value()
+            },
+        )
+    }
+
+    /// Invoke NAC3's builtin `uint64()`.
+    #[must_use]
+    pub fn cast_to_uint64<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Self {
+        cast_to_int_conversion(
+            generator,
+            ctx,
+            *self,
+            ctx.primitives.uint64,
+            |_generator, ctx, n| {
+                let val_gez = ctx
+                    .builder
+                    .build_float_compare(FloatPredicate::OGE, n, n.get_type().const_zero(), "")
+                    .unwrap();
+
+                let to_int64 =
+                    ctx.builder.build_float_to_signed_int(n, ctx.ctx.i64_type(), "").unwrap();
+                let to_uint64 =
+                    ctx.builder.build_float_to_unsigned_int(n, ctx.ctx.i64_type(), "").unwrap();
+                ctx.builder
+                    .build_select(val_gez, to_uint64, to_int64, "conv")
+                    .unwrap()
+                    .into_int_value()
+            },
+        )
+    }
+
+    /// Invoke NAC3's builtin `bool()`.
+    #[must_use]
+    pub fn cast_to_bool<G: CodeGenerator + ?Sized>(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Self {
+        // TODO: Why is the original code being so lax about i1 and i8 for the returned int type?
+        let result = if ctx.unifier.unioned(self.dtype, ctx.primitives.bool) {
+            self.value.into_int_value()
+        } else if ctx.unifier.unioned_any(self.dtype, ints(ctx)) {
+            let n = self.value.into_int_value();
+            ctx.builder
+                .build_int_compare(inkwell::IntPredicate::NE, n, n.get_type().const_zero(), "bool")
+                .unwrap()
+        } else if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
+            let n = self.value.into_float_value();
+            ctx.builder
+                .build_float_compare(FloatPredicate::UNE, n, n.get_type().const_zero(), "bool")
+                .unwrap()
+        } else {
+            unsupported_type(ctx, [self.dtype])
+        };
+
+        ScalarObject { dtype: ctx.primitives.bool, value: result.as_basic_value_enum() }
+    }
+
+    /// Invoke NAC3's builtin `round()`.
+    #[must_use]
+    pub fn round<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        target_int_dtype: Type,
+    ) -> Self {
+        let target_int_dtype_llvm = ctx.get_llvm_type(generator, target_int_dtype).into_int_type();
+
+        let result = if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
+            let n = self.value.into_float_value();
+            let n = llvm_intrinsics::call_float_round(ctx, n, None);
+            ctx.builder.build_float_to_signed_int(n, target_int_dtype_llvm, "round").unwrap()
+        } else {
+            unsupported_type(ctx, [self.dtype, target_int_dtype])
+        };
+        ScalarObject { dtype: target_int_dtype, value: result.as_basic_value_enum() }
+    }
+
+    /// Invoke NAC3's builtin `np_round()`.
+    ///
+    /// NOTE: `np.round()` has different behaviors than `round()` in terms of their result
+    /// on "tie" cases and return type.
+    #[must_use]
+    pub fn np_round<G: CodeGenerator + ?Sized>(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
+        let result = if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
+            let n = self.value.into_float_value();
+            llvm_intrinsics::call_float_rint(ctx, n, None)
+        } else {
+            unsupported_type(ctx, [self.dtype])
+        };
+        ScalarObject { dtype: ctx.primitives.float, value: result.as_basic_value_enum() }
+    }
+
+    /// Invoke NAC3's builtin `min()` or `max()`.
+    fn min_or_max_helper(
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        kind: MinOrMax,
+        a: Self,
+        b: Self,
+    ) -> Self {
+        if !ctx.unifier.unioned(a.dtype, b.dtype) {
+            unsupported_type(ctx, [a.dtype, b.dtype])
+        }
+
+        let common_dtype = a.dtype;
+
+        if ctx.unifier.unioned(common_dtype, ctx.primitives.float) {
+            let function = match kind {
+                MinOrMax::Min => llvm_intrinsics::call_float_minnum,
+                MinOrMax::Max => llvm_intrinsics::call_float_maxnum,
+            };
+            let result =
+                function(ctx, a.value.into_float_value(), b.value.into_float_value(), None);
+            ScalarObject { value: result.as_basic_value_enum(), dtype: ctx.primitives.float }
+        } else if ctx.unifier.unioned_any(
+            common_dtype,
+            [unsigned_ints(ctx).as_slice(), &[ctx.primitives.bool]].concat(),
+        ) {
+            // Treating bool has an unsigned int since that is convenient
+            let function = match kind {
+                MinOrMax::Min => llvm_intrinsics::call_int_umin,
+                MinOrMax::Max => llvm_intrinsics::call_int_umax,
+            };
+            let result = function(ctx, a.value.into_int_value(), b.value.into_int_value(), None);
+            ScalarObject { value: result.as_basic_value_enum(), dtype: common_dtype }
+        } else {
+            unsupported_type(ctx, [common_dtype])
+        }
+    }
+
+    /// Invoke NAC3's builtin `floor()` or `ceil()`.
+    #[must_use]
+    pub fn floor_or_ceil<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        kind: FloorOrCeil,
+        target_int_dtype: Type,
+    ) -> Self {
+        let target_int_dtype_llvm = ctx.get_llvm_type(generator, target_int_dtype).into_int_type();
+
+        if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
+            let function = match kind {
+                FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
+                FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
+            };
+            let n = self.value.into_float_value();
+            let n = function(ctx, n, None);
+            let n = ctx.builder.build_float_to_signed_int(n, target_int_dtype_llvm, "").unwrap();
+            ScalarObject { dtype: target_int_dtype, value: n.as_basic_value_enum() }
+        } else {
+            unsupported_type(ctx, [self.dtype])
+        }
+    }
+}
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// Helper function for NAC3's builtin `np_min()`, `np_max()`, `np_argmin()`, and `np_argmax()`.
+    ///
+    /// Generate LLVM IR to find the extremum and index of the **first** extremum value.
+    ///
+    /// Care has also been taken to make the error messages match that of NumPy.
+    fn min_or_max_helper<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        kind: MinOrMax,
+        on_empty_err_msg: &str,
+    ) -> (ScalarObject<'ctx>, Int<'ctx, SizeT>) {
+        let sizet_model = IntModel(SizeT);
+        let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
+
+        // If the ndarray is empty, throw an error.
+        let is_empty = self.is_empty(generator, ctx);
+        ctx.make_assert(
+            generator,
+            is_empty.value,
+            "0:ValueError",
+            on_empty_err_msg,
+            [None, None, None],
+            ctx.current_loc,
+        );
+
+        // Setup and initialize the extremum to be the first element in the ndarray
+        let pextremum_index = sizet_model.alloca(generator, ctx, "extremum_index");
+        let pextremum = ctx.builder.build_alloca(dtype_llvm, "extremum").unwrap();
+
+        let zero = sizet_model.const_0(generator, ctx.ctx);
+        pextremum_index.store(ctx, zero);
+
+        let first_scalar = self.get_nth(generator, ctx, zero);
+        ctx.builder.build_store(pextremum, first_scalar.value).unwrap();
+
+        // Find extremum
+        let start = sizet_model.const_1(generator, ctx.ctx); // Start on 1
+        let stop = self.size(generator, ctx);
+        let step = sizet_model.const_1(generator, ctx.ctx);
+        gen_for_model_auto(generator, ctx, start, stop, step, |generator, ctx, _hooks, i| {
+            // Worth reading on "Notes" in <https://numpy.org/doc/stable/reference/generated/numpy.min.html#numpy.min>
+            // on how `NaN` values have to be handled.
+
+            let scalar = self.get_nth(generator, ctx, i);
+
+            let old_extremum = ctx.builder.build_load(pextremum, "current_extremum").unwrap();
+            let old_extremum = ScalarObject { dtype: self.dtype, value: old_extremum };
+
+            let new_extremum = ScalarObject::min_or_max_helper(ctx, kind, old_extremum, scalar);
+
+            // Check if new_extremum is more extreme than old_extremum.
+            let update_index = ScalarObject::compare(
+                generator,
+                ctx,
+                new_extremum,
+                old_extremum,
+                IntPredicate::NE,
+                FloatPredicate::ONE,
+                "",
+            );
+
+            gen_if_model(generator, ctx, update_index, |_generator, ctx| {
+                pextremum_index.store(ctx, i);
+                Ok(())
+            })
+            .unwrap();
+            Ok(())
+        })
+        .unwrap();
+
+        // Finally return the extremum and extremum index.
+        let extremum_index = pextremum_index.load(generator, ctx, "extremum_index");
+
+        let extremum = ctx.builder.build_load(pextremum, "extremum_value").unwrap();
+        let extremum = ScalarObject { dtype: self.dtype, value: extremum };
+
+        (extremum, extremum_index)
+    }
+
+    /// Invoke NAC3's builtin `np_min()` or `np_max()`.
+    pub fn min_or_max<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        kind: MinOrMax,
+    ) -> ScalarObject<'ctx> {
+        let on_empty_err_msg = format!(
+            "zero-size array to reduction operation {} which has no identity",
+            match kind {
+                MinOrMax::Min => "minimum",
+                MinOrMax::Max => "maximum",
+            }
+        );
+        self.min_or_max_helper(generator, ctx, kind, &on_empty_err_msg).0
+    }
+
+    /// Invoke NAC3's builtin `np_argmin()` or `np_argmax()`.
+    pub fn argmin_or_argmax<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        kind: MinOrMax,
+    ) -> Int<'ctx, SizeT> {
+        let on_empty_err_msg = format!(
+            "attempt to get {} of an empty sequence",
+            match kind {
+                MinOrMax::Min => "argmin",
+                MinOrMax::Max => "argmax",
+            }
+        );
+        self.min_or_max_helper(generator, ctx, kind, &on_empty_err_msg).1
+    }
+}

nac3core/src/codegen/structure/ndarray/indexing.rs

@@ -1,7 +1,4 @@
-use crate::codegen::{
-    irrt::call_nac3_ndarray_index, model::*, structure::ndarray::scalar::ScalarObject,
-    CodeGenContext, CodeGenerator,
-};
+use crate::codegen::{irrt::call_nac3_ndarray_index, model::*, CodeGenContext, CodeGenerator};
 
 use super::{scalar::ScalarOrNDArray, NDArrayObject};
 
@@ -130,7 +127,7 @@ impl<'ctx> RustNDIndex<'ctx> {
         dst_ndindex_ptr: Ptr<'ctx, StructModel<NDIndex>>,
     ) {
         let ndindex_type_model = IntModel(NDIndexType::default());
-        let i32_model = IntModel(Int32::default());
+        let i32_model = IntModel(Int32);
         let user_slice_model = StructModel(UserSlice);
 
         // Set `dst_ndindex_ptr->type`
@@ -178,6 +175,7 @@ impl<'ctx> RustNDIndex<'ctx> {
 
 impl<'ctx> NDArrayObject<'ctx> {
     /// Get the ndims [`Type`] after indexing with a given slice.
+    #[must_use]
     pub fn deduce_ndims_after_indexing_with(&self, indexes: &[RustNDIndex<'ctx>]) -> u64 {
         let mut ndims = self.ndims;
         for index in indexes {
@@ -235,11 +233,8 @@ impl<'ctx> NDArrayObject<'ctx> {
 
         let subndarray = self.index(generator, ctx, indexes, name);
         if subndarray.is_unsized() {
-            // NOTE: `np.size(self) == 0` is impossible.
-            let pfirst = subndarray.get_nth_pelement(generator, ctx, zero, "pfirst");
-            let first = ctx.builder.build_load(pfirst, "first").unwrap();
-
-            ScalarOrNDArray::Scalar(ScalarObject { dtype: self.dtype, value: first })
+            // NOTE: `np.size(self) == 0` here is never possible.
+            ScalarOrNDArray::Scalar(subndarray.get_nth(generator, ctx, zero))
         } else {
             ScalarOrNDArray::NDArray(subndarray)
         }
@@ -319,9 +314,9 @@ pub mod util {
                         })
                     };
 
-                    let start = help(&start)?;
-                    let stop = help(&stop)?;
-                    let step = help(&step)?;
+                    let start = help(start)?;
+                    let stop = help(stop)?;
+                    let step = help(step)?;
 
                     RustNDIndex::Slice(RustUserSlice { start, stop, step })
                 } else {

nac3core/src/codegen/structure/ndarray/mapping.rs

@@ -1,12 +1,11 @@
+use inkwell::values::BasicValueEnum;
 use itertools::Itertools;
 use util::gen_for_model_auto;
 
 use crate::{
     codegen::{
         model::*,
-        structure::ndarray::{
-            broadcast::broadcast_all_ndarrays, scalar::ScalarObject, NDArrayObject,
-        },
+        structure::ndarray::{scalar::ScalarObject, NDArrayObject},
         CodeGenContext, CodeGenerator,
     },
     typecheck::typedef::Type,
@@ -14,179 +13,126 @@ use crate::{
 
 use super::scalar::ScalarOrNDArray;
 
-pub fn starmap_scalars_array_like<'ctx, 'a, F, G>(
+impl<'ctx> NDArrayObject<'ctx> {
+    /// TODO: Document me. Has complex behavior.
+    pub fn broadcasting_starmap<'a, G, MappingFn>(
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, 'a>,
-    inputs: &Vec<ScalarOrNDArray<'ctx>>,
-    mapping: F,
-) -> Result<ScalarOrNDArray<'ctx>, String>
-where
-    F: FnOnce(
+        ndarrays: &[Self],
+        ret_dtype: Type,
+        name: &str,
+        mapping: MappingFn,
+    ) -> Result<Self, String>
+    where
+        G: CodeGenerator + ?Sized,
+        MappingFn: FnOnce(
             &mut G,
             &mut CodeGenContext<'ctx, 'a>,
             Int<'ctx, SizeT>,
-        &Vec<ScalarObject<'ctx>>,
+            &[ScalarObject<'ctx>],
         ) -> Result<ScalarObject<'ctx>, String>,
-    G: CodeGenerator + ?Sized,
-{
-    assert!(!inputs.is_empty());
-
+    {
         let sizet_model = IntModel(SizeT);
 
-    // Check if all inputs are ScalarObjects
-    let scalars: Option<Vec<_>> =
-        inputs.iter().map(|input| ScalarObject::try_from(input)).try_collect().ok();
+        // Broadcast inputs
+        let broadcast_result = NDArrayObject::broadcast_all(generator, ctx, ndarrays);
 
-    match scalars {
-        Some(scalars) => {
-            // When inputs are all scalars, return a ScalarObject back
-
-            let i = sizet_model.const_0(generator, ctx.ctx);
-
-            let scalar = mapping(generator, ctx, i, &scalars)?;
-            Ok(ScalarOrNDArray::Scalar(scalar))
-        }
-        None => {
-            // When not all inputs are scalars, promote all non-ndarray inputs
-            // to ndarrays, do broadcast_shapes on them, and map.
-
-            let ndarrays =
-                inputs.into_iter().map(|input| input.as_ndarray(generator, ctx)).collect_vec();
-
-            let broadcast_result = broadcast_all_ndarrays(generator, ctx, &ndarrays);
-
-            let start = sizet_model.const_0(generator, ctx.ctx);
-            let stop = broadcast_result.ndarrays[0].size(generator, ctx); // They all should have the same `np.size`.
-            let step = sizet_model.const_1(generator, ctx.ctx);
-
-            // Map element-wise and store results into `mapped_ndarray`.
-            let mapped_ndarray = gen_for_model_auto(
-                generator,
-                ctx,
-                start,
-                stop,
-                step,
-                move |generator, ctx, _hooks, i| {
-                    let elements = ndarrays
-                        .iter()
-                        .map(|ndarray| {
-                            let pelement = ndarray.get_nth_pelement(generator, ctx, i, "pelement");
-                            let element = ctx.builder.build_load(pelement, "element").unwrap();
-                            ScalarObject { value: element, dtype: ndarray.dtype }
-                        })
-                        .collect_vec();
-
-                    let ret = mapping(generator, ctx, i, &elements)?;
-
-                    // It might look weird but it is perfectly fine putting the allocation codegen
-                    // here within `for`.
-                    // The reason for doing this is to get the `dtype` out of `ret`, which is only
-                    // available after running `mapping`.
+        // Allocate the resulting ndarray
         let mapped_ndarray = NDArrayObject::alloca_uninitialized(
             generator,
             ctx,
-                        ret.dtype,
+            ret_dtype,
             broadcast_result.ndims,
-                        "mapped_ndarray",
+            name,
         );
         mapped_ndarray.copy_shape_from_array(generator, ctx, broadcast_result.shape);
         mapped_ndarray.create_data(generator, ctx);
 
-                    let pret = mapped_ndarray.get_nth_pelement(generator, ctx, i, "pret");
+        // Map element-wise and store results into `mapped_ndarray`.
+        let start = sizet_model.const_0(generator, ctx.ctx);
+        let stop = broadcast_result.ndarrays[0].size(generator, ctx); // They all should have the same `np.size`.
+        let step = sizet_model.const_1(generator, ctx.ctx);
+        gen_for_model_auto(generator, ctx, start, stop, step, move |generator, ctx, _hooks, i| {
+            let elements =
+                ndarrays.iter().map(|ndarray| ndarray.get_nth(generator, ctx, i)).collect_vec();
+
+            let ret = mapping(generator, ctx, i, &elements)?;
+
+            let pret = mapped_ndarray.get_nth_pointer(generator, ctx, i, "pret");
             ctx.builder.build_store(pret, ret.value).unwrap();
+            Ok(())
+        })?;
+
         Ok(mapped_ndarray)
-                },
-            )?;
-            Ok(ScalarOrNDArray::NDArray(mapped_ndarray))
     }
-    }
-}
 
-impl<'ctx> ScalarObject<'ctx> {
-    pub fn map<'a, F, G>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, 'a>,
-        mapping: F,
-    ) -> Result<Self, String>
-    where
-        F: FnOnce(
-            &mut G,
-            &mut CodeGenContext<'ctx, 'a>,
-            Int<'ctx, SizeT>,
-            ScalarObject<'ctx>,
-        ) -> Result<ScalarObject<'ctx>, String>,
-        G: CodeGenerator + ?Sized,
-    {
-        let ScalarOrNDArray::Scalar(ret) = starmap_scalars_array_like(
-            generator,
-            ctx,
-            &vec![ScalarOrNDArray::Scalar(*self)],
-            |generator, ctx, i, scalars| mapping(generator, ctx, i, scalars[0]),
-        )?
-        else {
-            unreachable!()
-        };
-        Ok(ret)
-    }
-}
-
-impl<'ctx> NDArrayObject<'ctx> {
-    pub fn map<'a, F, G>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, 'a>,
-        mapping: F,
-    ) -> Result<Self, String>
-    where
-        F: FnOnce(
-            &mut G,
-            &mut CodeGenContext<'ctx, 'a>,
-            Int<'ctx, SizeT>,
-            ScalarObject<'ctx>,
-        ) -> Result<ScalarObject<'ctx>, String>,
-        G: CodeGenerator + ?Sized,
-    {
-        let ScalarOrNDArray::NDArray(ret) = starmap_scalars_array_like(
-            generator,
-            ctx,
-            &vec![ScalarOrNDArray::NDArray(*self)],
-            |generator, ctx, i, scalars| mapping(generator, ctx, i, scalars[0]),
-        )?
-        else {
-            unreachable!()
-        };
-        Ok(ret)
-    }
-}
-
-impl<'ctx> ScalarOrNDArray<'ctx> {
-    pub fn map<'a, F, G>(
+    pub fn map<'a, G, Mapping>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, 'a>,
         ret_dtype: Type,
-        mapping: F,
+        name: &str,
+        mapping: Mapping,
     ) -> Result<Self, String>
     where
-        F: FnOnce(
+        G: CodeGenerator + ?Sized,
+        Mapping: FnOnce(
             &mut G,
             &mut CodeGenContext<'ctx, 'a>,
             Int<'ctx, SizeT>,
             ScalarObject<'ctx>,
-        ) -> Result<ScalarObject<'ctx>, String>,
-        G: CodeGenerator + ?Sized,
+        ) -> Result<BasicValueEnum<'ctx>, String>,
     {
-        match self {
-            ScalarOrNDArray::Scalar(scalar) => starmap_scalars_array_like(
+        NDArrayObject::broadcasting_starmap(
             generator,
             ctx,
-                &vec![ScalarOrNDArray::Scalar(*scalar)],
-                |generator, ctx, i, scalars| mapping(generator, ctx, i, scalars[0]),
-            ),
-            ScalarOrNDArray::NDArray(ndarray) => {
-                ndarray.map(generator, ctx, mapping).map(ScalarOrNDArray::NDArray)
+            &[*self],
+            ret_dtype,
+            name,
+            |generator, ctx, i, scalars| {
+                let value = mapping(generator, ctx, i, scalars[0])?;
+                Ok(ScalarObject { dtype: ret_dtype, value })
+            },
+        )
     }
+}
+
+impl<'ctx> ScalarOrNDArray<'ctx> {
+    /// TODO: Document me. Has complex behavior.
+    pub fn broadcasting_starmap<'a, G, MappingFn>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        inputs: &[Self],
+        ret_dtype: Type,
+        name: &str,
+        mapping: MappingFn,
+    ) -> Result<Self, String>
+    where
+        G: CodeGenerator + ?Sized,
+        MappingFn: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            Int<'ctx, SizeT>,
+            &[ScalarObject<'ctx>],
+        ) -> Result<ScalarObject<'ctx>, String>,
+    {
+        let sizet_model = IntModel(SizeT);
+
+        // Check if all inputs are ScalarObjects
+        let all_scalars: Option<Vec<_>> =
+            inputs.iter().map(ScalarObject::try_from).try_collect().ok();
+
+        if let Some(scalars) = all_scalars {
+            let i = sizet_model.const_0(generator, ctx.ctx); // Pass 0 as the index
+            let scalar = mapping(generator, ctx, i, &scalars)?;
+            Ok(ScalarOrNDArray::Scalar(scalar))
+        } else {
+            // Promote all input to ndarrays and map through them.
+            let inputs = inputs.iter().map(|input| input.as_ndarray(generator, ctx)).collect_vec();
+            let ndarray = NDArrayObject::broadcasting_starmap(
+                generator, ctx, &inputs, ret_dtype, name, mapping,
+            )?;
+            Ok(ScalarOrNDArray::NDArray(ndarray))
         }
     }
 }

nac3core/src/codegen/structure/ndarray/mod.rs

@@ -1,4 +1,5 @@
 pub mod broadcast;
+pub mod functions;
 pub mod indexing;
 pub mod mapping;
 pub mod scalar;
@@ -22,8 +23,9 @@ use inkwell::{
     context::Context,
     types::BasicType,
     values::{BasicValue, BasicValueEnum, PointerValue},
-    AddressSpace,
+    AddressSpace, IntPredicate,
 };
+use scalar::ScalarObject;
 use util::{call_memcpy_model, gen_for_model_auto};
 
 pub struct NpArrayFields<'ctx, F: FieldTraversal<'ctx>> {
@@ -52,6 +54,7 @@ impl<'ctx> StructKind<'ctx> for NpArray {
     }
 }
 
+/// A NAC3 Python ndarray object.
 #[derive(Debug, Clone, Copy)]
 pub struct NDArrayObject<'ctx> {
     pub dtype: Type,
@@ -116,7 +119,7 @@ impl<'ctx> NDArrayObject<'ctx> {
     /// Get the pointer to the n-th (0-based) element.
     ///
     /// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
-    pub fn get_nth_pelement<G: CodeGenerator + ?Sized>(
+    pub fn get_nth_pointer<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
@@ -131,6 +134,18 @@ impl<'ctx> NDArrayObject<'ctx> {
             .unwrap()
     }
 
+    /// Get the n-th (0-based) scalar.
+    pub fn get_nth<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        nth: Int<'ctx, SizeT>,
+    ) -> ScalarObject<'ctx> {
+        let p = self.get_nth_pointer(generator, ctx, nth, "value");
+        let value = ctx.builder.build_load(p, "value").unwrap();
+        ScalarObject { dtype: self.dtype, value }
+    }
+
     /// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
     ///
     /// Please refer to the IRRT implementation to see its purpose.
@@ -210,7 +225,7 @@ impl<'ctx> NDArrayObject<'ctx> {
         name: &str,
     ) -> Self {
         let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
-        let ndims = extract_ndims(&mut ctx.unifier, ndims);
+        let ndims = extract_ndims(&ctx.unifier, ndims);
         Self::alloca_uninitialized(generator, ctx, dtype, ndims, name)
     }
 
@@ -224,7 +239,22 @@ impl<'ctx> NDArrayObject<'ctx> {
         sizet_model.constant(generator, ctx, self.ndims)
     }
 
-    /// Return true if this ndarray is unsized.
+    /// Get if this ndarray's `np.size` is `0` - containing no content.
+    pub fn is_empty<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Int<'ctx, Bool> {
+        let sizet_model = IntModel(SizeT);
+
+        let size = self.size(generator, ctx);
+        size.compare(ctx, IntPredicate::EQ, sizet_model.const_0(generator, ctx.ctx), "is_empty")
+    }
+
+    /// Return true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
+    ///
+    /// This is a staticially known property of ndarrays. This is why it is returning
+    /// a Rust boolean instead of a [`BasicValue`].
     #[must_use]
     pub fn is_unsized(&self) -> bool {
         self.ndims == 0
@@ -273,7 +303,7 @@ impl<'ctx> NDArrayObject<'ctx> {
     ) {
         assert_eq!(self.ndims, src_ndarray.ndims);
         let src_shape = src_ndarray.value.get(generator, ctx, |f| f.shape, "src_shape");
-        self.copy_shape_from_array(generator, ctx, src_shape)
+        self.copy_shape_from_array(generator, ctx, src_shape);
     }
 
     /// Copy strides dimensions from an array.
@@ -298,14 +328,14 @@ impl<'ctx> NDArrayObject<'ctx> {
     ) {
         assert_eq!(self.ndims, src_ndarray.ndims);
         let src_strides = src_ndarray.value.get(generator, ctx, |f| f.strides, "src_strides");
-        self.copy_strides_from_array(generator, ctx, src_strides)
+        self.copy_strides_from_array(generator, ctx, src_strides);
     }
 
-    /// Loop through every element pointer in the ndarray in its flatten view.
+    /// Iterate through every element pointer in the ndarray in its flatten view.
     ///
     /// `body` also access to [`BreakContinueHooks`] to short-circuit and an element's
     /// index. The given element pointer also has been casted to the LLVM type of this ndarray's `dtype`.
-    pub fn foreach<'a, G, F>(
+    pub fn foreach_pointer<'a, G, F>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, 'a>,
@@ -328,12 +358,36 @@ impl<'ctx> NDArrayObject<'ctx> {
         let step = sizet_model.const_1(generator, ctx.ctx);
 
         gen_for_model_auto(generator, ctx, start, stop, step, |generator, ctx, hooks, i| {
-            let pelement = self.get_nth_pelement(generator, ctx, i, "element");
+            let pelement = self.get_nth_pointer(generator, ctx, i, "element");
             body(generator, ctx, hooks, i, pelement)
         })
     }
 
-    /// Fill the NDArray with a value.
+    /// Iterate through every scalar in this ndarray.
+    pub fn foreach<'a, G, F>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        body: F,
+    ) -> Result<(), String>
+    where
+        G: CodeGenerator + ?Sized,
+        F: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            BreakContinueHooks<'ctx>,
+            Int<'ctx, SizeT>,
+            ScalarObject<'ctx>,
+        ) -> Result<(), String>,
+    {
+        self.foreach_pointer(generator, ctx, |generator, ctx, hooks, i, p| {
+            let value = ctx.builder.build_load(p, "value").unwrap();
+            let scalar = ScalarObject { dtype: self.dtype, value };
+            body(generator, ctx, hooks, i, scalar)
+        })
+    }
+
+    /// Fill the ndarray with a value.
     ///
     /// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
     pub fn fill<G: CodeGenerator + ?Sized>(
@@ -342,11 +396,11 @@ impl<'ctx> NDArrayObject<'ctx> {
         ctx: &mut CodeGenContext<'ctx, '_>,
         fill_value: BasicValueEnum<'ctx>,
     ) {
-        self.foreach(generator, ctx, |_generator, ctx, _hooks, _i, pelement| {
+        self.foreach_pointer(generator, ctx, |_generator, ctx, _hooks, _i, pelement| {
             ctx.builder.build_store(pelement, fill_value).unwrap();
             Ok(())
         })
-        .unwrap()
+        .unwrap();
     }
 
     /// Create a reshaped view on this ndarray like `np.reshape()`.

nac3core/src/codegen/structure/ndarray/scalar.rs

@@ -8,6 +8,10 @@ use crate::{
 use super::NDArrayObject;
 
 /// An LLVM numpy scalar with its [`Type`].
+///
+/// Intended to be used with [`ScalarOrNDArray`].
+///
+/// A scalar does not have to be an actual number. It could be arbitrary objects.
 #[derive(Debug, Clone, Copy)]
 pub struct ScalarObject<'ctx> {
     pub dtype: Type,
@@ -55,6 +59,22 @@ impl<'ctx> ScalarOrNDArray<'ctx> {
         }
     }
 
+    #[must_use]
+    pub fn into_scalar(&self) -> ScalarObject<'ctx> {
+        match self {
+            ScalarOrNDArray::NDArray(_ndarray) => panic!("Got NDArray"),
+            ScalarOrNDArray::Scalar(scalar) => *scalar,
+        }
+    }
+
+    #[must_use]
+    pub fn into_ndarray(&self) -> NDArrayObject<'ctx> {
+        match self {
+            ScalarOrNDArray::NDArray(ndarray) => *ndarray,
+            ScalarOrNDArray::Scalar(_scalar) => panic!("Got Scalar"),
+        }
+    }
+
     /// Convert this [`ScalarOrNDArray`] to an ndarray - behaves like `np.asarray`.
     /// - If this is an ndarray, the ndarray is returned.
     /// - If this is a scalar, an unsized ndarray view is created on it.
@@ -68,6 +88,14 @@ impl<'ctx> ScalarOrNDArray<'ctx> {
             ScalarOrNDArray::Scalar(scalar) => scalar.as_ndarray(generator, ctx),
         }
     }
+
+    #[must_use]
+    pub fn dtype(&self) -> Type {
+        match self {
+            ScalarOrNDArray::Scalar(scalar) => scalar.dtype,
+            ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
+        }
+    }
 }
 
 impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for ScalarObject<'ctx> {
@@ -76,7 +104,18 @@ impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for ScalarObject<'ctx> {
     fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
         match value {
             ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
-            ScalarOrNDArray::NDArray(_) => Err(()),
+            ScalarOrNDArray::NDArray(_ndarray) => Err(()),
+        }
+    }
+}
+
+impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayObject<'ctx> {
+    type Error = ();
+
+    fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
+        match value {
+            ScalarOrNDArray::Scalar(_scalar) => Err(()),
+            ScalarOrNDArray::NDArray(ndarray) => Ok(*ndarray),
         }
     }
 }

nac3core/src/codegen/structure/ndarray/shape_util.rs

@@ -2,15 +2,11 @@ use inkwell::values::BasicValueEnum;
 use util::gen_for_model_auto;
 
 use crate::{
-    codegen::{
-        model::*,
-        structure::list::{List, ListObject},
-        CodeGenContext, CodeGenerator,
-    },
+    codegen::{model::*, structure::list::ListObject, CodeGenContext, CodeGenerator},
     typecheck::typedef::{Type, TypeEnum},
 };
 
-/// Parse a NumPy-like "int sequence" input and return the int sequence as a [`ListObject`]
+/// Parse a NumPy-like "int sequence" input and return the int sequence as an array and its length.
 ///
 /// * `sequence` - The `sequence` parameter.
 /// * `sequence_ty` - The typechecker type of `sequence`
@@ -20,99 +16,97 @@ use crate::{
 ///   2. A tuple of `int32`;  e.g., `np.empty((600, 800, 3))`
 ///   3. A scalar `int32`;    e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
 ///
-/// `int32` values will be sign-extended to `SizeT`
+/// All `int32` values will be sign-extended to `SizeT`.
 pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
-    sequence: BasicValueEnum<'ctx>,
-    sequence_ty: Type,
-) -> ListObject<'ctx, IntModel<SizeT>, SizeT> {
+    input_sequence: BasicValueEnum<'ctx>,
+    input_sequence_ty: Type,
+) -> (Int<'ctx, SizeT>, Ptr<'ctx, IntModel<SizeT>>) {
     let sizet_model = IntModel(SizeT);
-    let list_model = StructModel(List { len: SizeT, item: IntModel(SizeT) });
 
     let zero = sizet_model.const_0(generator, ctx.ctx);
     let one = sizet_model.const_1(generator, ctx.ctx);
 
     // The result `list` to return.
-    let result = list_model.alloca(generator, ctx, "result_sequence");
-    match &*ctx.unifier.get_ty(sequence_ty) {
+    match &*ctx.unifier.get_ty(input_sequence_ty) {
         TypeEnum::TObj { obj_id, .. }
             if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
         {
             // 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
-            let in_sequence_model =
-                PtrModel(StructModel(List { item: IntModel(Int32), len: SizeT }));
-            let in_sequence = in_sequence_model.check_value(generator, ctx.ctx, sequence).unwrap();
 
-            /*
-                Reference code:
-                ```
-                result.size = sequence.size;
-                result.data = __builtin_alloca(sizeof(SizeT) * sequence.size);
-                for (SizeT i = 0; i < sequence.size; i++) {
-                    result.data[i] = (SizeT) sequence.data[i];
-                }
-                return result
-                ```
-            */
+            // Check `input_sequence`
+            let input_sequence =
+                ListObject::from_value_and_type(generator, ctx, input_sequence, input_sequence_ty);
 
-            let ndims = in_sequence.get(generator, ctx, |f| f.len, "size");
-            result.set(ctx, |f| f.len, ndims);
+            let len = input_sequence.value.gep(ctx, |f| f.len).load(generator, ctx, "len");
+            let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
 
-            let result_data = sizet_model.array_alloca(generator, ctx, ndims.value, "data");
-            result.set(ctx, |f| f.items, result_data);
+            // Load all the `int32`s from the input_sequence, cast them to `SizeT`, and store them into `result`
+            gen_for_model_auto(generator, ctx, zero, len, one, |generator, ctx, _hooks, i| {
+                // Load the i-th int32 in the input sequence
+                let int = input_sequence
+                    .value
+                    .get(generator, ctx, |f| f.items, "int")
+                    .ix(generator, ctx, i.value, "int")
+                    .value
+                    .into_int_value();
+
+                // Cast to SizeT
+                let int = sizet_model.s_extend_or_bit_cast(generator, ctx, int, "int");
+
+                // Store
+                result.offset(generator, ctx, i.value, "int").store(ctx, int);
 
-            gen_for_model_auto(generator, ctx, zero, ndims, one, |generator, ctx, _hooks, i| {
-                let in_dim = in_sequence
-                    .get(generator, ctx, |f| f.items, "in_dim")
-                    .ix(generator, ctx, i.value, "in_dim")
-                    .s_extend_or_bit_cast(generator, ctx, SizeT, "in_dim");
-                result_data.offset(generator, ctx, i.value, "dim").store(ctx, in_dim);
                 Ok(())
             })
             .unwrap();
+
+            (len, result)
         }
         TypeEnum::TTuple { ty: tuple_types } => {
             // 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
-            let ndims_int = tuple_types.len();
-            let ndims = sizet_model.constant(generator, ctx.ctx, ndims_int as u64);
-            result.set(ctx, |f| f.len, ndims);
+            let input_sequence = input_sequence.into_struct_value(); // A tuple is a struct
 
-            // A tuple has to be a StructValue
-            // Read [`codegen::expr::gen_expr`] to see how `nac3core` translates a Python tuple into LLVM.
-            let tuple = sequence.into_struct_value();
-            let data = sizet_model.array_alloca(generator, ctx, ndims.value, "sequence_data");
-            result.set(ctx, |f| f.items, data);
+            let len_int = tuple_types.len();
 
-            for i in 0..ndims_int {
-                // Get the i-th (0-based) element off of the tuple and load it
-                // into `result`.
-                let dim = ctx
+            let len = sizet_model.constant(generator, ctx.ctx, len_int as u64);
+            let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
+
+            for i in 0..len_int {
+                // Get the i-th element off of the tuple and load it into `result`.
+                let int = ctx
                     .builder
-                    .build_extract_value(tuple, i as u32, format!("dim").as_str())
+                    .build_extract_value(input_sequence, i as u32, "int")
                     .unwrap()
                     .into_int_value();
-                let dim = sizet_model.s_extend_or_bit_cast(generator, ctx, dim, "dim");
+                let int = sizet_model.s_extend_or_bit_cast(generator, ctx, int, "int");
 
                 let offset = sizet_model.constant(generator, ctx.ctx, i as u64);
-                data.offset(generator, ctx, offset.value, "dim").store(ctx, dim);
+                result.offset(generator, ctx, offset.value, "int").store(ctx, int);
             }
+
+            (len, result)
         }
         TypeEnum::TObj { obj_id, .. }
             if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
         {
             // 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
-            let sequence_int = sizet_model.check_value(generator, ctx.ctx, sequence).unwrap();
+            let input_int = input_sequence.into_int_value();
 
-            // Size is 1
-            result.set(ctx, |f| f.len, one);
+            let len = sizet_model.const_1(generator, ctx.ctx);
+            let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
 
-            // Alloca an array of length 1 and store the sole integer input into the array.
-            let data = sizet_model.array_alloca(generator, ctx, one.value, "data");
-            data.offset(generator, ctx, zero.value, "dim").store(ctx, sequence_int);
+            let int = sizet_model.s_extend_or_bit_cast(generator, ctx, input_int, "int");
+
+            // Storing into result[0]
+            result.store(ctx, int);
+
+            (len, result)
         }
-        _ => panic!("encountered unknown sequence type: {}", ctx.unifier.stringify(sequence_ty)),
+        _ => panic!(
+            "encountered unknown sequence type: {}",
+            ctx.unifier.stringify(input_sequence_ty)
+        ),
     }
-
-    ListObject { item_type: ctx.primitives.usize(), value: result }
 }

nac3core/src/toplevel/builtins.rs

@@ -1180,7 +1180,7 @@ impl<'a> BuiltinBuilder<'a> {
                 let ret_elem_ty = size_variant.of_int(&ctx.primitives);
                 let func = match kind {
                     Kind::Ceil => builtin_fns::call_ceil,
-                    Kind::Floor => builtin_fns::call_ceil_or_floor,
+                    Kind::Floor => builtin_fns::call_floor,
                 };
                 Ok(Some(func(generator, ctx, (arg_ty, arg), ret_elem_ty)?))
             }),
@@ -1361,7 +1361,7 @@ impl<'a> BuiltinBuilder<'a> {
                 let ndims1 = create_ndims(self.unifier, 1);
                 let ndarray_float_1d = make_ndarray_ty(
                     self.unifier,
-                    &self.primitives,
+                    self.primitives,
                     Some(self.primitives.float),
                     Some(ndims1),
                 );
@@ -1470,7 +1470,7 @@ impl<'a> BuiltinBuilder<'a> {
             PrimDef::FunNpSize => {
                 // TODO: Make the return type usize
                 create_fn_by_codegen(
-                    &mut self.unifier,
+                    self.unifier,
                     &VarMap::new(),
                     prim.name(),
                     self.primitives.int32,
@@ -1485,7 +1485,7 @@ impl<'a> BuiltinBuilder<'a> {
                 // of the input ndarray.
                 let ret_ty = self.unifier.get_dummy_var().ty;
                 create_fn_by_codegen(
-                    &mut self.unifier,
+                    self.unifier,
                     &VarMap::new(),
                     prim.name(),
                     ret_ty,
@@ -1548,7 +1548,7 @@ impl<'a> BuiltinBuilder<'a> {
 
                 let func = match prim {
                     PrimDef::FunNpCeil => builtin_fns::call_ceil,
-                    PrimDef::FunNpFloor => builtin_fns::call_ceil_or_floor,
+                    PrimDef::FunNpFloor => builtin_fns::call_floor,
                     _ => unreachable!(),
                 };
                 Ok(Some(func(generator, ctx, (arg_ty, arg), ctx.primitives.float)?))