core: Add ArrayLikeValue

For exposing LLVM values that can be accessed like an array.

nac3core/build.rs

@@ -21,7 +21,7 @@ fn main() {
         match env::var("PROFILE").as_deref() {
             Ok("debug") => "-O0",
             Ok("release") => "-O3",
-            flavor => panic!("Unknown or missing build flavor {:?}", flavor),
+            flavor => panic!("Unknown or missing build flavor {flavor:?}"),
         },
         "-emit-llvm",
         "-S",

nac3core/src/codegen/expr.rs

@@ -2,7 +2,14 @@ use std::{collections::HashMap, convert::TryInto, iter::once, iter::zip};
 
 use crate::{
     codegen::{
-        classes::{ListValue, NDArrayValue, RangeValue},
+        classes::{
+            ArrayLikeIndexer,
+            ArrayLikeValue,
+            ListValue,
+            NDArrayValue,
+            RangeValue,
+            UntypedArrayLikeAccessor,
+        },
         concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
         gen_in_range_check,
         get_llvm_type,
@@ -103,9 +110,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
         index
     }
 
-    pub fn gen_symbol_val(
+    pub fn gen_symbol_val<G: CodeGenerator + ?Sized>(
         &mut self,
-        generator: &mut dyn CodeGenerator,
+        generator: &mut G,
         val: &SymbolValue,
         ty: Type,
     ) -> BasicValueEnum<'ctx> {
@@ -174,9 +181,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
     }
 
     /// See [`get_llvm_type`].
-    pub fn get_llvm_type(
+    pub fn get_llvm_type<G: CodeGenerator + ?Sized>(
         &mut self,
-        generator: &mut dyn CodeGenerator,
+        generator: &mut G,
         ty: Type,
     ) -> BasicTypeEnum<'ctx> {
         get_llvm_type(
@@ -191,9 +198,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
     }
 
     /// See [`get_llvm_abi_type`].
-    pub fn get_llvm_abi_type(
+    pub fn get_llvm_abi_type<G: CodeGenerator + ?Sized>(
         &mut self,
-        generator: &mut dyn CodeGenerator,
+        generator: &mut G,
         ty: Type,
     ) -> BasicTypeEnum<'ctx> {
         get_llvm_abi_type(
@@ -209,9 +216,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
     }
 
     /// Generates an LLVM variable for a [constant value][value] with a given [type][ty].
-    pub fn gen_const(
+    pub fn gen_const<G: CodeGenerator + ?Sized>(
         &mut self,
-        generator: &mut dyn CodeGenerator,
+        generator: &mut G,
         value: &Constant,
         ty: Type,
     ) -> Option<BasicValueEnum<'ctx>> {
@@ -291,9 +298,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
     }
 
     /// Generates a binary operation `op` between two integral operands `lhs` and `rhs`.
-    pub fn gen_int_ops(
+    pub fn gen_int_ops<G: CodeGenerator + ?Sized>(
         &mut self,
-        generator: &mut dyn CodeGenerator,
+        generator: &mut G,
         op: &Operator,
         lhs: BasicValueEnum<'ctx>,
         rhs: BasicValueEnum<'ctx>,
@@ -492,17 +499,21 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
     }
 
     /// Helper function for generating a LLVM variable storing a [String].
-    pub fn gen_string<S: Into<String>>(
+    pub fn gen_string<G, S>(
         &mut self,
-        generator: &mut dyn CodeGenerator,
+        generator: &mut G,
         s: S,
-    ) -> BasicValueEnum<'ctx> {
+    ) -> BasicValueEnum<'ctx>
+        where
+            G: CodeGenerator + ?Sized,
+            S: Into<String>,
+    {
         self.gen_const(generator, &Constant::Str(s.into()), self.primitives.str).unwrap()
     }
 
-    pub fn raise_exn(
+    pub fn raise_exn<G: CodeGenerator + ?Sized>(
         &mut self,
-        generator: &mut dyn CodeGenerator,
+        generator: &mut G,
         name: &str,
         msg: BasicValueEnum<'ctx>,
         params: [Option<IntValue<'ctx>>; 3],
@@ -546,9 +557,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
         gen_raise(generator, self, Some(&zelf.into()), loc);
     }
 
-    pub fn make_assert(
+    pub fn make_assert<G: CodeGenerator + ?Sized>(
         &mut self,
-        generator: &mut dyn CodeGenerator,
+        generator: &mut G,
         cond: IntValue<'ctx>,
         err_name: &str,
         err_msg: &str,
@@ -559,9 +570,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
         self.make_assert_impl(generator, cond, err_name, err_msg, params, loc);
     }
 
-    pub fn make_assert_impl(
+    pub fn make_assert_impl<G: CodeGenerator + ?Sized>(
         &mut self,
-        generator: &mut dyn CodeGenerator,
+        generator: &mut G,
         cond: IntValue<'ctx>,
         err_name: &str,
         err_msg: BasicValueEnum<'ctx>,
@@ -878,7 +889,7 @@ pub fn destructure_range<'ctx>(
 /// Returns an instance of [`PointerValue`] pointing to the List structure. The List structure is
 /// defined as `type { ty*, size_t }` in LLVM, where the first element stores the pointer to the
 /// data, and the second element stores the size of the List.
-pub fn allocate_list<'ctx, G: CodeGenerator>(
+pub fn allocate_list<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     ty: BasicTypeEnum<'ctx>,
@@ -978,7 +989,7 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
             list_alloc_size.into_int_value(),
             Some("listcomp.addr")
         );
-        list_content = list.data().as_ptr_value(ctx);
+        list_content = list.data().base_ptr(ctx, generator);
 
         let i = generator.gen_store_target(ctx, target, Some("i.addr"))?.unwrap();
         ctx.builder
@@ -1011,7 +1022,7 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
             )
             .into_int_value();
         list = allocate_list(generator, ctx, elem_ty, length, Some("listcomp"));
-        list_content = list.data().as_ptr_value(ctx);
+        list_content = list.data().base_ptr(ctx, generator);
         let counter = generator.gen_var_alloc(ctx, size_t.into(), Some("counter.addr"))?;
         // counter = -1
         ctx.builder.build_store(counter, size_t.const_int(u64::MAX, true)).unwrap();
@@ -1256,7 +1267,7 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
         };
 
         Ok(Some(v.data()
-            .get_const(
+            .get(
                 ctx,
                 generator,
                 ctx.ctx.i32_type().const_array(&[index]),
@@ -1300,15 +1311,17 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
         ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
 
         let ndarray_num_dims = ndarray.load_ndims(ctx);
-        let v_dims_src_ptr = v.dim_sizes().ptr_offset(
+        let v_dims_src_ptr = unsafe {
-            ctx,
+            v.dim_sizes().ptr_offset_unchecked(
-            generator,
+                ctx,
-            llvm_usize.const_int(1, false),
+                generator,
-            None,
+                llvm_usize.const_int(1, false),
-        );
+                None,
+            )
+        };
         call_memcpy_generic(
             ctx,
-            ndarray.dim_sizes().as_ptr_value(ctx),
+            ndarray.dim_sizes().base_ptr(ctx, generator),
             v_dims_src_ptr,
             ctx.builder
                 .build_int_mul(ndarray_num_dims, llvm_usize.size_of(), "")
@@ -1320,12 +1333,11 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
         let ndarray_num_elems = call_ndarray_calc_size(
             generator,
             ctx,
-            ndarray.load_ndims(ctx),
+            &ndarray.dim_sizes().as_slice_value(ctx, generator),
-            ndarray.dim_sizes().as_ptr_value(ctx),
         );
         ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
 
-        let v_data_src_ptr = v.data().ptr_offset_const(
+        let v_data_src_ptr = v.data().ptr_offset(
             ctx,
             generator,
             ctx.ctx.i32_type().const_array(&[index]),
@@ -1333,7 +1345,7 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
         );
         call_memcpy_generic(
             ctx,
-            ndarray.data().as_ptr_value(ctx),
+            ndarray.data().base_ptr(ctx, generator),
             v_data_src_ptr,
             ctx.builder
                 .build_int_mul(ndarray_num_elems, llvm_ndarray_data_t.size_of().unwrap(), "")

nac3core/src/codegen/generator.rs

@@ -1,5 +1,5 @@
 use crate::{
-    codegen::{expr::*, stmt::*, bool_to_i1, bool_to_i8, CodeGenContext},
+    codegen::{classes::ArraySliceValue, expr::*, stmt::*, bool_to_i1, bool_to_i8, CodeGenContext},
     symbol_resolver::ValueEnum,
     toplevel::{DefinitionId, TopLevelDef},
     typecheck::typedef::{FunSignature, Type},
@@ -99,8 +99,8 @@ pub trait CodeGenerator {
         ctx: &mut CodeGenContext<'ctx, '_>,
         ty: BasicTypeEnum<'ctx>,
         size: IntValue<'ctx>,
-        name: Option<&str>,
+        name: Option<&'ctx str>,
-    ) -> Result<PointerValue<'ctx>, String> {
+    ) -> Result<ArraySliceValue<'ctx>, String> {
         gen_array_var(ctx, ty, size, name)
     }
 

nac3core/src/codegen/irrt/mod.rs

@@ -1,7 +1,7 @@
 use crate::typecheck::typedef::Type;
 
 use super::{
-    classes::{ListValue, NDArrayValue},
+    classes::{ArrayLikeIndexer, ArrayLikeValue, ListValue, NDArrayValue, UntypedArrayLikeMutator},
     CodeGenContext,
     CodeGenerator,
 };
@@ -39,8 +39,8 @@ pub fn load_irrt(ctx: &Context) -> Module {
 
 // repeated squaring method adapted from GNU Scientific Library:
 // https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
-pub fn integer_power<'ctx>(
+pub fn integer_power<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut dyn CodeGenerator,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     base: IntValue<'ctx>,
     exp: IntValue<'ctx>,
@@ -81,8 +81,8 @@ pub fn integer_power<'ctx>(
         .unwrap()
 }
 
-pub fn calculate_len_for_slice_range<'ctx>(
+pub fn calculate_len_for_slice_range<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut dyn CodeGenerator,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     start: IntValue<'ctx>,
     end: IntValue<'ctx>,
@@ -303,8 +303,8 @@ pub fn handle_slice_index_bound<'ctx, G: CodeGenerator>(
 /// This function handles 'end' **inclusively**.
 /// Order of tuples `assign_idx` and `value_idx` is ('start', 'end', 'step').
 /// Negative index should be handled before entering this function
-pub fn list_slice_assignment<'ctx>(
+pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut dyn CodeGenerator,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     ty: BasicTypeEnum<'ctx>,
     dest_arr: ListValue<'ctx>,
@@ -338,7 +338,7 @@ pub fn list_slice_assignment<'ctx>(
 
     let zero = int32.const_zero();
     let one = int32.const_int(1, false);
-    let dest_arr_ptr = dest_arr.data().as_ptr_value(ctx);
+    let dest_arr_ptr = dest_arr.data().base_ptr(ctx, generator);
     let dest_arr_ptr = ctx.builder.build_pointer_cast(
         dest_arr_ptr,
         elem_ptr_type,
@@ -346,7 +346,7 @@ pub fn list_slice_assignment<'ctx>(
     ).unwrap();
     let dest_len = dest_arr.load_size(ctx, Some("dest.len"));
     let dest_len = ctx.builder.build_int_truncate_or_bit_cast(dest_len, int32, "srclen32").unwrap();
-    let src_arr_ptr = src_arr.data().as_ptr_value(ctx);
+    let src_arr_ptr = src_arr.data().base_ptr(ctx, generator);
     let src_arr_ptr = ctx.builder.build_pointer_cast(
         src_arr_ptr,
         elem_ptr_type,
@@ -468,8 +468,8 @@ pub fn list_slice_assignment<'ctx>(
 }
 
 /// Generates a call to `isinf` in IR. Returns an `i1` representing the result.
-pub fn call_isinf<'ctx>(
+pub fn call_isinf<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut dyn CodeGenerator,
+    generator: &mut G,
     ctx: &CodeGenContext<'ctx, '_>,
     v: FloatValue<'ctx>,
 ) -> IntValue<'ctx> {
@@ -489,8 +489,8 @@ pub fn call_isinf<'ctx>(
 }
 
 /// Generates a call to `isnan` in IR. Returns an `i1` representing the result.
-pub fn call_isnan<'ctx>(
+pub fn call_isnan<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut dyn CodeGenerator,
+    generator: &mut G,
     ctx: &CodeGenContext<'ctx, '_>,
     v: FloatValue<'ctx>,
 ) -> IntValue<'ctx> {
@@ -574,12 +574,14 @@ pub fn call_j0<'ctx>(
 ///
 /// * `num_dims` - An [`IntValue`] containing the number of dimensions.
 /// * `dims` - A [`PointerValue`] to an array containing the size of each dimension.
-pub fn call_ndarray_calc_size<'ctx>(
+pub fn call_ndarray_calc_size<'ctx, G, Dims>(
-    generator: &dyn CodeGenerator,
+    generator: &G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
+    ctx: &CodeGenContext<'ctx, '_>,
-    num_dims: IntValue<'ctx>,
+    dims: &Dims,
-    dims: PointerValue<'ctx>,
+) -> IntValue<'ctx>
-) -> IntValue<'ctx> {
+    where
+        G: CodeGenerator + ?Sized,
+        Dims: ArrayLikeIndexer<'ctx>, {
     let llvm_i64 = ctx.ctx.i64_type();
     let llvm_usize = generator.get_size_type(ctx.ctx);
 
@@ -606,8 +608,8 @@ pub fn call_ndarray_calc_size<'ctx>(
         .build_call(
             ndarray_calc_size_fn,
             &[
-                dims.into(),
+                dims.base_ptr(ctx, generator).into(),
-                num_dims.into(),
+                dims.size(ctx, generator).into(),
             ],
             "",
         )
@@ -622,8 +624,8 @@ pub fn call_ndarray_calc_size<'ctx>(
 /// * `index` - The index to compute the multidimensional index for.
 /// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
 /// `NDArray`.
-pub fn call_ndarray_calc_nd_indices<'ctx>(
+pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &dyn CodeGenerator,
+    generator: &G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     index: IntValue<'ctx>,
     ndarray: NDArrayValue<'ctx>,
@@ -666,7 +668,7 @@ pub fn call_ndarray_calc_nd_indices<'ctx>(
             ndarray_calc_nd_indices_fn,
             &[
                 index.into(),
-                ndarray_dims.as_ptr_value(ctx).into(),
+                ndarray_dims.base_ptr(ctx, generator).into(),
                 ndarray_num_dims.into(),
                 indices.into(),
             ],
@@ -677,13 +679,15 @@ pub fn call_ndarray_calc_nd_indices<'ctx>(
     indices
 }
 
-fn call_ndarray_flatten_index_impl<'ctx>(
+fn call_ndarray_flatten_index_impl<'ctx, G, Indices>(
-    generator: &dyn CodeGenerator,
+    generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,
     ndarray: NDArrayValue<'ctx>,
-    indices: PointerValue<'ctx>,
+    indices: &Indices,
-    indices_size: IntValue<'ctx>,
+) -> IntValue<'ctx>
-) -> IntValue<'ctx> {
+    where
+        G: CodeGenerator + ?Sized,
+        Indices: ArrayLikeIndexer<'ctx>, {
     let llvm_i32 = ctx.ctx.i32_type();
     let llvm_usize = generator.get_size_type(ctx.ctx);
 
@@ -691,14 +695,14 @@ fn call_ndarray_flatten_index_impl<'ctx>(
     let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
 
     debug_assert_eq!(
-        IntType::try_from(indices.get_type().get_element_type())
+        IntType::try_from(indices.element_type(ctx, generator))
             .map(IntType::get_bit_width)
             .unwrap_or_default(),
         llvm_i32.get_bit_width(),
         "Expected i32 value for argument `indices` to `call_ndarray_flatten_index_impl`"
     );
     debug_assert_eq!(
-        indices_size.get_type().get_bit_width(),
+        indices.size(ctx, generator).get_type().get_bit_width(),
         llvm_usize.get_bit_width(),
         "Expected usize integer value for argument `indices_size` to `call_ndarray_flatten_index_impl`"
     );
@@ -729,10 +733,10 @@ fn call_ndarray_flatten_index_impl<'ctx>(
         .build_call(
             ndarray_flatten_index_fn,
             &[
-                ndarray_dims.as_ptr_value(ctx).into(),
+                ndarray_dims.base_ptr(ctx, generator).into(),
                 ndarray_num_dims.into(),
-                indices.into(),
+                indices.base_ptr(ctx, generator).into(),
-                indices_size.into(),
+                indices.size(ctx, generator).into(),
             ],
             "",
         )
@@ -750,21 +754,21 @@ fn call_ndarray_flatten_index_impl<'ctx>(
 /// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
 /// `NDArray`.
 /// * `indices` - The multidimensional index to compute the flattened index for.
-pub fn call_ndarray_flatten_index<'ctx>(
+pub fn call_ndarray_flatten_index<'ctx, G, Index>(
-    generator: &dyn CodeGenerator,
+    generator: &mut G,
-    ctx: &CodeGenContext<'ctx, '_>,
+    ctx: &mut CodeGenContext<'ctx, '_>,
     ndarray: NDArrayValue<'ctx>,
-    indices: ListValue<'ctx>,
+    indices: &Index,
-) -> IntValue<'ctx> {
+) -> IntValue<'ctx>
-    let indices_size = indices.load_size(ctx, None);
+    where
-    let indices_data = indices.data();
+        G: CodeGenerator + ?Sized,
+        Index: ArrayLikeIndexer<'ctx>, {
 
     call_ndarray_flatten_index_impl(
         generator,
         ctx,
         ndarray,
-        indices_data.as_ptr_value(ctx),
+        indices,
-        indices_size,
     )
 }
 /// Generates a call to `__nac3_ndarray_flatten_index`. Returns the flattened index for the
@@ -773,8 +777,8 @@ pub fn call_ndarray_flatten_index<'ctx>(
 /// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
 /// `NDArray`.
 /// * `indices` - The multidimensional index to compute the flattened index for.
-pub fn call_ndarray_flatten_index_const<'ctx>(
+pub fn call_ndarray_flatten_index_const<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut dyn CodeGenerator,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     ndarray: NDArrayValue<'ctx>,
     indices: ArrayValue<'ctx>,
@@ -786,27 +790,27 @@ pub fn call_ndarray_flatten_index_const<'ctx>(
         ctx,
         indices.get_type().get_element_type(),
         llvm_usize.const_int(indices_size as u64, false),
-        None
+        None,
     ).unwrap();
     for i in 0..indices_size {
         let v = ctx.builder.build_extract_value(indices, i, "")
             .unwrap()
             .into_int_value();
-        let elem_ptr = unsafe {
+
-            ctx.builder.build_in_bounds_gep(
+        unsafe {
-                indices_alloca,
+            indices_alloca.set_unchecked(
-                &[ctx.ctx.i32_type().const_int(i as u64, false)],
+                ctx,
-                ""
+                generator,
-            )
+                ctx.ctx.i32_type().const_int(i as u64, false),
-        }.unwrap();
+                v.into(),
-        ctx.builder.build_store(elem_ptr, v).unwrap();
+            );
+        }
     }
 
     call_ndarray_flatten_index_impl(
         generator,
         ctx,
         ndarray,
-        indices_alloca,
+        &indices_alloca,
-        llvm_usize.const_int(indices_size as u64, false),
     )
 }

nac3core/src/codegen/mod.rs

@@ -45,6 +45,7 @@ pub mod expr;
 mod generator;
 pub mod irrt;
 pub mod llvm_intrinsics;
+pub mod numpy;
 pub mod stmt;
 
 #[cfg(test)]
@@ -415,10 +416,10 @@ pub struct CodeGenTask {
 /// This function is used to obtain the in-memory representation of `ty`, e.g. a `bool` variable
 /// would be represented by an `i8`.
 #[allow(clippy::too_many_arguments)]
-fn get_llvm_type<'ctx>(
+fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
     ctx: &'ctx Context,
     module: &Module<'ctx>,
-    generator: &mut dyn CodeGenerator,
+    generator: &mut G,
     unifier: &mut Unifier,
     top_level: &TopLevelContext,
     type_cache: &mut HashMap<Type, BasicTypeEnum<'ctx>>,
@@ -553,10 +554,10 @@ fn get_llvm_type<'ctx>(
 /// be byte-aligned for the variable to be addressable in memory, whereas there is no such
 /// restriction for ABI representations.
 #[allow(clippy::too_many_arguments)]
-fn get_llvm_abi_type<'ctx>(
+fn get_llvm_abi_type<'ctx, G: CodeGenerator + ?Sized>(
     ctx: &'ctx Context,
     module: &Module<'ctx>,
-    generator: &mut dyn CodeGenerator,
+    generator: &mut G,
     unifier: &mut Unifier,
     top_level: &TopLevelContext,
     type_cache: &mut HashMap<Type, BasicTypeEnum<'ctx>>,

nac3core/src/codegen/numpy.rs

@@ -0,0 +1,837 @@
+use inkwell::{
+    IntPredicate, 
+    types::BasicType, 
+    values::{AggregateValueEnum, ArrayValue, BasicValueEnum, IntValue, PointerValue}
+};
+use nac3parser::ast::StrRef;
+use crate::{
+    codegen::{
+        classes::{
+            ArrayLikeIndexer,
+            ArrayLikeValue,
+            ListValue,
+            NDArrayValue,
+            TypedArrayLikeAccessor,
+            UntypedArrayLikeAccessor,
+        },
+        CodeGenContext,
+        CodeGenerator,
+        irrt::{
+            call_ndarray_calc_nd_indices,
+            call_ndarray_calc_size,
+        },
+        llvm_intrinsics::call_memcpy_generic,
+        stmt::gen_for_callback_incrementing,
+    },
+    symbol_resolver::ValueEnum,
+    toplevel::{
+        DefinitionId,
+        numpy::{make_ndarray_ty, unpack_ndarray_tvars},
+    },
+    typecheck::typedef::{FunSignature, Type},
+};
+
+/// Creates an `NDArray` instance from a dynamic shape.
+///
+/// * `elem_ty` - The element type of the `NDArray`.
+/// * `shape` - The shape of the `NDArray`.
+/// * `shape_len_fn` - A function that retrieves the number of dimensions from `shape`.
+/// * `shape_data_fn` - A function that retrieves the size of a dimension from `shape`.
+fn create_ndarray_dyn_shape<'ctx, 'a, G, V, LenFn, DataFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, 'a>,
+    elem_ty: Type,
+    shape: &V,
+    shape_len_fn: LenFn,
+    shape_data_fn: DataFn,
+) -> Result<NDArrayValue<'ctx>, String>
+    where
+        G: CodeGenerator + ?Sized,
+        LenFn: Fn(&mut G, &mut CodeGenContext<'ctx, 'a>, &V) -> Result<IntValue<'ctx>, String>,
+        DataFn: Fn(&mut G, &mut CodeGenContext<'ctx, 'a>, &V, IntValue<'ctx>) -> Result<IntValue<'ctx>, String>,
+{
+    let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
+
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
+    let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
+    let llvm_ndarray_data_t = ctx.get_llvm_type(generator, elem_ty).as_basic_type_enum();
+    assert!(llvm_ndarray_data_t.is_sized());
+
+    // Assert that all dimensions are non-negative
+    let shape_len = shape_len_fn(generator, ctx, shape)?;
+    gen_for_callback_incrementing(
+        generator,
+        ctx,
+        llvm_usize.const_zero(),
+        (shape_len, false),
+        |generator, ctx, i| {
+            let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
+            debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
+
+            let shape_dim_gez = ctx.builder
+                .build_int_compare(IntPredicate::SGE, shape_dim, shape_dim.get_type().const_zero(), "")
+                .unwrap();
+
+            ctx.make_assert(
+                generator,
+                shape_dim_gez,
+                "0:ValueError",
+                "negative dimensions not supported",
+                [None, None, None],
+                ctx.current_loc,
+            );
+
+            Ok(())
+        },
+        llvm_usize.const_int(1, false),
+    )?;
+
+    let ndarray = generator.gen_var_alloc(
+        ctx,
+        llvm_ndarray_t.into(),
+        None,
+    )?;
+    let ndarray = NDArrayValue::from_ptr_val(ndarray, llvm_usize, None);
+
+    let num_dims = shape_len_fn(generator, ctx, shape)?;
+    ndarray.store_ndims(ctx, generator, num_dims);
+
+    let ndarray_num_dims = ndarray.load_ndims(ctx);
+    ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
+
+    // Copy the dimension sizes from shape to ndarray.dims
+    let shape_len = shape_len_fn(generator, ctx, shape)?;
+    gen_for_callback_incrementing(
+        generator,
+        ctx,
+        llvm_usize.const_zero(),
+        (shape_len, false),
+        |generator, ctx, i| {
+            let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
+            debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
+            let shape_dim = ctx.builder
+                .build_int_z_extend(shape_dim, llvm_usize, "")
+                .unwrap();
+
+            let ndarray_pdim = unsafe {
+                ndarray.dim_sizes().ptr_offset_unchecked(ctx, generator, i, None)
+            };
+
+            ctx.builder.build_store(ndarray_pdim, shape_dim).unwrap();
+
+            Ok(())
+        },
+        llvm_usize.const_int(1, false),
+    )?;
+
+    let ndarray_num_elems = call_ndarray_calc_size(
+        generator,
+        ctx,
+        &ndarray.dim_sizes().as_slice_value(ctx, generator),
+    );
+    ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
+
+    Ok(ndarray)
+}
+
+/// Creates an `NDArray` instance from a constant shape.
+///
+/// * `elem_ty` - The element type of the `NDArray`.
+/// * `shape` - The shape of the `NDArray`, represented as an LLVM [`ArrayValue`].
+fn create_ndarray_const_shape<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    shape: ArrayValue<'ctx>
+) -> Result<NDArrayValue<'ctx>, String> {
+    let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
+
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
+    let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
+    let llvm_ndarray_data_t = ctx.get_llvm_type(generator, elem_ty).as_basic_type_enum();
+    assert!(llvm_ndarray_data_t.is_sized());
+
+    for i in 0..shape.get_type().len() {
+        let shape_dim = ctx.builder
+            .build_extract_value(shape, i, "")
+            .map(BasicValueEnum::into_int_value)
+            .unwrap();
+
+        let shape_dim_gez = ctx.builder
+            .build_int_compare(IntPredicate::SGE, shape_dim, llvm_usize.const_zero(), "")
+            .unwrap();
+
+        ctx.make_assert(
+            generator,
+            shape_dim_gez,
+            "0:ValueError",
+            "negative dimensions not supported",
+            [None, None, None],
+            ctx.current_loc,
+        );
+    }
+
+    let ndarray = generator.gen_var_alloc(
+        ctx,
+        llvm_ndarray_t.into(),
+        None,
+    )?;
+    let ndarray = NDArrayValue::from_ptr_val(ndarray, llvm_usize, None);
+
+    let num_dims = llvm_usize.const_int(shape.get_type().len() as u64, false);
+    ndarray.store_ndims(ctx, generator, num_dims);
+
+    let ndarray_num_dims = ndarray.load_ndims(ctx);
+    ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
+
+    for i in 0..shape.get_type().len() {
+        let ndarray_dim = ndarray
+            .dim_sizes()
+            .ptr_offset(ctx, generator, llvm_usize.const_int(i as u64, true), None);
+        let shape_dim = ctx.builder.build_extract_value(shape, i, "")
+            .map(BasicValueEnum::into_int_value)
+            .unwrap();
+
+        ctx.builder.build_store(ndarray_dim, shape_dim).unwrap();
+    }
+
+    let ndarray_num_elems = call_ndarray_calc_size(
+        generator,
+        ctx,
+        &ndarray.dim_sizes().as_slice_value(ctx, generator),
+    );
+    ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
+
+    Ok(ndarray)
+}
+
+fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+) -> BasicValueEnum<'ctx> {
+    if [ctx.primitives.int32, ctx.primitives.uint32].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
+        ctx.ctx.i32_type().const_zero().into()
+    } else if [ctx.primitives.int64, ctx.primitives.uint64].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
+        ctx.ctx.i64_type().const_zero().into()
+    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
+        ctx.ctx.f64_type().const_zero().into()
+    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
+        ctx.ctx.bool_type().const_zero().into()
+    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
+        ctx.gen_string(generator, "")
+    } else {
+        unreachable!()
+    }
+}
+
+fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+) -> BasicValueEnum<'ctx> {
+    if [ctx.primitives.int32, ctx.primitives.uint32].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
+        let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int32);
+        ctx.ctx.i32_type().const_int(1, is_signed).into()
+    } else if [ctx.primitives.int64, ctx.primitives.uint64].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
+        let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int64);
+        ctx.ctx.i64_type().const_int(1, is_signed).into()
+    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
+        ctx.ctx.f64_type().const_float(1.0).into()
+    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
+        ctx.ctx.bool_type().const_int(1, false).into()
+    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
+        ctx.gen_string(generator, "1")
+    } else {
+        unreachable!()
+    }
+}
+
+/// LLVM-typed implementation for generating the implementation for constructing an `NDArray`.
+///
+/// * `elem_ty` - The element type of the `NDArray`.
+/// * `shape` - The `shape` parameter used to construct the `NDArray`.
+fn call_ndarray_empty_impl<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    shape: ListValue<'ctx>,
+) -> Result<NDArrayValue<'ctx>, String> {
+    create_ndarray_dyn_shape(
+        generator,
+        ctx,
+        elem_ty,
+        &shape,
+        |_, ctx, shape| {
+            Ok(shape.load_size(ctx, None))
+        },
+        |generator, ctx, shape, idx| {
+            Ok(shape.data().get(ctx, generator, idx, None).into_int_value())
+        },
+    )
+}
+
+/// Generates LLVM IR for populating the entire `NDArray` using a lambda with its flattened index as
+/// its input.
+fn ndarray_fill_flattened<'ctx, 'a, G, ValueFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, 'a>,
+    ndarray: NDArrayValue<'ctx>,
+    value_fn: ValueFn,
+) -> Result<(), String>
+    where
+        G: CodeGenerator + ?Sized,
+        ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, 'a>, IntValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
+{
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    let ndarray_num_elems = call_ndarray_calc_size(
+        generator,
+        ctx,
+        &ndarray.dim_sizes().as_slice_value(ctx, generator),
+    );
+
+    gen_for_callback_incrementing(
+        generator,
+        ctx,
+        llvm_usize.const_zero(),
+        (ndarray_num_elems, false),
+        |generator, ctx, i| {
+            let elem = unsafe {
+                ndarray.data().ptr_offset_unchecked(ctx, generator, i, None)
+            };
+
+            let value = value_fn(generator, ctx, i)?;
+            ctx.builder.build_store(elem, value).unwrap();
+
+            Ok(())
+        },
+        llvm_usize.const_int(1, false),
+    )
+}
+
+/// Generates LLVM IR for populating the entire `NDArray` using a lambda with the dimension-indices
+/// as its input.
+fn ndarray_fill_indexed<'ctx, G, ValueFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ndarray: NDArrayValue<'ctx>,
+    value_fn: ValueFn,
+) -> Result<(), String>
+    where
+        G: CodeGenerator + ?Sized,
+        ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, PointerValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
+{
+    ndarray_fill_flattened(
+        generator,
+        ctx,
+        ndarray,
+        |generator, ctx, idx| {
+            let indices = call_ndarray_calc_nd_indices(
+                generator,
+                ctx,
+                idx,
+                ndarray,
+            );
+
+            value_fn(generator, ctx, indices)
+        }
+    )
+}
+
+/// LLVM-typed implementation for generating the implementation for `ndarray.zeros`.
+///
+/// * `elem_ty` - The element type of the `NDArray`.
+/// * `shape` - The `shape` parameter used to construct the `NDArray`.
+fn call_ndarray_zeros_impl<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    shape: ListValue<'ctx>,
+) -> Result<NDArrayValue<'ctx>, String> {
+    let supported_types = [
+        ctx.primitives.int32,
+        ctx.primitives.int64,
+        ctx.primitives.uint32,
+        ctx.primitives.uint64,
+        ctx.primitives.float,
+        ctx.primitives.bool,
+        ctx.primitives.str,
+    ];
+    assert!(supported_types.iter().any(|supported_ty| ctx.unifier.unioned(*supported_ty, elem_ty)));
+
+    let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
+    ndarray_fill_flattened(
+        generator,
+        ctx,
+        ndarray,
+        |generator, ctx, _| {
+            let value = ndarray_zero_value(generator, ctx, elem_ty);
+
+            Ok(value)
+        }
+    )?;
+
+    Ok(ndarray)
+}
+
+/// LLVM-typed implementation for generating the implementation for `ndarray.ones`.
+///
+/// * `elem_ty` - The element type of the `NDArray`.
+/// * `shape` - The `shape` parameter used to construct the `NDArray`.
+fn call_ndarray_ones_impl<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    shape: ListValue<'ctx>,
+) -> Result<NDArrayValue<'ctx>, String> {
+    let supported_types = [
+        ctx.primitives.int32,
+        ctx.primitives.int64,
+        ctx.primitives.uint32,
+        ctx.primitives.uint64,
+        ctx.primitives.float,
+        ctx.primitives.bool,
+        ctx.primitives.str,
+    ];
+    assert!(supported_types.iter().any(|supported_ty| ctx.unifier.unioned(*supported_ty, elem_ty)));
+
+    let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
+    ndarray_fill_flattened(
+        generator,
+        ctx,
+        ndarray,
+        |generator, ctx, _| {
+            let value = ndarray_one_value(generator, ctx, elem_ty);
+
+            Ok(value)
+        }
+    )?;
+
+    Ok(ndarray)
+}
+
+/// LLVM-typed implementation for generating the implementation for `ndarray.full`.
+///
+/// * `elem_ty` - The element type of the `NDArray`.
+/// * `shape` - The `shape` parameter used to construct the `NDArray`.
+fn call_ndarray_full_impl<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    shape: ListValue<'ctx>,
+    fill_value: BasicValueEnum<'ctx>,
+) -> Result<NDArrayValue<'ctx>, String> {
+    let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
+    ndarray_fill_flattened(
+        generator,
+        ctx,
+        ndarray,
+        |generator, ctx, _| {
+            let value = if fill_value.is_pointer_value() {
+                let llvm_i1 = ctx.ctx.bool_type();
+
+                let copy = generator.gen_var_alloc(ctx, fill_value.get_type(), None)?;
+
+                call_memcpy_generic(
+                    ctx,
+                    copy,
+                    fill_value.into_pointer_value(),
+                    fill_value.get_type().size_of().map(Into::into).unwrap(),
+                    llvm_i1.const_zero(),
+                );
+
+                copy.into()
+            } else if fill_value.is_int_value() || fill_value.is_float_value() {
+                fill_value
+            } else {
+                unreachable!()
+            };
+
+            Ok(value)
+        }
+    )?;
+
+    Ok(ndarray)
+}
+
+/// LLVM-typed implementation for generating the implementation for `ndarray.eye`.
+///
+/// * `elem_ty` - The element type of the `NDArray`.
+fn call_ndarray_eye_impl<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    nrows: IntValue<'ctx>,
+    ncols: IntValue<'ctx>,
+    offset: IntValue<'ctx>,
+) -> Result<NDArrayValue<'ctx>, String> {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_usize_2 = llvm_usize.array_type(2);
+
+    let shape_addr = generator.gen_var_alloc(ctx, llvm_usize_2.into(), None)?;
+
+    let shape = ctx.builder.build_load(shape_addr, "")
+        .map(BasicValueEnum::into_array_value)
+        .unwrap();
+
+    let nrows = ctx.builder.build_int_z_extend_or_bit_cast(nrows, llvm_usize, "").unwrap();
+    let shape = ctx.builder
+        .build_insert_value(shape, nrows, 0, "")
+        .map(AggregateValueEnum::into_array_value)
+        .unwrap();
+
+    let ncols = ctx.builder.build_int_z_extend_or_bit_cast(ncols, llvm_usize, "").unwrap();
+    let shape = ctx.builder
+        .build_insert_value(shape, ncols, 1, "")
+        .map(AggregateValueEnum::into_array_value)
+        .unwrap();
+
+    let ndarray = create_ndarray_const_shape(generator, ctx, elem_ty, shape)?;
+
+    ndarray_fill_indexed(
+        generator,
+        ctx,
+        ndarray,
+        |generator, ctx, indices| {
+            let row = ctx.build_gep_and_load(
+                indices,
+                &[llvm_usize.const_int(0, false)],
+                None,
+            ).into_int_value();
+            let col = ctx.build_gep_and_load(
+                indices,
+                &[llvm_usize.const_int(1, false)],
+                None,
+            ).into_int_value();
+
+            let col_with_offset = ctx.builder
+                .build_int_add(
+                    col,
+                    ctx.builder.build_int_s_extend_or_bit_cast(offset, llvm_usize, "").unwrap(),
+                    "",
+                )
+                .unwrap();
+            let is_on_diag = ctx.builder
+                .build_int_compare(IntPredicate::EQ, row, col_with_offset, "")
+                .unwrap();
+
+            let zero = ndarray_zero_value(generator, ctx, elem_ty);
+            let one = ndarray_one_value(generator, ctx, elem_ty);
+
+            let value = ctx.builder.build_select(is_on_diag, one, zero, "").unwrap();
+
+            Ok(value)
+        },
+    )?;
+
+    Ok(ndarray)
+}
+
+/// LLVM-typed implementation for generating the implementation for `ndarray.copy`.
+///
+/// * `elem_ty` - The element type of the `NDArray`.
+fn ndarray_copy_impl<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    this: NDArrayValue<'ctx>,
+) -> Result<NDArrayValue<'ctx>, String> {
+    let llvm_i1 = ctx.ctx.bool_type();
+
+    let ndarray = create_ndarray_dyn_shape(
+        generator,
+        ctx,
+        elem_ty,
+        &this,
+        |_, ctx, shape| {
+            Ok(shape.load_ndims(ctx))
+        },
+        |generator, ctx, shape, idx| {
+            unsafe { Ok(shape.dim_sizes().get_typed_unchecked(ctx, generator, idx, None)) }
+        },
+    )?;
+
+    let len = call_ndarray_calc_size(
+        generator,
+        ctx,
+        &ndarray.dim_sizes().as_slice_value(ctx, generator),
+    );
+    let sizeof_ty = ctx.get_llvm_type(generator, elem_ty);
+    let len_bytes = ctx.builder
+        .build_int_mul(
+            len,
+            sizeof_ty.size_of().unwrap(),
+            "",
+        )
+        .unwrap();
+
+    call_memcpy_generic(
+        ctx,
+        ndarray.data().base_ptr(ctx, generator),
+        this.data().base_ptr(ctx, generator),
+        len_bytes,
+        llvm_i1.const_zero(),
+    );
+
+    Ok(ndarray)
+}
+
+/// Generates LLVM IR for `ndarray.empty`.
+pub fn gen_ndarray_empty<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<PointerValue<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    let llvm_usize = generator.get_size_type(context.ctx);
+    let shape_ty = fun.0.args[0].ty;
+    let shape_arg = args[0].1.clone()
+        .to_basic_value_enum(context, generator, shape_ty)?;
+
+    call_ndarray_empty_impl(
+        generator,
+        context,
+        context.primitives.float,
+        ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
+    ).map(NDArrayValue::into)
+}
+
+/// Generates LLVM IR for `ndarray.zeros`.
+pub fn gen_ndarray_zeros<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<PointerValue<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    let llvm_usize = generator.get_size_type(context.ctx);
+    let shape_ty = fun.0.args[0].ty;
+    let shape_arg = args[0].1.clone()
+        .to_basic_value_enum(context, generator, shape_ty)?;
+
+    call_ndarray_zeros_impl(
+        generator,
+        context,
+        context.primitives.float,
+        ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
+    ).map(NDArrayValue::into)
+}
+
+/// Generates LLVM IR for `ndarray.ones`.
+pub fn gen_ndarray_ones<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<PointerValue<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    let llvm_usize = generator.get_size_type(context.ctx);
+    let shape_ty = fun.0.args[0].ty;
+    let shape_arg = args[0].1.clone()
+        .to_basic_value_enum(context, generator, shape_ty)?;
+
+    call_ndarray_ones_impl(
+        generator,
+        context,
+        context.primitives.float,
+        ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
+    ).map(NDArrayValue::into)
+}
+
+/// Generates LLVM IR for `ndarray.full`.
+pub fn gen_ndarray_full<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<PointerValue<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 2);
+
+    let llvm_usize = generator.get_size_type(context.ctx);
+    let shape_ty = fun.0.args[0].ty;
+    let shape_arg = args[0].1.clone()
+        .to_basic_value_enum(context, generator, shape_ty)?;
+    let fill_value_ty = fun.0.args[1].ty;
+    let fill_value_arg = args[1].1.clone()
+        .to_basic_value_enum(context, generator, fill_value_ty)?;
+
+    call_ndarray_full_impl(
+        generator,
+        context,
+        fill_value_ty,
+        ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
+        fill_value_arg,
+    ).map(NDArrayValue::into)
+}
+
+/// Generates LLVM IR for `ndarray.eye`.
+pub fn gen_ndarray_eye<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<PointerValue<'ctx>, String> {
+    assert!(obj.is_none());
+    assert!(matches!(args.len(), 1..=3));
+
+    let nrows_ty = fun.0.args[0].ty;
+    let nrows_arg = args[0].1.clone()
+        .to_basic_value_enum(context, generator, nrows_ty)?;
+
+    let ncols_ty = fun.0.args[1].ty;
+    let ncols_arg = args.iter()
+        .find(|arg| arg.0.is_some_and(|name| name == fun.0.args[1].name))
+        .map(|arg| arg.1.clone().to_basic_value_enum(context, generator, ncols_ty))
+        .unwrap_or_else(|| {
+            args[0].1.clone().to_basic_value_enum(context, generator, nrows_ty)
+        })?;
+
+    let offset_ty = fun.0.args[2].ty;
+    let offset_arg = args.iter()
+        .find(|arg| arg.0.is_some_and(|name| name == fun.0.args[2].name))
+        .map(|arg| arg.1.clone().to_basic_value_enum(context, generator, offset_ty))
+        .unwrap_or_else(|| {
+            Ok(context.gen_symbol_val(
+                generator,
+                fun.0.args[2].default_value.as_ref().unwrap(),
+                offset_ty
+            ))
+        })?;
+
+    call_ndarray_eye_impl(
+        generator,
+        context,
+        context.primitives.float,
+        nrows_arg.into_int_value(),
+        ncols_arg.into_int_value(),
+        offset_arg.into_int_value(),
+    ).map(NDArrayValue::into)
+}
+
+/// Generates LLVM IR for `ndarray.identity`.
+pub fn gen_ndarray_identity<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<PointerValue<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    let llvm_usize = generator.get_size_type(context.ctx);
+
+    let n_ty = fun.0.args[0].ty;
+    let n_arg = args[0].1.clone()
+        .to_basic_value_enum(context, generator, n_ty)?;
+
+    call_ndarray_eye_impl(
+        generator,
+        context,
+        context.primitives.float,
+        n_arg.into_int_value(),
+        n_arg.into_int_value(),
+        llvm_usize.const_zero(),
+    ).map(NDArrayValue::into)
+}
+
+/// Generates LLVM IR for `ndarray.copy`.
+pub fn gen_ndarray_copy<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    _fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<PointerValue<'ctx>, String> {
+    assert!(obj.is_some());
+    assert!(args.is_empty());
+
+    let llvm_usize = generator.get_size_type(context.ctx);
+
+    let this_ty = obj.as_ref().unwrap().0;
+    let (this_elem_ty, _) = unpack_ndarray_tvars(&mut context.unifier, this_ty);
+    let this_arg = obj
+        .as_ref()
+        .unwrap()
+        .1
+        .clone()
+        .to_basic_value_enum(context, generator, this_ty)?;
+
+    ndarray_copy_impl(
+        generator,
+        context,
+        this_elem_ty,
+        NDArrayValue::from_ptr_val(this_arg.into_pointer_value(), llvm_usize, None),
+    ).map(NDArrayValue::into)
+}
+
+/// Generates LLVM IR for `ndarray.fill`.
+pub fn gen_ndarray_fill<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<(), String> {
+    assert!(obj.is_some());
+    assert_eq!(args.len(), 1);
+
+    let llvm_usize = generator.get_size_type(context.ctx);
+
+    let this_ty = obj.as_ref().unwrap().0;
+    let this_arg = obj.as_ref().unwrap().1.clone()
+        .to_basic_value_enum(context, generator, this_ty)?
+        .into_pointer_value();
+    let value_ty = fun.0.args[0].ty;
+    let value_arg = args[0].1.clone()
+        .to_basic_value_enum(context, generator, value_ty)?;
+
+    ndarray_fill_flattened(
+        generator,
+        context,
+        NDArrayValue::from_ptr_val(this_arg, llvm_usize, None),
+        |generator, ctx, _| {
+            let value = if value_arg.is_pointer_value() {
+                let llvm_i1 = ctx.ctx.bool_type();
+
+                let copy = generator.gen_var_alloc(ctx, value_arg.get_type(), None)?;
+
+                call_memcpy_generic(
+                    ctx,
+                    copy,
+                    value_arg.into_pointer_value(),
+                    value_arg.get_type().size_of().map(Into::into).unwrap(),
+                    llvm_i1.const_zero(),
+                );
+
+                copy.into()
+            } else if value_arg.is_int_value() || value_arg.is_float_value() {
+                value_arg
+            } else {
+                unreachable!()
+            };
+
+            Ok(value)
+        }
+    )?;
+
+    Ok(())
+}

nac3core/src/codegen/stmt.rs

@@ -6,7 +6,7 @@ use super::{
 };
 use crate::{
     codegen::{
-        classes::{ListValue, RangeValue},
+        classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
         expr::gen_binop_expr,
         gen_in_range_check,
     },
@@ -65,8 +65,8 @@ pub fn gen_array_var<'ctx, 'a, T: BasicType<'ctx>>(
     ctx: &mut CodeGenContext<'ctx, 'a>,
     ty: T,
     size: IntValue<'ctx>,
-    name: Option<&str>,
+    name: Option<&'ctx str>,
-) -> Result<PointerValue<'ctx>, String> {
+) -> Result<ArraySliceValue<'ctx>, String> {
     // Restore debug location
     let di_loc = ctx.debug_info.0.create_debug_location(
         ctx.ctx,
@@ -84,6 +84,7 @@ pub fn gen_array_var<'ctx, 'a, T: BasicType<'ctx>>(
     ctx.builder.set_current_debug_location(di_loc);
 
     let ptr = ctx.builder.build_array_alloca(ty, size, name.unwrap_or("")).unwrap();
+    let ptr = ArraySliceValue::from_ptr_val(ptr, size, name);
 
     ctx.builder.position_at_end(current);
     ctx.builder.set_current_debug_location(di_loc);
@@ -478,8 +479,8 @@ pub fn gen_for<G: CodeGenerator>(
 /// executing. The result value must be an `i1` indicating if the loop should continue.
 /// * `body` - A lambda containing IR statements within the loop body.
 /// * `update` - A lambda containing IR statements updating loop variables.
-pub fn gen_for_callback<'ctx, 'a, I, InitFn, CondFn, BodyFn, UpdateFn>(
+pub fn gen_for_callback<'ctx, 'a, G, I, InitFn, CondFn, BodyFn, UpdateFn>(
-    generator: &mut dyn CodeGenerator,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, 'a>,
     init: InitFn,
     cond: CondFn,
@@ -487,11 +488,12 @@ pub fn gen_for_callback<'ctx, 'a, I, InitFn, CondFn, BodyFn, UpdateFn>(
     update: UpdateFn,
 ) -> Result<(), String>
     where
+        G: CodeGenerator + ?Sized,
         I: Clone,
-        InitFn: FnOnce(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>) -> Result<I, String>,
+        InitFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<I, String>,
-        CondFn: FnOnce(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, I) -> Result<IntValue<'ctx>, String>,
+        CondFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<IntValue<'ctx>, String>,
-        BodyFn: FnOnce(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
+        BodyFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
-        UpdateFn: FnOnce(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
+        UpdateFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
 {
     let current = ctx.builder.get_insert_block().and_then(BasicBlock::get_parent).unwrap();
     let init_bb = ctx.ctx.append_basic_block(current, "for.init");
@@ -536,6 +538,85 @@ pub fn gen_for_callback<'ctx, 'a, I, InitFn, CondFn, BodyFn, UpdateFn>(
     Ok(())
 }
 
+/// Generates a C-style monotonically-increasing `for` construct using lambdas, similar to the 
+/// following C code:
+///
+/// ```c
+/// for (int x = init_val; x /* < or <= ; see `max_val` */ max_val; x += incr_val) {
+///     body(x);
+/// }
+/// ```
+/// 
+/// * `init_val` - The initial value of the loop variable. The type of this value will also be used 
+/// as the type of the loop variable.
+/// * `max_val` - A tuple containing the maximum value of the loop variable, and whether the maximum
+/// value should be treated as inclusive (as opposed to exclusive).
+/// * `body` - A lambda containing IR statements within the loop body.
+/// * `incr_val` - The value to increment the loop variable on each iteration.
+pub fn gen_for_callback_incrementing<'ctx, 'a, G, BodyFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, 'a>,
+    init_val: IntValue<'ctx>,
+    max_val: (IntValue<'ctx>, bool),
+    body: BodyFn,
+    incr_val: IntValue<'ctx>,
+) -> Result<(), String>
+    where
+        G: CodeGenerator + ?Sized,
+        BodyFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, IntValue<'ctx>) -> Result<(), String>,
+{
+    let init_val_t = init_val.get_type();
+
+    gen_for_callback(
+        generator,
+        ctx,
+        |generator, ctx| {
+            let i_addr = generator.gen_var_alloc(ctx, init_val_t.into(), None)?;
+            ctx.builder.build_store(i_addr, init_val).unwrap();
+
+            Ok(i_addr)
+        },
+        |_, ctx, i_addr| {
+            let cmp_op = if max_val.1 {
+                IntPredicate::ULE
+            } else {
+                IntPredicate::ULT
+            };
+
+            let i = ctx.builder
+                .build_load(i_addr, "")
+                .map(BasicValueEnum::into_int_value)
+                .unwrap();
+            let max_val = ctx.builder
+                .build_int_z_extend_or_bit_cast(max_val.0, init_val_t, "")
+                .unwrap();
+
+            Ok(ctx.builder.build_int_compare(cmp_op, i, max_val, "").unwrap())
+        },
+        |generator, ctx, i_addr| {
+            let i = ctx.builder
+                .build_load(i_addr, "")
+                .map(BasicValueEnum::into_int_value)
+                .unwrap();
+
+            body(generator, ctx, i)
+        },
+        |_, ctx, i_addr| {
+            let i = ctx.builder
+                .build_load(i_addr, "")
+                .map(BasicValueEnum::into_int_value)
+                .unwrap();
+            let incr_val = ctx.builder
+                .build_int_z_extend_or_bit_cast(incr_val, init_val_t, "")
+                .unwrap();
+            let i = ctx.builder.build_int_add(i, incr_val, "").unwrap();
+            ctx.builder.build_store(i_addr, i).unwrap();
+
+            Ok(())
+        },
+    )
+}
+
 /// See [`CodeGenerator::gen_while`].
 pub fn gen_while<G: CodeGenerator>(
     generator: &mut G,
@@ -701,8 +782,8 @@ pub fn final_proxy<'ctx>(
 
 /// Inserts the declaration of the builtin function with the specified `symbol` name, and returns
 /// the function.
-pub fn get_builtins<'ctx>(
+pub fn get_builtins<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut dyn CodeGenerator,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     symbol: &str,
 ) -> FunctionValue<'ctx> {
@@ -795,8 +876,8 @@ pub fn exn_constructor<'ctx>(
 ///
 /// * `exception` - The exception thrown by the `raise` statement.
 /// * `loc` - The location where the exception is raised from.
-pub fn gen_raise<'ctx>(
+pub fn gen_raise<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut dyn CodeGenerator,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     exception: Option<&BasicValueEnum<'ctx>>,
     loc: Location,

nac3core/src/toplevel/builtins.rs

@@ -5,11 +5,14 @@ use crate::{
         expr::destructure_range,
         irrt::*,
         llvm_intrinsics::*,
+        numpy::*,
         stmt::exn_constructor,
     },
     symbol_resolver::SymbolValue,
-    toplevel::helper::PRIMITIVE_DEF_IDS,
+    toplevel::{
-    toplevel::numpy::*,
+        helper::PRIMITIVE_DEF_IDS,
+        numpy::make_ndarray_ty,
+    },
     typecheck::typedef::VarMap,
 };
 use inkwell::{

nac3core/src/toplevel/numpy.rs

@@ -1,24 +1,9 @@
-use inkwell::{IntPredicate, types::BasicType, values::{BasicValueEnum, PointerValue}};
-use inkwell::values::{AggregateValueEnum, ArrayValue, IntValue};
 use itertools::Itertools;
-use nac3parser::ast::StrRef;
 use crate::{
-    codegen::{
+    toplevel::helper::PRIMITIVE_DEF_IDS,
-        classes::{ListValue, NDArrayValue},
-        CodeGenContext,
-        CodeGenerator,
-        irrt::{
-            call_ndarray_calc_nd_indices,
-            call_ndarray_calc_size,
-        },
-        llvm_intrinsics::call_memcpy_generic,
-        stmt::gen_for_callback
-    },
-    symbol_resolver::ValueEnum,
-    toplevel::{DefinitionId, helper::PRIMITIVE_DEF_IDS},
     typecheck::{
         type_inferencer::PrimitiveStore,
-        typedef::{FunSignature, Type, TypeEnum, Unifier, VarMap},
+        typedef::{Type, TypeEnum, Unifier, VarMap},
     },
 };
 
@@ -76,885 +61,3 @@ pub fn unpack_ndarray_tvars(
         .collect_tuple()
         .unwrap()
 }
-
-/// Creates an `NDArray` instance from a dynamic shape.
-///
-/// * `elem_ty` - The element type of the `NDArray`.
-/// * `shape` - The shape of the `NDArray`.
-/// * `shape_len_fn` - A function that retrieves the number of dimensions from `shape`.
-/// * `shape_data_fn` - A function that retrieves the size of a dimension from `shape`.
-fn create_ndarray_dyn_shape<'ctx, 'a, V, LenFn, DataFn>(
-    generator: &mut dyn CodeGenerator,
-    ctx: &mut CodeGenContext<'ctx, 'a>,
-    elem_ty: Type,
-    shape: &V,
-    shape_len_fn: LenFn,
-    shape_data_fn: DataFn,
-) -> Result<NDArrayValue<'ctx>, String>
-    where
-        LenFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, &V) -> Result<IntValue<'ctx>, String>,
-        DataFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, &V, IntValue<'ctx>) -> Result<IntValue<'ctx>, String>,
-{
-    let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
-
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-
-    let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
-    let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
-    let llvm_ndarray_data_t = ctx.get_llvm_type(generator, elem_ty).as_basic_type_enum();
-    assert!(llvm_ndarray_data_t.is_sized());
-
-    // Assert that all dimensions are non-negative
-    gen_for_callback(
-        generator,
-        ctx,
-        |generator, ctx| {
-            let i = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
-            ctx.builder.build_store(i, llvm_usize.const_zero()).unwrap();
-
-            Ok(i)
-        },
-        |generator, ctx, i_addr| {
-            let i = ctx.builder
-                .build_load(i_addr, "")
-                .map(BasicValueEnum::into_int_value)
-                .unwrap();
-            let shape_len = shape_len_fn(generator, ctx, shape)?;
-            debug_assert!(shape_len.get_type().get_bit_width() <= llvm_usize.get_bit_width());
-
-            Ok(ctx.builder.build_int_compare(IntPredicate::ULT, i, shape_len, "").unwrap())
-        },
-        |generator, ctx, i_addr| {
-            let i = ctx.builder
-                .build_load(i_addr, "")
-                .map(BasicValueEnum::into_int_value)
-                .unwrap();
-            let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
-            debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
-
-            let shape_dim_gez = ctx.builder
-                .build_int_compare(IntPredicate::SGE, shape_dim, shape_dim.get_type().const_zero(), "")
-                .unwrap();
-
-            ctx.make_assert(
-                generator,
-                shape_dim_gez,
-                "0:ValueError",
-                "negative dimensions not supported",
-                [None, None, None],
-                ctx.current_loc,
-            );
-
-            Ok(())
-        },
-        |_, ctx, i_addr| {
-            let i = ctx.builder
-                .build_load(i_addr, "")
-                .map(BasicValueEnum::into_int_value)
-                .unwrap();
-            let i = ctx.builder.build_int_add(i, llvm_usize.const_int(1, true), "").unwrap();
-            ctx.builder.build_store(i_addr, i).unwrap();
-
-            Ok(())
-        },
-    )?;
-
-    let ndarray = generator.gen_var_alloc(
-        ctx,
-        llvm_ndarray_t.into(),
-        None,
-    )?;
-    let ndarray = NDArrayValue::from_ptr_val(ndarray, llvm_usize, None);
-
-    let num_dims = shape_len_fn(generator, ctx, shape)?;
-    ndarray.store_ndims(ctx, generator, num_dims);
-
-    let ndarray_num_dims = ndarray.load_ndims(ctx);
-    ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
-
-    // Copy the dimension sizes from shape to ndarray.dims
-    gen_for_callback(
-        generator,
-        ctx,
-        |generator, ctx| {
-            let i = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
-            ctx.builder.build_store(i, llvm_usize.const_zero()).unwrap();
-
-            Ok(i)
-        },
-        |generator, ctx, i_addr| {
-            let i = ctx.builder
-                .build_load(i_addr, "")
-                .map(BasicValueEnum::into_int_value)
-                .unwrap();
-            let shape_len = shape_len_fn(generator, ctx, shape)?;
-            debug_assert!(shape_len.get_type().get_bit_width() <= llvm_usize.get_bit_width());
-
-            Ok(ctx.builder.build_int_compare(IntPredicate::ULT, i, shape_len, "").unwrap())
-        },
-        |generator, ctx, i_addr| {
-            let i = ctx.builder
-                .build_load(i_addr, "")
-                .map(BasicValueEnum::into_int_value)
-                .unwrap();
-            let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
-            debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
-            let shape_dim = ctx.builder
-                .build_int_z_extend(shape_dim, llvm_usize, "")
-                .unwrap();
-
-            let ndarray_pdim = ndarray.dim_sizes().ptr_offset(ctx, generator, i, None);
-
-            ctx.builder.build_store(ndarray_pdim, shape_dim).unwrap();
-
-            Ok(())
-        },
-        |_, ctx, i_addr| {
-            let i = ctx.builder
-                .build_load(i_addr, "")
-                .map(BasicValueEnum::into_int_value)
-                .unwrap();
-            let i = ctx.builder.build_int_add(i, llvm_usize.const_int(1, true), "").unwrap();
-            ctx.builder.build_store(i_addr, i).unwrap();
-
-            Ok(())
-        },
-    )?;
-
-    let ndarray_num_elems = call_ndarray_calc_size(
-        generator,
-        ctx,
-        ndarray.load_ndims(ctx),
-        ndarray.dim_sizes().as_ptr_value(ctx),
-    );
-    ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
-
-    Ok(ndarray)
-}
-
-/// Creates an `NDArray` instance from a constant shape.
-///
-/// * `elem_ty` - The element type of the `NDArray`.
-/// * `shape` - The shape of the `NDArray`, represented as an LLVM [`ArrayValue`].
-fn create_ndarray_const_shape<'ctx>(
-    generator: &mut dyn CodeGenerator,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    elem_ty: Type,
-    shape: ArrayValue<'ctx>
-) -> Result<NDArrayValue<'ctx>, String> {
-    let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
-
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-
-    let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
-    let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
-    let llvm_ndarray_data_t = ctx.get_llvm_type(generator, elem_ty).as_basic_type_enum();
-    assert!(llvm_ndarray_data_t.is_sized());
-
-    for i in 0..shape.get_type().len() {
-        let shape_dim = ctx.builder
-            .build_extract_value(shape, i, "")
-            .map(BasicValueEnum::into_int_value)
-            .unwrap();
-
-        let shape_dim_gez = ctx.builder
-            .build_int_compare(IntPredicate::SGE, shape_dim, llvm_usize.const_zero(), "")
-            .unwrap();
-
-        ctx.make_assert(
-            generator,
-            shape_dim_gez,
-            "0:ValueError",
-            "negative dimensions not supported",
-            [None, None, None],
-            ctx.current_loc,
-        );
-    }
-
-    let ndarray = generator.gen_var_alloc(
-        ctx,
-        llvm_ndarray_t.into(),
-        None,
-    )?;
-    let ndarray = NDArrayValue::from_ptr_val(ndarray, llvm_usize, None);
-
-    let num_dims = llvm_usize.const_int(shape.get_type().len() as u64, false);
-    ndarray.store_ndims(ctx, generator, num_dims);
-
-    let ndarray_num_dims = ndarray.load_ndims(ctx);
-    ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
-
-    for i in 0..shape.get_type().len() {
-        let ndarray_dim = ndarray
-            .dim_sizes()
-            .ptr_offset(ctx, generator, llvm_usize.const_int(i as u64, true), None);
-        let shape_dim = ctx.builder.build_extract_value(shape, i, "")
-            .map(BasicValueEnum::into_int_value)
-            .unwrap();
-
-        ctx.builder.build_store(ndarray_dim, shape_dim).unwrap();
-    }
-
-    let ndarray_dims = ndarray.dim_sizes().as_ptr_value(ctx);
-    let ndarray_num_elems = call_ndarray_calc_size(
-        generator,
-        ctx,
-        ndarray.load_ndims(ctx),
-        ndarray_dims,
-    );
-    ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
-
-    Ok(ndarray)
-}
-
-fn ndarray_zero_value<'ctx>(
-    generator: &mut dyn CodeGenerator,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    elem_ty: Type,
-) -> BasicValueEnum<'ctx> {
-    if [ctx.primitives.int32, ctx.primitives.uint32].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
-        ctx.ctx.i32_type().const_zero().into()
-    } else if [ctx.primitives.int64, ctx.primitives.uint64].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
-        ctx.ctx.i64_type().const_zero().into()
-    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
-        ctx.ctx.f64_type().const_zero().into()
-    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
-        ctx.ctx.bool_type().const_zero().into()
-    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
-        ctx.gen_string(generator, "")
-    } else {
-        unreachable!()
-    }
-}
-
-fn ndarray_one_value<'ctx>(
-    generator: &mut dyn CodeGenerator,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    elem_ty: Type,
-) -> BasicValueEnum<'ctx> {
-    if [ctx.primitives.int32, ctx.primitives.uint32].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
-        let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int32);
-        ctx.ctx.i32_type().const_int(1, is_signed).into()
-    } else if [ctx.primitives.int64, ctx.primitives.uint64].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
-        let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int64);
-        ctx.ctx.i64_type().const_int(1, is_signed).into()
-    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
-        ctx.ctx.f64_type().const_float(1.0).into()
-    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
-        ctx.ctx.bool_type().const_int(1, false).into()
-    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
-        ctx.gen_string(generator, "1")
-    } else {
-        unreachable!()
-    }
-}
-
-/// LLVM-typed implementation for generating the implementation for constructing an `NDArray`.
-///
-/// * `elem_ty` - The element type of the `NDArray`.
-/// * `shape` - The `shape` parameter used to construct the `NDArray`.
-fn call_ndarray_empty_impl<'ctx>(
-    generator: &mut dyn CodeGenerator,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    elem_ty: Type,
-    shape: ListValue<'ctx>,
-) -> Result<NDArrayValue<'ctx>, String> {
-    create_ndarray_dyn_shape(
-        generator,
-        ctx,
-        elem_ty,
-        &shape,
-        |_, ctx, shape| {
-            Ok(shape.load_size(ctx, None))
-        },
-        |generator, ctx, shape, idx| {
-            Ok(shape.data().get(ctx, generator, idx, None).into_int_value())
-        },
-    )
-}
-
-/// Generates LLVM IR for populating the entire `NDArray` using a lambda with its flattened index as
-/// its input.
-fn ndarray_fill_flattened<'ctx, 'a, ValueFn>(
-    generator: &mut dyn CodeGenerator,
-    ctx: &mut CodeGenContext<'ctx, 'a>,
-    ndarray: NDArrayValue<'ctx>,
-    value_fn: ValueFn,
-) -> Result<(), String>
-    where
-        ValueFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, IntValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
-{
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-
-    let ndarray_num_elems = call_ndarray_calc_size(
-        generator,
-        ctx,
-        ndarray.load_ndims(ctx),
-        ndarray.dim_sizes().as_ptr_value(ctx),
-    );
-
-    gen_for_callback(
-        generator,
-        ctx,
-        |generator, ctx| {
-            let i = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
-            ctx.builder.build_store(i, llvm_usize.const_zero()).unwrap();
-
-            Ok(i)
-        },
-        |_, ctx, i_addr| {
-            let i = ctx.builder
-                .build_load(i_addr, "")
-                .map(BasicValueEnum::into_int_value)
-                .unwrap();
-
-            Ok(ctx.builder.build_int_compare(IntPredicate::ULT, i, ndarray_num_elems, "").unwrap())
-        },
-        |generator, ctx, i_addr| {
-            let i = ctx.builder
-                .build_load(i_addr, "")
-                .map(BasicValueEnum::into_int_value)
-                .unwrap();
-            let elem = unsafe {
-                ndarray.data().ptr_to_data_flattened_unchecked(ctx, i, None)
-            };
-
-            let value = value_fn(generator, ctx, i)?;
-            ctx.builder.build_store(elem, value).unwrap();
-
-            Ok(())
-        },
-        |_, ctx, i_addr| {
-            let i = ctx.builder
-                .build_load(i_addr, "")
-                .map(BasicValueEnum::into_int_value)
-                .unwrap();
-            let i = ctx.builder.build_int_add(i, llvm_usize.const_int(1, true), "").unwrap();
-            ctx.builder.build_store(i_addr, i).unwrap();
-
-            Ok(())
-        },
-    )
-}
-
-/// Generates LLVM IR for populating the entire `NDArray` using a lambda with the dimension-indices
-/// as its input.
-fn ndarray_fill_indexed<'ctx, ValueFn>(
-    generator: &mut dyn CodeGenerator,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ndarray: NDArrayValue<'ctx>,
-    value_fn: ValueFn,
-) -> Result<(), String>
-    where
-        ValueFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, '_>, PointerValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
-{
-    ndarray_fill_flattened(
-        generator,
-        ctx,
-        ndarray,
-        |generator, ctx, idx| {
-            let indices = call_ndarray_calc_nd_indices(
-                generator,
-                ctx,
-                idx,
-                ndarray,
-            );
-
-            value_fn(generator, ctx, indices)
-        }
-    )
-}
-
-/// LLVM-typed implementation for generating the implementation for `ndarray.zeros`.
-///
-/// * `elem_ty` - The element type of the `NDArray`.
-/// * `shape` - The `shape` parameter used to construct the `NDArray`.
-fn call_ndarray_zeros_impl<'ctx>(
-    generator: &mut dyn CodeGenerator,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    elem_ty: Type,
-    shape: ListValue<'ctx>,
-) -> Result<NDArrayValue<'ctx>, String> {
-    let supported_types = [
-        ctx.primitives.int32,
-        ctx.primitives.int64,
-        ctx.primitives.uint32,
-        ctx.primitives.uint64,
-        ctx.primitives.float,
-        ctx.primitives.bool,
-        ctx.primitives.str,
-    ];
-    assert!(supported_types.iter().any(|supported_ty| ctx.unifier.unioned(*supported_ty, elem_ty)));
-
-    let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
-    ndarray_fill_flattened(
-        generator,
-        ctx,
-        ndarray,
-        |generator, ctx, _| {
-            let value = ndarray_zero_value(generator, ctx, elem_ty);
-
-            Ok(value)
-        }
-    )?;
-
-    Ok(ndarray)
-}
-
-/// LLVM-typed implementation for generating the implementation for `ndarray.ones`.
-///
-/// * `elem_ty` - The element type of the `NDArray`.
-/// * `shape` - The `shape` parameter used to construct the `NDArray`.
-fn call_ndarray_ones_impl<'ctx>(
-    generator: &mut dyn CodeGenerator,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    elem_ty: Type,
-    shape: ListValue<'ctx>,
-) -> Result<NDArrayValue<'ctx>, String> {
-    let supported_types = [
-        ctx.primitives.int32,
-        ctx.primitives.int64,
-        ctx.primitives.uint32,
-        ctx.primitives.uint64,
-        ctx.primitives.float,
-        ctx.primitives.bool,
-        ctx.primitives.str,
-    ];
-    assert!(supported_types.iter().any(|supported_ty| ctx.unifier.unioned(*supported_ty, elem_ty)));
-
-    let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
-    ndarray_fill_flattened(
-        generator,
-        ctx,
-        ndarray,
-        |generator, ctx, _| {
-            let value = ndarray_one_value(generator, ctx, elem_ty);
-
-            Ok(value)
-        }
-    )?;
-
-    Ok(ndarray)
-}
-
-/// LLVM-typed implementation for generating the implementation for `ndarray.full`.
-///
-/// * `elem_ty` - The element type of the `NDArray`.
-/// * `shape` - The `shape` parameter used to construct the `NDArray`.
-fn call_ndarray_full_impl<'ctx>(
-    generator: &mut dyn CodeGenerator,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    elem_ty: Type,
-    shape: ListValue<'ctx>,
-    fill_value: BasicValueEnum<'ctx>,
-) -> Result<NDArrayValue<'ctx>, String> {
-    let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
-    ndarray_fill_flattened(
-        generator,
-        ctx,
-        ndarray,
-        |generator, ctx, _| {
-            let value = if fill_value.is_pointer_value() {
-                let llvm_i1 = ctx.ctx.bool_type();
-
-                let copy = generator.gen_var_alloc(ctx, fill_value.get_type(), None)?;
-
-                call_memcpy_generic(
-                    ctx,
-                    copy,
-                    fill_value.into_pointer_value(),
-                    fill_value.get_type().size_of().map(Into::into).unwrap(),
-                    llvm_i1.const_zero(),
-                );
-
-                copy.into()
-            } else if fill_value.is_int_value() || fill_value.is_float_value() {
-                fill_value
-            } else {
-                unreachable!()
-            };
-
-            Ok(value)
-        }
-    )?;
-
-    Ok(ndarray)
-}
-
-/// LLVM-typed implementation for generating the implementation for `ndarray.eye`.
-///
-/// * `elem_ty` - The element type of the `NDArray`.
-fn call_ndarray_eye_impl<'ctx>(
-    generator: &mut dyn CodeGenerator,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    elem_ty: Type,
-    nrows: IntValue<'ctx>,
-    ncols: IntValue<'ctx>,
-    offset: IntValue<'ctx>,
-) -> Result<NDArrayValue<'ctx>, String> {
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-    let llvm_usize_2 = llvm_usize.array_type(2);
-
-    let shape_addr = generator.gen_var_alloc(ctx, llvm_usize_2.into(), None)?;
-
-    let shape = ctx.builder.build_load(shape_addr, "")
-        .map(BasicValueEnum::into_array_value)
-        .unwrap();
-
-    let nrows = ctx.builder.build_int_z_extend_or_bit_cast(nrows, llvm_usize, "").unwrap();
-    let shape = ctx.builder
-        .build_insert_value(shape, nrows, 0, "")
-        .map(AggregateValueEnum::into_array_value)
-        .unwrap();
-
-    let ncols = ctx.builder.build_int_z_extend_or_bit_cast(ncols, llvm_usize, "").unwrap();
-    let shape = ctx.builder
-        .build_insert_value(shape, ncols, 1, "")
-        .map(AggregateValueEnum::into_array_value)
-        .unwrap();
-
-    let ndarray = create_ndarray_const_shape(generator, ctx, elem_ty, shape)?;
-
-    ndarray_fill_indexed(
-        generator,
-        ctx,
-        ndarray,
-        |generator, ctx, indices| {
-            let row = ctx.build_gep_and_load(
-                indices,
-                &[llvm_usize.const_int(0, false)],
-                None,
-            ).into_int_value();
-            let col = ctx.build_gep_and_load(
-                indices,
-                &[llvm_usize.const_int(1, false)],
-                None,
-            ).into_int_value();
-
-            let col_with_offset = ctx.builder
-                .build_int_add(
-                    col,
-                    ctx.builder.build_int_s_extend_or_bit_cast(offset, llvm_usize, "").unwrap(),
-                    "",
-                )
-                .unwrap();
-            let is_on_diag = ctx.builder
-                .build_int_compare(IntPredicate::EQ, row, col_with_offset, "")
-                .unwrap();
-
-            let zero = ndarray_zero_value(generator, ctx, elem_ty);
-            let one = ndarray_one_value(generator, ctx, elem_ty);
-
-            let value = ctx.builder.build_select(is_on_diag, one, zero, "").unwrap();
-
-            Ok(value)
-        },
-    )?;
-
-    Ok(ndarray)
-}
-
-/// LLVM-typed implementation for generating the implementation for `ndarray.copy`.
-///
-/// * `elem_ty` - The element type of the `NDArray`.
-fn ndarray_copy_impl<'ctx>(
-    generator: &mut dyn CodeGenerator,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    elem_ty: Type,
-    this: NDArrayValue<'ctx>,
-) -> Result<NDArrayValue<'ctx>, String> {
-    let llvm_i1 = ctx.ctx.bool_type();
-
-    let ndarray = create_ndarray_dyn_shape(
-        generator,
-        ctx,
-        elem_ty,
-        &this,
-        |_, ctx, shape| {
-            Ok(shape.load_ndims(ctx))
-        },
-        |generator, ctx, shape, idx| {
-            Ok(shape.dim_sizes().get(ctx, generator, idx, None))
-        },
-    )?;
-
-    let len = call_ndarray_calc_size(
-        generator,
-        ctx,
-        ndarray.load_ndims(ctx),
-        ndarray.dim_sizes().as_ptr_value(ctx),
-    );
-    let sizeof_ty = ctx.get_llvm_type(generator, elem_ty);
-    let len_bytes = ctx.builder
-        .build_int_mul(
-            len,
-            sizeof_ty.size_of().unwrap(),
-            "",
-        )
-        .unwrap();
-
-    call_memcpy_generic(
-        ctx,
-        ndarray.data().as_ptr_value(ctx),
-        this.data().as_ptr_value(ctx),
-        len_bytes,
-        llvm_i1.const_zero(),
-    );
-
-    Ok(ndarray)
-}
-
-/// Generates LLVM IR for `ndarray.empty`.
-pub fn gen_ndarray_empty<'ctx>(
-    context: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
-    fun: (&FunSignature, DefinitionId),
-    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
-    generator: &mut dyn CodeGenerator,
-) -> Result<PointerValue<'ctx>, String> {
-    assert!(obj.is_none());
-    assert_eq!(args.len(), 1);
-
-    let llvm_usize = generator.get_size_type(context.ctx);
-    let shape_ty = fun.0.args[0].ty;
-    let shape_arg = args[0].1.clone()
-        .to_basic_value_enum(context, generator, shape_ty)?;
-
-    call_ndarray_empty_impl(
-        generator,
-        context,
-        context.primitives.float,
-        ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
-    ).map(NDArrayValue::into)
-}
-
-/// Generates LLVM IR for `ndarray.zeros`.
-pub fn gen_ndarray_zeros<'ctx>(
-    context: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
-    fun: (&FunSignature, DefinitionId),
-    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
-    generator: &mut dyn CodeGenerator,
-) -> Result<PointerValue<'ctx>, String> {
-    assert!(obj.is_none());
-    assert_eq!(args.len(), 1);
-
-    let llvm_usize = generator.get_size_type(context.ctx);
-    let shape_ty = fun.0.args[0].ty;
-    let shape_arg = args[0].1.clone()
-        .to_basic_value_enum(context, generator, shape_ty)?;
-
-    call_ndarray_zeros_impl(
-        generator,
-        context,
-        context.primitives.float,
-        ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
-    ).map(NDArrayValue::into)
-}
-
-/// Generates LLVM IR for `ndarray.ones`.
-pub fn gen_ndarray_ones<'ctx>(
-    context: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
-    fun: (&FunSignature, DefinitionId),
-    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
-    generator: &mut dyn CodeGenerator,
-) -> Result<PointerValue<'ctx>, String> {
-    assert!(obj.is_none());
-    assert_eq!(args.len(), 1);
-
-    let llvm_usize = generator.get_size_type(context.ctx);
-    let shape_ty = fun.0.args[0].ty;
-    let shape_arg = args[0].1.clone()
-        .to_basic_value_enum(context, generator, shape_ty)?;
-
-    call_ndarray_ones_impl(
-        generator,
-        context,
-        context.primitives.float,
-        ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
-    ).map(NDArrayValue::into)
-}
-
-/// Generates LLVM IR for `ndarray.full`.
-pub fn gen_ndarray_full<'ctx>(
-    context: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
-    fun: (&FunSignature, DefinitionId),
-    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
-    generator: &mut dyn CodeGenerator,
-) -> Result<PointerValue<'ctx>, String> {
-    assert!(obj.is_none());
-    assert_eq!(args.len(), 2);
-
-    let llvm_usize = generator.get_size_type(context.ctx);
-    let shape_ty = fun.0.args[0].ty;
-    let shape_arg = args[0].1.clone()
-        .to_basic_value_enum(context, generator, shape_ty)?;
-    let fill_value_ty = fun.0.args[1].ty;
-    let fill_value_arg = args[1].1.clone()
-        .to_basic_value_enum(context, generator, fill_value_ty)?;
-
-    call_ndarray_full_impl(
-        generator,
-        context,
-        fill_value_ty,
-        ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
-        fill_value_arg,
-    ).map(NDArrayValue::into)
-}
-
-/// Generates LLVM IR for `ndarray.eye`.
-pub fn gen_ndarray_eye<'ctx>(
-    context: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
-    fun: (&FunSignature, DefinitionId),
-    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
-    generator: &mut dyn CodeGenerator,
-) -> Result<PointerValue<'ctx>, String> {
-    assert!(obj.is_none());
-    assert!(matches!(args.len(), 1..=3));
-
-    let nrows_ty = fun.0.args[0].ty;
-    let nrows_arg = args[0].1.clone()
-        .to_basic_value_enum(context, generator, nrows_ty)?;
-
-    let ncols_ty = fun.0.args[1].ty;
-    let ncols_arg = args.iter()
-        .find(|arg| arg.0.is_some_and(|name| name == fun.0.args[1].name))
-        .map(|arg| arg.1.clone().to_basic_value_enum(context, generator, ncols_ty))
-        .unwrap_or_else(|| {
-            args[0].1.clone().to_basic_value_enum(context, generator, nrows_ty)
-        })?;
-
-    let offset_ty = fun.0.args[2].ty;
-    let offset_arg = args.iter()
-        .find(|arg| arg.0.is_some_and(|name| name == fun.0.args[2].name))
-        .map(|arg| arg.1.clone().to_basic_value_enum(context, generator, offset_ty))
-        .unwrap_or_else(|| {
-            Ok(context.gen_symbol_val(
-                generator,
-                fun.0.args[2].default_value.as_ref().unwrap(),
-                offset_ty
-            ))
-        })?;
-
-    call_ndarray_eye_impl(
-        generator,
-        context,
-        context.primitives.float,
-        nrows_arg.into_int_value(),
-        ncols_arg.into_int_value(),
-        offset_arg.into_int_value(),
-    ).map(NDArrayValue::into)
-}
-
-/// Generates LLVM IR for `ndarray.identity`.
-pub fn gen_ndarray_identity<'ctx>(
-    context: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
-    fun: (&FunSignature, DefinitionId),
-    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
-    generator: &mut dyn CodeGenerator,
-) -> Result<PointerValue<'ctx>, String> {
-    assert!(obj.is_none());
-    assert_eq!(args.len(), 1);
-
-    let llvm_usize = generator.get_size_type(context.ctx);
-
-    let n_ty = fun.0.args[0].ty;
-    let n_arg = args[0].1.clone()
-        .to_basic_value_enum(context, generator, n_ty)?;
-
-    call_ndarray_eye_impl(
-        generator,
-        context,
-        context.primitives.float,
-        n_arg.into_int_value(),
-        n_arg.into_int_value(),
-        llvm_usize.const_zero(),
-    ).map(NDArrayValue::into)
-}
-
-/// Generates LLVM IR for `ndarray.copy`.
-pub fn gen_ndarray_copy<'ctx>(
-    context: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
-    _fun: (&FunSignature, DefinitionId),
-    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
-    generator: &mut dyn CodeGenerator,
-) -> Result<PointerValue<'ctx>, String> {
-    assert!(obj.is_some());
-    assert!(args.is_empty());
-
-    let llvm_usize = generator.get_size_type(context.ctx);
-
-    let this_ty = obj.as_ref().unwrap().0;
-    let (this_elem_ty, _) = unpack_ndarray_tvars(&mut context.unifier, this_ty);
-    let this_arg = obj
-        .as_ref()
-        .unwrap()
-        .1
-        .clone()
-        .to_basic_value_enum(context, generator, this_ty)?;
-
-    ndarray_copy_impl(
-        generator,
-        context,
-        this_elem_ty,
-        NDArrayValue::from_ptr_val(this_arg.into_pointer_value(), llvm_usize, None),
-    ).map(NDArrayValue::into)
-}
-
-/// Generates LLVM IR for `ndarray.fill`.
-pub fn gen_ndarray_fill<'ctx>(
-    context: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
-    fun: (&FunSignature, DefinitionId),
-    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
-    generator: &mut dyn CodeGenerator,
-) -> Result<(), String> {
-    assert!(obj.is_some());
-    assert_eq!(args.len(), 1);
-
-    let llvm_usize = generator.get_size_type(context.ctx);
-
-    let this_ty = obj.as_ref().unwrap().0;
-    let this_arg = obj.as_ref().unwrap().1.clone()
-        .to_basic_value_enum(context, generator, this_ty)?
-        .into_pointer_value();
-    let value_ty = fun.0.args[0].ty;
-    let value_arg = args[0].1.clone()
-        .to_basic_value_enum(context, generator, value_ty)?;
-
-    ndarray_fill_flattened(
-        generator,
-        context,
-        NDArrayValue::from_ptr_val(this_arg, llvm_usize, None),
-        |generator, ctx, _| {
-            let value = if value_arg.is_pointer_value() {
-                let llvm_i1 = ctx.ctx.bool_type();
-
-                let copy = generator.gen_var_alloc(ctx, value_arg.get_type(), None)?;
-
-                call_memcpy_generic(
-                    ctx,
-                    copy,
-                    value_arg.into_pointer_value(),
-                    value_arg.get_type().size_of().map(Into::into).unwrap(),
-                    llvm_i1.const_zero(),
-                );
-
-                copy.into()
-            } else if value_arg.is_int_value() || value_arg.is_float_value() {
-                value_arg
-            } else {
-                unreachable!()
-            };
-
-            Ok(value)
-        }
-    )?;
-
-    Ok(())
-}

nac3core/src/typecheck/typedef/mod.rs

@@ -699,7 +699,7 @@ impl Unifier {
                 self.set_a_to_b(a, x);
             }
             (TVar { fields: Some(fields), range, is_const_generic: false, .. }, TTuple { ty }) => {
-                let len = ty.len() as i32;
+                let len = i32::try_from(ty.len()).unwrap();
                 for (k, v) in fields {
                     match *k {
                         RecordKey::Int(i) => {

nac3standalone/src/basic_symbol_resolver.rs

@@ -74,7 +74,8 @@ impl SymbolResolver for Resolver {
         if let Some(id) = str_store.get(s) {
             *id
         } else {
-            let id = str_store.len() as i32;
+            let id = i32::try_from(str_store.len())
+                .expect("Symbol resolver string store size exceeds max capacity (i32::MAX)");
             str_store.insert(s.to_string(), id);
             id
         }

nac3standalone/src/main.rs

@@ -247,6 +247,8 @@ fn handle_assignment_pattern(
 }
 
 fn main() {
+    const SIZE_T: u32 = usize::BITS;
+
     let cli = CommandLineArgs::parse();
     let CommandLineArgs {
         file_name,
@@ -287,7 +289,6 @@ fn main() {
         // The default behavior for -O<n> where n>3 defaults to O3 for both Clang and GCC
         _ => OptimizationLevel::Aggressive,
     };
-    const SIZE_T: u32 = 64;
 
     let program = match fs::read_to_string(file_name.clone()) {
         Ok(program) => program,