core/ndstrides: implement cmpop

Currently this is used to interop with nalgebra.

nac3core/src/codegen/expr.rs

@@ -1,8 +1,8 @@
 use crate::{
     codegen::{
         classes::{
-            ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, NDArrayValue, ProxyType,
-            ProxyValue, RangeValue, UntypedArrayLikeAccessor,
+            ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, ProxyType, ProxyValue,
+            RangeValue, UntypedArrayLikeAccessor,
         },
         concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
         gen_in_range_check, get_llvm_abi_type, get_llvm_type, get_va_count_arg_name,
@@ -11,7 +11,7 @@ use crate::{
             call_expect, call_float_floor, call_float_pow, call_float_powi, call_int_smax,
             call_memcpy_generic,
         },
-        need_sret, numpy,
+        need_sret,
         object::ndarray::{NDArrayOut, ScalarOrNDArray},
         stmt::{
             gen_for_callback_incrementing, gen_if_callback, gen_if_else_expr_callback, gen_raise,
@@ -20,7 +20,7 @@ use crate::{
         CodeGenContext, CodeGenTask, CodeGenerator,
     },
     symbol_resolver::{SymbolValue, ValueEnum},
-    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, TopLevelDef},
+    toplevel::{helper::PrimDef, DefinitionId, TopLevelDef},
     typecheck::{
         magic_methods::{Binop, BinopVariant, HasOpInfo},
         typedef::{FunSignature, FuncArg, Type, TypeEnum, TypeVarId, Unifier, VarMap},
@@ -1773,14 +1773,12 @@ pub fn gen_unaryop_expr_with_values<'ctx, G: CodeGenerator>(
             _ => val.into(),
         }
     } else if ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) {
-        let llvm_usize = generator.get_size_type(ctx.ctx);
-        let (ndarray_dtype, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
-
-        let val = NDArrayValue::from_ptr_val(val.into_pointer_value(), llvm_usize, None);
+        let ndarray = AnyObject { value: val, ty };
+        let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
 
         // ndarray uses `~` rather than `not` to perform elementwise inversion, convert it before
         // passing it to the elementwise codegen function
-        let op = if ndarray_dtype.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::Bool.id()) {
+        let op = if ndarray.dtype.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::Bool.id()) {
             if op == ast::Unaryop::Invert {
                 ast::Unaryop::Not
             } else {
@@ -1793,20 +1791,18 @@ pub fn gen_unaryop_expr_with_values<'ctx, G: CodeGenerator>(
             op
         };
 
-        let res = numpy::ndarray_elementwise_unaryop_impl(
+        let mapped_ndarray = ndarray.map(
             generator,
             ctx,
-            ndarray_dtype,
-            None,
-            val,
-            |generator, ctx, val| {
-                gen_unaryop_expr_with_values(generator, ctx, op, (&Some(ndarray_dtype), val))?
+            NDArrayOut::NewNDArray { dtype: ndarray.dtype },
+            |generator, ctx, scalar| {
+                gen_unaryop_expr_with_values(generator, ctx, op, (&Some(ndarray.dtype), scalar))?
                     .unwrap()
-                    .to_basic_value_enum(ctx, generator, ndarray_dtype)
+                    .to_basic_value_enum(ctx, generator, ndarray.dtype)
             },
         )?;
 
-        res.as_base_value().into()
+        ValueEnum::Dynamic(mapped_ndarray.instance.value.as_basic_value_enum())
     } else {
         unimplemented!()
     }))
@@ -1849,85 +1845,46 @@ pub fn gen_cmpop_expr_with_values<'ctx, G: CodeGenerator>(
         if left_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
             || right_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
         {
-            let llvm_usize = generator.get_size_type(ctx.ctx);
-
-            let (Some(left_ty), lhs) = left else { unreachable!() };
-            let (Some(right_ty), rhs) = comparators[0] else { unreachable!() };
+            let (Some(left_ty), left) = left else { unreachable!() };
+            let (Some(right_ty), right) = comparators[0] else { unreachable!() };
             let op = ops[0];
 
-            let is_ndarray1 =
-                left_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
-            let is_ndarray2 =
-                right_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
+            let left = AnyObject { value: left, ty: left_ty };
+            let left =
+                ScalarOrNDArray::split_object(generator, ctx, left).to_ndarray(generator, ctx);
 
-            return if is_ndarray1 && is_ndarray2 {
-                let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, left_ty);
-                let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, right_ty);
+            let right = AnyObject { value: right, ty: right_ty };
+            let right =
+                ScalarOrNDArray::split_object(generator, ctx, right).to_ndarray(generator, ctx);
 
-                assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
+            let result_ndarray = NDArrayObject::broadcast_starmap(
+                generator,
+                ctx,
+                &[left, right],
+                NDArrayOut::NewNDArray { dtype: ctx.primitives.bool },
+                |generator, ctx, scalars| {
+                    let left_scalar = scalars[0];
+                    let right_scalar = scalars[1];
 
-                let left_val =
-                    NDArrayValue::from_ptr_val(lhs.into_pointer_value(), llvm_usize, None);
-                let res = numpy::ndarray_elementwise_binop_impl(
-                    generator,
-                    ctx,
-                    ctx.primitives.bool,
-                    None,
-                    (left_val.as_base_value().into(), false),
-                    (rhs, false),
-                    |generator, ctx, (lhs, rhs)| {
-                        let val = gen_cmpop_expr_with_values(
-                            generator,
-                            ctx,
-                            (Some(ndarray_dtype1), lhs),
-                            &[op],
-                            &[(Some(ndarray_dtype2), rhs)],
-                        )?
-                        .unwrap()
-                        .to_basic_value_enum(
-                            ctx,
-                            generator,
-                            ctx.primitives.bool,
-                        )?;
+                    let val = gen_cmpop_expr_with_values(
+                        generator,
+                        ctx,
+                        (Some(left.dtype), left_scalar),
+                        &[op],
+                        &[(Some(right.dtype), right_scalar)],
+                    )?
+                    .unwrap()
+                    .to_basic_value_enum(
+                        ctx,
+                        generator,
+                        ctx.primitives.bool,
+                    )?;
 
-                        Ok(generator.bool_to_i8(ctx, val.into_int_value()).into())
-                    },
-                )?;
+                    Ok(generator.bool_to_i8(ctx, val.into_int_value()).into())
+                },
+            )?;
 
-                Ok(Some(res.as_base_value().into()))
-            } else {
-                let (ndarray_dtype, _) = unpack_ndarray_var_tys(
-                    &mut ctx.unifier,
-                    if is_ndarray1 { left_ty } else { right_ty },
-                );
-                let res = numpy::ndarray_elementwise_binop_impl(
-                    generator,
-                    ctx,
-                    ctx.primitives.bool,
-                    None,
-                    (lhs, !is_ndarray1),
-                    (rhs, !is_ndarray2),
-                    |generator, ctx, (lhs, rhs)| {
-                        let val = gen_cmpop_expr_with_values(
-                            generator,
-                            ctx,
-                            (Some(ndarray_dtype), lhs),
-                            &[op],
-                            &[(Some(ndarray_dtype), rhs)],
-                        )?
-                        .unwrap()
-                        .to_basic_value_enum(
-                            ctx,
-                            generator,
-                            ctx.primitives.bool,
-                        )?;
-
-                        Ok(generator.bool_to_i8(ctx, val.into_int_value()).into())
-                    },
-                )?;
-
-                Ok(Some(res.as_base_value().into()))
-            };
+            return Ok(Some(result_ndarray.instance.value.into()));
         }
     }
 

nac3core/src/codegen/numpy.rs

@@ -5,7 +5,6 @@ use crate::{
             ProxyType, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeAdapter,
             TypedArrayLikeMutator, UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
         },
-        expr::gen_binop_expr_with_values,
         irrt::{
             calculate_len_for_slice_range, call_ndarray_calc_broadcast,
             call_ndarray_calc_broadcast_index, call_ndarray_calc_nd_indices,
@@ -15,9 +14,12 @@ use crate::{
         model::*,
         object::{
             any::AnyObject,
-            ndarray::{shape_util::parse_numpy_int_sequence, NDArrayObject},
+            ndarray::{nditer::NDIterHandle, shape_util::parse_numpy_int_sequence, NDArrayObject},
+        },
+        stmt::{
+            gen_for_callback, gen_for_callback_incrementing, gen_for_range_callback,
+            gen_if_else_expr_callback,
         },
-        stmt::{gen_for_callback_incrementing, gen_for_range_callback, gen_if_else_expr_callback},
         CodeGenContext, CodeGenerator,
     },
     symbol_resolver::ValueEnum,
@@ -26,21 +28,18 @@ use crate::{
         numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
         DefinitionId,
     },
-    typecheck::{
-        magic_methods::Binop,
-        typedef::{FunSignature, Type},
-    },
+    typecheck::typedef::{FunSignature, Type},
 };
 use inkwell::{
     types::BasicType,
     values::{BasicValueEnum, IntValue, PointerValue},
-    AddressSpace, IntPredicate, OptimizationLevel,
+    AddressSpace, IntPredicate,
 };
 use inkwell::{
     types::{AnyTypeEnum, BasicTypeEnum, PointerType},
     values::BasicValue,
 };
-use nac3parser::ast::{Operator, StrRef};
+use nac3parser::ast::StrRef;
 
 /// Creates an uninitialized `NDArray` instance.
 fn create_ndarray_uninitialized<'ctx, G: CodeGenerator + ?Sized>(
@@ -1708,77 +1707,88 @@ pub fn ndarray_dot<'ctx, G: CodeGenerator + ?Sized>(
 ) -> Result<BasicValueEnum<'ctx>, String> {
     const FN_NAME: &str = "ndarray_dot";
     let (x1_ty, x1) = x1;
-    let (_, x2) = x2;
-
-    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let (x2_ty, x2) = x2;
 
     match (x1, x2) {
-        (BasicValueEnum::PointerValue(n1), BasicValueEnum::PointerValue(n2)) => {
-            let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-            let n2 = NDArrayValue::from_ptr_val(n2, llvm_usize, None);
+        (BasicValueEnum::PointerValue(_), BasicValueEnum::PointerValue(_)) => {
+            let a = AnyObject { ty: x1_ty, value: x1 };
+            let b = AnyObject { ty: x2_ty, value: x2 };
 
-            let n1_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
-            let n2_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
+            let a = NDArrayObject::from_object(generator, ctx, a);
+            let b = NDArrayObject::from_object(generator, ctx, b);
 
+            // TODO: General `np.dot()` https://numpy.org/doc/stable/reference/generated/numpy.dot.html.
+            assert_eq!(a.ndims, 1);
+            assert_eq!(b.ndims, 1);
+            let common_dtype = a.dtype;
+
+            // Check shapes.
+            let a_size = a.size(generator, ctx);
+            let b_size = b.size(generator, ctx);
+            let same_shape = a_size.compare(ctx, IntPredicate::EQ, b_size);
             ctx.make_assert(
                 generator,
-                ctx.builder.build_int_compare(IntPredicate::EQ, n1_sz, n2_sz, "").unwrap(),
+                same_shape.value,
                 "0:ValueError",
-                "shapes ({0}), ({1}) not aligned",
-                [Some(n1_sz), Some(n2_sz), None],
+                "shapes ({0},) and ({1},) not aligned: {0} (dim 0) != {1} (dim 1)",
+                [Some(a_size.value), Some(b_size.value), None],
                 ctx.current_loc,
             );
 
-            let identity =
-                unsafe { n1.data().get_unchecked(ctx, generator, &llvm_usize.const_zero(), None) };
-            let acc = ctx.builder.build_alloca(identity.get_type(), "").unwrap();
-            ctx.builder.build_store(acc, identity.get_type().const_zero()).unwrap();
+            let dtype_llvm = ctx.get_llvm_type(generator, common_dtype);
 
-            gen_for_callback_incrementing(
+            let result = ctx.builder.build_alloca(dtype_llvm, "np_dot_result").unwrap();
+            ctx.builder.build_store(result, dtype_llvm.const_zero()).unwrap();
+
+            // Do dot product.
+            gen_for_callback(
                 generator,
                 ctx,
-                None,
-                llvm_usize.const_zero(),
-                (n1_sz, false),
-                |generator, ctx, _, idx| {
-                    let elem1 = unsafe { n1.data().get_unchecked(ctx, generator, &idx, None) };
-                    let elem2 = unsafe { n2.data().get_unchecked(ctx, generator, &idx, None) };
+                Some("np_dot"),
+                |generator, ctx| {
+                    let a_iter = NDIterHandle::new(generator, ctx, a);
+                    let b_iter = NDIterHandle::new(generator, ctx, b);
+                    Ok((a_iter, b_iter))
+                },
+                |generator, ctx, (a_iter, _b_iter)| {
+                    // Only a_iter drives the condition, b_iter should have the same status.
+                    Ok(a_iter.has_next(generator, ctx).value)
+                },
+                |generator, ctx, _hooks, (a_iter, b_iter)| {
+                    let a_scalar = a_iter.get_scalar(generator, ctx).value;
+                    let b_scalar = b_iter.get_scalar(generator, ctx).value;
 
-                    let product = match elem1 {
-                        BasicValueEnum::IntValue(e1) => ctx
-                            .builder
-                            .build_int_mul(e1, elem2.into_int_value(), "")
-                            .unwrap()
-                            .as_basic_value_enum(),
-                        BasicValueEnum::FloatValue(e1) => ctx
-                            .builder
-                            .build_float_mul(e1, elem2.into_float_value(), "")
-                            .unwrap()
-                            .as_basic_value_enum(),
-                        _ => unreachable!(),
+                    let old_result = ctx.builder.build_load(result, "").unwrap();
+                    let new_result: BasicValueEnum<'ctx> = match old_result {
+                        BasicValueEnum::IntValue(old_result) => {
+                            let a_scalar = a_scalar.into_int_value();
+                            let b_scalar = b_scalar.into_int_value();
+                            let x = ctx.builder.build_int_mul(a_scalar, b_scalar, "").unwrap();
+                            ctx.builder.build_int_add(old_result, x, "").unwrap().into()
+                        }
+                        BasicValueEnum::FloatValue(old_result) => {
+                            let a_scalar = a_scalar.into_float_value();
+                            let b_scalar = b_scalar.into_float_value();
+                            let x = ctx.builder.build_float_mul(a_scalar, b_scalar, "").unwrap();
+                            ctx.builder.build_float_add(old_result, x, "").unwrap().into()
+                        }
+                        _ => {
+                            panic!("Unrecognized dtype: {}", ctx.unifier.stringify(common_dtype));
+                        }
                     };
-                    let acc_val = ctx.builder.build_load(acc, "").unwrap();
-                    let acc_val = match acc_val {
-                        BasicValueEnum::IntValue(e1) => ctx
-                            .builder
-                            .build_int_add(e1, product.into_int_value(), "")
-                            .unwrap()
-                            .as_basic_value_enum(),
-                        BasicValueEnum::FloatValue(e1) => ctx
-                            .builder
-                            .build_float_add(e1, product.into_float_value(), "")
-                            .unwrap()
-                            .as_basic_value_enum(),
-                        _ => unreachable!(),
-                    };
-                    ctx.builder.build_store(acc, acc_val).unwrap();
 
+                    ctx.builder.build_store(result, new_result).unwrap();
                     Ok(())
                 },
-                llvm_usize.const_int(1, false),
-            )?;
-            let acc_val = ctx.builder.build_load(acc, "").unwrap();
-            Ok(acc_val)
+                |generator, ctx, (a_iter, b_iter)| {
+                    a_iter.next(generator, ctx);
+                    b_iter.next(generator, ctx);
+                    Ok(())
+                },
+            )
+            .unwrap();
+
+            Ok(ctx.builder.build_load(result, "").unwrap())
         }
         (BasicValueEnum::IntValue(e1), BasicValueEnum::IntValue(e2)) => {
             Ok(ctx.builder.build_int_mul(e1, e2, "").unwrap().as_basic_value_enum())

nac3core/src/codegen/object/ndarray/contiguous.rs

@@ -0,0 +1,134 @@
+use crate::{
+    codegen::{model::*, CodeGenContext, CodeGenerator},
+    typecheck::typedef::Type,
+};
+
+use super::NDArrayObject;
+
+/// Fields of [`ContiguousNDArray`]
+pub struct ContiguousNDArrayFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
+    pub ndims: F::Out<Int<SizeT>>,
+    pub shape: F::Out<Ptr<Int<SizeT>>>,
+    pub data: F::Out<Ptr<Item>>,
+}
+
+/// An ndarray without strides and non-opaque `data` field in NAC3.
+#[derive(Debug, Clone, Copy)]
+pub struct ContiguousNDArray<M> {
+    /// [`Model`] of the items.
+    pub item: M,
+}
+
+impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for ContiguousNDArray<Item> {
+    type Fields<F: FieldTraversal<'ctx>> = ContiguousNDArrayFields<'ctx, F, Item>;
+
+    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
+        Self::Fields {
+            ndims: traversal.add_auto("ndims"),
+            shape: traversal.add_auto("shape"),
+            data: traversal.add("data", Ptr(self.item)),
+        }
+    }
+}
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// Create a [`ContiguousNDArray`] from the contents of this ndarray.
+    ///
+    /// This function may or may not be expensive depending on if this ndarray has contiguous data.
+    ///
+    /// If this ndarray is not C-contiguous, this function will allocate memory on the stack for the `data` field of
+    /// the returned [`ContiguousNDArray`] and copy contents of this ndarray to there.
+    ///
+    /// If this ndarray is C-contiguous, contents of this ndarray will not be copied. The created [`ContiguousNDArray`]
+    /// will share memory with this ndarray.
+    ///
+    /// The `item_model` sets the [`Model`] of the returned [`ContiguousNDArray`]'s `Item` model for type-safety, and
+    /// should match the `ctx.get_llvm_type()` of this ndarray's `dtype`. Otherwise this function panics. Use model [`Any`]
+    /// if you don't care/cannot know the [`Model`] in advance.
+    pub fn make_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        item_model: Item,
+    ) -> Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>> {
+        // Sanity check on `self.dtype` and `item_model`.
+        let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
+        item_model.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
+
+        let cdarray_model = Struct(ContiguousNDArray { item: item_model });
+
+        let current_bb = ctx.builder.get_insert_block().unwrap();
+        let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
+        let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
+        let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
+
+        // Allocate and setup the resulting [`ContiguousNDArray`].
+        let result = cdarray_model.alloca(generator, ctx);
+
+        // Set ndims and shape.
+        let ndims = self.ndims_llvm(generator, ctx.ctx);
+        result.set(ctx, |f| f.ndims, ndims);
+
+        let shape = self.instance.get(generator, ctx, |f| f.shape);
+        result.set(ctx, |f| f.shape, shape);
+
+        let is_contiguous = self.is_c_contiguous(generator, ctx);
+        ctx.builder.build_conditional_branch(is_contiguous.value, then_bb, else_bb).unwrap();
+
+        // Inserting into then_bb; This ndarray is contiguous.
+        ctx.builder.position_at_end(then_bb);
+        let data = self.instance.get(generator, ctx, |f| f.data);
+        let data = data.pointer_cast(generator, ctx, item_model);
+        result.set(ctx, |f| f.data, data);
+        ctx.builder.build_unconditional_branch(end_bb).unwrap();
+
+        // Inserting into else_bb; This ndarray is not contiguous. Do a full-copy on `data`.
+        // `make_copy` produces an ndarray with contiguous `data`.
+        ctx.builder.position_at_end(else_bb);
+        let copied_ndarray = self.make_copy(generator, ctx);
+        let data = copied_ndarray.instance.get(generator, ctx, |f| f.data);
+        let data = data.pointer_cast(generator, ctx, item_model);
+        result.set(ctx, |f| f.data, data);
+        ctx.builder.build_unconditional_branch(end_bb).unwrap();
+
+        // Reposition to end_bb for continuation
+        ctx.builder.position_at_end(end_bb);
+
+        result
+    }
+
+    /// Create an [`NDArrayObject`] from a [`ContiguousNDArray`].
+    ///
+    /// The operation is super cheap. The newly created [`NDArrayObject`] will share the
+    /// same memory as the [`ContiguousNDArray`].
+    ///
+    /// `ndims` has to be provided as [`NDArrayObject`] requires a statically known `ndims` value, despite
+    /// the fact that the information should be contained within the [`ContiguousNDArray`].
+    pub fn from_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        carray: Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>>,
+        dtype: Type,
+        ndims: u64,
+    ) -> Self {
+        // Sanity check on `dtype` and `contiguous_array`'s `Item` model.
+        let dtype_llvm = ctx.get_llvm_type(generator, dtype);
+        carray.model.0 .0.item.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
+
+        // TODO: Debug assert `ndims == carray.ndims` to catch bugs.
+
+        // Allocate the resulting ndarray.
+        let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
+
+        // Copy shape and update strides
+        let shape = carray.get(generator, ctx, |f| f.shape);
+        ndarray.copy_shape_from_array(generator, ctx, shape);
+        ndarray.set_strides_contiguous(generator, ctx);
+
+        // Share data
+        let data = carray.get(generator, ctx, |f| f.data).pointer_cast(generator, ctx, Int(Byte));
+        ndarray.instance.set(ctx, |f| f.data, data);
+
+        ndarray
+    }
+}

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

@@ -1,5 +1,6 @@
 pub mod array;
 pub mod broadcast;
+pub mod contiguous;
 pub mod factory;
 pub mod indexing;
 pub mod map;
@@ -26,7 +27,10 @@ use crate::{
         model::*,
         CodeGenContext, CodeGenerator,
     },
-    toplevel::{helper::extract_ndims, numpy::unpack_ndarray_var_tys},
+    toplevel::{
+        helper::{create_ndims, extract_ndims},
+        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
+    },
     typecheck::typedef::{Type, TypeEnum},
 };
 
@@ -108,6 +112,18 @@ impl<'ctx> NDArrayObject<'ctx> {
         Int(SizeT).const_int(generator, ctx, self.ndims)
     }
 
+    /// Get the typechecker ndarray type of this [`NDArrayObject`].
+    pub fn get_type(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Type {
+        let ndims = create_ndims(&mut ctx.unifier, self.ndims);
+        make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(self.dtype), Some(ndims))
+    }
+
+    /// Forget that this is an ndarray and convert into an [`AnyObject`].
+    pub fn to_any(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
+        let ty = self.get_type(ctx);
+        AnyObject { value: self.instance.value.as_basic_value_enum(), ty }
+    }
+
     /// Allocate an ndarray on the stack given its `ndims` and `dtype`.
     ///
     /// `shape` and `strides` will be automatically allocated on the stack.

nac3core/src/toplevel/builtins.rs

@@ -2076,10 +2076,12 @@ impl<'a> BuiltinBuilder<'a> {
                 Box::new(move |ctx, _, fun, args, generator| {
                     let x1_ty = fun.0.args[0].ty;
                     let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
+
                     let x2_ty = fun.0.args[1].ty;
                     let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
 
-                    Ok(Some(ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
+                    let result = ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?;
+                    Ok(Some(result))
                 }),
             ),