nac3core/src/codegen/expr.rs

@@ -384,7 +384,7 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
         rhs: BasicValueEnum<'ctx>,
     ) -> BasicValueEnum<'ctx> {
         let (BasicValueEnum::FloatValue(lhs), BasicValueEnum::FloatValue(rhs)) = (lhs, rhs) else {
-            unreachable!()
+            unreachable!("Expected (FloatValue, FloatValue), got ({}, {})", lhs.get_type(), rhs.get_type())
         };
         match op {
             Operator::Add => self.builder.build_float_add(lhs, rhs, "fadd").map(Into::into).unwrap(),
@@ -589,8 +589,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
         // even if this assumption is violated, it does not matter as exception unwinding is
         // slow anyway...
         let cond = call_expect(self, cond, i1_true, Some("expect"));
-        let current_fun = self.builder.get_insert_block().unwrap().get_parent().unwrap();
-        let then_block = self.ctx.append_basic_block(current_fun, "succ");
+        let current_bb = self.builder.get_insert_block().unwrap();
+        let current_fun = current_bb.get_parent().unwrap();
+        let then_block = self.ctx.insert_basic_block_after(current_bb, "succ");
         let exn_block = self.ctx.append_basic_block(current_fun, "fail");
         self.builder.build_conditional_branch(cond, then_block, exn_block).unwrap();
         self.builder.position_at_end(exn_block);
@@ -1148,27 +1149,45 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
             let left_val = NDArrayValue::from_ptr_val(
                 left_val.into_pointer_value(),
                 llvm_usize,
-                None
+                None,
             );
-            let res = numpy::ndarray_elementwise_binop_impl(
-                generator,
-                ctx,
-                ndarray_dtype1,
-                if is_aug_assign { Some(left_val) } else { None },
-                (left_val.as_ptr_value().into(), false),
-                (right_val, false),
-                |generator, ctx, (lhs, rhs)| {
-                    gen_binop_expr_with_values(
-                        generator,
-                        ctx,
-                        (&Some(ndarray_dtype1), lhs),
-                        op,
-                        (&Some(ndarray_dtype2), rhs),
-                        ctx.current_loc,
-                        is_aug_assign,
-                    )?.unwrap().to_basic_value_enum(ctx, generator, ndarray_dtype1)
-                },
-            )?;
+            let right_val = NDArrayValue::from_ptr_val(
+                right_val.into_pointer_value(),
+                llvm_usize,
+                None,
+            );
+
+            let res = if *op == Operator::MatMult {
+                // MatMult is the only binop which is not an elementwise op
+                numpy::ndarray_matmul_2d(
+                    generator,
+                    ctx,
+                    ndarray_dtype1,
+                    if is_aug_assign { Some(left_val) } else { None },
+                    left_val,
+                    right_val,
+                )?
+            } else {
+                numpy::ndarray_elementwise_binop_impl(
+                    generator,
+                    ctx,
+                    ndarray_dtype1,
+                    if is_aug_assign { Some(left_val) } else { None },
+                    (left_val.as_ptr_value().into(), false),
+                    (right_val.as_ptr_value().into(), false),
+                    |generator, ctx, (lhs, rhs)| {
+                        gen_binop_expr_with_values(
+                            generator,
+                            ctx,
+                            (&Some(ndarray_dtype1), lhs),
+                            op,
+                            (&Some(ndarray_dtype2), rhs),
+                            ctx.current_loc,
+                            is_aug_assign,
+                        )?.unwrap().to_basic_value_enum(ctx, generator, ndarray_dtype1)
+                    },
+                )?
+            };
 
             Ok(Some(res.as_ptr_value().into()))
         } else {

nac3core/src/codegen/numpy.rs

@@ -1,9 +1,5 @@
-use inkwell::{
-    IntPredicate, 
-    types::BasicType, 
-    values::{BasicValueEnum, IntValue, PointerValue}
-};
-use nac3parser::ast::StrRef;
+use inkwell::{IntPredicate, OptimizationLevel, types::BasicType, values::{BasicValueEnum, IntValue, PointerValue}};
+use nac3parser::ast::{Operator, StrRef};
 use crate::{
     codegen::{
         classes::{
@@ -14,17 +10,20 @@ use crate::{
             TypedArrayLikeAccessor,
             TypedArrayLikeAdapter,
             UntypedArrayLikeAccessor,
+            UntypedArrayLikeMutator,
         },
         CodeGenContext,
         CodeGenerator,
+        expr::gen_binop_expr_with_values,
         irrt::{
             call_ndarray_calc_broadcast,
             call_ndarray_calc_broadcast_index,
             call_ndarray_calc_nd_indices,
             call_ndarray_calc_size,
         },
-        llvm_intrinsics::call_memcpy_generic,
-        stmt::gen_for_callback_incrementing,
+        llvm_intrinsics,
+        llvm_intrinsics::{call_memcpy_generic},
+        stmt::{gen_for_callback_incrementing, gen_if_else_expr_callback},
     },
     symbol_resolver::ValueEnum,
     toplevel::{
@@ -85,6 +84,8 @@ fn create_ndarray_dyn_shape<'ctx, 'a, G, V, LenFn, DataFn>(
                 [None, None, None],
                 ctx.current_loc,
             );
+            
+            // TODO: Disallow dim_sz > u32_MAX
 
             Ok(())
         },
@@ -171,6 +172,8 @@ fn create_ndarray_const_shape<'ctx, G: CodeGenerator + ?Sized>(
             [None, None, None],
             ctx.current_loc,
         );
+
+        // TODO: Disallow dim_sz > u32_MAX
     }
 
     let ndarray = generator.gen_var_alloc(
@@ -824,6 +827,319 @@ pub fn ndarray_elementwise_binop_impl<'ctx, G, ValueFn>(
     Ok(ndarray)
 }
 
+/// LLVM-typed implementation for computing matrix multiplication between two 2D `ndarray`s.
+///
+/// * `elem_ty` - The element type of the `NDArray`.
+/// * `res` - The `ndarray` instance to write results into, or [`None`] if the result should be
+/// written to a new `ndarray`.
+pub fn ndarray_matmul_2d<'ctx, G: CodeGenerator>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    res: Option<NDArrayValue<'ctx>>,
+    lhs: NDArrayValue<'ctx>,
+    rhs: NDArrayValue<'ctx>,
+) -> Result<NDArrayValue<'ctx>, String> {
+    let llvm_i32 = ctx.ctx.i32_type();
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    if cfg!(debug_assertions) {
+        let lhs_ndims = lhs.load_ndims(ctx);
+        let rhs_ndims = rhs.load_ndims(ctx);
+
+        // lhs.ndims == 2
+        ctx.make_assert(
+            generator,
+            ctx.builder.build_int_compare(
+                IntPredicate::EQ,
+                lhs_ndims,
+                llvm_usize.const_int(2, false),
+                "",
+            ).unwrap(),
+            "0:ValueError",
+            "",
+            [None, None, None],
+            ctx.current_loc,
+        );
+
+        // rhs.ndims == 2
+        ctx.make_assert(
+            generator,
+            ctx.builder.build_int_compare(
+                IntPredicate::EQ,
+                rhs_ndims,
+                llvm_usize.const_int(2, false),
+                "",
+            ).unwrap(),
+            "0:ValueError",
+            "",
+            [None, None, None],
+            ctx.current_loc,
+        );
+
+        if let Some(res) = res {
+            let res_ndims = res.load_ndims(ctx);
+            let res_dim0 = unsafe {
+                res.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
+            };
+            let res_dim1 = unsafe {
+                res.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
+            };
+            let lhs_dim0 = unsafe {
+                lhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
+            };
+            let rhs_dim1 = unsafe {
+                rhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
+            };
+
+            // res.ndims == 2
+            ctx.make_assert(
+                generator,
+                ctx.builder.build_int_compare(
+                    IntPredicate::EQ,
+                    res_ndims,
+                    llvm_usize.const_int(2, false),
+                    "",
+                ).unwrap(),
+                "0:ValueError",
+                "",
+                [None, None, None],
+                ctx.current_loc,
+            );
+
+            // res.dims[0] == lhs.dims[0]
+            ctx.make_assert(
+                generator,
+                ctx.builder.build_int_compare(
+                    IntPredicate::EQ,
+                    lhs_dim0,
+                    res_dim0,
+                    "",
+                ).unwrap(),
+                "0:ValueError",
+                "",
+                [None, None, None],
+                ctx.current_loc,
+            );
+
+            // res.dims[1] == rhs.dims[0]
+            ctx.make_assert(
+                generator,
+                ctx.builder.build_int_compare(
+                    IntPredicate::EQ,
+                    rhs_dim1,
+                    res_dim1,
+                    "",
+                ).unwrap(),
+                "0:ValueError",
+                "",
+                [None, None, None],
+                ctx.current_loc,
+            );
+        }
+    }
+
+    if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
+        let lhs_dim1 = unsafe {
+            lhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
+        };
+        let rhs_dim0 = unsafe {
+            rhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
+        };
+
+        // lhs.dims[1] == rhs.dims[0]
+        ctx.make_assert(
+            generator,
+            ctx.builder.build_int_compare(
+                IntPredicate::EQ,
+                lhs_dim1,
+                rhs_dim0,
+                "",
+            ).unwrap(),
+            "0:ValueError",
+            "",
+            [None, None, None],
+            ctx.current_loc,
+        );
+    }
+
+    let lhs = if res.is_some_and(|res| res.as_ptr_value() == lhs.as_ptr_value()) {
+        ndarray_copy_impl(generator, ctx, elem_ty, lhs)?
+    } else {
+        lhs
+    };
+
+    let ndarray = res.unwrap_or_else(|| {
+        create_ndarray_dyn_shape(
+            generator,
+            ctx,
+            elem_ty,
+            &(lhs, rhs),
+            |_, _, _| {
+                Ok(llvm_usize.const_int(2, false))
+            },
+            |generator, ctx, (lhs, rhs), idx| {
+                gen_if_else_expr_callback(
+                    generator,
+                    ctx,
+                    |_, ctx| {
+                        Ok(ctx.builder.build_int_compare(
+                            IntPredicate::EQ,
+                            idx,
+                            llvm_usize.const_zero(),
+                            "",
+                        ).unwrap())
+                    },
+                    |generator, ctx| {
+                        Ok(Some(unsafe {
+                            lhs.dim_sizes().get_typed_unchecked(
+                                ctx,
+                                generator,
+                                &llvm_usize.const_zero(),
+                                None,
+                            )
+                        }))
+                    },
+                    |generator, ctx| {
+                        Ok(Some(unsafe {
+                            rhs.dim_sizes().get_typed_unchecked(
+                                ctx,
+                                generator,
+                                &llvm_usize.const_int(1, false),
+                                None,
+                            )
+                        }))
+                    },
+                ).map(|v| v.map(BasicValueEnum::into_int_value).unwrap())
+            },
+        ).unwrap()
+    });
+
+    let llvm_ndarray_ty = ctx.get_llvm_type(generator, elem_ty);
+
+    ndarray_fill_indexed(
+        generator,
+        ctx,
+        ndarray,
+        |generator, ctx, idx| {
+            llvm_intrinsics::call_expect(
+                ctx,
+                idx.size(ctx, generator).get_type().const_int(2, false),
+                idx.size(ctx, generator),
+                None,
+            );
+
+            let common_dim = {
+                let lhs_idx1 = unsafe {
+                    lhs.dim_sizes().get_typed_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(1, false),
+                        None,
+                    )
+                };
+                let rhs_idx0 = unsafe {
+                    rhs.dim_sizes().get_typed_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_zero(),
+                        None,
+                    )
+                };
+
+                let idx = llvm_intrinsics::call_expect(ctx, rhs_idx0, lhs_idx1, None);
+
+                ctx.builder.build_int_truncate(idx, llvm_i32, "").unwrap()
+            };
+
+            let idx0 = unsafe {
+                let idx0 = idx.get_typed_unchecked(
+                    ctx,
+                    generator,
+                    &llvm_usize.const_zero(),
+                    None,
+                );
+
+                ctx.builder.build_int_truncate(idx0, llvm_i32, "").unwrap()
+            };
+            let idx1 = unsafe {
+                let idx1 = idx.get_typed_unchecked(
+                    ctx,
+                    generator,
+                    &llvm_usize.const_int(1, false),
+                    None,
+                );
+
+                ctx.builder.build_int_truncate(idx1, llvm_i32, "").unwrap()
+            };
+
+            let result_addr = generator.gen_var_alloc(ctx, llvm_ndarray_ty, None)?;
+            let result_identity = ndarray_zero_value(generator, ctx, elem_ty);
+            ctx.builder.build_store(result_addr, result_identity).unwrap();
+
+            gen_for_callback_incrementing(
+                generator,
+                ctx,
+                llvm_i32.const_zero(),
+                (common_dim, false),
+                |generator, ctx, i| {
+                    let i = ctx.builder.build_int_truncate(i, llvm_i32, "").unwrap();
+
+                    let ab_idx = generator.gen_array_var_alloc(
+                        ctx,
+                        llvm_i32.into(),
+                        llvm_usize.const_int(2, false),
+                        None,
+                    )?;
+
+                    let a = unsafe {
+                        ab_idx.set_unchecked(ctx, generator, &llvm_usize.const_zero(), idx0.into());
+                        ab_idx.set_unchecked(ctx, generator, &llvm_usize.const_int(1, false), i.into());
+
+                        lhs.data().get_unchecked(ctx, generator, &ab_idx, None)
+                    };
+                    let b = unsafe {
+                        ab_idx.set_unchecked(ctx, generator, &llvm_usize.const_zero(), i.into());
+                        ab_idx.set_unchecked(ctx, generator, &llvm_usize.const_int(1, false), idx1.into());
+
+                        rhs.data().get_unchecked(ctx, generator, &ab_idx, None)
+                    };
+
+                    let a_mul_b = gen_binop_expr_with_values(
+                        generator,
+                        ctx,
+                        (&Some(elem_ty), a),
+                        &Operator::Mult,
+                        (&Some(elem_ty), b),
+                        ctx.current_loc,
+                        false,
+                    )?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)?;
+
+                    let result = ctx.builder.build_load(result_addr, "").unwrap();
+                    let result = gen_binop_expr_with_values(
+                        generator,
+                        ctx,
+                        (&Some(elem_ty), result),
+                        &Operator::Add,
+                        (&Some(elem_ty), a_mul_b),
+                        ctx.current_loc,
+                        false,
+                    )?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)?;
+                    ctx.builder.build_store(result_addr, result).unwrap();
+
+                    Ok(())
+                },
+                llvm_usize.const_int(1, false),
+            )?;
+
+            let result = ctx.builder.build_load(result_addr, "").unwrap();
+            Ok(result)
+        }
+    )?;
+
+    Ok(ndarray)
+}
+
 /// Generates LLVM IR for `ndarray.empty`.
 pub fn gen_ndarray_empty<'ctx>(
     context: &mut CodeGenContext<'ctx, '_>,

nac3core/src/codegen/stmt.rs

@@ -495,14 +495,14 @@ pub fn gen_for_callback<'ctx, 'a, G, I, InitFn, CondFn, BodyFn, UpdateFn>(
         BodyFn: FnOnce(&mut G, &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");
+    let current_bb = ctx.builder.get_insert_block().unwrap();
+    let init_bb = ctx.ctx.insert_basic_block_after(current_bb, "for.init");
     // The BB containing the loop condition check
-    let cond_bb = ctx.ctx.append_basic_block(current, "for.cond");
-    let body_bb = ctx.ctx.append_basic_block(current, "for.body");
+    let cond_bb = ctx.ctx.insert_basic_block_after(init_bb, "for.cond");
+    let body_bb = ctx.ctx.insert_basic_block_after(cond_bb, "for.body");
     // The BB containing the increment expression
-    let update_bb = ctx.ctx.append_basic_block(current, "for.update");
-    let cont_bb = ctx.ctx.append_basic_block(current, "for.end");
+    let update_bb = ctx.ctx.insert_basic_block_after(body_bb, "for.update");
+    let cont_bb = ctx.ctx.insert_basic_block_after(update_bb, "for.end");
 
     // store loop bb information and restore it later
     let loop_bb = ctx.loop_target.replace((update_bb, cont_bb));
@@ -719,12 +719,10 @@ pub fn gen_if_else_expr_callback<'ctx, 'a, G, CondFn, ThenFn, ElseFn, R>(
         R: BasicValue<'ctx>,
 {
     let current_bb = ctx.builder.get_insert_block().unwrap();
-    let current_fn = current_bb.get_parent().unwrap();
-
-    let end_bb = ctx.ctx.append_basic_block(current_fn, "if.end");
 
     let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "if.then");
-    let else_bb = ctx.ctx.insert_basic_block_after(current_bb, "if.else");
+    let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "if.else");
+    let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "if.end");
 
     let cond = cond_fn(generator, ctx)?;
     assert_eq!(cond.get_type().get_bit_width(), ctx.ctx.bool_type().get_bit_width());
@@ -742,6 +740,7 @@ pub fn gen_if_else_expr_callback<'ctx, 'a, G, CondFn, ThenFn, ElseFn, R>(
         ctx.builder.build_unconditional_branch(end_bb).unwrap();
     }
 
+    ctx.builder.position_at_end(end_bb);
     let phi = match (then_val, else_val) {
         (Some(tv), Some(ev)) => {
             let tv_ty = tv.as_basic_value_enum().get_type();

nac3core/src/typecheck/magic_methods.rs

@@ -291,6 +291,17 @@ pub fn impl_mod(
     impl_binop(unifier, store, ty, other_ty, ret_ty, &[Operator::Mod]);
 }
 
+/// [Operator::MatMult]
+pub fn impl_matmul(
+    unifier: &mut Unifier,
+    store: &PrimitiveStore,
+    ty: Type,
+    other_ty: &[Type],
+    ret_ty: Option<Type>, 
+) {
+    impl_binop(unifier, store, ty, other_ty, ret_ty, &[Operator::MatMult])
+}
+
 /// `UAdd`, `USub`
 pub fn impl_sign(unifier: &mut Unifier, _store: &PrimitiveStore, ty: Type, ret_ty: Option<Type>) {
     impl_unaryop(unifier, ty, ret_ty, &[Unaryop::UAdd, Unaryop::USub]);
@@ -431,7 +442,38 @@ pub fn typeof_binop(
             }
         }
 
-        Operator::MatMult => typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?,
+        Operator::MatMult => {
+            let (_, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
+            let lhs_ndims = match &*unifier.get_ty_immutable(lhs_ndims) {
+                TypeEnum::TLiteral { values, .. } => {
+                    assert_eq!(values.len(), 1);
+                    u64::try_from(values[0].clone()).unwrap()
+                }
+                _ => unreachable!(),
+            };
+            let (_, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
+            let rhs_ndims = match &*unifier.get_ty_immutable(rhs_ndims) {
+                TypeEnum::TLiteral { values, .. } => {
+                    assert_eq!(values.len(), 1);
+                    u64::try_from(values[0].clone()).unwrap()
+                }
+                _ => unreachable!(),
+            };
+
+            match (lhs_ndims, rhs_ndims) {
+                (2, 2) => typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?,
+                (lhs, rhs) if lhs == 0 || rhs == 0 => {
+                    return Err(format!(
+                        "Input operand {} does not have enough dimensions (has {lhs}, requires {rhs})",
+                        (rhs == 0) as u8
+                    ))
+                }
+                (lhs, rhs) => {
+                    return Err(format!("ndarray.__matmul__ on {lhs}D and {rhs}D operands not supported"))
+                }
+            }
+        }
+
         Operator::Div => {
             if is_left_ndarray || is_right_ndarray {
                 typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?
@@ -610,6 +652,7 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
     impl_div(unifier, store, ndarray_t, &[ndarray_t, ndarray_dtype_t], None);
     impl_floordiv(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
     impl_mod(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
+    impl_matmul(unifier, store, ndarray_t, &[ndarray_t], Some(ndarray_t));
     impl_sign(unifier, store, ndarray_t, Some(ndarray_t));
     impl_invert(unifier, store, ndarray_t, Some(ndarray_t));
     impl_eq(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);

nac3standalone/demo/src/ndarray.py

@@ -429,6 +429,19 @@ def test_ndarray_ipow_broadcast_scalar():
 
     output_ndarray_float_2(x)
 
+def test_ndarray_matmul():
+    x = np_identity(2)
+    y = x @ np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_imatmul():
+    x = np_identity(2)
+    x @= np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+
 def test_ndarray_pos():
     x_int32 = np_full([2, 2], -2)
     y_int32 = +x_int32
@@ -696,6 +709,8 @@ def run() -> int32:
     test_ndarray_ipow()
     test_ndarray_ipow_broadcast()
     test_ndarray_ipow_broadcast_scalar()
+    test_ndarray_matmul()
+    test_ndarray_imatmul()
     test_ndarray_pos()
     test_ndarray_neg()
     test_ndarray_inv()