core: Implement elementwise binary operators

Including immediate variants of these operators.

nac3core/src/codegen/classes.rs

@@ -11,7 +11,7 @@ use crate::codegen::{
     stmt::gen_for_callback_incrementing,
 };
 
-/// An LLVM value that is array-like, i.e. it contains a contiguous, sequenced collection of 
+/// An LLVM value that is array-like, i.e. it contains a contiguous, sequenced collection of
 /// elements.
 pub trait ArrayLikeValue<'ctx> {
     /// Returns the element type of this array-like value.

nac3core/src/codegen/expr.rs

@@ -17,6 +17,7 @@ use crate::{
         get_llvm_abi_type,
         irrt::*,
         llvm_intrinsics::{call_expect, call_float_floor, call_float_pow, call_float_powi},
+        numpy,
         stmt::{gen_raise, gen_var},
         CodeGenContext, CodeGenTask,
     },
@@ -24,7 +25,7 @@ use crate::{
     toplevel::{
         DefinitionId, 
         helper::PRIMITIVE_DEF_IDS,
-        numpy::make_ndarray_ty,
+        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
         TopLevelDef,
     },
     typecheck::{
@@ -1129,6 +1130,78 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
             Some("f_pow_i")
         );
         Ok(Some(res.into()))
+    } else if ty1.obj_id(&ctx.unifier).is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray) || ty2.obj_id(&ctx.unifier).is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray) {
+        let llvm_usize = generator.get_size_type(ctx.ctx);
+
+        let is_ndarray1 = ty1.obj_id(&ctx.unifier)
+            .is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray);
+        let is_ndarray2 = ty2.obj_id(&ctx.unifier)
+            .is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray);
+
+        if is_ndarray1 && is_ndarray2 {
+            let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
+            let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty2);
+
+            assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
+
+            let left_val = NDArrayValue::from_ptr_val(
+                left_val.into_pointer_value(),
+                llvm_usize,
+                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)
+                },
+            )?;
+
+            Ok(Some(res.as_ptr_value().into()))
+        } else {
+            let (ndarray_dtype, _) = unpack_ndarray_var_tys(
+                &mut ctx.unifier,
+                if is_ndarray1 { ty1 } else { ty2 },
+            );
+            let ndarray_val = NDArrayValue::from_ptr_val(
+                if is_ndarray1 { left_val } else { right_val }.into_pointer_value(),
+                llvm_usize,
+                None,
+            );
+            let res = numpy::ndarray_elementwise_binop_impl(
+                generator,
+                ctx,
+                ndarray_dtype,
+                if is_aug_assign { Some(ndarray_val) } else { None },
+                (left_val, !is_ndarray1),
+                (right_val, !is_ndarray2),
+                |generator, ctx, (lhs, rhs)| {
+                    gen_binop_expr_with_values(
+                        generator,
+                        ctx,
+                        (&Some(ndarray_dtype), lhs),
+                        op,
+                        (&Some(ndarray_dtype), rhs),
+                        ctx.current_loc,
+                        is_aug_assign,
+                    )?.unwrap().to_basic_value_enum(ctx, generator, ndarray_dtype)
+                },
+            )?;
+
+            Ok(Some(res.as_ptr_value().into()))
+        }
     } else {
         let left_ty_enum = ctx.unifier.get_ty_immutable(left_ty.unwrap());
         let TypeEnum::TObj { fields, obj_id, .. } = left_ty_enum.as_ref() else {

nac3core/src/codegen/numpy.rs

@@ -18,6 +18,8 @@ use crate::{
         CodeGenContext,
         CodeGenerator,
         irrt::{
+            call_ndarray_calc_broadcast,
+            call_ndarray_calc_broadcast_index,
             call_ndarray_calc_nd_indices,
             call_ndarray_calc_size,
         },
@@ -338,6 +340,98 @@ fn ndarray_fill_indexed<'ctx, G, ValueFn>(
     )
 }
 
+/// Generates the LLVM IR for checking whether the source `ndarray` can be broadcast to the shape of
+/// the target `ndarray`.
+fn ndarray_assert_is_broadcastable<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    target: NDArrayValue<'ctx>,
+    source: NDArrayValue<'ctx>,
+) {
+    let array_ndims = source.load_ndims(ctx);
+    let broadcast_size = target.load_ndims(ctx);
+
+    ctx.make_assert(
+        generator,
+        ctx.builder.build_int_compare(IntPredicate::ULE, array_ndims, broadcast_size, "").unwrap(),
+        "0:ValueError",
+        "operands cannot be broadcast together",
+        [None, None, None],
+        ctx.current_loc,
+    );
+}
+
+/// Generates the LLVM IR for populating the entire `NDArray` from two `ndarray` or scalar value
+/// with broadcast-compatible shapes.
+fn ndarray_broadcast_fill<'ctx, G, ValueFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    res: NDArrayValue<'ctx>,
+    lhs: (BasicValueEnum<'ctx>, bool),
+    rhs: (BasicValueEnum<'ctx>, bool),
+    value_fn: ValueFn,
+) -> Result<NDArrayValue<'ctx>, String>
+    where
+        G: CodeGenerator + ?Sized,
+        ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, (BasicValueEnum<'ctx>, BasicValueEnum<'ctx>)) -> Result<BasicValueEnum<'ctx>, String>,
+{
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    let (lhs_val, lhs_scalar) = lhs;
+    let (rhs_val, rhs_scalar) = rhs;
+
+    assert!(!(lhs_scalar && rhs_scalar),
+            "One of the operands must be a ndarray instance: `{}`, `{}`",
+            lhs_val.get_type(),
+            rhs_val.get_type());
+
+    // Assert that all ndarray operands are broadcastable to the target size
+    if !lhs_scalar {
+        let lhs_val = NDArrayValue::from_ptr_val(lhs_val.into_pointer_value(), llvm_usize, None);
+        ndarray_assert_is_broadcastable(generator, ctx, res, lhs_val);
+    }
+
+    if !rhs_scalar {
+        let rhs_val = NDArrayValue::from_ptr_val(rhs_val.into_pointer_value(), llvm_usize, None);
+        ndarray_assert_is_broadcastable(generator, ctx, res, rhs_val);
+    }
+
+    ndarray_fill_indexed(
+        generator,
+        ctx,
+        res,
+        |generator, ctx, idx| {
+            let lhs_elem = if lhs_scalar {
+                lhs_val
+            } else {
+                let lhs = NDArrayValue::from_ptr_val(lhs_val.into_pointer_value(), llvm_usize, None);
+                let lhs_idx = call_ndarray_calc_broadcast_index(generator, ctx, lhs, &idx);
+
+                unsafe {
+                    lhs.data().get_unchecked(ctx, generator, lhs_idx, None)
+                }
+            };
+
+            let rhs_elem = if rhs_scalar {
+                rhs_val
+            } else {
+                let rhs = NDArrayValue::from_ptr_val(rhs_val.into_pointer_value(), llvm_usize, None);
+                let rhs_idx = call_ndarray_calc_broadcast_index(generator, ctx, rhs, &idx);
+                
+                unsafe {
+                    rhs.data().get_unchecked(ctx, generator, rhs_idx, None)
+                }
+            };
+
+            debug_assert_eq!(lhs_elem.get_type(), rhs_elem.get_type());
+
+            value_fn(generator, ctx, (lhs_elem, rhs_elem))
+        },
+    )?;
+
+    Ok(res)
+}
+
 /// LLVM-typed implementation for generating the implementation for `ndarray.zeros`.
 ///
 /// * `elem_ty` - The element type of the `NDArray`.
@@ -562,6 +656,107 @@ fn ndarray_copy_impl<'ctx, G: CodeGenerator + ?Sized>(
     Ok(ndarray)
 }
 
+/// LLVM-typed implementation for computing elementwise binary operations on two input operands.
+///
+/// If the operand is a `ndarray`, the broadcast index corresponding to each element in the output 
+/// is computed, the element accessed and used as an operand of the `value_fn` arguments tuple. 
+/// Otherwise, the operand is treated as a scalar value, and is used as an operand of the 
+/// `value_fn` arguments tuple for all output elements.
+///
+/// The second element of the tuple indicates whether to treat the operand value as a `ndarray`
+/// (which would be accessed by its broadcast index) or as a scalar value (which would be 
+/// broadcast to all elements).
+///
+/// * `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`.
+/// * `value_fn` - Function mapping the two input elements into the result.
+///
+/// # Panic
+///
+/// This function will panic if neither input operands (`lhs` or `rhs`) is a `ndarray`.
+pub fn ndarray_elementwise_binop_impl<'ctx, G, ValueFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    res: Option<NDArrayValue<'ctx>>,
+    lhs: (BasicValueEnum<'ctx>, bool),
+    rhs: (BasicValueEnum<'ctx>, bool),
+    value_fn: ValueFn,
+) -> Result<NDArrayValue<'ctx>, String>
+    where
+        G: CodeGenerator,
+        ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, (BasicValueEnum<'ctx>, BasicValueEnum<'ctx>)) -> Result<BasicValueEnum<'ctx>, String>,
+{
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    let (lhs_val, lhs_scalar) = lhs;
+    let (rhs_val, rhs_scalar) = rhs;
+
+    assert!(!(lhs_scalar && rhs_scalar),
+            "One of the operands must be a ndarray instance: `{}`, `{}`",
+            lhs_val.get_type(),
+            rhs_val.get_type());
+
+    let ndarray = res.unwrap_or_else(|| {
+        if lhs_scalar && rhs_scalar {
+            let lhs_val = NDArrayValue::from_ptr_val(lhs_val.into_pointer_value(), llvm_usize, None);
+            let rhs_val = NDArrayValue::from_ptr_val(rhs_val.into_pointer_value(), llvm_usize, None);
+
+            let ndarray_dims = call_ndarray_calc_broadcast(generator, ctx, lhs_val, rhs_val);
+
+            create_ndarray_dyn_shape(
+                generator,
+                ctx,
+                elem_ty,
+                &ndarray_dims,
+                |generator, ctx, v| {
+                    Ok(v.size(ctx, generator))
+                },
+                |generator, ctx, v, idx| {
+                    unsafe {
+                        Ok(v.get_typed_unchecked(ctx, generator, idx, None))
+                    }
+                },
+            ).unwrap()
+        } else {
+            let ndarray = NDArrayValue::from_ptr_val(
+                if lhs_scalar { rhs_val } else { lhs_val }.into_pointer_value(),
+                llvm_usize,
+                None,
+            );
+
+            create_ndarray_dyn_shape(
+                generator,
+                ctx,
+                elem_ty,
+                &ndarray,
+                |_, ctx, v| {
+                    Ok(v.load_ndims(ctx))
+                },
+                |generator, ctx, v, idx| {
+                    unsafe {
+                        Ok(v.dim_sizes().get_typed_unchecked(ctx, generator, idx, None))
+                    }
+                },
+            ).unwrap()
+        }
+    });
+
+    ndarray_broadcast_fill(
+        generator,
+        ctx,
+        ndarray,
+        lhs,
+        rhs,
+        |generator, ctx, elems| {
+            value_fn(generator, ctx, elems)
+        },
+    )?;
+
+    Ok(ndarray)
+}
+
 /// Generates LLVM IR for `ndarray.empty`.
 pub fn gen_ndarray_empty<'ctx>(
     context: &mut CodeGenContext<'ctx, '_>,

nac3core/src/codegen/stmt.rs

@@ -546,7 +546,7 @@ pub fn gen_for_callback<'ctx, 'a, G, I, InitFn, CondFn, BodyFn, UpdateFn>(
 ///     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

nac3core/src/toplevel/builtins.rs

@@ -299,6 +299,8 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
         Some("N".into()),
         None,
     );
+    let size_t = primitives.0.usize();
+
     let var_map: VarMap = vec![(num_ty.1, num_ty.0)].into_iter().collect();
     let exception_fields = vec![
         ("__name__".into(), int32, true),
@@ -345,8 +347,27 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
         .nth(1)
         .map(|(var_id, ty)| (*ty, *var_id))
         .unwrap();
+    let ndarray_usized_ndims_tvar = primitives.1.get_fresh_const_generic_var(
+        size_t, 
+        Some("ndarray_ndims".into()), 
+        None,
+    );
     let ndarray_copy_ty = *ndarray_fields.get(&"copy".into()).unwrap();
     let ndarray_fill_ty = *ndarray_fields.get(&"fill".into()).unwrap();
+    let ndarray_add_ty = *ndarray_fields.get(&"__add__".into()).unwrap();
+    let ndarray_sub_ty = *ndarray_fields.get(&"__sub__".into()).unwrap();
+    let ndarray_mul_ty = *ndarray_fields.get(&"__mul__".into()).unwrap();
+    let ndarray_truediv_ty = *ndarray_fields.get(&"__truediv__".into()).unwrap();
+    let ndarray_floordiv_ty = *ndarray_fields.get(&"__floordiv__".into()).unwrap();
+    let ndarray_mod_ty = *ndarray_fields.get(&"__mod__".into()).unwrap();
+    let ndarray_pow_ty = *ndarray_fields.get(&"__pow__".into()).unwrap();
+    let ndarray_iadd_ty = *ndarray_fields.get(&"__iadd__".into()).unwrap();
+    let ndarray_isub_ty = *ndarray_fields.get(&"__isub__".into()).unwrap();
+    let ndarray_imul_ty = *ndarray_fields.get(&"__imul__".into()).unwrap();
+    let ndarray_itruediv_ty = *ndarray_fields.get(&"__itruediv__".into()).unwrap();
+    let ndarray_ifloordiv_ty = *ndarray_fields.get(&"__ifloordiv__".into()).unwrap();
+    let ndarray_imod_ty = *ndarray_fields.get(&"__imod__".into()).unwrap();
+    let ndarray_ipow_ty = *ndarray_fields.get(&"__ipow__".into()).unwrap();
 
     let top_level_def_list = vec![
         Arc::new(RwLock::new(TopLevelComposer::make_top_level_class_def(
@@ -524,6 +545,20 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
             methods: vec![
                 ("copy".into(), ndarray_copy_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 1)),
                 ("fill".into(), ndarray_fill_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 2)),
+                ("__add__".into(), ndarray_add_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 3)),
+                ("__sub__".into(), ndarray_sub_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 4)),
+                ("__mul__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 5)),
+                ("__truediv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 6)),
+                ("__floordiv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 7)),
+                ("__mod__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 8)),
+                ("__pow__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 9)),
+                ("__iadd__".into(), ndarray_iadd_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 10)),
+                ("__isub__".into(), ndarray_isub_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 11)),
+                ("__imul__".into(), ndarray_imul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 12)),
+                ("__itruediv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 13)),
+                ("__ifloordiv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 14)),
+                ("__imod__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 15)),
+                ("__ipow__".into(), ndarray_imul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 16)),
             ],
             ancestors: Vec::default(),
             constructor: None,
@@ -562,6 +597,216 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
             )))),
             loc: None,
         })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__add__".into(),
+            simple_name: "__add__".into(),
+            signature: ndarray_add_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__sub__".into(),
+            simple_name: "__sub__".into(),
+            signature: ndarray_sub_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__mul__".into(),
+            simple_name: "__mul__".into(),
+            signature: ndarray_mul_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__truediv__".into(),
+            simple_name: "__truediv__".into(),
+            signature: ndarray_truediv_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__floordiv__".into(),
+            simple_name: "__floordiv__".into(),
+            signature: ndarray_floordiv_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__mod__".into(),
+            simple_name: "__mod__".into(),
+            signature: ndarray_mod_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__pow__".into(),
+            simple_name: "__pow__".into(),
+            signature: ndarray_pow_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__iadd__".into(),
+            simple_name: "__iadd__".into(),
+            signature: ndarray_iadd_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id, ndarray_usized_ndims_tvar.1],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__isub__".into(),
+            simple_name: "__isub__".into(),
+            signature: ndarray_isub_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__imul__".into(),
+            simple_name: "__imul__".into(),
+            signature: ndarray_imul_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__itruediv__".into(),
+            simple_name: "__itruediv__".into(),
+            signature: ndarray_itruediv_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__ifloordiv__".into(),
+            simple_name: "__ifloordiv__".into(),
+            signature: ndarray_ifloordiv_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__imod__".into(),
+            simple_name: "__imod__".into(),
+            signature: ndarray_imod_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__ipow__".into(),
+            simple_name: "__ipow__".into(),
+            signature: ndarray_ipow_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
         Arc::new(RwLock::new(TopLevelDef::Function {
             name: "int32".into(),
             simple_name: "int32".into(),

nac3core/src/toplevel/helper.rs

@@ -1,6 +1,7 @@
 use std::convert::TryInto;
 
 use crate::symbol_resolver::SymbolValue;
+use crate::toplevel::numpy::subst_ndarray_tvars;
 use crate::typecheck::typedef::{Mapping, VarMap};
 use nac3parser::ast::{Constant, Location};
 
@@ -231,11 +232,57 @@ impl TopLevelComposer {
                 (ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
             ]),
         }));
+        let ndarray_binop_fun_other_ty = unifier.get_fresh_var(None, None);
+        let ndarray_binop_fun_ret_ty = unifier.get_fresh_var(None, None);
+        let ndarray_binop_fun_ty = unifier.add_ty(TypeEnum::TFunc(FunSignature {
+            args: vec![
+                FuncArg {
+                    name: "other".into(),
+                    ty: ndarray_binop_fun_other_ty.0,
+                    default_value: None,
+                },
+            ],
+            ret: ndarray_binop_fun_ret_ty.0,
+            vars: VarMap::from([
+                (ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
+                (ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
+            ]),
+        }));
+        let ndarray_truediv_fun_other_ty = unifier.get_fresh_var(None, None);
+        let ndarray_truediv_fun_ret_ty = unifier.get_fresh_var(None, None);
+        let ndarray_truediv_fun_ty = unifier.add_ty(TypeEnum::TFunc(FunSignature {
+            args: vec![
+                FuncArg {
+                    name: "other".into(),
+                    ty: ndarray_truediv_fun_other_ty.0,
+                    default_value: None,
+                },
+            ],
+            ret: ndarray_truediv_fun_ret_ty.0,
+            vars: VarMap::from([
+                (ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
+                (ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
+            ]),
+        }));
         let ndarray = unifier.add_ty(TypeEnum::TObj {
             obj_id: PRIMITIVE_DEF_IDS.ndarray,
             fields: Mapping::from([
                 ("copy".into(), (ndarray_copy_fun_ty, true)),
                 ("fill".into(), (ndarray_fill_fun_ty, true)),
+                ("__add__".into(), (ndarray_binop_fun_ty, true)),
+                ("__sub__".into(), (ndarray_binop_fun_ty, true)),
+                ("__mul__".into(), (ndarray_binop_fun_ty, true)),
+                ("__truediv__".into(), (ndarray_truediv_fun_ty, true)),
+                ("__floordiv__".into(), (ndarray_binop_fun_ty, true)),
+                ("__mod__".into(), (ndarray_binop_fun_ty, true)),
+                ("__pow__".into(), (ndarray_binop_fun_ty, true)),
+                ("__iadd__".into(), (ndarray_binop_fun_ty, true)),
+                ("__isub__".into(), (ndarray_binop_fun_ty, true)),
+                ("__imul__".into(), (ndarray_binop_fun_ty, true)),
+                ("__itruediv__".into(), (ndarray_truediv_fun_ty, true)),
+                ("__ifloordiv__".into(), (ndarray_binop_fun_ty, true)),
+                ("__imod__".into(), (ndarray_binop_fun_ty, true)),
+                ("__ipow__".into(), (ndarray_binop_fun_ty, true)),
             ]),
             params: VarMap::from([
                 (ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
@@ -243,7 +290,16 @@ impl TopLevelComposer {
             ]),
         });
 
+        let ndarray_usized_ndims_tvar = unifier.get_fresh_const_generic_var(size_t_ty, Some("ndarray_ndims".into()), None);
+        let ndarray_unsized = subst_ndarray_tvars(&mut unifier, ndarray, Some(ndarray_usized_ndims_tvar.0), None);
+
         unifier.unify(ndarray_copy_fun_ret_ty.0, ndarray).unwrap();
+        unifier.unify(ndarray_binop_fun_other_ty.0, ndarray_unsized).unwrap();
+        unifier.unify(ndarray_binop_fun_ret_ty.0, ndarray).unwrap();
+
+        let ndarray_float = subst_ndarray_tvars(&mut unifier, ndarray, Some(float), None);
+        unifier.unify(ndarray_truediv_fun_other_ty.0, ndarray).unwrap();
+        unifier.unify(ndarray_truediv_fun_ret_ty.0, ndarray_float).unwrap();
 
         let primitives = PrimitiveStore {
             int32,

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__generic_class.snap

@@ -5,7 +5,7 @@ expression: res_vec
 [
     "Class {\nname: \"Generic_A\",\nancestors: [\"Generic_A[V]\", \"B\"],\nfields: [\"aa\", \"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\"), (\"fun\", \"fn[[a:int32], V]\")],\ntype_vars: [\"V\"]\n}\n",
     "Function {\nname: \"Generic_A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [30]\n}\n",
+    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [124]\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [\"aa\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__inheritance_override.snap

@@ -7,7 +7,7 @@ expression: res_vec
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[t:T], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.foo\",\nsig: \"fn[[c:C], none]\",\nvar_id: []\n}\n",
-    "Class {\nname: \"B\",\nancestors: [\"B[typevar19]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar19\"]\n}\n",
+    "Class {\nname: \"B\",\nancestors: [\"B[typevar113]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar113\"]\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"B.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"C\",\nancestors: [\"C\", \"B[bool]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\", \"e\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__list_tuple_generic.snap

@@ -5,8 +5,8 @@ expression: res_vec
 [
     "Function {\nname: \"foo\",\nsig: \"fn[[a:list[int32], b:tuple[T, float]], A[B, bool]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"A\",\nancestors: [\"A[T, V]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[v:V], none]\"), (\"fun\", \"fn[[a:T], V]\")],\ntype_vars: [\"T\", \"V\"]\n}\n",
-    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [32]\n}\n",
+    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [126]\n}\n",
-    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [37]\n}\n",
+    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [131]\n}\n",
     "Function {\nname: \"gfun\",\nsig: \"fn[[a:A[list[float], int32]], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [],\nmethods: [(\"__init__\", \"fn[[], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__self1.snap

@@ -3,7 +3,7 @@ source: nac3core/src/toplevel/test.rs
 expression: res_vec
 ---
 [
-    "Class {\nname: \"A\",\nancestors: [\"A[typevar18, typevar19]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar18\", \"typevar19\"]\n}\n",
+    "Class {\nname: \"A\",\nancestors: [\"A[typevar112, typevar113]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar112\", \"typevar113\"]\n}\n",
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[float, bool], b:B], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[float, bool]], A[bool, int32]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\")],\ntype_vars: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__simple_class_compose.snap

@@ -6,12 +6,12 @@ expression: res_vec
     "Class {\nname: \"A\",\nancestors: [\"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [38]\n}\n",
+    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [132]\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\", \"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"C\",\nancestors: [\"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"C.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"C.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [46]\n}\n",
+    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [140]\n}\n",
 ]

nac3core/src/typecheck/magic_methods.rs

@@ -453,8 +453,8 @@ pub fn typeof_binop(
         }
 
         Operator::LShift
-        | Operator::RShift
+        | Operator::RShift => lhs,
-        | Operator::BitOr
+        Operator::BitOr
         | Operator::BitXor
         | Operator::BitAnd => {
             if unifier.unioned(lhs, rhs) {
@@ -474,18 +474,21 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
         bool: bool_t,
         uint32: uint32_t,
         uint64: uint64_t,
+        ndarray: ndarray_t,
         ..
     } = *store;
+    let size_t = store.usize();
 
     /* int ======== */
     for t in [int32_t, int64_t, uint32_t, uint64_t] {
-        impl_basic_arithmetic(unifier, store, t, &[t], Some(t));
+        let ndarray_int_t = make_ndarray_ty(unifier, store, Some(t), None);
-        impl_pow(unifier, store, t, &[t], Some(t));
+        impl_basic_arithmetic(unifier, store, t, &[t, ndarray_int_t], None);
+        impl_pow(unifier, store, t, &[t, ndarray_int_t], None);
         impl_bitwise_arithmetic(unifier, store, t);
         impl_bitwise_shift(unifier, store, t);
-        impl_div(unifier, store, t, &[t], Some(float_t));
+        impl_div(unifier, store, t, &[t, ndarray_int_t], None);
-        impl_floordiv(unifier, store, t, &[t], Some(t));
+        impl_floordiv(unifier, store, t, &[t, ndarray_int_t], None);
-        impl_mod(unifier, store, t, &[t], Some(t));
+        impl_mod(unifier, store, t, &[t, ndarray_int_t], None);
         impl_invert(unifier, store, t, Some(t));
         impl_not(unifier, store, t, Some(bool_t));
         impl_comparison(unifier, store, t, &[t], Some(bool_t));
@@ -496,11 +499,13 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
     }
 
     /* float ======== */
-    impl_basic_arithmetic(unifier, store, float_t, &[float_t], Some(float_t));
+    let ndarray_float_t = make_ndarray_ty(unifier, store, Some(float_t), None);
-    impl_pow(unifier, store, float_t, &[int32_t, float_t], Some(float_t));
+    let ndarray_int32_t = make_ndarray_ty(unifier, store, Some(int32_t), None);
-    impl_div(unifier, store, float_t, &[float_t], Some(float_t));
+    impl_basic_arithmetic(unifier, store, float_t, &[float_t, ndarray_float_t], None);
-    impl_floordiv(unifier, store, float_t, &[float_t], Some(float_t));
+    impl_pow(unifier, store, float_t, &[int32_t, float_t, ndarray_int32_t, ndarray_float_t], None);
-    impl_mod(unifier, store, float_t, &[float_t], Some(float_t));
+    impl_div(unifier, store, float_t, &[float_t, ndarray_float_t], None);
+    impl_floordiv(unifier, store, float_t, &[float_t, ndarray_float_t], None);
+    impl_mod(unifier, store, float_t, &[float_t, ndarray_float_t], None);
     impl_sign(unifier, store, float_t, Some(float_t));
     impl_not(unifier, store, float_t, Some(bool_t));
     impl_comparison(unifier, store, float_t, &[float_t], Some(bool_t));
@@ -509,4 +514,15 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
     /* bool ======== */
     impl_not(unifier, store, bool_t, Some(bool_t));
     impl_eq(unifier, store, bool_t, &[bool_t], Some(bool_t));
+
+    /* ndarray ===== */
+    let ndarray_usized_ndims_tvar = unifier.get_fresh_const_generic_var(size_t, Some("ndarray_ndims".into()), None);
+    let ndarray_unsized_t = make_ndarray_ty(unifier, store, None, Some(ndarray_usized_ndims_tvar.0));
+    let (ndarray_dtype_t, _) = unpack_ndarray_var_tys(unifier, ndarray_t);
+    let (ndarray_unsized_dtype_t, _) = unpack_ndarray_var_tys(unifier, ndarray_unsized_t);
+    impl_basic_arithmetic(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
+    impl_pow(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
+    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);
 }

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -1203,8 +1203,11 @@ impl<'a> Inferencer<'a> {
         right: &ast::Expr<Option<Type>>,
         is_aug_assign: bool,
     ) -> InferenceResult {
+        let left_ty = left.custom.unwrap();
+        let right_ty = right.custom.unwrap();
+
         let method = if let TypeEnum::TObj { fields, .. } =
-            self.unifier.get_ty_immutable(left.custom.unwrap()).as_ref()
+            self.unifier.get_ty_immutable(left_ty).as_ref()
         {
             let (binop_name, binop_assign_name) = (
                 binop_name(op).into(),
@@ -1219,12 +1222,26 @@ impl<'a> Inferencer<'a> {
         } else {
             binop_name(op).into()
         };
+
+        let ret = if is_aug_assign {
+            // The type of augmented assignment operator should never change
+            Some(left_ty)
+        } else {
+            typeof_binop(
+                self.unifier,
+                self.primitives,
+                op,
+                left_ty,
+                right_ty,
+            ).map_err(|e| HashSet::from([format!("{e} (at {location})")]))?
+        };
+
         self.build_method_call(
             location,
             method,
-            left.custom.unwrap(),
+            left_ty,
-            vec![right.custom.unwrap()],
+            vec![right_ty],
-            None,
+            ret,
         )
     }
 

nac3core/src/typecheck/type_inferencer/test.rs

@@ -135,10 +135,15 @@ impl TestEnvironment {
             fields: HashMap::new(),
             params: VarMap::new(),
         });
+        let ndarray_dtype_tvar = unifier.get_fresh_var(Some("ndarray_dtype".into()), None);
+        let ndarray_ndims_tvar = unifier.get_fresh_const_generic_var(uint64, Some("ndarray_ndims".into()), None);
         let ndarray = unifier.add_ty(TypeEnum::TObj {
             obj_id: PRIMITIVE_DEF_IDS.ndarray,
             fields: HashMap::new(),
-            params: VarMap::new(),
+            params: VarMap::from([
+                (ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
+                (ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
+            ]),
         });
         let primitives = PrimitiveStore {
             int32,

nac3core/src/typecheck/typedef/mod.rs

@@ -774,12 +774,8 @@ impl Unifier {
 
                 // If the types don't match, try to implicitly promote integers
                 if !self.unioned(ty, value_ty) {
-                    let num_val = match *value {
+                    let Ok(num_val) = i128::try_from(value.clone()) else {
-                        SymbolValue::I32(v) => v as i128,
+                        return Self::incompatible_types(a, b)
-                        SymbolValue::I64(v) => v as i128,
-                        SymbolValue::U32(v) => v as i128,
-                        SymbolValue::U64(v) => v as i128,
-                        _ => return Self::incompatible_types(a, b),
                     };
 
                     let can_convert = if self.unioned(ty, primitives.int32) {

nac3standalone/demo/src/ndarray.py

@@ -6,6 +6,19 @@ def output_int32(x: int32):
 def output_float64(x: float):
     ...
 
+def output_ndarray_int32_1(n: ndarray[int32, Literal[1]]):
+    for i in range(len(n)):
+        output_int32(n[i])
+
+def output_ndarray_float_1(n: ndarray[float, Literal[1]]):
+    for i in range(len(n)):
+        output_float64(n[i])
+
+def output_ndarray_float_2(n: ndarray[float, Literal[2]]):
+    for r in range(len(n)):
+        for c in range(len(n[r])):
+            output_float64(n[r][c])
+
 def consume_ndarray_1(n: ndarray[float, Literal[1]]):
     pass
 
@@ -19,53 +32,381 @@ def test_ndarray_empty():
 
 def test_ndarray_zeros():
     n: ndarray[float, 1] = np_zeros([1])
-    output_float64(n[0])
+    output_ndarray_float_1(n)
 
 def test_ndarray_ones():
     n: ndarray[float, 1] = np_ones([1])
-    output_float64(n[0])
+    output_ndarray_float_1(n)
 
 def test_ndarray_full():
     n_float: ndarray[float, 1] = np_full([1], 2.0)
-    output_float64(n_float[0])
+    output_ndarray_float_1(n_float)
     n_i32: ndarray[int32, 1] = np_full([1], 2)
-    output_int32(n_i32[0])
+    output_ndarray_int32_1(n_i32)
 
 def test_ndarray_eye():
     n: ndarray[float, 2] = np_eye(2)
-    n0: ndarray[float, 1] = n[0]
+    output_ndarray_float_2(n)
-    v: float = n0[0]
-    output_float64(v)
 
 def test_ndarray_identity():
     n: ndarray[float, 2] = np_identity(2)
-    output_float64(n[0][0])
+    output_ndarray_float_2(n)
-    output_float64(n[0][1])
-    output_float64(n[1][0])
-    output_float64(n[1][1])
 
 def test_ndarray_fill():
     n: ndarray[float, 2] = np_empty([2, 2])
     n.fill(1.0)
-    output_float64(n[0][0])
+    output_ndarray_float_2(n)
-    output_float64(n[0][1])
-    output_float64(n[1][0])
-    output_float64(n[1][1])
 
 def test_ndarray_copy():
     x: ndarray[float, 2] = np_identity(2)
     y = x.copy()
     x.fill(0.0)
 
-    output_float64(x[0][0])
+    output_ndarray_float_2(x)
-    output_float64(x[0][1])
+    output_ndarray_float_2(y)
-    output_float64(x[1][0])
-    output_float64(x[1][1])
 
-    output_float64(y[0][0])
+def test_ndarray_add():
-    output_float64(y[0][1])
+    x = np_identity(2)
-    output_float64(y[1][0])
+    y = x + np_ones([2, 2])
-    output_float64(y[1][1])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_add_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x + np_ones([2])
+    y = x + np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_add_broadcast_lhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = 1.0 + x
+    y = 1.0 + x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_add_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x + 1.0
+    y = x + 1.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_iadd():
+    x = np_identity(2)
+    x += np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_iadd_broadcast():
+    x = np_identity(2)
+    x += np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_iadd_broadcast_scalar():
+    x = np_identity(2)
+    x += 1.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_sub():
+    x = np_ones([2, 2])
+    y = x - np_identity(2)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_sub_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x - np_ones([2])
+    y = x - np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_sub_broadcast_lhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = 1.0 - x
+    y = 1.0 - x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_sub_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x - 1
+    y = x - 1.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_isub():
+    x = np_ones([2, 2])
+    x -= np_identity(2)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_isub_broadcast():
+    x = np_identity(2)
+    x -= np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_isub_broadcast_scalar():
+    x = np_identity(2)
+    x -= 1.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_mul():
+    x = np_ones([2, 2])
+    y = x * np_identity(2)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mul_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x * np_ones([2])
+    y = x * np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mul_broadcast_lhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = 2.0 * x
+    y = 2.0 * x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mul_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x * 2.0
+    y = x * 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_imul():
+    x = np_ones([2, 2])
+    x *= np_identity(2)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_imul_broadcast():
+    x = np_identity(2)
+    x *= np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_imul_broadcast_scalar():
+    x = np_identity(2)
+    x *= 2.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_truediv():
+    x = np_identity(2)
+    y = x / np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_truediv_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x / np_ones([2])
+    y = x / np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_truediv_broadcast_lhs_scalar():
+    x = np_ones([2, 2])
+    # y: ndarray[float, 2] = 2.0 / x
+    y = 2.0 / x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_truediv_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x / 2.0
+    y = x / 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_itruediv():
+    x = np_identity(2)
+    x /= np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_itruediv_broadcast():
+    x = np_identity(2)
+    x /= np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_itruediv_broadcast_scalar():
+    x = np_identity(2)
+    x /= 2.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_floordiv():
+    x = np_identity(2)
+    y = x // np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_floordiv_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x // np_ones([2])
+    y = x // np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_floordiv_broadcast_lhs_scalar():
+    x = np_ones([2, 2])
+    # y: ndarray[float, 2] = 2.0 // x
+    y = 2.0 // x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_floordiv_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x // 2.0
+    y = x // 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_ifloordiv():
+    x = np_identity(2)
+    x //= np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_ifloordiv_broadcast():
+    x = np_identity(2)
+    x //= np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_ifloordiv_broadcast_scalar():
+    x = np_identity(2)
+    x //= 2.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_mod():
+    x = np_identity(2)
+    y = x % np_full([2, 2], 2.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mod_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x % np_ones([2])
+    y = x % np_full([2], 2.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mod_broadcast_lhs_scalar():
+    x = np_ones([2, 2])
+    # y: ndarray[float, 2] = 2.0 % x
+    y = 2.0 % x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mod_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x % 2.0
+    y = x % 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_imod():
+    x = np_identity(2)
+    x %= np_full([2, 2], 2.0)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_imod_broadcast():
+    x = np_identity(2)
+    x %= np_full([2], 2.0)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_imod_broadcast_scalar():
+    x = np_identity(2)
+    x %= 2.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_pow():
+    x = np_identity(2)
+    y = x ** np_full([2, 2], 2.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_pow_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x ** np_full([2], 2.0)
+    y = x ** np_full([2], 2.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_pow_broadcast_lhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = 2.0 ** x
+    y = 2.0 ** x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_pow_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x % 2.0
+    y = x ** 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_ipow():
+    x = np_identity(2)
+    x **= np_full([2, 2], 2.0)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_ipow_broadcast():
+    x = np_identity(2)
+    x **= np_full([2], 2.0)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_ipow_broadcast_scalar():
+    x = np_identity(2)
+    x **= 2.0
+
+    output_ndarray_float_2(x)
 
 def run() -> int32:
     test_ndarray_ctor()
@@ -77,5 +418,54 @@ def run() -> int32:
     test_ndarray_identity()
     test_ndarray_fill()
     test_ndarray_copy()
+    test_ndarray_add()
+    test_ndarray_add_broadcast()
+    test_ndarray_add_broadcast_lhs_scalar()
+    test_ndarray_add_broadcast_rhs_scalar()
+    test_ndarray_iadd()
+    test_ndarray_iadd_broadcast()
+    test_ndarray_iadd_broadcast_scalar()
+    test_ndarray_sub()
+    test_ndarray_sub_broadcast()
+    test_ndarray_sub_broadcast_lhs_scalar()
+    test_ndarray_sub_broadcast_rhs_scalar()
+    test_ndarray_isub()
+    test_ndarray_isub_broadcast()
+    test_ndarray_isub_broadcast_scalar()
+    test_ndarray_mul()
+    test_ndarray_mul_broadcast()
+    test_ndarray_mul_broadcast_lhs_scalar()
+    test_ndarray_mul_broadcast_rhs_scalar()
+    test_ndarray_imul()
+    test_ndarray_imul_broadcast()
+    test_ndarray_imul_broadcast_scalar()
+    test_ndarray_truediv()
+    test_ndarray_truediv_broadcast()
+    test_ndarray_truediv_broadcast_lhs_scalar()
+    test_ndarray_truediv_broadcast_rhs_scalar()
+    test_ndarray_itruediv()
+    test_ndarray_itruediv_broadcast()
+    test_ndarray_itruediv_broadcast_scalar()
+    test_ndarray_floordiv()
+    test_ndarray_floordiv_broadcast()
+    test_ndarray_floordiv_broadcast_lhs_scalar()
+    test_ndarray_floordiv_broadcast_rhs_scalar()
+    test_ndarray_ifloordiv()
+    test_ndarray_ifloordiv_broadcast()
+    test_ndarray_ifloordiv_broadcast_scalar()
+    test_ndarray_mod()
+    test_ndarray_mod_broadcast()
+    test_ndarray_mod_broadcast_lhs_scalar()
+    test_ndarray_mod_broadcast_rhs_scalar()
+    test_ndarray_imod()
+    test_ndarray_imod_broadcast()
+    test_ndarray_imod_broadcast_scalar()
+    test_ndarray_pow()
+    test_ndarray_pow_broadcast()
+    test_ndarray_pow_broadcast_lhs_scalar()
+    test_ndarray_pow_broadcast_rhs_scalar()
+    test_ndarray_ipow()
+    test_ndarray_ipow_broadcast()
+    test_ndarray_ipow_broadcast_scalar()
 
     return 0