WIP

nac3core/irrt/irrt/numpy/ndarray_basic.hpp

@@ -126,6 +126,54 @@ namespace { namespace ndarray { namespace basic {
 
         *dst_length = (SliceIndex) ndarray->shape[0];
     }
+
+    // Copy data from one ndarray to another *OF THE EXACT SAME* ndims, shape, and itemsize.
+    template <typename SizeT>
+    void copy_data(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
+        __builtin_assume(src_ndarray->ndims == dst_ndarray->ndims);
+        __builtin_assume(src_ndarray->itemsize == dst_ndarray->itemsize);
+
+        for (SizeT i = 0; i < src_ndarray->size; i++) {
+            auto src_element = ndarray::basic::get_nth_pelement(src_ndarray, i);
+            auto dst_element = ndarray::basic::get_nth_pelement(dst_ndarray, i);
+            ndarray::basic::set_pelement_value(dst_ndarray, dst_element, src_element);
+        }
+    }
+
+    // `copy_data()` with assertions to check ndims, shape, and itemsize between the two ndarrays.
+    template <typename SizeT>
+    void copy_data_checked(ErrorContext* errctx, const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
+        // NOTE: Out of all error types, runtime error seems appropriate
+
+        // Check ndims
+        if (src_ndarray->ndims != dst_ndarray->ndims) {
+            errctx->set_error(
+                errctx->error_ids->runtime_error,
+                "IRRT copy_data_checked input arrays `ndims` are mismatched"
+            );
+            return; // Terminate
+        }
+
+        // Check shape
+        if (!arrays_match(src_ndarray->ndims, src_ndarray->shape, dst_ndarray->shape)) {
+            errctx->set_error(
+                errctx->error_ids->runtime_error,
+                "IRRT copy_data_checked input arrays `shape` are mismatched"
+            );
+            return; // Terminate
+        }
+
+        // Check itemsize
+        if (src_ndarray->itemsize != dst_ndarray->itemsize) {
+            errctx->set_error(
+                errctx->error_ids->runtime_error,
+                "IRRT copy_data_checked input arrays `itemsize` are mismatched"
+            );
+            return; // Terminate
+        }
+
+        copy_data(src_ndarray, dst_ndarray);
+    }
 } } }
 
 extern "C" {

nac3core/irrt/irrt/numpy/ndarray_broadcast.hpp

@@ -3,11 +3,12 @@
 namespace { namespace ndarray { namespace broadcast {
     namespace util {
         template <typename SizeT>
-        bool can_broadcast_shape_to(
+        void assert_broadcast_shape_to(
+            ErrorContext* errctx,
             const SizeT target_ndims,
-            const SizeT *target_shape,
+            const SizeT* target_shape,
             const SizeT src_ndims,
-            const SizeT *src_shape
+            const SizeT* src_shape
         ) {
             /*
                 // See https://numpy.org/doc/stable/user/basics.broadcasting.html
@@ -20,23 +21,33 @@ namespace { namespace ndarray { namespace broadcast {
                 ```
 
                 Other interesting examples to consider:
-                - `can_broadcast_shape_to([3], [1, 1, 1, 1, 3]) == true`
+                - `can_broadcast_shape_to([3], [1, 1, 1, 1, 3]) ... ok`
                 - `can_broadcast_shape_to([3], [3, 1]) == false`
-                - `can_broadcast_shape_to([256, 256, 3], [256, 1, 3]) == true`
+                - `can_broadcast_shape_to([256, 256, 3], [256, 1, 3]) ... ok`
 
                 In cases when the shapes contain zero(es):
-                - `can_broadcast_shape_to([0], [1]) == true`
+                - `can_broadcast_shape_to([0], [1]) ... ok`
                 - `can_broadcast_shape_to([0], [2]) == false`
-                - `can_broadcast_shape_to([0, 4, 0, 0], [1]) == true`
+                - `can_broadcast_shape_to([0, 4, 0, 0], [1]) ... ok`
-                - `can_broadcast_shape_to([0, 4, 0, 0], [1, 1, 1, 1]) == true`
+                - `can_broadcast_shape_to([0, 4, 0, 0], [1, 1, 1, 1]) ... ok`
-                - `can_broadcast_shape_to([0, 4, 0, 0], [1, 4, 1, 1]) == true`
+                - `can_broadcast_shape_to([0, 4, 0, 0], [1, 4, 1, 1]) ... ok`
                 - `can_broadcast_shape_to([4, 3], [0, 3]) == false`
                 - `can_broadcast_shape_to([4, 3], [0, 0]) == false`
             */
 
-            // This is essentially doing the following in Python:
+            // Target ndims must not be smaller than source ndims
-            // `for target_dim, src_dim in itertools.zip_longest(target_shape[::-1], src_shape[::-1], fillvalue=1)`
+            // e.g., `np.broadcast_to(np.zeros((1, 1, 1, 1)), (1, ))` is prohibited by numpy
-            for (SizeT i = 0; i < max(target_ndims, src_ndims); i++) {
+            if (target_ndims < src_ndims) {
+                // Error copied from python by doing the `np.broadcast_to(np.zeros((1, 1, 1, 1)), (1, ))`
+                errctx->set_error(
+                    errctx->error_ids->value_error,
+                    "input operand has more dimensions than allowed by the axis remapping"
+                );
+                return; // Terminate
+            }
+
+            // Implements the rules in https://numpy.org/doc/stable/user/basics.broadcasting.html
+            for (SizeT i = 0; i < src_ndims; i++) {
                 SizeT target_axis = target_ndims - i - 1;
                 SizeT src_axis = src_ndims - i - 1;
 
@@ -47,10 +58,18 @@ namespace { namespace ndarray { namespace broadcast {
                 SizeT src_dim = src_dim_exists ? src_shape[src_axis] : 1;
 
                 bool ok = src_dim == 1 || target_dim == src_dim;
-                if (!ok) return false;
+                if (!ok) {
+                    // Error copied from python by doing `np.broadcast_to(np.zeros((3, 1)), (1, 1)),
+                    // but this is the true numpy error:
+                    // "ValueError: operands could not be broadcast together with remapped shapes [original->remapped]: (3,1)  and requested shape (1,1)"
+                    // TODO: we cannot show more than 3 parameters!!
+                    errctx->set_error(
+                        errctx->error_ids->value_error,
+                        "operands could not be broadcast together with remapping shapes [original->remapped]"
+                    );
+                    return; // Terminate
+                }
             }
-
-            return true;
         }
     }
 
@@ -79,18 +98,20 @@ namespace { namespace ndarray { namespace broadcast {
     //   # This implementation will NOT support this assignment.
     //   ```
     template <typename SizeT>
-    void broadcast_to(NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
+    void broadcast_to(ErrorContext* errctx, NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
         dst_ndarray->data = src_ndarray->data;
         dst_ndarray->itemsize = src_ndarray->itemsize;
 
-        // irrt_assert(
+        ndarray::broadcast::util::assert_broadcast_shape_to(
-        //     ndarray_util::can_broadcast_shape_to(
+            errctx,
-        //         dst_ndarray->ndims,
+            dst_ndarray->ndims,
-        //         dst_ndarray->shape,
+            dst_ndarray->shape,
-        //         src_ndarray->ndims,
+            src_ndarray->ndims,
-        //         src_ndarray->shape
+            src_ndarray->shape
-        //     )
+        );
-        // );
+        if (errctx->has_error()) {
+            return; // Propagate error
+        }
 
         SizeT stride_product = 1;
         for (SizeT i = 0; i < max(src_ndarray->ndims, dst_ndarray->ndims); i++) {

nac3core/irrt/irrt/numpy/ndarray_subscript.hpp

@@ -6,8 +6,6 @@
 #include <irrt/error_context.hpp>
 
 namespace {
-    typedef uint32_t NumNDSubscriptsType;
-
     typedef uint8_t NDSubscriptType;
 
     const NDSubscriptType INPUT_SUBSCRIPT_TYPE_INDEX = 0;
@@ -72,7 +70,7 @@ namespace { namespace ndarray { namespace subscript {
     // - `dst_ndarray->itemsize` does not have to be set, it will be set to `src_ndarray->itemsize`
     // - `dst_ndarray->shape` and `dst_ndarray.strides` can contain empty values
     template <typename SizeT>
-    void subscript(ErrorContext* errctx, NumNDSubscriptsType num_subscripts, NDSubscript* subscripts, NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
+    void subscript(ErrorContext* errctx, SliceIndex num_subscripts, NDSubscript* subscripts, NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
         // REFERENCE CODE (check out `_index_helper` in `__getitem__`):
         //   https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
 
@@ -84,7 +82,7 @@ namespace { namespace ndarray { namespace subscript {
         SizeT src_axis = 0;
         SizeT dst_axis = 0;
 
-        for (SizeT i = 0; i < num_subscripts; i++) {
+        for (SliceIndex i = 0; i < num_subscripts; i++) {
             NDSubscript *ndsubscript = &subscripts[i];
             if (ndsubscript->type == INPUT_SUBSCRIPT_TYPE_INDEX) {
                 // Handle when the ndsubscript is just a single (possibly negative) integer
@@ -161,11 +159,11 @@ extern "C" {
         ndarray::subscript::util::deduce_ndims_after_slicing(errctx, result, ndims, num_ndsubscripts, ndsubscripts);
     }
 
-    void __nac3_ndarray_subscript(ErrorContext* errctx, NumNDSubscriptsType num_subscripts, NDSubscript* subscripts, NDArray<int32_t>* src_ndarray, NDArray<int32_t> *dst_ndarray) {
+    void __nac3_ndarray_subscript(ErrorContext* errctx, SliceIndex num_subscripts, NDSubscript* subscripts, NDArray<int32_t>* src_ndarray, NDArray<int32_t> *dst_ndarray) {
         subscript(errctx, num_subscripts, subscripts, src_ndarray, dst_ndarray);
     }
 
-    void __nac3_ndarray_subscript64(ErrorContext* errctx, NumNDSubscriptsType num_subscripts, NDSubscript* subscripts, NDArray<int64_t>* src_ndarray, NDArray<int64_t> *dst_ndarray) {
+    void __nac3_ndarray_subscript64(ErrorContext* errctx, SliceIndex num_subscripts, NDSubscript* subscripts, NDArray<int64_t>* src_ndarray, NDArray<int64_t> *dst_ndarray) {
         subscript(errctx, num_subscripts, subscripts, src_ndarray, dst_ndarray);
     }
 }

nac3core/irrt/irrt_test.cpp

@@ -11,6 +11,7 @@
 #include <test/test_core.hpp>
 #include <test/test_ndarray_basic.hpp>
 #include <test/test_ndarray_subscript.hpp>
+#include <test/test_ndarray_broadcast.hpp>
 #include <test/test_slice.hpp>
 
 int main() {
@@ -19,5 +20,6 @@ int main() {
     test::slice::run();
     test::ndarray_basic::run();
     test::ndarray_subscript::run();
+    test::ndarray_broadcast::run();
     return 0;
 }

nac3core/irrt/test/test_ndarray_broadcast.hpp

@@ -0,0 +1,72 @@
+#pragma once
+
+#include <test/core.hpp>
+#include <irrt_everything.hpp>
+
+namespace test { namespace ndarray_broadcast {
+    void test_ndarray_broadcast_1() {
+        /*
+            ```python
+            array = np.array([[19.9, 29.9, 39.9, 49.9]], dtype=np.float64)
+            >>> [[19.9 29.9 39.9 49.9]]
+            
+            array = np.broadcast_to(array, (2, 3, 4))
+            >>> [[[19.9 29.9 39.9 49.9]
+            >>>   [19.9 29.9 39.9 49.9]
+            >>>   [19.9 29.9 39.9 49.9]]
+            >>>  [[19.9 29.9 39.9 49.9]
+            >>>   [19.9 29.9 39.9 49.9]
+            >>>   [19.9 29.9 39.9 49.9]]]
+            
+            assert array.strides == (0, 0, 8)
+            # and then pick some values in `array` and check them...
+            ```
+        */
+        BEGIN_TEST();
+
+        // Prepare src_ndarray
+        double src_data[4] = { 19.9, 29.9, 39.9, 49.9 };
+        const int32_t src_ndims = 2;
+        int32_t src_shape[src_ndims] = {1, 4};
+        int32_t src_strides[src_ndims] = {};
+        NDArray<int32_t> src_ndarray = {
+            .data = (uint8_t*) src_data,
+            .itemsize = sizeof(double),
+            .ndims = src_ndims,
+            .shape = src_shape,
+            .strides = src_strides
+        };
+        ndarray::basic::set_strides_by_shape(&src_ndarray);
+
+        // Prepare dst_ndarray
+        const int32_t dst_ndims = 3;
+        int32_t dst_shape[dst_ndims] = {2, 3, 4};
+        int32_t dst_strides[dst_ndims] = {};
+        NDArray<int32_t> dst_ndarray = {
+            .ndims = dst_ndims,
+            .shape = dst_shape,
+            .strides = dst_strides
+        };
+
+        // Broadcast
+        ErrorContext errctx = create_testing_errctx();
+        ndarray::broadcast::broadcast_to(&errctx, &src_ndarray, &dst_ndarray);
+        assert_errctx_no_error(&errctx);
+
+        assert_arrays_match(dst_ndims, ((int32_t[]) { 0, 0, 8 }), dst_ndarray.strides);
+
+        assert_values_match(19.9, *((double*) ndarray::basic::get_pelement_by_indices(&dst_ndarray, ((int32_t[]) {0, 0, 0}))));
+        assert_values_match(29.9, *((double*) ndarray::basic::get_pelement_by_indices(&dst_ndarray, ((int32_t[]) {0, 0, 1}))));
+        assert_values_match(39.9, *((double*) ndarray::basic::get_pelement_by_indices(&dst_ndarray, ((int32_t[]) {0, 0, 2}))));
+        assert_values_match(49.9, *((double*) ndarray::basic::get_pelement_by_indices(&dst_ndarray, ((int32_t[]) {0, 0, 3}))));
+        assert_values_match(19.9, *((double*) ndarray::basic::get_pelement_by_indices(&dst_ndarray, ((int32_t[]) {0, 1, 0}))));
+        assert_values_match(29.9, *((double*) ndarray::basic::get_pelement_by_indices(&dst_ndarray, ((int32_t[]) {0, 1, 1}))));
+        assert_values_match(39.9, *((double*) ndarray::basic::get_pelement_by_indices(&dst_ndarray, ((int32_t[]) {0, 1, 2}))));
+        assert_values_match(49.9, *((double*) ndarray::basic::get_pelement_by_indices(&dst_ndarray, ((int32_t[]) {0, 1, 3}))));
+        assert_values_match(49.9, *((double*) ndarray::basic::get_pelement_by_indices(&dst_ndarray, ((int32_t[]) {1, 2, 3}))));
+    }
+
+    void run() {
+        test_ndarray_broadcast_1();
+    }
+}}

nac3core/src/codegen/expr.rs

@@ -2189,7 +2189,7 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
                     Ok(match value_expr {
                         None => None,
                         Some(value_expr) => Some(
-                            slice_index_model.check_llvm_value(
+                            slice_index_model.review(
                                 generator
                                     .gen_expr(ctx, value_expr)?
                                     .unwrap()
@@ -2209,7 +2209,7 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
                 // Anything else that is not a slice (might be illegal values),
                 // For nac3core, this should be e.g., an int32 constant, an int32 variable, otherwise its an error
 
-                let index = slice_index_model.check_llvm_value(
+                let index = slice_index_model.review(
                     generator
                         .gen_expr(ctx, subscript_expr)?
                         .unwrap()
@@ -2931,7 +2931,7 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
                         let ndarray_ptr_model = PointerModel(StructModel(NpArray { sizet }));
 
                         let v = v.to_basic_value_enum(ctx, generator, value.custom.unwrap())?;
-                        ndarray_ptr_model.check_llvm_value(v.as_any_value_enum())
+                        ndarray_ptr_model.review(v.as_any_value_enum())
                     } else {
                         return Ok(None);
                     };

nac3core/src/codegen/irrt/numpy/subscript.rs

@@ -156,7 +156,7 @@ pub fn call_nac3_ndarray_subscript_deduce_ndims_after_slicing<'ctx, G: CodeGener
 pub fn call_nac3_ndarray_subscript<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
-    num_subscripts: FixedInt<'ctx, Int32>,
+    num_subscripts: SliceIndex<'ctx>,
     subscripts: Pointer<'ctx, StructModel<NDSubscript>>,
     src_ndarray: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
     dst_ndarray: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
@@ -171,7 +171,7 @@ pub fn call_nac3_ndarray_subscript<'ctx, G: CodeGenerator + ?Sized>(
         &get_sized_dependent_function_name(sizet, "__nac3_ndarray_subscript"),
     )
     .arg("errctx", PointerModel(StructModel(ErrorContext)), errctx_ptr)
-    .arg("num_subscripts", FixedIntModel(Int32), num_subscripts)
+    .arg("num_subscripts", SliceIndexModel::default(), num_subscripts)
     .arg("subscripts", PointerModel(StructModel(NDSubscript)), subscripts)
     .arg("src_ndarray", PointerModel(StructModel(NpArray { sizet })), src_ndarray)
     .arg("dst_ndarray", PointerModel(StructModel(NpArray { sizet })), dst_ndarray)

nac3core/src/codegen/irrt/util.rs

@@ -61,7 +61,7 @@ impl<'ctx, 'a> FunctionBuilder<'ctx, 'a> {
         });
 
         let ret = self.ctx.builder.build_call(function, &param_vals, name).unwrap();
-        return_model.check_llvm_value(ret.as_any_value_enum())
+        return_model.review(ret.as_any_value_enum())
     }
 
     // TODO: Code duplication, but otherwise returning<S: Optic<'ctx>> cannot resolve S if return_optic = None

nac3core/src/codegen/model/core.rs

@@ -22,11 +22,18 @@ pub trait ModelValue<'ctx>: Clone + Copy {
     fn get_llvm_value(&self) -> BasicValueEnum<'ctx>;
 }
 
-pub trait Model<'ctx>: Clone + Copy {
+// Should have been within [`Model<ctx>`],
+// but rust object safety requirements made it necessary to
+// split this interface out
+pub trait CanCheckLLVMType {
+    fn check_llvm_type<'ctx>(&self, ctx: &'ctx Context) -> Result<(), String>;
+}
+
+pub trait Model<'ctx>: Clone + Copy + CanCheckLLVMType + Sized {
     type Value: ModelValue<'ctx>;
 
     fn get_llvm_type(&self, ctx: &'ctx Context) -> BasicTypeEnum<'ctx>;
-    fn check_llvm_value(&self, value: AnyValueEnum<'ctx>) -> Self::Value;
+    fn review(&self, value: AnyValueEnum<'ctx>) -> Self::Value;
 
     fn alloca(&self, ctx: &CodeGenContext<'ctx, '_>, name: &str) -> Pointer<'ctx, Self> {
         Pointer {

nac3core/src/codegen/model/gep.rs

@@ -7,7 +7,7 @@ use itertools::Itertools;
 
 use crate::codegen::CodeGenContext;
 
-use super::{Model, ModelValue, Pointer};
+use super::{core::CanCheckLLVMType, Model, ModelValue, Pointer};
 
 #[derive(Debug, Clone, Copy)]
 pub struct Field<E> {
@@ -17,14 +17,12 @@ pub struct Field<E> {
 }
 
 // Like [`Field<E>`] but element must be [`BasicTypeEnum<'ctx>`]
-#[derive(Debug, Clone, Copy)]
 struct FieldLLVM<'ctx> {
     gep_index: u64,
     name: &'ctx str,
-    llvm_type: BasicTypeEnum<'ctx>,
+    llvm_type: Box<dyn CanCheckLLVMType>,
 }
 
-#[derive(Debug)]
 pub struct FieldBuilder<'ctx> {
     pub ctx: &'ctx Context,
     gep_index_counter: u64,
@@ -57,6 +55,33 @@ impl<'ctx> FieldBuilder<'ctx> {
     }
 }
 
+fn check_basic_types_match<'ctx, A, B>(expected: A, got: B) -> Result<(), String>
+where
+    A: BasicType<'ctx>,
+    B: BasicType<'ctx>,
+{
+    let expected = expected.as_basic_type_enum();
+    let got = got.as_basic_type_enum();
+
+    // Put those logic into here,
+    // otherwise there is always a fallback reporting on any kind of mismatch
+    match (expected, got) {
+        (BasicTypeEnum::IntType(expected), BasicTypeEnum::IntType(got)) => {
+            if expected.get_bit_width() != got.get_bit_width() {
+                return Err(format!(
+                    "Expected IntType ({expected}-bit(s)), got IntType ({got}-bit(s))"
+                ));
+            }
+        }
+        (expected, got) => {
+            if expected != got {
+                return Err(format!("Expected {expected}, got {got}"));
+            }
+        }
+    }
+    Ok(())
+}
+
 pub trait IsStruct<'ctx>: Clone + Copy {
     type Fields;
 
@@ -75,7 +100,12 @@ pub trait IsStruct<'ctx>: Clone + Copy {
 
         let field_types =
             builder.fields.iter().map(|field_info| field_info.llvm_type).collect_vec();
-        ctx.struct_type(&field_types, false)
+        ctx.struct_type(&field_types, false).as_basic_type_enum().into_pointer_type().get_el
+    }
+
+    fn check_struct_type(&self) {
+        // Datatypes behind
+        // check_basic_types_match
     }
 }
 
@@ -94,6 +124,12 @@ impl<'ctx, S: IsStruct<'ctx>> ModelValue<'ctx> for Struct<'ctx, S> {
     }
 }
 
+impl<'ctx, S: IsStruct<'ctx>> CanCheckLLVMType<'ctx> for StructModel<S> {
+    fn check_llvm_type<'ctx>(&self, ctx: &'ctx Context) -> Result<(), String> {
+        todo!()
+    }
+}
+
 impl<'ctx, S: IsStruct<'ctx>> Model<'ctx> for StructModel<S> {
     type Value = Struct<'ctx, S>; // TODO: enrich it
 
@@ -101,7 +137,7 @@ impl<'ctx, S: IsStruct<'ctx>> Model<'ctx> for StructModel<S> {
         self.0.get_struct_type(ctx).as_basic_type_enum()
     }
 
-    fn check_llvm_value(&self, value: AnyValueEnum<'ctx>) -> Self::Value {
+    fn review(&self, value: AnyValueEnum<'ctx>) -> Self::Value {
         // TODO: check structure
         Struct { structure: self.0, value: value.into_struct_value() }
     }

nac3core/src/codegen/model/int.rs

@@ -27,7 +27,7 @@ impl<'ctx> Model<'ctx> for IntModel<'ctx> {
         self.0.as_basic_type_enum()
     }
 
-    fn check_llvm_value(&self, value: AnyValueEnum<'ctx>) -> Self::Value {
+    fn review(&self, value: AnyValueEnum<'ctx>) -> Self::Value {
         let int = value.into_int_value();
         assert_eq!(int.get_type().get_bit_width(), self.0.get_bit_width());
         Int(int)
@@ -130,7 +130,7 @@ impl<'ctx, T: IsFixedInt> Model<'ctx> for FixedIntModel<T> {
         T::get_int_type(ctx).as_basic_type_enum()
     }
 
-    fn check_llvm_value(&self, value: AnyValueEnum<'ctx>) -> Self::Value {
+    fn review(&self, value: AnyValueEnum<'ctx>) -> Self::Value {
         let value = value.into_int_value();
         assert_eq!(value.get_type().get_bit_width(), T::get_bit_width());
         FixedInt { int: self.0, value }

nac3core/src/codegen/model/pointer.rs

@@ -31,7 +31,7 @@ impl<'ctx, E: Model<'ctx>> Pointer<'ctx, E> {
 
     pub fn load(&self, ctx: &CodeGenContext<'ctx, '_>, name: &str) -> E::Value {
         let val = ctx.builder.build_load(self.value, name).unwrap();
-        self.element.check_llvm_value(val.as_any_value_enum())
+        self.element.review(val.as_any_value_enum())
     }
 
     pub fn to_opaque(self) -> OpaquePointer<'ctx> {
@@ -66,7 +66,7 @@ impl<'ctx, E: Model<'ctx>> Model<'ctx> for PointerModel<E> {
         self.0.get_llvm_type(ctx).ptr_type(AddressSpace::default()).as_basic_type_enum()
     }
 
-    fn check_llvm_value(&self, value: AnyValueEnum<'ctx>) -> Self::Value {
+    fn review(&self, value: AnyValueEnum<'ctx>) -> Self::Value {
         // TODO: Check get_element_type()? for LLVM 14 at least...
         Pointer { element: self.0, value: value.into_pointer_value() }
     }
@@ -92,7 +92,7 @@ impl<'ctx> Model<'ctx> for OpaquePointerModel {
         ctx.i8_type().ptr_type(AddressSpace::default()).as_basic_type_enum()
     }
 
-    fn check_llvm_value(&self, value: AnyValueEnum<'ctx>) -> Self::Value {
+    fn review(&self, value: AnyValueEnum<'ctx>) -> Self::Value {
         let ptr = value.into_pointer_value();
         // TODO: remove this check once LLVM pointers do not have `get_element_type()`
         assert_eq!(ptr.get_type().get_element_type().into_int_type().get_bit_width(), 8);

nac3core/src/codegen/stmt.rs

@@ -189,10 +189,6 @@ pub fn gen_store_target<'ctx, G: CodeGenerator>(
                     v.data().ptr_offset(ctx, generator, &index, name)
                 }
 
-                TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-                    todo!()
-                }
-
                 _ => unreachable!(),
             }
         }
@@ -207,10 +203,26 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
     target: &Expr<Option<Type>>,
     value: ValueEnum<'ctx>,
 ) -> Result<(), String> {
+    /*
+        To handle assignment statements `target = value`, with
+        special care taken for targets `gen_store_target` cannot handle, these are:
+        - Case 1. target is a Tuple
+            - e.g., `(x, y, z, w) = value`
+        - Case 2. *Sliced* list assignment `list.__setitem__`
+            - e.g., `my_list[1:3] = [100, 101]`, BUT NOT `my_list[0] = 99` (gen_store_target knows how to handle these),
+        - Case 3. Indexed ndarray assignment `ndarray.__setitem__`
+            - e.g., `my_ndarray[::-1, :] = 3`, `my_ndarray[:, 3::-1] = their_ndarray[10::2]`
+            - NOTE: Technically speaking, if `target` is sliced in such as way that it is referencing a
+                    single element/scalar, we *could* implement gen_store_target for this special case;
+                    but it is much, *much* simpler to generalize all indexed ndarray assignment without
+                    special handling on that edgecase.
+        - Otherwise, use `gen_store_target`
+    */
+
     let llvm_usize = generator.get_size_type(ctx.ctx);
 
-    match &target.node {
+    if let ExprKind::Tuple { elts, .. } = &target.node {
-        ExprKind::Tuple { elts, .. } => {
+        // Handle Case 1. target is a Tuple
         let BasicValueEnum::StructValue(v) =
             value.to_basic_value_enum(ctx, generator, target.custom.unwrap())?
         else {
@@ -224,20 +236,39 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
                 .unwrap();
             generator.gen_assign(ctx, elt, v.into())?;
         }
+
+        return Ok(()); // Terminate
     }
-        ExprKind::Subscript { value: ls, slice, .. }
+
-            if matches!(&slice.node, ExprKind::Slice { .. }) =>
+    // Else, try checking if it's Case 2 or 3, and they *ONLY*
+    // happen if `target.node` is a `ExprKind::Subscript`, so do a special check
+    if let ExprKind::Subscript { value: target_without_slice, slice, .. } = &target.node {
+        // Get the type of target
+        let target_ty = target.custom.unwrap();
+        let target_ty_enum = &*ctx.unifier.get_ty(target_ty);
+
+        // Pattern match on this pair.
+        // This is done like this because of Case 2 - slice.node has to be in a specific pattern
+        match (target_ty_enum, &slice.node) {
+            (TypeEnum::TObj { obj_id, .. }, ExprKind::Slice { lower, upper, step })
+                if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
             {
-            let ExprKind::Slice { lower, upper, step } = &slice.node else { unreachable!() };
+                // Case 2. *Sliced* list assignment
 
                 let ls = generator
-                .gen_expr(ctx, ls)?
+                    .gen_expr(ctx, target_without_slice)?
                     .unwrap()
-                .to_basic_value_enum(ctx, generator, ls.custom.unwrap())?
+                    .to_basic_value_enum(ctx, generator, target_without_slice.custom.unwrap())?
                     .into_pointer_value();
                 let ls = ListValue::from_ptr_val(ls, llvm_usize, None);
-            let Some((start, end, step)) =
+                let Some((start, end, step)) = handle_slice_indices(
-                handle_slice_indices(lower, upper, step, ctx, generator, ls.load_size(ctx, None))?
+                    lower,
+                    upper,
+                    step,
+                    ctx,
+                    generator,
+                    ls.load_size(ctx, None),
+                )?
                 else {
                     return Ok(());
                 };
@@ -268,8 +299,33 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
                     return Ok(());
                 };
                 list_slice_assignment(generator, ctx, ty, ls, (start, end, step), value, src_ind);
+
+                return Ok(()); // Terminate
+            }
+            (TypeEnum::TObj { obj_id, .. }, _)
+                if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
+            {
+                // Case 3. Indexed ndarray assignment
+
+                let target = generator.gen_expr(ctx, target)?.unwrap().to_basic_value_enum(
+                    ctx,
+                    generator,
+                    target.custom.unwrap(),
+                );
+
+                // let value = value.to_basic_value_enum(ctx, generator, value);
+
+                todo!();
+
+                return Ok(()); // Terminate
             }
             _ => {
+                // Fallthrough
+            }
+        }
+    }
+
+    // None of the cases match. We should actually use `gen_store_target`.
     let name = if let ExprKind::Name { id, .. } = &target.node {
         format!("{id}.addr")
     } else {
@@ -288,8 +344,6 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
     }
     let val = value.to_basic_value_enum(ctx, generator, target.custom.unwrap())?;
     ctx.builder.build_store(ptr, val).unwrap();
-        }
-    };
     Ok(())
 }
 

nac3core/src/toplevel/builtins.rs

@@ -1470,7 +1470,7 @@ impl<'a> BuiltinBuilder<'a> {
                                 let ndarray_ptr_model =
                                     PointerModel(StructModel(NpArray { sizet }));
                                 let ndarray_ptr =
-                                    ndarray_ptr_model.check_llvm_value(arg.as_any_value_enum());
+                                    ndarray_ptr_model.review(arg.as_any_value_enum());
 
                                 // Calculate len
                                 // NOTE: Unsized object is asserted in IRRT