WIP: core/ndstrides: checkpoint 6

nac3core/irrt/irrt/core.hpp

@@ -4,7 +4,7 @@
 #include <irrt/util.hpp>
 
 // NDArray indices are always `uint32_t`.
-using NDIndex = uint32_t;
+using NDIndexInt = uint32_t;
 // The type of an index or a value describing the length of a
 // range/slice is always `int32_t`.
 using SliceIndex = int32_t;
@@ -43,7 +43,7 @@ SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len,
 
 template <typename SizeT>
 void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims,
-                                         SizeT num_dims, NDIndex* idxs) {
+                                         SizeT num_dims, NDIndexInt* idxs) {
     SizeT stride = 1;
     for (SizeT dim = 0; dim < num_dims; dim++) {
         SizeT i = num_dims - dim - 1;
@@ -55,7 +55,7 @@ void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims,
 
 template <typename SizeT>
 SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims,
-                                        const NDIndex* indices,
+                                        const NDIndexInt* indices,
                                         SizeT num_indices) {
     SizeT idx = 0;
     SizeT stride = 1;
@@ -104,8 +104,8 @@ void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims, SizeT lhs_ndims,
 template <typename SizeT>
 void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
                                             SizeT src_ndims,
-                                            const NDIndex* in_idx,
+                                            const NDIndexInt* in_idx,
-                                            NDIndex* out_idx) {
+                                            NDIndexInt* out_idx) {
     for (SizeT i = 0; i < src_ndims; ++i) {
         SizeT src_i = src_ndims - i - 1;
         out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
@@ -293,24 +293,24 @@ uint64_t __nac3_ndarray_calc_size64(const uint64_t* list_data,
 }
 
 void __nac3_ndarray_calc_nd_indices(uint32_t index, const uint32_t* dims,
-                                    uint32_t num_dims, NDIndex* idxs) {
+                                    uint32_t num_dims, NDIndexInt* idxs) {
     __nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
 }
 
 void __nac3_ndarray_calc_nd_indices64(uint64_t index, const uint64_t* dims,
-                                      uint64_t num_dims, NDIndex* idxs) {
+                                      uint64_t num_dims, NDIndexInt* idxs) {
     __nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
 }
 
 uint32_t __nac3_ndarray_flatten_index(const uint32_t* dims, uint32_t num_dims,
-                                      const NDIndex* indices,
+                                      const NDIndexInt* indices,
                                       uint32_t num_indices) {
     return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices,
                                              num_indices);
 }
 
 uint64_t __nac3_ndarray_flatten_index64(const uint64_t* dims, uint64_t num_dims,
-                                        const NDIndex* indices,
+                                        const NDIndexInt* indices,
                                         uint64_t num_indices) {
     return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices,
                                              num_indices);
@@ -333,16 +333,16 @@ void __nac3_ndarray_calc_broadcast64(const uint64_t* lhs_dims,
 
 void __nac3_ndarray_calc_broadcast_idx(const uint32_t* src_dims,
                                        uint32_t src_ndims,
-                                       const NDIndex* in_idx,
+                                       const NDIndexInt* in_idx,
-                                       NDIndex* out_idx) {
+                                       NDIndexInt* out_idx) {
     __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
                                            out_idx);
 }
 
 void __nac3_ndarray_calc_broadcast_idx64(const uint64_t* src_dims,
                                          uint64_t src_ndims,
-                                         const NDIndex* in_idx,
+                                         const NDIndexInt* in_idx,
-                                         NDIndex* out_idx) {
+                                         NDIndexInt* out_idx) {
     __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
                                            out_idx);
 }

nac3core/irrt/irrt/ndarray/basic.hpp

@@ -50,9 +50,9 @@ SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
 template <typename SizeT>
 void set_indices_by_nth(SizeT ndims, const SizeT* shape, SizeT* indices,
                         SizeT nth) {
-    for (int32_t i = 0; i < ndims; i++) {
+    for (SizeT i = 0; i < ndims; i++) {
-        int32_t axis = ndims - i - 1;
+        SizeT axis = ndims - i - 1;
-        int32_t dim = shape[axis];
+        SizeT dim = shape[axis];
 
         indices[axis] = nth % dim;
         nth /= dim;
@@ -93,8 +93,9 @@ SizeT len(const NDArray<SizeT>* ndarray) {
     if (ndarray->ndims == 0) {
         raise_exception(SizeT, EXN_TYPE_ERROR, "len() of unsized object",
                         NO_PARAM, NO_PARAM, NO_PARAM);
-    }
+    } else {
         return ndarray->shape[0];
+    }
 }
 
 /**
@@ -156,6 +157,8 @@ uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray,
     return element;
 }
 
+int counter = 0;
+
 /**
  * @brief Return the pointer to the nth (0-based) element in a flattened view of `ndarray`.
  *
@@ -163,9 +166,14 @@ uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray,
  */
 template <typename SizeT>
 uint8_t* get_nth_pelement(const NDArray<SizeT>* ndarray, SizeT nth) {
-    SizeT* indices = (SizeT*)__builtin_alloca(sizeof(SizeT) * ndarray->ndims);
+    uint8_t* element = ndarray->data;
-    util::set_indices_by_nth(ndarray->ndims, ndarray->shape, indices, nth);
+    for (SizeT i = 0; i < ndarray->ndims; i++) {
-    return get_pelement_by_indices(ndarray, indices);
+        SizeT axis = ndarray->ndims - i - 1;
+        SizeT dim = ndarray->shape[axis];
+        element += ndarray->strides[axis] * (nth % dim);
+        nth /= dim;
+    }
+    return element;
 }
 
 /**
@@ -259,7 +267,8 @@ bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t>* ndarray) {
     return is_c_contiguous(ndarray);
 }
 
-uint8_t* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray, int32_t nth) {
+uint8_t* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray,
+                                         int32_t nth) {
     return get_nth_pelement(ndarray, nth);
 }
 

nac3core/irrt/irrt/ndarray/broadcast.hpp

@@ -0,0 +1,157 @@
+#pragma once
+
+#include <irrt/int_defs.hpp>
+#include <irrt/ndarray/def.hpp>
+#include <irrt/slice.hpp>
+
+namespace {
+template <typename SizeT>
+struct ShapeEntry {
+    SizeT ndims;
+    SizeT* shape;
+};
+}  // namespace
+
+namespace {
+namespace ndarray {
+namespace broadcast {
+namespace util {
+/**
+ * @brief Return true if `src_shape` can broadcast to `dst_shape`.
+ *
+ * See https://numpy.org/doc/stable/user/basics.broadcasting.html
+ */
+template <typename SizeT>
+bool can_broadcast_shape_to(SizeT target_ndims, const SizeT* target_shape,
+                            SizeT src_ndims, const SizeT* src_shape) {
+    if (src_ndims > target_ndims) {
+        return false;
+    }
+
+    for (SizeT i = 0; i < src_ndims; i++) {
+        SizeT target_dim = target_shape[target_ndims - i - 1];
+        SizeT src_dim = src_shape[src_ndims - i - 1];
+        if (!(src_dim == 1 || target_dim == src_dim)) {
+            return false;
+        }
+    }
+    return true;
+}
+
+/**
+ * @brief Performs `np.broadcast_shapes`
+ */
+template <typename SizeT>
+void broadcast_shapes(SizeT num_shapes, const ShapeEntry<SizeT>* shapes,
+                      SizeT dst_ndims, SizeT* dst_shape) {
+    // `dst_ndims` must be `max([shape.ndims for shape in shapes])`, but the caller has to calculate it/provide it
+    //   for this function since they should already know in order to allocate `dst_shape` in the first place.
+    // `dst_shape` must be pre-allocated.
+    // `dst_shape` does not have to be initialized
+    for (SizeT dst_axis = 0; dst_axis < dst_ndims; dst_axis++) {
+        dst_shape[dst_axis] = 1;
+    }
+
+    for (SizeT i = 0; i < num_shapes; i++) {
+        ShapeEntry<SizeT> entry = shapes[i];
+
+        for (SizeT j = 0; j < entry.ndims; j++) {
+            SizeT entry_axis = entry.ndims - j - 1;
+            SizeT dst_axis = dst_ndims - j - 1;
+
+            SizeT entry_dim = entry.shape[entry_axis];
+            SizeT dst_dim = dst_shape[dst_axis];
+
+            if (dst_dim == 1) {
+                dst_shape[dst_axis] = entry_dim;
+            } else if (entry_dim == 1 || entry_dim == dst_dim) {
+                // Do nothing
+            } else {
+                raise_exception(SizeT, EXN_VALUE_ERROR,
+                                "shape mismatch: objects cannot be broadcast "
+                                "to a single shape.",
+                                NO_PARAM, NO_PARAM, NO_PARAM);
+            }
+        }
+    }
+}
+}  // namespace util
+
+/**
+ * @brief Perform `np.broadcast_to(<ndarray>, <target_shape>)` and appropriate assertions.
+ *
+ * This function attempts to broadcast `src_ndarray` to a new shape defined by `dst_ndarray.shape`,
+ * and return the result by modifying `dst_ndarray`.
+ *
+ * # Notes on `dst_ndarray`
+ * The caller is responsible for allocating space for the resulting ndarray.
+ * Here is what this function expects from `dst_ndarray` when called:
+ *   - `dst_ndarray->data` does not have to be initialized.
+ *   - `dst_ndarray->itemsize` does not have to be initialized.
+ *   - `dst_ndarray->ndims` must be initialized, determining the length of `dst_ndarray->shape`
+ *   - `dst_ndarray->shape` must be allocated, and must contain the desired target broadcast shape.
+ *   - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
+ * When this function call ends:
+ *   - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
+ *   - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
+ *   - `dst_ndarray->ndims` is unchanged.
+ *   - `dst_ndarray->shape` is unchanged.
+ *   - `dst_ndarray->strides` is updated accordingly by how ndarray broadcast_to works.
+ */
+template <typename SizeT>
+void broadcast_to(const NDArray<SizeT>* src_ndarray,
+                  NDArray<SizeT>* dst_ndarray) {
+    if (!ndarray::broadcast::util::can_broadcast_shape_to(
+            dst_ndarray->ndims, dst_ndarray->shape, src_ndarray->ndims,
+            src_ndarray->shape)) {
+        raise_exception(SizeT, EXN_VALUE_ERROR,
+                        "operands could not be broadcast together",
+                        dst_ndarray->shape[0], src_ndarray->shape[0], NO_PARAM);
+    }
+
+    dst_ndarray->data = src_ndarray->data;
+    dst_ndarray->itemsize = src_ndarray->itemsize;
+
+    for (SizeT i = 0; i < dst_ndarray->ndims; i++) {
+        SizeT src_axis = src_ndarray->ndims - i - 1;
+        SizeT dst_axis = dst_ndarray->ndims - i - 1;
+        if (src_axis < 0 || (src_ndarray->shape[src_axis] == 1 &&
+                             dst_ndarray->shape[dst_axis] != 1)) {
+            // Freeze the steps in-place
+            dst_ndarray->strides[dst_axis] = 0;
+        } else {
+            dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
+        }
+    }
+}
+}  // namespace broadcast
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+using namespace ndarray::broadcast;
+
+void __nac3_ndarray_broadcast_to(NDArray<int32_t>* src_ndarray,
+                                 NDArray<int32_t>* dst_ndarray) {
+    broadcast_to(src_ndarray, dst_ndarray);
+}
+
+void __nac3_ndarray_broadcast_to64(NDArray<int64_t>* src_ndarray,
+                                   NDArray<int64_t>* dst_ndarray) {
+    broadcast_to(src_ndarray, dst_ndarray);
+}
+
+void __nac3_ndarray_broadcast_shapes(int32_t num_shapes,
+                                     const ShapeEntry<int32_t>* shapes,
+                                     int32_t dst_ndims, int32_t* dst_shape) {
+    ndarray::broadcast::util::broadcast_shapes(num_shapes, shapes, dst_ndims,
+                                               dst_shape);
+}
+
+void __nac3_ndarray_broadcast_shapes64(int64_t num_shapes,
+                                       const ShapeEntry<int64_t>* shapes,
+                                       int64_t dst_ndims, int64_t* dst_shape) {
+    ndarray::broadcast::util::broadcast_shapes(num_shapes, shapes, dst_ndims,
+                                               dst_shape);
+}
+}

nac3core/irrt/irrt/ndarray/indexing.hpp

@@ -0,0 +1,228 @@
+#pragma once
+
+#include <irrt/exception.hpp>
+#include <irrt/int_defs.hpp>
+#include <irrt/ndarray/basic.hpp>
+#include <irrt/ndarray/def.hpp>
+#include <irrt/slice.hpp>
+
+namespace {
+typedef uint8_t NDIndexType;
+
+/**
+ * @brief A single element index
+ *
+ * `data` points to a `SliceIndex`.
+ */
+
+const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
+/**
+ * @brief A slice index
+ *
+ * `data` points to a `UserRange`.
+ */
+const NDIndexType ND_INDEX_TYPE_SLICE = 1;
+
+/**
+ * @brief `np.newaxis` / `None`
+ *
+ * `data` is unused.
+ */
+const NDIndexType ND_INDEX_TYPE_NEWAXIS = 2;
+
+/**
+ * @brief `Ellipsis` / `...`
+ *
+ * `data` is unused.
+ */
+const NDIndexType ND_INDEX_TYPE_ELLIPSIS = 3;
+
+/**
+ * @brief An index used in ndarray indexing
+ */
+struct NDIndex {
+    /**
+     * @brief Enum tag to specify the type of index.
+     *
+     * Please see comments of each enum constant.
+     */
+    NDIndexType type;
+
+    /**
+     * @brief The accompanying data associated with `type`.
+     *
+     * Please see comments of each enum constant.
+     */
+    uint8_t* data;
+};
+}  // namespace
+
+namespace {
+namespace ndarray {
+namespace indexing {
+namespace util {
+
+/**
+ * @brief Return the expected rank of the resulting ndarray
+ * created by indexing an ndarray of rank `ndims` using `indexes`.
+ *
+ * An IndexError is raised if the indexes are invalid.
+ */
+template <typename SizeT>
+SizeT validate_and_deduce_ndims_after_indexing(SizeT ndims, SizeT num_indexes,
+                                               const NDIndex* indexes) {
+    if (num_indexes > ndims) {
+        raise_exception(SizeT, EXN_INDEX_ERROR,
+                        "too many indices for array: array is {0}-dimensional, "
+                        "but {1} were indexed",
+                        ndims, num_indexes, NO_PARAM);
+    }
+
+    // There may be ellipsis `...` in `indexes`. There can only be 0 or 1 ellipsis.
+    SizeT num_ellipsis = 0;
+
+    for (SizeT i = 0; i < num_indexes; i++) {
+        if (indexes[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
+            ndims--;
+        } else if (indexes[i].type == ND_INDEX_TYPE_SLICE) {
+            // Nothing
+        } else if (indexes[i].type == ND_INDEX_TYPE_NEWAXIS) {
+            ndims++;
+        } else if (indexes[i].type == ND_INDEX_TYPE_ELLIPSIS) {
+            // `...` doesn't do anything to `ndims`.
+            num_ellipsis++;
+            if (num_ellipsis > 1) {
+                raise_exception(
+                    SizeT, EXN_INDEX_ERROR,
+                    "an index can only have a single ellipsis ('...')",
+                    NO_PARAM, NO_PARAM, NO_PARAM);
+            }
+        } else {
+            __builtin_unreachable();
+        }
+    }
+    return ndims;
+}
+}  // namespace util
+
+/**
+ * @brief Perform ndarray "basic indexing" (https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
+ *
+ * This is function very similar to performing `dst_ndarray = src_ndarray[indexes]` in Python (where the variables
+ * can all be found in the parameter of this function).
+ *
+ * In other words, this function takes in an ndarray (`src_ndarray`), index it with `indexes`, and return the
+ * indexed array (by writing the result to `dst_ndarray`).
+ *
+ * This function also does proper assertions on `indexes`.
+ *
+ * # Notes on `dst_ndarray`
+ * The caller is responsible for allocating space for the resulting ndarray.
+ * Here is what this function expects from `dst_ndarray` when called:
+ *   - `dst_ndarray->data` does not have to be initialized.
+ *   - `dst_ndarray->itemsize` does not have to be initialized.
+ *   - `dst_ndarray->ndims` must be initialized, and it must be equal to the expected `ndims` of the `dst_ndarray` after
+ *       indexing `src_ndarray` with `indexes`.
+ *   - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
+ *   - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
+ * When this function call ends:
+ *   - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
+ *   - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
+ *   - `dst_ndarray->ndims` is unchanged.
+ *   - `dst_ndarray->shape` is updated according to how `src_ndarray` is indexed.
+ *   - `dst_ndarray->strides` is updated accordingly by how ndarray indexing works.
+ *
+ * @param indexes Indexes to index `src_ndarray`, ordered in the same way you would write them in Python.
+ * @param src_ndarray The NDArray to be indexed.
+ * @param dst_ndarray The resulting NDArray after indexing. Further details in the comments above,
+ */
+template <typename SizeT>
+void index(SizeT num_indexes, const NDIndex* indexes,
+           const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
+    SizeT expected_dst_ndarray_ndims =
+        util::validate_and_deduce_ndims_after_indexing(src_ndarray->ndims,
+                                                       num_indexes, indexes);
+
+    dst_ndarray->data = src_ndarray->data;
+    dst_ndarray->itemsize = src_ndarray->itemsize;
+
+    // Reference code: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
+    SizeT src_axis = 0;
+    SizeT dst_axis = 0;
+
+    for (SliceIndex i = 0; i < num_indexes; i++) {
+        const NDIndex* index = &indexes[i];
+        if (index->type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
+            SliceIndex input = *((SliceIndex*)index->data);
+            SliceIndex k = slice::resolve_index_in_length(
+                src_ndarray->shape[src_axis], input);
+
+            if (k == slice::OUT_OF_BOUNDS) {
+                raise_exception(SizeT, EXN_INDEX_ERROR,
+                                "index {0} is out of bounds for axis {1} "
+                                "with size {2}",
+                                input, src_axis, src_ndarray->shape[src_axis]);
+            }
+
+            dst_ndarray->data += k * src_ndarray->strides[src_axis];
+
+            src_axis++;
+        } else if (index->type == ND_INDEX_TYPE_SLICE) {
+            UserSlice* input = (UserSlice*)index->data;
+
+            Slice slice;
+            input->indices_checked<SizeT>(src_ndarray->shape[src_axis], &slice);
+
+            dst_ndarray->data +=
+                (SizeT)slice.start * src_ndarray->strides[src_axis];
+            dst_ndarray->strides[dst_axis] =
+                ((SizeT)slice.step) * src_ndarray->strides[src_axis];
+            dst_ndarray->shape[dst_axis] = (SizeT)slice.len();
+
+            dst_axis++;
+            src_axis++;
+        } else if (index->type == ND_INDEX_TYPE_NEWAXIS) {
+            dst_ndarray->strides[dst_axis] = 0;
+            dst_ndarray->shape[dst_axis] = 1;
+
+            dst_axis++;
+        } else if (index->type == ND_INDEX_TYPE_ELLIPSIS) {
+            // The number of ':' entries this '...' implies.
+            SizeT ellipsis_size = src_ndarray->ndims - (num_indexes - 1);
+
+            for (SizeT j = 0; j < ellipsis_size; j++) {
+                dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
+                dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
+
+                dst_axis++;
+                src_axis++;
+            }
+        } else {
+            __builtin_unreachable();
+        }
+    }
+
+    for (; dst_axis < dst_ndarray->ndims; dst_axis++, src_axis++) {
+        dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
+        dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
+    }
+}
+}  // namespace indexing
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+using namespace ndarray::indexing;
+
+void __nac3_ndarray_index(int32_t num_indexes, NDIndex* indexes,
+                          NDArray<int32_t>* src_ndarray,
+                          NDArray<int32_t>* dst_ndarray) {
+    index(num_indexes, indexes, src_ndarray, dst_ndarray);
+}
+
+void __nac3_ndarray_index64(int64_t num_indexes, NDIndex* indexes,
+                            NDArray<int64_t>* src_ndarray,
+                            NDArray<int64_t>* dst_ndarray) {
+    index(num_indexes, indexes, src_ndarray, dst_ndarray);
+}
+}

nac3core/irrt/irrt/ndarray/product.hpp

@@ -0,0 +1,26 @@
+
+namespace {
+namespace ndarray {
+namespace matmul {
+namespace util {
+
+template <typename SizeT>
+void broadcast_shape(SizeT a_ndims, SizeT* a_shape, SizeT b_ndims,
+                     SizeT* b_shape, SizeT* dst_shape) {
+    __builtin_assume(!(a_ndims == 1 && b_ndims == 1));
+    __builtin_assume(a_ndims >= 1);
+    __builtin_assume(b_ndims >= 1);
+}
+}  // namespace util
+
+template <typename SizeT, typename T>
+void matmul_at_least_2d(NDArray<SizeT>* a_ndarray, NDArray<SizeT>* b_ndarray,
+            NDArray<SizeT>* dst_ndarray) {
+    __builtin_assume(sizeof(T) == a_ndarray->itemsize);
+    __builtin_assume(sizeof(T) == b_ndarray->itemsize);
+
+    // See https://numpy.org/doc/stable/reference/generated/numpy.matmul.html#numpy-matmul
+}
+}  // namespace matmul
+}  // namespace ndarray
+}  // namespace

nac3core/irrt/irrt/ndarray/reshape.hpp

@@ -0,0 +1,111 @@
+#pragma once
+
+#include <irrt/int_defs.hpp>
+#include <irrt/ndarray/def.hpp>
+
+namespace {
+namespace ndarray {
+namespace reshape {
+namespace util {
+
+/**
+ * @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
+ *
+ * If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
+ * modified to contain the resolved dimension.
+ *
+ * To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
+ * `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
+ *
+ * @param size The `.size` of `<ndarray>`
+ * @param new_ndims Number of elements in `new_shape`
+ * @param new_shape Target shape to reshape to
+ */
+template <typename SizeT>
+void resolve_and_check_new_shape(SizeT size, SizeT new_ndims,
+                                 SizeT* new_shape) {
+    // Is there a -1 in `new_shape`?
+    bool neg1_exists = false;
+    // Location of -1, only initialized if `neg1_exists` is true
+    SizeT neg1_axis_i;
+    // The computed ndarray size of `new_shape`
+    SizeT new_size = 1;
+
+    for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++) {
+        SizeT dim = new_shape[axis_i];
+        if (dim < 0) {
+            if (dim == -1) {
+                if (neg1_exists) {
+                    // Multiple `-1` found. Throw an error.
+                    raise_exception(SizeT, EXN_VALUE_ERROR,
+                                    "can only specify one unknown dimension",
+                                    NO_PARAM, NO_PARAM, NO_PARAM);
+                } else {
+                    neg1_exists = true;
+                    neg1_axis_i = axis_i;
+                }
+            } else {
+                // TODO: What? In `np.reshape` any negative dimensions is
+                // treated like its `-1`.
+                //
+                // Try running `np.zeros((3, 4)).reshape((-999, 2))`
+                //
+                // It is not documented by numpy.
+                // Throw an error for now...
+
+                raise_exception(
+                    SizeT, EXN_VALUE_ERROR,
+                    "Found non -1 negative dimension {0} on axis {1}", dim,
+                    axis_i, NO_PARAM);
+            }
+        } else {
+            new_size *= dim;
+        }
+    }
+
+    bool can_reshape;
+    if (neg1_exists) {
+        // Let `x` be the unknown dimension
+        // solve `x * <new_size> = <size>`
+        if (new_size == 0 && size == 0) {
+            // `x` has infinitely many solutions
+            can_reshape = false;
+        } else if (new_size == 0 && size != 0) {
+            // `x` has no solutions
+            can_reshape = false;
+        } else if (size % new_size != 0) {
+            // `x` has no integer solutions
+            can_reshape = false;
+        } else {
+            can_reshape = true;
+            new_shape[neg1_axis_i] = size / new_size;  // Resolve dimension
+        }
+    } else {
+        can_reshape = (new_size == size);
+    }
+
+    if (!can_reshape) {
+        raise_exception(SizeT, EXN_VALUE_ERROR,
+                        "cannot reshape array of size {0} into given shape",
+                        size, NO_PARAM, NO_PARAM);
+    }
+}
+}  // namespace util
+}  // namespace reshape
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+void __nac3_ndarray_resolve_and_check_new_shape(int32_t size, int32_t new_ndims,
+                                                int32_t* new_shape) {
+    ndarray::reshape::util::resolve_and_check_new_shape(size, new_ndims,
+                                                        new_shape);
+}
+
+void __nac3_ndarray_resolve_and_check_new_shape64(int64_t size,
+                                                  int64_t new_ndims,
+                                                  int64_t* new_shape) {
+    ndarray::reshape::util::resolve_and_check_new_shape(size, new_ndims,
+                                                        new_shape);
+}
+}

nac3core/irrt/irrt/ndarray/transpose.hpp

@@ -0,0 +1,148 @@
+#pragma once
+
+#include <irrt/int_defs.hpp>
+#include <irrt/ndarray/def.hpp>
+#include <irrt/slice.hpp>
+
+/*
+ * Notes on `np.transpose(<array>, <axes>)`
+ *
+ * TODO: `axes`, if specified, can actually contain negative indices,
+ * but it is not documented in numpy.
+ *
+ * Supporting it for now.
+ */
+
+namespace {
+namespace ndarray {
+namespace transpose {
+namespace util {
+
+/**
+ * @brief Do assertions on `<axes>` in `np.transpose(<array>, <axes>)`.
+ *
+ * Note that `np.transpose`'s `<axe>` argument is optional. If the argument
+ * is specified but the user, use this function to do assertions on it.
+ *
+ * @param ndims The number of dimensions of `<array>`
+ * @param num_axes Number of elements in `<axes>` as specified by the user.
+ *   This should be equal to `ndims`. If not, a "ValueError: axes don't match array" is thrown.
+ * @param axes The user specified `<axes>`.
+ */
+template <typename SizeT>
+void assert_transpose_axes(SizeT ndims, SizeT num_axes, const SizeT* axes) {
+    if (ndims != num_axes) {
+        raise_exception(SizeT, EXN_VALUE_ERROR, "axes don't match array",
+                        NO_PARAM, NO_PARAM, NO_PARAM);
+    }
+
+    // TODO: Optimize this
+    bool* axe_specified = (bool*)__builtin_alloca(sizeof(bool) * ndims);
+    for (SizeT i = 0; i < ndims; i++) axe_specified[i] = false;
+
+    for (SizeT i = 0; i < ndims; i++) {
+        SizeT axis = slice::resolve_index_in_length(ndims, axes[i]);
+        if (axis == slice::OUT_OF_BOUNDS) {
+            // TODO: numpy actually throws a `numpy.exceptions.AxisError`
+            raise_exception(
+                SizeT, EXN_VALUE_ERROR,
+                "axis {0} is out of bounds for array of dimension {1}", axis,
+                ndims, NO_PARAM);
+        }
+
+        if (axe_specified[axis]) {
+            raise_exception(SizeT, EXN_VALUE_ERROR,
+                            "repeated axis in transpose", NO_PARAM, NO_PARAM,
+                            NO_PARAM);
+        }
+
+        axe_specified[axis] = true;
+    }
+}
+}  // namespace util
+
+/**
+ * @brief Create a transpose view of `src_ndarray` and perform proper assertions.
+ *
+ * This function is very similar to doing `dst_ndarray = np.transpose(src_ndarray, <axes>)`.
+ * If `<axes>` is supposed to be `None`, caller can pass in a `nullptr` to `<axes>`.
+ *
+ * The transpose view created is returned by modifying `dst_ndarray`.
+ *
+ * The caller is responsible for setting up `dst_ndarray` before calling this function.
+ * Here is what this function expects from `dst_ndarray` when called:
+ *   - `dst_ndarray->data` does not have to be initialized.
+ *   - `dst_ndarray->itemsize` does not have to be initialized.
+ *   - `dst_ndarray->ndims` must be initialized, must be equal to `src_ndarray->ndims`.
+ *   - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
+ *   - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
+ * When this function call ends:
+ *   - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
+ *   - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
+ *   - `dst_ndarray->ndims` is unchanged
+ *   - `dst_ndarray->shape` is updated according to how `np.transpose` works
+ *   - `dst_ndarray->strides` is updated according to how `np.transpose` works
+ *
+ * @param src_ndarray The NDArray to build a transpose view on
+ * @param dst_ndarray The resulting NDArray after transpose. Further details in the comments above,
+ * @param num_axes Number of elements in axes. Unused if `axes` is nullptr.
+ * @param axes Axes permutation. Set it to `nullptr` if `<axes>` is `None`.
+ */
+template <typename SizeT>
+void transpose(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray,
+               SizeT num_axes, const SizeT* axes) {
+    __builtin_assume(src_ndarray->ndims == dst_ndarray->ndims);
+    const auto ndims = src_ndarray->ndims;
+
+    if (axes != nullptr) util::assert_transpose_axes(ndims, num_axes, axes);
+
+    dst_ndarray->data = src_ndarray->data;
+    dst_ndarray->itemsize = src_ndarray->itemsize;
+
+    // Check out https://ajcr.net/stride-guide-part-2/ to see how `np.transpose` works behind the scenes.
+    if (axes == nullptr) {
+        // `np.transpose(<array>, axes=None)`
+
+        /*
+         * Minor note: `np.transpose(<array>, axes=None)` is equivalent to
+         * `np.transpose(<array>, axes=[N-1, N-2, ..., 0])` - basically it
+         * is reversing the order of strides and shape.
+         *
+         * This is a fast implementation to handle this special (but very common) case.
+         */
+
+        for (SizeT axis = 0; axis < ndims; axis++) {
+            dst_ndarray->shape[axis] = src_ndarray->shape[ndims - axis - 1];
+            dst_ndarray->strides[axis] = src_ndarray->strides[ndims - axis - 1];
+        }
+    } else {
+        // `np.transpose(<array>, <axes>)`
+
+        // Permute strides and shape according to `axes`, while resolving negative indices in `axes`
+        for (SizeT axis = 0; axis < ndims; axis++) {
+            // `i` cannot be OUT_OF_BOUNDS because of assertions
+            SizeT i = slice::resolve_index_in_length(ndims, axes[axis]);
+
+            dst_ndarray->shape[axis] = src_ndarray->shape[i];
+            dst_ndarray->strides[axis] = src_ndarray->strides[i];
+        }
+    }
+}
+}  // namespace transpose
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+using namespace ndarray::transpose;
+void __nac3_ndarray_transpose(const NDArray<int32_t>* src_ndarray,
+                              NDArray<int32_t>* dst_ndarray, int32_t num_axes,
+                              const int32_t* axes) {
+    transpose(src_ndarray, dst_ndarray, num_axes, axes);
+}
+
+void __nac3_ndarray_transpose64(const NDArray<int64_t>* src_ndarray,
+                                NDArray<int64_t>* dst_ndarray, int64_t num_axes,
+                                const int64_t* axes) {
+    transpose(src_ndarray, dst_ndarray, num_axes, axes);
+}
+}

nac3core/irrt/irrt/slice.hpp

@@ -0,0 +1,165 @@
+#pragma once
+
+#include <irrt/int_defs.hpp>
+#include <irrt/slice.hpp>
+#include <irrt/util.hpp>
+
+#include "exception.hpp"
+
+// The type of an index or a value describing the length of a
+// range/slice is always `int32_t`.
+using SliceIndex = int32_t;
+
+namespace {
+
+/**
+ * @brief A Python-like slice with resolved indices.
+ *
+ * "Resolved indices" means that `start` and `stop` must be positive and are
+ * bound to a known length.
+ */
+struct Slice {
+    SliceIndex start;
+    SliceIndex stop;
+    SliceIndex step;
+
+    /**
+     * @brief Calculate and return the length / the number of the slice.
+     * 
+     * If this were a Python range, this function would be `len(range(start, stop, step))`.
+     */
+    SliceIndex len() {
+        SliceIndex diff = stop - start;
+        if (diff > 0 && step > 0) {
+            return ((diff - 1) / step) + 1;
+        } else if (diff < 0 && step < 0) {
+            return ((diff + 1) / step) + 1;
+        } else {
+            return 0;
+        }
+    }
+};
+
+namespace slice {
+/**
+ * @brief Resolve a slice index under a given length like Python indexing.
+ *
+ * In Python, if you have a `list` of length 100, `list[-1]` resolves to
+ * `list[99]`, so `resolve_index_in_length_clamped(100, -1)` returns `99`.
+ *
+ * If `length` is 0, 0 is returned for any value of `index`.
+ *
+ * If `index` is out of bounds, clamps the returned value between `0` and
+ * `length - 1` (inclusive).
+ *
+ */
+SliceIndex resolve_index_in_length_clamped(SliceIndex length,
+                                           SliceIndex index) {
+    if (index < 0) {
+        return max<SliceIndex>(length + index, 0);
+    } else {
+        return min<SliceIndex>(length, index);
+    }
+}
+
+const SliceIndex OUT_OF_BOUNDS = -1;
+
+/**
+ * @brief Like `resolve_index_in_length_clamped`, but returns `OUT_OF_BOUNDS`
+ * if `index` is out of bounds.
+ */
+SliceIndex resolve_index_in_length(SliceIndex length, SliceIndex index) {
+    SliceIndex resolved = index < 0 ? length + index : index;
+    if (0 <= resolved && resolved < length) {
+        return resolved;
+    } else {
+        return OUT_OF_BOUNDS;
+    }
+}
+}  // namespace slice
+
+/**
+ * @brief A Python-like slice with **unresolved** indices.
+ */
+struct UserSlice {
+    bool start_defined;
+    SliceIndex start;
+
+    bool stop_defined;
+    SliceIndex stop;
+
+    bool step_defined;
+    SliceIndex step;
+
+    UserSlice() { this->reset(); }
+
+    void reset() {
+        this->start_defined = false;
+        this->stop_defined = false;
+        this->step_defined = false;
+    }
+
+    void set_start(SliceIndex start) {
+        this->start_defined = true;
+        this->start = start;
+    }
+
+    void set_stop(SliceIndex stop) {
+        this->stop_defined = true;
+        this->stop = stop;
+    }
+
+    void set_step(SliceIndex step) {
+        this->step_defined = true;
+        this->step = step;
+    }
+
+    /**
+     * @brief Resolve this slice.
+     *
+     * In Python, this would be `slice(start, stop, step).indices(length)`.
+     *
+     * @return A `Slice` with the resolved indices.
+     */
+    Slice indices(SliceIndex length) {
+        Slice result;
+
+        result.step = step_defined ? step : 1;
+        bool step_is_negative = result.step < 0;
+
+        if (start_defined) {
+            result.start =
+                slice::resolve_index_in_length_clamped(length, start);
+        } else {
+            result.start = step_is_negative ? length - 1 : 0;
+        }
+
+        if (stop_defined) {
+            result.stop = slice::resolve_index_in_length_clamped(length, stop);
+        } else {
+            result.stop = step_is_negative ? -1 : length;
+        }
+
+        return result;
+    }
+
+    /**
+     * @brief Like `.indices()` but with assertions.
+     */
+    template <typename SizeT>
+    void indices_checked(SliceIndex length, Slice* result) {
+        if (length < 0) {
+            raise_exception(SizeT, EXN_VALUE_ERROR,
+                            "length should not be negative, got {0}", length,
+                            NO_PARAM, NO_PARAM);
+        }
+
+        if (this->step_defined && this->step == 0) {
+            raise_exception(SizeT, EXN_VALUE_ERROR, "slice step cannot be zero",
+                            NO_PARAM, NO_PARAM, NO_PARAM);
+        }
+
+        *result = this->indices(length);
+    }
+};
+}  // namespace

nac3core/irrt/irrt_everything.hpp

@@ -4,5 +4,9 @@
 #include <irrt/exception.hpp>
 #include <irrt/int_defs.hpp>
 #include <irrt/ndarray/basic.hpp>
+#include <irrt/ndarray/broadcast.hpp>
 #include <irrt/ndarray/def.hpp>
+#include <irrt/ndarray/indexing.hpp>
+#include <irrt/ndarray/reshape.hpp>
+#include <irrt/ndarray/transpose.hpp>
 #include <irrt/util.hpp>

nac3core/irrt/irrt_test.cpp

@@ -6,15 +6,20 @@
 #include <cstdio>
 #include <cstdlib>
 
-// Special macro to inform `#include <irrt/*>` that we
+// Special macro to inform `#include <irrt/*>` that we are testing.
-// are testing.
 #define IRRT_TESTING
 
+// Note that failure unit tests are not supported.
+
 #include <test/test_core.hpp>
 #include <test/test_ndarray_basic.hpp>
+#include <test/test_ndarray_broadcast.hpp>
+#include <test/test_ndarray_indexing.hpp>
 
 int main() {
     test::core::run();
     test::ndarray_basic::run();
+    test::ndarray_indexing::run();
+    test::ndarray_broadcast::run();
     return 0;
 }

nac3core/irrt/test/test_core.hpp

@@ -7,8 +7,8 @@ namespace core {
 void test_int_exp() {
     BEGIN_TEST();
 
-    assert_values_match(125, __nac3_int_exp_impl<int32_t>(5, 3));
+    assert_values_match(125L, __nac3_int_exp_impl<int64_t>(5, 3));
-    assert_values_match(3125, __nac3_int_exp_impl<int32_t>(5, 5));
+    assert_values_match(3125L, __nac3_int_exp_impl<int64_t>(5, 5));
 }
 
 void run() { test_int_exp(); }

nac3core/irrt/test/test_ndarray_basic.hpp

@@ -8,18 +8,18 @@ void test_calc_size_from_shape_normal() {
     // Test shapes with normal values
     BEGIN_TEST();
 
-    int32_t shape[4] = {2, 3, 5, 7};
+    int64_t shape[4] = {2, 3, 5, 7};
     assert_values_match(
-        210, ndarray::basic::util::calc_size_from_shape<int32_t>(4, shape));
+        210L, ndarray::basic::util::calc_size_from_shape<int64_t>(4, shape));
 }
 
 void test_calc_size_from_shape_has_zero() {
     // Test shapes with 0 in them
     BEGIN_TEST();
 
-    int32_t shape[4] = {2, 0, 5, 7};
+    int64_t shape[4] = {2, 0, 5, 7};
     assert_values_match(
-        0, ndarray::basic::util::calc_size_from_shape<int32_t>(4, shape));
+        0L, ndarray::basic::util::calc_size_from_shape<int64_t>(4, shape));
 }
 
 void run() {

nac3core/irrt/test/test_ndarray_broadcast.hpp

@@ -0,0 +1,127 @@
+#pragma once
+
+#include <test/includes.hpp>
+
+namespace test {
+namespace ndarray_broadcast {
+void test_can_broadcast_shape() {
+    BEGIN_TEST();
+
+    assert_values_match(true,
+                        ndarray::broadcast::util::can_broadcast_shape_to(
+                            1, (int32_t[]){3}, 5, (int32_t[]){1, 1, 1, 1, 3}));
+    assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
+                                   1, (int32_t[]){3}, 2, (int32_t[]){3, 1}));
+    assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
+                                  1, (int32_t[]){3}, 1, (int32_t[]){3}));
+    assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
+                                   1, (int32_t[]){1}, 1, (int32_t[]){3}));
+    assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
+                                  1, (int32_t[]){1}, 1, (int32_t[]){1}));
+    assert_values_match(
+        true, ndarray::broadcast::util::can_broadcast_shape_to(
+                  3, (int32_t[]){256, 256, 3}, 3, (int32_t[]){256, 1, 3}));
+    assert_values_match(true,
+                        ndarray::broadcast::util::can_broadcast_shape_to(
+                            3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){3}));
+    assert_values_match(false,
+                        ndarray::broadcast::util::can_broadcast_shape_to(
+                            3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){2}));
+    assert_values_match(true,
+                        ndarray::broadcast::util::can_broadcast_shape_to(
+                            3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){1}));
+
+    // In cases when the shapes contain zero(es)
+    assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
+                                  1, (int32_t[]){0}, 1, (int32_t[]){1}));
+    assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
+                                   1, (int32_t[]){0}, 1, (int32_t[]){2}));
+    assert_values_match(true,
+                        ndarray::broadcast::util::can_broadcast_shape_to(
+                            4, (int32_t[]){0, 4, 0, 0}, 1, (int32_t[]){1}));
+    assert_values_match(
+        true, ndarray::broadcast::util::can_broadcast_shape_to(
+                  4, (int32_t[]){0, 4, 0, 0}, 4, (int32_t[]){1, 1, 1, 1}));
+    assert_values_match(
+        true, ndarray::broadcast::util::can_broadcast_shape_to(
+                  4, (int32_t[]){0, 4, 0, 0}, 4, (int32_t[]){1, 4, 1, 1}));
+    assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
+                                   2, (int32_t[]){4, 3}, 2, (int32_t[]){0, 3}));
+    assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
+                                   2, (int32_t[]){4, 3}, 2, (int32_t[]){0, 0}));
+}
+
+void test_ndarray_broadcast() {
+    /*
+    # 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]]]
+    #
+    # assery array.strides == (0, 0, 8)
+
+    */
+    BEGIN_TEST();
+
+    double in_data[4] = {19.9, 29.9, 39.9, 49.9};
+    const int32_t in_ndims = 2;
+    int32_t in_shape[in_ndims] = {1, 4};
+    int32_t in_strides[in_ndims] = {};
+    NDArray<int32_t> ndarray = {.data = (uint8_t*)in_data,
+                                .itemsize = sizeof(double),
+                                .ndims = in_ndims,
+                                .shape = in_shape,
+                                .strides = in_strides};
+    ndarray::basic::set_strides_by_shape(&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};
+
+    ndarray::broadcast::broadcast_to(&ndarray, &dst_ndarray);
+
+    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_can_broadcast_shape();
+    test_ndarray_broadcast();
+}
+}  // namespace ndarray_broadcast
+}  // namespace test

nac3core/irrt/test/test_ndarray_indexing.hpp

@@ -0,0 +1,165 @@
+#pragma once
+
+#include <test/includes.hpp>
+
+namespace test {
+namespace ndarray_indexing {
+void test_normal_1() {
+    /*
+        Reference Python code:
+        ```python
+        ndarray = np.arange(12, dtype=np.float64).reshape((3, 4));
+        # array([[ 0.,  1.,  2.,  3.],
+        #        [ 4.,  5.,  6.,  7.],
+        #        [ 8.,  9., 10., 11.]])
+
+        dst_ndarray = ndarray[-2:, 1::2]
+        # array([[ 5.,  7.],
+        #        [ 9., 11.]])
+
+        assert dst_ndarray.shape == (2, 2)
+        assert dst_ndarray.strides == (32, 16)
+        assert dst_ndarray[0, 0] == 5.0
+        assert dst_ndarray[0, 1] == 7.0
+        assert dst_ndarray[1, 0] == 9.0
+        assert dst_ndarray[1, 1] == 11.0
+        ```
+    */
+    BEGIN_TEST();
+
+    // Prepare src_ndarray
+    double src_data[12] = {0.0, 1.0, 2.0, 3.0, 4.0,  5.0,
+                           6.0, 7.0, 8.0, 9.0, 10.0, 11.0};
+    int64_t src_itemsize = sizeof(double);
+    const int64_t src_ndims = 2;
+    int64_t src_shape[src_ndims] = {3, 4};
+    int64_t src_strides[src_ndims] = {};
+    NDArray<int64_t> src_ndarray = {.data = (uint8_t *)src_data,
+                                    .itemsize = src_itemsize,
+                                    .ndims = src_ndims,
+                                    .shape = src_shape,
+                                    .strides = src_strides};
+    ndarray::basic::set_strides_by_shape(&src_ndarray);
+
+    // Prepare dst_ndarray
+    const int64_t dst_ndims = 2;
+    int64_t dst_shape[dst_ndims] = {999, 999};    // Empty values
+    int64_t dst_strides[dst_ndims] = {999, 999};  // Empty values
+    NDArray<int64_t> dst_ndarray = {.data = nullptr,
+                                    .ndims = dst_ndims,
+                                    .shape = dst_shape,
+                                    .strides = dst_strides};
+
+    // Create the subscripts in `ndarray[-2::, 1::2]`
+    UserSlice subscript_1;
+    subscript_1.set_start(-2);
+
+    UserSlice subscript_2;
+    subscript_2.set_start(1);
+    subscript_2.set_step(2);
+
+    const int64_t num_indexes = 2;
+    NDIndex indexes[num_indexes] = {
+        {.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_1},
+        {.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
+
+    ndarray::indexing::index(num_indexes, indexes, &src_ndarray, &dst_ndarray);
+
+    int64_t expected_shape[dst_ndims] = {2, 2};
+    int64_t expected_strides[dst_ndims] = {32, 16};
+
+    assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
+    assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
+
+    // dst_ndarray[0, 0]
+    assert_values_match(5.0,
+                        *((double *)ndarray::basic::get_pelement_by_indices(
+                            &dst_ndarray, (int64_t[dst_ndims]){0, 0})));
+    // dst_ndarray[0, 1]
+    assert_values_match(7.0,
+                        *((double *)ndarray::basic::get_pelement_by_indices(
+                            &dst_ndarray, (int64_t[dst_ndims]){0, 1})));
+    // dst_ndarray[1, 0]
+    assert_values_match(9.0,
+                        *((double *)ndarray::basic::get_pelement_by_indices(
+                            &dst_ndarray, (int64_t[dst_ndims]){1, 0})));
+    // dst_ndarray[1, 1]
+    assert_values_match(11.0,
+                        *((double *)ndarray::basic::get_pelement_by_indices(
+                            &dst_ndarray, (int64_t[dst_ndims]){1, 1})));
+}
+
+void test_normal_2() {
+    /*
+        ```python
+        ndarray = np.arange(12, dtype=np.float64).reshape((3, 4))
+        # array([[ 0.,  1.,  2.,  3.],
+        #        [ 4.,  5.,  6.,  7.],
+        #        [ 8.,  9., 10., 11.]])
+
+        dst_ndarray = ndarray[2, ::-2]
+        # array([11.,  9.])
+
+        assert dst_ndarray.shape == (2,)
+        assert dst_ndarray.strides == (-16,)
+        assert dst_ndarray[0] == 11.0
+        assert dst_ndarray[1] == 9.0
+        ```
+    */
+    BEGIN_TEST();
+
+    // Prepare src_ndarray
+    double src_data[12] = {0.0, 1.0, 2.0, 3.0, 4.0,  5.0,
+                           6.0, 7.0, 8.0, 9.0, 10.0, 11.0};
+    int64_t src_itemsize = sizeof(double);
+    const int64_t src_ndims = 2;
+    int64_t src_shape[src_ndims] = {3, 4};
+    int64_t src_strides[src_ndims] = {};
+    NDArray<int64_t> src_ndarray = {.data = (uint8_t *)src_data,
+                                    .itemsize = src_itemsize,
+                                    .ndims = src_ndims,
+                                    .shape = src_shape,
+                                    .strides = src_strides};
+    ndarray::basic::set_strides_by_shape(&src_ndarray);
+
+    // Prepare dst_ndarray
+    const int64_t dst_ndims = 1;
+    int64_t dst_shape[dst_ndims] = {999};    // Empty values
+    int64_t dst_strides[dst_ndims] = {999};  // Empty values
+    NDArray<int64_t> dst_ndarray = {.data = nullptr,
+                                    .ndims = dst_ndims,
+                                    .shape = dst_shape,
+                                    .strides = dst_strides};
+
+    // Create the subscripts in `ndarray[2, ::-2]`
+    int64_t subscript_1 = 2;
+
+    UserSlice subscript_2;
+    subscript_2.set_step(-2);
+
+    const int64_t num_indexes = 2;
+    NDIndex indexes[num_indexes] = {
+        {.type = ND_INDEX_TYPE_SINGLE_ELEMENT, .data = (uint8_t *)&subscript_1},
+        {.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
+
+    ndarray::indexing::index(num_indexes, indexes, &src_ndarray, &dst_ndarray);
+
+    int64_t expected_shape[dst_ndims] = {2};
+    int64_t expected_strides[dst_ndims] = {-16};
+    assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
+    assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
+
+    assert_values_match(11.0,
+                        *((double *)ndarray::basic::get_pelement_by_indices(
+                            &dst_ndarray, (int64_t[dst_ndims]){0})));
+    assert_values_match(9.0,
+                        *((double *)ndarray::basic::get_pelement_by_indices(
+                            &dst_ndarray, (int64_t[dst_ndims]){1})));
+}
+
+void run() {
+    test_normal_1();
+    test_normal_2();
+}
+}  // namespace ndarray_indexing
+}  // namespace test

nac3core/irrt/test/util.hpp

@@ -6,6 +6,11 @@
 template <class T>
 void print_value(const T& value);
 
+template <>
+void print_value(const bool& value) {
+    printf("%s", value ? "true" : "false");
+}
+
 template <>
 void print_value(const int8_t& value) {
     printf("%d", value);
@@ -16,6 +21,11 @@ void print_value(const int32_t& value) {
     printf("%d", value);
 }
 
+template <>
+void print_value(const int64_t& value) {
+    printf("%d", value);
+}
+
 template <>
 void print_value(const uint8_t& value) {
     printf("%u", value);
@@ -26,6 +36,11 @@ void print_value(const uint32_t& value) {
     printf("%u", value);
 }
 
+template <>
+void print_value(const uint64_t& value) {
+    printf("%d", value);
+}
+
 template <>
 void print_value(const float& value) {
     printf("%f", value);

nac3core/src/codegen/expr.rs

@@ -2,7 +2,7 @@ use crate::{
     codegen::{
         classes::{
             ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, NDArrayValue, ProxyType,
-            ProxyValue, RangeValue, TypedArrayLikeAccessor, UntypedArrayLikeAccessor,
+            ProxyValue, RangeValue, UntypedArrayLikeAccessor,
         },
         concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
         gen_in_range_check, get_llvm_abi_type, get_llvm_type, get_va_count_arg_name,
@@ -16,14 +16,11 @@ use crate::{
             gen_for_callback_incrementing, gen_if_callback, gen_if_else_expr_callback, gen_raise,
             gen_var,
         },
+        structure::ndarray::NDArrayObject,
         CodeGenContext, CodeGenTask, CodeGenerator,
     },
     symbol_resolver::{SymbolValue, ValueEnum},
-    toplevel::{
+    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, TopLevelDef},
-        helper::PrimDef,
-        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
-        DefinitionId, TopLevelDef,
-    },
     typecheck::{
         magic_methods::{Binop, BinopVariant, HasOpInfo},
         typedef::{FunSignature, FuncArg, Type, TypeEnum, TypeVarId, Unifier, VarMap},
@@ -43,7 +40,7 @@ use nac3parser::ast::{
 use std::iter::{repeat, repeat_with};
 use std::{collections::HashMap, convert::TryInto, iter::once, iter::zip};
 
-use super::structure::cslice::CSlice;
+use super::structure::{cslice::CSlice, ndarray::indexing::util::gen_ndarray_subscript_ndindexes};
 use super::{
     model::*,
     structure::exception::{Exception, ExceptionId},
@@ -562,7 +559,6 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
         G: CodeGenerator + ?Sized,
     {
         self.const_strings.get(string).copied().unwrap_or_else(|| {
-            let type_context = generator.type_context(self.ctx);
             let sizet_model = IntModel(SizeT);
             let pbyte_model = PtrModel(IntModel(Byte));
             let cslice_model = StructModel(CSlice);
@@ -570,9 +566,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
             let base = self.builder.build_global_string_ptr(string, "constant_string").unwrap();
             let base = pbyte_model.believe_value(base.as_pointer_value());
 
-            let len = sizet_model.constant(type_context, self.ctx, string.len() as u64);
+            let len = sizet_model.constant(generator, self.ctx, string.len() as u64);
 
-            let cslice = cslice_model.create_const(type_context, self, base, len);
+            let cslice = cslice_model.create_const(generator, self.ctx, base, len);
 
             self.const_strings.insert(string.to_owned(), cslice);
 
@@ -588,12 +584,11 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
         params: [Option<Int<'ctx, Int64>>; 3],
         loc: Location,
     ) {
-        let type_context = generator.type_context(self.ctx);
         let exn_model = StructModel(Exception);
         let exn_id_model = IntModel(ExceptionId::default());
 
         let exn_id =
-            exn_id_model.constant(type_context, self.ctx, self.resolver.get_string_id(name) as u64);
+            exn_id_model.constant(generator, self.ctx, self.resolver.get_string_id(name) as u64);
         let exn = self.exception_val.unwrap_or_else(|| {
             let exn = exn_model.var_alloca(generator, self, Some("exn")).unwrap();
             *self.exception_val.insert(exn)
@@ -619,15 +614,11 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
         params: [Option<IntValue<'ctx>>; 3],
         loc: Location,
     ) {
-        let type_context = generator.type_context(self.ctx);
         let param_model = IntModel(Int64);
+        let params =
+            params.map(|p| p.map(|p| param_model.check_value(generator, self.ctx, p).unwrap()));
 
         let err_msg = self.gen_string(generator, err_msg);
-
-        let ctx = self.ctx;
-        let params =
-            params.map(|p| p.map(|p| param_model.check_value(type_context, ctx, p).unwrap()));
-
         self.make_assert_impl(generator, cond, err_name, err_msg, params, loc);
     }
 
@@ -640,7 +631,6 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
         params: [Option<Int<'ctx, Int64>>; 3],
         loc: Location,
     ) {
-        let type_context = generator.type_context(self.ctx);
         let bool_model = IntModel(Bool);
 
         // We assume that the condition is most probably true, so the normal path is the most
@@ -648,7 +638,7 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
         let cond = call_expect(
             self,
             generator.bool_to_i1(self, cond),
-            bool_model.const_true(type_context, self.ctx).value,
+            bool_model.const_true(generator, self.ctx).value,
             Some("expect"),
         );
 
@@ -2257,338 +2247,6 @@ pub fn gen_cmpop_expr<'ctx, G: CodeGenerator>(
     )
 }
 
-/// Generates code for a subscript expression on an `ndarray`.
-///
-/// * `ty` - The `Type` of the `NDArray` elements.
-/// * `ndims` - The `Type` of the `NDArray` number-of-dimensions `Literal`.
-/// * `v` - The `NDArray` value.
-/// * `slice` - The slice expression used to subscript into the `ndarray`.
-fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ty: Type,
-    ndims: Type,
-    v: NDArrayValue<'ctx>,
-    slice: &Expr<Option<Type>>,
-) -> Result<Option<ValueEnum<'ctx>>, String> {
-    let llvm_i1 = ctx.ctx.bool_type();
-    let llvm_i32 = ctx.ctx.i32_type();
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-
-    let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) else {
-        unreachable!()
-    };
-
-    let ndims = values
-        .iter()
-        .map(|ndim| u64::try_from(ndim.clone()).map_err(|()| ndim.clone()))
-        .collect::<Result<Vec<_>, _>>()
-        .map_err(|val| {
-            format!(
-                "Expected non-negative literal for ndarray.ndims, got {}",
-                i128::try_from(val).unwrap()
-            )
-        })?;
-
-    assert!(!ndims.is_empty());
-
-    // The number of dimensions subscripted by the index expression.
-    // Slicing a ndarray will yield the same number of dimensions, whereas indexing into a
-    // dimension will remove a dimension.
-    let subscripted_dims = match &slice.node {
-        ExprKind::Tuple { elts, .. } => elts.iter().fold(0, |acc, value_subexpr| {
-            if let ExprKind::Slice { .. } = &value_subexpr.node {
-                acc
-            } else {
-                acc + 1
-            }
-        }),
-
-        ExprKind::Slice { .. } => 0,
-        _ => 1,
-    };
-
-    let ndarray_ndims_ty = ctx.unifier.get_fresh_literal(
-        ndims.iter().map(|v| SymbolValue::U64(v - subscripted_dims)).collect(),
-        None,
-    );
-    let ndarray_ty =
-        make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(ty), Some(ndarray_ndims_ty));
-    let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
-    let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
-    let llvm_ndarray_data_t = ctx.get_llvm_type(generator, ty).as_basic_type_enum();
-    let sizeof_elem = llvm_ndarray_data_t.size_of().unwrap();
-
-    // Check that len is non-zero
-    let len = v.load_ndims(ctx);
-    ctx.make_assert(
-        generator,
-        ctx.builder.build_int_compare(IntPredicate::SGT, len, llvm_usize.const_zero(), "").unwrap(),
-        "0:IndexError",
-        "too many indices for array: array is {0}-dimensional but 1 were indexed",
-        [Some(len), None, None],
-        slice.location,
-    );
-
-    // Normalizes a possibly-negative index to its corresponding positive index
-    let normalize_index = |generator: &mut G,
-                           ctx: &mut CodeGenContext<'ctx, '_>,
-                           index: IntValue<'ctx>,
-                           dim: u64| {
-        gen_if_else_expr_callback(
-            generator,
-            ctx,
-            |_, ctx| {
-                Ok(ctx
-                    .builder
-                    .build_int_compare(IntPredicate::SGE, index, index.get_type().const_zero(), "")
-                    .unwrap())
-            },
-            |_, _| Ok(Some(index)),
-            |generator, ctx| {
-                let llvm_i32 = ctx.ctx.i32_type();
-
-                let len = unsafe {
-                    v.dim_sizes().get_typed_unchecked(
-                        ctx,
-                        generator,
-                        &llvm_usize.const_int(dim, true),
-                        None,
-                    )
-                };
-
-                let index = ctx
-                    .builder
-                    .build_int_add(
-                        len,
-                        ctx.builder.build_int_s_extend(index, llvm_usize, "").unwrap(),
-                        "",
-                    )
-                    .unwrap();
-
-                Ok(Some(ctx.builder.build_int_truncate(index, llvm_i32, "").unwrap()))
-            },
-        )
-        .map(|v| v.map(BasicValueEnum::into_int_value))
-    };
-
-    // Converts a slice expression into a slice-range tuple
-    let expr_to_slice = |generator: &mut G,
-                         ctx: &mut CodeGenContext<'ctx, '_>,
-                         node: &ExprKind<Option<Type>>,
-                         dim: u64| {
-        match node {
-            ExprKind::Constant { value: Constant::Int(v), .. } => {
-                let Some(index) =
-                    normalize_index(generator, ctx, llvm_i32.const_int(*v as u64, true), dim)?
-                else {
-                    return Ok(None);
-                };
-
-                Ok(Some((index, index, llvm_i32.const_int(1, true))))
-            }
-
-            ExprKind::Slice { lower, upper, step } => {
-                let dim_sz = unsafe {
-                    v.dim_sizes().get_typed_unchecked(
-                        ctx,
-                        generator,
-                        &llvm_usize.const_int(dim, false),
-                        None,
-                    )
-                };
-
-                handle_slice_indices(lower, upper, step, ctx, generator, dim_sz)
-            }
-
-            _ => {
-                let Some(index) = generator.gen_expr(ctx, slice)? else { return Ok(None) };
-                let index = index
-                    .to_basic_value_enum(ctx, generator, slice.custom.unwrap())?
-                    .into_int_value();
-                let Some(index) = normalize_index(generator, ctx, index, dim)? else {
-                    return Ok(None);
-                };
-
-                Ok(Some((index, index, llvm_i32.const_int(1, true))))
-            }
-        }
-    };
-
-    let make_indices_arr = |generator: &mut G,
-                            ctx: &mut CodeGenContext<'ctx, '_>|
-     -> Result<_, String> {
-        Ok(if let ExprKind::Tuple { elts, .. } = &slice.node {
-            let llvm_int_ty = ctx.get_llvm_type(generator, elts[0].custom.unwrap());
-            let index_addr = generator.gen_array_var_alloc(
-                ctx,
-                llvm_int_ty,
-                llvm_usize.const_int(elts.len() as u64, false),
-                None,
-            )?;
-
-            for (i, elt) in elts.iter().enumerate() {
-                let Some(index) = generator.gen_expr(ctx, elt)? else {
-                    return Ok(None);
-                };
-
-                let index = index
-                    .to_basic_value_enum(ctx, generator, elt.custom.unwrap())?
-                    .into_int_value();
-                let Some(index) = normalize_index(generator, ctx, index, 0)? else {
-                    return Ok(None);
-                };
-
-                let store_ptr = unsafe {
-                    index_addr.ptr_offset_unchecked(
-                        ctx,
-                        generator,
-                        &llvm_usize.const_int(i as u64, false),
-                        None,
-                    )
-                };
-                ctx.builder.build_store(store_ptr, index).unwrap();
-            }
-
-            Some(index_addr)
-        } else if let Some(index) = generator.gen_expr(ctx, slice)? {
-            let llvm_int_ty = ctx.get_llvm_type(generator, slice.custom.unwrap());
-            let index_addr = generator.gen_array_var_alloc(
-                ctx,
-                llvm_int_ty,
-                llvm_usize.const_int(1u64, false),
-                None,
-            )?;
-
-            let index =
-                index.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value();
-            let Some(index) = normalize_index(generator, ctx, index, 0)? else { return Ok(None) };
-
-            let store_ptr = unsafe {
-                index_addr.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-            };
-            ctx.builder.build_store(store_ptr, index).unwrap();
-
-            Some(index_addr)
-        } else {
-            None
-        })
-    };
-
-    Ok(Some(if ndims.len() == 1 && ndims[0] - subscripted_dims == 0 {
-        let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
-
-        v.data().get(ctx, generator, &index_addr, None).into()
-    } else {
-        match &slice.node {
-            ExprKind::Tuple { elts, .. } => {
-                let slices = elts
-                    .iter()
-                    .enumerate()
-                    .map(|(dim, elt)| expr_to_slice(generator, ctx, &elt.node, dim as u64))
-                    .take_while_inclusive(|slice| slice.as_ref().is_ok_and(Option::is_some))
-                    .collect::<Result<Vec<_>, _>>()?;
-                if slices.len() < elts.len() {
-                    return Ok(None);
-                }
-
-                let slices = slices.into_iter().map(Option::unwrap).collect_vec();
-
-                numpy::ndarray_sliced_copy(generator, ctx, ty, v, &slices)?.as_base_value().into()
-            }
-
-            ExprKind::Slice { .. } => {
-                let Some(slice) = expr_to_slice(generator, ctx, &slice.node, 0)? else {
-                    return Ok(None);
-                };
-
-                numpy::ndarray_sliced_copy(generator, ctx, ty, v, &[slice])?.as_base_value().into()
-            }
-
-            _ => {
-                // Accessing an element from a multi-dimensional `ndarray`
-
-                let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
-
-                // Create a new array, remove the top dimension from the dimension-size-list, and copy the
-                // elements over
-                let subscripted_ndarray =
-                    generator.gen_var_alloc(ctx, llvm_ndarray_t.into(), None)?;
-                let ndarray = NDArrayValue::from_ptr_val(subscripted_ndarray, llvm_usize, None);
-
-                let num_dims = v.load_ndims(ctx);
-                ndarray.store_ndims(
-                    ctx,
-                    generator,
-                    ctx.builder
-                        .build_int_sub(num_dims, llvm_usize.const_int(1, false), "")
-                        .unwrap(),
-                );
-
-                let ndarray_num_dims = ndarray.load_ndims(ctx);
-                ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
-
-                let ndarray_num_dims = ctx
-                    .builder
-                    .build_int_z_extend_or_bit_cast(
-                        ndarray.load_ndims(ctx),
-                        llvm_usize.size_of().get_type(),
-                        "",
-                    )
-                    .unwrap();
-                let v_dims_src_ptr = unsafe {
-                    v.dim_sizes().ptr_offset_unchecked(
-                        ctx,
-                        generator,
-                        &llvm_usize.const_int(1, false),
-                        None,
-                    )
-                };
-                call_memcpy_generic(
-                    ctx,
-                    ndarray.dim_sizes().base_ptr(ctx, generator),
-                    v_dims_src_ptr,
-                    ctx.builder
-                        .build_int_mul(ndarray_num_dims, llvm_usize.size_of(), "")
-                        .map(Into::into)
-                        .unwrap(),
-                    llvm_i1.const_zero(),
-                );
-
-                let ndarray_num_elems = call_ndarray_calc_size(
-                    generator,
-                    ctx,
-                    &ndarray.dim_sizes().as_slice_value(ctx, generator),
-                    (None, None),
-                );
-                let ndarray_num_elems = ctx
-                    .builder
-                    .build_int_z_extend_or_bit_cast(ndarray_num_elems, sizeof_elem.get_type(), "")
-                    .unwrap();
-                ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
-
-                let v_data_src_ptr = v.data().ptr_offset(ctx, generator, &index_addr, None);
-                call_memcpy_generic(
-                    ctx,
-                    ndarray.data().base_ptr(ctx, generator),
-                    v_data_src_ptr,
-                    ctx.builder
-                        .build_int_mul(
-                            ndarray_num_elems,
-                            llvm_ndarray_data_t.size_of().unwrap(),
-                            "",
-                        )
-                        .map(Into::into)
-                        .unwrap(),
-                    llvm_i1.const_zero(),
-                );
-
-                ndarray.as_base_value().into()
-            }
-        }
-    }))
-}
-
 /// See [`CodeGenerator::gen_expr`].
 pub fn gen_expr<'ctx, G: CodeGenerator>(
     generator: &mut G,
@@ -3228,18 +2886,20 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
                         v.data().get(ctx, generator, &index, None).into()
                     }
                 }
-                TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::NDArray.id() => {
+                TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-                    let (ty, ndims) = params.iter().map(|(_, ty)| ty).collect_tuple().unwrap();
+                    let Some(ndarray) = generator.gen_expr(ctx, value)? else {
-
-                    let v = if let Some(v) = generator.gen_expr(ctx, value)? {
-                        v.to_basic_value_enum(ctx, generator, value.custom.unwrap())?
-                            .into_pointer_value()
-                    } else {
                         return Ok(None);
                     };
-                    let v = NDArrayValue::from_ptr_val(v, usize, None);
 
-                    return gen_ndarray_subscript_expr(generator, ctx, *ty, *ndims, v, slice);
+                    let ndarray_ty = value.custom.unwrap();
+                    let ndarray = ndarray.to_basic_value_enum(ctx, generator, ndarray_ty)?;
+                    let ndarray =
+                        NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
+
+                    let indexes = gen_ndarray_subscript_ndindexes(generator, ctx, slice)?;
+                    let result = ndarray.index_or_scalar(generator, ctx, &indexes, "index_result");
+                    let result = result.to_basic_value_enum();
+                    return Ok(Some(ValueEnum::Dynamic(result)));
                 }
                 TypeEnum::TTuple { .. } => {
                     let index: u32 =

nac3core/src/codegen/irrt/mod.rs

@@ -5,6 +5,8 @@ mod test;
 pub mod util;
 
 use super::model::*;
+use super::structure::ndarray::broadcast::ShapeEntry;
+use super::structure::ndarray::indexing::NDIndex;
 use super::structure::ndarray::NpArray;
 use super::{
     classes::{
@@ -427,15 +429,13 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
     // TODO: Temporary fix. Rewrite `list_slice_assignment` later
     // Exception params should have been i64
     {
-        let type_context = generator.type_context(ctx.ctx);
         let param_model = IntModel(Int64);
 
         let src_slice_len =
-            param_model.s_extend_or_bit_cast(type_context, ctx, src_slice_len, "src_slice_len");
+            param_model.s_extend_or_bit_cast(generator, ctx, src_slice_len, "src_slice_len");
         let dest_slice_len =
-            param_model.s_extend_or_bit_cast(type_context, ctx, dest_slice_len, "dest_slice_len");
+            param_model.s_extend_or_bit_cast(generator, ctx, dest_slice_len, "dest_slice_len");
-        let dest_idx_2 =
+        let dest_idx_2 = param_model.s_extend_or_bit_cast(generator, ctx, dest_idx.2, "dest_idx_2");
-            param_model.s_extend_or_bit_cast(type_context, ctx, dest_idx.2, "dest_idx_2");
 
         ctx.make_assert(
             generator,
@@ -897,7 +897,7 @@ pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
 }
 
 /// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
-/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
+/// containing the indices used for accessing `array` corresponding to the index of the broadcast
 /// array `broadcast_idx`.
 pub fn call_ndarray_calc_broadcast_index<
     'ctx,
@@ -953,13 +953,12 @@ pub fn call_ndarray_calc_broadcast_index<
     )
 }
 
-pub fn call_nac3_throw_dummy_error<'ctx>(tyctx: TypeContext<'ctx>, ctx: &CodeGenContext<'ctx, '_>) {
+pub fn call_nac3_throw_dummy_error<G: CodeGenerator + ?Sized>(
-    CallFunction::begin(
+    generator: &mut G,
-        tyctx,
+    ctx: &CodeGenContext<'_, '_>,
-        ctx,
+) {
-        &get_sizet_dependent_function_name(tyctx, "__nac3_throw_dummy_error"),
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_throw_dummy_error");
-    )
+    CallFunction::begin(generator, ctx, &name).returning_void();
-    .returning_void();
 }
 
 /// Initialize all global `EXN_*` exception IDs in IRRT with the [`SymbolResolver`].
@@ -989,116 +988,176 @@ pub fn setup_irrt_exceptions<'ctx>(
     }
 }
 
-pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx>(
+pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
-    tyctx: TypeContext<'ctx>,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     ndims: Int<'ctx, SizeT>,
     shape: Ptr<'ctx, IntModel<SizeT>>,
 ) {
-    CallFunction::begin(
+    let name = get_sizet_dependent_function_name(
-        tyctx,
+        generator,
         ctx,
-        &get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_util_assert_shape_no_negative"),
+        "__nac3_ndarray_util_assert_shape_no_negative",
-    )
+    );
+    CallFunction::begin(generator, ctx, &name)
         .arg("ndims", ndims)
         .arg("shape", shape)
         .returning_void();
 }
 
-pub fn call_nac3_ndarray_size<'ctx>(
+pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
-    tyctx: TypeContext<'ctx>,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     pndarray: Ptr<'ctx, StructModel<NpArray>>,
 ) -> Int<'ctx, SizeT> {
-    CallFunction::begin(
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_size");
-        tyctx,
+    CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("size")
-        ctx,
-        &get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_size"),
-    )
-    .arg("ndarray", pndarray)
-    .returning_auto("size")
 }
 
-pub fn call_nac3_ndarray_nbytes<'ctx>(
+pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
-    tyctx: TypeContext<'ctx>,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     pndarray: Ptr<'ctx, StructModel<NpArray>>,
 ) -> Int<'ctx, SizeT> {
-    CallFunction::begin(
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_nbytes");
-        tyctx,
+    CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("nbytes")
-        ctx,
-        &get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_nbytes"),
-    )
-    .arg("ndarray", pndarray)
-    .returning_auto("nbytes")
 }
 
-pub fn call_nac3_ndarray_len<'ctx>(
+pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
-    tyctx: TypeContext<'ctx>,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     pndarray: Ptr<'ctx, StructModel<NpArray>>,
 ) -> Int<'ctx, SizeT> {
-    CallFunction::begin(tyctx, ctx, &get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_len"))
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_len");
-        .arg("ndarray", pndarray)
+    CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("len")
-        .returning_auto("len")
 }
 
-pub fn call_nac3_ndarray_is_c_contiguous<'ctx>(
+pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
-    tyctx: TypeContext<'ctx>,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
 ) -> Int<'ctx, Bool> {
-    CallFunction::begin(
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_is_c_contiguous");
-        tyctx,
+    CallFunction::begin(generator, ctx, &name)
-        ctx,
-        &get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_is_c_contiguous"),
-    )
         .arg("ndarray", ndarray_ptr)
         .returning_auto("is_c_contiguous")
 }
 
-pub fn call_nac3_ndarray_get_nth_pelement<'ctx>(
+pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
-    tyctx: TypeContext<'ctx>,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     pndarray: Ptr<'ctx, StructModel<NpArray>>,
     index: Int<'ctx, SizeT>,
 ) -> Ptr<'ctx, IntModel<Byte>> {
-    CallFunction::begin(
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_nth_pelement");
-        tyctx,
+    CallFunction::begin(generator, ctx, &name)
-        ctx,
-        &get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_get_nth_pelement"),
-    )
         .arg("ndarray", pndarray)
         .arg("index", index)
         .returning_auto("pelement")
 }
 
-pub fn call_nac3_ndarray_set_strides_by_shape<'ctx>(
+pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
-    tyctx: TypeContext<'ctx>,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     pdnarray: Ptr<'ctx, StructModel<NpArray>>,
 ) {
-    CallFunction::begin(
+    let name =
-        tyctx,
+        get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_set_strides_by_shape");
-        ctx,
+    CallFunction::begin(generator, ctx, &name).arg("ndarray", pdnarray).returning_void();
-        &get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_set_strides_by_shape"),
-    )
-    .arg("ndarray", pdnarray)
-    .returning_void();
 }
 
-pub fn call_nac3_ndarray_copy_data<'ctx>(
+pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
-    tyctx: TypeContext<'ctx>,
+    generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
     dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
 ) {
-    CallFunction::begin(
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_copy_data");
-        tyctx,
+    CallFunction::begin(generator, ctx, &name)
-        ctx,
-        &get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_copy_data"),
-    )
         .arg("src_ndarray", src_ndarray)
         .arg("dst_ndarray", dst_ndarray)
         .returning_void();
 }
+
+pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    num_indexes: Int<'ctx, SizeT>,
+    indexes: Ptr<'ctx, StructModel<NDIndex>>,
+    src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
+    dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
+) {
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
+    CallFunction::begin(generator, ctx, &name)
+        .arg("num_indexes", num_indexes)
+        .arg("indexes", indexes)
+        .arg("src_ndarray", src_ndarray)
+        .arg("dst_ndarray", dst_ndarray)
+        .returning_void();
+}
+
+pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
+    dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
+) {
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
+    CallFunction::begin(generator, ctx, &name)
+        .arg("src_ndarray", src_ndarray)
+        .arg("dst_ndarray", dst_ndarray)
+        .returning_void();
+}
+
+pub fn call_nac3_ndarray_broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    num_shape_entries: Int<'ctx, SizeT>,
+    shape_entries: Ptr<'ctx, StructModel<ShapeEntry>>,
+    dst_ndims: Int<'ctx, SizeT>,
+    dst_shape: Ptr<'ctx, IntModel<SizeT>>,
+) {
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
+    CallFunction::begin(generator, ctx, &name)
+        .arg("num_shapes", num_shape_entries)
+        .arg("shapes", shape_entries)
+        .arg("dst_ndims", dst_ndims)
+        .arg("dst_shape", dst_shape)
+        .returning_void();
+}
+
+pub fn call_nac3_ndarray_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    size: Int<'ctx, SizeT>,
+    new_ndims: Int<'ctx, SizeT>,
+    new_shape: Ptr<'ctx, IntModel<SizeT>>,
+) {
+    let name = get_sizet_dependent_function_name(
+        generator,
+        ctx,
+        "__nac3_ndarray_resolve_and_check_new_shape",
+    );
+    CallFunction::begin(generator, ctx, &name)
+        .arg("size", size)
+        .arg("new_ndims", new_ndims)
+        .arg("new_shape", new_shape)
+        .returning_void();
+}
+
+pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
+    dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
+    num_axes: Int<'ctx, SizeT>,
+    axes: Ptr<'ctx, IntModel<SizeT>>,
+) {
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
+    CallFunction::begin(generator, ctx, &name)
+        .arg("src_ndarray", src_ndarray)
+        .arg("dst_ndarray", dst_ndarray)
+        .arg("num_axes", num_axes)
+        .arg("axes", axes)
+        .returning_void();
+}

nac3core/src/codegen/irrt/util.rs

@@ -1,11 +1,15 @@
-use crate::codegen::model::*;
+use crate::codegen::{CodeGenContext, CodeGenerator};
 
 // When [`TypeContext::size_type`] is 32-bits, the function name is "{fn_name}".
 // When [`TypeContext::size_type`] is 64-bits, the function name is "{fn_name}64".
 #[must_use]
-pub fn get_sizet_dependent_function_name(tyctx: TypeContext<'_>, name: &str) -> String {
+pub fn get_sizet_dependent_function_name<G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &CodeGenContext<'_, '_>,
+    name: &str,
+) -> String {
     let mut name = name.to_owned();
-    match tyctx.size_type.get_bit_width() {
+    match generator.get_size_type(ctx.ctx).get_bit_width() {
         32 => {}
         64 => name.push_str("64"),
         bit_width => {
@@ -16,7 +20,7 @@ pub fn get_sizet_dependent_function_name(tyctx: TypeContext<'_>, name: &str) ->
 }
 
 pub mod function {
-    use crate::codegen::{model::*, CodeGenContext};
+    use crate::codegen::{model::*, CodeGenContext, CodeGenerator};
     use inkwell::{
         types::{BasicMetadataTypeEnum, BasicType, FunctionType},
         values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
@@ -30,8 +34,8 @@ pub mod function {
     }
 
     /// Helper structure to reduce IRRT Inkwell function call boilerplate
-    pub struct CallFunction<'ctx, 'a, 'b, 'c> {
+    pub struct CallFunction<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> {
-        tyctx: TypeContext<'ctx>,
+        generator: &'d mut G,
         ctx: &'b CodeGenContext<'ctx, 'a>,
         /// Function name
         name: &'c str,
@@ -39,13 +43,13 @@ pub mod function {
         args: Vec<Arg<'ctx>>,
     }
 
-    impl<'ctx, 'a, 'b, 'c> CallFunction<'ctx, 'a, 'b, 'c> {
+    impl<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> CallFunction<'ctx, 'a, 'b, 'c, 'd, G> {
         pub fn begin(
-            tyctx: TypeContext<'ctx>,
+            generator: &'d mut G,
             ctx: &'b CodeGenContext<'ctx, 'a>,
             name: &'c str,
         ) -> Self {
-            CallFunction { tyctx, ctx, name, args: Vec::new() }
+            CallFunction { generator, ctx, name, args: Vec::new() }
         }
 
         /// Push a call argument to the function call.
@@ -55,7 +59,7 @@ pub mod function {
         #[must_use]
         pub fn arg<M: Model<'ctx>>(mut self, _name: &str, arg: Instance<'ctx, M>) -> Self {
             let arg = Arg {
-                ty: arg.model.get_type(self.tyctx, self.ctx.ctx).as_basic_type_enum().into(),
+                ty: arg.model.get_type(self.generator, self.ctx.ctx).as_basic_type_enum().into(),
                 val: arg.value.as_basic_value_enum().into(),
             };
             self.args.push(arg);
@@ -65,11 +69,11 @@ pub mod function {
         /// Call the function and expect the function to return a value of type of `return_model`.
         #[must_use]
         pub fn returning<M: Model<'ctx>>(self, name: &str, return_model: M) -> Instance<'ctx, M> {
-            let ret_ty = return_model.get_type(self.tyctx, self.ctx.ctx);
+            let ret_ty = return_model.get_type(self.generator, self.ctx.ctx);
 
             let ret = self.get_function(|tys| ret_ty.fn_type(tys, false), name);
             let ret = BasicValueEnum::try_from(ret.as_any_value_enum()).unwrap(); // Must work
-            let ret = return_model.check_value(self.tyctx, self.ctx.ctx, ret).unwrap(); // Must work
+            let ret = return_model.check_value(self.generator, self.ctx.ctx, ret).unwrap(); // Must work
             ret
         }
 

nac3core/src/codegen/mod.rs

@@ -1,7 +1,7 @@
 use crate::{
-    codegen::classes::{ListType, NDArrayType, ProxyType, RangeType},
+    codegen::classes::{ListType, ProxyType, RangeType},
     symbol_resolver::{StaticValue, SymbolResolver},
-    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
+    toplevel::{helper::PrimDef, TopLevelContext, TopLevelDef},
     typecheck::{
         type_inferencer::{CodeLocation, PrimitiveStore},
         typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
@@ -494,10 +494,8 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
                         }
 
                         TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-                            let tyctx = generator.type_context(ctx);
-
                             let pndarray_model = PtrModel(StructModel(NpArray));
-                            pndarray_model.get_type(tyctx, ctx).as_basic_type_enum()
+                            pndarray_model.get_type(generator, ctx).as_basic_type_enum()
                         }
 
                         _ => unreachable!(
@@ -703,7 +701,6 @@ pub fn gen_func_impl<
         ..primitives
     };
 
-    let type_context = generator.type_context(context);
     let cslice_model = StructModel(CSlice);
     let pexn_model = PtrModel(StructModel(Exception));
 
@@ -714,9 +711,9 @@ pub fn gen_func_impl<
         (primitives.uint64, context.i64_type().into()),
         (primitives.float, context.f64_type().into()),
         (primitives.bool, context.i8_type().into()),
-        (primitives.str, cslice_model.get_type(type_context, context).into()),
+        (primitives.str, cslice_model.get_type(generator, context).into()),
         (primitives.range, RangeType::new(context).as_base_type().into()),
-        (primitives.exception, pexn_model.get_type(type_context, context).into()),
+        (primitives.exception, pexn_model.get_type(generator, context).into()),
     ]
     .iter()
     .copied()

nac3core/src/codegen/model/any.rs

@@ -4,6 +4,8 @@ use inkwell::{
     values::BasicValueEnum,
 };
 
+use crate::codegen::CodeGenerator;
+
 use super::*;
 
 #[derive(Debug, Clone, Copy)]
@@ -14,13 +16,17 @@ impl<'ctx> Model<'ctx> for AnyModel<'ctx> {
     type Value = BasicValueEnum<'ctx>;
     type Type = BasicTypeEnum<'ctx>;
 
-    fn get_type(&self, _tyctx: TypeContext<'ctx>, _ctx: &'ctx Context) -> Self::Type {
+    fn get_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        _ctx: &'ctx Context,
+    ) -> Self::Type {
         self.0
     }
 
-    fn check_type<T: BasicType<'ctx>>(
+    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
         &self,
-        _tyctx: TypeContext<'ctx>,
+        _generator: &mut G,
         _ctx: &'ctx Context,
         ty: T,
     ) -> Result<(), ModelError> {

nac3core/src/codegen/model/core.rs

@@ -5,21 +5,6 @@ use inkwell::{context::Context, types::*, values::*};
 use super::*;
 use crate::codegen::{CodeGenContext, CodeGenerator};
 
-#[derive(Clone, Copy)]
-pub struct TypeContext<'ctx> {
-    pub size_type: IntType<'ctx>,
-}
-
-pub trait HasTypeContext {
-    fn type_context<'ctx>(&self, ctx: &'ctx Context) -> TypeContext<'ctx>;
-}
-
-impl<T: CodeGenerator + ?Sized> HasTypeContext for T {
-    fn type_context<'ctx>(&self, ctx: &'ctx Context) -> TypeContext<'ctx> {
-        TypeContext { size_type: self.get_size_type(ctx) }
-    }
-}
-
 #[derive(Debug, Clone)]
 pub struct ModelError(pub String);
 
@@ -36,12 +21,16 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
     type Type: BasicType<'ctx>;
 
     /// Return the [`BasicType`] of this model.
-    fn get_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type;
+    fn get_type<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> Self::Type;
 
     /// Check if a [`BasicType`] is the same type of this model.
-    fn check_type<T: BasicType<'ctx>>(
+    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &'ctx Context,
         ty: T,
     ) -> Result<(), ModelError>;
@@ -55,15 +44,15 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
 
     /// Check if a [`BasicValue`]'s type is equivalent to the type of this model.
     /// Wrap it into an [`Instance`] if it is.
-    fn check_value<V: BasicValue<'ctx>>(
+    fn check_value<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &'ctx Context,
         value: V,
     ) -> Result<Instance<'ctx, Self>, ModelError> {
         let value = value.as_basic_value_enum();
-        self.check_type(tyctx, ctx, value.get_type())
+        self.check_type(generator, ctx, value.get_type())
-            .map_err(|err| err.under_context("the value {value:?}"))?;
+            .map_err(|err| err.under_context(format!("the value {value:?}").as_str()))?;
 
         let Ok(value) = Self::Value::try_from(value) else {
             unreachable!("check_type() has bad implementation")
@@ -72,27 +61,28 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
     }
 
     // Allocate a value on the stack and return its pointer.
-    fn alloca(
+    fn alloca<G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &CodeGenContext<'ctx, '_>,
         name: &str,
     ) -> Ptr<'ctx, Self> {
         let pmodel = PtrModel(*self);
-        let p = ctx.builder.build_alloca(self.get_type(tyctx, ctx.ctx), name).unwrap();
+        let p = ctx.builder.build_alloca(self.get_type(generator, ctx.ctx), name).unwrap();
         pmodel.believe_value(p)
     }
 
     // Allocate an array on the stack and return its pointer.
-    fn array_alloca(
+    fn array_alloca<G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &CodeGenContext<'ctx, '_>,
         len: IntValue<'ctx>,
         name: &str,
     ) -> Ptr<'ctx, Self> {
         let pmodel = PtrModel(*self);
-        let p = ctx.builder.build_array_alloca(self.get_type(tyctx, ctx.ctx), len, name).unwrap();
+        let p =
+            ctx.builder.build_array_alloca(self.get_type(generator, ctx.ctx), len, name).unwrap();
         pmodel.believe_value(p)
     }
 
@@ -102,14 +92,9 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
         ctx: &mut CodeGenContext<'ctx, '_>,
         name: Option<&str>,
     ) -> Result<Ptr<'ctx, Self>, String> {
-        let tyctx = generator.type_context(ctx.ctx);
-
         let pmodel = PtrModel(*self);
-        let p = generator.gen_var_alloc(
+        let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
-            ctx,
+        let p = generator.gen_var_alloc(ctx, ty, name)?;
-            self.get_type(tyctx, ctx.ctx).as_basic_type_enum(),
-            name,
-        )?;
         Ok(pmodel.believe_value(p))
     }
 
@@ -120,16 +105,10 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
         len: IntValue<'ctx>,
         name: Option<&'ctx str>,
     ) -> Result<Ptr<'ctx, Self>, String> {
-        let tyctx = generator.type_context(ctx.ctx);
-
         // TODO: Remove ArraySliceValue
         let pmodel = PtrModel(*self);
-        let p = generator.gen_array_var_alloc(
+        let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
-            ctx,
+        let p = generator.gen_array_var_alloc(ctx, ty, len, name)?;
-            self.get_type(tyctx, ctx.ctx).as_basic_type_enum(),
-            len,
-            name,
-        )?;
         Ok(pmodel.believe_value(PointerValue::from(p)))
     }
 }

nac3core/src/codegen/model/int.rs

@@ -7,7 +7,11 @@ use crate::codegen::{CodeGenContext, CodeGenerator};
 use super::*;
 
 pub trait IntKind<'ctx>: fmt::Debug + Clone + Copy {
-    fn get_int_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx>;
+    fn get_int_type<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> IntType<'ctx>;
 }
 
 #[derive(Debug, Clone, Copy, Default)]
@@ -22,32 +26,52 @@ pub struct Int64;
 pub struct SizeT;
 
 impl<'ctx> IntKind<'ctx> for Bool {
-    fn get_int_type(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
+    fn get_int_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        ctx: &'ctx Context,
+    ) -> IntType<'ctx> {
         ctx.bool_type()
     }
 }
 
 impl<'ctx> IntKind<'ctx> for Byte {
-    fn get_int_type(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
+    fn get_int_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        ctx: &'ctx Context,
+    ) -> IntType<'ctx> {
         ctx.i8_type()
     }
 }
 
 impl<'ctx> IntKind<'ctx> for Int32 {
-    fn get_int_type(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
+    fn get_int_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        ctx: &'ctx Context,
+    ) -> IntType<'ctx> {
         ctx.i32_type()
     }
 }
 
 impl<'ctx> IntKind<'ctx> for Int64 {
-    fn get_int_type(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
+    fn get_int_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        ctx: &'ctx Context,
+    ) -> IntType<'ctx> {
         ctx.i64_type()
     }
 }
 
 impl<'ctx> IntKind<'ctx> for SizeT {
-    fn get_int_type(&self, tyctx: TypeContext<'ctx>, _ctx: &'ctx Context) -> IntType<'ctx> {
+    fn get_int_type<G: CodeGenerator + ?Sized>(
-        tyctx.size_type
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> IntType<'ctx> {
+        generator.get_size_type(ctx)
     }
 }
 
@@ -55,7 +79,11 @@ impl<'ctx> IntKind<'ctx> for SizeT {
 pub struct AnyInt<'ctx>(pub IntType<'ctx>);
 
 impl<'ctx> IntKind<'ctx> for AnyInt<'ctx> {
-    fn get_int_type(&self, _tyctx: TypeContext<'ctx>, _ctx: &'ctx Context) -> IntType<'ctx> {
+    fn get_int_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        _ctx: &'ctx Context,
+    ) -> IntType<'ctx> {
         self.0
     }
 }
@@ -69,13 +97,17 @@ impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for IntModel<N> {
     type Type = IntType<'ctx>;
 
     #[must_use]
-    fn get_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type {
+    fn get_type<G: CodeGenerator + ?Sized>(
-        self.0.get_int_type(tyctx, ctx)
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> Self::Type {
+        self.0.get_int_type(generator, ctx)
     }
 
-    fn check_type<T: inkwell::types::BasicType<'ctx>>(
+    fn check_type<T: inkwell::types::BasicType<'ctx>, G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &'ctx Context,
         ty: T,
     ) -> Result<(), ModelError> {
@@ -84,7 +116,7 @@ impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for IntModel<N> {
             return Err(ModelError(format!("Expecting IntType, but got {ty:?}")));
         };
 
-        let exp_ty = self.0.get_int_type(tyctx, ctx);
+        let exp_ty = self.0.get_int_type(generator, ctx);
         if ty.get_bit_width() != exp_ty.get_bit_width() {
             return Err(ModelError(format!(
                 "Expecting IntType to have {} bit(s), but got {} bit(s)",
@@ -98,90 +130,107 @@ impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for IntModel<N> {
 }
 
 impl<'ctx, N: IntKind<'ctx>> IntModel<N> {
-    pub fn constant(
+    pub fn constant<G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &'ctx Context,
         value: u64,
     ) -> Int<'ctx, N> {
-        let value = self.get_type(tyctx, ctx).const_int(value, false);
+        let value = self.get_type(generator, ctx).const_int(value, false);
         self.believe_value(value)
     }
 
-    pub fn const_0(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Int<'ctx, N> {
+    pub fn const_0<G: CodeGenerator + ?Sized>(
-        self.constant(tyctx, ctx, 0)
-    }
-
-    pub fn const_1(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Int<'ctx, N> {
-        self.constant(tyctx, ctx, 1)
-    }
-
-    pub fn s_extend_or_bit_cast(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
+        ctx: &'ctx Context,
+    ) -> Int<'ctx, N> {
+        self.constant(generator, ctx, 0)
+    }
+
+    pub fn const_1<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+    ) -> Int<'ctx, N> {
+        self.constant(generator, ctx, 1)
+    }
+
+    pub fn const_all_1s<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+    ) -> Int<'ctx, N> {
+        let value = self.get_type(generator, ctx).const_all_ones();
+        self.believe_value(value)
+    }
+
+    pub fn s_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
         ctx: &CodeGenContext<'ctx, '_>,
         value: IntValue<'ctx>,
         name: &str,
     ) -> Int<'ctx, N> {
         let value = ctx
             .builder
-            .build_int_s_extend_or_bit_cast(value, self.get_type(tyctx, ctx.ctx), name)
+            .build_int_s_extend_or_bit_cast(value, self.get_type(generator, ctx.ctx), name)
             .unwrap();
         self.believe_value(value)
     }
 
-    pub fn truncate(
+    pub fn truncate<G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &CodeGenContext<'ctx, '_>,
         value: IntValue<'ctx>,
         name: &str,
     ) -> Int<'ctx, N> {
         let value =
-            ctx.builder.build_int_truncate(value, self.get_type(tyctx, ctx.ctx), name).unwrap();
+            ctx.builder.build_int_truncate(value, self.get_type(generator, ctx.ctx), name).unwrap();
         self.believe_value(value)
     }
 }
 
 impl IntModel<Bool> {
     #[must_use]
-    pub fn const_false<'ctx>(
+    pub fn const_false<'ctx, G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &'ctx Context,
     ) -> Int<'ctx, Bool> {
-        self.constant(tyctx, ctx, 0)
+        self.constant(generator, ctx, 0)
     }
 
     #[must_use]
-    pub fn const_true<'ctx>(
+    pub fn const_true<'ctx, G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &'ctx Context,
     ) -> Int<'ctx, Bool> {
-        self.constant(tyctx, ctx, 1)
+        self.constant(generator, ctx, 1)
     }
 }
 
 impl<'ctx, N: IntKind<'ctx>> Int<'ctx, N> {
-    pub fn s_extend_or_bit_cast<NewN: IntKind<'ctx>>(
+    pub fn s_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &CodeGenContext<'ctx, '_>,
         to_int_kind: NewN,
         name: &str,
     ) -> Int<'ctx, NewN> {
-        IntModel(to_int_kind).s_extend_or_bit_cast(tyctx, ctx, self.value, name)
+        IntModel(to_int_kind).s_extend_or_bit_cast(generator, ctx, self.value, name)
     }
 
-    pub fn truncate<NewN: IntKind<'ctx>>(
+    pub fn truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &CodeGenContext<'ctx, '_>,
         to_int_kind: NewN,
         name: &str,
     ) -> Int<'ctx, NewN> {
-        IntModel(to_int_kind).truncate(tyctx, ctx, self.value, name)
+        IntModel(to_int_kind).truncate(generator, ctx, self.value, name)
     }
 
     #[must_use]

nac3core/src/codegen/model/mod.rs

@@ -3,6 +3,7 @@ mod core;
 mod int;
 mod ptr;
 mod structure;
+pub mod util;
 
 pub use any::*;
 pub use core::*;

nac3core/src/codegen/model/ptr.rs

@@ -5,7 +5,7 @@ use inkwell::{
     AddressSpace,
 };
 
-use crate::codegen::CodeGenContext;
+use crate::codegen::{CodeGenContext, CodeGenerator};
 
 use super::*;
 
@@ -17,13 +17,17 @@ impl<'ctx, Element: Model<'ctx>> Model<'ctx> for PtrModel<Element> {
     type Value = PointerValue<'ctx>;
     type Type = PointerType<'ctx>;
 
-    fn get_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type {
+    fn get_type<G: CodeGenerator + ?Sized>(
-        self.0.get_type(tyctx, ctx).ptr_type(AddressSpace::default())
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> Self::Type {
+        self.0.get_type(generator, ctx).ptr_type(AddressSpace::default())
     }
 
-    fn check_type<T: BasicType<'ctx>>(
+    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &'ctx Context,
         ty: T,
     ) -> Result<(), ModelError> {
@@ -41,7 +45,9 @@ impl<'ctx, Element: Model<'ctx>> Model<'ctx> for PtrModel<Element> {
 
         // TODO: inkwell `get_element_type()` will be deprecated.
         // Remove the check for `get_element_type()` when the time comes.
-        self.0.check_type(tyctx, ctx, elem_ty).map_err(|err| err.under_context("a PointerType"))?;
+        self.0
+            .check_type(generator, ctx, elem_ty)
+            .map_err(|err| err.under_context("a PointerType"))?;
 
         Ok(())
     }
@@ -49,20 +55,25 @@ impl<'ctx, Element: Model<'ctx>> Model<'ctx> for PtrModel<Element> {
 
 impl<'ctx, Element: Model<'ctx>> PtrModel<Element> {
     /// Return a ***constant*** nullptr.
-    pub fn nullptr(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Ptr<'ctx, Element> {
+    pub fn nullptr<G: CodeGenerator + ?Sized>(
-        let ptr = self.get_type(tyctx, ctx).const_null();
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+    ) -> Ptr<'ctx, Element> {
+        let ptr = self.get_type(generator, ctx).const_null();
         self.believe_value(ptr)
     }
 
     /// Cast a pointer into this model with [`inkwell::builder::Builder::build_pointer_cast`]
-    pub fn pointer_cast(
+    pub fn pointer_cast<G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &CodeGenContext<'ctx, '_>,
         ptr: PointerValue<'ctx>,
         name: &str,
     ) -> Ptr<'ctx, Element> {
-        let ptr = ctx.builder.build_pointer_cast(ptr, self.get_type(tyctx, ctx.ctx), name).unwrap();
+        let ptr =
+            ctx.builder.build_pointer_cast(ptr, self.get_type(generator, ctx.ctx), name).unwrap();
         self.believe_value(ptr)
     }
 }
@@ -70,38 +81,38 @@ impl<'ctx, Element: Model<'ctx>> PtrModel<Element> {
 impl<'ctx, Element: Model<'ctx>> Ptr<'ctx, Element> {
     /// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`].
     #[must_use]
-    pub fn offset(
+    pub fn offset<G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &CodeGenContext<'ctx, '_>,
         offset: IntValue<'ctx>,
         name: &str,
     ) -> Ptr<'ctx, Element> {
         let new_ptr =
             unsafe { ctx.builder.build_in_bounds_gep(self.value, &[offset], name).unwrap() };
-        self.model.check_value(tyctx, ctx.ctx, new_ptr).unwrap()
+        self.model.check_value(generator, ctx.ctx, new_ptr).unwrap()
     }
 
     // Load the `i`-th element (0-based) on the array with [`inkwell::builder::Builder::build_in_bounds_gep`].
-    pub fn ix(
+    pub fn ix<G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &CodeGenContext<'ctx, '_>,
         i: IntValue<'ctx>,
         name: &str,
     ) -> Instance<'ctx, Element> {
-        self.offset(tyctx, ctx, i, name).load(tyctx, ctx, name)
+        self.offset(generator, ctx, i, name).load(generator, ctx, name)
     }
 
     /// Load the value with [`inkwell::builder::Builder::build_load`].
-    pub fn load(
+    pub fn load<G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &CodeGenContext<'ctx, '_>,
         name: &str,
     ) -> Instance<'ctx, Element> {
         let value = ctx.builder.build_load(self.value, name).unwrap();
-        self.model.0.check_value(tyctx, ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error.
+        self.model.0.check_value(generator, ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error.
     }
 
     /// Store a value with [`inkwell::builder::Builder::build_store`].
@@ -110,14 +121,14 @@ impl<'ctx, Element: Model<'ctx>> Ptr<'ctx, Element> {
     }
 
     /// Return a casted pointer of element type `NewElement` with [`inkwell::builder::Builder::build_pointer_cast`].
-    pub fn transmute<NewElement: Model<'ctx>>(
+    pub fn transmute<NewElement: Model<'ctx>, G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &CodeGenContext<'ctx, '_>,
         new_model: NewElement,
         name: &str,
     ) -> Ptr<'ctx, NewElement> {
-        PtrModel(new_model).pointer_cast(tyctx, ctx, self.value, name)
+        PtrModel(new_model).pointer_cast(generator, ctx, self.value, name)
     }
 
     /// Check if the pointer is null with [`inkwell::builder::Builder::build_is_null`].

nac3core/src/codegen/model/structure.rs

@@ -6,7 +6,7 @@ use inkwell::{
     values::StructValue,
 };
 
-use crate::codegen::CodeGenContext;
+use crate::codegen::{CodeGenContext, CodeGenerator};
 
 use super::*;
 
@@ -42,30 +42,32 @@ impl<'ctx> FieldTraversal<'ctx> for GepFieldTraversal {
     }
 }
 
-struct TypeFieldTraversal<'ctx> {
+struct TypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
-    tyctx: TypeContext<'ctx>,
+    generator: &'a G,
     ctx: &'ctx Context,
     field_types: Vec<BasicTypeEnum<'ctx>>,
 }
 
-impl<'ctx> FieldTraversal<'ctx> for TypeFieldTraversal<'ctx> {
+impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx> for TypeFieldTraversal<'ctx, 'a, G> {
     type Out<M> = ();
 
     fn add<M: Model<'ctx>>(&mut self, _name: &'static str, model: M) -> Self::Out<M> {
-        let t = model.get_type(self.tyctx, self.ctx).as_basic_type_enum();
+        let t = model.get_type(self.generator, self.ctx).as_basic_type_enum();
         self.field_types.push(t);
     }
 }
 
-struct CheckTypeFieldTraversal<'ctx> {
+struct CheckTypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
-    tyctx: TypeContext<'ctx>,
+    generator: &'a mut G,
     ctx: &'ctx Context,
     index: u32,
     scrutinee: StructType<'ctx>,
     errors: Vec<ModelError>,
 }
 
-impl<'ctx> FieldTraversal<'ctx> for CheckTypeFieldTraversal<'ctx> {
+impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx>
+    for CheckTypeFieldTraversal<'ctx, 'a, G>
+{
     type Out<M> = ();
 
     fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M> {
@@ -73,8 +75,8 @@ impl<'ctx> FieldTraversal<'ctx> for CheckTypeFieldTraversal<'ctx> {
         self.index += 1;
 
         if let Some(t) = self.scrutinee.get_field_type_at_index(i) {
-            if let Err(err) = model.check_type(self.tyctx, self.ctx, t) {
+            if let Err(err) = model.check_type(self.generator, self.ctx, t) {
-                self.errors.push(err.under_context(format!("At field #{i} '{name}'").as_str()));
+                self.errors.push(err.under_context(format!("field #{i} '{name}'").as_str()));
             }
         } // Otherwise, it will be caught
     }
@@ -89,8 +91,12 @@ pub trait StructKind<'ctx>: fmt::Debug + Clone + Copy {
         self.traverse_fields(&mut GepFieldTraversal { gep_index_counter: 0 })
     }
 
-    fn get_struct_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> StructType<'ctx> {
+    fn get_struct_type<G: CodeGenerator + ?Sized>(
-        let mut traversal = TypeFieldTraversal { tyctx, ctx, field_types: Vec::new() };
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> StructType<'ctx> {
+        let mut traversal = TypeFieldTraversal { generator, ctx, field_types: Vec::new() };
         self.traverse_fields(&mut traversal);
 
         ctx.struct_type(&traversal.field_types, false)
@@ -105,13 +111,17 @@ impl<'ctx, S: StructKind<'ctx>> Model<'ctx> for StructModel<S> {
     type Value = StructValue<'ctx>;
     type Type = StructType<'ctx>;
 
-    fn get_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type {
+    fn get_type<G: CodeGenerator + ?Sized>(
-        self.0.get_struct_type(tyctx, ctx)
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> Self::Type {
+        self.0.get_struct_type(generator, ctx)
     }
 
-    fn check_type<T: BasicType<'ctx>>(
+    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &'ctx Context,
         ty: T,
     ) -> Result<(), ModelError> {
@@ -121,7 +131,7 @@ impl<'ctx, S: StructKind<'ctx>> Model<'ctx> for StructModel<S> {
         };
 
         let mut traversal =
-            CheckTypeFieldTraversal { tyctx, ctx, index: 0, errors: Vec::new(), scrutinee: ty };
+            CheckTypeFieldTraversal { generator, ctx, index: 0, errors: Vec::new(), scrutinee: ty };
         self.0.traverse_fields(&mut traversal);
 
         let exp_num_fields = traversal.index;
@@ -168,9 +178,9 @@ impl<'ctx, S: StructKind<'ctx>> Ptr<'ctx, StructModel<S>> {
     }
 
     /// Convenience function equivalent to `.gep(...).load(...)`.
-    pub fn get<M, GetField>(
+    pub fn get<M, GetField, G: CodeGenerator + ?Sized>(
         &self,
-        tyctx: TypeContext<'ctx>,
+        generator: &mut G,
         ctx: &CodeGenContext<'ctx, '_>,
         get_field: GetField,
         name: &str,
@@ -179,7 +189,7 @@ impl<'ctx, S: StructKind<'ctx>> Ptr<'ctx, StructModel<S>> {
         M: Model<'ctx>,
         GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
     {
-        self.gep(ctx, get_field).load(tyctx, ctx, name)
+        self.gep(ctx, get_field).load(generator, ctx, name)
     }
 
     /// Convenience function equivalent to `.gep(...).store(...)`.
@@ -192,6 +202,6 @@ impl<'ctx, S: StructKind<'ctx>> Ptr<'ctx, StructModel<S>> {
         M: Model<'ctx>,
         GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
     {
-        self.gep(ctx, get_field).store(ctx, value)
+        self.gep(ctx, get_field).store(ctx, value);
     }
 }

nac3core/src/codegen/model/util.rs

@@ -0,0 +1,91 @@
+use inkwell::{types::BasicType, values::IntValue};
+
+/// `llvm.memcpy` but under the [`Model`] abstraction
+use crate::codegen::{
+    llvm_intrinsics::call_memcpy_generic,
+    stmt::{gen_for_callback_incrementing, BreakContinueHooks},
+    CodeGenContext, CodeGenerator,
+};
+
+use super::*;
+
+/// Convenience function.
+///
+/// Like [`call_memcpy_generic`] but with model abstractions and `is_volatile` set to `false`.
+pub fn call_memcpy_model<'ctx, Item: Model<'ctx> + Default, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    dst_array: Ptr<'ctx, Item>,
+    src_array: Ptr<'ctx, Item>,
+    num_items: IntValue<'ctx>,
+) {
+    let itemsize = Item::default().get_type(generator, ctx.ctx).size_of().unwrap();
+    let totalsize = ctx.builder.build_int_mul(itemsize, num_items, "totalsize").unwrap(); // TODO: Int types may not match.
+    let is_volatile = ctx.ctx.bool_type().const_zero();
+    call_memcpy_generic(ctx, dst_array.value, src_array.value, totalsize, is_volatile);
+}
+
+/// Like [`gen_for_callback_incrementing`] with [`Model`] abstractions.
+/// The [`IntKind`] is automatically inferred.
+pub fn gen_for_model_auto<'ctx, 'a, G, F, I>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, 'a>,
+    start: Int<'ctx, I>,
+    stop: Int<'ctx, I>,
+    step: Int<'ctx, I>,
+    body: F,
+) -> Result<(), String>
+where
+    G: CodeGenerator + ?Sized,
+    F: FnOnce(
+        &mut G,
+        &mut CodeGenContext<'ctx, 'a>,
+        BreakContinueHooks<'ctx>,
+        Int<'ctx, I>,
+    ) -> Result<(), String>,
+    I: IntKind<'ctx> + Default,
+{
+    let int_model = IntModel(I::default());
+
+    gen_for_callback_incrementing(
+        generator,
+        ctx,
+        None,
+        start.value,
+        (stop.value, false),
+        |g, ctx, hooks, i| {
+            let i = int_model.believe_value(i);
+            body(g, ctx, hooks, i)
+        },
+        step.value,
+    )
+}
+
+/// Like [`gen_if_callback`] with [`Model`] abstractions and without the `else` block.
+pub fn gen_if_model<'ctx, 'a, G, ThenFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, 'a>,
+    cond: Int<'ctx, Bool>,
+    then: ThenFn,
+) -> Result<(), String>
+where
+    G: CodeGenerator + ?Sized,
+    ThenFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<(), String>,
+{
+    let current_bb = ctx.builder.get_insert_block().unwrap();
+    let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "if.then");
+    let end_bb = ctx.ctx.insert_basic_block_after(then_bb, "if.end");
+
+    // Inserting into `current_bb`.
+    ctx.builder.build_conditional_branch(cond.value, then_bb, end_bb).unwrap();
+
+    // Inserting into `then_bb`
+    ctx.builder.position_at_end(then_bb);
+    then(generator, ctx)?;
+    ctx.builder.build_unconditional_branch(end_bb).unwrap();
+
+    // Reposition to `end_bb` for continuation.
+    ctx.builder.position_at_end(end_bb);
+
+    Ok(())
+}

nac3core/src/codegen/numpy_new.rs

@@ -0,0 +1,483 @@
+// TODO: Replace numpy.rs
+
+use inkwell::values::{BasicValue, BasicValueEnum};
+use nac3parser::ast::StrRef;
+
+use crate::{
+    codegen::{
+        structure::{
+            ndarray::{
+                scalar::split_scalar_or_ndarray, shape_util::parse_numpy_int_sequence,
+                NDArrayObject,
+            },
+            tuple::TupleObject,
+        },
+    },
+    symbol_resolver::ValueEnum,
+    toplevel::{
+        numpy::{extract_ndims, unpack_ndarray_var_tys},
+        DefinitionId,
+    },
+    typecheck::typedef::{FunSignature, Type},
+};
+
+use super::{
+    irrt::call_nac3_ndarray_util_assert_shape_no_negative, model::*, CodeGenContext, CodeGenerator,
+};
+
+/// Get the zero value in `np.zeros()` of a `dtype`.
+fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    dtype: Type,
+) -> BasicValueEnum<'ctx> {
+    if [ctx.primitives.int32, ctx.primitives.uint32]
+        .iter()
+        .any(|ty| ctx.unifier.unioned(dtype, *ty))
+    {
+        ctx.ctx.i32_type().const_zero().into()
+    } else if [ctx.primitives.int64, ctx.primitives.uint64]
+        .iter()
+        .any(|ty| ctx.unifier.unioned(dtype, *ty))
+    {
+        ctx.ctx.i64_type().const_zero().into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
+        ctx.ctx.f64_type().const_zero().into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
+        ctx.ctx.bool_type().const_zero().into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
+        ctx.gen_string(generator, "").value.into()
+    } else {
+        unreachable!()
+    }
+}
+
+/// Get the one value in `np.ones()` of a `dtype`.
+fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+) -> BasicValueEnum<'ctx> {
+    if [ctx.primitives.int32, ctx.primitives.uint32]
+        .iter()
+        .any(|ty| ctx.unifier.unioned(elem_ty, *ty))
+    {
+        let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int32);
+        ctx.ctx.i32_type().const_int(1, is_signed).into()
+    } else if [ctx.primitives.int64, ctx.primitives.uint64]
+        .iter()
+        .any(|ty| ctx.unifier.unioned(elem_ty, *ty))
+    {
+        let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int64);
+        ctx.ctx.i64_type().const_int(1, is_signed).into()
+    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
+        ctx.ctx.f64_type().const_float(1.0).into()
+    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
+        ctx.ctx.bool_type().const_int(1, false).into()
+    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
+        ctx.gen_string(generator, "1").value.into()
+    } else {
+        unreachable!()
+    }
+}
+
+/// Helper function to create an ndarray with uninitialized values.
+///
+/// * `ndarray_ty` - The [`Type`] of the ndarray
+/// * `shape` - The user input shape argument
+/// * `shape_ty` - The [`Type`] of the shape argument
+///
+/// This function does data validation the `shape` input.
+fn create_empty_ndarray<'ctx, G>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ndarray_ty: Type,
+    shape: BasicValueEnum<'ctx>,
+    shape_ty: Type,
+) -> NDArrayObject<'ctx>
+where
+    G: CodeGenerator + ?Sized,
+{
+    let (_, shape) = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
+
+    let ndarray =
+        NDArrayObject::alloca_uninitialized_of_type(generator, ctx, ndarray_ty, "ndarray");
+
+    // Validate `shape`
+    let ndims = ndarray.get_ndims(generator, ctx.ctx);
+    call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, ndims, shape);
+
+    // Setup `ndarray` with `shape`
+    ndarray.copy_shape_from_array(generator, ctx, shape);
+    ndarray.create_data(generator, ctx); // `shape` has to be set
+
+    ndarray
+}
+
+/// Generates LLVM IR for `np.empty`.
+pub fn gen_ndarray_empty<'ctx>(
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<BasicValueEnum<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    // Parse arguments
+    let shape_ty = fun.0.args[0].ty;
+    let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
+
+    // Implementation
+    let ndarray_ty = fun.0.ret;
+    let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
+
+    Ok(ndarray.value.value.as_basic_value_enum())
+}
+
+/// Generates LLVM IR for `np.zero`.
+pub fn gen_ndarray_zeros<'ctx>(
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<BasicValueEnum<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    // Parse arguments
+    let shape_ty = fun.0.args[0].ty;
+    let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
+
+    // Implementation
+    let ndarray_ty = fun.0.ret;
+    let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
+
+    let fill_value = ndarray_zero_value(generator, ctx, ndarray.dtype);
+    ndarray.fill(generator, ctx, fill_value);
+
+    Ok(ndarray.value.value.as_basic_value_enum())
+}
+
+/// Generates LLVM IR for `np.ones`.
+pub fn gen_ndarray_ones<'ctx>(
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<BasicValueEnum<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    // Parse arguments
+    let shape_ty = fun.0.args[0].ty;
+    let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
+
+    // Implementation
+    let ndarray_ty = fun.0.ret;
+    let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
+
+    let fill_value = ndarray_zero_value(generator, ctx, ndarray.dtype);
+    ndarray.fill(generator, ctx, fill_value);
+
+    Ok(ndarray.value.value.as_basic_value_enum())
+}
+
+/// Generates LLVM IR for `np.full`.
+pub fn gen_ndarray_full<'ctx>(
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<BasicValueEnum<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 2);
+
+    // Parse argument #1 shape
+    let shape_ty = fun.0.args[0].ty;
+    let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
+
+    // Parse argument #2 fill_value
+    let fill_value_ty = fun.0.args[1].ty;
+    let fill_value = args[1].1.clone().to_basic_value_enum(ctx, generator, fill_value_ty)?;
+
+    // Implementation
+    let ndarray_ty = fun.0.ret;
+    let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
+
+    ndarray.fill(generator, ctx, fill_value);
+
+    Ok(ndarray.value.value.as_basic_value_enum())
+}
+
+/// Generates LLVM IR for `np.broadcast_to`.
+pub fn gen_ndarray_broadcast_to<'ctx>(
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<BasicValueEnum<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 2);
+
+    // Parse argument #1 input
+    let input_ty = fun.0.args[0].ty;
+    let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?;
+
+    // Parse argument #2 shape
+    let shape_ty = fun.0.args[1].ty;
+    let shape = args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
+
+    // Define models
+    let sizet_model = IntModel(SizeT);
+
+    // Extract broadcast_ndims, this is the only way to get the
+    // ndims of the ndarray result statically.
+    let (_, broadcast_ndims_ty) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
+    let broadcast_ndims = extract_ndims(&ctx.unifier, broadcast_ndims_ty);
+
+    // Process `input`
+    let in_ndarray =
+        split_scalar_or_ndarray(generator, ctx, input, input_ty).as_ndarray(generator, ctx);
+
+    // Process `shape`
+    let (_, broadcast_shape) = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
+    // NOTE: shape.size should equal to `broadcasted_ndims`.
+    let broadcast_ndims_llvm = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
+    call_nac3_ndarray_util_assert_shape_no_negative(
+        generator,
+        ctx,
+        broadcast_ndims_llvm,
+        broadcast_shape,
+    );
+
+    // Create broadcast view
+    let broadcast_ndarray =
+        in_ndarray.broadcast_to(generator, ctx, broadcast_ndims, broadcast_shape);
+
+    Ok(broadcast_ndarray.value.value.as_basic_value_enum())
+}
+
+/// Generates LLVM IR for `np.reshape`.
+pub fn gen_ndarray_reshape<'ctx>(
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<BasicValueEnum<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 2);
+
+    // Parse argument #1 input
+    let input_ty = fun.0.args[0].ty;
+    let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?;
+
+    // Parse argument #2 shape
+    let shape_ty = fun.0.args[1].ty;
+    let shape = args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
+
+    // Extract reshaped_ndims
+    let (_, reshaped_ndims_ty) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
+    let reshaped_ndims = extract_ndims(&ctx.unifier, reshaped_ndims_ty);
+
+    // Process `input`
+    let in_ndarray =
+        split_scalar_or_ndarray(generator, ctx, input, input_ty).as_ndarray(generator, ctx);
+
+    // Process the shape input from user and resolve negative indices.
+    // The resulting `new_shape`'s size should be equal to reshaped_ndims.
+    // This is ensured by the typechecker.
+    let (_, new_shape) = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
+    let reshaped_ndarray = in_ndarray.reshape_or_copy(generator, ctx, reshaped_ndims, new_shape);
+
+    Ok(reshaped_ndarray.value.value.as_basic_value_enum())
+}
+
+/// Generates LLVM IR for `np.arange`.
+pub fn gen_ndarray_arange<'ctx>(
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<BasicValueEnum<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    // Parse argument #1 len
+    let input_ty = fun.0.args[0].ty;
+    let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?.into_int_value();
+
+    // Define models
+    let sizet_model = IntModel(SizeT);
+
+    // Process input
+    let input = sizet_model.s_extend_or_bit_cast(generator, ctx, input, "input_dim");
+
+    // Allocate the resulting ndarray
+    let ndarray = NDArrayObject::alloca_uninitialized(
+        generator,
+        ctx,
+        ctx.primitives.float,
+        1, // ndims = 1
+        "arange_ndarray",
+    );
+
+    // `ndarray.shape[0] = input`
+    let zero = sizet_model.const_0(generator, ctx.ctx);
+    ndarray
+        .value
+        .get(generator, ctx, |f| f.shape, "shape")
+        .offset(generator, ctx, zero.value, "dim")
+        .store(ctx, input);
+
+    // Create data and set elements
+    ndarray.create_data(generator, ctx);
+    ndarray.foreach_pointer(generator, ctx, |_generator, ctx, _hooks, i, pelement| {
+        let val =
+            ctx.builder.build_unsigned_int_to_float(i.value, ctx.ctx.f64_type(), "val").unwrap();
+        ctx.builder.build_store(pelement, val).unwrap();
+        Ok(())
+    })?;
+
+    Ok(ndarray.value.value.as_basic_value_enum())
+}
+
+/// Generates LLVM IR for `np.size`.
+pub fn gen_ndarray_size<'ctx>(
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<BasicValueEnum<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    let ndarray_ty = fun.0.args[0].ty;
+    let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
+
+    let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
+
+    let size = ndarray.size(generator, ctx).truncate(generator, ctx, Int32, "size");
+    Ok(size.value.as_basic_value_enum())
+}
+
+/// Generates LLVM IR for `np.shape`.
+pub fn gen_ndarray_shape<'ctx>(
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<BasicValueEnum<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    // Parse argument #1 ndarray
+    let ndarray_ty = fun.0.args[0].ty;
+    let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
+
+    // Define models
+    let sizet_model = IntModel(SizeT);
+
+    // Process ndarray
+    let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
+
+    let mut items = Vec::with_capacity(ndarray.ndims as usize);
+
+    for i in 0..ndarray.ndims {
+        let i = sizet_model.constant(generator, ctx.ctx, i);
+        let dim =
+            ndarray.value.get(generator, ctx, |f| f.shape, "").ix(generator, ctx, i.value, "dim");
+        let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
+
+        items.push((dim.value.as_basic_value_enum(), ctx.primitives.int32));
+    }
+
+    let shape = TupleObject::create(generator, ctx, items, "shape");
+    Ok(shape.value.as_basic_value_enum())
+}
+
+/// Generates LLVM IR for `<ndarray>.strides`.
+pub fn gen_ndarray_strides<'ctx>(
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<BasicValueEnum<'ctx>, String> {
+    // TODO: This function looks exactly like `gen_ndarray_shapes`, code duplication?
+
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    // Parse argument #1 ndarray
+    let ndarray_ty = fun.0.args[0].ty;
+    let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
+
+    // Define models
+    let sizet_model = IntModel(SizeT);
+
+    // Process ndarray
+    let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
+
+    let mut items = Vec::with_capacity(ndarray.ndims as usize);
+
+    for i in 0..ndarray.ndims {
+        let i = sizet_model.constant(generator, ctx.ctx, i);
+        let dim =
+            ndarray.value.get(generator, ctx, |f| f.strides, "").ix(generator, ctx, i.value, "dim");
+        let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
+
+        items.push((dim.value.as_basic_value_enum(), ctx.primitives.int32));
+    }
+
+    let strides = TupleObject::create(generator, ctx, items, "strides");
+    Ok(strides.value.as_basic_value_enum())
+}
+
+/// Generates LLVM IR for `np.transpose`.
+pub fn gen_ndarray_transpose<'ctx>(
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<BasicValueEnum<'ctx>, String> {
+    // TODO: The implementation will be changed once default values start working again.
+    // Read the comment on this function in BuiltinBuilder.
+
+    // TODO: Change axes values to `SizeT`
+
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    // Parse argument #1 ndarray
+    let ndarray_ty = fun.0.args[0].ty;
+    let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
+
+    // Implementation
+    let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
+
+    let has_axes = args.len() >= 2;
+    let transposed_ndarray = if has_axes {
+        // Parse argument #2 axes
+        let in_axes_ty = fun.0.args[1].ty;
+        let in_axes = args[1].1.clone().to_basic_value_enum(ctx, generator, in_axes_ty)?;
+
+        let (_, axes) = parse_numpy_int_sequence(generator, ctx, in_axes, in_axes_ty);
+
+        ndarray.transpose(generator, ctx, Some(axes))
+    } else {
+        ndarray.transpose(generator, ctx, None)
+    };
+
+    Ok(transposed_ndarray.value.value.as_basic_value_enum())
+}

nac3core/src/codegen/stmt.rs

@@ -7,6 +7,9 @@ use super::{
     structure::exception::Exception,
     CodeGenContext, CodeGenerator, Int32, IntModel, Ptr, StructModel,
 };
+use crate::codegen::structure::ndarray::indexing::util::gen_ndarray_subscript_ndindexes;
+use crate::codegen::structure::ndarray::scalar::split_scalar_or_ndarray;
+use crate::codegen::structure::ndarray::NDArrayObject;
 use crate::{
     codegen::{
         classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
@@ -404,7 +407,43 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
             if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
         {
             // Handle NDArray item assignment
-            todo!("ndarray subscript assignment is not yet implemented");
+            // Process target
+            let target = generator
+                .gen_expr(ctx, target)?
+                .unwrap()
+                .to_basic_value_enum(ctx, generator, target_ty)?;
+            let target = NDArrayObject::from_value_and_type(generator, ctx, target, target_ty);
+
+            // Process key
+            let key = gen_ndarray_subscript_ndindexes(generator, ctx, key)?;
+
+            // Process value
+            let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
+
+            /*
+                Reference code:
+                ```python
+                target = target[key]
+                value = np.asarray(value)
+
+                shape = np.broadcast_shape((target, value))
+
+                target = np.broadcast_to(target, shape)
+                value = np.broadcast_to(value, shape)
+
+                ...and finally copy 1-1 from value to target.
+                ```
+            */
+            let target = target.index(generator, ctx, &key, "assign_target_ndarray");
+            let value =
+                split_scalar_or_ndarray(generator, ctx, value, value_ty).as_ndarray(generator, ctx);
+
+            let broadcast_result = NDArrayObject::broadcast_all(generator, ctx, &[target, value]);
+
+            let target = broadcast_result.ndarrays[0];
+            let value = broadcast_result.ndarrays[1];
+
+            target.copy_data_from(generator, ctx, value);
         }
         _ => {
             panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));
@@ -641,8 +680,12 @@ where
     I: Clone,
     InitFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<I, String>,
     CondFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<IntValue<'ctx>, String>,
-    BodyFn:
+    BodyFn: FnOnce(
-        FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, BreakContinueHooks, I) -> Result<(), String>,
+        &mut G,
+        &mut CodeGenContext<'ctx, 'a>,
+        BreakContinueHooks<'ctx>,
+        I,
+    ) -> Result<(), String>,
     UpdateFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
 {
     let label = label.unwrap_or("for");
@@ -722,7 +765,7 @@ where
     BodyFn: FnOnce(
         &mut G,
         &mut CodeGenContext<'ctx, 'a>,
-        BreakContinueHooks,
+        BreakContinueHooks<'ctx>,
         IntValue<'ctx>,
     ) -> Result<(), String>,
 {
@@ -1266,20 +1309,19 @@ pub fn gen_raise<'ctx, G: CodeGenerator + ?Sized>(
     loc: Location,
 ) {
     if let Some(pexn) = exception {
-        let type_context = generator.type_context(ctx.ctx);
         let i32_model = IntModel(Int32);
         let cslice_model = StructModel(CSlice);
 
         // Get and store filename
         let filename = loc.file.0;
         let filename = ctx.gen_string(generator, &String::from(filename)).value;
-        let filename = cslice_model.check_value(type_context, ctx.ctx, filename).unwrap();
+        let filename = cslice_model.check_value(generator, ctx.ctx, filename).unwrap();
         pexn.set(ctx, |f| f.filename, filename);
 
-        let row = i32_model.constant(type_context, ctx.ctx, loc.row as u64);
+        let row = i32_model.constant(generator, ctx.ctx, loc.row as u64);
         pexn.set(ctx, |f| f.line, row);
 
-        let column = i32_model.constant(type_context, ctx.ctx, loc.column as u64);
+        let column = i32_model.constant(generator, ctx.ctx, loc.column as u64);
         pexn.set(ctx, |f| f.column, column);
 
         let current_fn = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
@@ -1755,9 +1797,8 @@ pub fn gen_stmt<G: CodeGenerator>(
                     return Ok(());
                 };
 
-                let type_context = generator.type_context(ctx.ctx);
                 let pexn_model = PtrModel(StructModel(Exception));
-                let exn = pexn_model.check_value(type_context, ctx.ctx, exc).unwrap();
+                let exn = pexn_model.check_value(generator, ctx.ctx, exc).unwrap();
 
                 gen_raise(generator, ctx, Some(exn), stmt.location);
             } else {
@@ -1765,7 +1806,6 @@ pub fn gen_stmt<G: CodeGenerator>(
             }
         }
         StmtKind::Assert { test, msg, .. } => {
-            let type_context = generator.type_context(ctx.ctx);
             let byte_model = IntModel(Byte);
             let cslice_model = StructModel(CSlice);
 
@@ -1773,7 +1813,7 @@ pub fn gen_stmt<G: CodeGenerator>(
                 return Ok(());
             };
             let test = test.to_basic_value_enum(ctx, generator, ctx.primitives.bool)?;
-            let test = byte_model.check_value(type_context, ctx.ctx, test).unwrap(); // Python `bool` is represented as `i8` in nac3core
+            let test = byte_model.check_value(generator, ctx.ctx, test).unwrap(); // Python `bool` is represented as `i8` in nac3core
 
             // Check `msg`
             let err_msg = match msg {
@@ -1783,7 +1823,7 @@ pub fn gen_stmt<G: CodeGenerator>(
                     };
 
                     let msg = msg.to_basic_value_enum(ctx, generator, ctx.primitives.str)?;
-                    cslice_model.check_value(type_context, ctx.ctx, msg).unwrap()
+                    cslice_model.check_value(generator, ctx.ctx, msg).unwrap()
                 }
                 None => ctx.gen_string(generator, ""),
             };

nac3core/src/codegen/structure/cslice.rs

@@ -1,4 +1,6 @@
-use crate::codegen::{model::*, CodeGenContext};
+use inkwell::context::Context;
+
+use crate::codegen::{model::*, CodeGenerator};
 
 /// Fields of [`CSlice<'ctx>`].
 pub struct CSliceFields<'ctx, F: FieldTraversal<'ctx>> {
@@ -27,16 +29,16 @@ impl StructModel<CSlice> {
     /// Create a [`CSlice`].
     ///
     /// `base` and `len` must be LLVM global constants.
-    pub fn create_const<'ctx>(
+    pub fn create_const<'ctx, G: CodeGenerator + ?Sized>(
         &self,
-        type_context: TypeContext<'ctx>,
+        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
+        ctx: &'ctx Context,
         base: Ptr<'ctx, IntModel<Byte>>,
         len: Int<'ctx, SizeT>,
     ) -> Struct<'ctx, CSlice> {
         let value = self
             .0
-            .get_struct_type(type_context, ctx.ctx)
+            .get_struct_type(generator, ctx)
             .const_named_struct(&[base.value.into(), len.value.into()]);
         self.believe_value(value)
     }

nac3core/src/codegen/structure/list.rs

@@ -0,0 +1,70 @@
+use inkwell::values::BasicValue;
+
+use crate::{
+    codegen::{model::*, CodeGenContext, CodeGenerator},
+    typecheck::typedef::{iter_type_vars, Type, TypeEnum},
+};
+
+/// Fields of [`List`]
+pub struct ListFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>, Size: IntKind<'ctx>> {
+    /// Array pointer to content
+    pub items: F::Out<PtrModel<Item>>,
+    /// Number of items in the array
+    pub len: F::Out<IntModel<Size>>,
+}
+
+/// A list in NAC3.
+#[derive(Debug, Clone, Copy, Default)]
+pub struct List<Item, Len> {
+    /// Model of the list items
+    pub item: Item,
+    /// Model of type of integer storing the number of items on the list
+    pub len: Len,
+}
+
+impl<'ctx, Item: Model<'ctx>, Size: IntKind<'ctx>> StructKind<'ctx> for List<Item, Size> {
+    type Fields<F: FieldTraversal<'ctx>> = ListFields<'ctx, F, Item, Size>;
+
+    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
+        Self::Fields {
+            items: traversal.add("data", PtrModel(self.item)),
+            len: traversal.add("len", IntModel(self.len)),
+        }
+    }
+}
+
+/// A NAC3 Python List object.
+pub struct ListObject<'ctx> {
+    /// Typechecker type of the list items
+    pub item_type: Type,
+    pub value: Ptr<'ctx, StructModel<List<AnyModel<'ctx>, SizeT>>>,
+}
+
+impl<'ctx> ListObject<'ctx> {
+    /// Create a [`ListObject`] from an LLVM value and its typechecker [`Type`].
+    pub fn from_value_and_type<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        list_val: V,
+        list_type: Type,
+    ) -> Self {
+        // Check typechecker type and extract `item_type`
+        let item_type = match &*ctx.unifier.get_ty(list_type) {
+            TypeEnum::TObj { obj_id, params, .. }
+                if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
+            {
+                iter_type_vars(params).next().unwrap().ty // Extract `item_type`
+            }
+            _ => {
+                panic!("Expecting type to be a list, but got {}", ctx.unifier.stringify(list_type))
+            }
+        };
+
+        let item_model = AnyModel(ctx.get_llvm_type(generator, item_type));
+        let plist_model = PtrModel(StructModel(List { item: item_model, len: SizeT }));
+
+        // Create object
+        let value = plist_model.check_value(generator, ctx.ctx, list_val).unwrap();
+        ListObject { item_type, value }
+    }
+}

nac3core/src/codegen/structure/mod.rs

@@ -1,3 +1,5 @@
 pub mod cslice;
 pub mod exception;
+pub mod list;
 pub mod ndarray;
+pub mod tuple;

nac3core/src/codegen/structure/ndarray.rs

@@ -1,224 +0,0 @@
-use irrt::{
-    call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
-    call_nac3_ndarray_is_c_contiguous, call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
-    call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
-};
-
-use crate::{codegen::*, symbol_resolver::SymbolValue};
-
-pub struct NpArrayFields<'ctx, F: FieldTraversal<'ctx>> {
-    pub data: F::Out<PtrModel<IntModel<Byte>>>,
-    pub itemsize: F::Out<IntModel<SizeT>>,
-    pub ndims: F::Out<IntModel<SizeT>>,
-    pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
-    pub strides: F::Out<PtrModel<IntModel<SizeT>>>,
-}
-
-// TODO: Rename to `NDArray` when the old NDArray is removed.
-#[derive(Debug, Clone, Copy, Default)]
-pub struct NpArray;
-
-impl<'ctx> StructKind<'ctx> for NpArray {
-    type Fields<F: FieldTraversal<'ctx>> = NpArrayFields<'ctx, F>;
-
-    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
-        Self::Fields {
-            data: traversal.add_auto("data"),
-            itemsize: traversal.add_auto("itemsize"),
-            ndims: traversal.add_auto("ndims"),
-            shape: traversal.add_auto("shape"),
-            strides: traversal.add_auto("strides"),
-        }
-    }
-}
-
-/// Extract an ndarray's `ndims` [type][`Type`] in `u64`. Panic if not possible.
-/// The `ndims` must only contain 1 value.
-#[must_use]
-pub fn extract_ndims(unifier: &Unifier, ndims_ty: Type) -> u64 {
-    let ndims_ty_enum = unifier.get_ty_immutable(ndims_ty);
-    let TypeEnum::TLiteral { values, .. } = &*ndims_ty_enum else {
-        panic!("ndims_ty should be a TLiteral");
-    };
-
-    assert_eq!(values.len(), 1, "ndims_ty TLiteral should only contain 1 value");
-
-    let ndims = values[0].clone();
-    u64::try_from(ndims).unwrap()
-}
-
-/// Return an ndarray's `ndims` as a typechecker [`Type`] from its `u64` value.
-pub fn create_ndims(unifier: &mut Unifier, ndims: u64) -> Type {
-    unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None)
-}
-
-#[derive(Debug, Clone, Copy)]
-pub struct NDArrayObject<'ctx> {
-    pub dtype: Type,
-    pub ndims: Type,
-    pub value: Ptr<'ctx, StructModel<NpArray>>,
-}
-
-impl<'ctx> NDArrayObject<'ctx> {
-    /// Allocate an ndarray on the stack given its `ndims` and `dtype`.
-    ///
-    /// `shape` and `strides` will be automatically allocated on the stack.
-    ///
-    /// The returned ndarray's content will be:
-    /// - `data`: set to `nullptr`.
-    /// - `itemsize`: set to the `sizeof()` of `dtype`.
-    /// - `ndims`: set to the value of  `ndims`.
-    /// - `shape`: allocated with an array of length `ndims` with uninitialized values.
-    /// - `strides`: allocated with an array of length `ndims` with uninitialized values.
-    pub fn alloca_uninitialized<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        dtype: Type,
-        ndims: Type,
-        name: &str,
-    ) -> Self {
-        let tyctx = generator.type_context(ctx.ctx);
-        let sizet_model = IntModel(SizeT);
-        let ndarray_model = StructModel(NpArray);
-        let ndarray_data_model = PtrModel(IntModel(Byte));
-
-        let pndarray = ndarray_model.alloca(tyctx, ctx, name);
-
-        let data = ndarray_data_model.nullptr(tyctx, ctx.ctx);
-
-        let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
-        let itemsize = sizet_model.s_extend_or_bit_cast(tyctx, ctx, itemsize, "itemsize");
-
-        let ndims_val = extract_ndims(&ctx.unifier, ndims);
-        let ndims_val = sizet_model.constant(tyctx, ctx.ctx, ndims_val);
-
-        let shape = sizet_model.array_alloca(tyctx, ctx, ndims_val.value, "shape");
-        let strides = sizet_model.array_alloca(tyctx, ctx, ndims_val.value, "strides");
-
-        pndarray.set(ctx, |f| f.data, data);
-        pndarray.set(ctx, |f| f.itemsize, itemsize);
-        pndarray.set(ctx, |f| f.ndims, ndims_val);
-        pndarray.set(ctx, |f| f.shape, shape);
-        pndarray.set(ctx, |f| f.strides, strides);
-
-        NDArrayObject { dtype, ndims, value: pndarray }
-    }
-
-    /// Get this ndarray's `ndims` as an LLVM constant.
-    pub fn get_ndims(
-        &self,
-        tyctx: TypeContext<'ctx>,
-        ctx: &CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, SizeT> {
-        let sizet_model = IntModel(SizeT);
-
-        let ndims_val = extract_ndims(&ctx.unifier, self.ndims);
-        sizet_model.constant(tyctx, ctx.ctx, ndims_val)
-    }
-
-    /// Return true if this ndarray is unsized.
-    #[must_use]
-    pub fn is_unsized(&self, unifier: &Unifier) -> bool {
-        extract_ndims(unifier, self.ndims) == 0
-    }
-
-    /// Initialize an ndarray's `data` by allocating a buffer on the stack.
-    /// The allocated data buffer is considered to be *owned* by the ndarray.
-    ///
-    /// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
-    ///
-    /// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
-    pub fn create_data<G: CodeGenerator + ?Sized>(
-        &self,
-        tyctx: TypeContext<'ctx>,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) {
-        let byte_model = IntModel(Byte);
-
-        let data = byte_model.array_alloca(tyctx, ctx, self.get_ndims(tyctx, ctx).value, "data");
-        self.value.set(ctx, |f| f.data, data);
-
-        self.update_strides_by_shape(tyctx, ctx);
-    }
-
-    /// Get the `np.size()` of this ndarray.
-    pub fn size(
-        &self,
-        tyctx: TypeContext<'ctx>,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, SizeT> {
-        call_nac3_ndarray_size(tyctx, ctx, self.value)
-    }
-
-    /// Get the `ndarray.nbytes` of this ndarray.
-    pub fn nbytes(
-        &self,
-        tyctx: TypeContext<'ctx>,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, SizeT> {
-        call_nac3_ndarray_nbytes(tyctx, ctx, self.value)
-    }
-
-    /// Get the `len()` of this ndarray.
-    pub fn len(
-        &self,
-        tyctx: TypeContext<'ctx>,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, SizeT> {
-        call_nac3_ndarray_len(tyctx, ctx, self.value)
-    }
-
-    /// Check if this ndarray is C-contiguous.
-    ///
-    /// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
-    pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
-        &self,
-        tyctx: TypeContext<'ctx>,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, Bool> {
-        call_nac3_ndarray_is_c_contiguous(tyctx, ctx, self.value)
-    }
-
-    /// Get the pointer to the n-th (0-based) element.
-    ///
-    /// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
-    pub fn get_nth_pelement<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        nth: Int<'ctx, SizeT>,
-        name: &str,
-    ) -> PointerValue<'ctx> {
-        let tyctx = generator.type_context(ctx.ctx);
-        let elem_ty = ctx.get_llvm_type(generator, self.dtype);
-
-        let p = call_nac3_ndarray_get_nth_pelement(tyctx, ctx, self.value, nth);
-        ctx.builder
-            .build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), name)
-            .unwrap()
-    }
-
-    /// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
-    ///
-    /// Please refer to the IRRT implementation to see its purpose.
-    pub fn update_strides_by_shape(
-        &self,
-        tyctx: TypeContext<'ctx>,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) {
-        call_nac3_ndarray_set_strides_by_shape(tyctx, ctx, self.value);
-    }
-
-    /// Copy data from another ndarray.
-    ///
-    /// Panics if the `dtype`s of ndarrays are different.
-    pub fn copy_data_from(
-        &self,
-        tyctx: TypeContext<'ctx>,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        src: NDArrayObject<'ctx>,
-    ) {
-        assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
-        call_nac3_ndarray_copy_data(tyctx, ctx, src.value, self.value);
-    }
-}

nac3core/src/codegen/structure/ndarray/broadcast.rs

@@ -0,0 +1,134 @@
+use itertools::Itertools;
+
+use crate::{
+    codegen::{
+        irrt::{call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to},
+        model::*,
+        CodeGenContext, CodeGenerator,
+    },
+    toplevel::numpy::get_broadcast_all_ndims,
+};
+
+use super::NDArrayObject;
+
+/// Fields of [`ShapeEntry`]
+pub struct ShapeEntryFields<'ctx, F: FieldTraversal<'ctx>> {
+    pub ndims: F::Out<IntModel<SizeT>>,
+    pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
+}
+
+/// An IRRT structure used in broadcasting.
+#[derive(Debug, Clone, Copy, Default)]
+pub struct ShapeEntry;
+
+impl<'ctx> StructKind<'ctx> for ShapeEntry {
+    type Fields<F: FieldTraversal<'ctx>> = ShapeEntryFields<'ctx, F>;
+
+    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
+        Self::Fields { ndims: traversal.add_auto("ndims"), shape: traversal.add_auto("shape") }
+    }
+}
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// Create a broadcast view on this ndarray with a target shape.
+    ///
+    /// * `target_ndims` - The ndims type after broadcasting to the given shape.
+    /// The caller has to figure this out for this function.
+    /// * `target_shape` - An array pointer pointing to the target shape.
+    #[must_use]
+    pub fn broadcast_to<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        target_ndims: u64,
+        target_shape: Ptr<'ctx, IntModel<SizeT>>,
+    ) -> Self {
+        let broadcast_ndarray = NDArrayObject::alloca_uninitialized(
+            generator,
+            ctx,
+            self.dtype,
+            target_ndims,
+            "broadcast_ndarray_to_dst",
+        );
+        broadcast_ndarray.copy_shape_from_array(generator, ctx, target_shape);
+
+        call_nac3_ndarray_broadcast_to(generator, ctx, self.value, broadcast_ndarray.value);
+        broadcast_ndarray
+    }
+}
+/// A result produced by [`broadcast_all_ndarrays`]
+#[derive(Debug, Clone)]
+pub struct BroadcastAllResult<'ctx> {
+    /// The statically known `ndims` of the broadcast result.
+    pub ndims: u64,
+    /// The broadcasting shape.
+    pub shape: Ptr<'ctx, IntModel<SizeT>>,
+    /// Broadcasted views on the inputs.
+    ///
+    /// All of them will have `shape` [`BroadcastAllResult::shape`] and
+    /// `ndims` [`BroadcastAllResult::ndims`]. The length of the vector
+    /// is the same as the input.
+    pub ndarrays: Vec<NDArrayObject<'ctx>>,
+}
+
+impl<'ctx> NDArrayObject<'ctx> {
+    // TODO: DOCUMENT: Behaves like `np.broadcast()`, except returns results differently.
+    pub fn broadcast_all<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ndarrays: &[Self],
+    ) -> BroadcastAllResult<'ctx> {
+        assert!(!ndarrays.is_empty());
+
+        let sizet_model = IntModel(SizeT);
+        let shape_model = StructModel(ShapeEntry);
+
+        let broadcast_ndims = get_broadcast_all_ndims(ndarrays.iter().map(|ndarray| ndarray.ndims));
+
+        // Prepare input shape entries
+        let num_shape_entries =
+            sizet_model.constant(generator, ctx.ctx, u64::try_from(ndarrays.len()).unwrap());
+        let shape_entries =
+            shape_model.array_alloca(generator, ctx, num_shape_entries.value, "shape_entries");
+        for (i, ndarray) in ndarrays.iter().enumerate() {
+            let i = sizet_model.constant(generator, ctx.ctx, i as u64).value;
+
+            let shape_entry = shape_entries.offset(generator, ctx, i, "shape_entry");
+
+            let this_ndims = ndarray.value.get(generator, ctx, |f| f.ndims, "this_ndims");
+            shape_entry.set(ctx, |f| f.ndims, this_ndims);
+
+            let this_shape = ndarray.value.get(generator, ctx, |f| f.shape, "this_shape");
+            shape_entry.set(ctx, |f| f.shape, this_shape);
+        }
+
+        // Prepare destination
+        let broadcast_ndims_llvm = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
+        let broadcast_shape =
+            sizet_model.array_alloca(generator, ctx, broadcast_ndims_llvm.value, "dst_shape");
+
+        // Compute the target broadcast shape `dst_shape` for all ndarrays.
+        call_nac3_ndarray_broadcast_shapes(
+            generator,
+            ctx,
+            num_shape_entries,
+            shape_entries,
+            broadcast_ndims_llvm,
+            broadcast_shape,
+        );
+
+        // Now that we know about the broadcasting shape, broadcast all the inputs.
+
+        // Broadcast all the inputs to shape `dst_shape`.
+        let broadcast_ndarrays: Vec<_> = ndarrays
+            .iter()
+            .map(|ndarray| ndarray.broadcast_to(generator, ctx, broadcast_ndims, broadcast_shape))
+            .collect_vec();
+
+        BroadcastAllResult {
+            ndims: broadcast_ndims,
+            shape: broadcast_shape,
+            ndarrays: broadcast_ndarrays,
+        }
+    }
+}

nac3core/src/codegen/structure/ndarray/functions.rs

@@ -0,0 +1,562 @@
+use inkwell::{
+    values::{BasicValue, FloatValue, IntValue},
+    FloatPredicate, IntPredicate,
+};
+use itertools::Itertools;
+
+use crate::{
+    codegen::{
+        llvm_intrinsics,
+        model::{
+            util::{gen_for_model_auto, gen_if_model},
+            *,
+        },
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::Type,
+};
+
+use super::{scalar::ScalarObject, NDArrayObject};
+
+/// Convenience function to crash the program when types of arguments are not supported.
+/// Used to be debugged with a stacktrace.
+fn unsupported_type<I>(ctx: &CodeGenContext<'_, '_>, tys: I) -> !
+where
+    I: IntoIterator<Item = Type>,
+{
+    unreachable!(
+        "unsupported types found '{}'",
+        tys.into_iter().map(|ty| format!("'{}'", ctx.unifier.stringify(ty))).join(", "),
+    )
+}
+
+#[derive(Debug, Clone, Copy)]
+pub enum FloorOrCeil {
+    Floor,
+    Ceil,
+}
+
+#[derive(Debug, Clone, Copy)]
+pub enum MinOrMax {
+    Min,
+    Max,
+}
+
+fn signed_ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
+    vec![ctx.primitives.int32, ctx.primitives.int64]
+}
+
+fn unsigned_ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
+    vec![ctx.primitives.uint32, ctx.primitives.uint64]
+}
+
+fn ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
+    vec![ctx.primitives.int32, ctx.primitives.int64, ctx.primitives.uint32, ctx.primitives.uint64]
+}
+
+fn int_like(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
+    vec![
+        ctx.primitives.bool,
+        ctx.primitives.int32,
+        ctx.primitives.int64,
+        ctx.primitives.uint32,
+        ctx.primitives.uint64,
+    ]
+}
+
+fn cast_to_int_conversion<'ctx, 'a, G, HandleFloatFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, 'a>,
+    scalar: ScalarObject<'ctx>,
+    ret_int_dtype: Type,
+    handle_float: HandleFloatFn,
+) -> ScalarObject<'ctx>
+where
+    G: CodeGenerator + ?Sized,
+    HandleFloatFn:
+        FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, FloatValue<'ctx>) -> IntValue<'ctx>,
+{
+    let ret_int_dtype_llvm = ctx.get_llvm_type(generator, ret_int_dtype).into_int_type();
+
+    let result = if ctx.unifier.unioned(scalar.dtype, ctx.primitives.float) {
+        // Special handling for floats
+        let n = scalar.value.into_float_value();
+        handle_float(generator, ctx, n)
+    } else if ctx.unifier.unioned_any(scalar.dtype, int_like(ctx)) {
+        let n = scalar.value.into_int_value();
+
+        if n.get_type().get_bit_width() <= ret_int_dtype_llvm.get_bit_width() {
+            ctx.builder.build_int_z_extend(n, ret_int_dtype_llvm, "zext").unwrap()
+        } else {
+            ctx.builder.build_int_truncate(n, ret_int_dtype_llvm, "trunc").unwrap()
+        }
+    } else {
+        unsupported_type(ctx, [scalar.dtype]);
+    };
+
+    assert_eq!(ret_int_dtype_llvm.get_bit_width(), result.get_type().get_bit_width()); // Sanity check
+    ScalarObject { value: result.into(), dtype: ret_int_dtype }
+}
+
+impl<'ctx> ScalarObject<'ctx> {
+    /// Convenience function. Assume this scalar has typechecker type float64, get its underlying LLVM value.
+    ///
+    /// Panic if the type is wrong.
+    pub fn into_float64(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> FloatValue<'ctx> {
+        if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
+            self.value.into_float_value() // self.value must be a FloatValue
+        } else {
+            panic!("not a float type")
+        }
+    }
+
+    /// Convenience function. Assume this scalar has typechecker type int32, get its underlying LLVM value.
+    ///
+    /// Panic if the type is wrong.
+    pub fn into_int32(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
+        if ctx.unifier.unioned(self.dtype, ctx.primitives.int32) {
+            let value = self.value.into_int_value();
+            debug_assert_eq!(value.get_type().get_bit_width(), 32); // Sanity check
+            value
+        } else {
+            panic!("not a float type")
+        }
+    }
+
+    /// Compare two scalars. Only int-to-int and float-to-float comparisons are allowed.
+    /// Panic otherwise.
+    pub fn compare<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        lhs: ScalarObject<'ctx>,
+        rhs: ScalarObject<'ctx>,
+        int_predicate: IntPredicate,
+        float_predicate: FloatPredicate,
+        name: &str,
+    ) -> Int<'ctx, Bool> {
+        if !ctx.unifier.unioned(lhs.dtype, rhs.dtype) {
+            unsupported_type(ctx, [lhs.dtype, rhs.dtype]);
+        }
+
+        let bool_model = IntModel(Bool);
+
+        let common_ty = lhs.dtype;
+        let result = if ctx.unifier.unioned(common_ty, ctx.primitives.float) {
+            let lhs = lhs.value.into_float_value();
+            let rhs = rhs.value.into_float_value();
+            ctx.builder.build_float_compare(float_predicate, lhs, rhs, name).unwrap()
+        } else if ctx.unifier.unioned_any(common_ty, int_like(ctx)) {
+            let lhs = lhs.value.into_int_value();
+            let rhs = rhs.value.into_int_value();
+            ctx.builder.build_int_compare(int_predicate, lhs, rhs, name).unwrap()
+        } else {
+            unsupported_type(ctx, [lhs.dtype, rhs.dtype]);
+        };
+
+        bool_model.check_value(generator, ctx.ctx, result).unwrap()
+    }
+
+    /// Invoke NAC3's builtin `int32()`.
+    #[must_use]
+    pub fn cast_to_int32<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Self {
+        cast_to_int_conversion(
+            generator,
+            ctx,
+            *self,
+            ctx.primitives.int32,
+            |_generator, ctx, input| {
+                let n =
+                    ctx.builder.build_float_to_signed_int(input, ctx.ctx.i64_type(), "").unwrap();
+                ctx.builder.build_int_truncate(n, ctx.ctx.i32_type(), "conv").unwrap()
+            },
+        )
+    }
+
+    /// Invoke NAC3's builtin `int64()`.
+    #[must_use]
+    pub fn cast_to_int64<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Self {
+        cast_to_int_conversion(
+            generator,
+            ctx,
+            *self,
+            ctx.primitives.int64,
+            |_generator, ctx, input| {
+                ctx.builder.build_float_to_signed_int(input, ctx.ctx.i64_type(), "").unwrap()
+            },
+        )
+    }
+
+    /// Invoke NAC3's builtin `uint32()`.
+    #[must_use]
+    pub fn cast_to_uint32<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Self {
+        cast_to_int_conversion(
+            generator,
+            ctx,
+            *self,
+            ctx.primitives.uint32,
+            |_generator, ctx, n| {
+                let n_gez = ctx
+                    .builder
+                    .build_float_compare(FloatPredicate::OGE, n, n.get_type().const_zero(), "")
+                    .unwrap();
+
+                let to_int32 =
+                    ctx.builder.build_float_to_signed_int(n, ctx.ctx.i32_type(), "").unwrap();
+                let to_uint64 =
+                    ctx.builder.build_float_to_unsigned_int(n, ctx.ctx.i64_type(), "").unwrap();
+
+                ctx.builder
+                    .build_select(
+                        n_gez,
+                        ctx.builder.build_int_truncate(to_uint64, ctx.ctx.i32_type(), "").unwrap(),
+                        to_int32,
+                        "conv",
+                    )
+                    .unwrap()
+                    .into_int_value()
+            },
+        )
+    }
+
+    /// Invoke NAC3's builtin `uint64()`.
+    #[must_use]
+    pub fn cast_to_uint64<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Self {
+        cast_to_int_conversion(
+            generator,
+            ctx,
+            *self,
+            ctx.primitives.uint64,
+            |_generator, ctx, n| {
+                let val_gez = ctx
+                    .builder
+                    .build_float_compare(FloatPredicate::OGE, n, n.get_type().const_zero(), "")
+                    .unwrap();
+
+                let to_int64 =
+                    ctx.builder.build_float_to_signed_int(n, ctx.ctx.i64_type(), "").unwrap();
+                let to_uint64 =
+                    ctx.builder.build_float_to_unsigned_int(n, ctx.ctx.i64_type(), "").unwrap();
+                ctx.builder
+                    .build_select(val_gez, to_uint64, to_int64, "conv")
+                    .unwrap()
+                    .into_int_value()
+            },
+        )
+    }
+
+    /// Invoke NAC3's builtin `bool()`.
+    #[must_use]
+    pub fn cast_to_bool(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
+        // TODO: Why is the original code being so lax about i1 and i8 for the returned int type?
+        let result = if ctx.unifier.unioned(self.dtype, ctx.primitives.bool) {
+            self.value.into_int_value()
+        } else if ctx.unifier.unioned_any(self.dtype, ints(ctx)) {
+            let n = self.value.into_int_value();
+            ctx.builder
+                .build_int_compare(inkwell::IntPredicate::NE, n, n.get_type().const_zero(), "bool")
+                .unwrap()
+        } else if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
+            let n = self.value.into_float_value();
+            ctx.builder
+                .build_float_compare(FloatPredicate::UNE, n, n.get_type().const_zero(), "bool")
+                .unwrap()
+        } else {
+            unsupported_type(ctx, [self.dtype])
+        };
+
+        ScalarObject { dtype: ctx.primitives.bool, value: result.as_basic_value_enum() }
+    }
+
+    /// Invoke NAC3's builtin `float()`.
+    #[must_use]
+    pub fn cast_to_float(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
+        let llvm_f64 = ctx.ctx.f64_type();
+
+        let result: FloatValue<'_> = if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
+            self.value.into_float_value()
+        } else if ctx
+            .unifier
+            .unioned_any(self.dtype, [signed_ints(ctx).as_slice(), &[ctx.primitives.bool]].concat())
+        {
+            let n = self.value.into_int_value();
+            ctx.builder.build_signed_int_to_float(n, llvm_f64, "sitofp").unwrap()
+        } else if ctx.unifier.unioned_any(self.dtype, unsigned_ints(ctx)) {
+            let n = self.value.into_int_value();
+            ctx.builder.build_unsigned_int_to_float(n, llvm_f64, "uitofp").unwrap()
+        } else {
+            unsupported_type(ctx, [self.dtype]);
+        };
+
+        ScalarObject { value: result.as_basic_value_enum(), dtype: ctx.primitives.float }
+    }
+
+    /// Invoke NAC3's builtin `round()`.
+    #[must_use]
+    pub fn round<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ret_int_dtype: Type,
+    ) -> Self {
+        let ret_int_dtype_llvm = ctx.get_llvm_type(generator, ret_int_dtype).into_int_type();
+
+        let result = if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
+            let n = self.value.into_float_value();
+            let n = llvm_intrinsics::call_float_round(ctx, n, None);
+            ctx.builder.build_float_to_signed_int(n, ret_int_dtype_llvm, "round").unwrap()
+        } else {
+            unsupported_type(ctx, [self.dtype, ret_int_dtype])
+        };
+        ScalarObject { dtype: ret_int_dtype, value: result.as_basic_value_enum() }
+    }
+
+    /// Invoke NAC3's builtin `np_round()`.
+    ///
+    /// NOTE: `np.round()` has different behaviors than `round()` in terms of their result
+    /// on "tie" cases and return type.
+    #[must_use]
+    pub fn np_round(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
+        let result = if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
+            let n = self.value.into_float_value();
+            llvm_intrinsics::call_float_rint(ctx, n, None)
+        } else {
+            unsupported_type(ctx, [self.dtype])
+        };
+        ScalarObject { dtype: ctx.primitives.float, value: result.as_basic_value_enum() }
+    }
+
+    /// Invoke NAC3's builtin `min()` or `max()`.
+    pub fn min_or_max(
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        kind: MinOrMax,
+        a: Self,
+        b: Self,
+    ) -> Self {
+        if !ctx.unifier.unioned(a.dtype, b.dtype) {
+            unsupported_type(ctx, [a.dtype, b.dtype])
+        }
+
+        let common_dtype = a.dtype;
+
+        if ctx.unifier.unioned(common_dtype, ctx.primitives.float) {
+            let function = match kind {
+                MinOrMax::Min => llvm_intrinsics::call_float_minnum,
+                MinOrMax::Max => llvm_intrinsics::call_float_maxnum,
+            };
+            let result =
+                function(ctx, a.value.into_float_value(), b.value.into_float_value(), None);
+            ScalarObject { value: result.as_basic_value_enum(), dtype: ctx.primitives.float }
+        } else if ctx.unifier.unioned_any(
+            common_dtype,
+            [unsigned_ints(ctx).as_slice(), &[ctx.primitives.bool]].concat(),
+        ) {
+            // Treating bool has an unsigned int since that is convenient
+            let function = match kind {
+                MinOrMax::Min => llvm_intrinsics::call_int_umin,
+                MinOrMax::Max => llvm_intrinsics::call_int_umax,
+            };
+            let result = function(ctx, a.value.into_int_value(), b.value.into_int_value(), None);
+            ScalarObject { value: result.as_basic_value_enum(), dtype: common_dtype }
+        } else {
+            unsupported_type(ctx, [common_dtype])
+        }
+    }
+
+    /// Invoke NAC3's builtin `floor()` or `ceil()`.
+    ///
+    /// * `ret_int_dtype` - The type of int to return.
+    ///
+    /// Takes in a float/int and returns an int of type `ret_int_dtype`
+    #[must_use]
+    pub fn floor_or_ceil<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        kind: FloorOrCeil,
+        ret_int_dtype: Type,
+    ) -> Self {
+        let ret_int_dtype_llvm = ctx.get_llvm_type(generator, ret_int_dtype).into_int_type();
+
+        if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
+            let function = match kind {
+                FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
+                FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
+            };
+            let n = self.value.into_float_value();
+            let n = function(ctx, n, None);
+
+            let n = ctx.builder.build_float_to_signed_int(n, ret_int_dtype_llvm, "").unwrap();
+            ScalarObject { dtype: ret_int_dtype, value: n.as_basic_value_enum() }
+        } else {
+            unsupported_type(ctx, [self.dtype])
+        }
+    }
+
+    /// Invoke NAC3's builtin `np_floor()`/ `np_ceil()`.
+    ///
+    /// Takes in a float/int and returns a float64 result.
+    #[must_use]
+    pub fn np_floor_or_ceil(&self, ctx: &mut CodeGenContext<'ctx, '_>, kind: FloorOrCeil) -> Self {
+        if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
+            let function = match kind {
+                FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
+                FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
+            };
+            let n = self.value.into_float_value();
+            let n = function(ctx, n, None);
+            ScalarObject { dtype: ctx.primitives.float, value: n.as_basic_value_enum() }
+        } else {
+            unsupported_type(ctx, [self.dtype])
+        }
+    }
+
+    /// Invoke NAC3's builtin `abs()`.
+    pub fn abs(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
+        if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
+            let n = self.value.into_float_value();
+            let n = llvm_intrinsics::call_float_fabs(ctx, n, Some("abs"));
+            ScalarObject { value: n.into(), dtype: ctx.primitives.float }
+        } else if ctx.unifier.unioned_any(self.dtype, ints(ctx)) {
+            let n = self.value.into_int_value();
+
+            let is_poisoned = ctx.ctx.bool_type().const_zero(); // is_poisoned = false
+            let n = llvm_intrinsics::call_int_abs(ctx, n, is_poisoned, Some("abs"));
+
+            ScalarObject { value: n.into(), dtype: self.dtype }
+        } else {
+            unsupported_type(ctx, [self.dtype])
+        }
+    }
+}
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// Helper function to implement NAC3's builtin `np_min()`, `np_max()`, `np_argmin()`, and `np_argmax()`.
+    ///
+    /// Generate LLVM IR to find the extremum and index of the **first** extremum value.
+    ///
+    /// Care has also been taken to make the error messages match that of NumPy.
+    fn min_max_argmin_argmax_helper<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        kind: MinOrMax,
+        on_empty_err_msg: &str,
+    ) -> (ScalarObject<'ctx>, Int<'ctx, SizeT>) {
+        let sizet_model = IntModel(SizeT);
+        let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
+
+        // If the ndarray is empty, throw an error.
+        let is_empty = self.is_empty(generator, ctx);
+        ctx.make_assert(
+            generator,
+            is_empty.value,
+            "0:ValueError",
+            on_empty_err_msg,
+            [None, None, None],
+            ctx.current_loc,
+        );
+
+        // Setup and initialize the extremum to be the first element in the ndarray
+        let pextremum_index = sizet_model.alloca(generator, ctx, "extremum_index");
+        let pextremum = ctx.builder.build_alloca(dtype_llvm, "extremum").unwrap();
+
+        let zero = sizet_model.const_0(generator, ctx.ctx);
+        pextremum_index.store(ctx, zero);
+
+        let first_scalar = self.get_nth(generator, ctx, zero);
+        ctx.builder.build_store(pextremum, first_scalar.value).unwrap();
+
+        // Find extremum
+        let start = sizet_model.const_1(generator, ctx.ctx); // Start on 1
+        let stop = self.size(generator, ctx);
+        let step = sizet_model.const_1(generator, ctx.ctx);
+        gen_for_model_auto(generator, ctx, start, stop, step, |generator, ctx, _hooks, i| {
+            // Worth reading on "Notes" in <https://numpy.org/doc/stable/reference/generated/numpy.min.html#numpy.min>
+            // on how `NaN` values have to be handled.
+
+            let scalar = self.get_nth(generator, ctx, i);
+
+            let old_extremum = ctx.builder.build_load(pextremum, "current_extremum").unwrap();
+            let old_extremum = ScalarObject { dtype: self.dtype, value: old_extremum };
+
+            let new_extremum = ScalarObject::min_or_max(ctx, kind, old_extremum, scalar);
+
+            // Check if new_extremum is more extreme than old_extremum.
+            let update_index = ScalarObject::compare(
+                generator,
+                ctx,
+                new_extremum,
+                old_extremum,
+                IntPredicate::NE,
+                FloatPredicate::ONE,
+                "",
+            );
+
+            gen_if_model(generator, ctx, update_index, |_generator, ctx| {
+                pextremum_index.store(ctx, i);
+                Ok(())
+            })
+            .unwrap();
+            Ok(())
+        })
+        .unwrap();
+
+        // Finally return the extremum and extremum index.
+        let extremum_index = pextremum_index.load(generator, ctx, "extremum_index");
+
+        let extremum = ctx.builder.build_load(pextremum, "extremum_value").unwrap();
+        let extremum = ScalarObject { dtype: self.dtype, value: extremum };
+
+        (extremum, extremum_index)
+    }
+
+    /// Invoke NAC3's builtin `np_min()` or `np_max()`.
+    pub fn min_or_max<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        kind: MinOrMax,
+    ) -> ScalarObject<'ctx> {
+        let on_empty_err_msg = format!(
+            "zero-size array to reduction operation {} which has no identity",
+            match kind {
+                MinOrMax::Min => "minimum",
+                MinOrMax::Max => "maximum",
+            }
+        );
+        self.min_max_argmin_argmax_helper(generator, ctx, kind, &on_empty_err_msg).0
+    }
+
+    /// Invoke NAC3's builtin `np_argmin()` or `np_argmax()`.
+    pub fn argmin_or_argmax<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        kind: MinOrMax,
+    ) -> Int<'ctx, SizeT> {
+        let on_empty_err_msg = format!(
+            "attempt to get {} of an empty sequence",
+            match kind {
+                MinOrMax::Min => "argmin",
+                MinOrMax::Max => "argmax",
+            }
+        );
+        self.min_max_argmin_argmax_helper(generator, ctx, kind, &on_empty_err_msg).1
+    }
+}

nac3core/src/codegen/structure/ndarray/indexing.rs

@@ -0,0 +1,353 @@
+use crate::codegen::{irrt::call_nac3_ndarray_index, model::*, CodeGenContext, CodeGenerator};
+
+use super::{scalar::ScalarOrNDArray, NDArrayObject};
+
+pub type NDIndexType = Byte;
+
+/// Fields of [`NDIndex`]
+#[derive(Debug, Clone, Copy)]
+pub struct NDIndexFields<'ctx, F: FieldTraversal<'ctx>> {
+    pub type_: F::Out<IntModel<NDIndexType>>, // Defined to be uint8_t in IRRT
+    pub data: F::Out<PtrModel<IntModel<Byte>>>,
+}
+
+/// An IRRT representation fo an ndarray subscript index.
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct NDIndex;
+
+impl<'ctx> StructKind<'ctx> for NDIndex {
+    type Fields<F: FieldTraversal<'ctx>> = NDIndexFields<'ctx, F>;
+
+    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
+        Self::Fields { type_: traversal.add_auto("type"), data: traversal.add_auto("data") }
+    }
+}
+
+/// Fields of [`UserSlice`]
+#[derive(Debug, Clone)]
+pub struct UserSliceFields<'ctx, F: FieldTraversal<'ctx>> {
+    pub start_defined: F::Out<IntModel<Bool>>,
+    pub start: F::Out<IntModel<Int32>>,
+    pub stop_defined: F::Out<IntModel<Bool>>,
+    pub stop: F::Out<IntModel<Int32>>,
+    pub step_defined: F::Out<IntModel<Bool>>,
+    pub step: F::Out<IntModel<Int32>>,
+}
+
+/// An IRRT representation of a user slice.
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct UserSlice;
+
+impl<'ctx> StructKind<'ctx> for UserSlice {
+    type Fields<F: FieldTraversal<'ctx>> = UserSliceFields<'ctx, F>;
+
+    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
+        Self::Fields {
+            start_defined: traversal.add_auto("start_defined"),
+            start: traversal.add_auto("start"),
+            stop_defined: traversal.add_auto("stop_defined"),
+            stop: traversal.add_auto("stop"),
+            step_defined: traversal.add_auto("step_defined"),
+            step: traversal.add_auto("step"),
+        }
+    }
+}
+
+/// A convenience structure to prepare a [`UserSlice`].
+#[derive(Debug, Clone)]
+pub struct RustUserSlice<'ctx> {
+    pub start: Option<Int<'ctx, Int32>>,
+    pub stop: Option<Int<'ctx, Int32>>,
+    pub step: Option<Int<'ctx, Int32>>,
+}
+
+impl<'ctx> RustUserSlice<'ctx> {
+    /// Write the contents to an LLVM [`UserSlice`].
+    pub fn write_to_user_slice<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        dst_slice_ptr: Ptr<'ctx, StructModel<UserSlice>>,
+    ) {
+        let bool_model = IntModel(Bool);
+
+        let false_ = bool_model.constant(generator, ctx.ctx, 0);
+        let true_ = bool_model.constant(generator, ctx.ctx, 1);
+
+        // TODO: Code duplication. Probably okay...?
+
+        match self.start {
+            Some(start) => {
+                dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, true_);
+                dst_slice_ptr.gep(ctx, |f| f.start).store(ctx, start);
+            }
+            None => dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, false_),
+        }
+
+        match self.stop {
+            Some(stop) => {
+                dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, true_);
+                dst_slice_ptr.gep(ctx, |f| f.stop).store(ctx, stop);
+            }
+            None => dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, false_),
+        }
+
+        match self.step {
+            Some(step) => {
+                dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, true_);
+                dst_slice_ptr.gep(ctx, |f| f.step).store(ctx, step);
+            }
+            None => dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, false_),
+        }
+    }
+}
+
+// A convenience enum variant to store the content and type of an NDIndex in high level.
+#[derive(Debug, Clone)]
+pub enum RustNDIndex<'ctx> {
+    SingleElement(Int<'ctx, Int32>), // TODO: To be SizeT
+    Slice(RustUserSlice<'ctx>),
+    NewAxis,
+    Ellipsis,
+}
+
+impl<'ctx> RustNDIndex<'ctx> {
+    /// Get the value to set `NDIndex::type` for this variant.
+    fn get_type_id(&self) -> u64 {
+        // Defined in IRRT, must be in sync
+        match self {
+            RustNDIndex::SingleElement(_) => 0,
+            RustNDIndex::Slice(_) => 1,
+            RustNDIndex::NewAxis => 2,
+            RustNDIndex::Ellipsis => 3,
+        }
+    }
+
+    /// Write the contents to an LLVM [`NDIndex`].
+    fn write_to_ndindex<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        dst_ndindex_ptr: Ptr<'ctx, StructModel<NDIndex>>,
+    ) {
+        let ndindex_type_model = IntModel(NDIndexType::default());
+        let i32_model = IntModel(Int32);
+        let user_slice_model = StructModel(UserSlice);
+
+        // Set `dst_ndindex_ptr->type`
+        dst_ndindex_ptr
+            .gep(ctx, |f| f.type_)
+            .store(ctx, ndindex_type_model.constant(generator, ctx.ctx, self.get_type_id()));
+
+        // Set `dst_ndindex_ptr->data`
+        match self {
+            RustNDIndex::SingleElement(in_index) => {
+                let index_ptr = i32_model.alloca(generator, ctx, "index");
+                index_ptr.store(ctx, *in_index);
+
+                dst_ndindex_ptr
+                    .gep(ctx, |f| f.data)
+                    .store(ctx, index_ptr.transmute(generator, ctx, IntModel(Byte), ""));
+            }
+            RustNDIndex::Slice(in_rust_slice) => {
+                let user_slice_ptr = user_slice_model.alloca(generator, ctx, "user_slice");
+                in_rust_slice.write_to_user_slice(generator, ctx, user_slice_ptr);
+
+                dst_ndindex_ptr
+                    .gep(ctx, |f| f.data)
+                    .store(ctx, user_slice_ptr.transmute(generator, ctx, IntModel(Byte), ""));
+            }
+            RustNDIndex::NewAxis => {}
+            RustNDIndex::Ellipsis => {}
+        }
+    }
+
+    /// Allocate an array of `NDIndex`es on the stack and return its stack pointer.
+    pub fn alloca_ndindexes<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        in_ndindexes: &[RustNDIndex<'ctx>],
+    ) -> (Int<'ctx, SizeT>, Ptr<'ctx, StructModel<NDIndex>>) {
+        let sizet_model = IntModel(SizeT);
+        let ndindex_model = StructModel(NDIndex);
+
+        let num_ndindexes = sizet_model.constant(generator, ctx.ctx, in_ndindexes.len() as u64);
+        let ndindexes =
+            ndindex_model.array_alloca(generator, ctx, num_ndindexes.value, "ndindexes");
+        for (i, in_ndindex) in in_ndindexes.iter().enumerate() {
+            let i = sizet_model.constant(generator, ctx.ctx, i as u64);
+            let pndindex = ndindexes.offset(generator, ctx, i.value, "");
+            in_ndindex.write_to_ndindex(generator, ctx, pndindex);
+        }
+
+        (num_ndindexes, ndindexes)
+    }
+}
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// Get the ndims [`Type`] after indexing with a given slice.
+    #[must_use]
+    pub fn deduce_ndims_after_indexing_with(&self, indexes: &[RustNDIndex<'ctx>]) -> u64 {
+        let mut ndims = self.ndims;
+        for index in indexes {
+            match index {
+                RustNDIndex::SingleElement(_) => {
+                    ndims -= 1; // Single elements decrements ndims
+                }
+                RustNDIndex::Slice(_) => {}
+                RustNDIndex::NewAxis => {
+                    ndims += 1; // `np.newaxis` / `none` adds a new axis
+                }
+                RustNDIndex::Ellipsis => {}
+            }
+        }
+        ndims
+    }
+
+    /// Index into the ndarray, and return a newly-allocated view on this ndarray.
+    ///
+    /// This function behaves like NumPy's ndarray indexing, but if the indexes index
+    /// into a single element, an unsized ndarray is returned.
+    #[must_use]
+    pub fn index<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        indexes: &[RustNDIndex<'ctx>],
+        name: &str,
+    ) -> Self {
+        let dst_ndims = self.deduce_ndims_after_indexing_with(indexes);
+        let dst_ndarray =
+            NDArrayObject::alloca_uninitialized(generator, ctx, self.dtype, dst_ndims, name);
+
+        let (num_indexes, indexes) = RustNDIndex::alloca_ndindexes(generator, ctx, indexes);
+        call_nac3_ndarray_index(
+            generator,
+            ctx,
+            num_indexes,
+            indexes,
+            self.value,
+            dst_ndarray.value,
+        );
+
+        dst_ndarray
+    }
+
+    /// Like [`NDArrayObject::index`] but returns a scalar if the indexes index
+    /// into a single element.
+    #[must_use]
+    pub fn index_or_scalar<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        indexes: &[RustNDIndex<'ctx>],
+        name: &str,
+    ) -> ScalarOrNDArray<'ctx> {
+        let sizet_model = IntModel(SizeT);
+        let zero = sizet_model.const_0(generator, ctx.ctx);
+
+        let subndarray = self.index(generator, ctx, indexes, name);
+        if subndarray.is_unsized() {
+            // NOTE: `np.size(self) == 0` here is never possible.
+            ScalarOrNDArray::Scalar(subndarray.get_nth(generator, ctx, zero))
+        } else {
+            ScalarOrNDArray::NDArray(subndarray)
+        }
+    }
+}
+
+pub mod util {
+    use itertools::Itertools;
+    use nac3parser::ast::{Expr, ExprKind};
+
+    use crate::{
+        codegen::{model::*, CodeGenContext, CodeGenerator},
+        typecheck::typedef::Type,
+    };
+
+    use super::{RustNDIndex, RustUserSlice};
+
+    /// Generate LLVM code to transform an ndarray subscript expression to
+    /// its list of [`RustNDIndex`]
+    ///
+    /// i.e.,
+    /// ```python
+    /// my_ndarray[::3, 1, :2:]
+    ///            ^^^^^^^^^^^ Then these into a three `RustNDIndex`es
+    /// ```
+    pub fn gen_ndarray_subscript_ndindexes<'ctx, G: CodeGenerator>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        subscript: &Expr<Option<Type>>,
+    ) -> Result<Vec<RustNDIndex<'ctx>>, String> {
+        // TODO: Support https://numpy.org/doc/stable/user/basics.indexing.html#dimensional-indexing-tools
+        let i32_model = IntModel(Int32);
+
+        // Annoying notes about `slice`
+        //  - `my_array[5]`
+        //    - slice is a `Constant`
+        //  - `my_array[:5]`
+        //    - slice is a `Slice`
+        //  - `my_array[:]`
+        //    - slice is a `Slice`, but lower upper step would all be `Option::None`
+        //  - `my_array[:, :]`
+        //    - slice is now a `Tuple` of two `Slice`-s
+        //
+        // In summary:
+        //  - when there is a comma "," within [], `slice` will be a `Tuple` of the entries.
+        //  - when there is not comma "," within [] (i.e., just a single entry), `slice` will be that entry itself.
+        //
+        // So we first "flatten" out the slice expression
+        let index_exprs = match &subscript.node {
+            ExprKind::Tuple { elts, .. } => elts.iter().collect_vec(),
+            _ => vec![subscript],
+        };
+
+        // Process all index expressions
+        let mut rust_ndindexes: Vec<RustNDIndex> = Vec::with_capacity(index_exprs.len()); // Not using iterators here because `?` is used here.
+        for index_expr in index_exprs {
+            // NOTE: Currently nac3core's slices do not have an object representation,
+            // so the code/implementation looks awkward - we have to do pattern matching on the expression
+            let ndindex =
+                if let ExprKind::Slice { lower: start, upper: stop, step } = &index_expr.node {
+                    // Helper function here to deduce code duplication
+                    type ValueExpr = Option<Box<Expr<Option<Type>>>>;
+                    let mut help = |value_expr: &ValueExpr| -> Result<_, String> {
+                        Ok(match value_expr {
+                            None => None,
+                            Some(value_expr) => {
+                                let value_expr = generator
+                                    .gen_expr(ctx, value_expr)?
+                                    .unwrap()
+                                    .to_basic_value_enum(ctx, generator, ctx.primitives.int32)?;
+
+                                let value_expr =
+                                    i32_model.check_value(generator, ctx.ctx, value_expr).unwrap();
+
+                                Some(value_expr)
+                            }
+                        })
+                    };
+
+                    let start = help(start)?;
+                    let stop = help(stop)?;
+                    let step = help(step)?;
+
+                    RustNDIndex::Slice(RustUserSlice { start, stop, step })
+                } else {
+                    // 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 = generator.gen_expr(ctx, index_expr)?.unwrap().to_basic_value_enum(
+                        ctx,
+                        generator,
+                        ctx.primitives.int32,
+                    )?;
+                    let index = i32_model.check_value(generator, ctx.ctx, index).unwrap();
+
+                    RustNDIndex::SingleElement(index)
+                };
+            rust_ndindexes.push(ndindex);
+        }
+        Ok(rust_ndindexes)
+    }
+}

nac3core/src/codegen/structure/ndarray/mapping.rs

@@ -0,0 +1,158 @@
+use inkwell::values::BasicValueEnum;
+use itertools::Itertools;
+use util::gen_for_model_auto;
+
+use crate::{
+    codegen::{
+        model::*,
+        structure::ndarray::{scalar::ScalarObject, NDArrayObject},
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::Type,
+};
+
+use super::scalar::ScalarOrNDArray;
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// TODO: Document me. Has complex behavior.
+    /// and explain why `ret_dtype` has to be specified beforehand.
+    pub fn broadcasting_starmap<'a, G, MappingFn>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        ndarrays: &[Self],
+        ret_dtype: Type,
+        mapping: MappingFn,
+    ) -> Result<Self, String>
+    where
+        G: CodeGenerator + ?Sized,
+        MappingFn: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            Int<'ctx, SizeT>,
+            &[ScalarObject<'ctx>],
+        ) -> Result<BasicValueEnum<'ctx>, String>,
+    {
+        let sizet_model = IntModel(SizeT);
+
+        // Broadcast inputs
+        let broadcast_result = NDArrayObject::broadcast_all(generator, ctx, ndarrays);
+
+        // Allocate the resulting ndarray
+        let mapped_ndarray = NDArrayObject::alloca_uninitialized(
+            generator,
+            ctx,
+            ret_dtype,
+            broadcast_result.ndims,
+            "mapped_ndarray",
+        );
+        mapped_ndarray.copy_shape_from_array(generator, ctx, broadcast_result.shape);
+        mapped_ndarray.create_data(generator, ctx);
+
+        // Map element-wise and store results into `mapped_ndarray`.
+        let start = sizet_model.const_0(generator, ctx.ctx);
+        let stop = broadcast_result.ndarrays[0].size(generator, ctx); // They all should have the same `np.size`.
+        let step = sizet_model.const_1(generator, ctx.ctx);
+        gen_for_model_auto(generator, ctx, start, stop, step, move |generator, ctx, _hooks, i| {
+            let elements =
+                ndarrays.iter().map(|ndarray| ndarray.get_nth(generator, ctx, i)).collect_vec();
+
+            let ret = mapping(generator, ctx, i, &elements)?;
+
+            let pret = mapped_ndarray.get_nth_pointer(generator, ctx, i, "pret");
+            ctx.builder.build_store(pret, ret).unwrap();
+            Ok(())
+        })?;
+
+        Ok(mapped_ndarray)
+    }
+
+    pub fn map<'a, G, Mapping>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        ret_dtype: Type,
+        mapping: Mapping,
+    ) -> Result<Self, String>
+    where
+        G: CodeGenerator + ?Sized,
+        Mapping: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            Int<'ctx, SizeT>,
+            ScalarObject<'ctx>,
+        ) -> Result<BasicValueEnum<'ctx>, String>,
+    {
+        NDArrayObject::broadcasting_starmap(
+            generator,
+            ctx,
+            &[*self],
+            ret_dtype,
+            |generator, ctx, i, scalars| mapping(generator, ctx, i, scalars[0]),
+        )
+    }
+}
+
+impl<'ctx> ScalarOrNDArray<'ctx> {
+    /// TODO: Document me. Has complex behavior.
+    /// and explain why `ret_dtype` has to be specified beforehand.
+    pub fn broadcasting_starmap<'a, G, MappingFn>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        inputs: &[Self],
+        ret_dtype: Type,
+        mapping: MappingFn,
+    ) -> Result<Self, String>
+    where
+        G: CodeGenerator + ?Sized,
+        MappingFn: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            Int<'ctx, SizeT>,
+            &[ScalarObject<'ctx>],
+        ) -> Result<BasicValueEnum<'ctx>, String>,
+    {
+        let sizet_model = IntModel(SizeT);
+
+        // Check if all inputs are ScalarObjects
+        let all_scalars: Option<Vec<_>> =
+            inputs.iter().map(ScalarObject::try_from).try_collect().ok();
+
+        if let Some(scalars) = all_scalars {
+            let i = sizet_model.const_0(generator, ctx.ctx); // Pass 0 as the index
+            let scalar =
+                ScalarObject { value: mapping(generator, ctx, i, &scalars)?, dtype: ret_dtype };
+            Ok(ScalarOrNDArray::Scalar(scalar))
+        } else {
+            // Promote all input to ndarrays and map through them.
+            let inputs = inputs.iter().map(|input| input.as_ndarray(generator, ctx)).collect_vec();
+            let ndarray =
+                NDArrayObject::broadcasting_starmap(generator, ctx, &inputs, ret_dtype, mapping)?;
+            Ok(ScalarOrNDArray::NDArray(ndarray))
+        }
+    }
+
+    pub fn map<'a, G, Mapping>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        ret_dtype: Type,
+        mapping: Mapping,
+    ) -> Result<Self, String>
+    where
+        G: CodeGenerator + ?Sized,
+        Mapping: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            Int<'ctx, SizeT>,
+            ScalarObject<'ctx>,
+        ) -> Result<BasicValueEnum<'ctx>, String>,
+    {
+        ScalarOrNDArray::broadcasting_starmap(
+            generator,
+            ctx,
+            &[*self],
+            ret_dtype,
+            |generator, ctx, i, scalars| mapping(generator, ctx, i, scalars[0]),
+        )
+    }
+}

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

@@ -0,0 +1,543 @@
+pub mod broadcast;
+pub mod functions;
+pub mod indexing;
+pub mod mapping;
+pub mod product;
+pub mod scalar;
+pub mod shape_util;
+
+use crate::{
+    codegen::{
+        irrt::{
+            call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
+            call_nac3_ndarray_is_c_contiguous, call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
+            call_nac3_ndarray_resolve_and_check_new_shape, call_nac3_ndarray_set_strides_by_shape,
+            call_nac3_ndarray_size, call_nac3_ndarray_transpose,
+        },
+        model::*,
+        stmt::BreakContinueHooks,
+        CodeGenContext, CodeGenerator,
+    },
+    toplevel::numpy::{extract_ndims, unpack_ndarray_var_tys},
+    typecheck::typedef::Type,
+};
+use inkwell::{
+    context::Context,
+    types::BasicType,
+    values::{BasicValue, BasicValueEnum, PointerValue},
+    AddressSpace, IntPredicate,
+};
+use scalar::ScalarObject;
+use util::{call_memcpy_model, gen_for_model_auto};
+
+pub struct NpArrayFields<'ctx, F: FieldTraversal<'ctx>> {
+    pub data: F::Out<PtrModel<IntModel<Byte>>>,
+    pub itemsize: F::Out<IntModel<SizeT>>,
+    pub ndims: F::Out<IntModel<SizeT>>,
+    pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
+    pub strides: F::Out<PtrModel<IntModel<SizeT>>>,
+}
+
+// TODO: Rename to `NDArray` when the old NDArray is removed.
+#[derive(Debug, Clone, Copy, Default)]
+pub struct NpArray;
+
+impl<'ctx> StructKind<'ctx> for NpArray {
+    type Fields<F: FieldTraversal<'ctx>> = NpArrayFields<'ctx, F>;
+
+    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
+        Self::Fields {
+            data: traversal.add_auto("data"),
+            itemsize: traversal.add_auto("itemsize"),
+            ndims: traversal.add_auto("ndims"),
+            shape: traversal.add_auto("shape"),
+            strides: traversal.add_auto("strides"),
+        }
+    }
+}
+
+/// A NAC3 Python ndarray object.
+#[derive(Debug, Clone, Copy)]
+pub struct NDArrayObject<'ctx> {
+    pub dtype: Type,
+    pub ndims: u64,
+    pub value: Ptr<'ctx, StructModel<NpArray>>,
+}
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// Create an [`NDArrayObject`] from an LLVM value and its typechecker [`Type`].
+    pub fn from_value_and_type<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        value: V,
+        ty: Type,
+    ) -> Self {
+        let pndarray_model = PtrModel(StructModel(NpArray));
+
+        let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
+        let ndims = extract_ndims(&ctx.unifier, ndims);
+        let value = pndarray_model.check_value(generator, ctx.ctx, value).unwrap();
+        NDArrayObject { dtype, ndims, value }
+    }
+
+    /// Get the `np.size()` of this ndarray.
+    pub fn size<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Int<'ctx, SizeT> {
+        call_nac3_ndarray_size(generator, ctx, self.value)
+    }
+
+    /// Get the `ndarray.nbytes` of this ndarray.
+    pub fn nbytes<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Int<'ctx, SizeT> {
+        call_nac3_ndarray_nbytes(generator, ctx, self.value)
+    }
+
+    /// Get the `len()` of this ndarray.
+    pub fn len<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Int<'ctx, SizeT> {
+        call_nac3_ndarray_len(generator, ctx, self.value)
+    }
+
+    /// Check if this ndarray is C-contiguous.
+    ///
+    /// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
+    pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Int<'ctx, Bool> {
+        call_nac3_ndarray_is_c_contiguous(generator, ctx, self.value)
+    }
+
+    /// Get the pointer to the n-th (0-based) element.
+    ///
+    /// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
+    pub fn get_nth_pointer<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        nth: Int<'ctx, SizeT>,
+        name: &str,
+    ) -> PointerValue<'ctx> {
+        let elem_ty = ctx.get_llvm_type(generator, self.dtype);
+
+        let p = call_nac3_ndarray_get_nth_pelement(generator, ctx, self.value, nth);
+        ctx.builder
+            .build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), name)
+            .unwrap()
+    }
+
+    /// Get the n-th (0-based) scalar.
+    pub fn get_nth<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        nth: Int<'ctx, SizeT>,
+    ) -> ScalarObject<'ctx> {
+        let p = self.get_nth_pointer(generator, ctx, nth, "value");
+        let value = ctx.builder.build_load(p, "value").unwrap();
+        ScalarObject { dtype: self.dtype, value }
+    }
+
+    /// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
+    ///
+    /// Please refer to the IRRT implementation to see its purpose.
+    pub fn update_strides_by_shape<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) {
+        call_nac3_ndarray_set_strides_by_shape(generator, ctx, self.value);
+    }
+
+    /// Copy data from another ndarray.
+    ///
+    /// This ndarray and `src` is that their `np.size()` should be the same. Their shapes
+    /// do not matter. The copying order is determined by how their flattened views look.
+    ///
+    /// Panics if the `dtype`s of ndarrays are different.
+    pub fn copy_data_from<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        src: NDArrayObject<'ctx>,
+    ) {
+        assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
+        call_nac3_ndarray_copy_data(generator, ctx, src.value, self.value);
+    }
+
+    /// Allocate an ndarray on the stack given its `ndims` and `dtype`.
+    ///
+    /// `shape` and `strides` will be automatically allocated on the stack.
+    ///
+    /// The returned ndarray's content will be:
+    /// - `data`: set to `nullptr`.
+    /// - `itemsize`: set to the `sizeof()` of `dtype`.
+    /// - `ndims`: set to the value of  `ndims`.
+    /// - `shape`: allocated with an array of length `ndims` with uninitialized values.
+    /// - `strides`: allocated with an array of length `ndims` with uninitialized values.
+    pub fn alloca_uninitialized<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        ndims: u64,
+        name: &str,
+    ) -> Self {
+        let sizet_model = IntModel(SizeT);
+        let ndarray_model = StructModel(NpArray);
+        let ndarray_data_model = PtrModel(IntModel(Byte));
+
+        let pndarray = ndarray_model.alloca(generator, ctx, name);
+
+        let data = ndarray_data_model.nullptr(generator, ctx.ctx);
+        pndarray.set(ctx, |f| f.data, data);
+
+        let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
+        let itemsize =
+            sizet_model.s_extend_or_bit_cast(generator, ctx, itemsize, "alloca_itemsize");
+        pndarray.set(ctx, |f| f.itemsize, itemsize);
+
+        let ndims_val = sizet_model.constant(generator, ctx.ctx, ndims);
+        pndarray.set(ctx, |f| f.ndims, ndims_val);
+
+        let shape = sizet_model.array_alloca(generator, ctx, ndims_val.value, "alloca_shape");
+        pndarray.set(ctx, |f| f.shape, shape);
+
+        let strides = sizet_model.array_alloca(generator, ctx, ndims_val.value, "alloca_strides");
+        pndarray.set(ctx, |f| f.strides, strides);
+
+        NDArrayObject { dtype, ndims, value: pndarray }
+    }
+
+    /// Convenience function.
+    /// Like [`NDArrayObject::alloca_uninitialized`] but directly takes the typechecker type of the ndarray.
+    pub fn alloca_uninitialized_of_type<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ndarray_ty: Type,
+        name: &str,
+    ) -> Self {
+        let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
+        let ndims = extract_ndims(&ctx.unifier, ndims);
+        Self::alloca_uninitialized(generator, ctx, dtype, ndims, name)
+    }
+
+    /// Clone this ndaarray - Allocate a new ndarray with the same shape as this ndarray and copy the contents
+    /// over.
+    ///
+    /// The new ndarray will own its data and will be C-contiguous.
+    pub fn make_clone<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: &str,
+    ) -> Self {
+        let clone =
+            NDArrayObject::alloca_uninitialized(generator, ctx, self.dtype, self.ndims, name);
+
+        let shape = self.value.gep(ctx, |f| f.shape).load(generator, ctx, "shape");
+        clone.copy_shape_from_array(generator, ctx, shape);
+        clone.create_data(generator, ctx);
+        clone.copy_data_from(generator, ctx, *self);
+        clone
+    }
+
+    /// Get this ndarray's `ndims` as an LLVM constant.
+    pub fn get_ndims<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+    ) -> Int<'ctx, SizeT> {
+        let sizet_model = IntModel(SizeT);
+        sizet_model.constant(generator, ctx, self.ndims)
+    }
+
+    /// Get if this ndarray's `np.size` is `0` - containing no content.
+    pub fn is_empty<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Int<'ctx, Bool> {
+        let sizet_model = IntModel(SizeT);
+
+        let size = self.size(generator, ctx);
+        size.compare(ctx, IntPredicate::EQ, sizet_model.const_0(generator, ctx.ctx), "is_empty")
+    }
+
+    /// Return true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
+    ///
+    /// This is a staticially known property of ndarrays. This is why it is returning
+    /// a Rust boolean instead of a [`BasicValue`].
+    #[must_use]
+    pub fn is_unsized(&self) -> bool {
+        self.ndims == 0
+    }
+
+    /// Initialize an ndarray's `data` by allocating a buffer on the stack.
+    /// The allocated data buffer is considered to be *owned* by the ndarray.
+    ///
+    /// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
+    ///
+    /// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
+    pub fn create_data<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) {
+        let byte_model = IntModel(Byte);
+
+        let nbytes = self.nbytes(generator, ctx);
+
+        let data = byte_model.array_alloca(generator, ctx, nbytes.value, "data");
+        self.value.set(ctx, |f| f.data, data);
+
+        self.update_strides_by_shape(generator, ctx);
+    }
+
+    /// Copy shape dimensions from an array.
+    pub fn copy_shape_from_array<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        src_shape: Ptr<'ctx, IntModel<SizeT>>,
+    ) {
+        let dst_shape = self.value.get(generator, ctx, |f| f.shape, "dst_shape");
+        let num_items = self.get_ndims(generator, ctx.ctx).value;
+        call_memcpy_model(generator, ctx, dst_shape, src_shape, num_items);
+    }
+
+    /// Copy shape dimensions from an ndarray.
+    /// Panics if `ndims` mismatches.
+    pub fn copy_shape_from_ndarray<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        src_ndarray: NDArrayObject<'ctx>,
+    ) {
+        assert_eq!(self.ndims, src_ndarray.ndims);
+        let src_shape = src_ndarray.value.get(generator, ctx, |f| f.shape, "src_shape");
+        self.copy_shape_from_array(generator, ctx, src_shape);
+    }
+
+    /// Copy strides dimensions from an array.
+    pub fn copy_strides_from_array<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        src_strides: Ptr<'ctx, IntModel<SizeT>>,
+    ) {
+        let dst_strides = self.value.get(generator, ctx, |f| f.strides, "dst_strides");
+        let num_items = self.get_ndims(generator, ctx.ctx).value;
+        call_memcpy_model(generator, ctx, dst_strides, src_strides, num_items);
+    }
+
+    /// Copy strides dimensions from an ndarray.
+    /// Panics if `ndims` mismatches.
+    pub fn copy_strides_from_ndarray<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        src_ndarray: NDArrayObject<'ctx>,
+    ) {
+        assert_eq!(self.ndims, src_ndarray.ndims);
+        let src_strides = src_ndarray.value.get(generator, ctx, |f| f.strides, "src_strides");
+        self.copy_strides_from_array(generator, ctx, src_strides);
+    }
+
+    /// Iterate through every element pointer in the ndarray in its flatten view.
+    ///
+    /// `body` also access to [`BreakContinueHooks`] to short-circuit and an element's
+    /// index. The given element pointer also has been casted to the LLVM type of this ndarray's `dtype`.
+    pub fn foreach_pointer<'a, G, F>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        body: F,
+    ) -> Result<(), String>
+    where
+        G: CodeGenerator + ?Sized,
+        F: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            BreakContinueHooks<'ctx>,
+            Int<'ctx, SizeT>,
+            PointerValue<'ctx>,
+        ) -> Result<(), String>,
+    {
+        let sizet_model = IntModel(SizeT);
+
+        let start = sizet_model.const_0(generator, ctx.ctx);
+        let stop = self.size(generator, ctx);
+        let step = sizet_model.const_1(generator, ctx.ctx);
+
+        gen_for_model_auto(generator, ctx, start, stop, step, |generator, ctx, hooks, i| {
+            let pelement = self.get_nth_pointer(generator, ctx, i, "element");
+            body(generator, ctx, hooks, i, pelement)
+        })
+    }
+
+    /// Iterate through every scalar in this ndarray.
+    pub fn foreach<'a, G, F>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        body: F,
+    ) -> Result<(), String>
+    where
+        G: CodeGenerator + ?Sized,
+        F: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            BreakContinueHooks<'ctx>,
+            Int<'ctx, SizeT>,
+            ScalarObject<'ctx>,
+        ) -> Result<(), String>,
+    {
+        self.foreach_pointer(generator, ctx, |generator, ctx, hooks, i, p| {
+            let value = ctx.builder.build_load(p, "value").unwrap();
+            let scalar = ScalarObject { dtype: self.dtype, value };
+            body(generator, ctx, hooks, i, scalar)
+        })
+    }
+
+    /// Fill the ndarray with a value.
+    ///
+    /// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
+    pub fn fill<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        fill_value: BasicValueEnum<'ctx>,
+    ) {
+        self.foreach_pointer(generator, ctx, |_generator, ctx, _hooks, _i, pelement| {
+            ctx.builder.build_store(pelement, fill_value).unwrap();
+            Ok(())
+        })
+        .unwrap();
+    }
+
+    /// Create a reshaped view on this ndarray like `np.reshape()`.
+    ///
+    /// If there is a `-1` in `new_shape`, it will be resolved; `new_shape` would **NOT** be modified as a result.
+    ///
+    /// If reshape without copying is impossible, this function will allocate a new ndarray and copy contents.
+    ///
+    /// * `new_ndims` - The number of dimensions of `new_shape` as a [`Type`].
+    /// * `new_shape` - The target shape to do `np.reshape()`.
+    #[must_use]
+    pub fn reshape_or_copy<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        new_ndims: u64,
+        new_shape: Ptr<'ctx, IntModel<SizeT>>,
+    ) -> Self {
+        // TODO: The current criterion for whether to do a full copy or not is by checking `is_c_contiguous`,
+        // but this is not optimal. Look into how numpy does it.
+
+        let current_bb = ctx.builder.get_insert_block().unwrap();
+        let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
+        let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
+        let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
+
+        let dst_ndarray = NDArrayObject::alloca_uninitialized(
+            generator,
+            ctx,
+            self.dtype,
+            new_ndims,
+            "reshaped_ndarray",
+        );
+        dst_ndarray.copy_shape_from_array(generator, ctx, new_shape);
+
+        let size = self.size(generator, ctx);
+        let new_ndims = dst_ndarray.get_ndims(generator, ctx.ctx);
+        call_nac3_ndarray_resolve_and_check_new_shape(generator, ctx, size, new_ndims, new_shape);
+
+        let is_c_contiguous = self.is_c_contiguous(generator, ctx);
+        ctx.builder.build_conditional_branch(is_c_contiguous.value, then_bb, else_bb).unwrap();
+
+        // Inserting into then_bb: reshape is possible without copying
+        ctx.builder.position_at_end(then_bb);
+        dst_ndarray.update_strides_by_shape(generator, ctx);
+        dst_ndarray.value.set(ctx, |f| f.data, self.value.get(generator, ctx, |f| f.data, "data"));
+        ctx.builder.build_unconditional_branch(end_bb).unwrap();
+
+        // Inserting into else_bb: reshape is impossible without copying
+        ctx.builder.position_at_end(else_bb);
+        dst_ndarray.create_data(generator, ctx);
+        dst_ndarray.copy_data_from(generator, ctx, *self);
+        ctx.builder.build_unconditional_branch(end_bb).unwrap();
+
+        // Reposition for continuation
+        ctx.builder.position_at_end(end_bb);
+
+        dst_ndarray
+    }
+
+    /// Create a flattened view of this ndarray, like `np.ravel()`.
+    ///
+    /// Uses [`NDArrayObject::reshape_or_copy`] under-the-hood so ndarray may or may not be copied.
+    pub fn ravel_or_copy<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Self {
+        // Define models
+        let sizet_model = IntModel(SizeT);
+
+        let num0 = sizet_model.const_0(generator, ctx.ctx);
+        let num1 = sizet_model.const_1(generator, ctx.ctx);
+        let num_neg1 = sizet_model.const_all_1s(generator, ctx.ctx);
+
+        // Create `[-1]` and pass to `reshape_or_copy`.
+        let new_shape = sizet_model.array_alloca(generator, ctx, num1.value, "new_shape");
+        new_shape.offset(generator, ctx, num0.value, "").store(ctx, num_neg1);
+
+        self.reshape_or_copy(generator, ctx, 1, new_shape)
+    }
+
+    /// Create a transposed view on this ndarray like `np.transpose(<ndarray>, <axes> = None)`.
+    /// * `axes` - If specified, should be an array of the permutation (negative indices are **allowed**).
+    pub fn transpose<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        axes: Option<Ptr<'ctx, IntModel<SizeT>>>,
+    ) -> Self {
+        // Define models
+        let sizet_model = IntModel(SizeT);
+
+        let transposed_ndarray = NDArrayObject::alloca_uninitialized(
+            generator,
+            ctx,
+            self.dtype,
+            self.ndims,
+            "transposed_ndarray",
+        );
+
+        let num_axes = self.get_ndims(generator, ctx.ctx);
+
+        // `axes = nullptr` if `axes` is unspecified.
+        let axes = axes.unwrap_or_else(|| PtrModel(sizet_model).nullptr(generator, ctx.ctx));
+
+        call_nac3_ndarray_transpose(
+            generator,
+            ctx,
+            self.value,
+            transposed_ndarray.value,
+            num_axes,
+            axes,
+        );
+
+        transposed_ndarray
+    }
+}

nac3core/src/codegen/structure/ndarray/product.rs

@@ -0,0 +1,20 @@
+use crate::codegen::{CodeGenContext, CodeGenerator};
+
+use super::NDArrayObject;
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// TODO: Document me
+    pub fn matmul_at_least_2d<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        a: Self,
+        b: Self,
+    ) -> Self {
+        assert!(a.ndims >= 2);
+        assert!(b.ndims >= 2);
+
+        
+
+        todo!()
+    }
+}

nac3core/src/codegen/structure/ndarray/scalar.rs

@@ -0,0 +1,143 @@
+use inkwell::values::{BasicValue, BasicValueEnum};
+
+use crate::{
+    codegen::{model::*, CodeGenContext, CodeGenerator},
+    typecheck::typedef::{Type, TypeEnum},
+};
+
+use super::NDArrayObject;
+
+/// An LLVM numpy scalar with its [`Type`].
+///
+/// Intended to be used with [`ScalarOrNDArray`].
+///
+/// A scalar does not have to be an actual number. It could be arbitrary objects.
+#[derive(Debug, Clone, Copy)]
+pub struct ScalarObject<'ctx> {
+    pub dtype: Type,
+    pub value: BasicValueEnum<'ctx>,
+}
+
+impl<'ctx> ScalarObject<'ctx> {
+    /// Promote this scalar to an unsized ndarray (like doing `np.asarray`).
+    ///
+    /// The scalar value is allocated onto the stack, and the ndarray's `data` will point to that
+    /// allocated value.
+    pub fn as_ndarray<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> NDArrayObject<'ctx> {
+        let pbyte_model = PtrModel(IntModel(Byte));
+
+        // We have to put the value on the stack to get a data pointer.
+        let data = ctx.builder.build_alloca(self.value.get_type(), "as_ndarray_scalar").unwrap();
+        ctx.builder.build_store(data, self.value).unwrap();
+        let data = pbyte_model.pointer_cast(generator, ctx, data, "data");
+
+        let ndarray =
+            NDArrayObject::alloca_uninitialized(generator, ctx, self.dtype, 0, "scalar_ndarray");
+        ndarray.value.set(ctx, |f| f.data, data);
+        ndarray
+    }
+}
+
+/// A convenience enum for implementing scalar/ndarray agnostic utilities.
+#[derive(Debug, Clone, Copy)]
+pub enum ScalarOrNDArray<'ctx> {
+    Scalar(ScalarObject<'ctx>),
+    NDArray(NDArrayObject<'ctx>),
+}
+
+impl<'ctx> ScalarOrNDArray<'ctx> {
+    /// Get the underlying [`BasicValueEnum<'ctx>`] of this [`ScalarOrNDArray`].
+    #[must_use]
+    pub fn to_basic_value_enum(self) -> BasicValueEnum<'ctx> {
+        match self {
+            ScalarOrNDArray::Scalar(scalar) => scalar.value,
+            ScalarOrNDArray::NDArray(ndarray) => ndarray.value.value.as_basic_value_enum(),
+        }
+    }
+
+    #[must_use]
+    pub fn into_scalar(&self) -> ScalarObject<'ctx> {
+        match self {
+            ScalarOrNDArray::NDArray(_ndarray) => panic!("Got NDArray"),
+            ScalarOrNDArray::Scalar(scalar) => *scalar,
+        }
+    }
+
+    #[must_use]
+    pub fn into_ndarray(&self) -> NDArrayObject<'ctx> {
+        match self {
+            ScalarOrNDArray::NDArray(ndarray) => *ndarray,
+            ScalarOrNDArray::Scalar(_scalar) => panic!("Got Scalar"),
+        }
+    }
+
+    /// Convert this [`ScalarOrNDArray`] to an ndarray - behaves like `np.asarray`.
+    /// - If this is an ndarray, the ndarray is returned.
+    /// - If this is a scalar, an unsized ndarray view is created on it.
+    pub fn as_ndarray<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> NDArrayObject<'ctx> {
+        match self {
+            ScalarOrNDArray::NDArray(ndarray) => *ndarray,
+            ScalarOrNDArray::Scalar(scalar) => scalar.as_ndarray(generator, ctx),
+        }
+    }
+
+    #[must_use]
+    pub fn dtype(&self) -> Type {
+        match self {
+            ScalarOrNDArray::Scalar(scalar) => scalar.dtype,
+            ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
+        }
+    }
+}
+
+impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for ScalarObject<'ctx> {
+    type Error = ();
+
+    fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
+        match value {
+            ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
+            ScalarOrNDArray::NDArray(_ndarray) => Err(()),
+        }
+    }
+}
+
+impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayObject<'ctx> {
+    type Error = ();
+
+    fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
+        match value {
+            ScalarOrNDArray::Scalar(_scalar) => Err(()),
+            ScalarOrNDArray::NDArray(ndarray) => Ok(*ndarray),
+        }
+    }
+}
+
+/// Split an [`BasicValueEnum<'ctx>`] into a [`ScalarOrNDArray`] depending
+/// on its [`Type`].
+pub fn split_scalar_or_ndarray<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    input: BasicValueEnum<'ctx>,
+    input_ty: Type,
+) -> ScalarOrNDArray<'ctx> {
+    match &*ctx.unifier.get_ty(input_ty) {
+        TypeEnum::TObj { obj_id, .. }
+            if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
+        {
+            let ndarray = NDArrayObject::from_value_and_type(generator, ctx, input, input_ty);
+            ScalarOrNDArray::NDArray(ndarray)
+        }
+        _ => {
+            let scalar = ScalarObject { dtype: input_ty, value: input };
+            ScalarOrNDArray::Scalar(scalar)
+        }
+    }
+}

nac3core/src/codegen/structure/ndarray/shape_util.rs

@@ -0,0 +1,112 @@
+use inkwell::values::BasicValueEnum;
+use util::gen_for_model_auto;
+
+use crate::{
+    codegen::{model::*, structure::list::ListObject, CodeGenContext, CodeGenerator},
+    typecheck::typedef::{Type, TypeEnum},
+};
+
+/// Parse a NumPy-like "int sequence" input and return the int sequence as an array and its length.
+///
+/// * `sequence` - The `sequence` parameter.
+/// * `sequence_ty` - The typechecker type of `sequence`
+///
+/// The `sequence` argument type may only be one of the following:
+///   1. A list of `int32`;   e.g., `np.empty([600, 800, 3])`
+///   2. A tuple of `int32`;  e.g., `np.empty((600, 800, 3))`
+///   3. A scalar `int32`;    e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
+///
+/// All `int32` values will be sign-extended to `SizeT`.
+pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    input_sequence: BasicValueEnum<'ctx>,
+    input_sequence_ty: Type,
+) -> (Int<'ctx, SizeT>, Ptr<'ctx, IntModel<SizeT>>) {
+    let sizet_model = IntModel(SizeT);
+
+    let zero = sizet_model.const_0(generator, ctx.ctx);
+    let one = sizet_model.const_1(generator, ctx.ctx);
+
+    // The result `list` to return.
+    match &*ctx.unifier.get_ty(input_sequence_ty) {
+        TypeEnum::TObj { obj_id, .. }
+            if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
+        {
+            // 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
+
+            // Check `input_sequence`
+            let input_sequence =
+                ListObject::from_value_and_type(generator, ctx, input_sequence, input_sequence_ty);
+
+            let len = input_sequence.value.gep(ctx, |f| f.len).load(generator, ctx, "len");
+            let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
+
+            // Load all the `int32`s from the input_sequence, cast them to `SizeT`, and store them into `result`
+            gen_for_model_auto(generator, ctx, zero, len, one, |generator, ctx, _hooks, i| {
+                // Load the i-th int32 in the input sequence
+                let int = input_sequence
+                    .value
+                    .get(generator, ctx, |f| f.items, "int")
+                    .ix(generator, ctx, i.value, "int")
+                    .value
+                    .into_int_value();
+
+                // Cast to SizeT
+                let int = sizet_model.s_extend_or_bit_cast(generator, ctx, int, "int");
+
+                // Store
+                result.offset(generator, ctx, i.value, "int").store(ctx, int);
+
+                Ok(())
+            })
+            .unwrap();
+
+            (len, result)
+        }
+        TypeEnum::TTuple { ty: tuple_types, .. } => {
+            // 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
+            let input_sequence = input_sequence.into_struct_value(); // A tuple is a struct
+
+            let len_int = tuple_types.len();
+
+            let len = sizet_model.constant(generator, ctx.ctx, len_int as u64);
+            let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
+
+            for i in 0..len_int {
+                // Get the i-th element off of the tuple and load it into `result`.
+                let int = ctx
+                    .builder
+                    .build_extract_value(input_sequence, i as u32, "int")
+                    .unwrap()
+                    .into_int_value();
+                let int = sizet_model.s_extend_or_bit_cast(generator, ctx, int, "int");
+
+                let offset = sizet_model.constant(generator, ctx.ctx, i as u64);
+                result.offset(generator, ctx, offset.value, "int").store(ctx, int);
+            }
+
+            (len, result)
+        }
+        TypeEnum::TObj { obj_id, .. }
+            if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
+        {
+            // 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
+            let input_int = input_sequence.into_int_value();
+
+            let len = sizet_model.const_1(generator, ctx.ctx);
+            let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
+
+            let int = sizet_model.s_extend_or_bit_cast(generator, ctx, input_int, "int");
+
+            // Storing into result[0]
+            result.store(ctx, int);
+
+            (len, result)
+        }
+        _ => panic!(
+            "encountered unknown sequence type: {}",
+            ctx.unifier.stringify(input_sequence_ty)
+        ),
+    }
+}

nac3core/src/codegen/structure/tuple.rs

@@ -0,0 +1,39 @@
+use inkwell::values::{BasicValueEnum, StructValue};
+use itertools::Itertools;
+
+use crate::{
+    codegen::{CodeGenContext, CodeGenerator},
+    typecheck::typedef::Type,
+};
+
+pub struct TupleObject<'ctx> {
+    pub tys: Vec<Type>,
+    pub value: StructValue<'ctx>,
+}
+
+impl<'ctx> TupleObject<'ctx> {
+    pub fn create<I, G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        items: I,
+        name: &str,
+    ) -> Self
+    where
+        I: IntoIterator<Item = (BasicValueEnum<'ctx>, Type)>,
+    {
+        let (vals, tys): (Vec<_>, Vec<_>) = items.into_iter().unzip();
+
+        // let tuple_ty = ctx.unifier.add_ty(TypeEnum::TTuple { ty: tys });
+        let llvm_tys = tys.iter().map(|ty| ctx.get_llvm_type(generator, *ty)).collect_vec();
+        let llvm_tuple_ty = ctx.ctx.struct_type(&llvm_tys, false);
+        let pllvm_tuple = ctx.builder.build_alloca(llvm_tuple_ty, "tuple").unwrap();
+        for (i, val) in vals.into_iter().enumerate() {
+            // Store the dim value into the tuple
+            let pval = ctx.builder.build_struct_gep(pllvm_tuple, i as u32, "value").unwrap();
+            ctx.builder.build_store(pval, val).unwrap();
+        }
+
+        let value = ctx.builder.build_load(pllvm_tuple, name).unwrap().into_struct_value();
+        TupleObject { tys, value }
+    }
+}

nac3core/src/toplevel/builtins.rs

@@ -13,15 +13,27 @@ use strum::IntoEnumIterator;
 
 use crate::{
     codegen::{
-        builtin_fns,
+        builtin_fns::{self},
-        classes::{ArrayLikeValue, NDArrayValue, ProxyValue, RangeValue, TypedArrayLikeAccessor},
+        classes::{ProxyValue, RangeValue},
         expr::destructure_range,
-        irrt::*,
+        extern_fns,
+        irrt::{self, *},
+        llvm_intrinsics,
+        model::Int32,
         numpy::*,
+        numpy_new::{self, gen_ndarray_transpose},
         stmt::exn_constructor,
+        structure::ndarray::{
+            functions::{FloorOrCeil, MinOrMax},
+            scalar::{split_scalar_or_ndarray, ScalarObject, ScalarOrNDArray},
+            NDArrayObject,
+        },
     },
     symbol_resolver::SymbolValue,
-    toplevel::{helper::PrimDef, numpy::make_ndarray_ty},
+    toplevel::{
+        helper::PrimDef,
+        numpy::{create_ndims, make_ndarray_ty},
+    },
     typecheck::typedef::{into_var_map, iter_type_vars, TypeVar, VarMap},
 };
 
@@ -511,7 +523,16 @@ impl<'a> BuiltinBuilder<'a> {
             PrimDef::FunNpArray
             | PrimDef::FunNpFull
             | PrimDef::FunNpEye
-            | PrimDef::FunNpIdentity => self.build_ndarray_other_factory_function(prim),
+            | PrimDef::FunNpIdentity
+            | PrimDef::FunNpArange => self.build_ndarray_other_factory_function(prim),
+
+            PrimDef::FunNpBroadcastTo | PrimDef::FunNpReshape | PrimDef::FunNpTranspose => {
+                self.build_ndarray_view_function(prim)
+            }
+
+            PrimDef::FunNpSize | PrimDef::FunNpShape | PrimDef::FunNpStrides => {
+                self.build_ndarray_property_getter_function(prim)
+            }
 
             PrimDef::FunStr => self.build_str_function(),
 
@@ -578,10 +599,6 @@ impl<'a> BuiltinBuilder<'a> {
             | PrimDef::FunNpHypot
             | PrimDef::FunNpNextAfter => self.build_np_2ary_function(prim),
 
-            PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
-                self.build_np_sp_ndarray_function(prim)
-            }
-
             PrimDef::FunNpDot
             | PrimDef::FunNpLinalgCholesky
             | PrimDef::FunNpLinalgQr
@@ -1072,16 +1089,34 @@ impl<'a> BuiltinBuilder<'a> {
                     let arg_ty = fun.0.args[0].ty;
                     let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
 
-                    let func = match prim {
+                    let ret_dtype = match prim {
-                        PrimDef::FunInt32 => builtin_fns::call_int32,
+                        PrimDef::FunInt32 => ctx.primitives.int32,
-                        PrimDef::FunInt64 => builtin_fns::call_int64,
+                        PrimDef::FunInt64 => ctx.primitives.int64,
-                        PrimDef::FunUInt32 => builtin_fns::call_uint32,
+                        PrimDef::FunUInt32 => ctx.primitives.uint32,
-                        PrimDef::FunUInt64 => builtin_fns::call_uint64,
+                        PrimDef::FunUInt64 => ctx.primitives.uint64,
-                        PrimDef::FunFloat => builtin_fns::call_float,
+                        PrimDef::FunFloat => ctx.primitives.float,
-                        PrimDef::FunBool => builtin_fns::call_bool,
+                        PrimDef::FunBool => ctx.primitives.bool,
                         _ => unreachable!(),
                     };
-                    Ok(Some(func(generator, ctx, (arg_ty, arg))?))
+
+                    let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
+                        generator,
+                        ctx,
+                        ret_dtype,
+                        |generator, ctx, _i, scalar| {
+                            let result = match prim {
+                                PrimDef::FunInt32 => scalar.cast_to_int32(generator, ctx),
+                                PrimDef::FunInt64 => scalar.cast_to_int64(generator, ctx),
+                                PrimDef::FunUInt32 => scalar.cast_to_uint32(generator, ctx),
+                                PrimDef::FunUInt64 => scalar.cast_to_uint64(generator, ctx),
+                                PrimDef::FunFloat => scalar.cast_to_float(ctx),
+                                PrimDef::FunBool => scalar.cast_to_bool(ctx),
+                                _ => unreachable!(),
+                            };
+                            Ok(result.value)
+                        },
+                    )?;
+                    Ok(Some(result.to_basic_value_enum()))
                 },
             )))),
             loc: None,
@@ -1132,20 +1167,23 @@ impl<'a> BuiltinBuilder<'a> {
                 let arg_ty = fun.0.args[0].ty;
                 let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
 
-                let ret_elem_ty = size_variant.of_int(&ctx.primitives);
+                let ret_int_dtype = size_variant.of_int(&ctx.primitives);
-                Ok(Some(builtin_fns::call_round(generator, ctx, (arg_ty, arg), ret_elem_ty)?))
+
+                let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
+                    generator,
+                    ctx,
+                    ret_int_dtype,
+                    |generator, ctx, _i, scalar| {
+                        Ok(scalar.round(generator, ctx, ret_int_dtype).value)
+                    },
+                )?;
+                Ok(Some(result.to_basic_value_enum()))
             }),
         )
     }
 
     /// Build the functions `ceil()` and `floor()` and their 64 bit variants.
     fn build_ceil_floor_function(&mut self, prim: PrimDef) -> TopLevelDef {
-        #[derive(Clone, Copy)]
-        enum Kind {
-            Floor,
-            Ceil,
-        }
-
         debug_assert_prim_is_allowed(
             prim,
             &[PrimDef::FunFloor, PrimDef::FunFloor64, PrimDef::FunCeil, PrimDef::FunCeil64],
@@ -1153,10 +1191,10 @@ impl<'a> BuiltinBuilder<'a> {
 
         let (size_variant, kind) = {
             match prim {
-                PrimDef::FunFloor => (SizeVariant::Bits32, Kind::Floor),
+                PrimDef::FunFloor => (SizeVariant::Bits32, FloorOrCeil::Floor),
-                PrimDef::FunFloor64 => (SizeVariant::Bits64, Kind::Floor),
+                PrimDef::FunFloor64 => (SizeVariant::Bits64, FloorOrCeil::Floor),
-                PrimDef::FunCeil => (SizeVariant::Bits32, Kind::Ceil),
+                PrimDef::FunCeil => (SizeVariant::Bits32, FloorOrCeil::Ceil),
-                PrimDef::FunCeil64 => (SizeVariant::Bits64, Kind::Ceil),
+                PrimDef::FunCeil64 => (SizeVariant::Bits64, FloorOrCeil::Ceil),
                 _ => unreachable!(),
             }
         };
@@ -1196,12 +1234,15 @@ impl<'a> BuiltinBuilder<'a> {
                 let arg_ty = fun.0.args[0].ty;
                 let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
 
-                let ret_elem_ty = size_variant.of_int(&ctx.primitives);
+                let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
-                let func = match kind {
+                    generator,
-                    Kind::Ceil => builtin_fns::call_ceil,
+                    ctx,
-                    Kind::Floor => builtin_fns::call_floor,
+                    int_sized,
-                };
+                    |generator, ctx, _i, scalar| {
-                Ok(Some(func(generator, ctx, (arg_ty, arg), ret_elem_ty)?))
+                        Ok(scalar.floor_or_ceil(generator, ctx, kind, int_sized).value)
+                    },
+                )?;
+                Ok(Some(result.to_basic_value_enum()))
             }),
         )
     }
@@ -1248,9 +1289,9 @@ impl<'a> BuiltinBuilder<'a> {
             &[(self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
             Box::new(move |ctx, obj, fun, args, generator| {
                 let func = match prim {
-                    PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => gen_ndarray_empty,
+                    PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => numpy_new::gen_ndarray_empty,
-                    PrimDef::FunNpZeros => gen_ndarray_zeros,
+                    PrimDef::FunNpZeros => numpy_new::gen_ndarray_zeros,
-                    PrimDef::FunNpOnes => gen_ndarray_ones,
+                    PrimDef::FunNpOnes => numpy_new::gen_ndarray_ones,
                     _ => unreachable!(),
                 };
                 func(ctx, &obj, fun, &args, generator).map(|val| Some(val.as_basic_value_enum()))
@@ -1264,7 +1305,13 @@ impl<'a> BuiltinBuilder<'a> {
     fn build_ndarray_other_factory_function(&mut self, prim: PrimDef) -> TopLevelDef {
         debug_assert_prim_is_allowed(
             prim,
-            &[PrimDef::FunNpArray, PrimDef::FunNpFull, PrimDef::FunNpEye, PrimDef::FunNpIdentity],
+            &[
+                PrimDef::FunNpArray,
+                PrimDef::FunNpFull,
+                PrimDef::FunNpEye,
+                PrimDef::FunNpIdentity,
+                PrimDef::FunNpArange,
+            ],
         );
 
         let PrimitiveStore { int32, bool, ndarray, .. } = *self.primitives;
@@ -1325,7 +1372,7 @@ impl<'a> BuiltinBuilder<'a> {
                     // type variable
                     &[(self.list_int32, "shape"), (tv.ty, "fill_value")],
                     Box::new(move |ctx, obj, fun, args, generator| {
-                        gen_ndarray_full(ctx, &obj, fun, &args, generator)
+                        numpy_new::gen_ndarray_full(ctx, &obj, fun, &args, generator)
                             .map(|val| Some(val.as_basic_value_enum()))
                     }),
                 )
@@ -1383,6 +1430,152 @@ impl<'a> BuiltinBuilder<'a> {
                         .map(|val| Some(val.as_basic_value_enum()))
                 }),
             ),
+            PrimDef::FunNpArange => {
+                // TODO: Support `np.arange(start, stop, step)`
+                let ndims1 = create_ndims(self.unifier, 1);
+                let ndarray_float_1d = make_ndarray_ty(
+                    self.unifier,
+                    self.primitives,
+                    Some(self.primitives.float),
+                    Some(ndims1),
+                );
+                create_fn_by_codegen(
+                    self.unifier,
+                    &VarMap::new(),
+                    prim.name(),
+                    ndarray_float_1d,
+                    &[(int32, "n")],
+                    Box::new(|ctx, obj, fun, args, generator| {
+                        numpy_new::gen_ndarray_arange(ctx, &obj, fun, &args, generator)
+                            .map(|val| Some(val.as_basic_value_enum()))
+                    }),
+                )
+            }
+            _ => unreachable!(),
+        }
+    }
+
+    fn build_ndarray_view_function(&mut self, prim: PrimDef) -> TopLevelDef {
+        debug_assert_prim_is_allowed(
+            prim,
+            &[PrimDef::FunNpBroadcastTo, PrimDef::FunNpReshape, PrimDef::FunNpTranspose],
+        );
+
+        match prim {
+            PrimDef::FunNpBroadcastTo | PrimDef::FunNpReshape => {
+                // `array_ty` can be ndarrays and arbitrary scalars and objects.
+                let array_tvar = self.unifier.get_dummy_var();
+
+                // The return type is handled by special folding in the type inferencer,
+                // since the returned `ndims` depends on input shape.
+                let return_tvar = self.unifier.get_dummy_var();
+
+                create_fn_by_codegen(
+                    self.unifier,
+                    &into_var_map([array_tvar, return_tvar]),
+                    prim.name(),
+                    return_tvar.ty,
+                    &[
+                        (array_tvar.ty, "array"),
+                        (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape"),
+                    ],
+                    Box::new(move |ctx, obj, fun, args, generator| {
+                        let f = match prim {
+                            PrimDef::FunNpBroadcastTo => numpy_new::gen_ndarray_broadcast_to,
+                            PrimDef::FunNpReshape => numpy_new::gen_ndarray_reshape,
+                            _ => unreachable!(),
+                        };
+                        f(ctx, &obj, fun, &args, generator).map(Some)
+                    }),
+                )
+            }
+            PrimDef::FunNpTranspose => {
+                // TODO: Allow tuple inputs.
+                // TODO: Support scalar inputs (difficult)
+
+                // TODO: Default values don't work for some reason.
+                // `axes` should have been `Option[List[int32]]` with default `None`.
+                // Workaround with some bogus types and values for now.
+
+                let axes_ty = self.list_int32;
+
+                TopLevelDef::Function {
+                    name: prim.name().into(),
+                    simple_name: prim.simple_name().into(),
+                    signature: self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
+                        args: vec![
+                            FuncArg {
+                                name: "a".into(),
+                                ty: self.primitives.ndarray,
+                                default_value: None,
+                                is_vararg: false,
+                            },
+                            FuncArg {
+                                name: "axes".into(),
+                                ty: axes_ty,
+                                default_value: Some(SymbolValue::OptionNone), // Bogus
+                                is_vararg: false,
+                            },
+                        ],
+                        ret: self.primitives.ndarray,
+                        vars: VarMap::new(),
+                    })),
+                    var_id: Vec::default(),
+                    instance_to_symbol: HashMap::default(),
+                    instance_to_stmt: HashMap::default(),
+                    resolver: None,
+                    codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                        |ctx, obj, fun, args, generator| {
+                            gen_ndarray_transpose(ctx, &obj, fun, &args, generator).map(Some)
+                        },
+                    )))),
+                    loc: None,
+                }
+            }
+            _ => unreachable!(),
+        }
+    }
+
+    fn build_ndarray_property_getter_function(&mut self, prim: PrimDef) -> TopLevelDef {
+        debug_assert_prim_is_allowed(
+            prim,
+            &[PrimDef::FunNpSize, PrimDef::FunNpShape, PrimDef::FunNpStrides],
+        );
+
+        match prim {
+            PrimDef::FunNpSize => {
+                // TODO: Make the return type usize
+                create_fn_by_codegen(
+                    self.unifier,
+                    &VarMap::new(),
+                    prim.name(),
+                    self.primitives.int32,
+                    &[(self.primitives.ndarray, "a")],
+                    Box::new(|ctx, obj, fun, args, generator| {
+                        numpy_new::gen_ndarray_size(ctx, &obj, fun, &args, generator).map(Some)
+                    }),
+                )
+            }
+            PrimDef::FunNpShape | PrimDef::FunNpStrides => {
+                // The return type is a tuple of variable length depending on the ndims
+                // of the input ndarray.
+                let ret_ty = self.unifier.get_dummy_var().ty;
+                create_fn_by_codegen(
+                    self.unifier,
+                    &VarMap::new(),
+                    prim.name(),
+                    ret_ty,
+                    &[(self.primitives.ndarray, "a")],
+                    Box::new(move |ctx, obj, fun, args, generator| {
+                        let f = match prim {
+                            PrimDef::FunNpShape => numpy_new::gen_ndarray_shape,
+                            PrimDef::FunNpStrides => numpy_new::gen_ndarray_strides,
+                            _ => unreachable!(),
+                        };
+                        f(ctx, &obj, fun, &args, generator).map(Some)
+                    }),
+                )
+            }
             _ => unreachable!(),
         }
     }
@@ -1434,12 +1627,22 @@ impl<'a> BuiltinBuilder<'a> {
                 let arg_ty = fun.0.args[0].ty;
                 let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
 
-                let func = match prim {
+                let kind = match prim {
-                    PrimDef::FunNpCeil => builtin_fns::call_ceil,
+                    PrimDef::FunNpFloor => FloorOrCeil::Floor,
-                    PrimDef::FunNpFloor => builtin_fns::call_floor,
+                    PrimDef::FunNpCeil => FloorOrCeil::Ceil,
                     _ => unreachable!(),
                 };
-                Ok(Some(func(generator, ctx, (arg_ty, arg), ctx.primitives.float)?))
+
+                let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
+                    generator,
+                    ctx,
+                    ctx.primitives.float,
+                    move |_generator, ctx, _i, scalar| {
+                        let result = scalar.np_floor_or_ceil(ctx, kind);
+                        Ok(result.value)
+                    },
+                )?;
+                Ok(Some(result.to_basic_value_enum()))
             }),
         )
     }
@@ -1457,7 +1660,17 @@ impl<'a> BuiltinBuilder<'a> {
             Box::new(|ctx, _, fun, args, generator| {
                 let arg_ty = fun.0.args[0].ty;
                 let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
-                Ok(Some(builtin_fns::call_numpy_round(generator, ctx, (arg_ty, arg))?))
+
+                let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
+                    generator,
+                    ctx,
+                    ctx.primitives.float,
+                    |_generator, ctx, _i, scalar| {
+                        let result = scalar.np_round(ctx);
+                        Ok(result.value)
+                    },
+                )?;
+                Ok(Some(result.to_basic_value_enum()))
             }),
         )
     }
@@ -1534,51 +1747,11 @@ impl<'a> BuiltinBuilder<'a> {
                                 }
                             }
                             TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-                                let llvm_i32 = ctx.ctx.i32_type();
+                                let ndarray =
-                                let llvm_usize = generator.get_size_type(ctx.ctx);
+                                    NDArrayObject::from_value_and_type(generator, ctx, arg, arg_ty);
-
+                                let len = ndarray.len(generator, ctx);
-                                let arg = NDArrayValue::from_ptr_val(
+                                let len = len.truncate(generator, ctx, Int32, "len"); // TODO: Currently `len()` returns an int32. It should have been SizeT
-                                    arg.into_pointer_value(),
+                                Some(len.value.as_basic_value_enum())
-                                    llvm_usize,
-                                    None,
-                                );
-
-                                let ndims = arg.dim_sizes().size(ctx, generator);
-                                ctx.make_assert(
-                                    generator,
-                                    ctx.builder
-                                        .build_int_compare(
-                                            IntPredicate::NE,
-                                            ndims,
-                                            llvm_usize.const_zero(),
-                                            "",
-                                        )
-                                        .unwrap(),
-                                    "0:TypeError",
-                                    &format!("{name}() of unsized object", name = prim.name()),
-                                    [None, None, None],
-                                    ctx.current_loc,
-                                );
-
-                                let len = unsafe {
-                                    arg.dim_sizes().get_typed_unchecked(
-                                        ctx,
-                                        generator,
-                                        &llvm_usize.const_zero(),
-                                        None,
-                                    )
-                                };
-
-                                if len.get_type().get_bit_width() == 32 {
-                                    Some(len.into())
-                                } else {
-                                    Some(
-                                        ctx.builder
-                                            .build_int_truncate(len, llvm_i32, "len")
-                                            .map(Into::into)
-                                            .unwrap(),
-                                    )
-                                }
                             }
                             _ => unreachable!(),
                         }
@@ -1621,16 +1794,21 @@ impl<'a> BuiltinBuilder<'a> {
             codegen_callback: Some(Arc::new(GenCall::new(Box::new(
                 move |ctx, _, fun, args, generator| {
                     let m_ty = fun.0.args[0].ty;
-                    let n_ty = fun.0.args[1].ty;
                     let m_val = args[0].1.clone().to_basic_value_enum(ctx, generator, m_ty)?;
+
+                    let n_ty = fun.0.args[1].ty;
                     let n_val = args[1].1.clone().to_basic_value_enum(ctx, generator, n_ty)?;
 
-                    let func = match prim {
+                    let kind = match prim {
-                        PrimDef::FunMin => builtin_fns::call_min,
+                        PrimDef::FunMin => MinOrMax::Min,
-                        PrimDef::FunMax => builtin_fns::call_max,
+                        PrimDef::FunMax => MinOrMax::Max,
                         _ => unreachable!(),
                     };
-                    Ok(Some(func(ctx, (m_ty, m_val), (n_ty, n_val))))
+
+                    let m = ScalarObject { dtype: m_ty, value: m_val };
+                    let n = ScalarObject { dtype: n_ty, value: n_val };
+                    let result = ScalarObject::min_or_max(ctx, kind, m, n);
+                    Ok(Some(result.value))
                 },
             )))),
             loc: None,
@@ -1672,7 +1850,25 @@ impl<'a> BuiltinBuilder<'a> {
                 let a_ty = fun.0.args[0].ty;
                 let a = args[0].1.clone().to_basic_value_enum(ctx, generator, a_ty)?;
 
-                Ok(Some(builtin_fns::call_numpy_max_min(generator, ctx, (a_ty, a), prim.name())?))
+                let a = split_scalar_or_ndarray(generator, ctx, a, a_ty).as_ndarray(generator, ctx);
+                let result = match prim {
+                    PrimDef::FunNpArgmin => a
+                        .argmin_or_argmax(generator, ctx, MinOrMax::Min)
+                        .value
+                        .as_basic_value_enum(),
+                    PrimDef::FunNpArgmax => a
+                        .argmin_or_argmax(generator, ctx, MinOrMax::Max)
+                        .value
+                        .as_basic_value_enum(),
+                    PrimDef::FunNpMin => {
+                        a.min_or_max(generator, ctx, MinOrMax::Min).value.as_basic_value_enum()
+                    }
+                    PrimDef::FunNpMax => {
+                        a.min_or_max(generator, ctx, MinOrMax::Max).value.as_basic_value_enum()
+                    }
+                    _ => unreachable!(),
+                };
+                Ok(Some(result))
             }),
         )
     }
@@ -1712,13 +1908,32 @@ impl<'a> BuiltinBuilder<'a> {
                     let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
                     let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
 
-                    let func = match prim {
+                    let kind = match prim {
-                        PrimDef::FunNpMinimum => builtin_fns::call_numpy_minimum,
+                        PrimDef::FunNpMinimum => MinOrMax::Min,
-                        PrimDef::FunNpMaximum => builtin_fns::call_numpy_maximum,
+                        PrimDef::FunNpMaximum => MinOrMax::Max,
                         _ => unreachable!(),
                     };
 
-                    Ok(Some(func(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
+                    let x1 = split_scalar_or_ndarray(generator, ctx, x1_val, x1_ty);
+                    let x2 = split_scalar_or_ndarray(generator, ctx, x2_val, x2_ty);
+
+                    // NOTE: x1.dtype() and x2.dtype() should be the same
+                    let common_ty = x1.dtype();
+
+                    let result = ScalarOrNDArray::broadcasting_starmap(
+                        generator,
+                        ctx,
+                        &[x1, x2],
+                        common_ty,
+                        |_generator, ctx, _i, scalars| {
+                            let x1 = scalars[0];
+                            let x2 = scalars[1];
+
+                            let result = ScalarObject::min_or_max(ctx, kind, x1, x2);
+                            Ok(result.value)
+                        },
+                    )?;
+                    Ok(Some(result.to_basic_value_enum()))
                 },
             )))),
             loc: None,
@@ -1729,6 +1944,7 @@ impl<'a> BuiltinBuilder<'a> {
     fn build_abs_function(&mut self) -> TopLevelDef {
         let prim = PrimDef::FunAbs;
 
+        let num_ty = self.num_ty; // To move into codegen_callback
         TopLevelDef::Function {
             name: prim.name().into(),
             simple_name: prim.simple_name().into(),
@@ -1747,11 +1963,17 @@ impl<'a> BuiltinBuilder<'a> {
             instance_to_stmt: HashMap::default(),
             resolver: None,
             codegen_callback: Some(Arc::new(GenCall::new(Box::new(
-                |ctx, _, fun, args, generator| {
+                move |ctx, _, fun, args, generator| {
                     let n_ty = fun.0.args[0].ty;
                     let n_val = args[0].1.clone().to_basic_value_enum(ctx, generator, n_ty)?;
 
-                    Ok(Some(builtin_fns::call_abs(generator, ctx, (n_ty, n_val))?))
+                    let result = split_scalar_or_ndarray(generator, ctx, n_val, n_ty).map(
+                        generator,
+                        ctx,
+                        num_ty.ty,
+                        |_generator, ctx, _i, scalar| Ok(scalar.abs(ctx).value),
+                    )?;
+                    Ok(Some(result.to_basic_value_enum()))
                 },
             )))),
             loc: None,
@@ -1774,13 +1996,23 @@ impl<'a> BuiltinBuilder<'a> {
                 let x_ty = fun.0.args[0].ty;
                 let x_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x_ty)?;
 
-                let func = match prim {
+                let function = match prim {
-                    PrimDef::FunNpIsInf => builtin_fns::call_numpy_isinf,
+                    PrimDef::FunNpIsInf => irrt::call_isnan,
-                    PrimDef::FunNpIsNan => builtin_fns::call_numpy_isnan,
+                    PrimDef::FunNpIsNan => irrt::call_isinf,
                     _ => unreachable!(),
                 };
 
-                Ok(Some(func(generator, ctx, (x_ty, x_val))?))
+                let result = split_scalar_or_ndarray(generator, ctx, x_val, x_ty).map(
+                    generator,
+                    ctx,
+                    ctx.primitives.bool,
+                    |generator, ctx, _i, scalar| {
+                        let n = scalar.into_float64(ctx);
+                        let n = function(generator, ctx, n);
+                        Ok(n.as_basic_value_enum())
+                    },
+                )?;
+                Ok(Some(result.to_basic_value_enum()))
             }),
         )
     }
@@ -1838,49 +2070,58 @@ impl<'a> BuiltinBuilder<'a> {
                 let arg_ty = fun.0.args[0].ty;
                 let arg_val = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
 
-                let func = match prim {
+                let result = split_scalar_or_ndarray(generator, ctx, arg_val, arg_ty).map(
-                    PrimDef::FunNpSin => builtin_fns::call_numpy_sin,
+                    generator,
-                    PrimDef::FunNpCos => builtin_fns::call_numpy_cos,
+                    ctx,
-                    PrimDef::FunNpTan => builtin_fns::call_numpy_tan,
+                    ctx.primitives.float,
+                    |_generator, ctx, _i, scalar| {
+                        let n = scalar.into_float64(ctx);
+                        let n = match prim {
+                            PrimDef::FunNpSin => llvm_intrinsics::call_float_sin(ctx, n, None),
+                            PrimDef::FunNpCos => llvm_intrinsics::call_float_cos(ctx, n, None),
+                            PrimDef::FunNpTan => extern_fns::call_tan(ctx, n, None),
 
-                    PrimDef::FunNpArcsin => builtin_fns::call_numpy_arcsin,
+                            PrimDef::FunNpArcsin => extern_fns::call_asin(ctx, n, None),
-                    PrimDef::FunNpArccos => builtin_fns::call_numpy_arccos,
+                            PrimDef::FunNpArccos => extern_fns::call_acos(ctx, n, None),
-                    PrimDef::FunNpArctan => builtin_fns::call_numpy_arctan,
+                            PrimDef::FunNpArctan => extern_fns::call_atan(ctx, n, None),
 
-                    PrimDef::FunNpSinh => builtin_fns::call_numpy_sinh,
+                            PrimDef::FunNpSinh => extern_fns::call_sinh(ctx, n, None),
-                    PrimDef::FunNpCosh => builtin_fns::call_numpy_cosh,
+                            PrimDef::FunNpCosh => extern_fns::call_cosh(ctx, n, None),
-                    PrimDef::FunNpTanh => builtin_fns::call_numpy_tanh,
+                            PrimDef::FunNpTanh => extern_fns::call_tanh(ctx, n, None),
 
-                    PrimDef::FunNpArcsinh => builtin_fns::call_numpy_arcsinh,
+                            PrimDef::FunNpArcsinh => extern_fns::call_asinh(ctx, n, None),
-                    PrimDef::FunNpArccosh => builtin_fns::call_numpy_arccosh,
+                            PrimDef::FunNpArccosh => extern_fns::call_acosh(ctx, n, None),
-                    PrimDef::FunNpArctanh => builtin_fns::call_numpy_arctanh,
+                            PrimDef::FunNpArctanh => extern_fns::call_atanh(ctx, n, None),
 
-                    PrimDef::FunNpExp => builtin_fns::call_numpy_exp,
+                            PrimDef::FunNpExp => llvm_intrinsics::call_float_exp(ctx, n, None),
-                    PrimDef::FunNpExp2 => builtin_fns::call_numpy_exp2,
+                            PrimDef::FunNpExp2 => llvm_intrinsics::call_float_exp2(ctx, n, None),
-                    PrimDef::FunNpExpm1 => builtin_fns::call_numpy_expm1,
+                            PrimDef::FunNpExpm1 => extern_fns::call_expm1(ctx, n, None),
 
-                    PrimDef::FunNpLog => builtin_fns::call_numpy_log,
+                            PrimDef::FunNpLog => llvm_intrinsics::call_float_log(ctx, n, None),
-                    PrimDef::FunNpLog2 => builtin_fns::call_numpy_log2,
+                            PrimDef::FunNpLog2 => llvm_intrinsics::call_float_log2(ctx, n, None),
-                    PrimDef::FunNpLog10 => builtin_fns::call_numpy_log10,
+                            PrimDef::FunNpLog10 => llvm_intrinsics::call_float_log10(ctx, n, None),
 
-                    PrimDef::FunNpSqrt => builtin_fns::call_numpy_sqrt,
+                            PrimDef::FunNpSqrt => llvm_intrinsics::call_float_sqrt(ctx, n, None),
-                    PrimDef::FunNpCbrt => builtin_fns::call_numpy_cbrt,
+                            PrimDef::FunNpCbrt => extern_fns::call_cbrt(ctx, n, None),
 
-                    PrimDef::FunNpFabs => builtin_fns::call_numpy_fabs,
+                            PrimDef::FunNpFabs => llvm_intrinsics::call_float_fabs(ctx, n, None),
-                    PrimDef::FunNpRint => builtin_fns::call_numpy_rint,
+                            PrimDef::FunNpRint => llvm_intrinsics::call_float_rint(ctx, n, None),
 
-                    PrimDef::FunSpSpecErf => builtin_fns::call_scipy_special_erf,
+                            PrimDef::FunSpSpecErf => extern_fns::call_erf(ctx, n, None),
-                    PrimDef::FunSpSpecErfc => builtin_fns::call_scipy_special_erfc,
+                            PrimDef::FunSpSpecErfc => extern_fns::call_erfc(ctx, n, None),
 
-                    PrimDef::FunSpSpecGamma => builtin_fns::call_scipy_special_gamma,
+                            PrimDef::FunSpSpecGamma => irrt::call_gamma(ctx, n),
-                    PrimDef::FunSpSpecGammaln => builtin_fns::call_scipy_special_gammaln,
+                            PrimDef::FunSpSpecGammaln => irrt::call_gammaln(ctx, n),
 
-                    PrimDef::FunSpSpecJ0 => builtin_fns::call_scipy_special_j0,
+                            PrimDef::FunSpSpecJ0 => irrt::call_j0(ctx, n),
-                    PrimDef::FunSpSpecJ1 => builtin_fns::call_scipy_special_j1,
+                            PrimDef::FunSpSpecJ1 => extern_fns::call_j1(ctx, n, None),
 
                             _ => unreachable!(),
                         };
-                Ok(Some(func(generator, ctx, (arg_ty, arg_val))?))
+                        Ok(n.as_basic_value_enum())
+                    },
+                )?;
+                Ok(Some(result.to_basic_value_enum()))
             }),
         )
     }
@@ -1902,20 +2143,20 @@ impl<'a> BuiltinBuilder<'a> {
 
         let PrimitiveStore { float, int32, .. } = *self.primitives;
 
-        // The argument types of the two input arguments are controlled here.
+        // The argument types of the two input arguments + the return type
-        let (x1_ty, x2_ty) = match prim {
+        let (x1_dtype, x2_dtype, ret_dtype) = match prim {
             PrimDef::FunNpArctan2
             | PrimDef::FunNpCopysign
             | PrimDef::FunNpFmax
             | PrimDef::FunNpFmin
             | PrimDef::FunNpHypot
-            | PrimDef::FunNpNextAfter => (float, float),
+            | PrimDef::FunNpNextAfter => (float, float, float),
-            PrimDef::FunNpLdExp => (float, int32),
+            PrimDef::FunNpLdExp => (float, int32, float),
             _ => unreachable!(),
         };
 
-        let x1_ty = self.new_type_or_ndarray_ty(x1_ty);
+        let x1_ty = self.new_type_or_ndarray_ty(x1_dtype);
-        let x2_ty = self.new_type_or_ndarray_ty(x2_ty);
+        let x2_ty = self.new_type_or_ndarray_ty(x2_dtype);
 
         let param_ty = &[(x1_ty.ty, "x1"), (x2_ty.ty, "x2")];
         let ret_ty = self.unifier.get_fresh_var(None, None);
@@ -1944,78 +2185,78 @@ impl<'a> BuiltinBuilder<'a> {
                 move |ctx, _, fun, args, generator| {
                     let x1_ty = fun.0.args[0].ty;
                     let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
+
                     let x2_ty = fun.0.args[1].ty;
                     let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
 
-                    let func = match prim {
+                    let x1 = split_scalar_or_ndarray(generator, ctx, x1_val, x1_ty);
-                        PrimDef::FunNpArctan2 => builtin_fns::call_numpy_arctan2,
+                    let x2 = split_scalar_or_ndarray(generator, ctx, x2_val, x2_ty);
-                        PrimDef::FunNpCopysign => builtin_fns::call_numpy_copysign,
+
-                        PrimDef::FunNpFmax => builtin_fns::call_numpy_fmax,
+                    let result = ScalarOrNDArray::broadcasting_starmap(
-                        PrimDef::FunNpFmin => builtin_fns::call_numpy_fmin,
+                        generator,
-                        PrimDef::FunNpLdExp => builtin_fns::call_numpy_ldexp,
+                        ctx,
-                        PrimDef::FunNpHypot => builtin_fns::call_numpy_hypot,
+                        &[x1, x2],
-                        PrimDef::FunNpNextAfter => builtin_fns::call_numpy_nextafter,
+                        ret_dtype,
+                        |_generator, ctx, _i, scalars| {
+                            let x1 = scalars[0];
+                            let x2 = scalars[1];
+
+                            // TODO: This looks ugly
+                            let result = match prim {
+                                PrimDef::FunNpArctan2 => {
+                                    let x1 = x1.into_float64(ctx);
+                                    let x2 = x2.into_float64(ctx);
+                                    extern_fns::call_atan2(ctx, x1, x2, None).as_basic_value_enum()
+                                }
+                                PrimDef::FunNpCopysign => {
+                                    let x1 = x1.into_float64(ctx);
+                                    let x2 = x2.into_float64(ctx);
+                                    llvm_intrinsics::call_float_copysign(ctx, x1, x2, None)
+                                        .as_basic_value_enum()
+                                }
+                                PrimDef::FunNpFmax => {
+                                    let x1 = x1.into_float64(ctx);
+                                    let x2 = x2.into_float64(ctx);
+                                    llvm_intrinsics::call_float_maxnum(ctx, x1, x2, None)
+                                        .as_basic_value_enum()
+                                }
+                                PrimDef::FunNpFmin => {
+                                    let x1 = x1.into_float64(ctx);
+                                    let x2 = x2.into_float64(ctx);
+                                    llvm_intrinsics::call_float_minnum(ctx, x1, x2, None)
+                                        .as_basic_value_enum()
+                                }
+                                PrimDef::FunNpHypot => {
+                                    let x1 = x1.into_float64(ctx);
+                                    let x2 = x2.into_float64(ctx);
+                                    llvm_intrinsics::call_float_minnum(ctx, x1, x2, None)
+                                        .as_basic_value_enum()
+                                }
+                                PrimDef::FunNpNextAfter => {
+                                    let x1 = x1.into_float64(ctx);
+                                    let x2 = x2.into_float64(ctx);
+                                    extern_fns::call_nextafter(ctx, x1, x2, None)
+                                        .as_basic_value_enum()
+                                }
+                                PrimDef::FunNpLdExp => {
+                                    let x1 = x1.into_float64(ctx);
+                                    let x2 = x2.into_int32(ctx);
+                                    extern_fns::call_ldexp(ctx, x1, x2, None).as_basic_value_enum()
+                                }
                                 _ => unreachable!(),
                             };
 
-                    Ok(Some(func(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
+                            Ok(result)
+                        },
+                    )?;
+
+                    Ok(Some(result.to_basic_value_enum()))
                 },
             )))),
             loc: None,
         }
     }
 
-    /// Build np/sp functions that take as input `NDArray` only
-    fn build_np_sp_ndarray_function(&mut self, prim: PrimDef) -> TopLevelDef {
-        debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpTranspose, PrimDef::FunNpReshape]);
-
-        match prim {
-            PrimDef::FunNpTranspose => {
-                let ndarray_ty = self.unifier.get_fresh_var_with_range(
-                    &[self.ndarray_num_ty],
-                    Some("T".into()),
-                    None,
-                );
-                create_fn_by_codegen(
-                    self.unifier,
-                    &into_var_map([ndarray_ty]),
-                    prim.name(),
-                    ndarray_ty.ty,
-                    &[(ndarray_ty.ty, "x")],
-                    Box::new(move |ctx, _, fun, args, generator| {
-                        let arg_ty = fun.0.args[0].ty;
-                        let arg_val =
-                            args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
-                        Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
-                    }),
-                )
-            }
-
-            // NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
-            // the `param_ty` for `create_fn_by_codegen`.
-            //
-            // Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
-            // to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
-            // and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
-            PrimDef::FunNpReshape => create_fn_by_codegen(
-                self.unifier,
-                &VarMap::new(),
-                prim.name(),
-                self.ndarray_num_ty,
-                &[(self.ndarray_num_ty, "x"), (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
-                Box::new(move |ctx, _, fun, args, generator| {
-                    let x1_ty = fun.0.args[0].ty;
-                    let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
-                    let x2_ty = fun.0.args[1].ty;
-                    let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
-                    Ok(Some(ndarray_reshape(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
-                }),
-            ),
-
-            _ => unreachable!(),
-        }
-    }
-
     /// Build `np_linalg` and `sp_linalg` functions
     ///
     /// The input to these functions must be floating point `NDArray`

nac3core/src/toplevel/helper.rs

@@ -51,6 +51,17 @@ pub enum PrimDef {
     FunNpArray,
     FunNpEye,
     FunNpIdentity,
+    FunNpArange,
+
+    // NumPy view functions
+    FunNpBroadcastTo,
+    FunNpReshape,
+    FunNpTranspose,
+
+    // NumPy NDArray property getters
+    FunNpSize,
+    FunNpShape,
+    FunNpStrides,
 
     // Miscellaneous NumPy & SciPy functions
     FunNpRound,
@@ -99,8 +110,6 @@ pub enum PrimDef {
     FunNpLdExp,
     FunNpHypot,
     FunNpNextAfter,
-    FunNpTranspose,
-    FunNpReshape,
 
     // Linalg functions
     FunNpDot,
@@ -237,6 +246,17 @@ impl PrimDef {
             PrimDef::FunNpArray => fun("np_array", None),
             PrimDef::FunNpEye => fun("np_eye", None),
             PrimDef::FunNpIdentity => fun("np_identity", None),
+            PrimDef::FunNpArange => fun("np_arange", None),
+
+            // NumPy view functions
+            PrimDef::FunNpBroadcastTo => fun("np_broadcast_to", None),
+            PrimDef::FunNpReshape => fun("np_reshape", None),
+            PrimDef::FunNpTranspose => fun("np_transpose", None),
+
+            // NumPy NDArray property getters
+            PrimDef::FunNpSize => fun("np_size", None),
+            PrimDef::FunNpShape => fun("np_shape", None),
+            PrimDef::FunNpStrides => fun("np_strides", None),
 
             // Miscellaneous NumPy & SciPy functions
             PrimDef::FunNpRound => fun("np_round", None),
@@ -285,8 +305,6 @@ impl PrimDef {
             PrimDef::FunNpLdExp => fun("np_ldexp", None),
             PrimDef::FunNpHypot => fun("np_hypot", None),
             PrimDef::FunNpNextAfter => fun("np_nextafter", None),
-            PrimDef::FunNpTranspose => fun("np_transpose", None),
-            PrimDef::FunNpReshape => fun("np_reshape", None),
 
             // Linalg functions
             PrimDef::FunNpDot => fun("np_dot", None),

nac3core/src/toplevel/numpy.rs

@@ -1,4 +1,7 @@
+use std::sync::Arc;
+
 use crate::{
+    symbol_resolver::SymbolValue,
     toplevel::helper::PrimDef,
     typecheck::{
         type_inferencer::PrimitiveStore,
@@ -83,3 +86,33 @@ pub fn unpack_ndarray_var_ids(unifier: &mut Unifier, ndarray: Type) -> (TypeVarI
 pub fn unpack_ndarray_var_tys(unifier: &mut Unifier, ndarray: Type) -> (Type, Type) {
     unpack_ndarray_tvars(unifier, ndarray).into_iter().map(|v| v.1).collect_tuple().unwrap()
 }
+
+/// Extract an ndarray's `ndims` [type][`Type`] in `u64`. Panic if not possible.
+/// The `ndims` must only contain 1 value.
+#[must_use]
+pub fn extract_ndims(unifier: &Unifier, ndims_ty: Type) -> u64 {
+    let ndims_ty_enum = unifier.get_ty_immutable(ndims_ty);
+    let TypeEnum::TLiteral { values, .. } = &*ndims_ty_enum else {
+        panic!("ndims_ty should be a TLiteral");
+    };
+
+    assert_eq!(values.len(), 1, "ndims_ty TLiteral should only contain 1 value");
+
+    let ndims = values[0].clone();
+    u64::try_from(ndims).unwrap()
+}
+
+/// Return an ndarray's `ndims` as a typechecker [`Type`] from its `u64` value.
+pub fn create_ndims(unifier: &mut Unifier, ndims: u64) -> Type {
+    unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None)
+}
+
+/// Return the ndims after broadcasting ndarrays of different ndims.
+///
+/// Panics if the input list is empty.
+pub fn get_broadcast_all_ndims<I>(ndims: I) -> u64
+where
+    I: IntoIterator<Item = u64>,
+{
+    ndims.into_iter().max().unwrap()
+}

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -1,6 +1,6 @@
 use std::collections::{HashMap, HashSet};
 use std::convert::{From, TryInto};
-use std::iter::once;
+use std::iter::{self, once};
 use std::{cell::RefCell, sync::Arc};
 
 use super::{
@@ -15,12 +15,13 @@ use crate::{
     symbol_resolver::{SymbolResolver, SymbolValue},
     toplevel::{
         helper::{arraylike_flatten_element_type, arraylike_get_ndims, PrimDef},
-        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
+        numpy::{extract_ndims, make_ndarray_ty, unpack_ndarray_var_tys},
         TopLevelContext, TopLevelDef,
     },
     typecheck::typedef::Mapping,
 };
 use itertools::{izip, Itertools};
+use nac3parser::ast::Constant;
 use nac3parser::ast::{
     self,
     fold::{self, Fold},
@@ -1272,7 +1273,7 @@ impl<'a> Inferencer<'a> {
                 arg_ty.obj_id(self.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
             }) {
                 // typeof_ndarray_broadcast requires both dtypes to be the same, but ldexp accepts
-                // (float, int32), so convert it to align with the dtype of the first arg
+                // (float, int32), so convert it to align with t#he dtype of the first arg
                 let arg1_ty = if id == &"np_ldexp".into() {
                     if arg1_ty.obj_id(self.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) {
                         let (_, ndims) = unpack_ndarray_var_tys(self.unifier, arg1_ty);
@@ -1448,7 +1449,7 @@ impl<'a> Inferencer<'a> {
                 },
             }));
         }
-        // 2-argument ndarray n-dimensional factory functions
+
         if id == &"np_reshape".into() && args.len() == 2 {
             let arg0 = self.fold_expr(args.remove(0))?;
 
@@ -1493,6 +1494,51 @@ impl<'a> Inferencer<'a> {
                 },
             }));
         }
+
+        if ["np_shape".into(), "np_strides".into()].contains(id) && args.len() == 1 {
+            // Output tuple size depends on input ndarray's ndims.
+
+            let ndarray = self.fold_expr(args.remove(0))?;
+
+            let (_, ndims) = unpack_ndarray_var_tys(self.unifier, ndarray.custom.unwrap());
+            let ndims = extract_ndims(self.unifier, ndims);
+
+            // Create a tuple of size `ndims` full of int32
+            // TODO: Make it usize
+
+            let ret_ty = TypeEnum::TTuple {
+                ty: iter::repeat(self.primitives.int32).take(ndims as usize).collect_vec(),
+                is_vararg_ctx: false,
+            };
+            let ret_ty = self.unifier.add_ty(ret_ty);
+
+            let func_ty = TypeEnum::TFunc(FunSignature {
+                args: vec![FuncArg {
+                    name: "a".into(),
+                    default_value: None,
+                    ty: ndarray.custom.unwrap(),
+                    is_vararg: false,
+                }],
+                ret: ret_ty,
+                vars: VarMap::new(),
+            });
+            let func_ty = self.unifier.add_ty(func_ty);
+
+            return Ok(Some(Located {
+                location,
+                custom: Some(ret_ty),
+                node: ExprKind::Call {
+                    func: Box::new(Located {
+                        custom: Some(func_ty),
+                        location: func.location,
+                        node: ExprKind::Name { id: *id, ctx: *ctx },
+                    }),
+                    args: vec![ndarray],
+                    keywords: vec![],
+                },
+            }));
+        }
+
         // 2-argument ndarray n-dimensional creation functions
         if id == &"np_full".into() && args.len() == 2 {
             let ExprKind::List { elts, .. } = &args[0].node else {
@@ -2210,16 +2256,27 @@ impl<'a> Inferencer<'a> {
         // We will also take the opportunity to deduce `dims_to_subtract` as well
         let mut dims_to_subtract: i128 = 0;
         for index in indices {
-            if let ExprKind::Slice { lower, upper, step } = &index.node {
+            match &index.node {
+                ExprKind::Slice { lower, upper, step } => {
+                    // Handle slices
                     for v in [lower.as_ref(), upper.as_ref(), step.as_ref()].iter().flatten() {
                         self.constrain(v.custom.unwrap(), self.primitives.int32, &v.location)?;
                     }
-            } else {
+                }
+                ExprKind::Constant { value: Constant::Ellipsis, .. } => {
+                    // Handle `...`. Do nothing.
+                }
+                ExprKind::Name { id, .. } if id == &"none".into() => {
+                    // Handle `np.newaxis` / `None`.
+                    dims_to_subtract -= 1;
+                }
+                _ => {
                     // Treat anything else as an integer index, and force unify their type to int32.
                     self.unify(index.custom.unwrap(), self.primitives.int32, &index.location)?;
                     dims_to_subtract += 1;
                 }
             }
+        }
 
         Ok(dims_to_subtract)
     }

nac3core/src/typecheck/typedef/mod.rs

@@ -342,6 +342,14 @@ impl Unifier {
         self.unification_table.unioned(a, b)
     }
 
+    /// Determine if a type unions with a type in `tys`.
+    pub fn unioned_any<I>(&mut self, a: Type, tys: I) -> bool
+    where
+        I: IntoIterator<Item = Type>,
+    {
+        tys.into_iter().any(|ty| self.unioned(a, ty))
+    }
+
     pub fn from_shared_unifier(unifier: &SharedUnifier) -> Unifier {
         let lock = unifier.lock().unwrap();
         Unifier {

nac3standalone/demo/interpret_demo.py

@@ -218,8 +218,16 @@ def patch(module):
     module.np_ldexp = np.ldexp
     module.np_hypot = np.hypot
     module.np_nextafter = np.nextafter
-    module.np_transpose = np.transpose
+
+    # NumPy view functions
+    module.np_broadcast_to = np.broadcast_to
     module.np_reshape = np.reshape
+    module.np_transpose = np.transpose
+
+    # NumPy NDArray property getter functions
+    module.np_size = np.size
+    module.np_shape = np.shape
+    module.np_strides = lambda ndarray: ndarray.strides
 
     # SciPy Math functions
     module.sp_spec_erf = special.erf