core/ndstrides: implement np_transpose() (no axes argument)

nac3core/irrt/irrt.cpp

@@ -4,9 +4,11 @@
 #include <irrt/math_util.hpp>
 #include <irrt/ndarray/array.hpp>
 #include <irrt/ndarray/basic.hpp>
+#include <irrt/ndarray/broadcast.hpp>
 #include <irrt/ndarray/def.hpp>
 #include <irrt/ndarray/indexing.hpp>
 #include <irrt/ndarray/iter.hpp>
 #include <irrt/ndarray/reshape.hpp>
+#include <irrt/ndarray/transpose.hpp>
 #include <irrt/original.hpp>
 #include <irrt/slice.hpp>

nac3core/irrt/irrt/ndarray/broadcast.hpp

@@ -0,0 +1,188 @@
+#pragma once
+
+#include <irrt/int_types.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
+{
+/**
+ * @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(<shapes>)`
+ *
+ * @param num_shapes Number of entries in `shapes`
+ * @param shapes The list of shape to do `np.broadcast_shapes` on.
+ * @param dst_ndims The length of `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.
+ * @param dst_shape The resulting shape. Must be pre-allocated by the caller. This function calculate the result
+ *   of `np.broadcast_shapes` and write it here.
+ */
+template <typename SizeT>
+void broadcast_shapes(SizeT num_shapes, const ShapeEntry<SizeT> *shapes, SizeT dst_ndims, SizeT *dst_shape)
+{
+    for (SizeT dst_axis = 0; dst_axis < dst_ndims; dst_axis++)
+    {
+        dst_shape[dst_axis] = 1;
+    }
+
+#ifdef IRRT_DEBUG_ASSERT
+    SizeT max_ndims_found = 0;
+#endif
+
+    for (SizeT i = 0; i < num_shapes; i++)
+    {
+        ShapeEntry<SizeT> entry = shapes[i];
+
+        // Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
+        debug_assert(SizeT, entry.ndims <= dst_ndims);
+
+#ifdef IRRT_DEBUG_ASSERT
+        max_ndims_found = max(max_ndims_found, entry.ndims);
+#endif
+
+        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);
+            }
+        }
+    }
+
+    // Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
+    debug_assert_eq(SizeT, max_ndims_found, dst_ndims);
+}
+
+/**
+ * @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::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", NO_PARAM, NO_PARAM,
+                        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)
+    {
+        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)
+    {
+        broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
+    }
+}

nac3core/irrt/irrt/ndarray/transpose.hpp

@@ -0,0 +1,155 @@
+#pragma once
+
+#include <irrt/int_types.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
+{
+/**
+ * @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;
+    }
+}
+
+/**
+ * @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)
+{
+    debug_assert_eq(SizeT, src_ndarray->ndims, dst_ndarray->ndims);
+    const auto ndims = src_ndarray->ndims;
+
+    if (axes != nullptr)
+        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/src/codegen/irrt/mod.rs

@@ -9,7 +9,7 @@ use super::{
     model::*,
     object::{
         list::List,
-        ndarray::{indexing::NDIndex, nditer::NDIter, NDArray},
+        ndarray::{broadcast::ShapeEntry, indexing::NDIndex, nditer::NDIter, NDArray},
     },
     CodeGenContext, CodeGenerator,
 };
@@ -1183,3 +1183,47 @@ pub fn call_nac3_ndarray_reshape_resolve_and_check_new_shape<'ctx, G: CodeGenera
         .arg(new_shape)
         .returning_void();
 }
+
+pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
+    dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
+) {
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
+    CallFunction::begin(generator, ctx, &name).arg(src_ndarray).arg(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: Instance<'ctx, Int<SizeT>>,
+    shape_entries: Instance<'ctx, Ptr<Struct<ShapeEntry>>>,
+    dst_ndims: Instance<'ctx, Int<SizeT>>,
+    dst_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+) {
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
+    CallFunction::begin(generator, ctx, &name)
+        .arg(num_shape_entries)
+        .arg(shape_entries)
+        .arg(dst_ndims)
+        .arg(dst_shape)
+        .returning_void();
+}
+
+pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
+    dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
+    num_axes: Instance<'ctx, Int<SizeT>>,
+    axes: Instance<'ctx, Ptr<Int<SizeT>>>,
+) {
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
+    CallFunction::begin(generator, ctx, &name)
+        .arg(src_ndarray)
+        .arg(dst_ndarray)
+        .arg(num_axes)
+        .arg(axes)
+        .returning_void();
+}

nac3core/src/codegen/numpy.rs

@@ -1990,112 +1990,6 @@ pub fn gen_ndarray_fill<'ctx>(
     Ok(())
 }
 
-/// Generates LLVM IR for `ndarray.transpose`.
-pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    x1: (Type, BasicValueEnum<'ctx>),
-) -> Result<BasicValueEnum<'ctx>, String> {
-    const FN_NAME: &str = "ndarray_transpose";
-    let (x1_ty, x1) = x1;
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-
-    if let BasicValueEnum::PointerValue(n1) = x1 {
-        let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-        let n_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
-
-        // Dimensions are reversed in the transposed array
-        let out = create_ndarray_dyn_shape(
-            generator,
-            ctx,
-            elem_ty,
-            &n1,
-            |_, ctx, n| Ok(n.load_ndims(ctx)),
-            |generator, ctx, n, idx| {
-                let new_idx = ctx.builder.build_int_sub(n.load_ndims(ctx), idx, "").unwrap();
-                let new_idx = ctx
-                    .builder
-                    .build_int_sub(new_idx, new_idx.get_type().const_int(1, false), "")
-                    .unwrap();
-                unsafe { Ok(n.dim_sizes().get_typed_unchecked(ctx, generator, &new_idx, None)) }
-            },
-        )
-        .unwrap();
-
-        gen_for_callback_incrementing(
-            generator,
-            ctx,
-            None,
-            llvm_usize.const_zero(),
-            (n_sz, false),
-            |generator, ctx, _, idx| {
-                let elem = unsafe { n1.data().get_unchecked(ctx, generator, &idx, None) };
-
-                let new_idx = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
-                let rem_idx = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
-                ctx.builder.build_store(new_idx, llvm_usize.const_zero()).unwrap();
-                ctx.builder.build_store(rem_idx, idx).unwrap();
-
-                // Incrementally calculate the new index in the transposed array
-                // For each index, we first decompose it into the n-dims and use those to reconstruct the new index
-                // The formula used for indexing is:
-                // idx = dim_n * ( ... (dim2 * (dim0 * dim1) + dim1) + dim2 ... ) + dim_n
-                gen_for_callback_incrementing(
-                    generator,
-                    ctx,
-                    None,
-                    llvm_usize.const_zero(),
-                    (n1.load_ndims(ctx), false),
-                    |generator, ctx, _, ndim| {
-                        let ndim_rev =
-                            ctx.builder.build_int_sub(n1.load_ndims(ctx), ndim, "").unwrap();
-                        let ndim_rev = ctx
-                            .builder
-                            .build_int_sub(ndim_rev, llvm_usize.const_int(1, false), "")
-                            .unwrap();
-                        let dim = unsafe {
-                            n1.dim_sizes().get_typed_unchecked(ctx, generator, &ndim_rev, None)
-                        };
-
-                        let rem_idx_val =
-                            ctx.builder.build_load(rem_idx, "").unwrap().into_int_value();
-                        let new_idx_val =
-                            ctx.builder.build_load(new_idx, "").unwrap().into_int_value();
-
-                        let add_component =
-                            ctx.builder.build_int_unsigned_rem(rem_idx_val, dim, "").unwrap();
-                        let rem_idx_val =
-                            ctx.builder.build_int_unsigned_div(rem_idx_val, dim, "").unwrap();
-
-                        let new_idx_val = ctx.builder.build_int_mul(new_idx_val, dim, "").unwrap();
-                        let new_idx_val =
-                            ctx.builder.build_int_add(new_idx_val, add_component, "").unwrap();
-
-                        ctx.builder.build_store(rem_idx, rem_idx_val).unwrap();
-                        ctx.builder.build_store(new_idx, new_idx_val).unwrap();
-
-                        Ok(())
-                    },
-                    llvm_usize.const_int(1, false),
-                )?;
-
-                let new_idx_val = ctx.builder.build_load(new_idx, "").unwrap().into_int_value();
-                unsafe { out.data().set_unchecked(ctx, generator, &new_idx_val, elem) };
-                Ok(())
-            },
-            llvm_usize.const_int(1, false),
-        )?;
-
-        Ok(out.as_base_value().into())
-    } else {
-        unreachable!(
-            "{FN_NAME}() not supported for '{}'",
-            format!("'{}'", ctx.unifier.stringify(x1_ty))
-        )
-    }
-}
-
 /// Generates LLVM IR for `ndarray.dot`.
 /// Calculate inner product of two vectors or literals
 /// For matrix multiplication use `np_matmul`

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

@@ -0,0 +1,135 @@
+use itertools::Itertools;
+
+use crate::codegen::{
+    irrt::{call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to},
+    model::*,
+    CodeGenContext, CodeGenerator,
+};
+
+use super::NDArrayObject;
+
+/// Fields of [`ShapeEntry`]
+pub struct ShapeEntryFields<'ctx, F: FieldTraversal<'ctx>> {
+    pub ndims: F::Out<Int<SizeT>>,
+    pub shape: F::Out<Ptr<Int<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.
+    ///
+    /// The input shape will be checked to make sure that it contains no negative values.
+    ///
+    /// * `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: Instance<'ctx, Ptr<Int<SizeT>>>,
+    ) -> Self {
+        let broadcast_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, target_ndims);
+        broadcast_ndarray.copy_shape_from_array(generator, ctx, target_shape);
+
+        call_nac3_ndarray_broadcast_to(generator, ctx, self.instance, broadcast_ndarray.instance);
+        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: Instance<'ctx, Ptr<Int<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>>,
+}
+
+/// Helper function to call `call_nac3_ndarray_broadcast_shapes`
+fn broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    in_shape_entries: &[(Instance<'ctx, Ptr<Int<SizeT>>>, u64)], // (shape, shape's length/ndims)
+    broadcast_ndims: u64,
+    broadcast_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+) {
+    // Prepare input shape entries to be passed to `call_nac3_ndarray_broadcast_shapes`.
+    let num_shape_entries =
+        Int(SizeT).const_int(generator, ctx.ctx, u64::try_from(in_shape_entries.len()).unwrap());
+    let shape_entries = Struct(ShapeEntry).array_alloca(generator, ctx, num_shape_entries.value);
+    for (i, (in_shape, in_ndims)) in in_shape_entries.iter().enumerate() {
+        let pshape_entry = shape_entries.offset_const(ctx, i as u64);
+
+        let in_ndims = Int(SizeT).const_int(generator, ctx.ctx, *in_ndims);
+        pshape_entry.set(ctx, |f| f.ndims, in_ndims);
+
+        pshape_entry.set(ctx, |f| f.shape, *in_shape);
+    }
+
+    let broadcast_ndims = Int(SizeT).const_int(generator, ctx.ctx, broadcast_ndims);
+    call_nac3_ndarray_broadcast_shapes(
+        generator,
+        ctx,
+        num_shape_entries,
+        shape_entries,
+        broadcast_ndims,
+        broadcast_shape,
+    );
+}
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// Broadcast all ndarrays according to `np.broadcast()` and return a [`BroadcastAllResult`]
+    /// containing all the information of the result of the broadcast operation.
+    pub fn broadcast<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ndarrays: &[Self],
+    ) -> BroadcastAllResult<'ctx> {
+        assert!(!ndarrays.is_empty());
+
+        // Infer the broadcast output ndims.
+        let broadcast_ndims_int = ndarrays.iter().map(|ndarray| ndarray.ndims).max().unwrap();
+
+        let broadcast_ndims = Int(SizeT).const_int(generator, ctx.ctx, broadcast_ndims_int);
+        let broadcast_shape = Int(SizeT).array_alloca(generator, ctx, broadcast_ndims.value);
+
+        let shape_entries = ndarrays
+            .iter()
+            .map(|ndarray| (ndarray.instance.get(generator, ctx, |f| f.shape), ndarray.ndims))
+            .collect_vec();
+        broadcast_shapes(generator, ctx, &shape_entries, broadcast_ndims_int, broadcast_shape);
+
+        // Broadcast all the inputs to shape `dst_shape`.
+        let broadcast_ndarrays: Vec<_> = ndarrays
+            .iter()
+            .map(|ndarray| {
+                ndarray.broadcast_to(generator, ctx, broadcast_ndims_int, broadcast_shape)
+            })
+            .collect_vec();
+
+        BroadcastAllResult {
+            ndims: broadcast_ndims_int,
+            shape: broadcast_shape,
+            ndarrays: broadcast_ndarrays,
+        }
+    }
+}

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

@@ -1,4 +1,5 @@
 pub mod array;
+pub mod broadcast;
 pub mod factory;
 pub mod indexing;
 pub mod nditer;

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

@@ -1,6 +1,7 @@
 use crate::codegen::{
-    irrt::call_nac3_ndarray_reshape_resolve_and_check_new_shape, model::*, CodeGenContext,
-    CodeGenerator,
+    irrt::{call_nac3_ndarray_reshape_resolve_and_check_new_shape, call_nac3_ndarray_transpose},
+    model::*,
+    CodeGenContext, CodeGenerator,
 };
 
 use super::{indexing::RustNDIndex, NDArrayObject};
@@ -86,4 +87,33 @@ impl<'ctx> NDArrayObject<'ctx> {
 
         dst_ndarray
     }
+
+    /// 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**).
+    #[must_use]
+    pub fn transpose<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        axes: Option<Instance<'ctx, Ptr<Int<SizeT>>>>,
+    ) -> Self {
+        // Define models
+        let transposed_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims);
+
+        let num_axes = self.ndims_llvm(generator, ctx.ctx);
+
+        // `axes = nullptr` if `axes` is unspecified.
+        let axes = axes.unwrap_or_else(|| Ptr(Int(SizeT)).nullptr(generator, ctx.ctx));
+
+        call_nac3_ndarray_transpose(
+            generator,
+            ctx,
+            self.instance,
+            transposed_ndarray.instance,
+            num_axes,
+            axes,
+        );
+
+        transposed_ndarray
+    }
 }

nac3core/src/toplevel/builtins.rs

@@ -521,7 +521,7 @@ impl<'a> BuiltinBuilder<'a> {
                 self.build_ndarray_property_getter_function(prim)
             }
 
-            PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
+            PrimDef::FunNpBroadcastTo | PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
                 self.build_ndarray_view_function(prim)
             }
 
@@ -1470,7 +1470,10 @@ impl<'a> BuiltinBuilder<'a> {
 
     /// Build np/sp functions that take as input `NDArray` only
     fn build_ndarray_view_function(&mut self, prim: PrimDef) -> TopLevelDef {
-        debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpTranspose, PrimDef::FunNpReshape]);
+        debug_assert_prim_is_allowed(
+            prim,
+            &[PrimDef::FunNpBroadcastTo, PrimDef::FunNpTranspose, PrimDef::FunNpReshape],
+        );
 
         let in_ndarray_ty = self.unifier.get_fresh_var_with_range(
             &[self.primitives.ndarray],
@@ -1479,18 +1482,26 @@ impl<'a> BuiltinBuilder<'a> {
         );
 
         match prim {
-            PrimDef::FunNpTranspose => create_fn_by_codegen(
-                self.unifier,
-                &into_var_map([in_ndarray_ty]),
-                prim.name(),
-                in_ndarray_ty.ty,
-                &[(in_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))?))
-                }),
-            ),
+            PrimDef::FunNpTranspose => {
+                create_fn_by_codegen(
+                    self.unifier,
+                    &VarMap::new(),
+                    prim.name(),
+                    in_ndarray_ty.ty,
+                    &[(in_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)?;
+
+                        let arg = AnyObject { ty: arg_ty, value: arg_val };
+                        let ndarray = NDArrayObject::from_object(generator, ctx, arg);
+
+                        let ndarray = ndarray.transpose(generator, ctx, None); // TODO: Add axes argument
+                        Ok(Some(ndarray.instance.value.as_basic_value_enum()))
+                    }),
+                )
+            }
 
             // NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
             // the `param_ty` for `create_fn_by_codegen`.
@@ -1498,7 +1509,7 @@ impl<'a> BuiltinBuilder<'a> {
             // 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 => {
+            PrimDef::FunNpBroadcastTo | PrimDef::FunNpReshape => {
                 let ret_ty = self.unifier.get_dummy_var().ty; // Handled by special holding
 
                 create_fn_by_codegen(
@@ -1529,7 +1540,15 @@ impl<'a> BuiltinBuilder<'a> {
                         let (_, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
                         let ndims = extract_ndims(&ctx.unifier, ndims);
 
-                        let new_ndarray = ndarray.reshape_or_copy(generator, ctx, ndims, shape);
+                        let new_ndarray = match prim {
+                            PrimDef::FunNpBroadcastTo => {
+                                ndarray.broadcast_to(generator, ctx, ndims, shape)
+                            }
+                            PrimDef::FunNpReshape => {
+                                ndarray.reshape_or_copy(generator, ctx, ndims, shape)
+                            }
+                            _ => unreachable!(),
+                        };
                         Ok(Some(new_ndarray.instance.value.as_basic_value_enum()))
                     }),
                 )

nac3core/src/toplevel/helper.rs

@@ -58,6 +58,7 @@ pub enum PrimDef {
     FunNpStrides,
 
     // NumPy ndarray view functions
+    FunNpBroadcastTo,
     FunNpTranspose,
     FunNpReshape,
 
@@ -251,6 +252,7 @@ impl PrimDef {
             PrimDef::FunNpStrides => fun("np_strides", None),
 
             // NumPy NDArray view functions
+            PrimDef::FunNpBroadcastTo => fun("np_broadcast_to", None),
             PrimDef::FunNpTranspose => fun("np_transpose", None),
             PrimDef::FunNpReshape => fun("np_reshape", None),
 

nac3standalone/demo/interpret_demo.py

@@ -180,6 +180,7 @@ def patch(module):
     module.np_array = np.array
 
     # NumPy NDArray view functions
+    module.np_broadcast_to = np.broadcast_to
     module.np_transpose = np.transpose
     module.np_reshape = np.reshape