core/ndstrides: implement np_reshape()

These functions are not important, but they are handy for debugging.

`np.strides()` is not an actual NumPy function, but `ndarray.strides` is used.

nac3core/irrt/irrt.cpp

@@ -8,4 +8,6 @@
 #include "irrt/ndarray/basic.hpp"
 #include "irrt/ndarray/def.hpp"
 #include "irrt/ndarray/iter.hpp"
-#include "irrt/ndarray/indexing.hpp"
+#include "irrt/ndarray/indexing.hpp"
+#include "irrt/ndarray/array.hpp"
+#include "irrt/ndarray/reshape.hpp"

nac3core/irrt/irrt/ndarray/array.hpp

@@ -0,0 +1,134 @@
+#pragma once
+
+#include "irrt/debug.hpp"
+#include "irrt/exception.hpp"
+#include "irrt/int_types.hpp"
+#include "irrt/list.hpp"
+#include "irrt/ndarray/basic.hpp"
+#include "irrt/ndarray/def.hpp"
+
+namespace {
+namespace ndarray {
+namespace array {
+/**
+ * @brief In the context of `np.array(<list>)`, deduce the ndarray's shape produced by `<list>` and raise
+ * an exception if there is anything wrong with `<shape>` (e.g., inconsistent dimensions `np.array([[1.0, 2.0],
+ * [3.0]])`)
+ *
+ * If this function finds no issues with `<list>`, the deduced shape is written to `shape`. The caller has the
+ * responsibility to allocate `[SizeT; ndims]` for `shape`. The caller must also initialize `shape` with `-1`s because
+ * of implementation details.
+ */
+template<typename SizeT>
+void set_and_validate_list_shape_helper(SizeT axis, List<SizeT>* list, SizeT ndims, SizeT* shape) {
+    if (shape[axis] == -1) {
+        // Dimension is unspecified. Set it.
+        shape[axis] = list->len;
+    } else {
+        // Dimension is specified. Check.
+        if (shape[axis] != list->len) {
+            // Mismatch, throw an error.
+            // NOTE: NumPy's error message is more complex and needs more PARAMS to display.
+            raise_exception(SizeT, EXN_VALUE_ERROR,
+                            "The requested array has an inhomogenous shape "
+                            "after {0} dimension(s).",
+                            axis, shape[axis], list->len);
+        }
+    }
+
+    if (axis + 1 == ndims) {
+        // `list` has type `list[ItemType]`
+        // Do nothing
+    } else {
+        // `list` has type `list[list[...]]`
+        List<SizeT>** lists = (List<SizeT>**)(list->items);
+        for (SizeT i = 0; i < list->len; i++) {
+            set_and_validate_list_shape_helper<SizeT>(axis + 1, lists[i], ndims, shape);
+        }
+    }
+}
+
+/**
+ * @brief See `set_and_validate_list_shape_helper`.
+ */
+template<typename SizeT>
+void set_and_validate_list_shape(List<SizeT>* list, SizeT ndims, SizeT* shape) {
+    for (SizeT axis = 0; axis < ndims; axis++) {
+        shape[axis] = -1;  // Sentinel to say this dimension is unspecified.
+    }
+    set_and_validate_list_shape_helper<SizeT>(0, list, ndims, shape);
+}
+
+/**
+ * @brief In the context of `np.array(<list>)`, copied the contents stored in `list` to `ndarray`.
+ *
+ * `list` is assumed to be "legal". (i.e., no inconsistent dimensions)
+ *
+ * # Notes on `ndarray`
+ * The caller is responsible for allocating space for `ndarray`.
+ * Here is what this function expects from `ndarray` when called:
+ *   - `ndarray->data` has to be allocated, contiguous, and may contain uninitialized values.
+ *   - `ndarray->itemsize` has to be initialized.
+ *   - `ndarray->ndims` has to be initialized.
+ *   - `ndarray->shape` has to be initialized.
+ *   - `ndarray->strides` is ignored, but note that `ndarray->data` is contiguous.
+ * When this function call ends:
+ *   - `ndarray->data` is written with contents from `<list>`.
+ */
+template<typename SizeT>
+void write_list_to_array_helper(SizeT axis, SizeT* index, List<SizeT>* list, NDArray<SizeT>* ndarray) {
+    debug_assert_eq(SizeT, list->len, ndarray->shape[axis]);
+    if (IRRT_DEBUG_ASSERT_BOOL) {
+        if (!ndarray::basic::is_c_contiguous(ndarray)) {
+            raise_debug_assert(SizeT, "ndarray is not C-contiguous", ndarray->strides[0], ndarray->strides[1],
+                               NO_PARAM);
+        }
+    }
+
+    if (axis + 1 == ndarray->ndims) {
+        // `list` has type `list[scalar]`
+        // `ndarray` is contiguous, so we can do this, and this is fast.
+        uint8_t* dst = ndarray->data + (ndarray->itemsize * (*index));
+        __builtin_memcpy(dst, list->items, ndarray->itemsize * list->len);
+        *index += list->len;
+    } else {
+        // `list` has type `list[list[...]]`
+        List<SizeT>** lists = (List<SizeT>**)(list->items);
+
+        for (SizeT i = 0; i < list->len; i++) {
+            write_list_to_array_helper<SizeT>(axis + 1, index, lists[i], ndarray);
+        }
+    }
+}
+
+/**
+ * @brief See `write_list_to_array_helper`.
+ */
+template<typename SizeT>
+void write_list_to_array(List<SizeT>* list, NDArray<SizeT>* ndarray) {
+    SizeT index = 0;
+    write_list_to_array_helper<SizeT>((SizeT)0, &index, list, ndarray);
+}
+}  // namespace array
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+using namespace ndarray::array;
+
+void __nac3_ndarray_array_set_and_validate_list_shape(List<int32_t>* list, int32_t ndims, int32_t* shape) {
+    set_and_validate_list_shape(list, ndims, shape);
+}
+
+void __nac3_ndarray_array_set_and_validate_list_shape64(List<int64_t>* list, int64_t ndims, int64_t* shape) {
+    set_and_validate_list_shape(list, ndims, shape);
+}
+
+void __nac3_ndarray_array_write_list_to_array(List<int32_t>* list, NDArray<int32_t>* ndarray) {
+    write_list_to_array(list, ndarray);
+}
+
+void __nac3_ndarray_array_write_list_to_array64(List<int64_t>* list, NDArray<int64_t>* ndarray) {
+    write_list_to_array(list, ndarray);
+}
+}

nac3core/irrt/irrt/ndarray/reshape.hpp

@@ -0,0 +1,99 @@
+#pragma once
+
+#include "irrt/exception.hpp"
+#include "irrt/int_types.hpp"
+#include "irrt/ndarray/def.hpp"
+
+namespace {
+namespace ndarray {
+namespace reshape {
+/**
+ * @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 reshape
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+void __nac3_ndarray_reshape_resolve_and_check_new_shape(int32_t size, int32_t new_ndims, int32_t* new_shape) {
+    ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
+}
+
+void __nac3_ndarray_reshape_resolve_and_check_new_shape64(int64_t size, int64_t new_ndims, int64_t* new_shape) {
+    ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
+}
+}

nac3core/src/codegen/irrt/mod.rs

@@ -8,7 +8,10 @@ use super::{
     llvm_intrinsics,
     macros::codegen_unreachable,
     model::*,
-    object::ndarray::{indexing::NDIndex, nditer::NDIter, NDArray},
+    object::{
+        list::List,
+        ndarray::{indexing::NDIndex, nditer::NDIter, NDArray},
+    },
     stmt::gen_for_callback_incrementing,
     CodeGenContext, CodeGenerator,
 };
@@ -1129,3 +1132,47 @@ pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
         .arg(dst_ndarray)
         .returning_void();
 }
+
+pub fn call_nac3_ndarray_array_set_and_validate_list_shape<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    list: Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>>,
+    ndims: Instance<'ctx, Int<SizeT>>,
+    shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+) {
+    let name = get_sizet_dependent_function_name(
+        generator,
+        ctx,
+        "__nac3_ndarray_array_set_and_validate_list_shape",
+    );
+    FnCall::builder(generator, ctx, &name).arg(list).arg(ndims).arg(shape).returning_void();
+}
+
+pub fn call_nac3_ndarray_array_write_list_to_array<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    list: Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>>,
+    ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
+) {
+    let name = get_sizet_dependent_function_name(
+        generator,
+        ctx,
+        "__nac3_ndarray_array_write_list_to_array",
+    );
+    FnCall::builder(generator, ctx, &name).arg(list).arg(ndarray).returning_void();
+}
+
+pub fn call_nac3_ndarray_reshape_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    size: Instance<'ctx, Int<SizeT>>,
+    new_ndims: Instance<'ctx, Int<SizeT>>,
+    new_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+) {
+    let name = get_sizet_dependent_function_name(
+        generator,
+        ctx,
+        "__nac3_ndarray_reshape_resolve_and_check_new_shape",
+    );
+    FnCall::builder(generator, ctx, &name).arg(size).arg(new_ndims).arg(new_shape).returning_void();
+}

nac3core/src/codegen/model/ptr.rs

@@ -182,6 +182,15 @@ impl<'ctx, Item: Model<'ctx>> Instance<'ctx, Ptr<Item>> {
         Ptr(new_item).pointer_cast(generator, ctx, self.value)
     }
 
+    /// Cast this pointer to `uint8_t*`
+    pub fn cast_to_pi8<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> Instance<'ctx, Ptr<Int<Byte>>> {
+        Ptr(Int(Byte)).pointer_cast(generator, ctx, self.value)
+    }
+
     /// Check if the pointer is null with [`inkwell::builder::Builder::build_is_null`].
     pub fn is_null(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Int<Bool>> {
         let value = ctx.builder.build_is_null(self.value, "").unwrap();

nac3core/src/codegen/numpy.rs

@@ -13,6 +13,7 @@ use crate::{
         },
         llvm_intrinsics::{self, call_memcpy_generic},
         macros::codegen_unreachable,
+        model::*,
         object::{
             any::AnyObject,
             ndarray::{shape_util::parse_numpy_int_sequence, NDArrayObject},
@@ -22,13 +23,13 @@ use crate::{
     },
     symbol_resolver::ValueEnum,
     toplevel::{
-        helper::{extract_ndims, PrimDef},
+        helper::extract_ndims,
         numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
         DefinitionId,
     },
     typecheck::{
         magic_methods::Binop,
-        typedef::{FunSignature, Type, TypeEnum},
+        typedef::{FunSignature, Type},
     },
 };
 use inkwell::{
@@ -1840,26 +1841,6 @@ pub fn gen_ndarray_array<'ctx>(
     assert!(matches!(args.len(), 1..=3));
 
     let obj_ty = fun.0.args[0].ty;
-    let obj_elem_ty = match &*context.unifier.get_ty(obj_ty) {
-        TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-            unpack_ndarray_var_tys(&mut context.unifier, obj_ty).0
-        }
-
-        TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::List.id() => {
-            let mut ty = *params.iter().next().unwrap().1;
-            while let TypeEnum::TObj { obj_id, params, .. } = &*context.unifier.get_ty_immutable(ty)
-            {
-                if *obj_id != PrimDef::List.id() {
-                    break;
-                }
-
-                ty = *params.iter().next().unwrap().1;
-            }
-            ty
-        }
-
-        _ => obj_ty,
-    };
     let obj_arg = args[0].1.clone().to_basic_value_enum(context, generator, obj_ty)?;
 
     let copy_arg = if let Some(arg) =
@@ -1875,28 +1856,18 @@ pub fn gen_ndarray_array<'ctx>(
         )
     };
 
-    let ndmin_arg = if let Some(arg) =
-        args.iter().find(|arg| arg.0.is_some_and(|name| name == fun.0.args[2].name))
-    {
-        let ndmin_ty = fun.0.args[2].ty;
-        arg.1.clone().to_basic_value_enum(context, generator, ndmin_ty)?
-    } else {
-        context.gen_symbol_val(
-            generator,
-            fun.0.args[2].default_value.as_ref().unwrap(),
-            fun.0.args[2].ty,
-        )
-    };
+    // The ndmin argument is ignored. We can simply force the ndarray's number of dimensions to be
+    // the `ndims` of the function return type.
+    let (_, ndims) = unpack_ndarray_var_tys(&mut context.unifier, fun.0.ret);
+    let ndims = extract_ndims(&context.unifier, ndims);
 
-    call_ndarray_array_impl(
-        generator,
-        context,
-        obj_elem_ty,
-        obj_arg,
-        copy_arg.into_int_value(),
-        ndmin_arg.into_int_value(),
-    )
-    .map(NDArrayValue::into)
+    let object = AnyObject { value: obj_arg, ty: obj_ty };
+    // NAC3 booleans are i8.
+    let copy = Int(Bool).truncate(generator, context, copy_arg.into_int_value());
+    let ndarray = NDArrayObject::make_np_array(generator, context, object, copy)
+        .atleast_nd(generator, context, ndims);
+
+    Ok(ndarray.instance.value)
 }
 
 /// Generates LLVM IR for `ndarray.eye`.
@@ -1935,15 +1906,23 @@ pub fn gen_ndarray_eye<'ctx>(
         ))
     }?;
 
-    call_ndarray_eye_impl(
-        generator,
-        context,
-        context.primitives.float,
-        nrows_arg.into_int_value(),
-        ncols_arg.into_int_value(),
-        offset_arg.into_int_value(),
-    )
-    .map(NDArrayValue::into)
+    let (dtype, _) = unpack_ndarray_var_tys(&mut context.unifier, fun.0.ret);
+
+    let nrows = Int(Int32)
+        .check_value(generator, context.ctx, nrows_arg)
+        .unwrap()
+        .s_extend_or_bit_cast(generator, context, SizeT);
+    let ncols = Int(Int32)
+        .check_value(generator, context.ctx, ncols_arg)
+        .unwrap()
+        .s_extend_or_bit_cast(generator, context, SizeT);
+    let offset = Int(Int32)
+        .check_value(generator, context.ctx, offset_arg)
+        .unwrap()
+        .s_extend_or_bit_cast(generator, context, SizeT);
+
+    let ndarray = NDArrayObject::make_np_eye(generator, context, dtype, nrows, ncols, offset);
+    Ok(ndarray.instance.value)
 }
 
 /// Generates LLVM IR for `ndarray.identity`.
@@ -1957,20 +1936,15 @@ pub fn gen_ndarray_identity<'ctx>(
     assert!(obj.is_none());
     assert_eq!(args.len(), 1);
 
-    let llvm_usize = generator.get_size_type(context.ctx);
+    let (dtype, _) = unpack_ndarray_var_tys(&mut context.unifier, fun.0.ret);
 
     let n_ty = fun.0.args[0].ty;
     let n_arg = args[0].1.clone().to_basic_value_enum(context, generator, n_ty)?;
 
-    call_ndarray_eye_impl(
-        generator,
-        context,
-        context.primitives.float,
-        n_arg.into_int_value(),
-        n_arg.into_int_value(),
-        llvm_usize.const_zero(),
-    )
-    .map(NDArrayValue::into)
+    let n = Int(Int32).check_value(generator, context.ctx, n_arg).unwrap();
+    let n = n.s_extend_or_bit_cast(generator, context, SizeT);
+    let ndarray = NDArrayObject::make_np_identity(generator, context, dtype, n);
+    Ok(ndarray.instance.value)
 }
 
 /// Generates LLVM IR for `ndarray.copy`.
@@ -1984,20 +1958,14 @@ pub fn gen_ndarray_copy<'ctx>(
     assert!(obj.is_some());
     assert!(args.is_empty());
 
-    let llvm_usize = generator.get_size_type(context.ctx);
-
     let this_ty = obj.as_ref().unwrap().0;
-    let (this_elem_ty, _) = unpack_ndarray_var_tys(&mut context.unifier, this_ty);
     let this_arg =
         obj.as_ref().unwrap().1.clone().to_basic_value_enum(context, generator, this_ty)?;
 
-    ndarray_copy_impl(
-        generator,
-        context,
-        this_elem_ty,
-        NDArrayValue::from_ptr_val(this_arg.into_pointer_value(), llvm_usize, None),
-    )
-    .map(NDArrayValue::into)
+    let this = AnyObject { value: this_arg, ty: this_ty };
+    let this = NDArrayObject::from_object(generator, context, this);
+    let ndarray = this.make_copy(generator, context);
+    Ok(ndarray.instance.value)
 }
 
 /// Generates LLVM IR for `ndarray.fill`.
@@ -2011,48 +1979,15 @@ pub fn gen_ndarray_fill<'ctx>(
     assert!(obj.is_some());
     assert_eq!(args.len(), 1);
 
-    let llvm_usize = generator.get_size_type(context.ctx);
-
     let this_ty = obj.as_ref().unwrap().0;
-    let this_arg = obj
-        .as_ref()
-        .unwrap()
-        .1
-        .clone()
-        .to_basic_value_enum(context, generator, this_ty)?
-        .into_pointer_value();
+    let this_arg =
+        obj.as_ref().unwrap().1.clone().to_basic_value_enum(context, generator, this_ty)?;
     let value_ty = fun.0.args[0].ty;
     let value_arg = args[0].1.clone().to_basic_value_enum(context, generator, value_ty)?;
 
-    ndarray_fill_flattened(
-        generator,
-        context,
-        NDArrayValue::from_ptr_val(this_arg, llvm_usize, None),
-        |generator, ctx, _| {
-            let value = if value_arg.is_pointer_value() {
-                let llvm_i1 = ctx.ctx.bool_type();
-
-                let copy = generator.gen_var_alloc(ctx, value_arg.get_type(), None)?;
-
-                call_memcpy_generic(
-                    ctx,
-                    copy,
-                    value_arg.into_pointer_value(),
-                    value_arg.get_type().size_of().map(Into::into).unwrap(),
-                    llvm_i1.const_zero(),
-                );
-
-                copy.into()
-            } else if value_arg.is_int_value() || value_arg.is_float_value() {
-                value_arg
-            } else {
-                codegen_unreachable!(ctx)
-            };
-
-            Ok(value)
-        },
-    )?;
-
+    let this = AnyObject { value: this_arg, ty: this_ty };
+    let this = NDArrayObject::from_object(generator, context, this);
+    this.fill(generator, context, value_arg);
     Ok(())
 }
 
@@ -2163,293 +2098,6 @@ pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
     }
 }
 
-/// LLVM-typed implementation for generating the implementation for `ndarray.reshape`.
-///
-/// * `x1` - `NDArray` to reshape.
-/// * `shape` - The `shape` parameter used to construct the new `NDArray`.
-///   Just like numpy, the `shape` argument can be:
-///   1. A list of `int32`;   e.g., `np.reshape(arr, [600, -1, 3])`
-///   2. A tuple of `int32`;  e.g., `np.reshape(arr, (-1, 800, 3))`
-///   3. A scalar `int32`;     e.g., `np.reshape(arr, 3)`
-///
-/// Note that unlike other generating functions, one of the dimensions in the shape can be negative.
-pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    x1: (Type, BasicValueEnum<'ctx>),
-    shape: (Type, BasicValueEnum<'ctx>),
-) -> Result<BasicValueEnum<'ctx>, String> {
-    const FN_NAME: &str = "ndarray_reshape";
-    let (x1_ty, x1) = x1;
-    let (_, shape) = shape;
-
-    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));
-
-        let acc = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
-        let num_neg = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
-        ctx.builder.build_store(acc, llvm_usize.const_int(1, false)).unwrap();
-        ctx.builder.build_store(num_neg, llvm_usize.const_zero()).unwrap();
-
-        let out = match shape {
-            BasicValueEnum::PointerValue(shape_list_ptr)
-                if ListValue::is_instance(shape_list_ptr, llvm_usize).is_ok() =>
-            {
-                // 1. A list of ints; e.g., `np.reshape(arr, [int64(600), int64(800, -1])`
-
-                let shape_list = ListValue::from_ptr_val(shape_list_ptr, llvm_usize, None);
-                // Check for -1 in dimensions
-                gen_for_callback_incrementing(
-                    generator,
-                    ctx,
-                    None,
-                    llvm_usize.const_zero(),
-                    (shape_list.load_size(ctx, None), false),
-                    |generator, ctx, _, idx| {
-                        let ele =
-                            shape_list.data().get(ctx, generator, &idx, None).into_int_value();
-                        let ele = ctx.builder.build_int_s_extend(ele, llvm_usize, "").unwrap();
-
-                        gen_if_else_expr_callback(
-                            generator,
-                            ctx,
-                            |_, ctx| {
-                                Ok(ctx
-                                    .builder
-                                    .build_int_compare(
-                                        IntPredicate::SLT,
-                                        ele,
-                                        llvm_usize.const_zero(),
-                                        "",
-                                    )
-                                    .unwrap())
-                            },
-                            |_, ctx| -> Result<Option<IntValue>, String> {
-                                let num_neg_value =
-                                    ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
-                                let num_neg_value = ctx
-                                    .builder
-                                    .build_int_add(
-                                        num_neg_value,
-                                        llvm_usize.const_int(1, false),
-                                        "",
-                                    )
-                                    .unwrap();
-                                ctx.builder.build_store(num_neg, num_neg_value).unwrap();
-                                Ok(None)
-                            },
-                            |_, ctx| {
-                                let acc_value =
-                                    ctx.builder.build_load(acc, "").unwrap().into_int_value();
-                                let acc_value =
-                                    ctx.builder.build_int_mul(acc_value, ele, "").unwrap();
-                                ctx.builder.build_store(acc, acc_value).unwrap();
-                                Ok(None)
-                            },
-                        )?;
-                        Ok(())
-                    },
-                    llvm_usize.const_int(1, false),
-                )?;
-                let acc_val = ctx.builder.build_load(acc, "").unwrap().into_int_value();
-                let rem = ctx.builder.build_int_unsigned_div(n_sz, acc_val, "").unwrap();
-                // Generate the output shape by filling -1 with `rem`
-                create_ndarray_dyn_shape(
-                    generator,
-                    ctx,
-                    elem_ty,
-                    &shape_list,
-                    |_, ctx, _| Ok(shape_list.load_size(ctx, None)),
-                    |generator, ctx, shape_list, idx| {
-                        let dim =
-                            shape_list.data().get(ctx, generator, &idx, None).into_int_value();
-                        let dim = ctx.builder.build_int_s_extend(dim, llvm_usize, "").unwrap();
-
-                        Ok(gen_if_else_expr_callback(
-                            generator,
-                            ctx,
-                            |_, ctx| {
-                                Ok(ctx
-                                    .builder
-                                    .build_int_compare(
-                                        IntPredicate::SLT,
-                                        dim,
-                                        llvm_usize.const_zero(),
-                                        "",
-                                    )
-                                    .unwrap())
-                            },
-                            |_, _| Ok(Some(rem)),
-                            |_, _| Ok(Some(dim)),
-                        )?
-                        .unwrap()
-                        .into_int_value())
-                    },
-                )
-            }
-            BasicValueEnum::StructValue(shape_tuple) => {
-                // 2. A tuple of `int32`;  e.g., `np.reshape(arr, (-1, 800, 3))`
-
-                let ndims = shape_tuple.get_type().count_fields();
-                // Check for -1 in dims
-                for dim_i in 0..ndims {
-                    let dim = ctx
-                        .builder
-                        .build_extract_value(shape_tuple, dim_i, "")
-                        .unwrap()
-                        .into_int_value();
-                    let dim = ctx.builder.build_int_s_extend(dim, llvm_usize, "").unwrap();
-
-                    gen_if_else_expr_callback(
-                        generator,
-                        ctx,
-                        |_, ctx| {
-                            Ok(ctx
-                                .builder
-                                .build_int_compare(
-                                    IntPredicate::SLT,
-                                    dim,
-                                    llvm_usize.const_zero(),
-                                    "",
-                                )
-                                .unwrap())
-                        },
-                        |_, ctx| -> Result<Option<IntValue>, String> {
-                            let num_negs =
-                                ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
-                            let num_negs = ctx
-                                .builder
-                                .build_int_add(num_negs, llvm_usize.const_int(1, false), "")
-                                .unwrap();
-                            ctx.builder.build_store(num_neg, num_negs).unwrap();
-                            Ok(None)
-                        },
-                        |_, ctx| {
-                            let acc_val = ctx.builder.build_load(acc, "").unwrap().into_int_value();
-                            let acc_val = ctx.builder.build_int_mul(acc_val, dim, "").unwrap();
-                            ctx.builder.build_store(acc, acc_val).unwrap();
-                            Ok(None)
-                        },
-                    )?;
-                }
-
-                let acc_val = ctx.builder.build_load(acc, "").unwrap().into_int_value();
-                let rem = ctx.builder.build_int_unsigned_div(n_sz, acc_val, "").unwrap();
-                let mut shape = Vec::with_capacity(ndims as usize);
-
-                // Reconstruct shape filling negatives with rem
-                for dim_i in 0..ndims {
-                    let dim = ctx
-                        .builder
-                        .build_extract_value(shape_tuple, dim_i, "")
-                        .unwrap()
-                        .into_int_value();
-                    let dim = ctx.builder.build_int_s_extend(dim, llvm_usize, "").unwrap();
-
-                    let dim = gen_if_else_expr_callback(
-                        generator,
-                        ctx,
-                        |_, ctx| {
-                            Ok(ctx
-                                .builder
-                                .build_int_compare(
-                                    IntPredicate::SLT,
-                                    dim,
-                                    llvm_usize.const_zero(),
-                                    "",
-                                )
-                                .unwrap())
-                        },
-                        |_, _| Ok(Some(rem)),
-                        |_, _| Ok(Some(dim)),
-                    )?
-                    .unwrap()
-                    .into_int_value();
-                    shape.push(dim);
-                }
-                create_ndarray_const_shape(generator, ctx, elem_ty, shape.as_slice())
-            }
-            BasicValueEnum::IntValue(shape_int) => {
-                // 3. A scalar `int32`;     e.g., `np.reshape(arr, 3)`
-                let shape_int = gen_if_else_expr_callback(
-                    generator,
-                    ctx,
-                    |_, ctx| {
-                        Ok(ctx
-                            .builder
-                            .build_int_compare(
-                                IntPredicate::SLT,
-                                shape_int,
-                                llvm_usize.const_zero(),
-                                "",
-                            )
-                            .unwrap())
-                    },
-                    |_, _| Ok(Some(n_sz)),
-                    |_, ctx| {
-                        Ok(Some(ctx.builder.build_int_s_extend(shape_int, llvm_usize, "").unwrap()))
-                    },
-                )?
-                .unwrap()
-                .into_int_value();
-                create_ndarray_const_shape(generator, ctx, elem_ty, &[shape_int])
-            }
-            _ => codegen_unreachable!(ctx),
-        }
-        .unwrap();
-
-        // Only allow one dimension to be negative
-        let num_negs = ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
-        ctx.make_assert(
-            generator,
-            ctx.builder
-                .build_int_compare(IntPredicate::ULT, num_negs, llvm_usize.const_int(2, false), "")
-                .unwrap(),
-            "0:ValueError",
-            "can only specify one unknown dimension",
-            [None, None, None],
-            ctx.current_loc,
-        );
-
-        // The new shape must be compatible with the old shape
-        let out_sz = call_ndarray_calc_size(generator, ctx, &out.dim_sizes(), (None, None));
-        ctx.make_assert(
-            generator,
-            ctx.builder.build_int_compare(IntPredicate::EQ, out_sz, n_sz, "").unwrap(),
-            "0:ValueError",
-            "cannot reshape array of size {0} into provided shape of size {1}",
-            [Some(n_sz), Some(out_sz), None],
-            ctx.current_loc,
-        );
-
-        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) };
-                unsafe { out.data().set_unchecked(ctx, generator, &idx, elem) };
-                Ok(())
-            },
-            llvm_usize.const_int(1, false),
-        )?;
-
-        Ok(out.as_base_value().into())
-    } else {
-        codegen_unreachable!(
-            ctx,
-            "{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/list.rs

@@ -31,6 +31,17 @@ impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for List<Item> {
     }
 }
 
+impl<'ctx, Item: Model<'ctx>> Instance<'ctx, Ptr<Struct<List<Item>>>> {
+    /// Cast the items pointer to `uint8_t*`.
+    pub fn with_pi8_items<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>> {
+        self.pointer_cast(generator, ctx, Struct(List { item: Int(Byte) }))
+    }
+}
+
 /// A NAC3 Python List object.
 #[derive(Debug, Clone, Copy)]
 pub struct ListObject<'ctx> {

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

@@ -0,0 +1,184 @@
+use super::NDArrayObject;
+use crate::{
+    codegen::{
+        irrt::{
+            call_nac3_ndarray_array_set_and_validate_list_shape,
+            call_nac3_ndarray_array_write_list_to_array,
+        },
+        model::*,
+        object::{any::AnyObject, list::ListObject},
+        stmt::gen_if_else_expr_callback,
+        CodeGenContext, CodeGenerator,
+    },
+    toplevel::helper::{arraylike_flatten_element_type, arraylike_get_ndims},
+    typecheck::typedef::{Type, TypeEnum},
+};
+
+/// Get the expected `dtype` and `ndims` of the ndarray returned by `np_array(list)`.
+fn get_list_object_dtype_and_ndims<'ctx>(
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    list: ListObject<'ctx>,
+) -> (Type, u64) {
+    let dtype = arraylike_flatten_element_type(&mut ctx.unifier, list.item_type);
+
+    let ndims = arraylike_get_ndims(&mut ctx.unifier, list.item_type);
+    let ndims = ndims + 1; // To count `list` itself.
+
+    (dtype, ndims)
+}
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// Implementation of `np_array(<list>, copy=True)`
+    fn make_np_array_list_copy_true_impl<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        list: ListObject<'ctx>,
+    ) -> Self {
+        let (dtype, ndims_int) = get_list_object_dtype_and_ndims(ctx, list);
+        let list_value = list.instance.with_pi8_items(generator, ctx);
+
+        // Validate `list` has a consistent shape.
+        // Raise an exception if `list` is something abnormal like `[[1, 2], [3]]`.
+        // If `list` has a consistent shape, deduce the shape and write it to `shape`.
+        let ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims_int, false);
+        let shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
+        call_nac3_ndarray_array_set_and_validate_list_shape(
+            generator, ctx, list_value, ndims, shape,
+        );
+
+        let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims_int);
+        ndarray.copy_shape_from_array(generator, ctx, shape);
+        ndarray.create_data(generator, ctx);
+
+        // Copy all contents from the list.
+        call_nac3_ndarray_array_write_list_to_array(generator, ctx, list_value, ndarray.instance);
+
+        ndarray
+    }
+
+    /// Implementation of `np_array(<list>, copy=None)`
+    fn make_np_array_list_copy_none_impl<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        list: ListObject<'ctx>,
+    ) -> Self {
+        // np_array without copying is only possible `list` is not nested.
+        //
+        // If `list` is `list[T]`, we can create an ndarray with `data` set
+        // to the array pointer of `list`.
+        //
+        // If `list` is `list[list[T]]` or worse, copy.
+
+        let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
+        if ndims == 1 {
+            // `list` is not nested
+            let ndarray = NDArrayObject::alloca(generator, ctx, dtype, 1);
+
+            // Set data
+            let data = list.instance.get(generator, ctx, |f| f.items).cast_to_pi8(generator, ctx);
+            ndarray.instance.set(ctx, |f| f.data, data);
+
+            // ndarray->shape[0] = list->len;
+            let shape = ndarray.instance.get(generator, ctx, |f| f.shape);
+            let list_len = list.instance.get(generator, ctx, |f| f.len);
+            shape.set_index_const(ctx, 0, list_len);
+
+            // Set strides, the `data` is contiguous
+            ndarray.set_strides_contiguous(generator, ctx);
+
+            ndarray
+        } else {
+            // `list` is nested, copy
+            NDArrayObject::make_np_array_list_copy_true_impl(generator, ctx, list)
+        }
+    }
+
+    /// Implementation of `np_array(<list>, copy=copy)`
+    fn make_np_array_list_impl<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        list: ListObject<'ctx>,
+        copy: Instance<'ctx, Int<Bool>>,
+    ) -> Self {
+        let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
+
+        let ndarray = gen_if_else_expr_callback(
+            generator,
+            ctx,
+            |_generator, _ctx| Ok(copy.value),
+            |generator, ctx| {
+                let ndarray =
+                    NDArrayObject::make_np_array_list_copy_true_impl(generator, ctx, list);
+                Ok(Some(ndarray.instance.value))
+            },
+            |generator, ctx| {
+                let ndarray =
+                    NDArrayObject::make_np_array_list_copy_none_impl(generator, ctx, list);
+                Ok(Some(ndarray.instance.value))
+            },
+        )
+        .unwrap()
+        .unwrap();
+
+        NDArrayObject::from_value_and_unpacked_types(generator, ctx, ndarray, dtype, ndims)
+    }
+
+    /// Implementation of `np_array(<ndarray>, copy=copy)`.
+    pub fn make_np_array_ndarray_impl<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ndarray: NDArrayObject<'ctx>,
+        copy: Instance<'ctx, Int<Bool>>,
+    ) -> Self {
+        let ndarray_val = gen_if_else_expr_callback(
+            generator,
+            ctx,
+            |_generator, _ctx| Ok(copy.value),
+            |generator, ctx| {
+                let ndarray = ndarray.make_copy(generator, ctx); // Force copy
+                Ok(Some(ndarray.instance.value))
+            },
+            |_generator, _ctx| {
+                // No need to copy. Return `ndarray` itself.
+                Ok(Some(ndarray.instance.value))
+            },
+        )
+        .unwrap()
+        .unwrap();
+
+        NDArrayObject::from_value_and_unpacked_types(
+            generator,
+            ctx,
+            ndarray_val,
+            ndarray.dtype,
+            ndarray.ndims,
+        )
+    }
+
+    /// Create a new ndarray like `np.array()`.
+    ///
+    /// NOTE: The `ndmin` argument is not here. You may want to
+    /// do [`NDArrayObject::atleast_nd`] to achieve that.
+    pub fn make_np_array<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        object: AnyObject<'ctx>,
+        copy: Instance<'ctx, Int<Bool>>,
+    ) -> Self {
+        match &*ctx.unifier.get_ty(object.ty) {
+            TypeEnum::TObj { obj_id, .. }
+                if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
+            {
+                let list = ListObject::from_object(generator, ctx, object);
+                NDArrayObject::make_np_array_list_impl(generator, ctx, list, copy)
+            }
+            TypeEnum::TObj { obj_id, .. }
+                if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
+            {
+                let ndarray = NDArrayObject::from_object(generator, ctx, object);
+                NDArrayObject::make_np_array_ndarray_impl(generator, ctx, ndarray, copy)
+            }
+            _ => panic!("Unrecognized object type: {}", ctx.unifier.stringify(object.ty)), // Typechecker ensures this
+        }
+    }
+}

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

@@ -1,4 +1,4 @@
-use inkwell::values::BasicValueEnum;
+use inkwell::{values::BasicValueEnum, IntPredicate};
 
 use crate::{
     codegen::{
@@ -123,4 +123,54 @@ impl<'ctx> NDArrayObject<'ctx> {
         let fill_value = ndarray_one_value(generator, ctx, dtype);
         NDArrayObject::make_np_full(generator, ctx, dtype, ndims, shape, fill_value)
     }
+
+    /// Create an ndarray like `np.eye`.
+    pub fn make_np_eye<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        nrows: Instance<'ctx, Int<SizeT>>,
+        ncols: Instance<'ctx, Int<SizeT>>,
+        offset: Instance<'ctx, Int<SizeT>>,
+    ) -> Self {
+        let ndzero = ndarray_zero_value(generator, ctx, dtype);
+        let ndone = ndarray_one_value(generator, ctx, dtype);
+
+        let ndarray = NDArrayObject::alloca_dynamic_shape(generator, ctx, dtype, &[nrows, ncols]);
+
+        // Create data and make the matrix like look np.eye()
+        ndarray.create_data(generator, ctx);
+        ndarray
+            .foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
+                // NOTE: rows and cols can never be zero here, since this ndarray's `np.size` would be zero
+                // and this loop would not execute.
+
+                // Load up `row_i` and `col_i` from indices.
+                let row_i = nditer.get_indices().get_index_const(generator, ctx, 0);
+                let col_i = nditer.get_indices().get_index_const(generator, ctx, 1);
+
+                let be_one = row_i.add(ctx, offset).compare(ctx, IntPredicate::EQ, col_i);
+                let value = ctx.builder.build_select(be_one.value, ndone, ndzero, "value").unwrap();
+
+                let p = nditer.get_pointer(generator, ctx);
+                ctx.builder.build_store(p, value).unwrap();
+
+                Ok(())
+            })
+            .unwrap();
+
+        ndarray
+    }
+
+    /// Create an ndarray like `np.identity`.
+    pub fn make_np_identity<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        size: Instance<'ctx, Int<SizeT>>,
+    ) -> Self {
+        // Convenient implementation
+        let offset = Int(SizeT).const_0(generator, ctx.ctx);
+        NDArrayObject::make_np_eye(generator, ctx, dtype, size, size, offset)
+    }
 }

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

@@ -1,3 +1,4 @@
+pub mod array;
 pub mod factory;
 pub mod indexing;
 pub mod nditer;
@@ -26,7 +27,7 @@ use crate::{
     typecheck::typedef::Type,
 };
 
-use super::any::AnyObject;
+use super::{any::AnyObject, tuple::TupleObject};
 
 /// Fields of [`NDArray`]
 pub struct NDArrayFields<'ctx, F: FieldTraversal<'ctx>> {
@@ -74,8 +75,19 @@ impl<'ctx> NDArrayObject<'ctx> {
     ) -> NDArrayObject<'ctx> {
         let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, object.ty);
         let ndims = extract_ndims(&ctx.unifier, ndims);
+        Self::from_value_and_unpacked_types(generator, ctx, object.value, dtype, ndims)
+    }
 
-        let value = Ptr(Struct(NDArray)).check_value(generator, ctx.ctx, object.value).unwrap();
+    /// Like [`NDArrayObject::from_object`] but you directly supply the ndarray's
+    /// `dtype` and `ndims`.
+    pub fn from_value_and_unpacked_types<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        value: V,
+        dtype: Type,
+        ndims: u64,
+    ) -> Self {
+        let value = Ptr(Struct(NDArray)).check_value(generator, ctx.ctx, value).unwrap();
         NDArrayObject { dtype, ndims, instance: value }
     }
 
@@ -406,6 +418,8 @@ impl<'ctx> NDArrayObject<'ctx> {
         ctx: &mut CodeGenContext<'ctx, '_>,
         value: BasicValueEnum<'ctx>,
     ) {
+        // TODO: It is possible to optimize this by exploiting contiguous strides with memset.
+        // Probably best to implement in IRRT.
         self.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
             let p = nditer.get_pointer(generator, ctx);
             ctx.builder.build_store(p, value).unwrap();
@@ -413,6 +427,62 @@ impl<'ctx> NDArrayObject<'ctx> {
         })
         .unwrap();
     }
+
+    /// Create the shape tuple of this ndarray like `np.shape(<ndarray>)`.
+    ///
+    /// The returned integers in the tuple are in int32.
+    pub fn make_shape_tuple<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> TupleObject<'ctx> {
+        // TODO: Return a tuple of SizeT
+
+        let mut objects = Vec::with_capacity(self.ndims as usize);
+
+        for i in 0..self.ndims {
+            let dim = self
+                .instance
+                .get(generator, ctx, |f| f.shape)
+                .get_index_const(generator, ctx, i64::try_from(i).unwrap())
+                .truncate_or_bit_cast(generator, ctx, Int32);
+
+            objects.push(AnyObject {
+                ty: ctx.primitives.int32,
+                value: dim.value.as_basic_value_enum(),
+            });
+        }
+
+        TupleObject::from_objects(generator, ctx, objects)
+    }
+
+    /// Create the strides tuple of this ndarray like `<ndarray>.strides`.
+    ///
+    /// The returned integers in the tuple are in int32.
+    pub fn make_strides_tuple<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> TupleObject<'ctx> {
+        // TODO: Return a tuple of SizeT.
+
+        let mut objects = Vec::with_capacity(self.ndims as usize);
+
+        for i in 0..self.ndims {
+            let dim = self
+                .instance
+                .get(generator, ctx, |f| f.strides)
+                .get_index_const(generator, ctx, i64::try_from(i).unwrap())
+                .truncate_or_bit_cast(generator, ctx, Int32);
+
+            objects.push(AnyObject {
+                ty: ctx.primitives.int32,
+                value: dim.value.as_basic_value_enum(),
+            });
+        }
+
+        TupleObject::from_objects(generator, ctx, objects)
+    }
 }
 
 /// A convenience enum for implementing functions that acts on scalars or ndarrays or both.

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

@@ -1,4 +1,7 @@
-use crate::codegen::{CodeGenContext, CodeGenerator};
+use crate::codegen::{
+    irrt::call_nac3_ndarray_reshape_resolve_and_check_new_shape, model::*, CodeGenContext,
+    CodeGenerator,
+};
 
 use super::{indexing::RustNDIndex, NDArrayObject};
 
@@ -26,4 +29,61 @@ impl<'ctx> NDArrayObject<'ctx> {
             *self
         }
     }
+
+    /// 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: Instance<'ctx, Ptr<Int<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 - there are cases when the ndarray is not contiguous but could be reshaped
+        // without copying data. 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(generator, ctx, self.dtype, new_ndims);
+        dst_ndarray.copy_shape_from_array(generator, ctx, new_shape);
+
+        // Reolsve negative indices
+        let size = self.size(generator, ctx);
+        let dst_ndims = dst_ndarray.ndims_llvm(generator, ctx.ctx);
+        let dst_shape = dst_ndarray.instance.get(generator, ctx, |f| f.shape);
+        call_nac3_ndarray_reshape_resolve_and_check_new_shape(
+            generator, ctx, size, dst_ndims, dst_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.set_strides_contiguous(generator, ctx);
+        dst_ndarray.instance.set(ctx, |f| f.data, self.instance.get(generator, ctx, |f| f.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
+    }
 }

nac3core/src/toplevel/builtins.rs

@@ -1,6 +1,6 @@
 use std::iter::once;
 
-use helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDefDetails};
+use helper::{debug_assert_prim_is_allowed, extract_ndims, make_exception_fields, PrimDefDetails};
 use indexmap::IndexMap;
 use inkwell::{
     attributes::{Attribute, AttributeLoc},
@@ -9,13 +9,19 @@ use inkwell::{
     IntPredicate,
 };
 use itertools::Either;
+use numpy::unpack_ndarray_var_tys;
 use strum::IntoEnumIterator;
 
 use crate::{
     codegen::{
         builtin_fns,
         classes::{ProxyValue, RangeValue},
+        model::*,
         numpy::*,
+        object::{
+            any::AnyObject,
+            ndarray::{shape_util::parse_numpy_int_sequence, NDArrayObject},
+        },
         stmt::exn_constructor,
     },
     symbol_resolver::SymbolValue,
@@ -511,6 +517,14 @@ impl<'a> BuiltinBuilder<'a> {
             | PrimDef::FunNpEye
             | PrimDef::FunNpIdentity => self.build_ndarray_other_factory_function(prim),
 
+            PrimDef::FunNpSize | PrimDef::FunNpShape | PrimDef::FunNpStrides => {
+                self.build_ndarray_property_getter_function(prim)
+            }
+
+            PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
+                self.build_ndarray_view_function(prim)
+            }
+
             PrimDef::FunStr => self.build_str_function(),
 
             PrimDef::FunFloor | PrimDef::FunFloor64 | PrimDef::FunCeil | PrimDef::FunCeil64 => {
@@ -576,10 +590,6 @@ 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
@@ -1385,6 +1395,149 @@ impl<'a> BuiltinBuilder<'a> {
         }
     }
 
+    fn build_ndarray_property_getter_function(&mut self, prim: PrimDef) -> TopLevelDef {
+        debug_assert_prim_is_allowed(
+            prim,
+            &[PrimDef::FunNpSize, PrimDef::FunNpShape, PrimDef::FunNpStrides],
+        );
+
+        let in_ndarray_ty = self.unifier.get_fresh_var_with_range(
+            &[self.primitives.ndarray],
+            Some("T".into()),
+            None,
+        );
+
+        match prim {
+            PrimDef::FunNpSize => create_fn_by_codegen(
+                self.unifier,
+                &into_var_map([in_ndarray_ty]),
+                prim.name(),
+                self.primitives.int32,
+                &[(in_ndarray_ty.ty, "a")],
+                Box::new(|ctx, obj, fun, args, generator| {
+                    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 = AnyObject { ty: ndarray_ty, value: ndarray };
+                    let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
+
+                    let size =
+                        ndarray.size(generator, ctx).truncate_or_bit_cast(generator, ctx, Int32);
+                    Ok(Some(size.value.as_basic_value_enum()))
+                }),
+            ),
+            PrimDef::FunNpShape | PrimDef::FunNpStrides => {
+                // The function signatures of `np_shape` an `np_size` are the same.
+                // Mixed together for convenience.
+
+                // 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; // Handled by special folding
+
+                create_fn_by_codegen(
+                    self.unifier,
+                    &into_var_map([in_ndarray_ty]),
+                    prim.name(),
+                    ret_ty,
+                    &[(in_ndarray_ty.ty, "a")],
+                    Box::new(move |ctx, obj, fun, args, generator| {
+                        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 = AnyObject { ty: ndarray_ty, value: ndarray };
+                        let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
+
+                        let result_tuple = match prim {
+                            PrimDef::FunNpShape => ndarray.make_shape_tuple(generator, ctx),
+                            PrimDef::FunNpStrides => ndarray.make_strides_tuple(generator, ctx),
+                            _ => unreachable!(),
+                        };
+
+                        Ok(Some(result_tuple.value.as_basic_value_enum()))
+                    }),
+                )
+            }
+            _ => unreachable!(),
+        }
+    }
+
+    /// 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]);
+
+        let in_ndarray_ty = self.unifier.get_fresh_var_with_range(
+            &[self.primitives.ndarray],
+            Some("T".into()),
+            None,
+        );
+
+        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))?))
+                }),
+            ),
+
+            // 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 => {
+                let ret_ty = self.unifier.get_dummy_var().ty; // Handled by special holding
+
+                create_fn_by_codegen(
+                    self.unifier,
+                    &VarMap::new(),
+                    prim.name(),
+                    ret_ty,
+                    &[
+                        (in_ndarray_ty.ty, "x"),
+                        (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape"), // Handled by special folding
+                    ],
+                    Box::new(move |ctx, _, fun, args, generator| {
+                        let ndarray_ty = fun.0.args[0].ty;
+                        let ndarray_val =
+                            args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
+
+                        let shape_ty = fun.0.args[1].ty;
+                        let shape_val =
+                            args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
+
+                        let ndarray = AnyObject { value: ndarray_val, ty: ndarray_ty };
+                        let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
+
+                        let shape = AnyObject { value: shape_val, ty: shape_ty };
+                        let (_, shape) = parse_numpy_int_sequence(generator, ctx, shape);
+
+                        // The ndims after reshaping is gotten from the return type of the call.
+                        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);
+                        Ok(Some(new_ndarray.instance.value.as_basic_value_enum()))
+                    }),
+                )
+            }
+
+            _ => unreachable!(),
+        }
+    }
+
     /// Build the `str()` function.
     fn build_str_function(&mut self) -> TopLevelDef {
         let prim = PrimDef::FunStr;
@@ -1872,57 +2025,6 @@ impl<'a> BuiltinBuilder<'a> {
         }
     }
 
-    /// 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

@@ -53,6 +53,15 @@ pub enum PrimDef {
     FunNpEye,
     FunNpIdentity,
 
+    // NumPy ndarray property getters
+    FunNpSize,
+    FunNpShape,
+    FunNpStrides,
+
+    // NumPy ndarray view functions
+    FunNpTranspose,
+    FunNpReshape,
+
     // Miscellaneous NumPy & SciPy functions
     FunNpRound,
     FunNpFloor,
@@ -100,8 +109,6 @@ pub enum PrimDef {
     FunNpLdExp,
     FunNpHypot,
     FunNpNextAfter,
-    FunNpTranspose,
-    FunNpReshape,
 
     // Linalg functions
     FunNpDot,
@@ -239,6 +246,15 @@ impl PrimDef {
             PrimDef::FunNpEye => fun("np_eye", None),
             PrimDef::FunNpIdentity => fun("np_identity", None),
 
+            // NumPy NDArray property getters,
+            PrimDef::FunNpSize => fun("np_size", None),
+            PrimDef::FunNpShape => fun("np_shape", None),
+            PrimDef::FunNpStrides => fun("np_strides", None),
+
+            // NumPy NDArray view functions
+            PrimDef::FunNpTranspose => fun("np_transpose", None),
+            PrimDef::FunNpReshape => fun("np_reshape", None),
+
             // Miscellaneous NumPy & SciPy functions
             PrimDef::FunNpRound => fun("np_round", None),
             PrimDef::FunNpFloor => fun("np_floor", None),
@@ -286,8 +302,6 @@ 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/typecheck/type_inferencer/mod.rs

@@ -1,7 +1,7 @@
 use std::cmp::max;
 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::{
@@ -1183,6 +1183,45 @@ impl<'a> Inferencer<'a> {
             }));
         }
 
+        if ["np_shape".into(), "np_strides".into()].contains(id) && args.len() == 1 {
+            let ndarray = self.fold_expr(args.remove(0))?;
+
+            let ndims = arraylike_get_ndims(self.unifier, ndarray.custom.unwrap());
+
+            // Make 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![],
+                },
+            }));
+        }
+
         if id == &"np_dot".into() {
             let arg0 = self.fold_expr(args.remove(0))?;
             let arg1 = self.fold_expr(args.remove(0))?;

nac3standalone/demo/interpret_demo.py

@@ -179,6 +179,15 @@ def patch(module):
     module.np_identity = np.identity
     module.np_array = np.array
 
+    # NumPy NDArray view functions
+    module.np_transpose = np.transpose
+    module.np_reshape = np.reshape
+
+    # NumPy NDArray property getters
+    module.np_size = np.size
+    module.np_shape = np.shape
+    module.np_strides = lambda ndarray: ndarray.strides
+
     # NumPy Math functions
     module.np_isnan = np.isnan
     module.np_isinf = np.isinf
@@ -218,8 +227,6 @@ def patch(module):
     module.np_ldexp = np.ldexp
     module.np_hypot = np.hypot
     module.np_nextafter = np.nextafter
-    module.np_transpose = np.transpose
-    module.np_reshape = np.reshape
 
     # SciPy Math functions
     module.sp_spec_erf = special.erf