[meta] Apply clippy suggestions

This is needed as ndarray.{shapes,strides} are ArrayValues, and so a GEP
operation is required to convert them into pointers to their first
elements.

Cargo.lock

@@ -126,9 +126,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
 
 [[package]]
 name = "cc"
-version = "1.2.3"
+version = "1.2.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "27f657647bcff5394bf56c7317665bbf790a137a50eaaa5c6bfbb9e27a518f2d"
+checksum = "a012a0df96dd6d06ba9a1b29d6402d1a5d77c6befd2566afdc26e10603dc93d7"
 dependencies = [
  "shlex",
 ]
@@ -170,7 +170,7 @@ dependencies = [
  "heck 0.5.0",
  "proc-macro2",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
@@ -187,14 +187,14 @@ checksum = "5b63caa9aa9397e2d9480a9b13673856c78d8ac123288526c37d7839f2a86990"
 
 [[package]]
 name = "console"
-version = "0.15.8"
+version = "0.15.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0e1f83fc076bd6dd27517eacdf25fef6c4dfe5f1d7448bafaaf3a26f13b5e4eb"
+checksum = "ea3c6ecd8059b57859df5c69830340ed3c41d30e3da0c1cbed90a96ac853041b"
 dependencies = [
  "encode_unicode",
- "lazy_static",
  "libc",
- "windows-sys 0.52.0",
+ "once_cell",
+ "windows-sys 0.59.0",
 ]
 
 [[package]]
@@ -221,18 +221,18 @@ dependencies = [
 
 [[package]]
 name = "crossbeam-channel"
-version = "0.5.13"
+version = "0.5.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "33480d6946193aa8033910124896ca395333cae7e2d1113d1fef6c3272217df2"
+checksum = "06ba6d68e24814cb8de6bb986db8222d3a027d15872cabc0d18817bc3c0e4471"
 dependencies = [
  "crossbeam-utils",
 ]
 
 [[package]]
 name = "crossbeam-deque"
-version = "0.8.5"
+version = "0.8.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "613f8cc01fe9cf1a3eb3d7f488fd2fa8388403e97039e2f73692932e291a770d"
+checksum = "9dd111b7b7f7d55b72c0a6ae361660ee5853c9af73f70c3c2ef6858b950e2e51"
 dependencies = [
  "crossbeam-epoch",
  "crossbeam-utils",
@@ -249,18 +249,18 @@ dependencies = [
 
 [[package]]
 name = "crossbeam-queue"
-version = "0.3.11"
+version = "0.3.12"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "df0346b5d5e76ac2fe4e327c5fd1118d6be7c51dfb18f9b7922923f287471e35"
+checksum = "0f58bbc28f91df819d0aa2a2c00cd19754769c2fad90579b3592b1c9ba7a3115"
 dependencies = [
  "crossbeam-utils",
 ]
 
 [[package]]
 name = "crossbeam-utils"
-version = "0.8.20"
+version = "0.8.21"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "22ec99545bb0ed0ea7bb9b8e1e9122ea386ff8a48c0922e43f36d45ab09e0e80"
+checksum = "d0a5c400df2834b80a4c3327b3aad3a4c4cd4de0629063962b03235697506a28"
 
 [[package]]
 name = "crypto-common"
@@ -305,9 +305,9 @@ dependencies = [
 
 [[package]]
 name = "encode_unicode"
-version = "0.3.6"
+version = "1.0.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a357d28ed41a50f9c765dbfe56cbc04a64e53e5fc58ba79fbc34c10ef3df831f"
+checksum = "34aa73646ffb006b8f5147f3dc182bd4bcb190227ce861fc4a4844bf8e3cb2c0"
 
 [[package]]
 name = "equivalent"
@@ -378,9 +378,9 @@ dependencies = [
 
 [[package]]
 name = "glob"
-version = "0.3.1"
+version = "0.3.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d2fabcfbdc87f4758337ca535fb41a6d701b65693ce38287d856d1674551ec9b"
+checksum = "a8d1add55171497b4705a648c6b583acafb01d58050a51727785f0b2c8e0a2b2"
 
 [[package]]
 name = "hashbrown"
@@ -417,11 +417,11 @@ checksum = "2304e00983f87ffb38b55b444b5e3b60a884b5d30c0fca7d82fe33449bbe55ea"
 
 [[package]]
 name = "home"
-version = "0.5.9"
+version = "0.5.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e3d1354bf6b7235cb4a0576c2619fd4ed18183f689b12b006a0ee7329eeff9a5"
+checksum = "589533453244b0995c858700322199b2becb13b627df2851f64a2775d024abcf"
 dependencies = [
- "windows-sys 0.52.0",
+ "windows-sys 0.59.0",
 ]
 
 [[package]]
@@ -472,7 +472,7 @@ checksum = "9dd28cfd4cfba665d47d31c08a6ba637eed16770abca2eccbbc3ca831fef1e44"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
@@ -559,9 +559,9 @@ checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe"
 
 [[package]]
 name = "libc"
-version = "0.2.167"
+version = "0.2.169"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "09d6582e104315a817dff97f75133544b2e094ee22447d2acf4a74e189ba06fc"
+checksum = "b5aba8db14291edd000dfcc4d620c7ebfb122c613afb886ca8803fa4e128a20a"
 
 [[package]]
 name = "libloading"
@@ -678,7 +678,7 @@ dependencies = [
  "proc-macro-error",
  "proc-macro2",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
  "trybuild",
 ]
 
@@ -799,7 +799,7 @@ dependencies = [
  "phf_shared 0.11.2",
  "proc-macro2",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
@@ -927,7 +927,7 @@ dependencies = [
  "proc-macro2",
  "pyo3-macros-backend",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
@@ -940,14 +940,14 @@ dependencies = [
  "proc-macro2",
  "pyo3-build-config",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
 name = "quote"
-version = "1.0.37"
+version = "1.0.38"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b5b9d34b8991d19d98081b46eacdd8eb58c6f2b201139f7c5f643cc155a633af"
+checksum = "0e4dccaaaf89514f546c693ddc140f729f958c247918a13380cccc6078391acc"
 dependencies = [
  "proc-macro2",
 ]
@@ -1004,9 +1004,9 @@ dependencies = [
 
 [[package]]
 name = "redox_syscall"
-version = "0.5.7"
+version = "0.5.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9b6dfecf2c74bce2466cabf93f6664d6998a69eb21e39f4207930065b27b771f"
+checksum = "03a862b389f93e68874fbf580b9de08dd02facb9a788ebadaf4a3fd33cf58834"
 dependencies = [
  "bitflags",
 ]
@@ -1062,9 +1062,9 @@ dependencies = [
 
 [[package]]
 name = "rustversion"
-version = "1.0.18"
+version = "1.0.19"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0e819f2bc632f285be6d7cd36e25940d45b2391dd6d9b939e79de557f7014248"
+checksum = "f7c45b9784283f1b2e7fb61b42047c2fd678ef0960d4f6f1eba131594cc369d4"
 
 [[package]]
 name = "ryu"
@@ -1089,35 +1089,35 @@ checksum = "94143f37725109f92c262ed2cf5e59bce7498c01bcc1502d7b9afe439a4e9f49"
 
 [[package]]
 name = "semver"
-version = "1.0.23"
+version = "1.0.24"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "61697e0a1c7e512e84a621326239844a24d8207b4669b41bc18b32ea5cbf988b"
+checksum = "3cb6eb87a131f756572d7fb904f6e7b68633f09cca868c5df1c4b8d1a694bbba"
 
 [[package]]
 name = "serde"
-version = "1.0.215"
+version = "1.0.217"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6513c1ad0b11a9376da888e3e0baa0077f1aed55c17f50e7b2397136129fb88f"
+checksum = "02fc4265df13d6fa1d00ecff087228cc0a2b5f3c0e87e258d8b94a156e984c70"
 dependencies = [
  "serde_derive",
 ]
 
 [[package]]
 name = "serde_derive"
-version = "1.0.215"
+version = "1.0.217"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ad1e866f866923f252f05c889987993144fb74e722403468a4ebd70c3cd756c0"
+checksum = "5a9bf7cf98d04a2b28aead066b7496853d4779c9cc183c440dbac457641e19a0"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
 name = "serde_json"
-version = "1.0.133"
+version = "1.0.134"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c7fceb2473b9166b2294ef05efcb65a3db80803f0b03ef86a5fc88a2b85ee377"
+checksum = "d00f4175c42ee48b15416f6193a959ba3a0d67fc699a0db9ad12df9f83991c7d"
 dependencies = [
  "itoa",
  "memchr",
@@ -1226,7 +1226,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "rustversion",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
@@ -1242,9 +1242,9 @@ dependencies = [
 
 [[package]]
 name = "syn"
-version = "2.0.90"
+version = "2.0.94"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "919d3b74a5dd0ccd15aeb8f93e7006bd9e14c295087c9896a110f490752bcf31"
+checksum = "987bc0be1cdea8b10216bd06e2ca407d40b9543468fafd3ddfb02f36e77f71f3"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -1265,12 +1265,13 @@ checksum = "42a4d50cdb458045afc8131fd91b64904da29548bcb63c7236e0844936c13078"
 
 [[package]]
 name = "tempfile"
-version = "3.14.0"
+version = "3.15.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "28cce251fcbc87fac86a866eeb0d6c2d536fc16d06f184bb61aeae11aa4cee0c"
+checksum = "9a8a559c81686f576e8cd0290cd2a24a2a9ad80c98b3478856500fcbd7acd704"
 dependencies = [
  "cfg-if",
  "fastrand",
+ "getrandom",
  "once_cell",
  "rustix",
  "windows-sys 0.59.0",
@@ -1278,9 +1279,9 @@ dependencies = [
 
 [[package]]
 name = "term"
-version = "1.0.0"
+version = "1.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4df4175de05129f31b80458c6df371a15e7fc3fd367272e6bf938e5c351c7ea0"
+checksum = "a3bb6001afcea98122260987f8b7b5da969ecad46dbf0b5453702f776b491a41"
 dependencies = [
  "home",
  "windows-sys 0.52.0",
@@ -1325,7 +1326,7 @@ checksum = "4fee6c4efc90059e10f81e6d42c60a18f76588c3d74cb83a0b242a2b6c7504c1"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
@@ -1603,9 +1604,9 @@ checksum = "589f6da84c646204747d1270a2a5661ea66ed1cced2631d546fdfb155959f9ec"
 
 [[package]]
 name = "winnow"
-version = "0.6.20"
+version = "0.6.22"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "36c1fec1a2bb5866f07c25f68c26e565c4c200aebb96d7e55710c19d3e8ac49b"
+checksum = "39281189af81c07ec09db316b302a3e67bf9bd7cbf6c820b50e35fee9c2fa980"
 dependencies = [
  "memchr",
 ]
@@ -1637,5 +1638,5 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]

flake.lock

@@ -2,11 +2,11 @@
   "nodes": {
     "nixpkgs": {
       "locked": {
-        "lastModified": 1731319897,
-        "narHash": "sha256-PbABj4tnbWFMfBp6OcUK5iGy1QY+/Z96ZcLpooIbuEI=",
+        "lastModified": 1735834308,
+        "narHash": "sha256-dklw3AXr3OGO4/XT1Tu3Xz9n/we8GctZZ75ZWVqAVhk=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "dc460ec76cbff0e66e269457d7b728432263166c",
+        "rev": "6df24922a1400241dae323af55f30e4318a6ca65",
         "type": "github"
       },
       "original": {

nac3artiq/demo/numpy_primitives_decay.py

@@ -0,0 +1,29 @@
+from min_artiq import *
+import numpy
+from numpy import int32
+
+
+@nac3
+class NumpyBoolDecay:
+    core: KernelInvariant[Core]
+    np_true: KernelInvariant[bool]
+    np_false: KernelInvariant[bool]
+    np_int: KernelInvariant[int32]
+    np_float: KernelInvariant[float]
+    np_str: KernelInvariant[str]
+
+    def __init__(self):
+        self.core = Core()
+        self.np_true = numpy.True_
+        self.np_false = numpy.False_
+        self.np_int = numpy.int32(0)
+        self.np_float = numpy.float64(0.0)
+        self.np_str = numpy.str_("")
+
+    @kernel
+    def run(self):
+        pass
+
+
+if __name__ == "__main__":
+    NumpyBoolDecay().run()

nac3artiq/src/codegen.rs

@@ -12,16 +12,17 @@ use pyo3::{
     PyObject, PyResult, Python,
 };
 
+use super::{symbol_resolver::InnerResolver, timeline::TimeFns};
 use nac3core::{
     codegen::{
         expr::{destructure_range, gen_call},
-        irrt::call_ndarray_calc_size,
-        llvm_intrinsics::{call_int_smax, call_memcpy_generic, call_stackrestore, call_stacksave},
+        llvm_intrinsics::{call_int_smax, call_memcpy, call_stackrestore, call_stacksave},
         stmt::{gen_block, gen_for_callback_incrementing, gen_if_callback, gen_with},
-        types::NDArrayType,
+        type_aligned_alloca,
+        types::ndarray::NDArrayType,
         values::{
-            ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue, ProxyValue,
-            RangeValue, UntypedArrayLikeAccessor,
+            ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, ProxyValue, RangeValue,
+            UntypedArrayLikeAccessor,
         },
         CodeGenContext, CodeGenerator,
     },
@@ -34,12 +35,14 @@ use nac3core::{
     },
     nac3parser::ast::{Expr, ExprKind, Located, Stmt, StmtKind, StrRef},
     symbol_resolver::ValueEnum,
-    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, GenCall},
+    toplevel::{
+        helper::{extract_ndims, PrimDef},
+        numpy::unpack_ndarray_var_tys,
+        DefinitionId, GenCall,
+    },
     typecheck::typedef::{iter_type_vars, FunSignature, FuncArg, Type, TypeEnum, VarMap},
 };
 
-use super::{symbol_resolver::InnerResolver, timeline::TimeFns};
-
 /// The parallelism mode within a block.
 #[derive(Copy, Clone, Eq, PartialEq)]
 enum ParallelMode {
@@ -159,7 +162,7 @@ impl<'a> ArtiqCodeGenerator<'a> {
     }
 }
 
-impl<'b> CodeGenerator for ArtiqCodeGenerator<'b> {
+impl CodeGenerator for ArtiqCodeGenerator<'_> {
     fn get_name(&self) -> &str {
         &self.name
     }
@@ -458,60 +461,49 @@ fn format_rpc_arg<'ctx>(
             let llvm_i1 = ctx.ctx.bool_type();
             let llvm_usize = generator.get_size_type(ctx.ctx);
 
-            let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, arg_ty);
-            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
-            let llvm_arg = NDArrayValue::from_pointer_value(
-                arg.into_pointer_value(),
-                llvm_elem_ty,
-                llvm_usize,
-                None,
-            );
+            let (elem_ty, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, arg_ty);
+            let ndims = extract_ndims(&ctx.unifier, ndims);
+            let dtype = ctx.get_llvm_type(generator, elem_ty);
+            let ndarray = NDArrayType::new(generator, ctx.ctx, dtype, ndims)
+                .map_value(arg.into_pointer_value(), None);
 
-            let llvm_usize_sizeof = ctx
-                .builder
-                .build_int_truncate_or_bit_cast(
-                    llvm_arg.get_type().size_type().size_of(),
-                    llvm_usize,
-                    "",
-                )
-                .unwrap();
-            let llvm_pdata_sizeof = ctx
-                .builder
-                .build_int_truncate_or_bit_cast(
-                    llvm_elem_ty.ptr_type(AddressSpace::default()).size_of(),
-                    llvm_usize,
-                    "",
-                )
-                .unwrap();
+            let ndims = llvm_usize.const_int(ndims, false);
 
-            let dims_buf_sz =
-                ctx.builder.build_int_mul(llvm_arg.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
+            // `ndarray.data` is possibly not contiguous, and we need it to be contiguous for
+            // the reader.
+            // Turning it into a ContiguousNDArray to get a `data` that is contiguous.
+            let carray = ndarray.make_contiguous_ndarray(generator, ctx);
 
-            let buffer_size =
-                ctx.builder.build_int_add(dims_buf_sz, llvm_pdata_sizeof, "").unwrap();
+            let sizeof_usize = llvm_usize.size_of();
+            let sizeof_usize =
+                ctx.builder.build_int_z_extend_or_bit_cast(sizeof_usize, llvm_usize, "").unwrap();
 
-            let buffer = ctx.builder.build_array_alloca(llvm_i8, buffer_size, "rpc.arg").unwrap();
-            let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, Some("rpc.arg"));
+            let sizeof_pdata = dtype.ptr_type(AddressSpace::default()).size_of();
+            let sizeof_pdata =
+                ctx.builder.build_int_z_extend_or_bit_cast(sizeof_pdata, llvm_usize, "").unwrap();
 
-            call_memcpy_generic(
-                ctx,
-                buffer.base_ptr(ctx, generator),
-                llvm_arg.ptr_to_data(ctx),
-                llvm_pdata_sizeof,
-                llvm_i1.const_zero(),
-            );
+            let sizeof_buf_shape = ctx.builder.build_int_mul(sizeof_usize, ndims, "").unwrap();
+            let sizeof_buf = ctx.builder.build_int_add(sizeof_buf_shape, sizeof_pdata, "").unwrap();
 
-            let pbuffer_dims_begin =
-                unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
-            call_memcpy_generic(
-                ctx,
-                pbuffer_dims_begin,
-                llvm_arg.shape().base_ptr(ctx, generator),
-                dims_buf_sz,
-                llvm_i1.const_zero(),
-            );
+            // buf = { data: void*, shape: [size_t; ndims]; }
+            let buf = ctx.builder.build_array_alloca(llvm_i8, sizeof_buf, "rpc.arg").unwrap();
+            let buf = ArraySliceValue::from_ptr_val(buf, sizeof_buf, Some("rpc.arg"));
+            let buf_data = buf.base_ptr(ctx, generator);
+            let buf_shape =
+                unsafe { buf.ptr_offset_unchecked(ctx, generator, &sizeof_pdata, None) };
 
-            buffer.base_ptr(ctx, generator)
+            // Write to `buf->data`
+            let carray_data = carray.load_data(ctx);
+            let carray_data = ctx.builder.build_pointer_cast(carray_data, llvm_pi8, "").unwrap();
+            call_memcpy(ctx, buf_data, carray_data, sizeof_pdata, llvm_i1.const_zero());
+
+            // Write to `buf->shape`
+            let carray_shape = ndarray.shape().base_ptr(ctx, generator);
+            let carray_shape_i8 =
+                ctx.builder.build_pointer_cast(carray_shape, llvm_pi8, "").unwrap();
+            call_memcpy(ctx, buf_shape, carray_shape_i8, sizeof_buf_shape, llvm_i1.const_zero());
+
+            buf.base_ptr(ctx, generator)
         }
 
         _ => {
@@ -552,6 +544,8 @@ fn format_rpc_ret<'ctx>(
     let llvm_i32 = ctx.ctx.i32_type();
     let llvm_i8_8 = ctx.ctx.struct_type(&[llvm_i8.array_type(8).into()], false);
     let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
 
     let rpc_recv = ctx.module.get_function("rpc_recv").unwrap_or_else(|| {
         ctx.module.add_function("rpc_recv", llvm_i32.fn_type(&[llvm_pi8.into()], false), None)
@@ -572,8 +566,7 @@ fn format_rpc_ret<'ctx>(
 
     let result = match &*ctx.unifier.get_ty_immutable(ret_ty) {
         TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-            let llvm_i1 = ctx.ctx.bool_type();
-            let llvm_usize = generator.get_size_type(ctx.ctx);
+            let num_0 = llvm_usize.const_zero();
 
             // Round `val` up to its modulo `power_of_two`
             let round_up = |ctx: &mut CodeGenContext<'ctx, '_>,
@@ -599,79 +592,49 @@ fn format_rpc_ret<'ctx>(
                     .unwrap()
             };
 
-            // Setup types
-            let (elem_ty, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ret_ty);
-            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
-            let llvm_ret_ty = NDArrayType::new(generator, ctx.ctx, llvm_elem_ty);
-
             // Allocate the resulting ndarray
             // A condition after format_rpc_ret ensures this will not be popped this off.
-            let ndarray = llvm_ret_ty.alloca(generator, ctx, Some("rpc.result"));
+            let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ret_ty);
+            let dtype_llvm = ctx.get_llvm_type(generator, dtype);
+            let ndims = extract_ndims(&ctx.unifier, ndims);
+            let ndarray = NDArrayType::new(generator, ctx.ctx, dtype_llvm, ndims)
+                .construct_uninitialized(generator, ctx, None);
 
-            // Setup ndims
-            let ndims =
-                if let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) {
-                    assert_eq!(values.len(), 1);
+            // NOTE: Current content of `ndarray`:
+            //   - * `data` - **NOT YET** allocated.
+            //   - * `itemsize` - initialized to be size_of(dtype).
+            //   - * `ndims` - initialized.
+            //   - * `shape` - allocated; has uninitialized values.
+            //   - * `strides` - allocated; has uninitialized values.
 
-                    u64::try_from(values[0].clone()).unwrap()
-                } else {
-                    unreachable!();
-                };
-            // Set `ndarray.ndims`
-            ndarray.store_ndims(ctx, generator, llvm_usize.const_int(ndims, false));
-            // Allocate `ndarray.shape` [size_t; ndims]
-            ndarray.create_shape(ctx, llvm_usize, ndarray.load_ndims(ctx));
-
-            /*
-                ndarray now:
-                  - .ndims: initialized
-                  - .shape: allocated but uninitialized .shape
-                  - .data: uninitialized
-            */
-
-            let llvm_usize_sizeof = ctx
-                .builder
-                .build_int_truncate_or_bit_cast(llvm_usize.size_of(), llvm_usize, "")
-                .unwrap();
-            let llvm_pdata_sizeof = ctx
-                .builder
-                .build_int_truncate_or_bit_cast(
-                    llvm_elem_ty.ptr_type(AddressSpace::default()).size_of(),
-                    llvm_usize,
-                    "",
-                )
-                .unwrap();
-            let llvm_elem_sizeof = ctx
-                .builder
-                .build_int_truncate_or_bit_cast(llvm_elem_ty.size_of().unwrap(), llvm_usize, "")
-                .unwrap();
+            let itemsize = ndarray.load_itemsize(ctx); // Same as doing a `ctx.get_llvm_type` on `dtype` and get its `size_of()`.
 
             // Allocates a buffer for the initial RPC'ed object, which is guaranteed to be
             // (4 + 4 * ndims) bytes with 8-byte alignment
-            let sizeof_dims =
-                ctx.builder.build_int_mul(ndarray.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
+            let sizeof_usize = llvm_usize.size_of();
+            let sizeof_usize =
+                ctx.builder.build_int_truncate_or_bit_cast(sizeof_usize, llvm_usize, "").unwrap();
+
+            let sizeof_ptr = llvm_i8.ptr_type(AddressSpace::default()).size_of();
+            let sizeof_ptr =
+                ctx.builder.build_int_z_extend_or_bit_cast(sizeof_ptr, llvm_usize, "").unwrap();
+
+            let sizeof_shape =
+                ctx.builder.build_int_mul(ndarray.load_ndims(ctx), sizeof_usize, "").unwrap();
+
+            // Size of the buffer for the initial `rpc_recv()`.
             let unaligned_buffer_size =
-                ctx.builder.build_int_add(sizeof_dims, llvm_pdata_sizeof, "").unwrap();
-            let buffer_size = round_up(ctx, unaligned_buffer_size, llvm_usize.const_int(8, false));
+                ctx.builder.build_int_add(sizeof_ptr, sizeof_shape, "").unwrap();
 
             let stackptr = call_stacksave(ctx, None);
-            // Just to be absolutely sure, alloca in [i8 x 8] slices to force 8-byte alignment
-            let buffer = ctx
-                .builder
-                .build_array_alloca(
-                    llvm_i8_8,
-                    ctx.builder
-                        .build_int_unsigned_div(buffer_size, llvm_usize.const_int(8, false), "")
-                        .unwrap(),
-                    "rpc.buffer",
-                )
-                .unwrap();
-            let buffer = ctx
-                .builder
-                .build_bit_cast(buffer, llvm_pi8, "")
-                .map(BasicValueEnum::into_pointer_value)
-                .unwrap();
-            let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, None);
+            let buffer = type_aligned_alloca(
+                generator,
+                ctx,
+                llvm_i8_8,
+                unaligned_buffer_size,
+                Some("rpc.buffer"),
+            );
+            let buffer = ArraySliceValue::from_ptr_val(buffer, unaligned_buffer_size, None);
 
             // The first call to `rpc_recv` reads the top-level ndarray object: [pdata, shape]
             //
@@ -679,7 +642,7 @@ fn format_rpc_ret<'ctx>(
             let ndarray_nbytes = ctx
                 .build_call_or_invoke(
                     rpc_recv,
-                    &[buffer.base_ptr(ctx, generator).into()], // Reads [usize; ndims]. NOTE: We are allocated [size_t; ndims].
+                    &[buffer.base_ptr(ctx, generator).into()], // Reads [usize; ndims]
                     "rpc.size.next",
                 )
                 .map(BasicValueEnum::into_int_value)
@@ -687,16 +650,14 @@ fn format_rpc_ret<'ctx>(
 
             // debug_assert(ndarray_nbytes > 0)
             if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
+                let cmp = ctx
+                    .builder
+                    .build_int_compare(IntPredicate::UGT, ndarray_nbytes, num_0, "")
+                    .unwrap();
+
                 ctx.make_assert(
                     generator,
-                    ctx.builder
-                        .build_int_compare(
-                            IntPredicate::UGT,
-                            ndarray_nbytes,
-                            ndarray_nbytes.get_type().const_zero(),
-                            "",
-                        )
-                        .unwrap(),
+                    cmp,
                     "0:AssertionError",
                     "Unexpected RPC termination for ndarray - Expected data buffer next",
                     [None, None, None],
@@ -705,49 +666,50 @@ fn format_rpc_ret<'ctx>(
             }
 
             // Copy shape from the buffer to `ndarray.shape`.
-            let pbuffer_dims =
-                unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
+            // We need to skip the first `sizeof(uint8_t*)` bytes to skip the `pdata` in `[pdata, shape]`.
+            let pbuffer_shape =
+                unsafe { buffer.ptr_offset_unchecked(ctx, generator, &sizeof_ptr, None) };
+            let pbuffer_shape =
+                ctx.builder.build_pointer_cast(pbuffer_shape, llvm_pusize, "").unwrap();
+
+            // Copy shape from buffer to `ndarray.shape`
+            ndarray.copy_shape_from_array(generator, ctx, pbuffer_shape);
 
-            call_memcpy_generic(
-                ctx,
-                ndarray.shape().base_ptr(ctx, generator),
-                pbuffer_dims,
-                sizeof_dims,
-                llvm_i1.const_zero(),
-            );
             // Restore stack from before allocation of buffer
             call_stackrestore(ctx, stackptr);
 
             // Allocate `ndarray.data`.
             // `ndarray.shape` must be initialized beforehand in this implementation
             //   (for ndarray.create_data() to know how many elements to allocate)
-            let num_elements =
-                call_ndarray_calc_size(generator, ctx, &ndarray.shape(), (None, None));
+            unsafe { ndarray.create_data(generator, ctx) }; // NOTE: the strides of `ndarray` has also been set to contiguous in `create_data`.
 
             // debug_assert(nelems * sizeof(T) >= ndarray_nbytes)
             if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
-                let sizeof_data =
-                    ctx.builder.build_int_mul(num_elements, llvm_elem_sizeof, "").unwrap();
+                let num_elements = ndarray.size(generator, ctx);
+
+                let expected_ndarray_nbytes =
+                    ctx.builder.build_int_mul(num_elements, itemsize, "").unwrap();
+                let cmp = ctx
+                    .builder
+                    .build_int_compare(
+                        IntPredicate::UGE,
+                        expected_ndarray_nbytes,
+                        ndarray_nbytes,
+                        "",
+                    )
+                    .unwrap();
 
                 ctx.make_assert(
                     generator,
-                    ctx.builder.build_int_compare(IntPredicate::UGE,
-                        sizeof_data,
-                        ndarray_nbytes,
-                        "",
-                    ).unwrap(),
+                    cmp,
                     "0:AssertionError",
                     "Unexpected allocation size request for ndarray data - Expected up to {0} bytes, got {1} bytes",
-                    [Some(sizeof_data), Some(ndarray_nbytes), None],
+                    [Some(expected_ndarray_nbytes), Some(ndarray_nbytes), None],
                     ctx.current_loc,
                 );
             }
 
-            ndarray.create_data(ctx, llvm_elem_ty, num_elements);
-
             let ndarray_data = ndarray.data().base_ptr(ctx, generator);
-            let ndarray_data_i8 =
-                ctx.builder.build_pointer_cast(ndarray_data, llvm_pi8, "").unwrap();
 
             // NOTE: Currently on `prehead_bb`
             ctx.builder.build_unconditional_branch(head_bb).unwrap();
@@ -756,7 +718,7 @@ fn format_rpc_ret<'ctx>(
             ctx.builder.position_at_end(head_bb);
 
             let phi = ctx.builder.build_phi(llvm_pi8, "rpc.ptr").unwrap();
-            phi.add_incoming(&[(&ndarray_data_i8, prehead_bb)]);
+            phi.add_incoming(&[(&ndarray_data, prehead_bb)]);
 
             let alloc_size = ctx
                 .build_call_or_invoke(rpc_recv, &[phi.as_basic_value()], "rpc.size.next")
@@ -771,12 +733,13 @@ fn format_rpc_ret<'ctx>(
 
             ctx.builder.position_at_end(alloc_bb);
             // Align the allocation to sizeof(T)
-            let alloc_size = round_up(ctx, alloc_size, llvm_elem_sizeof);
+            let alloc_size = round_up(ctx, alloc_size, itemsize);
+            // TODO(Derppening): Candidate for refactor into type_aligned_alloca
             let alloc_ptr = ctx
                 .builder
                 .build_array_alloca(
-                    llvm_elem_ty,
-                    ctx.builder.build_int_unsigned_div(alloc_size, llvm_elem_sizeof, "").unwrap(),
+                    dtype_llvm,
+                    ctx.builder.build_int_unsigned_div(alloc_size, itemsize, "").unwrap(),
                     "rpc.alloc",
                 )
                 .unwrap();
@@ -1375,62 +1338,50 @@ fn polymorphic_print<'ctx>(
             }
 
             TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-                let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
-                let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
-
                 fmt.push_str("array([");
                 flush(ctx, generator, &mut fmt, &mut args);
 
-                let val = NDArrayValue::from_pointer_value(
-                    value.into_pointer_value(),
-                    llvm_elem_ty,
-                    llvm_usize,
-                    None,
-                );
-                let len = call_ndarray_calc_size(generator, ctx, &val.shape(), (None, None));
-                let last =
-                    ctx.builder.build_int_sub(len, llvm_usize.const_int(1, false), "").unwrap();
+                let (dtype, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
+                let ndarray = NDArrayType::from_unifier_type(generator, ctx, ty)
+                    .map_value(value.into_pointer_value(), None);
 
-                gen_for_callback_incrementing(
-                    generator,
-                    ctx,
-                    None,
-                    llvm_usize.const_zero(),
-                    (len, false),
-                    |generator, ctx, _, i| {
-                        let elem = unsafe { val.data().get_unchecked(ctx, generator, &i, None) };
+                let num_0 = llvm_usize.const_zero();
 
-                        polymorphic_print(
-                            ctx,
-                            generator,
-                            &[(elem_ty, elem.into())],
-                            "",
-                            None,
-                            true,
-                            as_rtio,
-                        )?;
+                // Print `ndarray` as a flat list delimited by interspersed with ", \0"
+                ndarray.foreach(generator, ctx, |generator, ctx, _, hdl| {
+                    let i = hdl.get_index(ctx);
+                    let scalar = hdl.get_scalar(ctx);
 
-                        gen_if_callback(
-                            generator,
-                            ctx,
-                            |_, ctx| {
-                                Ok(ctx
-                                    .builder
-                                    .build_int_compare(IntPredicate::ULT, i, last, "")
-                                    .unwrap())
-                            },
-                            |generator, ctx| {
-                                printf(ctx, generator, ", \0".into(), Vec::default());
+                    // if (i != 0) puts(", ");
+                    gen_if_callback(
+                        generator,
+                        ctx,
+                        |_, ctx| {
+                            let not_first = ctx
+                                .builder
+                                .build_int_compare(IntPredicate::NE, i, num_0, "")
+                                .unwrap();
+                            Ok(not_first)
+                        },
+                        |generator, ctx| {
+                            printf(ctx, generator, ", \0".into(), Vec::default());
+                            Ok(())
+                        },
+                        |_, _| Ok(()),
+                    )?;
 
-                                Ok(())
-                            },
-                            |_, _| Ok(()),
-                        )?;
-
-                        Ok(())
-                    },
-                    llvm_usize.const_int(1, false),
-                )?;
+                    // Print element
+                    polymorphic_print(
+                        ctx,
+                        generator,
+                        &[(dtype, scalar.into())],
+                        "",
+                        None,
+                        true,
+                        as_rtio,
+                    )?;
+                    Ok(())
+                })?;
 
                 fmt.push_str(")]");
                 flush(ctx, generator, &mut fmt, &mut args);
@@ -1554,7 +1505,7 @@ pub fn call_rtio_log_impl<'ctx>(
 /// Generates a call to `core_log`.
 pub fn gen_core_log<'ctx>(
     ctx: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    obj: Option<&(Type, ValueEnum<'ctx>)>,
     fun: (&FunSignature, DefinitionId),
     args: &[(Option<StrRef>, ValueEnum<'ctx>)],
     generator: &mut dyn CodeGenerator,
@@ -1571,7 +1522,7 @@ pub fn gen_core_log<'ctx>(
 /// Generates a call to `rtio_log`.
 pub fn gen_rtio_log<'ctx>(
     ctx: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    obj: Option<&(Type, ValueEnum<'ctx>)>,
     fun: (&FunSignature, DefinitionId),
     args: &[(Option<StrRef>, ValueEnum<'ctx>)],
     generator: &mut dyn CodeGenerator,

nac3artiq/src/lib.rs

@@ -330,7 +330,7 @@ impl Nac3 {
                         vars: into_var_map([arg_ty]),
                     },
                     Arc::new(GenCall::new(Box::new(move |ctx, obj, fun, args, generator| {
-                        gen_core_log(ctx, &obj, fun, &args, generator)?;
+                        gen_core_log(ctx, obj.as_ref(), fun, &args, generator)?;
 
                         Ok(None)
                     }))),
@@ -360,7 +360,7 @@ impl Nac3 {
                         vars: into_var_map([arg_ty]),
                     },
                     Arc::new(GenCall::new(Box::new(move |ctx, obj, fun, args, generator| {
-                        gen_rtio_log(ctx, &obj, fun, &args, generator)?;
+                        gen_rtio_log(ctx, obj.as_ref(), fun, &args, generator)?;
 
                         Ok(None)
                     }))),

nac3artiq/src/symbol_resolver.rs

@@ -10,12 +10,14 @@ use itertools::Itertools;
 use parking_lot::RwLock;
 use pyo3::{
     types::{PyDict, PyTuple},
-    PyAny, PyObject, PyResult, Python,
+    PyAny, PyErr, PyObject, PyResult, Python,
 };
 
+use super::PrimitivePythonId;
 use nac3core::{
     codegen::{
-        types::{NDArrayType, ProxyType},
+        types::{ndarray::NDArrayType, ProxyType},
+        values::ndarray::make_contiguous_strides,
         CodeGenContext, CodeGenerator,
     },
     inkwell::{
@@ -37,8 +39,6 @@ use nac3core::{
     },
 };
 
-use super::PrimitivePythonId;
-
 pub enum PrimitiveValue {
     I32(i32),
     I64(i64),
@@ -931,10 +931,13 @@ impl InnerResolver {
                         |_| Ok(Ok(extracted_ty)),
                     )
                 } else if unifier.unioned(extracted_ty, primitives.bool) {
-                    obj.extract::<bool>().map_or_else(
-                        |_| Ok(Err(format!("{obj} is not in the range of bool"))),
-                        |_| Ok(Ok(extracted_ty)),
-                    )
+                    if obj.extract::<bool>().is_ok()
+                        || obj.call_method("__bool__", (), None)?.extract::<bool>().is_ok()
+                    {
+                        Ok(Ok(extracted_ty))
+                    } else {
+                        Ok(Err(format!("{obj} is not in the range of bool")))
+                    }
                 } else if unifier.unioned(extracted_ty, primitives.float) {
                     obj.extract::<f64>().map_or_else(
                         |_| Ok(Err(format!("{obj} is not in the range of float64"))),
@@ -974,10 +977,14 @@ impl InnerResolver {
             let val: u64 = obj.extract().unwrap();
             self.id_to_primitive.write().insert(id, PrimitiveValue::U64(val));
             Ok(Some(ctx.ctx.i64_type().const_int(val, false).into()))
-        } else if ty_id == self.primitive_ids.bool || ty_id == self.primitive_ids.np_bool_ {
+        } else if ty_id == self.primitive_ids.bool {
             let val: bool = obj.extract().unwrap();
             self.id_to_primitive.write().insert(id, PrimitiveValue::Bool(val));
             Ok(Some(ctx.ctx.i8_type().const_int(u64::from(val), false).into()))
+        } else if ty_id == self.primitive_ids.np_bool_ {
+            let val: bool = obj.call_method("__bool__", (), None)?.extract().unwrap();
+            self.id_to_primitive.write().insert(id, PrimitiveValue::Bool(val));
+            Ok(Some(ctx.ctx.i8_type().const_int(u64::from(val), false).into()))
         } else if ty_id == self.primitive_ids.string || ty_id == self.primitive_ids.np_str_ {
             let val: String = obj.extract().unwrap();
             self.id_to_primitive.write().insert(id, PrimitiveValue::Str(val.clone()));
@@ -1085,18 +1092,19 @@ impl InnerResolver {
             } else {
                 unreachable!("must be ndarray")
             };
-            let (ndarray_dtype, ndarray_ndims) =
-                unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
+            let (ndarray_dtype, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
 
+            let llvm_i8 = ctx.ctx.i8_type();
+            let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
             let llvm_usize = generator.get_size_type(ctx.ctx);
-            let ndarray_dtype_llvm_ty = ctx.get_llvm_type(generator, ndarray_dtype);
-            let ndarray_llvm_ty = NDArrayType::new(generator, ctx.ctx, ndarray_dtype_llvm_ty);
+            let llvm_ndarray = NDArrayType::from_unifier_type(generator, ctx, ndarray_ty);
+            let dtype = llvm_ndarray.element_type();
 
             {
                 if self.global_value_ids.read().contains_key(&id) {
                     let global = ctx.module.get_global(&id_str).unwrap_or_else(|| {
                         ctx.module.add_global(
-                            ndarray_llvm_ty.as_base_type().get_element_type().into_struct_type(),
+                            llvm_ndarray.as_base_type().get_element_type().into_struct_type(),
                             Some(AddressSpace::default()),
                             &id_str,
                         )
@@ -1106,40 +1114,44 @@ impl InnerResolver {
                 self.global_value_ids.write().insert(id, obj.into());
             }
 
-            let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndarray_ndims)
-            else {
-                unreachable!("Expected Literal for ndarray_ndims")
-            };
-
-            let ndarray_ndims = if values.len() == 1 {
-                values[0].clone()
-            } else {
-                todo!("Unpacking literal of more than one element unimplemented")
-            };
-            let Ok(ndarray_ndims) = u64::try_from(ndarray_ndims) else {
-                unreachable!("Expected u64 value for ndarray_ndims")
-            };
+            let ndims = llvm_ndarray.ndims();
 
             // Obtain the shape of the ndarray
             let shape_tuple: &PyTuple = obj.getattr("shape")?.downcast()?;
-            assert_eq!(shape_tuple.len(), ndarray_ndims as usize);
-            let shape_values: Result<Option<Vec<_>>, _> = shape_tuple
+            assert_eq!(shape_tuple.len(), ndims as usize);
+
+            // The Rust type inferencer cannot figure this out
+            let shape_values = shape_tuple
                 .iter()
                 .enumerate()
                 .map(|(i, elem)| {
-                    self.get_obj_value(py, elem, ctx, generator, ctx.primitives.usize()).map_err(
-                        |e| super::CompileError::new_err(format!("Error getting element {i}: {e}")),
-                    )
+                    let value = self
+                        .get_obj_value(py, elem, ctx, generator, ctx.primitives.usize())
+                        .map_err(|e| {
+                            super::CompileError::new_err(format!("Error getting element {i}: {e}"))
+                        })?
+                        .unwrap();
+                    let value = ctx
+                        .builder
+                        .build_int_z_extend(value.into_int_value(), llvm_usize, "")
+                        .unwrap();
+                    Ok(value)
                 })
-                .collect();
-            let shape_values = shape_values?.unwrap();
-            let shape_values = llvm_usize.const_array(
-                &shape_values.into_iter().map(BasicValueEnum::into_int_value).collect_vec(),
-            );
+                .collect::<Result<Vec<_>, PyErr>>()?;
+
+            // Also use this opportunity to get the constant values of `shape_values` for calculating strides.
+            let shape_u64s = shape_values
+                .iter()
+                .map(|dim| {
+                    assert!(dim.is_const());
+                    dim.get_zero_extended_constant().unwrap()
+                })
+                .collect_vec();
+            let shape_values = llvm_usize.const_array(&shape_values);
 
             // create a global for ndarray.shape and initialize it using the shape
             let shape_global = ctx.module.add_global(
-                llvm_usize.array_type(ndarray_ndims as u32),
+                llvm_usize.array_type(ndims as u32),
                 Some(AddressSpace::default()),
                 &(id_str.clone() + ".shape"),
             );
@@ -1147,17 +1159,25 @@ impl InnerResolver {
 
             // Obtain the (flattened) elements of the ndarray
             let sz: usize = obj.getattr("size")?.extract()?;
-            let data: Result<Option<Vec<_>>, _> = (0..sz)
+            let data: Vec<_> = (0..sz)
                 .map(|i| {
                     obj.getattr("flat")?.get_item(i).and_then(|elem| {
-                        self.get_obj_value(py, elem, ctx, generator, ndarray_dtype).map_err(|e| {
-                            super::CompileError::new_err(format!("Error getting element {i}: {e}"))
-                        })
+                        let value = self
+                            .get_obj_value(py, elem, ctx, generator, ndarray_dtype)
+                            .map_err(|e| {
+                                super::CompileError::new_err(format!(
+                                    "Error getting element {i}: {e}"
+                                ))
+                            })?
+                            .unwrap();
+
+                        assert_eq!(value.get_type(), dtype);
+                        Ok(value)
                     })
                 })
-                .collect();
-            let data = data?.unwrap().into_iter();
-            let data = match ndarray_dtype_llvm_ty {
+                .try_collect()?;
+            let data = data.into_iter();
+            let data = match dtype {
                 BasicTypeEnum::ArrayType(ty) => {
                     ty.const_array(&data.map(BasicValueEnum::into_array_value).collect_vec())
                 }
@@ -1182,38 +1202,97 @@ impl InnerResolver {
             };
 
             // create a global for ndarray.data and initialize it using the elements
+            //
+            // NOTE: NDArray's `data` is `u8*`. Here, `data_global` is an array of `dtype`.
+            // We will have to cast it to an `u8*` later.
             let data_global = ctx.module.add_global(
-                ndarray_dtype_llvm_ty.array_type(sz as u32),
+                dtype.array_type(sz as u32),
                 Some(AddressSpace::default()),
                 &(id_str.clone() + ".data"),
             );
             data_global.set_initializer(&data);
 
+            // Get the constant itemsize.
+            //
+            // NOTE: dtype.size_of() may return a non-constant, where `TargetData::get_store_size`
+            // will always return a constant size.
+            let itemsize = ctx
+                .registry
+                .llvm_options
+                .create_target_machine()
+                .map(|tm| tm.get_target_data().get_store_size(&dtype))
+                .unwrap();
+            assert_ne!(itemsize, 0);
+
+            // Create the strides needed for ndarray.strides
+            let strides = make_contiguous_strides(itemsize, ndims, &shape_u64s);
+            let strides =
+                strides.into_iter().map(|stride| llvm_usize.const_int(stride, false)).collect_vec();
+            let strides = llvm_usize.const_array(&strides);
+
+            // create a global for ndarray.strides and initialize it
+            let strides_global = ctx.module.add_global(
+                llvm_usize.array_type(ndims as u32),
+                Some(AddressSpace::default()),
+                &format!("${id_str}.strides"),
+            );
+            strides_global.set_initializer(&strides);
+
             // create a global for the ndarray object and initialize it
-            let value = ndarray_llvm_ty
+
+            // NOTE: data_global is an array of dtype, we want a `u8*`.
+            let ndarray_data = data_global.as_pointer_value();
+            let ndarray_data = ctx.builder.build_pointer_cast(ndarray_data, llvm_pi8, "").unwrap();
+
+            let ndarray_itemsize = llvm_usize.const_int(itemsize, false);
+
+            let ndarray_ndims = llvm_usize.const_int(ndims, false);
+
+            // calling as_pointer_value on shape and strides returns [i64 x ndims]*
+            // convert into i64* to conform with expected layout of ndarray
+
+            let ndarray_shape = shape_global.as_pointer_value();
+            let ndarray_shape = unsafe {
+                ctx.builder
+                    .build_in_bounds_gep(
+                        ndarray_shape,
+                        &[llvm_usize.const_zero(), llvm_usize.const_zero()],
+                        "",
+                    )
+                    .unwrap()
+            };
+
+            let ndarray_strides = strides_global.as_pointer_value();
+            let ndarray_strides = unsafe {
+                ctx.builder
+                    .build_in_bounds_gep(
+                        ndarray_strides,
+                        &[llvm_usize.const_zero(), llvm_usize.const_zero()],
+                        "",
+                    )
+                    .unwrap()
+            };
+
+            let ndarray = llvm_ndarray
                 .as_base_type()
                 .get_element_type()
                 .into_struct_type()
                 .const_named_struct(&[
-                    llvm_usize.const_int(ndarray_ndims, false).into(),
-                    shape_global
-                        .as_pointer_value()
-                        .const_cast(llvm_usize.ptr_type(AddressSpace::default()))
-                        .into(),
-                    data_global
-                        .as_pointer_value()
-                        .const_cast(ndarray_dtype_llvm_ty.ptr_type(AddressSpace::default()))
-                        .into(),
+                    ndarray_itemsize.into(),
+                    ndarray_ndims.into(),
+                    ndarray_shape.into(),
+                    ndarray_strides.into(),
+                    ndarray_data.into(),
                 ]);
 
-            let ndarray = ctx.module.add_global(
-                ndarray_llvm_ty.as_base_type().get_element_type().into_struct_type(),
+            let ndarray_global = ctx.module.add_global(
+                llvm_ndarray.as_base_type().get_element_type().into_struct_type(),
                 Some(AddressSpace::default()),
                 &id_str,
             );
-            ndarray.set_initializer(&value);
+            ndarray_global.set_initializer(&ndarray);
 
-            Ok(Some(ndarray.as_pointer_value().into()))
+            Ok(Some(ndarray_global.as_pointer_value().into()))
         } else if ty_id == self.primitive_ids.tuple {
             let expected_ty_enum = ctx.unifier.get_ty_immutable(expected_ty);
             let TypeEnum::TTuple { ty, is_vararg_ctx: false } = expected_ty_enum.as_ref() else {
@@ -1373,9 +1452,12 @@ impl InnerResolver {
         } else if ty_id == self.primitive_ids.uint64 {
             let val: u64 = obj.extract()?;
             Ok(SymbolValue::U64(val))
-        } else if ty_id == self.primitive_ids.bool || ty_id == self.primitive_ids.np_bool_ {
+        } else if ty_id == self.primitive_ids.bool {
             let val: bool = obj.extract()?;
             Ok(SymbolValue::Bool(val))
+        } else if ty_id == self.primitive_ids.np_bool_ {
+            let val: bool = obj.call_method("__bool__", (), None)?.extract()?;
+            Ok(SymbolValue::Bool(val))
         } else if ty_id == self.primitive_ids.string || ty_id == self.primitive_ids.np_str_ {
             let val: String = obj.extract()?;
             Ok(SymbolValue::Str(val))

nac3core/irrt/irrt.cpp

@@ -1,5 +1,15 @@
 #include "irrt/exception.hpp"
 #include "irrt/list.hpp"
 #include "irrt/math.hpp"
-#include "irrt/ndarray.hpp"
+#include "irrt/range.hpp"
 #include "irrt/slice.hpp"
+#include "irrt/string.hpp"
+#include "irrt/ndarray/basic.hpp"
+#include "irrt/ndarray/def.hpp"
+#include "irrt/ndarray/iter.hpp"
+#include "irrt/ndarray/indexing.hpp"
+#include "irrt/ndarray/array.hpp"
+#include "irrt/ndarray/reshape.hpp"
+#include "irrt/ndarray/broadcast.hpp"
+#include "irrt/ndarray/transpose.hpp"
+#include "irrt/ndarray/matmul.hpp"

nac3core/irrt/irrt/int_types.hpp

@@ -21,7 +21,5 @@ using uint64_t = unsigned _ExtInt(64);
 
 #endif
 
-// NDArray indices are always `uint32_t`.
-using NDIndex = uint32_t;
 // The type of an index or a value describing the length of a range/slice is always `int32_t`.
 using SliceIndex = int32_t;

nac3core/irrt/irrt/list.hpp

@@ -2,6 +2,21 @@
 
 #include "irrt/int_types.hpp"
 #include "irrt/math_util.hpp"
+#include "irrt/slice.hpp"
+
+namespace {
+/**
+ * @brief A list in NAC3.
+ *
+ * The `items` field is opaque. You must rely on external contexts to
+ * know how to interpret it.
+ */
+template<typename SizeT>
+struct List {
+    uint8_t* items;
+    SizeT len;
+};
+}  // namespace
 
 extern "C" {
 // Handle list assignment and dropping part of the list when

nac3core/irrt/irrt/math.hpp

@@ -1,5 +1,7 @@
 #pragma once
 
+#include "irrt/int_types.hpp"
+
 namespace {
 // adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
 // need to make sure `exp >= 0` before calling this function

nac3core/irrt/irrt/ndarray.hpp

@@ -1,144 +0,0 @@
-#pragma once
-
-#include "irrt/int_types.hpp"
-
-namespace {
-template<typename SizeT>
-SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len, SizeT begin_idx, SizeT end_idx) {
-    __builtin_assume(end_idx <= list_len);
-
-    SizeT num_elems = 1;
-    for (SizeT i = begin_idx; i < end_idx; ++i) {
-        SizeT val = list_data[i];
-        __builtin_assume(val > 0);
-        num_elems *= val;
-    }
-    return num_elems;
-}
-
-template<typename SizeT>
-void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims, SizeT num_dims, NDIndex* idxs) {
-    SizeT stride = 1;
-    for (SizeT dim = 0; dim < num_dims; dim++) {
-        SizeT i = num_dims - dim - 1;
-        __builtin_assume(dims[i] > 0);
-        idxs[i] = (index / stride) % dims[i];
-        stride *= dims[i];
-    }
-}
-
-template<typename SizeT>
-SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims, const NDIndex* indices, SizeT num_indices) {
-    SizeT idx = 0;
-    SizeT stride = 1;
-    for (SizeT i = 0; i < num_dims; ++i) {
-        SizeT ri = num_dims - i - 1;
-        if (ri < num_indices) {
-            idx += stride * indices[ri];
-        }
-
-        __builtin_assume(dims[i] > 0);
-        stride *= dims[ri];
-    }
-    return idx;
-}
-
-template<typename SizeT>
-void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims,
-                                        SizeT lhs_ndims,
-                                        const SizeT* rhs_dims,
-                                        SizeT rhs_ndims,
-                                        SizeT* out_dims) {
-    SizeT max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
-
-    for (SizeT i = 0; i < max_ndims; ++i) {
-        const SizeT* lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : nullptr;
-        const SizeT* rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : nullptr;
-        SizeT* out_dim = &out_dims[max_ndims - i - 1];
-
-        if (lhs_dim_sz == nullptr) {
-            *out_dim = *rhs_dim_sz;
-        } else if (rhs_dim_sz == nullptr) {
-            *out_dim = *lhs_dim_sz;
-        } else if (*lhs_dim_sz == 1) {
-            *out_dim = *rhs_dim_sz;
-        } else if (*rhs_dim_sz == 1) {
-            *out_dim = *lhs_dim_sz;
-        } else if (*lhs_dim_sz == *rhs_dim_sz) {
-            *out_dim = *lhs_dim_sz;
-        } else {
-            __builtin_unreachable();
-        }
-    }
-}
-
-template<typename SizeT>
-void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
-                                            SizeT src_ndims,
-                                            const NDIndex* in_idx,
-                                            NDIndex* out_idx) {
-    for (SizeT i = 0; i < src_ndims; ++i) {
-        SizeT src_i = src_ndims - i - 1;
-        out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
-    }
-}
-}  // namespace
-
-extern "C" {
-uint32_t __nac3_ndarray_calc_size(const uint32_t* list_data, uint32_t list_len, uint32_t begin_idx, uint32_t end_idx) {
-    return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
-}
-
-uint64_t
-__nac3_ndarray_calc_size64(const uint64_t* list_data, uint64_t list_len, uint64_t begin_idx, uint64_t end_idx) {
-    return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
-}
-
-void __nac3_ndarray_calc_nd_indices(uint32_t index, const uint32_t* dims, uint32_t num_dims, NDIndex* idxs) {
-    __nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
-}
-
-void __nac3_ndarray_calc_nd_indices64(uint64_t index, const uint64_t* dims, uint64_t num_dims, NDIndex* idxs) {
-    __nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
-}
-
-uint32_t
-__nac3_ndarray_flatten_index(const uint32_t* dims, uint32_t num_dims, const NDIndex* indices, uint32_t num_indices) {
-    return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
-}
-
-uint64_t
-__nac3_ndarray_flatten_index64(const uint64_t* dims, uint64_t num_dims, const NDIndex* indices, uint64_t num_indices) {
-    return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
-}
-
-void __nac3_ndarray_calc_broadcast(const uint32_t* lhs_dims,
-                                   uint32_t lhs_ndims,
-                                   const uint32_t* rhs_dims,
-                                   uint32_t rhs_ndims,
-                                   uint32_t* out_dims) {
-    return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
-}
-
-void __nac3_ndarray_calc_broadcast64(const uint64_t* lhs_dims,
-                                     uint64_t lhs_ndims,
-                                     const uint64_t* rhs_dims,
-                                     uint64_t rhs_ndims,
-                                     uint64_t* out_dims) {
-    return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
-}
-
-void __nac3_ndarray_calc_broadcast_idx(const uint32_t* src_dims,
-                                       uint32_t src_ndims,
-                                       const NDIndex* in_idx,
-                                       NDIndex* out_idx) {
-    __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
-}
-
-void __nac3_ndarray_calc_broadcast_idx64(const uint64_t* src_dims,
-                                         uint64_t src_ndims,
-                                         const NDIndex* in_idx,
-                                         NDIndex* out_idx) {
-    __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
-}
-}  // namespace

nac3core/irrt/irrt/ndarray/array.hpp

@@ -0,0 +1,132 @@
+#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::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 = static_cast<uint8_t*>(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 ndarray::array
+}  // 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/basic.hpp

@@ -0,0 +1,340 @@
+#pragma once
+
+#include "irrt/debug.hpp"
+#include "irrt/exception.hpp"
+#include "irrt/int_types.hpp"
+#include "irrt/ndarray/def.hpp"
+
+namespace {
+namespace ndarray::basic {
+/**
+ * @brief Assert that `shape` does not contain negative dimensions.
+ *
+ * @param ndims Number of dimensions in `shape`
+ * @param shape The shape to check on
+ */
+template<typename SizeT>
+void assert_shape_no_negative(SizeT ndims, const SizeT* shape) {
+    for (SizeT axis = 0; axis < ndims; axis++) {
+        if (shape[axis] < 0) {
+            raise_exception(SizeT, EXN_VALUE_ERROR,
+                            "negative dimensions are not allowed; axis {0} "
+                            "has dimension {1}",
+                            axis, shape[axis], NO_PARAM);
+        }
+    }
+}
+
+/**
+ * @brief Assert that two shapes are the same in the context of writing output to an ndarray.
+ */
+template<typename SizeT>
+void assert_output_shape_same(SizeT ndarray_ndims,
+                              const SizeT* ndarray_shape,
+                              SizeT output_ndims,
+                              const SizeT* output_shape) {
+    if (ndarray_ndims != output_ndims) {
+        // There is no corresponding NumPy error message like this.
+        raise_exception(SizeT, EXN_VALUE_ERROR, "Cannot write output of ndims {0} to an ndarray with ndims {1}",
+                        output_ndims, ndarray_ndims, NO_PARAM);
+    }
+
+    for (SizeT axis = 0; axis < ndarray_ndims; axis++) {
+        if (ndarray_shape[axis] != output_shape[axis]) {
+            // There is no corresponding NumPy error message like this.
+            raise_exception(SizeT, EXN_VALUE_ERROR,
+                            "Mismatched dimensions on axis {0}, output has "
+                            "dimension {1}, but destination ndarray has dimension {2}.",
+                            axis, output_shape[axis], ndarray_shape[axis]);
+        }
+    }
+}
+
+/**
+ * @brief Return the number of elements of an ndarray given its shape.
+ *
+ * @param ndims Number of dimensions in `shape`
+ * @param shape The shape of the ndarray
+ */
+template<typename SizeT>
+SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
+    SizeT size = 1;
+    for (SizeT axis = 0; axis < ndims; axis++)
+        size *= shape[axis];
+    return size;
+}
+
+/**
+ * @brief Compute the array indices of the `nth` (0-based) element of an ndarray given only its shape.
+ *
+ * @param ndims Number of elements in `shape` and `indices`
+ * @param shape The shape of the ndarray
+ * @param indices The returned indices indexing the ndarray with shape `shape`.
+ * @param nth The index of the element of interest.
+ */
+template<typename SizeT>
+void set_indices_by_nth(SizeT ndims, const SizeT* shape, SizeT* indices, SizeT nth) {
+    for (SizeT i = 0; i < ndims; i++) {
+        SizeT axis = ndims - i - 1;
+        SizeT dim = shape[axis];
+
+        indices[axis] = nth % dim;
+        nth /= dim;
+    }
+}
+
+/**
+ * @brief Return the number of elements of an `ndarray`
+ *
+ * This function corresponds to `<an_ndarray>.size`
+ */
+template<typename SizeT>
+SizeT size(const NDArray<SizeT>* ndarray) {
+    return calc_size_from_shape(ndarray->ndims, ndarray->shape);
+}
+
+/**
+ * @brief Return of the number of its content of an `ndarray`.
+ *
+ * This function corresponds to `<an_ndarray>.nbytes`.
+ */
+template<typename SizeT>
+SizeT nbytes(const NDArray<SizeT>* ndarray) {
+    return size(ndarray) * ndarray->itemsize;
+}
+
+/**
+ * @brief Get the `len()` of an ndarray, and asserts that `ndarray` is a sized object.
+ *
+ * This function corresponds to `<an_ndarray>.__len__`.
+ *
+ * @param dst_length The length.
+ */
+template<typename SizeT>
+SizeT len(const NDArray<SizeT>* ndarray) {
+    if (ndarray->ndims != 0) {
+        return ndarray->shape[0];
+    }
+
+    // numpy prohibits `__len__` on unsized objects
+    raise_exception(SizeT, EXN_TYPE_ERROR, "len() of unsized object", NO_PARAM, NO_PARAM, NO_PARAM);
+    __builtin_unreachable();
+}
+
+/**
+ * @brief Return a boolean indicating if `ndarray` is (C-)contiguous.
+ *
+ * You may want to see ndarray's rules for C-contiguity:
+ * https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
+ */
+template<typename SizeT>
+bool is_c_contiguous(const NDArray<SizeT>* ndarray) {
+    // References:
+    // - tinynumpy's implementation:
+    // https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L102
+    // - ndarray's flags["C_CONTIGUOUS"]:
+    // https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags
+    // - ndarray's rules for C-contiguity:
+    // https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
+
+    // From
+    // https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45:
+    //
+    // The traditional rule is that for an array to be flagged as C contiguous,
+    // the following must hold:
+    //
+    // strides[-1] == itemsize
+    // strides[i] == shape[i+1] * strides[i + 1]
+    // [...]
+    // According to these rules, a 0- or 1-dimensional array is either both
+    // C- and F-contiguous, or neither; and an array with 2+ dimensions
+    // can be C- or F- contiguous, or neither, but not both. Though there
+    // there are exceptions for arrays with zero or one item, in the first
+    // case the check is relaxed up to and including the first dimension
+    // with shape[i] == 0. In the second case `strides == itemsize` will
+    // can be true for all dimensions and both flags are set.
+
+    if (ndarray->ndims == 0) {
+        return true;
+    }
+
+    if (ndarray->strides[ndarray->ndims - 1] != ndarray->itemsize) {
+        return false;
+    }
+
+    for (SizeT i = 1; i < ndarray->ndims; i++) {
+        SizeT axis_i = ndarray->ndims - i - 1;
+        if (ndarray->strides[axis_i] != ndarray->shape[axis_i + 1] * ndarray->strides[axis_i + 1]) {
+            return false;
+        }
+    }
+
+    return true;
+}
+
+/**
+ * @brief Return the pointer to the element indexed by `indices` along the ndarray's axes.
+ *
+ * This function does no bound check.
+ */
+template<typename SizeT>
+void* get_pelement_by_indices(const NDArray<SizeT>* ndarray, const SizeT* indices) {
+    void* element = ndarray->data;
+    for (SizeT dim_i = 0; dim_i < ndarray->ndims; dim_i++)
+        element = static_cast<uint8_t*>(element) + indices[dim_i] * ndarray->strides[dim_i];
+    return element;
+}
+
+/**
+ * @brief Return the pointer to the nth (0-based) element of `ndarray` in flattened view.
+ *
+ * This function does no bound check.
+ */
+template<typename SizeT>
+void* get_nth_pelement(const NDArray<SizeT>* ndarray, SizeT nth) {
+    void* element = ndarray->data;
+    for (SizeT i = 0; i < ndarray->ndims; i++) {
+        SizeT axis = ndarray->ndims - i - 1;
+        SizeT dim = ndarray->shape[axis];
+        element = static_cast<uint8_t*>(element) + ndarray->strides[axis] * (nth % dim);
+        nth /= dim;
+    }
+    return element;
+}
+
+/**
+ * @brief Update the strides of an ndarray given an ndarray `shape` to be contiguous.
+ *
+ * You might want to read https://ajcr.net/stride-guide-part-1/.
+ */
+template<typename SizeT>
+void set_strides_by_shape(NDArray<SizeT>* ndarray) {
+    SizeT stride_product = 1;
+    for (SizeT i = 0; i < ndarray->ndims; i++) {
+        SizeT axis = ndarray->ndims - i - 1;
+        ndarray->strides[axis] = stride_product * ndarray->itemsize;
+        stride_product *= ndarray->shape[axis];
+    }
+}
+
+/**
+ * @brief Set an element in `ndarray`.
+ *
+ * @param pelement Pointer to the element in `ndarray` to be set.
+ * @param pvalue Pointer to the value `pelement` will be set to.
+ */
+template<typename SizeT>
+void set_pelement_value(NDArray<SizeT>* ndarray, void* pelement, const void* pvalue) {
+    __builtin_memcpy(pelement, pvalue, ndarray->itemsize);
+}
+
+/**
+ * @brief Copy data from one ndarray to another of the exact same size and itemsize.
+ *
+ * Both ndarrays will be viewed in their flatten views when copying the elements.
+ */
+template<typename SizeT>
+void copy_data(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
+    // TODO: Make this faster with memcpy when we see a contiguous segment.
+    // TODO: Handle overlapping.
+
+    debug_assert_eq(SizeT, src_ndarray->itemsize, dst_ndarray->itemsize);
+
+    for (SizeT i = 0; i < size(src_ndarray); i++) {
+        auto src_element = ndarray::basic::get_nth_pelement(src_ndarray, i);
+        auto dst_element = ndarray::basic::get_nth_pelement(dst_ndarray, i);
+        ndarray::basic::set_pelement_value(dst_ndarray, dst_element, src_element);
+    }
+}
+}  // namespace ndarray::basic
+}  // namespace
+
+extern "C" {
+using namespace ndarray::basic;
+
+void __nac3_ndarray_util_assert_shape_no_negative(int32_t ndims, int32_t* shape) {
+    assert_shape_no_negative(ndims, shape);
+}
+
+void __nac3_ndarray_util_assert_shape_no_negative64(int64_t ndims, int64_t* shape) {
+    assert_shape_no_negative(ndims, shape);
+}
+
+void __nac3_ndarray_util_assert_output_shape_same(int32_t ndarray_ndims,
+                                                  const int32_t* ndarray_shape,
+                                                  int32_t output_ndims,
+                                                  const int32_t* output_shape) {
+    assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims, output_shape);
+}
+
+void __nac3_ndarray_util_assert_output_shape_same64(int64_t ndarray_ndims,
+                                                    const int64_t* ndarray_shape,
+                                                    int64_t output_ndims,
+                                                    const int64_t* output_shape) {
+    assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims, output_shape);
+}
+
+uint32_t __nac3_ndarray_size(NDArray<int32_t>* ndarray) {
+    return size(ndarray);
+}
+
+uint64_t __nac3_ndarray_size64(NDArray<int64_t>* ndarray) {
+    return size(ndarray);
+}
+
+uint32_t __nac3_ndarray_nbytes(NDArray<int32_t>* ndarray) {
+    return nbytes(ndarray);
+}
+
+uint64_t __nac3_ndarray_nbytes64(NDArray<int64_t>* ndarray) {
+    return nbytes(ndarray);
+}
+
+int32_t __nac3_ndarray_len(NDArray<int32_t>* ndarray) {
+    return len(ndarray);
+}
+
+int64_t __nac3_ndarray_len64(NDArray<int64_t>* ndarray) {
+    return len(ndarray);
+}
+
+bool __nac3_ndarray_is_c_contiguous(NDArray<int32_t>* ndarray) {
+    return is_c_contiguous(ndarray);
+}
+
+bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t>* ndarray) {
+    return is_c_contiguous(ndarray);
+}
+
+void* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray, int32_t nth) {
+    return get_nth_pelement(ndarray, nth);
+}
+
+void* __nac3_ndarray_get_nth_pelement64(const NDArray<int64_t>* ndarray, int64_t nth) {
+    return get_nth_pelement(ndarray, nth);
+}
+
+void* __nac3_ndarray_get_pelement_by_indices(const NDArray<int32_t>* ndarray, int32_t* indices) {
+    return get_pelement_by_indices(ndarray, indices);
+}
+
+void* __nac3_ndarray_get_pelement_by_indices64(const NDArray<int64_t>* ndarray, int64_t* indices) {
+    return get_pelement_by_indices(ndarray, indices);
+}
+
+void __nac3_ndarray_set_strides_by_shape(NDArray<int32_t>* ndarray) {
+    set_strides_by_shape(ndarray);
+}
+
+void __nac3_ndarray_set_strides_by_shape64(NDArray<int64_t>* ndarray) {
+    set_strides_by_shape(ndarray);
+}
+
+void __nac3_ndarray_copy_data(NDArray<int32_t>* src_ndarray, NDArray<int32_t>* dst_ndarray) {
+    copy_data(src_ndarray, dst_ndarray);
+}
+
+void __nac3_ndarray_copy_data64(NDArray<int64_t>* src_ndarray, NDArray<int64_t>* dst_ndarray) {
+    copy_data(src_ndarray, dst_ndarray);
+}
+}

nac3core/irrt/irrt/ndarray/broadcast.hpp

@@ -0,0 +1,165 @@
+#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::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);
+            }
+        }
+    }
+
+#ifdef IRRT_DEBUG_ASSERT
+    // Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
+    debug_assert_eq(SizeT, max_ndims_found, dst_ndims);
+#endif
+}
+
+/**
+ * @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 ndarray::broadcast
+}  // 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/def.hpp

@@ -0,0 +1,51 @@
+#pragma once
+
+#include "irrt/int_types.hpp"
+
+namespace {
+/**
+ * @brief The NDArray object
+ *
+ * Official numpy implementation:
+ * https://github.com/numpy/numpy/blob/735a477f0bc2b5b84d0e72d92f224bde78d4e069/doc/source/reference/c-api/types-and-structures.rst#pyarrayinterface
+ *
+ * Note that this implementation is based on `PyArrayInterface` rather of `PyArrayObject`. The
+ * difference between `PyArrayInterface` and `PyArrayObject` (relevant to our implementation) is
+ * that `PyArrayInterface` *has* `itemsize` and uses `void*` for its `data`, whereas `PyArrayObject`
+ * does not require `itemsize` (probably using `strides[-1]` instead) and uses `char*` for its
+ * `data`. There are also minor differences in the struct layout.
+ */
+template<typename SizeT>
+struct NDArray {
+    /**
+     * @brief The number of bytes of a single element in `data`.
+     */
+    SizeT itemsize;
+
+    /**
+     * @brief The number of dimensions of this shape.
+     */
+    SizeT ndims;
+
+    /**
+     * @brief The NDArray shape, with length equal to `ndims`.
+     *
+     * Note that it may contain 0.
+     */
+    SizeT* shape;
+
+    /**
+     * @brief Array strides, with length equal to `ndims`
+     *
+     * The stride values are in units of bytes, not number of elements.
+     *
+     * Note that `strides` can have negative values or contain 0.
+     */
+    SizeT* strides;
+
+    /**
+     * @brief The underlying data this `ndarray` is pointing to.
+     */
+    void* data;
+};
+}  // namespace

nac3core/irrt/irrt/ndarray/indexing.hpp

@@ -0,0 +1,219 @@
+#pragma once
+
+#include "irrt/exception.hpp"
+#include "irrt/int_types.hpp"
+#include "irrt/ndarray/basic.hpp"
+#include "irrt/ndarray/def.hpp"
+#include "irrt/range.hpp"
+#include "irrt/slice.hpp"
+
+namespace {
+typedef uint8_t NDIndexType;
+
+/**
+ * @brief A single element index
+ *
+ * `data` points to a `int32_t`.
+ */
+const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
+
+/**
+ * @brief A slice index
+ *
+ * `data` points to a `Slice<int32_t>`.
+ */
+const NDIndexType ND_INDEX_TYPE_SLICE = 1;
+
+/**
+ * @brief `np.newaxis` / `None`
+ *
+ * `data` is unused.
+ */
+const NDIndexType ND_INDEX_TYPE_NEWAXIS = 2;
+
+/**
+ * @brief `Ellipsis` / `...`
+ *
+ * `data` is unused.
+ */
+const NDIndexType ND_INDEX_TYPE_ELLIPSIS = 3;
+
+/**
+ * @brief An index used in ndarray indexing
+ *
+ * That is:
+ * ```
+ * my_ndarray[::-1, 3, ..., np.newaxis]
+ *            ^^^^  ^  ^^^  ^^^^^^^^^^ each of these is represented by an NDIndex.
+ * ```
+ */
+struct NDIndex {
+    /**
+     * @brief Enum tag to specify the type of index.
+     *
+     * Please see the comment of each enum constant.
+     */
+    NDIndexType type;
+
+    /**
+     * @brief The accompanying data associated with `type`.
+     *
+     * Please see the comment of each enum constant.
+     */
+    uint8_t* data;
+};
+}  // namespace
+
+namespace {
+namespace ndarray::indexing {
+/**
+ * @brief Perform ndarray "basic indexing" (https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
+ *
+ * This function is very similar to performing `dst_ndarray = src_ndarray[indices]` in Python.
+ *
+ * This function also does proper assertions on `indices` to check for out of bounds access and more.
+ *
+ * # Notes on `dst_ndarray`
+ * The caller is responsible for allocating space for the resulting ndarray.
+ * Here is what this function expects from `dst_ndarray` when called:
+ *   - `dst_ndarray->data` does not have to be initialized.
+ *   - `dst_ndarray->itemsize` does not have to be initialized.
+ *   - `dst_ndarray->ndims` must be initialized, and it must be equal to the expected `ndims` of the `dst_ndarray` after
+ *       indexing `src_ndarray` with `indices`.
+ *   - `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->itemsize` is set to `src_ndarray->itemsize`.
+ *   - `dst_ndarray->ndims` is unchanged.
+ *   - `dst_ndarray->shape` is updated according to how `src_ndarray` is indexed.
+ *   - `dst_ndarray->strides` is updated accordingly by how ndarray indexing works.
+ *
+ * @param indices indices to index `src_ndarray`, ordered in the same way you would write them in Python.
+ * @param src_ndarray The NDArray to be indexed.
+ * @param dst_ndarray The resulting NDArray after indexing. Further details in the comments above,
+ */
+template<typename SizeT>
+void index(SizeT num_indices, const NDIndex* indices, const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
+    // Validate `indices`.
+
+    // Expected value of `dst_ndarray->ndims`.
+    SizeT expected_dst_ndims = src_ndarray->ndims;
+    // To check for "too many indices for array: array is ?-dimensional, but ? were indexed"
+    SizeT num_indexed = 0;
+    // There may be ellipsis `...` in `indices`. There can only be 0 or 1 ellipsis.
+    SizeT num_ellipsis = 0;
+
+    for (SizeT i = 0; i < num_indices; i++) {
+        if (indices[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
+            expected_dst_ndims--;
+            num_indexed++;
+        } else if (indices[i].type == ND_INDEX_TYPE_SLICE) {
+            num_indexed++;
+        } else if (indices[i].type == ND_INDEX_TYPE_NEWAXIS) {
+            expected_dst_ndims++;
+        } else if (indices[i].type == ND_INDEX_TYPE_ELLIPSIS) {
+            num_ellipsis++;
+            if (num_ellipsis > 1) {
+                raise_exception(SizeT, EXN_INDEX_ERROR, "an index can only have a single ellipsis ('...')", NO_PARAM,
+                                NO_PARAM, NO_PARAM);
+            }
+        } else {
+            __builtin_unreachable();
+        }
+    }
+
+    debug_assert_eq(SizeT, expected_dst_ndims, dst_ndarray->ndims);
+
+    if (src_ndarray->ndims - num_indexed < 0) {
+        raise_exception(SizeT, EXN_INDEX_ERROR,
+                        "too many indices for array: array is {0}-dimensional, "
+                        "but {1} were indexed",
+                        src_ndarray->ndims, num_indices, NO_PARAM);
+    }
+
+    dst_ndarray->data = src_ndarray->data;
+    dst_ndarray->itemsize = src_ndarray->itemsize;
+
+    // Reference code:
+    // https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
+    SizeT src_axis = 0;
+    SizeT dst_axis = 0;
+
+    for (int32_t i = 0; i < num_indices; i++) {
+        const NDIndex* index = &indices[i];
+        if (index->type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
+            SizeT input = (SizeT) * ((int32_t*)index->data);
+
+            SizeT k = slice::resolve_index_in_length(src_ndarray->shape[src_axis], input);
+            if (k == -1) {
+                raise_exception(SizeT, EXN_INDEX_ERROR,
+                                "index {0} is out of bounds for axis {1} "
+                                "with size {2}",
+                                input, src_axis, src_ndarray->shape[src_axis]);
+            }
+
+            dst_ndarray->data = static_cast<uint8_t*>(dst_ndarray->data) + k * src_ndarray->strides[src_axis];
+
+            src_axis++;
+        } else if (index->type == ND_INDEX_TYPE_SLICE) {
+            Slice<int32_t>* slice = (Slice<int32_t>*)index->data;
+
+            Range<int32_t> range = slice->indices_checked<SizeT>(src_ndarray->shape[src_axis]);
+
+            dst_ndarray->data =
+                static_cast<uint8_t*>(dst_ndarray->data) + (SizeT)range.start * src_ndarray->strides[src_axis];
+            dst_ndarray->strides[dst_axis] = ((SizeT)range.step) * src_ndarray->strides[src_axis];
+            dst_ndarray->shape[dst_axis] = (SizeT)range.len<SizeT>();
+
+            dst_axis++;
+            src_axis++;
+        } else if (index->type == ND_INDEX_TYPE_NEWAXIS) {
+            dst_ndarray->strides[dst_axis] = 0;
+            dst_ndarray->shape[dst_axis] = 1;
+
+            dst_axis++;
+        } else if (index->type == ND_INDEX_TYPE_ELLIPSIS) {
+            // The number of ':' entries this '...' implies.
+            SizeT ellipsis_size = src_ndarray->ndims - num_indexed;
+
+            for (SizeT j = 0; j < ellipsis_size; j++) {
+                dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
+                dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
+
+                dst_axis++;
+                src_axis++;
+            }
+        } else {
+            __builtin_unreachable();
+        }
+    }
+
+    for (; dst_axis < dst_ndarray->ndims; dst_axis++, src_axis++) {
+        dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
+        dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
+    }
+
+    debug_assert_eq(SizeT, src_ndarray->ndims, src_axis);
+    debug_assert_eq(SizeT, dst_ndarray->ndims, dst_axis);
+}
+}  // namespace ndarray::indexing
+}  // namespace
+
+extern "C" {
+using namespace ndarray::indexing;
+
+void __nac3_ndarray_index(int32_t num_indices,
+                          NDIndex* indices,
+                          NDArray<int32_t>* src_ndarray,
+                          NDArray<int32_t>* dst_ndarray) {
+    index(num_indices, indices, src_ndarray, dst_ndarray);
+}
+
+void __nac3_ndarray_index64(int64_t num_indices,
+                            NDIndex* indices,
+                            NDArray<int64_t>* src_ndarray,
+                            NDArray<int64_t>* dst_ndarray) {
+    index(num_indices, indices, src_ndarray, dst_ndarray);
+}
+}

nac3core/irrt/irrt/ndarray/iter.hpp

@@ -0,0 +1,146 @@
+#pragma once
+
+#include "irrt/int_types.hpp"
+#include "irrt/ndarray/def.hpp"
+
+namespace {
+/**
+ * @brief Helper struct to enumerate through an ndarray *efficiently*.
+ *
+ * Example usage (in pseudo-code):
+ * ```
+ * // Suppose my_ndarray has been initialized, with shape [2, 3] and dtype `double`
+ * NDIter nditer;
+ * nditer.initialize(my_ndarray);
+ * while (nditer.has_element()) {
+ *    // This body is run 6 (= my_ndarray.size) times.
+ *
+ *    // [0, 0] -> [0, 1] -> [0, 2] -> [1, 0] -> [1, 1] -> [1, 2] -> end
+ *    print(nditer.indices);
+ *
+ *    // 0 -> 1 -> 2 -> 3 -> 4 -> 5
+ *    print(nditer.nth);
+ *
+ *    // <1st element> -> <2nd element> -> ... -> <6th element> -> end
+ *    print(*((double *) nditer.element))
+ *
+ *    nditer.next(); // Go to next element.
+ * }
+ * ```
+ *
+ * Interesting cases:
+ * - If `my_ndarray.ndims` == 0, there is one iteration.
+ * - If `my_ndarray.shape` contains zeroes, there are no iterations.
+ */
+template<typename SizeT>
+struct NDIter {
+    // Information about the ndarray being iterated over.
+    SizeT ndims;
+    SizeT* shape;
+    SizeT* strides;
+
+    /**
+     * @brief The current indices.
+     *
+     * Must be allocated by the caller.
+     */
+    SizeT* indices;
+
+    /**
+     * @brief The nth (0-based) index of the current indices.
+     *
+     * Initially this is 0.
+     */
+    SizeT nth;
+
+    /**
+     * @brief Pointer to the current element.
+     *
+     * Initially this points to first element of the ndarray.
+     */
+    void* element;
+
+    /**
+     * @brief Cache for the product of shape.
+     *
+     * Could be 0 if `shape` has 0s in it.
+     */
+    SizeT size;
+
+    void initialize(SizeT ndims, SizeT* shape, SizeT* strides, void* element, SizeT* indices) {
+        this->ndims = ndims;
+        this->shape = shape;
+        this->strides = strides;
+
+        this->indices = indices;
+        this->element = element;
+
+        // Compute size
+        this->size = 1;
+        for (SizeT i = 0; i < ndims; i++) {
+            this->size *= shape[i];
+        }
+
+        // `indices` starts on all 0s.
+        for (SizeT axis = 0; axis < ndims; axis++)
+            indices[axis] = 0;
+        nth = 0;
+    }
+
+    void initialize_by_ndarray(NDArray<SizeT>* ndarray, SizeT* indices) {
+        // NOTE: ndarray->data is pointing to the first element, and `NDIter`'s `element` should also point to the first
+        // element as well.
+        this->initialize(ndarray->ndims, ndarray->shape, ndarray->strides, ndarray->data, indices);
+    }
+
+    // Is the current iteration valid?
+    // If true, then `element`, `indices` and `nth` contain details about the current element.
+    bool has_element() { return nth < size; }
+
+    // Go to the next element.
+    void next() {
+        for (SizeT i = 0; i < ndims; i++) {
+            SizeT axis = ndims - i - 1;
+            indices[axis]++;
+            if (indices[axis] >= shape[axis]) {
+                indices[axis] = 0;
+
+                // TODO: There is something called backstrides to speedup iteration.
+                // See https://ajcr.net/stride-guide-part-1/, and
+                // https://docs.scipy.org/doc/numpy-1.13.0/reference/c-api.types-and-structures.html#c.PyArrayIterObject.PyArrayIterObject.backstrides.
+                element = static_cast<void*>(reinterpret_cast<uint8_t*>(element) - strides[axis] * (shape[axis] - 1));
+            } else {
+                element = static_cast<void*>(reinterpret_cast<uint8_t*>(element) + strides[axis]);
+                break;
+            }
+        }
+        nth++;
+    }
+};
+}  // namespace
+
+extern "C" {
+void __nac3_nditer_initialize(NDIter<int32_t>* iter, NDArray<int32_t>* ndarray, int32_t* indices) {
+    iter->initialize_by_ndarray(ndarray, indices);
+}
+
+void __nac3_nditer_initialize64(NDIter<int64_t>* iter, NDArray<int64_t>* ndarray, int64_t* indices) {
+    iter->initialize_by_ndarray(ndarray, indices);
+}
+
+bool __nac3_nditer_has_element(NDIter<int32_t>* iter) {
+    return iter->has_element();
+}
+
+bool __nac3_nditer_has_element64(NDIter<int64_t>* iter) {
+    return iter->has_element();
+}
+
+void __nac3_nditer_next(NDIter<int32_t>* iter) {
+    iter->next();
+}
+
+void __nac3_nditer_next64(NDIter<int64_t>* iter) {
+    iter->next();
+}
+}

nac3core/irrt/irrt/ndarray/matmul.hpp

@@ -0,0 +1,98 @@
+#pragma once
+
+#include "irrt/debug.hpp"
+#include "irrt/exception.hpp"
+#include "irrt/int_types.hpp"
+#include "irrt/ndarray/basic.hpp"
+#include "irrt/ndarray/broadcast.hpp"
+#include "irrt/ndarray/iter.hpp"
+
+// NOTE: Everything would be much easier and elegant if einsum is implemented.
+
+namespace {
+namespace ndarray::matmul {
+
+/**
+ * @brief Perform the broadcast in `np.einsum("...ij,...jk->...ik", a, b)`.
+ *
+ * Example:
+ *   Suppose      `a_shape ==      [1, 97, 4, 2]`
+ *       and      `b_shape == [99, 98,  1, 2, 5]`,
+ *
+ *   ...then  `new_a_shape == [99, 98, 97, 4, 2]`,
+ *            `new_b_shape == [99, 98, 97, 2, 5]`,
+ *      and     `dst_shape == [99, 98, 97, 4, 5]`.
+ *                             ^^^^^^^^^^  ^^^^
+ *                          (broadcasted)  (4x2 @ 2x5 => 4x5)
+ *
+ * @param a_ndims Length of `a_shape`.
+ * @param a_shape Shape of `a`.
+ * @param b_ndims Length of `b_shape`.
+ * @param b_shape Shape of `b`.
+ * @param final_ndims Should be equal to `max(a_ndims, b_ndims)`. This is the length of `new_a_shape`,
+ * `new_b_shape`, and `dst_shape` - the number of dimensions after broadcasting.
+ */
+template<typename SizeT>
+void calculate_shapes(SizeT a_ndims,
+                      SizeT* a_shape,
+                      SizeT b_ndims,
+                      SizeT* b_shape,
+                      SizeT final_ndims,
+                      SizeT* new_a_shape,
+                      SizeT* new_b_shape,
+                      SizeT* dst_shape) {
+    debug_assert(SizeT, a_ndims >= 2);
+    debug_assert(SizeT, b_ndims >= 2);
+    debug_assert_eq(SizeT, max(a_ndims, b_ndims), final_ndims);
+
+    // Check that a and b are compatible for matmul
+    if (a_shape[a_ndims - 1] != b_shape[b_ndims - 2]) {
+        // This is a custom error message. Different from NumPy.
+        raise_exception(SizeT, EXN_VALUE_ERROR, "Cannot multiply LHS (shape ?x{0}) with RHS (shape {1}x?})",
+                        a_shape[a_ndims - 1], b_shape[b_ndims - 2], NO_PARAM);
+    }
+
+    const SizeT num_entries = 2;
+    ShapeEntry<SizeT> entries[num_entries] = {{.ndims = a_ndims - 2, .shape = a_shape},
+                                              {.ndims = b_ndims - 2, .shape = b_shape}};
+
+    // TODO: Optimize this
+    ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, new_a_shape);
+    ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, new_b_shape);
+    ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, dst_shape);
+
+    new_a_shape[final_ndims - 2] = a_shape[a_ndims - 2];
+    new_a_shape[final_ndims - 1] = a_shape[a_ndims - 1];
+    new_b_shape[final_ndims - 2] = b_shape[b_ndims - 2];
+    new_b_shape[final_ndims - 1] = b_shape[b_ndims - 1];
+    dst_shape[final_ndims - 2] = a_shape[a_ndims - 2];
+    dst_shape[final_ndims - 1] = b_shape[b_ndims - 1];
+}
+}  // namespace ndarray::matmul
+}  // namespace
+
+extern "C" {
+using namespace ndarray::matmul;
+
+void __nac3_ndarray_matmul_calculate_shapes(int32_t a_ndims,
+                                            int32_t* a_shape,
+                                            int32_t b_ndims,
+                                            int32_t* b_shape,
+                                            int32_t final_ndims,
+                                            int32_t* new_a_shape,
+                                            int32_t* new_b_shape,
+                                            int32_t* dst_shape) {
+    calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims, new_a_shape, new_b_shape, dst_shape);
+}
+
+void __nac3_ndarray_matmul_calculate_shapes64(int64_t a_ndims,
+                                              int64_t* a_shape,
+                                              int64_t b_ndims,
+                                              int64_t* b_shape,
+                                              int64_t final_ndims,
+                                              int64_t* new_a_shape,
+                                              int64_t* new_b_shape,
+                                              int64_t* dst_shape) {
+    calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims, new_a_shape, new_b_shape, dst_shape);
+}
+}

nac3core/irrt/irrt/ndarray/reshape.hpp

@@ -0,0 +1,97 @@
+#pragma once
+
+#include "irrt/exception.hpp"
+#include "irrt/int_types.hpp"
+#include "irrt/ndarray/def.hpp"
+
+namespace {
+namespace ndarray::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 ndarray::reshape
+}  // 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/irrt/irrt/ndarray/transpose.hpp

@@ -0,0 +1,143 @@
+#pragma once
+
+#include "irrt/debug.hpp"
+#include "irrt/exception.hpp"
+#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::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 == -1) {
+            // 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 ndarray::transpose
+}  // namespace
+
+extern "C" {
+using namespace ndarray::transpose;
+void __nac3_ndarray_transpose(const NDArray<int32_t>* src_ndarray,
+                              NDArray<int32_t>* dst_ndarray,
+                              int32_t num_axes,
+                              const int32_t* axes) {
+    transpose(src_ndarray, dst_ndarray, num_axes, axes);
+}
+
+void __nac3_ndarray_transpose64(const NDArray<int64_t>* src_ndarray,
+                                NDArray<int64_t>* dst_ndarray,
+                                int64_t num_axes,
+                                const int64_t* axes) {
+    transpose(src_ndarray, dst_ndarray, num_axes, axes);
+}
+}

nac3core/irrt/irrt/range.hpp

@@ -0,0 +1,47 @@
+#pragma once
+
+#include "irrt/debug.hpp"
+#include "irrt/int_types.hpp"
+
+namespace {
+namespace range {
+template<typename T>
+T len(T start, T stop, T step) {
+    // Reference:
+    // https://github.com/python/cpython/blob/9dbd12375561a393eaec4b21ee4ac568a407cdb0/Objects/rangeobject.c#L933
+    if (step > 0 && start < stop)
+        return 1 + (stop - 1 - start) / step;
+    else if (step < 0 && start > stop)
+        return 1 + (start - 1 - stop) / (-step);
+    else
+        return 0;
+}
+}  // namespace range
+
+/**
+ * @brief A Python range.
+ */
+template<typename T>
+struct Range {
+    T start;
+    T stop;
+    T step;
+
+    /**
+     * @brief Calculate the `len()` of this range.
+     */
+    template<typename SizeT>
+    T len() {
+        debug_assert(SizeT, step != 0);
+        return range::len(start, stop, step);
+    }
+};
+}  // namespace
+
+extern "C" {
+using namespace range;
+
+SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end, const SliceIndex step) {
+    return len(start, end, step);
+}
+}

nac3core/irrt/irrt/slice.hpp

@@ -1,6 +1,145 @@
 #pragma once
 
+#include "irrt/debug.hpp"
+#include "irrt/exception.hpp"
 #include "irrt/int_types.hpp"
+#include "irrt/math_util.hpp"
+#include "irrt/range.hpp"
+
+namespace {
+namespace slice {
+/**
+ * @brief Resolve a possibly negative index in a list of a known length.
+ *
+ * Returns -1 if the resolved index is out of the list's bounds.
+ */
+template<typename T>
+T resolve_index_in_length(T length, T index) {
+    T resolved = index < 0 ? length + index : index;
+    if (0 <= resolved && resolved < length) {
+        return resolved;
+    } else {
+        return -1;
+    }
+}
+
+/**
+ * @brief Resolve a slice as a range.
+ *
+ * This is equivalent to `range(*slice(start, stop, step).indices(length))` in Python.
+ */
+template<typename T>
+void indices(bool start_defined,
+             T start,
+             bool stop_defined,
+             T stop,
+             bool step_defined,
+             T step,
+             T length,
+             T* range_start,
+             T* range_stop,
+             T* range_step) {
+    // Reference: https://github.com/python/cpython/blob/main/Objects/sliceobject.c#L388
+    *range_step = step_defined ? step : 1;
+    bool step_is_negative = *range_step < 0;
+
+    T lower, upper;
+    if (step_is_negative) {
+        lower = -1;
+        upper = length - 1;
+    } else {
+        lower = 0;
+        upper = length;
+    }
+
+    if (start_defined) {
+        *range_start = start < 0 ? max(lower, start + length) : min(upper, start);
+    } else {
+        *range_start = step_is_negative ? upper : lower;
+    }
+
+    if (stop_defined) {
+        *range_stop = stop < 0 ? max(lower, stop + length) : min(upper, stop);
+    } else {
+        *range_stop = step_is_negative ? lower : upper;
+    }
+}
+}  // namespace slice
+
+/**
+ * @brief A Python-like slice with **unresolved** indices.
+ */
+template<typename T>
+struct Slice {
+    bool start_defined;
+    T start;
+
+    bool stop_defined;
+    T stop;
+
+    bool step_defined;
+    T step;
+
+    Slice() { this->reset(); }
+
+    void reset() {
+        this->start_defined = false;
+        this->stop_defined = false;
+        this->step_defined = false;
+    }
+
+    void set_start(T start) {
+        this->start_defined = true;
+        this->start = start;
+    }
+
+    void set_stop(T stop) {
+        this->stop_defined = true;
+        this->stop = stop;
+    }
+
+    void set_step(T step) {
+        this->step_defined = true;
+        this->step = step;
+    }
+
+    /**
+     * @brief Resolve this slice as a range.
+     *
+     * In Python, this would be `range(*slice(start, stop, step).indices(length))`.
+     */
+    template<typename SizeT>
+    Range<T> indices(T length) {
+        // Reference:
+        // https://github.com/python/cpython/blob/main/Objects/sliceobject.c#L388
+        debug_assert(SizeT, length >= 0);
+
+        Range<T> result;
+        slice::indices(start_defined, start, stop_defined, stop, step_defined, step, length, &result.start,
+                       &result.stop, &result.step);
+        return result;
+    }
+
+    /**
+     * @brief Like `.indices()` but with assertions.
+     */
+    template<typename SizeT>
+    Range<T> indices_checked(T length) {
+        // TODO: Switch to `SizeT length`
+
+        if (length < 0) {
+            raise_exception(SizeT, EXN_VALUE_ERROR, "length should not be negative, got {0}", length, NO_PARAM,
+                            NO_PARAM);
+        }
+
+        if (this->step_defined && this->step == 0) {
+            raise_exception(SizeT, EXN_VALUE_ERROR, "slice step cannot be zero", NO_PARAM, NO_PARAM, NO_PARAM);
+        }
+
+        return this->indices<SizeT>(length);
+    }
+};
+}  // namespace
 
 extern "C" {
 SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
@@ -14,15 +153,4 @@ SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
     }
     return i;
 }
-
-SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end, const SliceIndex step) {
-    SliceIndex diff = end - start;
-    if (diff > 0 && step > 0) {
-        return ((diff - 1) / step) + 1;
-    } else if (diff < 0 && step < 0) {
-        return ((diff + 1) / step) + 1;
-    } else {
-        return 0;
-    }
 }
-}  // namespace

nac3core/irrt/irrt/string.hpp

@@ -0,0 +1,23 @@
+#pragma once
+
+#include "irrt/int_types.hpp"
+
+namespace {
+template<typename SizeT>
+bool __nac3_str_eq_impl(const char* str1, SizeT len1, const char* str2, SizeT len2) {
+    if (len1 != len2) {
+        return 0;
+    }
+    return __builtin_memcmp(str1, str2, static_cast<SizeT>(len1)) == 0;
+}
+}  // namespace
+
+extern "C" {
+bool nac3_str_eq(const char* str1, uint32_t len1, const char* str2, uint32_t len2) {
+    return __nac3_str_eq_impl<uint32_t>(str1, len1, str2, len2);
+}
+
+bool nac3_str_eq64(const char* str1, uint64_t len1, const char* str2, uint64_t len2) {
+    return __nac3_str_eq_impl<uint64_t>(str1, len1, str2, len2);
+}
+}

nac3core/src/codegen/generator.rs

@@ -17,6 +17,7 @@ pub trait CodeGenerator {
     /// Return the module name for the code generator.
     fn get_name(&self) -> &str;
 
+    /// Return an instance of [`IntType`] corresponding to the type of `size_t` for this instance.
     fn get_size_type<'ctx>(&self, ctx: &'ctx Context) -> IntType<'ctx>;
 
     /// Generate function call and returns the function return value.

nac3core/src/codegen/irrt/list.rs

@@ -24,42 +24,52 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
     src_arr: ListValue<'ctx>,
     src_idx: (IntValue<'ctx>, IntValue<'ctx>, IntValue<'ctx>),
 ) {
-    let size_ty = generator.get_size_type(ctx.ctx);
-    let int8_ptr = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
-    let int32 = ctx.ctx.i32_type();
-    let (fun_symbol, elem_ptr_type) = ("__nac3_list_slice_assign_var_size", int8_ptr);
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_pi8 = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
+    let llvm_i32 = ctx.ctx.i32_type();
+
+    assert_eq!(dest_idx.0.get_type(), llvm_i32);
+    assert_eq!(dest_idx.1.get_type(), llvm_i32);
+    assert_eq!(dest_idx.2.get_type(), llvm_i32);
+    assert_eq!(src_idx.0.get_type(), llvm_i32);
+    assert_eq!(src_idx.1.get_type(), llvm_i32);
+    assert_eq!(src_idx.2.get_type(), llvm_i32);
+
+    let (fun_symbol, elem_ptr_type) = ("__nac3_list_slice_assign_var_size", llvm_pi8);
     let slice_assign_fun = {
         let ty_vec = vec![
-            int32.into(),         // dest start idx
-            int32.into(),         // dest end idx
-            int32.into(),         // dest step
+            llvm_i32.into(),      // dest start idx
+            llvm_i32.into(),      // dest end idx
+            llvm_i32.into(),      // dest step
             elem_ptr_type.into(), // dest arr ptr
-            int32.into(),         // dest arr len
-            int32.into(),         // src start idx
-            int32.into(),         // src end idx
-            int32.into(),         // src step
+            llvm_i32.into(),      // dest arr len
+            llvm_i32.into(),      // src start idx
+            llvm_i32.into(),      // src end idx
+            llvm_i32.into(),      // src step
             elem_ptr_type.into(), // src arr ptr
-            int32.into(),         // src arr len
-            int32.into(),         // size
+            llvm_i32.into(),      // src arr len
+            llvm_i32.into(),      // size
         ];
         ctx.module.get_function(fun_symbol).unwrap_or_else(|| {
-            let fn_t = int32.fn_type(ty_vec.as_slice(), false);
+            let fn_t = llvm_i32.fn_type(ty_vec.as_slice(), false);
             ctx.module.add_function(fun_symbol, fn_t, None)
         })
     };
 
-    let zero = int32.const_zero();
-    let one = int32.const_int(1, false);
+    let zero = llvm_i32.const_zero();
+    let one = llvm_i32.const_int(1, false);
     let dest_arr_ptr = dest_arr.data().base_ptr(ctx, generator);
     let dest_arr_ptr =
         ctx.builder.build_pointer_cast(dest_arr_ptr, elem_ptr_type, "dest_arr_ptr_cast").unwrap();
     let dest_len = dest_arr.load_size(ctx, Some("dest.len"));
-    let dest_len = ctx.builder.build_int_truncate_or_bit_cast(dest_len, int32, "srclen32").unwrap();
+    let dest_len =
+        ctx.builder.build_int_truncate_or_bit_cast(dest_len, llvm_i32, "srclen32").unwrap();
     let src_arr_ptr = src_arr.data().base_ptr(ctx, generator);
     let src_arr_ptr =
         ctx.builder.build_pointer_cast(src_arr_ptr, elem_ptr_type, "src_arr_ptr_cast").unwrap();
     let src_len = src_arr.load_size(ctx, Some("src.len"));
-    let src_len = ctx.builder.build_int_truncate_or_bit_cast(src_len, int32, "srclen32").unwrap();
+    let src_len =
+        ctx.builder.build_int_truncate_or_bit_cast(src_len, llvm_i32, "srclen32").unwrap();
 
     // index in bound and positive should be done
     // assert if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest), and
@@ -136,7 +146,7 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
                     BasicTypeEnum::StructType(t) => t.size_of().unwrap(),
                     _ => codegen_unreachable!(ctx),
                 };
-                ctx.builder.build_int_truncate_or_bit_cast(s, int32, "size").unwrap()
+                ctx.builder.build_int_truncate_or_bit_cast(s, llvm_i32, "size").unwrap()
             }
             .into(),
         ];
@@ -147,6 +157,7 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
             .map(Either::unwrap_left)
             .unwrap()
     };
+
     // update length
     let need_update =
         ctx.builder.build_int_compare(IntPredicate::NE, new_len, dest_len, "need_update").unwrap();
@@ -155,7 +166,8 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
     let cont_bb = ctx.ctx.append_basic_block(current, "cont");
     ctx.builder.build_conditional_branch(need_update, update_bb, cont_bb).unwrap();
     ctx.builder.position_at_end(update_bb);
-    let new_len = ctx.builder.build_int_z_extend_or_bit_cast(new_len, size_ty, "new_len").unwrap();
+    let new_len =
+        ctx.builder.build_int_z_extend_or_bit_cast(new_len, llvm_usize, "new_len").unwrap();
     dest_arr.store_size(ctx, generator, new_len);
     ctx.builder.build_unconditional_branch(cont_bb).unwrap();
     ctx.builder.position_at_end(cont_bb);

nac3core/src/codegen/irrt/math.rs

@@ -62,8 +62,13 @@ pub fn call_isinf<'ctx, G: CodeGenerator + ?Sized>(
     ctx: &CodeGenContext<'ctx, '_>,
     v: FloatValue<'ctx>,
 ) -> IntValue<'ctx> {
+    let llvm_i32 = ctx.ctx.i32_type();
+    let llvm_f64 = ctx.ctx.f64_type();
+
+    assert_eq!(v.get_type(), llvm_f64);
+
     let intrinsic_fn = ctx.module.get_function("__nac3_isinf").unwrap_or_else(|| {
-        let fn_type = ctx.ctx.i32_type().fn_type(&[ctx.ctx.f64_type().into()], false);
+        let fn_type = llvm_i32.fn_type(&[llvm_f64.into()], false);
         ctx.module.add_function("__nac3_isinf", fn_type, None)
     });
 
@@ -84,8 +89,13 @@ pub fn call_isnan<'ctx, G: CodeGenerator + ?Sized>(
     ctx: &CodeGenContext<'ctx, '_>,
     v: FloatValue<'ctx>,
 ) -> IntValue<'ctx> {
+    let llvm_i32 = ctx.ctx.i32_type();
+    let llvm_f64 = ctx.ctx.f64_type();
+
+    assert_eq!(v.get_type(), llvm_f64);
+
     let intrinsic_fn = ctx.module.get_function("__nac3_isnan").unwrap_or_else(|| {
-        let fn_type = ctx.ctx.i32_type().fn_type(&[ctx.ctx.f64_type().into()], false);
+        let fn_type = llvm_i32.fn_type(&[llvm_f64.into()], false);
         ctx.module.add_function("__nac3_isnan", fn_type, None)
     });
 
@@ -104,6 +114,8 @@ pub fn call_isnan<'ctx, G: CodeGenerator + ?Sized>(
 pub fn call_gamma<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
     let llvm_f64 = ctx.ctx.f64_type();
 
+    assert_eq!(v.get_type(), llvm_f64);
+
     let intrinsic_fn = ctx.module.get_function("__nac3_gamma").unwrap_or_else(|| {
         let fn_type = llvm_f64.fn_type(&[llvm_f64.into()], false);
         ctx.module.add_function("__nac3_gamma", fn_type, None)
@@ -121,6 +133,8 @@ pub fn call_gamma<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) ->
 pub fn call_gammaln<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
     let llvm_f64 = ctx.ctx.f64_type();
 
+    assert_eq!(v.get_type(), llvm_f64);
+
     let intrinsic_fn = ctx.module.get_function("__nac3_gammaln").unwrap_or_else(|| {
         let fn_type = llvm_f64.fn_type(&[llvm_f64.into()], false);
         ctx.module.add_function("__nac3_gammaln", fn_type, None)
@@ -138,6 +152,8 @@ pub fn call_gammaln<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -
 pub fn call_j0<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
     let llvm_f64 = ctx.ctx.f64_type();
 
+    assert_eq!(v.get_type(), llvm_f64);
+
     let intrinsic_fn = ctx.module.get_function("__nac3_j0").unwrap_or_else(|| {
         let fn_type = llvm_f64.fn_type(&[llvm_f64.into()], false);
         ctx.module.add_function("__nac3_j0", fn_type, None)

nac3core/src/codegen/irrt/mod.rs

@@ -13,13 +13,16 @@ use super::{CodeGenContext, CodeGenerator};
 use crate::{symbol_resolver::SymbolResolver, typecheck::typedef::Type};
 pub use list::*;
 pub use math::*;
-pub use ndarray::*;
+pub use range::*;
 pub use slice::*;
+pub use string::*;
 
 mod list;
 mod math;
-mod ndarray;
+pub mod ndarray;
+mod range;
 mod slice;
+mod string;
 
 #[must_use]
 pub fn load_irrt<'ctx>(ctx: &'ctx Context, symbol_resolver: &dyn SymbolResolver) -> Module<'ctx> {
@@ -60,6 +63,27 @@ pub fn load_irrt<'ctx>(ctx: &'ctx Context, symbol_resolver: &dyn SymbolResolver)
     irrt_mod
 }
 
+/// Returns the name of a function which contains variants for 32-bit and 64-bit `size_t`.
+///
+/// - When [`TypeContext::size_type`] is 32-bits, the function name is `fn_name}`.
+/// - When [`TypeContext::size_type`] is 64-bits, the function name is `{fn_name}64`.
+#[must_use]
+pub fn get_usize_dependent_function_name<G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'_, '_>,
+    name: &str,
+) -> String {
+    let mut name = name.to_owned();
+    match generator.get_size_type(ctx.ctx).get_bit_width() {
+        32 => {}
+        64 => name.push_str("64"),
+        bit_width => {
+            panic!("Unsupported int type bit width {bit_width}, must be either 32-bits or 64-bits")
+        }
+    }
+    name
+}
+
 /// NOTE: the output value of the end index of this function should be compared ***inclusively***,
 /// because python allows `a[2::-1]`, whose semantic is `[a[2], a[1], a[0]]`, which is equivalent to
 /// NO numeric slice in python.
@@ -108,10 +132,11 @@ pub fn handle_slice_indices<'ctx, G: CodeGenerator>(
     generator: &mut G,
     length: IntValue<'ctx>,
 ) -> Result<Option<(IntValue<'ctx>, IntValue<'ctx>, IntValue<'ctx>)>, String> {
-    let int32 = ctx.ctx.i32_type();
-    let zero = int32.const_zero();
-    let one = int32.const_int(1, false);
-    let length = ctx.builder.build_int_truncate_or_bit_cast(length, int32, "leni32").unwrap();
+    let llvm_i32 = ctx.ctx.i32_type();
+
+    let zero = llvm_i32.const_zero();
+    let one = llvm_i32.const_int(1, false);
+    let length = ctx.builder.build_int_truncate_or_bit_cast(length, llvm_i32, "leni32").unwrap();
     Ok(Some(match (start, end, step) {
         (s, e, None) => (
             if let Some(s) = s.as_ref() {
@@ -120,7 +145,7 @@ pub fn handle_slice_indices<'ctx, G: CodeGenerator>(
                     None => return Ok(None),
                 }
             } else {
-                int32.const_zero()
+                llvm_i32.const_zero()
             },
             {
                 let e = if let Some(s) = e.as_ref() {

nac3core/src/codegen/irrt/ndarray.rs

@@ -1,384 +0,0 @@
-use inkwell::{
-    types::IntType,
-    values::{BasicValueEnum, CallSiteValue, IntValue},
-    AddressSpace, IntPredicate,
-};
-use itertools::Either;
-
-use crate::codegen::{
-    llvm_intrinsics,
-    macros::codegen_unreachable,
-    stmt::gen_for_callback_incrementing,
-    values::{
-        ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, NDArrayValue, TypedArrayLikeAccessor,
-        TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
-    },
-    CodeGenContext, CodeGenerator,
-};
-
-/// Generates a call to `__nac3_ndarray_calc_size`. Returns an [`IntValue`] representing the
-/// calculated total size.
-///
-/// * `dims` - An [`ArrayLikeIndexer`] containing the size of each dimension.
-/// * `range` - The dimension index to begin and end (exclusively) calculating the dimensions for,
-///   or [`None`] if starting from the first dimension and ending at the last dimension
-///   respectively.
-pub fn call_ndarray_calc_size<'ctx, G, Dims>(
-    generator: &G,
-    ctx: &CodeGenContext<'ctx, '_>,
-    dims: &Dims,
-    (begin, end): (Option<IntValue<'ctx>>, Option<IntValue<'ctx>>),
-) -> IntValue<'ctx>
-where
-    G: CodeGenerator + ?Sized,
-    Dims: ArrayLikeIndexer<'ctx>,
-{
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
-
-    let ndarray_calc_size_fn_name = match llvm_usize.get_bit_width() {
-        32 => "__nac3_ndarray_calc_size",
-        64 => "__nac3_ndarray_calc_size64",
-        bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
-    };
-    let ndarray_calc_size_fn_t = llvm_usize.fn_type(
-        &[llvm_pusize.into(), llvm_usize.into(), llvm_usize.into(), llvm_usize.into()],
-        false,
-    );
-    let ndarray_calc_size_fn =
-        ctx.module.get_function(ndarray_calc_size_fn_name).unwrap_or_else(|| {
-            ctx.module.add_function(ndarray_calc_size_fn_name, ndarray_calc_size_fn_t, None)
-        });
-
-    let begin = begin.unwrap_or_else(|| llvm_usize.const_zero());
-    let end = end.unwrap_or_else(|| dims.size(ctx, generator));
-    ctx.builder
-        .build_call(
-            ndarray_calc_size_fn,
-            &[
-                dims.base_ptr(ctx, generator).into(),
-                dims.size(ctx, generator).into(),
-                begin.into(),
-                end.into(),
-            ],
-            "",
-        )
-        .map(CallSiteValue::try_as_basic_value)
-        .map(|v| v.map_left(BasicValueEnum::into_int_value))
-        .map(Either::unwrap_left)
-        .unwrap()
-}
-
-/// Generates a call to `__nac3_ndarray_calc_nd_indices`. Returns a [`TypeArrayLikeAdpater`]
-/// containing `i32` indices of the flattened index.
-///
-/// * `index` - The index to compute the multidimensional index for.
-/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
-///   `NDArray`.
-pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    index: IntValue<'ctx>,
-    ndarray: NDArrayValue<'ctx>,
-) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
-    let llvm_void = ctx.ctx.void_type();
-    let llvm_i32 = ctx.ctx.i32_type();
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-    let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
-    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
-
-    let ndarray_calc_nd_indices_fn_name = match llvm_usize.get_bit_width() {
-        32 => "__nac3_ndarray_calc_nd_indices",
-        64 => "__nac3_ndarray_calc_nd_indices64",
-        bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
-    };
-    let ndarray_calc_nd_indices_fn =
-        ctx.module.get_function(ndarray_calc_nd_indices_fn_name).unwrap_or_else(|| {
-            let fn_type = llvm_void.fn_type(
-                &[llvm_usize.into(), llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into()],
-                false,
-            );
-
-            ctx.module.add_function(ndarray_calc_nd_indices_fn_name, fn_type, None)
-        });
-
-    let ndarray_num_dims = ndarray.load_ndims(ctx);
-    let ndarray_dims = ndarray.shape();
-
-    let indices = ctx.builder.build_array_alloca(llvm_i32, ndarray_num_dims, "").unwrap();
-
-    ctx.builder
-        .build_call(
-            ndarray_calc_nd_indices_fn,
-            &[
-                index.into(),
-                ndarray_dims.base_ptr(ctx, generator).into(),
-                ndarray_num_dims.into(),
-                indices.into(),
-            ],
-            "",
-        )
-        .unwrap();
-
-    TypedArrayLikeAdapter::from(
-        ArraySliceValue::from_ptr_val(indices, ndarray_num_dims, None),
-        Box::new(|_, v| v.into_int_value()),
-        Box::new(|_, v| v.into()),
-    )
-}
-
-fn call_ndarray_flatten_index_impl<'ctx, G, Indices>(
-    generator: &G,
-    ctx: &CodeGenContext<'ctx, '_>,
-    ndarray: NDArrayValue<'ctx>,
-    indices: &Indices,
-) -> IntValue<'ctx>
-where
-    G: CodeGenerator + ?Sized,
-    Indices: ArrayLikeIndexer<'ctx>,
-{
-    let llvm_i32 = ctx.ctx.i32_type();
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-
-    let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
-    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
-
-    debug_assert_eq!(
-        IntType::try_from(indices.element_type(ctx, generator))
-            .map(IntType::get_bit_width)
-            .unwrap_or_default(),
-        llvm_i32.get_bit_width(),
-        "Expected i32 value for argument `indices` to `call_ndarray_flatten_index_impl`"
-    );
-    debug_assert_eq!(
-        indices.size(ctx, generator).get_type().get_bit_width(),
-        llvm_usize.get_bit_width(),
-        "Expected usize integer value for argument `indices_size` to `call_ndarray_flatten_index_impl`"
-    );
-
-    let ndarray_flatten_index_fn_name = match llvm_usize.get_bit_width() {
-        32 => "__nac3_ndarray_flatten_index",
-        64 => "__nac3_ndarray_flatten_index64",
-        bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
-    };
-    let ndarray_flatten_index_fn =
-        ctx.module.get_function(ndarray_flatten_index_fn_name).unwrap_or_else(|| {
-            let fn_type = llvm_usize.fn_type(
-                &[llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into(), llvm_usize.into()],
-                false,
-            );
-
-            ctx.module.add_function(ndarray_flatten_index_fn_name, fn_type, None)
-        });
-
-    let ndarray_num_dims = ndarray.load_ndims(ctx);
-    let ndarray_dims = ndarray.shape();
-
-    let index = ctx
-        .builder
-        .build_call(
-            ndarray_flatten_index_fn,
-            &[
-                ndarray_dims.base_ptr(ctx, generator).into(),
-                ndarray_num_dims.into(),
-                indices.base_ptr(ctx, generator).into(),
-                indices.size(ctx, generator).into(),
-            ],
-            "",
-        )
-        .map(CallSiteValue::try_as_basic_value)
-        .map(|v| v.map_left(BasicValueEnum::into_int_value))
-        .map(Either::unwrap_left)
-        .unwrap();
-
-    index
-}
-
-/// Generates a call to `__nac3_ndarray_flatten_index`. Returns the flattened index for the
-/// multidimensional index.
-///
-/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
-///   `NDArray`.
-/// * `indices` - The multidimensional index to compute the flattened index for.
-pub fn call_ndarray_flatten_index<'ctx, G, Index>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ndarray: NDArrayValue<'ctx>,
-    indices: &Index,
-) -> IntValue<'ctx>
-where
-    G: CodeGenerator + ?Sized,
-    Index: ArrayLikeIndexer<'ctx>,
-{
-    call_ndarray_flatten_index_impl(generator, ctx, ndarray, indices)
-}
-
-/// Generates a call to `__nac3_ndarray_calc_broadcast`. Returns a tuple containing the number of
-/// dimension and size of each dimension of the resultant `ndarray`.
-pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    lhs: NDArrayValue<'ctx>,
-    rhs: NDArrayValue<'ctx>,
-) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
-
-    let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
-        32 => "__nac3_ndarray_calc_broadcast",
-        64 => "__nac3_ndarray_calc_broadcast64",
-        bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
-    };
-    let ndarray_calc_broadcast_fn =
-        ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
-            let fn_type = llvm_usize.fn_type(
-                &[
-                    llvm_pusize.into(),
-                    llvm_usize.into(),
-                    llvm_pusize.into(),
-                    llvm_usize.into(),
-                    llvm_pusize.into(),
-                ],
-                false,
-            );
-
-            ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
-        });
-
-    let lhs_ndims = lhs.load_ndims(ctx);
-    let rhs_ndims = rhs.load_ndims(ctx);
-    let min_ndims = llvm_intrinsics::call_int_umin(ctx, lhs_ndims, rhs_ndims, None);
-
-    gen_for_callback_incrementing(
-        generator,
-        ctx,
-        None,
-        llvm_usize.const_zero(),
-        (min_ndims, false),
-        |generator, ctx, _, idx| {
-            let idx = ctx.builder.build_int_sub(min_ndims, idx, "").unwrap();
-            let (lhs_dim_sz, rhs_dim_sz) = unsafe {
-                (
-                    lhs.shape().get_typed_unchecked(ctx, generator, &idx, None),
-                    rhs.shape().get_typed_unchecked(ctx, generator, &idx, None),
-                )
-            };
-
-            let llvm_usize_const_one = llvm_usize.const_int(1, false);
-            let lhs_eqz = ctx
-                .builder
-                .build_int_compare(IntPredicate::EQ, lhs_dim_sz, llvm_usize_const_one, "")
-                .unwrap();
-            let rhs_eqz = ctx
-                .builder
-                .build_int_compare(IntPredicate::EQ, rhs_dim_sz, llvm_usize_const_one, "")
-                .unwrap();
-            let lhs_or_rhs_eqz = ctx.builder.build_or(lhs_eqz, rhs_eqz, "").unwrap();
-
-            let lhs_eq_rhs = ctx
-                .builder
-                .build_int_compare(IntPredicate::EQ, lhs_dim_sz, rhs_dim_sz, "")
-                .unwrap();
-
-            let is_compatible = ctx.builder.build_or(lhs_or_rhs_eqz, lhs_eq_rhs, "").unwrap();
-
-            ctx.make_assert(
-                generator,
-                is_compatible,
-                "0:ValueError",
-                "operands could not be broadcast together",
-                [None, None, None],
-                ctx.current_loc,
-            );
-
-            Ok(())
-        },
-        llvm_usize.const_int(1, false),
-    )
-    .unwrap();
-
-    let max_ndims = llvm_intrinsics::call_int_umax(ctx, lhs_ndims, rhs_ndims, None);
-    let lhs_dims = lhs.shape().base_ptr(ctx, generator);
-    let lhs_ndims = lhs.load_ndims(ctx);
-    let rhs_dims = rhs.shape().base_ptr(ctx, generator);
-    let rhs_ndims = rhs.load_ndims(ctx);
-    let out_dims = ctx.builder.build_array_alloca(llvm_usize, max_ndims, "").unwrap();
-    let out_dims = ArraySliceValue::from_ptr_val(out_dims, max_ndims, None);
-
-    ctx.builder
-        .build_call(
-            ndarray_calc_broadcast_fn,
-            &[
-                lhs_dims.into(),
-                lhs_ndims.into(),
-                rhs_dims.into(),
-                rhs_ndims.into(),
-                out_dims.base_ptr(ctx, generator).into(),
-            ],
-            "",
-        )
-        .unwrap();
-
-    TypedArrayLikeAdapter::from(
-        out_dims,
-        Box::new(|_, v| v.into_int_value()),
-        Box::new(|_, v| v.into()),
-    )
-}
-
-/// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
-/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
-/// array `broadcast_idx`.
-pub fn call_ndarray_calc_broadcast_index<
-    'ctx,
-    G: CodeGenerator + ?Sized,
-    BroadcastIdx: UntypedArrayLikeAccessor<'ctx>,
->(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    array: NDArrayValue<'ctx>,
-    broadcast_idx: &BroadcastIdx,
-) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
-    let llvm_i32 = ctx.ctx.i32_type();
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-    let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
-    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
-
-    let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
-        32 => "__nac3_ndarray_calc_broadcast_idx",
-        64 => "__nac3_ndarray_calc_broadcast_idx64",
-        bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
-    };
-    let ndarray_calc_broadcast_fn =
-        ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
-            let fn_type = llvm_usize.fn_type(
-                &[llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into(), llvm_pi32.into()],
-                false,
-            );
-
-            ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
-        });
-
-    let broadcast_size = broadcast_idx.size(ctx, generator);
-    let out_idx = ctx.builder.build_array_alloca(llvm_i32, broadcast_size, "").unwrap();
-
-    let array_dims = array.shape().base_ptr(ctx, generator);
-    let array_ndims = array.load_ndims(ctx);
-    let broadcast_idx_ptr = unsafe {
-        broadcast_idx.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-    };
-
-    ctx.builder
-        .build_call(
-            ndarray_calc_broadcast_fn,
-            &[array_dims.into(), array_ndims.into(), broadcast_idx_ptr.into(), out_idx.into()],
-            "",
-        )
-        .unwrap();
-
-    TypedArrayLikeAdapter::from(
-        ArraySliceValue::from_ptr_val(out_idx, broadcast_size, None),
-        Box::new(|_, v| v.into_int_value()),
-        Box::new(|_, v| v.into()),
-    )
-}

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

@@ -0,0 +1,80 @@
+use inkwell::{types::BasicTypeEnum, values::IntValue};
+
+use crate::codegen::{
+    expr::infer_and_call_function,
+    irrt::get_usize_dependent_function_name,
+    values::{ndarray::NDArrayValue, ListValue, ProxyValue, TypedArrayLikeAccessor},
+    CodeGenContext, CodeGenerator,
+};
+
+/// Generates a call to `__nac3_ndarray_array_set_and_validate_list_shape`.
+///
+/// Deduces the target shape of the `ndarray` from the provided `list`, raising an exception if
+/// there is any issue with the resultant `shape`.
+///
+/// `shape` must be pre-allocated by the caller of this function to `[usize; ndims]`, and must be
+/// initialized to all `-1`s.
+pub fn call_nac3_ndarray_array_set_and_validate_list_shape<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    list: ListValue<'ctx>,
+    ndims: IntValue<'ctx>,
+    shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+) {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    assert_eq!(list.get_type().element_type().unwrap(), ctx.ctx.i8_type().into());
+    assert_eq!(ndims.get_type(), llvm_usize);
+    assert_eq!(
+        BasicTypeEnum::try_from(shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+
+    let name = get_usize_dependent_function_name(
+        generator,
+        ctx,
+        "__nac3_ndarray_array_set_and_validate_list_shape",
+    );
+
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[list.as_base_value().into(), ndims.into(), shape.base_ptr(ctx, generator).into()],
+        None,
+        None,
+    );
+}
+
+/// Generates a call to `__nac3_ndarray_array_write_list_to_array`.
+///
+/// Copies the contents stored in `list` into `ndarray`.
+///
+/// The `ndarray` must fulfill the following preconditions:
+///
+/// - `ndarray.itemsize`: Must be initialized.
+/// - `ndarray.ndims`: Must be initialized.
+/// - `ndarray.shape`: Must be initialized.
+/// - `ndarray.data`: Must be allocated and contiguous.
+pub fn call_nac3_ndarray_array_write_list_to_array<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    list: ListValue<'ctx>,
+    ndarray: NDArrayValue<'ctx>,
+) {
+    assert_eq!(list.get_type().element_type().unwrap(), ctx.ctx.i8_type().into());
+
+    let name = get_usize_dependent_function_name(
+        generator,
+        ctx,
+        "__nac3_ndarray_array_write_list_to_array",
+    );
+
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[list.as_base_value().into(), ndarray.as_base_value().into()],
+        None,
+        None,
+    );
+}

nac3core/src/codegen/irrt/ndarray/basic.rs

@@ -0,0 +1,310 @@
+use inkwell::{
+    types::BasicTypeEnum,
+    values::{BasicValueEnum, IntValue, PointerValue},
+    AddressSpace,
+};
+
+use crate::codegen::{
+    expr::{create_and_call_function, infer_and_call_function},
+    irrt::get_usize_dependent_function_name,
+    types::ProxyType,
+    values::{ndarray::NDArrayValue, ProxyValue, TypedArrayLikeAccessor},
+    CodeGenContext, CodeGenerator,
+};
+
+/// Generates a call to `__nac3_ndarray_util_assert_shape_no_negative`.
+///
+/// Assets that `shape` does not contain negative dimensions.
+pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+) {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
+
+    assert_eq!(
+        BasicTypeEnum::try_from(shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+
+    let name = get_usize_dependent_function_name(
+        generator,
+        ctx,
+        "__nac3_ndarray_util_assert_shape_no_negative",
+    );
+
+    create_and_call_function(
+        ctx,
+        &name,
+        Some(llvm_usize.into()),
+        &[
+            (llvm_usize.into(), shape.size(ctx, generator).into()),
+            (llvm_pusize.into(), shape.base_ptr(ctx, generator).into()),
+        ],
+        None,
+        None,
+    );
+}
+
+/// Generates a call to `__nac3_ndarray_util_assert_shape_output_shape_same`.
+///
+/// Asserts that `ndarray_shape` and `output_shape` are the same in the context of writing output to
+/// an `ndarray`.
+pub fn call_nac3_ndarray_util_assert_output_shape_same<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    ndarray_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    output_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+) {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
+
+    assert_eq!(
+        BasicTypeEnum::try_from(ndarray_shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+    assert_eq!(
+        BasicTypeEnum::try_from(output_shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+
+    let name = get_usize_dependent_function_name(
+        generator,
+        ctx,
+        "__nac3_ndarray_util_assert_output_shape_same",
+    );
+
+    create_and_call_function(
+        ctx,
+        &name,
+        Some(llvm_usize.into()),
+        &[
+            (llvm_usize.into(), ndarray_shape.size(ctx, generator).into()),
+            (llvm_pusize.into(), ndarray_shape.base_ptr(ctx, generator).into()),
+            (llvm_usize.into(), output_shape.size(ctx, generator).into()),
+            (llvm_pusize.into(), output_shape.base_ptr(ctx, generator).into()),
+        ],
+        None,
+        None,
+    );
+}
+
+/// Generates a call to `__nac3_ndarray_size`.
+///
+/// Returns a [`usize`][CodeGenerator::get_size_type] value of the number of elements of an
+/// `ndarray`, corresponding to the value of `ndarray.size`.
+pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    ndarray: NDArrayValue<'ctx>,
+) -> IntValue<'ctx> {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_ndarray = ndarray.get_type().as_base_type();
+
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_size");
+
+    create_and_call_function(
+        ctx,
+        &name,
+        Some(llvm_usize.into()),
+        &[(llvm_ndarray.into(), ndarray.as_base_value().into())],
+        Some("size"),
+        None,
+    )
+    .map(BasicValueEnum::into_int_value)
+    .unwrap()
+}
+
+/// Generates a call to `__nac3_ndarray_nbytes`.
+///
+/// Returns a [`usize`][CodeGenerator::get_size_type] value of the number of bytes consumed by the
+/// data of the `ndarray`, corresponding to the value of `ndarray.nbytes`.
+pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    ndarray: NDArrayValue<'ctx>,
+) -> IntValue<'ctx> {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_ndarray = ndarray.get_type().as_base_type();
+
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_nbytes");
+
+    create_and_call_function(
+        ctx,
+        &name,
+        Some(llvm_usize.into()),
+        &[(llvm_ndarray.into(), ndarray.as_base_value().into())],
+        Some("nbytes"),
+        None,
+    )
+    .map(BasicValueEnum::into_int_value)
+    .unwrap()
+}
+
+/// Generates a call to `__nac3_ndarray_len`.
+///
+/// Returns a [`usize`][CodeGenerator::get_size_type] value of the size of the topmost dimension of
+/// the `ndarray`, corresponding to the value of `ndarray.__len__`.
+pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    ndarray: NDArrayValue<'ctx>,
+) -> IntValue<'ctx> {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_ndarray = ndarray.get_type().as_base_type();
+
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_len");
+
+    create_and_call_function(
+        ctx,
+        &name,
+        Some(llvm_usize.into()),
+        &[(llvm_ndarray.into(), ndarray.as_base_value().into())],
+        Some("len"),
+        None,
+    )
+    .map(BasicValueEnum::into_int_value)
+    .unwrap()
+}
+
+/// Generates a call to `__nac3_ndarray_is_c_contiguous`.
+///
+/// Returns an `i1` value indicating whether the `ndarray` is C-contiguous.
+pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    ndarray: NDArrayValue<'ctx>,
+) -> IntValue<'ctx> {
+    let llvm_i1 = ctx.ctx.bool_type();
+    let llvm_ndarray = ndarray.get_type().as_base_type();
+
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_is_c_contiguous");
+
+    create_and_call_function(
+        ctx,
+        &name,
+        Some(llvm_i1.into()),
+        &[(llvm_ndarray.into(), ndarray.as_base_value().into())],
+        Some("is_c_contiguous"),
+        None,
+    )
+    .map(BasicValueEnum::into_int_value)
+    .unwrap()
+}
+
+/// Generates a call to `__nac3_ndarray_get_nth_pelement`.
+///
+/// Returns a [`PointerValue`] to the `index`-th flattened element of the `ndarray`.
+pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    ndarray: NDArrayValue<'ctx>,
+    index: IntValue<'ctx>,
+) -> PointerValue<'ctx> {
+    let llvm_i8 = ctx.ctx.i8_type();
+    let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_ndarray = ndarray.get_type().as_base_type();
+
+    assert_eq!(index.get_type(), llvm_usize);
+
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_get_nth_pelement");
+
+    create_and_call_function(
+        ctx,
+        &name,
+        Some(llvm_pi8.into()),
+        &[(llvm_ndarray.into(), ndarray.as_base_value().into()), (llvm_usize.into(), index.into())],
+        Some("pelement"),
+        None,
+    )
+    .map(BasicValueEnum::into_pointer_value)
+    .unwrap()
+}
+
+/// Generates a call to `__nac3_ndarray_get_pelement_by_indices`.
+///
+/// `indices` must have the same number of elements as the number of dimensions in `ndarray`.
+///
+/// Returns a [`PointerValue`] to the element indexed by `indices`.
+pub fn call_nac3_ndarray_get_pelement_by_indices<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    ndarray: NDArrayValue<'ctx>,
+    indices: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+) -> PointerValue<'ctx> {
+    let llvm_i8 = ctx.ctx.i8_type();
+    let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
+    let llvm_ndarray = ndarray.get_type().as_base_type();
+
+    assert_eq!(
+        BasicTypeEnum::try_from(indices.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+
+    let name =
+        get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_get_pelement_by_indices");
+
+    create_and_call_function(
+        ctx,
+        &name,
+        Some(llvm_pi8.into()),
+        &[
+            (llvm_ndarray.into(), ndarray.as_base_value().into()),
+            (llvm_pusize.into(), indices.base_ptr(ctx, generator).into()),
+        ],
+        Some("pelement"),
+        None,
+    )
+    .map(BasicValueEnum::into_pointer_value)
+    .unwrap()
+}
+
+/// Generates a call to `__nac3_ndarray_set_strides_by_shape`.
+///
+/// Sets `ndarray.strides` assuming that `ndarray.shape` is C-contiguous.
+pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    ndarray: NDArrayValue<'ctx>,
+) {
+    let llvm_ndarray = ndarray.get_type().as_base_type();
+
+    let name =
+        get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_set_strides_by_shape");
+
+    create_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[(llvm_ndarray.into(), ndarray.as_base_value().into())],
+        None,
+        None,
+    );
+}
+
+/// Generates a call to `__nac3_ndarray_copy_data`.
+///
+/// Copies all elements from `src_ndarray` to `dst_ndarray` using their flattened views. The number
+/// of elements in `src_ndarray` must be greater than or equal to the number of elements in
+/// `dst_ndarray`.
+pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    src_ndarray: NDArrayValue<'ctx>,
+    dst_ndarray: NDArrayValue<'ctx>,
+) {
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_copy_data");
+
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[src_ndarray.as_base_value().into(), dst_ndarray.as_base_value().into()],
+        None,
+        None,
+    );
+}

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

@@ -0,0 +1,82 @@
+use inkwell::values::IntValue;
+
+use crate::codegen::{
+    expr::infer_and_call_function,
+    irrt::get_usize_dependent_function_name,
+    types::{ndarray::ShapeEntryType, ProxyType},
+    values::{
+        ndarray::NDArrayValue, ArrayLikeValue, ArraySliceValue, ProxyValue, TypedArrayLikeAccessor,
+        TypedArrayLikeMutator,
+    },
+    CodeGenContext, CodeGenerator,
+};
+
+/// Generates a call to `__nac3_ndarray_broadcast_to`.
+///
+/// Attempts to broadcast `src_ndarray` to the new shape defined by `dst_ndarray`.
+///
+/// `dst_ndarray` must meet the following preconditions:
+///
+/// - `dst_ndarray.ndims` must be initialized and matching the length of `dst_ndarray.shape`.
+/// - `dst_ndarray.shape` must be initialized and contains the target broadcast shape.
+/// - `dst_ndarray.strides` must be allocated and may contain uninitialized values.
+pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    src_ndarray: NDArrayValue<'ctx>,
+    dst_ndarray: NDArrayValue<'ctx>,
+) {
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[src_ndarray.as_base_value().into(), dst_ndarray.as_base_value().into()],
+        None,
+        None,
+    );
+}
+
+/// Generates a call to `__nac3_ndarray_broadcast_shapes`.
+///
+/// Attempts to calculate the resultant shape from broadcasting all shapes in `shape_entries`,
+/// writing the result to `dst_shape`.
+pub fn call_nac3_ndarray_broadcast_shapes<'ctx, G, Shape>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    num_shape_entries: IntValue<'ctx>,
+    shape_entries: ArraySliceValue<'ctx>,
+    dst_ndims: IntValue<'ctx>,
+    dst_shape: &Shape,
+) where
+    G: CodeGenerator + ?Sized,
+    Shape: TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>
+        + TypedArrayLikeMutator<'ctx, G, IntValue<'ctx>>,
+{
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    assert_eq!(num_shape_entries.get_type(), llvm_usize);
+    assert!(ShapeEntryType::is_type(
+        generator,
+        ctx.ctx,
+        shape_entries.base_ptr(ctx, generator).get_type()
+    )
+    .is_ok());
+    assert_eq!(dst_ndims.get_type(), llvm_usize);
+    assert_eq!(dst_shape.element_type(ctx, generator), llvm_usize.into());
+
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[
+            num_shape_entries.into(),
+            shape_entries.base_ptr(ctx, generator).into(),
+            dst_ndims.into(),
+            dst_shape.base_ptr(ctx, generator).into(),
+        ],
+        None,
+        None,
+    );
+}

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

@@ -0,0 +1,34 @@
+use crate::codegen::{
+    expr::infer_and_call_function,
+    irrt::get_usize_dependent_function_name,
+    values::{ndarray::NDArrayValue, ArrayLikeValue, ArraySliceValue, ProxyValue},
+    CodeGenContext, CodeGenerator,
+};
+
+/// Generates a call to `__nac3_ndarray_index`.
+///
+/// Performs [basic indexing](https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
+/// on `src_ndarray` using `indices`, writing the result to `dst_ndarray`, corresponding to the
+/// operation `dst_ndarray = src_ndarray[indices]`.
+pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    indices: ArraySliceValue<'ctx>,
+    src_ndarray: NDArrayValue<'ctx>,
+    dst_ndarray: NDArrayValue<'ctx>,
+) {
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[
+            indices.size(ctx, generator).into(),
+            indices.base_ptr(ctx, generator).into(),
+            src_ndarray.as_base_value().into(),
+            dst_ndarray.as_base_value().into(),
+        ],
+        None,
+        None,
+    );
+}

nac3core/src/codegen/irrt/ndarray/iter.rs

@@ -0,0 +1,86 @@
+use inkwell::{
+    types::BasicTypeEnum,
+    values::{BasicValueEnum, IntValue},
+    AddressSpace,
+};
+
+use crate::codegen::{
+    expr::{create_and_call_function, infer_and_call_function},
+    irrt::get_usize_dependent_function_name,
+    types::ProxyType,
+    values::{
+        ndarray::{NDArrayValue, NDIterValue},
+        ProxyValue, TypedArrayLikeAccessor,
+    },
+    CodeGenContext, CodeGenerator,
+};
+
+/// Generates a call to `__nac3_nditer_initialize`.
+///
+/// Initializes the `iter` object.
+pub fn call_nac3_nditer_initialize<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    iter: NDIterValue<'ctx>,
+    ndarray: NDArrayValue<'ctx>,
+    indices: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+) {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
+
+    assert_eq!(
+        BasicTypeEnum::try_from(indices.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_nditer_initialize");
+
+    create_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[
+            (iter.get_type().as_base_type().into(), iter.as_base_value().into()),
+            (ndarray.get_type().as_base_type().into(), ndarray.as_base_value().into()),
+            (llvm_pusize.into(), indices.base_ptr(ctx, generator).into()),
+        ],
+        None,
+        None,
+    );
+}
+
+/// Generates a call to `__nac3_nditer_initialize_has_element`.
+///
+/// Returns an `i1` value indicating whether there are elements left to traverse for the `iter`
+/// object.
+pub fn call_nac3_nditer_has_element<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    iter: NDIterValue<'ctx>,
+) -> IntValue<'ctx> {
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_nditer_has_element");
+
+    infer_and_call_function(
+        ctx,
+        &name,
+        Some(ctx.ctx.bool_type().into()),
+        &[iter.as_base_value().into()],
+        None,
+        None,
+    )
+    .map(BasicValueEnum::into_int_value)
+    .unwrap()
+}
+
+/// Generates a call to `__nac3_nditer_next`.
+///
+/// Moves `iter` to point to the next element.
+pub fn call_nac3_nditer_next<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    iter: NDIterValue<'ctx>,
+) {
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_nditer_next");
+
+    infer_and_call_function(ctx, &name, None, &[iter.as_base_value().into()], None, None);
+}

nac3core/src/codegen/irrt/ndarray/matmul.rs

@@ -0,0 +1,66 @@
+use inkwell::{types::BasicTypeEnum, values::IntValue};
+
+use crate::codegen::{
+    expr::infer_and_call_function, irrt::get_usize_dependent_function_name,
+    values::TypedArrayLikeAccessor, CodeGenContext, CodeGenerator,
+};
+
+/// Generates a call to `__nac3_ndarray_matmul_calculate_shapes`.
+///
+/// Calculates the broadcasted shapes for `a`, `b`, and the `ndarray` holding the final values of
+/// `a @ b`.
+#[allow(clippy::too_many_arguments)]
+pub fn call_nac3_ndarray_matmul_calculate_shapes<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    a_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    b_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    final_ndims: IntValue<'ctx>,
+    new_a_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    new_b_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    dst_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+) {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    assert_eq!(
+        BasicTypeEnum::try_from(a_shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+    assert_eq!(
+        BasicTypeEnum::try_from(b_shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+    assert_eq!(
+        BasicTypeEnum::try_from(new_a_shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+    assert_eq!(
+        BasicTypeEnum::try_from(new_b_shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+    assert_eq!(
+        BasicTypeEnum::try_from(dst_shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+
+    let name =
+        get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_matmul_calculate_shapes");
+
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[
+            a_shape.size(ctx, generator).into(),
+            a_shape.base_ptr(ctx, generator).into(),
+            b_shape.size(ctx, generator).into(),
+            b_shape.base_ptr(ctx, generator).into(),
+            final_ndims.into(),
+            new_a_shape.base_ptr(ctx, generator).into(),
+            new_b_shape.base_ptr(ctx, generator).into(),
+            dst_shape.base_ptr(ctx, generator).into(),
+        ],
+        None,
+        None,
+    );
+}

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

@@ -0,0 +1,17 @@
+pub use array::*;
+pub use basic::*;
+pub use broadcast::*;
+pub use indexing::*;
+pub use iter::*;
+pub use matmul::*;
+pub use reshape::*;
+pub use transpose::*;
+
+mod array;
+mod basic;
+mod broadcast;
+mod indexing;
+mod iter;
+mod matmul;
+mod reshape;
+mod transpose;

nac3core/src/codegen/irrt/ndarray/reshape.rs

@@ -0,0 +1,40 @@
+use inkwell::values::IntValue;
+
+use crate::codegen::{
+    expr::infer_and_call_function,
+    irrt::get_usize_dependent_function_name,
+    values::{ArrayLikeValue, ArraySliceValue},
+    CodeGenContext, CodeGenerator,
+};
+
+/// Generates a call to `__nac3_ndarray_reshape_resolve_and_check_new_shape`.
+///
+/// Resolves unknown dimensions in `new_shape` for `numpy.reshape(<ndarray>, new_shape)`, raising an
+/// assertion if multiple dimensions are unknown (`-1`).
+pub fn call_nac3_ndarray_reshape_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    size: IntValue<'ctx>,
+    new_ndims: IntValue<'ctx>,
+    new_shape: ArraySliceValue<'ctx>,
+) {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    assert_eq!(size.get_type(), llvm_usize);
+    assert_eq!(new_ndims.get_type(), llvm_usize);
+    assert_eq!(new_shape.element_type(ctx, generator), llvm_usize.into());
+
+    let name = get_usize_dependent_function_name(
+        generator,
+        ctx,
+        "__nac3_ndarray_reshape_resolve_and_check_new_shape",
+    );
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[size.into(), new_ndims.into(), new_shape.base_ptr(ctx, generator).into()],
+        None,
+        None,
+    );
+}

nac3core/src/codegen/irrt/ndarray/transpose.rs

@@ -0,0 +1,48 @@
+use inkwell::{values::IntValue, AddressSpace};
+
+use crate::codegen::{
+    expr::infer_and_call_function,
+    irrt::get_usize_dependent_function_name,
+    values::{ndarray::NDArrayValue, ProxyValue, TypedArrayLikeAccessor},
+    CodeGenContext, CodeGenerator,
+};
+
+/// Generates a call to `__nac3_ndarray_transpose`.
+///
+/// Creates a transpose view of `src_ndarray` and writes the result to `dst_ndarray`.
+///
+/// `dst_ndarray` must fulfill the following preconditions:
+///
+/// - `dst_ndarray.ndims` must be initialized and must be equal to `src_ndarray.ndims`.
+/// - `dst_ndarray.shape` must be allocated and may contain uninitialized values.
+/// - `dst_ndarray.strides` must be allocated and may contain uninitialized values.
+pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    src_ndarray: NDArrayValue<'ctx>,
+    dst_ndarray: NDArrayValue<'ctx>,
+    axes: Option<&impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>>,
+) {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    assert!(axes.is_none_or(|axes| axes.size(ctx, generator).get_type() == llvm_usize));
+    assert!(axes.is_none_or(|axes| axes.element_type(ctx, generator) == llvm_usize.into()));
+
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[
+            src_ndarray.as_base_value().into(),
+            dst_ndarray.as_base_value().into(),
+            axes.map_or(llvm_usize.const_zero(), |axes| axes.size(ctx, generator)).into(),
+            axes.map_or(llvm_usize.ptr_type(AddressSpace::default()).const_null(), |axes| {
+                axes.base_ptr(ctx, generator)
+            })
+            .into(),
+        ],
+        None,
+        None,
+    );
+}

nac3core/src/codegen/irrt/range.rs

@@ -0,0 +1,56 @@
+use inkwell::{
+    values::{BasicValueEnum, CallSiteValue, IntValue},
+    IntPredicate,
+};
+use itertools::Either;
+
+use crate::codegen::{CodeGenContext, CodeGenerator};
+
+/// Invokes the `__nac3_range_slice_len` in IRRT.
+///
+/// - `start`: The `i32` start value for the slice.
+/// - `end`: The `i32` end value for the slice.
+/// - `step`: The `i32` step value for the slice.
+///
+/// Returns an `i32` value of the length of the slice.
+pub fn calculate_len_for_slice_range<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    start: IntValue<'ctx>,
+    end: IntValue<'ctx>,
+    step: IntValue<'ctx>,
+) -> IntValue<'ctx> {
+    const SYMBOL: &str = "__nac3_range_slice_len";
+
+    let llvm_i32 = ctx.ctx.i32_type();
+
+    assert_eq!(start.get_type(), llvm_i32);
+    assert_eq!(end.get_type(), llvm_i32);
+    assert_eq!(step.get_type(), llvm_i32);
+
+    let len_func = ctx.module.get_function(SYMBOL).unwrap_or_else(|| {
+        let fn_t = llvm_i32.fn_type(&[llvm_i32.into(), llvm_i32.into(), llvm_i32.into()], false);
+        ctx.module.add_function(SYMBOL, fn_t, None)
+    });
+
+    // assert step != 0, throw exception if not
+    let not_zero = ctx
+        .builder
+        .build_int_compare(IntPredicate::NE, step, step.get_type().const_zero(), "range_step_ne")
+        .unwrap();
+    ctx.make_assert(
+        generator,
+        not_zero,
+        "0:ValueError",
+        "step must not be zero",
+        [None, None, None],
+        ctx.current_loc,
+    );
+
+    ctx.builder
+        .build_call(len_func, &[start.into(), end.into(), step.into()], "calc_len")
+        .map(CallSiteValue::try_as_basic_value)
+        .map(|v| v.map_left(BasicValueEnum::into_int_value))
+        .map(Either::unwrap_left)
+        .unwrap()
+}

nac3core/src/codegen/irrt/slice.rs

@@ -1,8 +1,6 @@
-use inkwell::{
-    values::{BasicValueEnum, CallSiteValue, IntValue},
-    IntPredicate,
-};
+use inkwell::values::{BasicValueEnum, CallSiteValue, IntValue};
 use itertools::Either;
+
 use nac3parser::ast::Expr;
 
 use crate::{
@@ -39,38 +37,3 @@ pub fn handle_slice_index_bound<'ctx, G: CodeGenerator>(
             .unwrap(),
     ))
 }
-
-pub fn calculate_len_for_slice_range<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    start: IntValue<'ctx>,
-    end: IntValue<'ctx>,
-    step: IntValue<'ctx>,
-) -> IntValue<'ctx> {
-    const SYMBOL: &str = "__nac3_range_slice_len";
-    let len_func = ctx.module.get_function(SYMBOL).unwrap_or_else(|| {
-        let i32_t = ctx.ctx.i32_type();
-        let fn_t = i32_t.fn_type(&[i32_t.into(), i32_t.into(), i32_t.into()], false);
-        ctx.module.add_function(SYMBOL, fn_t, None)
-    });
-
-    // assert step != 0, throw exception if not
-    let not_zero = ctx
-        .builder
-        .build_int_compare(IntPredicate::NE, step, step.get_type().const_zero(), "range_step_ne")
-        .unwrap();
-    ctx.make_assert(
-        generator,
-        not_zero,
-        "0:ValueError",
-        "step must not be zero",
-        [None, None, None],
-        ctx.current_loc,
-    );
-    ctx.builder
-        .build_call(len_func, &[start.into(), end.into(), step.into()], "calc_len")
-        .map(CallSiteValue::try_as_basic_value)
-        .map(|v| v.map_left(BasicValueEnum::into_int_value))
-        .map(Either::unwrap_left)
-        .unwrap()
-}

nac3core/src/codegen/irrt/string.rs

@@ -0,0 +1,46 @@
+use inkwell::values::{BasicValueEnum, CallSiteValue, IntValue, PointerValue};
+use itertools::Either;
+
+use super::get_usize_dependent_function_name;
+use crate::codegen::{CodeGenContext, CodeGenerator};
+
+/// Generates a call to string equality comparison. Returns an `i1` representing whether the strings are equal.
+pub fn call_string_eq<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    str1_ptr: PointerValue<'ctx>,
+    str1_len: IntValue<'ctx>,
+    str2_ptr: PointerValue<'ctx>,
+    str2_len: IntValue<'ctx>,
+) -> IntValue<'ctx> {
+    let llvm_i1 = ctx.ctx.bool_type();
+
+    let func_name = get_usize_dependent_function_name(generator, ctx, "nac3_str_eq");
+
+    let func = ctx.module.get_function(&func_name).unwrap_or_else(|| {
+        ctx.module.add_function(
+            &func_name,
+            llvm_i1.fn_type(
+                &[
+                    str1_ptr.get_type().into(),
+                    str1_len.get_type().into(),
+                    str2_ptr.get_type().into(),
+                    str2_len.get_type().into(),
+                ],
+                false,
+            ),
+            None,
+        )
+    });
+
+    ctx.builder
+        .build_call(
+            func,
+            &[str1_ptr.into(), str1_len.into(), str2_ptr.into(), str2_len.into()],
+            "str_eq_call",
+        )
+        .map(CallSiteValue::try_as_basic_value)
+        .map(|v| v.map_left(BasicValueEnum::into_int_value))
+        .map(Either::unwrap_left)
+        .unwrap()
+}

nac3core/src/codegen/llvm_intrinsics.rs

@@ -1,7 +1,6 @@
 use inkwell::{
-    context::Context,
     intrinsics::Intrinsic,
-    types::{AnyTypeEnum::IntType, FloatType},
+    types::AnyTypeEnum::IntType,
     values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue, PointerValue},
     AddressSpace,
 };
@@ -9,34 +8,6 @@ use itertools::Either;
 
 use super::CodeGenContext;
 
-/// Returns the string representation for the floating-point type `ft` when used in intrinsic
-/// functions.
-fn get_float_intrinsic_repr(ctx: &Context, ft: FloatType) -> &'static str {
-    // Standard LLVM floating-point types
-    if ft == ctx.f16_type() {
-        return "f16";
-    }
-    if ft == ctx.f32_type() {
-        return "f32";
-    }
-    if ft == ctx.f64_type() {
-        return "f64";
-    }
-    if ft == ctx.f128_type() {
-        return "f128";
-    }
-
-    // Non-standard floating-point types
-    if ft == ctx.x86_f80_type() {
-        return "f80";
-    }
-    if ft == ctx.ppc_f128_type() {
-        return "ppcf128";
-    }
-
-    unreachable!()
-}
-
 /// Invokes the [`llvm.va_start`](https://llvm.org/docs/LangRef.html#llvm-va-start-intrinsic)
 /// intrinsic.
 pub fn call_va_start<'ctx>(ctx: &CodeGenContext<'ctx, '_>, arglist: PointerValue<'ctx>) {
@@ -54,7 +25,7 @@ pub fn call_va_start<'ctx>(ctx: &CodeGenContext<'ctx, '_>, arglist: PointerValue
     ctx.builder.build_call(intrinsic_fn, &[arglist.into()], "").unwrap();
 }
 
-/// Invokes the [`llvm.va_start`](https://llvm.org/docs/LangRef.html#llvm-va-start-intrinsic)
+/// Invokes the [`llvm.va_end`](https://llvm.org/docs/LangRef.html#llvm-va-end-intrinsic)
 /// intrinsic.
 pub fn call_va_end<'ctx>(ctx: &CodeGenContext<'ctx, '_>, arglist: PointerValue<'ctx>) {
     const FN_NAME: &str = "llvm.va_end";
@@ -201,6 +172,49 @@ pub fn call_memcpy_generic<'ctx>(
     call_memcpy(ctx, dest, src, len, is_volatile);
 }
 
+/// Invokes the `llvm.memcpy` intrinsic.
+///
+/// Unlike [`call_memcpy`], this function accepts any type of pointer value. If `dest` or `src` is
+/// not a pointer to an integer, the pointer(s) will be cast to `i8*` before invoking `memcpy`.
+/// Moreover, `len` now refers to the number of elements to copy (rather than number of bytes to
+/// copy).
+pub fn call_memcpy_generic_array<'ctx>(
+    ctx: &CodeGenContext<'ctx, '_>,
+    dest: PointerValue<'ctx>,
+    src: PointerValue<'ctx>,
+    len: IntValue<'ctx>,
+    is_volatile: IntValue<'ctx>,
+) {
+    let llvm_i8 = ctx.ctx.i8_type();
+    let llvm_p0i8 = llvm_i8.ptr_type(AddressSpace::default());
+    let llvm_sizeof_expr_t = llvm_i8.size_of().get_type();
+
+    let dest_elem_t = dest.get_type().get_element_type();
+    let src_elem_t = src.get_type().get_element_type();
+
+    let dest = if matches!(dest_elem_t, IntType(t) if t.get_bit_width() == 8) {
+        dest
+    } else {
+        ctx.builder
+            .build_bit_cast(dest, llvm_p0i8, "")
+            .map(BasicValueEnum::into_pointer_value)
+            .unwrap()
+    };
+    let src = if matches!(src_elem_t, IntType(t) if t.get_bit_width() == 8) {
+        src
+    } else {
+        ctx.builder
+            .build_bit_cast(src, llvm_p0i8, "")
+            .map(BasicValueEnum::into_pointer_value)
+            .unwrap()
+    };
+
+    let len = ctx.builder.build_int_z_extend_or_bit_cast(len, llvm_sizeof_expr_t, "").unwrap();
+    let len = ctx.builder.build_int_mul(len, src_elem_t.size_of().unwrap(), "").unwrap();
+
+    call_memcpy(ctx, dest, src, len, is_volatile);
+}
+
 /// Macro to find and generate build call for llvm intrinsic (body of llvm intrinsic function)
 ///
 /// Arguments:
@@ -343,3 +357,25 @@ pub fn call_float_powi<'ctx>(
         .map(Either::unwrap_left)
         .unwrap()
 }
+
+/// Invokes the [`llvm.ctpop`](https://llvm.org/docs/LangRef.html#llvm-ctpop-intrinsic) intrinsic.
+pub fn call_int_ctpop<'ctx>(
+    ctx: &CodeGenContext<'ctx, '_>,
+    src: IntValue<'ctx>,
+    name: Option<&str>,
+) -> IntValue<'ctx> {
+    const FN_NAME: &str = "llvm.ctpop";
+
+    let llvm_src_t = src.get_type();
+
+    let intrinsic_fn = Intrinsic::find(FN_NAME)
+        .and_then(|intrinsic| intrinsic.get_declaration(&ctx.module, &[llvm_src_t.into()]))
+        .unwrap();
+
+    ctx.builder
+        .build_call(intrinsic_fn, &[src.into()], name.unwrap_or_default())
+        .map(CallSiteValue::try_as_basic_value)
+        .map(|v| v.map_left(BasicValueEnum::into_int_value))
+        .map(Either::unwrap_left)
+        .unwrap()
+}

nac3core/src/codegen/mod.rs

@@ -30,7 +30,11 @@ use nac3parser::ast::{Location, Stmt, StrRef};
 
 use crate::{
     symbol_resolver::{StaticValue, SymbolResolver},
-    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
+    toplevel::{
+        helper::{extract_ndims, PrimDef},
+        numpy::unpack_ndarray_var_tys,
+        TopLevelContext, TopLevelDef,
+    },
     typecheck::{
         type_inferencer::{CodeLocation, PrimitiveStore},
         typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
@@ -38,7 +42,7 @@ use crate::{
 };
 use concrete_type::{ConcreteType, ConcreteTypeEnum, ConcreteTypeStore};
 pub use generator::{CodeGenerator, DefaultCodeGenerator};
-use types::{ListType, NDArrayType, ProxyType, RangeType};
+use types::{ndarray::NDArrayType, ListType, ProxyType, RangeType, TupleType};
 
 pub mod builtin_fns;
 pub mod concrete_type;
@@ -224,7 +228,7 @@ pub struct CodeGenContext<'ctx, 'a> {
     pub current_loc: Location,
 }
 
-impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
+impl CodeGenContext<'_, '_> {
     /// Whether the [current basic block][Builder::get_insert_block] referenced by `builder`
     /// contains a [terminator statement][BasicBlock::get_terminator].
     pub fn is_terminated(&self) -> bool {
@@ -510,12 +514,13 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
                         }
 
                         TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-                            let (dtype, _) = unpack_ndarray_var_tys(unifier, ty);
+                            let (dtype, ndims) = unpack_ndarray_var_tys(unifier, ty);
+                            let ndims = extract_ndims(unifier, ndims);
                             let element_type = get_llvm_type(
                                 ctx, module, generator, unifier, top_level, type_cache, dtype,
                             );
 
-                            NDArrayType::new(generator, ctx, element_type).as_base_type().into()
+                            NDArrayType::new(generator, ctx, element_type, ndims).as_base_type().into()
                         }
 
                         _ => unreachable!(
@@ -569,7 +574,7 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
                         get_llvm_type(ctx, module, generator, unifier, top_level, type_cache, *ty)
                     })
                     .collect_vec();
-                ctx.struct_type(&fields, false).into()
+                TupleType::new(generator, ctx, &fields).as_base_type().into()
             }
             TVirtual { .. } => unimplemented!(),
             _ => unreachable!("{}", ty_enum.get_type_name()),
@@ -1119,3 +1124,106 @@ fn gen_in_range_check<'ctx>(
 fn get_va_count_arg_name(arg_name: StrRef) -> StrRef {
     format!("__{}_va_count", &arg_name).into()
 }
+
+/// Returns the alignment of the type.
+///
+/// This is necessary as `get_alignment` is not implemented as part of [`BasicType`].
+pub fn get_type_alignment<'ctx>(ty: impl Into<BasicTypeEnum<'ctx>>) -> IntValue<'ctx> {
+    match ty.into() {
+        BasicTypeEnum::ArrayType(ty) => ty.get_alignment(),
+        BasicTypeEnum::FloatType(ty) => ty.get_alignment(),
+        BasicTypeEnum::IntType(ty) => ty.get_alignment(),
+        BasicTypeEnum::PointerType(ty) => ty.get_alignment(),
+        BasicTypeEnum::StructType(ty) => ty.get_alignment(),
+        BasicTypeEnum::VectorType(ty) => ty.get_alignment(),
+    }
+}
+
+/// Inserts an `alloca` instruction with allocation `size` given in bytes and the alignment of the
+/// given type.
+///
+/// The returned [`PointerValue`] will have a type of `i8*`, a size of at least `size`, and will be
+/// aligned with the alignment of `align_ty`.
+pub fn type_aligned_alloca<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    align_ty: impl Into<BasicTypeEnum<'ctx>>,
+    size: IntValue<'ctx>,
+    name: Option<&str>,
+) -> PointerValue<'ctx> {
+    /// Round `val` up to its modulo `power_of_two`.
+    fn round_up<'ctx>(
+        ctx: &CodeGenContext<'ctx, '_>,
+        val: IntValue<'ctx>,
+        power_of_two: IntValue<'ctx>,
+    ) -> IntValue<'ctx> {
+        debug_assert_eq!(
+            val.get_type().get_bit_width(),
+            power_of_two.get_type().get_bit_width(),
+            "`val` ({}) and `power_of_two` ({}) must be the same type",
+            val.get_type(),
+            power_of_two.get_type(),
+        );
+
+        let llvm_val_t = val.get_type();
+
+        let max_rem =
+            ctx.builder.build_int_sub(power_of_two, llvm_val_t.const_int(1, false), "").unwrap();
+        ctx.builder
+            .build_and(
+                ctx.builder.build_int_add(val, max_rem, "").unwrap(),
+                ctx.builder.build_not(max_rem, "").unwrap(),
+                "",
+            )
+            .unwrap()
+    }
+
+    let llvm_i8 = ctx.ctx.i8_type();
+    let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let align_ty = align_ty.into();
+
+    let size = ctx.builder.build_int_truncate_or_bit_cast(size, llvm_usize, "").unwrap();
+
+    debug_assert_eq!(
+        size.get_type().get_bit_width(),
+        llvm_usize.get_bit_width(),
+        "Expected size_t ({}) for parameter `size` of `aligned_alloca`, got {}",
+        llvm_usize,
+        size.get_type(),
+    );
+
+    let alignment = get_type_alignment(align_ty);
+    let alignment = ctx.builder.build_int_truncate_or_bit_cast(alignment, llvm_usize, "").unwrap();
+
+    if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
+        let alignment_bitcount = llvm_intrinsics::call_int_ctpop(ctx, alignment, None);
+
+        ctx.make_assert(
+            generator,
+            ctx.builder
+                .build_int_compare(
+                    IntPredicate::EQ,
+                    alignment_bitcount,
+                    alignment_bitcount.get_type().const_int(1, false),
+                    "",
+                )
+                .unwrap(),
+            "0:AssertionError",
+            "Expected power-of-two alignment for aligned_alloca, got {0}",
+            [Some(alignment), None, None],
+            ctx.current_loc,
+        );
+    }
+
+    let buffer_size = round_up(ctx, size, alignment);
+    let aligned_slices = ctx.builder.build_int_unsigned_div(buffer_size, alignment, "").unwrap();
+
+    // Just to be absolutely sure, alloca in [i8 x alignment] slices
+    let buffer = ctx.builder.build_array_alloca(align_ty, aligned_slices, "").unwrap();
+
+    ctx.builder
+        .build_bit_cast(buffer, llvm_pi8, name.unwrap_or_default())
+        .map(BasicValueEnum::into_pointer_value)
+        .unwrap()
+}

nac3core/src/codegen/stmt.rs

@@ -16,7 +16,11 @@ use super::{
     gen_in_range_check,
     irrt::{handle_slice_indices, list_slice_assignment},
     macros::codegen_unreachable,
-    values::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
+    types::ndarray::NDArrayType,
+    values::{
+        ndarray::{RustNDIndex, ScalarOrNDArray},
+        ArrayLikeIndexer, ArraySliceValue, ListValue, ProxyValue, RangeValue,
+    },
     CodeGenContext, CodeGenerator,
 };
 use crate::{
@@ -411,7 +415,52 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
             if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
         {
             // Handle NDArray item assignment
-            todo!("ndarray subscript assignment is not yet implemented");
+            // Process target
+            let target = generator
+                .gen_expr(ctx, target)?
+                .unwrap()
+                .to_basic_value_enum(ctx, generator, target_ty)?;
+
+            // Process key
+            let key = RustNDIndex::from_subscript_expr(generator, ctx, key)?;
+
+            // Process value
+            let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
+
+            // Reference code:
+            // ```python
+            // target = target[key]
+            // value = np.asarray(value)
+            //
+            // shape = np.broadcast_shape((target, value))
+            //
+            // target = np.broadcast_to(target, shape)
+            // value = np.broadcast_to(value, shape)
+            //
+            // # ...and finally copy 1-1 from value to target.
+            // ```
+
+            let target = NDArrayType::from_unifier_type(generator, ctx, target_ty)
+                .map_value(target.into_pointer_value(), None);
+            let target = target.index(generator, ctx, &key);
+
+            let value = ScalarOrNDArray::from_value(generator, ctx, (value_ty, value))
+                .to_ndarray(generator, ctx);
+
+            let broadcast_ndims =
+                [target.get_type().ndims(), value.get_type().ndims()].into_iter().max().unwrap();
+            let broadcast_result = NDArrayType::new(
+                generator,
+                ctx.ctx,
+                value.get_type().element_type(),
+                broadcast_ndims,
+            )
+            .broadcast(generator, ctx, &[target, value]);
+
+            let target = broadcast_result.ndarrays[0];
+            let value = broadcast_result.ndarrays[1];
+
+            target.copy_data_from(generator, ctx, value);
         }
         _ => {
             panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));

nac3core/src/codegen/test.rs

@@ -17,7 +17,7 @@ use parking_lot::RwLock;
 
 use super::{
     concrete_type::ConcreteTypeStore,
-    types::{ListType, NDArrayType, ProxyType, RangeType},
+    types::{ndarray::NDArrayType, ListType, ProxyType, RangeType},
     CodeGenContext, CodeGenLLVMOptions, CodeGenTargetMachineOptions, CodeGenTask, CodeGenerator,
     DefaultCodeGenerator, WithCall, WorkerRegistry,
 };
@@ -36,7 +36,6 @@ use crate::{
 struct Resolver {
     id_to_type: HashMap<StrRef, Type>,
     id_to_def: RwLock<HashMap<StrRef, DefinitionId>>,
-    class_names: HashMap<StrRef, Type>,
 }
 
 impl Resolver {
@@ -104,11 +103,9 @@ fn test_primitives() {
     let top_level = Arc::new(composer.make_top_level_context());
     unifier.top_level = Some(top_level.clone());
 
-    let resolver = Arc::new(Resolver {
-        id_to_type: HashMap::new(),
-        id_to_def: RwLock::new(HashMap::new()),
-        class_names: HashMap::default(),
-    }) as Arc<dyn SymbolResolver + Send + Sync>;
+    let resolver =
+        Arc::new(Resolver { id_to_type: HashMap::new(), id_to_def: RwLock::new(HashMap::new()) })
+            as Arc<dyn SymbolResolver + Send + Sync>;
 
     let threads = vec![DefaultCodeGenerator::new("test".into(), 32).into()];
     let signature = FunSignature {
@@ -298,11 +295,7 @@ fn test_simple_call() {
         loc: None,
     })));
 
-    let resolver = Resolver {
-        id_to_type: HashMap::new(),
-        id_to_def: RwLock::new(HashMap::new()),
-        class_names: HashMap::default(),
-    };
+    let resolver = Resolver { id_to_type: HashMap::new(), id_to_def: RwLock::new(HashMap::new()) };
     resolver.add_id_def("foo".into(), DefinitionId(foo_id));
     let resolver = Arc::new(resolver) as Arc<dyn SymbolResolver + Send + Sync>;
 
@@ -471,6 +464,6 @@ fn test_classes_ndarray_type_new() {
     let llvm_i32 = ctx.i32_type();
     let llvm_usize = generator.get_size_type(&ctx);
 
-    let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into());
+    let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into(), 2);
     assert!(NDArrayType::is_representable(llvm_ndarray.as_base_type(), llvm_usize).is_ok());
 }

nac3core/src/codegen/types/list.rs

@@ -1,69 +1,113 @@
 use inkwell::{
-    context::Context,
+    context::{AsContextRef, Context},
     types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
-    values::IntValue,
-    AddressSpace,
+    values::{IntValue, PointerValue},
+    AddressSpace, IntPredicate, OptimizationLevel,
 };
+use itertools::Itertools;
+
+use nac3core_derive::StructFields;
 
 use super::ProxyType;
-use crate::codegen::{
-    values::{ArraySliceValue, ListValue, ProxyValue},
-    CodeGenContext, CodeGenerator,
+use crate::{
+    codegen::{
+        types::structure::{
+            check_struct_type_matches_fields, FieldIndexCounter, StructField, StructFields,
+        },
+        values::{ListValue, ProxyValue},
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::{iter_type_vars, Type, TypeEnum},
 };
 
 /// Proxy type for a `list` type in LLVM.
 #[derive(Debug, PartialEq, Eq, Clone, Copy)]
 pub struct ListType<'ctx> {
     ty: PointerType<'ctx>,
+    item: Option<BasicTypeEnum<'ctx>>,
     llvm_usize: IntType<'ctx>,
 }
 
+#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+pub struct ListStructFields<'ctx> {
+    /// Array pointer to content.
+    #[value_type(i8_type().ptr_type(AddressSpace::default()))]
+    pub items: StructField<'ctx, PointerValue<'ctx>>,
+
+    /// Number of items in the array.
+    #[value_type(usize)]
+    pub len: StructField<'ctx, IntValue<'ctx>>,
+}
+
+impl<'ctx> ListStructFields<'ctx> {
+    #[must_use]
+    pub fn new_typed(item: BasicTypeEnum<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
+        let mut counter = FieldIndexCounter::default();
+
+        ListStructFields {
+            items: StructField::create(
+                &mut counter,
+                "items",
+                item.ptr_type(AddressSpace::default()),
+            ),
+            len: StructField::create(&mut counter, "len", llvm_usize),
+        }
+    }
+}
+
 impl<'ctx> ListType<'ctx> {
     /// Checks whether `llvm_ty` represents a `list` type, returning [Err] if it does not.
     pub fn is_representable(
         llvm_ty: PointerType<'ctx>,
         llvm_usize: IntType<'ctx>,
     ) -> Result<(), String> {
-        let llvm_list_ty = llvm_ty.get_element_type();
-        let AnyTypeEnum::StructType(llvm_list_ty) = llvm_list_ty else {
-            return Err(format!("Expected struct type for `list` type, got {llvm_list_ty}"));
-        };
-        if llvm_list_ty.count_fields() != 2 {
-            return Err(format!(
-                "Expected 2 fields in `list`, got {}",
-                llvm_list_ty.count_fields()
-            ));
-        }
+        let ctx = llvm_ty.get_context();
 
-        let list_size_ty = llvm_list_ty.get_field_type_at_index(0).unwrap();
-        let Ok(_) = PointerType::try_from(list_size_ty) else {
-            return Err(format!("Expected pointer type for `list.0`, got {list_size_ty}"));
+        let llvm_ty = llvm_ty.get_element_type();
+        let AnyTypeEnum::StructType(llvm_ty) = llvm_ty else {
+            return Err(format!("Expected struct type for `list` type, got {llvm_ty}"));
         };
 
-        let list_data_ty = llvm_list_ty.get_field_type_at_index(1).unwrap();
-        let Ok(list_data_ty) = IntType::try_from(list_data_ty) else {
-            return Err(format!("Expected int type for `list.1`, got {list_data_ty}"));
-        };
-        if list_data_ty.get_bit_width() != llvm_usize.get_bit_width() {
-            return Err(format!(
-                "Expected {}-bit int type for `list.1`, got {}-bit int",
-                llvm_usize.get_bit_width(),
-                list_data_ty.get_bit_width()
-            ));
-        }
+        let fields = ListStructFields::new(ctx, llvm_usize);
 
-        Ok(())
+        check_struct_type_matches_fields(
+            fields,
+            llvm_ty,
+            "list",
+            &[(fields.items.name(), &|ty| {
+                if ty.is_pointer_type() {
+                    Ok(())
+                } else {
+                    Err(format!("Expected T* for `list.items`, got {ty}"))
+                }
+            })],
+        )
+    }
+
+    /// Returns an instance of [`StructFields`] containing all field accessors for this type.
+    #[must_use]
+    fn fields(item: BasicTypeEnum<'ctx>, llvm_usize: IntType<'ctx>) -> ListStructFields<'ctx> {
+        ListStructFields::new_typed(item, llvm_usize)
+    }
+
+    /// See [`ListType::fields`].
+    // TODO: Move this into e.g. StructProxyType
+    #[must_use]
+    pub fn get_fields(&self, _ctx: &impl AsContextRef<'ctx>) -> ListStructFields<'ctx> {
+        Self::fields(self.item.unwrap_or(self.llvm_usize.into()), self.llvm_usize)
     }
 
     /// Creates an LLVM type corresponding to the expected structure of a `List`.
     #[must_use]
     fn llvm_type(
         ctx: &'ctx Context,
-        element_type: BasicTypeEnum<'ctx>,
+        element_type: Option<BasicTypeEnum<'ctx>>,
         llvm_usize: IntType<'ctx>,
     ) -> PointerType<'ctx> {
-        // struct List { data: T*, size: size_t }
-        let field_tys = [element_type.ptr_type(AddressSpace::default()).into(), llvm_usize.into()];
+        let element_type = element_type.unwrap_or(llvm_usize.into());
+
+        let field_tys =
+            Self::fields(element_type, llvm_usize).into_iter().map(|field| field.1).collect_vec();
 
         ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
     }
@@ -76,9 +120,50 @@ impl<'ctx> ListType<'ctx> {
         element_type: BasicTypeEnum<'ctx>,
     ) -> Self {
         let llvm_usize = generator.get_size_type(ctx);
-        let llvm_list = Self::llvm_type(ctx, element_type, llvm_usize);
+        let llvm_list = Self::llvm_type(ctx, Some(element_type), llvm_usize);
 
-        ListType::from_type(llvm_list, llvm_usize)
+        Self { ty: llvm_list, item: Some(element_type), llvm_usize }
+    }
+
+    /// Creates an instance of [`ListType`] with an unknown element type.
+    #[must_use]
+    pub fn new_untyped<G: CodeGenerator + ?Sized>(generator: &G, ctx: &'ctx Context) -> Self {
+        let llvm_usize = generator.get_size_type(ctx);
+        let llvm_list = Self::llvm_type(ctx, None, llvm_usize);
+
+        Self { ty: llvm_list, item: None, llvm_usize }
+    }
+
+    /// Creates an [`ListType`] from a [unifier type][Type].
+    #[must_use]
+    pub fn from_unifier_type<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ty: Type,
+    ) -> Self {
+        // Check unifier type and extract `item_type`
+        let elem_type = match &*ctx.unifier.get_ty_immutable(ty) {
+            TypeEnum::TObj { obj_id, params, .. }
+                if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
+            {
+                iter_type_vars(params).next().unwrap().ty
+            }
+
+            _ => panic!("Expected `list` type, but got {}", ctx.unifier.stringify(ty)),
+        };
+
+        let llvm_usize = generator.get_size_type(ctx.ctx);
+        let llvm_elem_type = if let TypeEnum::TVar { .. } = &*ctx.unifier.get_ty_immutable(ty) {
+            None
+        } else {
+            Some(ctx.get_llvm_type(generator, elem_type))
+        };
+
+        Self {
+            ty: Self::llvm_type(ctx.ctx, llvm_elem_type, llvm_usize),
+            item: llvm_elem_type,
+            llvm_usize,
+        }
     }
 
     /// Creates an [`ListType`] from a [`PointerType`].
@@ -86,47 +171,141 @@ impl<'ctx> ListType<'ctx> {
     pub fn from_type(ptr_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
         debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
 
-        ListType { ty: ptr_ty, llvm_usize }
+        let ctx = ptr_ty.get_context();
+
+        // We are just searching for the index off a field - Slot an arbitrary element type in.
+        let item_field_idx =
+            Self::fields(ctx.i8_type().into(), llvm_usize).index_of_field(|f| f.items);
+        let item = unsafe {
+            ptr_ty
+                .get_element_type()
+                .into_struct_type()
+                .get_field_type_at_index_unchecked(item_field_idx)
+                .into_pointer_type()
+                .get_element_type()
+        };
+        let item = BasicTypeEnum::try_from(item).unwrap_or_else(|()| {
+            panic!(
+                "Expected BasicTypeEnum for list element type, got {}",
+                ptr_ty.get_element_type().print_to_string()
+            )
+        });
+
+        ListType { ty: ptr_ty, item: Some(item), llvm_usize }
     }
 
     /// Returns the type of the `size` field of this `list` type.
     #[must_use]
     pub fn size_type(&self) -> IntType<'ctx> {
-        self.as_base_type()
-            .get_element_type()
-            .into_struct_type()
-            .get_field_type_at_index(1)
-            .map(BasicTypeEnum::into_int_type)
-            .unwrap()
+        self.llvm_usize
     }
 
     /// Returns the element type of this `list` type.
     #[must_use]
-    pub fn element_type(&self) -> AnyTypeEnum<'ctx> {
-        self.as_base_type()
-            .get_element_type()
-            .into_struct_type()
-            .get_field_type_at_index(0)
-            .map(BasicTypeEnum::into_pointer_type)
-            .map(PointerType::get_element_type)
-            .unwrap()
+    pub fn element_type(&self) -> Option<BasicTypeEnum<'ctx>> {
+        self.item
     }
 
     /// Allocates an instance of [`ListValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca`].
     #[must_use]
-    pub fn alloca<G: CodeGenerator + ?Sized>(
+    pub fn alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca(ctx, name),
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Allocates an instance of [`ListValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca_var`].
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
         name: Option<&'ctx str>,
     ) -> <Self as ProxyType<'ctx>>::Value {
         <Self as ProxyType<'ctx>>::Value::from_pointer_value(
-            self.raw_alloca(generator, ctx, name),
+            self.raw_alloca_var(generator, ctx, name),
             self.llvm_usize,
             name,
         )
     }
 
+    /// Allocates a [`ListValue`] on the stack using `item` of this [`ListType`] instance.
+    ///
+    /// The returned list will contain:
+    ///
+    /// - `data`: Allocated with `len` number of elements.
+    /// - `len`: Initialized to the value of `len` passed to this function.
+    #[must_use]
+    pub fn construct<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        len: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let len = ctx.builder.build_int_z_extend(len, self.llvm_usize, "").unwrap();
+
+        // Generate a runtime assertion if allocating a non-empty list with unknown element type
+        if ctx.registry.llvm_options.opt_level == OptimizationLevel::None && self.item.is_none() {
+            let len_eqz = ctx
+                .builder
+                .build_int_compare(IntPredicate::EQ, len, self.llvm_usize.const_zero(), "")
+                .unwrap();
+
+            ctx.make_assert(
+                generator,
+                len_eqz,
+                "0:AssertionError",
+                "Cannot allocate a non-empty list with unknown element type",
+                [None, None, None],
+                ctx.current_loc,
+            );
+        }
+
+        let plist = self.alloca_var(generator, ctx, name);
+        plist.store_size(ctx, generator, len);
+
+        let item = self.item.unwrap_or(self.llvm_usize.into());
+        plist.create_data(ctx, item, None);
+
+        plist
+    }
+
+    /// Convenience function for creating a list with zero elements.
+    ///
+    /// This function is preferred over [`ListType::construct`] if the length is known to always be
+    /// 0, as this function avoids injecting an IR assertion for checking if a non-empty untyped
+    /// list is being allocated.
+    ///
+    /// The returned list will contain:
+    ///
+    /// - `data`: Initialized to `(T*) 0`.
+    /// - `len`: Initialized to `0`.
+    #[must_use]
+    pub fn construct_empty<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let plist = self.alloca_var(generator, ctx, name);
+
+        plist.store_size(ctx, generator, self.llvm_usize.const_zero());
+        plist.create_data(ctx, self.item.unwrap_or(self.llvm_usize.into()), None);
+
+        plist
+    }
+
     /// Converts an existing value into a [`ListValue`].
     #[must_use]
     pub fn map_value(
@@ -162,36 +341,8 @@ impl<'ctx> ProxyType<'ctx> for ListType<'ctx> {
         Self::is_representable(llvm_ty, generator.get_size_type(ctx))
     }
 
-    fn raw_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&'ctx str>,
-    ) -> <Self::Value as ProxyValue<'ctx>>::Base {
-        generator
-            .gen_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                name,
-            )
-            .unwrap()
-    }
-
-    fn array_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        size: IntValue<'ctx>,
-        name: Option<&'ctx str>,
-    ) -> ArraySliceValue<'ctx> {
-        generator
-            .gen_array_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                size,
-                name,
-            )
-            .unwrap()
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
     }
 
     fn as_base_type(&self) -> Self::Base {

nac3core/src/codegen/types/mod.rs

@@ -16,20 +16,26 @@
 //!   the returned object. This is similar to a `new` expression in C++ but the object is allocated
 //!   on the stack.
 
-use inkwell::{context::Context, types::BasicType, values::IntValue};
+use inkwell::{
+    context::Context,
+    types::BasicType,
+    values::{IntValue, PointerValue},
+};
 
 use super::{
     values::{ArraySliceValue, ProxyValue},
     {CodeGenContext, CodeGenerator},
 };
 pub use list::*;
-pub use ndarray::*;
 pub use range::*;
+pub use tuple::*;
 
 mod list;
-mod ndarray;
+pub mod ndarray;
 mod range;
 pub mod structure;
+mod tuple;
+pub mod utils;
 
 /// A LLVM type that is used to represent a corresponding type in NAC3.
 pub trait ProxyType<'ctx>: Into<Self::Base> {
@@ -53,23 +59,66 @@ pub trait ProxyType<'ctx>: Into<Self::Base> {
         llvm_ty: Self::Base,
     ) -> Result<(), String>;
 
-    /// Creates a new value of this type, returning the LLVM instance of this value.
-    fn raw_alloca<G: CodeGenerator + ?Sized>(
+    /// Returns the type that should be used in `alloca` IR statements.
+    fn alloca_type(&self) -> impl BasicType<'ctx>;
+
+    /// Creates a new value of this type by invoking `alloca` at the current builder location,
+    /// returning a [`PointerValue`] instance representing the allocated value.
+    fn raw_alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> PointerValue<'ctx> {
+        ctx.builder
+            .build_alloca(self.alloca_type().as_basic_type_enum(), name.unwrap_or_default())
+            .unwrap()
+    }
+
+    /// Creates a new value of this type by invoking `alloca` at the beginning of the function,
+    /// returning a [`PointerValue`] instance representing the allocated value.
+    fn raw_alloca_var<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
         name: Option<&'ctx str>,
-    ) -> <Self::Value as ProxyValue<'ctx>>::Base;
+    ) -> PointerValue<'ctx> {
+        generator.gen_var_alloc(ctx, self.alloca_type().as_basic_type_enum(), name).unwrap()
+    }
 
-    /// Creates a new array value of this type, returning an [`ArraySliceValue`] encapsulating the
-    /// resulting array.
-    fn array_alloca<G: CodeGenerator + ?Sized>(
+    /// Creates a new array value of this type by invoking `alloca` at the current builder location,
+    /// returning an [`ArraySliceValue`] encapsulating the resulting array.
+    fn array_alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        size: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> ArraySliceValue<'ctx> {
+        ArraySliceValue::from_ptr_val(
+            ctx.builder
+                .build_array_alloca(
+                    self.alloca_type().as_basic_type_enum(),
+                    size,
+                    name.unwrap_or_default(),
+                )
+                .unwrap(),
+            size,
+            name,
+        )
+    }
+
+    /// Creates a new array value of this type by invoking `alloca` at the beginning of the
+    /// function, returning an [`ArraySliceValue`] encapsulating the resulting array.
+    fn array_alloca_var<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
         size: IntValue<'ctx>,
         name: Option<&'ctx str>,
-    ) -> ArraySliceValue<'ctx>;
+    ) -> ArraySliceValue<'ctx> {
+        generator
+            .gen_array_var_alloc(ctx, self.alloca_type().as_basic_type_enum(), size, name)
+            .unwrap()
+    }
 
     /// Returns the [base type][Self::Base] of this proxy.
     fn as_base_type(&self) -> Self::Base;

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

@@ -0,0 +1,243 @@
+use inkwell::{
+    types::BasicTypeEnum,
+    values::{BasicValueEnum, IntValue},
+    AddressSpace,
+};
+
+use crate::{
+    codegen::{
+        irrt,
+        stmt::gen_if_else_expr_callback,
+        types::{ndarray::NDArrayType, ListType, ProxyType},
+        values::{
+            ndarray::NDArrayValue, ArrayLikeValue, ArraySliceValue, ListValue, ProxyValue,
+            TypedArrayLikeAdapter, TypedArrayLikeMutator,
+        },
+        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, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    list_ty: Type,
+) -> (BasicTypeEnum<'ctx>, u64) {
+    let dtype = arraylike_flatten_element_type(&mut ctx.unifier, list_ty);
+    let ndims = arraylike_get_ndims(&mut ctx.unifier, list_ty);
+
+    (ctx.get_llvm_type(generator, dtype), ndims)
+}
+
+impl<'ctx> NDArrayType<'ctx> {
+    /// Implementation of `np_array(<list>, copy=True)`
+    fn construct_numpy_array_from_list_copy_true_impl<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        (list_ty, list): (Type, ListValue<'ctx>),
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let (dtype, ndims_int) = get_list_object_dtype_and_ndims(generator, ctx, list_ty);
+        assert!(self.ndims >= ndims_int);
+        assert_eq!(dtype, self.dtype);
+
+        let list_value = list.as_i8_list(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 = self.llvm_usize.const_int(ndims_int, false);
+        let shape = ctx.builder.build_array_alloca(self.llvm_usize, ndims, "").unwrap();
+        let shape = ArraySliceValue::from_ptr_val(shape, ndims, None);
+        let shape = TypedArrayLikeAdapter::from(
+            shape,
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        );
+        irrt::ndarray::call_nac3_ndarray_array_set_and_validate_list_shape(
+            generator, ctx, list_value, ndims, &shape,
+        );
+
+        let ndarray = Self::new(generator, ctx.ctx, dtype, ndims_int)
+            .construct_uninitialized(generator, ctx, name);
+        ndarray.copy_shape_from_array(generator, ctx, shape.base_ptr(ctx, generator));
+        unsafe { ndarray.create_data(generator, ctx) };
+
+        // Copy all contents from the list.
+        irrt::ndarray::call_nac3_ndarray_array_write_list_to_array(
+            generator, ctx, list_value, ndarray,
+        );
+
+        ndarray
+    }
+
+    /// Implementation of `np_array(<list>, copy=None)`
+    fn construct_numpy_array_from_list_copy_none_impl<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        (list_ty, list): (Type, ListValue<'ctx>),
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        // 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(generator, ctx, list_ty);
+        if ndims == 1 {
+            // `list` is not nested
+            assert_eq!(ndims, 1);
+            assert!(self.ndims >= ndims);
+            assert_eq!(dtype, self.dtype);
+
+            let llvm_pi8 = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
+
+            let ndarray = Self::new(generator, ctx.ctx, dtype, 1)
+                .construct_uninitialized(generator, ctx, name);
+
+            // Set data
+            let data = ctx
+                .builder
+                .build_pointer_cast(list.data().base_ptr(ctx, generator), llvm_pi8, "")
+                .unwrap();
+            ndarray.store_data(ctx, data);
+
+            // ndarray->shape[0] = list->len;
+            let shape = ndarray.shape();
+            let list_len = list.load_size(ctx, None);
+            unsafe {
+                shape.set_typed_unchecked(ctx, generator, &self.llvm_usize.const_zero(), list_len);
+            }
+
+            // Set strides, the `data` is contiguous
+            ndarray.set_strides_contiguous(generator, ctx);
+
+            ndarray
+        } else {
+            // `list` is nested, copy
+            self.construct_numpy_array_from_list_copy_true_impl(
+                generator,
+                ctx,
+                (list_ty, list),
+                name,
+            )
+        }
+    }
+
+    /// Implementation of `np_array(<list>, copy=copy)`
+    fn construct_numpy_array_list_impl<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        (list_ty, list): (Type, ListValue<'ctx>),
+        copy: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        assert_eq!(copy.get_type(), ctx.ctx.bool_type());
+
+        let (dtype, ndims) = get_list_object_dtype_and_ndims(generator, ctx, list_ty);
+
+        let ndarray = gen_if_else_expr_callback(
+            generator,
+            ctx,
+            |_generator, _ctx| Ok(copy),
+            |generator, ctx| {
+                let ndarray = self.construct_numpy_array_from_list_copy_true_impl(
+                    generator,
+                    ctx,
+                    (list_ty, list),
+                    name,
+                );
+                Ok(Some(ndarray.as_base_value()))
+            },
+            |generator, ctx| {
+                let ndarray = self.construct_numpy_array_from_list_copy_none_impl(
+                    generator,
+                    ctx,
+                    (list_ty, list),
+                    name,
+                );
+                Ok(Some(ndarray.as_base_value()))
+            },
+        )
+        .unwrap()
+        .map(BasicValueEnum::into_pointer_value)
+        .unwrap();
+
+        NDArrayType::new(generator, ctx.ctx, dtype, ndims).map_value(ndarray, None)
+    }
+
+    /// Implementation of `np_array(<ndarray>, copy=copy)`.
+    pub fn construct_numpy_array_ndarray_impl<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ndarray: NDArrayValue<'ctx>,
+        copy: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        assert_eq!(ndarray.get_type().dtype, self.dtype);
+        assert!(self.ndims >= ndarray.get_type().ndims);
+        assert_eq!(copy.get_type(), ctx.ctx.bool_type());
+
+        let ndarray_val = gen_if_else_expr_callback(
+            generator,
+            ctx,
+            |_generator, _ctx| Ok(copy),
+            |generator, ctx| {
+                let ndarray = ndarray.make_copy(generator, ctx); // Force copy
+                Ok(Some(ndarray.as_base_value()))
+            },
+            |_generator, _ctx| {
+                // No need to copy. Return `ndarray` itself.
+                Ok(Some(ndarray.as_base_value()))
+            },
+        )
+        .unwrap()
+        .map(BasicValueEnum::into_pointer_value)
+        .unwrap();
+
+        ndarray.get_type().map_value(ndarray_val, name)
+    }
+
+    /// Create a new ndarray like
+    /// [`np.array()`](https://numpy.org/doc/stable/reference/generated/numpy.array.html).
+    ///
+    /// Note that the returned [`NDArrayValue`] may have fewer dimensions than is specified by this
+    /// instance. Use [`NDArrayValue::atleast_nd`] on the returned value if an `ndarray` instance
+    /// with the exact number of dimensions is needed.
+    pub fn construct_numpy_array<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        (object_ty, object): (Type, BasicValueEnum<'ctx>),
+        copy: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        match &*ctx.unifier.get_ty_immutable(object_ty) {
+            TypeEnum::TObj { obj_id, .. }
+                if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
+            {
+                let list = ListType::from_unifier_type(generator, ctx, object_ty)
+                    .map_value(object.into_pointer_value(), None);
+                self.construct_numpy_array_list_impl(generator, ctx, (object_ty, list), copy, name)
+            }
+
+            TypeEnum::TObj { obj_id, .. }
+                if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
+            {
+                let ndarray = NDArrayType::from_unifier_type(generator, ctx, object_ty)
+                    .map_value(object.into_pointer_value(), None);
+                self.construct_numpy_array_ndarray_impl(generator, ctx, ndarray, copy, name)
+            }
+
+            _ => panic!("Unrecognized object type: {}", ctx.unifier.stringify(object_ty)), // Typechecker ensures this
+        }
+    }
+}

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

@@ -0,0 +1,176 @@
+use inkwell::{
+    context::{AsContextRef, Context},
+    types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
+    values::{IntValue, PointerValue},
+    AddressSpace,
+};
+use itertools::Itertools;
+
+use nac3core_derive::StructFields;
+
+use crate::codegen::{
+    types::{
+        structure::{check_struct_type_matches_fields, StructField, StructFields},
+        ProxyType,
+    },
+    values::{ndarray::ShapeEntryValue, ProxyValue},
+    CodeGenContext, CodeGenerator,
+};
+
+#[derive(Debug, PartialEq, Eq, Clone, Copy)]
+pub struct ShapeEntryType<'ctx> {
+    ty: PointerType<'ctx>,
+    llvm_usize: IntType<'ctx>,
+}
+
+#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+pub struct ShapeEntryStructFields<'ctx> {
+    #[value_type(usize)]
+    pub ndims: StructField<'ctx, IntValue<'ctx>>,
+    #[value_type(usize.ptr_type(AddressSpace::default()))]
+    pub shape: StructField<'ctx, PointerValue<'ctx>>,
+}
+
+impl<'ctx> ShapeEntryType<'ctx> {
+    /// Checks whether `llvm_ty` represents a [`ShapeEntryType`], returning [Err] if it does not.
+    pub fn is_representable(
+        llvm_ty: PointerType<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> Result<(), String> {
+        let ctx = llvm_ty.get_context();
+
+        let llvm_ndarray_ty = llvm_ty.get_element_type();
+        let AnyTypeEnum::StructType(llvm_ndarray_ty) = llvm_ndarray_ty else {
+            return Err(format!(
+                "Expected struct type for `ShapeEntry` type, got {llvm_ndarray_ty}"
+            ));
+        };
+
+        check_struct_type_matches_fields(
+            Self::fields(ctx, llvm_usize),
+            llvm_ndarray_ty,
+            "NDArray",
+            &[],
+        )
+    }
+
+    /// Returns an instance of [`StructFields`] containing all field accessors for this type.
+    #[must_use]
+    fn fields(
+        ctx: impl AsContextRef<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> ShapeEntryStructFields<'ctx> {
+        ShapeEntryStructFields::new(ctx, llvm_usize)
+    }
+
+    /// See [`ShapeEntryStructFields::fields`].
+    // TODO: Move this into e.g. StructProxyType
+    #[must_use]
+    pub fn get_fields(&self, ctx: impl AsContextRef<'ctx>) -> ShapeEntryStructFields<'ctx> {
+        Self::fields(ctx, self.llvm_usize)
+    }
+
+    /// Creates an LLVM type corresponding to the expected structure of a `ShapeEntry`.
+    #[must_use]
+    fn llvm_type(ctx: &'ctx Context, llvm_usize: IntType<'ctx>) -> PointerType<'ctx> {
+        let field_tys =
+            Self::fields(ctx, llvm_usize).into_iter().map(|field| field.1).collect_vec();
+
+        ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
+    }
+
+    /// Creates an instance of [`ShapeEntryType`].
+    #[must_use]
+    pub fn new<G: CodeGenerator + ?Sized>(generator: &G, ctx: &'ctx Context) -> Self {
+        let llvm_usize = generator.get_size_type(ctx);
+        let llvm_ty = Self::llvm_type(ctx, llvm_usize);
+
+        Self { ty: llvm_ty, llvm_usize }
+    }
+
+    /// Creates a [`ShapeEntryType`] from a [`PointerType`] representing an `ShapeEntry`.
+    #[must_use]
+    pub fn from_type(ptr_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
+        debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
+
+        Self { ty: ptr_ty, llvm_usize }
+    }
+
+    /// Allocates an instance of [`ShapeEntryValue`] as if by calling `alloca` on the base type.
+    #[must_use]
+    pub fn alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca(ctx, name),
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Allocates an instance of [`ShapeEntryValue`] as if by calling `alloca` on the base type.
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca_var(generator, ctx, name),
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Converts an existing value into a [`ShapeEntryValue`].
+    #[must_use]
+    pub fn map_value(
+        &self,
+        value: <<Self as ProxyType<'ctx>>::Value as ProxyValue<'ctx>>::Base,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(value, self.llvm_usize, name)
+    }
+}
+
+impl<'ctx> ProxyType<'ctx> for ShapeEntryType<'ctx> {
+    type Base = PointerType<'ctx>;
+    type Value = ShapeEntryValue<'ctx>;
+
+    fn is_type<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        llvm_ty: impl BasicType<'ctx>,
+    ) -> Result<(), String> {
+        if let BasicTypeEnum::PointerType(ty) = llvm_ty.as_basic_type_enum() {
+            <Self as ProxyType<'ctx>>::is_representable(generator, ctx, ty)
+        } else {
+            Err(format!("Expected pointer type, got {llvm_ty:?}"))
+        }
+    }
+
+    fn is_representable<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        llvm_ty: Self::Base,
+    ) -> Result<(), String> {
+        Self::is_representable(llvm_ty, generator.get_size_type(ctx))
+    }
+
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
+    }
+
+    fn as_base_type(&self) -> Self::Base {
+        self.ty
+    }
+}
+
+impl<'ctx> From<ShapeEntryType<'ctx>> for PointerType<'ctx> {
+    fn from(value: ShapeEntryType<'ctx>) -> Self {
+        value.as_base_type()
+    }
+}

nac3core/src/codegen/types/ndarray/contiguous.rs

@@ -0,0 +1,250 @@
+use inkwell::{
+    context::Context,
+    types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
+    values::{IntValue, PointerValue},
+    AddressSpace,
+};
+use itertools::Itertools;
+
+use nac3core_derive::StructFields;
+
+use crate::{
+    codegen::{
+        types::{
+            structure::{
+                check_struct_type_matches_fields, FieldIndexCounter, StructField, StructFields,
+            },
+            ProxyType,
+        },
+        values::{ndarray::ContiguousNDArrayValue, ProxyValue},
+        CodeGenContext, CodeGenerator,
+    },
+    toplevel::numpy::unpack_ndarray_var_tys,
+    typecheck::typedef::Type,
+};
+
+#[derive(Debug, PartialEq, Eq, Clone, Copy)]
+pub struct ContiguousNDArrayType<'ctx> {
+    ty: PointerType<'ctx>,
+    item: BasicTypeEnum<'ctx>,
+    llvm_usize: IntType<'ctx>,
+}
+
+#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+pub struct ContiguousNDArrayStructFields<'ctx> {
+    #[value_type(usize)]
+    pub ndims: StructField<'ctx, IntValue<'ctx>>,
+    #[value_type(usize.ptr_type(AddressSpace::default()))]
+    pub shape: StructField<'ctx, PointerValue<'ctx>>,
+    #[value_type(i8_type().ptr_type(AddressSpace::default()))]
+    pub data: StructField<'ctx, PointerValue<'ctx>>,
+}
+
+impl<'ctx> ContiguousNDArrayStructFields<'ctx> {
+    #[must_use]
+    pub fn new_typed(item: BasicTypeEnum<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
+        let mut counter = FieldIndexCounter::default();
+
+        ContiguousNDArrayStructFields {
+            ndims: StructField::create(&mut counter, "ndims", llvm_usize),
+            shape: StructField::create(
+                &mut counter,
+                "shape",
+                llvm_usize.ptr_type(AddressSpace::default()),
+            ),
+            data: StructField::create(&mut counter, "data", item.ptr_type(AddressSpace::default())),
+        }
+    }
+}
+
+impl<'ctx> ContiguousNDArrayType<'ctx> {
+    /// Checks whether `llvm_ty` represents a `ndarray` type, returning [Err] if it does not.
+    pub fn is_representable(
+        llvm_ty: PointerType<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> Result<(), String> {
+        let ctx = llvm_ty.get_context();
+
+        let llvm_ty = llvm_ty.get_element_type();
+        let AnyTypeEnum::StructType(llvm_ty) = llvm_ty else {
+            return Err(format!(
+                "Expected struct type for `ContiguousNDArray` type, got {llvm_ty}"
+            ));
+        };
+
+        let fields = ContiguousNDArrayStructFields::new(ctx, llvm_usize);
+
+        check_struct_type_matches_fields(
+            fields,
+            llvm_ty,
+            "ContiguousNDArray",
+            &[(fields.data.name(), &|ty| {
+                if ty.is_pointer_type() {
+                    Ok(())
+                } else {
+                    Err(format!("Expected T* for `ContiguousNDArray.data`, got {ty}"))
+                }
+            })],
+        )
+    }
+
+    /// Returns an instance of [`StructFields`] containing all field accessors for this type.
+    #[must_use]
+    fn fields(
+        item: BasicTypeEnum<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> ContiguousNDArrayStructFields<'ctx> {
+        ContiguousNDArrayStructFields::new_typed(item, llvm_usize)
+    }
+
+    /// See [`NDArrayType::fields`].
+    // TODO: Move this into e.g. StructProxyType
+    #[must_use]
+    pub fn get_fields(&self) -> ContiguousNDArrayStructFields<'ctx> {
+        Self::fields(self.item, self.llvm_usize)
+    }
+
+    /// Creates an LLVM type corresponding to the expected structure of an `NDArray`.
+    #[must_use]
+    fn llvm_type(
+        ctx: &'ctx Context,
+        item: BasicTypeEnum<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> PointerType<'ctx> {
+        let field_tys =
+            Self::fields(item, llvm_usize).into_iter().map(|field| field.1).collect_vec();
+
+        ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
+    }
+
+    /// Creates an instance of [`ContiguousNDArrayType`].
+    #[must_use]
+    pub fn new<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        item: BasicTypeEnum<'ctx>,
+    ) -> Self {
+        let llvm_usize = generator.get_size_type(ctx);
+        let llvm_cndarray = Self::llvm_type(ctx, item, llvm_usize);
+
+        Self { ty: llvm_cndarray, item, llvm_usize }
+    }
+
+    /// Creates an [`ContiguousNDArrayType`] from a [unifier type][Type].
+    #[must_use]
+    pub fn from_unifier_type<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ty: Type,
+    ) -> Self {
+        let (dtype, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
+
+        let llvm_dtype = ctx.get_llvm_type(generator, dtype);
+        let llvm_usize = generator.get_size_type(ctx.ctx);
+
+        Self { ty: Self::llvm_type(ctx.ctx, llvm_dtype, llvm_usize), item: llvm_dtype, llvm_usize }
+    }
+
+    /// Creates an [`ContiguousNDArrayType`] from a [`PointerType`] representing an `NDArray`.
+    #[must_use]
+    pub fn from_type(
+        ptr_ty: PointerType<'ctx>,
+        item: BasicTypeEnum<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> Self {
+        debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
+
+        Self { ty: ptr_ty, item, llvm_usize }
+    }
+
+    /// Allocates an instance of [`ContiguousNDArrayValue`] as if by calling `alloca` on the base
+    /// type.
+    ///
+    /// See [`ProxyType::raw_alloca`].
+    #[must_use]
+    pub fn alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca(ctx, name),
+            self.item,
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Allocates an instance of [`ContiguousNDArrayValue`] as if by calling `alloca` on the base
+    /// type.
+    ///
+    /// See [`ProxyType::raw_alloca_var`].
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca_var(generator, ctx, name),
+            self.item,
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Converts an existing value into a [`ContiguousNDArrayValue`].
+    #[must_use]
+    pub fn map_value(
+        &self,
+        value: <<Self as ProxyType<'ctx>>::Value as ProxyValue<'ctx>>::Base,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            value,
+            self.item,
+            self.llvm_usize,
+            name,
+        )
+    }
+}
+
+impl<'ctx> ProxyType<'ctx> for ContiguousNDArrayType<'ctx> {
+    type Base = PointerType<'ctx>;
+    type Value = ContiguousNDArrayValue<'ctx>;
+
+    fn is_type<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        llvm_ty: impl BasicType<'ctx>,
+    ) -> Result<(), String> {
+        if let BasicTypeEnum::PointerType(ty) = llvm_ty.as_basic_type_enum() {
+            <Self as ProxyType<'ctx>>::is_representable(generator, ctx, ty)
+        } else {
+            Err(format!("Expected pointer type, got {llvm_ty:?}"))
+        }
+    }
+
+    fn is_representable<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        llvm_ty: Self::Base,
+    ) -> Result<(), String> {
+        Self::is_representable(llvm_ty, generator.get_size_type(ctx))
+    }
+
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
+    }
+
+    fn as_base_type(&self) -> Self::Base {
+        self.ty
+    }
+}
+
+impl<'ctx> From<ContiguousNDArrayType<'ctx>> for PointerType<'ctx> {
+    fn from(value: ContiguousNDArrayType<'ctx>) -> Self {
+        value.as_base_type()
+    }
+}

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

@@ -0,0 +1,236 @@
+use inkwell::{
+    values::{BasicValueEnum, IntValue},
+    IntPredicate,
+};
+
+use super::NDArrayType;
+use crate::{
+    codegen::{
+        irrt, types::ProxyType, values::TypedArrayLikeAccessor, CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::Type,
+};
+
+/// Get the zero value in `np.zeros()` of a `dtype`.
+fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    dtype: Type,
+) -> BasicValueEnum<'ctx> {
+    if [ctx.primitives.int32, ctx.primitives.uint32]
+        .iter()
+        .any(|ty| ctx.unifier.unioned(dtype, *ty))
+    {
+        ctx.ctx.i32_type().const_zero().into()
+    } else if [ctx.primitives.int64, ctx.primitives.uint64]
+        .iter()
+        .any(|ty| ctx.unifier.unioned(dtype, *ty))
+    {
+        ctx.ctx.i64_type().const_zero().into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
+        ctx.ctx.f64_type().const_zero().into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
+        ctx.ctx.bool_type().const_zero().into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
+        ctx.gen_string(generator, "").into()
+    } else {
+        panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
+    }
+}
+
+/// Get the one value in `np.ones()` of a `dtype`.
+fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    dtype: Type,
+) -> BasicValueEnum<'ctx> {
+    if [ctx.primitives.int32, ctx.primitives.uint32]
+        .iter()
+        .any(|ty| ctx.unifier.unioned(dtype, *ty))
+    {
+        let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int32);
+        ctx.ctx.i32_type().const_int(1, is_signed).into()
+    } else if [ctx.primitives.int64, ctx.primitives.uint64]
+        .iter()
+        .any(|ty| ctx.unifier.unioned(dtype, *ty))
+    {
+        let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int64);
+        ctx.ctx.i64_type().const_int(1, is_signed).into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
+        ctx.ctx.f64_type().const_float(1.0).into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
+        ctx.ctx.bool_type().const_int(1, false).into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
+        ctx.gen_string(generator, "1").into()
+    } else {
+        panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
+    }
+}
+
+impl<'ctx> NDArrayType<'ctx> {
+    /// Create an ndarray like
+    /// [`np.empty`](https://numpy.org/doc/stable/reference/generated/numpy.empty.html).
+    pub fn construct_numpy_empty<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let ndarray = self.construct_uninitialized(generator, ctx, name);
+
+        // Validate `shape`
+        irrt::ndarray::call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, shape);
+
+        ndarray.copy_shape_from_array(generator, ctx, shape.base_ptr(ctx, generator));
+        unsafe { ndarray.create_data(generator, ctx) };
+
+        ndarray
+    }
+
+    /// Create an ndarray like
+    /// [`np.full`](https://numpy.org/doc/stable/reference/generated/numpy.full.html).
+    pub fn construct_numpy_full<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+        fill_value: BasicValueEnum<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let ndarray = self.construct_numpy_empty(generator, ctx, shape, name);
+        ndarray.fill(generator, ctx, fill_value);
+        ndarray
+    }
+
+    /// Create an ndarray like
+    /// [`np.zero`](https://numpy.org/doc/stable/reference/generated/numpy.zeros.html).
+    pub fn construct_numpy_zeros<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        assert_eq!(
+            ctx.get_llvm_type(generator, dtype),
+            self.dtype,
+            "Expected LLVM dtype={} but got {}",
+            self.dtype.print_to_string(),
+            ctx.get_llvm_type(generator, dtype).print_to_string(),
+        );
+
+        let fill_value = ndarray_zero_value(generator, ctx, dtype);
+        self.construct_numpy_full(generator, ctx, shape, fill_value, name)
+    }
+
+    /// Create an ndarray like
+    /// [`np.ones`](https://numpy.org/doc/stable/reference/generated/numpy.ones.html).
+    pub fn construct_numpy_ones<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        assert_eq!(
+            ctx.get_llvm_type(generator, dtype),
+            self.dtype,
+            "Expected LLVM dtype={} but got {}",
+            self.dtype.print_to_string(),
+            ctx.get_llvm_type(generator, dtype).print_to_string(),
+        );
+
+        let fill_value = ndarray_one_value(generator, ctx, dtype);
+        self.construct_numpy_full(generator, ctx, shape, fill_value, name)
+    }
+
+    /// Create an ndarray like
+    /// [`np.eye`](https://numpy.org/doc/stable/reference/generated/numpy.eye.html).
+    #[allow(clippy::too_many_arguments)]
+    pub fn construct_numpy_eye<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        nrows: IntValue<'ctx>,
+        ncols: IntValue<'ctx>,
+        offset: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        assert_eq!(
+            ctx.get_llvm_type(generator, dtype),
+            self.dtype,
+            "Expected LLVM dtype={} but got {}",
+            self.dtype.print_to_string(),
+            ctx.get_llvm_type(generator, dtype).print_to_string(),
+        );
+        assert_eq!(nrows.get_type(), self.llvm_usize);
+        assert_eq!(ncols.get_type(), self.llvm_usize);
+        assert_eq!(offset.get_type(), self.llvm_usize);
+
+        let ndzero = ndarray_zero_value(generator, ctx, dtype);
+        let ndone = ndarray_one_value(generator, ctx, dtype);
+
+        let ndarray = self.construct_dyn_shape(generator, ctx, &[nrows, ncols], name);
+
+        // Create data and make the matrix like look np.eye()
+        unsafe {
+            ndarray.create_data(generator, ctx);
+        }
+        ndarray
+            .foreach(generator, ctx, |generator, ctx, _, 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.
+
+                let indices = nditer.get_indices();
+
+                let row_i = unsafe {
+                    indices.get_typed_unchecked(ctx, generator, &self.llvm_usize.const_zero(), None)
+                };
+                let col_i = unsafe {
+                    indices.get_typed_unchecked(
+                        ctx,
+                        generator,
+                        &self.llvm_usize.const_int(1, false),
+                        None,
+                    )
+                };
+
+                let be_one = ctx
+                    .builder
+                    .build_int_compare(
+                        IntPredicate::EQ,
+                        ctx.builder.build_int_add(row_i, offset, "").unwrap(),
+                        col_i,
+                        "",
+                    )
+                    .unwrap();
+                let value = ctx.builder.build_select(be_one, ndone, ndzero, "value").unwrap();
+
+                let p = nditer.get_pointer(ctx);
+                ctx.builder.build_store(p, value).unwrap();
+
+                Ok(())
+            })
+            .unwrap();
+
+        ndarray
+    }
+
+    /// Create an ndarray like
+    /// [`np.identity`](https://numpy.org/doc/stable/reference/generated/numpy.identity.html).
+    pub fn construct_numpy_identity<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        size: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let offset = self.llvm_usize.const_zero();
+        self.construct_numpy_eye(generator, ctx, dtype, size, size, offset, name)
+    }
+}

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

@@ -0,0 +1,205 @@
+use inkwell::{
+    context::{AsContextRef, Context},
+    types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
+    values::{IntValue, PointerValue},
+    AddressSpace,
+};
+use itertools::Itertools;
+
+use nac3core_derive::StructFields;
+
+use crate::codegen::{
+    types::{
+        structure::{check_struct_type_matches_fields, StructField, StructFields},
+        ProxyType,
+    },
+    values::{
+        ndarray::{NDIndexValue, RustNDIndex},
+        ArrayLikeIndexer, ArraySliceValue, ProxyValue,
+    },
+    CodeGenContext, CodeGenerator,
+};
+
+#[derive(Debug, PartialEq, Eq, Clone, Copy)]
+pub struct NDIndexType<'ctx> {
+    ty: PointerType<'ctx>,
+    llvm_usize: IntType<'ctx>,
+}
+
+#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+pub struct NDIndexStructFields<'ctx> {
+    #[value_type(i8_type())]
+    pub type_: StructField<'ctx, IntValue<'ctx>>,
+    #[value_type(i8_type().ptr_type(AddressSpace::default()))]
+    pub data: StructField<'ctx, PointerValue<'ctx>>,
+}
+
+impl<'ctx> NDIndexType<'ctx> {
+    /// Checks whether `llvm_ty` represents a `ndindex` type, returning [Err] if it does not.
+    pub fn is_representable(
+        llvm_ty: PointerType<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> Result<(), String> {
+        let ctx = llvm_ty.get_context();
+
+        let llvm_ty = llvm_ty.get_element_type();
+        let AnyTypeEnum::StructType(llvm_ty) = llvm_ty else {
+            return Err(format!(
+                "Expected struct type for `ContiguousNDArray` type, got {llvm_ty}"
+            ));
+        };
+
+        let fields = NDIndexStructFields::new(ctx, llvm_usize);
+
+        check_struct_type_matches_fields(fields, llvm_ty, "NDIndex", &[])
+    }
+
+    #[must_use]
+    fn fields(
+        ctx: impl AsContextRef<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> NDIndexStructFields<'ctx> {
+        NDIndexStructFields::new(ctx, llvm_usize)
+    }
+
+    #[must_use]
+    pub fn get_fields(&self) -> NDIndexStructFields<'ctx> {
+        Self::fields(self.ty.get_context(), self.llvm_usize)
+    }
+
+    #[must_use]
+    fn llvm_type(ctx: &'ctx Context, llvm_usize: IntType<'ctx>) -> PointerType<'ctx> {
+        let field_tys =
+            Self::fields(ctx, llvm_usize).into_iter().map(|field| field.1).collect_vec();
+
+        ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
+    }
+
+    #[must_use]
+    pub fn new<G: CodeGenerator + ?Sized>(generator: &G, ctx: &'ctx Context) -> Self {
+        let llvm_usize = generator.get_size_type(ctx);
+        let llvm_ndindex = Self::llvm_type(ctx, llvm_usize);
+
+        Self { ty: llvm_ndindex, llvm_usize }
+    }
+
+    #[must_use]
+    pub fn from_type(ptr_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
+        debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
+
+        Self { ty: ptr_ty, llvm_usize }
+    }
+
+    /// Allocates an instance of [`NDIndexValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca`].
+    #[must_use]
+    pub fn alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca(ctx, name),
+            self.llvm_usize,
+            name,
+        )
+    }
+    /// Allocates an instance of [`NDIndexValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca_var`].
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca_var(generator, ctx, name),
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Serialize a list of [`RustNDIndex`] as a newly allocated LLVM array of [`NDIndexValue`].
+    #[must_use]
+    pub fn construct_ndindices<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        in_ndindices: &[RustNDIndex<'ctx>],
+    ) -> ArraySliceValue<'ctx> {
+        // Allocate the LLVM ndindices.
+        let num_ndindices = self.llvm_usize.const_int(in_ndindices.len() as u64, false);
+        let ndindices = self.array_alloca_var(generator, ctx, num_ndindices, None);
+
+        // Initialize all of them.
+        for (i, in_ndindex) in in_ndindices.iter().enumerate() {
+            let pndindex = unsafe {
+                ndindices.ptr_offset_unchecked(
+                    ctx,
+                    generator,
+                    &ctx.ctx.i64_type().const_int(u64::try_from(i).unwrap(), false),
+                    None,
+                )
+            };
+
+            in_ndindex.write_to_ndindex(
+                generator,
+                ctx,
+                NDIndexValue::from_pointer_value(pndindex, self.llvm_usize, None),
+            );
+        }
+
+        ndindices
+    }
+
+    #[must_use]
+    pub fn map_value(
+        &self,
+        value: <<Self as ProxyType<'ctx>>::Value as ProxyValue<'ctx>>::Base,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(value, self.llvm_usize, name)
+    }
+}
+
+impl<'ctx> ProxyType<'ctx> for NDIndexType<'ctx> {
+    type Base = PointerType<'ctx>;
+    type Value = NDIndexValue<'ctx>;
+
+    fn is_type<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        llvm_ty: impl BasicType<'ctx>,
+    ) -> Result<(), String> {
+        if let BasicTypeEnum::PointerType(ty) = llvm_ty.as_basic_type_enum() {
+            <Self as ProxyType<'ctx>>::is_representable(generator, ctx, ty)
+        } else {
+            Err(format!("Expected pointer type, got {llvm_ty:?}"))
+        }
+    }
+
+    fn is_representable<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        llvm_ty: Self::Base,
+    ) -> Result<(), String> {
+        Self::is_representable(llvm_ty, generator.get_size_type(ctx))
+    }
+
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
+    }
+
+    fn as_base_type(&self) -> Self::Base {
+        self.ty
+    }
+}
+
+impl<'ctx> From<NDIndexType<'ctx>> for PointerType<'ctx> {
+    fn from(value: NDIndexType<'ctx>) -> Self {
+        value.as_base_type()
+    }
+}

nac3core/src/codegen/types/ndarray/map.rs

@@ -0,0 +1,187 @@
+use inkwell::{types::BasicTypeEnum, values::BasicValueEnum};
+use itertools::Itertools;
+
+use crate::codegen::{
+    stmt::gen_for_callback,
+    types::{
+        ndarray::{NDArrayType, NDIterType},
+        ProxyType,
+    },
+    values::{
+        ndarray::{NDArrayOut, NDArrayValue, ScalarOrNDArray},
+        ArrayLikeValue, ProxyValue,
+    },
+    CodeGenContext, CodeGenerator,
+};
+
+impl<'ctx> NDArrayType<'ctx> {
+    /// Generate LLVM IR to broadcast `ndarray`s together, and starmap through them with `mapping`
+    /// elementwise.
+    ///
+    /// `mapping` is an LLVM IR generator. The input of `mapping` is the list of elements when
+    /// iterating through the input `ndarrays` after broadcasting. The output of `mapping` is the
+    /// result of the elementwise operation.
+    ///
+    /// `out` specifies whether the result should be a new ndarray or to be written an existing
+    /// ndarray.
+    pub fn broadcast_starmap<'a, G, MappingFn>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        ndarrays: &[NDArrayValue<'ctx>],
+        out: NDArrayOut<'ctx>,
+        mapping: MappingFn,
+    ) -> Result<<Self as ProxyType<'ctx>>::Value, String>
+    where
+        G: CodeGenerator + ?Sized,
+        MappingFn: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            &[BasicValueEnum<'ctx>],
+        ) -> Result<BasicValueEnum<'ctx>, String>,
+    {
+        // Broadcast inputs
+        let broadcast_result = self.broadcast(generator, ctx, ndarrays);
+
+        let out_ndarray = match out {
+            NDArrayOut::NewNDArray { dtype } => {
+                // Create a new ndarray based on the broadcast shape.
+                let result_ndarray =
+                    NDArrayType::new(generator, ctx.ctx, dtype, broadcast_result.ndims)
+                        .construct_uninitialized(generator, ctx, None);
+                result_ndarray.copy_shape_from_array(
+                    generator,
+                    ctx,
+                    broadcast_result.shape.base_ptr(ctx, generator),
+                );
+                unsafe {
+                    result_ndarray.create_data(generator, ctx);
+                }
+                result_ndarray
+            }
+
+            NDArrayOut::WriteToNDArray { ndarray: result_ndarray } => {
+                // Use an existing ndarray.
+
+                // Check that its shape is compatible with the broadcast shape.
+                result_ndarray.assert_can_be_written_by_out(generator, ctx, broadcast_result.shape);
+                result_ndarray
+            }
+        };
+
+        // Map element-wise and store results into `mapped_ndarray`.
+        let nditer = NDIterType::new(generator, ctx.ctx).construct(generator, ctx, out_ndarray);
+        gen_for_callback(
+            generator,
+            ctx,
+            Some("broadcast_starmap"),
+            |generator, ctx| {
+                // Create NDIters for all broadcasted input ndarrays.
+                let other_nditers = broadcast_result
+                    .ndarrays
+                    .iter()
+                    .map(|ndarray| {
+                        NDIterType::new(generator, ctx.ctx).construct(generator, ctx, *ndarray)
+                    })
+                    .collect_vec();
+                Ok((nditer, other_nditers))
+            },
+            |generator, ctx, (out_nditer, _in_nditers)| {
+                // We can simply use `out_nditer`'s `has_element()`.
+                // `in_nditers`' `has_element()`s should return the same value.
+                Ok(out_nditer.has_element(generator, ctx))
+            },
+            |generator, ctx, _hooks, (out_nditer, in_nditers)| {
+                // Get all the scalars from the broadcasted input ndarrays, pass them to `mapping`,
+                // and write to `out_ndarray`.
+                let in_scalars =
+                    in_nditers.iter().map(|nditer| nditer.get_scalar(ctx)).collect_vec();
+
+                let result = mapping(generator, ctx, &in_scalars)?;
+
+                let p = out_nditer.get_pointer(ctx);
+                ctx.builder.build_store(p, result).unwrap();
+
+                Ok(())
+            },
+            |generator, ctx, (out_nditer, in_nditers)| {
+                // Advance all iterators
+                out_nditer.next(generator, ctx);
+                in_nditers.iter().for_each(|nditer| nditer.next(generator, ctx));
+                Ok(())
+            },
+        )?;
+
+        Ok(out_ndarray)
+    }
+}
+
+impl<'ctx> ScalarOrNDArray<'ctx> {
+    /// Starmap through a list of inputs using `mapping`, where an input could be an ndarray, a
+    /// scalar.
+    ///
+    /// This function is very helpful when implementing NumPy functions that takes on either scalars
+    /// or ndarrays or a mix of them as their inputs and produces either an ndarray with broadcast,
+    /// or a scalar if all its inputs are all scalars.
+    ///
+    /// For example ,this function can be used to implement `np.add`, which has the following
+    /// behaviors:
+    ///
+    /// - `np.add(3, 4) = 7` # (scalar, scalar) -> scalar
+    /// - `np.add(3, np.array([4, 5, 6]))` # (scalar, ndarray) -> ndarray; the first `scalar` is
+    ///   converted into an ndarray and broadcasted.
+    /// - `np.add(np.array([[1], [2], [3]]), np.array([[4, 5, 6]]))` # (ndarray, ndarray) ->
+    ///   ndarray; there is broadcasting.
+    ///
+    /// ## Details:
+    ///
+    /// If `inputs` are all [`ScalarOrNDArray::Scalar`], the output will be a
+    /// [`ScalarOrNDArray::Scalar`] with type `ret_dtype`.
+    ///
+    /// Otherwise (if there are any [`ScalarOrNDArray::NDArray`] in `inputs`), all inputs will be
+    /// 'as-ndarray'-ed into ndarrays, then all inputs (now all ndarrays) will be passed to
+    /// [`NDArrayValue::broadcasting_starmap`] and **create** a new ndarray with dtype `ret_dtype`.
+    pub fn broadcasting_starmap<'a, G, MappingFn>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        inputs: &[ScalarOrNDArray<'ctx>],
+        ret_dtype: BasicTypeEnum<'ctx>,
+        mapping: MappingFn,
+    ) -> Result<ScalarOrNDArray<'ctx>, String>
+    where
+        G: CodeGenerator + ?Sized,
+        MappingFn: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            &[BasicValueEnum<'ctx>],
+        ) -> Result<BasicValueEnum<'ctx>, String>,
+    {
+        // Check if all inputs are Scalars
+        let all_scalars: Option<Vec<_>> =
+            inputs.iter().map(BasicValueEnum::<'ctx>::try_from).try_collect().ok();
+
+        if let Some(scalars) = all_scalars {
+            let scalars = scalars.iter().copied().collect_vec();
+            let value = mapping(generator, ctx, &scalars)?;
+
+            Ok(ScalarOrNDArray::Scalar(value))
+        } else {
+            // Promote all input to ndarrays and map through them.
+            let inputs = inputs.iter().map(|input| input.to_ndarray(generator, ctx)).collect_vec();
+            let ndarray = NDArrayType::new_broadcast(
+                generator,
+                ctx.ctx,
+                ret_dtype,
+                &inputs.iter().map(NDArrayValue::get_type).collect_vec(),
+            )
+            .broadcast_starmap(
+                generator,
+                ctx,
+                &inputs,
+                NDArrayOut::NewNDArray { dtype: ret_dtype },
+                mapping,
+            )?;
+            Ok(ScalarOrNDArray::NDArray(ndarray))
+        }
+    }
+}

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

@@ -1,7 +1,7 @@
 use inkwell::{
-    context::Context,
+    context::{AsContextRef, Context},
     types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
-    values::{IntValue, PointerValue},
+    values::{BasicValue, IntValue, PointerValue},
     AddressSpace,
 };
 use itertools::Itertools;
@@ -9,28 +9,54 @@ use itertools::Itertools;
 use nac3core_derive::StructFields;
 
 use super::{
-    structure::{StructField, StructFields},
+    structure::{check_struct_type_matches_fields, StructField, StructFields},
     ProxyType,
 };
-use crate::codegen::{
-    values::{ArraySliceValue, NDArrayValue, ProxyValue},
-    {CodeGenContext, CodeGenerator},
+use crate::{
+    codegen::{
+        values::{ndarray::NDArrayValue, ProxyValue, TypedArrayLikeMutator},
+        {CodeGenContext, CodeGenerator},
+    },
+    toplevel::{helper::extract_ndims, numpy::unpack_ndarray_var_tys},
+    typecheck::typedef::Type,
 };
+pub use broadcast::*;
+pub use contiguous::*;
+pub use indexing::*;
+pub use nditer::*;
+
+mod array;
+mod broadcast;
+mod contiguous;
+pub mod factory;
+mod indexing;
+mod map;
+mod nditer;
 
 /// Proxy type for a `ndarray` type in LLVM.
 #[derive(Debug, PartialEq, Eq, Clone, Copy)]
 pub struct NDArrayType<'ctx> {
     ty: PointerType<'ctx>,
     dtype: BasicTypeEnum<'ctx>,
+    ndims: u64,
     llvm_usize: IntType<'ctx>,
 }
 
 #[derive(PartialEq, Eq, Clone, Copy, StructFields)]
 pub struct NDArrayStructFields<'ctx> {
+    /// The size of each `NDArray` element in bytes.
+    #[value_type(usize)]
+    pub itemsize: StructField<'ctx, IntValue<'ctx>>,
+    /// Number of dimensions in the array.
     #[value_type(usize)]
     pub ndims: StructField<'ctx, IntValue<'ctx>>,
+    /// Pointer to an array containing the shape of the `NDArray`.
     #[value_type(usize.ptr_type(AddressSpace::default()))]
     pub shape: StructField<'ctx, PointerValue<'ctx>>,
+    /// Pointer to an array indicating the number of bytes between each element at a dimension
+    #[value_type(usize.ptr_type(AddressSpace::default()))]
+    pub strides: StructField<'ctx, PointerValue<'ctx>>,
+    /// Pointer to an array containing the array data
     #[value_type(i8_type().ptr_type(AddressSpace::default()))]
     pub data: StructField<'ctx, PointerValue<'ctx>>,
 }
@@ -41,90 +67,40 @@ impl<'ctx> NDArrayType<'ctx> {
         llvm_ty: PointerType<'ctx>,
         llvm_usize: IntType<'ctx>,
     ) -> Result<(), String> {
+        let ctx = llvm_ty.get_context();
+
         let llvm_ndarray_ty = llvm_ty.get_element_type();
         let AnyTypeEnum::StructType(llvm_ndarray_ty) = llvm_ndarray_ty else {
             return Err(format!("Expected struct type for `NDArray` type, got {llvm_ndarray_ty}"));
         };
-        if llvm_ndarray_ty.count_fields() != 3 {
-            return Err(format!(
-                "Expected 3 fields in `NDArray`, got {}",
-                llvm_ndarray_ty.count_fields()
-            ));
-        }
 
-        let ndarray_ndims_ty = llvm_ndarray_ty.get_field_type_at_index(0).unwrap();
-        let Ok(ndarray_ndims_ty) = IntType::try_from(ndarray_ndims_ty) else {
-            return Err(format!("Expected int type for `ndarray.0`, got {ndarray_ndims_ty}"));
-        };
-        if ndarray_ndims_ty.get_bit_width() != llvm_usize.get_bit_width() {
-            return Err(format!(
-                "Expected {}-bit int type for `ndarray.0`, got {}-bit int",
-                llvm_usize.get_bit_width(),
-                ndarray_ndims_ty.get_bit_width()
-            ));
-        }
-
-        let ndarray_dims_ty = llvm_ndarray_ty.get_field_type_at_index(1).unwrap();
-        let Ok(ndarray_pdims) = PointerType::try_from(ndarray_dims_ty) else {
-            return Err(format!("Expected pointer type for `ndarray.1`, got {ndarray_dims_ty}"));
-        };
-        let ndarray_dims = ndarray_pdims.get_element_type();
-        let Ok(ndarray_dims) = IntType::try_from(ndarray_dims) else {
-            return Err(format!(
-                "Expected pointer-to-int type for `ndarray.1`, got pointer-to-{ndarray_dims}"
-            ));
-        };
-        if ndarray_dims.get_bit_width() != llvm_usize.get_bit_width() {
-            return Err(format!(
-                "Expected pointer-to-{}-bit int type for `ndarray.1`, got pointer-to-{}-bit int",
-                llvm_usize.get_bit_width(),
-                ndarray_dims.get_bit_width()
-            ));
-        }
-
-        let ndarray_data_ty = llvm_ndarray_ty.get_field_type_at_index(2).unwrap();
-        let Ok(ndarray_pdata) = PointerType::try_from(ndarray_data_ty) else {
-            return Err(format!("Expected pointer type for `ndarray.2`, got {ndarray_data_ty}"));
-        };
-        let ndarray_data = ndarray_pdata.get_element_type();
-        let Ok(ndarray_data) = IntType::try_from(ndarray_data) else {
-            return Err(format!(
-                "Expected pointer-to-int type for `ndarray.2`, got pointer-to-{ndarray_data}"
-            ));
-        };
-        if ndarray_data.get_bit_width() != 8 {
-            return Err(format!(
-                "Expected pointer-to-8-bit int type for `ndarray.1`, got pointer-to-{}-bit int",
-                ndarray_data.get_bit_width()
-            ));
-        }
-
-        Ok(())
+        check_struct_type_matches_fields(
+            Self::fields(ctx, llvm_usize),
+            llvm_ndarray_ty,
+            "NDArray",
+            &[],
+        )
     }
 
     /// Returns an instance of [`StructFields`] containing all field accessors for this type.
     #[must_use]
-    fn fields(ctx: &'ctx Context, llvm_usize: IntType<'ctx>) -> NDArrayStructFields<'ctx> {
+    fn fields(
+        ctx: impl AsContextRef<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> NDArrayStructFields<'ctx> {
         NDArrayStructFields::new(ctx, llvm_usize)
     }
 
     /// See [`NDArrayType::fields`].
     // TODO: Move this into e.g. StructProxyType
     #[must_use]
-    pub fn get_fields(&self, ctx: &'ctx Context) -> NDArrayStructFields<'ctx> {
+    pub fn get_fields(&self, ctx: impl AsContextRef<'ctx>) -> NDArrayStructFields<'ctx> {
         Self::fields(ctx, self.llvm_usize)
     }
 
     /// Creates an LLVM type corresponding to the expected structure of an `NDArray`.
     #[must_use]
     fn llvm_type(ctx: &'ctx Context, llvm_usize: IntType<'ctx>) -> PointerType<'ctx> {
-        // struct NDArray { num_dims: size_t, dims: size_t*, data: i8* }
-        //
-        // * data    : Pointer to an array containing the array data
-        // * itemsize: The size of each NDArray elements in bytes
-        // * ndims   : Number of dimensions in the array
-        // * shape   : Pointer to an array containing the shape of the NDArray
-        // * strides : Pointer to an array indicating the number of bytes between each element at a dimension
         let field_tys =
             Self::fields(ctx, llvm_usize).into_iter().map(|field| field.1).collect_vec();
 
@@ -137,11 +113,60 @@ impl<'ctx> NDArrayType<'ctx> {
         generator: &G,
         ctx: &'ctx Context,
         dtype: BasicTypeEnum<'ctx>,
+        ndims: u64,
     ) -> Self {
         let llvm_usize = generator.get_size_type(ctx);
         let llvm_ndarray = Self::llvm_type(ctx, llvm_usize);
 
-        NDArrayType { ty: llvm_ndarray, dtype, llvm_usize }
+        NDArrayType { ty: llvm_ndarray, dtype, ndims, llvm_usize }
+    }
+
+    /// Creates an instance of [`NDArrayType`] as a result of a broadcast operation over one or more
+    /// `ndarray` operands.
+    #[must_use]
+    pub fn new_broadcast<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        dtype: BasicTypeEnum<'ctx>,
+        inputs: &[NDArrayType<'ctx>],
+    ) -> Self {
+        assert!(!inputs.is_empty());
+
+        Self::new(generator, ctx, dtype, inputs.iter().map(NDArrayType::ndims).max().unwrap())
+    }
+
+    /// Creates an instance of [`NDArrayType`] with `ndims` of 0.
+    #[must_use]
+    pub fn new_unsized<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        dtype: BasicTypeEnum<'ctx>,
+    ) -> Self {
+        let llvm_usize = generator.get_size_type(ctx);
+        let llvm_ndarray = Self::llvm_type(ctx, llvm_usize);
+
+        NDArrayType { ty: llvm_ndarray, dtype, ndims: 0, llvm_usize }
+    }
+
+    /// Creates an [`NDArrayType`] from a [unifier type][Type].
+    #[must_use]
+    pub fn from_unifier_type<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ty: Type,
+    ) -> Self {
+        let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
+
+        let llvm_dtype = ctx.get_llvm_type(generator, dtype);
+        let llvm_usize = generator.get_size_type(ctx.ctx);
+        let ndims = extract_ndims(&ctx.unifier, ndims);
+
+        NDArrayType {
+            ty: Self::llvm_type(ctx.ctx, llvm_usize),
+            dtype: llvm_dtype,
+            ndims,
+            llvm_usize,
+        }
     }
 
     /// Creates an [`NDArrayType`] from a [`PointerType`] representing an `NDArray`.
@@ -149,22 +174,18 @@ impl<'ctx> NDArrayType<'ctx> {
     pub fn from_type(
         ptr_ty: PointerType<'ctx>,
         dtype: BasicTypeEnum<'ctx>,
+        ndims: u64,
         llvm_usize: IntType<'ctx>,
     ) -> Self {
         debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
 
-        NDArrayType { ty: ptr_ty, dtype, llvm_usize }
+        NDArrayType { ty: ptr_ty, dtype, ndims, llvm_usize }
     }
 
     /// Returns the type of the `size` field of this `ndarray` type.
     #[must_use]
     pub fn size_type(&self) -> IntType<'ctx> {
-        self.as_base_type()
-            .get_element_type()
-            .into_struct_type()
-            .get_field_type_at_index(0)
-            .map(BasicTypeEnum::into_int_type)
-            .unwrap()
+        self.llvm_usize
     }
 
     /// Returns the element type of this `ndarray` type.
@@ -173,22 +194,207 @@ impl<'ctx> NDArrayType<'ctx> {
         self.dtype
     }
 
-    /// Allocates an instance of [`NDArrayValue`] as if by calling `alloca` on the base type.
+    /// Returns the number of dimensions of this `ndarray` type.
     #[must_use]
-    pub fn alloca<G: CodeGenerator + ?Sized>(
+    pub fn ndims(&self) -> u64 {
+        self.ndims
+    }
+
+    /// Allocates an instance of [`NDArrayValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca`].
+    #[must_use]
+    pub fn alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca(ctx, name),
+            self.dtype,
+            self.ndims,
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Allocates an instance of [`NDArrayValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca_var`].
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
         name: Option<&'ctx str>,
     ) -> <Self as ProxyType<'ctx>>::Value {
         <Self as ProxyType<'ctx>>::Value::from_pointer_value(
-            self.raw_alloca(generator, ctx, name),
+            self.raw_alloca_var(generator, ctx, name),
             self.dtype,
+            self.ndims,
             self.llvm_usize,
             name,
         )
     }
 
+    /// Allocates an [`NDArrayValue`] on the stack and initializes all fields as follows:
+    ///
+    /// - `data`: uninitialized.
+    /// - `itemsize`: set to the size of `self.dtype`.
+    /// - `ndims`: set to the value of  `ndims`.
+    /// - `shape`: allocated on the stack with an array of length `ndims` with uninitialized values.
+    /// - `strides`: allocated on the stack with an array of length `ndims` with uninitialized
+    ///   values.
+    #[must_use]
+    fn construct_impl<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ndims: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let ndarray = self.alloca_var(generator, ctx, name);
+
+        let itemsize = ctx
+            .builder
+            .build_int_truncate_or_bit_cast(self.dtype.size_of().unwrap(), self.llvm_usize, "")
+            .unwrap();
+        ndarray.store_itemsize(ctx, generator, itemsize);
+
+        ndarray.store_ndims(ctx, generator, ndims);
+
+        ndarray.create_shape(ctx, self.llvm_usize, ndims);
+        ndarray.create_strides(ctx, self.llvm_usize, ndims);
+
+        ndarray
+    }
+
+    /// Allocate an [`NDArrayValue`] on the stack using `dtype` and `ndims` of this [`NDArrayType`]
+    /// instance.
+    ///
+    /// The returned ndarray's content will be:
+    /// - `data`: uninitialized.
+    /// - `itemsize`: set to the size of `dtype`.
+    /// - `ndims`: set to the value of `self.ndims`.
+    /// - `shape`: allocated on the stack with an array of length `ndims` with uninitialized values.
+    /// - `strides`: allocated on the stack with an array of length `ndims` with uninitialized
+    ///   values.
+    #[must_use]
+    pub fn construct_uninitialized<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let ndims = self.llvm_usize.const_int(self.ndims, false);
+
+        self.construct_impl(generator, ctx, ndims, name)
+    }
+
+    /// Convenience function. Allocate an [`NDArrayValue`] with a statically known shape.
+    ///
+    /// The returned [`NDArrayValue`]'s `data` and `strides` are uninitialized.
+    #[must_use]
+    pub fn construct_const_shape<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        shape: &[u64],
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        assert_eq!(shape.len() as u64, self.ndims);
+
+        let ndarray = Self::new(generator, ctx.ctx, self.dtype, shape.len() as u64)
+            .construct_uninitialized(generator, ctx, name);
+
+        let llvm_usize = generator.get_size_type(ctx.ctx);
+
+        // Write shape
+        let ndarray_shape = ndarray.shape();
+        for (i, dim) in shape.iter().enumerate() {
+            let dim = llvm_usize.const_int(*dim, false);
+            unsafe {
+                ndarray_shape.set_typed_unchecked(
+                    ctx,
+                    generator,
+                    &llvm_usize.const_int(i as u64, false),
+                    dim,
+                );
+            }
+        }
+
+        ndarray
+    }
+
+    /// Convenience function. Allocate an [`NDArrayValue`] with a dynamically known shape.
+    ///
+    /// The returned [`NDArrayValue`]'s `data` and `strides` are uninitialized.
+    #[must_use]
+    pub fn construct_dyn_shape<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        shape: &[IntValue<'ctx>],
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        assert_eq!(shape.len() as u64, self.ndims);
+
+        let ndarray = Self::new(generator, ctx.ctx, self.dtype, shape.len() as u64)
+            .construct_uninitialized(generator, ctx, name);
+
+        let llvm_usize = generator.get_size_type(ctx.ctx);
+
+        // Write shape
+        let ndarray_shape = ndarray.shape();
+        for (i, dim) in shape.iter().enumerate() {
+            assert_eq!(
+                dim.get_type(),
+                llvm_usize,
+                "Expected {} but got {}",
+                llvm_usize.print_to_string(),
+                dim.get_type().print_to_string()
+            );
+            unsafe {
+                ndarray_shape.set_typed_unchecked(
+                    ctx,
+                    generator,
+                    &llvm_usize.const_int(i as u64, false),
+                    *dim,
+                );
+            }
+        }
+
+        ndarray
+    }
+
+    /// Create an unsized ndarray to contain `value`.
+    #[must_use]
+    pub fn construct_unsized<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        value: &impl BasicValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> NDArrayValue<'ctx> {
+        let value = value.as_basic_value_enum();
+
+        assert_eq!(value.get_type(), self.dtype);
+        assert_eq!(self.ndims, 0);
+
+        // We have to put the value on the stack to get a data pointer.
+        let data = ctx.builder.build_alloca(value.get_type(), "construct_unsized").unwrap();
+        ctx.builder.build_store(data, value).unwrap();
+        let data = ctx
+            .builder
+            .build_pointer_cast(data, ctx.ctx.i8_type().ptr_type(AddressSpace::default()), "")
+            .unwrap();
+
+        let ndarray = Self::new_unsized(generator, ctx.ctx, value.get_type())
+            .construct_uninitialized(generator, ctx, name);
+        ctx.builder.build_store(ndarray.ptr_to_data(ctx), data).unwrap();
+        ndarray
+    }
+
     /// Converts an existing value into a [`NDArrayValue`].
     #[must_use]
     pub fn map_value(
@@ -199,6 +405,7 @@ impl<'ctx> NDArrayType<'ctx> {
         <Self as ProxyType<'ctx>>::Value::from_pointer_value(
             value,
             self.dtype,
+            self.ndims,
             self.llvm_usize,
             name,
         )
@@ -229,36 +436,8 @@ impl<'ctx> ProxyType<'ctx> for NDArrayType<'ctx> {
         Self::is_representable(llvm_ty, generator.get_size_type(ctx))
     }
 
-    fn raw_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&'ctx str>,
-    ) -> <Self::Value as ProxyValue<'ctx>>::Base {
-        generator
-            .gen_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                name,
-            )
-            .unwrap()
-    }
-
-    fn array_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        size: IntValue<'ctx>,
-        name: Option<&'ctx str>,
-    ) -> ArraySliceValue<'ctx> {
-        generator
-            .gen_array_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                size,
-                name,
-            )
-            .unwrap()
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
     }
 
     fn as_base_type(&self) -> Self::Base {

nac3core/src/codegen/types/ndarray/nditer.rs

@@ -0,0 +1,237 @@
+use inkwell::{
+    context::{AsContextRef, Context},
+    types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
+    values::{IntValue, PointerValue},
+    AddressSpace,
+};
+use itertools::Itertools;
+
+use nac3core_derive::StructFields;
+
+use super::ProxyType;
+use crate::codegen::{
+    irrt,
+    types::structure::{check_struct_type_matches_fields, StructField, StructFields},
+    values::{
+        ndarray::{NDArrayValue, NDIterValue},
+        ArrayLikeValue, ArraySliceValue, ProxyValue, TypedArrayLikeAdapter,
+    },
+    CodeGenContext, CodeGenerator,
+};
+
+#[derive(Debug, PartialEq, Eq, Clone, Copy)]
+pub struct NDIterType<'ctx> {
+    ty: PointerType<'ctx>,
+    llvm_usize: IntType<'ctx>,
+}
+
+#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+pub struct NDIterStructFields<'ctx> {
+    #[value_type(usize)]
+    pub ndims: StructField<'ctx, IntValue<'ctx>>,
+    #[value_type(usize.ptr_type(AddressSpace::default()))]
+    pub shape: StructField<'ctx, PointerValue<'ctx>>,
+    #[value_type(usize.ptr_type(AddressSpace::default()))]
+    pub strides: StructField<'ctx, PointerValue<'ctx>>,
+    #[value_type(usize.ptr_type(AddressSpace::default()))]
+    pub indices: StructField<'ctx, PointerValue<'ctx>>,
+    #[value_type(usize)]
+    pub nth: StructField<'ctx, IntValue<'ctx>>,
+    #[value_type(i8_type().ptr_type(AddressSpace::default()))]
+    pub element: StructField<'ctx, PointerValue<'ctx>>,
+    #[value_type(usize)]
+    pub size: StructField<'ctx, IntValue<'ctx>>,
+}
+
+impl<'ctx> NDIterType<'ctx> {
+    /// Checks whether `llvm_ty` represents a `nditer` type, returning [Err] if it does not.
+    pub fn is_representable(
+        llvm_ty: PointerType<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> Result<(), String> {
+        let ctx = llvm_ty.get_context();
+
+        let llvm_ty = llvm_ty.get_element_type();
+        let AnyTypeEnum::StructType(llvm_ndarray_ty) = llvm_ty else {
+            return Err(format!("Expected struct type for `NDIter` type, got {llvm_ty}"));
+        };
+
+        check_struct_type_matches_fields(
+            Self::fields(ctx, llvm_usize),
+            llvm_ndarray_ty,
+            "NDIter",
+            &[],
+        )
+    }
+
+    /// Returns an instance of [`StructFields`] containing all field accessors for this type.
+    #[must_use]
+    fn fields(ctx: impl AsContextRef<'ctx>, llvm_usize: IntType<'ctx>) -> NDIterStructFields<'ctx> {
+        NDIterStructFields::new(ctx, llvm_usize)
+    }
+
+    /// See [`NDIterType::fields`].
+    // TODO: Move this into e.g. StructProxyType
+    #[must_use]
+    pub fn get_fields(&self, ctx: impl AsContextRef<'ctx>) -> NDIterStructFields<'ctx> {
+        Self::fields(ctx, self.llvm_usize)
+    }
+
+    /// Creates an LLVM type corresponding to the expected structure of an `NDIter`.
+    #[must_use]
+    fn llvm_type(ctx: &'ctx Context, llvm_usize: IntType<'ctx>) -> PointerType<'ctx> {
+        let field_tys =
+            Self::fields(ctx, llvm_usize).into_iter().map(|field| field.1).collect_vec();
+
+        ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
+    }
+
+    /// Creates an instance of [`NDIter`].
+    #[must_use]
+    pub fn new<G: CodeGenerator + ?Sized>(generator: &G, ctx: &'ctx Context) -> Self {
+        let llvm_usize = generator.get_size_type(ctx);
+        let llvm_nditer = Self::llvm_type(ctx, llvm_usize);
+
+        Self { ty: llvm_nditer, llvm_usize }
+    }
+
+    /// Creates an [`NDIterType`] from a [`PointerType`] representing an `NDIter`.
+    #[must_use]
+    pub fn from_type(ptr_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
+        debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
+
+        Self { ty: ptr_ty, llvm_usize }
+    }
+
+    /// Returns the type of the `size` field of this `nditer` type.
+    #[must_use]
+    pub fn size_type(&self) -> IntType<'ctx> {
+        self.llvm_usize
+    }
+
+    /// Allocates an instance of [`NDIterValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca`].
+    #[must_use]
+    pub fn alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        parent: NDArrayValue<'ctx>,
+        indices: ArraySliceValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca(ctx, name),
+            parent,
+            indices,
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Allocates an instance of [`NDIterValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca_var`].
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        parent: NDArrayValue<'ctx>,
+        indices: ArraySliceValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca_var(generator, ctx, name),
+            parent,
+            indices,
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Allocate an [`NDIter`] that iterates through the given `ndarray`.
+    ///
+    /// Note: This function allocates an array on the stack at the current builder location, which
+    /// may lead to stack explosion if called in a hot loop. Therefore, callers are recommended to
+    /// call `llvm.stacksave` before calling this function and call `llvm.stackrestore` after the
+    /// [`NDIter`] is no longer needed.
+    #[must_use]
+    pub fn construct<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ndarray: NDArrayValue<'ctx>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let nditer = self.raw_alloca_var(generator, ctx, None);
+        let ndims = self.llvm_usize.const_int(ndarray.get_type().ndims(), false);
+
+        // The caller has the responsibility to allocate 'indices' for `NDIter`.
+        let indices =
+            generator.gen_array_var_alloc(ctx, self.llvm_usize.into(), ndims, None).unwrap();
+        let indices =
+            TypedArrayLikeAdapter::from(indices, |_, _, v| v.into_int_value(), |_, _, v| v.into());
+
+        let nditer = self.map_value(nditer, ndarray, indices.as_slice_value(ctx, generator), None);
+
+        irrt::ndarray::call_nac3_nditer_initialize(generator, ctx, nditer, ndarray, &indices);
+
+        nditer
+    }
+
+    #[must_use]
+    pub fn map_value(
+        &self,
+        value: <<Self as ProxyType<'ctx>>::Value as ProxyValue<'ctx>>::Base,
+        parent: NDArrayValue<'ctx>,
+        indices: ArraySliceValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            value,
+            parent,
+            indices,
+            self.llvm_usize,
+            name,
+        )
+    }
+}
+
+impl<'ctx> ProxyType<'ctx> for NDIterType<'ctx> {
+    type Base = PointerType<'ctx>;
+    type Value = NDIterValue<'ctx>;
+
+    fn is_type<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        llvm_ty: impl BasicType<'ctx>,
+    ) -> Result<(), String> {
+        if let BasicTypeEnum::PointerType(ty) = llvm_ty.as_basic_type_enum() {
+            <Self as ProxyType<'ctx>>::is_representable(generator, ctx, ty)
+        } else {
+            Err(format!("Expected pointer type, got {llvm_ty:?}"))
+        }
+    }
+
+    fn is_representable<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        llvm_ty: Self::Base,
+    ) -> Result<(), String> {
+        Self::is_representable(llvm_ty, generator.get_size_type(ctx))
+    }
+
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
+    }
+
+    fn as_base_type(&self) -> Self::Base {
+        self.ty
+    }
+}
+
+impl<'ctx> From<NDIterType<'ctx>> for PointerType<'ctx> {
+    fn from(value: NDIterType<'ctx>) -> Self {
+        value.as_base_type()
+    }
+}

nac3core/src/codegen/types/range.rs

@@ -1,13 +1,12 @@
 use inkwell::{
     context::Context,
     types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
-    values::IntValue,
     AddressSpace,
 };
 
 use super::ProxyType;
 use crate::codegen::{
-    values::{ArraySliceValue, ProxyValue, RangeValue},
+    values::{ProxyValue, RangeValue},
     {CodeGenContext, CodeGenerator},
 };
 
@@ -78,15 +77,29 @@ impl<'ctx> RangeType<'ctx> {
     }
 
     /// Allocates an instance of [`RangeValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca`].
     #[must_use]
     pub fn alloca<G: CodeGenerator + ?Sized>(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(self.raw_alloca(ctx, name), name)
+    }
+
+    /// Allocates an instance of [`RangeValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca_var`].
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
         name: Option<&'ctx str>,
     ) -> <Self as ProxyType<'ctx>>::Value {
         <Self as ProxyType<'ctx>>::Value::from_pointer_value(
-            self.raw_alloca(generator, ctx, name),
+            self.raw_alloca_var(generator, ctx, name),
             name,
         )
     }
@@ -126,36 +139,8 @@ impl<'ctx> ProxyType<'ctx> for RangeType<'ctx> {
         Self::is_representable(llvm_ty)
     }
 
-    fn raw_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&'ctx str>,
-    ) -> <Self::Value as ProxyValue<'ctx>>::Base {
-        generator
-            .gen_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                name,
-            )
-            .unwrap()
-    }
-
-    fn array_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        size: IntValue<'ctx>,
-        name: Option<&'ctx str>,
-    ) -> ArraySliceValue<'ctx> {
-        generator
-            .gen_array_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                size,
-                name,
-            )
-            .unwrap()
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
     }
 
     fn as_base_type(&self) -> Self::Base {

nac3core/src/codegen/types/structure.rs

@@ -2,9 +2,10 @@ use std::marker::PhantomData;
 
 use inkwell::{
     context::AsContextRef,
-    types::{BasicTypeEnum, IntType},
+    types::{BasicTypeEnum, IntType, StructType},
     values::{BasicValue, BasicValueEnum, IntValue, PointerValue, StructValue},
 };
+use itertools::Itertools;
 
 use crate::codegen::CodeGenContext;
 
@@ -55,6 +56,20 @@ pub trait StructFields<'ctx>: Eq + Copy {
     {
         self.into_vec().into_iter()
     }
+
+    /// Returns the field index of a field in this structure.
+    fn index_of_field<V>(&self, name: impl FnOnce(&Self) -> StructField<'ctx, V>) -> u32
+    where
+        V: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>, Error = ()>,
+    {
+        let field_name = name(self).name;
+        self.index_of_field_name(field_name).unwrap()
+    }
+
+    /// Returns the field index of a field with the given name in this structure.
+    fn index_of_field_name(&self, field_name: &str) -> Option<u32> {
+        self.iter().find_position(|(name, _)| *name == field_name).map(|(idx, _)| idx as u32)
+    }
 }
 
 /// A single field of an LLVM structure.
@@ -103,6 +118,12 @@ where
         StructField { index, name, ty: ty.into(), _value_ty: PhantomData }
     }
 
+    /// Returns the name of this field.
+    #[must_use]
+    pub fn name(&self) -> &'static str {
+        self.name
+    }
+
     /// Creates a pointer to this field in an arbitrary structure by performing a `getelementptr i32
     /// {idx...}, i32 {self.index}`.
     pub fn ptr_by_array_gep(
@@ -201,3 +222,49 @@ impl FieldIndexCounter {
         v
     }
 }
+
+type FieldTypeVerifier<'ctx> = dyn Fn(BasicTypeEnum<'ctx>) -> Result<(), String>;
+
+/// Checks whether [`llvm_ty`][StructType] contains the fields described by the given
+/// [`StructFields`] instance.
+///
+/// By default, this function will compare the type of each field in `expected_fields` against
+/// `llvm_ty`. To override this behavior for individual fields, pass in overrides to
+/// `custom_verifiers`, which will use the specified verifier when a field with the matching field
+/// name is being checked.
+pub(super) fn check_struct_type_matches_fields<'ctx>(
+    expected_fields: impl StructFields<'ctx>,
+    llvm_ty: StructType<'ctx>,
+    ty_name: &'static str,
+    custom_verifiers: &[(&str, &FieldTypeVerifier<'ctx>)],
+) -> Result<(), String> {
+    let expected_fields = expected_fields.to_vec();
+
+    if llvm_ty.count_fields() != u32::try_from(expected_fields.len()).unwrap() {
+        return Err(format!(
+            "Expected {} fields in `{ty_name}`, got {}",
+            expected_fields.len(),
+            llvm_ty.count_fields(),
+        ));
+    }
+
+    expected_fields
+        .into_iter()
+        .enumerate()
+        .map(|(i, (field_name, expected_ty))| {
+            (field_name, expected_ty, llvm_ty.get_field_type_at_index(i as u32).unwrap())
+        })
+        .try_for_each(|(field_name, expected_ty, actual_ty)| {
+            if let Some((_, verifier)) =
+                custom_verifiers.iter().find(|verifier| verifier.0 == field_name)
+            {
+                verifier(actual_ty)
+            } else if expected_ty == actual_ty {
+                Ok(())
+            } else {
+                Err(format!("Expected {expected_ty} for `{ty_name}.{field_name}`, got {actual_ty}"))
+            }
+        })?;
+
+    Ok(())
+}

nac3core/src/codegen/types/tuple.rs

@@ -0,0 +1,184 @@
+use inkwell::{
+    context::Context,
+    types::{BasicType, BasicTypeEnum, IntType, StructType},
+    values::BasicValueEnum,
+};
+use itertools::Itertools;
+
+use super::ProxyType;
+use crate::{
+    codegen::{
+        values::{ProxyValue, TupleValue},
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::{Type, TypeEnum},
+};
+
+#[derive(Debug, PartialEq, Eq, Clone)]
+pub struct TupleType<'ctx> {
+    ty: StructType<'ctx>,
+    llvm_usize: IntType<'ctx>,
+}
+
+impl<'ctx> TupleType<'ctx> {
+    /// Checks whether `llvm_ty` represents any tuple type, returning [Err] if it does not.
+    pub fn is_representable(_value: StructType<'ctx>) -> Result<(), String> {
+        Ok(())
+    }
+
+    /// Creates an LLVM type corresponding to the expected structure of a tuple.
+    #[must_use]
+    fn llvm_type(ctx: &'ctx Context, tys: &[BasicTypeEnum<'ctx>]) -> StructType<'ctx> {
+        ctx.struct_type(tys, false)
+    }
+
+    /// Creates an instance of [`TupleType`].
+    #[must_use]
+    pub fn new<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        tys: &[BasicTypeEnum<'ctx>],
+    ) -> Self {
+        let llvm_usize = generator.get_size_type(ctx);
+        let llvm_tuple = Self::llvm_type(ctx, tys);
+
+        Self { ty: llvm_tuple, llvm_usize }
+    }
+
+    /// Creates an [`TupleType`] from a [unifier type][Type].
+    #[must_use]
+    pub fn from_unifier_type<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ty: Type,
+    ) -> Self {
+        let llvm_usize = generator.get_size_type(ctx.ctx);
+
+        // Sanity check on object type.
+        let TypeEnum::TTuple { ty: tys, .. } = &*ctx.unifier.get_ty_immutable(ty) else {
+            panic!("Expected type to be a TypeEnum::TTuple, got {}", ctx.unifier.stringify(ty));
+        };
+
+        let llvm_tys = tys.iter().map(|ty| ctx.get_llvm_type(generator, *ty)).collect_vec();
+        Self { ty: Self::llvm_type(ctx.ctx, &llvm_tys), llvm_usize }
+    }
+
+    /// Creates an [`TupleType`] from a [`StructType`].
+    #[must_use]
+    pub fn from_type(struct_ty: StructType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
+        debug_assert!(Self::is_representable(struct_ty).is_ok());
+
+        TupleType { ty: struct_ty, llvm_usize }
+    }
+
+    /// Returns the number of elements present in this [`TupleType`].
+    #[must_use]
+    pub fn num_elements(&self) -> u32 {
+        self.ty.count_fields()
+    }
+
+    /// Returns the type of the tuple element at the given `index`, or [`None`] if `index` is out of
+    /// range.
+    #[must_use]
+    pub fn type_at_index(&self, index: u32) -> Option<BasicTypeEnum<'ctx>> {
+        if index < self.num_elements() {
+            Some(unsafe { self.type_at_index_unchecked(index) })
+        } else {
+            None
+        }
+    }
+
+    /// Returns the type of the tuple element at the given `index`.
+    ///
+    /// # Safety
+    ///
+    /// The caller must ensure that the index is valid.
+    #[must_use]
+    pub unsafe fn type_at_index_unchecked(&self, index: u32) -> BasicTypeEnum<'ctx> {
+        self.ty.get_field_type_at_index_unchecked(index)
+    }
+
+    /// Constructs a [`TupleValue`] from this type by zero-initializing the tuple value.
+    #[must_use]
+    pub fn construct(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        self.map_value(Self::llvm_type(ctx.ctx, &self.ty.get_field_types()).const_zero(), name)
+    }
+
+    /// Constructs a [`TupleValue`] from `objects`. The resulting tuple preserves the order of
+    /// objects.
+    #[must_use]
+    pub fn construct_from_objects<I: IntoIterator<Item = BasicValueEnum<'ctx>>>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        objects: I,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let values = objects.into_iter().collect_vec();
+
+        assert_eq!(values.len(), self.num_elements() as usize);
+        assert!(values
+            .iter()
+            .enumerate()
+            .all(|(i, v)| { v.get_type() == unsafe { self.type_at_index_unchecked(i as u32) } }));
+
+        let mut value = self.construct(ctx, name);
+        for (i, val) in values.into_iter().enumerate() {
+            value.store_element(ctx, i as u32, val);
+        }
+
+        value
+    }
+
+    /// Converts an existing value into a [`ListValue`].
+    #[must_use]
+    pub fn map_value(
+        &self,
+        value: <<Self as ProxyType<'ctx>>::Value as ProxyValue<'ctx>>::Base,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_struct_value(value, self.llvm_usize, name)
+    }
+}
+
+impl<'ctx> ProxyType<'ctx> for TupleType<'ctx> {
+    type Base = StructType<'ctx>;
+    type Value = TupleValue<'ctx>;
+
+    fn is_type<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        llvm_ty: impl BasicType<'ctx>,
+    ) -> Result<(), String> {
+        if let BasicTypeEnum::StructType(ty) = llvm_ty.as_basic_type_enum() {
+            <Self as ProxyType<'ctx>>::is_representable(generator, ctx, ty)
+        } else {
+            Err(format!("Expected struct type, got {llvm_ty:?}"))
+        }
+    }
+
+    fn is_representable<G: CodeGenerator + ?Sized>(
+        _generator: &G,
+        _ctx: &'ctx Context,
+        llvm_ty: Self::Base,
+    ) -> Result<(), String> {
+        Self::is_representable(llvm_ty)
+    }
+
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type()
+    }
+
+    fn as_base_type(&self) -> Self::Base {
+        self.ty
+    }
+}
+
+impl<'ctx> From<TupleType<'ctx>> for StructType<'ctx> {
+    fn from(value: TupleType<'ctx>) -> Self {
+        value.as_base_type()
+    }
+}

nac3core/src/codegen/types/utils/mod.rs

@@ -0,0 +1,3 @@
+pub use slice::*;
+
+mod slice;

nac3core/src/codegen/types/utils/slice.rs

@@ -0,0 +1,245 @@
+use inkwell::{
+    context::{AsContextRef, Context, ContextRef},
+    types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
+    values::IntValue,
+    AddressSpace,
+};
+use itertools::Itertools;
+
+use nac3core_derive::StructFields;
+
+use crate::codegen::{
+    types::{
+        structure::{
+            check_struct_type_matches_fields, FieldIndexCounter, StructField, StructFields,
+        },
+        ProxyType,
+    },
+    values::{utils::SliceValue, ProxyValue},
+    CodeGenContext, CodeGenerator,
+};
+
+#[derive(Debug, PartialEq, Eq, Clone, Copy)]
+pub struct SliceType<'ctx> {
+    ty: PointerType<'ctx>,
+    int_ty: IntType<'ctx>,
+    llvm_usize: IntType<'ctx>,
+}
+
+#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+pub struct SliceFields<'ctx> {
+    #[value_type(bool_type())]
+    pub start_defined: StructField<'ctx, IntValue<'ctx>>,
+    #[value_type(usize)]
+    pub start: StructField<'ctx, IntValue<'ctx>>,
+    #[value_type(bool_type())]
+    pub stop_defined: StructField<'ctx, IntValue<'ctx>>,
+    #[value_type(usize)]
+    pub stop: StructField<'ctx, IntValue<'ctx>>,
+    #[value_type(bool_type())]
+    pub step_defined: StructField<'ctx, IntValue<'ctx>>,
+    #[value_type(usize)]
+    pub step: StructField<'ctx, IntValue<'ctx>>,
+}
+
+impl<'ctx> SliceFields<'ctx> {
+    /// Creates a new instance of [`SliceFields`] with a custom integer type for its range values.
+    #[must_use]
+    pub fn new_sized(ctx: &impl AsContextRef<'ctx>, int_ty: IntType<'ctx>) -> Self {
+        let ctx = unsafe { ContextRef::new(ctx.as_ctx_ref()) };
+        let mut counter = FieldIndexCounter::default();
+
+        SliceFields {
+            start_defined: StructField::create(&mut counter, "start_defined", ctx.bool_type()),
+            start: StructField::create(&mut counter, "start", int_ty),
+            stop_defined: StructField::create(&mut counter, "stop_defined", ctx.bool_type()),
+            stop: StructField::create(&mut counter, "stop", int_ty),
+            step_defined: StructField::create(&mut counter, "step_defined", ctx.bool_type()),
+            step: StructField::create(&mut counter, "step", int_ty),
+        }
+    }
+}
+
+impl<'ctx> SliceType<'ctx> {
+    /// Checks whether `llvm_ty` represents a `slice` type, returning [Err] if it does not.
+    pub fn is_representable(
+        llvm_ty: PointerType<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> Result<(), String> {
+        let ctx = llvm_ty.get_context();
+
+        let fields = SliceFields::new(ctx, llvm_usize);
+
+        let llvm_ty = llvm_ty.get_element_type();
+        let AnyTypeEnum::StructType(llvm_ty) = llvm_ty else {
+            return Err(format!("Expected struct type for `Slice` type, got {llvm_ty}"));
+        };
+
+        check_struct_type_matches_fields(
+            fields,
+            llvm_ty,
+            "Slice",
+            &[
+                (fields.start.name(), &|ty| {
+                    if ty.is_int_type() {
+                        Ok(())
+                    } else {
+                        Err(format!("Expected int type for `Slice.start`, got {ty}"))
+                    }
+                }),
+                (fields.stop.name(), &|ty| {
+                    if ty.is_int_type() {
+                        Ok(())
+                    } else {
+                        Err(format!("Expected int type for `Slice.stop`, got {ty}"))
+                    }
+                }),
+                (fields.step.name(), &|ty| {
+                    if ty.is_int_type() {
+                        Ok(())
+                    } else {
+                        Err(format!("Expected int type for `Slice.step`, got {ty}"))
+                    }
+                }),
+            ],
+        )
+    }
+
+    // TODO: Move this into e.g. StructProxyType
+    #[must_use]
+    pub fn get_fields(&self) -> SliceFields<'ctx> {
+        SliceFields::new_sized(&self.int_ty.get_context(), self.int_ty)
+    }
+
+    /// Creates an LLVM type corresponding to the expected structure of a `Slice`.
+    #[must_use]
+    fn llvm_type(ctx: &'ctx Context, int_ty: IntType<'ctx>) -> PointerType<'ctx> {
+        let field_tys = SliceFields::new_sized(&int_ty.get_context(), int_ty)
+            .into_iter()
+            .map(|field| field.1)
+            .collect_vec();
+
+        ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
+    }
+
+    /// Creates an instance of [`SliceType`] with `int_ty` as its backing integer type.
+    #[must_use]
+    pub fn new(ctx: &'ctx Context, int_ty: IntType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
+        let llvm_ty = Self::llvm_type(ctx, int_ty);
+
+        Self { ty: llvm_ty, int_ty, llvm_usize }
+    }
+
+    /// Creates an instance of [`SliceType`] with `usize` as its backing integer type.
+    #[must_use]
+    pub fn new_usize<G: CodeGenerator + ?Sized>(generator: &G, ctx: &'ctx Context) -> Self {
+        let llvm_usize = generator.get_size_type(ctx);
+        Self::new(ctx, llvm_usize, llvm_usize)
+    }
+
+    /// Creates an [`SliceType`] from a [`PointerType`] representing a `slice`.
+    #[must_use]
+    pub fn from_type(
+        ptr_ty: PointerType<'ctx>,
+        int_ty: IntType<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> Self {
+        debug_assert!(Self::is_representable(ptr_ty, int_ty).is_ok());
+
+        Self { ty: ptr_ty, int_ty, llvm_usize }
+    }
+
+    #[must_use]
+    pub fn element_type(&self) -> IntType<'ctx> {
+        self.int_ty
+    }
+
+    /// Allocates an instance of [`SliceValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca`].
+    #[must_use]
+    pub fn alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca(ctx, name),
+            self.int_ty,
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Allocates an instance of [`SliceValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca_var`].
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca_var(generator, ctx, name),
+            self.int_ty,
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Converts an existing value into a [`ContiguousNDArrayValue`].
+    #[must_use]
+    pub fn map_value(
+        &self,
+        value: <<Self as ProxyType<'ctx>>::Value as ProxyValue<'ctx>>::Base,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            value,
+            self.int_ty,
+            self.llvm_usize,
+            name,
+        )
+    }
+}
+
+impl<'ctx> ProxyType<'ctx> for SliceType<'ctx> {
+    type Base = PointerType<'ctx>;
+    type Value = SliceValue<'ctx>;
+
+    fn is_type<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        llvm_ty: impl BasicType<'ctx>,
+    ) -> Result<(), String> {
+        if let BasicTypeEnum::PointerType(ty) = llvm_ty.as_basic_type_enum() {
+            <Self as ProxyType<'ctx>>::is_representable(generator, ctx, ty)
+        } else {
+            Err(format!("Expected pointer type, got {llvm_ty:?}"))
+        }
+    }
+
+    fn is_representable<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        llvm_ty: Self::Base,
+    ) -> Result<(), String> {
+        Self::is_representable(llvm_ty, generator.get_size_type(ctx))
+    }
+
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
+    }
+
+    fn as_base_type(&self) -> Self::Base {
+        self.ty
+    }
+}
+
+impl<'ctx> From<SliceType<'ctx>> for PointerType<'ctx> {
+    fn from(value: SliceType<'ctx>) -> Self {
+        value.as_base_type()
+    }
+}

nac3core/src/codegen/values/array.rs

@@ -51,8 +51,8 @@ pub trait ArrayLikeIndexer<'ctx, Index = IntValue<'ctx>>: ArrayLikeValue<'ctx> {
     /// This function should be called with a valid index.
     unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &Index,
         name: Option<&str>,
     ) -> PointerValue<'ctx>;
@@ -76,8 +76,8 @@ pub trait UntypedArrayLikeAccessor<'ctx, Index = IntValue<'ctx>>:
     /// This function should be called with a valid index.
     unsafe fn get_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &Index,
         name: Option<&str>,
     ) -> BasicValueEnum<'ctx> {
@@ -107,8 +107,8 @@ pub trait UntypedArrayLikeMutator<'ctx, Index = IntValue<'ctx>>:
     /// This function should be called with a valid index.
     unsafe fn set_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &Index,
         value: BasicValueEnum<'ctx>,
     ) {
@@ -130,32 +130,33 @@ pub trait UntypedArrayLikeMutator<'ctx, Index = IntValue<'ctx>>:
 }
 
 /// An array-like value that can have its array elements accessed as an arbitrary type `T`.
-pub trait TypedArrayLikeAccessor<'ctx, T, Index = IntValue<'ctx>>:
+pub trait TypedArrayLikeAccessor<'ctx, G: CodeGenerator + ?Sized, T, Index = IntValue<'ctx>>:
     UntypedArrayLikeAccessor<'ctx, Index>
 {
     /// Casts an element from [`BasicValueEnum`] into `T`.
     fn downcast_to_type(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         value: BasicValueEnum<'ctx>,
     ) -> T;
 
     /// # Safety
     ///
     /// This function should be called with a valid index.
-    unsafe fn get_typed_unchecked<G: CodeGenerator + ?Sized>(
+    unsafe fn get_typed_unchecked(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &Index,
         name: Option<&str>,
     ) -> T {
         let value = unsafe { self.get_unchecked(ctx, generator, idx, name) };
-        self.downcast_to_type(ctx, value)
+        self.downcast_to_type(ctx, generator, value)
     }
 
     /// Returns the data at the `idx`-th index.
-    fn get_typed<G: CodeGenerator + ?Sized>(
+    fn get_typed(
         &self,
         ctx: &mut CodeGenContext<'ctx, '_>,
         generator: &mut G,
@@ -163,62 +164,63 @@ pub trait TypedArrayLikeAccessor<'ctx, T, Index = IntValue<'ctx>>:
         name: Option<&str>,
     ) -> T {
         let value = self.get(ctx, generator, idx, name);
-        self.downcast_to_type(ctx, value)
+        self.downcast_to_type(ctx, generator, value)
     }
 }
 
 /// An array-like value that can have its array elements mutated as an arbitrary type `T`.
-pub trait TypedArrayLikeMutator<'ctx, T, Index = IntValue<'ctx>>:
+pub trait TypedArrayLikeMutator<'ctx, G: CodeGenerator + ?Sized, T, Index = IntValue<'ctx>>:
     UntypedArrayLikeMutator<'ctx, Index>
 {
     /// Casts an element from T into [`BasicValueEnum`].
     fn upcast_from_type(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         value: T,
     ) -> BasicValueEnum<'ctx>;
 
     /// # Safety
     ///
     /// This function should be called with a valid index.
-    unsafe fn set_typed_unchecked<G: CodeGenerator + ?Sized>(
+    unsafe fn set_typed_unchecked(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &Index,
         value: T,
     ) {
-        let value = self.upcast_from_type(ctx, value);
+        let value = self.upcast_from_type(ctx, generator, value);
         unsafe { self.set_unchecked(ctx, generator, idx, value) }
     }
 
     /// Sets the data at the `idx`-th index.
-    fn set_typed<G: CodeGenerator + ?Sized>(
+    fn set_typed(
         &self,
         ctx: &mut CodeGenContext<'ctx, '_>,
         generator: &mut G,
         idx: &Index,
         value: T,
     ) {
-        let value = self.upcast_from_type(ctx, value);
+        let value = self.upcast_from_type(ctx, generator, value);
         self.set(ctx, generator, idx, value);
     }
 }
 
-/// Type alias for a function that casts a [`BasicValueEnum`] into a `T`.
-type ValueDowncastFn<'ctx, T> =
-    Box<dyn Fn(&mut CodeGenContext<'ctx, '_>, BasicValueEnum<'ctx>) -> T>;
-/// Type alias for a function that casts a `T` into a [`BasicValueEnum`].
-type ValueUpcastFn<'ctx, T> = Box<dyn Fn(&mut CodeGenContext<'ctx, '_>, T) -> BasicValueEnum<'ctx>>;
-
 /// An adapter for constraining untyped array values as typed values.
-pub struct TypedArrayLikeAdapter<'ctx, T, Adapted: ArrayLikeValue<'ctx> = ArraySliceValue<'ctx>> {
+#[derive(Copy, Clone)]
+pub struct TypedArrayLikeAdapter<
+    'ctx,
+    G: CodeGenerator + ?Sized,
+    T,
+    Adapted: ArrayLikeValue<'ctx> = ArraySliceValue<'ctx>,
+> {
     adapted: Adapted,
-    downcast_fn: ValueDowncastFn<'ctx, T>,
-    upcast_fn: ValueUpcastFn<'ctx, T>,
+    downcast_fn: fn(&CodeGenContext<'ctx, '_>, &G, BasicValueEnum<'ctx>) -> T,
+    upcast_fn: fn(&CodeGenContext<'ctx, '_>, &G, T) -> BasicValueEnum<'ctx>,
 }
 
-impl<'ctx, T, Adapted> TypedArrayLikeAdapter<'ctx, T, Adapted>
+impl<'ctx, G: CodeGenerator + ?Sized, T, Adapted> TypedArrayLikeAdapter<'ctx, G, T, Adapted>
 where
     Adapted: ArrayLikeValue<'ctx>,
 {
@@ -229,61 +231,70 @@ where
     /// * `upcast_fn` - The function converting a T into a [`BasicValueEnum`].
     pub fn from(
         adapted: Adapted,
-        downcast_fn: ValueDowncastFn<'ctx, T>,
-        upcast_fn: ValueUpcastFn<'ctx, T>,
+        downcast_fn: fn(&CodeGenContext<'ctx, '_>, &G, BasicValueEnum<'ctx>) -> T,
+        upcast_fn: fn(&CodeGenContext<'ctx, '_>, &G, T) -> BasicValueEnum<'ctx>,
     ) -> Self {
         TypedArrayLikeAdapter { adapted, downcast_fn, upcast_fn }
     }
 }
 
-impl<'ctx, T, Adapted> ArrayLikeValue<'ctx> for TypedArrayLikeAdapter<'ctx, T, Adapted>
+impl<'ctx, G: CodeGenerator + ?Sized, T, Adapted> ArrayLikeValue<'ctx>
+    for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
 where
     Adapted: ArrayLikeValue<'ctx>,
 {
-    fn element_type<G: CodeGenerator + ?Sized>(
+    fn element_type<CG: CodeGenerator + ?Sized>(
         &self,
         ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
+        generator: &CG,
     ) -> AnyTypeEnum<'ctx> {
         self.adapted.element_type(ctx, generator)
     }
 
-    fn base_ptr<G: CodeGenerator + ?Sized>(
+    fn base_ptr<CG: CodeGenerator + ?Sized>(
         &self,
         ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
+        generator: &CG,
     ) -> PointerValue<'ctx> {
         self.adapted.base_ptr(ctx, generator)
     }
 
-    fn size<G: CodeGenerator + ?Sized>(
+    fn size<CG: CodeGenerator + ?Sized>(
         &self,
         ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
+        generator: &CG,
     ) -> IntValue<'ctx> {
         self.adapted.size(ctx, generator)
     }
+
+    fn as_slice_value<CG: CodeGenerator + ?Sized>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &CG,
+    ) -> ArraySliceValue<'ctx> {
+        self.adapted.as_slice_value(ctx, generator)
+    }
 }
 
-impl<'ctx, T, Index, Adapted> ArrayLikeIndexer<'ctx, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
+impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> ArrayLikeIndexer<'ctx, Index>
+    for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
 where
     Adapted: ArrayLikeIndexer<'ctx, Index>,
 {
-    unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
+    unsafe fn ptr_offset_unchecked<CG: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &CG,
         idx: &Index,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {
         unsafe { self.adapted.ptr_offset_unchecked(ctx, generator, idx, name) }
     }
 
-    fn ptr_offset<G: CodeGenerator + ?Sized>(
+    fn ptr_offset<CG: CodeGenerator + ?Sized>(
         &self,
         ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        generator: &mut CG,
         idx: &Index,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {
@@ -291,44 +302,46 @@ where
     }
 }
 
-impl<'ctx, T, Index, Adapted> UntypedArrayLikeAccessor<'ctx, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
+impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> UntypedArrayLikeAccessor<'ctx, Index>
+    for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
 where
     Adapted: UntypedArrayLikeAccessor<'ctx, Index>,
 {
 }
-impl<'ctx, T, Index, Adapted> UntypedArrayLikeMutator<'ctx, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
+impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> UntypedArrayLikeMutator<'ctx, Index>
+    for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
 where
     Adapted: UntypedArrayLikeMutator<'ctx, Index>,
 {
 }
 
-impl<'ctx, T, Index, Adapted> TypedArrayLikeAccessor<'ctx, T, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
+impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> TypedArrayLikeAccessor<'ctx, G, T, Index>
+    for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
 where
     Adapted: UntypedArrayLikeAccessor<'ctx, Index>,
 {
     fn downcast_to_type(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         value: BasicValueEnum<'ctx>,
     ) -> T {
-        (self.downcast_fn)(ctx, value)
+        (self.downcast_fn)(ctx, generator, value)
     }
 }
 
-impl<'ctx, T, Index, Adapted> TypedArrayLikeMutator<'ctx, T, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
+impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> TypedArrayLikeMutator<'ctx, G, T, Index>
+    for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
 where
     Adapted: UntypedArrayLikeMutator<'ctx, Index>,
 {
     fn upcast_from_type(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         value: T,
     ) -> BasicValueEnum<'ctx> {
-        (self.upcast_fn)(ctx, value)
+        (self.upcast_fn)(ctx, generator, value)
     }
 }
 
@@ -384,12 +397,12 @@ impl<'ctx> ArrayLikeValue<'ctx> for ArraySliceValue<'ctx> {
 impl<'ctx> ArrayLikeIndexer<'ctx> for ArraySliceValue<'ctx> {
     unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &IntValue<'ctx>,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {
-        let var_name = name.map(|v| format!("{v}.addr")).unwrap_or_default();
+        let var_name = name.or(self.2).map(|v| format!("{v}.addr")).unwrap_or_default();
 
         unsafe {
             ctx.builder

nac3core/src/codegen/values/list.rs

@@ -8,7 +8,7 @@ use super::{
     ArrayLikeIndexer, ArrayLikeValue, ProxyValue, UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
 };
 use crate::codegen::{
-    types::ListType,
+    types::{structure::StructField, ListType, ProxyType},
     {CodeGenContext, CodeGenerator},
 };
 
@@ -42,48 +42,26 @@ impl<'ctx> ListValue<'ctx> {
         ListValue { value: ptr, llvm_usize, name }
     }
 
+    fn items_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, PointerValue<'ctx>> {
+        self.get_type().get_fields(&ctx.ctx).items
+    }
+
     /// Returns the double-indirection pointer to the `data` array, as if by calling `getelementptr`
     /// on the field.
     fn pptr_to_data(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
-        let llvm_i32 = ctx.ctx.i32_type();
-        let var_name = self.name.map(|v| format!("{v}.data.addr")).unwrap_or_default();
-
-        unsafe {
-            ctx.builder
-                .build_in_bounds_gep(
-                    self.as_base_value(),
-                    &[llvm_i32.const_zero(), llvm_i32.const_zero()],
-                    var_name.as_str(),
-                )
-                .unwrap()
-        }
-    }
-
-    /// Returns the pointer to the field storing the size of this `list`.
-    fn ptr_to_size(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
-        let llvm_i32 = ctx.ctx.i32_type();
-        let var_name = self.name.map(|v| format!("{v}.size.addr")).unwrap_or_default();
-
-        unsafe {
-            ctx.builder
-                .build_in_bounds_gep(
-                    self.as_base_value(),
-                    &[llvm_i32.const_zero(), llvm_i32.const_int(1, true)],
-                    var_name.as_str(),
-                )
-                .unwrap()
-        }
+        self.items_field(ctx).ptr_by_gep(ctx, self.value, self.name)
     }
 
     /// Stores the array of data elements `data` into this instance.
     fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, data: PointerValue<'ctx>) {
-        ctx.builder.build_store(self.pptr_to_data(ctx), data).unwrap();
+        self.items_field(ctx).set(ctx, self.value, data, self.name);
     }
 
     /// Convenience method for creating a new array storing data elements with the given element
     /// type `elem_ty` and `size`.
     ///
-    /// If `size` is [None], the size stored in the field of this instance is used instead.
+    /// If `size` is [None], the size stored in the field of this instance is used instead. If
+    /// `size` is resolved to `0` at runtime, `(T*) 0` will be assigned to `data`.
     pub fn create_data(
         &self,
         ctx: &mut CodeGenContext<'ctx, '_>,
@@ -114,6 +92,10 @@ impl<'ctx> ListValue<'ctx> {
         ListDataProxy(self)
     }
 
+    fn len_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, IntValue<'ctx>> {
+        self.get_type().get_fields(&ctx.ctx).len
+    }
+
     /// Stores the `size` of this `list` into this instance.
     pub fn store_size<G: CodeGenerator + ?Sized>(
         &self,
@@ -123,22 +105,33 @@ impl<'ctx> ListValue<'ctx> {
     ) {
         debug_assert_eq!(size.get_type(), generator.get_size_type(ctx.ctx));
 
-        let psize = self.ptr_to_size(ctx);
-        ctx.builder.build_store(psize, size).unwrap();
+        self.len_field(ctx).set(ctx, self.value, size, self.name);
     }
 
     /// Returns the size of this `list` as a value.
-    pub fn load_size(&self, ctx: &CodeGenContext<'ctx, '_>, name: Option<&str>) -> IntValue<'ctx> {
-        let psize = self.ptr_to_size(ctx);
-        let var_name = name
-            .map(ToString::to_string)
-            .or_else(|| self.name.map(|v| format!("{v}.size")))
-            .unwrap_or_default();
+    pub fn load_size(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> IntValue<'ctx> {
+        self.len_field(ctx).get(ctx, self.value, name)
+    }
 
-        ctx.builder
-            .build_load(psize, var_name.as_str())
-            .map(BasicValueEnum::into_int_value)
-            .unwrap()
+    /// Returns an instance of [`ListValue`] with the `items` pointer cast to `i8*`.
+    #[must_use]
+    pub fn as_i8_list<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> ListValue<'ctx> {
+        let llvm_i8 = ctx.ctx.i8_type();
+        let llvm_list_i8 = <Self as ProxyValue>::Type::new(generator, ctx.ctx, llvm_i8.into());
+
+        Self::from_pointer_value(
+            ctx.builder.build_pointer_cast(self.value, llvm_list_i8.as_base_type(), "").unwrap(),
+            self.llvm_usize,
+            self.name,
+        )
     }
 }
 
@@ -199,8 +192,8 @@ impl<'ctx> ArrayLikeValue<'ctx> for ListDataProxy<'ctx, '_> {
 impl<'ctx> ArrayLikeIndexer<'ctx> for ListDataProxy<'ctx, '_> {
     unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &IntValue<'ctx>,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {

nac3core/src/codegen/values/mod.rs

@@ -4,13 +4,15 @@ use super::types::ProxyType;
 use crate::codegen::CodeGenerator;
 pub use array::*;
 pub use list::*;
-pub use ndarray::*;
 pub use range::*;
+pub use tuple::*;
 
 mod array;
 mod list;
-mod ndarray;
+pub mod ndarray;
 mod range;
+mod tuple;
+pub mod utils;
 
 /// A LLVM type that is used to represent a non-primitive value in NAC3.
 pub trait ProxyValue<'ctx>: Into<Self::Base> {

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

@@ -0,0 +1,243 @@
+use inkwell::{
+    types::IntType,
+    values::{IntValue, PointerValue},
+};
+use itertools::Itertools;
+
+use crate::codegen::{
+    irrt,
+    types::{
+        ndarray::{NDArrayType, ShapeEntryType},
+        structure::StructField,
+        ProxyType,
+    },
+    values::{
+        ndarray::NDArrayValue, ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ProxyValue,
+        TypedArrayLikeAccessor, TypedArrayLikeAdapter, TypedArrayLikeMutator,
+    },
+    CodeGenContext, CodeGenerator,
+};
+
+#[derive(Copy, Clone)]
+pub struct ShapeEntryValue<'ctx> {
+    value: PointerValue<'ctx>,
+    llvm_usize: IntType<'ctx>,
+    name: Option<&'ctx str>,
+}
+
+impl<'ctx> ShapeEntryValue<'ctx> {
+    /// Checks whether `value` is an instance of `ShapeEntry`, returning [Err] if `value` is
+    /// not an instance.
+    pub fn is_representable(
+        value: PointerValue<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> Result<(), String> {
+        <Self as ProxyValue<'ctx>>::Type::is_representable(value.get_type(), llvm_usize)
+    }
+
+    /// Creates an [`ShapeEntryValue`] from a [`PointerValue`].
+    #[must_use]
+    pub fn from_pointer_value(
+        ptr: PointerValue<'ctx>,
+        llvm_usize: IntType<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> Self {
+        debug_assert!(Self::is_representable(ptr, llvm_usize).is_ok());
+
+        Self { value: ptr, llvm_usize, name }
+    }
+
+    fn ndims_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
+        self.get_type().get_fields(self.value.get_type().get_context()).ndims
+    }
+
+    /// Stores the number of dimensions into this value.
+    pub fn store_ndims(&self, ctx: &CodeGenContext<'ctx, '_>, value: IntValue<'ctx>) {
+        self.ndims_field().set(ctx, self.value, value, self.name);
+    }
+
+    fn shape_field(&self) -> StructField<'ctx, PointerValue<'ctx>> {
+        self.get_type().get_fields(self.value.get_type().get_context()).shape
+    }
+
+    /// Stores the shape into this value.
+    pub fn store_shape(&self, ctx: &CodeGenContext<'ctx, '_>, value: PointerValue<'ctx>) {
+        self.shape_field().set(ctx, self.value, value, self.name);
+    }
+}
+
+impl<'ctx> ProxyValue<'ctx> for ShapeEntryValue<'ctx> {
+    type Base = PointerValue<'ctx>;
+    type Type = ShapeEntryType<'ctx>;
+
+    fn get_type(&self) -> Self::Type {
+        Self::Type::from_type(self.value.get_type(), self.llvm_usize)
+    }
+
+    fn as_base_value(&self) -> Self::Base {
+        self.value
+    }
+}
+
+impl<'ctx> From<ShapeEntryValue<'ctx>> for PointerValue<'ctx> {
+    fn from(value: ShapeEntryValue<'ctx>) -> Self {
+        value.as_base_value()
+    }
+}
+
+impl<'ctx> NDArrayValue<'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: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    ) -> Self {
+        assert!(self.ndims <= target_ndims);
+        assert_eq!(target_shape.element_type(ctx, generator), self.llvm_usize.into());
+
+        let broadcast_ndarray = NDArrayType::new(generator, ctx.ctx, self.dtype, target_ndims)
+            .construct_uninitialized(generator, ctx, None);
+        broadcast_ndarray.copy_shape_from_array(
+            generator,
+            ctx,
+            target_shape.base_ptr(ctx, generator),
+        );
+
+        irrt::ndarray::call_nac3_ndarray_broadcast_to(generator, ctx, *self, broadcast_ndarray);
+        broadcast_ndarray
+    }
+}
+
+/// A result produced by [`broadcast_all_ndarrays`]
+#[derive(Clone)]
+pub struct BroadcastAllResult<'ctx, G: CodeGenerator + ?Sized> {
+    /// The statically known `ndims` of the broadcast result.
+    pub ndims: u64,
+
+    /// The broadcasting shape.
+    pub shape: TypedArrayLikeAdapter<'ctx, G, IntValue<'ctx>>,
+
+    /// 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<NDArrayValue<'ctx>>,
+}
+
+/// Helper function to call [`irrt::ndarray::call_nac3_ndarray_broadcast_shapes`].
+fn broadcast_shapes<'ctx, G, Shape>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    in_shape_entries: &[(ArraySliceValue<'ctx>, u64)], // (shape, shape's length/ndims)
+    broadcast_ndims: u64,
+    broadcast_shape: &Shape,
+) where
+    G: CodeGenerator + ?Sized,
+    Shape: TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>
+        + TypedArrayLikeMutator<'ctx, G, IntValue<'ctx>>,
+{
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_shape_ty = ShapeEntryType::new(generator, ctx.ctx);
+
+    assert!(in_shape_entries
+        .iter()
+        .all(|entry| entry.0.element_type(ctx, generator) == llvm_usize.into()));
+    assert_eq!(broadcast_shape.element_type(ctx, generator), llvm_usize.into());
+
+    // Prepare input shape entries to be passed to `call_nac3_ndarray_broadcast_shapes`.
+    let num_shape_entries =
+        llvm_usize.const_int(u64::try_from(in_shape_entries.len()).unwrap(), false);
+    let shape_entries = llvm_shape_ty.array_alloca(ctx, num_shape_entries, None);
+    for (i, (in_shape, in_ndims)) in in_shape_entries.iter().enumerate() {
+        let pshape_entry = unsafe {
+            shape_entries.ptr_offset_unchecked(
+                ctx,
+                generator,
+                &llvm_usize.const_int(i as u64, false),
+                None,
+            )
+        };
+        let shape_entry = llvm_shape_ty.map_value(pshape_entry, None);
+
+        let in_ndims = llvm_usize.const_int(*in_ndims, false);
+        shape_entry.store_ndims(ctx, in_ndims);
+
+        shape_entry.store_shape(ctx, in_shape.base_ptr(ctx, generator));
+    }
+
+    let broadcast_ndims = llvm_usize.const_int(broadcast_ndims, false);
+    irrt::ndarray::call_nac3_ndarray_broadcast_shapes(
+        generator,
+        ctx,
+        num_shape_entries,
+        shape_entries,
+        broadcast_ndims,
+        broadcast_shape,
+    );
+}
+
+impl<'ctx> NDArrayType<'ctx> {
+    /// Broadcast all ndarrays according to
+    /// [`np.broadcast()`](https://numpy.org/doc/stable/reference/generated/numpy.broadcast.html)
+    /// and return a [`BroadcastAllResult`] containing all the information of the result of the
+    /// broadcast operation.
+    pub fn broadcast<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ndarrays: &[NDArrayValue<'ctx>],
+    ) -> BroadcastAllResult<'ctx, G> {
+        assert!(!ndarrays.is_empty());
+
+        let llvm_usize = generator.get_size_type(ctx.ctx);
+
+        // Infer the broadcast output ndims.
+        let broadcast_ndims_int =
+            ndarrays.iter().map(|ndarray| ndarray.get_type().ndims()).max().unwrap();
+        assert!(self.ndims() >= broadcast_ndims_int);
+
+        let broadcast_ndims = llvm_usize.const_int(broadcast_ndims_int, false);
+        let broadcast_shape = ArraySliceValue::from_ptr_val(
+            ctx.builder.build_array_alloca(llvm_usize, broadcast_ndims, "").unwrap(),
+            broadcast_ndims,
+            None,
+        );
+        let broadcast_shape = TypedArrayLikeAdapter::from(
+            broadcast_shape,
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        );
+
+        let shape_entries = ndarrays
+            .iter()
+            .map(|ndarray| {
+                (ndarray.shape().as_slice_value(ctx, generator), ndarray.get_type().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 = 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/values/ndarray/contiguous.rs

@@ -0,0 +1,200 @@
+use inkwell::{
+    types::{BasicType, BasicTypeEnum, IntType},
+    values::{IntValue, PointerValue},
+    AddressSpace,
+};
+
+use super::{ArrayLikeValue, NDArrayValue, ProxyValue};
+use crate::codegen::{
+    stmt::gen_if_callback,
+    types::{
+        ndarray::{ContiguousNDArrayType, NDArrayType},
+        structure::StructField,
+    },
+    CodeGenContext, CodeGenerator,
+};
+
+#[derive(Copy, Clone)]
+pub struct ContiguousNDArrayValue<'ctx> {
+    value: PointerValue<'ctx>,
+    item: BasicTypeEnum<'ctx>,
+    llvm_usize: IntType<'ctx>,
+    name: Option<&'ctx str>,
+}
+
+impl<'ctx> ContiguousNDArrayValue<'ctx> {
+    /// Checks whether `value` is an instance of `ContiguousNDArray`, returning [Err] if `value` is
+    /// not an instance.
+    pub fn is_representable(
+        value: PointerValue<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> Result<(), String> {
+        <Self as ProxyValue<'ctx>>::Type::is_representable(value.get_type(), llvm_usize)
+    }
+
+    /// Creates an [`ContiguousNDArrayValue`] from a [`PointerValue`].
+    #[must_use]
+    pub fn from_pointer_value(
+        ptr: PointerValue<'ctx>,
+        dtype: BasicTypeEnum<'ctx>,
+        llvm_usize: IntType<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> Self {
+        debug_assert!(Self::is_representable(ptr, llvm_usize).is_ok());
+
+        Self { value: ptr, item: dtype, llvm_usize, name }
+    }
+
+    fn ndims_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
+        self.get_type().get_fields().ndims
+    }
+
+    pub fn store_ndims(&self, ctx: &CodeGenContext<'ctx, '_>, value: IntValue<'ctx>) {
+        self.ndims_field().set(ctx, self.as_base_value(), value, self.name);
+    }
+
+    fn shape_field(&self) -> StructField<'ctx, PointerValue<'ctx>> {
+        self.get_type().get_fields().shape
+    }
+
+    pub fn store_shape(&self, ctx: &CodeGenContext<'ctx, '_>, value: PointerValue<'ctx>) {
+        self.shape_field().set(ctx, self.as_base_value(), value, self.name);
+    }
+
+    pub fn load_shape(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
+        self.shape_field().get(ctx, self.value, self.name)
+    }
+
+    fn data_field(&self) -> StructField<'ctx, PointerValue<'ctx>> {
+        self.get_type().get_fields().data
+    }
+
+    pub fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, value: PointerValue<'ctx>) {
+        self.data_field().set(ctx, self.as_base_value(), value, self.name);
+    }
+
+    pub fn load_data(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
+        self.data_field().get(ctx, self.value, self.name)
+    }
+}
+
+impl<'ctx> ProxyValue<'ctx> for ContiguousNDArrayValue<'ctx> {
+    type Base = PointerValue<'ctx>;
+    type Type = ContiguousNDArrayType<'ctx>;
+
+    fn get_type(&self) -> Self::Type {
+        <Self as ProxyValue<'ctx>>::Type::from_type(
+            self.as_base_value().get_type(),
+            self.item,
+            self.llvm_usize,
+        )
+    }
+
+    fn as_base_value(&self) -> Self::Base {
+        self.value
+    }
+}
+
+impl<'ctx> From<ContiguousNDArrayValue<'ctx>> for PointerValue<'ctx> {
+    fn from(value: ContiguousNDArrayValue<'ctx>) -> Self {
+        value.as_base_value()
+    }
+}
+
+impl<'ctx> NDArrayValue<'ctx> {
+    /// Create a [`ContiguousNDArrayValue`] from the contents of this ndarray.
+    ///
+    /// This function may or may not be expensive depending on if this ndarray has contiguous data.
+    ///
+    /// If this ndarray is not C-contiguous, this function will allocate memory on the stack for the
+    /// `data` field of the returned [`ContiguousNDArrayValue`] and copy contents of this ndarray to
+    /// there.
+    ///
+    /// If this ndarray is C-contiguous, contents of this ndarray will not be copied. The created
+    /// [`ContiguousNDArrayValue`] will share memory with this ndarray.
+    pub fn make_contiguous_ndarray<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> ContiguousNDArrayValue<'ctx> {
+        let result = ContiguousNDArrayType::new(generator, ctx.ctx, self.dtype)
+            .alloca_var(generator, ctx, self.name);
+
+        // Set ndims and shape.
+        let ndims = self.llvm_usize.const_int(self.ndims, false);
+        result.store_ndims(ctx, ndims);
+
+        let shape = self.shape();
+        result.store_shape(ctx, shape.base_ptr(ctx, generator));
+
+        gen_if_callback(
+            generator,
+            ctx,
+            |generator, ctx| Ok(self.is_c_contiguous(generator, ctx)),
+            |_, ctx| {
+                // This ndarray is contiguous.
+                let data = self.data_field(ctx).get(ctx, self.as_base_value(), self.name);
+                let data = ctx
+                    .builder
+                    .build_pointer_cast(data, result.item.ptr_type(AddressSpace::default()), "")
+                    .unwrap();
+                result.store_data(ctx, data);
+
+                Ok(())
+            },
+            |generator, ctx| {
+                // This ndarray is not contiguous. Do a full-copy on `data`. `make_copy` produces an
+                // ndarray with contiguous `data`.
+                let copied_ndarray = self.make_copy(generator, ctx);
+                let data = copied_ndarray.data().base_ptr(ctx, generator);
+                let data = ctx
+                    .builder
+                    .build_pointer_cast(data, result.item.ptr_type(AddressSpace::default()), "")
+                    .unwrap();
+                result.store_data(ctx, data);
+
+                Ok(())
+            },
+        )
+        .unwrap();
+
+        result
+    }
+
+    /// Create an [`NDArrayValue`] from a [`ContiguousNDArrayValue`].
+    ///
+    /// The operation is cheap. The newly created [`NDArrayValue`] will share the same memory as the
+    /// [`ContiguousNDArrayValue`].
+    ///
+    /// `ndims` has to be provided as [`NDArrayValue`] requires a statically known `ndims` value,
+    /// despite the fact that the information should be contained within the
+    /// [`ContiguousNDArrayValue`].
+    pub fn from_contiguous_ndarray<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        carray: ContiguousNDArrayValue<'ctx>,
+        ndims: u64,
+    ) -> Self {
+        // TODO: Debug assert `ndims == carray.ndims` to catch bugs.
+
+        // Allocate the resulting ndarray.
+        let ndarray = NDArrayType::new(generator, ctx.ctx, carray.item, ndims)
+            .construct_uninitialized(generator, ctx, carray.name);
+
+        // Copy shape and update strides
+        let shape = carray.load_shape(ctx);
+        ndarray.copy_shape_from_array(generator, ctx, shape);
+        ndarray.set_strides_contiguous(generator, ctx);
+
+        // Share data
+        let data = carray.load_data(ctx);
+        ndarray.store_data(
+            ctx,
+            ctx.builder
+                .build_pointer_cast(data, ctx.ctx.i8_type().ptr_type(AddressSpace::default()), "")
+                .unwrap(),
+        );
+
+        ndarray
+    }
+}

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

@@ -0,0 +1,260 @@
+use inkwell::{
+    types::IntType,
+    values::{IntValue, PointerValue},
+    AddressSpace,
+};
+use itertools::Itertools;
+
+use nac3parser::ast::{Expr, ExprKind};
+
+use crate::{
+    codegen::{
+        irrt,
+        types::{
+            ndarray::{NDArrayType, NDIndexType},
+            structure::StructField,
+            utils::SliceType,
+        },
+        values::{ndarray::NDArrayValue, utils::RustSlice, ProxyValue},
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::Type,
+};
+
+/// An IRRT representation of an ndarray subscript index.
+#[derive(Copy, Clone)]
+pub struct NDIndexValue<'ctx> {
+    value: PointerValue<'ctx>,
+    llvm_usize: IntType<'ctx>,
+    name: Option<&'ctx str>,
+}
+
+impl<'ctx> NDIndexValue<'ctx> {
+    /// Checks whether `value` is an instance of `ndindex`, returning [Err] if `value` is not an
+    /// instance.
+    pub fn is_representable(
+        value: PointerValue<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> Result<(), String> {
+        <Self as ProxyValue<'ctx>>::Type::is_representable(value.get_type(), llvm_usize)
+    }
+
+    /// Creates an [`NDIndexValue`] from a [`PointerValue`].
+    #[must_use]
+    pub fn from_pointer_value(
+        ptr: PointerValue<'ctx>,
+        llvm_usize: IntType<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> Self {
+        debug_assert!(Self::is_representable(ptr, llvm_usize).is_ok());
+
+        Self { value: ptr, llvm_usize, name }
+    }
+
+    fn type_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
+        self.get_type().get_fields().type_
+    }
+
+    pub fn load_type(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
+        self.type_field().get(ctx, self.value, self.name)
+    }
+
+    pub fn store_type(&self, ctx: &CodeGenContext<'ctx, '_>, value: IntValue<'ctx>) {
+        self.type_field().set(ctx, self.value, value, self.name);
+    }
+
+    fn data_field(&self) -> StructField<'ctx, PointerValue<'ctx>> {
+        self.get_type().get_fields().data
+    }
+
+    pub fn load_data(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
+        self.data_field().get(ctx, self.value, self.name)
+    }
+
+    pub fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, value: PointerValue<'ctx>) {
+        self.data_field().set(ctx, self.value, value, self.name);
+    }
+}
+
+impl<'ctx> ProxyValue<'ctx> for NDIndexValue<'ctx> {
+    type Base = PointerValue<'ctx>;
+    type Type = NDIndexType<'ctx>;
+
+    fn get_type(&self) -> Self::Type {
+        Self::Type::from_type(self.value.get_type(), self.llvm_usize)
+    }
+
+    fn as_base_value(&self) -> Self::Base {
+        self.value
+    }
+}
+
+impl<'ctx> From<NDIndexValue<'ctx>> for PointerValue<'ctx> {
+    fn from(value: NDIndexValue<'ctx>) -> Self {
+        value.as_base_value()
+    }
+}
+
+impl<'ctx> NDArrayValue<'ctx> {
+    /// Get the expected `ndims` after indexing with `indices`.
+    #[must_use]
+    fn deduce_ndims_after_indexing_with(&self, indices: &[RustNDIndex<'ctx>]) -> u64 {
+        let mut ndims = self.ndims;
+
+        for index in indices {
+            match index {
+                RustNDIndex::SingleElement(_) => {
+                    ndims -= 1; // Single elements decrements ndims
+                }
+                RustNDIndex::NewAxis => {
+                    ndims += 1; // `np.newaxis` / `none` adds a new axis
+                }
+                RustNDIndex::Ellipsis | RustNDIndex::Slice(_) => {}
+            }
+        }
+
+        ndims
+    }
+
+    /// Index into the ndarray, and return a newly-allocated view on this ndarray.
+    ///
+    /// This function behaves like NumPy's ndarray indexing, but if the indices index
+    /// into a single element, an unsized ndarray is returned.
+    #[must_use]
+    pub fn index<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        indices: &[RustNDIndex<'ctx>],
+    ) -> Self {
+        let dst_ndims = self.deduce_ndims_after_indexing_with(indices);
+        let dst_ndarray = NDArrayType::new(generator, ctx.ctx, self.dtype, dst_ndims)
+            .construct_uninitialized(generator, ctx, None);
+
+        let indices =
+            NDIndexType::new(generator, ctx.ctx).construct_ndindices(generator, ctx, indices);
+        irrt::ndarray::call_nac3_ndarray_index(generator, ctx, indices, *self, dst_ndarray);
+
+        dst_ndarray
+    }
+}
+
+/// A convenience enum representing a [`NDIndexValue`].
+// TODO: Rename to CTConstNDIndex
+#[derive(Debug, Clone)]
+pub enum RustNDIndex<'ctx> {
+    SingleElement(IntValue<'ctx>),
+    Slice(RustSlice<'ctx>),
+    NewAxis,
+    Ellipsis,
+}
+
+impl<'ctx> RustNDIndex<'ctx> {
+    /// Generate LLVM code to transform an ndarray subscript expression to
+    /// its list of [`RustNDIndex`]
+    ///
+    /// i.e.,
+    /// ```python
+    /// my_ndarray[::3, 1, :2:]
+    ///            ^^^^^^^^^^^ Then these into a three `RustNDIndex`es
+    /// ```
+    pub fn from_subscript_expr<G: CodeGenerator>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        subscript: &Expr<Option<Type>>,
+    ) -> Result<Vec<RustNDIndex<'ctx>>, String> {
+        // Annoying notes about `slice`
+        //  - `my_array[5]`
+        //    - slice is a `Constant`
+        //  - `my_array[:5]`
+        //    - slice is a `Slice`
+        //  - `my_array[:]`
+        //    - slice is a `Slice`, but lower upper step would all be `Option::None`
+        //  - `my_array[:, :]`
+        //    - slice is now a `Tuple` of two `Slice`-s
+        //
+        // In summary:
+        //  - when there is a comma "," within [], `slice` will be a `Tuple` of the entries.
+        //  - when there is not comma "," within [] (i.e., just a single entry), `slice` will be that entry itself.
+        //
+        // So we first "flatten" out the slice expression
+        let index_exprs = match &subscript.node {
+            ExprKind::Tuple { elts, .. } => elts.iter().collect_vec(),
+            _ => vec![subscript],
+        };
+
+        // Process all index expressions
+        let mut rust_ndindices: Vec<RustNDIndex> = Vec::with_capacity(index_exprs.len()); // Not using iterators here because `?` is used here.
+        for index_expr in index_exprs {
+            // NOTE: Currently nac3core's slices do not have an object representation,
+            // so the code/implementation looks awkward - we have to do pattern matching on the expression
+            let ndindex = if let ExprKind::Slice { lower, upper, step } = &index_expr.node {
+                // Handle slices
+                let slice = RustSlice::from_slice_expr(generator, ctx, lower, upper, step)?;
+                RustNDIndex::Slice(slice)
+            } else {
+                // Treat and handle everything else as a single element index.
+                let index = generator.gen_expr(ctx, index_expr)?.unwrap().to_basic_value_enum(
+                    ctx,
+                    generator,
+                    ctx.primitives.int32, // Must be int32, this checks for illegal values
+                )?;
+                let index = index.into_int_value();
+
+                RustNDIndex::SingleElement(index)
+            };
+            rust_ndindices.push(ndindex);
+        }
+        Ok(rust_ndindices)
+    }
+
+    /// Get the value to set `NDIndex::type` for this variant.
+    #[must_use]
+    pub fn get_type_id(&self) -> u64 {
+        // Defined in IRRT, must be in sync
+        match self {
+            RustNDIndex::SingleElement(_) => 0,
+            RustNDIndex::Slice(_) => 1,
+            RustNDIndex::NewAxis => 2,
+            RustNDIndex::Ellipsis => 3,
+        }
+    }
+
+    /// Serialize this [`RustNDIndex`] by writing it into an LLVM [`NDIndexValue`].
+    pub fn write_to_ndindex<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dst_ndindex: NDIndexValue<'ctx>,
+    ) {
+        let llvm_pi8 = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
+
+        // Set `dst_ndindex.type`
+        dst_ndindex.store_type(ctx, ctx.ctx.i8_type().const_int(self.get_type_id(), false));
+
+        // Set `dst_ndindex_ptr->data`
+        match self {
+            RustNDIndex::SingleElement(in_index) => {
+                let index_ptr = ctx.builder.build_alloca(ctx.ctx.i32_type(), "").unwrap();
+                ctx.builder.build_store(index_ptr, *in_index).unwrap();
+
+                dst_ndindex.store_data(
+                    ctx,
+                    ctx.builder.build_pointer_cast(index_ptr, llvm_pi8, "").unwrap(),
+                );
+            }
+            RustNDIndex::Slice(in_rust_slice) => {
+                let user_slice_ptr =
+                    SliceType::new(ctx.ctx, ctx.ctx.i32_type(), generator.get_size_type(ctx.ctx))
+                        .alloca_var(generator, ctx, None);
+                in_rust_slice.write_to_slice(ctx, user_slice_ptr);
+
+                dst_ndindex.store_data(
+                    ctx,
+                    ctx.builder.build_pointer_cast(user_slice_ptr.into(), llvm_pi8, "").unwrap(),
+                );
+            }
+            RustNDIndex::NewAxis | RustNDIndex::Ellipsis => {}
+        }
+    }
+}

nac3core/src/codegen/values/ndarray/map.rs

@@ -0,0 +1,69 @@
+use inkwell::{types::BasicTypeEnum, values::BasicValueEnum};
+
+use crate::codegen::{
+    values::{
+        ndarray::{NDArrayOut, NDArrayValue, ScalarOrNDArray},
+        ProxyValue,
+    },
+    CodeGenContext, CodeGenerator,
+};
+
+impl<'ctx> NDArrayValue<'ctx> {
+    /// Map through this ndarray with an elementwise function.
+    pub fn map<'a, G, Mapping>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        out: NDArrayOut<'ctx>,
+        mapping: Mapping,
+    ) -> Result<Self, String>
+    where
+        G: CodeGenerator + ?Sized,
+        Mapping: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            BasicValueEnum<'ctx>,
+        ) -> Result<BasicValueEnum<'ctx>, String>,
+    {
+        self.get_type().broadcast_starmap(
+            generator,
+            ctx,
+            &[*self],
+            out,
+            |generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
+        )
+    }
+}
+
+impl<'ctx> ScalarOrNDArray<'ctx> {
+    /// Map through this [`ScalarOrNDArray`] with an elementwise function.
+    ///
+    /// If this is a scalar, `mapping` will directly act on the scalar. This function will return a
+    /// [`ScalarOrNDArray::Scalar`] of that result.
+    ///
+    /// If this is an ndarray, `mapping` will be applied to the elements of the ndarray. A new
+    /// ndarray of the results will be created and returned as a [`ScalarOrNDArray::NDArray`].
+    pub fn map<'a, G, Mapping>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        ret_dtype: BasicTypeEnum<'ctx>,
+        mapping: Mapping,
+    ) -> Result<ScalarOrNDArray<'ctx>, String>
+    where
+        G: CodeGenerator + ?Sized,
+        Mapping: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            BasicValueEnum<'ctx>,
+        ) -> Result<BasicValueEnum<'ctx>, String>,
+    {
+        ScalarOrNDArray::broadcasting_starmap(
+            generator,
+            ctx,
+            &[*self],
+            ret_dtype,
+            |generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
+        )
+    }
+}

nac3core/src/codegen/values/ndarray/matmul.rs

@@ -0,0 +1,323 @@
+use std::cmp::max;
+
+use nac3parser::ast::Operator;
+
+use super::{NDArrayOut, NDArrayValue, RustNDIndex};
+use crate::{
+    codegen::{
+        expr::gen_binop_expr_with_values,
+        irrt,
+        stmt::gen_for_callback_incrementing,
+        types::ndarray::NDArrayType,
+        values::{
+            ArrayLikeValue, ArraySliceValue, TypedArrayLikeAccessor, TypedArrayLikeAdapter,
+            UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
+        },
+        CodeGenContext, CodeGenerator,
+    },
+    toplevel::helper::arraylike_flatten_element_type,
+    typecheck::{magic_methods::Binop, typedef::Type},
+};
+
+/// Perform `np.einsum("...ij,...jk->...ik", in_a, in_b)`.
+///
+/// `dst_dtype` defines the dtype of the returned ndarray.
+fn matmul_at_least_2d<'ctx, G: CodeGenerator>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    dst_dtype: Type,
+    (in_a_ty, in_a): (Type, NDArrayValue<'ctx>),
+    (in_b_ty, in_b): (Type, NDArrayValue<'ctx>),
+) -> NDArrayValue<'ctx> {
+    assert!(in_a.ndims >= 2, "in_a (which is {}) must be >= 2", in_a.ndims);
+    assert!(in_b.ndims >= 2, "in_b (which is {}) must be >= 2", in_b.ndims);
+
+    let lhs_dtype = arraylike_flatten_element_type(&mut ctx.unifier, in_a_ty);
+    let rhs_dtype = arraylike_flatten_element_type(&mut ctx.unifier, in_b_ty);
+
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_dst_dtype = ctx.get_llvm_type(generator, dst_dtype);
+
+    // Deduce ndims of the result of matmul.
+    let ndims_int = max(in_a.ndims, in_b.ndims);
+    let ndims = llvm_usize.const_int(ndims_int, false);
+
+    // Broadcasts `in_a.shape[:-2]` and `in_b.shape[:-2]` together and allocate the
+    // destination ndarray to store the result of matmul.
+    let (lhs, rhs, dst) = {
+        let in_lhs_ndims = llvm_usize.const_int(in_a.ndims, false);
+        let in_lhs_shape = TypedArrayLikeAdapter::from(
+            ArraySliceValue::from_ptr_val(
+                in_a.shape().base_ptr(ctx, generator),
+                in_lhs_ndims,
+                None,
+            ),
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        );
+        let in_rhs_ndims = llvm_usize.const_int(in_b.ndims, false);
+        let in_rhs_shape = TypedArrayLikeAdapter::from(
+            ArraySliceValue::from_ptr_val(
+                in_b.shape().base_ptr(ctx, generator),
+                in_rhs_ndims,
+                None,
+            ),
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        );
+        let lhs_shape = TypedArrayLikeAdapter::from(
+            ArraySliceValue::from_ptr_val(
+                ctx.builder.build_array_alloca(llvm_usize, ndims, "").unwrap(),
+                ndims,
+                None,
+            ),
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        );
+        let rhs_shape = TypedArrayLikeAdapter::from(
+            ArraySliceValue::from_ptr_val(
+                ctx.builder.build_array_alloca(llvm_usize, ndims, "").unwrap(),
+                ndims,
+                None,
+            ),
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        );
+        let dst_shape = TypedArrayLikeAdapter::from(
+            ArraySliceValue::from_ptr_val(
+                ctx.builder.build_array_alloca(llvm_usize, ndims, "").unwrap(),
+                ndims,
+                None,
+            ),
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        );
+
+        // Matmul dimension compatibility is checked here.
+        irrt::ndarray::call_nac3_ndarray_matmul_calculate_shapes(
+            generator,
+            ctx,
+            &in_lhs_shape,
+            &in_rhs_shape,
+            ndims,
+            &lhs_shape,
+            &rhs_shape,
+            &dst_shape,
+        );
+
+        let lhs = in_a.broadcast_to(generator, ctx, ndims_int, &lhs_shape);
+        let rhs = in_b.broadcast_to(generator, ctx, ndims_int, &rhs_shape);
+
+        let dst = NDArrayType::new(generator, ctx.ctx, llvm_dst_dtype, ndims_int)
+            .construct_uninitialized(generator, ctx, None);
+        dst.copy_shape_from_array(generator, ctx, dst_shape.base_ptr(ctx, generator));
+        unsafe {
+            dst.create_data(generator, ctx);
+        }
+
+        (lhs, rhs, dst)
+    };
+
+    let len = unsafe {
+        lhs.shape().get_typed_unchecked(
+            ctx,
+            generator,
+            &llvm_usize.const_int(ndims_int - 1, false),
+            None,
+        )
+    };
+
+    let at_row = i64::try_from(ndims_int - 2).unwrap();
+    let at_col = i64::try_from(ndims_int - 1).unwrap();
+
+    let dst_dtype_llvm = ctx.get_llvm_type(generator, dst_dtype);
+    let dst_zero = dst_dtype_llvm.const_zero();
+
+    dst.foreach(generator, ctx, |generator, ctx, _, hdl| {
+        let pdst_ij = hdl.get_pointer(ctx);
+
+        ctx.builder.build_store(pdst_ij, dst_zero).unwrap();
+
+        let indices = hdl.get_indices::<G>();
+        let i = unsafe {
+            indices.get_unchecked(ctx, generator, &llvm_usize.const_int(at_row as u64, true), None)
+        };
+        let j = unsafe {
+            indices.get_unchecked(ctx, generator, &llvm_usize.const_int(at_col as u64, true), None)
+        };
+
+        let num_0 = llvm_usize.const_int(0, false);
+        let num_1 = llvm_usize.const_int(1, false);
+
+        gen_for_callback_incrementing(
+            generator,
+            ctx,
+            None,
+            num_0,
+            (len, false),
+            |generator, ctx, _, k| {
+                // `indices` is modified to index into `a` and `b`, and restored.
+                unsafe {
+                    indices.set_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(at_row as u64, true),
+                        i,
+                    );
+                    indices.set_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(at_col as u64, true),
+                        k.into(),
+                    );
+                }
+                let a_ik = unsafe { lhs.data().get_unchecked(ctx, generator, &indices, None) };
+
+                unsafe {
+                    indices.set_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(at_row as u64, true),
+                        k.into(),
+                    );
+                    indices.set_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(at_col as u64, true),
+                        j,
+                    );
+                }
+                let b_kj = unsafe { rhs.data().get_unchecked(ctx, generator, &indices, None) };
+
+                // Restore `indices`.
+                unsafe {
+                    indices.set_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(at_row as u64, true),
+                        i,
+                    );
+                    indices.set_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(at_col as u64, true),
+                        j,
+                    );
+                }
+
+                // x = a_[...]ik * b_[...]kj
+                let x = gen_binop_expr_with_values(
+                    generator,
+                    ctx,
+                    (&Some(lhs_dtype), a_ik),
+                    Binop::normal(Operator::Mult),
+                    (&Some(rhs_dtype), b_kj),
+                    ctx.current_loc,
+                )?
+                .unwrap()
+                .to_basic_value_enum(ctx, generator, dst_dtype)?;
+
+                // dst_[...]ij += x
+                let dst_ij = ctx.builder.build_load(pdst_ij, "").unwrap();
+                let dst_ij = gen_binop_expr_with_values(
+                    generator,
+                    ctx,
+                    (&Some(dst_dtype), dst_ij),
+                    Binop::normal(Operator::Add),
+                    (&Some(dst_dtype), x),
+                    ctx.current_loc,
+                )?
+                .unwrap()
+                .to_basic_value_enum(ctx, generator, dst_dtype)?;
+                ctx.builder.build_store(pdst_ij, dst_ij).unwrap();
+
+                Ok(())
+            },
+            num_1,
+        )
+    })
+    .unwrap();
+
+    dst
+}
+
+impl<'ctx> NDArrayValue<'ctx> {
+    /// Perform [`np.matmul`](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).
+    ///
+    /// This function always return an [`NDArrayValue`]. You may want to use
+    /// [`NDArrayValue::split_unsized`] to handle when the output could be a scalar.
+    ///
+    /// `dst_dtype` defines the dtype of the returned ndarray.
+    #[must_use]
+    pub fn matmul<G: CodeGenerator>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        self_ty: Type,
+        (other_ty, other): (Type, Self),
+        (out_dtype, out): (Type, NDArrayOut<'ctx>),
+    ) -> Self {
+        // Sanity check, but type inference should prevent this.
+        assert!(self.ndims > 0 && other.ndims > 0, "np.matmul disallows scalar input");
+
+        // If both arguments are 2-D they are multiplied like conventional matrices.
+        //
+        // If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the
+        // last two indices and broadcast accordingly.
+        //
+        // If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its
+        // dimensions. After matrix multiplication the prepended 1 is removed.
+        //
+        // If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its
+        // dimensions. After matrix multiplication the appended 1 is removed.
+
+        let new_a = if self.ndims == 1 {
+            // Prepend 1 to its dimensions
+            self.index(generator, ctx, &[RustNDIndex::NewAxis, RustNDIndex::Ellipsis])
+        } else {
+            *self
+        };
+
+        let new_b = if other.ndims == 1 {
+            // Append 1 to its dimensions
+            other.index(generator, ctx, &[RustNDIndex::Ellipsis, RustNDIndex::NewAxis])
+        } else {
+            other
+        };
+
+        // NOTE: `result` will always be a newly allocated ndarray.
+        // Current implementation cannot do in-place matrix muliplication.
+        let mut result =
+            matmul_at_least_2d(generator, ctx, out_dtype, (self_ty, new_a), (other_ty, new_b));
+
+        // Postprocessing on the result to remove prepended/appended axes.
+        let mut postindices = vec![];
+        let zero = ctx.ctx.i32_type().const_zero();
+
+        if self.ndims == 1 {
+            // Remove the prepended 1
+            postindices.push(RustNDIndex::SingleElement(zero));
+        }
+
+        if other.ndims == 1 {
+            // Remove the appended 1
+            postindices.push(RustNDIndex::Ellipsis);
+            postindices.push(RustNDIndex::SingleElement(zero));
+        }
+
+        if !postindices.is_empty() {
+            result = result.index(generator, ctx, &postindices);
+        }
+
+        match out {
+            NDArrayOut::NewNDArray { .. } => result,
+            NDArrayOut::WriteToNDArray { ndarray: out_ndarray } => {
+                let result_shape = result.shape();
+                out_ndarray.assert_can_be_written_by_out(generator, ctx, result_shape);
+
+                out_ndarray.copy_data_from(generator, ctx, result);
+                out_ndarray
+            }
+        }
+    }
+}

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

@@ -1,26 +1,48 @@
+use std::iter::repeat_n;
+
 use inkwell::{
     types::{AnyType, AnyTypeEnum, BasicType, BasicTypeEnum, IntType},
-    values::{BasicValueEnum, IntValue, PointerValue},
+    values::{BasicValue, BasicValueEnum, IntValue, PointerValue},
     AddressSpace, IntPredicate,
 };
+use itertools::Itertools;
 
 use super::{
-    ArrayLikeIndexer, ArrayLikeValue, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeMutator,
-    UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
+    ArrayLikeIndexer, ArrayLikeValue, ProxyValue, TupleValue, TypedArrayLikeAccessor,
+    TypedArrayLikeAdapter, TypedArrayLikeMutator, UntypedArrayLikeAccessor,
+    UntypedArrayLikeMutator,
 };
-use crate::codegen::{
-    irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
-    llvm_intrinsics::call_int_umin,
-    stmt::gen_for_callback_incrementing,
-    types::{structure::StructField, NDArrayType},
-    CodeGenContext, CodeGenerator,
+use crate::{
+    codegen::{
+        irrt,
+        llvm_intrinsics::{call_int_umin, call_memcpy_generic_array},
+        stmt::gen_for_callback_incrementing,
+        type_aligned_alloca,
+        types::{ndarray::NDArrayType, structure::StructField, TupleType},
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::{Type, TypeEnum},
 };
+pub use broadcast::*;
+pub use contiguous::*;
+pub use indexing::*;
+pub use nditer::*;
+
+mod broadcast;
+mod contiguous;
+mod indexing;
+mod map;
+mod matmul;
+mod nditer;
+pub mod shape;
+mod view;
 
 /// Proxy type for accessing an `NDArray` value in LLVM.
 #[derive(Copy, Clone)]
 pub struct NDArrayValue<'ctx> {
     value: PointerValue<'ctx>,
     dtype: BasicTypeEnum<'ctx>,
+    ndims: u64,
     llvm_usize: IntType<'ctx>,
     name: Option<&'ctx str>,
 }
@@ -40,12 +62,13 @@ impl<'ctx> NDArrayValue<'ctx> {
     pub fn from_pointer_value(
         ptr: PointerValue<'ctx>,
         dtype: BasicTypeEnum<'ctx>,
+        ndims: u64,
         llvm_usize: IntType<'ctx>,
         name: Option<&'ctx str>,
     ) -> Self {
         debug_assert!(Self::is_representable(ptr, llvm_usize).is_ok());
 
-        NDArrayValue { value: ptr, dtype, llvm_usize, name }
+        NDArrayValue { value: ptr, dtype, ndims, llvm_usize, name }
     }
 
     fn ndims_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, IntValue<'ctx>> {
@@ -76,14 +99,29 @@ impl<'ctx> NDArrayValue<'ctx> {
         ctx.builder.build_load(pndims, "").map(BasicValueEnum::into_int_value).unwrap()
     }
 
-    fn shape_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, PointerValue<'ctx>> {
-        self.get_type().get_fields(ctx.ctx).shape
+    fn itemsize_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, IntValue<'ctx>> {
+        self.get_type().get_fields(ctx.ctx).itemsize
     }
 
-    /// Returns the double-indirection pointer to the `shape` array, as if by calling
-    /// `getelementptr` on the field.
-    fn ptr_to_shape(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
-        self.shape_field(ctx).ptr_by_gep(ctx, self.value, self.name)
+    /// Stores the size of each element `itemsize` into this instance.
+    pub fn store_itemsize<G: CodeGenerator + ?Sized>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
+        itemsize: IntValue<'ctx>,
+    ) {
+        debug_assert_eq!(itemsize.get_type(), generator.get_size_type(ctx.ctx));
+
+        self.itemsize_field(ctx).set(ctx, self.value, itemsize, self.name);
+    }
+
+    /// Returns the size of each element of this `NDArray` as a value.
+    pub fn load_itemsize(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
+        self.itemsize_field(ctx).get(ctx, self.value, self.name)
+    }
+
+    fn shape_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, PointerValue<'ctx>> {
+        self.get_type().get_fields(ctx.ctx).shape
     }
 
     /// Stores the array of dimension sizes `dims` into this instance.
@@ -107,6 +145,34 @@ impl<'ctx> NDArrayValue<'ctx> {
         NDArrayShapeProxy(self)
     }
 
+    fn strides_field(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> StructField<'ctx, PointerValue<'ctx>> {
+        self.get_type().get_fields(ctx.ctx).strides
+    }
+
+    /// Stores the array of stride sizes `strides` into this instance.
+    fn store_strides(&self, ctx: &CodeGenContext<'ctx, '_>, strides: PointerValue<'ctx>) {
+        self.strides_field(ctx).set(ctx, self.as_base_value(), strides, self.name);
+    }
+
+    /// Convenience method for creating a new array storing the stride with the given `size`.
+    pub fn create_strides(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        llvm_usize: IntType<'ctx>,
+        size: IntValue<'ctx>,
+    ) {
+        self.store_strides(ctx, ctx.builder.build_array_alloca(llvm_usize, size, "").unwrap());
+    }
+
+    /// Returns a proxy object to the field storing the stride of each dimension of this `NDArray`.
+    #[must_use]
+    pub fn strides(&self) -> NDArrayStridesProxy<'ctx, '_> {
+        NDArrayStridesProxy(self)
+    }
+
     fn data_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, PointerValue<'ctx>> {
         self.get_type().get_fields(ctx.ctx).data
     }
@@ -118,7 +184,7 @@ impl<'ctx> NDArrayValue<'ctx> {
     }
 
     /// Stores the array of data elements `data` into this instance.
-    fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, data: PointerValue<'ctx>) {
+    pub fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, data: PointerValue<'ctx>) {
         let data = ctx
             .builder
             .build_bit_cast(data, ctx.ctx.i8_type().ptr_type(AddressSpace::default()), "")
@@ -128,23 +194,23 @@ impl<'ctx> NDArrayValue<'ctx> {
 
     /// Convenience method for creating a new array storing data elements with the given element
     /// type `elem_ty` and `size`.
-    pub fn create_data(
+    ///
+    /// The data buffer will be allocated on the stack, and is considered to be owned by this ndarray instance.
+    ///
+    /// # Safety
+    ///
+    /// The caller must ensure that `shape` and `itemsize` of this ndarray instance is initialized.
+    pub unsafe fn create_data<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        elem_ty: BasicTypeEnum<'ctx>,
-        size: IntValue<'ctx>,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
     ) {
-        let itemsize = ctx
-            .builder
-            .build_int_z_extend_or_bit_cast(elem_ty.size_of().unwrap(), size.get_type(), "")
-            .unwrap();
-        let nbytes = ctx.builder.build_int_mul(size, itemsize, "").unwrap();
+        let nbytes = self.nbytes(generator, ctx);
 
-        // TODO: What about alignment?
-        self.store_data(
-            ctx,
-            ctx.builder.build_array_alloca(ctx.ctx.i8_type(), nbytes, "").unwrap(),
-        );
+        let data = type_aligned_alloca(generator, ctx, self.dtype, nbytes, None);
+        self.store_data(ctx, data);
+
+        self.set_strides_contiguous(generator, ctx);
     }
 
     /// Returns a proxy object to the field storing the data of this `NDArray`.
@@ -152,6 +218,298 @@ impl<'ctx> NDArrayValue<'ctx> {
     pub fn data(&self) -> NDArrayDataProxy<'ctx, '_> {
         NDArrayDataProxy(self)
     }
+
+    /// Copy shape dimensions from an array.
+    pub fn copy_shape_from_array<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        shape: PointerValue<'ctx>,
+    ) {
+        let num_items = self.load_ndims(ctx);
+
+        call_memcpy_generic_array(
+            ctx,
+            self.shape().base_ptr(ctx, generator),
+            shape,
+            num_items,
+            ctx.ctx.bool_type().const_zero(),
+        );
+    }
+
+    /// Copy shape dimensions from an ndarray.
+    /// Panics if `ndims` mismatches.
+    pub fn copy_shape_from_ndarray<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        src_ndarray: NDArrayValue<'ctx>,
+    ) {
+        assert_eq!(self.ndims, src_ndarray.ndims);
+
+        let src_shape = src_ndarray.shape().base_ptr(ctx, generator);
+        self.copy_shape_from_array(generator, ctx, src_shape);
+    }
+
+    /// Copy strides dimensions from an array.
+    pub fn copy_strides_from_array<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        strides: PointerValue<'ctx>,
+    ) {
+        let num_items = self.load_ndims(ctx);
+
+        call_memcpy_generic_array(
+            ctx,
+            self.strides().base_ptr(ctx, generator),
+            strides,
+            num_items,
+            ctx.ctx.bool_type().const_zero(),
+        );
+    }
+
+    /// Copy strides dimensions from an ndarray.
+    /// Panics if `ndims` mismatches.
+    pub fn copy_strides_from_ndarray<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        src_ndarray: NDArrayValue<'ctx>,
+    ) {
+        assert_eq!(self.ndims, src_ndarray.ndims);
+
+        let src_strides = src_ndarray.strides().base_ptr(ctx, generator);
+        self.copy_strides_from_array(generator, ctx, src_strides);
+    }
+
+    /// Get the `np.size()` of this ndarray.
+    pub fn size<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> IntValue<'ctx> {
+        irrt::ndarray::call_nac3_ndarray_size(generator, ctx, *self)
+    }
+
+    /// Get the `ndarray.nbytes` of this ndarray.
+    pub fn nbytes<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> IntValue<'ctx> {
+        irrt::ndarray::call_nac3_ndarray_nbytes(generator, ctx, *self)
+    }
+
+    /// Get the `len()` of this ndarray.
+    pub fn len<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> IntValue<'ctx> {
+        irrt::ndarray::call_nac3_ndarray_len(generator, ctx, *self)
+    }
+
+    /// Check if this ndarray is C-contiguous.
+    ///
+    /// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
+    pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> IntValue<'ctx> {
+        irrt::ndarray::call_nac3_ndarray_is_c_contiguous(generator, ctx, *self)
+    }
+
+    /// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
+    ///
+    /// Update the ndarray's strides to make the ndarray contiguous.
+    pub fn set_strides_contiguous<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) {
+        irrt::ndarray::call_nac3_ndarray_set_strides_by_shape(generator, ctx, *self);
+    }
+
+    /// Clone/Copy this ndarray - Allocate a new ndarray with the same shape as this ndarray and
+    /// copy the contents over.
+    ///
+    /// The new ndarray will own its data and will be C-contiguous.
+    #[must_use]
+    pub fn make_copy<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Self {
+        let clone = self.get_type().construct_uninitialized(generator, ctx, None);
+
+        let shape = self.shape();
+        clone.copy_shape_from_array(generator, ctx, shape.base_ptr(ctx, generator));
+        unsafe { clone.create_data(generator, ctx) };
+        clone.copy_data_from(generator, ctx, *self);
+        clone
+    }
+
+    /// Copy data from another ndarray.
+    ///
+    /// This ndarray and `src` is that their `np.size()` should be the same. Their shapes
+    /// do not matter. The copying order is determined by how their flattened views look.
+    ///
+    /// Panics if the `dtype`s of ndarrays are different.
+    pub fn copy_data_from<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        src: NDArrayValue<'ctx>,
+    ) {
+        assert_eq!(self.dtype, src.dtype, "self and src dtype should match");
+        irrt::ndarray::call_nac3_ndarray_copy_data(generator, ctx, src, *self);
+    }
+
+    /// Fill the ndarray with a scalar.
+    ///
+    /// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
+    pub fn fill<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        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, |_, ctx, _, nditer| {
+            let p = nditer.get_pointer(ctx);
+            ctx.builder.build_store(p, value).unwrap();
+            Ok(())
+        })
+        .unwrap();
+    }
+
+    /// Create the shape tuple of this ndarray like
+    /// [`np.shape(<ndarray>)`](https://numpy.org/doc/stable/reference/generated/numpy.shape.html).
+    ///
+    /// All elements in the tuple are `i32`.
+    pub fn make_shape_tuple<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> TupleValue<'ctx> {
+        let llvm_i32 = ctx.ctx.i32_type();
+
+        let objects = (0..self.ndims)
+            .map(|i| {
+                let dim = unsafe {
+                    self.shape().get_typed_unchecked(
+                        ctx,
+                        generator,
+                        &self.llvm_usize.const_int(i, false),
+                        None,
+                    )
+                };
+                ctx.builder.build_int_truncate_or_bit_cast(dim, llvm_i32, "").unwrap()
+            })
+            .map(|obj| obj.as_basic_value_enum())
+            .collect_vec();
+
+        TupleType::new(
+            generator,
+            ctx.ctx,
+            &repeat_n(llvm_i32.into(), self.ndims as usize).collect_vec(),
+        )
+        .construct_from_objects(ctx, objects, None)
+    }
+
+    /// Create the strides tuple of this ndarray like
+    /// [`<ndarray>.strides`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.strides.html).
+    ///
+    /// All elements in the tuple are `i32`.
+    pub fn make_strides_tuple<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> TupleValue<'ctx> {
+        let llvm_i32 = ctx.ctx.i32_type();
+
+        let objects = (0..self.ndims)
+            .map(|i| {
+                let dim = unsafe {
+                    self.strides().get_typed_unchecked(
+                        ctx,
+                        generator,
+                        &self.llvm_usize.const_int(i, false),
+                        None,
+                    )
+                };
+                ctx.builder.build_int_truncate_or_bit_cast(dim, llvm_i32, "").unwrap()
+            })
+            .map(|obj| obj.as_basic_value_enum())
+            .collect_vec();
+
+        TupleType::new(
+            generator,
+            ctx.ctx,
+            &repeat_n(llvm_i32.into(), self.ndims as usize).collect_vec(),
+        )
+        .construct_from_objects(ctx, objects, None)
+    }
+
+    /// Returns true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
+    #[must_use]
+    pub fn is_unsized(&self) -> bool {
+        self.ndims == 0
+    }
+
+    /// Returns the element present in this `ndarray` if this is unsized.
+    pub fn get_unsized_element<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Option<BasicValueEnum<'ctx>> {
+        if self.is_unsized() {
+            // NOTE: `np.size(self) == 0` here is never possible.
+            let zero = generator.get_size_type(ctx.ctx).const_zero();
+            let value = unsafe { self.data().get_unchecked(ctx, generator, &zero, None) };
+
+            Some(value)
+        } else {
+            None
+        }
+    }
+
+    /// If this ndarray is unsized, return its sole value as an [`BasicValueEnum`].
+    /// Otherwise, do nothing and return the ndarray itself.
+    pub fn split_unsized<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> ScalarOrNDArray<'ctx> {
+        if let Some(unsized_elem) = self.get_unsized_element(generator, ctx) {
+            ScalarOrNDArray::Scalar(unsized_elem)
+        } else {
+            ScalarOrNDArray::NDArray(*self)
+        }
+    }
+
+    /// Check if this `NDArray` can be used as an `out` ndarray for an operation.
+    ///
+    /// Raise an exception if the shapes do not match.
+    pub fn assert_can_be_written_by_out<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        out_shape: impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    ) {
+        let ndarray_shape = self.shape();
+        let output_shape = out_shape;
+
+        irrt::ndarray::call_nac3_ndarray_util_assert_output_shape_same(
+            generator,
+            ctx,
+            &ndarray_shape,
+            &output_shape,
+        );
+    }
 }
 
 impl<'ctx> ProxyValue<'ctx> for NDArrayValue<'ctx> {
@@ -159,7 +517,12 @@ impl<'ctx> ProxyValue<'ctx> for NDArrayValue<'ctx> {
     type Type = NDArrayType<'ctx>;
 
     fn get_type(&self) -> Self::Type {
-        NDArrayType::from_type(self.as_base_value().get_type(), self.dtype, self.llvm_usize)
+        NDArrayType::from_type(
+            self.as_base_value().get_type(),
+            self.dtype,
+            self.ndims,
+            self.llvm_usize,
+        )
     }
 
     fn as_base_value(&self) -> Self::Base {
@@ -173,7 +536,7 @@ impl<'ctx> From<NDArrayValue<'ctx>> for PointerValue<'ctx> {
     }
 }
 
-/// Proxy type for accessing the `dims` array of an `NDArray` instance in LLVM.
+/// Proxy type for accessing the `shape` array of an `NDArray` instance in LLVM.
 #[derive(Copy, Clone)]
 pub struct NDArrayShapeProxy<'ctx, 'a>(&'a NDArrayValue<'ctx>);
 
@@ -206,8 +569,8 @@ impl<'ctx> ArrayLikeValue<'ctx> for NDArrayShapeProxy<'ctx, '_> {
 impl<'ctx> ArrayLikeIndexer<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {
     unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &IntValue<'ctx>,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {
@@ -245,20 +608,124 @@ impl<'ctx> ArrayLikeIndexer<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_
 impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {}
 impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {}
 
-impl<'ctx> TypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {
+impl<'ctx, G: CodeGenerator + ?Sized> TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>
+    for NDArrayShapeProxy<'ctx, '_>
+{
     fn downcast_to_type(
         &self,
-        _: &mut CodeGenContext<'ctx, '_>,
+        _: &CodeGenContext<'ctx, '_>,
+        _: &G,
         value: BasicValueEnum<'ctx>,
     ) -> IntValue<'ctx> {
         value.into_int_value()
     }
 }
 
-impl<'ctx> TypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {
+impl<'ctx, G: CodeGenerator + ?Sized> TypedArrayLikeMutator<'ctx, G, IntValue<'ctx>>
+    for NDArrayShapeProxy<'ctx, '_>
+{
     fn upcast_from_type(
         &self,
-        _: &mut CodeGenContext<'ctx, '_>,
+        _: &CodeGenContext<'ctx, '_>,
+        _: &G,
+        value: IntValue<'ctx>,
+    ) -> BasicValueEnum<'ctx> {
+        value.into()
+    }
+}
+
+/// Proxy type for accessing the `strides` array of an `NDArray` instance in LLVM.
+#[derive(Copy, Clone)]
+pub struct NDArrayStridesProxy<'ctx, 'a>(&'a NDArrayValue<'ctx>);
+
+impl<'ctx> ArrayLikeValue<'ctx> for NDArrayStridesProxy<'ctx, '_> {
+    fn element_type<G: CodeGenerator + ?Sized>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
+    ) -> AnyTypeEnum<'ctx> {
+        self.0.strides().base_ptr(ctx, generator).get_type().get_element_type()
+    }
+
+    fn base_ptr<G: CodeGenerator + ?Sized>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        _: &G,
+    ) -> PointerValue<'ctx> {
+        self.0.strides_field(ctx).get(ctx, self.0.as_base_value(), self.0.name)
+    }
+
+    fn size<G: CodeGenerator + ?Sized>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        _: &G,
+    ) -> IntValue<'ctx> {
+        self.0.load_ndims(ctx)
+    }
+}
+
+impl<'ctx> ArrayLikeIndexer<'ctx, IntValue<'ctx>> for NDArrayStridesProxy<'ctx, '_> {
+    unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
+        idx: &IntValue<'ctx>,
+        name: Option<&str>,
+    ) -> PointerValue<'ctx> {
+        let var_name = name.map(|v| format!("{v}.addr")).unwrap_or_default();
+
+        unsafe {
+            ctx.builder
+                .build_in_bounds_gep(self.base_ptr(ctx, generator), &[*idx], var_name.as_str())
+                .unwrap()
+        }
+    }
+
+    fn ptr_offset<G: CodeGenerator + ?Sized>(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        generator: &mut G,
+        idx: &IntValue<'ctx>,
+        name: Option<&str>,
+    ) -> PointerValue<'ctx> {
+        let size = self.size(ctx, generator);
+        let in_range = ctx.builder.build_int_compare(IntPredicate::ULT, *idx, size, "").unwrap();
+        ctx.make_assert(
+            generator,
+            in_range,
+            "0:IndexError",
+            "index {0} is out of bounds for axis 0 with size {1}",
+            [Some(*idx), Some(self.0.load_ndims(ctx)), None],
+            ctx.current_loc,
+        );
+
+        unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) }
+    }
+}
+
+impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayStridesProxy<'ctx, '_> {}
+impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayStridesProxy<'ctx, '_> {}
+
+impl<'ctx, G: CodeGenerator + ?Sized> TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>
+    for NDArrayStridesProxy<'ctx, '_>
+{
+    fn downcast_to_type(
+        &self,
+        _: &CodeGenContext<'ctx, '_>,
+        _: &G,
+        value: BasicValueEnum<'ctx>,
+    ) -> IntValue<'ctx> {
+        value.into_int_value()
+    }
+}
+
+impl<'ctx, G: CodeGenerator + ?Sized> TypedArrayLikeMutator<'ctx, G, IntValue<'ctx>>
+    for NDArrayStridesProxy<'ctx, '_>
+{
+    fn upcast_from_type(
+        &self,
+        _: &CodeGenContext<'ctx, '_>,
+        _: &G,
         value: IntValue<'ctx>,
     ) -> BasicValueEnum<'ctx> {
         value.into()
@@ -291,36 +758,19 @@ impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDataProxy<'ctx, '_> {
         ctx: &CodeGenContext<'ctx, '_>,
         generator: &G,
     ) -> IntValue<'ctx> {
-        call_ndarray_calc_size(generator, ctx, &self.as_slice_value(ctx, generator), (None, None))
+        irrt::ndarray::call_nac3_ndarray_len(generator, ctx, *self.0)
     }
 }
 
 impl<'ctx> ArrayLikeIndexer<'ctx> for NDArrayDataProxy<'ctx, '_> {
     unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &IntValue<'ctx>,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {
-        let sizeof_elem = ctx
-            .builder
-            .build_int_truncate_or_bit_cast(
-                self.element_type(ctx, generator).size_of().unwrap(),
-                idx.get_type(),
-                "",
-            )
-            .unwrap();
-        let idx = ctx.builder.build_int_mul(*idx, sizeof_elem, "").unwrap();
-        let ptr = unsafe {
-            ctx.builder
-                .build_in_bounds_gep(
-                    self.base_ptr(ctx, generator),
-                    &[idx],
-                    name.unwrap_or_default(),
-                )
-                .unwrap()
-        };
+        let ptr = irrt::ndarray::call_nac3_ndarray_get_nth_pelement(generator, ctx, *self.0, *idx);
 
         // Current implementation is transparent - The returned pointer type is
         // already cast into the expected type, allowing for immediately
@@ -331,7 +781,7 @@ impl<'ctx> ArrayLikeIndexer<'ctx> for NDArrayDataProxy<'ctx, '_> {
                 BasicTypeEnum::try_from(self.element_type(ctx, generator))
                     .unwrap()
                     .ptr_type(AddressSpace::default()),
-                "",
+                name.unwrap_or_default(),
             )
             .unwrap()
     }
@@ -379,55 +829,33 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
 {
     unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         indices: &Index,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {
-        let llvm_usize = generator.get_size_type(ctx.ctx);
+        assert_eq!(indices.element_type(ctx, generator), generator.get_size_type(ctx.ctx).into());
 
-        let indices_elem_ty = indices
-            .ptr_offset(ctx, generator, &llvm_usize.const_zero(), None)
-            .get_type()
-            .get_element_type();
-        let Ok(indices_elem_ty) = IntType::try_from(indices_elem_ty) else {
-            panic!("Expected list[int32] but got {indices_elem_ty}")
-        };
-        assert_eq!(
-            indices_elem_ty.get_bit_width(),
-            32,
-            "Expected list[int32] but got list[int{}]",
-            indices_elem_ty.get_bit_width()
+        let indices = TypedArrayLikeAdapter::from(
+            indices.as_slice_value(ctx, generator),
+            |_, _, v| v.into_int_value(),
+            |_, _, v| v.into(),
         );
 
-        let index = call_ndarray_flatten_index(generator, ctx, *self.0, indices);
-        let sizeof_elem = ctx
-            .builder
-            .build_int_truncate_or_bit_cast(
-                self.element_type(ctx, generator).size_of().unwrap(),
-                index.get_type(),
-                "",
-            )
-            .unwrap();
-        let index = ctx.builder.build_int_mul(index, sizeof_elem, "").unwrap();
+        let ptr = irrt::ndarray::call_nac3_ndarray_get_pelement_by_indices(
+            generator, ctx, *self.0, &indices,
+        );
 
-        let ptr = unsafe {
-            ctx.builder
-                .build_in_bounds_gep(
-                    self.base_ptr(ctx, generator),
-                    &[index],
-                    name.unwrap_or_default(),
-                )
-                .unwrap()
-        };
-        // TODO: Current implementation is transparent
+        // Current implementation is transparent - The returned pointer type is
+        // already cast into the expected type, allowing for immediately
+        // load/store.
         ctx.builder
             .build_pointer_cast(
                 ptr,
                 BasicTypeEnum::try_from(self.element_type(ctx, generator))
                     .unwrap()
                     .ptr_type(AddressSpace::default()),
-                "",
+                name.unwrap_or_default(),
             )
             .unwrap()
     }
@@ -516,3 +944,133 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> UntypedArrayLikeMutator<'ctx,
     for NDArrayDataProxy<'ctx, '_>
 {
 }
+
+/// A version of [`call_nac3_ndarray_set_strides_by_shape`] in Rust.
+///
+/// This function is used generating strides for globally defined contiguous ndarrays.
+#[must_use]
+pub fn make_contiguous_strides(itemsize: u64, ndims: u64, shape: &[u64]) -> Vec<u64> {
+    let mut strides = vec![0u64; ndims as usize];
+    let mut stride_product = 1u64;
+    for axis in (0..ndims).rev() {
+        strides[axis as usize] = stride_product * itemsize;
+        stride_product *= shape[axis as usize];
+    }
+    strides
+}
+
+/// A convenience enum for implementing functions that acts on scalars or ndarrays or both.
+#[derive(Clone, Copy)]
+pub enum ScalarOrNDArray<'ctx> {
+    Scalar(BasicValueEnum<'ctx>),
+    NDArray(NDArrayValue<'ctx>),
+}
+
+impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for BasicValueEnum<'ctx> {
+    type Error = ();
+
+    fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
+        match value {
+            ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
+            ScalarOrNDArray::NDArray(_) => Err(()),
+        }
+    }
+}
+
+impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayValue<'ctx> {
+    type Error = ();
+
+    fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
+        match value {
+            ScalarOrNDArray::Scalar(_) => Err(()),
+            ScalarOrNDArray::NDArray(ndarray) => Ok(*ndarray),
+        }
+    }
+}
+
+impl<'ctx> ScalarOrNDArray<'ctx> {
+    /// Split on `object` either into a scalar or an ndarray.
+    ///
+    /// If `object` is an ndarray, [`ScalarOrNDArray::NDArray`].
+    ///
+    /// For everything else, it is wrapped with [`ScalarOrNDArray::Scalar`].
+    pub fn from_value<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        (object_ty, object): (Type, BasicValueEnum<'ctx>),
+    ) -> ScalarOrNDArray<'ctx> {
+        match &*ctx.unifier.get_ty(object_ty) {
+            TypeEnum::TObj { obj_id, .. }
+                if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
+            {
+                let ndarray = NDArrayType::from_unifier_type(generator, ctx, object_ty)
+                    .map_value(object.into_pointer_value(), None);
+                ScalarOrNDArray::NDArray(ndarray)
+            }
+
+            _ => ScalarOrNDArray::Scalar(object),
+        }
+    }
+
+    /// Get the underlying [`BasicValueEnum<'ctx>`] of this [`ScalarOrNDArray`].
+    #[must_use]
+    pub fn to_basic_value_enum(self) -> BasicValueEnum<'ctx> {
+        match self {
+            ScalarOrNDArray::Scalar(scalar) => scalar,
+            ScalarOrNDArray::NDArray(ndarray) => ndarray.as_base_value().into(),
+        }
+    }
+
+    /// Convert this [`ScalarOrNDArray`] to an ndarray - behaves like `np.asarray`.
+    ///
+    /// - If this is an ndarray, the ndarray is returned.
+    /// - If this is a scalar, this function returns new ndarray created with
+    ///   [`NDArrayType::construct_unsized`].
+    pub fn to_ndarray<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> NDArrayValue<'ctx> {
+        match self {
+            ScalarOrNDArray::NDArray(ndarray) => *ndarray,
+            ScalarOrNDArray::Scalar(scalar) => {
+                NDArrayType::new_unsized(generator, ctx.ctx, scalar.get_type())
+                    .construct_unsized(generator, ctx, scalar, None)
+            }
+        }
+    }
+
+    /// Get the dtype of the ndarray created if this were called with
+    /// [`ScalarOrNDArray::to_ndarray`].
+    #[must_use]
+    pub fn get_dtype(&self) -> BasicTypeEnum<'ctx> {
+        match self {
+            ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
+            ScalarOrNDArray::Scalar(scalar) => scalar.get_type(),
+        }
+    }
+}
+
+/// An helper enum specifying how a function should produce its output.
+///
+/// Many functions in NumPy has an optional `out` parameter (e.g., `matmul`). If `out` is specified
+/// with an ndarray, the result of a function will be written to `out`. If `out` is not specified, a
+/// function will create a new ndarray and store the result in it.
+#[derive(Clone, Copy)]
+pub enum NDArrayOut<'ctx> {
+    /// Tell a function should create a new ndarray with the expected element type `dtype`.
+    NewNDArray { dtype: BasicTypeEnum<'ctx> },
+    /// Tell a function to write the result to `ndarray`.
+    WriteToNDArray { ndarray: NDArrayValue<'ctx> },
+}
+
+impl<'ctx> NDArrayOut<'ctx> {
+    /// Get the dtype of this output.
+    #[must_use]
+    pub fn get_dtype(&self) -> BasicTypeEnum<'ctx> {
+        match self {
+            NDArrayOut::NewNDArray { dtype } => *dtype,
+            NDArrayOut::WriteToNDArray { ndarray } => ndarray.dtype,
+        }
+    }
+}

nac3core/src/codegen/values/ndarray/nditer.rs

@@ -0,0 +1,178 @@
+use inkwell::{
+    types::{BasicType, IntType},
+    values::{BasicValueEnum, IntValue, PointerValue},
+    AddressSpace,
+};
+
+use super::{NDArrayValue, ProxyValue};
+use crate::codegen::{
+    irrt,
+    stmt::{gen_for_callback, BreakContinueHooks},
+    types::{ndarray::NDIterType, structure::StructField},
+    values::{ArraySliceValue, TypedArrayLikeAdapter},
+    CodeGenContext, CodeGenerator,
+};
+
+#[derive(Copy, Clone)]
+pub struct NDIterValue<'ctx> {
+    value: PointerValue<'ctx>,
+    parent: NDArrayValue<'ctx>,
+    indices: ArraySliceValue<'ctx>,
+    llvm_usize: IntType<'ctx>,
+    name: Option<&'ctx str>,
+}
+
+impl<'ctx> NDIterValue<'ctx> {
+    /// Checks whether `value` is an instance of `NDArray`, returning [Err] if `value` is not an
+    /// instance.
+    pub fn is_representable(
+        value: PointerValue<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> Result<(), String> {
+        <Self as ProxyValue>::Type::is_representable(value.get_type(), llvm_usize)
+    }
+
+    /// Creates an [`NDArrayValue`] from a [`PointerValue`].
+    #[must_use]
+    pub fn from_pointer_value(
+        ptr: PointerValue<'ctx>,
+        parent: NDArrayValue<'ctx>,
+        indices: ArraySliceValue<'ctx>,
+        llvm_usize: IntType<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> Self {
+        debug_assert!(Self::is_representable(ptr, llvm_usize).is_ok());
+
+        Self { value: ptr, parent, indices, llvm_usize, name }
+    }
+
+    /// Is the current iteration valid?
+    ///
+    /// If true, then `element`, `indices` and `nth` contain details about the current element.
+    ///
+    /// If `ndarray` is unsized, this returns true only for the first iteration.
+    /// If `ndarray` is 0-sized, this always returns false.
+    #[must_use]
+    pub fn has_element<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> IntValue<'ctx> {
+        irrt::ndarray::call_nac3_nditer_has_element(generator, ctx, *self)
+    }
+
+    /// Go to the next element. If `has_element()` is false, then this has undefined behavior.
+    ///
+    /// If `ndarray` is unsized, this can only be called once.
+    /// If `ndarray` is 0-sized, this can never be called.
+    pub fn next<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &CodeGenContext<'ctx, '_>) {
+        irrt::ndarray::call_nac3_nditer_next(generator, ctx, *self);
+    }
+
+    fn element_field(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> StructField<'ctx, PointerValue<'ctx>> {
+        self.get_type().get_fields(ctx.ctx).element
+    }
+
+    /// Get pointer to the current element.
+    #[must_use]
+    pub fn get_pointer(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
+        let elem_ty = self.parent.dtype;
+
+        let p = self.element_field(ctx).get(ctx, self.as_base_value(), self.name);
+        ctx.builder
+            .build_pointer_cast(p, elem_ty.ptr_type(AddressSpace::default()), "element")
+            .unwrap()
+    }
+
+    /// Get the value of the current element.
+    #[must_use]
+    pub fn get_scalar(&self, ctx: &CodeGenContext<'ctx, '_>) -> BasicValueEnum<'ctx> {
+        let p = self.get_pointer(ctx);
+        ctx.builder.build_load(p, "value").unwrap()
+    }
+
+    fn nth_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, IntValue<'ctx>> {
+        self.get_type().get_fields(ctx.ctx).nth
+    }
+
+    /// Get the index of the current element if this ndarray were a flat ndarray.
+    #[must_use]
+    pub fn get_index(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
+        self.nth_field(ctx).get(ctx, self.as_base_value(), self.name)
+    }
+
+    /// Get the indices of the current element.
+    #[must_use]
+    pub fn get_indices<G: CodeGenerator + ?Sized>(
+        &self,
+    ) -> TypedArrayLikeAdapter<'ctx, G, IntValue<'ctx>> {
+        TypedArrayLikeAdapter::from(
+            self.indices,
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        )
+    }
+}
+
+impl<'ctx> ProxyValue<'ctx> for NDIterValue<'ctx> {
+    type Base = PointerValue<'ctx>;
+    type Type = NDIterType<'ctx>;
+
+    fn get_type(&self) -> Self::Type {
+        NDIterType::from_type(self.as_base_value().get_type(), self.llvm_usize)
+    }
+
+    fn as_base_value(&self) -> Self::Base {
+        self.value
+    }
+}
+
+impl<'ctx> From<NDIterValue<'ctx>> for PointerValue<'ctx> {
+    fn from(value: NDIterValue<'ctx>) -> Self {
+        value.as_base_value()
+    }
+}
+
+impl<'ctx> NDArrayValue<'ctx> {
+    /// Iterate through every element in the ndarray.
+    ///
+    /// `body` has access to [`BreakContinueHooks`] to short-circuit and [`NDIterValue`] to
+    /// get properties of the current iteration (e.g., the current element, indices, etc.)
+    ///
+    /// Note: The caller is recommended to call `llvm.stacksave` and `llvm.stackrestore` before and
+    /// after invoking this function respectively. See [`NDIterType::construct`] for an explanation
+    /// on why this is suggested.
+    pub fn foreach<'a, G, F>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        body: F,
+    ) -> Result<(), String>
+    where
+        G: CodeGenerator + ?Sized,
+        F: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            BreakContinueHooks<'ctx>,
+            NDIterValue<'ctx>,
+        ) -> Result<(), String>,
+    {
+        gen_for_callback(
+            generator,
+            ctx,
+            Some("ndarray_foreach"),
+            |generator, ctx| {
+                Ok(NDIterType::new(generator, ctx.ctx).construct(generator, ctx, *self))
+            },
+            |generator, ctx, nditer| Ok(nditer.has_element(generator, ctx)),
+            |generator, ctx, hooks, nditer| body(generator, ctx, hooks, nditer),
+            |generator, ctx, nditer| {
+                nditer.next(generator, ctx);
+                Ok(())
+            },
+        )
+    }
+}

nac3core/src/codegen/values/ndarray/shape.rs

@@ -0,0 +1,152 @@
+use inkwell::values::{BasicValueEnum, IntValue};
+
+use crate::{
+    codegen::{
+        stmt::gen_for_callback_incrementing,
+        types::{ListType, TupleType},
+        values::{
+            ArraySliceValue, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeAdapter,
+            TypedArrayLikeMutator, UntypedArrayLikeAccessor,
+        },
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::{Type, TypeEnum},
+};
+
+/// Parse a NumPy-like "int sequence" input and return the int sequence as an array and its length.
+///
+/// * `sequence` - The `sequence` parameter.
+/// * `sequence_ty` - The typechecker type of `sequence`
+///
+/// The `sequence` argument type may only be one of the following:
+///   1. A list of `int32`;   e.g., `np.empty([600, 800, 3])`
+///   2. A tuple of `int32`;  e.g., `np.empty((600, 800, 3))`
+///   3. A scalar `int32`;    e.g., `np.empty(3)`, this is functionally equivalent to
+///      `np.empty([3])`
+///
+/// All `int32` values will be sign-extended to `SizeT`.
+pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    (input_seq_ty, input_seq): (Type, BasicValueEnum<'ctx>),
+) -> impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>> {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let zero = llvm_usize.const_zero();
+    let one = llvm_usize.const_int(1, false);
+
+    // The result `list` to return.
+    match &*ctx.unifier.get_ty_immutable(input_seq_ty) {
+        TypeEnum::TObj { obj_id, .. }
+            if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
+        {
+            // 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
+
+            let input_seq = ListType::from_unifier_type(generator, ctx, input_seq_ty)
+                .map_value(input_seq.into_pointer_value(), None);
+
+            let len = input_seq.load_size(ctx, None);
+            // TODO: Find a way to remove this mid-BB allocation
+            let result = ctx.builder.build_array_alloca(llvm_usize, len, "").unwrap();
+            let result = TypedArrayLikeAdapter::from(
+                ArraySliceValue::from_ptr_val(result, len, None),
+                |_, _, val| val.into_int_value(),
+                |_, _, val| val.into(),
+            );
+
+            // Load all the `int32`s from the input_sequence, cast them to `SizeT`, and store them into `result`
+            gen_for_callback_incrementing(
+                generator,
+                ctx,
+                None,
+                zero,
+                (len, false),
+                |generator, ctx, _, i| {
+                    // Load the i-th int32 in the input sequence
+                    let int = unsafe {
+                        input_seq.data().get_unchecked(ctx, generator, &i, None).into_int_value()
+                    };
+
+                    // Cast to SizeT
+                    let int =
+                        ctx.builder.build_int_s_extend_or_bit_cast(int, llvm_usize, "").unwrap();
+
+                    // Store
+                    unsafe { result.set_typed_unchecked(ctx, generator, &i, int) };
+
+                    Ok(())
+                },
+                one,
+            )
+            .unwrap();
+
+            result
+        }
+
+        TypeEnum::TTuple { .. } => {
+            // 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
+
+            let input_seq = TupleType::from_unifier_type(generator, ctx, input_seq_ty)
+                .map_value(input_seq.into_struct_value(), None);
+
+            let len = input_seq.get_type().num_elements();
+
+            let result = generator
+                .gen_array_var_alloc(
+                    ctx,
+                    llvm_usize.into(),
+                    llvm_usize.const_int(u64::from(len), false),
+                    None,
+                )
+                .unwrap();
+            let result = TypedArrayLikeAdapter::from(
+                result,
+                |_, _, val| val.into_int_value(),
+                |_, _, val| val.into(),
+            );
+
+            for i in 0..input_seq.get_type().num_elements() {
+                // Get the i-th element off of the tuple and load it into `result`.
+                let int = input_seq.load_element(ctx, i).into_int_value();
+                let int = ctx.builder.build_int_s_extend_or_bit_cast(int, llvm_usize, "").unwrap();
+
+                unsafe {
+                    result.set_typed_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(u64::from(i), false),
+                        int,
+                    );
+                }
+            }
+
+            result
+        }
+
+        TypeEnum::TObj { obj_id, .. }
+            if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
+        {
+            // 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
+
+            let input_int = input_seq.into_int_value();
+
+            let len = one;
+            let result = generator.gen_array_var_alloc(ctx, llvm_usize.into(), len, None).unwrap();
+            let result = TypedArrayLikeAdapter::from(
+                result,
+                |_, _, val| val.into_int_value(),
+                |_, _, val| val.into(),
+            );
+            let int =
+                ctx.builder.build_int_s_extend_or_bit_cast(input_int, llvm_usize, "").unwrap();
+
+            // Storing into result[0]
+            unsafe {
+                result.set_typed_unchecked(ctx, generator, &zero, int);
+            }
+
+            result
+        }
+
+        _ => panic!("encountered unknown sequence type: {}", ctx.unifier.stringify(input_seq_ty)),
+    }
+}

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

@@ -0,0 +1,154 @@
+use std::iter::{once, repeat_n};
+
+use inkwell::values::{IntValue, PointerValue};
+use itertools::Itertools;
+
+use crate::codegen::{
+    irrt,
+    stmt::gen_if_callback,
+    types::ndarray::NDArrayType,
+    values::{
+        ndarray::{NDArrayValue, RustNDIndex},
+        ArrayLikeValue, ArraySliceValue, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeAdapter,
+    },
+    CodeGenContext, CodeGenerator,
+};
+
+impl<'ctx> NDArrayValue<'ctx> {
+    /// Make sure the ndarray is at least `ndmin`-dimensional.
+    ///
+    /// If this ndarray's `ndims` is less than `ndmin`, a view is created on this with 1s prepended
+    /// to the shape. Otherwise, this function does nothing and return this ndarray.
+    #[must_use]
+    pub fn atleast_nd<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ndmin: u64,
+    ) -> Self {
+        let ndims = self.ndims;
+
+        if ndims < ndmin {
+            // Extend the dimensions with np.newaxis.
+            let indices = repeat_n(RustNDIndex::NewAxis, (ndmin - ndims) as usize)
+                .chain(once(RustNDIndex::Ellipsis))
+                .collect_vec();
+            self.index(generator, ctx, &indices)
+        } else {
+            *self
+        }
+    }
+
+    /// Create a reshaped view on this ndarray like
+    /// [`np.reshape()`](https://numpy.org/doc/stable/reference/generated/numpy.reshape.html).
+    ///
+    /// 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: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    ) -> Self {
+        assert_eq!(new_shape.element_type(ctx, generator), self.llvm_usize.into());
+
+        // 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 dst_ndarray = NDArrayType::new(generator, ctx.ctx, self.dtype, new_ndims)
+            .construct_uninitialized(generator, ctx, None);
+        dst_ndarray.copy_shape_from_array(generator, ctx, new_shape.base_ptr(ctx, generator));
+
+        // Resolve negative indices
+        let size = self.size(generator, ctx);
+        let dst_ndims = self.llvm_usize.const_int(dst_ndarray.get_type().ndims(), false);
+        let dst_shape = dst_ndarray.shape();
+        irrt::ndarray::call_nac3_ndarray_reshape_resolve_and_check_new_shape(
+            generator,
+            ctx,
+            size,
+            dst_ndims,
+            dst_shape.as_slice_value(ctx, generator),
+        );
+
+        gen_if_callback(
+            generator,
+            ctx,
+            |generator, ctx| Ok(self.is_c_contiguous(generator, ctx)),
+            |generator, ctx| {
+                // Reshape is possible without copying
+                dst_ndarray.set_strides_contiguous(generator, ctx);
+                dst_ndarray.store_data(ctx, self.data().base_ptr(ctx, generator));
+
+                Ok(())
+            },
+            |generator, ctx| {
+                // Reshape is impossible without copying
+                unsafe {
+                    dst_ndarray.create_data(generator, ctx);
+                }
+                dst_ndarray.copy_data_from(generator, ctx, *self);
+
+                Ok(())
+            },
+        )
+        .unwrap();
+
+        dst_ndarray
+    }
+
+    /// Create a transposed view on this ndarray like
+    /// [`np.transpose(<ndarray>, <axes> = None)`](https://numpy.org/doc/stable/reference/generated/numpy.transpose.html).
+    ///
+    /// * `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<PointerValue<'ctx>>,
+    ) -> Self {
+        assert!(
+            axes.is_none_or(|axes| axes.get_type().get_element_type() == self.llvm_usize.into())
+        );
+
+        // Define models
+        let transposed_ndarray = self.get_type().construct_uninitialized(generator, ctx, None);
+
+        let axes = if let Some(axes) = axes {
+            let num_axes = self.llvm_usize.const_int(self.ndims, false);
+
+            // `axes = nullptr` if `axes` is unspecified.
+            let axes = ArraySliceValue::from_ptr_val(axes, num_axes, None);
+
+            Some(TypedArrayLikeAdapter::from(
+                axes,
+                |_, _, val| val.into_int_value(),
+                |_, _, val| val.into(),
+            ))
+        } else {
+            None
+        };
+
+        irrt::ndarray::call_nac3_ndarray_transpose(
+            generator,
+            ctx,
+            *self,
+            transposed_ndarray,
+            axes.as_ref(),
+        );
+
+        transposed_ndarray
+    }
+}

nac3core/src/codegen/values/tuple.rs

@@ -0,0 +1,85 @@
+use inkwell::{
+    types::IntType,
+    values::{BasicValue, BasicValueEnum, StructValue},
+};
+
+use super::ProxyValue;
+use crate::codegen::{types::TupleType, CodeGenContext};
+
+#[derive(Copy, Clone)]
+pub struct TupleValue<'ctx> {
+    value: StructValue<'ctx>,
+    llvm_usize: IntType<'ctx>,
+    name: Option<&'ctx str>,
+}
+
+impl<'ctx> TupleValue<'ctx> {
+    /// Checks whether `value` is an instance of `tuple`, returning [Err] if `value` is not an
+    /// instance.
+    pub fn is_representable(
+        value: StructValue<'ctx>,
+        _llvm_usize: IntType<'ctx>,
+    ) -> Result<(), String> {
+        TupleType::is_representable(value.get_type())
+    }
+
+    /// Creates an [`TupleValue`] from a [`StructValue`].
+    #[must_use]
+    pub fn from_struct_value(
+        value: StructValue<'ctx>,
+        llvm_usize: IntType<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> Self {
+        debug_assert!(Self::is_representable(value, llvm_usize).is_ok());
+
+        Self { value, llvm_usize, name }
+    }
+
+    /// Stores a value into the tuple element at the given `index`.
+    pub fn store_element(
+        &mut self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        index: u32,
+        element: impl BasicValue<'ctx>,
+    ) {
+        assert_eq!(element.as_basic_value_enum().get_type(), unsafe {
+            self.get_type().type_at_index_unchecked(index)
+        });
+
+        let new_value = ctx
+            .builder
+            .build_insert_value(self.value, element, index, self.name.unwrap_or_default())
+            .unwrap();
+        self.value = new_value.into_struct_value();
+    }
+
+    /// Loads a value from the tuple element at the given `index`.
+    pub fn load_element(&self, ctx: &CodeGenContext<'ctx, '_>, index: u32) -> BasicValueEnum<'ctx> {
+        ctx.builder
+            .build_extract_value(
+                self.value,
+                index,
+                &format!("{}[{{i}}]", self.name.unwrap_or("tuple")),
+            )
+            .unwrap()
+    }
+}
+
+impl<'ctx> ProxyValue<'ctx> for TupleValue<'ctx> {
+    type Base = StructValue<'ctx>;
+    type Type = TupleType<'ctx>;
+
+    fn get_type(&self) -> Self::Type {
+        TupleType::from_type(self.as_base_value().get_type(), self.llvm_usize)
+    }
+
+    fn as_base_value(&self) -> Self::Base {
+        self.value
+    }
+}
+
+impl<'ctx> From<TupleValue<'ctx>> for StructValue<'ctx> {
+    fn from(value: TupleValue<'ctx>) -> Self {
+        value.as_base_value()
+    }
+}

nac3core/src/codegen/values/utils/mod.rs

@@ -0,0 +1,3 @@
+pub use slice::*;
+
+mod slice;

nac3core/src/codegen/values/utils/slice.rs

@@ -0,0 +1,231 @@
+use inkwell::{
+    types::IntType,
+    values::{IntValue, PointerValue},
+};
+
+use nac3parser::ast::Expr;
+
+use crate::{
+    codegen::{
+        types::{structure::StructField, utils::SliceType},
+        values::ProxyValue,
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::Type,
+};
+
+/// An IRRT representation of an (unresolved) slice.
+#[derive(Copy, Clone)]
+pub struct SliceValue<'ctx> {
+    value: PointerValue<'ctx>,
+    int_ty: IntType<'ctx>,
+    llvm_usize: IntType<'ctx>,
+    name: Option<&'ctx str>,
+}
+
+impl<'ctx> SliceValue<'ctx> {
+    /// Checks whether `value` is an instance of `ContiguousNDArray`, returning [Err] if `value` is
+    /// not an instance.
+    pub fn is_representable(
+        value: PointerValue<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> Result<(), String> {
+        <Self as ProxyValue<'ctx>>::Type::is_representable(value.get_type(), llvm_usize)
+    }
+
+    /// Creates an [`SliceValue`] from a [`PointerValue`].
+    #[must_use]
+    pub fn from_pointer_value(
+        ptr: PointerValue<'ctx>,
+        int_ty: IntType<'ctx>,
+        llvm_usize: IntType<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> Self {
+        debug_assert!(Self::is_representable(ptr, llvm_usize).is_ok());
+
+        Self { value: ptr, int_ty, llvm_usize, name }
+    }
+
+    fn start_defined_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
+        self.get_type().get_fields().start_defined
+    }
+
+    pub fn load_start_defined(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
+        self.start_defined_field().get(ctx, self.value, self.name)
+    }
+
+    fn start_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
+        self.get_type().get_fields().start
+    }
+
+    pub fn load_start(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
+        self.start_field().get(ctx, self.value, self.name)
+    }
+
+    pub fn store_start(&self, ctx: &CodeGenContext<'ctx, '_>, value: Option<IntValue<'ctx>>) {
+        match value {
+            Some(start) => {
+                self.start_defined_field().set(
+                    ctx,
+                    self.value,
+                    ctx.ctx.bool_type().const_all_ones(),
+                    self.name,
+                );
+                self.start_field().set(ctx, self.value, start, self.name);
+            }
+
+            None => self.start_defined_field().set(
+                ctx,
+                self.value,
+                ctx.ctx.bool_type().const_zero(),
+                self.name,
+            ),
+        }
+    }
+
+    fn stop_defined_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
+        self.get_type().get_fields().stop_defined
+    }
+
+    pub fn load_stop_defined(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
+        self.stop_defined_field().get(ctx, self.value, self.name)
+    }
+
+    fn stop_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
+        self.get_type().get_fields().stop
+    }
+
+    pub fn load_stop(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
+        self.stop_field().get(ctx, self.value, self.name)
+    }
+
+    pub fn store_stop(&self, ctx: &CodeGenContext<'ctx, '_>, value: Option<IntValue<'ctx>>) {
+        match value {
+            Some(stop) => {
+                self.stop_defined_field().set(
+                    ctx,
+                    self.value,
+                    ctx.ctx.bool_type().const_all_ones(),
+                    self.name,
+                );
+                self.stop_field().set(ctx, self.value, stop, self.name);
+            }
+
+            None => self.stop_defined_field().set(
+                ctx,
+                self.value,
+                ctx.ctx.bool_type().const_zero(),
+                self.name,
+            ),
+        }
+    }
+
+    fn step_defined_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
+        self.get_type().get_fields().step_defined
+    }
+
+    pub fn load_step_defined(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
+        self.step_defined_field().get(ctx, self.value, self.name)
+    }
+
+    fn step_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
+        self.get_type().get_fields().step
+    }
+
+    pub fn load_step(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
+        self.step_field().get(ctx, self.value, self.name)
+    }
+
+    pub fn store_step(&self, ctx: &CodeGenContext<'ctx, '_>, value: Option<IntValue<'ctx>>) {
+        match value {
+            Some(step) => {
+                self.step_defined_field().set(
+                    ctx,
+                    self.value,
+                    ctx.ctx.bool_type().const_all_ones(),
+                    self.name,
+                );
+                self.step_field().set(ctx, self.value, step, self.name);
+            }
+
+            None => self.step_defined_field().set(
+                ctx,
+                self.value,
+                ctx.ctx.bool_type().const_zero(),
+                self.name,
+            ),
+        }
+    }
+}
+
+impl<'ctx> ProxyValue<'ctx> for SliceValue<'ctx> {
+    type Base = PointerValue<'ctx>;
+    type Type = SliceType<'ctx>;
+
+    fn get_type(&self) -> Self::Type {
+        Self::Type::from_type(self.value.get_type(), self.int_ty, self.llvm_usize)
+    }
+
+    fn as_base_value(&self) -> Self::Base {
+        self.value
+    }
+}
+
+impl<'ctx> From<SliceValue<'ctx>> for PointerValue<'ctx> {
+    fn from(value: SliceValue<'ctx>) -> Self {
+        value.as_base_value()
+    }
+}
+
+/// A slice represented in compile-time by `start`, `stop` and `step`, all held as LLVM values.
+// TODO: Rename this to CTConstSlice
+#[derive(Debug, Copy, Clone)]
+pub struct RustSlice<'ctx> {
+    int_ty: IntType<'ctx>,
+    start: Option<IntValue<'ctx>>,
+    stop: Option<IntValue<'ctx>>,
+    step: Option<IntValue<'ctx>>,
+}
+
+impl<'ctx> RustSlice<'ctx> {
+    /// Generate LLVM IR for an [`ExprKind::Slice`] and convert it into a [`RustSlice`].
+    #[allow(clippy::type_complexity)]
+    pub fn from_slice_expr<G: CodeGenerator>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        lower: &Option<Box<Expr<Option<Type>>>>,
+        upper: &Option<Box<Expr<Option<Type>>>>,
+        step: &Option<Box<Expr<Option<Type>>>>,
+    ) -> Result<RustSlice<'ctx>, String> {
+        let mut value_mapper = |value_expr: &Option<Box<Expr<Option<Type>>>>| -> Result<_, String> {
+            Ok(match value_expr {
+                None => None,
+                Some(value_expr) => {
+                    let value_expr = generator
+                        .gen_expr(ctx, value_expr)?
+                        .map(|value| {
+                            value.to_basic_value_enum(ctx, generator, ctx.primitives.int32)
+                        })
+                        .unwrap()?;
+
+                    Some(value_expr.into_int_value())
+                }
+            })
+        };
+
+        let start = value_mapper(lower)?;
+        let stop = value_mapper(upper)?;
+        let step = value_mapper(step)?;
+
+        Ok(RustSlice { int_ty: ctx.ctx.i32_type(), start, stop, step })
+    }
+
+    /// Write the contents to an LLVM [`SliceValue`].
+    pub fn write_to_slice(&self, ctx: &CodeGenContext<'ctx, '_>, dst_slice_ptr: SliceValue<'ctx>) {
+        assert_eq!(self.int_ty, dst_slice_ptr.int_ty);
+
+        dst_slice_ptr.store_start(ctx, self.start);
+        dst_slice_ptr.store_stop(ctx, self.stop);
+        dst_slice_ptr.store_step(ctx, self.step);
+    }
+}

nac3core/src/symbol_resolver.rs

@@ -6,7 +6,7 @@ use std::{
 };
 
 use inkwell::values::{BasicValueEnum, FloatValue, IntValue, PointerValue, StructValue};
-use itertools::{chain, izip, Itertools};
+use itertools::{izip, Itertools};
 use parking_lot::RwLock;
 
 use nac3parser::ast::{Constant, Expr, Location, StrRef};
@@ -452,11 +452,11 @@ pub fn parse_type_annotation<T>(
                             type_vars.len()
                         )]));
                     }
-                    let fields = chain(
-                        fields.iter().map(|(k, v, m)| (*k, (*v, *m))),
-                        methods.iter().map(|(k, v, _)| (*k, (*v, false))),
-                    )
-                    .collect();
+                    let fields = fields
+                        .iter()
+                        .map(|(k, v, m)| (*k, (*v, *m)))
+                        .chain(methods.iter().map(|(k, v, _)| (*k, (*v, false))))
+                        .collect();
                     Ok(unifier.add_ty(TypeEnum::TObj { obj_id, fields, params: VarMap::default() }))
                 } else {
                     Err(HashSet::from([format!("Cannot use function name as type at {loc}")]))

nac3core/src/toplevel/builtins.rs

@@ -1,18 +1,14 @@
 use std::iter::once;
 
 use indexmap::IndexMap;
-use inkwell::{
-    attributes::{Attribute, AttributeLoc},
-    types::{BasicMetadataTypeEnum, BasicType},
-    values::{BasicMetadataValueEnum, BasicValue, CallSiteValue},
-    IntPredicate,
-};
-use itertools::Either;
+use inkwell::{values::BasicValue, IntPredicate};
 use strum::IntoEnumIterator;
 
 use super::{
-    helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDef, PrimDefDetails},
-    numpy::make_ndarray_ty,
+    helper::{
+        debug_assert_prim_is_allowed, extract_ndims, make_exception_fields, PrimDef, PrimDefDetails,
+    },
+    numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
     *,
 };
 use crate::{
@@ -20,7 +16,8 @@ use crate::{
         builtin_fns,
         numpy::*,
         stmt::exn_constructor,
-        values::{ProxyValue, RangeValue},
+        types::ndarray::NDArrayType,
+        values::{ndarray::shape::parse_numpy_int_sequence, ProxyValue, RangeValue},
     },
     symbol_resolver::SymbolValue,
     typecheck::typedef::{into_var_map, iter_type_vars, TypeVar, VarMap},
@@ -148,144 +145,6 @@ fn create_fn_by_codegen(
     }
 }
 
-/// Creates a NumPy [`TopLevelDef`] function using an LLVM intrinsic.
-///
-/// * `name`: The name of the implemented NumPy function.
-/// * `ret_ty`: The return type of this function.
-/// * `param_ty`: The parameters accepted by this function, represented by a tuple of the
-///   [parameter type][Type] and the parameter symbol name.
-/// * `intrinsic_fn`: The fully-qualified name of the LLVM intrinsic function.
-fn create_fn_by_intrinsic(
-    unifier: &mut Unifier,
-    var_map: &VarMap,
-    name: &'static str,
-    ret_ty: Type,
-    params: &[(Type, &'static str)],
-    intrinsic_fn: &'static str,
-) -> TopLevelDef {
-    let param_tys = params.iter().map(|p| p.0).collect_vec();
-
-    create_fn_by_codegen(
-        unifier,
-        var_map,
-        name,
-        ret_ty,
-        params,
-        Box::new(move |ctx, _, fun, args, generator| {
-            let args_ty = fun.0.args.iter().map(|a| a.ty).collect_vec();
-
-            assert!(param_tys
-                .iter()
-                .zip(&args_ty)
-                .all(|(expected, actual)| ctx.unifier.unioned(*expected, *actual)));
-
-            let args_val = args_ty
-                .iter()
-                .zip_eq(args.iter())
-                .map(|(ty, arg)| arg.1.clone().to_basic_value_enum(ctx, generator, *ty).unwrap())
-                .map_into::<BasicMetadataValueEnum>()
-                .collect_vec();
-
-            let intrinsic_fn = ctx.module.get_function(intrinsic_fn).unwrap_or_else(|| {
-                let ret_llvm_ty = ctx.get_llvm_abi_type(generator, ret_ty);
-                let param_llvm_ty = param_tys
-                    .iter()
-                    .map(|p| ctx.get_llvm_abi_type(generator, *p))
-                    .map_into::<BasicMetadataTypeEnum>()
-                    .collect_vec();
-                let fn_type = ret_llvm_ty.fn_type(param_llvm_ty.as_slice(), false);
-
-                ctx.module.add_function(intrinsic_fn, fn_type, None)
-            });
-
-            let val = ctx
-                .builder
-                .build_call(intrinsic_fn, args_val.as_slice(), name)
-                .map(CallSiteValue::try_as_basic_value)
-                .map(Either::unwrap_left)
-                .unwrap();
-            Ok(val.into())
-        }),
-    )
-}
-
-/// Creates a unary NumPy [`TopLevelDef`] function using an extern function (e.g. from `libc` or
-/// `libm`).
-///
-/// * `name`: The name of the implemented NumPy function.
-/// * `ret_ty`: The return type of this function.
-/// * `param_ty`: The parameters accepted by this function, represented by a tuple of the
-///   [parameter type][Type] and the parameter symbol name.
-/// * `extern_fn`: The fully-qualified name of the extern function used as the implementation.
-/// * `attrs`: The list of attributes to apply to this function declaration. Note that `nounwind` is
-///   already implied by the C ABI.
-fn create_fn_by_extern(
-    unifier: &mut Unifier,
-    var_map: &VarMap,
-    name: &'static str,
-    ret_ty: Type,
-    params: &[(Type, &'static str)],
-    extern_fn: &'static str,
-    attrs: &'static [&str],
-) -> TopLevelDef {
-    let param_tys = params.iter().map(|p| p.0).collect_vec();
-
-    create_fn_by_codegen(
-        unifier,
-        var_map,
-        name,
-        ret_ty,
-        params,
-        Box::new(move |ctx, _, fun, args, generator| {
-            let args_ty = fun.0.args.iter().map(|a| a.ty).collect_vec();
-
-            assert!(param_tys
-                .iter()
-                .zip(&args_ty)
-                .all(|(expected, actual)| ctx.unifier.unioned(*expected, *actual)));
-
-            let args_val = args_ty
-                .iter()
-                .zip_eq(args.iter())
-                .map(|(ty, arg)| arg.1.clone().to_basic_value_enum(ctx, generator, *ty).unwrap())
-                .map_into::<BasicMetadataValueEnum>()
-                .collect_vec();
-
-            let intrinsic_fn = ctx.module.get_function(extern_fn).unwrap_or_else(|| {
-                let ret_llvm_ty = ctx.get_llvm_abi_type(generator, ret_ty);
-                let param_llvm_ty = param_tys
-                    .iter()
-                    .map(|p| ctx.get_llvm_abi_type(generator, *p))
-                    .map_into::<BasicMetadataTypeEnum>()
-                    .collect_vec();
-                let fn_type = ret_llvm_ty.fn_type(param_llvm_ty.as_slice(), false);
-                let func = ctx.module.add_function(extern_fn, fn_type, None);
-                func.add_attribute(
-                    AttributeLoc::Function,
-                    ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id("nounwind"), 0),
-                );
-
-                for attr in attrs {
-                    func.add_attribute(
-                        AttributeLoc::Function,
-                        ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
-                    );
-                }
-
-                func
-            });
-
-            let val = ctx
-                .builder
-                .build_call(intrinsic_fn, &args_val, name)
-                .map(CallSiteValue::try_as_basic_value)
-                .map(Either::unwrap_left)
-                .unwrap();
-            Ok(val.into())
-        }),
-    )
-}
-
 pub fn get_builtins(unifier: &mut Unifier, primitives: &PrimitiveStore) -> BuiltinInfo {
     BuiltinBuilder::new(unifier, primitives)
         .build_all_builtins()
@@ -336,7 +195,6 @@ struct BuiltinBuilder<'a> {
 
     ndarray_float: Type,
     ndarray_float_2d: Type,
-    ndarray_num_ty: Type,
 
     float_or_ndarray_ty: TypeVar,
     float_or_ndarray_var_map: VarMap,
@@ -450,7 +308,6 @@ impl<'a> BuiltinBuilder<'a> {
 
             ndarray_float,
             ndarray_float_2d,
-            ndarray_num_ty,
 
             float_or_ndarray_ty,
             float_or_ndarray_var_map,
@@ -512,6 +369,14 @@ 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::FunNpBroadcastTo | PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
+                self.build_ndarray_view_function(prim)
+            }
+
             PrimDef::FunStr => self.build_str_function(),
 
             PrimDef::FunFloor | PrimDef::FunFloor64 | PrimDef::FunCeil | PrimDef::FunCeil64 => {
@@ -577,10 +442,6 @@ 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
@@ -1386,6 +1247,176 @@ 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 = NDArrayType::from_unifier_type(generator, ctx, ndarray_ty)
+                        .map_value(ndarray.into_pointer_value(), None);
+
+                    let size = ctx
+                        .builder
+                        .build_int_truncate_or_bit_cast(
+                            ndarray.size(generator, ctx),
+                            ctx.ctx.i32_type(),
+                            "",
+                        )
+                        .unwrap();
+                    Ok(Some(size.into()))
+                }),
+            ),
+
+            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 = NDArrayType::from_unifier_type(generator, ctx, ndarray_ty)
+                            .map_value(ndarray.into_pointer_value(), None);
+
+                        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.as_base_value().into()))
+                    }),
+                )
+            }
+            _ => 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::FunNpBroadcastTo, 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)?;
+
+                    let ndarray = NDArrayType::from_unifier_type(generator, ctx, arg_ty)
+                        .map_value(arg_val.into_pointer_value(), None);
+
+                    let ndarray = ndarray.transpose(generator, ctx, None); // TODO: Add axes argument
+                    Ok(Some(ndarray.as_base_value().into()))
+                }),
+            ),
+
+            // 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::FunNpBroadcastTo | PrimDef::FunNpReshape => {
+                // These two functions have the same function signature.
+                // Mixed together for convenience.
+
+                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 = NDArrayType::from_unifier_type(generator, ctx, ndarray_ty)
+                            .map_value(ndarray_val.into_pointer_value(), None);
+
+                        let shape = parse_numpy_int_sequence(generator, ctx, (shape_ty, shape_val));
+
+                        // 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 = 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.as_base_value().as_basic_value_enum()))
+                    }),
+                )
+            }
+
+            _ => unreachable!(),
+        }
+    }
+
     /// Build the `str()` function.
     fn build_str_function(&mut self) -> TopLevelDef {
         let prim = PrimDef::FunStr;
@@ -1873,57 +1904,6 @@ 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`
@@ -1955,10 +1935,12 @@ impl<'a> BuiltinBuilder<'a> {
                 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_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
+                    let result = ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?;
+                    Ok(Some(result))
                 }),
             ),
 

nac3core/src/toplevel/composer.rs

@@ -1052,7 +1052,7 @@ impl TopLevelComposer {
                         }
                         let mut result = Vec::new();
                         let no_defaults = args.args.len() - args.defaults.len() - 1;
-                        for (idx, x) in itertools::enumerate(args.args.iter().skip(1)) {
+                        for (idx, x) in args.args.iter().skip(1).enumerate() {
                             let type_ann = {
                                 let Some(annotation_expr) = x.node.annotation.as_ref() else {return Err(HashSet::from([format!("type annotation needed for `{}` (at {})", x.node.arg, x.location)]));};
                                 parse_ast_to_type_annotation_kinds(

nac3core/src/toplevel/helper.rs

@@ -54,6 +54,16 @@ pub enum PrimDef {
     FunNpEye,
     FunNpIdentity,
 
+    // NumPy ndarray property getters
+    FunNpSize,
+    FunNpShape,
+    FunNpStrides,
+
+    // NumPy ndarray view functions
+    FunNpBroadcastTo,
+    FunNpTranspose,
+    FunNpReshape,
+
     // Miscellaneous NumPy & SciPy functions
     FunNpRound,
     FunNpFloor,
@@ -101,8 +111,6 @@ pub enum PrimDef {
     FunNpLdExp,
     FunNpHypot,
     FunNpNextAfter,
-    FunNpTranspose,
-    FunNpReshape,
 
     // Linalg functions
     FunNpDot,
@@ -240,6 +248,16 @@ 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::FunNpBroadcastTo => fun("np_broadcast_to", None),
+            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),
@@ -287,8 +305,6 @@ impl PrimDef {
             PrimDef::FunNpLdExp => fun("np_ldexp", None),
             PrimDef::FunNpHypot => fun("np_hypot", None),
             PrimDef::FunNpNextAfter => fun("np_nextafter", None),
-            PrimDef::FunNpTranspose => fun("np_transpose", None),
-            PrimDef::FunNpReshape => fun("np_reshape", None),
 
             // Linalg functions
             PrimDef::FunNpDot => fun("np_dot", None),

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__generic_class.snap

@@ -8,5 +8,5 @@ expression: res_vec
     "Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"Generic_A\",\nancestors: [\"Generic_A[V]\", \"B\"],\nfields: [\"aa\", \"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\"), (\"fun\", \"fn[[a:int32], V]\")],\ntype_vars: [\"V\"]\n}\n",
     "Function {\nname: \"Generic_A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(246)]\n}\n",
+    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(261)]\n}\n",
 ]

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__inheritance_override.snap

@@ -7,7 +7,7 @@ expression: res_vec
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[t:T], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.foo\",\nsig: \"fn[[c:C], none]\",\nvar_id: []\n}\n",
-    "Class {\nname: \"B\",\nancestors: [\"B[typevar230]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar230\"]\n}\n",
+    "Class {\nname: \"B\",\nancestors: [\"B[typevar245]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar245\"]\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"B.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"C\",\nancestors: [\"C\", \"B[bool]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\", \"e\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__list_tuple_generic.snap

@@ -5,8 +5,8 @@ expression: res_vec
 [
     "Function {\nname: \"foo\",\nsig: \"fn[[a:list[int32], b:tuple[T, float]], A[B, bool]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"A\",\nancestors: [\"A[T, V]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[v:V], none]\"), (\"fun\", \"fn[[a:T], V]\")],\ntype_vars: [\"T\", \"V\"]\n}\n",
-    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(243)]\n}\n",
-    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(248)]\n}\n",
+    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(258)]\n}\n",
+    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(263)]\n}\n",
     "Function {\nname: \"gfun\",\nsig: \"fn[[a:A[list[float], int32]], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [],\nmethods: [(\"__init__\", \"fn[[], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__self1.snap

@@ -3,7 +3,7 @@ source: nac3core/src/toplevel/test.rs
 expression: res_vec
 ---
 [
-    "Class {\nname: \"A\",\nancestors: [\"A[typevar229, typevar230]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar229\", \"typevar230\"]\n}\n",
+    "Class {\nname: \"A\",\nancestors: [\"A[typevar244, typevar245]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar244\", \"typevar245\"]\n}\n",
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[float, bool], b:B], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[float, bool]], A[bool, int32]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\")],\ntype_vars: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__simple_class_compose.snap

@@ -6,12 +6,12 @@ expression: res_vec
     "Class {\nname: \"A\",\nancestors: [\"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(249)]\n}\n",
+    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(264)]\n}\n",
     "Class {\nname: \"C\",\nancestors: [\"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"C.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"C.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\", \"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(257)]\n}\n",
+    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(272)]\n}\n",
 ]

nac3core/src/toplevel/test.rs

@@ -15,14 +15,13 @@ use crate::{
     symbol_resolver::{SymbolResolver, ValueEnum},
     typecheck::{
         type_inferencer::PrimitiveStore,
-        typedef::{into_var_map, Type, Unifier},
+        typedef::{Type, Unifier},
     },
 };
 
 struct ResolverInternal {
     id_to_type: Mutex<HashMap<StrRef, Type>>,
     id_to_def: Mutex<HashMap<StrRef, DefinitionId>>,
-    class_names: Mutex<HashMap<StrRef, Type>>,
 }
 
 impl ResolverInternal {
@@ -179,11 +178,8 @@ fn test_simple_function_analyze(source: &[&str], tys: &[&str], names: &[&str]) {
     let mut composer =
         TopLevelComposer::new(Vec::new(), Vec::new(), ComposerConfig::default(), 64).0;
 
-    let internal_resolver = Arc::new(ResolverInternal {
-        id_to_def: Mutex::default(),
-        id_to_type: Mutex::default(),
-        class_names: Mutex::default(),
-    });
+    let internal_resolver =
+        Arc::new(ResolverInternal { id_to_def: Mutex::default(), id_to_type: Mutex::default() });
     let resolver =
         Arc::new(Resolver(internal_resolver.clone())) as Arc<dyn SymbolResolver + Send + Sync>;
 
@@ -784,13 +780,6 @@ fn make_internal_resolver_with_tvar(
     unifier: &mut Unifier,
     print: bool,
 ) -> Arc<ResolverInternal> {
-    let list_elem_tvar = unifier.get_fresh_var(Some("list_elem".into()), None);
-    let list = unifier.add_ty(TypeEnum::TObj {
-        obj_id: PrimDef::List.id(),
-        fields: HashMap::new(),
-        params: into_var_map([list_elem_tvar]),
-    });
-
     let res: Arc<ResolverInternal> = ResolverInternal {
         id_to_def: Mutex::new(HashMap::from([("list".into(), PrimDef::List.id())])),
         id_to_type: tvars
@@ -806,7 +795,6 @@ fn make_internal_resolver_with_tvar(
             })
             .collect::<HashMap<_, _>>()
             .into(),
-        class_names: Mutex::new(HashMap::from([("list".into(), list)])),
     }
     .into();
     if print {
@@ -819,7 +807,7 @@ struct TypeToStringFolder<'a> {
     unifier: &'a mut Unifier,
 }
 
-impl<'a> Fold<Option<Type>> for TypeToStringFolder<'a> {
+impl Fold<Option<Type>> for TypeToStringFolder<'_> {
     type TargetU = String;
     type Error = String;
     fn map_user(&mut self, user: Option<Type>) -> Result<Self::TargetU, Self::Error> {

nac3core/src/typecheck/function_check.rs

@@ -15,7 +15,7 @@ use super::{
 };
 use crate::toplevel::helper::PrimDef;
 
-impl<'a> Inferencer<'a> {
+impl Inferencer<'_> {
     fn should_have_value(&mut self, expr: &Expr<Option<Type>>) -> Result<(), HashSet<String>> {
         if matches!(expr.custom, Some(ty) if self.unifier.unioned(ty, self.primitives.none)) {
             Err(HashSet::from([format!("Error at {}: cannot have value none", expr.location)]))
@@ -94,7 +94,7 @@ impl<'a> Inferencer<'a> {
         // there are some cases where the custom field is None
         if let Some(ty) = &expr.custom {
             if !matches!(&expr.node, ExprKind::Constant { value: Constant::Ellipsis, .. })
-                && !ty.obj_id(self.unifier).is_some_and(|id| id == PrimDef::List.id())
+                && ty.obj_id(self.unifier).is_none_or(|id| id != PrimDef::List.id())
                 && !self.unifier.is_concrete(*ty, &self.function_data.bound_variables)
             {
                 return Err(HashSet::from([format!(

nac3core/src/typecheck/magic_methods.rs

@@ -7,12 +7,12 @@ use nac3parser::ast::{Cmpop, Operator, StrRef, Unaryop};
 
 use super::{
     type_inferencer::*,
-    typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
+    typedef::{into_var_map, FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
 };
 use crate::{
     symbol_resolver::SymbolValue,
     toplevel::{
-        helper::PrimDef,
+        helper::{extract_ndims, PrimDef},
         numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
     },
 };
@@ -175,19 +175,8 @@ pub fn impl_binop(
     ops: &[Operator],
 ) {
     with_fields(unifier, ty, |unifier, fields| {
-        let (other_ty, other_var_id) = if other_ty.len() == 1 {
-            (other_ty[0], None)
-        } else {
-            let tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
-            (tvar.ty, Some(tvar.id))
-        };
-
-        let function_vars = if let Some(var_id) = other_var_id {
-            vec![(var_id, other_ty)].into_iter().collect::<VarMap>()
-        } else {
-            VarMap::new()
-        };
-
+        let other_tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
+        let function_vars = into_var_map([other_tvar]);
         let ret_ty = ret_ty.unwrap_or_else(|| unifier.get_fresh_var(None, None).ty);
 
         for (base_op, variant) in iproduct!(ops, [BinopVariant::Normal, BinopVariant::AugAssign]) {
@@ -198,7 +187,7 @@ pub fn impl_binop(
                         ret: ret_ty,
                         vars: function_vars.clone(),
                         args: vec![FuncArg {
-                            ty: other_ty,
+                            ty: other_tvar.ty,
                             default_value: None,
                             name: "other".into(),
                             is_vararg: false,
@@ -541,36 +530,43 @@ pub fn typeof_binop(
                 }
             }
 
-            let (_, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
-            let lhs_ndims = match &*unifier.get_ty_immutable(lhs_ndims) {
-                TypeEnum::TLiteral { values, .. } => {
-                    assert_eq!(values.len(), 1);
-                    u64::try_from(values[0].clone()).unwrap()
+            let (lhs_dtype, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
+            let lhs_ndims = extract_ndims(unifier, lhs_ndims);
+
+            let (rhs_dtype, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
+            let rhs_ndims = extract_ndims(unifier, rhs_ndims);
+
+            if !(unifier.unioned(lhs_dtype, primitives.float)
+                && unifier.unioned(rhs_dtype, primitives.float))
+            {
+                return Err(format!(
+                    "ndarray.__matmul__ only supports float64 operations, but LHS has type {} and RHS has type {}",
+                    unifier.stringify(lhs),
+                    unifier.stringify(rhs)
+                ));
+            }
+
+            // Deduce the ndims of the resulting ndarray.
+            // If this is 0 (an unsized ndarray), matmul returns a scalar just like NumPy.
+            let result_ndims = match (lhs_ndims, rhs_ndims) {
+                (0, _) | (_, 0) => {
+                    return Err(
+                        "ndarray.__matmul__ does not allow unsized ndarray input".to_string()
+                    )
                 }
-                _ => unreachable!(),
-            };
-            let (_, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
-            let rhs_ndims = match &*unifier.get_ty_immutable(rhs_ndims) {
-                TypeEnum::TLiteral { values, .. } => {
-                    assert_eq!(values.len(), 1);
-                    u64::try_from(values[0].clone()).unwrap()
-                }
-                _ => unreachable!(),
+                (1, 1) => 0,
+                (1, _) => rhs_ndims - 1,
+                (_, 1) => lhs_ndims - 1,
+                (m, n) => max(m, n),
             };
 
-            match (lhs_ndims, rhs_ndims) {
-                (2, 2) => typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?,
-                (lhs, rhs) if lhs == 0 || rhs == 0 => {
-                    return Err(format!(
-                    "Input operand {} does not have enough dimensions (has {lhs}, requires {rhs})",
-                    u8::from(rhs == 0)
-                ))
-                }
-                (lhs, rhs) => {
-                    return Err(format!(
-                        "ndarray.__matmul__ on {lhs}D and {rhs}D operands not supported"
-                    ))
-                }
+            if result_ndims == 0 {
+                // If the result is unsized, NumPy returns a scalar.
+                primitives.float
+            } else {
+                let result_ndims_ty =
+                    unifier.get_fresh_literal(vec![SymbolValue::U64(result_ndims)], None);
+                make_ndarray_ty(unifier, primitives, Some(primitives.float), Some(result_ndims_ty))
             }
         }
 
@@ -773,7 +769,7 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
     impl_div(unifier, store, ndarray_t, &[ndarray_t, ndarray_dtype_t], None);
     impl_floordiv(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
     impl_mod(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
-    impl_matmul(unifier, store, ndarray_t, &[ndarray_t], Some(ndarray_t));
+    impl_matmul(unifier, store, ndarray_t, &[ndarray_unsized_t], None);
     impl_sign(unifier, store, ndarray_t, Some(ndarray_t));
     impl_invert(unifier, store, ndarray_t, Some(ndarray_t));
     impl_eq(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);

nac3core/src/typecheck/type_error.rs

@@ -94,7 +94,7 @@ fn loc_to_str(loc: Option<Location>) -> String {
     }
 }
 
-impl<'a> Display for DisplayTypeError<'a> {
+impl Display for DisplayTypeError<'_> {
     fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
         use TypeErrorKind::*;
         let mut notes = Some(HashMap::new());

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -3,7 +3,7 @@ use std::{
     cmp::max,
     collections::{HashMap, HashSet},
     convert::{From, TryInto},
-    iter::once,
+    iter::{once, repeat_n},
     sync::Arc,
 };
 
@@ -187,7 +187,7 @@ fn fix_assignment_target_context(node: &mut ast::Located<ExprKind>) {
     }
 }
 
-impl<'a> Fold<()> for Inferencer<'a> {
+impl Fold<()> for Inferencer<'_> {
     type TargetU = Option<Type>;
     type Error = InferenceError;
 
@@ -657,7 +657,7 @@ impl<'a> Fold<()> for Inferencer<'a> {
 
 type InferenceResult = Result<Type, InferenceError>;
 
-impl<'a> Inferencer<'a> {
+impl Inferencer<'_> {
     /// Constrain a <: b
     /// Currently implemented as unification
     fn constrain(&mut self, a: Type, b: Type, location: &Location) -> Result<(), InferenceError> {
@@ -1234,6 +1234,45 @@ 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: repeat_n(self.primitives.int32, 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))?;
@@ -1555,7 +1594,7 @@ impl<'a> Inferencer<'a> {
             }));
         }
         // 2-argument ndarray n-dimensional factory functions
-        if id == &"np_reshape".into() && args.len() == 2 {
+        if ["np_reshape".into(), "np_broadcast_to".into()].contains(id) && args.len() == 2 {
             let arg0 = self.fold_expr(args.remove(0))?;
 
             let shape_expr = args.remove(0);

nac3core/src/typecheck/type_inferencer/test.rs

@@ -18,7 +18,6 @@ use crate::{
 struct Resolver {
     id_to_type: HashMap<StrRef, Type>,
     id_to_def: HashMap<StrRef, DefinitionId>,
-    class_names: HashMap<StrRef, Type>,
 }
 
 impl SymbolResolver for Resolver {
@@ -198,7 +197,6 @@ impl TestEnvironment {
         let resolver = Arc::new(Resolver {
             id_to_type: identifier_mapping.clone(),
             id_to_def: HashMap::default(),
-            class_names: HashMap::default(),
         }) as Arc<dyn SymbolResolver + Send + Sync>;
 
         TestEnvironment {
@@ -454,7 +452,6 @@ impl TestEnvironment {
                 vars: IndexMap::default(),
             })),
         );
-        let class_names: HashMap<_, _> = [("Bar".into(), bar), ("Bar2".into(), bar2)].into();
 
         let id_to_name = [
             "int32".into(),
@@ -492,7 +489,6 @@ impl TestEnvironment {
                 ("Bar2".into(), DefinitionId(defs + 3)),
             ]
             .into(),
-            class_names,
         }) as Arc<dyn SymbolResolver + Send + Sync>;
 
         TestEnvironment {

nac3core/src/typecheck/typedef/mod.rs

@@ -3,13 +3,13 @@ use std::{
     cell::RefCell,
     collections::{HashMap, HashSet},
     fmt::{self, Display},
-    iter::{repeat, zip},
+    iter::{repeat, repeat_n, zip},
     rc::Rc,
     sync::{Arc, Mutex},
 };
 
 use indexmap::IndexMap;
-use itertools::{repeat_n, Itertools};
+use itertools::Itertools;
 
 use nac3parser::ast::{Cmpop, Location, StrRef, Unaryop};
 

nac3ld/src/dwarf.rs

@@ -30,7 +30,7 @@ pub struct DwarfReader<'a> {
     pub virt_addr: u32,
 }
 
-impl<'a> DwarfReader<'a> {
+impl DwarfReader<'_> {
     pub fn new(slice: &[u8], virt_addr: u32) -> DwarfReader {
         DwarfReader { slice, virt_addr }
     }
@@ -113,7 +113,7 @@ pub struct DwarfWriter<'a> {
     pub offset: usize,
 }
 
-impl<'a> DwarfWriter<'a> {
+impl DwarfWriter<'_> {
     pub fn new(slice: &mut [u8]) -> DwarfWriter {
         DwarfWriter { slice, offset: 0 }
     }
@@ -375,7 +375,7 @@ pub struct FDE_Records<'a> {
     available: usize,
 }
 
-impl<'a> Iterator for FDE_Records<'a> {
+impl Iterator for FDE_Records<'_> {
     type Item = (u32, u32);
 
     fn next(&mut self) -> Option<Self::Item> {
@@ -423,7 +423,7 @@ pub struct EH_Frame_Hdr<'a> {
     fdes: Vec<(u32, u32)>,
 }
 
-impl<'a> EH_Frame_Hdr<'a> {
+impl EH_Frame_Hdr<'_> {
     /// Create a [EH_Frame_Hdr] object, and write out the fixed fields of `.eh_frame_hdr` to memory.
     ///
     /// Load address is not known at this point.

nac3ld/src/lib.rs

@@ -159,7 +159,7 @@ struct SymbolTableReader<'a> {
     strtab: &'a [u8],
 }
 
-impl<'a> SymbolTableReader<'a> {
+impl SymbolTableReader<'_> {
     pub fn find_index_by_name(&self, sym_name: &[u8]) -> Option<usize> {
         self.symtab.iter().position(|sym| {
             if let Ok(dynsym_name) = name_starting_at_slice(self.strtab, sym.st_name as usize) {

nac3standalone/demo/interpret_demo.py

@@ -67,7 +67,7 @@ def _bool(x):
 
 def _float(x):
     if isinstance(x, np.ndarray):
-        return np.float_(x)
+        return np.float64(x)
     else:
         return float(x)
 
@@ -111,6 +111,9 @@ def patch(module):
     def output_strln(x):
         print(x, end='')
 
+    def output_int32_list(x):
+        print([int(e) for e in x])
+
     def dbg_stack_address(_):
         return 0
 
@@ -126,11 +129,12 @@ def patch(module):
             return output_float
         elif name == "output_str":
             return output_strln
+        elif name == "output_int32_list":
+            return output_int32_list
         elif name in {
             "output_bool",
             "output_int32",
             "output_int64",
-            "output_int32_list",
             "output_uint32",
             "output_uint64",
             "output_strln",
@@ -179,6 +183,16 @@ def patch(module):
     module.np_identity = np.identity
     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
+
+    # 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 +232,6 @@ 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

nac3standalone/demo/src/ndarray.py

@@ -68,6 +68,19 @@ def output_ndarray_float_2(n: ndarray[float, Literal[2]]):
         for c in range(len(n[r])):
             output_float64(n[r][c])
 
+def output_ndarray_float_3(n: ndarray[float, Literal[3]]):
+    for d in range(len(n)):
+        for r in range(len(n[d])):
+            for c in range(len(n[d][r])):
+                output_float64(n[d][r][c])
+
+def output_ndarray_float_4(n: ndarray[float, Literal[4]]):
+    for x in range(len(n)):
+        for y in range(len(n[x])):
+            for z in range(len(n[x][y])):
+                for w in range(len(n[x][y][z])):
+                    output_float64(n[x][y][z][w])
+
 def consume_ndarray_1(n: ndarray[float, Literal[1]]):
     pass
 
@@ -197,6 +210,104 @@ def test_ndarray_nd_idx():
     output_float64(x[1, 0])
     output_float64(x[1, 1])
 
+def test_ndarray_transpose():
+    x: ndarray[float, 2] = np_array([[1., 2., 3.], [4., 5., 6.]])
+    y = np_transpose(x)
+    z = np_transpose(y)
+
+    output_int32(np_shape(x)[0])
+    output_int32(np_shape(x)[1])
+    output_ndarray_float_2(x)
+
+    output_int32(np_shape(y)[0])
+    output_int32(np_shape(y)[1])
+    output_ndarray_float_2(y)
+
+    output_int32(np_shape(z)[0])
+    output_int32(np_shape(z)[1])
+    output_ndarray_float_2(z)
+
+def test_ndarray_reshape():
+    w: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
+    x = np_reshape(w, (1, 2, 1, -1))
+    y = np_reshape(x, [2, -1])
+    z = np_reshape(y, 10)
+
+    output_int32(np_shape(w)[0])
+    output_ndarray_float_1(w)
+
+    output_int32(np_shape(x)[0])
+    output_int32(np_shape(x)[1])
+    output_int32(np_shape(x)[2])
+    output_int32(np_shape(x)[3])
+    output_ndarray_float_4(x)
+
+    output_int32(np_shape(y)[0])
+    output_int32(np_shape(y)[1])
+    output_ndarray_float_2(y)
+
+    output_int32(np_shape(z)[0])
+    output_ndarray_float_1(z)
+
+    x1: ndarray[int32, 1] = np_array([1, 2, 3, 4])
+    x2: ndarray[int32, 2] = np_reshape(x1, (2, 2))
+
+    output_int32(np_shape(x1)[0])
+    output_ndarray_int32_1(x1)
+
+    output_int32(np_shape(x2)[0])
+    output_int32(np_shape(x2)[1])
+    output_ndarray_int32_2(x2)
+
+def test_ndarray_broadcast_to():
+    xs = np_array([1.0, 2.0, 3.0])
+    ys = np_broadcast_to(xs, (1, 3))
+    zs = np_broadcast_to(ys, (2, 4, 3))
+
+    output_int32(np_shape(xs)[0])
+    output_ndarray_float_1(xs)
+
+    output_int32(np_shape(ys)[0])
+    output_int32(np_shape(ys)[1])
+    output_ndarray_float_2(ys)
+
+    output_int32(np_shape(zs)[0])
+    output_int32(np_shape(zs)[1])
+    output_int32(np_shape(zs)[2])
+    output_ndarray_float_3(zs)
+
+def test_ndarray_subscript_assignment():
+    xs = np_array([[11.0, 22.0, 33.0, 44.0], [55.0, 66.0, 77.0, 88.0]])
+
+    xs[0, 0] = 99.0
+    output_ndarray_float_2(xs)
+
+    xs[0] = 100.0
+    output_ndarray_float_2(xs)
+
+    xs[:, ::2] = 101.0
+    output_ndarray_float_2(xs)
+
+    xs[1:, 0] = 102.0
+    output_ndarray_float_2(xs)
+
+    xs[0] = np_array([-1.0, -2.0, -3.0, -4.0])
+    output_ndarray_float_2(xs)
+
+    xs[:] = np_array([-5.0, -6.0, -7.0, -8.0])
+    output_ndarray_float_2(xs)
+
+    # Test assignment with memory sharing
+    ys1 = np_reshape(xs, (2, 4))
+    ys2 = np_transpose(ys1)
+    ys3 = ys2[::-1, 0]
+    ys3[0] = -999.0
+
+    output_ndarray_float_2(xs)
+    output_ndarray_float_2(ys1)
+    output_ndarray_float_2(ys2)
+    output_ndarray_float_1(ys3)
+
 def test_ndarray_add():
     x = np_identity(2)
     y = x + np_ones([2, 2])
@@ -1440,27 +1551,6 @@ def test_ndarray_nextafter_broadcast_rhs_scalar():
     output_ndarray_float_2(nextafter_x_zeros)
     output_ndarray_float_2(nextafter_x_ones)
 
-def test_ndarray_transpose():
-    x: ndarray[float, 2] = np_array([[1., 2., 3.], [4., 5., 6.]])
-    y = np_transpose(x)
-    z = np_transpose(y)
-
-    output_ndarray_float_2(x)
-    output_ndarray_float_2(y)
-
-def test_ndarray_reshape():
-    w: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
-    x = np_reshape(w, (1, 2, 1, -1))
-    y = np_reshape(x, [2, -1])
-    z = np_reshape(y, 10)
-
-    x1: ndarray[int32, 1] = np_array([1, 2, 3, 4])
-    x2: ndarray[int32, 2] = np_reshape(x1, (2, 2))
-
-    output_ndarray_float_1(w)
-    output_ndarray_float_2(y)
-    output_ndarray_float_1(z)
-
 def test_ndarray_dot():
     x1: ndarray[float, 1] = np_array([5.0, 1.0, 4.0, 2.0])
     y1: ndarray[float, 1] = np_array([5.0, 1.0, 6.0, 6.0])
@@ -1592,6 +1682,11 @@ def run() -> int32:
     test_ndarray_slices()
     test_ndarray_nd_idx()
 
+    test_ndarray_transpose()
+    test_ndarray_reshape()
+    test_ndarray_broadcast_to()
+    test_ndarray_subscript_assignment()
+
     test_ndarray_add()
     test_ndarray_add_broadcast()
     test_ndarray_add_broadcast_lhs_scalar()
@@ -1755,8 +1850,6 @@ def run() -> int32:
     test_ndarray_nextafter_broadcast()
     test_ndarray_nextafter_broadcast_lhs_scalar()
     test_ndarray_nextafter_broadcast_rhs_scalar()
-    test_ndarray_transpose()
-    test_ndarray_reshape()
 
     test_ndarray_dot()
     test_ndarray_cholesky()

nac3standalone/demo/src/numeric_primitives.py

@@ -29,10 +29,10 @@ def u32_max() -> uint32:
     return ~uint32(0)
 
 def i32_min() -> int32:
-    return int32(1 << 31)
+    return int32(-(1 << 31))
 
 def i32_max() -> int32:
-    return int32(~(1 << 31))
+    return int32((1 << 31)-1)
 
 def u64_min() -> uint64:
     return uint64(0)
@@ -63,8 +63,9 @@ def test_conv_from_i32():
         i32_max()
     ]:
         output_int64(int64(x))
-        output_uint32(uint32(x))
-        output_uint64(uint64(x))
+        if x >= 0:
+            output_uint32(uint32(x))
+            output_uint64(uint64(x))
         output_float64(float(x))
 
 def test_conv_from_u32():
@@ -108,7 +109,6 @@ def test_conv_from_u64():
 
 def test_f64toi32():
     for x in [
-        float(i32_min()) - 1.0,
         float(i32_min()),
         float(i32_min()) + 1.0,
         -1.5,
@@ -117,7 +117,6 @@ def test_f64toi32():
         1.5,
         float(i32_max()) - 1.0,
         float(i32_max()),
-        float(i32_max()) + 1.0
     ]:
         output_int32(int32(x))
 
@@ -138,24 +137,17 @@ def test_f64toi64():
 
 def test_f64tou32():
     for x in [
-        -1.5,
-        float(u32_min()) - 1.0,
-        -0.5,
         float(u32_min()),
         0.5,
         float(u32_min()) + 1.0,
         1.5,
         float(u32_max()) - 1.0,
         float(u32_max()),
-        float(u32_max()) + 1.0
     ]:
         output_uint32(uint32(x))
 
 def test_f64tou64():
     for x in [
-        -1.5,
-        float(u64_min()) - 1.0,
-        -0.5,
         float(u64_min()),
         0.5,
         float(u64_min()) + 1.0,

nix/windows/msys2_packages.nix

@@ -1,15 +1,15 @@
 { pkgs } : [
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libunwind-18.1.8-2-any.pkg.tar.zst";
-  sha256 = "0f9m76dx40iy794nfks0360gvjhdg6yngb2lyhwp4xd76rn5081m";
-  name = "mingw-w64-clang-x86_64-libunwind-18.1.8-2-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libunwind-19.1.4-1-any.pkg.tar.zst";
+  sha256 = "0frb5k16bbxdf8g379d16vl3qrh7n9pydn83gpfxpvwf3qlvnzyl";
+  name = "mingw-w64-clang-x86_64-libunwind-19.1.4-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libc++-18.1.8-2-any.pkg.tar.zst";
-  sha256 = "17savj9wys9my2ji7vyba7wwqkvzdjwnkb3k4858wxrjbzbfa6lk";
-  name = "mingw-w64-clang-x86_64-libc++-18.1.8-2-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libc++-19.1.4-1-any.pkg.tar.zst";
+  sha256 = "0wh5km0v8j50pqz9bxb4f0w7r8zhsvssrjvc94np53iq8wjagk86";
+  name = "mingw-w64-clang-x86_64-libc++-19.1.4-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -31,9 +31,9 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-xz-5.6.2-2-any.pkg.tar.zst";
-  sha256 = "0phb9hwqksk1rg29yhwlc7si78zav19c2kac0i841pc7mc2n9gzx";
-  name = "mingw-w64-clang-x86_64-xz-5.6.2-2-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-xz-5.6.3-3-any.pkg.tar.zst";
+  sha256 = "1a7gc462gnrjy5qb0zfkr9qm8bsnnf02y6wp3c59n618dhsq7rcf";
+  name = "mingw-w64-clang-x86_64-xz-5.6.3-3-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -43,9 +43,9 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libxml2-2.12.9-1-any.pkg.tar.zst";
-  sha256 = "0cjz2vj9yz6k5xj601cp0yk631rrr0z94ciamwqrvclb0yhakf25";
-  name = "mingw-w64-clang-x86_64-libxml2-2.12.9-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libxml2-2.12.9-2-any.pkg.tar.zst";
+  sha256 = "1b1r5llgqv88id8iwhqh23qwqmn5ic9hdamdc8xzij9hmcvdmmci";
+  name = "mingw-w64-clang-x86_64-libxml2-2.12.9-2-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -55,75 +55,87 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-llvm-libs-18.1.8-1-any.pkg.tar.zst";
-  sha256 = "0rpbgvvinsqflhd3nhfxk0g0yy8j80zzw5yx6573ak0m78a9fa06";
-  name = "mingw-w64-clang-x86_64-llvm-libs-18.1.8-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-llvm-libs-19.1.4-1-any.pkg.tar.zst";
+  sha256 = "1clrbm8dk893byj8s15pgcgqqijm2zkd10zgyakamd8m354kj9q4";
+  name = "mingw-w64-clang-x86_64-llvm-libs-19.1.4-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-llvm-18.1.8-1-any.pkg.tar.zst";
-  sha256 = "185g5h8q3x3rav9lp2njln58ny2idh2067fd02j3nsbik6glshpf";
-  name = "mingw-w64-clang-x86_64-llvm-18.1.8-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-llvm-19.1.4-1-any.pkg.tar.zst";
+  sha256 = "1iz2c9475h8p20ydpp0znbhyb62rlrk7wr7xl7cmwbam7wkwr8rn";
+  name = "mingw-w64-clang-x86_64-llvm-19.1.4-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-clang-libs-18.1.8-1-any.pkg.tar.zst";
-  sha256 = "089hji3yd7wsd03v9mdfgc99l5k1dql8kg7p3hy13vrbgfsabxhc";
-  name = "mingw-w64-clang-x86_64-clang-libs-18.1.8-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-clang-libs-19.1.4-1-any.pkg.tar.zst";
+  sha256 = "1hidciwlakxrp4kyb0j2v6g4lv76nn834g6b88w1j94fk3qc765d";
+  name = "mingw-w64-clang-x86_64-clang-libs-19.1.4-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-compiler-rt-18.1.8-1-any.pkg.tar.zst";
-  sha256 = "1dwcxnv1k5ljim5ys4h1c3jlrdpi0054z094ynav7if65i8zjj4a";
-  name = "mingw-w64-clang-x86_64-compiler-rt-18.1.8-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-compiler-rt-19.1.4-1-any.pkg.tar.zst";
+  sha256 = "1m1yhjkgzlbk10sv966qk4yji009ga0lr25gpgj2w7mcd2wixcr3";
+  name = "mingw-w64-clang-x86_64-compiler-rt-19.1.4-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-headers-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
-  sha256 = "0163jzjlvq7inpafy3h48pkwag3ysk6x56xm84yfcz5q52fnfzq5";
-  name = "mingw-w64-clang-x86_64-headers-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-headers-git-12.0.0.r423.g8bcd5fc1a-1-any.pkg.tar.zst";
+  sha256 = "08gxc7h2achckknn6fz3p6yi7gxxvbaday8fpm4j56c4sa04n0df";
+  name = "mingw-w64-clang-x86_64-headers-git-12.0.0.r423.g8bcd5fc1a-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-crt-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
-  sha256 = "00cn1mi29mfys7qy4hvgnjd0smqvnkdn3ibnrr6a3wy1h2vaykgq";
-  name = "mingw-w64-clang-x86_64-crt-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-crt-git-12.0.0.r423.g8bcd5fc1a-1-any.pkg.tar.zst";
+  sha256 = "0fxd1pb197ki0gzw6z8gmd6wgpd9d28js6cp5d31d55kw7d1vz13";
+  name = "mingw-w64-clang-x86_64-crt-git-12.0.0.r423.g8bcd5fc1a-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-lld-18.1.8-1-any.pkg.tar.zst";
-  sha256 = "1vpij5d06m4kjy3qv8bizwlkl21gcv6fv0r2f1j9bclgm6k3144x";
-  name = "mingw-w64-clang-x86_64-lld-18.1.8-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-lld-19.1.4-1-any.pkg.tar.zst";
+  sha256 = "1a8pjyhrzpc2z3784xxwix4i7yrz03ygnsk1wv9k0yq8m8wi9nbw";
+  name = "mingw-w64-clang-x86_64-lld-19.1.4-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libwinpthread-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
-  sha256 = "1zkzqqd31xpkv817wja3qssjjx891bsdxw07037hv2sk0qr4ffn9";
-  name = "mingw-w64-clang-x86_64-libwinpthread-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libwinpthread-git-12.0.0.r423.g8bcd5fc1a-1-any.pkg.tar.zst";
+  sha256 = "140m312jx1sywqjkvfij69d268m4jpdmilq5bb8khkf0ayb16036";
+  name = "mingw-w64-clang-x86_64-libwinpthread-git-12.0.0.r423.g8bcd5fc1a-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-winpthreads-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
-  sha256 = "02ynia88ad3l03r08nyldmnajwqkyxcjd191lyamkbj4d6zck323";
-  name = "mingw-w64-clang-x86_64-winpthreads-git-12.0.0.r250.gc6bf4bdf6-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-winpthreads-git-12.0.0.r423.g8bcd5fc1a-1-any.pkg.tar.zst";
+  sha256 = "017j4h511wg37bacym73f8g6s0jcfgzbzabzxpc6anr3gy4kkpbg";
+  name = "mingw-w64-clang-x86_64-winpthreads-git-12.0.0.r423.g8bcd5fc1a-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-clang-18.1.8-1-any.pkg.tar.zst";
-  sha256 = "1qny934nv4g75k9gb5sf31v24bgafkg6qw7r35xv3in491w6annq";
-  name = "mingw-w64-clang-x86_64-clang-18.1.8-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-clang-19.1.4-1-any.pkg.tar.zst";
+  sha256 = "11f4i4ai2bzvq6f06vxk1ymv7056c9707vdw489f1i2bdrf0c0ii";
+  name = "mingw-w64-clang-x86_64-clang-19.1.4-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-c-ares-1.33.1-1-any.pkg.tar.zst";
-  sha256 = "14r6jjsvfbapbkv2zqp2yglva4vz4srzkgk7f186ri3kcafjspgq";
-  name = "mingw-w64-clang-x86_64-c-ares-1.33.1-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-rust-1.83.0-3-any.pkg.tar.zst";
+  sha256 = "0nxs571vb4f1i5vp91134p5blns9ml2r25nx6kdlg0zhd5x85kvm";
+  name = "mingw-w64-clang-x86_64-rust-1.83.0-3-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-brotli-1.1.0-2-any.pkg.tar.zst";
-  sha256 = "1q01lz9lcyrjmkhv9rddgjazmk7warlcmwhc4qkq9y6h0yfsb71n";
-  name = "mingw-w64-clang-x86_64-brotli-1.1.0-2-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-cppdap-1.65-1-any.pkg.tar.zst";
+  sha256 = "0phhwkcqp30dsyj5vr6w99sgm1jfm5rzg0w5x5mv9md4x7lm9lmh";
+  name = "mingw-w64-clang-x86_64-cppdap-1.65-1-any.pkg.tar.zst";
+})
+
+(pkgs.fetchurl {
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-c-ares-1.34.3-1-any.pkg.tar.zst";
+  sha256 = "1mpn397qsdz3l2fav6ymwjlj96ialn9m8sldii3ymbcyhranl3xx";
+  name = "mingw-w64-clang-x86_64-c-ares-1.34.3-1-any.pkg.tar.zst";
+})
+
+(pkgs.fetchurl {
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-brotli-1.1.0-4-any.pkg.tar.zst";
+  sha256 = "0hx9gjzibacfx3fzk11n2vzz2pmnb956babh2ig8avx3hk7vlqrg";
+  name = "mingw-w64-clang-x86_64-brotli-1.1.0-4-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -139,9 +151,9 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libpsl-0.21.5-2-any.pkg.tar.zst";
-  sha256 = "1mpx77q5g8pj45s8wgc52c4ww2r93080p6d559p56f558a3cl317";
-  name = "mingw-w64-clang-x86_64-libpsl-0.21.5-2-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libpsl-0.21.5-3-any.pkg.tar.zst";
+  sha256 = "0hb7wgdliic3d7fa0cvr5pj946pmwfc0apmyb0yfb5d0hc1afwsc";
+  name = "mingw-w64-clang-x86_64-libpsl-0.21.5-3-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -163,57 +175,45 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-openssl-3.3.2-1-any.pkg.tar.zst";
-  sha256 = "1djgpcz447yvhdy1yq5wh8l5d0821izxklx9afyszbw0pbr7f24y";
-  name = "mingw-w64-clang-x86_64-openssl-3.3.2-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-openssl-3.4.0-1-any.pkg.tar.zst";
+  sha256 = "0cgiqjmgdnwnv9r88z634dmqrzh06dmsfncyzymw0s16nnv2k7k2";
+  name = "mingw-w64-clang-x86_64-openssl-3.4.0-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libssh2-1.11.0-2-any.pkg.tar.zst";
-  sha256 = "0l2m823gm1rvnjmqm5ads17mxz1bhpzai5ixyhnkpzrsjxd1ygy5";
-  name = "mingw-w64-clang-x86_64-libssh2-1.11.0-2-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libssh2-1.11.1-1-any.pkg.tar.zst";
+  sha256 = "01l23cn5brficjzba7ldscqkdvk4rdcvvdyybd90qr2hqzligmhn";
+  name = "mingw-w64-clang-x86_64-libssh2-1.11.1-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-nghttp2-1.63.0-1-any.pkg.tar.zst";
-  sha256 = "0lfqrlmapsc7ilxjmhr7hxi578vclqlhpqimbvzq0c70c0iwk864";
-  name = "mingw-w64-clang-x86_64-nghttp2-1.63.0-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-nghttp2-1.64.0-1-any.pkg.tar.zst";
+  sha256 = "1hv8fp496l018s5dx5v8nvxc0a6rswskwk1jsrfd94rh3kbq2ilc";
+  name = "mingw-w64-clang-x86_64-nghttp2-1.64.0-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-nghttp3-1.5.0-1-any.pkg.tar.zst";
-  sha256 = "1ljl9kdasf91bxkqcmbbjchp5g00ahv8jn2zab38899z6j3x43nz";
-  name = "mingw-w64-clang-x86_64-nghttp3-1.5.0-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-nghttp3-1.6.0-1-any.pkg.tar.zst";
+  sha256 = "1p7q47fin12vzyf126v1azbbpgpa0y6ighfh6mbfdb6zcyq74kbd";
+  name = "mingw-w64-clang-x86_64-nghttp3-1.6.0-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-curl-8.9.1-2-any.pkg.tar.zst";
-  sha256 = "1zr6kgqp9i4qqrfckh3kfmz4x1cwv4xis9sfqsx7xji88priax64";
-  name = "mingw-w64-clang-x86_64-curl-8.9.1-2-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-curl-8.11.1-1-any.pkg.tar.zst";
+  sha256 = "16yvyqjzxyzawgv26r1g145wphvhjil2b0pyhy4nj7v5d19n6wvh";
+  name = "mingw-w64-clang-x86_64-curl-8.11.1-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-rust-1.80.1-1-any.pkg.tar.zst";
-  sha256 = "1dm4vlrfi9m6xl09zpn0yjr7qcjjr4x738z1rjfwysfnc0awq4x8";
-  name = "mingw-w64-clang-x86_64-rust-1.80.1-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-expat-2.6.4-1-any.pkg.tar.zst";
+  sha256 = "03fp2yacv7gk0g049lffz6pbj93vpjmzqxxa312d4gxczi57nqdv";
+  name = "mingw-w64-clang-x86_64-expat-2.6.4-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-cppdap-1.65-1-any.pkg.tar.zst";
-  sha256 = "0phhwkcqp30dsyj5vr6w99sgm1jfm5rzg0w5x5mv9md4x7lm9lmh";
-  name = "mingw-w64-clang-x86_64-cppdap-1.65-1-any.pkg.tar.zst";
-})
-
-(pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-expat-2.6.3-1-any.pkg.tar.zst";
-  sha256 = "19xfl1q78q1k8j0lr5aspcf668pmfg01fgib73zq7ff7y5y5fcyi";
-  name = "mingw-w64-clang-x86_64-expat-2.6.3-1-any.pkg.tar.zst";
-})
-
-(pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-jsoncpp-1.9.5-3-any.pkg.tar.zst";
-  sha256 = "1a8mdn4ram9pgqpx5fwxmhcmzc6bh1fq1s4m37xh0d8p6fpncv10";
-  name = "mingw-w64-clang-x86_64-jsoncpp-1.9.5-3-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-jsoncpp-1.9.6-3-any.pkg.tar.zst";
+  sha256 = "1ipilhiza17vz5dhgi61l80w2klw9f21w6jbyhi9wmfd6nxqv13c";
+  name = "mingw-w64-clang-x86_64-jsoncpp-1.9.6-3-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -235,27 +235,27 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libtre-git-r177.07e66d0-2-any.pkg.tar.zst";
-  sha256 = "0fc9hxsdks1xy5fv0rcna433hlzf6jhs77hg0hfzkzhn06f9alp4";
-  name = "mingw-w64-clang-x86_64-libtre-git-r177.07e66d0-2-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libtre-0.9.0-1-any.pkg.tar.zst";
+  sha256 = "1y5d2cbkpd0ngqlsv6hvz47nxb1wry33i5s1clg2k53yhgjkg8vv";
+  name = "mingw-w64-clang-x86_64-libtre-0.9.0-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libsystre-1.0.1-5-any.pkg.tar.zst";
-  sha256 = "05qsn8fkks4f93jkas43s47axqqgx5m64b45p462si3nlb8cjirq";
-  name = "mingw-w64-clang-x86_64-libsystre-1.0.1-5-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libsystre-1.0.1-6-any.pkg.tar.zst";
+  sha256 = "19c71fs5gqjrf88mv7l702fjg228xd9lfbxg0mkzm3ljvv4ljn0q";
+  name = "mingw-w64-clang-x86_64-libsystre-1.0.1-6-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libarchive-3.7.4-1-any.pkg.tar.zst";
-  sha256 = "1ykw6imllgxv6lsgwxx1miqjr4l1iryqkrj286jcbfrb8ghpzhv5";
-  name = "mingw-w64-clang-x86_64-libarchive-3.7.4-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libarchive-3.7.7-1-any.pkg.tar.zst";
+  sha256 = "01glychb0k1yd878aq4y2fn08lqh2bjydh90xmq03z5qhig66mmn";
+  name = "mingw-w64-clang-x86_64-libarchive-3.7.7-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libuv-1.48.0-1-any.pkg.tar.zst";
-  sha256 = "0kfzanvx7hg7bvy35h2z2vcfxvwn44sikd36mvzhkv6c3c6y84sn";
-  name = "mingw-w64-clang-x86_64-libuv-1.48.0-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libuv-1.49.2-1-any.pkg.tar.zst";
+  sha256 = "1b9slshbcprxjaj2qqypaywr0f2pgajg1bgspjk83hk65sx6sklb";
+  name = "mingw-w64-clang-x86_64-libuv-1.49.2-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -277,9 +277,9 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-cmake-3.30.3-1-any.pkg.tar.zst";
-  sha256 = "0fjwf6xxzli6rcsbzr1razldmm538ibkyf5kw132lpaz5wma9bj8";
-  name = "mingw-w64-clang-x86_64-cmake-3.30.3-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-cmake-3.31.2-3-any.pkg.tar.zst";
+  sha256 = "139f91r392c68hsajm0c81690pmzkywb0p4x8ms8ms53ncxnz6gz";
+  name = "mingw-w64-clang-x86_64-cmake-3.31.2-3-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -289,9 +289,9 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-ncurses-6.4.20231217-1-any.pkg.tar.zst";
-  sha256 = "00046d52zsr8zjifl7h22jfihhh53h20ipvbqmvf9myssw2fwjza";
-  name = "mingw-w64-clang-x86_64-ncurses-6.4.20231217-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-ncurses-6.5.20240831-1-any.pkg.tar.zst";
+  sha256 = "1hlfj9g4s767s502sawwbcv4a0xd3ym3ip4jswmhq48wh5050iyb";
+  name = "mingw-w64-clang-x86_64-ncurses-6.5.20240831-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -307,15 +307,15 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-tcl-8.6.13-1-any.pkg.tar.zst";
-  sha256 = "0paaqwk0sfy2zxwlxkmxf2bqq46lyg0sx7cqgzknvazwx8xa2z4x";
-  name = "mingw-w64-clang-x86_64-tcl-8.6.13-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-sqlite3-3.47.2-1-any.pkg.tar.zst";
+  sha256 = "10pavblv9yjirlm5hix9aikpswhiamry097clba6jcvsajlx4azy";
+  name = "mingw-w64-clang-x86_64-sqlite3-3.47.2-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-sqlite3-3.46.1-1-any.pkg.tar.zst";
-  sha256 = "1axplxyjnaz411qzjjqwbj55fbrh4akq3plm2p1sx64jp844xpyq";
-  name = "mingw-w64-clang-x86_64-sqlite3-3.46.1-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-tcl-8.6.13-1-any.pkg.tar.zst";
+  sha256 = "0paaqwk0sfy2zxwlxkmxf2bqq46lyg0sx7cqgzknvazwx8xa2z4x";
+  name = "mingw-w64-clang-x86_64-tcl-8.6.13-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -325,44 +325,38 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-tzdata-2024a-1-any.pkg.tar.zst";
-  sha256 = "1lsfn3759cyf56zlmfvgy6ihs4iks6zhlnrbfmnq5wml02k936ji";
-  name = "mingw-w64-clang-x86_64-tzdata-2024a-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-tzdata-2024b-1-any.pkg.tar.zst";
+  sha256 = "0jihnr1i7vyzczxz60ds1x3gcm3p4ad2pq9d5vvpwjdwrxkvxmkc";
+  name = "mingw-w64-clang-x86_64-tzdata-2024b-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-python3.12-3.12.1-2-any.pkg.tar.zst";
-  sha256 = "0wmd39wl9z237w093a7c6hl5pclca9yvwxn0kiw6i2njk3sjv51a";
-  name = "mingw-w64-clang-x86_64-python3.12-3.12.1-2-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-python-3.12.7-3-any.pkg.tar.zst";
+  sha256 = "1v15j2pzy9wj4n1rjngdi2hf8h0l9z4lri3xb86yvdv1xl2msj6h";
+  name = "mingw-w64-clang-x86_64-python-3.12.7-3-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-python-3.11.9-1-any.pkg.tar.zst";
-  sha256 = "0ah1idjqxg7jc07a1gz9z766rjjd0f0c6ri4hpcsimsrbj1zjd3c";
-  name = "mingw-w64-clang-x86_64-python-3.11.9-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-llvm-openmp-19.1.4-2-any.pkg.tar.zst";
+  sha256 = "1pn1fbj74rx837s9z8gqs4b0cr7kqi5m1m2mi9ibjpw64m1aqwxv";
+  name = "mingw-w64-clang-x86_64-llvm-openmp-19.1.4-2-any.pkg.tar.zst";
 })
 
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