core/ndstrides: implement np_dot() for scalars and 1D

Overlapping is not handled. Currently it has undefined behavior.

.pre-commit-config.yaml

@@ -1,7 +1,7 @@
 # See https://pre-commit.com for more information
 # See https://pre-commit.com/hooks.html for more hooks
 
-default_stages: [pre-commit]
+default_stages: [commit]
 
 repos:
   - repo: local

Cargo.lock

@@ -26,9 +26,9 @@ dependencies = [
 
 [[package]]
 name = "anstream"
-version = "0.6.18"
+version = "0.6.15"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8acc5369981196006228e28809f761875c0327210a891e941f4c683b3a99529b"
+checksum = "64e15c1ab1f89faffbf04a634d5e1962e9074f2741eef6d97f3c4e322426d526"
 dependencies = [
  "anstyle",
  "anstyle-parse",
@@ -41,36 +41,36 @@ dependencies = [
 
 [[package]]
 name = "anstyle"
-version = "1.0.10"
+version = "1.0.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "55cc3b69f167a1ef2e161439aa98aed94e6028e5f9a59be9a6ffb47aef1651f9"
+checksum = "1bec1de6f59aedf83baf9ff929c98f2ad654b97c9510f4e70cf6f661d49fd5b1"
 
 [[package]]
 name = "anstyle-parse"
-version = "0.2.6"
+version = "0.2.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3b2d16507662817a6a20a9ea92df6652ee4f94f914589377d69f3b21bc5798a9"
+checksum = "eb47de1e80c2b463c735db5b217a0ddc39d612e7ac9e2e96a5aed1f57616c1cb"
 dependencies = [
  "utf8parse",
 ]
 
 [[package]]
 name = "anstyle-query"
-version = "1.1.2"
+version = "1.1.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "79947af37f4177cfead1110013d678905c37501914fba0efea834c3fe9a8d60c"
+checksum = "6d36fc52c7f6c869915e99412912f22093507da8d9e942ceaf66fe4b7c14422a"
 dependencies = [
- "windows-sys 0.59.0",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
 name = "anstyle-wincon"
-version = "3.0.6"
+version = "3.0.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2109dbce0e72be3ec00bed26e6a7479ca384ad226efdd66db8fa2e3a38c83125"
+checksum = "5bf74e1b6e971609db8ca7a9ce79fd5768ab6ae46441c572e46cf596f59e57f8"
 dependencies = [
  "anstyle",
- "windows-sys 0.59.0",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
@@ -126,9 +126,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
 
 [[package]]
 name = "cc"
-version = "1.2.1"
+version = "1.1.30"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fd9de9f2205d5ef3fd67e685b0df337994ddd4495e2a28d185500d0e1edfea47"
+checksum = "b16803a61b81d9eabb7eae2588776c4c1e584b738ede45fdbb4c972cec1e9945"
 dependencies = [
  "shlex",
 ]
@@ -141,9 +141,9 @@ checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
 
 [[package]]
 name = "clap"
-version = "4.5.21"
+version = "4.5.20"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fb3b4b9e5a7c7514dfa52869339ee98b3156b0bfb4e8a77c4ff4babb64b1604f"
+checksum = "b97f376d85a664d5837dbae44bf546e6477a679ff6610010f17276f686d867e8"
 dependencies = [
  "clap_builder",
  "clap_derive",
@@ -151,9 +151,9 @@ dependencies = [
 
 [[package]]
 name = "clap_builder"
-version = "4.5.21"
+version = "4.5.20"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b17a95aa67cc7b5ebd32aa5370189aa0d79069ef1c64ce893bd30fb24bff20ec"
+checksum = "19bc80abd44e4bed93ca373a0704ccbd1b710dc5749406201bb018272808dc54"
 dependencies = [
  "anstream",
  "anstyle",
@@ -170,20 +170,20 @@ dependencies = [
  "heck 0.5.0",
  "proc-macro2",
  "quote",
- "syn 2.0.87",
+ "syn 2.0.79",
 ]
 
 [[package]]
 name = "clap_lex"
-version = "0.7.3"
+version = "0.7.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "afb84c814227b90d6895e01398aee0d8033c00e7466aca416fb6a8e0eb19d8a7"
+checksum = "1462739cb27611015575c0c11df5df7601141071f07518d56fcc1be504cbec97"
 
 [[package]]
 name = "colorchoice"
-version = "1.0.3"
+version = "1.0.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5b63caa9aa9397e2d9480a9b13673856c78d8ac123288526c37d7839f2a86990"
+checksum = "d3fd119d74b830634cea2a0f58bbd0d54540518a14397557951e79340abc28c0"
 
 [[package]]
 name = "console"
@@ -199,9 +199,9 @@ dependencies = [
 
 [[package]]
 name = "cpufeatures"
-version = "0.2.15"
+version = "0.2.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0ca741a962e1b0bff6d724a1a0958b686406e853bb14061f218562e1896f95e6"
+checksum = "608697df725056feaccfa42cffdaeeec3fccc4ffc38358ecd19b243e716a78e0"
 dependencies = [
  "libc",
 ]
@@ -282,12 +282,6 @@ dependencies = [
  "crypto-common",
 ]
 
-[[package]]
-name = "dissimilar"
-version = "1.0.9"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "59f8e79d1fbf76bdfbde321e902714bf6c49df88a7dda6fc682fc2979226962d"
-
 [[package]]
 name = "either"
 version = "1.13.0"
@@ -327,9 +321,9 @@ dependencies = [
 
 [[package]]
 name = "fastrand"
-version = "2.2.0"
+version = "2.1.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "486f806e73c5707928240ddc295403b1b93c96a02038563881c4a2fd84b81ac4"
+checksum = "e8c02a5121d4ea3eb16a80748c74f5549a5665e4c21333c6098f283870fbdea6"
 
 [[package]]
 name = "fixedbitset"
@@ -376,12 +370,6 @@ dependencies = [
  "wasi",
 ]
 
-[[package]]
-name = "glob"
-version = "0.3.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d2fabcfbdc87f4758337ca535fb41a6d701b65693ce38287d856d1674551ec9b"
-
 [[package]]
 name = "hashbrown"
 version = "0.12.3"
@@ -399,9 +387,9 @@ dependencies = [
 
 [[package]]
 name = "hashbrown"
-version = "0.15.1"
+version = "0.15.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3a9bfc1af68b1726ea47d3d5109de126281def866b33970e10fbab11b5dafab3"
+checksum = "1e087f84d4f86bf4b218b927129862374b72199ae7d8657835f1e89000eea4fb"
 
 [[package]]
 name = "heck"
@@ -441,7 +429,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "707907fe3c25f5424cce2cb7e1cbcafee6bdbe735ca90ef77c29e84591e5b9da"
 dependencies = [
  "equivalent",
- "hashbrown 0.15.1",
+ "hashbrown 0.15.0",
 ]
 
 [[package]]
@@ -472,7 +460,7 @@ checksum = "9dd28cfd4cfba665d47d31c08a6ba637eed16770abca2eccbbc3ca831fef1e44"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.87",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -559,9 +547,9 @@ checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe"
 
 [[package]]
 name = "libc"
-version = "0.2.164"
+version = "0.2.161"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "433bfe06b8c75da9b2e3fbea6e5329ff87748f0b144ef75306e674c3f6f7c13f"
+checksum = "8e9489c2807c139ffd9c1794f4af0ebe86a828db53ecdc7fea2111d0fed085d1"
 
 [[package]]
 name = "libloading"
@@ -646,6 +634,7 @@ name = "nac3ast"
 version = "0.1.0"
 dependencies = [
  "fxhash",
+ "lazy_static",
  "parking_lot",
  "string-interner",
 ]
@@ -660,7 +649,6 @@ dependencies = [
  "inkwell",
  "insta",
  "itertools",
- "nac3core_derive",
  "nac3parser",
  "parking_lot",
  "rayon",
@@ -670,18 +658,6 @@ dependencies = [
  "test-case",
 ]
 
-[[package]]
-name = "nac3core_derive"
-version = "0.1.0"
-dependencies = [
- "nac3core",
- "proc-macro-error",
- "proc-macro2",
- "quote",
- "syn 2.0.87",
- "trybuild",
-]
-
 [[package]]
 name = "nac3ld"
 version = "0.1.0"
@@ -799,7 +775,7 @@ dependencies = [
  "phf_shared 0.11.2",
  "proc-macro2",
  "quote",
- "syn 2.0.87",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -847,35 +823,11 @@ version = "0.1.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "925383efa346730478fb4838dbe9137d2a47675ad789c546d150a6e1dd4ab31c"
 
-[[package]]
-name = "proc-macro-error"
-version = "1.0.4"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "da25490ff9892aab3fcf7c36f08cfb902dd3e71ca0f9f9517bea02a73a5ce38c"
-dependencies = [
- "proc-macro-error-attr",
- "proc-macro2",
- "quote",
- "syn 1.0.109",
- "version_check",
-]
-
-[[package]]
-name = "proc-macro-error-attr"
-version = "1.0.4"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a1be40180e52ecc98ad80b184934baf3d0d29f979574e439af5a55274b35f869"
-dependencies = [
- "proc-macro2",
- "quote",
- "version_check",
-]
-
 [[package]]
 name = "proc-macro2"
-version = "1.0.89"
+version = "1.0.88"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f139b0662de085916d1fb67d2b4169d1addddda1919e696f3252b740b629986e"
+checksum = "7c3a7fc5db1e57d5a779a352c8cdb57b29aa4c40cc69c3a68a7fedc815fbf2f9"
 dependencies = [
  "unicode-ident",
 ]
@@ -927,7 +879,7 @@ dependencies = [
  "proc-macro2",
  "pyo3-macros-backend",
  "quote",
- "syn 2.0.87",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -940,7 +892,7 @@ dependencies = [
  "proc-macro2",
  "pyo3-build-config",
  "quote",
- "syn 2.0.87",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -1013,9 +965,9 @@ dependencies = [
 
 [[package]]
 name = "regex"
-version = "1.11.1"
+version = "1.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b544ef1b4eac5dc2db33ea63606ae9ffcfac26c1416a2806ae0bf5f56b201191"
+checksum = "38200e5ee88914975b69f657f0801b6f6dccafd44fd9326302a4aaeecfacb1d8"
 dependencies = [
  "aho-corasick",
  "memchr",
@@ -1025,9 +977,9 @@ dependencies = [
 
 [[package]]
 name = "regex-automata"
-version = "0.4.9"
+version = "0.4.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "809e8dc61f6de73b46c85f4c96486310fe304c434cfa43669d7b40f711150908"
+checksum = "368758f23274712b504848e9d5a6f010445cc8b87a7cdb4d7cbee666c1288da3"
 dependencies = [
  "aho-corasick",
  "memchr",
@@ -1049,9 +1001,9 @@ dependencies = [
 
 [[package]]
 name = "rustix"
-version = "0.38.41"
+version = "0.38.37"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d7f649912bc1495e167a6edee79151c84b1bad49748cb4f1f1167f459f6224f6"
+checksum = "8acb788b847c24f28525660c4d7758620a7210875711f79e7f663cc152726811"
 dependencies = [
  "bitflags",
  "errno",
@@ -1095,29 +1047,29 @@ checksum = "61697e0a1c7e512e84a621326239844a24d8207b4669b41bc18b32ea5cbf988b"
 
 [[package]]
 name = "serde"
-version = "1.0.215"
+version = "1.0.210"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6513c1ad0b11a9376da888e3e0baa0077f1aed55c17f50e7b2397136129fb88f"
+checksum = "c8e3592472072e6e22e0a54d5904d9febf8508f65fb8552499a1abc7d1078c3a"
 dependencies = [
  "serde_derive",
 ]
 
 [[package]]
 name = "serde_derive"
-version = "1.0.215"
+version = "1.0.210"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ad1e866f866923f252f05c889987993144fb74e722403468a4ebd70c3cd756c0"
+checksum = "243902eda00fad750862fc144cea25caca5e20d615af0a81bee94ca738f1df1f"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.87",
+ "syn 2.0.79",
 ]
 
 [[package]]
 name = "serde_json"
-version = "1.0.133"
+version = "1.0.129"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c7fceb2473b9166b2294ef05efcb65a3db80803f0b03ef86a5fc88a2b85ee377"
+checksum = "6dbcf9b78a125ee667ae19388837dd12294b858d101fdd393cb9d5501ef09eb2"
 dependencies = [
  "itoa",
  "memchr",
@@ -1125,15 +1077,6 @@ dependencies = [
  "serde",
 ]
 
-[[package]]
-name = "serde_spanned"
-version = "0.6.8"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "87607cb1398ed59d48732e575a4c28a7a8ebf2454b964fe3f224f2afc07909e1"
-dependencies = [
- "serde",
-]
-
 [[package]]
 name = "serde_yaml"
 version = "0.8.26"
@@ -1226,7 +1169,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "rustversion",
- "syn 2.0.87",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -1242,9 +1185,9 @@ dependencies = [
 
 [[package]]
 name = "syn"
-version = "2.0.87"
+version = "2.0.79"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "25aa4ce346d03a6dcd68dd8b4010bcb74e54e62c90c573f394c46eae99aba32d"
+checksum = "89132cd0bf050864e1d38dc3bbc07a0eb8e7530af26344d3d2bbbef83499f590"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -1257,17 +1200,11 @@ version = "0.12.16"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "61c41af27dd6d1e27b1b16b489db798443478cef1f06a660c96db617ba5de3b1"
 
-[[package]]
-name = "target-triple"
-version = "0.1.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "42a4d50cdb458045afc8131fd91b64904da29548bcb63c7236e0844936c13078"
-
 [[package]]
 name = "tempfile"
-version = "3.14.0"
+version = "3.13.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "28cce251fcbc87fac86a866eeb0d6c2d536fc16d06f184bb61aeae11aa4cee0c"
+checksum = "f0f2c9fc62d0beef6951ccffd757e241266a2c833136efbe35af6cd2567dca5b"
 dependencies = [
  "cfg-if",
  "fastrand",
@@ -1286,15 +1223,6 @@ dependencies = [
  "windows-sys 0.52.0",
 ]
 
-[[package]]
-name = "termcolor"
-version = "1.4.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "06794f8f6c5c898b3275aebefa6b8a1cb24cd2c6c79397ab15774837a0bc5755"
-dependencies = [
- "winapi-util",
-]
-
 [[package]]
 name = "test-case"
 version = "1.2.3"
@@ -1310,72 +1238,22 @@ dependencies = [
 
 [[package]]
 name = "thiserror"
-version = "1.0.69"
+version = "1.0.64"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b6aaf5339b578ea85b50e080feb250a3e8ae8cfcdff9a461c9ec2904bc923f52"
+checksum = "d50af8abc119fb8bb6dbabcfa89656f46f84aa0ac7688088608076ad2b459a84"
 dependencies = [
  "thiserror-impl",
 ]
 
 [[package]]
 name = "thiserror-impl"
-version = "1.0.69"
+version = "1.0.64"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4fee6c4efc90059e10f81e6d42c60a18f76588c3d74cb83a0b242a2b6c7504c1"
+checksum = "08904e7672f5eb876eaaf87e0ce17857500934f4981c4a0ab2b4aa98baac7fc3"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.87",
+ "syn 2.0.79",
-]
-
-[[package]]
-name = "toml"
-version = "0.8.19"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a1ed1f98e3fdc28d6d910e6737ae6ab1a93bf1985935a1193e68f93eeb68d24e"
-dependencies = [
- "serde",
- "serde_spanned",
- "toml_datetime",
- "toml_edit",
-]
-
-[[package]]
-name = "toml_datetime"
-version = "0.6.8"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0dd7358ecb8fc2f8d014bf86f6f638ce72ba252a2c3a2572f2a795f1d23efb41"
-dependencies = [
- "serde",
-]
-
-[[package]]
-name = "toml_edit"
-version = "0.22.22"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4ae48d6208a266e853d946088ed816055e556cc6028c5e8e2b84d9fa5dd7c7f5"
-dependencies = [
- "indexmap 2.6.0",
- "serde",
- "serde_spanned",
- "toml_datetime",
- "winnow",
-]
-
-[[package]]
-name = "trybuild"
-version = "1.0.101"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8dcd332a5496c026f1e14b7f3d2b7bd98e509660c04239c58b0ba38a12daded4"
-dependencies = [
- "dissimilar",
- "glob",
- "serde",
- "serde_derive",
- "serde_json",
- "target-triple",
- "termcolor",
- "toml",
 ]
 
 [[package]]
@@ -1601,15 +1479,6 @@ version = "0.52.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "589f6da84c646204747d1270a2a5661ea66ed1cced2631d546fdfb155959f9ec"
 
-[[package]]
-name = "winnow"
-version = "0.6.20"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "36c1fec1a2bb5866f07c25f68c26e565c4c200aebb96d7e55710c19d3e8ac49b"
-dependencies = [
- "memchr",
-]
-
 [[package]]
 name = "yaml-rust"
 version = "0.4.5"
@@ -1637,5 +1506,5 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.87",
+ "syn 2.0.79",
 ]

Cargo.toml

@@ -4,7 +4,6 @@ members = [
   "nac3ast",
   "nac3parser",
   "nac3core",
-  "nac3core/nac3core_derive",
   "nac3standalone",
   "nac3artiq",
   "runkernel",

flake.lock

@@ -2,11 +2,11 @@
   "nodes": {
     "nixpkgs": {
       "locked": {
-        "lastModified": 1731319897,
+        "lastModified": 1727348695,
-        "narHash": "sha256-PbABj4tnbWFMfBp6OcUK5iGy1QY+/Z96ZcLpooIbuEI=",
+        "narHash": "sha256-J+PeFKSDV+pHL7ukkfpVzCOO7mBSrrpJ3svwBFABbhI=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "dc460ec76cbff0e66e269457d7b728432263166c",
+        "rev": "1925c603f17fc89f4c8f6bf6f631a802ad85d784",
         "type": "github"
       },
       "original": {

flake.nix

@@ -107,18 +107,18 @@
             (pkgs.fetchFromGitHub {
               owner = "m-labs";
               repo = "sipyco";
-              rev = "094a6cd63ffa980ef63698920170e50dc9ba77fd";
+              rev = "939f84f9b5eef7efbf7423c735d1834783b6140e";
-              sha256 = "sha256-PPnAyDedUQ7Og/Cby9x5OT9wMkNGTP8GS53V6N/dk4w=";
+              sha256 = "sha256-15Nun4EY35j+6SPZkjzZtyH/ncxLS60KuGJjFh5kSTc=";
             })
             (pkgs.fetchFromGitHub {
               owner = "m-labs";
               repo = "artiq";
-              rev = "28c9de3e251daa89a8c9fd79d5ab64a3ec03bac6";
+              rev = "923ca3377d42c815f979983134ec549dc39d3ca0";
-              sha256 = "sha256-vAvpbHc5B+1wtG8zqN7j9dQE1ON+i22v+uqA+tw6Gak=";
+              sha256 = "sha256-oJoEeNEeNFSUyh6jXG8Tzp6qHVikeHS0CzfE+mODPgw=";
             })
           ];
           buildInputs = [
-            (python3-mimalloc.withPackages(ps: [ ps.numpy ps.scipy ps.jsonschema ps.lmdb ps.platformdirs nac3artiq-instrumented ]))
+            (python3-mimalloc.withPackages(ps: [ ps.numpy ps.scipy ps.jsonschema ps.lmdb nac3artiq-instrumented ]))
             pkgs.llvmPackages_14.llvm.out
           ];
           phases = [ "buildPhase" "installPhase" ];

nac3artiq/demo/min_artiq.py

@@ -206,7 +206,7 @@ class Core:
         embedding = EmbeddingMap()
 
         if allow_registration:
-            compiler.analyze(registered_functions, registered_classes, set())
+            compiler.analyze(registered_functions, registered_classes)
             allow_registration = False
 
         if hasattr(method, "__self__"):

nac3artiq/src/codegen.rs

@@ -14,15 +14,14 @@ use pyo3::{
 
 use nac3core::{
     codegen::{
+        classes::{
+            ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayType,
+            NDArrayValue, ProxyType, ProxyValue, RangeValue, UntypedArrayLikeAccessor,
+        },
         expr::{destructure_range, gen_call},
         irrt::call_ndarray_calc_size,
         llvm_intrinsics::{call_int_smax, call_memcpy_generic, call_stackrestore, call_stacksave},
         stmt::{gen_block, gen_for_callback_incrementing, gen_if_callback, gen_with},
-        types::{NDArrayType, ProxyType},
-        values::{
-            ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue, ProxyValue,
-            RangeValue, UntypedArrayLikeAccessor,
-        },
         CodeGenContext, CodeGenerator,
     },
     inkwell::{
@@ -461,7 +460,7 @@ fn format_rpc_arg<'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_ty = NDArrayType::new(generator, ctx.ctx, llvm_elem_ty);
-            let llvm_arg = llvm_arg_ty.map_value(arg.into_pointer_value(), None);
+            let llvm_arg = NDArrayValue::from_ptr_val(arg.into_pointer_value(), llvm_usize, None);
 
             let llvm_usize_sizeof = ctx
                 .builder
@@ -498,7 +497,7 @@ fn format_rpc_arg<'ctx>(
             call_memcpy_generic(
                 ctx,
                 pbuffer_dims_begin,
-                llvm_arg.shape().base_ptr(ctx, generator),
+                llvm_arg.dim_sizes().base_ptr(ctx, generator),
                 dims_buf_sz,
                 llvm_i1.const_zero(),
             );
@@ -612,7 +611,7 @@ fn format_rpc_ret<'ctx>(
             // 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.create_dim_sizes(ctx, llvm_usize, ndarray.load_ndims(ctx));
 
             /*
                 ndarray now:
@@ -702,7 +701,7 @@ fn format_rpc_ret<'ctx>(
 
             call_memcpy_generic(
                 ctx,
-                ndarray.shape().base_ptr(ctx, generator),
+                ndarray.dim_sizes().base_ptr(ctx, generator),
                 pbuffer_dims,
                 sizeof_dims,
                 llvm_i1.const_zero(),
@@ -714,7 +713,7 @@ fn format_rpc_ret<'ctx>(
             // `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));
+                call_ndarray_calc_size(generator, ctx, &ndarray.dim_sizes(), (None, None));
 
             // debug_assert(nelems * sizeof(T) >= ndarray_nbytes)
             if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
@@ -991,12 +990,11 @@ fn rpc_codegen_callback_fn<'ctx>(
     }
 }
 
-pub fn attributes_writeback<'ctx>(
+pub fn attributes_writeback(
-    ctx: &mut CodeGenContext<'ctx, '_>,
+    ctx: &mut CodeGenContext<'_, '_>,
     generator: &mut dyn CodeGenerator,
     inner_resolver: &InnerResolver,
     host_attributes: &PyObject,
-    return_obj: Option<(Type, ValueEnum<'ctx>)>,
 ) -> Result<(), String> {
     Python::with_gil(|py| -> PyResult<Result<(), String>> {
         let host_attributes: &PyList = host_attributes.downcast(py)?;
@@ -1006,11 +1004,6 @@ pub fn attributes_writeback<'ctx>(
         let zero = int32.const_zero();
         let mut values = Vec::new();
         let mut scratch_buffer = Vec::new();
-
-        if let Some((ty, obj)) = return_obj {
-            values.push((ty, obj.to_basic_value_enum(ctx, generator, ty).unwrap()));
-        }
-
         for val in (*globals).values() {
             let val = val.as_ref(py);
             let ty = inner_resolver.get_obj_type(
@@ -1089,7 +1082,7 @@ pub fn attributes_writeback<'ctx>(
         let args: Vec<_> =
             values.into_iter().map(|(_, val)| (None, ValueEnum::Dynamic(val))).collect();
         if let Err(e) =
-            rpc_codegen_callback_fn(ctx, None, (&fun, PrimDef::Int32.id()), args, generator, true)
+            rpc_codegen_callback_fn(ctx, None, (&fun, PrimDef::Int32.id()), args, generator, false)
         {
             return Ok(Err(e));
         }
@@ -1315,8 +1308,7 @@ fn polymorphic_print<'ctx>(
                 fmt.push('[');
                 flush(ctx, generator, &mut fmt, &mut args);
 
-                let val =
+                let val = ListValue::from_ptr_val(value.into_pointer_value(), llvm_usize, None);
-                    ListValue::from_pointer_value(value.into_pointer_value(), llvm_usize, None);
                 let len = val.load_size(ctx, None);
                 let last =
                     ctx.builder.build_int_sub(len, llvm_usize.const_int(1, false), "").unwrap();
@@ -1368,18 +1360,12 @@ 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(
+                let val = NDArrayValue::from_ptr_val(value.into_pointer_value(), llvm_usize, None);
-                    value.into_pointer_value(),
+                let len = call_ndarray_calc_size(generator, ctx, &val.dim_sizes(), (None, None));
-                    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();
 
@@ -1432,7 +1418,7 @@ fn polymorphic_print<'ctx>(
                 fmt.push_str("range(");
                 flush(ctx, generator, &mut fmt, &mut args);
 
-                let val = RangeValue::from_pointer_value(value.into_pointer_value(), None);
+                let val = RangeValue::from_ptr_val(value.into_pointer_value(), None);
 
                 let (start, stop, step) = destructure_range(ctx, val);
 

nac3artiq/src/lib.rs

@@ -1,4 +1,10 @@
-#![deny(future_incompatible, let_underscore, nonstandard_style, clippy::all)]
+#![deny(
+    future_incompatible,
+    let_underscore,
+    nonstandard_style,
+    rust_2024_compatibility,
+    clippy::all
+)]
 #![warn(clippy::pedantic)]
 #![allow(
     unsafe_op_in_unsafe_fn,
@@ -24,19 +30,19 @@ use parking_lot::{Mutex, RwLock};
 use pyo3::{
     create_exception, exceptions,
     prelude::*,
-    types::{PyBytes, PyDict, PyNone, PySet},
+    types::{PyBytes, PyDict, PySet},
 };
 use tempfile::{self, TempDir};
 
 use nac3core::{
     codegen::{
         concrete_type::ConcreteTypeStore, gen_func_impl, irrt::load_irrt, CodeGenLLVMOptions,
-        CodeGenTargetMachineOptions, CodeGenTask, CodeGenerator, WithCall, WorkerRegistry,
+        CodeGenTargetMachineOptions, CodeGenTask, WithCall, WorkerRegistry,
     },
     inkwell::{
         context::Context,
         memory_buffer::MemoryBuffer,
-        module::{FlagBehavior, Linkage, Module},
+        module::{Linkage, Module},
         passes::PassBuilderOptions,
         support::is_multithreaded,
         targets::*,
@@ -142,32 +148,14 @@ impl Nac3 {
         module: &PyObject,
         registered_class_ids: &HashSet<u64>,
     ) -> PyResult<()> {
-        let (module_name, source_file, source) =
+        let (module_name, source_file) = Python::with_gil(|py| -> PyResult<(String, String)> {
-            Python::with_gil(|py| -> PyResult<(String, String, String)> {
             let module: &PyAny = module.extract(py)?;
-                let source_file = module.getattr("__file__");
+            Ok((module.getattr("__name__")?.extract()?, module.getattr("__file__")?.extract()?))
-                let (source_file, source) = if let Ok(source_file) = source_file {
-                    let source_file = source_file.extract()?;
-                    (
-                        source_file,
-                        fs::read_to_string(&source_file).map_err(|e| {
-                            exceptions::PyIOError::new_err(format!(
-                                "failed to read input file: {e}"
-                            ))
-                        })?,
-                    )
-                } else {
-                    // kernels submitted by content have no file
-                    // but still can provide source by StringLoader
-                    let get_src_fn = module
-                        .getattr("__loader__")?
-                        .extract::<PyObject>()?
-                        .getattr(py, "get_source")?;
-                    ("<expcontent>", get_src_fn.call1(py, (PyNone::get(py),))?.extract(py)?)
-                };
-                Ok((module.getattr("__name__")?.extract()?, source_file.to_string(), source))
         })?;
 
+        let source = fs::read_to_string(&source_file).map_err(|e| {
+            exceptions::PyIOError::new_err(format!("failed to read input file: {e}"))
+        })?;
         let parser_result = parse_program(&source, source_file.into())
             .map_err(|e| exceptions::PySyntaxError::new_err(format!("parse error: {e}")))?;
 
@@ -685,12 +673,33 @@ impl Nac3 {
         let task = CodeGenTask {
             subst: Vec::default(),
             symbol_name: "__modinit__".to_string(),
+            body: instance.body,
+            signature,
+            resolver: resolver.clone(),
+            store,
+            unifier_index: instance.unifier_id,
+            calls: instance.calls,
+            id: 0,
+        };
+
+        let mut store = ConcreteTypeStore::new();
+        let mut cache = HashMap::new();
+        let signature = store.from_signature(
+            &mut composer.unifier,
+            &self.primitive,
+            &fun_signature,
+            &mut cache,
+        );
+        let signature = store.add_cty(signature);
+        let attributes_writeback_task = CodeGenTask {
+            subst: Vec::default(),
+            symbol_name: "attributes_writeback".to_string(),
             body: Arc::new(Vec::default()),
             signature,
             resolver,
             store,
             unifier_index: instance.unifier_id,
-            calls: instance.calls,
+            calls: Arc::new(HashMap::default()),
             id: 0,
         };
 
@@ -714,27 +723,19 @@ impl Nac3 {
             .collect();
 
         let membuffer = membuffers.clone();
-        let mut has_return = false;
         py.allow_threads(|| {
             let (registry, handles) =
                 WorkerRegistry::create_workers(threads, top_level.clone(), &self.llvm_options, &f);
+            registry.add_task(task);
+            registry.wait_tasks_complete(handles);
 
-            let mut generator = ArtiqCodeGenerator::new("main".to_string(), size_t, self.time_fns);
+            let mut generator =
+                ArtiqCodeGenerator::new("attributes_writeback".to_string(), size_t, self.time_fns);
             let context = Context::create();
-            let module = context.create_module("main");
+            let module = context.create_module("attributes_writeback");
             let target_machine = self.llvm_options.create_target_machine().unwrap();
             module.set_data_layout(&target_machine.get_target_data().get_data_layout());
             module.set_triple(&target_machine.get_triple());
-            module.add_basic_value_flag(
-                "Debug Info Version",
-                FlagBehavior::Warning,
-                context.i32_type().const_int(3, false),
-            );
-            module.add_basic_value_flag(
-                "Dwarf Version",
-                FlagBehavior::Warning,
-                context.i32_type().const_int(4, false),
-            );
             let builder = context.create_builder();
             let (_, module, _) = gen_func_impl(
                 &context,
@@ -742,27 +743,9 @@ impl Nac3 {
                 &registry,
                 builder,
                 module,
-                task,
+                attributes_writeback_task,
                 |generator, ctx| {
-                    assert_eq!(instance.body.len(), 1, "toplevel module should have 1 statement");
+                    attributes_writeback(ctx, generator, inner_resolver.as_ref(), &host_attributes)
-                    let StmtKind::Expr { value: ref expr, .. } = instance.body[0].node else {
-                        unreachable!("toplevel statement must be an expression")
-                    };
-                    let ExprKind::Call { .. } = expr.node else {
-                        unreachable!("toplevel expression must be a function call")
-                    };
-
-                    let return_obj =
-                        generator.gen_expr(ctx, expr)?.map(|value| (expr.custom.unwrap(), value));
-                    has_return = return_obj.is_some();
-                    registry.wait_tasks_complete(handles);
-                    attributes_writeback(
-                        ctx,
-                        generator,
-                        inner_resolver.as_ref(),
-                        &host_attributes,
-                        return_obj,
-                    )
                 },
             )
             .unwrap();
@@ -771,23 +754,35 @@ impl Nac3 {
             membuffer.lock().push(buffer);
         });
 
-        embedding_map.setattr("expects_return", has_return).unwrap();
-
         // Link all modules into `main`.
         let buffers = membuffers.lock();
         let main = context
-            .create_module_from_ir(MemoryBuffer::create_from_memory_range(
+            .create_module_from_ir(MemoryBuffer::create_from_memory_range(&buffers[0], "main"))
-                buffers.last().unwrap(),
-                "main",
-            ))
             .unwrap();
-        for buffer in buffers.iter().rev().skip(1) {
+        for buffer in buffers.iter().skip(1) {
             let other = context
                 .create_module_from_ir(MemoryBuffer::create_from_memory_range(buffer, "main"))
                 .unwrap();
 
             main.link_in_module(other).map_err(|err| CompileError::new_err(err.to_string()))?;
         }
+        let builder = context.create_builder();
+        let modinit_return = main
+            .get_function("__modinit__")
+            .unwrap()
+            .get_last_basic_block()
+            .unwrap()
+            .get_terminator()
+            .unwrap();
+        builder.position_before(&modinit_return);
+        builder
+            .build_call(
+                main.get_function("attributes_writeback").unwrap(),
+                &[],
+                "attributes_writeback",
+            )
+            .unwrap();
+
         main.link_in_module(irrt).map_err(|err| CompileError::new_err(err.to_string()))?;
 
         let mut function_iter = main.get_first_function();
@@ -1090,12 +1085,7 @@ impl Nac3 {
         })
     }
 
-    fn analyze(
+    fn analyze(&mut self, functions: &PySet, classes: &PySet) -> PyResult<()> {
-        &mut self,
-        functions: &PySet,
-        classes: &PySet,
-        content_modules: &PySet,
-    ) -> PyResult<()> {
         let (modules, class_ids) =
             Python::with_gil(|py| -> PyResult<(HashMap<u64, PyObject>, HashSet<u64>)> {
                 let mut modules: HashMap<u64, PyObject> = HashMap::new();
@@ -1105,22 +1095,14 @@ impl Nac3 {
                 let getmodule_fn = PyModule::import(py, "inspect")?.getattr("getmodule")?;
 
                 for function in functions {
-                    let module: PyObject = getmodule_fn.call1((function,))?.extract()?;
+                    let module = getmodule_fn.call1((function,))?.extract()?;
-                    if !module.is_none(py) {
                     modules.insert(id_fn.call1((&module,))?.extract()?, module);
                 }
-                }
                 for class in classes {
-                    let module: PyObject = getmodule_fn.call1((class,))?.extract()?;
+                    let module = getmodule_fn.call1((class,))?.extract()?;
-                    if !module.is_none(py) {
                     modules.insert(id_fn.call1((&module,))?.extract()?, module);
-                    }
                     class_ids.insert(id_fn.call1((class,))?.extract()?);
                 }
-                for module in content_modules {
-                    let module: PyObject = module.extract()?;
-                    modules.insert(id_fn.call1((&module,))?.extract()?, module.into());
-                }
                 Ok((modules, class_ids))
             })?;
 

nac3artiq/src/symbol_resolver.rs

@@ -14,7 +14,10 @@ use pyo3::{
 };
 
 use nac3core::{
-    codegen::{types::NDArrayType, CodeGenContext, CodeGenerator},
+    codegen::{
+        classes::{NDArrayType, ProxyType},
+        CodeGenContext, CodeGenerator,
+    },
     inkwell::{
         module::Linkage,
         types::{BasicType, BasicTypeEnum},
@@ -1093,7 +1096,7 @@ impl InnerResolver {
                 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.element_type().into_struct_type(),
+                            ndarray_llvm_ty.as_underlying_type(),
                             Some(AddressSpace::default()),
                             &id_str,
                         )
@@ -1187,7 +1190,7 @@ impl InnerResolver {
             data_global.set_initializer(&data);
 
             // create a global for the ndarray object and initialize it
-            let value = ndarray_llvm_ty.element_type().into_struct_type().const_named_struct(&[
+            let value = ndarray_llvm_ty.as_underlying_type().const_named_struct(&[
                 llvm_usize.const_int(ndarray_ndims, false).into(),
                 shape_global
                     .as_pointer_value()
@@ -1200,7 +1203,7 @@ impl InnerResolver {
             ]);
 
             let ndarray = ctx.module.add_global(
-                ndarray_llvm_ty.element_type().into_struct_type(),
+                ndarray_llvm_ty.as_underlying_type(),
                 Some(AddressSpace::default()),
                 &id_str,
             );

nac3ast/Cargo.toml

@@ -10,6 +10,7 @@ constant-optimization = ["fold"]
 fold = []
 
 [dependencies]
+lazy_static = "1.5"
 parking_lot = "0.12"
 string-interner = "0.17"
 fxhash = "0.2"

nac3ast/src/ast_gen.rs

@@ -5,12 +5,14 @@ pub use crate::location::Location;
 
 use fxhash::FxBuildHasher;
 use parking_lot::{Mutex, MutexGuard};
-use std::{cell::RefCell, collections::HashMap, fmt, sync::LazyLock};
+use std::{cell::RefCell, collections::HashMap, fmt};
 use string_interner::{symbol::SymbolU32, DefaultBackend, StringInterner};
 
 pub type Interner = StringInterner<DefaultBackend, FxBuildHasher>;
-static INTERNER: LazyLock<Mutex<Interner>> =
+lazy_static! {
-    LazyLock::new(|| Mutex::new(StringInterner::with_hasher(FxBuildHasher::default())));
+    static ref INTERNER: Mutex<Interner> =
+        Mutex::new(StringInterner::with_hasher(FxBuildHasher::default()));
+}
 
 thread_local! {
     static LOCAL_INTERNER: RefCell<HashMap<String, StrRef>> = RefCell::default();

nac3ast/src/lib.rs

@@ -1,4 +1,10 @@
-#![deny(future_incompatible, let_underscore, nonstandard_style, clippy::all)]
+#![deny(
+    future_incompatible,
+    let_underscore,
+    nonstandard_style,
+    rust_2024_compatibility,
+    clippy::all
+)]
 #![warn(clippy::pedantic)]
 #![allow(
     clippy::missing_errors_doc,
@@ -8,6 +14,9 @@
     clippy::wildcard_imports
 )]
 
+#[macro_use]
+extern crate lazy_static;
+
 mod ast_gen;
 mod constant;
 #[cfg(feature = "fold")]

nac3core/Cargo.toml

@@ -5,8 +5,6 @@ authors = ["M-Labs"]
 edition = "2021"
 
 [features]
-default = ["derive"]
-derive = ["dep:nac3core_derive"]
 no-escape-analysis = []
 
 [dependencies]
@@ -15,7 +13,6 @@ crossbeam = "0.8"
 indexmap = "2.6"
 parking_lot = "0.12"
 rayon = "1.10"
-nac3core_derive = { path = "nac3core_derive", optional = true }
 nac3parser = { path = "../nac3parser" }
 strum = "0.26"
 strum_macros = "0.26"

nac3core/build.rs

@@ -56,8 +56,9 @@ fn main() {
     let output = Command::new("clang-irrt")
         .args(flags)
         .output()
-        .inspect(|o| {
+        .map(|o| {
             assert!(o.status.success(), "{}", std::str::from_utf8(&o.stderr).unwrap());
+            o
         })
         .unwrap();
 

nac3core/irrt/irrt.cpp

@@ -1,5 +1,16 @@
 #include "irrt/exception.hpp"
+#include "irrt/int_types.hpp"
 #include "irrt/list.hpp"
 #include "irrt/math.hpp"
 #include "irrt/ndarray.hpp"
+#include "irrt/range.hpp"
 #include "irrt/slice.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/cslice.hpp

@@ -4,6 +4,6 @@
 
 template<typename SizeT>
 struct CSlice {
-    void* base;
+    uint8_t* base;
     SizeT len;
 };

nac3core/irrt/irrt/exception.hpp

@@ -6,7 +6,7 @@
 /**
  * @brief The int type of ARTIQ exception IDs.
  */
-using ExceptionId = int32_t;
+typedef int32_t ExceptionId;
 
 /*
  * Set of exceptions C++ IRRT can use.
@@ -55,14 +55,14 @@ void _raise_exception_helper(ExceptionId id,
                              int64_t param2) {
     Exception<SizeT> e = {
         .id = id,
-        .filename = {.base = reinterpret_cast<void*>(const_cast<char*>(filename)),
+        .filename = {.base = reinterpret_cast<uint8_t*>(const_cast<char*>(filename)),
-                     .len = static_cast<SizeT>(__builtin_strlen(filename))},
+                     .len = static_cast<int32_t>(__builtin_strlen(filename))},
         .line = line,
         .column = 0,
-        .function = {.base = reinterpret_cast<void*>(const_cast<char*>(function)),
+        .function = {.base = reinterpret_cast<uint8_t*>(const_cast<char*>(function)),
-                     .len = static_cast<SizeT>(__builtin_strlen(function))},
+                     .len = static_cast<int32_t>(__builtin_strlen(function))},
-        .msg = {.base = reinterpret_cast<void*>(const_cast<char*>(msg)),
+        .msg = {.base = reinterpret_cast<uint8_t*>(const_cast<char*>(msg)),
-                .len = static_cast<SizeT>(__builtin_strlen(msg))},
+                .len = static_cast<int32_t>(__builtin_strlen(msg))},
     };
     e.params[0] = param0;
     e.params[1] = param1;
@@ -70,7 +70,6 @@ void _raise_exception_helper(ExceptionId id,
     __nac3_raise(reinterpret_cast<void*>(&e));
     __builtin_unreachable();
 }
-}  // namespace
 
 /**
  * @brief Raise an exception with location details (location in the IRRT source files).
@@ -83,3 +82,4 @@ void _raise_exception_helper(ExceptionId id,
  */
 #define raise_exception(SizeT, id, msg, param0, param1, param2) \
     _raise_exception_helper<SizeT>(id, __FILE__, __LINE__, __FUNCTION__, msg, param0, param1, param2)
+}  // namespace

nac3core/irrt/irrt/int_types.hpp

@@ -8,20 +8,15 @@ using uint32_t = unsigned _BitInt(32);
 using int64_t = _BitInt(64);
 using uint64_t = unsigned _BitInt(64);
 #else
-
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wdeprecated-type"
 using int8_t = _ExtInt(8);
 using uint8_t = unsigned _ExtInt(8);
 using int32_t = _ExtInt(32);
 using uint32_t = unsigned _ExtInt(32);
 using int64_t = _ExtInt(64);
 using uint64_t = unsigned _ExtInt(64);
-#pragma clang diagnostic pop
-
 #endif
 
 // NDArray indices are always `uint32_t`.
-using NDIndex = uint32_t;
+using NDIndexInt = uint32_t;
 // The type of an index or a value describing the length of a range/slice is always `int32_t`.
 using SliceIndex = int32_t;

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
@@ -13,12 +28,12 @@ extern "C" {
 SliceIndex __nac3_list_slice_assign_var_size(SliceIndex dest_start,
                                              SliceIndex dest_end,
                                              SliceIndex dest_step,
-                                             void* dest_arr,
+                                             uint8_t* dest_arr,
                                              SliceIndex dest_arr_len,
                                              SliceIndex src_start,
                                              SliceIndex src_end,
                                              SliceIndex src_step,
-                                             void* src_arr,
+                                             uint8_t* src_arr,
                                              SliceIndex src_arr_len,
                                              const SliceIndex size) {
     /* if dest_arr_len == 0, do nothing since we do not support extending list */
@@ -29,13 +44,11 @@ SliceIndex __nac3_list_slice_assign_var_size(SliceIndex dest_start,
         const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
         const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
         if (src_len > 0) {
-            __builtin_memmove(static_cast<uint8_t*>(dest_arr) + dest_start * size,
+            __builtin_memmove(dest_arr + dest_start * size, src_arr + src_start * size, src_len * size);
-                              static_cast<uint8_t*>(src_arr) + src_start * size, src_len * size);
         }
         if (dest_len > 0) {
             /* dropping */
-            __builtin_memmove(static_cast<uint8_t*>(dest_arr) + (dest_start + src_len) * size,
+            __builtin_memmove(dest_arr + (dest_start + src_len) * size, dest_arr + (dest_end + 1) * size,
-                              static_cast<uint8_t*>(dest_arr) + (dest_end + 1) * size,
                               (dest_arr_len - dest_end - 1) * size);
         }
         /* shrink size */
@@ -46,7 +59,7 @@ SliceIndex __nac3_list_slice_assign_var_size(SliceIndex dest_start,
                           && !(max(dest_start, dest_end) < min(src_start, src_end)
                                || max(src_start, src_end) < min(dest_start, dest_end));
     if (need_alloca) {
-        void* tmp = __builtin_alloca(src_arr_len * size);
+        uint8_t* tmp = reinterpret_cast<uint8_t*>(__builtin_alloca(src_arr_len * size));
         __builtin_memcpy(tmp, src_arr, src_arr_len * size);
         src_arr = tmp;
     }
@@ -55,24 +68,20 @@ SliceIndex __nac3_list_slice_assign_var_size(SliceIndex dest_start,
     for (; (src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end); src_ind += src_step, dest_ind += dest_step) {
         /* for constant optimization */
         if (size == 1) {
-            __builtin_memcpy(static_cast<uint8_t*>(dest_arr) + dest_ind, static_cast<uint8_t*>(src_arr) + src_ind, 1);
+            __builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
         } else if (size == 4) {
-            __builtin_memcpy(static_cast<uint8_t*>(dest_arr) + dest_ind * 4,
+            __builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
-                             static_cast<uint8_t*>(src_arr) + src_ind * 4, 4);
         } else if (size == 8) {
-            __builtin_memcpy(static_cast<uint8_t*>(dest_arr) + dest_ind * 8,
+            __builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
-                             static_cast<uint8_t*>(src_arr) + src_ind * 8, 8);
         } else {
             /* memcpy for var size, cannot overlap after previous alloca */
-            __builtin_memcpy(static_cast<uint8_t*>(dest_arr) + dest_ind * size,
+            __builtin_memcpy(dest_arr + dest_ind * size, src_arr + src_ind * size, size);
-                             static_cast<uint8_t*>(src_arr) + src_ind * size, size);
         }
     }
     /* only dest_step == 1 can we shrink the dest list. */
     /* size should be ensured prior to calling this function */
     if (dest_step == 1 && dest_end >= dest_start) {
-        __builtin_memmove(static_cast<uint8_t*>(dest_arr) + dest_ind * size,
+        __builtin_memmove(dest_arr + dest_ind * size, dest_arr + (dest_end + 1) * size,
-                          static_cast<uint8_t*>(dest_arr) + (dest_end + 1) * size,
                           (dest_arr_len - dest_end - 1) * size);
         return dest_arr_len - (dest_end - dest_ind) - 1;
     }

nac3core/irrt/irrt/math.hpp

@@ -90,4 +90,4 @@ double __nac3_j0(double x) {
 
     return j0(x);
 }
-}  // namespace
+}

nac3core/irrt/irrt/ndarray.hpp

@@ -2,6 +2,8 @@
 
 #include "irrt/int_types.hpp"
 
+// TODO: To be deleted since NDArray with strides is done.
+
 namespace {
 template<typename SizeT>
 SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len, SizeT begin_idx, SizeT end_idx) {
@@ -17,7 +19,7 @@ SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len, Size
 }
 
 template<typename SizeT>
-void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims, SizeT num_dims, NDIndex* idxs) {
+void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims, SizeT num_dims, NDIndexInt* idxs) {
     SizeT stride = 1;
     for (SizeT dim = 0; dim < num_dims; dim++) {
         SizeT i = num_dims - dim - 1;
@@ -28,7 +30,10 @@ void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims, SizeT n
 }
 
 template<typename SizeT>
-SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims, const NDIndex* indices, SizeT num_indices) {
+SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims,
+                                        SizeT num_dims,
+                                        const NDIndexInt* indices,
+                                        SizeT num_indices) {
     SizeT idx = 0;
     SizeT stride = 1;
     for (SizeT i = 0; i < num_dims; ++i) {
@@ -75,8 +80,8 @@ void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims,
 template<typename SizeT>
 void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
                                             SizeT src_ndims,
-                                            const NDIndex* in_idx,
+                                            const NDIndexInt* in_idx,
-                                            NDIndex* out_idx) {
+                                            NDIndexInt* out_idx) {
     for (SizeT i = 0; i < src_ndims; ++i) {
         SizeT src_i = src_ndims - i - 1;
         out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
@@ -94,21 +99,23 @@ __nac3_ndarray_calc_size64(const uint64_t* list_data, uint64_t list_len, uint64_
     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) {
+void __nac3_ndarray_calc_nd_indices(uint32_t index, const uint32_t* dims, uint32_t num_dims, NDIndexInt* idxs) {
     __nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
 }
 
-void __nac3_ndarray_calc_nd_indices64(uint64_t index, const uint64_t* dims, uint64_t num_dims, NDIndex* idxs) {
+void __nac3_ndarray_calc_nd_indices64(uint64_t index, const uint64_t* dims, uint64_t num_dims, NDIndexInt* idxs) {
     __nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
 }
 
 uint32_t
-__nac3_ndarray_flatten_index(const uint32_t* dims, uint32_t num_dims, const NDIndex* indices, uint32_t num_indices) {
+__nac3_ndarray_flatten_index(const uint32_t* dims, uint32_t num_dims, const NDIndexInt* indices, uint32_t num_indices) {
     return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
 }
 
-uint64_t
+uint64_t __nac3_ndarray_flatten_index64(const uint64_t* dims,
-__nac3_ndarray_flatten_index64(const uint64_t* dims, uint64_t num_dims, const NDIndex* indices, uint64_t num_indices) {
+                                        uint64_t num_dims,
+                                        const NDIndexInt* indices,
+                                        uint64_t num_indices) {
     return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
 }
 
@@ -130,15 +137,15 @@ void __nac3_ndarray_calc_broadcast64(const uint64_t* lhs_dims,
 
 void __nac3_ndarray_calc_broadcast_idx(const uint32_t* src_dims,
                                        uint32_t src_ndims,
-                                       const NDIndex* in_idx,
+                                       const NDIndexInt* in_idx,
-                                       NDIndex* out_idx) {
+                                       NDIndexInt* out_idx) {
     __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
 }
 
 void __nac3_ndarray_calc_broadcast_idx64(const uint64_t* src_dims,
                                          uint64_t src_ndims,
-                                         const NDIndex* in_idx,
+                                         const NDIndexInt* in_idx,
-                                         NDIndex* out_idx) {
+                                         NDIndexInt* out_idx) {
     __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
 }
-}  // namespace
+}

nac3core/irrt/irrt/ndarray/array.hpp

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

nac3core/irrt/irrt/ndarray/basic.hpp

@@ -0,0 +1,341 @@
+#pragma once
+
+#include "irrt/debug.hpp"
+#include "irrt/exception.hpp"
+#include "irrt/int_types.hpp"
+#include "irrt/ndarray/def.hpp"
+
+namespace {
+namespace ndarray {
+namespace 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) {
+    // numpy prohibits `__len__` on unsized objects
+    if (ndarray->ndims == 0) {
+        raise_exception(SizeT, EXN_TYPE_ERROR, "len() of unsized object", NO_PARAM, NO_PARAM, NO_PARAM);
+    } else {
+        return ndarray->shape[0];
+    }
+}
+
+/**
+ * @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>
+uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray, const SizeT* indices) {
+    uint8_t* element = ndarray->data;
+    for (SizeT dim_i = 0; dim_i < ndarray->ndims; dim_i++)
+        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>
+uint8_t* get_nth_pelement(const NDArray<SizeT>* ndarray, SizeT nth) {
+    uint8_t* element = ndarray->data;
+    for (SizeT i = 0; i < ndarray->ndims; i++) {
+        SizeT axis = ndarray->ndims - i - 1;
+        SizeT dim = ndarray->shape[axis];
+        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, uint8_t* pelement, const uint8_t* 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 basic
+}  // namespace ndarray
+}  // 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);
+}
+
+uint8_t* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray, int32_t nth) {
+    return get_nth_pelement(ndarray, nth);
+}
+
+uint8_t* __nac3_ndarray_get_nth_pelement64(const NDArray<int64_t>* ndarray, int64_t nth) {
+    return get_nth_pelement(ndarray, nth);
+}
+
+uint8_t* __nac3_ndarray_get_pelement_by_indices(const NDArray<int32_t>* ndarray, int32_t* indices) {
+    return get_pelement_by_indices(ndarray, indices);
+}
+
+uint8_t* __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 {
+namespace broadcast {
+/**
+ * @brief Return true if `src_shape` can broadcast to `dst_shape`.
+ *
+ * See https://numpy.org/doc/stable/user/basics.broadcasting.html
+ */
+template<typename SizeT>
+bool can_broadcast_shape_to(SizeT target_ndims, const SizeT* target_shape, SizeT src_ndims, const SizeT* src_shape) {
+    if (src_ndims > target_ndims) {
+        return false;
+    }
+
+    for (SizeT i = 0; i < src_ndims; i++) {
+        SizeT target_dim = target_shape[target_ndims - i - 1];
+        SizeT src_dim = src_shape[src_ndims - i - 1];
+        if (!(src_dim == 1 || target_dim == src_dim)) {
+            return false;
+        }
+    }
+    return true;
+}
+
+/**
+ * @brief Performs `np.broadcast_shapes(<shapes>)`
+ *
+ * @param num_shapes Number of entries in `shapes`
+ * @param shapes The list of shape to do `np.broadcast_shapes` on.
+ * @param dst_ndims The length of `dst_shape`.
+ *   `dst_ndims` must be `max([shape.ndims for shape in shapes])`, but the caller has to calculate it/provide it.
+ *   for this function since they should already know in order to allocate `dst_shape` in the first place.
+ * @param dst_shape The resulting shape. Must be pre-allocated by the caller. This function calculate the result
+ *   of `np.broadcast_shapes` and write it here.
+ */
+template<typename SizeT>
+void broadcast_shapes(SizeT num_shapes, const ShapeEntry<SizeT>* shapes, SizeT dst_ndims, SizeT* dst_shape) {
+    for (SizeT dst_axis = 0; dst_axis < dst_ndims; dst_axis++) {
+        dst_shape[dst_axis] = 1;
+    }
+
+#ifdef IRRT_DEBUG_ASSERT
+    SizeT max_ndims_found = 0;
+#endif
+
+    for (SizeT i = 0; i < num_shapes; i++) {
+        ShapeEntry<SizeT> entry = shapes[i];
+
+        // Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
+        debug_assert(SizeT, entry.ndims <= dst_ndims);
+
+#ifdef IRRT_DEBUG_ASSERT
+        max_ndims_found = max(max_ndims_found, entry.ndims);
+#endif
+
+        for (SizeT j = 0; j < entry.ndims; j++) {
+            SizeT entry_axis = entry.ndims - j - 1;
+            SizeT dst_axis = dst_ndims - j - 1;
+
+            SizeT entry_dim = entry.shape[entry_axis];
+            SizeT dst_dim = dst_shape[dst_axis];
+
+            if (dst_dim == 1) {
+                dst_shape[dst_axis] = entry_dim;
+            } else if (entry_dim == 1 || entry_dim == dst_dim) {
+                // Do nothing
+            } else {
+                raise_exception(SizeT, EXN_VALUE_ERROR,
+                                "shape mismatch: objects cannot be broadcast "
+                                "to a single shape.",
+                                NO_PARAM, NO_PARAM, NO_PARAM);
+            }
+        }
+    }
+
+    // Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
+    debug_assert_eq(SizeT, max_ndims_found, dst_ndims);
+}
+
+/**
+ * @brief Perform `np.broadcast_to(<ndarray>, <target_shape>)` and appropriate assertions.
+ *
+ * This function attempts to broadcast `src_ndarray` to a new shape defined by `dst_ndarray.shape`,
+ * and return the result by modifying `dst_ndarray`.
+ *
+ * # Notes on `dst_ndarray`
+ * The caller is responsible for allocating space for the resulting ndarray.
+ * Here is what this function expects from `dst_ndarray` when called:
+ *   - `dst_ndarray->data` does not have to be initialized.
+ *   - `dst_ndarray->itemsize` does not have to be initialized.
+ *   - `dst_ndarray->ndims` must be initialized, determining the length of `dst_ndarray->shape`
+ *   - `dst_ndarray->shape` must be allocated, and must contain the desired target broadcast shape.
+ *   - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
+ * When this function call ends:
+ *   - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
+ *   - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
+ *   - `dst_ndarray->ndims` is unchanged.
+ *   - `dst_ndarray->shape` is unchanged.
+ *   - `dst_ndarray->strides` is updated accordingly by how ndarray broadcast_to works.
+ */
+template<typename SizeT>
+void broadcast_to(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
+    if (!ndarray::broadcast::can_broadcast_shape_to(dst_ndarray->ndims, dst_ndarray->shape, src_ndarray->ndims,
+                                                    src_ndarray->shape)) {
+        raise_exception(SizeT, EXN_VALUE_ERROR, "operands could not be broadcast together", NO_PARAM, NO_PARAM,
+                        NO_PARAM);
+    }
+
+    dst_ndarray->data = src_ndarray->data;
+    dst_ndarray->itemsize = src_ndarray->itemsize;
+
+    for (SizeT i = 0; i < dst_ndarray->ndims; i++) {
+        SizeT src_axis = src_ndarray->ndims - i - 1;
+        SizeT dst_axis = dst_ndarray->ndims - i - 1;
+        if (src_axis < 0 || (src_ndarray->shape[src_axis] == 1 && dst_ndarray->shape[dst_axis] != 1)) {
+            // Freeze the steps in-place
+            dst_ndarray->strides[dst_axis] = 0;
+        } else {
+            dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
+        }
+    }
+}
+}  // namespace broadcast
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+using namespace ndarray::broadcast;
+
+void __nac3_ndarray_broadcast_to(NDArray<int32_t>* src_ndarray, NDArray<int32_t>* dst_ndarray) {
+    broadcast_to(src_ndarray, dst_ndarray);
+}
+
+void __nac3_ndarray_broadcast_to64(NDArray<int64_t>* src_ndarray, NDArray<int64_t>* dst_ndarray) {
+    broadcast_to(src_ndarray, dst_ndarray);
+}
+
+void __nac3_ndarray_broadcast_shapes(int32_t num_shapes,
+                                     const ShapeEntry<int32_t>* shapes,
+                                     int32_t dst_ndims,
+                                     int32_t* dst_shape) {
+    broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
+}
+
+void __nac3_ndarray_broadcast_shapes64(int64_t num_shapes,
+                                       const ShapeEntry<int64_t>* shapes,
+                                       int64_t dst_ndims,
+                                       int64_t* dst_shape) {
+    broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
+}
+}

nac3core/irrt/irrt/ndarray/def.hpp

@@ -0,0 +1,45 @@
+#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
+ */
+template<typename SizeT>
+struct NDArray {
+    /**
+     * @brief The underlying data this `ndarray` is pointing to.
+     */
+    uint8_t* data;
+
+    /**
+     * @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;
+};
+}  // namespace

nac3core/irrt/irrt/ndarray/indexing.hpp

@@ -0,0 +1,220 @@
+#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 {
+namespace 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 += 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 += (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 indexing
+}  // namespace ndarray
+}  // 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.
+     */
+    uint8_t* 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, uint8_t* 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 -= strides[axis] * (shape[axis] - 1);
+            } else {
+                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 +1,100 @@
+#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 {
+namespace 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 matmul
+}  // namespace ndarray
+}  // 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 +1,99 @@
+#pragma once
+
+#include "irrt/exception.hpp"
+#include "irrt/int_types.hpp"
+#include "irrt/ndarray/def.hpp"
+
+namespace {
+namespace ndarray {
+namespace reshape {
+/**
+ * @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
+ *
+ * If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
+ * modified to contain the resolved dimension.
+ *
+ * To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
+ * `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
+ *
+ * @param size The `.size` of `<ndarray>`
+ * @param new_ndims Number of elements in `new_shape`
+ * @param new_shape Target shape to reshape to
+ */
+template<typename SizeT>
+void resolve_and_check_new_shape(SizeT size, SizeT new_ndims, SizeT* new_shape) {
+    // Is there a -1 in `new_shape`?
+    bool neg1_exists = false;
+    // Location of -1, only initialized if `neg1_exists` is true
+    SizeT neg1_axis_i;
+    // The computed ndarray size of `new_shape`
+    SizeT new_size = 1;
+
+    for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++) {
+        SizeT dim = new_shape[axis_i];
+        if (dim < 0) {
+            if (dim == -1) {
+                if (neg1_exists) {
+                    // Multiple `-1` found. Throw an error.
+                    raise_exception(SizeT, EXN_VALUE_ERROR, "can only specify one unknown dimension", NO_PARAM,
+                                    NO_PARAM, NO_PARAM);
+                } else {
+                    neg1_exists = true;
+                    neg1_axis_i = axis_i;
+                }
+            } else {
+                // TODO: What? In `np.reshape` any negative dimensions is
+                // treated like its `-1`.
+                //
+                // Try running `np.zeros((3, 4)).reshape((-999, 2))`
+                //
+                // It is not documented by numpy.
+                // Throw an error for now...
+
+                raise_exception(SizeT, EXN_VALUE_ERROR, "Found non -1 negative dimension {0} on axis {1}", dim, axis_i,
+                                NO_PARAM);
+            }
+        } else {
+            new_size *= dim;
+        }
+    }
+
+    bool can_reshape;
+    if (neg1_exists) {
+        // Let `x` be the unknown dimension
+        // Solve `x * <new_size> = <size>`
+        if (new_size == 0 && size == 0) {
+            // `x` has infinitely many solutions
+            can_reshape = false;
+        } else if (new_size == 0 && size != 0) {
+            // `x` has no solutions
+            can_reshape = false;
+        } else if (size % new_size != 0) {
+            // `x` has no integer solutions
+            can_reshape = false;
+        } else {
+            can_reshape = true;
+            new_shape[neg1_axis_i] = size / new_size;  // Resolve dimension
+        }
+    } else {
+        can_reshape = (new_size == size);
+    }
+
+    if (!can_reshape) {
+        raise_exception(SizeT, EXN_VALUE_ERROR, "cannot reshape array of size {0} into given shape", size, NO_PARAM,
+                        NO_PARAM);
+    }
+}
+}  // namespace reshape
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+void __nac3_ndarray_reshape_resolve_and_check_new_shape(int32_t size, int32_t new_ndims, int32_t* new_shape) {
+    ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
+}
+
+void __nac3_ndarray_reshape_resolve_and_check_new_shape64(int64_t size, int64_t new_ndims, int64_t* new_shape) {
+    ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
+}
+}

nac3core/irrt/irrt/ndarray/transpose.hpp

@@ -0,0 +1,145 @@
+#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 {
+namespace transpose {
+/**
+ * @brief Do assertions on `<axes>` in `np.transpose(<array>, <axes>)`.
+ *
+ * Note that `np.transpose`'s `<axe>` argument is optional. If the argument
+ * is specified but the user, use this function to do assertions on it.
+ *
+ * @param ndims The number of dimensions of `<array>`
+ * @param num_axes Number of elements in `<axes>` as specified by the user.
+ *   This should be equal to `ndims`. If not, a "ValueError: axes don't match array" is thrown.
+ * @param axes The user specified `<axes>`.
+ */
+template<typename SizeT>
+void assert_transpose_axes(SizeT ndims, SizeT num_axes, const SizeT* axes) {
+    if (ndims != num_axes) {
+        raise_exception(SizeT, EXN_VALUE_ERROR, "axes don't match array", NO_PARAM, NO_PARAM, NO_PARAM);
+    }
+
+    // TODO: Optimize this
+    bool* axe_specified = (bool*)__builtin_alloca(sizeof(bool) * ndims);
+    for (SizeT i = 0; i < ndims; i++)
+        axe_specified[i] = false;
+
+    for (SizeT i = 0; i < ndims; i++) {
+        SizeT axis = slice::resolve_index_in_length(ndims, axes[i]);
+        if (axis == -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 transpose
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+using namespace ndarray::transpose;
+void __nac3_ndarray_transpose(const NDArray<int32_t>* src_ndarray,
+                              NDArray<int32_t>* dst_ndarray,
+                              int32_t num_axes,
+                              const int32_t* axes) {
+    transpose(src_ndarray, dst_ndarray, num_axes, axes);
+}
+
+void __nac3_ndarray_transpose64(const NDArray<int64_t>* src_ndarray,
+                                NDArray<int64_t>* dst_ndarray,
+                                int64_t num_axes,
+                                const int64_t* axes) {
+    transpose(src_ndarray, dst_ndarray, num_axes, axes);
+}
+}

nac3core/irrt/irrt/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/nac3core_derive/Cargo.toml

@@ -1,21 +0,0 @@
-[package]
-name = "nac3core_derive"
-version = "0.1.0"
-edition = "2021"
-
-[lib]
-proc-macro = true
-
-[[test]]
-name = "structfields_tests"
-path = "tests/structfields_test.rs"
-
-[dev-dependencies]
-nac3core = { path = ".." }
-trybuild = { version = "1.0", features = ["diff"] }
-
-[dependencies]
-proc-macro2 = "1.0"
-proc-macro-error = "1.0"
-syn = "2.0"
-quote = "1.0"

nac3core/nac3core_derive/src/lib.rs

@@ -1,320 +0,0 @@
-use proc_macro::TokenStream;
-use proc_macro_error::{abort, proc_macro_error};
-use quote::quote;
-use syn::{
-    parse_macro_input, spanned::Spanned, Data, DataStruct, Expr, ExprField, ExprMethodCall,
-    ExprPath, GenericArgument, Ident, LitStr, Path, PathArguments, Type, TypePath,
-};
-
-/// Extracts all generic arguments of a [`Type`] into a [`Vec`].
-///
-/// Returns [`Some`] of a possibly-empty [`Vec`] if the path of `ty` matches with
-/// `expected_ty_name`, otherwise returns [`None`].
-fn extract_generic_args(expected_ty_name: &'static str, ty: &Type) -> Option<Vec<GenericArgument>> {
-    let Type::Path(TypePath { qself: None, path, .. }) = ty else {
-        return None;
-    };
-
-    let segments = &path.segments;
-    if segments.len() != 1 {
-        return None;
-    };
-
-    let segment = segments.iter().next().unwrap();
-    if segment.ident != expected_ty_name {
-        return None;
-    }
-
-    let PathArguments::AngleBracketed(path_args) = &segment.arguments else {
-        return Some(Vec::new());
-    };
-    let args = &path_args.args;
-
-    Some(args.iter().cloned().collect::<Vec<_>>())
-}
-
-/// Maps a `path` matching one of the `target_idents` into the `replacement` [`Ident`].
-fn map_path_to_ident(path: &Path, target_idents: &[&str], replacement: &str) -> Option<Ident> {
-    path.require_ident()
-        .ok()
-        .filter(|ident| target_idents.iter().any(|target| ident == target))
-        .map(|ident| Ident::new(replacement, ident.span()))
-}
-
-/// Extracts the left-hand side of a dot-expression.
-fn extract_dot_operand(expr: &Expr) -> Option<&Expr> {
-    match expr {
-        Expr::MethodCall(ExprMethodCall { receiver: operand, .. })
-        | Expr::Field(ExprField { base: operand, .. }) => Some(operand),
-        _ => None,
-    }
-}
-
-/// Replaces the top-level receiver of a dot-expression with an [`Ident`], returning `Some(&mut expr)` if the
-/// replacement is performed.
-///
-/// The top-level receiver is the left-most receiver expression, e.g. the top-level receiver of `a.b.c.foo()` is `a`.
-fn replace_top_level_receiver(expr: &mut Expr, ident: Ident) -> Option<&mut Expr> {
-    if let Expr::MethodCall(ExprMethodCall { receiver: operand, .. })
-    | Expr::Field(ExprField { base: operand, .. }) = expr
-    {
-        return if extract_dot_operand(operand).is_some() {
-            if replace_top_level_receiver(operand, ident).is_some() {
-                Some(expr)
-            } else {
-                None
-            }
-        } else {
-            *operand = Box::new(Expr::Path(ExprPath {
-                attrs: Vec::default(),
-                qself: None,
-                path: ident.into(),
-            }));
-
-            Some(expr)
-        };
-    }
-
-    None
-}
-
-/// Iterates all operands to the left-hand side of the `.` of an [expression][`Expr`], i.e. the container operand of all
-/// [`Expr::Field`] and the receiver operand of all [`Expr::MethodCall`].
-///
-/// The iterator will return the operand expressions in reverse order of appearance. For example, `a.b.c.func()` will
-/// return `vec![c, b, a]`.
-fn iter_dot_operands(expr: &Expr) -> impl Iterator<Item = &Expr> {
-    let mut o = extract_dot_operand(expr);
-
-    std::iter::from_fn(move || {
-        let this = o;
-        o = o.as_ref().and_then(|o| extract_dot_operand(o));
-
-        this
-    })
-}
-
-/// Normalizes a value expression for use when creating an instance of this structure, returning a
-/// [`proc_macro2::TokenStream`] of tokens representing the normalized expression.
-fn normalize_value_expr(expr: &Expr) -> proc_macro2::TokenStream {
-    match &expr {
-        Expr::Path(ExprPath { qself: None, path, .. }) => {
-            if let Some(ident) = map_path_to_ident(path, &["usize", "size_t"], "llvm_usize") {
-                quote! { #ident }
-            } else {
-                abort!(
-                    path,
-                    format!(
-                        "Expected one of `size_t`, `usize`, or an implicit call expression in #[value_type(...)], found {}", 
-                        quote!(#expr).to_string(),
-                    )
-                )
-            }
-        }
-
-        Expr::Call(_) => {
-            quote! { ctx.#expr }
-        }
-
-        Expr::MethodCall(_) => {
-            let base_receiver = iter_dot_operands(expr).last();
-
-            match base_receiver {
-                // `usize.{...}`, `size_t.{...}` -> Rewrite the identifiers to `llvm_usize`
-                Some(Expr::Path(ExprPath { qself: None, path, .. }))
-                    if map_path_to_ident(path, &["usize", "size_t"], "llvm_usize").is_some() =>
-                {
-                    let ident =
-                        map_path_to_ident(path, &["usize", "size_t"], "llvm_usize").unwrap();
-
-                    let mut expr = expr.clone();
-                    let expr = replace_top_level_receiver(&mut expr, ident).unwrap();
-
-                    quote!(#expr)
-                }
-
-                // `ctx.{...}`, `context.{...}` -> Rewrite the identifiers to `ctx`
-                Some(Expr::Path(ExprPath { qself: None, path, .. }))
-                    if map_path_to_ident(path, &["ctx", "context"], "ctx").is_some() =>
-                {
-                    let ident = map_path_to_ident(path, &["ctx", "context"], "ctx").unwrap();
-
-                    let mut expr = expr.clone();
-                    let expr = replace_top_level_receiver(&mut expr, ident).unwrap();
-
-                    quote!(#expr)
-                }
-
-                // No reserved identifier prefix -> Prepend `ctx.` to the entire expression
-                _ => quote! { ctx.#expr },
-            }
-        }
-
-        _ => {
-            abort!(
-                expr,
-                format!(
-                    "Expected one of `size_t`, `usize`, or an implicit call expression in #[value_type(...)], found {}", 
-                    quote!(#expr).to_string(),
-                )
-            )
-        }
-    }
-}
-
-/// Derives an implementation of `codegen::types::structure::StructFields`.
-///
-/// The benefit of using `#[derive(StructFields)]` is that all index- or order-dependent logic required by
-/// `impl StructFields` is automatically generated by this implementation, including the field index as required by
-/// `StructField::new` and the fields as returned by `StructFields::to_vec`.
-///
-/// # Prerequisites
-///
-/// In order to derive from [`StructFields`], you must implement (or derive) [`Eq`] and [`Copy`] as required by
-/// `StructFields`.
-///
-/// Moreover, `#[derive(StructFields)]` can only be used for `struct`s with named fields, and may only contain fields
-/// with either `StructField` or [`PhantomData`] types.
-///
-/// # Attributes for [`StructFields`]
-///
-/// Each `StructField` field must be declared with the `#[value_type(...)]` attribute. The argument of `value_type`
-/// accepts one of the following:
-///
-/// - An expression returning an instance of `inkwell::types::BasicType` (with or without the receiver `ctx`/`context`).
-/// For example, `context.i8_type()`, `ctx.i8_type()`, and `i8_type()` all refer to `i8`.
-/// - The reserved identifiers `usize` and `size_t` referring to an `inkwell::types::IntType` of the platform-dependent
-/// integer size. `usize` and `size_t` can also be used as the receiver to other method calls, e.g.
-/// `usize.array_type(3)`.
-///
-/// # Example
-///
-/// The following is an example of an LLVM slice implemented using `#[derive(StructFields)]`.
-///
-/// ```rust,ignore
-/// use nac3core::{
-///     codegen::types::structure::StructField,
-///     inkwell::{
-///         values::{IntValue, PointerValue},
-///         AddressSpace,
-///     },
-/// };
-/// use nac3core_derive::StructFields;
-///
-/// // All classes that implement StructFields must also implement Eq and Copy
-/// #[derive(PartialEq, Eq, Clone, Copy, StructFields)]
-/// pub struct SliceValue<'ctx> {
-///     // Declares ptr have a value type of i8*
-///     //
-///     // Can also be written as `ctx.i8_type().ptr_type(...)` or `context.i8_type().ptr_type(...)`
-///     #[value_type(i8_type().ptr_type(AddressSpace::default()))]
-///     ptr: StructField<'ctx, PointerValue<'ctx>>,
-///
-///     // Declares len have a value type of usize, depending on the target compilation platform
-///     #[value_type(usize)]
-///     len: StructField<'ctx, IntValue<'ctx>>,
-/// }
-/// ```
-#[proc_macro_derive(StructFields, attributes(value_type))]
-#[proc_macro_error]
-pub fn derive(input: TokenStream) -> TokenStream {
-    let input = parse_macro_input!(input as syn::DeriveInput);
-    let ident = &input.ident;
-
-    let Data::Struct(DataStruct { fields, .. }) = &input.data else {
-        abort!(input, "Only structs with named fields are supported");
-    };
-    if let Err(err_span) =
-        fields
-            .iter()
-            .try_for_each(|field| if field.ident.is_some() { Ok(()) } else { Err(field.span()) })
-    {
-        abort!(err_span, "Only structs with named fields are supported");
-    };
-
-    // Check if struct<'ctx>
-    if input.generics.params.len() != 1 {
-        abort!(input.generics, "Expected exactly 1 generic parameter")
-    }
-
-    let phantom_info = fields
-        .iter()
-        .filter(|field| extract_generic_args("PhantomData", &field.ty).is_some())
-        .map(|field| field.ident.as_ref().unwrap())
-        .cloned()
-        .collect::<Vec<_>>();
-
-    let field_info = fields
-        .iter()
-        .filter(|field| extract_generic_args("PhantomData", &field.ty).is_none())
-        .map(|field| {
-            let ident = field.ident.as_ref().unwrap();
-            let ty = &field.ty;
-
-            let Some(_) = extract_generic_args("StructField", ty) else {
-                abort!(field, "Only StructField and PhantomData are allowed")
-            };
-
-            let attrs = &field.attrs;
-            let Some(value_type_attr) =
-                attrs.iter().find(|attr| attr.path().is_ident("value_type"))
-            else {
-                abort!(field, "Expected #[value_type(...)] attribute for field");
-            };
-
-            let Ok(value_type_expr) = value_type_attr.parse_args::<Expr>() else {
-                abort!(value_type_attr, "Expected expression in #[value_type(...)]");
-            };
-
-            let value_expr_toks = normalize_value_expr(&value_type_expr);
-
-            (ident.clone(), value_expr_toks)
-        })
-        .collect::<Vec<_>>();
-
-    // `<*>::new` impl of `StructField` and `PhantomData` for `StructFields::new`
-    let phantoms_create = phantom_info
-        .iter()
-        .map(|id| quote! { #id: ::std::marker::PhantomData })
-        .collect::<Vec<_>>();
-    let fields_create = field_info
-        .iter()
-        .map(|(id, ty)| {
-            let id_lit = LitStr::new(&id.to_string(), id.span());
-            quote! {
-                #id: ::nac3core::codegen::types::structure::StructField::create(
-                    &mut counter,
-                    #id_lit,
-                    #ty,
-                )
-            }
-        })
-        .collect::<Vec<_>>();
-
-    // `.into()` impl of `StructField` for `StructFields::to_vec`
-    let fields_into =
-        field_info.iter().map(|(id, _)| quote! { self.#id.into() }).collect::<Vec<_>>();
-
-    let impl_block = quote! {
-        impl<'ctx> ::nac3core::codegen::types::structure::StructFields<'ctx> for #ident<'ctx> {
-            fn new(ctx: impl ::nac3core::inkwell::context::AsContextRef<'ctx>, llvm_usize: ::nac3core::inkwell::types::IntType<'ctx>) -> Self {
-                let ctx = unsafe { ::nac3core::inkwell::context::ContextRef::new(ctx.as_ctx_ref()) };
-
-                let mut counter = ::nac3core::codegen::types::structure::FieldIndexCounter::default();
-
-                #ident {
-                    #(#fields_create),*
-                    #(#phantoms_create),*
-                }
-            }
-
-            fn to_vec(&self) -> ::std::vec::Vec<(&'static str, ::nac3core::inkwell::types::BasicTypeEnum<'ctx>)> {
-                vec![
-                    #(#fields_into),*
-                ]
-            }
-        }
-    };
-
-    impl_block.into()
-}

nac3core/nac3core_derive/tests/structfields_empty.rs

@@ -1,9 +0,0 @@
-use nac3core_derive::StructFields;
-use std::marker::PhantomData;
-
-#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
-pub struct EmptyValue<'ctx> {
-    _phantom: PhantomData<&'ctx ()>,
-}
-
-fn main() {}

nac3core/nac3core_derive/tests/structfields_ndarray.rs

@@ -1,20 +0,0 @@
-use nac3core::{
-    codegen::types::structure::StructField,
-    inkwell::{
-        values::{IntValue, PointerValue},
-        AddressSpace,
-    },
-};
-use nac3core_derive::StructFields;
-
-#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
-pub struct NDArrayValue<'ctx> {
-    #[value_type(usize)]
-    ndims: StructField<'ctx, IntValue<'ctx>>,
-    #[value_type(usize.ptr_type(AddressSpace::default()))]
-    shape: StructField<'ctx, PointerValue<'ctx>>,
-    #[value_type(i8_type().ptr_type(AddressSpace::default()))]
-    data: StructField<'ctx, PointerValue<'ctx>>,
-}
-
-fn main() {}

nac3core/nac3core_derive/tests/structfields_slice.rs

@@ -1,18 +0,0 @@
-use nac3core::{
-    codegen::types::structure::StructField,
-    inkwell::{
-        values::{IntValue, PointerValue},
-        AddressSpace,
-    },
-};
-use nac3core_derive::StructFields;
-
-#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
-pub struct SliceValue<'ctx> {
-    #[value_type(i8_type().ptr_type(AddressSpace::default()))]
-    ptr: StructField<'ctx, PointerValue<'ctx>>,
-    #[value_type(usize)]
-    len: StructField<'ctx, IntValue<'ctx>>,
-}
-
-fn main() {}

nac3core/nac3core_derive/tests/structfields_slice_context.rs

@@ -1,18 +0,0 @@
-use nac3core::{
-    codegen::types::structure::StructField,
-    inkwell::{
-        values::{IntValue, PointerValue},
-        AddressSpace,
-    },
-};
-use nac3core_derive::StructFields;
-
-#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
-pub struct SliceValue<'ctx> {
-    #[value_type(context.i8_type().ptr_type(AddressSpace::default()))]
-    ptr: StructField<'ctx, PointerValue<'ctx>>,
-    #[value_type(usize)]
-    len: StructField<'ctx, IntValue<'ctx>>,
-}
-
-fn main() {}

nac3core/nac3core_derive/tests/structfields_slice_ctx.rs

@@ -1,18 +0,0 @@
-use nac3core::{
-    codegen::types::structure::StructField,
-    inkwell::{
-        values::{IntValue, PointerValue},
-        AddressSpace,
-    },
-};
-use nac3core_derive::StructFields;
-
-#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
-pub struct SliceValue<'ctx> {
-    #[value_type(ctx.i8_type().ptr_type(AddressSpace::default()))]
-    ptr: StructField<'ctx, PointerValue<'ctx>>,
-    #[value_type(usize)]
-    len: StructField<'ctx, IntValue<'ctx>>,
-}
-
-fn main() {}

nac3core/nac3core_derive/tests/structfields_slice_sizet.rs

@@ -1,18 +0,0 @@
-use nac3core::{
-    codegen::types::structure::StructField,
-    inkwell::{
-        values::{IntValue, PointerValue},
-        AddressSpace,
-    },
-};
-use nac3core_derive::StructFields;
-
-#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
-pub struct SliceValue<'ctx> {
-    #[value_type(i8_type().ptr_type(AddressSpace::default()))]
-    ptr: StructField<'ctx, PointerValue<'ctx>>,
-    #[value_type(size_t)]
-    len: StructField<'ctx, IntValue<'ctx>>,
-}
-
-fn main() {}

nac3core/nac3core_derive/tests/structfields_test.rs

@@ -1,10 +0,0 @@
-#[test]
-fn test_parse_empty() {
-    let t = trybuild::TestCases::new();
-    t.pass("tests/structfields_empty.rs");
-    t.pass("tests/structfields_slice.rs");
-    t.pass("tests/structfields_slice_ctx.rs");
-    t.pass("tests/structfields_slice_context.rs");
-    t.pass("tests/structfields_slice_sizet.rs");
-    t.pass("tests/structfields_ndarray.rs");
-}

nac3core/src/codegen/builtin_fns.rs

@@ -6,25 +6,23 @@ use inkwell::{
 use itertools::Itertools;
 
 use super::{
+    classes::{
+        NDArrayValue, ProxyValue, RangeValue, UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
+    },
     expr::destructure_range,
     extern_fns, irrt,
     irrt::calculate_len_for_slice_range,
     llvm_intrinsics,
     macros::codegen_unreachable,
+    model::*,
     numpy,
     numpy::ndarray_elementwise_unaryop_impl,
+    object::{any::AnyObject, list::ListObject, ndarray::NDArrayObject, tuple::TupleObject},
     stmt::gen_for_callback_incrementing,
-    values::{
-        ArrayLikeValue, NDArrayValue, ProxyValue, RangeValue, TypedArrayLikeAccessor,
-        UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
-    },
     CodeGenContext, CodeGenerator,
 };
 use crate::{
-    toplevel::{
+    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys},
-        helper::{arraylike_flatten_element_type, PrimDef},
-        numpy::unpack_ndarray_var_tys,
-    },
     typecheck::typedef::{Type, TypeEnum},
 };
 
@@ -45,59 +43,33 @@ pub fn call_len<'ctx, G: CodeGenerator + ?Sized>(
     ctx: &mut CodeGenContext<'ctx, '_>,
     n: (Type, BasicValueEnum<'ctx>),
 ) -> Result<IntValue<'ctx>, String> {
-    let llvm_i32 = ctx.ctx.i32_type();
-    let range_ty = ctx.primitives.range;
     let (arg_ty, arg) = n;
-
+    Ok(if ctx.unifier.unioned(arg_ty, ctx.primitives.range) {
-    Ok(if ctx.unifier.unioned(arg_ty, range_ty) {
+        let arg = RangeValue::from_ptr_val(arg.into_pointer_value(), Some("range"));
-        let arg = RangeValue::from_pointer_value(arg.into_pointer_value(), Some("range"));
         let (start, end, step) = destructure_range(ctx, arg);
         calculate_len_for_slice_range(generator, ctx, start, end, step)
     } else {
-        match &*ctx.unifier.get_ty_immutable(arg_ty) {
+        let arg = AnyObject { ty: arg_ty, value: arg };
-            TypeEnum::TTuple { ty, .. } => llvm_i32.const_int(ty.len() as u64, false),
+        let len: Instance<'ctx, Int<Int32>> = match &*ctx.unifier.get_ty(arg_ty) {
-            TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::List.id() => {
+            TypeEnum::TTuple { .. } => {
-                let zero = llvm_i32.const_zero();
+                let tuple = TupleObject::from_object(ctx, arg);
-                let len = ctx
+                tuple.len(generator, ctx).truncate_or_bit_cast(generator, ctx, Int32)
-                    .build_gep_and_load(
-                        arg.into_pointer_value(),
-                        &[zero, llvm_i32.const_int(1, false)],
-                        None,
-                    )
-                    .into_int_value();
-                ctx.builder.build_int_truncate_or_bit_cast(len, llvm_i32, "len").unwrap()
             }
-            TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
+            TypeEnum::TObj { obj_id, .. }
-                let elem_ty = arraylike_flatten_element_type(&mut ctx.unifier, arg_ty);
+                if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
-                let llvm_usize = generator.get_size_type(ctx.ctx);
+            {
-
+                let ndarray = NDArrayObject::from_object(generator, ctx, arg);
-                let arg = NDArrayValue::from_pointer_value(
+                ndarray.len(generator, ctx).truncate_or_bit_cast(generator, ctx, Int32)
-                    arg.into_pointer_value(),
+            }
-                    ctx.get_llvm_type(generator, elem_ty),
+            TypeEnum::TObj { obj_id, .. }
-                    llvm_usize,
+                if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
-                    None,
+            {
-                );
+                let list = ListObject::from_object(generator, ctx, arg);
-
+                list.len(generator, ctx).truncate_or_bit_cast(generator, ctx, Int32)
-                let ndims = arg.shape().size(ctx, generator);
+            }
-                ctx.make_assert(
+            _ => unsupported_type(ctx, "len", &[arg_ty]),
-                    generator,
-                    ctx.builder
-                        .build_int_compare(IntPredicate::NE, ndims, llvm_usize.const_zero(), "")
-                        .unwrap(),
-                    "0:TypeError",
-                    "len() of unsized object",
-                    [None, None, None],
-                    ctx.current_loc,
-                );
-
-                let len = unsafe {
-                    arg.shape().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
         };
-
+        len.value
-                ctx.builder.build_int_truncate_or_bit_cast(len, llvm_i32, "len").unwrap()
-            }
-            _ => codegen_unreachable!(ctx),
-        }
     })
 }
 
@@ -146,14 +118,13 @@ pub fn call_int32<'ctx, G: CodeGenerator + ?Sized>(
             if n_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) =>
         {
             let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, n_ty);
-            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
             let ndarray = ndarray_elementwise_unaryop_impl(
                 generator,
                 ctx,
                 ctx.primitives.int32,
                 None,
-                NDArrayValue::from_pointer_value(n, llvm_elem_ty, llvm_usize, None),
+                NDArrayValue::from_ptr_val(n, llvm_usize, None),
                 |generator, ctx, val| call_int32(generator, ctx, (elem_ty, val)),
             )?;
 
@@ -209,14 +180,13 @@ pub fn call_int64<'ctx, G: CodeGenerator + ?Sized>(
             if n_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) =>
         {
             let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, n_ty);
-            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
             let ndarray = ndarray_elementwise_unaryop_impl(
                 generator,
                 ctx,
                 ctx.primitives.int64,
                 None,
-                NDArrayValue::from_pointer_value(n, llvm_elem_ty, llvm_usize, None),
+                NDArrayValue::from_ptr_val(n, llvm_usize, None),
                 |generator, ctx, val| call_int64(generator, ctx, (elem_ty, val)),
             )?;
 
@@ -288,14 +258,13 @@ pub fn call_uint32<'ctx, G: CodeGenerator + ?Sized>(
             if n_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) =>
         {
             let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, n_ty);
-            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
             let ndarray = ndarray_elementwise_unaryop_impl(
                 generator,
                 ctx,
                 ctx.primitives.uint32,
                 None,
-                NDArrayValue::from_pointer_value(n, llvm_elem_ty, llvm_usize, None),
+                NDArrayValue::from_ptr_val(n, llvm_usize, None),
                 |generator, ctx, val| call_uint32(generator, ctx, (elem_ty, val)),
             )?;
 
@@ -356,14 +325,13 @@ pub fn call_uint64<'ctx, G: CodeGenerator + ?Sized>(
             if n_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) =>
         {
             let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, n_ty);
-            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
             let ndarray = ndarray_elementwise_unaryop_impl(
                 generator,
                 ctx,
                 ctx.primitives.uint64,
                 None,
-                NDArrayValue::from_pointer_value(n, llvm_elem_ty, llvm_usize, None),
+                NDArrayValue::from_ptr_val(n, llvm_usize, None),
                 |generator, ctx, val| call_uint64(generator, ctx, (elem_ty, val)),
             )?;
 
@@ -423,14 +391,13 @@ pub fn call_float<'ctx, G: CodeGenerator + ?Sized>(
             if n_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) =>
         {
             let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, n_ty);
-            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
             let ndarray = ndarray_elementwise_unaryop_impl(
                 generator,
                 ctx,
                 ctx.primitives.float,
                 None,
-                NDArrayValue::from_pointer_value(n, llvm_elem_ty, llvm_usize, None),
+                NDArrayValue::from_ptr_val(n, llvm_usize, None),
                 |generator, ctx, val| call_float(generator, ctx, (elem_ty, val)),
             )?;
 
@@ -470,14 +437,13 @@ pub fn call_round<'ctx, G: CodeGenerator + ?Sized>(
             if n_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) =>
         {
             let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, n_ty);
-            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
             let ndarray = ndarray_elementwise_unaryop_impl(
                 generator,
                 ctx,
                 ret_elem_ty,
                 None,
-                NDArrayValue::from_pointer_value(n, llvm_elem_ty, llvm_usize, None),
+                NDArrayValue::from_ptr_val(n, llvm_usize, None),
                 |generator, ctx, val| call_round(generator, ctx, (elem_ty, val), ret_elem_ty),
             )?;
 
@@ -511,14 +477,13 @@ pub fn call_numpy_round<'ctx, G: CodeGenerator + ?Sized>(
             if n_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) =>
         {
             let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, n_ty);
-            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
             let ndarray = ndarray_elementwise_unaryop_impl(
                 generator,
                 ctx,
                 ctx.primitives.float,
                 None,
-                NDArrayValue::from_pointer_value(n, llvm_elem_ty, llvm_usize, None),
+                NDArrayValue::from_ptr_val(n, llvm_usize, None),
                 |generator, ctx, val| call_numpy_round(generator, ctx, (elem_ty, val)),
             )?;
 
@@ -577,14 +542,13 @@ pub fn call_bool<'ctx, G: CodeGenerator + ?Sized>(
             if n_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) =>
         {
             let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, n_ty);
-            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
             let ndarray = ndarray_elementwise_unaryop_impl(
                 generator,
                 ctx,
                 ctx.primitives.bool,
                 None,
-                NDArrayValue::from_pointer_value(n, llvm_elem_ty, llvm_usize, None),
+                NDArrayValue::from_ptr_val(n, llvm_usize, None),
                 |generator, ctx, val| {
                     let elem = call_bool(generator, ctx, (elem_ty, val))?;
 
@@ -632,14 +596,13 @@ pub fn call_floor<'ctx, G: CodeGenerator + ?Sized>(
             if n_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) =>
         {
             let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, n_ty);
-            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
             let ndarray = ndarray_elementwise_unaryop_impl(
                 generator,
                 ctx,
                 ret_elem_ty,
                 None,
-                NDArrayValue::from_pointer_value(n, llvm_elem_ty, llvm_usize, None),
+                NDArrayValue::from_ptr_val(n, llvm_usize, None),
                 |generator, ctx, val| call_floor(generator, ctx, (elem_ty, val), ret_elem_ty),
             )?;
 
@@ -683,14 +646,13 @@ pub fn call_ceil<'ctx, G: CodeGenerator + ?Sized>(
             if n_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) =>
         {
             let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, n_ty);
-            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
             let ndarray = ndarray_elementwise_unaryop_impl(
                 generator,
                 ctx,
                 ret_elem_ty,
                 None,
-                NDArrayValue::from_pointer_value(n, llvm_elem_ty, llvm_usize, None),
+                NDArrayValue::from_ptr_val(n, llvm_usize, None),
                 |generator, ctx, val| call_ceil(generator, ctx, (elem_ty, val), ret_elem_ty),
             )?;
 
@@ -819,8 +781,8 @@ pub fn call_numpy_minimum<'ctx, G: CodeGenerator + ?Sized>(
                 ctx,
                 dtype,
                 None,
-                (x1_ty, x1, !is_ndarray1),
+                (x1, !is_ndarray1),
-                (x2_ty, x2, !is_ndarray2),
+                (x2, !is_ndarray2),
                 |generator, ctx, (lhs, rhs)| {
                     call_numpy_minimum(generator, ctx, (x1_scalar_ty, lhs), (x2_scalar_ty, rhs))
                 },
@@ -919,10 +881,10 @@ pub fn call_numpy_max_min<'ctx, G: CodeGenerator + ?Sized>(
             if a_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) =>
         {
             let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, a_ty);
-            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
+            let llvm_ndarray_ty = ctx.get_llvm_type(generator, elem_ty);
 
-            let n = NDArrayValue::from_pointer_value(n, llvm_elem_ty, llvm_usize, None);
+            let n = NDArrayValue::from_ptr_val(n, llvm_usize, None);
-            let n_sz = irrt::call_ndarray_calc_size(generator, ctx, &n.shape(), (None, None));
+            let n_sz = irrt::call_ndarray_calc_size(generator, ctx, &n.dim_sizes(), (None, None));
             if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
                 let n_sz_eqz = ctx
                     .builder
@@ -939,7 +901,7 @@ pub fn call_numpy_max_min<'ctx, G: CodeGenerator + ?Sized>(
                 );
             }
 
-            let accumulator_addr = generator.gen_var_alloc(ctx, llvm_elem_ty, None)?;
+            let accumulator_addr = generator.gen_var_alloc(ctx, llvm_ndarray_ty, None)?;
             let res_idx = generator.gen_var_alloc(ctx, llvm_int64.into(), None)?;
 
             unsafe {
@@ -1081,8 +1043,8 @@ pub fn call_numpy_maximum<'ctx, G: CodeGenerator + ?Sized>(
                 ctx,
                 dtype,
                 None,
-                (x1_ty, x1, !is_ndarray1),
+                (x1, !is_ndarray1),
-                (x2_ty, x2, !is_ndarray2),
+                (x2, !is_ndarray2),
                 |generator, ctx, (lhs, rhs)| {
                     call_numpy_maximum(generator, ctx, (x1_scalar_ty, lhs), (x2_scalar_ty, rhs))
                 },
@@ -1127,7 +1089,6 @@ where
         {
             let llvm_usize = generator.get_size_type(ctx.ctx);
             let (arg_elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, arg_ty);
-            let llvm_arg_elem_ty = ctx.get_llvm_type(generator, arg_elem_ty);
             let ret_elem_ty = get_ret_elem_type(ctx, arg_elem_ty);
 
             let ndarray = ndarray_elementwise_unaryop_impl(
@@ -1135,7 +1096,7 @@ where
                 ctx,
                 ret_elem_ty,
                 None,
-                NDArrayValue::from_pointer_value(x, llvm_arg_elem_ty, llvm_usize, None),
+                NDArrayValue::from_ptr_val(x, llvm_usize, None),
                 |generator, ctx, elem_val| {
                     helper_call_numpy_unary_elementwise(
                         generator,
@@ -1522,8 +1483,8 @@ pub fn call_numpy_arctan2<'ctx, G: CodeGenerator + ?Sized>(
                 ctx,
                 dtype,
                 None,
-                (x1_ty, x1, !is_ndarray1),
+                (x1, !is_ndarray1),
-                (x2_ty, x2, !is_ndarray2),
+                (x2, !is_ndarray2),
                 |generator, ctx, (lhs, rhs)| {
                     call_numpy_arctan2(generator, ctx, (x1_scalar_ty, lhs), (x2_scalar_ty, rhs))
                 },
@@ -1589,8 +1550,8 @@ pub fn call_numpy_copysign<'ctx, G: CodeGenerator + ?Sized>(
                 ctx,
                 dtype,
                 None,
-                (x1_ty, x1, !is_ndarray1),
+                (x1, !is_ndarray1),
-                (x2_ty, x2, !is_ndarray2),
+                (x2, !is_ndarray2),
                 |generator, ctx, (lhs, rhs)| {
                     call_numpy_copysign(generator, ctx, (x1_scalar_ty, lhs), (x2_scalar_ty, rhs))
                 },
@@ -1656,8 +1617,8 @@ pub fn call_numpy_fmax<'ctx, G: CodeGenerator + ?Sized>(
                 ctx,
                 dtype,
                 None,
-                (x1_ty, x1, !is_ndarray1),
+                (x1, !is_ndarray1),
-                (x2_ty, x2, !is_ndarray2),
+                (x2, !is_ndarray2),
                 |generator, ctx, (lhs, rhs)| {
                     call_numpy_fmax(generator, ctx, (x1_scalar_ty, lhs), (x2_scalar_ty, rhs))
                 },
@@ -1723,8 +1684,8 @@ pub fn call_numpy_fmin<'ctx, G: CodeGenerator + ?Sized>(
                 ctx,
                 dtype,
                 None,
-                (x1_ty, x1, !is_ndarray1),
+                (x1, !is_ndarray1),
-                (x2_ty, x2, !is_ndarray2),
+                (x2, !is_ndarray2),
                 |generator, ctx, (lhs, rhs)| {
                     call_numpy_fmin(generator, ctx, (x1_scalar_ty, lhs), (x2_scalar_ty, rhs))
                 },
@@ -1779,8 +1740,8 @@ pub fn call_numpy_ldexp<'ctx, G: CodeGenerator + ?Sized>(
                 ctx,
                 dtype,
                 None,
-                (x1_ty, x1, !is_ndarray1),
+                (x1, !is_ndarray1),
-                (x2_ty, x2, !is_ndarray2),
+                (x2, !is_ndarray2),
                 |generator, ctx, (lhs, rhs)| {
                     call_numpy_ldexp(generator, ctx, (x1_scalar_ty, lhs), (x2_scalar_ty, rhs))
                 },
@@ -1846,8 +1807,8 @@ pub fn call_numpy_hypot<'ctx, G: CodeGenerator + ?Sized>(
                 ctx,
                 dtype,
                 None,
-                (x1_ty, x1, !is_ndarray1),
+                (x1, !is_ndarray1),
-                (x2_ty, x2, !is_ndarray2),
+                (x2, !is_ndarray2),
                 |generator, ctx, (lhs, rhs)| {
                     call_numpy_hypot(generator, ctx, (x1_scalar_ty, lhs), (x2_scalar_ty, rhs))
                 },
@@ -1913,8 +1874,8 @@ pub fn call_numpy_nextafter<'ctx, G: CodeGenerator + ?Sized>(
                 ctx,
                 dtype,
                 None,
-                (x1_ty, x1, !is_ndarray1),
+                (x1, !is_ndarray1),
-                (x2_ty, x2, !is_ndarray2),
+                (x2, !is_ndarray2),
                 |generator, ctx, (lhs, rhs)| {
                     call_numpy_nextafter(generator, ctx, (x1_scalar_ty, lhs), (x2_scalar_ty, rhs))
                 },
@@ -1974,14 +1935,14 @@ pub fn call_np_linalg_cholesky<'ctx, G: CodeGenerator + ?Sized>(
             unsupported_type(ctx, FN_NAME, &[x1_ty]);
         };
 
-        let n1 = NDArrayValue::from_pointer_value(n1, n1_elem_ty, llvm_usize, None);
+        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
         let dim0 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
                 .into_int_value()
         };
         let dim1 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
                 .into_int_value()
         };
@@ -2016,14 +1977,14 @@ pub fn call_np_linalg_qr<'ctx, G: CodeGenerator + ?Sized>(
             unimplemented!("{FN_NAME} operates on float type NdArrays only");
         };
 
-        let n1 = NDArrayValue::from_pointer_value(n1, n1_elem_ty, llvm_usize, None);
+        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
         let dim0 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
                 .into_int_value()
         };
         let dim1 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
                 .into_int_value()
         };
@@ -2066,15 +2027,15 @@ pub fn call_np_linalg_svd<'ctx, G: CodeGenerator + ?Sized>(
             unsupported_type(ctx, FN_NAME, &[x1_ty]);
         };
 
-        let n1 = NDArrayValue::from_pointer_value(n1, n1_elem_ty, llvm_usize, None);
+        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
 
         let dim0 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
                 .into_int_value()
         };
         let dim1 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
                 .into_int_value()
         };
@@ -2121,14 +2082,14 @@ pub fn call_np_linalg_inv<'ctx, G: CodeGenerator + ?Sized>(
             unsupported_type(ctx, FN_NAME, &[x1_ty]);
         };
 
-        let n1 = NDArrayValue::from_pointer_value(n1, n1_elem_ty, llvm_usize, None);
+        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
         let dim0 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
                 .into_int_value()
         };
         let dim1 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
                 .into_int_value()
         };
@@ -2163,15 +2124,15 @@ pub fn call_np_linalg_pinv<'ctx, G: CodeGenerator + ?Sized>(
             unsupported_type(ctx, FN_NAME, &[x1_ty]);
         };
 
-        let n1 = NDArrayValue::from_pointer_value(n1, n1_elem_ty, llvm_usize, None);
+        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
 
         let dim0 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
                 .into_int_value()
         };
         let dim1 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
                 .into_int_value()
         };
@@ -2206,15 +2167,15 @@ pub fn call_sp_linalg_lu<'ctx, G: CodeGenerator + ?Sized>(
             unsupported_type(ctx, FN_NAME, &[x1_ty]);
         };
 
-        let n1 = NDArrayValue::from_pointer_value(n1, n1_elem_ty, llvm_usize, None);
+        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
 
         let dim0 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
                 .into_int_value()
         };
         let dim1 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
                 .into_int_value()
         };
@@ -2259,7 +2220,7 @@ pub fn call_np_linalg_matrix_power<'ctx, G: CodeGenerator + ?Sized>(
             unsupported_type(ctx, FN_NAME, &[x1_ty, x2_ty]);
         };
 
-        let n1 = NDArrayValue::from_pointer_value(n1, n1_elem_ty, llvm_usize, None);
+        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
         // Changing second parameter to a `NDArray` for uniformity in function call
         let n2_array = numpy::create_ndarray_const_shape(
             generator,
@@ -2279,12 +2240,12 @@ pub fn call_np_linalg_matrix_power<'ctx, G: CodeGenerator + ?Sized>(
         let n2_array = n2_array.as_base_value().as_basic_value_enum();
 
         let outdim0 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
                 .into_int_value()
         };
         let outdim1 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
                 .into_int_value()
         };
@@ -2354,10 +2315,10 @@ pub fn call_sp_linalg_schur<'ctx, G: CodeGenerator + ?Sized>(
             unsupported_type(ctx, FN_NAME, &[x1_ty]);
         };
 
-        let n1 = NDArrayValue::from_pointer_value(n1, n1_elem_ty, llvm_usize, None);
+        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
 
         let dim0 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
                 .into_int_value()
         };
@@ -2397,10 +2358,10 @@ pub fn call_sp_linalg_hessenberg<'ctx, G: CodeGenerator + ?Sized>(
             unsupported_type(ctx, FN_NAME, &[x1_ty]);
         };
 
-        let n1 = NDArrayValue::from_pointer_value(n1, n1_elem_ty, llvm_usize, None);
+        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
 
         let dim0 = unsafe {
-            n1.shape()
+            n1.dim_sizes()
                 .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
                 .into_int_value()
         };

nac3core/src/codegen/expr.rs

@@ -8,7 +8,10 @@ use std::{
 use inkwell::{
     attributes::{Attribute, AttributeLoc},
     types::{AnyType, BasicType, BasicTypeEnum},
-    values::{BasicValueEnum, CallSiteValue, FunctionValue, IntValue, PointerValue, StructValue},
+    values::{
+        BasicValue, BasicValueEnum, CallSiteValue, FunctionValue, IntValue, PointerValue,
+        StructValue,
+    },
     AddressSpace, IntPredicate, OptimizationLevel,
 };
 use itertools::{chain, izip, Either, Itertools};
@@ -19,6 +22,10 @@ use nac3parser::ast::{
 };
 
 use super::{
+    classes::{
+        ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, ProxyType, ProxyValue, RangeValue,
+        UntypedArrayLikeAccessor,
+    },
     concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
     gen_in_range_check, get_llvm_abi_type, get_llvm_type, get_va_count_arg_name,
     irrt::*,
@@ -27,25 +34,23 @@ use super::{
         call_int_umin, call_memcpy_generic,
     },
     macros::codegen_unreachable,
-    need_sret, numpy,
+    need_sret,
+    object::{
+        any::AnyObject,
+        ndarray::{
+            indexing::util::gen_ndarray_subscript_ndindices, NDArrayObject, NDArrayOut,
+            ScalarOrNDArray,
+        },
+    },
     stmt::{
         gen_for_callback_incrementing, gen_if_callback, gen_if_else_expr_callback, gen_raise,
         gen_var,
     },
-    types::{ListType, ProxyType},
-    values::{
-        ArrayLikeIndexer, ArrayLikeValue, ListValue, NDArrayValue, ProxyValue, RangeValue,
-        TypedArrayLikeAccessor, UntypedArrayLikeAccessor,
-    },
     CodeGenContext, CodeGenTask, CodeGenerator,
 };
 use crate::{
     symbol_resolver::{SymbolValue, ValueEnum},
-    toplevel::{
+    toplevel::{helper::PrimDef, DefinitionId, TopLevelDef},
-        helper::PrimDef,
-        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
-        DefinitionId, TopLevelDef,
-    },
     typecheck::{
         magic_methods::{Binop, BinopVariant, HasOpInfo},
         typedef::{FunSignature, FuncArg, Type, TypeEnum, TypeVarId, Unifier, VarMap},
@@ -1168,8 +1173,7 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
         TypeEnum::TObj { obj_id, .. }
             if *obj_id == ctx.primitives.range.obj_id(&ctx.unifier).unwrap() =>
         {
-            let iter_val =
+            let iter_val = RangeValue::from_ptr_val(iter_val.into_pointer_value(), Some("range"));
-                RangeValue::from_pointer_value(iter_val.into_pointer_value(), Some("range"));
             let (start, stop, step) = destructure_range(ctx, iter_val);
             let diff = ctx.builder.build_int_sub(stop, start, "diff").unwrap();
             // add 1 to the length as the value is rounded to zero
@@ -1400,10 +1404,8 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
                 let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty1);
                 let sizeof_elem = llvm_elem_ty.size_of().unwrap();
 
-                let lhs =
+                let lhs = ListValue::from_ptr_val(left_val.into_pointer_value(), llvm_usize, None);
-                    ListValue::from_pointer_value(left_val.into_pointer_value(), llvm_usize, None);
+                let rhs = ListValue::from_ptr_val(right_val.into_pointer_value(), llvm_usize, None);
-                let rhs =
-                    ListValue::from_pointer_value(right_val.into_pointer_value(), llvm_usize, None);
 
                 let size = ctx
                     .builder
@@ -1486,7 +1488,7 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
                         codegen_unreachable!(ctx)
                     };
                 let list_val =
-                    ListValue::from_pointer_value(list_val.into_pointer_value(), llvm_usize, None);
+                    ListValue::from_ptr_val(list_val.into_pointer_value(), llvm_usize, None);
                 let int_val = ctx
                     .builder
                     .build_int_s_extend(int_val.into_int_value(), llvm_usize, "")
@@ -1553,112 +1555,75 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
     } else if ty1.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
         || ty2.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
     {
-        let llvm_usize = generator.get_size_type(ctx.ctx);
+        let left =
+            ScalarOrNDArray::split_object(generator, ctx, AnyObject { ty: ty1, value: left_val });
+        let right =
+            ScalarOrNDArray::split_object(generator, ctx, AnyObject { ty: ty2, value: right_val });
 
-        let is_ndarray1 = ty1.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
+        // Inhomogeneous binary operations are not supported.
-        let is_ndarray2 = ty2.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
+        assert!(ctx.unifier.unioned(left.get_dtype(), right.get_dtype()));
 
-        if is_ndarray1 && is_ndarray2 {
+        let common_dtype = left.get_dtype();
-            let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
-            let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty2);
 
-            assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
+        let out = match op.variant {
-
+            BinopVariant::Normal => NDArrayOut::NewNDArray { dtype: common_dtype },
-            let llvm_ndarray_dtype1 = ctx.get_llvm_type(generator, ndarray_dtype1);
+            BinopVariant::AugAssign => {
-            let llvm_ndarray_dtype2 = ctx.get_llvm_type(generator, ndarray_dtype2);
+                // If this is an augmented assignment.
-
+                // `left` has to be an ndarray. If it were a scalar then NAC3 simply doesn't support it.
-            let left_val = NDArrayValue::from_pointer_value(
+                if let ScalarOrNDArray::NDArray(out_ndarray) = left {
-                left_val.into_pointer_value(),
+                    NDArrayOut::WriteToNDArray { ndarray: out_ndarray }
-                llvm_ndarray_dtype1,
-                llvm_usize,
-                None,
-            );
-            let right_val = NDArrayValue::from_pointer_value(
-                right_val.into_pointer_value(),
-                llvm_ndarray_dtype2,
-                llvm_usize,
-                None,
-            );
-
-            let res = if op.base == Operator::MatMult {
-                // MatMult is the only binop which is not an elementwise op
-                numpy::ndarray_matmul_2d(
-                    generator,
-                    ctx,
-                    ndarray_dtype1,
-                    match op.variant {
-                        BinopVariant::Normal => None,
-                        BinopVariant::AugAssign => Some(left_val),
-                    },
-                    left_val,
-                    right_val,
-                )?
                 } else {
-                numpy::ndarray_elementwise_binop_impl(
+                    panic!("left must be an ndarray")
-                    generator,
+                }
-                    ctx,
+            }
-                    ndarray_dtype1,
-                    match op.variant {
-                        BinopVariant::Normal => None,
-                        BinopVariant::AugAssign => Some(left_val),
-                    },
-                    (ty1, left_val.as_base_value().into(), false),
-                    (ty2, right_val.as_base_value().into(), false),
-                    |generator, ctx, (lhs, rhs)| {
-                        gen_binop_expr_with_values(
-                            generator,
-                            ctx,
-                            (&Some(ndarray_dtype1), lhs),
-                            op,
-                            (&Some(ndarray_dtype2), rhs),
-                            ctx.current_loc,
-                        )?
-                        .unwrap()
-                        .to_basic_value_enum(
-                            ctx,
-                            generator,
-                            ndarray_dtype1,
-                        )
-                    },
-                )?
         };
 
-            Ok(Some(res.as_base_value().into()))
+        if op.base == Operator::MatMult {
+            // Handle matrix multiplication.
+            let left = left.to_ndarray(generator, ctx);
+            let right = right.to_ndarray(generator, ctx);
+            let result = NDArrayObject::matmul(generator, ctx, left, right, out)
+                .split_unsized(generator, ctx);
+            Ok(Some(ValueEnum::Dynamic(result.to_basic_value_enum())))
         } else {
-            let (ndarray_dtype, _) =
+            // For other operations, they are all elementwise operations.
-                unpack_ndarray_var_tys(&mut ctx.unifier, if is_ndarray1 { ty1 } else { ty2 });
+
-            let llvm_ndarray_dtype = ctx.get_llvm_type(generator, ndarray_dtype);
+            // There are only three cases:
-            let ndarray_val = NDArrayValue::from_pointer_value(
+            // - LHS is a scalar, RHS is an ndarray.
-                if is_ndarray1 { left_val } else { right_val }.into_pointer_value(),
+            // - LHS is an ndarray, RHS is a scalar.
-                llvm_ndarray_dtype,
+            // - LHS is an ndarray, RHS is an ndarray.
-                llvm_usize,
+            //
-                None,
+            // For all cases, the scalar operand is promoted to an ndarray,
-            );
+            // the two are then broadcasted, and starmapped through.
-            let res = numpy::ndarray_elementwise_binop_impl(
+
+            let left = left.to_ndarray(generator, ctx);
+            let right = right.to_ndarray(generator, ctx);
+
+            let result = NDArrayObject::broadcast_starmap(
                 generator,
                 ctx,
-                ndarray_dtype,
+                &[left, right],
-                match op.variant {
+                out,
-                    BinopVariant::Normal => None,
+                |generator, ctx, scalars| {
-                    BinopVariant::AugAssign => Some(ndarray_val),
+                    let left_value = scalars[0];
-                },
+                    let right_value = scalars[1];
-                (ty1, left_val, !is_ndarray1),
+
-                (ty2, right_val, !is_ndarray2),
+                    let result = gen_binop_expr_with_values(
-                |generator, ctx, (lhs, rhs)| {
-                    gen_binop_expr_with_values(
                         generator,
                         ctx,
-                        (&Some(ndarray_dtype), lhs),
+                        (&Some(left.dtype), left_value),
                         op,
-                        (&Some(ndarray_dtype), rhs),
+                        (&Some(right.dtype), right_value),
                         ctx.current_loc,
                     )?
                     .unwrap()
-                    .to_basic_value_enum(ctx, generator, ndarray_dtype)
+                    .to_basic_value_enum(ctx, generator, common_dtype)?;
-                },
-            )?;
 
-            Ok(Some(res.as_base_value().into()))
+                    Ok(result)
+                },
+            )
+            .unwrap();
+            Ok(Some(ValueEnum::Dynamic(result.instance.value.as_basic_value_enum())))
         }
     } else {
         let left_ty_enum = ctx.unifier.get_ty_immutable(left_ty.unwrap());
@@ -1794,12 +1759,7 @@ pub fn gen_unaryop_expr_with_values<'ctx, G: CodeGenerator>(
             ast::Unaryop::Invert => ctx.builder.build_not(val, "not").map(Into::into).unwrap(),
             ast::Unaryop::Not => ctx
                 .builder
-                .build_int_compare(
+                .build_xor(val, val.get_type().const_all_ones(), "not")
-                    inkwell::IntPredicate::EQ,
-                    val,
-                    val.get_type().const_zero(),
-                    "not",
-                )
                 .map(Into::into)
                 .unwrap(),
             ast::Unaryop::UAdd => val.into(),
@@ -1821,20 +1781,12 @@ pub fn gen_unaryop_expr_with_values<'ctx, G: CodeGenerator>(
             _ => val.into(),
         }
     } else if ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) {
-        let llvm_usize = generator.get_size_type(ctx.ctx);
+        let ndarray = AnyObject { value: val, ty };
-        let (ndarray_dtype, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
+        let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
-        let llvm_ndarray_dtype = ctx.get_llvm_type(generator, ndarray_dtype);
-
-        let val = NDArrayValue::from_pointer_value(
-            val.into_pointer_value(),
-            llvm_ndarray_dtype,
-            llvm_usize,
-            None,
-        );
 
         // ndarray uses `~` rather than `not` to perform elementwise inversion, convert it before
         // passing it to the elementwise codegen function
-        let op = if ndarray_dtype.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::Bool.id()) {
+        let op = if ndarray.dtype.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::Bool.id()) {
             if op == ast::Unaryop::Invert {
                 ast::Unaryop::Not
             } else {
@@ -1848,20 +1800,18 @@ pub fn gen_unaryop_expr_with_values<'ctx, G: CodeGenerator>(
             op
         };
 
-        let res = numpy::ndarray_elementwise_unaryop_impl(
+        let mapped_ndarray = ndarray.map(
             generator,
             ctx,
-            ndarray_dtype,
+            NDArrayOut::NewNDArray { dtype: ndarray.dtype },
-            None,
+            |generator, ctx, scalar| {
-            val,
+                gen_unaryop_expr_with_values(generator, ctx, op, (&Some(ndarray.dtype), scalar))?
-            |generator, ctx, val| {
-                gen_unaryop_expr_with_values(generator, ctx, op, (&Some(ndarray_dtype), val))?
                     .unwrap()
-                    .to_basic_value_enum(ctx, generator, ndarray_dtype)
+                    .to_basic_value_enum(ctx, generator, ndarray.dtype)
             },
         )?;
 
-        res.as_base_value().into()
+        ValueEnum::Dynamic(mapped_ndarray.instance.value.as_basic_value_enum())
     } else {
         unimplemented!()
     }))
@@ -1904,45 +1854,33 @@ pub fn gen_cmpop_expr_with_values<'ctx, G: CodeGenerator>(
         if left_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
             || right_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
         {
-            let llvm_usize = generator.get_size_type(ctx.ctx);
+            let (Some(left_ty), left) = left else { codegen_unreachable!(ctx) };
-
+            let (Some(right_ty), right) = comparators[0] else { codegen_unreachable!(ctx) };
-            let (Some(left_ty), lhs) = left else { codegen_unreachable!(ctx) };
-            let (Some(right_ty), rhs) = comparators[0] else { codegen_unreachable!(ctx) };
             let op = ops[0];
 
-            let is_ndarray1 =
+            let left = AnyObject { value: left, ty: left_ty };
-                left_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
+            let left =
-            let is_ndarray2 =
+                ScalarOrNDArray::split_object(generator, ctx, left).to_ndarray(generator, ctx);
-                right_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
 
-            return if is_ndarray1 && is_ndarray2 {
+            let right = AnyObject { value: right, ty: right_ty };
-                let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, left_ty);
+            let right =
-                let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, right_ty);
+                ScalarOrNDArray::split_object(generator, ctx, right).to_ndarray(generator, ctx);
 
-                assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
+            let result_ndarray = NDArrayObject::broadcast_starmap(
-
-                let llvm_ndarray_dtype1 = ctx.get_llvm_type(generator, ndarray_dtype1);
-
-                let left_val = NDArrayValue::from_pointer_value(
-                    lhs.into_pointer_value(),
-                    llvm_ndarray_dtype1,
-                    llvm_usize,
-                    None,
-                );
-                let res = numpy::ndarray_elementwise_binop_impl(
                 generator,
                 ctx,
-                    ctx.primitives.bool,
+                &[left, right],
-                    None,
+                NDArrayOut::NewNDArray { dtype: ctx.primitives.bool },
-                    (left_ty, left_val.as_base_value().into(), false),
+                |generator, ctx, scalars| {
-                    (right_ty, rhs, false),
+                    let left_scalar = scalars[0];
-                    |generator, ctx, (lhs, rhs)| {
+                    let right_scalar = scalars[1];
+
                     let val = gen_cmpop_expr_with_values(
                         generator,
                         ctx,
-                            (Some(ndarray_dtype1), lhs),
+                        (Some(left.dtype), left_scalar),
                         &[op],
-                            &[(Some(ndarray_dtype2), rhs)],
+                        &[(Some(right.dtype), right_scalar)],
                     )?
                     .unwrap()
                     .to_basic_value_enum(
@@ -1955,40 +1893,7 @@ pub fn gen_cmpop_expr_with_values<'ctx, G: CodeGenerator>(
                 },
             )?;
 
-                Ok(Some(res.as_base_value().into()))
+            return Ok(Some(result_ndarray.instance.value.into()));
-            } else {
-                let (ndarray_dtype, _) = unpack_ndarray_var_tys(
-                    &mut ctx.unifier,
-                    if is_ndarray1 { left_ty } else { right_ty },
-                );
-                let res = numpy::ndarray_elementwise_binop_impl(
-                    generator,
-                    ctx,
-                    ctx.primitives.bool,
-                    None,
-                    (left_ty, lhs, !is_ndarray1),
-                    (right_ty, rhs, !is_ndarray2),
-                    |generator, ctx, (lhs, rhs)| {
-                        let val = gen_cmpop_expr_with_values(
-                            generator,
-                            ctx,
-                            (Some(ndarray_dtype), lhs),
-                            &[op],
-                            &[(Some(ndarray_dtype), rhs)],
-                        )?
-                        .unwrap()
-                        .to_basic_value_enum(
-                            ctx,
-                            generator,
-                            ctx.primitives.bool,
-                        )?;
-
-                        Ok(generator.bool_to_i8(ctx, val.into_int_value()).into())
-                    },
-                )?;
-
-                Ok(Some(res.as_base_value().into()))
-            };
         }
     }
 
@@ -2230,9 +2135,9 @@ pub fn gen_cmpop_expr_with_values<'ctx, G: CodeGenerator>(
                     }
 
                     let left_val =
-                        ListValue::from_pointer_value(lhs.into_pointer_value(), llvm_usize, None);
+                        ListValue::from_ptr_val(lhs.into_pointer_value(), llvm_usize, None);
                     let right_val =
-                        ListValue::from_pointer_value(rhs.into_pointer_value(), llvm_usize, None);
+                        ListValue::from_ptr_val(rhs.into_pointer_value(), llvm_usize, None);
 
                     Ok(gen_if_else_expr_callback(
                         generator,
@@ -2539,343 +2444,6 @@ pub fn gen_cmpop_expr<'ctx, G: CodeGenerator>(
     )
 }
 
-/// Generates code for a subscript expression on an `ndarray`.
-///
-/// * `ty` - The `Type` of the `NDArray` elements.
-/// * `ndims` - The `Type` of the `NDArray` number-of-dimensions `Literal`.
-/// * `v` - The `NDArray` value.
-/// * `slice` - The slice expression used to subscript into the `ndarray`.
-fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ty: Type,
-    ndims: Type,
-    v: NDArrayValue<'ctx>,
-    slice: &Expr<Option<Type>>,
-) -> Result<Option<ValueEnum<'ctx>>, String> {
-    let llvm_i1 = ctx.ctx.bool_type();
-    let llvm_i32 = ctx.ctx.i32_type();
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-
-    let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) else {
-        codegen_unreachable!(ctx)
-    };
-
-    let ndims = values
-        .iter()
-        .map(|ndim| u64::try_from(ndim.clone()).map_err(|()| ndim.clone()))
-        .collect::<Result<Vec<_>, _>>()
-        .map_err(|val| {
-            format!(
-                "Expected non-negative literal for ndarray.ndims, got {}",
-                i128::try_from(val).unwrap()
-            )
-        })?;
-
-    assert!(!ndims.is_empty());
-
-    // The number of dimensions subscripted by the index expression.
-    // Slicing a ndarray will yield the same number of dimensions, whereas indexing into a
-    // dimension will remove a dimension.
-    let subscripted_dims = match &slice.node {
-        ExprKind::Tuple { elts, .. } => elts.iter().fold(0, |acc, value_subexpr| {
-            if let ExprKind::Slice { .. } = &value_subexpr.node {
-                acc
-            } else {
-                acc + 1
-            }
-        }),
-
-        ExprKind::Slice { .. } => 0,
-        _ => 1,
-    };
-
-    let ndarray_ndims_ty = ctx.unifier.get_fresh_literal(
-        ndims.iter().map(|v| SymbolValue::U64(v - subscripted_dims)).collect(),
-        None,
-    );
-    let ndarray_ty =
-        make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(ty), Some(ndarray_ndims_ty));
-    let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
-    let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
-    let llvm_ndarray_data_t = ctx.get_llvm_type(generator, ty).as_basic_type_enum();
-    let sizeof_elem = llvm_ndarray_data_t.size_of().unwrap();
-
-    // Check that len is non-zero
-    let len = v.load_ndims(ctx);
-    ctx.make_assert(
-        generator,
-        ctx.builder.build_int_compare(IntPredicate::SGT, len, llvm_usize.const_zero(), "").unwrap(),
-        "0:IndexError",
-        "too many indices for array: array is {0}-dimensional but 1 were indexed",
-        [Some(len), None, None],
-        slice.location,
-    );
-
-    // Normalizes a possibly-negative index to its corresponding positive index
-    let normalize_index = |generator: &mut G,
-                           ctx: &mut CodeGenContext<'ctx, '_>,
-                           index: IntValue<'ctx>,
-                           dim: u64| {
-        gen_if_else_expr_callback(
-            generator,
-            ctx,
-            |_, ctx| {
-                Ok(ctx
-                    .builder
-                    .build_int_compare(IntPredicate::SGE, index, index.get_type().const_zero(), "")
-                    .unwrap())
-            },
-            |_, _| Ok(Some(index)),
-            |generator, ctx| {
-                let llvm_i32 = ctx.ctx.i32_type();
-
-                let len = unsafe {
-                    v.shape().get_typed_unchecked(
-                        ctx,
-                        generator,
-                        &llvm_usize.const_int(dim, true),
-                        None,
-                    )
-                };
-
-                let index = ctx
-                    .builder
-                    .build_int_add(
-                        len,
-                        ctx.builder.build_int_s_extend(index, llvm_usize, "").unwrap(),
-                        "",
-                    )
-                    .unwrap();
-
-                Ok(Some(ctx.builder.build_int_truncate(index, llvm_i32, "").unwrap()))
-            },
-        )
-        .map(|v| v.map(BasicValueEnum::into_int_value))
-    };
-
-    // Converts a slice expression into a slice-range tuple
-    let expr_to_slice = |generator: &mut G,
-                         ctx: &mut CodeGenContext<'ctx, '_>,
-                         node: &ExprKind<Option<Type>>,
-                         dim: u64| {
-        match node {
-            ExprKind::Constant { value: Constant::Int(v), .. } => {
-                let Some(index) =
-                    normalize_index(generator, ctx, llvm_i32.const_int(*v as u64, true), dim)?
-                else {
-                    return Ok(None);
-                };
-
-                Ok(Some((index, index, llvm_i32.const_int(1, true))))
-            }
-
-            ExprKind::Slice { lower, upper, step } => {
-                let dim_sz = unsafe {
-                    v.shape().get_typed_unchecked(
-                        ctx,
-                        generator,
-                        &llvm_usize.const_int(dim, false),
-                        None,
-                    )
-                };
-
-                handle_slice_indices(lower, upper, step, ctx, generator, dim_sz)
-            }
-
-            _ => {
-                let Some(index) = generator.gen_expr(ctx, slice)? else { return Ok(None) };
-                let index = index
-                    .to_basic_value_enum(ctx, generator, slice.custom.unwrap())?
-                    .into_int_value();
-                let Some(index) = normalize_index(generator, ctx, index, dim)? else {
-                    return Ok(None);
-                };
-
-                Ok(Some((index, index, llvm_i32.const_int(1, true))))
-            }
-        }
-    };
-
-    let make_indices_arr = |generator: &mut G,
-                            ctx: &mut CodeGenContext<'ctx, '_>|
-     -> Result<_, String> {
-        Ok(if let ExprKind::Tuple { elts, .. } = &slice.node {
-            let llvm_int_ty = ctx.get_llvm_type(generator, elts[0].custom.unwrap());
-            let index_addr = generator.gen_array_var_alloc(
-                ctx,
-                llvm_int_ty,
-                llvm_usize.const_int(elts.len() as u64, false),
-                None,
-            )?;
-
-            for (i, elt) in elts.iter().enumerate() {
-                let Some(index) = generator.gen_expr(ctx, elt)? else {
-                    return Ok(None);
-                };
-
-                let index = index
-                    .to_basic_value_enum(ctx, generator, elt.custom.unwrap())?
-                    .into_int_value();
-                let Some(index) = normalize_index(generator, ctx, index, 0)? else {
-                    return Ok(None);
-                };
-
-                let store_ptr = unsafe {
-                    index_addr.ptr_offset_unchecked(
-                        ctx,
-                        generator,
-                        &llvm_usize.const_int(i as u64, false),
-                        None,
-                    )
-                };
-                ctx.builder.build_store(store_ptr, index).unwrap();
-            }
-
-            Some(index_addr)
-        } else if let Some(index) = generator.gen_expr(ctx, slice)? {
-            let llvm_int_ty = ctx.get_llvm_type(generator, slice.custom.unwrap());
-            let index_addr = generator.gen_array_var_alloc(
-                ctx,
-                llvm_int_ty,
-                llvm_usize.const_int(1u64, false),
-                None,
-            )?;
-
-            let index =
-                index.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value();
-            let Some(index) = normalize_index(generator, ctx, index, 0)? else { return Ok(None) };
-
-            let store_ptr = unsafe {
-                index_addr.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-            };
-            ctx.builder.build_store(store_ptr, index).unwrap();
-
-            Some(index_addr)
-        } else {
-            None
-        })
-    };
-
-    Ok(Some(if ndims.len() == 1 && ndims[0] - subscripted_dims == 0 {
-        let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
-
-        v.data().get(ctx, generator, &index_addr, None).into()
-    } else {
-        match &slice.node {
-            ExprKind::Tuple { elts, .. } => {
-                let slices = elts
-                    .iter()
-                    .enumerate()
-                    .map(|(dim, elt)| expr_to_slice(generator, ctx, &elt.node, dim as u64))
-                    .take_while_inclusive(|slice| slice.as_ref().is_ok_and(Option::is_some))
-                    .collect::<Result<Vec<_>, _>>()?;
-                if slices.len() < elts.len() {
-                    return Ok(None);
-                }
-
-                let slices = slices.into_iter().map(Option::unwrap).collect_vec();
-
-                numpy::ndarray_sliced_copy(generator, ctx, ty, v, &slices)?.as_base_value().into()
-            }
-
-            ExprKind::Slice { .. } => {
-                let Some(slice) = expr_to_slice(generator, ctx, &slice.node, 0)? else {
-                    return Ok(None);
-                };
-
-                numpy::ndarray_sliced_copy(generator, ctx, ty, v, &[slice])?.as_base_value().into()
-            }
-
-            _ => {
-                // Accessing an element from a multi-dimensional `ndarray`
-
-                let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
-
-                // Create a new array, remove the top dimension from the dimension-size-list, and copy the
-                // elements over
-                let subscripted_ndarray =
-                    generator.gen_var_alloc(ctx, llvm_ndarray_t.into(), None)?;
-                let ndarray = NDArrayValue::from_pointer_value(
-                    subscripted_ndarray,
-                    llvm_ndarray_data_t,
-                    llvm_usize,
-                    None,
-                );
-
-                let num_dims = v.load_ndims(ctx);
-                ndarray.store_ndims(
-                    ctx,
-                    generator,
-                    ctx.builder
-                        .build_int_sub(num_dims, llvm_usize.const_int(1, false), "")
-                        .unwrap(),
-                );
-
-                let ndarray_num_dims = ndarray.load_ndims(ctx);
-                ndarray.create_shape(ctx, llvm_usize, ndarray_num_dims);
-
-                let ndarray_num_dims = ctx
-                    .builder
-                    .build_int_z_extend_or_bit_cast(
-                        ndarray.load_ndims(ctx),
-                        llvm_usize.size_of().get_type(),
-                        "",
-                    )
-                    .unwrap();
-                let v_dims_src_ptr = unsafe {
-                    v.shape().ptr_offset_unchecked(
-                        ctx,
-                        generator,
-                        &llvm_usize.const_int(1, false),
-                        None,
-                    )
-                };
-                call_memcpy_generic(
-                    ctx,
-                    ndarray.shape().base_ptr(ctx, generator),
-                    v_dims_src_ptr,
-                    ctx.builder
-                        .build_int_mul(ndarray_num_dims, llvm_usize.size_of(), "")
-                        .map(Into::into)
-                        .unwrap(),
-                    llvm_i1.const_zero(),
-                );
-
-                let ndarray_num_elems = call_ndarray_calc_size(
-                    generator,
-                    ctx,
-                    &ndarray.shape().as_slice_value(ctx, generator),
-                    (None, None),
-                );
-                let ndarray_num_elems = ctx
-                    .builder
-                    .build_int_z_extend_or_bit_cast(ndarray_num_elems, sizeof_elem.get_type(), "")
-                    .unwrap();
-                ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
-
-                let v_data_src_ptr = v.data().ptr_offset(ctx, generator, &index_addr, None);
-                call_memcpy_generic(
-                    ctx,
-                    ndarray.data().base_ptr(ctx, generator),
-                    v_data_src_ptr,
-                    ctx.builder
-                        .build_int_mul(
-                            ndarray_num_elems,
-                            llvm_ndarray_data_t.size_of().unwrap(),
-                            "",
-                        )
-                        .map(Into::into)
-                        .unwrap(),
-                    llvm_i1.const_zero(),
-                );
-
-                ndarray.as_base_value().into()
-            }
-        }
-    }))
-}
-
 /// See [`CodeGenerator::gen_expr`].
 pub fn gen_expr<'ctx, G: CodeGenerator>(
     generator: &mut G,
@@ -3142,53 +2710,48 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
             };
             let left = generator.bool_to_i1(ctx, left);
             let current = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
-            let a_begin_bb = ctx.ctx.append_basic_block(current, "a_begin");
+            let a_bb = ctx.ctx.append_basic_block(current, "a");
-            let a_end_bb = ctx.ctx.append_basic_block(current, "a_end");
+            let b_bb = ctx.ctx.append_basic_block(current, "b");
-            let b_begin_bb = ctx.ctx.append_basic_block(current, "b_begin");
-            let b_end_bb = ctx.ctx.append_basic_block(current, "b_end");
             let cont_bb = ctx.ctx.append_basic_block(current, "cont");
-            ctx.builder.build_conditional_branch(left, a_begin_bb, b_begin_bb).unwrap();
+            ctx.builder.build_conditional_branch(left, a_bb, b_bb).unwrap();
-
-            ctx.builder.position_at_end(a_end_bb);
-            ctx.builder.build_unconditional_branch(cont_bb).unwrap();
-            ctx.builder.position_at_end(b_end_bb);
-            ctx.builder.build_unconditional_branch(cont_bb).unwrap();
             let (a, b) = match op {
                 Boolop::Or => {
-                    ctx.builder.position_at_end(a_begin_bb);
+                    ctx.builder.position_at_end(a_bb);
                     let a = ctx.ctx.i8_type().const_int(1, false);
-                    ctx.builder.build_unconditional_branch(a_end_bb).unwrap();
+                    ctx.builder.build_unconditional_branch(cont_bb).unwrap();
 
-                    ctx.builder.position_at_end(b_begin_bb);
+                    ctx.builder.position_at_end(b_bb);
                     let b = if let Some(v) = generator.gen_expr(ctx, &values[1])? {
                         let b = v
                             .to_basic_value_enum(ctx, generator, values[1].custom.unwrap())?
                             .into_int_value();
                         let b = generator.bool_to_i8(ctx, b);
+                        ctx.builder.build_unconditional_branch(cont_bb).unwrap();
+
                         Some(b)
                     } else {
                         None
                     };
-                    ctx.builder.build_unconditional_branch(b_end_bb).unwrap();
 
                     (Some(a), b)
                 }
                 Boolop::And => {
-                    ctx.builder.position_at_end(a_begin_bb);
+                    ctx.builder.position_at_end(a_bb);
                     let a = if let Some(v) = generator.gen_expr(ctx, &values[1])? {
                         let a = v
                             .to_basic_value_enum(ctx, generator, values[1].custom.unwrap())?
                             .into_int_value();
                         let a = generator.bool_to_i8(ctx, a);
+                        ctx.builder.build_unconditional_branch(cont_bb).unwrap();
+
                         Some(a)
                     } else {
                         None
                     };
-                    ctx.builder.build_unconditional_branch(a_end_bb).unwrap();
 
-                    ctx.builder.position_at_end(b_begin_bb);
+                    ctx.builder.position_at_end(b_bb);
                     let b = ctx.ctx.i8_type().const_zero();
-                    ctx.builder.build_unconditional_branch(b_end_bb).unwrap();
+                    ctx.builder.build_unconditional_branch(cont_bb).unwrap();
 
                     (a, Some(b))
                 }
@@ -3198,7 +2761,7 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
             match (a, b) {
                 (Some(a), Some(b)) => {
                     let phi = ctx.builder.build_phi(ctx.ctx.i8_type(), "").unwrap();
-                    phi.add_incoming(&[(&a, a_end_bb), (&b, b_end_bb)]);
+                    phi.add_incoming(&[(&a, a_bb), (&b, b_bb)]);
                     phi.as_basic_value().into()
                 }
                 (Some(a), None) => a.into(),
@@ -3436,7 +2999,7 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
                     } else {
                         return Ok(None);
                     };
-                    let v = ListValue::from_pointer_value(v, usize, Some("arr"));
+                    let v = ListValue::from_ptr_val(v, usize, Some("arr"));
                     let ty = ctx.get_llvm_type(generator, *ty);
                     if let ExprKind::Slice { lower, upper, step } = &slice.node {
                         let one = int32.const_int(1, false);
@@ -3537,19 +3100,26 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
                         v.data().get(ctx, generator, &index, None).into()
                     }
                 }
-                TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::NDArray.id() => {
+                TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-                    let (ty, ndims) = params.iter().map(|(_, ty)| ty).collect_tuple().unwrap();
+                    let Some(ndarray) = generator.gen_expr(ctx, value)? else {
-                    let llvm_ty = ctx.get_llvm_type(generator, *ty);
-
-                    let v = if let Some(v) = generator.gen_expr(ctx, value)? {
-                        v.to_basic_value_enum(ctx, generator, value.custom.unwrap())?
-                            .into_pointer_value()
-                    } else {
                         return Ok(None);
                     };
-                    let v = NDArrayValue::from_pointer_value(v, llvm_ty, usize, None);
 
-                    return gen_ndarray_subscript_expr(generator, ctx, *ty, *ndims, v, slice);
+                    let ndarray_ty = value.custom.unwrap();
+                    let ndarray = ndarray.to_basic_value_enum(ctx, generator, ndarray_ty)?;
+
+                    let ndarray = NDArrayObject::from_object(
+                        generator,
+                        ctx,
+                        AnyObject { ty: ndarray_ty, value: ndarray },
+                    );
+
+                    let indices = gen_ndarray_subscript_ndindices(generator, ctx, slice)?;
+                    let result = ndarray
+                        .index(generator, ctx, &indices)
+                        .split_unsized(generator, ctx)
+                        .to_basic_value_enum();
+                    return Ok(Some(ValueEnum::Dynamic(result)));
                 }
                 TypeEnum::TTuple { .. } => {
                     let index: u32 =

nac3core/src/codegen/generator.rs

@@ -6,7 +6,7 @@ use inkwell::{
 
 use nac3parser::ast::{Expr, Stmt, StrRef};
 
-use super::{bool_to_i1, bool_to_i8, expr::*, stmt::*, values::ArraySliceValue, CodeGenContext};
+use super::{bool_to_i1, bool_to_i8, classes::ArraySliceValue, expr::*, stmt::*, CodeGenContext};
 use crate::{
     symbol_resolver::ValueEnum,
     toplevel::{DefinitionId, TopLevelDef},

nac3core/src/codegen/irrt/list.rs

@@ -1,162 +0,0 @@
-use inkwell::{
-    types::BasicTypeEnum,
-    values::{BasicValueEnum, CallSiteValue, IntValue},
-    AddressSpace, IntPredicate,
-};
-use itertools::Either;
-
-use super::calculate_len_for_slice_range;
-use crate::codegen::{
-    macros::codegen_unreachable,
-    values::{ArrayLikeValue, ListValue},
-    CodeGenContext, CodeGenerator,
-};
-
-/// This function handles 'end' **inclusively**.
-/// Order of tuples `assign_idx` and `value_idx` is ('start', 'end', 'step').
-/// Negative index should be handled before entering this function
-pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ty: BasicTypeEnum<'ctx>,
-    dest_arr: ListValue<'ctx>,
-    dest_idx: (IntValue<'ctx>, IntValue<'ctx>, IntValue<'ctx>),
-    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 slice_assign_fun = {
-        let ty_vec = vec![
-            int32.into(),         // dest start idx
-            int32.into(),         // dest end idx
-            int32.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
-            elem_ptr_type.into(), // src arr ptr
-            int32.into(),         // src arr len
-            int32.into(),         // size
-        ];
-        ctx.module.get_function(fun_symbol).unwrap_or_else(|| {
-            let fn_t = int32.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 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 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();
-
-    // index in bound and positive should be done
-    // assert if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest), and
-    // throw exception if not satisfied
-    let src_end = ctx
-        .builder
-        .build_select(
-            ctx.builder.build_int_compare(IntPredicate::SLT, src_idx.2, zero, "is_neg").unwrap(),
-            ctx.builder.build_int_sub(src_idx.1, one, "e_min_one").unwrap(),
-            ctx.builder.build_int_add(src_idx.1, one, "e_add_one").unwrap(),
-            "final_e",
-        )
-        .map(BasicValueEnum::into_int_value)
-        .unwrap();
-    let dest_end = ctx
-        .builder
-        .build_select(
-            ctx.builder.build_int_compare(IntPredicate::SLT, dest_idx.2, zero, "is_neg").unwrap(),
-            ctx.builder.build_int_sub(dest_idx.1, one, "e_min_one").unwrap(),
-            ctx.builder.build_int_add(dest_idx.1, one, "e_add_one").unwrap(),
-            "final_e",
-        )
-        .map(BasicValueEnum::into_int_value)
-        .unwrap();
-    let src_slice_len =
-        calculate_len_for_slice_range(generator, ctx, src_idx.0, src_end, src_idx.2);
-    let dest_slice_len =
-        calculate_len_for_slice_range(generator, ctx, dest_idx.0, dest_end, dest_idx.2);
-    let src_eq_dest = ctx
-        .builder
-        .build_int_compare(IntPredicate::EQ, src_slice_len, dest_slice_len, "slice_src_eq_dest")
-        .unwrap();
-    let src_slt_dest = ctx
-        .builder
-        .build_int_compare(IntPredicate::SLT, src_slice_len, dest_slice_len, "slice_src_slt_dest")
-        .unwrap();
-    let dest_step_eq_one = ctx
-        .builder
-        .build_int_compare(
-            IntPredicate::EQ,
-            dest_idx.2,
-            dest_idx.2.get_type().const_int(1, false),
-            "slice_dest_step_eq_one",
-        )
-        .unwrap();
-    let cond_1 = ctx.builder.build_and(dest_step_eq_one, src_slt_dest, "slice_cond_1").unwrap();
-    let cond = ctx.builder.build_or(src_eq_dest, cond_1, "slice_cond").unwrap();
-    ctx.make_assert(
-        generator,
-        cond,
-        "0:ValueError",
-        "attempt to assign sequence of size {0} to slice of size {1} with step size {2}",
-        [Some(src_slice_len), Some(dest_slice_len), Some(dest_idx.2)],
-        ctx.current_loc,
-    );
-
-    let new_len = {
-        let args = vec![
-            dest_idx.0.into(),   // dest start idx
-            dest_idx.1.into(),   // dest end idx
-            dest_idx.2.into(),   // dest step
-            dest_arr_ptr.into(), // dest arr ptr
-            dest_len.into(),     // dest arr len
-            src_idx.0.into(),    // src start idx
-            src_idx.1.into(),    // src end idx
-            src_idx.2.into(),    // src step
-            src_arr_ptr.into(),  // src arr ptr
-            src_len.into(),      // src arr len
-            {
-                let s = match ty {
-                    BasicTypeEnum::FloatType(t) => t.size_of(),
-                    BasicTypeEnum::IntType(t) => t.size_of(),
-                    BasicTypeEnum::PointerType(t) => t.size_of(),
-                    BasicTypeEnum::StructType(t) => t.size_of().unwrap(),
-                    _ => codegen_unreachable!(ctx),
-                };
-                ctx.builder.build_int_truncate_or_bit_cast(s, int32, "size").unwrap()
-            }
-            .into(),
-        ];
-        ctx.builder
-            .build_call(slice_assign_fun, args.as_slice(), "slice_assign")
-            .map(CallSiteValue::try_as_basic_value)
-            .map(|v| v.map_left(BasicValueEnum::into_int_value))
-            .map(Either::unwrap_left)
-            .unwrap()
-    };
-    // update length
-    let need_update =
-        ctx.builder.build_int_compare(IntPredicate::NE, new_len, dest_len, "need_update").unwrap();
-    let current = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
-    let update_bb = ctx.ctx.append_basic_block(current, "update");
-    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();
-    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

@@ -1,152 +0,0 @@
-use inkwell::{
-    values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue},
-    IntPredicate,
-};
-use itertools::Either;
-
-use crate::codegen::{
-    macros::codegen_unreachable,
-    {CodeGenContext, CodeGenerator},
-};
-
-// repeated squaring method adapted from GNU Scientific Library:
-// https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
-pub fn integer_power<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    base: IntValue<'ctx>,
-    exp: IntValue<'ctx>,
-    signed: bool,
-) -> IntValue<'ctx> {
-    let symbol = match (base.get_type().get_bit_width(), exp.get_type().get_bit_width(), signed) {
-        (32, 32, true) => "__nac3_int_exp_int32_t",
-        (64, 64, true) => "__nac3_int_exp_int64_t",
-        (32, 32, false) => "__nac3_int_exp_uint32_t",
-        (64, 64, false) => "__nac3_int_exp_uint64_t",
-        _ => codegen_unreachable!(ctx),
-    };
-    let base_type = base.get_type();
-    let pow_fun = ctx.module.get_function(symbol).unwrap_or_else(|| {
-        let fn_type = base_type.fn_type(&[base_type.into(), base_type.into()], false);
-        ctx.module.add_function(symbol, fn_type, None)
-    });
-    // throw exception when exp < 0
-    let ge_zero = ctx
-        .builder
-        .build_int_compare(
-            IntPredicate::SGE,
-            exp,
-            exp.get_type().const_zero(),
-            "assert_int_pow_ge_0",
-        )
-        .unwrap();
-    ctx.make_assert(
-        generator,
-        ge_zero,
-        "0:ValueError",
-        "integer power must be positive or zero",
-        [None, None, None],
-        ctx.current_loc,
-    );
-    ctx.builder
-        .build_call(pow_fun, &[base.into(), exp.into()], "call_int_pow")
-        .map(CallSiteValue::try_as_basic_value)
-        .map(|v| v.map_left(BasicValueEnum::into_int_value))
-        .map(Either::unwrap_left)
-        .unwrap()
-}
-
-/// Generates a call to `isinf` in IR. Returns an `i1` representing the result.
-pub fn call_isinf<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &CodeGenContext<'ctx, '_>,
-    v: FloatValue<'ctx>,
-) -> IntValue<'ctx> {
-    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);
-        ctx.module.add_function("__nac3_isinf", fn_type, None)
-    });
-
-    let ret = ctx
-        .builder
-        .build_call(intrinsic_fn, &[v.into()], "isinf")
-        .map(CallSiteValue::try_as_basic_value)
-        .map(|v| v.map_left(BasicValueEnum::into_int_value))
-        .map(Either::unwrap_left)
-        .unwrap();
-
-    generator.bool_to_i1(ctx, ret)
-}
-
-/// Generates a call to `isnan` in IR. Returns an `i1` representing the result.
-pub fn call_isnan<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &CodeGenContext<'ctx, '_>,
-    v: FloatValue<'ctx>,
-) -> IntValue<'ctx> {
-    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);
-        ctx.module.add_function("__nac3_isnan", fn_type, None)
-    });
-
-    let ret = ctx
-        .builder
-        .build_call(intrinsic_fn, &[v.into()], "isnan")
-        .map(CallSiteValue::try_as_basic_value)
-        .map(|v| v.map_left(BasicValueEnum::into_int_value))
-        .map(Either::unwrap_left)
-        .unwrap();
-
-    generator.bool_to_i1(ctx, ret)
-}
-
-/// Generates a call to `gamma` in IR. Returns an `f64` representing the result.
-pub fn call_gamma<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
-    let llvm_f64 = ctx.ctx.f64_type();
-
-    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)
-    });
-
-    ctx.builder
-        .build_call(intrinsic_fn, &[v.into()], "gamma")
-        .map(CallSiteValue::try_as_basic_value)
-        .map(|v| v.map_left(BasicValueEnum::into_float_value))
-        .map(Either::unwrap_left)
-        .unwrap()
-}
-
-/// Generates a call to `gammaln` in IR. Returns an `f64` representing the result.
-pub fn call_gammaln<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
-    let llvm_f64 = ctx.ctx.f64_type();
-
-    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)
-    });
-
-    ctx.builder
-        .build_call(intrinsic_fn, &[v.into()], "gammaln")
-        .map(CallSiteValue::try_as_basic_value)
-        .map(|v| v.map_left(BasicValueEnum::into_float_value))
-        .map(Either::unwrap_left)
-        .unwrap()
-}
-
-/// Generates a call to `j0` in IR. Returns an `f64` representing the result.
-pub fn call_j0<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
-    let llvm_f64 = ctx.ctx.f64_type();
-
-    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)
-    });
-
-    ctx.builder
-        .build_call(intrinsic_fn, &[v.into()], "j0")
-        .map(CallSiteValue::try_as_basic_value)
-        .map(|v| v.map_left(BasicValueEnum::into_float_value))
-        .map(Either::unwrap_left)
-        .unwrap()
-}

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/slice.rs

@@ -1,76 +0,0 @@
-use inkwell::{
-    values::{BasicValueEnum, CallSiteValue, IntValue},
-    IntPredicate,
-};
-use itertools::Either;
-use nac3parser::ast::Expr;
-
-use crate::{
-    codegen::{CodeGenContext, CodeGenerator},
-    typecheck::typedef::Type,
-};
-
-/// this function allows index out of range, since python
-/// allows index out of range in slice (`a = [1,2,3]; a[1:10] == [2,3]`).
-pub fn handle_slice_index_bound<'ctx, G: CodeGenerator>(
-    i: &Expr<Option<Type>>,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    generator: &mut G,
-    length: IntValue<'ctx>,
-) -> Result<Option<IntValue<'ctx>>, String> {
-    const SYMBOL: &str = "__nac3_slice_index_bound";
-    let 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()], false);
-        ctx.module.add_function(SYMBOL, fn_t, None)
-    });
-
-    let i = if let Some(v) = generator.gen_expr(ctx, i)? {
-        v.to_basic_value_enum(ctx, generator, i.custom.unwrap())?
-    } else {
-        return Ok(None);
-    };
-    Ok(Some(
-        ctx.builder
-            .build_call(func, &[i.into(), length.into()], "bounded_ind")
-            .map(CallSiteValue::try_as_basic_value)
-            .map(|v| v.map_left(BasicValueEnum::into_int_value))
-            .map(Either::unwrap_left)
-            .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/mod.rs

@@ -30,27 +30,30 @@ use nac3parser::ast::{Location, Stmt, StrRef};
 
 use crate::{
     symbol_resolver::{StaticValue, SymbolResolver},
-    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
+    toplevel::{helper::PrimDef, TopLevelContext, TopLevelDef},
     typecheck::{
         type_inferencer::{CodeLocation, PrimitiveStore},
         typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
     },
 };
+use classes::{ListType, ProxyType, RangeType};
 use concrete_type::{ConcreteType, ConcreteTypeEnum, ConcreteTypeStore};
 pub use generator::{CodeGenerator, DefaultCodeGenerator};
-use types::{ListType, NDArrayType, ProxyType, RangeType};
+use model::*;
+use object::ndarray::NDArray;
 
 pub mod builtin_fns;
+pub mod classes;
 pub mod concrete_type;
 pub mod expr;
 pub mod extern_fns;
 mod generator;
 pub mod irrt;
 pub mod llvm_intrinsics;
+pub mod model;
 pub mod numpy;
+pub mod object;
 pub mod stmt;
-pub mod types;
-pub mod values;
 
 #[cfg(test)]
 mod test;
@@ -510,12 +513,7 @@ 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);
+                            Ptr(Struct(NDArray)).llvm_type(generator, ctx).as_basic_type_enum()
-                            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()
                         }
 
                         _ => unreachable!(

nac3core/src/codegen/model/any.rs

@@ -0,0 +1,41 @@
+use inkwell::{
+    context::Context,
+    types::{BasicType, BasicTypeEnum},
+    values::BasicValueEnum,
+};
+
+use super::*;
+use crate::codegen::CodeGenerator;
+
+/// A [`Model`] of any [`BasicTypeEnum`].
+///
+/// Use this when it is infeasible to use model abstractions.
+#[derive(Debug, Clone, Copy)]
+pub struct Any<'ctx>(pub BasicTypeEnum<'ctx>);
+
+impl<'ctx> Model<'ctx> for Any<'ctx> {
+    type Value = BasicValueEnum<'ctx>;
+    type Type = BasicTypeEnum<'ctx>;
+
+    fn llvm_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        _ctx: &'ctx Context,
+    ) -> Self::Type {
+        self.0
+    }
+
+    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &mut G,
+        _ctx: &'ctx Context,
+        ty: T,
+    ) -> Result<(), ModelError> {
+        let ty = ty.as_basic_type_enum();
+        if ty == self.0 {
+            Ok(())
+        } else {
+            Err(ModelError(format!("Expecting {}, but got {}", self.0, ty)))
+        }
+    }
+}

nac3core/src/codegen/model/array.rs

@@ -0,0 +1,146 @@
+use std::fmt;
+
+use inkwell::{
+    context::Context,
+    types::{ArrayType, BasicType, BasicTypeEnum},
+    values::{ArrayValue, IntValue},
+};
+
+use super::*;
+use crate::codegen::{CodeGenContext, CodeGenerator};
+
+/// Trait for Rust structs identifying length values for [`Array`].
+pub trait ArrayLen: fmt::Debug + Clone + Copy {
+    fn length(&self) -> u32;
+}
+
+/// A statically known length.
+#[derive(Debug, Clone, Copy, Default)]
+pub struct Len<const N: u32>;
+
+/// A dynamically known length.
+#[derive(Debug, Clone, Copy)]
+pub struct AnyLen(pub u32);
+
+impl<const N: u32> ArrayLen for Len<N> {
+    fn length(&self) -> u32 {
+        N
+    }
+}
+
+impl ArrayLen for AnyLen {
+    fn length(&self) -> u32 {
+        self.0
+    }
+}
+
+/// A Model for an [`ArrayType`].
+///
+/// `Len` should be of a [`LenKind`] and `Item` should be a of [`Model`].
+#[derive(Debug, Clone, Copy, Default)]
+pub struct Array<Len, Item> {
+    /// Length of this array.
+    pub len: Len,
+    /// [`Model`] of the array items.
+    pub item: Item,
+}
+
+impl<'ctx, Len: ArrayLen, Item: Model<'ctx>> Model<'ctx> for Array<Len, Item> {
+    type Value = ArrayValue<'ctx>;
+    type Type = ArrayType<'ctx>;
+
+    fn llvm_type<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> Self::Type {
+        self.item.llvm_type(generator, ctx).array_type(self.len.length())
+    }
+
+    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+        ty: T,
+    ) -> Result<(), ModelError> {
+        let ty = ty.as_basic_type_enum();
+        let BasicTypeEnum::ArrayType(ty) = ty else {
+            return Err(ModelError(format!("Expecting ArrayType, but got {ty:?}")));
+        };
+
+        if ty.len() != self.len.length() {
+            return Err(ModelError(format!(
+                "Expecting ArrayType with size {}, but got an ArrayType with size {}",
+                ty.len(),
+                self.len.length()
+            )));
+        }
+
+        self.item
+            .check_type(generator, ctx, ty.get_element_type())
+            .map_err(|err| err.under_context("an ArrayType"))?;
+
+        Ok(())
+    }
+}
+
+impl<'ctx, Len: ArrayLen, Item: Model<'ctx>> Instance<'ctx, Ptr<Array<Len, Item>>> {
+    /// Get the pointer to the `i`-th (0-based) array element.
+    pub fn gep(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        i: IntValue<'ctx>,
+    ) -> Instance<'ctx, Ptr<Item>> {
+        let zero = ctx.ctx.i32_type().const_zero();
+        let ptr = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[zero, i], "").unwrap() };
+
+        unsafe { Ptr(self.model.0.item).believe_value(ptr) }
+    }
+
+    /// Like `gep` but `i` is a constant.
+    pub fn gep_const(&self, ctx: &CodeGenContext<'ctx, '_>, i: u64) -> Instance<'ctx, Ptr<Item>> {
+        assert!(
+            i < u64::from(self.model.0.len.length()),
+            "Index {i} is out of bounds. Array length = {}",
+            self.model.0.len.length()
+        );
+
+        let i = ctx.ctx.i32_type().const_int(i, false);
+        self.gep(ctx, i)
+    }
+
+    /// Convenience function equivalent to `.gep(...).load(...)`.
+    pub fn get<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        i: IntValue<'ctx>,
+    ) -> Instance<'ctx, Item> {
+        self.gep(ctx, i).load(generator, ctx)
+    }
+
+    /// Like `get` but `i` is a constant.
+    pub fn get_const<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        i: u64,
+    ) -> Instance<'ctx, Item> {
+        self.gep_const(ctx, i).load(generator, ctx)
+    }
+
+    /// Convenience function equivalent to `.gep(...).store(...)`.
+    pub fn set(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        i: IntValue<'ctx>,
+        value: Instance<'ctx, Item>,
+    ) {
+        self.gep(ctx, i).store(ctx, value);
+    }
+
+    /// Like `set` but `i` is a constant.
+    pub fn set_const(&self, ctx: &CodeGenContext<'ctx, '_>, i: u64, value: Instance<'ctx, Item>) {
+        self.gep_const(ctx, i).store(ctx, value);
+    }
+}

nac3core/src/codegen/model/core.rs

@@ -0,0 +1,207 @@
+use std::fmt;
+
+use inkwell::{context::Context, types::*, values::*};
+use itertools::Itertools;
+
+use super::*;
+use crate::codegen::{CodeGenContext, CodeGenerator};
+
+/// A error type for reporting any [`Model`]-related error (e.g., a [`BasicType`] mismatch).
+#[derive(Debug, Clone)]
+pub struct ModelError(pub String);
+
+impl ModelError {
+    /// Append a context message to the error.
+    pub(super) fn under_context(mut self, context: &str) -> Self {
+        self.0.push_str(" ... in ");
+        self.0.push_str(context);
+        self
+    }
+}
+
+/// Trait for Rust structs identifying [`BasicType`]s in the context of a known [`CodeGenerator`] and [`CodeGenContext`].
+///
+/// For instance,
+/// - [`Int<Int32>`] identifies an [`IntType`] with 32-bits.
+/// - [`Int<SizeT>`] identifies an [`IntType`] with bit-width [`CodeGenerator::get_size_type`].
+/// - [`Ptr<Int<SizeT>>`] identifies a [`PointerType`] that points to an [`IntType`] with bit-width [`CodeGenerator::get_size_type`].
+/// - [`Int<AnyInt>`] identifies an [`IntType`] with bit-width of whatever is set in the [`AnyInt`] object.
+/// - [`Any`] identifies a [`BasicType`] set in the [`Any`] object itself.
+///
+/// You can get the [`BasicType`] out of a model with [`Model::get_type`].
+///
+/// Furthermore, [`Instance<'ctx, M>`] is a simple structure that carries a [`BasicValue`] with [`BasicType`] identified by model `M`.
+///
+/// The main purpose of this abstraction is to have a more Rust type-safe way to use Inkwell and give type-hints for programmers.
+///
+/// ### Notes on `Default` trait
+///
+/// For some models like [`Int<Int32>`] or [`Int<SizeT>`], they have a [`Default`] trait since just by looking at their types, it is possible
+/// to tell the [`BasicType`]s they are identifying.
+///
+/// This can be used to create strongly-typed interfaces accepting only values of a specific [`BasicType`] without having to worry about
+/// writing debug assertions to check, for example, if the programmer has passed in an [`IntValue`] with the wrong bit-width.
+/// ```ignore
+/// fn give_me_i32_and_get_a_size_t_back<'ctx>(i32: Instance<'ctx, Int<Int32>>) -> Instance<'ctx, Int<SizeT>> {
+///     // code...
+/// }
+/// ```
+///
+/// ### Notes on converting between Inkwell and model/ge.
+///
+/// Suppose you have an [`IntValue`], and you want to pass it into a function that takes a [`Instance<'ctx, Int<Int32>>`]. You can do use
+/// [`Model::check_value`] or [`Model::believe_value`].
+/// ```ignore
+/// let my_value: IntValue<'ctx>;
+///
+/// let my_value = Int(Int32).check_value(my_value).unwrap(); // Panics if `my_value` is not 32-bit with a descriptive error message.
+///
+/// // or, if you are absolutely certain that `my_value` is 32-bit and doing extra checks is a waste of time:
+/// let my_value = Int(Int32).believe_value(my_value);
+/// ```
+pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
+    /// The [`BasicType`] *variant* this model is identifying.
+    type Type: BasicType<'ctx>;
+
+    /// The [`BasicValue`] type of the [`BasicType`] of this model.
+    type Value: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>>;
+
+    /// Return the [`BasicType`] of this model.
+    #[must_use]
+    fn llvm_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context)
+        -> Self::Type;
+
+    /// Get the number of bytes of the [`BasicType`] of this model.
+    fn size_of<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+    ) -> IntValue<'ctx> {
+        self.llvm_type(generator, ctx).size_of().unwrap()
+    }
+
+    /// Check if a [`BasicType`] matches the [`BasicType`] of this model.
+    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+        ty: T,
+    ) -> Result<(), ModelError>;
+
+    /// Create an instance from a value.
+    ///
+    /// # Safety
+    ///
+    /// Caller must make sure the type of `value` and the type of this `model` are equivalent.
+    #[must_use]
+    unsafe fn believe_value(&self, value: Self::Value) -> Instance<'ctx, Self> {
+        Instance { model: *self, value }
+    }
+
+    /// Check if a [`BasicValue`]'s type is equivalent to the type of this model.
+    /// Wrap the [`BasicValue`] into an [`Instance`] if it is.
+    fn check_value<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+        value: V,
+    ) -> Result<Instance<'ctx, Self>, ModelError> {
+        let value = value.as_basic_value_enum();
+        self.check_type(generator, ctx, value.get_type())
+            .map_err(|err| err.under_context(format!("the value {value:?}").as_str()))?;
+
+        let Ok(value) = Self::Value::try_from(value) else {
+            unreachable!("check_type() has bad implementation")
+        };
+        unsafe { Ok(self.believe_value(value)) }
+    }
+
+    // Allocate a value on the stack and return its pointer.
+    fn alloca<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> Instance<'ctx, Ptr<Self>> {
+        let p = ctx.builder.build_alloca(self.llvm_type(generator, ctx.ctx), "").unwrap();
+        unsafe { Ptr(*self).believe_value(p) }
+    }
+
+    // Allocate an array on the stack and return its pointer.
+    fn array_alloca<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        len: IntValue<'ctx>,
+    ) -> Instance<'ctx, Ptr<Self>> {
+        let p =
+            ctx.builder.build_array_alloca(self.llvm_type(generator, ctx.ctx), len, "").unwrap();
+        unsafe { Ptr(*self).believe_value(p) }
+    }
+
+    fn var_alloca<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&str>,
+    ) -> Result<Instance<'ctx, Ptr<Self>>, String> {
+        let ty = self.llvm_type(generator, ctx.ctx).as_basic_type_enum();
+        let p = generator.gen_var_alloc(ctx, ty, name)?;
+        unsafe { Ok(Ptr(*self).believe_value(p)) }
+    }
+
+    fn array_var_alloca<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        len: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> Result<Instance<'ctx, Ptr<Self>>, String> {
+        // TODO: Remove ArraySliceValue
+        let ty = self.llvm_type(generator, ctx.ctx).as_basic_type_enum();
+        let p = generator.gen_array_var_alloc(ctx, ty, len, name)?;
+        unsafe { Ok(Ptr(*self).believe_value(PointerValue::from(p))) }
+    }
+
+    /// Allocate a constant array.
+    fn const_array<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+        values: &[Instance<'ctx, Self>],
+    ) -> Instance<'ctx, Array<AnyLen, Self>> {
+        macro_rules! make {
+            ($t:expr, $into_value:expr) => {
+                $t.const_array(
+                    &values
+                        .iter()
+                        .map(|x| $into_value(x.value.as_basic_value_enum()))
+                        .collect_vec(),
+                )
+            };
+        }
+
+        let value = match self.llvm_type(generator, ctx).as_basic_type_enum() {
+            BasicTypeEnum::ArrayType(t) => make!(t, BasicValueEnum::into_array_value),
+            BasicTypeEnum::IntType(t) => make!(t, BasicValueEnum::into_int_value),
+            BasicTypeEnum::FloatType(t) => make!(t, BasicValueEnum::into_float_value),
+            BasicTypeEnum::PointerType(t) => make!(t, BasicValueEnum::into_pointer_value),
+            BasicTypeEnum::StructType(t) => make!(t, BasicValueEnum::into_struct_value),
+            BasicTypeEnum::VectorType(t) => make!(t, BasicValueEnum::into_vector_value),
+        };
+
+        Array { len: AnyLen(values.len() as u32), item: *self }
+            .check_value(generator, ctx, value)
+            .unwrap()
+    }
+}
+
+#[derive(Debug, Clone, Copy)]
+pub struct Instance<'ctx, M: Model<'ctx>> {
+    /// The model of this instance.
+    pub model: M,
+
+    /// The value of this instance.
+    ///
+    /// It is guaranteed the [`BasicType`] of `value` is consistent with that of `model`.
+    pub value: M::Value,
+}

nac3core/src/codegen/model/float.rs

@@ -0,0 +1,93 @@
+use std::fmt;
+
+use inkwell::{
+    context::Context,
+    types::{BasicType, FloatType},
+    values::FloatValue,
+};
+
+use super::*;
+use crate::codegen::CodeGenerator;
+
+pub trait FloatKind<'ctx>: fmt::Debug + Clone + Copy {
+    fn get_float_type<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> FloatType<'ctx>;
+}
+
+#[derive(Debug, Clone, Copy, Default)]
+pub struct Float32;
+#[derive(Debug, Clone, Copy, Default)]
+pub struct Float64;
+
+impl<'ctx> FloatKind<'ctx> for Float32 {
+    fn get_float_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        ctx: &'ctx Context,
+    ) -> FloatType<'ctx> {
+        ctx.f32_type()
+    }
+}
+
+impl<'ctx> FloatKind<'ctx> for Float64 {
+    fn get_float_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        ctx: &'ctx Context,
+    ) -> FloatType<'ctx> {
+        ctx.f64_type()
+    }
+}
+
+#[derive(Debug, Clone, Copy)]
+pub struct AnyFloat<'ctx>(FloatType<'ctx>);
+
+impl<'ctx> FloatKind<'ctx> for AnyFloat<'ctx> {
+    fn get_float_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        _ctx: &'ctx Context,
+    ) -> FloatType<'ctx> {
+        self.0
+    }
+}
+
+#[derive(Debug, Clone, Copy, Default)]
+pub struct Float<N>(pub N);
+
+impl<'ctx, N: FloatKind<'ctx>> Model<'ctx> for Float<N> {
+    type Value = FloatValue<'ctx>;
+    type Type = FloatType<'ctx>;
+
+    fn llvm_type<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> Self::Type {
+        self.0.get_float_type(generator, ctx)
+    }
+
+    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+        ty: T,
+    ) -> Result<(), ModelError> {
+        let ty = ty.as_basic_type_enum();
+        let Ok(ty) = FloatType::try_from(ty) else {
+            return Err(ModelError(format!("Expecting FloatType, but got {ty:?}")));
+        };
+
+        let exp_ty = self.0.get_float_type(generator, ctx);
+
+        // TODO: Inkwell does not have get_bit_width for FloatType?
+        if ty != exp_ty {
+            return Err(ModelError(format!("Expecting {exp_ty:?}, but got {ty:?}")));
+        }
+
+        Ok(())
+    }
+}

nac3core/src/codegen/model/function.rs

@@ -0,0 +1,121 @@
+use inkwell::{
+    attributes::{Attribute, AttributeLoc},
+    types::{BasicMetadataTypeEnum, BasicType, FunctionType},
+    values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
+};
+use itertools::Itertools;
+
+use super::*;
+use crate::codegen::{CodeGenContext, CodeGenerator};
+
+#[derive(Debug, Clone, Copy)]
+struct Arg<'ctx> {
+    ty: BasicMetadataTypeEnum<'ctx>,
+    val: BasicMetadataValueEnum<'ctx>,
+}
+
+/// A convenience structure to construct & call an LLVM function.
+///
+/// ### Usage
+///
+/// The syntax is like this:
+/// ```ignore
+/// let result = CallFunction::begin("my_function_name")
+///   .attrs(...)
+///   .arg(arg1)
+///   .arg(arg2)
+///   .arg(arg3)
+///   .returning("my_function_result", Int32);
+/// ```
+///
+/// The function `my_function_name` is called when `.returning()` (or its variants) is called, returning
+/// the result as an `Instance<'ctx, Int<Int32>>`.
+///
+/// If `my_function_name` has not been declared in `ctx.module`, once `.returning()` is called, a function
+/// declaration of `my_function_name` is added to `ctx.module`, where the [`FunctionType`] is deduced from
+/// the argument types and returning type.
+pub struct FnCall<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> {
+    generator: &'d mut G,
+    ctx: &'b CodeGenContext<'ctx, 'a>,
+    /// Function name
+    name: &'c str,
+    /// Call arguments
+    args: Vec<Arg<'ctx>>,
+    /// LLVM function Attributes
+    attrs: Vec<&'static str>,
+}
+
+impl<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> FnCall<'ctx, 'a, 'b, 'c, 'd, G> {
+    pub fn builder(generator: &'d mut G, ctx: &'b CodeGenContext<'ctx, 'a>, name: &'c str) -> Self {
+        FnCall { generator, ctx, name, args: Vec::new(), attrs: Vec::new() }
+    }
+
+    /// Push a list of LLVM function attributes to the function declaration.
+    #[must_use]
+    pub fn attrs(mut self, attrs: Vec<&'static str>) -> Self {
+        self.attrs = attrs;
+        self
+    }
+
+    /// Push a call argument to the function call.
+    #[allow(clippy::needless_pass_by_value)]
+    #[must_use]
+    pub fn arg<M: Model<'ctx>>(mut self, arg: Instance<'ctx, M>) -> Self {
+        let arg = Arg {
+            ty: arg.model.llvm_type(self.generator, self.ctx.ctx).as_basic_type_enum().into(),
+            val: arg.value.as_basic_value_enum().into(),
+        };
+        self.args.push(arg);
+        self
+    }
+
+    /// Call the function and expect the function to return a value of type of `return_model`.
+    #[must_use]
+    pub fn returning<M: Model<'ctx>>(self, name: &str, return_model: M) -> Instance<'ctx, M> {
+        let ret_ty = return_model.llvm_type(self.generator, self.ctx.ctx);
+
+        let ret = self.call(|tys| ret_ty.fn_type(tys, false), name);
+        let ret = BasicValueEnum::try_from(ret.as_any_value_enum()).unwrap(); // Must work
+        let ret = return_model.check_value(self.generator, self.ctx.ctx, ret).unwrap(); // Must work
+        ret
+    }
+
+    /// Like [`CallFunction::returning_`] but `return_model` is automatically inferred.
+    #[must_use]
+    pub fn returning_auto<M: Model<'ctx> + Default>(self, name: &str) -> Instance<'ctx, M> {
+        self.returning(name, M::default())
+    }
+
+    /// Call the function and expect the function to return a void-type.
+    pub fn returning_void(self) {
+        let ret_ty = self.ctx.ctx.void_type();
+
+        let _ = self.call(|tys| ret_ty.fn_type(tys, false), "");
+    }
+
+    fn call<F>(&self, make_fn_type: F, return_value_name: &str) -> CallSiteValue<'ctx>
+    where
+        F: FnOnce(&[BasicMetadataTypeEnum<'ctx>]) -> FunctionType<'ctx>,
+    {
+        // Get the LLVM function.
+        let func = self.ctx.module.get_function(self.name).unwrap_or_else(|| {
+            // Declare the function if it doesn't exist.
+            let tys = self.args.iter().map(|arg| arg.ty).collect_vec();
+
+            let func_type = make_fn_type(&tys);
+            let func = self.ctx.module.add_function(self.name, func_type, None);
+
+            for attr in &self.attrs {
+                func.add_attribute(
+                    AttributeLoc::Function,
+                    self.ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
+                );
+            }
+
+            func
+        });
+
+        let vals = self.args.iter().map(|arg| arg.val).collect_vec();
+        self.ctx.builder.build_call(func, &vals, return_value_name).unwrap()
+    }
+}

nac3core/src/codegen/model/int.rs

@@ -0,0 +1,421 @@
+use std::{cmp::Ordering, fmt};
+
+use inkwell::{
+    context::Context,
+    types::{BasicType, IntType},
+    values::IntValue,
+    IntPredicate,
+};
+
+use super::*;
+use crate::codegen::{CodeGenContext, CodeGenerator};
+
+pub trait IntKind<'ctx>: fmt::Debug + Clone + Copy {
+    fn get_int_type<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> IntType<'ctx>;
+}
+
+#[derive(Debug, Clone, Copy, Default)]
+pub struct Bool;
+#[derive(Debug, Clone, Copy, Default)]
+pub struct Byte;
+#[derive(Debug, Clone, Copy, Default)]
+pub struct Int32;
+#[derive(Debug, Clone, Copy, Default)]
+pub struct Int64;
+#[derive(Debug, Clone, Copy, Default)]
+pub struct SizeT;
+
+impl<'ctx> IntKind<'ctx> for Bool {
+    fn get_int_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        ctx: &'ctx Context,
+    ) -> IntType<'ctx> {
+        ctx.bool_type()
+    }
+}
+
+impl<'ctx> IntKind<'ctx> for Byte {
+    fn get_int_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        ctx: &'ctx Context,
+    ) -> IntType<'ctx> {
+        ctx.i8_type()
+    }
+}
+
+impl<'ctx> IntKind<'ctx> for Int32 {
+    fn get_int_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        ctx: &'ctx Context,
+    ) -> IntType<'ctx> {
+        ctx.i32_type()
+    }
+}
+
+impl<'ctx> IntKind<'ctx> for Int64 {
+    fn get_int_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        ctx: &'ctx Context,
+    ) -> IntType<'ctx> {
+        ctx.i64_type()
+    }
+}
+
+impl<'ctx> IntKind<'ctx> for SizeT {
+    fn get_int_type<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> IntType<'ctx> {
+        generator.get_size_type(ctx)
+    }
+}
+
+#[derive(Debug, Clone, Copy)]
+pub struct AnyInt<'ctx>(pub IntType<'ctx>);
+
+impl<'ctx> IntKind<'ctx> for AnyInt<'ctx> {
+    fn get_int_type<G: CodeGenerator + ?Sized>(
+        &self,
+        _generator: &G,
+        _ctx: &'ctx Context,
+    ) -> IntType<'ctx> {
+        self.0
+    }
+}
+
+#[derive(Debug, Clone, Copy, Default)]
+pub struct Int<N>(pub N);
+
+impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for Int<N> {
+    type Value = IntValue<'ctx>;
+    type Type = IntType<'ctx>;
+
+    fn llvm_type<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> Self::Type {
+        self.0.get_int_type(generator, ctx)
+    }
+
+    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+        ty: T,
+    ) -> Result<(), ModelError> {
+        let ty = ty.as_basic_type_enum();
+        let Ok(ty) = IntType::try_from(ty) else {
+            return Err(ModelError(format!("Expecting IntType, but got {ty:?}")));
+        };
+
+        let exp_ty = self.0.get_int_type(generator, ctx);
+        if ty.get_bit_width() != exp_ty.get_bit_width() {
+            return Err(ModelError(format!(
+                "Expecting IntType to have {} bit(s), but got {} bit(s)",
+                exp_ty.get_bit_width(),
+                ty.get_bit_width()
+            )));
+        }
+
+        Ok(())
+    }
+}
+
+impl<'ctx, N: IntKind<'ctx>> Int<N> {
+    pub fn const_int<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+        value: u64,
+        sign_extend: bool,
+    ) -> Instance<'ctx, Self> {
+        let value = self.llvm_type(generator, ctx).const_int(value, sign_extend);
+        unsafe { self.believe_value(value) }
+    }
+
+    pub fn const_0<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+    ) -> Instance<'ctx, Self> {
+        let value = self.llvm_type(generator, ctx).const_zero();
+        unsafe { self.believe_value(value) }
+    }
+
+    pub fn const_1<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+    ) -> Instance<'ctx, Self> {
+        self.const_int(generator, ctx, 1, false)
+    }
+
+    pub fn const_all_ones<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+    ) -> Instance<'ctx, Self> {
+        let value = self.llvm_type(generator, ctx).const_all_ones();
+        unsafe { self.believe_value(value) }
+    }
+
+    pub fn s_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        value: IntValue<'ctx>,
+    ) -> Instance<'ctx, Self> {
+        assert!(
+            value.get_type().get_bit_width()
+                <= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
+        );
+        let value = ctx
+            .builder
+            .build_int_s_extend_or_bit_cast(value, self.llvm_type(generator, ctx.ctx), "")
+            .unwrap();
+        unsafe { self.believe_value(value) }
+    }
+
+    pub fn s_extend<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        value: IntValue<'ctx>,
+    ) -> Instance<'ctx, Self> {
+        assert!(
+            value.get_type().get_bit_width()
+                < self.0.get_int_type(generator, ctx.ctx).get_bit_width()
+        );
+        let value =
+            ctx.builder.build_int_s_extend(value, self.llvm_type(generator, ctx.ctx), "").unwrap();
+        unsafe { self.believe_value(value) }
+    }
+
+    pub fn z_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        value: IntValue<'ctx>,
+    ) -> Instance<'ctx, Self> {
+        assert!(
+            value.get_type().get_bit_width()
+                <= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
+        );
+        let value = ctx
+            .builder
+            .build_int_z_extend_or_bit_cast(value, self.llvm_type(generator, ctx.ctx), "")
+            .unwrap();
+        unsafe { self.believe_value(value) }
+    }
+
+    pub fn z_extend<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        value: IntValue<'ctx>,
+    ) -> Instance<'ctx, Self> {
+        assert!(
+            value.get_type().get_bit_width()
+                < self.0.get_int_type(generator, ctx.ctx).get_bit_width()
+        );
+        let value =
+            ctx.builder.build_int_z_extend(value, self.llvm_type(generator, ctx.ctx), "").unwrap();
+        unsafe { self.believe_value(value) }
+    }
+
+    pub fn truncate_or_bit_cast<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        value: IntValue<'ctx>,
+    ) -> Instance<'ctx, Self> {
+        assert!(
+            value.get_type().get_bit_width()
+                >= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
+        );
+        let value = ctx
+            .builder
+            .build_int_truncate_or_bit_cast(value, self.llvm_type(generator, ctx.ctx), "")
+            .unwrap();
+        unsafe { self.believe_value(value) }
+    }
+
+    pub fn truncate<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        value: IntValue<'ctx>,
+    ) -> Instance<'ctx, Self> {
+        assert!(
+            value.get_type().get_bit_width()
+                > self.0.get_int_type(generator, ctx.ctx).get_bit_width()
+        );
+        let value =
+            ctx.builder.build_int_truncate(value, self.llvm_type(generator, ctx.ctx), "").unwrap();
+        unsafe { self.believe_value(value) }
+    }
+
+    /// `sext` or `trunc` an int to this model's int type. Does nothing if equal bit-widths.
+    pub fn s_extend_or_truncate<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        value: IntValue<'ctx>,
+    ) -> Instance<'ctx, Self> {
+        let their_width = value.get_type().get_bit_width();
+        let our_width = self.0.get_int_type(generator, ctx.ctx).get_bit_width();
+        match their_width.cmp(&our_width) {
+            Ordering::Less => self.s_extend(generator, ctx, value),
+            Ordering::Equal => unsafe { self.believe_value(value) },
+            Ordering::Greater => self.truncate(generator, ctx, value),
+        }
+    }
+
+    /// `zext` or `trunc` an int to this model's int type. Does nothing if equal bit-widths.
+    pub fn z_extend_or_truncate<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        value: IntValue<'ctx>,
+    ) -> Instance<'ctx, Self> {
+        let their_width = value.get_type().get_bit_width();
+        let our_width = self.0.get_int_type(generator, ctx.ctx).get_bit_width();
+        match their_width.cmp(&our_width) {
+            Ordering::Less => self.z_extend(generator, ctx, value),
+            Ordering::Equal => unsafe { self.believe_value(value) },
+            Ordering::Greater => self.truncate(generator, ctx, value),
+        }
+    }
+}
+
+impl Int<Bool> {
+    #[must_use]
+    pub fn const_false<'ctx, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+    ) -> Instance<'ctx, Self> {
+        self.const_int(generator, ctx, 0, false)
+    }
+
+    #[must_use]
+    pub fn const_true<'ctx, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+    ) -> Instance<'ctx, Self> {
+        self.const_int(generator, ctx, 1, false)
+    }
+}
+
+impl<'ctx, N: IntKind<'ctx>> Instance<'ctx, Int<N>> {
+    pub fn s_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        to_int_kind: NewN,
+    ) -> Instance<'ctx, Int<NewN>> {
+        Int(to_int_kind).s_extend_or_bit_cast(generator, ctx, self.value)
+    }
+
+    pub fn s_extend<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        to_int_kind: NewN,
+    ) -> Instance<'ctx, Int<NewN>> {
+        Int(to_int_kind).s_extend(generator, ctx, self.value)
+    }
+
+    pub fn z_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        to_int_kind: NewN,
+    ) -> Instance<'ctx, Int<NewN>> {
+        Int(to_int_kind).z_extend_or_bit_cast(generator, ctx, self.value)
+    }
+
+    pub fn z_extend<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        to_int_kind: NewN,
+    ) -> Instance<'ctx, Int<NewN>> {
+        Int(to_int_kind).z_extend(generator, ctx, self.value)
+    }
+
+    pub fn truncate_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        to_int_kind: NewN,
+    ) -> Instance<'ctx, Int<NewN>> {
+        Int(to_int_kind).truncate_or_bit_cast(generator, ctx, self.value)
+    }
+
+    pub fn truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        to_int_kind: NewN,
+    ) -> Instance<'ctx, Int<NewN>> {
+        Int(to_int_kind).truncate(generator, ctx, self.value)
+    }
+
+    pub fn s_extend_or_truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        to_int_kind: NewN,
+    ) -> Instance<'ctx, Int<NewN>> {
+        Int(to_int_kind).s_extend_or_truncate(generator, ctx, self.value)
+    }
+
+    pub fn z_extend_or_truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        to_int_kind: NewN,
+    ) -> Instance<'ctx, Int<NewN>> {
+        Int(to_int_kind).z_extend_or_truncate(generator, ctx, self.value)
+    }
+
+    #[must_use]
+    pub fn add(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
+        let value = ctx.builder.build_int_add(self.value, other.value, "").unwrap();
+        unsafe { self.model.believe_value(value) }
+    }
+
+    #[must_use]
+    pub fn sub(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
+        let value = ctx.builder.build_int_sub(self.value, other.value, "").unwrap();
+        unsafe { self.model.believe_value(value) }
+    }
+
+    #[must_use]
+    pub fn mul(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
+        let value = ctx.builder.build_int_mul(self.value, other.value, "").unwrap();
+        unsafe { self.model.believe_value(value) }
+    }
+
+    pub fn compare(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        op: IntPredicate,
+        other: Self,
+    ) -> Instance<'ctx, Int<Bool>> {
+        let value = ctx.builder.build_int_compare(op, self.value, other.value, "").unwrap();
+        unsafe { Int(Bool).believe_value(value) }
+    }
+}

nac3core/src/codegen/model/mod.rs

@@ -0,0 +1,17 @@
+mod any;
+mod array;
+mod core;
+mod float;
+pub mod function;
+mod int;
+mod ptr;
+mod structure;
+pub mod util;
+
+pub use any::*;
+pub use array::*;
+pub use core::*;
+pub use float::*;
+pub use int::*;
+pub use ptr::*;
+pub use structure::*;

nac3core/src/codegen/model/ptr.rs

@@ -0,0 +1,222 @@
+use inkwell::{
+    context::Context,
+    types::{BasicType, BasicTypeEnum, PointerType},
+    values::{IntValue, PointerValue},
+    AddressSpace,
+};
+
+use super::*;
+use crate::codegen::{llvm_intrinsics::call_memcpy_generic, CodeGenContext, CodeGenerator};
+
+/// A model for [`PointerType`].
+///
+/// `Item` is the element type this pointer is pointing to, and should be of a [`Model`].
+///
+// TODO: LLVM 15: `Item` is a Rust type-hint for the LLVM type of value the `.store()/.load()` family
+// of functions return. If a truly opaque pointer is needed, tell the programmer to use `OpaquePtr`.
+#[derive(Debug, Clone, Copy, Default)]
+pub struct Ptr<Item>(pub Item);
+
+/// An opaque pointer. Like [`Ptr`] but without any Rust type-hints about its element type.
+///
+/// `.load()/.store()` is not available for [`Instance`]s of opaque pointers.
+pub type OpaquePtr = Ptr<()>;
+
+// TODO: LLVM 15: `Item: Model<'ctx>` don't even need to be a model anymore. It will only be
+// a type-hint for the `.load()/.store()` functions for the `pointee_ty`.
+//
+// See https://thedan64.github.io/inkwell/inkwell/builder/struct.Builder.html#method.build_load.
+impl<'ctx, Item: Model<'ctx>> Model<'ctx> for Ptr<Item> {
+    type Value = PointerValue<'ctx>;
+    type Type = PointerType<'ctx>;
+
+    fn llvm_type<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> Self::Type {
+        // TODO: LLVM 15: ctx.ptr_type(AddressSpace::default())
+        self.0.llvm_type(generator, ctx).ptr_type(AddressSpace::default())
+    }
+
+    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+        ty: T,
+    ) -> Result<(), ModelError> {
+        let ty = ty.as_basic_type_enum();
+        let Ok(ty) = PointerType::try_from(ty) else {
+            return Err(ModelError(format!("Expecting PointerType, but got {ty:?}")));
+        };
+
+        let elem_ty = ty.get_element_type();
+        let Ok(elem_ty) = BasicTypeEnum::try_from(elem_ty) else {
+            return Err(ModelError(format!(
+                "Expecting pointer element type to be a BasicTypeEnum, but got {elem_ty:?}"
+            )));
+        };
+
+        // TODO: inkwell `get_element_type()` will be deprecated.
+        // Remove the check for `get_element_type()` when the time comes.
+        self.0
+            .check_type(generator, ctx, elem_ty)
+            .map_err(|err| err.under_context("a PointerType"))?;
+
+        Ok(())
+    }
+}
+
+impl<'ctx, Item: Model<'ctx>> Ptr<Item> {
+    /// Return a ***constant*** nullptr.
+    pub fn nullptr<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+    ) -> Instance<'ctx, Ptr<Item>> {
+        let ptr = self.llvm_type(generator, ctx).const_null();
+        unsafe { self.believe_value(ptr) }
+    }
+
+    /// Cast a pointer into this model with [`inkwell::builder::Builder::build_pointer_cast`]
+    pub fn pointer_cast<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        ptr: PointerValue<'ctx>,
+    ) -> Instance<'ctx, Ptr<Item>> {
+        // TODO: LLVM 15: Write in an impl where `Item` does not have to be `Model<'ctx>`.
+        // TODO: LLVM 15: This function will only have to be:
+        // ```
+        // return self.believe_value(ptr);
+        // ```
+        let t = self.llvm_type(generator, ctx.ctx);
+        let ptr = ctx.builder.build_pointer_cast(ptr, t, "").unwrap();
+        unsafe { self.believe_value(ptr) }
+    }
+}
+
+impl<'ctx, Item: Model<'ctx>> Instance<'ctx, Ptr<Item>> {
+    /// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`].
+    #[must_use]
+    pub fn offset(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        offset: IntValue<'ctx>,
+    ) -> Instance<'ctx, Ptr<Item>> {
+        let p = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[offset], "").unwrap() };
+        unsafe { self.model.believe_value(p) }
+    }
+
+    /// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`] by a constant offset.
+    #[must_use]
+    pub fn offset_const(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        offset: i64,
+    ) -> Instance<'ctx, Ptr<Item>> {
+        let offset = ctx.ctx.i32_type().const_int(offset as u64, true);
+        self.offset(ctx, offset)
+    }
+
+    pub fn set_index(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        index: IntValue<'ctx>,
+        value: Instance<'ctx, Item>,
+    ) {
+        self.offset(ctx, index).store(ctx, value);
+    }
+
+    pub fn set_index_const(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        index: i64,
+        value: Instance<'ctx, Item>,
+    ) {
+        self.offset_const(ctx, index).store(ctx, value);
+    }
+
+    pub fn get_index<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        index: IntValue<'ctx>,
+    ) -> Instance<'ctx, Item> {
+        self.offset(ctx, index).load(generator, ctx)
+    }
+
+    pub fn get_index_const<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        index: i64,
+    ) -> Instance<'ctx, Item> {
+        self.offset_const(ctx, index).load(generator, ctx)
+    }
+
+    /// Load the value with [`inkwell::builder::Builder::build_load`].
+    pub fn load<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> Instance<'ctx, Item> {
+        let value = ctx.builder.build_load(self.value, "").unwrap();
+        self.model.0.check_value(generator, ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error.
+    }
+
+    /// Store a value with [`inkwell::builder::Builder::build_store`].
+    pub fn store(&self, ctx: &CodeGenContext<'ctx, '_>, value: Instance<'ctx, Item>) {
+        ctx.builder.build_store(self.value, value.value).unwrap();
+    }
+
+    /// Return a casted pointer of element type `NewElement` with [`inkwell::builder::Builder::build_pointer_cast`].
+    pub fn pointer_cast<NewItem: Model<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        new_item: NewItem,
+    ) -> Instance<'ctx, Ptr<NewItem>> {
+        // TODO: LLVM 15: Write in an impl where `Item` does not have to be `Model<'ctx>`.
+        Ptr(new_item).pointer_cast(generator, ctx, self.value)
+    }
+
+    /// Cast this pointer to `uint8_t*`
+    pub fn cast_to_pi8<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> Instance<'ctx, Ptr<Int<Byte>>> {
+        Ptr(Int(Byte)).pointer_cast(generator, ctx, self.value)
+    }
+
+    /// Check if the pointer is null with [`inkwell::builder::Builder::build_is_null`].
+    pub fn is_null(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Int<Bool>> {
+        let value = ctx.builder.build_is_null(self.value, "").unwrap();
+        unsafe { Int(Bool).believe_value(value) }
+    }
+
+    /// Check if the pointer is not null with [`inkwell::builder::Builder::build_is_not_null`].
+    pub fn is_not_null(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Int<Bool>> {
+        let value = ctx.builder.build_is_not_null(self.value, "").unwrap();
+        unsafe { Int(Bool).believe_value(value) }
+    }
+
+    /// `memcpy` from another pointer.
+    pub fn copy_from<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        source: Self,
+        num_items: IntValue<'ctx>,
+    ) {
+        // Force extend `num_items` and `itemsize` to `i64` so their types would match.
+        let itemsize = self.model.size_of(generator, ctx.ctx);
+        let itemsize = Int(SizeT).z_extend_or_truncate(generator, ctx, itemsize);
+        let num_items = Int(SizeT).z_extend_or_truncate(generator, ctx, num_items);
+        let totalsize = itemsize.mul(ctx, num_items);
+
+        let is_volatile = ctx.ctx.bool_type().const_zero(); // is_volatile = false
+        call_memcpy_generic(ctx, self.value, source.value, totalsize.value, is_volatile);
+    }
+}

nac3core/src/codegen/model/structure.rs

@@ -0,0 +1,363 @@
+use std::fmt;
+
+use inkwell::{
+    context::Context,
+    types::{BasicType, BasicTypeEnum, StructType},
+    values::{BasicValueEnum, StructValue},
+};
+
+use super::*;
+use crate::codegen::{CodeGenContext, CodeGenerator};
+
+/// A traveral that traverses a Rust `struct` that is used to declare an LLVM's struct's field types.
+pub trait FieldTraversal<'ctx> {
+    /// Output type of [`FieldTraversal::add`].
+    type Output<M>;
+
+    /// Traverse through the type of a declared field and do something with it.
+    ///
+    /// * `name` - The cosmetic name of the LLVM field. Used for debugging.
+    /// * `model` - The [`Model`] representing the LLVM type of this field.
+    fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Output<M>;
+
+    /// Like [`FieldTraversal::add`] but [`Model`] is automatically inferred from its [`Default`] trait.
+    fn add_auto<M: Model<'ctx> + Default>(&mut self, name: &'static str) -> Self::Output<M> {
+        self.add(name, M::default())
+    }
+}
+
+/// Descriptor of an LLVM struct field.
+#[derive(Debug, Clone, Copy)]
+pub struct GepField<M> {
+    /// The GEP index of this field. This is the index to use with `build_gep`.
+    pub gep_index: u32,
+    /// The cosmetic name of this field.
+    pub name: &'static str,
+    /// The [`Model`] of this field's type.
+    pub model: M,
+}
+
+/// A traversal to calculate the GEP index of fields.
+pub struct GepFieldTraversal {
+    /// The current GEP index.
+    gep_index_counter: u32,
+}
+
+impl<'ctx> FieldTraversal<'ctx> for GepFieldTraversal {
+    type Output<M> = GepField<M>;
+
+    fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Output<M> {
+        let gep_index = self.gep_index_counter;
+        self.gep_index_counter += 1;
+        Self::Output { gep_index, name, model }
+    }
+}
+
+/// A traversal to collect the field types of a struct.
+///
+/// This is used to collect field types and construct the LLVM struct type with [`Context::struct_type`].
+struct TypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
+    generator: &'a G,
+    ctx: &'ctx Context,
+    /// The collected field types so far in exact order.
+    field_types: Vec<BasicTypeEnum<'ctx>>,
+}
+
+impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx> for TypeFieldTraversal<'ctx, 'a, G> {
+    type Output<M> = (); // Checking types return nothing.
+
+    fn add<M: Model<'ctx>>(&mut self, _name: &'static str, model: M) -> Self::Output<M> {
+        let t = model.llvm_type(self.generator, self.ctx).as_basic_type_enum();
+        self.field_types.push(t);
+    }
+}
+
+/// A traversal to check the types of fields.
+struct CheckTypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
+    generator: &'a mut G,
+    ctx: &'ctx Context,
+    /// The current GEP index, so we can tell the index of the field we are checking
+    /// and report the GEP index.
+    gep_index_counter: u32,
+    /// The [`StructType`] to check.
+    scrutinee: StructType<'ctx>,
+    /// The list of collected errors so far.
+    errors: Vec<ModelError>,
+}
+
+impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx>
+    for CheckTypeFieldTraversal<'ctx, 'a, G>
+{
+    type Output<M> = (); // Checking types return nothing.
+
+    fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Output<M> {
+        let gep_index = self.gep_index_counter;
+        self.gep_index_counter += 1;
+
+        if let Some(t) = self.scrutinee.get_field_type_at_index(gep_index) {
+            if let Err(err) = model.check_type(self.generator, self.ctx, t) {
+                self.errors
+                    .push(err.under_context(format!("field #{gep_index} '{name}'").as_str()));
+            }
+        }
+        // Otherwise, it will be caught by Struct's `check_type`.
+    }
+}
+
+/// A trait for Rust structs identifying LLVM structures.
+///
+/// ### Example
+///
+/// Suppose you want to define this structure:
+/// ```c
+/// template <typename T>
+/// struct ContiguousNDArray {
+///     size_t ndims;
+///     size_t* shape;
+///     T* data;
+/// }
+/// ```
+///
+/// This is how it should be done:
+/// ```ignore
+/// pub struct ContiguousNDArrayFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
+///     pub ndims: F::Out<Int<SizeT>>,
+///     pub shape: F::Out<Ptr<Int<SizeT>>>,
+///     pub data: F::Out<Ptr<Item>>,
+/// }
+///
+/// /// An ndarray without strides and non-opaque `data` field in NAC3.
+/// #[derive(Debug, Clone, Copy)]
+/// pub struct ContiguousNDArray<M> {
+///     /// [`Model`] of the items.
+///     pub item: M,
+/// }
+///
+/// impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for ContiguousNDArray<Item> {
+///     type Fields<F: FieldTraversal<'ctx>> = ContiguousNDArrayFields<'ctx, F, Item>;
+///
+///     fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
+///         // The order of `traversal.add*` is important
+///         Self::Fields {
+///             ndims: traversal.add_auto("ndims"),
+///             shape: traversal.add_auto("shape"),
+///             data: traversal.add("data", Ptr(self.item)),
+///         }
+///     }
+/// }
+/// ```
+///
+/// The [`FieldTraversal`] here is a mechanism to allow the fields of `ContiguousNDArrayFields` to be
+/// traversed to do useful work such as:
+///
+/// - To create the [`StructType`] of `ContiguousNDArray` by collecting [`BasicType`]s of the fields.
+/// - To enable the `.gep(ctx, |f| f.ndims).store(ctx, ...)` syntax.
+///
+/// Suppose now that you have defined `ContiguousNDArray` and you want to allocate a `ContiguousNDArray`
+/// with dtype `float64` in LLVM, this is how you do it:
+/// ```ignore
+/// type F64NDArray = Struct<ContiguousNDArray<Float<Float64>>>; // Type alias for leaner documentation
+/// let model: F64NDArray = Struct(ContigousNDArray { item: Float(Float64) });
+/// let ndarray: Instance<'ctx, Ptr<F64NDArray>> = model.alloca(generator, ctx);
+/// ```
+///
+/// ...and here is how you may manipulate/access `ndarray`:
+///
+/// (NOTE: some arguments have been omitted)
+///
+/// ```ignore
+/// // Get `&ndarray->data`
+/// ndarray.gep(|f| f.data); // type: Instance<'ctx, Ptr<Float<Float64>>>
+///
+/// // Get `ndarray->ndims`
+/// ndarray.get(|f| f.ndims); // type: Instance<'ctx, Int<SizeT>>
+///
+/// // Get `&ndarray->ndims`
+/// ndarray.gep(|f| f.ndims); // type: Instance<'ctx, Ptr<Int<SizeT>>>
+///
+/// // Get `ndarray->shape[0]`
+/// ndarray.get(|f| f.shape).get_index_const(0); // Instance<'ctx, Int<SizeT>>
+///
+/// // Get `&ndarray->shape[2]`
+/// ndarray.get(|f| f.shape).offset_const(2); // Instance<'ctx, Ptr<Int<SizeT>>>
+///
+/// // Do `ndarray->ndims = 3;`
+/// let num_3 = Int(SizeT).const_int(3);
+/// ndarray.set(|f| f.ndims, num_3);
+/// ```
+pub trait StructKind<'ctx>: fmt::Debug + Clone + Copy {
+    /// The associated fields of this struct.
+    type Fields<F: FieldTraversal<'ctx>>;
+
+    /// Traverse through all fields of this [`StructKind`].
+    ///
+    /// Only used internally in this module for implementing other components.
+    fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F>;
+
+    /// Get a convenience structure to get a struct field's GEP index through its corresponding Rust field.
+    ///
+    /// Only used internally in this module for implementing other components.
+    fn fields(&self) -> Self::Fields<GepFieldTraversal> {
+        self.iter_fields(&mut GepFieldTraversal { gep_index_counter: 0 })
+    }
+
+    /// Get the LLVM [`StructType`] of this [`StructKind`].
+    fn get_struct_type<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> StructType<'ctx> {
+        let mut traversal = TypeFieldTraversal { generator, ctx, field_types: Vec::new() };
+        self.iter_fields(&mut traversal);
+
+        ctx.struct_type(&traversal.field_types, false)
+    }
+}
+
+/// A model for LLVM struct.
+///
+/// `S` should be of a [`StructKind`].
+#[derive(Debug, Clone, Copy, Default)]
+pub struct Struct<S>(pub S);
+
+impl<'ctx, S: StructKind<'ctx>> Struct<S> {
+    /// Create a constant struct value from its fields.
+    ///
+    /// This function also validates `fields` and panic when there is something wrong.
+    pub fn const_struct<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+        fields: &[BasicValueEnum<'ctx>],
+    ) -> Instance<'ctx, Self> {
+        // NOTE: There *could* have been a functor `F<M> = Instance<'ctx, M>` for `S::Fields<F>`
+        // to create a more user-friendly interface, but Rust's type system is not sophisticated enough
+        // and if you try doing that Rust would force you put lifetimes everywhere.
+        let val = ctx.const_struct(fields, false);
+        self.check_value(generator, ctx, val).unwrap()
+    }
+}
+
+impl<'ctx, S: StructKind<'ctx>> Model<'ctx> for Struct<S> {
+    type Value = StructValue<'ctx>;
+    type Type = StructType<'ctx>;
+
+    fn llvm_type<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &'ctx Context,
+    ) -> Self::Type {
+        self.0.get_struct_type(generator, ctx)
+    }
+
+    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+        ty: T,
+    ) -> Result<(), ModelError> {
+        let ty = ty.as_basic_type_enum();
+        let Ok(ty) = StructType::try_from(ty) else {
+            return Err(ModelError(format!("Expecting StructType, but got {ty:?}")));
+        };
+
+        // Check each field individually.
+        let mut traversal = CheckTypeFieldTraversal {
+            generator,
+            ctx,
+            gep_index_counter: 0,
+            errors: Vec::new(),
+            scrutinee: ty,
+        };
+        self.0.iter_fields(&mut traversal);
+
+        // Check the number of fields.
+        let exp_num_fields = traversal.gep_index_counter;
+        let got_num_fields = u32::try_from(ty.get_field_types().len()).unwrap();
+        if exp_num_fields != got_num_fields {
+            return Err(ModelError(format!(
+                "Expecting StructType with {exp_num_fields} field(s), but got {got_num_fields}"
+            )));
+        }
+
+        if !traversal.errors.is_empty() {
+            // Currently, only the first error is reported.
+            return Err(traversal.errors[0].clone());
+        }
+
+        Ok(())
+    }
+}
+
+impl<'ctx, S: StructKind<'ctx>> Instance<'ctx, Struct<S>> {
+    /// Get a field with [`StructValue::get_field_at_index`].
+    pub fn get_field<G: CodeGenerator + ?Sized, M, GetField>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+        get_field: GetField,
+    ) -> Instance<'ctx, M>
+    where
+        M: Model<'ctx>,
+        GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
+    {
+        let field = get_field(self.model.0.fields());
+        let val = self.value.get_field_at_index(field.gep_index).unwrap();
+        field.model.check_value(generator, ctx, val).unwrap()
+    }
+}
+
+impl<'ctx, S: StructKind<'ctx>> Instance<'ctx, Ptr<Struct<S>>> {
+    /// Get a pointer to a field with [`Builder::build_in_bounds_gep`].
+    pub fn gep<M, GetField>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        get_field: GetField,
+    ) -> Instance<'ctx, Ptr<M>>
+    where
+        M: Model<'ctx>,
+        GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
+    {
+        let field = get_field(self.model.0 .0.fields());
+        let llvm_i32 = ctx.ctx.i32_type();
+
+        let ptr = unsafe {
+            ctx.builder
+                .build_in_bounds_gep(
+                    self.value,
+                    &[llvm_i32.const_zero(), llvm_i32.const_int(u64::from(field.gep_index), false)],
+                    field.name,
+                )
+                .unwrap()
+        };
+
+        unsafe { Ptr(field.model).believe_value(ptr) }
+    }
+
+    /// Convenience function equivalent to `.gep(...).load(...)`.
+    pub fn get<M, GetField, G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        get_field: GetField,
+    ) -> Instance<'ctx, M>
+    where
+        M: Model<'ctx>,
+        GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
+    {
+        self.gep(ctx, get_field).load(generator, ctx)
+    }
+
+    /// Convenience function equivalent to `.gep(...).store(...)`.
+    pub fn set<M, GetField>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        get_field: GetField,
+        value: Instance<'ctx, M>,
+    ) where
+        M: Model<'ctx>,
+        GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
+    {
+        self.gep(ctx, get_field).store(ctx, value);
+    }
+}

nac3core/src/codegen/model/util.rs

@@ -0,0 +1,41 @@
+use super::*;
+use crate::codegen::{
+    stmt::{gen_for_callback_incrementing, BreakContinueHooks},
+    CodeGenContext, CodeGenerator,
+};
+
+/// Like [`gen_for_callback_incrementing`] with [`Model`] abstractions.
+///
+/// The value for `stop` is exclusive.
+pub fn gen_for_model<'ctx, 'a, G, F, N>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, 'a>,
+    start: Instance<'ctx, Int<N>>,
+    stop: Instance<'ctx, Int<N>>,
+    step: Instance<'ctx, Int<N>>,
+    body: F,
+) -> Result<(), String>
+where
+    G: CodeGenerator + ?Sized,
+    F: FnOnce(
+        &mut G,
+        &mut CodeGenContext<'ctx, 'a>,
+        BreakContinueHooks<'ctx>,
+        Instance<'ctx, Int<N>>,
+    ) -> Result<(), String>,
+    N: IntKind<'ctx> + Default,
+{
+    let int_model = Int(N::default());
+    gen_for_callback_incrementing(
+        generator,
+        ctx,
+        None,
+        start.value,
+        (stop.value, false),
+        |g, ctx, hooks, i| {
+            let i = unsafe { int_model.believe_value(i) };
+            body(g, ctx, hooks, i)
+        },
+        step.value,
+    )
+}

nac3core/src/codegen/object/any.rs

@@ -0,0 +1,12 @@
+use inkwell::values::BasicValueEnum;
+
+use crate::typecheck::typedef::Type;
+
+/// A NAC3 LLVM Python object of any type.
+#[derive(Debug, Clone, Copy)]
+pub struct AnyObject<'ctx> {
+    /// Typechecker type of the object.
+    pub ty: Type,
+    /// LLVM value of the object.
+    pub value: BasicValueEnum<'ctx>,
+}

nac3core/src/codegen/object/list.rs

@@ -0,0 +1,86 @@
+use super::any::AnyObject;
+use crate::{
+    codegen::{model::*, CodeGenContext, CodeGenerator},
+    typecheck::typedef::{iter_type_vars, Type, TypeEnum},
+};
+
+/// Fields of [`List`]
+pub struct ListFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
+    /// Array pointer to content
+    pub items: F::Output<Ptr<Item>>,
+    /// Number of items in the array
+    pub len: F::Output<Int<SizeT>>,
+}
+
+/// A list in NAC3.
+#[derive(Debug, Clone, Copy, Default)]
+pub struct List<Item> {
+    /// Model of the list items
+    pub item: Item,
+}
+
+impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for List<Item> {
+    type Fields<F: FieldTraversal<'ctx>> = ListFields<'ctx, F, Item>;
+
+    fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
+        Self::Fields {
+            items: traversal.add("items", Ptr(self.item)),
+            len: traversal.add_auto("len"),
+        }
+    }
+}
+
+impl<'ctx, Item: Model<'ctx>> Instance<'ctx, Ptr<Struct<List<Item>>>> {
+    /// Cast the items pointer to `uint8_t*`.
+    pub fn with_pi8_items<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>> {
+        self.pointer_cast(generator, ctx, Struct(List { item: Int(Byte) }))
+    }
+}
+
+/// A NAC3 Python List object.
+#[derive(Debug, Clone, Copy)]
+pub struct ListObject<'ctx> {
+    /// Typechecker type of the list items
+    pub item_type: Type,
+    pub instance: Instance<'ctx, Ptr<Struct<List<Any<'ctx>>>>>,
+}
+
+impl<'ctx> ListObject<'ctx> {
+    /// Create a [`ListObject`] from an LLVM value and its typechecker [`Type`].
+    pub fn from_object<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        object: AnyObject<'ctx>,
+    ) -> Self {
+        // Check typechecker type and extract `item_type`
+        let item_type = match &*ctx.unifier.get_ty(object.ty) {
+            TypeEnum::TObj { obj_id, params, .. }
+                if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
+            {
+                iter_type_vars(params).next().unwrap().ty // Extract `item_type`
+            }
+            _ => {
+                panic!("Expecting type to be a list, but got {}", ctx.unifier.stringify(object.ty))
+            }
+        };
+
+        let plist = Ptr(Struct(List { item: Any(ctx.get_llvm_type(generator, item_type)) }));
+
+        // Create object
+        let value = plist.check_value(generator, ctx.ctx, object.value).unwrap();
+        ListObject { item_type, instance: value }
+    }
+
+    /// Get the `len()` of this list.
+    pub fn len<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Instance<'ctx, Int<SizeT>> {
+        self.instance.get(generator, ctx, |f| f.len)
+    }
+}

nac3core/src/codegen/object/mod.rs

@@ -0,0 +1,5 @@
+pub mod any;
+pub mod list;
+pub mod ndarray;
+pub mod tuple;
+pub mod utils;

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

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

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

@@ -0,0 +1,138 @@
+use itertools::Itertools;
+
+use super::NDArrayObject;
+use crate::codegen::{
+    irrt::{call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to},
+    model::*,
+    CodeGenContext, CodeGenerator,
+};
+
+/// Fields of [`ShapeEntry`]
+pub struct ShapeEntryFields<'ctx, F: FieldTraversal<'ctx>> {
+    pub ndims: F::Output<Int<SizeT>>,
+    pub shape: F::Output<Ptr<Int<SizeT>>>,
+}
+
+/// An IRRT structure used in broadcasting.
+#[derive(Debug, Clone, Copy, Default)]
+pub struct ShapeEntry;
+
+impl<'ctx> StructKind<'ctx> for ShapeEntry {
+    type Fields<F: FieldTraversal<'ctx>> = ShapeEntryFields<'ctx, F>;
+
+    fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
+        Self::Fields { ndims: traversal.add_auto("ndims"), shape: traversal.add_auto("shape") }
+    }
+}
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// Create a broadcast view on this ndarray with a target shape.
+    ///
+    /// The input shape will be checked to make sure that it contains no negative values.
+    ///
+    /// * `target_ndims` - The ndims type after broadcasting to the given shape.
+    ///   The caller has to figure this out for this function.
+    /// * `target_shape` - An array pointer pointing to the target shape.
+    #[must_use]
+    pub fn broadcast_to<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        target_ndims: u64,
+        target_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+    ) -> Self {
+        let broadcast_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, target_ndims);
+        broadcast_ndarray.copy_shape_from_array(generator, ctx, target_shape);
+
+        call_nac3_ndarray_broadcast_to(generator, ctx, self.instance, broadcast_ndarray.instance);
+        broadcast_ndarray
+    }
+}
+/// A result produced by [`broadcast_all_ndarrays`]
+#[derive(Debug, Clone)]
+pub struct BroadcastAllResult<'ctx> {
+    /// The statically known `ndims` of the broadcast result.
+    pub ndims: u64,
+    /// The broadcasting shape.
+    pub shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+    /// Broadcasted views on the inputs.
+    ///
+    /// All of them will have `shape` [`BroadcastAllResult::shape`] and
+    /// `ndims` [`BroadcastAllResult::ndims`]. The length of the vector
+    /// is the same as the input.
+    pub ndarrays: Vec<NDArrayObject<'ctx>>,
+}
+
+/// Helper function to call `call_nac3_ndarray_broadcast_shapes`
+fn broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    in_shape_entries: &[(Instance<'ctx, Ptr<Int<SizeT>>>, u64)], // (shape, shape's length/ndims)
+    broadcast_ndims: u64,
+    broadcast_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+) {
+    // Prepare input shape entries to be passed to `call_nac3_ndarray_broadcast_shapes`.
+    let num_shape_entries = Int(SizeT).const_int(
+        generator,
+        ctx.ctx,
+        u64::try_from(in_shape_entries.len()).unwrap(),
+        false,
+    );
+    let shape_entries = Struct(ShapeEntry).array_alloca(generator, ctx, num_shape_entries.value);
+    for (i, (in_shape, in_ndims)) in in_shape_entries.iter().enumerate() {
+        let pshape_entry = shape_entries.offset_const(ctx, i64::try_from(i).unwrap());
+
+        let in_ndims = Int(SizeT).const_int(generator, ctx.ctx, *in_ndims, false);
+        pshape_entry.set(ctx, |f| f.ndims, in_ndims);
+
+        pshape_entry.set(ctx, |f| f.shape, *in_shape);
+    }
+
+    let broadcast_ndims = Int(SizeT).const_int(generator, ctx.ctx, broadcast_ndims, false);
+    call_nac3_ndarray_broadcast_shapes(
+        generator,
+        ctx,
+        num_shape_entries,
+        shape_entries,
+        broadcast_ndims,
+        broadcast_shape,
+    );
+}
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// Broadcast all ndarrays according to `np.broadcast()` and return a [`BroadcastAllResult`]
+    /// containing all the information of the result of the broadcast operation.
+    pub fn broadcast<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ndarrays: &[Self],
+    ) -> BroadcastAllResult<'ctx> {
+        assert!(!ndarrays.is_empty());
+
+        // Infer the broadcast output ndims.
+        let broadcast_ndims_int = ndarrays.iter().map(|ndarray| ndarray.ndims).max().unwrap();
+
+        let broadcast_ndims = Int(SizeT).const_int(generator, ctx.ctx, broadcast_ndims_int, false);
+        let broadcast_shape = Int(SizeT).array_alloca(generator, ctx, broadcast_ndims.value);
+
+        let shape_entries = ndarrays
+            .iter()
+            .map(|ndarray| (ndarray.instance.get(generator, ctx, |f| f.shape), ndarray.ndims))
+            .collect_vec();
+        broadcast_shapes(generator, ctx, &shape_entries, broadcast_ndims_int, broadcast_shape);
+
+        // Broadcast all the inputs to shape `dst_shape`.
+        let broadcast_ndarrays: Vec<_> = ndarrays
+            .iter()
+            .map(|ndarray| {
+                ndarray.broadcast_to(generator, ctx, broadcast_ndims_int, broadcast_shape)
+            })
+            .collect_vec();
+
+        BroadcastAllResult {
+            ndims: broadcast_ndims_int,
+            shape: broadcast_shape,
+            ndarrays: broadcast_ndarrays,
+        }
+    }
+}

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

@@ -0,0 +1,175 @@
+use inkwell::{values::BasicValueEnum, IntPredicate};
+
+use super::NDArrayObject;
+use crate::{
+    codegen::{
+        irrt::call_nac3_ndarray_util_assert_shape_no_negative, model::*, 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> NDArrayObject<'ctx> {
+    /// Create an ndarray like `np.empty`.
+    pub fn make_np_empty<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        ndims: u64,
+        shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+    ) -> Self {
+        // Validate `shape`
+        let ndims_llvm = Int(SizeT).const_int(generator, ctx.ctx, ndims, false);
+        call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, ndims_llvm, shape);
+
+        let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
+        ndarray.copy_shape_from_array(generator, ctx, shape);
+        ndarray.create_data(generator, ctx);
+
+        ndarray
+    }
+
+    /// Create an ndarray like `np.full`.
+    pub fn make_np_full<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        ndims: u64,
+        shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+        fill_value: BasicValueEnum<'ctx>,
+    ) -> Self {
+        let ndarray = NDArrayObject::make_np_empty(generator, ctx, dtype, ndims, shape);
+        ndarray.fill(generator, ctx, fill_value);
+        ndarray
+    }
+
+    /// Create an ndarray like `np.zero`.
+    pub fn make_np_zeros<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        ndims: u64,
+        shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+    ) -> Self {
+        let fill_value = ndarray_zero_value(generator, ctx, dtype);
+        NDArrayObject::make_np_full(generator, ctx, dtype, ndims, shape, fill_value)
+    }
+
+    /// Create an ndarray like `np.ones`.
+    pub fn make_np_ones<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        ndims: u64,
+        shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+    ) -> Self {
+        let fill_value = ndarray_one_value(generator, ctx, dtype);
+        NDArrayObject::make_np_full(generator, ctx, dtype, ndims, shape, fill_value)
+    }
+
+    /// Create an ndarray like `np.eye`.
+    pub fn make_np_eye<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        nrows: Instance<'ctx, Int<SizeT>>,
+        ncols: Instance<'ctx, Int<SizeT>>,
+        offset: Instance<'ctx, Int<SizeT>>,
+    ) -> Self {
+        let ndzero = ndarray_zero_value(generator, ctx, dtype);
+        let ndone = ndarray_one_value(generator, ctx, dtype);
+
+        let ndarray = NDArrayObject::alloca_dynamic_shape(generator, ctx, dtype, &[nrows, ncols]);
+
+        // Create data and make the matrix like look np.eye()
+        ndarray.create_data(generator, ctx);
+        ndarray
+            .foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
+                // NOTE: rows and cols can never be zero here, since this ndarray's `np.size` would be zero
+                // and this loop would not execute.
+
+                // Load up `row_i` and `col_i` from indices.
+                let row_i = nditer.get_indices().get_index_const(generator, ctx, 0);
+                let col_i = nditer.get_indices().get_index_const(generator, ctx, 1);
+
+                let be_one = row_i.add(ctx, offset).compare(ctx, IntPredicate::EQ, col_i);
+                let value = ctx.builder.build_select(be_one.value, ndone, ndzero, "value").unwrap();
+
+                let p = nditer.get_pointer(generator, ctx);
+                ctx.builder.build_store(p, value).unwrap();
+
+                Ok(())
+            })
+            .unwrap();
+
+        ndarray
+    }
+
+    /// Create an ndarray like `np.identity`.
+    pub fn make_np_identity<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        size: Instance<'ctx, Int<SizeT>>,
+    ) -> Self {
+        // Convenient implementation
+        let offset = Int(SizeT).const_0(generator, ctx.ctx);
+        NDArrayObject::make_np_eye(generator, ctx, dtype, size, size, offset)
+    }
+}

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

@@ -0,0 +1,226 @@
+use super::NDArrayObject;
+use crate::codegen::{
+    irrt::call_nac3_ndarray_index,
+    model::*,
+    object::utils::slice::{RustSlice, Slice},
+    CodeGenContext, CodeGenerator,
+};
+
+pub type NDIndexType = Byte;
+
+/// Fields of [`NDIndex`]
+#[derive(Debug, Clone, Copy)]
+pub struct NDIndexFields<'ctx, F: FieldTraversal<'ctx>> {
+    pub type_: F::Output<Int<NDIndexType>>,
+    pub data: F::Output<Ptr<Int<Byte>>>,
+}
+
+/// An IRRT representation of an ndarray subscript index.
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct NDIndex;
+
+impl<'ctx> StructKind<'ctx> for NDIndex {
+    type Fields<F: FieldTraversal<'ctx>> = NDIndexFields<'ctx, F>;
+
+    fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
+        Self::Fields { type_: traversal.add_auto("type"), data: traversal.add_auto("data") }
+    }
+}
+
+// A convenience enum representing a [`NDIndex`].
+#[derive(Debug, Clone)]
+pub enum RustNDIndex<'ctx> {
+    SingleElement(Instance<'ctx, Int<Int32>>),
+    Slice(RustSlice<'ctx, Int32>),
+    NewAxis,
+    Ellipsis,
+}
+
+impl<'ctx> RustNDIndex<'ctx> {
+    /// Get the value to set `NDIndex::type` for this variant.
+    fn get_type_id(&self) -> u64 {
+        // Defined in IRRT, must be in sync
+        match self {
+            RustNDIndex::SingleElement(_) => 0,
+            RustNDIndex::Slice(_) => 1,
+            RustNDIndex::NewAxis => 2,
+            RustNDIndex::Ellipsis => 3,
+        }
+    }
+
+    /// Serialize this [`RustNDIndex`] by writing it into an LLVM [`NDIndex`].
+    fn write_to_ndindex<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        dst_ndindex_ptr: Instance<'ctx, Ptr<Struct<NDIndex>>>,
+    ) {
+        // Set `dst_ndindex_ptr->type`
+        dst_ndindex_ptr.gep(ctx, |f| f.type_).store(
+            ctx,
+            Int(NDIndexType::default()).const_int(generator, ctx.ctx, self.get_type_id(), false),
+        );
+
+        // Set `dst_ndindex_ptr->data`
+        match self {
+            RustNDIndex::SingleElement(in_index) => {
+                let index_ptr = Int(Int32).alloca(generator, ctx);
+                index_ptr.store(ctx, *in_index);
+
+                dst_ndindex_ptr
+                    .gep(ctx, |f| f.data)
+                    .store(ctx, index_ptr.pointer_cast(generator, ctx, Int(Byte)));
+            }
+            RustNDIndex::Slice(in_rust_slice) => {
+                let user_slice_ptr = Struct(Slice(Int32)).alloca(generator, ctx);
+                in_rust_slice.write_to_slice(generator, ctx, user_slice_ptr);
+
+                dst_ndindex_ptr
+                    .gep(ctx, |f| f.data)
+                    .store(ctx, user_slice_ptr.pointer_cast(generator, ctx, Int(Byte)));
+            }
+            RustNDIndex::NewAxis | RustNDIndex::Ellipsis => {}
+        }
+    }
+
+    /// Serialize a list of `RustNDIndex` as a newly allocated LLVM array of `NDIndex`.
+    pub fn make_ndindices<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        in_ndindices: &[RustNDIndex<'ctx>],
+    ) -> (Instance<'ctx, Int<SizeT>>, Instance<'ctx, Ptr<Struct<NDIndex>>>) {
+        let ndindex_model = Struct(NDIndex);
+
+        // Allocate the LLVM ndindices.
+        let num_ndindices =
+            Int(SizeT).const_int(generator, ctx.ctx, in_ndindices.len() as u64, false);
+        let ndindices = ndindex_model.array_alloca(generator, ctx, num_ndindices.value);
+
+        // Initialize all of them.
+        for (i, in_ndindex) in in_ndindices.iter().enumerate() {
+            let pndindex = ndindices.offset_const(ctx, i64::try_from(i).unwrap());
+            in_ndindex.write_to_ndindex(generator, ctx, pndindex);
+        }
+
+        (num_ndindices, ndindices)
+    }
+}
+
+impl<'ctx> NDArrayObject<'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 = NDArrayObject::alloca(generator, ctx, self.dtype, dst_ndims);
+
+        let (num_indices, indices) = RustNDIndex::make_ndindices(generator, ctx, indices);
+        call_nac3_ndarray_index(
+            generator,
+            ctx,
+            num_indices,
+            indices,
+            self.instance,
+            dst_ndarray.instance,
+        );
+
+        dst_ndarray
+    }
+}
+
+pub mod util {
+    use itertools::Itertools;
+    use nac3parser::ast::{Expr, ExprKind};
+
+    use crate::{
+        codegen::{model::*, object::utils::slice::util::gen_slice, CodeGenContext, CodeGenerator},
+        typecheck::typedef::Type,
+    };
+
+    use super::RustNDIndex;
+
+    /// Generate LLVM code to transform an ndarray subscript expression to
+    /// its list of [`RustNDIndex`]
+    ///
+    /// i.e.,
+    /// ```python
+    /// my_ndarray[::3, 1, :2:]
+    ///            ^^^^^^^^^^^ Then these into a three `RustNDIndex`es
+    /// ```
+    pub fn gen_ndarray_subscript_ndindices<'ctx, G: CodeGenerator>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        subscript: &Expr<Option<Type>>,
+    ) -> Result<Vec<RustNDIndex<'ctx>>, String> {
+        // TODO: Support https://numpy.org/doc/stable/user/basics.indexing.html#dimensional-indexing-tools
+
+        // 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 = gen_slice(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 = Int(Int32).check_value(generator, ctx.ctx, index).unwrap();
+
+                RustNDIndex::SingleElement(index)
+            };
+            rust_ndindices.push(ndindex);
+        }
+        Ok(rust_ndindices)
+    }
+}

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

@@ -0,0 +1,219 @@
+use inkwell::values::BasicValueEnum;
+use itertools::Itertools;
+
+use crate::{
+    codegen::{
+        object::ndarray::{AnyObject, NDArrayObject},
+        stmt::gen_for_callback,
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::Type,
+};
+
+use super::{nditer::NDIterHandle, NDArrayOut, ScalarOrNDArray};
+
+impl<'ctx> NDArrayObject<'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>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        ndarrays: &[Self],
+        out: NDArrayOut<'ctx>,
+        mapping: MappingFn,
+    ) -> Result<Self, String>
+    where
+        G: CodeGenerator + ?Sized,
+        MappingFn: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            &[BasicValueEnum<'ctx>],
+        ) -> Result<BasicValueEnum<'ctx>, String>,
+    {
+        // Broadcast inputs
+        let broadcast_result = NDArrayObject::broadcast(generator, ctx, ndarrays);
+
+        let out_ndarray = match out {
+            NDArrayOut::NewNDArray { dtype } => {
+                // Create a new ndarray based on the broadcast shape.
+                let result_ndarray =
+                    NDArrayObject::alloca(generator, ctx, dtype, broadcast_result.ndims);
+                result_ndarray.copy_shape_from_array(generator, ctx, broadcast_result.shape);
+                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.ndims,
+                    broadcast_result.shape,
+                );
+                result_ndarray
+            }
+        };
+
+        // Map element-wise and store results into `mapped_ndarray`.
+        let nditer = NDIterHandle::new(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| NDIterHandle::new(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).value)
+            },
+            |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(generator, ctx).value)
+                    .collect_vec();
+
+                let result = mapping(generator, ctx, &in_scalars)?;
+
+                let p = out_nditer.get_pointer(generator, 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)
+    }
+
+    /// 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>,
+    {
+        NDArrayObject::broadcast_starmap(
+            generator,
+            ctx,
+            &[*self],
+            out,
+            |generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
+        )
+    }
+}
+
+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 [`NDArrayObject::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: Type,
+        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(AnyObject::try_from).try_collect().ok();
+
+        if let Some(scalars) = all_scalars {
+            let scalars = scalars.iter().map(|scalar| scalar.value).collect_vec();
+            let value = mapping(generator, ctx, &scalars)?;
+
+            Ok(ScalarOrNDArray::Scalar(AnyObject { ty: ret_dtype, 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 = NDArrayObject::broadcast_starmap(
+                generator,
+                ctx,
+                &inputs,
+                NDArrayOut::NewNDArray { dtype: ret_dtype },
+                mapping,
+            )?;
+            Ok(ScalarOrNDArray::NDArray(ndarray))
+        }
+    }
+
+    /// 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: Type,
+        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/object/ndarray/matmul.rs

@@ -0,0 +1,216 @@
+use std::cmp::max;
+
+use nac3parser::ast::Operator;
+
+use super::{util::gen_for_model, NDArrayObject, NDArrayOut};
+use crate::{
+    codegen::{
+        expr::gen_binop_expr_with_values, irrt::call_nac3_ndarray_matmul_calculate_shapes,
+        model::*, object::ndarray::indexing::RustNDIndex, CodeGenContext, CodeGenerator,
+    },
+    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: NDArrayObject<'ctx>,
+    in_b: NDArrayObject<'ctx>,
+) -> NDArrayObject<'ctx> {
+    assert!(in_a.ndims >= 2);
+    assert!(in_b.ndims >= 2);
+
+    // Deduce ndims of the result of matmul.
+    let ndims_int = max(in_a.ndims, in_b.ndims);
+    let ndims = Int(SizeT).const_int(generator, ctx.ctx, 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 = in_a.ndims_llvm(generator, ctx.ctx);
+        let in_lhs_shape = in_a.instance.get(generator, ctx, |f| f.shape);
+        let in_rhs_ndims = in_b.ndims_llvm(generator, ctx.ctx);
+        let in_rhs_shape = in_b.instance.get(generator, ctx, |f| f.shape);
+        let lhs_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
+        let rhs_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
+        let dst_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
+
+        // Matmul dimension compatibility is checked here.
+        call_nac3_ndarray_matmul_calculate_shapes(
+            generator,
+            ctx,
+            in_lhs_ndims,
+            in_lhs_shape,
+            in_rhs_ndims,
+            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 = NDArrayObject::alloca(generator, ctx, dst_dtype, ndims_int);
+        dst.copy_shape_from_array(generator, ctx, dst_shape);
+        dst.create_data(generator, ctx);
+
+        (lhs, rhs, dst)
+    };
+
+    let len = lhs.instance.get(generator, ctx, |f| f.shape).get_index_const(
+        generator,
+        ctx,
+        i64::try_from(ndims_int - 1).unwrap(),
+    );
+
+    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(generator, ctx);
+
+        ctx.builder.build_store(pdst_ij, dst_zero).unwrap();
+
+        let indices = hdl.get_indices();
+        let i = indices.get_index_const(generator, ctx, at_row);
+        let j = indices.get_index_const(generator, ctx, at_col);
+
+        let num_0 = Int(SizeT).const_int(generator, ctx.ctx, 0, false);
+        let num_1 = Int(SizeT).const_int(generator, ctx.ctx, 1, false);
+
+        gen_for_model(generator, ctx, num_0, len, num_1, |generator, ctx, _, k| {
+            // `indices` is modified to index into `a` and `b`, and restored.
+            indices.set_index_const(ctx, at_row, i);
+            indices.set_index_const(ctx, at_col, k);
+            let a_ik = lhs.get_scalar_by_indices(generator, ctx, indices);
+
+            indices.set_index_const(ctx, at_row, k);
+            indices.set_index_const(ctx, at_col, j);
+            let b_kj = rhs.get_scalar_by_indices(generator, ctx, indices);
+
+            // Restore `indices`.
+            indices.set_index_const(ctx, at_row, i);
+            indices.set_index_const(ctx, at_col, j);
+
+            // x = a_[...]ik * b_[...]kj
+            let x = gen_binop_expr_with_values(
+                generator,
+                ctx,
+                (&Some(lhs.dtype), a_ik.value),
+                Binop::normal(Operator::Mult),
+                (&Some(rhs.dtype), b_kj.value),
+                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(())
+        })
+    })
+    .unwrap();
+
+    dst
+}
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// Perform `np.matmul` according to the rules in
+    /// <https://numpy.org/doc/stable/reference/generated/numpy.matmul.html>.
+    ///
+    /// This function always return an [`NDArrayObject`]. You may want to use [`NDArrayObject::split_unsized`]
+    /// to handle when the output could be a scalar.
+    ///
+    /// `dst_dtype` defines the dtype of the returned ndarray.
+    pub fn matmul<G: CodeGenerator>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        a: Self,
+        b: Self,
+        out: NDArrayOut<'ctx>,
+    ) -> Self {
+        // Sanity check, but type inference should prevent this.
+        assert!(a.ndims > 0 && b.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 a.ndims == 1 {
+            // Prepend 1 to its dimensions
+            a.index(generator, ctx, &[RustNDIndex::NewAxis, RustNDIndex::Ellipsis])
+        } else {
+            a
+        };
+
+        let new_b = if b.ndims == 1 {
+            // Append 1 to its dimensions
+            b.index(generator, ctx, &[RustNDIndex::Ellipsis, RustNDIndex::NewAxis])
+        } else {
+            b
+        };
+
+        // 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.get_dtype(), new_a, new_b);
+
+        // Postprocessing on the result to remove prepended/appended axes.
+        let mut postindices = vec![];
+        let zero = Int(Int32).const_0(generator, ctx.ctx);
+
+        if a.ndims == 1 {
+            // Remove the prepended 1
+            postindices.push(RustNDIndex::SingleElement(zero));
+        }
+
+        if b.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.instance.get(generator, ctx, |f| f.shape);
+                out_ndarray.assert_can_be_written_by_out(
+                    generator,
+                    ctx,
+                    result.ndims,
+                    result_shape,
+                );
+
+                out_ndarray.copy_data_from(generator, ctx, result);
+                out_ndarray
+            }
+        }
+    }
+}

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

@@ -0,0 +1,638 @@
+use inkwell::{
+    context::Context,
+    types::BasicType,
+    values::{BasicValue, BasicValueEnum, PointerValue},
+    AddressSpace,
+};
+
+use super::{any::AnyObject, tuple::TupleObject};
+use crate::{
+    codegen::{
+        irrt::{
+            call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
+            call_nac3_ndarray_get_pelement_by_indices, call_nac3_ndarray_is_c_contiguous,
+            call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
+            call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
+            call_nac3_ndarray_util_assert_output_shape_same,
+        },
+        model::*,
+        CodeGenContext, CodeGenerator,
+    },
+    toplevel::{helper::extract_ndims, numpy::unpack_ndarray_var_tys},
+    typecheck::typedef::{Type, TypeEnum},
+};
+
+pub mod array;
+pub mod broadcast;
+pub mod factory;
+pub mod indexing;
+pub mod map;
+pub mod matmul;
+pub mod nditer;
+pub mod shape_util;
+pub mod view;
+
+/// Fields of [`NDArray`]
+pub struct NDArrayFields<'ctx, F: FieldTraversal<'ctx>> {
+    pub data: F::Output<Ptr<Int<Byte>>>,
+    pub itemsize: F::Output<Int<SizeT>>,
+    pub ndims: F::Output<Int<SizeT>>,
+    pub shape: F::Output<Ptr<Int<SizeT>>>,
+    pub strides: F::Output<Ptr<Int<SizeT>>>,
+}
+
+/// A strided ndarray in NAC3.
+///
+/// See IRRT implementation for details about its fields.
+#[derive(Debug, Clone, Copy, Default)]
+pub struct NDArray;
+
+impl<'ctx> StructKind<'ctx> for NDArray {
+    type Fields<F: FieldTraversal<'ctx>> = NDArrayFields<'ctx, F>;
+
+    fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
+        Self::Fields {
+            data: traversal.add_auto("data"),
+            itemsize: traversal.add_auto("itemsize"),
+            ndims: traversal.add_auto("ndims"),
+            shape: traversal.add_auto("shape"),
+            strides: traversal.add_auto("strides"),
+        }
+    }
+}
+
+/// A NAC3 Python ndarray object.
+#[derive(Debug, Clone, Copy)]
+pub struct NDArrayObject<'ctx> {
+    pub dtype: Type,
+    pub ndims: u64,
+    pub instance: Instance<'ctx, Ptr<Struct<NDArray>>>,
+}
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// Attempt to convert an [`AnyObject`] into an [`NDArrayObject`].
+    pub fn from_object<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        object: AnyObject<'ctx>,
+    ) -> NDArrayObject<'ctx> {
+        let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, object.ty);
+        let ndims = extract_ndims(&ctx.unifier, ndims);
+        Self::from_value_and_unpacked_types(generator, ctx, object.value, dtype, ndims)
+    }
+
+    /// Like [`NDArrayObject::from_object`] but you directly supply the ndarray's
+    /// `dtype` and `ndims`.
+    pub fn from_value_and_unpacked_types<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        value: V,
+        dtype: Type,
+        ndims: u64,
+    ) -> Self {
+        let value = Ptr(Struct(NDArray)).check_value(generator, ctx.ctx, value).unwrap();
+        NDArrayObject { dtype, ndims, instance: value }
+    }
+
+    /// Get this ndarray's `ndims` as an LLVM constant.
+    pub fn ndims_llvm<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &'ctx Context,
+    ) -> Instance<'ctx, Int<SizeT>> {
+        Int(SizeT).const_int(generator, ctx, self.ndims, false)
+    }
+
+    /// Allocate an ndarray on the stack given its `ndims` and `dtype`.
+    ///
+    /// `shape` and `strides` will be automatically allocated onto the stack.
+    ///
+    /// The returned ndarray's content will be:
+    /// - `data`: uninitialized.
+    /// - `itemsize`: set to the `sizeof()` of `dtype`.
+    /// - `ndims`: set to the value of  `ndims`.
+    /// - `shape`: allocated with an array of length `ndims` with uninitialized values.
+    /// - `strides`: allocated with an array of length `ndims` with uninitialized values.
+    pub fn alloca<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        ndims: u64,
+    ) -> Self {
+        let ndarray = Struct(NDArray).alloca(generator, ctx);
+
+        let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
+        let itemsize = Int(SizeT).z_extend_or_truncate(generator, ctx, itemsize);
+        ndarray.set(ctx, |f| f.itemsize, itemsize);
+
+        let ndims_val = Int(SizeT).const_int(generator, ctx.ctx, ndims, false);
+        ndarray.set(ctx, |f| f.ndims, ndims_val);
+
+        let shape = Int(SizeT).array_alloca(generator, ctx, ndims_val.value);
+        ndarray.set(ctx, |f| f.shape, shape);
+
+        let strides = Int(SizeT).array_alloca(generator, ctx, ndims_val.value);
+        ndarray.set(ctx, |f| f.strides, strides);
+
+        NDArrayObject { dtype, ndims, instance: ndarray }
+    }
+
+    /// Convenience function. Allocate an [`NDArrayObject`] with a statically known shape.
+    ///
+    /// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
+    pub fn alloca_constant_shape<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        shape: &[u64],
+    ) -> Self {
+        let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64);
+
+        // Write shape
+        let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
+        for (i, dim) in shape.iter().enumerate() {
+            let dim = Int(SizeT).const_int(generator, ctx.ctx, *dim, false);
+            dst_shape.offset_const(ctx, i64::try_from(i).unwrap()).store(ctx, dim);
+        }
+
+        ndarray
+    }
+
+    /// Convenience function. Allocate an [`NDArrayObject`] with a dynamically known shape.
+    ///
+    /// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
+    pub fn alloca_dynamic_shape<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        shape: &[Instance<'ctx, Int<SizeT>>],
+    ) -> Self {
+        let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64);
+
+        // Write shape
+        let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
+        for (i, dim) in shape.iter().enumerate() {
+            dst_shape.offset_const(ctx, i64::try_from(i).unwrap()).store(ctx, *dim);
+        }
+
+        ndarray
+    }
+
+    /// Initialize an ndarray's `data` by allocating a buffer on the stack.
+    /// The allocated data buffer is considered to be *owned* by the ndarray.
+    ///
+    /// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
+    ///
+    /// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
+    pub fn create_data<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) {
+        let nbytes = self.nbytes(generator, ctx);
+
+        let data = Int(Byte).array_alloca(generator, ctx, nbytes.value);
+        self.instance.set(ctx, |f| f.data, data);
+
+        self.set_strides_contiguous(generator, ctx);
+    }
+
+    /// Copy shape dimensions from an array.
+    pub fn copy_shape_from_array<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+    ) {
+        let num_items = self.ndims_llvm(generator, ctx.ctx).value;
+        self.instance.get(generator, ctx, |f| f.shape).copy_from(generator, ctx, shape, num_items);
+    }
+
+    /// Copy shape dimensions from an ndarray.
+    /// Panics if `ndims` mismatches.
+    pub fn copy_shape_from_ndarray<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        src_ndarray: NDArrayObject<'ctx>,
+    ) {
+        assert_eq!(self.ndims, src_ndarray.ndims);
+        let src_shape = src_ndarray.instance.get(generator, ctx, |f| f.shape);
+        self.copy_shape_from_array(generator, ctx, src_shape);
+    }
+
+    /// Copy strides dimensions from an array.
+    pub fn copy_strides_from_array<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        strides: Instance<'ctx, Ptr<Int<SizeT>>>,
+    ) {
+        let num_items = self.ndims_llvm(generator, ctx.ctx).value;
+        self.instance
+            .get(generator, ctx, |f| f.strides)
+            .copy_from(generator, ctx, strides, num_items);
+    }
+
+    /// Copy strides dimensions from an ndarray.
+    /// Panics if `ndims` mismatches.
+    pub fn copy_strides_from_ndarray<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        src_ndarray: NDArrayObject<'ctx>,
+    ) {
+        assert_eq!(self.ndims, src_ndarray.ndims);
+        let src_strides = src_ndarray.instance.get(generator, ctx, |f| f.strides);
+        self.copy_strides_from_array(generator, ctx, src_strides);
+    }
+
+    /// Get the `np.size()` of this ndarray.
+    pub fn size<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Instance<'ctx, Int<SizeT>> {
+        call_nac3_ndarray_size(generator, ctx, self.instance)
+    }
+
+    /// Get the `ndarray.nbytes` of this ndarray.
+    pub fn nbytes<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Instance<'ctx, Int<SizeT>> {
+        call_nac3_ndarray_nbytes(generator, ctx, self.instance)
+    }
+
+    /// Get the `len()` of this ndarray.
+    pub fn len<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Instance<'ctx, Int<SizeT>> {
+        call_nac3_ndarray_len(generator, ctx, self.instance)
+    }
+
+    /// Check if this ndarray is C-contiguous.
+    ///
+    /// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
+    pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Instance<'ctx, Int<Bool>> {
+        call_nac3_ndarray_is_c_contiguous(generator, ctx, self.instance)
+    }
+
+    /// Get the pointer to the n-th (0-based) element.
+    ///
+    /// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
+    pub fn get_nth_pelement<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        nth: Instance<'ctx, Int<SizeT>>,
+    ) -> PointerValue<'ctx> {
+        let elem_ty = ctx.get_llvm_type(generator, self.dtype);
+
+        let p = call_nac3_ndarray_get_nth_pelement(generator, ctx, self.instance, nth);
+        ctx.builder
+            .build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "")
+            .unwrap()
+    }
+
+    /// Get the n-th (0-based) scalar.
+    pub fn get_nth_scalar<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        nth: Instance<'ctx, Int<SizeT>>,
+    ) -> AnyObject<'ctx> {
+        let ptr = self.get_nth_pelement(generator, ctx, nth);
+        let value = ctx.builder.build_load(ptr, "").unwrap();
+        AnyObject { ty: self.dtype, value }
+    }
+
+    /// Get the pointer to the element indexed by `indices`.
+    ///
+    /// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
+    pub fn get_pelement_by_indices<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        indices: Instance<'ctx, Ptr<Int<SizeT>>>,
+    ) -> PointerValue<'ctx> {
+        let elem_ty = ctx.get_llvm_type(generator, self.dtype);
+
+        let p = call_nac3_ndarray_get_pelement_by_indices(generator, ctx, self.instance, indices);
+        ctx.builder
+            .build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "")
+            .unwrap()
+    }
+
+    /// Get the scalar indexed by `indices`.
+    pub fn get_scalar_by_indices<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        indices: Instance<'ctx, Ptr<Int<SizeT>>>,
+    ) -> AnyObject<'ctx> {
+        let ptr = self.get_pelement_by_indices(generator, ctx, indices);
+        let value = ctx.builder.build_load(ptr, "").unwrap();
+        AnyObject { ty: self.dtype, value }
+    }
+
+    /// 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: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) {
+        call_nac3_ndarray_set_strides_by_shape(generator, ctx, self.instance);
+    }
+
+    /// 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 = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims);
+
+        let shape = self.instance.gep(ctx, |f| f.shape).load(generator, ctx);
+        clone.copy_shape_from_array(generator, ctx, shape);
+        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: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        src: NDArrayObject<'ctx>,
+    ) {
+        assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
+        call_nac3_ndarray_copy_data(generator, ctx, src.instance, self.instance);
+    }
+
+    /// 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
+    }
+
+    /// If this ndarray is unsized, return its sole value as an [`AnyObject`].
+    /// 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 self.is_unsized() {
+            // NOTE: `np.size(self) == 0` here is never possible.
+            let zero = Int(SizeT).const_0(generator, ctx.ctx);
+            let value = self.get_nth_scalar(generator, ctx, zero).value;
+
+            ScalarOrNDArray::Scalar(AnyObject { ty: self.dtype, value })
+        } else {
+            ScalarOrNDArray::NDArray(*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, |generator, ctx, _hooks, nditer| {
+            let p = nditer.get_pointer(generator, ctx);
+            ctx.builder.build_store(p, value).unwrap();
+            Ok(())
+        })
+        .unwrap();
+    }
+
+    /// Create the shape tuple of this ndarray like `np.shape(<ndarray>)`.
+    ///
+    /// The returned integers in the tuple are in int32.
+    pub fn make_shape_tuple<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> TupleObject<'ctx> {
+        // TODO: Return a tuple of SizeT
+
+        let mut objects = Vec::with_capacity(self.ndims as usize);
+
+        for i in 0..self.ndims {
+            let dim = self
+                .instance
+                .get(generator, ctx, |f| f.shape)
+                .get_index_const(generator, ctx, i64::try_from(i).unwrap())
+                .truncate_or_bit_cast(generator, ctx, Int32);
+
+            objects.push(AnyObject {
+                ty: ctx.primitives.int32,
+                value: dim.value.as_basic_value_enum(),
+            });
+        }
+
+        TupleObject::from_objects(generator, ctx, objects)
+    }
+
+    /// Create the strides tuple of this ndarray like `<ndarray>.strides`.
+    ///
+    /// The returned integers in the tuple are in int32.
+    pub fn make_strides_tuple<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> TupleObject<'ctx> {
+        // TODO: Return a tuple of SizeT.
+
+        let mut objects = Vec::with_capacity(self.ndims as usize);
+
+        for i in 0..self.ndims {
+            let dim = self
+                .instance
+                .get(generator, ctx, |f| f.strides)
+                .get_index_const(generator, ctx, i64::try_from(i).unwrap())
+                .truncate_or_bit_cast(generator, ctx, Int32);
+
+            objects.push(AnyObject {
+                ty: ctx.primitives.int32,
+                value: dim.value.as_basic_value_enum(),
+            });
+        }
+
+        TupleObject::from_objects(generator, ctx, objects)
+    }
+
+    /// Create an unsized ndarray to contain `object`.
+    pub fn make_unsized<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        object: AnyObject<'ctx>,
+    ) -> NDArrayObject<'ctx> {
+        // We have to put the value on the stack to get a data pointer.
+        let data = ctx.builder.build_alloca(object.value.get_type(), "make_unsized").unwrap();
+        ctx.builder.build_store(data, object.value).unwrap();
+        let data = Ptr(Int(Byte)).pointer_cast(generator, ctx, data);
+
+        let ndarray = NDArrayObject::alloca(generator, ctx, object.ty, 0);
+        ndarray.instance.set(ctx, |f| f.data, data);
+        ndarray
+    }
+    /// 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_ndims: u64,
+        out_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+    ) {
+        let ndarray_ndims = self.ndims_llvm(generator, ctx.ctx);
+        let ndarray_shape = self.instance.get(generator, ctx, |f| f.shape);
+
+        let output_ndims = Int(SizeT).const_int(generator, ctx.ctx, out_ndims, false);
+        let output_shape = out_shape;
+
+        call_nac3_ndarray_util_assert_output_shape_same(
+            generator,
+            ctx,
+            ndarray_ndims,
+            ndarray_shape,
+            output_ndims,
+            output_shape,
+        );
+    }
+}
+
+/// A convenience enum for implementing functions that acts on scalars or ndarrays or both.
+#[derive(Debug, Clone, Copy)]
+pub enum ScalarOrNDArray<'ctx> {
+    Scalar(AnyObject<'ctx>),
+    NDArray(NDArrayObject<'ctx>),
+}
+
+impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for AnyObject<'ctx> {
+    type Error = ();
+
+    fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
+        match value {
+            ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
+            ScalarOrNDArray::NDArray(_ndarray) => Err(()),
+        }
+    }
+}
+
+impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayObject<'ctx> {
+    type Error = ();
+
+    fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
+        match value {
+            ScalarOrNDArray::Scalar(_scalar) => Err(()),
+            ScalarOrNDArray::NDArray(ndarray) => Ok(*ndarray),
+        }
+    }
+}
+
+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 split_object<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        object: AnyObject<'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 = NDArrayObject::from_object(generator, ctx, object);
+                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.value,
+            ScalarOrNDArray::NDArray(ndarray) => ndarray.instance.value.as_basic_value_enum(),
+        }
+    }
+
+    /// 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 [`NDArrayObject::make_unsized`].
+    pub fn to_ndarray<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> NDArrayObject<'ctx> {
+        match self {
+            ScalarOrNDArray::NDArray(ndarray) => *ndarray,
+            ScalarOrNDArray::Scalar(scalar) => NDArrayObject::make_unsized(generator, ctx, *scalar),
+        }
+    }
+
+    /// Get the dtype of the ndarray created if this were called with [`ScalarOrNDArray::to_ndarray`].
+    #[must_use]
+    pub fn get_dtype(&self) -> Type {
+        match self {
+            ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
+            ScalarOrNDArray::Scalar(scalar) => scalar.ty,
+        }
+    }
+}
+
+/// 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(Debug, Clone, Copy)]
+pub enum NDArrayOut<'ctx> {
+    /// Tell a function should create a new ndarray with the expected element type `dtype`.
+    NewNDArray { dtype: Type },
+    /// Tell a function to write the result to `ndarray`.
+    WriteToNDArray { ndarray: NDArrayObject<'ctx> },
+}
+
+impl<'ctx> NDArrayOut<'ctx> {
+    /// Get the dtype of this output.
+    #[must_use]
+    pub fn get_dtype(&self) -> Type {
+        match self {
+            NDArrayOut::NewNDArray { dtype } => *dtype,
+            NDArrayOut::WriteToNDArray { ndarray } => ndarray.dtype,
+        }
+    }
+}

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

@@ -0,0 +1,178 @@
+use inkwell::{types::BasicType, values::PointerValue, AddressSpace};
+
+use super::NDArrayObject;
+use crate::codegen::{
+    irrt::{call_nac3_nditer_has_element, call_nac3_nditer_initialize, call_nac3_nditer_next},
+    model::*,
+    object::any::AnyObject,
+    stmt::{gen_for_callback, BreakContinueHooks},
+    CodeGenContext, CodeGenerator,
+};
+
+/// Fields of [`NDIter`]
+pub struct NDIterFields<'ctx, F: FieldTraversal<'ctx>> {
+    pub ndims: F::Output<Int<SizeT>>,
+    pub shape: F::Output<Ptr<Int<SizeT>>>,
+    pub strides: F::Output<Ptr<Int<SizeT>>>,
+
+    pub indices: F::Output<Ptr<Int<SizeT>>>,
+    pub nth: F::Output<Int<SizeT>>,
+    pub element: F::Output<Ptr<Int<Byte>>>,
+
+    pub size: F::Output<Int<SizeT>>,
+}
+
+/// An IRRT helper structure used to iterate through an ndarray.
+#[derive(Debug, Clone, Copy, Default)]
+pub struct NDIter;
+
+impl<'ctx> StructKind<'ctx> for NDIter {
+    type Fields<F: FieldTraversal<'ctx>> = NDIterFields<'ctx, F>;
+
+    fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
+        Self::Fields {
+            ndims: traversal.add_auto("ndims"),
+            shape: traversal.add_auto("shape"),
+            strides: traversal.add_auto("strides"),
+
+            indices: traversal.add_auto("indices"),
+            nth: traversal.add_auto("nth"),
+            element: traversal.add_auto("element"),
+
+            size: traversal.add_auto("size"),
+        }
+    }
+}
+
+/// A helper structure with a convenient interface to interact with [`NDIter`].
+#[derive(Debug, Clone)]
+pub struct NDIterHandle<'ctx> {
+    instance: Instance<'ctx, Ptr<Struct<NDIter>>>,
+    /// The ndarray this [`NDIter`] to iterating over.
+    ndarray: NDArrayObject<'ctx>,
+    /// The current indices of [`NDIter`].
+    indices: Instance<'ctx, Ptr<Int<SizeT>>>,
+}
+
+impl<'ctx> NDIterHandle<'ctx> {
+    /// Allocate an [`NDIter`] that iterates through an ndarray.
+    pub fn new<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ndarray: NDArrayObject<'ctx>,
+    ) -> Self {
+        let nditer = Struct(NDIter).alloca(generator, ctx);
+        let ndims = ndarray.ndims_llvm(generator, ctx.ctx);
+
+        // The caller has the responsibility to allocate 'indices' for `NDIter`.
+        let indices = Int(SizeT).array_alloca(generator, ctx, ndims.value);
+        call_nac3_nditer_initialize(generator, ctx, nditer, ndarray.instance, indices);
+
+        NDIterHandle { ndarray, instance: nditer, indices }
+    }
+
+    /// 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: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Instance<'ctx, Int<Bool>> {
+        call_nac3_nditer_has_element(generator, ctx, self.instance)
+    }
+
+    /// 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: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) {
+        call_nac3_nditer_next(generator, ctx, self.instance);
+    }
+
+    /// Get pointer to the current element.
+    #[must_use]
+    pub fn get_pointer<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> PointerValue<'ctx> {
+        let elem_ty = ctx.get_llvm_type(generator, self.ndarray.dtype);
+
+        let p = self.instance.get(generator, ctx, |f| f.element);
+        ctx.builder
+            .build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "element")
+            .unwrap()
+    }
+
+    /// Get the value of the current element.
+    #[must_use]
+    pub fn get_scalar<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> AnyObject<'ctx> {
+        let p = self.get_pointer(generator, ctx);
+        let value = ctx.builder.build_load(p, "value").unwrap();
+        AnyObject { ty: self.ndarray.dtype, value }
+    }
+
+    /// Get the index of the current element if this ndarray were a flat ndarray.
+    #[must_use]
+    pub fn get_index<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Instance<'ctx, Int<SizeT>> {
+        self.instance.get(generator, ctx, |f| f.nth)
+    }
+
+    /// Get the indices of the current element.
+    #[must_use]
+    pub fn get_indices(&self) -> Instance<'ctx, Ptr<Int<SizeT>>> {
+        self.indices
+    }
+}
+
+impl<'ctx> NDArrayObject<'ctx> {
+    /// Iterate through every element in the ndarray.
+    ///
+    /// `body` has access to [`BreakContinueHooks`] to short-circuit and [`NDIterHandle`] to
+    /// get properties of the current iteration (e.g., the current element, indices, etc.)
+    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>,
+            NDIterHandle<'ctx>,
+        ) -> Result<(), String>,
+    {
+        gen_for_callback(
+            generator,
+            ctx,
+            Some("ndarray_foreach"),
+            |generator, ctx| Ok(NDIterHandle::new(generator, ctx, *self)),
+            |generator, ctx, nditer| Ok(nditer.has_element(generator, ctx).value),
+            |generator, ctx, hooks, nditer| body(generator, ctx, hooks, nditer),
+            |generator, ctx, nditer| {
+                nditer.next(generator, ctx);
+                Ok(())
+            },
+        )
+    }
+}

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

@@ -0,0 +1,104 @@
+use crate::{
+    codegen::{
+        model::*,
+        object::{any::AnyObject, list::ListObject, tuple::TupleObject},
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::TypeEnum,
+};
+use util::gen_for_model;
+
+/// Parse a NumPy-like "int sequence" input and return the int sequence as an array and its length.
+///
+/// * `sequence` - The `sequence` parameter.
+/// * `sequence_ty` - The typechecker type of `sequence`
+///
+/// The `sequence` argument type may only be one of the following:
+///   1. A list of `int32`;   e.g., `np.empty([600, 800, 3])`
+///   2. A tuple of `int32`;  e.g., `np.empty((600, 800, 3))`
+///   3. A scalar `int32`;    e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
+///
+/// All `int32` values will be sign-extended to `SizeT`.
+pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    input_sequence: AnyObject<'ctx>,
+) -> (Instance<'ctx, Int<SizeT>>, Instance<'ctx, Ptr<Int<SizeT>>>) {
+    let zero = Int(SizeT).const_0(generator, ctx.ctx);
+    let one = Int(SizeT).const_1(generator, ctx.ctx);
+
+    // The result `list` to return.
+    match &*ctx.unifier.get_ty(input_sequence.ty) {
+        TypeEnum::TObj { obj_id, .. }
+            if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
+        {
+            // 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
+
+            // Check `input_sequence`
+            let input_sequence = ListObject::from_object(generator, ctx, input_sequence);
+
+            let len = input_sequence.instance.get(generator, ctx, |f| f.len);
+            let result = Int(SizeT).array_alloca(generator, ctx, len.value);
+
+            // Load all the `int32`s from the input_sequence, cast them to `SizeT`, and store them into `result`
+            gen_for_model(generator, ctx, zero, len, one, |generator, ctx, _hooks, i| {
+                // Load the i-th int32 in the input sequence
+                let int = input_sequence
+                    .instance
+                    .get(generator, ctx, |f| f.items)
+                    .get_index(generator, ctx, i.value)
+                    .value
+                    .into_int_value();
+
+                // Cast to SizeT
+                let int = Int(SizeT).s_extend_or_bit_cast(generator, ctx, int);
+
+                // Store
+                result.set_index(ctx, i.value, int);
+
+                Ok(())
+            })
+            .unwrap();
+
+            (len, result)
+        }
+        TypeEnum::TTuple { .. } => {
+            // 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
+
+            let input_sequence = TupleObject::from_object(ctx, input_sequence);
+
+            let len = input_sequence.len(generator, ctx);
+
+            let result = Int(SizeT).array_alloca(generator, ctx, len.value);
+
+            for i in 0..input_sequence.num_elements() {
+                // Get the i-th element off of the tuple and load it into `result`.
+                let int = input_sequence.index(ctx, i).value.into_int_value();
+                let int = Int(SizeT).s_extend_or_bit_cast(generator, ctx, int);
+
+                result.set_index_const(ctx, i64::try_from(i).unwrap(), int);
+            }
+
+            (len, result)
+        }
+        TypeEnum::TObj { obj_id, .. }
+            if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
+        {
+            // 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
+            let input_int = input_sequence.value.into_int_value();
+
+            let len = Int(SizeT).const_1(generator, ctx.ctx);
+            let result = Int(SizeT).array_alloca(generator, ctx, len.value);
+            let int = Int(SizeT).s_extend_or_bit_cast(generator, ctx, input_int);
+
+            // Storing into result[0]
+            result.store(ctx, int);
+
+            (len, result)
+        }
+        _ => panic!(
+            "encountered unknown sequence type: {}",
+            ctx.unifier.stringify(input_sequence.ty)
+        ),
+    }
+}

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

@@ -0,0 +1,118 @@
+use super::{indexing::RustNDIndex, NDArrayObject};
+use crate::codegen::{
+    irrt::{call_nac3_ndarray_reshape_resolve_and_check_new_shape, call_nac3_ndarray_transpose},
+    model::*,
+    CodeGenContext, CodeGenerator,
+};
+
+impl<'ctx> NDArrayObject<'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.
+    /// If this ndarray's `ndims` is not less than `ndmin`, 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 {
+        if self.ndims < ndmin {
+            // Extend the dimensions with np.newaxis.
+            let mut indices = vec![];
+            for _ in self.ndims..ndmin {
+                indices.push(RustNDIndex::NewAxis);
+            }
+            indices.push(RustNDIndex::Ellipsis);
+            self.index(generator, ctx, &indices)
+        } else {
+            *self
+        }
+    }
+
+    /// Create a reshaped view on this ndarray like `np.reshape()`.
+    ///
+    /// If there is a `-1` in `new_shape`, it will be resolved; `new_shape` would **NOT** be modified as a result.
+    ///
+    /// If reshape without copying is impossible, this function will allocate a new ndarray and copy contents.
+    ///
+    /// * `new_ndims` - The number of dimensions of `new_shape` as a [`Type`].
+    /// * `new_shape` - The target shape to do `np.reshape()`.
+    #[must_use]
+    pub fn reshape_or_copy<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        new_ndims: u64,
+        new_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+    ) -> Self {
+        // TODO: The current criterion for whether to do a full copy or not is by checking `is_c_contiguous`,
+        // but this is not optimal - there are cases when the ndarray is not contiguous but could be reshaped
+        // without copying data. Look into how numpy does it.
+
+        let current_bb = ctx.builder.get_insert_block().unwrap();
+        let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
+        let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
+        let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
+
+        let dst_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, new_ndims);
+        dst_ndarray.copy_shape_from_array(generator, ctx, new_shape);
+
+        // Reolsve negative indices
+        let size = self.size(generator, ctx);
+        let dst_ndims = dst_ndarray.ndims_llvm(generator, ctx.ctx);
+        let dst_shape = dst_ndarray.instance.get(generator, ctx, |f| f.shape);
+        call_nac3_ndarray_reshape_resolve_and_check_new_shape(
+            generator, ctx, size, dst_ndims, dst_shape,
+        );
+
+        let is_c_contiguous = self.is_c_contiguous(generator, ctx);
+        ctx.builder.build_conditional_branch(is_c_contiguous.value, then_bb, else_bb).unwrap();
+
+        // Inserting into then_bb: reshape is possible without copying
+        ctx.builder.position_at_end(then_bb);
+        dst_ndarray.set_strides_contiguous(generator, ctx);
+        dst_ndarray.instance.set(ctx, |f| f.data, self.instance.get(generator, ctx, |f| f.data));
+        ctx.builder.build_unconditional_branch(end_bb).unwrap();
+
+        // Inserting into else_bb: reshape is impossible without copying
+        ctx.builder.position_at_end(else_bb);
+        dst_ndarray.create_data(generator, ctx);
+        dst_ndarray.copy_data_from(generator, ctx, *self);
+        ctx.builder.build_unconditional_branch(end_bb).unwrap();
+
+        // Reposition for continuation
+        ctx.builder.position_at_end(end_bb);
+
+        dst_ndarray
+    }
+
+    /// Create a transposed view on this ndarray like `np.transpose(<ndarray>, <axes> = None)`.
+    /// * `axes` - If specified, should be an array of the permutation (negative indices are **allowed**).
+    #[must_use]
+    pub fn transpose<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        axes: Option<Instance<'ctx, Ptr<Int<SizeT>>>>,
+    ) -> Self {
+        // Define models
+        let transposed_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims);
+
+        let num_axes = self.ndims_llvm(generator, ctx.ctx);
+
+        // `axes = nullptr` if `axes` is unspecified.
+        let axes = axes.unwrap_or_else(|| Ptr(Int(SizeT)).nullptr(generator, ctx.ctx));
+
+        call_nac3_ndarray_transpose(
+            generator,
+            ctx,
+            self.instance,
+            transposed_ndarray.instance,
+            num_axes,
+            axes,
+        );
+
+        transposed_ndarray
+    }
+}

nac3core/src/codegen/object/tuple.rs

@@ -0,0 +1,98 @@
+use inkwell::values::StructValue;
+use itertools::Itertools;
+
+use super::any::AnyObject;
+use crate::{
+    codegen::{model::*, CodeGenContext, CodeGenerator},
+    typecheck::typedef::{Type, TypeEnum},
+};
+
+/// A NAC3 tuple object.
+///
+/// NOTE: This struct has no copy trait.
+#[derive(Debug, Clone)]
+pub struct TupleObject<'ctx> {
+    /// The type of the tuple.
+    pub tys: Vec<Type>,
+    /// The underlying LLVM struct value of this tuple.
+    pub value: StructValue<'ctx>,
+}
+
+impl<'ctx> TupleObject<'ctx> {
+    pub fn from_object(ctx: &mut CodeGenContext<'ctx, '_>, object: AnyObject<'ctx>) -> Self {
+        // TODO: Keep `is_vararg_ctx` from TTuple?
+
+        // Sanity check on object type.
+        let TypeEnum::TTuple { ty: tys, .. } = &*ctx.unifier.get_ty(object.ty) else {
+            panic!(
+                "Expected type to be a TypeEnum::TTuple, got {}",
+                ctx.unifier.stringify(object.ty)
+            );
+        };
+
+        // Check number of fields
+        let value = object.value.into_struct_value();
+        let value_num_fields = value.get_type().count_fields() as usize;
+        assert!(
+            value_num_fields == tys.len(),
+            "Tuple type has {} item(s), but the LLVM struct value has {} field(s)",
+            tys.len(),
+            value_num_fields
+        );
+
+        TupleObject { tys: tys.clone(), value }
+    }
+
+    /// Convenience function. Create a [`TupleObject`] from an iterator of objects.
+    pub fn from_objects<I, G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        objects: I,
+    ) -> Self
+    where
+        I: IntoIterator<Item = AnyObject<'ctx>>,
+    {
+        let (values, tys): (Vec<_>, Vec<_>) =
+            objects.into_iter().map(|object| (object.value, object.ty)).unzip();
+
+        let llvm_tys = tys.iter().map(|ty| ctx.get_llvm_type(generator, *ty)).collect_vec();
+        let llvm_tuple_ty = ctx.ctx.struct_type(&llvm_tys, false);
+
+        let pllvm_tuple = ctx.builder.build_alloca(llvm_tuple_ty, "tuple").unwrap();
+        for (i, val) in values.into_iter().enumerate() {
+            let pval = ctx.builder.build_struct_gep(pllvm_tuple, i as u32, "value").unwrap();
+            ctx.builder.build_store(pval, val).unwrap();
+        }
+
+        let value = ctx.builder.build_load(pllvm_tuple, "").unwrap().into_struct_value();
+        TupleObject { tys, value }
+    }
+
+    #[must_use]
+    pub fn num_elements(&self) -> usize {
+        self.tys.len()
+    }
+
+    /// Get the `len()` of this tuple.
+    #[must_use]
+    pub fn len<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Instance<'ctx, Int<SizeT>> {
+        Int(SizeT).const_int(generator, ctx.ctx, self.num_elements() as u64, false)
+    }
+
+    /// Get the `i`-th (0-based) object in this tuple.
+    pub fn index(&self, ctx: &mut CodeGenContext<'ctx, '_>, i: usize) -> AnyObject<'ctx> {
+        assert!(
+            i < self.num_elements(),
+            "Tuple object with length {} have index {i}",
+            self.num_elements()
+        );
+
+        let value = ctx.builder.build_extract_value(self.value, i as u32, "tuple[{i}]").unwrap();
+        let ty = self.tys[i];
+        AnyObject { ty, value }
+    }
+}

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

@@ -0,0 +1 @@
+pub mod slice;

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

@@ -0,0 +1,125 @@
+use crate::codegen::{model::*, CodeGenContext, CodeGenerator};
+
+/// Fields of [`Slice`]
+#[derive(Debug, Clone)]
+pub struct SliceFields<'ctx, F: FieldTraversal<'ctx>, N: IntKind<'ctx>> {
+    pub start_defined: F::Output<Int<Bool>>,
+    pub start: F::Output<Int<N>>,
+    pub stop_defined: F::Output<Int<Bool>>,
+    pub stop: F::Output<Int<N>>,
+    pub step_defined: F::Output<Int<Bool>>,
+    pub step: F::Output<Int<N>>,
+}
+
+/// An IRRT representation of an (unresolved) slice.
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct Slice<N>(pub N);
+
+impl<'ctx, N: IntKind<'ctx>> StructKind<'ctx> for Slice<N> {
+    type Fields<F: FieldTraversal<'ctx>> = SliceFields<'ctx, F, N>;
+
+    fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
+        Self::Fields {
+            start_defined: traversal.add_auto("start_defined"),
+            start: traversal.add("start", Int(self.0)),
+            stop_defined: traversal.add_auto("stop_defined"),
+            stop: traversal.add("stop", Int(self.0)),
+            step_defined: traversal.add_auto("step_defined"),
+            step: traversal.add("step", Int(self.0)),
+        }
+    }
+}
+
+/// A Rust structure that has [`Slice`] utilities and looks like a [`Slice`] but
+/// `start`, `stop` and `step` are held by LLVM registers only and possibly
+/// [`Option::None`] if unspecified.
+#[derive(Debug, Clone)]
+pub struct RustSlice<'ctx, N: IntKind<'ctx>> {
+    // It is possible that `start`, `stop`, and `step` are all `None`.
+    // We need to know the `int_kind` even when that is the case.
+    pub int_kind: N,
+    pub start: Option<Instance<'ctx, Int<N>>>,
+    pub stop: Option<Instance<'ctx, Int<N>>>,
+    pub step: Option<Instance<'ctx, Int<N>>>,
+}
+
+impl<'ctx, N: IntKind<'ctx>> RustSlice<'ctx, N> {
+    /// Write the contents to an LLVM [`Slice`].
+    pub fn write_to_slice<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        dst_slice_ptr: Instance<'ctx, Ptr<Struct<Slice<N>>>>,
+    ) {
+        let false_ = Int(Bool).const_false(generator, ctx.ctx);
+        let true_ = Int(Bool).const_true(generator, ctx.ctx);
+
+        match self.start {
+            Some(start) => {
+                dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, true_);
+                dst_slice_ptr.gep(ctx, |f| f.start).store(ctx, start);
+            }
+            None => dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, false_),
+        }
+
+        match self.stop {
+            Some(stop) => {
+                dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, true_);
+                dst_slice_ptr.gep(ctx, |f| f.stop).store(ctx, stop);
+            }
+            None => dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, false_),
+        }
+
+        match self.step {
+            Some(step) => {
+                dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, true_);
+                dst_slice_ptr.gep(ctx, |f| f.step).store(ctx, step);
+            }
+            None => dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, false_),
+        }
+    }
+}
+
+pub mod util {
+    use nac3parser::ast::Expr;
+
+    use crate::{
+        codegen::{model::*, CodeGenContext, CodeGenerator},
+        typecheck::typedef::Type,
+    };
+
+    use super::RustSlice;
+
+    /// Generate LLVM IR for an [`ExprKind::Slice`] and convert it into a [`RustSlice`].
+    #[allow(clippy::type_complexity)]
+    pub fn gen_slice<'ctx, 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, Int32>, String> {
+        let mut help = |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)?
+                        .unwrap()
+                        .to_basic_value_enum(ctx, generator, ctx.primitives.int32)?;
+
+                    let value_expr =
+                        Int(Int32).check_value(generator, ctx.ctx, value_expr).unwrap();
+
+                    Some(value_expr)
+                }
+            })
+        };
+
+        let start = help(lower)?;
+        let stop = help(upper)?;
+        let step = help(step)?;
+
+        Ok(RustSlice { int_kind: Int32, start, stop, step })
+    }
+}

nac3core/src/codegen/stmt.rs

@@ -12,11 +12,17 @@ use nac3parser::ast::{
 };
 
 use super::{
+    classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
     expr::{destructure_range, gen_binop_expr},
     gen_in_range_check,
     irrt::{handle_slice_indices, list_slice_assignment},
     macros::codegen_unreachable,
-    values::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
+    object::{
+        any::AnyObject,
+        ndarray::{
+            indexing::util::gen_ndarray_subscript_ndindices, NDArrayObject, ScalarOrNDArray,
+        },
+    },
     CodeGenContext, CodeGenerator,
 };
 use crate::{
@@ -310,7 +316,7 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
                 .unwrap()
                 .to_basic_value_enum(ctx, generator, target_ty)?
                 .into_pointer_value();
-            let target = ListValue::from_pointer_value(target, llvm_usize, None);
+            let target = ListValue::from_ptr_val(target, llvm_usize, None);
 
             if let ExprKind::Slice { .. } = &key.node {
                 // Handle assigning to a slice
@@ -331,7 +337,7 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
 
                 let value =
                     value.to_basic_value_enum(ctx, generator, value_ty)?.into_pointer_value();
-                let value = ListValue::from_pointer_value(value, llvm_usize, None);
+                let value = ListValue::from_ptr_val(value, llvm_usize, None);
 
                 let target_item_ty = ctx.get_llvm_type(generator, target_item_ty);
                 let Some(src_ind) = handle_slice_indices(
@@ -411,7 +417,47 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
             if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
         {
             // Handle NDArray item assignment
-            todo!("ndarray subscript assignment is not yet implemented");
+            // Process target
+            let target = generator
+                .gen_expr(ctx, target)?
+                .unwrap()
+                .to_basic_value_enum(ctx, generator, target_ty)?;
+            let target = AnyObject { value: target, ty: target_ty };
+
+            // Process key
+            let key = gen_ndarray_subscript_ndindices(generator, ctx, key)?;
+
+            // Process value
+            let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
+            let value = AnyObject { value, ty: 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 = NDArrayObject::from_object(generator, ctx, target);
+            let target = target.index(generator, ctx, &key);
+
+            let value =
+                ScalarOrNDArray::split_object(generator, ctx, value).to_ndarray(generator, ctx);
+
+            let broadcast_result = NDArrayObject::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));
@@ -463,8 +509,7 @@ pub fn gen_for<G: CodeGenerator>(
         TypeEnum::TObj { obj_id, .. }
             if *obj_id == ctx.primitives.range.obj_id(&ctx.unifier).unwrap() =>
         {
-            let iter_val =
+            let iter_val = RangeValue::from_ptr_val(iter_val.into_pointer_value(), Some("range"));
-                RangeValue::from_pointer_value(iter_val.into_pointer_value(), Some("range"));
             // Internal variable for loop; Cannot be assigned
             let i = generator.gen_var_alloc(ctx, int32.into(), Some("for.i.addr"))?;
             // Variable declared in "target" expression of the loop; Can be reassigned *or* shadowed

nac3core/src/codegen/test.rs

@@ -16,8 +16,8 @@ use nac3parser::{
 use parking_lot::RwLock;
 
 use super::{
+    classes::{ListType, NDArrayType, ProxyType, RangeType},
     concrete_type::ConcreteTypeStore,
-    types::{ListType, NDArrayType, ProxyType, RangeType},
     CodeGenContext, CodeGenLLVMOptions, CodeGenTargetMachineOptions, CodeGenTask, CodeGenerator,
     DefaultCodeGenerator, WithCall, WorkerRegistry,
 };
@@ -452,7 +452,7 @@ fn test_classes_list_type_new() {
     let llvm_usize = generator.get_size_type(&ctx);
 
     let llvm_list = ListType::new(&generator, &ctx, llvm_i32.into());
-    assert!(ListType::is_representable(llvm_list.as_base_type(), llvm_usize).is_ok());
+    assert!(ListType::is_type(llvm_list.as_base_type(), llvm_usize).is_ok());
 }
 
 #[test]
@@ -460,7 +460,7 @@ fn test_classes_range_type_new() {
     let ctx = inkwell::context::Context::create();
 
     let llvm_range = RangeType::new(&ctx);
-    assert!(RangeType::is_representable(llvm_range.as_base_type()).is_ok());
+    assert!(RangeType::is_type(llvm_range.as_base_type()).is_ok());
 }
 
 #[test]
@@ -472,5 +472,5 @@ fn test_classes_ndarray_type_new() {
     let llvm_usize = generator.get_size_type(&ctx);
 
     let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into());
-    assert!(NDArrayType::is_representable(llvm_ndarray.as_base_type(), llvm_usize).is_ok());
+    assert!(NDArrayType::is_type(llvm_ndarray.as_base_type(), llvm_usize).is_ok());
 }

nac3core/src/codegen/types/list.rs

@@ -1,192 +0,0 @@
-use inkwell::{
-    context::Context,
-    types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
-    values::IntValue,
-    AddressSpace,
-};
-
-use super::ProxyType;
-use crate::codegen::{
-    values::{ArraySliceValue, ListValue, ProxyValue},
-    CodeGenContext, CodeGenerator,
-};
-
-/// Proxy type for a `list` type in LLVM.
-#[derive(Debug, PartialEq, Eq, Clone, Copy)]
-pub struct ListType<'ctx> {
-    ty: PointerType<'ctx>,
-    llvm_usize: IntType<'ctx>,
-}
-
-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 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 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()
-            ));
-        }
-
-        Ok(())
-    }
-
-    /// Creates an LLVM type corresponding to the expected structure of a `List`.
-    #[must_use]
-    fn llvm_type(
-        ctx: &'ctx Context,
-        element_type: 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()];
-
-        ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
-    }
-
-    /// Creates an instance of [`ListType`].
-    #[must_use]
-    pub fn new<G: CodeGenerator + ?Sized>(
-        generator: &G,
-        ctx: &'ctx Context,
-        element_type: BasicTypeEnum<'ctx>,
-    ) -> Self {
-        let llvm_usize = generator.get_size_type(ctx);
-        let llvm_list = Self::llvm_type(ctx, element_type, llvm_usize);
-
-        ListType::from_type(llvm_list, llvm_usize)
-    }
-
-    /// Creates an [`ListType`] from a [`PointerType`].
-    #[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());
-
-        ListType { ty: ptr_ty, 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()
-    }
-
-    /// 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()
-    }
-}
-
-impl<'ctx> ProxyType<'ctx> for ListType<'ctx> {
-    type Base = PointerType<'ctx>;
-    type Value = ListValue<'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 new_value<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&'ctx str>,
-    ) -> Self::Value {
-        self.map_value(
-            generator
-                .gen_var_alloc(
-                    ctx,
-                    self.as_base_type().get_element_type().into_struct_type().into(),
-                    name,
-                )
-                .unwrap(),
-            name,
-        )
-    }
-
-    fn new_array_value<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 map_value(
-        &self,
-        value: <Self::Value as ProxyValue<'ctx>>::Base,
-        name: Option<&'ctx str>,
-    ) -> Self::Value {
-        Self::Value::from_pointer_value(value, self.llvm_usize, name)
-    }
-
-    fn as_base_type(&self) -> Self::Base {
-        self.ty
-    }
-}
-
-impl<'ctx> From<ListType<'ctx>> for PointerType<'ctx> {
-    fn from(value: ListType<'ctx>) -> Self {
-        value.as_base_type()
-    }
-}

nac3core/src/codegen/types/mod.rs

@@ -1,64 +0,0 @@
-use inkwell::{context::Context, types::BasicType, values::IntValue};
-
-use super::{
-    values::{ArraySliceValue, ProxyValue},
-    {CodeGenContext, CodeGenerator},
-};
-pub use list::*;
-pub use ndarray::*;
-pub use range::*;
-
-mod list;
-mod ndarray;
-mod range;
-pub mod structure;
-
-/// A LLVM type that is used to represent a corresponding type in NAC3.
-pub trait ProxyType<'ctx>: Into<Self::Base> {
-    /// The LLVM type of which values of this type possess. This is usually a
-    /// [LLVM pointer type][PointerType] for any non-primitive types.
-    type Base: BasicType<'ctx>;
-
-    /// The type of values represented by this type.
-    type Value: ProxyValue<'ctx, Type = Self>;
-
-    fn is_type<G: CodeGenerator + ?Sized>(
-        generator: &G,
-        ctx: &'ctx Context,
-        llvm_ty: impl BasicType<'ctx>,
-    ) -> Result<(), String>;
-
-    /// Checks whether `llvm_ty` can be represented by this [`ProxyType`].
-    fn is_representable<G: CodeGenerator + ?Sized>(
-        generator: &G,
-        ctx: &'ctx Context,
-        llvm_ty: Self::Base,
-    ) -> Result<(), String>;
-
-    /// Creates a new value of this type.
-    fn new_value<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&'ctx str>,
-    ) -> Self::Value;
-
-    /// Creates a new array value of this type.
-    fn new_array_value<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        size: IntValue<'ctx>,
-        name: Option<&'ctx str>,
-    ) -> ArraySliceValue<'ctx>;
-
-    /// Converts an existing value into a [`ProxyValue`] of this type.
-    fn map_value(
-        &self,
-        value: <Self::Value as ProxyValue<'ctx>>::Base,
-        name: Option<&'ctx str>,
-    ) -> Self::Value;
-
-    /// Returns the [base type][Self::Base] of this proxy.
-    fn as_base_type(&self) -> Self::Base;
-}

nac3core/src/codegen/types/ndarray.rs

@@ -1,258 +0,0 @@
-use inkwell::{
-    context::Context,
-    types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
-    values::{IntValue, PointerValue},
-    AddressSpace,
-};
-use itertools::Itertools;
-
-use nac3core_derive::StructFields;
-
-use super::{
-    structure::{StructField, StructFields},
-    ProxyType,
-};
-use crate::codegen::{
-    values::{ArraySliceValue, NDArrayValue, ProxyValue},
-    {CodeGenContext, CodeGenerator},
-};
-
-/// Proxy type for a `ndarray` type in LLVM.
-#[derive(Debug, PartialEq, Eq, Clone, Copy)]
-pub struct NDArrayType<'ctx> {
-    ty: PointerType<'ctx>,
-    dtype: BasicTypeEnum<'ctx>,
-    llvm_usize: IntType<'ctx>,
-}
-
-#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
-pub struct NDArrayStructFields<'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> NDArrayType<'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 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(())
-    }
-
-    // TODO: Move this into e.g. StructProxyType
-    #[must_use]
-    fn fields(ctx: &'ctx Context, llvm_usize: IntType<'ctx>) -> NDArrayStructFields<'ctx> {
-        NDArrayStructFields::new(ctx, llvm_usize)
-    }
-
-    // TODO: Move this into e.g. StructProxyType
-    #[must_use]
-    pub fn get_fields(
-        &self,
-        ctx: &'ctx Context,
-        llvm_usize: IntType<'ctx>,
-    ) -> NDArrayStructFields<'ctx> {
-        Self::fields(ctx, 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();
-
-        ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
-    }
-
-    /// Creates an instance of [`NDArrayType`].
-    #[must_use]
-    pub fn new<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, llvm_usize }
-    }
-
-    /// Creates an [`NDArrayType`] from a [`PointerType`] representing an `NDArray`.
-    #[must_use]
-    pub fn from_type(
-        ptr_ty: PointerType<'ctx>,
-        dtype: BasicTypeEnum<'ctx>,
-        llvm_usize: IntType<'ctx>,
-    ) -> Self {
-        debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
-
-        NDArrayType { ty: ptr_ty, dtype, 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()
-    }
-
-    /// Returns the element type of this `ndarray` type.
-    #[must_use]
-    pub fn element_type(&self) -> BasicTypeEnum<'ctx> {
-        self.dtype
-    }
-}
-
-impl<'ctx> ProxyType<'ctx> for NDArrayType<'ctx> {
-    type Base = PointerType<'ctx>;
-    type Value = NDArrayValue<'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 new_value<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&'ctx str>,
-    ) -> Self::Value {
-        self.map_value(
-            generator
-                .gen_var_alloc(
-                    ctx,
-                    self.as_base_type().get_element_type().into_struct_type().into(),
-                    name,
-                )
-                .unwrap(),
-            name,
-        )
-    }
-
-    fn new_array_value<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 map_value(
-        &self,
-        value: <Self::Value as ProxyValue<'ctx>>::Base,
-        name: Option<&'ctx str>,
-    ) -> Self::Value {
-        debug_assert_eq!(value.get_type(), self.as_base_type());
-
-        NDArrayValue::from_pointer_value(value, self.dtype, self.llvm_usize, name)
-    }
-
-    fn as_base_type(&self) -> Self::Base {
-        self.ty
-    }
-}
-
-impl<'ctx> From<NDArrayType<'ctx>> for PointerType<'ctx> {
-    fn from(value: NDArrayType<'ctx>) -> Self {
-        value.as_base_type()
-    }
-}

nac3core/src/codegen/types/range.rs

@@ -1,159 +0,0 @@
-use inkwell::{
-    context::Context,
-    types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
-    values::IntValue,
-    AddressSpace,
-};
-
-use super::ProxyType;
-use crate::codegen::{
-    values::{ArraySliceValue, ProxyValue, RangeValue},
-    {CodeGenContext, CodeGenerator},
-};
-
-/// Proxy type for a `range` type in LLVM.
-#[derive(Debug, PartialEq, Eq, Clone, Copy)]
-pub struct RangeType<'ctx> {
-    ty: PointerType<'ctx>,
-}
-
-impl<'ctx> RangeType<'ctx> {
-    /// Checks whether `llvm_ty` represents a `range` type, returning [Err] if it does not.
-    pub fn is_representable(llvm_ty: PointerType<'ctx>) -> Result<(), String> {
-        let llvm_range_ty = llvm_ty.get_element_type();
-        let AnyTypeEnum::ArrayType(llvm_range_ty) = llvm_range_ty else {
-            return Err(format!("Expected array type for `range` type, got {llvm_range_ty}"));
-        };
-        if llvm_range_ty.len() != 3 {
-            return Err(format!(
-                "Expected 3 elements for `range` type, got {}",
-                llvm_range_ty.len()
-            ));
-        }
-
-        let llvm_range_elem_ty = llvm_range_ty.get_element_type();
-        let Ok(llvm_range_elem_ty) = IntType::try_from(llvm_range_elem_ty) else {
-            return Err(format!(
-                "Expected int type for `range` element type, got {llvm_range_elem_ty}"
-            ));
-        };
-        if llvm_range_elem_ty.get_bit_width() != 32 {
-            return Err(format!(
-                "Expected 32-bit int type for `range` element type, got {}",
-                llvm_range_elem_ty.get_bit_width()
-            ));
-        }
-
-        Ok(())
-    }
-
-    /// Creates an LLVM type corresponding to the expected structure of a `Range`.
-    #[must_use]
-    fn llvm_type(ctx: &'ctx Context) -> PointerType<'ctx> {
-        // typedef int32_t Range[3];
-        let llvm_i32 = ctx.i32_type();
-        llvm_i32.array_type(3).ptr_type(AddressSpace::default())
-    }
-
-    /// Creates an instance of [`RangeType`].
-    #[must_use]
-    pub fn new(ctx: &'ctx Context) -> Self {
-        let llvm_range = Self::llvm_type(ctx);
-
-        RangeType::from_type(llvm_range)
-    }
-
-    /// Creates an [`RangeType`] from a [`PointerType`].
-    #[must_use]
-    pub fn from_type(ptr_ty: PointerType<'ctx>) -> Self {
-        debug_assert!(Self::is_representable(ptr_ty).is_ok());
-
-        RangeType { ty: ptr_ty }
-    }
-
-    /// Returns the type of all fields of this `range` type.
-    #[must_use]
-    pub fn value_type(&self) -> IntType<'ctx> {
-        self.as_base_type().get_element_type().into_array_type().get_element_type().into_int_type()
-    }
-}
-
-impl<'ctx> ProxyType<'ctx> for RangeType<'ctx> {
-    type Base = PointerType<'ctx>;
-    type Value = RangeValue<'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>(
-        _: &G,
-        _: &'ctx Context,
-        llvm_ty: Self::Base,
-    ) -> Result<(), String> {
-        Self::is_representable(llvm_ty)
-    }
-
-    fn new_value<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&'ctx str>,
-    ) -> Self::Value {
-        self.map_value(
-            generator
-                .gen_var_alloc(
-                    ctx,
-                    self.as_base_type().get_element_type().into_struct_type().into(),
-                    name,
-                )
-                .unwrap(),
-            name,
-        )
-    }
-
-    fn new_array_value<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 map_value(
-        &self,
-        value: <Self::Value as ProxyValue<'ctx>>::Base,
-        name: Option<&'ctx str>,
-    ) -> Self::Value {
-        debug_assert_eq!(value.get_type(), self.as_base_type());
-
-        RangeValue::from_pointer_value(value, name)
-    }
-
-    fn as_base_type(&self) -> Self::Base {
-        self.ty
-    }
-}
-
-impl<'ctx> From<RangeType<'ctx>> for PointerType<'ctx> {
-    fn from(value: RangeType<'ctx>) -> Self {
-        value.as_base_type()
-    }
-}

nac3core/src/codegen/types/structure.rs

@@ -1,203 +0,0 @@
-use std::marker::PhantomData;
-
-use inkwell::{
-    context::AsContextRef,
-    types::{BasicTypeEnum, IntType},
-    values::{BasicValue, BasicValueEnum, IntValue, PointerValue, StructValue},
-};
-
-use crate::codegen::CodeGenContext;
-
-/// Trait indicating that the structure is a field-wise representation of an LLVM structure.
-///
-/// # Usage
-///
-/// For example, for a simple C-slice LLVM structure:
-///
-/// ```ignore
-/// struct CSliceFields<'ctx> {
-///     ptr: StructField<'ctx, PointerValue<'ctx>>,
-///     len: StructField<'ctx, IntValue<'ctx>>
-/// }
-/// ```
-pub trait StructFields<'ctx>: Eq + Copy {
-    /// Creates an instance of [`StructFields`] using the given `ctx` and `size_t` types.
-    fn new(ctx: impl AsContextRef<'ctx>, llvm_usize: IntType<'ctx>) -> Self;
-
-    /// Returns a [`Vec`] that contains the fields of the structure in the order as they appear in
-    /// the type definition.
-    #[must_use]
-    fn to_vec(&self) -> Vec<(&'static str, BasicTypeEnum<'ctx>)>;
-
-    /// Returns a [`Iterator`] that contains the fields of the structure in the order as they appear
-    /// in the type definition.
-    #[must_use]
-    fn iter(&self) -> impl Iterator<Item = (&'static str, BasicTypeEnum<'ctx>)> {
-        self.to_vec().into_iter()
-    }
-
-    /// Returns a [`Vec`] that contains the fields of the structure in the order as they appear in
-    /// the type definition.
-    #[must_use]
-    fn into_vec(self) -> Vec<(&'static str, BasicTypeEnum<'ctx>)>
-    where
-        Self: Sized,
-    {
-        self.to_vec()
-    }
-
-    /// Returns a [`Iterator`] that contains the fields of the structure in the order as they appear
-    /// in the type definition.
-    #[must_use]
-    fn into_iter(self) -> impl Iterator<Item = (&'static str, BasicTypeEnum<'ctx>)>
-    where
-        Self: Sized,
-    {
-        self.into_vec().into_iter()
-    }
-}
-
-/// A single field of an LLVM structure.
-#[derive(Debug, PartialEq, Eq, Clone, Copy)]
-pub struct StructField<'ctx, Value>
-where
-    Value: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>, Error = ()>,
-{
-    /// The index of this field within the structure.
-    index: u32,
-
-    /// The name of this field.
-    name: &'static str,
-
-    /// The type of this field.
-    ty: BasicTypeEnum<'ctx>,
-
-    /// Instance of [`PhantomData`] containing [`Value`], used to implement automatic downcasts.
-    _value_ty: PhantomData<Value>,
-}
-
-impl<'ctx, Value> StructField<'ctx, Value>
-where
-    Value: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>, Error = ()>,
-{
-    /// Creates an instance of [`StructField`].
-    ///
-    /// * `idx_counter` - The instance of [`FieldIndexCounter`] used to track the current field
-    ///   index.
-    /// * `name` - Name of the field.
-    /// * `ty` - The type of this field.
-    pub fn create(
-        idx_counter: &mut FieldIndexCounter,
-        name: &'static str,
-        ty: impl Into<BasicTypeEnum<'ctx>>,
-    ) -> Self {
-        StructField { index: idx_counter.increment(), name, ty: ty.into(), _value_ty: PhantomData }
-    }
-
-    /// Creates an instance of [`StructField`] with a given index.
-    ///
-    /// * `index` - The index of this field within its enclosing structure.
-    /// * `name` - Name of the field.
-    /// * `ty` - The type of this field.
-    pub fn create_at(index: u32, name: &'static str, ty: impl Into<BasicTypeEnum<'ctx>>) -> Self {
-        StructField { index, name, ty: ty.into(), _value_ty: PhantomData }
-    }
-
-    /// 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(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        pobj: PointerValue<'ctx>,
-        idx: &[IntValue<'ctx>],
-    ) -> PointerValue<'ctx> {
-        unsafe {
-            ctx.builder.build_in_bounds_gep(
-                pobj,
-                &[idx, &[ctx.ctx.i32_type().const_int(u64::from(self.index), false)]].concat(),
-                "",
-            )
-        }
-        .unwrap()
-    }
-
-    /// Creates a pointer to this field in an arbitrary structure by performing the equivalent of
-    /// `getelementptr i32 0, i32 {self.index}`.
-    pub fn ptr_by_gep(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        pobj: PointerValue<'ctx>,
-        obj_name: Option<&'ctx str>,
-    ) -> PointerValue<'ctx> {
-        ctx.builder
-            .build_struct_gep(
-                pobj,
-                self.index,
-                &obj_name.map(|name| format!("{name}.{}.addr", self.name)).unwrap_or_default(),
-            )
-            .unwrap()
-    }
-
-    /// Gets the value of this field for a given `obj`.
-    #[must_use]
-    pub fn get_from_value(&self, obj: StructValue<'ctx>) -> Value {
-        obj.get_field_at_index(self.index).and_then(|value| Value::try_from(value).ok()).unwrap()
-    }
-
-    /// Sets the value of this field for a given `obj`.
-    pub fn set_from_value(&self, obj: StructValue<'ctx>, value: Value) {
-        obj.set_field_at_index(self.index, value);
-    }
-
-    /// Gets the value of this field for a pointer-to-structure.
-    pub fn get(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        pobj: PointerValue<'ctx>,
-        obj_name: Option<&'ctx str>,
-    ) -> Value {
-        ctx.builder
-            .build_load(
-                self.ptr_by_gep(ctx, pobj, obj_name),
-                &obj_name.map(|name| format!("{name}.{}", self.name)).unwrap_or_default(),
-            )
-            .map_err(|_| ())
-            .and_then(|value| Value::try_from(value))
-            .unwrap()
-    }
-
-    /// Sets the value of this field for a pointer-to-structure.
-    pub fn set(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        pobj: PointerValue<'ctx>,
-        value: Value,
-        obj_name: Option<&'ctx str>,
-    ) {
-        ctx.builder.build_store(self.ptr_by_gep(ctx, pobj, obj_name), value).unwrap();
-    }
-}
-
-impl<'ctx, Value> From<StructField<'ctx, Value>> for (&'static str, BasicTypeEnum<'ctx>)
-where
-    Value: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>, Error = ()>,
-{
-    fn from(value: StructField<'ctx, Value>) -> Self {
-        (value.name, value.ty)
-    }
-}
-
-/// A counter that tracks the next index of a field using a monotonically increasing counter.
-#[derive(Default, Debug, PartialEq, Eq, Clone, Copy)]
-pub struct FieldIndexCounter(u32);
-
-impl FieldIndexCounter {
-    /// Increments the number stored by this counter, returning the previous value.
-    ///
-    /// Functionally equivalent to `i++` in C-based languages.
-    pub fn increment(&mut self) -> u32 {
-        let v = self.0;
-        self.0 += 1;
-        v
-    }
-}

nac3core/src/codegen/values/array.rs

@@ -1,426 +0,0 @@
-use inkwell::{
-    types::AnyTypeEnum,
-    values::{BasicValueEnum, IntValue, PointerValue},
-    IntPredicate,
-};
-
-use crate::codegen::{CodeGenContext, CodeGenerator};
-
-/// An LLVM value that is array-like, i.e. it contains a contiguous, sequenced collection of
-/// elements.
-pub trait ArrayLikeValue<'ctx> {
-    /// Returns the element type of this array-like value.
-    fn element_type<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
-    ) -> AnyTypeEnum<'ctx>;
-
-    /// Returns the base pointer to the array.
-    fn base_ptr<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
-    ) -> PointerValue<'ctx>;
-
-    /// Returns the size of this array-like value.
-    fn size<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
-    ) -> IntValue<'ctx>;
-
-    /// Returns a [`ArraySliceValue`] representing this value.
-    fn as_slice_value<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
-    ) -> ArraySliceValue<'ctx> {
-        ArraySliceValue::from_ptr_val(
-            self.base_ptr(ctx, generator),
-            self.size(ctx, generator),
-            None,
-        )
-    }
-}
-
-/// An array-like value that can be indexed by memory offset.
-pub trait ArrayLikeIndexer<'ctx, Index = IntValue<'ctx>>: ArrayLikeValue<'ctx> {
-    /// # Safety
-    ///
-    /// 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,
-        idx: &Index,
-        name: Option<&str>,
-    ) -> PointerValue<'ctx>;
-
-    /// Returns the pointer to the data at the `idx`-th index.
-    fn ptr_offset<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        idx: &Index,
-        name: Option<&str>,
-    ) -> PointerValue<'ctx>;
-}
-
-/// An array-like value that can have its array elements accessed as a [`BasicValueEnum`].
-pub trait UntypedArrayLikeAccessor<'ctx, Index = IntValue<'ctx>>:
-    ArrayLikeIndexer<'ctx, Index>
-{
-    /// # Safety
-    ///
-    /// This function should be called with a valid index.
-    unsafe fn get_unchecked<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        idx: &Index,
-        name: Option<&str>,
-    ) -> BasicValueEnum<'ctx> {
-        let ptr = unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) };
-        ctx.builder.build_load(ptr, name.unwrap_or_default()).unwrap()
-    }
-
-    /// Returns the data at the `idx`-th index.
-    fn get<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        idx: &Index,
-        name: Option<&str>,
-    ) -> BasicValueEnum<'ctx> {
-        let ptr = self.ptr_offset(ctx, generator, idx, name);
-        ctx.builder.build_load(ptr, name.unwrap_or_default()).unwrap()
-    }
-}
-
-/// An array-like value that can have its array elements mutated as a [`BasicValueEnum`].
-pub trait UntypedArrayLikeMutator<'ctx, Index = IntValue<'ctx>>:
-    ArrayLikeIndexer<'ctx, Index>
-{
-    /// # Safety
-    ///
-    /// This function should be called with a valid index.
-    unsafe fn set_unchecked<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        idx: &Index,
-        value: BasicValueEnum<'ctx>,
-    ) {
-        let ptr = unsafe { self.ptr_offset_unchecked(ctx, generator, idx, None) };
-        ctx.builder.build_store(ptr, value).unwrap();
-    }
-
-    /// Sets the data at the `idx`-th index.
-    fn set<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        idx: &Index,
-        value: BasicValueEnum<'ctx>,
-    ) {
-        let ptr = self.ptr_offset(ctx, generator, idx, None);
-        ctx.builder.build_store(ptr, value).unwrap();
-    }
-}
-
-/// An array-like value that can have its array elements accessed as an arbitrary type `T`.
-pub trait TypedArrayLikeAccessor<'ctx, T, Index = IntValue<'ctx>>:
-    UntypedArrayLikeAccessor<'ctx, Index>
-{
-    /// Casts an element from [`BasicValueEnum`] into `T`.
-    fn downcast_to_type(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        value: BasicValueEnum<'ctx>,
-    ) -> T;
-
-    /// # Safety
-    ///
-    /// This function should be called with a valid index.
-    unsafe fn get_typed_unchecked<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        idx: &Index,
-        name: Option<&str>,
-    ) -> T {
-        let value = unsafe { self.get_unchecked(ctx, generator, idx, name) };
-        self.downcast_to_type(ctx, value)
-    }
-
-    /// Returns the data at the `idx`-th index.
-    fn get_typed<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        idx: &Index,
-        name: Option<&str>,
-    ) -> T {
-        let value = self.get(ctx, generator, idx, name);
-        self.downcast_to_type(ctx, 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>>:
-    UntypedArrayLikeMutator<'ctx, Index>
-{
-    /// Casts an element from T into [`BasicValueEnum`].
-    fn upcast_from_type(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        value: T,
-    ) -> BasicValueEnum<'ctx>;
-
-    /// # Safety
-    ///
-    /// This function should be called with a valid index.
-    unsafe fn set_typed_unchecked<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        idx: &Index,
-        value: T,
-    ) {
-        let value = self.upcast_from_type(ctx, value);
-        unsafe { self.set_unchecked(ctx, generator, idx, value) }
-    }
-
-    /// Sets the data at the `idx`-th index.
-    fn set_typed<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        idx: &Index,
-        value: T,
-    ) {
-        let value = self.upcast_from_type(ctx, 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>> {
-    adapted: Adapted,
-    downcast_fn: ValueDowncastFn<'ctx, T>,
-    upcast_fn: ValueUpcastFn<'ctx, T>,
-}
-
-impl<'ctx, T, Adapted> TypedArrayLikeAdapter<'ctx, T, Adapted>
-where
-    Adapted: ArrayLikeValue<'ctx>,
-{
-    /// Creates a [`TypedArrayLikeAdapter`].
-    ///
-    /// * `adapted` - The value to be adapted.
-    /// * `downcast_fn` - The function converting a [`BasicValueEnum`] into a `T`.
-    /// * `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>,
-    ) -> Self {
-        TypedArrayLikeAdapter { adapted, downcast_fn, upcast_fn }
-    }
-}
-
-impl<'ctx, T, Adapted> ArrayLikeValue<'ctx> for TypedArrayLikeAdapter<'ctx, T, Adapted>
-where
-    Adapted: ArrayLikeValue<'ctx>,
-{
-    fn element_type<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
-    ) -> AnyTypeEnum<'ctx> {
-        self.adapted.element_type(ctx, generator)
-    }
-
-    fn base_ptr<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
-    ) -> PointerValue<'ctx> {
-        self.adapted.base_ptr(ctx, generator)
-    }
-
-    fn size<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
-    ) -> IntValue<'ctx> {
-        self.adapted.size(ctx, generator)
-    }
-}
-
-impl<'ctx, T, Index, Adapted> ArrayLikeIndexer<'ctx, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
-where
-    Adapted: ArrayLikeIndexer<'ctx, Index>,
-{
-    unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        idx: &Index,
-        name: Option<&str>,
-    ) -> PointerValue<'ctx> {
-        unsafe { self.adapted.ptr_offset_unchecked(ctx, generator, idx, name) }
-    }
-
-    fn ptr_offset<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        idx: &Index,
-        name: Option<&str>,
-    ) -> PointerValue<'ctx> {
-        self.adapted.ptr_offset(ctx, generator, idx, name)
-    }
-}
-
-impl<'ctx, T, Index, Adapted> UntypedArrayLikeAccessor<'ctx, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
-where
-    Adapted: UntypedArrayLikeAccessor<'ctx, Index>,
-{
-}
-impl<'ctx, T, Index, Adapted> UntypedArrayLikeMutator<'ctx, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
-where
-    Adapted: UntypedArrayLikeMutator<'ctx, Index>,
-{
-}
-
-impl<'ctx, T, Index, Adapted> TypedArrayLikeAccessor<'ctx, T, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
-where
-    Adapted: UntypedArrayLikeAccessor<'ctx, Index>,
-{
-    fn downcast_to_type(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        value: BasicValueEnum<'ctx>,
-    ) -> T {
-        (self.downcast_fn)(ctx, value)
-    }
-}
-
-impl<'ctx, T, Index, Adapted> TypedArrayLikeMutator<'ctx, T, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
-where
-    Adapted: UntypedArrayLikeMutator<'ctx, Index>,
-{
-    fn upcast_from_type(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        value: T,
-    ) -> BasicValueEnum<'ctx> {
-        (self.upcast_fn)(ctx, value)
-    }
-}
-
-/// An LLVM value representing an array slice, consisting of a pointer to the data and the size of
-/// the slice.
-#[derive(Copy, Clone)]
-pub struct ArraySliceValue<'ctx>(PointerValue<'ctx>, IntValue<'ctx>, Option<&'ctx str>);
-
-impl<'ctx> ArraySliceValue<'ctx> {
-    /// Creates an [`ArraySliceValue`] from a [`PointerValue`] and its size.
-    #[must_use]
-    pub fn from_ptr_val(
-        ptr: PointerValue<'ctx>,
-        size: IntValue<'ctx>,
-        name: Option<&'ctx str>,
-    ) -> Self {
-        ArraySliceValue(ptr, size, name)
-    }
-}
-
-impl<'ctx> From<ArraySliceValue<'ctx>> for PointerValue<'ctx> {
-    fn from(value: ArraySliceValue<'ctx>) -> Self {
-        value.0
-    }
-}
-
-impl<'ctx> ArrayLikeValue<'ctx> for ArraySliceValue<'ctx> {
-    fn element_type<G: CodeGenerator + ?Sized>(
-        &self,
-        _: &CodeGenContext<'ctx, '_>,
-        _: &G,
-    ) -> AnyTypeEnum<'ctx> {
-        self.0.get_type().get_element_type()
-    }
-
-    fn base_ptr<G: CodeGenerator + ?Sized>(
-        &self,
-        _: &CodeGenContext<'ctx, '_>,
-        _: &G,
-    ) -> PointerValue<'ctx> {
-        self.0
-    }
-
-    fn size<G: CodeGenerator + ?Sized>(
-        &self,
-        _: &CodeGenContext<'ctx, '_>,
-        _: &G,
-    ) -> IntValue<'ctx> {
-        self.1
-    }
-}
-
-impl<'ctx> ArrayLikeIndexer<'ctx> for ArraySliceValue<'ctx> {
-    unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut 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> {
-        debug_assert_eq!(idx.get_type(), generator.get_size_type(ctx.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",
-            "list index out of range",
-            [None, None, None],
-            ctx.current_loc,
-        );
-
-        unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) }
-    }
-}
-
-impl<'ctx> UntypedArrayLikeAccessor<'ctx> for ArraySliceValue<'ctx> {}
-impl<'ctx> UntypedArrayLikeMutator<'ctx> for ArraySliceValue<'ctx> {}

nac3core/src/codegen/values/list.rs

@@ -1,241 +0,0 @@
-use inkwell::{
-    types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType},
-    values::{BasicValueEnum, IntValue, PointerValue},
-    AddressSpace, IntPredicate,
-};
-
-use super::{
-    ArrayLikeIndexer, ArrayLikeValue, ProxyValue, UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
-};
-use crate::codegen::{
-    types::ListType,
-    {CodeGenContext, CodeGenerator},
-};
-
-/// Proxy type for accessing a `list` value in LLVM.
-#[derive(Copy, Clone)]
-pub struct ListValue<'ctx> {
-    value: PointerValue<'ctx>,
-    llvm_usize: IntType<'ctx>,
-    name: Option<&'ctx str>,
-}
-
-impl<'ctx> ListValue<'ctx> {
-    /// Checks whether `value` is an instance of `list`, returning [Err] if `value` is not an
-    /// instance.
-    pub fn is_representable(
-        value: PointerValue<'ctx>,
-        llvm_usize: IntType<'ctx>,
-    ) -> Result<(), String> {
-        ListType::is_representable(value.get_type(), llvm_usize)
-    }
-
-    /// Creates an [`ListValue`] 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());
-
-        ListValue { value: ptr, llvm_usize, name }
-    }
-
-    /// 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()
-        }
-    }
-
-    /// 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();
-    }
-
-    /// 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.
-    pub fn create_data(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        elem_ty: BasicTypeEnum<'ctx>,
-        size: Option<IntValue<'ctx>>,
-    ) {
-        let size = size.unwrap_or_else(|| self.load_size(ctx, None));
-
-        let data = ctx
-            .builder
-            .build_select(
-                ctx.builder
-                    .build_int_compare(IntPredicate::NE, size, self.llvm_usize.const_zero(), "")
-                    .unwrap(),
-                ctx.builder.build_array_alloca(elem_ty, size, "").unwrap(),
-                elem_ty.ptr_type(AddressSpace::default()).const_zero(),
-                "",
-            )
-            .map(BasicValueEnum::into_pointer_value)
-            .unwrap();
-        self.store_data(ctx, data);
-    }
-
-    /// Returns the double-indirection pointer to the `data` array, as if by calling `getelementptr`
-    /// on the field.
-    #[must_use]
-    pub fn data(&self) -> ListDataProxy<'ctx, '_> {
-        ListDataProxy(self)
-    }
-
-    /// Stores the `size` of this `list` into this instance.
-    pub fn store_size<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
-        size: IntValue<'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();
-    }
-
-    /// 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();
-
-        ctx.builder
-            .build_load(psize, var_name.as_str())
-            .map(BasicValueEnum::into_int_value)
-            .unwrap()
-    }
-}
-
-impl<'ctx> ProxyValue<'ctx> for ListValue<'ctx> {
-    type Base = PointerValue<'ctx>;
-    type Type = ListType<'ctx>;
-
-    fn get_type(&self) -> Self::Type {
-        ListType::from_type(self.as_base_value().get_type(), self.llvm_usize)
-    }
-
-    fn as_base_value(&self) -> Self::Base {
-        self.value
-    }
-}
-
-impl<'ctx> From<ListValue<'ctx>> for PointerValue<'ctx> {
-    fn from(value: ListValue<'ctx>) -> Self {
-        value.as_base_value()
-    }
-}
-
-/// Proxy type for accessing the `data` array of an `list` instance in LLVM.
-#[derive(Copy, Clone)]
-pub struct ListDataProxy<'ctx, 'a>(&'a ListValue<'ctx>);
-
-impl<'ctx> ArrayLikeValue<'ctx> for ListDataProxy<'ctx, '_> {
-    fn element_type<G: CodeGenerator + ?Sized>(
-        &self,
-        _: &CodeGenContext<'ctx, '_>,
-        _: &G,
-    ) -> AnyTypeEnum<'ctx> {
-        self.0.value.get_type().get_element_type()
-    }
-
-    fn base_ptr<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        _: &G,
-    ) -> PointerValue<'ctx> {
-        let var_name = self.0.name.map(|v| format!("{v}.data")).unwrap_or_default();
-
-        ctx.builder
-            .build_load(self.0.pptr_to_data(ctx), var_name.as_str())
-            .map(BasicValueEnum::into_pointer_value)
-            .unwrap()
-    }
-
-    fn size<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        _: &G,
-    ) -> IntValue<'ctx> {
-        self.0.load_size(ctx, None)
-    }
-}
-
-impl<'ctx> ArrayLikeIndexer<'ctx> for ListDataProxy<'ctx, '_> {
-    unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut 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> {
-        debug_assert_eq!(idx.get_type(), generator.get_size_type(ctx.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",
-            "list index out of range",
-            [None, None, None],
-            ctx.current_loc,
-        );
-
-        unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) }
-    }
-}
-
-impl<'ctx> UntypedArrayLikeAccessor<'ctx> for ListDataProxy<'ctx, '_> {}
-impl<'ctx> UntypedArrayLikeMutator<'ctx> for ListDataProxy<'ctx, '_> {}

nac3core/src/codegen/values/mod.rs

@@ -1,47 +0,0 @@
-use inkwell::{context::Context, values::BasicValue};
-
-use super::types::ProxyType;
-use crate::codegen::CodeGenerator;
-pub use array::*;
-pub use list::*;
-pub use ndarray::*;
-pub use range::*;
-
-mod array;
-mod list;
-mod ndarray;
-mod range;
-
-/// A LLVM type that is used to represent a non-primitive value in NAC3.
-pub trait ProxyValue<'ctx>: Into<Self::Base> {
-    /// The type of LLVM values represented by this instance. This is usually the
-    /// [LLVM pointer type][PointerValue].
-    type Base: BasicValue<'ctx>;
-
-    /// The type of this value.
-    type Type: ProxyType<'ctx, Value = Self>;
-
-    /// Checks whether `value` can be represented by this [`ProxyValue`].
-    fn is_instance<G: CodeGenerator + ?Sized>(
-        generator: &G,
-        ctx: &'ctx Context,
-        value: impl BasicValue<'ctx>,
-    ) -> Result<(), String> {
-        Self::Type::is_type(generator, ctx, value.as_basic_value_enum().get_type())
-    }
-
-    /// Checks whether `value` can be represented by this [`ProxyValue`].
-    fn is_representable<G: CodeGenerator + ?Sized>(
-        generator: &G,
-        ctx: &'ctx Context,
-        value: Self::Base,
-    ) -> Result<(), String> {
-        Self::is_instance(generator, ctx, value.as_basic_value_enum())
-    }
-
-    /// Returns the [type][ProxyType] of this value.
-    fn get_type(&self) -> Self::Type;
-
-    /// Returns the [base value][Self::Base] of this proxy.
-    fn as_base_value(&self) -> Self::Base;
-}

nac3core/src/codegen/values/ndarray.rs

@@ -1,523 +0,0 @@
-use inkwell::{
-    types::{AnyType, AnyTypeEnum, BasicType, BasicTypeEnum, IntType},
-    values::{BasicValueEnum, IntValue, PointerValue},
-    AddressSpace, IntPredicate,
-};
-
-use super::{
-    ArrayLikeIndexer, ArrayLikeValue, ProxyValue, TypedArrayLikeAccessor, 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::NDArrayType,
-    CodeGenContext, CodeGenerator,
-};
-
-/// Proxy type for accessing an `NDArray` value in LLVM.
-#[derive(Copy, Clone)]
-pub struct NDArrayValue<'ctx> {
-    value: PointerValue<'ctx>,
-    dtype: BasicTypeEnum<'ctx>,
-    llvm_usize: IntType<'ctx>,
-    name: Option<&'ctx str>,
-}
-
-impl<'ctx> NDArrayValue<'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> {
-        NDArrayType::is_representable(value.get_type(), llvm_usize)
-    }
-
-    /// Creates an [`NDArrayValue`] 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());
-
-        NDArrayValue { value: ptr, dtype, llvm_usize, name }
-    }
-
-    /// Returns the pointer to the field storing the number of dimensions of this `NDArray`.
-    fn ptr_to_ndims(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
-        self.get_type()
-            .get_fields(ctx.ctx, self.llvm_usize)
-            .ndims
-            .ptr_by_gep(ctx, self.value, self.name)
-    }
-
-    /// Stores the number of dimensions `ndims` into this instance.
-    pub fn store_ndims<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
-        ndims: IntValue<'ctx>,
-    ) {
-        debug_assert_eq!(ndims.get_type(), generator.get_size_type(ctx.ctx));
-
-        let pndims = self.ptr_to_ndims(ctx);
-        ctx.builder.build_store(pndims, ndims).unwrap();
-    }
-
-    /// Returns the number of dimensions of this `NDArray` as a value.
-    pub fn load_ndims(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
-        let pndims = self.ptr_to_ndims(ctx);
-        ctx.builder.build_load(pndims, "").map(BasicValueEnum::into_int_value).unwrap()
-    }
-
-    /// 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.get_type()
-            .get_fields(ctx.ctx, self.llvm_usize)
-            .shape
-            .ptr_by_gep(ctx, self.value, self.name)
-    }
-
-    /// Stores the array of dimension sizes `dims` into this instance.
-    fn store_shape(&self, ctx: &CodeGenContext<'ctx, '_>, dims: PointerValue<'ctx>) {
-        ctx.builder.build_store(self.ptr_to_shape(ctx), dims).unwrap();
-    }
-
-    /// Convenience method for creating a new array storing dimension sizes with the given `size`.
-    pub fn create_shape(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        llvm_usize: IntType<'ctx>,
-        size: IntValue<'ctx>,
-    ) {
-        self.store_shape(ctx, ctx.builder.build_array_alloca(llvm_usize, size, "").unwrap());
-    }
-
-    /// Returns a proxy object to the field storing the size of each dimension of this `NDArray`.
-    #[must_use]
-    pub fn shape(&self) -> NDArrayShapeProxy<'ctx, '_> {
-        NDArrayShapeProxy(self)
-    }
-
-    /// Returns the double-indirection pointer to the `data` array, as if by calling `getelementptr`
-    /// on the field.
-    pub fn ptr_to_data(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
-        self.get_type()
-            .get_fields(ctx.ctx, self.llvm_usize)
-            .data
-            .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>) {
-        let data = ctx
-            .builder
-            .build_bit_cast(data, ctx.ctx.i8_type().ptr_type(AddressSpace::default()), "")
-            .unwrap();
-        ctx.builder.build_store(self.ptr_to_data(ctx), data).unwrap();
-    }
-
-    /// Convenience method for creating a new array storing data elements with the given element
-    /// type `elem_ty` and `size`.
-    pub fn create_data(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        elem_ty: BasicTypeEnum<'ctx>,
-        size: IntValue<'ctx>,
-    ) {
-        let itemsize =
-            ctx.builder.build_int_cast(elem_ty.size_of().unwrap(), size.get_type(), "").unwrap();
-        let nbytes = ctx.builder.build_int_mul(size, itemsize, "").unwrap();
-
-        // TODO: What about alignment?
-        self.store_data(
-            ctx,
-            ctx.builder.build_array_alloca(ctx.ctx.i8_type(), nbytes, "").unwrap(),
-        );
-    }
-
-    /// Returns a proxy object to the field storing the data of this `NDArray`.
-    #[must_use]
-    pub fn data(&self) -> NDArrayDataProxy<'ctx, '_> {
-        NDArrayDataProxy(self)
-    }
-}
-
-impl<'ctx> ProxyValue<'ctx> for NDArrayValue<'ctx> {
-    type Base = PointerValue<'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)
-    }
-
-    fn as_base_value(&self) -> Self::Base {
-        self.value
-    }
-}
-
-impl<'ctx> From<NDArrayValue<'ctx>> for PointerValue<'ctx> {
-    fn from(value: NDArrayValue<'ctx>) -> Self {
-        value.as_base_value()
-    }
-}
-
-/// Proxy type for accessing the `dims` array of an `NDArray` instance in LLVM.
-#[derive(Copy, Clone)]
-pub struct NDArrayShapeProxy<'ctx, 'a>(&'a NDArrayValue<'ctx>);
-
-impl<'ctx> ArrayLikeValue<'ctx> for NDArrayShapeProxy<'ctx, '_> {
-    fn element_type<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
-    ) -> AnyTypeEnum<'ctx> {
-        self.0.shape().base_ptr(ctx, generator).get_type().get_element_type()
-    }
-
-    fn base_ptr<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        _: &G,
-    ) -> PointerValue<'ctx> {
-        let var_name = self.0.name.map(|v| format!("{v}.data")).unwrap_or_default();
-
-        ctx.builder
-            .build_load(self.0.ptr_to_shape(ctx), var_name.as_str())
-            .map(BasicValueEnum::into_pointer_value)
-            .unwrap()
-    }
-
-    fn size<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        _: &G,
-    ) -> IntValue<'ctx> {
-        self.0.load_ndims(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,
-        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 NDArrayShapeProxy<'ctx, '_> {}
-impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {}
-
-impl<'ctx> TypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {
-    fn downcast_to_type(
-        &self,
-        _: &mut CodeGenContext<'ctx, '_>,
-        value: BasicValueEnum<'ctx>,
-    ) -> IntValue<'ctx> {
-        value.into_int_value()
-    }
-}
-
-impl<'ctx> TypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {
-    fn upcast_from_type(
-        &self,
-        _: &mut CodeGenContext<'ctx, '_>,
-        value: IntValue<'ctx>,
-    ) -> BasicValueEnum<'ctx> {
-        value.into()
-    }
-}
-
-/// Proxy type for accessing the `data` array of an `NDArray` instance in LLVM.
-#[derive(Copy, Clone)]
-pub struct NDArrayDataProxy<'ctx, 'a>(&'a NDArrayValue<'ctx>);
-
-impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDataProxy<'ctx, '_> {
-    fn element_type<G: CodeGenerator + ?Sized>(
-        &self,
-        _: &CodeGenContext<'ctx, '_>,
-        _: &G,
-    ) -> AnyTypeEnum<'ctx> {
-        self.0.dtype.as_any_type_enum()
-    }
-
-    fn base_ptr<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        _: &G,
-    ) -> PointerValue<'ctx> {
-        let var_name = self.0.name.map(|v| format!("{v}.data")).unwrap_or_default();
-
-        ctx.builder
-            .build_load(self.0.ptr_to_data(ctx), var_name.as_str())
-            .map(BasicValueEnum::into_pointer_value)
-            .unwrap()
-    }
-
-    fn size<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
-    ) -> IntValue<'ctx> {
-        call_ndarray_calc_size(generator, ctx, &self.as_slice_value(ctx, generator), (None, None))
-    }
-}
-
-impl<'ctx> ArrayLikeIndexer<'ctx> for NDArrayDataProxy<'ctx, '_> {
-    unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut 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()
-        };
-
-        // 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()),
-                "",
-            )
-            .unwrap()
-    }
-
-    fn ptr_offset<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        idx: &IntValue<'ctx>,
-        name: Option<&str>,
-    ) -> PointerValue<'ctx> {
-        let data_sz = self.size(ctx, generator);
-        let in_range = ctx.builder.build_int_compare(IntPredicate::ULT, *idx, data_sz, "").unwrap();
-        ctx.make_assert(
-            generator,
-            in_range,
-            "0:IndexError",
-            "index {0} is out of bounds with size {1}",
-            [Some(*idx), Some(self.0.load_ndims(ctx)), None],
-            ctx.current_loc,
-        );
-
-        let ptr = unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) };
-
-        // 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()),
-                "",
-            )
-            .unwrap()
-    }
-}
-
-impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
-impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
-
-impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
-    for NDArrayDataProxy<'ctx, '_>
-{
-    unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        indices: &Index,
-        name: Option<&str>,
-    ) -> PointerValue<'ctx> {
-        let llvm_usize = generator.get_size_type(ctx.ctx);
-
-        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 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 = unsafe {
-            ctx.builder
-                .build_in_bounds_gep(
-                    self.base_ptr(ctx, generator),
-                    &[index],
-                    name.unwrap_or_default(),
-                )
-                .unwrap()
-        };
-        // TODO: Current implementation is transparent
-        ctx.builder
-            .build_pointer_cast(
-                ptr,
-                BasicTypeEnum::try_from(self.element_type(ctx, generator))
-                    .unwrap()
-                    .ptr_type(AddressSpace::default()),
-                "",
-            )
-            .unwrap()
-    }
-
-    fn ptr_offset<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        indices: &Index,
-        name: Option<&str>,
-    ) -> PointerValue<'ctx> {
-        let llvm_usize = generator.get_size_type(ctx.ctx);
-
-        let indices_size = indices.size(ctx, generator);
-        let nidx_leq_ndims = ctx
-            .builder
-            .build_int_compare(IntPredicate::SLE, indices_size, self.0.load_ndims(ctx), "")
-            .unwrap();
-        ctx.make_assert(
-            generator,
-            nidx_leq_ndims,
-            "0:IndexError",
-            "invalid index to scalar variable",
-            [None, None, None],
-            ctx.current_loc,
-        );
-
-        let indices_len = indices.size(ctx, generator);
-        let ndarray_len = self.0.load_ndims(ctx);
-        let len = call_int_umin(ctx, indices_len, ndarray_len, None);
-        gen_for_callback_incrementing(
-            generator,
-            ctx,
-            None,
-            llvm_usize.const_zero(),
-            (len, false),
-            |generator, ctx, _, i| {
-                let (dim_idx, dim_sz) = unsafe {
-                    (
-                        indices.get_unchecked(ctx, generator, &i, None).into_int_value(),
-                        self.0.shape().get_typed_unchecked(ctx, generator, &i, None),
-                    )
-                };
-                let dim_idx = ctx
-                    .builder
-                    .build_int_z_extend_or_bit_cast(dim_idx, dim_sz.get_type(), "")
-                    .unwrap();
-
-                let dim_lt =
-                    ctx.builder.build_int_compare(IntPredicate::SLT, dim_idx, dim_sz, "").unwrap();
-
-                ctx.make_assert(
-                    generator,
-                    dim_lt,
-                    "0:IndexError",
-                    "index {0} is out of bounds for axis 0 with size {1}",
-                    [Some(dim_idx), Some(dim_sz), None],
-                    ctx.current_loc,
-                );
-
-                Ok(())
-            },
-            llvm_usize.const_int(1, false),
-        )
-        .unwrap();
-
-        let ptr = unsafe { self.ptr_offset_unchecked(ctx, generator, indices, name) };
-        // TODO: Current implementation is transparent
-        ctx.builder
-            .build_pointer_cast(
-                ptr,
-                BasicTypeEnum::try_from(self.element_type(ctx, generator))
-                    .unwrap()
-                    .ptr_type(AddressSpace::default()),
-                "",
-            )
-            .unwrap()
-    }
-}
-
-impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> UntypedArrayLikeAccessor<'ctx, Index>
-    for NDArrayDataProxy<'ctx, '_>
-{
-}
-impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> UntypedArrayLikeMutator<'ctx, Index>
-    for NDArrayDataProxy<'ctx, '_>
-{
-}

nac3core/src/codegen/values/range.rs

@@ -1,153 +0,0 @@
-use inkwell::values::{BasicValueEnum, IntValue, PointerValue};
-
-use super::ProxyValue;
-use crate::codegen::{types::RangeType, CodeGenContext};
-
-/// Proxy type for accessing a `range` value in LLVM.
-#[derive(Copy, Clone)]
-pub struct RangeValue<'ctx> {
-    value: PointerValue<'ctx>,
-    name: Option<&'ctx str>,
-}
-
-impl<'ctx> RangeValue<'ctx> {
-    /// Checks whether `value` is an instance of `range`, returning [Err] if `value` is not an instance.
-    pub fn is_representable(value: PointerValue<'ctx>) -> Result<(), String> {
-        RangeType::is_representable(value.get_type())
-    }
-
-    /// Creates an [`RangeValue`] from a [`PointerValue`].
-    #[must_use]
-    pub fn from_pointer_value(ptr: PointerValue<'ctx>, name: Option<&'ctx str>) -> Self {
-        debug_assert!(Self::is_representable(ptr).is_ok());
-
-        RangeValue { value: ptr, name }
-    }
-
-    fn ptr_to_start(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
-        let llvm_i32 = ctx.ctx.i32_type();
-        let var_name = self.name.map(|v| format!("{v}.start.addr")).unwrap_or_default();
-
-        unsafe {
-            ctx.builder
-                .build_in_bounds_gep(
-                    self.as_base_value(),
-                    &[llvm_i32.const_zero(), llvm_i32.const_int(0, false)],
-                    var_name.as_str(),
-                )
-                .unwrap()
-        }
-    }
-
-    fn ptr_to_end(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
-        let llvm_i32 = ctx.ctx.i32_type();
-        let var_name = self.name.map(|v| format!("{v}.end.addr")).unwrap_or_default();
-
-        unsafe {
-            ctx.builder
-                .build_in_bounds_gep(
-                    self.as_base_value(),
-                    &[llvm_i32.const_zero(), llvm_i32.const_int(1, false)],
-                    var_name.as_str(),
-                )
-                .unwrap()
-        }
-    }
-
-    fn ptr_to_step(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
-        let llvm_i32 = ctx.ctx.i32_type();
-        let var_name = self.name.map(|v| format!("{v}.step.addr")).unwrap_or_default();
-
-        unsafe {
-            ctx.builder
-                .build_in_bounds_gep(
-                    self.as_base_value(),
-                    &[llvm_i32.const_zero(), llvm_i32.const_int(2, false)],
-                    var_name.as_str(),
-                )
-                .unwrap()
-        }
-    }
-
-    /// Stores the `start` value into this instance.
-    pub fn store_start(&self, ctx: &CodeGenContext<'ctx, '_>, start: IntValue<'ctx>) {
-        debug_assert_eq!(start.get_type().get_bit_width(), 32);
-
-        let pstart = self.ptr_to_start(ctx);
-        ctx.builder.build_store(pstart, start).unwrap();
-    }
-
-    /// Returns the `start` value of this `range`.
-    pub fn load_start(&self, ctx: &CodeGenContext<'ctx, '_>, name: Option<&str>) -> IntValue<'ctx> {
-        let pstart = self.ptr_to_start(ctx);
-        let var_name = name
-            .map(ToString::to_string)
-            .or_else(|| self.name.map(|v| format!("{v}.start")))
-            .unwrap_or_default();
-
-        ctx.builder
-            .build_load(pstart, var_name.as_str())
-            .map(BasicValueEnum::into_int_value)
-            .unwrap()
-    }
-
-    /// Stores the `end` value into this instance.
-    pub fn store_end(&self, ctx: &CodeGenContext<'ctx, '_>, end: IntValue<'ctx>) {
-        debug_assert_eq!(end.get_type().get_bit_width(), 32);
-
-        let pend = self.ptr_to_end(ctx);
-        ctx.builder.build_store(pend, end).unwrap();
-    }
-
-    /// Returns the `end` value of this `range`.
-    pub fn load_end(&self, ctx: &CodeGenContext<'ctx, '_>, name: Option<&str>) -> IntValue<'ctx> {
-        let pend = self.ptr_to_end(ctx);
-        let var_name = name
-            .map(ToString::to_string)
-            .or_else(|| self.name.map(|v| format!("{v}.end")))
-            .unwrap_or_default();
-
-        ctx.builder.build_load(pend, var_name.as_str()).map(BasicValueEnum::into_int_value).unwrap()
-    }
-
-    /// Stores the `step` value into this instance.
-    pub fn store_step(&self, ctx: &CodeGenContext<'ctx, '_>, step: IntValue<'ctx>) {
-        debug_assert_eq!(step.get_type().get_bit_width(), 32);
-
-        let pstep = self.ptr_to_step(ctx);
-        ctx.builder.build_store(pstep, step).unwrap();
-    }
-
-    /// Returns the `step` value of this `range`.
-    pub fn load_step(&self, ctx: &CodeGenContext<'ctx, '_>, name: Option<&str>) -> IntValue<'ctx> {
-        let pstep = self.ptr_to_step(ctx);
-        let var_name = name
-            .map(ToString::to_string)
-            .or_else(|| self.name.map(|v| format!("{v}.step")))
-            .unwrap_or_default();
-
-        ctx.builder
-            .build_load(pstep, var_name.as_str())
-            .map(BasicValueEnum::into_int_value)
-            .unwrap()
-    }
-}
-
-impl<'ctx> ProxyValue<'ctx> for RangeValue<'ctx> {
-    type Base = PointerValue<'ctx>;
-    type Type = RangeType<'ctx>;
-
-    fn get_type(&self) -> Self::Type {
-        RangeType::from_type(self.value.get_type())
-    }
-
-    fn as_base_value(&self) -> Self::Base {
-        self.value
-    }
-}
-
-impl<'ctx> From<RangeValue<'ctx>> for PointerValue<'ctx> {
-    fn from(value: RangeValue<'ctx>) -> Self {
-        value.as_base_value()
-    }
-}

nac3core/src/lib.rs

@@ -1,4 +1,10 @@
-#![deny(future_incompatible, let_underscore, nonstandard_style, clippy::all)]
+#![deny(
+    future_incompatible,
+    let_underscore,
+    nonstandard_style,
+    rust_2024_compatibility,
+    clippy::all
+)]
 #![warn(clippy::pedantic)]
 #![allow(
     dead_code,
@@ -21,5 +27,3 @@ pub mod codegen;
 pub mod symbol_resolver;
 pub mod toplevel;
 pub mod typecheck;
-
-extern crate self as nac3core;

nac3core/src/toplevel/builtins.rs

@@ -11,16 +11,23 @@ use itertools::Either;
 use strum::IntoEnumIterator;
 
 use super::{
-    helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDef, PrimDefDetails},
+    helper::{
-    numpy::make_ndarray_ty,
+        debug_assert_prim_is_allowed, extract_ndims, make_exception_fields, PrimDef, PrimDefDetails,
+    },
+    numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
     *,
 };
 use crate::{
     codegen::{
         builtin_fns,
+        classes::{ProxyValue, RangeValue},
+        model::*,
         numpy::*,
+        object::{
+            any::AnyObject,
+            ndarray::{shape_util::parse_numpy_int_sequence, NDArrayObject},
+        },
         stmt::exn_constructor,
-        values::{ProxyValue, RangeValue},
     },
     symbol_resolver::SymbolValue,
     typecheck::typedef::{into_var_map, iter_type_vars, TypeVar, VarMap},
@@ -512,6 +519,14 @@ 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 +592,6 @@ 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
@@ -710,7 +721,7 @@ impl<'a> BuiltinBuilder<'a> {
                         let (zelf_ty, zelf) = obj.unwrap();
                         let zelf =
                             zelf.to_basic_value_enum(ctx, generator, zelf_ty)?.into_pointer_value();
-                        let zelf = RangeValue::from_pointer_value(zelf, Some("range"));
+                        let zelf = RangeValue::from_ptr_val(zelf, Some("range"));
 
                         let mut start = None;
                         let mut stop = None;
@@ -1386,6 +1397,171 @@ impl<'a> BuiltinBuilder<'a> {
         }
     }
 
+    fn build_ndarray_property_getter_function(&mut self, prim: PrimDef) -> TopLevelDef {
+        debug_assert_prim_is_allowed(
+            prim,
+            &[PrimDef::FunNpSize, PrimDef::FunNpShape, PrimDef::FunNpStrides],
+        );
+
+        let in_ndarray_ty = self.unifier.get_fresh_var_with_range(
+            &[self.primitives.ndarray],
+            Some("T".into()),
+            None,
+        );
+
+        match prim {
+            PrimDef::FunNpSize => create_fn_by_codegen(
+                self.unifier,
+                &into_var_map([in_ndarray_ty]),
+                prim.name(),
+                self.primitives.int32,
+                &[(in_ndarray_ty.ty, "a")],
+                Box::new(|ctx, obj, fun, args, generator| {
+                    assert!(obj.is_none());
+                    assert_eq!(args.len(), 1);
+
+                    let ndarray_ty = fun.0.args[0].ty;
+                    let ndarray =
+                        args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
+                    let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
+                    let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
+
+                    let size =
+                        ndarray.size(generator, ctx).truncate_or_bit_cast(generator, ctx, Int32);
+                    Ok(Some(size.value.as_basic_value_enum()))
+                }),
+            ),
+            PrimDef::FunNpShape | PrimDef::FunNpStrides => {
+                // The function signatures of `np_shape` an `np_size` are the same.
+                // Mixed together for convenience.
+
+                // The return type is a tuple of variable length depending on the ndims of the input ndarray.
+                let ret_ty = self.unifier.get_dummy_var().ty; // Handled by special folding
+
+                create_fn_by_codegen(
+                    self.unifier,
+                    &into_var_map([in_ndarray_ty]),
+                    prim.name(),
+                    ret_ty,
+                    &[(in_ndarray_ty.ty, "a")],
+                    Box::new(move |ctx, obj, fun, args, generator| {
+                        assert!(obj.is_none());
+                        assert_eq!(args.len(), 1);
+
+                        let ndarray_ty = fun.0.args[0].ty;
+                        let ndarray =
+                            args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
+
+                        let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
+                        let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
+
+                        let result_tuple = match prim {
+                            PrimDef::FunNpShape => ndarray.make_shape_tuple(generator, ctx),
+                            PrimDef::FunNpStrides => ndarray.make_strides_tuple(generator, ctx),
+                            _ => unreachable!(),
+                        };
+
+                        Ok(Some(result_tuple.value.as_basic_value_enum()))
+                    }),
+                )
+            }
+            _ => unreachable!(),
+        }
+    }
+
+    /// Build np/sp functions that take as input `NDArray` only
+    fn build_ndarray_view_function(&mut self, prim: PrimDef) -> TopLevelDef {
+        debug_assert_prim_is_allowed(
+            prim,
+            &[PrimDef::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 arg = AnyObject { ty: arg_ty, value: arg_val };
+                        let ndarray = NDArrayObject::from_object(generator, ctx, arg);
+
+                        let ndarray = ndarray.transpose(generator, ctx, None); // TODO: Add axes argument
+                        Ok(Some(ndarray.instance.value.as_basic_value_enum()))
+                    }),
+                )
+            }
+
+            // NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
+            // the `param_ty` for `create_fn_by_codegen`.
+            //
+            // 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 = AnyObject { value: ndarray_val, ty: ndarray_ty };
+                        let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
+
+                        let shape = AnyObject { value: shape_val, ty: shape_ty };
+                        let (_, shape) = parse_numpy_int_sequence(generator, ctx, shape);
+
+                        // The ndims after reshaping is gotten from the return type of the call.
+                        let (_, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
+                        let ndims = extract_ndims(&ctx.unifier, ndims);
+
+                        let new_ndarray = match prim {
+                            PrimDef::FunNpBroadcastTo => {
+                                ndarray.broadcast_to(generator, ctx, ndims, shape)
+                            }
+                            PrimDef::FunNpReshape => {
+                                ndarray.reshape_or_copy(generator, ctx, ndims, shape)
+                            }
+                            _ => unreachable!(),
+                        };
+                        Ok(Some(new_ndarray.instance.value.as_basic_value_enum()))
+                    }),
+                )
+            }
+
+            _ => unreachable!(),
+        }
+    }
+
     /// Build the `str()` function.
     fn build_str_function(&mut self) -> TopLevelDef {
         let prim = PrimDef::FunStr;
@@ -1873,57 +2049,6 @@ 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 +2080,12 @@ 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/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

@@ -5,7 +5,7 @@ expression: res_vec
 [
     "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(241)]\n}\n",
+    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(257)]\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [\"aa\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\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[typevar246]\", \"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: [\"typevar246\"]\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.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(259)]\n}\n",
-    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(248)]\n}\n",
+    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(264)]\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[typevar245, typevar246]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar245\", \"typevar246\"]\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(265)]\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",
     "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",
     "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(273)]\n}\n",
 ]

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 {
+        let other_tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
-            (other_ty[0], None)
+        let function_vars = into_var_map([other_tvar]);
-        } 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 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_dtype, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
-            let lhs_ndims = match &*unifier.get_ty_immutable(lhs_ndims) {
+            let lhs_ndims = extract_ndims(unifier, lhs_ndims);
-                TypeEnum::TLiteral { values, .. } => {
+
-                    assert_eq!(values.len(), 1);
+            let (rhs_dtype, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
-                    u64::try_from(values[0].clone()).unwrap()
+            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)
+                ));
             }
-                _ => unreachable!(),
+
-            };
+            // Deduce the ndims of the resulting ndarray.
-            let (_, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
+            // If this is 0 (an unsized ndarray), matmul returns a scalar just like NumPy.
-            let rhs_ndims = match &*unifier.get_ty_immutable(rhs_ndims) {
+            let result_ndims = match (lhs_ndims, rhs_ndims) {
-                TypeEnum::TLiteral { values, .. } => {
+                (0, _) | (_, 0) => {
-                    assert_eq!(values.len(), 1);
+                    return Err(
-                    u64::try_from(values[0].clone()).unwrap()
+                        "ndarray.__matmul__ does not allow unsized ndarray input".to_string()
+                    )
                 }
-                _ => unreachable!(),
+                (1, 1) => 0,
+                (1, _) => rhs_ndims - 1,
+                (_, 1) => lhs_ndims - 1,
+                (m, n) => max(m, n),
             };
 
-            match (lhs_ndims, rhs_ndims) {
+            if result_ndims == 0 {
-                (2, 2) => typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?,
+                // If the result is unsized, NumPy returns a scalar.
-                (lhs, rhs) if lhs == 0 || rhs == 0 => {
+                primitives.float
-                    return Err(format!(
+            } else {
-                    "Input operand {} does not have enough dimensions (has {lhs}, requires {rhs})",
+                let result_ndims_ty =
-                    u8::from(rhs == 0)
+                    unifier.get_fresh_literal(vec![SymbolValue::U64(result_ndims)], None);
-                ))
+                make_ndarray_ty(unifier, primitives, Some(primitives.float), Some(result_ndims_ty))
-                }
-                (lhs, rhs) => {
-                    return Err(format!(
-                        "ndarray.__matmul__ on {lhs}D and {rhs}D operands not supported"
-                    ))
-                }
             }
         }
 
@@ -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_inferencer/mod.rs

@@ -3,7 +3,7 @@ use std::{
     cmp::max,
     collections::{HashMap, HashSet},
     convert::{From, TryInto},
-    iter::once,
+    iter::{self, once},
     sync::Arc,
 };
 
@@ -536,8 +536,9 @@ impl<'a> Fold<()> for Inferencer<'a> {
             }
             ast::StmtKind::Assert { test, msg, .. } => {
                 self.unify(test.custom.unwrap(), self.primitives.bool, &test.location)?;
-                if let Some(m) = msg {
+                match msg {
-                    self.unify(m.custom.unwrap(), self.primitives.str, &m.location)?;
+                    Some(m) => self.unify(m.custom.unwrap(), self.primitives.str, &m.location)?,
+                    None => (),
                 }
             }
             _ => return report_error("Unsupported statement type", stmt.location),
@@ -1234,6 +1235,45 @@ impl<'a> Inferencer<'a> {
             }));
         }
 
+        if ["np_shape".into(), "np_strides".into()].contains(id) && args.len() == 1 {
+            let ndarray = self.fold_expr(args.remove(0))?;
+
+            let ndims = arraylike_get_ndims(self.unifier, ndarray.custom.unwrap());
+
+            // Make a tuple of size `ndims` full of int32 (TODO: Make it usize)
+            let ret_ty = TypeEnum::TTuple {
+                ty: iter::repeat(self.primitives.int32).take(ndims as usize).collect_vec(),
+                is_vararg_ctx: false,
+            };
+            let ret_ty = self.unifier.add_ty(ret_ty);
+
+            let func_ty = TypeEnum::TFunc(FunSignature {
+                args: vec![FuncArg {
+                    name: "a".into(),
+                    default_value: None,
+                    ty: ndarray.custom.unwrap(),
+                    is_vararg: false,
+                }],
+                ret: ret_ty,
+                vars: VarMap::new(),
+            });
+            let func_ty = self.unifier.add_ty(func_ty);
+
+            return Ok(Some(Located {
+                location,
+                custom: Some(ret_ty),
+                node: ExprKind::Call {
+                    func: Box::new(Located {
+                        custom: Some(func_ty),
+                        location: func.location,
+                        node: ExprKind::Name { id: *id, ctx: *ctx },
+                    }),
+                    args: vec![ndarray],
+                    keywords: vec![],
+                },
+            }));
+        }
+
         if id == &"np_dot".into() {
             let arg0 = self.fold_expr(args.remove(0))?;
             let arg1 = self.fold_expr(args.remove(0))?;
@@ -1555,7 +1595,7 @@ 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);