[standalone] Update globals.py with type-inferred global var

Required for when globals are read without the use of a global
declaration.

.pre-commit-config.yaml

@@ -8,17 +8,17 @@ repos:
     hooks:
       - id: nac3-cargo-fmt
         name: nac3 cargo format
-        entry: cargo
+        entry: nix
         language: system
         types: [file, rust]
         pass_filenames: false
         description: Runs cargo fmt on the codebase.
-        args: [fmt]
+        args: [develop, -c, cargo, fmt, --all]
       - id: nac3-cargo-clippy
         name: nac3 cargo clippy
-        entry: cargo
+        entry: nix
         language: system
         types: [file, rust]
         pass_filenames: false
         description: Runs cargo clippy on the codebase.
-        args: [clippy, --tests]
+        args: [develop, -c, cargo, clippy, --tests]

Cargo.lock

@@ -75,33 +75,33 @@ dependencies = [
 
 [[package]]
 name = "ascii-canvas"
-version = "3.0.0"
+version = "4.0.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8824ecca2e851cec16968d54a01dd372ef8f95b244fb84b84e70128be347c3c6"
+checksum = "ef1e3e699d84ab1b0911a1010c5c106aa34ae89aeac103be5ce0c3859db1e891"
 dependencies = [
  "term",
 ]
 
 [[package]]
 name = "autocfg"
-version = "1.3.0"
+version = "1.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0c4b4d0bd25bd0b74681c0ad21497610ce1b7c91b1022cd21c80c6fbdd9476b0"
+checksum = "ace50bade8e6234aa140d9a2f552bbee1db4d353f69b8217bc503490fc1a9f26"
 
 [[package]]
 name = "bit-set"
-version = "0.5.3"
+version = "0.8.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0700ddab506f33b20a03b13996eccd309a48e5ff77d0d95926aa0210fb4e95f1"
+checksum = "08807e080ed7f9d5433fa9b275196cfc35414f66a0c79d864dc51a0d825231a3"
 dependencies = [
  "bit-vec",
 ]
 
 [[package]]
 name = "bit-vec"
-version = "0.6.3"
+version = "0.8.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "349f9b6a179ed607305526ca489b34ad0a41aed5f7980fa90eb03160b69598fb"
+checksum = "5e764a1d40d510daf35e07be9eb06e75770908c27d411ee6c92109c9840eaaf7"
 
 [[package]]
 name = "bitflags"
@@ -109,6 +109,15 @@ version = "2.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b048fb63fd8b5923fc5aa7b340d8e156aec7ec02f0c78fa8a6ddc2613f6f71de"
 
+[[package]]
+name = "block-buffer"
+version = "0.10.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "3078c7629b62d3f0439517fa394996acacc5cbc91c5a20d8c658e77abd503a71"
+dependencies = [
+ "generic-array",
+]
+
 [[package]]
 name = "byteorder"
 version = "1.5.0"
@@ -117,9 +126,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
 
 [[package]]
 name = "cc"
-version = "1.1.15"
+version = "1.1.24"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "57b6a275aa2903740dc87da01c62040406b8812552e97129a63ea8850a17c6e6"
+checksum = "812acba72f0a070b003d3697490d2b55b837230ae7c6c6497f05cc2ddbb8d938"
 dependencies = [
  "shlex",
 ]
@@ -132,9 +141,9 @@ checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
 
 [[package]]
 name = "clap"
-version = "4.5.16"
+version = "4.5.19"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ed6719fffa43d0d87e5fd8caeab59be1554fb028cd30edc88fc4369b17971019"
+checksum = "7be5744db7978a28d9df86a214130d106a89ce49644cbc4e3f0c22c3fba30615"
 dependencies = [
  "clap_builder",
  "clap_derive",
@@ -142,9 +151,9 @@ dependencies = [
 
 [[package]]
 name = "clap_builder"
-version = "4.5.15"
+version = "4.5.19"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "216aec2b177652e3846684cbfe25c9964d18ec45234f0f5da5157b207ed1aab6"
+checksum = "a5fbc17d3ef8278f55b282b2a2e75ae6f6c7d4bb70ed3d0382375104bfafdb4b"
 dependencies = [
  "anstream",
  "anstyle",
@@ -154,14 +163,14 @@ dependencies = [
 
 [[package]]
 name = "clap_derive"
-version = "4.5.13"
+version = "4.5.18"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "501d359d5f3dcaf6ecdeee48833ae73ec6e42723a1e52419c79abf9507eec0a0"
+checksum = "4ac6a0c7b1a9e9a5186361f67dfa1b88213572f427fb9ab038efb2bd8c582dab"
 dependencies = [
  "heck 0.5.0",
  "proc-macro2",
  "quote",
- "syn 2.0.76",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -188,6 +197,15 @@ dependencies = [
  "windows-sys 0.52.0",
 ]
 
+[[package]]
+name = "cpufeatures"
+version = "0.2.14"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "608697df725056feaccfa42cffdaeeec3fccc4ffc38358ecd19b243e716a78e0"
+dependencies = [
+ "libc",
+]
+
 [[package]]
 name = "crossbeam"
 version = "0.8.4"
@@ -245,30 +263,23 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "22ec99545bb0ed0ea7bb9b8e1e9122ea386ff8a48c0922e43f36d45ab09e0e80"
 
 [[package]]
-name = "crunchy"
+name = "crypto-common"
-version = "0.2.2"
+version = "0.1.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7a81dae078cea95a014a339291cec439d2f232ebe854a9d672b796c6afafa9b7"
+checksum = "1bfb12502f3fc46cca1bb51ac28df9d618d813cdc3d2f25b9fe775a34af26bb3"
-
-[[package]]
-name = "dirs-next"
-version = "2.0.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b98cf8ebf19c3d1b223e151f99a4f9f0690dca41414773390fc824184ac833e1"
 dependencies = [
- "cfg-if",
+ "generic-array",
- "dirs-sys-next",
+ "typenum",
 ]
 
 [[package]]
-name = "dirs-sys-next"
+name = "digest"
-version = "0.1.2"
+version = "0.10.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4ebda144c4fe02d1f7ea1a7d9641b6fc6b580adcfa024ae48797ecdeb6825b4d"
+checksum = "9ed9a281f7bc9b7576e61468ba615a66a5c8cfdff42420a70aa82701a3b1e292"
 dependencies = [
- "libc",
+ "block-buffer",
- "redox_users",
+ "crypto-common",
- "winapi",
 ]
 
 [[package]]
@@ -329,6 +340,16 @@ dependencies = [
  "byteorder",
 ]
 
+[[package]]
+name = "generic-array"
+version = "0.14.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "85649ca51fd72272d7821adaf274ad91c288277713d9c18820d8499a7ff69e9a"
+dependencies = [
+ "typenum",
+ "version_check",
+]
+
 [[package]]
 name = "getopts"
 version = "0.2.21"
@@ -364,6 +385,12 @@ dependencies = [
  "ahash",
 ]
 
+[[package]]
+name = "hashbrown"
+version = "0.15.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1e087f84d4f86bf4b218b927129862374b72199ae7d8657835f1e89000eea4fb"
+
 [[package]]
 name = "heck"
 version = "0.4.1"
@@ -376,6 +403,15 @@ version = "0.5.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "2304e00983f87ffb38b55b444b5e3b60a884b5d30c0fca7d82fe33449bbe55ea"
 
+[[package]]
+name = "home"
+version = "0.5.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e3d1354bf6b7235cb4a0576c2619fd4ed18183f689b12b006a0ee7329eeff9a5"
+dependencies = [
+ "windows-sys 0.52.0",
+]
+
 [[package]]
 name = "indexmap"
 version = "1.9.3"
@@ -388,12 +424,12 @@ dependencies = [
 
 [[package]]
 name = "indexmap"
-version = "2.4.0"
+version = "2.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "93ead53efc7ea8ed3cfb0c79fc8023fbb782a5432b52830b6518941cebe6505c"
+checksum = "707907fe3c25f5424cce2cb7e1cbcafee6bdbe735ca90ef77c29e84591e5b9da"
 dependencies = [
  "equivalent",
- "hashbrown 0.14.5",
+ "hashbrown 0.15.0",
 ]
 
 [[package]]
@@ -404,9 +440,9 @@ checksum = "b248f5224d1d606005e02c97f5aa4e88eeb230488bcc03bc9ca4d7991399f2b5"
 
 [[package]]
 name = "inkwell"
-version = "0.4.0"
+version = "0.5.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b597a7b2cdf279aeef6d7149071e35e4bc87c2cf05a5b7f2d731300bffe587ea"
+checksum = "40fb405537710d51f6bdbc8471365ddd4cd6d3a3c3ad6e0c8291691031ba94b2"
 dependencies = [
  "either",
  "inkwell_internals",
@@ -418,13 +454,13 @@ dependencies = [
 
 [[package]]
 name = "inkwell_internals"
-version = "0.9.0"
+version = "0.10.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4fa4d8d74483041a882adaa9a29f633253a66dde85055f0495c121620ac484b2"
+checksum = "9dd28cfd4cfba665d47d31c08a6ba637eed16770abca2eccbbc3ca831fef1e44"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.76",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -447,15 +483,6 @@ version = "1.70.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "7943c866cc5cd64cbc25b2e01621d07fa8eb2a1a23160ee81ce38704e97b8ecf"
 
-[[package]]
-name = "itertools"
-version = "0.11.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b1c173a5686ce8bfa551b3563d0c2170bf24ca44da99c7ca4bfdab5418c3fe57"
-dependencies = [
- "either",
-]
-
 [[package]]
 name = "itertools"
 version = "0.13.0"
@@ -472,34 +499,44 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "49f1f14873335454500d59611f1cf4a4b0f786f9ac11f4312a78e4cf2566695b"
 
 [[package]]
-name = "lalrpop"
+name = "keccak"
-version = "0.20.2"
+version = "0.1.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "55cb077ad656299f160924eb2912aa147d7339ea7d69e1b5517326fdcec3c1ca"
+checksum = "ecc2af9a1119c51f12a14607e783cb977bde58bc069ff0c3da1095e635d70654"
+dependencies = [
+ "cpufeatures",
+]
+
+[[package]]
+name = "lalrpop"
+version = "0.22.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "06093b57658c723a21da679530e061a8c25340fa5a6f98e313b542268c7e2a1f"
 dependencies = [
  "ascii-canvas",
  "bit-set",
  "ena",
- "itertools 0.11.0",
+ "itertools",
  "lalrpop-util",
  "petgraph",
  "pico-args",
  "regex",
  "regex-syntax",
+ "sha3",
  "string_cache",
  "term",
- "tiny-keccak",
  "unicode-xid",
  "walkdir",
 ]
 
 [[package]]
 name = "lalrpop-util"
-version = "0.20.2"
+version = "0.22.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "507460a910eb7b32ee961886ff48539633b788a36b65692b95f225b844c82553"
+checksum = "feee752d43abd0f4807a921958ab4131f692a44d4d599733d4419c5d586176ce"
 dependencies = [
  "regex-automata",
+ "rustversion",
 ]
 
 [[package]]
@@ -510,9 +547,9 @@ checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe"
 
 [[package]]
 name = "libc"
-version = "0.2.158"
+version = "0.2.159"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d8adc4bb1803a324070e64a98ae98f38934d91957a99cfb3a43dcbc01bc56439"
+checksum = "561d97a539a36e26a9a5fad1ea11a3039a67714694aaa379433e580854bc3dc5"
 
 [[package]]
 name = "libloading"
@@ -524,16 +561,6 @@ dependencies = [
  "windows-targets",
 ]
 
-[[package]]
-name = "libredox"
-version = "0.1.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c0ff37bd590ca25063e35af745c343cb7a0271906fb7b37e4813e8f79f00268d"
-dependencies = [
- "bitflags",
- "libc",
-]
-
 [[package]]
 name = "linked-hash-map"
 version = "0.5.6"
@@ -594,11 +621,9 @@ dependencies = [
 name = "nac3artiq"
 version = "0.1.0"
 dependencies = [
- "inkwell",
+ "itertools",
- "itertools 0.13.0",
  "nac3core",
  "nac3ld",
- "nac3parser",
  "parking_lot",
  "pyo3",
  "tempfile",
@@ -619,11 +644,11 @@ name = "nac3core"
 version = "0.1.0"
 dependencies = [
  "crossbeam",
- "indexmap 2.4.0",
+ "indexmap 2.6.0",
  "indoc",
  "inkwell",
  "insta",
- "itertools 0.13.0",
+ "itertools",
  "nac3parser",
  "parking_lot",
  "rayon",
@@ -661,9 +686,7 @@ name = "nac3standalone"
 version = "0.1.0"
 dependencies = [
  "clap",
- "inkwell",
  "nac3core",
- "nac3parser",
  "parking_lot",
 ]
 
@@ -675,9 +698,12 @@ checksum = "650eef8c711430f1a879fdd01d4745a7deea475becfb90269c06775983bbf086"
 
 [[package]]
 name = "once_cell"
-version = "1.19.0"
+version = "1.20.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3fdb12b2476b595f9358c5161aa467c2438859caa136dec86c26fdd2efe17b92"
+checksum = "82881c4be219ab5faaf2ad5e5e5ecdff8c66bd7402ca3160975c93b24961afd1"
+dependencies = [
+ "portable-atomic",
+]
 
 [[package]]
 name = "parking_lot"
@@ -709,7 +735,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b4c5cc86750666a3ed20bdaf5ca2a0344f9c67674cae0515bec2da16fbaa47db"
 dependencies = [
  "fixedbitset",
- "indexmap 2.4.0",
+ "indexmap 2.6.0",
 ]
 
 [[package]]
@@ -752,7 +778,7 @@ dependencies = [
  "phf_shared 0.11.2",
  "proc-macro2",
  "quote",
- "syn 2.0.76",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -781,9 +807,9 @@ checksum = "5be167a7af36ee22fe3115051bc51f6e6c7054c9348e28deb4f49bd6f705a315"
 
 [[package]]
 name = "portable-atomic"
-version = "1.7.0"
+version = "1.9.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "da544ee218f0d287a911e9c99a39a8c9bc8fcad3cb8db5959940044ecfc67265"
+checksum = "cc9c68a3f6da06753e9335d63e27f6b9754dd1920d941135b7ea8224f141adb2"
 
 [[package]]
 name = "ppv-lite86"
@@ -856,7 +882,7 @@ dependencies = [
  "proc-macro2",
  "pyo3-macros-backend",
  "quote",
- "syn 2.0.76",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -869,7 +895,7 @@ dependencies = [
  "proc-macro2",
  "pyo3-build-config",
  "quote",
- "syn 2.0.76",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -933,29 +959,18 @@ dependencies = [
 
 [[package]]
 name = "redox_syscall"
-version = "0.5.3"
+version = "0.5.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2a908a6e00f1fdd0dfd9c0eb08ce85126f6d8bbda50017e74bc4a4b7d4a926a4"
+checksum = "9b6dfecf2c74bce2466cabf93f6664d6998a69eb21e39f4207930065b27b771f"
 dependencies = [
  "bitflags",
 ]
 
-[[package]]
-name = "redox_users"
-version = "0.4.6"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ba009ff324d1fc1b900bd1fdb31564febe58a8ccc8a6fdbb93b543d33b13ca43"
-dependencies = [
- "getrandom",
- "libredox",
- "thiserror",
-]
-
 [[package]]
 name = "regex"
-version = "1.10.6"
+version = "1.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4219d74c6b67a3654a9fbebc4b419e22126d13d2f3c4a07ee0cb61ff79a79619"
+checksum = "38200e5ee88914975b69f657f0801b6f6dccafd44fd9326302a4aaeecfacb1d8"
 dependencies = [
  "aho-corasick",
  "memchr",
@@ -965,9 +980,9 @@ dependencies = [
 
 [[package]]
 name = "regex-automata"
-version = "0.4.7"
+version = "0.4.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "38caf58cc5ef2fed281f89292ef23f6365465ed9a41b7a7754eb4e26496c92df"
+checksum = "368758f23274712b504848e9d5a6f010445cc8b87a7cdb4d7cbee666c1288da3"
 dependencies = [
  "aho-corasick",
  "memchr",
@@ -976,9 +991,9 @@ dependencies = [
 
 [[package]]
 name = "regex-syntax"
-version = "0.8.4"
+version = "0.8.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7a66a03ae7c801facd77a29370b4faec201768915ac14a721ba36f20bc9c209b"
+checksum = "2b15c43186be67a4fd63bee50d0303afffcef381492ebe2c5d87f324e1b8815c"
 
 [[package]]
 name = "runkernel"
@@ -989,9 +1004,9 @@ dependencies = [
 
 [[package]]
 name = "rustix"
-version = "0.38.35"
+version = "0.38.37"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a85d50532239da68e9addb745ba38ff4612a242c1c7ceea689c4bc7c2f43c36f"
+checksum = "8acb788b847c24f28525660c4d7758620a7210875711f79e7f663cc152726811"
 dependencies = [
  "bitflags",
  "errno",
@@ -1035,29 +1050,29 @@ checksum = "61697e0a1c7e512e84a621326239844a24d8207b4669b41bc18b32ea5cbf988b"
 
 [[package]]
 name = "serde"
-version = "1.0.209"
+version = "1.0.210"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "99fce0ffe7310761ca6bf9faf5115afbc19688edd00171d81b1bb1b116c63e09"
+checksum = "c8e3592472072e6e22e0a54d5904d9febf8508f65fb8552499a1abc7d1078c3a"
 dependencies = [
  "serde_derive",
 ]
 
 [[package]]
 name = "serde_derive"
-version = "1.0.209"
+version = "1.0.210"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a5831b979fd7b5439637af1752d535ff49f4860c0f341d1baeb6faf0f4242170"
+checksum = "243902eda00fad750862fc144cea25caca5e20d615af0a81bee94ca738f1df1f"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.76",
+ "syn 2.0.79",
 ]
 
 [[package]]
 name = "serde_json"
-version = "1.0.127"
+version = "1.0.128"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8043c06d9f82bd7271361ed64f415fe5e12a77fdb52e573e7f06a516dea329ad"
+checksum = "6ff5456707a1de34e7e37f2a6fd3d3f808c318259cbd01ab6377795054b483d8"
 dependencies = [
  "itoa",
  "memchr",
@@ -1077,6 +1092,16 @@ dependencies = [
  "yaml-rust",
 ]
 
+[[package]]
+name = "sha3"
+version = "0.10.8"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "75872d278a8f37ef87fa0ddbda7802605cb18344497949862c0d4dcb291eba60"
+dependencies = [
+ "digest",
+ "keccak",
+]
+
 [[package]]
 name = "shlex"
 version = "1.3.0"
@@ -1147,7 +1172,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "rustversion",
- "syn 2.0.76",
+ "syn 2.0.79",
 ]
 
 [[package]]
@@ -1163,9 +1188,9 @@ dependencies = [
 
 [[package]]
 name = "syn"
-version = "2.0.76"
+version = "2.0.79"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "578e081a14e0cefc3279b0472138c513f37b41a08d5a3cca9b6e4e8ceb6cd525"
+checksum = "89132cd0bf050864e1d38dc3bbc07a0eb8e7530af26344d3d2bbbef83499f590"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -1180,9 +1205,9 @@ checksum = "61c41af27dd6d1e27b1b16b489db798443478cef1f06a660c96db617ba5de3b1"
 
 [[package]]
 name = "tempfile"
-version = "3.12.0"
+version = "3.13.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "04cbcdd0c794ebb0d4cf35e88edd2f7d2c4c3e9a5a6dab322839b321c6a87a64"
+checksum = "f0f2c9fc62d0beef6951ccffd757e241266a2c833136efbe35af6cd2567dca5b"
 dependencies = [
  "cfg-if",
  "fastrand",
@@ -1193,13 +1218,12 @@ dependencies = [
 
 [[package]]
 name = "term"
-version = "0.7.0"
+version = "1.0.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c59df8ac95d96ff9bede18eb7300b0fda5e5d8d90960e76f8e14ae765eedbf1f"
+checksum = "4df4175de05129f31b80458c6df371a15e7fc3fd367272e6bf938e5c351c7ea0"
 dependencies = [
- "dirs-next",
+ "home",
- "rustversion",
+ "windows-sys 0.52.0",
- "winapi",
 ]
 
 [[package]]
@@ -1217,32 +1241,29 @@ dependencies = [
 
 [[package]]
 name = "thiserror"
-version = "1.0.63"
+version = "1.0.64"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c0342370b38b6a11b6cc11d6a805569958d54cfa061a29969c3b5ce2ea405724"
+checksum = "d50af8abc119fb8bb6dbabcfa89656f46f84aa0ac7688088608076ad2b459a84"
 dependencies = [
  "thiserror-impl",
 ]
 
 [[package]]
 name = "thiserror-impl"
-version = "1.0.63"
+version = "1.0.64"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a4558b58466b9ad7ca0f102865eccc95938dca1a74a856f2b57b6629050da261"
+checksum = "08904e7672f5eb876eaaf87e0ce17857500934f4981c4a0ab2b4aa98baac7fc3"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.76",
+ "syn 2.0.79",
 ]
 
 [[package]]
-name = "tiny-keccak"
+name = "typenum"
-version = "2.0.2"
+version = "1.17.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2c9d3793400a45f954c52e73d068316d76b6f4e36977e3fcebb13a2721e80237"
+checksum = "42ff0bf0c66b8238c6f3b578df37d0b7848e55df8577b3f74f92a69acceeb825"
-dependencies = [
- "crunchy",
-]
 
 [[package]]
 name = "unic-char-property"
@@ -1298,27 +1319,27 @@ dependencies = [
 
 [[package]]
 name = "unicode-ident"
-version = "1.0.12"
+version = "1.0.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3354b9ac3fae1ff6755cb6db53683adb661634f67557942dea4facebec0fee4b"
+checksum = "e91b56cd4cadaeb79bbf1a5645f6b4f8dc5bde8834ad5894a8db35fda9efa1fe"
 
 [[package]]
 name = "unicode-width"
-version = "0.1.13"
+version = "0.1.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0336d538f7abc86d282a4189614dfaa90810dfc2c6f6427eaf88e16311dd225d"
+checksum = "7dd6e30e90baa6f72411720665d41d89b9a3d039dc45b8faea1ddd07f617f6af"
 
 [[package]]
 name = "unicode-xid"
-version = "0.2.5"
+version = "0.2.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "229730647fbc343e3a80e463c1db7f78f3855d3f3739bee0dda773c9a037c90a"
+checksum = "ebc1c04c71510c7f702b52b7c350734c9ff1295c464a03335b00bb84fc54f853"
 
 [[package]]
 name = "unicode_names2"
-version = "1.2.2"
+version = "1.3.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "addeebf294df7922a1164f729fb27ebbbcea99cc32b3bf08afab62757f707677"
+checksum = "d1673eca9782c84de5f81b82e4109dcfb3611c8ba0d52930ec4a9478f547b2dd"
 dependencies = [
  "phf",
  "unicode_names2_generator",
@@ -1326,9 +1347,9 @@ dependencies = [
 
 [[package]]
 name = "unicode_names2_generator"
-version = "1.2.2"
+version = "1.3.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f444b8bba042fe3c1251ffaca35c603f2dc2ccc08d595c65a8c4f76f3e8426c0"
+checksum = "b91e5b84611016120197efd7dc93ef76774f4e084cd73c9fb3ea4a86c570c56e"
 dependencies = [
  "getopts",
  "log",
@@ -1370,22 +1391,6 @@ version = "0.11.0+wasi-snapshot-preview1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "9c8d87e72b64a3b4db28d11ce29237c246188f4f51057d65a7eab63b7987e423"
 
-[[package]]
-name = "winapi"
-version = "0.3.9"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5c839a674fcd7a98952e593242ea400abe93992746761e38641405d28b00f419"
-dependencies = [
- "winapi-i686-pc-windows-gnu",
- "winapi-x86_64-pc-windows-gnu",
-]
-
-[[package]]
-name = "winapi-i686-pc-windows-gnu"
-version = "0.4.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ac3b87c63620426dd9b991e5ce0329eff545bccbbb34f3be09ff6fb6ab51b7b6"
-
 [[package]]
 name = "winapi-util"
 version = "0.1.9"
@@ -1395,12 +1400,6 @@ dependencies = [
  "windows-sys 0.59.0",
 ]
 
-[[package]]
-name = "winapi-x86_64-pc-windows-gnu"
-version = "0.4.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "712e227841d057c1ee1cd2fb22fa7e5a5461ae8e48fa2ca79ec42cfc1931183f"
-
 [[package]]
 name = "windows-sys"
 version = "0.52.0"
@@ -1510,5 +1509,5 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.76",
+ "syn 2.0.79",
 ]

flake.lock

@@ -2,11 +2,11 @@
   "nodes": {
     "nixpkgs": {
       "locked": {
-        "lastModified": 1723637854,
+        "lastModified": 1727348695,
-        "narHash": "sha256-med8+5DSWa2UnOqtdICndjDAEjxr5D7zaIiK4pn0Q7c=",
+        "narHash": "sha256-J+PeFKSDV+pHL7ukkfpVzCOO7mBSrrpJ3svwBFABbhI=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "c3aa7b8938b17aebd2deecf7be0636000d62a2b9",
+        "rev": "1925c603f17fc89f4c8f6bf6f631a802ad85d784",
         "type": "github"
       },
       "original": {

nac3artiq/Cargo.toml

@@ -12,16 +12,10 @@ crate-type = ["cdylib"]
 itertools = "0.13"
 pyo3 = { version = "0.21", features = ["extension-module", "gil-refs"] }
 parking_lot = "0.12"
-tempfile = "3.10"
+tempfile = "3.13"
-nac3parser = { path = "../nac3parser" }
 nac3core = { path = "../nac3core" }
 nac3ld = { path = "../nac3ld" }
 
-[dependencies.inkwell]
-version = "0.4"
-default-features = false
-features = ["llvm14-0", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]
-
 [features]
 init-llvm-profile = []
 no-escape-analysis = ["nac3core/no-escape-analysis"]

nac3artiq/demo/min_artiq.py

@@ -112,10 +112,15 @@ def extern(function):
     register_function(function)
     return function
 
-def rpc(function):
+
-    """Decorates a function declaration defined by the core device runtime."""
+def rpc(arg=None, flags={}):
-    register_function(function)
+    """Decorates a function or method to be executed on the host interpreter."""
-    return function
+    if arg is None:
+        def inner_decorator(function):
+            return rpc(function, flags)
+        return inner_decorator
+    register_function(arg)
+    return arg
 
 def kernel(function_or_method):
     """Decorates a function or method to be executed on the core device."""

nac3artiq/src/codegen.rs

@@ -1,41 +1,3 @@
-use nac3core::{
-    codegen::{
-        classes::{ListValue, RangeValue, UntypedArrayLikeAccessor},
-        expr::{destructure_range, gen_call},
-        llvm_intrinsics::{call_int_smax, call_stackrestore, call_stacksave},
-        model::*,
-        object::{any::AnyObject, ndarray::NDArrayObject},
-        stmt::{gen_block, gen_for_callback_incrementing, gen_if_callback, gen_with},
-        CodeGenContext, CodeGenerator,
-    },
-    symbol_resolver::ValueEnum,
-    toplevel::{
-        helper::{extract_ndims, PrimDef},
-        numpy::unpack_ndarray_var_tys,
-        DefinitionId, GenCall,
-    },
-    typecheck::typedef::{iter_type_vars, FunSignature, FuncArg, Type, TypeEnum, VarMap},
-};
-
-use nac3parser::ast::{Expr, ExprKind, Located, Stmt, StmtKind, StrRef};
-
-use inkwell::{
-    context::Context,
-    module::Linkage,
-    types::IntType,
-    values::{BasicValue, BasicValueEnum, PointerValue, StructValue},
-    AddressSpace, IntPredicate, OptimizationLevel,
-};
-
-use pyo3::{
-    types::{PyDict, PyList},
-    PyObject, PyResult, Python,
-};
-
-use crate::{symbol_resolver::InnerResolver, timeline::TimeFns};
-
-use inkwell::values::IntValue;
-use itertools::Itertools;
 use std::{
     collections::{hash_map::DefaultHasher, HashMap},
     hash::{Hash, Hasher},
@@ -44,6 +6,39 @@ use std::{
     sync::Arc,
 };
 
+use itertools::Itertools;
+use pyo3::{
+    types::{PyDict, PyList},
+    PyObject, PyResult, Python,
+};
+
+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},
+        CodeGenContext, CodeGenerator,
+    },
+    inkwell::{
+        context::Context,
+        module::Linkage,
+        types::{BasicType, IntType},
+        values::{BasicValueEnum, IntValue, PointerValue, StructValue},
+        AddressSpace, IntPredicate, OptimizationLevel,
+    },
+    nac3parser::ast::{Expr, ExprKind, Located, Stmt, StmtKind, StrRef},
+    symbol_resolver::ValueEnum,
+    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, GenCall},
+    typecheck::typedef::{iter_type_vars, FunSignature, FuncArg, Type, TypeEnum, VarMap},
+};
+
+use crate::{symbol_resolver::InnerResolver, timeline::TimeFns};
+
 /// The parallelism mode within a block.
 #[derive(Copy, Clone, Eq, PartialEq)]
 enum ParallelMode {
@@ -459,42 +454,55 @@ fn format_rpc_arg<'ctx>(
             // NAC3: NDArray = { usize, usize*, T* }
             // libproto_artiq: NDArray = [data[..], dim_sz[..]]
 
-            let ndarray = AnyObject { ty: arg_ty, value: arg };
+            let llvm_i1 = ctx.ctx.bool_type();
-            let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
+            let llvm_usize = generator.get_size_type(ctx.ctx);
 
-            let dtype = ctx.get_llvm_type(generator, ndarray.dtype);
+            let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, arg_ty);
-            let ndims = ndarray.ndims_llvm(generator, ctx.ctx);
+            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 = NDArrayValue::from_ptr_val(arg.into_pointer_value(), llvm_usize, None);
 
-            // `ndarray.data` is possibly not contiguous, and we need it to be contiguous for
+            let llvm_usize_sizeof = ctx
-            // the reader.
+                .builder
-            // Turning it into a ContiguousNDArray to get a `data` that is contiguous.
+                .build_int_truncate_or_bit_cast(llvm_arg_ty.size_type().size_of(), llvm_usize, "")
-            let carray = ndarray.make_contiguous_ndarray(generator, ctx, Any(dtype));
+                .unwrap();
+            let llvm_pdata_sizeof = ctx
+                .builder
+                .build_int_truncate_or_bit_cast(
+                    llvm_elem_ty.ptr_type(AddressSpace::default()).size_of(),
+                    llvm_usize,
+                    "",
+                )
+                .unwrap();
 
-            let sizeof_sizet = Int(SizeT).size_of(generator, ctx.ctx);
+            let dims_buf_sz =
-            let sizeof_sizet = Int(SizeT).truncate_or_bit_cast(generator, ctx, sizeof_sizet);
+                ctx.builder.build_int_mul(llvm_arg.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
 
-            let sizeof_pdata = Ptr(Any(dtype)).size_of(generator, ctx.ctx);
+            let buffer_size =
-            let sizeof_pdata = Int(SizeT).truncate_or_bit_cast(generator, ctx, sizeof_pdata);
+                ctx.builder.build_int_add(dims_buf_sz, llvm_pdata_sizeof, "").unwrap();
 
-            let sizeof_buf_shape = sizeof_sizet.mul(ctx, ndims);
+            let buffer = ctx.builder.build_array_alloca(llvm_i8, buffer_size, "rpc.arg").unwrap();
-            let sizeof_buf = sizeof_buf_shape.add(ctx, sizeof_pdata);
+            let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, Some("rpc.arg"));
 
-            // buf = { data: void*, shape: [size_t; ndims]; }
+            call_memcpy_generic(
-            let buf = Int(Byte).array_alloca(generator, ctx, sizeof_buf.value);
+                ctx,
-            let buf_data = buf;
+                buffer.base_ptr(ctx, generator),
-            let buf_shape = buf_data.offset(ctx, sizeof_pdata.value);
+                llvm_arg.ptr_to_data(ctx),
+                llvm_pdata_sizeof,
+                llvm_i1.const_zero(),
+            );
 
-            // Write to `buf->data`
+            let pbuffer_dims_begin =
-            let carray_data = carray.get(generator, ctx, |f| f.data); // has type Ptr<Any>
+                unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
-            let carray_data = carray_data.pointer_cast(generator, ctx, Int(Byte));
+            call_memcpy_generic(
-            buf_data.copy_from(generator, ctx, carray_data, sizeof_pdata.value);
+                ctx,
+                pbuffer_dims_begin,
+                llvm_arg.dim_sizes().base_ptr(ctx, generator),
+                dims_buf_sz,
+                llvm_i1.const_zero(),
+            );
 
-            // Write to `buf->shape`
+            buffer.base_ptr(ctx, generator)
-            let carray_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
-            let carray_shape_i8 = carray_shape.pointer_cast(generator, ctx, Int(Byte));
-            buf_shape.copy_from(generator, ctx, carray_shape_i8, sizeof_buf_shape.value);
-
-            buf.value
         }
 
         _ => {
@@ -504,7 +512,7 @@ fn format_rpc_arg<'ctx>(
             ctx.builder.build_store(arg_slot, arg).unwrap();
 
             ctx.builder
-                .build_bitcast(arg_slot, llvm_pi8, "rpc.arg")
+                .build_bit_cast(arg_slot, llvm_pi8, "rpc.arg")
                 .map(BasicValueEnum::into_pointer_value)
                 .unwrap()
         }
@@ -555,10 +563,8 @@ fn format_rpc_ret<'ctx>(
 
     let result = match &*ctx.unifier.get_ty_immutable(ret_ty) {
         TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-            // FIXME: It is possible to rewrite everything more neatly with `Model<'ctx>`, but this is not too important.
+            let llvm_i1 = ctx.ctx.bool_type();
-
+            let llvm_usize = generator.get_size_type(ctx.ctx);
-            let num_0 = Int(SizeT).const_0(generator, ctx.ctx);
-            let num_8 = Int(SizeT).const_int(generator, ctx.ctx, 8, false);
 
             // Round `val` up to its modulo `power_of_two`
             let round_up = |ctx: &mut CodeGenContext<'ctx, '_>,
@@ -584,36 +590,60 @@ fn format_rpc_ret<'ctx>(
                     .unwrap()
             };
 
+            // Setup types
+            let (elem_ty, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ret_ty);
+            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
+            let llvm_ret_ty = NDArrayType::new(generator, ctx.ctx, llvm_elem_ty);
+
             // Allocate the resulting ndarray
             // A condition after format_rpc_ret ensures this will not be popped this off.
-            let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ret_ty);
+            let ndarray = llvm_ret_ty.new_value(generator, ctx, Some("rpc.result"));
-            let ndims = extract_ndims(&ctx.unifier, ndims);
-            let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
 
-            // NOTE: Current content of `ndarray`:
+            // Setup ndims
-            //   - * `data` - **NOT YET** allocated.
+            let ndims =
-            //   - * `itemsize` - initialized to be size_of(dtype).
+                if let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) {
-            //   - * `ndims` - initialized.
+                    assert_eq!(values.len(), 1);
-            //   - * `shape` - allocated; has uninitialized values.
-            //   - * `strides` - allocated; has uninitialized values.
 
-            let itemsize = ndarray.instance.get(generator, ctx, |f| f.itemsize); // Same as doing a `ctx.get_llvm_type` on `dtype` and get its `size_of()`.
+                    u64::try_from(values[0].clone()).unwrap()
-            let dtype_llvm = ctx.get_llvm_type(generator, dtype);
+                } else {
+                    unreachable!();
+                };
+            // Set `ndarray.ndims`
+            ndarray.store_ndims(ctx, generator, llvm_usize.const_int(ndims, false));
+            // Allocate `ndarray.shape` [size_t; ndims]
+            ndarray.create_dim_sizes(ctx, llvm_usize, ndarray.load_ndims(ctx));
+
+            /*
+                ndarray now:
+                  - .ndims: initialized
+                  - .shape: allocated but uninitialized .shape
+                  - .data: uninitialized
+            */
+
+            let llvm_usize_sizeof = ctx
+                .builder
+                .build_int_truncate_or_bit_cast(llvm_usize.size_of(), llvm_usize, "")
+                .unwrap();
+            let llvm_pdata_sizeof = ctx
+                .builder
+                .build_int_truncate_or_bit_cast(
+                    llvm_elem_ty.ptr_type(AddressSpace::default()).size_of(),
+                    llvm_usize,
+                    "",
+                )
+                .unwrap();
+            let llvm_elem_sizeof = ctx
+                .builder
+                .build_int_truncate_or_bit_cast(llvm_elem_ty.size_of().unwrap(), llvm_usize, "")
+                .unwrap();
 
             // Allocates a buffer for the initial RPC'ed object, which is guaranteed to be
             // (4 + 4 * ndims) bytes with 8-byte alignment
-            let sizeof_size_t = Int(SizeT).size_of(generator, ctx.ctx);
+            let sizeof_dims =
-            let sizeof_size_t = Int(SizeT).z_extend_or_truncate(generator, ctx, sizeof_size_t); // sizeof(size_t)
+                ctx.builder.build_int_mul(ndarray.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
-
+            let unaligned_buffer_size =
-            let sizeof_ptr = Ptr(Int(Byte)).size_of(generator, ctx.ctx);
+                ctx.builder.build_int_add(sizeof_dims, llvm_pdata_sizeof, "").unwrap();
-            let sizeof_ptr = Int(SizeT).z_extend_or_truncate(generator, ctx, sizeof_ptr); // sizeof(uint8_t*)
+            let buffer_size = round_up(ctx, unaligned_buffer_size, llvm_usize.const_int(8, false));
-
-            let sizeof_shape = ndarray.ndims_llvm(generator, ctx.ctx).mul(ctx, sizeof_size_t); // sizeof([size_t; ndims]); same as the # of bytes of `ndarray.shape`.
-
-            // Size of the buffer for the initial `rpc_recv()`.
-            let unaligned_buffer_size = sizeof_ptr.add(ctx, sizeof_shape); // sizeof(uint8_t*) + sizeof([size_t; ndims]).
-            let buffer_size = round_up(ctx, unaligned_buffer_size.value, num_8.value);
-            let buffer_size = unsafe { Int(SizeT).believe_value(buffer_size) };
 
             let stackptr = call_stacksave(ctx, None);
             // Just to be absolutely sure, alloca in [i8 x 8] slices to force 8-byte alignment
@@ -621,16 +651,18 @@ fn format_rpc_ret<'ctx>(
                 .builder
                 .build_array_alloca(
                     llvm_i8_8,
-                    ctx.builder.build_int_unsigned_div(buffer_size.value, num_8.value, "").unwrap(),
+                    ctx.builder
+                        .build_int_unsigned_div(buffer_size, llvm_usize.const_int(8, false), "")
+                        .unwrap(),
                     "rpc.buffer",
                 )
                 .unwrap();
             let buffer = ctx
                 .builder
-                .build_bitcast(buffer, llvm_pi8, "")
+                .build_bit_cast(buffer, llvm_pi8, "")
                 .map(BasicValueEnum::into_pointer_value)
                 .unwrap();
-            let buffer = unsafe { Ptr(Int(Byte)).believe_value(buffer) };
+            let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, None);
 
             // The first call to `rpc_recv` reads the top-level ndarray object: [pdata, shape]
             //
@@ -638,20 +670,24 @@ fn format_rpc_ret<'ctx>(
             let ndarray_nbytes = ctx
                 .build_call_or_invoke(
                     rpc_recv,
-                    &[buffer.value.into()], // Reads [usize; ndims]
+                    &[buffer.base_ptr(ctx, generator).into()], // Reads [usize; ndims]. NOTE: We are allocated [size_t; ndims].
                     "rpc.size.next",
                 )
                 .map(BasicValueEnum::into_int_value)
                 .unwrap();
-            let ndarray_nbytes = unsafe { Int(SizeT).believe_value(ndarray_nbytes) };
 
             // debug_assert(ndarray_nbytes > 0)
             if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
-                let cmp = ndarray_nbytes.compare(ctx, IntPredicate::UGT, num_0);
-
                 ctx.make_assert(
                     generator,
-                    cmp.value,
+                    ctx.builder
+                        .build_int_compare(
+                            IntPredicate::UGT,
+                            ndarray_nbytes,
+                            ndarray_nbytes.get_type().const_zero(),
+                            "",
+                        )
+                        .unwrap(),
                     "0:AssertionError",
                     "Unexpected RPC termination for ndarray - Expected data buffer next",
                     [None, None, None],
@@ -660,39 +696,49 @@ fn format_rpc_ret<'ctx>(
             }
 
             // Copy shape from the buffer to `ndarray.shape`.
-            // We need to skip the first `sizeof(uint8_t*)` bytes to skip the `pdata` in `[pdata, shape]`.
+            let pbuffer_dims =
-            let pbuffer_shape = buffer.offset(ctx, sizeof_ptr.value);
+                unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
-            let pbuffer_shape = pbuffer_shape.pointer_cast(generator, ctx, Int(SizeT));
-
-            // Copy shape from buffer to `ndarray.shape`
-            ndarray.copy_shape_from_array(generator, ctx, pbuffer_shape);
 
+            call_memcpy_generic(
+                ctx,
+                ndarray.dim_sizes().base_ptr(ctx, generator),
+                pbuffer_dims,
+                sizeof_dims,
+                llvm_i1.const_zero(),
+            );
             // Restore stack from before allocation of buffer
             call_stackrestore(ctx, stackptr);
 
             // Allocate `ndarray.data`.
             // `ndarray.shape` must be initialized beforehand in this implementation
             //   (for ndarray.create_data() to know how many elements to allocate)
-            ndarray.create_data(generator, ctx); // NOTE: the strides of `ndarray` has also been set to contiguous in `::create_data()`.
+            let num_elements =
+                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 {
-                let num_elements = ndarray.size(generator, ctx);
+                let sizeof_data =
-
+                    ctx.builder.build_int_mul(num_elements, llvm_elem_sizeof, "").unwrap();
-                let expected_ndarray_nbytes = num_elements.mul(ctx, itemsize);
-                let cmp = expected_ndarray_nbytes.compare(ctx, IntPredicate::UGE, ndarray_nbytes);
 
                 ctx.make_assert(
                     generator,
-                    cmp.value,
+                    ctx.builder.build_int_compare(IntPredicate::UGE,
+                        sizeof_data,
+                        ndarray_nbytes,
+                        "",
+                    ).unwrap(),
                     "0:AssertionError",
                     "Unexpected allocation size request for ndarray data - Expected up to {0} bytes, got {1} bytes",
-                    [Some(expected_ndarray_nbytes.value), Some(ndarray_nbytes.value), None],
+                    [Some(sizeof_data), Some(ndarray_nbytes), None],
                     ctx.current_loc,
                 );
             }
 
-            let ndarray_data = ndarray.instance.get(generator, ctx, |f| f.data);
+            ndarray.create_data(ctx, llvm_elem_ty, num_elements);
+
+            let ndarray_data = ndarray.data().base_ptr(ctx, generator);
+            let ndarray_data_i8 =
+                ctx.builder.build_pointer_cast(ndarray_data, llvm_pi8, "").unwrap();
 
             // NOTE: Currently on `prehead_bb`
             ctx.builder.build_unconditional_branch(head_bb).unwrap();
@@ -701,7 +747,7 @@ fn format_rpc_ret<'ctx>(
             ctx.builder.position_at_end(head_bb);
 
             let phi = ctx.builder.build_phi(llvm_pi8, "rpc.ptr").unwrap();
-            phi.add_incoming(&[(&ndarray_data.value, prehead_bb)]);
+            phi.add_incoming(&[(&ndarray_data_i8, prehead_bb)]);
 
             let alloc_size = ctx
                 .build_call_or_invoke(rpc_recv, &[phi.as_basic_value()], "rpc.size.next")
@@ -716,12 +762,12 @@ fn format_rpc_ret<'ctx>(
 
             ctx.builder.position_at_end(alloc_bb);
             // Align the allocation to sizeof(T)
-            let alloc_size = round_up(ctx, alloc_size, itemsize.value);
+            let alloc_size = round_up(ctx, alloc_size, llvm_elem_sizeof);
             let alloc_ptr = ctx
                 .builder
                 .build_array_alloca(
-                    dtype_llvm,
+                    llvm_elem_ty,
-                    ctx.builder.build_int_unsigned_div(alloc_size, itemsize.value, "").unwrap(),
+                    ctx.builder.build_int_unsigned_div(alloc_size, llvm_elem_sizeof, "").unwrap(),
                     "rpc.alloc",
                 )
                 .unwrap();
@@ -731,12 +777,12 @@ fn format_rpc_ret<'ctx>(
             ctx.builder.build_unconditional_branch(head_bb).unwrap();
 
             ctx.builder.position_at_end(tail_bb);
-            ndarray.instance.value.as_basic_value_enum()
+            ndarray.as_base_value().into()
         }
 
         _ => {
             let slot = ctx.builder.build_alloca(llvm_ret_ty, "rpc.ret.slot").unwrap();
-            let slotgen = ctx.builder.build_bitcast(slot, llvm_pi8, "rpc.ret.ptr").unwrap();
+            let slotgen = ctx.builder.build_bit_cast(slot, llvm_pi8, "rpc.ret.ptr").unwrap();
             ctx.builder.build_unconditional_branch(head_bb).unwrap();
             ctx.builder.position_at_end(head_bb);
 
@@ -757,7 +803,7 @@ fn format_rpc_ret<'ctx>(
             let alloc_ptr =
                 ctx.builder.build_array_alloca(llvm_pi8, alloc_size, "rpc.alloc").unwrap();
             let alloc_ptr =
-                ctx.builder.build_bitcast(alloc_ptr, llvm_pi8, "rpc.alloc.ptr").unwrap();
+                ctx.builder.build_bit_cast(alloc_ptr, llvm_pi8, "rpc.alloc.ptr").unwrap();
             phi.add_incoming(&[(&alloc_ptr, alloc_bb)]);
             ctx.builder.build_unconditional_branch(head_bb).unwrap();
 
@@ -775,6 +821,7 @@ fn rpc_codegen_callback_fn<'ctx>(
     fun: (&FunSignature, DefinitionId),
     args: Vec<(Option<StrRef>, ValueEnum<'ctx>)>,
     generator: &mut dyn CodeGenerator,
+    is_async: bool,
 ) -> Result<Option<BasicValueEnum<'ctx>>, String> {
     let int8 = ctx.ctx.i8_type();
     let int32 = ctx.ctx.i32_type();
@@ -883,6 +930,29 @@ fn rpc_codegen_callback_fn<'ctx>(
     }
 
     // call
+    if is_async {
+        let rpc_send_async = ctx.module.get_function("rpc_send_async").unwrap_or_else(|| {
+            ctx.module.add_function(
+                "rpc_send_async",
+                ctx.ctx.void_type().fn_type(
+                    &[
+                        int32.into(),
+                        tag_ptr_type.ptr_type(AddressSpace::default()).into(),
+                        ptr_type.ptr_type(AddressSpace::default()).into(),
+                    ],
+                    false,
+                ),
+                None,
+            )
+        });
+        ctx.builder
+            .build_call(
+                rpc_send_async,
+                &[service_id.into(), tag_ptr.into(), args_ptr.into()],
+                "rpc.send",
+            )
+            .unwrap();
+    } else {
         let rpc_send = ctx.module.get_function("rpc_send").unwrap_or_else(|| {
             ctx.module.add_function(
                 "rpc_send",
@@ -900,10 +970,15 @@ fn rpc_codegen_callback_fn<'ctx>(
         ctx.builder
             .build_call(rpc_send, &[service_id.into(), tag_ptr.into(), args_ptr.into()], "rpc.send")
             .unwrap();
+    }
 
     // reclaim stack space used by arguments
     call_stackrestore(ctx, stackptr);
 
+    if is_async {
+        // async RPCs do not return any values
+        Ok(None)
+    } else {
         let result = format_rpc_ret(generator, ctx, fun.0.ret);
 
         if !result.is_some_and(|res| res.get_type().is_pointer_type()) {
@@ -913,6 +988,7 @@ fn rpc_codegen_callback_fn<'ctx>(
 
         Ok(result)
     }
+}
 
 pub fn attributes_writeback(
     ctx: &mut CodeGenContext<'_, '_>,
@@ -1006,7 +1082,7 @@ pub fn attributes_writeback(
         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)
+            rpc_codegen_callback_fn(ctx, None, (&fun, PrimDef::Int32.id()), args, generator, false)
         {
             return Ok(Err(e));
         }
@@ -1016,9 +1092,9 @@ pub fn attributes_writeback(
     Ok(())
 }
 
-pub fn rpc_codegen_callback() -> Arc<GenCall> {
+pub fn rpc_codegen_callback(is_async: bool) -> Arc<GenCall> {
-    Arc::new(GenCall::new(Box::new(|ctx, obj, fun, args, generator| {
+    Arc::new(GenCall::new(Box::new(move |ctx, obj, fun, args, generator| {
-        rpc_codegen_callback_fn(ctx, obj, fun, args, generator)
+        rpc_codegen_callback_fn(ctx, obj, fun, args, generator, is_async)
     })))
 }
 
@@ -1283,46 +1359,56 @@ 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);
+
                 fmt.push_str("array([");
                 flush(ctx, generator, &mut fmt, &mut args);
 
-                let ndarray = AnyObject { ty, value };
+                let val = NDArrayValue::from_ptr_val(value.into_pointer_value(), llvm_usize, None);
-                let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
+                let len = call_ndarray_calc_size(generator, ctx, &val.dim_sizes(), (None, None));
+                let last =
+                    ctx.builder.build_int_sub(len, llvm_usize.const_int(1, false), "").unwrap();
 
-                let num_0 = Int(SizeT).const_0(generator, ctx.ctx);
+                gen_for_callback_incrementing(
-
-                // Print `ndarray` as a flat list delimited by interspersed with ", \0"
-                ndarray.foreach(generator, ctx, |generator, ctx, _, hdl| {
-                    let i = hdl.get_index(generator, ctx);
-                    let scalar = hdl.get_scalar(generator, ctx);
-
-                    // if (i != 0) { puts(", "); }
-                    gen_if_callback(
                     generator,
                     ctx,
-                        |_, ctx| {
+                    None,
-                            let not_first = i.compare(ctx, IntPredicate::NE, num_0);
+                    llvm_usize.const_zero(),
-                            Ok(not_first.value)
+                    (len, false),
-                        },
+                    |generator, ctx, _, i| {
-                        |generator, ctx| {
+                        let elem = unsafe { val.data().get_unchecked(ctx, generator, &i, None) };
-                            printf(ctx, generator, ", \0".into(), Vec::default());
-                            Ok(())
-                        },
-                        |_, _| Ok(()),
-                    )?;
 
-                    // Print element
                         polymorphic_print(
                             ctx,
                             generator,
-                        &[(scalar.ty, scalar.value.into())],
+                            &[(elem_ty, elem.into())],
                             "",
                             None,
                             true,
                             as_rtio,
                         )?;
+
+                        gen_if_callback(
+                            generator,
+                            ctx,
+                            |_, ctx| {
+                                Ok(ctx
+                                    .builder
+                                    .build_int_compare(IntPredicate::ULT, i, last, "")
+                                    .unwrap())
+                            },
+                            |generator, ctx| {
+                                printf(ctx, generator, ", \0".into(), Vec::default());
+
                                 Ok(())
-                })?;
+                            },
+                            |_, _| Ok(()),
+                        )?;
+
+                        Ok(())
+                    },
+                    llvm_usize.const_int(1, false),
+                )?;
 
                 fmt.push_str(")]");
                 flush(ctx, generator, &mut fmt, &mut args);

nac3artiq/src/lib.rs

@@ -16,14 +16,30 @@
     clippy::wildcard_imports
 )]
 
-use std::collections::{HashMap, HashSet};
+use std::{
-use std::fs;
+    collections::{HashMap, HashSet},
-use std::io::Write;
+    fs,
-use std::process::Command;
+    io::Write,
-use std::rc::Rc;
+    process::Command,
-use std::sync::Arc;
+    rc::Rc,
+    sync::Arc,
+};
 
-use inkwell::{
+use itertools::Itertools;
+use parking_lot::{Mutex, RwLock};
+use pyo3::{
+    create_exception, exceptions,
+    prelude::*,
+    types::{PyBytes, PyDict, PySet},
+};
+use tempfile::{self, TempDir};
+
+use nac3core::{
+    codegen::{
+        concrete_type::ConcreteTypeStore, gen_func_impl, irrt::load_irrt, CodeGenLLVMOptions,
+        CodeGenTargetMachineOptions, CodeGenTask, WithCall, WorkerRegistry,
+    },
+    inkwell::{
         context::Context,
         memory_buffer::MemoryBuffer,
         module::{Linkage, Module},
@@ -31,33 +47,22 @@ use inkwell::{
         support::is_multithreaded,
         targets::*,
         OptimizationLevel,
-};
+    },
-use itertools::Itertools;
+    nac3parser::{
-use nac3core::codegen::{gen_func_impl, CodeGenLLVMOptions, CodeGenTargetMachineOptions};
+        ast::{Constant, ExprKind, Located, Stmt, StmtKind, StrRef},
-use nac3core::toplevel::builtins::get_exn_constructor;
-use nac3core::typecheck::typedef::{into_var_map, TypeEnum, Unifier, VarMap};
-use nac3parser::{
-    ast::{ExprKind, Stmt, StmtKind, StrRef},
         parser::parse_program,
-};
+    },
-use pyo3::create_exception;
-use pyo3::prelude::*;
-use pyo3::{exceptions, types::PyBytes, types::PyDict, types::PySet};
-
-use parking_lot::{Mutex, RwLock};
-
-use nac3core::{
-    codegen::irrt::load_irrt,
-    codegen::{concrete_type::ConcreteTypeStore, CodeGenTask, WithCall, WorkerRegistry},
     symbol_resolver::SymbolResolver,
     toplevel::{
+        builtins::get_exn_constructor,
         composer::{BuiltinFuncCreator, BuiltinFuncSpec, ComposerConfig, TopLevelComposer},
         DefinitionId, GenCall, TopLevelDef,
     },
-    typecheck::typedef::{FunSignature, FuncArg},
+    typecheck::{
-    typecheck::{type_inferencer::PrimitiveStore, typedef::Type},
+        type_inferencer::PrimitiveStore,
+        typedef::{into_var_map, FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
+    },
 };
-
 use nac3ld::Linker;
 
 use crate::{
@@ -65,15 +70,13 @@ use crate::{
         attributes_writeback, gen_core_log, gen_rtio_log, rpc_codegen_callback, ArtiqCodeGenerator,
     },
     symbol_resolver::{DeferredEvaluationStore, InnerResolver, PythonHelper, Resolver},
+    timeline::TimeFns,
 };
-use tempfile::{self, TempDir};
 
 mod codegen;
 mod symbol_resolver;
 mod timeline;
 
-use timeline::TimeFns;
-
 #[derive(PartialEq, Clone, Copy)]
 enum Isa {
     Host,
@@ -194,10 +197,8 @@ impl Nac3 {
                     body.retain(|stmt| {
                         if let StmtKind::FunctionDef { ref decorator_list, .. } = stmt.node {
                             decorator_list.iter().any(|decorator| {
-                                if let ExprKind::Name { id, .. } = decorator.node {
+                                if let Some(id) = decorator_id_string(decorator) {
-                                    id.to_string() == "kernel"
+                                    id == "kernel" || id == "portable" || id == "rpc"
-                                        || id.to_string() == "portable"
-                                        || id.to_string() == "rpc"
                                 } else {
                                     false
                                 }
@@ -210,9 +211,8 @@ impl Nac3 {
                 }
                 StmtKind::FunctionDef { ref decorator_list, .. } => {
                     decorator_list.iter().any(|decorator| {
-                        if let ExprKind::Name { id, .. } = decorator.node {
+                        if let Some(id) = decorator_id_string(decorator) {
-                            let id = id.to_string();
+                            id == "extern" || id == "kernel" || id == "portable" || id == "rpc"
-                            id == "extern" || id == "portable" || id == "kernel" || id == "rpc"
                         } else {
                             false
                         }
@@ -478,9 +478,25 @@ impl Nac3 {
 
             match &stmt.node {
                 StmtKind::FunctionDef { decorator_list, .. } => {
-                    if decorator_list.iter().any(|decorator| matches!(decorator.node, ExprKind::Name { id, .. } if id == "rpc".into())) {
+                    if decorator_list
-                        store_fun.call1(py, (def_id.0.into_py(py), module.getattr(py, name.to_string().as_str()).unwrap())).unwrap();
+                        .iter()
-                        rpc_ids.push((None, def_id));
+                        .any(|decorator| decorator_id_string(decorator) == Some("rpc".to_string()))
+                    {
+                        store_fun
+                            .call1(
+                                py,
+                                (
+                                    def_id.0.into_py(py),
+                                    module.getattr(py, name.to_string().as_str()).unwrap(),
+                                ),
+                            )
+                            .unwrap();
+                        let is_async = decorator_list.iter().any(|decorator| {
+                            decorator_get_flags(decorator)
+                                .iter()
+                                .any(|constant| *constant == Constant::Str("async".into()))
+                        });
+                        rpc_ids.push((None, def_id, is_async));
                     }
                 }
                 StmtKind::ClassDef { name, body, .. } => {
@@ -488,19 +504,26 @@ impl Nac3 {
                     let class_obj = module.getattr(py, class_name.as_str()).unwrap();
                     for stmt in body {
                         if let StmtKind::FunctionDef { name, decorator_list, .. } = &stmt.node {
-                            if decorator_list.iter().any(|decorator| matches!(decorator.node, ExprKind::Name { id, .. } if id == "rpc".into())) {
+                            if decorator_list.iter().any(|decorator| {
+                                decorator_id_string(decorator) == Some("rpc".to_string())
+                            }) {
+                                let is_async = decorator_list.iter().any(|decorator| {
+                                    decorator_get_flags(decorator)
+                                        .iter()
+                                        .any(|constant| *constant == Constant::Str("async".into()))
+                                });
                                 if name == &"__init__".into() {
                                     return Err(CompileError::new_err(format!(
                                         "compilation failed\n----------\nThe constructor of class {} should not be decorated with rpc decorator (at {})",
                                         class_name, stmt.location
                                     )));
                                 }
-                                rpc_ids.push((Some((class_obj.clone(), *name)), def_id));
+                                rpc_ids.push((Some((class_obj.clone(), *name)), def_id, is_async));
                             }
                         }
                     }
                 }
-                _ => ()
+                _ => (),
             }
 
             let id = *name_to_pyid.get(&name).unwrap();
@@ -556,7 +579,7 @@ impl Nac3 {
             .unwrap();
 
         // Process IRRT
-        let context = inkwell::context::Context::create();
+        let context = Context::create();
         let irrt = load_irrt(&context, resolver.as_ref());
 
         let fun_signature =
@@ -596,13 +619,12 @@ impl Nac3 {
         let top_level = Arc::new(composer.make_top_level_context());
 
         {
-            let rpc_codegen = rpc_codegen_callback();
             let defs = top_level.definitions.read();
-            for (class_data, id) in &rpc_ids {
+            for (class_data, id, is_async) in &rpc_ids {
                 let mut def = defs[id.0].write();
                 match &mut *def {
                     TopLevelDef::Function { codegen_callback, .. } => {
-                        *codegen_callback = Some(rpc_codegen.clone());
+                        *codegen_callback = Some(rpc_codegen_callback(*is_async));
                     }
                     TopLevelDef::Class { methods, .. } => {
                         let (class_def, method_name) = class_data.as_ref().unwrap();
@@ -613,7 +635,7 @@ impl Nac3 {
                             if let TopLevelDef::Function { codegen_callback, .. } =
                                 &mut *defs[id.0].write()
                             {
-                                *codegen_callback = Some(rpc_codegen.clone());
+                                *codegen_callback = Some(rpc_codegen_callback(*is_async));
                                 store_fun
                                     .call1(
                                         py,
@@ -628,6 +650,11 @@ impl Nac3 {
                             }
                         }
                     }
+                    TopLevelDef::Variable { .. } => {
+                        return Err(CompileError::new_err(String::from(
+                            "Unsupported @rpc annotation on global variable",
+                        )))
+                    }
                 }
             }
         }
@@ -687,7 +714,7 @@ impl Nac3 {
             let buffer = buffer.as_slice().into();
             membuffer.lock().push(buffer);
         })));
-        let size_t = Context::create()
+        let size_t = context
             .ptr_sized_int_type(&self.get_llvm_target_machine().get_target_data(), None)
             .get_bit_width();
         let num_threads = if is_multithreaded() { 4 } else { 1 };
@@ -706,7 +733,7 @@ impl Nac3 {
 
             let mut generator =
                 ArtiqCodeGenerator::new("attributes_writeback".to_string(), size_t, self.time_fns);
-            let context = inkwell::context::Context::create();
+            let context = Context::create();
             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());
@@ -844,6 +871,41 @@ impl Nac3 {
     }
 }
 
+/// Retrieves the Name.id from a decorator, supports decorators with arguments.
+fn decorator_id_string(decorator: &Located<ExprKind>) -> Option<String> {
+    if let ExprKind::Name { id, .. } = decorator.node {
+        // Bare decorator
+        return Some(id.to_string());
+    } else if let ExprKind::Call { func, .. } = &decorator.node {
+        // Decorators that are calls (e.g. "@rpc()") have Call for the node,
+        // need to extract the id from within.
+        if let ExprKind::Name { id, .. } = func.node {
+            return Some(id.to_string());
+        }
+    }
+    None
+}
+
+/// Retrieves flags from a decorator, if any.
+fn decorator_get_flags(decorator: &Located<ExprKind>) -> Vec<Constant> {
+    let mut flags = vec![];
+    if let ExprKind::Call { keywords, .. } = &decorator.node {
+        for keyword in keywords {
+            if keyword.node.arg != Some("flags".into()) {
+                continue;
+            }
+            if let ExprKind::Set { elts } = &keyword.node.value.node {
+                for elt in elts {
+                    if let ExprKind::Constant { value, .. } = &elt.node {
+                        flags.push(value.clone());
+                    }
+                }
+            }
+        }
+    }
+    flags
+}
+
 fn link_with_lld(elf_filename: String, obj_filename: String) -> PyResult<()> {
     let linker_args = vec![
         "-shared".to_string(),

nac3artiq/src/symbol_resolver.rs

@@ -1,17 +1,30 @@
-use crate::PrimitivePythonId;
+use std::{
-use inkwell::{
+    collections::{HashMap, HashSet},
-    module::Linkage,
+    sync::{
-    types::BasicType,
+        atomic::{AtomicBool, Ordering::Relaxed},
-    values::{BasicValue, BasicValueEnum},
+        Arc,
-    AddressSpace,
+    },
 };
+
 use itertools::Itertools;
+use parking_lot::RwLock;
+use pyo3::{
+    types::{PyDict, PyTuple},
+    PyAny, PyObject, PyResult, Python,
+};
+
 use nac3core::{
     codegen::{
-        model::*,
+        classes::{NDArrayType, ProxyType},
-        object::ndarray::{make_contiguous_strides, NDArray},
         CodeGenContext, CodeGenerator,
     },
+    inkwell::{
+        module::Linkage,
+        types::{BasicType, BasicTypeEnum},
+        values::BasicValueEnum,
+        AddressSpace,
+    },
+    nac3parser::ast::{self, StrRef},
     symbol_resolver::{StaticValue, SymbolResolver, SymbolValue, ValueEnum},
     toplevel::{
         helper::PrimDef,
@@ -23,19 +36,8 @@ use nac3core::{
         typedef::{into_var_map, iter_type_vars, Type, TypeEnum, TypeVar, Unifier, VarMap},
     },
 };
-use nac3parser::ast::{self, StrRef};
+
-use parking_lot::RwLock;
+use crate::PrimitivePythonId;
-use pyo3::{
-    types::{PyDict, PyTuple},
-    PyAny, PyErr, PyObject, PyResult, Python,
-};
-use std::{
-    collections::{HashMap, HashSet},
-    sync::{
-        atomic::{AtomicBool, Ordering::Relaxed},
-        Arc,
-    },
-};
 
 pub enum PrimitiveValue {
     I32(i32),
@@ -1086,12 +1088,15 @@ impl InnerResolver {
             let (ndarray_dtype, ndarray_ndims) =
                 unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
 
-            let dtype = Any(ctx.get_llvm_type(generator, ndarray_dtype));
+            let llvm_usize = generator.get_size_type(ctx.ctx);
+            let ndarray_dtype_llvm_ty = ctx.get_llvm_type(generator, ndarray_dtype);
+            let ndarray_llvm_ty = NDArrayType::new(generator, ctx.ctx, ndarray_dtype_llvm_ty);
+
             {
                 if self.global_value_ids.read().contains_key(&id) {
                     let global = ctx.module.get_global(&id_str).unwrap_or_else(|| {
                         ctx.module.add_global(
-                            Struct(NDArray).llvm_type(generator, ctx.ctx),
+                            ndarray_llvm_ty.as_underlying_type(),
                             Some(AddressSpace::default()),
                             &id_str,
                         )
@@ -1111,138 +1116,100 @@ impl InnerResolver {
             } else {
                 todo!("Unpacking literal of more than one element unimplemented")
             };
-            let Ok(ndims) = u64::try_from(ndarray_ndims) else {
+            let Ok(ndarray_ndims) = u64::try_from(ndarray_ndims) else {
                 unreachable!("Expected u64 value for ndarray_ndims")
             };
 
             // Obtain the shape of the ndarray
             let shape_tuple: &PyTuple = obj.getattr("shape")?.downcast()?;
-            assert_eq!(shape_tuple.len(), ndims as usize);
+            assert_eq!(shape_tuple.len(), ndarray_ndims as usize);
-
+            let shape_values: Result<Option<Vec<_>>, _> = shape_tuple
-            // The Rust type inferencer cannot figure this out
-            let shape_values: Result<Vec<Instance<'ctx, Int<SizeT>>>, PyErr> = shape_tuple
                 .iter()
                 .enumerate()
                 .map(|(i, elem)| {
-                    let value = self
+                    self.get_obj_value(py, elem, ctx, generator, ctx.primitives.usize()).map_err(
-                        .get_obj_value(py, elem, ctx, generator, ctx.primitives.usize())
+                        |e| super::CompileError::new_err(format!("Error getting element {i}: {e}")),
-                        .map_err(|e| {
+                    )
-                            super::CompileError::new_err(format!("Error getting element {i}: {e}"))
-                        })?
-                        .unwrap();
-                    let value = Int(SizeT).check_value(generator, ctx.ctx, value).unwrap();
-                    Ok(value)
                 })
                 .collect();
-            let shape_values = shape_values?;
+            let shape_values = shape_values?.unwrap();
-
+            let shape_values = llvm_usize.const_array(
-            // Also use this opportunity to get the constant values of `shape_values` for calculating strides.
+                &shape_values.into_iter().map(BasicValueEnum::into_int_value).collect_vec(),
-            let shape_u64s = shape_values
+            );
-                .iter()
-                .map(|dim| {
-                    assert!(dim.value.is_const());
-                    dim.value.get_zero_extended_constant().unwrap()
-                })
-                .collect_vec();
-            let shape_values = Int(SizeT).const_array(generator, ctx.ctx, &shape_values);
 
             // create a global for ndarray.shape and initialize it using the shape
             let shape_global = ctx.module.add_global(
-                Array { len: AnyLen(ndims as u32), item: Int(SizeT) }.llvm_type(generator, ctx.ctx),
+                llvm_usize.array_type(ndarray_ndims as u32),
                 Some(AddressSpace::default()),
                 &(id_str.clone() + ".shape"),
             );
-            shape_global.set_initializer(&shape_values.value);
+            shape_global.set_initializer(&shape_values);
 
             // Obtain the (flattened) elements of the ndarray
             let sz: usize = obj.getattr("size")?.extract()?;
-            let data_values: Vec<Instance<'ctx, Any>> = (0..sz)
+            let data: Result<Option<Vec<_>>, _> = (0..sz)
                 .map(|i| {
                     obj.getattr("flat")?.get_item(i).and_then(|elem| {
-                        let value = self
+                        self.get_obj_value(py, elem, ctx, generator, ndarray_dtype).map_err(|e| {
-                            .get_obj_value(py, elem, ctx, generator, ndarray_dtype)
+                            super::CompileError::new_err(format!("Error getting element {i}: {e}"))
-                            .map_err(|e| {
+                        })
-                                super::CompileError::new_err(format!(
+                    })
-                                    "Error getting element {i}: {e}"
+                })
-                                ))
+                .collect();
-                            })?
+            let data = data?.unwrap().into_iter();
-                            .unwrap();
+            let data = match ndarray_dtype_llvm_ty {
+                BasicTypeEnum::ArrayType(ty) => {
+                    ty.const_array(&data.map(BasicValueEnum::into_array_value).collect_vec())
+                }
 
-                        let value = dtype.check_value(generator, ctx.ctx, value).unwrap();
+                BasicTypeEnum::FloatType(ty) => {
-                        Ok(value)
+                    ty.const_array(&data.map(BasicValueEnum::into_float_value).collect_vec())
-                    })
+                }
-                })
+
-                .try_collect()?;
+                BasicTypeEnum::IntType(ty) => {
-            let data = dtype.const_array(generator, ctx.ctx, &data_values);
+                    ty.const_array(&data.map(BasicValueEnum::into_int_value).collect_vec())
+                }
+
+                BasicTypeEnum::PointerType(ty) => {
+                    ty.const_array(&data.map(BasicValueEnum::into_pointer_value).collect_vec())
+                }
+
+                BasicTypeEnum::StructType(ty) => {
+                    ty.const_array(&data.map(BasicValueEnum::into_struct_value).collect_vec())
+                }
+
+                BasicTypeEnum::VectorType(_) => unreachable!(),
+            };
 
             // create a global for ndarray.data and initialize it using the elements
-            //
-            // NOTE: NDArray's `data` is `u8*`. Here, `data_global` is an array of `dtype`.
-            // We will have to cast it to an `u8*` later.
             let data_global = ctx.module.add_global(
-                Array { len: AnyLen(sz as u32), item: dtype }.llvm_type(generator, ctx.ctx),
+                ndarray_dtype_llvm_ty.array_type(sz as u32),
                 Some(AddressSpace::default()),
                 &(id_str.clone() + ".data"),
             );
-            data_global.set_initializer(&data.value);
+            data_global.set_initializer(&data);
-
-            // Get the constant itemsize.
-            let itemsize = dtype.llvm_type(generator, ctx.ctx).size_of().unwrap();
-            let itemsize = itemsize.get_zero_extended_constant().unwrap();
-
-            // Create the strides needed for ndarray.strides
-            let strides = make_contiguous_strides(itemsize, ndims, &shape_u64s);
-            let strides = strides
-                .into_iter()
-                .map(|stride| Int(SizeT).const_int(generator, ctx.ctx, stride, false))
-                .collect_vec();
-            let strides = Int(SizeT).const_array(generator, ctx.ctx, &strides);
-
-            // create a global for ndarray.strides and initialize it
-            let strides_global = ctx.module.add_global(
-                Array { len: AnyLen(ndims as u32), item: Int(Byte) }.llvm_type(generator, ctx.ctx),
-                Some(AddressSpace::default()),
-                &(id_str.clone() + ".strides"),
-            );
-            strides_global.set_initializer(&strides.value);
 
             // create a global for the ndarray object and initialize it
-            // We are also doing [`Model::check_value`] instead of [`Model::believe_value`] to catch bugs.
+            let value = ndarray_llvm_ty.as_underlying_type().const_named_struct(&[
+                llvm_usize.const_int(ndarray_ndims, false).into(),
+                shape_global
+                    .as_pointer_value()
+                    .const_cast(llvm_usize.ptr_type(AddressSpace::default()))
+                    .into(),
+                data_global
+                    .as_pointer_value()
+                    .const_cast(ndarray_dtype_llvm_ty.ptr_type(AddressSpace::default()))
+                    .into(),
+            ]);
 
-            // NOTE: data_global is an array of dtype, we want a `u8*`.
+            let ndarray = ctx.module.add_global(
-            let ndarray_data = Ptr(dtype).check_value(generator, ctx.ctx, data_global).unwrap();
+                ndarray_llvm_ty.as_underlying_type(),
-            let ndarray_data = Ptr(Int(Byte)).pointer_cast(generator, ctx, ndarray_data.value);
-
-            let ndarray_itemsize = Int(SizeT).const_int(generator, ctx.ctx, itemsize, false);
-
-            let ndarray_ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims, false);
-
-            let ndarray_shape =
-                Ptr(Int(SizeT)).check_value(generator, ctx.ctx, shape_global).unwrap();
-
-            let ndarray_strides =
-                Ptr(Int(SizeT)).check_value(generator, ctx.ctx, strides_global).unwrap();
-
-            let ndarray = Struct(NDArray).const_struct(
-                generator,
-                ctx.ctx,
-                &[
-                    ndarray_data.value.as_basic_value_enum(),
-                    ndarray_itemsize.value.as_basic_value_enum(),
-                    ndarray_ndims.value.as_basic_value_enum(),
-                    ndarray_shape.value.as_basic_value_enum(),
-                    ndarray_strides.value.as_basic_value_enum(),
-                ],
-            );
-
-            let ndarray_global = ctx.module.add_global(
-                Struct(NDArray).llvm_type(generator, ctx.ctx),
                 Some(AddressSpace::default()),
                 &id_str,
             );
-            ndarray_global.set_initializer(&ndarray.value);
+            ndarray.set_initializer(&value);
 
-            Ok(Some(ndarray_global.as_pointer_value().into()))
+            Ok(Some(ndarray.as_pointer_value().into()))
         } else if ty_id == self.primitive_ids.tuple {
             let expected_ty_enum = ctx.unifier.get_ty_immutable(expected_ty);
             let TypeEnum::TTuple { ty, is_vararg_ctx: false } = expected_ty_enum.as_ref() else {
@@ -1503,6 +1470,7 @@ impl SymbolResolver for Resolver {
         &self,
         id: StrRef,
         _: &mut CodeGenContext<'ctx, '_>,
+        _: &mut dyn CodeGenerator,
     ) -> Option<ValueEnum<'ctx>> {
         let sym_value = {
             let id_to_val = self.0.id_to_pyval.read();

nac3artiq/src/timeline.rs

@@ -1,9 +1,12 @@
-use inkwell::{
+use itertools::Either;
+
+use nac3core::{
+    codegen::CodeGenContext,
+    inkwell::{
         values::{BasicValueEnum, CallSiteValue},
         AddressSpace, AtomicOrdering,
+    },
 };
-use itertools::Either;
-use nac3core::codegen::CodeGenContext;
 
 /// Functions for manipulating the timeline.
 pub trait TimeFns {
@@ -31,7 +34,7 @@ impl TimeFns for NowPinningTimeFns64 {
             .unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
         let now_hiptr = ctx
             .builder
-            .build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
+            .build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
             .map(BasicValueEnum::into_pointer_value)
             .unwrap();
 
@@ -80,7 +83,7 @@ impl TimeFns for NowPinningTimeFns64 {
             .unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
         let now_hiptr = ctx
             .builder
-            .build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
+            .build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
             .map(BasicValueEnum::into_pointer_value)
             .unwrap();
 
@@ -109,7 +112,7 @@ impl TimeFns for NowPinningTimeFns64 {
             .unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
         let now_hiptr = ctx
             .builder
-            .build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
+            .build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
             .map(BasicValueEnum::into_pointer_value)
             .unwrap();
 
@@ -207,7 +210,7 @@ impl TimeFns for NowPinningTimeFns {
             .unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
         let now_hiptr = ctx
             .builder
-            .build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
+            .build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
             .map(BasicValueEnum::into_pointer_value)
             .unwrap();
 
@@ -258,7 +261,7 @@ impl TimeFns for NowPinningTimeFns {
         let time_lo = ctx.builder.build_int_truncate(time, i32_type, "time.lo").unwrap();
         let now_hiptr = ctx
             .builder
-            .build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
+            .build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
             .map(BasicValueEnum::into_pointer_value)
             .unwrap();
 

nac3core/Cargo.toml

@@ -10,17 +10,17 @@ no-escape-analysis = []
 [dependencies]
 itertools = "0.13"
 crossbeam = "0.8"
-indexmap = "2.2"
+indexmap = "2.6"
 parking_lot = "0.12"
-rayon = "1.8"
+rayon = "1.10"
 nac3parser = { path = "../nac3parser" }
 strum = "0.26"
 strum_macros = "0.26"
 
 [dependencies.inkwell]
-version = "0.4"
+version = "0.5"
 default-features = false
-features = ["llvm14-0", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]
+features = ["llvm14-0-prefer-dynamic", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]
 
 [dev-dependencies]
 test-case = "1.2.0"

nac3core/build.rs

@@ -1,4 +1,3 @@
-use regex::Regex;
 use std::{
     env,
     fs::File,
@@ -7,6 +6,8 @@ use std::{
     process::{Command, Stdio},
 };
 
+use regex::Regex;
+
 fn main() {
     let out_dir = env::var("OUT_DIR").unwrap();
     let out_dir = Path::new(&out_dir);

nac3core/irrt/irrt.cpp

@@ -2,14 +2,5 @@
 #include "irrt/int_types.hpp"
 #include "irrt/list.hpp"
 #include "irrt/math.hpp"
-#include "irrt/range.hpp"
+#include "irrt/ndarray.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/exception.hpp

@@ -55,14 +55,11 @@ void _raise_exception_helper(ExceptionId id,
                              int64_t param2) {
     Exception<SizeT> e = {
         .id = id,
-        .filename = {.base = reinterpret_cast<uint8_t*>(const_cast<char*>(filename)),
+        .filename = {.base = reinterpret_cast<const uint8_t*>(filename), .len = __builtin_strlen(filename)},
-                     .len = static_cast<int32_t>(__builtin_strlen(filename))},
         .line = line,
         .column = 0,
-        .function = {.base = reinterpret_cast<uint8_t*>(const_cast<char*>(function)),
+        .function = {.base = reinterpret_cast<const uint8_t*>(function), .len = __builtin_strlen(function)},
-                     .len = static_cast<int32_t>(__builtin_strlen(function))},
+        .msg = {.base = reinterpret_cast<const uint8_t*>(msg), .len = __builtin_strlen(msg)},
-        .msg = {.base = reinterpret_cast<uint8_t*>(const_cast<char*>(msg)),
-                .len = static_cast<int32_t>(__builtin_strlen(msg))},
     };
     e.params[0] = param0;
     e.params[1] = param1;

nac3core/irrt/irrt/int_types.hpp

@@ -1,13 +1,22 @@
 #pragma once
 
+#if __STDC_VERSION__ >= 202000
 using int8_t = _BitInt(8);
 using uint8_t = unsigned _BitInt(8);
 using int32_t = _BitInt(32);
 using uint32_t = unsigned _BitInt(32);
 using int64_t = _BitInt(64);
 using uint64_t = unsigned _BitInt(64);
+#else
+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);
+#endif
 
 // NDArray indices are always `uint32_t`.
-using NDIndexInt = uint32_t;
+using NDIndex = uint32_t;
 // The type of an index or a value describing the length of a range/slice is always `int32_t`.
 using SliceIndex = int32_t;

nac3core/irrt/irrt/list.hpp

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

nac3core/irrt/irrt/ndarray.hpp

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

nac3core/irrt/irrt/ndarray/array.hpp

@@ -1,134 +0,0 @@
-#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

@@ -1,341 +0,0 @@
-#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

@@ -1,165 +0,0 @@
-#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

@@ -1,45 +0,0 @@
-#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

@@ -1,220 +0,0 @@
-#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

@@ -1,146 +0,0 @@
-#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

@@ -1,100 +0,0 @@
-#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

@@ -1,99 +0,0 @@
-#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

@@ -1,145 +0,0 @@
-#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

@@ -1,47 +0,0 @@
-#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

@@ -1,145 +1,6 @@
 #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) {
@@ -153,4 +14,15 @@ 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;
+    }
+}
 }

nac3core/src/codegen/classes.rs

@@ -1,13 +1,17 @@
-use crate::codegen::{CodeGenContext, CodeGenerator};
-use inkwell::context::Context;
-use inkwell::types::{ArrayType, BasicType, StructType};
-use inkwell::values::{ArrayValue, BasicValue, StructValue};
 use inkwell::{
-    types::{AnyTypeEnum, BasicTypeEnum, IntType, PointerType},
+    context::Context,
-    values::{BasicValueEnum, IntValue, PointerValue},
+    types::{AnyTypeEnum, ArrayType, BasicType, BasicTypeEnum, IntType, PointerType, StructType},
+    values::{ArrayValue, BasicValue, BasicValueEnum, IntValue, PointerValue, StructValue},
     AddressSpace, IntPredicate,
 };
 
+use crate::codegen::{
+    irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
+    llvm_intrinsics::call_int_umin,
+    stmt::gen_for_callback_incrementing,
+    CodeGenContext, CodeGenerator,
+};
+
 /// A LLVM type that is used to represent a non-primitive type in NAC3.
 pub trait ProxyType<'ctx>: Into<Self::Base> {
     /// The LLVM type of which values of this type possess. This is usually a
@@ -1136,3 +1140,626 @@ impl<'ctx> From<RangeValue<'ctx>> for PointerValue<'ctx> {
         value.as_base_value()
     }
 }
+
+/// Proxy type for a `ndarray` type in LLVM.
+#[derive(Debug, PartialEq, Eq, Clone, Copy)]
+pub struct NDArrayType<'ctx> {
+    ty: PointerType<'ctx>,
+    llvm_usize: IntType<'ctx>,
+}
+
+impl<'ctx> NDArrayType<'ctx> {
+    /// Checks whether `llvm_ty` represents a `ndarray` type, returning [Err] if it does not.
+    pub fn is_type(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(_) = PointerType::try_from(ndarray_data_ty) else {
+            return Err(format!("Expected pointer type for `ndarray.2`, got {ndarray_data_ty}"));
+        };
+
+        Ok(())
+    }
+
+    /// Creates an instance of [`ListType`].
+    #[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);
+
+        // struct NDArray { num_dims: size_t, dims: size_t*, data: T* }
+        //
+        // * num_dims: Number of dimensions in the array
+        // * dims: Pointer to an array containing the size of each dimension
+        // * data: Pointer to an array containing the array data
+        let llvm_ndarray = ctx
+            .struct_type(
+                &[
+                    llvm_usize.into(),
+                    llvm_usize.ptr_type(AddressSpace::default()).into(),
+                    dtype.ptr_type(AddressSpace::default()).into(),
+                ],
+                false,
+            )
+            .ptr_type(AddressSpace::default());
+
+        NDArrayType::from_type(llvm_ndarray, llvm_usize)
+    }
+
+    /// Creates an [`NDArrayType`] from a [`PointerType`].
+    #[must_use]
+    pub fn from_type(ptr_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
+        debug_assert!(Self::is_type(ptr_ty, llvm_usize).is_ok());
+
+        NDArrayType { ty: ptr_ty, 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) -> AnyTypeEnum<'ctx> {
+        self.as_base_type()
+            .get_element_type()
+            .into_struct_type()
+            .get_field_type_at_index(2)
+            .map(BasicTypeEnum::into_pointer_type)
+            .map(PointerType::get_element_type)
+            .unwrap()
+    }
+}
+
+impl<'ctx> ProxyType<'ctx> for NDArrayType<'ctx> {
+    type Base = PointerType<'ctx>;
+    type Underlying = StructType<'ctx>;
+    type Value = NDArrayValue<'ctx>;
+
+    fn new_value<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> Self::Value {
+        self.create_value(
+            generator.gen_var_alloc(ctx, self.as_underlying_type().into(), name).unwrap(),
+            name,
+        )
+    }
+
+    fn create_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 { value, llvm_usize: self.llvm_usize, name }
+    }
+
+    fn as_base_type(&self) -> Self::Base {
+        self.ty
+    }
+
+    fn as_underlying_type(&self) -> Self::Underlying {
+        self.as_base_type().get_element_type().into_struct_type()
+    }
+}
+
+impl<'ctx> From<NDArrayType<'ctx>> for PointerType<'ctx> {
+    fn from(value: NDArrayType<'ctx>) -> Self {
+        value.as_base_type()
+    }
+}
+
+/// Proxy type for accessing an `NDArray` value in LLVM.
+#[derive(Copy, Clone)]
+pub struct NDArrayValue<'ctx> {
+    value: PointerValue<'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_instance(value: PointerValue<'ctx>, llvm_usize: IntType<'ctx>) -> Result<(), String> {
+        NDArrayType::is_type(value.get_type(), llvm_usize)
+    }
+
+    /// Creates an [`NDArrayValue`] from a [`PointerValue`].
+    #[must_use]
+    pub fn from_ptr_val(
+        ptr: PointerValue<'ctx>,
+        llvm_usize: IntType<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> Self {
+        debug_assert!(Self::is_instance(ptr, llvm_usize).is_ok());
+
+        <Self as ProxyValue<'ctx>>::Type::from_type(ptr.get_type(), llvm_usize)
+            .create_value(ptr, 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> {
+        let llvm_i32 = ctx.ctx.i32_type();
+        let var_name = self.name.map(|v| format!("{v}.ndims.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()
+        }
+    }
+
+    /// 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 `dims` array, as if by calling `getelementptr`
+    /// on the field.
+    fn ptr_to_dims(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
+        let llvm_i32 = ctx.ctx.i32_type();
+        let var_name = self.name.map(|v| format!("{v}.dims.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 dimension sizes `dims` into this instance.
+    fn store_dim_sizes(&self, ctx: &CodeGenContext<'ctx, '_>, dims: PointerValue<'ctx>) {
+        ctx.builder.build_store(self.ptr_to_dims(ctx), dims).unwrap();
+    }
+
+    /// Convenience method for creating a new array storing dimension sizes with the given `size`.
+    pub fn create_dim_sizes(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        llvm_usize: IntType<'ctx>,
+        size: IntValue<'ctx>,
+    ) {
+        self.store_dim_sizes(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 dim_sizes(&self) -> NDArrayDimsProxy<'ctx, '_> {
+        NDArrayDimsProxy(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> {
+        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_int(2, 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.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>,
+    ) {
+        self.store_data(ctx, ctx.builder.build_array_alloca(elem_ty, size, "").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 Underlying = StructValue<'ctx>;
+    type Type = NDArrayType<'ctx>;
+
+    fn get_type(&self) -> Self::Type {
+        NDArrayType::from_type(self.as_base_value().get_type(), self.llvm_usize)
+    }
+
+    fn as_base_value(&self) -> Self::Base {
+        self.value
+    }
+
+    fn as_underlying_value(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> Self::Underlying {
+        ctx.builder
+            .build_load(self.as_base_value(), name.unwrap_or_default())
+            .map(BasicValueEnum::into_struct_value)
+            .unwrap()
+    }
+}
+
+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 NDArrayDimsProxy<'ctx, 'a>(&'a NDArrayValue<'ctx>);
+
+impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDimsProxy<'ctx, '_> {
+    fn element_type<G: CodeGenerator + ?Sized>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
+    ) -> AnyTypeEnum<'ctx> {
+        self.0.dim_sizes().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_dims(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 NDArrayDimsProxy<'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 NDArrayDimsProxy<'ctx, '_> {}
+impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {}
+
+impl<'ctx> TypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'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 NDArrayDimsProxy<'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,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
+    ) -> AnyTypeEnum<'ctx> {
+        self.0.data().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_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> {
+        unsafe {
+            ctx.builder
+                .build_in_bounds_gep(
+                    self.base_ptr(ctx, generator),
+                    &[*idx],
+                    name.unwrap_or_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,
+        );
+
+        unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) }
+    }
+}
+
+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);
+
+        unsafe {
+            ctx.builder
+                .build_in_bounds_gep(
+                    self.base_ptr(ctx, generator),
+                    &[index],
+                    name.unwrap_or_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.dim_sizes().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();
+
+        unsafe { self.ptr_offset_unchecked(ctx, generator, indices, name) }
+    }
+}
+
+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/concrete_type.rs

@@ -1,3 +1,9 @@
+use std::collections::HashMap;
+
+use indexmap::IndexMap;
+
+use nac3parser::ast::StrRef;
+
 use crate::{
     symbol_resolver::SymbolValue,
     toplevel::DefinitionId,
@@ -9,10 +15,6 @@ use crate::{
     },
 };
 
-use indexmap::IndexMap;
-use nac3parser::ast::StrRef;
-use std::collections::HashMap;
-
 pub struct ConcreteTypeStore {
     store: Vec<ConcreteTypeEnum>,
 }

nac3core/src/codegen/expr.rs

@@ -1,8 +1,28 @@
+use std::{
+    cmp::min,
+    collections::HashMap,
+    convert::TryInto,
+    iter::{once, repeat, repeat_with, zip},
+};
+
+use inkwell::{
+    attributes::{Attribute, AttributeLoc},
+    types::{AnyType, BasicType, BasicTypeEnum},
+    values::{BasicValueEnum, CallSiteValue, FunctionValue, IntValue, PointerValue, StructValue},
+    AddressSpace, IntPredicate, OptimizationLevel,
+};
+use itertools::{chain, izip, Either, Itertools};
+
+use nac3parser::ast::{
+    self, Boolop, Cmpop, Comprehension, Constant, Expr, ExprKind, Location, Operator, StrRef,
+    Unaryop,
+};
+
 use crate::{
     codegen::{
         classes::{
-            ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, ProxyType, ProxyValue,
+            ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, NDArrayValue, ProxyType,
-            RangeValue, UntypedArrayLikeAccessor,
+            ProxyValue, RangeValue, TypedArrayLikeAccessor, UntypedArrayLikeAccessor,
         },
         concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
         gen_in_range_check, get_llvm_abi_type, get_llvm_type, get_va_count_arg_name,
@@ -12,8 +32,7 @@ use crate::{
             call_int_umin, call_memcpy_generic,
         },
         macros::codegen_unreachable,
-        need_sret,
+        need_sret, numpy,
-        object::ndarray::{NDArrayOut, ScalarOrNDArray},
         stmt::{
             gen_for_callback_incrementing, gen_if_callback, gen_if_else_expr_callback, gen_raise,
             gen_var,
@@ -21,34 +40,16 @@ use crate::{
         CodeGenContext, CodeGenTask, CodeGenerator,
     },
     symbol_resolver::{SymbolValue, ValueEnum},
-    toplevel::{helper::PrimDef, DefinitionId, TopLevelDef},
+    toplevel::{
+        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},
     },
 };
-use inkwell::{
-    attributes::{Attribute, AttributeLoc},
-    types::{AnyType, BasicType, BasicTypeEnum},
-    values::{
-        BasicValue, BasicValueEnum, CallSiteValue, FunctionValue, IntValue, PointerValue,
-        StructValue,
-    },
-    AddressSpace, IntPredicate, OptimizationLevel,
-};
-use itertools::{chain, izip, Either, Itertools};
-use nac3parser::ast::{
-    self, Boolop, Cmpop, Comprehension, Constant, Expr, ExprKind, Location, Operator, StrRef,
-    Unaryop,
-};
-use std::cmp::min;
-use std::iter::{repeat, repeat_with};
-use std::{collections::HashMap, convert::TryInto, iter::once, iter::zip};
-
-use super::object::{
-    any::AnyObject,
-    ndarray::{indexing::util::gen_ndarray_subscript_ndindices, NDArrayObject},
-};
 
 pub fn get_subst_key(
     unifier: &mut Unifier,
@@ -556,7 +557,7 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
                             && val_ty.get_element_type().is_struct_type()
                     } =>
                 {
-                    self.builder.build_bitcast(*val, arg_ty, "call_arg_cast").unwrap()
+                    self.builder.build_bit_cast(*val, arg_ty, "call_arg_cast").unwrap()
                 }
                 _ => *val,
             })
@@ -976,6 +977,7 @@ pub fn gen_call<'ctx, G: CodeGenerator>(
             TopLevelDef::Class { .. } => {
                 return Ok(Some(generator.gen_constructor(ctx, fun.0, &def, params)?))
             }
+            TopLevelDef::Variable { .. } => unreachable!(),
         }
     }
     .or_else(|_: String| {
@@ -1547,75 +1549,99 @@ 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 left =
+        let llvm_usize = generator.get_size_type(ctx.ctx);
-            ScalarOrNDArray::split_object(generator, ctx, AnyObject { ty: ty1, value: left_val });
-        let right =
-            ScalarOrNDArray::split_object(generator, ctx, AnyObject { ty: ty2, value: right_val });
 
-        // Inhomogeneous binary operations are not supported.
+        let is_ndarray1 = ty1.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
-        assert!(ctx.unifier.unioned(left.get_dtype(), right.get_dtype()));
+        let is_ndarray2 = ty2.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
 
-        let common_dtype = left.get_dtype();
+        if is_ndarray1 && is_ndarray2 {
+            let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
+            let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty2);
 
-        let out = match op.variant {
+            assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
-            BinopVariant::Normal => NDArrayOut::NewNDArray { dtype: common_dtype },
-            BinopVariant::AugAssign => {
-                // If this is an augmented assignment.
-                // `left` has to be an ndarray. If it were a scalar then NAC3 simply doesn't support it.
-                if let ScalarOrNDArray::NDArray(out_ndarray) = left {
-                    NDArrayOut::WriteToNDArray { ndarray: out_ndarray }
-                } else {
-                    panic!("left must be an ndarray")
-                }
-            }
-        };
 
-        if op.base == Operator::MatMult {
+            let left_val =
-            // Handle matrix multiplication.
+                NDArrayValue::from_ptr_val(left_val.into_pointer_value(), llvm_usize, None);
-            let left = left.to_ndarray(generator, ctx);
+            let right_val =
-            let right = right.to_ndarray(generator, ctx);
+                NDArrayValue::from_ptr_val(right_val.into_pointer_value(), llvm_usize, None);
-            let result = NDArrayObject::matmul(generator, ctx, left, right, out)
-                .split_unsized(generator, ctx);
-            Ok(Some(ValueEnum::Dynamic(result.to_basic_value_enum())))
-        } else {
-            // For other operations, they are all elementwise operations.
 
-            // There are only three cases:
+            let res = if op.base == Operator::MatMult {
-            // - LHS is a scalar, RHS is an ndarray.
+                // MatMult is the only binop which is not an elementwise op
-            // - LHS is an ndarray, RHS is a scalar.
+                numpy::ndarray_matmul_2d(
-            // - LHS is an ndarray, RHS is an ndarray.
-            //
-            // For all cases, the scalar operand is promoted to an ndarray,
-            // the two are then broadcasted, and starmapped through.
-
-            let left = left.to_ndarray(generator, ctx);
-            let right = right.to_ndarray(generator, ctx);
-
-            let result = NDArrayObject::broadcast_starmap(
                     generator,
                     ctx,
-                &[left, right],
+                    ndarray_dtype1,
-                out,
+                    match op.variant {
-                |generator, ctx, scalars| {
+                        BinopVariant::Normal => None,
-                    let left_value = scalars[0];
+                        BinopVariant::AugAssign => Some(left_val),
-                    let right_value = scalars[1];
+                    },
-
+                    left_val,
-                    let result = gen_binop_expr_with_values(
+                    right_val,
+                )?
+            } else {
+                numpy::ndarray_elementwise_binop_impl(
                     generator,
                     ctx,
-                        (&Some(left.dtype), left_value),
+                    ndarray_dtype1,
+                    match op.variant {
+                        BinopVariant::Normal => None,
+                        BinopVariant::AugAssign => Some(left_val),
+                    },
+                    (left_val.as_base_value().into(), false),
+                    (right_val.as_base_value().into(), false),
+                    |generator, ctx, (lhs, rhs)| {
+                        gen_binop_expr_with_values(
+                            generator,
+                            ctx,
+                            (&Some(ndarray_dtype1), lhs),
                             op,
-                        (&Some(right.dtype), right_value),
+                            (&Some(ndarray_dtype2), rhs),
                             ctx.current_loc,
                         )?
                         .unwrap()
-                    .to_basic_value_enum(ctx, generator, common_dtype)?;
+                        .to_basic_value_enum(
-
+                            ctx,
-                    Ok(result)
+                            generator,
-                },
+                            ndarray_dtype1,
                         )
-            .unwrap();
+                    },
-            Ok(Some(ValueEnum::Dynamic(result.instance.value.as_basic_value_enum())))
+                )?
+            };
+
+            Ok(Some(res.as_base_value().into()))
+        } else {
+            let (ndarray_dtype, _) =
+                unpack_ndarray_var_tys(&mut ctx.unifier, if is_ndarray1 { ty1 } else { ty2 });
+            let ndarray_val = NDArrayValue::from_ptr_val(
+                if is_ndarray1 { left_val } else { right_val }.into_pointer_value(),
+                llvm_usize,
+                None,
+            );
+            let res = numpy::ndarray_elementwise_binop_impl(
+                generator,
+                ctx,
+                ndarray_dtype,
+                match op.variant {
+                    BinopVariant::Normal => None,
+                    BinopVariant::AugAssign => Some(ndarray_val),
+                },
+                (left_val, !is_ndarray1),
+                (right_val, !is_ndarray2),
+                |generator, ctx, (lhs, rhs)| {
+                    gen_binop_expr_with_values(
+                        generator,
+                        ctx,
+                        (&Some(ndarray_dtype), lhs),
+                        op,
+                        (&Some(ndarray_dtype), rhs),
+                        ctx.current_loc,
+                    )?
+                    .unwrap()
+                    .to_basic_value_enum(ctx, generator, ndarray_dtype)
+                },
+            )?;
+
+            Ok(Some(res.as_base_value().into()))
         }
     } else {
         let left_ty_enum = ctx.unifier.get_ty_immutable(left_ty.unwrap());
@@ -1773,12 +1799,14 @@ 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 ndarray = AnyObject { value: val, ty };
+        let llvm_usize = generator.get_size_type(ctx.ctx);
-        let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
+        let (ndarray_dtype, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
+
+        let val = NDArrayValue::from_ptr_val(val.into_pointer_value(), 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 {
@@ -1792,18 +1820,20 @@ pub fn gen_unaryop_expr_with_values<'ctx, G: CodeGenerator>(
             op
         };
 
-        let mapped_ndarray = ndarray.map(
+        let res = numpy::ndarray_elementwise_unaryop_impl(
             generator,
             ctx,
-            NDArrayOut::NewNDArray { dtype: ndarray.dtype },
+            ndarray_dtype,
-            |generator, ctx, scalar| {
+            None,
-                gen_unaryop_expr_with_values(generator, ctx, op, (&Some(ndarray.dtype), scalar))?
+            val,
+            |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)
             },
         )?;
 
-        ValueEnum::Dynamic(mapped_ndarray.instance.value.as_basic_value_enum())
+        res.as_base_value().into()
     } else {
         unimplemented!()
     }))
@@ -1846,33 +1876,39 @@ 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 (Some(left_ty), left) = left else { codegen_unreachable!(ctx) };
+            let llvm_usize = generator.get_size_type(ctx.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 left = AnyObject { value: left, ty: left_ty };
+            let is_ndarray1 =
-            let left =
+                left_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
-                ScalarOrNDArray::split_object(generator, ctx, left).to_ndarray(generator, ctx);
+            let is_ndarray2 =
+                right_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
 
-            let right = AnyObject { value: right, ty: right_ty };
+            return if is_ndarray1 && is_ndarray2 {
-            let right =
+                let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, left_ty);
-                ScalarOrNDArray::split_object(generator, ctx, right).to_ndarray(generator, ctx);
+                let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, right_ty);
 
-            let result_ndarray = NDArrayObject::broadcast_starmap(
+                assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
+
+                let left_val =
+                    NDArrayValue::from_ptr_val(lhs.into_pointer_value(), llvm_usize, None);
+                let res = numpy::ndarray_elementwise_binop_impl(
                     generator,
                     ctx,
-                &[left, right],
+                    ctx.primitives.bool,
-                NDArrayOut::NewNDArray { dtype: ctx.primitives.bool },
+                    None,
-                |generator, ctx, scalars| {
+                    (left_val.as_base_value().into(), false),
-                    let left_scalar = scalars[0];
+                    (rhs, false),
-                    let right_scalar = scalars[1];
+                    |generator, ctx, (lhs, rhs)| {
-
                         let val = gen_cmpop_expr_with_values(
                             generator,
                             ctx,
-                        (Some(left.dtype), left_scalar),
+                            (Some(ndarray_dtype1), lhs),
                             &[op],
-                        &[(Some(right.dtype), right_scalar)],
+                            &[(Some(ndarray_dtype2), rhs)],
                         )?
                         .unwrap()
                         .to_basic_value_enum(
@@ -1885,7 +1921,40 @@ pub fn gen_cmpop_expr_with_values<'ctx, G: CodeGenerator>(
                     },
                 )?;
 
-            return Ok(Some(result_ndarray.instance.value.into()));
+                Ok(Some(res.as_base_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,
+                    (lhs, !is_ndarray1),
+                    (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()))
+            };
         }
     }
 
@@ -2436,6 +2505,338 @@ 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.dim_sizes().get_typed_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(dim, true),
+                        None,
+                    )
+                };
+
+                let index = ctx
+                    .builder
+                    .build_int_add(
+                        len,
+                        ctx.builder.build_int_s_extend(index, llvm_usize, "").unwrap(),
+                        "",
+                    )
+                    .unwrap();
+
+                Ok(Some(ctx.builder.build_int_truncate(index, llvm_i32, "").unwrap()))
+            },
+        )
+        .map(|v| v.map(BasicValueEnum::into_int_value))
+    };
+
+    // Converts a slice expression into a slice-range tuple
+    let expr_to_slice = |generator: &mut G,
+                         ctx: &mut CodeGenContext<'ctx, '_>,
+                         node: &ExprKind<Option<Type>>,
+                         dim: u64| {
+        match node {
+            ExprKind::Constant { value: Constant::Int(v), .. } => {
+                let Some(index) =
+                    normalize_index(generator, ctx, llvm_i32.const_int(*v as u64, true), dim)?
+                else {
+                    return Ok(None);
+                };
+
+                Ok(Some((index, index, llvm_i32.const_int(1, true))))
+            }
+
+            ExprKind::Slice { lower, upper, step } => {
+                let dim_sz = unsafe {
+                    v.dim_sizes().get_typed_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(dim, false),
+                        None,
+                    )
+                };
+
+                handle_slice_indices(lower, upper, step, ctx, generator, dim_sz)
+            }
+
+            _ => {
+                let Some(index) = generator.gen_expr(ctx, slice)? else { return Ok(None) };
+                let index = index
+                    .to_basic_value_enum(ctx, generator, slice.custom.unwrap())?
+                    .into_int_value();
+                let Some(index) = normalize_index(generator, ctx, index, dim)? else {
+                    return Ok(None);
+                };
+
+                Ok(Some((index, index, llvm_i32.const_int(1, true))))
+            }
+        }
+    };
+
+    let make_indices_arr = |generator: &mut G,
+                            ctx: &mut CodeGenContext<'ctx, '_>|
+     -> Result<_, String> {
+        Ok(if let ExprKind::Tuple { elts, .. } = &slice.node {
+            let llvm_int_ty = ctx.get_llvm_type(generator, elts[0].custom.unwrap());
+            let index_addr = generator.gen_array_var_alloc(
+                ctx,
+                llvm_int_ty,
+                llvm_usize.const_int(elts.len() as u64, false),
+                None,
+            )?;
+
+            for (i, elt) in elts.iter().enumerate() {
+                let Some(index) = generator.gen_expr(ctx, elt)? else {
+                    return Ok(None);
+                };
+
+                let index = index
+                    .to_basic_value_enum(ctx, generator, elt.custom.unwrap())?
+                    .into_int_value();
+                let Some(index) = normalize_index(generator, ctx, index, 0)? else {
+                    return Ok(None);
+                };
+
+                let store_ptr = unsafe {
+                    index_addr.ptr_offset_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(i as u64, false),
+                        None,
+                    )
+                };
+                ctx.builder.build_store(store_ptr, index).unwrap();
+            }
+
+            Some(index_addr)
+        } else if let Some(index) = generator.gen_expr(ctx, slice)? {
+            let llvm_int_ty = ctx.get_llvm_type(generator, slice.custom.unwrap());
+            let index_addr = generator.gen_array_var_alloc(
+                ctx,
+                llvm_int_ty,
+                llvm_usize.const_int(1u64, false),
+                None,
+            )?;
+
+            let index =
+                index.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value();
+            let Some(index) = normalize_index(generator, ctx, index, 0)? else { return Ok(None) };
+
+            let store_ptr = unsafe {
+                index_addr.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
+            };
+            ctx.builder.build_store(store_ptr, index).unwrap();
+
+            Some(index_addr)
+        } else {
+            None
+        })
+    };
+
+    Ok(Some(if ndims.len() == 1 && ndims[0] - subscripted_dims == 0 {
+        let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
+
+        v.data().get(ctx, generator, &index_addr, None).into()
+    } else {
+        match &slice.node {
+            ExprKind::Tuple { elts, .. } => {
+                let slices = elts
+                    .iter()
+                    .enumerate()
+                    .map(|(dim, elt)| expr_to_slice(generator, ctx, &elt.node, dim as u64))
+                    .take_while_inclusive(|slice| slice.as_ref().is_ok_and(Option::is_some))
+                    .collect::<Result<Vec<_>, _>>()?;
+                if slices.len() < elts.len() {
+                    return Ok(None);
+                }
+
+                let slices = slices.into_iter().map(Option::unwrap).collect_vec();
+
+                numpy::ndarray_sliced_copy(generator, ctx, ty, v, &slices)?.as_base_value().into()
+            }
+
+            ExprKind::Slice { .. } => {
+                let Some(slice) = expr_to_slice(generator, ctx, &slice.node, 0)? else {
+                    return Ok(None);
+                };
+
+                numpy::ndarray_sliced_copy(generator, ctx, ty, v, &[slice])?.as_base_value().into()
+            }
+
+            _ => {
+                // Accessing an element from a multi-dimensional `ndarray`
+
+                let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
+
+                // Create a new array, remove the top dimension from the dimension-size-list, and copy the
+                // elements over
+                let subscripted_ndarray =
+                    generator.gen_var_alloc(ctx, llvm_ndarray_t.into(), None)?;
+                let ndarray = NDArrayValue::from_ptr_val(subscripted_ndarray, llvm_usize, None);
+
+                let num_dims = v.load_ndims(ctx);
+                ndarray.store_ndims(
+                    ctx,
+                    generator,
+                    ctx.builder
+                        .build_int_sub(num_dims, llvm_usize.const_int(1, false), "")
+                        .unwrap(),
+                );
+
+                let ndarray_num_dims = ndarray.load_ndims(ctx);
+                ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
+
+                let ndarray_num_dims = ctx
+                    .builder
+                    .build_int_z_extend_or_bit_cast(
+                        ndarray.load_ndims(ctx),
+                        llvm_usize.size_of().get_type(),
+                        "",
+                    )
+                    .unwrap();
+                let v_dims_src_ptr = unsafe {
+                    v.dim_sizes().ptr_offset_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(1, false),
+                        None,
+                    )
+                };
+                call_memcpy_generic(
+                    ctx,
+                    ndarray.dim_sizes().base_ptr(ctx, generator),
+                    v_dims_src_ptr,
+                    ctx.builder
+                        .build_int_mul(ndarray_num_dims, llvm_usize.size_of(), "")
+                        .map(Into::into)
+                        .unwrap(),
+                    llvm_i1.const_zero(),
+                );
+
+                let ndarray_num_elems = call_ndarray_calc_size(
+                    generator,
+                    ctx,
+                    &ndarray.dim_sizes().as_slice_value(ctx, generator),
+                    (None, None),
+                );
+                let ndarray_num_elems = ctx
+                    .builder
+                    .build_int_z_extend_or_bit_cast(ndarray_num_elems, sizeof_elem.get_type(), "")
+                    .unwrap();
+                ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
+
+                let v_data_src_ptr = v.data().ptr_offset(ctx, generator, &index_addr, None);
+                call_memcpy_generic(
+                    ctx,
+                    ndarray.data().base_ptr(ctx, generator),
+                    v_data_src_ptr,
+                    ctx.builder
+                        .build_int_mul(
+                            ndarray_num_elems,
+                            llvm_ndarray_data_t.size_of().unwrap(),
+                            "",
+                        )
+                        .map(Into::into)
+                        .unwrap(),
+                    llvm_i1.const_zero(),
+                );
+
+                ndarray.as_base_value().into()
+            }
+        }
+    }))
+}
+
 /// See [`CodeGenerator::gen_expr`].
 pub fn gen_expr<'ctx, G: CodeGenerator>(
     generator: &mut G,
@@ -2485,7 +2886,31 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
             Some((_, Some(static_value), _)) => ValueEnum::Static(static_value.clone()),
             None => {
                 let resolver = ctx.resolver.clone();
-                resolver.get_symbol_value(*id, ctx).unwrap()
+                let value = resolver.get_symbol_value(*id, ctx, generator).unwrap();
+
+                let globals = ctx
+                    .top_level
+                    .definitions
+                    .read()
+                    .iter()
+                    .filter_map(|def| {
+                        if let TopLevelDef::Variable { simple_name, ty, .. } = &*def.read() {
+                            Some((*simple_name, *ty))
+                        } else {
+                            None
+                        }
+                    })
+                    .collect_vec();
+
+                if let Some((_, ty)) = globals.iter().find(|(name, _)| name == id) {
+                    let ptr = value
+                        .to_basic_value_enum(ctx, generator, *ty)
+                        .map(BasicValueEnum::into_pointer_value)?;
+
+                    ctx.builder.build_load(ptr, id.to_string().as_str()).map(Into::into).unwrap()
+                } else {
+                    value
+                }
             }
         },
         ExprKind::List { elts, .. } => {
@@ -3068,26 +3493,18 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
                         v.data().get(ctx, generator, &index, None).into()
                     }
                 }
-                TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
+                TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::NDArray.id() => {
-                    let Some(ndarray) = generator.gen_expr(ctx, value)? else {
+                    let (ty, ndims) = params.iter().map(|(_, ty)| ty).collect_tuple().unwrap();
+
+                    let v = if let Some(v) = generator.gen_expr(ctx, value)? {
+                        v.to_basic_value_enum(ctx, generator, value.custom.unwrap())?
+                            .into_pointer_value()
+                    } else {
                         return Ok(None);
                     };
+                    let v = NDArrayValue::from_ptr_val(v, usize, None);
 
-                    let ndarray_ty = value.custom.unwrap();
+                    return gen_ndarray_subscript_expr(generator, ctx, *ty, *ndims, v, slice);
-                    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/extern_fns.rs

@@ -1,5 +1,7 @@
-use inkwell::attributes::{Attribute, AttributeLoc};
+use inkwell::{
-use inkwell::values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue};
+    attributes::{Attribute, AttributeLoc},
+    values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue},
+};
 use itertools::Either;
 
 use crate::codegen::CodeGenContext;

nac3core/src/codegen/generator.rs

@@ -1,15 +1,17 @@
+use inkwell::{
+    context::Context,
+    types::{BasicTypeEnum, IntType},
+    values::{BasicValueEnum, IntValue, PointerValue},
+};
+
+use nac3parser::ast::{Expr, Stmt, StrRef};
+
 use crate::{
     codegen::{bool_to_i1, bool_to_i8, classes::ArraySliceValue, expr::*, stmt::*, CodeGenContext},
     symbol_resolver::ValueEnum,
     toplevel::{DefinitionId, TopLevelDef},
     typecheck::typedef::{FunSignature, Type},
 };
-use inkwell::{
-    context::Context,
-    types::{BasicTypeEnum, IntType},
-    values::{BasicValueEnum, IntValue, PointerValue},
-};
-use nac3parser::ast::{Expr, Stmt, StrRef};
 
 pub trait CodeGenerator {
     /// Return the module name for the code generator.

nac3core/src/codegen/irrt/mod.rs

@@ -1,27 +1,28 @@
-use crate::{symbol_resolver::SymbolResolver, typecheck::typedef::Type};
-
-use super::{
-    classes::{ArrayLikeValue, ListValue},
-    macros::codegen_unreachable,
-    model::{function::FnCall, *},
-    object::{
-        list::List,
-        ndarray::{broadcast::ShapeEntry, indexing::NDIndex, nditer::NDIter, NDArray},
-    },
-    CodeGenContext, CodeGenerator,
-};
 use inkwell::{
     attributes::{Attribute, AttributeLoc},
     context::Context,
     memory_buffer::MemoryBuffer,
     module::Module,
-    types::BasicTypeEnum,
+    types::{BasicTypeEnum, IntType},
     values::{BasicValue, BasicValueEnum, CallSiteValue, FloatValue, IntValue},
     AddressSpace, IntPredicate,
 };
 use itertools::Either;
+
 use nac3parser::ast::Expr;
 
+use super::{
+    classes::{
+        ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue,
+        TypedArrayLikeAccessor, TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
+    },
+    llvm_intrinsics,
+    macros::codegen_unreachable,
+    stmt::gen_for_callback_incrementing,
+    CodeGenContext, CodeGenerator,
+};
+use crate::{symbol_resolver::SymbolResolver, typecheck::typedef::Type};
+
 #[must_use]
 pub fn load_irrt<'ctx>(ctx: &'ctx Context, symbol_resolver: &dyn SymbolResolver) -> Module<'ctx> {
     let bitcode_buf = MemoryBuffer::create_from_memory_range(
@@ -583,294 +584,369 @@ pub fn call_j0<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> Flo
         .unwrap()
 }
 
-// When [`TypeContext::size_type`] is 32-bits, the function name is "{fn_name}".
+/// Generates a call to `__nac3_ndarray_calc_size`. Returns an [`IntValue`] representing the
-// When [`TypeContext::size_type`] is 64-bits, the function name is "{fn_name}64".
+/// calculated total size.
-#[must_use]
+///
-pub fn get_sizet_dependent_function_name<G: CodeGenerator + ?Sized>(
+/// * `dims` - An [`ArrayLikeIndexer`] containing the size of each dimension.
-    generator: &mut G,
+/// * `range` - The dimension index to begin and end (exclusively) calculating the dimensions for,
-    ctx: &CodeGenContext<'_, '_>,
+///   or [`None`] if starting from the first dimension and ending at the last dimension
-    name: &str,
+///   respectively.
-) -> String {
+pub fn call_ndarray_calc_size<'ctx, G, Dims>(
-    let mut name = name.to_owned();
+    generator: &G,
-    match generator.get_size_type(ctx.ctx).get_bit_width() {
+    ctx: &CodeGenContext<'ctx, '_>,
-        32 => {}
+    dims: &Dims,
-        64 => name.push_str("64"),
+    (begin, end): (Option<IntValue<'ctx>>, Option<IntValue<'ctx>>),
-        bit_width => {
+) -> IntValue<'ctx>
-            panic!("Unsupported int type bit width {bit_width}, must be either 32-bits or 64-bits")
+where
-        }
+    G: CodeGenerator + ?Sized,
-    }
+    Dims: ArrayLikeIndexer<'ctx>,
-    name
+{
+    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()
 }
 
-pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
+/// 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.dim_sizes();
+
+    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.dim_sizes();
+
+    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, '_>,
-    ndims: Instance<'ctx, Int<SizeT>>,
+    ndarray: NDArrayValue<'ctx>,
-    shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+    indices: &Index,
-) {
+) -> IntValue<'ctx>
-    let name = get_sizet_dependent_function_name(
+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,
-        "__nac3_ndarray_util_assert_shape_no_negative",
+        None,
-    );
+        llvm_usize.const_zero(),
-    FnCall::builder(generator, ctx, &name).arg(ndims).arg(shape).returning_void();
+        (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.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None),
+                    rhs.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None),
+                )
+            };
 
-pub fn call_nac3_ndarray_util_assert_output_shape_same<'ctx, G: CodeGenerator + ?Sized>(
+            let llvm_usize_const_one = llvm_usize.const_int(1, false);
-    generator: &mut G,
+            let lhs_eqz = ctx
-    ctx: &mut CodeGenContext<'ctx, '_>,
+                .builder
-    ndarray_ndims: Instance<'ctx, Int<SizeT>>,
+                .build_int_compare(IntPredicate::EQ, lhs_dim_sz, llvm_usize_const_one, "")
-    ndarray_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+                .unwrap();
-    output_ndims: Instance<'ctx, Int<SizeT>>,
+            let rhs_eqz = ctx
-    output_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+                .builder
-) {
+                .build_int_compare(IntPredicate::EQ, rhs_dim_sz, llvm_usize_const_one, "")
-    let name = get_sizet_dependent_function_name(
+                .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,
-        ctx,
+                is_compatible,
-        "__nac3_ndarray_util_assert_output_shape_same",
+                "0:ValueError",
+                "operands could not be broadcast together",
+                [None, None, None],
+                ctx.current_loc,
             );
-    FnCall::builder(generator, ctx, &name)
+
-        .arg(ndarray_ndims)
+            Ok(())
-        .arg(ndarray_shape)
+        },
-        .arg(output_ndims)
+        llvm_usize.const_int(1, false),
-        .arg(output_shape)
+    )
-        .returning_void();
+    .unwrap();
+
+    let max_ndims = llvm_intrinsics::call_int_umax(ctx, lhs_ndims, rhs_ndims, None);
+    let lhs_dims = lhs.dim_sizes().base_ptr(ctx, generator);
+    let lhs_ndims = lhs.load_ndims(ctx);
+    let rhs_dims = rhs.dim_sizes().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()),
+    )
 }
 
-pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
+/// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
+/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
+/// array `broadcast_idx`.
+pub fn call_ndarray_calc_broadcast_index<
+    'ctx,
+    G: CodeGenerator + ?Sized,
+    BroadcastIdx: UntypedArrayLikeAccessor<'ctx>,
+>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
-    ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
+    array: NDArrayValue<'ctx>,
-) -> Instance<'ctx, Int<SizeT>> {
+    broadcast_idx: &BroadcastIdx,
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_size");
+) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
-    FnCall::builder(generator, ctx, &name).arg(ndarray).returning_auto("size")
+    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());
 
-pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
+    let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
-    generator: &mut G,
+        32 => "__nac3_ndarray_calc_broadcast_idx",
-    ctx: &mut CodeGenContext<'ctx, '_>,
+        64 => "__nac3_ndarray_calc_broadcast_idx64",
-    ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
+        bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
-) -> Instance<'ctx, Int<SizeT>> {
+    };
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_nbytes");
+    let ndarray_calc_broadcast_fn =
-    FnCall::builder(generator, ctx, &name).arg(ndarray).returning_auto("nbytes")
+        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()],
-pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
+                false,
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
-) -> Instance<'ctx, Int<SizeT>> {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_len");
-    FnCall::builder(generator, ctx, &name).arg(ndarray).returning_auto("len")
-}
-
-pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
-) -> Instance<'ctx, Int<Bool>> {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_is_c_contiguous");
-    FnCall::builder(generator, ctx, &name).arg(ndarray).returning_auto("is_c_contiguous")
-}
-
-pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
-    index: Instance<'ctx, Int<SizeT>>,
-) -> Instance<'ctx, Ptr<Int<Byte>>> {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_nth_pelement");
-    FnCall::builder(generator, ctx, &name).arg(ndarray).arg(index).returning_auto("pelement")
-}
-
-pub fn call_nac3_ndarray_get_pelement_by_indices<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
-    indices: Instance<'ctx, Ptr<Int<SizeT>>>,
-) -> Instance<'ctx, Ptr<Int<Byte>>> {
-    let name =
-        get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_pelement_by_indices");
-    FnCall::builder(generator, ctx, &name).arg(ndarray).arg(indices).returning_auto("pelement")
-}
-
-pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
-) {
-    let name =
-        get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_set_strides_by_shape");
-    FnCall::builder(generator, ctx, &name).arg(ndarray).returning_void();
-}
-
-pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
-    dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
-) {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_copy_data");
-    FnCall::builder(generator, ctx, &name).arg(src_ndarray).arg(dst_ndarray).returning_void();
-}
-
-pub fn call_nac3_nditer_initialize<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    iter: Instance<'ctx, Ptr<Struct<NDIter>>>,
-    ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
-    indices: Instance<'ctx, Ptr<Int<SizeT>>>,
-) {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_initialize");
-    FnCall::builder(generator, ctx, &name).arg(iter).arg(ndarray).arg(indices).returning_void();
-}
-
-pub fn call_nac3_nditer_has_element<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    iter: Instance<'ctx, Ptr<Struct<NDIter>>>,
-) -> Instance<'ctx, Int<Bool>> {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_has_element");
-    FnCall::builder(generator, ctx, &name).arg(iter).returning_auto("has_element")
-}
-
-pub fn call_nac3_nditer_next<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    iter: Instance<'ctx, Ptr<Struct<NDIter>>>,
-) {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_next");
-    FnCall::builder(generator, ctx, &name).arg(iter).returning_void();
-}
-
-pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    num_indices: Instance<'ctx, Int<SizeT>>,
-    indices: Instance<'ctx, Ptr<Struct<NDIndex>>>,
-    src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
-    dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
-) {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
-    FnCall::builder(generator, ctx, &name)
-        .arg(num_indices)
-        .arg(indices)
-        .arg(src_ndarray)
-        .arg(dst_ndarray)
-        .returning_void();
-}
-
-pub fn call_nac3_ndarray_array_set_and_validate_list_shape<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    list: Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>>,
-    ndims: Instance<'ctx, Int<SizeT>>,
-    shape: Instance<'ctx, Ptr<Int<SizeT>>>,
-) {
-    let name = get_sizet_dependent_function_name(
-        generator,
-        ctx,
-        "__nac3_ndarray_array_set_and_validate_list_shape",
             );
-    FnCall::builder(generator, ctx, &name).arg(list).arg(ndims).arg(shape).returning_void();
-}
 
-pub fn call_nac3_ndarray_array_write_list_to_array<'ctx, G: CodeGenerator + ?Sized>(
+            ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
-    generator: &mut G,
+        });
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    list: Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>>,
-    ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
-) {
-    let name = get_sizet_dependent_function_name(
-        generator,
-        ctx,
-        "__nac3_ndarray_array_write_list_to_array",
-    );
-    FnCall::builder(generator, ctx, &name).arg(list).arg(ndarray).returning_void();
-}
 
-pub fn call_nac3_ndarray_reshape_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
+    let broadcast_size = broadcast_idx.size(ctx, generator);
-    generator: &mut G,
+    let out_idx = ctx.builder.build_array_alloca(llvm_i32, broadcast_size, "").unwrap();
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    size: Instance<'ctx, Int<SizeT>>,
-    new_ndims: Instance<'ctx, Int<SizeT>>,
-    new_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
-) {
-    let name = get_sizet_dependent_function_name(
-        generator,
-        ctx,
-        "__nac3_ndarray_reshape_resolve_and_check_new_shape",
-    );
-    FnCall::builder(generator, ctx, &name).arg(size).arg(new_ndims).arg(new_shape).returning_void();
-}
 
-pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
+    let array_dims = array.dim_sizes().base_ptr(ctx, generator);
-    generator: &mut G,
+    let array_ndims = array.load_ndims(ctx);
-    ctx: &mut CodeGenContext<'ctx, '_>,
+    let broadcast_idx_ptr = unsafe {
-    src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
+        broadcast_idx.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-    dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
+    };
-) {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
-    FnCall::builder(generator, ctx, &name).arg(src_ndarray).arg(dst_ndarray).returning_void();
-}
 
-pub fn call_nac3_ndarray_broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
+    ctx.builder
-    generator: &mut G,
+        .build_call(
-    ctx: &mut CodeGenContext<'ctx, '_>,
+            ndarray_calc_broadcast_fn,
-    num_shape_entries: Instance<'ctx, Int<SizeT>>,
+            &[array_dims.into(), array_ndims.into(), broadcast_idx_ptr.into(), out_idx.into()],
-    shape_entries: Instance<'ctx, Ptr<Struct<ShapeEntry>>>,
+            "",
-    dst_ndims: Instance<'ctx, Int<SizeT>>,
+        )
-    dst_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
+        .unwrap();
-) {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
-    FnCall::builder(generator, ctx, &name)
-        .arg(num_shape_entries)
-        .arg(shape_entries)
-        .arg(dst_ndims)
-        .arg(dst_shape)
-        .returning_void();
-}
 
-pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
+    TypedArrayLikeAdapter::from(
-    generator: &mut G,
+        ArraySliceValue::from_ptr_val(out_idx, broadcast_size, None),
-    ctx: &mut CodeGenContext<'ctx, '_>,
+        Box::new(|_, v| v.into_int_value()),
-    src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
+        Box::new(|_, v| v.into()),
-    dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
+    )
-    num_axes: Instance<'ctx, Int<SizeT>>,
-    axes: Instance<'ctx, Ptr<Int<SizeT>>>,
-) {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
-    FnCall::builder(generator, ctx, &name)
-        .arg(src_ndarray)
-        .arg(dst_ndarray)
-        .arg(num_axes)
-        .arg(axes)
-        .returning_void();
-}
-
-#[allow(clippy::too_many_arguments)]
-pub fn call_nac3_ndarray_matmul_calculate_shapes<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    a_ndims: Instance<'ctx, Int<SizeT>>,
-    a_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
-    b_ndims: Instance<'ctx, Int<SizeT>>,
-    b_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
-    final_ndims: Instance<'ctx, Int<SizeT>>,
-    new_a_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
-    new_b_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
-    dst_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
-) {
-    let name =
-        get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_matmul_calculate_shapes");
-    FnCall::builder(generator, ctx, &name)
-        .arg(a_ndims)
-        .arg(a_shape)
-        .arg(b_ndims)
-        .arg(b_shape)
-        .arg(final_ndims)
-        .arg(new_a_shape)
-        .arg(new_b_shape)
-        .arg(dst_shape)
-        .returning_void();
 }

nac3core/src/codegen/llvm_intrinsics.rs

@@ -1,12 +1,14 @@
-use crate::codegen::CodeGenContext;
+use inkwell::{
-use inkwell::context::Context;
+    context::Context,
-use inkwell::intrinsics::Intrinsic;
+    intrinsics::Intrinsic,
-use inkwell::types::AnyTypeEnum::IntType;
+    types::{AnyTypeEnum::IntType, FloatType},
-use inkwell::types::FloatType;
+    values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue, PointerValue},
-use inkwell::values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue, PointerValue};
+    AddressSpace,
-use inkwell::AddressSpace;
+};
 use itertools::Either;
 
+use crate::codegen::CodeGenContext;
+
 /// Returns the string representation for the floating-point type `ft` when used in intrinsic
 /// functions.
 fn get_float_intrinsic_repr(ctx: &Context, ft: FloatType) -> &'static str {
@@ -183,7 +185,7 @@ pub fn call_memcpy_generic<'ctx>(
         dest
     } else {
         ctx.builder
-            .build_bitcast(dest, llvm_p0i8, "")
+            .build_bit_cast(dest, llvm_p0i8, "")
             .map(BasicValueEnum::into_pointer_value)
             .unwrap()
     };
@@ -191,7 +193,7 @@ pub fn call_memcpy_generic<'ctx>(
         src
     } else {
         ctx.builder
-            .build_bitcast(src, llvm_p0i8, "")
+            .build_bit_cast(src, llvm_p0i8, "")
             .map(BasicValueEnum::into_pointer_value)
             .unwrap()
     };

nac3core/src/codegen/mod.rs

@@ -1,12 +1,12 @@
-use crate::{
+use std::{
-    codegen::classes::{ListType, ProxyType, RangeType},
+    collections::{HashMap, HashSet},
-    symbol_resolver::{StaticValue, SymbolResolver},
+    sync::{
-    toplevel::{helper::PrimDef, TopLevelContext, TopLevelDef},
+        atomic::{AtomicBool, Ordering},
-    typecheck::{
+        Arc,
-        type_inferencer::{CodeLocation, PrimitiveStore},
-        typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
     },
+    thread,
 };
+
 use crossbeam::channel::{unbounded, Receiver, Sender};
 use inkwell::{
     attributes::{Attribute, AttributeLoc},
@@ -24,16 +24,19 @@ use inkwell::{
     AddressSpace, IntPredicate, OptimizationLevel,
 };
 use itertools::Itertools;
-use model::*;
-use nac3parser::ast::{Location, Stmt, StrRef};
-use object::ndarray::NDArray;
 use parking_lot::{Condvar, Mutex};
-use std::collections::{HashMap, HashSet};
+
-use std::sync::{
+use nac3parser::ast::{Location, Stmt, StrRef};
-    atomic::{AtomicBool, Ordering},
+
-    Arc,
+use crate::{
+    codegen::classes::{ListType, NDArrayType, ProxyType, RangeType},
+    symbol_resolver::{StaticValue, SymbolResolver},
+    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
+    typecheck::{
+        type_inferencer::{CodeLocation, PrimitiveStore},
+        typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
+    },
 };
-use std::thread;
 
 pub mod builtin_fns;
 pub mod classes;
@@ -43,9 +46,7 @@ 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;
 
 #[cfg(test)]
@@ -509,7 +510,12 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
                         }
 
                         TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-                            Ptr(Struct(NDArray)).llvm_type(generator, ctx).as_basic_type_enum()
+                            let (dtype, _) = unpack_ndarray_var_tys(unifier, ty);
+                            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!(
@@ -847,10 +853,9 @@ pub fn gen_func_impl<
     builder.position_at_end(init_bb);
     let body_bb = context.append_basic_block(fn_val, "body");
 
+    // Store non-vararg argument values into local variables
     let mut var_assignment = HashMap::new();
     let offset = u32::from(has_sret);
-
-    // Store non-vararg argument values into local variables
     for (n, arg) in args.iter().enumerate().filter(|(_, arg)| !arg.is_vararg) {
         let param = fn_val.get_nth_param((n as u32) + offset).unwrap();
         let local_type = get_llvm_type(

nac3core/src/codegen/model/any.rs

@@ -1,42 +0,0 @@
-use inkwell::{
-    context::Context,
-    types::{BasicType, BasicTypeEnum},
-    values::BasicValueEnum,
-};
-
-use crate::codegen::CodeGenerator;
-
-use super::*;
-
-/// 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

@@ -1,147 +0,0 @@
-use std::fmt;
-
-use inkwell::{
-    context::Context,
-    types::{ArrayType, BasicType, BasicTypeEnum},
-    values::{ArrayValue, IntValue},
-};
-
-use crate::codegen::{CodeGenContext, CodeGenerator};
-
-use super::*;
-
-/// 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

@@ -1,207 +0,0 @@
-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

@@ -1,94 +0,0 @@
-use std::fmt;
-
-use inkwell::{
-    context::Context,
-    types::{BasicType, FloatType},
-    values::FloatValue,
-};
-
-use crate::codegen::CodeGenerator;
-
-use super::*;
-
-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

@@ -1,122 +0,0 @@
-use inkwell::{
-    attributes::{Attribute, AttributeLoc},
-    types::{BasicMetadataTypeEnum, BasicType, FunctionType},
-    values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
-};
-use itertools::Itertools;
-
-use crate::codegen::{CodeGenContext, CodeGenerator};
-
-use super::*;
-
-#[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

@@ -1,422 +0,0 @@
-use std::{cmp::Ordering, fmt};
-
-use inkwell::{
-    context::Context,
-    types::{BasicType, IntType},
-    values::IntValue,
-    IntPredicate,
-};
-
-use crate::codegen::{CodeGenContext, CodeGenerator};
-
-use super::*;
-
-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

@@ -1,17 +0,0 @@
-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

@@ -1,223 +0,0 @@
-use inkwell::{
-    context::Context,
-    types::{BasicType, BasicTypeEnum, PointerType},
-    values::{IntValue, PointerValue},
-    AddressSpace,
-};
-
-use crate::codegen::{llvm_intrinsics::call_memcpy_generic, CodeGenContext, CodeGenerator};
-
-use super::*;
-
-/// 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

@@ -1,364 +0,0 @@
-use std::fmt;
-
-use inkwell::{
-    context::Context,
-    types::{BasicType, BasicTypeEnum, StructType},
-    values::{BasicValueEnum, StructValue},
-};
-
-use crate::codegen::{CodeGenContext, CodeGenerator};
-
-use super::*;
-
-/// 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

@@ -1,42 +0,0 @@
-use crate::codegen::{
-    stmt::{gen_for_callback_incrementing, BreakContinueHooks},
-    CodeGenContext, CodeGenerator,
-};
-
-use super::*;
-
-/// Like [`gen_for_callback_incrementing`] with [`Model`] abstractions.
-///
-/// `stop` is not included.
-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

@@ -1,12 +0,0 @@
-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

@@ -1,87 +0,0 @@
-use crate::{
-    codegen::{model::*, CodeGenContext, CodeGenerator},
-    typecheck::typedef::{iter_type_vars, Type, TypeEnum},
-};
-
-use super::any::AnyObject;
-
-/// 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

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

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

@@ -1,184 +0,0 @@
-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

@@ -1,139 +0,0 @@
-use itertools::Itertools;
-
-use crate::codegen::{
-    irrt::{call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to},
-    model::*,
-    CodeGenContext, CodeGenerator,
-};
-
-use super::NDArrayObject;
-
-/// Fields of [`ShapeEntry`]
-pub struct ShapeEntryFields<'ctx, F: FieldTraversal<'ctx>> {
-    pub ndims: F::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/contiguous.rs

@@ -1,134 +0,0 @@
-use crate::{
-    codegen::{model::*, CodeGenContext, CodeGenerator},
-    typecheck::typedef::Type,
-};
-
-use super::NDArrayObject;
-
-/// Fields of [`ContiguousNDArray`]
-pub struct ContiguousNDArrayFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
-    pub ndims: F::Output<Int<SizeT>>,
-    pub shape: F::Output<Ptr<Int<SizeT>>>,
-    pub data: F::Output<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 iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
-        Self::Fields {
-            ndims: traversal.add_auto("ndims"),
-            shape: traversal.add_auto("shape"),
-            data: traversal.add("data", Ptr(self.item)),
-        }
-    }
-}
-
-impl<'ctx> NDArrayObject<'ctx> {
-    /// Create a [`ContiguousNDArray`] from the contents of this ndarray.
-    ///
-    /// This function may or may not be expensive depending on if this ndarray has contiguous data.
-    ///
-    /// If this ndarray is not C-contiguous, this function will allocate memory on the stack for the `data` field of
-    /// the returned [`ContiguousNDArray`] and copy contents of this ndarray to there.
-    ///
-    /// If this ndarray is C-contiguous, contents of this ndarray will not be copied. The created [`ContiguousNDArray`]
-    /// will share memory with this ndarray.
-    ///
-    /// The `item_model` sets the [`Model`] of the returned [`ContiguousNDArray`]'s `Item` model for type-safety, and
-    /// should match the `ctx.get_llvm_type()` of this ndarray's `dtype`. Otherwise this function panics. Use model [`Any`]
-    /// if you don't care/cannot know the [`Model`] in advance.
-    pub fn make_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        item_model: Item,
-    ) -> Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>> {
-        // Sanity check on `self.dtype` and `item_model`.
-        let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
-        item_model.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
-
-        let cdarray_model = Struct(ContiguousNDArray { item: item_model });
-
-        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");
-
-        // Allocate and setup the resulting [`ContiguousNDArray`].
-        let result = cdarray_model.alloca(generator, ctx);
-
-        // Set ndims and shape.
-        let ndims = self.ndims_llvm(generator, ctx.ctx);
-        result.set(ctx, |f| f.ndims, ndims);
-
-        let shape = self.instance.get(generator, ctx, |f| f.shape);
-        result.set(ctx, |f| f.shape, shape);
-
-        let is_contiguous = self.is_c_contiguous(generator, ctx);
-        ctx.builder.build_conditional_branch(is_contiguous.value, then_bb, else_bb).unwrap();
-
-        // Inserting into then_bb; This ndarray is contiguous.
-        ctx.builder.position_at_end(then_bb);
-        let data = self.instance.get(generator, ctx, |f| f.data);
-        let data = data.pointer_cast(generator, ctx, item_model);
-        result.set(ctx, |f| f.data, data);
-        ctx.builder.build_unconditional_branch(end_bb).unwrap();
-
-        // Inserting into else_bb; This ndarray is not contiguous. Do a full-copy on `data`.
-        // `make_copy` produces an ndarray with contiguous `data`.
-        ctx.builder.position_at_end(else_bb);
-        let copied_ndarray = self.make_copy(generator, ctx);
-        let data = copied_ndarray.instance.get(generator, ctx, |f| f.data);
-        let data = data.pointer_cast(generator, ctx, item_model);
-        result.set(ctx, |f| f.data, data);
-        ctx.builder.build_unconditional_branch(end_bb).unwrap();
-
-        // Reposition to end_bb for continuation
-        ctx.builder.position_at_end(end_bb);
-
-        result
-    }
-
-    /// Create an [`NDArrayObject`] from a [`ContiguousNDArray`].
-    ///
-    /// The operation is super cheap. The newly created [`NDArrayObject`] will share the
-    /// same memory as the [`ContiguousNDArray`].
-    ///
-    /// `ndims` has to be provided as [`NDArrayObject`] requires a statically known `ndims` value, despite
-    /// the fact that the information should be contained within the [`ContiguousNDArray`].
-    pub fn from_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        carray: Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>>,
-        dtype: Type,
-        ndims: u64,
-    ) -> Self {
-        // Sanity check on `dtype` and `contiguous_array`'s `Item` model.
-        let dtype_llvm = ctx.get_llvm_type(generator, dtype);
-        carray.model.0 .0.item.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
-
-        // TODO: Debug assert `ndims == carray.ndims` to catch bugs.
-
-        // Allocate the resulting ndarray.
-        let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
-
-        // Copy shape and update strides
-        let shape = carray.get(generator, ctx, |f| f.shape);
-        ndarray.copy_shape_from_array(generator, ctx, shape);
-        ndarray.set_strides_contiguous(generator, ctx);
-
-        // Share data
-        let data = carray.get(generator, ctx, |f| f.data).pointer_cast(generator, ctx, Int(Byte));
-        ndarray.instance.set(ctx, |f| f.data, data);
-
-        ndarray
-    }
-}

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

@@ -1,176 +0,0 @@
-use inkwell::{values::BasicValueEnum, IntPredicate};
-
-use crate::{
-    codegen::{
-        irrt::call_nac3_ndarray_util_assert_shape_no_negative, model::*, CodeGenContext,
-        CodeGenerator,
-    },
-    typecheck::typedef::Type,
-};
-
-use super::NDArrayObject;
-
-/// 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

@@ -1,227 +0,0 @@
-use crate::codegen::{
-    irrt::call_nac3_ndarray_index,
-    model::*,
-    object::utils::slice::{RustSlice, Slice},
-    CodeGenContext, CodeGenerator,
-};
-
-use super::NDArrayObject;
-
-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

@@ -1,219 +0,0 @@
-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

@@ -1,218 +0,0 @@
-use std::cmp::max;
-
-use nac3parser::ast::Operator;
-use util::gen_for_model;
-
-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},
-};
-
-use super::{NDArrayObject, NDArrayOut};
-
-/// 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

@@ -1,670 +0,0 @@
-pub mod array;
-pub mod broadcast;
-pub mod contiguous;
-pub mod factory;
-pub mod indexing;
-pub mod map;
-pub mod matmul;
-pub mod nditer;
-pub mod shape_util;
-pub mod view;
-
-use inkwell::{
-    context::Context,
-    types::BasicType,
-    values::{BasicValue, BasicValueEnum, PointerValue},
-    AddressSpace,
-};
-
-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::{create_ndims, extract_ndims},
-        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
-    },
-    typecheck::typedef::{Type, TypeEnum},
-};
-
-use super::{any::AnyObject, tuple::TupleObject};
-
-/// 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)
-    }
-
-    /// Get the typechecker ndarray type of this [`NDArrayObject`].
-    pub fn get_type(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Type {
-        let ndims = create_ndims(&mut ctx.unifier, self.ndims);
-        make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(self.dtype), Some(ndims))
-    }
-
-    /// Forget that this is an ndarray and convert into an [`AnyObject`].
-    pub fn to_any(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
-        let ty = self.get_type(ctx);
-        AnyObject { value: self.instance.value.as_basic_value_enum(), ty }
-    }
-
-    /// 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,
-        }
-    }
-}
-
-/// A version of [`call_nac3_ndarray_set_strides_by_shape`] in Rust.
-///
-/// This function is used generating strides for globally defined contiguous ndarrays.
-#[must_use]
-pub fn make_contiguous_strides(itemsize: u64, ndims: u64, shape: &[u64]) -> Vec<u64> {
-    let mut strides = Vec::with_capacity(ndims as usize);
-    let mut stride_product = 1u64;
-    for i in 0..ndims {
-        let axis = ndims - i - 1;
-        strides[axis as usize] = stride_product * itemsize;
-        stride_product *= shape[axis as usize];
-    }
-    strides
-}

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

@@ -1,179 +0,0 @@
-use inkwell::{types::BasicType, values::PointerValue, AddressSpace};
-
-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,
-};
-
-use super::NDArrayObject;
-
-/// 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

@@ -1,105 +0,0 @@
-use util::gen_for_model;
-
-use crate::{
-    codegen::{
-        model::*,
-        object::{any::AnyObject, list::ListObject, tuple::TupleObject},
-        CodeGenContext, CodeGenerator,
-    },
-    typecheck::typedef::TypeEnum,
-};
-
-/// Parse a NumPy-like "int sequence" input and return the int sequence as an array and its length.
-///
-/// * `sequence` - The `sequence` parameter.
-/// * `sequence_ty` - The typechecker type of `sequence`
-///
-/// The `sequence` argument type may only be one of the following:
-///   1. A list of `int32`;   e.g., `np.empty([600, 800, 3])`
-///   2. A tuple of `int32`;  e.g., `np.empty((600, 800, 3))`
-///   3. A scalar `int32`;    e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
-///
-/// All `int32` values will be sign-extended to `SizeT`.
-pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    input_sequence: 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

@@ -1,119 +0,0 @@
-use crate::codegen::{
-    irrt::{call_nac3_ndarray_reshape_resolve_and_check_new_shape, call_nac3_ndarray_transpose},
-    model::*,
-    CodeGenContext, CodeGenerator,
-};
-
-use super::{indexing::RustNDIndex, NDArrayObject};
-
-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

@@ -1,99 +0,0 @@
-use inkwell::values::StructValue;
-use itertools::Itertools;
-
-use crate::{
-    codegen::{model::*, CodeGenContext, CodeGenerator},
-    typecheck::typedef::{Type, TypeEnum},
-};
-
-use super::any::AnyObject;
-
-/// 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

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

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

@@ -1,125 +0,0 @@
-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

@@ -1,15 +1,22 @@
+use inkwell::{
+    attributes::{Attribute, AttributeLoc},
+    basic_block::BasicBlock,
+    types::{BasicType, BasicTypeEnum},
+    values::{BasicValue, BasicValueEnum, FunctionValue, IntValue, PointerValue},
+    IntPredicate,
+};
+use itertools::{izip, Itertools};
+
+use nac3parser::ast::{
+    Constant, ExcepthandlerKind, Expr, ExprKind, Location, Stmt, StmtKind, StrRef,
+};
+
 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,
-    object::{
-        any::AnyObject,
-        ndarray::{
-            indexing::util::gen_ndarray_subscript_ndindices, NDArrayObject, ScalarOrNDArray,
-        },
-    },
     CodeGenContext, CodeGenerator,
 };
 use crate::{
@@ -20,17 +27,6 @@ use crate::{
         typedef::{iter_type_vars, FunSignature, Type, TypeEnum},
     },
 };
-use inkwell::{
-    attributes::{Attribute, AttributeLoc},
-    basic_block::BasicBlock,
-    types::{BasicType, BasicTypeEnum},
-    values::{BasicValue, BasicValueEnum, FunctionValue, IntValue, PointerValue},
-    IntPredicate,
-};
-use itertools::{izip, Itertools};
-use nac3parser::ast::{
-    Constant, ExcepthandlerKind, Expr, ExprKind, Location, Stmt, StmtKind, StrRef,
-};
 
 /// See [`CodeGenerator::gen_var_alloc`].
 pub fn gen_var<'ctx>(
@@ -415,47 +411,7 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
             if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
         {
             // Handle NDArray item assignment
-            // Process target
+            todo!("ndarray subscript assignment is not yet implemented");
-            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));
@@ -1872,6 +1828,37 @@ pub fn gen_stmt<G: CodeGenerator>(
                 stmt.location,
             );
         }
+        StmtKind::Global { names, .. } => {
+            let registered_globals = ctx
+                .top_level
+                .definitions
+                .read()
+                .iter()
+                .filter_map(|def| {
+                    if let TopLevelDef::Variable { simple_name, ty, .. } = &*def.read() {
+                        Some((*simple_name, *ty))
+                    } else {
+                        None
+                    }
+                })
+                .collect_vec();
+
+            for id in names {
+                let Some((_, ty)) = registered_globals.iter().find(|(name, _)| name == id) else {
+                    return Err(format!("{id} is not a global at {}", stmt.location));
+                };
+
+                let resolver = ctx.resolver.clone();
+                let ptr = resolver
+                    .get_symbol_value(*id, ctx, generator)
+                    .map(|val| val.to_basic_value_enum(ctx, generator, *ty))
+                    .transpose()?
+                    .map(BasicValueEnum::into_pointer_value)
+                    .unwrap();
+
+                ctx.var_assignment.insert(*id, (ptr, None, 0));
+            }
+        }
         _ => unimplemented!(),
     };
     Ok(())

nac3core/src/codegen/test.rs

@@ -1,6 +1,23 @@
+use std::{
+    collections::{HashMap, HashSet},
+    sync::Arc,
+};
+
+use indexmap::IndexMap;
+use indoc::indoc;
+use inkwell::{
+    targets::{InitializationConfig, Target},
+    OptimizationLevel,
+};
+use nac3parser::{
+    ast::{fold::Fold, FileName, StrRef},
+    parser::parse_program,
+};
+use parking_lot::RwLock;
+
 use crate::{
     codegen::{
-        classes::{ListType, ProxyType, RangeType},
+        classes::{ListType, NDArrayType, ProxyType, RangeType},
         concrete_type::ConcreteTypeStore,
         CodeGenContext, CodeGenLLVMOptions, CodeGenTargetMachineOptions, CodeGenTask,
         CodeGenerator, DefaultCodeGenerator, WithCall, WorkerRegistry,
@@ -11,24 +28,10 @@ use crate::{
         DefinitionId, FunInstance, TopLevelContext, TopLevelDef,
     },
     typecheck::{
-        type_inferencer::{FunctionData, Inferencer, PrimitiveStore},
+        type_inferencer::{FunctionData, IdentifierInfo, Inferencer, PrimitiveStore},
         typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
     },
 };
-use indexmap::IndexMap;
-use indoc::indoc;
-use inkwell::{
-    targets::{InitializationConfig, Target},
-    OptimizationLevel,
-};
-use nac3parser::ast::FileName;
-use nac3parser::{
-    ast::{fold::Fold, StrRef},
-    parser::parse_program,
-};
-use parking_lot::RwLock;
-use std::collections::{HashMap, HashSet};
-use std::sync::Arc;
 
 struct Resolver {
     id_to_type: HashMap<StrRef, Type>,
@@ -64,6 +67,7 @@ impl SymbolResolver for Resolver {
         &self,
         _: StrRef,
         _: &mut CodeGenContext<'ctx, '_>,
+        _: &mut dyn CodeGenerator,
     ) -> Option<ValueEnum<'ctx>> {
         unimplemented!()
     }
@@ -138,7 +142,8 @@ fn test_primitives() {
     };
     let mut virtual_checks = Vec::new();
     let mut calls = HashMap::new();
-    let mut identifiers: HashSet<_> = ["a".into(), "b".into()].into();
+    let mut identifiers: HashMap<_, _> =
+        ["a".into(), "b".into()].map(|id| (id, IdentifierInfo::default())).into();
     let mut inferencer = Inferencer {
         top_level: &top_level,
         function_data: &mut function_data,
@@ -317,7 +322,8 @@ fn test_simple_call() {
     };
     let mut virtual_checks = Vec::new();
     let mut calls = HashMap::new();
-    let mut identifiers: HashSet<_> = ["a".into(), "foo".into()].into();
+    let mut identifiers: HashMap<_, _> =
+        ["a".into(), "foo".into()].map(|id| (id, IdentifierInfo::default())).into();
     let mut inferencer = Inferencer {
         top_level: &top_level,
         function_data: &mut function_data,
@@ -456,3 +462,15 @@ fn test_classes_range_type_new() {
     let llvm_range = RangeType::new(&ctx);
     assert!(RangeType::is_type(llvm_range.as_base_type()).is_ok());
 }
+
+#[test]
+fn test_classes_ndarray_type_new() {
+    let ctx = inkwell::context::Context::create();
+    let generator = DefaultCodeGenerator::new(String::new(), 64);
+
+    let llvm_i32 = ctx.i32_type();
+    let llvm_usize = generator.get_size_type(&ctx);
+
+    let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into());
+    assert!(NDArrayType::is_type(llvm_ndarray.as_base_type(), llvm_usize).is_ok());
+}

nac3core/src/lib.rs

@@ -19,6 +19,10 @@
     clippy::wildcard_imports
 )]
 
+// users of nac3core need to use the same version of these dependencies, so expose them as nac3core::*
+pub use inkwell;
+pub use nac3parser;
+
 pub mod codegen;
 pub mod symbol_resolver;
 pub mod toplevel;

nac3core/src/symbol_resolver.rs

@@ -1,7 +1,15 @@
-use std::fmt::Debug;
+use std::{
-use std::rc::Rc;
+    collections::{HashMap, HashSet},
-use std::sync::Arc;
+    fmt::{Debug, Display},
-use std::{collections::HashMap, collections::HashSet, fmt::Display};
+    rc::Rc,
+    sync::Arc,
+};
+
+use inkwell::values::{BasicValueEnum, FloatValue, IntValue, PointerValue, StructValue};
+use itertools::{chain, izip, Itertools};
+use parking_lot::RwLock;
+
+use nac3parser::ast::{Constant, Expr, Location, StrRef};
 
 use crate::{
     codegen::{CodeGenContext, CodeGenerator},
@@ -11,10 +19,6 @@ use crate::{
         typedef::{Type, TypeEnum, Unifier, VarMap},
     },
 };
-use inkwell::values::{BasicValueEnum, FloatValue, IntValue, PointerValue, StructValue};
-use itertools::{chain, izip, Itertools};
-use nac3parser::ast::{Constant, Expr, Location, StrRef};
-use parking_lot::RwLock;
 
 #[derive(Clone, PartialEq, Debug)]
 pub enum SymbolValue {
@@ -365,6 +369,7 @@ pub trait SymbolResolver {
         &self,
         str: StrRef,
         ctx: &mut CodeGenContext<'ctx, '_>,
+        generator: &mut dyn CodeGenerator,
     ) -> Option<ValueEnum<'ctx>>;
 
     fn get_default_param_value(&self, expr: &Expr) -> Option<SymbolValue>;

nac3core/src/toplevel/builtins.rs

@@ -1,6 +1,5 @@
 use std::iter::once;
 
-use helper::{debug_assert_prim_is_allowed, extract_ndims, make_exception_fields, PrimDefDetails};
 use indexmap::IndexMap;
 use inkwell::{
     attributes::{Attribute, AttributeLoc},
@@ -9,19 +8,17 @@ use inkwell::{
     IntPredicate,
 };
 use itertools::Either;
-use numpy::unpack_ndarray_var_tys;
 use strum::IntoEnumIterator;
 
+use super::{
+    helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDefDetails},
+    *,
+};
 use crate::{
     codegen::{
         builtin_fns,
         classes::{ProxyValue, RangeValue},
-        model::*,
         numpy::*,
-        object::{
-            any::AnyObject,
-            ndarray::{shape_util::parse_numpy_int_sequence, NDArrayObject},
-        },
         stmt::exn_constructor,
     },
     symbol_resolver::SymbolValue,
@@ -29,8 +26,6 @@ use crate::{
     typecheck::typedef::{into_var_map, iter_type_vars, TypeVar, VarMap},
 };
 
-use super::*;
-
 type BuiltinInfo = Vec<(Arc<RwLock<TopLevelDef>>, Option<Stmt>)>;
 
 pub fn get_exn_constructor(
@@ -517,14 +512,6 @@ 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 => {
@@ -590,6 +577,10 @@ 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
@@ -1395,171 +1386,6 @@ 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;
@@ -2047,6 +1873,57 @@ 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`
@@ -2078,12 +1955,10 @@ 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)?;
 
-                    let result = ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?;
+                    Ok(Some(ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
-                    Ok(Some(result))
                 }),
             ),
 

nac3core/src/toplevel/composer.rs

@@ -1,17 +1,17 @@
-use nac3parser::ast::fold::Fold;
 use std::rc::Rc;
 
+use nac3parser::ast::{fold::Fold, ExprKind, Ident};
+
+use super::*;
 use crate::{
     codegen::{expr::get_subst_key, stmt::exn_constructor},
     symbol_resolver::SymbolValue,
     typecheck::{
-        type_inferencer::{FunctionData, Inferencer},
+        type_inferencer::{FunctionData, IdentifierInfo, Inferencer},
         typedef::{TypeVar, VarMap},
     },
 };
 
-use super::*;
-
 pub struct ComposerConfig {
     pub kernel_ann: Option<&'static str>,
     pub kernel_invariant_ann: &'static str,
@@ -101,7 +101,8 @@ impl TopLevelComposer {
             .iter()
             .map(|def_ast| match *def_ast.0.read() {
                 TopLevelDef::Class { name, .. } => name.to_string(),
-                TopLevelDef::Function { simple_name, .. } => simple_name.to_string(),
+                TopLevelDef::Function { simple_name, .. }
+                | TopLevelDef::Variable { simple_name, .. } => simple_name.to_string(),
             })
             .collect_vec();
 
@@ -381,13 +382,87 @@ impl TopLevelComposer {
                 ))
             }
 
+            ast::StmtKind::Assign { .. } => {
+                // Assignment statements can assign to (and therefore create) more than one 
+                // variable, but this function only allows returning one set of symbol information.
+                // We want to avoid changing this to return a `Vec` of symbol info, as this would 
+                // require `iter().next().unwrap()` on every variable created from a non-Assign 
+                // statement.
+                //
+                // Make callers use `register_top_level_var` instead, as it provides more
+                // fine-grained control over which symbols to register, while also simplifying the
+                // usage of this function.
+                panic!("Registration of top-level Assign statements must use TopLevelComposer::register_top_level_var (at {})", ast.location);
+            }
+
+            ast::StmtKind::AnnAssign { target, annotation, .. } => {
+                let ExprKind::Name { id: name, .. } = target.node else {
+                    return Err(format!(
+                        "global variable declaration must be an identifier (at {})",
+                        target.location
+                    ));
+                };
+
+                self.register_top_level_var(
+                    name,
+                    Some(annotation.as_ref().clone()),
+                    resolver,
+                    mod_path,
+                    target.location,
+                )
+            }
+
             _ => Err(format!(
-                "registrations of constructs other than top level classes/functions are not supported (at {})",
+                "registrations of constructs other than top level classes/functions/variables are not supported (at {})",
                 ast.location
             )),
         }
     }
 
+    /// Registers a top-level variable with the given `name` into the composer.
+    ///
+    /// `annotation` - The type annotation of the top-level variable, or [`None`] if no type
+    /// annotation is provided.
+    /// `location` - The location of the top-level variable.
+    pub fn register_top_level_var(
+        &mut self,
+        name: Ident,
+        annotation: Option<Expr>,
+        resolver: Option<Arc<dyn SymbolResolver + Send + Sync>>,
+        mod_path: &str,
+        location: Location,
+    ) -> Result<(StrRef, DefinitionId, Option<Type>), String> {
+        if self.keyword_list.contains(&name) {
+            return Err(format!("cannot use keyword `{name}` as a class name (at {location})"));
+        }
+
+        let global_var_name =
+            if mod_path.is_empty() { name.to_string() } else { format!("{mod_path}.{name}") };
+
+        if !self.defined_names.insert(global_var_name.clone()) {
+            return Err(format!(
+                "global variable `{global_var_name}` defined twice (at {location})"
+            ));
+        }
+
+        let ty_to_be_unified = self.unifier.get_dummy_var().ty;
+        self.definition_ast_list.push((
+            RwLock::new(Self::make_top_level_variable_def(
+                global_var_name,
+                name,
+                // dummy here, unify with correct type later,
+                ty_to_be_unified,
+                annotation,
+                resolver,
+                Some(location),
+            ))
+            .into(),
+            None,
+        ));
+
+        Ok((name, DefinitionId(self.definition_ast_list.len() - 1), Some(ty_to_be_unified)))
+    }
+
     pub fn start_analysis(&mut self, inference: bool) -> Result<(), HashSet<String>> {
         self.analyze_top_level_class_type_var()?;
         self.analyze_top_level_class_bases()?;
@@ -396,6 +471,7 @@ impl TopLevelComposer {
         if inference {
             self.analyze_function_instance()?;
         }
+        self.analyze_top_level_variables()?;
         Ok(())
     }
 
@@ -433,7 +509,7 @@ impl TopLevelComposer {
                     // things like `class A(Generic[T, V, ImportedModule.T])` is not supported
                     // i.e. only simple names are allowed in the subscript
                     // should update the TopLevelDef::Class.typevars and the TypeEnum::TObj.params
-                    ast::ExprKind::Subscript { value, slice, .. }
+                    ExprKind::Subscript { value, slice, .. }
                         if {
                             matches!(
                                 &value.node,
@@ -449,9 +525,9 @@ impl TopLevelComposer {
                         }
                         is_generic = true;
 
-                        let type_var_list: Vec<&ast::Expr<()>>;
+                        let type_var_list: Vec<&Expr<()>>;
                         // if `class A(Generic[T, V, G])`
-                        if let ast::ExprKind::Tuple { elts, .. } = &slice.node {
+                        if let ExprKind::Tuple { elts, .. } = &slice.node {
                             type_var_list = elts.iter().collect_vec();
                         // `class A(Generic[T])`
                         } else {
@@ -500,6 +576,7 @@ impl TopLevelComposer {
             }
             Ok(())
         };
+
         let mut errors = HashSet::new();
         for (class_def, class_ast) in def_list.iter().skip(self.builtin_num) {
             if class_ast.is_none() {
@@ -853,7 +930,6 @@ impl TopLevelComposer {
         let unifier = self.unifier.borrow_mut();
         let primitives_store = &self.primitives_ty;
 
-        let mut errors = HashSet::new();
         let mut analyze = |function_def: &Arc<RwLock<TopLevelDef>>, function_ast: &Option<Stmt>| {
             let mut function_def = function_def.write();
             let function_def = &mut *function_def;
@@ -962,15 +1038,15 @@ impl TopLevelComposer {
                     }
                 }
 
-                let arg_with_default: Vec<(&ast::Located<ast::ArgData<()>>, Option<&ast::Expr>)> =
+                let arg_with_default: Vec<(&ast::Located<ast::ArgData<()>>, Option<&Expr>)> = args
-                    args.args
+                    .args
                     .iter()
                     .rev()
                     .zip(
                         args.defaults
                             .iter()
                             .rev()
-                                .map(|x| -> Option<&ast::Expr> { Some(x) })
+                            .map(|x| -> Option<&Expr> { Some(x) })
                             .chain(std::iter::repeat(None)),
                     )
                     .collect_vec();
@@ -1128,6 +1204,8 @@ impl TopLevelComposer {
             })?;
             Ok(())
         };
+
+        let mut errors = HashSet::new();
         for (function_def, function_ast) in def_list.iter().skip(self.builtin_num) {
             if function_ast.is_none() {
                 continue;
@@ -1229,7 +1307,7 @@ impl TopLevelComposer {
 
                         let arg_with_default: Vec<(
                             &ast::Located<ast::ArgData<()>>,
-                            Option<&ast::Expr>,
+                            Option<&Expr>,
                         )> = args
                             .args
                             .iter()
@@ -1238,7 +1316,7 @@ impl TopLevelComposer {
                                 args.defaults
                                     .iter()
                                     .rev()
-                                    .map(|x| -> Option<&ast::Expr> { Some(x) })
+                                    .map(|x| -> Option<&Expr> { Some(x) })
                                     .chain(std::iter::repeat(None)),
                             )
                             .collect_vec();
@@ -1395,7 +1473,7 @@ impl TopLevelComposer {
                         .map_err(|e| HashSet::from([e.to_display(unifier).to_string()]))?;
                 }
                 ast::StmtKind::AnnAssign { target, annotation, value, .. } => {
-                    if let ast::ExprKind::Name { id: attr, .. } = &target.node {
+                    if let ExprKind::Name { id: attr, .. } = &target.node {
                         if defined_fields.insert(attr.to_string()) {
                             let dummy_field_type = unifier.get_dummy_var().ty;
 
@@ -1403,7 +1481,7 @@ impl TopLevelComposer {
                                 None => {
                                     // handle Kernel[T], KernelInvariant[T]
                                     let (annotation, mutable) = match &annotation.node {
-                                        ast::ExprKind::Subscript { value, slice, .. }
+                                        ExprKind::Subscript { value, slice, .. }
                                             if matches!(
                                                 &value.node,
                                                 ast::ExprKind::Name { id, .. } if id == &core_config.kernel_invariant_ann.into()
@@ -1411,7 +1489,7 @@ impl TopLevelComposer {
                                         {
                                             (slice, false)
                                         }
-                                        ast::ExprKind::Subscript { value, slice, .. }
+                                        ExprKind::Subscript { value, slice, .. }
                                             if matches!(
                                                 &value.node,
                                                 ast::ExprKind::Name { id, .. } if core_config.kernel_ann.map_or(false, |c| id == &c.into())
@@ -1429,13 +1507,13 @@ impl TopLevelComposer {
                                 Some(boxed_expr) => {
                                     // Class attributes are set as immutable regardless
                                     let (annotation, _) = match &annotation.node {
-                                        ast::ExprKind::Subscript { slice, .. } => (slice, false),
+                                        ExprKind::Subscript { slice, .. } => (slice, false),
                                         _ if core_config.kernel_ann.is_none() => (annotation, false),
                                         _ => continue,
                                     };
 
                                     match &**boxed_expr {
-                                        ast::Located {location: _, custom: (), node: ast::ExprKind::Constant { value: v, kind: _ }} => {
+                                        ast::Located {location: _, custom: (), node: ExprKind::Constant { value: v, kind: _ }} => {
                                             // Restricting the types allowed to be defined as class attributes
                                             match v {
                                                 ast::Constant::Bool(_) | ast::Constant::Str(_) | ast::Constant::Int(_) | ast::Constant::Float(_) => {}
@@ -1702,7 +1780,6 @@ impl TopLevelComposer {
             }
         }
 
-        let mut errors = HashSet::new();
         let mut analyze = |i, def: &Arc<RwLock<TopLevelDef>>, ast: &Option<Stmt>| {
             let class_def = def.read();
             if let TopLevelDef::Class {
@@ -1845,6 +1922,8 @@ impl TopLevelComposer {
             }
             Ok(())
         };
+
+        let mut errors = HashSet::new();
         for (i, (def, ast)) in definition_ast_list.iter().enumerate().skip(self.builtin_num) {
             if ast.is_none() {
                 continue;
@@ -1882,19 +1961,20 @@ impl TopLevelComposer {
             if ast.is_none() {
                 return Ok(());
             }
-            let mut function_def = def.write();
+
-            if let TopLevelDef::Function {
+            let (name, simple_name, signature, resolver) = {
-                instance_to_stmt,
+                let function_def = def.read();
-                instance_to_symbol,
+                let TopLevelDef::Function { name, simple_name, signature, resolver, .. } =
-                name,
+                    &*function_def
-                simple_name,
+                else {
-                signature,
+                    return Ok(());
-                resolver,
+                };
-                ..
+
-            } = &mut *function_def
+                (name.clone(), *simple_name, *signature, resolver.clone())
-            {
+            };
-                let signature_ty_enum = unifier.get_ty(*signature);
+
-                let TypeEnum::TFunc(FunSignature { args, ret, vars }) = signature_ty_enum.as_ref()
+            let signature_ty_enum = unifier.get_ty(signature);
+            let TypeEnum::TFunc(FunSignature { args, ret, vars, .. }) = signature_ty_enum.as_ref()
             else {
                 unreachable!("must be typeenum::tfunc")
             };
@@ -2002,11 +2082,11 @@ impl TopLevelComposer {
                     })
                 };
                 let mut identifiers = {
-                        let mut result: HashSet<_> = HashSet::new();
+                    let mut result = HashMap::new();
                     if self_type.is_some() {
-                            result.insert("self".into());
+                        result.insert("self".into(), IdentifierInfo::default());
                     }
-                        result.extend(inst_args.iter().map(|x| x.name));
+                    result.extend(inst_args.iter().map(|x| (x.name, IdentifierInfo::default())));
                     result
                 };
                 let mut calls: HashMap<CodeLocation, CallId> = HashMap::new();
@@ -2043,23 +2123,43 @@ impl TopLevelComposer {
                 else {
                     unreachable!("must be function def ast")
                 };
-                    if !decorator_list.is_empty()
+
-                        && matches!(&decorator_list[0].node,
+                if !decorator_list.is_empty() {
-                            ast::ExprKind::Name{ id, .. } if id == &"extern".into())
+                    if matches!(&decorator_list[0].node, ExprKind::Name { id, .. } if id == &"extern".into())
-                    {
-                        instance_to_symbol.insert(String::new(), simple_name.to_string());
-                        continue;
-                    }
-                    if !decorator_list.is_empty()
-                        && matches!(&decorator_list[0].node,
-                            ast::ExprKind::Name{ id, .. } if id == &"rpc".into())
                     {
+                        let TopLevelDef::Function { instance_to_symbol, .. } = &mut *def.write()
+                        else {
+                            unreachable!()
+                        };
                         instance_to_symbol.insert(String::new(), simple_name.to_string());
                         continue;
                     }
 
-                    let fun_body = body
+                    if matches!(&decorator_list[0].node, ExprKind::Name { id, .. } if id == &"rpc".into())
-                        .into_iter()
+                    {
+                        let TopLevelDef::Function { instance_to_symbol, .. } = &mut *def.write()
+                        else {
+                            unreachable!()
+                        };
+                        instance_to_symbol.insert(String::new(), simple_name.to_string());
+                        continue;
+                    }
+
+                    if let ExprKind::Call { func, .. } = &decorator_list[0].node {
+                        if matches!(&func.node, ExprKind::Name { id, .. } if id == &"rpc".into()) {
+                            let TopLevelDef::Function { instance_to_symbol, .. } =
+                                &mut *def.write()
+                            else {
+                                unreachable!()
+                            };
+                            instance_to_symbol.insert(String::new(), simple_name.to_string());
+                            continue;
+                        }
+                    }
+                }
+
+                let fun_body =
+                    body.into_iter()
                         .map(|b| inferencer.fold_stmt(b))
                         .collect::<Result<Vec<_>, _>>()?;
 
@@ -2129,6 +2229,9 @@ impl TopLevelComposer {
                     )]));
                 }
 
+                let TopLevelDef::Function { instance_to_stmt, .. } = &mut *def.write() else {
+                    unreachable!()
+                };
                 instance_to_stmt.insert(
                     get_subst_key(
                         unifier,
@@ -2144,10 +2247,10 @@ impl TopLevelComposer {
                     },
                 );
             }
-            }
 
             Ok(())
         };
+
         for (id, (def, ast)) in self.definition_ast_list.iter().enumerate().skip(self.builtin_num) {
             if ast.is_none() {
                 continue;
@@ -2161,4 +2264,59 @@ impl TopLevelComposer {
         }
         Ok(())
     }
+
+    /// Step 6. Analyze and populate the types of global variables.
+    fn analyze_top_level_variables(&mut self) -> Result<(), HashSet<String>> {
+        let def_list = &self.definition_ast_list;
+        let temp_def_list = self.extract_def_list();
+        let unifier = &mut self.unifier;
+        let primitives_store = &self.primitives_ty;
+
+        let mut analyze = |variable_def: &Arc<RwLock<TopLevelDef>>| -> Result<_, HashSet<String>> {
+            let TopLevelDef::Variable { ty: dummy_ty, ty_decl, resolver, loc, .. } =
+                &*variable_def.read()
+            else {
+                // not top level variable def, skip
+                return Ok(());
+            };
+
+            let resolver = &**resolver.as_ref().unwrap();
+
+            if let Some(ty_decl) = ty_decl {
+                let ty_annotation = parse_ast_to_type_annotation_kinds(
+                    resolver,
+                    &temp_def_list,
+                    unifier,
+                    primitives_store,
+                    ty_decl,
+                    HashMap::new(),
+                )?;
+                let ty_from_ty_annotation = get_type_from_type_annotation_kinds(
+                    &temp_def_list,
+                    unifier,
+                    primitives_store,
+                    &ty_annotation,
+                    &mut None,
+                )?;
+
+                unifier.unify(*dummy_ty, ty_from_ty_annotation).map_err(|e| {
+                    HashSet::from([e.at(Some(loc.unwrap())).to_display(unifier).to_string()])
+                })?;
+            }
+
+            Ok(())
+        };
+
+        let mut errors = HashSet::new();
+        for (variable_def, _) in def_list.iter().skip(self.builtin_num) {
+            if let Err(e) = analyze(variable_def) {
+                errors.extend(e);
+            }
+        }
+        if !errors.is_empty() {
+            return Err(errors);
+        }
+
+        Ok(())
+    }
 }

nac3core/src/toplevel/helper.rs

@@ -1,14 +1,17 @@
 use std::convert::TryInto;
 
-use crate::symbol_resolver::SymbolValue;
-use crate::toplevel::numpy::unpack_ndarray_var_tys;
-use crate::typecheck::typedef::{into_var_map, iter_type_vars, Mapping, TypeVarId, VarMap};
-use ast::ExprKind;
-use nac3parser::ast::{Constant, Location};
 use strum::IntoEnumIterator;
 use strum_macros::EnumIter;
 
+use ast::ExprKind;
+use nac3parser::ast::{Constant, Location};
+
 use super::*;
+use crate::{
+    symbol_resolver::SymbolValue,
+    toplevel::numpy::unpack_ndarray_var_tys,
+    typecheck::typedef::{into_var_map, iter_type_vars, Mapping, TypeVarId, VarMap},
+};
 
 /// All primitive types and functions in nac3core.
 #[derive(Clone, Copy, Debug, EnumIter, PartialEq, Eq)]
@@ -53,16 +56,6 @@ 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,
@@ -110,6 +103,8 @@ pub enum PrimDef {
     FunNpLdExp,
     FunNpHypot,
     FunNpNextAfter,
+    FunNpTranspose,
+    FunNpReshape,
 
     // Linalg functions
     FunNpDot,
@@ -247,16 +242,6 @@ 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),
@@ -304,6 +289,8 @@ 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),
@@ -404,6 +391,9 @@ impl TopLevelDef {
                     r
                 }
             ),
+            TopLevelDef::Variable { name, ty, .. } => {
+                format!("Variable {{ name: {name:?}, ty: {:?} }}", unifier.stringify(*ty),)
+            }
         }
     }
 }
@@ -605,6 +595,18 @@ impl TopLevelComposer {
         }
     }
 
+    #[must_use]
+    pub fn make_top_level_variable_def(
+        name: String,
+        simple_name: StrRef,
+        ty: Type,
+        ty_decl: Option<Expr>,
+        resolver: Option<Arc<dyn SymbolResolver + Send + Sync>>,
+        loc: Option<Location>,
+    ) -> TopLevelDef {
+        TopLevelDef::Variable { name, simple_name, ty, ty_decl, resolver, loc }
+    }
+
     #[must_use]
     pub fn make_class_method_name(mut class_name: String, method_name: &str) -> String {
         class_name.push('.');
@@ -1134,23 +1136,3 @@ pub fn arraylike_get_ndims(unifier: &mut Unifier, ty: Type) -> u64 {
         _ => 0,
     }
 }
-
-/// Extract an ndarray's `ndims` [type][`Type`] in `u64`. Panic if not possible.
-/// The `ndims` must only contain 1 value.
-#[must_use]
-pub fn extract_ndims(unifier: &Unifier, ndims_ty: Type) -> u64 {
-    let ndims_ty_enum = unifier.get_ty_immutable(ndims_ty);
-    let TypeEnum::TLiteral { values, .. } = &*ndims_ty_enum else {
-        panic!("ndims_ty should be a TLiteral");
-    };
-
-    assert_eq!(values.len(), 1, "ndims_ty TLiteral should only contain 1 value");
-
-    let ndims = values[0].clone();
-    u64::try_from(ndims).unwrap()
-}
-
-/// Return an ndarray's `ndims` as a typechecker [`Type`] from its `u64` value.
-pub fn create_ndims(unifier: &mut Unifier, ndims: u64) -> Type {
-    unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None)
-}

nac3core/src/toplevel/mod.rs

@@ -6,24 +6,24 @@ use std::{
     sync::Arc,
 };
 
-use super::codegen::CodeGenContext;
-use super::typecheck::type_inferencer::PrimitiveStore;
-use super::typecheck::typedef::{
-    FunSignature, FuncArg, SharedUnifier, Type, TypeEnum, Unifier, VarMap,
-};
-use crate::{
-    codegen::CodeGenerator,
-    symbol_resolver::{SymbolResolver, ValueEnum},
-    typecheck::{
-        type_inferencer::CodeLocation,
-        typedef::{CallId, TypeVarId},
-    },
-};
 use inkwell::values::BasicValueEnum;
 use itertools::Itertools;
-use nac3parser::ast::{self, Location, Stmt, StrRef};
 use parking_lot::RwLock;
 
+use nac3parser::ast::{self, Expr, Location, Stmt, StrRef};
+
+use crate::{
+    codegen::{CodeGenContext, CodeGenerator},
+    symbol_resolver::{SymbolResolver, ValueEnum},
+    typecheck::{
+        type_inferencer::{CodeLocation, PrimitiveStore},
+        typedef::{
+            CallId, FunSignature, FuncArg, SharedUnifier, Type, TypeEnum, TypeVarId, Unifier,
+            VarMap,
+        },
+    },
+};
+
 #[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Copy, Hash, Debug)]
 pub struct DefinitionId(pub usize);
 
@@ -148,6 +148,25 @@ pub enum TopLevelDef {
         /// Definition location.
         loc: Option<Location>,
     },
+    Variable {
+        /// Qualified name of the global variable, should be unique globally.
+        name: String,
+
+        /// Simple name, the same as in method/function definition.
+        simple_name: StrRef,
+
+        /// Type of the global variable.
+        ty: Type,
+
+        /// The declared type of the global variable, or [`None`] if no type annotation is provided.
+        ty_decl: Option<Expr>,
+
+        /// Symbol resolver of the module defined the class.
+        resolver: Option<Arc<dyn SymbolResolver + Send + Sync>>,
+
+        /// Definition location.
+        loc: Option<Location>,
+    },
 }
 
 pub struct TopLevelContext {

nac3core/src/toplevel/numpy.rs

@@ -1,3 +1,5 @@
+use itertools::Itertools;
+
 use crate::{
     toplevel::helper::PrimDef,
     typecheck::{
@@ -5,7 +7,6 @@ use crate::{
         typedef::{Type, TypeEnum, TypeVarId, Unifier, VarMap},
     },
 };
-use itertools::Itertools;
 
 /// Creates a `ndarray` [`Type`] with the given type arguments.
 ///

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(257)]\n}\n",
+    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(241)]\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[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",
+    "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",
     "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(259)]\n}\n",
+    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(243)]\n}\n",
-    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(264)]\n}\n",
+    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(248)]\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[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",
+    "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",
     "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(265)]\n}\n",
+    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(249)]\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(273)]\n}\n",
+    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(257)]\n}\n",
 ]

nac3core/src/toplevel/test.rs

@@ -1,21 +1,24 @@
+use std::{collections::HashMap, sync::Arc};
+
+use indoc::indoc;
+use parking_lot::Mutex;
+use test_case::test_case;
+
+use nac3parser::{
+    ast::{fold::Fold, FileName},
+    parser::parse_program,
+};
+
 use super::*;
-use crate::toplevel::helper::PrimDef;
-use crate::typecheck::typedef::into_var_map;
 use crate::{
     codegen::CodeGenContext,
     symbol_resolver::{SymbolResolver, ValueEnum},
-    toplevel::DefinitionId,
+    toplevel::{helper::PrimDef, DefinitionId},
     typecheck::{
         type_inferencer::PrimitiveStore,
-        typedef::{Type, Unifier},
+        typedef::{into_var_map, Type, Unifier},
     },
 };
-use indoc::indoc;
-use nac3parser::ast::FileName;
-use nac3parser::{ast::fold::Fold, parser::parse_program};
-use parking_lot::Mutex;
-use std::{collections::HashMap, sync::Arc};
-use test_case::test_case;
 
 struct ResolverInternal {
     id_to_type: Mutex<HashMap<StrRef, Type>>,
@@ -62,6 +65,7 @@ impl SymbolResolver for Resolver {
         &self,
         _: StrRef,
         _: &mut CodeGenContext<'ctx, '_>,
+        _: &mut dyn CodeGenerator,
     ) -> Option<ValueEnum<'ctx>> {
         unimplemented!()
     }

nac3core/src/toplevel/type_annotation.rs

@@ -1,10 +1,14 @@
-use super::*;
-use crate::symbol_resolver::SymbolValue;
-use crate::toplevel::helper::{PrimDef, PrimDefDetails};
-use crate::typecheck::typedef::VarMap;
-use nac3parser::ast::Constant;
 use strum::IntoEnumIterator;
 
+use nac3parser::ast::Constant;
+
+use super::*;
+use crate::{
+    symbol_resolver::SymbolValue,
+    toplevel::helper::{PrimDef, PrimDefDetails},
+    typecheck::typedef::VarMap,
+};
+
 #[derive(Clone, Debug)]
 pub enum TypeAnnotation {
     Primitive(Type),

nac3core/src/typecheck/function_check.rs

@@ -1,13 +1,19 @@
-use crate::toplevel::helper::PrimDef;
+use std::{
+    collections::{HashMap, HashSet},
+    iter::once,
+};
 
-use super::type_inferencer::Inferencer;
-use super::typedef::{Type, TypeEnum};
 use nac3parser::ast::{
     self, Constant, Expr, ExprKind,
     Operator::{LShift, RShift},
     Stmt, StmtKind, StrRef,
 };
-use std::{collections::HashSet, iter::once};
+
+use super::{
+    type_inferencer::{DeclarationSource, IdentifierInfo, Inferencer},
+    typedef::{Type, TypeEnum},
+};
+use crate::toplevel::helper::PrimDef;
 
 impl<'a> Inferencer<'a> {
     fn should_have_value(&mut self, expr: &Expr<Option<Type>>) -> Result<(), HashSet<String>> {
@@ -21,15 +27,29 @@ impl<'a> Inferencer<'a> {
     fn check_pattern(
         &mut self,
         pattern: &Expr<Option<Type>>,
-        defined_identifiers: &mut HashSet<StrRef>,
+        defined_identifiers: &mut HashMap<StrRef, IdentifierInfo>,
     ) -> Result<(), HashSet<String>> {
         match &pattern.node {
             ExprKind::Name { id, .. } if id == &"none".into() => {
                 Err(HashSet::from([format!("cannot assign to a `none` (at {})", pattern.location)]))
             }
             ExprKind::Name { id, .. } => {
-                if !defined_identifiers.contains(id) {
+                // If `id` refers to a declared symbol, reject this assignment if it is used in the
-                    defined_identifiers.insert(*id);
+                // context of an (implicit) global variable
+                if let Some(id_info) = defined_identifiers.get(id) {
+                    if matches!(
+                        id_info.source,
+                        DeclarationSource::Global { is_explicit: Some(false) }
+                    ) {
+                        return Err(HashSet::from([format!(
+                            "cannot access local variable '{id}' before it is declared (at {})",
+                            pattern.location
+                        )]));
+                    }
+                }
+
+                if !defined_identifiers.contains_key(id) {
+                    defined_identifiers.insert(*id, IdentifierInfo::default());
                 }
                 self.should_have_value(pattern)?;
                 Ok(())
@@ -69,7 +89,7 @@ impl<'a> Inferencer<'a> {
     fn check_expr(
         &mut self,
         expr: &Expr<Option<Type>>,
-        defined_identifiers: &mut HashSet<StrRef>,
+        defined_identifiers: &mut HashMap<StrRef, IdentifierInfo>,
     ) -> Result<(), HashSet<String>> {
         // there are some cases where the custom field is None
         if let Some(ty) = &expr.custom {
@@ -90,7 +110,7 @@ impl<'a> Inferencer<'a> {
                     return Ok(());
                 }
                 self.should_have_value(expr)?;
-                if !defined_identifiers.contains(id) {
+                if !defined_identifiers.contains_key(id) {
                     match self.function_data.resolver.get_symbol_type(
                         self.unifier,
                         &self.top_level.definitions.read(),
@@ -98,7 +118,22 @@ impl<'a> Inferencer<'a> {
                         *id,
                     ) {
                         Ok(_) => {
-                            self.defined_identifiers.insert(*id);
+                            let is_global = self.is_id_global(*id);
+
+                            defined_identifiers.insert(
+                                *id,
+                                IdentifierInfo {
+                                    source: match is_global {
+                                        Some(true) => {
+                                            DeclarationSource::Global { is_explicit: Some(false) }
+                                        }
+                                        Some(false) => {
+                                            DeclarationSource::Global { is_explicit: None }
+                                        }
+                                        None => DeclarationSource::Local,
+                                    },
+                                },
+                            );
                         }
                         Err(e) => {
                             return Err(HashSet::from([format!(
@@ -171,9 +206,7 @@ impl<'a> Inferencer<'a> {
                 let mut defined_identifiers = defined_identifiers.clone();
                 for arg in &args.args {
                     // TODO: should we check the types here?
-                    if !defined_identifiers.contains(&arg.node.arg) {
+                    defined_identifiers.entry(arg.node.arg).or_default();
-                        defined_identifiers.insert(arg.node.arg);
-                    }
                 }
                 self.check_expr(body, &mut defined_identifiers)?;
             }
@@ -236,7 +269,7 @@ impl<'a> Inferencer<'a> {
     fn check_stmt(
         &mut self,
         stmt: &Stmt<Option<Type>>,
-        defined_identifiers: &mut HashSet<StrRef>,
+        defined_identifiers: &mut HashMap<StrRef, IdentifierInfo>,
     ) -> Result<bool, HashSet<String>> {
         match &stmt.node {
             StmtKind::For { target, iter, body, orelse, .. } => {
@@ -262,9 +295,11 @@ impl<'a> Inferencer<'a> {
                 let body_returned = self.check_block(body, &mut body_identifiers)?;
                 let orelse_returned = self.check_block(orelse, &mut orelse_identifiers)?;
 
-                for ident in &body_identifiers {
+                for ident in body_identifiers.keys() {
-                    if !defined_identifiers.contains(ident) && orelse_identifiers.contains(ident) {
+                    if !defined_identifiers.contains_key(ident)
-                        defined_identifiers.insert(*ident);
+                        && orelse_identifiers.contains_key(ident)
+                    {
+                        defined_identifiers.insert(*ident, IdentifierInfo::default());
                     }
                 }
                 Ok(body_returned && orelse_returned)
@@ -295,7 +330,7 @@ impl<'a> Inferencer<'a> {
                     let mut defined_identifiers = defined_identifiers.clone();
                     let ast::ExcepthandlerKind::ExceptHandler { name, body, .. } = &handler.node;
                     if let Some(name) = name {
-                        defined_identifiers.insert(*name);
+                        defined_identifiers.insert(*name, IdentifierInfo::default());
                     }
                     self.check_block(body, &mut defined_identifiers)?;
                 }
@@ -359,6 +394,44 @@ impl<'a> Inferencer<'a> {
                 }
                 Ok(true)
             }
+            StmtKind::Global { names, .. } => {
+                for id in names {
+                    if let Some(id_info) = defined_identifiers.get(id) {
+                        if id_info.source == DeclarationSource::Local {
+                            return Err(HashSet::from([format!(
+                                "name '{id}' is referenced prior to global declaration at {}",
+                                stmt.location,
+                            )]));
+                        }
+
+                        continue;
+                    }
+
+                    match self.function_data.resolver.get_symbol_type(
+                        self.unifier,
+                        &self.top_level.definitions.read(),
+                        self.primitives,
+                        *id,
+                    ) {
+                        Ok(_) => {
+                            defined_identifiers.insert(
+                                *id,
+                                IdentifierInfo {
+                                    source: DeclarationSource::Global { is_explicit: Some(true) },
+                                },
+                            );
+                        }
+                        Err(e) => {
+                            return Err(HashSet::from([format!(
+                                "type error at identifier `{}` ({}) at {}",
+                                id, e, stmt.location
+                            )]))
+                        }
+                    }
+                }
+
+                Ok(false)
+            }
             // break, raise, etc.
             _ => Ok(false),
         }
@@ -367,7 +440,7 @@ impl<'a> Inferencer<'a> {
     pub fn check_block(
         &mut self,
         block: &[Stmt<Option<Type>>],
-        defined_identifiers: &mut HashSet<StrRef>,
+        defined_identifiers: &mut HashMap<StrRef, IdentifierInfo>,
     ) -> Result<bool, HashSet<String>> {
         let mut ret = false;
         for stmt in block {

nac3core/src/typecheck/magic_methods.rs

@@ -1,19 +1,21 @@
-use crate::symbol_resolver::SymbolValue;
+use std::{cmp::max, collections::HashMap, rc::Rc};
-use crate::toplevel::helper::{extract_ndims, PrimDef};
+
-use crate::toplevel::numpy::{make_ndarray_ty, unpack_ndarray_var_tys};
-use crate::typecheck::{
-    type_inferencer::*,
-    typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
-};
 use itertools::{iproduct, Itertools};
-use nac3parser::ast::StrRef;
-use nac3parser::ast::{Cmpop, Operator, Unaryop};
-use std::cmp::max;
-use std::collections::HashMap;
-use std::rc::Rc;
 use strum::IntoEnumIterator;
 
-use super::typedef::into_var_map;
+use nac3parser::ast::{Cmpop, Operator, StrRef, Unaryop};
+
+use crate::{
+    symbol_resolver::SymbolValue,
+    toplevel::{
+        helper::PrimDef,
+        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
+    },
+    typecheck::{
+        type_inferencer::*,
+        typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
+    },
+};
 
 /// The variant of a binary operator.
 #[derive(Debug, Clone, Copy, PartialEq, Eq)]
@@ -173,8 +175,19 @@ pub fn impl_binop(
     ops: &[Operator],
 ) {
     with_fields(unifier, ty, |unifier, fields| {
-        let other_tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
+        let (other_ty, other_var_id) = if other_ty.len() == 1 {
-        let function_vars = into_var_map([other_tvar]);
+            (other_ty[0], None)
+        } else {
+            let tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
+            (tvar.ty, Some(tvar.id))
+        };
+
+        let function_vars = if let Some(var_id) = other_var_id {
+            vec![(var_id, other_ty)].into_iter().collect::<VarMap>()
+        } else {
+            VarMap::new()
+        };
+
         let 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]) {
@@ -185,7 +198,7 @@ pub fn impl_binop(
                         ret: ret_ty,
                         vars: function_vars.clone(),
                         args: vec![FuncArg {
-                            ty: other_tvar.ty,
+                            ty: other_ty,
                             default_value: None,
                             name: "other".into(),
                             is_vararg: false,
@@ -528,43 +541,36 @@ pub fn typeof_binop(
                 }
             }
 
-            let (lhs_dtype, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
+            let (_, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
-            let lhs_ndims = extract_ndims(unifier, lhs_ndims);
+            let lhs_ndims = match &*unifier.get_ty_immutable(lhs_ndims) {
-
+                TypeEnum::TLiteral { values, .. } => {
-            let (rhs_dtype, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
+                    assert_eq!(values.len(), 1);
-            let rhs_ndims = extract_ndims(unifier, rhs_ndims);
+                    u64::try_from(values[0].clone()).unwrap()
-
-            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.
+            };
-            // If this is 0 (an unsized ndarray), matmul returns a scalar just like NumPy.
+            let (_, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
-            let result_ndims = match (lhs_ndims, rhs_ndims) {
+            let rhs_ndims = match &*unifier.get_ty_immutable(rhs_ndims) {
-                (0, _) | (_, 0) => {
+                TypeEnum::TLiteral { values, .. } => {
-                    return Err(
+                    assert_eq!(values.len(), 1);
-                        "ndarray.__matmul__ does not allow unsized ndarray input".to_string()
+                    u64::try_from(values[0].clone()).unwrap()
-                    )
                 }
-                (1, 1) => 0,
+                _ => unreachable!(),
-                (1, _) => rhs_ndims - 1,
-                (_, 1) => lhs_ndims - 1,
-                (m, n) => max(m, n),
             };
 
-            if result_ndims == 0 {
+            match (lhs_ndims, rhs_ndims) {
-                // If the result is unsized, NumPy returns a scalar.
+                (2, 2) => typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?,
-                primitives.float
+                (lhs, rhs) if lhs == 0 || rhs == 0 => {
-            } else {
+                    return Err(format!(
-                let result_ndims_ty =
+                    "Input operand {} does not have enough dimensions (has {lhs}, requires {rhs})",
-                    unifier.get_fresh_literal(vec![SymbolValue::U64(result_ndims)], None);
+                    u8::from(rhs == 0)
-                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"
+                    ))
+                }
             }
         }
 
@@ -767,7 +773,7 @@ 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_unsized_t], None);
+    impl_matmul(unifier, store, ndarray_t, &[ndarray_t], Some(ndarray_t));
     impl_sign(unifier, store, ndarray_t, Some(ndarray_t));
     impl_invert(unifier, store, ndarray_t, Some(ndarray_t));
     impl_eq(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);

nac3core/src/typecheck/type_error.rs

@@ -1,14 +1,14 @@
-use std::collections::HashMap;
+use std::{collections::HashMap, fmt::Display};
-use std::fmt::Display;
 
-use crate::typecheck::{magic_methods::HasOpInfo, typedef::TypeEnum};
+use itertools::Itertools;
+
+use nac3parser::ast::{Cmpop, Location, StrRef};
 
 use super::{
     magic_methods::Binop,
     typedef::{RecordKey, Type, Unifier},
 };
-use itertools::Itertools;
+use crate::typecheck::{magic_methods::HasOpInfo, typedef::TypeEnum};
-use nac3parser::ast::{Cmpop, Location, StrRef};
 
 #[derive(Debug, Clone)]
 pub enum TypeErrorKind {

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -1,32 +1,36 @@
-use std::cmp::max;
+use std::{
-use std::collections::{HashMap, HashSet};
+    cell::RefCell,
-use std::convert::{From, TryInto};
+    cmp::max,
-use std::iter::{self, once};
+    collections::{HashMap, HashSet},
-use std::{cell::RefCell, sync::Arc};
+    convert::{From, TryInto},
+    iter::once,
+    sync::Arc,
+};
+
+use itertools::{izip, Itertools};
+
+use nac3parser::ast::{
+    self,
+    fold::{self, Fold},
+    Arguments, Comprehension, ExprContext, ExprKind, Ident, Located, Location, StrRef,
+};
 
 use super::{
     magic_methods::*,
     type_error::{TypeError, TypeErrorKind},
     typedef::{
-        into_var_map, iter_type_vars, Call, CallId, FunSignature, FuncArg, OperatorInfo,
+        into_var_map, iter_type_vars, Call, CallId, FunSignature, FuncArg, Mapping, OperatorInfo,
         RecordField, RecordKey, Type, TypeEnum, TypeVar, Unifier, VarMap,
     },
 };
-use crate::toplevel::type_annotation::TypeAnnotation;
 use crate::{
     symbol_resolver::{SymbolResolver, SymbolValue},
     toplevel::{
         helper::{arraylike_flatten_element_type, arraylike_get_ndims, PrimDef},
         numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
+        type_annotation::TypeAnnotation,
         TopLevelContext, TopLevelDef,
     },
-    typecheck::typedef::Mapping,
-};
-use itertools::{izip, Itertools};
-use nac3parser::ast::{
-    self,
-    fold::{self, Fold},
-    Arguments, Comprehension, ExprContext, ExprKind, Located, Location, StrRef,
 };
 
 #[cfg(test)]
@@ -84,6 +88,40 @@ impl PrimitiveStore {
     }
 }
 
+/// The location where an identifier declaration refers to.
+#[derive(Clone, Copy, Debug, Eq, PartialEq)]
+pub enum DeclarationSource {
+    /// Local scope.
+    Local,
+
+    /// Global scope.
+    Global {
+        /// Whether the identifier is declared by the use of `global` statement. This field is
+        /// [`None`] if the identifier does not refer to a variable.
+        is_explicit: Option<bool>,
+    },
+}
+
+/// Information regarding a defined identifier.
+#[derive(Clone, Copy, Debug)]
+pub struct IdentifierInfo {
+    /// Whether this identifier refers to a global variable.
+    pub source: DeclarationSource,
+}
+
+impl Default for IdentifierInfo {
+    fn default() -> Self {
+        IdentifierInfo { source: DeclarationSource::Local }
+    }
+}
+
+impl IdentifierInfo {
+    #[must_use]
+    pub fn new() -> IdentifierInfo {
+        IdentifierInfo::default()
+    }
+}
+
 pub struct FunctionData {
     pub resolver: Arc<dyn SymbolResolver + Send + Sync>,
     pub return_type: Option<Type>,
@@ -92,7 +130,7 @@ pub struct FunctionData {
 
 pub struct Inferencer<'a> {
     pub top_level: &'a TopLevelContext,
-    pub defined_identifiers: HashSet<StrRef>,
+    pub defined_identifiers: HashMap<StrRef, IdentifierInfo>,
     pub function_data: &'a mut FunctionData,
     pub unifier: &'a mut Unifier,
     pub primitives: &'a PrimitiveStore,
@@ -224,9 +262,7 @@ impl<'a> Fold<()> for Inferencer<'a> {
                                 handler.location,
                             ));
                             if let Some(name) = name {
-                                if !self.defined_identifiers.contains(&name) {
+                                self.defined_identifiers.entry(name).or_default();
-                                    self.defined_identifiers.insert(name);
-                                }
                                 if let Some(old_typ) = self.variable_mapping.insert(name, typ) {
                                     let loc = handler.location;
                                     self.unifier.unify(old_typ, typ).map_err(|e| {
@@ -378,6 +414,7 @@ impl<'a> Fold<()> for Inferencer<'a> {
             | ast::StmtKind::Continue { .. }
             | ast::StmtKind::Expr { .. }
             | ast::StmtKind::For { .. }
+            | ast::StmtKind::Global { .. }
             | ast::StmtKind::Pass { .. }
             | ast::StmtKind::Try { .. } => {}
             ast::StmtKind::If { test, .. } | ast::StmtKind::While { test, .. } => {
@@ -549,7 +586,7 @@ impl<'a> Fold<()> for Inferencer<'a> {
                         unreachable!("must be tobj")
                     }
                 } else {
-                    if !self.defined_identifiers.contains(id) {
+                    if !self.defined_identifiers.contains_key(id) {
                         match self.function_data.resolver.get_symbol_type(
                             self.unifier,
                             &self.top_level.definitions.read(),
@@ -557,7 +594,22 @@ impl<'a> Fold<()> for Inferencer<'a> {
                             *id,
                         ) {
                             Ok(_) => {
-                                self.defined_identifiers.insert(*id);
+                                let is_global = self.is_id_global(*id);
+
+                                self.defined_identifiers.insert(
+                                    *id,
+                                    IdentifierInfo {
+                                        source: match is_global {
+                                            Some(true) => DeclarationSource::Global {
+                                                is_explicit: Some(false),
+                                            },
+                                            Some(false) => {
+                                                DeclarationSource::Global { is_explicit: None }
+                                            }
+                                            None => DeclarationSource::Local,
+                                        },
+                                    },
+                                );
                             }
                             Err(e) => {
                                 return report_error(
@@ -622,8 +674,8 @@ impl<'a> Inferencer<'a> {
     fn infer_pattern<T>(&mut self, pattern: &ast::Expr<T>) -> Result<(), InferenceError> {
         match &pattern.node {
             ExprKind::Name { id, .. } => {
-                if !self.defined_identifiers.contains(id) {
+                if !self.defined_identifiers.contains_key(id) {
-                    self.defined_identifiers.insert(*id);
+                    self.defined_identifiers.insert(*id, IdentifierInfo::default());
                 }
                 Ok(())
             }
@@ -732,8 +784,8 @@ impl<'a> Inferencer<'a> {
         let mut defined_identifiers = self.defined_identifiers.clone();
         for arg in &args.args {
             let name = &arg.node.arg;
-            if !defined_identifiers.contains(name) {
+            if !defined_identifiers.contains_key(name) {
-                defined_identifiers.insert(*name);
+                defined_identifiers.insert(*name, IdentifierInfo::default());
             }
         }
         let fn_args: Vec<_> = args
@@ -1183,45 +1235,6 @@ 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))?;
@@ -1543,7 +1556,7 @@ impl<'a> Inferencer<'a> {
             }));
         }
         // 2-argument ndarray n-dimensional factory functions
-        if ["np_reshape".into(), "np_broadcast_to".into()].contains(id) && args.len() == 2 {
+        if id == &"np_reshape".into() && args.len() == 2 {
             let arg0 = self.fold_expr(args.remove(0))?;
 
             let shape_expr = args.remove(0);
@@ -1589,36 +1602,29 @@ impl<'a> Inferencer<'a> {
         }
         // 2-argument ndarray n-dimensional creation functions
         if id == &"np_full".into() && args.len() == 2 {
-            let ExprKind::List { elts, .. } = &args[0].node else {
+            // Parse arguments
-                return report_error(
+            let shape_expr = args.remove(0);
-                    format!(
+            let (ndims, shape) =
-                        "Expected List literal for first argument of {id}, got {}",
+                self.fold_numpy_function_call_shape_argument(*id, 0, shape_expr)?; // Special handling for `shape`
-                        args[0].node.name()
-                    )
-                    .as_str(),
-                    args[0].location,
-                );
-            };
 
-            let ndims = elts.len() as u64;
+            let fill_value = self.fold_expr(args.remove(0))?;
 
-            let arg0 = self.fold_expr(args.remove(0))?;
+            // Build the return type
-            let arg1 = self.fold_expr(args.remove(0))?;
+            let dtype = fill_value.custom.unwrap();
-
-            let ty = arg1.custom.unwrap();
             let ndims = self.unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None);
-            let ret = make_ndarray_ty(self.unifier, self.primitives, Some(ty), Some(ndims));
+            let ret = make_ndarray_ty(self.unifier, self.primitives, Some(dtype), Some(ndims));
+
             let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
                 args: vec![
                     FuncArg {
                         name: "shape".into(),
-                        ty: arg0.custom.unwrap(),
+                        ty: shape.custom.unwrap(),
                         default_value: None,
                         is_vararg: false,
                     },
                     FuncArg {
                         name: "fill_value".into(),
-                        ty: arg1.custom.unwrap(),
+                        ty: fill_value.custom.unwrap(),
                         default_value: None,
                         is_vararg: false,
                     },
@@ -1636,7 +1642,7 @@ impl<'a> Inferencer<'a> {
                         location: func.location,
                         node: ExprKind::Name { id: *id, ctx: *ctx },
                     }),
-                    args: vec![arg0, arg1],
+                    args: vec![shape, fill_value],
                     keywords: vec![],
                 },
             }));
@@ -2679,4 +2685,22 @@ impl<'a> Inferencer<'a> {
         self.constrain(body.custom.unwrap(), orelse.custom.unwrap(), &body.location)?;
         Ok(body.custom.unwrap())
     }
+
+    /// Determines whether the given `id` refers to a global symbol.
+    ///
+    /// Returns `Some(true)` if `id` refers to a global variable, `Some(false)` if `id` refers to a
+    /// class/function, and `None` if `id` refers to a local symbol.
+    pub(super) fn is_id_global(&self, id: Ident) -> Option<bool> {
+        self.top_level
+            .definitions
+            .read()
+            .iter()
+            .map(|def| match *def.read() {
+                TopLevelDef::Class { name, .. } => (name, false),
+                TopLevelDef::Function { simple_name, .. } => (simple_name, false),
+                TopLevelDef::Variable { simple_name, .. } => (simple_name, true),
+            })
+            .find(|(global, _)| global == &id)
+            .map(|(_, has_explicit_prop)| has_explicit_prop)
+    }
 }

nac3core/src/typecheck/type_inferencer/test.rs

@@ -1,18 +1,20 @@
-use super::super::{magic_methods::with_fields, typedef::*};
+use std::iter::zip;
-use super::*;
+
-use crate::{
-    codegen::CodeGenContext,
-    symbol_resolver::ValueEnum,
-    toplevel::{helper::PrimDef, DefinitionId, TopLevelDef},
-};
 use indexmap::IndexMap;
 use indoc::indoc;
-use nac3parser::ast::FileName;
-use nac3parser::parser::parse_program;
 use parking_lot::RwLock;
-use std::iter::zip;
 use test_case::test_case;
 
+use nac3parser::{ast::FileName, parser::parse_program};
+
+use super::*;
+use crate::{
+    codegen::{CodeGenContext, CodeGenerator},
+    symbol_resolver::ValueEnum,
+    toplevel::{helper::PrimDef, DefinitionId, TopLevelDef},
+    typecheck::{magic_methods::with_fields, typedef::*},
+};
+
 struct Resolver {
     id_to_type: HashMap<StrRef, Type>,
     id_to_def: HashMap<StrRef, DefinitionId>,
@@ -41,6 +43,7 @@ impl SymbolResolver for Resolver {
         &self,
         _: StrRef,
         _: &mut CodeGenContext<'ctx, '_>,
+        _: &mut dyn CodeGenerator,
     ) -> Option<ValueEnum<'ctx>> {
         unimplemented!()
     }
@@ -517,7 +520,7 @@ impl TestEnvironment {
             primitives: &mut self.primitives,
             virtual_checks: &mut self.virtual_checks,
             calls: &mut self.calls,
-            defined_identifiers: HashSet::default(),
+            defined_identifiers: HashMap::default(),
             in_handler: false,
         }
     }
@@ -593,8 +596,9 @@ fn test_basic(source: &str, mapping: &HashMap<&str, &str>, virtuals: &[(&str, &s
     println!("source:\n{source}");
     let mut env = TestEnvironment::new();
     let id_to_name = std::mem::take(&mut env.id_to_name);
-    let mut defined_identifiers: HashSet<_> = env.identifier_mapping.keys().copied().collect();
+    let mut defined_identifiers: HashMap<_, _> =
-    defined_identifiers.insert("virtual".into());
+        env.identifier_mapping.keys().copied().map(|id| (id, IdentifierInfo::default())).collect();
+    defined_identifiers.insert("virtual".into(), IdentifierInfo::default());
     let mut inferencer = env.get_inferencer();
     inferencer.defined_identifiers.clone_from(&defined_identifiers);
     let statements = parse_program(source, FileName::default()).unwrap();
@@ -739,8 +743,9 @@ fn test_primitive_magic_methods(source: &str, mapping: &HashMap<&str, &str>) {
     println!("source:\n{source}");
     let mut env = TestEnvironment::basic_test_env();
     let id_to_name = std::mem::take(&mut env.id_to_name);
-    let mut defined_identifiers: HashSet<_> = env.identifier_mapping.keys().copied().collect();
+    let mut defined_identifiers: HashMap<_, _> =
-    defined_identifiers.insert("virtual".into());
+        env.identifier_mapping.keys().copied().map(|id| (id, IdentifierInfo::default())).collect();
+    defined_identifiers.insert("virtual".into(), IdentifierInfo::default());
     let mut inferencer = env.get_inferencer();
     inferencer.defined_identifiers.clone_from(&defined_identifiers);
     let statements = parse_program(source, FileName::default()).unwrap();

nac3core/src/typecheck/typedef/mod.rs

@@ -1,21 +1,28 @@
-use super::magic_methods::{Binop, HasOpInfo};
+use std::{
-use super::type_error::{TypeError, TypeErrorKind};
+    borrow::Cow,
-use super::unification_table::{UnificationKey, UnificationTable};
+    cell::RefCell,
-use crate::symbol_resolver::SymbolValue;
+    collections::{HashMap, HashSet},
-use crate::toplevel::helper::PrimDef;
+    fmt::{self, Display},
-use crate::toplevel::{DefinitionId, TopLevelContext, TopLevelDef};
+    iter::{repeat, zip},
-use crate::typecheck::magic_methods::OpInfo;
+    rc::Rc,
-use crate::typecheck::type_inferencer::PrimitiveStore;
+    sync::{Arc, Mutex},
+};
+
 use indexmap::IndexMap;
 use itertools::{repeat_n, Itertools};
+
 use nac3parser::ast::{Cmpop, Location, StrRef, Unaryop};
-use std::cell::RefCell;
+
-use std::collections::HashMap;
+use super::{
-use std::fmt::{self, Display};
+    magic_methods::{Binop, HasOpInfo},
-use std::iter::{repeat, zip};
+    type_error::{TypeError, TypeErrorKind},
-use std::rc::Rc;
+    unification_table::{UnificationKey, UnificationTable},
-use std::sync::{Arc, Mutex};
+};
-use std::{borrow::Cow, collections::HashSet};
+use crate::{
+    symbol_resolver::SymbolValue,
+    toplevel::{helper::PrimDef, DefinitionId, TopLevelContext, TopLevelDef},
+    typecheck::{magic_methods::OpInfo, type_inferencer::PrimitiveStore},
+};
 
 #[cfg(test)]
 mod test;
@@ -670,8 +677,8 @@ impl Unifier {
         let num_args = posargs.len() + kwargs.len();
 
         // Now we check the arguments against the parameters,
-        // and depending on what `call_info` is, we might change how the behavior `unify_call()`
+        // and depending on what `call_info` is, we might change how `unify_call()` behaves
-        // in hopes to improve user error messages when type checking fails.
+        // to improve user error messages when type checking fails.
         match operator_info {
             Some(OperatorInfo::IsBinaryOp { self_type, operator }) => {
                 // The call is written in the form of (say) `a + b`.

nac3core/src/typecheck/typedef/test.rs

@@ -1,10 +1,12 @@
-use super::super::magic_methods::with_fields;
+use std::collections::HashMap;
-use super::*;
+
 use indoc::indoc;
 use itertools::Itertools;
-use std::collections::HashMap;
 use test_case::test_case;
 
+use super::*;
+use crate::typecheck::magic_methods::with_fields;
+
 impl Unifier {
     /// Check whether two types are equal.
     fn eq(&mut self, a: Type, b: Type) -> bool {

nac3ld/src/lib.rs

@@ -21,13 +21,12 @@
     clippy::wildcard_imports
 )]
 
+use std::{collections::HashMap, mem, ptr, slice, str};
+
+use byteorder::{ByteOrder, LittleEndian};
+
 use dwarf::*;
 use elf::*;
-use std::collections::HashMap;
-use std::{mem, ptr, slice, str};
-
-extern crate byteorder;
-use byteorder::{ByteOrder, LittleEndian};
 
 mod dwarf;
 mod elf;

nac3parser/Cargo.toml

@@ -8,15 +8,15 @@ license = "MIT"
 edition = "2021"
 
 [build-dependencies]
-lalrpop = "0.20"
+lalrpop = "0.22"
 
 [dependencies]
 nac3ast = { path = "../nac3ast" }
-lalrpop-util = "0.20"
+lalrpop-util = "0.22"
 log = "0.4"
 unic-emoji-char = "0.9"
 unic-ucd-ident  = "0.9"
-unicode_names2 = "1.2"
+unicode_names2 = "1.3"
 phf = { version = "0.11", features = ["macros"] }
 ahash = "0.8"
 

nac3parser/src/config_comment_helper.rs

@@ -1,8 +1,10 @@
-use crate::ast::Ident;
+use crate::{
-use crate::ast::Location;
+    ast::{Ident, Location},
-use crate::error::*;
+    error::*,
-use crate::token::Tok;
+    token::Tok,
+};
 use lalrpop_util::ParseError;
+
 use nac3ast::*;
 
 pub fn make_config_comment(

nac3parser/src/error.rs

@@ -1,13 +1,12 @@
 //! Define internal parse error types
 //! The goal is to provide a matching and a safe error API, maksing errors from LALR
-use lalrpop_util::ParseError as LalrpopError;
-
-use crate::ast::Location;
-use crate::token::Tok;
-
 use std::error::Error;
 use std::fmt;
 
+use lalrpop_util::ParseError as LalrpopError;
+
+use crate::{ast::Location, token::Tok};
+
 /// Represents an error during lexical scanning.
 #[derive(Debug, PartialEq)]
 pub struct LexicalError {

nac3parser/src/fstring.rs

@@ -1,12 +1,11 @@
-use std::iter;
+use std::{iter, mem, str};
-use std::mem;
-use std::str;
-
-use crate::ast::{Constant, ConversionFlag, Expr, ExprKind, Location};
-use crate::error::{FStringError, FStringErrorType, ParseError};
-use crate::parser::parse_expression;
 
 use self::FStringErrorType::*;
+use crate::{
+    ast::{Constant, ConversionFlag, Expr, ExprKind, Location},
+    error::{FStringError, FStringErrorType, ParseError},
+    parser::parse_expression,
+};
 
 struct FStringParser<'a> {
     chars: iter::Peekable<str::Chars<'a>>,

nac3parser/src/function.rs

@@ -1,8 +1,11 @@
-use ahash::RandomState;
 use std::collections::HashSet;
 
-use crate::ast;
+use ahash::RandomState;
-use crate::error::{LexicalError, LexicalErrorType};
+
+use crate::{
+    ast,
+    error::{LexicalError, LexicalErrorType},
+};
 
 pub struct ArgumentList {
     pub args: Vec<ast::Expr>,

nac3parser/src/lexer.rs

@@ -1,17 +1,17 @@
 //! This module takes care of lexing python source text.
 //!
 //! This means source code is translated into separate tokens.
+use std::{char, cmp::Ordering, num::IntErrorKind, str::FromStr};
 
-pub use super::token::Tok;
-use crate::ast::{FileName, Location};
-use crate::error::{LexicalError, LexicalErrorType};
-use std::char;
-use std::cmp::Ordering;
-use std::num::IntErrorKind;
-use std::str::FromStr;
 use unic_emoji_char::is_emoji_presentation;
 use unic_ucd_ident::{is_xid_continue, is_xid_start};
 
+pub use super::token::Tok;
+use crate::{
+    ast::{FileName, Location},
+    error::{LexicalError, LexicalErrorType},
+};
+
 #[derive(Clone, Copy, PartialEq, Debug, Default)]
 struct IndentationLevel {
     tabs: usize,

nac3parser/src/parser.rs

@@ -5,14 +5,16 @@
 //! parse a whole program, a single statement, or a single
 //! expression.
 
-use nac3ast::Location;
 use std::iter;
 
-use crate::ast::{self, FileName};
+use nac3ast::Location;
-use crate::error::ParseError;
+
-use crate::lexer;
 pub use crate::mode::Mode;
-use crate::python;
+use crate::{
+    ast::{self, FileName},
+    error::ParseError,
+    lexer, python,
+};
 
 /*
  * Parse python code.

nac3parser/src/token.rs

@@ -1,8 +1,9 @@
 //! Different token definitions.
 //! Loosely based on token.h from CPython source:
-use crate::ast;
 use std::fmt::{self, Write};
 
+use crate::ast;
+
 /// Python source code can be tokenized in a sequence of these tokens.
 #[derive(Clone, Debug, PartialEq)]
 pub enum Tok {

nac3standalone/Cargo.toml

@@ -9,14 +9,8 @@ no-escape-analysis = ["nac3core/no-escape-analysis"]
 
 [dependencies]
 parking_lot = "0.12"
-nac3parser = { path = "../nac3parser" }
 nac3core = { path = "../nac3core" }
 
 [dependencies.clap]
 version = "4.5"
 features = ["derive"]
-
-[dependencies.inkwell]
-version = "0.4"
-default-features = false
-features = ["llvm14-0", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]

nac3standalone/demo/interpret_demo.py

@@ -179,16 +179,6 @@ def patch(module):
     module.np_identity = np.identity
     module.np_array = np.array
 
-    # NumPy NDArray view functions
-    module.np_broadcast_to = np.broadcast_to
-    module.np_transpose = np.transpose
-    module.np_reshape = np.reshape
-
-    # NumPy NDArray property getters
-    module.np_size = np.size
-    module.np_shape = np.shape
-    module.np_strides = lambda ndarray: ndarray.strides
-
     # NumPy Math functions
     module.np_isnan = np.isnan
     module.np_isinf = np.isinf
@@ -228,6 +218,8 @@ def patch(module):
     module.np_ldexp = np.ldexp
     module.np_hypot = np.hypot
     module.np_nextafter = np.nextafter
+    module.np_transpose = np.transpose
+    module.np_reshape = np.reshape
 
     # SciPy Math functions
     module.sp_spec_erf = special.erf

nac3standalone/demo/src/globals.py

@@ -0,0 +1,31 @@
+@extern
+def output_int32(x: int32):
+    ...
+
+@extern
+def output_int64(x: int64):
+    ...
+
+X: int32 = 0
+Y = int64(1)
+
+def f():
+    global X, Y
+    X = 1
+    Y = int64(2)
+
+def run() -> int32:
+    global X, Y
+
+    output_int32(X)
+    output_int64(Y)
+    f()
+    output_int32(X)
+    output_int64(Y)
+
+    X = 0
+    Y = int64(0)
+    output_int32(X)
+    output_int64(Y)
+
+    return 0

nac3standalone/demo/src/ndarray.py

@@ -68,19 +68,6 @@ def output_ndarray_float_2(n: ndarray[float, Literal[2]]):
         for c in range(len(n[r])):
             output_float64(n[r][c])
 
-def output_ndarray_float_3(n: ndarray[float, Literal[3]]):
-    for d in range(len(n)):
-        for r in range(len(n[d])):
-            for c in range(len(n[d][r])):
-                output_float64(n[d][r][c])
-
-def output_ndarray_float_4(n: ndarray[float, Literal[4]]):
-    for x in range(len(n)):
-        for y in range(len(n[x])):
-            for z in range(len(n[x][y])):
-                for w in range(len(n[x][y][z])):
-                    output_float64(n[x][y][z][w])
-
 def consume_ndarray_1(n: ndarray[float, Literal[1]]):
     pass
 
@@ -127,12 +114,22 @@ def test_ndarray_ones():
     n: ndarray[float, 1] = np_ones([1])
     output_ndarray_float_1(n)
 
+    dim = (1,)
+    n_tup: ndarray[float, 1] = np_ones(dim)
+    output_ndarray_float_1(n_tup)
+
 def test_ndarray_full():
     n_float: ndarray[float, 1] = np_full([1], 2.0)
     output_ndarray_float_1(n_float)
     n_i32: ndarray[int32, 1] = np_full([1], 2)
     output_ndarray_int32_1(n_i32)
 
+    dim = (1,)
+    n_float_tup: ndarray[float, 1] = np_full(dim, 2.0)
+    output_ndarray_float_1(n_float_tup)
+    n_i32_tup: ndarray[int32, 1] = np_full(dim, 2)
+    output_ndarray_int32_1(n_i32_tup)
+
 def test_ndarray_eye():
     n: ndarray[float, 2] = np_eye(2)
     output_ndarray_float_2(n)
@@ -199,104 +196,6 @@ def test_ndarray_nd_idx():
     output_float64(x[1, 0])
     output_float64(x[1, 1])
 
-def test_ndarray_transpose():
-    x: ndarray[float, 2] = np_array([[1., 2., 3.], [4., 5., 6.]])
-    y = np_transpose(x)
-    z = np_transpose(y)
-
-    output_int32(np_shape(x)[0])
-    output_int32(np_shape(x)[1])
-    output_ndarray_float_2(x)
-
-    output_int32(np_shape(y)[0])
-    output_int32(np_shape(y)[1])
-    output_ndarray_float_2(y)
-
-    output_int32(np_shape(z)[0])
-    output_int32(np_shape(z)[1])
-    output_ndarray_float_2(z)
-
-def test_ndarray_reshape():
-    w: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
-    x = np_reshape(w, (1, 2, 1, -1))
-    y = np_reshape(x, [2, -1])
-    z = np_reshape(y, 10)
-
-    output_int32(np_shape(w)[0])
-    output_ndarray_float_1(w)
-
-    output_int32(np_shape(x)[0])
-    output_int32(np_shape(x)[1])
-    output_int32(np_shape(x)[2])
-    output_int32(np_shape(x)[3])
-    output_ndarray_float_4(x)
-
-    output_int32(np_shape(y)[0])
-    output_int32(np_shape(y)[1])
-    output_ndarray_float_2(y)
-
-    output_int32(np_shape(z)[0])
-    output_ndarray_float_1(z)
-
-    x1: ndarray[int32, 1] = np_array([1, 2, 3, 4])
-    x2: ndarray[int32, 2] = np_reshape(x1, (2, 2))
-
-    output_int32(np_shape(x1)[0])
-    output_ndarray_int32_1(x1)
-
-    output_int32(np_shape(x2)[0])
-    output_int32(np_shape(x2)[1])
-    output_ndarray_int32_2(x2)
-
-def test_ndarray_broadcast_to():
-    xs = np_array([1.0, 2.0, 3.0])
-    ys = np_broadcast_to(xs, (1, 3))
-    zs = np_broadcast_to(ys, (2, 4, 3))
-
-    output_int32(np_shape(xs)[0])
-    output_ndarray_float_1(xs)
-
-    output_int32(np_shape(ys)[0])
-    output_int32(np_shape(ys)[1])
-    output_ndarray_float_2(ys)
-
-    output_int32(np_shape(zs)[0])
-    output_int32(np_shape(zs)[1])
-    output_int32(np_shape(zs)[2])
-    output_ndarray_float_3(zs)
-
-def test_ndarray_subscript_assignment():
-    xs = np_array([[11.0, 22.0, 33.0, 44.0], [55.0, 66.0, 77.0, 88.0]])
-
-    xs[0, 0] = 99.0
-    output_ndarray_float_2(xs)
-
-    xs[0] = 100.0
-    output_ndarray_float_2(xs)
-
-    xs[:, ::2] = 101.0
-    output_ndarray_float_2(xs)
-
-    xs[1:, 0] = 102.0
-    output_ndarray_float_2(xs)
-
-    xs[0] = np_array([-1.0, -2.0, -3.0, -4.0])
-    output_ndarray_float_2(xs)
-
-    xs[:] = np_array([-5.0, -6.0, -7.0, -8.0])
-    output_ndarray_float_2(xs)
-
-    # Test assignment with memory sharing
-    ys1 = np_reshape(xs, (2, 4))
-    ys2 = np_transpose(ys1)
-    ys3 = ys2[::-1, 0]
-    ys3[0] = -999.0
-
-    output_ndarray_float_2(xs)
-    output_ndarray_float_2(ys1)
-    output_ndarray_float_2(ys2)
-    output_ndarray_float_1(ys3)
-
 def test_ndarray_add():
     x = np_identity(2)
     y = x + np_ones([2, 2])
@@ -641,59 +540,11 @@ def test_ndarray_ipow_broadcast_scalar():
     output_ndarray_float_2(x)
 
 def test_ndarray_matmul():
-    # 2D @ 2D -> 2D
+    x = np_identity(2)
-    a1 = np_array([[2.0, 3.0], [5.0, 7.0]])
+    y = x @ np_ones([2, 2])
-    b1 = np_array([[11.0, 13.0], [17.0, 23.0]])
-    c1 = a1 @ b1
-    output_int32(np_shape(c1)[0])
-    output_int32(np_shape(c1)[1])
-    output_ndarray_float_2(c1)
 
-    # 1D @ 1D -> Scalar
+    output_ndarray_float_2(x)
-    a2 = np_array([2.0, 3.0, 5.0])
+    output_ndarray_float_2(y)
-    b2 = np_array([7.0, 11.0, 13.0])
-    c2 = a2 @ b2
-    output_float64(c2)
-
-    # 2D @ 1D -> 1D
-    a3 = np_array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]])
-    b3 = np_array([4.0, 5.0, 6.0])
-    c3 = a3 @ b3
-    output_int32(np_shape(c3)[0])
-    output_ndarray_float_1(c3)
-
-    # 1D @ 2D -> 1D
-    a4 = np_array([1.0, 2.0, 3.0])
-    b4 = np_array([[4.0, 5.0], [6.0, 7.0], [8.0, 9.0]])
-    c4 = a4 @ b4
-    output_int32(np_shape(c4)[0])
-    output_ndarray_float_1(c4)
-
-    # Broadcasting
-    a5 = np_array([
-        [[ 0.0,  1.0,  2.0,  3.0],
-         [ 4.0,  5.0,  6.0,  7.0]],
-        [[ 8.0,  9.0, 10.0, 11.0],
-         [12.0, 13.0, 14.0, 15.0]],
-        [[16.0, 17.0, 18.0, 19.0],
-         [20.0, 21.0, 22.0, 23.0]]
-    ])
-    b5 = np_array([
-        [[[ 0.0,  1.0,  2.0],
-          [ 3.0,  4.0,  5.0],
-          [ 6.0,  7.0,  8.0],
-          [ 9.0, 10.0, 11.0]]],
-        [[[12.0, 13.0, 14.0],
-          [15.0, 16.0, 17.0],
-          [18.0, 19.0, 20.0],
-          [21.0, 22.0, 23.0]]]
-    ])
-    c5 = a5 @ b5
-    output_int32(np_shape(c5)[0])
-    output_int32(np_shape(c5)[1])
-    output_int32(np_shape(c5)[2])
-    output_int32(np_shape(c5)[3])
-    output_ndarray_float_4(c5)
 
 def test_ndarray_imatmul():
     x = np_identity(2)
@@ -1588,6 +1439,27 @@ def test_ndarray_nextafter_broadcast_rhs_scalar():
     output_ndarray_float_2(nextafter_x_zeros)
     output_ndarray_float_2(nextafter_x_ones)
 
+def test_ndarray_transpose():
+    x: ndarray[float, 2] = np_array([[1., 2., 3.], [4., 5., 6.]])
+    y = np_transpose(x)
+    z = np_transpose(y)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_reshape():
+    w: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
+    x = np_reshape(w, (1, 2, 1, -1))
+    y = np_reshape(x, [2, -1])
+    z = np_reshape(y, 10)
+
+    x1: ndarray[int32, 1] = np_array([1, 2, 3, 4])
+    x2: ndarray[int32, 2] = np_reshape(x1, (2, 2))
+
+    output_ndarray_float_1(w)
+    output_ndarray_float_2(y)
+    output_ndarray_float_1(z)
+
 def test_ndarray_dot():
     x1: ndarray[float, 1] = np_array([5.0, 1.0, 4.0, 2.0])
     y1: ndarray[float, 1] = np_array([5.0, 1.0, 6.0, 6.0])
@@ -1719,11 +1591,6 @@ def run() -> int32:
     test_ndarray_slices()
     test_ndarray_nd_idx()
 
-    test_ndarray_transpose()
-    test_ndarray_reshape()
-    test_ndarray_broadcast_to()
-    test_ndarray_subscript_assignment()
-
     test_ndarray_add()
     test_ndarray_add_broadcast()
     test_ndarray_add_broadcast_lhs_scalar()
@@ -1887,6 +1754,8 @@ def run() -> int32:
     test_ndarray_nextafter_broadcast()
     test_ndarray_nextafter_broadcast_lhs_scalar()
     test_ndarray_nextafter_broadcast_rhs_scalar()
+    test_ndarray_transpose()
+    test_ndarray_reshape()
     
     test_ndarray_dot()
     test_ndarray_cholesky()