forked from M-Labs/nac3
Compare commits
72 Commits
ndarray-st
...
nds3
Author | SHA1 | Date |
---|---|---|
|
858b4b9f3f | |
|
937b36dcfd | |
|
bcd35544cc | |
|
7afc9ff7fb | |
|
7c69015aaf | |
|
5acba1c4ef | |
|
133e25de50 | |
|
f9e360a3f4 | |
|
79f66e8517 | |
|
3886dffe68 | |
|
49ab9087d8 | |
|
cb2b7bec3e | |
|
40387b9a66 | |
|
925685fb69 | |
|
65419194cb | |
|
a343bef2ad | |
|
1617a61480 | |
|
99eaef4dbd | |
|
5016b95972 | |
|
6139bec658 | |
|
051684c921 | |
|
3a8c385e01 | |
|
221de4d06a | |
|
fb9fe8edf2 | |
|
894083c6a3 | |
|
669c6aca6b | |
|
63d2b49b09 | |
|
bf709889c4 | |
|
1c72698d02 | |
|
54f883f0a5 | |
|
4a6845dac6 | |
|
00236f48bc | |
|
a3e6bb2292 | |
|
17171065b1 | |
|
540b35ec84 | |
|
4bb00c52e3 | |
|
faf07527cb | |
|
d6a4d0a634 | |
|
2242c5af43 | |
|
318a675ea6 | |
|
32e52ce198 | |
|
665ca8e32d | |
|
12c12b1d80 | |
|
72972fa909 | |
|
142cd48594 | |
|
8adfe781c5 | |
|
339b74161b | |
|
8c5ba37d09 | |
|
05a8948ff2 | |
|
6d171ec284 | |
|
0ba68f6657 | |
|
693b2a8863 | |
|
5faeede0e5 | |
|
266707df9d | |
|
3d3c258756 | |
|
ed1182cb24 | |
|
fd025c1137 | |
|
f139db9af9 | |
|
44487b76ae | |
|
1332f113e8 | |
|
7632d6f72a | |
|
4948395ca2 | |
|
3db3061d99 | |
|
51c2175c80 | |
|
1a31a50b8a | |
|
6c10e3d056 | |
|
2dbc1ec659 | |
|
c80378063a | |
|
513d30152b | |
|
45e9360c4d | |
|
2e01b77fc8 | |
|
cea7cade51 |
|
@ -0,0 +1,3 @@
|
|||
BasedOnStyle: Google
|
||||
IndentWidth: 4
|
||||
ReflowComments: false
|
|
@ -1,3 +1,4 @@
|
|||
__pycache__
|
||||
/target
|
||||
/nac3standalone/demo/linalg/target
|
||||
nix/windows/msys2
|
||||
|
|
|
@ -26,9 +26,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "anstream"
|
||||
version = "0.6.14"
|
||||
version = "0.6.15"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "418c75fa768af9c03be99d17643f93f79bbba589895012a80e3452a19ddda15b"
|
||||
checksum = "64e15c1ab1f89faffbf04a634d5e1962e9074f2741eef6d97f3c4e322426d526"
|
||||
dependencies = [
|
||||
"anstyle",
|
||||
"anstyle-parse",
|
||||
|
@ -41,33 +41,33 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "anstyle"
|
||||
version = "1.0.7"
|
||||
version = "1.0.8"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "038dfcf04a5feb68e9c60b21c9625a54c2c0616e79b72b0fd87075a056ae1d1b"
|
||||
checksum = "1bec1de6f59aedf83baf9ff929c98f2ad654b97c9510f4e70cf6f661d49fd5b1"
|
||||
|
||||
[[package]]
|
||||
name = "anstyle-parse"
|
||||
version = "0.2.4"
|
||||
version = "0.2.5"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "c03a11a9034d92058ceb6ee011ce58af4a9bf61491aa7e1e59ecd24bd40d22d4"
|
||||
checksum = "eb47de1e80c2b463c735db5b217a0ddc39d612e7ac9e2e96a5aed1f57616c1cb"
|
||||
dependencies = [
|
||||
"utf8parse",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anstyle-query"
|
||||
version = "1.1.0"
|
||||
version = "1.1.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "ad186efb764318d35165f1758e7dcef3b10628e26d41a44bc5550652e6804391"
|
||||
checksum = "6d36fc52c7f6c869915e99412912f22093507da8d9e942ceaf66fe4b7c14422a"
|
||||
dependencies = [
|
||||
"windows-sys",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anstyle-wincon"
|
||||
version = "3.0.3"
|
||||
version = "3.0.4"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "61a38449feb7068f52bb06c12759005cf459ee52bb4adc1d5a7c4322d716fb19"
|
||||
checksum = "5bf74e1b6e971609db8ca7a9ce79fd5768ab6ae46441c572e46cf596f59e57f8"
|
||||
dependencies = [
|
||||
"anstyle",
|
||||
"windows-sys",
|
||||
|
@ -117,9 +117,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
|
|||
|
||||
[[package]]
|
||||
name = "cc"
|
||||
version = "1.1.0"
|
||||
version = "1.1.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "eaff6f8ce506b9773fa786672d63fc7a191ffea1be33f72bbd4aeacefca9ffc8"
|
||||
checksum = "2aba8f4e9906c7ce3c73463f62a7f0c65183ada1a2d47e397cc8810827f9694f"
|
||||
|
||||
[[package]]
|
||||
name = "cfg-if"
|
||||
|
@ -129,9 +129,9 @@ checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
|
|||
|
||||
[[package]]
|
||||
name = "clap"
|
||||
version = "4.5.9"
|
||||
version = "4.5.11"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "64acc1846d54c1fe936a78dc189c34e28d3f5afc348403f28ecf53660b9b8462"
|
||||
checksum = "35723e6a11662c2afb578bcf0b88bf6ea8e21282a953428f240574fcc3a2b5b3"
|
||||
dependencies = [
|
||||
"clap_builder",
|
||||
"clap_derive",
|
||||
|
@ -139,9 +139,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "clap_builder"
|
||||
version = "4.5.9"
|
||||
version = "4.5.11"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "6fb8393d67ba2e7bfaf28a23458e4e2b543cc73a99595511eb207fdb8aede942"
|
||||
checksum = "49eb96cbfa7cfa35017b7cd548c75b14c3118c98b423041d70562665e07fb0fa"
|
||||
dependencies = [
|
||||
"anstream",
|
||||
"anstyle",
|
||||
|
@ -151,27 +151,27 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "clap_derive"
|
||||
version = "4.5.8"
|
||||
version = "4.5.11"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "2bac35c6dafb060fd4d275d9a4ffae97917c13a6327903a8be2153cd964f7085"
|
||||
checksum = "5d029b67f89d30bbb547c89fd5161293c0aec155fc691d7924b64550662db93e"
|
||||
dependencies = [
|
||||
"heck 0.5.0",
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.70",
|
||||
"syn 2.0.72",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "clap_lex"
|
||||
version = "0.7.1"
|
||||
version = "0.7.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "4b82cf0babdbd58558212896d1a4272303a57bdb245c2bf1147185fb45640e70"
|
||||
checksum = "1462739cb27611015575c0c11df5df7601141071f07518d56fcc1be504cbec97"
|
||||
|
||||
[[package]]
|
||||
name = "colorchoice"
|
||||
version = "1.0.1"
|
||||
version = "1.0.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "0b6a852b24ab71dffc585bcb46eaf7959d175cb865a7152e35b348d1b2960422"
|
||||
checksum = "d3fd119d74b830634cea2a0f58bbd0d54540518a14397557951e79340abc28c0"
|
||||
|
||||
[[package]]
|
||||
name = "console"
|
||||
|
@ -421,7 +421,7 @@ checksum = "4fa4d8d74483041a882adaa9a29f633253a66dde85055f0495c121620ac484b2"
|
|||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.70",
|
||||
"syn 2.0.72",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
@ -440,9 +440,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "is_terminal_polyfill"
|
||||
version = "1.70.0"
|
||||
version = "1.70.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "f8478577c03552c21db0e2724ffb8986a5ce7af88107e6be5d2ee6e158c12800"
|
||||
checksum = "7943c866cc5cd64cbc25b2e01621d07fa8eb2a1a23160ee81ce38704e97b8ecf"
|
||||
|
||||
[[package]]
|
||||
name = "itertools"
|
||||
|
@ -513,9 +513,9 @@ checksum = "97b3888a4aecf77e811145cadf6eef5901f4782c53886191b2f693f24761847c"
|
|||
|
||||
[[package]]
|
||||
name = "libloading"
|
||||
version = "0.8.4"
|
||||
version = "0.8.5"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "e310b3a6b5907f99202fcdb4960ff45b93735d7c7d96b760fcff8db2dc0e103d"
|
||||
checksum = "4979f22fdb869068da03c9f7528f8297c6fd2606bc3a4affe42e6a823fdb8da4"
|
||||
dependencies = [
|
||||
"cfg-if",
|
||||
"windows-targets",
|
||||
|
@ -749,7 +749,7 @@ dependencies = [
|
|||
"phf_shared 0.11.2",
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.70",
|
||||
"syn 2.0.72",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
@ -778,9 +778,9 @@ checksum = "5be167a7af36ee22fe3115051bc51f6e6c7054c9348e28deb4f49bd6f705a315"
|
|||
|
||||
[[package]]
|
||||
name = "portable-atomic"
|
||||
version = "1.6.0"
|
||||
version = "1.7.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "7170ef9988bc169ba16dd36a7fa041e5c4cbeb6a35b76d4c03daded371eae7c0"
|
||||
checksum = "da544ee218f0d287a911e9c99a39a8c9bc8fcad3cb8db5959940044ecfc67265"
|
||||
|
||||
[[package]]
|
||||
name = "ppv-lite86"
|
||||
|
@ -850,7 +850,7 @@ dependencies = [
|
|||
"proc-macro2",
|
||||
"pyo3-macros-backend",
|
||||
"quote",
|
||||
"syn 2.0.70",
|
||||
"syn 2.0.72",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
@ -863,7 +863,7 @@ dependencies = [
|
|||
"proc-macro2",
|
||||
"pyo3-build-config",
|
||||
"quote",
|
||||
"syn 2.0.70",
|
||||
"syn 2.0.72",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
@ -927,9 +927,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "redox_syscall"
|
||||
version = "0.5.2"
|
||||
version = "0.5.3"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "c82cf8cff14456045f55ec4241383baeff27af886adb72ffb2162f99911de0fd"
|
||||
checksum = "2a908a6e00f1fdd0dfd9c0eb08ce85126f6d8bbda50017e74bc4a4b7d4a926a4"
|
||||
dependencies = [
|
||||
"bitflags",
|
||||
]
|
||||
|
@ -1044,7 +1044,7 @@ checksum = "e0cd7e117be63d3c3678776753929474f3b04a43a080c744d6b0ae2a8c28e222"
|
|||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.70",
|
||||
"syn 2.0.72",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
@ -1072,9 +1072,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "similar"
|
||||
version = "2.5.0"
|
||||
version = "2.6.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "fa42c91313f1d05da9b26f267f931cf178d4aba455b4c4622dd7355eb80c6640"
|
||||
checksum = "1de1d4f81173b03af4c0cbed3c898f6bff5b870e4a7f5d6f4057d62a7a4b686e"
|
||||
|
||||
[[package]]
|
||||
name = "siphasher"
|
||||
|
@ -1134,7 +1134,7 @@ dependencies = [
|
|||
"proc-macro2",
|
||||
"quote",
|
||||
"rustversion",
|
||||
"syn 2.0.70",
|
||||
"syn 2.0.72",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
@ -1150,9 +1150,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "syn"
|
||||
version = "2.0.70"
|
||||
version = "2.0.72"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "2f0209b68b3613b093e0ec905354eccaedcfe83b8cb37cbdeae64026c3064c16"
|
||||
checksum = "dc4b9b9bf2add8093d3f2c0204471e951b2285580335de42f9d2534f3ae7a8af"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
|
@ -1203,22 +1203,22 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "thiserror"
|
||||
version = "1.0.61"
|
||||
version = "1.0.63"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "c546c80d6be4bc6a00c0f01730c08df82eaa7a7a61f11d656526506112cc1709"
|
||||
checksum = "c0342370b38b6a11b6cc11d6a805569958d54cfa061a29969c3b5ce2ea405724"
|
||||
dependencies = [
|
||||
"thiserror-impl",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "thiserror-impl"
|
||||
version = "1.0.61"
|
||||
version = "1.0.63"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "46c3384250002a6d5af4d114f2845d37b57521033f30d5c3f46c4d70e1197533"
|
||||
checksum = "a4558b58466b9ad7ca0f102865eccc95938dca1a74a856f2b57b6629050da261"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.70",
|
||||
"syn 2.0.72",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
@ -1336,9 +1336,9 @@ checksum = "06abde3611657adf66d383f00b093d7faecc7fa57071cce2578660c9f1010821"
|
|||
|
||||
[[package]]
|
||||
name = "version_check"
|
||||
version = "0.9.4"
|
||||
version = "0.9.5"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "49874b5167b65d7193b8aba1567f5c7d93d001cafc34600cee003eda787e483f"
|
||||
checksum = "0b928f33d975fc6ad9f86c8f283853ad26bdd5b10b7f1542aa2fa15e2289105a"
|
||||
|
||||
[[package]]
|
||||
name = "walkdir"
|
||||
|
@ -1486,5 +1486,5 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
|
|||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.70",
|
||||
"syn 2.0.72",
|
||||
]
|
||||
|
|
|
@ -2,11 +2,11 @@
|
|||
"nodes": {
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1720418205,
|
||||
"narHash": "sha256-cPJoFPXU44GlhWg4pUk9oUPqurPlCFZ11ZQPk21GTPU=",
|
||||
"lastModified": 1721924956,
|
||||
"narHash": "sha256-Sb1jlyRO+N8jBXEX9Pg9Z1Qb8Bw9QyOgLDNMEpmjZ2M=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "655a58a72a6601292512670343087c2d75d859c1",
|
||||
"rev": "5ad6a14c6bf098e98800b091668718c336effc95",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
|
|
32
flake.nix
32
flake.nix
|
@ -6,6 +6,7 @@
|
|||
outputs = { self, nixpkgs }:
|
||||
let
|
||||
pkgs = import nixpkgs { system = "x86_64-linux"; };
|
||||
pkgs32 = import nixpkgs { system = "i686-linux"; };
|
||||
in rec {
|
||||
packages.x86_64-linux = rec {
|
||||
llvm-nac3 = pkgs.callPackage ./nix/llvm {};
|
||||
|
@ -13,8 +14,25 @@
|
|||
''
|
||||
mkdir -p $out/bin
|
||||
ln -s ${pkgs.llvmPackages_14.clang-unwrapped}/bin/clang $out/bin/clang-irrt
|
||||
ln -s ${pkgs.llvmPackages_14.clang}/bin/clang $out/bin/clang-irrt-test
|
||||
ln -s ${pkgs.llvmPackages_14.llvm.out}/bin/llvm-as $out/bin/llvm-as-irrt
|
||||
'';
|
||||
demo-linalg-stub = pkgs.rustPlatform.buildRustPackage {
|
||||
name = "demo-linalg-stub";
|
||||
src = ./nac3standalone/demo/linalg;
|
||||
cargoLock = {
|
||||
lockFile = ./nac3standalone/demo/linalg/Cargo.lock;
|
||||
};
|
||||
doCheck = false;
|
||||
};
|
||||
demo-linalg-stub32 = pkgs32.rustPlatform.buildRustPackage {
|
||||
name = "demo-linalg-stub32";
|
||||
src = ./nac3standalone/demo/linalg;
|
||||
cargoLock = {
|
||||
lockFile = ./nac3standalone/demo/linalg/Cargo.lock;
|
||||
};
|
||||
doCheck = false;
|
||||
};
|
||||
nac3artiq = pkgs.python3Packages.toPythonModule (
|
||||
pkgs.rustPlatform.buildRustPackage rec {
|
||||
name = "nac3artiq";
|
||||
|
@ -23,8 +41,9 @@
|
|||
cargoLock = {
|
||||
lockFile = ./Cargo.lock;
|
||||
};
|
||||
cargoTestFlags = [ "--features" "test" ];
|
||||
passthru.cargoLock = cargoLock;
|
||||
nativeBuildInputs = [ pkgs.python3 pkgs.llvmPackages_14.clang llvm-tools-irrt pkgs.llvmPackages_14.llvm.out llvm-nac3 ];
|
||||
nativeBuildInputs = [ pkgs.python3 (pkgs.wrapClangMulti pkgs.llvmPackages_14.clang) llvm-tools-irrt pkgs.llvmPackages_14.llvm.out llvm-nac3 ];
|
||||
buildInputs = [ pkgs.python3 llvm-nac3 ];
|
||||
checkInputs = [ (pkgs.python3.withPackages(ps: [ ps.numpy ps.scipy ])) ];
|
||||
checkPhase =
|
||||
|
@ -32,7 +51,9 @@
|
|||
echo "Checking nac3standalone demos..."
|
||||
pushd nac3standalone/demo
|
||||
patchShebangs .
|
||||
./check_demos.sh
|
||||
export DEMO_LINALG_STUB=${demo-linalg-stub}/lib/liblinalg.a
|
||||
export DEMO_LINALG_STUB32=${demo-linalg-stub32}/lib/liblinalg.a
|
||||
./check_demos.sh -i686
|
||||
popd
|
||||
echo "Running Cargo tests..."
|
||||
cargoCheckHook
|
||||
|
@ -149,7 +170,7 @@
|
|||
buildInputs = with pkgs; [
|
||||
# build dependencies
|
||||
packages.x86_64-linux.llvm-nac3
|
||||
llvmPackages_14.clang llvmPackages_14.llvm.out # for running nac3standalone demos
|
||||
(pkgs.wrapClangMulti llvmPackages_14.clang) llvmPackages_14.llvm.out # for running nac3standalone demos
|
||||
packages.x86_64-linux.llvm-tools-irrt
|
||||
cargo
|
||||
rustc
|
||||
|
@ -162,6 +183,11 @@
|
|||
pre-commit
|
||||
rustfmt
|
||||
];
|
||||
shellHook =
|
||||
''
|
||||
export DEMO_LINALG_STUB=${packages.x86_64-linux.demo-linalg-stub}/lib/liblinalg.a
|
||||
export DEMO_LINALG_STUB32=${packages.x86_64-linux.demo-linalg-stub32}/lib/liblinalg.a
|
||||
'';
|
||||
};
|
||||
devShells.x86_64-linux.msys2 = pkgs.mkShell {
|
||||
name = "nac3-dev-shell-msys2";
|
||||
|
|
|
@ -0,0 +1,24 @@
|
|||
from min_artiq import *
|
||||
from numpy import int32
|
||||
|
||||
|
||||
@nac3
|
||||
class EmptyList:
|
||||
core: KernelInvariant[Core]
|
||||
|
||||
def __init__(self):
|
||||
self.core = Core()
|
||||
|
||||
@rpc
|
||||
def get_empty(self) -> list[int32]:
|
||||
return []
|
||||
|
||||
@kernel
|
||||
def run(self):
|
||||
a: list[int32] = self.get_empty()
|
||||
if a != []:
|
||||
raise ValueError
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
EmptyList().run()
|
|
@ -24,6 +24,7 @@ use std::rc::Rc;
|
|||
use std::sync::Arc;
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
memory_buffer::MemoryBuffer,
|
||||
module::{Linkage, Module},
|
||||
passes::PassBuilderOptions,
|
||||
|
@ -32,6 +33,7 @@ use inkwell::{
|
|||
OptimizationLevel,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
use nac3core::codegen::irrt::setup_irrt_exceptions;
|
||||
use nac3core::codegen::{gen_func_impl, CodeGenLLVMOptions, CodeGenTargetMachineOptions};
|
||||
use nac3core::toplevel::builtins::get_exn_constructor;
|
||||
use nac3core::typecheck::typedef::{TypeEnum, Unifier, VarMap};
|
||||
|
@ -497,6 +499,11 @@ impl Nac3 {
|
|||
.register_top_level(synthesized.pop().unwrap(), Some(resolver.clone()), "", false)
|
||||
.unwrap();
|
||||
|
||||
// Process IRRT
|
||||
let context = inkwell::context::Context::create();
|
||||
let irrt = load_irrt(&context);
|
||||
setup_irrt_exceptions(&context, &irrt, resolver.as_ref());
|
||||
|
||||
let fun_signature =
|
||||
FunSignature { args: vec![], ret: self.primitive.none, vars: VarMap::new() };
|
||||
let mut store = ConcreteTypeStore::new();
|
||||
|
@ -625,7 +632,9 @@ impl Nac3 {
|
|||
let buffer = buffer.as_slice().into();
|
||||
membuffer.lock().push(buffer);
|
||||
})));
|
||||
let size_t = if self.isa == Isa::Host { 64 } else { 32 };
|
||||
let size_t = Context::create()
|
||||
.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 };
|
||||
let thread_names: Vec<String> = (0..num_threads).map(|_| "main".to_string()).collect();
|
||||
let threads: Vec<_> = thread_names
|
||||
|
@ -644,6 +653,9 @@ impl Nac3 {
|
|||
ArtiqCodeGenerator::new("attributes_writeback".to_string(), size_t, self.time_fns);
|
||||
let context = inkwell::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());
|
||||
module.set_triple(&target_machine.get_triple());
|
||||
let builder = context.create_builder();
|
||||
let (_, module, _) = gen_func_impl(
|
||||
&context,
|
||||
|
@ -662,7 +674,7 @@ impl Nac3 {
|
|||
membuffer.lock().push(buffer);
|
||||
});
|
||||
|
||||
let context = inkwell::context::Context::create();
|
||||
// Link all modules into `main`.
|
||||
let buffers = membuffers.lock();
|
||||
let main = context
|
||||
.create_module_from_ir(MemoryBuffer::create_from_memory_range(&buffers[0], "main"))
|
||||
|
@ -691,8 +703,7 @@ impl Nac3 {
|
|||
)
|
||||
.unwrap();
|
||||
|
||||
main.link_in_module(load_irrt(&context))
|
||||
.map_err(|err| CompileError::new_err(err.to_string()))?;
|
||||
main.link_in_module(irrt).map_err(|err| CompileError::new_err(err.to_string()))?;
|
||||
|
||||
let mut function_iter = main.get_first_function();
|
||||
while let Some(func) = function_iter {
|
||||
|
|
|
@ -991,8 +991,15 @@ impl InnerResolver {
|
|||
}
|
||||
_ => unreachable!("must be list"),
|
||||
};
|
||||
let ty = ctx.get_llvm_type(generator, elem_ty);
|
||||
let size_t = generator.get_size_type(ctx.ctx);
|
||||
let ty = if len == 0
|
||||
&& matches!(&*ctx.unifier.get_ty_immutable(elem_ty), TypeEnum::TVar { .. })
|
||||
{
|
||||
// The default type for zero-length lists of unknown element type is size_t
|
||||
size_t.into()
|
||||
} else {
|
||||
ctx.get_llvm_type(generator, elem_ty)
|
||||
};
|
||||
let arr_ty = ctx
|
||||
.ctx
|
||||
.struct_type(&[ty.ptr_type(AddressSpace::default()).into(), size_t.into()], false);
|
||||
|
|
|
@ -1,3 +1,6 @@
|
|||
[features]
|
||||
test = []
|
||||
|
||||
[package]
|
||||
name = "nac3core"
|
||||
version = "0.1.0"
|
||||
|
|
|
@ -3,20 +3,34 @@ use std::{
|
|||
env,
|
||||
fs::File,
|
||||
io::Write,
|
||||
path::Path,
|
||||
path::{Path, PathBuf},
|
||||
process::{Command, Stdio},
|
||||
};
|
||||
|
||||
fn main() {
|
||||
const FILE: &str = "src/codegen/irrt/irrt.cpp";
|
||||
const CMD_IRRT_CLANG: &str = "clang-irrt";
|
||||
const CMD_IRRT_CLANG_TEST: &str = "clang-irrt-test";
|
||||
const CMD_IRRT_LLVM_AS: &str = "llvm-as-irrt";
|
||||
|
||||
fn get_out_dir() -> PathBuf {
|
||||
PathBuf::from(env::var("OUT_DIR").unwrap())
|
||||
}
|
||||
|
||||
fn get_irrt_dir() -> &'static Path {
|
||||
Path::new("irrt")
|
||||
}
|
||||
|
||||
/// Compile `irrt.cpp` for use in `src/codegen`
|
||||
fn compile_irrt_cpp() {
|
||||
let out_dir = get_out_dir();
|
||||
let irrt_dir = get_irrt_dir();
|
||||
|
||||
/*
|
||||
* HACK: Sadly, clang doesn't let us emit generic LLVM bitcode.
|
||||
* Compiling for WASM32 and filtering the output with regex is the closest we can get.
|
||||
*/
|
||||
let irrt_cpp_path = irrt_dir.join("irrt.cpp");
|
||||
let flags: &[&str] = &[
|
||||
"--target=wasm32",
|
||||
FILE,
|
||||
"-x",
|
||||
"c++",
|
||||
"-fno-discard-value-names",
|
||||
|
@ -33,13 +47,16 @@ fn main() {
|
|||
"-Wextra",
|
||||
"-o",
|
||||
"-",
|
||||
"-I",
|
||||
irrt_dir.to_str().unwrap(),
|
||||
irrt_cpp_path.to_str().unwrap(),
|
||||
];
|
||||
|
||||
println!("cargo:rerun-if-changed={FILE}");
|
||||
let out_dir = env::var("OUT_DIR").unwrap();
|
||||
let out_path = Path::new(&out_dir);
|
||||
// Tell Cargo to rerun if any file under `irrt_dir` (recursive) changes
|
||||
println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
|
||||
|
||||
let output = Command::new("clang-irrt")
|
||||
// Compile IRRT and capture the LLVM IR output
|
||||
let output = Command::new(CMD_IRRT_CLANG)
|
||||
.args(flags)
|
||||
.output()
|
||||
.map(|o| {
|
||||
|
@ -52,7 +69,17 @@ fn main() {
|
|||
let output = std::str::from_utf8(&output.stdout).unwrap().replace("\r\n", "\n");
|
||||
let mut filtered_output = String::with_capacity(output.len());
|
||||
|
||||
let regex_filter = Regex::new(r"(?ms:^define.*?\}$)|(?m:^declare.*?$)").unwrap();
|
||||
// Filter out irrelevant IR
|
||||
//
|
||||
// Regex:
|
||||
// - `(?ms:^define.*?\}$)` captures LLVM `define` blocks
|
||||
// - `(?m:^declare.*?$)` captures LLVM `declare` lines
|
||||
// - `(?m:^%.+?=\s*type\s*\{.+?\}$)` captures LLVM `type` declarations
|
||||
// - `(?m:^@.+?=.+$)` captures global constants
|
||||
let regex_filter = Regex::new(
|
||||
r"(?ms:^define.*?\}$)|(?m:^declare.*?$)|(?m:^%.+?=\s*type\s*\{.+?\}$)|(?m:^@.+?=.+$)",
|
||||
)
|
||||
.unwrap();
|
||||
for f in regex_filter.captures_iter(&output) {
|
||||
assert_eq!(f.len(), 1);
|
||||
filtered_output.push_str(&f[0]);
|
||||
|
@ -63,20 +90,71 @@ fn main() {
|
|||
.unwrap()
|
||||
.replace_all(&filtered_output, "");
|
||||
|
||||
println!("cargo:rerun-if-env-changed=DEBUG_DUMP_IRRT");
|
||||
if env::var("DEBUG_DUMP_IRRT").is_ok() {
|
||||
let mut file = File::create(out_path.join("irrt.ll")).unwrap();
|
||||
// For debugging
|
||||
// Doing `DEBUG_DUMP_IRRT=1 cargo build -p nac3core` dumps the LLVM IR generated
|
||||
const DEBUG_DUMP_IRRT: &str = "DEBUG_DUMP_IRRT";
|
||||
println!("cargo:rerun-if-env-changed={DEBUG_DUMP_IRRT}");
|
||||
if env::var(DEBUG_DUMP_IRRT).is_ok() {
|
||||
let mut file = File::create(out_dir.join("irrt.ll")).unwrap();
|
||||
file.write_all(output.as_bytes()).unwrap();
|
||||
let mut file = File::create(out_path.join("irrt-filtered.ll")).unwrap();
|
||||
|
||||
let mut file = File::create(out_dir.join("irrt-filtered.ll")).unwrap();
|
||||
file.write_all(filtered_output.as_bytes()).unwrap();
|
||||
}
|
||||
|
||||
let mut llvm_as = Command::new("llvm-as-irrt")
|
||||
// Assemble the emitted and filtered IR to .bc
|
||||
// That .bc will be integrated into nac3core's codegen
|
||||
let mut llvm_as = Command::new(CMD_IRRT_LLVM_AS)
|
||||
.stdin(Stdio::piped())
|
||||
.arg("-o")
|
||||
.arg(out_path.join("irrt.bc"))
|
||||
.arg(out_dir.join("irrt.bc"))
|
||||
.spawn()
|
||||
.unwrap();
|
||||
llvm_as.stdin.as_mut().unwrap().write_all(filtered_output.as_bytes()).unwrap();
|
||||
assert!(llvm_as.wait().unwrap().success());
|
||||
}
|
||||
|
||||
/// Compile `irrt_test.cpp` for testing
|
||||
fn compile_irrt_test_cpp() {
|
||||
let out_dir = get_out_dir();
|
||||
let irrt_dir = get_irrt_dir();
|
||||
|
||||
let exe_path = out_dir.join("irrt_test.out"); // Output path of the compiled test executable
|
||||
let irrt_test_cpp_path = irrt_dir.join("irrt_test.cpp");
|
||||
let flags: &[&str] = &[
|
||||
irrt_test_cpp_path.to_str().unwrap(),
|
||||
"-x",
|
||||
"c++",
|
||||
"-I",
|
||||
irrt_dir.to_str().unwrap(),
|
||||
"-g",
|
||||
"-fno-discard-value-names",
|
||||
"-O0",
|
||||
"-Wall",
|
||||
"-Wextra",
|
||||
"-Werror=return-type",
|
||||
"-lm", // for `tgamma()`, `lgamma()`
|
||||
"-o",
|
||||
exe_path.to_str().unwrap(),
|
||||
];
|
||||
|
||||
Command::new(CMD_IRRT_CLANG_TEST)
|
||||
.args(flags)
|
||||
.output()
|
||||
.map(|o| {
|
||||
assert!(o.status.success(), "{}", std::str::from_utf8(&o.stderr).unwrap());
|
||||
o
|
||||
})
|
||||
.unwrap();
|
||||
println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
|
||||
}
|
||||
|
||||
fn main() {
|
||||
compile_irrt_cpp();
|
||||
|
||||
// https://github.com/rust-lang/cargo/issues/2549
|
||||
// `cargo test -F test` to also build `irrt_test.cpp
|
||||
if cfg!(feature = "test") {
|
||||
compile_irrt_test_cpp();
|
||||
}
|
||||
}
|
||||
|
|
|
@ -0,0 +1,10 @@
|
|||
#define IRRT_DEFINE_TYPEDEF_INTS
|
||||
#include <irrt_everything.hpp>
|
||||
|
||||
/*
|
||||
* All IRRT implementations.
|
||||
*
|
||||
* We don't have pre-compiled objects, so we are writing all implementations in
|
||||
* headers and concatenate them with `#include` into one massive source file that
|
||||
* contains all the IRRT stuff.
|
||||
*/
|
|
@ -1,27 +1,17 @@
|
|||
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);
|
||||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/util.hpp>
|
||||
|
||||
// NDArray indices are always `uint32_t`.
|
||||
using NDIndex = uint32_t;
|
||||
// The type of an index or a value describing the length of a range/slice is always `int32_t`.
|
||||
using NDIndexInt = uint32_t;
|
||||
// The type of an index or a value describing the length of a
|
||||
// range/slice is always `int32_t`.
|
||||
using SliceIndex = int32_t;
|
||||
|
||||
namespace {
|
||||
template <typename T>
|
||||
const T& max(const T& a, const T& b) {
|
||||
return a > b ? a : b;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
const T& min(const T& a, const T& b) {
|
||||
return a > b ? b : a;
|
||||
}
|
||||
|
||||
// adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
|
||||
// adapted from GNU Scientific Library:
|
||||
// https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
|
||||
// need to make sure `exp >= 0` before calling this function
|
||||
template <typename T>
|
||||
T __nac3_int_exp_impl(T base, T exp) {
|
||||
|
@ -38,12 +28,8 @@ T __nac3_int_exp_impl(T base, T exp) {
|
|||
}
|
||||
|
||||
template <typename SizeT>
|
||||
SizeT __nac3_ndarray_calc_size_impl(
|
||||
const SizeT* list_data,
|
||||
SizeT list_len,
|
||||
SizeT begin_idx,
|
||||
SizeT end_idx
|
||||
) {
|
||||
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;
|
||||
|
@ -56,12 +42,8 @@ SizeT __nac3_ndarray_calc_size_impl(
|
|||
}
|
||||
|
||||
template <typename SizeT>
|
||||
void __nac3_ndarray_calc_nd_indices_impl(
|
||||
SizeT index,
|
||||
const SizeT* dims,
|
||||
SizeT num_dims,
|
||||
NDIndex* idxs
|
||||
) {
|
||||
void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims,
|
||||
SizeT num_dims, NDIndexInt* idxs) {
|
||||
SizeT stride = 1;
|
||||
for (SizeT dim = 0; dim < num_dims; dim++) {
|
||||
SizeT i = num_dims - dim - 1;
|
||||
|
@ -72,12 +54,9 @@ void __nac3_ndarray_calc_nd_indices_impl(
|
|||
}
|
||||
|
||||
template <typename SizeT>
|
||||
SizeT __nac3_ndarray_flatten_index_impl(
|
||||
const SizeT* dims,
|
||||
SizeT num_dims,
|
||||
const NDIndex* indices,
|
||||
SizeT num_indices
|
||||
) {
|
||||
SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims,
|
||||
const NDIndexInt* indices,
|
||||
SizeT num_indices) {
|
||||
SizeT idx = 0;
|
||||
SizeT stride = 1;
|
||||
for (SizeT i = 0; i < num_dims; ++i) {
|
||||
|
@ -93,18 +72,17 @@ SizeT __nac3_ndarray_flatten_index_impl(
|
|||
}
|
||||
|
||||
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
|
||||
) {
|
||||
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;
|
||||
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) {
|
||||
|
@ -124,12 +102,10 @@ void __nac3_ndarray_calc_broadcast_impl(
|
|||
}
|
||||
|
||||
template <typename SizeT>
|
||||
void __nac3_ndarray_calc_broadcast_idx_impl(
|
||||
const SizeT* src_dims,
|
||||
void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
|
||||
SizeT src_ndims,
|
||||
const NDIndex* in_idx,
|
||||
NDIndex* out_idx
|
||||
) {
|
||||
const NDIndexInt* in_idx,
|
||||
NDIndexInt* out_idx) {
|
||||
for (SizeT i = 0; i < src_ndims; ++i) {
|
||||
SizeT src_i = src_ndims - i - 1;
|
||||
out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
|
||||
|
@ -143,10 +119,10 @@ extern "C" {
|
|||
return __nac3_int_exp_impl(base, exp); \
|
||||
}
|
||||
|
||||
DEF_nac3_int_exp_(int32_t)
|
||||
DEF_nac3_int_exp_(int64_t)
|
||||
DEF_nac3_int_exp_(uint32_t)
|
||||
DEF_nac3_int_exp_(uint64_t)
|
||||
DEF_nac3_int_exp_(int32_t);
|
||||
DEF_nac3_int_exp_(int64_t);
|
||||
DEF_nac3_int_exp_(uint32_t);
|
||||
DEF_nac3_int_exp_(uint64_t);
|
||||
|
||||
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
|
||||
if (i < 0) {
|
||||
|
@ -160,11 +136,8 @@ 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 __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;
|
||||
|
@ -180,62 +153,52 @@ SliceIndex __nac3_range_slice_len(
|
|||
// - All the index must *not* be out-of-bound or negative,
|
||||
// - The end index is *inclusive*,
|
||||
// - The length of src and dest slice size should already
|
||||
// be checked: if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest)
|
||||
// be checked: if dest.step == 1 then len(src) <= len(dest) else
|
||||
// len(src) == len(dest)
|
||||
SliceIndex __nac3_list_slice_assign_var_size(
|
||||
SliceIndex dest_start,
|
||||
SliceIndex dest_end,
|
||||
SliceIndex dest_step,
|
||||
uint8_t* dest_arr,
|
||||
SliceIndex dest_arr_len,
|
||||
SliceIndex src_start,
|
||||
SliceIndex src_end,
|
||||
SliceIndex src_step,
|
||||
uint8_t* src_arr,
|
||||
SliceIndex src_arr_len,
|
||||
const SliceIndex size
|
||||
) {
|
||||
/* if dest_arr_len == 0, do nothing since we do not support extending list */
|
||||
SliceIndex dest_start, SliceIndex dest_end, SliceIndex dest_step,
|
||||
uint8_t* dest_arr, SliceIndex dest_arr_len, SliceIndex src_start,
|
||||
SliceIndex src_end, SliceIndex src_step, uint8_t* src_arr,
|
||||
SliceIndex src_arr_len, const SliceIndex size) {
|
||||
/* if dest_arr_len == 0, do nothing since we do not support
|
||||
* extending list
|
||||
*/
|
||||
if (dest_arr_len == 0) return dest_arr_len;
|
||||
/* if both step is 1, memmove directly, handle the dropping of the list, and shrink size */
|
||||
/* if both step is 1, memmove directly, handle the dropping of
|
||||
* the list, and shrink size */
|
||||
if (src_step == dest_step && dest_step == 1) {
|
||||
const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
|
||||
const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
|
||||
const SliceIndex src_len =
|
||||
(src_end >= src_start) ? (src_end - src_start + 1) : 0;
|
||||
const SliceIndex dest_len =
|
||||
(dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
|
||||
if (src_len > 0) {
|
||||
__builtin_memmove(
|
||||
dest_arr + dest_start * size,
|
||||
src_arr + src_start * size,
|
||||
src_len * size
|
||||
);
|
||||
__builtin_memmove(dest_arr + dest_start * size,
|
||||
src_arr + src_start * size, src_len * size);
|
||||
}
|
||||
if (dest_len > 0) {
|
||||
/* dropping */
|
||||
__builtin_memmove(
|
||||
dest_arr + (dest_start + src_len) * size,
|
||||
__builtin_memmove(dest_arr + (dest_start + src_len) * size,
|
||||
dest_arr + (dest_end + 1) * size,
|
||||
(dest_arr_len - dest_end - 1) * size
|
||||
);
|
||||
(dest_arr_len - dest_end - 1) * size);
|
||||
}
|
||||
/* shrink size */
|
||||
return dest_arr_len - (dest_len - src_len);
|
||||
}
|
||||
/* if two range overlaps, need alloca */
|
||||
uint8_t need_alloca =
|
||||
(dest_arr == src_arr)
|
||||
&& !(
|
||||
max(dest_start, dest_end) < min(src_start, src_end)
|
||||
|| max(src_start, src_end) < min(dest_start, dest_end)
|
||||
);
|
||||
(dest_arr == src_arr) &&
|
||||
!(max(dest_start, dest_end) < min(src_start, src_end) ||
|
||||
max(src_start, src_end) < min(dest_start, dest_end));
|
||||
if (need_alloca) {
|
||||
uint8_t* tmp = reinterpret_cast<uint8_t *>(__builtin_alloca(src_arr_len * size));
|
||||
uint8_t* tmp =
|
||||
reinterpret_cast<uint8_t*>(__builtin_alloca(src_arr_len * size));
|
||||
__builtin_memcpy(tmp, src_arr, src_arr_len * size);
|
||||
src_arr = tmp;
|
||||
}
|
||||
SliceIndex src_ind = src_start;
|
||||
SliceIndex dest_ind = dest_start;
|
||||
for (;
|
||||
(src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end);
|
||||
src_ind += src_step, dest_ind += dest_step
|
||||
) {
|
||||
for (; (src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end);
|
||||
src_ind += src_step, dest_ind += dest_step) {
|
||||
/* for constant optimization */
|
||||
if (size == 1) {
|
||||
__builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
|
||||
|
@ -244,30 +207,26 @@ SliceIndex __nac3_list_slice_assign_var_size(
|
|||
} else if (size == 8) {
|
||||
__builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
|
||||
} else {
|
||||
/* memcpy for var size, cannot overlap after previous alloca */
|
||||
__builtin_memcpy(dest_arr + dest_ind * size, src_arr + src_ind * size, size);
|
||||
/* memcpy for var size, cannot overlap after previous
|
||||
* alloca */
|
||||
__builtin_memcpy(dest_arr + dest_ind * size,
|
||||
src_arr + src_ind * size, size);
|
||||
}
|
||||
}
|
||||
/* only dest_step == 1 can we shrink the dest list. */
|
||||
/* size should be ensured prior to calling this function */
|
||||
if (dest_step == 1 && dest_end >= dest_start) {
|
||||
__builtin_memmove(
|
||||
dest_arr + dest_ind * size,
|
||||
dest_arr + (dest_end + 1) * size,
|
||||
(dest_arr_len - dest_end - 1) * size
|
||||
);
|
||||
dest_arr + dest_ind * size, dest_arr + (dest_end + 1) * size,
|
||||
(dest_arr_len - dest_end - 1) * size + size + size + size);
|
||||
return dest_arr_len - (dest_end - dest_ind) - 1;
|
||||
}
|
||||
return dest_arr_len;
|
||||
}
|
||||
|
||||
int32_t __nac3_isinf(double x) {
|
||||
return __builtin_isinf(x);
|
||||
}
|
||||
int32_t __nac3_isinf(double x) { return __builtin_isinf(x); }
|
||||
|
||||
int32_t __nac3_isnan(double x) {
|
||||
return __builtin_isnan(x);
|
||||
}
|
||||
int32_t __nac3_isnan(double x) { return __builtin_isnan(x); }
|
||||
|
||||
double tgamma(double arg);
|
||||
|
||||
|
@ -320,95 +279,71 @@ double __nac3_j0(double x) {
|
|||
return j0(x);
|
||||
}
|
||||
|
||||
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);
|
||||
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);
|
||||
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
|
||||
) {
|
||||
void __nac3_ndarray_calc_nd_indices(uint32_t index, const uint32_t* dims,
|
||||
uint32_t num_dims, NDIndexInt* idxs) {
|
||||
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_nd_indices64(
|
||||
uint64_t index,
|
||||
const uint64_t* dims,
|
||||
uint64_t num_dims,
|
||||
NDIndex* idxs
|
||||
) {
|
||||
void __nac3_ndarray_calc_nd_indices64(uint64_t index, const uint64_t* dims,
|
||||
uint64_t num_dims, NDIndexInt* idxs) {
|
||||
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
|
||||
}
|
||||
|
||||
uint32_t __nac3_ndarray_flatten_index(
|
||||
const uint32_t* dims,
|
||||
uint32_t num_dims,
|
||||
const NDIndex* indices,
|
||||
uint32_t num_indices
|
||||
) {
|
||||
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
|
||||
uint32_t __nac3_ndarray_flatten_index(const uint32_t* dims, uint32_t num_dims,
|
||||
const NDIndexInt* indices,
|
||||
uint32_t num_indices) {
|
||||
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices,
|
||||
num_indices);
|
||||
}
|
||||
|
||||
uint64_t __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);
|
||||
uint64_t __nac3_ndarray_flatten_index64(const uint64_t* dims, uint64_t num_dims,
|
||||
const NDIndexInt* 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_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,
|
||||
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);
|
||||
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,
|
||||
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);
|
||||
const NDIndexInt* in_idx,
|
||||
NDIndexInt* out_idx) {
|
||||
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
|
||||
out_idx);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast_idx64(
|
||||
const uint64_t* src_dims,
|
||||
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);
|
||||
const NDIndexInt* in_idx,
|
||||
NDIndexInt* out_idx) {
|
||||
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
|
||||
out_idx);
|
||||
}
|
||||
} // extern "C"
|
|
@ -0,0 +1,9 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
|
||||
template <typename SizeT>
|
||||
struct CSlice {
|
||||
uint8_t* base;
|
||||
SizeT len;
|
||||
};
|
|
@ -0,0 +1,123 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/cslice.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/util.hpp>
|
||||
|
||||
/**
|
||||
* @brief The int type of ARTIQ exception IDs.
|
||||
*
|
||||
* It is always `int32_t`
|
||||
*/
|
||||
typedef int32_t ExceptionId;
|
||||
|
||||
/*
|
||||
* A set of exceptions IRRT can use.
|
||||
* Must be synchronized with `setup_irrt_exceptions` in `nac3core/src/codegen/irrt/mod.rs`.
|
||||
* All exception IDs are initialized by `setup_irrt_exceptions`.
|
||||
*/
|
||||
#ifdef IRRT_TESTING
|
||||
// If we are doing IRRT tests (i.e., running `cargo test -F test`), define them with a fake set of IDs.
|
||||
ExceptionId EXN_INDEX_ERROR = 0;
|
||||
ExceptionId EXN_VALUE_ERROR = 1;
|
||||
ExceptionId EXN_ASSERTION_ERROR = 2;
|
||||
ExceptionId EXN_RUNTIME_ERROR = 3;
|
||||
ExceptionId EXN_TYPE_ERROR = 4;
|
||||
#else
|
||||
extern "C" {
|
||||
ExceptionId EXN_INDEX_ERROR;
|
||||
ExceptionId EXN_VALUE_ERROR;
|
||||
ExceptionId EXN_ASSERTION_ERROR;
|
||||
ExceptionId EXN_RUNTIME_ERROR;
|
||||
ExceptionId EXN_TYPE_ERROR;
|
||||
}
|
||||
#endif
|
||||
|
||||
namespace {
|
||||
/**
|
||||
* @brief NAC3's Exception struct
|
||||
*/
|
||||
template <typename SizeT>
|
||||
struct Exception {
|
||||
ExceptionId id;
|
||||
CSlice<SizeT> filename;
|
||||
int32_t line;
|
||||
int32_t column;
|
||||
CSlice<SizeT> function;
|
||||
CSlice<SizeT> msg;
|
||||
int64_t params[3];
|
||||
};
|
||||
} // namespace
|
||||
|
||||
// Declare/Define `__nac3_raise`
|
||||
#ifdef IRRT_TESTING
|
||||
#include <cstdio>
|
||||
void __nac3_raise(void* err) {
|
||||
// TODO: Print the error content?
|
||||
printf("__nac3_raise called. Exiting...\n");
|
||||
exit(1);
|
||||
}
|
||||
#else
|
||||
/**
|
||||
* @brief Extern function to `__nac3_raise`
|
||||
*
|
||||
* The parameter `err` could be `Exception<int32_t>` or `Exception<int64_t>`. The caller
|
||||
* must make sure to pass `Exception`s with the correct `SizeT` depending on the `size_t` of the runtime.
|
||||
*/
|
||||
extern "C" void __nac3_raise(void* err);
|
||||
#endif
|
||||
|
||||
namespace {
|
||||
const int64_t NO_PARAM = 0;
|
||||
|
||||
// Helper function to raise an exception with `__nac3_raise`
|
||||
// Do not use this function directly. See `raise_exception`.
|
||||
template <typename SizeT>
|
||||
void _raise_exception_helper(ExceptionId id, const char* filename, int32_t line,
|
||||
const char* function, const char* msg,
|
||||
int64_t param0, int64_t param1, int64_t param2) {
|
||||
Exception<SizeT> e = {
|
||||
.id = id,
|
||||
.filename = {.base = (uint8_t*)filename,
|
||||
.len = (int32_t)cstr_utils::length(filename)},
|
||||
.line = line,
|
||||
.column = 0,
|
||||
.function = {.base = (uint8_t*)function,
|
||||
.len = (int32_t)cstr_utils::length(function)},
|
||||
.msg = {.base = (uint8_t*)msg, .len = (int32_t)cstr_utils::length(msg)},
|
||||
};
|
||||
e.params[0] = param0;
|
||||
e.params[1] = param1;
|
||||
e.params[2] = param2;
|
||||
__nac3_raise((void*)&e);
|
||||
__builtin_unreachable();
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Raise an exception with location details (location in the IRRT source files).
|
||||
* @param SizeT The runtime `size_t` type.
|
||||
* @param id The ID of the exception to raise.
|
||||
* @param msg A global constant C-string of the error message.
|
||||
*
|
||||
* `param0` and `param2` are optional format arguments of `msg`. They should be set to
|
||||
* `NO_PARAM` to indicate they are unused.
|
||||
*/
|
||||
#define raise_exception(SizeT, id, msg, param0, param1, param2) \
|
||||
_raise_exception_helper<SizeT>(id, __FILE__, __LINE__, __FUNCTION__, msg, \
|
||||
param0, param1, param2)
|
||||
|
||||
/**
|
||||
* @brief Throw a dummy error for testing.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void throw_dummy_error() {
|
||||
raise_exception(SizeT, EXN_RUNTIME_ERROR, "dummy error", NO_PARAM, NO_PARAM,
|
||||
NO_PARAM);
|
||||
}
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
void __nac3_throw_dummy_error() { throw_dummy_error<int32_t>(); }
|
||||
|
||||
void __nac3_throw_dummy_error64() { throw_dummy_error<int64_t>(); }
|
||||
}
|
|
@ -0,0 +1,12 @@
|
|||
#pragma once
|
||||
|
||||
// This is made toggleable since `irrt_test.cpp` itself would include
|
||||
// headers that define these typedefs
|
||||
#ifdef IRRT_DEFINE_TYPEDEF_INTS
|
||||
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);
|
||||
#endif
|
|
@ -0,0 +1,297 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace basic {
|
||||
namespace util {
|
||||
/**
|
||||
* @brief Asserts 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 Returns 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;
|
||||
}
|
||||
}
|
||||
} // namespace util
|
||||
|
||||
/**
|
||||
* @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 util::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 returned result
|
||||
*/
|
||||
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) {
|
||||
// Other 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`.
|
||||
*/
|
||||
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;
|
||||
}
|
||||
|
||||
int counter = 0;
|
||||
|
||||
/**
|
||||
* @brief Return the pointer to the nth (0-based) element in a flattened view of `ndarray`.
|
||||
*
|
||||
* 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`
|
||||
* and assuming that the ndarray is fully c-contagious.
|
||||
*
|
||||
* 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++) {
|
||||
int 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
|
||||
|
||||
__builtin_assume(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) {
|
||||
util::assert_shape_no_negative(ndims, shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_util_assert_shape_no_negative64(int64_t ndims,
|
||||
int64_t* shape) {
|
||||
util::assert_shape_no_negative(ndims, 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);
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
|
@ -0,0 +1,157 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.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 {
|
||||
namespace util {
|
||||
/**
|
||||
* @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`
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void broadcast_shapes(SizeT num_shapes, const ShapeEntry<SizeT>* shapes,
|
||||
SizeT dst_ndims, SizeT* 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.
|
||||
// `dst_shape` must be pre-allocated.
|
||||
// `dst_shape` does not have to be initialized
|
||||
for (SizeT dst_axis = 0; dst_axis < dst_ndims; dst_axis++) {
|
||||
dst_shape[dst_axis] = 1;
|
||||
}
|
||||
|
||||
for (SizeT i = 0; i < num_shapes; i++) {
|
||||
ShapeEntry<SizeT> entry = shapes[i];
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace util
|
||||
|
||||
/**
|
||||
* @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::util::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",
|
||||
dst_ndarray->shape[0], src_ndarray->shape[0], 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) {
|
||||
ndarray::broadcast::util::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) {
|
||||
ndarray::broadcast::util::broadcast_shapes(num_shapes, shapes, dst_ndims,
|
||||
dst_shape);
|
||||
}
|
||||
}
|
|
@ -0,0 +1,44 @@
|
|||
#pragma once
|
||||
|
||||
namespace {
|
||||
/**
|
||||
* @brief The NDArray object
|
||||
*
|
||||
* The official numpy implementations: 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.
|
||||
*
|
||||
* Must be set to `nullptr` to indicate that this NDArray's `data` is uninitialized.
|
||||
*/
|
||||
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.
|
||||
*/
|
||||
SizeT* strides;
|
||||
};
|
||||
} // namespace
|
|
@ -0,0 +1,182 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
|
||||
namespace {
|
||||
typedef uint8_t NDIndexType;
|
||||
|
||||
/**
|
||||
* @brief A single element index
|
||||
*
|
||||
* See https://numpy.org/doc/stable/user/basics.indexing.html#single-element-indexing
|
||||
*
|
||||
* `data` points to a `SliceIndex`.
|
||||
*/
|
||||
const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
|
||||
/**
|
||||
* @brief A slice index
|
||||
*
|
||||
* See https://numpy.org/doc/stable/user/basics.indexing.html#slicing-and-striding
|
||||
*
|
||||
* `data` points to a `UserRange`.
|
||||
*/
|
||||
const NDIndexType ND_INDEX_TYPE_SLICE = 1;
|
||||
|
||||
/**
|
||||
* @brief An index used in ndarray indexing
|
||||
*/
|
||||
struct NDIndex {
|
||||
/**
|
||||
* @brief Enum tag to specify the type of index.
|
||||
*
|
||||
* Please see comments of each enum constant.
|
||||
*/
|
||||
NDIndexType type;
|
||||
|
||||
/**
|
||||
* @brief The accompanying data associated with `type`.
|
||||
*
|
||||
* Please see comments of each enum constant.
|
||||
*/
|
||||
uint8_t* data;
|
||||
};
|
||||
} // namespace
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace indexing {
|
||||
namespace util {
|
||||
|
||||
/**
|
||||
* @brief Return the expected rank of the resulting ndarray
|
||||
* created by indexing an ndarray of rank `ndims` using `indexes`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
SizeT deduce_ndims_after_indexing(SizeT ndims, SizeT num_indexes,
|
||||
const NDIndex* indexes) {
|
||||
if (num_indexes > ndims) {
|
||||
raise_exception(SizeT, EXN_INDEX_ERROR,
|
||||
"too many indices for array: array is {0}-dimensional, "
|
||||
"but {1} were indexed",
|
||||
ndims, num_indexes, NO_PARAM);
|
||||
}
|
||||
|
||||
for (SizeT i = 0; i < num_indexes; i++) {
|
||||
if (indexes[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
|
||||
// An index demotes the rank by 1
|
||||
ndims--;
|
||||
}
|
||||
}
|
||||
return ndims;
|
||||
}
|
||||
} // namespace util
|
||||
|
||||
/**
|
||||
* @brief Perform ndarray "basic indexing" (https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
|
||||
*
|
||||
* This is function very similar to performing `dst_ndarray = src_ndarray[indexes]` in Python (where the variables
|
||||
* can all be found in the parameter of this function).
|
||||
*
|
||||
* In other words, this function takes in an ndarray (`src_ndarray`), index it with `indexes`, and return the
|
||||
* indexed array (by writing the result to `dst_ndarray`).
|
||||
*
|
||||
* This function also does proper assertions on `indexes`.
|
||||
*
|
||||
* # 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 `indexes`.
|
||||
* - `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 `src_ndarray` is indexed.
|
||||
* - `dst_ndarray->strides` is updated accordingly by how ndarray indexing works.
|
||||
*
|
||||
* @param indexes Indexes 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_indexes, const NDIndex* indexes,
|
||||
const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
|
||||
// Reference code: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
|
||||
|
||||
SizeT expected_dst_ndarray_ndims = util::deduce_ndims_after_indexing(
|
||||
src_ndarray->ndims, num_indexes, indexes);
|
||||
|
||||
dst_ndarray->data = src_ndarray->data;
|
||||
dst_ndarray->itemsize = src_ndarray->itemsize;
|
||||
|
||||
SizeT src_axis = 0;
|
||||
SizeT dst_axis = 0;
|
||||
|
||||
for (SliceIndex i = 0; i < num_indexes; i++) {
|
||||
const NDIndex* index = &indexes[i];
|
||||
if (index->type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
|
||||
SliceIndex input = *((SliceIndex*)index->data);
|
||||
SliceIndex k = slice::resolve_index_in_length(
|
||||
src_ndarray->shape[src_axis], input);
|
||||
|
||||
if (k == slice::OUT_OF_BOUNDS) {
|
||||
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) {
|
||||
UserSlice* input = (UserSlice*)index->data;
|
||||
|
||||
Slice slice;
|
||||
input->indices_checked<SizeT>(src_ndarray->shape[src_axis], &slice);
|
||||
|
||||
dst_ndarray->data +=
|
||||
(SizeT)slice.start * src_ndarray->strides[src_axis];
|
||||
dst_ndarray->strides[dst_axis] =
|
||||
((SizeT)slice.step) * src_ndarray->strides[src_axis];
|
||||
dst_ndarray->shape[dst_axis] = (SizeT)slice.len();
|
||||
|
||||
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];
|
||||
}
|
||||
}
|
||||
} // namespace indexing
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::indexing;
|
||||
|
||||
void __nac3_ndarray_index(int32_t num_indexes, NDIndex* indexes,
|
||||
NDArray<int32_t>* src_ndarray,
|
||||
NDArray<int32_t>* dst_ndarray) {
|
||||
index(num_indexes, indexes, src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_index64(int64_t num_indexes, NDIndex* indexes,
|
||||
NDArray<int64_t>* src_ndarray,
|
||||
NDArray<int64_t>* dst_ndarray) {
|
||||
index(num_indexes, indexes, src_ndarray, dst_ndarray);
|
||||
}
|
||||
}
|
|
@ -0,0 +1,111 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace reshape {
|
||||
namespace util {
|
||||
|
||||
/**
|
||||
* @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 util
|
||||
} // namespace reshape
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
void __nac3_ndarray_resolve_and_check_new_shape(int32_t size, int32_t new_ndims,
|
||||
int32_t* new_shape) {
|
||||
ndarray::reshape::util::resolve_and_check_new_shape(size, new_ndims,
|
||||
new_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_resolve_and_check_new_shape64(int64_t size,
|
||||
int64_t new_ndims,
|
||||
int64_t* new_shape) {
|
||||
ndarray::reshape::util::resolve_and_check_new_shape(size, new_ndims,
|
||||
new_shape);
|
||||
}
|
||||
}
|
|
@ -0,0 +1,148 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.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 {
|
||||
namespace util {
|
||||
|
||||
/**
|
||||
* @brief Do assertions on `<axes>` in `np.transpose(<array>, <axes>)`.
|
||||
*
|
||||
* Note that `np.transpose`'s `<axe>` argument is optional. If the argument
|
||||
* is specified but the user, use this function to do assertions on it.
|
||||
*
|
||||
* @param ndims The number of dimensions of `<array>`
|
||||
* @param num_axes Number of elements in `<axes>` as specified by the user.
|
||||
* This should be equal to `ndims`. If not, a "ValueError: axes don't match array" is thrown.
|
||||
* @param axes The user specified `<axes>`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void assert_transpose_axes(SizeT ndims, SizeT num_axes, const SizeT* axes) {
|
||||
if (ndims != num_axes) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "axes don't match array",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
// TODO: Optimize this
|
||||
bool* axe_specified = (bool*)__builtin_alloca(sizeof(bool) * ndims);
|
||||
for (SizeT i = 0; i < ndims; i++) axe_specified[i] = false;
|
||||
|
||||
for (SizeT i = 0; i < ndims; i++) {
|
||||
SizeT axis = slice::resolve_index_in_length(ndims, axes[i]);
|
||||
if (axis == slice::OUT_OF_BOUNDS) {
|
||||
// TODO: numpy actually throws a `numpy.exceptions.AxisError`
|
||||
raise_exception(
|
||||
SizeT, EXN_VALUE_ERROR,
|
||||
"axis {0} is out of bounds for array of dimension {1}", axis,
|
||||
ndims, NO_PARAM);
|
||||
}
|
||||
|
||||
if (axe_specified[axis]) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"repeated axis in transpose", NO_PARAM, NO_PARAM,
|
||||
NO_PARAM);
|
||||
}
|
||||
|
||||
axe_specified[axis] = true;
|
||||
}
|
||||
}
|
||||
} // namespace util
|
||||
|
||||
/**
|
||||
* @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, can be undefined 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) {
|
||||
__builtin_assume(src_ndarray->ndims == dst_ndarray->ndims);
|
||||
const auto ndims = src_ndarray->ndims;
|
||||
|
||||
if (axes != nullptr) util::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);
|
||||
}
|
||||
}
|
|
@ -0,0 +1,165 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
#include <irrt/util.hpp>
|
||||
|
||||
#include "exception.hpp"
|
||||
|
||||
// The type of an index or a value describing the length of a
|
||||
// range/slice is always `int32_t`.
|
||||
using SliceIndex = int32_t;
|
||||
|
||||
namespace {
|
||||
|
||||
/**
|
||||
* @brief A Python-like slice with resolved indices.
|
||||
*
|
||||
* "Resolved indices" means that `start` and `stop` must be positive and are
|
||||
* bound to a known length.
|
||||
*/
|
||||
struct Slice {
|
||||
SliceIndex start;
|
||||
SliceIndex stop;
|
||||
SliceIndex step;
|
||||
|
||||
/**
|
||||
* @brief Calculate and return the length / the number of the slice.
|
||||
*
|
||||
* If this were a Python range, this function would be `len(range(start, stop, step))`.
|
||||
*/
|
||||
SliceIndex len() {
|
||||
SliceIndex diff = stop - start;
|
||||
if (diff > 0 && step > 0) {
|
||||
return ((diff - 1) / step) + 1;
|
||||
} else if (diff < 0 && step < 0) {
|
||||
return ((diff + 1) / step) + 1;
|
||||
} else {
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
namespace slice {
|
||||
/**
|
||||
* @brief Resolve a slice index under a given length like Python indexing.
|
||||
*
|
||||
* In Python, if you have a `list` of length 100, `list[-1]` resolves to
|
||||
* `list[99]`, so `resolve_index_in_length_clamped(100, -1)` returns `99`.
|
||||
*
|
||||
* If `length` is 0, 0 is returned for any value of `index`.
|
||||
*
|
||||
* If `index` is out of bounds, clamps the returned value between `0` and
|
||||
* `length - 1` (inclusive).
|
||||
*
|
||||
*/
|
||||
SliceIndex resolve_index_in_length_clamped(SliceIndex length,
|
||||
SliceIndex index) {
|
||||
if (index < 0) {
|
||||
return max<SliceIndex>(length + index, 0);
|
||||
} else {
|
||||
return min<SliceIndex>(length, index);
|
||||
}
|
||||
}
|
||||
|
||||
const SliceIndex OUT_OF_BOUNDS = -1;
|
||||
|
||||
/**
|
||||
* @brief Like `resolve_index_in_length_clamped`, but returns `OUT_OF_BOUNDS`
|
||||
* if `index` is out of bounds.
|
||||
*/
|
||||
SliceIndex resolve_index_in_length(SliceIndex length, SliceIndex index) {
|
||||
SliceIndex resolved = index < 0 ? length + index : index;
|
||||
if (0 <= resolved && resolved < length) {
|
||||
return resolved;
|
||||
} else {
|
||||
return OUT_OF_BOUNDS;
|
||||
}
|
||||
}
|
||||
} // namespace slice
|
||||
|
||||
/**
|
||||
* @brief A Python-like slice with **unresolved** indices.
|
||||
*/
|
||||
struct UserSlice {
|
||||
bool start_defined;
|
||||
SliceIndex start;
|
||||
|
||||
bool stop_defined;
|
||||
SliceIndex stop;
|
||||
|
||||
bool step_defined;
|
||||
SliceIndex step;
|
||||
|
||||
UserSlice() { this->reset(); }
|
||||
|
||||
void reset() {
|
||||
this->start_defined = false;
|
||||
this->stop_defined = false;
|
||||
this->step_defined = false;
|
||||
}
|
||||
|
||||
void set_start(SliceIndex start) {
|
||||
this->start_defined = true;
|
||||
this->start = start;
|
||||
}
|
||||
|
||||
void set_stop(SliceIndex stop) {
|
||||
this->stop_defined = true;
|
||||
this->stop = stop;
|
||||
}
|
||||
|
||||
void set_step(SliceIndex step) {
|
||||
this->step_defined = true;
|
||||
this->step = step;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Resolve this slice.
|
||||
*
|
||||
* In Python, this would be `slice(start, stop, step).indices(length)`.
|
||||
*
|
||||
* @return A `Slice` with the resolved indices.
|
||||
*/
|
||||
Slice indices(SliceIndex length) {
|
||||
Slice result;
|
||||
|
||||
result.step = step_defined ? step : 1;
|
||||
bool step_is_negative = result.step < 0;
|
||||
|
||||
if (start_defined) {
|
||||
result.start =
|
||||
slice::resolve_index_in_length_clamped(length, start);
|
||||
} else {
|
||||
result.start = step_is_negative ? length - 1 : 0;
|
||||
}
|
||||
|
||||
if (stop_defined) {
|
||||
result.stop = slice::resolve_index_in_length_clamped(length, stop);
|
||||
} else {
|
||||
result.stop = step_is_negative ? -1 : length;
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Like `.indices()` but with assertions.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void indices_checked(SliceIndex length, Slice* result) {
|
||||
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);
|
||||
}
|
||||
|
||||
*result = this->indices(length);
|
||||
}
|
||||
};
|
||||
} // namespace
|
|
@ -0,0 +1,101 @@
|
|||
#pragma once
|
||||
|
||||
namespace {
|
||||
template <typename T>
|
||||
const T& max(const T& a, const T& b) {
|
||||
return a > b ? a : b;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
const T& min(const T& a, const T& b) {
|
||||
return a > b ? b : a;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
bool arrays_match(int len, T* as, T* bs) {
|
||||
for (int i = 0; i < len; i++) {
|
||||
if (as[i] != bs[i]) return false;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
namespace cstr_utils {
|
||||
/**
|
||||
* @brief Return true if `str` is empty.
|
||||
*/
|
||||
bool is_empty(const char* str) { return str[0] == '\0'; }
|
||||
|
||||
/**
|
||||
* @brief Implementation of `strcmp()`
|
||||
*/
|
||||
int8_t compare(const char* a, const char* b) {
|
||||
uint32_t i = 0;
|
||||
while (true) {
|
||||
if (a[i] < b[i]) {
|
||||
return -1;
|
||||
} else if (a[i] > b[i]) {
|
||||
return 1;
|
||||
} else {
|
||||
if (a[i] == '\0') {
|
||||
return 0;
|
||||
} else {
|
||||
i++;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return true two strings have the same content.
|
||||
*/
|
||||
int8_t equal(const char* a, const char* b) { return compare(a, b) == 0; }
|
||||
|
||||
/**
|
||||
* @brief Implementation of `strlen()`.
|
||||
*/
|
||||
uint32_t length(const char* str) {
|
||||
uint32_t length = 0;
|
||||
while (*str != '\0') {
|
||||
length++;
|
||||
str++;
|
||||
}
|
||||
return length;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Copy a null-terminated string to a buffer with limited size and guaranteed null-termination.
|
||||
*
|
||||
* `dst_max_size` must be greater than 0, otherwise this function has undefined behavior.
|
||||
*
|
||||
* This function attempts to copy everything from `src` from `dst`, and *always* null-terminates `dst`.
|
||||
*
|
||||
* If the size of `dst` is too small, the final byte (`dst[dst_max_size - 1]`) of `dst` will be set to
|
||||
* the null terminator.
|
||||
*
|
||||
* @param src String to copy from.
|
||||
* @param dst Buffer to copy string to.
|
||||
* @param dst_max_size
|
||||
* Number of bytes of this buffer, including the space needed for the null terminator.
|
||||
* Must be greater than 0.
|
||||
* @return If `dst` is too small to contain everything in `src`.
|
||||
*/
|
||||
bool copy(const char* src, char* dst, uint32_t dst_max_size) {
|
||||
for (uint32_t i = 0; i < dst_max_size; i++) {
|
||||
bool is_last = i + 1 == dst_max_size;
|
||||
if (is_last && src[i] != '\0') {
|
||||
dst[i] = '\0';
|
||||
return false;
|
||||
}
|
||||
|
||||
if (src[i] == '\0') {
|
||||
dst[i] = '\0';
|
||||
return true;
|
||||
}
|
||||
|
||||
dst[i] = src[i];
|
||||
}
|
||||
|
||||
__builtin_unreachable();
|
||||
}
|
||||
} // namespace cstr_utils
|
||||
} // namespace
|
|
@ -0,0 +1,12 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/core.hpp>
|
||||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/broadcast.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/ndarray/indexing.hpp>
|
||||
#include <irrt/ndarray/reshape.hpp>
|
||||
#include <irrt/ndarray/transpose.hpp>
|
||||
#include <irrt/util.hpp>
|
|
@ -0,0 +1,25 @@
|
|||
// This file will be compiled like a real C++ program,
|
||||
// and we do have the luxury to use the standard libraries.
|
||||
// That is if the nix flakes do not have issues... especially on msys2...
|
||||
|
||||
#include <cstdint>
|
||||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
|
||||
// Special macro to inform `#include <irrt/*>` that we are testing.
|
||||
#define IRRT_TESTING
|
||||
|
||||
// Note that failure unit tests are not supported.
|
||||
|
||||
#include <test/test_core.hpp>
|
||||
#include <test/test_ndarray_basic.hpp>
|
||||
#include <test/test_ndarray_broadcast.hpp>
|
||||
#include <test/test_ndarray_indexing.hpp>
|
||||
|
||||
int main() {
|
||||
test::core::run();
|
||||
test::ndarray_basic::run();
|
||||
test::ndarray_indexing::run();
|
||||
test::ndarray_broadcast::run();
|
||||
return 0;
|
||||
}
|
|
@ -0,0 +1,11 @@
|
|||
#pragma once
|
||||
|
||||
#include <cstdint>
|
||||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
#include <irrt_everything.hpp>
|
||||
#include <test/util.hpp>
|
||||
|
||||
/*
|
||||
Include this header for every test_*.cpp
|
||||
*/
|
|
@ -0,0 +1,16 @@
|
|||
#pragma once
|
||||
|
||||
#include <test/includes.hpp>
|
||||
|
||||
namespace test {
|
||||
namespace core {
|
||||
void test_int_exp() {
|
||||
BEGIN_TEST();
|
||||
|
||||
assert_values_match(125L, __nac3_int_exp_impl<int64_t>(5, 3));
|
||||
assert_values_match(3125L, __nac3_int_exp_impl<int64_t>(5, 5));
|
||||
}
|
||||
|
||||
void run() { test_int_exp(); }
|
||||
} // namespace core
|
||||
} // namespace test
|
|
@ -0,0 +1,30 @@
|
|||
#pragma once
|
||||
|
||||
#include <test/includes.hpp>
|
||||
|
||||
namespace test {
|
||||
namespace ndarray_basic {
|
||||
void test_calc_size_from_shape_normal() {
|
||||
// Test shapes with normal values
|
||||
BEGIN_TEST();
|
||||
|
||||
int64_t shape[4] = {2, 3, 5, 7};
|
||||
assert_values_match(
|
||||
210L, ndarray::basic::util::calc_size_from_shape<int64_t>(4, shape));
|
||||
}
|
||||
|
||||
void test_calc_size_from_shape_has_zero() {
|
||||
// Test shapes with 0 in them
|
||||
BEGIN_TEST();
|
||||
|
||||
int64_t shape[4] = {2, 0, 5, 7};
|
||||
assert_values_match(
|
||||
0L, ndarray::basic::util::calc_size_from_shape<int64_t>(4, shape));
|
||||
}
|
||||
|
||||
void run() {
|
||||
test_calc_size_from_shape_normal();
|
||||
test_calc_size_from_shape_has_zero();
|
||||
}
|
||||
} // namespace ndarray_basic
|
||||
} // namespace test
|
|
@ -0,0 +1,127 @@
|
|||
#pragma once
|
||||
|
||||
#include <test/includes.hpp>
|
||||
|
||||
namespace test {
|
||||
namespace ndarray_broadcast {
|
||||
void test_can_broadcast_shape() {
|
||||
BEGIN_TEST();
|
||||
|
||||
assert_values_match(true,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){3}, 5, (int32_t[]){1, 1, 1, 1, 3}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){3}, 2, (int32_t[]){3, 1}));
|
||||
assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){3}, 1, (int32_t[]){3}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){1}, 1, (int32_t[]){3}));
|
||||
assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){1}, 1, (int32_t[]){1}));
|
||||
assert_values_match(
|
||||
true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
3, (int32_t[]){256, 256, 3}, 3, (int32_t[]){256, 1, 3}));
|
||||
assert_values_match(true,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){3}));
|
||||
assert_values_match(false,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){2}));
|
||||
assert_values_match(true,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){1}));
|
||||
|
||||
// In cases when the shapes contain zero(es)
|
||||
assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){0}, 1, (int32_t[]){1}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){0}, 1, (int32_t[]){2}));
|
||||
assert_values_match(true,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
4, (int32_t[]){0, 4, 0, 0}, 1, (int32_t[]){1}));
|
||||
assert_values_match(
|
||||
true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
4, (int32_t[]){0, 4, 0, 0}, 4, (int32_t[]){1, 1, 1, 1}));
|
||||
assert_values_match(
|
||||
true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
4, (int32_t[]){0, 4, 0, 0}, 4, (int32_t[]){1, 4, 1, 1}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
2, (int32_t[]){4, 3}, 2, (int32_t[]){0, 3}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
2, (int32_t[]){4, 3}, 2, (int32_t[]){0, 0}));
|
||||
}
|
||||
|
||||
void test_ndarray_broadcast() {
|
||||
/*
|
||||
# array = np.array([[19.9, 29.9, 39.9, 49.9]], dtype=np.float64)
|
||||
# >>> [[19.9 29.9 39.9 49.9]]
|
||||
#
|
||||
# array = np.broadcast_to(array, (2, 3, 4))
|
||||
# >>> [[[19.9 29.9 39.9 49.9]
|
||||
# >>> [19.9 29.9 39.9 49.9]
|
||||
# >>> [19.9 29.9 39.9 49.9]]
|
||||
# >>> [[19.9 29.9 39.9 49.9]
|
||||
# >>> [19.9 29.9 39.9 49.9]
|
||||
# >>> [19.9 29.9 39.9 49.9]]]
|
||||
#
|
||||
# assery array.strides == (0, 0, 8)
|
||||
|
||||
*/
|
||||
BEGIN_TEST();
|
||||
|
||||
double in_data[4] = {19.9, 29.9, 39.9, 49.9};
|
||||
const int32_t in_ndims = 2;
|
||||
int32_t in_shape[in_ndims] = {1, 4};
|
||||
int32_t in_strides[in_ndims] = {};
|
||||
NDArray<int32_t> ndarray = {.data = (uint8_t*)in_data,
|
||||
.itemsize = sizeof(double),
|
||||
.ndims = in_ndims,
|
||||
.shape = in_shape,
|
||||
.strides = in_strides};
|
||||
ndarray::basic::set_strides_by_shape(&ndarray);
|
||||
|
||||
const int32_t dst_ndims = 3;
|
||||
int32_t dst_shape[dst_ndims] = {2, 3, 4};
|
||||
int32_t dst_strides[dst_ndims] = {};
|
||||
NDArray<int32_t> dst_ndarray = {
|
||||
.ndims = dst_ndims, .shape = dst_shape, .strides = dst_strides};
|
||||
|
||||
ndarray::broadcast::broadcast_to(&ndarray, &dst_ndarray);
|
||||
|
||||
assert_arrays_match(dst_ndims, ((int32_t[]){0, 0, 8}), dst_ndarray.strides);
|
||||
|
||||
assert_values_match(19.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 0, 0}))));
|
||||
assert_values_match(29.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 0, 1}))));
|
||||
assert_values_match(39.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 0, 2}))));
|
||||
assert_values_match(49.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 0, 3}))));
|
||||
assert_values_match(19.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 1, 0}))));
|
||||
assert_values_match(29.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 1, 1}))));
|
||||
assert_values_match(39.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 1, 2}))));
|
||||
assert_values_match(49.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 1, 3}))));
|
||||
assert_values_match(49.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){1, 2, 3}))));
|
||||
}
|
||||
|
||||
void run() {
|
||||
test_can_broadcast_shape();
|
||||
test_ndarray_broadcast();
|
||||
}
|
||||
} // namespace ndarray_broadcast
|
||||
} // namespace test
|
|
@ -0,0 +1,165 @@
|
|||
#pragma once
|
||||
|
||||
#include <test/includes.hpp>
|
||||
|
||||
namespace test {
|
||||
namespace ndarray_indexing {
|
||||
void test_normal_1() {
|
||||
/*
|
||||
Reference Python code:
|
||||
```python
|
||||
ndarray = np.arange(12, dtype=np.float64).reshape((3, 4));
|
||||
# array([[ 0., 1., 2., 3.],
|
||||
# [ 4., 5., 6., 7.],
|
||||
# [ 8., 9., 10., 11.]])
|
||||
|
||||
dst_ndarray = ndarray[-2:, 1::2]
|
||||
# array([[ 5., 7.],
|
||||
# [ 9., 11.]])
|
||||
|
||||
assert dst_ndarray.shape == (2, 2)
|
||||
assert dst_ndarray.strides == (32, 16)
|
||||
assert dst_ndarray[0, 0] == 5.0
|
||||
assert dst_ndarray[0, 1] == 7.0
|
||||
assert dst_ndarray[1, 0] == 9.0
|
||||
assert dst_ndarray[1, 1] == 11.0
|
||||
```
|
||||
*/
|
||||
BEGIN_TEST();
|
||||
|
||||
// Prepare src_ndarray
|
||||
double src_data[12] = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0,
|
||||
6.0, 7.0, 8.0, 9.0, 10.0, 11.0};
|
||||
int64_t src_itemsize = sizeof(double);
|
||||
const int64_t src_ndims = 2;
|
||||
int64_t src_shape[src_ndims] = {3, 4};
|
||||
int64_t src_strides[src_ndims] = {};
|
||||
NDArray<int64_t> src_ndarray = {.data = (uint8_t *)src_data,
|
||||
.itemsize = src_itemsize,
|
||||
.ndims = src_ndims,
|
||||
.shape = src_shape,
|
||||
.strides = src_strides};
|
||||
ndarray::basic::set_strides_by_shape(&src_ndarray);
|
||||
|
||||
// Prepare dst_ndarray
|
||||
const int64_t dst_ndims = 2;
|
||||
int64_t dst_shape[dst_ndims] = {999, 999}; // Empty values
|
||||
int64_t dst_strides[dst_ndims] = {999, 999}; // Empty values
|
||||
NDArray<int64_t> dst_ndarray = {.data = nullptr,
|
||||
.ndims = dst_ndims,
|
||||
.shape = dst_shape,
|
||||
.strides = dst_strides};
|
||||
|
||||
// Create the subscripts in `ndarray[-2::, 1::2]`
|
||||
UserSlice subscript_1;
|
||||
subscript_1.set_start(-2);
|
||||
|
||||
UserSlice subscript_2;
|
||||
subscript_2.set_start(1);
|
||||
subscript_2.set_step(2);
|
||||
|
||||
const int64_t num_indexes = 2;
|
||||
NDIndex indexes[num_indexes] = {
|
||||
{.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_1},
|
||||
{.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
|
||||
|
||||
ndarray::indexing::index(num_indexes, indexes, &src_ndarray, &dst_ndarray);
|
||||
|
||||
int64_t expected_shape[dst_ndims] = {2, 2};
|
||||
int64_t expected_strides[dst_ndims] = {32, 16};
|
||||
|
||||
assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
|
||||
assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
|
||||
|
||||
// dst_ndarray[0, 0]
|
||||
assert_values_match(5.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){0, 0})));
|
||||
// dst_ndarray[0, 1]
|
||||
assert_values_match(7.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){0, 1})));
|
||||
// dst_ndarray[1, 0]
|
||||
assert_values_match(9.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){1, 0})));
|
||||
// dst_ndarray[1, 1]
|
||||
assert_values_match(11.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){1, 1})));
|
||||
}
|
||||
|
||||
void test_normal_2() {
|
||||
/*
|
||||
```python
|
||||
ndarray = np.arange(12, dtype=np.float64).reshape((3, 4))
|
||||
# array([[ 0., 1., 2., 3.],
|
||||
# [ 4., 5., 6., 7.],
|
||||
# [ 8., 9., 10., 11.]])
|
||||
|
||||
dst_ndarray = ndarray[2, ::-2]
|
||||
# array([11., 9.])
|
||||
|
||||
assert dst_ndarray.shape == (2,)
|
||||
assert dst_ndarray.strides == (-16,)
|
||||
assert dst_ndarray[0] == 11.0
|
||||
assert dst_ndarray[1] == 9.0
|
||||
```
|
||||
*/
|
||||
BEGIN_TEST();
|
||||
|
||||
// Prepare src_ndarray
|
||||
double src_data[12] = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0,
|
||||
6.0, 7.0, 8.0, 9.0, 10.0, 11.0};
|
||||
int64_t src_itemsize = sizeof(double);
|
||||
const int64_t src_ndims = 2;
|
||||
int64_t src_shape[src_ndims] = {3, 4};
|
||||
int64_t src_strides[src_ndims] = {};
|
||||
NDArray<int64_t> src_ndarray = {.data = (uint8_t *)src_data,
|
||||
.itemsize = src_itemsize,
|
||||
.ndims = src_ndims,
|
||||
.shape = src_shape,
|
||||
.strides = src_strides};
|
||||
ndarray::basic::set_strides_by_shape(&src_ndarray);
|
||||
|
||||
// Prepare dst_ndarray
|
||||
const int64_t dst_ndims = 1;
|
||||
int64_t dst_shape[dst_ndims] = {999}; // Empty values
|
||||
int64_t dst_strides[dst_ndims] = {999}; // Empty values
|
||||
NDArray<int64_t> dst_ndarray = {.data = nullptr,
|
||||
.ndims = dst_ndims,
|
||||
.shape = dst_shape,
|
||||
.strides = dst_strides};
|
||||
|
||||
// Create the subscripts in `ndarray[2, ::-2]`
|
||||
int64_t subscript_1 = 2;
|
||||
|
||||
UserSlice subscript_2;
|
||||
subscript_2.set_step(-2);
|
||||
|
||||
const int64_t num_indexes = 2;
|
||||
NDIndex indexes[num_indexes] = {
|
||||
{.type = ND_INDEX_TYPE_SINGLE_ELEMENT, .data = (uint8_t *)&subscript_1},
|
||||
{.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
|
||||
|
||||
ndarray::indexing::index(num_indexes, indexes, &src_ndarray, &dst_ndarray);
|
||||
|
||||
int64_t expected_shape[dst_ndims] = {2};
|
||||
int64_t expected_strides[dst_ndims] = {-16};
|
||||
assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
|
||||
assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
|
||||
|
||||
assert_values_match(11.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){0})));
|
||||
assert_values_match(9.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){1})));
|
||||
}
|
||||
|
||||
void run() {
|
||||
test_normal_1();
|
||||
test_normal_2();
|
||||
}
|
||||
} // namespace ndarray_indexing
|
||||
} // namespace test
|
|
@ -0,0 +1,131 @@
|
|||
#pragma once
|
||||
|
||||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
|
||||
template <class T>
|
||||
void print_value(const T& value);
|
||||
|
||||
template <>
|
||||
void print_value(const bool& value) {
|
||||
printf("%s", value ? "true" : "false");
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const int8_t& value) {
|
||||
printf("%d", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const int32_t& value) {
|
||||
printf("%d", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const int64_t& value) {
|
||||
printf("%d", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const uint8_t& value) {
|
||||
printf("%u", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const uint32_t& value) {
|
||||
printf("%u", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const uint64_t& value) {
|
||||
printf("%d", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const float& value) {
|
||||
printf("%f", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const double& value) {
|
||||
printf("%f", value);
|
||||
}
|
||||
|
||||
void __begin_test(const char* function_name, const char* file, int line) {
|
||||
printf("######### Running %s @ %s:%d\n", function_name, file, line);
|
||||
}
|
||||
|
||||
#define BEGIN_TEST() __begin_test(__FUNCTION__, __FILE__, __LINE__)
|
||||
|
||||
void test_fail() {
|
||||
printf("[!] Test failed. Exiting with status code 1.\n");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void debug_print_array(int len, const T* as) {
|
||||
printf("[");
|
||||
for (int i = 0; i < len; i++) {
|
||||
if (i != 0) printf(", ");
|
||||
print_value(as[i]);
|
||||
}
|
||||
printf("]");
|
||||
}
|
||||
|
||||
void print_assertion_passed(const char* file, int line) {
|
||||
printf("[*] Assertion passed on %s:%d\n", file, line);
|
||||
}
|
||||
|
||||
void print_assertion_failed(const char* file, int line) {
|
||||
printf("[!] Assertion failed on %s:%d\n", file, line);
|
||||
}
|
||||
|
||||
void __assert_true(const char* file, int line, bool cond) {
|
||||
if (cond) {
|
||||
print_assertion_passed(file, line);
|
||||
} else {
|
||||
print_assertion_failed(file, line);
|
||||
test_fail();
|
||||
}
|
||||
}
|
||||
|
||||
#define assert_true(cond) __assert_true(__FILE__, __LINE__, cond)
|
||||
|
||||
template <typename T>
|
||||
void __assert_arrays_match(const char* file, int line, int len,
|
||||
const T* expected, const T* got) {
|
||||
if (arrays_match(len, expected, got)) {
|
||||
print_assertion_passed(file, line);
|
||||
} else {
|
||||
print_assertion_failed(file, line);
|
||||
printf("Expect = ");
|
||||
debug_print_array(len, expected);
|
||||
printf("\n");
|
||||
printf(" Got = ");
|
||||
debug_print_array(len, got);
|
||||
printf("\n");
|
||||
test_fail();
|
||||
}
|
||||
}
|
||||
|
||||
#define assert_arrays_match(len, expected, got) \
|
||||
__assert_arrays_match(__FILE__, __LINE__, len, expected, got)
|
||||
|
||||
template <typename T>
|
||||
void __assert_values_match(const char* file, int line, T expected, T got) {
|
||||
if (expected == got) {
|
||||
print_assertion_passed(file, line);
|
||||
} else {
|
||||
print_assertion_failed(file, line);
|
||||
printf("Expect = ");
|
||||
print_value(expected);
|
||||
printf("\n");
|
||||
printf(" Got = ");
|
||||
print_value(got);
|
||||
printf("\n");
|
||||
test_fail();
|
||||
}
|
||||
}
|
||||
|
||||
#define assert_values_match(expected, got) \
|
||||
__assert_values_match(__FILE__, __LINE__, expected, got)
|
File diff suppressed because it is too large
Load Diff
|
@ -1717,6 +1717,7 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
|
|||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(len, false),
|
||||
|generator, ctx, _, i| {
|
||||
|
|
|
@ -4,7 +4,7 @@ use crate::{
|
|||
codegen::{
|
||||
classes::{
|
||||
ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, NDArrayValue, ProxyType,
|
||||
ProxyValue, RangeValue, TypedArrayLikeAccessor, UntypedArrayLikeAccessor,
|
||||
ProxyValue, RangeValue, UntypedArrayLikeAccessor,
|
||||
},
|
||||
concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
|
||||
gen_in_range_check, get_llvm_abi_type, get_llvm_type,
|
||||
|
@ -18,14 +18,11 @@ use crate::{
|
|||
gen_for_callback_incrementing, gen_if_callback, gen_if_else_expr_callback, gen_raise,
|
||||
gen_var,
|
||||
},
|
||||
structure::ndarray::NDArrayObject,
|
||||
CodeGenContext, CodeGenTask, CodeGenerator,
|
||||
},
|
||||
symbol_resolver::{SymbolValue, ValueEnum},
|
||||
toplevel::{
|
||||
helper::PrimDef,
|
||||
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
|
||||
DefinitionId, TopLevelDef,
|
||||
},
|
||||
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, TopLevelDef},
|
||||
typecheck::{
|
||||
magic_methods::{Binop, BinopVariant, HasOpInfo},
|
||||
typedef::{FunSignature, FuncArg, Type, TypeEnum, TypeVarId, Unifier, VarMap},
|
||||
|
@ -43,6 +40,12 @@ use nac3parser::ast::{
|
|||
Unaryop,
|
||||
};
|
||||
|
||||
use super::structure::{cslice::CSlice, ndarray::indexing::util::gen_ndarray_subscript_ndindexes};
|
||||
use super::{
|
||||
model::*,
|
||||
structure::exception::{Exception, ExceptionId},
|
||||
};
|
||||
|
||||
pub fn get_subst_key(
|
||||
unifier: &mut Unifier,
|
||||
obj: Option<Type>,
|
||||
|
@ -281,24 +284,7 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
|
|||
None
|
||||
}
|
||||
}
|
||||
Constant::Str(v) => {
|
||||
assert!(self.unifier.unioned(ty, self.primitives.str));
|
||||
if let Some(v) = self.const_strings.get(v) {
|
||||
Some(*v)
|
||||
} else {
|
||||
let str_ptr = self
|
||||
.builder
|
||||
.build_global_string_ptr(v, "const")
|
||||
.map(|v| v.as_pointer_value().into())
|
||||
.unwrap();
|
||||
let size = generator.get_size_type(self.ctx).const_int(v.len() as u64, false);
|
||||
let ty = self.get_llvm_type(generator, self.primitives.str);
|
||||
let val =
|
||||
ty.into_struct_type().const_named_struct(&[str_ptr, size.into()]).into();
|
||||
self.const_strings.insert(v.to_string(), val);
|
||||
Some(val)
|
||||
}
|
||||
}
|
||||
Constant::Str(s) => Some(self.gen_string(generator, s).value.into()),
|
||||
Constant::Ellipsis => {
|
||||
let msg = self.gen_string(generator, "NotImplementedError");
|
||||
|
||||
|
@ -560,69 +546,70 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
|
|||
}
|
||||
|
||||
/// Helper function for generating a LLVM variable storing a [String].
|
||||
pub fn gen_string<G, S>(&mut self, generator: &mut G, s: S) -> BasicValueEnum<'ctx>
|
||||
pub fn gen_string<G>(&mut self, generator: &mut G, string: &str) -> Struct<'ctx, CSlice>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
S: Into<String>,
|
||||
{
|
||||
self.gen_const(generator, &Constant::Str(s.into()), self.primitives.str).unwrap()
|
||||
self.const_strings.get(string).copied().unwrap_or_else(|| {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let pbyte_model = PtrModel(IntModel(Byte));
|
||||
let cslice_model = StructModel(CSlice);
|
||||
|
||||
let base = self.builder.build_global_string_ptr(string, "constant_string").unwrap();
|
||||
let base = pbyte_model.believe_value(base.as_pointer_value());
|
||||
|
||||
let len = sizet_model.constant(generator, self.ctx, string.len() as u64);
|
||||
|
||||
let cslice = cslice_model.create_const(generator, self.ctx, base, len);
|
||||
|
||||
self.const_strings.insert(string.to_owned(), cslice);
|
||||
|
||||
cslice
|
||||
})
|
||||
}
|
||||
|
||||
pub fn raise_exn<G: CodeGenerator + ?Sized>(
|
||||
&mut self,
|
||||
generator: &mut G,
|
||||
name: &str,
|
||||
msg: BasicValueEnum<'ctx>,
|
||||
params: [Option<IntValue<'ctx>>; 3],
|
||||
msg: Struct<'ctx, CSlice>,
|
||||
params: [Option<Int<'ctx, Int64>>; 3],
|
||||
loc: Location,
|
||||
) {
|
||||
let zelf = if let Some(exception_val) = self.exception_val {
|
||||
exception_val
|
||||
} else {
|
||||
let ty = self.get_llvm_type(generator, self.primitives.exception).into_pointer_type();
|
||||
let zelf_ty: BasicTypeEnum = ty.get_element_type().into_struct_type().into();
|
||||
let zelf = generator.gen_var_alloc(self, zelf_ty, Some("exn")).unwrap();
|
||||
*self.exception_val.insert(zelf)
|
||||
};
|
||||
let int32 = self.ctx.i32_type();
|
||||
let zero = int32.const_zero();
|
||||
unsafe {
|
||||
let id_ptr = self.builder.build_in_bounds_gep(zelf, &[zero, zero], "exn.id").unwrap();
|
||||
let id = self.resolver.get_string_id(name);
|
||||
self.builder.build_store(id_ptr, int32.const_int(id as u64, false)).unwrap();
|
||||
let ptr = self
|
||||
.builder
|
||||
.build_in_bounds_gep(zelf, &[zero, int32.const_int(5, false)], "exn.msg")
|
||||
.unwrap();
|
||||
self.builder.build_store(ptr, msg).unwrap();
|
||||
let i64_zero = self.ctx.i64_type().const_zero();
|
||||
for (i, attr_ind) in [6, 7, 8].iter().enumerate() {
|
||||
let ptr = self
|
||||
.builder
|
||||
.build_in_bounds_gep(
|
||||
zelf,
|
||||
&[zero, int32.const_int(*attr_ind, false)],
|
||||
"exn.param",
|
||||
)
|
||||
.unwrap();
|
||||
let val = params[i].map_or(i64_zero, |v| {
|
||||
self.builder.build_int_s_extend(v, self.ctx.i64_type(), "sext").unwrap()
|
||||
let exn_model = StructModel(Exception);
|
||||
let exn_id_model = IntModel(ExceptionId::default());
|
||||
|
||||
let exn_id =
|
||||
exn_id_model.constant(generator, self.ctx, self.resolver.get_string_id(name) as u64);
|
||||
let exn = self.exception_val.unwrap_or_else(|| {
|
||||
let exn = exn_model.var_alloca(generator, self, Some("exn")).unwrap();
|
||||
*self.exception_val.insert(exn)
|
||||
});
|
||||
self.builder.build_store(ptr, val).unwrap();
|
||||
|
||||
exn.set(self, |f| f.id, exn_id);
|
||||
exn.set(self, |f| f.msg, msg);
|
||||
for (i, param) in params.iter().enumerate() {
|
||||
if let Some(param) = param {
|
||||
exn.set(self, |f| f.params[i], *param);
|
||||
}
|
||||
}
|
||||
gen_raise(generator, self, Some(&zelf.into()), loc);
|
||||
|
||||
gen_raise(generator, self, Some(exn), loc);
|
||||
}
|
||||
|
||||
pub fn make_assert<G: CodeGenerator + ?Sized>(
|
||||
&mut self,
|
||||
generator: &mut G,
|
||||
cond: IntValue<'ctx>,
|
||||
cond: IntValue<'ctx>, // IntType can have arbitrary bit width
|
||||
err_name: &str,
|
||||
err_msg: &str,
|
||||
params: [Option<IntValue<'ctx>>; 3],
|
||||
loc: Location,
|
||||
) {
|
||||
let param_model = IntModel(Int64);
|
||||
let params =
|
||||
params.map(|p| p.map(|p| param_model.check_value(generator, self.ctx, p).unwrap()));
|
||||
|
||||
let err_msg = self.gen_string(generator, err_msg);
|
||||
self.make_assert_impl(generator, cond, err_name, err_msg, params, loc);
|
||||
}
|
||||
|
@ -630,26 +617,36 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
|
|||
pub fn make_assert_impl<G: CodeGenerator + ?Sized>(
|
||||
&mut self,
|
||||
generator: &mut G,
|
||||
cond: IntValue<'ctx>,
|
||||
cond: IntValue<'ctx>, // IntType can have arbitrary bit width
|
||||
err_name: &str,
|
||||
err_msg: BasicValueEnum<'ctx>,
|
||||
params: [Option<IntValue<'ctx>>; 3],
|
||||
err_msg: Struct<'ctx, CSlice>,
|
||||
params: [Option<Int<'ctx, Int64>>; 3],
|
||||
loc: Location,
|
||||
) {
|
||||
let i1 = self.ctx.bool_type();
|
||||
let i1_true = i1.const_all_ones();
|
||||
// we assume that the condition is most probably true, so the normal path is the most
|
||||
// probable path
|
||||
// even if this assumption is violated, it does not matter as exception unwinding is
|
||||
// slow anyway...
|
||||
let cond = call_expect(self, cond, i1_true, Some("expect"));
|
||||
let bool_model = IntModel(Bool);
|
||||
|
||||
// We assume that the condition is most probably true, so the normal path is the most
|
||||
// probable path even if this assumption is violated, it does not matter as exception unwinding is.
|
||||
let cond = call_expect(
|
||||
self,
|
||||
generator.bool_to_i1(self, cond),
|
||||
bool_model.const_true(generator, self.ctx).value,
|
||||
Some("expect"),
|
||||
);
|
||||
|
||||
let current_bb = self.builder.get_insert_block().unwrap();
|
||||
let current_fun = current_bb.get_parent().unwrap();
|
||||
|
||||
let then_block = self.ctx.insert_basic_block_after(current_bb, "succ");
|
||||
let exn_block = self.ctx.append_basic_block(current_fun, "fail");
|
||||
|
||||
self.builder.build_conditional_branch(cond, then_block, exn_block).unwrap();
|
||||
|
||||
// Inserting into `exn_block`
|
||||
self.builder.position_at_end(exn_block);
|
||||
self.raise_exn(generator, err_name, err_msg, params, loc);
|
||||
|
||||
// Continuation
|
||||
self.builder.position_at_end(then_block);
|
||||
}
|
||||
}
|
||||
|
@ -951,9 +948,9 @@ pub fn destructure_range<'ctx>(
|
|||
/// Allocates a List structure with the given [type][ty] and [length]. The name of the resulting
|
||||
/// LLVM value is `{name}.addr`, or `list.addr` if [name] is not specified.
|
||||
///
|
||||
/// Setting `ty` to [`None`] implies that the list does not have a known element type, which is only
|
||||
/// valid for empty lists. It is undefined behavior to generate a sized list with an unknown element
|
||||
/// type.
|
||||
/// Setting `ty` to [`None`] implies that the list is empty **and** does not have a known element
|
||||
/// type, and will therefore set the `list.data` type as `size_t*`. It is undefined behavior to
|
||||
/// generate a sized list with an unknown element type.
|
||||
pub fn allocate_list<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
|
@ -995,8 +992,10 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
|
|||
ctx.builder.position_at_end(init_bb);
|
||||
|
||||
let Comprehension { target, iter, ifs, .. } = &generators[0];
|
||||
|
||||
let iter_ty = iter.custom.unwrap();
|
||||
let iter_val = if let Some(v) = generator.gen_expr(ctx, iter)? {
|
||||
v.to_basic_value_enum(ctx, generator, iter.custom.unwrap())?
|
||||
v.to_basic_value_enum(ctx, generator, iter_ty)?
|
||||
} else {
|
||||
for bb in [test_bb, body_bb, cont_bb] {
|
||||
ctx.builder.position_at_end(bb);
|
||||
|
@ -1014,11 +1013,12 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
|
|||
ctx.builder.build_store(index, zero_size_t).unwrap();
|
||||
|
||||
let elem_ty = ctx.get_llvm_type(generator, elt.custom.unwrap());
|
||||
let is_range = ctx.unifier.unioned(iter.custom.unwrap(), ctx.primitives.range);
|
||||
let list;
|
||||
let list_content;
|
||||
|
||||
if is_range {
|
||||
match &*ctx.unifier.get_ty(iter_ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.range.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let iter_val = RangeValue::from_ptr_val(iter_val.into_pointer_value(), Some("range"));
|
||||
let (start, stop, step) = destructure_range(ctx, iter_val);
|
||||
let diff = ctx.builder.build_int_sub(stop, start, "diff").unwrap();
|
||||
|
@ -1026,7 +1026,8 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
|
|||
// the length may be 1 more than the actual length if the division is exact, but the
|
||||
// length is a upper bound only anyway so it does not matter.
|
||||
let length = ctx.builder.build_int_signed_div(diff, step, "div").unwrap();
|
||||
let length = ctx.builder.build_int_add(length, int32.const_int(1, false), "add1").unwrap();
|
||||
let length =
|
||||
ctx.builder.build_int_add(length, int32.const_int(1, false), "add1").unwrap();
|
||||
// in case length is non-positive
|
||||
let is_valid =
|
||||
ctx.builder.build_int_compare(IntPredicate::SGT, length, zero_32, "check").unwrap();
|
||||
|
@ -1035,7 +1036,9 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
|
|||
.builder
|
||||
.build_select(
|
||||
is_valid,
|
||||
ctx.builder.build_int_z_extend_or_bit_cast(length, size_t, "z_ext_len").unwrap(),
|
||||
ctx.builder
|
||||
.build_int_z_extend_or_bit_cast(length, size_t, "z_ext_len")
|
||||
.unwrap(),
|
||||
zero_size_t,
|
||||
"listcomp.alloc_size",
|
||||
)
|
||||
|
@ -1047,7 +1050,6 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
|
|||
list_alloc_size.into_int_value(),
|
||||
Some("listcomp.addr"),
|
||||
);
|
||||
list_content = list.data().base_ptr(ctx, generator);
|
||||
|
||||
let i = generator.gen_store_target(ctx, target, Some("i.addr"))?.unwrap();
|
||||
ctx.builder
|
||||
|
@ -1055,7 +1057,11 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
|
|||
.unwrap();
|
||||
|
||||
ctx.builder
|
||||
.build_conditional_branch(gen_in_range_check(ctx, start, stop, step), test_bb, cont_bb)
|
||||
.build_conditional_branch(
|
||||
gen_in_range_check(ctx, start, stop, step),
|
||||
test_bb,
|
||||
cont_bb,
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
ctx.builder.position_at_end(test_bb);
|
||||
|
@ -1070,11 +1076,18 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
|
|||
.unwrap();
|
||||
ctx.builder.build_store(i, tmp).unwrap();
|
||||
ctx.builder
|
||||
.build_conditional_branch(gen_in_range_check(ctx, tmp, stop, step), body_bb, cont_bb)
|
||||
.build_conditional_branch(
|
||||
gen_in_range_check(ctx, tmp, stop, step),
|
||||
body_bb,
|
||||
cont_bb,
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
ctx.builder.position_at_end(body_bb);
|
||||
} else {
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let length = ctx
|
||||
.build_gep_and_load(
|
||||
iter_val.into_pointer_value(),
|
||||
|
@ -1083,14 +1096,15 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
|
|||
)
|
||||
.into_int_value();
|
||||
list = allocate_list(generator, ctx, Some(elem_ty), length, Some("listcomp"));
|
||||
list_content = list.data().base_ptr(ctx, generator);
|
||||
|
||||
let counter = generator.gen_var_alloc(ctx, size_t.into(), Some("counter.addr"))?;
|
||||
// counter = -1
|
||||
ctx.builder.build_store(counter, size_t.const_int(u64::MAX, true)).unwrap();
|
||||
ctx.builder.build_store(counter, size_t.const_all_ones()).unwrap();
|
||||
ctx.builder.build_unconditional_branch(test_bb).unwrap();
|
||||
|
||||
ctx.builder.position_at_end(test_bb);
|
||||
let tmp = ctx.builder.build_load(counter, "i").map(BasicValueEnum::into_int_value).unwrap();
|
||||
let tmp =
|
||||
ctx.builder.build_load(counter, "i").map(BasicValueEnum::into_int_value).unwrap();
|
||||
let tmp = ctx.builder.build_int_add(tmp, size_t.const_int(1, false), "inc").unwrap();
|
||||
ctx.builder.build_store(counter, tmp).unwrap();
|
||||
let cmp = ctx.builder.build_int_compare(IntPredicate::SLT, tmp, length, "cmp").unwrap();
|
||||
|
@ -1105,7 +1119,14 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
|
|||
)
|
||||
.into_pointer_value();
|
||||
let val = ctx.build_gep_and_load(arr_ptr, &[tmp], Some("val"));
|
||||
generator.gen_assign(ctx, target, val.into())?;
|
||||
generator.gen_assign(ctx, target, val.into(), elt.custom.unwrap())?;
|
||||
}
|
||||
_ => {
|
||||
panic!(
|
||||
"unsupported list comprehension iterator type: {}",
|
||||
ctx.unifier.stringify(iter_ty)
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
// Emits the content of `cont_bb`
|
||||
|
@ -1143,7 +1164,8 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
|
|||
return Ok(None);
|
||||
};
|
||||
let i = ctx.builder.build_load(index, "i").map(BasicValueEnum::into_int_value).unwrap();
|
||||
let elem_ptr = unsafe { ctx.builder.build_gep(list_content, &[i], "elem_ptr") }.unwrap();
|
||||
let elem_ptr =
|
||||
unsafe { list.data().ptr_offset_unchecked(ctx, generator, &i, Some("elem_ptr")) };
|
||||
let val = elem.to_basic_value_enum(ctx, generator, elt.custom.unwrap())?;
|
||||
ctx.builder.build_store(elem_ptr, val).unwrap();
|
||||
ctx.builder
|
||||
|
@ -1226,6 +1248,7 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
|
|||
debug_assert!(ctx.unifier.unioned(elem_ty1, elem_ty2));
|
||||
|
||||
let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty1);
|
||||
let sizeof_elem = llvm_elem_ty.size_of().unwrap();
|
||||
|
||||
let lhs = ListValue::from_ptr_val(left_val.into_pointer_value(), llvm_usize, None);
|
||||
let rhs = ListValue::from_ptr_val(right_val.into_pointer_value(), llvm_usize, None);
|
||||
|
@ -1237,14 +1260,25 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
|
|||
|
||||
let new_list = allocate_list(generator, ctx, Some(llvm_elem_ty), size, None);
|
||||
|
||||
let lhs_len = ctx
|
||||
let lhs_size = ctx
|
||||
.builder
|
||||
.build_int_mul(lhs.load_size(ctx, None), llvm_elem_ty.size_of().unwrap(), "")
|
||||
.build_int_z_extend_or_bit_cast(
|
||||
lhs.load_size(ctx, None),
|
||||
sizeof_elem.get_type(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
let rhs_len = ctx
|
||||
let lhs_len = ctx.builder.build_int_mul(lhs_size, sizeof_elem, "").unwrap();
|
||||
|
||||
let rhs_size = ctx
|
||||
.builder
|
||||
.build_int_mul(rhs.load_size(ctx, None), llvm_elem_ty.size_of().unwrap(), "")
|
||||
.build_int_z_extend_or_bit_cast(
|
||||
rhs.load_size(ctx, None),
|
||||
sizeof_elem.get_type(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
let rhs_len = ctx.builder.build_int_mul(rhs_size, sizeof_elem, "").unwrap();
|
||||
|
||||
let list_ptr = new_list.data().base_ptr(ctx, generator);
|
||||
call_memcpy_generic(
|
||||
|
@ -1309,6 +1343,7 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
|
|||
let int_val = call_int_smax(ctx, int_val, llvm_usize.const_zero(), None);
|
||||
|
||||
let elem_llvm_ty = ctx.get_llvm_type(generator, elem_ty);
|
||||
let sizeof_elem = elem_llvm_ty.size_of().unwrap();
|
||||
|
||||
let new_list = allocate_list(
|
||||
generator,
|
||||
|
@ -1321,6 +1356,7 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
|
|||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(int_val, false),
|
||||
|generator, ctx, _, i| {
|
||||
|
@ -1332,15 +1368,18 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
|
|||
new_list.data().ptr_offset_unchecked(ctx, generator, &offset, None)
|
||||
};
|
||||
|
||||
let memcpy_sz = ctx
|
||||
let list_size = ctx
|
||||
.builder
|
||||
.build_int_mul(
|
||||
.build_int_z_extend_or_bit_cast(
|
||||
list_val.load_size(ctx, None),
|
||||
elem_llvm_ty.size_of().unwrap(),
|
||||
sizeof_elem.get_type(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
let memcpy_sz =
|
||||
ctx.builder.build_int_mul(list_size, sizeof_elem, "").unwrap();
|
||||
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
ptr,
|
||||
|
@ -1928,6 +1967,7 @@ pub fn gen_cmpop_expr_with_values<'ctx, G: CodeGenerator>(
|
|||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(left_val.load_size(ctx, None), false),
|
||||
|generator, ctx, hooks, i| {
|
||||
|
@ -2088,326 +2128,6 @@ pub fn gen_cmpop_expr<'ctx, G: CodeGenerator>(
|
|||
)
|
||||
}
|
||||
|
||||
/// Generates code for a subscript expression on an `ndarray`.
|
||||
///
|
||||
/// * `ty` - The `Type` of the `NDArray` elements.
|
||||
/// * `ndims` - The `Type` of the `NDArray` number-of-dimensions `Literal`.
|
||||
/// * `v` - The `NDArray` value.
|
||||
/// * `slice` - The slice expression used to subscript into the `ndarray`.
|
||||
fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ty: Type,
|
||||
ndims: Type,
|
||||
v: NDArrayValue<'ctx>,
|
||||
slice: &Expr<Option<Type>>,
|
||||
) -> Result<Option<ValueEnum<'ctx>>, String> {
|
||||
let llvm_i1 = ctx.ctx.bool_type();
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
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();
|
||||
|
||||
// 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 = ndarray.load_ndims(ctx);
|
||||
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),
|
||||
);
|
||||
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,
|
||||
|
@ -2457,7 +2177,29 @@ 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()
|
||||
if let Some(res) = resolver.get_symbol_value(*id, ctx) {
|
||||
res
|
||||
} else {
|
||||
// Allow "raise Exception" short form
|
||||
let def_id = resolver.get_identifier_def(*id).map_err(|e| {
|
||||
format!("{} (at {})", e.iter().next().unwrap(), expr.location)
|
||||
})?;
|
||||
let def = ctx.top_level.definitions.read();
|
||||
if let TopLevelDef::Class { constructor, .. } = *def[def_id.0].read() {
|
||||
let TypeEnum::TFunc(signature) =
|
||||
ctx.unifier.get_ty(constructor.unwrap()).as_ref().clone()
|
||||
else {
|
||||
return Err(format!(
|
||||
"Failed to resolve symbol {} (at {})",
|
||||
id, expr.location
|
||||
));
|
||||
};
|
||||
return Ok(generator
|
||||
.gen_call(ctx, None, (&signature, def_id), Vec::default())?
|
||||
.map(Into::into));
|
||||
}
|
||||
return Err(format!("Failed to resolve symbol {} (at {})", id, expr.location));
|
||||
}
|
||||
}
|
||||
},
|
||||
ExprKind::List { elts, .. } => {
|
||||
|
@ -3025,18 +2767,20 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
|
|||
v.data().get(ctx, generator, &index, None).into()
|
||||
}
|
||||
}
|
||||
TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
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 {
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let Some(ndarray) = generator.gen_expr(ctx, value)? else {
|
||||
return Ok(None);
|
||||
};
|
||||
let v = NDArrayValue::from_ptr_val(v, usize, None);
|
||||
|
||||
return gen_ndarray_subscript_expr(generator, ctx, *ty, *ndims, v, slice);
|
||||
let ndarray_ty = value.custom.unwrap();
|
||||
let ndarray = ndarray.to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
let ndarray =
|
||||
NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
|
||||
|
||||
let indexes = gen_ndarray_subscript_ndindexes(generator, ctx, slice)?;
|
||||
let result = ndarray.index_or_scalar(generator, ctx, &indexes, "index_result");
|
||||
let result = result.to_basic_value_enum();
|
||||
return Ok(Some(ValueEnum::Dynamic(result)));
|
||||
}
|
||||
TypeEnum::TTuple { .. } => {
|
||||
let index: u32 =
|
||||
|
|
|
@ -130,3 +130,62 @@ pub fn call_ldexp<'ctx>(
|
|||
.map(Either::unwrap_left)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Macro to generate `np_linalg` and `sp_linalg` functions
|
||||
/// The function takes as input `NDArray` and returns ()
|
||||
///
|
||||
/// Arguments:
|
||||
/// * `$fn_name:ident`: The identifier of the rust function to be generated
|
||||
/// * `$extern_fn:literal`: Name of underlying extern function
|
||||
/// * (2/3/4): Number of `NDArray` that function takes as input
|
||||
///
|
||||
/// Note:
|
||||
/// The operands and resulting `NDArray` are both passed as input to the funcion
|
||||
/// It is the responsibility of caller to ensure that output `NDArray` is properly allocated on stack
|
||||
/// The function changes the content of the output `NDArray` in-place
|
||||
macro_rules! generate_linalg_extern_fn {
|
||||
($fn_name:ident, $extern_fn:literal, 2) => {
|
||||
generate_linalg_extern_fn!($fn_name, $extern_fn, mat1, mat2);
|
||||
};
|
||||
($fn_name:ident, $extern_fn:literal, 3) => {
|
||||
generate_linalg_extern_fn!($fn_name, $extern_fn, mat1, mat2, mat3);
|
||||
};
|
||||
($fn_name:ident, $extern_fn:literal, 4) => {
|
||||
generate_linalg_extern_fn!($fn_name, $extern_fn, mat1, mat2, mat3, mat4);
|
||||
};
|
||||
($fn_name:ident, $extern_fn:literal $(,$input_matrix:ident)*) => {
|
||||
#[doc = concat!("Invokes the linalg `", stringify!($extern_fn), " function." )]
|
||||
pub fn $fn_name<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>
|
||||
$(,$input_matrix: BasicValueEnum<'ctx>)*,
|
||||
name: Option<&str>,
|
||||
){
|
||||
const FN_NAME: &str = $extern_fn;
|
||||
let extern_fn = ctx.module.get_function(FN_NAME).unwrap_or_else(|| {
|
||||
let fn_type = ctx.ctx.void_type().fn_type(&[$($input_matrix.get_type().into()),*], false);
|
||||
|
||||
let func = ctx.module.add_function(FN_NAME, fn_type, None);
|
||||
for attr in ["mustprogress", "nofree", "nounwind", "willreturn", "writeonly"] {
|
||||
func.add_attribute(
|
||||
AttributeLoc::Function,
|
||||
ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
|
||||
);
|
||||
}
|
||||
func
|
||||
});
|
||||
|
||||
ctx.builder.build_call(extern_fn, &[$($input_matrix.into(),)*], name.unwrap_or_default()).unwrap();
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
generate_linalg_extern_fn!(call_np_linalg_cholesky, "np_linalg_cholesky", 2);
|
||||
generate_linalg_extern_fn!(call_np_linalg_qr, "np_linalg_qr", 3);
|
||||
generate_linalg_extern_fn!(call_np_linalg_svd, "np_linalg_svd", 4);
|
||||
generate_linalg_extern_fn!(call_np_linalg_inv, "np_linalg_inv", 2);
|
||||
generate_linalg_extern_fn!(call_np_linalg_pinv, "np_linalg_pinv", 2);
|
||||
generate_linalg_extern_fn!(call_np_linalg_matrix_power, "np_linalg_matrix_power", 3);
|
||||
generate_linalg_extern_fn!(call_np_linalg_det, "np_linalg_det", 2);
|
||||
generate_linalg_extern_fn!(call_sp_linalg_lu, "sp_linalg_lu", 3);
|
||||
generate_linalg_extern_fn!(call_sp_linalg_schur, "sp_linalg_schur", 3);
|
||||
generate_linalg_extern_fn!(call_sp_linalg_hessenberg, "sp_linalg_hessenberg", 3);
|
||||
|
|
|
@ -123,11 +123,45 @@ pub trait CodeGenerator {
|
|||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
target: &Expr<Option<Type>>,
|
||||
value: ValueEnum<'ctx>,
|
||||
value_ty: Type,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
Self: Sized,
|
||||
{
|
||||
gen_assign(self, ctx, target, value)
|
||||
gen_assign(self, ctx, target, value, value_ty)
|
||||
}
|
||||
|
||||
/// Generate code for an assignment expression where LHS is a `"target_list"`.
|
||||
///
|
||||
/// See <https://docs.python.org/3/reference/simple_stmts.html#assignment-statements>.
|
||||
fn gen_assign_target_list<'ctx>(
|
||||
&mut self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
targets: &Vec<Expr<Option<Type>>>,
|
||||
value: ValueEnum<'ctx>,
|
||||
value_ty: Type,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
Self: Sized,
|
||||
{
|
||||
gen_assign_target_list(self, ctx, targets, value, value_ty)
|
||||
}
|
||||
|
||||
/// Generate code for an item assignment.
|
||||
///
|
||||
/// i.e., `target[key] = value`
|
||||
fn gen_setitem<'ctx>(
|
||||
&mut self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
target: &Expr<Option<Type>>,
|
||||
key: &Expr<Option<Type>>,
|
||||
value: ValueEnum<'ctx>,
|
||||
value_ty: Type,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
Self: Sized,
|
||||
{
|
||||
gen_setitem(self, ctx, target, key, value, value_ty)
|
||||
}
|
||||
|
||||
/// Generate code for a while expression.
|
||||
|
|
|
@ -1,5 +1,13 @@
|
|||
use crate::symbol_resolver::SymbolResolver;
|
||||
use crate::typecheck::typedef::Type;
|
||||
|
||||
mod test;
|
||||
pub mod util;
|
||||
|
||||
use super::model::*;
|
||||
use super::structure::ndarray::broadcast::ShapeEntry;
|
||||
use super::structure::ndarray::indexing::NDIndex;
|
||||
use super::structure::ndarray::NpArray;
|
||||
use super::{
|
||||
classes::{
|
||||
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue,
|
||||
|
@ -9,6 +17,7 @@ use super::{
|
|||
};
|
||||
use crate::codegen::classes::TypedArrayLikeAccessor;
|
||||
use crate::codegen::stmt::gen_for_callback_incrementing;
|
||||
use inkwell::values::BasicValue;
|
||||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
context::Context,
|
||||
|
@ -20,6 +29,8 @@ use inkwell::{
|
|||
};
|
||||
use itertools::Either;
|
||||
use nac3parser::ast::Expr;
|
||||
use util::function::CallFunction;
|
||||
use util::get_sizet_dependent_function_name;
|
||||
|
||||
#[must_use]
|
||||
pub fn load_irrt(ctx: &Context) -> Module {
|
||||
|
@ -414,14 +425,27 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
|
|||
.unwrap();
|
||||
let cond_1 = ctx.builder.build_and(dest_step_eq_one, src_slt_dest, "slice_cond_1").unwrap();
|
||||
let cond = ctx.builder.build_or(src_eq_dest, cond_1, "slice_cond").unwrap();
|
||||
|
||||
// TODO: Temporary fix. Rewrite `list_slice_assignment` later
|
||||
// Exception params should have been i64
|
||||
{
|
||||
let param_model = IntModel(Int64);
|
||||
|
||||
let src_slice_len =
|
||||
param_model.s_extend_or_bit_cast(generator, ctx, src_slice_len, "src_slice_len");
|
||||
let dest_slice_len =
|
||||
param_model.s_extend_or_bit_cast(generator, ctx, dest_slice_len, "dest_slice_len");
|
||||
let dest_idx_2 = param_model.s_extend_or_bit_cast(generator, ctx, dest_idx.2, "dest_idx_2");
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
cond,
|
||||
"0:ValueError",
|
||||
"attempt to assign sequence of size {0} to slice of size {1} with step size {2}",
|
||||
[Some(src_slice_len), Some(dest_slice_len), Some(dest_idx.2)],
|
||||
[Some(src_slice_len.value), Some(dest_slice_len.value), Some(dest_idx_2.value)],
|
||||
ctx.current_loc,
|
||||
);
|
||||
}
|
||||
|
||||
let new_len = {
|
||||
let args = vec![
|
||||
|
@ -798,6 +822,7 @@ pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
|
|||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(min_ndims, false),
|
||||
|generator, ctx, _, idx| {
|
||||
|
@ -872,7 +897,7 @@ pub fn call_ndarray_calc_broadcast<'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
|
||||
/// containing the indices used for accessing `array` corresponding to the index of the broadcast
|
||||
/// array `broadcast_idx`.
|
||||
pub fn call_ndarray_calc_broadcast_index<
|
||||
'ctx,
|
||||
|
@ -927,3 +952,212 @@ pub fn call_ndarray_calc_broadcast_index<
|
|||
Box::new(|_, v| v.into()),
|
||||
)
|
||||
}
|
||||
|
||||
pub fn call_nac3_throw_dummy_error<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'_, '_>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_throw_dummy_error");
|
||||
CallFunction::begin(generator, ctx, &name).returning_void();
|
||||
}
|
||||
|
||||
/// Initialize all global `EXN_*` exception IDs in IRRT with the [`SymbolResolver`].
|
||||
pub fn setup_irrt_exceptions<'ctx>(
|
||||
ctx: &'ctx Context,
|
||||
module: &Module<'ctx>,
|
||||
symbol_resolver: &dyn SymbolResolver,
|
||||
) {
|
||||
let exn_id_type = ctx.i32_type();
|
||||
|
||||
let errors = &[
|
||||
("EXN_INDEX_ERROR", "0:IndexError"),
|
||||
("EXN_VALUE_ERROR", "0:ValueError"),
|
||||
("EXN_ASSERTION_ERROR", "0:AssertionError"),
|
||||
("EXN_RUNTIME_ERROR", "0:RuntimeError"),
|
||||
("EXN_TYPE_ERROR", "0:TypeError"),
|
||||
];
|
||||
|
||||
for (irrt_name, symbol_name) in errors {
|
||||
let exn_id = symbol_resolver.get_string_id(symbol_name);
|
||||
let exn_id = exn_id_type.const_int(exn_id as u64, false).as_basic_value_enum();
|
||||
|
||||
let global = module.get_global(irrt_name).unwrap_or_else(|| {
|
||||
panic!("Exception symbol name '{irrt_name}' should exist in the IRRT LLVM module")
|
||||
});
|
||||
global.set_initializer(&exn_id);
|
||||
}
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndims: Int<'ctx, SizeT>,
|
||||
shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_util_assert_shape_no_negative",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("ndims", ndims)
|
||||
.arg("shape", shape)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_size");
|
||||
CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("size")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_nbytes");
|
||||
CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("nbytes")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_len");
|
||||
CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("len")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_is_c_contiguous");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("ndarray", ndarray_ptr)
|
||||
.returning_auto("is_c_contiguous")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
index: Int<'ctx, SizeT>,
|
||||
) -> Ptr<'ctx, IntModel<Byte>> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_nth_pelement");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("ndarray", pndarray)
|
||||
.arg("index", index)
|
||||
.returning_auto("pelement")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pdnarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
let name =
|
||||
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_set_strides_by_shape");
|
||||
CallFunction::begin(generator, ctx, &name).arg("ndarray", pdnarray).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_copy_data");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("src_ndarray", src_ndarray)
|
||||
.arg("dst_ndarray", dst_ndarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
num_indexes: Int<'ctx, SizeT>,
|
||||
indexes: Ptr<'ctx, StructModel<NDIndex>>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("num_indexes", num_indexes)
|
||||
.arg("indexes", indexes)
|
||||
.arg("src_ndarray", src_ndarray)
|
||||
.arg("dst_ndarray", dst_ndarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("src_ndarray", src_ndarray)
|
||||
.arg("dst_ndarray", dst_ndarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
num_shape_entries: Int<'ctx, SizeT>,
|
||||
shape_entries: Ptr<'ctx, StructModel<ShapeEntry>>,
|
||||
dst_ndims: Int<'ctx, SizeT>,
|
||||
dst_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("num_shapes", num_shape_entries)
|
||||
.arg("shapes", shape_entries)
|
||||
.arg("dst_ndims", dst_ndims)
|
||||
.arg("dst_shape", dst_shape)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
size: Int<'ctx, SizeT>,
|
||||
new_ndims: Int<'ctx, SizeT>,
|
||||
new_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_resolve_and_check_new_shape",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("size", size)
|
||||
.arg("new_ndims", new_ndims)
|
||||
.arg("new_shape", new_shape)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
num_axes: Int<'ctx, SizeT>,
|
||||
axes: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("src_ndarray", src_ndarray)
|
||||
.arg("dst_ndarray", dst_ndarray)
|
||||
.arg("num_axes", num_axes)
|
||||
.arg("axes", axes)
|
||||
.returning_void();
|
||||
}
|
||||
|
|
|
@ -0,0 +1,26 @@
|
|||
#[cfg(test)]
|
||||
mod tests {
|
||||
use std::{path::Path, process::Command};
|
||||
|
||||
#[test]
|
||||
fn run_irrt_test() {
|
||||
assert!(
|
||||
cfg!(feature = "test"),
|
||||
"Please do `cargo test -F test` to compile `irrt_test.out` and run test"
|
||||
);
|
||||
|
||||
let irrt_test_out_path = Path::new(concat!(env!("OUT_DIR"), "/irrt_test.out"));
|
||||
let output = Command::new(irrt_test_out_path.to_str().unwrap()).output().unwrap();
|
||||
|
||||
if !output.status.success() {
|
||||
eprintln!("irrt_test failed with status {}:", output.status);
|
||||
eprintln!("====== stdout ======");
|
||||
eprintln!("{}", String::from_utf8(output.stdout).unwrap());
|
||||
eprintln!("====== stderr ======");
|
||||
eprintln!("{}", String::from_utf8(output.stderr).unwrap());
|
||||
eprintln!("====================");
|
||||
|
||||
panic!("irrt_test failed");
|
||||
}
|
||||
}
|
||||
}
|
|
@ -0,0 +1,109 @@
|
|||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
// When [`TypeContext::size_type`] is 32-bits, the function name is "{fn_name}".
|
||||
// When [`TypeContext::size_type`] is 64-bits, the function name is "{fn_name}64".
|
||||
#[must_use]
|
||||
pub fn get_sizet_dependent_function_name<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'_, '_>,
|
||||
name: &str,
|
||||
) -> String {
|
||||
let mut name = name.to_owned();
|
||||
match generator.get_size_type(ctx.ctx).get_bit_width() {
|
||||
32 => {}
|
||||
64 => name.push_str("64"),
|
||||
bit_width => {
|
||||
panic!("Unsupported int type bit width {bit_width}, must be either 32-bits or 64-bits")
|
||||
}
|
||||
}
|
||||
name
|
||||
}
|
||||
|
||||
pub mod function {
|
||||
use crate::codegen::{model::*, CodeGenContext, CodeGenerator};
|
||||
use inkwell::{
|
||||
types::{BasicMetadataTypeEnum, BasicType, FunctionType},
|
||||
values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
struct Arg<'ctx> {
|
||||
ty: BasicMetadataTypeEnum<'ctx>,
|
||||
val: BasicMetadataValueEnum<'ctx>,
|
||||
}
|
||||
|
||||
/// Helper structure to reduce IRRT Inkwell function call boilerplate
|
||||
pub struct CallFunction<'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>>,
|
||||
}
|
||||
|
||||
impl<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> CallFunction<'ctx, 'a, 'b, 'c, 'd, G> {
|
||||
pub fn begin(
|
||||
generator: &'d mut G,
|
||||
ctx: &'b CodeGenContext<'ctx, 'a>,
|
||||
name: &'c str,
|
||||
) -> Self {
|
||||
CallFunction { generator, ctx, name, args: Vec::new() }
|
||||
}
|
||||
|
||||
/// Push a call argument to the function call.
|
||||
///
|
||||
/// The `_name` parameter is there for self-documentation purposes.
|
||||
#[allow(clippy::needless_pass_by_value)]
|
||||
#[must_use]
|
||||
pub fn arg<M: Model<'ctx>>(mut self, _name: &str, arg: Instance<'ctx, M>) -> Self {
|
||||
let arg = Arg {
|
||||
ty: arg.model.get_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.get_type(self.generator, self.ctx.ctx);
|
||||
|
||||
let ret = self.get_function(|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.get_function(|tys| ret_ty.fn_type(tys, false), "");
|
||||
}
|
||||
|
||||
fn get_function<F>(&self, make_fn_type: F, return_value_name: &str) -> CallSiteValue<'ctx>
|
||||
where
|
||||
F: FnOnce(&[BasicMetadataTypeEnum<'ctx>]) -> FunctionType<'ctx>,
|
||||
{
|
||||
// Get the LLVM function, declare the function if it doesn't exist - it will be defined by other
|
||||
// components of NAC3.
|
||||
let func = self.ctx.module.get_function(self.name).unwrap_or_else(|| {
|
||||
let tys = self.args.iter().map(|arg| arg.ty).collect_vec();
|
||||
let fn_type = make_fn_type(&tys);
|
||||
self.ctx.module.add_function(self.name, fn_type, None)
|
||||
});
|
||||
|
||||
let vals = self.args.iter().map(|arg| arg.val).collect_vec();
|
||||
self.ctx.builder.build_call(func, &vals, return_value_name).unwrap()
|
||||
}
|
||||
}
|
||||
}
|
|
@ -1,7 +1,7 @@
|
|||
use crate::{
|
||||
codegen::classes::{ListType, NDArrayType, ProxyType, RangeType},
|
||||
codegen::classes::{ListType, ProxyType, RangeType},
|
||||
symbol_resolver::{StaticValue, SymbolResolver},
|
||||
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
|
||||
toplevel::{helper::PrimDef, TopLevelContext, TopLevelDef},
|
||||
typecheck::{
|
||||
type_inferencer::{CodeLocation, PrimitiveStore},
|
||||
typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
|
||||
|
@ -24,6 +24,7 @@ use inkwell::{
|
|||
AddressSpace, IntPredicate, OptimizationLevel,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
use model::*;
|
||||
use nac3parser::ast::{Location, Stmt, StrRef};
|
||||
use parking_lot::{Condvar, Mutex};
|
||||
use std::collections::{HashMap, HashSet};
|
||||
|
@ -32,6 +33,7 @@ use std::sync::{
|
|||
Arc,
|
||||
};
|
||||
use std::thread;
|
||||
use structure::{cslice::CSlice, exception::Exception, ndarray::NpArray};
|
||||
|
||||
pub mod builtin_fns;
|
||||
pub mod classes;
|
||||
|
@ -41,8 +43,11 @@ pub mod extern_fns;
|
|||
mod generator;
|
||||
pub mod irrt;
|
||||
pub mod llvm_intrinsics;
|
||||
pub mod model;
|
||||
pub mod numpy;
|
||||
pub mod numpy_new;
|
||||
pub mod stmt;
|
||||
pub mod structure;
|
||||
|
||||
#[cfg(test)]
|
||||
mod test;
|
||||
|
@ -68,6 +73,16 @@ pub struct CodeGenLLVMOptions {
|
|||
pub target: CodeGenTargetMachineOptions,
|
||||
}
|
||||
|
||||
impl CodeGenLLVMOptions {
|
||||
/// Creates a [`TargetMachine`] using the target options specified by this struct.
|
||||
///
|
||||
/// See [`Target::create_target_machine`].
|
||||
#[must_use]
|
||||
pub fn create_target_machine(&self) -> Option<TargetMachine> {
|
||||
self.target.create_target_machine(self.opt_level)
|
||||
}
|
||||
}
|
||||
|
||||
/// Additional options for code generation for the target machine.
|
||||
#[derive(Clone, Debug, Eq, PartialEq)]
|
||||
pub struct CodeGenTargetMachineOptions {
|
||||
|
@ -158,11 +173,11 @@ pub struct CodeGenContext<'ctx, 'a> {
|
|||
pub registry: &'a WorkerRegistry,
|
||||
|
||||
/// Cache for constant strings.
|
||||
pub const_strings: HashMap<String, BasicValueEnum<'ctx>>,
|
||||
pub const_strings: HashMap<String, Struct<'ctx, CSlice>>,
|
||||
|
||||
/// [`BasicBlock`] containing all `alloca` statements for the current function.
|
||||
pub init_bb: BasicBlock<'ctx>,
|
||||
pub exception_val: Option<PointerValue<'ctx>>,
|
||||
pub exception_val: Option<Ptr<'ctx, StructModel<Exception>>>,
|
||||
|
||||
/// The header and exit basic blocks of a loop in this context. See
|
||||
/// <https://llvm.org/docs/LoopTerminology.html> for explanation of these terminology.
|
||||
|
@ -338,6 +353,10 @@ impl WorkerRegistry {
|
|||
let mut builder = context.create_builder();
|
||||
let mut module = context.create_module(generator.get_name());
|
||||
|
||||
let target_machine = self.llvm_options.create_target_machine().unwrap();
|
||||
module.set_data_layout(&target_machine.get_target_data().get_data_layout());
|
||||
module.set_triple(&target_machine.get_triple());
|
||||
|
||||
module.add_basic_value_flag(
|
||||
"Debug Info Version",
|
||||
inkwell::module::FlagBehavior::Warning,
|
||||
|
@ -361,6 +380,10 @@ impl WorkerRegistry {
|
|||
errors.insert(e);
|
||||
// create a new empty module just to continue codegen and collect errors
|
||||
module = context.create_module(&format!("{}_recover", generator.get_name()));
|
||||
|
||||
let target_machine = self.llvm_options.create_target_machine().unwrap();
|
||||
module.set_data_layout(&target_machine.get_target_data().get_data_layout());
|
||||
module.set_triple(&target_machine.get_triple());
|
||||
}
|
||||
}
|
||||
*self.task_count.lock() -= 1;
|
||||
|
@ -471,12 +494,8 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
|
|||
}
|
||||
|
||||
TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let (dtype, _) = unpack_ndarray_var_tys(unifier, ty);
|
||||
let element_type = get_llvm_type(
|
||||
ctx, module, generator, unifier, top_level, type_cache, dtype,
|
||||
);
|
||||
|
||||
NDArrayType::new(generator, ctx, element_type).as_base_type().into()
|
||||
let pndarray_model = PtrModel(StructModel(NpArray));
|
||||
pndarray_model.get_type(generator, ctx).as_basic_type_enum()
|
||||
}
|
||||
|
||||
_ => unreachable!(
|
||||
|
@ -646,43 +665,19 @@ pub fn gen_func_impl<
|
|||
..primitives
|
||||
};
|
||||
|
||||
let mut type_cache: HashMap<_, _> = [
|
||||
let cslice_model = StructModel(CSlice);
|
||||
let pexn_model = PtrModel(StructModel(Exception));
|
||||
|
||||
let mut type_cache: HashMap<_, BasicTypeEnum<'ctx>> = [
|
||||
(primitives.int32, context.i32_type().into()),
|
||||
(primitives.int64, context.i64_type().into()),
|
||||
(primitives.uint32, context.i32_type().into()),
|
||||
(primitives.uint64, context.i64_type().into()),
|
||||
(primitives.float, context.f64_type().into()),
|
||||
(primitives.bool, context.i8_type().into()),
|
||||
(primitives.str, {
|
||||
let name = "str";
|
||||
match module.get_struct_type(name) {
|
||||
None => {
|
||||
let str_type = context.opaque_struct_type("str");
|
||||
let fields = [
|
||||
context.i8_type().ptr_type(AddressSpace::default()).into(),
|
||||
generator.get_size_type(context).into(),
|
||||
];
|
||||
str_type.set_body(&fields, false);
|
||||
str_type.into()
|
||||
}
|
||||
Some(t) => t.as_basic_type_enum(),
|
||||
}
|
||||
}),
|
||||
(primitives.str, cslice_model.get_type(generator, context).into()),
|
||||
(primitives.range, RangeType::new(context).as_base_type().into()),
|
||||
(primitives.exception, {
|
||||
let name = "Exception";
|
||||
if let Some(t) = module.get_struct_type(name) {
|
||||
t.ptr_type(AddressSpace::default()).as_basic_type_enum()
|
||||
} else {
|
||||
let exception = context.opaque_struct_type("Exception");
|
||||
let int32 = context.i32_type().into();
|
||||
let int64 = context.i64_type().into();
|
||||
let str_ty = module.get_struct_type("str").unwrap().as_basic_type_enum();
|
||||
let fields = [int32, str_ty, int32, int32, str_ty, str_ty, int64, int64, int64];
|
||||
exception.set_body(&fields, false);
|
||||
exception.ptr_type(AddressSpace::default()).as_basic_type_enum()
|
||||
}
|
||||
}),
|
||||
(primitives.exception, pexn_model.get_type(generator, context).into()),
|
||||
]
|
||||
.iter()
|
||||
.copied()
|
||||
|
|
|
@ -0,0 +1,40 @@
|
|||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicType, BasicTypeEnum},
|
||||
values::BasicValueEnum,
|
||||
};
|
||||
|
||||
use crate::codegen::CodeGenerator;
|
||||
|
||||
use super::*;
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AnyModel<'ctx>(pub BasicTypeEnum<'ctx>);
|
||||
pub type Anything<'ctx> = Instance<'ctx, AnyModel<'ctx>>;
|
||||
|
||||
impl<'ctx> Model<'ctx> for AnyModel<'ctx> {
|
||||
type Value = BasicValueEnum<'ctx>;
|
||||
type Type = BasicTypeEnum<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &mut 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)))
|
||||
}
|
||||
}
|
||||
}
|
|
@ -0,0 +1,125 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{context::Context, types::*, values::*};
|
||||
|
||||
use super::*;
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct ModelError(pub String);
|
||||
|
||||
impl ModelError {
|
||||
pub(super) fn under_context(mut self, context: &str) -> Self {
|
||||
self.0.push_str(" ... in ");
|
||||
self.0.push_str(context);
|
||||
self
|
||||
}
|
||||
}
|
||||
|
||||
pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
|
||||
type Value: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>>;
|
||||
type Type: BasicType<'ctx>;
|
||||
|
||||
/// Return the [`BasicType`] of this model.
|
||||
fn get_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Self::Type;
|
||||
|
||||
/// Check if a [`BasicType`] is the same type 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 with [`Instance::model`] being this model.
|
||||
///
|
||||
/// Caller must make sure the type of `value` and the type of this `model` are equivalent.
|
||||
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 it 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")
|
||||
};
|
||||
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, '_>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Self> {
|
||||
let pmodel = PtrModel(*self);
|
||||
let p = ctx.builder.build_alloca(self.get_type(generator, ctx.ctx), name).unwrap();
|
||||
pmodel.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>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Self> {
|
||||
let pmodel = PtrModel(*self);
|
||||
let p =
|
||||
ctx.builder.build_array_alloca(self.get_type(generator, ctx.ctx), len, name).unwrap();
|
||||
pmodel.believe_value(p)
|
||||
}
|
||||
|
||||
fn var_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&str>,
|
||||
) -> Result<Ptr<'ctx, Self>, String> {
|
||||
let pmodel = PtrModel(*self);
|
||||
let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
|
||||
let p = generator.gen_var_alloc(ctx, ty, name)?;
|
||||
Ok(pmodel.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<Ptr<'ctx, Self>, String> {
|
||||
// TODO: Remove ArraySliceValue
|
||||
let pmodel = PtrModel(*self);
|
||||
let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
|
||||
let p = generator.gen_array_var_alloc(ctx, ty, len, name)?;
|
||||
Ok(pmodel.believe_value(PointerValue::from(p)))
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct Instance<'ctx, M: Model<'ctx>> {
|
||||
/// The model of this instance.
|
||||
pub model: M,
|
||||
/// The value of this instance.
|
||||
///
|
||||
/// Caller must make sure the type of `value` and the type of this `model` are equivalent,
|
||||
/// down to having the same [`IntType::get_bit_width`] in case of [`IntType`] for example.
|
||||
pub value: M::Value,
|
||||
}
|
|
@ -0,0 +1,271 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{context::Context, types::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: &mut 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: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.bool_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Byte {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i8_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Int32 {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i32_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Int64 {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i64_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for SizeT {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut 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: &mut G,
|
||||
_ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
self.0
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct IntModel<N>(pub N);
|
||||
pub type Int<'ctx, N> = Instance<'ctx, IntModel<N>>;
|
||||
|
||||
impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for IntModel<N> {
|
||||
type Value = IntValue<'ctx>;
|
||||
type Type = IntType<'ctx>;
|
||||
|
||||
#[must_use]
|
||||
fn get_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Self::Type {
|
||||
self.0.get_int_type(generator, ctx)
|
||||
}
|
||||
|
||||
fn check_type<T: inkwell::types::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>> IntModel<N> {
|
||||
pub fn constant<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
value: u64,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = self.get_type(generator, ctx).const_int(value, false);
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn const_0<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, N> {
|
||||
self.constant(generator, ctx, 0)
|
||||
}
|
||||
|
||||
pub fn const_1<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, N> {
|
||||
self.constant(generator, ctx, 1)
|
||||
}
|
||||
|
||||
pub fn s_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = ctx
|
||||
.builder
|
||||
.build_int_s_extend_or_bit_cast(value, self.get_type(generator, ctx.ctx), name)
|
||||
.unwrap();
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn truncate<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, N> {
|
||||
let value =
|
||||
ctx.builder.build_int_truncate(value, self.get_type(generator, ctx.ctx), name).unwrap();
|
||||
self.believe_value(value)
|
||||
}
|
||||
}
|
||||
|
||||
impl IntModel<Bool> {
|
||||
#[must_use]
|
||||
pub fn const_false<'ctx, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, Bool> {
|
||||
self.constant(generator, ctx, 0)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn const_true<'ctx, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, Bool> {
|
||||
self.constant(generator, ctx, 1)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, N: IntKind<'ctx>> Int<'ctx, 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,
|
||||
name: &str,
|
||||
) -> Int<'ctx, NewN> {
|
||||
IntModel(to_int_kind).s_extend_or_bit_cast(generator, ctx, self.value, name)
|
||||
}
|
||||
|
||||
pub fn truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
name: &str,
|
||||
) -> Int<'ctx, NewN> {
|
||||
IntModel(to_int_kind).truncate(generator, ctx, self.value, name)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn add(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
other: Int<'ctx, N>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = ctx.builder.build_int_add(self.value, other.value, name).unwrap();
|
||||
self.model.believe_value(value)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn sub(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
other: Int<'ctx, N>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = ctx.builder.build_int_sub(self.value, other.value, name).unwrap();
|
||||
self.model.believe_value(value)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn mul(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
other: Int<'ctx, N>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = ctx.builder.build_int_mul(self.value, other.value, name).unwrap();
|
||||
self.model.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn compare(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
op: IntPredicate,
|
||||
other: Int<'ctx, N>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, Bool> {
|
||||
let bool_model = IntModel(Bool);
|
||||
let value = ctx.builder.build_int_compare(op, self.value, other.value, name).unwrap();
|
||||
bool_model.believe_value(value)
|
||||
}
|
||||
}
|
|
@ -0,0 +1,12 @@
|
|||
mod any;
|
||||
mod core;
|
||||
mod int;
|
||||
mod ptr;
|
||||
mod structure;
|
||||
pub mod util;
|
||||
|
||||
pub use any::*;
|
||||
pub use core::*;
|
||||
pub use int::*;
|
||||
pub use ptr::*;
|
||||
pub use structure::*;
|
|
@ -0,0 +1,147 @@
|
|||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicType, BasicTypeEnum, PointerType},
|
||||
values::{IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct PtrModel<Element>(pub Element);
|
||||
pub type Ptr<'ctx, Element> = Instance<'ctx, PtrModel<Element>>;
|
||||
|
||||
impl<'ctx, Element: Model<'ctx>> Model<'ctx> for PtrModel<Element> {
|
||||
type Value = PointerValue<'ctx>;
|
||||
type Type = PointerType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Self::Type {
|
||||
self.0.get_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, Element: Model<'ctx>> PtrModel<Element> {
|
||||
/// Return a ***constant*** nullptr.
|
||||
pub fn nullptr<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
let ptr = self.get_type(generator, ctx).const_null();
|
||||
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>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
let ptr =
|
||||
ctx.builder.build_pointer_cast(ptr, self.get_type(generator, ctx.ctx), name).unwrap();
|
||||
self.believe_value(ptr)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, Element: Model<'ctx>> Ptr<'ctx, Element> {
|
||||
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`].
|
||||
#[must_use]
|
||||
pub fn offset<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
offset: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
let new_ptr =
|
||||
unsafe { ctx.builder.build_in_bounds_gep(self.value, &[offset], name).unwrap() };
|
||||
self.model.check_value(generator, ctx.ctx, new_ptr).unwrap()
|
||||
}
|
||||
|
||||
// Load the `i`-th element (0-based) on the array with [`inkwell::builder::Builder::build_in_bounds_gep`].
|
||||
pub fn ix<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
i: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Instance<'ctx, Element> {
|
||||
self.offset(generator, ctx, i, name).load(generator, ctx, name)
|
||||
}
|
||||
|
||||
/// Load the value with [`inkwell::builder::Builder::build_load`].
|
||||
pub fn load<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Instance<'ctx, Element> {
|
||||
let value = ctx.builder.build_load(self.value, name).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, Element>) {
|
||||
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 transmute<NewElement: Model<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
new_model: NewElement,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, NewElement> {
|
||||
PtrModel(new_model).pointer_cast(generator, ctx, self.value, name)
|
||||
}
|
||||
|
||||
/// Check if the pointer is null with [`inkwell::builder::Builder::build_is_null`].
|
||||
pub fn is_null(&self, ctx: &CodeGenContext<'ctx, '_>, name: &str) -> Int<'ctx, Bool> {
|
||||
let bool_model = IntModel(Bool);
|
||||
let value = ctx.builder.build_is_null(self.value, name).unwrap();
|
||||
bool_model.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, '_>, name: &str) -> Int<'ctx, Bool> {
|
||||
let bool_model = IntModel(Bool);
|
||||
let value = ctx.builder.build_is_not_null(self.value, name).unwrap();
|
||||
bool_model.believe_value(value)
|
||||
}
|
||||
}
|
|
@ -0,0 +1,207 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicType, BasicTypeEnum, StructType},
|
||||
values::StructValue,
|
||||
};
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct GepField<M> {
|
||||
pub gep_index: u64,
|
||||
pub name: &'static str,
|
||||
pub model: M,
|
||||
}
|
||||
|
||||
pub trait FieldTraversal<'ctx> {
|
||||
type Out<M>;
|
||||
|
||||
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M>;
|
||||
|
||||
/// Like [`FieldTraversal::visit`] but [`Model`] is automatically inferred.
|
||||
fn add_auto<M: Model<'ctx> + Default>(&mut self, name: &'static str) -> Self::Out<M> {
|
||||
self.add(name, M::default())
|
||||
}
|
||||
}
|
||||
|
||||
pub struct GepFieldTraversal {
|
||||
gep_index_counter: u64,
|
||||
}
|
||||
|
||||
impl<'ctx> FieldTraversal<'ctx> for GepFieldTraversal {
|
||||
type Out<M> = GepField<M>;
|
||||
|
||||
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M> {
|
||||
let gep_index = self.gep_index_counter;
|
||||
self.gep_index_counter += 1;
|
||||
Self::Out { gep_index, name, model }
|
||||
}
|
||||
}
|
||||
|
||||
struct TypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
|
||||
generator: &'a mut G,
|
||||
ctx: &'ctx Context,
|
||||
field_types: Vec<BasicTypeEnum<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx> for TypeFieldTraversal<'ctx, 'a, G> {
|
||||
type Out<M> = ();
|
||||
|
||||
fn add<M: Model<'ctx>>(&mut self, _name: &'static str, model: M) -> Self::Out<M> {
|
||||
let t = model.get_type(self.generator, self.ctx).as_basic_type_enum();
|
||||
self.field_types.push(t);
|
||||
}
|
||||
}
|
||||
|
||||
struct CheckTypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
|
||||
generator: &'a mut G,
|
||||
ctx: &'ctx Context,
|
||||
index: u32,
|
||||
scrutinee: StructType<'ctx>,
|
||||
errors: Vec<ModelError>,
|
||||
}
|
||||
|
||||
impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx>
|
||||
for CheckTypeFieldTraversal<'ctx, 'a, G>
|
||||
{
|
||||
type Out<M> = ();
|
||||
|
||||
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M> {
|
||||
let i = self.index;
|
||||
self.index += 1;
|
||||
|
||||
if let Some(t) = self.scrutinee.get_field_type_at_index(i) {
|
||||
if let Err(err) = model.check_type(self.generator, self.ctx, t) {
|
||||
self.errors.push(err.under_context(format!("field #{i} '{name}'").as_str()));
|
||||
}
|
||||
} // Otherwise, it will be caught
|
||||
}
|
||||
}
|
||||
|
||||
pub trait StructKind<'ctx>: fmt::Debug + Clone + Copy {
|
||||
type Fields<F: FieldTraversal<'ctx>>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F>;
|
||||
|
||||
fn fields(&self) -> Self::Fields<GepFieldTraversal> {
|
||||
self.traverse_fields(&mut GepFieldTraversal { gep_index_counter: 0 })
|
||||
}
|
||||
|
||||
fn get_struct_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> StructType<'ctx> {
|
||||
let mut traversal = TypeFieldTraversal { generator, ctx, field_types: Vec::new() };
|
||||
self.traverse_fields(&mut traversal);
|
||||
|
||||
ctx.struct_type(&traversal.field_types, false)
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct StructModel<S>(pub S);
|
||||
pub type Struct<'ctx, S> = Instance<'ctx, StructModel<S>>;
|
||||
|
||||
impl<'ctx, S: StructKind<'ctx>> Model<'ctx> for StructModel<S> {
|
||||
type Value = StructValue<'ctx>;
|
||||
type Type = StructType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut 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:?}")));
|
||||
};
|
||||
|
||||
let mut traversal =
|
||||
CheckTypeFieldTraversal { generator, ctx, index: 0, errors: Vec::new(), scrutinee: ty };
|
||||
self.0.traverse_fields(&mut traversal);
|
||||
|
||||
let exp_num_fields = traversal.index;
|
||||
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() {
|
||||
return Err(traversal.errors[0].clone()); // TODO: Return other errors as well
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, S: StructKind<'ctx>> Ptr<'ctx, StructModel<S>> {
|
||||
pub fn gep<M, GetField>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
get_field: GetField,
|
||||
) -> Ptr<'ctx, 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(); // i64 would segfault
|
||||
|
||||
let ptr = unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
self.value,
|
||||
&[llvm_i32.const_zero(), llvm_i32.const_int(field.gep_index, false)],
|
||||
field.name,
|
||||
)
|
||||
.unwrap()
|
||||
};
|
||||
|
||||
let ptr_model = PtrModel(field.model);
|
||||
ptr_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,
|
||||
name: &str,
|
||||
) -> Instance<'ctx, M>
|
||||
where
|
||||
M: Model<'ctx>,
|
||||
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
|
||||
{
|
||||
self.gep(ctx, get_field).load(generator, ctx, name)
|
||||
}
|
||||
|
||||
/// 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);
|
||||
}
|
||||
}
|
|
@ -0,0 +1,91 @@
|
|||
use inkwell::{types::BasicType, values::IntValue};
|
||||
|
||||
/// `llvm.memcpy` but under the [`Model`] abstraction
|
||||
use crate::codegen::{
|
||||
llvm_intrinsics::call_memcpy_generic,
|
||||
stmt::{gen_for_callback_incrementing, BreakContinueHooks},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::*;
|
||||
|
||||
/// Convenience function.
|
||||
///
|
||||
/// Like [`call_memcpy_generic`] but with model abstractions and `is_volatile` set to `false`.
|
||||
pub fn call_memcpy_model<'ctx, Item: Model<'ctx> + Default, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_array: Ptr<'ctx, Item>,
|
||||
src_array: Ptr<'ctx, Item>,
|
||||
num_items: IntValue<'ctx>,
|
||||
) {
|
||||
let itemsize = Item::default().get_type(generator, ctx.ctx).size_of().unwrap();
|
||||
let totalsize = ctx.builder.build_int_mul(itemsize, num_items, "totalsize").unwrap(); // TODO: Int types may not match.
|
||||
let is_volatile = ctx.ctx.bool_type().const_zero();
|
||||
call_memcpy_generic(ctx, dst_array.value, src_array.value, totalsize, is_volatile);
|
||||
}
|
||||
|
||||
/// Like [`gen_for_callback_incrementing`] with [`Model`] abstractions.
|
||||
/// The [`IntKind`] is automatically inferred.
|
||||
pub fn gen_for_model_auto<'ctx, 'a, G, F, I>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
start: Int<'ctx, I>,
|
||||
stop: Int<'ctx, I>,
|
||||
step: Int<'ctx, I>,
|
||||
body: F,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
F: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks<'ctx>,
|
||||
Int<'ctx, I>,
|
||||
) -> Result<(), String>,
|
||||
I: IntKind<'ctx> + Default,
|
||||
{
|
||||
let int_model = IntModel(I::default());
|
||||
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
start.value,
|
||||
(stop.value, false),
|
||||
|g, ctx, hooks, i| {
|
||||
let i = int_model.believe_value(i);
|
||||
body(g, ctx, hooks, i)
|
||||
},
|
||||
step.value,
|
||||
)
|
||||
}
|
||||
|
||||
/// Like [`gen_if_callback`] with [`Model`] abstractions and without the `else` block.
|
||||
pub fn gen_if_model<'ctx, 'a, G, ThenFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
cond: Int<'ctx, Bool>,
|
||||
then: ThenFn,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
ThenFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<(), String>,
|
||||
{
|
||||
let current_bb = ctx.builder.get_insert_block().unwrap();
|
||||
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "if.then");
|
||||
let end_bb = ctx.ctx.insert_basic_block_after(then_bb, "if.end");
|
||||
|
||||
// Inserting into `current_bb`.
|
||||
ctx.builder.build_conditional_branch(cond.value, then_bb, end_bb).unwrap();
|
||||
|
||||
// Inserting into `then_bb`
|
||||
ctx.builder.position_at_end(then_bb);
|
||||
then(generator, ctx)?;
|
||||
ctx.builder.build_unconditional_branch(end_bb).unwrap();
|
||||
|
||||
// Reposition to `end_bb` for continuation.
|
||||
ctx.builder.position_at_end(end_bb);
|
||||
|
||||
Ok(())
|
||||
}
|
|
@ -26,12 +26,15 @@ use crate::{
|
|||
typedef::{FunSignature, Type, TypeEnum},
|
||||
},
|
||||
};
|
||||
use inkwell::types::{AnyTypeEnum, BasicTypeEnum, PointerType};
|
||||
use inkwell::{
|
||||
types::BasicType,
|
||||
values::{BasicValueEnum, IntValue, PointerValue},
|
||||
AddressSpace, IntPredicate, OptimizationLevel,
|
||||
};
|
||||
use inkwell::{
|
||||
types::{AnyTypeEnum, BasicTypeEnum, PointerType},
|
||||
values::BasicValue,
|
||||
};
|
||||
use nac3parser::ast::{Operator, StrRef};
|
||||
|
||||
/// Creates an uninitialized `NDArray` instance.
|
||||
|
@ -86,6 +89,7 @@ where
|
|||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(shape_len, false),
|
||||
|generator, ctx, _, i| {
|
||||
|
@ -131,6 +135,7 @@ where
|
|||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(shape_len, false),
|
||||
|generator, ctx, _, i| {
|
||||
|
@ -157,7 +162,7 @@ where
|
|||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `shape` - The shape of the `NDArray`, represented am array of [`IntValue`]s.
|
||||
fn create_ndarray_const_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
pub fn create_ndarray_const_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
|
@ -252,7 +257,7 @@ fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
|
|||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "")
|
||||
ctx.gen_string(generator, "").value.into()
|
||||
} else {
|
||||
unreachable!()
|
||||
}
|
||||
|
@ -280,7 +285,7 @@ fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
|
|||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_int(1, false).into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "1")
|
||||
ctx.gen_string(generator, "1").value.into()
|
||||
} else {
|
||||
unreachable!()
|
||||
}
|
||||
|
@ -382,6 +387,7 @@ where
|
|||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(ndarray_num_elems, false),
|
||||
|generator, ctx, _, i| {
|
||||
|
@ -703,11 +709,12 @@ fn ndarray_from_ndlist_impl<'ctx, G: CodeGenerator + ?Sized>(
|
|||
gen_for_range_callback(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
true,
|
||||
|_, _| Ok(llvm_usize.const_zero()),
|
||||
(|_, ctx| Ok(src_lst.load_size(ctx, None)), false),
|
||||
|_, _| Ok(llvm_usize.const_int(1, false)),
|
||||
|generator, ctx, i| {
|
||||
|generator, ctx, _, i| {
|
||||
let offset = ctx.builder.build_int_mul(stride, i, "").unwrap();
|
||||
|
||||
let dst_ptr =
|
||||
|
@ -943,11 +950,12 @@ fn call_ndarray_array_impl<'ctx, G: CodeGenerator + ?Sized>(
|
|||
gen_for_range_callback(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
true,
|
||||
|_, _| Ok(llvm_usize.const_zero()),
|
||||
(|_, _| Ok(stop), false),
|
||||
|_, _| Ok(llvm_usize.const_int(1, false)),
|
||||
|generator, ctx, _| {
|
||||
|generator, ctx, _, _| {
|
||||
let plist_plist_i8 = make_llvm_list(llvm_plist_i8.into())
|
||||
.ptr_type(AddressSpace::default());
|
||||
|
||||
|
@ -1086,13 +1094,17 @@ fn ndarray_sliced_copyto_impl<'ctx, G: CodeGenerator + ?Sized>(
|
|||
|
||||
// If there are no (remaining) slice expressions, memcpy the entire dimension
|
||||
if slices.is_empty() {
|
||||
let sizeof_elem = ctx.get_llvm_type(generator, elem_ty).size_of().unwrap();
|
||||
|
||||
let stride = call_ndarray_calc_size(
|
||||
generator,
|
||||
ctx,
|
||||
&src_arr.dim_sizes(),
|
||||
(Some(llvm_usize.const_int(dim, false)), None),
|
||||
);
|
||||
let sizeof_elem = ctx.get_llvm_type(generator, elem_ty).size_of().unwrap();
|
||||
let stride =
|
||||
ctx.builder.build_int_z_extend_or_bit_cast(stride, sizeof_elem.get_type(), "").unwrap();
|
||||
|
||||
let cpy_len = ctx.builder.build_int_mul(stride, sizeof_elem, "").unwrap();
|
||||
|
||||
call_memcpy_generic(ctx, dst_slice_ptr, src_slice_ptr, cpy_len, llvm_i1.const_zero());
|
||||
|
@ -1126,11 +1138,12 @@ fn ndarray_sliced_copyto_impl<'ctx, G: CodeGenerator + ?Sized>(
|
|||
gen_for_range_callback(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
false,
|
||||
|_, _| Ok(start),
|
||||
(|_, _| Ok(stop), true),
|
||||
|_, _| Ok(step),
|
||||
|generator, ctx, src_i| {
|
||||
|generator, ctx, _, src_i| {
|
||||
// Calculate the offset of the active slice
|
||||
let src_data_offset = ctx.builder.build_int_mul(src_stride, src_i, "").unwrap();
|
||||
let dst_i =
|
||||
|
@ -1243,6 +1256,7 @@ pub fn ndarray_sliced_copy<'ctx, G: CodeGenerator + ?Sized>(
|
|||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_int(slices.len() as u64, false),
|
||||
(this.load_ndims(ctx), false),
|
||||
|generator, ctx, _, idx| {
|
||||
|
@ -1647,6 +1661,7 @@ pub fn ndarray_matmul_2d<'ctx, G: CodeGenerator>(
|
|||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_i32.const_zero(),
|
||||
(common_dim, false),
|
||||
|generator, ctx, _, i| {
|
||||
|
@ -2014,3 +2029,493 @@ pub fn gen_ndarray_fill<'ctx>(
|
|||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.transpose`.
|
||||
pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
x1: (Type, BasicValueEnum<'ctx>),
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
const FN_NAME: &str = "ndarray_transpose";
|
||||
let (x1_ty, x1) = x1;
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
if let BasicValueEnum::PointerValue(n1) = x1 {
|
||||
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
|
||||
let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
|
||||
let n_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
|
||||
|
||||
// Dimensions are reversed in the transposed array
|
||||
let out = create_ndarray_dyn_shape(
|
||||
generator,
|
||||
ctx,
|
||||
elem_ty,
|
||||
&n1,
|
||||
|_, ctx, n| Ok(n.load_ndims(ctx)),
|
||||
|generator, ctx, n, idx| {
|
||||
let new_idx = ctx.builder.build_int_sub(n.load_ndims(ctx), idx, "").unwrap();
|
||||
let new_idx = ctx
|
||||
.builder
|
||||
.build_int_sub(new_idx, new_idx.get_type().const_int(1, false), "")
|
||||
.unwrap();
|
||||
unsafe { Ok(n.dim_sizes().get_typed_unchecked(ctx, generator, &new_idx, None)) }
|
||||
},
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(n_sz, false),
|
||||
|generator, ctx, _, idx| {
|
||||
let elem = unsafe { n1.data().get_unchecked(ctx, generator, &idx, None) };
|
||||
|
||||
let new_idx = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
|
||||
let rem_idx = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
|
||||
ctx.builder.build_store(new_idx, llvm_usize.const_zero()).unwrap();
|
||||
ctx.builder.build_store(rem_idx, idx).unwrap();
|
||||
|
||||
// Incrementally calculate the new index in the transposed array
|
||||
// For each index, we first decompose it into the n-dims and use those to reconstruct the new index
|
||||
// The formula used for indexing is:
|
||||
// idx = dim_n * ( ... (dim2 * (dim0 * dim1) + dim1) + dim2 ... ) + dim_n
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(n1.load_ndims(ctx), false),
|
||||
|generator, ctx, _, ndim| {
|
||||
let ndim_rev =
|
||||
ctx.builder.build_int_sub(n1.load_ndims(ctx), ndim, "").unwrap();
|
||||
let ndim_rev = ctx
|
||||
.builder
|
||||
.build_int_sub(ndim_rev, llvm_usize.const_int(1, false), "")
|
||||
.unwrap();
|
||||
let dim = unsafe {
|
||||
n1.dim_sizes().get_typed_unchecked(ctx, generator, &ndim_rev, None)
|
||||
};
|
||||
|
||||
let rem_idx_val =
|
||||
ctx.builder.build_load(rem_idx, "").unwrap().into_int_value();
|
||||
let new_idx_val =
|
||||
ctx.builder.build_load(new_idx, "").unwrap().into_int_value();
|
||||
|
||||
let add_component =
|
||||
ctx.builder.build_int_unsigned_rem(rem_idx_val, dim, "").unwrap();
|
||||
let rem_idx_val =
|
||||
ctx.builder.build_int_unsigned_div(rem_idx_val, dim, "").unwrap();
|
||||
|
||||
let new_idx_val = ctx.builder.build_int_mul(new_idx_val, dim, "").unwrap();
|
||||
let new_idx_val =
|
||||
ctx.builder.build_int_add(new_idx_val, add_component, "").unwrap();
|
||||
|
||||
ctx.builder.build_store(rem_idx, rem_idx_val).unwrap();
|
||||
ctx.builder.build_store(new_idx, new_idx_val).unwrap();
|
||||
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)?;
|
||||
|
||||
let new_idx_val = ctx.builder.build_load(new_idx, "").unwrap().into_int_value();
|
||||
unsafe { out.data().set_unchecked(ctx, generator, &new_idx_val, elem) };
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)?;
|
||||
|
||||
Ok(out.as_base_value().into())
|
||||
} else {
|
||||
unreachable!(
|
||||
"{FN_NAME}() not supported for '{}'",
|
||||
format!("'{}'", ctx.unifier.stringify(x1_ty))
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for `ndarray.reshape`.
|
||||
///
|
||||
/// * `x1` - `NDArray` to reshape.
|
||||
/// * `shape` - The `shape` parameter used to construct the new `NDArray`.
|
||||
/// Just like numpy, the `shape` argument can be:
|
||||
/// 1. A list of `int32`; e.g., `np.reshape(arr, [600, -1, 3])`
|
||||
/// 2. A tuple of `int32`; e.g., `np.reshape(arr, (-1, 800, 3))`
|
||||
/// 3. A scalar `int32`; e.g., `np.reshape(arr, 3)`
|
||||
/// Note that unlike other generating functions, one of the dimesions in the shape can be negative
|
||||
pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
x1: (Type, BasicValueEnum<'ctx>),
|
||||
shape: (Type, BasicValueEnum<'ctx>),
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
const FN_NAME: &str = "ndarray_reshape";
|
||||
let (x1_ty, x1) = x1;
|
||||
let (_, shape) = shape;
|
||||
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
if let BasicValueEnum::PointerValue(n1) = x1 {
|
||||
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
|
||||
let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
|
||||
let n_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
|
||||
|
||||
let acc = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
|
||||
let num_neg = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
|
||||
ctx.builder.build_store(acc, llvm_usize.const_int(1, false)).unwrap();
|
||||
ctx.builder.build_store(num_neg, llvm_usize.const_zero()).unwrap();
|
||||
|
||||
let out = match shape {
|
||||
BasicValueEnum::PointerValue(shape_list_ptr)
|
||||
if ListValue::is_instance(shape_list_ptr, llvm_usize).is_ok() =>
|
||||
{
|
||||
// 1. A list of ints; e.g., `np.reshape(arr, [int64(600), int64(800, -1])`
|
||||
|
||||
let shape_list = ListValue::from_ptr_val(shape_list_ptr, llvm_usize, None);
|
||||
// Check for -1 in dimensions
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(shape_list.load_size(ctx, None), false),
|
||||
|generator, ctx, _, idx| {
|
||||
let ele =
|
||||
shape_list.data().get(ctx, generator, &idx, None).into_int_value();
|
||||
let ele = ctx.builder.build_int_s_extend(ele, llvm_usize, "").unwrap();
|
||||
|
||||
gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_, ctx| {
|
||||
Ok(ctx
|
||||
.builder
|
||||
.build_int_compare(
|
||||
IntPredicate::SLT,
|
||||
ele,
|
||||
llvm_usize.const_zero(),
|
||||
"",
|
||||
)
|
||||
.unwrap())
|
||||
},
|
||||
|_, ctx| -> Result<Option<IntValue>, String> {
|
||||
let num_neg_value =
|
||||
ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
|
||||
let num_neg_value = ctx
|
||||
.builder
|
||||
.build_int_add(
|
||||
num_neg_value,
|
||||
llvm_usize.const_int(1, false),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
ctx.builder.build_store(num_neg, num_neg_value).unwrap();
|
||||
Ok(None)
|
||||
},
|
||||
|_, ctx| {
|
||||
let acc_value =
|
||||
ctx.builder.build_load(acc, "").unwrap().into_int_value();
|
||||
let acc_value =
|
||||
ctx.builder.build_int_mul(acc_value, ele, "").unwrap();
|
||||
ctx.builder.build_store(acc, acc_value).unwrap();
|
||||
Ok(None)
|
||||
},
|
||||
)?;
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)?;
|
||||
let acc_val = ctx.builder.build_load(acc, "").unwrap().into_int_value();
|
||||
let rem = ctx.builder.build_int_unsigned_div(n_sz, acc_val, "").unwrap();
|
||||
// Generate the output shape by filling -1 with `rem`
|
||||
create_ndarray_dyn_shape(
|
||||
generator,
|
||||
ctx,
|
||||
elem_ty,
|
||||
&shape_list,
|
||||
|_, ctx, _| Ok(shape_list.load_size(ctx, None)),
|
||||
|generator, ctx, shape_list, idx| {
|
||||
let dim =
|
||||
shape_list.data().get(ctx, generator, &idx, None).into_int_value();
|
||||
let dim = ctx.builder.build_int_s_extend(dim, llvm_usize, "").unwrap();
|
||||
|
||||
Ok(gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_, ctx| {
|
||||
Ok(ctx
|
||||
.builder
|
||||
.build_int_compare(
|
||||
IntPredicate::SLT,
|
||||
dim,
|
||||
llvm_usize.const_zero(),
|
||||
"",
|
||||
)
|
||||
.unwrap())
|
||||
},
|
||||
|_, _| Ok(Some(rem)),
|
||||
|_, _| Ok(Some(dim)),
|
||||
)?
|
||||
.unwrap()
|
||||
.into_int_value())
|
||||
},
|
||||
)
|
||||
}
|
||||
BasicValueEnum::StructValue(shape_tuple) => {
|
||||
// 2. A tuple of `int32`; e.g., `np.reshape(arr, (-1, 800, 3))`
|
||||
|
||||
let ndims = shape_tuple.get_type().count_fields();
|
||||
// Check for -1 in dims
|
||||
for dim_i in 0..ndims {
|
||||
let dim = ctx
|
||||
.builder
|
||||
.build_extract_value(shape_tuple, dim_i, "")
|
||||
.unwrap()
|
||||
.into_int_value();
|
||||
let dim = ctx.builder.build_int_s_extend(dim, llvm_usize, "").unwrap();
|
||||
|
||||
gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_, ctx| {
|
||||
Ok(ctx
|
||||
.builder
|
||||
.build_int_compare(
|
||||
IntPredicate::SLT,
|
||||
dim,
|
||||
llvm_usize.const_zero(),
|
||||
"",
|
||||
)
|
||||
.unwrap())
|
||||
},
|
||||
|_, ctx| -> Result<Option<IntValue>, String> {
|
||||
let num_negs =
|
||||
ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
|
||||
let num_negs = ctx
|
||||
.builder
|
||||
.build_int_add(num_negs, llvm_usize.const_int(1, false), "")
|
||||
.unwrap();
|
||||
ctx.builder.build_store(num_neg, num_negs).unwrap();
|
||||
Ok(None)
|
||||
},
|
||||
|_, ctx| {
|
||||
let acc_val = ctx.builder.build_load(acc, "").unwrap().into_int_value();
|
||||
let acc_val = ctx.builder.build_int_mul(acc_val, dim, "").unwrap();
|
||||
ctx.builder.build_store(acc, acc_val).unwrap();
|
||||
Ok(None)
|
||||
},
|
||||
)?;
|
||||
}
|
||||
|
||||
let acc_val = ctx.builder.build_load(acc, "").unwrap().into_int_value();
|
||||
let rem = ctx.builder.build_int_unsigned_div(n_sz, acc_val, "").unwrap();
|
||||
let mut shape = Vec::with_capacity(ndims as usize);
|
||||
|
||||
// Reconstruct shape filling negatives with rem
|
||||
for dim_i in 0..ndims {
|
||||
let dim = ctx
|
||||
.builder
|
||||
.build_extract_value(shape_tuple, dim_i, "")
|
||||
.unwrap()
|
||||
.into_int_value();
|
||||
let dim = ctx.builder.build_int_s_extend(dim, llvm_usize, "").unwrap();
|
||||
|
||||
let dim = gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_, ctx| {
|
||||
Ok(ctx
|
||||
.builder
|
||||
.build_int_compare(
|
||||
IntPredicate::SLT,
|
||||
dim,
|
||||
llvm_usize.const_zero(),
|
||||
"",
|
||||
)
|
||||
.unwrap())
|
||||
},
|
||||
|_, _| Ok(Some(rem)),
|
||||
|_, _| Ok(Some(dim)),
|
||||
)?
|
||||
.unwrap()
|
||||
.into_int_value();
|
||||
shape.push(dim);
|
||||
}
|
||||
create_ndarray_const_shape(generator, ctx, elem_ty, shape.as_slice())
|
||||
}
|
||||
BasicValueEnum::IntValue(shape_int) => {
|
||||
// 3. A scalar `int32`; e.g., `np.reshape(arr, 3)`
|
||||
let shape_int = gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_, ctx| {
|
||||
Ok(ctx
|
||||
.builder
|
||||
.build_int_compare(
|
||||
IntPredicate::SLT,
|
||||
shape_int,
|
||||
llvm_usize.const_zero(),
|
||||
"",
|
||||
)
|
||||
.unwrap())
|
||||
},
|
||||
|_, _| Ok(Some(n_sz)),
|
||||
|_, ctx| {
|
||||
Ok(Some(ctx.builder.build_int_s_extend(shape_int, llvm_usize, "").unwrap()))
|
||||
},
|
||||
)?
|
||||
.unwrap()
|
||||
.into_int_value();
|
||||
create_ndarray_const_shape(generator, ctx, elem_ty, &[shape_int])
|
||||
}
|
||||
_ => unreachable!(),
|
||||
}
|
||||
.unwrap();
|
||||
|
||||
// Only allow one dimension to be negative
|
||||
let num_negs = ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
ctx.builder
|
||||
.build_int_compare(IntPredicate::ULT, num_negs, llvm_usize.const_int(2, false), "")
|
||||
.unwrap(),
|
||||
"0:ValueError",
|
||||
"can only specify one unknown dimension",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
// The new shape must be compatible with the old shape
|
||||
let out_sz = call_ndarray_calc_size(generator, ctx, &out.dim_sizes(), (None, None));
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
ctx.builder.build_int_compare(IntPredicate::EQ, out_sz, n_sz, "").unwrap(),
|
||||
"0:ValueError",
|
||||
"cannot reshape array of size {0} into provided shape of size {1}",
|
||||
[Some(n_sz), Some(out_sz), None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(n_sz, false),
|
||||
|generator, ctx, _, idx| {
|
||||
let elem = unsafe { n1.data().get_unchecked(ctx, generator, &idx, None) };
|
||||
unsafe { out.data().set_unchecked(ctx, generator, &idx, elem) };
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)?;
|
||||
|
||||
Ok(out.as_base_value().into())
|
||||
} else {
|
||||
unreachable!(
|
||||
"{FN_NAME}() not supported for '{}'",
|
||||
format!("'{}'", ctx.unifier.stringify(x1_ty))
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.dot`.
|
||||
/// Calculate inner product of two vectors or literals
|
||||
/// For matrix multiplication use `np_matmul`
|
||||
///
|
||||
/// The input `NDArray` are flattened and treated as 1D
|
||||
/// The operation is equivalent to `np.dot(arr1.ravel(), arr2.ravel())`
|
||||
pub fn ndarray_dot<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
x1: (Type, BasicValueEnum<'ctx>),
|
||||
x2: (Type, BasicValueEnum<'ctx>),
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
const FN_NAME: &str = "ndarray_dot";
|
||||
let (x1_ty, x1) = x1;
|
||||
let (_, x2) = x2;
|
||||
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
match (x1, x2) {
|
||||
(BasicValueEnum::PointerValue(n1), BasicValueEnum::PointerValue(n2)) => {
|
||||
let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
|
||||
let n2 = NDArrayValue::from_ptr_val(n2, llvm_usize, None);
|
||||
|
||||
let n1_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
|
||||
let n2_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
ctx.builder.build_int_compare(IntPredicate::EQ, n1_sz, n2_sz, "").unwrap(),
|
||||
"0:ValueError",
|
||||
"shapes ({0}), ({1}) not aligned",
|
||||
[Some(n1_sz), Some(n2_sz), None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
let identity =
|
||||
unsafe { n1.data().get_unchecked(ctx, generator, &llvm_usize.const_zero(), None) };
|
||||
let acc = ctx.builder.build_alloca(identity.get_type(), "").unwrap();
|
||||
ctx.builder.build_store(acc, identity.get_type().const_zero()).unwrap();
|
||||
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(n1_sz, false),
|
||||
|generator, ctx, _, idx| {
|
||||
let elem1 = unsafe { n1.data().get_unchecked(ctx, generator, &idx, None) };
|
||||
let elem2 = unsafe { n2.data().get_unchecked(ctx, generator, &idx, None) };
|
||||
|
||||
let product = match elem1 {
|
||||
BasicValueEnum::IntValue(e1) => ctx
|
||||
.builder
|
||||
.build_int_mul(e1, elem2.into_int_value(), "")
|
||||
.unwrap()
|
||||
.as_basic_value_enum(),
|
||||
BasicValueEnum::FloatValue(e1) => ctx
|
||||
.builder
|
||||
.build_float_mul(e1, elem2.into_float_value(), "")
|
||||
.unwrap()
|
||||
.as_basic_value_enum(),
|
||||
_ => unreachable!(),
|
||||
};
|
||||
let acc_val = ctx.builder.build_load(acc, "").unwrap();
|
||||
let acc_val = match acc_val {
|
||||
BasicValueEnum::IntValue(e1) => ctx
|
||||
.builder
|
||||
.build_int_add(e1, product.into_int_value(), "")
|
||||
.unwrap()
|
||||
.as_basic_value_enum(),
|
||||
BasicValueEnum::FloatValue(e1) => ctx
|
||||
.builder
|
||||
.build_float_add(e1, product.into_float_value(), "")
|
||||
.unwrap()
|
||||
.as_basic_value_enum(),
|
||||
_ => unreachable!(),
|
||||
};
|
||||
ctx.builder.build_store(acc, acc_val).unwrap();
|
||||
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)?;
|
||||
let acc_val = ctx.builder.build_load(acc, "").unwrap();
|
||||
Ok(acc_val)
|
||||
}
|
||||
(BasicValueEnum::IntValue(e1), BasicValueEnum::IntValue(e2)) => {
|
||||
Ok(ctx.builder.build_int_mul(e1, e2, "").unwrap().as_basic_value_enum())
|
||||
}
|
||||
(BasicValueEnum::FloatValue(e1), BasicValueEnum::FloatValue(e2)) => {
|
||||
Ok(ctx.builder.build_float_mul(e1, e2, "").unwrap().as_basic_value_enum())
|
||||
}
|
||||
_ => unreachable!(
|
||||
"{FN_NAME}() not supported for '{}'",
|
||||
format!("'{}'", ctx.unifier.stringify(x1_ty))
|
||||
),
|
||||
}
|
||||
}
|
||||
|
|
|
@ -0,0 +1,523 @@
|
|||
// TODO: Replace numpy.rs
|
||||
|
||||
use inkwell::values::{BasicValue, BasicValueEnum};
|
||||
use nac3parser::ast::StrRef;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::{call_nac3_ndarray_resolve_and_check_new_shape, call_nac3_ndarray_transpose},
|
||||
structure::{
|
||||
ndarray::{
|
||||
scalar::split_scalar_or_ndarray, shape_util::parse_numpy_int_sequence,
|
||||
NDArrayObject,
|
||||
},
|
||||
tuple::TupleObject,
|
||||
},
|
||||
},
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{
|
||||
numpy::{extract_ndims, unpack_ndarray_var_tys},
|
||||
DefinitionId,
|
||||
},
|
||||
typecheck::typedef::{FunSignature, Type},
|
||||
};
|
||||
|
||||
use super::{
|
||||
irrt::call_nac3_ndarray_util_assert_shape_no_negative, model::*, CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
/// 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, "").value.into()
|
||||
} else {
|
||||
unreachable!()
|
||||
}
|
||||
}
|
||||
|
||||
/// 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, '_>,
|
||||
elem_ty: Type,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
if [ctx.primitives.int32, ctx.primitives.uint32]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(elem_ty, *ty))
|
||||
{
|
||||
let is_signed = ctx.unifier.unioned(elem_ty, 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(elem_ty, *ty))
|
||||
{
|
||||
let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int64);
|
||||
ctx.ctx.i64_type().const_int(1, is_signed).into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
|
||||
ctx.ctx.f64_type().const_float(1.0).into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_int(1, false).into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "1").value.into()
|
||||
} else {
|
||||
unreachable!()
|
||||
}
|
||||
}
|
||||
|
||||
/// Helper function to create an ndarray with uninitialized values.
|
||||
///
|
||||
/// * `ndarray_ty` - The [`Type`] of the ndarray
|
||||
/// * `shape` - The user input shape argument
|
||||
/// * `shape_ty` - The [`Type`] of the shape argument
|
||||
///
|
||||
/// This function does data validation the `shape` input.
|
||||
fn create_empty_ndarray<'ctx, G>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ty: Type,
|
||||
shape: BasicValueEnum<'ctx>,
|
||||
shape_ty: Type,
|
||||
) -> NDArrayObject<'ctx>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
{
|
||||
let (_, shape) = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
|
||||
|
||||
let ndarray =
|
||||
NDArrayObject::alloca_uninitialized_of_type(generator, ctx, ndarray_ty, "ndarray");
|
||||
|
||||
// Validate `shape`
|
||||
let ndims = ndarray.get_ndims(generator, ctx.ctx);
|
||||
call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, ndims, shape);
|
||||
|
||||
// Setup `ndarray` with `shape`
|
||||
ndarray.copy_shape_from_array(generator, ctx, shape);
|
||||
ndarray.create_data(generator, ctx); // `shape` has to be set
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.empty`.
|
||||
pub fn gen_ndarray_empty<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse arguments
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
|
||||
// Implementation
|
||||
let ndarray_ty = fun.0.ret;
|
||||
let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
|
||||
|
||||
Ok(ndarray.value.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.zero`.
|
||||
pub fn gen_ndarray_zeros<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse arguments
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
|
||||
// Implementation
|
||||
let ndarray_ty = fun.0.ret;
|
||||
let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
|
||||
|
||||
let fill_value = ndarray_zero_value(generator, ctx, ndarray.dtype);
|
||||
ndarray.fill(generator, ctx, fill_value);
|
||||
|
||||
Ok(ndarray.value.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.ones`.
|
||||
pub fn gen_ndarray_ones<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse arguments
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
|
||||
// Implementation
|
||||
let ndarray_ty = fun.0.ret;
|
||||
let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
|
||||
|
||||
let fill_value = ndarray_zero_value(generator, ctx, ndarray.dtype);
|
||||
ndarray.fill(generator, ctx, fill_value);
|
||||
|
||||
Ok(ndarray.value.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.full`.
|
||||
pub fn gen_ndarray_full<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 2);
|
||||
|
||||
// Parse argument #1 shape
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
|
||||
// Parse argument #2 fill_value
|
||||
let fill_value_ty = fun.0.args[1].ty;
|
||||
let fill_value = args[1].1.clone().to_basic_value_enum(ctx, generator, fill_value_ty)?;
|
||||
|
||||
// Implementation
|
||||
let ndarray_ty = fun.0.ret;
|
||||
let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
|
||||
|
||||
ndarray.fill(generator, ctx, fill_value);
|
||||
|
||||
Ok(ndarray.value.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.broadcast_to`.
|
||||
pub fn gen_ndarray_broadcast_to<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 2);
|
||||
|
||||
// Parse argument #1 input
|
||||
let input_ty = fun.0.args[0].ty;
|
||||
let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?;
|
||||
|
||||
// Parse argument #2 shape
|
||||
let shape_ty = fun.0.args[1].ty;
|
||||
let shape = args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Extract broadcast_ndims, this is the only way to get the
|
||||
// ndims of the ndarray result statically.
|
||||
let (_, broadcast_ndims_ty) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
|
||||
let broadcast_ndims = extract_ndims(&ctx.unifier, broadcast_ndims_ty);
|
||||
|
||||
// Process `input`
|
||||
let in_ndarray =
|
||||
split_scalar_or_ndarray(generator, ctx, input, input_ty).as_ndarray(generator, ctx);
|
||||
|
||||
// Process `shape`
|
||||
let (_, broadcast_shape) = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
|
||||
// NOTE: shape.size should equal to `broadcasted_ndims`.
|
||||
let broadcast_ndims_llvm = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
|
||||
call_nac3_ndarray_util_assert_shape_no_negative(
|
||||
generator,
|
||||
ctx,
|
||||
broadcast_ndims_llvm,
|
||||
broadcast_shape,
|
||||
);
|
||||
|
||||
// Create broadcast view
|
||||
let broadcast_ndarray =
|
||||
in_ndarray.broadcast_to(generator, ctx, broadcast_ndims, broadcast_shape);
|
||||
|
||||
Ok(broadcast_ndarray.value.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.reshape`.
|
||||
pub fn gen_ndarray_reshape<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 2);
|
||||
|
||||
// Parse argument #1 input
|
||||
let input_ty = fun.0.args[0].ty;
|
||||
let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?;
|
||||
|
||||
// Parse argument #2 shape
|
||||
let shape_ty = fun.0.args[1].ty;
|
||||
let shape = args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Extract reshaped_ndims
|
||||
let (_, reshaped_ndims_ty) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
|
||||
let reshaped_ndims = extract_ndims(&ctx.unifier, reshaped_ndims_ty);
|
||||
|
||||
// Process `input`
|
||||
let in_ndarray =
|
||||
split_scalar_or_ndarray(generator, ctx, input, input_ty).as_ndarray(generator, ctx);
|
||||
let in_size = in_ndarray.size(generator, ctx);
|
||||
|
||||
// Process the shape input from user and resolve negative indices.
|
||||
// The resulting `new_shape`'s size should be equal to reshaped_ndims.
|
||||
// This is ensured by the typechecker.
|
||||
let (_, new_shape) = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
|
||||
|
||||
// Resolve unknown dimensions & validate `new_shape`.
|
||||
let new_ndims = sizet_model.constant(generator, ctx.ctx, reshaped_ndims);
|
||||
call_nac3_ndarray_resolve_and_check_new_shape(generator, ctx, in_size, new_ndims, new_shape);
|
||||
|
||||
// Reshape or copy
|
||||
let reshaped_ndarray = in_ndarray.reshape_or_copy(generator, ctx, reshaped_ndims, new_shape);
|
||||
|
||||
Ok(reshaped_ndarray.value.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.arange`.
|
||||
pub fn gen_ndarray_arange<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse argument #1 len
|
||||
let input_ty = fun.0.args[0].ty;
|
||||
let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?.into_int_value();
|
||||
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Process input
|
||||
let input = sizet_model.s_extend_or_bit_cast(generator, ctx, input, "input_dim");
|
||||
|
||||
// Allocate the resulting ndarray
|
||||
let ndarray = NDArrayObject::alloca_uninitialized(
|
||||
generator,
|
||||
ctx,
|
||||
ctx.primitives.float,
|
||||
1, // ndims = 1
|
||||
"arange_ndarray",
|
||||
);
|
||||
|
||||
// `ndarray.shape[0] = input`
|
||||
let zero = sizet_model.const_0(generator, ctx.ctx);
|
||||
ndarray
|
||||
.value
|
||||
.get(generator, ctx, |f| f.shape, "shape")
|
||||
.offset(generator, ctx, zero.value, "dim")
|
||||
.store(ctx, input);
|
||||
|
||||
// Create data and set elements
|
||||
ndarray.create_data(generator, ctx);
|
||||
ndarray.foreach_pointer(generator, ctx, |_generator, ctx, _hooks, i, pelement| {
|
||||
let val =
|
||||
ctx.builder.build_unsigned_int_to_float(i.value, ctx.ctx.f64_type(), "val").unwrap();
|
||||
ctx.builder.build_store(pelement, val).unwrap();
|
||||
Ok(())
|
||||
})?;
|
||||
|
||||
Ok(ndarray.value.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.size`.
|
||||
pub fn gen_ndarray_size<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
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 = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
|
||||
|
||||
let size = ndarray.size(generator, ctx).truncate(generator, ctx, Int32, "size");
|
||||
Ok(size.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.shape`.
|
||||
pub fn gen_ndarray_shape<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse argument #1 ndarray
|
||||
let ndarray_ty = fun.0.args[0].ty;
|
||||
let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Process ndarray
|
||||
let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
|
||||
|
||||
let mut items = Vec::with_capacity(ndarray.ndims as usize);
|
||||
|
||||
for i in 0..ndarray.ndims {
|
||||
let i = sizet_model.constant(generator, ctx.ctx, i);
|
||||
let dim =
|
||||
ndarray.value.get(generator, ctx, |f| f.shape, "").ix(generator, ctx, i.value, "dim");
|
||||
let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
|
||||
|
||||
items.push((dim.value.as_basic_value_enum(), ctx.primitives.int32));
|
||||
}
|
||||
|
||||
let shape = TupleObject::create(generator, ctx, items, "shape");
|
||||
Ok(shape.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `<ndarray>.strides`.
|
||||
pub fn gen_ndarray_strides<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
// TODO: This function looks exactly like `gen_ndarray_shapes`, code duplication?
|
||||
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse argument #1 ndarray
|
||||
let ndarray_ty = fun.0.args[0].ty;
|
||||
let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Process ndarray
|
||||
let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
|
||||
|
||||
let mut items = Vec::with_capacity(ndarray.ndims as usize);
|
||||
|
||||
for i in 0..ndarray.ndims {
|
||||
let i = sizet_model.constant(generator, ctx.ctx, i);
|
||||
let dim =
|
||||
ndarray.value.get(generator, ctx, |f| f.strides, "").ix(generator, ctx, i.value, "dim");
|
||||
let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
|
||||
|
||||
items.push((dim.value.as_basic_value_enum(), ctx.primitives.int32));
|
||||
}
|
||||
|
||||
let strides = TupleObject::create(generator, ctx, items, "strides");
|
||||
Ok(strides.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.transpose`.
|
||||
pub fn gen_ndarray_transpose<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
// TODO: The implementation will be changed once default values start working again.
|
||||
// Read the comment on this function in BuiltinBuilder.
|
||||
|
||||
// TODO: Change axes values to `SizeT`
|
||||
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse argument #1 ndarray
|
||||
let ndarray_ty = fun.0.args[0].ty;
|
||||
let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Implementation
|
||||
let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
|
||||
let transposed_ndarray = NDArrayObject::alloca_uninitialized(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray.dtype,
|
||||
ndarray.ndims,
|
||||
"transposed_ndarray",
|
||||
);
|
||||
|
||||
let has_axes = args.len() >= 2;
|
||||
if has_axes {
|
||||
// Parse argument #2 axes
|
||||
let in_axes_ty = fun.0.args[1].ty;
|
||||
let in_axes = args[1].1.clone().to_basic_value_enum(ctx, generator, in_axes_ty)?;
|
||||
|
||||
let (_, axes) = parse_numpy_int_sequence(generator, ctx, in_axes, in_axes_ty);
|
||||
let num_axes = ndarray.get_ndims(generator, ctx.ctx);
|
||||
|
||||
call_nac3_ndarray_transpose(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray.value,
|
||||
transposed_ndarray.value,
|
||||
num_axes,
|
||||
axes,
|
||||
);
|
||||
} else {
|
||||
let num_axes = sizet_model.const_0(generator, ctx.ctx); // Placeholder value
|
||||
let axes = PtrModel(sizet_model).nullptr(generator, ctx.ctx);
|
||||
|
||||
// See IRRT implementation for argument requirements when axes is None
|
||||
call_nac3_ndarray_transpose(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray.value,
|
||||
transposed_ndarray.value,
|
||||
num_axes,
|
||||
axes,
|
||||
);
|
||||
}
|
||||
|
||||
Ok(transposed_ndarray.value.value.as_basic_value_enum())
|
||||
}
|
|
@ -1,19 +1,25 @@
|
|||
use super::model::*;
|
||||
use super::structure::cslice::CSlice;
|
||||
use super::{
|
||||
super::symbol_resolver::ValueEnum,
|
||||
expr::destructure_range,
|
||||
irrt::{handle_slice_indices, list_slice_assignment},
|
||||
CodeGenContext, CodeGenerator,
|
||||
structure::exception::Exception,
|
||||
CodeGenContext, CodeGenerator, Int32, IntModel, Ptr, StructModel,
|
||||
};
|
||||
use crate::codegen::structure::ndarray::indexing::util::gen_ndarray_subscript_ndindexes;
|
||||
use crate::codegen::structure::ndarray::scalar::split_scalar_or_ndarray;
|
||||
use crate::codegen::structure::ndarray::NDArrayObject;
|
||||
use crate::{
|
||||
codegen::{
|
||||
classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
|
||||
expr::gen_binop_expr,
|
||||
gen_in_range_check,
|
||||
},
|
||||
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, TopLevelDef},
|
||||
toplevel::{DefinitionId, TopLevelDef},
|
||||
typecheck::{
|
||||
magic_methods::Binop,
|
||||
typedef::{FunSignature, Type, TypeEnum},
|
||||
typedef::{iter_type_vars, FunSignature, Type, TypeEnum},
|
||||
},
|
||||
};
|
||||
use inkwell::{
|
||||
|
@ -23,10 +29,10 @@ use inkwell::{
|
|||
values::{BasicValue, BasicValueEnum, FunctionValue, IntValue, PointerValue},
|
||||
IntPredicate,
|
||||
};
|
||||
use itertools::{izip, Itertools};
|
||||
use nac3parser::ast::{
|
||||
Constant, ExcepthandlerKind, Expr, ExprKind, Location, Stmt, StmtKind, StrRef,
|
||||
};
|
||||
use std::convert::TryFrom;
|
||||
|
||||
/// See [`CodeGenerator::gen_var_alloc`].
|
||||
pub fn gen_var<'ctx>(
|
||||
|
@ -97,8 +103,6 @@ pub fn gen_store_target<'ctx, G: CodeGenerator>(
|
|||
pattern: &Expr<Option<Type>>,
|
||||
name: Option<&str>,
|
||||
) -> Result<Option<PointerValue<'ctx>>, String> {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
// very similar to gen_expr, but we don't do an extra load at the end
|
||||
// and we flatten nested tuples
|
||||
Ok(Some(match &pattern.node {
|
||||
|
@ -137,65 +141,6 @@ pub fn gen_store_target<'ctx, G: CodeGenerator>(
|
|||
}
|
||||
.unwrap()
|
||||
}
|
||||
ExprKind::Subscript { value, slice, .. } => {
|
||||
match ctx.unifier.get_ty_immutable(value.custom.unwrap()).as_ref() {
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::List.id() => {
|
||||
let v = generator
|
||||
.gen_expr(ctx, value)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, value.custom.unwrap())?
|
||||
.into_pointer_value();
|
||||
let v = ListValue::from_ptr_val(v, llvm_usize, None);
|
||||
let len = v.load_size(ctx, Some("len"));
|
||||
let raw_index = generator
|
||||
.gen_expr(ctx, slice)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?
|
||||
.into_int_value();
|
||||
let raw_index = ctx
|
||||
.builder
|
||||
.build_int_s_extend(raw_index, generator.get_size_type(ctx.ctx), "sext")
|
||||
.unwrap();
|
||||
// handle negative index
|
||||
let is_negative = ctx
|
||||
.builder
|
||||
.build_int_compare(
|
||||
IntPredicate::SLT,
|
||||
raw_index,
|
||||
generator.get_size_type(ctx.ctx).const_zero(),
|
||||
"is_neg",
|
||||
)
|
||||
.unwrap();
|
||||
let adjusted = ctx.builder.build_int_add(raw_index, len, "adjusted").unwrap();
|
||||
let index = ctx
|
||||
.builder
|
||||
.build_select(is_negative, adjusted, raw_index, "index")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
// unsigned less than is enough, because negative index after adjustment is
|
||||
// bigger than the length (for unsigned cmp)
|
||||
let bound_check = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::ULT, index, len, "inbound")
|
||||
.unwrap();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
bound_check,
|
||||
"0:IndexError",
|
||||
"index {0} out of bounds 0:{1}",
|
||||
[Some(raw_index), Some(len), None],
|
||||
slice.location,
|
||||
);
|
||||
v.data().ptr_offset(ctx, generator, &index, name)
|
||||
}
|
||||
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
todo!()
|
||||
}
|
||||
|
||||
_ => unreachable!(),
|
||||
}
|
||||
}
|
||||
_ => unreachable!(),
|
||||
}))
|
||||
}
|
||||
|
@ -206,70 +151,20 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
|
|||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
target: &Expr<Option<Type>>,
|
||||
value: ValueEnum<'ctx>,
|
||||
value_ty: Type,
|
||||
) -> Result<(), String> {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
// See https://docs.python.org/3/reference/simple_stmts.html#assignment-statements.
|
||||
match &target.node {
|
||||
ExprKind::Tuple { elts, .. } => {
|
||||
let BasicValueEnum::StructValue(v) =
|
||||
value.to_basic_value_enum(ctx, generator, target.custom.unwrap())?
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
for (i, elt) in elts.iter().enumerate() {
|
||||
let v = ctx
|
||||
.builder
|
||||
.build_extract_value(v, u32::try_from(i).unwrap(), "struct_elem")
|
||||
.unwrap();
|
||||
generator.gen_assign(ctx, elt, v.into())?;
|
||||
ExprKind::Subscript { value: target, slice: key, .. } => {
|
||||
// Handle "slicing" or "subscription"
|
||||
generator.gen_setitem(ctx, target, key, value, value_ty)?;
|
||||
}
|
||||
}
|
||||
ExprKind::Subscript { value: ls, slice, .. }
|
||||
if matches!(&slice.node, ExprKind::Slice { .. }) =>
|
||||
{
|
||||
let ExprKind::Slice { lower, upper, step } = &slice.node else { unreachable!() };
|
||||
|
||||
let ls = generator
|
||||
.gen_expr(ctx, ls)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, ls.custom.unwrap())?
|
||||
.into_pointer_value();
|
||||
let ls = ListValue::from_ptr_val(ls, llvm_usize, None);
|
||||
let Some((start, end, step)) =
|
||||
handle_slice_indices(lower, upper, step, ctx, generator, ls.load_size(ctx, None))?
|
||||
else {
|
||||
return Ok(());
|
||||
};
|
||||
let value = value
|
||||
.to_basic_value_enum(ctx, generator, target.custom.unwrap())?
|
||||
.into_pointer_value();
|
||||
let value = ListValue::from_ptr_val(value, llvm_usize, None);
|
||||
let ty = match &*ctx.unifier.get_ty_immutable(target.custom.unwrap()) {
|
||||
TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::List.id() => {
|
||||
*params.iter().next().unwrap().1
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
unpack_ndarray_var_tys(&mut ctx.unifier, target.custom.unwrap()).0
|
||||
}
|
||||
_ => unreachable!(),
|
||||
};
|
||||
|
||||
let ty = ctx.get_llvm_type(generator, ty);
|
||||
let Some(src_ind) = handle_slice_indices(
|
||||
&None,
|
||||
&None,
|
||||
&None,
|
||||
ctx,
|
||||
generator,
|
||||
value.load_size(ctx, None),
|
||||
)?
|
||||
else {
|
||||
return Ok(());
|
||||
};
|
||||
list_slice_assignment(generator, ctx, ty, ls, (start, end, step), value, src_ind);
|
||||
ExprKind::Tuple { elts, .. } | ExprKind::List { elts, .. } => {
|
||||
// Fold on `"[" [target_list] "]"` and `"(" [target_list] ")"`
|
||||
generator.gen_assign_target_list(ctx, elts, value, value_ty)?;
|
||||
}
|
||||
_ => {
|
||||
// Handle attribute and direct variable assignments.
|
||||
let name = if let ExprKind::Name { id, .. } = &target.node {
|
||||
format!("{id}.addr")
|
||||
} else {
|
||||
|
@ -293,6 +188,269 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
|
|||
Ok(())
|
||||
}
|
||||
|
||||
/// See [`CodeGenerator::gen_assign_target_list`].
|
||||
pub fn gen_assign_target_list<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
targets: &Vec<Expr<Option<Type>>>,
|
||||
value: ValueEnum<'ctx>,
|
||||
value_ty: Type,
|
||||
) -> Result<(), String> {
|
||||
// Deconstruct the tuple `value`
|
||||
let BasicValueEnum::StructValue(tuple) = value.to_basic_value_enum(ctx, generator, value_ty)?
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
// NOTE: Currently, RHS's type is forced to be a Tuple by the type inferencer.
|
||||
let TypeEnum::TTuple { ty: tuple_tys } = &*ctx.unifier.get_ty(value_ty) else {
|
||||
unreachable!();
|
||||
};
|
||||
|
||||
assert_eq!(tuple.get_type().count_fields() as usize, tuple_tys.len());
|
||||
|
||||
let tuple = (0..tuple.get_type().count_fields())
|
||||
.map(|i| ctx.builder.build_extract_value(tuple, i, "item").unwrap())
|
||||
.collect_vec();
|
||||
|
||||
// Find the starred target if it exists.
|
||||
let mut starred_target_index: Option<usize> = None; // Index of the "starred" target. If it exists, there may only be one.
|
||||
for (i, target) in targets.iter().enumerate() {
|
||||
if matches!(target.node, ExprKind::Starred { .. }) {
|
||||
assert!(starred_target_index.is_none()); // The typechecker ensures this
|
||||
starred_target_index = Some(i);
|
||||
}
|
||||
}
|
||||
|
||||
if let Some(starred_target_index) = starred_target_index {
|
||||
assert!(tuple_tys.len() >= targets.len() - 1); // The typechecker ensures this
|
||||
|
||||
let a = starred_target_index; // Number of RHS values before the starred target
|
||||
let b = tuple_tys.len() - (targets.len() - 1 - starred_target_index); // Number of RHS values after the starred target
|
||||
// Thus `tuple[a..b]` is assigned to the starred target.
|
||||
|
||||
// Handle assignment before the starred target
|
||||
for (target, val, val_ty) in
|
||||
izip!(&targets[..starred_target_index], &tuple[..a], &tuple_tys[..a])
|
||||
{
|
||||
generator.gen_assign(ctx, target, ValueEnum::Dynamic(*val), *val_ty)?;
|
||||
}
|
||||
|
||||
// Handle assignment to the starred target
|
||||
if let ExprKind::Starred { value: target, .. } = &targets[starred_target_index].node {
|
||||
let vals = &tuple[a..b];
|
||||
let val_tys = &tuple_tys[a..b];
|
||||
|
||||
// Create a sub-tuple from `value` for the starred target.
|
||||
let sub_tuple_ty = ctx
|
||||
.ctx
|
||||
.struct_type(&vals.iter().map(BasicValueEnum::get_type).collect_vec(), false);
|
||||
let psub_tuple_val =
|
||||
ctx.builder.build_alloca(sub_tuple_ty, "starred_target_value_ptr").unwrap();
|
||||
for (i, val) in vals.iter().enumerate() {
|
||||
let pitem = ctx
|
||||
.builder
|
||||
.build_struct_gep(psub_tuple_val, i as u32, "starred_target_value_item")
|
||||
.unwrap();
|
||||
ctx.builder.build_store(pitem, *val).unwrap();
|
||||
}
|
||||
let sub_tuple_val =
|
||||
ctx.builder.build_load(psub_tuple_val, "starred_target_value").unwrap();
|
||||
|
||||
// Create the typechecker type of the sub-tuple
|
||||
let sub_tuple_ty = ctx.unifier.add_ty(TypeEnum::TTuple { ty: val_tys.to_vec() });
|
||||
|
||||
// Now assign with that sub-tuple to the starred target.
|
||||
generator.gen_assign(ctx, target, ValueEnum::Dynamic(sub_tuple_val), sub_tuple_ty)?;
|
||||
} else {
|
||||
unreachable!() // The typechecker ensures this
|
||||
}
|
||||
|
||||
// Handle assignment after the starred target
|
||||
for (target, val, val_ty) in
|
||||
izip!(&targets[starred_target_index + 1..], &tuple[b..], &tuple_tys[b..])
|
||||
{
|
||||
generator.gen_assign(ctx, target, ValueEnum::Dynamic(*val), *val_ty)?;
|
||||
}
|
||||
} else {
|
||||
assert_eq!(tuple_tys.len(), targets.len()); // The typechecker ensures this
|
||||
|
||||
for (target, val, val_ty) in izip!(targets, tuple, tuple_tys) {
|
||||
generator.gen_assign(ctx, target, ValueEnum::Dynamic(val), *val_ty)?;
|
||||
}
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// See [`CodeGenerator::gen_setitem`].
|
||||
pub fn gen_setitem<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
target: &Expr<Option<Type>>,
|
||||
key: &Expr<Option<Type>>,
|
||||
value: ValueEnum<'ctx>,
|
||||
value_ty: Type,
|
||||
) -> Result<(), String> {
|
||||
let target_ty = target.custom.unwrap();
|
||||
let key_ty = key.custom.unwrap();
|
||||
|
||||
match &*ctx.unifier.get_ty(target_ty) {
|
||||
TypeEnum::TObj { obj_id, params: list_params, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
// Handle list item assignment
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let target_item_ty = iter_type_vars(list_params).next().unwrap().ty;
|
||||
|
||||
let target = generator
|
||||
.gen_expr(ctx, target)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, target_ty)?
|
||||
.into_pointer_value();
|
||||
let target = ListValue::from_ptr_val(target, llvm_usize, None);
|
||||
|
||||
if let ExprKind::Slice { .. } = &key.node {
|
||||
// Handle assigning to a slice
|
||||
let ExprKind::Slice { lower, upper, step } = &key.node else { unreachable!() };
|
||||
let Some((start, end, step)) = handle_slice_indices(
|
||||
lower,
|
||||
upper,
|
||||
step,
|
||||
ctx,
|
||||
generator,
|
||||
target.load_size(ctx, None),
|
||||
)?
|
||||
else {
|
||||
return Ok(());
|
||||
};
|
||||
|
||||
let value =
|
||||
value.to_basic_value_enum(ctx, generator, value_ty)?.into_pointer_value();
|
||||
let value = ListValue::from_ptr_val(value, llvm_usize, None);
|
||||
|
||||
let target_item_ty = ctx.get_llvm_type(generator, target_item_ty);
|
||||
let Some(src_ind) = handle_slice_indices(
|
||||
&None,
|
||||
&None,
|
||||
&None,
|
||||
ctx,
|
||||
generator,
|
||||
value.load_size(ctx, None),
|
||||
)?
|
||||
else {
|
||||
return Ok(());
|
||||
};
|
||||
list_slice_assignment(
|
||||
generator,
|
||||
ctx,
|
||||
target_item_ty,
|
||||
target,
|
||||
(start, end, step),
|
||||
value,
|
||||
src_ind,
|
||||
);
|
||||
} else {
|
||||
// Handle assigning to an index
|
||||
let len = target.load_size(ctx, Some("len"));
|
||||
|
||||
let index = generator
|
||||
.gen_expr(ctx, key)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, key_ty)?
|
||||
.into_int_value();
|
||||
let index = ctx
|
||||
.builder
|
||||
.build_int_s_extend(index, generator.get_size_type(ctx.ctx), "sext")
|
||||
.unwrap();
|
||||
|
||||
// handle negative index
|
||||
let is_negative = ctx
|
||||
.builder
|
||||
.build_int_compare(
|
||||
IntPredicate::SLT,
|
||||
index,
|
||||
generator.get_size_type(ctx.ctx).const_zero(),
|
||||
"is_neg",
|
||||
)
|
||||
.unwrap();
|
||||
let adjusted = ctx.builder.build_int_add(index, len, "adjusted").unwrap();
|
||||
let index = ctx
|
||||
.builder
|
||||
.build_select(is_negative, adjusted, index, "index")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
|
||||
// unsigned less than is enough, because negative index after adjustment is
|
||||
// bigger than the length (for unsigned cmp)
|
||||
let bound_check = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::ULT, index, len, "inbound")
|
||||
.unwrap();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
bound_check,
|
||||
"0:IndexError",
|
||||
"index {0} out of bounds 0:{1}",
|
||||
[Some(index), Some(len), None],
|
||||
key.location,
|
||||
);
|
||||
|
||||
// Write value to index on list
|
||||
let item_ptr =
|
||||
target.data().ptr_offset(ctx, generator, &index, Some("list_item_ptr"));
|
||||
let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
|
||||
ctx.builder.build_store(item_ptr, value).unwrap();
|
||||
}
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
// Handle NDArray item assignment
|
||||
// Process target
|
||||
let target = generator
|
||||
.gen_expr(ctx, target)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, target_ty)?;
|
||||
let target = NDArrayObject::from_value_and_type(generator, ctx, target, target_ty);
|
||||
|
||||
// Process key
|
||||
let key = gen_ndarray_subscript_ndindexes(generator, ctx, key)?;
|
||||
|
||||
// Process value
|
||||
let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
|
||||
|
||||
/*
|
||||
Reference code:
|
||||
```python
|
||||
target = target[key]
|
||||
value = np.asarray(value)
|
||||
|
||||
shape = np.broadcast_shape((target, value))
|
||||
|
||||
target = np.broadcast_to(target, shape)
|
||||
value = np.broadcast_to(value, shape)
|
||||
|
||||
...and finally copy 1-1 from value to target.
|
||||
```
|
||||
*/
|
||||
let target = target.index(generator, ctx, &key, "assign_target_ndarray");
|
||||
let value =
|
||||
split_scalar_or_ndarray(generator, ctx, value, value_ty).as_ndarray(generator, ctx);
|
||||
|
||||
let broadcast_result = NDArrayObject::broadcast_all(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));
|
||||
}
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// See [`CodeGenerator::gen_for`].
|
||||
pub fn gen_for<G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
|
@ -315,9 +473,6 @@ pub fn gen_for<G: CodeGenerator>(
|
|||
let orelse_bb =
|
||||
if orelse.is_empty() { cont_bb } else { ctx.ctx.append_basic_block(current, "for.orelse") };
|
||||
|
||||
// Whether the iterable is a range() expression
|
||||
let is_iterable_range_expr = ctx.unifier.unioned(iter.custom.unwrap(), ctx.primitives.range);
|
||||
|
||||
// The BB containing the increment expression
|
||||
let incr_bb = ctx.ctx.append_basic_block(current, "for.incr");
|
||||
// The BB containing the loop condition check
|
||||
|
@ -326,17 +481,23 @@ pub fn gen_for<G: CodeGenerator>(
|
|||
// store loop bb information and restore it later
|
||||
let loop_bb = ctx.loop_target.replace((incr_bb, cont_bb));
|
||||
|
||||
let iter_ty = iter.custom.unwrap();
|
||||
let iter_val = if let Some(v) = generator.gen_expr(ctx, iter)? {
|
||||
v.to_basic_value_enum(ctx, generator, iter.custom.unwrap())?
|
||||
v.to_basic_value_enum(ctx, generator, iter_ty)?
|
||||
} else {
|
||||
return Ok(());
|
||||
};
|
||||
if is_iterable_range_expr {
|
||||
|
||||
match &*ctx.unifier.get_ty(iter_ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.range.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let iter_val = RangeValue::from_ptr_val(iter_val.into_pointer_value(), Some("range"));
|
||||
// Internal variable for loop; Cannot be assigned
|
||||
let i = generator.gen_var_alloc(ctx, int32.into(), Some("for.i.addr"))?;
|
||||
// Variable declared in "target" expression of the loop; Can be reassigned *or* shadowed
|
||||
let Some(target_i) = generator.gen_store_target(ctx, target, Some("for.target.addr"))?
|
||||
let Some(target_i) =
|
||||
generator.gen_store_target(ctx, target, Some("for.target.addr"))?
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
|
@ -345,8 +506,10 @@ pub fn gen_for<G: CodeGenerator>(
|
|||
ctx.builder.build_store(i, start).unwrap();
|
||||
|
||||
// Check "If step is zero, ValueError is raised."
|
||||
let rangenez =
|
||||
ctx.builder.build_int_compare(IntPredicate::NE, step, int32.const_zero(), "").unwrap();
|
||||
let rangenez = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::NE, step, int32.const_zero(), "")
|
||||
.unwrap();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
rangenez,
|
||||
|
@ -363,7 +526,10 @@ pub fn gen_for<G: CodeGenerator>(
|
|||
.build_conditional_branch(
|
||||
gen_in_range_check(
|
||||
ctx,
|
||||
ctx.builder.build_load(i, "").map(BasicValueEnum::into_int_value).unwrap(),
|
||||
ctx.builder
|
||||
.build_load(i, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap(),
|
||||
stop,
|
||||
step,
|
||||
),
|
||||
|
@ -393,7 +559,10 @@ pub fn gen_for<G: CodeGenerator>(
|
|||
)
|
||||
.unwrap();
|
||||
generator.gen_block(ctx, body.iter())?;
|
||||
} else {
|
||||
}
|
||||
TypeEnum::TObj { obj_id, params: list_params, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let index_addr = generator.gen_var_alloc(ctx, size_t.into(), Some("for.index.addr"))?;
|
||||
ctx.builder.build_store(index_addr, size_t.const_zero()).unwrap();
|
||||
let len = ctx
|
||||
|
@ -431,9 +600,14 @@ pub fn gen_for<G: CodeGenerator>(
|
|||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
let val = ctx.build_gep_and_load(arr_ptr, &[index], Some("val"));
|
||||
generator.gen_assign(ctx, target, val.into())?;
|
||||
let val_ty = iter_type_vars(list_params).next().unwrap().ty;
|
||||
generator.gen_assign(ctx, target, val.into(), val_ty)?;
|
||||
generator.gen_block(ctx, body.iter())?;
|
||||
}
|
||||
_ => {
|
||||
panic!("unsupported for loop iterator type: {}", ctx.unifier.stringify(iter_ty));
|
||||
}
|
||||
}
|
||||
|
||||
for (k, (_, _, counter)) in &var_assignment {
|
||||
let (_, static_val, counter2) = ctx.var_assignment.get_mut(k).unwrap();
|
||||
|
@ -494,6 +668,7 @@ pub struct BreakContinueHooks<'ctx> {
|
|||
pub fn gen_for_callback<'ctx, 'a, G, I, InitFn, CondFn, BodyFn, UpdateFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
label: Option<&str>,
|
||||
init: InitFn,
|
||||
cond: CondFn,
|
||||
body: BodyFn,
|
||||
|
@ -504,18 +679,24 @@ where
|
|||
I: Clone,
|
||||
InitFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<I, String>,
|
||||
CondFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<IntValue<'ctx>, String>,
|
||||
BodyFn:
|
||||
FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, BreakContinueHooks, I) -> Result<(), String>,
|
||||
BodyFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks<'ctx>,
|
||||
I,
|
||||
) -> Result<(), String>,
|
||||
UpdateFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
|
||||
{
|
||||
let label = label.unwrap_or("for");
|
||||
|
||||
let current_bb = ctx.builder.get_insert_block().unwrap();
|
||||
let init_bb = ctx.ctx.insert_basic_block_after(current_bb, "for.init");
|
||||
let init_bb = ctx.ctx.insert_basic_block_after(current_bb, &format!("{label}.init"));
|
||||
// The BB containing the loop condition check
|
||||
let cond_bb = ctx.ctx.insert_basic_block_after(init_bb, "for.cond");
|
||||
let body_bb = ctx.ctx.insert_basic_block_after(cond_bb, "for.body");
|
||||
let cond_bb = ctx.ctx.insert_basic_block_after(init_bb, &format!("{label}.cond"));
|
||||
let body_bb = ctx.ctx.insert_basic_block_after(cond_bb, &format!("{label}.body"));
|
||||
// The BB containing the increment expression
|
||||
let update_bb = ctx.ctx.insert_basic_block_after(body_bb, "for.update");
|
||||
let cont_bb = ctx.ctx.insert_basic_block_after(update_bb, "for.end");
|
||||
let update_bb = ctx.ctx.insert_basic_block_after(body_bb, &format!("{label}.update"));
|
||||
let cont_bb = ctx.ctx.insert_basic_block_after(update_bb, &format!("{label}.end"));
|
||||
|
||||
// store loop bb information and restore it later
|
||||
let loop_bb = ctx.loop_target.replace((update_bb, cont_bb));
|
||||
|
@ -572,6 +753,7 @@ where
|
|||
pub fn gen_for_callback_incrementing<'ctx, 'a, G, BodyFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
label: Option<&str>,
|
||||
init_val: IntValue<'ctx>,
|
||||
max_val: (IntValue<'ctx>, bool),
|
||||
body: BodyFn,
|
||||
|
@ -582,7 +764,7 @@ where
|
|||
BodyFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks,
|
||||
BreakContinueHooks<'ctx>,
|
||||
IntValue<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
{
|
||||
|
@ -591,6 +773,7 @@ where
|
|||
gen_for_callback(
|
||||
generator,
|
||||
ctx,
|
||||
label,
|
||||
|generator, ctx| {
|
||||
let i_addr = generator.gen_var_alloc(ctx, init_val_t.into(), None)?;
|
||||
ctx.builder.build_store(i_addr, init_val).unwrap();
|
||||
|
@ -642,9 +825,11 @@ where
|
|||
/// - `step_fn`: A lambda of IR statements that retrieves the `step` value of the `range`-like
|
||||
/// iterable. This value will be extended to the size of `start`.
|
||||
/// - `body_fn`: A lambda of IR statements within the loop body.
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
pub fn gen_for_range_callback<'ctx, 'a, G, StartFn, StopFn, StepFn, BodyFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
label: Option<&str>,
|
||||
is_unsigned: bool,
|
||||
start_fn: StartFn,
|
||||
(stop_fn, stop_inclusive): (StopFn, bool),
|
||||
|
@ -656,13 +841,19 @@ where
|
|||
StartFn: Fn(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<IntValue<'ctx>, String>,
|
||||
StopFn: Fn(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<IntValue<'ctx>, String>,
|
||||
StepFn: Fn(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<IntValue<'ctx>, String>,
|
||||
BodyFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, IntValue<'ctx>) -> Result<(), String>,
|
||||
BodyFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks,
|
||||
IntValue<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
{
|
||||
let init_val_t = start_fn(generator, ctx).map(IntValue::get_type).unwrap();
|
||||
|
||||
gen_for_callback(
|
||||
generator,
|
||||
ctx,
|
||||
label,
|
||||
|generator, ctx| {
|
||||
let i_addr = generator.gen_var_alloc(ctx, init_val_t.into(), None)?;
|
||||
|
||||
|
@ -720,10 +911,10 @@ where
|
|||
|
||||
Ok(cond)
|
||||
},
|
||||
|generator, ctx, _, (i_addr, _)| {
|
||||
|generator, ctx, hooks, (i_addr, _)| {
|
||||
let i = ctx.builder.build_load(i_addr, "").map(BasicValueEnum::into_int_value).unwrap();
|
||||
|
||||
body_fn(generator, ctx, i)
|
||||
body_fn(generator, ctx, hooks, i)
|
||||
},
|
||||
|generator, ctx, (i_addr, _)| {
|
||||
let i = ctx.builder.build_load(i_addr, "").map(BasicValueEnum::into_int_value).unwrap();
|
||||
|
@ -1113,47 +1304,36 @@ pub fn exn_constructor<'ctx>(
|
|||
pub fn gen_raise<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
exception: Option<&BasicValueEnum<'ctx>>,
|
||||
exception: Option<Ptr<'ctx, StructModel<Exception>>>,
|
||||
loc: Location,
|
||||
) {
|
||||
if let Some(exception) = exception {
|
||||
unsafe {
|
||||
let int32 = ctx.ctx.i32_type();
|
||||
let zero = int32.const_zero();
|
||||
let exception = exception.into_pointer_value();
|
||||
let file_ptr = ctx
|
||||
.builder
|
||||
.build_in_bounds_gep(exception, &[zero, int32.const_int(1, false)], "file_ptr")
|
||||
.unwrap();
|
||||
let filename = ctx.gen_string(generator, loc.file.0);
|
||||
ctx.builder.build_store(file_ptr, filename).unwrap();
|
||||
let row_ptr = ctx
|
||||
.builder
|
||||
.build_in_bounds_gep(exception, &[zero, int32.const_int(2, false)], "row_ptr")
|
||||
.unwrap();
|
||||
ctx.builder.build_store(row_ptr, int32.const_int(loc.row as u64, false)).unwrap();
|
||||
let col_ptr = ctx
|
||||
.builder
|
||||
.build_in_bounds_gep(exception, &[zero, int32.const_int(3, false)], "col_ptr")
|
||||
.unwrap();
|
||||
ctx.builder.build_store(col_ptr, int32.const_int(loc.column as u64, false)).unwrap();
|
||||
if let Some(pexn) = exception {
|
||||
let i32_model = IntModel(Int32);
|
||||
let cslice_model = StructModel(CSlice);
|
||||
|
||||
let current_fun = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
|
||||
let fun_name = ctx.gen_string(generator, current_fun.get_name().to_str().unwrap());
|
||||
let name_ptr = ctx
|
||||
.builder
|
||||
.build_in_bounds_gep(exception, &[zero, int32.const_int(4, false)], "name_ptr")
|
||||
.unwrap();
|
||||
ctx.builder.build_store(name_ptr, fun_name).unwrap();
|
||||
}
|
||||
// Get and store filename
|
||||
let filename = loc.file.0;
|
||||
let filename = ctx.gen_string(generator, &String::from(filename)).value;
|
||||
let filename = cslice_model.check_value(generator, ctx.ctx, filename).unwrap();
|
||||
pexn.set(ctx, |f| f.filename, filename);
|
||||
|
||||
let row = i32_model.constant(generator, ctx.ctx, loc.row as u64);
|
||||
pexn.set(ctx, |f| f.line, row);
|
||||
|
||||
let column = i32_model.constant(generator, ctx.ctx, loc.column as u64);
|
||||
pexn.set(ctx, |f| f.column, column);
|
||||
|
||||
let current_fn = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
|
||||
let fn_name = ctx.gen_string(generator, current_fn.get_name().to_str().unwrap());
|
||||
pexn.set(ctx, |f| f.function, fn_name);
|
||||
|
||||
let raise = get_builtins(generator, ctx, "__nac3_raise");
|
||||
let exception = *exception;
|
||||
ctx.build_call_or_invoke(raise, &[exception], "raise");
|
||||
ctx.build_call_or_invoke(raise, &[pexn.value.into()], "raise");
|
||||
} else {
|
||||
let resume = get_builtins(generator, ctx, "__nac3_resume");
|
||||
ctx.build_call_or_invoke(resume, &[], "resume");
|
||||
}
|
||||
|
||||
ctx.builder.build_unreachable().unwrap();
|
||||
}
|
||||
|
||||
|
@ -1575,14 +1755,14 @@ pub fn gen_stmt<G: CodeGenerator>(
|
|||
}
|
||||
StmtKind::AnnAssign { target, value, .. } => {
|
||||
if let Some(value) = value {
|
||||
let Some(value) = generator.gen_expr(ctx, value)? else { return Ok(()) };
|
||||
generator.gen_assign(ctx, target, value)?;
|
||||
let Some(value_enum) = generator.gen_expr(ctx, value)? else { return Ok(()) };
|
||||
generator.gen_assign(ctx, target, value_enum, value.custom.unwrap())?;
|
||||
}
|
||||
}
|
||||
StmtKind::Assign { targets, value, .. } => {
|
||||
let Some(value) = generator.gen_expr(ctx, value)? else { return Ok(()) };
|
||||
let Some(value_enum) = generator.gen_expr(ctx, value)? else { return Ok(()) };
|
||||
for target in targets {
|
||||
generator.gen_assign(ctx, target, value.clone())?;
|
||||
generator.gen_assign(ctx, target, value_enum.clone(), value.custom.unwrap())?;
|
||||
}
|
||||
}
|
||||
StmtKind::Continue { .. } => {
|
||||
|
@ -1596,15 +1776,16 @@ pub fn gen_stmt<G: CodeGenerator>(
|
|||
StmtKind::For { .. } => generator.gen_for(ctx, stmt)?,
|
||||
StmtKind::With { .. } => generator.gen_with(ctx, stmt)?,
|
||||
StmtKind::AugAssign { target, op, value, .. } => {
|
||||
let value = gen_binop_expr(
|
||||
let value_enum = gen_binop_expr(
|
||||
generator,
|
||||
ctx,
|
||||
target,
|
||||
Binop::aug_assign(*op),
|
||||
value,
|
||||
stmt.location,
|
||||
)?;
|
||||
generator.gen_assign(ctx, target, value.unwrap())?;
|
||||
)?
|
||||
.unwrap();
|
||||
generator.gen_assign(ctx, target, value_enum, value.custom.unwrap())?;
|
||||
}
|
||||
StmtKind::Try { .. } => gen_try(generator, ctx, stmt)?,
|
||||
StmtKind::Raise { exc, .. } => {
|
||||
|
@ -1614,30 +1795,41 @@ pub fn gen_stmt<G: CodeGenerator>(
|
|||
} else {
|
||||
return Ok(());
|
||||
};
|
||||
gen_raise(generator, ctx, Some(&exc), stmt.location);
|
||||
|
||||
let pexn_model = PtrModel(StructModel(Exception));
|
||||
let exn = pexn_model.check_value(generator, ctx.ctx, exc).unwrap();
|
||||
|
||||
gen_raise(generator, ctx, Some(exn), stmt.location);
|
||||
} else {
|
||||
gen_raise(generator, ctx, None, stmt.location);
|
||||
}
|
||||
}
|
||||
StmtKind::Assert { test, msg, .. } => {
|
||||
let test = if let Some(v) = generator.gen_expr(ctx, test)? {
|
||||
v.to_basic_value_enum(ctx, generator, test.custom.unwrap())?
|
||||
} else {
|
||||
let byte_model = IntModel(Byte);
|
||||
let cslice_model = StructModel(CSlice);
|
||||
|
||||
let Some(test) = generator.gen_expr(ctx, test)? else {
|
||||
return Ok(());
|
||||
};
|
||||
let test = test.to_basic_value_enum(ctx, generator, ctx.primitives.bool)?;
|
||||
let test = byte_model.check_value(generator, ctx.ctx, test).unwrap(); // Python `bool` is represented as `i8` in nac3core
|
||||
|
||||
// Check `msg`
|
||||
let err_msg = match msg {
|
||||
Some(msg) => {
|
||||
if let Some(v) = generator.gen_expr(ctx, msg)? {
|
||||
v.to_basic_value_enum(ctx, generator, msg.custom.unwrap())?
|
||||
} else {
|
||||
let Some(msg) = generator.gen_expr(ctx, msg)? else {
|
||||
return Ok(());
|
||||
}
|
||||
};
|
||||
|
||||
let msg = msg.to_basic_value_enum(ctx, generator, ctx.primitives.str)?;
|
||||
cslice_model.check_value(generator, ctx.ctx, msg).unwrap()
|
||||
}
|
||||
None => ctx.gen_string(generator, ""),
|
||||
};
|
||||
|
||||
ctx.make_assert_impl(
|
||||
generator,
|
||||
test.into_int_value(),
|
||||
test.value,
|
||||
"0:AssertionError",
|
||||
err_msg,
|
||||
[None, None, None],
|
||||
|
|
|
@ -0,0 +1,45 @@
|
|||
use inkwell::context::Context;
|
||||
|
||||
use crate::codegen::{model::*, CodeGenerator};
|
||||
|
||||
/// Fields of [`CSlice<'ctx>`].
|
||||
pub struct CSliceFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
/// Pointer to data.
|
||||
pub base: F::Out<PtrModel<IntModel<Byte>>>,
|
||||
/// Number of bytes of data.
|
||||
pub len: F::Out<IntModel<SizeT>>,
|
||||
}
|
||||
|
||||
/// See <https://crates.io/crates/cslice>.
|
||||
///
|
||||
/// Additionally, see <https://github.com/m-labs/artiq/blob/b0d2705c385f64b6e6711c1726cd9178f40b598e/artiq/firmware/libeh/eh_artiq.rs>)
|
||||
/// for ARTIQ-specific notes.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct CSlice;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for CSlice {
|
||||
type Fields<F: FieldTraversal<'ctx>> = CSliceFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields { base: traversal.add_auto("base"), len: traversal.add_auto("len") }
|
||||
}
|
||||
}
|
||||
|
||||
impl StructModel<CSlice> {
|
||||
/// Create a [`CSlice`].
|
||||
///
|
||||
/// `base` and `len` must be LLVM global constants.
|
||||
pub fn create_const<'ctx, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
base: Ptr<'ctx, IntModel<Byte>>,
|
||||
len: Int<'ctx, SizeT>,
|
||||
) -> Struct<'ctx, CSlice> {
|
||||
let value = self
|
||||
.0
|
||||
.get_struct_type(generator, ctx)
|
||||
.const_named_struct(&[base.value.into(), len.value.into()]);
|
||||
self.believe_value(value)
|
||||
}
|
||||
}
|
|
@ -0,0 +1,61 @@
|
|||
use crate::codegen::model::*;
|
||||
|
||||
use super::cslice::CSlice;
|
||||
|
||||
/// The LLVM int type of an Exception ID.
|
||||
pub type ExceptionId = Int32;
|
||||
|
||||
/// Fields of [`Exception<'ctx>`]
|
||||
///
|
||||
/// The definition came from `pub struct Exception<'a>` in
|
||||
/// <https://github.com/m-labs/artiq/blob/master/artiq/firmware/libeh/eh_artiq.rs>.
|
||||
pub struct ExceptionFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
/// nac3core's ID of the exception
|
||||
pub id: F::Out<IntModel<ExceptionId>>,
|
||||
/// The name of the file this `Exception` was raised in.
|
||||
pub filename: F::Out<StructModel<CSlice>>,
|
||||
/// The line number in the file this `Exception` was raised in.
|
||||
pub line: F::Out<IntModel<Int32>>,
|
||||
/// The column number in the file this `Exception` was raised in.
|
||||
pub column: F::Out<IntModel<Int32>>,
|
||||
/// The name of the Python function this `Exception` was raised in.
|
||||
pub function: F::Out<StructModel<CSlice>>,
|
||||
/// The message of this Exception.
|
||||
///
|
||||
/// The message can optionally contain integer parameters `{0}`, `{1}`, and `{2}` in its string,
|
||||
/// where they will be substituted by `params[0]`, `params[1]`, and `params[2]` respectively (as `int64_t`s).
|
||||
/// Here is an example:
|
||||
///
|
||||
/// ```ignore
|
||||
/// "Index {0} is out of bounds! List only has {1} element(s)."
|
||||
/// ```
|
||||
///
|
||||
/// In this case, `params[0]` and `params[1]` must be specified, and `params[2]` is ***unused***.
|
||||
/// Having only 3 parameters is a constraint in ARTIQ.
|
||||
pub msg: F::Out<StructModel<CSlice>>,
|
||||
pub params: [F::Out<IntModel<Int64>>; 3],
|
||||
}
|
||||
|
||||
/// nac3core & ARTIQ's Exception
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Exception;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for Exception {
|
||||
type Fields<F: FieldTraversal<'ctx>> = ExceptionFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
id: traversal.add_auto("id"),
|
||||
filename: traversal.add_auto("filename"),
|
||||
line: traversal.add_auto("line"),
|
||||
column: traversal.add_auto("column"),
|
||||
function: traversal.add_auto("function"),
|
||||
msg: traversal.add_auto("msg"),
|
||||
params: [
|
||||
traversal.add_auto("params[0]"),
|
||||
traversal.add_auto("params[1]"),
|
||||
traversal.add_auto("params[2]"),
|
||||
],
|
||||
}
|
||||
}
|
||||
}
|
|
@ -0,0 +1,70 @@
|
|||
use inkwell::values::BasicValue;
|
||||
|
||||
use crate::{
|
||||
codegen::{model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::{iter_type_vars, Type, TypeEnum},
|
||||
};
|
||||
|
||||
/// Fields of [`List`]
|
||||
pub struct ListFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>, Size: IntKind<'ctx>> {
|
||||
/// Array pointer to content
|
||||
pub items: F::Out<PtrModel<Item>>,
|
||||
/// Number of items in the array
|
||||
pub len: F::Out<IntModel<Size>>,
|
||||
}
|
||||
|
||||
/// A list in NAC3.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct List<Item, Len> {
|
||||
/// Model of the list items
|
||||
pub item: Item,
|
||||
/// Model of type of integer storing the number of items on the list
|
||||
pub len: Len,
|
||||
}
|
||||
|
||||
impl<'ctx, Item: Model<'ctx>, Size: IntKind<'ctx>> StructKind<'ctx> for List<Item, Size> {
|
||||
type Fields<F: FieldTraversal<'ctx>> = ListFields<'ctx, F, Item, Size>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
items: traversal.add("data", PtrModel(self.item)),
|
||||
len: traversal.add("len", IntModel(self.len)),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A NAC3 Python List object.
|
||||
pub struct ListObject<'ctx> {
|
||||
/// Typechecker type of the list items
|
||||
pub item_type: Type,
|
||||
pub value: Ptr<'ctx, StructModel<List<AnyModel<'ctx>, SizeT>>>,
|
||||
}
|
||||
|
||||
impl<'ctx> ListObject<'ctx> {
|
||||
/// Create a [`ListObject`] from an LLVM value and its typechecker [`Type`].
|
||||
pub fn from_value_and_type<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list_val: V,
|
||||
list_type: Type,
|
||||
) -> Self {
|
||||
// Check typechecker type and extract `item_type`
|
||||
let item_type = match &*ctx.unifier.get_ty(list_type) {
|
||||
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(list_type))
|
||||
}
|
||||
};
|
||||
|
||||
let item_model = AnyModel(ctx.get_llvm_type(generator, item_type));
|
||||
let plist_model = PtrModel(StructModel(List { item: item_model, len: SizeT }));
|
||||
|
||||
// Create object
|
||||
let value = plist_model.check_value(generator, ctx.ctx, list_val).unwrap();
|
||||
ListObject { item_type, value }
|
||||
}
|
||||
}
|
|
@ -0,0 +1,5 @@
|
|||
pub mod cslice;
|
||||
pub mod exception;
|
||||
pub mod list;
|
||||
pub mod ndarray;
|
||||
pub mod tuple;
|
|
@ -0,0 +1,134 @@
|
|||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::{call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to},
|
||||
model::*,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::numpy::get_broadcast_all_ndims,
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
/// Fields of [`ShapeEntry`]
|
||||
pub struct ShapeEntryFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub ndims: F::Out<IntModel<SizeT>>,
|
||||
pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
}
|
||||
|
||||
/// An IRRT structure used in broadcasting.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct ShapeEntry;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for ShapeEntry {
|
||||
type Fields<F: FieldTraversal<'ctx>> = ShapeEntryFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields { ndims: traversal.add_auto("ndims"), shape: traversal.add_auto("shape") }
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Create a broadcast view on this ndarray with a target shape.
|
||||
///
|
||||
/// * `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: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) -> Self {
|
||||
let broadcast_ndarray = NDArrayObject::alloca_uninitialized(
|
||||
generator,
|
||||
ctx,
|
||||
self.dtype,
|
||||
target_ndims,
|
||||
"broadcast_ndarray_to_dst",
|
||||
);
|
||||
broadcast_ndarray.copy_shape_from_array(generator, ctx, target_shape);
|
||||
|
||||
call_nac3_ndarray_broadcast_to(generator, ctx, self.value, broadcast_ndarray.value);
|
||||
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: Ptr<'ctx, IntModel<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>>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
// TODO: DOCUMENT: Behaves like `np.broadcast()`, except returns results differently.
|
||||
pub fn broadcast_all<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarrays: &[Self],
|
||||
) -> BroadcastAllResult<'ctx> {
|
||||
assert!(!ndarrays.is_empty());
|
||||
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let shape_model = StructModel(ShapeEntry);
|
||||
|
||||
let broadcast_ndims = get_broadcast_all_ndims(ndarrays.iter().map(|ndarray| ndarray.ndims));
|
||||
|
||||
// Prepare input shape entries
|
||||
let num_shape_entries =
|
||||
sizet_model.constant(generator, ctx.ctx, u64::try_from(ndarrays.len()).unwrap());
|
||||
let shape_entries =
|
||||
shape_model.array_alloca(generator, ctx, num_shape_entries.value, "shape_entries");
|
||||
for (i, ndarray) in ndarrays.iter().enumerate() {
|
||||
let i = sizet_model.constant(generator, ctx.ctx, i as u64).value;
|
||||
|
||||
let shape_entry = shape_entries.offset(generator, ctx, i, "shape_entry");
|
||||
|
||||
let this_ndims = ndarray.value.get(generator, ctx, |f| f.ndims, "this_ndims");
|
||||
shape_entry.set(ctx, |f| f.ndims, this_ndims);
|
||||
|
||||
let this_shape = ndarray.value.get(generator, ctx, |f| f.shape, "this_shape");
|
||||
shape_entry.set(ctx, |f| f.shape, this_shape);
|
||||
}
|
||||
|
||||
// Prepare destination
|
||||
let broadcast_ndims_llvm = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
|
||||
let broadcast_shape =
|
||||
sizet_model.array_alloca(generator, ctx, broadcast_ndims_llvm.value, "dst_shape");
|
||||
|
||||
// Compute the target broadcast shape `dst_shape` for all ndarrays.
|
||||
call_nac3_ndarray_broadcast_shapes(
|
||||
generator,
|
||||
ctx,
|
||||
num_shape_entries,
|
||||
shape_entries,
|
||||
broadcast_ndims_llvm,
|
||||
broadcast_shape,
|
||||
);
|
||||
|
||||
// Now that we know about the broadcasting shape, broadcast all the inputs.
|
||||
|
||||
// Broadcast all the inputs to shape `dst_shape`.
|
||||
let broadcast_ndarrays: Vec<_> = ndarrays
|
||||
.iter()
|
||||
.map(|ndarray| ndarray.broadcast_to(generator, ctx, broadcast_ndims, broadcast_shape))
|
||||
.collect_vec();
|
||||
|
||||
BroadcastAllResult {
|
||||
ndims: broadcast_ndims,
|
||||
shape: broadcast_shape,
|
||||
ndarrays: broadcast_ndarrays,
|
||||
}
|
||||
}
|
||||
}
|
|
@ -0,0 +1,562 @@
|
|||
use inkwell::{
|
||||
values::{BasicValue, FloatValue, IntValue},
|
||||
FloatPredicate, IntPredicate,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
llvm_intrinsics,
|
||||
model::{
|
||||
util::{gen_for_model_auto, gen_if_model},
|
||||
*,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
use super::{scalar::ScalarObject, NDArrayObject};
|
||||
|
||||
/// Convenience function to crash the program when types of arguments are not supported.
|
||||
/// Used to be debugged with a stacktrace.
|
||||
fn unsupported_type<I>(ctx: &CodeGenContext<'_, '_>, tys: I) -> !
|
||||
where
|
||||
I: IntoIterator<Item = Type>,
|
||||
{
|
||||
unreachable!(
|
||||
"unsupported types found '{}'",
|
||||
tys.into_iter().map(|ty| format!("'{}'", ctx.unifier.stringify(ty))).join(", "),
|
||||
)
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub enum FloorOrCeil {
|
||||
Floor,
|
||||
Ceil,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub enum MinOrMax {
|
||||
Min,
|
||||
Max,
|
||||
}
|
||||
|
||||
fn signed_ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
|
||||
vec![ctx.primitives.int32, ctx.primitives.int64]
|
||||
}
|
||||
|
||||
fn unsigned_ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
|
||||
vec![ctx.primitives.uint32, ctx.primitives.uint64]
|
||||
}
|
||||
|
||||
fn ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
|
||||
vec![ctx.primitives.int32, ctx.primitives.int64, ctx.primitives.uint32, ctx.primitives.uint64]
|
||||
}
|
||||
|
||||
fn int_like(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
|
||||
vec![
|
||||
ctx.primitives.bool,
|
||||
ctx.primitives.int32,
|
||||
ctx.primitives.int64,
|
||||
ctx.primitives.uint32,
|
||||
ctx.primitives.uint64,
|
||||
]
|
||||
}
|
||||
|
||||
fn cast_to_int_conversion<'ctx, 'a, G, HandleFloatFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
scalar: ScalarObject<'ctx>,
|
||||
ret_int_dtype: Type,
|
||||
handle_float: HandleFloatFn,
|
||||
) -> ScalarObject<'ctx>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
HandleFloatFn:
|
||||
FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, FloatValue<'ctx>) -> IntValue<'ctx>,
|
||||
{
|
||||
let ret_int_dtype_llvm = ctx.get_llvm_type(generator, ret_int_dtype).into_int_type();
|
||||
|
||||
let result = if ctx.unifier.unioned(scalar.dtype, ctx.primitives.float) {
|
||||
// Special handling for floats
|
||||
let n = scalar.value.into_float_value();
|
||||
handle_float(generator, ctx, n)
|
||||
} else if ctx.unifier.unioned_any(scalar.dtype, int_like(ctx)) {
|
||||
let n = scalar.value.into_int_value();
|
||||
|
||||
if n.get_type().get_bit_width() <= ret_int_dtype_llvm.get_bit_width() {
|
||||
ctx.builder.build_int_z_extend(n, ret_int_dtype_llvm, "zext").unwrap()
|
||||
} else {
|
||||
ctx.builder.build_int_truncate(n, ret_int_dtype_llvm, "trunc").unwrap()
|
||||
}
|
||||
} else {
|
||||
unsupported_type(ctx, [scalar.dtype]);
|
||||
};
|
||||
|
||||
assert_eq!(ret_int_dtype_llvm.get_bit_width(), result.get_type().get_bit_width()); // Sanity check
|
||||
ScalarObject { value: result.into(), dtype: ret_int_dtype }
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarObject<'ctx> {
|
||||
/// Convenience function. Assume this scalar has typechecker type float64, get its underlying LLVM value.
|
||||
///
|
||||
/// Panic if the type is wrong.
|
||||
pub fn into_float64(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> FloatValue<'ctx> {
|
||||
if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
|
||||
self.value.into_float_value() // self.value must be a FloatValue
|
||||
} else {
|
||||
panic!("not a float type")
|
||||
}
|
||||
}
|
||||
|
||||
/// Convenience function. Assume this scalar has typechecker type int32, get its underlying LLVM value.
|
||||
///
|
||||
/// Panic if the type is wrong.
|
||||
pub fn into_int32(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
if ctx.unifier.unioned(self.dtype, ctx.primitives.int32) {
|
||||
let value = self.value.into_int_value();
|
||||
debug_assert_eq!(value.get_type().get_bit_width(), 32); // Sanity check
|
||||
value
|
||||
} else {
|
||||
panic!("not a float type")
|
||||
}
|
||||
}
|
||||
|
||||
/// Compare two scalars. Only int-to-int and float-to-float comparisons are allowed.
|
||||
/// Panic otherwise.
|
||||
pub fn compare<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
lhs: ScalarObject<'ctx>,
|
||||
rhs: ScalarObject<'ctx>,
|
||||
int_predicate: IntPredicate,
|
||||
float_predicate: FloatPredicate,
|
||||
name: &str,
|
||||
) -> Int<'ctx, Bool> {
|
||||
if !ctx.unifier.unioned(lhs.dtype, rhs.dtype) {
|
||||
unsupported_type(ctx, [lhs.dtype, rhs.dtype]);
|
||||
}
|
||||
|
||||
let bool_model = IntModel(Bool);
|
||||
|
||||
let common_ty = lhs.dtype;
|
||||
let result = if ctx.unifier.unioned(common_ty, ctx.primitives.float) {
|
||||
let lhs = lhs.value.into_float_value();
|
||||
let rhs = rhs.value.into_float_value();
|
||||
ctx.builder.build_float_compare(float_predicate, lhs, rhs, name).unwrap()
|
||||
} else if ctx.unifier.unioned_any(common_ty, int_like(ctx)) {
|
||||
let lhs = lhs.value.into_int_value();
|
||||
let rhs = rhs.value.into_int_value();
|
||||
ctx.builder.build_int_compare(int_predicate, lhs, rhs, name).unwrap()
|
||||
} else {
|
||||
unsupported_type(ctx, [lhs.dtype, rhs.dtype]);
|
||||
};
|
||||
|
||||
bool_model.check_value(generator, ctx.ctx, result).unwrap()
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `int32()`.
|
||||
#[must_use]
|
||||
pub fn cast_to_int32<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Self {
|
||||
cast_to_int_conversion(
|
||||
generator,
|
||||
ctx,
|
||||
*self,
|
||||
ctx.primitives.int32,
|
||||
|_generator, ctx, input| {
|
||||
let n =
|
||||
ctx.builder.build_float_to_signed_int(input, ctx.ctx.i64_type(), "").unwrap();
|
||||
ctx.builder.build_int_truncate(n, ctx.ctx.i32_type(), "conv").unwrap()
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `int64()`.
|
||||
#[must_use]
|
||||
pub fn cast_to_int64<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Self {
|
||||
cast_to_int_conversion(
|
||||
generator,
|
||||
ctx,
|
||||
*self,
|
||||
ctx.primitives.int64,
|
||||
|_generator, ctx, input| {
|
||||
ctx.builder.build_float_to_signed_int(input, ctx.ctx.i64_type(), "").unwrap()
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `uint32()`.
|
||||
#[must_use]
|
||||
pub fn cast_to_uint32<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Self {
|
||||
cast_to_int_conversion(
|
||||
generator,
|
||||
ctx,
|
||||
*self,
|
||||
ctx.primitives.uint32,
|
||||
|_generator, ctx, n| {
|
||||
let n_gez = ctx
|
||||
.builder
|
||||
.build_float_compare(FloatPredicate::OGE, n, n.get_type().const_zero(), "")
|
||||
.unwrap();
|
||||
|
||||
let to_int32 =
|
||||
ctx.builder.build_float_to_signed_int(n, ctx.ctx.i32_type(), "").unwrap();
|
||||
let to_uint64 =
|
||||
ctx.builder.build_float_to_unsigned_int(n, ctx.ctx.i64_type(), "").unwrap();
|
||||
|
||||
ctx.builder
|
||||
.build_select(
|
||||
n_gez,
|
||||
ctx.builder.build_int_truncate(to_uint64, ctx.ctx.i32_type(), "").unwrap(),
|
||||
to_int32,
|
||||
"conv",
|
||||
)
|
||||
.unwrap()
|
||||
.into_int_value()
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `uint64()`.
|
||||
#[must_use]
|
||||
pub fn cast_to_uint64<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Self {
|
||||
cast_to_int_conversion(
|
||||
generator,
|
||||
ctx,
|
||||
*self,
|
||||
ctx.primitives.uint64,
|
||||
|_generator, ctx, n| {
|
||||
let val_gez = ctx
|
||||
.builder
|
||||
.build_float_compare(FloatPredicate::OGE, n, n.get_type().const_zero(), "")
|
||||
.unwrap();
|
||||
|
||||
let to_int64 =
|
||||
ctx.builder.build_float_to_signed_int(n, ctx.ctx.i64_type(), "").unwrap();
|
||||
let to_uint64 =
|
||||
ctx.builder.build_float_to_unsigned_int(n, ctx.ctx.i64_type(), "").unwrap();
|
||||
ctx.builder
|
||||
.build_select(val_gez, to_uint64, to_int64, "conv")
|
||||
.unwrap()
|
||||
.into_int_value()
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `bool()`.
|
||||
#[must_use]
|
||||
pub fn cast_to_bool(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
|
||||
// TODO: Why is the original code being so lax about i1 and i8 for the returned int type?
|
||||
let result = if ctx.unifier.unioned(self.dtype, ctx.primitives.bool) {
|
||||
self.value.into_int_value()
|
||||
} else if ctx.unifier.unioned_any(self.dtype, ints(ctx)) {
|
||||
let n = self.value.into_int_value();
|
||||
ctx.builder
|
||||
.build_int_compare(inkwell::IntPredicate::NE, n, n.get_type().const_zero(), "bool")
|
||||
.unwrap()
|
||||
} else if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
|
||||
let n = self.value.into_float_value();
|
||||
ctx.builder
|
||||
.build_float_compare(FloatPredicate::UNE, n, n.get_type().const_zero(), "bool")
|
||||
.unwrap()
|
||||
} else {
|
||||
unsupported_type(ctx, [self.dtype])
|
||||
};
|
||||
|
||||
ScalarObject { dtype: ctx.primitives.bool, value: result.as_basic_value_enum() }
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `float()`.
|
||||
#[must_use]
|
||||
pub fn cast_to_float(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
|
||||
let llvm_f64 = ctx.ctx.f64_type();
|
||||
|
||||
let result: FloatValue<'_> = if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
|
||||
self.value.into_float_value()
|
||||
} else if ctx
|
||||
.unifier
|
||||
.unioned_any(self.dtype, [signed_ints(ctx).as_slice(), &[ctx.primitives.bool]].concat())
|
||||
{
|
||||
let n = self.value.into_int_value();
|
||||
ctx.builder.build_signed_int_to_float(n, llvm_f64, "sitofp").unwrap()
|
||||
} else if ctx.unifier.unioned_any(self.dtype, unsigned_ints(ctx)) {
|
||||
let n = self.value.into_int_value();
|
||||
ctx.builder.build_unsigned_int_to_float(n, llvm_f64, "uitofp").unwrap()
|
||||
} else {
|
||||
unsupported_type(ctx, [self.dtype]);
|
||||
};
|
||||
|
||||
ScalarObject { value: result.as_basic_value_enum(), dtype: ctx.primitives.float }
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `round()`.
|
||||
#[must_use]
|
||||
pub fn round<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ret_int_dtype: Type,
|
||||
) -> Self {
|
||||
let ret_int_dtype_llvm = ctx.get_llvm_type(generator, ret_int_dtype).into_int_type();
|
||||
|
||||
let result = if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
|
||||
let n = self.value.into_float_value();
|
||||
let n = llvm_intrinsics::call_float_round(ctx, n, None);
|
||||
ctx.builder.build_float_to_signed_int(n, ret_int_dtype_llvm, "round").unwrap()
|
||||
} else {
|
||||
unsupported_type(ctx, [self.dtype, ret_int_dtype])
|
||||
};
|
||||
ScalarObject { dtype: ret_int_dtype, value: result.as_basic_value_enum() }
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `np_round()`.
|
||||
///
|
||||
/// NOTE: `np.round()` has different behaviors than `round()` in terms of their result
|
||||
/// on "tie" cases and return type.
|
||||
#[must_use]
|
||||
pub fn np_round(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
|
||||
let result = if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
|
||||
let n = self.value.into_float_value();
|
||||
llvm_intrinsics::call_float_rint(ctx, n, None)
|
||||
} else {
|
||||
unsupported_type(ctx, [self.dtype])
|
||||
};
|
||||
ScalarObject { dtype: ctx.primitives.float, value: result.as_basic_value_enum() }
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `min()` or `max()`.
|
||||
pub fn min_or_max(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
kind: MinOrMax,
|
||||
a: Self,
|
||||
b: Self,
|
||||
) -> Self {
|
||||
if !ctx.unifier.unioned(a.dtype, b.dtype) {
|
||||
unsupported_type(ctx, [a.dtype, b.dtype])
|
||||
}
|
||||
|
||||
let common_dtype = a.dtype;
|
||||
|
||||
if ctx.unifier.unioned(common_dtype, ctx.primitives.float) {
|
||||
let function = match kind {
|
||||
MinOrMax::Min => llvm_intrinsics::call_float_minnum,
|
||||
MinOrMax::Max => llvm_intrinsics::call_float_maxnum,
|
||||
};
|
||||
let result =
|
||||
function(ctx, a.value.into_float_value(), b.value.into_float_value(), None);
|
||||
ScalarObject { value: result.as_basic_value_enum(), dtype: ctx.primitives.float }
|
||||
} else if ctx.unifier.unioned_any(
|
||||
common_dtype,
|
||||
[unsigned_ints(ctx).as_slice(), &[ctx.primitives.bool]].concat(),
|
||||
) {
|
||||
// Treating bool has an unsigned int since that is convenient
|
||||
let function = match kind {
|
||||
MinOrMax::Min => llvm_intrinsics::call_int_umin,
|
||||
MinOrMax::Max => llvm_intrinsics::call_int_umax,
|
||||
};
|
||||
let result = function(ctx, a.value.into_int_value(), b.value.into_int_value(), None);
|
||||
ScalarObject { value: result.as_basic_value_enum(), dtype: common_dtype }
|
||||
} else {
|
||||
unsupported_type(ctx, [common_dtype])
|
||||
}
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `floor()` or `ceil()`.
|
||||
///
|
||||
/// * `ret_int_dtype` - The type of int to return.
|
||||
///
|
||||
/// Takes in a float/int and returns an int of type `ret_int_dtype`
|
||||
#[must_use]
|
||||
pub fn floor_or_ceil<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
kind: FloorOrCeil,
|
||||
ret_int_dtype: Type,
|
||||
) -> Self {
|
||||
let ret_int_dtype_llvm = ctx.get_llvm_type(generator, ret_int_dtype).into_int_type();
|
||||
|
||||
if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
|
||||
let function = match kind {
|
||||
FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
|
||||
FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
|
||||
};
|
||||
let n = self.value.into_float_value();
|
||||
let n = function(ctx, n, None);
|
||||
|
||||
let n = ctx.builder.build_float_to_signed_int(n, ret_int_dtype_llvm, "").unwrap();
|
||||
ScalarObject { dtype: ret_int_dtype, value: n.as_basic_value_enum() }
|
||||
} else {
|
||||
unsupported_type(ctx, [self.dtype])
|
||||
}
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `np_floor()`/ `np_ceil()`.
|
||||
///
|
||||
/// Takes in a float/int and returns a float64 result.
|
||||
#[must_use]
|
||||
pub fn np_floor_or_ceil(&self, ctx: &mut CodeGenContext<'ctx, '_>, kind: FloorOrCeil) -> Self {
|
||||
if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
|
||||
let function = match kind {
|
||||
FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
|
||||
FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
|
||||
};
|
||||
let n = self.value.into_float_value();
|
||||
let n = function(ctx, n, None);
|
||||
ScalarObject { dtype: ctx.primitives.float, value: n.as_basic_value_enum() }
|
||||
} else {
|
||||
unsupported_type(ctx, [self.dtype])
|
||||
}
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `abs()`.
|
||||
pub fn abs(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
|
||||
if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
|
||||
let n = self.value.into_float_value();
|
||||
let n = llvm_intrinsics::call_float_fabs(ctx, n, Some("abs"));
|
||||
ScalarObject { value: n.into(), dtype: ctx.primitives.float }
|
||||
} else if ctx.unifier.unioned_any(self.dtype, ints(ctx)) {
|
||||
let n = self.value.into_int_value();
|
||||
|
||||
let is_poisoned = ctx.ctx.bool_type().const_zero(); // is_poisoned = false
|
||||
let n = llvm_intrinsics::call_int_abs(ctx, n, is_poisoned, Some("abs"));
|
||||
|
||||
ScalarObject { value: n.into(), dtype: self.dtype }
|
||||
} else {
|
||||
unsupported_type(ctx, [self.dtype])
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Helper function to implement NAC3's builtin `np_min()`, `np_max()`, `np_argmin()`, and `np_argmax()`.
|
||||
///
|
||||
/// Generate LLVM IR to find the extremum and index of the **first** extremum value.
|
||||
///
|
||||
/// Care has also been taken to make the error messages match that of NumPy.
|
||||
fn min_max_argmin_argmax_helper<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
kind: MinOrMax,
|
||||
on_empty_err_msg: &str,
|
||||
) -> (ScalarObject<'ctx>, Int<'ctx, SizeT>) {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
|
||||
|
||||
// If the ndarray is empty, throw an error.
|
||||
let is_empty = self.is_empty(generator, ctx);
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
is_empty.value,
|
||||
"0:ValueError",
|
||||
on_empty_err_msg,
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
// Setup and initialize the extremum to be the first element in the ndarray
|
||||
let pextremum_index = sizet_model.alloca(generator, ctx, "extremum_index");
|
||||
let pextremum = ctx.builder.build_alloca(dtype_llvm, "extremum").unwrap();
|
||||
|
||||
let zero = sizet_model.const_0(generator, ctx.ctx);
|
||||
pextremum_index.store(ctx, zero);
|
||||
|
||||
let first_scalar = self.get_nth(generator, ctx, zero);
|
||||
ctx.builder.build_store(pextremum, first_scalar.value).unwrap();
|
||||
|
||||
// Find extremum
|
||||
let start = sizet_model.const_1(generator, ctx.ctx); // Start on 1
|
||||
let stop = self.size(generator, ctx);
|
||||
let step = sizet_model.const_1(generator, ctx.ctx);
|
||||
gen_for_model_auto(generator, ctx, start, stop, step, |generator, ctx, _hooks, i| {
|
||||
// Worth reading on "Notes" in <https://numpy.org/doc/stable/reference/generated/numpy.min.html#numpy.min>
|
||||
// on how `NaN` values have to be handled.
|
||||
|
||||
let scalar = self.get_nth(generator, ctx, i);
|
||||
|
||||
let old_extremum = ctx.builder.build_load(pextremum, "current_extremum").unwrap();
|
||||
let old_extremum = ScalarObject { dtype: self.dtype, value: old_extremum };
|
||||
|
||||
let new_extremum = ScalarObject::min_or_max(ctx, kind, old_extremum, scalar);
|
||||
|
||||
// Check if new_extremum is more extreme than old_extremum.
|
||||
let update_index = ScalarObject::compare(
|
||||
generator,
|
||||
ctx,
|
||||
new_extremum,
|
||||
old_extremum,
|
||||
IntPredicate::NE,
|
||||
FloatPredicate::ONE,
|
||||
"",
|
||||
);
|
||||
|
||||
gen_if_model(generator, ctx, update_index, |_generator, ctx| {
|
||||
pextremum_index.store(ctx, i);
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
// Finally return the extremum and extremum index.
|
||||
let extremum_index = pextremum_index.load(generator, ctx, "extremum_index");
|
||||
|
||||
let extremum = ctx.builder.build_load(pextremum, "extremum_value").unwrap();
|
||||
let extremum = ScalarObject { dtype: self.dtype, value: extremum };
|
||||
|
||||
(extremum, extremum_index)
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `np_min()` or `np_max()`.
|
||||
pub fn min_or_max<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
kind: MinOrMax,
|
||||
) -> ScalarObject<'ctx> {
|
||||
let on_empty_err_msg = format!(
|
||||
"zero-size array to reduction operation {} which has no identity",
|
||||
match kind {
|
||||
MinOrMax::Min => "minimum",
|
||||
MinOrMax::Max => "maximum",
|
||||
}
|
||||
);
|
||||
self.min_max_argmin_argmax_helper(generator, ctx, kind, &on_empty_err_msg).0
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `np_argmin()` or `np_argmax()`.
|
||||
pub fn argmin_or_argmax<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
kind: MinOrMax,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let on_empty_err_msg = format!(
|
||||
"attempt to get {} of an empty sequence",
|
||||
match kind {
|
||||
MinOrMax::Min => "argmin",
|
||||
MinOrMax::Max => "argmax",
|
||||
}
|
||||
);
|
||||
self.min_max_argmin_argmax_helper(generator, ctx, kind, &on_empty_err_msg).1
|
||||
}
|
||||
}
|
|
@ -0,0 +1,338 @@
|
|||
use crate::codegen::{irrt::call_nac3_ndarray_index, model::*, CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::{scalar::ScalarOrNDArray, NDArrayObject};
|
||||
|
||||
pub type NDIndexType = Byte;
|
||||
|
||||
/// Fields of [`NDIndex`]
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDIndexFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub type_: F::Out<IntModel<NDIndexType>>, // Defined to be uint8_t in IRRT
|
||||
pub data: F::Out<PtrModel<IntModel<Byte>>>,
|
||||
}
|
||||
|
||||
/// An IRRT representation fo 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 traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields { type_: traversal.add_auto("type"), data: traversal.add_auto("data") }
|
||||
}
|
||||
}
|
||||
|
||||
/// Fields of [`UserSlice`]
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct UserSliceFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub start_defined: F::Out<IntModel<Bool>>,
|
||||
pub start: F::Out<IntModel<Int32>>,
|
||||
pub stop_defined: F::Out<IntModel<Bool>>,
|
||||
pub stop: F::Out<IntModel<Int32>>,
|
||||
pub step_defined: F::Out<IntModel<Bool>>,
|
||||
pub step: F::Out<IntModel<Int32>>,
|
||||
}
|
||||
|
||||
/// An IRRT representation of a user slice.
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct UserSlice;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for UserSlice {
|
||||
type Fields<F: FieldTraversal<'ctx>> = UserSliceFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
start_defined: traversal.add_auto("start_defined"),
|
||||
start: traversal.add_auto("start"),
|
||||
stop_defined: traversal.add_auto("stop_defined"),
|
||||
stop: traversal.add_auto("stop"),
|
||||
step_defined: traversal.add_auto("step_defined"),
|
||||
step: traversal.add_auto("step"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A convenience structure to prepare a [`UserSlice`].
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct RustUserSlice<'ctx> {
|
||||
pub start: Option<Int<'ctx, Int32>>,
|
||||
pub stop: Option<Int<'ctx, Int32>>,
|
||||
pub step: Option<Int<'ctx, Int32>>,
|
||||
}
|
||||
|
||||
impl<'ctx> RustUserSlice<'ctx> {
|
||||
/// Write the contents to an LLVM [`UserSlice`].
|
||||
pub fn write_to_user_slice<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_slice_ptr: Ptr<'ctx, StructModel<UserSlice>>,
|
||||
) {
|
||||
let bool_model = IntModel(Bool);
|
||||
|
||||
let false_ = bool_model.constant(generator, ctx.ctx, 0);
|
||||
let true_ = bool_model.constant(generator, ctx.ctx, 1);
|
||||
|
||||
// TODO: Code duplication. Probably okay...?
|
||||
|
||||
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_),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// A convenience enum variant to store the content and type of an NDIndex in high level.
|
||||
#[derive(Debug, Clone)]
|
||||
pub enum RustNDIndex<'ctx> {
|
||||
SingleElement(Int<'ctx, Int32>), // TODO: To be SizeT
|
||||
Slice(RustUserSlice<'ctx>),
|
||||
}
|
||||
|
||||
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,
|
||||
}
|
||||
}
|
||||
|
||||
/// Write the contents to an LLVM [`NDIndex`].
|
||||
fn write_to_ndindex<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_ndindex_ptr: Ptr<'ctx, StructModel<NDIndex>>,
|
||||
) {
|
||||
let ndindex_type_model = IntModel(NDIndexType::default());
|
||||
let i32_model = IntModel(Int32);
|
||||
let user_slice_model = StructModel(UserSlice);
|
||||
|
||||
// Set `dst_ndindex_ptr->type`
|
||||
dst_ndindex_ptr
|
||||
.gep(ctx, |f| f.type_)
|
||||
.store(ctx, ndindex_type_model.constant(generator, ctx.ctx, self.get_type_id()));
|
||||
|
||||
// Set `dst_ndindex_ptr->data`
|
||||
let data = match self {
|
||||
RustNDIndex::SingleElement(in_index) => {
|
||||
let index_ptr = i32_model.alloca(generator, ctx, "index");
|
||||
index_ptr.store(ctx, *in_index);
|
||||
index_ptr.transmute(generator, ctx, IntModel(Byte), "")
|
||||
}
|
||||
RustNDIndex::Slice(in_rust_slice) => {
|
||||
let user_slice_ptr = user_slice_model.alloca(generator, ctx, "user_slice");
|
||||
in_rust_slice.write_to_user_slice(generator, ctx, user_slice_ptr);
|
||||
user_slice_ptr.transmute(generator, ctx, IntModel(Byte), "")
|
||||
}
|
||||
};
|
||||
dst_ndindex_ptr.gep(ctx, |f| f.data).store(ctx, data);
|
||||
}
|
||||
|
||||
/// Allocate an array of `NDIndex`es on the stack and return its stack pointer.
|
||||
pub fn alloca_ndindexes<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
in_ndindexes: &[RustNDIndex<'ctx>],
|
||||
) -> (Int<'ctx, SizeT>, Ptr<'ctx, StructModel<NDIndex>>) {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let ndindex_model = StructModel(NDIndex);
|
||||
|
||||
let num_ndindexes = sizet_model.constant(generator, ctx.ctx, in_ndindexes.len() as u64);
|
||||
let ndindexes =
|
||||
ndindex_model.array_alloca(generator, ctx, num_ndindexes.value, "ndindexes");
|
||||
for (i, in_ndindex) in in_ndindexes.iter().enumerate() {
|
||||
let i = sizet_model.constant(generator, ctx.ctx, i as u64);
|
||||
let pndindex = ndindexes.offset(generator, ctx, i.value, "");
|
||||
in_ndindex.write_to_ndindex(generator, ctx, pndindex);
|
||||
}
|
||||
|
||||
(num_ndindexes, ndindexes)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Get the ndims [`Type`] after indexing with a given slice.
|
||||
#[must_use]
|
||||
pub fn deduce_ndims_after_indexing_with(&self, indexes: &[RustNDIndex<'ctx>]) -> u64 {
|
||||
let mut ndims = self.ndims;
|
||||
for index in indexes {
|
||||
match index {
|
||||
RustNDIndex::SingleElement(_) => {
|
||||
ndims -= 1; // Single elements decrements ndims
|
||||
}
|
||||
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 indexes 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, '_>,
|
||||
indexes: &[RustNDIndex<'ctx>],
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let dst_ndims = self.deduce_ndims_after_indexing_with(indexes);
|
||||
let dst_ndarray =
|
||||
NDArrayObject::alloca_uninitialized(generator, ctx, self.dtype, dst_ndims, name);
|
||||
|
||||
let (num_indexes, indexes) = RustNDIndex::alloca_ndindexes(generator, ctx, indexes);
|
||||
call_nac3_ndarray_index(
|
||||
generator,
|
||||
ctx,
|
||||
num_indexes,
|
||||
indexes,
|
||||
self.value,
|
||||
dst_ndarray.value,
|
||||
);
|
||||
|
||||
dst_ndarray
|
||||
}
|
||||
|
||||
/// Like [`NDArrayObject::index`] but returns a scalar if the indexes index
|
||||
/// into a single element.
|
||||
#[must_use]
|
||||
pub fn index_or_scalar<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
indexes: &[RustNDIndex<'ctx>],
|
||||
name: &str,
|
||||
) -> ScalarOrNDArray<'ctx> {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let zero = sizet_model.const_0(generator, ctx.ctx);
|
||||
|
||||
let subndarray = self.index(generator, ctx, indexes, name);
|
||||
if subndarray.is_unsized() {
|
||||
// NOTE: `np.size(self) == 0` here is never possible.
|
||||
ScalarOrNDArray::Scalar(subndarray.get_nth(generator, ctx, zero))
|
||||
} else {
|
||||
ScalarOrNDArray::NDArray(subndarray)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub mod util {
|
||||
use itertools::Itertools;
|
||||
use nac3parser::ast::{Expr, ExprKind};
|
||||
|
||||
use crate::{
|
||||
codegen::{model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
use super::{RustNDIndex, RustUserSlice};
|
||||
|
||||
/// 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_ndindexes<'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
|
||||
let i32_model = IntModel(Int32);
|
||||
|
||||
// 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_ndindexes: 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: start, upper: stop, step } = &index_expr.node {
|
||||
// Helper function here to deduce code duplication
|
||||
type ValueExpr = Option<Box<Expr<Option<Type>>>>;
|
||||
let mut help = |value_expr: &ValueExpr| -> 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 =
|
||||
i32_model.check_value(generator, ctx.ctx, value_expr).unwrap();
|
||||
|
||||
Some(value_expr)
|
||||
}
|
||||
})
|
||||
};
|
||||
|
||||
let start = help(start)?;
|
||||
let stop = help(stop)?;
|
||||
let step = help(step)?;
|
||||
|
||||
RustNDIndex::Slice(RustUserSlice { start, stop, step })
|
||||
} else {
|
||||
// Anything else that is not a slice (might be illegal values),
|
||||
// For nac3core, this should be e.g., an int32 constant, an int32 variable, otherwise its an error
|
||||
let index = generator.gen_expr(ctx, index_expr)?.unwrap().to_basic_value_enum(
|
||||
ctx,
|
||||
generator,
|
||||
ctx.primitives.int32,
|
||||
)?;
|
||||
let index = i32_model.check_value(generator, ctx.ctx, index).unwrap();
|
||||
|
||||
RustNDIndex::SingleElement(index)
|
||||
};
|
||||
rust_ndindexes.push(ndindex);
|
||||
}
|
||||
Ok(rust_ndindexes)
|
||||
}
|
||||
}
|
|
@ -0,0 +1,158 @@
|
|||
use inkwell::values::BasicValueEnum;
|
||||
use itertools::Itertools;
|
||||
use util::gen_for_model_auto;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
model::*,
|
||||
structure::ndarray::{scalar::ScalarObject, NDArrayObject},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
use super::scalar::ScalarOrNDArray;
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// TODO: Document me. Has complex behavior.
|
||||
/// and explain why `ret_dtype` has to be specified beforehand.
|
||||
pub fn broadcasting_starmap<'a, G, MappingFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ndarrays: &[Self],
|
||||
ret_dtype: Type,
|
||||
mapping: MappingFn,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
MappingFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
Int<'ctx, SizeT>,
|
||||
&[ScalarObject<'ctx>],
|
||||
) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Broadcast inputs
|
||||
let broadcast_result = NDArrayObject::broadcast_all(generator, ctx, ndarrays);
|
||||
|
||||
// Allocate the resulting ndarray
|
||||
let mapped_ndarray = NDArrayObject::alloca_uninitialized(
|
||||
generator,
|
||||
ctx,
|
||||
ret_dtype,
|
||||
broadcast_result.ndims,
|
||||
"mapped_ndarray",
|
||||
);
|
||||
mapped_ndarray.copy_shape_from_array(generator, ctx, broadcast_result.shape);
|
||||
mapped_ndarray.create_data(generator, ctx);
|
||||
|
||||
// Map element-wise and store results into `mapped_ndarray`.
|
||||
let start = sizet_model.const_0(generator, ctx.ctx);
|
||||
let stop = broadcast_result.ndarrays[0].size(generator, ctx); // They all should have the same `np.size`.
|
||||
let step = sizet_model.const_1(generator, ctx.ctx);
|
||||
gen_for_model_auto(generator, ctx, start, stop, step, move |generator, ctx, _hooks, i| {
|
||||
let elements =
|
||||
ndarrays.iter().map(|ndarray| ndarray.get_nth(generator, ctx, i)).collect_vec();
|
||||
|
||||
let ret = mapping(generator, ctx, i, &elements)?;
|
||||
|
||||
let pret = mapped_ndarray.get_nth_pointer(generator, ctx, i, "pret");
|
||||
ctx.builder.build_store(pret, ret).unwrap();
|
||||
Ok(())
|
||||
})?;
|
||||
|
||||
Ok(mapped_ndarray)
|
||||
}
|
||||
|
||||
pub fn map<'a, G, Mapping>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ret_dtype: Type,
|
||||
mapping: Mapping,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Mapping: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
Int<'ctx, SizeT>,
|
||||
ScalarObject<'ctx>,
|
||||
) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
NDArrayObject::broadcasting_starmap(
|
||||
generator,
|
||||
ctx,
|
||||
&[*self],
|
||||
ret_dtype,
|
||||
|generator, ctx, i, scalars| mapping(generator, ctx, i, scalars[0]),
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarOrNDArray<'ctx> {
|
||||
/// TODO: Document me. Has complex behavior.
|
||||
/// and explain why `ret_dtype` has to be specified beforehand.
|
||||
pub fn broadcasting_starmap<'a, G, MappingFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
inputs: &[Self],
|
||||
ret_dtype: Type,
|
||||
mapping: MappingFn,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
MappingFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
Int<'ctx, SizeT>,
|
||||
&[ScalarObject<'ctx>],
|
||||
) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Check if all inputs are ScalarObjects
|
||||
let all_scalars: Option<Vec<_>> =
|
||||
inputs.iter().map(ScalarObject::try_from).try_collect().ok();
|
||||
|
||||
if let Some(scalars) = all_scalars {
|
||||
let i = sizet_model.const_0(generator, ctx.ctx); // Pass 0 as the index
|
||||
let scalar =
|
||||
ScalarObject { value: mapping(generator, ctx, i, &scalars)?, dtype: ret_dtype };
|
||||
Ok(ScalarOrNDArray::Scalar(scalar))
|
||||
} else {
|
||||
// Promote all input to ndarrays and map through them.
|
||||
let inputs = inputs.iter().map(|input| input.as_ndarray(generator, ctx)).collect_vec();
|
||||
let ndarray =
|
||||
NDArrayObject::broadcasting_starmap(generator, ctx, &inputs, ret_dtype, mapping)?;
|
||||
Ok(ScalarOrNDArray::NDArray(ndarray))
|
||||
}
|
||||
}
|
||||
|
||||
pub fn map<'a, G, Mapping>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ret_dtype: Type,
|
||||
mapping: Mapping,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Mapping: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
Int<'ctx, SizeT>,
|
||||
ScalarObject<'ctx>,
|
||||
) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
ScalarOrNDArray::broadcasting_starmap(
|
||||
generator,
|
||||
ctx,
|
||||
&[*self],
|
||||
ret_dtype,
|
||||
|generator, ctx, i, scalars| mapping(generator, ctx, i, scalars[0]),
|
||||
)
|
||||
}
|
||||
}
|
|
@ -0,0 +1,478 @@
|
|||
pub mod broadcast;
|
||||
pub mod functions;
|
||||
pub mod indexing;
|
||||
pub mod mapping;
|
||||
pub mod scalar;
|
||||
pub mod shape_util;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::{
|
||||
call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
|
||||
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,
|
||||
},
|
||||
model::*,
|
||||
stmt::BreakContinueHooks,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
toplevel::numpy::unpack_ndarray_var_tys,
|
||||
typecheck::{numpy::extract_ndims, typedef::Type},
|
||||
};
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::BasicType,
|
||||
values::{BasicValue, BasicValueEnum, PointerValue},
|
||||
AddressSpace, IntPredicate,
|
||||
};
|
||||
use scalar::ScalarObject;
|
||||
use util::{call_memcpy_model, gen_for_model_auto};
|
||||
|
||||
pub struct NpArrayFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub data: F::Out<PtrModel<IntModel<Byte>>>,
|
||||
pub itemsize: F::Out<IntModel<SizeT>>,
|
||||
pub ndims: F::Out<IntModel<SizeT>>,
|
||||
pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
pub strides: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
}
|
||||
|
||||
// TODO: Rename to `NDArray` when the old NDArray is removed.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct NpArray;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for NpArray {
|
||||
type Fields<F: FieldTraversal<'ctx>> = NpArrayFields<'ctx, F>;
|
||||
|
||||
fn traverse_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 value: Ptr<'ctx, StructModel<NpArray>>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Create an [`NDArrayObject`] from an LLVM value and its typechecker [`Type`].
|
||||
pub fn from_value_and_type<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
value: V,
|
||||
ty: Type,
|
||||
) -> Self {
|
||||
let pndarray_model = PtrModel(StructModel(NpArray));
|
||||
|
||||
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
|
||||
let ndims = extract_ndims(&ctx.unifier, ndims);
|
||||
let value = pndarray_model.check_value(generator, ctx.ctx, value).unwrap();
|
||||
NDArrayObject { dtype, ndims, value }
|
||||
}
|
||||
|
||||
/// Get the `np.size()` of this ndarray.
|
||||
pub fn size<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_size(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Get the `ndarray.nbytes` of this ndarray.
|
||||
pub fn nbytes<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_nbytes(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Get the `len()` of this ndarray.
|
||||
pub fn len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_len(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
/// 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, '_>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
call_nac3_ndarray_is_c_contiguous(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
/// 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_pointer<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
nth: Int<'ctx, SizeT>,
|
||||
name: &str,
|
||||
) -> PointerValue<'ctx> {
|
||||
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
|
||||
|
||||
let p = call_nac3_ndarray_get_nth_pelement(generator, ctx, self.value, nth);
|
||||
ctx.builder
|
||||
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), name)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Get the n-th (0-based) scalar.
|
||||
pub fn get_nth<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
nth: Int<'ctx, SizeT>,
|
||||
) -> ScalarObject<'ctx> {
|
||||
let p = self.get_nth_pointer(generator, ctx, nth, "value");
|
||||
let value = ctx.builder.build_load(p, "value").unwrap();
|
||||
ScalarObject { dtype: self.dtype, value }
|
||||
}
|
||||
|
||||
/// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
|
||||
///
|
||||
/// Please refer to the IRRT implementation to see its purpose.
|
||||
pub fn update_strides_by_shape<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
call_nac3_ndarray_set_strides_by_shape(generator, ctx, self.value);
|
||||
}
|
||||
|
||||
/// 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.value, self.value);
|
||||
}
|
||||
|
||||
/// Allocate an ndarray on the stack given its `ndims` and `dtype`.
|
||||
///
|
||||
/// `shape` and `strides` will be automatically allocated on the stack.
|
||||
///
|
||||
/// The returned ndarray's content will be:
|
||||
/// - `data`: set to `nullptr`.
|
||||
/// - `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_uninitialized<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let ndarray_model = StructModel(NpArray);
|
||||
let ndarray_data_model = PtrModel(IntModel(Byte));
|
||||
|
||||
let pndarray = ndarray_model.alloca(generator, ctx, name);
|
||||
|
||||
let data = ndarray_data_model.nullptr(generator, ctx.ctx);
|
||||
pndarray.set(ctx, |f| f.data, data);
|
||||
|
||||
let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
|
||||
let itemsize =
|
||||
sizet_model.s_extend_or_bit_cast(generator, ctx, itemsize, "alloca_itemsize");
|
||||
pndarray.set(ctx, |f| f.itemsize, itemsize);
|
||||
|
||||
let ndims_val = sizet_model.constant(generator, ctx.ctx, ndims);
|
||||
pndarray.set(ctx, |f| f.ndims, ndims_val);
|
||||
|
||||
let shape = sizet_model.array_alloca(generator, ctx, ndims_val.value, "alloca_shape");
|
||||
pndarray.set(ctx, |f| f.shape, shape);
|
||||
|
||||
let strides = sizet_model.array_alloca(generator, ctx, ndims_val.value, "alloca_strides");
|
||||
pndarray.set(ctx, |f| f.strides, strides);
|
||||
|
||||
NDArrayObject { dtype, ndims, value: pndarray }
|
||||
}
|
||||
|
||||
/// Convenience function.
|
||||
/// Like [`NDArrayObject::alloca_uninitialized`] but directly takes the typechecker type of the ndarray.
|
||||
pub fn alloca_uninitialized_of_type<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ty: Type,
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
|
||||
let ndims = extract_ndims(&ctx.unifier, ndims);
|
||||
Self::alloca_uninitialized(generator, ctx, dtype, ndims, name)
|
||||
}
|
||||
|
||||
/// Clone this ndaarray - 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.
|
||||
pub fn make_clone<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let clone =
|
||||
NDArrayObject::alloca_uninitialized(generator, ctx, self.dtype, self.ndims, name);
|
||||
|
||||
let shape = self.value.gep(ctx, |f| f.shape).load(generator, ctx, "shape");
|
||||
clone.copy_shape_from_array(generator, ctx, shape);
|
||||
clone.create_data(generator, ctx);
|
||||
clone.copy_data_from(generator, ctx, *self);
|
||||
clone
|
||||
}
|
||||
|
||||
/// Get this ndarray's `ndims` as an LLVM constant.
|
||||
pub fn get_ndims<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
sizet_model.constant(generator, ctx, self.ndims)
|
||||
}
|
||||
|
||||
/// Get if this ndarray's `np.size` is `0` - containing no content.
|
||||
pub fn is_empty<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let size = self.size(generator, ctx);
|
||||
size.compare(ctx, IntPredicate::EQ, sizet_model.const_0(generator, ctx.ctx), "is_empty")
|
||||
}
|
||||
|
||||
/// Return true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
|
||||
///
|
||||
/// This is a staticially known property of ndarrays. This is why it is returning
|
||||
/// a Rust boolean instead of a [`BasicValue`].
|
||||
#[must_use]
|
||||
pub fn is_unsized(&self) -> bool {
|
||||
self.ndims == 0
|
||||
}
|
||||
|
||||
/// 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 byte_model = IntModel(Byte);
|
||||
|
||||
let nbytes = self.nbytes(generator, ctx);
|
||||
|
||||
let data = byte_model.array_alloca(generator, ctx, nbytes.value, "data");
|
||||
self.value.set(ctx, |f| f.data, data);
|
||||
|
||||
self.update_strides_by_shape(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, '_>,
|
||||
src_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let dst_shape = self.value.get(generator, ctx, |f| f.shape, "dst_shape");
|
||||
let num_items = self.get_ndims(generator, ctx.ctx).value;
|
||||
call_memcpy_model(generator, ctx, dst_shape, src_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.value.get(generator, ctx, |f| f.shape, "src_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, '_>,
|
||||
src_strides: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let dst_strides = self.value.get(generator, ctx, |f| f.strides, "dst_strides");
|
||||
let num_items = self.get_ndims(generator, ctx.ctx).value;
|
||||
call_memcpy_model(generator, ctx, dst_strides, src_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.value.get(generator, ctx, |f| f.strides, "src_strides");
|
||||
self.copy_strides_from_array(generator, ctx, src_strides);
|
||||
}
|
||||
|
||||
/// Iterate through every element pointer in the ndarray in its flatten view.
|
||||
///
|
||||
/// `body` also access to [`BreakContinueHooks`] to short-circuit and an element's
|
||||
/// index. The given element pointer also has been casted to the LLVM type of this ndarray's `dtype`.
|
||||
pub fn foreach_pointer<'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>,
|
||||
Int<'ctx, SizeT>,
|
||||
PointerValue<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
{
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let start = sizet_model.const_0(generator, ctx.ctx);
|
||||
let stop = self.size(generator, ctx);
|
||||
let step = sizet_model.const_1(generator, ctx.ctx);
|
||||
|
||||
gen_for_model_auto(generator, ctx, start, stop, step, |generator, ctx, hooks, i| {
|
||||
let pelement = self.get_nth_pointer(generator, ctx, i, "element");
|
||||
body(generator, ctx, hooks, i, pelement)
|
||||
})
|
||||
}
|
||||
|
||||
/// Iterate through every scalar in this ndarray.
|
||||
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>,
|
||||
Int<'ctx, SizeT>,
|
||||
ScalarObject<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
{
|
||||
self.foreach_pointer(generator, ctx, |generator, ctx, hooks, i, p| {
|
||||
let value = ctx.builder.build_load(p, "value").unwrap();
|
||||
let scalar = ScalarObject { dtype: self.dtype, value };
|
||||
body(generator, ctx, hooks, i, scalar)
|
||||
})
|
||||
}
|
||||
|
||||
/// Fill the ndarray with a value.
|
||||
///
|
||||
/// `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, '_>,
|
||||
fill_value: BasicValueEnum<'ctx>,
|
||||
) {
|
||||
self.foreach_pointer(generator, ctx, |_generator, ctx, _hooks, _i, pelement| {
|
||||
ctx.builder.build_store(pelement, fill_value).unwrap();
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
}
|
||||
|
||||
/// Create a reshaped view on this ndarray like `np.reshape()`.
|
||||
///
|
||||
/// 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: Ptr<'ctx, IntModel<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. 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_uninitialized(
|
||||
generator,
|
||||
ctx,
|
||||
self.dtype,
|
||||
new_ndims,
|
||||
"reshaped_ndarray",
|
||||
);
|
||||
dst_ndarray.copy_shape_from_array(generator, ctx, new_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.update_strides_by_shape(generator, ctx);
|
||||
dst_ndarray.value.set(ctx, |f| f.data, self.value.get(generator, ctx, |f| f.data, "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
|
||||
}
|
||||
}
|
|
@ -0,0 +1,143 @@
|
|||
use inkwell::values::{BasicValue, BasicValueEnum};
|
||||
|
||||
use crate::{
|
||||
codegen::{model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
/// An LLVM numpy scalar with its [`Type`].
|
||||
///
|
||||
/// Intended to be used with [`ScalarOrNDArray`].
|
||||
///
|
||||
/// A scalar does not have to be an actual number. It could be arbitrary objects.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct ScalarObject<'ctx> {
|
||||
pub dtype: Type,
|
||||
pub value: BasicValueEnum<'ctx>,
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarObject<'ctx> {
|
||||
/// Promote this scalar to an unsized ndarray (like doing `np.asarray`).
|
||||
///
|
||||
/// The scalar value is allocated onto the stack, and the ndarray's `data` will point to that
|
||||
/// allocated value.
|
||||
pub fn as_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> NDArrayObject<'ctx> {
|
||||
let pbyte_model = PtrModel(IntModel(Byte));
|
||||
|
||||
// We have to put the value on the stack to get a data pointer.
|
||||
let data = ctx.builder.build_alloca(self.value.get_type(), "as_ndarray_scalar").unwrap();
|
||||
ctx.builder.build_store(data, self.value).unwrap();
|
||||
let data = pbyte_model.pointer_cast(generator, ctx, data, "data");
|
||||
|
||||
let ndarray =
|
||||
NDArrayObject::alloca_uninitialized(generator, ctx, self.dtype, 0, "scalar_ndarray");
|
||||
ndarray.value.set(ctx, |f| f.data, data);
|
||||
ndarray
|
||||
}
|
||||
}
|
||||
|
||||
/// A convenience enum for implementing scalar/ndarray agnostic utilities.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub enum ScalarOrNDArray<'ctx> {
|
||||
Scalar(ScalarObject<'ctx>),
|
||||
NDArray(NDArrayObject<'ctx>),
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarOrNDArray<'ctx> {
|
||||
/// 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.value.value.as_basic_value_enum(),
|
||||
}
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn into_scalar(&self) -> ScalarObject<'ctx> {
|
||||
match self {
|
||||
ScalarOrNDArray::NDArray(_ndarray) => panic!("Got NDArray"),
|
||||
ScalarOrNDArray::Scalar(scalar) => *scalar,
|
||||
}
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn into_ndarray(&self) -> NDArrayObject<'ctx> {
|
||||
match self {
|
||||
ScalarOrNDArray::NDArray(ndarray) => *ndarray,
|
||||
ScalarOrNDArray::Scalar(_scalar) => panic!("Got Scalar"),
|
||||
}
|
||||
}
|
||||
|
||||
/// Convert this [`ScalarOrNDArray`] to an ndarray - behaves like `np.asarray`.
|
||||
/// - If this is an ndarray, the ndarray is returned.
|
||||
/// - If this is a scalar, an unsized ndarray view is created on it.
|
||||
pub fn as_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> NDArrayObject<'ctx> {
|
||||
match self {
|
||||
ScalarOrNDArray::NDArray(ndarray) => *ndarray,
|
||||
ScalarOrNDArray::Scalar(scalar) => scalar.as_ndarray(generator, ctx),
|
||||
}
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn dtype(&self) -> Type {
|
||||
match self {
|
||||
ScalarOrNDArray::Scalar(scalar) => scalar.dtype,
|
||||
ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for ScalarObject<'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),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Split an [`BasicValueEnum<'ctx>`] into a [`ScalarOrNDArray`] depending
|
||||
/// on its [`Type`].
|
||||
pub fn split_scalar_or_ndarray<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
input: BasicValueEnum<'ctx>,
|
||||
input_ty: Type,
|
||||
) -> ScalarOrNDArray<'ctx> {
|
||||
match &*ctx.unifier.get_ty(input_ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let ndarray = NDArrayObject::from_value_and_type(generator, ctx, input, input_ty);
|
||||
ScalarOrNDArray::NDArray(ndarray)
|
||||
}
|
||||
_ => {
|
||||
let scalar = ScalarObject { dtype: input_ty, value: input };
|
||||
ScalarOrNDArray::Scalar(scalar)
|
||||
}
|
||||
}
|
||||
}
|
|
@ -0,0 +1,112 @@
|
|||
use inkwell::values::BasicValueEnum;
|
||||
use util::gen_for_model_auto;
|
||||
|
||||
use crate::{
|
||||
codegen::{model::*, structure::list::ListObject, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
/// Parse a NumPy-like "int sequence" input and return the int sequence as an array and its length.
|
||||
///
|
||||
/// * `sequence` - The `sequence` parameter.
|
||||
/// * `sequence_ty` - The typechecker type of `sequence`
|
||||
///
|
||||
/// The `sequence` argument type may only be one of the following:
|
||||
/// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
|
||||
/// 2. A tuple of `int32`; e.g., `np.empty((600, 800, 3))`
|
||||
/// 3. A scalar `int32`; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
|
||||
///
|
||||
/// All `int32` values will be sign-extended to `SizeT`.
|
||||
pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
input_sequence: BasicValueEnum<'ctx>,
|
||||
input_sequence_ty: Type,
|
||||
) -> (Int<'ctx, SizeT>, Ptr<'ctx, IntModel<SizeT>>) {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let zero = sizet_model.const_0(generator, ctx.ctx);
|
||||
let one = sizet_model.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_value_and_type(generator, ctx, input_sequence, input_sequence_ty);
|
||||
|
||||
let len = input_sequence.value.gep(ctx, |f| f.len).load(generator, ctx, "len");
|
||||
let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
|
||||
|
||||
// Load all the `int32`s from the input_sequence, cast them to `SizeT`, and store them into `result`
|
||||
gen_for_model_auto(generator, ctx, zero, len, one, |generator, ctx, _hooks, i| {
|
||||
// Load the i-th int32 in the input sequence
|
||||
let int = input_sequence
|
||||
.value
|
||||
.get(generator, ctx, |f| f.items, "int")
|
||||
.ix(generator, ctx, i.value, "int")
|
||||
.value
|
||||
.into_int_value();
|
||||
|
||||
// Cast to SizeT
|
||||
let int = sizet_model.s_extend_or_bit_cast(generator, ctx, int, "int");
|
||||
|
||||
// Store
|
||||
result.offset(generator, ctx, i.value, "int").store(ctx, int);
|
||||
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
(len, result)
|
||||
}
|
||||
TypeEnum::TTuple { ty: tuple_types } => {
|
||||
// 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
|
||||
let input_sequence = input_sequence.into_struct_value(); // A tuple is a struct
|
||||
|
||||
let len_int = tuple_types.len();
|
||||
|
||||
let len = sizet_model.constant(generator, ctx.ctx, len_int as u64);
|
||||
let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
|
||||
|
||||
for i in 0..len_int {
|
||||
// Get the i-th element off of the tuple and load it into `result`.
|
||||
let int = ctx
|
||||
.builder
|
||||
.build_extract_value(input_sequence, i as u32, "int")
|
||||
.unwrap()
|
||||
.into_int_value();
|
||||
let int = sizet_model.s_extend_or_bit_cast(generator, ctx, int, "int");
|
||||
|
||||
let offset = sizet_model.constant(generator, ctx.ctx, i as u64);
|
||||
result.offset(generator, ctx, offset.value, "int").store(ctx, 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.into_int_value();
|
||||
|
||||
let len = sizet_model.const_1(generator, ctx.ctx);
|
||||
let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
|
||||
|
||||
let int = sizet_model.s_extend_or_bit_cast(generator, ctx, input_int, "int");
|
||||
|
||||
// Storing into result[0]
|
||||
result.store(ctx, int);
|
||||
|
||||
(len, result)
|
||||
}
|
||||
_ => panic!(
|
||||
"encountered unknown sequence type: {}",
|
||||
ctx.unifier.stringify(input_sequence_ty)
|
||||
),
|
||||
}
|
||||
}
|
|
@ -0,0 +1,67 @@
|
|||
use inkwell::values::{BasicValueEnum, StructValue};
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
codegen::{CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
pub struct TupleObject<'ctx> {
|
||||
pub tys: Vec<Type>,
|
||||
pub value: StructValue<'ctx>,
|
||||
}
|
||||
|
||||
impl<'ctx> TupleObject<'ctx> {
|
||||
pub fn create<I, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
items: I,
|
||||
name: &str,
|
||||
) -> Self
|
||||
where
|
||||
I: IntoIterator<Item = (BasicValueEnum<'ctx>, Type)>,
|
||||
{
|
||||
let (vals, tys): (Vec<_>, Vec<_>) = items.into_iter().unzip();
|
||||
|
||||
// let tuple_ty = ctx.unifier.add_ty(TypeEnum::TTuple { ty: tys });
|
||||
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 vals.into_iter().enumerate() {
|
||||
// Store the dim value into the tuple
|
||||
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, name).unwrap().into_struct_value();
|
||||
TupleObject { tys, value }
|
||||
}
|
||||
|
||||
// pub fn create_from_array<G: CodeGenerator + ?Sized>(
|
||||
// generator: &mut G,
|
||||
// ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
// array: PointerValue<'ctx>,
|
||||
// elem_ty: Type,
|
||||
// count: u64,
|
||||
// name: &str,
|
||||
// ) -> Self {
|
||||
// let i32_type = ctx.ctx.i32_type();
|
||||
|
||||
// let mut items: Vec<(BasicValueEnum<'ctx>, Type)> = Vec::with_capacity(count as usize);
|
||||
|
||||
// for i in 0..count {
|
||||
// let pval = unsafe {
|
||||
// ctx.builder.build_in_bounds_gep(
|
||||
// array,
|
||||
// &[i32_type.const_zero(), i32_type.const_int(i as u64, false)],
|
||||
// name,
|
||||
// )
|
||||
// }
|
||||
// .unwrap();
|
||||
// let val = ctx.builder.build_load(pval, "value").unwrap();
|
||||
// items.push((val, elem_ty));
|
||||
// }
|
||||
|
||||
// Self::create(generator, ctx, items, name)
|
||||
// }
|
||||
}
|
|
@ -189,6 +189,8 @@ fn test_primitives() {
|
|||
let expected = indoc! {"
|
||||
; ModuleID = 'test'
|
||||
source_filename = \"test\"
|
||||
target datalayout = \"e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-f80:128-n8:16:32:64-S128\"
|
||||
target triple = \"x86_64-unknown-linux-gnu\"
|
||||
|
||||
; Function Attrs: mustprogress nofree norecurse nosync nounwind readnone willreturn
|
||||
define i32 @testing(i32 %0, i32 %1) local_unnamed_addr #0 !dbg !4 {
|
||||
|
@ -368,6 +370,8 @@ fn test_simple_call() {
|
|||
let expected = indoc! {"
|
||||
; ModuleID = 'test'
|
||||
source_filename = \"test\"
|
||||
target datalayout = \"e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-f80:128-n8:16:32:64-S128\"
|
||||
target triple = \"x86_64-unknown-linux-gnu\"
|
||||
|
||||
; Function Attrs: mustprogress nofree norecurse nosync nounwind readnone willreturn
|
||||
define i32 @testing(i32 %0) local_unnamed_addr #0 !dbg !5 {
|
||||
|
|
File diff suppressed because it is too large
Load Diff
|
@ -766,6 +766,7 @@ impl TopLevelComposer {
|
|||
let target_ty = get_type_from_type_annotation_kinds(
|
||||
&temp_def_list,
|
||||
unifier,
|
||||
primitives,
|
||||
&def,
|
||||
&mut subst_list,
|
||||
)?;
|
||||
|
@ -936,6 +937,7 @@ impl TopLevelComposer {
|
|||
let ty = get_type_from_type_annotation_kinds(
|
||||
temp_def_list.as_ref(),
|
||||
unifier,
|
||||
primitives_store,
|
||||
&type_annotation,
|
||||
&mut None,
|
||||
)?;
|
||||
|
@ -1002,6 +1004,7 @@ impl TopLevelComposer {
|
|||
get_type_from_type_annotation_kinds(
|
||||
&temp_def_list,
|
||||
unifier,
|
||||
primitives_store,
|
||||
&return_ty_annotation,
|
||||
&mut None,
|
||||
)?
|
||||
|
@ -1622,6 +1625,7 @@ impl TopLevelComposer {
|
|||
let self_type = get_type_from_type_annotation_kinds(
|
||||
&def_list,
|
||||
unifier,
|
||||
primitives_ty,
|
||||
&make_self_type_annotation(type_vars, *object_id),
|
||||
&mut None,
|
||||
)?;
|
||||
|
@ -1803,7 +1807,11 @@ impl TopLevelComposer {
|
|||
|
||||
let ty_ann = make_self_type_annotation(type_vars, *class_id);
|
||||
let self_ty = get_type_from_type_annotation_kinds(
|
||||
&def_list, unifier, &ty_ann, &mut None,
|
||||
&def_list,
|
||||
unifier,
|
||||
primitives_ty,
|
||||
&ty_ann,
|
||||
&mut None,
|
||||
)?;
|
||||
vars.extend(type_vars.iter().map(|ty| {
|
||||
let TypeEnum::TVar { id, .. } = &*unifier.get_ty(*ty) else {
|
||||
|
|
|
@ -27,17 +27,22 @@ pub enum PrimDef {
|
|||
List,
|
||||
NDArray,
|
||||
|
||||
// Member Functions
|
||||
OptionIsSome,
|
||||
OptionIsNone,
|
||||
OptionUnwrap,
|
||||
NDArrayCopy,
|
||||
NDArrayFill,
|
||||
FunInt32,
|
||||
FunInt64,
|
||||
FunUInt32,
|
||||
FunUInt64,
|
||||
FunFloat,
|
||||
// Option methods
|
||||
FunOptionIsSome,
|
||||
FunOptionIsNone,
|
||||
FunOptionUnwrap,
|
||||
|
||||
// Option-related functions
|
||||
FunSome,
|
||||
|
||||
// NDArray methods
|
||||
FunNDArrayCopy,
|
||||
FunNDArrayFill,
|
||||
|
||||
// Range methods
|
||||
FunRangeInit,
|
||||
|
||||
// NumPy factory functions
|
||||
FunNpNDArray,
|
||||
FunNpEmpty,
|
||||
FunNpZeros,
|
||||
|
@ -46,26 +51,28 @@ pub enum PrimDef {
|
|||
FunNpArray,
|
||||
FunNpEye,
|
||||
FunNpIdentity,
|
||||
FunRound,
|
||||
FunRound64,
|
||||
FunNpArange,
|
||||
|
||||
// NumPy view functions
|
||||
FunNpBroadcastTo,
|
||||
FunNpReshape,
|
||||
FunNpTranspose,
|
||||
|
||||
// NumPy NDArray property getters
|
||||
FunNpSize,
|
||||
FunNpShape,
|
||||
FunNpStrides,
|
||||
|
||||
// Miscellaneous NumPy & SciPy functions
|
||||
FunNpRound,
|
||||
FunRangeInit,
|
||||
FunStr,
|
||||
FunBool,
|
||||
FunFloor,
|
||||
FunFloor64,
|
||||
FunNpFloor,
|
||||
FunCeil,
|
||||
FunCeil64,
|
||||
FunNpCeil,
|
||||
FunLen,
|
||||
FunMin,
|
||||
FunNpMin,
|
||||
FunNpMinimum,
|
||||
FunMax,
|
||||
FunNpArgmin,
|
||||
FunNpMax,
|
||||
FunNpMaximum,
|
||||
FunAbs,
|
||||
FunNpArgmax,
|
||||
FunNpIsNan,
|
||||
FunNpIsInf,
|
||||
FunNpSin,
|
||||
|
@ -104,14 +111,43 @@ pub enum PrimDef {
|
|||
FunNpHypot,
|
||||
FunNpNextAfter,
|
||||
|
||||
// Top-Level Functions
|
||||
FunSome,
|
||||
// Linalg functions
|
||||
FunNpDot,
|
||||
FunNpLinalgCholesky,
|
||||
FunNpLinalgQr,
|
||||
FunNpLinalgSvd,
|
||||
FunNpLinalgInv,
|
||||
FunNpLinalgPinv,
|
||||
FunNpLinalgMatrixPower,
|
||||
FunNpLinalgDet,
|
||||
FunSpLinalgLu,
|
||||
FunSpLinalgSchur,
|
||||
FunSpLinalgHessenberg,
|
||||
|
||||
// Miscellaneous Python & NAC3 functions
|
||||
FunInt32,
|
||||
FunInt64,
|
||||
FunUInt32,
|
||||
FunUInt64,
|
||||
FunFloat,
|
||||
FunRound,
|
||||
FunRound64,
|
||||
FunStr,
|
||||
FunBool,
|
||||
FunFloor,
|
||||
FunFloor64,
|
||||
FunCeil,
|
||||
FunCeil64,
|
||||
FunLen,
|
||||
FunMin,
|
||||
FunMax,
|
||||
FunAbs,
|
||||
}
|
||||
|
||||
/// Associated details of a [`PrimDef`]
|
||||
pub enum PrimDefDetails {
|
||||
PrimFunction { name: &'static str, simple_name: &'static str },
|
||||
PrimClass { name: &'static str },
|
||||
PrimClass { name: &'static str, get_ty_fn: fn(&PrimitiveStore) -> Type },
|
||||
}
|
||||
|
||||
impl PrimDef {
|
||||
|
@ -153,15 +189,17 @@ impl PrimDef {
|
|||
#[must_use]
|
||||
pub fn name(&self) -> &'static str {
|
||||
match self.details() {
|
||||
PrimDefDetails::PrimFunction { name, .. } | PrimDefDetails::PrimClass { name } => name,
|
||||
PrimDefDetails::PrimFunction { name, .. } | PrimDefDetails::PrimClass { name, .. } => {
|
||||
name
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the associated details of this [`PrimDef`]
|
||||
#[must_use]
|
||||
pub fn details(self) -> PrimDefDetails {
|
||||
fn class(name: &'static str) -> PrimDefDetails {
|
||||
PrimDefDetails::PrimClass { name }
|
||||
fn class(name: &'static str, get_ty_fn: fn(&PrimitiveStore) -> Type) -> PrimDefDetails {
|
||||
PrimDefDetails::PrimClass { name, get_ty_fn }
|
||||
}
|
||||
|
||||
fn fun(name: &'static str, simple_name: Option<&'static str>) -> PrimDefDetails {
|
||||
|
@ -169,29 +207,37 @@ impl PrimDef {
|
|||
}
|
||||
|
||||
match self {
|
||||
PrimDef::Int32 => class("int32"),
|
||||
PrimDef::Int64 => class("int64"),
|
||||
PrimDef::Float => class("float"),
|
||||
PrimDef::Bool => class("bool"),
|
||||
PrimDef::None => class("none"),
|
||||
PrimDef::Range => class("range"),
|
||||
PrimDef::Str => class("str"),
|
||||
PrimDef::Exception => class("Exception"),
|
||||
PrimDef::UInt32 => class("uint32"),
|
||||
PrimDef::UInt64 => class("uint64"),
|
||||
PrimDef::Option => class("Option"),
|
||||
PrimDef::OptionIsSome => fun("Option.is_some", Some("is_some")),
|
||||
PrimDef::OptionIsNone => fun("Option.is_none", Some("is_none")),
|
||||
PrimDef::OptionUnwrap => fun("Option.unwrap", Some("unwrap")),
|
||||
PrimDef::List => class("list"),
|
||||
PrimDef::NDArray => class("ndarray"),
|
||||
PrimDef::NDArrayCopy => fun("ndarray.copy", Some("copy")),
|
||||
PrimDef::NDArrayFill => fun("ndarray.fill", Some("fill")),
|
||||
PrimDef::FunInt32 => fun("int32", None),
|
||||
PrimDef::FunInt64 => fun("int64", None),
|
||||
PrimDef::FunUInt32 => fun("uint32", None),
|
||||
PrimDef::FunUInt64 => fun("uint64", None),
|
||||
PrimDef::FunFloat => fun("float", None),
|
||||
// Classes
|
||||
PrimDef::Int32 => class("int32", |primitives| primitives.int32),
|
||||
PrimDef::Int64 => class("int64", |primitives| primitives.int64),
|
||||
PrimDef::Float => class("float", |primitives| primitives.float),
|
||||
PrimDef::Bool => class("bool", |primitives| primitives.bool),
|
||||
PrimDef::None => class("none", |primitives| primitives.none),
|
||||
PrimDef::Range => class("range", |primitives| primitives.range),
|
||||
PrimDef::Str => class("str", |primitives| primitives.str),
|
||||
PrimDef::Exception => class("Exception", |primitives| primitives.exception),
|
||||
PrimDef::UInt32 => class("uint32", |primitives| primitives.uint32),
|
||||
PrimDef::UInt64 => class("uint64", |primitives| primitives.uint64),
|
||||
PrimDef::Option => class("Option", |primitives| primitives.option),
|
||||
PrimDef::List => class("list", |primitives| primitives.list),
|
||||
PrimDef::NDArray => class("ndarray", |primitives| primitives.ndarray),
|
||||
|
||||
// Option methods
|
||||
PrimDef::FunOptionIsSome => fun("Option.is_some", Some("is_some")),
|
||||
PrimDef::FunOptionIsNone => fun("Option.is_none", Some("is_none")),
|
||||
PrimDef::FunOptionUnwrap => fun("Option.unwrap", Some("unwrap")),
|
||||
|
||||
// Option-related functions
|
||||
PrimDef::FunSome => fun("Some", None),
|
||||
|
||||
// NDArray methods
|
||||
PrimDef::FunNDArrayCopy => fun("ndarray.copy", Some("copy")),
|
||||
PrimDef::FunNDArrayFill => fun("ndarray.fill", Some("fill")),
|
||||
|
||||
// Range methods
|
||||
PrimDef::FunRangeInit => fun("range.__init__", Some("__init__")),
|
||||
|
||||
// NumPy factory functions
|
||||
PrimDef::FunNpNDArray => fun("np_ndarray", None),
|
||||
PrimDef::FunNpEmpty => fun("np_empty", None),
|
||||
PrimDef::FunNpZeros => fun("np_zeros", None),
|
||||
|
@ -200,26 +246,28 @@ impl PrimDef {
|
|||
PrimDef::FunNpArray => fun("np_array", None),
|
||||
PrimDef::FunNpEye => fun("np_eye", None),
|
||||
PrimDef::FunNpIdentity => fun("np_identity", None),
|
||||
PrimDef::FunRound => fun("round", None),
|
||||
PrimDef::FunRound64 => fun("round64", None),
|
||||
PrimDef::FunNpArange => fun("np_arange", None),
|
||||
|
||||
// NumPy view functions
|
||||
PrimDef::FunNpBroadcastTo => fun("np_broadcast_to", None),
|
||||
PrimDef::FunNpReshape => fun("np_reshape", None),
|
||||
PrimDef::FunNpTranspose => fun("np_transpose", None),
|
||||
|
||||
// NumPy NDArray property getters
|
||||
PrimDef::FunNpSize => fun("np_size", None),
|
||||
PrimDef::FunNpShape => fun("np_shape", None),
|
||||
PrimDef::FunNpStrides => fun("np_strides", None),
|
||||
|
||||
// Miscellaneous NumPy & SciPy functions
|
||||
PrimDef::FunNpRound => fun("np_round", None),
|
||||
PrimDef::FunRangeInit => fun("range.__init__", Some("__init__")),
|
||||
PrimDef::FunStr => fun("str", None),
|
||||
PrimDef::FunBool => fun("bool", None),
|
||||
PrimDef::FunFloor => fun("floor", None),
|
||||
PrimDef::FunFloor64 => fun("floor64", None),
|
||||
PrimDef::FunNpFloor => fun("np_floor", None),
|
||||
PrimDef::FunCeil => fun("ceil", None),
|
||||
PrimDef::FunCeil64 => fun("ceil64", None),
|
||||
PrimDef::FunNpCeil => fun("np_ceil", None),
|
||||
PrimDef::FunLen => fun("len", None),
|
||||
PrimDef::FunMin => fun("min", None),
|
||||
PrimDef::FunNpMin => fun("np_min", None),
|
||||
PrimDef::FunNpMinimum => fun("np_minimum", None),
|
||||
PrimDef::FunMax => fun("max", None),
|
||||
PrimDef::FunNpArgmin => fun("np_argmin", None),
|
||||
PrimDef::FunNpMax => fun("np_max", None),
|
||||
PrimDef::FunNpMaximum => fun("np_maximum", None),
|
||||
PrimDef::FunAbs => fun("abs", None),
|
||||
PrimDef::FunNpArgmax => fun("np_argmax", None),
|
||||
PrimDef::FunNpIsNan => fun("np_isnan", None),
|
||||
PrimDef::FunNpIsInf => fun("np_isinf", None),
|
||||
PrimDef::FunNpSin => fun("np_sin", None),
|
||||
|
@ -257,7 +305,38 @@ impl PrimDef {
|
|||
PrimDef::FunNpLdExp => fun("np_ldexp", None),
|
||||
PrimDef::FunNpHypot => fun("np_hypot", None),
|
||||
PrimDef::FunNpNextAfter => fun("np_nextafter", None),
|
||||
PrimDef::FunSome => fun("Some", None),
|
||||
|
||||
// Linalg functions
|
||||
PrimDef::FunNpDot => fun("np_dot", None),
|
||||
PrimDef::FunNpLinalgCholesky => fun("np_linalg_cholesky", None),
|
||||
PrimDef::FunNpLinalgQr => fun("np_linalg_qr", None),
|
||||
PrimDef::FunNpLinalgSvd => fun("np_linalg_svd", None),
|
||||
PrimDef::FunNpLinalgInv => fun("np_linalg_inv", None),
|
||||
PrimDef::FunNpLinalgPinv => fun("np_linalg_pinv", None),
|
||||
PrimDef::FunNpLinalgMatrixPower => fun("np_linalg_matrix_power", None),
|
||||
PrimDef::FunNpLinalgDet => fun("np_linalg_det", None),
|
||||
PrimDef::FunSpLinalgLu => fun("sp_linalg_lu", None),
|
||||
PrimDef::FunSpLinalgSchur => fun("sp_linalg_schur", None),
|
||||
PrimDef::FunSpLinalgHessenberg => fun("sp_linalg_hessenberg", None),
|
||||
|
||||
// Miscellaneous Python & NAC3 functions
|
||||
PrimDef::FunInt32 => fun("int32", None),
|
||||
PrimDef::FunInt64 => fun("int64", None),
|
||||
PrimDef::FunUInt32 => fun("uint32", None),
|
||||
PrimDef::FunUInt64 => fun("uint64", None),
|
||||
PrimDef::FunFloat => fun("float", None),
|
||||
PrimDef::FunRound => fun("round", None),
|
||||
PrimDef::FunRound64 => fun("round64", None),
|
||||
PrimDef::FunStr => fun("str", None),
|
||||
PrimDef::FunBool => fun("bool", None),
|
||||
PrimDef::FunFloor => fun("floor", None),
|
||||
PrimDef::FunFloor64 => fun("floor64", None),
|
||||
PrimDef::FunCeil => fun("ceil", None),
|
||||
PrimDef::FunCeil64 => fun("ceil64", None),
|
||||
PrimDef::FunLen => fun("len", None),
|
||||
PrimDef::FunMin => fun("min", None),
|
||||
PrimDef::FunMax => fun("max", None),
|
||||
PrimDef::FunAbs => fun("abs", None),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
@ -408,9 +487,9 @@ impl TopLevelComposer {
|
|||
let option = unifier.add_ty(TypeEnum::TObj {
|
||||
obj_id: PrimDef::Option.id(),
|
||||
fields: vec![
|
||||
(PrimDef::OptionIsSome.simple_name().into(), (is_some_type_fun_ty, true)),
|
||||
(PrimDef::OptionIsNone.simple_name().into(), (is_some_type_fun_ty, true)),
|
||||
(PrimDef::OptionUnwrap.simple_name().into(), (unwrap_fun_ty, true)),
|
||||
(PrimDef::FunOptionIsSome.simple_name().into(), (is_some_type_fun_ty, true)),
|
||||
(PrimDef::FunOptionIsNone.simple_name().into(), (is_some_type_fun_ty, true)),
|
||||
(PrimDef::FunOptionUnwrap.simple_name().into(), (unwrap_fun_ty, true)),
|
||||
]
|
||||
.into_iter()
|
||||
.collect::<HashMap<_, _>>(),
|
||||
|
@ -451,8 +530,8 @@ impl TopLevelComposer {
|
|||
let ndarray = unifier.add_ty(TypeEnum::TObj {
|
||||
obj_id: PrimDef::NDArray.id(),
|
||||
fields: Mapping::from([
|
||||
(PrimDef::NDArrayCopy.simple_name().into(), (ndarray_copy_fun_ty, true)),
|
||||
(PrimDef::NDArrayFill.simple_name().into(), (ndarray_fill_fun_ty, true)),
|
||||
(PrimDef::FunNDArrayCopy.simple_name().into(), (ndarray_copy_fun_ty, true)),
|
||||
(PrimDef::FunNDArrayFill.simple_name().into(), (ndarray_fill_fun_ty, true)),
|
||||
]),
|
||||
params: into_var_map([ndarray_dtype_tvar, ndarray_ndims_tvar]),
|
||||
});
|
||||
|
|
|
@ -1,4 +1,7 @@
|
|||
use std::sync::Arc;
|
||||
|
||||
use crate::{
|
||||
symbol_resolver::SymbolValue,
|
||||
toplevel::helper::PrimDef,
|
||||
typecheck::{
|
||||
type_inferencer::PrimitiveStore,
|
||||
|
@ -83,3 +86,33 @@ pub fn unpack_ndarray_var_ids(unifier: &mut Unifier, ndarray: Type) -> (TypeVarI
|
|||
pub fn unpack_ndarray_var_tys(unifier: &mut Unifier, ndarray: Type) -> (Type, Type) {
|
||||
unpack_ndarray_tvars(unifier, ndarray).into_iter().map(|v| v.1).collect_tuple().unwrap()
|
||||
}
|
||||
|
||||
/// 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)
|
||||
}
|
||||
|
||||
/// Return the ndims after broadcasting ndarrays of different ndims.
|
||||
///
|
||||
/// Panics if the input list is empty.
|
||||
pub fn get_broadcast_all_ndims<I>(ndims: I) -> u64
|
||||
where
|
||||
I: IntoIterator<Item = u64>,
|
||||
{
|
||||
ndims.into_iter().max().unwrap()
|
||||
}
|
||||
|
|
|
@ -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(245)]\n}\n",
|
||||
"Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(246)]\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",
|
||||
|
|
|
@ -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[typevar234]\", \"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: [\"typevar234\"]\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B[typevar235]\", \"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: [\"typevar235\"]\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",
|
||||
|
|
|
@ -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(247)]\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(252)]\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(248)]\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(253)]\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",
|
||||
|
|
|
@ -3,7 +3,7 @@ source: nac3core/src/toplevel/test.rs
|
|||
expression: res_vec
|
||||
---
|
||||
[
|
||||
"Class {\nname: \"A\",\nancestors: [\"A[typevar233, typevar234]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar233\", \"typevar234\"]\n}\n",
|
||||
"Class {\nname: \"A\",\nancestors: [\"A[typevar234, typevar235]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar234\", \"typevar235\"]\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",
|
||||
|
|
|
@ -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(253)]\n}\n",
|
||||
"Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(254)]\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(261)]\n}\n",
|
||||
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(262)]\n}\n",
|
||||
]
|
||||
|
|
|
@ -1,8 +1,9 @@
|
|||
use super::*;
|
||||
use crate::symbol_resolver::SymbolValue;
|
||||
use crate::toplevel::helper::PrimDef;
|
||||
use crate::toplevel::helper::{PrimDef, PrimDefDetails};
|
||||
use crate::typecheck::typedef::VarMap;
|
||||
use nac3parser::ast::Constant;
|
||||
use strum::IntoEnumIterator;
|
||||
|
||||
#[derive(Clone, Debug)]
|
||||
pub enum TypeAnnotation {
|
||||
|
@ -357,6 +358,7 @@ pub fn parse_ast_to_type_annotation_kinds<T, S: std::hash::BuildHasher + Clone>(
|
|||
pub fn get_type_from_type_annotation_kinds(
|
||||
top_level_defs: &[Arc<RwLock<TopLevelDef>>],
|
||||
unifier: &mut Unifier,
|
||||
primitives: &PrimitiveStore,
|
||||
ann: &TypeAnnotation,
|
||||
subst_list: &mut Option<Vec<Type>>,
|
||||
) -> Result<Type, HashSet<String>> {
|
||||
|
@ -379,10 +381,43 @@ pub fn get_type_from_type_annotation_kinds(
|
|||
let param_ty = params
|
||||
.iter()
|
||||
.map(|x| {
|
||||
get_type_from_type_annotation_kinds(top_level_defs, unifier, x, subst_list)
|
||||
get_type_from_type_annotation_kinds(
|
||||
top_level_defs,
|
||||
unifier,
|
||||
primitives,
|
||||
x,
|
||||
subst_list,
|
||||
)
|
||||
})
|
||||
.collect::<Result<Vec<_>, _>>()?;
|
||||
|
||||
let ty = if let Some(prim_def) = PrimDef::iter().find(|prim| prim.id() == *obj_id) {
|
||||
// Primitive TopLevelDefs do not contain all fields that are present in their Type
|
||||
// counterparts, so directly perform subst on the Type instead.
|
||||
|
||||
let PrimDefDetails::PrimClass { get_ty_fn, .. } = prim_def.details() else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
let base_ty = get_ty_fn(primitives);
|
||||
let params =
|
||||
if let TypeEnum::TObj { params, .. } = &*unifier.get_ty_immutable(base_ty) {
|
||||
params.clone()
|
||||
} else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
unifier
|
||||
.subst(
|
||||
get_ty_fn(primitives),
|
||||
¶ms
|
||||
.iter()
|
||||
.zip(param_ty)
|
||||
.map(|(obj_tv, param)| (*obj_tv.0, param))
|
||||
.collect(),
|
||||
)
|
||||
.unwrap_or(base_ty)
|
||||
} else {
|
||||
let subst = {
|
||||
// check for compatible range
|
||||
// TODO: if allow type var to be applied(now this disallowed in the parse_to_type_annotation), need more check
|
||||
|
@ -424,12 +459,15 @@ pub fn get_type_from_type_annotation_kinds(
|
|||
}
|
||||
}
|
||||
|
||||
TypeEnum::TVar { id, range, name, loc, is_const_generic: true, .. } => {
|
||||
TypeEnum::TVar {
|
||||
id, range, name, loc, is_const_generic: true, ..
|
||||
} => {
|
||||
let ty = range[0];
|
||||
let ok: bool = {
|
||||
// create a temp type var and unify to check compatibility
|
||||
p == *tvar || {
|
||||
let temp = unifier.get_fresh_const_generic_var(ty, *name, *loc);
|
||||
let temp =
|
||||
unifier.get_fresh_const_generic_var(ty, *name, *loc);
|
||||
unifier.unify(temp.ty, p).is_ok()
|
||||
}
|
||||
};
|
||||
|
@ -468,11 +506,16 @@ pub fn get_type_from_type_annotation_kinds(
|
|||
fields: tobj_fields,
|
||||
params: subst,
|
||||
});
|
||||
|
||||
if need_subst {
|
||||
if let Some(wl) = subst_list.as_mut() {
|
||||
wl.push(ty);
|
||||
}
|
||||
}
|
||||
|
||||
ty
|
||||
};
|
||||
|
||||
Ok(ty)
|
||||
}
|
||||
TypeAnnotation::Primitive(ty) | TypeAnnotation::TypeVar(ty) => Ok(*ty),
|
||||
|
@ -490,6 +533,7 @@ pub fn get_type_from_type_annotation_kinds(
|
|||
let ty = get_type_from_type_annotation_kinds(
|
||||
top_level_defs,
|
||||
unifier,
|
||||
primitives,
|
||||
ty.as_ref(),
|
||||
subst_list,
|
||||
)?;
|
||||
|
@ -499,7 +543,13 @@ pub fn get_type_from_type_annotation_kinds(
|
|||
let tys = tys
|
||||
.iter()
|
||||
.map(|x| {
|
||||
get_type_from_type_annotation_kinds(top_level_defs, unifier, x, subst_list)
|
||||
get_type_from_type_annotation_kinds(
|
||||
top_level_defs,
|
||||
unifier,
|
||||
primitives,
|
||||
x,
|
||||
subst_list,
|
||||
)
|
||||
})
|
||||
.collect::<Result<Vec<_>, _>>()?;
|
||||
Ok(unifier.add_ty(TypeEnum::TTuple { ty: tys }))
|
||||
|
|
|
@ -34,13 +34,18 @@ impl<'a> Inferencer<'a> {
|
|||
self.should_have_value(pattern)?;
|
||||
Ok(())
|
||||
}
|
||||
ExprKind::Tuple { elts, .. } => {
|
||||
ExprKind::List { elts, .. } | ExprKind::Tuple { elts, .. } => {
|
||||
for elt in elts {
|
||||
self.check_pattern(elt, defined_identifiers)?;
|
||||
self.should_have_value(elt)?;
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
ExprKind::Starred { value, .. } => {
|
||||
self.check_pattern(value, defined_identifiers)?;
|
||||
self.should_have_value(value)?;
|
||||
Ok(())
|
||||
}
|
||||
ExprKind::Subscript { value, slice, .. } => {
|
||||
self.check_expr(value, defined_identifiers)?;
|
||||
self.should_have_value(value)?;
|
||||
|
|
File diff suppressed because it is too large
Load Diff
|
@ -336,6 +336,14 @@ impl Unifier {
|
|||
self.unification_table.unioned(a, b)
|
||||
}
|
||||
|
||||
/// Determine if a type unions with a type in `tys`.
|
||||
pub fn unioned_any<I>(&mut self, a: Type, tys: I) -> bool
|
||||
where
|
||||
I: IntoIterator<Item = Type>,
|
||||
{
|
||||
tys.into_iter().any(|ty| self.unioned(a, ty))
|
||||
}
|
||||
|
||||
pub fn from_shared_unifier(unifier: &SharedUnifier) -> Unifier {
|
||||
let lock = unifier.lock().unwrap();
|
||||
Unifier {
|
||||
|
|
|
@ -3,23 +3,55 @@
|
|||
set -e
|
||||
|
||||
if [ -z "$1" ]; then
|
||||
echo "Requires at least one argument"
|
||||
echo "No argument supplied"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
declare -a nac3args
|
||||
while [ $# -ge 2 ]; do
|
||||
case "$1" in
|
||||
--help)
|
||||
echo "Usage: check_demo.sh [-i686] -- demo [NAC3ARGS...]"
|
||||
exit
|
||||
;;
|
||||
-i686)
|
||||
i686=1
|
||||
;;
|
||||
--)
|
||||
shift
|
||||
break
|
||||
;;
|
||||
*)
|
||||
break
|
||||
;;
|
||||
esac
|
||||
shift
|
||||
done
|
||||
|
||||
demo="$1"
|
||||
shift
|
||||
while [ $# -gt 1 ]; do
|
||||
nac3args+=("$1")
|
||||
shift
|
||||
done
|
||||
demo="$1"
|
||||
|
||||
echo -n "Checking $demo... "
|
||||
|
||||
echo "### Checking $demo..."
|
||||
|
||||
echo ">>>>>> Running $demo with the Python interpreter"
|
||||
./interpret_demo.py "$demo" > interpreted.log
|
||||
./run_demo.sh --out run.log "${nac3args[@]}" "$demo"
|
||||
./run_demo.sh --lli --out run_lli.log "${nac3args[@]}" "$demo"
|
||||
diff -Nau interpreted.log run.log
|
||||
diff -Nau interpreted.log run_lli.log
|
||||
echo "ok"
|
||||
|
||||
rm -f interpreted.log run.log run_lli.log
|
||||
if [ -n "$i686" ]; then
|
||||
echo "...... Trying NAC3's 32-bit code generator output"
|
||||
./run_demo.sh -i686 --out run_32.log "${nac3args[@]}" "$demo"
|
||||
diff -Nau interpreted.log run_32.log
|
||||
fi
|
||||
|
||||
echo "...... Trying NAC3's 64-bit code generator output"
|
||||
./run_demo.sh --out run_64.log "${nac3args[@]}" "$demo"
|
||||
diff -Nau interpreted.log run_64.log
|
||||
|
||||
echo "...... OK"
|
||||
|
||||
rm -f interpreted.log \
|
||||
run_32.log run_64.log
|
||||
|
|
|
@ -6,8 +6,6 @@
|
|||
#include <stdlib.h>
|
||||
#include <string.h>
|
||||
|
||||
#define usize size_t
|
||||
|
||||
double dbl_nan(void) {
|
||||
return NAN;
|
||||
}
|
||||
|
@ -64,14 +62,14 @@ void output_asciiart(int32_t x) {
|
|||
|
||||
struct cslice {
|
||||
void *data;
|
||||
usize len;
|
||||
size_t len;
|
||||
};
|
||||
|
||||
void output_int32_list(struct cslice *slice) {
|
||||
const int32_t *data = (int32_t *) slice->data;
|
||||
|
||||
putchar('[');
|
||||
for (usize i = 0; i < slice->len; ++i) {
|
||||
for (size_t i = 0; i < slice->len; ++i) {
|
||||
if (i == slice->len - 1) {
|
||||
printf("%d", data[i]);
|
||||
} else {
|
||||
|
@ -85,7 +83,7 @@ void output_int32_list(struct cslice *slice) {
|
|||
void output_str(struct cslice *slice) {
|
||||
const char *data = (const char *) slice->data;
|
||||
|
||||
for (usize i = 0; i < slice->len; ++i) {
|
||||
for (size_t i = 0; i < slice->len; ++i) {
|
||||
putchar(data[i]);
|
||||
}
|
||||
}
|
||||
|
@ -107,8 +105,25 @@ uint32_t __nac3_personality(uint32_t state, uint32_t exception_object, uint32_t
|
|||
__builtin_unreachable();
|
||||
}
|
||||
|
||||
uint32_t __nac3_raise(uint32_t state, uint32_t exception_object, uint32_t context) {
|
||||
printf("__nac3_raise(state: %u, exception_object: %u, context: %u)\n", state, exception_object, context);
|
||||
// See `struct Exception<'a>` in
|
||||
// https://github.com/m-labs/artiq/blob/master/artiq/firmware/libeh/eh_artiq.rs
|
||||
struct Exception {
|
||||
uint32_t id;
|
||||
struct cslice file;
|
||||
uint32_t line;
|
||||
uint32_t column;
|
||||
struct cslice function;
|
||||
struct cslice message;
|
||||
int64_t param[3];
|
||||
};
|
||||
|
||||
uint32_t __nac3_raise(struct Exception* e) {
|
||||
printf("__nac3_raise called. Exception details:\n");
|
||||
printf(" ID: %"PRIu32"\n", e->id);
|
||||
printf(" Location: %*s:%"PRIu32":%"PRIu32"\n" , (int) e->file.len, (const char*) e->file.data, e->line, e->column);
|
||||
printf(" Function: %*s\n" , (int) e->function.len, (const char*) e->function.data);
|
||||
printf(" Message: \"%*s\"\n" , (int) e->message.len, (const char*) e->message.data);
|
||||
printf(" Params: {0}=%"PRId64", {1}=%"PRId64", {2}=%"PRId64"\n", e->param[0], e->param[1], e->param[2]);
|
||||
exit(101);
|
||||
__builtin_unreachable();
|
||||
}
|
||||
|
|
|
@ -6,6 +6,7 @@ import importlib.machinery
|
|||
import math
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
import scipy as sp
|
||||
import pathlib
|
||||
|
||||
from numpy import int32, int64, uint32, uint64
|
||||
|
@ -167,7 +168,7 @@ def patch(module):
|
|||
module.ceil64 = _ceil
|
||||
module.np_ceil = np.ceil
|
||||
|
||||
# NumPy ndarray functions
|
||||
# NumPy NDArray factory functions
|
||||
module.ndarray = NDArray
|
||||
module.np_ndarray = np.ndarray
|
||||
module.np_empty = np.empty
|
||||
|
@ -183,8 +184,10 @@ def patch(module):
|
|||
module.np_isinf = np.isinf
|
||||
module.np_min = np.min
|
||||
module.np_minimum = np.minimum
|
||||
module.np_argmin = np.argmin
|
||||
module.np_max = np.max
|
||||
module.np_maximum = np.maximum
|
||||
module.np_argmax = np.argmax
|
||||
module.np_sin = np.sin
|
||||
module.np_cos = np.cos
|
||||
module.np_exp = np.exp
|
||||
|
@ -216,7 +219,17 @@ def patch(module):
|
|||
module.np_hypot = np.hypot
|
||||
module.np_nextafter = np.nextafter
|
||||
|
||||
# SciPy Math Functions
|
||||
# NumPy view functions
|
||||
module.np_broadcast_to = np.broadcast_to
|
||||
module.np_reshape = np.reshape
|
||||
module.np_transpose = np.transpose
|
||||
|
||||
# NumPy NDArray property getter functions
|
||||
module.np_size = np.size
|
||||
module.np_shape = np.shape
|
||||
module.np_strides = lambda ndarray: ndarray.strides
|
||||
|
||||
# SciPy Math functions
|
||||
module.sp_spec_erf = special.erf
|
||||
module.sp_spec_erfc = special.erfc
|
||||
module.sp_spec_gamma = special.gamma
|
||||
|
@ -224,14 +237,19 @@ def patch(module):
|
|||
module.sp_spec_j0 = special.j0
|
||||
module.sp_spec_j1 = special.j1
|
||||
|
||||
# NumPy NDArray Functions
|
||||
module.np_ndarray = np.ndarray
|
||||
module.np_empty = np.empty
|
||||
module.np_zeros = np.zeros
|
||||
module.np_ones = np.ones
|
||||
module.np_full = np.full
|
||||
module.np_eye = np.eye
|
||||
module.np_identity = np.identity
|
||||
# Linalg functions
|
||||
module.np_dot = np.dot
|
||||
module.np_linalg_cholesky = np.linalg.cholesky
|
||||
module.np_linalg_qr = np.linalg.qr
|
||||
module.np_linalg_svd = np.linalg.svd
|
||||
module.np_linalg_inv = np.linalg.inv
|
||||
module.np_linalg_pinv = np.linalg.pinv
|
||||
module.np_linalg_matrix_power = np.linalg.matrix_power
|
||||
module.np_linalg_det = np.linalg.det
|
||||
|
||||
module.sp_linalg_lu = lambda x: sp.linalg.lu(x, True)
|
||||
module.sp_linalg_schur = sp.linalg.schur
|
||||
module.sp_linalg_hessenberg = lambda x: sp.linalg.hessenberg(x, True)
|
||||
|
||||
def file_import(filename, prefix="file_import_"):
|
||||
filename = pathlib.Path(filename)
|
||||
|
|
|
@ -0,0 +1,114 @@
|
|||
# This file is automatically @generated by Cargo.
|
||||
# It is not intended for manual editing.
|
||||
version = 3
|
||||
|
||||
[[package]]
|
||||
name = "approx"
|
||||
version = "0.5.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "cab112f0a86d568ea0e627cc1d6be74a1e9cd55214684db5561995f6dad897c6"
|
||||
dependencies = [
|
||||
"num-traits",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "autocfg"
|
||||
version = "1.3.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "0c4b4d0bd25bd0b74681c0ad21497610ce1b7c91b1022cd21c80c6fbdd9476b0"
|
||||
|
||||
[[package]]
|
||||
name = "cslice"
|
||||
version = "0.3.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "0f8cb7306107e4b10e64994de6d3274bd08996a7c1322a27b86482392f96be0a"
|
||||
|
||||
[[package]]
|
||||
name = "libm"
|
||||
version = "0.2.8"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "4ec2a862134d2a7d32d7983ddcdd1c4923530833c9f2ea1a44fc5fa473989058"
|
||||
|
||||
[[package]]
|
||||
name = "linalg"
|
||||
version = "0.1.0"
|
||||
dependencies = [
|
||||
"cslice",
|
||||
"nalgebra",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nalgebra"
|
||||
version = "0.32.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "7b5c17de023a86f59ed79891b2e5d5a94c705dbe904a5b5c9c952ea6221b03e4"
|
||||
dependencies = [
|
||||
"approx",
|
||||
"num-complex",
|
||||
"num-rational",
|
||||
"num-traits",
|
||||
"simba",
|
||||
"typenum",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "num-complex"
|
||||
version = "0.4.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "73f88a1307638156682bada9d7604135552957b7818057dcef22705b4d509495"
|
||||
dependencies = [
|
||||
"num-traits",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "num-integer"
|
||||
version = "0.1.46"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "7969661fd2958a5cb096e56c8e1ad0444ac2bbcd0061bd28660485a44879858f"
|
||||
dependencies = [
|
||||
"num-traits",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "num-rational"
|
||||
version = "0.4.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "f83d14da390562dca69fc84082e73e548e1ad308d24accdedd2720017cb37824"
|
||||
dependencies = [
|
||||
"num-integer",
|
||||
"num-traits",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "num-traits"
|
||||
version = "0.2.19"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "071dfc062690e90b734c0b2273ce72ad0ffa95f0c74596bc250dcfd960262841"
|
||||
dependencies = [
|
||||
"autocfg",
|
||||
"libm",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "paste"
|
||||
version = "1.0.15"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "57c0d7b74b563b49d38dae00a0c37d4d6de9b432382b2892f0574ddcae73fd0a"
|
||||
|
||||
[[package]]
|
||||
name = "simba"
|
||||
version = "0.8.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "061507c94fc6ab4ba1c9a0305018408e312e17c041eb63bef8aa726fa33aceae"
|
||||
dependencies = [
|
||||
"approx",
|
||||
"num-complex",
|
||||
"num-traits",
|
||||
"paste",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typenum"
|
||||
version = "1.17.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "42ff0bf0c66b8238c6f3b578df37d0b7848e55df8577b3f74f92a69acceeb825"
|
|
@ -0,0 +1,13 @@
|
|||
[package]
|
||||
name = "linalg"
|
||||
version = "0.1.0"
|
||||
edition = "2021"
|
||||
|
||||
[lib]
|
||||
crate-type = ["staticlib"]
|
||||
|
||||
[dependencies]
|
||||
nalgebra = {version = "0.32.6", default-features = false, features = ["libm", "alloc"]}
|
||||
cslice = "0.3.0"
|
||||
|
||||
[workspace]
|
|
@ -0,0 +1,406 @@
|
|||
// Uses `nalgebra` crate to invoke `np_linalg` and `sp_linalg` functions
|
||||
// When converting between `nalgebra::Matrix` and `NDArray` following considerations are necessary
|
||||
//
|
||||
// * Both `nalgebra::Matrix` and `NDArray` require their content to be stored in row-major order
|
||||
// * `NDArray` data pointer can be directly read and converted to `nalgebra::Matrix` (row and column number must be known)
|
||||
// * `nalgebra::Matrix::as_slice` returns the content of matrix in column-major order and initial data needs to be transposed before storing it in `NDArray` data pointer
|
||||
|
||||
use core::slice;
|
||||
use nalgebra::DMatrix;
|
||||
|
||||
fn report_error(
|
||||
error_name: &str,
|
||||
fn_name: &str,
|
||||
file_name: &str,
|
||||
line_num: u32,
|
||||
col_num: u32,
|
||||
err_msg: &str,
|
||||
) -> ! {
|
||||
panic!(
|
||||
"Exception {} from {} in {}:{}:{}, message: {}",
|
||||
error_name, fn_name, file_name, line_num, col_num, err_msg
|
||||
);
|
||||
}
|
||||
|
||||
pub struct InputMatrix {
|
||||
pub ndims: usize,
|
||||
pub dims: *const usize,
|
||||
pub data: *mut f64,
|
||||
}
|
||||
impl InputMatrix {
|
||||
fn get_dims(&mut self) -> Vec<usize> {
|
||||
let dims = unsafe { slice::from_raw_parts(self.dims, self.ndims) };
|
||||
dims.to_vec()
|
||||
}
|
||||
}
|
||||
|
||||
/// # Safety
|
||||
///
|
||||
/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
|
||||
#[no_mangle]
|
||||
pub unsafe extern "C" fn np_linalg_cholesky(mat1: *mut InputMatrix, out: *mut InputMatrix) {
|
||||
let mat1 = mat1.as_mut().unwrap();
|
||||
let out = out.as_mut().unwrap();
|
||||
|
||||
if mat1.ndims != 2 {
|
||||
let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
|
||||
report_error("ValueError", "np_linalg_cholesky", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
|
||||
let dim1 = (*mat1).get_dims();
|
||||
if dim1[0] != dim1[1] {
|
||||
let err_msg =
|
||||
format!("last 2 dimensions of the array must be square: {0} != {1}", dim1[0], dim1[1]);
|
||||
report_error("LinAlgError", "np_linalg_cholesky", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
|
||||
let outdim = out.get_dims();
|
||||
let out_slice = unsafe { slice::from_raw_parts_mut(out.data, outdim[0] * outdim[1]) };
|
||||
let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
|
||||
|
||||
let matrix1 = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
|
||||
let result = matrix1.cholesky();
|
||||
match result {
|
||||
Some(res) => {
|
||||
out_slice.copy_from_slice(res.unpack().transpose().as_slice());
|
||||
}
|
||||
None => {
|
||||
report_error(
|
||||
"LinAlgError",
|
||||
"np_linalg_cholesky",
|
||||
file!(),
|
||||
line!(),
|
||||
column!(),
|
||||
"Matrix is not positive definite",
|
||||
);
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
/// # Safety
|
||||
///
|
||||
/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
|
||||
#[no_mangle]
|
||||
pub unsafe extern "C" fn np_linalg_qr(
|
||||
mat1: *mut InputMatrix,
|
||||
out_q: *mut InputMatrix,
|
||||
out_r: *mut InputMatrix,
|
||||
) {
|
||||
let mat1 = mat1.as_mut().unwrap();
|
||||
let out_q = out_q.as_mut().unwrap();
|
||||
let out_r = out_r.as_mut().unwrap();
|
||||
|
||||
if mat1.ndims != 2 {
|
||||
let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
|
||||
report_error("ValueError", "np_linalg_cholesky", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
|
||||
let dim1 = (*mat1).get_dims();
|
||||
let outq_dim = (*out_q).get_dims();
|
||||
let outr_dim = (*out_r).get_dims();
|
||||
|
||||
let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
|
||||
let out_q_slice = unsafe { slice::from_raw_parts_mut(out_q.data, outq_dim[0] * outq_dim[1]) };
|
||||
let out_r_slice = unsafe { slice::from_raw_parts_mut(out_r.data, outr_dim[0] * outr_dim[1]) };
|
||||
|
||||
// Refer to https://github.com/dimforge/nalgebra/issues/735
|
||||
let matrix1 = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
|
||||
|
||||
let res = matrix1.qr();
|
||||
let (q, r) = res.unpack();
|
||||
|
||||
// Uses different algo need to match numpy
|
||||
out_q_slice.copy_from_slice(q.transpose().as_slice());
|
||||
out_r_slice.copy_from_slice(r.transpose().as_slice());
|
||||
}
|
||||
|
||||
/// # Safety
|
||||
///
|
||||
/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
|
||||
#[no_mangle]
|
||||
pub unsafe extern "C" fn np_linalg_svd(
|
||||
mat1: *mut InputMatrix,
|
||||
outu: *mut InputMatrix,
|
||||
outs: *mut InputMatrix,
|
||||
outvh: *mut InputMatrix,
|
||||
) {
|
||||
let mat1 = mat1.as_mut().unwrap();
|
||||
let outu = outu.as_mut().unwrap();
|
||||
let outs = outs.as_mut().unwrap();
|
||||
let outvh = outvh.as_mut().unwrap();
|
||||
|
||||
if mat1.ndims != 2 {
|
||||
let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
|
||||
report_error("ValueError", "np_linalg_svd", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
|
||||
let dim1 = (*mat1).get_dims();
|
||||
let outu_dim = (*outu).get_dims();
|
||||
let outs_dim = (*outs).get_dims();
|
||||
let outvh_dim = (*outvh).get_dims();
|
||||
|
||||
let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
|
||||
let out_u_slice = unsafe { slice::from_raw_parts_mut(outu.data, outu_dim[0] * outu_dim[1]) };
|
||||
let out_s_slice = unsafe { slice::from_raw_parts_mut(outs.data, outs_dim[0]) };
|
||||
let out_vh_slice =
|
||||
unsafe { slice::from_raw_parts_mut(outvh.data, outvh_dim[0] * outvh_dim[1]) };
|
||||
|
||||
let matrix = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
|
||||
let result = matrix.svd(true, true);
|
||||
out_u_slice.copy_from_slice(result.u.unwrap().transpose().as_slice());
|
||||
out_s_slice.copy_from_slice(result.singular_values.as_slice());
|
||||
out_vh_slice.copy_from_slice(result.v_t.unwrap().transpose().as_slice());
|
||||
}
|
||||
|
||||
/// # Safety
|
||||
///
|
||||
/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
|
||||
#[no_mangle]
|
||||
pub unsafe extern "C" fn np_linalg_inv(mat1: *mut InputMatrix, out: *mut InputMatrix) {
|
||||
let mat1 = mat1.as_mut().unwrap();
|
||||
let out = out.as_mut().unwrap();
|
||||
|
||||
if mat1.ndims != 2 {
|
||||
let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
|
||||
report_error("ValueError", "np_linalg_inv", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
let dim1 = (*mat1).get_dims();
|
||||
|
||||
if dim1[0] != dim1[1] {
|
||||
let err_msg =
|
||||
format!("last 2 dimensions of the array must be square: {0} != {1}", dim1[0], dim1[1]);
|
||||
report_error("LinAlgError", "np_linalg_inv", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
|
||||
let outdim = out.get_dims();
|
||||
let out_slice = unsafe { slice::from_raw_parts_mut(out.data, outdim[0] * outdim[1]) };
|
||||
let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
|
||||
|
||||
let matrix = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
|
||||
if !matrix.is_invertible() {
|
||||
report_error(
|
||||
"LinAlgError",
|
||||
"np_linalg_inv",
|
||||
file!(),
|
||||
line!(),
|
||||
column!(),
|
||||
"no inverse for Singular Matrix",
|
||||
);
|
||||
}
|
||||
let inv = matrix.try_inverse().unwrap();
|
||||
out_slice.copy_from_slice(inv.transpose().as_slice());
|
||||
}
|
||||
|
||||
/// # Safety
|
||||
///
|
||||
/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
|
||||
#[no_mangle]
|
||||
pub unsafe extern "C" fn np_linalg_pinv(mat1: *mut InputMatrix, out: *mut InputMatrix) {
|
||||
let mat1 = mat1.as_mut().unwrap();
|
||||
let out = out.as_mut().unwrap();
|
||||
|
||||
if mat1.ndims != 2 {
|
||||
let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
|
||||
report_error("ValueError", "np_linalg_pinv", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
let dim1 = (*mat1).get_dims();
|
||||
let outdim = out.get_dims();
|
||||
let out_slice = unsafe { slice::from_raw_parts_mut(out.data, outdim[0] * outdim[1]) };
|
||||
let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
|
||||
|
||||
let matrix = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
|
||||
let svd = matrix.svd(true, true);
|
||||
let inv = svd.pseudo_inverse(1e-15);
|
||||
|
||||
match inv {
|
||||
Ok(m) => {
|
||||
out_slice.copy_from_slice(m.transpose().as_slice());
|
||||
}
|
||||
Err(err_msg) => {
|
||||
report_error("LinAlgError", "np_linalg_pinv", file!(), line!(), column!(), err_msg);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// # Safety
|
||||
///
|
||||
/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
|
||||
#[no_mangle]
|
||||
pub unsafe extern "C" fn np_linalg_matrix_power(
|
||||
mat1: *mut InputMatrix,
|
||||
mat2: *mut InputMatrix,
|
||||
out: *mut InputMatrix,
|
||||
) {
|
||||
let mat1 = mat1.as_mut().unwrap();
|
||||
let mat2 = mat2.as_mut().unwrap();
|
||||
let out = out.as_mut().unwrap();
|
||||
|
||||
if mat1.ndims != 2 {
|
||||
let err_msg = format!("expected 2D Vector Input, but received {}D", mat1.ndims);
|
||||
report_error("ValueError", "np_linalg_matrix_power", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
|
||||
let dim1 = (*mat1).get_dims();
|
||||
let power = unsafe { slice::from_raw_parts_mut(mat2.data, 1) };
|
||||
let power = power[0];
|
||||
let outdim = out.get_dims();
|
||||
let out_slice = unsafe { slice::from_raw_parts_mut(out.data, outdim[0] * outdim[1]) };
|
||||
let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
|
||||
|
||||
let abs_pow = power.abs();
|
||||
let matrix1 = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
|
||||
let mut result = matrix1.pow(abs_pow as u32);
|
||||
|
||||
if power < 0.0 {
|
||||
if !result.is_invertible() {
|
||||
report_error(
|
||||
"LinAlgError",
|
||||
"np_linalg_inv",
|
||||
file!(),
|
||||
line!(),
|
||||
column!(),
|
||||
"no inverse for Singular Matrix",
|
||||
);
|
||||
}
|
||||
result = result.try_inverse().unwrap();
|
||||
}
|
||||
out_slice.copy_from_slice(result.transpose().as_slice());
|
||||
}
|
||||
|
||||
/// # Safety
|
||||
///
|
||||
/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
|
||||
#[no_mangle]
|
||||
pub unsafe extern "C" fn np_linalg_det(mat1: *mut InputMatrix, out: *mut InputMatrix) {
|
||||
let mat1 = mat1.as_mut().unwrap();
|
||||
let out = out.as_mut().unwrap();
|
||||
|
||||
if mat1.ndims != 2 {
|
||||
let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
|
||||
report_error("ValueError", "np_linalg_det", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
let dim1 = (*mat1).get_dims();
|
||||
let out_slice = unsafe { slice::from_raw_parts_mut(out.data, 1) };
|
||||
let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
|
||||
|
||||
let matrix = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
|
||||
if !matrix.is_square() {
|
||||
let err_msg =
|
||||
format!("last 2 dimensions of the array must be square: {0} != {1}", dim1[0], dim1[1]);
|
||||
report_error("LinAlgError", "np_linalg_inv", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
out_slice[0] = matrix.determinant();
|
||||
}
|
||||
|
||||
/// # Safety
|
||||
///
|
||||
/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
|
||||
#[no_mangle]
|
||||
pub unsafe extern "C" fn sp_linalg_lu(
|
||||
mat1: *mut InputMatrix,
|
||||
out_l: *mut InputMatrix,
|
||||
out_u: *mut InputMatrix,
|
||||
) {
|
||||
let mat1 = mat1.as_mut().unwrap();
|
||||
let out_l = out_l.as_mut().unwrap();
|
||||
let out_u = out_u.as_mut().unwrap();
|
||||
|
||||
if mat1.ndims != 2 {
|
||||
let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
|
||||
report_error("ValueError", "sp_linalg_lu", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
|
||||
let dim1 = (*mat1).get_dims();
|
||||
let outl_dim = (*out_l).get_dims();
|
||||
let outu_dim = (*out_u).get_dims();
|
||||
|
||||
let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
|
||||
let out_l_slice = unsafe { slice::from_raw_parts_mut(out_l.data, outl_dim[0] * outl_dim[1]) };
|
||||
let out_u_slice = unsafe { slice::from_raw_parts_mut(out_u.data, outu_dim[0] * outu_dim[1]) };
|
||||
|
||||
let matrix = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
|
||||
let (_, l, u) = matrix.lu().unpack();
|
||||
|
||||
out_l_slice.copy_from_slice(l.transpose().as_slice());
|
||||
out_u_slice.copy_from_slice(u.transpose().as_slice());
|
||||
}
|
||||
|
||||
/// # Safety
|
||||
///
|
||||
/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
|
||||
#[no_mangle]
|
||||
pub unsafe extern "C" fn sp_linalg_schur(
|
||||
mat1: *mut InputMatrix,
|
||||
out_t: *mut InputMatrix,
|
||||
out_z: *mut InputMatrix,
|
||||
) {
|
||||
let mat1 = mat1.as_mut().unwrap();
|
||||
let out_t = out_t.as_mut().unwrap();
|
||||
let out_z = out_z.as_mut().unwrap();
|
||||
|
||||
if mat1.ndims != 2 {
|
||||
let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
|
||||
report_error("ValueError", "sp_linalg_schur", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
|
||||
let dim1 = (*mat1).get_dims();
|
||||
|
||||
if dim1[0] != dim1[1] {
|
||||
let err_msg =
|
||||
format!("last 2 dimensions of the array must be square: {0} != {1}", dim1[0], dim1[1]);
|
||||
report_error("LinAlgError", "np_linalg_schur", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
|
||||
let out_t_dim = (*out_t).get_dims();
|
||||
let out_z_dim = (*out_z).get_dims();
|
||||
|
||||
let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
|
||||
let out_t_slice = unsafe { slice::from_raw_parts_mut(out_t.data, out_t_dim[0] * out_t_dim[1]) };
|
||||
let out_z_slice = unsafe { slice::from_raw_parts_mut(out_z.data, out_z_dim[0] * out_z_dim[1]) };
|
||||
|
||||
let matrix = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
|
||||
let (z, t) = matrix.schur().unpack();
|
||||
|
||||
out_t_slice.copy_from_slice(t.transpose().as_slice());
|
||||
out_z_slice.copy_from_slice(z.transpose().as_slice());
|
||||
}
|
||||
|
||||
/// # Safety
|
||||
///
|
||||
/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
|
||||
#[no_mangle]
|
||||
pub unsafe extern "C" fn sp_linalg_hessenberg(
|
||||
mat1: *mut InputMatrix,
|
||||
out_h: *mut InputMatrix,
|
||||
out_q: *mut InputMatrix,
|
||||
) {
|
||||
let mat1 = mat1.as_mut().unwrap();
|
||||
let out_h = out_h.as_mut().unwrap();
|
||||
let out_q = out_q.as_mut().unwrap();
|
||||
|
||||
if mat1.ndims != 2 {
|
||||
let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
|
||||
report_error("ValueError", "sp_linalg_hessenberg", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
|
||||
let dim1 = (*mat1).get_dims();
|
||||
|
||||
if dim1[0] != dim1[1] {
|
||||
let err_msg =
|
||||
format!("last 2 dimensions of the array must be square: {} != {}", dim1[0], dim1[1]);
|
||||
report_error("LinAlgError", "sp_linalg_hessenberg", file!(), line!(), column!(), &err_msg);
|
||||
}
|
||||
|
||||
let out_h_dim = (*out_h).get_dims();
|
||||
let out_q_dim = (*out_q).get_dims();
|
||||
|
||||
let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
|
||||
let out_h_slice = unsafe { slice::from_raw_parts_mut(out_h.data, out_h_dim[0] * out_h_dim[1]) };
|
||||
let out_q_slice = unsafe { slice::from_raw_parts_mut(out_q.data, out_q_dim[0] * out_q_dim[1]) };
|
||||
|
||||
let matrix = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
|
||||
let (q, h) = matrix.hessenberg().unpack();
|
||||
|
||||
out_h_slice.copy_from_slice(h.transpose().as_slice());
|
||||
out_q_slice.copy_from_slice(q.transpose().as_slice());
|
||||
}
|
|
@ -2,6 +2,9 @@
|
|||
|
||||
set -e
|
||||
|
||||
: "${DEMO_LINALG_STUB:=linalg/target/release/liblinalg.a}"
|
||||
: "${DEMO_LINALG_STUB32:=linalg/target/i686-unknown-linux-gnu/release/liblinalg.a}"
|
||||
|
||||
if [ -z "$1" ]; then
|
||||
echo "No argument supplied"
|
||||
exit 1
|
||||
|
@ -11,19 +14,19 @@ declare -a nac3args
|
|||
while [ $# -ge 1 ]; do
|
||||
case "$1" in
|
||||
--help)
|
||||
echo "Usage: run_demo.sh [--help] [--out OUTFILE] [--lli] [--debug] -- [NAC3ARGS...]"
|
||||
echo "Usage: run_demo.sh [--help] [--out OUTFILE] [--debug] [-i686] -- [NAC3ARGS...]"
|
||||
exit
|
||||
;;
|
||||
--out)
|
||||
shift
|
||||
outfile="$1"
|
||||
;;
|
||||
--lli)
|
||||
use_lli=1
|
||||
;;
|
||||
--debug)
|
||||
debug=1
|
||||
;;
|
||||
-i686)
|
||||
i686=1
|
||||
;;
|
||||
--)
|
||||
shift
|
||||
break
|
||||
|
@ -50,29 +53,19 @@ else
|
|||
fi
|
||||
|
||||
rm -f ./*.o ./*.bc demo
|
||||
if [ -z "$use_lli" ]; then
|
||||
$nac3standalone "${nac3args[@]}"
|
||||
|
||||
if [ -z "$i686" ]; then
|
||||
$nac3standalone "${nac3args[@]}"
|
||||
clang -c -std=gnu11 -Wall -Wextra -O3 -o demo.o demo.c
|
||||
clang -lm -o demo module.o demo.o
|
||||
clang -o demo module.o demo.o $DEMO_LINALG_STUB -lm -Wl,--no-warn-search-mismatch
|
||||
else
|
||||
$nac3standalone --triple i686-unknown-linux-gnu "${nac3args[@]}"
|
||||
clang -m32 -c -std=gnu11 -Wall -Wextra -O3 -msse2 -o demo.o demo.c
|
||||
clang -m32 -o demo module.o demo.o $DEMO_LINALG_STUB32 -lm -Wl,--no-warn-search-mismatch
|
||||
fi
|
||||
|
||||
if [ -z "$outfile" ]; then
|
||||
./demo
|
||||
else
|
||||
./demo > "$outfile"
|
||||
fi
|
||||
else
|
||||
$nac3standalone --emit-llvm "${nac3args[@]}"
|
||||
|
||||
clang -c -std=gnu11 -Wall -Wextra -O3 -emit-llvm -o demo.bc demo.c
|
||||
|
||||
shopt -s nullglob
|
||||
llvm-link -o nac3out.bc module*.bc main.bc
|
||||
shopt -u nullglob
|
||||
|
||||
if [ -z "$outfile" ]; then
|
||||
lli --extra-module demo.bc --extra-module irrt.bc nac3out.bc
|
||||
else
|
||||
lli --extra-module demo.bc --extra-module irrt.bc nac3out.bc > "$outfile"
|
||||
fi
|
||||
fi
|
||||
|
|
|
@ -0,0 +1,66 @@
|
|||
@extern
|
||||
def output_int32(x: int32):
|
||||
...
|
||||
|
||||
@extern
|
||||
def output_bool(x: bool):
|
||||
...
|
||||
|
||||
def example1():
|
||||
x, *ys, z = (1, 2, 3, 4, 5)
|
||||
output_int32(x)
|
||||
output_int32(ys[0])
|
||||
output_int32(ys[1])
|
||||
output_int32(ys[2])
|
||||
output_int32(z)
|
||||
|
||||
def example2():
|
||||
x, y, *zs = (1, 2, 3, 4, 5)
|
||||
output_int32(x)
|
||||
output_int32(y)
|
||||
output_int32(zs[0])
|
||||
output_int32(zs[1])
|
||||
output_int32(zs[2])
|
||||
|
||||
def example3():
|
||||
*xs, y, z = (1, 2, 3, 4, 5)
|
||||
output_int32(xs[0])
|
||||
output_int32(xs[1])
|
||||
output_int32(xs[2])
|
||||
output_int32(y)
|
||||
output_int32(z)
|
||||
|
||||
def example4():
|
||||
# Example from: https://docs.python.org/3/reference/simple_stmts.html#assignment-statements
|
||||
x = [0, 1]
|
||||
i = 0
|
||||
i, x[i] = 1, 2 # i is updated, then x[i] is updated
|
||||
output_int32(i)
|
||||
output_int32(x[0])
|
||||
output_int32(x[1])
|
||||
|
||||
class A:
|
||||
value: int32
|
||||
def __init__(self):
|
||||
self.value = 1000
|
||||
|
||||
def example5():
|
||||
ws = [88, 7, 8]
|
||||
a = A()
|
||||
x, [y, *ys, a.value], ws[0], (ws[0],) = 1, (2, False, 4, 5), 99, (6,)
|
||||
output_int32(x)
|
||||
output_int32(y)
|
||||
output_bool(ys[0])
|
||||
output_int32(ys[1])
|
||||
output_int32(a.value)
|
||||
output_int32(ws[0])
|
||||
output_int32(ws[1])
|
||||
output_int32(ws[2])
|
||||
|
||||
def run() -> int32:
|
||||
example1()
|
||||
example2()
|
||||
example3()
|
||||
example4()
|
||||
example5()
|
||||
return 0
|
|
@ -867,6 +867,13 @@ def test_ndarray_minimum_broadcast_rhs_scalar():
|
|||
output_ndarray_float_2(min_x_zeros)
|
||||
output_ndarray_float_2(min_x_ones)
|
||||
|
||||
def test_ndarray_argmin():
|
||||
x = np_array([[1., 2.], [3., 4.]])
|
||||
y = np_argmin(x)
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
output_int64(y)
|
||||
|
||||
def test_ndarray_max():
|
||||
x = np_identity(2)
|
||||
y = np_max(x)
|
||||
|
@ -910,6 +917,13 @@ def test_ndarray_maximum_broadcast_rhs_scalar():
|
|||
output_ndarray_float_2(max_x_zeros)
|
||||
output_ndarray_float_2(max_x_ones)
|
||||
|
||||
def test_ndarray_argmax():
|
||||
x = np_array([[1., 2.], [3., 4.]])
|
||||
y = np_argmax(x)
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
output_int64(y)
|
||||
|
||||
def test_ndarray_abs():
|
||||
x = np_identity(2)
|
||||
y = abs(x)
|
||||
|
@ -1415,6 +1429,142 @@ 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])
|
||||
z1 = np_dot(x1, y1)
|
||||
|
||||
x2: ndarray[int32, 1] = np_array([5, 1, 4, 2])
|
||||
y2: ndarray[int32, 1] = np_array([5, 1, 6, 6])
|
||||
z2 = np_dot(x2, y2)
|
||||
|
||||
x3: ndarray[bool, 1] = np_array([True, True, True, True])
|
||||
y3: ndarray[bool, 1] = np_array([True, True, True, True])
|
||||
z3 = np_dot(x3, y3)
|
||||
|
||||
z4 = np_dot(2, 3)
|
||||
z5 = np_dot(2., 3.)
|
||||
z6 = np_dot(True, False)
|
||||
|
||||
output_float64(z1)
|
||||
output_int32(z2)
|
||||
output_bool(z3)
|
||||
output_int32(z4)
|
||||
output_float64(z5)
|
||||
output_bool(z6)
|
||||
|
||||
def test_ndarray_cholesky():
|
||||
x: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
|
||||
y = np_linalg_cholesky(x)
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
output_ndarray_float_2(y)
|
||||
|
||||
def test_ndarray_qr():
|
||||
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
|
||||
y, z = np_linalg_qr(x)
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
|
||||
# QR Factorization is not unique and gives different results in numpy and nalgebra
|
||||
# Reverting the decomposition to compare the initial arrays
|
||||
a = y @ z
|
||||
output_ndarray_float_2(a)
|
||||
|
||||
def test_ndarray_linalg_inv():
|
||||
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
|
||||
y = np_linalg_inv(x)
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
output_ndarray_float_2(y)
|
||||
|
||||
def test_ndarray_pinv():
|
||||
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5]])
|
||||
y = np_linalg_pinv(x)
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
output_ndarray_float_2(y)
|
||||
|
||||
def test_ndarray_matrix_power():
|
||||
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
|
||||
y = np_linalg_matrix_power(x, -9)
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
output_ndarray_float_2(y)
|
||||
|
||||
def test_ndarray_det():
|
||||
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
|
||||
y = np_linalg_det(x)
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
output_float64(y)
|
||||
|
||||
def test_ndarray_schur():
|
||||
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
|
||||
t, z = sp_linalg_schur(x)
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
|
||||
# Schur Factorization is not unique and gives different results in scipy and nalgebra
|
||||
# Reverting the decomposition to compare the initial arrays
|
||||
a = (z @ t) @ np_linalg_inv(z)
|
||||
output_ndarray_float_2(a)
|
||||
|
||||
def test_ndarray_hessenberg():
|
||||
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 5.0, 8.5]])
|
||||
h, q = sp_linalg_hessenberg(x)
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
|
||||
# Hessenberg Factorization is not unique and gives different results in scipy and nalgebra
|
||||
# Reverting the decomposition to compare the initial arrays
|
||||
a = (q @ h) @ np_linalg_inv(q)
|
||||
output_ndarray_float_2(a)
|
||||
|
||||
|
||||
def test_ndarray_lu():
|
||||
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5]])
|
||||
l, u = sp_linalg_lu(x)
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
output_ndarray_float_2(l)
|
||||
output_ndarray_float_2(u)
|
||||
|
||||
|
||||
def test_ndarray_svd():
|
||||
w: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
|
||||
x, y, z = np_linalg_svd(w)
|
||||
|
||||
output_ndarray_float_2(w)
|
||||
|
||||
# SVD Factorization is not unique and gives different results in numpy and nalgebra
|
||||
# Reverting the decomposition to compare the initial arrays
|
||||
a = x @ z
|
||||
output_ndarray_float_2(a)
|
||||
output_ndarray_float_1(y)
|
||||
|
||||
|
||||
def run() -> int32:
|
||||
test_ndarray_ctor()
|
||||
test_ndarray_empty()
|
||||
|
@ -1524,11 +1674,13 @@ def run() -> int32:
|
|||
test_ndarray_minimum_broadcast()
|
||||
test_ndarray_minimum_broadcast_lhs_scalar()
|
||||
test_ndarray_minimum_broadcast_rhs_scalar()
|
||||
test_ndarray_argmin()
|
||||
test_ndarray_max()
|
||||
test_ndarray_maximum()
|
||||
test_ndarray_maximum_broadcast()
|
||||
test_ndarray_maximum_broadcast_lhs_scalar()
|
||||
test_ndarray_maximum_broadcast_rhs_scalar()
|
||||
test_ndarray_argmax()
|
||||
test_ndarray_abs()
|
||||
test_ndarray_isnan()
|
||||
test_ndarray_isinf()
|
||||
|
@ -1591,5 +1743,18 @@ 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()
|
||||
test_ndarray_qr()
|
||||
test_ndarray_svd()
|
||||
test_ndarray_linalg_inv()
|
||||
test_ndarray_pinv()
|
||||
test_ndarray_matrix_power()
|
||||
test_ndarray_det()
|
||||
test_ndarray_lu()
|
||||
test_ndarray_schur()
|
||||
test_ndarray_hessenberg()
|
||||
return 0
|
||||
|
|
|
@ -9,15 +9,12 @@
|
|||
#![allow(clippy::too_many_lines, clippy::wildcard_imports)]
|
||||
|
||||
use clap::Parser;
|
||||
use inkwell::context::Context;
|
||||
use inkwell::{
|
||||
memory_buffer::MemoryBuffer, passes::PassBuilderOptions, support::is_multithreaded, targets::*,
|
||||
OptimizationLevel,
|
||||
};
|
||||
use parking_lot::{Mutex, RwLock};
|
||||
use std::collections::HashSet;
|
||||
use std::num::NonZeroUsize;
|
||||
use std::{collections::HashMap, fs, path::Path, sync::Arc};
|
||||
|
||||
use nac3core::codegen::irrt::setup_irrt_exceptions;
|
||||
use nac3core::{
|
||||
codegen::{
|
||||
concrete_type::ConcreteTypeStore, irrt::load_irrt, CodeGenLLVMOptions,
|
||||
|
@ -39,6 +36,10 @@ use nac3parser::{
|
|||
ast::{Constant, Expr, ExprKind, StmtKind, StrRef},
|
||||
parser,
|
||||
};
|
||||
use parking_lot::{Mutex, RwLock};
|
||||
use std::collections::HashSet;
|
||||
use std::num::NonZeroUsize;
|
||||
use std::{collections::HashMap, fs, path::Path, sync::Arc};
|
||||
|
||||
mod basic_symbol_resolver;
|
||||
use basic_symbol_resolver::*;
|
||||
|
@ -113,7 +114,9 @@ fn handle_typevar_definition(
|
|||
x,
|
||||
HashMap::new(),
|
||||
)?;
|
||||
get_type_from_type_annotation_kinds(def_list, unifier, &ty, &mut None)
|
||||
get_type_from_type_annotation_kinds(
|
||||
def_list, unifier, primitives, &ty, &mut None,
|
||||
)
|
||||
})
|
||||
.collect::<Result<Vec<_>, _>>()?;
|
||||
let loc = func.location;
|
||||
|
@ -152,7 +155,7 @@ fn handle_typevar_definition(
|
|||
HashMap::new(),
|
||||
)?;
|
||||
let constraint =
|
||||
get_type_from_type_annotation_kinds(def_list, unifier, &ty, &mut None)?;
|
||||
get_type_from_type_annotation_kinds(def_list, unifier, primitives, &ty, &mut None)?;
|
||||
let loc = func.location;
|
||||
|
||||
Ok(unifier.get_fresh_const_generic_var(constraint, Some(generic_name), Some(loc)).ty)
|
||||
|
@ -239,8 +242,6 @@ fn handle_assignment_pattern(
|
|||
}
|
||||
|
||||
fn main() {
|
||||
const SIZE_T: u32 = usize::BITS;
|
||||
|
||||
let cli = CommandLineArgs::parse();
|
||||
let CommandLineArgs { file_name, threads, opt_level, emit_llvm, triple, mcpu, target_features } =
|
||||
cli;
|
||||
|
@ -273,6 +274,24 @@ fn main() {
|
|||
_ => OptimizationLevel::Aggressive,
|
||||
};
|
||||
|
||||
let target_machine_options = CodeGenTargetMachineOptions {
|
||||
triple,
|
||||
cpu: mcpu,
|
||||
features: target_features,
|
||||
reloc_mode: RelocMode::PIC,
|
||||
..host_target_machine
|
||||
};
|
||||
|
||||
let size_t = Context::create()
|
||||
.ptr_sized_int_type(
|
||||
&target_machine_options
|
||||
.create_target_machine(opt_level)
|
||||
.map(|tm| tm.get_target_data())
|
||||
.unwrap(),
|
||||
None,
|
||||
)
|
||||
.get_bit_width();
|
||||
|
||||
let program = match fs::read_to_string(file_name.clone()) {
|
||||
Ok(program) => program,
|
||||
Err(err) => {
|
||||
|
@ -281,9 +300,9 @@ fn main() {
|
|||
}
|
||||
};
|
||||
|
||||
let primitive: PrimitiveStore = TopLevelComposer::make_primitives(SIZE_T).0;
|
||||
let primitive: PrimitiveStore = TopLevelComposer::make_primitives(size_t).0;
|
||||
let (mut composer, builtins_def, builtins_ty) =
|
||||
TopLevelComposer::new(vec![], ComposerConfig::default(), SIZE_T);
|
||||
TopLevelComposer::new(vec![], ComposerConfig::default(), size_t);
|
||||
|
||||
let internal_resolver: Arc<ResolverInternal> = ResolverInternal {
|
||||
id_to_type: builtins_ty.into(),
|
||||
|
@ -296,6 +315,16 @@ fn main() {
|
|||
let resolver =
|
||||
Arc::new(Resolver(internal_resolver.clone())) as Arc<dyn SymbolResolver + Send + Sync>;
|
||||
|
||||
let context = inkwell::context::Context::create();
|
||||
|
||||
// Process IRRT
|
||||
let irrt = load_irrt(&context);
|
||||
setup_irrt_exceptions(&context, &irrt, resolver.as_ref());
|
||||
if emit_llvm {
|
||||
irrt.write_bitcode_to_path(Path::new("irrt.bc"));
|
||||
}
|
||||
|
||||
// Process the Python script
|
||||
let parser_result = parser::parse_program(&program, file_name.into()).unwrap();
|
||||
|
||||
for stmt in parser_result {
|
||||
|
@ -371,16 +400,7 @@ fn main() {
|
|||
instance_to_stmt[""].clone()
|
||||
};
|
||||
|
||||
let llvm_options = CodeGenLLVMOptions {
|
||||
opt_level,
|
||||
target: CodeGenTargetMachineOptions {
|
||||
triple,
|
||||
cpu: mcpu,
|
||||
features: target_features,
|
||||
reloc_mode: RelocMode::PIC,
|
||||
..host_target_machine
|
||||
},
|
||||
};
|
||||
let llvm_options = CodeGenLLVMOptions { opt_level, target: target_machine_options };
|
||||
|
||||
let task = CodeGenTask {
|
||||
subst: Vec::default(),
|
||||
|
@ -403,14 +423,14 @@ fn main() {
|
|||
membuffer.lock().push(buffer);
|
||||
})));
|
||||
let threads = (0..threads)
|
||||
.map(|i| Box::new(DefaultCodeGenerator::new(format!("module{i}"), SIZE_T)))
|
||||
.map(|i| Box::new(DefaultCodeGenerator::new(format!("module{i}"), size_t)))
|
||||
.collect();
|
||||
let (registry, handles) = WorkerRegistry::create_workers(threads, top_level, &llvm_options, &f);
|
||||
registry.add_task(task);
|
||||
registry.wait_tasks_complete(handles);
|
||||
|
||||
// Link all modules together into `main`
|
||||
let buffers = membuffers.lock();
|
||||
let context = inkwell::context::Context::create();
|
||||
let main = context
|
||||
.create_module_from_ir(MemoryBuffer::create_from_memory_range(&buffers[0], "main"))
|
||||
.unwrap();
|
||||
|
@ -430,12 +450,9 @@ fn main() {
|
|||
main.link_in_module(other).unwrap();
|
||||
}
|
||||
|
||||
let irrt = load_irrt(&context);
|
||||
if emit_llvm {
|
||||
irrt.write_bitcode_to_path(Path::new("irrt.bc"));
|
||||
}
|
||||
main.link_in_module(irrt).unwrap();
|
||||
|
||||
// Private all functions except "run"
|
||||
let mut function_iter = main.get_first_function();
|
||||
while let Some(func) = function_iter {
|
||||
if func.count_basic_blocks() > 0 && func.get_name().to_str().unwrap() != "run" {
|
||||
|
@ -444,6 +461,7 @@ fn main() {
|
|||
function_iter = func.get_next_function();
|
||||
}
|
||||
|
||||
// Optimize `main`
|
||||
let target_machine = llvm_options
|
||||
.target
|
||||
.create_target_machine(llvm_options.opt_level)
|
||||
|
@ -457,6 +475,7 @@ fn main() {
|
|||
panic!("Failed to run optimization for module `main`: {}", err.to_string());
|
||||
}
|
||||
|
||||
// Write output
|
||||
target_machine
|
||||
.write_to_file(&main, FileType::Object, Path::new("module.o"))
|
||||
.expect("couldn't write module to file");
|
||||
|
|
Loading…
Reference in New Issue