forked from M-Labs/nac3
Compare commits
9 Commits
ndstrides-
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| Author | SHA1 | Date |
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1303265785 | |
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bc91ab9b13 | |
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1e06a3d199 | |
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87511ac749 |
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@ -1,3 +0,0 @@
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BasedOnStyle: Google
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@ -1,4 +1,3 @@
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__pycache__
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/target
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/nac3standalone/demo/linalg/target
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nix/windows/msys2
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@ -26,9 +26,9 @@ dependencies = [
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|||
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||||
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||||
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||||
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||||
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||||
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||||
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@ -41,36 +41,36 @@ dependencies = [
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||||
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||||
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@ -117,9 +117,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
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||||
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||||
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@ -129,9 +129,9 @@ checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
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||||
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@ -139,9 +139,9 @@ dependencies = [
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||||
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||||
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||||
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||||
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||||
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@ -151,27 +151,27 @@ dependencies = [
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|
||||
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||||
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||||
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||||
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||||
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@ -182,7 +182,7 @@ dependencies = [
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|||
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||||
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|
||||
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|
||||
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@ -302,7 +302,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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@ -385,9 +385,9 @@ dependencies = [
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|
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@ -421,7 +421,7 @@ checksum = "4fa4d8d74483041a882adaa9a29f633253a66dde85055f0495c121620ac484b2"
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[[package]]
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@ -440,9 +440,9 @@ dependencies = [
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|
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@ -513,9 +513,9 @@ checksum = "97b3888a4aecf77e811145cadf6eef5901f4782c53886191b2f693f24761847c"
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|
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@ -616,7 +616,7 @@ name = "nac3core"
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|||
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|
||||
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|
||||
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|
||||
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@ -706,7 +706,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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@ -749,7 +749,7 @@ dependencies = [
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@ -778,18 +778,15 @@ checksum = "5be167a7af36ee22fe3115051bc51f6e6c7054c9348e28deb4f49bd6f705a315"
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@ -853,7 +850,7 @@ dependencies = [
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|||
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|
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@ -866,7 +863,7 @@ dependencies = [
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@ -930,9 +927,9 @@ dependencies = [
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|
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@ -950,9 +947,9 @@ dependencies = [
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@ -994,7 +991,7 @@ dependencies = [
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|
||||
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|
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|
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@ -1032,32 +1029,31 @@ checksum = "61697e0a1c7e512e84a621326239844a24d8207b4669b41bc18b32ea5cbf988b"
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||||
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@ -1076,9 +1072,9 @@ dependencies = [
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|||
|
||||
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|
||||
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|
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||||
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@ -1138,7 +1134,7 @@ dependencies = [
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|||
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|
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|
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@ -1154,9 +1150,9 @@ dependencies = [
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|
||||
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checksum = "1fceb41e3d546d0bd83421d3409b1460cc7444cd389341a4c880fe7a042cb3d7"
|
||||
checksum = "2f0209b68b3613b093e0ec905354eccaedcfe83b8cb37cbdeae64026c3064c16"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
|
|
@ -1165,21 +1161,20 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "target-lexicon"
|
||||
version = "0.12.16"
|
||||
version = "0.12.15"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "61c41af27dd6d1e27b1b16b489db798443478cef1f06a660c96db617ba5de3b1"
|
||||
checksum = "4873307b7c257eddcb50c9bedf158eb669578359fb28428bef438fec8e6ba7c2"
|
||||
|
||||
[[package]]
|
||||
name = "tempfile"
|
||||
version = "3.12.0"
|
||||
version = "3.10.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "04cbcdd0c794ebb0d4cf35e88edd2f7d2c4c3e9a5a6dab322839b321c6a87a64"
|
||||
checksum = "85b77fafb263dd9d05cbeac119526425676db3784113aa9295c88498cbf8bff1"
|
||||
dependencies = [
|
||||
"cfg-if",
|
||||
"fastrand",
|
||||
"once_cell",
|
||||
"rustix",
|
||||
"windows-sys 0.59.0",
|
||||
"windows-sys",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1208,22 +1203,22 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "thiserror"
|
||||
version = "1.0.63"
|
||||
version = "1.0.61"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "c0342370b38b6a11b6cc11d6a805569958d54cfa061a29969c3b5ce2ea405724"
|
||||
checksum = "c546c80d6be4bc6a00c0f01730c08df82eaa7a7a61f11d656526506112cc1709"
|
||||
dependencies = [
|
||||
"thiserror-impl",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "thiserror-impl"
|
||||
version = "1.0.63"
|
||||
version = "1.0.61"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "a4558b58466b9ad7ca0f102865eccc95938dca1a74a856f2b57b6629050da261"
|
||||
checksum = "46c3384250002a6d5af4d114f2845d37b57521033f30d5c3f46c4d70e1197533"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.74",
|
||||
"syn 2.0.70",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1341,9 +1336,9 @@ checksum = "06abde3611657adf66d383f00b093d7faecc7fa57071cce2578660c9f1010821"
|
|||
|
||||
[[package]]
|
||||
name = "version_check"
|
||||
version = "0.9.5"
|
||||
version = "0.9.4"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "0b928f33d975fc6ad9f86c8f283853ad26bdd5b10b7f1542aa2fa15e2289105a"
|
||||
checksum = "49874b5167b65d7193b8aba1567f5c7d93d001cafc34600cee003eda787e483f"
|
||||
|
||||
[[package]]
|
||||
name = "walkdir"
|
||||
|
|
@ -1379,11 +1374,11 @@ checksum = "ac3b87c63620426dd9b991e5ce0329eff545bccbbb34f3be09ff6fb6ab51b7b6"
|
|||
|
||||
[[package]]
|
||||
name = "winapi-util"
|
||||
version = "0.1.9"
|
||||
version = "0.1.8"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "cf221c93e13a30d793f7645a0e7762c55d169dbb0a49671918a2319d289b10bb"
|
||||
checksum = "4d4cc384e1e73b93bafa6fb4f1df8c41695c8a91cf9c4c64358067d15a7b6c6b"
|
||||
dependencies = [
|
||||
"windows-sys 0.59.0",
|
||||
"windows-sys",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1401,15 +1396,6 @@ dependencies = [
|
|||
"windows-targets",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "windows-sys"
|
||||
version = "0.59.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1e38bc4d79ed67fd075bcc251a1c39b32a1776bbe92e5bef1f0bf1f8c531853b"
|
||||
dependencies = [
|
||||
"windows-targets",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "windows-targets"
|
||||
version = "0.52.6"
|
||||
|
|
@ -1489,7 +1475,6 @@ version = "0.7.35"
|
|||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1b9b4fd18abc82b8136838da5d50bae7bdea537c574d8dc1a34ed098d6c166f0"
|
||||
dependencies = [
|
||||
"byteorder",
|
||||
"zerocopy-derive",
|
||||
]
|
||||
|
||||
|
|
@ -1501,5 +1486,5 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
|
|||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.74",
|
||||
"syn 2.0.70",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -2,11 +2,11 @@
|
|||
"nodes": {
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1721924956,
|
||||
"narHash": "sha256-Sb1jlyRO+N8jBXEX9Pg9Z1Qb8Bw9QyOgLDNMEpmjZ2M=",
|
||||
"lastModified": 1720418205,
|
||||
"narHash": "sha256-cPJoFPXU44GlhWg4pUk9oUPqurPlCFZ11ZQPk21GTPU=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "5ad6a14c6bf098e98800b091668718c336effc95",
|
||||
"rev": "655a58a72a6601292512670343087c2d75d859c1",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
|
|
|
|||
33
flake.nix
33
flake.nix
|
|
@ -6,7 +6,6 @@
|
|||
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 {};
|
||||
|
|
@ -17,22 +16,6 @@
|
|||
ln -s ${pkgs.llvmPackages_14.clang}/bin/clang $out/bin/clang-irrt-test
|
||||
ln -s ${pkgs.llvmPackages_14.llvm.out}/bin/llvm-as $out/bin/llvm-as-irrt
|
||||
'';
|
||||
demo-linalg-stub = pkgs.rustPlatform.buildRustPackage {
|
||||
name = "demo-linalg-stub";
|
||||
src = ./nac3standalone/demo/linalg;
|
||||
cargoLock = {
|
||||
lockFile = ./nac3standalone/demo/linalg/Cargo.lock;
|
||||
};
|
||||
doCheck = false;
|
||||
};
|
||||
demo-linalg-stub32 = pkgs32.rustPlatform.buildRustPackage {
|
||||
name = "demo-linalg-stub32";
|
||||
src = ./nac3standalone/demo/linalg;
|
||||
cargoLock = {
|
||||
lockFile = ./nac3standalone/demo/linalg/Cargo.lock;
|
||||
};
|
||||
doCheck = false;
|
||||
};
|
||||
nac3artiq = pkgs.python3Packages.toPythonModule (
|
||||
pkgs.rustPlatform.buildRustPackage rec {
|
||||
name = "nac3artiq";
|
||||
|
|
@ -43,7 +26,7 @@
|
|||
};
|
||||
cargoTestFlags = [ "--features" "test" ];
|
||||
passthru.cargoLock = cargoLock;
|
||||
nativeBuildInputs = [ pkgs.python3 (pkgs.wrapClangMulti pkgs.llvmPackages_14.clang) llvm-tools-irrt pkgs.llvmPackages_14.llvm.out llvm-nac3 ];
|
||||
nativeBuildInputs = [ pkgs.python3 pkgs.llvmPackages_14.clang llvm-tools-irrt pkgs.llvmPackages_14.llvm.out llvm-nac3 ];
|
||||
buildInputs = [ pkgs.python3 llvm-nac3 ];
|
||||
checkInputs = [ (pkgs.python3.withPackages(ps: [ ps.numpy ps.scipy ])) ];
|
||||
checkPhase =
|
||||
|
|
@ -51,9 +34,7 @@
|
|||
echo "Checking nac3standalone demos..."
|
||||
pushd nac3standalone/demo
|
||||
patchShebangs .
|
||||
export DEMO_LINALG_STUB=${demo-linalg-stub}/lib/liblinalg.a
|
||||
export DEMO_LINALG_STUB32=${demo-linalg-stub32}/lib/liblinalg.a
|
||||
./check_demos.sh -i686
|
||||
./check_demos.sh
|
||||
popd
|
||||
echo "Running Cargo tests..."
|
||||
cargoCheckHook
|
||||
|
|
@ -170,7 +151,7 @@
|
|||
buildInputs = with pkgs; [
|
||||
# build dependencies
|
||||
packages.x86_64-linux.llvm-nac3
|
||||
(pkgs.wrapClangMulti llvmPackages_14.clang) llvmPackages_14.llvm.out # for running nac3standalone demos
|
||||
llvmPackages_14.clang llvmPackages_14.llvm.out # for running nac3standalone demos
|
||||
packages.x86_64-linux.llvm-tools-irrt
|
||||
cargo
|
||||
rustc
|
||||
|
|
@ -182,12 +163,10 @@
|
|||
clippy
|
||||
pre-commit
|
||||
rustfmt
|
||||
rust-analyzer
|
||||
];
|
||||
shellHook =
|
||||
''
|
||||
export DEMO_LINALG_STUB=${packages.x86_64-linux.demo-linalg-stub}/lib/liblinalg.a
|
||||
export DEMO_LINALG_STUB32=${packages.x86_64-linux.demo-linalg-stub32}/lib/liblinalg.a
|
||||
'';
|
||||
# https://nixos.wiki/wiki/Rust#Shell.nix_example
|
||||
RUST_SRC_PATH = "${pkgs.rust.packages.stable.rustPlatform.rustLibSrc}";
|
||||
};
|
||||
devShells.x86_64-linux.msys2 = pkgs.mkShell {
|
||||
name = "nac3-dev-shell-msys2";
|
||||
|
|
|
|||
|
|
@ -1,24 +0,0 @@
|
|||
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()
|
||||
|
|
@ -1,10 +1,8 @@
|
|||
use nac3core::{
|
||||
codegen::{
|
||||
classes::{ListValue, NDArrayValue, RangeValue, UntypedArrayLikeAccessor},
|
||||
expr::{destructure_range, gen_call},
|
||||
irrt::call_ndarray_calc_size,
|
||||
expr::gen_call,
|
||||
llvm_intrinsics::{call_int_smax, call_stackrestore, call_stacksave},
|
||||
stmt::{gen_block, gen_for_callback_incrementing, gen_if_callback, gen_with},
|
||||
stmt::{gen_block, gen_with},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
symbol_resolver::ValueEnum,
|
||||
|
|
@ -15,11 +13,7 @@ use nac3core::{
|
|||
use nac3parser::ast::{Expr, ExprKind, Located, Stmt, StmtKind, StrRef};
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
module::Linkage,
|
||||
types::IntType,
|
||||
values::{BasicValueEnum, StructValue},
|
||||
AddressSpace, IntPredicate,
|
||||
context::Context, module::Linkage, types::IntType, values::BasicValueEnum, AddressSpace,
|
||||
};
|
||||
|
||||
use pyo3::{
|
||||
|
|
@ -29,12 +23,10 @@ use pyo3::{
|
|||
|
||||
use crate::{symbol_resolver::InnerResolver, timeline::TimeFns};
|
||||
|
||||
use itertools::Itertools;
|
||||
use std::{
|
||||
collections::{hash_map::DefaultHasher, HashMap},
|
||||
collections::hash_map::DefaultHasher,
|
||||
collections::HashMap,
|
||||
hash::{Hash, Hasher},
|
||||
iter::once,
|
||||
mem,
|
||||
sync::Arc,
|
||||
};
|
||||
|
||||
|
|
@ -394,7 +386,7 @@ fn gen_rpc_tag(
|
|||
} else {
|
||||
let ty_enum = ctx.unifier.get_ty(ty);
|
||||
match &*ty_enum {
|
||||
TTuple { ty, is_vararg_ctx: false } => {
|
||||
TTuple { ty } => {
|
||||
buffer.push(b't');
|
||||
buffer.push(ty.len() as u8);
|
||||
for ty in ty {
|
||||
|
|
@ -708,7 +700,6 @@ pub fn attributes_writeback(
|
|||
name: i.to_string().into(),
|
||||
ty: *ty,
|
||||
default_value: None,
|
||||
is_vararg: false,
|
||||
})
|
||||
.collect(),
|
||||
ret: ctx.primitives.none,
|
||||
|
|
@ -732,475 +723,3 @@ pub fn rpc_codegen_callback() -> Arc<GenCall> {
|
|||
rpc_codegen_callback_fn(ctx, obj, fun, args, generator)
|
||||
})))
|
||||
}
|
||||
|
||||
/// Returns the `fprintf` format constant for the given [`llvm_int_t`][`IntType`] on a platform with
|
||||
/// [`llvm_usize`] as its native word size.
|
||||
///
|
||||
/// Note that, similar to format constants in `<inttypes.h>`, these constants need to be prepended
|
||||
/// with `%`.
|
||||
#[must_use]
|
||||
fn get_fprintf_format_constant<'ctx>(
|
||||
llvm_usize: IntType<'ctx>,
|
||||
llvm_int_t: IntType<'ctx>,
|
||||
is_unsigned: bool,
|
||||
) -> String {
|
||||
debug_assert!(matches!(llvm_usize.get_bit_width(), 8 | 16 | 32 | 64));
|
||||
|
||||
let conv_spec = if is_unsigned { 'u' } else { 'd' };
|
||||
|
||||
// https://en.cppreference.com/w/c/language/arithmetic_types
|
||||
// Note that NAC3 does **not** support LP32 and LLP64 configurations
|
||||
match llvm_int_t.get_bit_width() {
|
||||
8 => format!("hh{conv_spec}"),
|
||||
16 => format!("h{conv_spec}"),
|
||||
32 => conv_spec.to_string(),
|
||||
64 => format!("{}{conv_spec}", if llvm_usize.get_bit_width() == 64 { "l" } else { "ll" }),
|
||||
_ => todo!(
|
||||
"Not yet implemented for i{} on {}-bit platform",
|
||||
llvm_int_t.get_bit_width(),
|
||||
llvm_usize.get_bit_width()
|
||||
),
|
||||
}
|
||||
}
|
||||
|
||||
/// Prints one or more `values` to `core_log` or `rtio_log`.
|
||||
///
|
||||
/// * `separator` - The separator between multiple values.
|
||||
/// * `suffix` - String to terminate the printed string, if any.
|
||||
/// * `as_repr` - Whether the `repr()` output of values instead of `str()`.
|
||||
/// * `as_rtio` - Whether to print to `rtio_log` instead of `core_log`.
|
||||
fn polymorphic_print<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut dyn CodeGenerator,
|
||||
values: &[(Type, ValueEnum<'ctx>)],
|
||||
separator: &str,
|
||||
suffix: Option<&str>,
|
||||
as_repr: bool,
|
||||
as_rtio: bool,
|
||||
) -> Result<(), String> {
|
||||
let printf = |ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut dyn CodeGenerator,
|
||||
fmt: String,
|
||||
args: Vec<BasicValueEnum<'ctx>>| {
|
||||
debug_assert!(!fmt.is_empty());
|
||||
debug_assert_eq!(fmt.as_bytes().last().unwrap(), &0u8);
|
||||
|
||||
let fn_name = if as_rtio { "rtio_log" } else { "core_log" };
|
||||
let print_fn = ctx.module.get_function(fn_name).unwrap_or_else(|| {
|
||||
let llvm_pi8 = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
|
||||
let fn_t = if as_rtio {
|
||||
let llvm_void = ctx.ctx.void_type();
|
||||
llvm_void.fn_type(&[llvm_pi8.into()], true)
|
||||
} else {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
llvm_i32.fn_type(&[llvm_pi8.into()], true)
|
||||
};
|
||||
ctx.module.add_function(fn_name, fn_t, None)
|
||||
});
|
||||
|
||||
let fmt = ctx.gen_string(generator, &fmt).get_field(generator, ctx.ctx, |f| f.base).value;
|
||||
|
||||
ctx.builder
|
||||
.build_call(
|
||||
print_fn,
|
||||
&once(fmt.into()).chain(args).map(BasicValueEnum::into).collect_vec(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
};
|
||||
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_i64 = ctx.ctx.i64_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let suffix = suffix.unwrap_or_default();
|
||||
|
||||
let mut fmt = String::new();
|
||||
let mut args = Vec::new();
|
||||
|
||||
let flush = |ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut dyn CodeGenerator,
|
||||
fmt: &mut String,
|
||||
args: &mut Vec<BasicValueEnum<'ctx>>| {
|
||||
if !fmt.is_empty() {
|
||||
fmt.push('\0');
|
||||
printf(ctx, generator, mem::take(fmt), mem::take(args));
|
||||
}
|
||||
};
|
||||
|
||||
for (ty, value) in values {
|
||||
let ty = *ty;
|
||||
let value = value.clone().to_basic_value_enum(ctx, generator, ty).unwrap();
|
||||
|
||||
if !fmt.is_empty() {
|
||||
fmt.push_str(separator);
|
||||
}
|
||||
|
||||
match &*ctx.unifier.get_ty_immutable(ty) {
|
||||
TypeEnum::TTuple { ty: tys, is_vararg_ctx: false } => {
|
||||
let pvalue = {
|
||||
let pvalue = generator.gen_var_alloc(ctx, value.get_type(), None).unwrap();
|
||||
ctx.builder.build_store(pvalue, value).unwrap();
|
||||
pvalue
|
||||
};
|
||||
|
||||
fmt.push('(');
|
||||
flush(ctx, generator, &mut fmt, &mut args);
|
||||
|
||||
let tuple_vals = tys
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(i, ty)| {
|
||||
(*ty, {
|
||||
let pfield =
|
||||
ctx.builder.build_struct_gep(pvalue, i as u32, "").unwrap();
|
||||
|
||||
ValueEnum::from(ctx.builder.build_load(pfield, "").unwrap())
|
||||
})
|
||||
})
|
||||
.collect_vec();
|
||||
|
||||
polymorphic_print(ctx, generator, &tuple_vals, ", ", None, true, as_rtio)?;
|
||||
|
||||
if tuple_vals.len() == 1 {
|
||||
fmt.push_str(",)");
|
||||
} else {
|
||||
fmt.push(')');
|
||||
}
|
||||
}
|
||||
|
||||
TypeEnum::TFunc { .. } => todo!(),
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::None.id() => {
|
||||
fmt.push_str("None");
|
||||
}
|
||||
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::Bool.id() => {
|
||||
fmt.push_str("%.*s");
|
||||
|
||||
let true_str = ctx.gen_string(generator, "True");
|
||||
|
||||
let true_data = true_str.get_field(generator, ctx.ctx, |f| f.base);
|
||||
let true_len = true_str.get_field(generator, ctx.ctx, |f| f.len);
|
||||
|
||||
let false_str = ctx.gen_string(generator, "False");
|
||||
|
||||
let false_data = false_str.get_field(generator, ctx.ctx, |f| f.base);
|
||||
let false_len = false_str.get_field(generator, ctx.ctx, |f| f.len);
|
||||
|
||||
let bool_val = generator.bool_to_i1(ctx, value.into_int_value());
|
||||
|
||||
args.extend([
|
||||
ctx.builder
|
||||
.build_select(bool_val, true_len.value, false_len.value, "")
|
||||
.unwrap(),
|
||||
ctx.builder
|
||||
.build_select(bool_val, true_data.value, false_data.value, "")
|
||||
.unwrap(),
|
||||
]);
|
||||
}
|
||||
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == PrimDef::Int32.id()
|
||||
|| *obj_id == PrimDef::Int64.id()
|
||||
|| *obj_id == PrimDef::UInt32.id()
|
||||
|| *obj_id == PrimDef::UInt64.id() =>
|
||||
{
|
||||
let is_unsigned =
|
||||
*obj_id == PrimDef::UInt32.id() || *obj_id == PrimDef::UInt64.id();
|
||||
|
||||
let llvm_int_t = value.get_type().into_int_type();
|
||||
debug_assert!(matches!(llvm_usize.get_bit_width(), 32 | 64));
|
||||
debug_assert!(matches!(llvm_int_t.get_bit_width(), 32 | 64));
|
||||
|
||||
let fmt_spec = format!(
|
||||
"%{}",
|
||||
get_fprintf_format_constant(llvm_usize, llvm_int_t, is_unsigned)
|
||||
);
|
||||
|
||||
fmt.push_str(fmt_spec.as_str());
|
||||
args.push(value);
|
||||
}
|
||||
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::Float.id() => {
|
||||
fmt.push_str("%g");
|
||||
args.push(value);
|
||||
}
|
||||
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::Str.id() => {
|
||||
if as_repr {
|
||||
fmt.push_str("\"%.*s\"");
|
||||
} else {
|
||||
fmt.push_str("%.*s");
|
||||
}
|
||||
|
||||
let str = value.into_struct_value();
|
||||
let str_data = unsafe { str.get_field_at_index_unchecked(0) }.into_pointer_value();
|
||||
let str_len = unsafe { str.get_field_at_index_unchecked(1) }.into_int_value();
|
||||
|
||||
args.extend(&[str_len.into(), str_data.into()]);
|
||||
}
|
||||
|
||||
TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::List.id() => {
|
||||
let elem_ty = *params.iter().next().unwrap().1;
|
||||
|
||||
fmt.push('[');
|
||||
flush(ctx, generator, &mut fmt, &mut args);
|
||||
|
||||
let val = ListValue::from_ptr_val(value.into_pointer_value(), llvm_usize, None);
|
||||
let len = val.load_size(ctx, None);
|
||||
let last =
|
||||
ctx.builder.build_int_sub(len, llvm_usize.const_int(1, false), "").unwrap();
|
||||
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(len, false),
|
||||
|generator, ctx, _, i| {
|
||||
let elem = unsafe { val.data().get_unchecked(ctx, generator, &i, None) };
|
||||
|
||||
polymorphic_print(
|
||||
ctx,
|
||||
generator,
|
||||
&[(elem_ty, elem.into())],
|
||||
"",
|
||||
None,
|
||||
true,
|
||||
as_rtio,
|
||||
)?;
|
||||
|
||||
gen_if_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_, ctx| {
|
||||
Ok(ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::ULT, i, last, "")
|
||||
.unwrap())
|
||||
},
|
||||
|generator, ctx| {
|
||||
printf(ctx, generator, ", \0".into(), Vec::default());
|
||||
|
||||
Ok(())
|
||||
},
|
||||
|_, _| Ok(()),
|
||||
)?;
|
||||
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)?;
|
||||
|
||||
fmt.push(']');
|
||||
flush(ctx, generator, &mut fmt, &mut args);
|
||||
}
|
||||
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
|
||||
|
||||
fmt.push_str("array([");
|
||||
flush(ctx, generator, &mut fmt, &mut args);
|
||||
|
||||
let val = NDArrayValue::from_ptr_val(value.into_pointer_value(), llvm_usize, None);
|
||||
let len = call_ndarray_calc_size(generator, ctx, &val.dim_sizes(), (None, None));
|
||||
let last =
|
||||
ctx.builder.build_int_sub(len, llvm_usize.const_int(1, false), "").unwrap();
|
||||
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(len, false),
|
||||
|generator, ctx, _, i| {
|
||||
let elem = unsafe { val.data().get_unchecked(ctx, generator, &i, None) };
|
||||
|
||||
polymorphic_print(
|
||||
ctx,
|
||||
generator,
|
||||
&[(elem_ty, elem.into())],
|
||||
"",
|
||||
None,
|
||||
true,
|
||||
as_rtio,
|
||||
)?;
|
||||
|
||||
gen_if_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_, ctx| {
|
||||
Ok(ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::ULT, i, last, "")
|
||||
.unwrap())
|
||||
},
|
||||
|generator, ctx| {
|
||||
printf(ctx, generator, ", \0".into(), Vec::default());
|
||||
|
||||
Ok(())
|
||||
},
|
||||
|_, _| Ok(()),
|
||||
)?;
|
||||
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)?;
|
||||
|
||||
fmt.push_str(")]");
|
||||
flush(ctx, generator, &mut fmt, &mut args);
|
||||
}
|
||||
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::Range.id() => {
|
||||
fmt.push_str("range(");
|
||||
flush(ctx, generator, &mut fmt, &mut args);
|
||||
|
||||
let val = RangeValue::from_ptr_val(value.into_pointer_value(), None);
|
||||
|
||||
let (start, stop, step) = destructure_range(ctx, val);
|
||||
|
||||
polymorphic_print(
|
||||
ctx,
|
||||
generator,
|
||||
&[
|
||||
(ctx.primitives.int32, start.into()),
|
||||
(ctx.primitives.int32, stop.into()),
|
||||
(ctx.primitives.int32, step.into()),
|
||||
],
|
||||
", ",
|
||||
None,
|
||||
false,
|
||||
as_rtio,
|
||||
)?;
|
||||
|
||||
fmt.push(')');
|
||||
}
|
||||
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::Exception.id() => {
|
||||
let fmt_str = format!(
|
||||
"%{}(%{}, %{1:}, %{1:})",
|
||||
get_fprintf_format_constant(llvm_usize, llvm_i32, false),
|
||||
get_fprintf_format_constant(llvm_usize, llvm_i64, false),
|
||||
);
|
||||
|
||||
let exn = value.into_pointer_value();
|
||||
let name = ctx
|
||||
.build_in_bounds_gep_and_load(
|
||||
exn,
|
||||
&[llvm_i32.const_zero(), llvm_i32.const_zero()],
|
||||
None,
|
||||
)
|
||||
.into_int_value();
|
||||
let param0 = ctx
|
||||
.build_in_bounds_gep_and_load(
|
||||
exn,
|
||||
&[llvm_i32.const_zero(), llvm_i32.const_int(6, false)],
|
||||
None,
|
||||
)
|
||||
.into_int_value();
|
||||
let param1 = ctx
|
||||
.build_in_bounds_gep_and_load(
|
||||
exn,
|
||||
&[llvm_i32.const_zero(), llvm_i32.const_int(7, false)],
|
||||
None,
|
||||
)
|
||||
.into_int_value();
|
||||
let param2 = ctx
|
||||
.build_in_bounds_gep_and_load(
|
||||
exn,
|
||||
&[llvm_i32.const_zero(), llvm_i32.const_int(8, false)],
|
||||
None,
|
||||
)
|
||||
.into_int_value();
|
||||
|
||||
fmt.push_str(fmt_str.as_str());
|
||||
args.extend_from_slice(&[name.into(), param0.into(), param1.into(), param2.into()]);
|
||||
}
|
||||
|
||||
_ => unreachable!(
|
||||
"Unsupported object type for polymorphic_print: {}",
|
||||
ctx.unifier.stringify(ty)
|
||||
),
|
||||
}
|
||||
}
|
||||
|
||||
fmt.push_str(suffix);
|
||||
flush(ctx, generator, &mut fmt, &mut args);
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Invokes the `core_log` intrinsic function.
|
||||
pub fn call_core_log_impl<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut dyn CodeGenerator,
|
||||
arg: (Type, BasicValueEnum<'ctx>),
|
||||
) -> Result<(), String> {
|
||||
let (arg_ty, arg_val) = arg;
|
||||
|
||||
polymorphic_print(ctx, generator, &[(arg_ty, arg_val.into())], " ", Some("\n"), false, false)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Invokes the `rtio_log` intrinsic function.
|
||||
pub fn call_rtio_log_impl<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut dyn CodeGenerator,
|
||||
channel: StructValue<'ctx>,
|
||||
arg: (Type, BasicValueEnum<'ctx>),
|
||||
) -> Result<(), String> {
|
||||
let (arg_ty, arg_val) = arg;
|
||||
|
||||
polymorphic_print(
|
||||
ctx,
|
||||
generator,
|
||||
&[(ctx.primitives.str, channel.into())],
|
||||
" ",
|
||||
Some("\x1E"),
|
||||
false,
|
||||
true,
|
||||
)?;
|
||||
polymorphic_print(ctx, generator, &[(arg_ty, arg_val.into())], " ", Some("\x1D"), false, true)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Generates a call to `core_log`.
|
||||
pub fn gen_core_log<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<(), String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let value_ty = fun.0.args[0].ty;
|
||||
let value_arg = args[0].1.clone().to_basic_value_enum(ctx, generator, value_ty)?;
|
||||
|
||||
call_core_log_impl(ctx, generator, (value_ty, value_arg))
|
||||
}
|
||||
|
||||
/// Generates a call to `rtio_log`.
|
||||
pub fn gen_rtio_log<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<(), String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 2);
|
||||
|
||||
let channel_ty = fun.0.args[0].ty;
|
||||
assert!(ctx.unifier.unioned(channel_ty, ctx.primitives.str));
|
||||
let channel_arg =
|
||||
args[0].1.clone().to_basic_value_enum(ctx, generator, channel_ty)?.into_struct_value();
|
||||
let value_ty = fun.0.args[1].ty;
|
||||
let value_arg = args[1].1.clone().to_basic_value_enum(ctx, generator, value_ty)?;
|
||||
|
||||
call_rtio_log_impl(ctx, generator, channel_arg, (value_ty, value_arg))
|
||||
}
|
||||
|
|
|
|||
|
|
@ -24,7 +24,6 @@ use std::rc::Rc;
|
|||
use std::sync::Arc;
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
memory_buffer::MemoryBuffer,
|
||||
module::{Linkage, Module},
|
||||
passes::PassBuilderOptions,
|
||||
|
|
@ -33,10 +32,9 @@ 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::{into_var_map, TypeEnum, Unifier, VarMap};
|
||||
use nac3core::typecheck::typedef::{TypeEnum, Unifier, VarMap};
|
||||
use nac3parser::{
|
||||
ast::{ExprKind, Stmt, StmtKind, StrRef},
|
||||
parser::parse_program,
|
||||
|
|
@ -52,7 +50,7 @@ use nac3core::{
|
|||
codegen::{concrete_type::ConcreteTypeStore, CodeGenTask, WithCall, WorkerRegistry},
|
||||
symbol_resolver::SymbolResolver,
|
||||
toplevel::{
|
||||
composer::{BuiltinFuncCreator, BuiltinFuncSpec, ComposerConfig, TopLevelComposer},
|
||||
composer::{ComposerConfig, TopLevelComposer},
|
||||
DefinitionId, GenCall, TopLevelDef,
|
||||
},
|
||||
typecheck::typedef::{FunSignature, FuncArg},
|
||||
|
|
@ -61,13 +59,13 @@ use nac3core::{
|
|||
|
||||
use nac3ld::Linker;
|
||||
|
||||
use tempfile::{self, TempDir};
|
||||
|
||||
use crate::codegen::attributes_writeback;
|
||||
use crate::{
|
||||
codegen::{
|
||||
attributes_writeback, gen_core_log, gen_rtio_log, rpc_codegen_callback, ArtiqCodeGenerator,
|
||||
},
|
||||
codegen::{rpc_codegen_callback, ArtiqCodeGenerator},
|
||||
symbol_resolver::{DeferredEvaluationStore, InnerResolver, PythonHelper, Resolver},
|
||||
};
|
||||
use tempfile::{self, TempDir};
|
||||
|
||||
mod codegen;
|
||||
mod symbol_resolver;
|
||||
|
|
@ -128,7 +126,7 @@ struct Nac3 {
|
|||
isa: Isa,
|
||||
time_fns: &'static (dyn TimeFns + Sync),
|
||||
primitive: PrimitiveStore,
|
||||
builtins: Vec<BuiltinFuncSpec>,
|
||||
builtins: Vec<(StrRef, FunSignature, Arc<GenCall>)>,
|
||||
pyid_to_def: Arc<RwLock<HashMap<u64, DefinitionId>>>,
|
||||
primitive_ids: PrimitivePythonId,
|
||||
working_directory: TempDir,
|
||||
|
|
@ -266,7 +264,7 @@ impl Nac3 {
|
|||
arg_names.len(),
|
||||
));
|
||||
}
|
||||
for (i, FuncArg { ty, default_value, name, .. }) in args.iter().enumerate() {
|
||||
for (i, FuncArg { ty, default_value, name }) in args.iter().enumerate() {
|
||||
let in_name = match arg_names.get(i) {
|
||||
Some(n) => n,
|
||||
None if default_value.is_none() => {
|
||||
|
|
@ -302,64 +300,6 @@ impl Nac3 {
|
|||
None
|
||||
}
|
||||
|
||||
/// Returns a [`Vec`] of builtins that needs to be initialized during method compilation time.
|
||||
fn get_lateinit_builtins() -> Vec<Box<BuiltinFuncCreator>> {
|
||||
vec![
|
||||
Box::new(|primitives, unifier| {
|
||||
let arg_ty = unifier.get_fresh_var(Some("T".into()), None);
|
||||
|
||||
(
|
||||
"core_log".into(),
|
||||
FunSignature {
|
||||
args: vec![FuncArg {
|
||||
name: "arg".into(),
|
||||
ty: arg_ty.ty,
|
||||
default_value: None,
|
||||
is_vararg: false,
|
||||
}],
|
||||
ret: primitives.none,
|
||||
vars: into_var_map([arg_ty]),
|
||||
},
|
||||
Arc::new(GenCall::new(Box::new(move |ctx, obj, fun, args, generator| {
|
||||
gen_core_log(ctx, &obj, fun, &args, generator)?;
|
||||
|
||||
Ok(None)
|
||||
}))),
|
||||
)
|
||||
}),
|
||||
Box::new(|primitives, unifier| {
|
||||
let arg_ty = unifier.get_fresh_var(Some("T".into()), None);
|
||||
|
||||
(
|
||||
"rtio_log".into(),
|
||||
FunSignature {
|
||||
args: vec![
|
||||
FuncArg {
|
||||
name: "channel".into(),
|
||||
ty: primitives.str,
|
||||
default_value: None,
|
||||
is_vararg: false,
|
||||
},
|
||||
FuncArg {
|
||||
name: "arg".into(),
|
||||
ty: arg_ty.ty,
|
||||
default_value: None,
|
||||
is_vararg: false,
|
||||
},
|
||||
],
|
||||
ret: primitives.none,
|
||||
vars: into_var_map([arg_ty]),
|
||||
},
|
||||
Arc::new(GenCall::new(Box::new(move |ctx, obj, fun, args, generator| {
|
||||
gen_rtio_log(ctx, &obj, fun, &args, generator)?;
|
||||
|
||||
Ok(None)
|
||||
}))),
|
||||
)
|
||||
}),
|
||||
]
|
||||
}
|
||||
|
||||
fn compile_method<T>(
|
||||
&self,
|
||||
obj: &PyAny,
|
||||
|
|
@ -372,7 +312,6 @@ impl Nac3 {
|
|||
let size_t = self.isa.get_size_type();
|
||||
let (mut composer, mut builtins_def, mut builtins_ty) = TopLevelComposer::new(
|
||||
self.builtins.clone(),
|
||||
Self::get_lateinit_builtins(),
|
||||
ComposerConfig { kernel_ann: Some("Kernel"), kernel_invariant_ann: "KernelInvariant" },
|
||||
size_t,
|
||||
);
|
||||
|
|
@ -558,11 +497,6 @@ 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();
|
||||
|
|
@ -691,9 +625,7 @@ impl Nac3 {
|
|||
let buffer = buffer.as_slice().into();
|
||||
membuffer.lock().push(buffer);
|
||||
})));
|
||||
let size_t = Context::create()
|
||||
.ptr_sized_int_type(&self.get_llvm_target_machine().get_target_data(), None)
|
||||
.get_bit_width();
|
||||
let size_t = if self.isa == Isa::Host { 64 } else { 32 };
|
||||
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
|
||||
|
|
@ -712,9 +644,6 @@ 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,
|
||||
|
|
@ -733,7 +662,7 @@ impl Nac3 {
|
|||
membuffer.lock().push(buffer);
|
||||
});
|
||||
|
||||
// Link all modules into `main`.
|
||||
let context = inkwell::context::Context::create();
|
||||
let buffers = membuffers.lock();
|
||||
let main = context
|
||||
.create_module_from_ir(MemoryBuffer::create_from_memory_range(&buffers[0], "main"))
|
||||
|
|
@ -762,7 +691,8 @@ impl Nac3 {
|
|||
)
|
||||
.unwrap();
|
||||
|
||||
main.link_in_module(irrt).map_err(|err| CompileError::new_err(err.to_string()))?;
|
||||
main.link_in_module(load_irrt(&context))
|
||||
.map_err(|err| CompileError::new_err(err.to_string()))?;
|
||||
|
||||
let mut function_iter = main.get_first_function();
|
||||
while let Some(func) = function_iter {
|
||||
|
|
@ -917,7 +847,7 @@ impl Nac3 {
|
|||
Isa::RiscV32IMA => &timeline::NOW_PINNING_TIME_FNS,
|
||||
Isa::CortexA9 | Isa::Host => &timeline::EXTERN_TIME_FNS,
|
||||
};
|
||||
let (primitive, _) = TopLevelComposer::make_primitives(isa.get_size_type());
|
||||
let primitive: PrimitiveStore = TopLevelComposer::make_primitives(isa.get_size_type()).0;
|
||||
let builtins = vec![
|
||||
(
|
||||
"now_mu".into(),
|
||||
|
|
@ -933,7 +863,6 @@ impl Nac3 {
|
|||
name: "t".into(),
|
||||
ty: primitive.int64,
|
||||
default_value: None,
|
||||
is_vararg: false,
|
||||
}],
|
||||
ret: primitive.none,
|
||||
vars: VarMap::new(),
|
||||
|
|
@ -953,7 +882,6 @@ impl Nac3 {
|
|||
name: "dt".into(),
|
||||
ty: primitive.int64,
|
||||
default_value: None,
|
||||
is_vararg: false,
|
||||
}],
|
||||
ret: primitive.none,
|
||||
vars: VarMap::new(),
|
||||
|
|
|
|||
|
|
@ -351,7 +351,7 @@ impl InnerResolver {
|
|||
Ok(Ok((ndarray, false)))
|
||||
} else if ty_id == self.primitive_ids.tuple {
|
||||
// do not handle type var param and concrete check here
|
||||
Ok(Ok((unifier.add_ty(TypeEnum::TTuple { ty: vec![], is_vararg_ctx: false }), false)))
|
||||
Ok(Ok((unifier.add_ty(TypeEnum::TTuple { ty: vec![] }), false)))
|
||||
} else if ty_id == self.primitive_ids.option {
|
||||
Ok(Ok((primitives.option, false)))
|
||||
} else if ty_id == self.primitive_ids.none {
|
||||
|
|
@ -555,10 +555,7 @@ impl InnerResolver {
|
|||
Err(err) => return Ok(Err(err)),
|
||||
_ => return Ok(Err("tuple type needs at least 1 type parameters".to_string()))
|
||||
};
|
||||
Ok(Ok((
|
||||
unifier.add_ty(TypeEnum::TTuple { ty: args, is_vararg_ctx: false }),
|
||||
true,
|
||||
)))
|
||||
Ok(Ok((unifier.add_ty(TypeEnum::TTuple { ty: args }), true)))
|
||||
}
|
||||
TypeEnum::TObj { params, obj_id, .. } => {
|
||||
let subst = {
|
||||
|
|
@ -800,9 +797,7 @@ impl InnerResolver {
|
|||
.map(|elem| self.get_obj_type(py, elem, unifier, defs, primitives))
|
||||
.collect();
|
||||
let types = types?;
|
||||
Ok(types.map(|types| {
|
||||
unifier.add_ty(TypeEnum::TTuple { ty: types, is_vararg_ctx: false })
|
||||
}))
|
||||
Ok(types.map(|types| unifier.add_ty(TypeEnum::TTuple { ty: types })))
|
||||
}
|
||||
// special handling for option type since its class member layout in python side
|
||||
// is special and cannot be mapped directly to a nac3 type as below
|
||||
|
|
@ -996,15 +991,8 @@ 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);
|
||||
|
|
@ -1208,9 +1196,7 @@ impl InnerResolver {
|
|||
Ok(Some(ndarray.as_pointer_value().into()))
|
||||
} else if ty_id == self.primitive_ids.tuple {
|
||||
let expected_ty_enum = ctx.unifier.get_ty_immutable(expected_ty);
|
||||
let TypeEnum::TTuple { ty, is_vararg_ctx: false } = expected_ty_enum.as_ref() else {
|
||||
unreachable!()
|
||||
};
|
||||
let TypeEnum::TTuple { ty } = expected_ty_enum.as_ref() else { unreachable!() };
|
||||
|
||||
let tup_tys = ty.iter();
|
||||
let elements: &PyTuple = obj.downcast()?;
|
||||
|
|
|
|||
|
|
@ -3,60 +3,44 @@ use std::{
|
|||
env,
|
||||
fs::File,
|
||||
io::Write,
|
||||
path::{Path, PathBuf},
|
||||
path::Path,
|
||||
process::{Command, Stdio},
|
||||
};
|
||||
|
||||
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();
|
||||
fn compile_irrt(irrt_dir: &Path, out_dir: &Path) {
|
||||
let irrt_cpp_path = irrt_dir.join("irrt.cpp");
|
||||
|
||||
/*
|
||||
* 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",
|
||||
irrt_cpp_path.to_str().unwrap(),
|
||||
"-x",
|
||||
"c++",
|
||||
"-fno-discard-value-names",
|
||||
"-fno-exceptions",
|
||||
"-fno-rtti",
|
||||
match env::var("PROFILE").as_deref() {
|
||||
Ok("debug") => "-O0",
|
||||
Ok("release") => "-O3",
|
||||
flavor => panic!("Unknown or missing build flavor {flavor:?}"),
|
||||
},
|
||||
"-emit-llvm",
|
||||
"-S",
|
||||
"-Wall",
|
||||
"-Wextra",
|
||||
"-Werror=return-type",
|
||||
"-I",
|
||||
irrt_dir.to_str().unwrap(),
|
||||
"-o",
|
||||
"-",
|
||||
];
|
||||
|
||||
let mut flags = vec![];
|
||||
flags.push("--target=wasm32");
|
||||
flags.extend(&["-x", "c++"]);
|
||||
flags.extend(&["-fno-discard-value-names", "-fno-exceptions", "-fno-rtti"]);
|
||||
flags.push("-emit-llvm");
|
||||
flags.push("-S");
|
||||
flags.extend(&["-Wall", "-Wextra"]);
|
||||
flags.extend(&["-o", "-"]);
|
||||
flags.extend(&["-I", irrt_dir.to_str().unwrap()]);
|
||||
flags.push(irrt_cpp_path.to_str().unwrap());
|
||||
println!("cargo:rerun-if-changed={}", out_dir.to_str().unwrap());
|
||||
|
||||
match env::var("PROFILE").as_deref() {
|
||||
Ok("debug") => {
|
||||
flags.push("-O0");
|
||||
flags.push("-DIRRT_DEBUG");
|
||||
}
|
||||
Ok("release") => {
|
||||
flags.push("-O3");
|
||||
}
|
||||
flavor => panic!("Unknown or missing build flavor {flavor:?}"),
|
||||
};
|
||||
|
||||
// Tell Cargo to rerun if any file under `irrt_dir` (recursive) changes
|
||||
println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
|
||||
|
||||
// Compile IRRT and capture the LLVM IR output
|
||||
let output = Command::new(CMD_IRRT_CLANG)
|
||||
let output = Command::new("clang-irrt")
|
||||
.args(flags)
|
||||
.output()
|
||||
.map(|o| {
|
||||
|
|
@ -69,17 +53,11 @@ fn compile_irrt_cpp() {
|
|||
let output = std::str::from_utf8(&output.stdout).unwrap().replace("\r\n", "\n");
|
||||
let mut filtered_output = String::with_capacity(output.len());
|
||||
|
||||
// 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();
|
||||
// (?ms:^define.*?\}$) to capture `define` blocks
|
||||
// (?m:^declare.*?$) to capture `declare` blocks
|
||||
// (?m:^%.+?=\s*type\s*\{.+?\}$) to capture `type` declarations
|
||||
let regex_filter =
|
||||
Regex::new(r"(?ms:^define.*?\}$)|(?m:^declare.*?$)|(?m:^%.+?=\s*type\s*\{.+?\}$)").unwrap();
|
||||
for f in regex_filter.captures_iter(&output) {
|
||||
assert_eq!(f.len(), 1);
|
||||
filtered_output.push_str(&f[0]);
|
||||
|
|
@ -90,21 +68,15 @@ fn compile_irrt_cpp() {
|
|||
.unwrap()
|
||||
.replace_all(&filtered_output, "");
|
||||
|
||||
// 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() {
|
||||
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_dir.join("irrt-filtered.ll")).unwrap();
|
||||
file.write_all(filtered_output.as_bytes()).unwrap();
|
||||
}
|
||||
|
||||
// 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)
|
||||
let mut llvm_as = Command::new("llvm-as-irrt")
|
||||
.stdin(Stdio::piped())
|
||||
.arg("-o")
|
||||
.arg(out_dir.join("irrt.bc"))
|
||||
|
|
@ -114,13 +86,10 @@ fn compile_irrt_cpp() {
|
|||
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
|
||||
fn compile_irrt_test(irrt_dir: &Path, out_dir: &Path) {
|
||||
let irrt_test_cpp_path = irrt_dir.join("irrt_test.cpp");
|
||||
let exe_path = out_dir.join("irrt_test.out");
|
||||
|
||||
let flags: &[&str] = &[
|
||||
irrt_test_cpp_path.to_str().unwrap(),
|
||||
"-x",
|
||||
|
|
@ -138,7 +107,7 @@ fn compile_irrt_test_cpp() {
|
|||
exe_path.to_str().unwrap(),
|
||||
];
|
||||
|
||||
Command::new(CMD_IRRT_CLANG_TEST)
|
||||
Command::new("clang-irrt-test")
|
||||
.args(flags)
|
||||
.output()
|
||||
.map(|o| {
|
||||
|
|
@ -146,15 +115,20 @@ fn compile_irrt_test_cpp() {
|
|||
o
|
||||
})
|
||||
.unwrap();
|
||||
println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
|
||||
println!("cargo:rerun-if-changed={}", out_dir.to_str().unwrap());
|
||||
}
|
||||
|
||||
fn main() {
|
||||
compile_irrt_cpp();
|
||||
let out_dir = env::var("OUT_DIR").unwrap();
|
||||
let out_dir = Path::new(&out_dir);
|
||||
|
||||
let irrt_dir = Path::new("./irrt");
|
||||
|
||||
compile_irrt(irrt_dir, out_dir);
|
||||
|
||||
// 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();
|
||||
compile_irrt_test(irrt_dir, out_dir);
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,10 +1,5 @@
|
|||
#define IRRT_DEFINE_TYPEDEF_INTS
|
||||
#include <irrt_everything.hpp>
|
||||
#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.
|
||||
*/
|
||||
This file will be read by `clang-irrt` to conveniently produce LLVM IR for `nac3core/codegen`.
|
||||
*/
|
||||
|
|
|
|||
|
|
@ -0,0 +1,437 @@
|
|||
#ifndef IRRT_DONT_TYPEDEF_INTS
|
||||
typedef _BitInt(8) int8_t;
|
||||
typedef unsigned _BitInt(8) uint8_t;
|
||||
typedef _BitInt(32) int32_t;
|
||||
typedef unsigned _BitInt(32) uint32_t;
|
||||
typedef _BitInt(64) int64_t;
|
||||
typedef unsigned _BitInt(64) uint64_t;
|
||||
#endif
|
||||
|
||||
// NDArray indices are always `uint32_t`.
|
||||
typedef uint32_t NDIndex;
|
||||
// The type of an index or a value describing the length of a range/slice is
|
||||
// always `int32_t`.
|
||||
typedef int32_t SliceIndex;
|
||||
|
||||
template <typename T>
|
||||
static T max(T a, T b) {
|
||||
return a > b ? a : b;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
static T min(T a, 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
|
||||
// need to make sure `exp >= 0` before calling this function
|
||||
template <typename T>
|
||||
static T __nac3_int_exp_impl(T base, T exp) {
|
||||
T res = 1;
|
||||
/* repeated squaring method */
|
||||
do {
|
||||
if (exp & 1) {
|
||||
res *= base; /* for n odd */
|
||||
}
|
||||
exp >>= 1;
|
||||
base *= base;
|
||||
} while (exp);
|
||||
return res;
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
static SizeT __nac3_ndarray_calc_size_impl(
|
||||
const SizeT *list_data,
|
||||
SizeT list_len,
|
||||
SizeT begin_idx,
|
||||
SizeT end_idx
|
||||
) {
|
||||
__builtin_assume(end_idx <= list_len);
|
||||
|
||||
SizeT num_elems = 1;
|
||||
for (SizeT i = begin_idx; i < end_idx; ++i) {
|
||||
SizeT val = list_data[i];
|
||||
__builtin_assume(val > 0);
|
||||
num_elems *= val;
|
||||
}
|
||||
return num_elems;
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
static void __nac3_ndarray_calc_nd_indices_impl(
|
||||
SizeT index,
|
||||
const SizeT *dims,
|
||||
SizeT num_dims,
|
||||
NDIndex *idxs
|
||||
) {
|
||||
SizeT stride = 1;
|
||||
for (SizeT dim = 0; dim < num_dims; dim++) {
|
||||
SizeT i = num_dims - dim - 1;
|
||||
__builtin_assume(dims[i] > 0);
|
||||
idxs[i] = (index / stride) % dims[i];
|
||||
stride *= dims[i];
|
||||
}
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
static SizeT __nac3_ndarray_flatten_index_impl(
|
||||
const SizeT *dims,
|
||||
SizeT num_dims,
|
||||
const NDIndex *indices,
|
||||
SizeT num_indices
|
||||
) {
|
||||
SizeT idx = 0;
|
||||
SizeT stride = 1;
|
||||
for (SizeT i = 0; i < num_dims; ++i) {
|
||||
SizeT ri = num_dims - i - 1;
|
||||
if (ri < num_indices) {
|
||||
idx += stride * indices[ri];
|
||||
}
|
||||
|
||||
__builtin_assume(dims[i] > 0);
|
||||
stride *= dims[ri];
|
||||
}
|
||||
return idx;
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
static void __nac3_ndarray_calc_broadcast_impl(
|
||||
const SizeT *lhs_dims,
|
||||
SizeT lhs_ndims,
|
||||
const SizeT *rhs_dims,
|
||||
SizeT rhs_ndims,
|
||||
SizeT *out_dims
|
||||
) {
|
||||
SizeT max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
|
||||
|
||||
for (SizeT i = 0; i < max_ndims; ++i) {
|
||||
const SizeT *lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : nullptr;
|
||||
const SizeT *rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : nullptr;
|
||||
SizeT *out_dim = &out_dims[max_ndims - i - 1];
|
||||
|
||||
if (lhs_dim_sz == nullptr) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (rhs_dim_sz == nullptr) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == 1) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (*rhs_dim_sz == 1) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == *rhs_dim_sz) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
static void __nac3_ndarray_calc_broadcast_idx_impl(
|
||||
const SizeT *src_dims,
|
||||
SizeT src_ndims,
|
||||
const NDIndex *in_idx,
|
||||
NDIndex *out_idx
|
||||
) {
|
||||
for (SizeT i = 0; i < src_ndims; ++i) {
|
||||
SizeT src_i = src_ndims - i - 1;
|
||||
out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
|
||||
}
|
||||
}
|
||||
|
||||
template<typename SizeT>
|
||||
static void __nac3_ndarray_strides_from_shape_impl(
|
||||
SizeT ndims,
|
||||
SizeT *shape,
|
||||
SizeT *dst_strides
|
||||
) {
|
||||
SizeT stride_product = 1;
|
||||
for (SizeT i = 0; i < ndims; i++) {
|
||||
int dim_i = ndims - i - 1;
|
||||
dst_strides[dim_i] = stride_product;
|
||||
stride_product *= shape[dim_i];
|
||||
}
|
||||
}
|
||||
|
||||
extern "C" {
|
||||
#define DEF_nac3_int_exp_(T) \
|
||||
T __nac3_int_exp_##T(T base, T exp) {\
|
||||
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)
|
||||
|
||||
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
|
||||
if (i < 0) {
|
||||
i = len + i;
|
||||
}
|
||||
if (i < 0) {
|
||||
return 0;
|
||||
} else if (i > len) {
|
||||
return len;
|
||||
}
|
||||
return i;
|
||||
}
|
||||
|
||||
SliceIndex __nac3_range_slice_len(
|
||||
const SliceIndex start,
|
||||
const SliceIndex end,
|
||||
const SliceIndex step
|
||||
) {
|
||||
SliceIndex diff = end - start;
|
||||
if (diff > 0 && step > 0) {
|
||||
return ((diff - 1) / step) + 1;
|
||||
} else if (diff < 0 && step < 0) {
|
||||
return ((diff + 1) / step) + 1;
|
||||
} else {
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
||||
// Handle list assignment and dropping part of the list when
|
||||
// both dest_step and src_step are +1.
|
||||
// - 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)
|
||||
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 */
|
||||
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 (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;
|
||||
if (src_len > 0) {
|
||||
__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,
|
||||
dest_arr + (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)
|
||||
);
|
||||
if (need_alloca) {
|
||||
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 constant optimization */
|
||||
if (size == 1) {
|
||||
__builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
|
||||
} else if (size == 4) {
|
||||
__builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
|
||||
} 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);
|
||||
}
|
||||
}
|
||||
/* 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
|
||||
);
|
||||
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_isnan(double x) {
|
||||
return __builtin_isnan(x);
|
||||
}
|
||||
|
||||
double tgamma(double arg);
|
||||
|
||||
double __nac3_gamma(double z) {
|
||||
// Handling for denormals
|
||||
// | x | Python gamma(x) | C tgamma(x) |
|
||||
// --- | ----------------- | --------------- | ----------- |
|
||||
// (1) | nan | nan | nan |
|
||||
// (2) | -inf | -inf | inf |
|
||||
// (3) | inf | inf | inf |
|
||||
// (4) | 0.0 | inf | inf |
|
||||
// (5) | {-1.0, -2.0, ...} | inf | nan |
|
||||
|
||||
// (1)-(3)
|
||||
if (__builtin_isinf(z) || __builtin_isnan(z)) {
|
||||
return z;
|
||||
}
|
||||
|
||||
double v = tgamma(z);
|
||||
|
||||
// (4)-(5)
|
||||
return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
|
||||
}
|
||||
|
||||
double lgamma(double arg);
|
||||
|
||||
double __nac3_gammaln(double x) {
|
||||
// libm's handling of value overflows differs from scipy:
|
||||
// - scipy: gammaln(-inf) -> -inf
|
||||
// - libm : lgamma(-inf) -> inf
|
||||
|
||||
if (__builtin_isinf(x)) {
|
||||
return x;
|
||||
}
|
||||
|
||||
return lgamma(x);
|
||||
}
|
||||
|
||||
double j0(double x);
|
||||
|
||||
double __nac3_j0(double x) {
|
||||
// libm's handling of value overflows differs from scipy:
|
||||
// - scipy: j0(inf) -> nan
|
||||
// - libm : j0(inf) -> 0.0
|
||||
|
||||
if (__builtin_isinf(x)) {
|
||||
return __builtin_nan("");
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
uint64_t __nac3_ndarray_calc_size64(
|
||||
const uint64_t *list_data,
|
||||
uint64_t list_len,
|
||||
uint64_t begin_idx,
|
||||
uint64_t end_idx
|
||||
) {
|
||||
return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_nd_indices(
|
||||
uint32_t index,
|
||||
const uint32_t* dims,
|
||||
uint32_t num_dims,
|
||||
NDIndex* idxs
|
||||
) {
|
||||
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_nd_indices64(
|
||||
uint64_t index,
|
||||
const uint64_t* dims,
|
||||
uint64_t num_dims,
|
||||
NDIndex* idxs
|
||||
) {
|
||||
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
|
||||
}
|
||||
|
||||
uint32_t __nac3_ndarray_flatten_index(
|
||||
const uint32_t* dims,
|
||||
uint32_t num_dims,
|
||||
const NDIndex* indices,
|
||||
uint32_t num_indices
|
||||
) {
|
||||
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
|
||||
}
|
||||
|
||||
uint64_t __nac3_ndarray_flatten_index64(
|
||||
const uint64_t* dims,
|
||||
uint64_t num_dims,
|
||||
const NDIndex* indices,
|
||||
uint64_t num_indices
|
||||
) {
|
||||
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast(
|
||||
const uint32_t *lhs_dims,
|
||||
uint32_t lhs_ndims,
|
||||
const uint32_t *rhs_dims,
|
||||
uint32_t rhs_ndims,
|
||||
uint32_t *out_dims
|
||||
) {
|
||||
return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast64(
|
||||
const uint64_t *lhs_dims,
|
||||
uint64_t lhs_ndims,
|
||||
const uint64_t *rhs_dims,
|
||||
uint64_t rhs_ndims,
|
||||
uint64_t *out_dims
|
||||
) {
|
||||
return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast_idx(
|
||||
const uint32_t *src_dims,
|
||||
uint32_t src_ndims,
|
||||
const NDIndex *in_idx,
|
||||
NDIndex *out_idx
|
||||
) {
|
||||
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast_idx64(
|
||||
const uint64_t *src_dims,
|
||||
uint64_t src_ndims,
|
||||
const NDIndex *in_idx,
|
||||
NDIndex *out_idx
|
||||
) {
|
||||
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_strides_from_shape(uint32_t ndims, uint32_t* shape, uint32_t* dst_strides) {
|
||||
__nac3_ndarray_strides_from_shape_impl(ndims, shape, dst_strides);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_strides_from_shape64(uint64_t ndims, uint64_t* shape, uint64_t* dst_strides) {
|
||||
__nac3_ndarray_strides_from_shape_impl(ndims, shape, dst_strides);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,349 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/util.hpp>
|
||||
|
||||
// NDArray indices are always `uint32_t`.
|
||||
using NDIndexInt = uint32_t;
|
||||
// The type of an index or a value describing the length of a
|
||||
// range/slice is always `int32_t`.
|
||||
using SliceIndex = int32_t;
|
||||
|
||||
namespace {
|
||||
// adapted from GNU Scientific Library:
|
||||
// https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
|
||||
// need to make sure `exp >= 0` before calling this function
|
||||
template <typename T>
|
||||
T __nac3_int_exp_impl(T base, T exp) {
|
||||
T res = 1;
|
||||
/* repeated squaring method */
|
||||
do {
|
||||
if (exp & 1) {
|
||||
res *= base; /* for n odd */
|
||||
}
|
||||
exp >>= 1;
|
||||
base *= base;
|
||||
} while (exp);
|
||||
return res;
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len,
|
||||
SizeT begin_idx, SizeT end_idx) {
|
||||
__builtin_assume(end_idx <= list_len);
|
||||
|
||||
SizeT num_elems = 1;
|
||||
for (SizeT i = begin_idx; i < end_idx; ++i) {
|
||||
SizeT val = list_data[i];
|
||||
__builtin_assume(val > 0);
|
||||
num_elems *= val;
|
||||
}
|
||||
return num_elems;
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims,
|
||||
SizeT num_dims, NDIndexInt* idxs) {
|
||||
SizeT stride = 1;
|
||||
for (SizeT dim = 0; dim < num_dims; dim++) {
|
||||
SizeT i = num_dims - dim - 1;
|
||||
__builtin_assume(dims[i] > 0);
|
||||
idxs[i] = (index / stride) % dims[i];
|
||||
stride *= dims[i];
|
||||
}
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims,
|
||||
const NDIndexInt* indices,
|
||||
SizeT num_indices) {
|
||||
SizeT idx = 0;
|
||||
SizeT stride = 1;
|
||||
for (SizeT i = 0; i < num_dims; ++i) {
|
||||
SizeT ri = num_dims - i - 1;
|
||||
if (ri < num_indices) {
|
||||
idx += stride * indices[ri];
|
||||
}
|
||||
|
||||
__builtin_assume(dims[i] > 0);
|
||||
stride *= dims[ri];
|
||||
}
|
||||
return idx;
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims, SizeT lhs_ndims,
|
||||
const SizeT* rhs_dims, SizeT rhs_ndims,
|
||||
SizeT* out_dims) {
|
||||
SizeT max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
|
||||
|
||||
for (SizeT i = 0; i < max_ndims; ++i) {
|
||||
const SizeT* lhs_dim_sz =
|
||||
i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : nullptr;
|
||||
const SizeT* rhs_dim_sz =
|
||||
i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : nullptr;
|
||||
|
||||
SizeT* out_dim = &out_dims[max_ndims - i - 1];
|
||||
|
||||
if (lhs_dim_sz == nullptr) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (rhs_dim_sz == nullptr) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == 1) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (*rhs_dim_sz == 1) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == *rhs_dim_sz) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
|
||||
SizeT src_ndims,
|
||||
const 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];
|
||||
}
|
||||
}
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
#define DEF_nac3_int_exp_(T) \
|
||||
T __nac3_int_exp_##T(T base, T exp) { \
|
||||
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);
|
||||
|
||||
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
|
||||
if (i < 0) {
|
||||
i = len + i;
|
||||
}
|
||||
if (i < 0) {
|
||||
return 0;
|
||||
} else if (i > len) {
|
||||
return len;
|
||||
}
|
||||
return i;
|
||||
}
|
||||
|
||||
SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end,
|
||||
const SliceIndex step) {
|
||||
SliceIndex diff = end - start;
|
||||
if (diff > 0 && step > 0) {
|
||||
return ((diff - 1) / step) + 1;
|
||||
} else if (diff < 0 && step < 0) {
|
||||
return ((diff + 1) / step) + 1;
|
||||
} else {
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
||||
// Handle list assignment and dropping part of the list when
|
||||
// both dest_step and src_step are +1.
|
||||
// - 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)
|
||||
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
|
||||
*/
|
||||
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 (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;
|
||||
if (src_len > 0) {
|
||||
__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,
|
||||
dest_arr + (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));
|
||||
if (need_alloca) {
|
||||
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 constant optimization */
|
||||
if (size == 1) {
|
||||
__builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
|
||||
} else if (size == 4) {
|
||||
__builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
|
||||
} 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);
|
||||
}
|
||||
}
|
||||
/* 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 + 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_isnan(double x) { return __builtin_isnan(x); }
|
||||
|
||||
double tgamma(double arg);
|
||||
|
||||
double __nac3_gamma(double z) {
|
||||
// Handling for denormals
|
||||
// | x | Python gamma(x) | C tgamma(x) |
|
||||
// --- | ----------------- | --------------- | ----------- |
|
||||
// (1) | nan | nan | nan |
|
||||
// (2) | -inf | -inf | inf |
|
||||
// (3) | inf | inf | inf |
|
||||
// (4) | 0.0 | inf | inf |
|
||||
// (5) | {-1.0, -2.0, ...} | inf | nan |
|
||||
|
||||
// (1)-(3)
|
||||
if (__builtin_isinf(z) || __builtin_isnan(z)) {
|
||||
return z;
|
||||
}
|
||||
|
||||
double v = tgamma(z);
|
||||
|
||||
// (4)-(5)
|
||||
return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
|
||||
}
|
||||
|
||||
double lgamma(double arg);
|
||||
|
||||
double __nac3_gammaln(double x) {
|
||||
// libm's handling of value overflows differs from scipy:
|
||||
// - scipy: gammaln(-inf) -> -inf
|
||||
// - libm : lgamma(-inf) -> inf
|
||||
|
||||
if (__builtin_isinf(x)) {
|
||||
return x;
|
||||
}
|
||||
|
||||
return lgamma(x);
|
||||
}
|
||||
|
||||
double j0(double x);
|
||||
|
||||
double __nac3_j0(double x) {
|
||||
// libm's handling of value overflows differs from scipy:
|
||||
// - scipy: j0(inf) -> nan
|
||||
// - libm : j0(inf) -> 0.0
|
||||
|
||||
if (__builtin_isinf(x)) {
|
||||
return __builtin_nan("");
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
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, 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, 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 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 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_broadcast64(const uint64_t* lhs_dims,
|
||||
uint64_t lhs_ndims,
|
||||
const uint64_t* rhs_dims,
|
||||
uint64_t rhs_ndims, uint64_t* out_dims) {
|
||||
return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims,
|
||||
rhs_ndims, out_dims);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast_idx(const uint32_t* src_dims,
|
||||
uint32_t src_ndims,
|
||||
const 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,
|
||||
uint64_t src_ndims,
|
||||
const NDIndexInt* in_idx,
|
||||
NDIndexInt* out_idx) {
|
||||
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
|
||||
out_idx);
|
||||
}
|
||||
} // extern "C"
|
||||
|
|
@ -1,9 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
|
||||
template <typename SizeT>
|
||||
struct CSlice {
|
||||
uint8_t* base;
|
||||
SizeT len;
|
||||
};
|
||||
|
|
@ -1,15 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#define raise_debug_assert(SizeT, msg, param1, param2, param3) \
|
||||
raise_exception(SizeT, EXN_ASSERTION_ERROR, \
|
||||
"IRRT debug assert failed: " msg, param1, param2, param3);
|
||||
|
||||
#define debug_assert_eq(SizeT, lhs, rhs) \
|
||||
if (IRRT_DEBUG_ASSERT_BOOL && (lhs) != (rhs)) { \
|
||||
raise_debug_assert(SizeT, "LHS = {0}. RHS = {1}", lhs, rhs, NO_PARAM); \
|
||||
}
|
||||
|
||||
#define debug_assert(SizeT, expr) \
|
||||
if (IRRT_DEBUG_ASSERT_BOOL && !(expr)) { \
|
||||
raise_debug_assert(SizeT, "Got false.", NO_PARAM, NO_PARAM, NO_PARAM); \
|
||||
}
|
||||
|
|
@ -1,123 +0,0 @@
|
|||
#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>(); }
|
||||
}
|
||||
|
|
@ -1,12 +0,0 @@
|
|||
#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
|
||||
|
|
@ -1,56 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
|
||||
namespace {
|
||||
/**
|
||||
* @brief A list in NAC3.
|
||||
*
|
||||
* The `items` field is opaque. You must rely on external contexts to
|
||||
* know how to interpret it.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
struct List {
|
||||
uint8_t* items;
|
||||
SizeT len;
|
||||
};
|
||||
|
||||
namespace list {
|
||||
template <typename SizeT>
|
||||
void slice_assign(List<SizeT>* dst, List<SizeT>* src, SizeT itemsize,
|
||||
UserSlice* user_slice) {
|
||||
Slice slice = user_slice->indices_checked<SizeT>(dst->len);
|
||||
|
||||
// NOTE: Python does not have this restriction.
|
||||
if (slice.len() != src->len) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"List destination has {} item(s), but source has {} "
|
||||
"item(s). The lengths must match.",
|
||||
slice.len(), src->len, NO_PARAM);
|
||||
}
|
||||
|
||||
// TODO: Look into how the original implementation was implemented and optimized.
|
||||
SizeT dst_i = slice.start;
|
||||
SizeT src_i = 0;
|
||||
while (src_i < slice.len()) {
|
||||
__builtin_memcpy(dst->items + dst_i, src->items + src_i, itemsize);
|
||||
|
||||
src_i += 1;
|
||||
dst_i += slice.step;
|
||||
}
|
||||
}
|
||||
} // namespace list
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
void __nac3_list_slice_assign(List<int32_t>* dst, List<int32_t>* src,
|
||||
int32_t itemsize, UserSlice* user_slice) {
|
||||
list::slice_assign(dst, src, itemsize, user_slice);
|
||||
}
|
||||
|
||||
void __nac3_list_slice_assign64(List<int64_t>* dst, List<int64_t>* src,
|
||||
int64_t itemsize, UserSlice* user_slice) {
|
||||
list::slice_assign(dst, src, itemsize, user_slice);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,119 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/list.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace array {
|
||||
// TODO: Document me
|
||||
template <typename SizeT>
|
||||
void set_and_validate_list_shape_helper(SizeT axis, List<SizeT>* list,
|
||||
SizeT ndims, SizeT* shape) {
|
||||
if (shape[axis] == -1) {
|
||||
// Dimension is unspecified. Set it.
|
||||
shape[axis] = list->len;
|
||||
} else {
|
||||
// Dimension is specified. Check.
|
||||
if (shape[axis] != list->len) {
|
||||
// Mismatch, throw an error.
|
||||
// NOTE: NumPy's error message is more complex and needs more PARAMS to display.
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"The requested array has an inhomogenous shape "
|
||||
"after {0} dimension(s).",
|
||||
axis, shape[axis], list->len);
|
||||
}
|
||||
}
|
||||
|
||||
if (axis + 1 == ndims) {
|
||||
// `list` has type `list[ItemType]`
|
||||
// Do nothing
|
||||
} else {
|
||||
// `list` has type `list[list[...]]`
|
||||
List<SizeT>** lists = (List<SizeT>**)(list->items);
|
||||
for (SizeT i = 0; i < list->len; i++) {
|
||||
set_and_validate_list_shape_helper<SizeT>(axis + 1, lists[i], ndims,
|
||||
shape);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// TODO: Document me
|
||||
template <typename SizeT>
|
||||
void set_and_validate_list_shape(List<SizeT>* list, SizeT ndims, SizeT* shape) {
|
||||
for (SizeT axis = 0; axis < ndims; axis++) {
|
||||
shape[axis] = -1; // Sentinel to say this dimension is unspecified.
|
||||
}
|
||||
set_and_validate_list_shape_helper<SizeT>(0, list, ndims, shape);
|
||||
}
|
||||
|
||||
// TODO: Document me
|
||||
template <typename SizeT>
|
||||
void write_list_to_array_helper(SizeT axis, SizeT* index, List<SizeT>* list,
|
||||
NDArray<SizeT>* ndarray) {
|
||||
debug_assert_eq(SizeT, list->len, ndarray->shape[axis]);
|
||||
if (IRRT_DEBUG_ASSERT_BOOL) {
|
||||
if (!ndarray::basic::is_c_contiguous(ndarray)) {
|
||||
raise_debug_assert(SizeT, "ndarray is not C-contiguous", ndarray->strides[0],
|
||||
ndarray->strides[1], NO_PARAM);
|
||||
}
|
||||
}
|
||||
|
||||
if (axis + 1 == ndarray->ndims) {
|
||||
// `list` has type `list[ItemType]`
|
||||
// `ndarray` is contiguous, so we can do this, and this is fast.
|
||||
uint8_t* dst = ndarray->data + (ndarray->itemsize * (*index));
|
||||
__builtin_memcpy(dst, list->items, ndarray->itemsize * list->len);
|
||||
*index += list->len;
|
||||
} else {
|
||||
// `list` has type `list[list[...]]`
|
||||
List<SizeT>** lists = (List<SizeT>**)(list->items);
|
||||
|
||||
for (SizeT i = 0; i < list->len; i++) {
|
||||
write_list_to_array_helper<SizeT>(axis + 1, index, lists[i],
|
||||
ndarray);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// TODO: Document me
|
||||
template <typename SizeT>
|
||||
void write_list_to_array(List<SizeT>* list, NDArray<SizeT>* ndarray) {
|
||||
// done after set_and_validate(list, ndims, shape), list is well-formed
|
||||
// ndarray->data is allocated and owned
|
||||
// ndarray->itemsize is set
|
||||
// ndarray->ndims is set
|
||||
// ndarray->shape is set
|
||||
// ndarray->strides is ???
|
||||
SizeT index = 0;
|
||||
write_list_to_array_helper<SizeT>((SizeT)0, &index, list, ndarray);
|
||||
}
|
||||
} // namespace array
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::array;
|
||||
|
||||
void __nac3_array_set_and_validate_list_shape(List<int32_t>* list,
|
||||
int32_t ndims, int32_t* shape) {
|
||||
set_and_validate_list_shape(list, ndims, shape);
|
||||
}
|
||||
|
||||
void __nac3_array_set_and_validate_list_shape64(List<int64_t>* list,
|
||||
int64_t ndims, int64_t* shape) {
|
||||
set_and_validate_list_shape(list, ndims, shape);
|
||||
}
|
||||
|
||||
void __nac3_array_write_list_to_array(List<int32_t>* list,
|
||||
NDArray<int32_t>* ndarray) {
|
||||
write_list_to_array(list, ndarray);
|
||||
}
|
||||
|
||||
void __nac3_array_write_list_to_array64(List<int64_t>* list,
|
||||
NDArray<int64_t>* ndarray) {
|
||||
write_list_to_array(list, ndarray);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,345 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace basic {
|
||||
/**
|
||||
* @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 Check two shapes are the same in the context of writing outputting to an ndarray.
|
||||
*
|
||||
* This function throws error messages for output shape mismatches.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void assert_output_shape_same(SizeT ndarray_ndims, const SizeT* ndarray_shape,
|
||||
SizeT output_ndims, const SizeT* output_shape) {
|
||||
if (ndarray_ndims != output_ndims) {
|
||||
// There is no corresponding NumPy error message like this.
|
||||
raise_exception(
|
||||
SizeT, EXN_VALUE_ERROR,
|
||||
"Cannot write output of ndims {0} to an ndarray with ndims {1}",
|
||||
output_ndims, ndarray_ndims, NO_PARAM);
|
||||
}
|
||||
|
||||
for (SizeT axis = 0; axis < ndarray_ndims; axis++) {
|
||||
if (ndarray_shape[axis] != output_shape[axis]) {
|
||||
// There is no corresponding NumPy error message like this.
|
||||
raise_exception(
|
||||
SizeT, EXN_VALUE_ERROR,
|
||||
"Mismatched dimensions on axis {0}, output has "
|
||||
"dimension {1}, but destination ndarray has dimension {2}.",
|
||||
axis, output_shape[axis], ndarray_shape[axis]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief 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;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return the number of elements of an `ndarray`
|
||||
*
|
||||
* This function corresponds to `<an_ndarray>.size`
|
||||
*/
|
||||
template <typename SizeT>
|
||||
SizeT size(const NDArray<SizeT>* ndarray) {
|
||||
return calc_size_from_shape(ndarray->ndims, ndarray->shape);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return of the number of its content of an `ndarray`.
|
||||
*
|
||||
* This function corresponds to `<an_ndarray>.nbytes`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
SizeT nbytes(const NDArray<SizeT>* ndarray) {
|
||||
return size(ndarray) * ndarray->itemsize;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Get the `len()` of an ndarray, and asserts that `ndarray` is a sized object.
|
||||
*
|
||||
* This function corresponds to `<an_ndarray>.__len__`.
|
||||
*
|
||||
* @param dst_length The 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;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Convenience function. Like `get_pelement_by_indices` but
|
||||
* reinterprets the element pointer.
|
||||
*/
|
||||
template <typename SizeT, typename T>
|
||||
T* get_ptr(const NDArray<SizeT>* ndarray, const SizeT* indices) {
|
||||
return (T*)get_pelement_by_indices(ndarray, indices);
|
||||
}
|
||||
|
||||
/**
|
||||
* @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++) {
|
||||
SizeT axis = ndarray->ndims - i - 1;
|
||||
ndarray->strides[axis] = stride_product * ndarray->itemsize;
|
||||
stride_product *= ndarray->shape[axis];
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Set an element in `ndarray`.
|
||||
*
|
||||
* @param pelement Pointer to the element in `ndarray` to be set.
|
||||
* @param pvalue Pointer to the value `pelement` will be set to.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void set_pelement_value(NDArray<SizeT>* ndarray, uint8_t* pelement,
|
||||
const uint8_t* pvalue) {
|
||||
__builtin_memcpy(pelement, pvalue, ndarray->itemsize);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Copy data from one ndarray to another of the exact same size and itemsize.
|
||||
*
|
||||
* Both ndarrays will be viewed in their flatten views when copying the elements.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void copy_data(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
|
||||
// TODO: Make this faster with memcpy
|
||||
|
||||
debug_assert_eq(SizeT, src_ndarray->itemsize, dst_ndarray->itemsize);
|
||||
|
||||
for (SizeT i = 0; i < size(src_ndarray); i++) {
|
||||
auto src_element = ndarray::basic::get_nth_pelement(src_ndarray, i);
|
||||
auto dst_element = ndarray::basic::get_nth_pelement(dst_ndarray, i);
|
||||
ndarray::basic::set_pelement_value(dst_ndarray, dst_element,
|
||||
src_element);
|
||||
}
|
||||
}
|
||||
} // namespace basic
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::basic;
|
||||
|
||||
void __nac3_ndarray_util_assert_shape_no_negative(int32_t ndims,
|
||||
int32_t* shape) {
|
||||
assert_shape_no_negative(ndims, shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_util_assert_shape_no_negative64(int64_t ndims,
|
||||
int64_t* shape) {
|
||||
assert_shape_no_negative(ndims, shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_util_assert_output_shape_same(int32_t ndarray_ndims,
|
||||
const int32_t* ndarray_shape,
|
||||
int32_t output_ndims,
|
||||
const int32_t* output_shape) {
|
||||
assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims,
|
||||
output_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_util_assert_output_shape_same64(
|
||||
int64_t ndarray_ndims, const int64_t* ndarray_shape, int64_t output_ndims,
|
||||
const int64_t* output_shape) {
|
||||
assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims,
|
||||
output_shape);
|
||||
}
|
||||
|
||||
uint32_t __nac3_ndarray_size(NDArray<int32_t>* ndarray) {
|
||||
return size(ndarray);
|
||||
}
|
||||
|
||||
uint64_t __nac3_ndarray_size64(NDArray<int64_t>* ndarray) {
|
||||
return size(ndarray);
|
||||
}
|
||||
|
||||
uint32_t __nac3_ndarray_nbytes(NDArray<int32_t>* ndarray) {
|
||||
return nbytes(ndarray);
|
||||
}
|
||||
|
||||
uint64_t __nac3_ndarray_nbytes64(NDArray<int64_t>* ndarray) {
|
||||
return nbytes(ndarray);
|
||||
}
|
||||
|
||||
int32_t __nac3_ndarray_len(NDArray<int32_t>* ndarray) { return len(ndarray); }
|
||||
|
||||
int64_t __nac3_ndarray_len64(NDArray<int64_t>* ndarray) { return len(ndarray); }
|
||||
|
||||
bool __nac3_ndarray_is_c_contiguous(NDArray<int32_t>* ndarray) {
|
||||
return is_c_contiguous(ndarray);
|
||||
}
|
||||
|
||||
bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t>* ndarray) {
|
||||
return is_c_contiguous(ndarray);
|
||||
}
|
||||
|
||||
uint8_t* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray,
|
||||
int32_t nth) {
|
||||
return get_nth_pelement(ndarray, nth);
|
||||
}
|
||||
|
||||
uint8_t* __nac3_ndarray_get_nth_pelement64(const NDArray<int64_t>* ndarray,
|
||||
int64_t nth) {
|
||||
return get_nth_pelement(ndarray, nth);
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,171 +0,0 @@
|
|||
#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 {
|
||||
/**
|
||||
* @brief Return true if `src_shape` can broadcast to `dst_shape`.
|
||||
*
|
||||
* See https://numpy.org/doc/stable/user/basics.broadcasting.html
|
||||
*/
|
||||
template <typename SizeT>
|
||||
bool can_broadcast_shape_to(SizeT target_ndims, const SizeT* target_shape,
|
||||
SizeT src_ndims, const SizeT* src_shape) {
|
||||
if (src_ndims > target_ndims) {
|
||||
return false;
|
||||
}
|
||||
|
||||
for (SizeT i = 0; i < src_ndims; i++) {
|
||||
SizeT target_dim = target_shape[target_ndims - i - 1];
|
||||
SizeT src_dim = src_shape[src_ndims - i - 1];
|
||||
if (!(src_dim == 1 || target_dim == src_dim)) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Performs `np.broadcast_shapes(<shapes>)`
|
||||
*
|
||||
* @param num_shapes Number of entries in `shapes`
|
||||
* @param shapes The list of shape to do `np.broadcast_shapes` on.
|
||||
* @param dst_ndims The length of `dst_shape`.
|
||||
* `dst_ndims` must be `max([shape.ndims for shape in shapes])`, but the caller has to calculate it/provide it.
|
||||
* for this function since they should already know in order to allocate `dst_shape` in the first place.
|
||||
* @param dst_shape The resulting shape. Must be pre-allocated by the caller. This function calculate the result
|
||||
* of `np.broadcast_shapes` and write it here.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void broadcast_shapes(SizeT num_shapes, const ShapeEntry<SizeT>* shapes,
|
||||
SizeT dst_ndims, SizeT* dst_shape) {
|
||||
for (SizeT dst_axis = 0; dst_axis < dst_ndims; dst_axis++) {
|
||||
dst_shape[dst_axis] = 1;
|
||||
}
|
||||
|
||||
#ifdef IRRT_DEBUG_ASSERT
|
||||
SizeT max_ndims_found = 0;
|
||||
#endif
|
||||
|
||||
for (SizeT i = 0; i < num_shapes; i++) {
|
||||
ShapeEntry<SizeT> entry = shapes[i];
|
||||
|
||||
// Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
|
||||
debug_assert(SizeT, entry.ndims <= dst_ndims);
|
||||
|
||||
#ifdef IRRT_DEBUG_ASSERT
|
||||
max_ndims_found = max(max_ndims_found, entry.ndims);
|
||||
#endif
|
||||
|
||||
for (SizeT j = 0; j < entry.ndims; j++) {
|
||||
SizeT entry_axis = entry.ndims - j - 1;
|
||||
SizeT dst_axis = dst_ndims - j - 1;
|
||||
|
||||
SizeT entry_dim = entry.shape[entry_axis];
|
||||
SizeT dst_dim = dst_shape[dst_axis];
|
||||
|
||||
if (dst_dim == 1) {
|
||||
dst_shape[dst_axis] = entry_dim;
|
||||
} else if (entry_dim == 1 || entry_dim == dst_dim) {
|
||||
// Do nothing
|
||||
} else {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"shape mismatch: objects cannot be broadcast "
|
||||
"to a single shape.",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
|
||||
debug_assert_eq(SizeT, max_ndims_found, dst_ndims);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Perform `np.broadcast_to(<ndarray>, <target_shape>)` and appropriate assertions.
|
||||
*
|
||||
* This function attempts to broadcast `src_ndarray` to a new shape defined by `dst_ndarray.shape`,
|
||||
* and return the result by modifying `dst_ndarray`.
|
||||
*
|
||||
* # Notes on `dst_ndarray`
|
||||
* The caller is responsible for allocating space for the resulting ndarray.
|
||||
* Here is what this function expects from `dst_ndarray` when called:
|
||||
* - `dst_ndarray->data` does not have to be initialized.
|
||||
* - `dst_ndarray->itemsize` does not have to be initialized.
|
||||
* - `dst_ndarray->ndims` must be initialized, determining the length of `dst_ndarray->shape`
|
||||
* - `dst_ndarray->shape` must be allocated, and must contain the desired target broadcast shape.
|
||||
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
|
||||
* When this function call ends:
|
||||
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
|
||||
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
|
||||
* - `dst_ndarray->ndims` is unchanged.
|
||||
* - `dst_ndarray->shape` is unchanged.
|
||||
* - `dst_ndarray->strides` is updated accordingly by how ndarray broadcast_to works.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void broadcast_to(const NDArray<SizeT>* src_ndarray,
|
||||
NDArray<SizeT>* dst_ndarray) {
|
||||
if (!ndarray::broadcast::can_broadcast_shape_to(
|
||||
dst_ndarray->ndims, dst_ndarray->shape, src_ndarray->ndims,
|
||||
src_ndarray->shape)) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"operands could not be broadcast together", NO_PARAM,
|
||||
NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
dst_ndarray->data = src_ndarray->data;
|
||||
dst_ndarray->itemsize = src_ndarray->itemsize;
|
||||
|
||||
for (SizeT i = 0; i < dst_ndarray->ndims; i++) {
|
||||
SizeT src_axis = src_ndarray->ndims - i - 1;
|
||||
SizeT dst_axis = dst_ndarray->ndims - i - 1;
|
||||
if (src_axis < 0 || (src_ndarray->shape[src_axis] == 1 &&
|
||||
dst_ndarray->shape[dst_axis] != 1)) {
|
||||
// Freeze the steps in-place
|
||||
dst_ndarray->strides[dst_axis] = 0;
|
||||
} else {
|
||||
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace broadcast
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::broadcast;
|
||||
|
||||
void __nac3_ndarray_broadcast_to(NDArray<int32_t>* src_ndarray,
|
||||
NDArray<int32_t>* dst_ndarray) {
|
||||
broadcast_to(src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_broadcast_to64(NDArray<int64_t>* src_ndarray,
|
||||
NDArray<int64_t>* dst_ndarray) {
|
||||
broadcast_to(src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_broadcast_shapes(int32_t num_shapes,
|
||||
const ShapeEntry<int32_t>* shapes,
|
||||
int32_t dst_ndims, int32_t* dst_shape) {
|
||||
broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_broadcast_shapes64(int64_t num_shapes,
|
||||
const ShapeEntry<int64_t>* shapes,
|
||||
int64_t dst_ndims, int64_t* dst_shape) {
|
||||
broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,44 +0,0 @@
|
|||
#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
|
||||
|
|
@ -1,221 +0,0 @@
|
|||
#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
|
||||
*
|
||||
* `data` points to a `SliceIndex`.
|
||||
*/
|
||||
|
||||
const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
|
||||
/**
|
||||
* @brief A slice index
|
||||
*
|
||||
* `data` points to a `UserRange`.
|
||||
*/
|
||||
const NDIndexType ND_INDEX_TYPE_SLICE = 1;
|
||||
|
||||
/**
|
||||
* @brief `np.newaxis` / `None`
|
||||
*
|
||||
* `data` is unused.
|
||||
*/
|
||||
const NDIndexType ND_INDEX_TYPE_NEWAXIS = 2;
|
||||
|
||||
/**
|
||||
* @brief `Ellipsis` / `...`
|
||||
*
|
||||
* `data` is unused.
|
||||
*/
|
||||
const NDIndexType ND_INDEX_TYPE_ELLIPSIS = 3;
|
||||
|
||||
/**
|
||||
* @brief An index used in ndarray indexing
|
||||
*/
|
||||
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 {
|
||||
/**
|
||||
* @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) {
|
||||
// First, validate `indexes`.
|
||||
|
||||
// Expected value of `dst_ndarray->ndims`.
|
||||
SizeT expected_dst_ndims = src_ndarray->ndims;
|
||||
// To check for "too many indices for array: array is ?-dimensional, but ? were indexed"
|
||||
SizeT num_indexed = 0;
|
||||
// There may be ellipsis `...` in `indexes`. There can only be 0 or 1 ellipsis.
|
||||
SizeT num_ellipsis = 0;
|
||||
|
||||
for (SizeT i = 0; i < num_indexes; i++) {
|
||||
if (indexes[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
|
||||
expected_dst_ndims--;
|
||||
num_indexed++;
|
||||
} else if (indexes[i].type == ND_INDEX_TYPE_SLICE) {
|
||||
num_indexed++;
|
||||
} else if (indexes[i].type == ND_INDEX_TYPE_NEWAXIS) {
|
||||
expected_dst_ndims++;
|
||||
} else if (indexes[i].type == ND_INDEX_TYPE_ELLIPSIS) {
|
||||
num_ellipsis++;
|
||||
if (num_ellipsis > 1) {
|
||||
raise_exception(
|
||||
SizeT, EXN_INDEX_ERROR,
|
||||
"an index can only have a single ellipsis ('...')",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
|
||||
debug_assert_eq(SizeT, expected_dst_ndims, dst_ndarray->ndims);
|
||||
|
||||
if (src_ndarray->ndims - num_indexed < 0) {
|
||||
raise_exception(SizeT, EXN_INDEX_ERROR,
|
||||
"too many indices for array: array is {0}-dimensional, "
|
||||
"but {1} were indexed",
|
||||
src_ndarray->ndims, num_indexes, NO_PARAM);
|
||||
}
|
||||
|
||||
dst_ndarray->data = src_ndarray->data;
|
||||
dst_ndarray->itemsize = src_ndarray->itemsize;
|
||||
|
||||
// Reference code: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
|
||||
SizeT src_axis = 0;
|
||||
SizeT dst_axis = 0;
|
||||
|
||||
for (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]);
|
||||
|
||||
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 if (index->type == ND_INDEX_TYPE_NEWAXIS) {
|
||||
dst_ndarray->strides[dst_axis] = 0;
|
||||
dst_ndarray->shape[dst_axis] = 1;
|
||||
|
||||
dst_axis++;
|
||||
} else if (index->type == ND_INDEX_TYPE_ELLIPSIS) {
|
||||
// The number of ':' entries this '...' implies.
|
||||
SizeT ellipsis_size = src_ndarray->ndims - num_indexed;
|
||||
|
||||
for (SizeT j = 0; j < ellipsis_size; j++) {
|
||||
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
|
||||
dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
|
||||
|
||||
dst_axis++;
|
||||
src_axis++;
|
||||
}
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
|
||||
for (; dst_axis < dst_ndarray->ndims; dst_axis++, src_axis++) {
|
||||
dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
|
||||
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
|
||||
}
|
||||
|
||||
debug_assert_eq(SizeT, src_ndarray->ndims, src_axis);
|
||||
debug_assert_eq(SizeT, dst_ndarray->ndims, dst_axis);
|
||||
}
|
||||
} // namespace indexing
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::indexing;
|
||||
|
||||
void __nac3_ndarray_index(int32_t num_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);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,118 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace {
|
||||
/**
|
||||
* @brief Helper struct to enumerate through all indices under a shape.
|
||||
*
|
||||
* i.e., If `shape` is `[3, 2]`, by repeating `next()`, then you get:
|
||||
* - `[0, 0]`
|
||||
* - `[0, 1]`
|
||||
* - `[1, 0]`
|
||||
* - `[1, 1]`
|
||||
* - `[2, 0]`
|
||||
* - `[2, 1]`
|
||||
* - end.
|
||||
*
|
||||
* Interesting cases:
|
||||
* - If ndims == 0, there is one enumeration.
|
||||
* - If shape contains zeroes, there are no enumerations.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
struct NDIter {
|
||||
SizeT ndims;
|
||||
SizeT* shape;
|
||||
SizeT* strides;
|
||||
|
||||
/**
|
||||
* @brief The current indices.
|
||||
*
|
||||
* Must be allocated by the caller.
|
||||
*/
|
||||
SizeT* indices;
|
||||
|
||||
/**
|
||||
* @brief The nth (0-based) index of the current indices.
|
||||
*/
|
||||
SizeT nth;
|
||||
|
||||
/**
|
||||
* @brief Pointer to the current element.
|
||||
*/
|
||||
uint8_t* element;
|
||||
|
||||
/**
|
||||
* @brief The product of shape.
|
||||
*/
|
||||
SizeT size;
|
||||
|
||||
// TODO:: There is something called backstrides to speedup iteration.
|
||||
// See https://ajcr.net/stride-guide-part-1/, and https://docs.scipy.org/doc/numpy-1.13.0/reference/c-api.types-and-structures.html#c.PyArrayIterObject.PyArrayIterObject.backstrides.
|
||||
// Maybe LLVM is clever and knows how to optimize.
|
||||
|
||||
void initialize(SizeT ndims, SizeT* shape, SizeT* strides, uint8_t* element,
|
||||
SizeT* indices) {
|
||||
this->ndims = ndims;
|
||||
this->shape = shape;
|
||||
this->strides = strides;
|
||||
|
||||
this->indices = indices;
|
||||
this->element = element;
|
||||
|
||||
// Compute size and backstrides
|
||||
this->size = 1;
|
||||
for (SizeT i = 0; i < ndims; i++) {
|
||||
this->size *= shape[i];
|
||||
}
|
||||
|
||||
for (SizeT axis = 0; axis < ndims; axis++) indices[axis] = 0;
|
||||
nth = 0;
|
||||
}
|
||||
|
||||
void initialize_by_ndarray(NDArray<SizeT>* ndarray, SizeT* indices) {
|
||||
this->initialize(ndarray->ndims, ndarray->shape, ndarray->strides,
|
||||
ndarray->data, indices);
|
||||
}
|
||||
|
||||
bool has_next() { return nth < size; }
|
||||
|
||||
void next() {
|
||||
for (SizeT i = 0; i < ndims; i++) {
|
||||
SizeT axis = ndims - i - 1;
|
||||
indices[axis]++;
|
||||
if (indices[axis] >= shape[axis]) {
|
||||
indices[axis] = 0;
|
||||
|
||||
// TODO: Can be optimized with backstrides.
|
||||
element -= strides[axis] * (shape[axis] - 1);
|
||||
} else {
|
||||
element += strides[axis];
|
||||
break;
|
||||
}
|
||||
}
|
||||
nth++;
|
||||
}
|
||||
};
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
void __nac3_nditer_initialize(NDIter<int32_t>* iter, NDArray<int32_t>* ndarray,
|
||||
int32_t* indices) {
|
||||
iter->initialize_by_ndarray(ndarray, indices);
|
||||
}
|
||||
|
||||
void __nac3_nditer_initialize64(NDIter<int64_t>* iter,
|
||||
NDArray<int64_t>* ndarray, int64_t* indices) {
|
||||
iter->initialize_by_ndarray(ndarray, indices);
|
||||
}
|
||||
|
||||
bool __nac3_nditer_has_next(NDIter<int32_t>* iter) { return iter->has_next(); }
|
||||
|
||||
bool __nac3_nditer_has_next64(NDIter<int64_t>* iter) { return iter->has_next(); }
|
||||
|
||||
void __nac3_nditer_next(NDIter<int32_t>* iter) { iter->next(); }
|
||||
|
||||
void __nac3_nditer_next64(NDIter<int64_t>* iter) { iter->next(); }
|
||||
}
|
||||
|
|
@ -1,194 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/broadcast.hpp>
|
||||
#include <irrt/ndarray/iter.hpp>
|
||||
|
||||
// NOTE: Everything would be much easier and elegant if einsum is implemented.
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace matmul {
|
||||
|
||||
/*
|
||||
* In einsum notation, the output is the broadcasts performed by `np.einsum("...ij,...jk->...ik", a, b)`.
|
||||
*
|
||||
* Example:
|
||||
* Suppose `a_shape == [99, 1, 97, 4, 2]`
|
||||
* and `b_shape == [ 1, 98, 1, 2, 5]`,
|
||||
*
|
||||
* ...then `new_a_shape == [99, 98, 97, 4, 2]`,
|
||||
* `new_b_shape == [99, 98, 97, 2, 5]`,
|
||||
* and `dst_shape == [99, 98, 97, 4, 5]`.
|
||||
* ^^^^^^^^^^ ^^^^
|
||||
* (by broadcast) (4x2 @ 2x5 => 4x5)
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void calculate_shapes(SizeT a_ndims, SizeT* a_shape, SizeT b_ndims,
|
||||
SizeT* b_shape, SizeT final_ndims, SizeT* new_a_shape,
|
||||
SizeT* new_b_shape, SizeT* dst_shape) {
|
||||
debug_assert(SizeT, a_ndims >= 2);
|
||||
debug_assert(SizeT, b_ndims >= 2);
|
||||
debug_assert_eq(SizeT, max(a_ndims, b_ndims), final_ndims);
|
||||
|
||||
const SizeT num_entries = 2;
|
||||
ShapeEntry<SizeT> entries[num_entries] = {
|
||||
{.ndims = a_ndims - 2, .shape = a_shape},
|
||||
{.ndims = b_ndims - 2, .shape = b_shape}};
|
||||
|
||||
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries,
|
||||
final_ndims - 2, new_a_shape);
|
||||
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries,
|
||||
final_ndims - 2, new_b_shape);
|
||||
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries,
|
||||
final_ndims - 2, dst_shape);
|
||||
|
||||
new_a_shape[final_ndims - 2] = a_shape[a_ndims - 2];
|
||||
new_a_shape[final_ndims - 1] = a_shape[a_ndims - 1];
|
||||
new_b_shape[final_ndims - 2] = b_shape[b_ndims - 2];
|
||||
new_b_shape[final_ndims - 1] = b_shape[b_ndims - 1];
|
||||
dst_shape[final_ndims - 2] = a_shape[a_ndims - 2];
|
||||
dst_shape[final_ndims - 1] = b_shape[b_ndims - 1];
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Perform `np.matmul(a, b)` but the inputs are both rank >=2 matrices and `a.shape[:-2] == b.shape[:-2]`.
|
||||
*
|
||||
* The compatibility of `a` and `b` (for their `.shape[-2:]`) are asserted.
|
||||
*
|
||||
* Also see https://numpy.org/doc/stable/reference/generated/numpy.matmul.html#numpy-matmul.
|
||||
*
|
||||
* This function expects `dst_ndarray` to contain the following content when called:
|
||||
* - `dst_ndarray->data` is allocated. Can be uninitialized.
|
||||
* - `dst_ndarray->itemsize` is set to `sizeof(T)`.
|
||||
* - `dst_ndarray->ndims` is set appropriately.
|
||||
* - `dst_ndarray->shape` is set appropriately.
|
||||
* - `dst_ndarray->strides` is ignored.
|
||||
*
|
||||
* Moreover, the shapes of `a_ndarray`, `b_ndarray`, and `dst_ndarray` **must be the same**. This implies
|
||||
*/
|
||||
template <typename SizeT, typename T>
|
||||
void matmul_at_least_2d(NDArray<SizeT>* a_ndarray, NDArray<SizeT>* b_ndarray,
|
||||
NDArray<SizeT>* dst_ndarray) {
|
||||
// All inputs' ndims should be >= 2 and be the same.
|
||||
debug_assert_eq(SizeT, a_ndarray->ndims, b_ndarray->ndims);
|
||||
debug_assert_eq(SizeT, a_ndarray->ndims, dst_ndarray->ndims);
|
||||
debug_assert(SizeT, a_ndarray->ndims >= 2);
|
||||
|
||||
debug_assert_eq(SizeT, a_ndarray->itemsize, sizeof(T));
|
||||
debug_assert_eq(SizeT, b_ndarray->itemsize, sizeof(T));
|
||||
debug_assert_eq(SizeT, dst_ndarray->itemsize, sizeof(T));
|
||||
|
||||
if (IRRT_DEBUG_ASSERT_BOOL) {
|
||||
// Check that the shapes are the same.
|
||||
for (SizeT i = 0; i < a_ndarray->ndims - 2; i++) {
|
||||
if (dst_ndarray->shape[0] != a_ndarray->shape[0]) {
|
||||
raise_debug_assert(
|
||||
SizeT, "Bad shape. At axis {0}, a has {1}, dst has {2}", i,
|
||||
a_ndarray->shape[i], dst_ndarray->shape[i]);
|
||||
}
|
||||
if (dst_ndarray->shape[0] != b_ndarray->shape[0]) {
|
||||
raise_debug_assert(
|
||||
SizeT, "Bad shape. At axis {0}, b has {1}, dst has {2}", i,
|
||||
b_ndarray->shape[i], dst_ndarray->shape[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Number of dimensions dedicated to stacking
|
||||
// e.g., [4, 6, 1, 2, 3]
|
||||
// ^^^^^^^ count these
|
||||
const SizeT u = a_ndarray->ndims - 2; // Alias
|
||||
|
||||
SizeT* a_mat_shape = a_ndarray->shape + u;
|
||||
SizeT* b_mat_shape = b_ndarray->shape + u;
|
||||
SizeT* dst_mat_shape = dst_ndarray->shape + u;
|
||||
|
||||
// Assert that dst_ndarray has the correct shape
|
||||
debug_assert_eq(SizeT, dst_mat_shape[0], a_mat_shape[0]);
|
||||
debug_assert_eq(SizeT, dst_mat_shape[1], b_mat_shape[1]);
|
||||
|
||||
// Check that a and b are compatible for matmul
|
||||
if (a_mat_shape[1] != b_mat_shape[0]) {
|
||||
// This is a custom error message. Different from NumPy.
|
||||
raise_exception(
|
||||
SizeT, EXN_VALUE_ERROR,
|
||||
"Cannot multiply LHS (shape ?x{0}) with RHS (shape {1}x?})",
|
||||
a_mat_shape[1], b_mat_shape[0], NO_PARAM);
|
||||
}
|
||||
|
||||
// Iterate through shape[:-2]. i.e,
|
||||
// Given a = [5, 4, 3, m, p] and b = [5, 4, 3, p, n]. We iterate through [5, 4, 3].
|
||||
SizeT* indices =
|
||||
(SizeT*)__builtin_alloca(sizeof(SizeT) * dst_ndarray->ndims);
|
||||
SizeT* mat_indices = indices + u;
|
||||
NDIter<SizeT> iter;
|
||||
iter.initialize(u, dst_ndarray->shape, dst_ndarray->strides,
|
||||
dst_ndarray->data, indices);
|
||||
|
||||
for (; iter.has_next(); iter.next()) {
|
||||
for (SizeT i = 0; i < dst_mat_shape[0]; i++) {
|
||||
for (SizeT j = 0; j < dst_mat_shape[1]; j++) {
|
||||
// `indices` is being reused to index into different ndarrays.
|
||||
mat_indices[0] = i;
|
||||
mat_indices[1] = j;
|
||||
T* d = ndarray::basic::get_ptr<SizeT, T>(dst_ndarray, indices);
|
||||
*d = 0;
|
||||
|
||||
for (SizeT k = 0; k < a_ndarray->shape[1]; k++) {
|
||||
mat_indices[0] = i;
|
||||
mat_indices[1] = k;
|
||||
T* a =
|
||||
ndarray::basic::get_ptr<SizeT, T>(a_ndarray, indices);
|
||||
|
||||
mat_indices[0] = k;
|
||||
mat_indices[1] = j;
|
||||
T* b =
|
||||
ndarray::basic::get_ptr<SizeT, T>(b_ndarray, indices);
|
||||
|
||||
*d += (*a) * (*b);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace matmul
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::matmul;
|
||||
|
||||
void __nac3_ndarray_matmul_calculate_shapes(int32_t a_ndims, int32_t* a_shape,
|
||||
int32_t b_ndims, int32_t* b_shape,
|
||||
int32_t final_ndims,
|
||||
int32_t* new_a_shape,
|
||||
int32_t* new_b_shape,
|
||||
int32_t* dst_shape) {
|
||||
calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims,
|
||||
new_a_shape, new_b_shape, dst_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_matmul_calculate_shapes64(int64_t a_ndims, int64_t* a_shape,
|
||||
int64_t b_ndims, int64_t* b_shape,
|
||||
int64_t final_ndims,
|
||||
int64_t* new_a_shape,
|
||||
int64_t* new_b_shape,
|
||||
int64_t* dst_shape) {
|
||||
calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims,
|
||||
new_a_shape, new_b_shape, dst_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_float64_matmul_at_least_2d(NDArray<int32_t>* a_ndarray,
|
||||
NDArray<int32_t>* b_ndarray,
|
||||
NDArray<int32_t>* dst_ndarray) {
|
||||
matmul_at_least_2d<int32_t, double>(a_ndarray, b_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_float64_matmul_at_least_2d64(
|
||||
NDArray<int64_t>* a_ndarray, NDArray<int64_t>* b_ndarray,
|
||||
NDArray<int64_t>* dst_ndarray) {
|
||||
matmul_at_least_2d<int64_t, double>(a_ndarray, b_ndarray, dst_ndarray);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,106 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace reshape {
|
||||
/**
|
||||
* @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
|
||||
*
|
||||
* If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
|
||||
* modified to contain the resolved dimension.
|
||||
*
|
||||
* To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
|
||||
* `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
|
||||
*
|
||||
* @param size The `.size` of `<ndarray>`
|
||||
* @param new_ndims Number of elements in `new_shape`
|
||||
* @param new_shape Target shape to reshape to
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void resolve_and_check_new_shape(SizeT size, SizeT new_ndims,
|
||||
SizeT* new_shape) {
|
||||
// Is there a -1 in `new_shape`?
|
||||
bool neg1_exists = false;
|
||||
// Location of -1, only initialized if `neg1_exists` is true
|
||||
SizeT neg1_axis_i;
|
||||
// The computed ndarray size of `new_shape`
|
||||
SizeT new_size = 1;
|
||||
|
||||
for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++) {
|
||||
SizeT dim = new_shape[axis_i];
|
||||
if (dim < 0) {
|
||||
if (dim == -1) {
|
||||
if (neg1_exists) {
|
||||
// Multiple `-1` found. Throw an error.
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"can only specify one unknown dimension",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
} else {
|
||||
neg1_exists = true;
|
||||
neg1_axis_i = axis_i;
|
||||
}
|
||||
} else {
|
||||
// TODO: What? In `np.reshape` any negative dimensions is
|
||||
// treated like its `-1`.
|
||||
//
|
||||
// Try running `np.zeros((3, 4)).reshape((-999, 2))`
|
||||
//
|
||||
// It is not documented by numpy.
|
||||
// Throw an error for now...
|
||||
|
||||
raise_exception(
|
||||
SizeT, EXN_VALUE_ERROR,
|
||||
"Found non -1 negative dimension {0} on axis {1}", dim,
|
||||
axis_i, NO_PARAM);
|
||||
}
|
||||
} else {
|
||||
new_size *= dim;
|
||||
}
|
||||
}
|
||||
|
||||
bool can_reshape;
|
||||
if (neg1_exists) {
|
||||
// Let `x` be the unknown dimension
|
||||
// Solve `x * <new_size> = <size>`
|
||||
if (new_size == 0 && size == 0) {
|
||||
// `x` has infinitely many solutions
|
||||
can_reshape = false;
|
||||
} else if (new_size == 0 && size != 0) {
|
||||
// `x` has no solutions
|
||||
can_reshape = false;
|
||||
} else if (size % new_size != 0) {
|
||||
// `x` has no integer solutions
|
||||
can_reshape = false;
|
||||
} else {
|
||||
can_reshape = true;
|
||||
new_shape[neg1_axis_i] = size / new_size; // Resolve dimension
|
||||
}
|
||||
} else {
|
||||
can_reshape = (new_size == size);
|
||||
}
|
||||
|
||||
if (!can_reshape) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"cannot reshape array of size {0} into given shape",
|
||||
size, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
}
|
||||
} // namespace reshape
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
void __nac3_ndarray_resolve_and_check_new_shape(int32_t size, int32_t new_ndims,
|
||||
int32_t* new_shape) {
|
||||
ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_resolve_and_check_new_shape64(int64_t size,
|
||||
int64_t new_ndims,
|
||||
int64_t* new_shape) {
|
||||
ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,145 +0,0 @@
|
|||
#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 {
|
||||
/**
|
||||
* @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;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Create a transpose view of `src_ndarray` and perform proper assertions.
|
||||
*
|
||||
* This function is very similar to doing `dst_ndarray = np.transpose(src_ndarray, <axes>)`.
|
||||
* If `<axes>` is supposed to be `None`, caller can pass in a `nullptr` to `<axes>`.
|
||||
*
|
||||
* The transpose view created is returned by modifying `dst_ndarray`.
|
||||
*
|
||||
* The caller is responsible for setting up `dst_ndarray` before calling this function.
|
||||
* Here is what this function expects from `dst_ndarray` when called:
|
||||
* - `dst_ndarray->data` does not have to be initialized.
|
||||
* - `dst_ndarray->itemsize` does not have to be initialized.
|
||||
* - `dst_ndarray->ndims` must be initialized, must be equal to `src_ndarray->ndims`.
|
||||
* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
|
||||
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
|
||||
* When this function call ends:
|
||||
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
|
||||
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
|
||||
* - `dst_ndarray->ndims` is unchanged
|
||||
* - `dst_ndarray->shape` is updated according to how `np.transpose` works
|
||||
* - `dst_ndarray->strides` is updated according to how `np.transpose` works
|
||||
*
|
||||
* @param src_ndarray The NDArray to build a transpose view on
|
||||
* @param dst_ndarray The resulting NDArray after transpose. Further details in the comments above,
|
||||
* @param num_axes Number of elements in axes. Unused if `axes` is nullptr.
|
||||
* @param axes Axes permutation. Set it to `nullptr` if `<axes>` is `None`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void transpose(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray,
|
||||
SizeT num_axes, const SizeT* axes) {
|
||||
debug_assert_eq(SizeT, src_ndarray->ndims, dst_ndarray->ndims);
|
||||
const auto ndims = src_ndarray->ndims;
|
||||
|
||||
if (axes != nullptr) assert_transpose_axes(ndims, num_axes, axes);
|
||||
|
||||
dst_ndarray->data = src_ndarray->data;
|
||||
dst_ndarray->itemsize = src_ndarray->itemsize;
|
||||
|
||||
// Check out https://ajcr.net/stride-guide-part-2/ to see how `np.transpose` works behind the scenes.
|
||||
if (axes == nullptr) {
|
||||
// `np.transpose(<array>, axes=None)`
|
||||
|
||||
/*
|
||||
* Minor note: `np.transpose(<array>, axes=None)` is equivalent to
|
||||
* `np.transpose(<array>, axes=[N-1, N-2, ..., 0])` - basically it
|
||||
* is reversing the order of strides and shape.
|
||||
*
|
||||
* This is a fast implementation to handle this special (but very common) case.
|
||||
*/
|
||||
|
||||
for (SizeT axis = 0; axis < ndims; axis++) {
|
||||
dst_ndarray->shape[axis] = src_ndarray->shape[ndims - axis - 1];
|
||||
dst_ndarray->strides[axis] = src_ndarray->strides[ndims - axis - 1];
|
||||
}
|
||||
} else {
|
||||
// `np.transpose(<array>, <axes>)`
|
||||
|
||||
// Permute strides and shape according to `axes`, while resolving negative indices in `axes`
|
||||
for (SizeT axis = 0; axis < ndims; axis++) {
|
||||
// `i` cannot be OUT_OF_BOUNDS because of assertions
|
||||
SizeT i = slice::resolve_index_in_length(ndims, axes[axis]);
|
||||
|
||||
dst_ndarray->shape[axis] = src_ndarray->shape[i];
|
||||
dst_ndarray->strides[axis] = src_ndarray->strides[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace transpose
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::transpose;
|
||||
void __nac3_ndarray_transpose(const NDArray<int32_t>* src_ndarray,
|
||||
NDArray<int32_t>* dst_ndarray, int32_t num_axes,
|
||||
const int32_t* axes) {
|
||||
transpose(src_ndarray, dst_ndarray, num_axes, axes);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_transpose64(const NDArray<int64_t>* src_ndarray,
|
||||
NDArray<int64_t>* dst_ndarray, int64_t num_axes,
|
||||
const int64_t* axes) {
|
||||
transpose(src_ndarray, dst_ndarray, num_axes, axes);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,167 +0,0 @@
|
|||
#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>
|
||||
Slice indices_checked(SliceIndex length) {
|
||||
// TODO: Switch to `SizeT length`
|
||||
|
||||
if (length < 0) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"length should not be negative, got {0}", length,
|
||||
NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
if (this->step_defined && this->step == 0) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "slice step cannot be zero",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
return this->indices(length);
|
||||
}
|
||||
};
|
||||
} // namespace
|
||||
|
|
@ -1,101 +0,0 @@
|
|||
#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 +1,216 @@
|
|||
#pragma once
|
||||
|
||||
#include "irrt_utils.hpp"
|
||||
#include "irrt_typedefs.hpp"
|
||||
|
||||
/*
|
||||
This header contains IRRT implementations
|
||||
that do not deserved to be categorized (e.g., into numpy, etc.)
|
||||
|
||||
Check out other *.hpp files before including them here!!
|
||||
*/
|
||||
|
||||
// The type of an index or a value describing the length of a range/slice is
|
||||
// always `int32_t`.
|
||||
|
||||
namespace {
|
||||
// adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
|
||||
// need to make sure `exp >= 0` before calling this function
|
||||
template <typename T>
|
||||
T __nac3_int_exp_impl(T base, T exp) {
|
||||
T res = 1;
|
||||
/* repeated squaring method */
|
||||
do {
|
||||
if (exp & 1) {
|
||||
res *= base; /* for n odd */
|
||||
}
|
||||
exp >>= 1;
|
||||
base *= base;
|
||||
} while (exp);
|
||||
return res;
|
||||
}
|
||||
}
|
||||
|
||||
extern "C" {
|
||||
#define DEF_nac3_int_exp_(T) \
|
||||
T __nac3_int_exp_##T(T base, T exp) {\
|
||||
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)
|
||||
|
||||
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
|
||||
if (i < 0) {
|
||||
i = len + i;
|
||||
}
|
||||
if (i < 0) {
|
||||
return 0;
|
||||
} else if (i > len) {
|
||||
return len;
|
||||
}
|
||||
return i;
|
||||
}
|
||||
|
||||
SliceIndex __nac3_range_slice_len(
|
||||
const SliceIndex start,
|
||||
const SliceIndex end,
|
||||
const SliceIndex step
|
||||
) {
|
||||
SliceIndex diff = end - start;
|
||||
if (diff > 0 && step > 0) {
|
||||
return ((diff - 1) / step) + 1;
|
||||
} else if (diff < 0 && step < 0) {
|
||||
return ((diff + 1) / step) + 1;
|
||||
} else {
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
||||
// Handle list assignment and dropping part of the list when
|
||||
// both dest_step and src_step are +1.
|
||||
// - 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)
|
||||
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 */
|
||||
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 (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;
|
||||
if (src_len > 0) {
|
||||
__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,
|
||||
dest_arr + (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)
|
||||
);
|
||||
if (need_alloca) {
|
||||
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 constant optimization */
|
||||
if (size == 1) {
|
||||
__builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
|
||||
} else if (size == 4) {
|
||||
__builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
|
||||
} 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);
|
||||
}
|
||||
}
|
||||
/* 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
|
||||
);
|
||||
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_isnan(double x) {
|
||||
return __builtin_isnan(x);
|
||||
}
|
||||
|
||||
double tgamma(double arg);
|
||||
|
||||
double __nac3_gamma(double z) {
|
||||
// Handling for denormals
|
||||
// | x | Python gamma(x) | C tgamma(x) |
|
||||
// --- | ----------------- | --------------- | ----------- |
|
||||
// (1) | nan | nan | nan |
|
||||
// (2) | -inf | -inf | inf |
|
||||
// (3) | inf | inf | inf |
|
||||
// (4) | 0.0 | inf | inf |
|
||||
// (5) | {-1.0, -2.0, ...} | inf | nan |
|
||||
|
||||
// (1)-(3)
|
||||
if (__builtin_isinf(z) || __builtin_isnan(z)) {
|
||||
return z;
|
||||
}
|
||||
|
||||
double v = tgamma(z);
|
||||
|
||||
// (4)-(5)
|
||||
return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
|
||||
}
|
||||
|
||||
double lgamma(double arg);
|
||||
|
||||
double __nac3_gammaln(double x) {
|
||||
// libm's handling of value overflows differs from scipy:
|
||||
// - scipy: gammaln(-inf) -> -inf
|
||||
// - libm : lgamma(-inf) -> inf
|
||||
|
||||
if (__builtin_isinf(x)) {
|
||||
return x;
|
||||
}
|
||||
|
||||
return lgamma(x);
|
||||
}
|
||||
|
||||
double j0(double x);
|
||||
|
||||
double __nac3_j0(double x) {
|
||||
// libm's handling of value overflows differs from scipy:
|
||||
// - scipy: j0(inf) -> nan
|
||||
// - libm : j0(inf) -> 0.0
|
||||
|
||||
if (__builtin_isinf(x)) {
|
||||
return __builtin_nan("");
|
||||
}
|
||||
|
||||
return j0(x);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,24 +1,14 @@
|
|||
#pragma once
|
||||
|
||||
#ifdef IRRT_DEBUG
|
||||
#define IRRT_DEBUG_ASSERT
|
||||
#define IRRT_DEBUG_ASSERT_BOOL true
|
||||
#else
|
||||
#define IRRT_DEBUG_ASSERT_BOOL false
|
||||
#endif
|
||||
#include "irrt_utils.hpp"
|
||||
#include "irrt_typedefs.hpp"
|
||||
#include "irrt_basic.hpp"
|
||||
#include "irrt_slice.hpp"
|
||||
#include "irrt_numpy_ndarray.hpp"
|
||||
|
||||
#include <irrt/core.hpp>
|
||||
#include <irrt/debug.hpp>
|
||||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/list.hpp>
|
||||
#include <irrt/ndarray/array.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/broadcast.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/ndarray/indexing.hpp>
|
||||
#include <irrt/ndarray/iter.hpp>
|
||||
#include <irrt/ndarray/product.hpp>
|
||||
#include <irrt/ndarray/reshape.hpp>
|
||||
#include <irrt/ndarray/transpose.hpp>
|
||||
#include <irrt/util.hpp>
|
||||
/*
|
||||
All IRRT implementations.
|
||||
|
||||
We don't have any 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.
|
||||
*/
|
||||
|
|
@ -0,0 +1,466 @@
|
|||
#pragma once
|
||||
|
||||
#include "irrt_utils.hpp"
|
||||
#include "irrt_typedefs.hpp"
|
||||
#include "irrt_slice.hpp"
|
||||
|
||||
/*
|
||||
NDArray-related implementations.
|
||||
`*/
|
||||
|
||||
// NDArray indices are always `uint32_t`.
|
||||
using NDIndex = uint32_t;
|
||||
|
||||
namespace {
|
||||
namespace ndarray_util {
|
||||
template <typename SizeT>
|
||||
static void set_indices_by_nth(SizeT ndims, const SizeT* shape, SizeT* indices, SizeT nth) {
|
||||
for (int32_t i = 0; i < ndims; i++) {
|
||||
int32_t dim_i = ndims - i - 1;
|
||||
int32_t dim = shape[dim_i];
|
||||
|
||||
indices[dim_i] = nth % dim;
|
||||
nth /= dim;
|
||||
}
|
||||
}
|
||||
|
||||
// Compute the strides of an ndarray given an ndarray `shape`
|
||||
// and assuming that the ndarray is *fully C-contagious*.
|
||||
//
|
||||
// You might want to read up on https://ajcr.net/stride-guide-part-1/.
|
||||
template <typename SizeT>
|
||||
static void set_strides_by_shape(SizeT itemsize, SizeT ndims, SizeT* dst_strides, const SizeT* shape) {
|
||||
SizeT stride_product = 1;
|
||||
for (SizeT i = 0; i < ndims; i++) {
|
||||
int dim_i = ndims - i - 1;
|
||||
dst_strides[dim_i] = stride_product * itemsize;
|
||||
stride_product *= shape[dim_i];
|
||||
}
|
||||
}
|
||||
|
||||
// Compute the size/# of elements of an ndarray given its shape
|
||||
template <typename SizeT>
|
||||
static SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
|
||||
SizeT size = 1;
|
||||
for (SizeT dim_i = 0; dim_i < ndims; dim_i++) size *= shape[dim_i];
|
||||
return size;
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
static bool can_broadcast_shape_to(
|
||||
const SizeT target_ndims,
|
||||
const SizeT *target_shape,
|
||||
const SizeT src_ndims,
|
||||
const SizeT *src_shape
|
||||
) {
|
||||
/*
|
||||
// See https://numpy.org/doc/stable/user/basics.broadcasting.html
|
||||
|
||||
This function handles this example:
|
||||
```
|
||||
Image (3d array): 256 x 256 x 3
|
||||
Scale (1d array): 3
|
||||
Result (3d array): 256 x 256 x 3
|
||||
```
|
||||
|
||||
Other interesting examples to consider:
|
||||
- `can_broadcast_shape_to([3], [1, 1, 1, 1, 3]) == true`
|
||||
- `can_broadcast_shape_to([3], [3, 1]) == false`
|
||||
- `can_broadcast_shape_to([256, 256, 3], [256, 1, 3]) == true`
|
||||
|
||||
In cases when the shapes contain zero(es):
|
||||
- `can_broadcast_shape_to([0], [1]) == true`
|
||||
- `can_broadcast_shape_to([0], [2]) == false`
|
||||
- `can_broadcast_shape_to([0, 4, 0, 0], [1]) == true`
|
||||
- `can_broadcast_shape_to([0, 4, 0, 0], [1, 1, 1, 1]) == true`
|
||||
- `can_broadcast_shape_to([0, 4, 0, 0], [1, 4, 1, 1]) == true`
|
||||
- `can_broadcast_shape_to([4, 3], [0, 3]) == false`
|
||||
- `can_broadcast_shape_to([4, 3], [0, 0]) == false`
|
||||
*/
|
||||
|
||||
// This is essentially doing the following in Python:
|
||||
// `for target_dim, src_dim in itertools.zip_longest(target_shape[::-1], src_shape[::-1], fillvalue=1)`
|
||||
for (SizeT i = 0; i < max(target_ndims, src_ndims); i++) {
|
||||
SizeT target_dim_i = target_ndims - i - 1;
|
||||
SizeT src_dim_i = src_ndims - i - 1;
|
||||
|
||||
bool target_dim_exists = target_dim_i >= 0;
|
||||
bool src_dim_exists = src_dim_i >= 0;
|
||||
|
||||
SizeT target_dim = target_dim_exists ? target_shape[target_dim_i] : 1;
|
||||
SizeT src_dim = src_dim_exists ? src_shape[src_dim_i] : 1;
|
||||
|
||||
bool ok = src_dim == 1 || target_dim == src_dim;
|
||||
if (!ok) return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
}
|
||||
|
||||
typedef uint8_t NDSliceType;
|
||||
extern "C" {
|
||||
const NDSliceType INPUT_SLICE_TYPE_INDEX = 0;
|
||||
const NDSliceType INPUT_SLICE_TYPE_SLICE = 1;
|
||||
}
|
||||
|
||||
struct NDSlice {
|
||||
// A poor-man's `std::variant<int, UserRange>`
|
||||
NDSliceType type;
|
||||
|
||||
/*
|
||||
if type == INPUT_SLICE_TYPE_INDEX => `slice` points to a single `SizeT`
|
||||
if type == INPUT_SLICE_TYPE_SLICE => `slice` points to a single `UserRange`
|
||||
*/
|
||||
uint8_t *slice;
|
||||
};
|
||||
|
||||
namespace ndarray_util {
|
||||
template<typename SizeT>
|
||||
SizeT deduce_ndims_after_slicing(SizeT ndims, SizeT num_slices, const NDSlice *slices) {
|
||||
irrt_assert(num_slices <= ndims);
|
||||
|
||||
SizeT final_ndims = ndims;
|
||||
for (SizeT i = 0; i < num_slices; i++) {
|
||||
if (slices[i].type == INPUT_SLICE_TYPE_INDEX) {
|
||||
final_ndims--; // An integer slice demotes the rank by 1
|
||||
}
|
||||
}
|
||||
return final_ndims;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
struct NDArrayIndicesIter {
|
||||
SizeT ndims;
|
||||
const SizeT *shape;
|
||||
SizeT *indices;
|
||||
|
||||
void set_indices_zero() {
|
||||
__builtin_memset(indices, 0, sizeof(SizeT) * ndims);
|
||||
}
|
||||
|
||||
void next() {
|
||||
for (SizeT i = 0; i < ndims; i++) {
|
||||
SizeT dim_i = ndims - i - 1;
|
||||
|
||||
indices[dim_i]++;
|
||||
if (indices[dim_i] < shape[dim_i]) {
|
||||
break;
|
||||
} else {
|
||||
indices[dim_i] = 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
// The NDArray object. `SizeT` is the *signed* size type of this ndarray.
|
||||
//
|
||||
// NOTE: The order of fields is IMPORTANT. DON'T TOUCH IT
|
||||
//
|
||||
// Some resources you might find helpful:
|
||||
// - The official numpy implementations:
|
||||
// - https://github.com/numpy/numpy/blob/735a477f0bc2b5b84d0e72d92f224bde78d4e069/doc/source/reference/c-api/types-and-structures.rst
|
||||
// - On strides (about reshaping, slicing, C-contagiousness, etc)
|
||||
// - https://ajcr.net/stride-guide-part-1/.
|
||||
// - https://ajcr.net/stride-guide-part-2/.
|
||||
// - https://ajcr.net/stride-guide-part-3/.
|
||||
template <typename SizeT>
|
||||
struct NDArray {
|
||||
// The underlying data this `ndarray` is pointing to.
|
||||
//
|
||||
// NOTE: Formally this should be of type `void *`, but clang
|
||||
// translates `void *` to `i8 *` when run with `-S -emit-llvm`,
|
||||
// so we will put `uint8_t *` here for clarity.
|
||||
uint8_t *data;
|
||||
|
||||
// The number of bytes of a single element in `data`.
|
||||
//
|
||||
// The `SizeT` is treated as `unsigned`.
|
||||
SizeT itemsize;
|
||||
|
||||
// The number of dimensions of this shape.
|
||||
//
|
||||
// The `SizeT` is treated as `unsigned`.
|
||||
SizeT ndims;
|
||||
|
||||
// Array shape, with length equal to `ndims`.
|
||||
//
|
||||
// The `SizeT` is treated as `unsigned`.
|
||||
//
|
||||
// NOTE: `shape` can contain 0.
|
||||
// (those appear when the user makes an out of bounds slice into an ndarray, e.g., `np.zeros((3, 3))[400:].shape == (0, 3)`)
|
||||
SizeT *shape;
|
||||
|
||||
// Array strides (stride value is in number of bytes, NOT number of elements), with length equal to `ndims`.
|
||||
//
|
||||
// The `SizeT` is treated as `signed`.
|
||||
//
|
||||
// NOTE: `strides` can have negative numbers.
|
||||
// (those appear when there is a slice with a negative step, e.g., `my_array[::-1]`)
|
||||
SizeT *strides;
|
||||
|
||||
// Calculate the size/# of elements of an `ndarray`.
|
||||
// This function corresponds to `np.size(<ndarray>)` or `ndarray.size`
|
||||
SizeT size() {
|
||||
return ndarray_util::calc_size_from_shape(ndims, shape);
|
||||
}
|
||||
|
||||
// Calculate the number of bytes of its content of an `ndarray` *in its view*.
|
||||
// This function corresponds to `ndarray.nbytes`
|
||||
SizeT nbytes() {
|
||||
return this->size() * itemsize;
|
||||
}
|
||||
|
||||
void set_value_at_pelement(uint8_t* pelement, const uint8_t* pvalue) {
|
||||
__builtin_memcpy(pelement, pvalue, itemsize);
|
||||
}
|
||||
|
||||
uint8_t* get_pelement(const SizeT *indices) {
|
||||
uint8_t* element = data;
|
||||
for (SizeT dim_i = 0; dim_i < ndims; dim_i++)
|
||||
element += indices[dim_i] * strides[dim_i];
|
||||
return element;
|
||||
}
|
||||
|
||||
uint8_t* get_nth_pelement(SizeT nth) {
|
||||
irrt_assert(0 <= nth);
|
||||
irrt_assert(nth < this->size());
|
||||
|
||||
SizeT* indices = (SizeT*) __builtin_alloca(sizeof(SizeT) * this->ndims);
|
||||
ndarray_util::set_indices_by_nth(this->ndims, this->shape, indices, nth);
|
||||
return get_pelement(indices);
|
||||
}
|
||||
|
||||
// Get pointer to the first element of this ndarray, assuming
|
||||
// `this->size() > 0`, i.e., not "degenerate" due to zeroes in `this->shape`)
|
||||
//
|
||||
// This is particularly useful for when the ndarray is just containing a single scalar.
|
||||
uint8_t* get_first_pelement() {
|
||||
irrt_assert(this->size() > 0);
|
||||
return this->data; // ...It is simply `this->data`
|
||||
}
|
||||
|
||||
// Is the given `indices` valid/in-bounds?
|
||||
bool in_bounds(const SizeT *indices) {
|
||||
for (SizeT dim_i = 0; dim_i < ndims; dim_i++) {
|
||||
bool dim_ok = indices[dim_i] < shape[dim_i];
|
||||
if (!dim_ok) return false;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
// Fill the ndarray with a value
|
||||
void fill_generic(const uint8_t* pvalue) {
|
||||
NDArrayIndicesIter<SizeT> iter;
|
||||
iter.ndims = this->ndims;
|
||||
iter.shape = this->shape;
|
||||
iter.indices = (SizeT*) __builtin_alloca(sizeof(SizeT) * ndims);
|
||||
iter.set_indices_zero();
|
||||
|
||||
for (SizeT i = 0; i < this->size(); i++, iter.next()) {
|
||||
uint8_t* pelement = get_pelement(iter.indices);
|
||||
set_value_at_pelement(pelement, pvalue);
|
||||
}
|
||||
}
|
||||
|
||||
// Set the strides of the ndarray with `ndarray_util::set_strides_by_shape`
|
||||
void set_strides_by_shape() {
|
||||
ndarray_util::set_strides_by_shape(itemsize, ndims, strides, shape);
|
||||
}
|
||||
|
||||
// https://numpy.org/doc/stable/reference/generated/numpy.eye.html
|
||||
void set_to_eye(SizeT k, const uint8_t* zero_pvalue, const uint8_t* one_pvalue) {
|
||||
__builtin_assume(ndims == 2);
|
||||
|
||||
// TODO: Better implementation
|
||||
|
||||
fill_generic(zero_pvalue);
|
||||
for (SizeT i = 0; i < min(shape[0], shape[1]); i++) {
|
||||
SizeT row = i;
|
||||
SizeT col = i + k;
|
||||
SizeT indices[2] = { row, col };
|
||||
|
||||
if (!in_bounds(indices)) continue;
|
||||
|
||||
uint8_t* pelement = get_pelement(indices);
|
||||
set_value_at_pelement(pelement, one_pvalue);
|
||||
}
|
||||
}
|
||||
|
||||
// To support numpy complex slices (e.g., `my_array[:50:2,4,:2:-1]`)
|
||||
//
|
||||
// Things assumed by this function:
|
||||
// - `dst_ndarray` is allocated by the caller
|
||||
// - `dst_ndarray.ndims` has the correct value (according to `ndarray_util::deduce_ndims_after_slicing`).
|
||||
// - ... and `dst_ndarray.shape` and `dst_ndarray.strides` have been allocated by the caller as well
|
||||
//
|
||||
// Other notes:
|
||||
// - `dst_ndarray->data` does not have to be set, it will be derived.
|
||||
// - `dst_ndarray->itemsize` does not have to be set, it will be set to `this->itemsize`
|
||||
// - `dst_ndarray->shape` and `dst_ndarray.strides` can contain empty values
|
||||
void slice(SizeT num_ndslices, NDSlice* ndslices, NDArray<SizeT>* dst_ndarray) {
|
||||
// REFERENCE CODE (check out `_index_helper` in `__getitem__`):
|
||||
// https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
|
||||
|
||||
irrt_assert(dst_ndarray->ndims == ndarray_util::deduce_ndims_after_slicing(this->ndims, num_ndslices, ndslices));
|
||||
|
||||
dst_ndarray->data = this->data;
|
||||
|
||||
SizeT this_axis = 0;
|
||||
SizeT dst_axis = 0;
|
||||
|
||||
for (SizeT i = 0; i < num_ndslices; i++) {
|
||||
NDSlice *ndslice = &ndslices[i];
|
||||
if (ndslice->type == INPUT_SLICE_TYPE_INDEX) {
|
||||
// Handle when the ndslice is just a single (possibly negative) integer
|
||||
// e.g., `my_array[::2, -5, ::-1]`
|
||||
// ^^------ like this
|
||||
SizeT index_user = *((SizeT*) ndslice->slice);
|
||||
SizeT index = resolve_index_in_length(this->shape[this_axis], index_user);
|
||||
dst_ndarray->data += index * this->strides[this_axis]; // Add offset
|
||||
|
||||
// Next
|
||||
this_axis++;
|
||||
} else if (ndslice->type == INPUT_SLICE_TYPE_SLICE) {
|
||||
// Handle when the ndslice is a slice (represented by UserSlice in IRRT)
|
||||
// e.g., `my_array[::2, -5, ::-1]`
|
||||
// ^^^------^^^^----- like these
|
||||
UserSlice<SizeT>* user_slice = (UserSlice<SizeT>*) ndslice->slice;
|
||||
Slice<SizeT> slice = user_slice->indices(this->shape[this_axis]); // To resolve negative indices and other funny stuff written by the user
|
||||
|
||||
// NOTE: There is no need to write special code to handle negative steps/strides.
|
||||
// This simple implementation meticulously handles both positive and negative steps/strides.
|
||||
// Check out the tinynumpy and IRRT's test cases if you are not convinced.
|
||||
dst_ndarray->data += slice.start * this->strides[this_axis]; // Add offset (NOTE: no need to `* itemsize`, strides count in # of bytes)
|
||||
dst_ndarray->strides[dst_axis] = slice.step * this->strides[this_axis]; // Determine stride
|
||||
dst_ndarray->shape[dst_axis] = slice.len(); // Determine shape dimension
|
||||
|
||||
// Next
|
||||
dst_axis++;
|
||||
this_axis++;
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
|
||||
irrt_assert(dst_axis == dst_ndarray->ndims); // Sanity check on the implementation
|
||||
}
|
||||
|
||||
// Similar to `np.broadcast_to(<ndarray>, <target_shape>)`
|
||||
// Assumptions:
|
||||
// - `this` has to be fully initialized.
|
||||
// - `dst_ndarray->ndims` has to be set.
|
||||
// - `dst_ndarray->shape` has to be set, this determines the shape `this` broadcasts to.
|
||||
//
|
||||
// Other notes:
|
||||
// - `dst_ndarray->data` does not have to be set, it will be set to `this->data`.
|
||||
// - `dst_ndarray->itemsize` does not have to be set, it will be set to `this->data`.
|
||||
// - `dst_ndarray->strides` does not have to be set, it will be overwritten.
|
||||
//
|
||||
// Cautions:
|
||||
// ```
|
||||
// xs = np.zeros((4,))
|
||||
// ys = np.zero((4, 1))
|
||||
// ys[:] = xs # ok
|
||||
//
|
||||
// xs = np.zeros((1, 4))
|
||||
// ys = np.zero((4,))
|
||||
// ys[:] = xs # allowed
|
||||
// # However `np.broadcast_to(xs, (4,))` would fails, as per numpy's broadcasting rule.
|
||||
// # and apparently numpy will "deprecate" this? SEE https://github.com/numpy/numpy/issues/21744
|
||||
// # This implementation will NOT support this assignment.
|
||||
// ```
|
||||
void broadcast_to(NDArray<SizeT>* dst_ndarray) {
|
||||
dst_ndarray->data = this->data;
|
||||
dst_ndarray->itemsize = this->itemsize;
|
||||
|
||||
irrt_assert(
|
||||
ndarray_util::can_broadcast_shape_to(
|
||||
dst_ndarray->ndims,
|
||||
dst_ndarray->shape,
|
||||
this->ndims,
|
||||
this->shape
|
||||
)
|
||||
);
|
||||
|
||||
SizeT stride_product = 1;
|
||||
for (SizeT i = 0; i < max(this->ndims, dst_ndarray->ndims); i++) {
|
||||
SizeT this_dim_i = this->ndims - i - 1;
|
||||
SizeT dst_dim_i = dst_ndarray->ndims - i - 1;
|
||||
|
||||
bool this_dim_exists = this_dim_i >= 0;
|
||||
bool dst_dim_exists = dst_dim_i >= 0;
|
||||
|
||||
// TODO: Explain how this works
|
||||
bool c1 = this_dim_exists && this->shape[this_dim_i] == 1;
|
||||
bool c2 = dst_dim_exists && dst_ndarray->shape[dst_dim_i] != 1;
|
||||
if (!this_dim_exists || (c1 && c2)) {
|
||||
dst_ndarray->strides[dst_dim_i] = 0; // Freeze it in-place
|
||||
} else {
|
||||
dst_ndarray->strides[dst_dim_i] = stride_product * this->itemsize;
|
||||
stride_product *= this->shape[this_dim_i]; // NOTE: this_dim_exist must be true here.
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Simulates `this_ndarray[:] = src_ndarray`, with automatic broadcasting.
|
||||
// Caution on https://github.com/numpy/numpy/issues/21744
|
||||
// Also see `NDArray::broadcast_to`
|
||||
void assign_with(NDArray<SizeT>* src_ndarray) {
|
||||
irrt_assert(
|
||||
ndarray_util::can_broadcast_shape_to(
|
||||
this->ndims,
|
||||
this->shape,
|
||||
src_ndarray->ndims,
|
||||
src_ndarray->shape
|
||||
)
|
||||
);
|
||||
|
||||
// Broadcast the `src_ndarray` to make the reading process *much* easier
|
||||
SizeT* broadcasted_src_ndarray_strides = __builtin_alloca(sizeof(SizeT) * this->ndims); // Remember to allocate strides beforehand
|
||||
NDArray<SizeT> broadcasted_src_ndarray = {
|
||||
.ndims = this->ndims,
|
||||
.shape = this->shape,
|
||||
.strides = broadcasted_src_ndarray_strides
|
||||
};
|
||||
src_ndarray->broadcast_to(&broadcasted_src_ndarray);
|
||||
|
||||
// Using iter instead of `get_nth_pelement` because it is slightly faster
|
||||
SizeT* indices = __builtin_alloca(sizeof(SizeT) * this->ndims);
|
||||
auto iter = NDArrayIndicesIter<SizeT> {
|
||||
.ndims = this->ndims,
|
||||
.shape = this->shape,
|
||||
.indices = indices
|
||||
};
|
||||
const SizeT this_size = this->size();
|
||||
for (SizeT i = 0; i < this_size; i++, iter.next()) {
|
||||
uint8_t* src_pelement = broadcasted_src_ndarray_strides->get_pelement(indices);
|
||||
uint8_t* this_pelement = this->get_pelement(indices);
|
||||
this->set_value_at_pelement(src_pelement, src_pelement);
|
||||
}
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
extern "C" {
|
||||
uint32_t __nac3_ndarray_size(NDArray<int32_t>* ndarray) {
|
||||
return ndarray->size();
|
||||
}
|
||||
|
||||
uint64_t __nac3_ndarray_size64(NDArray<int64_t>* ndarray) {
|
||||
return ndarray->size();
|
||||
}
|
||||
|
||||
void __nac3_ndarray_fill_generic(NDArray<int32_t>* ndarray, uint8_t* pvalue) {
|
||||
ndarray->fill_generic(pvalue);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_fill_generic64(NDArray<int64_t>* ndarray, uint8_t* pvalue) {
|
||||
ndarray->fill_generic(pvalue);
|
||||
}
|
||||
|
||||
// void __nac3_ndarray_slice(NDArray<int32_t>* ndarray, int32_t num_slices, NDSlice<int32_t> *slices, NDArray<int32_t> *dst_ndarray) {
|
||||
// // ndarray->slice(num_slices, slices, dst_ndarray);
|
||||
// }
|
||||
}
|
||||
|
|
@ -0,0 +1,80 @@
|
|||
#pragma once
|
||||
|
||||
#include "irrt_utils.hpp"
|
||||
#include "irrt_typedefs.hpp"
|
||||
|
||||
namespace {
|
||||
// A proper slice in IRRT, all negative indices have be resolved to absolute values.
|
||||
// Even though nac3core's slices are always `int32_t`, we will template slice anyway
|
||||
// since this struct is used as a general utility.
|
||||
template <typename T>
|
||||
struct Slice {
|
||||
T start;
|
||||
T stop;
|
||||
T step;
|
||||
|
||||
// The length/The number of elements of the slice if it were a range,
|
||||
// i.e., the value of `len(range(this->start, this->stop, this->end))`
|
||||
T len() {
|
||||
T 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;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
template<typename T>
|
||||
T resolve_index_in_length(T length, T index) {
|
||||
irrt_assert(length >= 0);
|
||||
if (index < 0) {
|
||||
// Remember that index is negative, so do a plus here
|
||||
return max(length + index, 0);
|
||||
} else {
|
||||
return min(length, index);
|
||||
}
|
||||
}
|
||||
|
||||
// NOTE: using a bitfield for the `*_defined` is better, at the
|
||||
// cost of a more annoying implementation in nac3core inkwell
|
||||
template <typename T>
|
||||
struct UserSlice {
|
||||
uint8_t start_defined;
|
||||
T start;
|
||||
|
||||
uint8_t stop_defined;
|
||||
T stop;
|
||||
|
||||
uint8_t step_defined;
|
||||
T step;
|
||||
|
||||
// Like Python's `slice(start, stop, step).indices(length)`
|
||||
Slice<T> indices(T length) {
|
||||
// NOTE: This function implements Python's `slice.indices` *FAITHFULLY*.
|
||||
// SEE: https://github.com/python/cpython/blob/f62161837e68c1c77961435f1b954412dd5c2b65/Objects/sliceobject.c#L546
|
||||
irrt_assert(length >= 0);
|
||||
irrt_assert(!step_defined || step != 0); // step_defined -> step != 0; step cannot be zero if specified by user
|
||||
|
||||
Slice<T> result;
|
||||
result.step = step_defined ? step : 1;
|
||||
bool step_is_negative = result.step < 0;
|
||||
|
||||
if (start_defined) {
|
||||
result.start = resolve_index_in_length(length, start);
|
||||
} else {
|
||||
result.start = step_is_negative ? length - 1 : 0;
|
||||
}
|
||||
|
||||
if (stop_defined) {
|
||||
result.stop = resolve_index_in_length(length, stop);
|
||||
} else {
|
||||
result.stop = step_is_negative ? -1 : length;
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
};
|
||||
}
|
||||
|
|
@ -1,25 +1,658 @@
|
|||
// 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
|
||||
// Set `IRRT_DONT_TYPEDEF_INTS` because `cstdint` defines them
|
||||
#define IRRT_DONT_TYPEDEF_INTS
|
||||
#include "irrt_everything.hpp"
|
||||
|
||||
// Note that failure unit tests are not supported.
|
||||
void test_fail() {
|
||||
printf("[!] Test failed\n");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
#include <test/test_core.hpp>
|
||||
#include <test/test_ndarray_basic.hpp>
|
||||
#include <test/test_ndarray_broadcast.hpp>
|
||||
#include <test/test_ndarray_indexing.hpp>
|
||||
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__)
|
||||
|
||||
template <typename T>
|
||||
void debug_print_array(const char* format, int len, T* as) {
|
||||
printf("[");
|
||||
for (int i = 0; i < len; i++) {
|
||||
if (i != 0) printf(", ");
|
||||
printf(format, as[i]);
|
||||
}
|
||||
printf("]");
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void assert_arrays_match(const char* label, const char* format, int len, T* expected, T* got) {
|
||||
if (!arrays_match(len, expected, got)) {
|
||||
printf(">>>>>>> %s\n", label);
|
||||
printf(" Expecting = ");
|
||||
debug_print_array(format, len, expected);
|
||||
printf("\n");
|
||||
printf(" Got = ");
|
||||
debug_print_array(format, len, got);
|
||||
printf("\n");
|
||||
test_fail();
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void assert_values_match(const char* label, const char* format, T expected, T got) {
|
||||
if (expected != got) {
|
||||
printf(">>>>>>> %s\n", label);
|
||||
printf(" Expecting = ");
|
||||
printf(format, expected);
|
||||
printf("\n");
|
||||
printf(" Got = ");
|
||||
printf(format, got);
|
||||
printf("\n");
|
||||
test_fail();
|
||||
}
|
||||
}
|
||||
|
||||
void print_repeated(const char *str, int count) {
|
||||
for (int i = 0; i < count; i++) {
|
||||
printf("%s", str);
|
||||
}
|
||||
}
|
||||
|
||||
template<typename SizeT, typename ElementT>
|
||||
void __print_ndarray_aux(const char *format, bool first, bool last, SizeT* cursor, SizeT depth, NDArray<SizeT>* ndarray) {
|
||||
// A really lazy recursive implementation
|
||||
|
||||
// Add left padding unless its the first entry (since there would be "[[[" before it)
|
||||
if (!first) {
|
||||
print_repeated(" ", depth);
|
||||
}
|
||||
|
||||
const SizeT dim = ndarray->shape[depth];
|
||||
if (depth + 1 == ndarray->ndims) {
|
||||
// Recursed down to last dimension, print the values in a nice list
|
||||
printf("[");
|
||||
|
||||
SizeT* indices = (SizeT*) __builtin_alloca(sizeof(SizeT) * ndarray->ndims);
|
||||
for (SizeT i = 0; i < dim; i++) {
|
||||
ndarray_util::set_indices_by_nth(ndarray->ndims, ndarray->shape, indices, *cursor);
|
||||
ElementT* pelement = (ElementT*) ndarray->get_pelement(indices);
|
||||
ElementT element = *pelement;
|
||||
|
||||
if (i != 0) printf(", "); // List delimiter
|
||||
printf(format, element);
|
||||
printf("(@");
|
||||
debug_print_array("%d", ndarray->ndims, indices);
|
||||
printf(")");
|
||||
|
||||
(*cursor)++;
|
||||
}
|
||||
printf("]");
|
||||
} else {
|
||||
printf("[");
|
||||
for (SizeT i = 0; i < ndarray->shape[depth]; i++) {
|
||||
__print_ndarray_aux<SizeT, ElementT>(
|
||||
format,
|
||||
i == 0, // first?
|
||||
i + 1 == dim, // last?
|
||||
cursor,
|
||||
depth + 1,
|
||||
ndarray
|
||||
);
|
||||
}
|
||||
printf("]");
|
||||
}
|
||||
|
||||
// Add newline unless its the last entry (since there will be "]]]" after it)
|
||||
if (!last) {
|
||||
print_repeated("\n", depth);
|
||||
}
|
||||
}
|
||||
|
||||
template<typename SizeT, typename ElementT>
|
||||
void print_ndarray(const char *format, NDArray<SizeT>* ndarray) {
|
||||
if (ndarray->ndims == 0) {
|
||||
printf("<empty ndarray>");
|
||||
} else {
|
||||
SizeT cursor = 0;
|
||||
__print_ndarray_aux<SizeT, ElementT>(format, true, true, &cursor, 0, ndarray);
|
||||
}
|
||||
printf("\n");
|
||||
}
|
||||
|
||||
void test_calc_size_from_shape_normal() {
|
||||
// Test shapes with normal values
|
||||
BEGIN_TEST();
|
||||
|
||||
int32_t shape[4] = { 2, 3, 5, 7 };
|
||||
assert_values_match("size", "%d", 210, ndarray_util::calc_size_from_shape<int32_t>(4, shape));
|
||||
}
|
||||
|
||||
void test_calc_size_from_shape_has_zero() {
|
||||
// Test shapes with 0 in them
|
||||
BEGIN_TEST();
|
||||
|
||||
int32_t shape[4] = { 2, 0, 5, 7 };
|
||||
assert_values_match("size", "%d", 0, ndarray_util::calc_size_from_shape<int32_t>(4, shape));
|
||||
}
|
||||
|
||||
void test_set_strides_by_shape() {
|
||||
// Test `set_strides_by_shape()`
|
||||
BEGIN_TEST();
|
||||
|
||||
int32_t shape[4] = { 99, 3, 5, 7 };
|
||||
int32_t strides[4] = { 0 };
|
||||
ndarray_util::set_strides_by_shape((int32_t) sizeof(int32_t), 4, strides, shape);
|
||||
|
||||
int32_t expected_strides[4] = {
|
||||
105 * sizeof(int32_t),
|
||||
35 * sizeof(int32_t),
|
||||
7 * sizeof(int32_t),
|
||||
1 * sizeof(int32_t)
|
||||
};
|
||||
assert_arrays_match("strides", "%u", 4u, expected_strides, strides);
|
||||
}
|
||||
|
||||
void test_ndarray_indices_iter_normal() {
|
||||
// Test NDArrayIndicesIter normal behavior
|
||||
BEGIN_TEST();
|
||||
|
||||
int32_t shape[3] = { 1, 2, 3 };
|
||||
int32_t indices[3] = { 0, 0, 0 };
|
||||
auto iter = NDArrayIndicesIter<int32_t> {
|
||||
.ndims = 3,
|
||||
.shape = shape,
|
||||
.indices = indices
|
||||
};
|
||||
|
||||
assert_arrays_match("indices #0", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 0 });
|
||||
iter.next();
|
||||
assert_arrays_match("indices #1", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 1 });
|
||||
iter.next();
|
||||
assert_arrays_match("indices #2", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 2 });
|
||||
iter.next();
|
||||
assert_arrays_match("indices #3", "%u", 3u, iter.indices, (int32_t[3]) { 0, 1, 0 });
|
||||
iter.next();
|
||||
assert_arrays_match("indices #4", "%u", 3u, iter.indices, (int32_t[3]) { 0, 1, 1 });
|
||||
iter.next();
|
||||
assert_arrays_match("indices #5", "%u", 3u, iter.indices, (int32_t[3]) { 0, 1, 2 });
|
||||
iter.next();
|
||||
assert_arrays_match("indices #6", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 0 }); // Loops back
|
||||
iter.next();
|
||||
assert_arrays_match("indices #7", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 1 });
|
||||
}
|
||||
|
||||
void test_ndarray_fill_generic() {
|
||||
// Test ndarray fill_generic
|
||||
BEGIN_TEST();
|
||||
|
||||
// Choose a type that's neither int32_t nor uint64_t (candidates of SizeT) to spice it up
|
||||
// Also make all the octets non-zero, to see if `memcpy` in `fill_generic` is working perfectly.
|
||||
uint16_t fill_value = 0xFACE;
|
||||
|
||||
uint16_t in_data[6] = { 100, 101, 102, 103, 104, 105 }; // Fill `data` with values that != `999`
|
||||
int32_t in_itemsize = sizeof(uint16_t);
|
||||
const int32_t in_ndims = 2;
|
||||
int32_t in_shape[in_ndims] = { 2, 3 };
|
||||
int32_t in_strides[in_ndims] = {};
|
||||
NDArray<int32_t> ndarray = {
|
||||
.data = (uint8_t*) in_data,
|
||||
.itemsize = in_itemsize,
|
||||
.ndims = in_ndims,
|
||||
.shape = in_shape,
|
||||
.strides = in_strides,
|
||||
};
|
||||
ndarray.set_strides_by_shape();
|
||||
ndarray.fill_generic((uint8_t*) &fill_value); // `fill_generic` here
|
||||
|
||||
uint16_t expected_data[6] = { fill_value, fill_value, fill_value, fill_value, fill_value, fill_value };
|
||||
assert_arrays_match("data", "0x%hX", 6, expected_data, in_data);
|
||||
}
|
||||
|
||||
void test_ndarray_set_to_eye() {
|
||||
// Test `set_to_eye` behavior (helper function to implement `np.eye()`)
|
||||
BEGIN_TEST();
|
||||
|
||||
double in_data[9] = { 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0 };
|
||||
int32_t in_itemsize = sizeof(double);
|
||||
const int32_t in_ndims = 2;
|
||||
int32_t in_shape[in_ndims] = { 3, 3 };
|
||||
int32_t in_strides[in_ndims] = {};
|
||||
NDArray<int32_t> ndarray = {
|
||||
.data = (uint8_t*) in_data,
|
||||
.itemsize = in_itemsize,
|
||||
.ndims = in_ndims,
|
||||
.shape = in_shape,
|
||||
.strides = in_strides,
|
||||
};
|
||||
ndarray.set_strides_by_shape();
|
||||
|
||||
double zero = 0.0;
|
||||
double one = 1.0;
|
||||
ndarray.set_to_eye(1, (uint8_t*) &zero, (uint8_t*) &one);
|
||||
|
||||
assert_values_match("in_data[0]", "%f", 0.0, in_data[0]);
|
||||
assert_values_match("in_data[1]", "%f", 1.0, in_data[1]);
|
||||
assert_values_match("in_data[2]", "%f", 0.0, in_data[2]);
|
||||
assert_values_match("in_data[3]", "%f", 0.0, in_data[3]);
|
||||
assert_values_match("in_data[4]", "%f", 0.0, in_data[4]);
|
||||
assert_values_match("in_data[5]", "%f", 1.0, in_data[5]);
|
||||
assert_values_match("in_data[6]", "%f", 0.0, in_data[6]);
|
||||
assert_values_match("in_data[7]", "%f", 0.0, in_data[7]);
|
||||
assert_values_match("in_data[8]", "%f", 0.0, in_data[8]);
|
||||
}
|
||||
|
||||
void test_slice_1() {
|
||||
// Test `slice(5, None, None).indices(100) == slice(5, 100, 1)`
|
||||
BEGIN_TEST();
|
||||
|
||||
UserSlice<int> user_slice = {
|
||||
.start_defined = 1,
|
||||
.start = 5,
|
||||
.stop_defined = 0,
|
||||
.step_defined = 0,
|
||||
};
|
||||
|
||||
auto slice = user_slice.indices(100);
|
||||
assert_values_match("start", "%d", 5, slice.start);
|
||||
assert_values_match("stop", "%d", 100, slice.stop);
|
||||
assert_values_match("step", "%d", 1, slice.step);
|
||||
}
|
||||
|
||||
void test_slice_2() {
|
||||
// Test `slice(400, 999, None).indices(100) == slice(100, 100, 1)`
|
||||
BEGIN_TEST();
|
||||
|
||||
UserSlice<int> user_slice = {
|
||||
.start_defined = 1,
|
||||
.start = 400,
|
||||
.stop_defined = 0,
|
||||
.step_defined = 0,
|
||||
};
|
||||
|
||||
auto slice = user_slice.indices(100);
|
||||
assert_values_match("start", "%d", 100, slice.start);
|
||||
assert_values_match("stop", "%d", 100, slice.stop);
|
||||
assert_values_match("step", "%d", 1, slice.step);
|
||||
}
|
||||
|
||||
void test_slice_3() {
|
||||
// Test `slice(-10, -5, None).indices(100) == slice(90, 95, 1)`
|
||||
BEGIN_TEST();
|
||||
|
||||
UserSlice<int> user_slice = {
|
||||
.start_defined = 1,
|
||||
.start = -10,
|
||||
.stop_defined = 1,
|
||||
.stop = -5,
|
||||
.step_defined = 0,
|
||||
};
|
||||
|
||||
auto slice = user_slice.indices(100);
|
||||
assert_values_match("start", "%d", 90, slice.start);
|
||||
assert_values_match("stop", "%d", 95, slice.stop);
|
||||
assert_values_match("step", "%d", 1, slice.step);
|
||||
}
|
||||
|
||||
void test_slice_4() {
|
||||
// Test `slice(None, None, -5).indices(100) == (99, -1, -5)`
|
||||
BEGIN_TEST();
|
||||
|
||||
UserSlice<int> user_slice = {
|
||||
.start_defined = 0,
|
||||
.stop_defined = 0,
|
||||
.step_defined = 1,
|
||||
.step = -5
|
||||
};
|
||||
|
||||
auto slice = user_slice.indices(100);
|
||||
assert_values_match("start", "%d", 99, slice.start);
|
||||
assert_values_match("stop", "%d", -1, slice.stop);
|
||||
assert_values_match("step", "%d", -5, slice.step);
|
||||
}
|
||||
|
||||
void test_ndslice_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();
|
||||
|
||||
double in_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 };
|
||||
int32_t in_itemsize = sizeof(double);
|
||||
const int32_t in_ndims = 2;
|
||||
int32_t in_shape[in_ndims] = { 3, 4 };
|
||||
int32_t in_strides[in_ndims] = {};
|
||||
NDArray<int32_t> ndarray = {
|
||||
.data = (uint8_t*) in_data,
|
||||
.itemsize = in_itemsize,
|
||||
.ndims = in_ndims,
|
||||
.shape = in_shape,
|
||||
.strides = in_strides
|
||||
};
|
||||
ndarray.set_strides_by_shape();
|
||||
|
||||
// Destination ndarray
|
||||
// As documented, ndims and shape & strides must be allocated and determined by the caller.
|
||||
const int32_t dst_ndims = 2;
|
||||
int32_t dst_shape[dst_ndims] = {999, 999}; // Empty values
|
||||
int32_t dst_strides[dst_ndims] = {999, 999}; // Empty values
|
||||
NDArray<int32_t> dst_ndarray = {
|
||||
.data = nullptr,
|
||||
.ndims = dst_ndims,
|
||||
.shape = dst_shape,
|
||||
.strides = dst_strides
|
||||
};
|
||||
|
||||
// Create the slice in `ndarray[-2::, 1::2]`
|
||||
UserSlice<int32_t> user_slice_1 = {
|
||||
.start_defined = 1,
|
||||
.start = -2,
|
||||
.stop_defined = 0,
|
||||
.step_defined = 0
|
||||
};
|
||||
|
||||
UserSlice<int32_t> user_slice_2 = {
|
||||
.start_defined = 1,
|
||||
.start = 1,
|
||||
.stop_defined = 0,
|
||||
.step_defined = 1,
|
||||
.step = 2
|
||||
};
|
||||
|
||||
const int32_t num_ndslices = 2;
|
||||
NDSlice ndslices[num_ndslices] = {
|
||||
{ .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_1 },
|
||||
{ .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_2 }
|
||||
};
|
||||
|
||||
ndarray.slice(num_ndslices, ndslices, &dst_ndarray);
|
||||
|
||||
int32_t expected_shape[dst_ndims] = { 2, 2 };
|
||||
int32_t expected_strides[dst_ndims] = { 32, 16 };
|
||||
assert_arrays_match("shape", "%d", dst_ndims, expected_shape, dst_ndarray.shape);
|
||||
assert_arrays_match("strides", "%d", dst_ndims, expected_strides, dst_ndarray.strides);
|
||||
|
||||
assert_values_match("dst_ndarray[0, 0]", "%f", 5.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 0, 0 })));
|
||||
assert_values_match("dst_ndarray[0, 1]", "%f", 7.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 0, 1 })));
|
||||
assert_values_match("dst_ndarray[1, 0]", "%f", 9.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 1, 0 })));
|
||||
assert_values_match("dst_ndarray[1, 1]", "%f", 11.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 1, 1 })));
|
||||
}
|
||||
|
||||
void test_ndslice_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
|
||||
|
||||
dst_ndarray[1, 0] == 99 # If you write to `dst_ndarray`
|
||||
assert ndarray[1, 3] == 99 # `ndarray` also updates!!
|
||||
```
|
||||
*/
|
||||
BEGIN_TEST();
|
||||
|
||||
double in_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 };
|
||||
int32_t in_itemsize = sizeof(double);
|
||||
const int32_t in_ndims = 2;
|
||||
int32_t in_shape[in_ndims] = { 3, 4 };
|
||||
int32_t in_strides[in_ndims] = {};
|
||||
NDArray<int32_t> ndarray = {
|
||||
.data = (uint8_t*) in_data,
|
||||
.itemsize = in_itemsize,
|
||||
.ndims = in_ndims,
|
||||
.shape = in_shape,
|
||||
.strides = in_strides
|
||||
};
|
||||
ndarray.set_strides_by_shape();
|
||||
|
||||
// Destination ndarray
|
||||
// As documented, ndims and shape & strides must be allocated and determined by the caller.
|
||||
const int32_t dst_ndims = 1;
|
||||
int32_t dst_shape[dst_ndims] = {999}; // Empty values
|
||||
int32_t dst_strides[dst_ndims] = {999}; // Empty values
|
||||
NDArray<int32_t> dst_ndarray = {
|
||||
.data = nullptr,
|
||||
.ndims = dst_ndims,
|
||||
.shape = dst_shape,
|
||||
.strides = dst_strides
|
||||
};
|
||||
|
||||
// Create the slice in `ndarray[2, ::-2]`
|
||||
int32_t user_slice_1 = 2;
|
||||
UserSlice<int32_t> user_slice_2 = {
|
||||
.start_defined = 0,
|
||||
.stop_defined = 0,
|
||||
.step_defined = 1,
|
||||
.step = -2
|
||||
};
|
||||
|
||||
const int32_t num_ndslices = 2;
|
||||
NDSlice ndslices[num_ndslices] = {
|
||||
{ .type = INPUT_SLICE_TYPE_INDEX, .slice = (uint8_t*) &user_slice_1 },
|
||||
{ .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_2 }
|
||||
};
|
||||
|
||||
ndarray.slice(num_ndslices, ndslices, &dst_ndarray);
|
||||
|
||||
int32_t expected_shape[dst_ndims] = { 2 };
|
||||
int32_t expected_strides[dst_ndims] = { -16 };
|
||||
assert_arrays_match("shape", "%d", dst_ndims, expected_shape, dst_ndarray.shape);
|
||||
assert_arrays_match("strides", "%d", dst_ndims, expected_strides, dst_ndarray.strides);
|
||||
|
||||
// [5.0, 3.0]
|
||||
assert_values_match("dst_ndarray[0]", "%f", 11.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 0 })));
|
||||
assert_values_match("dst_ndarray[1]", "%f", 9.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 1 })));
|
||||
}
|
||||
|
||||
void test_can_broadcast_shape() {
|
||||
BEGIN_TEST();
|
||||
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([3], [1, 1, 1, 1, 3]) == true",
|
||||
"%d",
|
||||
true,
|
||||
ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 3 }, 5, (int32_t[]) { 1, 1, 1, 1, 3 })
|
||||
);
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([3], [3, 1]) == false",
|
||||
"%d",
|
||||
false,
|
||||
ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 3 }, 2, (int32_t[]) { 3, 1 }));
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([3], [3]) == true",
|
||||
"%d",
|
||||
true,
|
||||
ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 3 }, 1, (int32_t[]) { 3 }));
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([1], [3]) == false",
|
||||
"%d",
|
||||
false,
|
||||
ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 1 }, 1, (int32_t[]) { 3 }));
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([1], [1]) == true",
|
||||
"%d",
|
||||
true,
|
||||
ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 1 }, 1, (int32_t[]) { 1 }));
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([256, 256, 3], [256, 1, 3]) == true",
|
||||
"%d",
|
||||
true,
|
||||
ndarray_util::can_broadcast_shape_to(3, (int32_t[]) { 256, 256, 3 }, 3, (int32_t[]) { 256, 1, 3 })
|
||||
);
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([256, 256, 3], [3]) == true",
|
||||
"%d",
|
||||
true,
|
||||
ndarray_util::can_broadcast_shape_to(3, (int32_t[]) { 256, 256, 3 }, 1, (int32_t[]) { 3 })
|
||||
);
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([256, 256, 3], [2]) == false",
|
||||
"%d",
|
||||
false,
|
||||
ndarray_util::can_broadcast_shape_to(3, (int32_t[]) { 256, 256, 3 }, 1, (int32_t[]) { 2 })
|
||||
);
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([256, 256, 3], [1]) == true",
|
||||
"%d",
|
||||
true,
|
||||
ndarray_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(
|
||||
"can_broadcast_shape_to([0], [1]) == true",
|
||||
"%d",
|
||||
true,
|
||||
ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 0 }, 1, (int32_t[]) { 1 })
|
||||
);
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([0], [2]) == false",
|
||||
"%d",
|
||||
false,
|
||||
ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 0 }, 1, (int32_t[]) { 2 })
|
||||
);
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([0, 4, 0, 0], [1]) == true",
|
||||
"%d",
|
||||
true,
|
||||
ndarray_util::can_broadcast_shape_to(4, (int32_t[]) { 0, 4, 0, 0 }, 1, (int32_t[]) { 1 })
|
||||
);
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([0, 4, 0, 0], [1, 1, 1, 1]) == true",
|
||||
"%d",
|
||||
true,
|
||||
ndarray_util::can_broadcast_shape_to(4, (int32_t[]) { 0, 4, 0, 0 }, 4, (int32_t[]) { 1, 1, 1, 1 })
|
||||
);
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([0, 4, 0, 0], [1, 4, 1, 1]) == true",
|
||||
"%d",
|
||||
true,
|
||||
ndarray_util::can_broadcast_shape_to(4, (int32_t[]) { 0, 4, 0, 0 }, 4, (int32_t[]) { 1, 4, 1, 1 })
|
||||
);
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([4, 3], [0, 3]) == false",
|
||||
"%d",
|
||||
false,
|
||||
ndarray_util::can_broadcast_shape_to(2, (int32_t[]) { 4, 3 }, 2, (int32_t[]) { 0, 3 })
|
||||
);
|
||||
assert_values_match(
|
||||
"can_broadcast_shape_to([4, 3], [0, 0]) == false",
|
||||
"%d",
|
||||
false,
|
||||
ndarray_util::can_broadcast_shape_to(2, (int32_t[]) { 4, 3 }, 2, (int32_t[]) { 0, 0 })
|
||||
);
|
||||
}
|
||||
|
||||
void test_ndarray_broadcast_1() {
|
||||
/*
|
||||
# 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.set_strides_by_shape();
|
||||
|
||||
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_to(&dst_ndarray);
|
||||
|
||||
assert_arrays_match("dst_ndarray->strides", "%d", dst_ndims, (int32_t[]) { 0, 0, 8 }, dst_ndarray.strides);
|
||||
|
||||
assert_values_match("dst_ndarray[0, 0, 0]", "%f", 19.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 0, 0})));
|
||||
assert_values_match("dst_ndarray[0, 0, 1]", "%f", 29.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 0, 1})));
|
||||
assert_values_match("dst_ndarray[0, 0, 2]", "%f", 39.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 0, 2})));
|
||||
assert_values_match("dst_ndarray[0, 0, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 0, 3})));
|
||||
assert_values_match("dst_ndarray[0, 1, 0]", "%f", 19.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 1, 0})));
|
||||
assert_values_match("dst_ndarray[0, 1, 1]", "%f", 29.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 1, 1})));
|
||||
assert_values_match("dst_ndarray[0, 1, 2]", "%f", 39.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 1, 2})));
|
||||
assert_values_match("dst_ndarray[0, 1, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 1, 3})));
|
||||
assert_values_match("dst_ndarray[1, 2, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {1, 2, 3})));
|
||||
}
|
||||
|
||||
void test_assign_with() {
|
||||
/*
|
||||
```
|
||||
xs = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], dtype=np.float64)
|
||||
ys = xs.shape
|
||||
```
|
||||
*/
|
||||
}
|
||||
|
||||
int main() {
|
||||
test::core::run();
|
||||
test::ndarray_basic::run();
|
||||
test::ndarray_indexing::run();
|
||||
test::ndarray_broadcast::run();
|
||||
test_calc_size_from_shape_normal();
|
||||
test_calc_size_from_shape_has_zero();
|
||||
test_set_strides_by_shape();
|
||||
test_ndarray_indices_iter_normal();
|
||||
test_ndarray_fill_generic();
|
||||
test_ndarray_set_to_eye();
|
||||
test_slice_1();
|
||||
test_slice_2();
|
||||
test_slice_3();
|
||||
test_slice_4();
|
||||
test_ndslice_1();
|
||||
test_ndslice_2();
|
||||
test_can_broadcast_shape();
|
||||
test_ndarray_broadcast_1();
|
||||
test_assign_with();
|
||||
return 0;
|
||||
}
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
#pragma once
|
||||
|
||||
// This is made toggleable since `irrt_test.cpp` itself would include
|
||||
// headers that define the `int_t` family.
|
||||
#ifndef IRRT_DONT_TYPEDEF_INTS
|
||||
typedef _BitInt(8) int8_t;
|
||||
typedef unsigned _BitInt(8) uint8_t;
|
||||
typedef _BitInt(32) int32_t;
|
||||
typedef unsigned _BitInt(32) uint32_t;
|
||||
typedef _BitInt(64) int64_t;
|
||||
typedef unsigned _BitInt(64) uint64_t;
|
||||
#endif
|
||||
|
||||
typedef int32_t SliceIndex;
|
||||
|
|
@ -0,0 +1,37 @@
|
|||
#pragma once
|
||||
|
||||
#include "irrt_typedefs.hpp"
|
||||
|
||||
namespace {
|
||||
template <typename T>
|
||||
T max(T a, T b) {
|
||||
return a > b ? a : b;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
T min(T a, 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;
|
||||
}
|
||||
|
||||
void irrt_panic() {
|
||||
// Crash the program for now.
|
||||
// TODO: Don't crash the program
|
||||
// ... or at least produce a good message when doing testing IRRT
|
||||
|
||||
uint8_t* death = nullptr;
|
||||
*death = 0; // TODO: address 0 on hardware might be writable?
|
||||
}
|
||||
|
||||
// TODO: Make this a macro and allow it to be toggled on/off (e.g., debug vs release)
|
||||
void irrt_assert(bool condition) {
|
||||
if (!condition) irrt_panic();
|
||||
}
|
||||
}
|
||||
|
|
@ -1,11 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <cstdint>
|
||||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
#include <irrt_everything.hpp>
|
||||
#include <test/util.hpp>
|
||||
|
||||
/*
|
||||
Include this header for every test_*.cpp
|
||||
*/
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
#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
|
||||
|
|
@ -1,30 +0,0 @@
|
|||
#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
|
||||
|
|
@ -1,127 +0,0 @@
|
|||
#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
|
||||
|
|
@ -1,165 +0,0 @@
|
|||
#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
|
||||
|
|
@ -1,131 +0,0 @@
|
|||
#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
|
|
@ -1,8 +1,6 @@
|
|||
use crate::codegen::{
|
||||
irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
|
||||
llvm_intrinsics::call_int_umin,
|
||||
stmt::gen_for_callback_incrementing,
|
||||
CodeGenContext, CodeGenerator,
|
||||
llvm_intrinsics::call_int_umin, stmt::gen_for_callback_incrementing, CodeGenContext,
|
||||
CodeGenerator,
|
||||
};
|
||||
use inkwell::context::Context;
|
||||
use inkwell::types::{ArrayType, BasicType, StructType};
|
||||
|
|
@ -12,6 +10,7 @@ use inkwell::{
|
|||
values::{BasicValueEnum, IntValue, PointerValue},
|
||||
AddressSpace, IntPredicate,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
/// A LLVM type that is used to represent a non-primitive type in NAC3.
|
||||
pub trait ProxyType<'ctx>: Into<Self::Base> {
|
||||
|
|
@ -1601,7 +1600,8 @@ impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDataProxy<'ctx, '_> {
|
|||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
) -> IntValue<'ctx> {
|
||||
call_ndarray_calc_size(generator, ctx, &self.as_slice_value(ctx, generator), (None, None))
|
||||
todo!()
|
||||
// call_ndarray_calc_size(generator, ctx, &self.as_slice_value(ctx, generator), (None, None))
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -1675,17 +1675,19 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
|
|||
indices_elem_ty.get_bit_width()
|
||||
);
|
||||
|
||||
let index = call_ndarray_flatten_index(generator, ctx, *self.0, indices);
|
||||
todo!()
|
||||
|
||||
unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
self.base_ptr(ctx, generator),
|
||||
&[index],
|
||||
name.unwrap_or_default(),
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
// let index = call_ndarray_flatten_index(generator, ctx, *self.0, indices);
|
||||
|
||||
// unsafe {
|
||||
// ctx.builder
|
||||
// .build_in_bounds_gep(
|
||||
// self.base_ptr(ctx, generator),
|
||||
// &[index],
|
||||
// name.unwrap_or_default(),
|
||||
// )
|
||||
// .unwrap()
|
||||
// }
|
||||
}
|
||||
|
||||
fn ptr_offset<G: CodeGenerator + ?Sized>(
|
||||
|
|
@ -1717,7 +1719,6 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
|
|||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(len, false),
|
||||
|generator, ctx, _, i| {
|
||||
|
|
@ -1762,3 +1763,307 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> UntypedArrayLikeMutator<'ctx,
|
|||
for NDArrayDataProxy<'ctx, '_>
|
||||
{
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct StructField<'ctx> {
|
||||
/// The GEP index of this struct field.
|
||||
pub gep_index: u32,
|
||||
/// Name of this struct field.
|
||||
///
|
||||
/// Used for generating names.
|
||||
pub name: &'static str,
|
||||
/// The type of this struct field.
|
||||
pub ty: BasicTypeEnum<'ctx>,
|
||||
}
|
||||
|
||||
pub struct StructFields<'ctx> {
|
||||
/// Name of the struct.
|
||||
///
|
||||
/// Used for generating names.
|
||||
pub name: &'static str,
|
||||
|
||||
/// All the [`StructField`]s of this struct.
|
||||
///
|
||||
/// **NOTE:** The index position of a [`StructField`]
|
||||
/// matches the element's [`StructField::index`].
|
||||
pub fields: Vec<StructField<'ctx>>,
|
||||
}
|
||||
|
||||
struct StructFieldsBuilder<'ctx> {
|
||||
gep_index_counter: u32,
|
||||
/// Name of the struct to be built.
|
||||
name: &'static str,
|
||||
fields: Vec<StructField<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx> StructField<'ctx> {
|
||||
pub fn gep(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ptr: PointerValue<'ctx>,
|
||||
) -> PointerValue<'ctx> {
|
||||
ctx.builder.build_struct_gep(ptr, self.gep_index, self.name).unwrap()
|
||||
}
|
||||
|
||||
pub fn load(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ptr: PointerValue<'ctx>,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
ctx.builder.build_load(self.gep(ctx, ptr), self.name).unwrap()
|
||||
}
|
||||
|
||||
pub fn store<V>(&self, ctx: &CodeGenContext<'ctx, '_>, ptr: PointerValue<'ctx>, value: V)
|
||||
where
|
||||
V: BasicValue<'ctx>,
|
||||
{
|
||||
ctx.builder.build_store(ptr, value).unwrap();
|
||||
}
|
||||
}
|
||||
|
||||
type IsInstanceError = String;
|
||||
type IsInstanceResult = Result<(), IsInstanceError>;
|
||||
|
||||
pub fn check_basic_types_match<'ctx, A, B>(expected: A, got: B) -> IsInstanceResult
|
||||
where
|
||||
A: BasicType<'ctx>,
|
||||
B: BasicType<'ctx>,
|
||||
{
|
||||
let expected = expected.as_basic_type_enum();
|
||||
let got = got.as_basic_type_enum();
|
||||
|
||||
// Put those logic into here,
|
||||
// otherwise there is always a fallback reporting on any kind of mismatch
|
||||
match (expected, got) {
|
||||
(BasicTypeEnum::IntType(expected), BasicTypeEnum::IntType(got)) => {
|
||||
if expected.get_bit_width() != got.get_bit_width() {
|
||||
return Err(format!(
|
||||
"Expected IntType ({expected}-bit(s)), got IntType ({got}-bit(s))"
|
||||
));
|
||||
}
|
||||
}
|
||||
(expected, got) => {
|
||||
if expected != got {
|
||||
return Err(format!("Expected {expected}, got {got}"));
|
||||
}
|
||||
}
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
impl<'ctx> StructFields<'ctx> {
|
||||
pub fn num_fields(&self) -> u32 {
|
||||
self.fields.len() as u32
|
||||
}
|
||||
|
||||
pub fn as_struct_type(&self, ctx: &'ctx Context) -> StructType<'ctx> {
|
||||
let llvm_fields = self.fields.iter().map(|field| field.ty).collect_vec();
|
||||
ctx.struct_type(llvm_fields.as_slice(), false)
|
||||
}
|
||||
|
||||
pub fn is_type(&self, scrutinee: StructType<'ctx>) -> IsInstanceResult {
|
||||
// Check scrutinee's number of struct fields
|
||||
if scrutinee.count_fields() != self.num_fields() {
|
||||
return Err(format!(
|
||||
"Expected {expected_count} field(s) in `{struct_name}` type, got {got_count}",
|
||||
struct_name = self.name,
|
||||
expected_count = self.num_fields(),
|
||||
got_count = scrutinee.count_fields(),
|
||||
));
|
||||
}
|
||||
|
||||
// Check the scrutinee's field types
|
||||
for field in self.fields.iter() {
|
||||
let expected_field_ty = field.ty;
|
||||
let got_field_ty = scrutinee.get_field_type_at_index(field.gep_index).unwrap();
|
||||
|
||||
if let Err(field_err) = check_basic_types_match(expected_field_ty, got_field_ty) {
|
||||
return Err(format!(
|
||||
"Field GEP index {gep_index} does not match the expected type of ({struct_name}::{field_name}): {field_err}",
|
||||
gep_index = field.gep_index,
|
||||
struct_name = self.name,
|
||||
field_name = field.name,
|
||||
));
|
||||
}
|
||||
}
|
||||
|
||||
// Done
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> StructFieldsBuilder<'ctx> {
|
||||
fn start(name: &'static str) -> Self {
|
||||
StructFieldsBuilder { gep_index_counter: 0, name, fields: Vec::new() }
|
||||
}
|
||||
|
||||
fn add_field(&mut self, name: &'static str, ty: BasicTypeEnum<'ctx>) -> StructField<'ctx> {
|
||||
let index = self.gep_index_counter;
|
||||
self.gep_index_counter += 1;
|
||||
StructField { gep_index: index, name, ty }
|
||||
}
|
||||
|
||||
fn end(self) -> StructFields<'ctx> {
|
||||
StructFields { name: self.name, fields: self.fields }
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NpArrayType<'ctx> {
|
||||
pub size_type: IntType<'ctx>,
|
||||
pub elem_type: BasicTypeEnum<'ctx>,
|
||||
}
|
||||
|
||||
pub struct NpArrayStructFields<'ctx> {
|
||||
pub whole_struct: StructFields<'ctx>,
|
||||
pub data: StructField<'ctx>,
|
||||
pub itemsize: StructField<'ctx>,
|
||||
pub ndims: StructField<'ctx>,
|
||||
pub shape: StructField<'ctx>,
|
||||
pub strides: StructField<'ctx>,
|
||||
}
|
||||
|
||||
impl<'ctx> NpArrayType<'ctx> {
|
||||
pub fn new_opaque_elem(
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
size_type: IntType<'ctx>,
|
||||
) -> NpArrayType<'ctx> {
|
||||
NpArrayType { size_type, elem_type: ctx.ctx.i8_type().as_basic_type_enum() }
|
||||
}
|
||||
|
||||
pub fn struct_type(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructType<'ctx> {
|
||||
self.fields().whole_struct.as_struct_type(ctx.ctx)
|
||||
}
|
||||
|
||||
pub fn fields(&self) -> NpArrayStructFields<'ctx> {
|
||||
let mut builder = StructFieldsBuilder::start("NpArray");
|
||||
|
||||
let addrspace = AddressSpace::default();
|
||||
|
||||
let byte_type = self.size_type.get_context().i8_type();
|
||||
|
||||
// Make sure the struct matches PERFECTLY with that defined in `nac3core/irrt`.
|
||||
let data = builder.add_field("data", byte_type.ptr_type(addrspace).into());
|
||||
let itemsize = builder.add_field("itemsize", self.size_type.into());
|
||||
let ndims = builder.add_field("ndims", self.size_type.into());
|
||||
let shape = builder.add_field("shape", self.size_type.ptr_type(addrspace).into());
|
||||
let strides = builder.add_field("strides", self.size_type.ptr_type(addrspace).into());
|
||||
|
||||
NpArrayStructFields { whole_struct: builder.end(), data, itemsize, ndims, shape, strides }
|
||||
}
|
||||
|
||||
/// Allocate an `ndarray` on stack, with the following notes:
|
||||
///
|
||||
/// - `ndarray.ndims` will be initialized to `in_ndims`.
|
||||
/// - `ndarray.itemsize` will be initialized to the size of `self.elem_type.size_of()`.
|
||||
/// - `ndarray.shape` and `ndarray.strides` will be allocated on the stack with number of elements being `in_ndims`,
|
||||
/// all with empty/uninitialized values.
|
||||
pub fn alloca(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
in_ndims: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> NpArrayValue<'ctx> {
|
||||
let fields = self.fields();
|
||||
let ptr =
|
||||
ctx.builder.build_alloca(fields.whole_struct.as_struct_type(ctx.ctx), name).unwrap();
|
||||
|
||||
// Allocate `in_dims` number of `size_type` on the stack for `shape` and `strides`
|
||||
let allocated_shape =
|
||||
ctx.builder.build_array_alloca(fields.shape.ty, in_ndims, "allocated_shape").unwrap();
|
||||
let allocated_strides = ctx
|
||||
.builder
|
||||
.build_array_alloca(fields.strides.ty, in_ndims, "allocated_strides")
|
||||
.unwrap();
|
||||
|
||||
let value = NpArrayValue { ty: *self, ptr };
|
||||
value.store_ndims(ctx, in_ndims);
|
||||
value.store_itemsize(ctx, self.elem_type.size_of().unwrap());
|
||||
value.store_shape(ctx, allocated_shape);
|
||||
value.store_strides(ctx, allocated_strides);
|
||||
|
||||
return value;
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NpArrayValue<'ctx> {
|
||||
pub ty: NpArrayType<'ctx>,
|
||||
pub ptr: PointerValue<'ctx>,
|
||||
}
|
||||
|
||||
impl<'ctx> NpArrayValue<'ctx> {
|
||||
pub fn load_ndims(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
let field = self.ty.fields().ndims;
|
||||
field.load(ctx, self.ptr).into_int_value()
|
||||
}
|
||||
|
||||
pub fn store_ndims(&self, ctx: &CodeGenContext<'ctx, '_>, value: IntValue<'ctx>) {
|
||||
let field = self.ty.fields().ndims;
|
||||
field.store(ctx, self.ptr, value);
|
||||
}
|
||||
|
||||
pub fn load_itemsize(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
let field = self.ty.fields().itemsize;
|
||||
field.load(ctx, self.ptr).into_int_value()
|
||||
}
|
||||
|
||||
pub fn store_itemsize(&self, ctx: &CodeGenContext<'ctx, '_>, value: IntValue<'ctx>) {
|
||||
let field = self.ty.fields().itemsize;
|
||||
field.store(ctx, self.ptr, value);
|
||||
}
|
||||
|
||||
pub fn load_shape(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
|
||||
let field = self.ty.fields().shape;
|
||||
field.load(ctx, self.ptr).into_pointer_value()
|
||||
}
|
||||
|
||||
pub fn store_shape(&self, ctx: &CodeGenContext<'ctx, '_>, value: PointerValue<'ctx>) {
|
||||
let field = self.ty.fields().shape;
|
||||
field.store(ctx, self.ptr, value);
|
||||
}
|
||||
|
||||
pub fn load_strides(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
|
||||
let field = self.ty.fields().strides;
|
||||
field.load(ctx, self.ptr).into_pointer_value()
|
||||
}
|
||||
|
||||
pub fn store_strides(&self, ctx: &CodeGenContext<'ctx, '_>, value: PointerValue<'ctx>) {
|
||||
let field = self.ty.fields().strides;
|
||||
field.store(ctx, self.ptr, value);
|
||||
}
|
||||
|
||||
/// TODO: DOCUMENT ME -- NDIMS WOULD NEVER CHANGE!!!!!
|
||||
pub fn shape_slice(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
|
||||
let field = self.ty.fields().shape;
|
||||
field.gep(ctx, self.ptr);
|
||||
|
||||
let ndims = self.load_ndims(ctx);
|
||||
|
||||
TypedArrayLikeAdapter {
|
||||
adapted: ArraySliceValue(self.ptr, ndims, Some(field.name)),
|
||||
downcast_fn: Box::new(|_ctx, x| x.into_int_value()),
|
||||
upcast_fn: Box::new(|_ctx, x| x.as_basic_value_enum()),
|
||||
}
|
||||
}
|
||||
|
||||
/// TODO: DOCUMENT ME -- NDIMS WOULD NEVER CHANGE!!!!!
|
||||
pub fn strides_slice(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
|
||||
let field = self.ty.fields().strides;
|
||||
field.gep(ctx, self.ptr);
|
||||
|
||||
let ndims = self.load_ndims(ctx);
|
||||
|
||||
TypedArrayLikeAdapter {
|
||||
adapted: ArraySliceValue(self.ptr, ndims, Some(field.name)),
|
||||
downcast_fn: Box::new(|_ctx, x| x.into_int_value()),
|
||||
upcast_fn: Box::new(|_ctx, x| x.as_basic_value_enum()),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -25,7 +25,6 @@ pub struct ConcreteFuncArg {
|
|||
pub name: StrRef,
|
||||
pub ty: ConcreteType,
|
||||
pub default_value: Option<SymbolValue>,
|
||||
pub is_vararg: bool,
|
||||
}
|
||||
|
||||
#[derive(Clone, Debug)]
|
||||
|
|
@ -47,7 +46,6 @@ pub enum ConcreteTypeEnum {
|
|||
TPrimitive(Primitive),
|
||||
TTuple {
|
||||
ty: Vec<ConcreteType>,
|
||||
is_vararg_ctx: bool,
|
||||
},
|
||||
TObj {
|
||||
obj_id: DefinitionId,
|
||||
|
|
@ -104,16 +102,8 @@ impl ConcreteTypeStore {
|
|||
.iter()
|
||||
.map(|arg| ConcreteFuncArg {
|
||||
name: arg.name,
|
||||
ty: if arg.is_vararg {
|
||||
let tuple_ty = unifier
|
||||
.add_ty(TypeEnum::TTuple { ty: vec![arg.ty], is_vararg_ctx: true });
|
||||
|
||||
self.from_unifier_type(unifier, primitives, tuple_ty, cache)
|
||||
} else {
|
||||
self.from_unifier_type(unifier, primitives, arg.ty, cache)
|
||||
},
|
||||
ty: self.from_unifier_type(unifier, primitives, arg.ty, cache),
|
||||
default_value: arg.default_value.clone(),
|
||||
is_vararg: arg.is_vararg,
|
||||
})
|
||||
.collect(),
|
||||
ret: self.from_unifier_type(unifier, primitives, signature.ret, cache),
|
||||
|
|
@ -168,12 +158,11 @@ impl ConcreteTypeStore {
|
|||
cache.insert(ty, None);
|
||||
let ty_enum = unifier.get_ty(ty);
|
||||
let result = match &*ty_enum {
|
||||
TypeEnum::TTuple { ty, is_vararg_ctx } => ConcreteTypeEnum::TTuple {
|
||||
TypeEnum::TTuple { ty } => ConcreteTypeEnum::TTuple {
|
||||
ty: ty
|
||||
.iter()
|
||||
.map(|t| self.from_unifier_type(unifier, primitives, *t, cache))
|
||||
.collect(),
|
||||
is_vararg_ctx: *is_vararg_ctx,
|
||||
},
|
||||
TypeEnum::TObj { obj_id, fields, params } => ConcreteTypeEnum::TObj {
|
||||
obj_id: *obj_id,
|
||||
|
|
@ -259,12 +248,11 @@ impl ConcreteTypeStore {
|
|||
*cache.get_mut(&cty).unwrap() = Some(ty);
|
||||
return ty;
|
||||
}
|
||||
ConcreteTypeEnum::TTuple { ty, is_vararg_ctx } => TypeEnum::TTuple {
|
||||
ConcreteTypeEnum::TTuple { ty } => TypeEnum::TTuple {
|
||||
ty: ty
|
||||
.iter()
|
||||
.map(|cty| self.to_unifier_type(unifier, primitives, *cty, cache))
|
||||
.collect(),
|
||||
is_vararg_ctx: *is_vararg_ctx,
|
||||
},
|
||||
ConcreteTypeEnum::TVirtual { ty } => {
|
||||
TypeEnum::TVirtual { ty: self.to_unifier_type(unifier, primitives, *ty, cache) }
|
||||
|
|
@ -289,7 +277,6 @@ impl ConcreteTypeStore {
|
|||
name: arg.name,
|
||||
ty: self.to_unifier_type(unifier, primitives, arg.ty, cache),
|
||||
default_value: arg.default_value.clone(),
|
||||
is_vararg: false,
|
||||
})
|
||||
.collect(),
|
||||
ret: self.to_unifier_type(unifier, primitives, *ret, cache),
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -130,62 +130,3 @@ 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);
|
||||
|
|
|
|||
|
|
@ -123,45 +123,11 @@ 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, 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)
|
||||
gen_assign(self, ctx, target, value)
|
||||
}
|
||||
|
||||
/// Generate code for a while expression.
|
||||
|
|
|
|||
|
|
@ -1,30 +1,23 @@
|
|||
use crate::symbol_resolver::SymbolResolver;
|
||||
use crate::typecheck::typedef::Type;
|
||||
use crate::{typecheck::typedef::Type, util::SizeVariant};
|
||||
|
||||
mod test;
|
||||
|
||||
use super::model::*;
|
||||
use super::object::ndarray::broadcast::ShapeEntry;
|
||||
use super::object::ndarray::indexing::{NDIndex, UserSlice};
|
||||
use super::structure::{List, NDArray, NDIter};
|
||||
use super::{
|
||||
classes::{
|
||||
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue,
|
||||
TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
|
||||
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue, NpArrayType,
|
||||
NpArrayValue, TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
|
||||
},
|
||||
llvm_intrinsics, CodeGenContext, CodeGenerator,
|
||||
};
|
||||
use crate::codegen::classes::TypedArrayLikeAccessor;
|
||||
use crate::codegen::stmt::gen_for_callback_incrementing;
|
||||
use function::{get_sizet_dependent_function_name, CallFunction};
|
||||
use inkwell::values::BasicValue;
|
||||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
context::Context,
|
||||
memory_buffer::MemoryBuffer,
|
||||
module::Module,
|
||||
types::{BasicTypeEnum, IntType},
|
||||
values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue},
|
||||
types::{BasicType, BasicTypeEnum, FunctionType, IntType, PointerType},
|
||||
values::{BasicValueEnum, CallSiteValue, FloatValue, FunctionValue, IntValue},
|
||||
AddressSpace, IntPredicate,
|
||||
};
|
||||
use itertools::Either;
|
||||
|
|
@ -423,27 +416,14 @@ 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.value), Some(dest_slice_len.value), Some(dest_idx_2.value)],
|
||||
ctx.current_loc,
|
||||
);
|
||||
}
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
cond,
|
||||
"0:ValueError",
|
||||
"attempt to assign sequence of size {0} to slice of size {1} with step size {2}",
|
||||
[Some(src_slice_len), Some(dest_slice_len), Some(dest_idx.2)],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
let new_len = {
|
||||
let args = vec![
|
||||
|
|
@ -820,7 +800,6 @@ 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| {
|
||||
|
|
@ -895,7 +874,7 @@ 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 broadcast
|
||||
/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
|
||||
/// array `broadcast_idx`.
|
||||
pub fn call_ndarray_calc_broadcast_index<
|
||||
'ctx,
|
||||
|
|
@ -951,336 +930,62 @@ pub fn call_ndarray_calc_broadcast_index<
|
|||
)
|
||||
}
|
||||
|
||||
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);
|
||||
fn get_size_variant<'ctx>(ty: IntType<'ctx>) -> SizeVariant {
|
||||
match ty.get_bit_width() {
|
||||
32 => SizeVariant::Bits32,
|
||||
64 => SizeVariant::Bits64,
|
||||
_ => unreachable!("Unsupported int type bit width {}", ty.get_bit_width()),
|
||||
}
|
||||
}
|
||||
|
||||
pub fn call_nac3_list_slice_assign<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dst: Ptr<'ctx, StructModel<List<IntModel<Byte>>>>,
|
||||
src: Ptr<'ctx, StructModel<List<IntModel<Byte>>>>,
|
||||
itemsize: Int<'ctx, SizeT>,
|
||||
user_slice: Ptr<'ctx, StructModel<UserSlice>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_list_slice_assign");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(dst)
|
||||
.arg(src)
|
||||
.arg(itemsize)
|
||||
.arg(user_slice)
|
||||
.returning_void();
|
||||
fn get_size_type_dependent_function<'ctx, BuildFuncTypeFn>(
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
size_type: IntType<'ctx>,
|
||||
base_name: &str,
|
||||
build_func_type: BuildFuncTypeFn,
|
||||
) -> FunctionValue<'ctx>
|
||||
where
|
||||
BuildFuncTypeFn: Fn() -> FunctionType<'ctx>,
|
||||
{
|
||||
let mut fn_name = base_name.to_owned();
|
||||
match get_size_variant(size_type) {
|
||||
SizeVariant::Bits32 => {
|
||||
// The original fn_name is the correct function name
|
||||
}
|
||||
SizeVariant::Bits64 => {
|
||||
// Append "64" at the end, this is the naming convention for 64-bit
|
||||
fn_name.push_str("64");
|
||||
}
|
||||
}
|
||||
|
||||
// Get (or declare then get if does not exist) the corresponding function
|
||||
ctx.module.get_function(&fn_name).unwrap_or_else(|| {
|
||||
let fn_type = build_func_type();
|
||||
ctx.module.add_function(&fn_name, fn_type, None)
|
||||
})
|
||||
}
|
||||
|
||||
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).arg(shape).returning_void();
|
||||
fn get_ndarray_struct_ptr<'ctx>(ctx: &'ctx Context, size_type: IntType<'ctx>) -> PointerType<'ctx> {
|
||||
let i8_type = ctx.i8_type();
|
||||
|
||||
let ndarray_ty = NpArrayType { size_type, elem_type: i8_type.as_basic_type_enum() };
|
||||
let struct_ty = ndarray_ty.fields().whole_struct.as_struct_type(ctx);
|
||||
struct_ty.ptr_type(AddressSpace::default())
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_util_assert_output_shape_same<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ndims: Int<'ctx, SizeT>,
|
||||
ndarray_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
output_ndims: Int<'ctx, SizeT>,
|
||||
output_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_util_assert_output_shape_same",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(ndarray_ndims)
|
||||
.arg(ndarray_shape)
|
||||
.arg(output_ndims)
|
||||
.arg(output_shape)
|
||||
.returning_void();
|
||||
}
|
||||
pub fn call_nac3_ndarray_size<'ctx>(
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ndarray: NpArrayValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
let size_type = ndarray.ty.size_type;
|
||||
let function = get_size_type_dependent_function(ctx, size_type, "__nac3_ndarray_size", || {
|
||||
size_type.fn_type(&[get_ndarray_struct_ptr(ctx.ctx, size_type).into()], false)
|
||||
});
|
||||
|
||||
pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_size");
|
||||
CallFunction::begin(generator, ctx, &name).arg(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<NDArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_nbytes");
|
||||
CallFunction::begin(generator, ctx, &name).arg(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<NDArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_len");
|
||||
CallFunction::begin(generator, ctx, &name).arg(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<NDArray>>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_is_c_contiguous");
|
||||
CallFunction::begin(generator, ctx, &name).arg(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<NDArray>>,
|
||||
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(pndarray).arg(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<NDArray>>,
|
||||
) {
|
||||
let name =
|
||||
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_set_strides_by_shape");
|
||||
CallFunction::begin(generator, ctx, &name).arg(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<NDArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_copy_data");
|
||||
CallFunction::begin(generator, ctx, &name).arg(src_ndarray).arg(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<NDArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(num_indexes)
|
||||
.arg(indexes)
|
||||
.arg(src_ndarray)
|
||||
.arg(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<NDArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
|
||||
CallFunction::begin(generator, ctx, &name).arg(src_ndarray).arg(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_shape_entries)
|
||||
.arg(shape_entries)
|
||||
.arg(dst_ndims)
|
||||
.arg(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)
|
||||
.arg(new_ndims)
|
||||
.arg(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<NDArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
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)
|
||||
.arg(dst_ndarray)
|
||||
.arg(num_axes)
|
||||
.arg(axes)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
pub fn call_nac3_ndarray_matmul_calculate_shapes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
a_ndims: Int<'ctx, SizeT>,
|
||||
a_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
b_ndims: Int<'ctx, SizeT>,
|
||||
b_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
final_ndims: Int<'ctx, SizeT>,
|
||||
new_a_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
new_b_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
dst_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name =
|
||||
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_matmul_calculate_shapes");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(a_ndims)
|
||||
.arg(a_shape)
|
||||
.arg(b_ndims)
|
||||
.arg(b_shape)
|
||||
.arg(final_ndims)
|
||||
.arg(new_a_shape)
|
||||
.arg(new_b_shape)
|
||||
.arg(dst_shape)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_float64_matmul_at_least_2d<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
a_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
b_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_float64_matmul_at_least_2d",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(a_ndarray)
|
||||
.arg(b_ndarray)
|
||||
.arg(dst_ndarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_array_set_and_validate_list_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: Ptr<'ctx, StructModel<List<IntModel<Byte>>>>,
|
||||
ndims: Int<'ctx, SizeT>,
|
||||
shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_array_set_and_validate_list_shape",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name).arg(list).arg(ndims).arg(shape).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_array_write_list_to_array<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: Ptr<'ctx, StructModel<List<IntModel<Byte>>>>,
|
||||
ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) {
|
||||
let name =
|
||||
get_sizet_dependent_function_name(generator, ctx, "__nac3_array_write_list_to_array");
|
||||
CallFunction::begin(generator, ctx, &name).arg(list).arg(ndarray).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_nditer_initialize<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
iter: Ptr<'ctx, StructModel<NDIter>>,
|
||||
ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
indices: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_initialize");
|
||||
CallFunction::begin(generator, ctx, &name).arg(iter).arg(ndarray).arg(indices).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_nditer_has_next<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
iter: Ptr<'ctx, StructModel<NDIter>>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_has_next");
|
||||
CallFunction::begin(generator, ctx, &name).arg(iter).returning_auto("has_next")
|
||||
}
|
||||
|
||||
pub fn call_nac3_nditer_next<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
iter: Ptr<'ctx, StructModel<NDIter>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_next");
|
||||
CallFunction::begin(generator, ctx, &name).arg(iter).returning_void();
|
||||
ctx.builder
|
||||
.build_call(function, &[ndarray.ptr.into()], "size")
|
||||
.unwrap()
|
||||
.try_as_basic_value()
|
||||
.unwrap_left()
|
||||
.into_int_value()
|
||||
}
|
||||
|
|
|
|||
|
|
@ -35,40 +35,6 @@ fn get_float_intrinsic_repr(ctx: &Context, ft: FloatType) -> &'static str {
|
|||
unreachable!()
|
||||
}
|
||||
|
||||
/// Invokes the [`llvm.va_start`](https://llvm.org/docs/LangRef.html#llvm-va-start-intrinsic)
|
||||
/// intrinsic.
|
||||
pub fn call_va_start<'ctx>(ctx: &CodeGenContext<'ctx, '_>, arglist: PointerValue<'ctx>) {
|
||||
const FN_NAME: &str = "llvm.va_start";
|
||||
|
||||
let intrinsic_fn = ctx.module.get_function(FN_NAME).unwrap_or_else(|| {
|
||||
let llvm_void = ctx.ctx.void_type();
|
||||
let llvm_i8 = ctx.ctx.i8_type();
|
||||
let llvm_p0i8 = llvm_i8.ptr_type(AddressSpace::default());
|
||||
let fn_type = llvm_void.fn_type(&[llvm_p0i8.into()], false);
|
||||
|
||||
ctx.module.add_function(FN_NAME, fn_type, None)
|
||||
});
|
||||
|
||||
ctx.builder.build_call(intrinsic_fn, &[arglist.into()], "").unwrap();
|
||||
}
|
||||
|
||||
/// Invokes the [`llvm.va_start`](https://llvm.org/docs/LangRef.html#llvm-va-start-intrinsic)
|
||||
/// intrinsic.
|
||||
pub fn call_va_end<'ctx>(ctx: &CodeGenContext<'ctx, '_>, arglist: PointerValue<'ctx>) {
|
||||
const FN_NAME: &str = "llvm.va_end";
|
||||
|
||||
let intrinsic_fn = ctx.module.get_function(FN_NAME).unwrap_or_else(|| {
|
||||
let llvm_void = ctx.ctx.void_type();
|
||||
let llvm_i8 = ctx.ctx.i8_type();
|
||||
let llvm_p0i8 = llvm_i8.ptr_type(AddressSpace::default());
|
||||
let fn_type = llvm_void.fn_type(&[llvm_p0i8.into()], false);
|
||||
|
||||
ctx.module.add_function(FN_NAME, fn_type, None)
|
||||
});
|
||||
|
||||
ctx.builder.build_call(intrinsic_fn, &[arglist.into()], "").unwrap();
|
||||
}
|
||||
|
||||
/// Invokes the [`llvm.stacksave`](https://llvm.org/docs/LangRef.html#llvm-stacksave-intrinsic)
|
||||
/// intrinsic.
|
||||
pub fn call_stacksave<'ctx>(
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
use crate::{
|
||||
codegen::classes::{ListType, ProxyType, RangeType},
|
||||
codegen::classes::{ListType, NDArrayType, ProxyType, RangeType},
|
||||
symbol_resolver::{StaticValue, SymbolResolver},
|
||||
toplevel::{helper::PrimDef, TopLevelContext, TopLevelDef},
|
||||
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
|
||||
typecheck::{
|
||||
type_inferencer::{CodeLocation, PrimitiveStore},
|
||||
typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
|
||||
|
|
@ -24,7 +24,6 @@ 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};
|
||||
|
|
@ -33,8 +32,8 @@ use std::sync::{
|
|||
Arc,
|
||||
};
|
||||
use std::thread;
|
||||
use structure::{CSlice, Exception, NDArray};
|
||||
|
||||
pub mod builtin_fns;
|
||||
pub mod classes;
|
||||
pub mod concrete_type;
|
||||
pub mod expr;
|
||||
|
|
@ -42,12 +41,8 @@ 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 object;
|
||||
pub mod stmt;
|
||||
pub mod structure;
|
||||
|
||||
#[cfg(test)]
|
||||
mod test;
|
||||
|
|
@ -73,16 +68,6 @@ 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 {
|
||||
|
|
@ -173,11 +158,11 @@ pub struct CodeGenContext<'ctx, 'a> {
|
|||
pub registry: &'a WorkerRegistry,
|
||||
|
||||
/// Cache for constant strings.
|
||||
pub const_strings: HashMap<String, Struct<'ctx, CSlice>>,
|
||||
pub const_strings: HashMap<String, BasicValueEnum<'ctx>>,
|
||||
|
||||
/// [`BasicBlock`] containing all `alloca` statements for the current function.
|
||||
pub init_bb: BasicBlock<'ctx>,
|
||||
pub exception_val: Option<Ptr<'ctx, StructModel<Exception>>>,
|
||||
pub exception_val: Option<PointerValue<'ctx>>,
|
||||
|
||||
/// The header and exit basic blocks of a loop in this context. See
|
||||
/// <https://llvm.org/docs/LoopTerminology.html> for explanation of these terminology.
|
||||
|
|
@ -353,10 +338,6 @@ 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,
|
||||
|
|
@ -380,10 +361,6 @@ 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;
|
||||
|
|
@ -449,7 +426,7 @@ pub struct CodeGenTask {
|
|||
fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
|
||||
ctx: &'ctx Context,
|
||||
module: &Module<'ctx>,
|
||||
generator: &G,
|
||||
generator: &mut G,
|
||||
unifier: &mut Unifier,
|
||||
top_level: &TopLevelContext,
|
||||
type_cache: &mut HashMap<Type, BasicTypeEnum<'ctx>>,
|
||||
|
|
@ -494,8 +471,12 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
|
|||
}
|
||||
|
||||
TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let pndarray_model = PtrModel(StructModel(NDArray));
|
||||
pndarray_model.get_type(generator, ctx).as_basic_type_enum()
|
||||
let (dtype, _) = unpack_ndarray_var_tys(unifier, ty);
|
||||
let element_type = get_llvm_type(
|
||||
ctx, module, generator, unifier, top_level, type_cache, dtype,
|
||||
);
|
||||
|
||||
NDArrayType::new(generator, ctx, element_type).as_base_type().into()
|
||||
}
|
||||
|
||||
_ => unreachable!(
|
||||
|
|
@ -539,10 +520,8 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
|
|||
};
|
||||
return ty;
|
||||
}
|
||||
TTuple { ty, is_vararg_ctx } => {
|
||||
TTuple { ty } => {
|
||||
// a struct with fields in the order present in the tuple
|
||||
assert!(!is_vararg_ctx, "Tuples in vararg context must be instantiated with the correct number of arguments before calling get_llvm_type");
|
||||
|
||||
let fields = ty
|
||||
.iter()
|
||||
.map(|ty| {
|
||||
|
|
@ -572,7 +551,7 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
|
|||
fn get_llvm_abi_type<'ctx, G: CodeGenerator + ?Sized>(
|
||||
ctx: &'ctx Context,
|
||||
module: &Module<'ctx>,
|
||||
generator: &G,
|
||||
generator: &mut G,
|
||||
unifier: &mut Unifier,
|
||||
top_level: &TopLevelContext,
|
||||
type_cache: &mut HashMap<Type, BasicTypeEnum<'ctx>>,
|
||||
|
|
@ -610,40 +589,6 @@ fn need_sret(ty: BasicTypeEnum) -> bool {
|
|||
need_sret_impl(ty, true)
|
||||
}
|
||||
|
||||
/// Returns the [`BasicTypeEnum`] representing a `va_list` struct for variadic arguments.
|
||||
fn get_llvm_valist_type<'ctx>(ctx: &'ctx Context, triple: &TargetTriple) -> BasicTypeEnum<'ctx> {
|
||||
let triple = TargetMachine::normalize_triple(triple);
|
||||
let triple = triple.as_str().to_str().unwrap();
|
||||
let arch = triple.split('-').next().unwrap();
|
||||
|
||||
let llvm_pi8 = ctx.i8_type().ptr_type(AddressSpace::default());
|
||||
|
||||
// Referenced from parseArch() in llvm/lib/Support/Triple.cpp
|
||||
match arch {
|
||||
"i386" | "i486" | "i586" | "i686" | "riscv32" => {
|
||||
ctx.i8_type().ptr_type(AddressSpace::default()).into()
|
||||
}
|
||||
"amd64" | "x86_64" | "x86_64h" => {
|
||||
let llvm_i32 = ctx.i32_type();
|
||||
|
||||
let va_list_tag = ctx.opaque_struct_type("struct.__va_list_tag");
|
||||
va_list_tag.set_body(
|
||||
&[llvm_i32.into(), llvm_i32.into(), llvm_pi8.into(), llvm_pi8.into()],
|
||||
false,
|
||||
);
|
||||
va_list_tag.into()
|
||||
}
|
||||
"armv7" => {
|
||||
let va_list = ctx.opaque_struct_type("struct.__va_list");
|
||||
va_list.set_body(&[llvm_pi8.into()], false);
|
||||
va_list.into()
|
||||
}
|
||||
triple => {
|
||||
todo!("Unsupported platform for varargs: {triple}")
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Implementation for generating LLVM IR for a function.
|
||||
pub fn gen_func_impl<
|
||||
'ctx,
|
||||
|
|
@ -701,19 +646,43 @@ pub fn gen_func_impl<
|
|||
..primitives
|
||||
};
|
||||
|
||||
let cslice_model = StructModel(CSlice);
|
||||
let pexn_model = PtrModel(StructModel(Exception));
|
||||
|
||||
let mut type_cache: HashMap<_, BasicTypeEnum<'ctx>> = [
|
||||
let mut type_cache: HashMap<_, _> = [
|
||||
(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, cslice_model.get_type(generator, context).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.range, RangeType::new(context).as_base_type().into()),
|
||||
(primitives.exception, pexn_model.get_type(generator, context).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()
|
||||
}
|
||||
}),
|
||||
]
|
||||
.iter()
|
||||
.copied()
|
||||
|
|
@ -731,7 +700,6 @@ pub fn gen_func_impl<
|
|||
name: arg.name,
|
||||
ty: task.store.to_unifier_type(&mut unifier, &primitives, arg.ty, &mut cache),
|
||||
default_value: arg.default_value.clone(),
|
||||
is_vararg: arg.is_vararg,
|
||||
})
|
||||
.collect_vec(),
|
||||
task.store.to_unifier_type(&mut unifier, &primitives, *ret, &mut cache),
|
||||
|
|
@ -754,10 +722,7 @@ pub fn gen_func_impl<
|
|||
let has_sret = ret_type.map_or(false, |ty| need_sret(ty));
|
||||
let mut params = args
|
||||
.iter()
|
||||
.filter(|arg| !arg.is_vararg)
|
||||
.map(|arg| {
|
||||
debug_assert!(!arg.is_vararg);
|
||||
|
||||
get_llvm_abi_type(
|
||||
context,
|
||||
&module,
|
||||
|
|
@ -776,12 +741,9 @@ pub fn gen_func_impl<
|
|||
params.insert(0, ret_type.unwrap().ptr_type(AddressSpace::default()).into());
|
||||
}
|
||||
|
||||
debug_assert!(matches!(args.iter().filter(|arg| arg.is_vararg).count(), 0..=1));
|
||||
let vararg_arg = args.iter().find(|arg| arg.is_vararg);
|
||||
|
||||
let fn_type = match ret_type {
|
||||
Some(ret_type) if !has_sret => ret_type.fn_type(¶ms, vararg_arg.is_some()),
|
||||
_ => context.void_type().fn_type(¶ms, vararg_arg.is_some()),
|
||||
Some(ret_type) if !has_sret => ret_type.fn_type(¶ms, false),
|
||||
_ => context.void_type().fn_type(¶ms, false),
|
||||
};
|
||||
|
||||
let symbol = &task.symbol_name;
|
||||
|
|
@ -811,9 +773,7 @@ pub fn gen_func_impl<
|
|||
|
||||
let mut var_assignment = HashMap::new();
|
||||
let offset = u32::from(has_sret);
|
||||
|
||||
// Store non-vararg argument values into local variables
|
||||
for (n, arg) in args.iter().enumerate().filter(|(_, arg)| !arg.is_vararg) {
|
||||
for (n, arg) in args.iter().enumerate() {
|
||||
let param = fn_val.get_nth_param((n as u32) + offset).unwrap();
|
||||
let local_type = get_llvm_type(
|
||||
context,
|
||||
|
|
@ -846,8 +806,6 @@ pub fn gen_func_impl<
|
|||
var_assignment.insert(arg.name, (alloca, None, 0));
|
||||
}
|
||||
|
||||
// TODO: Save vararg parameters as list
|
||||
|
||||
let return_buffer = if has_sret {
|
||||
Some(fn_val.get_nth_param(0).unwrap().into_pointer_value())
|
||||
} else {
|
||||
|
|
@ -1070,9 +1028,3 @@ fn gen_in_range_check<'ctx>(
|
|||
|
||||
ctx.builder.build_int_compare(IntPredicate::SLT, lo, hi, "cmp").unwrap()
|
||||
}
|
||||
|
||||
/// Returns the internal name for the `va_count` argument, used to indicate the number of arguments
|
||||
/// passed to the variadic function.
|
||||
fn get_va_count_arg_name(arg_name: StrRef) -> StrRef {
|
||||
format!("__{}_va_count", &arg_name).into()
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,40 +0,0 @@
|
|||
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: &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)))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,122 +0,0 @@
|
|||
use inkwell::{
|
||||
context::Context,
|
||||
types::{ArrayType, BasicType, BasicTypeEnum},
|
||||
values::ArrayValue,
|
||||
};
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
/// A Model for an [`ArrayType`].
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct ArrayModel<Element> {
|
||||
pub len: u32,
|
||||
pub element: Element,
|
||||
}
|
||||
pub type Array<'ctx, Element> = Instance<'ctx, ArrayModel<Element>>;
|
||||
|
||||
impl<'ctx, Element: Model<'ctx>> Model<'ctx> for ArrayModel<Element> {
|
||||
type Value = ArrayValue<'ctx>;
|
||||
type Type = ArrayType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
|
||||
self.element.get_type(generator, ctx).array_type(self.len)
|
||||
}
|
||||
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
let ty = ty.as_basic_type_enum();
|
||||
let BasicTypeEnum::ArrayType(ty) = ty else {
|
||||
return Err(ModelError(format!("Expecting ArrayType, but got {ty:?}")));
|
||||
};
|
||||
|
||||
if ty.len() != self.len {
|
||||
return Err(ModelError(format!(
|
||||
"Expecting ArrayType with size {}, but got an ArrayType with size {}",
|
||||
ty.len(),
|
||||
self.len
|
||||
)));
|
||||
}
|
||||
|
||||
self.element
|
||||
.check_type(generator, ctx, ty.get_element_type())
|
||||
.map_err(|err| err.under_context("an ArrayType"))?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, Element: Model<'ctx>> Ptr<'ctx, ArrayModel<Element>> {
|
||||
/// Get the pointer to the `i`-th (0-based) array element.
|
||||
pub fn at<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
i: u32,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
assert!(i < self.model.0.len);
|
||||
|
||||
let zero = ctx.ctx.i32_type().const_zero();
|
||||
let i = ctx.ctx.i32_type().const_int(u64::from(i), false);
|
||||
let ptr = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[zero, i], name).unwrap() };
|
||||
|
||||
PtrModel(self.model.0.element).check_value(generator, ctx.ctx, ptr).unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
/// Like [`ArrayModel`] but length is strongly-typed.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct NArrayModel<const LEN: u32, Element>(pub Element);
|
||||
pub type NArray<'ctx, const LEN: u32, Element> = Instance<'ctx, NArrayModel<LEN, Element>>;
|
||||
|
||||
impl<'ctx, const LEN: u32, Element: Model<'ctx>> NArrayModel<LEN, Element> {
|
||||
/// Forget the `LEN` constant generic and get an [`ArrayModel`] with the same length.
|
||||
pub fn forget_len(&self) -> ArrayModel<Element> {
|
||||
ArrayModel { element: self.0, len: LEN }
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, const LEN: u32, Element: Model<'ctx>> Model<'ctx> for NArrayModel<LEN, Element> {
|
||||
type Value = ArrayValue<'ctx>;
|
||||
type Type = ArrayType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
|
||||
// Convenient implementation
|
||||
self.forget_len().get_type(generator, ctx)
|
||||
}
|
||||
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
// Convenient implementation
|
||||
self.forget_len().check_type(generator, ctx, ty)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, const LEN: u32, Element: Model<'ctx>> Ptr<'ctx, NArrayModel<LEN, Element>> {
|
||||
/// Get the pointer to the `i`-th (0-based) array element.
|
||||
pub fn at_const<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
i: u32,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
assert!(i < LEN);
|
||||
|
||||
let zero = ctx.ctx.i32_type().const_zero();
|
||||
let i = ctx.ctx.i32_type().const_int(u64::from(i), false);
|
||||
let ptr = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[zero, i], name).unwrap() };
|
||||
|
||||
PtrModel(self.model.0 .0).check_value(generator, ctx.ctx, ptr).unwrap()
|
||||
}
|
||||
}
|
||||
|
|
@ -1,123 +0,0 @@
|
|||
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.
|
||||
#[must_use]
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &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.
|
||||
#[must_use]
|
||||
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,
|
||||
}
|
||||
|
|
@ -1,88 +0,0 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{context::Context, types::FloatType, values::FloatValue};
|
||||
|
||||
use crate::codegen::CodeGenerator;
|
||||
|
||||
use super::*;
|
||||
|
||||
pub trait FloatKind<'ctx>: fmt::Debug + Clone + Copy {
|
||||
fn get_float_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> FloatType<'ctx>;
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Float32;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Float64;
|
||||
|
||||
impl<'ctx> FloatKind<'ctx> for Float32 {
|
||||
fn get_float_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> FloatType<'ctx> {
|
||||
ctx.f32_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> FloatKind<'ctx> for Float64 {
|
||||
fn get_float_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> FloatType<'ctx> {
|
||||
ctx.f64_type()
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AnyFloat<'ctx>(FloatType<'ctx>);
|
||||
|
||||
impl<'ctx> FloatKind<'ctx> for AnyFloat<'ctx> {
|
||||
fn get_float_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
_ctx: &'ctx Context,
|
||||
) -> FloatType<'ctx> {
|
||||
self.0
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct FloatModel<N>(pub N);
|
||||
pub type Float<'ctx, N> = Instance<'ctx, FloatModel<N>>;
|
||||
|
||||
impl<'ctx, N: FloatKind<'ctx>> Model<'ctx> for FloatModel<N> {
|
||||
type Value = FloatValue<'ctx>;
|
||||
type Type = FloatType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
|
||||
self.0.get_float_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) = FloatType::try_from(ty) else {
|
||||
return Err(ModelError(format!("Expecting FloatType, but got {ty:?}")));
|
||||
};
|
||||
|
||||
let exp_ty = self.0.get_float_type(generator, ctx);
|
||||
|
||||
// TODO: Inkwell does not have get_bit_width for FloatType?
|
||||
// TODO: Quick hack for now, but does this actually work?
|
||||
if ty != exp_ty {
|
||||
return Err(ModelError(format!("Expecting {exp_ty:?}, but got {ty:?}")));
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
|
@ -1,125 +0,0 @@
|
|||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
types::{BasicMetadataTypeEnum, BasicType, FunctionType},
|
||||
values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
// 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
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
struct Arg<'ctx> {
|
||||
ty: BasicMetadataTypeEnum<'ctx>,
|
||||
val: BasicMetadataValueEnum<'ctx>,
|
||||
}
|
||||
|
||||
/// A structure to construct & call an LLVM function.
|
||||
///
|
||||
/// This is a helper to reduce IRRT Inkwell function call boilerplate
|
||||
// TODO: Remove the lifetimes somehow? There is 4 of them.
|
||||
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>>,
|
||||
/// LLVM function Attributes
|
||||
attrs: Vec<&'static str>,
|
||||
}
|
||||
|
||||
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(), attrs: Vec::new() }
|
||||
}
|
||||
|
||||
/// Push a list of LLVM function attributes to the function declaration.
|
||||
#[must_use]
|
||||
pub fn attrs(mut self, attrs: Vec<&'static str>) -> Self {
|
||||
self.attrs = attrs;
|
||||
self
|
||||
}
|
||||
|
||||
/// Push a call argument to the function call.
|
||||
#[allow(clippy::needless_pass_by_value)]
|
||||
#[must_use]
|
||||
pub fn arg<M: Model<'ctx>>(mut self, arg: Instance<'ctx, M>) -> Self {
|
||||
let arg = Arg {
|
||||
ty: arg.model.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.
|
||||
let func = self.ctx.module.get_function(self.name).unwrap_or_else(|| {
|
||||
// Declare the function if it doesn't exist.
|
||||
let tys = self.args.iter().map(|arg| arg.ty).collect_vec();
|
||||
|
||||
let func_type = make_fn_type(&tys);
|
||||
let func = self.ctx.module.add_function(self.name, func_type, None);
|
||||
|
||||
for attr in &self.attrs {
|
||||
func.add_attribute(
|
||||
AttributeLoc::Function,
|
||||
self.ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
|
||||
);
|
||||
}
|
||||
|
||||
func
|
||||
});
|
||||
|
||||
let vals = self.args.iter().map(|arg| arg.val).collect_vec();
|
||||
self.ctx.builder.build_call(func, &vals, return_value_name).unwrap()
|
||||
}
|
||||
}
|
||||
|
|
@ -1,275 +0,0 @@
|
|||
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: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx>;
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Bool;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Byte;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Int32;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Int64;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct SizeT;
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Bool {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.bool_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Byte {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i8_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Int32 {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i32_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Int64 {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i64_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for SizeT {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
generator.get_size_type(ctx)
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AnyInt<'ctx>(pub IntType<'ctx>);
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for AnyInt<'ctx> {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
_ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
self.0
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct 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>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &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 const_all_1s<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = self.get_type(generator, ctx).const_all_ones();
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
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)
|
||||
}
|
||||
}
|
||||
|
|
@ -1,17 +0,0 @@
|
|||
mod any;
|
||||
mod array;
|
||||
mod core;
|
||||
mod float;
|
||||
pub mod function;
|
||||
mod int;
|
||||
mod ptr;
|
||||
mod structure;
|
||||
pub mod util;
|
||||
|
||||
pub use any::*;
|
||||
pub use array::*;
|
||||
pub use core::*;
|
||||
pub use float::*;
|
||||
pub use int::*;
|
||||
pub use ptr::*;
|
||||
pub use structure::*;
|
||||
|
|
@ -1,145 +0,0 @@
|
|||
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: &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()
|
||||
}
|
||||
|
||||
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`] by a constant offset.
|
||||
#[must_use]
|
||||
pub fn offset_const<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
offset: u64,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
let offset = ctx.ctx.i32_type().const_int(offset, false);
|
||||
self.offset(generator, ctx, offset, 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 pointer_cast<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)
|
||||
}
|
||||
}
|
||||
|
|
@ -1,222 +0,0 @@
|
|||
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 from [`Default`] trait.
|
||||
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 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: &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: &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>> Struct<'ctx, S> {
|
||||
pub fn get_field<G: CodeGenerator + ?Sized, M, GetField>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
get_field: GetField,
|
||||
) -> Instance<'ctx, M>
|
||||
where
|
||||
M: Model<'ctx>,
|
||||
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
|
||||
{
|
||||
let field = get_field(self.model.0.fields());
|
||||
let val = self.value.get_field_at_index(field.gep_index as u32).unwrap();
|
||||
field.model.check_value(generator, ctx, val).unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
// TODO: Add an opaque struct type?
|
||||
|
|
@ -1,62 +0,0 @@
|
|||
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,
|
||||
)
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,210 +0,0 @@
|
|||
// TODO: Replace numpy.rs
|
||||
|
||||
use inkwell::values::{BasicValue, BasicValueEnum};
|
||||
use nac3parser::ast::StrRef;
|
||||
|
||||
use crate::{
|
||||
codegen::object::{ndarray::scalar::split_scalar_or_ndarray, tuple::TupleObject},
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{helper::extract_ndims, numpy::unpack_ndarray_var_tys, DefinitionId},
|
||||
typecheck::typedef::{FunSignature, Type},
|
||||
};
|
||||
|
||||
use super::{
|
||||
irrt::call_nac3_ndarray_util_assert_shape_no_negative,
|
||||
model::*,
|
||||
object::{
|
||||
ndarray::{shape_util::parse_numpy_int_sequence, NDArrayObject},
|
||||
AnyObject,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
/// 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)?;
|
||||
let input = AnyObject { ty: input_ty, value: input };
|
||||
|
||||
// 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)?;
|
||||
let shape = AnyObject { ty: shape_ty, value: shape };
|
||||
|
||||
// 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).as_ndarray(generator, ctx);
|
||||
|
||||
// Process `shape`
|
||||
let (_, broadcast_shape) = parse_numpy_int_sequence(generator, ctx, shape);
|
||||
// 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.instance.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)?;
|
||||
let input = AnyObject { ty: input_ty, value: input };
|
||||
|
||||
// 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)?;
|
||||
let shape = AnyObject { ty: shape_ty, value: shape };
|
||||
|
||||
// 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).as_ndarray(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);
|
||||
let reshaped_ndarray = in_ndarray.reshape_or_copy(generator, ctx, reshaped_ndims, new_shape);
|
||||
|
||||
Ok(reshaped_ndarray.instance.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();
|
||||
|
||||
// Implementation
|
||||
let input_dim = IntModel(SizeT).s_extend_or_bit_cast(generator, ctx, input, "input_dim");
|
||||
let ndarray = NDArrayObject::from_np_arange(generator, ctx, input_dim);
|
||||
|
||||
Ok(ndarray.instance.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 = AnyObject { ty: ndarray_ty, value: ndarray };
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
|
||||
|
||||
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)?;
|
||||
let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
|
||||
|
||||
// Process ndarray
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
|
||||
Ok(ndarray.make_shape_tuple(generator, ctx).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: Code duplication: This function looks exactly like `gen_ndarray_shapes`.
|
||||
|
||||
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)?;
|
||||
let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
|
||||
|
||||
// Process ndarray
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
|
||||
|
||||
let mut objects = Vec::with_capacity(ndarray.ndims as usize);
|
||||
|
||||
for i in 0..ndarray.ndims {
|
||||
let dim = ndarray
|
||||
.instance
|
||||
.get(generator, ctx, |f| f.strides, "")
|
||||
.offset_const(generator, ctx, i, "")
|
||||
.load(generator, ctx, "dim");
|
||||
let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
|
||||
|
||||
objects
|
||||
.push(AnyObject { ty: ctx.primitives.int32, value: dim.value.as_basic_value_enum() });
|
||||
}
|
||||
|
||||
let strides = TupleObject::create(generator, ctx, objects, "strides");
|
||||
Ok(strides.value.as_basic_value_enum())
|
||||
}
|
||||
|
|
@ -1,121 +0,0 @@
|
|||
use crate::{
|
||||
codegen::{
|
||||
irrt::{call_nac3_list_slice_assign, list_slice_assignment},
|
||||
model::*,
|
||||
object::ndarray::indexing::UserSlice,
|
||||
structure::List,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::{iter_type_vars, Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::{ndarray::indexing::RustUserSlice, AnyObject};
|
||||
|
||||
/// A NAC3 Python List object.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct ListObject<'ctx> {
|
||||
/// Typechecker type of the list items
|
||||
pub item_type: Type,
|
||||
pub instance: Ptr<'ctx, StructModel<List<AnyModel<'ctx>>>>,
|
||||
}
|
||||
|
||||
impl<'ctx> ListObject<'ctx> {
|
||||
/// Create a [`ListObject`] from an LLVM value and its typechecker [`Type`].
|
||||
pub fn from_object<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
) -> Self {
|
||||
// Check typechecker type and extract `item_type`
|
||||
let item_type = match &*ctx.unifier.get_ty(object.ty) {
|
||||
TypeEnum::TObj { obj_id, params, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
iter_type_vars(params).next().unwrap().ty // Extract `item_type`
|
||||
}
|
||||
_ => {
|
||||
panic!("Expecting type to be a list, but got {}", ctx.unifier.stringify(object.ty))
|
||||
}
|
||||
};
|
||||
|
||||
let item_model = AnyModel(ctx.get_llvm_type(generator, item_type));
|
||||
let plist_model = PtrModel(StructModel(List { item: item_model }));
|
||||
|
||||
// Create object
|
||||
let value = plist_model.check_value(generator, ctx.ctx, object.value).unwrap();
|
||||
ListObject { item_type, instance: value }
|
||||
}
|
||||
|
||||
/// Get the `items` field as an opaque pointer.
|
||||
pub fn get_opaque_items_ptr<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Ptr<'ctx, IntModel<Byte>> {
|
||||
self.instance.get(generator, ctx, |f| f.items, "items").pointer_cast(
|
||||
generator,
|
||||
ctx,
|
||||
IntModel(Byte),
|
||||
"items_opaque",
|
||||
)
|
||||
}
|
||||
|
||||
/// Get the value of this [`ListObject`] as a list with opaque items.
|
||||
///
|
||||
/// This function allocates on the stack to create the list, but the
|
||||
/// reference to the `items` are preserved.
|
||||
pub fn get_opaque_list_ptr<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Ptr<'ctx, StructModel<List<IntModel<Byte>>>> {
|
||||
let opaque_list_model = StructModel(List { item: IntModel(Byte) });
|
||||
let opaque_list_ptr = opaque_list_model.alloca(generator, ctx, "opaque_list_ptr");
|
||||
|
||||
// Copy items pointer
|
||||
let items = self.get_opaque_items_ptr(generator, ctx);
|
||||
opaque_list_ptr.set(ctx, |f| f.items, items);
|
||||
|
||||
// Copy len
|
||||
let len = self.instance.get(generator, ctx, |f| f.len, "len");
|
||||
opaque_list_ptr.set(ctx, |f| f.len, len);
|
||||
|
||||
opaque_list_ptr
|
||||
}
|
||||
|
||||
/// Get the `len()` of this list.
|
||||
pub fn len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
self.instance.get(generator, ctx, |f| f.len, "list_len")
|
||||
}
|
||||
|
||||
pub fn slice_assign_from<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
user_slice: &RustUserSlice<'ctx>,
|
||||
source: ListObject<'ctx>,
|
||||
) {
|
||||
// Sanity check
|
||||
assert!(ctx.unifier.unioned(self.item_type, source.item_type));
|
||||
|
||||
let user_slice_model = StructModel(UserSlice);
|
||||
let puser_slice = user_slice_model.alloca(generator, ctx, "user_slice");
|
||||
user_slice.write_to_user_slice(generator, ctx, puser_slice);
|
||||
|
||||
let itemsize = self.instance.model.get_type(generator, ctx.ctx).size_of();
|
||||
|
||||
call_nac3_list_slice_assign(
|
||||
generator,
|
||||
ctx,
|
||||
self.get_opaque_list_ptr(generator, ctx),
|
||||
source.instance.value,
|
||||
itemsize,
|
||||
user_slice,
|
||||
);
|
||||
todo!()
|
||||
}
|
||||
}
|
||||
|
|
@ -1,608 +0,0 @@
|
|||
use inkwell::{
|
||||
values::{BasicValue, BasicValueEnum, FloatValue, IntValue},
|
||||
FloatPredicate, IntPredicate,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
use list::ListObject;
|
||||
use ndarray::{NDArrayObject, NDArrayOut};
|
||||
use range::RangeObject;
|
||||
use tuple::TupleObject;
|
||||
|
||||
use crate::{
|
||||
toplevel::helper::PrimDef,
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::{llvm_intrinsics, model::*, CodeGenContext, CodeGenerator};
|
||||
|
||||
pub mod list;
|
||||
pub mod ndarray;
|
||||
pub mod range;
|
||||
pub mod tuple;
|
||||
|
||||
/// 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,
|
||||
]
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AnyObject<'ctx> {
|
||||
pub ty: Type,
|
||||
pub value: BasicValueEnum<'ctx>,
|
||||
}
|
||||
|
||||
impl<'ctx> AnyObject<'ctx> {
|
||||
/// Returns true if this object's type is a [`TypeEnum::TObj`] and has the object ID as `prim`.
|
||||
pub fn is_obj(&self, ctx: &mut CodeGenContext<'ctx, '_>, prim: PrimDef) -> bool {
|
||||
match &*ctx.unifier.get_ty(self.ty) {
|
||||
TypeEnum::TObj { obj_id, .. } => *obj_id == prim.id(),
|
||||
_ => false,
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns true if this object's type is a [`TypeEnum::TTuple`]
|
||||
pub fn is_tuple(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
matches!(&*ctx.unifier.get_ty(self.ty), TypeEnum::TTuple { .. })
|
||||
}
|
||||
|
||||
pub fn into_tuple() {}
|
||||
|
||||
pub fn is_int32(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned(self.ty, ctx.primitives.int32)
|
||||
}
|
||||
|
||||
pub fn into_int32(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Int<'ctx, Int32> {
|
||||
assert!(self.is_int32(ctx));
|
||||
IntModel(Int32).believe_value(self.value.into_int_value())
|
||||
}
|
||||
|
||||
pub fn is_uint32(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned(self.ty, ctx.primitives.uint32)
|
||||
}
|
||||
|
||||
pub fn is_int64(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned(self.ty, ctx.primitives.int64)
|
||||
}
|
||||
|
||||
pub fn is_uint64(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned(self.ty, ctx.primitives.uint64)
|
||||
}
|
||||
|
||||
pub fn is_bool(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned(self.ty, ctx.primitives.bool)
|
||||
}
|
||||
|
||||
/// Returns true if the object type is `bool`, `int32`, `int64`, `uint32`, or `uint64`.
|
||||
pub fn is_int_like(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned_any(self.ty, int_like(ctx))
|
||||
}
|
||||
|
||||
/// Returns true if the object type is `int32`, `int64`.
|
||||
pub fn is_signed_int(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned_any(self.ty, signed_ints(ctx))
|
||||
}
|
||||
|
||||
/// Returns true if the object type is `uint32`, `uint64`.
|
||||
pub fn is_unsigned_int(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned_any(self.ty, unsigned_ints(ctx))
|
||||
}
|
||||
|
||||
pub fn into_int(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
assert!(self.is_int_like(ctx));
|
||||
self.value.into_int_value()
|
||||
}
|
||||
|
||||
pub fn is_float(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
self.is_obj(ctx, PrimDef::Float)
|
||||
}
|
||||
|
||||
pub fn into_float(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Float<'ctx, Float64> {
|
||||
assert!(self.is_float(ctx));
|
||||
FloatModel(Float64).believe_value(self.value.into_float_value())
|
||||
}
|
||||
|
||||
pub fn is_ndarray(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
self.is_obj(ctx, PrimDef::NDArray)
|
||||
}
|
||||
|
||||
pub fn into_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> NDArrayObject<'ctx> {
|
||||
NDArrayObject::from_object(generator, ctx, *self)
|
||||
}
|
||||
|
||||
/// Create an object from a boolean from an i1.
|
||||
///
|
||||
/// NOTE: In NAC3, booleans are i8. This function does converts the input i1 to an i8.
|
||||
pub fn from_bool(ctx: &mut CodeGenContext<'ctx, '_>, n: Int<'ctx, Bool>) -> AnyObject<'ctx> {
|
||||
let llvm_i8 = ctx.ctx.i8_type();
|
||||
let value = ctx.builder.build_int_z_extend(n.value, llvm_i8, "bool").unwrap();
|
||||
AnyObject { value: value.as_basic_value_enum(), ty: ctx.primitives.bool }
|
||||
}
|
||||
|
||||
/// Helper function to compare two scalars.
|
||||
///
|
||||
/// Only int-to-int and float-to-float comparisons are allowed.
|
||||
///
|
||||
/// Panic otherwise.
|
||||
pub fn compare_int_or_float_by_predicate<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
lhs: AnyObject<'ctx>,
|
||||
rhs: AnyObject<'ctx>,
|
||||
int_predicate: IntPredicate,
|
||||
float_predicate: FloatPredicate,
|
||||
name: &str,
|
||||
) -> Int<'ctx, Bool> {
|
||||
assert!(ctx.unifier.unioned(lhs.ty, rhs.ty), "lhs and rhs type should be the same");
|
||||
|
||||
let bool_model = IntModel(Bool);
|
||||
|
||||
let common_ty = lhs.ty;
|
||||
let result = if lhs.is_float(ctx) {
|
||||
let lhs = lhs.into_float(ctx);
|
||||
let rhs = rhs.into_float(ctx);
|
||||
ctx.builder.build_float_compare(float_predicate, lhs.value, rhs.value, name).unwrap()
|
||||
} else if ctx.unifier.unioned_any(common_ty, int_like(ctx)) {
|
||||
let lhs = lhs.into_int(ctx);
|
||||
let rhs = rhs.into_int(ctx);
|
||||
ctx.builder.build_int_compare(int_predicate, lhs, rhs, name).unwrap()
|
||||
} else {
|
||||
unsupported_type(ctx, [lhs.ty, rhs.ty]);
|
||||
};
|
||||
|
||||
bool_model.check_value(generator, ctx.ctx, result).unwrap()
|
||||
}
|
||||
|
||||
/// Helper function for `int32()`, `int64()`, `uint32()`, and `uint64()`.
|
||||
///
|
||||
/// TODO: Document me
|
||||
fn cast_to_int_conversion<'a, G, HandleFloatFn>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ret_int_ty: Type,
|
||||
handle_float: HandleFloatFn,
|
||||
) -> AnyObject<'ctx>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
HandleFloatFn:
|
||||
FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, FloatValue<'ctx>) -> IntValue<'ctx>,
|
||||
{
|
||||
let ret_int_ty_llvm = ctx.get_llvm_type(generator, ret_int_ty).into_int_type();
|
||||
|
||||
let result = if self.is_float(ctx) {
|
||||
// Handle float to int
|
||||
let n = self.into_float(ctx);
|
||||
handle_float(generator, ctx, n.value)
|
||||
} else if self.is_int_like(ctx) {
|
||||
// Handle int to a new int type
|
||||
let n = self.into_int(ctx);
|
||||
if n.get_type().get_bit_width() <= ret_int_ty_llvm.get_bit_width() {
|
||||
ctx.builder.build_int_z_extend(n, ret_int_ty_llvm, "zext").unwrap()
|
||||
} else {
|
||||
ctx.builder.build_int_truncate(n, ret_int_ty_llvm, "trunc").unwrap()
|
||||
}
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty]);
|
||||
};
|
||||
|
||||
assert_eq!(ret_int_ty_llvm.get_bit_width(), result.get_type().get_bit_width()); // Sanity check
|
||||
AnyObject { value: result.into(), ty: ret_int_ty }
|
||||
}
|
||||
|
||||
/// Call `int32()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_int32<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
self.cast_to_int_conversion(
|
||||
generator,
|
||||
ctx,
|
||||
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()
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
/// Call `int64()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_int64<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
self.cast_to_int_conversion(
|
||||
generator,
|
||||
ctx,
|
||||
ctx.primitives.int64,
|
||||
|_generator, ctx, input| {
|
||||
ctx.builder.build_float_to_signed_int(input, ctx.ctx.i64_type(), "").unwrap()
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
/// Call `uint32()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_uint32<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
self.cast_to_int_conversion(generator, ctx, 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()
|
||||
})
|
||||
}
|
||||
|
||||
/// Call `uint64()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_uint64<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
self.cast_to_int_conversion(generator, ctx, 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()
|
||||
})
|
||||
}
|
||||
|
||||
// Get the `len()` of this object.
|
||||
#[must_use]
|
||||
pub fn call_len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
// TODO: Switch to returning SizeT
|
||||
let result = match &*ctx.unifier.get_ty_immutable(self.ty) {
|
||||
TypeEnum::TTuple { .. } => {
|
||||
let tuple = TupleObject::from_object(ctx, *self);
|
||||
tuple.len(generator, ctx).truncate(generator, ctx, Int32, "tuple_len_32")
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.range.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let range = RangeObject::from_object(generator, ctx, *self);
|
||||
range.len(generator, ctx)
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let list = ListObject::from_object(generator, ctx, *self);
|
||||
list.len(generator, ctx).truncate(generator, ctx, Int32, "list_len_i32")
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, *self);
|
||||
ndarray.len(generator, ctx).truncate(generator, ctx, Int32, "ndarray_len_i32")
|
||||
}
|
||||
_ => unreachable!(),
|
||||
};
|
||||
|
||||
AnyObject { ty: ctx.primitives.int32, value: result.value.as_basic_value_enum() }
|
||||
}
|
||||
|
||||
/// Like [`AnyObject::call_bool`] but this returns an `Int<'ctx, Bool>` instead of an object.
|
||||
pub fn bool(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Int<'ctx, Bool> {
|
||||
let bool_model = IntModel(Bool);
|
||||
if self.is_int_like(ctx) {
|
||||
let n = self.into_int(ctx);
|
||||
let n = ctx
|
||||
.builder
|
||||
.build_int_compare(inkwell::IntPredicate::NE, n, n.get_type().const_zero(), "bool")
|
||||
.unwrap();
|
||||
bool_model.believe_value(n)
|
||||
} else if self.is_float(ctx) {
|
||||
let n = self.value.into_float_value();
|
||||
let n = ctx
|
||||
.builder
|
||||
.build_float_compare(FloatPredicate::UNE, n, n.get_type().const_zero(), "bool")
|
||||
.unwrap();
|
||||
bool_model.believe_value(n)
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty])
|
||||
}
|
||||
}
|
||||
|
||||
/// Call `bool()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_bool(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
|
||||
let n = self.bool(ctx);
|
||||
AnyObject::from_bool(ctx, n)
|
||||
}
|
||||
|
||||
/// Call `float()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_float(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
|
||||
let f64_model = FloatModel(Float64);
|
||||
let llvm_f64 = ctx.ctx.f64_type();
|
||||
|
||||
let result = if self.is_float(ctx) {
|
||||
self.into_float(ctx)
|
||||
} else if self.is_signed_int(ctx) || self.is_bool(ctx) {
|
||||
let n = self.into_int(ctx);
|
||||
let n = ctx.builder.build_signed_int_to_float(n, llvm_f64, "sitofp").unwrap();
|
||||
f64_model.believe_value(n)
|
||||
} else if self.is_unsigned_int(ctx) {
|
||||
let n = self.into_int(ctx);
|
||||
let n = ctx.builder.build_unsigned_int_to_float(n, llvm_f64, "uitofp").unwrap();
|
||||
f64_model.believe_value(n)
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty]);
|
||||
};
|
||||
|
||||
AnyObject { ty: ctx.primitives.float, value: result.value.as_basic_value_enum() }
|
||||
}
|
||||
|
||||
// Call `abs()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_abs<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Self {
|
||||
if self.is_float(ctx) {
|
||||
let n = self.value.into_float_value();
|
||||
let n = llvm_intrinsics::call_float_fabs(ctx, n, Some("abs"));
|
||||
AnyObject { value: n.into(), ty: ctx.primitives.float }
|
||||
} else if self.is_unsigned_int(ctx) || self.is_signed_int(ctx) {
|
||||
let is_poisoned = ctx.ctx.bool_type().const_zero(); // is_poisoned = false
|
||||
|
||||
let n = self.value.into_int_value();
|
||||
let n = llvm_intrinsics::call_int_abs(ctx, n, is_poisoned, Some("abs"));
|
||||
AnyObject { value: n.into(), ty: self.ty }
|
||||
} else if self.is_ndarray(ctx) {
|
||||
let ndarray = self.into_ndarray(generator, ctx);
|
||||
ndarray
|
||||
.map(
|
||||
generator,
|
||||
ctx,
|
||||
NDArrayOut::NewNDArray { dtype: ndarray.dtype },
|
||||
|generator, ctx, scalar| Ok(scalar.call_abs(generator, ctx)),
|
||||
)
|
||||
.unwrap()
|
||||
.to_any_object(ctx)
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty])
|
||||
}
|
||||
}
|
||||
|
||||
// Call `round()` on this object.
|
||||
//
|
||||
// It is possible to specify which kind of int type to return.
|
||||
#[must_use]
|
||||
pub fn call_round<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ret_int_ty: Type,
|
||||
) -> AnyObject<'ctx> {
|
||||
let ret_int_ty_llvm = ctx.get_llvm_type(generator, ret_int_ty).into_int_type();
|
||||
|
||||
let result = if ctx.unifier.unioned(self.ty, 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_ty_llvm, "round").unwrap()
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty])
|
||||
};
|
||||
AnyObject { ty: ret_int_ty, value: result.as_basic_value_enum() }
|
||||
}
|
||||
|
||||
/// Call `np_round()` on this object.
|
||||
///
|
||||
/// NOTE: `np.round()` has different behaviors than `round()` when in comes to "tie" cases and return type.
|
||||
#[must_use]
|
||||
pub fn call_np_round<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
if self.is_float(ctx) {
|
||||
let n = self.into_float(ctx);
|
||||
let n = llvm_intrinsics::call_float_rint(ctx, n.value, None);
|
||||
AnyObject { ty: ctx.primitives.float, value: n.as_basic_value_enum() }
|
||||
} else if self.is_ndarray(ctx) {
|
||||
let ndarray = self.into_ndarray(generator, ctx);
|
||||
ndarray
|
||||
.map(
|
||||
generator,
|
||||
ctx,
|
||||
NDArrayOut::NewNDArray { dtype: ndarray.dtype },
|
||||
|generator, ctx, scalar| Ok(scalar.call_np_round(generator, ctx)),
|
||||
)
|
||||
.unwrap()
|
||||
.to_any_object(ctx)
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty])
|
||||
}
|
||||
}
|
||||
|
||||
/// Call `min()` or `max()` on two objects.
|
||||
#[must_use]
|
||||
pub fn call_min_or_max(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
kind: MinOrMax,
|
||||
a: AnyObject<'ctx>,
|
||||
b: AnyObject<'ctx>,
|
||||
) -> AnyObject<'ctx> {
|
||||
if !ctx.unifier.unioned(a.ty, b.ty) {
|
||||
unsupported_type(ctx, [a.ty, b.ty])
|
||||
}
|
||||
|
||||
let common_ty = a.ty;
|
||||
|
||||
if a.is_float(ctx) {
|
||||
let function = match kind {
|
||||
MinOrMax::Min => llvm_intrinsics::call_float_minnum,
|
||||
MinOrMax::Max => llvm_intrinsics::call_float_maxnum,
|
||||
};
|
||||
|
||||
let a = a.into_float(ctx).value;
|
||||
let b = b.into_float(ctx).value;
|
||||
let result = function(ctx, a, b, None);
|
||||
AnyObject { value: result.as_basic_value_enum(), ty: ctx.primitives.float }
|
||||
} else if a.is_unsigned_int(ctx) || a.is_bool(ctx) {
|
||||
// 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 a = a.into_int(ctx);
|
||||
let b = b.into_int(ctx);
|
||||
let result = function(ctx, a, b, None);
|
||||
AnyObject { value: result.as_basic_value_enum(), ty: common_ty }
|
||||
} else if a.is_signed_int(ctx) {
|
||||
let function = match kind {
|
||||
MinOrMax::Min => llvm_intrinsics::call_int_smin,
|
||||
MinOrMax::Max => llvm_intrinsics::call_int_smax,
|
||||
};
|
||||
|
||||
let a = a.into_int(ctx);
|
||||
let b = b.into_int(ctx);
|
||||
let result = function(ctx, a, b, None);
|
||||
AnyObject { value: result.as_basic_value_enum(), ty: common_ty }
|
||||
} else {
|
||||
unsupported_type(ctx, [common_ty])
|
||||
}
|
||||
}
|
||||
|
||||
/// Call `floor()` or `ceil()` on this object.
|
||||
///
|
||||
/// It is possible to specify which kind of int type to return.
|
||||
#[must_use]
|
||||
pub fn call_floor_or_ceil<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
kind: FloorOrCeil,
|
||||
ret_int_ty: Type,
|
||||
) -> Self {
|
||||
let ret_int_ty_llvm = ctx.get_llvm_type(generator, ret_int_ty).into_int_type();
|
||||
|
||||
if self.is_float(ctx) {
|
||||
let function = match kind {
|
||||
FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
|
||||
FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
|
||||
};
|
||||
|
||||
let n = self.into_float(ctx).value;
|
||||
let n = function(ctx, n, None);
|
||||
let n = ctx.builder.build_float_to_signed_int(n, ret_int_ty_llvm, "").unwrap();
|
||||
AnyObject { ty: ret_int_ty, value: n.as_basic_value_enum() }
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty])
|
||||
}
|
||||
}
|
||||
|
||||
/// Call `np_floor()` or `np_ceil()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_np_floor_or_ceil<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
kind: FloorOrCeil,
|
||||
) -> Self {
|
||||
// TODO:
|
||||
if self.is_float(ctx) {
|
||||
let function = match kind {
|
||||
FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
|
||||
FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
|
||||
};
|
||||
let n = self.into_float(ctx).value;
|
||||
let n = function(ctx, n, None);
|
||||
AnyObject { ty: ctx.primitives.float, value: n.as_basic_value_enum() }
|
||||
} else if self.is_ndarray(ctx) {
|
||||
let ndarray = self.into_ndarray(generator, ctx);
|
||||
ndarray
|
||||
.map(
|
||||
generator,
|
||||
ctx,
|
||||
NDArrayOut::NewNDArray { dtype: ctx.primitives.float },
|
||||
|generator, ctx, scalar| Ok(scalar.call_np_floor_or_ceil(generator, ctx, kind)),
|
||||
)
|
||||
.unwrap()
|
||||
.to_any_object(ctx)
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty])
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,176 +0,0 @@
|
|||
use super::NDArrayObject;
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::{call_nac3_array_set_and_validate_list_shape, call_nac3_array_write_list_to_array},
|
||||
model::*,
|
||||
object::{list::ListObject, AnyObject},
|
||||
stmt::gen_if_else_expr_callback,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
toplevel::helper::{arraylike_flatten_element_type, arraylike_get_ndims},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
fn get_list_object_dtype_and_ndims<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: ListObject<'ctx>,
|
||||
) -> (Type, u64) {
|
||||
let dtype = arraylike_flatten_element_type(&mut ctx.unifier, list.item_type);
|
||||
|
||||
let ndims = arraylike_get_ndims(&mut ctx.unifier, list.item_type);
|
||||
let ndims = ndims + 1; // To count `list` itself.
|
||||
|
||||
(dtype, ndims)
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
fn from_np_array_list_copy_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: ListObject<'ctx>,
|
||||
) -> Self {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let (dtype, ndims_int) = get_list_object_dtype_and_ndims(ctx, list);
|
||||
let list_value = list.get_opaque_list_ptr(generator, ctx);
|
||||
|
||||
// Validate `list` has a consistent shape.
|
||||
// Raise an exception if `list` is something abnormal like `[[1, 2], [3]]`.
|
||||
// If `list` has a consistent shape, deduce the shape and write it to `shape`.
|
||||
let ndims = sizet_model.constant(generator, ctx.ctx, ndims_int);
|
||||
let shape = sizet_model.array_alloca(generator, ctx, ndims.value, "shape");
|
||||
call_nac3_array_set_and_validate_list_shape(generator, ctx, list_value, ndims, shape);
|
||||
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims_int, "ndarray_from_list");
|
||||
ndarray.copy_shape_from_array(generator, ctx, shape);
|
||||
ndarray.create_data(generator, ctx);
|
||||
|
||||
// Copy all contents from the list.
|
||||
call_nac3_array_write_list_to_array(generator, ctx, list_value, ndarray.instance);
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
fn from_np_array_list_try_no_copy_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: ListObject<'ctx>,
|
||||
) -> Self {
|
||||
// np_array without copy is only possible `list` is not nested.
|
||||
|
||||
// If `list` is `list[T]`, we can create an ndarray with `data` set
|
||||
// to the array pointer of `list`.
|
||||
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let zero = sizet_model.const_0(generator, ctx.ctx);
|
||||
|
||||
let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
|
||||
if ndims == 1 {
|
||||
// `list` is not nested, does not need to copy.
|
||||
let ndarray =
|
||||
NDArrayObject::alloca(generator, ctx, dtype, 1, "ndarray_from_list_no_copy");
|
||||
|
||||
// Set data
|
||||
let data = list.get_opaque_items_ptr(generator, ctx);
|
||||
ndarray.instance.set(ctx, |f| f.data, data);
|
||||
|
||||
// Set shape
|
||||
// dim = list->len;
|
||||
// shape[0] = dim;
|
||||
let shape = ndarray.instance.get(generator, ctx, |f| f.shape, "shape");
|
||||
let dim = list.instance.get(generator, ctx, |f| f.len, "dim");
|
||||
shape.offset(generator, ctx, zero.value, "pdim").store(ctx, dim);
|
||||
|
||||
// Set strides, the `data` is contiguous
|
||||
ndarray.update_strides_by_shape(generator, ctx);
|
||||
|
||||
// Done
|
||||
ndarray
|
||||
} else {
|
||||
// `list` is nested, it is impossible to not copy.
|
||||
NDArrayObject::from_np_array_list_copy_impl(generator, ctx, list)
|
||||
}
|
||||
}
|
||||
|
||||
fn from_np_array_list_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: ListObject<'ctx>,
|
||||
copy: Int<'ctx, Bool>,
|
||||
) -> Self {
|
||||
let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
|
||||
|
||||
let ndarray = gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_generator, _ctx| Ok(copy.value),
|
||||
|generator, ctx| {
|
||||
let ndarray = NDArrayObject::from_np_array_list_copy_impl(generator, ctx, list);
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
|generator, ctx| {
|
||||
let ndarray =
|
||||
NDArrayObject::from_np_array_list_try_no_copy_impl(generator, ctx, list);
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
)
|
||||
.unwrap()
|
||||
.unwrap();
|
||||
|
||||
NDArrayObject::from_value_and_unpacked_types(generator, ctx, ndarray, dtype, ndims)
|
||||
}
|
||||
|
||||
pub fn from_np_array_ndarray_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayObject<'ctx>,
|
||||
copy: Int<'ctx, Bool>,
|
||||
) -> Self {
|
||||
let ndarray_val = gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_generator, _ctx| Ok(copy.value),
|
||||
|generator, ctx| {
|
||||
let ndarray = ndarray.make_copy(generator, ctx, "np_array_copied_ndarray"); // Force copy
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
|_generator, _ctx| {
|
||||
// No need to copy. Return `ndarray` itself.
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
)
|
||||
.unwrap()
|
||||
.unwrap();
|
||||
|
||||
NDArrayObject::from_value_and_unpacked_types(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray_val,
|
||||
ndarray.dtype,
|
||||
ndarray.ndims,
|
||||
)
|
||||
}
|
||||
|
||||
pub fn from_np_array<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
copy: Int<'ctx, Bool>,
|
||||
) -> Self {
|
||||
match &*ctx.unifier.get_ty(object.ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let list = ListObject::from_object(generator, ctx, object);
|
||||
NDArrayObject::from_np_array_list_impl(generator, ctx, list, copy)
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, object);
|
||||
NDArrayObject::from_np_array_ndarray_impl(generator, ctx, ndarray, copy)
|
||||
}
|
||||
_ => panic!("Unrecognized object type: {}", ctx.unifier.stringify(object.ty)), // Typechecker ensures this
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,161 +0,0 @@
|
|||
use itertools::Itertools;
|
||||
|
||||
use crate::codegen::{
|
||||
irrt::{
|
||||
call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to,
|
||||
call_nac3_ndarray_util_assert_shape_no_negative,
|
||||
},
|
||||
model::*,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
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.
|
||||
///
|
||||
/// The input shape will be checked to make sure that it contains no negative values.
|
||||
///
|
||||
/// * `target_ndims` - The ndims type after broadcasting to the given shape.
|
||||
/// The caller has to figure this out for this function.
|
||||
/// * `target_shape` - An array pointer pointing to the target shape.
|
||||
#[must_use]
|
||||
pub fn broadcast_to<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
target_ndims: u64,
|
||||
target_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) -> Self {
|
||||
let target_ndims_llvm = IntModel(SizeT).constant(generator, ctx.ctx, target_ndims);
|
||||
call_nac3_ndarray_util_assert_shape_no_negative(
|
||||
generator,
|
||||
ctx,
|
||||
target_ndims_llvm,
|
||||
target_shape,
|
||||
);
|
||||
|
||||
let broadcast_ndarray = NDArrayObject::alloca(
|
||||
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.instance, broadcast_ndarray.instance);
|
||||
broadcast_ndarray
|
||||
}
|
||||
}
|
||||
/// A result produced by [`broadcast_all_ndarrays`]
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct BroadcastAllResult<'ctx> {
|
||||
/// The statically known `ndims` of the broadcast result.
|
||||
pub ndims: u64,
|
||||
/// The broadcasting shape.
|
||||
pub shape: 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>>,
|
||||
}
|
||||
|
||||
pub fn broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
in_entries: &[(Ptr<'ctx, IntModel<SizeT>>, u64)],
|
||||
broadcast_ndims: u64,
|
||||
broadcast_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let shape_model = StructModel(ShapeEntry);
|
||||
|
||||
// Prepare input shape entries
|
||||
let num_shape_entries =
|
||||
sizet_model.constant(generator, ctx.ctx, u64::try_from(in_entries.len()).unwrap());
|
||||
let shape_entries =
|
||||
shape_model.array_alloca(generator, ctx, num_shape_entries.value, "shape_entries");
|
||||
for (i, (in_shape, in_ndims)) in in_entries.iter().enumerate() {
|
||||
let i = sizet_model.constant(generator, ctx.ctx, i as u64).value;
|
||||
let pshape_entry = shape_entries.offset(generator, ctx, i, "shape_entry");
|
||||
|
||||
let in_ndims = sizet_model.constant(generator, ctx.ctx, *in_ndims);
|
||||
pshape_entry.set(ctx, |f| f.ndims, in_ndims);
|
||||
|
||||
pshape_entry.set(ctx, |f| f.shape, *in_shape);
|
||||
}
|
||||
|
||||
let broadcast_ndims = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
|
||||
|
||||
call_nac3_ndarray_broadcast_shapes(
|
||||
generator,
|
||||
ctx,
|
||||
num_shape_entries,
|
||||
shape_entries,
|
||||
broadcast_ndims,
|
||||
broadcast_shape,
|
||||
);
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
// TODO: DOCUMENT: Behaves like `np.broadcast()`, except returns results differently.
|
||||
pub fn broadcast<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarrays: &[Self],
|
||||
) -> BroadcastAllResult<'ctx> {
|
||||
assert!(!ndarrays.is_empty());
|
||||
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Infer the broadcast output ndims.
|
||||
let broadcast_ndims_int = ndarrays.iter().map(|ndarray| ndarray.ndims).max().unwrap();
|
||||
|
||||
let broadcast_ndims = sizet_model.constant(generator, ctx.ctx, broadcast_ndims_int);
|
||||
let broadcast_shape =
|
||||
sizet_model.array_alloca(generator, ctx, broadcast_ndims.value, "broadcast_shape");
|
||||
|
||||
let shape_entries = ndarrays
|
||||
.iter()
|
||||
.map(|ndarray| {
|
||||
(ndarray.instance.get(generator, ctx, |f| f.shape, "shape"), ndarray.ndims)
|
||||
})
|
||||
.collect_vec();
|
||||
broadcast_shapes(generator, ctx, &shape_entries, broadcast_ndims_int, broadcast_shape);
|
||||
|
||||
// Broadcast all the inputs to shape `dst_shape`.
|
||||
let broadcast_ndarrays: Vec<_> = ndarrays
|
||||
.iter()
|
||||
.map(|ndarray| {
|
||||
ndarray.broadcast_to(generator, ctx, broadcast_ndims_int, broadcast_shape)
|
||||
})
|
||||
.collect_vec();
|
||||
|
||||
BroadcastAllResult {
|
||||
ndims: broadcast_ndims_int,
|
||||
shape: broadcast_shape,
|
||||
ndarrays: broadcast_ndarrays,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,238 +0,0 @@
|
|||
use inkwell::{values::BasicValueEnum, IntPredicate};
|
||||
|
||||
use super::NDArrayObject;
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::call_nac3_ndarray_util_assert_shape_no_negative, model::*, object::AnyObject,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
/// Get the zero value in `np.zeros()` of a `dtype`.
|
||||
fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
if [ctx.primitives.int32, ctx.primitives.uint32]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
ctx.ctx.i32_type().const_zero().into()
|
||||
} else if [ctx.primitives.int64, ctx.primitives.uint64]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
ctx.ctx.i64_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
|
||||
ctx.ctx.f64_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "").value.into()
|
||||
} else {
|
||||
panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the one value in `np.ones()` of a `dtype`.
|
||||
fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
if [ctx.primitives.int32, ctx.primitives.uint32]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int32);
|
||||
ctx.ctx.i32_type().const_int(1, is_signed).into()
|
||||
} else if [ctx.primitives.int64, ctx.primitives.uint64]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int64);
|
||||
ctx.ctx.i64_type().const_int(1, is_signed).into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
|
||||
ctx.ctx.f64_type().const_float(1.0).into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_int(1, false).into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "1").value.into()
|
||||
} else {
|
||||
panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Create an ndarray like `np.empty`.
|
||||
pub fn from_np_empty<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) -> Self {
|
||||
// Validate `shape`
|
||||
// TODO: Should the caller be responsible for this instead?
|
||||
let ndims_llvm = IntModel(SizeT).constant(generator, ctx.ctx, ndims);
|
||||
call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, ndims_llvm, shape);
|
||||
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims, "full_ndarray");
|
||||
ndarray.copy_shape_from_array(generator, ctx, shape);
|
||||
ndarray.create_data(generator, ctx);
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.full`.
|
||||
pub fn from_np_full<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
fill_value: AnyObject<'ctx>,
|
||||
) -> Self {
|
||||
// Sanity check on `fill_value`'s dtype.
|
||||
assert!(ctx.unifier.unioned(dtype, fill_value.ty));
|
||||
|
||||
let ndarray = NDArrayObject::from_np_empty(generator, ctx, dtype, ndims, shape);
|
||||
ndarray.fill(generator, ctx, fill_value);
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.zero`.
|
||||
pub fn from_np_zero<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) -> Self {
|
||||
let fill_value = ndarray_zero_value(generator, ctx, dtype);
|
||||
let fill_value = AnyObject { value: fill_value, ty: dtype };
|
||||
NDArrayObject::from_np_full(generator, ctx, dtype, ndims, shape, fill_value)
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.ones`.
|
||||
pub fn from_np_ones<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) -> Self {
|
||||
let fill_value = ndarray_one_value(generator, ctx, dtype);
|
||||
let fill_value = AnyObject { value: fill_value, ty: dtype };
|
||||
NDArrayObject::from_np_full(generator, ctx, dtype, ndims, shape, fill_value)
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.arange`.
|
||||
/// The returned ndarray's `dtype` is always `float`
|
||||
pub fn from_np_arange<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
length: Int<'ctx, SizeT>,
|
||||
) -> Self {
|
||||
let ndarray = NDArrayObject::alloca(
|
||||
generator,
|
||||
ctx,
|
||||
ctx.primitives.float,
|
||||
1, // ndims = 1
|
||||
"arange_ndarray",
|
||||
);
|
||||
|
||||
// `ndarray.shape[0] = length`
|
||||
ndarray
|
||||
.instance
|
||||
.get(generator, ctx, |f| f.shape, "shape")
|
||||
.offset_const(generator, ctx, 0, "dim")
|
||||
.store(ctx, length);
|
||||
|
||||
// Create data and set elements
|
||||
ndarray.create_data(generator, ctx);
|
||||
ndarray
|
||||
.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
|
||||
// Get the index of the current element, convert that index to float, and write it.
|
||||
// This is how we get [0.0, 1.0, 2.0, ...].
|
||||
let index = nditer.get_index(generator, ctx);
|
||||
let pelement = nditer.get_pointer(generator, ctx);
|
||||
|
||||
let val = ctx
|
||||
.builder
|
||||
.build_unsigned_int_to_float(index.value, ctx.ctx.f64_type(), "val")
|
||||
.unwrap();
|
||||
ctx.builder.build_store(pelement, val).unwrap();
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.eye`.
|
||||
pub fn from_np_eye<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
nrows: Int<'ctx, SizeT>,
|
||||
ncols: Int<'ctx, SizeT>,
|
||||
offset: Int<'ctx, SizeT>,
|
||||
) -> Self {
|
||||
let ndzero = ndarray_zero_value(generator, ctx, dtype);
|
||||
let ndone = ndarray_one_value(generator, ctx, dtype);
|
||||
|
||||
let ndarray = NDArrayObject::alloca_dynamic_shape(
|
||||
generator,
|
||||
ctx,
|
||||
dtype,
|
||||
&[nrows, ncols],
|
||||
"eye_ndarray",
|
||||
);
|
||||
|
||||
// Create data and make the matrix like look np.eye()
|
||||
ndarray.create_data(generator, ctx);
|
||||
ndarray
|
||||
.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
|
||||
// NOTE: rows and cols can never be zero here, since this ndarray's `np.size` would be zero
|
||||
// and this loop would not execute.
|
||||
|
||||
// Load up `row_i` and `col_i` from indices.
|
||||
let row_i = nditer
|
||||
.get_indices()
|
||||
.offset_const(generator, ctx, 0, "")
|
||||
.load(generator, ctx, "row_i");
|
||||
let col_i = nditer
|
||||
.get_indices()
|
||||
.offset_const(generator, ctx, 1, "")
|
||||
.load(generator, ctx, "col_i");
|
||||
|
||||
// Write to element
|
||||
let be_one =
|
||||
row_i.add(ctx, offset, "").compare(ctx, IntPredicate::EQ, col_i, "write_one");
|
||||
let value = ctx.builder.build_select(be_one.value, ndone, ndzero, "value").unwrap();
|
||||
|
||||
let p = nditer.get_pointer(generator, ctx);
|
||||
ctx.builder.build_store(p, value).unwrap();
|
||||
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.identity`.
|
||||
pub fn from_np_identity<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
size: Int<'ctx, SizeT>,
|
||||
) -> Self {
|
||||
// Convenient implementation
|
||||
let offset = IntModel(SizeT).const_0(generator, ctx.ctx);
|
||||
NDArrayObject::from_np_eye(generator, ctx, dtype, size, size, offset)
|
||||
}
|
||||
}
|
||||
|
|
@ -1,128 +0,0 @@
|
|||
use inkwell::{FloatPredicate, IntPredicate};
|
||||
|
||||
use crate::codegen::{
|
||||
model::*,
|
||||
object::{AnyObject, MinOrMax},
|
||||
stmt::gen_if_callback,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
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,
|
||||
) -> (AnyObject<'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_scalar(generator, ctx, zero);
|
||||
ctx.builder.build_store(pextremum, first_scalar.value).unwrap();
|
||||
|
||||
self.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
|
||||
let old_extremum = ctx.builder.build_load(pextremum, "current_extremum").unwrap();
|
||||
let old_extremum = AnyObject { ty: self.dtype, value: old_extremum };
|
||||
|
||||
let scalar = nditer.get_scalar(generator, ctx);
|
||||
let new_extremum = AnyObject::call_min_or_max(ctx, kind, old_extremum, scalar);
|
||||
|
||||
gen_if_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|generator, ctx| {
|
||||
// Is new_extremum is more extreme than old_extremum?
|
||||
let cmp = AnyObject::compare_int_or_float_by_predicate(
|
||||
generator,
|
||||
ctx,
|
||||
new_extremum,
|
||||
old_extremum,
|
||||
IntPredicate::NE,
|
||||
FloatPredicate::ONE,
|
||||
"",
|
||||
);
|
||||
Ok(cmp.value)
|
||||
},
|
||||
|generator, ctx| {
|
||||
// Yes, update the extremum index
|
||||
let index = nditer.get_index(generator, ctx);
|
||||
pextremum_index.store(ctx, index);
|
||||
Ok(())
|
||||
},
|
||||
|_generator, _ctx| {
|
||||
// No, do nothing
|
||||
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 = AnyObject { ty: 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,
|
||||
) -> AnyObject<'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
|
||||
}
|
||||
}
|
||||
|
|
@ -1,321 +0,0 @@
|
|||
use crate::codegen::{irrt::call_nac3_ndarray_index, model::*, CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
pub type NDIndexType = Byte;
|
||||
|
||||
/// Fields of [`NDIndex`]
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDIndexFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub type_: F::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>),
|
||||
NewAxis,
|
||||
Ellipsis,
|
||||
}
|
||||
|
||||
impl<'ctx> RustNDIndex<'ctx> {
|
||||
/// Get the value to set `NDIndex::type` for this variant.
|
||||
fn get_type_id(&self) -> u64 {
|
||||
// Defined in IRRT, must be in sync
|
||||
match self {
|
||||
RustNDIndex::SingleElement(_) => 0,
|
||||
RustNDIndex::Slice(_) => 1,
|
||||
RustNDIndex::NewAxis => 2,
|
||||
RustNDIndex::Ellipsis => 3,
|
||||
}
|
||||
}
|
||||
|
||||
/// 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`
|
||||
match self {
|
||||
RustNDIndex::SingleElement(in_index) => {
|
||||
let index_ptr = i32_model.alloca(generator, ctx, "index");
|
||||
index_ptr.store(ctx, *in_index);
|
||||
|
||||
dst_ndindex_ptr
|
||||
.gep(ctx, |f| f.data)
|
||||
.store(ctx, index_ptr.pointer_cast(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);
|
||||
|
||||
dst_ndindex_ptr
|
||||
.gep(ctx, |f| f.data)
|
||||
.store(ctx, user_slice_ptr.pointer_cast(generator, ctx, IntModel(Byte), ""));
|
||||
}
|
||||
RustNDIndex::NewAxis | RustNDIndex::Ellipsis => {}
|
||||
}
|
||||
}
|
||||
|
||||
/// 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::NewAxis => {
|
||||
ndims += 1; // `np.newaxis` / `none` adds a new axis
|
||||
}
|
||||
RustNDIndex::Ellipsis | RustNDIndex::Slice(_) => {}
|
||||
}
|
||||
}
|
||||
ndims
|
||||
}
|
||||
|
||||
/// Index into the ndarray, and return a newly-allocated view on this ndarray.
|
||||
///
|
||||
/// This function behaves like NumPy's ndarray indexing, but if the 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(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.instance,
|
||||
dst_ndarray.instance,
|
||||
);
|
||||
|
||||
dst_ndarray
|
||||
}
|
||||
}
|
||||
|
||||
pub mod util {
|
||||
use itertools::Itertools;
|
||||
use nac3parser::ast::{Constant, Expr, ExprKind};
|
||||
|
||||
use crate::{
|
||||
codegen::{expr::gen_slice, model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
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, upper, step } = &index_expr.node {
|
||||
// Handle slices
|
||||
|
||||
// Helper function here to deduce code duplication
|
||||
let (lower, upper, step) = gen_slice(generator, ctx, lower, upper, step)?;
|
||||
RustNDIndex::Slice(RustUserSlice { start: lower, stop: upper, step })
|
||||
} else if let ExprKind::Constant { value: Constant::Ellipsis, .. } = &index_expr.node {
|
||||
// Handle '...'
|
||||
RustNDIndex::Ellipsis
|
||||
} else {
|
||||
match &*ctx.unifier.get_ty(index_expr.custom.unwrap()) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.option.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
// Handle `np.newaxis` / `None`
|
||||
RustNDIndex::NewAxis
|
||||
}
|
||||
_ => {
|
||||
// Treat and handle everything else as a single element index.
|
||||
let index =
|
||||
generator.gen_expr(ctx, index_expr)?.unwrap().to_basic_value_enum(
|
||||
ctx,
|
||||
generator,
|
||||
ctx.primitives.int32, // Must be int32, this checks for illegal values
|
||||
)?;
|
||||
let index = i32_model.check_value(generator, ctx.ctx, index).unwrap();
|
||||
|
||||
RustNDIndex::SingleElement(index)
|
||||
}
|
||||
}
|
||||
};
|
||||
rust_ndindexes.push(ndindex);
|
||||
}
|
||||
Ok(rust_ndindexes)
|
||||
}
|
||||
}
|
||||
|
|
@ -1,200 +0,0 @@
|
|||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
object::ndarray::{AnyObject, NDArrayObject},
|
||||
stmt::gen_for_callback,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
use super::{nditer::NDIterHandle, scalar::ScalarOrNDArray, NDArrayOut};
|
||||
|
||||
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],
|
||||
out: NDArrayOut<'ctx>,
|
||||
mapping: MappingFn,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
MappingFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
&[AnyObject<'ctx>],
|
||||
) -> Result<AnyObject<'ctx>, String>,
|
||||
{
|
||||
// Broadcast inputs
|
||||
let broadcast_result = NDArrayObject::broadcast(generator, ctx, ndarrays);
|
||||
|
||||
let out_ndarray = match out {
|
||||
NDArrayOut::NewNDArray { dtype } => {
|
||||
// Create a new ndarray based on the broadcast shape.
|
||||
let result_ndarray = NDArrayObject::alloca(
|
||||
generator,
|
||||
ctx,
|
||||
dtype,
|
||||
broadcast_result.ndims,
|
||||
"mapped_ndarray",
|
||||
);
|
||||
result_ndarray.copy_shape_from_array(generator, ctx, broadcast_result.shape);
|
||||
result_ndarray.create_data(generator, ctx);
|
||||
result_ndarray
|
||||
}
|
||||
NDArrayOut::WriteToNDArray { ndarray: result_ndarray } => {
|
||||
// Use an existing ndarray.
|
||||
|
||||
// Check that its shape is compatible with the broadcast shape.
|
||||
result_ndarray.check_can_be_written_by_out(
|
||||
generator,
|
||||
ctx,
|
||||
broadcast_result.ndims,
|
||||
broadcast_result.shape,
|
||||
);
|
||||
result_ndarray
|
||||
}
|
||||
};
|
||||
|
||||
// Map element-wise and store results into `mapped_ndarray`.
|
||||
let nditer = NDIterHandle::new(generator, ctx, out_ndarray);
|
||||
gen_for_callback(
|
||||
generator,
|
||||
ctx,
|
||||
Some("broadcast_starmap"),
|
||||
|generator, ctx| {
|
||||
// Create NDIters for all broadcasted input ndarrays.
|
||||
let other_nditers = broadcast_result
|
||||
.ndarrays
|
||||
.iter()
|
||||
.map(|ndarray| NDIterHandle::new(generator, ctx, *ndarray))
|
||||
.collect_vec();
|
||||
Ok((nditer, other_nditers))
|
||||
},
|
||||
|generator, ctx, (out_nditer, _in_nditers)| {
|
||||
// We can simply use `out_nditer`'s `has_next()`.
|
||||
// `in_nditers`' `has_next()`s should return the same value.
|
||||
Ok(out_nditer.has_next(generator, ctx).value)
|
||||
},
|
||||
|generator, ctx, _hooks, (out_nditer, in_nditers)| {
|
||||
// Get all the scalars from the broadcasted input ndarrays, pass them to `mapping`,
|
||||
// and write to `out_ndarray`.
|
||||
|
||||
let in_scalars =
|
||||
in_nditers.iter().map(|nditer| nditer.get_scalar(generator, ctx)).collect_vec();
|
||||
|
||||
let result = mapping(generator, ctx, &in_scalars)?;
|
||||
// Sanity check on result's ty
|
||||
assert!(ctx.unifier.unioned(result.ty, out_ndarray.dtype));
|
||||
|
||||
let p = out_nditer.get_pointer(generator, ctx);
|
||||
ctx.builder.build_store(p, result.value).unwrap();
|
||||
|
||||
Ok(())
|
||||
},
|
||||
|generator, ctx, (out_nditer, in_nditers)| {
|
||||
// Advance all iterators
|
||||
out_nditer.next(generator, ctx);
|
||||
in_nditers.iter().for_each(|nditer| nditer.next(generator, ctx));
|
||||
Ok(())
|
||||
},
|
||||
)?;
|
||||
|
||||
Ok(out_ndarray)
|
||||
}
|
||||
|
||||
pub fn map<'a, G, Mapping>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
out: NDArrayOut<'ctx>,
|
||||
mapping: Mapping,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Mapping: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
AnyObject<'ctx>,
|
||||
) -> Result<AnyObject<'ctx>, String>,
|
||||
{
|
||||
NDArrayObject::broadcasting_starmap(
|
||||
generator,
|
||||
ctx,
|
||||
&[*self],
|
||||
out,
|
||||
|generator, ctx, scalars| mapping(generator, ctx, 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>,
|
||||
&[AnyObject<'ctx>],
|
||||
) -> Result<AnyObject<'ctx>, String>,
|
||||
{
|
||||
// Check if all inputs are AnyObjects
|
||||
let all_scalars: Option<Vec<_>> = inputs.iter().map(AnyObject::try_from).try_collect().ok();
|
||||
|
||||
if let Some(scalars) = all_scalars {
|
||||
let scalar = mapping(generator, ctx, &scalars)?;
|
||||
|
||||
// Sanity check on scalar's type
|
||||
assert!(ctx.unifier.unioned(scalar.ty, 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,
|
||||
NDArrayOut::NewNDArray { dtype: 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>,
|
||||
AnyObject<'ctx>,
|
||||
) -> Result<AnyObject<'ctx>, String>,
|
||||
{
|
||||
ScalarOrNDArray::broadcasting_starmap(
|
||||
generator,
|
||||
ctx,
|
||||
&[*self],
|
||||
ret_dtype,
|
||||
|generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
|
||||
)
|
||||
}
|
||||
}
|
||||
|
|
@ -1,831 +0,0 @@
|
|||
pub mod array;
|
||||
pub mod broadcast;
|
||||
pub mod factory;
|
||||
pub mod functions;
|
||||
pub mod indexing;
|
||||
pub mod mapping;
|
||||
pub mod nalgebra;
|
||||
pub mod nditer;
|
||||
pub mod product;
|
||||
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_resolve_and_check_new_shape, call_nac3_ndarray_set_strides_by_shape,
|
||||
call_nac3_ndarray_size, call_nac3_ndarray_transpose,
|
||||
call_nac3_ndarray_util_assert_output_shape_same,
|
||||
},
|
||||
model::*,
|
||||
stmt::{gen_for_callback, BreakContinueHooks},
|
||||
structure::{NDArray, SimpleNDArray},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
toplevel::{
|
||||
helper::{create_ndims, extract_ndims},
|
||||
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
use indexing::RustNDIndex;
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::BasicType,
|
||||
values::{BasicValue, PointerValue},
|
||||
AddressSpace, IntPredicate,
|
||||
};
|
||||
use nditer::NDIterHandle;
|
||||
use scalar::ScalarOrNDArray;
|
||||
use util::call_memcpy_model;
|
||||
|
||||
use super::{tuple::TupleObject, AnyObject};
|
||||
|
||||
/// A NAC3 Python ndarray object.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDArrayObject<'ctx> {
|
||||
pub dtype: Type,
|
||||
pub ndims: u64,
|
||||
pub instance: Ptr<'ctx, StructModel<NDArray>>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Create an [`NDArrayObject`] from an LLVM value and its typechecker [`Type`].
|
||||
pub fn from_object<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
) -> Self {
|
||||
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, object.ty);
|
||||
let ndims = extract_ndims(&ctx.unifier, ndims);
|
||||
Self::from_value_and_unpacked_types(generator, ctx, object.value, dtype, ndims)
|
||||
}
|
||||
|
||||
/// Like [`NDArrayObject::from_object`] but you directly supply the ndarray's
|
||||
/// `dtype` and `ndims`.
|
||||
pub fn from_value_and_unpacked_types<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
value: V,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
) -> Self {
|
||||
let pndarray_model = PtrModel(StructModel(NDArray));
|
||||
let value = pndarray_model.check_value(generator, ctx.ctx, value).unwrap();
|
||||
NDArrayObject { dtype, ndims, instance: value }
|
||||
}
|
||||
|
||||
/// Forget that this is an ndarray and convert to an [`AnyObject`].
|
||||
pub fn to_any_object(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
|
||||
let ty = self.get_ndarray_type(ctx);
|
||||
AnyObject { value: self.instance.value.as_basic_value_enum(), ty }
|
||||
}
|
||||
|
||||
/// Create a [`SimpleNDArray`] from the contents of this ndarray.
|
||||
///
|
||||
/// This function may or may not be expensive depending on if this ndarray has contiguous data.
|
||||
///
|
||||
/// If this ndarray is not C-contiguous, this function will allocate memory on the stack for the `data` field of
|
||||
/// the returned [`SimpleNDArray`] and copy contents of this ndarray to there.
|
||||
///
|
||||
/// If this ndarray is C-contiguous, contents of this ndarray will not be copied. The created [`SimpleNDArray`]
|
||||
/// will have the same `data` field as this ndarray.
|
||||
///
|
||||
/// The `item_model` sets the [`Model`] of the returned [`SimpleNDArray`]'s `Item` model, and should match the
|
||||
/// `ctx.get_llvm_type()` of this ndarray's `dtype`. Otherwise this function panics.
|
||||
pub fn make_simple_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
item_model: Item,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, StructModel<SimpleNDArray<Item>>> {
|
||||
// Sanity check on `self.dtype` and `item_model`.
|
||||
let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
|
||||
item_model.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
|
||||
|
||||
let simple_ndarray_model = StructModel(SimpleNDArray { item: item_model });
|
||||
|
||||
let current_bb = ctx.builder.get_insert_block().unwrap();
|
||||
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
|
||||
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
|
||||
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
|
||||
|
||||
// Allocate and setup the resulting [`SimpleNDArray`].
|
||||
let result = simple_ndarray_model.alloca(generator, ctx, name);
|
||||
|
||||
// Set ndims and shape.
|
||||
let ndims = self.get_ndims(generator, ctx.ctx);
|
||||
result.set(ctx, |f| f.ndims, ndims);
|
||||
|
||||
let shape = self.instance.get(generator, ctx, |f| f.shape, "shape");
|
||||
result.set(ctx, |f| f.shape, shape);
|
||||
|
||||
// Set data, but we do things differently if this ndarray is contiguous.
|
||||
let is_contiguous = self.is_c_contiguous(generator, ctx);
|
||||
ctx.builder.build_conditional_branch(is_contiguous.value, then_bb, else_bb).unwrap();
|
||||
|
||||
// Inserting into then_bb; This ndarray is contiguous.
|
||||
let data = self.instance.get(generator, ctx, |f| f.data, "");
|
||||
let data = data.pointer_cast(generator, ctx, item_model, "");
|
||||
result.set(ctx, |f| f.data, data);
|
||||
ctx.builder.build_unconditional_branch(end_bb).unwrap();
|
||||
|
||||
// Inserting into else_bb; This ndarray is not contiguous. Do a full-copy on `data`.
|
||||
// TODO: Reimplement this? This method does give us the contiguous `data`, but
|
||||
// this creates a few extra bytes of useless information because an entire NDArray
|
||||
// is allocated. Though this is super convenient.
|
||||
let data = self.make_copy(generator, ctx, "").instance.get(generator, ctx, |f| f.data, "");
|
||||
let data = data.pointer_cast(generator, ctx, item_model, "");
|
||||
result.set(ctx, |f| f.data, data);
|
||||
ctx.builder.build_unconditional_branch(end_bb).unwrap();
|
||||
|
||||
// Reposition to end_bb for continuation
|
||||
ctx.builder.position_at_end(end_bb);
|
||||
|
||||
result
|
||||
}
|
||||
|
||||
/// Create an [`NDArrayObject`] from a [`SimpleNDArray`].
|
||||
///
|
||||
/// This operation is super cheap. The newly created [`NDArray`] will not copy contents
|
||||
/// from `simple_ndarray`, but only having its `data` and `shape` pointing to `simple_array`.
|
||||
pub fn from_simple_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
simple_ndarray: Ptr<'ctx, StructModel<SimpleNDArray<Item>>>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
) -> Self {
|
||||
// Sanity check on `dtype` and `simple_array`'s `Item` model.
|
||||
let dtype_llvm = ctx.get_llvm_type(generator, dtype);
|
||||
simple_ndarray.model.0 .0.item.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
|
||||
|
||||
let byte_model = IntModel(Byte);
|
||||
|
||||
// TODO: Check if `ndims` is consistent with that in `simple_array`?
|
||||
|
||||
// Allocate the resulting ndarray.
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims, "from_simple_ndarray");
|
||||
|
||||
// Set data, shape by simply copying addresses.
|
||||
let data = simple_ndarray
|
||||
.get(generator, ctx, |f| f.data, "")
|
||||
.pointer_cast(generator, ctx, byte_model, "data");
|
||||
ndarray.instance.set(ctx, |f| f.data, data);
|
||||
|
||||
let shape = simple_ndarray.get(generator, ctx, |f| f.shape, "shape");
|
||||
ndarray.instance.set(ctx, |f| f.shape, shape);
|
||||
|
||||
// Set strides. We know `data` is contiguous.
|
||||
ndarray.update_strides_by_shape(generator, ctx);
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Get the typechecker ndarray type of this [`NDArrayObject`].
|
||||
pub fn get_ndarray_type(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Type {
|
||||
let ndims = create_ndims(&mut ctx.unifier, self.ndims);
|
||||
make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(self.dtype), Some(ndims))
|
||||
}
|
||||
|
||||
/// 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.instance)
|
||||
}
|
||||
|
||||
/// 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.instance)
|
||||
}
|
||||
|
||||
/// 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.instance)
|
||||
}
|
||||
|
||||
/// Check if this ndarray is C-contiguous.
|
||||
///
|
||||
/// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
|
||||
pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
call_nac3_ndarray_is_c_contiguous(generator, ctx, self.instance)
|
||||
}
|
||||
|
||||
/// Get the pointer to the n-th (0-based) element.
|
||||
///
|
||||
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
|
||||
///
|
||||
/// There is no out-of-bounds check.
|
||||
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.instance, nth);
|
||||
ctx.builder
|
||||
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), name)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Get the n-th (0-based) scalar.
|
||||
///
|
||||
/// There is no out-of-bounds check.
|
||||
pub fn get_nth_scalar<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
nth: Int<'ctx, SizeT>,
|
||||
) -> AnyObject<'ctx> {
|
||||
let p = self.get_nth_pointer(generator, ctx, nth, "value");
|
||||
let value = ctx.builder.build_load(p, "value").unwrap();
|
||||
AnyObject { ty: self.dtype, value }
|
||||
}
|
||||
|
||||
/// Set the n-th (0-based) scalar.
|
||||
///
|
||||
/// There is no out-of-bounds check.
|
||||
pub fn set_nth_scalar<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
nth: Int<'ctx, SizeT>,
|
||||
scalar: AnyObject<'ctx>,
|
||||
) {
|
||||
// Sanity check on scalar's `dtype`
|
||||
assert!(ctx.unifier.unioned(scalar.ty, self.dtype));
|
||||
|
||||
let pscalar = self.get_nth_pointer(generator, ctx, nth, "pscalar");
|
||||
ctx.builder.build_store(pscalar, scalar.value).unwrap();
|
||||
}
|
||||
|
||||
/// 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.instance);
|
||||
}
|
||||
|
||||
/// Copy data from another ndarray.
|
||||
///
|
||||
/// This ndarray and `src` is that their `np.size()` should be the same. Their shapes
|
||||
/// do not matter. The copying order is determined by how their flattened views look.
|
||||
///
|
||||
/// Panics if the `dtype`s of ndarrays are different.
|
||||
pub fn copy_data_from<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src: NDArrayObject<'ctx>,
|
||||
) {
|
||||
assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
|
||||
call_nac3_ndarray_copy_data(generator, ctx, src.instance, self.instance);
|
||||
}
|
||||
|
||||
/// Allocate an ndarray on the stack given its `ndims` and `dtype`.
|
||||
///
|
||||
/// `shape` and `strides` will be automatically allocated on the stack.
|
||||
//e
|
||||
/// 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<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(NDArray);
|
||||
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, instance: pndarray }
|
||||
}
|
||||
|
||||
/// Convenience function.
|
||||
/// Like [`NDArrayObject::alloca_uninitialized`] but directly takes the typechecker type of the ndarray.
|
||||
pub fn alloca_ndarray_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(generator, ctx, dtype, ndims, name)
|
||||
}
|
||||
|
||||
/// Convenience function. Allocate an [`NDArrayObject`] with a statically known shape.
|
||||
///
|
||||
/// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
|
||||
pub fn alloca_constant_shape<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
shape: &[u64],
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64, name);
|
||||
|
||||
// Write shape
|
||||
let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape, "shape");
|
||||
for (i, dim) in shape.iter().enumerate() {
|
||||
let dim = sizet_model.constant(generator, ctx.ctx, *dim);
|
||||
dst_shape.offset_const(generator, ctx, i as u64, "").store(ctx, dim);
|
||||
}
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Convenience function. Allocate an [`NDArrayObject`] with a dynamically known shape.
|
||||
///
|
||||
/// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
|
||||
pub fn alloca_dynamic_shape<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
shape: &[Int<'ctx, SizeT>],
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64, name);
|
||||
|
||||
// Write shape
|
||||
let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape, "shape");
|
||||
for (i, dim) in shape.iter().enumerate() {
|
||||
dst_shape.offset_const(generator, ctx, i as u64, "").store(ctx, *dim);
|
||||
}
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Clone/Copy this ndarray - Allocate a new ndarray with the same shape as this ndarray and copy the contents over.
|
||||
///
|
||||
/// The new ndarray will own its data and will be C-contiguous.
|
||||
#[must_use]
|
||||
pub fn make_copy<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let clone = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims, name);
|
||||
|
||||
let shape = self.instance.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
|
||||
}
|
||||
|
||||
/// If this ndarray is unsized, return its sole value as a [`AnyObject`]. Otherwise, do nothing.
|
||||
pub fn split_unsized<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> ScalarOrNDArray<'ctx> {
|
||||
if self.is_unsized() {
|
||||
// NOTE: `np.size(self) == 0` here is never possible.
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let zero = sizet_model.const_0(generator, ctx.ctx);
|
||||
ScalarOrNDArray::Scalar(self.get_nth_scalar(generator, ctx, zero))
|
||||
} else {
|
||||
ScalarOrNDArray::NDArray(*self)
|
||||
}
|
||||
}
|
||||
|
||||
/// 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.instance.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.instance.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.instance.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.instance.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.instance.get(generator, ctx, |f| f.strides, "src_strides");
|
||||
self.copy_strides_from_array(generator, ctx, src_strides);
|
||||
}
|
||||
|
||||
/// Iterate through every element in the ndarray.
|
||||
///
|
||||
/// `body` also access to [`BreakContinueHooks`] to short-circuit.
|
||||
pub fn foreach<'a, G, F>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
body: F,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
F: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks<'ctx>,
|
||||
NDIterHandle<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
{
|
||||
gen_for_callback(
|
||||
generator,
|
||||
ctx,
|
||||
Some("ndarray_foreach"),
|
||||
|generator, ctx| Ok(NDIterHandle::new(generator, ctx, *self)),
|
||||
|generator, ctx, nditer| Ok(nditer.has_next(generator, ctx).value),
|
||||
|generator, ctx, hooks, nditer| body(generator, ctx, hooks, nditer),
|
||||
|generator, ctx, nditer| {
|
||||
nditer.next(generator, ctx);
|
||||
Ok(())
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
/// Make sure the ndarray is at least `ndmin`-dimensional.
|
||||
///
|
||||
/// If this ndarray's `ndims` is less than `ndmin`, a view is created on this with 1s prepended to the shape.
|
||||
/// If this ndarray's `ndims` is not less than `ndmin`, this function does nothing and return this ndarray.
|
||||
#[must_use]
|
||||
pub fn atleast_nd<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndmin: u64,
|
||||
) -> Self {
|
||||
if self.ndims < ndmin {
|
||||
let mut indices = vec![];
|
||||
for _ in self.ndims..ndmin {
|
||||
indices.push(RustNDIndex::NewAxis);
|
||||
}
|
||||
indices.push(RustNDIndex::Ellipsis);
|
||||
self.index(generator, ctx, &indices, "atleast_nd_ndarray")
|
||||
} else {
|
||||
*self
|
||||
}
|
||||
}
|
||||
|
||||
/// Fill the ndarray with a scalar.
|
||||
///
|
||||
/// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
|
||||
pub fn fill<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
scalar: AnyObject<'ctx>,
|
||||
) {
|
||||
// Sanity check on scalar's type.
|
||||
assert!(ctx.unifier.unioned(self.dtype, scalar.ty));
|
||||
|
||||
self.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
|
||||
let p = nditer.get_pointer(generator, ctx);
|
||||
ctx.builder.build_store(p, scalar.value).unwrap();
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
}
|
||||
|
||||
/// Create a reshaped view on this ndarray like `np.reshape()`.
|
||||
///
|
||||
/// If there is a `-1` in `new_shape`, it will be resolved; `new_shape` would **NOT** be modified as a result.
|
||||
///
|
||||
/// If reshape without copying is impossible, this function will allocate a new ndarray and copy contents.
|
||||
///
|
||||
/// * `new_ndims` - The number of dimensions of `new_shape` as a [`Type`].
|
||||
/// * `new_shape` - The target shape to do `np.reshape()`.
|
||||
#[must_use]
|
||||
pub fn reshape_or_copy<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
new_ndims: u64,
|
||||
new_shape: 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 - there are cases when the ndarray is not contiguous but could be reshaped
|
||||
// without copying data. Look into how numpy does it.
|
||||
|
||||
let current_bb = ctx.builder.get_insert_block().unwrap();
|
||||
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
|
||||
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
|
||||
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
|
||||
|
||||
let dst_ndarray =
|
||||
NDArrayObject::alloca(generator, ctx, self.dtype, new_ndims, "reshaped_ndarray");
|
||||
dst_ndarray.copy_shape_from_array(generator, ctx, new_shape);
|
||||
|
||||
// Reolsve negative indices
|
||||
let size = self.size(generator, ctx);
|
||||
let dst_ndims = dst_ndarray.get_ndims(generator, ctx.ctx);
|
||||
let dst_shape =
|
||||
dst_ndarray.instance.get(generator, ctx, |f| f.shape, "reshaped_ndarray_shape");
|
||||
call_nac3_ndarray_resolve_and_check_new_shape(generator, ctx, size, dst_ndims, dst_shape);
|
||||
|
||||
let is_c_contiguous = self.is_c_contiguous(generator, ctx);
|
||||
ctx.builder.build_conditional_branch(is_c_contiguous.value, then_bb, else_bb).unwrap();
|
||||
|
||||
// Inserting into then_bb: reshape is possible without copying
|
||||
ctx.builder.position_at_end(then_bb);
|
||||
dst_ndarray.update_strides_by_shape(generator, ctx);
|
||||
dst_ndarray.instance.set(
|
||||
ctx,
|
||||
|f| f.data,
|
||||
self.instance.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
|
||||
}
|
||||
|
||||
/// Create a flattened view of this ndarray, like `np.ravel()`.
|
||||
///
|
||||
/// Uses [`NDArrayObject::reshape_or_copy`] under-the-hood so ndarray may or may not be copied.
|
||||
#[must_use]
|
||||
pub fn ravel_or_copy<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Self {
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let num0 = sizet_model.const_0(generator, ctx.ctx);
|
||||
let num1 = sizet_model.const_1(generator, ctx.ctx);
|
||||
let num_neg1 = sizet_model.const_all_1s(generator, ctx.ctx);
|
||||
|
||||
// Create `[-1]` and pass to `reshape_or_copy`.
|
||||
let new_shape = sizet_model.array_alloca(generator, ctx, num1.value, "new_shape");
|
||||
new_shape.offset(generator, ctx, num0.value, "").store(ctx, num_neg1);
|
||||
|
||||
self.reshape_or_copy(generator, ctx, 1, new_shape)
|
||||
}
|
||||
|
||||
/// Create a transposed view on this ndarray like `np.transpose(<ndarray>, <axes> = None)`.
|
||||
/// * `axes` - If specified, should be an array of the permutation (negative indices are **allowed**).
|
||||
#[must_use]
|
||||
pub fn transpose<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
axes: Option<Ptr<'ctx, IntModel<SizeT>>>,
|
||||
) -> Self {
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let transposed_ndarray =
|
||||
NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims, "transposed_ndarray");
|
||||
|
||||
let num_axes = self.get_ndims(generator, ctx.ctx);
|
||||
|
||||
// `axes = nullptr` if `axes` is unspecified.
|
||||
let axes = axes.unwrap_or_else(|| PtrModel(sizet_model).nullptr(generator, ctx.ctx));
|
||||
|
||||
call_nac3_ndarray_transpose(
|
||||
generator,
|
||||
ctx,
|
||||
self.instance,
|
||||
transposed_ndarray.instance,
|
||||
num_axes,
|
||||
axes,
|
||||
);
|
||||
|
||||
transposed_ndarray
|
||||
}
|
||||
|
||||
/// Check if this `NDArray` can be used as an `out` ndarray for an operation.
|
||||
///
|
||||
/// Raise an exception if the shapes do not match.
|
||||
pub fn check_can_be_written_by_out<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
out_ndims: u64,
|
||||
out_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let ndarray_ndims = self.get_ndims(generator, ctx.ctx);
|
||||
let ndarray_shape = self.instance.get(generator, ctx, |f| f.shape, "shape");
|
||||
|
||||
let output_ndims = sizet_model.constant(generator, ctx.ctx, out_ndims);
|
||||
let output_shape = out_shape;
|
||||
|
||||
call_nac3_ndarray_util_assert_output_shape_same(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray_ndims,
|
||||
ndarray_shape,
|
||||
output_ndims,
|
||||
output_shape,
|
||||
);
|
||||
}
|
||||
|
||||
/// Create the shape tuple of this ndarray like `np.shape(<ndarray>)`.
|
||||
///
|
||||
/// The returned integers in the tuple are in int32.
|
||||
pub fn make_shape_tuple<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> TupleObject<'ctx> {
|
||||
// TODO: Don't return a tuple of int32s.
|
||||
|
||||
let mut objects = Vec::with_capacity(self.ndims as usize);
|
||||
|
||||
for i in 0..self.ndims {
|
||||
let dim = self
|
||||
.instance
|
||||
.get(generator, ctx, |f| f.shape, "")
|
||||
.offset_const(generator, ctx, i, "")
|
||||
.load(generator, ctx, "dim");
|
||||
let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
|
||||
|
||||
objects.push(AnyObject {
|
||||
ty: ctx.primitives.int32,
|
||||
value: dim.value.as_basic_value_enum(),
|
||||
});
|
||||
}
|
||||
|
||||
TupleObject::create(generator, ctx, objects, "shape")
|
||||
}
|
||||
|
||||
/// Create the strides tuple of this ndarray like `np.strides(<ndarray>)`.
|
||||
///
|
||||
/// The returned integers in the tuple are in int32.
|
||||
pub fn make_strides_tuple<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> TupleObject<'ctx> {
|
||||
// TODO: Don't return a tuple of int32s.
|
||||
|
||||
let mut objects = Vec::with_capacity(self.ndims as usize);
|
||||
|
||||
for i in 0..self.ndims {
|
||||
let dim = self
|
||||
.instance
|
||||
.get(generator, ctx, |f| f.strides, "")
|
||||
.offset_const(generator, ctx, i, "")
|
||||
.load(generator, ctx, "dim");
|
||||
let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
|
||||
|
||||
objects.push(AnyObject {
|
||||
ty: ctx.primitives.int32,
|
||||
value: dim.value.as_basic_value_enum(),
|
||||
});
|
||||
}
|
||||
|
||||
TupleObject::create(generator, ctx, objects, "strides")
|
||||
}
|
||||
}
|
||||
|
||||
/// TODO: Document me
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub enum NDArrayOut<'ctx> {
|
||||
NewNDArray { dtype: Type },
|
||||
WriteToNDArray { ndarray: NDArrayObject<'ctx> },
|
||||
}
|
||||
|
|
@ -1,53 +0,0 @@
|
|||
use inkwell::values::{BasicValue, BasicValueEnum};
|
||||
|
||||
use crate::codegen::{model::*, structure::SimpleNDArray, CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
pub fn perform_nalgebra_call<'ctx, 'a, const NUM_INPUTS: usize, const NUM_OUTPUTS: usize, G, F>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
inputs: [NDArrayObject<'ctx>; NUM_INPUTS],
|
||||
output_ndims: [u64; NUM_OUTPUTS],
|
||||
invoke_function: F,
|
||||
) -> [NDArrayObject<'ctx>; NUM_OUTPUTS]
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
F: FnOnce(
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
[BasicValueEnum<'ctx>; NUM_INPUTS],
|
||||
[BasicValueEnum<'ctx>; NUM_OUTPUTS],
|
||||
),
|
||||
{
|
||||
// TODO: Allow stacked inputs. See NumPy docs.
|
||||
|
||||
let f64_model = FloatModel(Float64);
|
||||
let simple_ndarray_model = StructModel(SimpleNDArray { item: f64_model });
|
||||
|
||||
// Prepare inputs & outputs and invoke
|
||||
let inputs = inputs.map(|input| {
|
||||
// Sanity check. Typechecker ensures this.
|
||||
assert!(ctx.unifier.unioned(input.dtype, ctx.primitives.float));
|
||||
|
||||
input
|
||||
.make_simple_ndarray(generator, ctx, FloatModel(Float64), "nalgebra_input")
|
||||
.value
|
||||
.as_basic_value_enum()
|
||||
});
|
||||
let outputs = [simple_ndarray_model.alloca(generator, ctx, "nalgebra_output"); NUM_OUTPUTS];
|
||||
invoke_function(ctx, inputs, outputs.map(|output| output.value.as_basic_value_enum()));
|
||||
|
||||
// Turn the outputs into strided NDArrays
|
||||
let mut output_i = 0;
|
||||
outputs.map(|output| {
|
||||
let out = NDArrayObject::from_simple_ndarray(
|
||||
generator,
|
||||
ctx,
|
||||
output,
|
||||
ctx.primitives.float,
|
||||
output_ndims[output_i],
|
||||
);
|
||||
output_i += 1;
|
||||
out
|
||||
})
|
||||
}
|
||||
|
|
@ -1,88 +0,0 @@
|
|||
use inkwell::{types::BasicType, values::PointerValue, AddressSpace};
|
||||
|
||||
use crate::codegen::{
|
||||
irrt::{call_nac3_nditer_has_next, call_nac3_nditer_initialize, call_nac3_nditer_next},
|
||||
model::*,
|
||||
object::AnyObject,
|
||||
structure::NDIter,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct NDIterHandle<'ctx> {
|
||||
ndarray: NDArrayObject<'ctx>,
|
||||
instance: Ptr<'ctx, StructModel<NDIter>>,
|
||||
indices: Ptr<'ctx, IntModel<SizeT>>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDIterHandle<'ctx> {
|
||||
pub fn new<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayObject<'ctx>,
|
||||
) -> Self {
|
||||
let nditer = StructModel(NDIter).alloca(generator, ctx, "nditer");
|
||||
let ndims = ndarray.get_ndims(generator, ctx.ctx);
|
||||
let indices = IntModel(SizeT).array_alloca(generator, ctx, ndims.value, "indices");
|
||||
call_nac3_nditer_initialize(generator, ctx, nditer, ndarray.instance, indices);
|
||||
NDIterHandle { ndarray, instance: nditer, indices }
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn has_next<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
call_nac3_nditer_has_next(generator, ctx, self.instance)
|
||||
}
|
||||
|
||||
pub fn next<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
call_nac3_nditer_next(generator, ctx, self.instance);
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn get_pointer<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let elem_ty = ctx.get_llvm_type(generator, self.ndarray.dtype);
|
||||
|
||||
let p = self.instance.get(generator, ctx, |f| f.element, "element");
|
||||
ctx.builder
|
||||
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "element")
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn get_scalar<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
let p = self.get_pointer(generator, ctx);
|
||||
let value = ctx.builder.build_load(p, "value").unwrap();
|
||||
AnyObject { ty: self.ndarray.dtype, value }
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn get_index<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
self.instance.get(generator, ctx, |f| f.nth, "index")
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn get_indices(&self) -> Ptr<'ctx, IntModel<SizeT>> {
|
||||
self.indices
|
||||
}
|
||||
}
|
||||
|
|
@ -1,159 +0,0 @@
|
|||
use std::cmp::max;
|
||||
|
||||
use crate::codegen::{
|
||||
irrt::{
|
||||
call_nac3_ndarray_float64_matmul_at_least_2d, call_nac3_ndarray_matmul_calculate_shapes,
|
||||
},
|
||||
model::*,
|
||||
object::ndarray::indexing::RustNDIndex,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::{NDArrayObject, NDArrayOut};
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// TODO: Document me
|
||||
fn matmul_helper<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
a: Self,
|
||||
b: Self,
|
||||
) -> Self {
|
||||
assert!(a.ndims >= 2);
|
||||
assert!(b.ndims >= 2);
|
||||
|
||||
assert!(ctx.unifier.unioned(ctx.primitives.float, a.dtype));
|
||||
assert!(ctx.unifier.unioned(ctx.primitives.float, b.dtype));
|
||||
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let final_ndims_int = max(a.ndims, b.ndims);
|
||||
|
||||
let a_ndims = a.get_ndims(generator, ctx.ctx);
|
||||
let a_shape = a.instance.get(generator, ctx, |f| f.shape, "a_shape");
|
||||
let b_ndims = b.get_ndims(generator, ctx.ctx);
|
||||
let b_shape = b.instance.get(generator, ctx, |f| f.shape, "b_shape");
|
||||
let final_ndims = sizet_model.constant(generator, ctx.ctx, final_ndims_int);
|
||||
let new_a_shape =
|
||||
sizet_model.array_alloca(generator, ctx, final_ndims.value, "new_a_shape");
|
||||
let new_b_shape =
|
||||
sizet_model.array_alloca(generator, ctx, final_ndims.value, "new_b_shape");
|
||||
let dst_shape = sizet_model.array_alloca(generator, ctx, final_ndims.value, "dst_shape");
|
||||
|
||||
call_nac3_ndarray_matmul_calculate_shapes(
|
||||
generator,
|
||||
ctx,
|
||||
a_ndims,
|
||||
a_shape,
|
||||
b_ndims,
|
||||
b_shape,
|
||||
final_ndims,
|
||||
new_a_shape,
|
||||
new_b_shape,
|
||||
dst_shape,
|
||||
);
|
||||
|
||||
let new_a = a.broadcast_to(generator, ctx, final_ndims_int, new_a_shape);
|
||||
let new_b = b.broadcast_to(generator, ctx, final_ndims_int, new_b_shape);
|
||||
let dst = NDArrayObject::alloca(
|
||||
generator,
|
||||
ctx,
|
||||
ctx.primitives.float,
|
||||
final_ndims_int,
|
||||
"matmul_result",
|
||||
);
|
||||
dst.copy_shape_from_array(generator, ctx, dst_shape);
|
||||
dst.create_data(generator, ctx);
|
||||
|
||||
call_nac3_ndarray_float64_matmul_at_least_2d(
|
||||
generator,
|
||||
ctx,
|
||||
new_a.instance,
|
||||
new_b.instance,
|
||||
dst.instance,
|
||||
);
|
||||
|
||||
dst
|
||||
}
|
||||
|
||||
/// Perform `np.matmul` according to the rules in
|
||||
/// <https://numpy.org/doc/stable/reference/generated/numpy.matmul.html>.
|
||||
///
|
||||
/// This function always return an [`NDArrayObject`]. You may want to use [`NDArrayObject::split_unsized`].
|
||||
pub fn matmul<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
a: Self,
|
||||
b: Self,
|
||||
out: NDArrayOut<'ctx>,
|
||||
) -> Self {
|
||||
// Sanity check, but type inference should prevent this.
|
||||
assert!(a.ndims > 0 && b.ndims > 0, "np.matmul disallows scalar input");
|
||||
|
||||
/*
|
||||
If both arguments are 2-D they are multiplied like conventional matrices.
|
||||
If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indexes and broadcast accordingly.
|
||||
If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed.
|
||||
If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed.
|
||||
*/
|
||||
|
||||
let new_a = if a.ndims == 1 {
|
||||
// Prepend 1 to its dimensions
|
||||
a.index(generator, ctx, &[RustNDIndex::NewAxis, RustNDIndex::Ellipsis], "new_a")
|
||||
} else {
|
||||
a
|
||||
};
|
||||
|
||||
let new_b = if b.ndims == 1 {
|
||||
// Append 1 to its dimensions
|
||||
b.index(generator, ctx, &[RustNDIndex::Ellipsis, RustNDIndex::NewAxis], "new_a")
|
||||
} else {
|
||||
b
|
||||
};
|
||||
|
||||
// NOTE: `result` will always be a newly allocated ndarray.
|
||||
// Current implementation cannot do in-place matrix muliplication.
|
||||
let mut result = NDArrayObject::matmul_helper(generator, ctx, new_a, new_b);
|
||||
|
||||
let i32_model = IntModel(Int32); // TODO: Upgrade to SizeT
|
||||
let zero = i32_model.const_0(generator, ctx.ctx);
|
||||
|
||||
if a.ndims == 1 {
|
||||
// Remove the prepended 1
|
||||
result = result.index(
|
||||
generator,
|
||||
ctx,
|
||||
&[RustNDIndex::SingleElement(zero), RustNDIndex::Ellipsis],
|
||||
"result_no_prepend_1",
|
||||
);
|
||||
}
|
||||
|
||||
if b.ndims == 1 {
|
||||
// Remove the appended 1
|
||||
result = result.index(
|
||||
generator,
|
||||
ctx,
|
||||
&[RustNDIndex::Ellipsis, RustNDIndex::SingleElement(zero)],
|
||||
"result_no_append_1",
|
||||
);
|
||||
}
|
||||
|
||||
match out {
|
||||
NDArrayOut::NewNDArray { dtype } => {
|
||||
// We don't support auto-casting right now, nor anything other than float64.
|
||||
// Force the output dtype to be float64.
|
||||
assert!(ctx.unifier.unioned(ctx.primitives.float, dtype));
|
||||
result
|
||||
}
|
||||
NDArrayOut::WriteToNDArray { ndarray: out_ndarray } => {
|
||||
// TODO: It is possible to check the shapes before computing the matmul to save resources.
|
||||
let result_shape = result.instance.get(generator, ctx, |f| f.shape, "result_shape");
|
||||
out_ndarray.check_can_be_written_by_out(generator, ctx, result.ndims, result_shape);
|
||||
|
||||
// TODO: We can just set `out_ndarray.data` to `result.data`. Should we?
|
||||
out_ndarray.copy_data_from(generator, ctx, result);
|
||||
out_ndarray
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,131 +0,0 @@
|
|||
use inkwell::values::{BasicValue, BasicValueEnum};
|
||||
|
||||
use crate::{
|
||||
codegen::{model::*, object::AnyObject, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
impl<'ctx> AnyObject<'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(generator, ctx, self.ty, 0, "scalar_ndarray");
|
||||
ndarray.instance.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(AnyObject<'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.instance.value.as_basic_value_enum(),
|
||||
}
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn into_scalar(&self) -> AnyObject<'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.ty,
|
||||
ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for AnyObject<'ctx> {
|
||||
type Error = ();
|
||||
|
||||
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
|
||||
match value {
|
||||
ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
|
||||
ScalarOrNDArray::NDArray(_ndarray) => Err(()),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayObject<'ctx> {
|
||||
type Error = ();
|
||||
|
||||
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
|
||||
match value {
|
||||
ScalarOrNDArray::Scalar(_scalar) => Err(()),
|
||||
ScalarOrNDArray::NDArray(ndarray) => Ok(*ndarray),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Split an [`AnyObject`] into a [`ScalarOrNDArray`] depending on its [`Type`].
|
||||
pub fn split_scalar_or_ndarray<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
) -> ScalarOrNDArray<'ctx> {
|
||||
// TODO: Automatically convert a list into an ndarray?
|
||||
|
||||
match &*ctx.unifier.get_ty(object.ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, object);
|
||||
ScalarOrNDArray::NDArray(ndarray)
|
||||
}
|
||||
_ => {
|
||||
let scalar = AnyObject { ty: object.ty, value: object.value };
|
||||
ScalarOrNDArray::Scalar(scalar)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,111 +0,0 @@
|
|||
use util::gen_for_model_auto;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
model::*,
|
||||
object::{list::ListObject, tuple::TupleObject, AnyObject},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::TypeEnum,
|
||||
};
|
||||
|
||||
/// Parse a NumPy-like "int sequence" input and return the int sequence as an array and its length.
|
||||
///
|
||||
/// * `sequence` - The `sequence` parameter.
|
||||
/// * `sequence_ty` - The typechecker type of `sequence`
|
||||
///
|
||||
/// The `sequence` argument type may only be one of the following:
|
||||
/// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
|
||||
/// 2. A tuple of `int32`; e.g., `np.empty((600, 800, 3))`
|
||||
/// 3. A scalar `int32`; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
|
||||
///
|
||||
/// All `int32` values will be sign-extended to `SizeT`.
|
||||
pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
input_sequence: AnyObject<'ctx>,
|
||||
) -> (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_object(generator, ctx, input_sequence);
|
||||
|
||||
let len = input_sequence.instance.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
|
||||
.instance
|
||||
.get(generator, ctx, |f| f.items, "")
|
||||
.offset(generator, ctx, i.value, "")
|
||||
.load(generator, ctx, "")
|
||||
.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 { .. } => {
|
||||
// 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
|
||||
|
||||
let input_sequence = TupleObject::from_object(ctx, input_sequence);
|
||||
|
||||
let len_int = input_sequence.len_static();
|
||||
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 = input_sequence.get(ctx, i, "dim").value.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.value.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)
|
||||
),
|
||||
}
|
||||
}
|
||||
|
|
@ -1,41 +0,0 @@
|
|||
use crate::codegen::{
|
||||
irrt::calculate_len_for_slice_range, model::*, structure::RangeModel, CodeGenContext,
|
||||
CodeGenerator,
|
||||
};
|
||||
|
||||
use super::AnyObject;
|
||||
|
||||
/// A `range` in NAC3
|
||||
pub struct RangeObject<'ctx> {
|
||||
pub instance: Ptr<'ctx, RangeModel>,
|
||||
}
|
||||
|
||||
impl<'ctx> RangeObject<'ctx> {
|
||||
pub fn from_object<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
) -> Self {
|
||||
assert!(ctx.unifier.unioned(ctx.primitives.range, object.ty)); // Sanity check on type.
|
||||
|
||||
let model = PtrModel(RangeModel::default());
|
||||
let instance = model.check_value(generator, ctx.ctx, object.value).unwrap();
|
||||
RangeObject { instance }
|
||||
}
|
||||
|
||||
/// Get the `len()` of this range.
|
||||
pub fn len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, Int32> {
|
||||
let start = self.instance.gep_start(generator, ctx, "").load(generator, ctx, "start");
|
||||
let stop = self.instance.gep_stop(generator, ctx, "").load(generator, ctx, "stop");
|
||||
let step = self.instance.gep_step(generator, ctx, "").load(generator, ctx, "step");
|
||||
|
||||
// TODO: Refactor this
|
||||
let len =
|
||||
calculate_len_for_slice_range(generator, ctx, start.value, stop.value, step.value);
|
||||
IntModel(Int32).check_value(generator, ctx.ctx, len).unwrap()
|
||||
}
|
||||
}
|
||||
|
|
@ -1,113 +0,0 @@
|
|||
use core::panic;
|
||||
|
||||
use inkwell::values::StructValue;
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
codegen::{model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::AnyObject;
|
||||
|
||||
/// A NAC3 tuple object.
|
||||
///
|
||||
/// NOTE: This struct has no copy trait.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct TupleObject<'ctx> {
|
||||
/// The type of the tuple.
|
||||
pub tys: Vec<Type>,
|
||||
/// The underlying LLVM value of this tuple.
|
||||
pub value: StructValue<'ctx>,
|
||||
}
|
||||
|
||||
impl<'ctx> TupleObject<'ctx> {
|
||||
// NOTE: There is no Model abstraction for Tuples with arbitrary lengths.
|
||||
// Everything has to be done raw with Inkwell.
|
||||
|
||||
pub fn from_object(ctx: &mut CodeGenContext<'ctx, '_>, object: AnyObject<'ctx>) -> Self {
|
||||
// TODO: Keep `is_vararg_ctx` from TTuple?
|
||||
|
||||
// Sanity check on object type.
|
||||
let TypeEnum::TTuple { ty: tys, .. } = &*ctx.unifier.get_ty(object.ty) else {
|
||||
panic!(
|
||||
"Expected type to be a TypeEnum::TTuple, got {}",
|
||||
ctx.unifier.stringify(object.ty)
|
||||
);
|
||||
};
|
||||
|
||||
let value = object.value.into_struct_value();
|
||||
let value_num_fields = value.get_type().count_fields() as usize;
|
||||
assert!(
|
||||
value_num_fields == tys.len(),
|
||||
"Tuple type has {} item(s), but the LLVM struct value has {} field(s)",
|
||||
tys.len(),
|
||||
value_num_fields
|
||||
);
|
||||
|
||||
TupleObject { tys: tys.clone(), value }
|
||||
}
|
||||
|
||||
/// Convenience function. Create a [`TupleObject`] from an iterator of objects.
|
||||
pub fn create<I, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
objects: I,
|
||||
name: &str,
|
||||
) -> Self
|
||||
where
|
||||
I: IntoIterator<Item = AnyObject<'ctx>>,
|
||||
{
|
||||
let (values, tys): (Vec<_>, Vec<_>) =
|
||||
objects.into_iter().map(|object| (object.value, object.ty)).unzip();
|
||||
|
||||
let llvm_tys = tys.iter().map(|ty| ctx.get_llvm_type(generator, *ty)).collect_vec();
|
||||
let llvm_tuple_ty = ctx.ctx.struct_type(&llvm_tys, false);
|
||||
|
||||
let pllvm_tuple = ctx.builder.build_alloca(llvm_tuple_ty, "tuple").unwrap();
|
||||
for (i, val) in values.into_iter().enumerate() {
|
||||
let pval = ctx.builder.build_struct_gep(pllvm_tuple, i as u32, "value").unwrap();
|
||||
ctx.builder.build_store(pval, val).unwrap();
|
||||
}
|
||||
|
||||
let value = ctx.builder.build_load(pllvm_tuple, name).unwrap().into_struct_value();
|
||||
TupleObject { tys, value }
|
||||
}
|
||||
|
||||
/// Get the `len()` of this tuple statically.
|
||||
///
|
||||
/// We statically know the lengths of tuples in NAC3 when compiling.
|
||||
#[must_use]
|
||||
pub fn len_static(&self) -> usize {
|
||||
self.tys.len()
|
||||
}
|
||||
|
||||
/// Get the `len()` of this tuple.
|
||||
#[must_use]
|
||||
pub fn len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
IntModel(SizeT).constant(generator, ctx.ctx, self.len_static() as u64)
|
||||
}
|
||||
|
||||
/// Check if this tuple is an empty/unit tuple.
|
||||
#[must_use]
|
||||
pub fn is_empty(&self) -> bool {
|
||||
self.len_static() == 0
|
||||
}
|
||||
|
||||
/// Get the `i`-th (0-based) object in this tuple.
|
||||
pub fn get(&self, ctx: &mut CodeGenContext<'ctx, '_>, i: usize, name: &str) -> AnyObject<'ctx> {
|
||||
assert!(
|
||||
i < self.len_static(),
|
||||
"Tuple object with length {} have index {i}",
|
||||
self.len_static()
|
||||
);
|
||||
|
||||
let value = ctx.builder.build_extract_value(self.value, i as u32, name).unwrap();
|
||||
let ty = self.tys[i];
|
||||
AnyObject { ty, value }
|
||||
}
|
||||
}
|
||||
|
|
@ -1,14 +1,8 @@
|
|||
use super::model::*;
|
||||
use super::object::ndarray::indexing::util::gen_ndarray_subscript_ndindexes;
|
||||
use super::object::ndarray::scalar::split_scalar_or_ndarray;
|
||||
use super::object::ndarray::NDArrayObject;
|
||||
use super::object::AnyObject;
|
||||
use super::{
|
||||
super::symbol_resolver::ValueEnum,
|
||||
expr::destructure_range,
|
||||
irrt::{handle_slice_indices, list_slice_assignment},
|
||||
structure::{CSlice, Exception},
|
||||
CodeGenContext, CodeGenerator, Int32, IntModel, Ptr, StructModel,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
use crate::{
|
||||
codegen::{
|
||||
|
|
@ -16,10 +10,10 @@ use crate::{
|
|||
expr::gen_binop_expr,
|
||||
gen_in_range_check,
|
||||
},
|
||||
toplevel::{DefinitionId, TopLevelDef},
|
||||
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, TopLevelDef},
|
||||
typecheck::{
|
||||
magic_methods::Binop,
|
||||
typedef::{iter_type_vars, FunSignature, Type, TypeEnum},
|
||||
typedef::{FunSignature, Type, TypeEnum},
|
||||
},
|
||||
};
|
||||
use inkwell::{
|
||||
|
|
@ -29,10 +23,10 @@ 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>(
|
||||
|
|
@ -103,6 +97,8 @@ 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 {
|
||||
|
|
@ -141,6 +137,65 @@ 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!(),
|
||||
}))
|
||||
}
|
||||
|
|
@ -151,20 +206,70 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
|
|||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
target: &Expr<Option<Type>>,
|
||||
value: ValueEnum<'ctx>,
|
||||
value_ty: Type,
|
||||
) -> Result<(), String> {
|
||||
// See https://docs.python.org/3/reference/simple_stmts.html#assignment-statements.
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
match &target.node {
|
||||
ExprKind::Subscript { value: target, slice: key, .. } => {
|
||||
// Handle "slicing" or "subscription"
|
||||
generator.gen_setitem(ctx, target, key, value, value_ty)?;
|
||||
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::Tuple { elts, .. } | ExprKind::List { elts, .. } => {
|
||||
// Fold on `"[" [target_list] "]"` and `"(" [target_list] ")"`
|
||||
generator.gen_assign_target_list(ctx, elts, 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);
|
||||
}
|
||||
_ => {
|
||||
// Handle attribute and direct variable assignments.
|
||||
let name = if let ExprKind::Name { id, .. } = &target.node {
|
||||
format!("{id}.addr")
|
||||
} else {
|
||||
|
|
@ -188,272 +293,6 @@ 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(), is_vararg_ctx: false });
|
||||
|
||||
// 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 = AnyObject { value: target, ty: 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)?;
|
||||
let value = AnyObject { value, ty: value_ty };
|
||||
|
||||
/*
|
||||
Reference code:
|
||||
```python
|
||||
target = target[key]
|
||||
value = np.asarray(value)
|
||||
|
||||
shape = np.broadcast_shape((target, value))
|
||||
|
||||
target = np.broadcast_to(target, shape)
|
||||
value = np.broadcast_to(value, shape)
|
||||
|
||||
...and finally copy 1-1 from value to target.
|
||||
```
|
||||
*/
|
||||
let target = NDArrayObject::from_object(generator, ctx, target);
|
||||
let target = target.index(generator, ctx, &key, "assign_target_ndarray");
|
||||
|
||||
let value = split_scalar_or_ndarray(generator, ctx, value).as_ndarray(generator, ctx);
|
||||
|
||||
let broadcast_result = NDArrayObject::broadcast(generator, ctx, &[target, value]);
|
||||
|
||||
let target = broadcast_result.ndarrays[0];
|
||||
let value = broadcast_result.ndarrays[1];
|
||||
|
||||
target.copy_data_from(generator, ctx, value);
|
||||
}
|
||||
_ => {
|
||||
panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));
|
||||
}
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// See [`CodeGenerator::gen_for`].
|
||||
pub fn gen_for<G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
|
|
@ -476,6 +315,9 @@ 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
|
||||
|
|
@ -484,132 +326,113 @@ 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_ty)?
|
||||
v.to_basic_value_enum(ctx, generator, iter.custom.unwrap())?
|
||||
} else {
|
||||
return Ok(());
|
||||
};
|
||||
if is_iterable_range_expr {
|
||||
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"))?
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
let (start, stop, step) = destructure_range(ctx, iter_val);
|
||||
|
||||
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();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
rangenez,
|
||||
"ValueError",
|
||||
"range() arg 3 must not be zero",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
ctx.builder.build_unconditional_branch(cond_bb).unwrap();
|
||||
|
||||
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"))?
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
let (start, stop, step) = destructure_range(ctx, iter_val);
|
||||
|
||||
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();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
rangenez,
|
||||
"ValueError",
|
||||
"range() arg 3 must not be zero",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
ctx.builder.build_unconditional_branch(cond_bb).unwrap();
|
||||
|
||||
{
|
||||
ctx.builder.position_at_end(cond_bb);
|
||||
ctx.builder
|
||||
.build_conditional_branch(
|
||||
gen_in_range_check(
|
||||
ctx,
|
||||
ctx.builder
|
||||
.build_load(i, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap(),
|
||||
stop,
|
||||
step,
|
||||
),
|
||||
body_bb,
|
||||
orelse_bb,
|
||||
)
|
||||
.unwrap();
|
||||
}
|
||||
|
||||
ctx.builder.position_at_end(incr_bb);
|
||||
let next_i = ctx
|
||||
.builder
|
||||
.build_int_add(
|
||||
ctx.builder.build_load(i, "").map(BasicValueEnum::into_int_value).unwrap(),
|
||||
step,
|
||||
"inc",
|
||||
)
|
||||
.unwrap();
|
||||
ctx.builder.build_store(i, next_i).unwrap();
|
||||
ctx.builder.build_unconditional_branch(cond_bb).unwrap();
|
||||
|
||||
ctx.builder.position_at_end(body_bb);
|
||||
ctx.builder
|
||||
.build_store(
|
||||
target_i,
|
||||
ctx.builder.build_load(i, "").map(BasicValueEnum::into_int_value).unwrap(),
|
||||
)
|
||||
.unwrap();
|
||||
generator.gen_block(ctx, body.iter())?;
|
||||
}
|
||||
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
|
||||
.build_gep_and_load(
|
||||
iter_val.into_pointer_value(),
|
||||
&[zero, int32.const_int(1, false)],
|
||||
Some("len"),
|
||||
)
|
||||
.into_int_value();
|
||||
ctx.builder.build_unconditional_branch(cond_bb).unwrap();
|
||||
|
||||
ctx.builder.position_at_end(cond_bb);
|
||||
let index = ctx
|
||||
.builder
|
||||
.build_load(index_addr, "for.index")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
ctx.builder
|
||||
.build_conditional_branch(
|
||||
gen_in_range_check(
|
||||
ctx,
|
||||
ctx.builder.build_load(i, "").map(BasicValueEnum::into_int_value).unwrap(),
|
||||
stop,
|
||||
step,
|
||||
),
|
||||
body_bb,
|
||||
orelse_bb,
|
||||
)
|
||||
.unwrap();
|
||||
let cmp = ctx.builder.build_int_compare(IntPredicate::SLT, index, len, "cond").unwrap();
|
||||
ctx.builder.build_conditional_branch(cmp, body_bb, orelse_bb).unwrap();
|
||||
|
||||
ctx.builder.position_at_end(incr_bb);
|
||||
let index =
|
||||
ctx.builder.build_load(index_addr, "").map(BasicValueEnum::into_int_value).unwrap();
|
||||
let inc = ctx.builder.build_int_add(index, size_t.const_int(1, true), "inc").unwrap();
|
||||
ctx.builder.build_store(index_addr, inc).unwrap();
|
||||
ctx.builder.build_unconditional_branch(cond_bb).unwrap();
|
||||
|
||||
ctx.builder.position_at_end(body_bb);
|
||||
let arr_ptr = ctx
|
||||
.build_gep_and_load(iter_val.into_pointer_value(), &[zero, zero], Some("arr.addr"))
|
||||
.into_pointer_value();
|
||||
let index = ctx
|
||||
.builder
|
||||
.build_load(index_addr, "for.index")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
let val = ctx.build_gep_and_load(arr_ptr, &[index], Some("val"));
|
||||
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));
|
||||
}
|
||||
|
||||
ctx.builder.position_at_end(incr_bb);
|
||||
let next_i = ctx
|
||||
.builder
|
||||
.build_int_add(
|
||||
ctx.builder.build_load(i, "").map(BasicValueEnum::into_int_value).unwrap(),
|
||||
step,
|
||||
"inc",
|
||||
)
|
||||
.unwrap();
|
||||
ctx.builder.build_store(i, next_i).unwrap();
|
||||
ctx.builder.build_unconditional_branch(cond_bb).unwrap();
|
||||
|
||||
ctx.builder.position_at_end(body_bb);
|
||||
ctx.builder
|
||||
.build_store(
|
||||
target_i,
|
||||
ctx.builder.build_load(i, "").map(BasicValueEnum::into_int_value).unwrap(),
|
||||
)
|
||||
.unwrap();
|
||||
generator.gen_block(ctx, body.iter())?;
|
||||
} else {
|
||||
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
|
||||
.build_gep_and_load(
|
||||
iter_val.into_pointer_value(),
|
||||
&[zero, int32.const_int(1, false)],
|
||||
Some("len"),
|
||||
)
|
||||
.into_int_value();
|
||||
ctx.builder.build_unconditional_branch(cond_bb).unwrap();
|
||||
|
||||
ctx.builder.position_at_end(cond_bb);
|
||||
let index = ctx
|
||||
.builder
|
||||
.build_load(index_addr, "for.index")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
let cmp = ctx.builder.build_int_compare(IntPredicate::SLT, index, len, "cond").unwrap();
|
||||
ctx.builder.build_conditional_branch(cmp, body_bb, orelse_bb).unwrap();
|
||||
|
||||
ctx.builder.position_at_end(incr_bb);
|
||||
let index =
|
||||
ctx.builder.build_load(index_addr, "").map(BasicValueEnum::into_int_value).unwrap();
|
||||
let inc = ctx.builder.build_int_add(index, size_t.const_int(1, true), "inc").unwrap();
|
||||
ctx.builder.build_store(index_addr, inc).unwrap();
|
||||
ctx.builder.build_unconditional_branch(cond_bb).unwrap();
|
||||
|
||||
ctx.builder.position_at_end(body_bb);
|
||||
let arr_ptr = ctx
|
||||
.build_gep_and_load(iter_val.into_pointer_value(), &[zero, zero], Some("arr.addr"))
|
||||
.into_pointer_value();
|
||||
let index = ctx
|
||||
.builder
|
||||
.build_load(index_addr, "for.index")
|
||||
.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())?;
|
||||
generator.gen_block(ctx, body.iter())?;
|
||||
}
|
||||
|
||||
for (k, (_, _, counter)) in &var_assignment {
|
||||
|
|
@ -671,7 +494,6 @@ 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,
|
||||
|
|
@ -682,24 +504,18 @@ 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<'ctx>,
|
||||
I,
|
||||
) -> Result<(), String>,
|
||||
BodyFn:
|
||||
FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, BreakContinueHooks, 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, &format!("{label}.init"));
|
||||
let init_bb = ctx.ctx.insert_basic_block_after(current_bb, "for.init");
|
||||
// The BB containing the loop condition check
|
||||
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"));
|
||||
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");
|
||||
// The BB containing the increment expression
|
||||
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"));
|
||||
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");
|
||||
|
||||
// store loop bb information and restore it later
|
||||
let loop_bb = ctx.loop_target.replace((update_bb, cont_bb));
|
||||
|
|
@ -756,7 +572,6 @@ 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,
|
||||
|
|
@ -767,7 +582,7 @@ where
|
|||
BodyFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks<'ctx>,
|
||||
BreakContinueHooks,
|
||||
IntValue<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
{
|
||||
|
|
@ -776,7 +591,6 @@ 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();
|
||||
|
|
@ -828,11 +642,9 @@ 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),
|
||||
|
|
@ -844,19 +656,13 @@ 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>,
|
||||
BreakContinueHooks,
|
||||
IntValue<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
BodyFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, 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)?;
|
||||
|
||||
|
|
@ -914,10 +720,10 @@ where
|
|||
|
||||
Ok(cond)
|
||||
},
|
||||
|generator, ctx, hooks, (i_addr, _)| {
|
||||
|generator, ctx, _, (i_addr, _)| {
|
||||
let i = ctx.builder.build_load(i_addr, "").map(BasicValueEnum::into_int_value).unwrap();
|
||||
|
||||
body_fn(generator, ctx, hooks, i)
|
||||
body_fn(generator, ctx, i)
|
||||
},
|
||||
|generator, ctx, (i_addr, _)| {
|
||||
let i = ctx.builder.build_load(i_addr, "").map(BasicValueEnum::into_int_value).unwrap();
|
||||
|
|
@ -1307,36 +1113,47 @@ pub fn exn_constructor<'ctx>(
|
|||
pub fn gen_raise<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
exception: Option<Ptr<'ctx, StructModel<Exception>>>,
|
||||
exception: Option<&BasicValueEnum<'ctx>>,
|
||||
loc: Location,
|
||||
) {
|
||||
if let Some(pexn) = exception {
|
||||
let i32_model = IntModel(Int32);
|
||||
let cslice_model = StructModel(CSlice);
|
||||
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();
|
||||
|
||||
// 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 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();
|
||||
}
|
||||
|
||||
let raise = get_builtins(generator, ctx, "__nac3_raise");
|
||||
ctx.build_call_or_invoke(raise, &[pexn.value.into()], "raise");
|
||||
let exception = *exception;
|
||||
ctx.build_call_or_invoke(raise, &[exception], "raise");
|
||||
} else {
|
||||
let resume = get_builtins(generator, ctx, "__nac3_resume");
|
||||
ctx.build_call_or_invoke(resume, &[], "resume");
|
||||
}
|
||||
|
||||
ctx.builder.build_unreachable().unwrap();
|
||||
}
|
||||
|
||||
|
|
@ -1758,14 +1575,14 @@ pub fn gen_stmt<G: CodeGenerator>(
|
|||
}
|
||||
StmtKind::AnnAssign { target, value, .. } => {
|
||||
if let Some(value) = value {
|
||||
let Some(value_enum) = generator.gen_expr(ctx, value)? else { return Ok(()) };
|
||||
generator.gen_assign(ctx, target, value_enum, value.custom.unwrap())?;
|
||||
let Some(value) = generator.gen_expr(ctx, value)? else { return Ok(()) };
|
||||
generator.gen_assign(ctx, target, value)?;
|
||||
}
|
||||
}
|
||||
StmtKind::Assign { targets, value, .. } => {
|
||||
let Some(value_enum) = generator.gen_expr(ctx, value)? else { return Ok(()) };
|
||||
let Some(value) = generator.gen_expr(ctx, value)? else { return Ok(()) };
|
||||
for target in targets {
|
||||
generator.gen_assign(ctx, target, value_enum.clone(), value.custom.unwrap())?;
|
||||
generator.gen_assign(ctx, target, value.clone())?;
|
||||
}
|
||||
}
|
||||
StmtKind::Continue { .. } => {
|
||||
|
|
@ -1779,16 +1596,15 @@ 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_enum = gen_binop_expr(
|
||||
let value = gen_binop_expr(
|
||||
generator,
|
||||
ctx,
|
||||
target,
|
||||
Binop::aug_assign(*op),
|
||||
value,
|
||||
stmt.location,
|
||||
)?
|
||||
.unwrap();
|
||||
generator.gen_assign(ctx, target, value_enum, value.custom.unwrap())?;
|
||||
)?;
|
||||
generator.gen_assign(ctx, target, value.unwrap())?;
|
||||
}
|
||||
StmtKind::Try { .. } => gen_try(generator, ctx, stmt)?,
|
||||
StmtKind::Raise { exc, .. } => {
|
||||
|
|
@ -1798,41 +1614,30 @@ pub fn gen_stmt<G: CodeGenerator>(
|
|||
} else {
|
||||
return Ok(());
|
||||
};
|
||||
|
||||
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);
|
||||
gen_raise(generator, ctx, Some(&exc), stmt.location);
|
||||
} else {
|
||||
gen_raise(generator, ctx, None, stmt.location);
|
||||
}
|
||||
}
|
||||
StmtKind::Assert { test, msg, .. } => {
|
||||
let byte_model = IntModel(Byte);
|
||||
let cslice_model = StructModel(CSlice);
|
||||
|
||||
let Some(test) = generator.gen_expr(ctx, test)? else {
|
||||
let test = if let Some(v) = generator.gen_expr(ctx, test)? {
|
||||
v.to_basic_value_enum(ctx, generator, test.custom.unwrap())?
|
||||
} 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) => {
|
||||
let Some(msg) = generator.gen_expr(ctx, msg)? else {
|
||||
if let Some(v) = generator.gen_expr(ctx, msg)? {
|
||||
v.to_basic_value_enum(ctx, generator, msg.custom.unwrap())?
|
||||
} 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.value,
|
||||
test.into_int_value(),
|
||||
"0:AssertionError",
|
||||
err_msg,
|
||||
[None, None, None],
|
||||
|
|
|
|||
|
|
@ -1,256 +0,0 @@
|
|||
use inkwell::context::Context;
|
||||
|
||||
use crate::codegen::model::*;
|
||||
|
||||
use super::{CodeGenContext, CodeGenerator};
|
||||
|
||||
/// Fields of [`CSlice`]
|
||||
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)
|
||||
}
|
||||
}
|
||||
|
||||
/// 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]"),
|
||||
],
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Fields of [`List`]
|
||||
pub struct ListFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
|
||||
/// Array pointer to content
|
||||
pub items: F::Out<PtrModel<Item>>,
|
||||
/// Number of items in the array
|
||||
pub len: F::Out<IntModel<SizeT>>,
|
||||
}
|
||||
|
||||
/// A list in NAC3.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct List<Item> {
|
||||
/// Model of the list items
|
||||
pub item: Item,
|
||||
}
|
||||
|
||||
impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for List<Item> {
|
||||
type Fields<F: FieldTraversal<'ctx>> = ListFields<'ctx, F, Item>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
items: traversal.add("data", PtrModel(self.item)),
|
||||
len: traversal.add_auto("len"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Fields of [`NDArray`]
|
||||
pub struct NDArrayFields<'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>>>,
|
||||
}
|
||||
|
||||
/// A strided ndarray in NAC3.
|
||||
///
|
||||
/// See IRRT implementation for details about its fields.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct NDArray;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for NDArray {
|
||||
type Fields<F: FieldTraversal<'ctx>> = NDArrayFields<'ctx, F>;
|
||||
|
||||
fn 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"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Fields of [`SimpleNDArray`]
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct SimpleNDArrayFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
|
||||
pub ndims: F::Out<IntModel<SizeT>>,
|
||||
pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
pub data: F::Out<PtrModel<Item>>,
|
||||
}
|
||||
|
||||
/// An ndarray without strides and non-opaque `data` field in NAC3.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct SimpleNDArray<M> {
|
||||
/// [`Model`] of the items.
|
||||
pub item: M,
|
||||
}
|
||||
|
||||
impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for SimpleNDArray<Item> {
|
||||
type Fields<F: FieldTraversal<'ctx>> = SimpleNDArrayFields<'ctx, F, Item>;
|
||||
|
||||
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"),
|
||||
data: traversal.add("data", PtrModel(self.item)),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Fields of [`NDIter`]
|
||||
pub struct NDIterFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub ndims: F::Out<IntModel<SizeT>>,
|
||||
pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
pub strides: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
|
||||
pub indices: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
pub nth: F::Out<IntModel<SizeT>>,
|
||||
pub element: F::Out<PtrModel<IntModel<Byte>>>,
|
||||
|
||||
pub size: F::Out<IntModel<SizeT>>,
|
||||
}
|
||||
|
||||
/// An IRRT helper structure used when iterating through an ndarray.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct NDIter;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for NDIter {
|
||||
type Fields<F: FieldTraversal<'ctx>> = NDIterFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
ndims: traversal.add_auto("ndims"),
|
||||
shape: traversal.add_auto("shape"),
|
||||
strides: traversal.add_auto("strides"),
|
||||
|
||||
indices: traversal.add_auto("indices"),
|
||||
nth: traversal.add_auto("nth"),
|
||||
element: traversal.add_auto("element"),
|
||||
|
||||
size: traversal.add_auto("size"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A NAC3 `range`. It is an array of 3 int32s.
|
||||
// TODO: Use `pub type RangeModel<N> = NArrayModel<3, IntModel<N>>` in the future when
|
||||
// `range` type is type dependent.
|
||||
pub type RangeModel = NArrayModel<3, IntModel<Int32>>;
|
||||
|
||||
impl<'ctx> Ptr<'ctx, RangeModel> {
|
||||
pub fn gep_start<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, IntModel<Int32>> {
|
||||
self.at_const(generator, ctx, 0, name)
|
||||
}
|
||||
|
||||
pub fn gep_stop<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, IntModel<Int32>> {
|
||||
self.at_const(generator, ctx, 1, name)
|
||||
}
|
||||
|
||||
pub fn gep_step<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, IntModel<Int32>> {
|
||||
self.at_const(generator, ctx, 2, name)
|
||||
}
|
||||
}
|
||||
|
|
@ -94,7 +94,7 @@ fn test_primitives() {
|
|||
"};
|
||||
let statements = parse_program(source, FileName::default()).unwrap();
|
||||
|
||||
let composer = TopLevelComposer::new(Vec::new(), Vec::new(), ComposerConfig::default(), 32).0;
|
||||
let composer = TopLevelComposer::new(Vec::new(), ComposerConfig::default(), 32).0;
|
||||
let mut unifier = composer.unifier.clone();
|
||||
let primitives = composer.primitives_ty;
|
||||
let top_level = Arc::new(composer.make_top_level_context());
|
||||
|
|
@ -109,18 +109,8 @@ fn test_primitives() {
|
|||
let threads = vec![DefaultCodeGenerator::new("test".into(), 32).into()];
|
||||
let signature = FunSignature {
|
||||
args: vec![
|
||||
FuncArg {
|
||||
name: "a".into(),
|
||||
ty: primitives.int32,
|
||||
default_value: None,
|
||||
is_vararg: false,
|
||||
},
|
||||
FuncArg {
|
||||
name: "b".into(),
|
||||
ty: primitives.int32,
|
||||
default_value: None,
|
||||
is_vararg: false,
|
||||
},
|
||||
FuncArg { name: "a".into(), ty: primitives.int32, default_value: None },
|
||||
FuncArg { name: "b".into(), ty: primitives.int32, default_value: None },
|
||||
],
|
||||
ret: primitives.int32,
|
||||
vars: VarMap::new(),
|
||||
|
|
@ -199,8 +189,6 @@ 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 {
|
||||
|
|
@ -258,19 +246,14 @@ fn test_simple_call() {
|
|||
"};
|
||||
let statements_2 = parse_program(source_2, FileName::default()).unwrap();
|
||||
|
||||
let composer = TopLevelComposer::new(Vec::new(), Vec::new(), ComposerConfig::default(), 32).0;
|
||||
let composer = TopLevelComposer::new(Vec::new(), ComposerConfig::default(), 32).0;
|
||||
let mut unifier = composer.unifier.clone();
|
||||
let primitives = composer.primitives_ty;
|
||||
let top_level = Arc::new(composer.make_top_level_context());
|
||||
unifier.top_level = Some(top_level.clone());
|
||||
|
||||
let signature = FunSignature {
|
||||
args: vec![FuncArg {
|
||||
name: "a".into(),
|
||||
ty: primitives.int32,
|
||||
default_value: None,
|
||||
is_vararg: false,
|
||||
}],
|
||||
args: vec![FuncArg { name: "a".into(), ty: primitives.int32, default_value: None }],
|
||||
ret: primitives.int32,
|
||||
vars: VarMap::new(),
|
||||
};
|
||||
|
|
@ -385,8 +368,6 @@ 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 {
|
||||
|
|
|
|||
|
|
@ -23,3 +23,4 @@ pub mod codegen;
|
|||
pub mod symbol_resolver;
|
||||
pub mod toplevel;
|
||||
pub mod typecheck;
|
||||
pub mod util;
|
||||
|
|
@ -78,14 +78,14 @@ impl SymbolValue {
|
|||
}
|
||||
Constant::Tuple(t) => {
|
||||
let expected_ty = unifier.get_ty(expected_ty);
|
||||
let TypeEnum::TTuple { ty, is_vararg_ctx } = expected_ty.as_ref() else {
|
||||
let TypeEnum::TTuple { ty } = expected_ty.as_ref() else {
|
||||
return Err(format!(
|
||||
"Expected {:?}, but got Tuple",
|
||||
expected_ty.get_type_name()
|
||||
));
|
||||
};
|
||||
|
||||
assert!(*is_vararg_ctx || ty.len() == t.len());
|
||||
assert_eq!(ty.len(), t.len());
|
||||
|
||||
let elems = t
|
||||
.iter()
|
||||
|
|
@ -155,7 +155,7 @@ impl SymbolValue {
|
|||
SymbolValue::Bool(_) => primitives.bool,
|
||||
SymbolValue::Tuple(vs) => {
|
||||
let vs_tys = vs.iter().map(|v| v.get_type(primitives, unifier)).collect::<Vec<_>>();
|
||||
unifier.add_ty(TypeEnum::TTuple { ty: vs_tys, is_vararg_ctx: false })
|
||||
unifier.add_ty(TypeEnum::TTuple { ty: vs_tys })
|
||||
}
|
||||
SymbolValue::OptionSome(_) | SymbolValue::OptionNone => primitives.option,
|
||||
}
|
||||
|
|
@ -482,7 +482,7 @@ pub fn parse_type_annotation<T>(
|
|||
parse_type_annotation(resolver, top_level_defs, unifier, primitives, elt)
|
||||
})
|
||||
.collect::<Result<Vec<_>, _>>()?;
|
||||
Ok(unifier.add_ty(TypeEnum::TTuple { ty, is_vararg_ctx: false }))
|
||||
Ok(unifier.add_ty(TypeEnum::TTuple { ty }))
|
||||
} else {
|
||||
Err(HashSet::from(["Expected multiple elements for tuple".into()]))
|
||||
}
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -44,27 +44,12 @@ pub struct TopLevelComposer {
|
|||
pub size_t: u32,
|
||||
}
|
||||
|
||||
/// The specification for a builtin function, consisting of the function name, the function
|
||||
/// signature, and a [code generation callback][`GenCall`].
|
||||
pub type BuiltinFuncSpec = (StrRef, FunSignature, Arc<GenCall>);
|
||||
|
||||
/// A function that creates a [`BuiltinFuncSpec`] using the provided [`PrimitiveStore`] and
|
||||
/// [`Unifier`].
|
||||
pub type BuiltinFuncCreator = dyn Fn(&PrimitiveStore, &mut Unifier) -> BuiltinFuncSpec;
|
||||
|
||||
impl TopLevelComposer {
|
||||
/// return a composer and things to make a "primitive" symbol resolver, so that the symbol
|
||||
/// resolver can later figure out primitive tye definitions when passed a primitive type name
|
||||
///
|
||||
/// `lateinit_builtins` are specifically for the ARTIQ module. Since the [`Unifier`] instance
|
||||
/// used to create builtin functions do not persist until method compilation, any types
|
||||
/// created (e.g. [`TypeEnum::TVar`]) also do not persist. Those functions should be instead put
|
||||
/// in `lateinit_builtins`, where they will be instantiated with the [`Unifier`] instance used
|
||||
/// for method compilation.
|
||||
/// resolver can later figure out primitive type definitions when passed a primitive type name
|
||||
#[must_use]
|
||||
pub fn new(
|
||||
builtins: Vec<BuiltinFuncSpec>,
|
||||
lateinit_builtins: Vec<Box<BuiltinFuncCreator>>,
|
||||
builtins: Vec<(StrRef, FunSignature, Arc<GenCall>)>,
|
||||
core_config: ComposerConfig,
|
||||
size_t: u32,
|
||||
) -> (Self, HashMap<StrRef, DefinitionId>, HashMap<StrRef, Type>) {
|
||||
|
|
@ -134,13 +119,7 @@ impl TopLevelComposer {
|
|||
}
|
||||
}
|
||||
|
||||
// Materialize lateinit_builtins, now that the unifier is ready
|
||||
let lateinit_builtins = lateinit_builtins
|
||||
.into_iter()
|
||||
.map(|builtin| builtin(&primitives_ty, &mut unifier))
|
||||
.collect_vec();
|
||||
|
||||
for (name, sig, codegen_callback) in builtins.into_iter().chain(lateinit_builtins) {
|
||||
for (name, sig, codegen_callback) in builtins {
|
||||
let fun_sig = unifier.add_ty(TypeEnum::TFunc(sig));
|
||||
builtin_ty.insert(name, fun_sig);
|
||||
builtin_id.insert(name, DefinitionId(definition_ast_list.len()));
|
||||
|
|
@ -787,7 +766,6 @@ impl TopLevelComposer {
|
|||
let target_ty = get_type_from_type_annotation_kinds(
|
||||
&temp_def_list,
|
||||
unifier,
|
||||
primitives,
|
||||
&def,
|
||||
&mut subst_list,
|
||||
)?;
|
||||
|
|
@ -881,73 +859,7 @@ impl TopLevelComposer {
|
|||
let resolver = &**resolver;
|
||||
|
||||
let mut function_var_map = VarMap::new();
|
||||
|
||||
let vararg = args
|
||||
.vararg
|
||||
.as_ref()
|
||||
.map(|vararg| -> Result<_, HashSet<String>> {
|
||||
let vararg = vararg.as_ref();
|
||||
|
||||
let annotation = vararg
|
||||
.node
|
||||
.annotation
|
||||
.as_ref()
|
||||
.ok_or_else(|| {
|
||||
HashSet::from([format!(
|
||||
"function parameter `{}` needs type annotation at {}",
|
||||
vararg.node.arg, vararg.location
|
||||
)])
|
||||
})?
|
||||
.as_ref();
|
||||
|
||||
let type_annotation = parse_ast_to_type_annotation_kinds(
|
||||
resolver,
|
||||
temp_def_list.as_slice(),
|
||||
unifier,
|
||||
primitives_store,
|
||||
annotation,
|
||||
// NOTE: since only class need this, for function
|
||||
// it should be fine to be empty map
|
||||
HashMap::new(),
|
||||
)?;
|
||||
|
||||
let type_vars_within =
|
||||
get_type_var_contained_in_type_annotation(&type_annotation)
|
||||
.into_iter()
|
||||
.map(|x| -> Result<TypeVar, HashSet<String>> {
|
||||
let TypeAnnotation::TypeVar(ty) = x else {
|
||||
unreachable!("must be type var annotation kind")
|
||||
};
|
||||
|
||||
let id = Self::get_var_id(ty, unifier)?;
|
||||
Ok(TypeVar { id, ty })
|
||||
})
|
||||
.collect::<Result<Vec<_>, _>>()?;
|
||||
for var in type_vars_within {
|
||||
if let Some(prev_ty) = function_var_map.insert(var.id, var.ty) {
|
||||
// if already have the type inserted, make sure they are the same thing
|
||||
assert_eq!(prev_ty, var.ty);
|
||||
}
|
||||
}
|
||||
|
||||
let ty = get_type_from_type_annotation_kinds(
|
||||
temp_def_list.as_ref(),
|
||||
unifier,
|
||||
primitives_store,
|
||||
&type_annotation,
|
||||
&mut None,
|
||||
)?;
|
||||
|
||||
Ok(FuncArg {
|
||||
name: vararg.node.arg,
|
||||
ty,
|
||||
default_value: Some(SymbolValue::Tuple(Vec::default())),
|
||||
is_vararg: true,
|
||||
})
|
||||
})
|
||||
.transpose()?;
|
||||
|
||||
let mut arg_types = {
|
||||
let arg_types = {
|
||||
// make sure no duplicate parameter
|
||||
let mut defined_parameter_name: HashSet<_> = HashSet::new();
|
||||
for x in &args.args {
|
||||
|
|
@ -1024,7 +936,6 @@ impl TopLevelComposer {
|
|||
let ty = get_type_from_type_annotation_kinds(
|
||||
temp_def_list.as_ref(),
|
||||
unifier,
|
||||
primitives_store,
|
||||
&type_annotation,
|
||||
&mut None,
|
||||
)?;
|
||||
|
|
@ -1048,18 +959,11 @@ impl TopLevelComposer {
|
|||
v
|
||||
}),
|
||||
},
|
||||
is_vararg: false,
|
||||
})
|
||||
})
|
||||
.collect::<Result<Vec<_>, _>>()?
|
||||
};
|
||||
|
||||
if let Some(vararg) = vararg {
|
||||
arg_types.push(vararg);
|
||||
};
|
||||
|
||||
let arg_types = arg_types;
|
||||
|
||||
let return_ty = {
|
||||
if let Some(returns) = returns {
|
||||
let return_ty_annotation = {
|
||||
|
|
@ -1098,7 +1002,6 @@ impl TopLevelComposer {
|
|||
get_type_from_type_annotation_kinds(
|
||||
&temp_def_list,
|
||||
unifier,
|
||||
primitives_store,
|
||||
&return_ty_annotation,
|
||||
&mut None,
|
||||
)?
|
||||
|
|
@ -1311,7 +1214,6 @@ impl TopLevelComposer {
|
|||
})
|
||||
}
|
||||
},
|
||||
is_vararg: false,
|
||||
};
|
||||
// push the dummy type and the type annotation
|
||||
// into the list for later unification
|
||||
|
|
@ -1720,7 +1622,6 @@ 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,
|
||||
)?;
|
||||
|
|
@ -1737,25 +1638,21 @@ impl TopLevelComposer {
|
|||
name: "msg".into(),
|
||||
ty: string,
|
||||
default_value: Some(SymbolValue::Str(String::new())),
|
||||
is_vararg: false,
|
||||
},
|
||||
FuncArg {
|
||||
name: "param0".into(),
|
||||
ty: int64,
|
||||
default_value: Some(SymbolValue::I64(0)),
|
||||
is_vararg: false,
|
||||
},
|
||||
FuncArg {
|
||||
name: "param1".into(),
|
||||
ty: int64,
|
||||
default_value: Some(SymbolValue::I64(0)),
|
||||
is_vararg: false,
|
||||
},
|
||||
FuncArg {
|
||||
name: "param2".into(),
|
||||
ty: int64,
|
||||
default_value: Some(SymbolValue::I64(0)),
|
||||
is_vararg: false,
|
||||
},
|
||||
],
|
||||
ret: self_type,
|
||||
|
|
@ -1906,11 +1803,7 @@ 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,
|
||||
primitives_ty,
|
||||
&ty_ann,
|
||||
&mut None,
|
||||
&def_list, unifier, &ty_ann, &mut None,
|
||||
)?;
|
||||
vars.extend(type_vars.iter().map(|ty| {
|
||||
let TypeEnum::TVar { id, .. } = &*unifier.get_ty(*ty) else {
|
||||
|
|
@ -1965,7 +1858,6 @@ impl TopLevelComposer {
|
|||
name: a.name,
|
||||
ty: unifier.subst(a.ty, &subst).unwrap_or(a.ty),
|
||||
default_value: a.default_value.clone(),
|
||||
is_vararg: false,
|
||||
})
|
||||
.collect_vec()
|
||||
};
|
||||
|
|
|
|||
|
|
@ -27,22 +27,17 @@ pub enum PrimDef {
|
|||
List,
|
||||
NDArray,
|
||||
|
||||
// Option methods
|
||||
FunOptionIsSome,
|
||||
FunOptionIsNone,
|
||||
FunOptionUnwrap,
|
||||
|
||||
// Option-related functions
|
||||
FunSome,
|
||||
|
||||
// NDArray methods
|
||||
FunNDArrayCopy,
|
||||
FunNDArrayFill,
|
||||
|
||||
// Range methods
|
||||
FunRangeInit,
|
||||
|
||||
// NumPy factory functions
|
||||
// Member Functions
|
||||
OptionIsSome,
|
||||
OptionIsNone,
|
||||
OptionUnwrap,
|
||||
NDArrayCopy,
|
||||
NDArrayFill,
|
||||
FunInt32,
|
||||
FunInt64,
|
||||
FunUInt32,
|
||||
FunUInt64,
|
||||
FunFloat,
|
||||
FunNpNDArray,
|
||||
FunNpEmpty,
|
||||
FunNpZeros,
|
||||
|
|
@ -51,28 +46,26 @@ pub enum PrimDef {
|
|||
FunNpArray,
|
||||
FunNpEye,
|
||||
FunNpIdentity,
|
||||
FunNpArange,
|
||||
|
||||
// NumPy view functions
|
||||
FunNpBroadcastTo,
|
||||
FunNpReshape,
|
||||
FunNpTranspose,
|
||||
|
||||
// NumPy NDArray property getters
|
||||
FunNpSize,
|
||||
FunNpShape,
|
||||
FunNpStrides,
|
||||
|
||||
// Miscellaneous NumPy & SciPy functions
|
||||
FunRound,
|
||||
FunRound64,
|
||||
FunNpRound,
|
||||
FunRangeInit,
|
||||
FunStr,
|
||||
FunBool,
|
||||
FunFloor,
|
||||
FunFloor64,
|
||||
FunNpFloor,
|
||||
FunCeil,
|
||||
FunCeil64,
|
||||
FunNpCeil,
|
||||
FunLen,
|
||||
FunMin,
|
||||
FunNpMin,
|
||||
FunNpMinimum,
|
||||
FunNpArgmin,
|
||||
FunMax,
|
||||
FunNpMax,
|
||||
FunNpMaximum,
|
||||
FunNpArgmax,
|
||||
FunAbs,
|
||||
FunNpIsNan,
|
||||
FunNpIsInf,
|
||||
FunNpSin,
|
||||
|
|
@ -111,43 +104,14 @@ pub enum PrimDef {
|
|||
FunNpHypot,
|
||||
FunNpNextAfter,
|
||||
|
||||
// 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,
|
||||
// Top-Level Functions
|
||||
FunSome,
|
||||
}
|
||||
|
||||
/// Associated details of a [`PrimDef`]
|
||||
pub enum PrimDefDetails {
|
||||
PrimFunction { name: &'static str, simple_name: &'static str },
|
||||
PrimClass { name: &'static str, get_ty_fn: fn(&PrimitiveStore) -> Type },
|
||||
PrimClass { name: &'static str },
|
||||
}
|
||||
|
||||
impl PrimDef {
|
||||
|
|
@ -189,17 +153,15 @@ 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, get_ty_fn: fn(&PrimitiveStore) -> Type) -> PrimDefDetails {
|
||||
PrimDefDetails::PrimClass { name, get_ty_fn }
|
||||
fn class(name: &'static str) -> PrimDefDetails {
|
||||
PrimDefDetails::PrimClass { name }
|
||||
}
|
||||
|
||||
fn fun(name: &'static str, simple_name: Option<&'static str>) -> PrimDefDetails {
|
||||
|
|
@ -207,37 +169,29 @@ impl PrimDef {
|
|||
}
|
||||
|
||||
match self {
|
||||
// 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::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),
|
||||
PrimDef::FunNpNDArray => fun("np_ndarray", None),
|
||||
PrimDef::FunNpEmpty => fun("np_empty", None),
|
||||
PrimDef::FunNpZeros => fun("np_zeros", None),
|
||||
|
|
@ -246,28 +200,26 @@ impl PrimDef {
|
|||
PrimDef::FunNpArray => fun("np_array", None),
|
||||
PrimDef::FunNpEye => fun("np_eye", None),
|
||||
PrimDef::FunNpIdentity => fun("np_identity", 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::FunRound => fun("round", None),
|
||||
PrimDef::FunRound64 => fun("round64", None),
|
||||
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::FunNpArgmin => fun("np_argmin", None),
|
||||
PrimDef::FunMax => fun("max", None),
|
||||
PrimDef::FunNpMax => fun("np_max", None),
|
||||
PrimDef::FunNpMaximum => fun("np_maximum", None),
|
||||
PrimDef::FunNpArgmax => fun("np_argmax", None),
|
||||
PrimDef::FunAbs => fun("abs", None),
|
||||
PrimDef::FunNpIsNan => fun("np_isnan", None),
|
||||
PrimDef::FunNpIsInf => fun("np_isinf", None),
|
||||
PrimDef::FunNpSin => fun("np_sin", None),
|
||||
|
|
@ -305,38 +257,7 @@ impl PrimDef {
|
|||
PrimDef::FunNpLdExp => fun("np_ldexp", None),
|
||||
PrimDef::FunNpHypot => fun("np_hypot", None),
|
||||
PrimDef::FunNpNextAfter => fun("np_nextafter", 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),
|
||||
PrimDef::FunSome => fun("Some", None),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -487,9 +408,9 @@ impl TopLevelComposer {
|
|||
let option = unifier.add_ty(TypeEnum::TObj {
|
||||
obj_id: PrimDef::Option.id(),
|
||||
fields: vec![
|
||||
(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)),
|
||||
(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)),
|
||||
]
|
||||
.into_iter()
|
||||
.collect::<HashMap<_, _>>(),
|
||||
|
|
@ -523,7 +444,6 @@ impl TopLevelComposer {
|
|||
name: "value".into(),
|
||||
ty: ndarray_dtype_tvar.ty,
|
||||
default_value: None,
|
||||
is_vararg: false,
|
||||
}],
|
||||
ret: none,
|
||||
vars: into_var_map([ndarray_dtype_tvar, ndarray_ndims_tvar]),
|
||||
|
|
@ -531,8 +451,8 @@ impl TopLevelComposer {
|
|||
let ndarray = unifier.add_ty(TypeEnum::TObj {
|
||||
obj_id: PrimDef::NDArray.id(),
|
||||
fields: Mapping::from([
|
||||
(PrimDef::FunNDArrayCopy.simple_name().into(), (ndarray_copy_fun_ty, true)),
|
||||
(PrimDef::FunNDArrayFill.simple_name().into(), (ndarray_fill_fun_ty, true)),
|
||||
(PrimDef::NDArrayCopy.simple_name().into(), (ndarray_copy_fun_ty, true)),
|
||||
(PrimDef::NDArrayFill.simple_name().into(), (ndarray_fill_fun_ty, true)),
|
||||
]),
|
||||
params: into_var_map([ndarray_dtype_tvar, ndarray_ndims_tvar]),
|
||||
});
|
||||
|
|
@ -1018,23 +938,3 @@ pub fn arraylike_get_ndims(unifier: &mut Unifier, ty: Type) -> u64 {
|
|||
_ => 0,
|
||||
}
|
||||
}
|
||||
|
||||
/// Extract an ndarray's `ndims` [type][`Type`] in `u64`. Panic if not possible.
|
||||
/// The `ndims` must only contain 1 value.
|
||||
#[must_use]
|
||||
pub fn extract_ndims(unifier: &Unifier, ndims_ty: Type) -> u64 {
|
||||
let ndims_ty_enum = unifier.get_ty_immutable(ndims_ty);
|
||||
let TypeEnum::TLiteral { values, .. } = &*ndims_ty_enum else {
|
||||
panic!("ndims_ty should be a TLiteral");
|
||||
};
|
||||
|
||||
assert_eq!(values.len(), 1, "ndims_ty TLiteral should only contain 1 value");
|
||||
|
||||
let ndims = values[0].clone();
|
||||
u64::try_from(ndims).unwrap()
|
||||
}
|
||||
|
||||
/// Return an ndarray's `ndims` as a typechecker [`Type`] from its `u64` value.
|
||||
pub fn create_ndims(unifier: &mut Unifier, ndims: u64) -> Type {
|
||||
unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None)
|
||||
}
|
||||
|
|
|
|||
|
|
@ -31,7 +31,6 @@ pub mod builtins;
|
|||
pub mod composer;
|
||||
pub mod helper;
|
||||
pub mod numpy;
|
||||
pub mod option;
|
||||
pub mod type_annotation;
|
||||
use composer::*;
|
||||
use type_annotation::*;
|
||||
|
|
|
|||
|
|
@ -1,46 +0,0 @@
|
|||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
toplevel::helper::PrimDef,
|
||||
typecheck::{
|
||||
type_inferencer::PrimitiveStore,
|
||||
typedef::{Type, TypeEnum, Unifier, VarMap},
|
||||
},
|
||||
};
|
||||
|
||||
// TODO: This entire module is duplicated code (numpy.rs also has these kinds of things)
|
||||
|
||||
/// Creates a `option` [`Type`] with the given type arguments.
|
||||
///
|
||||
/// * `dtype` - The element type of the `option`, or [`None`] if the type variable is not
|
||||
/// specialized.
|
||||
/// * `ndims` - The number of dimensions of the `option`, or [`None`] if the type variable is not
|
||||
/// specialized.
|
||||
pub fn make_option_ty(
|
||||
unifier: &mut Unifier,
|
||||
primitives: &PrimitiveStore,
|
||||
dtype: Option<Type>,
|
||||
) -> Type {
|
||||
subst_option_tvars(unifier, primitives.option, dtype)
|
||||
}
|
||||
|
||||
/// Substitutes type variables in `option`.
|
||||
///
|
||||
/// * `dtype` - The element type of the `option`, or [`None`] if the type variable is not
|
||||
/// specialized.
|
||||
pub fn subst_option_tvars(unifier: &mut Unifier, option: Type, dtype: Option<Type>) -> Type {
|
||||
let TypeEnum::TObj { obj_id, params, .. } = &*unifier.get_ty_immutable(option) else {
|
||||
panic!("Expected `option` to be TObj, but got {}", unifier.stringify(option))
|
||||
};
|
||||
debug_assert_eq!(*obj_id, PrimDef::Option.id());
|
||||
|
||||
let tvar_ids = params.iter().map(|(obj_id, _)| *obj_id).collect_vec();
|
||||
debug_assert_eq!(tvar_ids.len(), 1);
|
||||
|
||||
let mut tvar_subst = VarMap::new();
|
||||
if let Some(dtype) = dtype {
|
||||
tvar_subst.insert(tvar_ids[0], dtype);
|
||||
}
|
||||
|
||||
unifier.subst(option, &tvar_subst).unwrap_or(option)
|
||||
}
|
||||
|
|
@ -5,7 +5,7 @@ expression: res_vec
|
|||
[
|
||||
"Class {\nname: \"Generic_A\",\nancestors: [\"Generic_A[V]\", \"B\"],\nfields: [\"aa\", \"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\"), (\"fun\", \"fn[[a:int32], V]\")],\ntype_vars: [\"V\"]\n}\n",
|
||||
"Function {\nname: \"Generic_A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(241)]\n}\n",
|
||||
"Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(245)]\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[typevar230]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar230\"]\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B[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",
|
||||
"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(243)]\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(248)]\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(247)]\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(252)]\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[typevar229, typevar230]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar229\", \"typevar230\"]\n}\n",
|
||||
"Class {\nname: \"A\",\nancestors: [\"A[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",
|
||||
"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(249)]\n}\n",
|
||||
"Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(253)]\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B\", \"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"C\",\nancestors: [\"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"C.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"C.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(257)]\n}\n",
|
||||
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(261)]\n}\n",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -117,8 +117,7 @@ impl SymbolResolver for Resolver {
|
|||
"register"
|
||||
)]
|
||||
fn test_simple_register(source: Vec<&str>) {
|
||||
let mut composer =
|
||||
TopLevelComposer::new(Vec::new(), Vec::new(), ComposerConfig::default(), 64).0;
|
||||
let mut composer = TopLevelComposer::new(Vec::new(), ComposerConfig::default(), 64).0;
|
||||
|
||||
for s in source {
|
||||
let ast = parse_program(s, FileName::default()).unwrap();
|
||||
|
|
@ -138,8 +137,7 @@ fn test_simple_register(source: Vec<&str>) {
|
|||
"register"
|
||||
)]
|
||||
fn test_simple_register_without_constructor(source: &str) {
|
||||
let mut composer =
|
||||
TopLevelComposer::new(Vec::new(), Vec::new(), ComposerConfig::default(), 64).0;
|
||||
let mut composer = TopLevelComposer::new(Vec::new(), ComposerConfig::default(), 64).0;
|
||||
let ast = parse_program(source, FileName::default()).unwrap();
|
||||
let ast = ast[0].clone();
|
||||
composer.register_top_level(ast, None, "", true).unwrap();
|
||||
|
|
@ -173,8 +171,7 @@ fn test_simple_register_without_constructor(source: &str) {
|
|||
"function compose"
|
||||
)]
|
||||
fn test_simple_function_analyze(source: &[&str], tys: &[&str], names: &[&str]) {
|
||||
let mut composer =
|
||||
TopLevelComposer::new(Vec::new(), Vec::new(), ComposerConfig::default(), 64).0;
|
||||
let mut composer = TopLevelComposer::new(Vec::new(), ComposerConfig::default(), 64).0;
|
||||
|
||||
let internal_resolver = Arc::new(ResolverInternal {
|
||||
id_to_def: Mutex::default(),
|
||||
|
|
@ -522,8 +519,7 @@ fn test_simple_function_analyze(source: &[&str], tys: &[&str], names: &[&str]) {
|
|||
)]
|
||||
fn test_analyze(source: &[&str], res: &[&str]) {
|
||||
let print = false;
|
||||
let mut composer =
|
||||
TopLevelComposer::new(Vec::new(), Vec::new(), ComposerConfig::default(), 64).0;
|
||||
let mut composer = TopLevelComposer::new(Vec::new(), ComposerConfig::default(), 64).0;
|
||||
|
||||
let internal_resolver = make_internal_resolver_with_tvar(
|
||||
vec![
|
||||
|
|
@ -700,8 +696,7 @@ fn test_analyze(source: &[&str], res: &[&str]) {
|
|||
)]
|
||||
fn test_inference(source: Vec<&str>, res: &[&str]) {
|
||||
let print = true;
|
||||
let mut composer =
|
||||
TopLevelComposer::new(Vec::new(), Vec::new(), ComposerConfig::default(), 64).0;
|
||||
let mut composer = TopLevelComposer::new(Vec::new(), ComposerConfig::default(), 64).0;
|
||||
|
||||
let internal_resolver = make_internal_resolver_with_tvar(
|
||||
vec![
|
||||
|
|
|
|||
|
|
@ -1,9 +1,8 @@
|
|||
use super::*;
|
||||
use crate::symbol_resolver::SymbolValue;
|
||||
use crate::toplevel::helper::{PrimDef, PrimDefDetails};
|
||||
use crate::toplevel::helper::PrimDef;
|
||||
use crate::typecheck::typedef::VarMap;
|
||||
use nac3parser::ast::Constant;
|
||||
use strum::IntoEnumIterator;
|
||||
|
||||
#[derive(Clone, Debug)]
|
||||
pub enum TypeAnnotation {
|
||||
|
|
@ -358,7 +357,6 @@ 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>> {
|
||||
|
|
@ -381,141 +379,100 @@ 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,
|
||||
primitives,
|
||||
x,
|
||||
subst_list,
|
||||
)
|
||||
get_type_from_type_annotation_kinds(top_level_defs, unifier, 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
|
||||
let mut result = VarMap::new();
|
||||
for (tvar, p) in type_vars.iter().zip(param_ty) {
|
||||
match unifier.get_ty(*tvar).as_ref() {
|
||||
TypeEnum::TVar {
|
||||
id,
|
||||
range,
|
||||
fields: None,
|
||||
name,
|
||||
loc,
|
||||
is_const_generic: false,
|
||||
} => {
|
||||
let ok: bool = {
|
||||
// create a temp type var and unify to check compatibility
|
||||
p == *tvar || {
|
||||
let temp = unifier.get_fresh_var_with_range(
|
||||
range.as_slice(),
|
||||
*name,
|
||||
*loc,
|
||||
);
|
||||
unifier.unify(temp.ty, p).is_ok()
|
||||
}
|
||||
};
|
||||
if ok {
|
||||
result.insert(*id, p);
|
||||
} else {
|
||||
return Err(HashSet::from([format!(
|
||||
"cannot apply type {} to type variable with id {:?}",
|
||||
unifier.internal_stringify(
|
||||
p,
|
||||
&mut |id| format!("class{id}"),
|
||||
&mut |id| format!("typevar{id}"),
|
||||
&mut None
|
||||
),
|
||||
*id
|
||||
)]));
|
||||
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
|
||||
let mut result = VarMap::new();
|
||||
for (tvar, p) in type_vars.iter().zip(param_ty) {
|
||||
match unifier.get_ty(*tvar).as_ref() {
|
||||
TypeEnum::TVar {
|
||||
id,
|
||||
range,
|
||||
fields: None,
|
||||
name,
|
||||
loc,
|
||||
is_const_generic: false,
|
||||
} => {
|
||||
let ok: bool = {
|
||||
// create a temp type var and unify to check compatibility
|
||||
p == *tvar || {
|
||||
let temp = unifier.get_fresh_var_with_range(
|
||||
range.as_slice(),
|
||||
*name,
|
||||
*loc,
|
||||
);
|
||||
unifier.unify(temp.ty, p).is_ok()
|
||||
}
|
||||
};
|
||||
if ok {
|
||||
result.insert(*id, p);
|
||||
} else {
|
||||
return Err(HashSet::from([format!(
|
||||
"cannot apply type {} to type variable with id {:?}",
|
||||
unifier.internal_stringify(
|
||||
p,
|
||||
&mut |id| format!("class{id}"),
|
||||
&mut |id| format!("typevar{id}"),
|
||||
&mut None
|
||||
),
|
||||
*id
|
||||
)]));
|
||||
}
|
||||
|
||||
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);
|
||||
unifier.unify(temp.ty, p).is_ok()
|
||||
}
|
||||
};
|
||||
if ok {
|
||||
result.insert(*id, p);
|
||||
} else {
|
||||
return Err(HashSet::from([format!(
|
||||
"cannot apply type {} to type variable {}",
|
||||
unifier.stringify(p),
|
||||
name.unwrap_or_else(|| format!("typevar{id}").into()),
|
||||
)]));
|
||||
}
|
||||
}
|
||||
|
||||
_ => unreachable!("must be generic type var"),
|
||||
}
|
||||
}
|
||||
result
|
||||
};
|
||||
// Class Attributes keep a copy with Class Definition and are not added to objects
|
||||
let mut tobj_fields = methods
|
||||
.iter()
|
||||
.map(|(name, ty, _)| {
|
||||
let subst_ty = unifier.subst(*ty, &subst).unwrap_or(*ty);
|
||||
// methods are immutable
|
||||
(*name, (subst_ty, false))
|
||||
})
|
||||
.collect::<HashMap<_, _>>();
|
||||
tobj_fields.extend(fields.iter().map(|(name, ty, mutability)| {
|
||||
let subst_ty = unifier.subst(*ty, &subst).unwrap_or(*ty);
|
||||
(*name, (subst_ty, *mutability))
|
||||
}));
|
||||
let need_subst = !subst.is_empty();
|
||||
let ty = unifier.add_ty(TypeEnum::TObj {
|
||||
obj_id: *obj_id,
|
||||
fields: tobj_fields,
|
||||
params: subst,
|
||||
});
|
||||
|
||||
if need_subst {
|
||||
if let Some(wl) = subst_list.as_mut() {
|
||||
wl.push(ty);
|
||||
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);
|
||||
unifier.unify(temp.ty, p).is_ok()
|
||||
}
|
||||
};
|
||||
if ok {
|
||||
result.insert(*id, p);
|
||||
} else {
|
||||
return Err(HashSet::from([format!(
|
||||
"cannot apply type {} to type variable {}",
|
||||
unifier.stringify(p),
|
||||
name.unwrap_or_else(|| format!("typevar{id}").into()),
|
||||
)]));
|
||||
}
|
||||
}
|
||||
|
||||
_ => unreachable!("must be generic type var"),
|
||||
}
|
||||
}
|
||||
|
||||
ty
|
||||
result
|
||||
};
|
||||
|
||||
// Class Attributes keep a copy with Class Definition and are not added to objects
|
||||
let mut tobj_fields = methods
|
||||
.iter()
|
||||
.map(|(name, ty, _)| {
|
||||
let subst_ty = unifier.subst(*ty, &subst).unwrap_or(*ty);
|
||||
// methods are immutable
|
||||
(*name, (subst_ty, false))
|
||||
})
|
||||
.collect::<HashMap<_, _>>();
|
||||
tobj_fields.extend(fields.iter().map(|(name, ty, mutability)| {
|
||||
let subst_ty = unifier.subst(*ty, &subst).unwrap_or(*ty);
|
||||
(*name, (subst_ty, *mutability))
|
||||
}));
|
||||
let need_subst = !subst.is_empty();
|
||||
let ty = unifier.add_ty(TypeEnum::TObj {
|
||||
obj_id: *obj_id,
|
||||
fields: tobj_fields,
|
||||
params: subst,
|
||||
});
|
||||
if need_subst {
|
||||
if let Some(wl) = subst_list.as_mut() {
|
||||
wl.push(ty);
|
||||
}
|
||||
}
|
||||
Ok(ty)
|
||||
}
|
||||
TypeAnnotation::Primitive(ty) | TypeAnnotation::TypeVar(ty) => Ok(*ty),
|
||||
|
|
@ -533,7 +490,6 @@ 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,
|
||||
)?;
|
||||
|
|
@ -543,16 +499,10 @@ pub fn get_type_from_type_annotation_kinds(
|
|||
let tys = tys
|
||||
.iter()
|
||||
.map(|x| {
|
||||
get_type_from_type_annotation_kinds(
|
||||
top_level_defs,
|
||||
unifier,
|
||||
primitives,
|
||||
x,
|
||||
subst_list,
|
||||
)
|
||||
get_type_from_type_annotation_kinds(top_level_defs, unifier, x, subst_list)
|
||||
})
|
||||
.collect::<Result<Vec<_>, _>>()?;
|
||||
Ok(unifier.add_ty(TypeEnum::TTuple { ty: tys, is_vararg_ctx: false }))
|
||||
Ok(unifier.add_ty(TypeEnum::TTuple { ty: tys }))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -34,18 +34,13 @@ impl<'a> Inferencer<'a> {
|
|||
self.should_have_value(pattern)?;
|
||||
Ok(())
|
||||
}
|
||||
ExprKind::List { elts, .. } | ExprKind::Tuple { 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)?;
|
||||
|
|
@ -80,7 +75,7 @@ impl<'a> Inferencer<'a> {
|
|||
return Err(HashSet::from([format!(
|
||||
"expected concrete type at {} but got {}",
|
||||
expr.location,
|
||||
self.unifier.stringify(*ty)
|
||||
self.unifier.get_ty(*ty).get_type_name()
|
||||
)]));
|
||||
}
|
||||
}
|
||||
|
|
@ -223,7 +218,7 @@ impl<'a> Inferencer<'a> {
|
|||
]
|
||||
.iter()
|
||||
.any(|allowed_ty| self.unifier.unioned(ret_ty, *allowed_ty)),
|
||||
TypeEnum::TTuple { ty, .. } => ty.iter().all(|t| self.check_return_value_ty(*t)),
|
||||
TypeEnum::TTuple { ty } => ty.iter().all(|t| self.check_return_value_ty(*t)),
|
||||
_ => false,
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
use crate::symbol_resolver::SymbolValue;
|
||||
use crate::toplevel::helper::{arraylike_flatten_element_type, arraylike_get_ndims, PrimDef};
|
||||
use crate::toplevel::helper::PrimDef;
|
||||
use crate::toplevel::numpy::{make_ndarray_ty, unpack_ndarray_var_tys};
|
||||
use crate::typecheck::{
|
||||
type_inferencer::*,
|
||||
|
|
@ -197,7 +197,6 @@ pub fn impl_binop(
|
|||
ty: other_ty,
|
||||
default_value: None,
|
||||
name: "other".into(),
|
||||
is_vararg: false,
|
||||
}],
|
||||
})),
|
||||
false,
|
||||
|
|
@ -262,7 +261,6 @@ pub fn impl_cmpop(
|
|||
ty: other_ty,
|
||||
default_value: None,
|
||||
name: "other".into(),
|
||||
is_vararg: false,
|
||||
}],
|
||||
})),
|
||||
false,
|
||||
|
|
@ -520,41 +518,36 @@ pub fn typeof_binop(
|
|||
}
|
||||
|
||||
Operator::MatMult => {
|
||||
let lhs_dtype = arraylike_flatten_element_type(unifier, lhs);
|
||||
let rhs_dtype = arraylike_flatten_element_type(unifier, rhs);
|
||||
|
||||
let lhs_ndims = arraylike_get_ndims(unifier, lhs);
|
||||
let rhs_ndims = arraylike_get_ndims(unifier, rhs);
|
||||
|
||||
if !(unifier.unioned(lhs_dtype, primitives.float)
|
||||
&& unifier.unioned(rhs_dtype, primitives.float))
|
||||
{
|
||||
return Err(format!(
|
||||
"ndarray.__matmul__ only supports float64 operations, but LHS has type {} and RHS has type {}",
|
||||
unifier.stringify(lhs),
|
||||
unifier.stringify(rhs)
|
||||
));
|
||||
}
|
||||
|
||||
let result_ndims = match (lhs_ndims, rhs_ndims) {
|
||||
(0, _) | (_, 0) => {
|
||||
return Err(
|
||||
"ndarray.__matmul__ does not allow unsized ndarray input".to_string()
|
||||
)
|
||||
let (_, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
|
||||
let lhs_ndims = match &*unifier.get_ty_immutable(lhs_ndims) {
|
||||
TypeEnum::TLiteral { values, .. } => {
|
||||
assert_eq!(values.len(), 1);
|
||||
u64::try_from(values[0].clone()).unwrap()
|
||||
}
|
||||
(1, 1) => 0,
|
||||
(1, _) => rhs_ndims - 1,
|
||||
(_, 1) => lhs_ndims - 1,
|
||||
(m, n) => max(m, n),
|
||||
_ => unreachable!(),
|
||||
};
|
||||
let (_, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
|
||||
let rhs_ndims = match &*unifier.get_ty_immutable(rhs_ndims) {
|
||||
TypeEnum::TLiteral { values, .. } => {
|
||||
assert_eq!(values.len(), 1);
|
||||
u64::try_from(values[0].clone()).unwrap()
|
||||
}
|
||||
_ => unreachable!(),
|
||||
};
|
||||
|
||||
if result_ndims == 0 {
|
||||
// If the result is unsized, NumPy returns a scalar.
|
||||
primitives.float
|
||||
} else {
|
||||
let result_ndims_ty =
|
||||
unifier.get_fresh_literal(vec![SymbolValue::U64(result_ndims)], None);
|
||||
make_ndarray_ty(unifier, primitives, Some(primitives.float), Some(result_ndims_ty))
|
||||
match (lhs_ndims, rhs_ndims) {
|
||||
(2, 2) => typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?,
|
||||
(lhs, rhs) if lhs == 0 || rhs == 0 => {
|
||||
return Err(format!(
|
||||
"Input operand {} does not have enough dimensions (has {lhs}, requires {rhs})",
|
||||
u8::from(rhs == 0)
|
||||
))
|
||||
}
|
||||
(lhs, rhs) => {
|
||||
return Err(format!(
|
||||
"ndarray.__matmul__ on {lhs}D and {rhs}D operands not supported"
|
||||
))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -753,7 +746,7 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
|
|||
impl_div(unifier, store, ndarray_t, &[ndarray_t, ndarray_dtype_t], None);
|
||||
impl_floordiv(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
|
||||
impl_mod(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
|
||||
impl_matmul(unifier, store, ndarray_t, &[ndarray_unsized_t], Some(ndarray_t));
|
||||
impl_matmul(unifier, store, ndarray_t, &[ndarray_t], Some(ndarray_t));
|
||||
impl_sign(unifier, store, ndarray_t, Some(ndarray_t));
|
||||
impl_invert(unifier, store, ndarray_t, Some(ndarray_t));
|
||||
impl_eq(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
|
||||
|
|
|
|||
|
|
@ -183,10 +183,9 @@ impl<'a> Display for DisplayTypeError<'a> {
|
|||
}
|
||||
result
|
||||
}
|
||||
(
|
||||
TypeEnum::TTuple { ty: ty1, is_vararg_ctx: is_vararg1 },
|
||||
TypeEnum::TTuple { ty: ty2, is_vararg_ctx: is_vararg2 },
|
||||
) if !is_vararg1 && !is_vararg2 && ty1.len() != ty2.len() => {
|
||||
(TypeEnum::TTuple { ty: ty1 }, TypeEnum::TTuple { ty: ty2 })
|
||||
if ty1.len() != ty2.len() =>
|
||||
{
|
||||
let t1 = self.unifier.stringify_with_notes(*t1, &mut notes);
|
||||
let t2 = self.unifier.stringify_with_notes(*t2, &mut notes);
|
||||
write!(f, "Tuple length mismatch: got {t1} and {t2}")
|
||||
|
|
|
|||
File diff suppressed because it is too large
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Reference in New Issue