forked from M-Labs/nac3
Compare commits
9 Commits
ndstrides-
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ndarray-st
| Author | SHA1 | Date |
|---|---|---|
 lyken
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87d2a4ed59 | |
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lyken
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9aae290727 | |
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lyken
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d18c769cdc | |
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lyken
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f41f06aec7 | |
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lyken
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1303265785 | |
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lyken
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e9cf6ce1e5 | |
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lyken
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bc91ab9b13 | |
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lyken
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1e06a3d199 | |
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lyken
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87511ac749 |
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@ -1,3 +0,0 @@
|
|||
BasedOnStyle: Google
|
||||
IndentWidth: 4
|
||||
ReflowComments: false
|
||||
|
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@ -26,9 +26,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "anstream"
|
||||
version = "0.6.15"
|
||||
version = "0.6.14"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "64e15c1ab1f89faffbf04a634d5e1962e9074f2741eef6d97f3c4e322426d526"
|
||||
checksum = "418c75fa768af9c03be99d17643f93f79bbba589895012a80e3452a19ddda15b"
|
||||
dependencies = [
|
||||
"anstyle",
|
||||
"anstyle-parse",
|
||||
|
|
@ -41,33 +41,33 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "anstyle"
|
||||
version = "1.0.8"
|
||||
version = "1.0.7"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1bec1de6f59aedf83baf9ff929c98f2ad654b97c9510f4e70cf6f661d49fd5b1"
|
||||
checksum = "038dfcf04a5feb68e9c60b21c9625a54c2c0616e79b72b0fd87075a056ae1d1b"
|
||||
|
||||
[[package]]
|
||||
name = "anstyle-parse"
|
||||
version = "0.2.5"
|
||||
version = "0.2.4"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "eb47de1e80c2b463c735db5b217a0ddc39d612e7ac9e2e96a5aed1f57616c1cb"
|
||||
checksum = "c03a11a9034d92058ceb6ee011ce58af4a9bf61491aa7e1e59ecd24bd40d22d4"
|
||||
dependencies = [
|
||||
"utf8parse",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anstyle-query"
|
||||
version = "1.1.1"
|
||||
version = "1.1.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "6d36fc52c7f6c869915e99412912f22093507da8d9e942ceaf66fe4b7c14422a"
|
||||
checksum = "ad186efb764318d35165f1758e7dcef3b10628e26d41a44bc5550652e6804391"
|
||||
dependencies = [
|
||||
"windows-sys",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anstyle-wincon"
|
||||
version = "3.0.4"
|
||||
version = "3.0.3"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "5bf74e1b6e971609db8ca7a9ce79fd5768ab6ae46441c572e46cf596f59e57f8"
|
||||
checksum = "61a38449feb7068f52bb06c12759005cf459ee52bb4adc1d5a7c4322d716fb19"
|
||||
dependencies = [
|
||||
"anstyle",
|
||||
"windows-sys",
|
||||
|
|
@ -117,9 +117,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
|
|||
|
||||
[[package]]
|
||||
name = "cc"
|
||||
version = "1.1.6"
|
||||
version = "1.1.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "2aba8f4e9906c7ce3c73463f62a7f0c65183ada1a2d47e397cc8810827f9694f"
|
||||
checksum = "eaff6f8ce506b9773fa786672d63fc7a191ffea1be33f72bbd4aeacefca9ffc8"
|
||||
|
||||
[[package]]
|
||||
name = "cfg-if"
|
||||
|
|
@ -129,9 +129,9 @@ checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
|
|||
|
||||
[[package]]
|
||||
name = "clap"
|
||||
version = "4.5.11"
|
||||
version = "4.5.9"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "35723e6a11662c2afb578bcf0b88bf6ea8e21282a953428f240574fcc3a2b5b3"
|
||||
checksum = "64acc1846d54c1fe936a78dc189c34e28d3f5afc348403f28ecf53660b9b8462"
|
||||
dependencies = [
|
||||
"clap_builder",
|
||||
"clap_derive",
|
||||
|
|
@ -139,9 +139,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "clap_builder"
|
||||
version = "4.5.11"
|
||||
version = "4.5.9"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "49eb96cbfa7cfa35017b7cd548c75b14c3118c98b423041d70562665e07fb0fa"
|
||||
checksum = "6fb8393d67ba2e7bfaf28a23458e4e2b543cc73a99595511eb207fdb8aede942"
|
||||
dependencies = [
|
||||
"anstream",
|
||||
"anstyle",
|
||||
|
|
@ -151,27 +151,27 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "clap_derive"
|
||||
version = "4.5.11"
|
||||
version = "4.5.8"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "5d029b67f89d30bbb547c89fd5161293c0aec155fc691d7924b64550662db93e"
|
||||
checksum = "2bac35c6dafb060fd4d275d9a4ffae97917c13a6327903a8be2153cd964f7085"
|
||||
dependencies = [
|
||||
"heck 0.5.0",
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.72",
|
||||
"syn 2.0.70",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "clap_lex"
|
||||
version = "0.7.2"
|
||||
version = "0.7.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1462739cb27611015575c0c11df5df7601141071f07518d56fcc1be504cbec97"
|
||||
checksum = "4b82cf0babdbd58558212896d1a4272303a57bdb245c2bf1147185fb45640e70"
|
||||
|
||||
[[package]]
|
||||
name = "colorchoice"
|
||||
version = "1.0.2"
|
||||
version = "1.0.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "d3fd119d74b830634cea2a0f58bbd0d54540518a14397557951e79340abc28c0"
|
||||
checksum = "0b6a852b24ab71dffc585bcb46eaf7959d175cb865a7152e35b348d1b2960422"
|
||||
|
||||
[[package]]
|
||||
name = "console"
|
||||
|
|
@ -421,7 +421,7 @@ checksum = "4fa4d8d74483041a882adaa9a29f633253a66dde85055f0495c121620ac484b2"
|
|||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.72",
|
||||
"syn 2.0.70",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -440,9 +440,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "is_terminal_polyfill"
|
||||
version = "1.70.1"
|
||||
version = "1.70.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "7943c866cc5cd64cbc25b2e01621d07fa8eb2a1a23160ee81ce38704e97b8ecf"
|
||||
checksum = "f8478577c03552c21db0e2724ffb8986a5ce7af88107e6be5d2ee6e158c12800"
|
||||
|
||||
[[package]]
|
||||
name = "itertools"
|
||||
|
|
@ -513,9 +513,9 @@ checksum = "97b3888a4aecf77e811145cadf6eef5901f4782c53886191b2f693f24761847c"
|
|||
|
||||
[[package]]
|
||||
name = "libloading"
|
||||
version = "0.8.5"
|
||||
version = "0.8.4"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "4979f22fdb869068da03c9f7528f8297c6fd2606bc3a4affe42e6a823fdb8da4"
|
||||
checksum = "e310b3a6b5907f99202fcdb4960ff45b93735d7c7d96b760fcff8db2dc0e103d"
|
||||
dependencies = [
|
||||
"cfg-if",
|
||||
"windows-targets",
|
||||
|
|
@ -749,7 +749,7 @@ dependencies = [
|
|||
"phf_shared 0.11.2",
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.72",
|
||||
"syn 2.0.70",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -778,9 +778,9 @@ checksum = "5be167a7af36ee22fe3115051bc51f6e6c7054c9348e28deb4f49bd6f705a315"
|
|||
|
||||
[[package]]
|
||||
name = "portable-atomic"
|
||||
version = "1.7.0"
|
||||
version = "1.6.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "da544ee218f0d287a911e9c99a39a8c9bc8fcad3cb8db5959940044ecfc67265"
|
||||
checksum = "7170ef9988bc169ba16dd36a7fa041e5c4cbeb6a35b76d4c03daded371eae7c0"
|
||||
|
||||
[[package]]
|
||||
name = "ppv-lite86"
|
||||
|
|
@ -850,7 +850,7 @@ dependencies = [
|
|||
"proc-macro2",
|
||||
"pyo3-macros-backend",
|
||||
"quote",
|
||||
"syn 2.0.72",
|
||||
"syn 2.0.70",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -863,7 +863,7 @@ dependencies = [
|
|||
"proc-macro2",
|
||||
"pyo3-build-config",
|
||||
"quote",
|
||||
"syn 2.0.72",
|
||||
"syn 2.0.70",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -927,9 +927,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "redox_syscall"
|
||||
version = "0.5.3"
|
||||
version = "0.5.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "2a908a6e00f1fdd0dfd9c0eb08ce85126f6d8bbda50017e74bc4a4b7d4a926a4"
|
||||
checksum = "c82cf8cff14456045f55ec4241383baeff27af886adb72ffb2162f99911de0fd"
|
||||
dependencies = [
|
||||
"bitflags",
|
||||
]
|
||||
|
|
@ -1044,7 +1044,7 @@ checksum = "e0cd7e117be63d3c3678776753929474f3b04a43a080c744d6b0ae2a8c28e222"
|
|||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.72",
|
||||
"syn 2.0.70",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1072,9 +1072,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "similar"
|
||||
version = "2.6.0"
|
||||
version = "2.5.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1de1d4f81173b03af4c0cbed3c898f6bff5b870e4a7f5d6f4057d62a7a4b686e"
|
||||
checksum = "fa42c91313f1d05da9b26f267f931cf178d4aba455b4c4622dd7355eb80c6640"
|
||||
|
||||
[[package]]
|
||||
name = "siphasher"
|
||||
|
|
@ -1134,7 +1134,7 @@ dependencies = [
|
|||
"proc-macro2",
|
||||
"quote",
|
||||
"rustversion",
|
||||
"syn 2.0.72",
|
||||
"syn 2.0.70",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1150,9 +1150,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "syn"
|
||||
version = "2.0.72"
|
||||
version = "2.0.70"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "dc4b9b9bf2add8093d3f2c0204471e951b2285580335de42f9d2534f3ae7a8af"
|
||||
checksum = "2f0209b68b3613b093e0ec905354eccaedcfe83b8cb37cbdeae64026c3064c16"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
|
|
@ -1203,22 +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 2.0.72",
|
||||
"syn 2.0.70",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1336,9 +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"
|
||||
|
|
@ -1486,5 +1486,5 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
|
|||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.72",
|
||||
"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": {
|
||||
|
|
|
|||
|
|
@ -26,7 +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 =
|
||||
|
|
@ -151,7 +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
|
||||
|
|
@ -163,7 +163,10 @@
|
|||
clippy
|
||||
pre-commit
|
||||
rustfmt
|
||||
rust-analyzer
|
||||
];
|
||||
# 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()
|
||||
|
|
@ -24,7 +24,6 @@ use std::rc::Rc;
|
|||
use std::sync::Arc;
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
memory_buffer::MemoryBuffer,
|
||||
module::{Linkage, Module},
|
||||
passes::PassBuilderOptions,
|
||||
|
|
@ -626,9 +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
|
||||
|
|
@ -647,9 +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,
|
||||
|
|
|
|||
|
|
@ -991,15 +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);
|
||||
|
|
|
|||
|
|
@ -3,34 +3,20 @@ 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",
|
||||
|
|
@ -45,18 +31,16 @@ fn compile_irrt_cpp() {
|
|||
"-S",
|
||||
"-Wall",
|
||||
"-Wextra",
|
||||
"-o",
|
||||
"-",
|
||||
"-Werror=return-type",
|
||||
"-I",
|
||||
irrt_dir.to_str().unwrap(),
|
||||
irrt_cpp_path.to_str().unwrap(),
|
||||
"-o",
|
||||
"-",
|
||||
];
|
||||
|
||||
// Tell Cargo to rerun if any file under `irrt_dir` (recursive) changes
|
||||
println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
|
||||
println!("cargo:rerun-if-changed={}", out_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);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,39 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
|
||||
/*
|
||||
This file defines all ARTIQ-specific structures
|
||||
*/
|
||||
|
||||
/**
|
||||
* @brief ARTIQ's `cslice` object
|
||||
*
|
||||
* See https://docs.rs/cslice/0.3.0/src/cslice/lib.rs.html#33-37
|
||||
*/
|
||||
template <typename SizeT>
|
||||
struct CSlice {
|
||||
const char *base;
|
||||
SizeT len;
|
||||
};
|
||||
|
||||
/**
|
||||
* @brief Int type of ARTIQ's `Exception` IDs.
|
||||
*/
|
||||
typedef uint32_t ExceptionId;
|
||||
|
||||
/**
|
||||
* @brief ARTIQ's `Exception` object
|
||||
*
|
||||
* See https://github.com/m-labs/artiq/blob/b0d2705c385f64b6e6711c1726cd9178f40b598e/artiq/firmware/libeh/eh_artiq.rs#L1C1-L17C1
|
||||
*/
|
||||
template <typename SizeT>
|
||||
struct Exception {
|
||||
ExceptionId id;
|
||||
CSlice<SizeT> file;
|
||||
uint32_t line;
|
||||
uint32_t column;
|
||||
CSlice<SizeT> function;
|
||||
CSlice<SizeT> message;
|
||||
uint32_t param;
|
||||
};
|
||||
|
|
@ -1,347 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
#include <irrt/utils.hpp>
|
||||
|
||||
// NDArray indices are always `uint32_t`.
|
||||
using NDIndexInt = uint32_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"
|
||||
|
|
@ -1,92 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/artiq_defs.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/utils.hpp>
|
||||
|
||||
namespace {
|
||||
/**
|
||||
* @brief A (limited) set of known Exception IDs usable in IRRT
|
||||
*/
|
||||
struct ErrorContextExceptions {
|
||||
ExceptionId index_error;
|
||||
ExceptionId value_error;
|
||||
ExceptionId assertion_error;
|
||||
ExceptionId runtime_error;
|
||||
ExceptionId type_error;
|
||||
};
|
||||
|
||||
/**
|
||||
* @brief The IRRT error context object
|
||||
*
|
||||
* This object contains all the details needed to propagate Python-like Exceptions in
|
||||
* IRRT - within IRRT itself or propagate out of extern calls from nac3core.
|
||||
*/
|
||||
struct ErrorContext {
|
||||
const ErrorContextExceptions *exceptions;
|
||||
|
||||
// Exception thrown by IRRT
|
||||
ExceptionId exception_id;
|
||||
// Points to empty c-string if there is no thrown Exception
|
||||
const char *msg;
|
||||
uint64_t param1;
|
||||
uint64_t param2;
|
||||
uint64_t param3;
|
||||
|
||||
void initialize(const ErrorContextExceptions *exceptions) {
|
||||
this->exceptions = exceptions;
|
||||
clear_error();
|
||||
}
|
||||
|
||||
void clear_error() {
|
||||
// NOTE: Point the msg to an empty str.
|
||||
// Don't set it to nullptr - to implement `has_exception`
|
||||
this->msg = "";
|
||||
}
|
||||
|
||||
void set_exception(ExceptionId exception_id, const char *msg,
|
||||
uint64_t param1 = 0, uint64_t param2 = 0,
|
||||
uint64_t param3 = 0) {
|
||||
this->exception_id = exception_id;
|
||||
this->msg = msg;
|
||||
this->param1 = param1;
|
||||
this->param2 = param2;
|
||||
this->param3 = param3;
|
||||
}
|
||||
|
||||
bool has_exception() { return !cstr_utils::is_empty(msg); }
|
||||
|
||||
template <typename SizeT>
|
||||
void get_exception_str(CSlice<SizeT> *dst_str) {
|
||||
dst_str->base = msg;
|
||||
dst_str->len = (SizeT)cstr_utils::length(msg);
|
||||
}
|
||||
};
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
void __nac3_error_context_initialize(ErrorContext *errctx,
|
||||
ErrorContextExceptions *exceptions) {
|
||||
errctx->initialize(exceptions);
|
||||
}
|
||||
|
||||
bool __nac3_error_context_has_exception(ErrorContext *errctx) {
|
||||
return errctx->has_exception();
|
||||
}
|
||||
|
||||
void __nac3_error_context_get_exception_str(ErrorContext *errctx,
|
||||
CSlice<int32_t> *dst_str) {
|
||||
errctx->get_exception_str<int32_t>(dst_str);
|
||||
}
|
||||
|
||||
void __nac3_error_context_get_exception_str64(ErrorContext *errctx,
|
||||
CSlice<int64_t> *dst_str) {
|
||||
errctx->get_exception_str<int64_t>(dst_str);
|
||||
}
|
||||
|
||||
// Used for testing
|
||||
void __nac3_error_dummy_raise(ErrorContext *errctx) {
|
||||
errctx->set_exception(errctx->exceptions->runtime_error,
|
||||
"Error thrown from __nac3_error_dummy_raise");
|
||||
}
|
||||
}
|
||||
|
|
@ -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
|
||||
|
|
@ -1,315 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/error_context.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace basic {
|
||||
namespace util {
|
||||
/**
|
||||
* @brief Asserts that `shape` does not contain negative dimensions.
|
||||
*
|
||||
* @param ndims Number of dimensions in `shape`
|
||||
* @param shape The shape to check on
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void assert_shape_no_negative(ErrorContext* errctx, SizeT ndims,
|
||||
const SizeT* shape) {
|
||||
for (SizeT axis = 0; axis < ndims; axis++) {
|
||||
if (shape[axis] < 0) {
|
||||
errctx->set_exception(
|
||||
errctx->exceptions->value_error,
|
||||
"negative dimensions are not allowed; axis {0} "
|
||||
"has dimension {1}",
|
||||
axis, shape[axis]);
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @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 (int32_t i = 0; i < ndims; i++) {
|
||||
int32_t axis = ndims - i - 1;
|
||||
int32_t dim = shape[axis];
|
||||
|
||||
indices[axis] = nth % dim;
|
||||
nth /= dim;
|
||||
}
|
||||
}
|
||||
} // namespace util
|
||||
|
||||
/**
|
||||
* @brief Return the number of elements of an `ndarray`
|
||||
*
|
||||
* This function corresponds to `<an_ndarray>.size`
|
||||
*/
|
||||
template <typename SizeT>
|
||||
SizeT size(const NDArray<SizeT>* ndarray) {
|
||||
return util::calc_size_from_shape(ndarray->ndims, ndarray->shape);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return of the number of its content of an `ndarray`.
|
||||
*
|
||||
* This function corresponds to `<an_ndarray>.nbytes`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
SizeT nbytes(const NDArray<SizeT>* ndarray) {
|
||||
return size(ndarray) * ndarray->itemsize;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Update the strides of an ndarray given an ndarray `shape`
|
||||
* and assuming that the ndarray is fully c-contagious.
|
||||
*
|
||||
* You might want to read https://ajcr.net/stride-guide-part-1/.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void set_strides_by_shape(NDArray<SizeT>* ndarray) {
|
||||
SizeT stride_product = 1;
|
||||
for (SizeT i = 0; i < ndarray->ndims; i++) {
|
||||
int axis = ndarray->ndims - i - 1;
|
||||
ndarray->strides[axis] = stride_product * ndarray->itemsize;
|
||||
stride_product *= ndarray->shape[axis];
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief 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 Return the pointer to the nth (0-based) element in a flattened view of `ndarray`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
uint8_t* get_nth_pelement(const NDArray<SizeT>* ndarray, SizeT nth) {
|
||||
SizeT* indices = (SizeT*)__builtin_alloca(sizeof(SizeT) * ndarray->ndims);
|
||||
util::set_indices_by_nth(ndarray->ndims, ndarray->shape, indices, nth);
|
||||
return get_pelement_by_indices(ndarray, indices);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Like `get_nth_pelement` but asserts that `nth` is in bounds.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
uint8_t* checked_get_nth_pelement(ErrorContext* errctx,
|
||||
const NDArray<SizeT>* ndarray, SizeT nth) {
|
||||
SizeT arr_size = ndarray->size();
|
||||
if (!(0 <= nth && nth < arr_size)) {
|
||||
errctx->set_exception(
|
||||
errctx->exceptions->index_error,
|
||||
"index {0} is out of bounds, valid range is {1} <= index < {2}",
|
||||
nth, 0, arr_size);
|
||||
return 0;
|
||||
}
|
||||
return get_nth_pelement(ndarray, nth);
|
||||
}
|
||||
|
||||
/**
|
||||
* @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 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>
|
||||
void len(ErrorContext* errctx, const NDArray<SizeT>* ndarray,
|
||||
SliceIndex* dst_length) {
|
||||
// numpy prohibits `__len__` on unsized objects
|
||||
if (ndarray->ndims == 0) {
|
||||
errctx->set_exception(errctx->exceptions->type_error,
|
||||
"len() of unsized object");
|
||||
return;
|
||||
}
|
||||
|
||||
*dst_length = (SliceIndex)ndarray->shape[0];
|
||||
}
|
||||
|
||||
/**
|
||||
* @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) {
|
||||
__builtin_assume(src_ndarray->itemsize == dst_ndarray->itemsize);
|
||||
|
||||
for (SizeT i = 0; i < size(src_ndarray); i++) {
|
||||
auto src_element = ndarray::basic::get_nth_pelement(src_ndarray, i);
|
||||
auto dst_element = ndarray::basic::get_nth_pelement(dst_ndarray, i);
|
||||
ndarray::basic::set_pelement_value(dst_ndarray, dst_element,
|
||||
src_element);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @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 = 0; i < ndarray->ndims - 1; i++) {
|
||||
if (ndarray->strides[i] !=
|
||||
ndarray->shape[i + 1] + ndarray->strides[i + 1]) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
} // namespace basic
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::basic;
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_len(ErrorContext* errctx, NDArray<int32_t>* ndarray,
|
||||
SliceIndex* dst_len) {
|
||||
return len(errctx, ndarray, dst_len);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_len64(ErrorContext* errctx, NDArray<int64_t>* ndarray,
|
||||
SliceIndex* dst_len) {
|
||||
return len(errctx, ndarray, dst_len);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_util_assert_shape_no_negative(ErrorContext* errctx,
|
||||
int32_t ndims,
|
||||
int32_t* shape) {
|
||||
util::assert_shape_no_negative(errctx, ndims, shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_util_assert_shape_no_negative64(ErrorContext* errctx,
|
||||
int64_t ndims,
|
||||
int64_t* shape) {
|
||||
util::assert_shape_no_negative(errctx, ndims, shape);
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
bool __nac3_ndarray_is_c_contiguous(NDArray<int32_t>* ndarray) {
|
||||
return is_c_contiguous(ndarray);
|
||||
}
|
||||
|
||||
bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t>* ndarray) {
|
||||
return is_c_contiguous(ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_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);
|
||||
}
|
||||
|
||||
uint8_t* __nac3_ndarray_get_nth_pelement(NDArray<int32_t>* ndarray,
|
||||
int32_t index) {
|
||||
return get_nth_pelement(ndarray, index);
|
||||
}
|
||||
|
||||
uint8_t* __nac3_ndarray_get_nth_pelement64(NDArray<int64_t>* ndarray,
|
||||
int64_t index) {
|
||||
return get_nth_pelement(ndarray, index);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,221 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/error_context.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
|
||||
namespace {
|
||||
template <typename SizeT>
|
||||
struct ShapeEntry {
|
||||
SizeT ndims;
|
||||
SizeT* shape;
|
||||
};
|
||||
} // namespace
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace broadcast {
|
||||
namespace util {
|
||||
/**
|
||||
* @brief Return true if `src_shape` can broadcast to `dst_shape`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
bool can_broadcast_shape_to(SizeT target_ndims, const SizeT* target_shape,
|
||||
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;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Performs `np.broadcast_shapes`
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void broadcast_shapes(ErrorContext* errctx, SizeT num_shapes,
|
||||
const ShapeEntry<SizeT>* shapes, SizeT dst_ndims,
|
||||
SizeT* dst_shape) {
|
||||
// `dst_ndims` must be `max([shape.ndims for shape in shapes])`, but the caller has to calculate it/provide it
|
||||
// for this function since it should already know in order to allocate `dst_shape` in the first place.
|
||||
// `dst_shape` must be pre-allocated.
|
||||
// `dst_shape` does not have to be initialized
|
||||
|
||||
// TODO: Implementation is not obvious
|
||||
|
||||
// This is essentially a `mconcat` where the neutral element is `[1, 1, 1, 1, ...]`, and the operation is commutative.
|
||||
|
||||
// Set `dst_shape` to all `1`s.
|
||||
for (SizeT dst_axis = 0; dst_axis < dst_ndims; dst_axis++) {
|
||||
dst_shape[dst_axis] = 0;
|
||||
}
|
||||
|
||||
for (SizeT i = 0; i < num_shapes; i++) {
|
||||
ShapeEntry<SizeT> entry = shapes[i];
|
||||
SizeT entry_axis = entry.ndims - i;
|
||||
SizeT dst_axis = dst_ndims - i;
|
||||
|
||||
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) {
|
||||
// Do nothing
|
||||
} else if (entry_dim == dst_dim) {
|
||||
// Do nothing
|
||||
} else {
|
||||
errctx->set_exception(errctx->exceptions->value_error,
|
||||
"shape mismatch: objects cannot be broadcast "
|
||||
"to a single shape.");
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace util
|
||||
|
||||
/**
|
||||
* @brief Perform `np.broadcast_to(<ndarray>, <target_shape>)` and appropriate assertions.
|
||||
*
|
||||
* Cautious note on https://github.com/numpy/numpy/issues/21744..
|
||||
*
|
||||
* 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(ErrorContext* errctx, const NDArray<SizeT>* src_ndarray,
|
||||
NDArray<SizeT>* dst_ndarray) {
|
||||
/*
|
||||
* 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.
|
||||
* ```
|
||||
*/
|
||||
|
||||
if (!ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
dst_ndarray->ndims, dst_ndarray->shape, src_ndarray->ndims,
|
||||
src_ndarray->shape)) {
|
||||
errctx->set_exception(errctx->exceptions->value_error,
|
||||
"operands could not be broadcast together");
|
||||
return;
|
||||
}
|
||||
|
||||
dst_ndarray->data = src_ndarray->data;
|
||||
dst_ndarray->itemsize = src_ndarray->itemsize;
|
||||
|
||||
// TODO: Implementation is not obvious
|
||||
SizeT stride_product = 1;
|
||||
for (SizeT i = 0; i < max(src_ndarray->ndims, dst_ndarray->ndims); i++) {
|
||||
SizeT src_ndarray_dim_i = src_ndarray->ndims - i - 1;
|
||||
SizeT dst_dim_i = dst_ndarray->ndims - i - 1;
|
||||
|
||||
bool src_ndarray_dim_exists = src_ndarray_dim_i >= 0;
|
||||
bool dst_dim_exists = dst_dim_i >= 0;
|
||||
|
||||
bool c1 = src_ndarray_dim_exists &&
|
||||
src_ndarray->shape[src_ndarray_dim_i] == 1;
|
||||
bool c2 = dst_dim_exists && dst_ndarray->shape[dst_dim_i] != 1;
|
||||
if (!src_ndarray_dim_exists || (c1 && c2)) {
|
||||
dst_ndarray->strides[dst_dim_i] = 0; // Freeze it in-place
|
||||
} else {
|
||||
dst_ndarray->strides[dst_dim_i] =
|
||||
stride_product * src_ndarray->itemsize;
|
||||
stride_product *= src_ndarray->shape[src_ndarray_dim_i];
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace broadcast
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::broadcast;
|
||||
|
||||
void __nac3_ndarray_broadcast_to(ErrorContext* errctx,
|
||||
NDArray<int32_t>* src_ndarray,
|
||||
NDArray<int32_t>* dst_ndarray) {
|
||||
broadcast_to(errctx, src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_broadcast_to64(ErrorContext* errctx,
|
||||
NDArray<int64_t>* src_ndarray,
|
||||
NDArray<int64_t>* dst_ndarray) {
|
||||
broadcast_to(errctx, src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_broadcast_shapes(ErrorContext* errctx, int32_t num_shapes,
|
||||
const ShapeEntry<int32_t>* shapes,
|
||||
int32_t dst_ndims, int32_t* dst_shape) {
|
||||
ndarray::broadcast::util::broadcast_shapes(errctx, num_shapes, shapes,
|
||||
dst_ndims, dst_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_broadcast_shapes64(ErrorContext* errctx, int64_t num_shapes,
|
||||
const ShapeEntry<int64_t>* shapes,
|
||||
int64_t dst_ndims, int64_t* dst_shape) {
|
||||
ndarray::broadcast::util::broadcast_shapes(errctx, 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
|
||||
|
|
@ -1,200 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/error_context.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
|
||||
namespace {
|
||||
typedef uint8_t NDIndexType;
|
||||
|
||||
/**
|
||||
* @brief A single element index
|
||||
*
|
||||
* See https://numpy.org/doc/stable/user/basics.indexing.html#single-element-indexing
|
||||
*
|
||||
* `data` points to a `SliceIndex`.
|
||||
*/
|
||||
const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
|
||||
/**
|
||||
* @brief A slice index
|
||||
*
|
||||
* See https://numpy.org/doc/stable/user/basics.indexing.html#slicing-and-striding
|
||||
*
|
||||
* `data` points to a `UserRange`.
|
||||
*/
|
||||
const NDIndexType ND_INDEX_TYPE_SLICE = 1;
|
||||
|
||||
/**
|
||||
* @brief An index used in ndarray indexing
|
||||
*/
|
||||
struct NDIndex {
|
||||
/**
|
||||
* @brief Enum tag to specify the type of index.
|
||||
*
|
||||
* Please see comments of each enum constant.
|
||||
*/
|
||||
NDIndexType type;
|
||||
|
||||
/**
|
||||
* @brief The accompanying data associated with `type`.
|
||||
*
|
||||
* Please see comments of each enum constant.
|
||||
*/
|
||||
uint8_t* data;
|
||||
};
|
||||
} // namespace
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace indexing {
|
||||
namespace util {
|
||||
|
||||
/**
|
||||
* @brief Return the expected rank of the resulting ndarray
|
||||
* created by indexing an ndarray of rank `ndims` using `indexes`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void deduce_ndims_after_indexing(ErrorContext* errctx, SizeT* final_ndims,
|
||||
SizeT ndims, SizeT num_indexes,
|
||||
const NDIndex* indexes) {
|
||||
if (num_indexes > ndims) {
|
||||
errctx->set_exception(errctx->exceptions->index_error,
|
||||
"too many indices for array: array is "
|
||||
"{0}-dimensional, but {1} were indexed",
|
||||
ndims, num_indexes);
|
||||
return;
|
||||
}
|
||||
|
||||
*final_ndims = ndims;
|
||||
for (SizeT i = 0; i < num_indexes; i++) {
|
||||
if (indexes[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
|
||||
// An index demotes the rank by 1
|
||||
(*final_ndims)--;
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace util
|
||||
|
||||
/**
|
||||
* @brief Perform ndarray "basic indexing" (https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
|
||||
*
|
||||
* This is function very similar to performing `dst_ndarray = src_ndarray[indexes]` in Python (where the variables
|
||||
* can all be found in the parameter of this function).
|
||||
*
|
||||
* In other words, this function takes in an ndarray (`src_ndarray`), index it with `indexes`, and return the
|
||||
* indexed array (by writing the result to `dst_ndarray`).
|
||||
*
|
||||
* This function also does proper assertions on `indexes`.
|
||||
*
|
||||
* # Notes on `dst_ndarray`
|
||||
* The caller is responsible for allocating space for the resulting ndarray.
|
||||
* Here is what this function expects from `dst_ndarray` when called:
|
||||
* - `dst_ndarray->data` does not have to be initialized.
|
||||
* - `dst_ndarray->itemsize` does not have to be initialized.
|
||||
* - `dst_ndarray->ndims` must be initialized, and it must be equal to the expected `ndims` of the `dst_ndarray` after
|
||||
* indexing `src_ndarray` with `indexes`.
|
||||
* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
|
||||
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
|
||||
* When this function call ends:
|
||||
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
|
||||
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
|
||||
* - `dst_ndarray->ndims` is unchanged.
|
||||
* - `dst_ndarray->shape` is updated according to how `src_ndarray` is indexed.
|
||||
* - `dst_ndarray->strides` is updated accordingly by how ndarray indexing works.
|
||||
*
|
||||
* @param indexes Indexes to index `src_ndarray`, ordered in the same way you would write them in Python.
|
||||
* @param src_ndarray The NDArray to be indexed.
|
||||
* @param dst_ndarray The resulting NDArray after indexing. Further details in the comments above,
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void index(ErrorContext* errctx, SizeT num_indexes, const NDIndex* indexes,
|
||||
const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
|
||||
// Reference code: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
|
||||
|
||||
dst_ndarray->data = src_ndarray->data;
|
||||
dst_ndarray->itemsize = src_ndarray->itemsize;
|
||||
|
||||
SizeT src_axis = 0;
|
||||
SizeT dst_axis = 0;
|
||||
|
||||
for (SliceIndex i = 0; i < num_indexes; i++) {
|
||||
const NDIndex* index = &indexes[i];
|
||||
if (index->type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
|
||||
SliceIndex input = *((SliceIndex*)index->data);
|
||||
SliceIndex k = slice::resolve_index_in_length(
|
||||
src_ndarray->shape[src_axis], input);
|
||||
|
||||
if (k == slice::OUT_OF_BOUNDS) {
|
||||
errctx->set_exception(errctx->exceptions->index_error,
|
||||
"index {0} is out of bounds for axis {1} "
|
||||
"with size {2}",
|
||||
input, src_axis,
|
||||
src_ndarray->shape[src_axis]);
|
||||
return;
|
||||
}
|
||||
|
||||
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(errctx, src_ndarray->shape[src_axis],
|
||||
&slice);
|
||||
if (errctx->has_exception()) {
|
||||
return;
|
||||
}
|
||||
|
||||
dst_ndarray->data +=
|
||||
(SizeT)slice.start * src_ndarray->strides[src_axis];
|
||||
dst_ndarray->strides[dst_axis] =
|
||||
((SizeT)slice.step) * src_ndarray->strides[src_axis];
|
||||
dst_ndarray->shape[dst_axis] = (SizeT)slice.len();
|
||||
|
||||
dst_axis++;
|
||||
src_axis++;
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
|
||||
for (; dst_axis < dst_ndarray->ndims; dst_axis++, src_axis++) {
|
||||
dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
|
||||
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
|
||||
}
|
||||
}
|
||||
} // namespace indexing
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::indexing;
|
||||
|
||||
void __nac3_ndarray_indexing_deduce_ndims_after_indexing(
|
||||
ErrorContext* errctx, int32_t* result, int32_t ndims, int32_t num_indexes,
|
||||
const NDIndex* indexes) {
|
||||
ndarray::indexing::util::deduce_ndims_after_indexing(errctx, result, ndims,
|
||||
num_indexes, indexes);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_indexing_deduce_ndims_after_indexing64(
|
||||
ErrorContext* errctx, int64_t* result, int64_t ndims, int64_t num_indexes,
|
||||
const NDIndex* indexes) {
|
||||
ndarray::indexing::util::deduce_ndims_after_indexing(errctx, result, ndims,
|
||||
num_indexes, indexes);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_index(ErrorContext* errctx, int32_t num_indexes,
|
||||
NDIndex* indexes, NDArray<int32_t>* src_ndarray,
|
||||
NDArray<int32_t>* dst_ndarray) {
|
||||
index(errctx, num_indexes, indexes, src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_index64(ErrorContext* errctx, int64_t num_indexes,
|
||||
NDIndex* indexes, NDArray<int64_t>* src_ndarray,
|
||||
NDArray<int64_t>* dst_ndarray) {
|
||||
index(errctx, num_indexes, indexes, src_ndarray, dst_ndarray);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,117 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/error_context.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace reshape {
|
||||
namespace util {
|
||||
|
||||
/**
|
||||
* @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
|
||||
*
|
||||
* If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
|
||||
* modified to contain the resolved dimension.
|
||||
*
|
||||
* To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
|
||||
* `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
|
||||
*
|
||||
* @param size The `.size` of `<ndarray>`
|
||||
* @param new_ndims Number of elements in `new_shape`
|
||||
* @param new_shape Target shape to reshape to
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void resolve_and_check_new_shape(ErrorContext* errctx, 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.
|
||||
errctx->set_exception(
|
||||
errctx->exceptions->value_error,
|
||||
"can only specify one unknown dimension");
|
||||
return;
|
||||
} 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...
|
||||
|
||||
errctx->set_exception(
|
||||
errctx->exceptions->value_error,
|
||||
"Found negative dimension {0} on axis {1}", dim, axis_i);
|
||||
return;
|
||||
}
|
||||
} 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) {
|
||||
errctx->set_exception(
|
||||
errctx->exceptions->value_error,
|
||||
"cannot reshape array of size {0} into given shape", size);
|
||||
return;
|
||||
}
|
||||
}
|
||||
} // namespace util
|
||||
} // namespace reshape
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
|
||||
void __nac3_ndarray_resolve_and_check_new_shape(ErrorContext* errctx,
|
||||
int32_t size, int32_t new_ndims,
|
||||
int32_t* new_shape) {
|
||||
ndarray::reshape::util::resolve_and_check_new_shape(errctx, size, new_ndims,
|
||||
new_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_resolve_and_check_new_shape64(ErrorContext* errctx,
|
||||
int64_t size,
|
||||
int64_t new_ndims,
|
||||
int64_t* new_shape) {
|
||||
ndarray::reshape::util::resolve_and_check_new_shape(errctx, size, new_ndims,
|
||||
new_shape);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,166 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/error_context.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
#include <irrt/utils.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.
|
||||
*/
|
||||
void indices_checked(ErrorContext* errctx, SliceIndex length,
|
||||
Slice* result) {
|
||||
if (length < 0) {
|
||||
errctx->set_exception(errctx->exceptions->value_error,
|
||||
"length should not be negative, got {0}",
|
||||
length);
|
||||
return;
|
||||
}
|
||||
|
||||
if (this->step_defined && this->step == 0) {
|
||||
errctx->set_exception(errctx->exceptions->value_error,
|
||||
"slice step cannot be zero");
|
||||
return;
|
||||
}
|
||||
|
||||
*result = this->indices(length);
|
||||
}
|
||||
};
|
||||
} // namespace
|
||||
|
|
@ -1,104 +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;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Compare contents of two arrays with the same length.
|
||||
*/
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,13 +1,14 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/artiq_defs.hpp>
|
||||
#include <irrt/core.hpp>
|
||||
#include <irrt/error_context.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/broadcast.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/ndarray/indexing.hpp>
|
||||
#include <irrt/ndarray/reshape.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
#include <irrt/utils.hpp>
|
||||
#include "irrt_utils.hpp"
|
||||
#include "irrt_typedefs.hpp"
|
||||
#include "irrt_basic.hpp"
|
||||
#include "irrt_slice.hpp"
|
||||
#include "irrt_numpy_ndarray.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;
|
||||
}
|
||||
};
|
||||
}
|
||||
|
|
@ -4,17 +4,655 @@
|
|||
#include <cstdint>
|
||||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
#include <test/test_core.hpp>
|
||||
#include <test/test_ndarray_basic.hpp>
|
||||
#include <test/test_ndarray_broadcast.hpp>
|
||||
#include <test/test_ndarray_indexing.hpp>
|
||||
#include <test/test_slice.hpp>
|
||||
|
||||
// Set `IRRT_DONT_TYPEDEF_INTS` because `cstdint` defines them
|
||||
#define IRRT_DONT_TYPEDEF_INTS
|
||||
#include "irrt_everything.hpp"
|
||||
|
||||
void test_fail() {
|
||||
printf("[!] Test failed\n");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
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::slice::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(125, __nac3_int_exp_impl<int32_t>(5, 3));
|
||||
assert_values_match(3125, __nac3_int_exp_impl<int32_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();
|
||||
|
||||
int32_t shape[4] = {2, 3, 5, 7};
|
||||
assert_values_match(
|
||||
210, ndarray::basic::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(
|
||||
0, ndarray::basic::util::calc_size_from_shape<int32_t>(4, shape));
|
||||
}
|
||||
|
||||
void run() {
|
||||
test_calc_size_from_shape_normal();
|
||||
test_calc_size_from_shape_has_zero();
|
||||
}
|
||||
} // namespace ndarray_basic
|
||||
} // namespace test
|
||||
|
|
@ -1,129 +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};
|
||||
|
||||
ErrorContext errctx = create_testing_errctx();
|
||||
ndarray::broadcast::broadcast_to(&errctx, &ndarray, &dst_ndarray);
|
||||
assert_errctx_no_exception(&errctx);
|
||||
|
||||
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
|
||||
|
|
@ -1,220 +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};
|
||||
int32_t src_itemsize = sizeof(double);
|
||||
const int32_t src_ndims = 2;
|
||||
int32_t src_shape[src_ndims] = {3, 4};
|
||||
int32_t src_strides[src_ndims] = {};
|
||||
NDArray<int32_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 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 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 int32_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}};
|
||||
|
||||
ErrorContext errctx = create_testing_errctx();
|
||||
ndarray::indexing::index(&errctx, num_indexes, indexes, &src_ndarray,
|
||||
&dst_ndarray);
|
||||
assert_errctx_no_exception(&errctx);
|
||||
|
||||
int32_t expected_shape[dst_ndims] = {2, 2};
|
||||
int32_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, (int32_t[dst_ndims]){0, 0})));
|
||||
// dst_ndarray[0, 1]
|
||||
assert_values_match(7.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int32_t[dst_ndims]){0, 1})));
|
||||
// dst_ndarray[1, 0]
|
||||
assert_values_match(9.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int32_t[dst_ndims]){1, 0})));
|
||||
// dst_ndarray[1, 1]
|
||||
assert_values_match(11.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int32_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};
|
||||
int32_t src_itemsize = sizeof(double);
|
||||
const int32_t src_ndims = 2;
|
||||
int32_t src_shape[src_ndims] = {3, 4};
|
||||
int32_t src_strides[src_ndims] = {};
|
||||
NDArray<int32_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 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 subscripts in `ndarray[2, ::-2]`
|
||||
int32_t subscript_1 = 2;
|
||||
|
||||
UserSlice subscript_2;
|
||||
subscript_2.set_step(-2);
|
||||
|
||||
const int32_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}};
|
||||
|
||||
ErrorContext errctx = create_testing_errctx();
|
||||
ndarray::indexing::index(&errctx, num_indexes, indexes, &src_ndarray,
|
||||
&dst_ndarray);
|
||||
assert_errctx_no_exception(&errctx);
|
||||
|
||||
int32_t expected_shape[dst_ndims] = {2};
|
||||
int32_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, (int32_t[dst_ndims]){0})));
|
||||
assert_values_match(9.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int32_t[dst_ndims]){1})));
|
||||
}
|
||||
|
||||
void test_index_subscript_out_of_bounds() {
|
||||
/*
|
||||
# Consider `my_array`
|
||||
|
||||
print(my_array.shape)
|
||||
# (4, 5, 6)
|
||||
|
||||
my_array[2, 100] # error, index subscript at axis 1 is out of bounds
|
||||
*/
|
||||
BEGIN_TEST();
|
||||
|
||||
// Prepare src_ndarray
|
||||
const int32_t src_ndims = 2;
|
||||
int32_t src_shape[src_ndims] = {3, 4};
|
||||
int32_t src_strides[src_ndims] = {};
|
||||
NDArray<int32_t> src_ndarray = {
|
||||
.data = (uint8_t *)nullptr, // placeholder, we wouldn't access it
|
||||
.itemsize = sizeof(double), // placeholder
|
||||
.ndims = src_ndims,
|
||||
.shape = src_shape,
|
||||
.strides = src_strides};
|
||||
ndarray::basic::set_strides_by_shape(&src_ndarray);
|
||||
|
||||
// Create the subscripts in `my_array[2, 100]`
|
||||
int32_t subscript_1 = 2;
|
||||
int32_t subscript_2 = 100;
|
||||
|
||||
const int32_t num_indexes = 2;
|
||||
NDIndex indexes[num_indexes] = {
|
||||
{.type = ND_INDEX_TYPE_SINGLE_ELEMENT, .data = (uint8_t *)&subscript_1},
|
||||
{.type = ND_INDEX_TYPE_SINGLE_ELEMENT,
|
||||
.data = (uint8_t *)&subscript_2}};
|
||||
|
||||
// Prepare dst_ndarray
|
||||
const int32_t dst_ndims = 0;
|
||||
int32_t dst_shape[dst_ndims] = {};
|
||||
int32_t dst_strides[dst_ndims] = {};
|
||||
NDArray<int32_t> dst_ndarray = {.data = nullptr, // placehloder
|
||||
.ndims = dst_ndims,
|
||||
.shape = dst_shape,
|
||||
.strides = dst_strides};
|
||||
|
||||
ErrorContext errctx = create_testing_errctx();
|
||||
ndarray::indexing::index(&errctx, num_indexes, indexes, &src_ndarray,
|
||||
&dst_ndarray);
|
||||
assert_errctx_has_exception(&errctx, errctx.exceptions->index_error);
|
||||
}
|
||||
|
||||
void run() {
|
||||
test_normal_1();
|
||||
test_normal_2();
|
||||
test_index_subscript_out_of_bounds();
|
||||
}
|
||||
} // namespace ndarray_indexing
|
||||
} // namespace test
|
||||
|
|
@ -1,92 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt_everything.hpp>
|
||||
#include <test/includes.hpp>
|
||||
|
||||
namespace test {
|
||||
namespace slice {
|
||||
void test_slice_normal() {
|
||||
// Normal situation
|
||||
BEGIN_TEST();
|
||||
|
||||
UserSlice user_slice;
|
||||
user_slice.set_stop(5);
|
||||
|
||||
Slice slice = user_slice.indices(100);
|
||||
|
||||
printf("%d, %d, %d\n", slice.start, slice.stop, slice.step);
|
||||
|
||||
assert_values_match(0, slice.start);
|
||||
assert_values_match(5, slice.stop);
|
||||
assert_values_match(1, slice.step);
|
||||
}
|
||||
|
||||
void test_slice_start_too_large() {
|
||||
// Start is too large and should be clamped to length
|
||||
BEGIN_TEST();
|
||||
|
||||
UserSlice user_slice;
|
||||
user_slice.set_start(400);
|
||||
|
||||
Slice slice = user_slice.indices(100);
|
||||
|
||||
assert_values_match(100, slice.start);
|
||||
assert_values_match(100, slice.stop);
|
||||
assert_values_match(1, slice.step);
|
||||
}
|
||||
|
||||
void test_slice_negative_start_stop() {
|
||||
// Negative start/stop should be resolved
|
||||
BEGIN_TEST();
|
||||
|
||||
UserSlice user_slice;
|
||||
user_slice.set_start(-10);
|
||||
user_slice.set_stop(-5);
|
||||
|
||||
Slice slice = user_slice.indices(100);
|
||||
|
||||
assert_values_match(90, slice.start);
|
||||
assert_values_match(95, slice.stop);
|
||||
assert_values_match(1, slice.step);
|
||||
}
|
||||
|
||||
void test_slice_only_negative_step() {
|
||||
// Things like `[::-5]` should be handled correctly
|
||||
BEGIN_TEST();
|
||||
|
||||
UserSlice user_slice;
|
||||
user_slice.set_step(-5);
|
||||
|
||||
Slice slice = user_slice.indices(100);
|
||||
|
||||
assert_values_match(99, slice.start);
|
||||
assert_values_match(-1, slice.stop);
|
||||
assert_values_match(-5, slice.step);
|
||||
}
|
||||
|
||||
void test_slice_step_zero() {
|
||||
// Step = 0 is a value error
|
||||
BEGIN_TEST();
|
||||
|
||||
ErrorContext errctx = create_testing_errctx();
|
||||
|
||||
UserSlice user_slice;
|
||||
user_slice.set_start(2);
|
||||
user_slice.set_stop(12);
|
||||
user_slice.set_step(0);
|
||||
|
||||
Slice slice;
|
||||
user_slice.indices_checked(&errctx, 100, &slice);
|
||||
|
||||
assert_errctx_has_exception(&errctx, errctx.exceptions->value_error);
|
||||
}
|
||||
|
||||
void run() {
|
||||
test_slice_normal();
|
||||
test_slice_start_too_large();
|
||||
test_slice_negative_start_stop();
|
||||
test_slice_only_negative_step();
|
||||
test_slice_step_zero();
|
||||
}
|
||||
} // namespace slice
|
||||
} // namespace test
|
||||
|
|
@ -1,188 +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 uint8_t& value) {
|
||||
printf("%u", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const uint32_t& value) {
|
||||
printf("%u", 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)
|
||||
|
||||
// A fake set of ExceptionIds for testing only
|
||||
const ErrorContextExceptions TEST_ERROR_CONTEXT_EXCEPTIONS = {
|
||||
.index_error = 0,
|
||||
.value_error = 1,
|
||||
.assertion_error = 2,
|
||||
.runtime_error = 3,
|
||||
.type_error = 4,
|
||||
};
|
||||
|
||||
ErrorContext create_testing_errctx() {
|
||||
// Everything is global so it is fine to directly return a struct
|
||||
// ErrorContext
|
||||
ErrorContext errctx;
|
||||
errctx.initialize(&TEST_ERROR_CONTEXT_EXCEPTIONS);
|
||||
return errctx;
|
||||
}
|
||||
|
||||
void print_errctx_content(ErrorContext* errctx) {
|
||||
if (errctx->has_exception()) {
|
||||
printf(
|
||||
"(Exception ID %d): %s ... where param1 = %ld, param2 = %ld, "
|
||||
"param3 = "
|
||||
"%ld\n",
|
||||
errctx->exception_id, errctx->msg, errctx->param1, errctx->param2,
|
||||
errctx->param3);
|
||||
} else {
|
||||
printf("<no exception>\n");
|
||||
}
|
||||
}
|
||||
|
||||
void __assert_errctx_no_exception(const char* file, int line,
|
||||
ErrorContext* errctx) {
|
||||
if (errctx->has_exception()) {
|
||||
print_assertion_failed(file, line);
|
||||
printf("Expecting no exception but caught the following:\n\n");
|
||||
print_errctx_content(errctx);
|
||||
test_fail();
|
||||
}
|
||||
}
|
||||
|
||||
#define assert_errctx_no_exception(errctx) \
|
||||
__assert_errctx_no_exception(__FILE__, __LINE__, errctx)
|
||||
|
||||
void __assert_errctx_has_exception(const char* file, int line,
|
||||
ErrorContext* errctx,
|
||||
ExceptionId expected_exception_id) {
|
||||
if (errctx->has_exception()) {
|
||||
if (errctx->exception_id != expected_exception_id) {
|
||||
print_assertion_failed(file, line);
|
||||
printf(
|
||||
"Expecting exception id %d but got exception id %d. Error "
|
||||
"caught:\n\n",
|
||||
expected_exception_id, errctx->exception_id);
|
||||
print_errctx_content(errctx);
|
||||
test_fail();
|
||||
}
|
||||
} else {
|
||||
print_assertion_failed(file, line);
|
||||
printf("Expecting an exception, but there is none.");
|
||||
test_fail();
|
||||
}
|
||||
}
|
||||
|
||||
#define assert_errctx_has_exception(errctx, expected_exception_id) \
|
||||
__assert_errctx_has_exception(__FILE__, __LINE__, errctx, \
|
||||
expected_exception_id)
|
||||
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()),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -123,12 +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)
|
||||
gen_assign(self, ctx, target, value)
|
||||
}
|
||||
|
||||
/// Generate code for a while expression.
|
||||
|
|
|
|||
|
|
@ -1,198 +0,0 @@
|
|||
use super::util::{function::CallFunction, get_sizet_dependent_function_name};
|
||||
use crate::codegen::{
|
||||
model::*,
|
||||
structure::{cslice::CSlice, exception::ExceptionId},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
#[allow(clippy::struct_field_names)]
|
||||
pub struct ErrorContextExceptionsFields<F: FieldVisitor> {
|
||||
pub index_error: F::Field<IntModel<ExceptionId>>,
|
||||
pub value_error: F::Field<IntModel<ExceptionId>>,
|
||||
pub assertion_error: F::Field<IntModel<ExceptionId>>,
|
||||
pub runtime_error: F::Field<IntModel<ExceptionId>>,
|
||||
pub type_error: F::Field<IntModel<ExceptionId>>,
|
||||
}
|
||||
|
||||
/// Corresponds to IRRT's `struct ErrorContextExceptions`
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct ErrorContextExceptions;
|
||||
|
||||
impl StructKind for ErrorContextExceptions {
|
||||
type Fields<F: FieldVisitor> = ErrorContextExceptionsFields<F>;
|
||||
|
||||
fn visit_fields<F: FieldVisitor>(&self, visitor: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
index_error: visitor.add("index_error"),
|
||||
value_error: visitor.add("value_error"),
|
||||
assertion_error: visitor.add("assertion_error"),
|
||||
runtime_error: visitor.add("runtime_error"),
|
||||
type_error: visitor.add("type_error"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub struct ErrorContextFields<F: FieldVisitor> {
|
||||
pub exceptions: F::Field<PtrModel<StructModel<ErrorContextExceptions>>>,
|
||||
pub exception_id: F::Field<IntModel<ExceptionId>>,
|
||||
pub msg: F::Field<PtrModel<IntModel<Byte>>>,
|
||||
pub param1: F::Field<IntModel<Int64>>,
|
||||
pub param2: F::Field<IntModel<Int64>>,
|
||||
pub param3: F::Field<IntModel<Int64>>,
|
||||
}
|
||||
|
||||
/// Corresponds to IRRT's `struct ErrorContext`
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct ErrorContext;
|
||||
|
||||
impl StructKind for ErrorContext {
|
||||
type Fields<F: FieldVisitor> = ErrorContextFields<F>;
|
||||
|
||||
fn visit_fields<F: FieldVisitor>(&self, visitor: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
exceptions: visitor.add("exceptions"),
|
||||
exception_id: visitor.add("exception_id"),
|
||||
msg: visitor.add("msg"),
|
||||
param1: visitor.add("param1"),
|
||||
param2: visitor.add("param2"),
|
||||
param3: visitor.add("param3"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Build an [`ErrorContextExceptions`] loaded with resolved [`ExceptionID`]s according to the [`SymbolResolver`].
|
||||
fn build_error_context_exceptions<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> Ptr<'ctx, StructModel<ErrorContextExceptions>> {
|
||||
let exceptions =
|
||||
StructModel(ErrorContextExceptions).alloca(tyctx, ctx, "error_context_exceptions");
|
||||
let i32_model = IntModel(Int32);
|
||||
|
||||
let get_string_id = |string_id| {
|
||||
i32_model.constant(tyctx, ctx.ctx, ctx.resolver.get_string_id(string_id) as u64)
|
||||
};
|
||||
|
||||
exceptions.gep(ctx, |f| f.index_error).store(ctx, get_string_id("0:IndexError"));
|
||||
exceptions.gep(ctx, |f| f.value_error).store(ctx, get_string_id("0:ValueError"));
|
||||
exceptions.gep(ctx, |f| f.assertion_error).store(ctx, get_string_id("0:AssertionError"));
|
||||
exceptions.gep(ctx, |f| f.runtime_error).store(ctx, get_string_id("0:RuntimeError"));
|
||||
exceptions.gep(ctx, |f| f.type_error).store(ctx, get_string_id("0:TypeError"));
|
||||
|
||||
exceptions
|
||||
}
|
||||
|
||||
pub fn call_nac3_error_context_initialize<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
perrctx: Ptr<'ctx, StructModel<ErrorContext>>,
|
||||
pexceptions: Ptr<'ctx, StructModel<ErrorContextExceptions>>,
|
||||
) {
|
||||
CallFunction::begin(tyctx, ctx, "__nac3_error_context_initialize")
|
||||
.arg("errctx", perrctx)
|
||||
.arg("exceptions", pexceptions)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_error_context_has_exception<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
perrctx: Ptr<'ctx, StructModel<ErrorContext>>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
CallFunction::begin(tyctx, ctx, "__nac3_error_context_has_exception")
|
||||
.arg("errctx", perrctx)
|
||||
.returning("has_exception")
|
||||
}
|
||||
|
||||
pub fn call_nac3_error_context_get_exception_str<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
perrctx: Ptr<'ctx, StructModel<ErrorContext>>,
|
||||
dst_str: Ptr<'ctx, StructModel<CSlice>>,
|
||||
) {
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_error_context_get_exception_str"),
|
||||
)
|
||||
.arg("errctx", perrctx)
|
||||
.arg("dst_str", dst_str)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
/// Setup a [`ErrorContext`] that could be passed to IRRT functions taking in a `ErrorContext* errctx`
|
||||
/// for error reporting purposes.
|
||||
///
|
||||
/// Also see: [`check_error_context`]
|
||||
pub fn setup_error_context<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> Ptr<'ctx, StructModel<ErrorContext>> {
|
||||
let errctx_model = StructModel(ErrorContext);
|
||||
|
||||
let exceptions = build_error_context_exceptions(tyctx, ctx);
|
||||
let errctx_ptr = errctx_model.alloca(tyctx, ctx, "errctx");
|
||||
|
||||
call_nac3_error_context_initialize(tyctx, ctx, errctx_ptr, exceptions);
|
||||
|
||||
errctx_ptr
|
||||
}
|
||||
|
||||
/// Check a [`ErrorContext`] to see if it contains error. **If there is an error,
|
||||
/// a Pythonic exception will be raised in the firmware**.
|
||||
pub fn check_error_context<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
perrctx: Ptr<'ctx, StructModel<ErrorContext>>,
|
||||
) {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let cslice_model = StructModel(CSlice);
|
||||
|
||||
let current_bb = ctx.builder.get_insert_block().unwrap();
|
||||
let irrt_has_exception_bb = ctx.ctx.insert_basic_block_after(current_bb, "irrt_has_exception");
|
||||
let end_bb = ctx.ctx.insert_basic_block_after(irrt_has_exception_bb, "end");
|
||||
|
||||
// Inserting into `current_bb`
|
||||
let has_exception = call_nac3_error_context_has_exception(tyctx, ctx, perrctx);
|
||||
ctx.builder
|
||||
.build_conditional_branch(has_exception.value, irrt_has_exception_bb, end_bb)
|
||||
.unwrap();
|
||||
|
||||
// Inserting into `irrt_has_exception_bb`
|
||||
ctx.builder.position_at_end(irrt_has_exception_bb);
|
||||
|
||||
// Load all the values for `ctx.make_assert_impl_by_id`
|
||||
let pexception_str = cslice_model.alloca(tyctx, ctx, "exception_str");
|
||||
call_nac3_error_context_get_exception_str(tyctx, ctx, perrctx, pexception_str);
|
||||
|
||||
let exception_id = perrctx.gep(ctx, |f| f.exception_id).load(tyctx, ctx, "exception_id");
|
||||
let msg = pexception_str.load(tyctx, ctx, "msg");
|
||||
let param1 = perrctx.gep(ctx, |f| f.param1).load(tyctx, ctx, "param1");
|
||||
let param2 = perrctx.gep(ctx, |f| f.param2).load(tyctx, ctx, "param2");
|
||||
let param3 = perrctx.gep(ctx, |f| f.param3).load(tyctx, ctx, "param3");
|
||||
|
||||
ctx.raise_exn_impl(
|
||||
generator,
|
||||
exception_id,
|
||||
msg,
|
||||
[Some(param1), Some(param2), Some(param3)],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
// Position to `end_bb` for continuation
|
||||
ctx.builder.position_at_end(end_bb);
|
||||
}
|
||||
|
||||
pub fn call_nac3_dummy_raise<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext,
|
||||
) {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
let errctx = setup_error_context(tyctx, ctx);
|
||||
CallFunction::begin(tyctx, ctx, "__nac3_error_dummy_raise")
|
||||
.arg("errctx", errctx)
|
||||
.returning_void();
|
||||
|
||||
check_error_context(generator, ctx, errctx);
|
||||
}
|
||||
|
|
@ -1,16 +1,11 @@
|
|||
use crate::typecheck::typedef::Type;
|
||||
use crate::{typecheck::typedef::Type, util::SizeVariant};
|
||||
|
||||
pub mod error_context;
|
||||
pub mod ndarray;
|
||||
pub mod slice;
|
||||
mod test;
|
||||
mod util;
|
||||
|
||||
use super::model::*;
|
||||
use super::{
|
||||
classes::{
|
||||
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue,
|
||||
TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
|
||||
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue, NpArrayType,
|
||||
NpArrayValue, TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
|
||||
},
|
||||
llvm_intrinsics, CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
|
@ -21,8 +16,8 @@ use inkwell::{
|
|||
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;
|
||||
|
|
@ -421,29 +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 type_context = generator.type_context(ctx.ctx);
|
||||
let param_model = IntModel(Int64);
|
||||
|
||||
let src_slice_len =
|
||||
param_model.s_extend_or_bit_cast(type_context, ctx, src_slice_len, "src_slice_len");
|
||||
let dest_slice_len =
|
||||
param_model.s_extend_or_bit_cast(type_context, ctx, dest_slice_len, "dest_slice_len");
|
||||
let dest_idx_2 =
|
||||
param_model.s_extend_or_bit_cast(type_context, 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)],
|
||||
[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| {
|
||||
|
|
@ -950,3 +929,63 @@ pub fn call_ndarray_calc_broadcast_index<
|
|||
Box::new(|_, v| v.into()),
|
||||
)
|
||||
}
|
||||
|
||||
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()),
|
||||
}
|
||||
}
|
||||
|
||||
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)
|
||||
})
|
||||
}
|
||||
|
||||
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_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)
|
||||
});
|
||||
|
||||
ctx.builder
|
||||
.build_call(function, &[ndarray.ptr.into()], "size")
|
||||
.unwrap()
|
||||
.try_as_basic_value()
|
||||
.unwrap_left()
|
||||
.into_int_value()
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,153 +0,0 @@
|
|||
use crate::codegen::irrt::error_context::{check_error_context, setup_error_context};
|
||||
use crate::codegen::irrt::slice::SliceIndex;
|
||||
use crate::codegen::irrt::util::function::CallFunction;
|
||||
use crate::codegen::irrt::util::get_sizet_dependent_function_name;
|
||||
use crate::codegen::model::*;
|
||||
use crate::codegen::structure::ndarray::NpArray;
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_size"),
|
||||
)
|
||||
.arg("ndarray", ndarray_ptr)
|
||||
.returning("size")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_nbytes"),
|
||||
)
|
||||
.arg("ndarray", ndarray_ptr)
|
||||
.returning("nbytes")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, SliceIndex> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let slice_index_model = IntModel(SliceIndex::default());
|
||||
|
||||
let dst_len = slice_index_model.alloca(tyctx, ctx, "dst_len");
|
||||
|
||||
let errctx = setup_error_context(tyctx, ctx);
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_len"),
|
||||
)
|
||||
.arg("errctx", errctx)
|
||||
.arg("ndarray", ndarray_ptr)
|
||||
.arg("dst_len", dst_len)
|
||||
.returning_void();
|
||||
check_error_context(generator, ctx, errctx);
|
||||
|
||||
dst_len.load(tyctx, ctx, "len")
|
||||
}
|
||||
|
||||
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 tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
let errctx = setup_error_context(tyctx, ctx);
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_util_assert_shape_no_negative"),
|
||||
)
|
||||
.arg("errctx", errctx)
|
||||
.arg("ndims", ndims)
|
||||
.arg("shape", shape)
|
||||
.returning_void();
|
||||
check_error_context(generator, ctx, errctx);
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_set_strides_by_shape"),
|
||||
)
|
||||
.arg("ndarray", ndarray_ptr)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_is_c_contiguous"),
|
||||
)
|
||||
.arg("ndarray", ndarray_ptr)
|
||||
.returning("is_c_contiguous")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_copy_data"),
|
||||
)
|
||||
.arg("src_ndarray", src_ndarray)
|
||||
.arg("dst_ndarray", dst_ndarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
index: Int<'ctx, SizeT>,
|
||||
) -> Ptr<'ctx, IntModel<Byte>> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_get_nth_pelement"),
|
||||
)
|
||||
.arg("ndarray", pndarray)
|
||||
.arg("index", index)
|
||||
.returning("pelement")
|
||||
}
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
use crate::codegen::{
|
||||
irrt::{
|
||||
error_context::{check_error_context, setup_error_context},
|
||||
util::{function::CallFunction, get_sizet_dependent_function_name},
|
||||
},
|
||||
model::*,
|
||||
structure::ndarray::NpArray,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
let perrctx = setup_error_context(tyctx, ctx);
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_broadcast_to"),
|
||||
)
|
||||
.arg("errctx", perrctx)
|
||||
.arg("src_ndarray", src_ndarray)
|
||||
.arg("dst_ndarray", dst_ndarray)
|
||||
.returning_void();
|
||||
|
||||
check_error_context(generator, ctx, perrctx);
|
||||
}
|
||||
|
||||
/// Fields of [`ShapeEntry`]
|
||||
pub struct ShapeEntryFields<F: FieldVisitor> {
|
||||
pub ndims: F::Field<IntModel<SizeT>>,
|
||||
pub shape: F::Field<PtrModel<IntModel<SizeT>>>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct ShapeEntry;
|
||||
|
||||
impl StructKind for ShapeEntry {
|
||||
type Fields<F: FieldVisitor> = ShapeEntryFields<F>;
|
||||
|
||||
fn visit_fields<F: FieldVisitor>(&self, visitor: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields { ndims: visitor.add("ndims"), shape: visitor.add("shape") }
|
||||
}
|
||||
}
|
||||
|
||||
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 tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
let perrctx = setup_error_context(tyctx, ctx);
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_broadcast_shapes"),
|
||||
)
|
||||
.arg("errctx", perrctx)
|
||||
.arg("num_shapes", num_shape_entries)
|
||||
.arg("shapes", shape_entries)
|
||||
.arg("dst_ndims", dst_ndims)
|
||||
.arg("dst_shape", dst_shape)
|
||||
.returning_void();
|
||||
|
||||
check_error_context(generator, ctx, perrctx);
|
||||
}
|
||||
|
|
@ -1,170 +0,0 @@
|
|||
use crate::codegen::{
|
||||
irrt::{
|
||||
error_context::{check_error_context, setup_error_context},
|
||||
slice::{RustUserSlice, SliceIndex, UserSlice},
|
||||
util::{function::CallFunction, get_sizet_dependent_function_name},
|
||||
},
|
||||
model::*,
|
||||
structure::ndarray::NpArray,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
pub type NDIndexType = Byte;
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDIndexFields<F: FieldVisitor> {
|
||||
pub type_: F::Field<IntModel<NDIndexType>>, // Defined to be uint8_t in IRRT
|
||||
pub data: F::Field<PtrModel<IntModel<Byte>>>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct NDIndex;
|
||||
|
||||
impl StructKind for NDIndex {
|
||||
type Fields<F: FieldVisitor> = NDIndexFields<F>;
|
||||
|
||||
fn visit_fields<F: FieldVisitor>(&self, visitor: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields { type_: visitor.add("type"), data: visitor.add("data") }
|
||||
}
|
||||
}
|
||||
|
||||
// An enum variant to store the content
|
||||
// and type of an NDIndex in high level.
|
||||
#[derive(Debug, Clone)]
|
||||
pub enum RustNDIndex<'ctx> {
|
||||
SingleElement(Int<'ctx, SliceIndex>),
|
||||
Slice(RustUserSlice<'ctx>),
|
||||
}
|
||||
|
||||
impl<'ctx> RustNDIndex<'ctx> {
|
||||
fn get_type_id(&self) -> u64 {
|
||||
// Defined in IRRT, must be in sync
|
||||
match self {
|
||||
RustNDIndex::SingleElement(_) => 0,
|
||||
RustNDIndex::Slice(_) => 1,
|
||||
}
|
||||
}
|
||||
|
||||
fn write_to_ndindex(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_ndindex_ptr: Ptr<'ctx, StructModel<NDIndex>>,
|
||||
) {
|
||||
let ndindex_type_model = IntModel(NDIndexType::default());
|
||||
let slice_index_model = IntModel(SliceIndex::default());
|
||||
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(tyctx, ctx.ctx, self.get_type_id()));
|
||||
|
||||
// Set `dst_ndindex_ptr->data`
|
||||
let data = match self {
|
||||
RustNDIndex::SingleElement(in_index) => {
|
||||
let index_ptr = slice_index_model.alloca(tyctx, ctx, "index");
|
||||
index_ptr.store(ctx, *in_index);
|
||||
index_ptr.transmute(tyctx, ctx, IntModel(Byte), "")
|
||||
}
|
||||
RustNDIndex::Slice(in_rust_slice) => {
|
||||
let user_slice_ptr = user_slice_model.alloca(tyctx, ctx, "user_slice");
|
||||
in_rust_slice.write_to_user_slice(tyctx, ctx, user_slice_ptr);
|
||||
user_slice_ptr.transmute(tyctx, ctx, IntModel(Byte), "")
|
||||
}
|
||||
};
|
||||
dst_ndindex_ptr.gep(ctx, |f| f.data).store(ctx, data);
|
||||
}
|
||||
|
||||
/// Allocate an array of `NDIndex`es on the stack and return its stack pointer.
|
||||
pub fn alloca_ndindexes(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
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(tyctx, ctx.ctx, in_ndindexes.len() as u64);
|
||||
let ndindexes = ndindex_model.array_alloca(tyctx, ctx, num_ndindexes.value, "ndindexes");
|
||||
for (i, in_ndindex) in in_ndindexes.iter().enumerate() {
|
||||
let i = sizet_model.constant(tyctx, ctx.ctx, i as u64);
|
||||
let pndindex = ndindexes.offset(tyctx, ctx, i.value, "");
|
||||
in_ndindex.write_to_ndindex(tyctx, ctx, pndindex);
|
||||
}
|
||||
|
||||
(num_ndindexes, ndindexes)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn deduce_ndims_after_indexing(indices: &[RustNDIndex], original_ndims: u64) -> u64 {
|
||||
let mut final_ndims = original_ndims;
|
||||
for index in indices {
|
||||
match index {
|
||||
RustNDIndex::SingleElement(_) => {
|
||||
final_ndims -= 1;
|
||||
}
|
||||
RustNDIndex::Slice(_) => {}
|
||||
}
|
||||
}
|
||||
final_ndims
|
||||
}
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_indexing_deduce_ndims_after_indexing<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndims: Int<'ctx, SizeT>,
|
||||
num_ndindexes: Int<'ctx, SizeT>,
|
||||
ndindexs: Ptr<'ctx, StructModel<NDIndex>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let pfinal_ndims = sizet_model.alloca(tyctx, ctx, "pfinal_ndims");
|
||||
|
||||
let errctx_ptr = setup_error_context(tyctx, ctx);
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(
|
||||
tyctx,
|
||||
"__nac3_ndarray_indexing_deduce_ndims_after_indexing",
|
||||
),
|
||||
)
|
||||
.arg("errctx", errctx_ptr)
|
||||
.arg("result", pfinal_ndims)
|
||||
.arg("ndims", ndims)
|
||||
.arg("num_ndindexs", num_ndindexes)
|
||||
.arg("ndindexs", ndindexs)
|
||||
.returning_void();
|
||||
check_error_context(generator, ctx, errctx_ptr);
|
||||
|
||||
pfinal_ndims.load(tyctx, ctx, "final_ndims")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
num_indexes: Int<'ctx, SizeT>,
|
||||
indexes: Ptr<'ctx, StructModel<NDIndex>>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
let perrctx = setup_error_context(tyctx, ctx);
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_index"),
|
||||
)
|
||||
.arg("errctx", perrctx)
|
||||
.arg("num_indexes", num_indexes)
|
||||
.arg("indexes", indexes)
|
||||
.arg("src_ndarray", src_ndarray)
|
||||
.arg("dst_ndarray", dst_ndarray)
|
||||
.returning_void();
|
||||
|
||||
check_error_context(generator, ctx, perrctx);
|
||||
}
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
pub mod basic;
|
||||
pub mod broadcast;
|
||||
pub mod indexing;
|
||||
pub mod reshape;
|
||||
|
|
@ -1,31 +0,0 @@
|
|||
use crate::codegen::{
|
||||
irrt::{
|
||||
error_context::{check_error_context, setup_error_context},
|
||||
util::{function::CallFunction, get_sizet_dependent_function_name},
|
||||
},
|
||||
model::*,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
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 tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
let perrctx = setup_error_context(tyctx, ctx);
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_resolve_and_check_new_shape"),
|
||||
)
|
||||
.arg("errctx", perrctx)
|
||||
.arg("size", size)
|
||||
.arg("new_ndims", new_ndims)
|
||||
.arg("new_shape", new_shape)
|
||||
.returning_void();
|
||||
check_error_context(generator, ctx, perrctx);
|
||||
}
|
||||
|
|
@ -1,81 +0,0 @@
|
|||
use crate::codegen::{model::*, CodeGenContext};
|
||||
|
||||
// nac3core's slicing index/length values are always int32_t
|
||||
pub type SliceIndex = Int32;
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct UserSliceFields<F: FieldVisitor> {
|
||||
pub start_defined: F::Field<IntModel<Bool>>,
|
||||
pub start: F::Field<IntModel<SliceIndex>>,
|
||||
pub stop_defined: F::Field<IntModel<Bool>>,
|
||||
pub stop: F::Field<IntModel<SliceIndex>>,
|
||||
pub step_defined: F::Field<IntModel<Bool>>,
|
||||
pub step: F::Field<IntModel<SliceIndex>>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct UserSlice;
|
||||
|
||||
impl StructKind for UserSlice {
|
||||
type Fields<F: FieldVisitor> = UserSliceFields<F>;
|
||||
|
||||
fn visit_fields<F: FieldVisitor>(&self, visitor: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
start_defined: visitor.add("start_defined"),
|
||||
start: visitor.add("start"),
|
||||
stop_defined: visitor.add("stop_defined"),
|
||||
stop: visitor.add("stop"),
|
||||
step_defined: visitor.add("step_defined"),
|
||||
step: visitor.add("step"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct RustUserSlice<'ctx> {
|
||||
pub start: Option<Int<'ctx, SliceIndex>>,
|
||||
pub stop: Option<Int<'ctx, SliceIndex>>,
|
||||
pub step: Option<Int<'ctx, SliceIndex>>,
|
||||
}
|
||||
|
||||
impl<'ctx> RustUserSlice<'ctx> {
|
||||
// Set the values of an LLVM UserSlice
|
||||
// in the format of Python's `slice()`
|
||||
pub fn write_to_user_slice(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_slice_ptr: Ptr<'ctx, StructModel<UserSlice>>,
|
||||
) {
|
||||
let bool_model = IntModel(Bool);
|
||||
|
||||
let false_ = bool_model.constant(tyctx, ctx.ctx, 0);
|
||||
let true_ = bool_model.constant(tyctx, 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_),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,103 +0,0 @@
|
|||
use crate::codegen::model::*;
|
||||
|
||||
// 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(tyctx: TypeContext<'_>, name: &str) -> String {
|
||||
let mut name = name.to_owned();
|
||||
match tyctx.size_type.get_bit_width() {
|
||||
32 => {}
|
||||
64 => name.push_str("64"),
|
||||
bit_width => {
|
||||
panic!("Unsupported int type bit width {bit_width}, must be either 32-bits or 64-bits")
|
||||
}
|
||||
}
|
||||
name
|
||||
}
|
||||
|
||||
pub mod function {
|
||||
use crate::codegen::{model::*, CodeGenContext};
|
||||
use inkwell::{
|
||||
types::{BasicMetadataTypeEnum, BasicType, FunctionType},
|
||||
values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
struct Arg<'ctx> {
|
||||
ty: BasicMetadataTypeEnum<'ctx>,
|
||||
val: BasicMetadataValueEnum<'ctx>,
|
||||
}
|
||||
|
||||
/// Helper structure to reduce IRRT Inkwell function call boilerplate
|
||||
/// TODO: Optimize
|
||||
pub struct CallFunction<'ctx, 'a, 'b, 'c> {
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'b CodeGenContext<'ctx, 'a>,
|
||||
/// Function name
|
||||
name: &'c str,
|
||||
/// Call arguments
|
||||
args: Vec<Arg<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx, 'a, 'b, 'c> CallFunction<'ctx, 'a, 'b, 'c> {
|
||||
pub fn begin(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'b CodeGenContext<'ctx, 'a>,
|
||||
name: &'c str,
|
||||
) -> Self {
|
||||
CallFunction { tyctx, ctx, name, args: Vec::new() }
|
||||
}
|
||||
|
||||
/// Push a call argument to the function call.
|
||||
///
|
||||
/// The `_name` parameter is there for self-documentation purposes.
|
||||
#[allow(clippy::needless_pass_by_value)]
|
||||
pub fn arg<M: Model>(mut self, _name: &str, arg: Instance<'ctx, M>) -> Self {
|
||||
let arg = Arg {
|
||||
ty: arg.model.get_type(self.tyctx, self.ctx.ctx).as_basic_type_enum().into(),
|
||||
val: arg.value.as_basic_value_enum().into(),
|
||||
};
|
||||
self.args.push(arg);
|
||||
self
|
||||
}
|
||||
|
||||
/// Like [`CallFunction::returning_`] but `return_model` is automatically inferred.
|
||||
pub fn returning<M: Model>(self, name: &str) -> Instance<'ctx, M> {
|
||||
self.returning_(name, M::default())
|
||||
}
|
||||
|
||||
/// Call the function and expect the function to return a value of type of `return_model`.
|
||||
pub fn returning_<M: Model>(self, name: &str, return_model: M) -> Instance<'ctx, M> {
|
||||
let ret_ty = return_model.get_type(self.tyctx, 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.tyctx, self.ctx.ctx, ret).unwrap(); // Must work
|
||||
ret
|
||||
}
|
||||
|
||||
/// Call the function and expect the function to return a void-type.
|
||||
pub fn returning_void(self) {
|
||||
let ret_ty = self.ctx.ctx.void_type();
|
||||
|
||||
let _ = self.get_function(|tys| ret_ty.fn_type(tys, false), "");
|
||||
}
|
||||
|
||||
fn get_function<F>(&self, make_fn_type: F, return_value_name: &str) -> CallSiteValue<'ctx>
|
||||
where
|
||||
F: FnOnce(&[BasicMetadataTypeEnum<'ctx>]) -> FunctionType<'ctx>,
|
||||
{
|
||||
// Get the LLVM function, declare the function if it doesn't exist - it will be defined by other
|
||||
// components of NAC3.
|
||||
let func = self.ctx.module.get_function(self.name).unwrap_or_else(|| {
|
||||
let tys = self.args.iter().map(|arg| arg.ty).collect_vec();
|
||||
let fn_type = make_fn_type(&tys);
|
||||
self.ctx.module.add_function(self.name, fn_type, None)
|
||||
});
|
||||
|
||||
let vals = self.args.iter().map(|arg| arg.val).collect_vec();
|
||||
self.ctx.builder.build_call(func, &vals, return_value_name).unwrap()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,7 +1,7 @@
|
|||
use crate::{
|
||||
codegen::classes::{ListType, 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};
|
||||
|
|
@ -33,7 +32,6 @@ use std::sync::{
|
|||
Arc,
|
||||
};
|
||||
use std::thread;
|
||||
use structure::{cslice::CSlice, exception::Exception, ndarray::NpArray};
|
||||
|
||||
pub mod builtin_fns;
|
||||
pub mod classes;
|
||||
|
|
@ -43,12 +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 stmt;
|
||||
pub mod structure;
|
||||
pub mod util;
|
||||
|
||||
#[cfg(test)]
|
||||
mod test;
|
||||
|
|
@ -74,16 +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 {
|
||||
|
|
@ -174,11 +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.
|
||||
|
|
@ -354,10 +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,
|
||||
|
|
@ -381,10 +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;
|
||||
|
|
@ -495,9 +471,12 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
|
|||
}
|
||||
|
||||
TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let tyctx = generator.type_context(ctx);
|
||||
let pndarray_model = PtrModel(StructModel(NpArray));
|
||||
pndarray_model.get_type(tyctx, ctx).into()
|
||||
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!(
|
||||
|
|
@ -667,20 +646,43 @@ pub fn gen_func_impl<
|
|||
..primitives
|
||||
};
|
||||
|
||||
let type_context = generator.type_context(context);
|
||||
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(type_context, 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(type_context, 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()
|
||||
|
|
|
|||
|
|
@ -1,161 +0,0 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{context::Context, types::*, values::*};
|
||||
|
||||
use super::*;
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
#[derive(Clone, Copy)]
|
||||
pub struct TypeContext<'ctx> {
|
||||
pub size_type: IntType<'ctx>,
|
||||
}
|
||||
|
||||
pub trait HasTypeContext {
|
||||
fn type_context<'ctx>(&self, ctx: &'ctx Context) -> TypeContext<'ctx>;
|
||||
}
|
||||
|
||||
impl<T: CodeGenerator + ?Sized> HasTypeContext for T {
|
||||
fn type_context<'ctx>(&self, ctx: &'ctx Context) -> TypeContext<'ctx> {
|
||||
TypeContext { size_type: self.get_size_type(ctx) }
|
||||
}
|
||||
}
|
||||
|
||||
#[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
|
||||
}
|
||||
}
|
||||
|
||||
/// A [`Model`] is a singleton object that uniquely identifies a [`BasicType`]
|
||||
/// solely from a [`CodeGenerator`] and a [`Context`].
|
||||
pub trait Model: CheckType + fmt::Debug + Clone + Copy + Default {
|
||||
type Value<'ctx>: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>>;
|
||||
type Type<'ctx>: BasicType<'ctx>;
|
||||
|
||||
/// Return the [`BasicType`] of this model.
|
||||
fn get_type<'ctx>(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type<'ctx>;
|
||||
|
||||
/// Check if a [`BasicType`] is the same type of this model.
|
||||
fn check_type<'ctx, T: BasicType<'ctx>>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
let ty = ty.as_basic_type_enum();
|
||||
self.check_type_impl(tyctx, ctx, ty.as_basic_type_enum())
|
||||
}
|
||||
|
||||
/// Create an instance from a value with [`Instance::model`] being this model.
|
||||
///
|
||||
/// Caller must make sure the type of `value` and the type of this `model` are equivalent.
|
||||
fn believe_value<'ctx>(&self, value: Self::Value<'ctx>) -> 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<'ctx, V: BasicValue<'ctx>>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'ctx Context,
|
||||
value: V,
|
||||
) -> Result<Instance<'ctx, Self>, ModelError> {
|
||||
let value = value.as_basic_value_enum();
|
||||
self.check_type(tyctx, ctx, value.get_type())
|
||||
.map_err(|err| err.under_context("the value {value:?}"))?;
|
||||
|
||||
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<'ctx>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Self> {
|
||||
let pmodel = PtrModel(*self);
|
||||
let p = ctx.builder.build_alloca(self.get_type(tyctx, ctx.ctx), name).unwrap();
|
||||
pmodel.believe_value(p)
|
||||
}
|
||||
|
||||
// Allocate an array on the stack and return its pointer.
|
||||
fn array_alloca<'ctx>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
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(tyctx, ctx.ctx), len, name).unwrap();
|
||||
pmodel.believe_value(p)
|
||||
}
|
||||
|
||||
fn var_alloca<'ctx, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&str>,
|
||||
) -> Result<Ptr<'ctx, Self>, String> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
let pmodel = PtrModel(*self);
|
||||
let p = generator.gen_var_alloc(
|
||||
ctx,
|
||||
self.get_type(tyctx, ctx.ctx).as_basic_type_enum(),
|
||||
name,
|
||||
)?;
|
||||
Ok(pmodel.believe_value(p))
|
||||
}
|
||||
|
||||
fn array_var_alloca<'ctx, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
len: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Result<Ptr<'ctx, Self>, String> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
// TODO: Remove ArraySliceValue
|
||||
let pmodel = PtrModel(*self);
|
||||
let p = generator.gen_array_var_alloc(
|
||||
ctx,
|
||||
self.get_type(tyctx, ctx.ctx).as_basic_type_enum(),
|
||||
len,
|
||||
name,
|
||||
)?;
|
||||
Ok(pmodel.believe_value(PointerValue::from(p)))
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct Instance<'ctx, M: Model> {
|
||||
/// 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<'ctx>,
|
||||
}
|
||||
|
||||
// NOTE: Must be Rust object-safe - This must be typeable for a Rust trait object.
|
||||
pub trait CheckType {
|
||||
fn check_type_impl<'ctx>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'ctx Context,
|
||||
ty: BasicTypeEnum<'ctx>,
|
||||
) -> Result<(), ModelError>;
|
||||
}
|
||||
|
|
@ -1,228 +0,0 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicTypeEnum, IntType},
|
||||
values::IntValue,
|
||||
IntPredicate,
|
||||
};
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
pub trait IntKind: fmt::Debug + Clone + Copy + Default {
|
||||
fn get_int_type<'ctx>(&self, tyctx: TypeContext<'ctx>, 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 IntKind for Bool {
|
||||
fn get_int_type<'ctx>(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
|
||||
ctx.bool_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl IntKind for Byte {
|
||||
fn get_int_type<'ctx>(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
|
||||
ctx.i8_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl IntKind for Int32 {
|
||||
fn get_int_type<'ctx>(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
|
||||
ctx.i32_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl IntKind for Int64 {
|
||||
fn get_int_type<'ctx>(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
|
||||
ctx.i64_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl IntKind for SizeT {
|
||||
fn get_int_type<'ctx>(&self, tyctx: TypeContext<'ctx>, _ctx: &'ctx Context) -> IntType<'ctx> {
|
||||
tyctx.size_type
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct IntModel<N: IntKind>(pub N);
|
||||
pub type Int<'ctx, N> = Instance<'ctx, IntModel<N>>;
|
||||
|
||||
impl<N: IntKind> CheckType for IntModel<N> {
|
||||
fn check_type_impl<'ctx>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'ctx Context,
|
||||
ty: BasicTypeEnum<'ctx>,
|
||||
) -> Result<(), ModelError> {
|
||||
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(tyctx, 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<N: IntKind> Model for IntModel<N> {
|
||||
type Value<'ctx> = IntValue<'ctx>;
|
||||
type Type<'ctx> = IntType<'ctx>;
|
||||
|
||||
#[must_use]
|
||||
fn get_type<'ctx>(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type<'ctx> {
|
||||
self.0.get_int_type(tyctx, ctx)
|
||||
}
|
||||
}
|
||||
|
||||
impl<N: IntKind> IntModel<N> {
|
||||
pub fn constant<'ctx>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'ctx Context,
|
||||
value: u64,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = self.get_type(tyctx, ctx).const_int(value, false);
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn const_0<'ctx>(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Int<'ctx, N> {
|
||||
self.constant(tyctx, ctx, 0)
|
||||
}
|
||||
|
||||
pub fn const_1<'ctx>(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Int<'ctx, N> {
|
||||
self.constant(tyctx, ctx, 1)
|
||||
}
|
||||
|
||||
pub fn s_extend_or_bit_cast<'ctx>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
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(tyctx, ctx.ctx), name)
|
||||
.unwrap();
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn truncate<'ctx>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, N> {
|
||||
let value =
|
||||
ctx.builder.build_int_truncate(value, self.get_type(tyctx, ctx.ctx), name).unwrap();
|
||||
self.believe_value(value)
|
||||
}
|
||||
}
|
||||
|
||||
impl IntModel<Bool> {
|
||||
#[must_use]
|
||||
pub fn const_false<'ctx>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, Bool> {
|
||||
self.constant(tyctx, ctx, 0)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn const_true<'ctx>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, Bool> {
|
||||
self.constant(tyctx, ctx, 1)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, N: IntKind> Int<'ctx, N> {
|
||||
pub fn s_extend_or_bit_cast<NewN: IntKind, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
name: &str,
|
||||
) -> Int<'ctx, NewN> {
|
||||
IntModel(to_int_kind).s_extend_or_bit_cast(tyctx, ctx, self.value, name)
|
||||
}
|
||||
|
||||
pub fn truncate<NewN: IntKind, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
name: &str,
|
||||
) -> Int<'ctx, NewN> {
|
||||
IntModel(to_int_kind).truncate(tyctx, ctx, self.value, name)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn add<G: CodeGenerator + ?Sized>(
|
||||
&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<G: CodeGenerator + ?Sized>(
|
||||
&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<G: CodeGenerator + ?Sized>(
|
||||
&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<G: CodeGenerator + ?Sized>(
|
||||
&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)
|
||||
}
|
||||
}
|
||||
|
|
@ -1,12 +0,0 @@
|
|||
mod core;
|
||||
mod int;
|
||||
mod ptr;
|
||||
mod slice;
|
||||
mod structure;
|
||||
pub mod util;
|
||||
|
||||
pub use core::*;
|
||||
pub use int::*;
|
||||
pub use ptr::*;
|
||||
pub use slice::*;
|
||||
pub use structure::*;
|
||||
|
|
@ -1,142 +0,0 @@
|
|||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicType, BasicTypeEnum, PointerType},
|
||||
values::{IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
|
||||
use crate::codegen::CodeGenContext;
|
||||
|
||||
use super::*;
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct PtrModel<Element>(pub Element);
|
||||
pub type Ptr<'ctx, Element> = Instance<'ctx, PtrModel<Element>>;
|
||||
|
||||
impl<Element: CheckType> CheckType for PtrModel<Element> {
|
||||
fn check_type_impl<'ctx>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'ctx Context,
|
||||
ty: BasicTypeEnum<'ctx>,
|
||||
) -> Result<(), super::ModelError> {
|
||||
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_impl(tyctx, ctx, elem_ty)
|
||||
.map_err(|err| err.under_context("a PointerType"))?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
impl<Element: Model> Model for PtrModel<Element> {
|
||||
type Value<'ctx> = PointerValue<'ctx>;
|
||||
type Type<'ctx> = PointerType<'ctx>;
|
||||
|
||||
fn get_type<'ctx>(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type<'ctx> {
|
||||
self.0.get_type(tyctx, ctx).ptr_type(AddressSpace::default())
|
||||
}
|
||||
}
|
||||
|
||||
impl<Element: Model> PtrModel<Element> {
|
||||
/// Return a ***constant*** nullptr.
|
||||
pub fn nullptr<'ctx>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'ctx Context,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
let ptr = self.get_type(tyctx, ctx).const_null();
|
||||
self.believe_value(ptr)
|
||||
}
|
||||
|
||||
pub fn transmute<'ctx>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ptr: PointerValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
let ptr = ctx.builder.build_pointer_cast(ptr, self.get_type(tyctx, ctx.ctx), name).unwrap();
|
||||
self.believe_value(ptr)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, Element: Model> Ptr<'ctx, Element> {
|
||||
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`].
|
||||
#[must_use]
|
||||
pub fn offset(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
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(tyctx, ctx.ctx, new_ptr).unwrap()
|
||||
}
|
||||
|
||||
// Load the `i`-th element (0-based) on the array with [`inkwell::builder::Builder::build_in_bounds_gep`].
|
||||
pub fn ix(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
i: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Instance<'ctx, Element> {
|
||||
self.offset(tyctx, ctx, i, name).load(tyctx, ctx, name)
|
||||
}
|
||||
|
||||
/// Load the value with [`inkwell::builder::Builder::build_load`].
|
||||
pub fn load(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Instance<'ctx, Element> {
|
||||
let value = ctx.builder.build_load(self.value, name).unwrap();
|
||||
self.model.0.check_value(tyctx, ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error.
|
||||
}
|
||||
|
||||
/// Store a value with [`inkwell::builder::Builder::build_store`].
|
||||
pub fn store(&self, ctx: &CodeGenContext<'ctx, '_>, value: Instance<'ctx, Element>) {
|
||||
ctx.builder.build_store(self.value, value.value).unwrap();
|
||||
}
|
||||
|
||||
/// Return a casted pointer of element type `NewElement` with [`inkwell::builder::Builder::build_pointer_cast`].
|
||||
pub fn transmute<NewElement: Model>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
new_model: NewElement,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, NewElement> {
|
||||
PtrModel(new_model).transmute(tyctx, 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)
|
||||
}
|
||||
}
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
/// A slice - literally just a pointer and a length value.
|
||||
///
|
||||
/// NOTE: This is NOT a [`Model`].
|
||||
pub struct ArraySlice<'ctx, Len: IntKind, Item: Model> {
|
||||
pub base: Ptr<'ctx, Item>,
|
||||
pub len: Int<'ctx, Len>,
|
||||
}
|
||||
|
||||
impl<'ctx, Len: IntKind, Item: Model> ArraySlice<'ctx, Len, Item> {
|
||||
/// Get the `idx`-nth element of this [`ArraySlice`], but doesn't do an assertion to see if `idx` is out of bounds or not.
|
||||
///
|
||||
/// Also see [`ArraySlice::ix`].
|
||||
pub fn ix_unchecked(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
idx: Int<'ctx, Len>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Item> {
|
||||
let element_ptr = unsafe {
|
||||
ctx.builder.build_in_bounds_gep(self.base.value, &[idx.value], name).unwrap()
|
||||
};
|
||||
self.base.model.check_value(tyctx, ctx.ctx, element_ptr).unwrap()
|
||||
}
|
||||
|
||||
/// Call [`ArraySlice::ix_unchecked`], but checks if `idx` is in bounds, otherwise a runtime `IndexError` will be thrown.
|
||||
pub fn ix<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
idx: Int<'ctx, Len>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Item> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let len_model = IntModel(Len::default());
|
||||
|
||||
// Assert `0 <= idx < length` and throw an Exception if `idx` is out of bounds
|
||||
let lower_bounded = ctx
|
||||
.builder
|
||||
.build_int_compare(
|
||||
inkwell::IntPredicate::SLE,
|
||||
len_model.constant(tyctx, ctx.ctx, 0).value,
|
||||
idx.value,
|
||||
"lower_bounded",
|
||||
)
|
||||
.unwrap();
|
||||
let upper_bounded = ctx
|
||||
.builder
|
||||
.build_int_compare(
|
||||
inkwell::IntPredicate::SLT,
|
||||
idx.value,
|
||||
self.len.value,
|
||||
"upper_bounded",
|
||||
)
|
||||
.unwrap();
|
||||
let bounded = ctx.builder.build_and(lower_bounded, upper_bounded, "bounded").unwrap();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
bounded,
|
||||
"0:IndexError",
|
||||
"nac3core LLVM codegen attempting to access out of bounds array index {0}. Must satisfy 0 <= index < {2}",
|
||||
[ Some(idx.value), Some(self.len.value), None],
|
||||
ctx.current_loc
|
||||
);
|
||||
|
||||
self.ix_unchecked(tyctx, ctx, idx, name)
|
||||
}
|
||||
}
|
||||
|
|
@ -1,174 +0,0 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicType, BasicTypeEnum, StructType},
|
||||
values::StructValue,
|
||||
};
|
||||
use itertools::izip;
|
||||
|
||||
use crate::codegen::CodeGenContext;
|
||||
|
||||
use super::*;
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct GepField<M: Model> {
|
||||
pub gep_index: u64,
|
||||
pub name: &'static str,
|
||||
pub model: M,
|
||||
}
|
||||
|
||||
pub trait FieldVisitor {
|
||||
type Field<M: Model + 'static>;
|
||||
|
||||
fn add<M: Model + 'static>(&mut self, name: &'static str) -> Self::Field<M>;
|
||||
}
|
||||
|
||||
pub struct GepFieldVisitor {
|
||||
gep_index_counter: u64,
|
||||
}
|
||||
|
||||
impl FieldVisitor for GepFieldVisitor {
|
||||
type Field<M: Model + 'static> = GepField<M>;
|
||||
|
||||
fn add<M: Model + 'static>(&mut self, name: &'static str) -> Self::Field<M> {
|
||||
let gep_index = self.gep_index_counter;
|
||||
self.gep_index_counter += 1;
|
||||
Self::Field { gep_index, name, model: M::default() }
|
||||
}
|
||||
}
|
||||
|
||||
struct TypeFieldVisitor<'ctx> {
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'ctx Context,
|
||||
field_types: Vec<BasicTypeEnum<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx> FieldVisitor for TypeFieldVisitor<'ctx> {
|
||||
type Field<M: Model + 'static> = ();
|
||||
|
||||
fn add<M: Model + 'static>(&mut self, _name: &'static str) -> Self::Field<M> {
|
||||
self.field_types.push(M::default().get_type(self.tyctx, self.ctx).as_basic_type_enum());
|
||||
}
|
||||
}
|
||||
|
||||
struct CheckTypeEntry {
|
||||
check_type: Box<dyn CheckType + 'static>,
|
||||
name: &'static str,
|
||||
}
|
||||
|
||||
struct CheckTypeFieldVisitor<'ctx> {
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'ctx Context,
|
||||
check_types: Vec<CheckTypeEntry>,
|
||||
}
|
||||
|
||||
impl<'ctx> FieldVisitor for CheckTypeFieldVisitor<'ctx> {
|
||||
type Field<M: Model + 'static> = ();
|
||||
|
||||
fn add<M: Model + 'static>(&mut self, name: &'static str) -> Self::Field<M> {
|
||||
self.check_types.push(CheckTypeEntry { check_type: Box::<M>::default(), name });
|
||||
}
|
||||
}
|
||||
|
||||
pub trait StructKind: fmt::Debug + Clone + Copy + Default {
|
||||
type Fields<F: FieldVisitor>;
|
||||
|
||||
fn visit_fields<F: FieldVisitor>(&self, visitor: &mut F) -> Self::Fields<F>;
|
||||
|
||||
fn fields(&self) -> Self::Fields<GepFieldVisitor> {
|
||||
self.visit_fields(&mut GepFieldVisitor { gep_index_counter: 0 })
|
||||
}
|
||||
|
||||
fn get_struct_type<'ctx>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'ctx Context,
|
||||
) -> StructType<'ctx> {
|
||||
let mut visitor = TypeFieldVisitor { tyctx, ctx, field_types: Vec::new() };
|
||||
self.visit_fields(&mut visitor);
|
||||
|
||||
ctx.struct_type(&visitor.field_types, false)
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct StructModel<S: StructKind>(pub S);
|
||||
pub type Struct<'ctx, S> = Instance<'ctx, StructModel<S>>;
|
||||
|
||||
impl<S: StructKind> CheckType for StructModel<S> {
|
||||
fn check_type_impl<'ctx>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &'ctx Context,
|
||||
ty: BasicTypeEnum<'ctx>,
|
||||
) -> 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 field_types = ty.get_field_types();
|
||||
|
||||
let check_types = {
|
||||
let mut builder = CheckTypeFieldVisitor { tyctx, ctx, check_types: Vec::new() };
|
||||
self.0.visit_fields(&mut builder);
|
||||
builder.check_types
|
||||
};
|
||||
|
||||
if check_types.len() != field_types.len() {
|
||||
return Err(ModelError(format!(
|
||||
"Expecting StructType to have {} field(s), but got {} field(s)",
|
||||
check_types.len(),
|
||||
field_types.len()
|
||||
)));
|
||||
}
|
||||
|
||||
for (field_i, (entry, field_type)) in izip!(check_types, field_types).enumerate() {
|
||||
let field_at = field_i + 1;
|
||||
|
||||
entry.check_type.check_type_impl(tyctx, ctx, field_type).map_err(|err| {
|
||||
err.under_context(format!("struct field #{field_at} '{}'", entry.name).as_str())
|
||||
})?;
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
impl<S: StructKind> Model for StructModel<S> {
|
||||
type Value<'ctx> = StructValue<'ctx>;
|
||||
type Type<'ctx> = StructType<'ctx>;
|
||||
|
||||
fn get_type<'ctx>(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type<'ctx> {
|
||||
self.0.get_struct_type(tyctx, ctx)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, S: StructKind> Ptr<'ctx, StructModel<S>> {
|
||||
pub fn gep<M, GetField>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
get_field: GetField,
|
||||
) -> Ptr<'ctx, M>
|
||||
where
|
||||
M: Model,
|
||||
GetField: FnOnce(S::Fields<GepFieldVisitor>) -> GepField<M>,
|
||||
{
|
||||
let field = get_field(self.model.0 .0.fields());
|
||||
let llvm_i32 = ctx.ctx.i32_type(); // must be i32, if its i64 then rust segfaults
|
||||
|
||||
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)
|
||||
}
|
||||
}
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
use inkwell::{types::BasicType, values::IntValue};
|
||||
|
||||
use crate::codegen::{llvm_intrinsics::call_memcpy_generic, CodeGenContext};
|
||||
|
||||
use super::*;
|
||||
|
||||
pub fn gen_model_memcpy<'ctx, M: Model>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst: Ptr<'ctx, M>,
|
||||
src: Ptr<'ctx, M>,
|
||||
num_elements: IntValue<'ctx>,
|
||||
volatile: bool,
|
||||
) {
|
||||
let bool_model = IntModel(Bool);
|
||||
|
||||
let itemsize = M::default().get_type(tyctx, ctx.ctx).size_of().unwrap();
|
||||
let totalsize =
|
||||
ctx.builder.build_int_mul(itemsize, num_elements, "model_memcpy_totalsize").unwrap();
|
||||
let is_volatile = bool_model.constant(tyctx, ctx.ctx, u64::from(volatile));
|
||||
|
||||
call_memcpy_generic(ctx, dst.value, src.value, totalsize, is_volatile.value);
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,113 +0,0 @@
|
|||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::ndarray::broadcast::{
|
||||
call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to, ShapeEntry,
|
||||
},
|
||||
model::*,
|
||||
numpy_new::util::{create_ndims, extract_ndims},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
use super::object::NDArrayObject;
|
||||
|
||||
#[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>>,
|
||||
}
|
||||
|
||||
// TODO: DOCUMENT: Behaves like `np.broadcast()`, except returns results differently.
|
||||
pub fn broadcast_all_ndarrays<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarrays: Vec<NDArrayObject<'ctx>>,
|
||||
) -> BroadcastAllResult<'ctx> {
|
||||
assert!(!ndarrays.is_empty());
|
||||
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let shape_model = StructModel(ShapeEntry);
|
||||
|
||||
// We can deduce the final ndims statically and immediately.
|
||||
// It should be `max([ ndarray.ndims for ndarray in ndarrays ])`.
|
||||
let broadcast_ndims =
|
||||
ndarrays.iter().map(|ndarray| extract_ndims(&ctx.unifier, ndarray.ndims)).max().unwrap();
|
||||
let broadcast_ndims_ty = create_ndims(&mut ctx.unifier, broadcast_ndims);
|
||||
|
||||
// NOTE: Now prepare before calling `call_nac3_ndarray_broadcast_shapes`
|
||||
|
||||
// Prepare input shape entries
|
||||
let num_shape_entries =
|
||||
sizet_model.constant(tyctx, ctx.ctx, u64::try_from(ndarrays.len()).unwrap());
|
||||
let shape_entries =
|
||||
shape_model.array_alloca(tyctx, ctx, num_shape_entries.value, "shape_entries");
|
||||
for (i, ndarray) in ndarrays.iter().enumerate() {
|
||||
let i = sizet_model.constant(tyctx, ctx.ctx, i as u64).value;
|
||||
|
||||
let this_shape = ndarray.instance.gep(ctx, |f| f.shape).load(tyctx, ctx, "this_shape");
|
||||
let this_ndims = ndarray.instance.gep(ctx, |f| f.ndims).load(tyctx, ctx, "this_ndims");
|
||||
|
||||
let shape_entry = shape_entries.offset(tyctx, ctx, i, "shape_entry");
|
||||
shape_entry.gep(ctx, |f| f.shape).store(ctx, this_shape);
|
||||
shape_entry.gep(ctx, |f| f.ndims).store(ctx, this_ndims);
|
||||
}
|
||||
|
||||
// Prepare destination
|
||||
let dst_ndims = sizet_model.constant(tyctx, ctx.ctx, broadcast_ndims);
|
||||
let dst_shape = sizet_model.array_alloca(tyctx, ctx, dst_ndims.value, "dst_shape");
|
||||
|
||||
call_nac3_ndarray_broadcast_shapes(
|
||||
generator,
|
||||
ctx,
|
||||
num_shape_entries,
|
||||
shape_entries,
|
||||
dst_ndims,
|
||||
dst_shape,
|
||||
);
|
||||
|
||||
// Now that we know about the broadcasting shape, broadcast all the inputs.
|
||||
|
||||
// Broadcast all the inputs to shape `dst_shape`
|
||||
let broadcasted_ndarrays = ndarrays
|
||||
.into_iter()
|
||||
.map(|ndarray| ndarray.broadcast_to(generator, ctx, broadcast_ndims_ty, dst_shape))
|
||||
.collect_vec();
|
||||
|
||||
BroadcastAllResult { ndims: broadcast_ndims, shape: dst_shape, ndarrays: broadcasted_ndarrays }
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Broadcast an ndarray to a target shape.
|
||||
#[must_use]
|
||||
pub fn broadcast_to<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
target_ndims_ty: Type,
|
||||
target_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) -> Self {
|
||||
// Please see comment in IRRT on how the caller should prepare `dst_ndarray`
|
||||
let dst_ndarray = NDArrayObject::alloca(
|
||||
generator,
|
||||
ctx,
|
||||
target_ndims_ty,
|
||||
self.dtype,
|
||||
"broadcast_ndarray_to_dst",
|
||||
);
|
||||
dst_ndarray.copy_shape(generator, ctx, target_shape);
|
||||
call_nac3_ndarray_broadcast_to(generator, ctx, self.instance, dst_ndarray.instance);
|
||||
dst_ndarray
|
||||
}
|
||||
}
|
||||
|
|
@ -1,217 +0,0 @@
|
|||
use inkwell::{
|
||||
types::BasicType,
|
||||
values::{BasicValue, BasicValueEnum, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
use nac3parser::ast::StrRef;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
model::*,
|
||||
numpy_new::util::{alloca_ndarray, init_ndarray_data_by_alloca, init_ndarray_shape},
|
||||
structure::ndarray::NpArray,
|
||||
util::shape::make_shape_writer,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::DefinitionId,
|
||||
typecheck::typedef::{FunSignature, Type},
|
||||
};
|
||||
|
||||
use super::util::gen_foreach_ndarray_elements;
|
||||
|
||||
/// Helper function to create an ndarray with uninitialized values
|
||||
///
|
||||
/// * `elem_ty` - The [`Type`] of the ndarray elements
|
||||
/// * `shape` - The user input shape argument
|
||||
/// * `shape_ty` - The [`Type`] of the shape argument
|
||||
/// * `name` - LLVM IR name of the returned ndarray
|
||||
fn create_empty_ndarray<'ctx, G>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
shape: BasicValueEnum<'ctx>,
|
||||
shape_ty: Type,
|
||||
name: &str,
|
||||
) -> Result<Ptr<'ctx, StructModel<NpArray>>, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
{
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let shape_writer = make_shape_writer(generator, ctx, shape, shape_ty);
|
||||
let ndims = shape_writer.len;
|
||||
|
||||
let ndarray = alloca_ndarray(generator, ctx, ndims, name);
|
||||
init_ndarray_shape(generator, ctx, ndarray, &shape_writer)?;
|
||||
|
||||
let itemsize = ctx.get_llvm_type(generator, elem_ty).size_of().unwrap();
|
||||
let itemsize = sizet_model.check_value(tyctx, ctx.ctx, itemsize).unwrap();
|
||||
ndarray.gep(ctx, |f| f.itemsize).store(ctx, itemsize);
|
||||
|
||||
// Needs `itemsize` and `shape` initialized
|
||||
init_ndarray_data_by_alloca(generator, ctx, ndarray);
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
/// Helper function to create an ndarray full of a value.
|
||||
///
|
||||
/// * `elem_ty` - The [`Type`] of the ndarray elements and the fill value
|
||||
/// * `shape` - The user input shape argument
|
||||
/// * `shape_ty` - The [`Type`] of the shape argument
|
||||
/// * `fill_value` - The user specified fill value
|
||||
/// * `name` - LLVM IR name of the returned ndarray
|
||||
fn create_full_ndarray<'ctx, G>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
shape: BasicValueEnum<'ctx>,
|
||||
shape_ty: Type,
|
||||
fill_value: BasicValueEnum<'ctx>,
|
||||
name: &str,
|
||||
) -> Result<Ptr<'ctx, StructModel<NpArray>>, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
{
|
||||
let pndarray = create_empty_ndarray(generator, ctx, elem_ty, shape, shape_ty, name)?;
|
||||
gen_foreach_ndarray_elements(
|
||||
generator,
|
||||
ctx,
|
||||
pndarray,
|
||||
|_generator, ctx, _hooks, _i, pelement| {
|
||||
// Cannot use Model here, fill_value's type is not statically known.
|
||||
let pfill_value_ty = fill_value.get_type().ptr_type(AddressSpace::default());
|
||||
let pelement =
|
||||
ctx.builder.build_pointer_cast(pelement.value, pfill_value_ty, "pelement").unwrap();
|
||||
ctx.builder.build_store(pelement, fill_value).unwrap();
|
||||
Ok(())
|
||||
},
|
||||
)?;
|
||||
Ok(pndarray)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.empty`.
|
||||
pub fn gen_ndarray_empty<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse arguments
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape = args[0].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
|
||||
|
||||
// Implementation
|
||||
let ndarray_ptr = create_empty_ndarray(
|
||||
generator,
|
||||
context,
|
||||
context.primitives.float,
|
||||
shape,
|
||||
shape_ty,
|
||||
"ndarray",
|
||||
)?;
|
||||
Ok(ndarray_ptr.value)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.zeros`.
|
||||
pub fn gen_ndarray_zeros<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse arguments
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape = args[0].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
|
||||
|
||||
// Implementation
|
||||
// NOTE: Currently nac3's `np.zeros` is always `float64`.
|
||||
let float64_ty = context.primitives.float;
|
||||
let float64_llvm_type = context.get_llvm_type(generator, float64_ty).into_float_type();
|
||||
|
||||
let ndarray_ptr = create_full_ndarray(
|
||||
generator,
|
||||
context,
|
||||
float64_ty, // `elem_ty` is always `float64`
|
||||
shape,
|
||||
shape_ty,
|
||||
float64_llvm_type.const_zero().as_basic_value_enum(),
|
||||
"ndarray",
|
||||
)?;
|
||||
Ok(ndarray_ptr.value)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.ones`.
|
||||
pub fn gen_ndarray_ones<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse arguments
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape = args[0].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
|
||||
|
||||
// Implementation
|
||||
// NOTE: Currently nac3's `np.ones` is always `float64`.
|
||||
let float64_ty = context.primitives.float;
|
||||
let float64_llvm_type = context.get_llvm_type(generator, float64_ty).into_float_type();
|
||||
|
||||
let ndarray_ptr = create_full_ndarray(
|
||||
generator,
|
||||
context,
|
||||
float64_ty, // `elem_ty` is always `float64`
|
||||
shape,
|
||||
shape_ty,
|
||||
float64_llvm_type.const_float(1.0).as_basic_value_enum(),
|
||||
"ndarray",
|
||||
)?;
|
||||
Ok(ndarray_ptr.value)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.full`.
|
||||
pub fn gen_ndarray_full<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 2);
|
||||
|
||||
// Parse argument #1 shape
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape_arg = args[0].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
|
||||
|
||||
// Parse argument #2 fill_value
|
||||
let fill_value_ty = fun.0.args[1].ty;
|
||||
let fill_value_arg =
|
||||
args[1].1.clone().to_basic_value_enum(context, generator, fill_value_ty)?;
|
||||
|
||||
// Implementation
|
||||
let ndarray_ptr = create_full_ndarray(
|
||||
generator,
|
||||
context,
|
||||
fill_value_ty,
|
||||
shape_arg,
|
||||
shape_ty,
|
||||
fill_value_arg,
|
||||
"ndarray",
|
||||
)?;
|
||||
Ok(ndarray_ptr.value)
|
||||
}
|
||||
|
|
@ -1,76 +0,0 @@
|
|||
use crate::{
|
||||
codegen::{
|
||||
irrt::ndarray::indexing::{call_nac3_ndarray_index, RustNDIndex},
|
||||
model::*,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::{Type, Unifier},
|
||||
};
|
||||
|
||||
use super::{
|
||||
object::{NDArrayObject, ScalarObject, ScalarOrNDArray},
|
||||
util::{create_ndims, extract_ndims},
|
||||
};
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
pub fn deduce_ndims_after_indexing_with(
|
||||
&self,
|
||||
unifier: &mut Unifier,
|
||||
indexes: &[RustNDIndex<'ctx>],
|
||||
) -> Type {
|
||||
let ndims = extract_ndims(unifier, self.ndims);
|
||||
let new_ndims = RustNDIndex::deduce_ndims_after_indexing(indexes, ndims);
|
||||
create_ndims(unifier, new_ndims)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn index_always_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
indexes: &[RustNDIndex<'ctx>],
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
let dst_ndims = self.deduce_ndims_after_indexing_with(&mut ctx.unifier, indexes);
|
||||
let dst_ndarray = NDArrayObject::alloca(generator, ctx, dst_ndims, self.dtype, name);
|
||||
|
||||
let (num_indexes, indexes) = RustNDIndex::alloca_ndindexes(tyctx, ctx, indexes);
|
||||
call_nac3_ndarray_index(
|
||||
generator,
|
||||
ctx,
|
||||
num_indexes,
|
||||
indexes,
|
||||
self.instance,
|
||||
dst_ndarray.instance,
|
||||
);
|
||||
|
||||
dst_ndarray
|
||||
}
|
||||
|
||||
pub fn index<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
indexes: &[RustNDIndex<'ctx>],
|
||||
name: &str,
|
||||
) -> ScalarOrNDArray<'ctx> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let subndarray = self.index_always_ndarray(generator, ctx, indexes, name);
|
||||
if subndarray.is_unsized(&ctx.unifier) {
|
||||
// TODO: This actually never fails, don't use the `checked_` version.
|
||||
let value = subndarray.checked_get_nth_element(
|
||||
generator,
|
||||
ctx,
|
||||
sizet_model.const_0(tyctx, ctx.ctx),
|
||||
name,
|
||||
);
|
||||
ScalarOrNDArray::Scalar(ScalarObject { dtype: self.dtype, value })
|
||||
} else {
|
||||
ScalarOrNDArray::NDArray(subndarray)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,6 +0,0 @@
|
|||
pub mod broadcast;
|
||||
pub mod factory;
|
||||
pub mod indexing;
|
||||
pub mod object;
|
||||
pub mod util;
|
||||
pub mod view;
|
||||
|
|
@ -1,69 +0,0 @@
|
|||
use inkwell::values::{BasicValue, BasicValueEnum};
|
||||
|
||||
use crate::{
|
||||
codegen::{model::*, structure::ndarray::NpArray, CodeGenContext},
|
||||
toplevel::numpy::unpack_ndarray_var_tys,
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
/// An LLVM ndarray instance with its typechecker [`Type`]s.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDArrayObject<'ctx> {
|
||||
pub dtype: Type,
|
||||
pub ndims: Type,
|
||||
pub instance: Ptr<'ctx, StructModel<NpArray>>,
|
||||
}
|
||||
|
||||
/// An LLVM numpy scalar with its [`Type`].
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct ScalarObject<'ctx> {
|
||||
pub dtype: Type,
|
||||
pub value: BasicValueEnum<'ctx>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub enum ScalarOrNDArray<'ctx> {
|
||||
Scalar(ScalarObject<'ctx>),
|
||||
NDArray(NDArrayObject<'ctx>),
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarOrNDArray<'ctx> {
|
||||
/// Get the underlying [`BasicValueEnum<'ctx>`] of this [`ScalarOrNDArray`].
|
||||
#[must_use]
|
||||
pub fn to_basic_value_enum(self) -> BasicValueEnum<'ctx> {
|
||||
match self {
|
||||
ScalarOrNDArray::Scalar(scalar) => scalar.value,
|
||||
ScalarOrNDArray::NDArray(ndarray) => ndarray.instance.value.as_basic_value_enum(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<ScalarOrNDArray<'ctx>> for BasicValueEnum<'ctx> {
|
||||
fn from(input: ScalarOrNDArray<'ctx>) -> BasicValueEnum<'ctx> {
|
||||
input.to_basic_value_enum()
|
||||
}
|
||||
}
|
||||
|
||||
/// Split an [`BasicValueEnum<'ctx>`] into a [`ScalarOrNDArray`] depending
|
||||
/// on its [`Type`].
|
||||
pub fn split_scalar_or_ndarray<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
input: BasicValueEnum<'ctx>,
|
||||
input_ty: Type,
|
||||
) -> ScalarOrNDArray<'ctx> {
|
||||
let pndarray_model = PtrModel(StructModel(NpArray));
|
||||
|
||||
let input_ty_enum = ctx.unifier.get_ty(input_ty);
|
||||
match &*input_ty_enum {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let value = pndarray_model.check_value(tyctx, ctx.ctx, input).unwrap();
|
||||
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, input_ty);
|
||||
|
||||
ScalarOrNDArray::NDArray(NDArrayObject { dtype, ndims, instance: value })
|
||||
}
|
||||
_ => ScalarOrNDArray::Scalar(ScalarObject { dtype: input_ty, value: input }),
|
||||
}
|
||||
}
|
||||
|
|
@ -1,328 +0,0 @@
|
|||
use inkwell::{
|
||||
types::BasicType,
|
||||
values::{BasicValueEnum, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
use util::gen_model_memcpy;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::ndarray::basic::{
|
||||
call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
|
||||
call_nac3_ndarray_is_c_contiguous, call_nac3_ndarray_nbytes,
|
||||
call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
|
||||
call_nac3_ndarray_util_assert_shape_no_negative,
|
||||
},
|
||||
model::*,
|
||||
stmt::BreakContinueHooks,
|
||||
structure::ndarray::NpArray,
|
||||
util::{array_writer::ArrayWriter, control::gen_model_for},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
symbol_resolver::SymbolValue,
|
||||
typecheck::typedef::{Type, TypeEnum, Unifier},
|
||||
};
|
||||
|
||||
use super::object::{NDArrayObject, ScalarOrNDArray};
|
||||
|
||||
/// Extract an ndarray's `ndims` [type][`Type`] in `u64`. Panic if not possible.
|
||||
#[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)
|
||||
}
|
||||
|
||||
/// Allocate an ndarray on the stack given its `ndims`.
|
||||
///
|
||||
/// `shape` and `strides` will be automatically allocated on the stack.
|
||||
///
|
||||
/// The returned ndarray's content will be:
|
||||
/// - `data`: `nullptr`
|
||||
/// - `itemsize`: **uninitialized** value
|
||||
/// - `ndims`: initialized value, set to the input `ndims`
|
||||
/// - `shape`: initialized pointer to an allocated stack with **uninitialized** values
|
||||
/// - `strides`: initialized pointer to an allocated stack with **uninitialized** values
|
||||
pub fn alloca_ndarray<'ctx, G>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndims: Int<'ctx, SizeT>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, StructModel<NpArray>>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
{
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let ndarray_model = StructModel(NpArray);
|
||||
let ndarray_data_model = PtrModel(IntModel(Byte));
|
||||
|
||||
// Setup ndarray
|
||||
let ndarray_ptr = ndarray_model.alloca(tyctx, ctx, name);
|
||||
let shape = sizet_model.array_alloca(tyctx, ctx, ndims.value, "shape");
|
||||
let strides = sizet_model.array_alloca(tyctx, ctx, ndims.value, "strides");
|
||||
|
||||
ndarray_ptr.gep(ctx, |f| f.data).store(ctx, ndarray_data_model.nullptr(tyctx, ctx.ctx));
|
||||
ndarray_ptr.gep(ctx, |f| f.ndims).store(ctx, ndims);
|
||||
ndarray_ptr.gep(ctx, |f| f.shape).store(ctx, shape);
|
||||
ndarray_ptr.gep(ctx, |f| f.strides).store(ctx, strides);
|
||||
|
||||
ndarray_ptr
|
||||
}
|
||||
|
||||
/// Initialize an ndarray's `shape` and asserts on.
|
||||
/// `shape`'s values and prohibit illegal inputs like negative dimensions.
|
||||
pub fn init_ndarray_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
shape_writer: &ArrayWriter<'ctx, G, SizeT, IntModel<SizeT>>,
|
||||
) -> Result<(), String> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let shape = pndarray.gep(ctx, |f| f.shape).load(tyctx, ctx, "shape");
|
||||
(shape_writer.write)(generator, ctx, shape)?;
|
||||
call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, shape_writer.len, shape);
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 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 init_ndarray_data_by_alloca<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let ndarray_data_model = IntModel(Byte);
|
||||
|
||||
let nbytes = call_nac3_ndarray_nbytes(generator, ctx, pndarray);
|
||||
let data = ndarray_data_model.array_alloca(tyctx, ctx, nbytes.value, "data");
|
||||
pndarray.gep(ctx, |f| f.data).store(ctx, data);
|
||||
call_nac3_ndarray_set_strides_by_shape(generator, ctx, pndarray);
|
||||
}
|
||||
|
||||
/// Iterate through all elements in an ndarray.
|
||||
///
|
||||
/// `body` is given the index of an element and an opaque pointer (as an `uint8_t*`, you might want to cast it) to the element.
|
||||
///
|
||||
/// Short-circuiting is possible with the given [`BreakContinueHooks`].
|
||||
pub fn gen_foreach_ndarray_elements<'ctx, G, F>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
body: F,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
F: Fn(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, '_>,
|
||||
BreakContinueHooks<'ctx>,
|
||||
Int<'ctx, SizeT>,
|
||||
Ptr<'ctx, IntModel<Byte>>,
|
||||
) -> Result<(), String>,
|
||||
{
|
||||
// TODO: Make this more efficient - use a special NDArray iterator?
|
||||
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let size = call_nac3_ndarray_size(generator, ctx, pndarray);
|
||||
|
||||
gen_model_for(
|
||||
generator,
|
||||
ctx,
|
||||
sizet_model.const_0(tyctx, ctx.ctx),
|
||||
size,
|
||||
sizet_model.const_1(tyctx, ctx.ctx),
|
||||
|generator, ctx, hooks, index| {
|
||||
let pelement = call_nac3_ndarray_get_nth_pelement(generator, ctx, pndarray, index);
|
||||
body(generator, ctx, hooks, index, pelement)
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarOrNDArray<'ctx> {
|
||||
/// Convert `input` to an ndarray - behaves like `np.asarray`.
|
||||
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) => {
|
||||
let tyctx = generator.type_context(ctx.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(scalar.value.get_type(), "as_ndarray_scalar").unwrap();
|
||||
ctx.builder.build_store(data, scalar.value).unwrap();
|
||||
let data = pbyte_model.transmute(tyctx, ctx, data, "data");
|
||||
|
||||
let ndims_ty = create_ndims(&mut ctx.unifier, 0);
|
||||
let ndarray = NDArrayObject::alloca(
|
||||
generator,
|
||||
ctx,
|
||||
ndims_ty,
|
||||
scalar.dtype,
|
||||
"scalar_as_ndarray",
|
||||
);
|
||||
ndarray.instance.gep(ctx, |f| f.data).store(ctx, data);
|
||||
|
||||
// No need to initialize/setup strides or shapes - because `ndims` is 0.
|
||||
// So we only have to set `data`, `itemsize`, and `ndims = 0`.
|
||||
|
||||
ndarray
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
pub fn alloca<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndims: Type,
|
||||
dtype: Type,
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let ndims_int = sizet_model.constant(tyctx, ctx.ctx, extract_ndims(&ctx.unifier, ndims));
|
||||
let instance = alloca_ndarray(generator, ctx, ndims_int, name);
|
||||
|
||||
// Set itemsize
|
||||
let dtype_ty = ctx.get_llvm_type(generator, dtype);
|
||||
let itemsize = dtype_ty.size_of().unwrap();
|
||||
let itemsize = sizet_model.s_extend_or_bit_cast(tyctx, ctx, itemsize, "itemsize");
|
||||
instance.gep(ctx, |f| f.itemsize).store(ctx, itemsize);
|
||||
|
||||
NDArrayObject { dtype, ndims, instance }
|
||||
}
|
||||
|
||||
pub fn copy_shape<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let self_shape = self.instance.gep(ctx, |f| f.shape).load(tyctx, ctx, "self_shape");
|
||||
let ndims_int =
|
||||
sizet_model.constant(tyctx, ctx.ctx, extract_ndims(&ctx.unifier, self.ndims));
|
||||
gen_model_memcpy(tyctx, ctx, self_shape, src_shape, ndims_int.value, false);
|
||||
}
|
||||
|
||||
pub fn copy_shape_from<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: NDArrayObject<'ctx>,
|
||||
) {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let src_shape = src_ndarray.instance.gep(ctx, |f| f.shape).load(tyctx, ctx, "src_shape");
|
||||
self.copy_shape(generator, ctx, src_shape);
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
pub fn checked_get_nth_pelement<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
i: 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, i);
|
||||
ctx.builder
|
||||
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), name)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
pub fn checked_get_nth_element<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
i: Int<'ctx, SizeT>,
|
||||
name: &str,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
let pelement = self.checked_get_nth_pelement(generator, ctx, i, "pelement");
|
||||
ctx.builder.build_load(pelement, name).unwrap()
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn is_unsized(&self, unifier: &Unifier) -> bool {
|
||||
extract_ndims(unifier, self.ndims) == 0
|
||||
}
|
||||
|
||||
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)
|
||||
}
|
||||
|
||||
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)
|
||||
}
|
||||
|
||||
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)
|
||||
}
|
||||
|
||||
pub fn alloca_owned_data<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
init_ndarray_data_by_alloca(generator, ctx, self.instance);
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,114 +0,0 @@
|
|||
use inkwell::values::PointerValue;
|
||||
use nac3parser::ast::StrRef;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::ndarray::reshape::call_nac3_ndarray_resolve_and_check_new_shape,
|
||||
model::*,
|
||||
numpy_new::{object::split_scalar_or_ndarray, util::extract_ndims},
|
||||
util::shape::make_shape_writer,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{numpy::unpack_ndarray_var_tys, DefinitionId},
|
||||
typecheck::typedef::{FunSignature, Type},
|
||||
};
|
||||
|
||||
use super::object::NDArrayObject;
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
#[must_use]
|
||||
pub fn reshape_or_copy<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
new_ndims: Type,
|
||||
new_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) -> Self {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
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, new_ndims, self.dtype, "reshaped_ndarray");
|
||||
dst_ndarray.copy_shape(generator, ctx, new_shape);
|
||||
dst_ndarray.update_strides_by_shape(generator, ctx);
|
||||
|
||||
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
|
||||
.instance
|
||||
.gep(ctx, |f| f.data)
|
||||
.store(ctx, dst_ndarray.instance.gep(ctx, |f| f.data).load(tyctx, ctx, "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.alloca_owned_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
|
||||
}
|
||||
}
|
||||
|
||||
/// 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<PointerValue<'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_arg = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?;
|
||||
|
||||
// Parse argument #2 shape
|
||||
let shape_ty = fun.0.args[1].ty;
|
||||
let shape_arg = args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
|
||||
// Define models
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Extract reshaped_ndims
|
||||
let (_, reshaped_ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
|
||||
let reshaped_ndims_int = extract_ndims(&ctx.unifier, reshaped_ndims);
|
||||
|
||||
// Process `input`
|
||||
let ndarray =
|
||||
split_scalar_or_ndarray(tyctx, ctx, input_arg, input_ty).as_ndarray(generator, ctx);
|
||||
|
||||
// Process the shape input from user and resolve negative indices
|
||||
let new_shape = make_shape_writer(generator, ctx, shape_arg, shape_ty).alloca_array_and_write(
|
||||
generator,
|
||||
ctx,
|
||||
"new_shape",
|
||||
)?;
|
||||
let size = ndarray.size(generator, ctx);
|
||||
call_nac3_ndarray_resolve_and_check_new_shape(
|
||||
generator,
|
||||
ctx,
|
||||
size,
|
||||
sizet_model.constant(tyctx, ctx.ctx, reshaped_ndims_int),
|
||||
new_shape,
|
||||
);
|
||||
|
||||
// Reshape
|
||||
let reshaped_ndarray = ndarray.reshape_or_copy(generator, ctx, reshaped_ndims, new_shape);
|
||||
|
||||
Ok(reshaped_ndarray.instance.value)
|
||||
}
|
||||
|
|
@ -1,11 +1,8 @@
|
|||
use super::model::*;
|
||||
use super::structure::cslice::CSlice;
|
||||
use super::{
|
||||
super::symbol_resolver::ValueEnum,
|
||||
expr::destructure_range,
|
||||
irrt::{handle_slice_indices, list_slice_assignment},
|
||||
structure::exception::Exception,
|
||||
CodeGenContext, CodeGenerator, Int32, IntModel, Ptr, StructModel,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
use crate::{
|
||||
codegen::{
|
||||
|
|
@ -209,7 +206,6 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
|
|||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
target: &Expr<Option<Type>>,
|
||||
value: ValueEnum<'ctx>,
|
||||
value_ty: Type,
|
||||
) -> Result<(), String> {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
|
|
@ -226,7 +222,7 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
|
|||
.builder
|
||||
.build_extract_value(v, u32::try_from(i).unwrap(), "struct_elem")
|
||||
.unwrap();
|
||||
generator.gen_assign(ctx, elt, v.into(), value_ty)?;
|
||||
generator.gen_assign(ctx, elt, v.into())?;
|
||||
}
|
||||
}
|
||||
ExprKind::Subscript { value: ls, slice, .. }
|
||||
|
|
@ -435,7 +431,7 @@ pub fn gen_for<G: CodeGenerator>(
|
|||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
let val = ctx.build_gep_and_load(arr_ptr, &[index], Some("val"));
|
||||
generator.gen_assign(ctx, target, val.into(), ctx.primitives.int32)?;
|
||||
generator.gen_assign(ctx, target, val.into())?;
|
||||
generator.gen_block(ctx, body.iter())?;
|
||||
}
|
||||
|
||||
|
|
@ -498,7 +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,
|
||||
|
|
@ -509,24 +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));
|
||||
|
|
@ -583,7 +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,
|
||||
|
|
@ -594,7 +582,7 @@ where
|
|||
BodyFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks<'ctx>,
|
||||
BreakContinueHooks,
|
||||
IntValue<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
{
|
||||
|
|
@ -603,7 +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();
|
||||
|
|
@ -655,11 +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),
|
||||
|
|
@ -671,19 +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)?;
|
||||
|
||||
|
|
@ -741,10 +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();
|
||||
|
|
@ -1134,37 +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 type_context = generator.type_context(ctx.ctx);
|
||||
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(type_context, ctx.ctx, filename).unwrap();
|
||||
pexn.gep(ctx, |f| f.filename).store(ctx, filename);
|
||||
|
||||
let row = i32_model.constant(type_context, ctx.ctx, loc.row as u64);
|
||||
pexn.gep(ctx, |f| f.line).store(ctx, row);
|
||||
|
||||
let column = i32_model.constant(type_context, ctx.ctx, loc.column as u64);
|
||||
pexn.gep(ctx, |f| f.column).store(ctx, 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.gep(ctx, |f| f.function).store(ctx, 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();
|
||||
}
|
||||
|
||||
|
|
@ -1586,16 +1575,14 @@ pub fn gen_stmt<G: CodeGenerator>(
|
|||
}
|
||||
StmtKind::AnnAssign { target, value, .. } => {
|
||||
if let Some(value) = value {
|
||||
let value_ty = value.custom.unwrap();
|
||||
let Some(value) = generator.gen_expr(ctx, value)? else { return Ok(()) };
|
||||
generator.gen_assign(ctx, target, value, value_ty)?;
|
||||
generator.gen_assign(ctx, target, value)?;
|
||||
}
|
||||
}
|
||||
StmtKind::Assign { targets, value, .. } => {
|
||||
let value_ty = value.custom.unwrap();
|
||||
let Some(value) = generator.gen_expr(ctx, value)? else { return Ok(()) };
|
||||
for target in targets {
|
||||
generator.gen_assign(ctx, target, value.clone(), value_ty)?;
|
||||
generator.gen_assign(ctx, target, value.clone())?;
|
||||
}
|
||||
}
|
||||
StmtKind::Continue { .. } => {
|
||||
|
|
@ -1609,7 +1596,6 @@ 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_ty = value.custom.unwrap();
|
||||
let value = gen_binop_expr(
|
||||
generator,
|
||||
ctx,
|
||||
|
|
@ -1618,7 +1604,7 @@ pub fn gen_stmt<G: CodeGenerator>(
|
|||
value,
|
||||
stmt.location,
|
||||
)?;
|
||||
generator.gen_assign(ctx, target, value.unwrap(), value_ty)?;
|
||||
generator.gen_assign(ctx, target, value.unwrap())?;
|
||||
}
|
||||
StmtKind::Try { .. } => gen_try(generator, ctx, stmt)?,
|
||||
StmtKind::Raise { exc, .. } => {
|
||||
|
|
@ -1628,43 +1614,30 @@ pub fn gen_stmt<G: CodeGenerator>(
|
|||
} else {
|
||||
return Ok(());
|
||||
};
|
||||
|
||||
let type_context = generator.type_context(ctx.ctx);
|
||||
let pexn_model = PtrModel(StructModel(Exception));
|
||||
let exn = pexn_model.check_value(type_context, 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 type_context = generator.type_context(ctx.ctx);
|
||||
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(type_context, 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(type_context, 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],
|
||||
|
|
|
|||
|
|
@ -1,43 +0,0 @@
|
|||
use crate::codegen::{model::*, CodeGenContext};
|
||||
|
||||
/// Fields of [`CSlice<'ctx>`].
|
||||
pub struct CSliceFields<F: FieldVisitor> {
|
||||
/// Pointer to the data.
|
||||
pub base: F::Field<PtrModel<IntModel<Byte>>>,
|
||||
/// Number of bytes of the data.
|
||||
pub len: F::Field<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 StructKind for CSlice {
|
||||
type Fields<F: FieldVisitor> = CSliceFields<F>;
|
||||
|
||||
fn visit_fields<F: FieldVisitor>(&self, visitor: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields { base: visitor.add("base"), len: visitor.add("len") }
|
||||
}
|
||||
}
|
||||
|
||||
impl StructModel<CSlice> {
|
||||
/// Create a [`CSlice`].
|
||||
///
|
||||
/// `base` and `len` must be LLVM global constants.
|
||||
pub fn create_const<'ctx>(
|
||||
&self,
|
||||
type_context: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
base: Ptr<'ctx, IntModel<Byte>>,
|
||||
len: Int<'ctx, SizeT>,
|
||||
) -> Struct<'ctx, CSlice> {
|
||||
let value = self
|
||||
.0
|
||||
.get_struct_type(type_context, ctx.ctx)
|
||||
.const_named_struct(&[base.value.into(), len.value.into()]);
|
||||
self.believe_value(value)
|
||||
}
|
||||
}
|
||||
|
|
@ -1,57 +0,0 @@
|
|||
use crate::codegen::model::*;
|
||||
|
||||
use super::cslice::CSlice;
|
||||
|
||||
/// The LLVM int type of an Exception ID.
|
||||
pub type ExceptionId = Int32;
|
||||
|
||||
/// Fields of [`Exception<'ctx>`]
|
||||
///
|
||||
/// The definition came from `pub struct Exception<'a>` in
|
||||
/// <https://github.com/m-labs/artiq/blob/master/artiq/firmware/libeh/eh_artiq.rs>.
|
||||
pub struct ExceptionFields<F: FieldVisitor> {
|
||||
/// nac3core's ID of the exception
|
||||
pub id: F::Field<IntModel<ExceptionId>>,
|
||||
/// The name of the file this `Exception` was raised in.
|
||||
pub filename: F::Field<StructModel<CSlice>>,
|
||||
/// The line number in the file this `Exception` was raised in.
|
||||
pub line: F::Field<IntModel<Int32>>,
|
||||
/// The column number in the file this `Exception` was raised in.
|
||||
pub column: F::Field<IntModel<Int32>>,
|
||||
/// The name of the Python function this `Exception` was raised in.
|
||||
pub function: F::Field<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::Field<StructModel<CSlice>>,
|
||||
pub params: [F::Field<IntModel<Int64>>; 3],
|
||||
}
|
||||
|
||||
/// nac3core & ARTIQ's Exception
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Exception;
|
||||
|
||||
impl StructKind for Exception {
|
||||
type Fields<F: FieldVisitor> = ExceptionFields<F>;
|
||||
|
||||
fn visit_fields<F: FieldVisitor>(&self, visitor: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
id: visitor.add("id"),
|
||||
filename: visitor.add("filename"),
|
||||
line: visitor.add("line"),
|
||||
column: visitor.add("column"),
|
||||
function: visitor.add("function"),
|
||||
msg: visitor.add("msg"),
|
||||
params: [visitor.add("params[0]"), visitor.add("params[1]"), visitor.add("params[2]")],
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
pub mod cslice;
|
||||
pub mod exception;
|
||||
pub mod ndarray;
|
||||
|
|
@ -1,27 +0,0 @@
|
|||
use crate::codegen::*;
|
||||
|
||||
pub struct NpArrayFields<F: FieldVisitor> {
|
||||
pub data: F::Field<PtrModel<IntModel<Byte>>>,
|
||||
pub itemsize: F::Field<IntModel<SizeT>>,
|
||||
pub ndims: F::Field<IntModel<SizeT>>,
|
||||
pub shape: F::Field<PtrModel<IntModel<SizeT>>>,
|
||||
pub strides: F::Field<PtrModel<IntModel<SizeT>>>,
|
||||
}
|
||||
|
||||
// TODO: Rename to `NDArray` when the old NDArray is removed.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct NpArray;
|
||||
|
||||
impl StructKind for NpArray {
|
||||
type Fields<F: FieldVisitor> = NpArrayFields<F>;
|
||||
|
||||
fn visit_fields<F: FieldVisitor>(&self, visitor: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
data: visitor.add("data"),
|
||||
itemsize: visitor.add("itemsize"),
|
||||
ndims: visitor.add("ndims"),
|
||||
shape: visitor.add("shape"),
|
||||
strides: visitor.add("strides"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -189,8 +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 {
|
||||
|
|
@ -370,8 +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 {
|
||||
|
|
|
|||
|
|
@ -1,34 +0,0 @@
|
|||
use crate::codegen::{model::*, CodeGenContext, CodeGenerator};
|
||||
|
||||
/// A closure containing details on how to write to/initialize an array.
|
||||
#[allow(clippy::type_complexity)]
|
||||
pub struct ArrayWriter<'ctx, G: CodeGenerator + ?Sized, Len: IntKind, Item: Model> {
|
||||
/// Number of items to write
|
||||
pub len: Int<'ctx, Len>,
|
||||
/// Implementation to write to an array given its base pointer.
|
||||
pub write: Box<
|
||||
dyn Fn(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, '_>,
|
||||
Ptr<'ctx, Item>, // Base pointer
|
||||
) -> Result<(), String>
|
||||
+ 'ctx,
|
||||
>,
|
||||
}
|
||||
|
||||
impl<'ctx, G: CodeGenerator + ?Sized, Len: IntKind, Item: Model> ArrayWriter<'ctx, G, Len, Item> {
|
||||
pub fn alloca_array_and_write(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Result<Ptr<'ctx, Item>, String> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
let item_model = Item::default();
|
||||
|
||||
let item_array = item_model.array_alloca(tyctx, ctx, self.len.value, name);
|
||||
(self.write)(generator, ctx, item_array)?;
|
||||
Ok(item_array)
|
||||
}
|
||||
}
|
||||
|
|
@ -1,42 +0,0 @@
|
|||
use crate::codegen::{
|
||||
model::*,
|
||||
stmt::{gen_for_callback_incrementing, BreakContinueHooks},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
// TODO: Document
|
||||
// TODO: Rename function
|
||||
/// Only allows positive steps
|
||||
pub fn gen_model_for<'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,
|
||||
{
|
||||
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,
|
||||
)
|
||||
}
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
pub mod array_writer;
|
||||
pub mod control;
|
||||
pub mod shape;
|
||||
|
|
@ -1,127 +0,0 @@
|
|||
use inkwell::values::BasicValueEnum;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
classes::{ListValue, UntypedArrayLikeAccessor},
|
||||
model::*,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::{array_writer::ArrayWriter, control::gen_model_for};
|
||||
|
||||
// TODO: Generalize to complex iterables under a common interface
|
||||
/// Create an [`ArrayWriter`] from a NumPy-like `shape` argument input.
|
||||
/// * `shape` - The `shape` parameter.
|
||||
/// * `shape_ty` - The element type of the `NDArray`.
|
||||
///
|
||||
/// The `shape` 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])`
|
||||
///
|
||||
/// The `int32` values will be sign-extended to `SizeT`
|
||||
pub fn make_shape_writer<'ctx, G>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
shape: BasicValueEnum<'ctx>,
|
||||
shape_ty: Type,
|
||||
) -> ArrayWriter<'ctx, G, SizeT, IntModel<SizeT>>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
{
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
match &*ctx.unifier.get_ty(shape_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])`
|
||||
|
||||
// TODO: Remove ListValue with Model
|
||||
|
||||
let shape = ListValue::from_ptr_val(shape.into_pointer_value(), tyctx.size_type, None);
|
||||
let len =
|
||||
sizet_model.check_value(tyctx, ctx.ctx, shape.load_size(ctx, Some("len"))).unwrap();
|
||||
|
||||
ArrayWriter {
|
||||
len,
|
||||
write: Box::new(move |generator, ctx, dst_array| {
|
||||
gen_model_for(
|
||||
generator,
|
||||
ctx,
|
||||
sizet_model.constant(tyctx, ctx.ctx, 0),
|
||||
len,
|
||||
sizet_model.constant(tyctx, ctx.ctx, 1),
|
||||
|generator, ctx, _hooks, i| {
|
||||
let dim =
|
||||
shape.data().get(ctx, generator, &i.value, None).into_int_value();
|
||||
let dim = sizet_model.s_extend_or_bit_cast(tyctx, ctx, dim, "");
|
||||
|
||||
dst_array.offset(tyctx, ctx, i.value, "pdim").store(ctx, dim);
|
||||
Ok(())
|
||||
},
|
||||
)
|
||||
}),
|
||||
}
|
||||
}
|
||||
TypeEnum::TTuple { ty: tuple_types } => {
|
||||
// 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
|
||||
|
||||
let ndims = tuple_types.len();
|
||||
|
||||
// A tuple has to be a StructValue
|
||||
// Read [`codegen::expr::gen_expr`] to see how `nac3core` translates a Python tuple into LLVM.
|
||||
let shape = shape.into_struct_value();
|
||||
|
||||
ArrayWriter {
|
||||
len: sizet_model.constant(tyctx, ctx.ctx, ndims as u64),
|
||||
write: Box::new(move |_generator, ctx, dst_array| {
|
||||
for axis in 0..ndims {
|
||||
let dim = ctx
|
||||
.builder
|
||||
.build_extract_value(shape, axis as u32, format!("dim{axis}").as_str())
|
||||
.unwrap()
|
||||
.into_int_value();
|
||||
let dim = sizet_model.s_extend_or_bit_cast(tyctx, ctx, dim, "");
|
||||
|
||||
dst_array
|
||||
.offset(
|
||||
tyctx,
|
||||
ctx,
|
||||
sizet_model.constant(tyctx, ctx.ctx, axis as u64).value,
|
||||
"pdim",
|
||||
)
|
||||
.store(ctx, dim);
|
||||
}
|
||||
Ok(())
|
||||
}),
|
||||
}
|
||||
}
|
||||
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])`
|
||||
|
||||
// The value has to be an integer
|
||||
let shape_int = shape.into_int_value();
|
||||
|
||||
ArrayWriter {
|
||||
len: sizet_model.constant(tyctx, ctx.ctx, 1),
|
||||
write: Box::new(move |_generator, ctx, dst_array| {
|
||||
let dim = sizet_model.s_extend_or_bit_cast(tyctx, ctx, shape_int, "");
|
||||
|
||||
// Set shape[0] = shape_int
|
||||
dst_array
|
||||
.offset(tyctx, ctx, sizet_model.constant(tyctx, ctx.ctx, 0).value, "pdim")
|
||||
.store(ctx, dim);
|
||||
|
||||
Ok(())
|
||||
}),
|
||||
}
|
||||
}
|
||||
_ => panic!("encountered shape type"),
|
||||
}
|
||||
}
|
||||
|
|
@ -23,3 +23,4 @@ pub mod codegen;
|
|||
pub mod symbol_resolver;
|
||||
pub mod toplevel;
|
||||
pub mod typecheck;
|
||||
pub mod util;
|
||||
|
|
@ -1,5 +1,6 @@
|
|||
use std::iter::once;
|
||||
|
||||
use crate::util::SizeVariant;
|
||||
use helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDefDetails};
|
||||
use indexmap::IndexMap;
|
||||
use inkwell::{
|
||||
|
|
@ -9,20 +10,16 @@ use inkwell::{
|
|||
IntPredicate,
|
||||
};
|
||||
use itertools::Either;
|
||||
use ndarray::basic::call_nac3_ndarray_len;
|
||||
use strum::IntoEnumIterator;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
builtin_fns,
|
||||
classes::{ProxyValue, RangeValue},
|
||||
classes::{ArrayLikeValue, NDArrayValue, ProxyValue, RangeValue, TypedArrayLikeAccessor},
|
||||
expr::destructure_range,
|
||||
irrt::*,
|
||||
model::*,
|
||||
numpy::*,
|
||||
numpy_new,
|
||||
stmt::exn_constructor,
|
||||
structure::ndarray::NpArray,
|
||||
},
|
||||
symbol_resolver::SymbolValue,
|
||||
toplevel::{helper::PrimDef, numpy::make_ndarray_ty},
|
||||
|
|
@ -282,20 +279,11 @@ pub fn get_builtins(unifier: &mut Unifier, primitives: &PrimitiveStore) -> Built
|
|||
.collect()
|
||||
}
|
||||
|
||||
/// A helper enum used by [`BuiltinBuilder`]
|
||||
#[derive(Clone, Copy)]
|
||||
enum SizeVariant {
|
||||
Bits32,
|
||||
Bits64,
|
||||
}
|
||||
|
||||
impl SizeVariant {
|
||||
fn of_int(self, primitives: &PrimitiveStore) -> Type {
|
||||
match self {
|
||||
fn size_variant_to_int_type(variant: SizeVariant, primitives: &PrimitiveStore) -> Type {
|
||||
match variant {
|
||||
SizeVariant::Bits32 => primitives.int32,
|
||||
SizeVariant::Bits64 => primitives.int64,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
struct BuiltinBuilder<'a> {
|
||||
|
|
@ -350,8 +338,8 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
let (is_some_ty, unwrap_ty, option_tvar) =
|
||||
if let TypeEnum::TObj { fields, params, .. } = unifier.get_ty(option).as_ref() {
|
||||
(
|
||||
*fields.get(&PrimDef::FunOptionIsSome.simple_name().into()).unwrap(),
|
||||
*fields.get(&PrimDef::FunOptionUnwrap.simple_name().into()).unwrap(),
|
||||
*fields.get(&PrimDef::OptionIsSome.simple_name().into()).unwrap(),
|
||||
*fields.get(&PrimDef::OptionUnwrap.simple_name().into()).unwrap(),
|
||||
iter_type_vars(params).next().unwrap(),
|
||||
)
|
||||
} else {
|
||||
|
|
@ -366,9 +354,9 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
let ndarray_dtype_tvar = iter_type_vars(ndarray_params).next().unwrap();
|
||||
let ndarray_ndims_tvar = iter_type_vars(ndarray_params).nth(1).unwrap();
|
||||
let ndarray_copy_ty =
|
||||
*ndarray_fields.get(&PrimDef::FunNDArrayCopy.simple_name().into()).unwrap();
|
||||
*ndarray_fields.get(&PrimDef::NDArrayCopy.simple_name().into()).unwrap();
|
||||
let ndarray_fill_ty =
|
||||
*ndarray_fields.get(&PrimDef::FunNDArrayFill.simple_name().into()).unwrap();
|
||||
*ndarray_fields.get(&PrimDef::NDArrayFill.simple_name().into()).unwrap();
|
||||
|
||||
let num_ty = unifier.get_fresh_var_with_range(
|
||||
&[int32, int64, float, boolean, uint32, uint64],
|
||||
|
|
@ -468,14 +456,14 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
PrimDef::Exception => self.build_exception_class_related(prim),
|
||||
|
||||
PrimDef::Option
|
||||
| PrimDef::FunOptionIsSome
|
||||
| PrimDef::FunOptionIsNone
|
||||
| PrimDef::FunOptionUnwrap
|
||||
| PrimDef::OptionIsSome
|
||||
| PrimDef::OptionIsNone
|
||||
| PrimDef::OptionUnwrap
|
||||
| PrimDef::FunSome => self.build_option_class_related(prim),
|
||||
|
||||
PrimDef::List => self.build_list_class_related(prim),
|
||||
|
||||
PrimDef::NDArray | PrimDef::FunNDArrayCopy | PrimDef::FunNDArrayFill => {
|
||||
PrimDef::NDArray | PrimDef::NDArrayCopy | PrimDef::NDArrayFill => {
|
||||
self.build_ndarray_class_related(prim)
|
||||
}
|
||||
|
||||
|
|
@ -496,8 +484,6 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
| PrimDef::FunNpEye
|
||||
| PrimDef::FunNpIdentity => self.build_ndarray_other_factory_function(prim),
|
||||
|
||||
PrimDef::FunNpReshape => self.build_ndarray_view_functions(prim),
|
||||
|
||||
PrimDef::FunStr => self.build_str_function(),
|
||||
|
||||
PrimDef::FunFloor | PrimDef::FunFloor64 | PrimDef::FunCeil | PrimDef::FunCeil64 => {
|
||||
|
|
@ -516,9 +502,7 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
|
||||
PrimDef::FunMin | PrimDef::FunMax => self.build_min_max_function(prim),
|
||||
|
||||
PrimDef::FunNpArgmin | PrimDef::FunNpArgmax | PrimDef::FunNpMin | PrimDef::FunNpMax => {
|
||||
self.build_np_max_min_function(prim)
|
||||
}
|
||||
PrimDef::FunNpMin | PrimDef::FunNpMax => self.build_np_min_max_function(prim),
|
||||
|
||||
PrimDef::FunNpMinimum | PrimDef::FunNpMaximum => {
|
||||
self.build_np_minimum_maximum_function(prim)
|
||||
|
|
@ -570,7 +554,7 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
match (&tld, prim.details()) {
|
||||
(
|
||||
TopLevelDef::Class { name, object_id, .. },
|
||||
PrimDefDetails::PrimClass { name: exp_name, .. },
|
||||
PrimDefDetails::PrimClass { name: exp_name },
|
||||
) => {
|
||||
let exp_object_id = prim.id();
|
||||
assert_eq!(name, &exp_name.into());
|
||||
|
|
@ -800,9 +784,9 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
prim,
|
||||
&[
|
||||
PrimDef::Option,
|
||||
PrimDef::FunOptionIsSome,
|
||||
PrimDef::FunOptionIsNone,
|
||||
PrimDef::FunOptionUnwrap,
|
||||
PrimDef::OptionIsSome,
|
||||
PrimDef::OptionIsNone,
|
||||
PrimDef::OptionUnwrap,
|
||||
PrimDef::FunSome,
|
||||
],
|
||||
);
|
||||
|
|
@ -815,9 +799,9 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
fields: Vec::default(),
|
||||
attributes: Vec::default(),
|
||||
methods: vec![
|
||||
Self::create_method(PrimDef::FunOptionIsSome, self.is_some_ty.0),
|
||||
Self::create_method(PrimDef::FunOptionIsNone, self.is_some_ty.0),
|
||||
Self::create_method(PrimDef::FunOptionUnwrap, self.unwrap_ty.0),
|
||||
Self::create_method(PrimDef::OptionIsSome, self.is_some_ty.0),
|
||||
Self::create_method(PrimDef::OptionIsNone, self.is_some_ty.0),
|
||||
Self::create_method(PrimDef::OptionUnwrap, self.unwrap_ty.0),
|
||||
],
|
||||
ancestors: vec![TypeAnnotation::CustomClass {
|
||||
id: prim.id(),
|
||||
|
|
@ -828,7 +812,7 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
loc: None,
|
||||
},
|
||||
|
||||
PrimDef::FunOptionUnwrap => TopLevelDef::Function {
|
||||
PrimDef::OptionUnwrap => TopLevelDef::Function {
|
||||
name: prim.name().into(),
|
||||
simple_name: prim.simple_name().into(),
|
||||
signature: self.unwrap_ty.0,
|
||||
|
|
@ -842,7 +826,7 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
loc: None,
|
||||
},
|
||||
|
||||
PrimDef::FunOptionIsNone | PrimDef::FunOptionIsSome => TopLevelDef::Function {
|
||||
PrimDef::OptionIsNone | PrimDef::OptionIsSome => TopLevelDef::Function {
|
||||
name: prim.name().to_string(),
|
||||
simple_name: prim.simple_name().into(),
|
||||
signature: self.is_some_ty.0,
|
||||
|
|
@ -863,10 +847,10 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
};
|
||||
|
||||
let returned_int = match prim {
|
||||
PrimDef::FunOptionIsNone => {
|
||||
PrimDef::OptionIsNone => {
|
||||
ctx.builder.build_is_null(ptr, prim.simple_name())
|
||||
}
|
||||
PrimDef::FunOptionIsSome => {
|
||||
PrimDef::OptionIsSome => {
|
||||
ctx.builder.build_is_not_null(ptr, prim.simple_name())
|
||||
}
|
||||
_ => unreachable!(),
|
||||
|
|
@ -939,7 +923,7 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
fn build_ndarray_class_related(&self, prim: PrimDef) -> TopLevelDef {
|
||||
debug_assert_prim_is_allowed(
|
||||
prim,
|
||||
&[PrimDef::NDArray, PrimDef::FunNDArrayCopy, PrimDef::FunNDArrayFill],
|
||||
&[PrimDef::NDArray, PrimDef::NDArrayCopy, PrimDef::NDArrayFill],
|
||||
);
|
||||
|
||||
match prim {
|
||||
|
|
@ -950,8 +934,8 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
fields: Vec::default(),
|
||||
attributes: Vec::default(),
|
||||
methods: vec![
|
||||
Self::create_method(PrimDef::FunNDArrayCopy, self.ndarray_copy_ty.0),
|
||||
Self::create_method(PrimDef::FunNDArrayFill, self.ndarray_fill_ty.0),
|
||||
Self::create_method(PrimDef::NDArrayCopy, self.ndarray_copy_ty.0),
|
||||
Self::create_method(PrimDef::NDArrayFill, self.ndarray_fill_ty.0),
|
||||
],
|
||||
ancestors: Vec::default(),
|
||||
constructor: None,
|
||||
|
|
@ -959,7 +943,7 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
loc: None,
|
||||
},
|
||||
|
||||
PrimDef::FunNDArrayCopy => TopLevelDef::Function {
|
||||
PrimDef::NDArrayCopy => TopLevelDef::Function {
|
||||
name: prim.name().into(),
|
||||
simple_name: prim.simple_name().into(),
|
||||
signature: self.ndarray_copy_ty.0,
|
||||
|
|
@ -969,14 +953,15 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|ctx, obj, fun, args, generator| {
|
||||
gen_ndarray_copy(ctx, &obj, fun, &args, generator)
|
||||
.map(|val| Some(val.as_basic_value_enum()))
|
||||
todo!()
|
||||
// gen_ndarray_copy(ctx, &obj, fun, &args, generator)
|
||||
// .map(|val| Some(val.as_basic_value_enum()))
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
},
|
||||
|
||||
PrimDef::FunNDArrayFill => TopLevelDef::Function {
|
||||
PrimDef::NDArrayFill => TopLevelDef::Function {
|
||||
name: prim.name().into(),
|
||||
simple_name: prim.simple_name().into(),
|
||||
signature: self.ndarray_fill_ty.0,
|
||||
|
|
@ -986,8 +971,9 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|ctx, obj, fun, args, generator| {
|
||||
gen_ndarray_fill(ctx, &obj, fun, &args, generator)?;
|
||||
Ok(None)
|
||||
todo!()
|
||||
// gen_ndarray_fill(ctx, &obj, fun, &args, generator)?;
|
||||
// Ok(None)
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
|
|
@ -1067,7 +1053,7 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
);
|
||||
|
||||
// The size variant of the function determines the size of the returned int.
|
||||
let int_sized = size_variant.of_int(self.primitives);
|
||||
let int_sized = size_variant_to_int_type(size_variant, self.primitives);
|
||||
|
||||
let ndarray_int_sized =
|
||||
make_ndarray_ty(self.unifier, self.primitives, Some(int_sized), Some(common_ndim.ty));
|
||||
|
|
@ -1092,7 +1078,7 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
let arg_ty = fun.0.args[0].ty;
|
||||
let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
|
||||
|
||||
let ret_elem_ty = size_variant.of_int(&ctx.primitives);
|
||||
let ret_elem_ty = size_variant_to_int_type(size_variant, &ctx.primitives);
|
||||
Ok(Some(builtin_fns::call_round(generator, ctx, (arg_ty, arg), ret_elem_ty)?))
|
||||
}),
|
||||
)
|
||||
|
|
@ -1133,7 +1119,7 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
make_ndarray_ty(self.unifier, self.primitives, Some(float), Some(common_ndim.ty));
|
||||
|
||||
// The size variant of the function determines the type of int returned
|
||||
let int_sized = size_variant.of_int(self.primitives);
|
||||
let int_sized = size_variant_to_int_type(size_variant, self.primitives);
|
||||
let ndarray_int_sized =
|
||||
make_ndarray_ty(self.unifier, self.primitives, Some(int_sized), Some(common_ndim.ty));
|
||||
|
||||
|
|
@ -1156,7 +1142,7 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
let arg_ty = fun.0.args[0].ty;
|
||||
let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
|
||||
|
||||
let ret_elem_ty = size_variant.of_int(&ctx.primitives);
|
||||
let ret_elem_ty = size_variant_to_int_type(size_variant, &ctx.primitives);
|
||||
let func = match kind {
|
||||
Kind::Ceil => builtin_fns::call_ceil,
|
||||
Kind::Floor => builtin_fns::call_floor,
|
||||
|
|
@ -1207,15 +1193,14 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
self.ndarray_float,
|
||||
&[(self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
|
||||
Box::new(move |ctx, obj, fun, args, generator| {
|
||||
let func = match prim {
|
||||
PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => {
|
||||
numpy_new::factory::gen_ndarray_empty
|
||||
}
|
||||
PrimDef::FunNpZeros => numpy_new::factory::gen_ndarray_zeros,
|
||||
PrimDef::FunNpOnes => numpy_new::factory::gen_ndarray_ones,
|
||||
_ => unreachable!(),
|
||||
};
|
||||
func(ctx, &obj, fun, &args, generator).map(|val| Some(val.as_basic_value_enum()))
|
||||
todo!()
|
||||
// let func = match prim {
|
||||
// PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => gen_ndarray_empty,
|
||||
// PrimDef::FunNpZeros => gen_ndarray_zeros,
|
||||
// PrimDef::FunNpOnes => gen_ndarray_ones,
|
||||
// _ => unreachable!(),
|
||||
// };
|
||||
// func(ctx, &obj, fun, &args, generator).map(|val| Some(val.as_basic_value_enum()))
|
||||
}),
|
||||
)
|
||||
}
|
||||
|
|
@ -1261,8 +1246,9 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|ctx, obj, fun, args, generator| {
|
||||
gen_ndarray_array(ctx, &obj, fun, &args, generator)
|
||||
.map(|val| Some(val.as_basic_value_enum()))
|
||||
todo!()
|
||||
// gen_ndarray_array(ctx, &obj, fun, &args, generator)
|
||||
// .map(|val| Some(val.as_basic_value_enum()))
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
|
|
@ -1280,8 +1266,9 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
// type variable
|
||||
&[(self.list_int32, "shape"), (tv.ty, "fill_value")],
|
||||
Box::new(move |ctx, obj, fun, args, generator| {
|
||||
numpy_new::factory::gen_ndarray_full(ctx, &obj, fun, &args, generator)
|
||||
.map(|val| Some(val.as_basic_value_enum()))
|
||||
todo!()
|
||||
// gen_ndarray_full(ctx, &obj, fun, &args, generator)
|
||||
// .map(|val| Some(val.as_basic_value_enum()))
|
||||
}),
|
||||
)
|
||||
}
|
||||
|
|
@ -1313,8 +1300,9 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|ctx, obj, fun, args, generator| {
|
||||
gen_ndarray_eye(ctx, &obj, fun, &args, generator)
|
||||
.map(|val| Some(val.as_basic_value_enum()))
|
||||
todo!()
|
||||
// gen_ndarray_eye(ctx, &obj, fun, &args, generator)
|
||||
// .map(|val| Some(val.as_basic_value_enum()))
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
|
|
@ -1327,49 +1315,15 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
self.ndarray_float_2d,
|
||||
&[(int32, "n")],
|
||||
Box::new(|ctx, obj, fun, args, generator| {
|
||||
gen_ndarray_identity(ctx, &obj, fun, &args, generator)
|
||||
.map(|val| Some(val.as_basic_value_enum()))
|
||||
todo!()
|
||||
// gen_ndarray_identity(ctx, &obj, fun, &args, generator)
|
||||
// .map(|val| Some(val.as_basic_value_enum()))
|
||||
}),
|
||||
),
|
||||
_ => unreachable!(),
|
||||
}
|
||||
}
|
||||
|
||||
// Build functions related to NDArray views
|
||||
fn build_ndarray_view_functions(&mut self, prim: PrimDef) -> TopLevelDef {
|
||||
debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpReshape]);
|
||||
|
||||
match prim {
|
||||
PrimDef::FunNpReshape => {
|
||||
// TODO: Support scalar inputs, e.g., `np.reshape(99, (1, 1, 1, 1))`
|
||||
|
||||
let new_ndim_ty = self.unifier.get_fresh_var(Some("NewNDim".into()), None);
|
||||
let returned_ndarray_ty = make_ndarray_ty(
|
||||
self.unifier,
|
||||
self.primitives,
|
||||
Some(self.ndarray_dtype_tvar.ty),
|
||||
Some(new_ndim_ty.ty),
|
||||
);
|
||||
|
||||
create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&into_var_map([self.ndarray_dtype_tvar, self.ndarray_ndims_tvar, new_ndim_ty]),
|
||||
prim.name(),
|
||||
returned_ndarray_ty,
|
||||
&[
|
||||
(self.primitives.ndarray, "array"),
|
||||
(self.ndarray_factory_fn_shape_arg_tvar.ty, "shape"),
|
||||
],
|
||||
Box::new(|ctx, obj, fun, args, generator| {
|
||||
numpy_new::view::gen_ndarray_reshape(ctx, &obj, fun, &args, generator)
|
||||
.map(|val| Some(val.as_basic_value_enum()))
|
||||
}),
|
||||
)
|
||||
}
|
||||
_ => unreachable!(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Build the `str()` function.
|
||||
fn build_str_function(&mut self) -> TopLevelDef {
|
||||
let prim = PrimDef::FunStr;
|
||||
|
|
@ -1507,13 +1461,51 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
}
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let pndarray_model = PtrModel(StructModel(NpArray));
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let ndarray =
|
||||
pndarray_model.check_value(tyctx, ctx.ctx, arg).unwrap();
|
||||
let len = call_nac3_ndarray_len(generator, ctx, ndarray);
|
||||
Some(len.value.as_basic_value_enum())
|
||||
let arg = NDArrayValue::from_ptr_val(
|
||||
arg.into_pointer_value(),
|
||||
llvm_usize,
|
||||
None,
|
||||
);
|
||||
|
||||
let ndims = arg.dim_sizes().size(ctx, generator);
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
ctx.builder
|
||||
.build_int_compare(
|
||||
IntPredicate::NE,
|
||||
ndims,
|
||||
llvm_usize.const_zero(),
|
||||
"",
|
||||
)
|
||||
.unwrap(),
|
||||
"0:TypeError",
|
||||
&format!("{name}() of unsized object", name = prim.name()),
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
let len = unsafe {
|
||||
arg.dim_sizes().get_typed_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_zero(),
|
||||
None,
|
||||
)
|
||||
};
|
||||
|
||||
if len.get_type().get_bit_width() == 32 {
|
||||
Some(len.into())
|
||||
} else {
|
||||
Some(
|
||||
ctx.builder
|
||||
.build_int_truncate(len, llvm_i32, "len")
|
||||
.map(Into::into)
|
||||
.unwrap(),
|
||||
)
|
||||
}
|
||||
}
|
||||
_ => unreachable!(),
|
||||
}
|
||||
|
|
@ -1562,19 +1554,10 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
}
|
||||
}
|
||||
|
||||
/// Build the functions `np_max()`, `np_min()`, `np_argmax()` and `np_argmin()`
|
||||
/// Calls `call_numpy_max_min` with the function name
|
||||
fn build_np_max_min_function(&mut self, prim: PrimDef) -> TopLevelDef {
|
||||
debug_assert_prim_is_allowed(
|
||||
prim,
|
||||
&[PrimDef::FunNpArgmin, PrimDef::FunNpArgmax, PrimDef::FunNpMin, PrimDef::FunNpMax],
|
||||
);
|
||||
/// Build the functions `np_min()` and `np_max()`.
|
||||
fn build_np_min_max_function(&mut self, prim: PrimDef) -> TopLevelDef {
|
||||
debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpMin, PrimDef::FunNpMax]);
|
||||
|
||||
let (var_map, ret_ty) = match prim {
|
||||
PrimDef::FunNpArgmax | PrimDef::FunNpArgmin => {
|
||||
(self.num_or_ndarray_var_map.clone(), self.primitives.int64)
|
||||
}
|
||||
PrimDef::FunNpMax | PrimDef::FunNpMin => {
|
||||
let ret_ty = self.unifier.get_fresh_var(Some("R".into()), None);
|
||||
let var_map = self
|
||||
.num_or_ndarray_var_map
|
||||
|
|
@ -1582,25 +1565,28 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
.into_iter()
|
||||
.chain(once((ret_ty.id, ret_ty.ty)))
|
||||
.collect::<IndexMap<_, _>>();
|
||||
(var_map, ret_ty.ty)
|
||||
}
|
||||
_ => unreachable!(),
|
||||
};
|
||||
|
||||
create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&var_map,
|
||||
prim.name(),
|
||||
ret_ty,
|
||||
&[(self.num_or_ndarray_ty.ty, "a")],
|
||||
ret_ty.ty,
|
||||
&[(self.float_or_ndarray_ty.ty, "a")],
|
||||
Box::new(move |ctx, _, fun, args, generator| {
|
||||
let a_ty = fun.0.args[0].ty;
|
||||
let a = args[0].1.clone().to_basic_value_enum(ctx, generator, a_ty)?;
|
||||
|
||||
Ok(Some(builtin_fns::call_numpy_max_min(generator, ctx, (a_ty, a), prim.name())?))
|
||||
let func = match prim {
|
||||
PrimDef::FunNpMin => builtin_fns::call_numpy_min,
|
||||
PrimDef::FunNpMax => builtin_fns::call_numpy_max,
|
||||
_ => unreachable!(),
|
||||
};
|
||||
|
||||
Ok(Some(func(generator, ctx, (a_ty, a))?))
|
||||
}),
|
||||
)
|
||||
}
|
||||
|
||||
/// Build the functions `np_minimum()` and `np_maximum()`.
|
||||
fn build_np_minimum_maximum_function(&mut self, prim: PrimDef) -> TopLevelDef {
|
||||
debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpMinimum, PrimDef::FunNpMaximum]);
|
||||
|
|
|
|||
|
|
@ -766,7 +766,6 @@ impl TopLevelComposer {
|
|||
let target_ty = get_type_from_type_annotation_kinds(
|
||||
&temp_def_list,
|
||||
unifier,
|
||||
primitives,
|
||||
&def,
|
||||
&mut subst_list,
|
||||
)?;
|
||||
|
|
@ -937,7 +936,6 @@ impl TopLevelComposer {
|
|||
let ty = get_type_from_type_annotation_kinds(
|
||||
temp_def_list.as_ref(),
|
||||
unifier,
|
||||
primitives_store,
|
||||
&type_annotation,
|
||||
&mut None,
|
||||
)?;
|
||||
|
|
@ -1004,7 +1002,6 @@ impl TopLevelComposer {
|
|||
get_type_from_type_annotation_kinds(
|
||||
&temp_def_list,
|
||||
unifier,
|
||||
primitives_store,
|
||||
&return_ty_annotation,
|
||||
&mut None,
|
||||
)?
|
||||
|
|
@ -1625,7 +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,
|
||||
)?;
|
||||
|
|
@ -1807,11 +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 {
|
||||
|
|
|
|||
|
|
@ -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,
|
||||
|
|
@ -51,20 +46,26 @@ pub enum PrimDef {
|
|||
FunNpArray,
|
||||
FunNpEye,
|
||||
FunNpIdentity,
|
||||
|
||||
// NumPy view functions
|
||||
FunNpReshape,
|
||||
|
||||
// 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,
|
||||
|
|
@ -103,30 +104,14 @@ pub enum PrimDef {
|
|||
FunNpHypot,
|
||||
FunNpNextAfter,
|
||||
|
||||
// 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 {
|
||||
|
|
@ -168,17 +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 {
|
||||
|
|
@ -186,37 +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),
|
||||
|
|
@ -225,20 +200,26 @@ impl PrimDef {
|
|||
PrimDef::FunNpArray => fun("np_array", None),
|
||||
PrimDef::FunNpEye => fun("np_eye", None),
|
||||
PrimDef::FunNpIdentity => fun("np_identity", None),
|
||||
|
||||
// NumPy view functions
|
||||
PrimDef::FunNpReshape => fun("np_reshape", 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),
|
||||
|
|
@ -276,25 +257,7 @@ impl PrimDef {
|
|||
PrimDef::FunNpLdExp => fun("np_ldexp", None),
|
||||
PrimDef::FunNpHypot => fun("np_hypot", None),
|
||||
PrimDef::FunNpNextAfter => fun("np_nextafter", 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),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -445,9 +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<_, _>>(),
|
||||
|
|
@ -488,8 +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]),
|
||||
});
|
||||
|
|
|
|||
|
|
@ -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(248)]\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[typevar237]\", \"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: [\"typevar237\"]\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(250)]\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(255)]\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[typevar236, typevar237]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar236\", \"typevar237\"]\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(256)]\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(264)]\n}\n",
|
||||
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(261)]\n}\n",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -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>> {
|
||||
|
|
@ -381,43 +379,10 @@ 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
|
||||
|
|
@ -459,15 +424,12 @@ pub fn get_type_from_type_annotation_kinds(
|
|||
}
|
||||
}
|
||||
|
||||
TypeEnum::TVar {
|
||||
id, range, name, loc, is_const_generic: true, ..
|
||||
} => {
|
||||
TypeEnum::TVar { id, range, name, loc, is_const_generic: true, .. } => {
|
||||
let ty = range[0];
|
||||
let ok: bool = {
|
||||
// create a temp type var and unify to check compatibility
|
||||
p == *tvar || {
|
||||
let temp =
|
||||
unifier.get_fresh_const_generic_var(ty, *name, *loc);
|
||||
let temp = unifier.get_fresh_const_generic_var(ty, *name, *loc);
|
||||
unifier.unify(temp.ty, p).is_ok()
|
||||
}
|
||||
};
|
||||
|
|
@ -506,16 +468,11 @@ pub fn get_type_from_type_annotation_kinds(
|
|||
fields: tobj_fields,
|
||||
params: subst,
|
||||
});
|
||||
|
||||
if need_subst {
|
||||
if let Some(wl) = subst_list.as_mut() {
|
||||
wl.push(ty);
|
||||
}
|
||||
}
|
||||
|
||||
ty
|
||||
};
|
||||
|
||||
Ok(ty)
|
||||
}
|
||||
TypeAnnotation::Primitive(ty) | TypeAnnotation::TypeVar(ty) => Ok(*ty),
|
||||
|
|
@ -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,
|
||||
)?;
|
||||
|
|
@ -543,13 +499,7 @@ 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 }))
|
||||
|
|
|
|||
|
|
@ -389,7 +389,7 @@ impl<'a> Fold<()> for Inferencer<'a> {
|
|||
}
|
||||
ast::StmtKind::Assign { targets, value, .. } => {
|
||||
for target in targets {
|
||||
self.fold_assign(target, value)?;
|
||||
self.unify(target.custom.unwrap(), value.custom.unwrap(), &target.location)?;
|
||||
}
|
||||
}
|
||||
ast::StmtKind::Raise { exc, cause, .. } => {
|
||||
|
|
@ -398,10 +398,7 @@ impl<'a> Fold<()> for Inferencer<'a> {
|
|||
}
|
||||
if let Some(exc) = exc {
|
||||
self.virtual_checks.push((
|
||||
match &*self.unifier.get_ty(exc.custom.unwrap()) {
|
||||
TypeEnum::TFunc(sign) => sign.ret,
|
||||
_ => exc.custom.unwrap(),
|
||||
},
|
||||
exc.custom.unwrap(),
|
||||
self.primitives.exception,
|
||||
exc.location,
|
||||
));
|
||||
|
|
@ -1390,55 +1387,6 @@ impl<'a> Inferencer<'a> {
|
|||
}));
|
||||
}
|
||||
|
||||
// Handle `np.reshape(<array>, <shape>)`
|
||||
if ["np_reshape".into()].contains(id) && args.len() == 2 {
|
||||
// Extract arguments
|
||||
let array_expr = args.remove(0);
|
||||
let shape_expr = args.remove(0);
|
||||
|
||||
// Fold `<array>`
|
||||
let array = self.fold_expr(array_expr)?;
|
||||
let array_ty = array.custom.unwrap();
|
||||
let (array_dtype, _) = unpack_ndarray_var_tys(self.unifier, array_ty);
|
||||
|
||||
// Fold `<shape>`
|
||||
let (target_ndims, target_shape) =
|
||||
self.fold_numpy_function_call_shape_argument(*id, 0, shape_expr)?;
|
||||
let target_shape_ty = target_shape.custom.unwrap();
|
||||
// ... and deduce the return type of the call
|
||||
let target_ndims_ty =
|
||||
self.unifier.get_fresh_literal(vec![SymbolValue::U64(target_ndims)], None);
|
||||
let ret = make_ndarray_ty(
|
||||
self.unifier,
|
||||
self.primitives,
|
||||
Some(array_dtype),
|
||||
Some(target_ndims_ty),
|
||||
);
|
||||
|
||||
let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
|
||||
args: vec![
|
||||
FuncArg { name: "array".into(), ty: array_ty, default_value: None },
|
||||
FuncArg { name: "shape".into(), ty: target_shape_ty, default_value: None },
|
||||
],
|
||||
ret,
|
||||
vars: VarMap::new(),
|
||||
}));
|
||||
|
||||
return Ok(Some(Located {
|
||||
location,
|
||||
custom: Some(ret),
|
||||
node: ExprKind::Call {
|
||||
func: Box::new(Located {
|
||||
custom: Some(custom),
|
||||
location: func.location,
|
||||
node: ExprKind::Name { id: *id, ctx: *ctx },
|
||||
}),
|
||||
args: vec![array, target_shape],
|
||||
keywords: vec![],
|
||||
},
|
||||
}));
|
||||
}
|
||||
|
||||
// 2-argument ndarray n-dimensional creation functions
|
||||
if id == &"np_full".into() && args.len() == 2 {
|
||||
let ExprKind::List { elts, .. } = &args[0].node else {
|
||||
|
|
@ -2159,58 +2107,4 @@ impl<'a> Inferencer<'a> {
|
|||
self.constrain(body.custom.unwrap(), orelse.custom.unwrap(), &body.location)?;
|
||||
Ok(body.custom.unwrap())
|
||||
}
|
||||
|
||||
fn fold_assign(
|
||||
&mut self,
|
||||
target: &ast::Expr<Option<Type>>,
|
||||
value: &ast::Expr<Option<Type>>,
|
||||
) -> Result<(), HashSet<String>> {
|
||||
let target_ty = target.custom.unwrap();
|
||||
let value_ty = value.custom.unwrap();
|
||||
|
||||
match (&target.node, &*self.unifier.get_ty(target_ty)) {
|
||||
(ExprKind::Subscript { .. }, TypeEnum::TObj { obj_id: target_obj_id, .. })
|
||||
if *target_obj_id == self.primitives.ndarray.obj_id(self.unifier).unwrap() =>
|
||||
{
|
||||
// Pattern match expressions like `my_ndarray[slices] = value`.
|
||||
// TODO: `(my_ndarray[slices1], my_ndarray[slices2]) = (value1, value2)` are not supported for now.
|
||||
|
||||
// Suppose `my_ndarray` has type `ndarray[target_dtype, ndims]`
|
||||
// value's type could be one of the following:
|
||||
// Case 1. `target_dtype`
|
||||
// Case 2. `ndarray[target_dtype, ?]`
|
||||
// Case 3. list, tuple, iterables (TODO: NOT IMPLEMENTED)
|
||||
|
||||
let (target_dtype, _) = unpack_ndarray_var_tys(self.unifier, target_ty);
|
||||
|
||||
// Typecheck `value_ty`
|
||||
match &*self.unifier.get_ty(value_ty) {
|
||||
TypeEnum::TObj { obj_id: value_obj_id, .. }
|
||||
if *value_obj_id
|
||||
== self.primitives.ndarray.obj_id(self.unifier).unwrap() =>
|
||||
{
|
||||
// Case 2
|
||||
// - `dtype` of `target_ty` and `value_ty` must unify.
|
||||
// - `ndims` of `value_ty` is ignored.
|
||||
let (value_dtype, _) = unpack_ndarray_var_tys(self.unifier, value_ty);
|
||||
|
||||
self.unify(target_dtype, value_dtype, &target.location)?;
|
||||
}
|
||||
_ => {
|
||||
// If `value_ty` is not an ndarray, simply typecheck as through it has to be Case 1.
|
||||
self.unify(target_dtype, value_ty, &target.location)?;
|
||||
}
|
||||
}
|
||||
}
|
||||
_ => {
|
||||
// To handle
|
||||
// - variable assignments `target = value`
|
||||
// - and attribute assignments `target.my_attr = value`
|
||||
//
|
||||
// For these cases in nac3core, types of LHS and RHS must unify
|
||||
self.unify(target_ty, value_ty, &target.location)?;
|
||||
}
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -14,21 +14,12 @@ while [ $# -gt 1 ]; do
|
|||
done
|
||||
demo="$1"
|
||||
|
||||
echo "### Checking $demo..."
|
||||
|
||||
# Get reference output
|
||||
echo ">>>>>> Running $demo with the Python interpreter"
|
||||
echo -n "Checking $demo... "
|
||||
./interpret_demo.py "$demo" > interpreted.log
|
||||
./run_demo.sh --out run.log "${nac3args[@]}" "$demo"
|
||||
./run_demo.sh --lli --out run_lli.log "${nac3args[@]}" "$demo"
|
||||
diff -Nau interpreted.log run.log
|
||||
diff -Nau interpreted.log run_lli.log
|
||||
echo "ok"
|
||||
|
||||
echo "...... Trying NAC3's 32-bit code generator output"
|
||||
./run_demo.sh -i386 --out run_32.log "${nac3args[@]}" "$demo"
|
||||
diff -Nau interpreted.log run_32.log
|
||||
|
||||
echo "...... Trying NAC3's 64-bit code generator output"
|
||||
./run_demo.sh --out run_64.log "${nac3args[@]}" "$demo"
|
||||
diff -Nau interpreted.log run_64.log
|
||||
|
||||
echo "...... OK"
|
||||
|
||||
rm -f interpreted.log \
|
||||
run_32.log run_64.log
|
||||
rm -f interpreted.log run.log run_lli.log
|
||||
|
|
@ -6,6 +6,8 @@
|
|||
#include <stdlib.h>
|
||||
#include <string.h>
|
||||
|
||||
#define usize size_t
|
||||
|
||||
double dbl_nan(void) {
|
||||
return NAN;
|
||||
}
|
||||
|
|
@ -62,14 +64,14 @@ void output_asciiart(int32_t x) {
|
|||
|
||||
struct cslice {
|
||||
void *data;
|
||||
size_t len;
|
||||
usize len;
|
||||
};
|
||||
|
||||
void output_int32_list(struct cslice *slice) {
|
||||
const int32_t *data = (int32_t *) slice->data;
|
||||
|
||||
putchar('[');
|
||||
for (size_t i = 0; i < slice->len; ++i) {
|
||||
for (usize i = 0; i < slice->len; ++i) {
|
||||
if (i == slice->len - 1) {
|
||||
printf("%d", data[i]);
|
||||
} else {
|
||||
|
|
@ -83,7 +85,7 @@ void output_int32_list(struct cslice *slice) {
|
|||
void output_str(struct cslice *slice) {
|
||||
const char *data = (const char *) slice->data;
|
||||
|
||||
for (size_t i = 0; i < slice->len; ++i) {
|
||||
for (usize i = 0; i < slice->len; ++i) {
|
||||
putchar(data[i]);
|
||||
}
|
||||
}
|
||||
|
|
@ -105,25 +107,8 @@ uint32_t __nac3_personality(uint32_t state, uint32_t exception_object, uint32_t
|
|||
__builtin_unreachable();
|
||||
}
|
||||
|
||||
// See `struct Exception<'a>` in
|
||||
// https://github.com/m-labs/artiq/blob/master/artiq/firmware/libeh/eh_artiq.rs
|
||||
struct Exception {
|
||||
uint32_t id;
|
||||
struct cslice file;
|
||||
uint32_t line;
|
||||
uint32_t column;
|
||||
struct cslice function;
|
||||
struct cslice message;
|
||||
int64_t param[3];
|
||||
};
|
||||
|
||||
uint32_t __nac3_raise(struct Exception* e) {
|
||||
printf("__nac3_raise called. Exception details:\n");
|
||||
printf(" ID: %"PRIu32"\n", e->id);
|
||||
printf(" Location: %*s:%"PRIu32":%"PRIu32"\n" , (int) e->file.len, (const char*) e->file.data, e->line, e->column);
|
||||
printf(" Function: %*s\n" , (int) e->function.len, (const char*) e->function.data);
|
||||
printf(" Message: \"%*s\"\n" , (int) e->message.len, (const char*) e->message.data);
|
||||
printf(" Params: {0}=%"PRId64", {1}=%"PRId64", {2}=%"PRId64"\n", e->param[0], e->param[1], e->param[2]);
|
||||
uint32_t __nac3_raise(uint32_t state, uint32_t exception_object, uint32_t context) {
|
||||
printf("__nac3_raise(state: %u, exception_object: %u, context: %u)\n", state, exception_object, context);
|
||||
exit(101);
|
||||
__builtin_unreachable();
|
||||
}
|
||||
|
|
|
|||
|
|
@ -167,7 +167,7 @@ def patch(module):
|
|||
module.ceil64 = _ceil
|
||||
module.np_ceil = np.ceil
|
||||
|
||||
# NumPy NDArray factory functions
|
||||
# NumPy ndarray functions
|
||||
module.ndarray = NDArray
|
||||
module.np_ndarray = np.ndarray
|
||||
module.np_empty = np.empty
|
||||
|
|
@ -178,18 +178,13 @@ def patch(module):
|
|||
module.np_identity = np.identity
|
||||
module.np_array = np.array
|
||||
|
||||
# NumPy view functions
|
||||
module.np_reshape = np.reshape
|
||||
|
||||
# NumPy Math functions
|
||||
module.np_isnan = np.isnan
|
||||
module.np_isinf = np.isinf
|
||||
module.np_min = np.min
|
||||
module.np_minimum = np.minimum
|
||||
module.np_argmin = np.argmin
|
||||
module.np_max = np.max
|
||||
module.np_maximum = np.maximum
|
||||
module.np_argmax = np.argmax
|
||||
module.np_sin = np.sin
|
||||
module.np_cos = np.cos
|
||||
module.np_exp = np.exp
|
||||
|
|
@ -221,7 +216,7 @@ def patch(module):
|
|||
module.np_hypot = np.hypot
|
||||
module.np_nextafter = np.nextafter
|
||||
|
||||
# SciPy Math functions
|
||||
# SciPy Math Functions
|
||||
module.sp_spec_erf = special.erf
|
||||
module.sp_spec_erfc = special.erfc
|
||||
module.sp_spec_gamma = special.gamma
|
||||
|
|
@ -229,6 +224,15 @@ def patch(module):
|
|||
module.sp_spec_j0 = special.j0
|
||||
module.sp_spec_j1 = special.j1
|
||||
|
||||
# NumPy NDArray Functions
|
||||
module.np_ndarray = np.ndarray
|
||||
module.np_empty = np.empty
|
||||
module.np_zeros = np.zeros
|
||||
module.np_ones = np.ones
|
||||
module.np_full = np.full
|
||||
module.np_eye = np.eye
|
||||
module.np_identity = np.identity
|
||||
|
||||
def file_import(filename, prefix="file_import_"):
|
||||
filename = pathlib.Path(filename)
|
||||
modname = prefix + filename.stem
|
||||
|
|
|
|||
|
|
@ -11,19 +11,19 @@ declare -a nac3args
|
|||
while [ $# -ge 1 ]; do
|
||||
case "$1" in
|
||||
--help)
|
||||
echo "Usage: run_demo.sh [--help] [--out OUTFILE] [--debug] [-i386] -- [NAC3ARGS...]"
|
||||
echo "Usage: run_demo.sh [--help] [--out OUTFILE] [--lli] [--debug] -- [NAC3ARGS...]"
|
||||
exit
|
||||
;;
|
||||
--out)
|
||||
shift
|
||||
outfile="$1"
|
||||
;;
|
||||
--lli)
|
||||
use_lli=1
|
||||
;;
|
||||
--debug)
|
||||
debug=1
|
||||
;;
|
||||
-i386)
|
||||
i386=1
|
||||
;;
|
||||
--)
|
||||
shift
|
||||
break
|
||||
|
|
@ -50,23 +50,29 @@ else
|
|||
fi
|
||||
|
||||
rm -f ./*.o ./*.bc demo
|
||||
|
||||
if [ -z "$i386" ]; then
|
||||
if [ -z "$use_lli" ]; then
|
||||
$nac3standalone "${nac3args[@]}"
|
||||
|
||||
clang -c -std=gnu11 -Wall -Wextra -O3 -o demo.o demo.c
|
||||
clang -lm -Wl,--no-warn-search-mismatch -o demo module.o demo.o
|
||||
else
|
||||
# Enable SSE2 to avoid rounding errors with X87's 80-bit fp precision computations
|
||||
clang -lm -o demo module.o demo.o
|
||||
|
||||
$nac3standalone --triple i386-pc-linux-gnu --target-features +sse2 "${nac3args[@]}"
|
||||
|
||||
clang -m32 -c -std=gnu11 -Wall -Wextra -O3 -msse2 -o demo.o demo.c
|
||||
clang -m32 -lm -Wl,--no-warn-search-mismatch -o demo module.o demo.o
|
||||
fi
|
||||
|
||||
if [ -z "$outfile" ]; then
|
||||
if [ -z "$outfile" ]; then
|
||||
./demo
|
||||
else
|
||||
else
|
||||
./demo > "$outfile"
|
||||
fi
|
||||
else
|
||||
$nac3standalone --emit-llvm "${nac3args[@]}"
|
||||
|
||||
clang -c -std=gnu11 -Wall -Wextra -O3 -emit-llvm -o demo.bc demo.c
|
||||
|
||||
shopt -s nullglob
|
||||
llvm-link -o nac3out.bc module*.bc main.bc
|
||||
shopt -u nullglob
|
||||
|
||||
if [ -z "$outfile" ]; then
|
||||
lli --extra-module demo.bc --extra-module irrt.bc nac3out.bc
|
||||
else
|
||||
lli --extra-module demo.bc --extra-module irrt.bc nac3out.bc > "$outfile"
|
||||
fi
|
||||
fi
|
||||
|
|
|
|||
|
|
@ -867,13 +867,6 @@ def test_ndarray_minimum_broadcast_rhs_scalar():
|
|||
output_ndarray_float_2(min_x_zeros)
|
||||
output_ndarray_float_2(min_x_ones)
|
||||
|
||||
def test_ndarray_argmin():
|
||||
x = np_array([[1., 2.], [3., 4.]])
|
||||
y = np_argmin(x)
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
output_int64(y)
|
||||
|
||||
def test_ndarray_max():
|
||||
x = np_identity(2)
|
||||
y = np_max(x)
|
||||
|
|
@ -917,13 +910,6 @@ def test_ndarray_maximum_broadcast_rhs_scalar():
|
|||
output_ndarray_float_2(max_x_zeros)
|
||||
output_ndarray_float_2(max_x_ones)
|
||||
|
||||
def test_ndarray_argmax():
|
||||
x = np_array([[1., 2.], [3., 4.]])
|
||||
y = np_argmax(x)
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
output_int64(y)
|
||||
|
||||
def test_ndarray_abs():
|
||||
x = np_identity(2)
|
||||
y = abs(x)
|
||||
|
|
@ -1538,13 +1524,11 @@ def run() -> int32:
|
|||
test_ndarray_minimum_broadcast()
|
||||
test_ndarray_minimum_broadcast_lhs_scalar()
|
||||
test_ndarray_minimum_broadcast_rhs_scalar()
|
||||
test_ndarray_argmin()
|
||||
test_ndarray_max()
|
||||
test_ndarray_maximum()
|
||||
test_ndarray_maximum_broadcast()
|
||||
test_ndarray_maximum_broadcast_lhs_scalar()
|
||||
test_ndarray_maximum_broadcast_rhs_scalar()
|
||||
test_ndarray_argmax()
|
||||
test_ndarray_abs()
|
||||
test_ndarray_isnan()
|
||||
test_ndarray_isinf()
|
||||
|
|
|
|||
|
|
@ -9,11 +9,15 @@
|
|||
#![allow(clippy::too_many_lines, clippy::wildcard_imports)]
|
||||
|
||||
use clap::Parser;
|
||||
use inkwell::context::Context;
|
||||
use inkwell::{
|
||||
memory_buffer::MemoryBuffer, passes::PassBuilderOptions, support::is_multithreaded, targets::*,
|
||||
OptimizationLevel,
|
||||
};
|
||||
use parking_lot::{Mutex, RwLock};
|
||||
use std::collections::HashSet;
|
||||
use std::num::NonZeroUsize;
|
||||
use std::{collections::HashMap, fs, path::Path, sync::Arc};
|
||||
|
||||
use nac3core::{
|
||||
codegen::{
|
||||
concrete_type::ConcreteTypeStore, irrt::load_irrt, CodeGenLLVMOptions,
|
||||
|
|
@ -35,10 +39,6 @@ use nac3parser::{
|
|||
ast::{Constant, Expr, ExprKind, StmtKind, StrRef},
|
||||
parser,
|
||||
};
|
||||
use parking_lot::{Mutex, RwLock};
|
||||
use std::collections::HashSet;
|
||||
use std::num::NonZeroUsize;
|
||||
use std::{collections::HashMap, fs, path::Path, sync::Arc};
|
||||
|
||||
mod basic_symbol_resolver;
|
||||
use basic_symbol_resolver::*;
|
||||
|
|
@ -113,9 +113,7 @@ fn handle_typevar_definition(
|
|||
x,
|
||||
HashMap::new(),
|
||||
)?;
|
||||
get_type_from_type_annotation_kinds(
|
||||
def_list, unifier, primitives, &ty, &mut None,
|
||||
)
|
||||
get_type_from_type_annotation_kinds(def_list, unifier, &ty, &mut None)
|
||||
})
|
||||
.collect::<Result<Vec<_>, _>>()?;
|
||||
let loc = func.location;
|
||||
|
|
@ -154,7 +152,7 @@ fn handle_typevar_definition(
|
|||
HashMap::new(),
|
||||
)?;
|
||||
let constraint =
|
||||
get_type_from_type_annotation_kinds(def_list, unifier, primitives, &ty, &mut None)?;
|
||||
get_type_from_type_annotation_kinds(def_list, unifier, &ty, &mut None)?;
|
||||
let loc = func.location;
|
||||
|
||||
Ok(unifier.get_fresh_const_generic_var(constraint, Some(generic_name), Some(loc)).ty)
|
||||
|
|
@ -241,6 +239,8 @@ fn handle_assignment_pattern(
|
|||
}
|
||||
|
||||
fn main() {
|
||||
const SIZE_T: u32 = usize::BITS;
|
||||
|
||||
let cli = CommandLineArgs::parse();
|
||||
let CommandLineArgs { file_name, threads, opt_level, emit_llvm, triple, mcpu, target_features } =
|
||||
cli;
|
||||
|
|
@ -273,24 +273,6 @@ fn main() {
|
|||
_ => OptimizationLevel::Aggressive,
|
||||
};
|
||||
|
||||
let target_machine_options = CodeGenTargetMachineOptions {
|
||||
triple,
|
||||
cpu: mcpu,
|
||||
features: target_features,
|
||||
reloc_mode: RelocMode::PIC,
|
||||
..host_target_machine
|
||||
};
|
||||
|
||||
let size_t = Context::create()
|
||||
.ptr_sized_int_type(
|
||||
&target_machine_options
|
||||
.create_target_machine(opt_level)
|
||||
.map(|tm| tm.get_target_data())
|
||||
.unwrap(),
|
||||
None,
|
||||
)
|
||||
.get_bit_width();
|
||||
|
||||
let program = match fs::read_to_string(file_name.clone()) {
|
||||
Ok(program) => program,
|
||||
Err(err) => {
|
||||
|
|
@ -299,9 +281,9 @@ fn main() {
|
|||
}
|
||||
};
|
||||
|
||||
let primitive: PrimitiveStore = TopLevelComposer::make_primitives(size_t).0;
|
||||
let primitive: PrimitiveStore = TopLevelComposer::make_primitives(SIZE_T).0;
|
||||
let (mut composer, builtins_def, builtins_ty) =
|
||||
TopLevelComposer::new(vec![], ComposerConfig::default(), size_t);
|
||||
TopLevelComposer::new(vec![], ComposerConfig::default(), SIZE_T);
|
||||
|
||||
let internal_resolver: Arc<ResolverInternal> = ResolverInternal {
|
||||
id_to_type: builtins_ty.into(),
|
||||
|
|
@ -389,7 +371,16 @@ fn main() {
|
|||
instance_to_stmt[""].clone()
|
||||
};
|
||||
|
||||
let llvm_options = CodeGenLLVMOptions { opt_level, target: target_machine_options };
|
||||
let llvm_options = CodeGenLLVMOptions {
|
||||
opt_level,
|
||||
target: CodeGenTargetMachineOptions {
|
||||
triple,
|
||||
cpu: mcpu,
|
||||
features: target_features,
|
||||
reloc_mode: RelocMode::PIC,
|
||||
..host_target_machine
|
||||
},
|
||||
};
|
||||
|
||||
let task = CodeGenTask {
|
||||
subst: Vec::default(),
|
||||
|
|
@ -412,7 +403,7 @@ fn main() {
|
|||
membuffer.lock().push(buffer);
|
||||
})));
|
||||
let threads = (0..threads)
|
||||
.map(|i| Box::new(DefaultCodeGenerator::new(format!("module{i}"), size_t)))
|
||||
.map(|i| Box::new(DefaultCodeGenerator::new(format!("module{i}"), SIZE_T)))
|
||||
.collect();
|
||||
let (registry, handles) = WorkerRegistry::create_workers(threads, top_level, &llvm_options, &f);
|
||||
registry.add_task(task);
|
||||
|
|
|
|||
|
|
@ -81,6 +81,7 @@ in rec {
|
|||
''
|
||||
mkdir -p $out/bin
|
||||
ln -s ${llvm-nac3}/bin/clang.exe $out/bin/clang-irrt.exe
|
||||
ln -s ${llvm-nac3}/bin/clang.exe $out/bin/clang-irrt-test.exe
|
||||
ln -s ${llvm-nac3}/bin/llvm-as.exe $out/bin/llvm-as-irrt.exe
|
||||
'';
|
||||
nac3artiq = pkgs.rustPlatform.buildRustPackage {
|
||||
|
|
|
|||
Loading…
Reference in New Issue