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base: newell:ndstrides-14-end
Branches Tags
newell:master
newell:enhance/global-var
newell:ndstrides-2-basic
newell:ndstrides-1-model
newell:refactor_composer
newell:ndstrides
newell:misc/update-pyo3
newell:misc/impl-tracert
newell:misc/mold
newell:ndstrides-14-end
newell:ndstrides-13-tests
newell:ndstrides-12-builtins
newell:ndstrides-11-matmul
newell:ndstrides-10-ops
newell:ndstrides-9-subassign
newell:ndstrides-8-transpose
newell:ndstrides-7-broadcasting
newell:ndstrides-6-reshape
newell:ndstrides-5-miscfuncs
newell:ndstrides-4-nparray
newell:ndstrides-3-indexing
newell:ndstrides-1-model-lifetime
newell:ndstrides-enhance-irrt
newell:ndstrides-intro
newell:issue-313
newell:refactor-primstore
newell:numpy-anyall
newell:enhance/cargo-test-standalone-demos
newell:issue-136
newell:rpc-obj-as-param
newell:iterators
newell:escape-analysis
newell:refactor_anto
M-Labs:ndstrides-14-end
M-Labs:ndstrides-13-tests
M-Labs:ndstrides-12-builtins
M-Labs:ndstrides-11-matmul
M-Labs:ndstrides-10-ops
M-Labs:ndstrides-9-subassign
M-Labs:ndstrides-8-transpose
M-Labs:ndstrides-7-broadcasting
M-Labs:ndstrides-6-reshape
M-Labs:ndstrides-5-miscfuncs
M-Labs:ndstrides-4-nparray
M-Labs:ndstrides-3-indexing
M-Labs:ndstrides-2-basic
M-Labs:ndstrides-1-model
M-Labs:master
M-Labs:ndstrides
M-Labs:refactor_composer
M-Labs:misc/update-pyo3
M-Labs:misc/impl-tracert
M-Labs:misc/mold
M-Labs:ndstrides-1-model-lifetime
M-Labs:ndstrides-enhance-irrt
M-Labs:ndstrides-intro
M-Labs:issue-313
M-Labs:refactor-primstore
M-Labs:numpy-anyall
M-Labs:enhance/cargo-test-standalone-demos
M-Labs:issue-136
M-Labs:rpc-obj-as-param
M-Labs:iterators
M-Labs:escape-analysis
M-Labs:refactor_anto
..
compare: newell:master
Branches Tags
newell:enhance/global-var
newell:ndstrides-2-basic
newell:ndstrides-1-model
newell:refactor_composer
newell:master
newell:ndstrides
newell:misc/update-pyo3
newell:misc/impl-tracert
newell:misc/mold
newell:ndstrides-14-end
newell:ndstrides-13-tests
newell:ndstrides-12-builtins
newell:ndstrides-11-matmul
newell:ndstrides-10-ops
newell:ndstrides-9-subassign
newell:ndstrides-8-transpose
newell:ndstrides-7-broadcasting
newell:ndstrides-6-reshape
newell:ndstrides-5-miscfuncs
newell:ndstrides-4-nparray
newell:ndstrides-3-indexing
newell:ndstrides-1-model-lifetime
newell:ndstrides-enhance-irrt
newell:ndstrides-intro
newell:issue-313
newell:refactor-primstore
newell:numpy-anyall
newell:enhance/cargo-test-standalone-demos
newell:issue-136
newell:rpc-obj-as-param
newell:iterators
newell:escape-analysis
newell:refactor_anto
M-Labs:ndstrides-14-end
M-Labs:ndstrides-13-tests
M-Labs:ndstrides-12-builtins
M-Labs:ndstrides-11-matmul
M-Labs:ndstrides-10-ops
M-Labs:ndstrides-9-subassign
M-Labs:ndstrides-8-transpose
M-Labs:ndstrides-7-broadcasting
M-Labs:ndstrides-6-reshape
M-Labs:ndstrides-5-miscfuncs
M-Labs:ndstrides-4-nparray
M-Labs:ndstrides-3-indexing
M-Labs:ndstrides-2-basic
M-Labs:ndstrides-1-model
M-Labs:master
M-Labs:ndstrides
M-Labs:refactor_composer
M-Labs:misc/update-pyo3
M-Labs:misc/impl-tracert
M-Labs:misc/mold
M-Labs:ndstrides-1-model-lifetime
M-Labs:ndstrides-enhance-irrt
M-Labs:ndstrides-intro
M-Labs:issue-313
M-Labs:refactor-primstore
M-Labs:numpy-anyall
M-Labs:enhance/cargo-test-standalone-demos
M-Labs:issue-136
M-Labs:rpc-obj-as-param
M-Labs:iterators
M-Labs:escape-analysis
M-Labs:refactor_anto

31 Commits
ndstrides- ... master

Author SHA1 Message Date
David Mak 581b2f7bb2 [standalone] Add demo for global variables 2024-10-04 13:24:30 +08:00
David Mak 746329ec5d [standalone] Implement symbol resolution for globals 2024-10-04 13:24:30 +08:00
David Mak e60e8e837f [core] Add support for global statements 2024-10-04 13:24:27 +08:00
David Mak 9fdbe9695d [core] Add generator to SymbolResolver::get_symbol_value 
Needed in a future commit.
 2024-10-04 13:20:29 +08:00
David Mak 8065e73598 [core] toplevel/composer: Add type analysis for global variables 2024-10-04 13:20:29 +08:00
David Mak 192290889b [core] Add IdentifierInfo 
Keeps track of whether an identifier refers to a global or local
variable.
 2024-10-04 13:20:24 +08:00
David Mak 1407553a2f [core] Implement parsing of global variables 
Globals are now parsed into symbol resolver and top level definitions.
 2024-10-04 13:18:29 +08:00
David Mak c7697606e1 [core] Add TopLevelDef::Variable 2024-10-04 13:09:25 +08:00
David Mak 88d0ccbf69 [standalone] Explicit panic when encountering a compilation error 
Otherwise scripts will continue to execute.
 2024-10-04 13:00:16 +08:00
David Mak a43b59539c [meta] Move variables declarations closer to where they are first used 2024-10-04 13:00:16 +08:00
David Mak fe06b2806f [meta] Reorganize order of use declarations 
Use declarations are now grouped into 4 groups:

- Declarations from the standard library
- Declarations from external crates
- Declarations from other crates in this project
- Declarations from within this module

Furthermore, all use declarations are grouped together to enhance
readability. super::super is also replaced by an equivalent crate::
declaration.
 2024-10-04 12:52:01 +08:00
David Mak 7f6c9a25ac [meta] Update Cargo dependencies 2024-10-04 12:52:01 +08:00
Sébastien Bourdeauducq 6c8382219f msys2: get python via numpy dependencies 2024-09-30 14:27:30 +08:00
Sebastien Bourdeauducq 9274a7b96b flake: update nixpkgs 2024-09-30 14:22:40 +08:00
Sébastien Bourdeauducq d1c0fe2900 cargo: update dependencies 2024-09-30 14:14:43 +08:00
mwojcik f2c047ba57 artiq: support async rpcs 
Co-authored-by: mwojcik <mw@m-labs.hk>
Co-committed-by: mwojcik <mw@m-labs.hk>
 2024-09-13 12:12:13 +08:00
David Mak 5e2e77a500 [meta] Bump inkwell to v0.5 2024-09-13 11:11:14 +08:00
David Mak f3cc4702b9 [meta] Update dependencies 2024-09-13 11:11:14 +08:00
David Mak 3e92c491f5 [standalone] Add tests creating ndarrays with tuple dims 2024-09-11 15:52:43 +08:00
lyken 7f629f1579 core: fix comment in unify_call 2024-09-11 15:46:19 +08:00
lyken 5640a793e2 core: allow np_full to take tuple shapes 2024-09-11 15:46:19 +08:00
David Mak abbaa506ad [standalone] Remove redundant recreation of TargetMachine 2024-09-09 14:27:10 +08:00
David Mak f3dc02d646 [meta] Apply cargo fmt 2024-09-09 14:24:52 +08:00
David Mak ea217eaea1 [meta] Update pre-commit config 
Directly invoke cargo using nix develop to avoid using the system cargo.
 2024-09-09 14:24:38 +08:00
Sébastien Bourdeauducq 5a34551905 allow the use of the LLVM shared library 
Which in turns allows working around the incompatibility of the LLVM static library
with Rust link-args=-rdynamic, which produces binaries that either fail to link (OpenBSD)
or segfault on startup (Linux).

The year is 2024 and compiler toolchains are still a trash fire like this.
 2024-09-09 11:17:31 +08:00
Sebastien Bourdeauducq 6098b1b853 fix previous commit 2024-09-06 11:32:08 +08:00
Sebastien Bourdeauducq 668ccb1c95 nac3core: expose inkwell and nac3parser 2024-09-06 11:06:26 +08:00
Sebastien Bourdeauducq a3c624d69d update all dependencies 2024-09-06 10:21:58 +08:00
Sébastien Bourdeauducq bd06155f34 irrt: compatibility with pre-C23 compilers 2024-09-05 18:54:55 +08:00
David Mak 9c33c4209c [core] Fix type of ndarray.element_type 
Should be the element type of the NDArray itself, not the pointer to its
type.
 2024-08-30 22:47:38 +08:00
Sebastien Bourdeauducq 122983f11c flake: update dependencies 2024-08-30 14:45:38 +08:00
106 changed files with 6772 additions and 8736 deletions
Show Stats Expand all files Collapse all files

8
.pre-commit-config.yaml
View File

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

333
Cargo.lock generated
View File

@ -75,33 +75,33 @@ dependencies = [
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@ -109,6 +109,15 @@ version = "2.6.0"
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@ -132,9 +141,9 @@ checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
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@ -142,9 +151,9 @@ dependencies = [
[[package]]
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version = "4.5.15"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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dependencies = [
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@ -154,14 +163,14 @@ dependencies = [
[[package]]
name = "clap_derive"
version = "4.5.13"
version = "4.5.18"
source = "registry+https://github.com/rust-lang/crates.io-index"
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[[package]]
@ -188,6 +197,15 @@ dependencies = [
"windows-sys 0.52.0",
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[[package]]
name = "cpufeatures"
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source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "608697df725056feaccfa42cffdaeeec3fccc4ffc38358ecd19b243e716a78e0"
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[[package]]
name = "crossbeam"
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@ -245,30 +263,23 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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@ -329,6 +340,16 @@ dependencies = [
"byteorder",
]
[[package]]
name = "generic-array"
version = "0.14.7"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "85649ca51fd72272d7821adaf274ad91c288277713d9c18820d8499a7ff69e9a"
dependencies = [
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]
[[package]]
name = "getopts"
version = "0.2.21"
@ -364,6 +385,12 @@ dependencies = [
"ahash",
]
[[package]]
name = "hashbrown"
version = "0.15.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "1e087f84d4f86bf4b218b927129862374b72199ae7d8657835f1e89000eea4fb"
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@ -376,6 +403,15 @@ version = "0.5.0"
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@ -388,12 +424,12 @@ dependencies = [
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@ -404,9 +440,9 @@ checksum = "b248f5224d1d606005e02c97f5aa4e88eeb230488bcc03bc9ca4d7991399f2b5"
[[package]]
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dependencies = [
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@ -510,9 +547,9 @@ checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe"
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@ -524,16 +561,6 @@ dependencies = [
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]
[[package]]
name = "libredox"
version = "0.1.3"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "c0ff37bd590ca25063e35af745c343cb7a0271906fb7b37e4813e8f79f00268d"
dependencies = [
"bitflags",
"libc",
]
[[package]]
name = "linked-hash-map"
version = "0.5.6"
@ -594,11 +621,9 @@ dependencies = [
name = "nac3artiq"
version = "0.1.0"
dependencies = [
"inkwell",
"itertools 0.13.0",
"itertools",
"nac3core",
"nac3ld",
"nac3parser",
"parking_lot",
"pyo3",
"tempfile",
@ -619,11 +644,11 @@ name = "nac3core"
version = "0.1.0"
dependencies = [
"crossbeam",
"indexmap 2.4.0",
"indexmap 2.6.0",
"indoc",
"inkwell",
"insta",
"itertools 0.13.0",
"itertools",
"nac3parser",
"parking_lot",
"rayon",
@ -661,9 +686,7 @@ name = "nac3standalone"
version = "0.1.0"
dependencies = [
"clap",
"inkwell",
"nac3core",
"nac3parser",
"parking_lot",
]
@ -675,9 +698,12 @@ checksum = "650eef8c711430f1a879fdd01d4745a7deea475becfb90269c06775983bbf086"
[[package]]
name = "once_cell"
version = "1.19.0"
version = "1.20.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "3fdb12b2476b595f9358c5161aa467c2438859caa136dec86c26fdd2efe17b92"
checksum = "82881c4be219ab5faaf2ad5e5e5ecdff8c66bd7402ca3160975c93b24961afd1"
dependencies = [
"portable-atomic",
]
[[package]]
name = "parking_lot"
@ -709,7 +735,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "b4c5cc86750666a3ed20bdaf5ca2a0344f9c67674cae0515bec2da16fbaa47db"
dependencies = [
"fixedbitset",
"indexmap 2.4.0",
"indexmap 2.6.0",
]
[[package]]
@ -752,7 +778,7 @@ dependencies = [
"phf_shared 0.11.2",
"proc-macro2",
"quote",
"syn 2.0.76",
"syn 2.0.79",
]
[[package]]
@ -781,9 +807,9 @@ checksum = "5be167a7af36ee22fe3115051bc51f6e6c7054c9348e28deb4f49bd6f705a315"
[[package]]
name = "portable-atomic"
version = "1.7.0"
version = "1.9.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "da544ee218f0d287a911e9c99a39a8c9bc8fcad3cb8db5959940044ecfc67265"
checksum = "cc9c68a3f6da06753e9335d63e27f6b9754dd1920d941135b7ea8224f141adb2"
[[package]]
name = "ppv-lite86"
@ -856,7 +882,7 @@ dependencies = [
"proc-macro2",
"pyo3-macros-backend",
"quote",
"syn 2.0.76",
"syn 2.0.79",
]
[[package]]
@ -869,7 +895,7 @@ dependencies = [
"proc-macro2",
"pyo3-build-config",
"quote",
"syn 2.0.76",
"syn 2.0.79",
]
[[package]]
@ -933,29 +959,18 @@ dependencies = [
[[package]]
name = "redox_syscall"
version = "0.5.3"
version = "0.5.7"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "2a908a6e00f1fdd0dfd9c0eb08ce85126f6d8bbda50017e74bc4a4b7d4a926a4"
checksum = "9b6dfecf2c74bce2466cabf93f6664d6998a69eb21e39f4207930065b27b771f"
dependencies = [
"bitflags",
]
[[package]]
name = "redox_users"
version = "0.4.6"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "ba009ff324d1fc1b900bd1fdb31564febe58a8ccc8a6fdbb93b543d33b13ca43"
dependencies = [
"getrandom",
"libredox",
"thiserror",
]
[[package]]
name = "regex"
version = "1.10.6"
version = "1.11.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "4219d74c6b67a3654a9fbebc4b419e22126d13d2f3c4a07ee0cb61ff79a79619"
checksum = "38200e5ee88914975b69f657f0801b6f6dccafd44fd9326302a4aaeecfacb1d8"
dependencies = [
"aho-corasick",
"memchr",
@ -965,9 +980,9 @@ dependencies = [
[[package]]
name = "regex-automata"
version = "0.4.7"
version = "0.4.8"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "38caf58cc5ef2fed281f89292ef23f6365465ed9a41b7a7754eb4e26496c92df"
checksum = "368758f23274712b504848e9d5a6f010445cc8b87a7cdb4d7cbee666c1288da3"
dependencies = [
"aho-corasick",
"memchr",
@ -976,9 +991,9 @@ dependencies = [
[[package]]
name = "regex-syntax"
version = "0.8.4"
version = "0.8.5"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "7a66a03ae7c801facd77a29370b4faec201768915ac14a721ba36f20bc9c209b"
checksum = "2b15c43186be67a4fd63bee50d0303afffcef381492ebe2c5d87f324e1b8815c"
[[package]]
name = "runkernel"
@ -989,9 +1004,9 @@ dependencies = [
[[package]]
name = "rustix"
version = "0.38.35"
version = "0.38.37"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "a85d50532239da68e9addb745ba38ff4612a242c1c7ceea689c4bc7c2f43c36f"
checksum = "8acb788b847c24f28525660c4d7758620a7210875711f79e7f663cc152726811"
dependencies = [
"bitflags",
"errno",
@ -1035,29 +1050,29 @@ checksum = "61697e0a1c7e512e84a621326239844a24d8207b4669b41bc18b32ea5cbf988b"
[[package]]
name = "serde"
version = "1.0.209"
version = "1.0.210"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "99fce0ffe7310761ca6bf9faf5115afbc19688edd00171d81b1bb1b116c63e09"
checksum = "c8e3592472072e6e22e0a54d5904d9febf8508f65fb8552499a1abc7d1078c3a"
dependencies = [
"serde_derive",
]
[[package]]
name = "serde_derive"
version = "1.0.209"
version = "1.0.210"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "a5831b979fd7b5439637af1752d535ff49f4860c0f341d1baeb6faf0f4242170"
checksum = "243902eda00fad750862fc144cea25caca5e20d615af0a81bee94ca738f1df1f"
dependencies = [
"proc-macro2",
"quote",
"syn 2.0.76",
"syn 2.0.79",
]
[[package]]
name = "serde_json"
version = "1.0.127"
version = "1.0.128"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "8043c06d9f82bd7271361ed64f415fe5e12a77fdb52e573e7f06a516dea329ad"
checksum = "6ff5456707a1de34e7e37f2a6fd3d3f808c318259cbd01ab6377795054b483d8"
dependencies = [
"itoa",
"memchr",
@ -1077,6 +1092,16 @@ dependencies = [
"yaml-rust",
]
[[package]]
name = "sha3"
version = "0.10.8"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "75872d278a8f37ef87fa0ddbda7802605cb18344497949862c0d4dcb291eba60"
dependencies = [
"digest",
"keccak",
]
[[package]]
name = "shlex"
version = "1.3.0"
@ -1147,7 +1172,7 @@ dependencies = [
"proc-macro2",
"quote",
"rustversion",
"syn 2.0.76",
"syn 2.0.79",
]
[[package]]
@ -1163,9 +1188,9 @@ dependencies = [
[[package]]
name = "syn"
version = "2.0.76"
version = "2.0.79"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "578e081a14e0cefc3279b0472138c513f37b41a08d5a3cca9b6e4e8ceb6cd525"
checksum = "89132cd0bf050864e1d38dc3bbc07a0eb8e7530af26344d3d2bbbef83499f590"
dependencies = [
"proc-macro2",
"quote",
@ -1180,9 +1205,9 @@ checksum = "61c41af27dd6d1e27b1b16b489db798443478cef1f06a660c96db617ba5de3b1"
[[package]]
name = "tempfile"
version = "3.12.0"
version = "3.13.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "04cbcdd0c794ebb0d4cf35e88edd2f7d2c4c3e9a5a6dab322839b321c6a87a64"
checksum = "f0f2c9fc62d0beef6951ccffd757e241266a2c833136efbe35af6cd2567dca5b"
dependencies = [
"cfg-if",
"fastrand",
@ -1193,13 +1218,12 @@ dependencies = [
[[package]]
name = "term"
version = "0.7.0"
version = "1.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "c59df8ac95d96ff9bede18eb7300b0fda5e5d8d90960e76f8e14ae765eedbf1f"
checksum = "4df4175de05129f31b80458c6df371a15e7fc3fd367272e6bf938e5c351c7ea0"
dependencies = [
"dirs-next",
"rustversion",
"winapi",
"home",
"windows-sys 0.52.0",
]
[[package]]
@ -1217,32 +1241,29 @@ dependencies = [
[[package]]
name = "thiserror"
version = "1.0.63"
version = "1.0.64"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "c0342370b38b6a11b6cc11d6a805569958d54cfa061a29969c3b5ce2ea405724"
checksum = "d50af8abc119fb8bb6dbabcfa89656f46f84aa0ac7688088608076ad2b459a84"
dependencies = [
"thiserror-impl",
]
[[package]]
name = "thiserror-impl"
version = "1.0.63"
version = "1.0.64"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "a4558b58466b9ad7ca0f102865eccc95938dca1a74a856f2b57b6629050da261"
checksum = "08904e7672f5eb876eaaf87e0ce17857500934f4981c4a0ab2b4aa98baac7fc3"
dependencies = [
"proc-macro2",
"quote",
"syn 2.0.76",
"syn 2.0.79",
]
[[package]]
name = "tiny-keccak"
version = "2.0.2"
name = "typenum"
version = "1.17.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "2c9d3793400a45f954c52e73d068316d76b6f4e36977e3fcebb13a2721e80237"
dependencies = [
"crunchy",
]
checksum = "42ff0bf0c66b8238c6f3b578df37d0b7848e55df8577b3f74f92a69acceeb825"
[[package]]
name = "unic-char-property"
@ -1298,27 +1319,27 @@ dependencies = [
[[package]]
name = "unicode-ident"
version = "1.0.12"
version = "1.0.13"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "3354b9ac3fae1ff6755cb6db53683adb661634f67557942dea4facebec0fee4b"
checksum = "e91b56cd4cadaeb79bbf1a5645f6b4f8dc5bde8834ad5894a8db35fda9efa1fe"
[[package]]
name = "unicode-width"
version = "0.1.13"
version = "0.1.14"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "0336d538f7abc86d282a4189614dfaa90810dfc2c6f6427eaf88e16311dd225d"
checksum = "7dd6e30e90baa6f72411720665d41d89b9a3d039dc45b8faea1ddd07f617f6af"
[[package]]
name = "unicode-xid"
version = "0.2.5"
version = "0.2.6"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "229730647fbc343e3a80e463c1db7f78f3855d3f3739bee0dda773c9a037c90a"
checksum = "ebc1c04c71510c7f702b52b7c350734c9ff1295c464a03335b00bb84fc54f853"
[[package]]
name = "unicode_names2"
version = "1.2.2"
version = "1.3.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "addeebf294df7922a1164f729fb27ebbbcea99cc32b3bf08afab62757f707677"
checksum = "d1673eca9782c84de5f81b82e4109dcfb3611c8ba0d52930ec4a9478f547b2dd"
dependencies = [
"phf",
"unicode_names2_generator",
@ -1326,9 +1347,9 @@ dependencies = [
[[package]]
name = "unicode_names2_generator"
version = "1.2.2"
version = "1.3.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "f444b8bba042fe3c1251ffaca35c603f2dc2ccc08d595c65a8c4f76f3e8426c0"
checksum = "b91e5b84611016120197efd7dc93ef76774f4e084cd73c9fb3ea4a86c570c56e"
dependencies = [
"getopts",
"log",
@ -1370,22 +1391,6 @@ version = "0.11.0+wasi-snapshot-preview1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "9c8d87e72b64a3b4db28d11ce29237c246188f4f51057d65a7eab63b7987e423"
[[package]]
name = "winapi"
version = "0.3.9"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "5c839a674fcd7a98952e593242ea400abe93992746761e38641405d28b00f419"
dependencies = [
"winapi-i686-pc-windows-gnu",
"winapi-x86_64-pc-windows-gnu",
]
[[package]]
name = "winapi-i686-pc-windows-gnu"
version = "0.4.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "ac3b87c63620426dd9b991e5ce0329eff545bccbbb34f3be09ff6fb6ab51b7b6"
[[package]]
name = "winapi-util"
version = "0.1.9"
@ -1395,12 +1400,6 @@ dependencies = [
"windows-sys 0.59.0",
]
[[package]]
name = "winapi-x86_64-pc-windows-gnu"
version = "0.4.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "712e227841d057c1ee1cd2fb22fa7e5a5461ae8e48fa2ca79ec42cfc1931183f"
[[package]]
name = "windows-sys"
version = "0.52.0"
@ -1510,5 +1509,5 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
dependencies = [
"proc-macro2",
"quote",
"syn 2.0.76",
"syn 2.0.79",
]

6
flake.lock
View File

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

8
nac3artiq/Cargo.toml
View File

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

13
nac3artiq/demo/min_artiq.py
View File

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

388
nac3artiq/src/codegen.rs
View File

@ -1,41 +1,3 @@
use nac3core::{
codegen::{
classes::{ListValue, RangeValue, UntypedArrayLikeAccessor},
expr::{destructure_range, gen_call},
llvm_intrinsics::{call_int_smax, call_stackrestore, call_stacksave},
model::*,
object::{any::AnyObject, ndarray::NDArrayObject},
stmt::{gen_block, gen_for_callback_incrementing, gen_if_callback, gen_with},
CodeGenContext, CodeGenerator,
},
symbol_resolver::ValueEnum,
toplevel::{
helper::{extract_ndims, PrimDef},
numpy::unpack_ndarray_var_tys,
DefinitionId, GenCall,
},
typecheck::typedef::{iter_type_vars, FunSignature, FuncArg, Type, TypeEnum, VarMap},
};
use nac3parser::ast::{Expr, ExprKind, Located, Stmt, StmtKind, StrRef};
use inkwell::{
context::Context,
module::Linkage,
types::IntType,
values::{BasicValue, BasicValueEnum, PointerValue, StructValue},
AddressSpace, IntPredicate, OptimizationLevel,
};
use pyo3::{
types::{PyDict, PyList},
PyObject, PyResult, Python,
};
use crate::{symbol_resolver::InnerResolver, timeline::TimeFns};
use inkwell::values::IntValue;
use itertools::Itertools;
use std::{
collections::{hash_map::DefaultHasher, HashMap},
hash::{Hash, Hasher},
@ -44,6 +6,39 @@ use std::{
sync::Arc,
};
use itertools::Itertools;
use pyo3::{
types::{PyDict, PyList},
PyObject, PyResult, Python,
};
use nac3core::{
codegen::{
classes::{
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayType,
NDArrayValue, ProxyType, ProxyValue, RangeValue, UntypedArrayLikeAccessor,
},
expr::{destructure_range, gen_call},
irrt::call_ndarray_calc_size,
llvm_intrinsics::{call_int_smax, call_memcpy_generic, call_stackrestore, call_stacksave},
stmt::{gen_block, gen_for_callback_incrementing, gen_if_callback, gen_with},
CodeGenContext, CodeGenerator,
},
inkwell::{
context::Context,
module::Linkage,
types::{BasicType, IntType},
values::{BasicValueEnum, IntValue, PointerValue, StructValue},
AddressSpace, IntPredicate, OptimizationLevel,
},
nac3parser::ast::{Expr, ExprKind, Located, Stmt, StmtKind, StrRef},
symbol_resolver::ValueEnum,
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, GenCall},
typecheck::typedef::{iter_type_vars, FunSignature, FuncArg, Type, TypeEnum, VarMap},
};
use crate::{symbol_resolver::InnerResolver, timeline::TimeFns};
/// The parallelism mode within a block.
#[derive(Copy, Clone, Eq, PartialEq)]
enum ParallelMode {
@ -459,42 +454,55 @@ fn format_rpc_arg<'ctx>(
// NAC3: NDArray = { usize, usize*, T* }
// libproto_artiq: NDArray = [data[..], dim_sz[..]]
let ndarray = AnyObject { ty: arg_ty, value: arg };
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
let llvm_i1 = ctx.ctx.bool_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
let dtype = ctx.get_llvm_type(generator, ndarray.dtype);
let ndims = ndarray.ndims_llvm(generator, ctx.ctx);
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, arg_ty);
let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
let llvm_arg_ty = NDArrayType::new(generator, ctx.ctx, llvm_elem_ty);
let llvm_arg = NDArrayValue::from_ptr_val(arg.into_pointer_value(), llvm_usize, None);
// `ndarray.data` is possibly not contiguous, and we need it to be contiguous for
// the reader.
// Turning it into a ContiguousNDArray to get a `data` that is contiguous.
let carray = ndarray.make_contiguous_ndarray(generator, ctx, Any(dtype));
let llvm_usize_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(llvm_arg_ty.size_type().size_of(), llvm_usize, "")
.unwrap();
let llvm_pdata_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(
llvm_elem_ty.ptr_type(AddressSpace::default()).size_of(),
llvm_usize,
"",
)
.unwrap();
let sizeof_sizet = Int(SizeT).size_of(generator, ctx.ctx);
let sizeof_sizet = Int(SizeT).truncate_or_bit_cast(generator, ctx, sizeof_sizet);
let dims_buf_sz =
ctx.builder.build_int_mul(llvm_arg.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
let sizeof_pdata = Ptr(Any(dtype)).size_of(generator, ctx.ctx);
let sizeof_pdata = Int(SizeT).truncate_or_bit_cast(generator, ctx, sizeof_pdata);
let buffer_size =
ctx.builder.build_int_add(dims_buf_sz, llvm_pdata_sizeof, "").unwrap();
let sizeof_buf_shape = sizeof_sizet.mul(ctx, ndims);
let sizeof_buf = sizeof_buf_shape.add(ctx, sizeof_pdata);
let buffer = ctx.builder.build_array_alloca(llvm_i8, buffer_size, "rpc.arg").unwrap();
let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, Some("rpc.arg"));
// buf = { data: void*, shape: [size_t; ndims]; }
let buf = Int(Byte).array_alloca(generator, ctx, sizeof_buf.value);
let buf_data = buf;
let buf_shape = buf_data.offset(ctx, sizeof_pdata.value);
call_memcpy_generic(
ctx,
buffer.base_ptr(ctx, generator),
llvm_arg.ptr_to_data(ctx),
llvm_pdata_sizeof,
llvm_i1.const_zero(),
);
// Write to `buf->data`
let carray_data = carray.get(generator, ctx, |f| f.data); // has type Ptr<Any>
let carray_data = carray_data.pointer_cast(generator, ctx, Int(Byte));
buf_data.copy_from(generator, ctx, carray_data, sizeof_pdata.value);
let pbuffer_dims_begin =
unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
call_memcpy_generic(
ctx,
pbuffer_dims_begin,
llvm_arg.dim_sizes().base_ptr(ctx, generator),
dims_buf_sz,
llvm_i1.const_zero(),
);
// Write to `buf->shape`
let carray_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
let carray_shape_i8 = carray_shape.pointer_cast(generator, ctx, Int(Byte));
buf_shape.copy_from(generator, ctx, carray_shape_i8, sizeof_buf_shape.value);
buf.value
buffer.base_ptr(ctx, generator)
}
_ => {
@ -504,7 +512,7 @@ fn format_rpc_arg<'ctx>(
ctx.builder.build_store(arg_slot, arg).unwrap();
ctx.builder
.build_bitcast(arg_slot, llvm_pi8, "rpc.arg")
.build_bit_cast(arg_slot, llvm_pi8, "rpc.arg")
.map(BasicValueEnum::into_pointer_value)
.unwrap()
}
@ -555,10 +563,8 @@ fn format_rpc_ret<'ctx>(
let result = match &*ctx.unifier.get_ty_immutable(ret_ty) {
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
// FIXME: It is possible to rewrite everything more neatly with `Model<'ctx>`, but this is not too important.
let num_0 = Int(SizeT).const_0(generator, ctx.ctx);
let num_8 = Int(SizeT).const_int(generator, ctx.ctx, 8, false);
let llvm_i1 = ctx.ctx.bool_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
// Round `val` up to its modulo `power_of_two`
let round_up = |ctx: &mut CodeGenContext<'ctx, '_>,
@ -584,36 +590,60 @@ fn format_rpc_ret<'ctx>(
.unwrap()
};
// Setup types
let (elem_ty, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ret_ty);
let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
let llvm_ret_ty = NDArrayType::new(generator, ctx.ctx, llvm_elem_ty);
// Allocate the resulting ndarray
// A condition after format_rpc_ret ensures this will not be popped this off.
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ret_ty);
let ndims = extract_ndims(&ctx.unifier, ndims);
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
let ndarray = llvm_ret_ty.new_value(generator, ctx, Some("rpc.result"));
// NOTE: Current content of `ndarray`:
// - * `data` - **NOT YET** allocated.
// - * `itemsize` - initialized to be size_of(dtype).
// - * `ndims` - initialized.
// - * `shape` - allocated; has uninitialized values.
// - * `strides` - allocated; has uninitialized values.
// Setup ndims
let ndims =
if let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) {
assert_eq!(values.len(), 1);
let itemsize = ndarray.instance.get(generator, ctx, |f| f.itemsize); // Same as doing a `ctx.get_llvm_type` on `dtype` and get its `size_of()`.
let dtype_llvm = ctx.get_llvm_type(generator, dtype);
u64::try_from(values[0].clone()).unwrap()
} else {
unreachable!();
};
// Set `ndarray.ndims`
ndarray.store_ndims(ctx, generator, llvm_usize.const_int(ndims, false));
// Allocate `ndarray.shape` [size_t; ndims]
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray.load_ndims(ctx));
/*
ndarray now:
- .ndims: initialized
- .shape: allocated but uninitialized .shape
- .data: uninitialized
*/
let llvm_usize_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(llvm_usize.size_of(), llvm_usize, "")
.unwrap();
let llvm_pdata_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(
llvm_elem_ty.ptr_type(AddressSpace::default()).size_of(),
llvm_usize,
"",
)
.unwrap();
let llvm_elem_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(llvm_elem_ty.size_of().unwrap(), llvm_usize, "")
.unwrap();
// Allocates a buffer for the initial RPC'ed object, which is guaranteed to be
// (4 + 4 * ndims) bytes with 8-byte alignment
let sizeof_size_t = Int(SizeT).size_of(generator, ctx.ctx);
let sizeof_size_t = Int(SizeT).z_extend_or_truncate(generator, ctx, sizeof_size_t); // sizeof(size_t)
let sizeof_ptr = Ptr(Int(Byte)).size_of(generator, ctx.ctx);
let sizeof_ptr = Int(SizeT).z_extend_or_truncate(generator, ctx, sizeof_ptr); // sizeof(uint8_t*)
let sizeof_shape = ndarray.ndims_llvm(generator, ctx.ctx).mul(ctx, sizeof_size_t); // sizeof([size_t; ndims]); same as the # of bytes of `ndarray.shape`.
// Size of the buffer for the initial `rpc_recv()`.
let unaligned_buffer_size = sizeof_ptr.add(ctx, sizeof_shape); // sizeof(uint8_t*) + sizeof([size_t; ndims]).
let buffer_size = round_up(ctx, unaligned_buffer_size.value, num_8.value);
let buffer_size = unsafe { Int(SizeT).believe_value(buffer_size) };
let sizeof_dims =
ctx.builder.build_int_mul(ndarray.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
let unaligned_buffer_size =
ctx.builder.build_int_add(sizeof_dims, llvm_pdata_sizeof, "").unwrap();
let buffer_size = round_up(ctx, unaligned_buffer_size, llvm_usize.const_int(8, false));
let stackptr = call_stacksave(ctx, None);
// Just to be absolutely sure, alloca in [i8 x 8] slices to force 8-byte alignment
@ -621,16 +651,18 @@ fn format_rpc_ret<'ctx>(
.builder
.build_array_alloca(
llvm_i8_8,
ctx.builder.build_int_unsigned_div(buffer_size.value, num_8.value, "").unwrap(),
ctx.builder
.build_int_unsigned_div(buffer_size, llvm_usize.const_int(8, false), "")
.unwrap(),
"rpc.buffer",
)
.unwrap();
let buffer = ctx
.builder
.build_bitcast(buffer, llvm_pi8, "")
.build_bit_cast(buffer, llvm_pi8, "")
.map(BasicValueEnum::into_pointer_value)
.unwrap();
let buffer = unsafe { Ptr(Int(Byte)).believe_value(buffer) };
let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, None);
// The first call to `rpc_recv` reads the top-level ndarray object: [pdata, shape]
//
@ -638,20 +670,24 @@ fn format_rpc_ret<'ctx>(
let ndarray_nbytes = ctx
.build_call_or_invoke(
rpc_recv,
&[buffer.value.into()], // Reads [usize; ndims]
&[buffer.base_ptr(ctx, generator).into()], // Reads [usize; ndims]. NOTE: We are allocated [size_t; ndims].
"rpc.size.next",
)
.map(BasicValueEnum::into_int_value)
.unwrap();
let ndarray_nbytes = unsafe { Int(SizeT).believe_value(ndarray_nbytes) };
// debug_assert(ndarray_nbytes > 0)
if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
let cmp = ndarray_nbytes.compare(ctx, IntPredicate::UGT, num_0);
ctx.make_assert(
generator,
cmp.value,
ctx.builder
.build_int_compare(
IntPredicate::UGT,
ndarray_nbytes,
ndarray_nbytes.get_type().const_zero(),
"",
)
.unwrap(),
"0:AssertionError",
"Unexpected RPC termination for ndarray - Expected data buffer next",
[None, None, None],
@ -660,39 +696,49 @@ fn format_rpc_ret<'ctx>(
}
// Copy shape from the buffer to `ndarray.shape`.
// We need to skip the first `sizeof(uint8_t*)` bytes to skip the `pdata` in `[pdata, shape]`.
let pbuffer_shape = buffer.offset(ctx, sizeof_ptr.value);
let pbuffer_shape = pbuffer_shape.pointer_cast(generator, ctx, Int(SizeT));
// Copy shape from buffer to `ndarray.shape`
ndarray.copy_shape_from_array(generator, ctx, pbuffer_shape);
let pbuffer_dims =
unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
call_memcpy_generic(
ctx,
ndarray.dim_sizes().base_ptr(ctx, generator),
pbuffer_dims,
sizeof_dims,
llvm_i1.const_zero(),
);
// Restore stack from before allocation of buffer
call_stackrestore(ctx, stackptr);
// Allocate `ndarray.data`.
// `ndarray.shape` must be initialized beforehand in this implementation
// (for ndarray.create_data() to know how many elements to allocate)
ndarray.create_data(generator, ctx); // NOTE: the strides of `ndarray` has also been set to contiguous in `::create_data()`.
let num_elements =
call_ndarray_calc_size(generator, ctx, &ndarray.dim_sizes(), (None, None));
// debug_assert(nelems * sizeof(T) >= ndarray_nbytes)
if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
let num_elements = ndarray.size(generator, ctx);
let expected_ndarray_nbytes = num_elements.mul(ctx, itemsize);
let cmp = expected_ndarray_nbytes.compare(ctx, IntPredicate::UGE, ndarray_nbytes);
let sizeof_data =
ctx.builder.build_int_mul(num_elements, llvm_elem_sizeof, "").unwrap();
ctx.make_assert(
generator,
cmp.value,
ctx.builder.build_int_compare(IntPredicate::UGE,
sizeof_data,
ndarray_nbytes,
"",
).unwrap(),
"0:AssertionError",
"Unexpected allocation size request for ndarray data - Expected up to {0} bytes, got {1} bytes",
[Some(expected_ndarray_nbytes.value), Some(ndarray_nbytes.value), None],
[Some(sizeof_data), Some(ndarray_nbytes), None],
ctx.current_loc,
);
}
let ndarray_data = ndarray.instance.get(generator, ctx, |f| f.data);
ndarray.create_data(ctx, llvm_elem_ty, num_elements);
let ndarray_data = ndarray.data().base_ptr(ctx, generator);
let ndarray_data_i8 =
ctx.builder.build_pointer_cast(ndarray_data, llvm_pi8, "").unwrap();
// NOTE: Currently on `prehead_bb`
ctx.builder.build_unconditional_branch(head_bb).unwrap();
@ -701,7 +747,7 @@ fn format_rpc_ret<'ctx>(
ctx.builder.position_at_end(head_bb);
let phi = ctx.builder.build_phi(llvm_pi8, "rpc.ptr").unwrap();
phi.add_incoming(&[(&ndarray_data.value, prehead_bb)]);
phi.add_incoming(&[(&ndarray_data_i8, prehead_bb)]);
let alloc_size = ctx
.build_call_or_invoke(rpc_recv, &[phi.as_basic_value()], "rpc.size.next")
@ -716,12 +762,12 @@ fn format_rpc_ret<'ctx>(
ctx.builder.position_at_end(alloc_bb);
// Align the allocation to sizeof(T)
let alloc_size = round_up(ctx, alloc_size, itemsize.value);
let alloc_size = round_up(ctx, alloc_size, llvm_elem_sizeof);
let alloc_ptr = ctx
.builder
.build_array_alloca(
dtype_llvm,
ctx.builder.build_int_unsigned_div(alloc_size, itemsize.value, "").unwrap(),
llvm_elem_ty,
ctx.builder.build_int_unsigned_div(alloc_size, llvm_elem_sizeof, "").unwrap(),
"rpc.alloc",
)
.unwrap();
@ -731,12 +777,12 @@ fn format_rpc_ret<'ctx>(
ctx.builder.build_unconditional_branch(head_bb).unwrap();
ctx.builder.position_at_end(tail_bb);
ndarray.instance.value.as_basic_value_enum()
ndarray.as_base_value().into()
}
_ => {
let slot = ctx.builder.build_alloca(llvm_ret_ty, "rpc.ret.slot").unwrap();
let slotgen = ctx.builder.build_bitcast(slot, llvm_pi8, "rpc.ret.ptr").unwrap();
let slotgen = ctx.builder.build_bit_cast(slot, llvm_pi8, "rpc.ret.ptr").unwrap();
ctx.builder.build_unconditional_branch(head_bb).unwrap();
ctx.builder.position_at_end(head_bb);
@ -757,7 +803,7 @@ fn format_rpc_ret<'ctx>(
let alloc_ptr =
ctx.builder.build_array_alloca(llvm_pi8, alloc_size, "rpc.alloc").unwrap();
let alloc_ptr =
ctx.builder.build_bitcast(alloc_ptr, llvm_pi8, "rpc.alloc.ptr").unwrap();
ctx.builder.build_bit_cast(alloc_ptr, llvm_pi8, "rpc.alloc.ptr").unwrap();
phi.add_incoming(&[(&alloc_ptr, alloc_bb)]);
ctx.builder.build_unconditional_branch(head_bb).unwrap();
@ -775,6 +821,7 @@ fn rpc_codegen_callback_fn<'ctx>(
fun: (&FunSignature, DefinitionId),
args: Vec<(Option<StrRef>, ValueEnum<'ctx>)>,
generator: &mut dyn CodeGenerator,
is_async: bool,
) -> Result<Option<BasicValueEnum<'ctx>>, String> {
let int8 = ctx.ctx.i8_type();
let int32 = ctx.ctx.i32_type();
@ -883,6 +930,29 @@ fn rpc_codegen_callback_fn<'ctx>(
}
// call
if is_async {
let rpc_send_async = ctx.module.get_function("rpc_send_async").unwrap_or_else(|| {
ctx.module.add_function(
"rpc_send_async",
ctx.ctx.void_type().fn_type(
&[
int32.into(),
tag_ptr_type.ptr_type(AddressSpace::default()).into(),
ptr_type.ptr_type(AddressSpace::default()).into(),
],
false,
),
None,
)
});
ctx.builder
.build_call(
rpc_send_async,
&[service_id.into(), tag_ptr.into(), args_ptr.into()],
"rpc.send",
)
.unwrap();
} else {
let rpc_send = ctx.module.get_function("rpc_send").unwrap_or_else(|| {
ctx.module.add_function(
"rpc_send",
@ -900,10 +970,15 @@ fn rpc_codegen_callback_fn<'ctx>(
ctx.builder
.build_call(rpc_send, &[service_id.into(), tag_ptr.into(), args_ptr.into()], "rpc.send")
.unwrap();
}
// reclaim stack space used by arguments
call_stackrestore(ctx, stackptr);
if is_async {
// async RPCs do not return any values
Ok(None)
} else {
let result = format_rpc_ret(generator, ctx, fun.0.ret);
if !result.is_some_and(|res| res.get_type().is_pointer_type()) {
@ -912,6 +987,7 @@ fn rpc_codegen_callback_fn<'ctx>(
}
Ok(result)
}
}
pub fn attributes_writeback(
@ -1006,7 +1082,7 @@ pub fn attributes_writeback(
let args: Vec<_> =
values.into_iter().map(|(_, val)| (None, ValueEnum::Dynamic(val))).collect();
if let Err(e) =
rpc_codegen_callback_fn(ctx, None, (&fun, PrimDef::Int32.id()), args, generator)
rpc_codegen_callback_fn(ctx, None, (&fun, PrimDef::Int32.id()), args, generator, false)
{
return Ok(Err(e));
}
@ -1016,9 +1092,9 @@ pub fn attributes_writeback(
Ok(())
}
pub fn rpc_codegen_callback() -> Arc<GenCall> {
Arc::new(GenCall::new(Box::new(|ctx, obj, fun, args, generator| {
rpc_codegen_callback_fn(ctx, obj, fun, args, generator)
pub fn rpc_codegen_callback(is_async: bool) -> Arc<GenCall> {
Arc::new(GenCall::new(Box::new(move |ctx, obj, fun, args, generator| {
rpc_codegen_callback_fn(ctx, obj, fun, args, generator, is_async)
})))
}
@ -1283,46 +1359,56 @@ fn polymorphic_print<'ctx>(
}
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
fmt.push_str("array([");
flush(ctx, generator, &mut fmt, &mut args);
let ndarray = AnyObject { ty, value };
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
let val = NDArrayValue::from_ptr_val(value.into_pointer_value(), llvm_usize, None);
let len = call_ndarray_calc_size(generator, ctx, &val.dim_sizes(), (None, None));
let last =
ctx.builder.build_int_sub(len, llvm_usize.const_int(1, false), "").unwrap();
let num_0 = Int(SizeT).const_0(generator, ctx.ctx);
// Print `ndarray` as a flat list delimited by interspersed with ", \0"
ndarray.foreach(generator, ctx, |generator, ctx, _, hdl| {
let i = hdl.get_index(generator, ctx);
let scalar = hdl.get_scalar(generator, ctx);
// if (i != 0) { puts(", "); }
gen_if_callback(
gen_for_callback_incrementing(
generator,
ctx,
|_, ctx| {
let not_first = i.compare(ctx, IntPredicate::NE, num_0);
Ok(not_first.value)
},
|generator, ctx| {
printf(ctx, generator, ", \0".into(), Vec::default());
Ok(())
},
|_, _| Ok(()),
)?;
None,
llvm_usize.const_zero(),
(len, false),
|generator, ctx, _, i| {
let elem = unsafe { val.data().get_unchecked(ctx, generator, &i, None) };
// Print element
polymorphic_print(
ctx,
generator,
&[(scalar.ty, scalar.value.into())],
&[(elem_ty, elem.into())],
"",
None,
true,
as_rtio,
)?;
gen_if_callback(
generator,
ctx,
|_, ctx| {
Ok(ctx
.builder
.build_int_compare(IntPredicate::ULT, i, last, "")
.unwrap())
},
|generator, ctx| {
printf(ctx, generator, ", \0".into(), Vec::default());
Ok(())
})?;
},
|_, _| Ok(()),
)?;
Ok(())
},
llvm_usize.const_int(1, false),
)?;
fmt.push_str(")]");
flush(ctx, generator, &mut fmt, &mut args);

162
nac3artiq/src/lib.rs
View File

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

208
nac3artiq/src/symbol_resolver.rs
View File

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

19
nac3artiq/src/timeline.rs
View File

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

8
nac3core/Cargo.toml
View File

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

3
nac3core/build.rs
View File

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

11
nac3core/irrt/irrt.cpp
View File

@ -2,14 +2,5 @@
#include "irrt/int_types.hpp"
#include "irrt/list.hpp"
#include "irrt/math.hpp"
#include "irrt/range.hpp"
#include "irrt/ndarray.hpp"
#include "irrt/slice.hpp"
#include "irrt/ndarray/basic.hpp"
#include "irrt/ndarray/def.hpp"
#include "irrt/ndarray/iter.hpp"
#include "irrt/ndarray/indexing.hpp"
#include "irrt/ndarray/array.hpp"
#include "irrt/ndarray/reshape.hpp"
#include "irrt/ndarray/broadcast.hpp"
#include "irrt/ndarray/transpose.hpp"
#include "irrt/ndarray/matmul.hpp"

9
nac3core/irrt/irrt/exception.hpp
View File

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

11
nac3core/irrt/irrt/int_types.hpp
View File

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

15
nac3core/irrt/irrt/list.hpp
View File

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

144
nac3core/irrt/irrt/ndarray.hpp Normal file
View File

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

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

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#pragma once
#include "irrt/debug.hpp"
#include "irrt/exception.hpp"
#include "irrt/int_types.hpp"
#include "irrt/ndarray/def.hpp"
namespace {
namespace ndarray {
namespace basic {
/**
* @brief Assert that `shape` does not contain negative dimensions.
*
* @param ndims Number of dimensions in `shape`
* @param shape The shape to check on
*/
template<typename SizeT>
void assert_shape_no_negative(SizeT ndims, const SizeT* shape) {
for (SizeT axis = 0; axis < ndims; axis++) {
if (shape[axis] < 0) {
raise_exception(SizeT, EXN_VALUE_ERROR,
"negative dimensions are not allowed; axis {0} "
"has dimension {1}",
axis, shape[axis], NO_PARAM);
}
}
}
/**
* @brief Assert that two shapes are the same in the context of writing output to an ndarray.
*/
template<typename SizeT>
void assert_output_shape_same(SizeT ndarray_ndims,
const SizeT* ndarray_shape,
SizeT output_ndims,
const SizeT* output_shape) {
if (ndarray_ndims != output_ndims) {
// There is no corresponding NumPy error message like this.
raise_exception(SizeT, EXN_VALUE_ERROR, "Cannot write output of ndims {0} to an ndarray with ndims {1}",
output_ndims, ndarray_ndims, NO_PARAM);
}
for (SizeT axis = 0; axis < ndarray_ndims; axis++) {
if (ndarray_shape[axis] != output_shape[axis]) {
// There is no corresponding NumPy error message like this.
raise_exception(SizeT, EXN_VALUE_ERROR,
"Mismatched dimensions on axis {0}, output has "
"dimension {1}, but destination ndarray has dimension {2}.",
axis, output_shape[axis], ndarray_shape[axis]);
}
}
}
/**
* @brief Return the number of elements of an ndarray given its shape.
*
* @param ndims Number of dimensions in `shape`
* @param shape The shape of the ndarray
*/
template<typename SizeT>
SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
SizeT size = 1;
for (SizeT axis = 0; axis < ndims; axis++)
size *= shape[axis];
return size;
}
/**
* @brief Compute the array indices of the `nth` (0-based) element of an ndarray given only its shape.
*
* @param ndims Number of elements in `shape` and `indices`
* @param shape The shape of the ndarray
* @param indices The returned indices indexing the ndarray with shape `shape`.
* @param nth The index of the element of interest.
*/
template<typename SizeT>
void set_indices_by_nth(SizeT ndims, const SizeT* shape, SizeT* indices, SizeT nth) {
for (SizeT i = 0; i < ndims; i++) {
SizeT axis = ndims - i - 1;
SizeT dim = shape[axis];
indices[axis] = nth % dim;
nth /= dim;
}
}
/**
* @brief Return the number of elements of an `ndarray`
*
* This function corresponds to `<an_ndarray>.size`
*/
template<typename SizeT>
SizeT size(const NDArray<SizeT>* ndarray) {
return calc_size_from_shape(ndarray->ndims, ndarray->shape);
}
/**
* @brief Return of the number of its content of an `ndarray`.
*
* This function corresponds to `<an_ndarray>.nbytes`.
*/
template<typename SizeT>
SizeT nbytes(const NDArray<SizeT>* ndarray) {
return size(ndarray) * ndarray->itemsize;
}
/**
* @brief Get the `len()` of an ndarray, and asserts that `ndarray` is a sized object.
*
* This function corresponds to `<an_ndarray>.__len__`.
*
* @param dst_length The length.
*/
template<typename SizeT>
SizeT len(const NDArray<SizeT>* ndarray) {
// numpy prohibits `__len__` on unsized objects
if (ndarray->ndims == 0) {
raise_exception(SizeT, EXN_TYPE_ERROR, "len() of unsized object", NO_PARAM, NO_PARAM, NO_PARAM);
} else {
return ndarray->shape[0];
}
}
/**
* @brief Return a boolean indicating if `ndarray` is (C-)contiguous.
*
* You may want to see ndarray's rules for C-contiguity:
* https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
*/
template<typename SizeT>
bool is_c_contiguous(const NDArray<SizeT>* ndarray) {
// References:
// - tinynumpy's implementation:
// https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L102
// - ndarray's flags["C_CONTIGUOUS"]:
// https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags
// - ndarray's rules for C-contiguity:
// https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
// From
// https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45:
//
// The traditional rule is that for an array to be flagged as C contiguous,
// the following must hold:
//
// strides[-1] == itemsize
// strides[i] == shape[i+1] * strides[i + 1]
// [...]
// According to these rules, a 0- or 1-dimensional array is either both
// C- and F-contiguous, or neither; and an array with 2+ dimensions
// can be C- or F- contiguous, or neither, but not both. Though there
// there are exceptions for arrays with zero or one item, in the first
// case the check is relaxed up to and including the first dimension
// with shape[i] == 0. In the second case `strides == itemsize` will
// can be true for all dimensions and both flags are set.
if (ndarray->ndims == 0) {
return true;
}
if (ndarray->strides[ndarray->ndims - 1] != ndarray->itemsize) {
return false;
}
for (SizeT i = 1; i < ndarray->ndims; i++) {
SizeT axis_i = ndarray->ndims - i - 1;
if (ndarray->strides[axis_i] != ndarray->shape[axis_i + 1] * ndarray->strides[axis_i + 1]) {
return false;
}
}
return true;
}
/**
* @brief Return the pointer to the element indexed by `indices` along the ndarray's axes.
*
* This function does no bound check.
*/
template<typename SizeT>
uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray, const SizeT* indices) {
uint8_t* element = ndarray->data;
for (SizeT dim_i = 0; dim_i < ndarray->ndims; dim_i++)
element += indices[dim_i] * ndarray->strides[dim_i];
return element;
}
/**
* @brief Return the pointer to the nth (0-based) element of `ndarray` in flattened view.
*
* This function does no bound check.
*/
template<typename SizeT>
uint8_t* get_nth_pelement(const NDArray<SizeT>* ndarray, SizeT nth) {
uint8_t* element = ndarray->data;
for (SizeT i = 0; i < ndarray->ndims; i++) {
SizeT axis = ndarray->ndims - i - 1;
SizeT dim = ndarray->shape[axis];
element += ndarray->strides[axis] * (nth % dim);
nth /= dim;
}
return element;
}
/**
* @brief Update the strides of an ndarray given an ndarray `shape` to be contiguous.
*
* You might want to read https://ajcr.net/stride-guide-part-1/.
*/
template<typename SizeT>
void set_strides_by_shape(NDArray<SizeT>* ndarray) {
SizeT stride_product = 1;
for (SizeT i = 0; i < ndarray->ndims; i++) {
SizeT axis = ndarray->ndims - i - 1;
ndarray->strides[axis] = stride_product * ndarray->itemsize;
stride_product *= ndarray->shape[axis];
}
}
/**
* @brief Set an element in `ndarray`.
*
* @param pelement Pointer to the element in `ndarray` to be set.
* @param pvalue Pointer to the value `pelement` will be set to.
*/
template<typename SizeT>
void set_pelement_value(NDArray<SizeT>* ndarray, uint8_t* pelement, const uint8_t* pvalue) {
__builtin_memcpy(pelement, pvalue, ndarray->itemsize);
}
/**
* @brief Copy data from one ndarray to another of the exact same size and itemsize.
*
* Both ndarrays will be viewed in their flatten views when copying the elements.
*/
template<typename SizeT>
void copy_data(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
// TODO: Make this faster with memcpy when we see a contiguous segment.
// TODO: Handle overlapping.
debug_assert_eq(SizeT, src_ndarray->itemsize, dst_ndarray->itemsize);
for (SizeT i = 0; i < size(src_ndarray); i++) {
auto src_element = ndarray::basic::get_nth_pelement(src_ndarray, i);
auto dst_element = ndarray::basic::get_nth_pelement(dst_ndarray, i);
ndarray::basic::set_pelement_value(dst_ndarray, dst_element, src_element);
}
}
} // namespace basic
} // namespace ndarray
} // namespace
extern "C" {
using namespace ndarray::basic;
void __nac3_ndarray_util_assert_shape_no_negative(int32_t ndims, int32_t* shape) {
assert_shape_no_negative(ndims, shape);
}
void __nac3_ndarray_util_assert_shape_no_negative64(int64_t ndims, int64_t* shape) {
assert_shape_no_negative(ndims, shape);
}
void __nac3_ndarray_util_assert_output_shape_same(int32_t ndarray_ndims,
const int32_t* ndarray_shape,
int32_t output_ndims,
const int32_t* output_shape) {
assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims, output_shape);
}
void __nac3_ndarray_util_assert_output_shape_same64(int64_t ndarray_ndims,
const int64_t* ndarray_shape,
int64_t output_ndims,
const int64_t* output_shape) {
assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims, output_shape);
}
uint32_t __nac3_ndarray_size(NDArray<int32_t>* ndarray) {
return size(ndarray);
}
uint64_t __nac3_ndarray_size64(NDArray<int64_t>* ndarray) {
return size(ndarray);
}
uint32_t __nac3_ndarray_nbytes(NDArray<int32_t>* ndarray) {
return nbytes(ndarray);
}
uint64_t __nac3_ndarray_nbytes64(NDArray<int64_t>* ndarray) {
return nbytes(ndarray);
}
int32_t __nac3_ndarray_len(NDArray<int32_t>* ndarray) {
return len(ndarray);
}
int64_t __nac3_ndarray_len64(NDArray<int64_t>* ndarray) {
return len(ndarray);
}
bool __nac3_ndarray_is_c_contiguous(NDArray<int32_t>* ndarray) {
return is_c_contiguous(ndarray);
}
bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t>* ndarray) {
return is_c_contiguous(ndarray);
}
uint8_t* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray, int32_t nth) {
return get_nth_pelement(ndarray, nth);
}
uint8_t* __nac3_ndarray_get_nth_pelement64(const NDArray<int64_t>* ndarray, int64_t nth) {
return get_nth_pelement(ndarray, nth);
}
uint8_t* __nac3_ndarray_get_pelement_by_indices(const NDArray<int32_t>* ndarray, int32_t* indices) {
return get_pelement_by_indices(ndarray, indices);
}
uint8_t* __nac3_ndarray_get_pelement_by_indices64(const NDArray<int64_t>* ndarray, int64_t* indices) {
return get_pelement_by_indices(ndarray, indices);
}
void __nac3_ndarray_set_strides_by_shape(NDArray<int32_t>* ndarray) {
set_strides_by_shape(ndarray);
}
void __nac3_ndarray_set_strides_by_shape64(NDArray<int64_t>* ndarray) {
set_strides_by_shape(ndarray);
}
void __nac3_ndarray_copy_data(NDArray<int32_t>* src_ndarray, NDArray<int32_t>* dst_ndarray) {
copy_data(src_ndarray, dst_ndarray);
}
void __nac3_ndarray_copy_data64(NDArray<int64_t>* src_ndarray, NDArray<int64_t>* dst_ndarray) {
copy_data(src_ndarray, dst_ndarray);
}
}

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

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#pragma once
#include "irrt/int_types.hpp"
namespace {
/**
* @brief The NDArray object
*
* Official numpy implementation:
* https://github.com/numpy/numpy/blob/735a477f0bc2b5b84d0e72d92f224bde78d4e069/doc/source/reference/c-api/types-and-structures.rst
*/
template<typename SizeT>
struct NDArray {
/**
* @brief The underlying data this `ndarray` is pointing to.
*/
uint8_t* data;
/**
* @brief The number of bytes of a single element in `data`.
*/
SizeT itemsize;
/**
* @brief The number of dimensions of this shape.
*/
SizeT ndims;
/**
* @brief The NDArray shape, with length equal to `ndims`.
*
* Note that it may contain 0.
*/
SizeT* shape;
/**
* @brief Array strides, with length equal to `ndims`
*
* The stride values are in units of bytes, not number of elements.
*
* Note that `strides` can have negative values or contain 0.
*/
SizeT* strides;
};
} // namespace

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#pragma once
#include "irrt/exception.hpp"
#include "irrt/int_types.hpp"
#include "irrt/ndarray/basic.hpp"
#include "irrt/ndarray/def.hpp"
#include "irrt/range.hpp"
#include "irrt/slice.hpp"
namespace {
typedef uint8_t NDIndexType;
/**
* @brief A single element index
*
* `data` points to a `int32_t`.
*/
const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
/**
* @brief A slice index
*
* `data` points to a `Slice<int32_t>`.
*/
const NDIndexType ND_INDEX_TYPE_SLICE = 1;
/**
* @brief `np.newaxis` / `None`
*
* `data` is unused.
*/
const NDIndexType ND_INDEX_TYPE_NEWAXIS = 2;
/**
* @brief `Ellipsis` / `...`
*
* `data` is unused.
*/
const NDIndexType ND_INDEX_TYPE_ELLIPSIS = 3;
/**
* @brief An index used in ndarray indexing
*
* That is:
* ```
* my_ndarray[::-1, 3, ..., np.newaxis]
* ^^^^ ^ ^^^ ^^^^^^^^^^ each of these is represented by an NDIndex.
* ```
*/
struct NDIndex {
/**
* @brief Enum tag to specify the type of index.
*
* Please see the comment of each enum constant.
*/
NDIndexType type;
/**
* @brief The accompanying data associated with `type`.
*
* Please see the comment of each enum constant.
*/
uint8_t* data;
};
} // namespace
namespace {
namespace ndarray {
namespace indexing {
/**
* @brief Perform ndarray "basic indexing" (https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
*
* This function is very similar to performing `dst_ndarray = src_ndarray[indices]` in Python.
*
* This function also does proper assertions on `indices` to check for out of bounds access and more.
*
* # Notes on `dst_ndarray`
* The caller is responsible for allocating space for the resulting ndarray.
* Here is what this function expects from `dst_ndarray` when called:
* - `dst_ndarray->data` does not have to be initialized.
* - `dst_ndarray->itemsize` does not have to be initialized.
* - `dst_ndarray->ndims` must be initialized, and it must be equal to the expected `ndims` of the `dst_ndarray` after
* indexing `src_ndarray` with `indices`.
* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
* When this function call ends:
* - `dst_ndarray->data` is set to `src_ndarray->data`.
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`.
* - `dst_ndarray->ndims` is unchanged.
* - `dst_ndarray->shape` is updated according to how `src_ndarray` is indexed.
* - `dst_ndarray->strides` is updated accordingly by how ndarray indexing works.
*
* @param indices indices to index `src_ndarray`, ordered in the same way you would write them in Python.
* @param src_ndarray The NDArray to be indexed.
* @param dst_ndarray The resulting NDArray after indexing. Further details in the comments above,
*/
template<typename SizeT>
void index(SizeT num_indices, const NDIndex* indices, const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
// Validate `indices`.
// Expected value of `dst_ndarray->ndims`.
SizeT expected_dst_ndims = src_ndarray->ndims;
// To check for "too many indices for array: array is ?-dimensional, but ? were indexed"
SizeT num_indexed = 0;
// There may be ellipsis `...` in `indices`. There can only be 0 or 1 ellipsis.
SizeT num_ellipsis = 0;
for (SizeT i = 0; i < num_indices; i++) {
if (indices[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
expected_dst_ndims--;
num_indexed++;
} else if (indices[i].type == ND_INDEX_TYPE_SLICE) {
num_indexed++;
} else if (indices[i].type == ND_INDEX_TYPE_NEWAXIS) {
expected_dst_ndims++;
} else if (indices[i].type == ND_INDEX_TYPE_ELLIPSIS) {
num_ellipsis++;
if (num_ellipsis > 1) {
raise_exception(SizeT, EXN_INDEX_ERROR, "an index can only have a single ellipsis ('...')", NO_PARAM,
NO_PARAM, NO_PARAM);
}
} else {
__builtin_unreachable();
}
}
debug_assert_eq(SizeT, expected_dst_ndims, dst_ndarray->ndims);
if (src_ndarray->ndims - num_indexed < 0) {
raise_exception(SizeT, EXN_INDEX_ERROR,
"too many indices for array: array is {0}-dimensional, "
"but {1} were indexed",
src_ndarray->ndims, num_indices, NO_PARAM);
}
dst_ndarray->data = src_ndarray->data;
dst_ndarray->itemsize = src_ndarray->itemsize;
// Reference code:
// https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
SizeT src_axis = 0;
SizeT dst_axis = 0;
for (int32_t i = 0; i < num_indices; i++) {
const NDIndex* index = &indices[i];
if (index->type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
SizeT input = (SizeT) * ((int32_t*)index->data);
SizeT k = slice::resolve_index_in_length(src_ndarray->shape[src_axis], input);
if (k == -1) {
raise_exception(SizeT, EXN_INDEX_ERROR,
"index {0} is out of bounds for axis {1} "
"with size {2}",
input, src_axis, src_ndarray->shape[src_axis]);
}
dst_ndarray->data += k * src_ndarray->strides[src_axis];
src_axis++;
} else if (index->type == ND_INDEX_TYPE_SLICE) {
Slice<int32_t>* slice = (Slice<int32_t>*)index->data;
Range<int32_t> range = slice->indices_checked<SizeT>(src_ndarray->shape[src_axis]);
dst_ndarray->data += (SizeT)range.start * src_ndarray->strides[src_axis];
dst_ndarray->strides[dst_axis] = ((SizeT)range.step) * src_ndarray->strides[src_axis];
dst_ndarray->shape[dst_axis] = (SizeT)range.len<SizeT>();
dst_axis++;
src_axis++;
} else if (index->type == ND_INDEX_TYPE_NEWAXIS) {
dst_ndarray->strides[dst_axis] = 0;
dst_ndarray->shape[dst_axis] = 1;
dst_axis++;
} else if (index->type == ND_INDEX_TYPE_ELLIPSIS) {
// The number of ':' entries this '...' implies.
SizeT ellipsis_size = src_ndarray->ndims - num_indexed;
for (SizeT j = 0; j < ellipsis_size; j++) {
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
dst_axis++;
src_axis++;
}
} else {
__builtin_unreachable();
}
}
for (; dst_axis < dst_ndarray->ndims; dst_axis++, src_axis++) {
dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
}
debug_assert_eq(SizeT, src_ndarray->ndims, src_axis);
debug_assert_eq(SizeT, dst_ndarray->ndims, dst_axis);
}
} // namespace indexing
} // namespace ndarray
} // namespace
extern "C" {
using namespace ndarray::indexing;
void __nac3_ndarray_index(int32_t num_indices,
NDIndex* indices,
NDArray<int32_t>* src_ndarray,
NDArray<int32_t>* dst_ndarray) {
index(num_indices, indices, src_ndarray, dst_ndarray);
}
void __nac3_ndarray_index64(int64_t num_indices,
NDIndex* indices,
NDArray<int64_t>* src_ndarray,
NDArray<int64_t>* dst_ndarray) {
index(num_indices, indices, src_ndarray, dst_ndarray);
}
}

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nac3core/irrt/irrt/ndarray/iter.hpp
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#pragma once
#include "irrt/int_types.hpp"
#include "irrt/ndarray/def.hpp"
namespace {
/**
* @brief Helper struct to enumerate through an ndarray *efficiently*.
*
* Example usage (in pseudo-code):
* ```
* // Suppose my_ndarray has been initialized, with shape [2, 3] and dtype `double`
* NDIter nditer;
* nditer.initialize(my_ndarray);
* while (nditer.has_element()) {
* // This body is run 6 (= my_ndarray.size) times.
*
* // [0, 0] -> [0, 1] -> [0, 2] -> [1, 0] -> [1, 1] -> [1, 2] -> end
* print(nditer.indices);
*
* // 0 -> 1 -> 2 -> 3 -> 4 -> 5
* print(nditer.nth);
*
* // <1st element> -> <2nd element> -> ... -> <6th element> -> end
* print(*((double *) nditer.element))
*
* nditer.next(); // Go to next element.
* }
* ```
*
* Interesting cases:
* - If `my_ndarray.ndims` == 0, there is one iteration.
* - If `my_ndarray.shape` contains zeroes, there are no iterations.
*/
template<typename SizeT>
struct NDIter {
// Information about the ndarray being iterated over.
SizeT ndims;
SizeT* shape;
SizeT* strides;
/**
* @brief The current indices.
*
* Must be allocated by the caller.
*/
SizeT* indices;
/**
* @brief The nth (0-based) index of the current indices.
*
* Initially this is 0.
*/
SizeT nth;
/**
* @brief Pointer to the current element.
*
* Initially this points to first element of the ndarray.
*/
uint8_t* element;
/**
* @brief Cache for the product of shape.
*
* Could be 0 if `shape` has 0s in it.
*/
SizeT size;
void initialize(SizeT ndims, SizeT* shape, SizeT* strides, uint8_t* element, SizeT* indices) {
this->ndims = ndims;
this->shape = shape;
this->strides = strides;
this->indices = indices;
this->element = element;
// Compute size
this->size = 1;
for (SizeT i = 0; i < ndims; i++) {
this->size *= shape[i];
}
// `indices` starts on all 0s.
for (SizeT axis = 0; axis < ndims; axis++)
indices[axis] = 0;
nth = 0;
}
void initialize_by_ndarray(NDArray<SizeT>* ndarray, SizeT* indices) {
// NOTE: ndarray->data is pointing to the first element, and `NDIter`'s `element` should also point to the first
// element as well.
this->initialize(ndarray->ndims, ndarray->shape, ndarray->strides, ndarray->data, indices);
}
// Is the current iteration valid?
// If true, then `element`, `indices` and `nth` contain details about the current element.
bool has_element() { return nth < size; }
// Go to the next element.
void next() {
for (SizeT i = 0; i < ndims; i++) {
SizeT axis = ndims - i - 1;
indices[axis]++;
if (indices[axis] >= shape[axis]) {
indices[axis] = 0;
// TODO: There is something called backstrides to speedup iteration.
// See https://ajcr.net/stride-guide-part-1/, and
// https://docs.scipy.org/doc/numpy-1.13.0/reference/c-api.types-and-structures.html#c.PyArrayIterObject.PyArrayIterObject.backstrides.
element -= strides[axis] * (shape[axis] - 1);
} else {
element += strides[axis];
break;
}
}
nth++;
}
};
} // namespace
extern "C" {
void __nac3_nditer_initialize(NDIter<int32_t>* iter, NDArray<int32_t>* ndarray, int32_t* indices) {
iter->initialize_by_ndarray(ndarray, indices);
}
void __nac3_nditer_initialize64(NDIter<int64_t>* iter, NDArray<int64_t>* ndarray, int64_t* indices) {
iter->initialize_by_ndarray(ndarray, indices);
}
bool __nac3_nditer_has_element(NDIter<int32_t>* iter) {
return iter->has_element();
}
bool __nac3_nditer_has_element64(NDIter<int64_t>* iter) {
return iter->has_element();
}
void __nac3_nditer_next(NDIter<int32_t>* iter) {
iter->next();
}
void __nac3_nditer_next64(NDIter<int64_t>* iter) {
iter->next();
}
}

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nac3core/irrt/irrt/ndarray/matmul.hpp
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#pragma once
#include "irrt/debug.hpp"
#include "irrt/exception.hpp"
#include "irrt/int_types.hpp"
#include "irrt/ndarray/basic.hpp"
#include "irrt/ndarray/broadcast.hpp"
#include "irrt/ndarray/iter.hpp"
// NOTE: Everything would be much easier and elegant if einsum is implemented.
namespace {
namespace ndarray {
namespace matmul {
/**
* @brief Perform the broadcast in `np.einsum("...ij,...jk->...ik", a, b)`.
*
* Example:
* Suppose `a_shape == [1, 97, 4, 2]`
* and `b_shape == [99, 98, 1, 2, 5]`,
*
* ...then `new_a_shape == [99, 98, 97, 4, 2]`,
* `new_b_shape == [99, 98, 97, 2, 5]`,
* and `dst_shape == [99, 98, 97, 4, 5]`.
* ^^^^^^^^^^ ^^^^
* (broadcasted) (4x2 @ 2x5 => 4x5)
*
* @param a_ndims Length of `a_shape`.
* @param a_shape Shape of `a`.
* @param b_ndims Length of `b_shape`.
* @param b_shape Shape of `b`.
* @param final_ndims Should be equal to `max(a_ndims, b_ndims)`. This is the length of `new_a_shape`,
* `new_b_shape`, and `dst_shape` - the number of dimensions after broadcasting.
*/
template<typename SizeT>
void calculate_shapes(SizeT a_ndims,
SizeT* a_shape,
SizeT b_ndims,
SizeT* b_shape,
SizeT final_ndims,
SizeT* new_a_shape,
SizeT* new_b_shape,
SizeT* dst_shape) {
debug_assert(SizeT, a_ndims >= 2);
debug_assert(SizeT, b_ndims >= 2);
debug_assert_eq(SizeT, max(a_ndims, b_ndims), final_ndims);
// Check that a and b are compatible for matmul
if (a_shape[a_ndims - 1] != b_shape[b_ndims - 2]) {
// This is a custom error message. Different from NumPy.
raise_exception(SizeT, EXN_VALUE_ERROR, "Cannot multiply LHS (shape ?x{0}) with RHS (shape {1}x?})",
a_shape[a_ndims - 1], b_shape[b_ndims - 2], NO_PARAM);
}
const SizeT num_entries = 2;
ShapeEntry<SizeT> entries[num_entries] = {{.ndims = a_ndims - 2, .shape = a_shape},
{.ndims = b_ndims - 2, .shape = b_shape}};
// TODO: Optimize this
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, new_a_shape);
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, new_b_shape);
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, dst_shape);
new_a_shape[final_ndims - 2] = a_shape[a_ndims - 2];
new_a_shape[final_ndims - 1] = a_shape[a_ndims - 1];
new_b_shape[final_ndims - 2] = b_shape[b_ndims - 2];
new_b_shape[final_ndims - 1] = b_shape[b_ndims - 1];
dst_shape[final_ndims - 2] = a_shape[a_ndims - 2];
dst_shape[final_ndims - 1] = b_shape[b_ndims - 1];
}
} // namespace matmul
} // namespace ndarray
} // namespace
extern "C" {
using namespace ndarray::matmul;
void __nac3_ndarray_matmul_calculate_shapes(int32_t a_ndims,
int32_t* a_shape,
int32_t b_ndims,
int32_t* b_shape,
int32_t final_ndims,
int32_t* new_a_shape,
int32_t* new_b_shape,
int32_t* dst_shape) {
calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims, new_a_shape, new_b_shape, dst_shape);
}
void __nac3_ndarray_matmul_calculate_shapes64(int64_t a_ndims,
int64_t* a_shape,
int64_t b_ndims,
int64_t* b_shape,
int64_t final_ndims,
int64_t* new_a_shape,
int64_t* new_b_shape,
int64_t* dst_shape) {
calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims, new_a_shape, new_b_shape, dst_shape);
}
}

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nac3core/irrt/irrt/ndarray/reshape.hpp
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#pragma once
#include "irrt/exception.hpp"
#include "irrt/int_types.hpp"
#include "irrt/ndarray/def.hpp"
namespace {
namespace ndarray {
namespace reshape {
/**
* @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
*
* If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
* modified to contain the resolved dimension.
*
* To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
* `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
*
* @param size The `.size` of `<ndarray>`
* @param new_ndims Number of elements in `new_shape`
* @param new_shape Target shape to reshape to
*/
template<typename SizeT>
void resolve_and_check_new_shape(SizeT size, SizeT new_ndims, SizeT* new_shape) {
// Is there a -1 in `new_shape`?
bool neg1_exists = false;
// Location of -1, only initialized if `neg1_exists` is true
SizeT neg1_axis_i;
// The computed ndarray size of `new_shape`
SizeT new_size = 1;
for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++) {
SizeT dim = new_shape[axis_i];
if (dim < 0) {
if (dim == -1) {
if (neg1_exists) {
// Multiple `-1` found. Throw an error.
raise_exception(SizeT, EXN_VALUE_ERROR, "can only specify one unknown dimension", NO_PARAM,
NO_PARAM, NO_PARAM);
} else {
neg1_exists = true;
neg1_axis_i = axis_i;
}
} else {
// TODO: What? In `np.reshape` any negative dimensions is
// treated like its `-1`.
//
// Try running `np.zeros((3, 4)).reshape((-999, 2))`
//
// It is not documented by numpy.
// Throw an error for now...
raise_exception(SizeT, EXN_VALUE_ERROR, "Found non -1 negative dimension {0} on axis {1}", dim, axis_i,
NO_PARAM);
}
} else {
new_size *= dim;
}
}
bool can_reshape;
if (neg1_exists) {
// Let `x` be the unknown dimension
// Solve `x * <new_size> = <size>`
if (new_size == 0 && size == 0) {
// `x` has infinitely many solutions
can_reshape = false;
} else if (new_size == 0 && size != 0) {
// `x` has no solutions
can_reshape = false;
} else if (size % new_size != 0) {
// `x` has no integer solutions
can_reshape = false;
} else {
can_reshape = true;
new_shape[neg1_axis_i] = size / new_size; // Resolve dimension
}
} else {
can_reshape = (new_size == size);
}
if (!can_reshape) {
raise_exception(SizeT, EXN_VALUE_ERROR, "cannot reshape array of size {0} into given shape", size, NO_PARAM,
NO_PARAM);
}
}
} // namespace reshape
} // namespace ndarray
} // namespace
extern "C" {
void __nac3_ndarray_reshape_resolve_and_check_new_shape(int32_t size, int32_t new_ndims, int32_t* new_shape) {
ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
}
void __nac3_ndarray_reshape_resolve_and_check_new_shape64(int64_t size, int64_t new_ndims, int64_t* new_shape) {
ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
}
}

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nac3core/irrt/irrt/ndarray/transpose.hpp
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#pragma once
#include "irrt/debug.hpp"
#include "irrt/exception.hpp"
#include "irrt/int_types.hpp"
#include "irrt/ndarray/def.hpp"
#include "irrt/slice.hpp"
/*
* Notes on `np.transpose(<array>, <axes>)`
*
* TODO: `axes`, if specified, can actually contain negative indices,
* but it is not documented in numpy.
*
* Supporting it for now.
*/
namespace {
namespace ndarray {
namespace transpose {
/**
* @brief Do assertions on `<axes>` in `np.transpose(<array>, <axes>)`.
*
* Note that `np.transpose`'s `<axe>` argument is optional. If the argument
* is specified but the user, use this function to do assertions on it.
*
* @param ndims The number of dimensions of `<array>`
* @param num_axes Number of elements in `<axes>` as specified by the user.
* This should be equal to `ndims`. If not, a "ValueError: axes don't match array" is thrown.
* @param axes The user specified `<axes>`.
*/
template<typename SizeT>
void assert_transpose_axes(SizeT ndims, SizeT num_axes, const SizeT* axes) {
if (ndims != num_axes) {
raise_exception(SizeT, EXN_VALUE_ERROR, "axes don't match array", NO_PARAM, NO_PARAM, NO_PARAM);
}
// TODO: Optimize this
bool* axe_specified = (bool*)__builtin_alloca(sizeof(bool) * ndims);
for (SizeT i = 0; i < ndims; i++)
axe_specified[i] = false;
for (SizeT i = 0; i < ndims; i++) {
SizeT axis = slice::resolve_index_in_length(ndims, axes[i]);
if (axis == -1) {
// TODO: numpy actually throws a `numpy.exceptions.AxisError`
raise_exception(SizeT, EXN_VALUE_ERROR, "axis {0} is out of bounds for array of dimension {1}", axis, ndims,
NO_PARAM);
}
if (axe_specified[axis]) {
raise_exception(SizeT, EXN_VALUE_ERROR, "repeated axis in transpose", NO_PARAM, NO_PARAM, NO_PARAM);
}
axe_specified[axis] = true;
}
}
/**
* @brief Create a transpose view of `src_ndarray` and perform proper assertions.
*
* This function is very similar to doing `dst_ndarray = np.transpose(src_ndarray, <axes>)`.
* If `<axes>` is supposed to be `None`, caller can pass in a `nullptr` to `<axes>`.
*
* The transpose view created is returned by modifying `dst_ndarray`.
*
* The caller is responsible for setting up `dst_ndarray` before calling this function.
* Here is what this function expects from `dst_ndarray` when called:
* - `dst_ndarray->data` does not have to be initialized.
* - `dst_ndarray->itemsize` does not have to be initialized.
* - `dst_ndarray->ndims` must be initialized, must be equal to `src_ndarray->ndims`.
* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
* When this function call ends:
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
* - `dst_ndarray->ndims` is unchanged
* - `dst_ndarray->shape` is updated according to how `np.transpose` works
* - `dst_ndarray->strides` is updated according to how `np.transpose` works
*
* @param src_ndarray The NDArray to build a transpose view on
* @param dst_ndarray The resulting NDArray after transpose. Further details in the comments above,
* @param num_axes Number of elements in axes. Unused if `axes` is nullptr.
* @param axes Axes permutation. Set it to `nullptr` if `<axes>` is `None`.
*/
template<typename SizeT>
void transpose(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray, SizeT num_axes, const SizeT* axes) {
debug_assert_eq(SizeT, src_ndarray->ndims, dst_ndarray->ndims);
const auto ndims = src_ndarray->ndims;
if (axes != nullptr)
assert_transpose_axes(ndims, num_axes, axes);
dst_ndarray->data = src_ndarray->data;
dst_ndarray->itemsize = src_ndarray->itemsize;
// Check out https://ajcr.net/stride-guide-part-2/ to see how `np.transpose` works behind the scenes.
if (axes == nullptr) {
// `np.transpose(<array>, axes=None)`
/*
* Minor note: `np.transpose(<array>, axes=None)` is equivalent to
* `np.transpose(<array>, axes=[N-1, N-2, ..., 0])` - basically it
* is reversing the order of strides and shape.
*
* This is a fast implementation to handle this special (but very common) case.
*/
for (SizeT axis = 0; axis < ndims; axis++) {
dst_ndarray->shape[axis] = src_ndarray->shape[ndims - axis - 1];
dst_ndarray->strides[axis] = src_ndarray->strides[ndims - axis - 1];
}
} else {
// `np.transpose(<array>, <axes>)`
// Permute strides and shape according to `axes`, while resolving negative indices in `axes`
for (SizeT axis = 0; axis < ndims; axis++) {
// `i` cannot be OUT_OF_BOUNDS because of assertions
SizeT i = slice::resolve_index_in_length(ndims, axes[axis]);
dst_ndarray->shape[axis] = src_ndarray->shape[i];
dst_ndarray->strides[axis] = src_ndarray->strides[i];
}
}
}
} // namespace transpose
} // namespace ndarray
} // namespace
extern "C" {
using namespace ndarray::transpose;
void __nac3_ndarray_transpose(const NDArray<int32_t>* src_ndarray,
NDArray<int32_t>* dst_ndarray,
int32_t num_axes,
const int32_t* axes) {
transpose(src_ndarray, dst_ndarray, num_axes, axes);
}
void __nac3_ndarray_transpose64(const NDArray<int64_t>* src_ndarray,
NDArray<int64_t>* dst_ndarray,
int64_t num_axes,
const int64_t* axes) {
transpose(src_ndarray, dst_ndarray, num_axes, axes);
}
}

47
nac3core/irrt/irrt/range.hpp
View File

@ -1,47 +0,0 @@
#pragma once
#include "irrt/debug.hpp"
#include "irrt/int_types.hpp"
namespace {
namespace range {
template<typename T>
T len(T start, T stop, T step) {
// Reference:
// https://github.com/python/cpython/blob/9dbd12375561a393eaec4b21ee4ac568a407cdb0/Objects/rangeobject.c#L933
if (step > 0 && start < stop)
return 1 + (stop - 1 - start) / step;
else if (step < 0 && start > stop)
return 1 + (start - 1 - stop) / (-step);
else
return 0;
}
} // namespace range
/**
* @brief A Python range.
*/
template<typename T>
struct Range {
T start;
T stop;
T step;
/**
* @brief Calculate the `len()` of this range.
*/
template<typename SizeT>
T len() {
debug_assert(SizeT, step != 0);
return range::len(start, stop, step);
}
};
} // namespace
extern "C" {
using namespace range;
SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end, const SliceIndex step) {
return len(start, end, step);
}
}

150
nac3core/irrt/irrt/slice.hpp
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@ -1,145 +1,6 @@
#pragma once
#include "irrt/debug.hpp"
#include "irrt/exception.hpp"
#include "irrt/int_types.hpp"
#include "irrt/math_util.hpp"
#include "irrt/range.hpp"
namespace {
namespace slice {
/**
* @brief Resolve a possibly negative index in a list of a known length.
*
* Returns -1 if the resolved index is out of the list's bounds.
*/
template<typename T>
T resolve_index_in_length(T length, T index) {
T resolved = index < 0 ? length + index : index;
if (0 <= resolved && resolved < length) {
return resolved;
} else {
return -1;
}
}
/**
* @brief Resolve a slice as a range.
*
* This is equivalent to `range(*slice(start, stop, step).indices(length))` in Python.
*/
template<typename T>
void indices(bool start_defined,
T start,
bool stop_defined,
T stop,
bool step_defined,
T step,
T length,
T* range_start,
T* range_stop,
T* range_step) {
// Reference: https://github.com/python/cpython/blob/main/Objects/sliceobject.c#L388
*range_step = step_defined ? step : 1;
bool step_is_negative = *range_step < 0;
T lower, upper;
if (step_is_negative) {
lower = -1;
upper = length - 1;
} else {
lower = 0;
upper = length;
}
if (start_defined) {
*range_start = start < 0 ? max(lower, start + length) : min(upper, start);
} else {
*range_start = step_is_negative ? upper : lower;
}
if (stop_defined) {
*range_stop = stop < 0 ? max(lower, stop + length) : min(upper, stop);
} else {
*range_stop = step_is_negative ? lower : upper;
}
}
} // namespace slice
/**
* @brief A Python-like slice with **unresolved** indices.
*/
template<typename T>
struct Slice {
bool start_defined;
T start;
bool stop_defined;
T stop;
bool step_defined;
T step;
Slice() { this->reset(); }
void reset() {
this->start_defined = false;
this->stop_defined = false;
this->step_defined = false;
}
void set_start(T start) {
this->start_defined = true;
this->start = start;
}
void set_stop(T stop) {
this->stop_defined = true;
this->stop = stop;
}
void set_step(T step) {
this->step_defined = true;
this->step = step;
}
/**
* @brief Resolve this slice as a range.
*
* In Python, this would be `range(*slice(start, stop, step).indices(length))`.
*/
template<typename SizeT>
Range<T> indices(T length) {
// Reference:
// https://github.com/python/cpython/blob/main/Objects/sliceobject.c#L388
debug_assert(SizeT, length >= 0);
Range<T> result;
slice::indices(start_defined, start, stop_defined, stop, step_defined, step, length, &result.start,
&result.stop, &result.step);
return result;
}
/**
* @brief Like `.indices()` but with assertions.
*/
template<typename SizeT>
Range<T> indices_checked(T length) {
// TODO: Switch to `SizeT length`
if (length < 0) {
raise_exception(SizeT, EXN_VALUE_ERROR, "length should not be negative, got {0}", length, NO_PARAM,
NO_PARAM);
}
if (this->step_defined && this->step == 0) {
raise_exception(SizeT, EXN_VALUE_ERROR, "slice step cannot be zero", NO_PARAM, NO_PARAM, NO_PARAM);
}
return this->indices<SizeT>(length);
}
};
} // namespace
extern "C" {
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
@ -153,4 +14,15 @@ SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
}
return i;
}
SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end, const SliceIndex step) {
SliceIndex diff = end - start;
if (diff > 0 && step > 0) {
return ((diff - 1) / step) + 1;
} else if (diff < 0 && step < 0) {
return ((diff + 1) / step) + 1;
} else {
return 0;
}
}
}

1702
nac3core/src/codegen/builtin_fns.rs
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File diff suppressed because it is too large Load Diff

639
nac3core/src/codegen/classes.rs
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@ -1,13 +1,17 @@
use crate::codegen::{CodeGenContext, CodeGenerator};
use inkwell::context::Context;
use inkwell::types::{ArrayType, BasicType, StructType};
use inkwell::values::{ArrayValue, BasicValue, StructValue};
use inkwell::{
types::{AnyTypeEnum, BasicTypeEnum, IntType, PointerType},
values::{BasicValueEnum, IntValue, PointerValue},
context::Context,
types::{AnyTypeEnum, ArrayType, BasicType, BasicTypeEnum, IntType, PointerType, StructType},
values::{ArrayValue, BasicValue, BasicValueEnum, IntValue, PointerValue, StructValue},
AddressSpace, IntPredicate,
};
use crate::codegen::{
irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
llvm_intrinsics::call_int_umin,
stmt::gen_for_callback_incrementing,
CodeGenContext, CodeGenerator,
};
/// A LLVM type that is used to represent a non-primitive type in NAC3.
pub trait ProxyType<'ctx>: Into<Self::Base> {
/// The LLVM type of which values of this type possess. This is usually a
@ -1136,3 +1140,626 @@ impl<'ctx> From<RangeValue<'ctx>> for PointerValue<'ctx> {
value.as_base_value()
}
}
/// Proxy type for a `ndarray` type in LLVM.
#[derive(Debug, PartialEq, Eq, Clone, Copy)]
pub struct NDArrayType<'ctx> {
ty: PointerType<'ctx>,
llvm_usize: IntType<'ctx>,
}
impl<'ctx> NDArrayType<'ctx> {
/// Checks whether `llvm_ty` represents a `ndarray` type, returning [Err] if it does not.
pub fn is_type(llvm_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Result<(), String> {
let llvm_ndarray_ty = llvm_ty.get_element_type();
let AnyTypeEnum::StructType(llvm_ndarray_ty) = llvm_ndarray_ty else {
return Err(format!("Expected struct type for `NDArray` type, got {llvm_ndarray_ty}"));
};
if llvm_ndarray_ty.count_fields() != 3 {
return Err(format!(
"Expected 3 fields in `NDArray`, got {}",
llvm_ndarray_ty.count_fields()
));
}
let ndarray_ndims_ty = llvm_ndarray_ty.get_field_type_at_index(0).unwrap();
let Ok(ndarray_ndims_ty) = IntType::try_from(ndarray_ndims_ty) else {
return Err(format!("Expected int type for `ndarray.0`, got {ndarray_ndims_ty}"));
};
if ndarray_ndims_ty.get_bit_width() != llvm_usize.get_bit_width() {
return Err(format!(
"Expected {}-bit int type for `ndarray.0`, got {}-bit int",
llvm_usize.get_bit_width(),
ndarray_ndims_ty.get_bit_width()
));
}
let ndarray_dims_ty = llvm_ndarray_ty.get_field_type_at_index(1).unwrap();
let Ok(ndarray_pdims) = PointerType::try_from(ndarray_dims_ty) else {
return Err(format!("Expected pointer type for `ndarray.1`, got {ndarray_dims_ty}"));
};
let ndarray_dims = ndarray_pdims.get_element_type();
let Ok(ndarray_dims) = IntType::try_from(ndarray_dims) else {
return Err(format!(
"Expected pointer-to-int type for `ndarray.1`, got pointer-to-{ndarray_dims}"
));
};
if ndarray_dims.get_bit_width() != llvm_usize.get_bit_width() {
return Err(format!(
"Expected pointer-to-{}-bit int type for `ndarray.1`, got pointer-to-{}-bit int",
llvm_usize.get_bit_width(),
ndarray_dims.get_bit_width()
));
}
let ndarray_data_ty = llvm_ndarray_ty.get_field_type_at_index(2).unwrap();
let Ok(_) = PointerType::try_from(ndarray_data_ty) else {
return Err(format!("Expected pointer type for `ndarray.2`, got {ndarray_data_ty}"));
};
Ok(())
}
/// Creates an instance of [`ListType`].
#[must_use]
pub fn new<G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &'ctx Context,
dtype: BasicTypeEnum<'ctx>,
) -> Self {
let llvm_usize = generator.get_size_type(ctx);
// struct NDArray { num_dims: size_t, dims: size_t*, data: T* }
//
// * num_dims: Number of dimensions in the array
// * dims: Pointer to an array containing the size of each dimension
// * data: Pointer to an array containing the array data
let llvm_ndarray = ctx
.struct_type(
&[
llvm_usize.into(),
llvm_usize.ptr_type(AddressSpace::default()).into(),
dtype.ptr_type(AddressSpace::default()).into(),
],
false,
)
.ptr_type(AddressSpace::default());
NDArrayType::from_type(llvm_ndarray, llvm_usize)
}
/// Creates an [`NDArrayType`] from a [`PointerType`].
#[must_use]
pub fn from_type(ptr_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
debug_assert!(Self::is_type(ptr_ty, llvm_usize).is_ok());
NDArrayType { ty: ptr_ty, llvm_usize }
}
/// Returns the type of the `size` field of this `ndarray` type.
#[must_use]
pub fn size_type(&self) -> IntType<'ctx> {
self.as_base_type()
.get_element_type()
.into_struct_type()
.get_field_type_at_index(0)
.map(BasicTypeEnum::into_int_type)
.unwrap()
}
/// Returns the element type of this `ndarray` type.
#[must_use]
pub fn element_type(&self) -> AnyTypeEnum<'ctx> {
self.as_base_type()
.get_element_type()
.into_struct_type()
.get_field_type_at_index(2)
.map(BasicTypeEnum::into_pointer_type)
.map(PointerType::get_element_type)
.unwrap()
}
}
impl<'ctx> ProxyType<'ctx> for NDArrayType<'ctx> {
type Base = PointerType<'ctx>;
type Underlying = StructType<'ctx>;
type Value = NDArrayValue<'ctx>;
fn new_value<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
name: Option<&'ctx str>,
) -> Self::Value {
self.create_value(
generator.gen_var_alloc(ctx, self.as_underlying_type().into(), name).unwrap(),
name,
)
}
fn create_value(
&self,
value: <Self::Value as ProxyValue<'ctx>>::Base,
name: Option<&'ctx str>,
) -> Self::Value {
debug_assert_eq!(value.get_type(), self.as_base_type());
NDArrayValue { value, llvm_usize: self.llvm_usize, name }
}
fn as_base_type(&self) -> Self::Base {
self.ty
}
fn as_underlying_type(&self) -> Self::Underlying {
self.as_base_type().get_element_type().into_struct_type()
}
}
impl<'ctx> From<NDArrayType<'ctx>> for PointerType<'ctx> {
fn from(value: NDArrayType<'ctx>) -> Self {
value.as_base_type()
}
}
/// Proxy type for accessing an `NDArray` value in LLVM.
#[derive(Copy, Clone)]
pub struct NDArrayValue<'ctx> {
value: PointerValue<'ctx>,
llvm_usize: IntType<'ctx>,
name: Option<&'ctx str>,
}
impl<'ctx> NDArrayValue<'ctx> {
/// Checks whether `value` is an instance of `NDArray`, returning [Err] if `value` is not an
/// instance.
pub fn is_instance(value: PointerValue<'ctx>, llvm_usize: IntType<'ctx>) -> Result<(), String> {
NDArrayType::is_type(value.get_type(), llvm_usize)
}
/// Creates an [`NDArrayValue`] from a [`PointerValue`].
#[must_use]
pub fn from_ptr_val(
ptr: PointerValue<'ctx>,
llvm_usize: IntType<'ctx>,
name: Option<&'ctx str>,
) -> Self {
debug_assert!(Self::is_instance(ptr, llvm_usize).is_ok());
<Self as ProxyValue<'ctx>>::Type::from_type(ptr.get_type(), llvm_usize)
.create_value(ptr, name)
}
/// Returns the pointer to the field storing the number of dimensions of this `NDArray`.
fn ptr_to_ndims(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
let llvm_i32 = ctx.ctx.i32_type();
let var_name = self.name.map(|v| format!("{v}.ndims.addr")).unwrap_or_default();
unsafe {
ctx.builder
.build_in_bounds_gep(
self.as_base_value(),
&[llvm_i32.const_zero(), llvm_i32.const_zero()],
var_name.as_str(),
)
.unwrap()
}
}
/// Stores the number of dimensions `ndims` into this instance.
pub fn store_ndims<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
ndims: IntValue<'ctx>,
) {
debug_assert_eq!(ndims.get_type(), generator.get_size_type(ctx.ctx));
let pndims = self.ptr_to_ndims(ctx);
ctx.builder.build_store(pndims, ndims).unwrap();
}
/// Returns the number of dimensions of this `NDArray` as a value.
pub fn load_ndims(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
let pndims = self.ptr_to_ndims(ctx);
ctx.builder.build_load(pndims, "").map(BasicValueEnum::into_int_value).unwrap()
}
/// Returns the double-indirection pointer to the `dims` array, as if by calling `getelementptr`
/// on the field.
fn ptr_to_dims(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
let llvm_i32 = ctx.ctx.i32_type();
let var_name = self.name.map(|v| format!("{v}.dims.addr")).unwrap_or_default();
unsafe {
ctx.builder
.build_in_bounds_gep(
self.as_base_value(),
&[llvm_i32.const_zero(), llvm_i32.const_int(1, true)],
var_name.as_str(),
)
.unwrap()
}
}
/// Stores the array of dimension sizes `dims` into this instance.
fn store_dim_sizes(&self, ctx: &CodeGenContext<'ctx, '_>, dims: PointerValue<'ctx>) {
ctx.builder.build_store(self.ptr_to_dims(ctx), dims).unwrap();
}
/// Convenience method for creating a new array storing dimension sizes with the given `size`.
pub fn create_dim_sizes(
&self,
ctx: &CodeGenContext<'ctx, '_>,
llvm_usize: IntType<'ctx>,
size: IntValue<'ctx>,
) {
self.store_dim_sizes(ctx, ctx.builder.build_array_alloca(llvm_usize, size, "").unwrap());
}
/// Returns a proxy object to the field storing the size of each dimension of this `NDArray`.
#[must_use]
pub fn dim_sizes(&self) -> NDArrayDimsProxy<'ctx, '_> {
NDArrayDimsProxy(self)
}
/// Returns the double-indirection pointer to the `data` array, as if by calling `getelementptr`
/// on the field.
pub fn ptr_to_data(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
let llvm_i32 = ctx.ctx.i32_type();
let var_name = self.name.map(|v| format!("{v}.data.addr")).unwrap_or_default();
unsafe {
ctx.builder
.build_in_bounds_gep(
self.as_base_value(),
&[llvm_i32.const_zero(), llvm_i32.const_int(2, true)],
var_name.as_str(),
)
.unwrap()
}
}
/// Stores the array of data elements `data` into this instance.
fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, data: PointerValue<'ctx>) {
ctx.builder.build_store(self.ptr_to_data(ctx), data).unwrap();
}
/// Convenience method for creating a new array storing data elements with the given element
/// type `elem_ty` and `size`.
pub fn create_data(
&self,
ctx: &CodeGenContext<'ctx, '_>,
elem_ty: BasicTypeEnum<'ctx>,
size: IntValue<'ctx>,
) {
self.store_data(ctx, ctx.builder.build_array_alloca(elem_ty, size, "").unwrap());
}
/// Returns a proxy object to the field storing the data of this `NDArray`.
#[must_use]
pub fn data(&self) -> NDArrayDataProxy<'ctx, '_> {
NDArrayDataProxy(self)
}
}
impl<'ctx> ProxyValue<'ctx> for NDArrayValue<'ctx> {
type Base = PointerValue<'ctx>;
type Underlying = StructValue<'ctx>;
type Type = NDArrayType<'ctx>;
fn get_type(&self) -> Self::Type {
NDArrayType::from_type(self.as_base_value().get_type(), self.llvm_usize)
}
fn as_base_value(&self) -> Self::Base {
self.value
}
fn as_underlying_value(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
name: Option<&'ctx str>,
) -> Self::Underlying {
ctx.builder
.build_load(self.as_base_value(), name.unwrap_or_default())
.map(BasicValueEnum::into_struct_value)
.unwrap()
}
}
impl<'ctx> From<NDArrayValue<'ctx>> for PointerValue<'ctx> {
fn from(value: NDArrayValue<'ctx>) -> Self {
value.as_base_value()
}
}
/// Proxy type for accessing the `dims` array of an `NDArray` instance in LLVM.
#[derive(Copy, Clone)]
pub struct NDArrayDimsProxy<'ctx, 'a>(&'a NDArrayValue<'ctx>);
impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDimsProxy<'ctx, '_> {
fn element_type<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
) -> AnyTypeEnum<'ctx> {
self.0.dim_sizes().base_ptr(ctx, generator).get_type().get_element_type()
}
fn base_ptr<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
_: &G,
) -> PointerValue<'ctx> {
let var_name = self.0.name.map(|v| format!("{v}.data")).unwrap_or_default();
ctx.builder
.build_load(self.0.ptr_to_dims(ctx), var_name.as_str())
.map(BasicValueEnum::into_pointer_value)
.unwrap()
}
fn size<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
_: &G,
) -> IntValue<'ctx> {
self.0.load_ndims(ctx)
}
}
impl<'ctx> ArrayLikeIndexer<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &IntValue<'ctx>,
name: Option<&str>,
) -> PointerValue<'ctx> {
let var_name = name.map(|v| format!("{v}.addr")).unwrap_or_default();
unsafe {
ctx.builder
.build_in_bounds_gep(self.base_ptr(ctx, generator), &[*idx], var_name.as_str())
.unwrap()
}
}
fn ptr_offset<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &IntValue<'ctx>,
name: Option<&str>,
) -> PointerValue<'ctx> {
let size = self.size(ctx, generator);
let in_range = ctx.builder.build_int_compare(IntPredicate::ULT, *idx, size, "").unwrap();
ctx.make_assert(
generator,
in_range,
"0:IndexError",
"index {0} is out of bounds for axis 0 with size {1}",
[Some(*idx), Some(self.0.load_ndims(ctx)), None],
ctx.current_loc,
);
unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) }
}
}
impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {}
impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {}
impl<'ctx> TypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {
fn downcast_to_type(
&self,
_: &mut CodeGenContext<'ctx, '_>,
value: BasicValueEnum<'ctx>,
) -> IntValue<'ctx> {
value.into_int_value()
}
}
impl<'ctx> TypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {
fn upcast_from_type(
&self,
_: &mut CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> BasicValueEnum<'ctx> {
value.into()
}
}
/// Proxy type for accessing the `data` array of an `NDArray` instance in LLVM.
#[derive(Copy, Clone)]
pub struct NDArrayDataProxy<'ctx, 'a>(&'a NDArrayValue<'ctx>);
impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDataProxy<'ctx, '_> {
fn element_type<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
) -> AnyTypeEnum<'ctx> {
self.0.data().base_ptr(ctx, generator).get_type().get_element_type()
}
fn base_ptr<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
_: &G,
) -> PointerValue<'ctx> {
let var_name = self.0.name.map(|v| format!("{v}.data")).unwrap_or_default();
ctx.builder
.build_load(self.0.ptr_to_data(ctx), var_name.as_str())
.map(BasicValueEnum::into_pointer_value)
.unwrap()
}
fn size<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
) -> IntValue<'ctx> {
call_ndarray_calc_size(generator, ctx, &self.as_slice_value(ctx, generator), (None, None))
}
}
impl<'ctx> ArrayLikeIndexer<'ctx> for NDArrayDataProxy<'ctx, '_> {
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &IntValue<'ctx>,
name: Option<&str>,
) -> PointerValue<'ctx> {
unsafe {
ctx.builder
.build_in_bounds_gep(
self.base_ptr(ctx, generator),
&[*idx],
name.unwrap_or_default(),
)
.unwrap()
}
}
fn ptr_offset<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &IntValue<'ctx>,
name: Option<&str>,
) -> PointerValue<'ctx> {
let data_sz = self.size(ctx, generator);
let in_range = ctx.builder.build_int_compare(IntPredicate::ULT, *idx, data_sz, "").unwrap();
ctx.make_assert(
generator,
in_range,
"0:IndexError",
"index {0} is out of bounds with size {1}",
[Some(*idx), Some(self.0.load_ndims(ctx)), None],
ctx.current_loc,
);
unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) }
}
}
impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
for NDArrayDataProxy<'ctx, '_>
{
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
indices: &Index,
name: Option<&str>,
) -> PointerValue<'ctx> {
let llvm_usize = generator.get_size_type(ctx.ctx);
let indices_elem_ty = indices
.ptr_offset(ctx, generator, &llvm_usize.const_zero(), None)
.get_type()
.get_element_type();
let Ok(indices_elem_ty) = IntType::try_from(indices_elem_ty) else {
panic!("Expected list[int32] but got {indices_elem_ty}")
};
assert_eq!(
indices_elem_ty.get_bit_width(),
32,
"Expected list[int32] but got list[int{}]",
indices_elem_ty.get_bit_width()
);
let index = call_ndarray_flatten_index(generator, ctx, *self.0, indices);
unsafe {
ctx.builder
.build_in_bounds_gep(
self.base_ptr(ctx, generator),
&[index],
name.unwrap_or_default(),
)
.unwrap()
}
}
fn ptr_offset<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
indices: &Index,
name: Option<&str>,
) -> PointerValue<'ctx> {
let llvm_usize = generator.get_size_type(ctx.ctx);
let indices_size = indices.size(ctx, generator);
let nidx_leq_ndims = ctx
.builder
.build_int_compare(IntPredicate::SLE, indices_size, self.0.load_ndims(ctx), "")
.unwrap();
ctx.make_assert(
generator,
nidx_leq_ndims,
"0:IndexError",
"invalid index to scalar variable",
[None, None, None],
ctx.current_loc,
);
let indices_len = indices.size(ctx, generator);
let ndarray_len = self.0.load_ndims(ctx);
let len = call_int_umin(ctx, indices_len, ndarray_len, None);
gen_for_callback_incrementing(
generator,
ctx,
None,
llvm_usize.const_zero(),
(len, false),
|generator, ctx, _, i| {
let (dim_idx, dim_sz) = unsafe {
(
indices.get_unchecked(ctx, generator, &i, None).into_int_value(),
self.0.dim_sizes().get_typed_unchecked(ctx, generator, &i, None),
)
};
let dim_idx = ctx
.builder
.build_int_z_extend_or_bit_cast(dim_idx, dim_sz.get_type(), "")
.unwrap();
let dim_lt =
ctx.builder.build_int_compare(IntPredicate::SLT, dim_idx, dim_sz, "").unwrap();
ctx.make_assert(
generator,
dim_lt,
"0:IndexError",
"index {0} is out of bounds for axis 0 with size {1}",
[Some(dim_idx), Some(dim_sz), None],
ctx.current_loc,
);
Ok(())
},
llvm_usize.const_int(1, false),
)
.unwrap();
unsafe { self.ptr_offset_unchecked(ctx, generator, indices, name) }
}
}
impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> UntypedArrayLikeAccessor<'ctx, Index>
for NDArrayDataProxy<'ctx, '_>
{
}
impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> UntypedArrayLikeMutator<'ctx, Index>
for NDArrayDataProxy<'ctx, '_>
{
}

10
nac3core/src/codegen/concrete_type.rs
View File

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

649
nac3core/src/codegen/expr.rs
View File

@ -1,8 +1,28 @@
use std::{
cmp::min,
collections::HashMap,
convert::TryInto,
iter::{once, repeat, repeat_with, zip},
};
use inkwell::{
attributes::{Attribute, AttributeLoc},
types::{AnyType, BasicType, BasicTypeEnum},
values::{BasicValueEnum, CallSiteValue, FunctionValue, IntValue, PointerValue, StructValue},
AddressSpace, IntPredicate, OptimizationLevel,
};
use itertools::{chain, izip, Either, Itertools};
use nac3parser::ast::{
self, Boolop, Cmpop, Comprehension, Constant, Expr, ExprKind, Location, Operator, StrRef,
Unaryop,
};
use crate::{
codegen::{
classes::{
ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, ProxyType, ProxyValue,
RangeValue, UntypedArrayLikeAccessor,
ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, NDArrayValue, ProxyType,
ProxyValue, RangeValue, TypedArrayLikeAccessor, UntypedArrayLikeAccessor,
},
concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
gen_in_range_check, get_llvm_abi_type, get_llvm_type, get_va_count_arg_name,
@ -12,8 +32,7 @@ use crate::{
call_int_umin, call_memcpy_generic,
},
macros::codegen_unreachable,
need_sret,
object::ndarray::{NDArrayOut, ScalarOrNDArray},
need_sret, numpy,
stmt::{
gen_for_callback_incrementing, gen_if_callback, gen_if_else_expr_callback, gen_raise,
gen_var,
@ -21,34 +40,16 @@ use crate::{
CodeGenContext, CodeGenTask, CodeGenerator,
},
symbol_resolver::{SymbolValue, ValueEnum},
toplevel::{helper::PrimDef, DefinitionId, TopLevelDef},
toplevel::{
helper::PrimDef,
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
DefinitionId, TopLevelDef,
},
typecheck::{
magic_methods::{Binop, BinopVariant, HasOpInfo},
typedef::{FunSignature, FuncArg, Type, TypeEnum, TypeVarId, Unifier, VarMap},
},
};
use inkwell::{
attributes::{Attribute, AttributeLoc},
types::{AnyType, BasicType, BasicTypeEnum},
values::{
BasicValue, BasicValueEnum, CallSiteValue, FunctionValue, IntValue, PointerValue,
StructValue,
},
AddressSpace, IntPredicate, OptimizationLevel,
};
use itertools::{chain, izip, Either, Itertools};
use nac3parser::ast::{
self, Boolop, Cmpop, Comprehension, Constant, Expr, ExprKind, Location, Operator, StrRef,
Unaryop,
};
use std::cmp::min;
use std::iter::{repeat, repeat_with};
use std::{collections::HashMap, convert::TryInto, iter::once, iter::zip};
use super::object::{
any::AnyObject,
ndarray::{indexing::util::gen_ndarray_subscript_ndindices, NDArrayObject},
};
pub fn get_subst_key(
unifier: &mut Unifier,
@ -556,7 +557,7 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
&& val_ty.get_element_type().is_struct_type()
} =>
{
self.builder.build_bitcast(*val, arg_ty, "call_arg_cast").unwrap()
self.builder.build_bit_cast(*val, arg_ty, "call_arg_cast").unwrap()
}
_ => *val,
})
@ -976,6 +977,7 @@ pub fn gen_call<'ctx, G: CodeGenerator>(
TopLevelDef::Class { .. } => {
return Ok(Some(generator.gen_constructor(ctx, fun.0, &def, params)?))
}
TopLevelDef::Variable { .. } => unreachable!(),
}
}
.or_else(|_: String| {
@ -1547,75 +1549,99 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
} else if ty1.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
|| ty2.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
{
let left =
ScalarOrNDArray::split_object(generator, ctx, AnyObject { ty: ty1, value: left_val });
let right =
ScalarOrNDArray::split_object(generator, ctx, AnyObject { ty: ty2, value: right_val });
let llvm_usize = generator.get_size_type(ctx.ctx);
// Inhomogeneous binary operations are not supported.
assert!(ctx.unifier.unioned(left.get_dtype(), right.get_dtype()));
let is_ndarray1 = ty1.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
let is_ndarray2 = ty2.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
let common_dtype = left.get_dtype();
if is_ndarray1 && is_ndarray2 {
let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty2);
let out = match op.variant {
BinopVariant::Normal => NDArrayOut::NewNDArray { dtype: common_dtype },
BinopVariant::AugAssign => {
// If this is an augmented assignment.
// `left` has to be an ndarray. If it were a scalar then NAC3 simply doesn't support it.
if let ScalarOrNDArray::NDArray(out_ndarray) = left {
NDArrayOut::WriteToNDArray { ndarray: out_ndarray }
} else {
panic!("left must be an ndarray")
}
}
};
assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
if op.base == Operator::MatMult {
// Handle matrix multiplication.
let left = left.to_ndarray(generator, ctx);
let right = right.to_ndarray(generator, ctx);
let result = NDArrayObject::matmul(generator, ctx, left, right, out)
.split_unsized(generator, ctx);
Ok(Some(ValueEnum::Dynamic(result.to_basic_value_enum())))
} else {
// For other operations, they are all elementwise operations.
let left_val =
NDArrayValue::from_ptr_val(left_val.into_pointer_value(), llvm_usize, None);
let right_val =
NDArrayValue::from_ptr_val(right_val.into_pointer_value(), llvm_usize, None);
// There are only three cases:
// - LHS is a scalar, RHS is an ndarray.
// - LHS is an ndarray, RHS is a scalar.
// - LHS is an ndarray, RHS is an ndarray.
//
// For all cases, the scalar operand is promoted to an ndarray,
// the two are then broadcasted, and starmapped through.
let left = left.to_ndarray(generator, ctx);
let right = right.to_ndarray(generator, ctx);
let result = NDArrayObject::broadcast_starmap(
let res = if op.base == Operator::MatMult {
// MatMult is the only binop which is not an elementwise op
numpy::ndarray_matmul_2d(
generator,
ctx,
&[left, right],
out,
|generator, ctx, scalars| {
let left_value = scalars[0];
let right_value = scalars[1];
let result = gen_binop_expr_with_values(
ndarray_dtype1,
match op.variant {
BinopVariant::Normal => None,
BinopVariant::AugAssign => Some(left_val),
},
left_val,
right_val,
)?
} else {
numpy::ndarray_elementwise_binop_impl(
generator,
ctx,
(&Some(left.dtype), left_value),
ndarray_dtype1,
match op.variant {
BinopVariant::Normal => None,
BinopVariant::AugAssign => Some(left_val),
},
(left_val.as_base_value().into(), false),
(right_val.as_base_value().into(), false),
|generator, ctx, (lhs, rhs)| {
gen_binop_expr_with_values(
generator,
ctx,
(&Some(ndarray_dtype1), lhs),
op,
(&Some(right.dtype), right_value),
(&Some(ndarray_dtype2), rhs),
ctx.current_loc,
)?
.unwrap()
.to_basic_value_enum(ctx, generator, common_dtype)?;
Ok(result)
},
.to_basic_value_enum(
ctx,
generator,
ndarray_dtype1,
)
.unwrap();
Ok(Some(ValueEnum::Dynamic(result.instance.value.as_basic_value_enum())))
},
)?
};
Ok(Some(res.as_base_value().into()))
} else {
let (ndarray_dtype, _) =
unpack_ndarray_var_tys(&mut ctx.unifier, if is_ndarray1 { ty1 } else { ty2 });
let ndarray_val = NDArrayValue::from_ptr_val(
if is_ndarray1 { left_val } else { right_val }.into_pointer_value(),
llvm_usize,
None,
);
let res = numpy::ndarray_elementwise_binop_impl(
generator,
ctx,
ndarray_dtype,
match op.variant {
BinopVariant::Normal => None,
BinopVariant::AugAssign => Some(ndarray_val),
},
(left_val, !is_ndarray1),
(right_val, !is_ndarray2),
|generator, ctx, (lhs, rhs)| {
gen_binop_expr_with_values(
generator,
ctx,
(&Some(ndarray_dtype), lhs),
op,
(&Some(ndarray_dtype), rhs),
ctx.current_loc,
)?
.unwrap()
.to_basic_value_enum(ctx, generator, ndarray_dtype)
},
)?;
Ok(Some(res.as_base_value().into()))
}
} else {
let left_ty_enum = ctx.unifier.get_ty_immutable(left_ty.unwrap());
@ -1773,12 +1799,14 @@ pub fn gen_unaryop_expr_with_values<'ctx, G: CodeGenerator>(
_ => val.into(),
}
} else if ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) {
let ndarray = AnyObject { value: val, ty };
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
let llvm_usize = generator.get_size_type(ctx.ctx);
let (ndarray_dtype, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
let val = NDArrayValue::from_ptr_val(val.into_pointer_value(), llvm_usize, None);
// ndarray uses `~` rather than `not` to perform elementwise inversion, convert it before
// passing it to the elementwise codegen function
let op = if ndarray.dtype.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::Bool.id()) {
let op = if ndarray_dtype.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::Bool.id()) {
if op == ast::Unaryop::Invert {
ast::Unaryop::Not
} else {
@ -1792,18 +1820,20 @@ pub fn gen_unaryop_expr_with_values<'ctx, G: CodeGenerator>(
op
};
let mapped_ndarray = ndarray.map(
let res = numpy::ndarray_elementwise_unaryop_impl(
generator,
ctx,
NDArrayOut::NewNDArray { dtype: ndarray.dtype },
|generator, ctx, scalar| {
gen_unaryop_expr_with_values(generator, ctx, op, (&Some(ndarray.dtype), scalar))?
ndarray_dtype,
None,
val,
|generator, ctx, val| {
gen_unaryop_expr_with_values(generator, ctx, op, (&Some(ndarray_dtype), val))?
.unwrap()
.to_basic_value_enum(ctx, generator, ndarray.dtype)
.to_basic_value_enum(ctx, generator, ndarray_dtype)
},
)?;
ValueEnum::Dynamic(mapped_ndarray.instance.value.as_basic_value_enum())
res.as_base_value().into()
} else {
unimplemented!()
}))
@ -1846,33 +1876,39 @@ pub fn gen_cmpop_expr_with_values<'ctx, G: CodeGenerator>(
if left_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
|| right_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
{
let (Some(left_ty), left) = left else { codegen_unreachable!(ctx) };
let (Some(right_ty), right) = comparators[0] else { codegen_unreachable!(ctx) };
let llvm_usize = generator.get_size_type(ctx.ctx);
let (Some(left_ty), lhs) = left else { codegen_unreachable!(ctx) };
let (Some(right_ty), rhs) = comparators[0] else { codegen_unreachable!(ctx) };
let op = ops[0];
let left = AnyObject { value: left, ty: left_ty };
let left =
ScalarOrNDArray::split_object(generator, ctx, left).to_ndarray(generator, ctx);
let is_ndarray1 =
left_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
let is_ndarray2 =
right_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
let right = AnyObject { value: right, ty: right_ty };
let right =
ScalarOrNDArray::split_object(generator, ctx, right).to_ndarray(generator, ctx);
return if is_ndarray1 && is_ndarray2 {
let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, left_ty);
let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, right_ty);
let result_ndarray = NDArrayObject::broadcast_starmap(
assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
let left_val =
NDArrayValue::from_ptr_val(lhs.into_pointer_value(), llvm_usize, None);
let res = numpy::ndarray_elementwise_binop_impl(
generator,
ctx,
&[left, right],
NDArrayOut::NewNDArray { dtype: ctx.primitives.bool },
|generator, ctx, scalars| {
let left_scalar = scalars[0];
let right_scalar = scalars[1];
ctx.primitives.bool,
None,
(left_val.as_base_value().into(), false),
(rhs, false),
|generator, ctx, (lhs, rhs)| {
let val = gen_cmpop_expr_with_values(
generator,
ctx,
(Some(left.dtype), left_scalar),
(Some(ndarray_dtype1), lhs),
&[op],
&[(Some(right.dtype), right_scalar)],
&[(Some(ndarray_dtype2), rhs)],
)?
.unwrap()
.to_basic_value_enum(
@ -1885,7 +1921,40 @@ pub fn gen_cmpop_expr_with_values<'ctx, G: CodeGenerator>(
},
)?;
return Ok(Some(result_ndarray.instance.value.into()));
Ok(Some(res.as_base_value().into()))
} else {
let (ndarray_dtype, _) = unpack_ndarray_var_tys(
&mut ctx.unifier,
if is_ndarray1 { left_ty } else { right_ty },
);
let res = numpy::ndarray_elementwise_binop_impl(
generator,
ctx,
ctx.primitives.bool,
None,
(lhs, !is_ndarray1),
(rhs, !is_ndarray2),
|generator, ctx, (lhs, rhs)| {
let val = gen_cmpop_expr_with_values(
generator,
ctx,
(Some(ndarray_dtype), lhs),
&[op],
&[(Some(ndarray_dtype), rhs)],
)?
.unwrap()
.to_basic_value_enum(
ctx,
generator,
ctx.primitives.bool,
)?;
Ok(generator.bool_to_i8(ctx, val.into_int_value()).into())
},
)?;
Ok(Some(res.as_base_value().into()))
};
}
}
@ -2436,6 +2505,338 @@ pub fn gen_cmpop_expr<'ctx, G: CodeGenerator>(
)
}
/// Generates code for a subscript expression on an `ndarray`.
///
/// * `ty` - The `Type` of the `NDArray` elements.
/// * `ndims` - The `Type` of the `NDArray` number-of-dimensions `Literal`.
/// * `v` - The `NDArray` value.
/// * `slice` - The slice expression used to subscript into the `ndarray`.
fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ty: Type,
ndims: Type,
v: NDArrayValue<'ctx>,
slice: &Expr<Option<Type>>,
) -> Result<Option<ValueEnum<'ctx>>, String> {
let llvm_i1 = ctx.ctx.bool_type();
let llvm_i32 = ctx.ctx.i32_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) else {
codegen_unreachable!(ctx)
};
let ndims = values
.iter()
.map(|ndim| u64::try_from(ndim.clone()).map_err(|()| ndim.clone()))
.collect::<Result<Vec<_>, _>>()
.map_err(|val| {
format!(
"Expected non-negative literal for ndarray.ndims, got {}",
i128::try_from(val).unwrap()
)
})?;
assert!(!ndims.is_empty());
// The number of dimensions subscripted by the index expression.
// Slicing a ndarray will yield the same number of dimensions, whereas indexing into a
// dimension will remove a dimension.
let subscripted_dims = match &slice.node {
ExprKind::Tuple { elts, .. } => elts.iter().fold(0, |acc, value_subexpr| {
if let ExprKind::Slice { .. } = &value_subexpr.node {
acc
} else {
acc + 1
}
}),
ExprKind::Slice { .. } => 0,
_ => 1,
};
let ndarray_ndims_ty = ctx.unifier.get_fresh_literal(
ndims.iter().map(|v| SymbolValue::U64(v - subscripted_dims)).collect(),
None,
);
let ndarray_ty =
make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(ty), Some(ndarray_ndims_ty));
let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
let llvm_ndarray_data_t = ctx.get_llvm_type(generator, ty).as_basic_type_enum();
let sizeof_elem = llvm_ndarray_data_t.size_of().unwrap();
// Check that len is non-zero
let len = v.load_ndims(ctx);
ctx.make_assert(
generator,
ctx.builder.build_int_compare(IntPredicate::SGT, len, llvm_usize.const_zero(), "").unwrap(),
"0:IndexError",
"too many indices for array: array is {0}-dimensional but 1 were indexed",
[Some(len), None, None],
slice.location,
);
// Normalizes a possibly-negative index to its corresponding positive index
let normalize_index = |generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
index: IntValue<'ctx>,
dim: u64| {
gen_if_else_expr_callback(
generator,
ctx,
|_, ctx| {
Ok(ctx
.builder
.build_int_compare(IntPredicate::SGE, index, index.get_type().const_zero(), "")
.unwrap())
},
|_, _| Ok(Some(index)),
|generator, ctx| {
let llvm_i32 = ctx.ctx.i32_type();
let len = unsafe {
v.dim_sizes().get_typed_unchecked(
ctx,
generator,
&llvm_usize.const_int(dim, true),
None,
)
};
let index = ctx
.builder
.build_int_add(
len,
ctx.builder.build_int_s_extend(index, llvm_usize, "").unwrap(),
"",
)
.unwrap();
Ok(Some(ctx.builder.build_int_truncate(index, llvm_i32, "").unwrap()))
},
)
.map(|v| v.map(BasicValueEnum::into_int_value))
};
// Converts a slice expression into a slice-range tuple
let expr_to_slice = |generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
node: &ExprKind<Option<Type>>,
dim: u64| {
match node {
ExprKind::Constant { value: Constant::Int(v), .. } => {
let Some(index) =
normalize_index(generator, ctx, llvm_i32.const_int(*v as u64, true), dim)?
else {
return Ok(None);
};
Ok(Some((index, index, llvm_i32.const_int(1, true))))
}
ExprKind::Slice { lower, upper, step } => {
let dim_sz = unsafe {
v.dim_sizes().get_typed_unchecked(
ctx,
generator,
&llvm_usize.const_int(dim, false),
None,
)
};
handle_slice_indices(lower, upper, step, ctx, generator, dim_sz)
}
_ => {
let Some(index) = generator.gen_expr(ctx, slice)? else { return Ok(None) };
let index = index
.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?
.into_int_value();
let Some(index) = normalize_index(generator, ctx, index, dim)? else {
return Ok(None);
};
Ok(Some((index, index, llvm_i32.const_int(1, true))))
}
}
};
let make_indices_arr = |generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>|
-> Result<_, String> {
Ok(if let ExprKind::Tuple { elts, .. } = &slice.node {
let llvm_int_ty = ctx.get_llvm_type(generator, elts[0].custom.unwrap());
let index_addr = generator.gen_array_var_alloc(
ctx,
llvm_int_ty,
llvm_usize.const_int(elts.len() as u64, false),
None,
)?;
for (i, elt) in elts.iter().enumerate() {
let Some(index) = generator.gen_expr(ctx, elt)? else {
return Ok(None);
};
let index = index
.to_basic_value_enum(ctx, generator, elt.custom.unwrap())?
.into_int_value();
let Some(index) = normalize_index(generator, ctx, index, 0)? else {
return Ok(None);
};
let store_ptr = unsafe {
index_addr.ptr_offset_unchecked(
ctx,
generator,
&llvm_usize.const_int(i as u64, false),
None,
)
};
ctx.builder.build_store(store_ptr, index).unwrap();
}
Some(index_addr)
} else if let Some(index) = generator.gen_expr(ctx, slice)? {
let llvm_int_ty = ctx.get_llvm_type(generator, slice.custom.unwrap());
let index_addr = generator.gen_array_var_alloc(
ctx,
llvm_int_ty,
llvm_usize.const_int(1u64, false),
None,
)?;
let index =
index.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value();
let Some(index) = normalize_index(generator, ctx, index, 0)? else { return Ok(None) };
let store_ptr = unsafe {
index_addr.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
};
ctx.builder.build_store(store_ptr, index).unwrap();
Some(index_addr)
} else {
None
})
};
Ok(Some(if ndims.len() == 1 && ndims[0] - subscripted_dims == 0 {
let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
v.data().get(ctx, generator, &index_addr, None).into()
} else {
match &slice.node {
ExprKind::Tuple { elts, .. } => {
let slices = elts
.iter()
.enumerate()
.map(|(dim, elt)| expr_to_slice(generator, ctx, &elt.node, dim as u64))
.take_while_inclusive(|slice| slice.as_ref().is_ok_and(Option::is_some))
.collect::<Result<Vec<_>, _>>()?;
if slices.len() < elts.len() {
return Ok(None);
}
let slices = slices.into_iter().map(Option::unwrap).collect_vec();
numpy::ndarray_sliced_copy(generator, ctx, ty, v, &slices)?.as_base_value().into()
}
ExprKind::Slice { .. } => {
let Some(slice) = expr_to_slice(generator, ctx, &slice.node, 0)? else {
return Ok(None);
};
numpy::ndarray_sliced_copy(generator, ctx, ty, v, &[slice])?.as_base_value().into()
}
_ => {
// Accessing an element from a multi-dimensional `ndarray`
let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
// Create a new array, remove the top dimension from the dimension-size-list, and copy the
// elements over
let subscripted_ndarray =
generator.gen_var_alloc(ctx, llvm_ndarray_t.into(), None)?;
let ndarray = NDArrayValue::from_ptr_val(subscripted_ndarray, llvm_usize, None);
let num_dims = v.load_ndims(ctx);
ndarray.store_ndims(
ctx,
generator,
ctx.builder
.build_int_sub(num_dims, llvm_usize.const_int(1, false), "")
.unwrap(),
);
let ndarray_num_dims = ndarray.load_ndims(ctx);
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
let ndarray_num_dims = ctx
.builder
.build_int_z_extend_or_bit_cast(
ndarray.load_ndims(ctx),
llvm_usize.size_of().get_type(),
"",
)
.unwrap();
let v_dims_src_ptr = unsafe {
v.dim_sizes().ptr_offset_unchecked(
ctx,
generator,
&llvm_usize.const_int(1, false),
None,
)
};
call_memcpy_generic(
ctx,
ndarray.dim_sizes().base_ptr(ctx, generator),
v_dims_src_ptr,
ctx.builder
.build_int_mul(ndarray_num_dims, llvm_usize.size_of(), "")
.map(Into::into)
.unwrap(),
llvm_i1.const_zero(),
);
let ndarray_num_elems = call_ndarray_calc_size(
generator,
ctx,
&ndarray.dim_sizes().as_slice_value(ctx, generator),
(None, None),
);
let ndarray_num_elems = ctx
.builder
.build_int_z_extend_or_bit_cast(ndarray_num_elems, sizeof_elem.get_type(), "")
.unwrap();
ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
let v_data_src_ptr = v.data().ptr_offset(ctx, generator, &index_addr, None);
call_memcpy_generic(
ctx,
ndarray.data().base_ptr(ctx, generator),
v_data_src_ptr,
ctx.builder
.build_int_mul(
ndarray_num_elems,
llvm_ndarray_data_t.size_of().unwrap(),
"",
)
.map(Into::into)
.unwrap(),
llvm_i1.const_zero(),
);
ndarray.as_base_value().into()
}
}
}))
}
/// See [`CodeGenerator::gen_expr`].
pub fn gen_expr<'ctx, G: CodeGenerator>(
generator: &mut G,
@ -2485,7 +2886,7 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
Some((_, Some(static_value), _)) => ValueEnum::Static(static_value.clone()),
None => {
let resolver = ctx.resolver.clone();
resolver.get_symbol_value(*id, ctx).unwrap()
resolver.get_symbol_value(*id, ctx, generator).unwrap()
}
},
ExprKind::List { elts, .. } => {
@ -3068,26 +3469,18 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
v.data().get(ctx, generator, &index, None).into()
}
}
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
let Some(ndarray) = generator.gen_expr(ctx, value)? else {
TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::NDArray.id() => {
let (ty, ndims) = params.iter().map(|(_, ty)| ty).collect_tuple().unwrap();
let v = if let Some(v) = generator.gen_expr(ctx, value)? {
v.to_basic_value_enum(ctx, generator, value.custom.unwrap())?
.into_pointer_value()
} else {
return Ok(None);
};
let v = NDArrayValue::from_ptr_val(v, usize, None);
let ndarray_ty = value.custom.unwrap();
let ndarray = ndarray.to_basic_value_enum(ctx, generator, ndarray_ty)?;
let ndarray = NDArrayObject::from_object(
generator,
ctx,
AnyObject { ty: ndarray_ty, value: ndarray },
);
let indices = gen_ndarray_subscript_ndindices(generator, ctx, slice)?;
let result = ndarray
.index(generator, ctx, &indices)
.split_unsized(generator, ctx)
.to_basic_value_enum();
return Ok(Some(ValueEnum::Dynamic(result)));
return gen_ndarray_subscript_expr(generator, ctx, *ty, *ndims, v, slice);
}
TypeEnum::TTuple { .. } => {
let index: u32 =

6
nac3core/src/codegen/extern_fns.rs
View File

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

14
nac3core/src/codegen/generator.rs
View File

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

642
nac3core/src/codegen/irrt/mod.rs
View File

@ -1,27 +1,28 @@
use crate::{symbol_resolver::SymbolResolver, typecheck::typedef::Type};
use super::{
classes::{ArrayLikeValue, ListValue},
macros::codegen_unreachable,
model::{function::FnCall, *},
object::{
list::List,
ndarray::{broadcast::ShapeEntry, indexing::NDIndex, nditer::NDIter, NDArray},
},
CodeGenContext, CodeGenerator,
};
use inkwell::{
attributes::{Attribute, AttributeLoc},
context::Context,
memory_buffer::MemoryBuffer,
module::Module,
types::BasicTypeEnum,
types::{BasicTypeEnum, IntType},
values::{BasicValue, BasicValueEnum, CallSiteValue, FloatValue, IntValue},
AddressSpace, IntPredicate,
};
use itertools::Either;
use nac3parser::ast::Expr;
use super::{
classes::{
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue,
TypedArrayLikeAccessor, TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
},
llvm_intrinsics,
macros::codegen_unreachable,
stmt::gen_for_callback_incrementing,
CodeGenContext, CodeGenerator,
};
use crate::{symbol_resolver::SymbolResolver, typecheck::typedef::Type};
#[must_use]
pub fn load_irrt<'ctx>(ctx: &'ctx Context, symbol_resolver: &dyn SymbolResolver) -> Module<'ctx> {
let bitcode_buf = MemoryBuffer::create_from_memory_range(
@ -583,294 +584,369 @@ pub fn call_j0<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> Flo
.unwrap()
}
// When [`TypeContext::size_type`] is 32-bits, the function name is "{fn_name}".
// When [`TypeContext::size_type`] is 64-bits, the function name is "{fn_name}64".
#[must_use]
pub fn get_sizet_dependent_function_name<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &CodeGenContext<'_, '_>,
name: &str,
) -> String {
let mut name = name.to_owned();
match generator.get_size_type(ctx.ctx).get_bit_width() {
32 => {}
64 => name.push_str("64"),
bit_width => {
panic!("Unsupported int type bit width {bit_width}, must be either 32-bits or 64-bits")
}
}
name
/// Generates a call to `__nac3_ndarray_calc_size`. Returns an [`IntValue`] representing the
/// calculated total size.
///
/// * `dims` - An [`ArrayLikeIndexer`] containing the size of each dimension.
/// * `range` - The dimension index to begin and end (exclusively) calculating the dimensions for,
/// or [`None`] if starting from the first dimension and ending at the last dimension
/// respectively.
pub fn call_ndarray_calc_size<'ctx, G, Dims>(
generator: &G,
ctx: &CodeGenContext<'ctx, '_>,
dims: &Dims,
(begin, end): (Option<IntValue<'ctx>>, Option<IntValue<'ctx>>),
) -> IntValue<'ctx>
where
G: CodeGenerator + ?Sized,
Dims: ArrayLikeIndexer<'ctx>,
{
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
let ndarray_calc_size_fn_name = match llvm_usize.get_bit_width() {
32 => "__nac3_ndarray_calc_size",
64 => "__nac3_ndarray_calc_size64",
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
};
let ndarray_calc_size_fn_t = llvm_usize.fn_type(
&[llvm_pusize.into(), llvm_usize.into(), llvm_usize.into(), llvm_usize.into()],
false,
);
let ndarray_calc_size_fn =
ctx.module.get_function(ndarray_calc_size_fn_name).unwrap_or_else(|| {
ctx.module.add_function(ndarray_calc_size_fn_name, ndarray_calc_size_fn_t, None)
});
let begin = begin.unwrap_or_else(|| llvm_usize.const_zero());
let end = end.unwrap_or_else(|| dims.size(ctx, generator));
ctx.builder
.build_call(
ndarray_calc_size_fn,
&[
dims.base_ptr(ctx, generator).into(),
dims.size(ctx, generator).into(),
begin.into(),
end.into(),
],
"",
)
.map(CallSiteValue::try_as_basic_value)
.map(|v| v.map_left(BasicValueEnum::into_int_value))
.map(Either::unwrap_left)
.unwrap()
}
pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
/// Generates a call to `__nac3_ndarray_calc_nd_indices`. Returns a [`TypeArrayLikeAdpater`]
/// containing `i32` indices of the flattened index.
///
/// * `index` - The index to compute the multidimensional index for.
/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
/// `NDArray`.
pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &mut CodeGenContext<'ctx, '_>,
index: IntValue<'ctx>,
ndarray: NDArrayValue<'ctx>,
) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
let llvm_void = ctx.ctx.void_type();
let llvm_i32 = ctx.ctx.i32_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
let ndarray_calc_nd_indices_fn_name = match llvm_usize.get_bit_width() {
32 => "__nac3_ndarray_calc_nd_indices",
64 => "__nac3_ndarray_calc_nd_indices64",
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
};
let ndarray_calc_nd_indices_fn =
ctx.module.get_function(ndarray_calc_nd_indices_fn_name).unwrap_or_else(|| {
let fn_type = llvm_void.fn_type(
&[llvm_usize.into(), llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into()],
false,
);
ctx.module.add_function(ndarray_calc_nd_indices_fn_name, fn_type, None)
});
let ndarray_num_dims = ndarray.load_ndims(ctx);
let ndarray_dims = ndarray.dim_sizes();
let indices = ctx.builder.build_array_alloca(llvm_i32, ndarray_num_dims, "").unwrap();
ctx.builder
.build_call(
ndarray_calc_nd_indices_fn,
&[
index.into(),
ndarray_dims.base_ptr(ctx, generator).into(),
ndarray_num_dims.into(),
indices.into(),
],
"",
)
.unwrap();
TypedArrayLikeAdapter::from(
ArraySliceValue::from_ptr_val(indices, ndarray_num_dims, None),
Box::new(|_, v| v.into_int_value()),
Box::new(|_, v| v.into()),
)
}
fn call_ndarray_flatten_index_impl<'ctx, G, Indices>(
generator: &G,
ctx: &CodeGenContext<'ctx, '_>,
ndarray: NDArrayValue<'ctx>,
indices: &Indices,
) -> IntValue<'ctx>
where
G: CodeGenerator + ?Sized,
Indices: ArrayLikeIndexer<'ctx>,
{
let llvm_i32 = ctx.ctx.i32_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
debug_assert_eq!(
IntType::try_from(indices.element_type(ctx, generator))
.map(IntType::get_bit_width)
.unwrap_or_default(),
llvm_i32.get_bit_width(),
"Expected i32 value for argument `indices` to `call_ndarray_flatten_index_impl`"
);
debug_assert_eq!(
indices.size(ctx, generator).get_type().get_bit_width(),
llvm_usize.get_bit_width(),
"Expected usize integer value for argument `indices_size` to `call_ndarray_flatten_index_impl`"
);
let ndarray_flatten_index_fn_name = match llvm_usize.get_bit_width() {
32 => "__nac3_ndarray_flatten_index",
64 => "__nac3_ndarray_flatten_index64",
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
};
let ndarray_flatten_index_fn =
ctx.module.get_function(ndarray_flatten_index_fn_name).unwrap_or_else(|| {
let fn_type = llvm_usize.fn_type(
&[llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into(), llvm_usize.into()],
false,
);
ctx.module.add_function(ndarray_flatten_index_fn_name, fn_type, None)
});
let ndarray_num_dims = ndarray.load_ndims(ctx);
let ndarray_dims = ndarray.dim_sizes();
let index = ctx
.builder
.build_call(
ndarray_flatten_index_fn,
&[
ndarray_dims.base_ptr(ctx, generator).into(),
ndarray_num_dims.into(),
indices.base_ptr(ctx, generator).into(),
indices.size(ctx, generator).into(),
],
"",
)
.map(CallSiteValue::try_as_basic_value)
.map(|v| v.map_left(BasicValueEnum::into_int_value))
.map(Either::unwrap_left)
.unwrap();
index
}
/// Generates a call to `__nac3_ndarray_flatten_index`. Returns the flattened index for the
/// multidimensional index.
///
/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
/// `NDArray`.
/// * `indices` - The multidimensional index to compute the flattened index for.
pub fn call_ndarray_flatten_index<'ctx, G, Index>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndims: Instance<'ctx, Int<SizeT>>,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(
ndarray: NDArrayValue<'ctx>,
indices: &Index,
) -> IntValue<'ctx>
where
G: CodeGenerator + ?Sized,
Index: ArrayLikeIndexer<'ctx>,
{
call_ndarray_flatten_index_impl(generator, ctx, ndarray, indices)
}
/// Generates a call to `__nac3_ndarray_calc_broadcast`. Returns a tuple containing the number of
/// dimension and size of each dimension of the resultant `ndarray`.
pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
lhs: NDArrayValue<'ctx>,
rhs: NDArrayValue<'ctx>,
) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
32 => "__nac3_ndarray_calc_broadcast",
64 => "__nac3_ndarray_calc_broadcast64",
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
};
let ndarray_calc_broadcast_fn =
ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
let fn_type = llvm_usize.fn_type(
&[
llvm_pusize.into(),
llvm_usize.into(),
llvm_pusize.into(),
llvm_usize.into(),
llvm_pusize.into(),
],
false,
);
ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
});
let lhs_ndims = lhs.load_ndims(ctx);
let rhs_ndims = rhs.load_ndims(ctx);
let min_ndims = llvm_intrinsics::call_int_umin(ctx, lhs_ndims, rhs_ndims, None);
gen_for_callback_incrementing(
generator,
ctx,
"__nac3_ndarray_util_assert_shape_no_negative",
);
FnCall::builder(generator, ctx, &name).arg(ndims).arg(shape).returning_void();
}
None,
llvm_usize.const_zero(),
(min_ndims, false),
|generator, ctx, _, idx| {
let idx = ctx.builder.build_int_sub(min_ndims, idx, "").unwrap();
let (lhs_dim_sz, rhs_dim_sz) = unsafe {
(
lhs.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None),
rhs.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None),
)
};
pub fn call_nac3_ndarray_util_assert_output_shape_same<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray_ndims: Instance<'ctx, Int<SizeT>>,
ndarray_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
output_ndims: Instance<'ctx, Int<SizeT>>,
output_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(
let llvm_usize_const_one = llvm_usize.const_int(1, false);
let lhs_eqz = ctx
.builder
.build_int_compare(IntPredicate::EQ, lhs_dim_sz, llvm_usize_const_one, "")
.unwrap();
let rhs_eqz = ctx
.builder
.build_int_compare(IntPredicate::EQ, rhs_dim_sz, llvm_usize_const_one, "")
.unwrap();
let lhs_or_rhs_eqz = ctx.builder.build_or(lhs_eqz, rhs_eqz, "").unwrap();
let lhs_eq_rhs = ctx
.builder
.build_int_compare(IntPredicate::EQ, lhs_dim_sz, rhs_dim_sz, "")
.unwrap();
let is_compatible = ctx.builder.build_or(lhs_or_rhs_eqz, lhs_eq_rhs, "").unwrap();
ctx.make_assert(
generator,
ctx,
"__nac3_ndarray_util_assert_output_shape_same",
is_compatible,
"0:ValueError",
"operands could not be broadcast together",
[None, None, None],
ctx.current_loc,
);
FnCall::builder(generator, ctx, &name)
.arg(ndarray_ndims)
.arg(ndarray_shape)
.arg(output_ndims)
.arg(output_shape)
.returning_void();
Ok(())
},
llvm_usize.const_int(1, false),
)
.unwrap();
let max_ndims = llvm_intrinsics::call_int_umax(ctx, lhs_ndims, rhs_ndims, None);
let lhs_dims = lhs.dim_sizes().base_ptr(ctx, generator);
let lhs_ndims = lhs.load_ndims(ctx);
let rhs_dims = rhs.dim_sizes().base_ptr(ctx, generator);
let rhs_ndims = rhs.load_ndims(ctx);
let out_dims = ctx.builder.build_array_alloca(llvm_usize, max_ndims, "").unwrap();
let out_dims = ArraySliceValue::from_ptr_val(out_dims, max_ndims, None);
ctx.builder
.build_call(
ndarray_calc_broadcast_fn,
&[
lhs_dims.into(),
lhs_ndims.into(),
rhs_dims.into(),
rhs_ndims.into(),
out_dims.base_ptr(ctx, generator).into(),
],
"",
)
.unwrap();
TypedArrayLikeAdapter::from(
out_dims,
Box::new(|_, v| v.into_int_value()),
Box::new(|_, v| v.into()),
)
}
pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
/// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
/// array `broadcast_idx`.
pub fn call_ndarray_calc_broadcast_index<
'ctx,
G: CodeGenerator + ?Sized,
BroadcastIdx: UntypedArrayLikeAccessor<'ctx>,
>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) -> Instance<'ctx, Int<SizeT>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_size");
FnCall::builder(generator, ctx, &name).arg(ndarray).returning_auto("size")
}
array: NDArrayValue<'ctx>,
broadcast_idx: &BroadcastIdx,
) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
let llvm_i32 = ctx.ctx.i32_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) -> Instance<'ctx, Int<SizeT>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_nbytes");
FnCall::builder(generator, ctx, &name).arg(ndarray).returning_auto("nbytes")
}
pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) -> Instance<'ctx, Int<SizeT>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_len");
FnCall::builder(generator, ctx, &name).arg(ndarray).returning_auto("len")
}
pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) -> Instance<'ctx, Int<Bool>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_is_c_contiguous");
FnCall::builder(generator, ctx, &name).arg(ndarray).returning_auto("is_c_contiguous")
}
pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
index: Instance<'ctx, Int<SizeT>>,
) -> Instance<'ctx, Ptr<Int<Byte>>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_nth_pelement");
FnCall::builder(generator, ctx, &name).arg(ndarray).arg(index).returning_auto("pelement")
}
pub fn call_nac3_ndarray_get_pelement_by_indices<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Instance<'ctx, Ptr<Int<Byte>>> {
let name =
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_pelement_by_indices");
FnCall::builder(generator, ctx, &name).arg(ndarray).arg(indices).returning_auto("pelement")
}
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name =
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_set_strides_by_shape");
FnCall::builder(generator, ctx, &name).arg(ndarray).returning_void();
}
pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_copy_data");
FnCall::builder(generator, ctx, &name).arg(src_ndarray).arg(dst_ndarray).returning_void();
}
pub fn call_nac3_nditer_initialize<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
iter: Instance<'ctx, Ptr<Struct<NDIter>>>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_initialize");
FnCall::builder(generator, ctx, &name).arg(iter).arg(ndarray).arg(indices).returning_void();
}
pub fn call_nac3_nditer_has_element<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
iter: Instance<'ctx, Ptr<Struct<NDIter>>>,
) -> Instance<'ctx, Int<Bool>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_has_element");
FnCall::builder(generator, ctx, &name).arg(iter).returning_auto("has_element")
}
pub fn call_nac3_nditer_next<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
iter: Instance<'ctx, Ptr<Struct<NDIter>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_next");
FnCall::builder(generator, ctx, &name).arg(iter).returning_void();
}
pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
num_indices: Instance<'ctx, Int<SizeT>>,
indices: Instance<'ctx, Ptr<Struct<NDIndex>>>,
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
FnCall::builder(generator, ctx, &name)
.arg(num_indices)
.arg(indices)
.arg(src_ndarray)
.arg(dst_ndarray)
.returning_void();
}
pub fn call_nac3_ndarray_array_set_and_validate_list_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
list: Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>>,
ndims: Instance<'ctx, Int<SizeT>>,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(
generator,
ctx,
"__nac3_ndarray_array_set_and_validate_list_shape",
let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
32 => "__nac3_ndarray_calc_broadcast_idx",
64 => "__nac3_ndarray_calc_broadcast_idx64",
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
};
let ndarray_calc_broadcast_fn =
ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
let fn_type = llvm_usize.fn_type(
&[llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into(), llvm_pi32.into()],
false,
);
FnCall::builder(generator, ctx, &name).arg(list).arg(ndims).arg(shape).returning_void();
}
pub fn call_nac3_ndarray_array_write_list_to_array<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
list: Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name = get_sizet_dependent_function_name(
generator,
ctx,
"__nac3_ndarray_array_write_list_to_array",
);
FnCall::builder(generator, ctx, &name).arg(list).arg(ndarray).returning_void();
}
ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
});
pub fn call_nac3_ndarray_reshape_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
size: Instance<'ctx, Int<SizeT>>,
new_ndims: Instance<'ctx, Int<SizeT>>,
new_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(
generator,
ctx,
"__nac3_ndarray_reshape_resolve_and_check_new_shape",
);
FnCall::builder(generator, ctx, &name).arg(size).arg(new_ndims).arg(new_shape).returning_void();
}
let broadcast_size = broadcast_idx.size(ctx, generator);
let out_idx = ctx.builder.build_array_alloca(llvm_i32, broadcast_size, "").unwrap();
pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
FnCall::builder(generator, ctx, &name).arg(src_ndarray).arg(dst_ndarray).returning_void();
}
let array_dims = array.dim_sizes().base_ptr(ctx, generator);
let array_ndims = array.load_ndims(ctx);
let broadcast_idx_ptr = unsafe {
broadcast_idx.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
};
pub fn call_nac3_ndarray_broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
num_shape_entries: Instance<'ctx, Int<SizeT>>,
shape_entries: Instance<'ctx, Ptr<Struct<ShapeEntry>>>,
dst_ndims: Instance<'ctx, Int<SizeT>>,
dst_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
FnCall::builder(generator, ctx, &name)
.arg(num_shape_entries)
.arg(shape_entries)
.arg(dst_ndims)
.arg(dst_shape)
.returning_void();
}
ctx.builder
.build_call(
ndarray_calc_broadcast_fn,
&[array_dims.into(), array_ndims.into(), broadcast_idx_ptr.into(), out_idx.into()],
"",
)
.unwrap();
pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
num_axes: Instance<'ctx, Int<SizeT>>,
axes: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
FnCall::builder(generator, ctx, &name)
.arg(src_ndarray)
.arg(dst_ndarray)
.arg(num_axes)
.arg(axes)
.returning_void();
}
#[allow(clippy::too_many_arguments)]
pub fn call_nac3_ndarray_matmul_calculate_shapes<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
a_ndims: Instance<'ctx, Int<SizeT>>,
a_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
b_ndims: Instance<'ctx, Int<SizeT>>,
b_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
final_ndims: Instance<'ctx, Int<SizeT>>,
new_a_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
new_b_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
dst_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name =
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_matmul_calculate_shapes");
FnCall::builder(generator, ctx, &name)
.arg(a_ndims)
.arg(a_shape)
.arg(b_ndims)
.arg(b_shape)
.arg(final_ndims)
.arg(new_a_shape)
.arg(new_b_shape)
.arg(dst_shape)
.returning_void();
TypedArrayLikeAdapter::from(
ArraySliceValue::from_ptr_val(out_idx, broadcast_size, None),
Box::new(|_, v| v.into_int_value()),
Box::new(|_, v| v.into()),
)
}

20
nac3core/src/codegen/llvm_intrinsics.rs
View File

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

45
nac3core/src/codegen/mod.rs
View File

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

42
nac3core/src/codegen/model/any.rs
View File

@ -1,42 +0,0 @@
use inkwell::{
context::Context,
types::{BasicType, BasicTypeEnum},
values::BasicValueEnum,
};
use crate::codegen::CodeGenerator;
use super::*;
/// A [`Model`] of any [`BasicTypeEnum`].
///
/// Use this when it is infeasible to use model abstractions.
#[derive(Debug, Clone, Copy)]
pub struct Any<'ctx>(pub BasicTypeEnum<'ctx>);
impl<'ctx> Model<'ctx> for Any<'ctx> {
type Value = BasicValueEnum<'ctx>;
type Type = BasicTypeEnum<'ctx>;
fn llvm_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
_ctx: &'ctx Context,
) -> Self::Type {
self.0
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
_generator: &mut G,
_ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
if ty == self.0 {
Ok(())
} else {
Err(ModelError(format!("Expecting {}, but got {}", self.0, ty)))
}
}
}

147
nac3core/src/codegen/model/array.rs
View File

@ -1,147 +0,0 @@
use std::fmt;
use inkwell::{
context::Context,
types::{ArrayType, BasicType, BasicTypeEnum},
values::{ArrayValue, IntValue},
};
use crate::codegen::{CodeGenContext, CodeGenerator};
use super::*;
/// Trait for Rust structs identifying length values for [`Array`].
pub trait ArrayLen: fmt::Debug + Clone + Copy {
fn length(&self) -> u32;
}
/// A statically known length.
#[derive(Debug, Clone, Copy, Default)]
pub struct Len<const N: u32>;
/// A dynamically known length.
#[derive(Debug, Clone, Copy)]
pub struct AnyLen(pub u32);
impl<const N: u32> ArrayLen for Len<N> {
fn length(&self) -> u32 {
N
}
}
impl ArrayLen for AnyLen {
fn length(&self) -> u32 {
self.0
}
}
/// A Model for an [`ArrayType`].
///
/// `Len` should be of a [`LenKind`] and `Item` should be a of [`Model`].
#[derive(Debug, Clone, Copy, Default)]
pub struct Array<Len, Item> {
/// Length of this array.
pub len: Len,
/// [`Model`] of the array items.
pub item: Item,
}
impl<'ctx, Len: ArrayLen, Item: Model<'ctx>> Model<'ctx> for Array<Len, Item> {
type Value = ArrayValue<'ctx>;
type Type = ArrayType<'ctx>;
fn llvm_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> Self::Type {
self.item.llvm_type(generator, ctx).array_type(self.len.length())
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
let BasicTypeEnum::ArrayType(ty) = ty else {
return Err(ModelError(format!("Expecting ArrayType, but got {ty:?}")));
};
if ty.len() != self.len.length() {
return Err(ModelError(format!(
"Expecting ArrayType with size {}, but got an ArrayType with size {}",
ty.len(),
self.len.length()
)));
}
self.item
.check_type(generator, ctx, ty.get_element_type())
.map_err(|err| err.under_context("an ArrayType"))?;
Ok(())
}
}
impl<'ctx, Len: ArrayLen, Item: Model<'ctx>> Instance<'ctx, Ptr<Array<Len, Item>>> {
/// Get the pointer to the `i`-th (0-based) array element.
pub fn gep(
&self,
ctx: &CodeGenContext<'ctx, '_>,
i: IntValue<'ctx>,
) -> Instance<'ctx, Ptr<Item>> {
let zero = ctx.ctx.i32_type().const_zero();
let ptr = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[zero, i], "").unwrap() };
unsafe { Ptr(self.model.0.item).believe_value(ptr) }
}
/// Like `gep` but `i` is a constant.
pub fn gep_const(&self, ctx: &CodeGenContext<'ctx, '_>, i: u64) -> Instance<'ctx, Ptr<Item>> {
assert!(
i < u64::from(self.model.0.len.length()),
"Index {i} is out of bounds. Array length = {}",
self.model.0.len.length()
);
let i = ctx.ctx.i32_type().const_int(i, false);
self.gep(ctx, i)
}
/// Convenience function equivalent to `.gep(...).load(...)`.
pub fn get<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
i: IntValue<'ctx>,
) -> Instance<'ctx, Item> {
self.gep(ctx, i).load(generator, ctx)
}
/// Like `get` but `i` is a constant.
pub fn get_const<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
i: u64,
) -> Instance<'ctx, Item> {
self.gep_const(ctx, i).load(generator, ctx)
}
/// Convenience function equivalent to `.gep(...).store(...)`.
pub fn set(
&self,
ctx: &CodeGenContext<'ctx, '_>,
i: IntValue<'ctx>,
value: Instance<'ctx, Item>,
) {
self.gep(ctx, i).store(ctx, value);
}
/// Like `set` but `i` is a constant.
pub fn set_const(&self, ctx: &CodeGenContext<'ctx, '_>, i: u64, value: Instance<'ctx, Item>) {
self.gep_const(ctx, i).store(ctx, value);
}
}

207
nac3core/src/codegen/model/core.rs
View File

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

94
nac3core/src/codegen/model/float.rs
View File

@ -1,94 +0,0 @@
use std::fmt;
use inkwell::{
context::Context,
types::{BasicType, FloatType},
values::FloatValue,
};
use crate::codegen::CodeGenerator;
use super::*;
pub trait FloatKind<'ctx>: fmt::Debug + Clone + Copy {
fn get_float_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> FloatType<'ctx>;
}
#[derive(Debug, Clone, Copy, Default)]
pub struct Float32;
#[derive(Debug, Clone, Copy, Default)]
pub struct Float64;
impl<'ctx> FloatKind<'ctx> for Float32 {
fn get_float_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> FloatType<'ctx> {
ctx.f32_type()
}
}
impl<'ctx> FloatKind<'ctx> for Float64 {
fn get_float_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> FloatType<'ctx> {
ctx.f64_type()
}
}
#[derive(Debug, Clone, Copy)]
pub struct AnyFloat<'ctx>(FloatType<'ctx>);
impl<'ctx> FloatKind<'ctx> for AnyFloat<'ctx> {
fn get_float_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
_ctx: &'ctx Context,
) -> FloatType<'ctx> {
self.0
}
}
#[derive(Debug, Clone, Copy, Default)]
pub struct Float<N>(pub N);
impl<'ctx, N: FloatKind<'ctx>> Model<'ctx> for Float<N> {
type Value = FloatValue<'ctx>;
type Type = FloatType<'ctx>;
fn llvm_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> Self::Type {
self.0.get_float_type(generator, ctx)
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
let Ok(ty) = FloatType::try_from(ty) else {
return Err(ModelError(format!("Expecting FloatType, but got {ty:?}")));
};
let exp_ty = self.0.get_float_type(generator, ctx);
// TODO: Inkwell does not have get_bit_width for FloatType?
if ty != exp_ty {
return Err(ModelError(format!("Expecting {exp_ty:?}, but got {ty:?}")));
}
Ok(())
}
}

122
nac3core/src/codegen/model/function.rs
View File

@ -1,122 +0,0 @@
use inkwell::{
attributes::{Attribute, AttributeLoc},
types::{BasicMetadataTypeEnum, BasicType, FunctionType},
values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
};
use itertools::Itertools;
use crate::codegen::{CodeGenContext, CodeGenerator};
use super::*;
#[derive(Debug, Clone, Copy)]
struct Arg<'ctx> {
ty: BasicMetadataTypeEnum<'ctx>,
val: BasicMetadataValueEnum<'ctx>,
}
/// A convenience structure to construct & call an LLVM function.
///
/// ### Usage
///
/// The syntax is like this:
/// ```ignore
/// let result = CallFunction::begin("my_function_name")
/// .attrs(...)
/// .arg(arg1)
/// .arg(arg2)
/// .arg(arg3)
/// .returning("my_function_result", Int32);
/// ```
///
/// The function `my_function_name` is called when `.returning()` (or its variants) is called, returning
/// the result as an `Instance<'ctx, Int<Int32>>`.
///
/// If `my_function_name` has not been declared in `ctx.module`, once `.returning()` is called, a function
/// declaration of `my_function_name` is added to `ctx.module`, where the [`FunctionType`] is deduced from
/// the argument types and returning type.
pub struct FnCall<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> {
generator: &'d mut G,
ctx: &'b CodeGenContext<'ctx, 'a>,
/// Function name
name: &'c str,
/// Call arguments
args: Vec<Arg<'ctx>>,
/// LLVM function Attributes
attrs: Vec<&'static str>,
}
impl<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> FnCall<'ctx, 'a, 'b, 'c, 'd, G> {
pub fn builder(generator: &'d mut G, ctx: &'b CodeGenContext<'ctx, 'a>, name: &'c str) -> Self {
FnCall { generator, ctx, name, args: Vec::new(), attrs: Vec::new() }
}
/// Push a list of LLVM function attributes to the function declaration.
#[must_use]
pub fn attrs(mut self, attrs: Vec<&'static str>) -> Self {
self.attrs = attrs;
self
}
/// Push a call argument to the function call.
#[allow(clippy::needless_pass_by_value)]
#[must_use]
pub fn arg<M: Model<'ctx>>(mut self, arg: Instance<'ctx, M>) -> Self {
let arg = Arg {
ty: arg.model.llvm_type(self.generator, self.ctx.ctx).as_basic_type_enum().into(),
val: arg.value.as_basic_value_enum().into(),
};
self.args.push(arg);
self
}
/// Call the function and expect the function to return a value of type of `return_model`.
#[must_use]
pub fn returning<M: Model<'ctx>>(self, name: &str, return_model: M) -> Instance<'ctx, M> {
let ret_ty = return_model.llvm_type(self.generator, self.ctx.ctx);
let ret = self.call(|tys| ret_ty.fn_type(tys, false), name);
let ret = BasicValueEnum::try_from(ret.as_any_value_enum()).unwrap(); // Must work
let ret = return_model.check_value(self.generator, self.ctx.ctx, ret).unwrap(); // Must work
ret
}
/// Like [`CallFunction::returning_`] but `return_model` is automatically inferred.
#[must_use]
pub fn returning_auto<M: Model<'ctx> + Default>(self, name: &str) -> Instance<'ctx, M> {
self.returning(name, M::default())
}
/// Call the function and expect the function to return a void-type.
pub fn returning_void(self) {
let ret_ty = self.ctx.ctx.void_type();
let _ = self.call(|tys| ret_ty.fn_type(tys, false), "");
}
fn call<F>(&self, make_fn_type: F, return_value_name: &str) -> CallSiteValue<'ctx>
where
F: FnOnce(&[BasicMetadataTypeEnum<'ctx>]) -> FunctionType<'ctx>,
{
// Get the LLVM function.
let func = self.ctx.module.get_function(self.name).unwrap_or_else(|| {
// Declare the function if it doesn't exist.
let tys = self.args.iter().map(|arg| arg.ty).collect_vec();
let func_type = make_fn_type(&tys);
let func = self.ctx.module.add_function(self.name, func_type, None);
for attr in &self.attrs {
func.add_attribute(
AttributeLoc::Function,
self.ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
);
}
func
});
let vals = self.args.iter().map(|arg| arg.val).collect_vec();
self.ctx.builder.build_call(func, &vals, return_value_name).unwrap()
}
}

422
nac3core/src/codegen/model/int.rs
View File

@ -1,422 +0,0 @@
use std::{cmp::Ordering, fmt};
use inkwell::{
context::Context,
types::{BasicType, IntType},
values::IntValue,
IntPredicate,
};
use crate::codegen::{CodeGenContext, CodeGenerator};
use super::*;
pub trait IntKind<'ctx>: fmt::Debug + Clone + Copy {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx>;
}
#[derive(Debug, Clone, Copy, Default)]
pub struct Bool;
#[derive(Debug, Clone, Copy, Default)]
pub struct Byte;
#[derive(Debug, Clone, Copy, Default)]
pub struct Int32;
#[derive(Debug, Clone, Copy, Default)]
pub struct Int64;
#[derive(Debug, Clone, Copy, Default)]
pub struct SizeT;
impl<'ctx> IntKind<'ctx> for Bool {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.bool_type()
}
}
impl<'ctx> IntKind<'ctx> for Byte {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.i8_type()
}
}
impl<'ctx> IntKind<'ctx> for Int32 {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.i32_type()
}
}
impl<'ctx> IntKind<'ctx> for Int64 {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.i64_type()
}
}
impl<'ctx> IntKind<'ctx> for SizeT {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
generator.get_size_type(ctx)
}
}
#[derive(Debug, Clone, Copy)]
pub struct AnyInt<'ctx>(pub IntType<'ctx>);
impl<'ctx> IntKind<'ctx> for AnyInt<'ctx> {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
_ctx: &'ctx Context,
) -> IntType<'ctx> {
self.0
}
}
#[derive(Debug, Clone, Copy, Default)]
pub struct Int<N>(pub N);
impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for Int<N> {
type Value = IntValue<'ctx>;
type Type = IntType<'ctx>;
fn llvm_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> Self::Type {
self.0.get_int_type(generator, ctx)
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
let Ok(ty) = IntType::try_from(ty) else {
return Err(ModelError(format!("Expecting IntType, but got {ty:?}")));
};
let exp_ty = self.0.get_int_type(generator, ctx);
if ty.get_bit_width() != exp_ty.get_bit_width() {
return Err(ModelError(format!(
"Expecting IntType to have {} bit(s), but got {} bit(s)",
exp_ty.get_bit_width(),
ty.get_bit_width()
)));
}
Ok(())
}
}
impl<'ctx, N: IntKind<'ctx>> Int<N> {
pub fn const_int<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
value: u64,
sign_extend: bool,
) -> Instance<'ctx, Self> {
let value = self.llvm_type(generator, ctx).const_int(value, sign_extend);
unsafe { self.believe_value(value) }
}
pub fn const_0<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
let value = self.llvm_type(generator, ctx).const_zero();
unsafe { self.believe_value(value) }
}
pub fn const_1<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
self.const_int(generator, ctx, 1, false)
}
pub fn const_all_ones<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
let value = self.llvm_type(generator, ctx).const_all_ones();
unsafe { self.believe_value(value) }
}
pub fn s_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
<= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value = ctx
.builder
.build_int_s_extend_or_bit_cast(value, self.llvm_type(generator, ctx.ctx), "")
.unwrap();
unsafe { self.believe_value(value) }
}
pub fn s_extend<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
< self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value =
ctx.builder.build_int_s_extend(value, self.llvm_type(generator, ctx.ctx), "").unwrap();
unsafe { self.believe_value(value) }
}
pub fn z_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
<= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value = ctx
.builder
.build_int_z_extend_or_bit_cast(value, self.llvm_type(generator, ctx.ctx), "")
.unwrap();
unsafe { self.believe_value(value) }
}
pub fn z_extend<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
< self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value =
ctx.builder.build_int_z_extend(value, self.llvm_type(generator, ctx.ctx), "").unwrap();
unsafe { self.believe_value(value) }
}
pub fn truncate_or_bit_cast<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
>= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value = ctx
.builder
.build_int_truncate_or_bit_cast(value, self.llvm_type(generator, ctx.ctx), "")
.unwrap();
unsafe { self.believe_value(value) }
}
pub fn truncate<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
> self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value =
ctx.builder.build_int_truncate(value, self.llvm_type(generator, ctx.ctx), "").unwrap();
unsafe { self.believe_value(value) }
}
/// `sext` or `trunc` an int to this model's int type. Does nothing if equal bit-widths.
pub fn s_extend_or_truncate<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
let their_width = value.get_type().get_bit_width();
let our_width = self.0.get_int_type(generator, ctx.ctx).get_bit_width();
match their_width.cmp(&our_width) {
Ordering::Less => self.s_extend(generator, ctx, value),
Ordering::Equal => unsafe { self.believe_value(value) },
Ordering::Greater => self.truncate(generator, ctx, value),
}
}
/// `zext` or `trunc` an int to this model's int type. Does nothing if equal bit-widths.
pub fn z_extend_or_truncate<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
let their_width = value.get_type().get_bit_width();
let our_width = self.0.get_int_type(generator, ctx.ctx).get_bit_width();
match their_width.cmp(&our_width) {
Ordering::Less => self.z_extend(generator, ctx, value),
Ordering::Equal => unsafe { self.believe_value(value) },
Ordering::Greater => self.truncate(generator, ctx, value),
}
}
}
impl Int<Bool> {
#[must_use]
pub fn const_false<'ctx, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
self.const_int(generator, ctx, 0, false)
}
#[must_use]
pub fn const_true<'ctx, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
self.const_int(generator, ctx, 1, false)
}
}
impl<'ctx, N: IntKind<'ctx>> Instance<'ctx, Int<N>> {
pub fn s_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).s_extend_or_bit_cast(generator, ctx, self.value)
}
pub fn s_extend<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).s_extend(generator, ctx, self.value)
}
pub fn z_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).z_extend_or_bit_cast(generator, ctx, self.value)
}
pub fn z_extend<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).z_extend(generator, ctx, self.value)
}
pub fn truncate_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).truncate_or_bit_cast(generator, ctx, self.value)
}
pub fn truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).truncate(generator, ctx, self.value)
}
pub fn s_extend_or_truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).s_extend_or_truncate(generator, ctx, self.value)
}
pub fn z_extend_or_truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).z_extend_or_truncate(generator, ctx, self.value)
}
#[must_use]
pub fn add(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
let value = ctx.builder.build_int_add(self.value, other.value, "").unwrap();
unsafe { self.model.believe_value(value) }
}
#[must_use]
pub fn sub(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
let value = ctx.builder.build_int_sub(self.value, other.value, "").unwrap();
unsafe { self.model.believe_value(value) }
}
#[must_use]
pub fn mul(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
let value = ctx.builder.build_int_mul(self.value, other.value, "").unwrap();
unsafe { self.model.believe_value(value) }
}
pub fn compare(
&self,
ctx: &CodeGenContext<'ctx, '_>,
op: IntPredicate,
other: Self,
) -> Instance<'ctx, Int<Bool>> {
let value = ctx.builder.build_int_compare(op, self.value, other.value, "").unwrap();
unsafe { Int(Bool).believe_value(value) }
}
}

17
nac3core/src/codegen/model/mod.rs
View File

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

223
nac3core/src/codegen/model/ptr.rs
View File

@ -1,223 +0,0 @@
use inkwell::{
context::Context,
types::{BasicType, BasicTypeEnum, PointerType},
values::{IntValue, PointerValue},
AddressSpace,
};
use crate::codegen::{llvm_intrinsics::call_memcpy_generic, CodeGenContext, CodeGenerator};
use super::*;
/// A model for [`PointerType`].
///
/// `Item` is the element type this pointer is pointing to, and should be of a [`Model`].
///
// TODO: LLVM 15: `Item` is a Rust type-hint for the LLVM type of value the `.store()/.load()` family
// of functions return. If a truly opaque pointer is needed, tell the programmer to use `OpaquePtr`.
#[derive(Debug, Clone, Copy, Default)]
pub struct Ptr<Item>(pub Item);
/// An opaque pointer. Like [`Ptr`] but without any Rust type-hints about its element type.
///
/// `.load()/.store()` is not available for [`Instance`]s of opaque pointers.
pub type OpaquePtr = Ptr<()>;
// TODO: LLVM 15: `Item: Model<'ctx>` don't even need to be a model anymore. It will only be
// a type-hint for the `.load()/.store()` functions for the `pointee_ty`.
//
// See https://thedan64.github.io/inkwell/inkwell/builder/struct.Builder.html#method.build_load.
impl<'ctx, Item: Model<'ctx>> Model<'ctx> for Ptr<Item> {
type Value = PointerValue<'ctx>;
type Type = PointerType<'ctx>;
fn llvm_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> Self::Type {
// TODO: LLVM 15: ctx.ptr_type(AddressSpace::default())
self.0.llvm_type(generator, ctx).ptr_type(AddressSpace::default())
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
let Ok(ty) = PointerType::try_from(ty) else {
return Err(ModelError(format!("Expecting PointerType, but got {ty:?}")));
};
let elem_ty = ty.get_element_type();
let Ok(elem_ty) = BasicTypeEnum::try_from(elem_ty) else {
return Err(ModelError(format!(
"Expecting pointer element type to be a BasicTypeEnum, but got {elem_ty:?}"
)));
};
// TODO: inkwell `get_element_type()` will be deprecated.
// Remove the check for `get_element_type()` when the time comes.
self.0
.check_type(generator, ctx, elem_ty)
.map_err(|err| err.under_context("a PointerType"))?;
Ok(())
}
}
impl<'ctx, Item: Model<'ctx>> Ptr<Item> {
/// Return a ***constant*** nullptr.
pub fn nullptr<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Ptr<Item>> {
let ptr = self.llvm_type(generator, ctx).const_null();
unsafe { self.believe_value(ptr) }
}
/// Cast a pointer into this model with [`inkwell::builder::Builder::build_pointer_cast`]
pub fn pointer_cast<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
ptr: PointerValue<'ctx>,
) -> Instance<'ctx, Ptr<Item>> {
// TODO: LLVM 15: Write in an impl where `Item` does not have to be `Model<'ctx>`.
// TODO: LLVM 15: This function will only have to be:
// ```
// return self.believe_value(ptr);
// ```
let t = self.llvm_type(generator, ctx.ctx);
let ptr = ctx.builder.build_pointer_cast(ptr, t, "").unwrap();
unsafe { self.believe_value(ptr) }
}
}
impl<'ctx, Item: Model<'ctx>> Instance<'ctx, Ptr<Item>> {
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`].
#[must_use]
pub fn offset(
&self,
ctx: &CodeGenContext<'ctx, '_>,
offset: IntValue<'ctx>,
) -> Instance<'ctx, Ptr<Item>> {
let p = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[offset], "").unwrap() };
unsafe { self.model.believe_value(p) }
}
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`] by a constant offset.
#[must_use]
pub fn offset_const(
&self,
ctx: &CodeGenContext<'ctx, '_>,
offset: i64,
) -> Instance<'ctx, Ptr<Item>> {
let offset = ctx.ctx.i32_type().const_int(offset as u64, true);
self.offset(ctx, offset)
}
pub fn set_index(
&self,
ctx: &CodeGenContext<'ctx, '_>,
index: IntValue<'ctx>,
value: Instance<'ctx, Item>,
) {
self.offset(ctx, index).store(ctx, value);
}
pub fn set_index_const(
&self,
ctx: &CodeGenContext<'ctx, '_>,
index: i64,
value: Instance<'ctx, Item>,
) {
self.offset_const(ctx, index).store(ctx, value);
}
pub fn get_index<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
index: IntValue<'ctx>,
) -> Instance<'ctx, Item> {
self.offset(ctx, index).load(generator, ctx)
}
pub fn get_index_const<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
index: i64,
) -> Instance<'ctx, Item> {
self.offset_const(ctx, index).load(generator, ctx)
}
/// Load the value with [`inkwell::builder::Builder::build_load`].
pub fn load<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Item> {
let value = ctx.builder.build_load(self.value, "").unwrap();
self.model.0.check_value(generator, ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error.
}
/// Store a value with [`inkwell::builder::Builder::build_store`].
pub fn store(&self, ctx: &CodeGenContext<'ctx, '_>, value: Instance<'ctx, Item>) {
ctx.builder.build_store(self.value, value.value).unwrap();
}
/// Return a casted pointer of element type `NewElement` with [`inkwell::builder::Builder::build_pointer_cast`].
pub fn pointer_cast<NewItem: Model<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
new_item: NewItem,
) -> Instance<'ctx, Ptr<NewItem>> {
// TODO: LLVM 15: Write in an impl where `Item` does not have to be `Model<'ctx>`.
Ptr(new_item).pointer_cast(generator, ctx, self.value)
}
/// Cast this pointer to `uint8_t*`
pub fn cast_to_pi8<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Ptr<Int<Byte>>> {
Ptr(Int(Byte)).pointer_cast(generator, ctx, self.value)
}
/// Check if the pointer is null with [`inkwell::builder::Builder::build_is_null`].
pub fn is_null(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Int<Bool>> {
let value = ctx.builder.build_is_null(self.value, "").unwrap();
unsafe { Int(Bool).believe_value(value) }
}
/// Check if the pointer is not null with [`inkwell::builder::Builder::build_is_not_null`].
pub fn is_not_null(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Int<Bool>> {
let value = ctx.builder.build_is_not_null(self.value, "").unwrap();
unsafe { Int(Bool).believe_value(value) }
}
/// `memcpy` from another pointer.
pub fn copy_from<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
source: Self,
num_items: IntValue<'ctx>,
) {
// Force extend `num_items` and `itemsize` to `i64` so their types would match.
let itemsize = self.model.size_of(generator, ctx.ctx);
let itemsize = Int(SizeT).z_extend_or_truncate(generator, ctx, itemsize);
let num_items = Int(SizeT).z_extend_or_truncate(generator, ctx, num_items);
let totalsize = itemsize.mul(ctx, num_items);
let is_volatile = ctx.ctx.bool_type().const_zero(); // is_volatile = false
call_memcpy_generic(ctx, self.value, source.value, totalsize.value, is_volatile);
}
}

364
nac3core/src/codegen/model/structure.rs
View File

@ -1,364 +0,0 @@
use std::fmt;
use inkwell::{
context::Context,
types::{BasicType, BasicTypeEnum, StructType},
values::{BasicValueEnum, StructValue},
};
use crate::codegen::{CodeGenContext, CodeGenerator};
use super::*;
/// A traveral that traverses a Rust `struct` that is used to declare an LLVM's struct's field types.
pub trait FieldTraversal<'ctx> {
/// Output type of [`FieldTraversal::add`].
type Output<M>;
/// Traverse through the type of a declared field and do something with it.
///
/// * `name` - The cosmetic name of the LLVM field. Used for debugging.
/// * `model` - The [`Model`] representing the LLVM type of this field.
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Output<M>;
/// Like [`FieldTraversal::add`] but [`Model`] is automatically inferred from its [`Default`] trait.
fn add_auto<M: Model<'ctx> + Default>(&mut self, name: &'static str) -> Self::Output<M> {
self.add(name, M::default())
}
}
/// Descriptor of an LLVM struct field.
#[derive(Debug, Clone, Copy)]
pub struct GepField<M> {
/// The GEP index of this field. This is the index to use with `build_gep`.
pub gep_index: u32,
/// The cosmetic name of this field.
pub name: &'static str,
/// The [`Model`] of this field's type.
pub model: M,
}
/// A traversal to calculate the GEP index of fields.
pub struct GepFieldTraversal {
/// The current GEP index.
gep_index_counter: u32,
}
impl<'ctx> FieldTraversal<'ctx> for GepFieldTraversal {
type Output<M> = GepField<M>;
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Output<M> {
let gep_index = self.gep_index_counter;
self.gep_index_counter += 1;
Self::Output { gep_index, name, model }
}
}
/// A traversal to collect the field types of a struct.
///
/// This is used to collect field types and construct the LLVM struct type with [`Context::struct_type`].
struct TypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
generator: &'a G,
ctx: &'ctx Context,
/// The collected field types so far in exact order.
field_types: Vec<BasicTypeEnum<'ctx>>,
}
impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx> for TypeFieldTraversal<'ctx, 'a, G> {
type Output<M> = (); // Checking types return nothing.
fn add<M: Model<'ctx>>(&mut self, _name: &'static str, model: M) -> Self::Output<M> {
let t = model.llvm_type(self.generator, self.ctx).as_basic_type_enum();
self.field_types.push(t);
}
}
/// A traversal to check the types of fields.
struct CheckTypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
generator: &'a mut G,
ctx: &'ctx Context,
/// The current GEP index, so we can tell the index of the field we are checking
/// and report the GEP index.
gep_index_counter: u32,
/// The [`StructType`] to check.
scrutinee: StructType<'ctx>,
/// The list of collected errors so far.
errors: Vec<ModelError>,
}
impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx>
for CheckTypeFieldTraversal<'ctx, 'a, G>
{
type Output<M> = (); // Checking types return nothing.
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Output<M> {
let gep_index = self.gep_index_counter;
self.gep_index_counter += 1;
if let Some(t) = self.scrutinee.get_field_type_at_index(gep_index) {
if let Err(err) = model.check_type(self.generator, self.ctx, t) {
self.errors
.push(err.under_context(format!("field #{gep_index} '{name}'").as_str()));
}
}
// Otherwise, it will be caught by Struct's `check_type`.
}
}
/// A trait for Rust structs identifying LLVM structures.
///
/// ### Example
///
/// Suppose you want to define this structure:
/// ```c
/// template <typename T>
/// struct ContiguousNDArray {
/// size_t ndims;
/// size_t* shape;
/// T* data;
/// }
/// ```
///
/// This is how it should be done:
/// ```ignore
/// pub struct ContiguousNDArrayFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
/// pub ndims: F::Out<Int<SizeT>>,
/// pub shape: F::Out<Ptr<Int<SizeT>>>,
/// pub data: F::Out<Ptr<Item>>,
/// }
///
/// /// An ndarray without strides and non-opaque `data` field in NAC3.
/// #[derive(Debug, Clone, Copy)]
/// pub struct ContiguousNDArray<M> {
/// /// [`Model`] of the items.
/// pub item: M,
/// }
///
/// impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for ContiguousNDArray<Item> {
/// type Fields<F: FieldTraversal<'ctx>> = ContiguousNDArrayFields<'ctx, F, Item>;
///
/// fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
/// // The order of `traversal.add*` is important
/// Self::Fields {
/// ndims: traversal.add_auto("ndims"),
/// shape: traversal.add_auto("shape"),
/// data: traversal.add("data", Ptr(self.item)),
/// }
/// }
/// }
/// ```
///
/// The [`FieldTraversal`] here is a mechanism to allow the fields of `ContiguousNDArrayFields` to be
/// traversed to do useful work such as:
///
/// - To create the [`StructType`] of `ContiguousNDArray` by collecting [`BasicType`]s of the fields.
/// - To enable the `.gep(ctx, |f| f.ndims).store(ctx, ...)` syntax.
///
/// Suppose now that you have defined `ContiguousNDArray` and you want to allocate a `ContiguousNDArray`
/// with dtype `float64` in LLVM, this is how you do it:
/// ```ignore
/// type F64NDArray = Struct<ContiguousNDArray<Float<Float64>>>; // Type alias for leaner documentation
/// let model: F64NDArray = Struct(ContigousNDArray { item: Float(Float64) });
/// let ndarray: Instance<'ctx, Ptr<F64NDArray>> = model.alloca(generator, ctx);
/// ```
///
/// ...and here is how you may manipulate/access `ndarray`:
///
/// (NOTE: some arguments have been omitted)
///
/// ```ignore
/// // Get `&ndarray->data`
/// ndarray.gep(|f| f.data); // type: Instance<'ctx, Ptr<Float<Float64>>>
///
/// // Get `ndarray->ndims`
/// ndarray.get(|f| f.ndims); // type: Instance<'ctx, Int<SizeT>>
///
/// // Get `&ndarray->ndims`
/// ndarray.gep(|f| f.ndims); // type: Instance<'ctx, Ptr<Int<SizeT>>>
///
/// // Get `ndarray->shape[0]`
/// ndarray.get(|f| f.shape).get_index_const(0); // Instance<'ctx, Int<SizeT>>
///
/// // Get `&ndarray->shape[2]`
/// ndarray.get(|f| f.shape).offset_const(2); // Instance<'ctx, Ptr<Int<SizeT>>>
///
/// // Do `ndarray->ndims = 3;`
/// let num_3 = Int(SizeT).const_int(3);
/// ndarray.set(|f| f.ndims, num_3);
/// ```
pub trait StructKind<'ctx>: fmt::Debug + Clone + Copy {
/// The associated fields of this struct.
type Fields<F: FieldTraversal<'ctx>>;
/// Traverse through all fields of this [`StructKind`].
///
/// Only used internally in this module for implementing other components.
fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F>;
/// Get a convenience structure to get a struct field's GEP index through its corresponding Rust field.
///
/// Only used internally in this module for implementing other components.
fn fields(&self) -> Self::Fields<GepFieldTraversal> {
self.iter_fields(&mut GepFieldTraversal { gep_index_counter: 0 })
}
/// Get the LLVM [`StructType`] of this [`StructKind`].
fn get_struct_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> StructType<'ctx> {
let mut traversal = TypeFieldTraversal { generator, ctx, field_types: Vec::new() };
self.iter_fields(&mut traversal);
ctx.struct_type(&traversal.field_types, false)
}
}
/// A model for LLVM struct.
///
/// `S` should be of a [`StructKind`].
#[derive(Debug, Clone, Copy, Default)]
pub struct Struct<S>(pub S);
impl<'ctx, S: StructKind<'ctx>> Struct<S> {
/// Create a constant struct value from its fields.
///
/// This function also validates `fields` and panic when there is something wrong.
pub fn const_struct<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
fields: &[BasicValueEnum<'ctx>],
) -> Instance<'ctx, Self> {
// NOTE: There *could* have been a functor `F<M> = Instance<'ctx, M>` for `S::Fields<F>`
// to create a more user-friendly interface, but Rust's type system is not sophisticated enough
// and if you try doing that Rust would force you put lifetimes everywhere.
let val = ctx.const_struct(fields, false);
self.check_value(generator, ctx, val).unwrap()
}
}
impl<'ctx, S: StructKind<'ctx>> Model<'ctx> for Struct<S> {
type Value = StructValue<'ctx>;
type Type = StructType<'ctx>;
fn llvm_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> Self::Type {
self.0.get_struct_type(generator, ctx)
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
let Ok(ty) = StructType::try_from(ty) else {
return Err(ModelError(format!("Expecting StructType, but got {ty:?}")));
};
// Check each field individually.
let mut traversal = CheckTypeFieldTraversal {
generator,
ctx,
gep_index_counter: 0,
errors: Vec::new(),
scrutinee: ty,
};
self.0.iter_fields(&mut traversal);
// Check the number of fields.
let exp_num_fields = traversal.gep_index_counter;
let got_num_fields = u32::try_from(ty.get_field_types().len()).unwrap();
if exp_num_fields != got_num_fields {
return Err(ModelError(format!(
"Expecting StructType with {exp_num_fields} field(s), but got {got_num_fields}"
)));
}
if !traversal.errors.is_empty() {
// Currently, only the first error is reported.
return Err(traversal.errors[0].clone());
}
Ok(())
}
}
impl<'ctx, S: StructKind<'ctx>> Instance<'ctx, Struct<S>> {
/// Get a field with [`StructValue::get_field_at_index`].
pub fn get_field<G: CodeGenerator + ?Sized, M, GetField>(
&self,
generator: &mut G,
ctx: &'ctx Context,
get_field: GetField,
) -> Instance<'ctx, M>
where
M: Model<'ctx>,
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
{
let field = get_field(self.model.0.fields());
let val = self.value.get_field_at_index(field.gep_index).unwrap();
field.model.check_value(generator, ctx, val).unwrap()
}
}
impl<'ctx, S: StructKind<'ctx>> Instance<'ctx, Ptr<Struct<S>>> {
/// Get a pointer to a field with [`Builder::build_in_bounds_gep`].
pub fn gep<M, GetField>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
get_field: GetField,
) -> Instance<'ctx, Ptr<M>>
where
M: Model<'ctx>,
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
{
let field = get_field(self.model.0 .0.fields());
let llvm_i32 = ctx.ctx.i32_type();
let ptr = unsafe {
ctx.builder
.build_in_bounds_gep(
self.value,
&[llvm_i32.const_zero(), llvm_i32.const_int(u64::from(field.gep_index), false)],
field.name,
)
.unwrap()
};
unsafe { Ptr(field.model).believe_value(ptr) }
}
/// Convenience function equivalent to `.gep(...).load(...)`.
pub fn get<M, GetField, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
get_field: GetField,
) -> Instance<'ctx, M>
where
M: Model<'ctx>,
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
{
self.gep(ctx, get_field).load(generator, ctx)
}
/// Convenience function equivalent to `.gep(...).store(...)`.
pub fn set<M, GetField>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
get_field: GetField,
value: Instance<'ctx, M>,
) where
M: Model<'ctx>,
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
{
self.gep(ctx, get_field).store(ctx, value);
}
}

42
nac3core/src/codegen/model/util.rs
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@ -1,42 +0,0 @@
use crate::codegen::{
stmt::{gen_for_callback_incrementing, BreakContinueHooks},
CodeGenContext, CodeGenerator,
};
use super::*;
/// Like [`gen_for_callback_incrementing`] with [`Model`] abstractions.
///
/// `stop` is not included.
pub fn gen_for_model<'ctx, 'a, G, F, N>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
start: Instance<'ctx, Int<N>>,
stop: Instance<'ctx, Int<N>>,
step: Instance<'ctx, Int<N>>,
body: F,
) -> Result<(), String>
where
G: CodeGenerator + ?Sized,
F: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
BreakContinueHooks<'ctx>,
Instance<'ctx, Int<N>>,
) -> Result<(), String>,
N: IntKind<'ctx> + Default,
{
let int_model = Int(N::default());
gen_for_callback_incrementing(
generator,
ctx,
None,
start.value,
(stop.value, false),
|g, ctx, hooks, i| {
let i = unsafe { int_model.believe_value(i) };
body(g, ctx, hooks, i)
},
step.value,
)
}

2434
nac3core/src/codegen/numpy.rs
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File diff suppressed because it is too large Load Diff

12
nac3core/src/codegen/object/any.rs
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@ -1,12 +0,0 @@
use inkwell::values::BasicValueEnum;
use crate::typecheck::typedef::Type;
/// A NAC3 LLVM Python object of any type.
#[derive(Debug, Clone, Copy)]
pub struct AnyObject<'ctx> {
/// Typechecker type of the object.
pub ty: Type,
/// LLVM value of the object.
pub value: BasicValueEnum<'ctx>,
}

87
nac3core/src/codegen/object/list.rs
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@ -1,87 +0,0 @@
use crate::{
codegen::{model::*, CodeGenContext, CodeGenerator},
typecheck::typedef::{iter_type_vars, Type, TypeEnum},
};
use super::any::AnyObject;
/// Fields of [`List`]
pub struct ListFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
/// Array pointer to content
pub items: F::Output<Ptr<Item>>,
/// Number of items in the array
pub len: F::Output<Int<SizeT>>,
}
/// A list in NAC3.
#[derive(Debug, Clone, Copy, Default)]
pub struct List<Item> {
/// Model of the list items
pub item: Item,
}
impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for List<Item> {
type Fields<F: FieldTraversal<'ctx>> = ListFields<'ctx, F, Item>;
fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
items: traversal.add("items", Ptr(self.item)),
len: traversal.add_auto("len"),
}
}
}
impl<'ctx, Item: Model<'ctx>> Instance<'ctx, Ptr<Struct<List<Item>>>> {
/// Cast the items pointer to `uint8_t*`.
pub fn with_pi8_items<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>> {
self.pointer_cast(generator, ctx, Struct(List { item: Int(Byte) }))
}
}
/// A NAC3 Python List object.
#[derive(Debug, Clone, Copy)]
pub struct ListObject<'ctx> {
/// Typechecker type of the list items
pub item_type: Type,
pub instance: Instance<'ctx, Ptr<Struct<List<Any<'ctx>>>>>,
}
impl<'ctx> ListObject<'ctx> {
/// Create a [`ListObject`] from an LLVM value and its typechecker [`Type`].
pub fn from_object<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> Self {
// Check typechecker type and extract `item_type`
let item_type = match &*ctx.unifier.get_ty(object.ty) {
TypeEnum::TObj { obj_id, params, .. }
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
{
iter_type_vars(params).next().unwrap().ty // Extract `item_type`
}
_ => {
panic!("Expecting type to be a list, but got {}", ctx.unifier.stringify(object.ty))
}
};
let plist = Ptr(Struct(List { item: Any(ctx.get_llvm_type(generator, item_type)) }));
// Create object
let value = plist.check_value(generator, ctx.ctx, object.value).unwrap();
ListObject { item_type, instance: value }
}
/// Get the `len()` of this list.
pub fn len<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
self.instance.get(generator, ctx, |f| f.len)
}
}

5
nac3core/src/codegen/object/mod.rs
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@ -1,5 +0,0 @@
pub mod any;
pub mod list;
pub mod ndarray;
pub mod tuple;
pub mod utils;

184
nac3core/src/codegen/object/ndarray/array.rs
View File

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

139
nac3core/src/codegen/object/ndarray/broadcast.rs
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@ -1,139 +0,0 @@
use itertools::Itertools;
use crate::codegen::{
irrt::{call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to},
model::*,
CodeGenContext, CodeGenerator,
};
use super::NDArrayObject;
/// Fields of [`ShapeEntry`]
pub struct ShapeEntryFields<'ctx, F: FieldTraversal<'ctx>> {
pub ndims: F::Output<Int<SizeT>>,
pub shape: F::Output<Ptr<Int<SizeT>>>,
}
/// An IRRT structure used in broadcasting.
#[derive(Debug, Clone, Copy, Default)]
pub struct ShapeEntry;
impl<'ctx> StructKind<'ctx> for ShapeEntry {
type Fields<F: FieldTraversal<'ctx>> = ShapeEntryFields<'ctx, F>;
fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields { ndims: traversal.add_auto("ndims"), shape: traversal.add_auto("shape") }
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Create a broadcast view on this ndarray with a target shape.
///
/// The input shape will be checked to make sure that it contains no negative values.
///
/// * `target_ndims` - The ndims type after broadcasting to the given shape.
/// The caller has to figure this out for this function.
/// * `target_shape` - An array pointer pointing to the target shape.
#[must_use]
pub fn broadcast_to<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
target_ndims: u64,
target_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Self {
let broadcast_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, target_ndims);
broadcast_ndarray.copy_shape_from_array(generator, ctx, target_shape);
call_nac3_ndarray_broadcast_to(generator, ctx, self.instance, broadcast_ndarray.instance);
broadcast_ndarray
}
}
/// A result produced by [`broadcast_all_ndarrays`]
#[derive(Debug, Clone)]
pub struct BroadcastAllResult<'ctx> {
/// The statically known `ndims` of the broadcast result.
pub ndims: u64,
/// The broadcasting shape.
pub shape: Instance<'ctx, Ptr<Int<SizeT>>>,
/// Broadcasted views on the inputs.
///
/// All of them will have `shape` [`BroadcastAllResult::shape`] and
/// `ndims` [`BroadcastAllResult::ndims`]. The length of the vector
/// is the same as the input.
pub ndarrays: Vec<NDArrayObject<'ctx>>,
}
/// Helper function to call `call_nac3_ndarray_broadcast_shapes`
fn broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
in_shape_entries: &[(Instance<'ctx, Ptr<Int<SizeT>>>, u64)], // (shape, shape's length/ndims)
broadcast_ndims: u64,
broadcast_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
// Prepare input shape entries to be passed to `call_nac3_ndarray_broadcast_shapes`.
let num_shape_entries = Int(SizeT).const_int(
generator,
ctx.ctx,
u64::try_from(in_shape_entries.len()).unwrap(),
false,
);
let shape_entries = Struct(ShapeEntry).array_alloca(generator, ctx, num_shape_entries.value);
for (i, (in_shape, in_ndims)) in in_shape_entries.iter().enumerate() {
let pshape_entry = shape_entries.offset_const(ctx, i64::try_from(i).unwrap());
let in_ndims = Int(SizeT).const_int(generator, ctx.ctx, *in_ndims, false);
pshape_entry.set(ctx, |f| f.ndims, in_ndims);
pshape_entry.set(ctx, |f| f.shape, *in_shape);
}
let broadcast_ndims = Int(SizeT).const_int(generator, ctx.ctx, broadcast_ndims, false);
call_nac3_ndarray_broadcast_shapes(
generator,
ctx,
num_shape_entries,
shape_entries,
broadcast_ndims,
broadcast_shape,
);
}
impl<'ctx> NDArrayObject<'ctx> {
/// Broadcast all ndarrays according to `np.broadcast()` and return a [`BroadcastAllResult`]
/// containing all the information of the result of the broadcast operation.
pub fn broadcast<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarrays: &[Self],
) -> BroadcastAllResult<'ctx> {
assert!(!ndarrays.is_empty());
// Infer the broadcast output ndims.
let broadcast_ndims_int = ndarrays.iter().map(|ndarray| ndarray.ndims).max().unwrap();
let broadcast_ndims = Int(SizeT).const_int(generator, ctx.ctx, broadcast_ndims_int, false);
let broadcast_shape = Int(SizeT).array_alloca(generator, ctx, broadcast_ndims.value);
let shape_entries = ndarrays
.iter()
.map(|ndarray| (ndarray.instance.get(generator, ctx, |f| f.shape), ndarray.ndims))
.collect_vec();
broadcast_shapes(generator, ctx, &shape_entries, broadcast_ndims_int, broadcast_shape);
// Broadcast all the inputs to shape `dst_shape`.
let broadcast_ndarrays: Vec<_> = ndarrays
.iter()
.map(|ndarray| {
ndarray.broadcast_to(generator, ctx, broadcast_ndims_int, broadcast_shape)
})
.collect_vec();
BroadcastAllResult {
ndims: broadcast_ndims_int,
shape: broadcast_shape,
ndarrays: broadcast_ndarrays,
}
}
}

134
nac3core/src/codegen/object/ndarray/contiguous.rs
View File

@ -1,134 +0,0 @@
use crate::{
codegen::{model::*, CodeGenContext, CodeGenerator},
typecheck::typedef::Type,
};
use super::NDArrayObject;
/// Fields of [`ContiguousNDArray`]
pub struct ContiguousNDArrayFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
pub ndims: F::Output<Int<SizeT>>,
pub shape: F::Output<Ptr<Int<SizeT>>>,
pub data: F::Output<Ptr<Item>>,
}
/// An ndarray without strides and non-opaque `data` field in NAC3.
#[derive(Debug, Clone, Copy)]
pub struct ContiguousNDArray<M> {
/// [`Model`] of the items.
pub item: M,
}
impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for ContiguousNDArray<Item> {
type Fields<F: FieldTraversal<'ctx>> = ContiguousNDArrayFields<'ctx, F, Item>;
fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
ndims: traversal.add_auto("ndims"),
shape: traversal.add_auto("shape"),
data: traversal.add("data", Ptr(self.item)),
}
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Create a [`ContiguousNDArray`] from the contents of this ndarray.
///
/// This function may or may not be expensive depending on if this ndarray has contiguous data.
///
/// If this ndarray is not C-contiguous, this function will allocate memory on the stack for the `data` field of
/// the returned [`ContiguousNDArray`] and copy contents of this ndarray to there.
///
/// If this ndarray is C-contiguous, contents of this ndarray will not be copied. The created [`ContiguousNDArray`]
/// will share memory with this ndarray.
///
/// The `item_model` sets the [`Model`] of the returned [`ContiguousNDArray`]'s `Item` model for type-safety, and
/// should match the `ctx.get_llvm_type()` of this ndarray's `dtype`. Otherwise this function panics. Use model [`Any`]
/// if you don't care/cannot know the [`Model`] in advance.
pub fn make_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
item_model: Item,
) -> Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>> {
// Sanity check on `self.dtype` and `item_model`.
let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
item_model.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
let cdarray_model = Struct(ContiguousNDArray { item: item_model });
let current_bb = ctx.builder.get_insert_block().unwrap();
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
// Allocate and setup the resulting [`ContiguousNDArray`].
let result = cdarray_model.alloca(generator, ctx);
// Set ndims and shape.
let ndims = self.ndims_llvm(generator, ctx.ctx);
result.set(ctx, |f| f.ndims, ndims);
let shape = self.instance.get(generator, ctx, |f| f.shape);
result.set(ctx, |f| f.shape, shape);
let is_contiguous = self.is_c_contiguous(generator, ctx);
ctx.builder.build_conditional_branch(is_contiguous.value, then_bb, else_bb).unwrap();
// Inserting into then_bb; This ndarray is contiguous.
ctx.builder.position_at_end(then_bb);
let data = self.instance.get(generator, ctx, |f| f.data);
let data = data.pointer_cast(generator, ctx, item_model);
result.set(ctx, |f| f.data, data);
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Inserting into else_bb; This ndarray is not contiguous. Do a full-copy on `data`.
// `make_copy` produces an ndarray with contiguous `data`.
ctx.builder.position_at_end(else_bb);
let copied_ndarray = self.make_copy(generator, ctx);
let data = copied_ndarray.instance.get(generator, ctx, |f| f.data);
let data = data.pointer_cast(generator, ctx, item_model);
result.set(ctx, |f| f.data, data);
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Reposition to end_bb for continuation
ctx.builder.position_at_end(end_bb);
result
}
/// Create an [`NDArrayObject`] from a [`ContiguousNDArray`].
///
/// The operation is super cheap. The newly created [`NDArrayObject`] will share the
/// same memory as the [`ContiguousNDArray`].
///
/// `ndims` has to be provided as [`NDArrayObject`] requires a statically known `ndims` value, despite
/// the fact that the information should be contained within the [`ContiguousNDArray`].
pub fn from_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
carray: Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>>,
dtype: Type,
ndims: u64,
) -> Self {
// Sanity check on `dtype` and `contiguous_array`'s `Item` model.
let dtype_llvm = ctx.get_llvm_type(generator, dtype);
carray.model.0 .0.item.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
// TODO: Debug assert `ndims == carray.ndims` to catch bugs.
// Allocate the resulting ndarray.
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
// Copy shape and update strides
let shape = carray.get(generator, ctx, |f| f.shape);
ndarray.copy_shape_from_array(generator, ctx, shape);
ndarray.set_strides_contiguous(generator, ctx);
// Share data
let data = carray.get(generator, ctx, |f| f.data).pointer_cast(generator, ctx, Int(Byte));
ndarray.instance.set(ctx, |f| f.data, data);
ndarray
}
}

176
nac3core/src/codegen/object/ndarray/factory.rs
View File

@ -1,176 +0,0 @@
use inkwell::{values::BasicValueEnum, IntPredicate};
use crate::{
codegen::{
irrt::call_nac3_ndarray_util_assert_shape_no_negative, model::*, CodeGenContext,
CodeGenerator,
},
typecheck::typedef::Type,
};
use super::NDArrayObject;
/// Get the zero value in `np.zeros()` of a `dtype`.
fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
) -> BasicValueEnum<'ctx> {
if [ctx.primitives.int32, ctx.primitives.uint32]
.iter()
.any(|ty| ctx.unifier.unioned(dtype, *ty))
{
ctx.ctx.i32_type().const_zero().into()
} else if [ctx.primitives.int64, ctx.primitives.uint64]
.iter()
.any(|ty| ctx.unifier.unioned(dtype, *ty))
{
ctx.ctx.i64_type().const_zero().into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
ctx.ctx.f64_type().const_zero().into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
ctx.ctx.bool_type().const_zero().into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
ctx.gen_string(generator, "").into()
} else {
panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
}
}
/// Get the one value in `np.ones()` of a `dtype`.
fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
) -> BasicValueEnum<'ctx> {
if [ctx.primitives.int32, ctx.primitives.uint32]
.iter()
.any(|ty| ctx.unifier.unioned(dtype, *ty))
{
let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int32);
ctx.ctx.i32_type().const_int(1, is_signed).into()
} else if [ctx.primitives.int64, ctx.primitives.uint64]
.iter()
.any(|ty| ctx.unifier.unioned(dtype, *ty))
{
let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int64);
ctx.ctx.i64_type().const_int(1, is_signed).into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
ctx.ctx.f64_type().const_float(1.0).into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
ctx.ctx.bool_type().const_int(1, false).into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
ctx.gen_string(generator, "1").into()
} else {
panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Create an ndarray like `np.empty`.
pub fn make_np_empty<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Self {
// Validate `shape`
let ndims_llvm = Int(SizeT).const_int(generator, ctx.ctx, ndims, false);
call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, ndims_llvm, shape);
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
ndarray.copy_shape_from_array(generator, ctx, shape);
ndarray.create_data(generator, ctx);
ndarray
}
/// Create an ndarray like `np.full`.
pub fn make_np_full<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
fill_value: BasicValueEnum<'ctx>,
) -> Self {
let ndarray = NDArrayObject::make_np_empty(generator, ctx, dtype, ndims, shape);
ndarray.fill(generator, ctx, fill_value);
ndarray
}
/// Create an ndarray like `np.zero`.
pub fn make_np_zeros<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Self {
let fill_value = ndarray_zero_value(generator, ctx, dtype);
NDArrayObject::make_np_full(generator, ctx, dtype, ndims, shape, fill_value)
}
/// Create an ndarray like `np.ones`.
pub fn make_np_ones<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Self {
let fill_value = ndarray_one_value(generator, ctx, dtype);
NDArrayObject::make_np_full(generator, ctx, dtype, ndims, shape, fill_value)
}
/// Create an ndarray like `np.eye`.
pub fn make_np_eye<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
nrows: Instance<'ctx, Int<SizeT>>,
ncols: Instance<'ctx, Int<SizeT>>,
offset: Instance<'ctx, Int<SizeT>>,
) -> Self {
let ndzero = ndarray_zero_value(generator, ctx, dtype);
let ndone = ndarray_one_value(generator, ctx, dtype);
let ndarray = NDArrayObject::alloca_dynamic_shape(generator, ctx, dtype, &[nrows, ncols]);
// Create data and make the matrix like look np.eye()
ndarray.create_data(generator, ctx);
ndarray
.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
// NOTE: rows and cols can never be zero here, since this ndarray's `np.size` would be zero
// and this loop would not execute.
// Load up `row_i` and `col_i` from indices.
let row_i = nditer.get_indices().get_index_const(generator, ctx, 0);
let col_i = nditer.get_indices().get_index_const(generator, ctx, 1);
let be_one = row_i.add(ctx, offset).compare(ctx, IntPredicate::EQ, col_i);
let value = ctx.builder.build_select(be_one.value, ndone, ndzero, "value").unwrap();
let p = nditer.get_pointer(generator, ctx);
ctx.builder.build_store(p, value).unwrap();
Ok(())
})
.unwrap();
ndarray
}
/// Create an ndarray like `np.identity`.
pub fn make_np_identity<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
size: Instance<'ctx, Int<SizeT>>,
) -> Self {
// Convenient implementation
let offset = Int(SizeT).const_0(generator, ctx.ctx);
NDArrayObject::make_np_eye(generator, ctx, dtype, size, size, offset)
}
}

227
nac3core/src/codegen/object/ndarray/indexing.rs
View File

@ -1,227 +0,0 @@
use crate::codegen::{
irrt::call_nac3_ndarray_index,
model::*,
object::utils::slice::{RustSlice, Slice},
CodeGenContext, CodeGenerator,
};
use super::NDArrayObject;
pub type NDIndexType = Byte;
/// Fields of [`NDIndex`]
#[derive(Debug, Clone, Copy)]
pub struct NDIndexFields<'ctx, F: FieldTraversal<'ctx>> {
pub type_: F::Output<Int<NDIndexType>>,
pub data: F::Output<Ptr<Int<Byte>>>,
}
/// An IRRT representation of an ndarray subscript index.
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
pub struct NDIndex;
impl<'ctx> StructKind<'ctx> for NDIndex {
type Fields<F: FieldTraversal<'ctx>> = NDIndexFields<'ctx, F>;
fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields { type_: traversal.add_auto("type"), data: traversal.add_auto("data") }
}
}
// A convenience enum representing a [`NDIndex`].
#[derive(Debug, Clone)]
pub enum RustNDIndex<'ctx> {
SingleElement(Instance<'ctx, Int<Int32>>),
Slice(RustSlice<'ctx, Int32>),
NewAxis,
Ellipsis,
}
impl<'ctx> RustNDIndex<'ctx> {
/// Get the value to set `NDIndex::type` for this variant.
fn get_type_id(&self) -> u64 {
// Defined in IRRT, must be in sync
match self {
RustNDIndex::SingleElement(_) => 0,
RustNDIndex::Slice(_) => 1,
RustNDIndex::NewAxis => 2,
RustNDIndex::Ellipsis => 3,
}
}
/// Serialize this [`RustNDIndex`] by writing it into an LLVM [`NDIndex`].
fn write_to_ndindex<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
dst_ndindex_ptr: Instance<'ctx, Ptr<Struct<NDIndex>>>,
) {
// Set `dst_ndindex_ptr->type`
dst_ndindex_ptr.gep(ctx, |f| f.type_).store(
ctx,
Int(NDIndexType::default()).const_int(generator, ctx.ctx, self.get_type_id(), false),
);
// Set `dst_ndindex_ptr->data`
match self {
RustNDIndex::SingleElement(in_index) => {
let index_ptr = Int(Int32).alloca(generator, ctx);
index_ptr.store(ctx, *in_index);
dst_ndindex_ptr
.gep(ctx, |f| f.data)
.store(ctx, index_ptr.pointer_cast(generator, ctx, Int(Byte)));
}
RustNDIndex::Slice(in_rust_slice) => {
let user_slice_ptr = Struct(Slice(Int32)).alloca(generator, ctx);
in_rust_slice.write_to_slice(generator, ctx, user_slice_ptr);
dst_ndindex_ptr
.gep(ctx, |f| f.data)
.store(ctx, user_slice_ptr.pointer_cast(generator, ctx, Int(Byte)));
}
RustNDIndex::NewAxis | RustNDIndex::Ellipsis => {}
}
}
/// Serialize a list of `RustNDIndex` as a newly allocated LLVM array of `NDIndex`.
pub fn make_ndindices<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
in_ndindices: &[RustNDIndex<'ctx>],
) -> (Instance<'ctx, Int<SizeT>>, Instance<'ctx, Ptr<Struct<NDIndex>>>) {
let ndindex_model = Struct(NDIndex);
// Allocate the LLVM ndindices.
let num_ndindices =
Int(SizeT).const_int(generator, ctx.ctx, in_ndindices.len() as u64, false);
let ndindices = ndindex_model.array_alloca(generator, ctx, num_ndindices.value);
// Initialize all of them.
for (i, in_ndindex) in in_ndindices.iter().enumerate() {
let pndindex = ndindices.offset_const(ctx, i64::try_from(i).unwrap());
in_ndindex.write_to_ndindex(generator, ctx, pndindex);
}
(num_ndindices, ndindices)
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Get the expected `ndims` after indexing with `indices`.
#[must_use]
fn deduce_ndims_after_indexing_with(&self, indices: &[RustNDIndex<'ctx>]) -> u64 {
let mut ndims = self.ndims;
for index in indices {
match index {
RustNDIndex::SingleElement(_) => {
ndims -= 1; // Single elements decrements ndims
}
RustNDIndex::NewAxis => {
ndims += 1; // `np.newaxis` / `none` adds a new axis
}
RustNDIndex::Ellipsis | RustNDIndex::Slice(_) => {}
}
}
ndims
}
/// Index into the ndarray, and return a newly-allocated view on this ndarray.
///
/// This function behaves like NumPy's ndarray indexing, but if the indices index
/// into a single element, an unsized ndarray is returned.
#[must_use]
pub fn index<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
indices: &[RustNDIndex<'ctx>],
) -> Self {
let dst_ndims = self.deduce_ndims_after_indexing_with(indices);
let dst_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, dst_ndims);
let (num_indices, indices) = RustNDIndex::make_ndindices(generator, ctx, indices);
call_nac3_ndarray_index(
generator,
ctx,
num_indices,
indices,
self.instance,
dst_ndarray.instance,
);
dst_ndarray
}
}
pub mod util {
use itertools::Itertools;
use nac3parser::ast::{Expr, ExprKind};
use crate::{
codegen::{model::*, object::utils::slice::util::gen_slice, CodeGenContext, CodeGenerator},
typecheck::typedef::Type,
};
use super::RustNDIndex;
/// Generate LLVM code to transform an ndarray subscript expression to
/// its list of [`RustNDIndex`]
///
/// i.e.,
/// ```python
/// my_ndarray[::3, 1, :2:]
/// ^^^^^^^^^^^ Then these into a three `RustNDIndex`es
/// ```
pub fn gen_ndarray_subscript_ndindices<'ctx, G: CodeGenerator>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
subscript: &Expr<Option<Type>>,
) -> Result<Vec<RustNDIndex<'ctx>>, String> {
// TODO: Support https://numpy.org/doc/stable/user/basics.indexing.html#dimensional-indexing-tools
// Annoying notes about `slice`
// - `my_array[5]`
// - slice is a `Constant`
// - `my_array[:5]`
// - slice is a `Slice`
// - `my_array[:]`
// - slice is a `Slice`, but lower upper step would all be `Option::None`
// - `my_array[:, :]`
// - slice is now a `Tuple` of two `Slice`-s
//
// In summary:
// - when there is a comma "," within [], `slice` will be a `Tuple` of the entries.
// - when there is not comma "," within [] (i.e., just a single entry), `slice` will be that entry itself.
//
// So we first "flatten" out the slice expression
let index_exprs = match &subscript.node {
ExprKind::Tuple { elts, .. } => elts.iter().collect_vec(),
_ => vec![subscript],
};
// Process all index expressions
let mut rust_ndindices: Vec<RustNDIndex> = Vec::with_capacity(index_exprs.len()); // Not using iterators here because `?` is used here.
for index_expr in index_exprs {
// NOTE: Currently nac3core's slices do not have an object representation,
// so the code/implementation looks awkward - we have to do pattern matching on the expression
let ndindex = if let ExprKind::Slice { lower, upper, step } = &index_expr.node {
// Handle slices
let slice = gen_slice(generator, ctx, lower, upper, step)?;
RustNDIndex::Slice(slice)
} else {
// Treat and handle everything else as a single element index.
let index = generator.gen_expr(ctx, index_expr)?.unwrap().to_basic_value_enum(
ctx,
generator,
ctx.primitives.int32, // Must be int32, this checks for illegal values
)?;
let index = Int(Int32).check_value(generator, ctx.ctx, index).unwrap();
RustNDIndex::SingleElement(index)
};
rust_ndindices.push(ndindex);
}
Ok(rust_ndindices)
}
}

219
nac3core/src/codegen/object/ndarray/map.rs
View File

@ -1,219 +0,0 @@
use inkwell::values::BasicValueEnum;
use itertools::Itertools;
use crate::{
codegen::{
object::ndarray::{AnyObject, NDArrayObject},
stmt::gen_for_callback,
CodeGenContext, CodeGenerator,
},
typecheck::typedef::Type,
};
use super::{nditer::NDIterHandle, NDArrayOut, ScalarOrNDArray};
impl<'ctx> NDArrayObject<'ctx> {
/// Generate LLVM IR to broadcast `ndarray`s together, and starmap through them with `mapping` elementwise.
///
/// `mapping` is an LLVM IR generator. The input of `mapping` is the list of elements when iterating through
/// the input `ndarrays` after broadcasting. The output of `mapping` is the result of the elementwise operation.
///
/// `out` specifies whether the result should be a new ndarray or to be written an existing ndarray.
pub fn broadcast_starmap<'a, G, MappingFn>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
ndarrays: &[Self],
out: NDArrayOut<'ctx>,
mapping: MappingFn,
) -> Result<Self, String>
where
G: CodeGenerator + ?Sized,
MappingFn: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
&[BasicValueEnum<'ctx>],
) -> Result<BasicValueEnum<'ctx>, String>,
{
// Broadcast inputs
let broadcast_result = NDArrayObject::broadcast(generator, ctx, ndarrays);
let out_ndarray = match out {
NDArrayOut::NewNDArray { dtype } => {
// Create a new ndarray based on the broadcast shape.
let result_ndarray =
NDArrayObject::alloca(generator, ctx, dtype, broadcast_result.ndims);
result_ndarray.copy_shape_from_array(generator, ctx, broadcast_result.shape);
result_ndarray.create_data(generator, ctx);
result_ndarray
}
NDArrayOut::WriteToNDArray { ndarray: result_ndarray } => {
// Use an existing ndarray.
// Check that its shape is compatible with the broadcast shape.
result_ndarray.assert_can_be_written_by_out(
generator,
ctx,
broadcast_result.ndims,
broadcast_result.shape,
);
result_ndarray
}
};
// Map element-wise and store results into `mapped_ndarray`.
let nditer = NDIterHandle::new(generator, ctx, out_ndarray);
gen_for_callback(
generator,
ctx,
Some("broadcast_starmap"),
|generator, ctx| {
// Create NDIters for all broadcasted input ndarrays.
let other_nditers = broadcast_result
.ndarrays
.iter()
.map(|ndarray| NDIterHandle::new(generator, ctx, *ndarray))
.collect_vec();
Ok((nditer, other_nditers))
},
|generator, ctx, (out_nditer, _in_nditers)| {
// We can simply use `out_nditer`'s `has_element()`.
// `in_nditers`' `has_element()`s should return the same value.
Ok(out_nditer.has_element(generator, ctx).value)
},
|generator, ctx, _hooks, (out_nditer, in_nditers)| {
// Get all the scalars from the broadcasted input ndarrays, pass them to `mapping`,
// and write to `out_ndarray`.
let in_scalars = in_nditers
.iter()
.map(|nditer| nditer.get_scalar(generator, ctx).value)
.collect_vec();
let result = mapping(generator, ctx, &in_scalars)?;
let p = out_nditer.get_pointer(generator, ctx);
ctx.builder.build_store(p, result).unwrap();
Ok(())
},
|generator, ctx, (out_nditer, in_nditers)| {
// Advance all iterators
out_nditer.next(generator, ctx);
in_nditers.iter().for_each(|nditer| nditer.next(generator, ctx));
Ok(())
},
)?;
Ok(out_ndarray)
}
/// Map through this ndarray with an elementwise function.
pub fn map<'a, G, Mapping>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
out: NDArrayOut<'ctx>,
mapping: Mapping,
) -> Result<Self, String>
where
G: CodeGenerator + ?Sized,
Mapping: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
BasicValueEnum<'ctx>,
) -> Result<BasicValueEnum<'ctx>, String>,
{
NDArrayObject::broadcast_starmap(
generator,
ctx,
&[*self],
out,
|generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
)
}
}
impl<'ctx> ScalarOrNDArray<'ctx> {
/// Starmap through a list of inputs using `mapping`, where an input could be an ndarray, a scalar.
///
/// This function is very helpful when implementing NumPy functions that takes on either scalars or ndarrays or a mix of them
/// as their inputs and produces either an ndarray with broadcast, or a scalar if all its inputs are all scalars.
///
/// For example ,this function can be used to implement `np.add`, which has the following behaviors:
/// - `np.add(3, 4) = 7` # (scalar, scalar) -> scalar
/// - `np.add(3, np.array([4, 5, 6]))` # (scalar, ndarray) -> ndarray; the first `scalar` is converted into an ndarray and broadcasted.
/// - `np.add(np.array([[1], [2], [3]]), np.array([[4, 5, 6]]))` # (ndarray, ndarray) -> ndarray; there is broadcasting.
///
/// ## Details:
///
/// If `inputs` are all [`ScalarOrNDArray::Scalar`], the output will be a [`ScalarOrNDArray::Scalar`] with type `ret_dtype`.
///
/// Otherwise (if there are any [`ScalarOrNDArray::NDArray`] in `inputs`), all inputs will be 'as-ndarray'-ed into ndarrays,
/// then all inputs (now all ndarrays) will be passed to [`NDArrayObject::broadcasting_starmap`] and **create** a new ndarray
/// with dtype `ret_dtype`.
pub fn broadcasting_starmap<'a, G, MappingFn>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
inputs: &[ScalarOrNDArray<'ctx>],
ret_dtype: Type,
mapping: MappingFn,
) -> Result<ScalarOrNDArray<'ctx>, String>
where
G: CodeGenerator + ?Sized,
MappingFn: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
&[BasicValueEnum<'ctx>],
) -> Result<BasicValueEnum<'ctx>, String>,
{
// Check if all inputs are Scalars
let all_scalars: Option<Vec<_>> = inputs.iter().map(AnyObject::try_from).try_collect().ok();
if let Some(scalars) = all_scalars {
let scalars = scalars.iter().map(|scalar| scalar.value).collect_vec();
let value = mapping(generator, ctx, &scalars)?;
Ok(ScalarOrNDArray::Scalar(AnyObject { ty: ret_dtype, value }))
} else {
// Promote all input to ndarrays and map through them.
let inputs = inputs.iter().map(|input| input.to_ndarray(generator, ctx)).collect_vec();
let ndarray = NDArrayObject::broadcast_starmap(
generator,
ctx,
&inputs,
NDArrayOut::NewNDArray { dtype: ret_dtype },
mapping,
)?;
Ok(ScalarOrNDArray::NDArray(ndarray))
}
}
/// Map through this [`ScalarOrNDArray`] with an elementwise function.
///
/// If this is a scalar, `mapping` will directly act on the scalar. This function will return a [`ScalarOrNDArray::Scalar`] of that result.
///
/// If this is an ndarray, `mapping` will be applied to the elements of the ndarray. A new ndarray of the results will be created and
/// returned as a [`ScalarOrNDArray::NDArray`].
pub fn map<'a, G, Mapping>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
ret_dtype: Type,
mapping: Mapping,
) -> Result<ScalarOrNDArray<'ctx>, String>
where
G: CodeGenerator + ?Sized,
Mapping: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
BasicValueEnum<'ctx>,
) -> Result<BasicValueEnum<'ctx>, String>,
{
ScalarOrNDArray::broadcasting_starmap(
generator,
ctx,
&[*self],
ret_dtype,
|generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
)
}
}

218
nac3core/src/codegen/object/ndarray/matmul.rs
View File

@ -1,218 +0,0 @@
use std::cmp::max;
use nac3parser::ast::Operator;
use util::gen_for_model;
use crate::{
codegen::{
expr::gen_binop_expr_with_values, irrt::call_nac3_ndarray_matmul_calculate_shapes,
model::*, object::ndarray::indexing::RustNDIndex, CodeGenContext, CodeGenerator,
},
typecheck::{magic_methods::Binop, typedef::Type},
};
use super::{NDArrayObject, NDArrayOut};
/// Perform `np.einsum("...ij,...jk->...ik", in_a, in_b)`.
///
/// `dst_dtype` defines the dtype of the returned ndarray.
fn matmul_at_least_2d<'ctx, G: CodeGenerator>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dst_dtype: Type,
in_a: NDArrayObject<'ctx>,
in_b: NDArrayObject<'ctx>,
) -> NDArrayObject<'ctx> {
assert!(in_a.ndims >= 2);
assert!(in_b.ndims >= 2);
// Deduce ndims of the result of matmul.
let ndims_int = max(in_a.ndims, in_b.ndims);
let ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims_int, false);
// Broadcasts `in_a.shape[:-2]` and `in_b.shape[:-2]` together and allocate the
// destination ndarray to store the result of matmul.
let (lhs, rhs, dst) = {
let in_lhs_ndims = in_a.ndims_llvm(generator, ctx.ctx);
let in_lhs_shape = in_a.instance.get(generator, ctx, |f| f.shape);
let in_rhs_ndims = in_b.ndims_llvm(generator, ctx.ctx);
let in_rhs_shape = in_b.instance.get(generator, ctx, |f| f.shape);
let lhs_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
let rhs_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
let dst_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
// Matmul dimension compatibility is checked here.
call_nac3_ndarray_matmul_calculate_shapes(
generator,
ctx,
in_lhs_ndims,
in_lhs_shape,
in_rhs_ndims,
in_rhs_shape,
ndims,
lhs_shape,
rhs_shape,
dst_shape,
);
let lhs = in_a.broadcast_to(generator, ctx, ndims_int, lhs_shape);
let rhs = in_b.broadcast_to(generator, ctx, ndims_int, rhs_shape);
let dst = NDArrayObject::alloca(generator, ctx, dst_dtype, ndims_int);
dst.copy_shape_from_array(generator, ctx, dst_shape);
dst.create_data(generator, ctx);
(lhs, rhs, dst)
};
let len = lhs.instance.get(generator, ctx, |f| f.shape).get_index_const(
generator,
ctx,
i64::try_from(ndims_int - 1).unwrap(),
);
let at_row = i64::try_from(ndims_int - 2).unwrap();
let at_col = i64::try_from(ndims_int - 1).unwrap();
let dst_dtype_llvm = ctx.get_llvm_type(generator, dst_dtype);
let dst_zero = dst_dtype_llvm.const_zero();
dst.foreach(generator, ctx, |generator, ctx, _, hdl| {
let pdst_ij = hdl.get_pointer(generator, ctx);
ctx.builder.build_store(pdst_ij, dst_zero).unwrap();
let indices = hdl.get_indices();
let i = indices.get_index_const(generator, ctx, at_row);
let j = indices.get_index_const(generator, ctx, at_col);
let num_0 = Int(SizeT).const_int(generator, ctx.ctx, 0, false);
let num_1 = Int(SizeT).const_int(generator, ctx.ctx, 1, false);
gen_for_model(generator, ctx, num_0, len, num_1, |generator, ctx, _, k| {
// `indices` is modified to index into `a` and `b`, and restored.
indices.set_index_const(ctx, at_row, i);
indices.set_index_const(ctx, at_col, k);
let a_ik = lhs.get_scalar_by_indices(generator, ctx, indices);
indices.set_index_const(ctx, at_row, k);
indices.set_index_const(ctx, at_col, j);
let b_kj = rhs.get_scalar_by_indices(generator, ctx, indices);
// Restore `indices`.
indices.set_index_const(ctx, at_row, i);
indices.set_index_const(ctx, at_col, j);
// x = a_[...]ik * b_[...]kj
let x = gen_binop_expr_with_values(
generator,
ctx,
(&Some(lhs.dtype), a_ik.value),
Binop::normal(Operator::Mult),
(&Some(rhs.dtype), b_kj.value),
ctx.current_loc,
)?
.unwrap()
.to_basic_value_enum(ctx, generator, dst_dtype)?;
// dst_[...]ij += x
let dst_ij = ctx.builder.build_load(pdst_ij, "").unwrap();
let dst_ij = gen_binop_expr_with_values(
generator,
ctx,
(&Some(dst_dtype), dst_ij),
Binop::normal(Operator::Add),
(&Some(dst_dtype), x),
ctx.current_loc,
)?
.unwrap()
.to_basic_value_enum(ctx, generator, dst_dtype)?;
ctx.builder.build_store(pdst_ij, dst_ij).unwrap();
Ok(())
})
})
.unwrap();
dst
}
impl<'ctx> NDArrayObject<'ctx> {
/// Perform `np.matmul` according to the rules in
/// <https://numpy.org/doc/stable/reference/generated/numpy.matmul.html>.
///
/// This function always return an [`NDArrayObject`]. You may want to use [`NDArrayObject::split_unsized`]
/// to handle when the output could be a scalar.
///
/// `dst_dtype` defines the dtype of the returned ndarray.
pub fn matmul<G: CodeGenerator>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
a: Self,
b: Self,
out: NDArrayOut<'ctx>,
) -> Self {
// Sanity check, but type inference should prevent this.
assert!(a.ndims > 0 && b.ndims > 0, "np.matmul disallows scalar input");
/*
If both arguments are 2-D they are multiplied like conventional matrices.
If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indices and broadcast accordingly.
If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed.
If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed.
*/
let new_a = if a.ndims == 1 {
// Prepend 1 to its dimensions
a.index(generator, ctx, &[RustNDIndex::NewAxis, RustNDIndex::Ellipsis])
} else {
a
};
let new_b = if b.ndims == 1 {
// Append 1 to its dimensions
b.index(generator, ctx, &[RustNDIndex::Ellipsis, RustNDIndex::NewAxis])
} else {
b
};
// NOTE: `result` will always be a newly allocated ndarray.
// Current implementation cannot do in-place matrix muliplication.
let mut result = matmul_at_least_2d(generator, ctx, out.get_dtype(), new_a, new_b);
// Postprocessing on the result to remove prepended/appended axes.
let mut postindices = vec![];
let zero = Int(Int32).const_0(generator, ctx.ctx);
if a.ndims == 1 {
// Remove the prepended 1
postindices.push(RustNDIndex::SingleElement(zero));
}
if b.ndims == 1 {
// Remove the appended 1
postindices.push(RustNDIndex::Ellipsis);
postindices.push(RustNDIndex::SingleElement(zero));
}
if !postindices.is_empty() {
result = result.index(generator, ctx, &postindices);
}
match out {
NDArrayOut::NewNDArray { .. } => result,
NDArrayOut::WriteToNDArray { ndarray: out_ndarray } => {
let result_shape = result.instance.get(generator, ctx, |f| f.shape);
out_ndarray.assert_can_be_written_by_out(
generator,
ctx,
result.ndims,
result_shape,
);
out_ndarray.copy_data_from(generator, ctx, result);
out_ndarray
}
}
}
}

670
nac3core/src/codegen/object/ndarray/mod.rs
View File

@ -1,670 +0,0 @@
pub mod array;
pub mod broadcast;
pub mod contiguous;
pub mod factory;
pub mod indexing;
pub mod map;
pub mod matmul;
pub mod nditer;
pub mod shape_util;
pub mod view;
use inkwell::{
context::Context,
types::BasicType,
values::{BasicValue, BasicValueEnum, PointerValue},
AddressSpace,
};
use crate::{
codegen::{
irrt::{
call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
call_nac3_ndarray_get_pelement_by_indices, call_nac3_ndarray_is_c_contiguous,
call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
call_nac3_ndarray_util_assert_output_shape_same,
},
model::*,
CodeGenContext, CodeGenerator,
},
toplevel::{
helper::{create_ndims, extract_ndims},
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
},
typecheck::typedef::{Type, TypeEnum},
};
use super::{any::AnyObject, tuple::TupleObject};
/// Fields of [`NDArray`]
pub struct NDArrayFields<'ctx, F: FieldTraversal<'ctx>> {
pub data: F::Output<Ptr<Int<Byte>>>,
pub itemsize: F::Output<Int<SizeT>>,
pub ndims: F::Output<Int<SizeT>>,
pub shape: F::Output<Ptr<Int<SizeT>>>,
pub strides: F::Output<Ptr<Int<SizeT>>>,
}
/// A strided ndarray in NAC3.
///
/// See IRRT implementation for details about its fields.
#[derive(Debug, Clone, Copy, Default)]
pub struct NDArray;
impl<'ctx> StructKind<'ctx> for NDArray {
type Fields<F: FieldTraversal<'ctx>> = NDArrayFields<'ctx, F>;
fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
data: traversal.add_auto("data"),
itemsize: traversal.add_auto("itemsize"),
ndims: traversal.add_auto("ndims"),
shape: traversal.add_auto("shape"),
strides: traversal.add_auto("strides"),
}
}
}
/// A NAC3 Python ndarray object.
#[derive(Debug, Clone, Copy)]
pub struct NDArrayObject<'ctx> {
pub dtype: Type,
pub ndims: u64,
pub instance: Instance<'ctx, Ptr<Struct<NDArray>>>,
}
impl<'ctx> NDArrayObject<'ctx> {
/// Attempt to convert an [`AnyObject`] into an [`NDArrayObject`].
pub fn from_object<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> NDArrayObject<'ctx> {
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, object.ty);
let ndims = extract_ndims(&ctx.unifier, ndims);
Self::from_value_and_unpacked_types(generator, ctx, object.value, dtype, ndims)
}
/// Like [`NDArrayObject::from_object`] but you directly supply the ndarray's
/// `dtype` and `ndims`.
pub fn from_value_and_unpacked_types<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
value: V,
dtype: Type,
ndims: u64,
) -> Self {
let value = Ptr(Struct(NDArray)).check_value(generator, ctx.ctx, value).unwrap();
NDArrayObject { dtype, ndims, instance: value }
}
/// Get this ndarray's `ndims` as an LLVM constant.
pub fn ndims_llvm<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Int<SizeT>> {
Int(SizeT).const_int(generator, ctx, self.ndims, false)
}
/// Get the typechecker ndarray type of this [`NDArrayObject`].
pub fn get_type(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Type {
let ndims = create_ndims(&mut ctx.unifier, self.ndims);
make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(self.dtype), Some(ndims))
}
/// Forget that this is an ndarray and convert into an [`AnyObject`].
pub fn to_any(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
let ty = self.get_type(ctx);
AnyObject { value: self.instance.value.as_basic_value_enum(), ty }
}
/// Allocate an ndarray on the stack given its `ndims` and `dtype`.
///
/// `shape` and `strides` will be automatically allocated onto the stack.
///
/// The returned ndarray's content will be:
/// - `data`: uninitialized.
/// - `itemsize`: set to the `sizeof()` of `dtype`.
/// - `ndims`: set to the value of `ndims`.
/// - `shape`: allocated with an array of length `ndims` with uninitialized values.
/// - `strides`: allocated with an array of length `ndims` with uninitialized values.
pub fn alloca<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
) -> Self {
let ndarray = Struct(NDArray).alloca(generator, ctx);
let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
let itemsize = Int(SizeT).z_extend_or_truncate(generator, ctx, itemsize);
ndarray.set(ctx, |f| f.itemsize, itemsize);
let ndims_val = Int(SizeT).const_int(generator, ctx.ctx, ndims, false);
ndarray.set(ctx, |f| f.ndims, ndims_val);
let shape = Int(SizeT).array_alloca(generator, ctx, ndims_val.value);
ndarray.set(ctx, |f| f.shape, shape);
let strides = Int(SizeT).array_alloca(generator, ctx, ndims_val.value);
ndarray.set(ctx, |f| f.strides, strides);
NDArrayObject { dtype, ndims, instance: ndarray }
}
/// Convenience function. Allocate an [`NDArrayObject`] with a statically known shape.
///
/// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
pub fn alloca_constant_shape<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
shape: &[u64],
) -> Self {
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64);
// Write shape
let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
for (i, dim) in shape.iter().enumerate() {
let dim = Int(SizeT).const_int(generator, ctx.ctx, *dim, false);
dst_shape.offset_const(ctx, i64::try_from(i).unwrap()).store(ctx, dim);
}
ndarray
}
/// Convenience function. Allocate an [`NDArrayObject`] with a dynamically known shape.
///
/// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
pub fn alloca_dynamic_shape<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
shape: &[Instance<'ctx, Int<SizeT>>],
) -> Self {
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64);
// Write shape
let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
for (i, dim) in shape.iter().enumerate() {
dst_shape.offset_const(ctx, i64::try_from(i).unwrap()).store(ctx, *dim);
}
ndarray
}
/// Initialize an ndarray's `data` by allocating a buffer on the stack.
/// The allocated data buffer is considered to be *owned* by the ndarray.
///
/// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
///
/// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
pub fn create_data<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
let nbytes = self.nbytes(generator, ctx);
let data = Int(Byte).array_alloca(generator, ctx, nbytes.value);
self.instance.set(ctx, |f| f.data, data);
self.set_strides_contiguous(generator, ctx);
}
/// Copy shape dimensions from an array.
pub fn copy_shape_from_array<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let num_items = self.ndims_llvm(generator, ctx.ctx).value;
self.instance.get(generator, ctx, |f| f.shape).copy_from(generator, ctx, shape, num_items);
}
/// Copy shape dimensions from an ndarray.
/// Panics if `ndims` mismatches.
pub fn copy_shape_from_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: NDArrayObject<'ctx>,
) {
assert_eq!(self.ndims, src_ndarray.ndims);
let src_shape = src_ndarray.instance.get(generator, ctx, |f| f.shape);
self.copy_shape_from_array(generator, ctx, src_shape);
}
/// Copy strides dimensions from an array.
pub fn copy_strides_from_array<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
strides: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let num_items = self.ndims_llvm(generator, ctx.ctx).value;
self.instance
.get(generator, ctx, |f| f.strides)
.copy_from(generator, ctx, strides, num_items);
}
/// Copy strides dimensions from an ndarray.
/// Panics if `ndims` mismatches.
pub fn copy_strides_from_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: NDArrayObject<'ctx>,
) {
assert_eq!(self.ndims, src_ndarray.ndims);
let src_strides = src_ndarray.instance.get(generator, ctx, |f| f.strides);
self.copy_strides_from_array(generator, ctx, src_strides);
}
/// Get the `np.size()` of this ndarray.
pub fn size<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
call_nac3_ndarray_size(generator, ctx, self.instance)
}
/// Get the `ndarray.nbytes` of this ndarray.
pub fn nbytes<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
call_nac3_ndarray_nbytes(generator, ctx, self.instance)
}
/// Get the `len()` of this ndarray.
pub fn len<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
call_nac3_ndarray_len(generator, ctx, self.instance)
}
/// Check if this ndarray is C-contiguous.
///
/// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<Bool>> {
call_nac3_ndarray_is_c_contiguous(generator, ctx, self.instance)
}
/// Get the pointer to the n-th (0-based) element.
///
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
pub fn get_nth_pelement<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
nth: Instance<'ctx, Int<SizeT>>,
) -> PointerValue<'ctx> {
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
let p = call_nac3_ndarray_get_nth_pelement(generator, ctx, self.instance, nth);
ctx.builder
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "")
.unwrap()
}
/// Get the n-th (0-based) scalar.
pub fn get_nth_scalar<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
nth: Instance<'ctx, Int<SizeT>>,
) -> AnyObject<'ctx> {
let ptr = self.get_nth_pelement(generator, ctx, nth);
let value = ctx.builder.build_load(ptr, "").unwrap();
AnyObject { ty: self.dtype, value }
}
/// Get the pointer to the element indexed by `indices`.
///
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
pub fn get_pelement_by_indices<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> PointerValue<'ctx> {
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
let p = call_nac3_ndarray_get_pelement_by_indices(generator, ctx, self.instance, indices);
ctx.builder
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "")
.unwrap()
}
/// Get the scalar indexed by `indices`.
pub fn get_scalar_by_indices<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> AnyObject<'ctx> {
let ptr = self.get_pelement_by_indices(generator, ctx, indices);
let value = ctx.builder.build_load(ptr, "").unwrap();
AnyObject { ty: self.dtype, value }
}
/// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
///
/// Update the ndarray's strides to make the ndarray contiguous.
pub fn set_strides_contiguous<G: CodeGenerator + ?Sized>(
self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
call_nac3_ndarray_set_strides_by_shape(generator, ctx, self.instance);
}
/// Clone/Copy this ndarray - Allocate a new ndarray with the same shape as this ndarray and copy the contents over.
///
/// The new ndarray will own its data and will be C-contiguous.
#[must_use]
pub fn make_copy<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Self {
let clone = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims);
let shape = self.instance.gep(ctx, |f| f.shape).load(generator, ctx);
clone.copy_shape_from_array(generator, ctx, shape);
clone.create_data(generator, ctx);
clone.copy_data_from(generator, ctx, *self);
clone
}
/// Copy data from another ndarray.
///
/// This ndarray and `src` is that their `np.size()` should be the same. Their shapes
/// do not matter. The copying order is determined by how their flattened views look.
///
/// Panics if the `dtype`s of ndarrays are different.
pub fn copy_data_from<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src: NDArrayObject<'ctx>,
) {
assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
call_nac3_ndarray_copy_data(generator, ctx, src.instance, self.instance);
}
/// Returns true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
#[must_use]
pub fn is_unsized(&self) -> bool {
self.ndims == 0
}
/// If this ndarray is unsized, return its sole value as an [`AnyObject`].
/// Otherwise, do nothing and return the ndarray itself.
pub fn split_unsized<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> ScalarOrNDArray<'ctx> {
if self.is_unsized() {
// NOTE: `np.size(self) == 0` here is never possible.
let zero = Int(SizeT).const_0(generator, ctx.ctx);
let value = self.get_nth_scalar(generator, ctx, zero).value;
ScalarOrNDArray::Scalar(AnyObject { ty: self.dtype, value })
} else {
ScalarOrNDArray::NDArray(*self)
}
}
/// Fill the ndarray with a scalar.
///
/// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
pub fn fill<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
value: BasicValueEnum<'ctx>,
) {
// TODO: It is possible to optimize this by exploiting contiguous strides with memset.
// Probably best to implement in IRRT.
self.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
let p = nditer.get_pointer(generator, ctx);
ctx.builder.build_store(p, value).unwrap();
Ok(())
})
.unwrap();
}
/// Create the shape tuple of this ndarray like `np.shape(<ndarray>)`.
///
/// The returned integers in the tuple are in int32.
pub fn make_shape_tuple<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> TupleObject<'ctx> {
// TODO: Return a tuple of SizeT
let mut objects = Vec::with_capacity(self.ndims as usize);
for i in 0..self.ndims {
let dim = self
.instance
.get(generator, ctx, |f| f.shape)
.get_index_const(generator, ctx, i64::try_from(i).unwrap())
.truncate_or_bit_cast(generator, ctx, Int32);
objects.push(AnyObject {
ty: ctx.primitives.int32,
value: dim.value.as_basic_value_enum(),
});
}
TupleObject::from_objects(generator, ctx, objects)
}
/// Create the strides tuple of this ndarray like `<ndarray>.strides`.
///
/// The returned integers in the tuple are in int32.
pub fn make_strides_tuple<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> TupleObject<'ctx> {
// TODO: Return a tuple of SizeT.
let mut objects = Vec::with_capacity(self.ndims as usize);
for i in 0..self.ndims {
let dim = self
.instance
.get(generator, ctx, |f| f.strides)
.get_index_const(generator, ctx, i64::try_from(i).unwrap())
.truncate_or_bit_cast(generator, ctx, Int32);
objects.push(AnyObject {
ty: ctx.primitives.int32,
value: dim.value.as_basic_value_enum(),
});
}
TupleObject::from_objects(generator, ctx, objects)
}
/// Create an unsized ndarray to contain `object`.
pub fn make_unsized<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> NDArrayObject<'ctx> {
// We have to put the value on the stack to get a data pointer.
let data = ctx.builder.build_alloca(object.value.get_type(), "make_unsized").unwrap();
ctx.builder.build_store(data, object.value).unwrap();
let data = Ptr(Int(Byte)).pointer_cast(generator, ctx, data);
let ndarray = NDArrayObject::alloca(generator, ctx, object.ty, 0);
ndarray.instance.set(ctx, |f| f.data, data);
ndarray
}
/// Check if this `NDArray` can be used as an `out` ndarray for an operation.
///
/// Raise an exception if the shapes do not match.
pub fn assert_can_be_written_by_out<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
out_ndims: u64,
out_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let ndarray_ndims = self.ndims_llvm(generator, ctx.ctx);
let ndarray_shape = self.instance.get(generator, ctx, |f| f.shape);
let output_ndims = Int(SizeT).const_int(generator, ctx.ctx, out_ndims, false);
let output_shape = out_shape;
call_nac3_ndarray_util_assert_output_shape_same(
generator,
ctx,
ndarray_ndims,
ndarray_shape,
output_ndims,
output_shape,
);
}
}
/// A convenience enum for implementing functions that acts on scalars or ndarrays or both.
#[derive(Debug, Clone, Copy)]
pub enum ScalarOrNDArray<'ctx> {
Scalar(AnyObject<'ctx>),
NDArray(NDArrayObject<'ctx>),
}
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for AnyObject<'ctx> {
type Error = ();
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
match value {
ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
ScalarOrNDArray::NDArray(_ndarray) => Err(()),
}
}
}
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayObject<'ctx> {
type Error = ();
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
match value {
ScalarOrNDArray::Scalar(_scalar) => Err(()),
ScalarOrNDArray::NDArray(ndarray) => Ok(*ndarray),
}
}
}
impl<'ctx> ScalarOrNDArray<'ctx> {
/// Split on `object` either into a scalar or an ndarray.
///
/// If `object` is an ndarray, [`ScalarOrNDArray::NDArray`].
///
/// For everything else, it is wrapped with [`ScalarOrNDArray::Scalar`].
pub fn split_object<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> ScalarOrNDArray<'ctx> {
match &*ctx.unifier.get_ty(object.ty) {
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
{
let ndarray = NDArrayObject::from_object(generator, ctx, object);
ScalarOrNDArray::NDArray(ndarray)
}
_ => ScalarOrNDArray::Scalar(object),
}
}
/// Get the underlying [`BasicValueEnum<'ctx>`] of this [`ScalarOrNDArray`].
#[must_use]
pub fn to_basic_value_enum(self) -> BasicValueEnum<'ctx> {
match self {
ScalarOrNDArray::Scalar(scalar) => scalar.value,
ScalarOrNDArray::NDArray(ndarray) => ndarray.instance.value.as_basic_value_enum(),
}
}
/// Convert this [`ScalarOrNDArray`] to an ndarray - behaves like `np.asarray`.
/// - If this is an ndarray, the ndarray is returned.
/// - If this is a scalar, this function returns new ndarray created with [`NDArrayObject::make_unsized`].
pub fn to_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> NDArrayObject<'ctx> {
match self {
ScalarOrNDArray::NDArray(ndarray) => *ndarray,
ScalarOrNDArray::Scalar(scalar) => NDArrayObject::make_unsized(generator, ctx, *scalar),
}
}
/// Get the dtype of the ndarray created if this were called with [`ScalarOrNDArray::to_ndarray`].
#[must_use]
pub fn get_dtype(&self) -> Type {
match self {
ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
ScalarOrNDArray::Scalar(scalar) => scalar.ty,
}
}
}
/// An helper enum specifying how a function should produce its output.
///
/// Many functions in NumPy has an optional `out` parameter (e.g., `matmul`). If `out` is specified
/// with an ndarray, the result of a function will be written to `out`. If `out` is not specified, a function will
/// create a new ndarray and store the result in it.
#[derive(Debug, Clone, Copy)]
pub enum NDArrayOut<'ctx> {
/// Tell a function should create a new ndarray with the expected element type `dtype`.
NewNDArray { dtype: Type },
/// Tell a function to write the result to `ndarray`.
WriteToNDArray { ndarray: NDArrayObject<'ctx> },
}
impl<'ctx> NDArrayOut<'ctx> {
/// Get the dtype of this output.
#[must_use]
pub fn get_dtype(&self) -> Type {
match self {
NDArrayOut::NewNDArray { dtype } => *dtype,
NDArrayOut::WriteToNDArray { ndarray } => ndarray.dtype,
}
}
}
/// A version of [`call_nac3_ndarray_set_strides_by_shape`] in Rust.
///
/// This function is used generating strides for globally defined contiguous ndarrays.
#[must_use]
pub fn make_contiguous_strides(itemsize: u64, ndims: u64, shape: &[u64]) -> Vec<u64> {
let mut strides = Vec::with_capacity(ndims as usize);
let mut stride_product = 1u64;
for i in 0..ndims {
let axis = ndims - i - 1;
strides[axis as usize] = stride_product * itemsize;
stride_product *= shape[axis as usize];
}
strides
}

179
nac3core/src/codegen/object/ndarray/nditer.rs
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@ -1,179 +0,0 @@
use inkwell::{types::BasicType, values::PointerValue, AddressSpace};
use crate::codegen::{
irrt::{call_nac3_nditer_has_element, call_nac3_nditer_initialize, call_nac3_nditer_next},
model::*,
object::any::AnyObject,
stmt::{gen_for_callback, BreakContinueHooks},
CodeGenContext, CodeGenerator,
};
use super::NDArrayObject;
/// Fields of [`NDIter`]
pub struct NDIterFields<'ctx, F: FieldTraversal<'ctx>> {
pub ndims: F::Output<Int<SizeT>>,
pub shape: F::Output<Ptr<Int<SizeT>>>,
pub strides: F::Output<Ptr<Int<SizeT>>>,
pub indices: F::Output<Ptr<Int<SizeT>>>,
pub nth: F::Output<Int<SizeT>>,
pub element: F::Output<Ptr<Int<Byte>>>,
pub size: F::Output<Int<SizeT>>,
}
/// An IRRT helper structure used to iterate through an ndarray.
#[derive(Debug, Clone, Copy, Default)]
pub struct NDIter;
impl<'ctx> StructKind<'ctx> for NDIter {
type Fields<F: FieldTraversal<'ctx>> = NDIterFields<'ctx, F>;
fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
ndims: traversal.add_auto("ndims"),
shape: traversal.add_auto("shape"),
strides: traversal.add_auto("strides"),
indices: traversal.add_auto("indices"),
nth: traversal.add_auto("nth"),
element: traversal.add_auto("element"),
size: traversal.add_auto("size"),
}
}
}
/// A helper structure with a convenient interface to interact with [`NDIter`].
#[derive(Debug, Clone)]
pub struct NDIterHandle<'ctx> {
instance: Instance<'ctx, Ptr<Struct<NDIter>>>,
/// The ndarray this [`NDIter`] to iterating over.
ndarray: NDArrayObject<'ctx>,
/// The current indices of [`NDIter`].
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
}
impl<'ctx> NDIterHandle<'ctx> {
/// Allocate an [`NDIter`] that iterates through an ndarray.
pub fn new<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: NDArrayObject<'ctx>,
) -> Self {
let nditer = Struct(NDIter).alloca(generator, ctx);
let ndims = ndarray.ndims_llvm(generator, ctx.ctx);
// The caller has the responsibility to allocate 'indices' for `NDIter`.
let indices = Int(SizeT).array_alloca(generator, ctx, ndims.value);
call_nac3_nditer_initialize(generator, ctx, nditer, ndarray.instance, indices);
NDIterHandle { ndarray, instance: nditer, indices }
}
/// Is the current iteration valid?
///
/// If true, then `element`, `indices` and `nth` contain details about the current element.
///
/// If `ndarray` is unsized, this returns true only for the first iteration.
/// If `ndarray` is 0-sized, this always returns false.
#[must_use]
pub fn has_element<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<Bool>> {
call_nac3_nditer_has_element(generator, ctx, self.instance)
}
/// Go to the next element. If `has_element()` is false, then this has undefined behavior.
///
/// If `ndarray` is unsized, this can only be called once.
/// If `ndarray` is 0-sized, this can never be called.
pub fn next<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
call_nac3_nditer_next(generator, ctx, self.instance);
}
/// Get pointer to the current element.
#[must_use]
pub fn get_pointer<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> PointerValue<'ctx> {
let elem_ty = ctx.get_llvm_type(generator, self.ndarray.dtype);
let p = self.instance.get(generator, ctx, |f| f.element);
ctx.builder
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "element")
.unwrap()
}
/// Get the value of the current element.
#[must_use]
pub fn get_scalar<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> AnyObject<'ctx> {
let p = self.get_pointer(generator, ctx);
let value = ctx.builder.build_load(p, "value").unwrap();
AnyObject { ty: self.ndarray.dtype, value }
}
/// Get the index of the current element if this ndarray were a flat ndarray.
#[must_use]
pub fn get_index<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
self.instance.get(generator, ctx, |f| f.nth)
}
/// Get the indices of the current element.
#[must_use]
pub fn get_indices(&self) -> Instance<'ctx, Ptr<Int<SizeT>>> {
self.indices
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Iterate through every element in the ndarray.
///
/// `body` has access to [`BreakContinueHooks`] to short-circuit and [`NDIterHandle`] to
/// get properties of the current iteration (e.g., the current element, indices, etc.)
pub fn foreach<'a, G, F>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
body: F,
) -> Result<(), String>
where
G: CodeGenerator + ?Sized,
F: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
BreakContinueHooks<'ctx>,
NDIterHandle<'ctx>,
) -> Result<(), String>,
{
gen_for_callback(
generator,
ctx,
Some("ndarray_foreach"),
|generator, ctx| Ok(NDIterHandle::new(generator, ctx, *self)),
|generator, ctx, nditer| Ok(nditer.has_element(generator, ctx).value),
|generator, ctx, hooks, nditer| body(generator, ctx, hooks, nditer),
|generator, ctx, nditer| {
nditer.next(generator, ctx);
Ok(())
},
)
}
}

105
nac3core/src/codegen/object/ndarray/shape_util.rs
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@ -1,105 +0,0 @@
use util::gen_for_model;
use crate::{
codegen::{
model::*,
object::{any::AnyObject, list::ListObject, tuple::TupleObject},
CodeGenContext, CodeGenerator,
},
typecheck::typedef::TypeEnum,
};
/// Parse a NumPy-like "int sequence" input and return the int sequence as an array and its length.
///
/// * `sequence` - The `sequence` parameter.
/// * `sequence_ty` - The typechecker type of `sequence`
///
/// The `sequence` argument type may only be one of the following:
/// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
/// 2. A tuple of `int32`; e.g., `np.empty((600, 800, 3))`
/// 3. A scalar `int32`; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
///
/// All `int32` values will be sign-extended to `SizeT`.
pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
input_sequence: AnyObject<'ctx>,
) -> (Instance<'ctx, Int<SizeT>>, Instance<'ctx, Ptr<Int<SizeT>>>) {
let zero = Int(SizeT).const_0(generator, ctx.ctx);
let one = Int(SizeT).const_1(generator, ctx.ctx);
// The result `list` to return.
match &*ctx.unifier.get_ty(input_sequence.ty) {
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
{
// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
// Check `input_sequence`
let input_sequence = ListObject::from_object(generator, ctx, input_sequence);
let len = input_sequence.instance.get(generator, ctx, |f| f.len);
let result = Int(SizeT).array_alloca(generator, ctx, len.value);
// Load all the `int32`s from the input_sequence, cast them to `SizeT`, and store them into `result`
gen_for_model(generator, ctx, zero, len, one, |generator, ctx, _hooks, i| {
// Load the i-th int32 in the input sequence
let int = input_sequence
.instance
.get(generator, ctx, |f| f.items)
.get_index(generator, ctx, i.value)
.value
.into_int_value();
// Cast to SizeT
let int = Int(SizeT).s_extend_or_bit_cast(generator, ctx, int);
// Store
result.set_index(ctx, i.value, int);
Ok(())
})
.unwrap();
(len, result)
}
TypeEnum::TTuple { .. } => {
// 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
let input_sequence = TupleObject::from_object(ctx, input_sequence);
let len = input_sequence.len(generator, ctx);
let result = Int(SizeT).array_alloca(generator, ctx, len.value);
for i in 0..input_sequence.num_elements() {
// Get the i-th element off of the tuple and load it into `result`.
let int = input_sequence.index(ctx, i).value.into_int_value();
let int = Int(SizeT).s_extend_or_bit_cast(generator, ctx, int);
result.set_index_const(ctx, i64::try_from(i).unwrap(), int);
}
(len, result)
}
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
{
// 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
let input_int = input_sequence.value.into_int_value();
let len = Int(SizeT).const_1(generator, ctx.ctx);
let result = Int(SizeT).array_alloca(generator, ctx, len.value);
let int = Int(SizeT).s_extend_or_bit_cast(generator, ctx, input_int);
// Storing into result[0]
result.store(ctx, int);
(len, result)
}
_ => panic!(
"encountered unknown sequence type: {}",
ctx.unifier.stringify(input_sequence.ty)
),
}
}

119
nac3core/src/codegen/object/ndarray/view.rs
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@ -1,119 +0,0 @@
use crate::codegen::{
irrt::{call_nac3_ndarray_reshape_resolve_and_check_new_shape, call_nac3_ndarray_transpose},
model::*,
CodeGenContext, CodeGenerator,
};
use super::{indexing::RustNDIndex, NDArrayObject};
impl<'ctx> NDArrayObject<'ctx> {
/// Make sure the ndarray is at least `ndmin`-dimensional.
///
/// If this ndarray's `ndims` is less than `ndmin`, a view is created on this with 1s prepended to the shape.
/// If this ndarray's `ndims` is not less than `ndmin`, this function does nothing and return this ndarray.
#[must_use]
pub fn atleast_nd<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndmin: u64,
) -> Self {
if self.ndims < ndmin {
// Extend the dimensions with np.newaxis.
let mut indices = vec![];
for _ in self.ndims..ndmin {
indices.push(RustNDIndex::NewAxis);
}
indices.push(RustNDIndex::Ellipsis);
self.index(generator, ctx, &indices)
} else {
*self
}
}
/// Create a reshaped view on this ndarray like `np.reshape()`.
///
/// If there is a `-1` in `new_shape`, it will be resolved; `new_shape` would **NOT** be modified as a result.
///
/// If reshape without copying is impossible, this function will allocate a new ndarray and copy contents.
///
/// * `new_ndims` - The number of dimensions of `new_shape` as a [`Type`].
/// * `new_shape` - The target shape to do `np.reshape()`.
#[must_use]
pub fn reshape_or_copy<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
new_ndims: u64,
new_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Self {
// TODO: The current criterion for whether to do a full copy or not is by checking `is_c_contiguous`,
// but this is not optimal - there are cases when the ndarray is not contiguous but could be reshaped
// without copying data. Look into how numpy does it.
let current_bb = ctx.builder.get_insert_block().unwrap();
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
let dst_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, new_ndims);
dst_ndarray.copy_shape_from_array(generator, ctx, new_shape);
// Reolsve negative indices
let size = self.size(generator, ctx);
let dst_ndims = dst_ndarray.ndims_llvm(generator, ctx.ctx);
let dst_shape = dst_ndarray.instance.get(generator, ctx, |f| f.shape);
call_nac3_ndarray_reshape_resolve_and_check_new_shape(
generator, ctx, size, dst_ndims, dst_shape,
);
let is_c_contiguous = self.is_c_contiguous(generator, ctx);
ctx.builder.build_conditional_branch(is_c_contiguous.value, then_bb, else_bb).unwrap();
// Inserting into then_bb: reshape is possible without copying
ctx.builder.position_at_end(then_bb);
dst_ndarray.set_strides_contiguous(generator, ctx);
dst_ndarray.instance.set(ctx, |f| f.data, self.instance.get(generator, ctx, |f| f.data));
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Inserting into else_bb: reshape is impossible without copying
ctx.builder.position_at_end(else_bb);
dst_ndarray.create_data(generator, ctx);
dst_ndarray.copy_data_from(generator, ctx, *self);
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Reposition for continuation
ctx.builder.position_at_end(end_bb);
dst_ndarray
}
/// Create a transposed view on this ndarray like `np.transpose(<ndarray>, <axes> = None)`.
/// * `axes` - If specified, should be an array of the permutation (negative indices are **allowed**).
#[must_use]
pub fn transpose<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
axes: Option<Instance<'ctx, Ptr<Int<SizeT>>>>,
) -> Self {
// Define models
let transposed_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims);
let num_axes = self.ndims_llvm(generator, ctx.ctx);
// `axes = nullptr` if `axes` is unspecified.
let axes = axes.unwrap_or_else(|| Ptr(Int(SizeT)).nullptr(generator, ctx.ctx));
call_nac3_ndarray_transpose(
generator,
ctx,
self.instance,
transposed_ndarray.instance,
num_axes,
axes,
);
transposed_ndarray
}
}

99
nac3core/src/codegen/object/tuple.rs
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@ -1,99 +0,0 @@
use inkwell::values::StructValue;
use itertools::Itertools;
use crate::{
codegen::{model::*, CodeGenContext, CodeGenerator},
typecheck::typedef::{Type, TypeEnum},
};
use super::any::AnyObject;
/// A NAC3 tuple object.
///
/// NOTE: This struct has no copy trait.
#[derive(Debug, Clone)]
pub struct TupleObject<'ctx> {
/// The type of the tuple.
pub tys: Vec<Type>,
/// The underlying LLVM struct value of this tuple.
pub value: StructValue<'ctx>,
}
impl<'ctx> TupleObject<'ctx> {
pub fn from_object(ctx: &mut CodeGenContext<'ctx, '_>, object: AnyObject<'ctx>) -> Self {
// TODO: Keep `is_vararg_ctx` from TTuple?
// Sanity check on object type.
let TypeEnum::TTuple { ty: tys, .. } = &*ctx.unifier.get_ty(object.ty) else {
panic!(
"Expected type to be a TypeEnum::TTuple, got {}",
ctx.unifier.stringify(object.ty)
);
};
// Check number of fields
let value = object.value.into_struct_value();
let value_num_fields = value.get_type().count_fields() as usize;
assert!(
value_num_fields == tys.len(),
"Tuple type has {} item(s), but the LLVM struct value has {} field(s)",
tys.len(),
value_num_fields
);
TupleObject { tys: tys.clone(), value }
}
/// Convenience function. Create a [`TupleObject`] from an iterator of objects.
pub fn from_objects<I, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
objects: I,
) -> Self
where
I: IntoIterator<Item = AnyObject<'ctx>>,
{
let (values, tys): (Vec<_>, Vec<_>) =
objects.into_iter().map(|object| (object.value, object.ty)).unzip();
let llvm_tys = tys.iter().map(|ty| ctx.get_llvm_type(generator, *ty)).collect_vec();
let llvm_tuple_ty = ctx.ctx.struct_type(&llvm_tys, false);
let pllvm_tuple = ctx.builder.build_alloca(llvm_tuple_ty, "tuple").unwrap();
for (i, val) in values.into_iter().enumerate() {
let pval = ctx.builder.build_struct_gep(pllvm_tuple, i as u32, "value").unwrap();
ctx.builder.build_store(pval, val).unwrap();
}
let value = ctx.builder.build_load(pllvm_tuple, "").unwrap().into_struct_value();
TupleObject { tys, value }
}
#[must_use]
pub fn num_elements(&self) -> usize {
self.tys.len()
}
/// Get the `len()` of this tuple.
#[must_use]
pub fn len<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
Int(SizeT).const_int(generator, ctx.ctx, self.num_elements() as u64, false)
}
/// Get the `i`-th (0-based) object in this tuple.
pub fn index(&self, ctx: &mut CodeGenContext<'ctx, '_>, i: usize) -> AnyObject<'ctx> {
assert!(
i < self.num_elements(),
"Tuple object with length {} have index {i}",
self.num_elements()
);
let value = ctx.builder.build_extract_value(self.value, i as u32, "tuple[{i}]").unwrap();
let ty = self.tys[i];
AnyObject { ty, value }
}
}

1
nac3core/src/codegen/object/utils/mod.rs
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@ -1 +0,0 @@
pub mod slice;

125
nac3core/src/codegen/object/utils/slice.rs
View File

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

103
nac3core/src/codegen/stmt.rs
View File

@ -1,15 +1,22 @@
use inkwell::{
attributes::{Attribute, AttributeLoc},
basic_block::BasicBlock,
types::{BasicType, BasicTypeEnum},
values::{BasicValue, BasicValueEnum, FunctionValue, IntValue, PointerValue},
IntPredicate,
};
use itertools::{izip, Itertools};
use nac3parser::ast::{
Constant, ExcepthandlerKind, Expr, ExprKind, Location, Stmt, StmtKind, StrRef,
};
use super::{
classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
expr::{destructure_range, gen_binop_expr},
gen_in_range_check,
irrt::{handle_slice_indices, list_slice_assignment},
macros::codegen_unreachable,
object::{
any::AnyObject,
ndarray::{
indexing::util::gen_ndarray_subscript_ndindices, NDArrayObject, ScalarOrNDArray,
},
},
CodeGenContext, CodeGenerator,
};
use crate::{
@ -20,17 +27,6 @@ use crate::{
typedef::{iter_type_vars, FunSignature, Type, TypeEnum},
},
};
use inkwell::{
attributes::{Attribute, AttributeLoc},
basic_block::BasicBlock,
types::{BasicType, BasicTypeEnum},
values::{BasicValue, BasicValueEnum, FunctionValue, IntValue, PointerValue},
IntPredicate,
};
use itertools::{izip, Itertools};
use nac3parser::ast::{
Constant, ExcepthandlerKind, Expr, ExprKind, Location, Stmt, StmtKind, StrRef,
};
/// See [`CodeGenerator::gen_var_alloc`].
pub fn gen_var<'ctx>(
@ -415,47 +411,7 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
{
// Handle NDArray item assignment
// Process target
let target = generator
.gen_expr(ctx, target)?
.unwrap()
.to_basic_value_enum(ctx, generator, target_ty)?;
let target = AnyObject { value: target, ty: target_ty };
// Process key
let key = gen_ndarray_subscript_ndindices(generator, ctx, key)?;
// Process value
let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
let value = AnyObject { value, ty: value_ty };
/*
Reference code:
```python
target = target[key]
value = np.asarray(value)
shape = np.broadcast_shape((target, value))
target = np.broadcast_to(target, shape)
value = np.broadcast_to(value, shape)
...and finally copy 1-1 from value to target.
```
*/
let target = NDArrayObject::from_object(generator, ctx, target);
let target = target.index(generator, ctx, &key);
let value =
ScalarOrNDArray::split_object(generator, ctx, value).to_ndarray(generator, ctx);
let broadcast_result = NDArrayObject::broadcast(generator, ctx, &[target, value]);
let target = broadcast_result.ndarrays[0];
let value = broadcast_result.ndarrays[1];
target.copy_data_from(generator, ctx, value);
todo!("ndarray subscript assignment is not yet implemented");
}
_ => {
panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));
@ -1872,6 +1828,37 @@ pub fn gen_stmt<G: CodeGenerator>(
stmt.location,
);
}
StmtKind::Global { names, .. } => {
let registered_globals = ctx
.top_level
.definitions
.read()
.iter()
.filter_map(|def| {
if let TopLevelDef::Variable { simple_name, ty, .. } = &*def.read() {
Some((*simple_name, *ty))
} else {
None
}
})
.collect_vec();
for id in names {
let Some((_, ty)) = registered_globals.iter().find(|(name, _)| name == id) else {
return Err(format!("{id} is not a global at {}", stmt.location));
};
let resolver = ctx.resolver.clone();
let ptr = resolver
.get_symbol_value(*id, ctx, generator)
.map(|val| val.to_basic_value_enum(ctx, generator, *ty))
.transpose()?
.map(BasicValueEnum::into_pointer_value)
.unwrap();
ctx.var_assignment.insert(*id, (ptr, None, 0));
}
}
_ => unimplemented!(),
};
Ok(())

54
nac3core/src/codegen/test.rs
View File

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

4
nac3core/src/lib.rs
View File

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

21
nac3core/src/symbol_resolver.rs
View File

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

245
nac3core/src/toplevel/builtins.rs
View File

@ -1,6 +1,5 @@
use std::iter::once;
use helper::{debug_assert_prim_is_allowed, extract_ndims, make_exception_fields, PrimDefDetails};
use indexmap::IndexMap;
use inkwell::{
attributes::{Attribute, AttributeLoc},
@ -9,19 +8,17 @@ use inkwell::{
IntPredicate,
};
use itertools::Either;
use numpy::unpack_ndarray_var_tys;
use strum::IntoEnumIterator;
use super::{
helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDefDetails},
*,
};
use crate::{
codegen::{
builtin_fns,
classes::{ProxyValue, RangeValue},
model::*,
numpy::*,
object::{
any::AnyObject,
ndarray::{shape_util::parse_numpy_int_sequence, NDArrayObject},
},
stmt::exn_constructor,
},
symbol_resolver::SymbolValue,
@ -29,8 +26,6 @@ use crate::{
typecheck::typedef::{into_var_map, iter_type_vars, TypeVar, VarMap},
};
use super::*;
type BuiltinInfo = Vec<(Arc<RwLock<TopLevelDef>>, Option<Stmt>)>;
pub fn get_exn_constructor(
@ -517,14 +512,6 @@ impl<'a> BuiltinBuilder<'a> {
| PrimDef::FunNpEye
| PrimDef::FunNpIdentity => self.build_ndarray_other_factory_function(prim),
PrimDef::FunNpSize | PrimDef::FunNpShape | PrimDef::FunNpStrides => {
self.build_ndarray_property_getter_function(prim)
}
PrimDef::FunNpBroadcastTo | PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
self.build_ndarray_view_function(prim)
}
PrimDef::FunStr => self.build_str_function(),
PrimDef::FunFloor | PrimDef::FunFloor64 | PrimDef::FunCeil | PrimDef::FunCeil64 => {
@ -590,6 +577,10 @@ impl<'a> BuiltinBuilder<'a> {
| PrimDef::FunNpHypot
| PrimDef::FunNpNextAfter => self.build_np_2ary_function(prim),
PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
self.build_np_sp_ndarray_function(prim)
}
PrimDef::FunNpDot
| PrimDef::FunNpLinalgCholesky
| PrimDef::FunNpLinalgQr
@ -1395,171 +1386,6 @@ impl<'a> BuiltinBuilder<'a> {
}
}
fn build_ndarray_property_getter_function(&mut self, prim: PrimDef) -> TopLevelDef {
debug_assert_prim_is_allowed(
prim,
&[PrimDef::FunNpSize, PrimDef::FunNpShape, PrimDef::FunNpStrides],
);
let in_ndarray_ty = self.unifier.get_fresh_var_with_range(
&[self.primitives.ndarray],
Some("T".into()),
None,
);
match prim {
PrimDef::FunNpSize => create_fn_by_codegen(
self.unifier,
&into_var_map([in_ndarray_ty]),
prim.name(),
self.primitives.int32,
&[(in_ndarray_ty.ty, "a")],
Box::new(|ctx, obj, fun, args, generator| {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
let ndarray_ty = fun.0.args[0].ty;
let ndarray =
args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
let size =
ndarray.size(generator, ctx).truncate_or_bit_cast(generator, ctx, Int32);
Ok(Some(size.value.as_basic_value_enum()))
}),
),
PrimDef::FunNpShape | PrimDef::FunNpStrides => {
// The function signatures of `np_shape` an `np_size` are the same.
// Mixed together for convenience.
// The return type is a tuple of variable length depending on the ndims of the input ndarray.
let ret_ty = self.unifier.get_dummy_var().ty; // Handled by special folding
create_fn_by_codegen(
self.unifier,
&into_var_map([in_ndarray_ty]),
prim.name(),
ret_ty,
&[(in_ndarray_ty.ty, "a")],
Box::new(move |ctx, obj, fun, args, generator| {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
let ndarray_ty = fun.0.args[0].ty;
let ndarray =
args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
let result_tuple = match prim {
PrimDef::FunNpShape => ndarray.make_shape_tuple(generator, ctx),
PrimDef::FunNpStrides => ndarray.make_strides_tuple(generator, ctx),
_ => unreachable!(),
};
Ok(Some(result_tuple.value.as_basic_value_enum()))
}),
)
}
_ => unreachable!(),
}
}
/// Build np/sp functions that take as input `NDArray` only
fn build_ndarray_view_function(&mut self, prim: PrimDef) -> TopLevelDef {
debug_assert_prim_is_allowed(
prim,
&[PrimDef::FunNpBroadcastTo, PrimDef::FunNpTranspose, PrimDef::FunNpReshape],
);
let in_ndarray_ty = self.unifier.get_fresh_var_with_range(
&[self.primitives.ndarray],
Some("T".into()),
None,
);
match prim {
PrimDef::FunNpTranspose => {
create_fn_by_codegen(
self.unifier,
&into_var_map([in_ndarray_ty]),
prim.name(),
in_ndarray_ty.ty,
&[(in_ndarray_ty.ty, "x")],
Box::new(move |ctx, _, fun, args, generator| {
let arg_ty = fun.0.args[0].ty;
let arg_val =
args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
let arg = AnyObject { ty: arg_ty, value: arg_val };
let ndarray = NDArrayObject::from_object(generator, ctx, arg);
let ndarray = ndarray.transpose(generator, ctx, None); // TODO: Add axes argument
Ok(Some(ndarray.instance.value.as_basic_value_enum()))
}),
)
}
// NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
// the `param_ty` for `create_fn_by_codegen`.
//
// Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
// to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
// and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
PrimDef::FunNpBroadcastTo | PrimDef::FunNpReshape => {
// These two functions have the same function signature.
// Mixed together for convenience.
let ret_ty = self.unifier.get_dummy_var().ty; // Handled by special holding
create_fn_by_codegen(
self.unifier,
&VarMap::new(),
prim.name(),
ret_ty,
&[
(in_ndarray_ty.ty, "x"),
(self.ndarray_factory_fn_shape_arg_tvar.ty, "shape"), // Handled by special folding
],
Box::new(move |ctx, _, fun, args, generator| {
let ndarray_ty = fun.0.args[0].ty;
let ndarray_val =
args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
let shape_ty = fun.0.args[1].ty;
let shape_val =
args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
let ndarray = AnyObject { value: ndarray_val, ty: ndarray_ty };
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
let shape = AnyObject { value: shape_val, ty: shape_ty };
let (_, shape) = parse_numpy_int_sequence(generator, ctx, shape);
// The ndims after reshaping is gotten from the return type of the call.
let (_, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
let ndims = extract_ndims(&ctx.unifier, ndims);
let new_ndarray = match prim {
PrimDef::FunNpBroadcastTo => {
ndarray.broadcast_to(generator, ctx, ndims, shape)
}
PrimDef::FunNpReshape => {
ndarray.reshape_or_copy(generator, ctx, ndims, shape)
}
_ => unreachable!(),
};
Ok(Some(new_ndarray.instance.value.as_basic_value_enum()))
}),
)
}
_ => unreachable!(),
}
}
/// Build the `str()` function.
fn build_str_function(&mut self) -> TopLevelDef {
let prim = PrimDef::FunStr;
@ -2047,6 +1873,57 @@ impl<'a> BuiltinBuilder<'a> {
}
}
/// Build np/sp functions that take as input `NDArray` only
fn build_np_sp_ndarray_function(&mut self, prim: PrimDef) -> TopLevelDef {
debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpTranspose, PrimDef::FunNpReshape]);
match prim {
PrimDef::FunNpTranspose => {
let ndarray_ty = self.unifier.get_fresh_var_with_range(
&[self.ndarray_num_ty],
Some("T".into()),
None,
);
create_fn_by_codegen(
self.unifier,
&into_var_map([ndarray_ty]),
prim.name(),
ndarray_ty.ty,
&[(ndarray_ty.ty, "x")],
Box::new(move |ctx, _, fun, args, generator| {
let arg_ty = fun.0.args[0].ty;
let arg_val =
args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
}),
)
}
// NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
// the `param_ty` for `create_fn_by_codegen`.
//
// Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
// to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
// and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
PrimDef::FunNpReshape => create_fn_by_codegen(
self.unifier,
&VarMap::new(),
prim.name(),
self.ndarray_num_ty,
&[(self.ndarray_num_ty, "x"), (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
Box::new(move |ctx, _, fun, args, generator| {
let x1_ty = fun.0.args[0].ty;
let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
let x2_ty = fun.0.args[1].ty;
let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
Ok(Some(ndarray_reshape(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
}),
),
_ => unreachable!(),
}
}
/// Build `np_linalg` and `sp_linalg` functions
///
/// The input to these functions must be floating point `NDArray`
@ -2078,12 +1955,10 @@ impl<'a> BuiltinBuilder<'a> {
Box::new(move |ctx, _, fun, args, generator| {
let x1_ty = fun.0.args[0].ty;
let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
let x2_ty = fun.0.args[1].ty;
let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
let result = ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?;
Ok(Some(result))
Ok(Some(ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
}),
),

144
nac3core/src/toplevel/composer.rs
View File

@ -1,17 +1,17 @@
use nac3parser::ast::fold::Fold;
use std::rc::Rc;
use nac3parser::ast::{fold::Fold, ExprKind};
use super::*;
use crate::{
codegen::{expr::get_subst_key, stmt::exn_constructor},
symbol_resolver::SymbolValue,
typecheck::{
type_inferencer::{FunctionData, Inferencer},
type_inferencer::{FunctionData, IdentifierInfo, Inferencer},
typedef::{TypeVar, VarMap},
},
};
use super::*;
pub struct ComposerConfig {
pub kernel_ann: Option<&'static str>,
pub kernel_invariant_ann: &'static str,
@ -101,7 +101,8 @@ impl TopLevelComposer {
.iter()
.map(|def_ast| match *def_ast.0.read() {
TopLevelDef::Class { name, .. } => name.to_string(),
TopLevelDef::Function { simple_name, .. } => simple_name.to_string(),
TopLevelDef::Function { simple_name, .. }
| TopLevelDef::Variable { simple_name, .. } => simple_name.to_string(),
})
.collect_vec();
@ -381,8 +382,58 @@ impl TopLevelComposer {
))
}
ast::StmtKind::AnnAssign { target, annotation, .. } => {
let ExprKind::Name { id: name, .. } = target.node else {
return Err(format!(
"global variable declaration must be an identifier (at {})",
ast.location
));
};
if self.keyword_list.contains(&name) {
return Err(format!(
"cannot use keyword `{}` as a class name (at {})",
name,
ast.location
));
}
let global_var_name = if mod_path.is_empty() {
name.to_string()
} else {
format!("{mod_path}.{name}")
};
if !defined_names.insert(global_var_name.clone()) {
return Err(format!(
"global variable `{}` defined twice (at {})",
global_var_name,
ast.location
));
}
let ty_to_be_unified = self.unifier.get_dummy_var().ty;
self.definition_ast_list.push((
RwLock::new(Self::make_top_level_variable_def(
global_var_name,
name,
// dummy here, unify with correct type later,
ty_to_be_unified,
*(annotation.clone()),
resolver,
Some(ast.location),
)).into(),
None,
));
Ok((
name,
DefinitionId(self.definition_ast_list.len() - 1),
Some(ty_to_be_unified),
))
}
_ => Err(format!(
"registrations of constructs other than top level classes/functions are not supported (at {})",
"registrations of constructs other than top level classes/functions/variables are not supported (at {})",
ast.location
)),
}
@ -396,6 +447,7 @@ impl TopLevelComposer {
if inference {
self.analyze_function_instance()?;
}
self.analyze_top_level_variables()?;
Ok(())
}
@ -500,6 +552,7 @@ impl TopLevelComposer {
}
Ok(())
};
let mut errors = HashSet::new();
for (class_def, class_ast) in def_list.iter().skip(self.builtin_num) {
if class_ast.is_none() {
@ -853,7 +906,6 @@ impl TopLevelComposer {
let unifier = self.unifier.borrow_mut();
let primitives_store = &self.primitives_ty;
let mut errors = HashSet::new();
let mut analyze = |function_def: &Arc<RwLock<TopLevelDef>>, function_ast: &Option<Stmt>| {
let mut function_def = function_def.write();
let function_def = &mut *function_def;
@ -1128,6 +1180,8 @@ impl TopLevelComposer {
})?;
Ok(())
};
let mut errors = HashSet::new();
for (function_def, function_ast) in def_list.iter().skip(self.builtin_num) {
if function_ast.is_none() {
continue;
@ -1702,7 +1756,6 @@ impl TopLevelComposer {
}
}
let mut errors = HashSet::new();
let mut analyze = |i, def: &Arc<RwLock<TopLevelDef>>, ast: &Option<Stmt>| {
let class_def = def.read();
if let TopLevelDef::Class {
@ -1845,6 +1898,8 @@ impl TopLevelComposer {
}
Ok(())
};
let mut errors = HashSet::new();
for (i, (def, ast)) in definition_ast_list.iter().enumerate().skip(self.builtin_num) {
if ast.is_none() {
continue;
@ -1894,7 +1949,8 @@ impl TopLevelComposer {
} = &mut *function_def
{
let signature_ty_enum = unifier.get_ty(*signature);
let TypeEnum::TFunc(FunSignature { args, ret, vars }) = signature_ty_enum.as_ref()
let TypeEnum::TFunc(FunSignature { args, ret, vars, .. }) =
signature_ty_enum.as_ref()
else {
unreachable!("must be typeenum::tfunc")
};
@ -2002,11 +2058,12 @@ impl TopLevelComposer {
})
};
let mut identifiers = {
let mut result: HashSet<_> = HashSet::new();
let mut result = HashMap::new();
if self_type.is_some() {
result.insert("self".into());
result.insert("self".into(), IdentifierInfo::default());
}
result.extend(inst_args.iter().map(|x| x.name));
result
.extend(inst_args.iter().map(|x| (x.name, IdentifierInfo::default())));
result
};
let mut calls: HashMap<CodeLocation, CallId> = HashMap::new();
@ -2057,6 +2114,16 @@ impl TopLevelComposer {
instance_to_symbol.insert(String::new(), simple_name.to_string());
continue;
}
if !decorator_list.is_empty() {
if let ast::ExprKind::Call { func, .. } = &decorator_list[0].node {
if matches!(&func.node,
ast::ExprKind::Name{ id, .. } if id == &"rpc".into())
{
instance_to_symbol.insert(String::new(), simple_name.to_string());
continue;
}
}
}
let fun_body = body
.into_iter()
@ -2161,4 +2228,57 @@ impl TopLevelComposer {
}
Ok(())
}
/// Step 6. Analyze and populate the types of global variables.
fn analyze_top_level_variables(&mut self) -> Result<(), HashSet<String>> {
let def_list = &self.definition_ast_list;
let temp_def_list = self.extract_def_list();
let unifier = &mut self.unifier;
let primitives_store = &self.primitives_ty;
let mut analyze = |variable_def: &Arc<RwLock<TopLevelDef>>| -> Result<_, HashSet<String>> {
let variable_def = &mut *variable_def.write();
let TopLevelDef::Variable { ty: dummy_ty, ty_decl, resolver, loc, .. } = variable_def
else {
// not top level variable def, skip
return Ok(());
};
let resolver = &**resolver.as_ref().unwrap();
let ty_annotation = parse_ast_to_type_annotation_kinds(
resolver,
&temp_def_list,
unifier,
primitives_store,
ty_decl,
HashMap::new(),
)?;
let ty_from_ty_annotation = get_type_from_type_annotation_kinds(
&temp_def_list,
unifier,
primitives_store,
&ty_annotation,
&mut None,
)?;
unifier.unify(*dummy_ty, ty_from_ty_annotation).map_err(|e| {
HashSet::from([e.at(Some(loc.unwrap())).to_display(unifier).to_string()])
})?;
Ok(())
};
let mut errors = HashSet::new();
for (variable_def, _) in def_list.iter().skip(self.builtin_num) {
if let Err(e) = analyze(variable_def) {
errors.extend(e);
}
}
if !errors.is_empty() {
return Err(errors);
}
Ok(())
}
}

72
nac3core/src/toplevel/helper.rs
View File

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

47
nac3core/src/toplevel/mod.rs
View File

@ -6,24 +6,24 @@ use std::{
sync::Arc,
};
use super::codegen::CodeGenContext;
use super::typecheck::type_inferencer::PrimitiveStore;
use super::typecheck::typedef::{
FunSignature, FuncArg, SharedUnifier, Type, TypeEnum, Unifier, VarMap,
};
use crate::{
codegen::CodeGenerator,
symbol_resolver::{SymbolResolver, ValueEnum},
typecheck::{
type_inferencer::CodeLocation,
typedef::{CallId, TypeVarId},
},
};
use inkwell::values::BasicValueEnum;
use itertools::Itertools;
use nac3parser::ast::{self, Location, Stmt, StrRef};
use parking_lot::RwLock;
use nac3parser::ast::{self, Expr, Location, Stmt, StrRef};
use crate::{
codegen::{CodeGenContext, CodeGenerator},
symbol_resolver::{SymbolResolver, ValueEnum},
typecheck::{
type_inferencer::{CodeLocation, PrimitiveStore},
typedef::{
CallId, FunSignature, FuncArg, SharedUnifier, Type, TypeEnum, TypeVarId, Unifier,
VarMap,
},
},
};
#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Copy, Hash, Debug)]
pub struct DefinitionId(pub usize);
@ -148,6 +148,25 @@ pub enum TopLevelDef {
/// Definition location.
loc: Option<Location>,
},
Variable {
/// Qualified name of the global variable, should be unique globally.
name: String,
/// Simple name, the same as in method/function definition.
simple_name: StrRef,
/// Type of the global variable.
ty: Type,
/// The declared type of the global variable.
ty_decl: Expr,
/// Symbol resolver of the module defined the class.
resolver: Option<Arc<dyn SymbolResolver + Send + Sync>>,
/// Definition location.
loc: Option<Location>,
},
}
pub struct TopLevelContext {

3
nac3core/src/toplevel/numpy.rs
View File

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

2
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__generic_class.snap
View File

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

2
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__inheritance_override.snap
View File

@ -7,7 +7,7 @@ expression: res_vec
"Function {\nname: \"A.__init__\",\nsig: \"fn[[t:T], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.foo\",\nsig: \"fn[[c:C], none]\",\nvar_id: []\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B[typevar246]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar246\"]\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B[typevar230]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar230\"]\n}\n",
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"B.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
"Class {\nname: \"C\",\nancestors: [\"C\", \"B[bool]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\", \"e\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: []\n}\n",

4
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__list_tuple_generic.snap
View File

@ -5,8 +5,8 @@ expression: res_vec
[
"Function {\nname: \"foo\",\nsig: \"fn[[a:list[int32], b:tuple[T, float]], A[B, bool]]\",\nvar_id: []\n}\n",
"Class {\nname: \"A\",\nancestors: [\"A[T, V]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[v:V], none]\"), (\"fun\", \"fn[[a:T], V]\")],\ntype_vars: [\"T\", \"V\"]\n}\n",
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(259)]\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(264)]\n}\n",
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(243)]\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(248)]\n}\n",
"Function {\nname: \"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",

2
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__self1.snap
View File

@ -3,7 +3,7 @@ source: nac3core/src/toplevel/test.rs
expression: res_vec
---
[
"Class {\nname: \"A\",\nancestors: [\"A[typevar245, typevar246]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar245\", \"typevar246\"]\n}\n",
"Class {\nname: \"A\",\nancestors: [\"A[typevar229, typevar230]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar229\", \"typevar230\"]\n}\n",
"Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[float, bool], b:B], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[float, bool]], A[bool, int32]]\",\nvar_id: []\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\")],\ntype_vars: []\n}\n",

4
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__simple_class_compose.snap
View File

@ -6,12 +6,12 @@ expression: res_vec
"Class {\nname: \"A\",\nancestors: [\"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
"Function {\nname: \"A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(265)]\n}\n",
"Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(249)]\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B\", \"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Class {\nname: \"C\",\nancestors: [\"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
"Function {\nname: \"C.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"C.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(273)]\n}\n",
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(257)]\n}\n",
]

24
nac3core/src/toplevel/test.rs
View File

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

14
nac3core/src/toplevel/type_annotation.rs
View File

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

78
nac3core/src/typecheck/function_check.rs
View File

@ -1,13 +1,19 @@
use crate::toplevel::helper::PrimDef;
use std::{
collections::{HashMap, HashSet},
iter::once,
};
use super::type_inferencer::Inferencer;
use super::typedef::{Type, TypeEnum};
use nac3parser::ast::{
self, Constant, Expr, ExprKind,
Operator::{LShift, RShift},
Stmt, StmtKind, StrRef,
};
use std::{collections::HashSet, iter::once};
use super::{
type_inferencer::{IdentifierInfo, Inferencer},
typedef::{Type, TypeEnum},
};
use crate::toplevel::helper::PrimDef;
impl<'a> Inferencer<'a> {
fn should_have_value(&mut self, expr: &Expr<Option<Type>>) -> Result<(), HashSet<String>> {
@ -21,15 +27,15 @@ impl<'a> Inferencer<'a> {
fn check_pattern(
&mut self,
pattern: &Expr<Option<Type>>,
defined_identifiers: &mut HashSet<StrRef>,
defined_identifiers: &mut HashMap<StrRef, IdentifierInfo>,
) -> Result<(), HashSet<String>> {
match &pattern.node {
ExprKind::Name { id, .. } if id == &"none".into() => {
Err(HashSet::from([format!("cannot assign to a `none` (at {})", pattern.location)]))
}
ExprKind::Name { id, .. } => {
if !defined_identifiers.contains(id) {
defined_identifiers.insert(*id);
if !defined_identifiers.contains_key(id) {
defined_identifiers.insert(*id, IdentifierInfo::default());
}
self.should_have_value(pattern)?;
Ok(())
@ -69,7 +75,7 @@ impl<'a> Inferencer<'a> {
fn check_expr(
&mut self,
expr: &Expr<Option<Type>>,
defined_identifiers: &mut HashSet<StrRef>,
defined_identifiers: &mut HashMap<StrRef, IdentifierInfo>,
) -> Result<(), HashSet<String>> {
// there are some cases where the custom field is None
if let Some(ty) = &expr.custom {
@ -90,7 +96,7 @@ impl<'a> Inferencer<'a> {
return Ok(());
}
self.should_have_value(expr)?;
if !defined_identifiers.contains(id) {
if !defined_identifiers.contains_key(id) {
match self.function_data.resolver.get_symbol_type(
self.unifier,
&self.top_level.definitions.read(),
@ -98,7 +104,7 @@ impl<'a> Inferencer<'a> {
*id,
) {
Ok(_) => {
self.defined_identifiers.insert(*id);
self.defined_identifiers.insert(*id, IdentifierInfo::default());
}
Err(e) => {
return Err(HashSet::from([format!(
@ -171,9 +177,7 @@ impl<'a> Inferencer<'a> {
let mut defined_identifiers = defined_identifiers.clone();
for arg in &args.args {
// TODO: should we check the types here?
if !defined_identifiers.contains(&arg.node.arg) {
defined_identifiers.insert(arg.node.arg);
}
defined_identifiers.entry(arg.node.arg).or_default();
}
self.check_expr(body, &mut defined_identifiers)?;
}
@ -236,7 +240,7 @@ impl<'a> Inferencer<'a> {
fn check_stmt(
&mut self,
stmt: &Stmt<Option<Type>>,
defined_identifiers: &mut HashSet<StrRef>,
defined_identifiers: &mut HashMap<StrRef, IdentifierInfo>,
) -> Result<bool, HashSet<String>> {
match &stmt.node {
StmtKind::For { target, iter, body, orelse, .. } => {
@ -262,9 +266,11 @@ impl<'a> Inferencer<'a> {
let body_returned = self.check_block(body, &mut body_identifiers)?;
let orelse_returned = self.check_block(orelse, &mut orelse_identifiers)?;
for ident in &body_identifiers {
if !defined_identifiers.contains(ident) && orelse_identifiers.contains(ident) {
defined_identifiers.insert(*ident);
for ident in body_identifiers.keys() {
if !defined_identifiers.contains_key(ident)
&& orelse_identifiers.contains_key(ident)
{
defined_identifiers.insert(*ident, IdentifierInfo::default());
}
}
Ok(body_returned && orelse_returned)
@ -295,7 +301,7 @@ impl<'a> Inferencer<'a> {
let mut defined_identifiers = defined_identifiers.clone();
let ast::ExcepthandlerKind::ExceptHandler { name, body, .. } = &handler.node;
if let Some(name) = name {
defined_identifiers.insert(*name);
defined_identifiers.insert(*name, IdentifierInfo::default());
}
self.check_block(body, &mut defined_identifiers)?;
}
@ -359,6 +365,40 @@ impl<'a> Inferencer<'a> {
}
Ok(true)
}
StmtKind::Global { names, .. } => {
for id in names {
if let Some(id_info) = defined_identifiers.get(id) {
if !id_info.is_global {
return Err(HashSet::from([format!(
"name '{id}' is assigned to before global declaration at {}",
stmt.location,
)]));
}
continue;
}
match self.function_data.resolver.get_symbol_type(
self.unifier,
&self.top_level.definitions.read(),
self.primitives,
*id,
) {
Ok(_) => {
self.defined_identifiers
.insert(*id, IdentifierInfo { is_global: true });
}
Err(e) => {
return Err(HashSet::from([format!(
"type error at identifier `{}` ({}) at {}",
id, e, stmt.location
)]))
}
}
}
Ok(false)
}
// break, raise, etc.
_ => Ok(false),
}
@ -367,7 +407,7 @@ impl<'a> Inferencer<'a> {
pub fn check_block(
&mut self,
block: &[Stmt<Option<Type>>],
defined_identifiers: &mut HashSet<StrRef>,
defined_identifiers: &mut HashMap<StrRef, IdentifierInfo>,
) -> Result<bool, HashSet<String>> {
let mut ret = false;
for stmt in block {

106
nac3core/src/typecheck/magic_methods.rs
View File

@ -1,19 +1,21 @@
use crate::symbol_resolver::SymbolValue;
use crate::toplevel::helper::{extract_ndims, PrimDef};
use crate::toplevel::numpy::{make_ndarray_ty, unpack_ndarray_var_tys};
use crate::typecheck::{
type_inferencer::*,
typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
};
use std::{cmp::max, collections::HashMap, rc::Rc};
use itertools::{iproduct, Itertools};
use nac3parser::ast::StrRef;
use nac3parser::ast::{Cmpop, Operator, Unaryop};
use std::cmp::max;
use std::collections::HashMap;
use std::rc::Rc;
use strum::IntoEnumIterator;
use super::typedef::into_var_map;
use nac3parser::ast::{Cmpop, Operator, StrRef, Unaryop};
use crate::{
symbol_resolver::SymbolValue,
toplevel::{
helper::PrimDef,
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
},
typecheck::{
type_inferencer::*,
typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
},
};
/// The variant of a binary operator.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
@ -173,8 +175,19 @@ pub fn impl_binop(
ops: &[Operator],
) {
with_fields(unifier, ty, |unifier, fields| {
let other_tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
let function_vars = into_var_map([other_tvar]);
let (other_ty, other_var_id) = if other_ty.len() == 1 {
(other_ty[0], None)
} else {
let tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
(tvar.ty, Some(tvar.id))
};
let function_vars = if let Some(var_id) = other_var_id {
vec![(var_id, other_ty)].into_iter().collect::<VarMap>()
} else {
VarMap::new()
};
let ret_ty = ret_ty.unwrap_or_else(|| unifier.get_fresh_var(None, None).ty);
for (base_op, variant) in iproduct!(ops, [BinopVariant::Normal, BinopVariant::AugAssign]) {
@ -185,7 +198,7 @@ pub fn impl_binop(
ret: ret_ty,
vars: function_vars.clone(),
args: vec![FuncArg {
ty: other_tvar.ty,
ty: other_ty,
default_value: None,
name: "other".into(),
is_vararg: false,
@ -528,43 +541,36 @@ pub fn typeof_binop(
}
}
let (lhs_dtype, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
let lhs_ndims = extract_ndims(unifier, lhs_ndims);
let (rhs_dtype, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
let rhs_ndims = extract_ndims(unifier, rhs_ndims);
if !(unifier.unioned(lhs_dtype, primitives.float)
&& unifier.unioned(rhs_dtype, primitives.float))
{
return Err(format!(
"ndarray.__matmul__ only supports float64 operations, but LHS has type {} and RHS has type {}",
unifier.stringify(lhs),
unifier.stringify(rhs)
));
let (_, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
let lhs_ndims = match &*unifier.get_ty_immutable(lhs_ndims) {
TypeEnum::TLiteral { values, .. } => {
assert_eq!(values.len(), 1);
u64::try_from(values[0].clone()).unwrap()
}
// Deduce the ndims of the resulting ndarray.
// If this is 0 (an unsized ndarray), matmul returns a scalar just like NumPy.
let result_ndims = match (lhs_ndims, rhs_ndims) {
(0, _) | (_, 0) => {
return Err(
"ndarray.__matmul__ does not allow unsized ndarray input".to_string()
)
_ => unreachable!(),
};
let (_, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
let rhs_ndims = match &*unifier.get_ty_immutable(rhs_ndims) {
TypeEnum::TLiteral { values, .. } => {
assert_eq!(values.len(), 1);
u64::try_from(values[0].clone()).unwrap()
}
(1, 1) => 0,
(1, _) => rhs_ndims - 1,
(_, 1) => lhs_ndims - 1,
(m, n) => max(m, n),
_ => unreachable!(),
};
if result_ndims == 0 {
// If the result is unsized, NumPy returns a scalar.
primitives.float
} else {
let result_ndims_ty =
unifier.get_fresh_literal(vec![SymbolValue::U64(result_ndims)], None);
make_ndarray_ty(unifier, primitives, Some(primitives.float), Some(result_ndims_ty))
match (lhs_ndims, rhs_ndims) {
(2, 2) => typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?,
(lhs, rhs) if lhs == 0 || rhs == 0 => {
return Err(format!(
"Input operand {} does not have enough dimensions (has {lhs}, requires {rhs})",
u8::from(rhs == 0)
))
}
(lhs, rhs) => {
return Err(format!(
"ndarray.__matmul__ on {lhs}D and {rhs}D operands not supported"
))
}
}
}
@ -767,7 +773,7 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
impl_div(unifier, store, ndarray_t, &[ndarray_t, ndarray_dtype_t], None);
impl_floordiv(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
impl_mod(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
impl_matmul(unifier, store, ndarray_t, &[ndarray_unsized_t], None);
impl_matmul(unifier, store, ndarray_t, &[ndarray_t], Some(ndarray_t));
impl_sign(unifier, store, ndarray_t, Some(ndarray_t));
impl_invert(unifier, store, ndarray_t, Some(ndarray_t));
impl_eq(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);

10
nac3core/src/typecheck/type_error.rs
View File

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

137
nac3core/src/typecheck/type_inferencer/mod.rs
View File

@ -1,32 +1,36 @@
use std::cmp::max;
use std::collections::{HashMap, HashSet};
use std::convert::{From, TryInto};
use std::iter::{self, once};
use std::{cell::RefCell, sync::Arc};
use std::{
cell::RefCell,
cmp::max,
collections::{HashMap, HashSet},
convert::{From, TryInto},
iter::once,
sync::Arc,
};
use itertools::{izip, Itertools};
use nac3parser::ast::{
self,
fold::{self, Fold},
Arguments, Comprehension, ExprContext, ExprKind, Located, Location, StrRef,
};
use super::{
magic_methods::*,
type_error::{TypeError, TypeErrorKind},
typedef::{
into_var_map, iter_type_vars, Call, CallId, FunSignature, FuncArg, OperatorInfo,
into_var_map, iter_type_vars, Call, CallId, FunSignature, FuncArg, Mapping, OperatorInfo,
RecordField, RecordKey, Type, TypeEnum, TypeVar, Unifier, VarMap,
},
};
use crate::toplevel::type_annotation::TypeAnnotation;
use crate::{
symbol_resolver::{SymbolResolver, SymbolValue},
toplevel::{
helper::{arraylike_flatten_element_type, arraylike_get_ndims, PrimDef},
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
type_annotation::TypeAnnotation,
TopLevelContext, TopLevelDef,
},
typecheck::typedef::Mapping,
};
use itertools::{izip, Itertools};
use nac3parser::ast::{
self,
fold::{self, Fold},
Arguments, Comprehension, ExprContext, ExprKind, Located, Location, StrRef,
};
#[cfg(test)]
@ -84,6 +88,20 @@ impl PrimitiveStore {
}
}
/// Information regarding a defined identifier.
#[derive(Clone, Copy, Debug, Default)]
pub struct IdentifierInfo {
/// Whether this identifier refers to a global variable.
pub is_global: bool,
}
impl IdentifierInfo {
#[must_use]
pub fn new() -> IdentifierInfo {
IdentifierInfo::default()
}
}
pub struct FunctionData {
pub resolver: Arc<dyn SymbolResolver + Send + Sync>,
pub return_type: Option<Type>,
@ -92,7 +110,7 @@ pub struct FunctionData {
pub struct Inferencer<'a> {
pub top_level: &'a TopLevelContext,
pub defined_identifiers: HashSet<StrRef>,
pub defined_identifiers: HashMap<StrRef, IdentifierInfo>,
pub function_data: &'a mut FunctionData,
pub unifier: &'a mut Unifier,
pub primitives: &'a PrimitiveStore,
@ -224,9 +242,7 @@ impl<'a> Fold<()> for Inferencer<'a> {
handler.location,
));
if let Some(name) = name {
if !self.defined_identifiers.contains(&name) {
self.defined_identifiers.insert(name);
}
self.defined_identifiers.entry(name).or_default();
if let Some(old_typ) = self.variable_mapping.insert(name, typ) {
let loc = handler.location;
self.unifier.unify(old_typ, typ).map_err(|e| {
@ -378,6 +394,7 @@ impl<'a> Fold<()> for Inferencer<'a> {
| ast::StmtKind::Continue { .. }
| ast::StmtKind::Expr { .. }
| ast::StmtKind::For { .. }
| ast::StmtKind::Global { .. }
| ast::StmtKind::Pass { .. }
| ast::StmtKind::Try { .. } => {}
ast::StmtKind::If { test, .. } | ast::StmtKind::While { test, .. } => {
@ -549,7 +566,7 @@ impl<'a> Fold<()> for Inferencer<'a> {
unreachable!("must be tobj")
}
} else {
if !self.defined_identifiers.contains(id) {
if !self.defined_identifiers.contains_key(id) {
match self.function_data.resolver.get_symbol_type(
self.unifier,
&self.top_level.definitions.read(),
@ -557,7 +574,7 @@ impl<'a> Fold<()> for Inferencer<'a> {
*id,
) {
Ok(_) => {
self.defined_identifiers.insert(*id);
self.defined_identifiers.insert(*id, IdentifierInfo::default());
}
Err(e) => {
return report_error(
@ -622,8 +639,8 @@ impl<'a> Inferencer<'a> {
fn infer_pattern<T>(&mut self, pattern: &ast::Expr<T>) -> Result<(), InferenceError> {
match &pattern.node {
ExprKind::Name { id, .. } => {
if !self.defined_identifiers.contains(id) {
self.defined_identifiers.insert(*id);
if !self.defined_identifiers.contains_key(id) {
self.defined_identifiers.insert(*id, IdentifierInfo::default());
}
Ok(())
}
@ -732,8 +749,8 @@ impl<'a> Inferencer<'a> {
let mut defined_identifiers = self.defined_identifiers.clone();
for arg in &args.args {
let name = &arg.node.arg;
if !defined_identifiers.contains(name) {
defined_identifiers.insert(*name);
if !defined_identifiers.contains_key(name) {
defined_identifiers.insert(*name, IdentifierInfo::default());
}
}
let fn_args: Vec<_> = args
@ -1183,45 +1200,6 @@ impl<'a> Inferencer<'a> {
}));
}
if ["np_shape".into(), "np_strides".into()].contains(id) && args.len() == 1 {
let ndarray = self.fold_expr(args.remove(0))?;
let ndims = arraylike_get_ndims(self.unifier, ndarray.custom.unwrap());
// Make a tuple of size `ndims` full of int32 (TODO: Make it usize)
let ret_ty = TypeEnum::TTuple {
ty: iter::repeat(self.primitives.int32).take(ndims as usize).collect_vec(),
is_vararg_ctx: false,
};
let ret_ty = self.unifier.add_ty(ret_ty);
let func_ty = TypeEnum::TFunc(FunSignature {
args: vec![FuncArg {
name: "a".into(),
default_value: None,
ty: ndarray.custom.unwrap(),
is_vararg: false,
}],
ret: ret_ty,
vars: VarMap::new(),
});
let func_ty = self.unifier.add_ty(func_ty);
return Ok(Some(Located {
location,
custom: Some(ret_ty),
node: ExprKind::Call {
func: Box::new(Located {
custom: Some(func_ty),
location: func.location,
node: ExprKind::Name { id: *id, ctx: *ctx },
}),
args: vec![ndarray],
keywords: vec![],
},
}));
}
if id == &"np_dot".into() {
let arg0 = self.fold_expr(args.remove(0))?;
let arg1 = self.fold_expr(args.remove(0))?;
@ -1543,7 +1521,7 @@ impl<'a> Inferencer<'a> {
}));
}
// 2-argument ndarray n-dimensional factory functions
if ["np_reshape".into(), "np_broadcast_to".into()].contains(id) && args.len() == 2 {
if id == &"np_reshape".into() && args.len() == 2 {
let arg0 = self.fold_expr(args.remove(0))?;
let shape_expr = args.remove(0);
@ -1589,36 +1567,29 @@ impl<'a> Inferencer<'a> {
}
// 2-argument ndarray n-dimensional creation functions
if id == &"np_full".into() && args.len() == 2 {
let ExprKind::List { elts, .. } = &args[0].node else {
return report_error(
format!(
"Expected List literal for first argument of {id}, got {}",
args[0].node.name()
)
.as_str(),
args[0].location,
);
};
// Parse arguments
let shape_expr = args.remove(0);
let (ndims, shape) =
self.fold_numpy_function_call_shape_argument(*id, 0, shape_expr)?; // Special handling for `shape`
let ndims = elts.len() as u64;
let fill_value = self.fold_expr(args.remove(0))?;
let arg0 = self.fold_expr(args.remove(0))?;
let arg1 = self.fold_expr(args.remove(0))?;
let ty = arg1.custom.unwrap();
// Build the return type
let dtype = fill_value.custom.unwrap();
let ndims = self.unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None);
let ret = make_ndarray_ty(self.unifier, self.primitives, Some(ty), Some(ndims));
let ret = make_ndarray_ty(self.unifier, self.primitives, Some(dtype), Some(ndims));
let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
args: vec![
FuncArg {
name: "shape".into(),
ty: arg0.custom.unwrap(),
ty: shape.custom.unwrap(),
default_value: None,
is_vararg: false,
},
FuncArg {
name: "fill_value".into(),
ty: arg1.custom.unwrap(),
ty: fill_value.custom.unwrap(),
default_value: None,
is_vararg: false,
},
@ -1636,7 +1607,7 @@ impl<'a> Inferencer<'a> {
location: func.location,
node: ExprKind::Name { id: *id, ctx: *ctx },
}),
args: vec![arg0, arg1],
args: vec![shape, fill_value],
keywords: vec![],
},
}));

35
nac3core/src/typecheck/type_inferencer/test.rs
View File

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

41
nac3core/src/typecheck/typedef/mod.rs
View File

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

8
nac3core/src/typecheck/typedef/test.rs
View File

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

9
nac3ld/src/lib.rs
View File

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

6
nac3parser/Cargo.toml
View File

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

10
nac3parser/src/config_comment_helper.rs
View File

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

9
nac3parser/src/error.rs
View File

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

13
nac3parser/src/fstring.rs
View File

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

9
nac3parser/src/function.rs
View File

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

14
nac3parser/src/lexer.rs
View File

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

12
nac3parser/src/parser.rs
View File

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

3
nac3parser/src/token.rs
View File

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

6
nac3standalone/Cargo.toml
View File

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

12
nac3standalone/demo/interpret_demo.py
View File

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

31
nac3standalone/demo/src/globals.py Normal file
View File

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

205
nac3standalone/demo/src/ndarray.py
View File

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

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