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@ -1,32 +1,3 @@
|
|||
BasedOnStyle: LLVM
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||||
|
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Language: Cpp
|
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Standard: Cpp11
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@ -8,17 +8,17 @@ repos:
|
|||
hooks:
|
||||
- id: nac3-cargo-fmt
|
||||
name: nac3 cargo format
|
||||
entry: nix
|
||||
entry: cargo
|
||||
language: system
|
||||
types: [file, rust]
|
||||
pass_filenames: false
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||||
description: Runs cargo fmt on the codebase.
|
||||
args: [develop, -c, cargo, fmt, --all]
|
||||
args: [fmt]
|
||||
- id: nac3-cargo-clippy
|
||||
name: nac3 cargo clippy
|
||||
entry: nix
|
||||
entry: cargo
|
||||
language: system
|
||||
types: [file, rust]
|
||||
pass_filenames: false
|
||||
description: Runs cargo clippy on the codebase.
|
||||
args: [develop, -c, cargo, clippy, --tests]
|
||||
args: [clippy, --tests]
|
||||
|
|
|
|||
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|||
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@ -41,67 +41,67 @@ dependencies = [
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@ -109,15 +109,6 @@ version = "2.6.0"
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|
@ -126,12 +117,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
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||||
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||||
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||||
[[package]]
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||||
name = "cfg-if"
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|
@ -141,9 +129,9 @@ checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
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|
@ -151,9 +139,9 @@ dependencies = [
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|
@ -163,14 +151,14 @@ dependencies = [
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[[package]]
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|
@ -181,9 +169,9 @@ checksum = "1462739cb27611015575c0c11df5df7601141071f07518d56fcc1be504cbec97"
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|
@ -197,15 +185,6 @@ dependencies = [
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@ -263,23 +242,30 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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@ -321,9 +307,9 @@ dependencies = [
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@ -340,16 +326,6 @@ dependencies = [
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|||
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|
||||
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|
@ -385,12 +361,6 @@ dependencies = [
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|||
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|
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|
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@ -403,15 +373,6 @@ version = "0.5.0"
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@ -424,12 +385,12 @@ dependencies = [
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|
|
@ -440,9 +401,9 @@ checksum = "b248f5224d1d606005e02c97f5aa4e88eeb230488bcc03bc9ca4d7991399f2b5"
|
|||
|
||||
[[package]]
|
||||
name = "inkwell"
|
||||
version = "0.5.0"
|
||||
version = "0.4.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "40fb405537710d51f6bdbc8471365ddd4cd6d3a3c3ad6e0c8291691031ba94b2"
|
||||
checksum = "b597a7b2cdf279aeef6d7149071e35e4bc87c2cf05a5b7f2d731300bffe587ea"
|
||||
dependencies = [
|
||||
"either",
|
||||
"inkwell_internals",
|
||||
|
|
@ -454,13 +415,13 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "inkwell_internals"
|
||||
version = "0.10.0"
|
||||
version = "0.9.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "9dd28cfd4cfba665d47d31c08a6ba637eed16770abca2eccbbc3ca831fef1e44"
|
||||
checksum = "4fa4d8d74483041a882adaa9a29f633253a66dde85055f0495c121620ac484b2"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.85",
|
||||
"syn 2.0.74",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -483,6 +444,15 @@ version = "1.70.1"
|
|||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "7943c866cc5cd64cbc25b2e01621d07fa8eb2a1a23160ee81ce38704e97b8ecf"
|
||||
|
||||
[[package]]
|
||||
name = "itertools"
|
||||
version = "0.11.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "b1c173a5686ce8bfa551b3563d0c2170bf24ca44da99c7ca4bfdab5418c3fe57"
|
||||
dependencies = [
|
||||
"either",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "itertools"
|
||||
version = "0.13.0"
|
||||
|
|
@ -498,45 +468,35 @@ version = "1.0.11"
|
|||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "49f1f14873335454500d59611f1cf4a4b0f786f9ac11f4312a78e4cf2566695b"
|
||||
|
||||
[[package]]
|
||||
name = "keccak"
|
||||
version = "0.1.5"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "ecc2af9a1119c51f12a14607e783cb977bde58bc069ff0c3da1095e635d70654"
|
||||
dependencies = [
|
||||
"cpufeatures",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "lalrpop"
|
||||
version = "0.22.0"
|
||||
version = "0.20.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "06093b57658c723a21da679530e061a8c25340fa5a6f98e313b542268c7e2a1f"
|
||||
checksum = "55cb077ad656299f160924eb2912aa147d7339ea7d69e1b5517326fdcec3c1ca"
|
||||
dependencies = [
|
||||
"ascii-canvas",
|
||||
"bit-set",
|
||||
"ena",
|
||||
"itertools",
|
||||
"itertools 0.11.0",
|
||||
"lalrpop-util",
|
||||
"petgraph",
|
||||
"pico-args",
|
||||
"regex",
|
||||
"regex-syntax",
|
||||
"sha3",
|
||||
"string_cache",
|
||||
"term",
|
||||
"tiny-keccak",
|
||||
"unicode-xid",
|
||||
"walkdir",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "lalrpop-util"
|
||||
version = "0.22.0"
|
||||
version = "0.20.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "feee752d43abd0f4807a921958ab4131f692a44d4d599733d4419c5d586176ce"
|
||||
checksum = "507460a910eb7b32ee961886ff48539633b788a36b65692b95f225b844c82553"
|
||||
dependencies = [
|
||||
"regex-automata",
|
||||
"rustversion",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -547,9 +507,9 @@ checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe"
|
|||
|
||||
[[package]]
|
||||
name = "libc"
|
||||
version = "0.2.161"
|
||||
version = "0.2.155"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "8e9489c2807c139ffd9c1794f4af0ebe86a828db53ecdc7fea2111d0fed085d1"
|
||||
checksum = "97b3888a4aecf77e811145cadf6eef5901f4782c53886191b2f693f24761847c"
|
||||
|
||||
[[package]]
|
||||
name = "libloading"
|
||||
|
|
@ -561,6 +521,16 @@ dependencies = [
|
|||
"windows-targets",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "libredox"
|
||||
version = "0.1.3"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "c0ff37bd590ca25063e35af745c343cb7a0271906fb7b37e4813e8f79f00268d"
|
||||
dependencies = [
|
||||
"bitflags",
|
||||
"libc",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "linked-hash-map"
|
||||
version = "0.5.6"
|
||||
|
|
@ -621,9 +591,11 @@ dependencies = [
|
|||
name = "nac3artiq"
|
||||
version = "0.1.0"
|
||||
dependencies = [
|
||||
"itertools",
|
||||
"inkwell",
|
||||
"itertools 0.13.0",
|
||||
"nac3core",
|
||||
"nac3ld",
|
||||
"nac3parser",
|
||||
"parking_lot",
|
||||
"pyo3",
|
||||
"tempfile",
|
||||
|
|
@ -634,6 +606,7 @@ name = "nac3ast"
|
|||
version = "0.1.0"
|
||||
dependencies = [
|
||||
"fxhash",
|
||||
"lazy_static",
|
||||
"parking_lot",
|
||||
"string-interner",
|
||||
]
|
||||
|
|
@ -643,11 +616,11 @@ name = "nac3core"
|
|||
version = "0.1.0"
|
||||
dependencies = [
|
||||
"crossbeam",
|
||||
"indexmap 2.6.0",
|
||||
"indexmap 2.3.0",
|
||||
"indoc",
|
||||
"inkwell",
|
||||
"insta",
|
||||
"itertools",
|
||||
"itertools 0.13.0",
|
||||
"nac3parser",
|
||||
"parking_lot",
|
||||
"rayon",
|
||||
|
|
@ -685,7 +658,9 @@ name = "nac3standalone"
|
|||
version = "0.1.0"
|
||||
dependencies = [
|
||||
"clap",
|
||||
"inkwell",
|
||||
"nac3core",
|
||||
"nac3parser",
|
||||
"parking_lot",
|
||||
]
|
||||
|
||||
|
|
@ -697,9 +672,9 @@ checksum = "650eef8c711430f1a879fdd01d4745a7deea475becfb90269c06775983bbf086"
|
|||
|
||||
[[package]]
|
||||
name = "once_cell"
|
||||
version = "1.20.2"
|
||||
version = "1.19.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1261fe7e33c73b354eab43b1273a57c8f967d0391e80353e51f764ac02cf6775"
|
||||
checksum = "3fdb12b2476b595f9358c5161aa467c2438859caa136dec86c26fdd2efe17b92"
|
||||
|
||||
[[package]]
|
||||
name = "parking_lot"
|
||||
|
|
@ -731,7 +706,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
|
|||
checksum = "b4c5cc86750666a3ed20bdaf5ca2a0344f9c67674cae0515bec2da16fbaa47db"
|
||||
dependencies = [
|
||||
"fixedbitset",
|
||||
"indexmap 2.6.0",
|
||||
"indexmap 2.3.0",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -774,7 +749,7 @@ dependencies = [
|
|||
"phf_shared 0.11.2",
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.85",
|
||||
"syn 2.0.74",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -803,9 +778,9 @@ checksum = "5be167a7af36ee22fe3115051bc51f6e6c7054c9348e28deb4f49bd6f705a315"
|
|||
|
||||
[[package]]
|
||||
name = "portable-atomic"
|
||||
version = "1.9.0"
|
||||
version = "1.7.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "cc9c68a3f6da06753e9335d63e27f6b9754dd1920d941135b7ea8224f141adb2"
|
||||
checksum = "da544ee218f0d287a911e9c99a39a8c9bc8fcad3cb8db5959940044ecfc67265"
|
||||
|
||||
[[package]]
|
||||
name = "ppv-lite86"
|
||||
|
|
@ -824,9 +799,9 @@ checksum = "925383efa346730478fb4838dbe9137d2a47675ad789c546d150a6e1dd4ab31c"
|
|||
|
||||
[[package]]
|
||||
name = "proc-macro2"
|
||||
version = "1.0.89"
|
||||
version = "1.0.86"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "f139b0662de085916d1fb67d2b4169d1addddda1919e696f3252b740b629986e"
|
||||
checksum = "5e719e8df665df0d1c8fbfd238015744736151d4445ec0836b8e628aae103b77"
|
||||
dependencies = [
|
||||
"unicode-ident",
|
||||
]
|
||||
|
|
@ -878,7 +853,7 @@ dependencies = [
|
|||
"proc-macro2",
|
||||
"pyo3-macros-backend",
|
||||
"quote",
|
||||
"syn 2.0.85",
|
||||
"syn 2.0.74",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -891,14 +866,14 @@ dependencies = [
|
|||
"proc-macro2",
|
||||
"pyo3-build-config",
|
||||
"quote",
|
||||
"syn 2.0.85",
|
||||
"syn 2.0.74",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "quote"
|
||||
version = "1.0.37"
|
||||
version = "1.0.36"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "b5b9d34b8991d19d98081b46eacdd8eb58c6f2b201139f7c5f643cc155a633af"
|
||||
checksum = "0fa76aaf39101c457836aec0ce2316dbdc3ab723cdda1c6bd4e6ad4208acaca7"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
]
|
||||
|
|
@ -955,18 +930,29 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "redox_syscall"
|
||||
version = "0.5.7"
|
||||
version = "0.5.3"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "9b6dfecf2c74bce2466cabf93f6664d6998a69eb21e39f4207930065b27b771f"
|
||||
checksum = "2a908a6e00f1fdd0dfd9c0eb08ce85126f6d8bbda50017e74bc4a4b7d4a926a4"
|
||||
dependencies = [
|
||||
"bitflags",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "regex"
|
||||
version = "1.11.1"
|
||||
name = "redox_users"
|
||||
version = "0.4.5"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "b544ef1b4eac5dc2db33ea63606ae9ffcfac26c1416a2806ae0bf5f56b201191"
|
||||
checksum = "bd283d9651eeda4b2a83a43c1c91b266c40fd76ecd39a50a8c630ae69dc72891"
|
||||
dependencies = [
|
||||
"getrandom",
|
||||
"libredox",
|
||||
"thiserror",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "regex"
|
||||
version = "1.10.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "4219d74c6b67a3654a9fbebc4b419e22126d13d2f3c4a07ee0cb61ff79a79619"
|
||||
dependencies = [
|
||||
"aho-corasick",
|
||||
"memchr",
|
||||
|
|
@ -976,9 +962,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "regex-automata"
|
||||
version = "0.4.8"
|
||||
version = "0.4.7"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "368758f23274712b504848e9d5a6f010445cc8b87a7cdb4d7cbee666c1288da3"
|
||||
checksum = "38caf58cc5ef2fed281f89292ef23f6365465ed9a41b7a7754eb4e26496c92df"
|
||||
dependencies = [
|
||||
"aho-corasick",
|
||||
"memchr",
|
||||
|
|
@ -987,9 +973,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "regex-syntax"
|
||||
version = "0.8.5"
|
||||
version = "0.8.4"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "2b15c43186be67a4fd63bee50d0303afffcef381492ebe2c5d87f324e1b8815c"
|
||||
checksum = "7a66a03ae7c801facd77a29370b4faec201768915ac14a721ba36f20bc9c209b"
|
||||
|
||||
[[package]]
|
||||
name = "runkernel"
|
||||
|
|
@ -1000,9 +986,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "rustix"
|
||||
version = "0.38.38"
|
||||
version = "0.38.34"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "aa260229e6538e52293eeb577aabd09945a09d6d9cc0fc550ed7529056c2e32a"
|
||||
checksum = "70dc5ec042f7a43c4a73241207cecc9873a06d45debb38b329f8541d85c2730f"
|
||||
dependencies = [
|
||||
"bitflags",
|
||||
"errno",
|
||||
|
|
@ -1013,9 +999,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "rustversion"
|
||||
version = "1.0.18"
|
||||
version = "1.0.17"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "0e819f2bc632f285be6d7cd36e25940d45b2391dd6d9b939e79de557f7014248"
|
||||
checksum = "955d28af4278de8121b7ebeb796b6a45735dc01436d898801014aced2773a3d6"
|
||||
|
||||
[[package]]
|
||||
name = "ryu"
|
||||
|
|
@ -1046,29 +1032,29 @@ checksum = "61697e0a1c7e512e84a621326239844a24d8207b4669b41bc18b32ea5cbf988b"
|
|||
|
||||
[[package]]
|
||||
name = "serde"
|
||||
version = "1.0.214"
|
||||
version = "1.0.206"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "f55c3193aca71c12ad7890f1785d2b73e1b9f63a0bbc353c08ef26fe03fc56b5"
|
||||
checksum = "5b3e4cd94123dd520a128bcd11e34d9e9e423e7e3e50425cb1b4b1e3549d0284"
|
||||
dependencies = [
|
||||
"serde_derive",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "serde_derive"
|
||||
version = "1.0.214"
|
||||
version = "1.0.206"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "de523f781f095e28fa605cdce0f8307e451cc0fd14e2eb4cd2e98a355b147766"
|
||||
checksum = "fabfb6138d2383ea8208cf98ccf69cdfb1aff4088460681d84189aa259762f97"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.85",
|
||||
"syn 2.0.74",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "serde_json"
|
||||
version = "1.0.132"
|
||||
version = "1.0.124"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "d726bfaff4b320266d395898905d0eba0345aae23b54aee3a737e260fd46db03"
|
||||
checksum = "66ad62847a56b3dba58cc891acd13884b9c61138d330c0d7b6181713d4fce38d"
|
||||
dependencies = [
|
||||
"itoa",
|
||||
"memchr",
|
||||
|
|
@ -1088,22 +1074,6 @@ 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"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "0fda2ff0d084019ba4d7c6f371c95d8fd75ce3524c3cb8fb653a3023f6323e64"
|
||||
|
||||
[[package]]
|
||||
name = "similar"
|
||||
version = "2.6.0"
|
||||
|
|
@ -1168,7 +1138,7 @@ dependencies = [
|
|||
"proc-macro2",
|
||||
"quote",
|
||||
"rustversion",
|
||||
"syn 2.0.85",
|
||||
"syn 2.0.74",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1184,9 +1154,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "syn"
|
||||
version = "2.0.85"
|
||||
version = "2.0.74"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "5023162dfcd14ef8f32034d8bcd4cc5ddc61ef7a247c024a33e24e1f24d21b56"
|
||||
checksum = "1fceb41e3d546d0bd83421d3409b1460cc7444cd389341a4c880fe7a042cb3d7"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
|
|
@ -1201,9 +1171,9 @@ checksum = "61c41af27dd6d1e27b1b16b489db798443478cef1f06a660c96db617ba5de3b1"
|
|||
|
||||
[[package]]
|
||||
name = "tempfile"
|
||||
version = "3.13.0"
|
||||
version = "3.12.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "f0f2c9fc62d0beef6951ccffd757e241266a2c833136efbe35af6cd2567dca5b"
|
||||
checksum = "04cbcdd0c794ebb0d4cf35e88edd2f7d2c4c3e9a5a6dab322839b321c6a87a64"
|
||||
dependencies = [
|
||||
"cfg-if",
|
||||
"fastrand",
|
||||
|
|
@ -1214,12 +1184,13 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "term"
|
||||
version = "1.0.0"
|
||||
version = "0.7.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "4df4175de05129f31b80458c6df371a15e7fc3fd367272e6bf938e5c351c7ea0"
|
||||
checksum = "c59df8ac95d96ff9bede18eb7300b0fda5e5d8d90960e76f8e14ae765eedbf1f"
|
||||
dependencies = [
|
||||
"home",
|
||||
"windows-sys 0.52.0",
|
||||
"dirs-next",
|
||||
"rustversion",
|
||||
"winapi",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1237,29 +1208,32 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "thiserror"
|
||||
version = "1.0.65"
|
||||
version = "1.0.63"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "5d11abd9594d9b38965ef50805c5e469ca9cc6f197f883f717e0269a3057b3d5"
|
||||
checksum = "c0342370b38b6a11b6cc11d6a805569958d54cfa061a29969c3b5ce2ea405724"
|
||||
dependencies = [
|
||||
"thiserror-impl",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "thiserror-impl"
|
||||
version = "1.0.65"
|
||||
version = "1.0.63"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "ae71770322cbd277e69d762a16c444af02aa0575ac0d174f0b9562d3b37f8602"
|
||||
checksum = "a4558b58466b9ad7ca0f102865eccc95938dca1a74a856f2b57b6629050da261"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.85",
|
||||
"syn 2.0.74",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typenum"
|
||||
version = "1.17.0"
|
||||
name = "tiny-keccak"
|
||||
version = "2.0.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "42ff0bf0c66b8238c6f3b578df37d0b7848e55df8577b3f74f92a69acceeb825"
|
||||
checksum = "2c9d3793400a45f954c52e73d068316d76b6f4e36977e3fcebb13a2721e80237"
|
||||
dependencies = [
|
||||
"crunchy",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "unic-char-property"
|
||||
|
|
@ -1315,27 +1289,27 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "unicode-ident"
|
||||
version = "1.0.13"
|
||||
version = "1.0.12"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "e91b56cd4cadaeb79bbf1a5645f6b4f8dc5bde8834ad5894a8db35fda9efa1fe"
|
||||
checksum = "3354b9ac3fae1ff6755cb6db53683adb661634f67557942dea4facebec0fee4b"
|
||||
|
||||
[[package]]
|
||||
name = "unicode-width"
|
||||
version = "0.1.14"
|
||||
version = "0.1.13"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "7dd6e30e90baa6f72411720665d41d89b9a3d039dc45b8faea1ddd07f617f6af"
|
||||
checksum = "0336d538f7abc86d282a4189614dfaa90810dfc2c6f6427eaf88e16311dd225d"
|
||||
|
||||
[[package]]
|
||||
name = "unicode-xid"
|
||||
version = "0.2.6"
|
||||
version = "0.2.4"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "ebc1c04c71510c7f702b52b7c350734c9ff1295c464a03335b00bb84fc54f853"
|
||||
checksum = "f962df74c8c05a667b5ee8bcf162993134c104e96440b663c8daa176dc772d8c"
|
||||
|
||||
[[package]]
|
||||
name = "unicode_names2"
|
||||
version = "1.3.0"
|
||||
version = "1.2.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "d1673eca9782c84de5f81b82e4109dcfb3611c8ba0d52930ec4a9478f547b2dd"
|
||||
checksum = "addeebf294df7922a1164f729fb27ebbbcea99cc32b3bf08afab62757f707677"
|
||||
dependencies = [
|
||||
"phf",
|
||||
"unicode_names2_generator",
|
||||
|
|
@ -1343,9 +1317,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "unicode_names2_generator"
|
||||
version = "1.3.0"
|
||||
version = "1.2.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "b91e5b84611016120197efd7dc93ef76774f4e084cd73c9fb3ea4a86c570c56e"
|
||||
checksum = "f444b8bba042fe3c1251ffaca35c603f2dc2ccc08d595c65a8c4f76f3e8426c0"
|
||||
dependencies = [
|
||||
"getopts",
|
||||
"log",
|
||||
|
|
@ -1387,6 +1361,22 @@ 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"
|
||||
|
|
@ -1396,6 +1386,12 @@ 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"
|
||||
|
|
@ -1505,5 +1501,5 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
|
|||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.85",
|
||||
"syn 2.0.74",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -2,11 +2,11 @@
|
|||
"nodes": {
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1727348695,
|
||||
"narHash": "sha256-J+PeFKSDV+pHL7ukkfpVzCOO7mBSrrpJ3svwBFABbhI=",
|
||||
"lastModified": 1721924956,
|
||||
"narHash": "sha256-Sb1jlyRO+N8jBXEX9Pg9Z1Qb8Bw9QyOgLDNMEpmjZ2M=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "1925c603f17fc89f4c8f6bf6f631a802ad85d784",
|
||||
"rev": "5ad6a14c6bf098e98800b091668718c336effc95",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
|
|
|
|||
|
|
@ -14,6 +14,7 @@
|
|||
''
|
||||
mkdir -p $out/bin
|
||||
ln -s ${pkgs.llvmPackages_14.clang-unwrapped}/bin/clang $out/bin/clang-irrt
|
||||
ln -s ${pkgs.llvmPackages_14.clang}/bin/clang $out/bin/clang-irrt-test
|
||||
ln -s ${pkgs.llvmPackages_14.llvm.out}/bin/llvm-as $out/bin/llvm-as-irrt
|
||||
'';
|
||||
demo-linalg-stub = pkgs.rustPlatform.buildRustPackage {
|
||||
|
|
@ -40,6 +41,7 @@
|
|||
cargoLock = {
|
||||
lockFile = ./Cargo.lock;
|
||||
};
|
||||
cargoTestFlags = [ "--features" "test" ];
|
||||
passthru.cargoLock = cargoLock;
|
||||
nativeBuildInputs = [ pkgs.python3 (pkgs.wrapClangMulti pkgs.llvmPackages_14.clang) llvm-tools-irrt pkgs.llvmPackages_14.llvm.out llvm-nac3 ];
|
||||
buildInputs = [ pkgs.python3 llvm-nac3 ];
|
||||
|
|
|
|||
|
|
@ -12,10 +12,15 @@ crate-type = ["cdylib"]
|
|||
itertools = "0.13"
|
||||
pyo3 = { version = "0.21", features = ["extension-module", "gil-refs"] }
|
||||
parking_lot = "0.12"
|
||||
tempfile = "3.13"
|
||||
tempfile = "3.10"
|
||||
nac3parser = { path = "../nac3parser" }
|
||||
nac3core = { path = "../nac3core" }
|
||||
nac3ld = { path = "../nac3ld" }
|
||||
|
||||
[dependencies.inkwell]
|
||||
version = "0.4"
|
||||
default-features = false
|
||||
features = ["llvm14-0", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]
|
||||
|
||||
[features]
|
||||
init-llvm-profile = []
|
||||
no-escape-analysis = ["nac3core/no-escape-analysis"]
|
||||
|
|
|
|||
|
|
@ -112,15 +112,10 @@ def extern(function):
|
|||
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 rpc(function):
|
||||
"""Decorates a function declaration defined by the core device runtime."""
|
||||
register_function(function)
|
||||
return function
|
||||
|
||||
def kernel(function_or_method):
|
||||
"""Decorates a function or method to be executed on the core device."""
|
||||
|
|
|
|||
|
|
@ -1,26 +0,0 @@
|
|||
from min_artiq import *
|
||||
from numpy import ndarray, zeros as np_zeros
|
||||
|
||||
|
||||
@nac3
|
||||
class StrFail:
|
||||
core: KernelInvariant[Core]
|
||||
|
||||
def __init__(self):
|
||||
self.core = Core()
|
||||
|
||||
@kernel
|
||||
def hello(self, arg: str):
|
||||
pass
|
||||
|
||||
@kernel
|
||||
def consume_ndarray(self, arg: ndarray[str, 1]):
|
||||
pass
|
||||
|
||||
def run(self):
|
||||
self.hello("world")
|
||||
self.consume_ndarray(np_zeros([10], dtype=str))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
StrFail().run()
|
||||
|
|
@ -1,3 +1,35 @@
|
|||
use nac3core::{
|
||||
codegen::{
|
||||
classes::{ListValue, NDArrayValue, RangeValue, UntypedArrayLikeAccessor},
|
||||
expr::{destructure_range, gen_call},
|
||||
irrt::call_ndarray_calc_size,
|
||||
llvm_intrinsics::{call_int_smax, call_stackrestore, call_stacksave},
|
||||
stmt::{gen_block, gen_for_callback_incrementing, gen_if_callback, gen_with},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{helper::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::{BasicValueEnum, StructValue},
|
||||
AddressSpace, IntPredicate,
|
||||
};
|
||||
|
||||
use pyo3::{
|
||||
types::{PyDict, PyList},
|
||||
PyObject, PyResult, Python,
|
||||
};
|
||||
|
||||
use crate::{symbol_resolver::InnerResolver, timeline::TimeFns};
|
||||
|
||||
use itertools::Itertools;
|
||||
use std::{
|
||||
collections::{hash_map::DefaultHasher, HashMap},
|
||||
hash::{Hash, Hasher},
|
||||
|
|
@ -6,39 +38,6 @@ 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 super::{symbol_resolver::InnerResolver, timeline::TimeFns};
|
||||
|
||||
/// The parallelism mode within a block.
|
||||
#[derive(Copy, Clone, Eq, PartialEq)]
|
||||
enum ParallelMode {
|
||||
|
|
@ -128,7 +127,7 @@ impl<'a> ArtiqCodeGenerator<'a> {
|
|||
/// (possibly indirect) `parallel` block.
|
||||
///
|
||||
/// * `store_name` - The LLVM value name for the pointer to `end`. `.addr` will be appended to
|
||||
/// the end of the provided value name.
|
||||
/// the end of the provided value name.
|
||||
fn timeline_update_end_max(
|
||||
&mut self,
|
||||
ctx: &mut CodeGenContext<'_, '_>,
|
||||
|
|
@ -423,10 +422,7 @@ fn gen_rpc_tag(
|
|||
} else {
|
||||
unreachable!()
|
||||
};
|
||||
assert!(
|
||||
(0u64..=u64::from(u8::MAX)).contains(&ndarray_ndims),
|
||||
"Only NDArrays of sizes between 0 and 255 can be RPCed"
|
||||
);
|
||||
assert!((0u64..=u64::from(u8::MAX)).contains(&ndarray_ndims));
|
||||
|
||||
buffer.push(b'a');
|
||||
buffer.push((ndarray_ndims & 0xFF) as u8);
|
||||
|
|
@ -438,395 +434,17 @@ fn gen_rpc_tag(
|
|||
Ok(())
|
||||
}
|
||||
|
||||
/// Formats an RPC argument to conform to the expected format required by `send_value`.
|
||||
///
|
||||
/// See `artiq/firmware/libproto_artiq/rpc_proto.rs` for the expected format.
|
||||
fn format_rpc_arg<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
(arg, arg_ty, arg_idx): (BasicValueEnum<'ctx>, Type, usize),
|
||||
) -> PointerValue<'ctx> {
|
||||
let llvm_i8 = ctx.ctx.i8_type();
|
||||
let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
|
||||
|
||||
let arg_slot = match &*ctx.unifier.get_ty_immutable(arg_ty) {
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
// NAC3: NDArray = { usize, usize*, T* }
|
||||
// libproto_artiq: NDArray = [data[..], dim_sz[..]]
|
||||
|
||||
let llvm_i1 = ctx.ctx.bool_type();
|
||||
let llvm_usize = generator.get_size_type(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);
|
||||
|
||||
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 dims_buf_sz =
|
||||
ctx.builder.build_int_mul(llvm_arg.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
|
||||
|
||||
let buffer_size =
|
||||
ctx.builder.build_int_add(dims_buf_sz, llvm_pdata_sizeof, "").unwrap();
|
||||
|
||||
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"));
|
||||
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
buffer.base_ptr(ctx, generator),
|
||||
llvm_arg.ptr_to_data(ctx),
|
||||
llvm_pdata_sizeof,
|
||||
llvm_i1.const_zero(),
|
||||
);
|
||||
|
||||
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(),
|
||||
);
|
||||
|
||||
buffer.base_ptr(ctx, generator)
|
||||
}
|
||||
|
||||
_ => {
|
||||
let arg_slot = generator
|
||||
.gen_var_alloc(ctx, arg.get_type(), Some(&format!("rpc.arg{arg_idx}")))
|
||||
.unwrap();
|
||||
ctx.builder.build_store(arg_slot, arg).unwrap();
|
||||
|
||||
ctx.builder
|
||||
.build_bit_cast(arg_slot, llvm_pi8, "rpc.arg")
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap()
|
||||
}
|
||||
};
|
||||
|
||||
debug_assert_eq!(arg_slot.get_type(), llvm_pi8);
|
||||
|
||||
arg_slot
|
||||
}
|
||||
|
||||
/// Formats an RPC return value to conform to the expected format required by NAC3.
|
||||
fn format_rpc_ret<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ret_ty: Type,
|
||||
) -> Option<BasicValueEnum<'ctx>> {
|
||||
// -- receive value:
|
||||
// T result = {
|
||||
// void *ret_ptr = alloca(sizeof(T));
|
||||
// void *ptr = ret_ptr;
|
||||
// loop: int size = rpc_recv(ptr);
|
||||
// // Non-zero: Provide `size` bytes of extra storage for variable-length data.
|
||||
// if(size) { ptr = alloca(size); goto loop; }
|
||||
// else *(T*)ret_ptr
|
||||
// }
|
||||
|
||||
let llvm_i8 = ctx.ctx.i8_type();
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_i8_8 = ctx.ctx.struct_type(&[llvm_i8.array_type(8).into()], false);
|
||||
let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
|
||||
|
||||
let rpc_recv = ctx.module.get_function("rpc_recv").unwrap_or_else(|| {
|
||||
ctx.module.add_function("rpc_recv", llvm_i32.fn_type(&[llvm_pi8.into()], false), None)
|
||||
});
|
||||
|
||||
if ctx.unifier.unioned(ret_ty, ctx.primitives.none) {
|
||||
ctx.build_call_or_invoke(rpc_recv, &[llvm_pi8.const_null().into()], "rpc_recv");
|
||||
return None;
|
||||
}
|
||||
|
||||
let prehead_bb = ctx.builder.get_insert_block().unwrap();
|
||||
let current_function = prehead_bb.get_parent().unwrap();
|
||||
let head_bb = ctx.ctx.append_basic_block(current_function, "rpc.head");
|
||||
let alloc_bb = ctx.ctx.append_basic_block(current_function, "rpc.continue");
|
||||
let tail_bb = ctx.ctx.append_basic_block(current_function, "rpc.tail");
|
||||
|
||||
let llvm_ret_ty = ctx.get_llvm_abi_type(generator, ret_ty);
|
||||
|
||||
let result = match &*ctx.unifier.get_ty_immutable(ret_ty) {
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
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, '_>,
|
||||
val: IntValue<'ctx>,
|
||||
power_of_two: IntValue<'ctx>| {
|
||||
debug_assert_eq!(
|
||||
val.get_type().get_bit_width(),
|
||||
power_of_two.get_type().get_bit_width()
|
||||
);
|
||||
|
||||
let llvm_val_t = val.get_type();
|
||||
|
||||
let max_rem = ctx
|
||||
.builder
|
||||
.build_int_sub(power_of_two, llvm_val_t.const_int(1, false), "")
|
||||
.unwrap();
|
||||
ctx.builder
|
||||
.build_and(
|
||||
ctx.builder.build_int_add(val, max_rem, "").unwrap(),
|
||||
ctx.builder.build_not(max_rem, "").unwrap(),
|
||||
"",
|
||||
)
|
||||
.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 ndarray = llvm_ret_ty.new_value(generator, ctx, Some("rpc.result"));
|
||||
|
||||
// Setup ndims
|
||||
let ndims =
|
||||
if let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) {
|
||||
assert_eq!(values.len(), 1);
|
||||
|
||||
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_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
|
||||
let buffer = ctx
|
||||
.builder
|
||||
.build_array_alloca(
|
||||
llvm_i8_8,
|
||||
ctx.builder
|
||||
.build_int_unsigned_div(buffer_size, llvm_usize.const_int(8, false), "")
|
||||
.unwrap(),
|
||||
"rpc.buffer",
|
||||
)
|
||||
.unwrap();
|
||||
let buffer = ctx
|
||||
.builder
|
||||
.build_bit_cast(buffer, llvm_pi8, "")
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap();
|
||||
let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, None);
|
||||
|
||||
// The first call to `rpc_recv` reads the top-level ndarray object: [pdata, shape]
|
||||
//
|
||||
// The returned value is the number of bytes for `ndarray.data`.
|
||||
let ndarray_nbytes = ctx
|
||||
.build_call_or_invoke(
|
||||
rpc_recv,
|
||||
&[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();
|
||||
|
||||
// debug_assert(ndarray_nbytes > 0)
|
||||
if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
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],
|
||||
ctx.current_loc,
|
||||
);
|
||||
}
|
||||
|
||||
// Copy shape from the buffer to `ndarray.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)
|
||||
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 sizeof_data =
|
||||
ctx.builder.build_int_mul(num_elements, llvm_elem_sizeof, "").unwrap();
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
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(sizeof_data), Some(ndarray_nbytes), None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
}
|
||||
|
||||
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();
|
||||
|
||||
// Inserting into `head_bb`. Do `rpc_recv` for `data` recursively.
|
||||
ctx.builder.position_at_end(head_bb);
|
||||
|
||||
let phi = ctx.builder.build_phi(llvm_pi8, "rpc.ptr").unwrap();
|
||||
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")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
|
||||
let is_done = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::EQ, llvm_i32.const_zero(), alloc_size, "rpc.done")
|
||||
.unwrap();
|
||||
ctx.builder.build_conditional_branch(is_done, tail_bb, alloc_bb).unwrap();
|
||||
|
||||
ctx.builder.position_at_end(alloc_bb);
|
||||
// Align the allocation to sizeof(T)
|
||||
let alloc_size = round_up(ctx, alloc_size, llvm_elem_sizeof);
|
||||
let alloc_ptr = ctx
|
||||
.builder
|
||||
.build_array_alloca(
|
||||
llvm_elem_ty,
|
||||
ctx.builder.build_int_unsigned_div(alloc_size, llvm_elem_sizeof, "").unwrap(),
|
||||
"rpc.alloc",
|
||||
)
|
||||
.unwrap();
|
||||
let alloc_ptr =
|
||||
ctx.builder.build_pointer_cast(alloc_ptr, llvm_pi8, "rpc.alloc.ptr").unwrap();
|
||||
phi.add_incoming(&[(&alloc_ptr, alloc_bb)]);
|
||||
ctx.builder.build_unconditional_branch(head_bb).unwrap();
|
||||
|
||||
ctx.builder.position_at_end(tail_bb);
|
||||
ndarray.as_base_value().into()
|
||||
}
|
||||
|
||||
_ => {
|
||||
let slot = ctx.builder.build_alloca(llvm_ret_ty, "rpc.ret.slot").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);
|
||||
|
||||
let phi = ctx.builder.build_phi(llvm_pi8, "rpc.ptr").unwrap();
|
||||
phi.add_incoming(&[(&slotgen, prehead_bb)]);
|
||||
let alloc_size = ctx
|
||||
.build_call_or_invoke(rpc_recv, &[phi.as_basic_value()], "rpc.size.next")
|
||||
.unwrap()
|
||||
.into_int_value();
|
||||
let is_done = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::EQ, llvm_i32.const_zero(), alloc_size, "rpc.done")
|
||||
.unwrap();
|
||||
|
||||
ctx.builder.build_conditional_branch(is_done, tail_bb, alloc_bb).unwrap();
|
||||
ctx.builder.position_at_end(alloc_bb);
|
||||
|
||||
let alloc_ptr =
|
||||
ctx.builder.build_array_alloca(llvm_pi8, alloc_size, "rpc.alloc").unwrap();
|
||||
let alloc_ptr =
|
||||
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();
|
||||
|
||||
ctx.builder.position_at_end(tail_bb);
|
||||
ctx.builder.build_load(slot, "rpc.result").unwrap()
|
||||
}
|
||||
};
|
||||
|
||||
Some(result)
|
||||
}
|
||||
|
||||
fn rpc_codegen_callback_fn<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: Vec<(Option<StrRef>, ValueEnum<'ctx>)>,
|
||||
generator: &mut dyn CodeGenerator,
|
||||
is_async: bool,
|
||||
) -> Result<Option<BasicValueEnum<'ctx>>, String> {
|
||||
let ptr_type = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
|
||||
let size_type = generator.get_size_type(ctx.ctx);
|
||||
let int8 = ctx.ctx.i8_type();
|
||||
let int32 = ctx.ctx.i32_type();
|
||||
let size_type = generator.get_size_type(ctx.ctx);
|
||||
let ptr_type = int8.ptr_type(AddressSpace::default());
|
||||
let tag_ptr_type = ctx.ctx.struct_type(&[ptr_type.into(), size_type.into()], false);
|
||||
|
||||
let service_id = int32.const_int(fun.1 .0 as u64, false);
|
||||
|
|
@ -899,25 +517,22 @@ fn rpc_codegen_callback_fn<'ctx>(
|
|||
.0
|
||||
.args
|
||||
.iter()
|
||||
.map(|arg| {
|
||||
mapping
|
||||
.remove(&arg.name)
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, arg.ty)
|
||||
.map(|llvm_val| (llvm_val, arg.ty))
|
||||
})
|
||||
.collect::<Result<Vec<(_, _)>, _>>()?;
|
||||
.map(|arg| mapping.remove(&arg.name).unwrap().to_basic_value_enum(ctx, generator, arg.ty))
|
||||
.collect::<Result<Vec<_>, _>>()?;
|
||||
if let Some(obj) = obj {
|
||||
if let ValueEnum::Static(obj_val) = obj.1 {
|
||||
real_params.insert(0, (obj_val.get_const_obj(ctx, generator), obj.0));
|
||||
if let ValueEnum::Static(obj) = obj.1 {
|
||||
real_params.insert(0, obj.get_const_obj(ctx, generator));
|
||||
} else {
|
||||
// should be an error here...
|
||||
panic!("only host object is allowed");
|
||||
}
|
||||
}
|
||||
|
||||
for (i, (arg, arg_ty)) in real_params.iter().enumerate() {
|
||||
let arg_slot = format_rpc_arg(generator, ctx, (*arg, *arg_ty, i));
|
||||
for (i, arg) in real_params.iter().enumerate() {
|
||||
let arg_slot =
|
||||
generator.gen_var_alloc(ctx, arg.get_type(), Some(&format!("rpc.arg{i}"))).unwrap();
|
||||
ctx.builder.build_store(arg_slot, *arg).unwrap();
|
||||
let arg_slot = ctx.builder.build_bitcast(arg_slot, ptr_type, "rpc.arg").unwrap();
|
||||
let arg_ptr = unsafe {
|
||||
ctx.builder.build_gep(
|
||||
args_ptr,
|
||||
|
|
@ -930,64 +545,84 @@ 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",
|
||||
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, &[service_id.into(), tag_ptr.into(), args_ptr.into()], "rpc.send")
|
||||
.unwrap();
|
||||
}
|
||||
let rpc_send = ctx.module.get_function("rpc_send").unwrap_or_else(|| {
|
||||
ctx.module.add_function(
|
||||
"rpc_send",
|
||||
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, &[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);
|
||||
// -- receive value:
|
||||
// T result = {
|
||||
// void *ret_ptr = alloca(sizeof(T));
|
||||
// void *ptr = ret_ptr;
|
||||
// loop: int size = rpc_recv(ptr);
|
||||
// // Non-zero: Provide `size` bytes of extra storage for variable-length data.
|
||||
// if(size) { ptr = alloca(size); goto loop; }
|
||||
// else *(T*)ret_ptr
|
||||
// }
|
||||
let rpc_recv = ctx.module.get_function("rpc_recv").unwrap_or_else(|| {
|
||||
ctx.module.add_function("rpc_recv", int32.fn_type(&[ptr_type.into()], false), None)
|
||||
});
|
||||
|
||||
if !result.is_some_and(|res| res.get_type().is_pointer_type()) {
|
||||
// An RPC returning an NDArray would not touch here.
|
||||
call_stackrestore(ctx, stackptr);
|
||||
}
|
||||
|
||||
Ok(result)
|
||||
if ctx.unifier.unioned(fun.0.ret, ctx.primitives.none) {
|
||||
ctx.build_call_or_invoke(rpc_recv, &[ptr_type.const_null().into()], "rpc_recv");
|
||||
return Ok(None);
|
||||
}
|
||||
|
||||
let prehead_bb = ctx.builder.get_insert_block().unwrap();
|
||||
let current_function = prehead_bb.get_parent().unwrap();
|
||||
let head_bb = ctx.ctx.append_basic_block(current_function, "rpc.head");
|
||||
let alloc_bb = ctx.ctx.append_basic_block(current_function, "rpc.continue");
|
||||
let tail_bb = ctx.ctx.append_basic_block(current_function, "rpc.tail");
|
||||
|
||||
let ret_ty = ctx.get_llvm_abi_type(generator, fun.0.ret);
|
||||
let need_load = !ret_ty.is_pointer_type();
|
||||
let slot = ctx.builder.build_alloca(ret_ty, "rpc.ret.slot").unwrap();
|
||||
let slotgen = ctx.builder.build_bitcast(slot, ptr_type, "rpc.ret.ptr").unwrap();
|
||||
ctx.builder.build_unconditional_branch(head_bb).unwrap();
|
||||
ctx.builder.position_at_end(head_bb);
|
||||
|
||||
let phi = ctx.builder.build_phi(ptr_type, "rpc.ptr").unwrap();
|
||||
phi.add_incoming(&[(&slotgen, prehead_bb)]);
|
||||
let alloc_size = ctx
|
||||
.build_call_or_invoke(rpc_recv, &[phi.as_basic_value()], "rpc.size.next")
|
||||
.unwrap()
|
||||
.into_int_value();
|
||||
let is_done = ctx
|
||||
.builder
|
||||
.build_int_compare(inkwell::IntPredicate::EQ, int32.const_zero(), alloc_size, "rpc.done")
|
||||
.unwrap();
|
||||
|
||||
ctx.builder.build_conditional_branch(is_done, tail_bb, alloc_bb).unwrap();
|
||||
ctx.builder.position_at_end(alloc_bb);
|
||||
|
||||
let alloc_ptr = ctx.builder.build_array_alloca(ptr_type, alloc_size, "rpc.alloc").unwrap();
|
||||
let alloc_ptr = ctx.builder.build_bitcast(alloc_ptr, ptr_type, "rpc.alloc.ptr").unwrap();
|
||||
phi.add_incoming(&[(&alloc_ptr, alloc_bb)]);
|
||||
ctx.builder.build_unconditional_branch(head_bb).unwrap();
|
||||
|
||||
ctx.builder.position_at_end(tail_bb);
|
||||
|
||||
let result = ctx.builder.build_load(slot, "rpc.result").unwrap();
|
||||
if need_load {
|
||||
call_stackrestore(ctx, stackptr);
|
||||
}
|
||||
Ok(Some(result))
|
||||
}
|
||||
|
||||
pub fn attributes_writeback(
|
||||
|
|
@ -1082,7 +717,7 @@ 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, false)
|
||||
rpc_codegen_callback_fn(ctx, None, (&fun, PrimDef::Int32.id()), args, generator)
|
||||
{
|
||||
return Ok(Err(e));
|
||||
}
|
||||
|
|
@ -1092,9 +727,9 @@ pub fn attributes_writeback(
|
|||
Ok(())
|
||||
}
|
||||
|
||||
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)
|
||||
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)
|
||||
})))
|
||||
}
|
||||
|
||||
|
|
@ -1163,8 +798,7 @@ fn polymorphic_print<'ctx>(
|
|||
ctx.module.add_function(fn_name, fn_t, None)
|
||||
});
|
||||
|
||||
let fmt = ctx.gen_string(generator, fmt);
|
||||
let fmt = unsafe { fmt.get_field_at_index_unchecked(0) }.into_pointer_value();
|
||||
let fmt = ctx.gen_string(generator, &fmt).get_field(generator, ctx.ctx, |f| f.base).value;
|
||||
|
||||
ctx.builder
|
||||
.build_call(
|
||||
|
|
@ -1244,20 +878,24 @@ fn polymorphic_print<'ctx>(
|
|||
fmt.push_str("%.*s");
|
||||
|
||||
let true_str = ctx.gen_string(generator, "True");
|
||||
let true_data =
|
||||
unsafe { true_str.get_field_at_index_unchecked(0) }.into_pointer_value();
|
||||
let true_len = unsafe { true_str.get_field_at_index_unchecked(1) }.into_int_value();
|
||||
|
||||
let true_data = true_str.get_field(generator, ctx.ctx, |f| f.base);
|
||||
let true_len = true_str.get_field(generator, ctx.ctx, |f| f.len);
|
||||
|
||||
let false_str = ctx.gen_string(generator, "False");
|
||||
let false_data =
|
||||
unsafe { false_str.get_field_at_index_unchecked(0) }.into_pointer_value();
|
||||
let false_len =
|
||||
unsafe { false_str.get_field_at_index_unchecked(1) }.into_int_value();
|
||||
|
||||
let false_data = false_str.get_field(generator, ctx.ctx, |f| f.base);
|
||||
let false_len = false_str.get_field(generator, ctx.ctx, |f| f.len);
|
||||
|
||||
let bool_val = generator.bool_to_i1(ctx, value.into_int_value());
|
||||
|
||||
args.extend([
|
||||
ctx.builder.build_select(bool_val, true_len, false_len, "").unwrap(),
|
||||
ctx.builder.build_select(bool_val, true_data, false_data, "").unwrap(),
|
||||
ctx.builder
|
||||
.build_select(bool_val, true_len.value, false_len.value, "")
|
||||
.unwrap(),
|
||||
ctx.builder
|
||||
.build_select(bool_val, true_data.value, false_data.value, "")
|
||||
.unwrap(),
|
||||
]);
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -16,65 +16,65 @@
|
|||
clippy::wildcard_imports
|
||||
)]
|
||||
|
||||
use std::{
|
||||
collections::{HashMap, HashSet},
|
||||
fs,
|
||||
io::Write,
|
||||
process::Command,
|
||||
rc::Rc,
|
||||
sync::Arc,
|
||||
};
|
||||
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 itertools::Itertools;
|
||||
use parking_lot::{Mutex, RwLock};
|
||||
use pyo3::{
|
||||
create_exception, exceptions,
|
||||
prelude::*,
|
||||
types::{PyBytes, PyDict, PySet},
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
memory_buffer::MemoryBuffer,
|
||||
module::{Linkage, Module},
|
||||
passes::PassBuilderOptions,
|
||||
support::is_multithreaded,
|
||||
targets::*,
|
||||
OptimizationLevel,
|
||||
};
|
||||
use tempfile::{self, TempDir};
|
||||
use itertools::Itertools;
|
||||
use nac3core::codegen::irrt::setup_irrt_exceptions;
|
||||
use nac3core::codegen::{gen_func_impl, CodeGenLLVMOptions, CodeGenTargetMachineOptions};
|
||||
use nac3core::toplevel::builtins::get_exn_constructor;
|
||||
use nac3core::typecheck::typedef::{into_var_map, TypeEnum, Unifier, VarMap};
|
||||
use nac3parser::{
|
||||
ast::{ExprKind, Stmt, StmtKind, StrRef},
|
||||
parser::parse_program,
|
||||
};
|
||||
use pyo3::create_exception;
|
||||
use pyo3::prelude::*;
|
||||
use pyo3::{exceptions, types::PyBytes, types::PyDict, types::PySet};
|
||||
|
||||
use parking_lot::{Mutex, RwLock};
|
||||
|
||||
use nac3core::{
|
||||
codegen::{
|
||||
concrete_type::ConcreteTypeStore, gen_func_impl, irrt::load_irrt, CodeGenLLVMOptions,
|
||||
CodeGenTargetMachineOptions, CodeGenTask, WithCall, WorkerRegistry,
|
||||
},
|
||||
inkwell::{
|
||||
context::Context,
|
||||
memory_buffer::MemoryBuffer,
|
||||
module::{Linkage, Module},
|
||||
passes::PassBuilderOptions,
|
||||
support::is_multithreaded,
|
||||
targets::*,
|
||||
OptimizationLevel,
|
||||
},
|
||||
nac3parser::{
|
||||
ast::{Constant, ExprKind, Located, Stmt, StmtKind, StrRef},
|
||||
parser::parse_program,
|
||||
},
|
||||
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::{
|
||||
type_inferencer::PrimitiveStore,
|
||||
typedef::{into_var_map, FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
|
||||
},
|
||||
typecheck::typedef::{FunSignature, FuncArg},
|
||||
typecheck::{type_inferencer::PrimitiveStore, typedef::Type},
|
||||
};
|
||||
|
||||
use nac3ld::Linker;
|
||||
|
||||
use codegen::{
|
||||
attributes_writeback, gen_core_log, gen_rtio_log, rpc_codegen_callback, ArtiqCodeGenerator,
|
||||
use crate::{
|
||||
codegen::{
|
||||
attributes_writeback, gen_core_log, gen_rtio_log, rpc_codegen_callback, ArtiqCodeGenerator,
|
||||
},
|
||||
symbol_resolver::{DeferredEvaluationStore, InnerResolver, PythonHelper, Resolver},
|
||||
};
|
||||
use symbol_resolver::{DeferredEvaluationStore, InnerResolver, PythonHelper, Resolver};
|
||||
use timeline::TimeFns;
|
||||
use tempfile::{self, TempDir};
|
||||
|
||||
mod codegen;
|
||||
mod symbol_resolver;
|
||||
mod timeline;
|
||||
|
||||
use timeline::TimeFns;
|
||||
|
||||
#[derive(PartialEq, Clone, Copy)]
|
||||
enum Isa {
|
||||
Host,
|
||||
|
|
@ -195,8 +195,10 @@ impl Nac3 {
|
|||
body.retain(|stmt| {
|
||||
if let StmtKind::FunctionDef { ref decorator_list, .. } = stmt.node {
|
||||
decorator_list.iter().any(|decorator| {
|
||||
if let Some(id) = decorator_id_string(decorator) {
|
||||
id == "kernel" || id == "portable" || id == "rpc"
|
||||
if let ExprKind::Name { id, .. } = decorator.node {
|
||||
id.to_string() == "kernel"
|
||||
|| id.to_string() == "portable"
|
||||
|| id.to_string() == "rpc"
|
||||
} else {
|
||||
false
|
||||
}
|
||||
|
|
@ -209,8 +211,9 @@ impl Nac3 {
|
|||
}
|
||||
StmtKind::FunctionDef { ref decorator_list, .. } => {
|
||||
decorator_list.iter().any(|decorator| {
|
||||
if let Some(id) = decorator_id_string(decorator) {
|
||||
id == "extern" || id == "kernel" || id == "portable" || id == "rpc"
|
||||
if let ExprKind::Name { id, .. } = decorator.node {
|
||||
let id = id.to_string();
|
||||
id == "extern" || id == "portable" || id == "kernel" || id == "rpc"
|
||||
} else {
|
||||
false
|
||||
}
|
||||
|
|
@ -446,6 +449,7 @@ impl Nac3 {
|
|||
pyid_to_type: pyid_to_type.clone(),
|
||||
primitive_ids: self.primitive_ids.clone(),
|
||||
global_value_ids: global_value_ids.clone(),
|
||||
class_names: Mutex::default(),
|
||||
name_to_pyid: name_to_pyid.clone(),
|
||||
module: module.clone(),
|
||||
id_to_pyval: RwLock::default(),
|
||||
|
|
@ -476,25 +480,9 @@ impl Nac3 {
|
|||
|
||||
match &stmt.node {
|
||||
StmtKind::FunctionDef { decorator_list, .. } => {
|
||||
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));
|
||||
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));
|
||||
}
|
||||
}
|
||||
StmtKind::ClassDef { name, body, .. } => {
|
||||
|
|
@ -502,26 +490,19 @@ 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| {
|
||||
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 decorator_list.iter().any(|decorator| matches!(decorator.node, ExprKind::Name { id, .. } if id == "rpc".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, is_async));
|
||||
rpc_ids.push((Some((class_obj.clone(), *name)), def_id));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
_ => (),
|
||||
_ => ()
|
||||
}
|
||||
|
||||
let id = *name_to_pyid.get(&name).unwrap();
|
||||
|
|
@ -560,6 +541,7 @@ impl Nac3 {
|
|||
pyid_to_type: pyid_to_type.clone(),
|
||||
primitive_ids: self.primitive_ids.clone(),
|
||||
global_value_ids: global_value_ids.clone(),
|
||||
class_names: Mutex::default(),
|
||||
id_to_pyval: RwLock::default(),
|
||||
id_to_primitive: RwLock::default(),
|
||||
field_to_val: RwLock::default(),
|
||||
|
|
@ -577,8 +559,9 @@ impl Nac3 {
|
|||
.unwrap();
|
||||
|
||||
// Process IRRT
|
||||
let context = Context::create();
|
||||
let irrt = load_irrt(&context, resolver.as_ref());
|
||||
let context = inkwell::context::Context::create();
|
||||
let irrt = load_irrt(&context);
|
||||
setup_irrt_exceptions(&context, &irrt, resolver.as_ref());
|
||||
|
||||
let fun_signature =
|
||||
FunSignature { args: vec![], ret: self.primitive.none, vars: VarMap::new() };
|
||||
|
|
@ -617,12 +600,13 @@ 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, is_async) in &rpc_ids {
|
||||
for (class_data, id) in &rpc_ids {
|
||||
let mut def = defs[id.0].write();
|
||||
match &mut *def {
|
||||
TopLevelDef::Function { codegen_callback, .. } => {
|
||||
*codegen_callback = Some(rpc_codegen_callback(*is_async));
|
||||
*codegen_callback = Some(rpc_codegen.clone());
|
||||
}
|
||||
TopLevelDef::Class { methods, .. } => {
|
||||
let (class_def, method_name) = class_data.as_ref().unwrap();
|
||||
|
|
@ -633,7 +617,7 @@ impl Nac3 {
|
|||
if let TopLevelDef::Function { codegen_callback, .. } =
|
||||
&mut *defs[id.0].write()
|
||||
{
|
||||
*codegen_callback = Some(rpc_codegen_callback(*is_async));
|
||||
*codegen_callback = Some(rpc_codegen.clone());
|
||||
store_fun
|
||||
.call1(
|
||||
py,
|
||||
|
|
@ -648,11 +632,6 @@ impl Nac3 {
|
|||
}
|
||||
}
|
||||
}
|
||||
TopLevelDef::Variable { .. } => {
|
||||
return Err(CompileError::new_err(String::from(
|
||||
"Unsupported @rpc annotation on global variable",
|
||||
)))
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -712,7 +691,7 @@ impl Nac3 {
|
|||
let buffer = buffer.as_slice().into();
|
||||
membuffer.lock().push(buffer);
|
||||
})));
|
||||
let size_t = context
|
||||
let size_t = Context::create()
|
||||
.ptr_sized_int_type(&self.get_llvm_target_machine().get_target_data(), None)
|
||||
.get_bit_width();
|
||||
let num_threads = if is_multithreaded() { 4 } else { 1 };
|
||||
|
|
@ -731,7 +710,7 @@ impl Nac3 {
|
|||
|
||||
let mut generator =
|
||||
ArtiqCodeGenerator::new("attributes_writeback".to_string(), size_t, self.time_fns);
|
||||
let context = Context::create();
|
||||
let context = inkwell::context::Context::create();
|
||||
let module = context.create_module("attributes_writeback");
|
||||
let target_machine = self.llvm_options.create_target_machine().unwrap();
|
||||
module.set_data_layout(&target_machine.get_target_data().get_data_layout());
|
||||
|
|
@ -869,41 +848,6 @@ 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(),
|
||||
|
|
|
|||
|
|
@ -1,30 +1,14 @@
|
|||
use std::{
|
||||
collections::{HashMap, HashSet},
|
||||
sync::{
|
||||
atomic::{AtomicBool, Ordering::Relaxed},
|
||||
Arc,
|
||||
},
|
||||
use inkwell::{
|
||||
types::{BasicType, BasicTypeEnum},
|
||||
values::BasicValueEnum,
|
||||
AddressSpace,
|
||||
};
|
||||
|
||||
use itertools::Itertools;
|
||||
use parking_lot::RwLock;
|
||||
use pyo3::{
|
||||
types::{PyDict, PyTuple},
|
||||
PyAny, PyObject, PyResult, Python,
|
||||
};
|
||||
|
||||
use nac3core::{
|
||||
codegen::{
|
||||
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,
|
||||
|
|
@ -36,8 +20,21 @@ use nac3core::{
|
|||
typedef::{into_var_map, iter_type_vars, Type, TypeEnum, TypeVar, Unifier, VarMap},
|
||||
},
|
||||
};
|
||||
use nac3parser::ast::{self, StrRef};
|
||||
use parking_lot::{Mutex, RwLock};
|
||||
use pyo3::{
|
||||
types::{PyDict, PyTuple},
|
||||
PyAny, PyObject, PyResult, Python,
|
||||
};
|
||||
use std::{
|
||||
collections::{HashMap, HashSet},
|
||||
sync::{
|
||||
atomic::{AtomicBool, Ordering::Relaxed},
|
||||
Arc,
|
||||
},
|
||||
};
|
||||
|
||||
use super::PrimitivePythonId;
|
||||
use crate::PrimitivePythonId;
|
||||
|
||||
pub enum PrimitiveValue {
|
||||
I32(i32),
|
||||
|
|
@ -82,6 +79,7 @@ pub struct InnerResolver {
|
|||
pub id_to_primitive: RwLock<HashMap<u64, PrimitiveValue>>,
|
||||
pub field_to_val: RwLock<HashMap<ResolverField, Option<PyFieldHandle>>>,
|
||||
pub global_value_ids: Arc<RwLock<HashMap<u64, PyObject>>>,
|
||||
pub class_names: Mutex<HashMap<StrRef, Type>>,
|
||||
pub pyid_to_def: Arc<RwLock<HashMap<u64, DefinitionId>>>,
|
||||
pub pyid_to_type: Arc<RwLock<HashMap<u64, Type>>>,
|
||||
pub primitive_ids: PrimitivePythonId,
|
||||
|
|
@ -135,8 +133,6 @@ impl StaticValue for PythonValue {
|
|||
format!("{}_const", self.id).as_str(),
|
||||
);
|
||||
global.set_constant(true);
|
||||
// Set linkage of global to private to avoid name collisions
|
||||
global.set_linkage(Linkage::Private);
|
||||
global.set_initializer(&ctx.ctx.const_struct(
|
||||
&[ctx.ctx.i32_type().const_int(u64::from(id), false).into()],
|
||||
false,
|
||||
|
|
@ -167,7 +163,7 @@ impl StaticValue for PythonValue {
|
|||
PrimitiveValue::Bool(val) => {
|
||||
ctx.ctx.i8_type().const_int(u64::from(*val), false).into()
|
||||
}
|
||||
PrimitiveValue::Str(val) => ctx.gen_string(generator, val).into(),
|
||||
PrimitiveValue::Str(val) => ctx.ctx.const_string(val.as_bytes(), true).into(),
|
||||
});
|
||||
}
|
||||
if let Some(global) = ctx.module.get_global(&self.id.to_string()) {
|
||||
|
|
@ -981,7 +977,7 @@ impl InnerResolver {
|
|||
} else if ty_id == self.primitive_ids.string || ty_id == self.primitive_ids.np_str_ {
|
||||
let val: String = obj.extract().unwrap();
|
||||
self.id_to_primitive.write().insert(id, PrimitiveValue::Str(val.clone()));
|
||||
Ok(Some(ctx.gen_string(generator, val).into()))
|
||||
Ok(Some(ctx.ctx.const_string(val.as_bytes(), true).into()))
|
||||
} else if ty_id == self.primitive_ids.float || ty_id == self.primitive_ids.float64 {
|
||||
let val: f64 = obj.extract().unwrap();
|
||||
self.id_to_primitive.write().insert(id, PrimitiveValue::F64(val));
|
||||
|
|
@ -1470,7 +1466,6 @@ 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();
|
||||
|
|
|
|||
|
|
@ -1,12 +1,9 @@
|
|||
use itertools::Either;
|
||||
|
||||
use nac3core::{
|
||||
codegen::CodeGenContext,
|
||||
inkwell::{
|
||||
values::{BasicValueEnum, CallSiteValue},
|
||||
AddressSpace, AtomicOrdering,
|
||||
},
|
||||
use inkwell::{
|
||||
values::{BasicValueEnum, CallSiteValue},
|
||||
AddressSpace, AtomicOrdering,
|
||||
};
|
||||
use itertools::Either;
|
||||
use nac3core::codegen::CodeGenContext;
|
||||
|
||||
/// Functions for manipulating the timeline.
|
||||
pub trait TimeFns {
|
||||
|
|
@ -34,7 +31,7 @@ impl TimeFns for NowPinningTimeFns64 {
|
|||
.unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
|
||||
let now_hiptr = ctx
|
||||
.builder
|
||||
.build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
|
||||
.build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap();
|
||||
|
||||
|
|
@ -83,7 +80,7 @@ impl TimeFns for NowPinningTimeFns64 {
|
|||
.unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
|
||||
let now_hiptr = ctx
|
||||
.builder
|
||||
.build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
|
||||
.build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap();
|
||||
|
||||
|
|
@ -112,7 +109,7 @@ impl TimeFns for NowPinningTimeFns64 {
|
|||
.unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
|
||||
let now_hiptr = ctx
|
||||
.builder
|
||||
.build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
|
||||
.build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap();
|
||||
|
||||
|
|
@ -210,7 +207,7 @@ impl TimeFns for NowPinningTimeFns {
|
|||
.unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
|
||||
let now_hiptr = ctx
|
||||
.builder
|
||||
.build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
|
||||
.build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap();
|
||||
|
||||
|
|
@ -261,7 +258,7 @@ impl TimeFns for NowPinningTimeFns {
|
|||
let time_lo = ctx.builder.build_int_truncate(time, i32_type, "time.lo").unwrap();
|
||||
let now_hiptr = ctx
|
||||
.builder
|
||||
.build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
|
||||
.build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap();
|
||||
|
||||
|
|
|
|||
|
|
@ -10,6 +10,7 @@ constant-optimization = ["fold"]
|
|||
fold = []
|
||||
|
||||
[dependencies]
|
||||
lazy_static = "1.5"
|
||||
parking_lot = "0.12"
|
||||
string-interner = "0.17"
|
||||
fxhash = "0.2"
|
||||
|
|
|
|||
|
|
@ -5,12 +5,14 @@ pub use crate::location::Location;
|
|||
|
||||
use fxhash::FxBuildHasher;
|
||||
use parking_lot::{Mutex, MutexGuard};
|
||||
use std::{cell::RefCell, collections::HashMap, fmt, sync::LazyLock};
|
||||
use std::{cell::RefCell, collections::HashMap, fmt};
|
||||
use string_interner::{symbol::SymbolU32, DefaultBackend, StringInterner};
|
||||
|
||||
pub type Interner = StringInterner<DefaultBackend, FxBuildHasher>;
|
||||
static INTERNER: LazyLock<Mutex<Interner>> =
|
||||
LazyLock::new(|| Mutex::new(StringInterner::with_hasher(FxBuildHasher::default())));
|
||||
lazy_static! {
|
||||
static ref INTERNER: Mutex<Interner> =
|
||||
Mutex::new(StringInterner::with_hasher(FxBuildHasher::default()));
|
||||
}
|
||||
|
||||
thread_local! {
|
||||
static LOCAL_INTERNER: RefCell<HashMap<String, StrRef>> = RefCell::default();
|
||||
|
|
|
|||
|
|
@ -14,6 +14,9 @@
|
|||
clippy::wildcard_imports
|
||||
)]
|
||||
|
||||
#[macro_use]
|
||||
extern crate lazy_static;
|
||||
|
||||
mod ast_gen;
|
||||
mod constant;
|
||||
#[cfg(feature = "fold")]
|
||||
|
|
|
|||
|
|
@ -1,26 +1,26 @@
|
|||
[features]
|
||||
test = []
|
||||
|
||||
[package]
|
||||
name = "nac3core"
|
||||
version = "0.1.0"
|
||||
authors = ["M-Labs"]
|
||||
edition = "2021"
|
||||
|
||||
[features]
|
||||
no-escape-analysis = []
|
||||
|
||||
[dependencies]
|
||||
itertools = "0.13"
|
||||
crossbeam = "0.8"
|
||||
indexmap = "2.6"
|
||||
indexmap = "2.2"
|
||||
parking_lot = "0.12"
|
||||
rayon = "1.10"
|
||||
rayon = "1.8"
|
||||
nac3parser = { path = "../nac3parser" }
|
||||
strum = "0.26"
|
||||
strum_macros = "0.26"
|
||||
strum = "0.26.2"
|
||||
strum_macros = "0.26.4"
|
||||
|
||||
[dependencies.inkwell]
|
||||
version = "0.5"
|
||||
version = "0.4"
|
||||
default-features = false
|
||||
features = ["llvm14-0-prefer-dynamic", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]
|
||||
features = ["llvm14-0", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]
|
||||
|
||||
[dev-dependencies]
|
||||
test-case = "1.2.0"
|
||||
|
|
|
|||
|
|
@ -1,59 +1,62 @@
|
|||
use regex::Regex;
|
||||
use std::{
|
||||
env,
|
||||
fs::File,
|
||||
io::Write,
|
||||
path::Path,
|
||||
path::{Path, PathBuf},
|
||||
process::{Command, Stdio},
|
||||
};
|
||||
|
||||
use regex::Regex;
|
||||
const CMD_IRRT_CLANG: &str = "clang-irrt";
|
||||
const CMD_IRRT_CLANG_TEST: &str = "clang-irrt-test";
|
||||
const CMD_IRRT_LLVM_AS: &str = "llvm-as-irrt";
|
||||
|
||||
fn main() {
|
||||
let out_dir = env::var("OUT_DIR").unwrap();
|
||||
let out_dir = Path::new(&out_dir);
|
||||
let irrt_dir = Path::new("irrt");
|
||||
fn get_out_dir() -> PathBuf {
|
||||
PathBuf::from(env::var("OUT_DIR").unwrap())
|
||||
}
|
||||
|
||||
let irrt_cpp_path = irrt_dir.join("irrt.cpp");
|
||||
fn get_irrt_dir() -> &'static Path {
|
||||
Path::new("irrt")
|
||||
}
|
||||
|
||||
/// Compile `irrt.cpp` for use in `src/codegen`
|
||||
fn compile_irrt_cpp() {
|
||||
let out_dir = get_out_dir();
|
||||
let irrt_dir = get_irrt_dir();
|
||||
|
||||
/*
|
||||
* HACK: Sadly, clang doesn't let us emit generic LLVM bitcode.
|
||||
* Compiling for WASM32 and filtering the output with regex is the closest we can get.
|
||||
*/
|
||||
let mut flags: Vec<&str> = vec![
|
||||
"--target=wasm32",
|
||||
"-x",
|
||||
"c++",
|
||||
"-std=c++20",
|
||||
"-fno-discard-value-names",
|
||||
"-fno-exceptions",
|
||||
"-fno-rtti",
|
||||
"-emit-llvm",
|
||||
"-S",
|
||||
"-Wall",
|
||||
"-Wextra",
|
||||
"-o",
|
||||
"-",
|
||||
"-I",
|
||||
irrt_dir.to_str().unwrap(),
|
||||
irrt_cpp_path.to_str().unwrap(),
|
||||
];
|
||||
let irrt_cpp_path = irrt_dir.join("irrt.cpp");
|
||||
|
||||
let mut flags = vec![];
|
||||
flags.push("--target=wasm32");
|
||||
flags.extend(&["-x", "c++"]);
|
||||
flags.extend(&["-fno-discard-value-names", "-fno-exceptions", "-fno-rtti"]);
|
||||
flags.push("-emit-llvm");
|
||||
flags.push("-S");
|
||||
flags.extend(&["-Wall", "-Wextra"]);
|
||||
flags.extend(&["-o", "-"]);
|
||||
flags.extend(&["-I", irrt_dir.to_str().unwrap()]);
|
||||
flags.push(irrt_cpp_path.to_str().unwrap());
|
||||
|
||||
match env::var("PROFILE").as_deref() {
|
||||
Ok("debug") => {
|
||||
flags.push("-O0");
|
||||
flags.push("-DIRRT_DEBUG_ASSERT");
|
||||
flags.push("-DIRRT_DEBUG");
|
||||
}
|
||||
Ok("release") => {
|
||||
flags.push("-O3");
|
||||
}
|
||||
flavor => panic!("Unknown or missing build flavor {flavor:?}"),
|
||||
}
|
||||
};
|
||||
|
||||
// Tell Cargo to rerun if any file under `irrt_dir` (recursive) changes
|
||||
println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
|
||||
|
||||
// Compile IRRT and capture the LLVM IR output
|
||||
let output = Command::new("clang-irrt")
|
||||
let output = Command::new(CMD_IRRT_CLANG)
|
||||
.args(flags)
|
||||
.output()
|
||||
.map(|o| {
|
||||
|
|
@ -99,7 +102,9 @@ fn main() {
|
|||
file.write_all(filtered_output.as_bytes()).unwrap();
|
||||
}
|
||||
|
||||
let mut llvm_as = Command::new("llvm-as-irrt")
|
||||
// Assemble the emitted and filtered IR to .bc
|
||||
// That .bc will be integrated into nac3core's codegen
|
||||
let mut llvm_as = Command::new(CMD_IRRT_LLVM_AS)
|
||||
.stdin(Stdio::piped())
|
||||
.arg("-o")
|
||||
.arg(out_dir.join("irrt.bc"))
|
||||
|
|
@ -108,3 +113,48 @@ fn main() {
|
|||
llvm_as.stdin.as_mut().unwrap().write_all(filtered_output.as_bytes()).unwrap();
|
||||
assert!(llvm_as.wait().unwrap().success());
|
||||
}
|
||||
|
||||
/// Compile `irrt_test.cpp` for testing
|
||||
fn compile_irrt_test_cpp() {
|
||||
let out_dir = get_out_dir();
|
||||
let irrt_dir = get_irrt_dir();
|
||||
|
||||
let exe_path = out_dir.join("irrt_test.out"); // Output path of the compiled test executable
|
||||
let irrt_test_cpp_path = irrt_dir.join("irrt_test.cpp");
|
||||
let flags: &[&str] = &[
|
||||
irrt_test_cpp_path.to_str().unwrap(),
|
||||
"-x",
|
||||
"c++",
|
||||
"-I",
|
||||
irrt_dir.to_str().unwrap(),
|
||||
"-g",
|
||||
"-fno-discard-value-names",
|
||||
"-O0",
|
||||
"-Wall",
|
||||
"-Wextra",
|
||||
"-Werror=return-type",
|
||||
"-lm", // for `tgamma()`, `lgamma()`
|
||||
"-o",
|
||||
exe_path.to_str().unwrap(),
|
||||
];
|
||||
|
||||
Command::new(CMD_IRRT_CLANG_TEST)
|
||||
.args(flags)
|
||||
.output()
|
||||
.map(|o| {
|
||||
assert!(o.status.success(), "{}", std::str::from_utf8(&o.stderr).unwrap());
|
||||
o
|
||||
})
|
||||
.unwrap();
|
||||
println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
|
||||
}
|
||||
|
||||
fn main() {
|
||||
compile_irrt_cpp();
|
||||
|
||||
// https://github.com/rust-lang/cargo/issues/2549
|
||||
// `cargo test -F test` to also build `irrt_test.cpp
|
||||
if cfg!(feature = "test") {
|
||||
compile_irrt_test_cpp();
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,6 +1,10 @@
|
|||
#include "irrt/exception.hpp"
|
||||
#include "irrt/int_types.hpp"
|
||||
#include "irrt/list.hpp"
|
||||
#include "irrt/math.hpp"
|
||||
#include "irrt/ndarray.hpp"
|
||||
#include "irrt/slice.hpp"
|
||||
#define IRRT_DEFINE_TYPEDEF_INTS
|
||||
#include <irrt_everything.hpp>
|
||||
|
||||
/*
|
||||
* All IRRT implementations.
|
||||
*
|
||||
* We don't have pre-compiled objects, so we are writing all implementations in
|
||||
* headers and concatenate them with `#include` into one massive source file that
|
||||
* contains all the IRRT stuff.
|
||||
*/
|
||||
|
|
@ -0,0 +1,349 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/util.hpp>
|
||||
|
||||
// NDArray indices are always `uint32_t`.
|
||||
using NDIndexInt = uint32_t;
|
||||
// The type of an index or a value describing the length of a
|
||||
// range/slice is always `int32_t`.
|
||||
using SliceIndex = int32_t;
|
||||
|
||||
namespace {
|
||||
// adapted from GNU Scientific Library:
|
||||
// https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
|
||||
// need to make sure `exp >= 0` before calling this function
|
||||
template <typename T>
|
||||
T __nac3_int_exp_impl(T base, T exp) {
|
||||
T res = 1;
|
||||
/* repeated squaring method */
|
||||
do {
|
||||
if (exp & 1) {
|
||||
res *= base; /* for n odd */
|
||||
}
|
||||
exp >>= 1;
|
||||
base *= base;
|
||||
} while (exp);
|
||||
return res;
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len,
|
||||
SizeT begin_idx, SizeT end_idx) {
|
||||
__builtin_assume(end_idx <= list_len);
|
||||
|
||||
SizeT num_elems = 1;
|
||||
for (SizeT i = begin_idx; i < end_idx; ++i) {
|
||||
SizeT val = list_data[i];
|
||||
__builtin_assume(val > 0);
|
||||
num_elems *= val;
|
||||
}
|
||||
return num_elems;
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims,
|
||||
SizeT num_dims, NDIndexInt* idxs) {
|
||||
SizeT stride = 1;
|
||||
for (SizeT dim = 0; dim < num_dims; dim++) {
|
||||
SizeT i = num_dims - dim - 1;
|
||||
__builtin_assume(dims[i] > 0);
|
||||
idxs[i] = (index / stride) % dims[i];
|
||||
stride *= dims[i];
|
||||
}
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims,
|
||||
const NDIndexInt* indices,
|
||||
SizeT num_indices) {
|
||||
SizeT idx = 0;
|
||||
SizeT stride = 1;
|
||||
for (SizeT i = 0; i < num_dims; ++i) {
|
||||
SizeT ri = num_dims - i - 1;
|
||||
if (ri < num_indices) {
|
||||
idx += stride * indices[ri];
|
||||
}
|
||||
|
||||
__builtin_assume(dims[i] > 0);
|
||||
stride *= dims[ri];
|
||||
}
|
||||
return idx;
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims, SizeT lhs_ndims,
|
||||
const SizeT* rhs_dims, SizeT rhs_ndims,
|
||||
SizeT* out_dims) {
|
||||
SizeT max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
|
||||
|
||||
for (SizeT i = 0; i < max_ndims; ++i) {
|
||||
const SizeT* lhs_dim_sz =
|
||||
i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : nullptr;
|
||||
const SizeT* rhs_dim_sz =
|
||||
i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : nullptr;
|
||||
|
||||
SizeT* out_dim = &out_dims[max_ndims - i - 1];
|
||||
|
||||
if (lhs_dim_sz == nullptr) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (rhs_dim_sz == nullptr) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == 1) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (*rhs_dim_sz == 1) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == *rhs_dim_sz) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename SizeT>
|
||||
void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
|
||||
SizeT src_ndims,
|
||||
const NDIndexInt* in_idx,
|
||||
NDIndexInt* out_idx) {
|
||||
for (SizeT i = 0; i < src_ndims; ++i) {
|
||||
SizeT src_i = src_ndims - i - 1;
|
||||
out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
|
||||
}
|
||||
}
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
#define DEF_nac3_int_exp_(T) \
|
||||
T __nac3_int_exp_##T(T base, T exp) { \
|
||||
return __nac3_int_exp_impl(base, exp); \
|
||||
}
|
||||
|
||||
DEF_nac3_int_exp_(int32_t);
|
||||
DEF_nac3_int_exp_(int64_t);
|
||||
DEF_nac3_int_exp_(uint32_t);
|
||||
DEF_nac3_int_exp_(uint64_t);
|
||||
|
||||
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
|
||||
if (i < 0) {
|
||||
i = len + i;
|
||||
}
|
||||
if (i < 0) {
|
||||
return 0;
|
||||
} else if (i > len) {
|
||||
return len;
|
||||
}
|
||||
return i;
|
||||
}
|
||||
|
||||
SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end,
|
||||
const SliceIndex step) {
|
||||
SliceIndex diff = end - start;
|
||||
if (diff > 0 && step > 0) {
|
||||
return ((diff - 1) / step) + 1;
|
||||
} else if (diff < 0 && step < 0) {
|
||||
return ((diff + 1) / step) + 1;
|
||||
} else {
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
||||
// Handle list assignment and dropping part of the list when
|
||||
// both dest_step and src_step are +1.
|
||||
// - All the index must *not* be out-of-bound or negative,
|
||||
// - The end index is *inclusive*,
|
||||
// - The length of src and dest slice size should already
|
||||
// be checked: if dest.step == 1 then len(src) <= len(dest) else
|
||||
// len(src) == len(dest)
|
||||
SliceIndex __nac3_list_slice_assign_var_size(
|
||||
SliceIndex dest_start, SliceIndex dest_end, SliceIndex dest_step,
|
||||
uint8_t* dest_arr, SliceIndex dest_arr_len, SliceIndex src_start,
|
||||
SliceIndex src_end, SliceIndex src_step, uint8_t* src_arr,
|
||||
SliceIndex src_arr_len, const SliceIndex size) {
|
||||
/* if dest_arr_len == 0, do nothing since we do not support
|
||||
* extending list
|
||||
*/
|
||||
if (dest_arr_len == 0) return dest_arr_len;
|
||||
/* if both step is 1, memmove directly, handle the dropping of
|
||||
* the list, and shrink size */
|
||||
if (src_step == dest_step && dest_step == 1) {
|
||||
const SliceIndex src_len =
|
||||
(src_end >= src_start) ? (src_end - src_start + 1) : 0;
|
||||
const SliceIndex dest_len =
|
||||
(dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
|
||||
if (src_len > 0) {
|
||||
__builtin_memmove(dest_arr + dest_start * size,
|
||||
src_arr + src_start * size, src_len * size);
|
||||
}
|
||||
if (dest_len > 0) {
|
||||
/* dropping */
|
||||
__builtin_memmove(dest_arr + (dest_start + src_len) * size,
|
||||
dest_arr + (dest_end + 1) * size,
|
||||
(dest_arr_len - dest_end - 1) * size);
|
||||
}
|
||||
/* shrink size */
|
||||
return dest_arr_len - (dest_len - src_len);
|
||||
}
|
||||
/* if two range overlaps, need alloca */
|
||||
uint8_t need_alloca =
|
||||
(dest_arr == src_arr) &&
|
||||
!(max(dest_start, dest_end) < min(src_start, src_end) ||
|
||||
max(src_start, src_end) < min(dest_start, dest_end));
|
||||
if (need_alloca) {
|
||||
uint8_t* tmp =
|
||||
reinterpret_cast<uint8_t*>(__builtin_alloca(src_arr_len * size));
|
||||
__builtin_memcpy(tmp, src_arr, src_arr_len * size);
|
||||
src_arr = tmp;
|
||||
}
|
||||
SliceIndex src_ind = src_start;
|
||||
SliceIndex dest_ind = dest_start;
|
||||
for (; (src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end);
|
||||
src_ind += src_step, dest_ind += dest_step) {
|
||||
/* for constant optimization */
|
||||
if (size == 1) {
|
||||
__builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
|
||||
} else if (size == 4) {
|
||||
__builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
|
||||
} else if (size == 8) {
|
||||
__builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
|
||||
} else {
|
||||
/* memcpy for var size, cannot overlap after previous
|
||||
* alloca */
|
||||
__builtin_memcpy(dest_arr + dest_ind * size,
|
||||
src_arr + src_ind * size, size);
|
||||
}
|
||||
}
|
||||
/* only dest_step == 1 can we shrink the dest list. */
|
||||
/* size should be ensured prior to calling this function */
|
||||
if (dest_step == 1 && dest_end >= dest_start) {
|
||||
__builtin_memmove(
|
||||
dest_arr + dest_ind * size, dest_arr + (dest_end + 1) * size,
|
||||
(dest_arr_len - dest_end - 1) * size + size + size + size);
|
||||
return dest_arr_len - (dest_end - dest_ind) - 1;
|
||||
}
|
||||
return dest_arr_len;
|
||||
}
|
||||
|
||||
int32_t __nac3_isinf(double x) { return __builtin_isinf(x); }
|
||||
|
||||
int32_t __nac3_isnan(double x) { return __builtin_isnan(x); }
|
||||
|
||||
double tgamma(double arg);
|
||||
|
||||
double __nac3_gamma(double z) {
|
||||
// Handling for denormals
|
||||
// | x | Python gamma(x) | C tgamma(x) |
|
||||
// --- | ----------------- | --------------- | ----------- |
|
||||
// (1) | nan | nan | nan |
|
||||
// (2) | -inf | -inf | inf |
|
||||
// (3) | inf | inf | inf |
|
||||
// (4) | 0.0 | inf | inf |
|
||||
// (5) | {-1.0, -2.0, ...} | inf | nan |
|
||||
|
||||
// (1)-(3)
|
||||
if (__builtin_isinf(z) || __builtin_isnan(z)) {
|
||||
return z;
|
||||
}
|
||||
|
||||
double v = tgamma(z);
|
||||
|
||||
// (4)-(5)
|
||||
return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
|
||||
}
|
||||
|
||||
double lgamma(double arg);
|
||||
|
||||
double __nac3_gammaln(double x) {
|
||||
// libm's handling of value overflows differs from scipy:
|
||||
// - scipy: gammaln(-inf) -> -inf
|
||||
// - libm : lgamma(-inf) -> inf
|
||||
|
||||
if (__builtin_isinf(x)) {
|
||||
return x;
|
||||
}
|
||||
|
||||
return lgamma(x);
|
||||
}
|
||||
|
||||
double j0(double x);
|
||||
|
||||
double __nac3_j0(double x) {
|
||||
// libm's handling of value overflows differs from scipy:
|
||||
// - scipy: j0(inf) -> nan
|
||||
// - libm : j0(inf) -> 0.0
|
||||
|
||||
if (__builtin_isinf(x)) {
|
||||
return __builtin_nan("");
|
||||
}
|
||||
|
||||
return j0(x);
|
||||
}
|
||||
|
||||
uint32_t __nac3_ndarray_calc_size(const uint32_t* list_data, uint32_t list_len,
|
||||
uint32_t begin_idx, uint32_t end_idx) {
|
||||
return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx,
|
||||
end_idx);
|
||||
}
|
||||
|
||||
uint64_t __nac3_ndarray_calc_size64(const uint64_t* list_data,
|
||||
uint64_t list_len, uint64_t begin_idx,
|
||||
uint64_t end_idx) {
|
||||
return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx,
|
||||
end_idx);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_nd_indices(uint32_t index, const uint32_t* dims,
|
||||
uint32_t num_dims, NDIndexInt* idxs) {
|
||||
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_nd_indices64(uint64_t index, const uint64_t* dims,
|
||||
uint64_t num_dims, NDIndexInt* idxs) {
|
||||
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
|
||||
}
|
||||
|
||||
uint32_t __nac3_ndarray_flatten_index(const uint32_t* dims, uint32_t num_dims,
|
||||
const NDIndexInt* indices,
|
||||
uint32_t num_indices) {
|
||||
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices,
|
||||
num_indices);
|
||||
}
|
||||
|
||||
uint64_t __nac3_ndarray_flatten_index64(const uint64_t* dims, uint64_t num_dims,
|
||||
const NDIndexInt* indices,
|
||||
uint64_t num_indices) {
|
||||
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices,
|
||||
num_indices);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast(const uint32_t* lhs_dims, uint32_t lhs_ndims,
|
||||
const uint32_t* rhs_dims, uint32_t rhs_ndims,
|
||||
uint32_t* out_dims) {
|
||||
return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims,
|
||||
rhs_ndims, out_dims);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast64(const uint64_t* lhs_dims,
|
||||
uint64_t lhs_ndims,
|
||||
const uint64_t* rhs_dims,
|
||||
uint64_t rhs_ndims, uint64_t* out_dims) {
|
||||
return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims,
|
||||
rhs_ndims, out_dims);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast_idx(const uint32_t* src_dims,
|
||||
uint32_t src_ndims,
|
||||
const NDIndexInt* in_idx,
|
||||
NDIndexInt* out_idx) {
|
||||
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
|
||||
out_idx);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast_idx64(const uint64_t* src_dims,
|
||||
uint64_t src_ndims,
|
||||
const NDIndexInt* in_idx,
|
||||
NDIndexInt* out_idx) {
|
||||
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
|
||||
out_idx);
|
||||
}
|
||||
} // extern "C"
|
||||
|
|
@ -1,8 +1,8 @@
|
|||
#pragma once
|
||||
|
||||
#include "irrt/int_types.hpp"
|
||||
#include <irrt/int_defs.hpp>
|
||||
|
||||
template<typename SizeT>
|
||||
template <typename SizeT>
|
||||
struct CSlice {
|
||||
uint8_t* base;
|
||||
SizeT len;
|
||||
|
|
|
|||
|
|
@ -1,25 +1,15 @@
|
|||
#pragma once
|
||||
|
||||
// Set in nac3core/build.rs
|
||||
#ifdef IRRT_DEBUG_ASSERT
|
||||
#define IRRT_DEBUG_ASSERT_BOOL true
|
||||
#else
|
||||
#define IRRT_DEBUG_ASSERT_BOOL false
|
||||
#endif
|
||||
|
||||
#define raise_debug_assert(SizeT, msg, param1, param2, param3) \
|
||||
raise_exception(SizeT, EXN_ASSERTION_ERROR, "IRRT debug assert failed: " msg, param1, param2, param3)
|
||||
raise_exception(SizeT, EXN_ASSERTION_ERROR, \
|
||||
"IRRT debug assert failed: " msg, param1, param2, param3);
|
||||
|
||||
#define debug_assert_eq(SizeT, lhs, rhs) \
|
||||
if constexpr (IRRT_DEBUG_ASSERT_BOOL) { \
|
||||
if ((lhs) != (rhs)) { \
|
||||
raise_debug_assert(SizeT, "LHS = {0}. RHS = {1}", lhs, rhs, NO_PARAM); \
|
||||
} \
|
||||
#define debug_assert_eq(SizeT, lhs, rhs) \
|
||||
if (IRRT_DEBUG_ASSERT_BOOL && (lhs) != (rhs)) { \
|
||||
raise_debug_assert(SizeT, "LHS = {0}. RHS = {1}", lhs, rhs, NO_PARAM); \
|
||||
}
|
||||
|
||||
#define debug_assert(SizeT, expr) \
|
||||
if constexpr (IRRT_DEBUG_ASSERT_BOOL) { \
|
||||
if (!(expr)) { \
|
||||
raise_debug_assert(SizeT, "Got false.", NO_PARAM, NO_PARAM, NO_PARAM); \
|
||||
} \
|
||||
#define debug_assert(SizeT, expr) \
|
||||
if (IRRT_DEBUG_ASSERT_BOOL && !(expr)) { \
|
||||
raise_debug_assert(SizeT, "Got false.", NO_PARAM, NO_PARAM, NO_PARAM); \
|
||||
}
|
||||
|
|
@ -1,37 +1,43 @@
|
|||
#pragma once
|
||||
|
||||
#include "irrt/cslice.hpp"
|
||||
#include "irrt/int_types.hpp"
|
||||
#include <irrt/cslice.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/util.hpp>
|
||||
|
||||
/**
|
||||
* @brief The int type of ARTIQ exception IDs.
|
||||
*
|
||||
* It is always `int32_t`
|
||||
*/
|
||||
typedef int32_t ExceptionId;
|
||||
|
||||
/*
|
||||
* Set of exceptions C++ IRRT can use.
|
||||
* A set of exceptions IRRT can use.
|
||||
* Must be synchronized with `setup_irrt_exceptions` in `nac3core/src/codegen/irrt/mod.rs`.
|
||||
* All exception IDs are initialized by `setup_irrt_exceptions`.
|
||||
*/
|
||||
#ifdef IRRT_TESTING
|
||||
// If we are doing IRRT tests (i.e., running `cargo test -F test`), define them with a fake set of IDs.
|
||||
ExceptionId EXN_INDEX_ERROR = 0;
|
||||
ExceptionId EXN_VALUE_ERROR = 1;
|
||||
ExceptionId EXN_ASSERTION_ERROR = 2;
|
||||
ExceptionId EXN_RUNTIME_ERROR = 3;
|
||||
ExceptionId EXN_TYPE_ERROR = 4;
|
||||
#else
|
||||
extern "C" {
|
||||
ExceptionId EXN_INDEX_ERROR;
|
||||
ExceptionId EXN_VALUE_ERROR;
|
||||
ExceptionId EXN_ASSERTION_ERROR;
|
||||
ExceptionId EXN_RUNTIME_ERROR;
|
||||
ExceptionId EXN_TYPE_ERROR;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Extern function to `__nac3_raise`
|
||||
*
|
||||
* The parameter `err` could be `Exception<int32_t>` or `Exception<int64_t>`. The caller
|
||||
* must make sure to pass `Exception`s with the correct `SizeT` depending on the `size_t` of the runtime.
|
||||
*/
|
||||
extern "C" void __nac3_raise(void* err);
|
||||
#endif
|
||||
|
||||
namespace {
|
||||
/**
|
||||
* @brief NAC3's Exception struct
|
||||
*/
|
||||
template<typename SizeT>
|
||||
template <typename SizeT>
|
||||
struct Exception {
|
||||
ExceptionId id;
|
||||
CSlice<SizeT> filename;
|
||||
|
|
@ -41,30 +47,49 @@ struct Exception {
|
|||
CSlice<SizeT> msg;
|
||||
int64_t params[3];
|
||||
};
|
||||
} // namespace
|
||||
|
||||
constexpr int64_t NO_PARAM = 0;
|
||||
// Declare/Define `__nac3_raise`
|
||||
#ifdef IRRT_TESTING
|
||||
#include <cstdio>
|
||||
void __nac3_raise(void* err) {
|
||||
// TODO: Print the error content?
|
||||
printf("__nac3_raise called. Exiting...\n");
|
||||
exit(1);
|
||||
}
|
||||
#else
|
||||
/**
|
||||
* @brief Extern function to `__nac3_raise`
|
||||
*
|
||||
* The parameter `err` could be `Exception<int32_t>` or `Exception<int64_t>`. The caller
|
||||
* must make sure to pass `Exception`s with the correct `SizeT` depending on the `size_t` of the runtime.
|
||||
*/
|
||||
extern "C" void __nac3_raise(void* err);
|
||||
#endif
|
||||
|
||||
template<typename SizeT>
|
||||
void _raise_exception_helper(ExceptionId id,
|
||||
const char* filename,
|
||||
int32_t line,
|
||||
const char* function,
|
||||
const char* msg,
|
||||
int64_t param0,
|
||||
int64_t param1,
|
||||
int64_t param2) {
|
||||
namespace {
|
||||
const int64_t NO_PARAM = 0;
|
||||
|
||||
// Helper function to raise an exception with `__nac3_raise`
|
||||
// Do not use this function directly. See `raise_exception`.
|
||||
template <typename SizeT>
|
||||
void _raise_exception_helper(ExceptionId id, const char* filename, int32_t line,
|
||||
const char* function, const char* msg,
|
||||
int64_t param0, int64_t param1, int64_t param2) {
|
||||
Exception<SizeT> e = {
|
||||
.id = id,
|
||||
.filename = {.base = reinterpret_cast<const uint8_t*>(filename), .len = __builtin_strlen(filename)},
|
||||
.filename = {.base = (uint8_t*)filename,
|
||||
.len = (int32_t)cstr_utils::length(filename)},
|
||||
.line = line,
|
||||
.column = 0,
|
||||
.function = {.base = reinterpret_cast<const uint8_t*>(function), .len = __builtin_strlen(function)},
|
||||
.msg = {.base = reinterpret_cast<const uint8_t*>(msg), .len = __builtin_strlen(msg)},
|
||||
.function = {.base = (uint8_t*)function,
|
||||
.len = (int32_t)cstr_utils::length(function)},
|
||||
.msg = {.base = (uint8_t*)msg, .len = (int32_t)cstr_utils::length(msg)},
|
||||
};
|
||||
e.params[0] = param0;
|
||||
e.params[1] = param1;
|
||||
e.params[2] = param2;
|
||||
__nac3_raise(reinterpret_cast<void*>(&e));
|
||||
__nac3_raise((void*)&e);
|
||||
__builtin_unreachable();
|
||||
}
|
||||
|
||||
|
|
@ -74,9 +99,25 @@ void _raise_exception_helper(ExceptionId id,
|
|||
* @param id The ID of the exception to raise.
|
||||
* @param msg A global constant C-string of the error message.
|
||||
*
|
||||
* `param0` to `param2` are optional format arguments of `msg`. They should be set to
|
||||
* `param0` and `param2` are optional format arguments of `msg`. They should be set to
|
||||
* `NO_PARAM` to indicate they are unused.
|
||||
*/
|
||||
#define raise_exception(SizeT, id, msg, param0, param1, param2) \
|
||||
_raise_exception_helper<SizeT>(id, __FILE__, __LINE__, __FUNCTION__, msg, param0, param1, param2)
|
||||
} // namespace
|
||||
#define raise_exception(SizeT, id, msg, param0, param1, param2) \
|
||||
_raise_exception_helper<SizeT>(id, __FILE__, __LINE__, __FUNCTION__, msg, \
|
||||
param0, param1, param2)
|
||||
|
||||
/**
|
||||
* @brief Throw a dummy error for testing.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void throw_dummy_error() {
|
||||
raise_exception(SizeT, EXN_RUNTIME_ERROR, "dummy error", NO_PARAM, NO_PARAM,
|
||||
NO_PARAM);
|
||||
}
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
void __nac3_throw_dummy_error() { throw_dummy_error<int32_t>(); }
|
||||
|
||||
void __nac3_throw_dummy_error64() { throw_dummy_error<int64_t>(); }
|
||||
}
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
#pragma once
|
||||
|
||||
// This is made toggleable since `irrt_test.cpp` itself would include
|
||||
// headers that define these typedefs
|
||||
#ifdef IRRT_DEFINE_TYPEDEF_INTS
|
||||
using int8_t = _BitInt(8);
|
||||
using uint8_t = unsigned _BitInt(8);
|
||||
using int32_t = _BitInt(32);
|
||||
using uint32_t = unsigned _BitInt(32);
|
||||
using int64_t = _BitInt(64);
|
||||
using uint64_t = unsigned _BitInt(64);
|
||||
#endif
|
||||
|
|
@ -1,22 +0,0 @@
|
|||
#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 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;
|
||||
|
|
@ -1,75 +1,56 @@
|
|||
#pragma once
|
||||
|
||||
#include "irrt/int_types.hpp"
|
||||
#include "irrt/math_util.hpp"
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
|
||||
namespace {
|
||||
/**
|
||||
* @brief A list in NAC3.
|
||||
*
|
||||
* The `items` field is opaque. You must rely on external contexts to
|
||||
* know how to interpret it.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
struct List {
|
||||
uint8_t* items;
|
||||
SizeT len;
|
||||
};
|
||||
|
||||
namespace list {
|
||||
template <typename SizeT>
|
||||
void slice_assign(List<SizeT>* dst, List<SizeT>* src, SizeT itemsize,
|
||||
UserSlice* user_slice) {
|
||||
Slice slice = user_slice->indices_checked<SizeT>(dst->len);
|
||||
|
||||
// NOTE: Python does not have this restriction.
|
||||
if (slice.len() != src->len) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"List destination has {} item(s), but source has {} "
|
||||
"item(s). The lengths must match.",
|
||||
slice.len(), src->len, NO_PARAM);
|
||||
}
|
||||
|
||||
// TODO: Look into how the original implementation was implemented and optimized.
|
||||
SizeT dst_i = slice.start;
|
||||
SizeT src_i = 0;
|
||||
while (src_i < slice.len()) {
|
||||
__builtin_memcpy(dst->items + dst_i, src->items + src_i, itemsize);
|
||||
|
||||
src_i += 1;
|
||||
dst_i += slice.step;
|
||||
}
|
||||
}
|
||||
} // namespace list
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
// Handle list assignment and dropping part of the list when
|
||||
// both dest_step and src_step are +1.
|
||||
// - All the index must *not* be out-of-bound or negative,
|
||||
// - The end index is *inclusive*,
|
||||
// - The length of src and dest slice size should already
|
||||
// be checked: if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest)
|
||||
SliceIndex __nac3_list_slice_assign_var_size(SliceIndex dest_start,
|
||||
SliceIndex dest_end,
|
||||
SliceIndex dest_step,
|
||||
uint8_t* dest_arr,
|
||||
SliceIndex dest_arr_len,
|
||||
SliceIndex src_start,
|
||||
SliceIndex src_end,
|
||||
SliceIndex src_step,
|
||||
uint8_t* src_arr,
|
||||
SliceIndex src_arr_len,
|
||||
const SliceIndex size) {
|
||||
/* if dest_arr_len == 0, do nothing since we do not support extending list */
|
||||
if (dest_arr_len == 0)
|
||||
return dest_arr_len;
|
||||
/* if both step is 1, memmove directly, handle the dropping of the list, and shrink size */
|
||||
if (src_step == dest_step && dest_step == 1) {
|
||||
const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
|
||||
const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
|
||||
if (src_len > 0) {
|
||||
__builtin_memmove(dest_arr + dest_start * size, src_arr + src_start * size, src_len * size);
|
||||
}
|
||||
if (dest_len > 0) {
|
||||
/* dropping */
|
||||
__builtin_memmove(dest_arr + (dest_start + src_len) * size, dest_arr + (dest_end + 1) * size,
|
||||
(dest_arr_len - dest_end - 1) * size);
|
||||
}
|
||||
/* shrink size */
|
||||
return dest_arr_len - (dest_len - src_len);
|
||||
}
|
||||
/* if two range overlaps, need alloca */
|
||||
uint8_t need_alloca = (dest_arr == src_arr)
|
||||
&& !(max(dest_start, dest_end) < min(src_start, src_end)
|
||||
|| max(src_start, src_end) < min(dest_start, dest_end));
|
||||
if (need_alloca) {
|
||||
uint8_t* tmp = reinterpret_cast<uint8_t*>(__builtin_alloca(src_arr_len * size));
|
||||
__builtin_memcpy(tmp, src_arr, src_arr_len * size);
|
||||
src_arr = tmp;
|
||||
}
|
||||
SliceIndex src_ind = src_start;
|
||||
SliceIndex dest_ind = dest_start;
|
||||
for (; (src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end); src_ind += src_step, dest_ind += dest_step) {
|
||||
/* for constant optimization */
|
||||
if (size == 1) {
|
||||
__builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
|
||||
} else if (size == 4) {
|
||||
__builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
|
||||
} else if (size == 8) {
|
||||
__builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
|
||||
} else {
|
||||
/* memcpy for var size, cannot overlap after previous alloca */
|
||||
__builtin_memcpy(dest_arr + dest_ind * size, src_arr + src_ind * size, size);
|
||||
}
|
||||
}
|
||||
/* only dest_step == 1 can we shrink the dest list. */
|
||||
/* size should be ensured prior to calling this function */
|
||||
if (dest_step == 1 && dest_end >= dest_start) {
|
||||
__builtin_memmove(dest_arr + dest_ind * size, dest_arr + (dest_end + 1) * size,
|
||||
(dest_arr_len - dest_end - 1) * size);
|
||||
return dest_arr_len - (dest_end - dest_ind) - 1;
|
||||
}
|
||||
return dest_arr_len;
|
||||
void __nac3_list_slice_assign(List<int32_t>* dst, List<int32_t>* src,
|
||||
int32_t itemsize, UserSlice* user_slice) {
|
||||
list::slice_assign(dst, src, itemsize, user_slice);
|
||||
}
|
||||
} // extern "C"
|
||||
|
||||
void __nac3_list_slice_assign64(List<int64_t>* dst, List<int64_t>* src,
|
||||
int64_t itemsize, UserSlice* user_slice) {
|
||||
list::slice_assign(dst, src, itemsize, user_slice);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,93 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
namespace {
|
||||
// adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
|
||||
// need to make sure `exp >= 0` before calling this function
|
||||
template<typename T>
|
||||
T __nac3_int_exp_impl(T base, T exp) {
|
||||
T res = 1;
|
||||
/* repeated squaring method */
|
||||
do {
|
||||
if (exp & 1) {
|
||||
res *= base; /* for n odd */
|
||||
}
|
||||
exp >>= 1;
|
||||
base *= base;
|
||||
} while (exp);
|
||||
return res;
|
||||
}
|
||||
} // namespace
|
||||
|
||||
#define DEF_nac3_int_exp_(T) \
|
||||
T __nac3_int_exp_##T(T base, T exp) { \
|
||||
return __nac3_int_exp_impl(base, exp); \
|
||||
}
|
||||
|
||||
extern "C" {
|
||||
|
||||
// Putting semicolons here to make clang-format not reformat this into
|
||||
// a stair shape.
|
||||
DEF_nac3_int_exp_(int32_t);
|
||||
DEF_nac3_int_exp_(int64_t);
|
||||
DEF_nac3_int_exp_(uint32_t);
|
||||
DEF_nac3_int_exp_(uint64_t);
|
||||
|
||||
int32_t __nac3_isinf(double x) {
|
||||
return __builtin_isinf(x);
|
||||
}
|
||||
|
||||
int32_t __nac3_isnan(double x) {
|
||||
return __builtin_isnan(x);
|
||||
}
|
||||
|
||||
double tgamma(double arg);
|
||||
|
||||
double __nac3_gamma(double z) {
|
||||
// Handling for denormals
|
||||
// | x | Python gamma(x) | C tgamma(x) |
|
||||
// --- | ----------------- | --------------- | ----------- |
|
||||
// (1) | nan | nan | nan |
|
||||
// (2) | -inf | -inf | inf |
|
||||
// (3) | inf | inf | inf |
|
||||
// (4) | 0.0 | inf | inf |
|
||||
// (5) | {-1.0, -2.0, ...} | inf | nan |
|
||||
|
||||
// (1)-(3)
|
||||
if (__builtin_isinf(z) || __builtin_isnan(z)) {
|
||||
return z;
|
||||
}
|
||||
|
||||
double v = tgamma(z);
|
||||
|
||||
// (4)-(5)
|
||||
return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
|
||||
}
|
||||
|
||||
double lgamma(double arg);
|
||||
|
||||
double __nac3_gammaln(double x) {
|
||||
// libm's handling of value overflows differs from scipy:
|
||||
// - scipy: gammaln(-inf) -> -inf
|
||||
// - libm : lgamma(-inf) -> inf
|
||||
|
||||
if (__builtin_isinf(x)) {
|
||||
return x;
|
||||
}
|
||||
|
||||
return lgamma(x);
|
||||
}
|
||||
|
||||
double j0(double x);
|
||||
|
||||
double __nac3_j0(double x) {
|
||||
// libm's handling of value overflows differs from scipy:
|
||||
// - scipy: j0(inf) -> nan
|
||||
// - libm : j0(inf) -> 0.0
|
||||
|
||||
if (__builtin_isinf(x)) {
|
||||
return __builtin_nan("");
|
||||
}
|
||||
|
||||
return j0(x);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,13 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
namespace {
|
||||
template<typename T>
|
||||
const T& max(const T& a, const T& b) {
|
||||
return a > b ? a : b;
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
const T& min(const T& a, const T& b) {
|
||||
return a > b ? b : a;
|
||||
}
|
||||
} // namespace
|
||||
|
|
@ -1,144 +0,0 @@
|
|||
#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);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,119 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/list.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace array {
|
||||
// TODO: Document me
|
||||
template <typename SizeT>
|
||||
void set_and_validate_list_shape_helper(SizeT axis, List<SizeT>* list,
|
||||
SizeT ndims, SizeT* shape) {
|
||||
if (shape[axis] == -1) {
|
||||
// Dimension is unspecified. Set it.
|
||||
shape[axis] = list->len;
|
||||
} else {
|
||||
// Dimension is specified. Check.
|
||||
if (shape[axis] != list->len) {
|
||||
// Mismatch, throw an error.
|
||||
// NOTE: NumPy's error message is more complex and needs more PARAMS to display.
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"The requested array has an inhomogenous shape "
|
||||
"after {0} dimension(s).",
|
||||
axis, shape[axis], list->len);
|
||||
}
|
||||
}
|
||||
|
||||
if (axis + 1 == ndims) {
|
||||
// `list` has type `list[ItemType]`
|
||||
// Do nothing
|
||||
} else {
|
||||
// `list` has type `list[list[...]]`
|
||||
List<SizeT>** lists = (List<SizeT>**)(list->items);
|
||||
for (SizeT i = 0; i < list->len; i++) {
|
||||
set_and_validate_list_shape_helper<SizeT>(axis + 1, lists[i], ndims,
|
||||
shape);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// TODO: Document me
|
||||
template <typename SizeT>
|
||||
void set_and_validate_list_shape(List<SizeT>* list, SizeT ndims, SizeT* shape) {
|
||||
for (SizeT axis = 0; axis < ndims; axis++) {
|
||||
shape[axis] = -1; // Sentinel to say this dimension is unspecified.
|
||||
}
|
||||
set_and_validate_list_shape_helper<SizeT>(0, list, ndims, shape);
|
||||
}
|
||||
|
||||
// TODO: Document me
|
||||
template <typename SizeT>
|
||||
void write_list_to_array_helper(SizeT axis, SizeT* index, List<SizeT>* list,
|
||||
NDArray<SizeT>* ndarray) {
|
||||
debug_assert_eq(SizeT, list->len, ndarray->shape[axis]);
|
||||
if (IRRT_DEBUG_ASSERT_BOOL) {
|
||||
if (!ndarray::basic::is_c_contiguous(ndarray)) {
|
||||
raise_debug_assert(SizeT, "ndarray is not C-contiguous", ndarray->strides[0],
|
||||
ndarray->strides[1], NO_PARAM);
|
||||
}
|
||||
}
|
||||
|
||||
if (axis + 1 == ndarray->ndims) {
|
||||
// `list` has type `list[ItemType]`
|
||||
// `ndarray` is contiguous, so we can do this, and this is fast.
|
||||
uint8_t* dst = ndarray->data + (ndarray->itemsize * (*index));
|
||||
__builtin_memcpy(dst, list->items, ndarray->itemsize * list->len);
|
||||
*index += list->len;
|
||||
} else {
|
||||
// `list` has type `list[list[...]]`
|
||||
List<SizeT>** lists = (List<SizeT>**)(list->items);
|
||||
|
||||
for (SizeT i = 0; i < list->len; i++) {
|
||||
write_list_to_array_helper<SizeT>(axis + 1, index, lists[i],
|
||||
ndarray);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// TODO: Document me
|
||||
template <typename SizeT>
|
||||
void write_list_to_array(List<SizeT>* list, NDArray<SizeT>* ndarray) {
|
||||
// done after set_and_validate(list, ndims, shape), list is well-formed
|
||||
// ndarray->data is allocated and owned
|
||||
// ndarray->itemsize is set
|
||||
// ndarray->ndims is set
|
||||
// ndarray->shape is set
|
||||
// ndarray->strides is ???
|
||||
SizeT index = 0;
|
||||
write_list_to_array_helper<SizeT>((SizeT)0, &index, list, ndarray);
|
||||
}
|
||||
} // namespace array
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::array;
|
||||
|
||||
void __nac3_array_set_and_validate_list_shape(List<int32_t>* list,
|
||||
int32_t ndims, int32_t* shape) {
|
||||
set_and_validate_list_shape(list, ndims, shape);
|
||||
}
|
||||
|
||||
void __nac3_array_set_and_validate_list_shape64(List<int64_t>* list,
|
||||
int64_t ndims, int64_t* shape) {
|
||||
set_and_validate_list_shape(list, ndims, shape);
|
||||
}
|
||||
|
||||
void __nac3_array_write_list_to_array(List<int32_t>* list,
|
||||
NDArray<int32_t>* ndarray) {
|
||||
write_list_to_array(list, ndarray);
|
||||
}
|
||||
|
||||
void __nac3_array_write_list_to_array64(List<int64_t>* list,
|
||||
NDArray<int64_t>* ndarray) {
|
||||
write_list_to_array(list, ndarray);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,345 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace basic {
|
||||
/**
|
||||
* @brief Asserts that `shape` does not contain negative dimensions.
|
||||
*
|
||||
* @param ndims Number of dimensions in `shape`
|
||||
* @param shape The shape to check on
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void assert_shape_no_negative(SizeT ndims, const SizeT* shape) {
|
||||
for (SizeT axis = 0; axis < ndims; axis++) {
|
||||
if (shape[axis] < 0) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"negative dimensions are not allowed; axis {0} "
|
||||
"has dimension {1}",
|
||||
axis, shape[axis], NO_PARAM);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Check two shapes are the same in the context of writing outputting to an ndarray.
|
||||
*
|
||||
* This function throws error messages for output shape mismatches.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void assert_output_shape_same(SizeT ndarray_ndims, const SizeT* ndarray_shape,
|
||||
SizeT output_ndims, const SizeT* output_shape) {
|
||||
if (ndarray_ndims != output_ndims) {
|
||||
// There is no corresponding NumPy error message like this.
|
||||
raise_exception(
|
||||
SizeT, EXN_VALUE_ERROR,
|
||||
"Cannot write output of ndims {0} to an ndarray with ndims {1}",
|
||||
output_ndims, ndarray_ndims, NO_PARAM);
|
||||
}
|
||||
|
||||
for (SizeT axis = 0; axis < ndarray_ndims; axis++) {
|
||||
if (ndarray_shape[axis] != output_shape[axis]) {
|
||||
// There is no corresponding NumPy error message like this.
|
||||
raise_exception(
|
||||
SizeT, EXN_VALUE_ERROR,
|
||||
"Mismatched dimensions on axis {0}, output has "
|
||||
"dimension {1}, but destination ndarray has dimension {2}.",
|
||||
axis, output_shape[axis], ndarray_shape[axis]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Returns the number of elements of an ndarray given its shape.
|
||||
*
|
||||
* @param ndims Number of dimensions in `shape`
|
||||
* @param shape The shape of the ndarray
|
||||
*/
|
||||
template <typename SizeT>
|
||||
SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
|
||||
SizeT size = 1;
|
||||
for (SizeT axis = 0; axis < ndims; axis++) size *= shape[axis];
|
||||
return size;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Compute the array indices of the `nth` (0-based) element of an ndarray given only its shape.
|
||||
*
|
||||
* @param ndims Number of elements in `shape` and `indices`
|
||||
* @param shape The shape of the ndarray
|
||||
* @param indices The returned indices indexing the ndarray with shape `shape`.
|
||||
* @param nth The index of the element of interest.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void set_indices_by_nth(SizeT ndims, const SizeT* shape, SizeT* indices,
|
||||
SizeT nth) {
|
||||
for (SizeT i = 0; i < ndims; i++) {
|
||||
SizeT axis = ndims - i - 1;
|
||||
SizeT dim = shape[axis];
|
||||
|
||||
indices[axis] = nth % dim;
|
||||
nth /= dim;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return the number of elements of an `ndarray`
|
||||
*
|
||||
* This function corresponds to `<an_ndarray>.size`
|
||||
*/
|
||||
template <typename SizeT>
|
||||
SizeT size(const NDArray<SizeT>* ndarray) {
|
||||
return calc_size_from_shape(ndarray->ndims, ndarray->shape);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return of the number of its content of an `ndarray`.
|
||||
*
|
||||
* This function corresponds to `<an_ndarray>.nbytes`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
SizeT nbytes(const NDArray<SizeT>* ndarray) {
|
||||
return size(ndarray) * ndarray->itemsize;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Get the `len()` of an ndarray, and asserts that `ndarray` is a sized object.
|
||||
*
|
||||
* This function corresponds to `<an_ndarray>.__len__`.
|
||||
*
|
||||
* @param dst_length The returned result
|
||||
*/
|
||||
template <typename SizeT>
|
||||
SizeT len(const NDArray<SizeT>* ndarray) {
|
||||
// numpy prohibits `__len__` on unsized objects
|
||||
if (ndarray->ndims == 0) {
|
||||
raise_exception(SizeT, EXN_TYPE_ERROR, "len() of unsized object",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
} else {
|
||||
return ndarray->shape[0];
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return a boolean indicating if `ndarray` is (C-)contiguous.
|
||||
*
|
||||
* You may want to see: ndarray's rules for C-contiguity: https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
|
||||
*/
|
||||
template <typename SizeT>
|
||||
bool is_c_contiguous(const NDArray<SizeT>* ndarray) {
|
||||
// Other references:
|
||||
// - tinynumpy's implementation: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L102
|
||||
// - ndarray's flags["C_CONTIGUOUS"]: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags
|
||||
// - ndarray's rules for C-contiguity: https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
|
||||
|
||||
// From https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45:
|
||||
//
|
||||
// The traditional rule is that for an array to be flagged as C contiguous,
|
||||
// the following must hold:
|
||||
//
|
||||
// strides[-1] == itemsize
|
||||
// strides[i] == shape[i+1] * strides[i + 1]
|
||||
// [...]
|
||||
// According to these rules, a 0- or 1-dimensional array is either both
|
||||
// C- and F-contiguous, or neither; and an array with 2+ dimensions
|
||||
// can be C- or F- contiguous, or neither, but not both. Though there
|
||||
// there are exceptions for arrays with zero or one item, in the first
|
||||
// case the check is relaxed up to and including the first dimension
|
||||
// with shape[i] == 0. In the second case `strides == itemsize` will
|
||||
// can be true for all dimensions and both flags are set.
|
||||
|
||||
if (ndarray->ndims == 0) {
|
||||
return true;
|
||||
}
|
||||
|
||||
if (ndarray->strides[ndarray->ndims - 1] != ndarray->itemsize) {
|
||||
return false;
|
||||
}
|
||||
|
||||
for (SizeT i = 1; i < ndarray->ndims; i++) {
|
||||
SizeT axis_i = ndarray->ndims - i - 1;
|
||||
if (ndarray->strides[axis_i] !=
|
||||
ndarray->shape[axis_i + 1] * ndarray->strides[axis_i + 1]) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return the pointer to the element indexed by `indices`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray,
|
||||
const SizeT* indices) {
|
||||
uint8_t* element = ndarray->data;
|
||||
for (SizeT dim_i = 0; dim_i < ndarray->ndims; dim_i++)
|
||||
element += indices[dim_i] * ndarray->strides[dim_i];
|
||||
return element;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Convenience function. Like `get_pelement_by_indices` but
|
||||
* reinterprets the element pointer.
|
||||
*/
|
||||
template <typename SizeT, typename T>
|
||||
T* get_ptr(const NDArray<SizeT>* ndarray, const SizeT* indices) {
|
||||
return (T*)get_pelement_by_indices(ndarray, indices);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return the pointer to the nth (0-based) element in a flattened view of `ndarray`.
|
||||
*
|
||||
* This function does no bound check.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
uint8_t* get_nth_pelement(const NDArray<SizeT>* ndarray, SizeT nth) {
|
||||
uint8_t* element = ndarray->data;
|
||||
for (SizeT i = 0; i < ndarray->ndims; i++) {
|
||||
SizeT axis = ndarray->ndims - i - 1;
|
||||
SizeT dim = ndarray->shape[axis];
|
||||
element += ndarray->strides[axis] * (nth % dim);
|
||||
nth /= dim;
|
||||
}
|
||||
return element;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Update the strides of an ndarray given an ndarray `shape`
|
||||
* and assuming that the ndarray is fully c-contagious.
|
||||
*
|
||||
* You might want to read https://ajcr.net/stride-guide-part-1/.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void set_strides_by_shape(NDArray<SizeT>* ndarray) {
|
||||
SizeT stride_product = 1;
|
||||
for (SizeT i = 0; i < ndarray->ndims; i++) {
|
||||
SizeT axis = ndarray->ndims - i - 1;
|
||||
ndarray->strides[axis] = stride_product * ndarray->itemsize;
|
||||
stride_product *= ndarray->shape[axis];
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Set an element in `ndarray`.
|
||||
*
|
||||
* @param pelement Pointer to the element in `ndarray` to be set.
|
||||
* @param pvalue Pointer to the value `pelement` will be set to.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void set_pelement_value(NDArray<SizeT>* ndarray, uint8_t* pelement,
|
||||
const uint8_t* pvalue) {
|
||||
__builtin_memcpy(pelement, pvalue, ndarray->itemsize);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Copy data from one ndarray to another of the exact same size and itemsize.
|
||||
*
|
||||
* Both ndarrays will be viewed in their flatten views when copying the elements.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void copy_data(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
|
||||
// TODO: Make this faster with memcpy
|
||||
|
||||
debug_assert_eq(SizeT, src_ndarray->itemsize, dst_ndarray->itemsize);
|
||||
|
||||
for (SizeT i = 0; i < size(src_ndarray); i++) {
|
||||
auto src_element = ndarray::basic::get_nth_pelement(src_ndarray, i);
|
||||
auto dst_element = ndarray::basic::get_nth_pelement(dst_ndarray, i);
|
||||
ndarray::basic::set_pelement_value(dst_ndarray, dst_element,
|
||||
src_element);
|
||||
}
|
||||
}
|
||||
} // namespace basic
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::basic;
|
||||
|
||||
void __nac3_ndarray_util_assert_shape_no_negative(int32_t ndims,
|
||||
int32_t* shape) {
|
||||
assert_shape_no_negative(ndims, shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_util_assert_shape_no_negative64(int64_t ndims,
|
||||
int64_t* shape) {
|
||||
assert_shape_no_negative(ndims, shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_util_assert_output_shape_same(int32_t ndarray_ndims,
|
||||
const int32_t* ndarray_shape,
|
||||
int32_t output_ndims,
|
||||
const int32_t* output_shape) {
|
||||
assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims,
|
||||
output_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_util_assert_output_shape_same64(
|
||||
int64_t ndarray_ndims, const int64_t* ndarray_shape, int64_t output_ndims,
|
||||
const int64_t* output_shape) {
|
||||
assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims,
|
||||
output_shape);
|
||||
}
|
||||
|
||||
uint32_t __nac3_ndarray_size(NDArray<int32_t>* ndarray) {
|
||||
return size(ndarray);
|
||||
}
|
||||
|
||||
uint64_t __nac3_ndarray_size64(NDArray<int64_t>* ndarray) {
|
||||
return size(ndarray);
|
||||
}
|
||||
|
||||
uint32_t __nac3_ndarray_nbytes(NDArray<int32_t>* ndarray) {
|
||||
return nbytes(ndarray);
|
||||
}
|
||||
|
||||
uint64_t __nac3_ndarray_nbytes64(NDArray<int64_t>* ndarray) {
|
||||
return nbytes(ndarray);
|
||||
}
|
||||
|
||||
int32_t __nac3_ndarray_len(NDArray<int32_t>* ndarray) { return len(ndarray); }
|
||||
|
||||
int64_t __nac3_ndarray_len64(NDArray<int64_t>* ndarray) { return len(ndarray); }
|
||||
|
||||
bool __nac3_ndarray_is_c_contiguous(NDArray<int32_t>* ndarray) {
|
||||
return is_c_contiguous(ndarray);
|
||||
}
|
||||
|
||||
bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t>* ndarray) {
|
||||
return is_c_contiguous(ndarray);
|
||||
}
|
||||
|
||||
uint8_t* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray,
|
||||
int32_t nth) {
|
||||
return get_nth_pelement(ndarray, nth);
|
||||
}
|
||||
|
||||
uint8_t* __nac3_ndarray_get_nth_pelement64(const NDArray<int64_t>* ndarray,
|
||||
int64_t nth) {
|
||||
return get_nth_pelement(ndarray, nth);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_set_strides_by_shape(NDArray<int32_t>* ndarray) {
|
||||
set_strides_by_shape(ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_set_strides_by_shape64(NDArray<int64_t>* ndarray) {
|
||||
set_strides_by_shape(ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_copy_data(NDArray<int32_t>* src_ndarray,
|
||||
NDArray<int32_t>* dst_ndarray) {
|
||||
copy_data(src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_copy_data64(NDArray<int64_t>* src_ndarray,
|
||||
NDArray<int64_t>* dst_ndarray) {
|
||||
copy_data(src_ndarray, dst_ndarray);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,171 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
|
||||
namespace {
|
||||
template <typename SizeT>
|
||||
struct ShapeEntry {
|
||||
SizeT ndims;
|
||||
SizeT* shape;
|
||||
};
|
||||
} // namespace
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace broadcast {
|
||||
/**
|
||||
* @brief Return true if `src_shape` can broadcast to `dst_shape`.
|
||||
*
|
||||
* See https://numpy.org/doc/stable/user/basics.broadcasting.html
|
||||
*/
|
||||
template <typename SizeT>
|
||||
bool can_broadcast_shape_to(SizeT target_ndims, const SizeT* target_shape,
|
||||
SizeT src_ndims, const SizeT* src_shape) {
|
||||
if (src_ndims > target_ndims) {
|
||||
return false;
|
||||
}
|
||||
|
||||
for (SizeT i = 0; i < src_ndims; i++) {
|
||||
SizeT target_dim = target_shape[target_ndims - i - 1];
|
||||
SizeT src_dim = src_shape[src_ndims - i - 1];
|
||||
if (!(src_dim == 1 || target_dim == src_dim)) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Performs `np.broadcast_shapes(<shapes>)`
|
||||
*
|
||||
* @param num_shapes Number of entries in `shapes`
|
||||
* @param shapes The list of shape to do `np.broadcast_shapes` on.
|
||||
* @param dst_ndims The length of `dst_shape`.
|
||||
* `dst_ndims` must be `max([shape.ndims for shape in shapes])`, but the caller has to calculate it/provide it.
|
||||
* for this function since they should already know in order to allocate `dst_shape` in the first place.
|
||||
* @param dst_shape The resulting shape. Must be pre-allocated by the caller. This function calculate the result
|
||||
* of `np.broadcast_shapes` and write it here.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void broadcast_shapes(SizeT num_shapes, const ShapeEntry<SizeT>* shapes,
|
||||
SizeT dst_ndims, SizeT* dst_shape) {
|
||||
for (SizeT dst_axis = 0; dst_axis < dst_ndims; dst_axis++) {
|
||||
dst_shape[dst_axis] = 1;
|
||||
}
|
||||
|
||||
#ifdef IRRT_DEBUG_ASSERT
|
||||
SizeT max_ndims_found = 0;
|
||||
#endif
|
||||
|
||||
for (SizeT i = 0; i < num_shapes; i++) {
|
||||
ShapeEntry<SizeT> entry = shapes[i];
|
||||
|
||||
// Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
|
||||
debug_assert(SizeT, entry.ndims <= dst_ndims);
|
||||
|
||||
#ifdef IRRT_DEBUG_ASSERT
|
||||
max_ndims_found = max(max_ndims_found, entry.ndims);
|
||||
#endif
|
||||
|
||||
for (SizeT j = 0; j < entry.ndims; j++) {
|
||||
SizeT entry_axis = entry.ndims - j - 1;
|
||||
SizeT dst_axis = dst_ndims - j - 1;
|
||||
|
||||
SizeT entry_dim = entry.shape[entry_axis];
|
||||
SizeT dst_dim = dst_shape[dst_axis];
|
||||
|
||||
if (dst_dim == 1) {
|
||||
dst_shape[dst_axis] = entry_dim;
|
||||
} else if (entry_dim == 1 || entry_dim == dst_dim) {
|
||||
// Do nothing
|
||||
} else {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"shape mismatch: objects cannot be broadcast "
|
||||
"to a single shape.",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
|
||||
debug_assert_eq(SizeT, max_ndims_found, dst_ndims);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Perform `np.broadcast_to(<ndarray>, <target_shape>)` and appropriate assertions.
|
||||
*
|
||||
* This function attempts to broadcast `src_ndarray` to a new shape defined by `dst_ndarray.shape`,
|
||||
* and return the result by modifying `dst_ndarray`.
|
||||
*
|
||||
* # Notes on `dst_ndarray`
|
||||
* The caller is responsible for allocating space for the resulting ndarray.
|
||||
* Here is what this function expects from `dst_ndarray` when called:
|
||||
* - `dst_ndarray->data` does not have to be initialized.
|
||||
* - `dst_ndarray->itemsize` does not have to be initialized.
|
||||
* - `dst_ndarray->ndims` must be initialized, determining the length of `dst_ndarray->shape`
|
||||
* - `dst_ndarray->shape` must be allocated, and must contain the desired target broadcast shape.
|
||||
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
|
||||
* When this function call ends:
|
||||
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
|
||||
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
|
||||
* - `dst_ndarray->ndims` is unchanged.
|
||||
* - `dst_ndarray->shape` is unchanged.
|
||||
* - `dst_ndarray->strides` is updated accordingly by how ndarray broadcast_to works.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void broadcast_to(const NDArray<SizeT>* src_ndarray,
|
||||
NDArray<SizeT>* dst_ndarray) {
|
||||
if (!ndarray::broadcast::can_broadcast_shape_to(
|
||||
dst_ndarray->ndims, dst_ndarray->shape, src_ndarray->ndims,
|
||||
src_ndarray->shape)) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"operands could not be broadcast together", NO_PARAM,
|
||||
NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
dst_ndarray->data = src_ndarray->data;
|
||||
dst_ndarray->itemsize = src_ndarray->itemsize;
|
||||
|
||||
for (SizeT i = 0; i < dst_ndarray->ndims; i++) {
|
||||
SizeT src_axis = src_ndarray->ndims - i - 1;
|
||||
SizeT dst_axis = dst_ndarray->ndims - i - 1;
|
||||
if (src_axis < 0 || (src_ndarray->shape[src_axis] == 1 &&
|
||||
dst_ndarray->shape[dst_axis] != 1)) {
|
||||
// Freeze the steps in-place
|
||||
dst_ndarray->strides[dst_axis] = 0;
|
||||
} else {
|
||||
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace broadcast
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::broadcast;
|
||||
|
||||
void __nac3_ndarray_broadcast_to(NDArray<int32_t>* src_ndarray,
|
||||
NDArray<int32_t>* dst_ndarray) {
|
||||
broadcast_to(src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_broadcast_to64(NDArray<int64_t>* src_ndarray,
|
||||
NDArray<int64_t>* dst_ndarray) {
|
||||
broadcast_to(src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_broadcast_shapes(int32_t num_shapes,
|
||||
const ShapeEntry<int32_t>* shapes,
|
||||
int32_t dst_ndims, int32_t* dst_shape) {
|
||||
broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_broadcast_shapes64(int64_t num_shapes,
|
||||
const ShapeEntry<int64_t>* shapes,
|
||||
int64_t dst_ndims, int64_t* dst_shape) {
|
||||
broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,44 @@
|
|||
#pragma once
|
||||
|
||||
namespace {
|
||||
/**
|
||||
* @brief The NDArray object
|
||||
*
|
||||
* The official numpy implementations: https://github.com/numpy/numpy/blob/735a477f0bc2b5b84d0e72d92f224bde78d4e069/doc/source/reference/c-api/types-and-structures.rst
|
||||
*/
|
||||
template <typename SizeT>
|
||||
struct NDArray {
|
||||
/**
|
||||
* @brief The underlying data this `ndarray` is pointing to.
|
||||
*
|
||||
* Must be set to `nullptr` to indicate that this NDArray's `data` is uninitialized.
|
||||
*/
|
||||
uint8_t* data;
|
||||
|
||||
/**
|
||||
* @brief The number of bytes of a single element in `data`.
|
||||
*/
|
||||
SizeT itemsize;
|
||||
|
||||
/**
|
||||
* @brief The number of dimensions of this shape.
|
||||
*/
|
||||
SizeT ndims;
|
||||
|
||||
/**
|
||||
* @brief The NDArray shape, with length equal to `ndims`.
|
||||
*
|
||||
* Note that it may contain 0.
|
||||
*/
|
||||
SizeT* shape;
|
||||
|
||||
/**
|
||||
* @brief Array strides, with length equal to `ndims`
|
||||
*
|
||||
* The stride values are in units of bytes, not number of elements.
|
||||
*
|
||||
* Note that `strides` can have negative values.
|
||||
*/
|
||||
SizeT* strides;
|
||||
};
|
||||
} // namespace
|
||||
|
|
@ -0,0 +1,221 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
|
||||
namespace {
|
||||
typedef uint8_t NDIndexType;
|
||||
|
||||
/**
|
||||
* @brief A single element index
|
||||
*
|
||||
* `data` points to a `SliceIndex`.
|
||||
*/
|
||||
|
||||
const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
|
||||
/**
|
||||
* @brief A slice index
|
||||
*
|
||||
* `data` points to a `UserRange`.
|
||||
*/
|
||||
const NDIndexType ND_INDEX_TYPE_SLICE = 1;
|
||||
|
||||
/**
|
||||
* @brief `np.newaxis` / `None`
|
||||
*
|
||||
* `data` is unused.
|
||||
*/
|
||||
const NDIndexType ND_INDEX_TYPE_NEWAXIS = 2;
|
||||
|
||||
/**
|
||||
* @brief `Ellipsis` / `...`
|
||||
*
|
||||
* `data` is unused.
|
||||
*/
|
||||
const NDIndexType ND_INDEX_TYPE_ELLIPSIS = 3;
|
||||
|
||||
/**
|
||||
* @brief An index used in ndarray indexing
|
||||
*/
|
||||
struct NDIndex {
|
||||
/**
|
||||
* @brief Enum tag to specify the type of index.
|
||||
*
|
||||
* Please see comments of each enum constant.
|
||||
*/
|
||||
NDIndexType type;
|
||||
|
||||
/**
|
||||
* @brief The accompanying data associated with `type`.
|
||||
*
|
||||
* Please see comments of each enum constant.
|
||||
*/
|
||||
uint8_t* data;
|
||||
};
|
||||
} // namespace
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace indexing {
|
||||
/**
|
||||
* @brief Perform ndarray "basic indexing" (https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
|
||||
*
|
||||
* This is function very similar to performing `dst_ndarray = src_ndarray[indexes]` in Python (where the variables
|
||||
* can all be found in the parameter of this function).
|
||||
*
|
||||
* In other words, this function takes in an ndarray (`src_ndarray`), index it with `indexes`, and return the
|
||||
* indexed array (by writing the result to `dst_ndarray`).
|
||||
*
|
||||
* This function also does proper assertions on `indexes`.
|
||||
*
|
||||
* # Notes on `dst_ndarray`
|
||||
* The caller is responsible for allocating space for the resulting ndarray.
|
||||
* Here is what this function expects from `dst_ndarray` when called:
|
||||
* - `dst_ndarray->data` does not have to be initialized.
|
||||
* - `dst_ndarray->itemsize` does not have to be initialized.
|
||||
* - `dst_ndarray->ndims` must be initialized, and it must be equal to the expected `ndims` of the `dst_ndarray` after
|
||||
* indexing `src_ndarray` with `indexes`.
|
||||
* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
|
||||
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
|
||||
* When this function call ends:
|
||||
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
|
||||
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
|
||||
* - `dst_ndarray->ndims` is unchanged.
|
||||
* - `dst_ndarray->shape` is updated according to how `src_ndarray` is indexed.
|
||||
* - `dst_ndarray->strides` is updated accordingly by how ndarray indexing works.
|
||||
*
|
||||
* @param indexes Indexes to index `src_ndarray`, ordered in the same way you would write them in Python.
|
||||
* @param src_ndarray The NDArray to be indexed.
|
||||
* @param dst_ndarray The resulting NDArray after indexing. Further details in the comments above,
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void index(SizeT num_indexes, const NDIndex* indexes,
|
||||
const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
|
||||
// First, validate `indexes`.
|
||||
|
||||
// Expected value of `dst_ndarray->ndims`.
|
||||
SizeT expected_dst_ndims = src_ndarray->ndims;
|
||||
// To check for "too many indices for array: array is ?-dimensional, but ? were indexed"
|
||||
SizeT num_indexed = 0;
|
||||
// There may be ellipsis `...` in `indexes`. There can only be 0 or 1 ellipsis.
|
||||
SizeT num_ellipsis = 0;
|
||||
|
||||
for (SizeT i = 0; i < num_indexes; i++) {
|
||||
if (indexes[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
|
||||
expected_dst_ndims--;
|
||||
num_indexed++;
|
||||
} else if (indexes[i].type == ND_INDEX_TYPE_SLICE) {
|
||||
num_indexed++;
|
||||
} else if (indexes[i].type == ND_INDEX_TYPE_NEWAXIS) {
|
||||
expected_dst_ndims++;
|
||||
} else if (indexes[i].type == ND_INDEX_TYPE_ELLIPSIS) {
|
||||
num_ellipsis++;
|
||||
if (num_ellipsis > 1) {
|
||||
raise_exception(
|
||||
SizeT, EXN_INDEX_ERROR,
|
||||
"an index can only have a single ellipsis ('...')",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
|
||||
debug_assert_eq(SizeT, expected_dst_ndims, dst_ndarray->ndims);
|
||||
|
||||
if (src_ndarray->ndims - num_indexed < 0) {
|
||||
raise_exception(SizeT, EXN_INDEX_ERROR,
|
||||
"too many indices for array: array is {0}-dimensional, "
|
||||
"but {1} were indexed",
|
||||
src_ndarray->ndims, num_indexes, NO_PARAM);
|
||||
}
|
||||
|
||||
dst_ndarray->data = src_ndarray->data;
|
||||
dst_ndarray->itemsize = src_ndarray->itemsize;
|
||||
|
||||
// Reference code: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
|
||||
SizeT src_axis = 0;
|
||||
SizeT dst_axis = 0;
|
||||
|
||||
for (SliceIndex i = 0; i < num_indexes; i++) {
|
||||
const NDIndex* index = &indexes[i];
|
||||
if (index->type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
|
||||
SliceIndex input = *((SliceIndex*)index->data);
|
||||
SliceIndex k = slice::resolve_index_in_length(
|
||||
src_ndarray->shape[src_axis], input);
|
||||
|
||||
if (k == slice::OUT_OF_BOUNDS) {
|
||||
raise_exception(SizeT, EXN_INDEX_ERROR,
|
||||
"index {0} is out of bounds for axis {1} "
|
||||
"with size {2}",
|
||||
input, src_axis, src_ndarray->shape[src_axis]);
|
||||
}
|
||||
|
||||
dst_ndarray->data += k * src_ndarray->strides[src_axis];
|
||||
|
||||
src_axis++;
|
||||
} else if (index->type == ND_INDEX_TYPE_SLICE) {
|
||||
UserSlice* input = (UserSlice*)index->data;
|
||||
|
||||
Slice slice =
|
||||
input->indices_checked<SizeT>(src_ndarray->shape[src_axis]);
|
||||
|
||||
dst_ndarray->data +=
|
||||
(SizeT)slice.start * src_ndarray->strides[src_axis];
|
||||
dst_ndarray->strides[dst_axis] =
|
||||
((SizeT)slice.step) * src_ndarray->strides[src_axis];
|
||||
dst_ndarray->shape[dst_axis] = (SizeT)slice.len();
|
||||
|
||||
dst_axis++;
|
||||
src_axis++;
|
||||
} else if (index->type == ND_INDEX_TYPE_NEWAXIS) {
|
||||
dst_ndarray->strides[dst_axis] = 0;
|
||||
dst_ndarray->shape[dst_axis] = 1;
|
||||
|
||||
dst_axis++;
|
||||
} else if (index->type == ND_INDEX_TYPE_ELLIPSIS) {
|
||||
// The number of ':' entries this '...' implies.
|
||||
SizeT ellipsis_size = src_ndarray->ndims - num_indexed;
|
||||
|
||||
for (SizeT j = 0; j < ellipsis_size; j++) {
|
||||
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
|
||||
dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
|
||||
|
||||
dst_axis++;
|
||||
src_axis++;
|
||||
}
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
|
||||
for (; dst_axis < dst_ndarray->ndims; dst_axis++, src_axis++) {
|
||||
dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
|
||||
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
|
||||
}
|
||||
|
||||
debug_assert_eq(SizeT, src_ndarray->ndims, src_axis);
|
||||
debug_assert_eq(SizeT, dst_ndarray->ndims, dst_axis);
|
||||
}
|
||||
} // namespace indexing
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::indexing;
|
||||
|
||||
void __nac3_ndarray_index(int32_t num_indexes, NDIndex* indexes,
|
||||
NDArray<int32_t>* src_ndarray,
|
||||
NDArray<int32_t>* dst_ndarray) {
|
||||
index(num_indexes, indexes, src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_index64(int64_t num_indexes, NDIndex* indexes,
|
||||
NDArray<int64_t>* src_ndarray,
|
||||
NDArray<int64_t>* dst_ndarray) {
|
||||
index(num_indexes, indexes, src_ndarray, dst_ndarray);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,118 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace {
|
||||
/**
|
||||
* @brief Helper struct to enumerate through all indices under a shape.
|
||||
*
|
||||
* i.e., If `shape` is `[3, 2]`, by repeating `next()`, then you get:
|
||||
* - `[0, 0]`
|
||||
* - `[0, 1]`
|
||||
* - `[1, 0]`
|
||||
* - `[1, 1]`
|
||||
* - `[2, 0]`
|
||||
* - `[2, 1]`
|
||||
* - end.
|
||||
*
|
||||
* Interesting cases:
|
||||
* - If ndims == 0, there is one enumeration.
|
||||
* - If shape contains zeroes, there are no enumerations.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
struct NDIter {
|
||||
SizeT ndims;
|
||||
SizeT* shape;
|
||||
SizeT* strides;
|
||||
|
||||
/**
|
||||
* @brief The current indices.
|
||||
*
|
||||
* Must be allocated by the caller.
|
||||
*/
|
||||
SizeT* indices;
|
||||
|
||||
/**
|
||||
* @brief The nth (0-based) index of the current indices.
|
||||
*/
|
||||
SizeT nth;
|
||||
|
||||
/**
|
||||
* @brief Pointer to the current element.
|
||||
*/
|
||||
uint8_t* element;
|
||||
|
||||
/**
|
||||
* @brief The product of shape.
|
||||
*/
|
||||
SizeT size;
|
||||
|
||||
// TODO:: There is something called backstrides to speedup iteration.
|
||||
// See https://ajcr.net/stride-guide-part-1/, and https://docs.scipy.org/doc/numpy-1.13.0/reference/c-api.types-and-structures.html#c.PyArrayIterObject.PyArrayIterObject.backstrides.
|
||||
// Maybe LLVM is clever and knows how to optimize.
|
||||
|
||||
void initialize(SizeT ndims, SizeT* shape, SizeT* strides, uint8_t* element,
|
||||
SizeT* indices) {
|
||||
this->ndims = ndims;
|
||||
this->shape = shape;
|
||||
this->strides = strides;
|
||||
|
||||
this->indices = indices;
|
||||
this->element = element;
|
||||
|
||||
// Compute size and backstrides
|
||||
this->size = 1;
|
||||
for (SizeT i = 0; i < ndims; i++) {
|
||||
this->size *= shape[i];
|
||||
}
|
||||
|
||||
for (SizeT axis = 0; axis < ndims; axis++) indices[axis] = 0;
|
||||
nth = 0;
|
||||
}
|
||||
|
||||
void initialize_by_ndarray(NDArray<SizeT>* ndarray, SizeT* indices) {
|
||||
this->initialize(ndarray->ndims, ndarray->shape, ndarray->strides,
|
||||
ndarray->data, indices);
|
||||
}
|
||||
|
||||
bool has_next() { return nth < size; }
|
||||
|
||||
void next() {
|
||||
for (SizeT i = 0; i < ndims; i++) {
|
||||
SizeT axis = ndims - i - 1;
|
||||
indices[axis]++;
|
||||
if (indices[axis] >= shape[axis]) {
|
||||
indices[axis] = 0;
|
||||
|
||||
// TODO: Can be optimized with backstrides.
|
||||
element -= strides[axis] * (shape[axis] - 1);
|
||||
} else {
|
||||
element += strides[axis];
|
||||
break;
|
||||
}
|
||||
}
|
||||
nth++;
|
||||
}
|
||||
};
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
void __nac3_nditer_initialize(NDIter<int32_t>* iter, NDArray<int32_t>* ndarray,
|
||||
int32_t* indices) {
|
||||
iter->initialize_by_ndarray(ndarray, indices);
|
||||
}
|
||||
|
||||
void __nac3_nditer_initialize64(NDIter<int64_t>* iter,
|
||||
NDArray<int64_t>* ndarray, int64_t* indices) {
|
||||
iter->initialize_by_ndarray(ndarray, indices);
|
||||
}
|
||||
|
||||
bool __nac3_nditer_has_next(NDIter<int32_t>* iter) { return iter->has_next(); }
|
||||
|
||||
bool __nac3_nditer_has_next64(NDIter<int64_t>* iter) { return iter->has_next(); }
|
||||
|
||||
void __nac3_nditer_next(NDIter<int32_t>* iter) { iter->next(); }
|
||||
|
||||
void __nac3_nditer_next64(NDIter<int64_t>* iter) { iter->next(); }
|
||||
}
|
||||
|
|
@ -0,0 +1,194 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/broadcast.hpp>
|
||||
#include <irrt/ndarray/iter.hpp>
|
||||
|
||||
// NOTE: Everything would be much easier and elegant if einsum is implemented.
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace matmul {
|
||||
|
||||
/*
|
||||
* In einsum notation, the output is the broadcasts performed by `np.einsum("...ij,...jk->...ik", a, b)`.
|
||||
*
|
||||
* Example:
|
||||
* Suppose `a_shape == [99, 1, 97, 4, 2]`
|
||||
* and `b_shape == [ 1, 98, 1, 2, 5]`,
|
||||
*
|
||||
* ...then `new_a_shape == [99, 98, 97, 4, 2]`,
|
||||
* `new_b_shape == [99, 98, 97, 2, 5]`,
|
||||
* and `dst_shape == [99, 98, 97, 4, 5]`.
|
||||
* ^^^^^^^^^^ ^^^^
|
||||
* (by broadcast) (4x2 @ 2x5 => 4x5)
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void calculate_shapes(SizeT a_ndims, SizeT* a_shape, SizeT b_ndims,
|
||||
SizeT* b_shape, SizeT final_ndims, SizeT* new_a_shape,
|
||||
SizeT* new_b_shape, SizeT* dst_shape) {
|
||||
debug_assert(SizeT, a_ndims >= 2);
|
||||
debug_assert(SizeT, b_ndims >= 2);
|
||||
debug_assert_eq(SizeT, max(a_ndims, b_ndims), final_ndims);
|
||||
|
||||
const SizeT num_entries = 2;
|
||||
ShapeEntry<SizeT> entries[num_entries] = {
|
||||
{.ndims = a_ndims - 2, .shape = a_shape},
|
||||
{.ndims = b_ndims - 2, .shape = b_shape}};
|
||||
|
||||
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries,
|
||||
final_ndims - 2, new_a_shape);
|
||||
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries,
|
||||
final_ndims - 2, new_b_shape);
|
||||
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries,
|
||||
final_ndims - 2, dst_shape);
|
||||
|
||||
new_a_shape[final_ndims - 2] = a_shape[a_ndims - 2];
|
||||
new_a_shape[final_ndims - 1] = a_shape[a_ndims - 1];
|
||||
new_b_shape[final_ndims - 2] = b_shape[b_ndims - 2];
|
||||
new_b_shape[final_ndims - 1] = b_shape[b_ndims - 1];
|
||||
dst_shape[final_ndims - 2] = a_shape[a_ndims - 2];
|
||||
dst_shape[final_ndims - 1] = b_shape[b_ndims - 1];
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Perform `np.matmul(a, b)` but the inputs are both rank >=2 matrices and `a.shape[:-2] == b.shape[:-2]`.
|
||||
*
|
||||
* The compatibility of `a` and `b` (for their `.shape[-2:]`) are asserted.
|
||||
*
|
||||
* Also see https://numpy.org/doc/stable/reference/generated/numpy.matmul.html#numpy-matmul.
|
||||
*
|
||||
* This function expects `dst_ndarray` to contain the following content when called:
|
||||
* - `dst_ndarray->data` is allocated. Can be uninitialized.
|
||||
* - `dst_ndarray->itemsize` is set to `sizeof(T)`.
|
||||
* - `dst_ndarray->ndims` is set appropriately.
|
||||
* - `dst_ndarray->shape` is set appropriately.
|
||||
* - `dst_ndarray->strides` is ignored.
|
||||
*
|
||||
* Moreover, the shapes of `a_ndarray`, `b_ndarray`, and `dst_ndarray` **must be the same**. This implies
|
||||
*/
|
||||
template <typename SizeT, typename T>
|
||||
void matmul_at_least_2d(NDArray<SizeT>* a_ndarray, NDArray<SizeT>* b_ndarray,
|
||||
NDArray<SizeT>* dst_ndarray) {
|
||||
// All inputs' ndims should be >= 2 and be the same.
|
||||
debug_assert_eq(SizeT, a_ndarray->ndims, b_ndarray->ndims);
|
||||
debug_assert_eq(SizeT, a_ndarray->ndims, dst_ndarray->ndims);
|
||||
debug_assert(SizeT, a_ndarray->ndims >= 2);
|
||||
|
||||
debug_assert_eq(SizeT, a_ndarray->itemsize, sizeof(T));
|
||||
debug_assert_eq(SizeT, b_ndarray->itemsize, sizeof(T));
|
||||
debug_assert_eq(SizeT, dst_ndarray->itemsize, sizeof(T));
|
||||
|
||||
if (IRRT_DEBUG_ASSERT_BOOL) {
|
||||
// Check that the shapes are the same.
|
||||
for (SizeT i = 0; i < a_ndarray->ndims - 2; i++) {
|
||||
if (dst_ndarray->shape[0] != a_ndarray->shape[0]) {
|
||||
raise_debug_assert(
|
||||
SizeT, "Bad shape. At axis {0}, a has {1}, dst has {2}", i,
|
||||
a_ndarray->shape[i], dst_ndarray->shape[i]);
|
||||
}
|
||||
if (dst_ndarray->shape[0] != b_ndarray->shape[0]) {
|
||||
raise_debug_assert(
|
||||
SizeT, "Bad shape. At axis {0}, b has {1}, dst has {2}", i,
|
||||
b_ndarray->shape[i], dst_ndarray->shape[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Number of dimensions dedicated to stacking
|
||||
// e.g., [4, 6, 1, 2, 3]
|
||||
// ^^^^^^^ count these
|
||||
const SizeT u = a_ndarray->ndims - 2; // Alias
|
||||
|
||||
SizeT* a_mat_shape = a_ndarray->shape + u;
|
||||
SizeT* b_mat_shape = b_ndarray->shape + u;
|
||||
SizeT* dst_mat_shape = dst_ndarray->shape + u;
|
||||
|
||||
// Assert that dst_ndarray has the correct shape
|
||||
debug_assert_eq(SizeT, dst_mat_shape[0], a_mat_shape[0]);
|
||||
debug_assert_eq(SizeT, dst_mat_shape[1], b_mat_shape[1]);
|
||||
|
||||
// Check that a and b are compatible for matmul
|
||||
if (a_mat_shape[1] != b_mat_shape[0]) {
|
||||
// This is a custom error message. Different from NumPy.
|
||||
raise_exception(
|
||||
SizeT, EXN_VALUE_ERROR,
|
||||
"Cannot multiply LHS (shape ?x{0}) with RHS (shape {1}x?})",
|
||||
a_mat_shape[1], b_mat_shape[0], NO_PARAM);
|
||||
}
|
||||
|
||||
// Iterate through shape[:-2]. i.e,
|
||||
// Given a = [5, 4, 3, m, p] and b = [5, 4, 3, p, n]. We iterate through [5, 4, 3].
|
||||
SizeT* indices =
|
||||
(SizeT*)__builtin_alloca(sizeof(SizeT) * dst_ndarray->ndims);
|
||||
SizeT* mat_indices = indices + u;
|
||||
NDIter<SizeT> iter;
|
||||
iter.initialize(u, dst_ndarray->shape, dst_ndarray->strides,
|
||||
dst_ndarray->data, indices);
|
||||
|
||||
for (; iter.has_next(); iter.next()) {
|
||||
for (SizeT i = 0; i < dst_mat_shape[0]; i++) {
|
||||
for (SizeT j = 0; j < dst_mat_shape[1]; j++) {
|
||||
// `indices` is being reused to index into different ndarrays.
|
||||
mat_indices[0] = i;
|
||||
mat_indices[1] = j;
|
||||
T* d = ndarray::basic::get_ptr<SizeT, T>(dst_ndarray, indices);
|
||||
*d = 0;
|
||||
|
||||
for (SizeT k = 0; k < a_ndarray->shape[1]; k++) {
|
||||
mat_indices[0] = i;
|
||||
mat_indices[1] = k;
|
||||
T* a =
|
||||
ndarray::basic::get_ptr<SizeT, T>(a_ndarray, indices);
|
||||
|
||||
mat_indices[0] = k;
|
||||
mat_indices[1] = j;
|
||||
T* b =
|
||||
ndarray::basic::get_ptr<SizeT, T>(b_ndarray, indices);
|
||||
|
||||
*d += (*a) * (*b);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace matmul
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::matmul;
|
||||
|
||||
void __nac3_ndarray_matmul_calculate_shapes(int32_t a_ndims, int32_t* a_shape,
|
||||
int32_t b_ndims, int32_t* b_shape,
|
||||
int32_t final_ndims,
|
||||
int32_t* new_a_shape,
|
||||
int32_t* new_b_shape,
|
||||
int32_t* dst_shape) {
|
||||
calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims,
|
||||
new_a_shape, new_b_shape, dst_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_matmul_calculate_shapes64(int64_t a_ndims, int64_t* a_shape,
|
||||
int64_t b_ndims, int64_t* b_shape,
|
||||
int64_t final_ndims,
|
||||
int64_t* new_a_shape,
|
||||
int64_t* new_b_shape,
|
||||
int64_t* dst_shape) {
|
||||
calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims,
|
||||
new_a_shape, new_b_shape, dst_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_float64_matmul_at_least_2d(NDArray<int32_t>* a_ndarray,
|
||||
NDArray<int32_t>* b_ndarray,
|
||||
NDArray<int32_t>* dst_ndarray) {
|
||||
matmul_at_least_2d<int32_t, double>(a_ndarray, b_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_float64_matmul_at_least_2d64(
|
||||
NDArray<int64_t>* a_ndarray, NDArray<int64_t>* b_ndarray,
|
||||
NDArray<int64_t>* dst_ndarray) {
|
||||
matmul_at_least_2d<int64_t, double>(a_ndarray, b_ndarray, dst_ndarray);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,106 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace reshape {
|
||||
/**
|
||||
* @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
|
||||
*
|
||||
* If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
|
||||
* modified to contain the resolved dimension.
|
||||
*
|
||||
* To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
|
||||
* `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
|
||||
*
|
||||
* @param size The `.size` of `<ndarray>`
|
||||
* @param new_ndims Number of elements in `new_shape`
|
||||
* @param new_shape Target shape to reshape to
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void resolve_and_check_new_shape(SizeT size, SizeT new_ndims,
|
||||
SizeT* new_shape) {
|
||||
// Is there a -1 in `new_shape`?
|
||||
bool neg1_exists = false;
|
||||
// Location of -1, only initialized if `neg1_exists` is true
|
||||
SizeT neg1_axis_i;
|
||||
// The computed ndarray size of `new_shape`
|
||||
SizeT new_size = 1;
|
||||
|
||||
for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++) {
|
||||
SizeT dim = new_shape[axis_i];
|
||||
if (dim < 0) {
|
||||
if (dim == -1) {
|
||||
if (neg1_exists) {
|
||||
// Multiple `-1` found. Throw an error.
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"can only specify one unknown dimension",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
} else {
|
||||
neg1_exists = true;
|
||||
neg1_axis_i = axis_i;
|
||||
}
|
||||
} else {
|
||||
// TODO: What? In `np.reshape` any negative dimensions is
|
||||
// treated like its `-1`.
|
||||
//
|
||||
// Try running `np.zeros((3, 4)).reshape((-999, 2))`
|
||||
//
|
||||
// It is not documented by numpy.
|
||||
// Throw an error for now...
|
||||
|
||||
raise_exception(
|
||||
SizeT, EXN_VALUE_ERROR,
|
||||
"Found non -1 negative dimension {0} on axis {1}", dim,
|
||||
axis_i, NO_PARAM);
|
||||
}
|
||||
} else {
|
||||
new_size *= dim;
|
||||
}
|
||||
}
|
||||
|
||||
bool can_reshape;
|
||||
if (neg1_exists) {
|
||||
// Let `x` be the unknown dimension
|
||||
// Solve `x * <new_size> = <size>`
|
||||
if (new_size == 0 && size == 0) {
|
||||
// `x` has infinitely many solutions
|
||||
can_reshape = false;
|
||||
} else if (new_size == 0 && size != 0) {
|
||||
// `x` has no solutions
|
||||
can_reshape = false;
|
||||
} else if (size % new_size != 0) {
|
||||
// `x` has no integer solutions
|
||||
can_reshape = false;
|
||||
} else {
|
||||
can_reshape = true;
|
||||
new_shape[neg1_axis_i] = size / new_size; // Resolve dimension
|
||||
}
|
||||
} else {
|
||||
can_reshape = (new_size == size);
|
||||
}
|
||||
|
||||
if (!can_reshape) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"cannot reshape array of size {0} into given shape",
|
||||
size, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
}
|
||||
} // namespace reshape
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
void __nac3_ndarray_resolve_and_check_new_shape(int32_t size, int32_t new_ndims,
|
||||
int32_t* new_shape) {
|
||||
ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_resolve_and_check_new_shape64(int64_t size,
|
||||
int64_t new_ndims,
|
||||
int64_t* new_shape) {
|
||||
ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,145 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
|
||||
/*
|
||||
* Notes on `np.transpose(<array>, <axes>)`
|
||||
*
|
||||
* TODO: `axes`, if specified, can actually contain negative indices,
|
||||
* but it is not documented in numpy.
|
||||
*
|
||||
* Supporting it for now.
|
||||
*/
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace transpose {
|
||||
/**
|
||||
* @brief Do assertions on `<axes>` in `np.transpose(<array>, <axes>)`.
|
||||
*
|
||||
* Note that `np.transpose`'s `<axe>` argument is optional. If the argument
|
||||
* is specified but the user, use this function to do assertions on it.
|
||||
*
|
||||
* @param ndims The number of dimensions of `<array>`
|
||||
* @param num_axes Number of elements in `<axes>` as specified by the user.
|
||||
* This should be equal to `ndims`. If not, a "ValueError: axes don't match array" is thrown.
|
||||
* @param axes The user specified `<axes>`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void assert_transpose_axes(SizeT ndims, SizeT num_axes, const SizeT* axes) {
|
||||
if (ndims != num_axes) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "axes don't match array",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
// TODO: Optimize this
|
||||
bool* axe_specified = (bool*)__builtin_alloca(sizeof(bool) * ndims);
|
||||
for (SizeT i = 0; i < ndims; i++) axe_specified[i] = false;
|
||||
|
||||
for (SizeT i = 0; i < ndims; i++) {
|
||||
SizeT axis = slice::resolve_index_in_length(ndims, axes[i]);
|
||||
if (axis == slice::OUT_OF_BOUNDS) {
|
||||
// TODO: numpy actually throws a `numpy.exceptions.AxisError`
|
||||
raise_exception(
|
||||
SizeT, EXN_VALUE_ERROR,
|
||||
"axis {0} is out of bounds for array of dimension {1}", axis,
|
||||
ndims, NO_PARAM);
|
||||
}
|
||||
|
||||
if (axe_specified[axis]) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"repeated axis in transpose", NO_PARAM, NO_PARAM,
|
||||
NO_PARAM);
|
||||
}
|
||||
|
||||
axe_specified[axis] = true;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Create a transpose view of `src_ndarray` and perform proper assertions.
|
||||
*
|
||||
* This function is very similar to doing `dst_ndarray = np.transpose(src_ndarray, <axes>)`.
|
||||
* If `<axes>` is supposed to be `None`, caller can pass in a `nullptr` to `<axes>`.
|
||||
*
|
||||
* The transpose view created is returned by modifying `dst_ndarray`.
|
||||
*
|
||||
* The caller is responsible for setting up `dst_ndarray` before calling this function.
|
||||
* Here is what this function expects from `dst_ndarray` when called:
|
||||
* - `dst_ndarray->data` does not have to be initialized.
|
||||
* - `dst_ndarray->itemsize` does not have to be initialized.
|
||||
* - `dst_ndarray->ndims` must be initialized, must be equal to `src_ndarray->ndims`.
|
||||
* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
|
||||
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
|
||||
* When this function call ends:
|
||||
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
|
||||
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
|
||||
* - `dst_ndarray->ndims` is unchanged
|
||||
* - `dst_ndarray->shape` is updated according to how `np.transpose` works
|
||||
* - `dst_ndarray->strides` is updated according to how `np.transpose` works
|
||||
*
|
||||
* @param src_ndarray The NDArray to build a transpose view on
|
||||
* @param dst_ndarray The resulting NDArray after transpose. Further details in the comments above,
|
||||
* @param num_axes Number of elements in axes. Unused if `axes` is nullptr.
|
||||
* @param axes Axes permutation. Set it to `nullptr` if `<axes>` is `None`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void transpose(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray,
|
||||
SizeT num_axes, const SizeT* axes) {
|
||||
debug_assert_eq(SizeT, src_ndarray->ndims, dst_ndarray->ndims);
|
||||
const auto ndims = src_ndarray->ndims;
|
||||
|
||||
if (axes != nullptr) assert_transpose_axes(ndims, num_axes, axes);
|
||||
|
||||
dst_ndarray->data = src_ndarray->data;
|
||||
dst_ndarray->itemsize = src_ndarray->itemsize;
|
||||
|
||||
// Check out https://ajcr.net/stride-guide-part-2/ to see how `np.transpose` works behind the scenes.
|
||||
if (axes == nullptr) {
|
||||
// `np.transpose(<array>, axes=None)`
|
||||
|
||||
/*
|
||||
* Minor note: `np.transpose(<array>, axes=None)` is equivalent to
|
||||
* `np.transpose(<array>, axes=[N-1, N-2, ..., 0])` - basically it
|
||||
* is reversing the order of strides and shape.
|
||||
*
|
||||
* This is a fast implementation to handle this special (but very common) case.
|
||||
*/
|
||||
|
||||
for (SizeT axis = 0; axis < ndims; axis++) {
|
||||
dst_ndarray->shape[axis] = src_ndarray->shape[ndims - axis - 1];
|
||||
dst_ndarray->strides[axis] = src_ndarray->strides[ndims - axis - 1];
|
||||
}
|
||||
} else {
|
||||
// `np.transpose(<array>, <axes>)`
|
||||
|
||||
// Permute strides and shape according to `axes`, while resolving negative indices in `axes`
|
||||
for (SizeT axis = 0; axis < ndims; axis++) {
|
||||
// `i` cannot be OUT_OF_BOUNDS because of assertions
|
||||
SizeT i = slice::resolve_index_in_length(ndims, axes[axis]);
|
||||
|
||||
dst_ndarray->shape[axis] = src_ndarray->shape[i];
|
||||
dst_ndarray->strides[axis] = src_ndarray->strides[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace transpose
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::transpose;
|
||||
void __nac3_ndarray_transpose(const NDArray<int32_t>* src_ndarray,
|
||||
NDArray<int32_t>* dst_ndarray, int32_t num_axes,
|
||||
const int32_t* axes) {
|
||||
transpose(src_ndarray, dst_ndarray, num_axes, axes);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_transpose64(const NDArray<int64_t>* src_ndarray,
|
||||
NDArray<int64_t>* dst_ndarray, int64_t num_axes,
|
||||
const int64_t* axes) {
|
||||
transpose(src_ndarray, dst_ndarray, num_axes, axes);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,28 +1,167 @@
|
|||
#pragma once
|
||||
|
||||
#include "irrt/int_types.hpp"
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
#include <irrt/util.hpp>
|
||||
|
||||
extern "C" {
|
||||
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
|
||||
if (i < 0) {
|
||||
i = len + i;
|
||||
}
|
||||
if (i < 0) {
|
||||
return 0;
|
||||
} else if (i > len) {
|
||||
return len;
|
||||
}
|
||||
return i;
|
||||
}
|
||||
#include "exception.hpp"
|
||||
|
||||
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;
|
||||
// The type of an index or a value describing the length of a
|
||||
// range/slice is always `int32_t`.
|
||||
using SliceIndex = int32_t;
|
||||
|
||||
namespace {
|
||||
|
||||
/**
|
||||
* @brief A Python-like slice with resolved indices.
|
||||
*
|
||||
* "Resolved indices" means that `start` and `stop` must be positive and are
|
||||
* bound to a known length.
|
||||
*/
|
||||
struct Slice {
|
||||
SliceIndex start;
|
||||
SliceIndex stop;
|
||||
SliceIndex step;
|
||||
|
||||
/**
|
||||
* @brief Calculate and return the length / the number of the slice.
|
||||
*
|
||||
* If this were a Python range, this function would be `len(range(start, stop, step))`.
|
||||
*/
|
||||
SliceIndex len() {
|
||||
SliceIndex diff = stop - start;
|
||||
if (diff > 0 && step > 0) {
|
||||
return ((diff - 1) / step) + 1;
|
||||
} else if (diff < 0 && step < 0) {
|
||||
return ((diff + 1) / step) + 1;
|
||||
} else {
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
namespace slice {
|
||||
/**
|
||||
* @brief Resolve a slice index under a given length like Python indexing.
|
||||
*
|
||||
* In Python, if you have a `list` of length 100, `list[-1]` resolves to
|
||||
* `list[99]`, so `resolve_index_in_length_clamped(100, -1)` returns `99`.
|
||||
*
|
||||
* If `length` is 0, 0 is returned for any value of `index`.
|
||||
*
|
||||
* If `index` is out of bounds, clamps the returned value between `0` and
|
||||
* `length - 1` (inclusive).
|
||||
*
|
||||
*/
|
||||
SliceIndex resolve_index_in_length_clamped(SliceIndex length,
|
||||
SliceIndex index) {
|
||||
if (index < 0) {
|
||||
return max<SliceIndex>(length + index, 0);
|
||||
} else {
|
||||
return 0;
|
||||
return min<SliceIndex>(length, index);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const SliceIndex OUT_OF_BOUNDS = -1;
|
||||
|
||||
/**
|
||||
* @brief Like `resolve_index_in_length_clamped`, but returns `OUT_OF_BOUNDS`
|
||||
* if `index` is out of bounds.
|
||||
*/
|
||||
SliceIndex resolve_index_in_length(SliceIndex length, SliceIndex index) {
|
||||
SliceIndex resolved = index < 0 ? length + index : index;
|
||||
if (0 <= resolved && resolved < length) {
|
||||
return resolved;
|
||||
} else {
|
||||
return OUT_OF_BOUNDS;
|
||||
}
|
||||
}
|
||||
} // namespace slice
|
||||
|
||||
/**
|
||||
* @brief A Python-like slice with **unresolved** indices.
|
||||
*/
|
||||
struct UserSlice {
|
||||
bool start_defined;
|
||||
SliceIndex start;
|
||||
|
||||
bool stop_defined;
|
||||
SliceIndex stop;
|
||||
|
||||
bool step_defined;
|
||||
SliceIndex step;
|
||||
|
||||
UserSlice() { this->reset(); }
|
||||
|
||||
void reset() {
|
||||
this->start_defined = false;
|
||||
this->stop_defined = false;
|
||||
this->step_defined = false;
|
||||
}
|
||||
|
||||
void set_start(SliceIndex start) {
|
||||
this->start_defined = true;
|
||||
this->start = start;
|
||||
}
|
||||
|
||||
void set_stop(SliceIndex stop) {
|
||||
this->stop_defined = true;
|
||||
this->stop = stop;
|
||||
}
|
||||
|
||||
void set_step(SliceIndex step) {
|
||||
this->step_defined = true;
|
||||
this->step = step;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Resolve this slice.
|
||||
*
|
||||
* In Python, this would be `slice(start, stop, step).indices(length)`.
|
||||
*
|
||||
* @return A `Slice` with the resolved indices.
|
||||
*/
|
||||
Slice indices(SliceIndex length) {
|
||||
Slice result;
|
||||
|
||||
result.step = step_defined ? step : 1;
|
||||
bool step_is_negative = result.step < 0;
|
||||
|
||||
if (start_defined) {
|
||||
result.start =
|
||||
slice::resolve_index_in_length_clamped(length, start);
|
||||
} else {
|
||||
result.start = step_is_negative ? length - 1 : 0;
|
||||
}
|
||||
|
||||
if (stop_defined) {
|
||||
result.stop = slice::resolve_index_in_length_clamped(length, stop);
|
||||
} else {
|
||||
result.stop = step_is_negative ? -1 : length;
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Like `.indices()` but with assertions.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
Slice indices_checked(SliceIndex length) {
|
||||
// TODO: Switch to `SizeT length`
|
||||
|
||||
if (length < 0) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"length should not be negative, got {0}", length,
|
||||
NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
if (this->step_defined && this->step == 0) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "slice step cannot be zero",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
return this->indices(length);
|
||||
}
|
||||
};
|
||||
} // namespace
|
||||
|
|
@ -0,0 +1,101 @@
|
|||
#pragma once
|
||||
|
||||
namespace {
|
||||
template <typename T>
|
||||
const T& max(const T& a, const T& b) {
|
||||
return a > b ? a : b;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
const T& min(const T& a, const T& b) {
|
||||
return a > b ? b : a;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
bool arrays_match(int len, T* as, T* bs) {
|
||||
for (int i = 0; i < len; i++) {
|
||||
if (as[i] != bs[i]) return false;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
namespace cstr_utils {
|
||||
/**
|
||||
* @brief Return true if `str` is empty.
|
||||
*/
|
||||
bool is_empty(const char* str) { return str[0] == '\0'; }
|
||||
|
||||
/**
|
||||
* @brief Implementation of `strcmp()`
|
||||
*/
|
||||
int8_t compare(const char* a, const char* b) {
|
||||
uint32_t i = 0;
|
||||
while (true) {
|
||||
if (a[i] < b[i]) {
|
||||
return -1;
|
||||
} else if (a[i] > b[i]) {
|
||||
return 1;
|
||||
} else {
|
||||
if (a[i] == '\0') {
|
||||
return 0;
|
||||
} else {
|
||||
i++;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return true two strings have the same content.
|
||||
*/
|
||||
int8_t equal(const char* a, const char* b) { return compare(a, b) == 0; }
|
||||
|
||||
/**
|
||||
* @brief Implementation of `strlen()`.
|
||||
*/
|
||||
uint32_t length(const char* str) {
|
||||
uint32_t length = 0;
|
||||
while (*str != '\0') {
|
||||
length++;
|
||||
str++;
|
||||
}
|
||||
return length;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Copy a null-terminated string to a buffer with limited size and guaranteed null-termination.
|
||||
*
|
||||
* `dst_max_size` must be greater than 0, otherwise this function has undefined behavior.
|
||||
*
|
||||
* This function attempts to copy everything from `src` from `dst`, and *always* null-terminates `dst`.
|
||||
*
|
||||
* If the size of `dst` is too small, the final byte (`dst[dst_max_size - 1]`) of `dst` will be set to
|
||||
* the null terminator.
|
||||
*
|
||||
* @param src String to copy from.
|
||||
* @param dst Buffer to copy string to.
|
||||
* @param dst_max_size
|
||||
* Number of bytes of this buffer, including the space needed for the null terminator.
|
||||
* Must be greater than 0.
|
||||
* @return If `dst` is too small to contain everything in `src`.
|
||||
*/
|
||||
bool copy(const char* src, char* dst, uint32_t dst_max_size) {
|
||||
for (uint32_t i = 0; i < dst_max_size; i++) {
|
||||
bool is_last = i + 1 == dst_max_size;
|
||||
if (is_last && src[i] != '\0') {
|
||||
dst[i] = '\0';
|
||||
return false;
|
||||
}
|
||||
|
||||
if (src[i] == '\0') {
|
||||
dst[i] = '\0';
|
||||
return true;
|
||||
}
|
||||
|
||||
dst[i] = src[i];
|
||||
}
|
||||
|
||||
__builtin_unreachable();
|
||||
}
|
||||
} // namespace cstr_utils
|
||||
} // namespace
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
#pragma once
|
||||
|
||||
#ifdef IRRT_DEBUG
|
||||
#define IRRT_DEBUG_ASSERT
|
||||
#define IRRT_DEBUG_ASSERT_BOOL true
|
||||
#else
|
||||
#define IRRT_DEBUG_ASSERT_BOOL false
|
||||
#endif
|
||||
|
||||
#include <irrt/core.hpp>
|
||||
#include <irrt/debug.hpp>
|
||||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/list.hpp>
|
||||
#include <irrt/ndarray/array.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/broadcast.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/ndarray/indexing.hpp>
|
||||
#include <irrt/ndarray/iter.hpp>
|
||||
#include <irrt/ndarray/product.hpp>
|
||||
#include <irrt/ndarray/reshape.hpp>
|
||||
#include <irrt/ndarray/transpose.hpp>
|
||||
#include <irrt/util.hpp>
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
// This file will be compiled like a real C++ program,
|
||||
// and we do have the luxury to use the standard libraries.
|
||||
// That is if the nix flakes do not have issues... especially on msys2...
|
||||
|
||||
#include <cstdint>
|
||||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
|
||||
// Special macro to inform `#include <irrt/*>` that we are testing.
|
||||
#define IRRT_TESTING
|
||||
|
||||
// Note that failure unit tests are not supported.
|
||||
|
||||
#include <test/test_core.hpp>
|
||||
#include <test/test_ndarray_basic.hpp>
|
||||
#include <test/test_ndarray_broadcast.hpp>
|
||||
#include <test/test_ndarray_indexing.hpp>
|
||||
|
||||
int main() {
|
||||
test::core::run();
|
||||
test::ndarray_basic::run();
|
||||
test::ndarray_indexing::run();
|
||||
test::ndarray_broadcast::run();
|
||||
return 0;
|
||||
}
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
#pragma once
|
||||
|
||||
#include <cstdint>
|
||||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
#include <irrt_everything.hpp>
|
||||
#include <test/util.hpp>
|
||||
|
||||
/*
|
||||
Include this header for every test_*.cpp
|
||||
*/
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
#pragma once
|
||||
|
||||
#include <test/includes.hpp>
|
||||
|
||||
namespace test {
|
||||
namespace core {
|
||||
void test_int_exp() {
|
||||
BEGIN_TEST();
|
||||
|
||||
assert_values_match(125L, __nac3_int_exp_impl<int64_t>(5, 3));
|
||||
assert_values_match(3125L, __nac3_int_exp_impl<int64_t>(5, 5));
|
||||
}
|
||||
|
||||
void run() { test_int_exp(); }
|
||||
} // namespace core
|
||||
} // namespace test
|
||||
|
|
@ -0,0 +1,30 @@
|
|||
#pragma once
|
||||
|
||||
#include <test/includes.hpp>
|
||||
|
||||
namespace test {
|
||||
namespace ndarray_basic {
|
||||
void test_calc_size_from_shape_normal() {
|
||||
// Test shapes with normal values
|
||||
BEGIN_TEST();
|
||||
|
||||
int64_t shape[4] = {2, 3, 5, 7};
|
||||
assert_values_match(
|
||||
210L, ndarray::basic::util::calc_size_from_shape<int64_t>(4, shape));
|
||||
}
|
||||
|
||||
void test_calc_size_from_shape_has_zero() {
|
||||
// Test shapes with 0 in them
|
||||
BEGIN_TEST();
|
||||
|
||||
int64_t shape[4] = {2, 0, 5, 7};
|
||||
assert_values_match(
|
||||
0L, ndarray::basic::util::calc_size_from_shape<int64_t>(4, shape));
|
||||
}
|
||||
|
||||
void run() {
|
||||
test_calc_size_from_shape_normal();
|
||||
test_calc_size_from_shape_has_zero();
|
||||
}
|
||||
} // namespace ndarray_basic
|
||||
} // namespace test
|
||||
|
|
@ -0,0 +1,127 @@
|
|||
#pragma once
|
||||
|
||||
#include <test/includes.hpp>
|
||||
|
||||
namespace test {
|
||||
namespace ndarray_broadcast {
|
||||
void test_can_broadcast_shape() {
|
||||
BEGIN_TEST();
|
||||
|
||||
assert_values_match(true,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){3}, 5, (int32_t[]){1, 1, 1, 1, 3}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){3}, 2, (int32_t[]){3, 1}));
|
||||
assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){3}, 1, (int32_t[]){3}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){1}, 1, (int32_t[]){3}));
|
||||
assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){1}, 1, (int32_t[]){1}));
|
||||
assert_values_match(
|
||||
true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
3, (int32_t[]){256, 256, 3}, 3, (int32_t[]){256, 1, 3}));
|
||||
assert_values_match(true,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){3}));
|
||||
assert_values_match(false,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){2}));
|
||||
assert_values_match(true,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){1}));
|
||||
|
||||
// In cases when the shapes contain zero(es)
|
||||
assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){0}, 1, (int32_t[]){1}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){0}, 1, (int32_t[]){2}));
|
||||
assert_values_match(true,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
4, (int32_t[]){0, 4, 0, 0}, 1, (int32_t[]){1}));
|
||||
assert_values_match(
|
||||
true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
4, (int32_t[]){0, 4, 0, 0}, 4, (int32_t[]){1, 1, 1, 1}));
|
||||
assert_values_match(
|
||||
true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
4, (int32_t[]){0, 4, 0, 0}, 4, (int32_t[]){1, 4, 1, 1}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
2, (int32_t[]){4, 3}, 2, (int32_t[]){0, 3}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
2, (int32_t[]){4, 3}, 2, (int32_t[]){0, 0}));
|
||||
}
|
||||
|
||||
void test_ndarray_broadcast() {
|
||||
/*
|
||||
# array = np.array([[19.9, 29.9, 39.9, 49.9]], dtype=np.float64)
|
||||
# >>> [[19.9 29.9 39.9 49.9]]
|
||||
#
|
||||
# array = np.broadcast_to(array, (2, 3, 4))
|
||||
# >>> [[[19.9 29.9 39.9 49.9]
|
||||
# >>> [19.9 29.9 39.9 49.9]
|
||||
# >>> [19.9 29.9 39.9 49.9]]
|
||||
# >>> [[19.9 29.9 39.9 49.9]
|
||||
# >>> [19.9 29.9 39.9 49.9]
|
||||
# >>> [19.9 29.9 39.9 49.9]]]
|
||||
#
|
||||
# assery array.strides == (0, 0, 8)
|
||||
|
||||
*/
|
||||
BEGIN_TEST();
|
||||
|
||||
double in_data[4] = {19.9, 29.9, 39.9, 49.9};
|
||||
const int32_t in_ndims = 2;
|
||||
int32_t in_shape[in_ndims] = {1, 4};
|
||||
int32_t in_strides[in_ndims] = {};
|
||||
NDArray<int32_t> ndarray = {.data = (uint8_t*)in_data,
|
||||
.itemsize = sizeof(double),
|
||||
.ndims = in_ndims,
|
||||
.shape = in_shape,
|
||||
.strides = in_strides};
|
||||
ndarray::basic::set_strides_by_shape(&ndarray);
|
||||
|
||||
const int32_t dst_ndims = 3;
|
||||
int32_t dst_shape[dst_ndims] = {2, 3, 4};
|
||||
int32_t dst_strides[dst_ndims] = {};
|
||||
NDArray<int32_t> dst_ndarray = {
|
||||
.ndims = dst_ndims, .shape = dst_shape, .strides = dst_strides};
|
||||
|
||||
ndarray::broadcast::broadcast_to(&ndarray, &dst_ndarray);
|
||||
|
||||
assert_arrays_match(dst_ndims, ((int32_t[]){0, 0, 8}), dst_ndarray.strides);
|
||||
|
||||
assert_values_match(19.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 0, 0}))));
|
||||
assert_values_match(29.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 0, 1}))));
|
||||
assert_values_match(39.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 0, 2}))));
|
||||
assert_values_match(49.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 0, 3}))));
|
||||
assert_values_match(19.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 1, 0}))));
|
||||
assert_values_match(29.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 1, 1}))));
|
||||
assert_values_match(39.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 1, 2}))));
|
||||
assert_values_match(49.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 1, 3}))));
|
||||
assert_values_match(49.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){1, 2, 3}))));
|
||||
}
|
||||
|
||||
void run() {
|
||||
test_can_broadcast_shape();
|
||||
test_ndarray_broadcast();
|
||||
}
|
||||
} // namespace ndarray_broadcast
|
||||
} // namespace test
|
||||
|
|
@ -0,0 +1,165 @@
|
|||
#pragma once
|
||||
|
||||
#include <test/includes.hpp>
|
||||
|
||||
namespace test {
|
||||
namespace ndarray_indexing {
|
||||
void test_normal_1() {
|
||||
/*
|
||||
Reference Python code:
|
||||
```python
|
||||
ndarray = np.arange(12, dtype=np.float64).reshape((3, 4));
|
||||
# array([[ 0., 1., 2., 3.],
|
||||
# [ 4., 5., 6., 7.],
|
||||
# [ 8., 9., 10., 11.]])
|
||||
|
||||
dst_ndarray = ndarray[-2:, 1::2]
|
||||
# array([[ 5., 7.],
|
||||
# [ 9., 11.]])
|
||||
|
||||
assert dst_ndarray.shape == (2, 2)
|
||||
assert dst_ndarray.strides == (32, 16)
|
||||
assert dst_ndarray[0, 0] == 5.0
|
||||
assert dst_ndarray[0, 1] == 7.0
|
||||
assert dst_ndarray[1, 0] == 9.0
|
||||
assert dst_ndarray[1, 1] == 11.0
|
||||
```
|
||||
*/
|
||||
BEGIN_TEST();
|
||||
|
||||
// Prepare src_ndarray
|
||||
double src_data[12] = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0,
|
||||
6.0, 7.0, 8.0, 9.0, 10.0, 11.0};
|
||||
int64_t src_itemsize = sizeof(double);
|
||||
const int64_t src_ndims = 2;
|
||||
int64_t src_shape[src_ndims] = {3, 4};
|
||||
int64_t src_strides[src_ndims] = {};
|
||||
NDArray<int64_t> src_ndarray = {.data = (uint8_t *)src_data,
|
||||
.itemsize = src_itemsize,
|
||||
.ndims = src_ndims,
|
||||
.shape = src_shape,
|
||||
.strides = src_strides};
|
||||
ndarray::basic::set_strides_by_shape(&src_ndarray);
|
||||
|
||||
// Prepare dst_ndarray
|
||||
const int64_t dst_ndims = 2;
|
||||
int64_t dst_shape[dst_ndims] = {999, 999}; // Empty values
|
||||
int64_t dst_strides[dst_ndims] = {999, 999}; // Empty values
|
||||
NDArray<int64_t> dst_ndarray = {.data = nullptr,
|
||||
.ndims = dst_ndims,
|
||||
.shape = dst_shape,
|
||||
.strides = dst_strides};
|
||||
|
||||
// Create the subscripts in `ndarray[-2::, 1::2]`
|
||||
UserSlice subscript_1;
|
||||
subscript_1.set_start(-2);
|
||||
|
||||
UserSlice subscript_2;
|
||||
subscript_2.set_start(1);
|
||||
subscript_2.set_step(2);
|
||||
|
||||
const int64_t num_indexes = 2;
|
||||
NDIndex indexes[num_indexes] = {
|
||||
{.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_1},
|
||||
{.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
|
||||
|
||||
ndarray::indexing::index(num_indexes, indexes, &src_ndarray, &dst_ndarray);
|
||||
|
||||
int64_t expected_shape[dst_ndims] = {2, 2};
|
||||
int64_t expected_strides[dst_ndims] = {32, 16};
|
||||
|
||||
assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
|
||||
assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
|
||||
|
||||
// dst_ndarray[0, 0]
|
||||
assert_values_match(5.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){0, 0})));
|
||||
// dst_ndarray[0, 1]
|
||||
assert_values_match(7.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){0, 1})));
|
||||
// dst_ndarray[1, 0]
|
||||
assert_values_match(9.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){1, 0})));
|
||||
// dst_ndarray[1, 1]
|
||||
assert_values_match(11.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){1, 1})));
|
||||
}
|
||||
|
||||
void test_normal_2() {
|
||||
/*
|
||||
```python
|
||||
ndarray = np.arange(12, dtype=np.float64).reshape((3, 4))
|
||||
# array([[ 0., 1., 2., 3.],
|
||||
# [ 4., 5., 6., 7.],
|
||||
# [ 8., 9., 10., 11.]])
|
||||
|
||||
dst_ndarray = ndarray[2, ::-2]
|
||||
# array([11., 9.])
|
||||
|
||||
assert dst_ndarray.shape == (2,)
|
||||
assert dst_ndarray.strides == (-16,)
|
||||
assert dst_ndarray[0] == 11.0
|
||||
assert dst_ndarray[1] == 9.0
|
||||
```
|
||||
*/
|
||||
BEGIN_TEST();
|
||||
|
||||
// Prepare src_ndarray
|
||||
double src_data[12] = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0,
|
||||
6.0, 7.0, 8.0, 9.0, 10.0, 11.0};
|
||||
int64_t src_itemsize = sizeof(double);
|
||||
const int64_t src_ndims = 2;
|
||||
int64_t src_shape[src_ndims] = {3, 4};
|
||||
int64_t src_strides[src_ndims] = {};
|
||||
NDArray<int64_t> src_ndarray = {.data = (uint8_t *)src_data,
|
||||
.itemsize = src_itemsize,
|
||||
.ndims = src_ndims,
|
||||
.shape = src_shape,
|
||||
.strides = src_strides};
|
||||
ndarray::basic::set_strides_by_shape(&src_ndarray);
|
||||
|
||||
// Prepare dst_ndarray
|
||||
const int64_t dst_ndims = 1;
|
||||
int64_t dst_shape[dst_ndims] = {999}; // Empty values
|
||||
int64_t dst_strides[dst_ndims] = {999}; // Empty values
|
||||
NDArray<int64_t> dst_ndarray = {.data = nullptr,
|
||||
.ndims = dst_ndims,
|
||||
.shape = dst_shape,
|
||||
.strides = dst_strides};
|
||||
|
||||
// Create the subscripts in `ndarray[2, ::-2]`
|
||||
int64_t subscript_1 = 2;
|
||||
|
||||
UserSlice subscript_2;
|
||||
subscript_2.set_step(-2);
|
||||
|
||||
const int64_t num_indexes = 2;
|
||||
NDIndex indexes[num_indexes] = {
|
||||
{.type = ND_INDEX_TYPE_SINGLE_ELEMENT, .data = (uint8_t *)&subscript_1},
|
||||
{.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
|
||||
|
||||
ndarray::indexing::index(num_indexes, indexes, &src_ndarray, &dst_ndarray);
|
||||
|
||||
int64_t expected_shape[dst_ndims] = {2};
|
||||
int64_t expected_strides[dst_ndims] = {-16};
|
||||
assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
|
||||
assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
|
||||
|
||||
assert_values_match(11.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){0})));
|
||||
assert_values_match(9.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){1})));
|
||||
}
|
||||
|
||||
void run() {
|
||||
test_normal_1();
|
||||
test_normal_2();
|
||||
}
|
||||
} // namespace ndarray_indexing
|
||||
} // namespace test
|
||||
|
|
@ -0,0 +1,131 @@
|
|||
#pragma once
|
||||
|
||||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
|
||||
template <class T>
|
||||
void print_value(const T& value);
|
||||
|
||||
template <>
|
||||
void print_value(const bool& value) {
|
||||
printf("%s", value ? "true" : "false");
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const int8_t& value) {
|
||||
printf("%d", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const int32_t& value) {
|
||||
printf("%d", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const int64_t& value) {
|
||||
printf("%d", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const uint8_t& value) {
|
||||
printf("%u", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const uint32_t& value) {
|
||||
printf("%u", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const uint64_t& value) {
|
||||
printf("%d", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const float& value) {
|
||||
printf("%f", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const double& value) {
|
||||
printf("%f", value);
|
||||
}
|
||||
|
||||
void __begin_test(const char* function_name, const char* file, int line) {
|
||||
printf("######### Running %s @ %s:%d\n", function_name, file, line);
|
||||
}
|
||||
|
||||
#define BEGIN_TEST() __begin_test(__FUNCTION__, __FILE__, __LINE__)
|
||||
|
||||
void test_fail() {
|
||||
printf("[!] Test failed. Exiting with status code 1.\n");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void debug_print_array(int len, const T* as) {
|
||||
printf("[");
|
||||
for (int i = 0; i < len; i++) {
|
||||
if (i != 0) printf(", ");
|
||||
print_value(as[i]);
|
||||
}
|
||||
printf("]");
|
||||
}
|
||||
|
||||
void print_assertion_passed(const char* file, int line) {
|
||||
printf("[*] Assertion passed on %s:%d\n", file, line);
|
||||
}
|
||||
|
||||
void print_assertion_failed(const char* file, int line) {
|
||||
printf("[!] Assertion failed on %s:%d\n", file, line);
|
||||
}
|
||||
|
||||
void __assert_true(const char* file, int line, bool cond) {
|
||||
if (cond) {
|
||||
print_assertion_passed(file, line);
|
||||
} else {
|
||||
print_assertion_failed(file, line);
|
||||
test_fail();
|
||||
}
|
||||
}
|
||||
|
||||
#define assert_true(cond) __assert_true(__FILE__, __LINE__, cond)
|
||||
|
||||
template <typename T>
|
||||
void __assert_arrays_match(const char* file, int line, int len,
|
||||
const T* expected, const T* got) {
|
||||
if (arrays_match(len, expected, got)) {
|
||||
print_assertion_passed(file, line);
|
||||
} else {
|
||||
print_assertion_failed(file, line);
|
||||
printf("Expect = ");
|
||||
debug_print_array(len, expected);
|
||||
printf("\n");
|
||||
printf(" Got = ");
|
||||
debug_print_array(len, got);
|
||||
printf("\n");
|
||||
test_fail();
|
||||
}
|
||||
}
|
||||
|
||||
#define assert_arrays_match(len, expected, got) \
|
||||
__assert_arrays_match(__FILE__, __LINE__, len, expected, got)
|
||||
|
||||
template <typename T>
|
||||
void __assert_values_match(const char* file, int line, T expected, T got) {
|
||||
if (expected == got) {
|
||||
print_assertion_passed(file, line);
|
||||
} else {
|
||||
print_assertion_failed(file, line);
|
||||
printf("Expect = ");
|
||||
print_value(expected);
|
||||
printf("\n");
|
||||
printf(" Got = ");
|
||||
print_value(got);
|
||||
printf("\n");
|
||||
test_fail();
|
||||
}
|
||||
}
|
||||
|
||||
#define assert_values_match(expected, got) \
|
||||
__assert_values_match(__FILE__, __LINE__, expected, got)
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,16 +1,17 @@
|
|||
use inkwell::{
|
||||
context::Context,
|
||||
types::{AnyTypeEnum, ArrayType, BasicType, BasicTypeEnum, IntType, PointerType, StructType},
|
||||
values::{ArrayValue, BasicValue, BasicValueEnum, IntValue, PointerValue, StructValue},
|
||||
AddressSpace, IntPredicate,
|
||||
};
|
||||
|
||||
use super::{
|
||||
use crate::codegen::{
|
||||
irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
|
||||
llvm_intrinsics::call_int_umin,
|
||||
stmt::gen_for_callback_incrementing,
|
||||
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},
|
||||
AddressSpace, IntPredicate,
|
||||
};
|
||||
|
||||
/// A LLVM type that is used to represent a non-primitive type in NAC3.
|
||||
pub trait ProxyType<'ctx>: Into<Self::Base> {
|
||||
|
|
@ -1249,13 +1250,11 @@ impl<'ctx> NDArrayType<'ctx> {
|
|||
|
||||
/// Returns the element type of this `ndarray` type.
|
||||
#[must_use]
|
||||
pub fn element_type(&self) -> AnyTypeEnum<'ctx> {
|
||||
pub fn element_type(&self) -> BasicTypeEnum<'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()
|
||||
}
|
||||
}
|
||||
|
|
@ -1405,7 +1404,7 @@ impl<'ctx> NDArrayValue<'ctx> {
|
|||
|
||||
/// 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> {
|
||||
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();
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +1,3 @@
|
|||
use std::collections::HashMap;
|
||||
|
||||
use indexmap::IndexMap;
|
||||
|
||||
use nac3parser::ast::StrRef;
|
||||
|
||||
use crate::{
|
||||
symbol_resolver::SymbolValue,
|
||||
toplevel::DefinitionId,
|
||||
|
|
@ -15,6 +9,10 @@ use crate::{
|
|||
},
|
||||
};
|
||||
|
||||
use indexmap::IndexMap;
|
||||
use nac3parser::ast::StrRef;
|
||||
use std::collections::HashMap;
|
||||
|
||||
pub struct ConcreteTypeStore {
|
||||
store: Vec<ConcreteTypeEnum>,
|
||||
}
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,10 +1,8 @@
|
|||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue},
|
||||
};
|
||||
use inkwell::attributes::{Attribute, AttributeLoc};
|
||||
use inkwell::values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue};
|
||||
use itertools::Either;
|
||||
|
||||
use super::CodeGenContext;
|
||||
use crate::codegen::CodeGenContext;
|
||||
|
||||
/// Macro to generate extern function
|
||||
/// Both function return type and function parameter type are `FloatValue`
|
||||
|
|
@ -15,11 +13,11 @@ use super::CodeGenContext;
|
|||
/// * `$extern_fn:literal`: Name of underlying extern function
|
||||
///
|
||||
/// Optional Arguments:
|
||||
/// * `$(,$attributes:literal)*)`: Attributes linked with the extern function.
|
||||
/// The default attributes are "mustprogress", "nofree", "nounwind", "willreturn", and "writeonly".
|
||||
/// These will be used unless other attributes are specified
|
||||
/// * `$(,$attributes:literal)*)`: Attributes linked with the extern function
|
||||
/// The default attributes are "mustprogress", "nofree", "nounwind", "willreturn", and "writeonly"
|
||||
/// These will be used unless other attributes are specified
|
||||
/// * `$(,$args:ident)*`: Operands of the extern function
|
||||
/// The data type of these operands will be set to `FloatValue`
|
||||
/// The data type of these operands will be set to `FloatValue`
|
||||
///
|
||||
macro_rules! generate_extern_fn {
|
||||
("unary", $fn_name:ident, $extern_fn:literal) => {
|
||||
|
|
|
|||
|
|
@ -1,18 +1,16 @@
|
|||
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};
|
||||
|
||||
use super::{bool_to_i1, bool_to_i8, classes::ArraySliceValue, expr::*, stmt::*, CodeGenContext};
|
||||
use crate::{
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{DefinitionId, TopLevelDef},
|
||||
typecheck::typedef::{FunSignature, Type},
|
||||
};
|
||||
|
||||
pub trait CodeGenerator {
|
||||
/// Return the module name for the code generator.
|
||||
fn get_name(&self) -> &str;
|
||||
|
|
@ -59,7 +57,6 @@ pub trait CodeGenerator {
|
|||
/// - fun: Function signature, definition ID and the substitution key.
|
||||
/// - params: Function parameters. Note that this does not include the object even if the
|
||||
/// function is a class method.
|
||||
///
|
||||
/// Note that this function should check if the function is generated in another thread (due to
|
||||
/// possible race condition), see the default implementation for an example.
|
||||
fn gen_func_instance<'ctx>(
|
||||
|
|
|
|||
|
|
@ -1,30 +1,37 @@
|
|||
use crate::symbol_resolver::SymbolResolver;
|
||||
use crate::typecheck::typedef::Type;
|
||||
|
||||
mod test;
|
||||
|
||||
use super::model::*;
|
||||
use super::object::ndarray::broadcast::ShapeEntry;
|
||||
use super::object::ndarray::indexing::{NDIndex, UserSlice};
|
||||
use super::structure::{List, NDArray, NDIter};
|
||||
use super::{
|
||||
classes::{
|
||||
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue,
|
||||
TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
|
||||
},
|
||||
llvm_intrinsics, CodeGenContext, CodeGenerator,
|
||||
};
|
||||
use crate::codegen::classes::TypedArrayLikeAccessor;
|
||||
use crate::codegen::stmt::gen_for_callback_incrementing;
|
||||
use function::{get_sizet_dependent_function_name, CallFunction};
|
||||
use inkwell::values::BasicValue;
|
||||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
context::Context,
|
||||
memory_buffer::MemoryBuffer,
|
||||
module::Module,
|
||||
types::{BasicTypeEnum, IntType},
|
||||
values::{BasicValue, BasicValueEnum, CallSiteValue, FloatValue, IntValue},
|
||||
values::{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> {
|
||||
pub fn load_irrt(ctx: &Context) -> Module {
|
||||
let bitcode_buf = MemoryBuffer::create_from_memory_range(
|
||||
include_bytes!(concat!(env!("OUT_DIR"), "/irrt.bc")),
|
||||
"irrt_bitcode_buffer",
|
||||
|
|
@ -40,25 +47,6 @@ pub fn load_irrt<'ctx>(ctx: &'ctx Context, symbol_resolver: &dyn SymbolResolver)
|
|||
let function = irrt_mod.get_function(symbol).unwrap();
|
||||
function.add_attribute(AttributeLoc::Function, ctx.create_enum_attribute(inline_attr, 0));
|
||||
}
|
||||
|
||||
// Initialize all global `EXN_*` exception IDs in IRRT with the [`SymbolResolver`].
|
||||
let exn_id_type = ctx.i32_type();
|
||||
let errors = &[
|
||||
("EXN_INDEX_ERROR", "0:IndexError"),
|
||||
("EXN_VALUE_ERROR", "0:ValueError"),
|
||||
("EXN_ASSERTION_ERROR", "0:AssertionError"),
|
||||
("EXN_TYPE_ERROR", "0:TypeError"),
|
||||
];
|
||||
for (irrt_name, symbol_name) in errors {
|
||||
let exn_id = symbol_resolver.get_string_id(symbol_name);
|
||||
let exn_id = exn_id_type.const_int(exn_id as u64, false).as_basic_value_enum();
|
||||
|
||||
let global = irrt_mod.get_global(irrt_name).unwrap_or_else(|| {
|
||||
panic!("Exception symbol name '{irrt_name}' should exist in the IRRT LLVM module")
|
||||
});
|
||||
global.set_initializer(&exn_id);
|
||||
}
|
||||
|
||||
irrt_mod
|
||||
}
|
||||
|
||||
|
|
@ -76,7 +64,7 @@ pub fn integer_power<'ctx, G: CodeGenerator + ?Sized>(
|
|||
(64, 64, true) => "__nac3_int_exp_int64_t",
|
||||
(32, 32, false) => "__nac3_int_exp_uint32_t",
|
||||
(64, 64, false) => "__nac3_int_exp_uint64_t",
|
||||
_ => codegen_unreachable!(ctx),
|
||||
_ => unreachable!(),
|
||||
};
|
||||
let base_type = base.get_type();
|
||||
let pow_fun = ctx.module.get_function(symbol).unwrap_or_else(|| {
|
||||
|
|
@ -435,14 +423,27 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
|
|||
.unwrap();
|
||||
let cond_1 = ctx.builder.build_and(dest_step_eq_one, src_slt_dest, "slice_cond_1").unwrap();
|
||||
let cond = ctx.builder.build_or(src_eq_dest, cond_1, "slice_cond").unwrap();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
cond,
|
||||
"0:ValueError",
|
||||
"attempt to assign sequence of size {0} to slice of size {1} with step size {2}",
|
||||
[Some(src_slice_len), Some(dest_slice_len), Some(dest_idx.2)],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
// TODO: Temporary fix. Rewrite `list_slice_assignment` later
|
||||
// Exception params should have been i64
|
||||
{
|
||||
let param_model = IntModel(Int64);
|
||||
|
||||
let src_slice_len =
|
||||
param_model.s_extend_or_bit_cast(generator, ctx, src_slice_len, "src_slice_len");
|
||||
let dest_slice_len =
|
||||
param_model.s_extend_or_bit_cast(generator, ctx, dest_slice_len, "dest_slice_len");
|
||||
let dest_idx_2 = param_model.s_extend_or_bit_cast(generator, ctx, dest_idx.2, "dest_idx_2");
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
cond,
|
||||
"0:ValueError",
|
||||
"attempt to assign sequence of size {0} to slice of size {1} with step size {2}",
|
||||
[Some(src_slice_len.value), Some(dest_slice_len.value), Some(dest_idx_2.value)],
|
||||
ctx.current_loc,
|
||||
);
|
||||
}
|
||||
|
||||
let new_len = {
|
||||
let args = vec![
|
||||
|
|
@ -462,7 +463,7 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
|
|||
BasicTypeEnum::IntType(t) => t.size_of(),
|
||||
BasicTypeEnum::PointerType(t) => t.size_of(),
|
||||
BasicTypeEnum::StructType(t) => t.size_of().unwrap(),
|
||||
_ => codegen_unreachable!(ctx),
|
||||
_ => unreachable!(),
|
||||
};
|
||||
ctx.builder.build_int_truncate_or_bit_cast(s, int32, "size").unwrap()
|
||||
}
|
||||
|
|
@ -589,8 +590,7 @@ pub fn call_j0<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> Flo
|
|||
///
|
||||
/// * `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.
|
||||
/// 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, '_>,
|
||||
|
|
@ -607,7 +607,7 @@ where
|
|||
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),
|
||||
bw => unreachable!("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()],
|
||||
|
|
@ -642,7 +642,7 @@ where
|
|||
///
|
||||
/// * `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`.
|
||||
/// `NDArray`.
|
||||
pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
|
|
@ -658,7 +658,7 @@ pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
|
|||
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),
|
||||
bw => unreachable!("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(|| {
|
||||
|
|
@ -727,7 +727,7 @@ where
|
|||
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),
|
||||
bw => unreachable!("Unsupported size type bit width: {}", bw),
|
||||
};
|
||||
let ndarray_flatten_index_fn =
|
||||
ctx.module.get_function(ndarray_flatten_index_fn_name).unwrap_or_else(|| {
|
||||
|
|
@ -766,7 +766,7 @@ where
|
|||
/// multidimensional index.
|
||||
///
|
||||
/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
|
||||
/// `NDArray`.
|
||||
/// `NDArray`.
|
||||
/// * `indices` - The multidimensional index to compute the flattened index for.
|
||||
pub fn call_ndarray_flatten_index<'ctx, G, Index>(
|
||||
generator: &mut G,
|
||||
|
|
@ -795,7 +795,7 @@ pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
|
|||
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),
|
||||
bw => unreachable!("Unsupported size type bit width: {}", bw),
|
||||
};
|
||||
let ndarray_calc_broadcast_fn =
|
||||
ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
|
||||
|
|
@ -895,7 +895,7 @@ pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
|
|||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
|
||||
/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
|
||||
/// containing the indices used for accessing `array` corresponding to the index of the broadcast
|
||||
/// array `broadcast_idx`.
|
||||
pub fn call_ndarray_calc_broadcast_index<
|
||||
'ctx,
|
||||
|
|
@ -915,7 +915,7 @@ pub fn call_ndarray_calc_broadcast_index<
|
|||
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),
|
||||
bw => unreachable!("Unsupported size type bit width: {}", bw),
|
||||
};
|
||||
let ndarray_calc_broadcast_fn =
|
||||
ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
|
||||
|
|
@ -950,3 +950,337 @@ pub fn call_ndarray_calc_broadcast_index<
|
|||
Box::new(|_, v| v.into()),
|
||||
)
|
||||
}
|
||||
|
||||
pub fn call_nac3_throw_dummy_error<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'_, '_>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_throw_dummy_error");
|
||||
CallFunction::begin(generator, ctx, &name).returning_void();
|
||||
}
|
||||
|
||||
/// Initialize all global `EXN_*` exception IDs in IRRT with the [`SymbolResolver`].
|
||||
pub fn setup_irrt_exceptions<'ctx>(
|
||||
ctx: &'ctx Context,
|
||||
module: &Module<'ctx>,
|
||||
symbol_resolver: &dyn SymbolResolver,
|
||||
) {
|
||||
let exn_id_type = ctx.i32_type();
|
||||
|
||||
let errors = &[
|
||||
("EXN_INDEX_ERROR", "0:IndexError"),
|
||||
("EXN_VALUE_ERROR", "0:ValueError"),
|
||||
("EXN_ASSERTION_ERROR", "0:AssertionError"),
|
||||
("EXN_RUNTIME_ERROR", "0:RuntimeError"),
|
||||
("EXN_TYPE_ERROR", "0:TypeError"),
|
||||
];
|
||||
|
||||
for (irrt_name, symbol_name) in errors {
|
||||
let exn_id = symbol_resolver.get_string_id(symbol_name);
|
||||
let exn_id = exn_id_type.const_int(exn_id as u64, false).as_basic_value_enum();
|
||||
|
||||
let global = module.get_global(irrt_name).unwrap_or_else(|| {
|
||||
panic!("Exception symbol name '{irrt_name}' should exist in the IRRT LLVM module")
|
||||
});
|
||||
global.set_initializer(&exn_id);
|
||||
}
|
||||
}
|
||||
|
||||
pub fn call_nac3_list_slice_assign<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dst: Ptr<'ctx, StructModel<List<IntModel<Byte>>>>,
|
||||
src: Ptr<'ctx, StructModel<List<IntModel<Byte>>>>,
|
||||
itemsize: Int<'ctx, SizeT>,
|
||||
user_slice: Ptr<'ctx, StructModel<UserSlice>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_list_slice_assign");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(dst)
|
||||
.arg(src)
|
||||
.arg(itemsize)
|
||||
.arg(user_slice)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndims: Int<'ctx, SizeT>,
|
||||
shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_util_assert_shape_no_negative",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name).arg(ndims).arg(shape).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_util_assert_output_shape_same<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ndims: Int<'ctx, SizeT>,
|
||||
ndarray_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
output_ndims: Int<'ctx, SizeT>,
|
||||
output_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_util_assert_output_shape_same",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(ndarray_ndims)
|
||||
.arg(ndarray_shape)
|
||||
.arg(output_ndims)
|
||||
.arg(output_shape)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_size");
|
||||
CallFunction::begin(generator, ctx, &name).arg(pndarray).returning_auto("size")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_nbytes");
|
||||
CallFunction::begin(generator, ctx, &name).arg(pndarray).returning_auto("nbytes")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_len");
|
||||
CallFunction::begin(generator, ctx, &name).arg(pndarray).returning_auto("len")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ptr: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_is_c_contiguous");
|
||||
CallFunction::begin(generator, ctx, &name).arg(ndarray_ptr).returning_auto("is_c_contiguous")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
index: Int<'ctx, SizeT>,
|
||||
) -> Ptr<'ctx, IntModel<Byte>> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_nth_pelement");
|
||||
CallFunction::begin(generator, ctx, &name).arg(pndarray).arg(index).returning_auto("pelement")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pdnarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) {
|
||||
let name =
|
||||
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_set_strides_by_shape");
|
||||
CallFunction::begin(generator, ctx, &name).arg(pdnarray).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_copy_data");
|
||||
CallFunction::begin(generator, ctx, &name).arg(src_ndarray).arg(dst_ndarray).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
num_indexes: Int<'ctx, SizeT>,
|
||||
indexes: Ptr<'ctx, StructModel<NDIndex>>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(num_indexes)
|
||||
.arg(indexes)
|
||||
.arg(src_ndarray)
|
||||
.arg(dst_ndarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
|
||||
CallFunction::begin(generator, ctx, &name).arg(src_ndarray).arg(dst_ndarray).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
num_shape_entries: Int<'ctx, SizeT>,
|
||||
shape_entries: Ptr<'ctx, StructModel<ShapeEntry>>,
|
||||
dst_ndims: Int<'ctx, SizeT>,
|
||||
dst_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(num_shape_entries)
|
||||
.arg(shape_entries)
|
||||
.arg(dst_ndims)
|
||||
.arg(dst_shape)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
size: Int<'ctx, SizeT>,
|
||||
new_ndims: Int<'ctx, SizeT>,
|
||||
new_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_resolve_and_check_new_shape",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(size)
|
||||
.arg(new_ndims)
|
||||
.arg(new_shape)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
num_axes: Int<'ctx, SizeT>,
|
||||
axes: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(src_ndarray)
|
||||
.arg(dst_ndarray)
|
||||
.arg(num_axes)
|
||||
.arg(axes)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
pub fn call_nac3_ndarray_matmul_calculate_shapes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
a_ndims: Int<'ctx, SizeT>,
|
||||
a_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
b_ndims: Int<'ctx, SizeT>,
|
||||
b_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
final_ndims: Int<'ctx, SizeT>,
|
||||
new_a_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
new_b_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
dst_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name =
|
||||
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_matmul_calculate_shapes");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(a_ndims)
|
||||
.arg(a_shape)
|
||||
.arg(b_ndims)
|
||||
.arg(b_shape)
|
||||
.arg(final_ndims)
|
||||
.arg(new_a_shape)
|
||||
.arg(new_b_shape)
|
||||
.arg(dst_shape)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_float64_matmul_at_least_2d<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
a_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
b_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_float64_matmul_at_least_2d",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(a_ndarray)
|
||||
.arg(b_ndarray)
|
||||
.arg(dst_ndarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_array_set_and_validate_list_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: Ptr<'ctx, StructModel<List<IntModel<Byte>>>>,
|
||||
ndims: Int<'ctx, SizeT>,
|
||||
shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_array_set_and_validate_list_shape",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name).arg(list).arg(ndims).arg(shape).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_array_write_list_to_array<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: Ptr<'ctx, StructModel<List<IntModel<Byte>>>>,
|
||||
ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
) {
|
||||
let name =
|
||||
get_sizet_dependent_function_name(generator, ctx, "__nac3_array_write_list_to_array");
|
||||
CallFunction::begin(generator, ctx, &name).arg(list).arg(ndarray).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_nditer_initialize<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
iter: Ptr<'ctx, StructModel<NDIter>>,
|
||||
ndarray: Ptr<'ctx, StructModel<NDArray>>,
|
||||
indices: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_initialize");
|
||||
CallFunction::begin(generator, ctx, &name).arg(iter).arg(ndarray).arg(indices).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_nditer_has_next<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
iter: Ptr<'ctx, StructModel<NDIter>>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_has_next");
|
||||
CallFunction::begin(generator, ctx, &name).arg(iter).returning_auto("has_next")
|
||||
}
|
||||
|
||||
pub fn call_nac3_nditer_next<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
iter: Ptr<'ctx, StructModel<NDIter>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_next");
|
||||
CallFunction::begin(generator, ctx, &name).arg(iter).returning_void();
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,26 @@
|
|||
#[cfg(test)]
|
||||
mod tests {
|
||||
use std::{path::Path, process::Command};
|
||||
|
||||
#[test]
|
||||
fn run_irrt_test() {
|
||||
assert!(
|
||||
cfg!(feature = "test"),
|
||||
"Please do `cargo test -F test` to compile `irrt_test.out` and run test"
|
||||
);
|
||||
|
||||
let irrt_test_out_path = Path::new(concat!(env!("OUT_DIR"), "/irrt_test.out"));
|
||||
let output = Command::new(irrt_test_out_path.to_str().unwrap()).output().unwrap();
|
||||
|
||||
if !output.status.success() {
|
||||
eprintln!("irrt_test failed with status {}:", output.status);
|
||||
eprintln!("====== stdout ======");
|
||||
eprintln!("{}", String::from_utf8(output.stdout).unwrap());
|
||||
eprintln!("====== stderr ======");
|
||||
eprintln!("{}", String::from_utf8(output.stderr).unwrap());
|
||||
eprintln!("====================");
|
||||
|
||||
panic!("irrt_test failed");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,14 +1,12 @@
|
|||
use inkwell::{
|
||||
context::Context,
|
||||
intrinsics::Intrinsic,
|
||||
types::{AnyTypeEnum::IntType, FloatType},
|
||||
values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
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 itertools::Either;
|
||||
|
||||
use super::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 {
|
||||
|
|
@ -185,7 +183,7 @@ pub fn call_memcpy_generic<'ctx>(
|
|||
dest
|
||||
} else {
|
||||
ctx.builder
|
||||
.build_bit_cast(dest, llvm_p0i8, "")
|
||||
.build_bitcast(dest, llvm_p0i8, "")
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap()
|
||||
};
|
||||
|
|
@ -193,7 +191,7 @@ pub fn call_memcpy_generic<'ctx>(
|
|||
src
|
||||
} else {
|
||||
ctx.builder
|
||||
.build_bit_cast(src, llvm_p0i8, "")
|
||||
.build_bitcast(src, llvm_p0i8, "")
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap()
|
||||
};
|
||||
|
|
@ -207,9 +205,8 @@ pub fn call_memcpy_generic<'ctx>(
|
|||
/// * `$ctx:ident`: Reference to the current Code Generation Context
|
||||
/// * `$name:ident`: Optional name to be assigned to the llvm build call (Option<&str>)
|
||||
/// * `$llvm_name:literal`: Name of underlying llvm intrinsic function
|
||||
/// * `$map_fn:ident`: Mapping function to be applied on `BasicValue` (`BasicValue` -> Function Return Type).
|
||||
/// Use `BasicValueEnum::into_int_value` for Integer return type and
|
||||
/// `BasicValueEnum::into_float_value` for Float return type
|
||||
/// * `$map_fn:ident`: Mapping function to be applied on `BasicValue` (`BasicValue` -> Function Return Type)
|
||||
/// Use `BasicValueEnum::into_int_value` for Integer return type and `BasicValueEnum::into_float_value` for Float return type
|
||||
/// * `$llvm_ty:ident`: Type of first operand
|
||||
/// * `,($val:ident)*`: Comma separated list of operands
|
||||
macro_rules! generate_llvm_intrinsic_fn_body {
|
||||
|
|
@ -225,8 +222,8 @@ macro_rules! generate_llvm_intrinsic_fn_body {
|
|||
/// Arguments:
|
||||
/// * `float/int`: Indicates the return and argument type of the function
|
||||
/// * `$fn_name:ident`: The identifier of the rust function to be generated
|
||||
/// * `$llvm_name:literal`: Name of underlying llvm intrinsic function.
|
||||
/// Omit "llvm." prefix from the function name i.e. use "ceil" instead of "llvm.ceil"
|
||||
/// * `$llvm_name:literal`: Name of underlying llvm intrinsic function
|
||||
/// Omit "llvm." prefix from the function name i.e. use "ceil" instead of "llvm.ceil"
|
||||
/// * `$val:ident`: The operand for unary operations
|
||||
/// * `$val1:ident`, `$val2:ident`: The operands for binary operations
|
||||
macro_rules! generate_llvm_intrinsic_fn {
|
||||
|
|
|
|||
|
|
@ -1,12 +1,12 @@
|
|||
use std::{
|
||||
collections::{HashMap, HashSet},
|
||||
sync::{
|
||||
atomic::{AtomicBool, Ordering},
|
||||
Arc,
|
||||
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},
|
||||
},
|
||||
thread,
|
||||
};
|
||||
|
||||
use crossbeam::channel::{unbounded, Receiver, Sender};
|
||||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
|
|
@ -24,23 +24,17 @@ use inkwell::{
|
|||
AddressSpace, IntPredicate, OptimizationLevel,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
use parking_lot::{Condvar, Mutex};
|
||||
|
||||
use model::*;
|
||||
use nac3parser::ast::{Location, Stmt, StrRef};
|
||||
|
||||
use crate::{
|
||||
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 parking_lot::{Condvar, Mutex};
|
||||
use std::collections::{HashMap, HashSet};
|
||||
use std::sync::{
|
||||
atomic::{AtomicBool, Ordering},
|
||||
Arc,
|
||||
};
|
||||
use classes::{ListType, NDArrayType, ProxyType, RangeType};
|
||||
use concrete_type::{ConcreteType, ConcreteTypeEnum, ConcreteTypeStore};
|
||||
pub use generator::{CodeGenerator, DefaultCodeGenerator};
|
||||
use std::thread;
|
||||
use structure::{CSlice, Exception, NDArray};
|
||||
|
||||
pub mod builtin_fns;
|
||||
pub mod classes;
|
||||
pub mod concrete_type;
|
||||
pub mod expr;
|
||||
|
|
@ -48,27 +42,18 @@ pub mod extern_fns;
|
|||
mod generator;
|
||||
pub mod irrt;
|
||||
pub mod llvm_intrinsics;
|
||||
pub mod model;
|
||||
pub mod numpy;
|
||||
pub mod numpy_new;
|
||||
pub mod object;
|
||||
pub mod stmt;
|
||||
pub mod structure;
|
||||
|
||||
#[cfg(test)]
|
||||
mod test;
|
||||
|
||||
mod macros {
|
||||
/// Codegen-variant of [`std::unreachable`] which accepts an instance of [`CodeGenContext`] as
|
||||
/// its first argument to provide Python source information to indicate the codegen location
|
||||
/// causing the assertion.
|
||||
macro_rules! codegen_unreachable {
|
||||
($ctx:expr $(,)?) => {
|
||||
std::unreachable!("unreachable code while processing {}", &$ctx.current_loc)
|
||||
};
|
||||
($ctx:expr, $($arg:tt)*) => {
|
||||
std::unreachable!("unreachable code while processing {}: {}", &$ctx.current_loc, std::format!("{}", std::format_args!($($arg)+)))
|
||||
};
|
||||
}
|
||||
|
||||
pub(crate) use codegen_unreachable;
|
||||
}
|
||||
use concrete_type::{ConcreteType, ConcreteTypeEnum, ConcreteTypeStore};
|
||||
pub use generator::{CodeGenerator, DefaultCodeGenerator};
|
||||
|
||||
#[derive(Default)]
|
||||
pub struct StaticValueStore {
|
||||
|
|
@ -188,11 +173,11 @@ pub struct CodeGenContext<'ctx, 'a> {
|
|||
pub registry: &'a WorkerRegistry,
|
||||
|
||||
/// Cache for constant strings.
|
||||
pub const_strings: HashMap<String, BasicValueEnum<'ctx>>,
|
||||
pub const_strings: HashMap<String, Struct<'ctx, CSlice>>,
|
||||
|
||||
/// [`BasicBlock`] containing all `alloca` statements for the current function.
|
||||
pub init_bb: BasicBlock<'ctx>,
|
||||
pub exception_val: Option<PointerValue<'ctx>>,
|
||||
pub exception_val: Option<Ptr<'ctx, StructModel<Exception>>>,
|
||||
|
||||
/// The header and exit basic blocks of a loop in this context. See
|
||||
/// <https://llvm.org/docs/LoopTerminology.html> for explanation of these terminology.
|
||||
|
|
@ -509,12 +494,8 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
|
|||
}
|
||||
|
||||
TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let (dtype, _) = unpack_ndarray_var_tys(unifier, ty);
|
||||
let element_type = get_llvm_type(
|
||||
ctx, module, generator, unifier, top_level, type_cache, dtype,
|
||||
);
|
||||
|
||||
NDArrayType::new(generator, ctx, element_type).as_base_type().into()
|
||||
let pndarray_model = PtrModel(StructModel(NDArray));
|
||||
pndarray_model.get_type(generator, ctx).as_basic_type_enum()
|
||||
}
|
||||
|
||||
_ => unreachable!(
|
||||
|
|
@ -600,11 +581,11 @@ fn get_llvm_abi_type<'ctx, G: CodeGenerator + ?Sized>(
|
|||
) -> BasicTypeEnum<'ctx> {
|
||||
// If the type is used in the definition of a function, return `i1` instead of `i8` for ABI
|
||||
// consistency.
|
||||
if unifier.unioned(ty, primitives.bool) {
|
||||
return if unifier.unioned(ty, primitives.bool) {
|
||||
ctx.bool_type().into()
|
||||
} else {
|
||||
get_llvm_type(ctx, module, generator, unifier, top_level, type_cache, ty)
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
/// Whether `sret` is needed for a return value with type `ty`.
|
||||
|
|
@ -720,43 +701,19 @@ pub fn gen_func_impl<
|
|||
..primitives
|
||||
};
|
||||
|
||||
let mut type_cache: HashMap<_, _> = [
|
||||
let cslice_model = StructModel(CSlice);
|
||||
let pexn_model = PtrModel(StructModel(Exception));
|
||||
|
||||
let mut type_cache: HashMap<_, BasicTypeEnum<'ctx>> = [
|
||||
(primitives.int32, context.i32_type().into()),
|
||||
(primitives.int64, context.i64_type().into()),
|
||||
(primitives.uint32, context.i32_type().into()),
|
||||
(primitives.uint64, context.i64_type().into()),
|
||||
(primitives.float, context.f64_type().into()),
|
||||
(primitives.bool, context.i8_type().into()),
|
||||
(primitives.str, {
|
||||
let name = "str";
|
||||
match module.get_struct_type(name) {
|
||||
None => {
|
||||
let str_type = context.opaque_struct_type("str");
|
||||
let fields = [
|
||||
context.i8_type().ptr_type(AddressSpace::default()).into(),
|
||||
generator.get_size_type(context).into(),
|
||||
];
|
||||
str_type.set_body(&fields, false);
|
||||
str_type.into()
|
||||
}
|
||||
Some(t) => t.as_basic_type_enum(),
|
||||
}
|
||||
}),
|
||||
(primitives.str, cslice_model.get_type(generator, context).into()),
|
||||
(primitives.range, RangeType::new(context).as_base_type().into()),
|
||||
(primitives.exception, {
|
||||
let name = "Exception";
|
||||
if let Some(t) = module.get_struct_type(name) {
|
||||
t.ptr_type(AddressSpace::default()).as_basic_type_enum()
|
||||
} else {
|
||||
let exception = context.opaque_struct_type("Exception");
|
||||
let int32 = context.i32_type().into();
|
||||
let int64 = context.i64_type().into();
|
||||
let str_ty = module.get_struct_type("str").unwrap().as_basic_type_enum();
|
||||
let fields = [int32, str_ty, int32, int32, str_ty, str_ty, int64, int64, int64];
|
||||
exception.set_body(&fields, false);
|
||||
exception.ptr_type(AddressSpace::default()).as_basic_type_enum()
|
||||
}
|
||||
}),
|
||||
(primitives.exception, pexn_model.get_type(generator, context).into()),
|
||||
]
|
||||
.iter()
|
||||
.copied()
|
||||
|
|
@ -852,9 +809,10 @@ 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(
|
||||
|
|
|
|||
|
|
@ -0,0 +1,40 @@
|
|||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicType, BasicTypeEnum},
|
||||
values::BasicValueEnum,
|
||||
};
|
||||
|
||||
use crate::codegen::CodeGenerator;
|
||||
|
||||
use super::*;
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AnyModel<'ctx>(pub BasicTypeEnum<'ctx>);
|
||||
pub type Anything<'ctx> = Instance<'ctx, AnyModel<'ctx>>;
|
||||
|
||||
impl<'ctx> Model<'ctx> for AnyModel<'ctx> {
|
||||
type Value = BasicValueEnum<'ctx>;
|
||||
type Type = BasicTypeEnum<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
_ctx: &'ctx Context,
|
||||
) -> Self::Type {
|
||||
self.0
|
||||
}
|
||||
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &mut G,
|
||||
_ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
let ty = ty.as_basic_type_enum();
|
||||
if ty == self.0 {
|
||||
Ok(())
|
||||
} else {
|
||||
Err(ModelError(format!("Expecting {}, but got {}", self.0, ty)))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,122 @@
|
|||
use inkwell::{
|
||||
context::Context,
|
||||
types::{ArrayType, BasicType, BasicTypeEnum},
|
||||
values::ArrayValue,
|
||||
};
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
/// A Model for an [`ArrayType`].
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct ArrayModel<Element> {
|
||||
pub len: u32,
|
||||
pub element: Element,
|
||||
}
|
||||
pub type Array<'ctx, Element> = Instance<'ctx, ArrayModel<Element>>;
|
||||
|
||||
impl<'ctx, Element: Model<'ctx>> Model<'ctx> for ArrayModel<Element> {
|
||||
type Value = ArrayValue<'ctx>;
|
||||
type Type = ArrayType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
|
||||
self.element.get_type(generator, ctx).array_type(self.len)
|
||||
}
|
||||
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
let ty = ty.as_basic_type_enum();
|
||||
let BasicTypeEnum::ArrayType(ty) = ty else {
|
||||
return Err(ModelError(format!("Expecting ArrayType, but got {ty:?}")));
|
||||
};
|
||||
|
||||
if ty.len() != self.len {
|
||||
return Err(ModelError(format!(
|
||||
"Expecting ArrayType with size {}, but got an ArrayType with size {}",
|
||||
ty.len(),
|
||||
self.len
|
||||
)));
|
||||
}
|
||||
|
||||
self.element
|
||||
.check_type(generator, ctx, ty.get_element_type())
|
||||
.map_err(|err| err.under_context("an ArrayType"))?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, Element: Model<'ctx>> Ptr<'ctx, ArrayModel<Element>> {
|
||||
/// Get the pointer to the `i`-th (0-based) array element.
|
||||
pub fn at<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
i: u32,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
assert!(i < self.model.0.len);
|
||||
|
||||
let zero = ctx.ctx.i32_type().const_zero();
|
||||
let i = ctx.ctx.i32_type().const_int(u64::from(i), false);
|
||||
let ptr = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[zero, i], name).unwrap() };
|
||||
|
||||
PtrModel(self.model.0.element).check_value(generator, ctx.ctx, ptr).unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
/// Like [`ArrayModel`] but length is strongly-typed.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct NArrayModel<const LEN: u32, Element>(pub Element);
|
||||
pub type NArray<'ctx, const LEN: u32, Element> = Instance<'ctx, NArrayModel<LEN, Element>>;
|
||||
|
||||
impl<'ctx, const LEN: u32, Element: Model<'ctx>> NArrayModel<LEN, Element> {
|
||||
/// Forget the `LEN` constant generic and get an [`ArrayModel`] with the same length.
|
||||
pub fn forget_len(&self) -> ArrayModel<Element> {
|
||||
ArrayModel { element: self.0, len: LEN }
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, const LEN: u32, Element: Model<'ctx>> Model<'ctx> for NArrayModel<LEN, Element> {
|
||||
type Value = ArrayValue<'ctx>;
|
||||
type Type = ArrayType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
|
||||
// Convenient implementation
|
||||
self.forget_len().get_type(generator, ctx)
|
||||
}
|
||||
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
// Convenient implementation
|
||||
self.forget_len().check_type(generator, ctx, ty)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, const LEN: u32, Element: Model<'ctx>> Ptr<'ctx, NArrayModel<LEN, Element>> {
|
||||
/// Get the pointer to the `i`-th (0-based) array element.
|
||||
pub fn at_const<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
i: u32,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
assert!(i < LEN);
|
||||
|
||||
let zero = ctx.ctx.i32_type().const_zero();
|
||||
let i = ctx.ctx.i32_type().const_int(u64::from(i), false);
|
||||
let ptr = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[zero, i], name).unwrap() };
|
||||
|
||||
PtrModel(self.model.0 .0).check_value(generator, ctx.ctx, ptr).unwrap()
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,123 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{context::Context, types::*, values::*};
|
||||
|
||||
use super::*;
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct ModelError(pub String);
|
||||
|
||||
impl ModelError {
|
||||
pub(super) fn under_context(mut self, context: &str) -> Self {
|
||||
self.0.push_str(" ... in ");
|
||||
self.0.push_str(context);
|
||||
self
|
||||
}
|
||||
}
|
||||
|
||||
pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
|
||||
type Value: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>>;
|
||||
type Type: BasicType<'ctx>;
|
||||
|
||||
/// Return the [`BasicType`] of this model.
|
||||
#[must_use]
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type;
|
||||
|
||||
/// Check if a [`BasicType`] is the same type of this model.
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError>;
|
||||
|
||||
/// Create an instance from a value with [`Instance::model`] being this model.
|
||||
///
|
||||
/// Caller must make sure the type of `value` and the type of this `model` are equivalent.
|
||||
#[must_use]
|
||||
fn believe_value(&self, value: Self::Value) -> Instance<'ctx, Self> {
|
||||
Instance { model: *self, value }
|
||||
}
|
||||
|
||||
/// Check if a [`BasicValue`]'s type is equivalent to the type of this model.
|
||||
/// Wrap it into an [`Instance`] if it is.
|
||||
fn check_value<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
value: V,
|
||||
) -> Result<Instance<'ctx, Self>, ModelError> {
|
||||
let value = value.as_basic_value_enum();
|
||||
self.check_type(generator, ctx, value.get_type())
|
||||
.map_err(|err| err.under_context(format!("the value {value:?}").as_str()))?;
|
||||
|
||||
let Ok(value) = Self::Value::try_from(value) else {
|
||||
unreachable!("check_type() has bad implementation")
|
||||
};
|
||||
Ok(self.believe_value(value))
|
||||
}
|
||||
|
||||
// Allocate a value on the stack and return its pointer.
|
||||
fn alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Self> {
|
||||
let pmodel = PtrModel(*self);
|
||||
let p = ctx.builder.build_alloca(self.get_type(generator, ctx.ctx), name).unwrap();
|
||||
pmodel.believe_value(p)
|
||||
}
|
||||
|
||||
// Allocate an array on the stack and return its pointer.
|
||||
fn array_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
len: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Self> {
|
||||
let pmodel = PtrModel(*self);
|
||||
let p =
|
||||
ctx.builder.build_array_alloca(self.get_type(generator, ctx.ctx), len, name).unwrap();
|
||||
pmodel.believe_value(p)
|
||||
}
|
||||
|
||||
fn var_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&str>,
|
||||
) -> Result<Ptr<'ctx, Self>, String> {
|
||||
let pmodel = PtrModel(*self);
|
||||
let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
|
||||
let p = generator.gen_var_alloc(ctx, ty, name)?;
|
||||
Ok(pmodel.believe_value(p))
|
||||
}
|
||||
|
||||
fn array_var_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
len: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Result<Ptr<'ctx, Self>, String> {
|
||||
// TODO: Remove ArraySliceValue
|
||||
let pmodel = PtrModel(*self);
|
||||
let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
|
||||
let p = generator.gen_array_var_alloc(ctx, ty, len, name)?;
|
||||
Ok(pmodel.believe_value(PointerValue::from(p)))
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct Instance<'ctx, M: Model<'ctx>> {
|
||||
/// The model of this instance.
|
||||
pub model: M,
|
||||
/// The value of this instance.
|
||||
///
|
||||
/// Caller must make sure the type of `value` and the type of this `model` are equivalent,
|
||||
/// down to having the same [`IntType::get_bit_width`] in case of [`IntType`] for example.
|
||||
pub value: M::Value,
|
||||
}
|
||||
|
|
@ -0,0 +1,88 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{context::Context, types::FloatType, values::FloatValue};
|
||||
|
||||
use crate::codegen::CodeGenerator;
|
||||
|
||||
use super::*;
|
||||
|
||||
pub trait FloatKind<'ctx>: fmt::Debug + Clone + Copy {
|
||||
fn get_float_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> FloatType<'ctx>;
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Float32;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Float64;
|
||||
|
||||
impl<'ctx> FloatKind<'ctx> for Float32 {
|
||||
fn get_float_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> FloatType<'ctx> {
|
||||
ctx.f32_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> FloatKind<'ctx> for Float64 {
|
||||
fn get_float_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> FloatType<'ctx> {
|
||||
ctx.f64_type()
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AnyFloat<'ctx>(FloatType<'ctx>);
|
||||
|
||||
impl<'ctx> FloatKind<'ctx> for AnyFloat<'ctx> {
|
||||
fn get_float_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
_ctx: &'ctx Context,
|
||||
) -> FloatType<'ctx> {
|
||||
self.0
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct FloatModel<N>(pub N);
|
||||
pub type Float<'ctx, N> = Instance<'ctx, FloatModel<N>>;
|
||||
|
||||
impl<'ctx, N: FloatKind<'ctx>> Model<'ctx> for FloatModel<N> {
|
||||
type Value = FloatValue<'ctx>;
|
||||
type Type = FloatType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
|
||||
self.0.get_float_type(generator, ctx)
|
||||
}
|
||||
|
||||
fn check_type<T: inkwell::types::BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
let ty = ty.as_basic_type_enum();
|
||||
let Ok(ty) = FloatType::try_from(ty) else {
|
||||
return Err(ModelError(format!("Expecting FloatType, but got {ty:?}")));
|
||||
};
|
||||
|
||||
let exp_ty = self.0.get_float_type(generator, ctx);
|
||||
|
||||
// TODO: Inkwell does not have get_bit_width for FloatType?
|
||||
// TODO: Quick hack for now, but does this actually work?
|
||||
if ty != exp_ty {
|
||||
return Err(ModelError(format!("Expecting {exp_ty:?}, but got {ty:?}")));
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,125 @@
|
|||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
types::{BasicMetadataTypeEnum, BasicType, FunctionType},
|
||||
values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
// When [`TypeContext::size_type`] is 32-bits, the function name is "{fn_name}".
|
||||
// When [`TypeContext::size_type`] is 64-bits, the function name is "{fn_name}64".
|
||||
#[must_use]
|
||||
pub fn get_sizet_dependent_function_name<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'_, '_>,
|
||||
name: &str,
|
||||
) -> String {
|
||||
let mut name = name.to_owned();
|
||||
match generator.get_size_type(ctx.ctx).get_bit_width() {
|
||||
32 => {}
|
||||
64 => name.push_str("64"),
|
||||
bit_width => {
|
||||
panic!("Unsupported int type bit width {bit_width}, must be either 32-bits or 64-bits")
|
||||
}
|
||||
}
|
||||
name
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
struct Arg<'ctx> {
|
||||
ty: BasicMetadataTypeEnum<'ctx>,
|
||||
val: BasicMetadataValueEnum<'ctx>,
|
||||
}
|
||||
|
||||
/// A structure to construct & call an LLVM function.
|
||||
///
|
||||
/// This is a helper to reduce IRRT Inkwell function call boilerplate
|
||||
// TODO: Remove the lifetimes somehow? There is 4 of them.
|
||||
pub struct CallFunction<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> {
|
||||
generator: &'d mut G,
|
||||
ctx: &'b CodeGenContext<'ctx, 'a>,
|
||||
/// Function name
|
||||
name: &'c str,
|
||||
/// Call arguments
|
||||
args: Vec<Arg<'ctx>>,
|
||||
/// LLVM function Attributes
|
||||
attrs: Vec<&'static str>,
|
||||
}
|
||||
|
||||
impl<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> CallFunction<'ctx, 'a, 'b, 'c, 'd, G> {
|
||||
pub fn begin(generator: &'d mut G, ctx: &'b CodeGenContext<'ctx, 'a>, name: &'c str) -> Self {
|
||||
CallFunction { generator, ctx, name, args: Vec::new(), attrs: Vec::new() }
|
||||
}
|
||||
|
||||
/// Push a list of LLVM function attributes to the function declaration.
|
||||
#[must_use]
|
||||
pub fn attrs(mut self, attrs: Vec<&'static str>) -> Self {
|
||||
self.attrs = attrs;
|
||||
self
|
||||
}
|
||||
|
||||
/// Push a call argument to the function call.
|
||||
#[allow(clippy::needless_pass_by_value)]
|
||||
#[must_use]
|
||||
pub fn arg<M: Model<'ctx>>(mut self, arg: Instance<'ctx, M>) -> Self {
|
||||
let arg = Arg {
|
||||
ty: arg.model.get_type(self.generator, self.ctx.ctx).as_basic_type_enum().into(),
|
||||
val: arg.value.as_basic_value_enum().into(),
|
||||
};
|
||||
self.args.push(arg);
|
||||
self
|
||||
}
|
||||
|
||||
/// Call the function and expect the function to return a value of type of `return_model`.
|
||||
#[must_use]
|
||||
pub fn returning<M: Model<'ctx>>(self, name: &str, return_model: M) -> Instance<'ctx, M> {
|
||||
let ret_ty = return_model.get_type(self.generator, self.ctx.ctx);
|
||||
|
||||
let ret = self.get_function(|tys| ret_ty.fn_type(tys, false), name);
|
||||
let ret = BasicValueEnum::try_from(ret.as_any_value_enum()).unwrap(); // Must work
|
||||
let ret = return_model.check_value(self.generator, self.ctx.ctx, ret).unwrap(); // Must work
|
||||
ret
|
||||
}
|
||||
|
||||
/// Like [`CallFunction::returning_`] but `return_model` is automatically inferred.
|
||||
#[must_use]
|
||||
pub fn returning_auto<M: Model<'ctx> + Default>(self, name: &str) -> Instance<'ctx, M> {
|
||||
self.returning(name, M::default())
|
||||
}
|
||||
|
||||
/// Call the function and expect the function to return a void-type.
|
||||
pub fn returning_void(self) {
|
||||
let ret_ty = self.ctx.ctx.void_type();
|
||||
|
||||
let _ = self.get_function(|tys| ret_ty.fn_type(tys, false), "");
|
||||
}
|
||||
|
||||
fn get_function<F>(&self, make_fn_type: F, return_value_name: &str) -> CallSiteValue<'ctx>
|
||||
where
|
||||
F: FnOnce(&[BasicMetadataTypeEnum<'ctx>]) -> FunctionType<'ctx>,
|
||||
{
|
||||
// Get the LLVM function.
|
||||
let func = self.ctx.module.get_function(self.name).unwrap_or_else(|| {
|
||||
// Declare the function if it doesn't exist.
|
||||
let tys = self.args.iter().map(|arg| arg.ty).collect_vec();
|
||||
|
||||
let func_type = make_fn_type(&tys);
|
||||
let func = self.ctx.module.add_function(self.name, func_type, None);
|
||||
|
||||
for attr in &self.attrs {
|
||||
func.add_attribute(
|
||||
AttributeLoc::Function,
|
||||
self.ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
|
||||
);
|
||||
}
|
||||
|
||||
func
|
||||
});
|
||||
|
||||
let vals = self.args.iter().map(|arg| arg.val).collect_vec();
|
||||
self.ctx.builder.build_call(func, &vals, return_value_name).unwrap()
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,275 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{context::Context, types::IntType, values::IntValue, IntPredicate};
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
pub trait IntKind<'ctx>: fmt::Debug + Clone + Copy {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx>;
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Bool;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Byte;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Int32;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Int64;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct SizeT;
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Bool {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.bool_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Byte {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i8_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Int32 {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i32_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Int64 {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i64_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for SizeT {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
generator.get_size_type(ctx)
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AnyInt<'ctx>(pub IntType<'ctx>);
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for AnyInt<'ctx> {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
_ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
self.0
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct IntModel<N>(pub N);
|
||||
pub type Int<'ctx, N> = Instance<'ctx, IntModel<N>>;
|
||||
|
||||
impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for IntModel<N> {
|
||||
type Value = IntValue<'ctx>;
|
||||
type Type = IntType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
|
||||
self.0.get_int_type(generator, ctx)
|
||||
}
|
||||
|
||||
fn check_type<T: inkwell::types::BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
let ty = ty.as_basic_type_enum();
|
||||
let Ok(ty) = IntType::try_from(ty) else {
|
||||
return Err(ModelError(format!("Expecting IntType, but got {ty:?}")));
|
||||
};
|
||||
|
||||
let exp_ty = self.0.get_int_type(generator, ctx);
|
||||
if ty.get_bit_width() != exp_ty.get_bit_width() {
|
||||
return Err(ModelError(format!(
|
||||
"Expecting IntType to have {} bit(s), but got {} bit(s)",
|
||||
exp_ty.get_bit_width(),
|
||||
ty.get_bit_width()
|
||||
)));
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, N: IntKind<'ctx>> IntModel<N> {
|
||||
pub fn constant<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
value: u64,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = self.get_type(generator, ctx).const_int(value, false);
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn const_0<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, N> {
|
||||
self.constant(generator, ctx, 0)
|
||||
}
|
||||
|
||||
pub fn const_1<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, N> {
|
||||
self.constant(generator, ctx, 1)
|
||||
}
|
||||
|
||||
pub fn const_all_1s<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = self.get_type(generator, ctx).const_all_ones();
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn s_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = ctx
|
||||
.builder
|
||||
.build_int_s_extend_or_bit_cast(value, self.get_type(generator, ctx.ctx), name)
|
||||
.unwrap();
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn truncate<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, N> {
|
||||
let value =
|
||||
ctx.builder.build_int_truncate(value, self.get_type(generator, ctx.ctx), name).unwrap();
|
||||
self.believe_value(value)
|
||||
}
|
||||
}
|
||||
|
||||
impl IntModel<Bool> {
|
||||
#[must_use]
|
||||
pub fn const_false<'ctx, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, Bool> {
|
||||
self.constant(generator, ctx, 0)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn const_true<'ctx, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, Bool> {
|
||||
self.constant(generator, ctx, 1)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, N: IntKind<'ctx>> Int<'ctx, N> {
|
||||
pub fn s_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
name: &str,
|
||||
) -> Int<'ctx, NewN> {
|
||||
IntModel(to_int_kind).s_extend_or_bit_cast(generator, ctx, self.value, name)
|
||||
}
|
||||
|
||||
pub fn truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
name: &str,
|
||||
) -> Int<'ctx, NewN> {
|
||||
IntModel(to_int_kind).truncate(generator, ctx, self.value, name)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn add(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
other: Int<'ctx, N>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = ctx.builder.build_int_add(self.value, other.value, name).unwrap();
|
||||
self.model.believe_value(value)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn sub(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
other: Int<'ctx, N>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = ctx.builder.build_int_sub(self.value, other.value, name).unwrap();
|
||||
self.model.believe_value(value)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn mul(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
other: Int<'ctx, N>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = ctx.builder.build_int_mul(self.value, other.value, name).unwrap();
|
||||
self.model.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn compare(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
op: IntPredicate,
|
||||
other: Int<'ctx, N>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, Bool> {
|
||||
let bool_model = IntModel(Bool);
|
||||
let value = ctx.builder.build_int_compare(op, self.value, other.value, name).unwrap();
|
||||
bool_model.believe_value(value)
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
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::*;
|
||||
|
|
@ -0,0 +1,145 @@
|
|||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicType, BasicTypeEnum, PointerType},
|
||||
values::{IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct PtrModel<Element>(pub Element);
|
||||
pub type Ptr<'ctx, Element> = Instance<'ctx, PtrModel<Element>>;
|
||||
|
||||
impl<'ctx, Element: Model<'ctx>> Model<'ctx> for PtrModel<Element> {
|
||||
type Value = PointerValue<'ctx>;
|
||||
type Type = PointerType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
|
||||
self.0.get_type(generator, ctx).ptr_type(AddressSpace::default())
|
||||
}
|
||||
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
let ty = ty.as_basic_type_enum();
|
||||
let Ok(ty) = PointerType::try_from(ty) else {
|
||||
return Err(ModelError(format!("Expecting PointerType, but got {ty:?}")));
|
||||
};
|
||||
|
||||
let elem_ty = ty.get_element_type();
|
||||
let Ok(elem_ty) = BasicTypeEnum::try_from(elem_ty) else {
|
||||
return Err(ModelError(format!(
|
||||
"Expecting pointer element type to be a BasicTypeEnum, but got {elem_ty:?}"
|
||||
)));
|
||||
};
|
||||
|
||||
// TODO: inkwell `get_element_type()` will be deprecated.
|
||||
// Remove the check for `get_element_type()` when the time comes.
|
||||
self.0
|
||||
.check_type(generator, ctx, elem_ty)
|
||||
.map_err(|err| err.under_context("a PointerType"))?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, Element: Model<'ctx>> PtrModel<Element> {
|
||||
/// Return a ***constant*** nullptr.
|
||||
pub fn nullptr<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
let ptr = self.get_type(generator, ctx).const_null();
|
||||
self.believe_value(ptr)
|
||||
}
|
||||
|
||||
/// Cast a pointer into this model with [`inkwell::builder::Builder::build_pointer_cast`]
|
||||
pub fn pointer_cast<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ptr: PointerValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
let ptr =
|
||||
ctx.builder.build_pointer_cast(ptr, self.get_type(generator, ctx.ctx), name).unwrap();
|
||||
self.believe_value(ptr)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, Element: Model<'ctx>> Ptr<'ctx, Element> {
|
||||
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`].
|
||||
#[must_use]
|
||||
pub fn offset<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
offset: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
let new_ptr =
|
||||
unsafe { ctx.builder.build_in_bounds_gep(self.value, &[offset], name).unwrap() };
|
||||
self.model.check_value(generator, ctx.ctx, new_ptr).unwrap()
|
||||
}
|
||||
|
||||
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`] by a constant offset.
|
||||
#[must_use]
|
||||
pub fn offset_const<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
offset: u64,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
let offset = ctx.ctx.i32_type().const_int(offset, false);
|
||||
self.offset(generator, ctx, offset, name)
|
||||
}
|
||||
|
||||
/// Load the value with [`inkwell::builder::Builder::build_load`].
|
||||
pub fn load<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Instance<'ctx, Element> {
|
||||
let value = ctx.builder.build_load(self.value, name).unwrap();
|
||||
self.model.0.check_value(generator, ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error.
|
||||
}
|
||||
|
||||
/// Store a value with [`inkwell::builder::Builder::build_store`].
|
||||
pub fn store(&self, ctx: &CodeGenContext<'ctx, '_>, value: Instance<'ctx, Element>) {
|
||||
ctx.builder.build_store(self.value, value.value).unwrap();
|
||||
}
|
||||
|
||||
/// Return a casted pointer of element type `NewElement` with [`inkwell::builder::Builder::build_pointer_cast`].
|
||||
pub fn pointer_cast<NewElement: Model<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
new_model: NewElement,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, NewElement> {
|
||||
PtrModel(new_model).pointer_cast(generator, ctx, self.value, name)
|
||||
}
|
||||
|
||||
/// Check if the pointer is null with [`inkwell::builder::Builder::build_is_null`].
|
||||
pub fn is_null(&self, ctx: &CodeGenContext<'ctx, '_>, name: &str) -> Int<'ctx, Bool> {
|
||||
let bool_model = IntModel(Bool);
|
||||
let value = ctx.builder.build_is_null(self.value, name).unwrap();
|
||||
bool_model.believe_value(value)
|
||||
}
|
||||
|
||||
/// Check if the pointer is not null with [`inkwell::builder::Builder::build_is_not_null`].
|
||||
pub fn is_not_null(&self, ctx: &CodeGenContext<'ctx, '_>, name: &str) -> Int<'ctx, Bool> {
|
||||
let bool_model = IntModel(Bool);
|
||||
let value = ctx.builder.build_is_not_null(self.value, name).unwrap();
|
||||
bool_model.believe_value(value)
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,222 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicType, BasicTypeEnum, StructType},
|
||||
values::StructValue,
|
||||
};
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct GepField<M> {
|
||||
pub gep_index: u64,
|
||||
pub name: &'static str,
|
||||
pub model: M,
|
||||
}
|
||||
|
||||
pub trait FieldTraversal<'ctx> {
|
||||
type Out<M>;
|
||||
|
||||
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M>;
|
||||
|
||||
/// Like [`FieldTraversal::visit`] but [`Model`] is automatically inferred from [`Default`] trait.
|
||||
fn add_auto<M: Model<'ctx> + Default>(&mut self, name: &'static str) -> Self::Out<M> {
|
||||
self.add(name, M::default())
|
||||
}
|
||||
}
|
||||
|
||||
pub struct GepFieldTraversal {
|
||||
gep_index_counter: u64,
|
||||
}
|
||||
|
||||
impl<'ctx> FieldTraversal<'ctx> for GepFieldTraversal {
|
||||
type Out<M> = GepField<M>;
|
||||
|
||||
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M> {
|
||||
let gep_index = self.gep_index_counter;
|
||||
self.gep_index_counter += 1;
|
||||
Self::Out { gep_index, name, model }
|
||||
}
|
||||
}
|
||||
|
||||
struct TypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
|
||||
generator: &'a G,
|
||||
ctx: &'ctx Context,
|
||||
field_types: Vec<BasicTypeEnum<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx> for TypeFieldTraversal<'ctx, 'a, G> {
|
||||
type Out<M> = ();
|
||||
|
||||
fn add<M: Model<'ctx>>(&mut self, _name: &'static str, model: M) -> Self::Out<M> {
|
||||
let t = model.get_type(self.generator, self.ctx).as_basic_type_enum();
|
||||
self.field_types.push(t);
|
||||
}
|
||||
}
|
||||
|
||||
struct CheckTypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
|
||||
generator: &'a mut G,
|
||||
ctx: &'ctx Context,
|
||||
index: u32,
|
||||
scrutinee: StructType<'ctx>,
|
||||
errors: Vec<ModelError>,
|
||||
}
|
||||
|
||||
impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx>
|
||||
for CheckTypeFieldTraversal<'ctx, 'a, G>
|
||||
{
|
||||
type Out<M> = ();
|
||||
|
||||
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M> {
|
||||
let i = self.index;
|
||||
self.index += 1;
|
||||
|
||||
if let Some(t) = self.scrutinee.get_field_type_at_index(i) {
|
||||
if let Err(err) = model.check_type(self.generator, self.ctx, t) {
|
||||
self.errors.push(err.under_context(format!("field #{i} '{name}'").as_str()));
|
||||
}
|
||||
} // Otherwise, it will be caught
|
||||
}
|
||||
}
|
||||
|
||||
pub trait StructKind<'ctx>: fmt::Debug + Clone + Copy {
|
||||
type Fields<F: FieldTraversal<'ctx>>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F>;
|
||||
|
||||
fn fields(&self) -> Self::Fields<GepFieldTraversal> {
|
||||
self.traverse_fields(&mut GepFieldTraversal { gep_index_counter: 0 })
|
||||
}
|
||||
|
||||
fn get_struct_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> StructType<'ctx> {
|
||||
let mut traversal = TypeFieldTraversal { generator, ctx, field_types: Vec::new() };
|
||||
self.traverse_fields(&mut traversal);
|
||||
|
||||
ctx.struct_type(&traversal.field_types, false)
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct StructModel<S>(pub S);
|
||||
pub type Struct<'ctx, S> = Instance<'ctx, StructModel<S>>;
|
||||
|
||||
impl<'ctx, S: StructKind<'ctx>> Model<'ctx> for StructModel<S> {
|
||||
type Value = StructValue<'ctx>;
|
||||
type Type = StructType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
|
||||
self.0.get_struct_type(generator, ctx)
|
||||
}
|
||||
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
let ty = ty.as_basic_type_enum();
|
||||
let Ok(ty) = StructType::try_from(ty) else {
|
||||
return Err(ModelError(format!("Expecting StructType, but got {ty:?}")));
|
||||
};
|
||||
|
||||
let mut traversal =
|
||||
CheckTypeFieldTraversal { generator, ctx, index: 0, errors: Vec::new(), scrutinee: ty };
|
||||
self.0.traverse_fields(&mut traversal);
|
||||
|
||||
let exp_num_fields = traversal.index;
|
||||
let got_num_fields = u32::try_from(ty.get_field_types().len()).unwrap();
|
||||
if exp_num_fields != got_num_fields {
|
||||
return Err(ModelError(format!(
|
||||
"Expecting StructType with {exp_num_fields} field(s), but got {got_num_fields}"
|
||||
)));
|
||||
}
|
||||
|
||||
if !traversal.errors.is_empty() {
|
||||
return Err(traversal.errors[0].clone()); // TODO: Return other errors as well
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, S: StructKind<'ctx>> Struct<'ctx, S> {
|
||||
pub fn get_field<G: CodeGenerator + ?Sized, M, GetField>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
get_field: GetField,
|
||||
) -> Instance<'ctx, M>
|
||||
where
|
||||
M: Model<'ctx>,
|
||||
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
|
||||
{
|
||||
let field = get_field(self.model.0.fields());
|
||||
let val = self.value.get_field_at_index(field.gep_index as u32).unwrap();
|
||||
field.model.check_value(generator, ctx, val).unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, S: StructKind<'ctx>> Ptr<'ctx, StructModel<S>> {
|
||||
pub fn gep<M, GetField>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
get_field: GetField,
|
||||
) -> Ptr<'ctx, M>
|
||||
where
|
||||
M: Model<'ctx>,
|
||||
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
|
||||
{
|
||||
let field = get_field(self.model.0 .0.fields());
|
||||
let llvm_i32 = ctx.ctx.i32_type(); // i64 would segfault
|
||||
|
||||
let ptr = unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
self.value,
|
||||
&[llvm_i32.const_zero(), llvm_i32.const_int(field.gep_index, false)],
|
||||
field.name,
|
||||
)
|
||||
.unwrap()
|
||||
};
|
||||
|
||||
let ptr_model = PtrModel(field.model);
|
||||
ptr_model.believe_value(ptr)
|
||||
}
|
||||
|
||||
/// Convenience function equivalent to `.gep(...).load(...)`.
|
||||
pub fn get<M, GetField, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
get_field: GetField,
|
||||
name: &str,
|
||||
) -> Instance<'ctx, M>
|
||||
where
|
||||
M: Model<'ctx>,
|
||||
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
|
||||
{
|
||||
self.gep(ctx, get_field).load(generator, ctx, name)
|
||||
}
|
||||
|
||||
/// Convenience function equivalent to `.gep(...).store(...)`.
|
||||
pub fn set<M, GetField>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
get_field: GetField,
|
||||
value: Instance<'ctx, M>,
|
||||
) where
|
||||
M: Model<'ctx>,
|
||||
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
|
||||
{
|
||||
self.gep(ctx, get_field).store(ctx, value);
|
||||
}
|
||||
}
|
||||
|
||||
// TODO: Add an opaque struct type?
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
use inkwell::{types::BasicType, values::IntValue};
|
||||
|
||||
/// `llvm.memcpy` but under the [`Model`] abstraction
|
||||
use crate::codegen::{
|
||||
llvm_intrinsics::call_memcpy_generic,
|
||||
stmt::{gen_for_callback_incrementing, BreakContinueHooks},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::*;
|
||||
|
||||
/// Convenience function.
|
||||
///
|
||||
/// Like [`call_memcpy_generic`] but with model abstractions and `is_volatile` set to `false`.
|
||||
pub fn call_memcpy_model<'ctx, Item: Model<'ctx> + Default, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_array: Ptr<'ctx, Item>,
|
||||
src_array: Ptr<'ctx, Item>,
|
||||
num_items: IntValue<'ctx>,
|
||||
) {
|
||||
let itemsize = Item::default().get_type(generator, ctx.ctx).size_of().unwrap();
|
||||
let totalsize = ctx.builder.build_int_mul(itemsize, num_items, "totalsize").unwrap(); // TODO: Int types may not match.
|
||||
let is_volatile = ctx.ctx.bool_type().const_zero();
|
||||
call_memcpy_generic(ctx, dst_array.value, src_array.value, totalsize, is_volatile);
|
||||
}
|
||||
|
||||
/// Like [`gen_for_callback_incrementing`] with [`Model`] abstractions.
|
||||
/// The [`IntKind`] is automatically inferred.
|
||||
pub fn gen_for_model_auto<'ctx, 'a, G, F, I>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
start: Int<'ctx, I>,
|
||||
stop: Int<'ctx, I>,
|
||||
step: Int<'ctx, I>,
|
||||
body: F,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
F: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks<'ctx>,
|
||||
Int<'ctx, I>,
|
||||
) -> Result<(), String>,
|
||||
I: IntKind<'ctx> + Default,
|
||||
{
|
||||
let int_model = IntModel(I::default());
|
||||
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
start.value,
|
||||
(stop.value, false),
|
||||
|g, ctx, hooks, i| {
|
||||
let i = int_model.believe_value(i);
|
||||
body(g, ctx, hooks, i)
|
||||
},
|
||||
step.value,
|
||||
)
|
||||
}
|
||||
|
|
@ -1,28 +1,20 @@
|
|||
use inkwell::{
|
||||
types::{AnyTypeEnum, BasicType, BasicTypeEnum, PointerType},
|
||||
values::{BasicValue, BasicValueEnum, IntValue, PointerValue},
|
||||
AddressSpace, IntPredicate, OptimizationLevel,
|
||||
};
|
||||
|
||||
use nac3parser::ast::{Operator, StrRef};
|
||||
|
||||
use super::{
|
||||
classes::{
|
||||
ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, NDArrayType, NDArrayValue,
|
||||
ProxyType, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeAdapter,
|
||||
TypedArrayLikeMutator, UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
|
||||
},
|
||||
expr::gen_binop_expr_with_values,
|
||||
irrt::{
|
||||
calculate_len_for_slice_range, call_ndarray_calc_broadcast,
|
||||
call_ndarray_calc_broadcast_index, call_ndarray_calc_nd_indices, call_ndarray_calc_size,
|
||||
},
|
||||
llvm_intrinsics::{self, call_memcpy_generic},
|
||||
macros::codegen_unreachable,
|
||||
stmt::{gen_for_callback_incrementing, gen_for_range_callback, gen_if_else_expr_callback},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
use crate::{
|
||||
codegen::{
|
||||
classes::{
|
||||
ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, NDArrayType, NDArrayValue,
|
||||
ProxyType, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeAdapter,
|
||||
TypedArrayLikeMutator, UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
|
||||
},
|
||||
expr::gen_binop_expr_with_values,
|
||||
irrt::{
|
||||
calculate_len_for_slice_range, call_ndarray_calc_broadcast,
|
||||
call_ndarray_calc_broadcast_index, call_ndarray_calc_nd_indices,
|
||||
call_ndarray_calc_size,
|
||||
},
|
||||
llvm_intrinsics::{self, call_memcpy_generic},
|
||||
stmt::{gen_for_callback_incrementing, gen_for_range_callback, gen_if_else_expr_callback},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{
|
||||
helper::PrimDef,
|
||||
|
|
@ -34,6 +26,16 @@ use crate::{
|
|||
typedef::{FunSignature, Type, TypeEnum},
|
||||
},
|
||||
};
|
||||
use inkwell::{
|
||||
types::BasicType,
|
||||
values::{BasicValueEnum, IntValue, PointerValue},
|
||||
AddressSpace, IntPredicate, OptimizationLevel,
|
||||
};
|
||||
use inkwell::{
|
||||
types::{AnyTypeEnum, BasicTypeEnum, PointerType},
|
||||
values::BasicValue,
|
||||
};
|
||||
use nac3parser::ast::{Operator, StrRef};
|
||||
|
||||
/// Creates an uninitialized `NDArray` instance.
|
||||
fn create_ndarray_uninitialized<'ctx, G: CodeGenerator + ?Sized>(
|
||||
|
|
@ -255,9 +257,9 @@ fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
|
|||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "").into()
|
||||
ctx.gen_string(generator, "").value.into()
|
||||
} else {
|
||||
codegen_unreachable!(ctx)
|
||||
unreachable!()
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -283,9 +285,9 @@ fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
|
|||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_int(1, false).into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "1").into()
|
||||
ctx.gen_string(generator, "1").value.into()
|
||||
} else {
|
||||
codegen_unreachable!(ctx)
|
||||
unreachable!()
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -353,7 +355,7 @@ fn call_ndarray_empty_impl<'ctx, G: CodeGenerator + ?Sized>(
|
|||
|
||||
create_ndarray_const_shape(generator, ctx, elem_ty, &[shape_int])
|
||||
}
|
||||
_ => codegen_unreachable!(ctx),
|
||||
_ => unreachable!(),
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -624,7 +626,7 @@ fn call_ndarray_full_impl<'ctx, G: CodeGenerator + ?Sized>(
|
|||
} else if fill_value.is_int_value() || fill_value.is_float_value() {
|
||||
fill_value
|
||||
} else {
|
||||
codegen_unreachable!(ctx)
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
Ok(value)
|
||||
|
|
@ -939,7 +941,7 @@ fn call_ndarray_array_impl<'ctx, G: CodeGenerator + ?Sized>(
|
|||
.build_store(
|
||||
lst,
|
||||
ctx.builder
|
||||
.build_bit_cast(object.as_base_value(), llvm_plist_i8, "")
|
||||
.build_bitcast(object.as_base_value(), llvm_plist_i8, "")
|
||||
.unwrap(),
|
||||
)
|
||||
.unwrap();
|
||||
|
|
@ -961,7 +963,7 @@ fn call_ndarray_array_impl<'ctx, G: CodeGenerator + ?Sized>(
|
|||
.builder
|
||||
.build_load(lst, "")
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.map(|v| ctx.builder.build_bit_cast(v, plist_plist_i8, "").unwrap())
|
||||
.map(|v| ctx.builder.build_bitcast(v, plist_plist_i8, "").unwrap())
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap();
|
||||
let this_dim = ListValue::from_ptr_val(this_dim, llvm_usize, None);
|
||||
|
|
@ -980,9 +982,7 @@ fn call_ndarray_array_impl<'ctx, G: CodeGenerator + ?Sized>(
|
|||
ctx.builder
|
||||
.build_store(
|
||||
lst,
|
||||
ctx.builder
|
||||
.build_bit_cast(next_dim, llvm_plist_i8, "")
|
||||
.unwrap(),
|
||||
ctx.builder.build_bitcast(next_dim, llvm_plist_i8, "").unwrap(),
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
|
|
@ -1071,15 +1071,15 @@ fn call_ndarray_eye_impl<'ctx, G: CodeGenerator + ?Sized>(
|
|||
/// Copies a slice of an [`NDArrayValue`] to another.
|
||||
///
|
||||
/// - `dst_arr`: The [`NDArrayValue`] instance of the destination array. The `ndims` and `dim_sz`
|
||||
/// fields should be populated before calling this function.
|
||||
/// fields should be populated before calling this function.
|
||||
/// - `dst_slice_ptr`: The [`PointerValue`] to the first element of the currently processing
|
||||
/// dimensional slice in the destination array.
|
||||
/// dimensional slice in the destination array.
|
||||
/// - `src_arr`: The [`NDArrayValue`] instance of the source array.
|
||||
/// - `src_slice_ptr`: The [`PointerValue`] to the first element of the currently processing
|
||||
/// dimensional slice in the source array.
|
||||
/// dimensional slice in the source array.
|
||||
/// - `dim`: The index of the currently processing dimension.
|
||||
/// - `slices`: List of all slices, with the first element corresponding to the slice applicable to
|
||||
/// this dimension. The `start`/`stop` values of each slice must be non-negative indices.
|
||||
/// this dimension. The `start`/`stop` values of each slice must be non-negative indices.
|
||||
fn ndarray_sliced_copyto_impl<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
|
|
@ -1184,7 +1184,7 @@ fn ndarray_sliced_copyto_impl<'ctx, G: CodeGenerator + ?Sized>(
|
|||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// - `slices`: List of all slices, with the first element corresponding to the slice applicable to
|
||||
/// this dimension. The `start`/`stop` values of each slice must be positive indices.
|
||||
/// this dimension. The `start`/`stop` values of each slice must be positive indices.
|
||||
pub fn ndarray_sliced_copy<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
|
|
@ -1349,7 +1349,7 @@ where
|
|||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `res` - The `ndarray` instance to write results into, or [`None`] if the result should be
|
||||
/// written to a new `ndarray`.
|
||||
/// written to a new `ndarray`.
|
||||
/// * `value_fn` - Function mapping the two input elements into the result.
|
||||
///
|
||||
/// # Panic
|
||||
|
|
@ -1436,7 +1436,7 @@ where
|
|||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `res` - The `ndarray` instance to write results into, or [`None`] if the result should be
|
||||
/// written to a new `ndarray`.
|
||||
/// written to a new `ndarray`.
|
||||
pub fn ndarray_matmul_2d<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
|
|
@ -2020,7 +2020,7 @@ pub fn gen_ndarray_fill<'ctx>(
|
|||
} else if value_arg.is_int_value() || value_arg.is_float_value() {
|
||||
value_arg
|
||||
} else {
|
||||
codegen_unreachable!(ctx)
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
Ok(value)
|
||||
|
|
@ -2129,8 +2129,7 @@ pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
|
|||
|
||||
Ok(out.as_base_value().into())
|
||||
} else {
|
||||
codegen_unreachable!(
|
||||
ctx,
|
||||
unreachable!(
|
||||
"{FN_NAME}() not supported for '{}'",
|
||||
format!("'{}'", ctx.unifier.stringify(x1_ty))
|
||||
)
|
||||
|
|
@ -2141,12 +2140,11 @@ pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
|
|||
///
|
||||
/// * `x1` - `NDArray` to reshape.
|
||||
/// * `shape` - The `shape` parameter used to construct the new `NDArray`.
|
||||
/// Just like numpy, the `shape` argument can be:
|
||||
/// Just like numpy, the `shape` argument can be:
|
||||
/// 1. A list of `int32`; e.g., `np.reshape(arr, [600, -1, 3])`
|
||||
/// 2. A tuple of `int32`; e.g., `np.reshape(arr, (-1, 800, 3))`
|
||||
/// 3. A scalar `int32`; e.g., `np.reshape(arr, 3)`
|
||||
///
|
||||
/// Note that unlike other generating functions, one of the dimensions in the shape can be negative.
|
||||
/// Note that unlike other generating functions, one of the dimesions in the shape can be negative
|
||||
pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
|
|
@ -2372,7 +2370,7 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
|
|||
.into_int_value();
|
||||
create_ndarray_const_shape(generator, ctx, elem_ty, &[shape_int])
|
||||
}
|
||||
_ => codegen_unreachable!(ctx),
|
||||
_ => unreachable!(),
|
||||
}
|
||||
.unwrap();
|
||||
|
||||
|
|
@ -2416,8 +2414,7 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
|
|||
|
||||
Ok(out.as_base_value().into())
|
||||
} else {
|
||||
codegen_unreachable!(
|
||||
ctx,
|
||||
unreachable!(
|
||||
"{FN_NAME}() not supported for '{}'",
|
||||
format!("'{}'", ctx.unifier.stringify(x1_ty))
|
||||
)
|
||||
|
|
@ -2485,7 +2482,7 @@ pub fn ndarray_dot<'ctx, G: CodeGenerator + ?Sized>(
|
|||
.build_float_mul(e1, elem2.into_float_value(), "")
|
||||
.unwrap()
|
||||
.as_basic_value_enum(),
|
||||
_ => codegen_unreachable!(ctx),
|
||||
_ => unreachable!(),
|
||||
};
|
||||
let acc_val = ctx.builder.build_load(acc, "").unwrap();
|
||||
let acc_val = match acc_val {
|
||||
|
|
@ -2499,7 +2496,7 @@ pub fn ndarray_dot<'ctx, G: CodeGenerator + ?Sized>(
|
|||
.build_float_add(e1, product.into_float_value(), "")
|
||||
.unwrap()
|
||||
.as_basic_value_enum(),
|
||||
_ => codegen_unreachable!(ctx),
|
||||
_ => unreachable!(),
|
||||
};
|
||||
ctx.builder.build_store(acc, acc_val).unwrap();
|
||||
|
||||
|
|
@ -2516,8 +2513,7 @@ pub fn ndarray_dot<'ctx, G: CodeGenerator + ?Sized>(
|
|||
(BasicValueEnum::FloatValue(e1), BasicValueEnum::FloatValue(e2)) => {
|
||||
Ok(ctx.builder.build_float_mul(e1, e2, "").unwrap().as_basic_value_enum())
|
||||
}
|
||||
_ => codegen_unreachable!(
|
||||
ctx,
|
||||
_ => unreachable!(
|
||||
"{FN_NAME}() not supported for '{}'",
|
||||
format!("'{}'", ctx.unifier.stringify(x1_ty))
|
||||
),
|
||||
|
|
|
|||
|
|
@ -0,0 +1,210 @@
|
|||
// TODO: Replace numpy.rs
|
||||
|
||||
use inkwell::values::{BasicValue, BasicValueEnum};
|
||||
use nac3parser::ast::StrRef;
|
||||
|
||||
use crate::{
|
||||
codegen::object::{ndarray::scalar::split_scalar_or_ndarray, tuple::TupleObject},
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{helper::extract_ndims, numpy::unpack_ndarray_var_tys, DefinitionId},
|
||||
typecheck::typedef::{FunSignature, Type},
|
||||
};
|
||||
|
||||
use super::{
|
||||
irrt::call_nac3_ndarray_util_assert_shape_no_negative,
|
||||
model::*,
|
||||
object::{
|
||||
ndarray::{shape_util::parse_numpy_int_sequence, NDArrayObject},
|
||||
AnyObject,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
/// Generates LLVM IR for `np.broadcast_to`.
|
||||
pub fn gen_ndarray_broadcast_to<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 2);
|
||||
|
||||
// Parse argument #1 input
|
||||
let input_ty = fun.0.args[0].ty;
|
||||
let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?;
|
||||
let input = AnyObject { ty: input_ty, value: input };
|
||||
|
||||
// Parse argument #2 shape
|
||||
let shape_ty = fun.0.args[1].ty;
|
||||
let shape = args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
let shape = AnyObject { ty: shape_ty, value: shape };
|
||||
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Extract broadcast_ndims, this is the only way to get the
|
||||
// ndims of the ndarray result statically.
|
||||
let (_, broadcast_ndims_ty) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
|
||||
let broadcast_ndims = extract_ndims(&ctx.unifier, broadcast_ndims_ty);
|
||||
|
||||
// Process `input`
|
||||
let in_ndarray = split_scalar_or_ndarray(generator, ctx, input).as_ndarray(generator, ctx);
|
||||
|
||||
// Process `shape`
|
||||
let (_, broadcast_shape) = parse_numpy_int_sequence(generator, ctx, shape);
|
||||
// NOTE: shape.size should equal to `broadcasted_ndims`.
|
||||
let broadcast_ndims_llvm = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
|
||||
call_nac3_ndarray_util_assert_shape_no_negative(
|
||||
generator,
|
||||
ctx,
|
||||
broadcast_ndims_llvm,
|
||||
broadcast_shape,
|
||||
);
|
||||
|
||||
// Create broadcast view
|
||||
let broadcast_ndarray =
|
||||
in_ndarray.broadcast_to(generator, ctx, broadcast_ndims, broadcast_shape);
|
||||
|
||||
Ok(broadcast_ndarray.instance.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.reshape`.
|
||||
pub fn gen_ndarray_reshape<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 2);
|
||||
|
||||
// Parse argument #1 input
|
||||
let input_ty = fun.0.args[0].ty;
|
||||
let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?;
|
||||
let input = AnyObject { ty: input_ty, value: input };
|
||||
|
||||
// Parse argument #2 shape
|
||||
let shape_ty = fun.0.args[1].ty;
|
||||
let shape = args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
let shape = AnyObject { ty: shape_ty, value: shape };
|
||||
|
||||
// Extract reshaped_ndims
|
||||
let (_, reshaped_ndims_ty) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
|
||||
let reshaped_ndims = extract_ndims(&ctx.unifier, reshaped_ndims_ty);
|
||||
|
||||
// Process `input`
|
||||
let in_ndarray = split_scalar_or_ndarray(generator, ctx, input).as_ndarray(generator, ctx);
|
||||
|
||||
// Process the shape input from user and resolve negative indices.
|
||||
// The resulting `new_shape`'s size should be equal to reshaped_ndims.
|
||||
// This is ensured by the typechecker.
|
||||
let (_, new_shape) = parse_numpy_int_sequence(generator, ctx, shape);
|
||||
let reshaped_ndarray = in_ndarray.reshape_or_copy(generator, ctx, reshaped_ndims, new_shape);
|
||||
|
||||
Ok(reshaped_ndarray.instance.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.arange`.
|
||||
pub fn gen_ndarray_arange<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse argument #1 len
|
||||
let input_ty = fun.0.args[0].ty;
|
||||
let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?.into_int_value();
|
||||
|
||||
// Implementation
|
||||
let input_dim = IntModel(SizeT).s_extend_or_bit_cast(generator, ctx, input, "input_dim");
|
||||
let ndarray = NDArrayObject::from_np_arange(generator, ctx, input_dim);
|
||||
|
||||
Ok(ndarray.instance.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.size`.
|
||||
pub fn gen_ndarray_size<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let ndarray_ty = fun.0.args[0].ty;
|
||||
let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
|
||||
|
||||
let size = ndarray.size(generator, ctx).truncate(generator, ctx, Int32, "size");
|
||||
Ok(size.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.shape`.
|
||||
pub fn gen_ndarray_shape<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse argument #1 ndarray
|
||||
let ndarray_ty = fun.0.args[0].ty;
|
||||
let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
|
||||
|
||||
// Process ndarray
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
|
||||
Ok(ndarray.make_shape_tuple(generator, ctx).value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `<ndarray>.strides`.
|
||||
pub fn gen_ndarray_strides<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
// TODO: Code duplication: This function looks exactly like `gen_ndarray_shapes`.
|
||||
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse argument #1 ndarray
|
||||
let ndarray_ty = fun.0.args[0].ty;
|
||||
let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
|
||||
|
||||
// Process ndarray
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
|
||||
|
||||
let mut objects = Vec::with_capacity(ndarray.ndims as usize);
|
||||
|
||||
for i in 0..ndarray.ndims {
|
||||
let dim = ndarray
|
||||
.instance
|
||||
.get(generator, ctx, |f| f.strides, "")
|
||||
.offset_const(generator, ctx, i, "")
|
||||
.load(generator, ctx, "dim");
|
||||
let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
|
||||
|
||||
objects
|
||||
.push(AnyObject { ty: ctx.primitives.int32, value: dim.value.as_basic_value_enum() });
|
||||
}
|
||||
|
||||
let strides = TupleObject::create(generator, ctx, objects, "strides");
|
||||
Ok(strides.value.as_basic_value_enum())
|
||||
}
|
||||
|
|
@ -0,0 +1,121 @@
|
|||
use crate::{
|
||||
codegen::{
|
||||
irrt::{call_nac3_list_slice_assign, list_slice_assignment},
|
||||
model::*,
|
||||
object::ndarray::indexing::UserSlice,
|
||||
structure::List,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::{iter_type_vars, Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::{ndarray::indexing::RustUserSlice, AnyObject};
|
||||
|
||||
/// A NAC3 Python List object.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct ListObject<'ctx> {
|
||||
/// Typechecker type of the list items
|
||||
pub item_type: Type,
|
||||
pub instance: Ptr<'ctx, StructModel<List<AnyModel<'ctx>>>>,
|
||||
}
|
||||
|
||||
impl<'ctx> ListObject<'ctx> {
|
||||
/// Create a [`ListObject`] from an LLVM value and its typechecker [`Type`].
|
||||
pub fn from_object<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
) -> Self {
|
||||
// Check typechecker type and extract `item_type`
|
||||
let item_type = match &*ctx.unifier.get_ty(object.ty) {
|
||||
TypeEnum::TObj { obj_id, params, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
iter_type_vars(params).next().unwrap().ty // Extract `item_type`
|
||||
}
|
||||
_ => {
|
||||
panic!("Expecting type to be a list, but got {}", ctx.unifier.stringify(object.ty))
|
||||
}
|
||||
};
|
||||
|
||||
let item_model = AnyModel(ctx.get_llvm_type(generator, item_type));
|
||||
let plist_model = PtrModel(StructModel(List { item: item_model }));
|
||||
|
||||
// Create object
|
||||
let value = plist_model.check_value(generator, ctx.ctx, object.value).unwrap();
|
||||
ListObject { item_type, instance: value }
|
||||
}
|
||||
|
||||
/// Get the `items` field as an opaque pointer.
|
||||
pub fn get_opaque_items_ptr<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Ptr<'ctx, IntModel<Byte>> {
|
||||
self.instance.get(generator, ctx, |f| f.items, "items").pointer_cast(
|
||||
generator,
|
||||
ctx,
|
||||
IntModel(Byte),
|
||||
"items_opaque",
|
||||
)
|
||||
}
|
||||
|
||||
/// Get the value of this [`ListObject`] as a list with opaque items.
|
||||
///
|
||||
/// This function allocates on the stack to create the list, but the
|
||||
/// reference to the `items` are preserved.
|
||||
pub fn get_opaque_list_ptr<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Ptr<'ctx, StructModel<List<IntModel<Byte>>>> {
|
||||
let opaque_list_model = StructModel(List { item: IntModel(Byte) });
|
||||
let opaque_list_ptr = opaque_list_model.alloca(generator, ctx, "opaque_list_ptr");
|
||||
|
||||
// Copy items pointer
|
||||
let items = self.get_opaque_items_ptr(generator, ctx);
|
||||
opaque_list_ptr.set(ctx, |f| f.items, items);
|
||||
|
||||
// Copy len
|
||||
let len = self.instance.get(generator, ctx, |f| f.len, "len");
|
||||
opaque_list_ptr.set(ctx, |f| f.len, len);
|
||||
|
||||
opaque_list_ptr
|
||||
}
|
||||
|
||||
/// Get the `len()` of this list.
|
||||
pub fn len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
self.instance.get(generator, ctx, |f| f.len, "list_len")
|
||||
}
|
||||
|
||||
pub fn slice_assign_from<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
user_slice: &RustUserSlice<'ctx>,
|
||||
source: ListObject<'ctx>,
|
||||
) {
|
||||
// Sanity check
|
||||
assert!(ctx.unifier.unioned(self.item_type, source.item_type));
|
||||
|
||||
let user_slice_model = StructModel(UserSlice);
|
||||
let puser_slice = user_slice_model.alloca(generator, ctx, "user_slice");
|
||||
user_slice.write_to_user_slice(generator, ctx, puser_slice);
|
||||
|
||||
let itemsize = self.instance.model.get_type(generator, ctx.ctx).size_of();
|
||||
|
||||
call_nac3_list_slice_assign(
|
||||
generator,
|
||||
ctx,
|
||||
self.get_opaque_list_ptr(generator, ctx),
|
||||
source.instance.value,
|
||||
itemsize,
|
||||
user_slice,
|
||||
);
|
||||
todo!()
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,608 @@
|
|||
use inkwell::{
|
||||
values::{BasicValue, BasicValueEnum, FloatValue, IntValue},
|
||||
FloatPredicate, IntPredicate,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
use list::ListObject;
|
||||
use ndarray::{NDArrayObject, NDArrayOut};
|
||||
use range::RangeObject;
|
||||
use tuple::TupleObject;
|
||||
|
||||
use crate::{
|
||||
toplevel::helper::PrimDef,
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::{llvm_intrinsics, model::*, CodeGenContext, CodeGenerator};
|
||||
|
||||
pub mod list;
|
||||
pub mod ndarray;
|
||||
pub mod range;
|
||||
pub mod tuple;
|
||||
|
||||
/// Convenience function to crash the program when types of arguments are not supported.
|
||||
/// Used to be debugged with a stacktrace.
|
||||
fn unsupported_type<I>(ctx: &CodeGenContext<'_, '_>, tys: I) -> !
|
||||
where
|
||||
I: IntoIterator<Item = Type>,
|
||||
{
|
||||
unreachable!(
|
||||
"unsupported types found '{}'",
|
||||
tys.into_iter().map(|ty| format!("'{}'", ctx.unifier.stringify(ty))).join(", "),
|
||||
)
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub enum FloorOrCeil {
|
||||
Floor,
|
||||
Ceil,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub enum MinOrMax {
|
||||
Min,
|
||||
Max,
|
||||
}
|
||||
|
||||
fn signed_ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
|
||||
vec![ctx.primitives.int32, ctx.primitives.int64]
|
||||
}
|
||||
|
||||
fn unsigned_ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
|
||||
vec![ctx.primitives.uint32, ctx.primitives.uint64]
|
||||
}
|
||||
|
||||
fn ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
|
||||
vec![ctx.primitives.int32, ctx.primitives.int64, ctx.primitives.uint32, ctx.primitives.uint64]
|
||||
}
|
||||
|
||||
fn int_like(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
|
||||
vec![
|
||||
ctx.primitives.bool,
|
||||
ctx.primitives.int32,
|
||||
ctx.primitives.int64,
|
||||
ctx.primitives.uint32,
|
||||
ctx.primitives.uint64,
|
||||
]
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AnyObject<'ctx> {
|
||||
pub ty: Type,
|
||||
pub value: BasicValueEnum<'ctx>,
|
||||
}
|
||||
|
||||
impl<'ctx> AnyObject<'ctx> {
|
||||
/// Returns true if this object's type is a [`TypeEnum::TObj`] and has the object ID as `prim`.
|
||||
pub fn is_obj(&self, ctx: &mut CodeGenContext<'ctx, '_>, prim: PrimDef) -> bool {
|
||||
match &*ctx.unifier.get_ty(self.ty) {
|
||||
TypeEnum::TObj { obj_id, .. } => *obj_id == prim.id(),
|
||||
_ => false,
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns true if this object's type is a [`TypeEnum::TTuple`]
|
||||
pub fn is_tuple(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
matches!(&*ctx.unifier.get_ty(self.ty), TypeEnum::TTuple { .. })
|
||||
}
|
||||
|
||||
pub fn into_tuple() {}
|
||||
|
||||
pub fn is_int32(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned(self.ty, ctx.primitives.int32)
|
||||
}
|
||||
|
||||
pub fn into_int32(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Int<'ctx, Int32> {
|
||||
assert!(self.is_int32(ctx));
|
||||
IntModel(Int32).believe_value(self.value.into_int_value())
|
||||
}
|
||||
|
||||
pub fn is_uint32(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned(self.ty, ctx.primitives.uint32)
|
||||
}
|
||||
|
||||
pub fn is_int64(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned(self.ty, ctx.primitives.int64)
|
||||
}
|
||||
|
||||
pub fn is_uint64(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned(self.ty, ctx.primitives.uint64)
|
||||
}
|
||||
|
||||
pub fn is_bool(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned(self.ty, ctx.primitives.bool)
|
||||
}
|
||||
|
||||
/// Returns true if the object type is `bool`, `int32`, `int64`, `uint32`, or `uint64`.
|
||||
pub fn is_int_like(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned_any(self.ty, int_like(ctx))
|
||||
}
|
||||
|
||||
/// Returns true if the object type is `int32`, `int64`.
|
||||
pub fn is_signed_int(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned_any(self.ty, signed_ints(ctx))
|
||||
}
|
||||
|
||||
/// Returns true if the object type is `uint32`, `uint64`.
|
||||
pub fn is_unsigned_int(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
ctx.unifier.unioned_any(self.ty, unsigned_ints(ctx))
|
||||
}
|
||||
|
||||
pub fn into_int(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
assert!(self.is_int_like(ctx));
|
||||
self.value.into_int_value()
|
||||
}
|
||||
|
||||
pub fn is_float(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
self.is_obj(ctx, PrimDef::Float)
|
||||
}
|
||||
|
||||
pub fn into_float(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Float<'ctx, Float64> {
|
||||
assert!(self.is_float(ctx));
|
||||
FloatModel(Float64).believe_value(self.value.into_float_value())
|
||||
}
|
||||
|
||||
pub fn is_ndarray(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
|
||||
self.is_obj(ctx, PrimDef::NDArray)
|
||||
}
|
||||
|
||||
pub fn into_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> NDArrayObject<'ctx> {
|
||||
NDArrayObject::from_object(generator, ctx, *self)
|
||||
}
|
||||
|
||||
/// Create an object from a boolean from an i1.
|
||||
///
|
||||
/// NOTE: In NAC3, booleans are i8. This function does converts the input i1 to an i8.
|
||||
pub fn from_bool(ctx: &mut CodeGenContext<'ctx, '_>, n: Int<'ctx, Bool>) -> AnyObject<'ctx> {
|
||||
let llvm_i8 = ctx.ctx.i8_type();
|
||||
let value = ctx.builder.build_int_z_extend(n.value, llvm_i8, "bool").unwrap();
|
||||
AnyObject { value: value.as_basic_value_enum(), ty: ctx.primitives.bool }
|
||||
}
|
||||
|
||||
/// Helper function to compare two scalars.
|
||||
///
|
||||
/// Only int-to-int and float-to-float comparisons are allowed.
|
||||
///
|
||||
/// Panic otherwise.
|
||||
pub fn compare_int_or_float_by_predicate<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
lhs: AnyObject<'ctx>,
|
||||
rhs: AnyObject<'ctx>,
|
||||
int_predicate: IntPredicate,
|
||||
float_predicate: FloatPredicate,
|
||||
name: &str,
|
||||
) -> Int<'ctx, Bool> {
|
||||
assert!(ctx.unifier.unioned(lhs.ty, rhs.ty), "lhs and rhs type should be the same");
|
||||
|
||||
let bool_model = IntModel(Bool);
|
||||
|
||||
let common_ty = lhs.ty;
|
||||
let result = if lhs.is_float(ctx) {
|
||||
let lhs = lhs.into_float(ctx);
|
||||
let rhs = rhs.into_float(ctx);
|
||||
ctx.builder.build_float_compare(float_predicate, lhs.value, rhs.value, name).unwrap()
|
||||
} else if ctx.unifier.unioned_any(common_ty, int_like(ctx)) {
|
||||
let lhs = lhs.into_int(ctx);
|
||||
let rhs = rhs.into_int(ctx);
|
||||
ctx.builder.build_int_compare(int_predicate, lhs, rhs, name).unwrap()
|
||||
} else {
|
||||
unsupported_type(ctx, [lhs.ty, rhs.ty]);
|
||||
};
|
||||
|
||||
bool_model.check_value(generator, ctx.ctx, result).unwrap()
|
||||
}
|
||||
|
||||
/// Helper function for `int32()`, `int64()`, `uint32()`, and `uint64()`.
|
||||
///
|
||||
/// TODO: Document me
|
||||
fn cast_to_int_conversion<'a, G, HandleFloatFn>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ret_int_ty: Type,
|
||||
handle_float: HandleFloatFn,
|
||||
) -> AnyObject<'ctx>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
HandleFloatFn:
|
||||
FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, FloatValue<'ctx>) -> IntValue<'ctx>,
|
||||
{
|
||||
let ret_int_ty_llvm = ctx.get_llvm_type(generator, ret_int_ty).into_int_type();
|
||||
|
||||
let result = if self.is_float(ctx) {
|
||||
// Handle float to int
|
||||
let n = self.into_float(ctx);
|
||||
handle_float(generator, ctx, n.value)
|
||||
} else if self.is_int_like(ctx) {
|
||||
// Handle int to a new int type
|
||||
let n = self.into_int(ctx);
|
||||
if n.get_type().get_bit_width() <= ret_int_ty_llvm.get_bit_width() {
|
||||
ctx.builder.build_int_z_extend(n, ret_int_ty_llvm, "zext").unwrap()
|
||||
} else {
|
||||
ctx.builder.build_int_truncate(n, ret_int_ty_llvm, "trunc").unwrap()
|
||||
}
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty]);
|
||||
};
|
||||
|
||||
assert_eq!(ret_int_ty_llvm.get_bit_width(), result.get_type().get_bit_width()); // Sanity check
|
||||
AnyObject { value: result.into(), ty: ret_int_ty }
|
||||
}
|
||||
|
||||
/// Call `int32()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_int32<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
self.cast_to_int_conversion(
|
||||
generator,
|
||||
ctx,
|
||||
ctx.primitives.int32,
|
||||
|_generator, ctx, input| {
|
||||
let n =
|
||||
ctx.builder.build_float_to_signed_int(input, ctx.ctx.i64_type(), "").unwrap();
|
||||
ctx.builder.build_int_truncate(n, ctx.ctx.i32_type(), "conv").unwrap()
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
/// Call `int64()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_int64<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
self.cast_to_int_conversion(
|
||||
generator,
|
||||
ctx,
|
||||
ctx.primitives.int64,
|
||||
|_generator, ctx, input| {
|
||||
ctx.builder.build_float_to_signed_int(input, ctx.ctx.i64_type(), "").unwrap()
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
/// Call `uint32()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_uint32<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
self.cast_to_int_conversion(generator, ctx, ctx.primitives.uint32, |_generator, ctx, n| {
|
||||
let n_gez = ctx
|
||||
.builder
|
||||
.build_float_compare(FloatPredicate::OGE, n, n.get_type().const_zero(), "")
|
||||
.unwrap();
|
||||
|
||||
let to_int32 =
|
||||
ctx.builder.build_float_to_signed_int(n, ctx.ctx.i32_type(), "").unwrap();
|
||||
let to_uint64 =
|
||||
ctx.builder.build_float_to_unsigned_int(n, ctx.ctx.i64_type(), "").unwrap();
|
||||
|
||||
ctx.builder
|
||||
.build_select(
|
||||
n_gez,
|
||||
ctx.builder.build_int_truncate(to_uint64, ctx.ctx.i32_type(), "").unwrap(),
|
||||
to_int32,
|
||||
"conv",
|
||||
)
|
||||
.unwrap()
|
||||
.into_int_value()
|
||||
})
|
||||
}
|
||||
|
||||
/// Call `uint64()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_uint64<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
self.cast_to_int_conversion(generator, ctx, ctx.primitives.uint64, |_generator, ctx, n| {
|
||||
let val_gez = ctx
|
||||
.builder
|
||||
.build_float_compare(FloatPredicate::OGE, n, n.get_type().const_zero(), "")
|
||||
.unwrap();
|
||||
|
||||
let to_int64 =
|
||||
ctx.builder.build_float_to_signed_int(n, ctx.ctx.i64_type(), "").unwrap();
|
||||
let to_uint64 =
|
||||
ctx.builder.build_float_to_unsigned_int(n, ctx.ctx.i64_type(), "").unwrap();
|
||||
|
||||
ctx.builder.build_select(val_gez, to_uint64, to_int64, "conv").unwrap().into_int_value()
|
||||
})
|
||||
}
|
||||
|
||||
// Get the `len()` of this object.
|
||||
#[must_use]
|
||||
pub fn call_len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
// TODO: Switch to returning SizeT
|
||||
let result = match &*ctx.unifier.get_ty_immutable(self.ty) {
|
||||
TypeEnum::TTuple { .. } => {
|
||||
let tuple = TupleObject::from_object(ctx, *self);
|
||||
tuple.len(generator, ctx).truncate(generator, ctx, Int32, "tuple_len_32")
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.range.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let range = RangeObject::from_object(generator, ctx, *self);
|
||||
range.len(generator, ctx)
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let list = ListObject::from_object(generator, ctx, *self);
|
||||
list.len(generator, ctx).truncate(generator, ctx, Int32, "list_len_i32")
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, *self);
|
||||
ndarray.len(generator, ctx).truncate(generator, ctx, Int32, "ndarray_len_i32")
|
||||
}
|
||||
_ => unreachable!(),
|
||||
};
|
||||
|
||||
AnyObject { ty: ctx.primitives.int32, value: result.value.as_basic_value_enum() }
|
||||
}
|
||||
|
||||
/// Like [`AnyObject::call_bool`] but this returns an `Int<'ctx, Bool>` instead of an object.
|
||||
pub fn bool(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Int<'ctx, Bool> {
|
||||
let bool_model = IntModel(Bool);
|
||||
if self.is_int_like(ctx) {
|
||||
let n = self.into_int(ctx);
|
||||
let n = ctx
|
||||
.builder
|
||||
.build_int_compare(inkwell::IntPredicate::NE, n, n.get_type().const_zero(), "bool")
|
||||
.unwrap();
|
||||
bool_model.believe_value(n)
|
||||
} else if self.is_float(ctx) {
|
||||
let n = self.value.into_float_value();
|
||||
let n = ctx
|
||||
.builder
|
||||
.build_float_compare(FloatPredicate::UNE, n, n.get_type().const_zero(), "bool")
|
||||
.unwrap();
|
||||
bool_model.believe_value(n)
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty])
|
||||
}
|
||||
}
|
||||
|
||||
/// Call `bool()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_bool(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
|
||||
let n = self.bool(ctx);
|
||||
AnyObject::from_bool(ctx, n)
|
||||
}
|
||||
|
||||
/// Call `float()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_float(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
|
||||
let f64_model = FloatModel(Float64);
|
||||
let llvm_f64 = ctx.ctx.f64_type();
|
||||
|
||||
let result = if self.is_float(ctx) {
|
||||
self.into_float(ctx)
|
||||
} else if self.is_signed_int(ctx) || self.is_bool(ctx) {
|
||||
let n = self.into_int(ctx);
|
||||
let n = ctx.builder.build_signed_int_to_float(n, llvm_f64, "sitofp").unwrap();
|
||||
f64_model.believe_value(n)
|
||||
} else if self.is_unsigned_int(ctx) {
|
||||
let n = self.into_int(ctx);
|
||||
let n = ctx.builder.build_unsigned_int_to_float(n, llvm_f64, "uitofp").unwrap();
|
||||
f64_model.believe_value(n)
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty]);
|
||||
};
|
||||
|
||||
AnyObject { ty: ctx.primitives.float, value: result.value.as_basic_value_enum() }
|
||||
}
|
||||
|
||||
// Call `abs()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_abs<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Self {
|
||||
if self.is_float(ctx) {
|
||||
let n = self.value.into_float_value();
|
||||
let n = llvm_intrinsics::call_float_fabs(ctx, n, Some("abs"));
|
||||
AnyObject { value: n.into(), ty: ctx.primitives.float }
|
||||
} else if self.is_unsigned_int(ctx) || self.is_signed_int(ctx) {
|
||||
let is_poisoned = ctx.ctx.bool_type().const_zero(); // is_poisoned = false
|
||||
|
||||
let n = self.value.into_int_value();
|
||||
let n = llvm_intrinsics::call_int_abs(ctx, n, is_poisoned, Some("abs"));
|
||||
AnyObject { value: n.into(), ty: self.ty }
|
||||
} else if self.is_ndarray(ctx) {
|
||||
let ndarray = self.into_ndarray(generator, ctx);
|
||||
ndarray
|
||||
.map(
|
||||
generator,
|
||||
ctx,
|
||||
NDArrayOut::NewNDArray { dtype: ndarray.dtype },
|
||||
|generator, ctx, scalar| Ok(scalar.call_abs(generator, ctx)),
|
||||
)
|
||||
.unwrap()
|
||||
.to_any_object(ctx)
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty])
|
||||
}
|
||||
}
|
||||
|
||||
// Call `round()` on this object.
|
||||
//
|
||||
// It is possible to specify which kind of int type to return.
|
||||
#[must_use]
|
||||
pub fn call_round<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ret_int_ty: Type,
|
||||
) -> AnyObject<'ctx> {
|
||||
let ret_int_ty_llvm = ctx.get_llvm_type(generator, ret_int_ty).into_int_type();
|
||||
|
||||
let result = if ctx.unifier.unioned(self.ty, ctx.primitives.float) {
|
||||
let n = self.value.into_float_value();
|
||||
let n = llvm_intrinsics::call_float_round(ctx, n, None);
|
||||
ctx.builder.build_float_to_signed_int(n, ret_int_ty_llvm, "round").unwrap()
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty])
|
||||
};
|
||||
AnyObject { ty: ret_int_ty, value: result.as_basic_value_enum() }
|
||||
}
|
||||
|
||||
/// Call `np_round()` on this object.
|
||||
///
|
||||
/// NOTE: `np.round()` has different behaviors than `round()` when in comes to "tie" cases and return type.
|
||||
#[must_use]
|
||||
pub fn call_np_round<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
if self.is_float(ctx) {
|
||||
let n = self.into_float(ctx);
|
||||
let n = llvm_intrinsics::call_float_rint(ctx, n.value, None);
|
||||
AnyObject { ty: ctx.primitives.float, value: n.as_basic_value_enum() }
|
||||
} else if self.is_ndarray(ctx) {
|
||||
let ndarray = self.into_ndarray(generator, ctx);
|
||||
ndarray
|
||||
.map(
|
||||
generator,
|
||||
ctx,
|
||||
NDArrayOut::NewNDArray { dtype: ndarray.dtype },
|
||||
|generator, ctx, scalar| Ok(scalar.call_np_round(generator, ctx)),
|
||||
)
|
||||
.unwrap()
|
||||
.to_any_object(ctx)
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty])
|
||||
}
|
||||
}
|
||||
|
||||
/// Call `min()` or `max()` on two objects.
|
||||
#[must_use]
|
||||
pub fn call_min_or_max(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
kind: MinOrMax,
|
||||
a: AnyObject<'ctx>,
|
||||
b: AnyObject<'ctx>,
|
||||
) -> AnyObject<'ctx> {
|
||||
if !ctx.unifier.unioned(a.ty, b.ty) {
|
||||
unsupported_type(ctx, [a.ty, b.ty])
|
||||
}
|
||||
|
||||
let common_ty = a.ty;
|
||||
|
||||
if a.is_float(ctx) {
|
||||
let function = match kind {
|
||||
MinOrMax::Min => llvm_intrinsics::call_float_minnum,
|
||||
MinOrMax::Max => llvm_intrinsics::call_float_maxnum,
|
||||
};
|
||||
|
||||
let a = a.into_float(ctx).value;
|
||||
let b = b.into_float(ctx).value;
|
||||
let result = function(ctx, a, b, None);
|
||||
AnyObject { value: result.as_basic_value_enum(), ty: ctx.primitives.float }
|
||||
} else if a.is_unsigned_int(ctx) || a.is_bool(ctx) {
|
||||
// Treating bool has an unsigned int since that is convenient
|
||||
let function = match kind {
|
||||
MinOrMax::Min => llvm_intrinsics::call_int_umin,
|
||||
MinOrMax::Max => llvm_intrinsics::call_int_umax,
|
||||
};
|
||||
|
||||
let a = a.into_int(ctx);
|
||||
let b = b.into_int(ctx);
|
||||
let result = function(ctx, a, b, None);
|
||||
AnyObject { value: result.as_basic_value_enum(), ty: common_ty }
|
||||
} else if a.is_signed_int(ctx) {
|
||||
let function = match kind {
|
||||
MinOrMax::Min => llvm_intrinsics::call_int_smin,
|
||||
MinOrMax::Max => llvm_intrinsics::call_int_smax,
|
||||
};
|
||||
|
||||
let a = a.into_int(ctx);
|
||||
let b = b.into_int(ctx);
|
||||
let result = function(ctx, a, b, None);
|
||||
AnyObject { value: result.as_basic_value_enum(), ty: common_ty }
|
||||
} else {
|
||||
unsupported_type(ctx, [common_ty])
|
||||
}
|
||||
}
|
||||
|
||||
/// Call `floor()` or `ceil()` on this object.
|
||||
///
|
||||
/// It is possible to specify which kind of int type to return.
|
||||
#[must_use]
|
||||
pub fn call_floor_or_ceil<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
kind: FloorOrCeil,
|
||||
ret_int_ty: Type,
|
||||
) -> Self {
|
||||
let ret_int_ty_llvm = ctx.get_llvm_type(generator, ret_int_ty).into_int_type();
|
||||
|
||||
if self.is_float(ctx) {
|
||||
let function = match kind {
|
||||
FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
|
||||
FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
|
||||
};
|
||||
|
||||
let n = self.into_float(ctx).value;
|
||||
let n = function(ctx, n, None);
|
||||
let n = ctx.builder.build_float_to_signed_int(n, ret_int_ty_llvm, "").unwrap();
|
||||
AnyObject { ty: ret_int_ty, value: n.as_basic_value_enum() }
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty])
|
||||
}
|
||||
}
|
||||
|
||||
/// Call `np_floor()` or `np_ceil()` on this object.
|
||||
#[must_use]
|
||||
pub fn call_np_floor_or_ceil<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
kind: FloorOrCeil,
|
||||
) -> Self {
|
||||
// TODO:
|
||||
if self.is_float(ctx) {
|
||||
let function = match kind {
|
||||
FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
|
||||
FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
|
||||
};
|
||||
let n = self.into_float(ctx).value;
|
||||
let n = function(ctx, n, None);
|
||||
AnyObject { ty: ctx.primitives.float, value: n.as_basic_value_enum() }
|
||||
} else if self.is_ndarray(ctx) {
|
||||
let ndarray = self.into_ndarray(generator, ctx);
|
||||
ndarray
|
||||
.map(
|
||||
generator,
|
||||
ctx,
|
||||
NDArrayOut::NewNDArray { dtype: ctx.primitives.float },
|
||||
|generator, ctx, scalar| Ok(scalar.call_np_floor_or_ceil(generator, ctx, kind)),
|
||||
)
|
||||
.unwrap()
|
||||
.to_any_object(ctx)
|
||||
} else {
|
||||
unsupported_type(ctx, [self.ty])
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,176 @@
|
|||
use super::NDArrayObject;
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::{call_nac3_array_set_and_validate_list_shape, call_nac3_array_write_list_to_array},
|
||||
model::*,
|
||||
object::{list::ListObject, AnyObject},
|
||||
stmt::gen_if_else_expr_callback,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
toplevel::helper::{arraylike_flatten_element_type, arraylike_get_ndims},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
fn get_list_object_dtype_and_ndims<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: ListObject<'ctx>,
|
||||
) -> (Type, u64) {
|
||||
let dtype = arraylike_flatten_element_type(&mut ctx.unifier, list.item_type);
|
||||
|
||||
let ndims = arraylike_get_ndims(&mut ctx.unifier, list.item_type);
|
||||
let ndims = ndims + 1; // To count `list` itself.
|
||||
|
||||
(dtype, ndims)
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
fn from_np_array_list_copy_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: ListObject<'ctx>,
|
||||
) -> Self {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let (dtype, ndims_int) = get_list_object_dtype_and_ndims(ctx, list);
|
||||
let list_value = list.get_opaque_list_ptr(generator, ctx);
|
||||
|
||||
// Validate `list` has a consistent shape.
|
||||
// Raise an exception if `list` is something abnormal like `[[1, 2], [3]]`.
|
||||
// If `list` has a consistent shape, deduce the shape and write it to `shape`.
|
||||
let ndims = sizet_model.constant(generator, ctx.ctx, ndims_int);
|
||||
let shape = sizet_model.array_alloca(generator, ctx, ndims.value, "shape");
|
||||
call_nac3_array_set_and_validate_list_shape(generator, ctx, list_value, ndims, shape);
|
||||
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims_int, "ndarray_from_list");
|
||||
ndarray.copy_shape_from_array(generator, ctx, shape);
|
||||
ndarray.create_data(generator, ctx);
|
||||
|
||||
// Copy all contents from the list.
|
||||
call_nac3_array_write_list_to_array(generator, ctx, list_value, ndarray.instance);
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
fn from_np_array_list_try_no_copy_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: ListObject<'ctx>,
|
||||
) -> Self {
|
||||
// np_array without copy is only possible `list` is not nested.
|
||||
|
||||
// If `list` is `list[T]`, we can create an ndarray with `data` set
|
||||
// to the array pointer of `list`.
|
||||
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let zero = sizet_model.const_0(generator, ctx.ctx);
|
||||
|
||||
let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
|
||||
if ndims == 1 {
|
||||
// `list` is not nested, does not need to copy.
|
||||
let ndarray =
|
||||
NDArrayObject::alloca(generator, ctx, dtype, 1, "ndarray_from_list_no_copy");
|
||||
|
||||
// Set data
|
||||
let data = list.get_opaque_items_ptr(generator, ctx);
|
||||
ndarray.instance.set(ctx, |f| f.data, data);
|
||||
|
||||
// Set shape
|
||||
// dim = list->len;
|
||||
// shape[0] = dim;
|
||||
let shape = ndarray.instance.get(generator, ctx, |f| f.shape, "shape");
|
||||
let dim = list.instance.get(generator, ctx, |f| f.len, "dim");
|
||||
shape.offset(generator, ctx, zero.value, "pdim").store(ctx, dim);
|
||||
|
||||
// Set strides, the `data` is contiguous
|
||||
ndarray.update_strides_by_shape(generator, ctx);
|
||||
|
||||
// Done
|
||||
ndarray
|
||||
} else {
|
||||
// `list` is nested, it is impossible to not copy.
|
||||
NDArrayObject::from_np_array_list_copy_impl(generator, ctx, list)
|
||||
}
|
||||
}
|
||||
|
||||
fn from_np_array_list_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: ListObject<'ctx>,
|
||||
copy: Int<'ctx, Bool>,
|
||||
) -> Self {
|
||||
let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
|
||||
|
||||
let ndarray = gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_generator, _ctx| Ok(copy.value),
|
||||
|generator, ctx| {
|
||||
let ndarray = NDArrayObject::from_np_array_list_copy_impl(generator, ctx, list);
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
|generator, ctx| {
|
||||
let ndarray =
|
||||
NDArrayObject::from_np_array_list_try_no_copy_impl(generator, ctx, list);
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
)
|
||||
.unwrap()
|
||||
.unwrap();
|
||||
|
||||
NDArrayObject::from_value_and_unpacked_types(generator, ctx, ndarray, dtype, ndims)
|
||||
}
|
||||
|
||||
pub fn from_np_array_ndarray_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayObject<'ctx>,
|
||||
copy: Int<'ctx, Bool>,
|
||||
) -> Self {
|
||||
let ndarray_val = gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_generator, _ctx| Ok(copy.value),
|
||||
|generator, ctx| {
|
||||
let ndarray = ndarray.make_copy(generator, ctx, "np_array_copied_ndarray"); // Force copy
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
|_generator, _ctx| {
|
||||
// No need to copy. Return `ndarray` itself.
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
)
|
||||
.unwrap()
|
||||
.unwrap();
|
||||
|
||||
NDArrayObject::from_value_and_unpacked_types(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray_val,
|
||||
ndarray.dtype,
|
||||
ndarray.ndims,
|
||||
)
|
||||
}
|
||||
|
||||
pub fn from_np_array<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
copy: Int<'ctx, Bool>,
|
||||
) -> Self {
|
||||
match &*ctx.unifier.get_ty(object.ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let list = ListObject::from_object(generator, ctx, object);
|
||||
NDArrayObject::from_np_array_list_impl(generator, ctx, list, copy)
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, object);
|
||||
NDArrayObject::from_np_array_ndarray_impl(generator, ctx, ndarray, copy)
|
||||
}
|
||||
_ => panic!("Unrecognized object type: {}", ctx.unifier.stringify(object.ty)), // Typechecker ensures this
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,161 @@
|
|||
use itertools::Itertools;
|
||||
|
||||
use crate::codegen::{
|
||||
irrt::{
|
||||
call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to,
|
||||
call_nac3_ndarray_util_assert_shape_no_negative,
|
||||
},
|
||||
model::*,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
/// Fields of [`ShapeEntry`]
|
||||
pub struct ShapeEntryFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub ndims: F::Out<IntModel<SizeT>>,
|
||||
pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
}
|
||||
|
||||
/// An IRRT structure used in broadcasting.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct ShapeEntry;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for ShapeEntry {
|
||||
type Fields<F: FieldTraversal<'ctx>> = ShapeEntryFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields { ndims: traversal.add_auto("ndims"), shape: traversal.add_auto("shape") }
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Create a broadcast view on this ndarray with a target shape.
|
||||
///
|
||||
/// The input shape will be checked to make sure that it contains no negative values.
|
||||
///
|
||||
/// * `target_ndims` - The ndims type after broadcasting to the given shape.
|
||||
/// The caller has to figure this out for this function.
|
||||
/// * `target_shape` - An array pointer pointing to the target shape.
|
||||
#[must_use]
|
||||
pub fn broadcast_to<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
target_ndims: u64,
|
||||
target_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) -> Self {
|
||||
let target_ndims_llvm = IntModel(SizeT).constant(generator, ctx.ctx, target_ndims);
|
||||
call_nac3_ndarray_util_assert_shape_no_negative(
|
||||
generator,
|
||||
ctx,
|
||||
target_ndims_llvm,
|
||||
target_shape,
|
||||
);
|
||||
|
||||
let broadcast_ndarray = NDArrayObject::alloca(
|
||||
generator,
|
||||
ctx,
|
||||
self.dtype,
|
||||
target_ndims,
|
||||
"broadcast_ndarray_to_dst",
|
||||
);
|
||||
broadcast_ndarray.copy_shape_from_array(generator, ctx, target_shape);
|
||||
|
||||
call_nac3_ndarray_broadcast_to(generator, ctx, self.instance, broadcast_ndarray.instance);
|
||||
broadcast_ndarray
|
||||
}
|
||||
}
|
||||
/// A result produced by [`broadcast_all_ndarrays`]
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct BroadcastAllResult<'ctx> {
|
||||
/// The statically known `ndims` of the broadcast result.
|
||||
pub ndims: u64,
|
||||
/// The broadcasting shape.
|
||||
pub shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
/// Broadcasted views on the inputs.
|
||||
///
|
||||
/// All of them will have `shape` [`BroadcastAllResult::shape`] and
|
||||
/// `ndims` [`BroadcastAllResult::ndims`]. The length of the vector
|
||||
/// is the same as the input.
|
||||
pub ndarrays: Vec<NDArrayObject<'ctx>>,
|
||||
}
|
||||
|
||||
pub fn broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
in_entries: &[(Ptr<'ctx, IntModel<SizeT>>, u64)],
|
||||
broadcast_ndims: u64,
|
||||
broadcast_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let shape_model = StructModel(ShapeEntry);
|
||||
|
||||
// Prepare input shape entries
|
||||
let num_shape_entries =
|
||||
sizet_model.constant(generator, ctx.ctx, u64::try_from(in_entries.len()).unwrap());
|
||||
let shape_entries =
|
||||
shape_model.array_alloca(generator, ctx, num_shape_entries.value, "shape_entries");
|
||||
for (i, (in_shape, in_ndims)) in in_entries.iter().enumerate() {
|
||||
let i = sizet_model.constant(generator, ctx.ctx, i as u64).value;
|
||||
let pshape_entry = shape_entries.offset(generator, ctx, i, "shape_entry");
|
||||
|
||||
let in_ndims = sizet_model.constant(generator, ctx.ctx, *in_ndims);
|
||||
pshape_entry.set(ctx, |f| f.ndims, in_ndims);
|
||||
|
||||
pshape_entry.set(ctx, |f| f.shape, *in_shape);
|
||||
}
|
||||
|
||||
let broadcast_ndims = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
|
||||
|
||||
call_nac3_ndarray_broadcast_shapes(
|
||||
generator,
|
||||
ctx,
|
||||
num_shape_entries,
|
||||
shape_entries,
|
||||
broadcast_ndims,
|
||||
broadcast_shape,
|
||||
);
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
// TODO: DOCUMENT: Behaves like `np.broadcast()`, except returns results differently.
|
||||
pub fn broadcast<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarrays: &[Self],
|
||||
) -> BroadcastAllResult<'ctx> {
|
||||
assert!(!ndarrays.is_empty());
|
||||
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Infer the broadcast output ndims.
|
||||
let broadcast_ndims_int = ndarrays.iter().map(|ndarray| ndarray.ndims).max().unwrap();
|
||||
|
||||
let broadcast_ndims = sizet_model.constant(generator, ctx.ctx, broadcast_ndims_int);
|
||||
let broadcast_shape =
|
||||
sizet_model.array_alloca(generator, ctx, broadcast_ndims.value, "broadcast_shape");
|
||||
|
||||
let shape_entries = ndarrays
|
||||
.iter()
|
||||
.map(|ndarray| {
|
||||
(ndarray.instance.get(generator, ctx, |f| f.shape, "shape"), ndarray.ndims)
|
||||
})
|
||||
.collect_vec();
|
||||
broadcast_shapes(generator, ctx, &shape_entries, broadcast_ndims_int, broadcast_shape);
|
||||
|
||||
// Broadcast all the inputs to shape `dst_shape`.
|
||||
let broadcast_ndarrays: Vec<_> = ndarrays
|
||||
.iter()
|
||||
.map(|ndarray| {
|
||||
ndarray.broadcast_to(generator, ctx, broadcast_ndims_int, broadcast_shape)
|
||||
})
|
||||
.collect_vec();
|
||||
|
||||
BroadcastAllResult {
|
||||
ndims: broadcast_ndims_int,
|
||||
shape: broadcast_shape,
|
||||
ndarrays: broadcast_ndarrays,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,238 @@
|
|||
use inkwell::{values::BasicValueEnum, IntPredicate};
|
||||
|
||||
use super::NDArrayObject;
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::call_nac3_ndarray_util_assert_shape_no_negative, model::*, object::AnyObject,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
/// Get the zero value in `np.zeros()` of a `dtype`.
|
||||
fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
if [ctx.primitives.int32, ctx.primitives.uint32]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
ctx.ctx.i32_type().const_zero().into()
|
||||
} else if [ctx.primitives.int64, ctx.primitives.uint64]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
ctx.ctx.i64_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
|
||||
ctx.ctx.f64_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "").value.into()
|
||||
} else {
|
||||
panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the one value in `np.ones()` of a `dtype`.
|
||||
fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
if [ctx.primitives.int32, ctx.primitives.uint32]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int32);
|
||||
ctx.ctx.i32_type().const_int(1, is_signed).into()
|
||||
} else if [ctx.primitives.int64, ctx.primitives.uint64]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int64);
|
||||
ctx.ctx.i64_type().const_int(1, is_signed).into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
|
||||
ctx.ctx.f64_type().const_float(1.0).into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_int(1, false).into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "1").value.into()
|
||||
} else {
|
||||
panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Create an ndarray like `np.empty`.
|
||||
pub fn from_np_empty<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) -> Self {
|
||||
// Validate `shape`
|
||||
// TODO: Should the caller be responsible for this instead?
|
||||
let ndims_llvm = IntModel(SizeT).constant(generator, ctx.ctx, ndims);
|
||||
call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, ndims_llvm, shape);
|
||||
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims, "full_ndarray");
|
||||
ndarray.copy_shape_from_array(generator, ctx, shape);
|
||||
ndarray.create_data(generator, ctx);
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.full`.
|
||||
pub fn from_np_full<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
fill_value: AnyObject<'ctx>,
|
||||
) -> Self {
|
||||
// Sanity check on `fill_value`'s dtype.
|
||||
assert!(ctx.unifier.unioned(dtype, fill_value.ty));
|
||||
|
||||
let ndarray = NDArrayObject::from_np_empty(generator, ctx, dtype, ndims, shape);
|
||||
ndarray.fill(generator, ctx, fill_value);
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.zero`.
|
||||
pub fn from_np_zero<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) -> Self {
|
||||
let fill_value = ndarray_zero_value(generator, ctx, dtype);
|
||||
let fill_value = AnyObject { value: fill_value, ty: dtype };
|
||||
NDArrayObject::from_np_full(generator, ctx, dtype, ndims, shape, fill_value)
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.ones`.
|
||||
pub fn from_np_ones<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) -> Self {
|
||||
let fill_value = ndarray_one_value(generator, ctx, dtype);
|
||||
let fill_value = AnyObject { value: fill_value, ty: dtype };
|
||||
NDArrayObject::from_np_full(generator, ctx, dtype, ndims, shape, fill_value)
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.arange`.
|
||||
/// The returned ndarray's `dtype` is always `float`
|
||||
pub fn from_np_arange<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
length: Int<'ctx, SizeT>,
|
||||
) -> Self {
|
||||
let ndarray = NDArrayObject::alloca(
|
||||
generator,
|
||||
ctx,
|
||||
ctx.primitives.float,
|
||||
1, // ndims = 1
|
||||
"arange_ndarray",
|
||||
);
|
||||
|
||||
// `ndarray.shape[0] = length`
|
||||
ndarray
|
||||
.instance
|
||||
.get(generator, ctx, |f| f.shape, "shape")
|
||||
.offset_const(generator, ctx, 0, "dim")
|
||||
.store(ctx, length);
|
||||
|
||||
// Create data and set elements
|
||||
ndarray.create_data(generator, ctx);
|
||||
ndarray
|
||||
.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
|
||||
// Get the index of the current element, convert that index to float, and write it.
|
||||
// This is how we get [0.0, 1.0, 2.0, ...].
|
||||
let index = nditer.get_index(generator, ctx);
|
||||
let pelement = nditer.get_pointer(generator, ctx);
|
||||
|
||||
let val = ctx
|
||||
.builder
|
||||
.build_unsigned_int_to_float(index.value, ctx.ctx.f64_type(), "val")
|
||||
.unwrap();
|
||||
ctx.builder.build_store(pelement, val).unwrap();
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.eye`.
|
||||
pub fn from_np_eye<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
nrows: Int<'ctx, SizeT>,
|
||||
ncols: Int<'ctx, SizeT>,
|
||||
offset: Int<'ctx, SizeT>,
|
||||
) -> Self {
|
||||
let ndzero = ndarray_zero_value(generator, ctx, dtype);
|
||||
let ndone = ndarray_one_value(generator, ctx, dtype);
|
||||
|
||||
let ndarray = NDArrayObject::alloca_dynamic_shape(
|
||||
generator,
|
||||
ctx,
|
||||
dtype,
|
||||
&[nrows, ncols],
|
||||
"eye_ndarray",
|
||||
);
|
||||
|
||||
// Create data and make the matrix like look np.eye()
|
||||
ndarray.create_data(generator, ctx);
|
||||
ndarray
|
||||
.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
|
||||
// NOTE: rows and cols can never be zero here, since this ndarray's `np.size` would be zero
|
||||
// and this loop would not execute.
|
||||
|
||||
// Load up `row_i` and `col_i` from indices.
|
||||
let row_i = nditer
|
||||
.get_indices()
|
||||
.offset_const(generator, ctx, 0, "")
|
||||
.load(generator, ctx, "row_i");
|
||||
let col_i = nditer
|
||||
.get_indices()
|
||||
.offset_const(generator, ctx, 1, "")
|
||||
.load(generator, ctx, "col_i");
|
||||
|
||||
// Write to element
|
||||
let be_one =
|
||||
row_i.add(ctx, offset, "").compare(ctx, IntPredicate::EQ, col_i, "write_one");
|
||||
let value = ctx.builder.build_select(be_one.value, ndone, ndzero, "value").unwrap();
|
||||
|
||||
let p = nditer.get_pointer(generator, ctx);
|
||||
ctx.builder.build_store(p, value).unwrap();
|
||||
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.identity`.
|
||||
pub fn from_np_identity<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
size: Int<'ctx, SizeT>,
|
||||
) -> Self {
|
||||
// Convenient implementation
|
||||
let offset = IntModel(SizeT).const_0(generator, ctx.ctx);
|
||||
NDArrayObject::from_np_eye(generator, ctx, dtype, size, size, offset)
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,128 @@
|
|||
use inkwell::{FloatPredicate, IntPredicate};
|
||||
|
||||
use crate::codegen::{
|
||||
model::*,
|
||||
object::{AnyObject, MinOrMax},
|
||||
stmt::gen_if_callback,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Helper function to implement NAC3's builtin `np_min()`, `np_max()`, `np_argmin()`, and `np_argmax()`.
|
||||
///
|
||||
/// Generate LLVM IR to find the extremum and index of the **first** extremum value.
|
||||
///
|
||||
/// Care has also been taken to make the error messages match that of NumPy.
|
||||
fn min_max_argmin_argmax_helper<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
kind: MinOrMax,
|
||||
on_empty_err_msg: &str,
|
||||
) -> (AnyObject<'ctx>, Int<'ctx, SizeT>) {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
|
||||
|
||||
// If the ndarray is empty, throw an error.
|
||||
let is_empty = self.is_empty(generator, ctx);
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
is_empty.value,
|
||||
"0:ValueError",
|
||||
on_empty_err_msg,
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
// Setup and initialize the extremum to be the first element in the ndarray
|
||||
let pextremum_index = sizet_model.alloca(generator, ctx, "extremum_index");
|
||||
let pextremum = ctx.builder.build_alloca(dtype_llvm, "extremum").unwrap();
|
||||
|
||||
let zero = sizet_model.const_0(generator, ctx.ctx);
|
||||
pextremum_index.store(ctx, zero);
|
||||
|
||||
let first_scalar = self.get_nth_scalar(generator, ctx, zero);
|
||||
ctx.builder.build_store(pextremum, first_scalar.value).unwrap();
|
||||
|
||||
self.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
|
||||
let old_extremum = ctx.builder.build_load(pextremum, "current_extremum").unwrap();
|
||||
let old_extremum = AnyObject { ty: self.dtype, value: old_extremum };
|
||||
|
||||
let scalar = nditer.get_scalar(generator, ctx);
|
||||
let new_extremum = AnyObject::call_min_or_max(ctx, kind, old_extremum, scalar);
|
||||
|
||||
gen_if_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|generator, ctx| {
|
||||
// Is new_extremum is more extreme than old_extremum?
|
||||
let cmp = AnyObject::compare_int_or_float_by_predicate(
|
||||
generator,
|
||||
ctx,
|
||||
new_extremum,
|
||||
old_extremum,
|
||||
IntPredicate::NE,
|
||||
FloatPredicate::ONE,
|
||||
"",
|
||||
);
|
||||
Ok(cmp.value)
|
||||
},
|
||||
|generator, ctx| {
|
||||
// Yes, update the extremum index
|
||||
let index = nditer.get_index(generator, ctx);
|
||||
pextremum_index.store(ctx, index);
|
||||
Ok(())
|
||||
},
|
||||
|_generator, _ctx| {
|
||||
// No, do nothing
|
||||
Ok(())
|
||||
},
|
||||
)
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
// Finally return the extremum and extremum index.
|
||||
let extremum_index = pextremum_index.load(generator, ctx, "extremum_index");
|
||||
|
||||
let extremum = ctx.builder.build_load(pextremum, "extremum_value").unwrap();
|
||||
let extremum = AnyObject { ty: self.dtype, value: extremum };
|
||||
|
||||
(extremum, extremum_index)
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `np_min()` or `np_max()`.
|
||||
pub fn min_or_max<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
kind: MinOrMax,
|
||||
) -> AnyObject<'ctx> {
|
||||
let on_empty_err_msg = format!(
|
||||
"zero-size array to reduction operation {} which has no identity",
|
||||
match kind {
|
||||
MinOrMax::Min => "minimum",
|
||||
MinOrMax::Max => "maximum",
|
||||
}
|
||||
);
|
||||
self.min_max_argmin_argmax_helper(generator, ctx, kind, &on_empty_err_msg).0
|
||||
}
|
||||
|
||||
/// Invoke NAC3's builtin `np_argmin()` or `np_argmax()`.
|
||||
pub fn argmin_or_argmax<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
kind: MinOrMax,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let on_empty_err_msg = format!(
|
||||
"attempt to get {} of an empty sequence",
|
||||
match kind {
|
||||
MinOrMax::Min => "argmin",
|
||||
MinOrMax::Max => "argmax",
|
||||
}
|
||||
);
|
||||
self.min_max_argmin_argmax_helper(generator, ctx, kind, &on_empty_err_msg).1
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,321 @@
|
|||
use crate::codegen::{irrt::call_nac3_ndarray_index, model::*, CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
pub type NDIndexType = Byte;
|
||||
|
||||
/// Fields of [`NDIndex`]
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDIndexFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub type_: F::Out<IntModel<NDIndexType>>, // Defined to be uint8_t in IRRT
|
||||
pub data: F::Out<PtrModel<IntModel<Byte>>>,
|
||||
}
|
||||
|
||||
/// An IRRT representation fo an ndarray subscript index.
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct NDIndex;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for NDIndex {
|
||||
type Fields<F: FieldTraversal<'ctx>> = NDIndexFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields { type_: traversal.add_auto("type"), data: traversal.add_auto("data") }
|
||||
}
|
||||
}
|
||||
|
||||
/// Fields of [`UserSlice`]
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct UserSliceFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub start_defined: F::Out<IntModel<Bool>>,
|
||||
pub start: F::Out<IntModel<Int32>>,
|
||||
pub stop_defined: F::Out<IntModel<Bool>>,
|
||||
pub stop: F::Out<IntModel<Int32>>,
|
||||
pub step_defined: F::Out<IntModel<Bool>>,
|
||||
pub step: F::Out<IntModel<Int32>>,
|
||||
}
|
||||
|
||||
/// An IRRT representation of a user slice.
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct UserSlice;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for UserSlice {
|
||||
type Fields<F: FieldTraversal<'ctx>> = UserSliceFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
start_defined: traversal.add_auto("start_defined"),
|
||||
start: traversal.add_auto("start"),
|
||||
stop_defined: traversal.add_auto("stop_defined"),
|
||||
stop: traversal.add_auto("stop"),
|
||||
step_defined: traversal.add_auto("step_defined"),
|
||||
step: traversal.add_auto("step"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A convenience structure to prepare a [`UserSlice`].
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct RustUserSlice<'ctx> {
|
||||
pub start: Option<Int<'ctx, Int32>>,
|
||||
pub stop: Option<Int<'ctx, Int32>>,
|
||||
pub step: Option<Int<'ctx, Int32>>,
|
||||
}
|
||||
|
||||
impl<'ctx> RustUserSlice<'ctx> {
|
||||
/// Write the contents to an LLVM [`UserSlice`].
|
||||
pub fn write_to_user_slice<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_slice_ptr: Ptr<'ctx, StructModel<UserSlice>>,
|
||||
) {
|
||||
let bool_model = IntModel(Bool);
|
||||
|
||||
let false_ = bool_model.constant(generator, ctx.ctx, 0);
|
||||
let true_ = bool_model.constant(generator, ctx.ctx, 1);
|
||||
|
||||
// TODO: Code duplication. Probably okay...?
|
||||
|
||||
match self.start {
|
||||
Some(start) => {
|
||||
dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, true_);
|
||||
dst_slice_ptr.gep(ctx, |f| f.start).store(ctx, start);
|
||||
}
|
||||
None => dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, false_),
|
||||
}
|
||||
|
||||
match self.stop {
|
||||
Some(stop) => {
|
||||
dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, true_);
|
||||
dst_slice_ptr.gep(ctx, |f| f.stop).store(ctx, stop);
|
||||
}
|
||||
None => dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, false_),
|
||||
}
|
||||
|
||||
match self.step {
|
||||
Some(step) => {
|
||||
dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, true_);
|
||||
dst_slice_ptr.gep(ctx, |f| f.step).store(ctx, step);
|
||||
}
|
||||
None => dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, false_),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// A convenience enum variant to store the content and type of an NDIndex in high level.
|
||||
#[derive(Debug, Clone)]
|
||||
pub enum RustNDIndex<'ctx> {
|
||||
SingleElement(Int<'ctx, Int32>), // TODO: To be SizeT
|
||||
Slice(RustUserSlice<'ctx>),
|
||||
NewAxis,
|
||||
Ellipsis,
|
||||
}
|
||||
|
||||
impl<'ctx> RustNDIndex<'ctx> {
|
||||
/// Get the value to set `NDIndex::type` for this variant.
|
||||
fn get_type_id(&self) -> u64 {
|
||||
// Defined in IRRT, must be in sync
|
||||
match self {
|
||||
RustNDIndex::SingleElement(_) => 0,
|
||||
RustNDIndex::Slice(_) => 1,
|
||||
RustNDIndex::NewAxis => 2,
|
||||
RustNDIndex::Ellipsis => 3,
|
||||
}
|
||||
}
|
||||
|
||||
/// Write the contents to an LLVM [`NDIndex`].
|
||||
fn write_to_ndindex<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_ndindex_ptr: Ptr<'ctx, StructModel<NDIndex>>,
|
||||
) {
|
||||
let ndindex_type_model = IntModel(NDIndexType::default());
|
||||
let i32_model = IntModel(Int32);
|
||||
let user_slice_model = StructModel(UserSlice);
|
||||
|
||||
// Set `dst_ndindex_ptr->type`
|
||||
dst_ndindex_ptr
|
||||
.gep(ctx, |f| f.type_)
|
||||
.store(ctx, ndindex_type_model.constant(generator, ctx.ctx, self.get_type_id()));
|
||||
|
||||
// Set `dst_ndindex_ptr->data`
|
||||
match self {
|
||||
RustNDIndex::SingleElement(in_index) => {
|
||||
let index_ptr = i32_model.alloca(generator, ctx, "index");
|
||||
index_ptr.store(ctx, *in_index);
|
||||
|
||||
dst_ndindex_ptr
|
||||
.gep(ctx, |f| f.data)
|
||||
.store(ctx, index_ptr.pointer_cast(generator, ctx, IntModel(Byte), ""));
|
||||
}
|
||||
RustNDIndex::Slice(in_rust_slice) => {
|
||||
let user_slice_ptr = user_slice_model.alloca(generator, ctx, "user_slice");
|
||||
in_rust_slice.write_to_user_slice(generator, ctx, user_slice_ptr);
|
||||
|
||||
dst_ndindex_ptr
|
||||
.gep(ctx, |f| f.data)
|
||||
.store(ctx, user_slice_ptr.pointer_cast(generator, ctx, IntModel(Byte), ""));
|
||||
}
|
||||
RustNDIndex::NewAxis | RustNDIndex::Ellipsis => {}
|
||||
}
|
||||
}
|
||||
|
||||
/// Allocate an array of `NDIndex`es on the stack and return its stack pointer.
|
||||
pub fn alloca_ndindexes<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
in_ndindexes: &[RustNDIndex<'ctx>],
|
||||
) -> (Int<'ctx, SizeT>, Ptr<'ctx, StructModel<NDIndex>>) {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let ndindex_model = StructModel(NDIndex);
|
||||
|
||||
let num_ndindexes = sizet_model.constant(generator, ctx.ctx, in_ndindexes.len() as u64);
|
||||
let ndindexes =
|
||||
ndindex_model.array_alloca(generator, ctx, num_ndindexes.value, "ndindexes");
|
||||
for (i, in_ndindex) in in_ndindexes.iter().enumerate() {
|
||||
let i = sizet_model.constant(generator, ctx.ctx, i as u64);
|
||||
let pndindex = ndindexes.offset(generator, ctx, i.value, "");
|
||||
in_ndindex.write_to_ndindex(generator, ctx, pndindex);
|
||||
}
|
||||
|
||||
(num_ndindexes, ndindexes)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Get the ndims [`Type`] after indexing with a given slice.
|
||||
#[must_use]
|
||||
pub fn deduce_ndims_after_indexing_with(&self, indexes: &[RustNDIndex<'ctx>]) -> u64 {
|
||||
let mut ndims = self.ndims;
|
||||
for index in indexes {
|
||||
match index {
|
||||
RustNDIndex::SingleElement(_) => {
|
||||
ndims -= 1; // Single elements decrements ndims
|
||||
}
|
||||
RustNDIndex::NewAxis => {
|
||||
ndims += 1; // `np.newaxis` / `none` adds a new axis
|
||||
}
|
||||
RustNDIndex::Ellipsis | RustNDIndex::Slice(_) => {}
|
||||
}
|
||||
}
|
||||
ndims
|
||||
}
|
||||
|
||||
/// Index into the ndarray, and return a newly-allocated view on this ndarray.
|
||||
///
|
||||
/// This function behaves like NumPy's ndarray indexing, but if the indexes index
|
||||
/// into a single element, an unsized ndarray is returned.
|
||||
#[must_use]
|
||||
pub fn index<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
indexes: &[RustNDIndex<'ctx>],
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let dst_ndims = self.deduce_ndims_after_indexing_with(indexes);
|
||||
let dst_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, dst_ndims, name);
|
||||
|
||||
let (num_indexes, indexes) = RustNDIndex::alloca_ndindexes(generator, ctx, indexes);
|
||||
call_nac3_ndarray_index(
|
||||
generator,
|
||||
ctx,
|
||||
num_indexes,
|
||||
indexes,
|
||||
self.instance,
|
||||
dst_ndarray.instance,
|
||||
);
|
||||
|
||||
dst_ndarray
|
||||
}
|
||||
}
|
||||
|
||||
pub mod util {
|
||||
use itertools::Itertools;
|
||||
use nac3parser::ast::{Constant, Expr, ExprKind};
|
||||
|
||||
use crate::{
|
||||
codegen::{expr::gen_slice, model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::{RustNDIndex, RustUserSlice};
|
||||
|
||||
/// Generate LLVM code to transform an ndarray subscript expression to
|
||||
/// its list of [`RustNDIndex`]
|
||||
///
|
||||
/// i.e.,
|
||||
/// ```python
|
||||
/// my_ndarray[::3, 1, :2:]
|
||||
/// ^^^^^^^^^^^ Then these into a three `RustNDIndex`es
|
||||
/// ```
|
||||
pub fn gen_ndarray_subscript_ndindexes<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
subscript: &Expr<Option<Type>>,
|
||||
) -> Result<Vec<RustNDIndex<'ctx>>, String> {
|
||||
// TODO: Support https://numpy.org/doc/stable/user/basics.indexing.html#dimensional-indexing-tools
|
||||
let i32_model = IntModel(Int32);
|
||||
|
||||
// Annoying notes about `slice`
|
||||
// - `my_array[5]`
|
||||
// - slice is a `Constant`
|
||||
// - `my_array[:5]`
|
||||
// - slice is a `Slice`
|
||||
// - `my_array[:]`
|
||||
// - slice is a `Slice`, but lower upper step would all be `Option::None`
|
||||
// - `my_array[:, :]`
|
||||
// - slice is now a `Tuple` of two `Slice`-s
|
||||
//
|
||||
// In summary:
|
||||
// - when there is a comma "," within [], `slice` will be a `Tuple` of the entries.
|
||||
// - when there is not comma "," within [] (i.e., just a single entry), `slice` will be that entry itself.
|
||||
//
|
||||
// So we first "flatten" out the slice expression
|
||||
let index_exprs = match &subscript.node {
|
||||
ExprKind::Tuple { elts, .. } => elts.iter().collect_vec(),
|
||||
_ => vec![subscript],
|
||||
};
|
||||
|
||||
// Process all index expressions
|
||||
let mut rust_ndindexes: Vec<RustNDIndex> = Vec::with_capacity(index_exprs.len()); // Not using iterators here because `?` is used here.
|
||||
for index_expr in index_exprs {
|
||||
// NOTE: Currently nac3core's slices do not have an object representation,
|
||||
// so the code/implementation looks awkward - we have to do pattern matching on the expression
|
||||
let ndindex = if let ExprKind::Slice { lower, upper, step } = &index_expr.node {
|
||||
// Handle slices
|
||||
|
||||
// Helper function here to deduce code duplication
|
||||
let (lower, upper, step) = gen_slice(generator, ctx, lower, upper, step)?;
|
||||
RustNDIndex::Slice(RustUserSlice { start: lower, stop: upper, step })
|
||||
} else if let ExprKind::Constant { value: Constant::Ellipsis, .. } = &index_expr.node {
|
||||
// Handle '...'
|
||||
RustNDIndex::Ellipsis
|
||||
} else {
|
||||
match &*ctx.unifier.get_ty(index_expr.custom.unwrap()) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.option.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
// Handle `np.newaxis` / `None`
|
||||
RustNDIndex::NewAxis
|
||||
}
|
||||
_ => {
|
||||
// Treat and handle everything else as a single element index.
|
||||
let index =
|
||||
generator.gen_expr(ctx, index_expr)?.unwrap().to_basic_value_enum(
|
||||
ctx,
|
||||
generator,
|
||||
ctx.primitives.int32, // Must be int32, this checks for illegal values
|
||||
)?;
|
||||
let index = i32_model.check_value(generator, ctx.ctx, index).unwrap();
|
||||
|
||||
RustNDIndex::SingleElement(index)
|
||||
}
|
||||
}
|
||||
};
|
||||
rust_ndindexes.push(ndindex);
|
||||
}
|
||||
Ok(rust_ndindexes)
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,200 @@
|
|||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
object::ndarray::{AnyObject, NDArrayObject},
|
||||
stmt::gen_for_callback,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
use super::{nditer::NDIterHandle, scalar::ScalarOrNDArray, NDArrayOut};
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// TODO: Document me. Has complex behavior.
|
||||
/// and explain why `ret_dtype` has to be specified beforehand.
|
||||
pub fn broadcasting_starmap<'a, G, MappingFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ndarrays: &[Self],
|
||||
out: NDArrayOut<'ctx>,
|
||||
mapping: MappingFn,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
MappingFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
&[AnyObject<'ctx>],
|
||||
) -> Result<AnyObject<'ctx>, String>,
|
||||
{
|
||||
// Broadcast inputs
|
||||
let broadcast_result = NDArrayObject::broadcast(generator, ctx, ndarrays);
|
||||
|
||||
let out_ndarray = match out {
|
||||
NDArrayOut::NewNDArray { dtype } => {
|
||||
// Create a new ndarray based on the broadcast shape.
|
||||
let result_ndarray = NDArrayObject::alloca(
|
||||
generator,
|
||||
ctx,
|
||||
dtype,
|
||||
broadcast_result.ndims,
|
||||
"mapped_ndarray",
|
||||
);
|
||||
result_ndarray.copy_shape_from_array(generator, ctx, broadcast_result.shape);
|
||||
result_ndarray.create_data(generator, ctx);
|
||||
result_ndarray
|
||||
}
|
||||
NDArrayOut::WriteToNDArray { ndarray: result_ndarray } => {
|
||||
// Use an existing ndarray.
|
||||
|
||||
// Check that its shape is compatible with the broadcast shape.
|
||||
result_ndarray.check_can_be_written_by_out(
|
||||
generator,
|
||||
ctx,
|
||||
broadcast_result.ndims,
|
||||
broadcast_result.shape,
|
||||
);
|
||||
result_ndarray
|
||||
}
|
||||
};
|
||||
|
||||
// Map element-wise and store results into `mapped_ndarray`.
|
||||
let nditer = NDIterHandle::new(generator, ctx, out_ndarray);
|
||||
gen_for_callback(
|
||||
generator,
|
||||
ctx,
|
||||
Some("broadcast_starmap"),
|
||||
|generator, ctx| {
|
||||
// Create NDIters for all broadcasted input ndarrays.
|
||||
let other_nditers = broadcast_result
|
||||
.ndarrays
|
||||
.iter()
|
||||
.map(|ndarray| NDIterHandle::new(generator, ctx, *ndarray))
|
||||
.collect_vec();
|
||||
Ok((nditer, other_nditers))
|
||||
},
|
||||
|generator, ctx, (out_nditer, _in_nditers)| {
|
||||
// We can simply use `out_nditer`'s `has_next()`.
|
||||
// `in_nditers`' `has_next()`s should return the same value.
|
||||
Ok(out_nditer.has_next(generator, ctx).value)
|
||||
},
|
||||
|generator, ctx, _hooks, (out_nditer, in_nditers)| {
|
||||
// Get all the scalars from the broadcasted input ndarrays, pass them to `mapping`,
|
||||
// and write to `out_ndarray`.
|
||||
|
||||
let in_scalars =
|
||||
in_nditers.iter().map(|nditer| nditer.get_scalar(generator, ctx)).collect_vec();
|
||||
|
||||
let result = mapping(generator, ctx, &in_scalars)?;
|
||||
// Sanity check on result's ty
|
||||
assert!(ctx.unifier.unioned(result.ty, out_ndarray.dtype));
|
||||
|
||||
let p = out_nditer.get_pointer(generator, ctx);
|
||||
ctx.builder.build_store(p, result.value).unwrap();
|
||||
|
||||
Ok(())
|
||||
},
|
||||
|generator, ctx, (out_nditer, in_nditers)| {
|
||||
// Advance all iterators
|
||||
out_nditer.next(generator, ctx);
|
||||
in_nditers.iter().for_each(|nditer| nditer.next(generator, ctx));
|
||||
Ok(())
|
||||
},
|
||||
)?;
|
||||
|
||||
Ok(out_ndarray)
|
||||
}
|
||||
|
||||
pub fn map<'a, G, Mapping>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
out: NDArrayOut<'ctx>,
|
||||
mapping: Mapping,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Mapping: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
AnyObject<'ctx>,
|
||||
) -> Result<AnyObject<'ctx>, String>,
|
||||
{
|
||||
NDArrayObject::broadcasting_starmap(
|
||||
generator,
|
||||
ctx,
|
||||
&[*self],
|
||||
out,
|
||||
|generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarOrNDArray<'ctx> {
|
||||
/// TODO: Document me. Has complex behavior.
|
||||
/// and explain why `ret_dtype` has to be specified beforehand.
|
||||
pub fn broadcasting_starmap<'a, G, MappingFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
inputs: &[Self],
|
||||
ret_dtype: Type,
|
||||
mapping: MappingFn,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
MappingFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
&[AnyObject<'ctx>],
|
||||
) -> Result<AnyObject<'ctx>, String>,
|
||||
{
|
||||
// Check if all inputs are AnyObjects
|
||||
let all_scalars: Option<Vec<_>> = inputs.iter().map(AnyObject::try_from).try_collect().ok();
|
||||
|
||||
if let Some(scalars) = all_scalars {
|
||||
let scalar = mapping(generator, ctx, &scalars)?;
|
||||
|
||||
// Sanity check on scalar's type
|
||||
assert!(ctx.unifier.unioned(scalar.ty, ret_dtype));
|
||||
|
||||
Ok(ScalarOrNDArray::Scalar(scalar))
|
||||
} else {
|
||||
// Promote all input to ndarrays and map through them.
|
||||
let inputs = inputs.iter().map(|input| input.as_ndarray(generator, ctx)).collect_vec();
|
||||
let ndarray = NDArrayObject::broadcasting_starmap(
|
||||
generator,
|
||||
ctx,
|
||||
&inputs,
|
||||
NDArrayOut::NewNDArray { dtype: ret_dtype },
|
||||
mapping,
|
||||
)?;
|
||||
Ok(ScalarOrNDArray::NDArray(ndarray))
|
||||
}
|
||||
}
|
||||
|
||||
pub fn map<'a, G, Mapping>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ret_dtype: Type,
|
||||
mapping: Mapping,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Mapping: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
AnyObject<'ctx>,
|
||||
) -> Result<AnyObject<'ctx>, String>,
|
||||
{
|
||||
ScalarOrNDArray::broadcasting_starmap(
|
||||
generator,
|
||||
ctx,
|
||||
&[*self],
|
||||
ret_dtype,
|
||||
|generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
|
||||
)
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,831 @@
|
|||
pub mod array;
|
||||
pub mod broadcast;
|
||||
pub mod factory;
|
||||
pub mod functions;
|
||||
pub mod indexing;
|
||||
pub mod mapping;
|
||||
pub mod nalgebra;
|
||||
pub mod nditer;
|
||||
pub mod product;
|
||||
pub mod scalar;
|
||||
pub mod shape_util;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::{
|
||||
call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
|
||||
call_nac3_ndarray_is_c_contiguous, call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
|
||||
call_nac3_ndarray_resolve_and_check_new_shape, call_nac3_ndarray_set_strides_by_shape,
|
||||
call_nac3_ndarray_size, call_nac3_ndarray_transpose,
|
||||
call_nac3_ndarray_util_assert_output_shape_same,
|
||||
},
|
||||
model::*,
|
||||
stmt::{gen_for_callback, BreakContinueHooks},
|
||||
structure::{NDArray, SimpleNDArray},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
toplevel::{
|
||||
helper::{create_ndims, extract_ndims},
|
||||
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
use indexing::RustNDIndex;
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::BasicType,
|
||||
values::{BasicValue, PointerValue},
|
||||
AddressSpace, IntPredicate,
|
||||
};
|
||||
use nditer::NDIterHandle;
|
||||
use scalar::ScalarOrNDArray;
|
||||
use util::call_memcpy_model;
|
||||
|
||||
use super::{tuple::TupleObject, AnyObject};
|
||||
|
||||
/// A NAC3 Python ndarray object.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDArrayObject<'ctx> {
|
||||
pub dtype: Type,
|
||||
pub ndims: u64,
|
||||
pub instance: Ptr<'ctx, StructModel<NDArray>>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Create an [`NDArrayObject`] from an LLVM value and its typechecker [`Type`].
|
||||
pub fn from_object<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
) -> Self {
|
||||
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, object.ty);
|
||||
let ndims = extract_ndims(&ctx.unifier, ndims);
|
||||
Self::from_value_and_unpacked_types(generator, ctx, object.value, dtype, ndims)
|
||||
}
|
||||
|
||||
/// Like [`NDArrayObject::from_object`] but you directly supply the ndarray's
|
||||
/// `dtype` and `ndims`.
|
||||
pub fn from_value_and_unpacked_types<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
value: V,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
) -> Self {
|
||||
let pndarray_model = PtrModel(StructModel(NDArray));
|
||||
let value = pndarray_model.check_value(generator, ctx.ctx, value).unwrap();
|
||||
NDArrayObject { dtype, ndims, instance: value }
|
||||
}
|
||||
|
||||
/// Forget that this is an ndarray and convert to an [`AnyObject`].
|
||||
pub fn to_any_object(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
|
||||
let ty = self.get_ndarray_type(ctx);
|
||||
AnyObject { value: self.instance.value.as_basic_value_enum(), ty }
|
||||
}
|
||||
|
||||
/// Create a [`SimpleNDArray`] from the contents of this ndarray.
|
||||
///
|
||||
/// This function may or may not be expensive depending on if this ndarray has contiguous data.
|
||||
///
|
||||
/// If this ndarray is not C-contiguous, this function will allocate memory on the stack for the `data` field of
|
||||
/// the returned [`SimpleNDArray`] and copy contents of this ndarray to there.
|
||||
///
|
||||
/// If this ndarray is C-contiguous, contents of this ndarray will not be copied. The created [`SimpleNDArray`]
|
||||
/// will have the same `data` field as this ndarray.
|
||||
///
|
||||
/// The `item_model` sets the [`Model`] of the returned [`SimpleNDArray`]'s `Item` model, and should match the
|
||||
/// `ctx.get_llvm_type()` of this ndarray's `dtype`. Otherwise this function panics.
|
||||
pub fn make_simple_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
item_model: Item,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, StructModel<SimpleNDArray<Item>>> {
|
||||
// Sanity check on `self.dtype` and `item_model`.
|
||||
let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
|
||||
item_model.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
|
||||
|
||||
let simple_ndarray_model = StructModel(SimpleNDArray { item: item_model });
|
||||
|
||||
let current_bb = ctx.builder.get_insert_block().unwrap();
|
||||
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
|
||||
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
|
||||
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
|
||||
|
||||
// Allocate and setup the resulting [`SimpleNDArray`].
|
||||
let result = simple_ndarray_model.alloca(generator, ctx, name);
|
||||
|
||||
// Set ndims and shape.
|
||||
let ndims = self.get_ndims(generator, ctx.ctx);
|
||||
result.set(ctx, |f| f.ndims, ndims);
|
||||
|
||||
let shape = self.instance.get(generator, ctx, |f| f.shape, "shape");
|
||||
result.set(ctx, |f| f.shape, shape);
|
||||
|
||||
// Set data, but we do things differently if this ndarray is contiguous.
|
||||
let is_contiguous = self.is_c_contiguous(generator, ctx);
|
||||
ctx.builder.build_conditional_branch(is_contiguous.value, then_bb, else_bb).unwrap();
|
||||
|
||||
// Inserting into then_bb; This ndarray is contiguous.
|
||||
let data = self.instance.get(generator, ctx, |f| f.data, "");
|
||||
let data = data.pointer_cast(generator, ctx, item_model, "");
|
||||
result.set(ctx, |f| f.data, data);
|
||||
ctx.builder.build_unconditional_branch(end_bb).unwrap();
|
||||
|
||||
// Inserting into else_bb; This ndarray is not contiguous. Do a full-copy on `data`.
|
||||
// TODO: Reimplement this? This method does give us the contiguous `data`, but
|
||||
// this creates a few extra bytes of useless information because an entire NDArray
|
||||
// is allocated. Though this is super convenient.
|
||||
let data = self.make_copy(generator, ctx, "").instance.get(generator, ctx, |f| f.data, "");
|
||||
let data = data.pointer_cast(generator, ctx, item_model, "");
|
||||
result.set(ctx, |f| f.data, data);
|
||||
ctx.builder.build_unconditional_branch(end_bb).unwrap();
|
||||
|
||||
// Reposition to end_bb for continuation
|
||||
ctx.builder.position_at_end(end_bb);
|
||||
|
||||
result
|
||||
}
|
||||
|
||||
/// Create an [`NDArrayObject`] from a [`SimpleNDArray`].
|
||||
///
|
||||
/// This operation is super cheap. The newly created [`NDArray`] will not copy contents
|
||||
/// from `simple_ndarray`, but only having its `data` and `shape` pointing to `simple_array`.
|
||||
pub fn from_simple_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
simple_ndarray: Ptr<'ctx, StructModel<SimpleNDArray<Item>>>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
) -> Self {
|
||||
// Sanity check on `dtype` and `simple_array`'s `Item` model.
|
||||
let dtype_llvm = ctx.get_llvm_type(generator, dtype);
|
||||
simple_ndarray.model.0 .0.item.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
|
||||
|
||||
let byte_model = IntModel(Byte);
|
||||
|
||||
// TODO: Check if `ndims` is consistent with that in `simple_array`?
|
||||
|
||||
// Allocate the resulting ndarray.
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims, "from_simple_ndarray");
|
||||
|
||||
// Set data, shape by simply copying addresses.
|
||||
let data = simple_ndarray
|
||||
.get(generator, ctx, |f| f.data, "")
|
||||
.pointer_cast(generator, ctx, byte_model, "data");
|
||||
ndarray.instance.set(ctx, |f| f.data, data);
|
||||
|
||||
let shape = simple_ndarray.get(generator, ctx, |f| f.shape, "shape");
|
||||
ndarray.instance.set(ctx, |f| f.shape, shape);
|
||||
|
||||
// Set strides. We know `data` is contiguous.
|
||||
ndarray.update_strides_by_shape(generator, ctx);
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Get the typechecker ndarray type of this [`NDArrayObject`].
|
||||
pub fn get_ndarray_type(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Type {
|
||||
let ndims = create_ndims(&mut ctx.unifier, self.ndims);
|
||||
make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(self.dtype), Some(ndims))
|
||||
}
|
||||
|
||||
/// Get the `np.size()` of this ndarray.
|
||||
pub fn size<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_size(generator, ctx, self.instance)
|
||||
}
|
||||
|
||||
/// Get the `ndarray.nbytes` of this ndarray.
|
||||
pub fn nbytes<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_nbytes(generator, ctx, self.instance)
|
||||
}
|
||||
|
||||
/// Get the `len()` of this ndarray.
|
||||
pub fn len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_len(generator, ctx, self.instance)
|
||||
}
|
||||
|
||||
/// Check if this ndarray is C-contiguous.
|
||||
///
|
||||
/// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
|
||||
pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
call_nac3_ndarray_is_c_contiguous(generator, ctx, self.instance)
|
||||
}
|
||||
|
||||
/// Get the pointer to the n-th (0-based) element.
|
||||
///
|
||||
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
|
||||
///
|
||||
/// There is no out-of-bounds check.
|
||||
pub fn get_nth_pointer<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
nth: Int<'ctx, SizeT>,
|
||||
name: &str,
|
||||
) -> PointerValue<'ctx> {
|
||||
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
|
||||
|
||||
let p = call_nac3_ndarray_get_nth_pelement(generator, ctx, self.instance, nth);
|
||||
ctx.builder
|
||||
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), name)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Get the n-th (0-based) scalar.
|
||||
///
|
||||
/// There is no out-of-bounds check.
|
||||
pub fn get_nth_scalar<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
nth: Int<'ctx, SizeT>,
|
||||
) -> AnyObject<'ctx> {
|
||||
let p = self.get_nth_pointer(generator, ctx, nth, "value");
|
||||
let value = ctx.builder.build_load(p, "value").unwrap();
|
||||
AnyObject { ty: self.dtype, value }
|
||||
}
|
||||
|
||||
/// Set the n-th (0-based) scalar.
|
||||
///
|
||||
/// There is no out-of-bounds check.
|
||||
pub fn set_nth_scalar<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
nth: Int<'ctx, SizeT>,
|
||||
scalar: AnyObject<'ctx>,
|
||||
) {
|
||||
// Sanity check on scalar's `dtype`
|
||||
assert!(ctx.unifier.unioned(scalar.ty, self.dtype));
|
||||
|
||||
let pscalar = self.get_nth_pointer(generator, ctx, nth, "pscalar");
|
||||
ctx.builder.build_store(pscalar, scalar.value).unwrap();
|
||||
}
|
||||
|
||||
/// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
|
||||
///
|
||||
/// Please refer to the IRRT implementation to see its purpose.
|
||||
pub fn update_strides_by_shape<G: CodeGenerator + ?Sized>(
|
||||
self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
call_nac3_ndarray_set_strides_by_shape(generator, ctx, self.instance);
|
||||
}
|
||||
|
||||
/// Copy data from another ndarray.
|
||||
///
|
||||
/// This ndarray and `src` is that their `np.size()` should be the same. Their shapes
|
||||
/// do not matter. The copying order is determined by how their flattened views look.
|
||||
///
|
||||
/// Panics if the `dtype`s of ndarrays are different.
|
||||
pub fn copy_data_from<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src: NDArrayObject<'ctx>,
|
||||
) {
|
||||
assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
|
||||
call_nac3_ndarray_copy_data(generator, ctx, src.instance, self.instance);
|
||||
}
|
||||
|
||||
/// Allocate an ndarray on the stack given its `ndims` and `dtype`.
|
||||
///
|
||||
/// `shape` and `strides` will be automatically allocated on the stack.
|
||||
//e
|
||||
/// The returned ndarray's content will be:
|
||||
/// - `data`: set to `nullptr`.
|
||||
/// - `itemsize`: set to the `sizeof()` of `dtype`.
|
||||
/// - `ndims`: set to the value of `ndims`.
|
||||
/// - `shape`: allocated with an array of length `ndims` with uninitialized values.
|
||||
/// - `strides`: allocated with an array of length `ndims` with uninitialized values.
|
||||
pub fn alloca<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let ndarray_model = StructModel(NDArray);
|
||||
let ndarray_data_model = PtrModel(IntModel(Byte));
|
||||
|
||||
let pndarray = ndarray_model.alloca(generator, ctx, name);
|
||||
|
||||
let data = ndarray_data_model.nullptr(generator, ctx.ctx);
|
||||
pndarray.set(ctx, |f| f.data, data);
|
||||
|
||||
let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
|
||||
let itemsize =
|
||||
sizet_model.s_extend_or_bit_cast(generator, ctx, itemsize, "alloca_itemsize");
|
||||
pndarray.set(ctx, |f| f.itemsize, itemsize);
|
||||
|
||||
let ndims_val = sizet_model.constant(generator, ctx.ctx, ndims);
|
||||
pndarray.set(ctx, |f| f.ndims, ndims_val);
|
||||
|
||||
let shape = sizet_model.array_alloca(generator, ctx, ndims_val.value, "alloca_shape");
|
||||
pndarray.set(ctx, |f| f.shape, shape);
|
||||
|
||||
let strides = sizet_model.array_alloca(generator, ctx, ndims_val.value, "alloca_strides");
|
||||
pndarray.set(ctx, |f| f.strides, strides);
|
||||
|
||||
NDArrayObject { dtype, ndims, instance: pndarray }
|
||||
}
|
||||
|
||||
/// Convenience function.
|
||||
/// Like [`NDArrayObject::alloca_uninitialized`] but directly takes the typechecker type of the ndarray.
|
||||
pub fn alloca_ndarray_type<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ty: Type,
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
|
||||
let ndims = extract_ndims(&ctx.unifier, ndims);
|
||||
Self::alloca(generator, ctx, dtype, ndims, name)
|
||||
}
|
||||
|
||||
/// Convenience function. Allocate an [`NDArrayObject`] with a statically known shape.
|
||||
///
|
||||
/// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
|
||||
pub fn alloca_constant_shape<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
shape: &[u64],
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64, name);
|
||||
|
||||
// Write shape
|
||||
let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape, "shape");
|
||||
for (i, dim) in shape.iter().enumerate() {
|
||||
let dim = sizet_model.constant(generator, ctx.ctx, *dim);
|
||||
dst_shape.offset_const(generator, ctx, i as u64, "").store(ctx, dim);
|
||||
}
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Convenience function. Allocate an [`NDArrayObject`] with a dynamically known shape.
|
||||
///
|
||||
/// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
|
||||
pub fn alloca_dynamic_shape<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
shape: &[Int<'ctx, SizeT>],
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64, name);
|
||||
|
||||
// Write shape
|
||||
let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape, "shape");
|
||||
for (i, dim) in shape.iter().enumerate() {
|
||||
dst_shape.offset_const(generator, ctx, i as u64, "").store(ctx, *dim);
|
||||
}
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Clone/Copy this ndarray - Allocate a new ndarray with the same shape as this ndarray and copy the contents over.
|
||||
///
|
||||
/// The new ndarray will own its data and will be C-contiguous.
|
||||
#[must_use]
|
||||
pub fn make_copy<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let clone = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims, name);
|
||||
|
||||
let shape = self.instance.gep(ctx, |f| f.shape).load(generator, ctx, "shape");
|
||||
clone.copy_shape_from_array(generator, ctx, shape);
|
||||
clone.create_data(generator, ctx);
|
||||
clone.copy_data_from(generator, ctx, *self);
|
||||
clone
|
||||
}
|
||||
|
||||
/// Get this ndarray's `ndims` as an LLVM constant.
|
||||
pub fn get_ndims<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
sizet_model.constant(generator, ctx, self.ndims)
|
||||
}
|
||||
|
||||
/// Get if this ndarray's `np.size` is `0` - containing no content.
|
||||
pub fn is_empty<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let size = self.size(generator, ctx);
|
||||
size.compare(ctx, IntPredicate::EQ, sizet_model.const_0(generator, ctx.ctx), "is_empty")
|
||||
}
|
||||
|
||||
/// Return true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
|
||||
///
|
||||
/// This is a staticially known property of ndarrays. This is why it is returning
|
||||
/// a Rust boolean instead of a [`BasicValue`].
|
||||
#[must_use]
|
||||
pub fn is_unsized(&self) -> bool {
|
||||
self.ndims == 0
|
||||
}
|
||||
|
||||
/// If this ndarray is unsized, return its sole value as a [`AnyObject`]. Otherwise, do nothing.
|
||||
pub fn split_unsized<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> ScalarOrNDArray<'ctx> {
|
||||
if self.is_unsized() {
|
||||
// NOTE: `np.size(self) == 0` here is never possible.
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let zero = sizet_model.const_0(generator, ctx.ctx);
|
||||
ScalarOrNDArray::Scalar(self.get_nth_scalar(generator, ctx, zero))
|
||||
} else {
|
||||
ScalarOrNDArray::NDArray(*self)
|
||||
}
|
||||
}
|
||||
|
||||
/// Initialize an ndarray's `data` by allocating a buffer on the stack.
|
||||
/// The allocated data buffer is considered to be *owned* by the ndarray.
|
||||
///
|
||||
/// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
|
||||
///
|
||||
/// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
|
||||
pub fn create_data<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
let byte_model = IntModel(Byte);
|
||||
|
||||
let nbytes = self.nbytes(generator, ctx);
|
||||
|
||||
let data = byte_model.array_alloca(generator, ctx, nbytes.value, "data");
|
||||
self.instance.set(ctx, |f| f.data, data);
|
||||
|
||||
self.update_strides_by_shape(generator, ctx);
|
||||
}
|
||||
|
||||
/// Copy shape dimensions from an array.
|
||||
pub fn copy_shape_from_array<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let dst_shape = self.instance.get(generator, ctx, |f| f.shape, "dst_shape");
|
||||
let num_items = self.get_ndims(generator, ctx.ctx).value;
|
||||
call_memcpy_model(generator, ctx, dst_shape, src_shape, num_items);
|
||||
}
|
||||
|
||||
/// Copy shape dimensions from an ndarray.
|
||||
/// Panics if `ndims` mismatches.
|
||||
pub fn copy_shape_from_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: NDArrayObject<'ctx>,
|
||||
) {
|
||||
assert_eq!(self.ndims, src_ndarray.ndims);
|
||||
let src_shape = src_ndarray.instance.get(generator, ctx, |f| f.shape, "src_shape");
|
||||
self.copy_shape_from_array(generator, ctx, src_shape);
|
||||
}
|
||||
|
||||
/// Copy strides dimensions from an array.
|
||||
pub fn copy_strides_from_array<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_strides: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let dst_strides = self.instance.get(generator, ctx, |f| f.strides, "dst_strides");
|
||||
let num_items = self.get_ndims(generator, ctx.ctx).value;
|
||||
call_memcpy_model(generator, ctx, dst_strides, src_strides, num_items);
|
||||
}
|
||||
|
||||
/// Copy strides dimensions from an ndarray.
|
||||
/// Panics if `ndims` mismatches.
|
||||
pub fn copy_strides_from_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: NDArrayObject<'ctx>,
|
||||
) {
|
||||
assert_eq!(self.ndims, src_ndarray.ndims);
|
||||
let src_strides = src_ndarray.instance.get(generator, ctx, |f| f.strides, "src_strides");
|
||||
self.copy_strides_from_array(generator, ctx, src_strides);
|
||||
}
|
||||
|
||||
/// Iterate through every element in the ndarray.
|
||||
///
|
||||
/// `body` also access to [`BreakContinueHooks`] to short-circuit.
|
||||
pub fn foreach<'a, G, F>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
body: F,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
F: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks<'ctx>,
|
||||
NDIterHandle<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
{
|
||||
gen_for_callback(
|
||||
generator,
|
||||
ctx,
|
||||
Some("ndarray_foreach"),
|
||||
|generator, ctx| Ok(NDIterHandle::new(generator, ctx, *self)),
|
||||
|generator, ctx, nditer| Ok(nditer.has_next(generator, ctx).value),
|
||||
|generator, ctx, hooks, nditer| body(generator, ctx, hooks, nditer),
|
||||
|generator, ctx, nditer| {
|
||||
nditer.next(generator, ctx);
|
||||
Ok(())
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
/// Make sure the ndarray is at least `ndmin`-dimensional.
|
||||
///
|
||||
/// If this ndarray's `ndims` is less than `ndmin`, a view is created on this with 1s prepended to the shape.
|
||||
/// If this ndarray's `ndims` is not less than `ndmin`, this function does nothing and return this ndarray.
|
||||
#[must_use]
|
||||
pub fn atleast_nd<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndmin: u64,
|
||||
) -> Self {
|
||||
if self.ndims < ndmin {
|
||||
let mut indices = vec![];
|
||||
for _ in self.ndims..ndmin {
|
||||
indices.push(RustNDIndex::NewAxis);
|
||||
}
|
||||
indices.push(RustNDIndex::Ellipsis);
|
||||
self.index(generator, ctx, &indices, "atleast_nd_ndarray")
|
||||
} else {
|
||||
*self
|
||||
}
|
||||
}
|
||||
|
||||
/// Fill the ndarray with a scalar.
|
||||
///
|
||||
/// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
|
||||
pub fn fill<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
scalar: AnyObject<'ctx>,
|
||||
) {
|
||||
// Sanity check on scalar's type.
|
||||
assert!(ctx.unifier.unioned(self.dtype, scalar.ty));
|
||||
|
||||
self.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
|
||||
let p = nditer.get_pointer(generator, ctx);
|
||||
ctx.builder.build_store(p, scalar.value).unwrap();
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
}
|
||||
|
||||
/// Create a reshaped view on this ndarray like `np.reshape()`.
|
||||
///
|
||||
/// If there is a `-1` in `new_shape`, it will be resolved; `new_shape` would **NOT** be modified as a result.
|
||||
///
|
||||
/// If reshape without copying is impossible, this function will allocate a new ndarray and copy contents.
|
||||
///
|
||||
/// * `new_ndims` - The number of dimensions of `new_shape` as a [`Type`].
|
||||
/// * `new_shape` - The target shape to do `np.reshape()`.
|
||||
#[must_use]
|
||||
pub fn reshape_or_copy<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
new_ndims: u64,
|
||||
new_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) -> Self {
|
||||
// TODO: The current criterion for whether to do a full copy or not is by checking `is_c_contiguous`,
|
||||
// but this is not optimal - there are cases when the ndarray is not contiguous but could be reshaped
|
||||
// without copying data. Look into how numpy does it.
|
||||
|
||||
let current_bb = ctx.builder.get_insert_block().unwrap();
|
||||
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
|
||||
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
|
||||
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
|
||||
|
||||
let dst_ndarray =
|
||||
NDArrayObject::alloca(generator, ctx, self.dtype, new_ndims, "reshaped_ndarray");
|
||||
dst_ndarray.copy_shape_from_array(generator, ctx, new_shape);
|
||||
|
||||
// Reolsve negative indices
|
||||
let size = self.size(generator, ctx);
|
||||
let dst_ndims = dst_ndarray.get_ndims(generator, ctx.ctx);
|
||||
let dst_shape =
|
||||
dst_ndarray.instance.get(generator, ctx, |f| f.shape, "reshaped_ndarray_shape");
|
||||
call_nac3_ndarray_resolve_and_check_new_shape(generator, ctx, size, dst_ndims, dst_shape);
|
||||
|
||||
let is_c_contiguous = self.is_c_contiguous(generator, ctx);
|
||||
ctx.builder.build_conditional_branch(is_c_contiguous.value, then_bb, else_bb).unwrap();
|
||||
|
||||
// Inserting into then_bb: reshape is possible without copying
|
||||
ctx.builder.position_at_end(then_bb);
|
||||
dst_ndarray.update_strides_by_shape(generator, ctx);
|
||||
dst_ndarray.instance.set(
|
||||
ctx,
|
||||
|f| f.data,
|
||||
self.instance.get(generator, ctx, |f| f.data, "data"),
|
||||
);
|
||||
ctx.builder.build_unconditional_branch(end_bb).unwrap();
|
||||
|
||||
// Inserting into else_bb: reshape is impossible without copying
|
||||
ctx.builder.position_at_end(else_bb);
|
||||
dst_ndarray.create_data(generator, ctx);
|
||||
dst_ndarray.copy_data_from(generator, ctx, *self);
|
||||
ctx.builder.build_unconditional_branch(end_bb).unwrap();
|
||||
|
||||
// Reposition for continuation
|
||||
ctx.builder.position_at_end(end_bb);
|
||||
|
||||
dst_ndarray
|
||||
}
|
||||
|
||||
/// Create a flattened view of this ndarray, like `np.ravel()`.
|
||||
///
|
||||
/// Uses [`NDArrayObject::reshape_or_copy`] under-the-hood so ndarray may or may not be copied.
|
||||
#[must_use]
|
||||
pub fn ravel_or_copy<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Self {
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let num0 = sizet_model.const_0(generator, ctx.ctx);
|
||||
let num1 = sizet_model.const_1(generator, ctx.ctx);
|
||||
let num_neg1 = sizet_model.const_all_1s(generator, ctx.ctx);
|
||||
|
||||
// Create `[-1]` and pass to `reshape_or_copy`.
|
||||
let new_shape = sizet_model.array_alloca(generator, ctx, num1.value, "new_shape");
|
||||
new_shape.offset(generator, ctx, num0.value, "").store(ctx, num_neg1);
|
||||
|
||||
self.reshape_or_copy(generator, ctx, 1, new_shape)
|
||||
}
|
||||
|
||||
/// Create a transposed view on this ndarray like `np.transpose(<ndarray>, <axes> = None)`.
|
||||
/// * `axes` - If specified, should be an array of the permutation (negative indices are **allowed**).
|
||||
#[must_use]
|
||||
pub fn transpose<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
axes: Option<Ptr<'ctx, IntModel<SizeT>>>,
|
||||
) -> Self {
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let transposed_ndarray =
|
||||
NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims, "transposed_ndarray");
|
||||
|
||||
let num_axes = self.get_ndims(generator, ctx.ctx);
|
||||
|
||||
// `axes = nullptr` if `axes` is unspecified.
|
||||
let axes = axes.unwrap_or_else(|| PtrModel(sizet_model).nullptr(generator, ctx.ctx));
|
||||
|
||||
call_nac3_ndarray_transpose(
|
||||
generator,
|
||||
ctx,
|
||||
self.instance,
|
||||
transposed_ndarray.instance,
|
||||
num_axes,
|
||||
axes,
|
||||
);
|
||||
|
||||
transposed_ndarray
|
||||
}
|
||||
|
||||
/// Check if this `NDArray` can be used as an `out` ndarray for an operation.
|
||||
///
|
||||
/// Raise an exception if the shapes do not match.
|
||||
pub fn check_can_be_written_by_out<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
out_ndims: u64,
|
||||
out_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let ndarray_ndims = self.get_ndims(generator, ctx.ctx);
|
||||
let ndarray_shape = self.instance.get(generator, ctx, |f| f.shape, "shape");
|
||||
|
||||
let output_ndims = sizet_model.constant(generator, ctx.ctx, out_ndims);
|
||||
let output_shape = out_shape;
|
||||
|
||||
call_nac3_ndarray_util_assert_output_shape_same(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray_ndims,
|
||||
ndarray_shape,
|
||||
output_ndims,
|
||||
output_shape,
|
||||
);
|
||||
}
|
||||
|
||||
/// Create the shape tuple of this ndarray like `np.shape(<ndarray>)`.
|
||||
///
|
||||
/// The returned integers in the tuple are in int32.
|
||||
pub fn make_shape_tuple<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> TupleObject<'ctx> {
|
||||
// TODO: Don't return a tuple of int32s.
|
||||
|
||||
let mut objects = Vec::with_capacity(self.ndims as usize);
|
||||
|
||||
for i in 0..self.ndims {
|
||||
let dim = self
|
||||
.instance
|
||||
.get(generator, ctx, |f| f.shape, "")
|
||||
.offset_const(generator, ctx, i, "")
|
||||
.load(generator, ctx, "dim");
|
||||
let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
|
||||
|
||||
objects.push(AnyObject {
|
||||
ty: ctx.primitives.int32,
|
||||
value: dim.value.as_basic_value_enum(),
|
||||
});
|
||||
}
|
||||
|
||||
TupleObject::create(generator, ctx, objects, "shape")
|
||||
}
|
||||
|
||||
/// Create the strides tuple of this ndarray like `np.strides(<ndarray>)`.
|
||||
///
|
||||
/// The returned integers in the tuple are in int32.
|
||||
pub fn make_strides_tuple<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> TupleObject<'ctx> {
|
||||
// TODO: Don't return a tuple of int32s.
|
||||
|
||||
let mut objects = Vec::with_capacity(self.ndims as usize);
|
||||
|
||||
for i in 0..self.ndims {
|
||||
let dim = self
|
||||
.instance
|
||||
.get(generator, ctx, |f| f.strides, "")
|
||||
.offset_const(generator, ctx, i, "")
|
||||
.load(generator, ctx, "dim");
|
||||
let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
|
||||
|
||||
objects.push(AnyObject {
|
||||
ty: ctx.primitives.int32,
|
||||
value: dim.value.as_basic_value_enum(),
|
||||
});
|
||||
}
|
||||
|
||||
TupleObject::create(generator, ctx, objects, "strides")
|
||||
}
|
||||
}
|
||||
|
||||
/// TODO: Document me
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub enum NDArrayOut<'ctx> {
|
||||
NewNDArray { dtype: Type },
|
||||
WriteToNDArray { ndarray: NDArrayObject<'ctx> },
|
||||
}
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
use inkwell::values::{BasicValue, BasicValueEnum};
|
||||
|
||||
use crate::codegen::{model::*, structure::SimpleNDArray, CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
pub fn perform_nalgebra_call<'ctx, 'a, const NUM_INPUTS: usize, const NUM_OUTPUTS: usize, G, F>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
inputs: [NDArrayObject<'ctx>; NUM_INPUTS],
|
||||
output_ndims: [u64; NUM_OUTPUTS],
|
||||
invoke_function: F,
|
||||
) -> [NDArrayObject<'ctx>; NUM_OUTPUTS]
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
F: FnOnce(
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
[BasicValueEnum<'ctx>; NUM_INPUTS],
|
||||
[BasicValueEnum<'ctx>; NUM_OUTPUTS],
|
||||
),
|
||||
{
|
||||
// TODO: Allow stacked inputs. See NumPy docs.
|
||||
|
||||
let f64_model = FloatModel(Float64);
|
||||
let simple_ndarray_model = StructModel(SimpleNDArray { item: f64_model });
|
||||
|
||||
// Prepare inputs & outputs and invoke
|
||||
let inputs = inputs.map(|input| {
|
||||
// Sanity check. Typechecker ensures this.
|
||||
assert!(ctx.unifier.unioned(input.dtype, ctx.primitives.float));
|
||||
|
||||
input
|
||||
.make_simple_ndarray(generator, ctx, FloatModel(Float64), "nalgebra_input")
|
||||
.value
|
||||
.as_basic_value_enum()
|
||||
});
|
||||
let outputs = [simple_ndarray_model.alloca(generator, ctx, "nalgebra_output"); NUM_OUTPUTS];
|
||||
invoke_function(ctx, inputs, outputs.map(|output| output.value.as_basic_value_enum()));
|
||||
|
||||
// Turn the outputs into strided NDArrays
|
||||
let mut output_i = 0;
|
||||
outputs.map(|output| {
|
||||
let out = NDArrayObject::from_simple_ndarray(
|
||||
generator,
|
||||
ctx,
|
||||
output,
|
||||
ctx.primitives.float,
|
||||
output_ndims[output_i],
|
||||
);
|
||||
output_i += 1;
|
||||
out
|
||||
})
|
||||
}
|
||||
|
|
@ -0,0 +1,88 @@
|
|||
use inkwell::{types::BasicType, values::PointerValue, AddressSpace};
|
||||
|
||||
use crate::codegen::{
|
||||
irrt::{call_nac3_nditer_has_next, call_nac3_nditer_initialize, call_nac3_nditer_next},
|
||||
model::*,
|
||||
object::AnyObject,
|
||||
structure::NDIter,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct NDIterHandle<'ctx> {
|
||||
ndarray: NDArrayObject<'ctx>,
|
||||
instance: Ptr<'ctx, StructModel<NDIter>>,
|
||||
indices: Ptr<'ctx, IntModel<SizeT>>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDIterHandle<'ctx> {
|
||||
pub fn new<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayObject<'ctx>,
|
||||
) -> Self {
|
||||
let nditer = StructModel(NDIter).alloca(generator, ctx, "nditer");
|
||||
let ndims = ndarray.get_ndims(generator, ctx.ctx);
|
||||
let indices = IntModel(SizeT).array_alloca(generator, ctx, ndims.value, "indices");
|
||||
call_nac3_nditer_initialize(generator, ctx, nditer, ndarray.instance, indices);
|
||||
NDIterHandle { ndarray, instance: nditer, indices }
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn has_next<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
call_nac3_nditer_has_next(generator, ctx, self.instance)
|
||||
}
|
||||
|
||||
pub fn next<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
call_nac3_nditer_next(generator, ctx, self.instance);
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn get_pointer<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let elem_ty = ctx.get_llvm_type(generator, self.ndarray.dtype);
|
||||
|
||||
let p = self.instance.get(generator, ctx, |f| f.element, "element");
|
||||
ctx.builder
|
||||
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "element")
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn get_scalar<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
let p = self.get_pointer(generator, ctx);
|
||||
let value = ctx.builder.build_load(p, "value").unwrap();
|
||||
AnyObject { ty: self.ndarray.dtype, value }
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn get_index<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
self.instance.get(generator, ctx, |f| f.nth, "index")
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn get_indices(&self) -> Ptr<'ctx, IntModel<SizeT>> {
|
||||
self.indices
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,159 @@
|
|||
use std::cmp::max;
|
||||
|
||||
use crate::codegen::{
|
||||
irrt::{
|
||||
call_nac3_ndarray_float64_matmul_at_least_2d, call_nac3_ndarray_matmul_calculate_shapes,
|
||||
},
|
||||
model::*,
|
||||
object::ndarray::indexing::RustNDIndex,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::{NDArrayObject, NDArrayOut};
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// TODO: Document me
|
||||
fn matmul_helper<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
a: Self,
|
||||
b: Self,
|
||||
) -> Self {
|
||||
assert!(a.ndims >= 2);
|
||||
assert!(b.ndims >= 2);
|
||||
|
||||
assert!(ctx.unifier.unioned(ctx.primitives.float, a.dtype));
|
||||
assert!(ctx.unifier.unioned(ctx.primitives.float, b.dtype));
|
||||
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let final_ndims_int = max(a.ndims, b.ndims);
|
||||
|
||||
let a_ndims = a.get_ndims(generator, ctx.ctx);
|
||||
let a_shape = a.instance.get(generator, ctx, |f| f.shape, "a_shape");
|
||||
let b_ndims = b.get_ndims(generator, ctx.ctx);
|
||||
let b_shape = b.instance.get(generator, ctx, |f| f.shape, "b_shape");
|
||||
let final_ndims = sizet_model.constant(generator, ctx.ctx, final_ndims_int);
|
||||
let new_a_shape =
|
||||
sizet_model.array_alloca(generator, ctx, final_ndims.value, "new_a_shape");
|
||||
let new_b_shape =
|
||||
sizet_model.array_alloca(generator, ctx, final_ndims.value, "new_b_shape");
|
||||
let dst_shape = sizet_model.array_alloca(generator, ctx, final_ndims.value, "dst_shape");
|
||||
|
||||
call_nac3_ndarray_matmul_calculate_shapes(
|
||||
generator,
|
||||
ctx,
|
||||
a_ndims,
|
||||
a_shape,
|
||||
b_ndims,
|
||||
b_shape,
|
||||
final_ndims,
|
||||
new_a_shape,
|
||||
new_b_shape,
|
||||
dst_shape,
|
||||
);
|
||||
|
||||
let new_a = a.broadcast_to(generator, ctx, final_ndims_int, new_a_shape);
|
||||
let new_b = b.broadcast_to(generator, ctx, final_ndims_int, new_b_shape);
|
||||
let dst = NDArrayObject::alloca(
|
||||
generator,
|
||||
ctx,
|
||||
ctx.primitives.float,
|
||||
final_ndims_int,
|
||||
"matmul_result",
|
||||
);
|
||||
dst.copy_shape_from_array(generator, ctx, dst_shape);
|
||||
dst.create_data(generator, ctx);
|
||||
|
||||
call_nac3_ndarray_float64_matmul_at_least_2d(
|
||||
generator,
|
||||
ctx,
|
||||
new_a.instance,
|
||||
new_b.instance,
|
||||
dst.instance,
|
||||
);
|
||||
|
||||
dst
|
||||
}
|
||||
|
||||
/// Perform `np.matmul` according to the rules in
|
||||
/// <https://numpy.org/doc/stable/reference/generated/numpy.matmul.html>.
|
||||
///
|
||||
/// This function always return an [`NDArrayObject`]. You may want to use [`NDArrayObject::split_unsized`].
|
||||
pub fn matmul<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
a: Self,
|
||||
b: Self,
|
||||
out: NDArrayOut<'ctx>,
|
||||
) -> Self {
|
||||
// Sanity check, but type inference should prevent this.
|
||||
assert!(a.ndims > 0 && b.ndims > 0, "np.matmul disallows scalar input");
|
||||
|
||||
/*
|
||||
If both arguments are 2-D they are multiplied like conventional matrices.
|
||||
If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indexes and broadcast accordingly.
|
||||
If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed.
|
||||
If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed.
|
||||
*/
|
||||
|
||||
let new_a = if a.ndims == 1 {
|
||||
// Prepend 1 to its dimensions
|
||||
a.index(generator, ctx, &[RustNDIndex::NewAxis, RustNDIndex::Ellipsis], "new_a")
|
||||
} else {
|
||||
a
|
||||
};
|
||||
|
||||
let new_b = if b.ndims == 1 {
|
||||
// Append 1 to its dimensions
|
||||
b.index(generator, ctx, &[RustNDIndex::Ellipsis, RustNDIndex::NewAxis], "new_a")
|
||||
} else {
|
||||
b
|
||||
};
|
||||
|
||||
// NOTE: `result` will always be a newly allocated ndarray.
|
||||
// Current implementation cannot do in-place matrix muliplication.
|
||||
let mut result = NDArrayObject::matmul_helper(generator, ctx, new_a, new_b);
|
||||
|
||||
let i32_model = IntModel(Int32); // TODO: Upgrade to SizeT
|
||||
let zero = i32_model.const_0(generator, ctx.ctx);
|
||||
|
||||
if a.ndims == 1 {
|
||||
// Remove the prepended 1
|
||||
result = result.index(
|
||||
generator,
|
||||
ctx,
|
||||
&[RustNDIndex::SingleElement(zero), RustNDIndex::Ellipsis],
|
||||
"result_no_prepend_1",
|
||||
);
|
||||
}
|
||||
|
||||
if b.ndims == 1 {
|
||||
// Remove the appended 1
|
||||
result = result.index(
|
||||
generator,
|
||||
ctx,
|
||||
&[RustNDIndex::Ellipsis, RustNDIndex::SingleElement(zero)],
|
||||
"result_no_append_1",
|
||||
);
|
||||
}
|
||||
|
||||
match out {
|
||||
NDArrayOut::NewNDArray { dtype } => {
|
||||
// We don't support auto-casting right now, nor anything other than float64.
|
||||
// Force the output dtype to be float64.
|
||||
assert!(ctx.unifier.unioned(ctx.primitives.float, dtype));
|
||||
result
|
||||
}
|
||||
NDArrayOut::WriteToNDArray { ndarray: out_ndarray } => {
|
||||
// TODO: It is possible to check the shapes before computing the matmul to save resources.
|
||||
let result_shape = result.instance.get(generator, ctx, |f| f.shape, "result_shape");
|
||||
out_ndarray.check_can_be_written_by_out(generator, ctx, result.ndims, result_shape);
|
||||
|
||||
// TODO: We can just set `out_ndarray.data` to `result.data`. Should we?
|
||||
out_ndarray.copy_data_from(generator, ctx, result);
|
||||
out_ndarray
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,131 @@
|
|||
use inkwell::values::{BasicValue, BasicValueEnum};
|
||||
|
||||
use crate::{
|
||||
codegen::{model::*, object::AnyObject, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
impl<'ctx> AnyObject<'ctx> {
|
||||
/// Promote this scalar to an unsized ndarray (like doing `np.asarray`).
|
||||
///
|
||||
/// The scalar value is allocated onto the stack, and the ndarray's `data` will point to that
|
||||
/// allocated value.
|
||||
pub fn as_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> NDArrayObject<'ctx> {
|
||||
let pbyte_model = PtrModel(IntModel(Byte));
|
||||
|
||||
// We have to put the value on the stack to get a data pointer.
|
||||
let data = ctx.builder.build_alloca(self.value.get_type(), "as_ndarray_scalar").unwrap();
|
||||
ctx.builder.build_store(data, self.value).unwrap();
|
||||
let data = pbyte_model.pointer_cast(generator, ctx, data, "data");
|
||||
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, self.ty, 0, "scalar_ndarray");
|
||||
ndarray.instance.set(ctx, |f| f.data, data);
|
||||
ndarray
|
||||
}
|
||||
}
|
||||
|
||||
/// A convenience enum for implementing scalar/ndarray agnostic utilities.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub enum ScalarOrNDArray<'ctx> {
|
||||
Scalar(AnyObject<'ctx>),
|
||||
NDArray(NDArrayObject<'ctx>),
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarOrNDArray<'ctx> {
|
||||
/// Get the underlying [`BasicValueEnum<'ctx>`] of this [`ScalarOrNDArray`].
|
||||
#[must_use]
|
||||
pub fn to_basic_value_enum(self) -> BasicValueEnum<'ctx> {
|
||||
match self {
|
||||
ScalarOrNDArray::Scalar(scalar) => scalar.value,
|
||||
ScalarOrNDArray::NDArray(ndarray) => ndarray.instance.value.as_basic_value_enum(),
|
||||
}
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn into_scalar(&self) -> AnyObject<'ctx> {
|
||||
match self {
|
||||
ScalarOrNDArray::NDArray(_ndarray) => panic!("Got NDArray"),
|
||||
ScalarOrNDArray::Scalar(scalar) => *scalar,
|
||||
}
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn into_ndarray(&self) -> NDArrayObject<'ctx> {
|
||||
match self {
|
||||
ScalarOrNDArray::NDArray(ndarray) => *ndarray,
|
||||
ScalarOrNDArray::Scalar(_scalar) => panic!("Got Scalar"),
|
||||
}
|
||||
}
|
||||
|
||||
/// Convert this [`ScalarOrNDArray`] to an ndarray - behaves like `np.asarray`.
|
||||
/// - If this is an ndarray, the ndarray is returned.
|
||||
/// - If this is a scalar, an unsized ndarray view is created on it.
|
||||
pub fn as_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> NDArrayObject<'ctx> {
|
||||
match self {
|
||||
ScalarOrNDArray::NDArray(ndarray) => *ndarray,
|
||||
ScalarOrNDArray::Scalar(scalar) => scalar.as_ndarray(generator, ctx),
|
||||
}
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn dtype(&self) -> Type {
|
||||
match self {
|
||||
ScalarOrNDArray::Scalar(scalar) => scalar.ty,
|
||||
ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for AnyObject<'ctx> {
|
||||
type Error = ();
|
||||
|
||||
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
|
||||
match value {
|
||||
ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
|
||||
ScalarOrNDArray::NDArray(_ndarray) => Err(()),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayObject<'ctx> {
|
||||
type Error = ();
|
||||
|
||||
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
|
||||
match value {
|
||||
ScalarOrNDArray::Scalar(_scalar) => Err(()),
|
||||
ScalarOrNDArray::NDArray(ndarray) => Ok(*ndarray),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Split an [`AnyObject`] into a [`ScalarOrNDArray`] depending on its [`Type`].
|
||||
pub fn split_scalar_or_ndarray<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
) -> ScalarOrNDArray<'ctx> {
|
||||
// TODO: Automatically convert a list into an ndarray?
|
||||
|
||||
match &*ctx.unifier.get_ty(object.ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, object);
|
||||
ScalarOrNDArray::NDArray(ndarray)
|
||||
}
|
||||
_ => {
|
||||
let scalar = AnyObject { ty: object.ty, value: object.value };
|
||||
ScalarOrNDArray::Scalar(scalar)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
use util::gen_for_model_auto;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
model::*,
|
||||
object::{list::ListObject, tuple::TupleObject, AnyObject},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::TypeEnum,
|
||||
};
|
||||
|
||||
/// Parse a NumPy-like "int sequence" input and return the int sequence as an array and its length.
|
||||
///
|
||||
/// * `sequence` - The `sequence` parameter.
|
||||
/// * `sequence_ty` - The typechecker type of `sequence`
|
||||
///
|
||||
/// The `sequence` argument type may only be one of the following:
|
||||
/// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
|
||||
/// 2. A tuple of `int32`; e.g., `np.empty((600, 800, 3))`
|
||||
/// 3. A scalar `int32`; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
|
||||
///
|
||||
/// All `int32` values will be sign-extended to `SizeT`.
|
||||
pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
input_sequence: AnyObject<'ctx>,
|
||||
) -> (Int<'ctx, SizeT>, Ptr<'ctx, IntModel<SizeT>>) {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let zero = sizet_model.const_0(generator, ctx.ctx);
|
||||
let one = sizet_model.const_1(generator, ctx.ctx);
|
||||
|
||||
// The result `list` to return.
|
||||
match &*ctx.unifier.get_ty(input_sequence.ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
|
||||
|
||||
// Check `input_sequence`
|
||||
let input_sequence = ListObject::from_object(generator, ctx, input_sequence);
|
||||
|
||||
let len = input_sequence.instance.gep(ctx, |f| f.len).load(generator, ctx, "len");
|
||||
let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
|
||||
|
||||
// Load all the `int32`s from the input_sequence, cast them to `SizeT`, and store them into `result`
|
||||
gen_for_model_auto(generator, ctx, zero, len, one, |generator, ctx, _hooks, i| {
|
||||
// Load the i-th int32 in the input sequence
|
||||
let int = input_sequence
|
||||
.instance
|
||||
.get(generator, ctx, |f| f.items, "")
|
||||
.offset(generator, ctx, i.value, "")
|
||||
.load(generator, ctx, "")
|
||||
.value
|
||||
.into_int_value();
|
||||
|
||||
// Cast to SizeT
|
||||
let int = sizet_model.s_extend_or_bit_cast(generator, ctx, int, "int");
|
||||
|
||||
// Store
|
||||
result.offset(generator, ctx, i.value, "int").store(ctx, int);
|
||||
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
(len, result)
|
||||
}
|
||||
TypeEnum::TTuple { .. } => {
|
||||
// 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
|
||||
|
||||
let input_sequence = TupleObject::from_object(ctx, input_sequence);
|
||||
|
||||
let len_int = input_sequence.len_static();
|
||||
let len = sizet_model.constant(generator, ctx.ctx, len_int as u64);
|
||||
|
||||
let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
|
||||
|
||||
for i in 0..len_int {
|
||||
// Get the i-th element off of the tuple and load it into `result`.
|
||||
let int = input_sequence.get(ctx, i, "dim").value.into_int_value();
|
||||
let int = sizet_model.s_extend_or_bit_cast(generator, ctx, int, "int");
|
||||
|
||||
let offset = sizet_model.constant(generator, ctx.ctx, i as u64);
|
||||
result.offset(generator, ctx, offset.value, "int").store(ctx, int);
|
||||
}
|
||||
|
||||
(len, result)
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
// 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
|
||||
let input_int = input_sequence.value.into_int_value();
|
||||
|
||||
let len = sizet_model.const_1(generator, ctx.ctx);
|
||||
let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
|
||||
|
||||
let int = sizet_model.s_extend_or_bit_cast(generator, ctx, input_int, "int");
|
||||
|
||||
// Storing into result[0]
|
||||
result.store(ctx, int);
|
||||
|
||||
(len, result)
|
||||
}
|
||||
_ => panic!(
|
||||
"encountered unknown sequence type: {}",
|
||||
ctx.unifier.stringify(input_sequence.ty)
|
||||
),
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,41 @@
|
|||
use crate::codegen::{
|
||||
irrt::calculate_len_for_slice_range, model::*, structure::RangeModel, CodeGenContext,
|
||||
CodeGenerator,
|
||||
};
|
||||
|
||||
use super::AnyObject;
|
||||
|
||||
/// A `range` in NAC3
|
||||
pub struct RangeObject<'ctx> {
|
||||
pub instance: Ptr<'ctx, RangeModel>,
|
||||
}
|
||||
|
||||
impl<'ctx> RangeObject<'ctx> {
|
||||
pub fn from_object<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
) -> Self {
|
||||
assert!(ctx.unifier.unioned(ctx.primitives.range, object.ty)); // Sanity check on type.
|
||||
|
||||
let model = PtrModel(RangeModel::default());
|
||||
let instance = model.check_value(generator, ctx.ctx, object.value).unwrap();
|
||||
RangeObject { instance }
|
||||
}
|
||||
|
||||
/// Get the `len()` of this range.
|
||||
pub fn len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, Int32> {
|
||||
let start = self.instance.gep_start(generator, ctx, "").load(generator, ctx, "start");
|
||||
let stop = self.instance.gep_stop(generator, ctx, "").load(generator, ctx, "stop");
|
||||
let step = self.instance.gep_step(generator, ctx, "").load(generator, ctx, "step");
|
||||
|
||||
// TODO: Refactor this
|
||||
let len =
|
||||
calculate_len_for_slice_range(generator, ctx, start.value, stop.value, step.value);
|
||||
IntModel(Int32).check_value(generator, ctx.ctx, len).unwrap()
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,113 @@
|
|||
use core::panic;
|
||||
|
||||
use inkwell::values::StructValue;
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
codegen::{model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::AnyObject;
|
||||
|
||||
/// A NAC3 tuple object.
|
||||
///
|
||||
/// NOTE: This struct has no copy trait.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct TupleObject<'ctx> {
|
||||
/// The type of the tuple.
|
||||
pub tys: Vec<Type>,
|
||||
/// The underlying LLVM value of this tuple.
|
||||
pub value: StructValue<'ctx>,
|
||||
}
|
||||
|
||||
impl<'ctx> TupleObject<'ctx> {
|
||||
// NOTE: There is no Model abstraction for Tuples with arbitrary lengths.
|
||||
// Everything has to be done raw with Inkwell.
|
||||
|
||||
pub fn from_object(ctx: &mut CodeGenContext<'ctx, '_>, object: AnyObject<'ctx>) -> Self {
|
||||
// TODO: Keep `is_vararg_ctx` from TTuple?
|
||||
|
||||
// Sanity check on object type.
|
||||
let TypeEnum::TTuple { ty: tys, .. } = &*ctx.unifier.get_ty(object.ty) else {
|
||||
panic!(
|
||||
"Expected type to be a TypeEnum::TTuple, got {}",
|
||||
ctx.unifier.stringify(object.ty)
|
||||
);
|
||||
};
|
||||
|
||||
let value = object.value.into_struct_value();
|
||||
let value_num_fields = value.get_type().count_fields() as usize;
|
||||
assert!(
|
||||
value_num_fields == tys.len(),
|
||||
"Tuple type has {} item(s), but the LLVM struct value has {} field(s)",
|
||||
tys.len(),
|
||||
value_num_fields
|
||||
);
|
||||
|
||||
TupleObject { tys: tys.clone(), value }
|
||||
}
|
||||
|
||||
/// Convenience function. Create a [`TupleObject`] from an iterator of objects.
|
||||
pub fn create<I, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
objects: I,
|
||||
name: &str,
|
||||
) -> Self
|
||||
where
|
||||
I: IntoIterator<Item = AnyObject<'ctx>>,
|
||||
{
|
||||
let (values, tys): (Vec<_>, Vec<_>) =
|
||||
objects.into_iter().map(|object| (object.value, object.ty)).unzip();
|
||||
|
||||
let llvm_tys = tys.iter().map(|ty| ctx.get_llvm_type(generator, *ty)).collect_vec();
|
||||
let llvm_tuple_ty = ctx.ctx.struct_type(&llvm_tys, false);
|
||||
|
||||
let pllvm_tuple = ctx.builder.build_alloca(llvm_tuple_ty, "tuple").unwrap();
|
||||
for (i, val) in values.into_iter().enumerate() {
|
||||
let pval = ctx.builder.build_struct_gep(pllvm_tuple, i as u32, "value").unwrap();
|
||||
ctx.builder.build_store(pval, val).unwrap();
|
||||
}
|
||||
|
||||
let value = ctx.builder.build_load(pllvm_tuple, name).unwrap().into_struct_value();
|
||||
TupleObject { tys, value }
|
||||
}
|
||||
|
||||
/// Get the `len()` of this tuple statically.
|
||||
///
|
||||
/// We statically know the lengths of tuples in NAC3 when compiling.
|
||||
#[must_use]
|
||||
pub fn len_static(&self) -> usize {
|
||||
self.tys.len()
|
||||
}
|
||||
|
||||
/// Get the `len()` of this tuple.
|
||||
#[must_use]
|
||||
pub fn len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
IntModel(SizeT).constant(generator, ctx.ctx, self.len_static() as u64)
|
||||
}
|
||||
|
||||
/// Check if this tuple is an empty/unit tuple.
|
||||
#[must_use]
|
||||
pub fn is_empty(&self) -> bool {
|
||||
self.len_static() == 0
|
||||
}
|
||||
|
||||
/// Get the `i`-th (0-based) object in this tuple.
|
||||
pub fn get(&self, ctx: &mut CodeGenContext<'ctx, '_>, i: usize, name: &str) -> AnyObject<'ctx> {
|
||||
assert!(
|
||||
i < self.len_static(),
|
||||
"Tuple object with length {} have index {i}",
|
||||
self.len_static()
|
||||
);
|
||||
|
||||
let value = ctx.builder.build_extract_value(self.value, i as u32, name).unwrap();
|
||||
let ty = self.tys[i];
|
||||
AnyObject { ty, value }
|
||||
}
|
||||
}
|
||||
|
|
@ -1,3 +1,27 @@
|
|||
use super::model::*;
|
||||
use super::object::ndarray::indexing::util::gen_ndarray_subscript_ndindexes;
|
||||
use super::object::ndarray::scalar::split_scalar_or_ndarray;
|
||||
use super::object::ndarray::NDArrayObject;
|
||||
use super::object::AnyObject;
|
||||
use super::{
|
||||
super::symbol_resolver::ValueEnum,
|
||||
expr::destructure_range,
|
||||
irrt::{handle_slice_indices, list_slice_assignment},
|
||||
structure::{CSlice, Exception},
|
||||
CodeGenContext, CodeGenerator, Int32, IntModel, Ptr, StructModel,
|
||||
};
|
||||
use crate::{
|
||||
codegen::{
|
||||
classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
|
||||
expr::gen_binop_expr,
|
||||
gen_in_range_check,
|
||||
},
|
||||
toplevel::{DefinitionId, TopLevelDef},
|
||||
typecheck::{
|
||||
magic_methods::Binop,
|
||||
typedef::{iter_type_vars, FunSignature, Type, TypeEnum},
|
||||
},
|
||||
};
|
||||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
basic_block::BasicBlock,
|
||||
|
|
@ -6,28 +30,10 @@ use inkwell::{
|
|||
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,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
use crate::{
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{DefinitionId, TopLevelDef},
|
||||
typecheck::{
|
||||
magic_methods::Binop,
|
||||
typedef::{iter_type_vars, FunSignature, Type, TypeEnum},
|
||||
},
|
||||
};
|
||||
|
||||
/// See [`CodeGenerator::gen_var_alloc`].
|
||||
pub fn gen_var<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
|
|
@ -121,7 +127,7 @@ pub fn gen_store_target<'ctx, G: CodeGenerator>(
|
|||
return Ok(None);
|
||||
};
|
||||
let BasicValueEnum::PointerValue(ptr) = val else {
|
||||
codegen_unreachable!(ctx);
|
||||
unreachable!();
|
||||
};
|
||||
unsafe {
|
||||
ctx.builder.build_in_bounds_gep(
|
||||
|
|
@ -135,7 +141,7 @@ pub fn gen_store_target<'ctx, G: CodeGenerator>(
|
|||
}
|
||||
.unwrap()
|
||||
}
|
||||
_ => codegen_unreachable!(ctx),
|
||||
_ => unreachable!(),
|
||||
}))
|
||||
}
|
||||
|
||||
|
|
@ -176,14 +182,6 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
|
|||
}
|
||||
}
|
||||
let val = value.to_basic_value_enum(ctx, generator, target.custom.unwrap())?;
|
||||
|
||||
// Perform i1 <-> i8 conversion as needed
|
||||
let val = if ctx.unifier.unioned(target.custom.unwrap(), ctx.primitives.bool) {
|
||||
generator.bool_to_i8(ctx, val.into_int_value()).into()
|
||||
} else {
|
||||
val
|
||||
};
|
||||
|
||||
ctx.builder.build_store(ptr, val).unwrap();
|
||||
}
|
||||
};
|
||||
|
|
@ -201,12 +199,12 @@ pub fn gen_assign_target_list<'ctx, G: CodeGenerator>(
|
|||
// Deconstruct the tuple `value`
|
||||
let BasicValueEnum::StructValue(tuple) = value.to_basic_value_enum(ctx, generator, value_ty)?
|
||||
else {
|
||||
codegen_unreachable!(ctx)
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
// NOTE: Currently, RHS's type is forced to be a Tuple by the type inferencer.
|
||||
let TypeEnum::TTuple { ty: tuple_tys, .. } = &*ctx.unifier.get_ty(value_ty) else {
|
||||
codegen_unreachable!(ctx);
|
||||
unreachable!();
|
||||
};
|
||||
|
||||
assert_eq!(tuple.get_type().count_fields() as usize, tuple_tys.len());
|
||||
|
|
@ -266,7 +264,7 @@ pub fn gen_assign_target_list<'ctx, G: CodeGenerator>(
|
|||
// Now assign with that sub-tuple to the starred target.
|
||||
generator.gen_assign(ctx, target, ValueEnum::Dynamic(sub_tuple_val), sub_tuple_ty)?;
|
||||
} else {
|
||||
codegen_unreachable!(ctx) // The typechecker ensures this
|
||||
unreachable!() // The typechecker ensures this
|
||||
}
|
||||
|
||||
// Handle assignment after the starred target
|
||||
|
|
@ -314,9 +312,7 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
|
|||
|
||||
if let ExprKind::Slice { .. } = &key.node {
|
||||
// Handle assigning to a slice
|
||||
let ExprKind::Slice { lower, upper, step } = &key.node else {
|
||||
codegen_unreachable!(ctx)
|
||||
};
|
||||
let ExprKind::Slice { lower, upper, step } = &key.node else { unreachable!() };
|
||||
let Some((start, end, step)) = handle_slice_indices(
|
||||
lower,
|
||||
upper,
|
||||
|
|
@ -411,7 +407,45 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
|
|||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
// Handle NDArray item assignment
|
||||
todo!("ndarray subscript assignment is not yet implemented");
|
||||
// Process target
|
||||
let target = generator
|
||||
.gen_expr(ctx, target)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, target_ty)?;
|
||||
let target = AnyObject { value: target, ty: target_ty };
|
||||
|
||||
// Process key
|
||||
let key = gen_ndarray_subscript_ndindexes(generator, ctx, key)?;
|
||||
|
||||
// Process value
|
||||
let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
|
||||
let value = AnyObject { value, ty: value_ty };
|
||||
|
||||
/*
|
||||
Reference code:
|
||||
```python
|
||||
target = target[key]
|
||||
value = np.asarray(value)
|
||||
|
||||
shape = np.broadcast_shape((target, value))
|
||||
|
||||
target = np.broadcast_to(target, shape)
|
||||
value = np.broadcast_to(value, shape)
|
||||
|
||||
...and finally copy 1-1 from value to target.
|
||||
```
|
||||
*/
|
||||
let target = NDArrayObject::from_object(generator, ctx, target);
|
||||
let target = target.index(generator, ctx, &key, "assign_target_ndarray");
|
||||
|
||||
let value = split_scalar_or_ndarray(generator, ctx, value).as_ndarray(generator, ctx);
|
||||
|
||||
let broadcast_result = NDArrayObject::broadcast(generator, ctx, &[target, value]);
|
||||
|
||||
let target = broadcast_result.ndarrays[0];
|
||||
let value = broadcast_result.ndarrays[1];
|
||||
|
||||
target.copy_data_from(generator, ctx, value);
|
||||
}
|
||||
_ => {
|
||||
panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));
|
||||
|
|
@ -426,9 +460,7 @@ pub fn gen_for<G: CodeGenerator>(
|
|||
ctx: &mut CodeGenContext<'_, '_>,
|
||||
stmt: &Stmt<Option<Type>>,
|
||||
) -> Result<(), String> {
|
||||
let StmtKind::For { iter, target, body, orelse, .. } = &stmt.node else {
|
||||
codegen_unreachable!(ctx)
|
||||
};
|
||||
let StmtKind::For { iter, target, body, orelse, .. } = &stmt.node else { unreachable!() };
|
||||
|
||||
// var_assignment static values may be changed in another branch
|
||||
// if so, remove the static value as it may not be correct in this branch
|
||||
|
|
@ -470,7 +502,7 @@ pub fn gen_for<G: CodeGenerator>(
|
|||
let Some(target_i) =
|
||||
generator.gen_store_target(ctx, target, Some("for.target.addr"))?
|
||||
else {
|
||||
codegen_unreachable!(ctx)
|
||||
unreachable!()
|
||||
};
|
||||
let (start, stop, step) = destructure_range(ctx, iter_val);
|
||||
|
||||
|
|
@ -631,9 +663,9 @@ pub struct BreakContinueHooks<'ctx> {
|
|||
/// ```
|
||||
///
|
||||
/// * `init` - A lambda containing IR statements declaring and initializing loop variables. The
|
||||
/// return value is a [Clone] value which will be passed to the other lambdas.
|
||||
/// return value is a [Clone] value which will be passed to the other lambdas.
|
||||
/// * `cond` - A lambda containing IR statements checking whether the loop should continue
|
||||
/// executing. The result value must be an `i1` indicating if the loop should continue.
|
||||
/// executing. The result value must be an `i1` indicating if the loop should continue.
|
||||
/// * `body` - A lambda containing IR statements within the loop body.
|
||||
/// * `update` - A lambda containing IR statements updating loop variables.
|
||||
pub fn gen_for_callback<'ctx, 'a, G, I, InitFn, CondFn, BodyFn, UpdateFn>(
|
||||
|
|
@ -716,9 +748,9 @@ where
|
|||
/// ```
|
||||
///
|
||||
/// * `init_val` - The initial value of the loop variable. The type of this value will also be used
|
||||
/// as the type of the loop variable.
|
||||
/// as the type of the loop variable.
|
||||
/// * `max_val` - A tuple containing the maximum value of the loop variable, and whether the maximum
|
||||
/// value should be treated as inclusive (as opposed to exclusive).
|
||||
/// value should be treated as inclusive (as opposed to exclusive).
|
||||
/// * `body` - A lambda containing IR statements within the loop body.
|
||||
/// * `incr_val` - The value to increment the loop variable on each iteration.
|
||||
pub fn gen_for_callback_incrementing<'ctx, 'a, G, BodyFn>(
|
||||
|
|
@ -789,12 +821,12 @@ where
|
|||
///
|
||||
/// - `is_unsigned`: Whether to treat the values of the `range` as unsigned.
|
||||
/// - `start_fn`: A lambda of IR statements that retrieves the `start` value of the `range`-like
|
||||
/// iterable.
|
||||
/// iterable.
|
||||
/// - `stop_fn`: A lambda of IR statements that retrieves the `stop` value of the `range`-like
|
||||
/// iterable. This value will be extended to the size of `start`.
|
||||
/// iterable. This value will be extended to the size of `start`.
|
||||
/// - `stop_inclusive`: Whether the stop value should be treated as inclusive.
|
||||
/// - `step_fn`: A lambda of IR statements that retrieves the `step` value of the `range`-like
|
||||
/// iterable. This value will be extended to the size of `start`.
|
||||
/// iterable. This value will be extended to the size of `start`.
|
||||
/// - `body_fn`: A lambda of IR statements within the loop body.
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
pub fn gen_for_range_callback<'ctx, 'a, G, StartFn, StopFn, StepFn, BodyFn>(
|
||||
|
|
@ -815,7 +847,7 @@ where
|
|||
BodyFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks<'ctx>,
|
||||
BreakContinueHooks,
|
||||
IntValue<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
{
|
||||
|
|
@ -913,7 +945,7 @@ pub fn gen_while<G: CodeGenerator>(
|
|||
ctx: &mut CodeGenContext<'_, '_>,
|
||||
stmt: &Stmt<Option<Type>>,
|
||||
) -> Result<(), String> {
|
||||
let StmtKind::While { test, body, orelse, .. } = &stmt.node else { codegen_unreachable!(ctx) };
|
||||
let StmtKind::While { test, body, orelse, .. } = &stmt.node else { unreachable!() };
|
||||
|
||||
// var_assignment static values may be changed in another branch
|
||||
// if so, remove the static value as it may not be correct in this branch
|
||||
|
|
@ -943,7 +975,7 @@ pub fn gen_while<G: CodeGenerator>(
|
|||
|
||||
return Ok(());
|
||||
};
|
||||
let BasicValueEnum::IntValue(test) = test else { codegen_unreachable!(ctx) };
|
||||
let BasicValueEnum::IntValue(test) = test else { unreachable!() };
|
||||
|
||||
ctx.builder
|
||||
.build_conditional_branch(generator.bool_to_i1(ctx, test), body_bb, orelse_bb)
|
||||
|
|
@ -1091,7 +1123,7 @@ pub fn gen_if<G: CodeGenerator>(
|
|||
ctx: &mut CodeGenContext<'_, '_>,
|
||||
stmt: &Stmt<Option<Type>>,
|
||||
) -> Result<(), String> {
|
||||
let StmtKind::If { test, body, orelse, .. } = &stmt.node else { codegen_unreachable!(ctx) };
|
||||
let StmtKind::If { test, body, orelse, .. } = &stmt.node else { unreachable!() };
|
||||
|
||||
// var_assignment static values may be changed in another branch
|
||||
// if so, remove the static value as it may not be correct in this branch
|
||||
|
|
@ -1214,11 +1246,11 @@ pub fn exn_constructor<'ctx>(
|
|||
let zelf_id = if let TypeEnum::TObj { obj_id, .. } = &*ctx.unifier.get_ty(zelf_ty) {
|
||||
obj_id.0
|
||||
} else {
|
||||
codegen_unreachable!(ctx)
|
||||
unreachable!()
|
||||
};
|
||||
let defs = ctx.top_level.definitions.read();
|
||||
let def = defs[zelf_id].read();
|
||||
let TopLevelDef::Class { name: zelf_name, .. } = &*def else { codegen_unreachable!(ctx) };
|
||||
let TopLevelDef::Class { name: zelf_name, .. } = &*def else { unreachable!() };
|
||||
let exception_name = format!("{}:{}", ctx.resolver.get_exception_id(zelf_id), zelf_name);
|
||||
unsafe {
|
||||
let id_ptr = ctx.builder.build_in_bounds_gep(zelf, &[zero, zero], "exn.id").unwrap();
|
||||
|
|
@ -1275,47 +1307,36 @@ pub fn exn_constructor<'ctx>(
|
|||
pub fn gen_raise<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
exception: Option<&BasicValueEnum<'ctx>>,
|
||||
exception: Option<Ptr<'ctx, StructModel<Exception>>>,
|
||||
loc: Location,
|
||||
) {
|
||||
if let Some(exception) = exception {
|
||||
unsafe {
|
||||
let int32 = ctx.ctx.i32_type();
|
||||
let zero = int32.const_zero();
|
||||
let exception = exception.into_pointer_value();
|
||||
let file_ptr = ctx
|
||||
.builder
|
||||
.build_in_bounds_gep(exception, &[zero, int32.const_int(1, false)], "file_ptr")
|
||||
.unwrap();
|
||||
let filename = ctx.gen_string(generator, loc.file.0);
|
||||
ctx.builder.build_store(file_ptr, filename).unwrap();
|
||||
let row_ptr = ctx
|
||||
.builder
|
||||
.build_in_bounds_gep(exception, &[zero, int32.const_int(2, false)], "row_ptr")
|
||||
.unwrap();
|
||||
ctx.builder.build_store(row_ptr, int32.const_int(loc.row as u64, false)).unwrap();
|
||||
let col_ptr = ctx
|
||||
.builder
|
||||
.build_in_bounds_gep(exception, &[zero, int32.const_int(3, false)], "col_ptr")
|
||||
.unwrap();
|
||||
ctx.builder.build_store(col_ptr, int32.const_int(loc.column as u64, false)).unwrap();
|
||||
if let Some(pexn) = exception {
|
||||
let i32_model = IntModel(Int32);
|
||||
let cslice_model = StructModel(CSlice);
|
||||
|
||||
let current_fun = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
|
||||
let fun_name = ctx.gen_string(generator, current_fun.get_name().to_str().unwrap());
|
||||
let name_ptr = ctx
|
||||
.builder
|
||||
.build_in_bounds_gep(exception, &[zero, int32.const_int(4, false)], "name_ptr")
|
||||
.unwrap();
|
||||
ctx.builder.build_store(name_ptr, fun_name).unwrap();
|
||||
}
|
||||
// Get and store filename
|
||||
let filename = loc.file.0;
|
||||
let filename = ctx.gen_string(generator, &String::from(filename)).value;
|
||||
let filename = cslice_model.check_value(generator, ctx.ctx, filename).unwrap();
|
||||
pexn.set(ctx, |f| f.filename, filename);
|
||||
|
||||
let row = i32_model.constant(generator, ctx.ctx, loc.row as u64);
|
||||
pexn.set(ctx, |f| f.line, row);
|
||||
|
||||
let column = i32_model.constant(generator, ctx.ctx, loc.column as u64);
|
||||
pexn.set(ctx, |f| f.column, column);
|
||||
|
||||
let current_fn = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
|
||||
let fn_name = ctx.gen_string(generator, current_fn.get_name().to_str().unwrap());
|
||||
pexn.set(ctx, |f| f.function, fn_name);
|
||||
|
||||
let raise = get_builtins(generator, ctx, "__nac3_raise");
|
||||
let exception = *exception;
|
||||
ctx.build_call_or_invoke(raise, &[exception], "raise");
|
||||
ctx.build_call_or_invoke(raise, &[pexn.value.into()], "raise");
|
||||
} else {
|
||||
let resume = get_builtins(generator, ctx, "__nac3_resume");
|
||||
ctx.build_call_or_invoke(resume, &[], "resume");
|
||||
}
|
||||
|
||||
ctx.builder.build_unreachable().unwrap();
|
||||
}
|
||||
|
||||
|
|
@ -1326,7 +1347,7 @@ pub fn gen_try<'ctx, 'a, G: CodeGenerator>(
|
|||
target: &Stmt<Option<Type>>,
|
||||
) -> Result<(), String> {
|
||||
let StmtKind::Try { body, handlers, orelse, finalbody, .. } = &target.node else {
|
||||
codegen_unreachable!(ctx)
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
// if we need to generate anything related to exception, we must have personality defined
|
||||
|
|
@ -1403,7 +1424,7 @@ pub fn gen_try<'ctx, 'a, G: CodeGenerator>(
|
|||
if let TypeEnum::TObj { obj_id, .. } = &*ctx.unifier.get_ty(type_.custom.unwrap()) {
|
||||
*obj_id
|
||||
} else {
|
||||
codegen_unreachable!(ctx)
|
||||
unreachable!()
|
||||
};
|
||||
let exception_name = format!("{}:{}", ctx.resolver.get_exception_id(obj_id.0), exn_name);
|
||||
let exn_id = ctx.resolver.get_string_id(&exception_name);
|
||||
|
|
@ -1675,23 +1696,6 @@ pub fn gen_return<G: CodeGenerator>(
|
|||
} else {
|
||||
None
|
||||
};
|
||||
|
||||
// Remap boolean return type into i1
|
||||
let value = value.map(|ret_val| {
|
||||
// The "return type" of a sret function is in the first parameter
|
||||
let expected_ty = if ctx.need_sret {
|
||||
func.get_type().get_param_types()[0]
|
||||
} else {
|
||||
func.get_type().get_return_type().unwrap()
|
||||
};
|
||||
|
||||
if matches!(expected_ty, BasicTypeEnum::IntType(ty) if ty.get_bit_width() == 1) {
|
||||
generator.bool_to_i1(ctx, ret_val.into_int_value()).into()
|
||||
} else {
|
||||
ret_val
|
||||
}
|
||||
});
|
||||
|
||||
if let Some(return_target) = ctx.return_target {
|
||||
if let Some(value) = value {
|
||||
ctx.builder.build_store(ctx.return_buffer.unwrap(), value).unwrap();
|
||||
|
|
@ -1702,6 +1706,25 @@ pub fn gen_return<G: CodeGenerator>(
|
|||
ctx.builder.build_store(ctx.return_buffer.unwrap(), value.unwrap()).unwrap();
|
||||
ctx.builder.build_return(None).unwrap();
|
||||
} else {
|
||||
// Remap boolean return type into i1
|
||||
let value = value.map(|v| {
|
||||
let expected_ty = func.get_type().get_return_type().unwrap();
|
||||
let ret_val = v.as_basic_value_enum();
|
||||
|
||||
if expected_ty.is_int_type() && ret_val.is_int_value() {
|
||||
let ret_type = expected_ty.into_int_type();
|
||||
let ret_val = ret_val.into_int_value();
|
||||
|
||||
if ret_type.get_bit_width() == 1 && ret_val.get_type().get_bit_width() != 1 {
|
||||
generator.bool_to_i1(ctx, ret_val)
|
||||
} else {
|
||||
ret_val
|
||||
}
|
||||
.into()
|
||||
} else {
|
||||
ret_val
|
||||
}
|
||||
});
|
||||
let value = value.as_ref().map(|v| v as &dyn BasicValue);
|
||||
ctx.builder.build_return(value).unwrap();
|
||||
}
|
||||
|
|
@ -1770,95 +1793,52 @@ pub fn gen_stmt<G: CodeGenerator>(
|
|||
StmtKind::Try { .. } => gen_try(generator, ctx, stmt)?,
|
||||
StmtKind::Raise { exc, .. } => {
|
||||
if let Some(exc) = exc {
|
||||
let exn = if let ExprKind::Name { id, .. } = &exc.node {
|
||||
// Handle "raise Exception" short form
|
||||
let def_id = ctx.resolver.get_identifier_def(*id).map_err(|e| {
|
||||
format!("{} (at {})", e.iter().next().unwrap(), exc.location)
|
||||
})?;
|
||||
let def = ctx.top_level.definitions.read();
|
||||
let TopLevelDef::Class { constructor, .. } = *def[def_id.0].read() else {
|
||||
return Err(format!("Failed to resolve symbol {id} (at {})", exc.location));
|
||||
};
|
||||
|
||||
let TypeEnum::TFunc(signature) =
|
||||
ctx.unifier.get_ty(constructor.unwrap()).as_ref().clone()
|
||||
else {
|
||||
return Err(format!("Failed to resolve symbol {id} (at {})", exc.location));
|
||||
};
|
||||
|
||||
generator
|
||||
.gen_call(ctx, None, (&signature, def_id), Vec::default())?
|
||||
.map(Into::into)
|
||||
} else {
|
||||
generator.gen_expr(ctx, exc)?
|
||||
};
|
||||
|
||||
let exc = if let Some(v) = exn {
|
||||
let exc = if let Some(v) = generator.gen_expr(ctx, exc)? {
|
||||
v.to_basic_value_enum(ctx, generator, exc.custom.unwrap())?
|
||||
} else {
|
||||
return Ok(());
|
||||
};
|
||||
gen_raise(generator, ctx, Some(&exc), stmt.location);
|
||||
|
||||
let pexn_model = PtrModel(StructModel(Exception));
|
||||
let exn = pexn_model.check_value(generator, ctx.ctx, exc).unwrap();
|
||||
|
||||
gen_raise(generator, ctx, Some(exn), stmt.location);
|
||||
} else {
|
||||
gen_raise(generator, ctx, None, stmt.location);
|
||||
}
|
||||
}
|
||||
StmtKind::Assert { test, msg, .. } => {
|
||||
let test = if let Some(v) = generator.gen_expr(ctx, test)? {
|
||||
v.to_basic_value_enum(ctx, generator, test.custom.unwrap())?
|
||||
} else {
|
||||
let byte_model = IntModel(Byte);
|
||||
let cslice_model = StructModel(CSlice);
|
||||
|
||||
let Some(test) = generator.gen_expr(ctx, test)? else {
|
||||
return Ok(());
|
||||
};
|
||||
let test = test.to_basic_value_enum(ctx, generator, ctx.primitives.bool)?;
|
||||
let test = byte_model.check_value(generator, ctx.ctx, test).unwrap(); // Python `bool` is represented as `i8` in nac3core
|
||||
|
||||
// Check `msg`
|
||||
let err_msg = match msg {
|
||||
Some(msg) => {
|
||||
if let Some(v) = generator.gen_expr(ctx, msg)? {
|
||||
v.to_basic_value_enum(ctx, generator, msg.custom.unwrap())?
|
||||
} else {
|
||||
let Some(msg) = generator.gen_expr(ctx, msg)? else {
|
||||
return Ok(());
|
||||
}
|
||||
};
|
||||
|
||||
let msg = msg.to_basic_value_enum(ctx, generator, ctx.primitives.str)?;
|
||||
cslice_model.check_value(generator, ctx.ctx, msg).unwrap()
|
||||
}
|
||||
None => ctx.gen_string(generator, "").into(),
|
||||
None => ctx.gen_string(generator, ""),
|
||||
};
|
||||
|
||||
ctx.make_assert_impl(
|
||||
generator,
|
||||
generator.bool_to_i1(ctx, test.into_int_value()),
|
||||
test.value,
|
||||
"0:AssertionError",
|
||||
err_msg,
|
||||
[None, None, None],
|
||||
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(())
|
||||
|
|
|
|||
|
|
@ -0,0 +1,256 @@
|
|||
use inkwell::context::Context;
|
||||
|
||||
use crate::codegen::model::*;
|
||||
|
||||
use super::{CodeGenContext, CodeGenerator};
|
||||
|
||||
/// Fields of [`CSlice`]
|
||||
pub struct CSliceFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
/// Pointer to data.
|
||||
pub base: F::Out<PtrModel<IntModel<Byte>>>,
|
||||
/// Number of bytes of data.
|
||||
pub len: F::Out<IntModel<SizeT>>,
|
||||
}
|
||||
|
||||
/// See <https://crates.io/crates/cslice>.
|
||||
///
|
||||
/// Additionally, see <https://github.com/m-labs/artiq/blob/b0d2705c385f64b6e6711c1726cd9178f40b598e/artiq/firmware/libeh/eh_artiq.rs>)
|
||||
/// for ARTIQ-specific notes.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct CSlice;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for CSlice {
|
||||
type Fields<F: FieldTraversal<'ctx>> = CSliceFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields { base: traversal.add_auto("base"), len: traversal.add_auto("len") }
|
||||
}
|
||||
}
|
||||
|
||||
impl StructModel<CSlice> {
|
||||
/// Create a [`CSlice`].
|
||||
///
|
||||
/// `base` and `len` must be LLVM global constants.
|
||||
pub fn create_const<'ctx, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
base: Ptr<'ctx, IntModel<Byte>>,
|
||||
len: Int<'ctx, SizeT>,
|
||||
) -> Struct<'ctx, CSlice> {
|
||||
let value = self
|
||||
.0
|
||||
.get_struct_type(generator, ctx)
|
||||
.const_named_struct(&[base.value.into(), len.value.into()]);
|
||||
self.believe_value(value)
|
||||
}
|
||||
}
|
||||
|
||||
/// The LLVM int type of an Exception ID.
|
||||
pub type ExceptionId = Int32;
|
||||
|
||||
/// Fields of [`Exception<'ctx>`]
|
||||
///
|
||||
/// The definition came from `pub struct Exception<'a>` in
|
||||
/// <https://github.com/m-labs/artiq/blob/master/artiq/firmware/libeh/eh_artiq.rs>.
|
||||
pub struct ExceptionFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
/// nac3core's ID of the exception
|
||||
pub id: F::Out<IntModel<ExceptionId>>,
|
||||
/// The name of the file this `Exception` was raised in.
|
||||
pub filename: F::Out<StructModel<CSlice>>,
|
||||
/// The line number in the file this `Exception` was raised in.
|
||||
pub line: F::Out<IntModel<Int32>>,
|
||||
/// The column number in the file this `Exception` was raised in.
|
||||
pub column: F::Out<IntModel<Int32>>,
|
||||
/// The name of the Python function this `Exception` was raised in.
|
||||
pub function: F::Out<StructModel<CSlice>>,
|
||||
/// The message of this Exception.
|
||||
///
|
||||
/// The message can optionally contain integer parameters `{0}`, `{1}`, and `{2}` in its string,
|
||||
/// where they will be substituted by `params[0]`, `params[1]`, and `params[2]` respectively (as `int64_t`s).
|
||||
/// Here is an example:
|
||||
///
|
||||
/// ```ignore
|
||||
/// "Index {0} is out of bounds! List only has {1} element(s)."
|
||||
/// ```
|
||||
///
|
||||
/// In this case, `params[0]` and `params[1]` must be specified, and `params[2]` is ***unused***.
|
||||
/// Having only 3 parameters is a constraint in ARTIQ.
|
||||
pub msg: F::Out<StructModel<CSlice>>,
|
||||
pub params: [F::Out<IntModel<Int64>>; 3],
|
||||
}
|
||||
|
||||
/// nac3core & ARTIQ's Exception
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Exception;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for Exception {
|
||||
type Fields<F: FieldTraversal<'ctx>> = ExceptionFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
id: traversal.add_auto("id"),
|
||||
filename: traversal.add_auto("filename"),
|
||||
line: traversal.add_auto("line"),
|
||||
column: traversal.add_auto("column"),
|
||||
function: traversal.add_auto("function"),
|
||||
msg: traversal.add_auto("msg"),
|
||||
params: [
|
||||
traversal.add_auto("params[0]"),
|
||||
traversal.add_auto("params[1]"),
|
||||
traversal.add_auto("params[2]"),
|
||||
],
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Fields of [`List`]
|
||||
pub struct ListFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
|
||||
/// Array pointer to content
|
||||
pub items: F::Out<PtrModel<Item>>,
|
||||
/// Number of items in the array
|
||||
pub len: F::Out<IntModel<SizeT>>,
|
||||
}
|
||||
|
||||
/// A list in NAC3.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct List<Item> {
|
||||
/// Model of the list items
|
||||
pub item: Item,
|
||||
}
|
||||
|
||||
impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for List<Item> {
|
||||
type Fields<F: FieldTraversal<'ctx>> = ListFields<'ctx, F, Item>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
items: traversal.add("data", PtrModel(self.item)),
|
||||
len: traversal.add_auto("len"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Fields of [`NDArray`]
|
||||
pub struct NDArrayFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub data: F::Out<PtrModel<IntModel<Byte>>>,
|
||||
pub itemsize: F::Out<IntModel<SizeT>>,
|
||||
pub ndims: F::Out<IntModel<SizeT>>,
|
||||
pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
pub strides: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
}
|
||||
|
||||
/// A strided ndarray in NAC3.
|
||||
///
|
||||
/// See IRRT implementation for details about its fields.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct NDArray;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for NDArray {
|
||||
type Fields<F: FieldTraversal<'ctx>> = NDArrayFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
data: traversal.add_auto("data"),
|
||||
itemsize: traversal.add_auto("itemsize"),
|
||||
ndims: traversal.add_auto("ndims"),
|
||||
shape: traversal.add_auto("shape"),
|
||||
strides: traversal.add_auto("strides"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Fields of [`SimpleNDArray`]
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct SimpleNDArrayFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
|
||||
pub ndims: F::Out<IntModel<SizeT>>,
|
||||
pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
pub data: F::Out<PtrModel<Item>>,
|
||||
}
|
||||
|
||||
/// An ndarray without strides and non-opaque `data` field in NAC3.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct SimpleNDArray<M> {
|
||||
/// [`Model`] of the items.
|
||||
pub item: M,
|
||||
}
|
||||
|
||||
impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for SimpleNDArray<Item> {
|
||||
type Fields<F: FieldTraversal<'ctx>> = SimpleNDArrayFields<'ctx, F, Item>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
ndims: traversal.add_auto("ndims"),
|
||||
shape: traversal.add_auto("shape"),
|
||||
data: traversal.add("data", PtrModel(self.item)),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Fields of [`NDIter`]
|
||||
pub struct NDIterFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub ndims: F::Out<IntModel<SizeT>>,
|
||||
pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
pub strides: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
|
||||
pub indices: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
pub nth: F::Out<IntModel<SizeT>>,
|
||||
pub element: F::Out<PtrModel<IntModel<Byte>>>,
|
||||
|
||||
pub size: F::Out<IntModel<SizeT>>,
|
||||
}
|
||||
|
||||
/// An IRRT helper structure used when iterating through an ndarray.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct NDIter;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for NDIter {
|
||||
type Fields<F: FieldTraversal<'ctx>> = NDIterFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
ndims: traversal.add_auto("ndims"),
|
||||
shape: traversal.add_auto("shape"),
|
||||
strides: traversal.add_auto("strides"),
|
||||
|
||||
indices: traversal.add_auto("indices"),
|
||||
nth: traversal.add_auto("nth"),
|
||||
element: traversal.add_auto("element"),
|
||||
|
||||
size: traversal.add_auto("size"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A NAC3 `range`. It is an array of 3 int32s.
|
||||
// TODO: Use `pub type RangeModel<N> = NArrayModel<3, IntModel<N>>` in the future when
|
||||
// `range` type is type dependent.
|
||||
pub type RangeModel = NArrayModel<3, IntModel<Int32>>;
|
||||
|
||||
impl<'ctx> Ptr<'ctx, RangeModel> {
|
||||
pub fn gep_start<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, IntModel<Int32>> {
|
||||
self.at_const(generator, ctx, 0, name)
|
||||
}
|
||||
|
||||
pub fn gep_stop<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, IntModel<Int32>> {
|
||||
self.at_const(generator, ctx, 1, name)
|
||||
}
|
||||
|
||||
pub fn gep_step<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, IntModel<Int32>> {
|
||||
self.at_const(generator, ctx, 2, name)
|
||||
}
|
||||
}
|
||||
|
|
@ -1,37 +1,34 @@
|
|||
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 super::{
|
||||
classes::{ListType, NDArrayType, ProxyType, RangeType},
|
||||
concrete_type::ConcreteTypeStore,
|
||||
CodeGenContext, CodeGenLLVMOptions, CodeGenTargetMachineOptions, CodeGenTask, CodeGenerator,
|
||||
DefaultCodeGenerator, WithCall, WorkerRegistry,
|
||||
};
|
||||
use crate::{
|
||||
codegen::{
|
||||
classes::{ListType, NDArrayType, ProxyType, RangeType},
|
||||
concrete_type::ConcreteTypeStore,
|
||||
CodeGenContext, CodeGenLLVMOptions, CodeGenTargetMachineOptions, CodeGenTask,
|
||||
CodeGenerator, DefaultCodeGenerator, WithCall, WorkerRegistry,
|
||||
},
|
||||
symbol_resolver::{SymbolResolver, ValueEnum},
|
||||
toplevel::{
|
||||
composer::{ComposerConfig, TopLevelComposer},
|
||||
DefinitionId, FunInstance, TopLevelContext, TopLevelDef,
|
||||
},
|
||||
typecheck::{
|
||||
type_inferencer::{FunctionData, IdentifierInfo, Inferencer, PrimitiveStore},
|
||||
type_inferencer::{FunctionData, 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>,
|
||||
|
|
@ -67,7 +64,6 @@ impl SymbolResolver for Resolver {
|
|||
&self,
|
||||
_: StrRef,
|
||||
_: &mut CodeGenContext<'ctx, '_>,
|
||||
_: &mut dyn CodeGenerator,
|
||||
) -> Option<ValueEnum<'ctx>> {
|
||||
unimplemented!()
|
||||
}
|
||||
|
|
@ -142,8 +138,7 @@ fn test_primitives() {
|
|||
};
|
||||
let mut virtual_checks = Vec::new();
|
||||
let mut calls = HashMap::new();
|
||||
let mut identifiers: HashMap<_, _> =
|
||||
["a".into(), "b".into()].map(|id| (id, IdentifierInfo::default())).into();
|
||||
let mut identifiers: HashSet<_> = ["a".into(), "b".into()].into();
|
||||
let mut inferencer = Inferencer {
|
||||
top_level: &top_level,
|
||||
function_data: &mut function_data,
|
||||
|
|
@ -322,8 +317,7 @@ fn test_simple_call() {
|
|||
};
|
||||
let mut virtual_checks = Vec::new();
|
||||
let mut calls = HashMap::new();
|
||||
let mut identifiers: HashMap<_, _> =
|
||||
["a".into(), "foo".into()].map(|id| (id, IdentifierInfo::default())).into();
|
||||
let mut identifiers: HashSet<_> = ["a".into(), "foo".into()].into();
|
||||
let mut inferencer = Inferencer {
|
||||
top_level: &top_level,
|
||||
function_data: &mut function_data,
|
||||
|
|
|
|||
|
|
@ -19,10 +19,6 @@
|
|||
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;
|
||||
|
|
|
|||
|
|
@ -1,15 +1,7 @@
|
|||
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 std::fmt::Debug;
|
||||
use std::rc::Rc;
|
||||
use std::sync::Arc;
|
||||
use std::{collections::HashMap, collections::HashSet, fmt::Display};
|
||||
|
||||
use crate::{
|
||||
codegen::{CodeGenContext, CodeGenerator},
|
||||
|
|
@ -19,6 +11,10 @@ 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 {
|
||||
|
|
@ -369,7 +365,6 @@ 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>;
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,17 +1,17 @@
|
|||
use nac3parser::ast::fold::Fold;
|
||||
use std::rc::Rc;
|
||||
|
||||
use nac3parser::ast::{fold::Fold, ExprKind, Ident};
|
||||
|
||||
use super::*;
|
||||
use crate::{
|
||||
codegen::{expr::get_subst_key, stmt::exn_constructor},
|
||||
symbol_resolver::SymbolValue,
|
||||
typecheck::{
|
||||
type_inferencer::{FunctionData, IdentifierInfo, Inferencer},
|
||||
type_inferencer::{FunctionData, Inferencer},
|
||||
typedef::{TypeVar, VarMap},
|
||||
},
|
||||
};
|
||||
|
||||
use super::*;
|
||||
|
||||
pub struct ComposerConfig {
|
||||
pub kernel_ann: Option<&'static str>,
|
||||
pub kernel_invariant_ann: &'static str,
|
||||
|
|
@ -23,7 +23,7 @@ impl Default for ComposerConfig {
|
|||
}
|
||||
}
|
||||
|
||||
pub type DefAst = (Arc<RwLock<TopLevelDef>>, Option<Stmt<()>>);
|
||||
type DefAst = (Arc<RwLock<TopLevelDef>>, Option<Stmt<()>>);
|
||||
pub struct TopLevelComposer {
|
||||
// list of top level definitions, same as top level context
|
||||
pub definition_ast_list: Vec<DefAst>,
|
||||
|
|
@ -101,8 +101,7 @@ impl TopLevelComposer {
|
|||
.iter()
|
||||
.map(|def_ast| match *def_ast.0.read() {
|
||||
TopLevelDef::Class { name, .. } => name.to_string(),
|
||||
TopLevelDef::Function { simple_name, .. }
|
||||
| TopLevelDef::Variable { simple_name, .. } => simple_name.to_string(),
|
||||
TopLevelDef::Function { simple_name, .. } => simple_name.to_string(),
|
||||
})
|
||||
.collect_vec();
|
||||
|
||||
|
|
@ -382,87 +381,13 @@ impl TopLevelComposer {
|
|||
))
|
||||
}
|
||||
|
||||
ast::StmtKind::Assign { .. } => {
|
||||
// Assignment statements can assign to (and therefore create) more than one
|
||||
// variable, but this function only allows returning one set of symbol information.
|
||||
// We want to avoid changing this to return a `Vec` of symbol info, as this would
|
||||
// require `iter().next().unwrap()` on every variable created from a non-Assign
|
||||
// statement.
|
||||
//
|
||||
// Make callers use `register_top_level_var` instead, as it provides more
|
||||
// fine-grained control over which symbols to register, while also simplifying the
|
||||
// usage of this function.
|
||||
panic!("Registration of top-level Assign statements must use TopLevelComposer::register_top_level_var (at {})", ast.location);
|
||||
}
|
||||
|
||||
ast::StmtKind::AnnAssign { target, annotation, .. } => {
|
||||
let ExprKind::Name { id: name, .. } = target.node else {
|
||||
return Err(format!(
|
||||
"global variable declaration must be an identifier (at {})",
|
||||
target.location
|
||||
));
|
||||
};
|
||||
|
||||
self.register_top_level_var(
|
||||
name,
|
||||
Some(annotation.as_ref().clone()),
|
||||
resolver,
|
||||
mod_path,
|
||||
target.location,
|
||||
)
|
||||
}
|
||||
|
||||
_ => Err(format!(
|
||||
"registrations of constructs other than top level classes/functions/variables are not supported (at {})",
|
||||
"registrations of constructs other than top level classes/functions are not supported (at {})",
|
||||
ast.location
|
||||
)),
|
||||
}
|
||||
}
|
||||
|
||||
/// Registers a top-level variable with the given `name` into the composer.
|
||||
///
|
||||
/// `annotation` - The type annotation of the top-level variable, or [`None`] if no type
|
||||
/// annotation is provided.
|
||||
/// `location` - The location of the top-level variable.
|
||||
pub fn register_top_level_var(
|
||||
&mut self,
|
||||
name: Ident,
|
||||
annotation: Option<Expr>,
|
||||
resolver: Option<Arc<dyn SymbolResolver + Send + Sync>>,
|
||||
mod_path: &str,
|
||||
location: Location,
|
||||
) -> Result<(StrRef, DefinitionId, Option<Type>), String> {
|
||||
if self.keyword_list.contains(&name) {
|
||||
return Err(format!("cannot use keyword `{name}` as a class name (at {location})"));
|
||||
}
|
||||
|
||||
let global_var_name =
|
||||
if mod_path.is_empty() { name.to_string() } else { format!("{mod_path}.{name}") };
|
||||
|
||||
if !self.defined_names.insert(global_var_name.clone()) {
|
||||
return Err(format!(
|
||||
"global variable `{global_var_name}` defined twice (at {location})"
|
||||
));
|
||||
}
|
||||
|
||||
let ty_to_be_unified = self.unifier.get_dummy_var().ty;
|
||||
self.definition_ast_list.push((
|
||||
RwLock::new(Self::make_top_level_variable_def(
|
||||
global_var_name,
|
||||
name,
|
||||
// dummy here, unify with correct type later,
|
||||
ty_to_be_unified,
|
||||
annotation,
|
||||
resolver,
|
||||
Some(location),
|
||||
))
|
||||
.into(),
|
||||
None,
|
||||
));
|
||||
|
||||
Ok((name, DefinitionId(self.definition_ast_list.len() - 1), Some(ty_to_be_unified)))
|
||||
}
|
||||
|
||||
pub fn start_analysis(&mut self, inference: bool) -> Result<(), HashSet<String>> {
|
||||
self.analyze_top_level_class_type_var()?;
|
||||
self.analyze_top_level_class_bases()?;
|
||||
|
|
@ -471,7 +396,6 @@ impl TopLevelComposer {
|
|||
if inference {
|
||||
self.analyze_function_instance()?;
|
||||
}
|
||||
self.analyze_top_level_variables()?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
|
|
@ -509,7 +433,7 @@ impl TopLevelComposer {
|
|||
// things like `class A(Generic[T, V, ImportedModule.T])` is not supported
|
||||
// i.e. only simple names are allowed in the subscript
|
||||
// should update the TopLevelDef::Class.typevars and the TypeEnum::TObj.params
|
||||
ExprKind::Subscript { value, slice, .. }
|
||||
ast::ExprKind::Subscript { value, slice, .. }
|
||||
if {
|
||||
matches!(
|
||||
&value.node,
|
||||
|
|
@ -525,9 +449,9 @@ impl TopLevelComposer {
|
|||
}
|
||||
is_generic = true;
|
||||
|
||||
let type_var_list: Vec<&Expr<()>>;
|
||||
let type_var_list: Vec<&ast::Expr<()>>;
|
||||
// if `class A(Generic[T, V, G])`
|
||||
if let ExprKind::Tuple { elts, .. } = &slice.node {
|
||||
if let ast::ExprKind::Tuple { elts, .. } = &slice.node {
|
||||
type_var_list = elts.iter().collect_vec();
|
||||
// `class A(Generic[T])`
|
||||
} else {
|
||||
|
|
@ -576,7 +500,6 @@ 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() {
|
||||
|
|
@ -930,6 +853,7 @@ 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;
|
||||
|
|
@ -1038,18 +962,18 @@ impl TopLevelComposer {
|
|||
}
|
||||
}
|
||||
|
||||
let arg_with_default: Vec<(&ast::Located<ast::ArgData<()>>, Option<&Expr>)> = args
|
||||
.args
|
||||
.iter()
|
||||
.rev()
|
||||
.zip(
|
||||
args.defaults
|
||||
.iter()
|
||||
.rev()
|
||||
.map(|x| -> Option<&Expr> { Some(x) })
|
||||
.chain(std::iter::repeat(None)),
|
||||
)
|
||||
.collect_vec();
|
||||
let arg_with_default: Vec<(&ast::Located<ast::ArgData<()>>, Option<&ast::Expr>)> =
|
||||
args.args
|
||||
.iter()
|
||||
.rev()
|
||||
.zip(
|
||||
args.defaults
|
||||
.iter()
|
||||
.rev()
|
||||
.map(|x| -> Option<&ast::Expr> { Some(x) })
|
||||
.chain(std::iter::repeat(None)),
|
||||
)
|
||||
.collect_vec();
|
||||
|
||||
arg_with_default
|
||||
.iter()
|
||||
|
|
@ -1204,8 +1128,6 @@ 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;
|
||||
|
|
@ -1307,7 +1229,7 @@ impl TopLevelComposer {
|
|||
|
||||
let arg_with_default: Vec<(
|
||||
&ast::Located<ast::ArgData<()>>,
|
||||
Option<&Expr>,
|
||||
Option<&ast::Expr>,
|
||||
)> = args
|
||||
.args
|
||||
.iter()
|
||||
|
|
@ -1316,7 +1238,7 @@ impl TopLevelComposer {
|
|||
args.defaults
|
||||
.iter()
|
||||
.rev()
|
||||
.map(|x| -> Option<&Expr> { Some(x) })
|
||||
.map(|x| -> Option<&ast::Expr> { Some(x) })
|
||||
.chain(std::iter::repeat(None)),
|
||||
)
|
||||
.collect_vec();
|
||||
|
|
@ -1473,7 +1395,7 @@ impl TopLevelComposer {
|
|||
.map_err(|e| HashSet::from([e.to_display(unifier).to_string()]))?;
|
||||
}
|
||||
ast::StmtKind::AnnAssign { target, annotation, value, .. } => {
|
||||
if let ExprKind::Name { id: attr, .. } = &target.node {
|
||||
if let ast::ExprKind::Name { id: attr, .. } = &target.node {
|
||||
if defined_fields.insert(attr.to_string()) {
|
||||
let dummy_field_type = unifier.get_dummy_var().ty;
|
||||
|
||||
|
|
@ -1481,7 +1403,7 @@ impl TopLevelComposer {
|
|||
None => {
|
||||
// handle Kernel[T], KernelInvariant[T]
|
||||
let (annotation, mutable) = match &annotation.node {
|
||||
ExprKind::Subscript { value, slice, .. }
|
||||
ast::ExprKind::Subscript { value, slice, .. }
|
||||
if matches!(
|
||||
&value.node,
|
||||
ast::ExprKind::Name { id, .. } if id == &core_config.kernel_invariant_ann.into()
|
||||
|
|
@ -1489,7 +1411,7 @@ impl TopLevelComposer {
|
|||
{
|
||||
(slice, false)
|
||||
}
|
||||
ExprKind::Subscript { value, slice, .. }
|
||||
ast::ExprKind::Subscript { value, slice, .. }
|
||||
if matches!(
|
||||
&value.node,
|
||||
ast::ExprKind::Name { id, .. } if core_config.kernel_ann.map_or(false, |c| id == &c.into())
|
||||
|
|
@ -1507,13 +1429,13 @@ impl TopLevelComposer {
|
|||
Some(boxed_expr) => {
|
||||
// Class attributes are set as immutable regardless
|
||||
let (annotation, _) = match &annotation.node {
|
||||
ExprKind::Subscript { slice, .. } => (slice, false),
|
||||
ast::ExprKind::Subscript { slice, .. } => (slice, false),
|
||||
_ if core_config.kernel_ann.is_none() => (annotation, false),
|
||||
_ => continue,
|
||||
};
|
||||
|
||||
match &**boxed_expr {
|
||||
ast::Located {location: _, custom: (), node: ExprKind::Constant { value: v, kind: _ }} => {
|
||||
ast::Located {location: _, custom: (), node: ast::ExprKind::Constant { value: v, kind: _ }} => {
|
||||
// Restricting the types allowed to be defined as class attributes
|
||||
match v {
|
||||
ast::Constant::Bool(_) | ast::Constant::Str(_) | ast::Constant::Int(_) | ast::Constant::Float(_) => {}
|
||||
|
|
@ -1780,6 +1702,7 @@ 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 {
|
||||
|
|
@ -1899,12 +1822,7 @@ impl TopLevelComposer {
|
|||
if *name != init_str_id {
|
||||
unreachable!("must be init function here")
|
||||
}
|
||||
|
||||
let all_inited = Self::get_all_assigned_field(
|
||||
object_id.0,
|
||||
definition_ast_list,
|
||||
body.as_slice(),
|
||||
)?;
|
||||
let all_inited = Self::get_all_assigned_field(body.as_slice())?;
|
||||
for (f, _, _) in fields {
|
||||
if !all_inited.contains(f) {
|
||||
return Err(HashSet::from([
|
||||
|
|
@ -1922,8 +1840,6 @@ 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;
|
||||
|
|
@ -1961,296 +1877,272 @@ impl TopLevelComposer {
|
|||
if ast.is_none() {
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
let (name, simple_name, signature, resolver) = {
|
||||
let function_def = def.read();
|
||||
let TopLevelDef::Function { name, simple_name, signature, resolver, .. } =
|
||||
&*function_def
|
||||
let mut function_def = def.write();
|
||||
if let TopLevelDef::Function {
|
||||
instance_to_stmt,
|
||||
instance_to_symbol,
|
||||
name,
|
||||
simple_name,
|
||||
signature,
|
||||
resolver,
|
||||
..
|
||||
} = &mut *function_def
|
||||
{
|
||||
let signature_ty_enum = unifier.get_ty(*signature);
|
||||
let TypeEnum::TFunc(FunSignature { args, ret, vars }) = signature_ty_enum.as_ref()
|
||||
else {
|
||||
return Ok(());
|
||||
unreachable!("must be typeenum::tfunc")
|
||||
};
|
||||
|
||||
(name.clone(), *simple_name, *signature, resolver.clone())
|
||||
};
|
||||
let mut vars = vars.clone();
|
||||
// None if is not class method
|
||||
let uninst_self_type = {
|
||||
if let Some(class_id) = method_class.get(&DefinitionId(id)) {
|
||||
let class_def = definition_ast_list.get(class_id.0).unwrap();
|
||||
let class_def = class_def.0.read();
|
||||
let TopLevelDef::Class { type_vars, .. } = &*class_def else {
|
||||
unreachable!("must be class def")
|
||||
};
|
||||
|
||||
let signature_ty_enum = unifier.get_ty(signature);
|
||||
let TypeEnum::TFunc(FunSignature { args, ret, vars, .. }) = signature_ty_enum.as_ref()
|
||||
else {
|
||||
unreachable!("must be typeenum::tfunc")
|
||||
};
|
||||
let ty_ann = make_self_type_annotation(type_vars, *class_id);
|
||||
let self_ty = get_type_from_type_annotation_kinds(
|
||||
&def_list,
|
||||
unifier,
|
||||
primitives_ty,
|
||||
&ty_ann,
|
||||
&mut None,
|
||||
)?;
|
||||
vars.extend(type_vars.iter().map(|ty| {
|
||||
let TypeEnum::TVar { id, .. } = &*unifier.get_ty(*ty) else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
let mut vars = vars.clone();
|
||||
// None if is not class method
|
||||
let uninst_self_type = {
|
||||
if let Some(class_id) = method_class.get(&DefinitionId(id)) {
|
||||
let class_def = definition_ast_list.get(class_id.0).unwrap();
|
||||
let class_def = class_def.0.read();
|
||||
let TopLevelDef::Class { type_vars, .. } = &*class_def else {
|
||||
unreachable!("must be class def")
|
||||
(*id, *ty)
|
||||
}));
|
||||
Some((self_ty, type_vars.clone()))
|
||||
} else {
|
||||
None
|
||||
}
|
||||
};
|
||||
// carefully handle those with bounds, without bounds and no typevars
|
||||
// if class methods, `vars` also contains all class typevars here
|
||||
let (type_var_subst_comb, no_range_vars) = {
|
||||
let mut no_ranges: Vec<Type> = Vec::new();
|
||||
let var_combs = vars
|
||||
.values()
|
||||
.map(|ty| {
|
||||
unifier.get_instantiations(*ty).unwrap_or_else(|| {
|
||||
let TypeEnum::TVar { name, loc, is_const_generic: false, .. } =
|
||||
&*unifier.get_ty(*ty)
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
let rigid = unifier.get_fresh_rigid_var(*name, *loc).ty;
|
||||
no_ranges.push(rigid);
|
||||
vec![rigid]
|
||||
})
|
||||
})
|
||||
.multi_cartesian_product()
|
||||
.collect_vec();
|
||||
let mut result: Vec<VarMap> = Vec::default();
|
||||
for comb in var_combs {
|
||||
result.push(vars.keys().copied().zip(comb).collect());
|
||||
}
|
||||
// NOTE: if is empty, means no type var, append a empty subst, ok to do this?
|
||||
if result.is_empty() {
|
||||
result.push(VarMap::new());
|
||||
}
|
||||
(result, no_ranges)
|
||||
};
|
||||
|
||||
for subst in type_var_subst_comb {
|
||||
// for each instance
|
||||
let inst_ret = unifier.subst(*ret, &subst).unwrap_or(*ret);
|
||||
let inst_args = {
|
||||
args.iter()
|
||||
.map(|a| FuncArg {
|
||||
name: a.name,
|
||||
ty: unifier.subst(a.ty, &subst).unwrap_or(a.ty),
|
||||
default_value: a.default_value.clone(),
|
||||
is_vararg: false,
|
||||
})
|
||||
.collect_vec()
|
||||
};
|
||||
let self_type = {
|
||||
uninst_self_type.clone().map(|(self_type, type_vars)| {
|
||||
let subst_for_self = {
|
||||
let class_ty_var_ids = type_vars
|
||||
.iter()
|
||||
.map(|x| {
|
||||
if let TypeEnum::TVar { id, .. } = &*unifier.get_ty(*x) {
|
||||
*id
|
||||
} else {
|
||||
unreachable!("must be type var here");
|
||||
}
|
||||
})
|
||||
.collect::<HashSet<_>>();
|
||||
subst
|
||||
.iter()
|
||||
.filter_map(|(ty_var_id, ty_var_target)| {
|
||||
if class_ty_var_ids.contains(ty_var_id) {
|
||||
Some((*ty_var_id, *ty_var_target))
|
||||
} else {
|
||||
None
|
||||
}
|
||||
})
|
||||
.collect::<VarMap>()
|
||||
};
|
||||
unifier.subst(self_type, &subst_for_self).unwrap_or(self_type)
|
||||
})
|
||||
};
|
||||
let mut identifiers = {
|
||||
let mut result: HashSet<_> = HashSet::new();
|
||||
if self_type.is_some() {
|
||||
result.insert("self".into());
|
||||
}
|
||||
result.extend(inst_args.iter().map(|x| x.name));
|
||||
result
|
||||
};
|
||||
let mut calls: HashMap<CodeLocation, CallId> = HashMap::new();
|
||||
let mut inferencer = Inferencer {
|
||||
top_level: ctx.as_ref(),
|
||||
defined_identifiers: identifiers.clone(),
|
||||
function_data: &mut FunctionData {
|
||||
resolver: resolver.as_ref().unwrap().clone(),
|
||||
return_type: if unifier.unioned(inst_ret, primitives_ty.none) {
|
||||
None
|
||||
} else {
|
||||
Some(inst_ret)
|
||||
},
|
||||
// NOTE: allowed type vars
|
||||
bound_variables: no_range_vars.clone(),
|
||||
},
|
||||
unifier,
|
||||
variable_mapping: {
|
||||
let mut result: HashMap<StrRef, Type> = HashMap::new();
|
||||
if let Some(self_ty) = self_type {
|
||||
result.insert("self".into(), self_ty);
|
||||
}
|
||||
result.extend(inst_args.iter().map(|x| (x.name, x.ty)));
|
||||
result
|
||||
},
|
||||
primitives: primitives_ty,
|
||||
virtual_checks: &mut Vec::new(),
|
||||
calls: &mut calls,
|
||||
in_handler: false,
|
||||
};
|
||||
|
||||
let ty_ann = make_self_type_annotation(type_vars, *class_id);
|
||||
let self_ty = get_type_from_type_annotation_kinds(
|
||||
&def_list,
|
||||
unifier,
|
||||
primitives_ty,
|
||||
&ty_ann,
|
||||
&mut None,
|
||||
)?;
|
||||
vars.extend(type_vars.iter().map(|ty| {
|
||||
let TypeEnum::TVar { id, .. } = &*unifier.get_ty(*ty) else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
(*id, *ty)
|
||||
}));
|
||||
Some((self_ty, type_vars.clone()))
|
||||
} else {
|
||||
None
|
||||
}
|
||||
};
|
||||
// carefully handle those with bounds, without bounds and no typevars
|
||||
// if class methods, `vars` also contains all class typevars here
|
||||
let (type_var_subst_comb, no_range_vars) = {
|
||||
let mut no_ranges: Vec<Type> = Vec::new();
|
||||
let var_combs = vars
|
||||
.values()
|
||||
.map(|ty| {
|
||||
unifier.get_instantiations(*ty).unwrap_or_else(|| {
|
||||
let TypeEnum::TVar { name, loc, is_const_generic: false, .. } =
|
||||
&*unifier.get_ty(*ty)
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
let rigid = unifier.get_fresh_rigid_var(*name, *loc).ty;
|
||||
no_ranges.push(rigid);
|
||||
vec![rigid]
|
||||
})
|
||||
})
|
||||
.multi_cartesian_product()
|
||||
.collect_vec();
|
||||
let mut result: Vec<VarMap> = Vec::default();
|
||||
for comb in var_combs {
|
||||
result.push(vars.keys().copied().zip(comb).collect());
|
||||
}
|
||||
// NOTE: if is empty, means no type var, append a empty subst, ok to do this?
|
||||
if result.is_empty() {
|
||||
result.push(VarMap::new());
|
||||
}
|
||||
(result, no_ranges)
|
||||
};
|
||||
|
||||
for subst in type_var_subst_comb {
|
||||
// for each instance
|
||||
let inst_ret = unifier.subst(*ret, &subst).unwrap_or(*ret);
|
||||
let inst_args = {
|
||||
args.iter()
|
||||
.map(|a| FuncArg {
|
||||
name: a.name,
|
||||
ty: unifier.subst(a.ty, &subst).unwrap_or(a.ty),
|
||||
default_value: a.default_value.clone(),
|
||||
is_vararg: false,
|
||||
})
|
||||
.collect_vec()
|
||||
};
|
||||
let self_type = {
|
||||
uninst_self_type.clone().map(|(self_type, type_vars)| {
|
||||
let subst_for_self = {
|
||||
let class_ty_var_ids = type_vars
|
||||
.iter()
|
||||
.map(|x| {
|
||||
if let TypeEnum::TVar { id, .. } = &*unifier.get_ty(*x) {
|
||||
*id
|
||||
} else {
|
||||
unreachable!("must be type var here");
|
||||
}
|
||||
})
|
||||
.collect::<HashSet<_>>();
|
||||
subst
|
||||
.iter()
|
||||
.filter_map(|(ty_var_id, ty_var_target)| {
|
||||
if class_ty_var_ids.contains(ty_var_id) {
|
||||
Some((*ty_var_id, *ty_var_target))
|
||||
} else {
|
||||
None
|
||||
}
|
||||
})
|
||||
.collect::<VarMap>()
|
||||
};
|
||||
unifier.subst(self_type, &subst_for_self).unwrap_or(self_type)
|
||||
})
|
||||
};
|
||||
let mut identifiers = {
|
||||
let mut result = HashMap::new();
|
||||
if self_type.is_some() {
|
||||
result.insert("self".into(), IdentifierInfo::default());
|
||||
}
|
||||
result.extend(inst_args.iter().map(|x| (x.name, IdentifierInfo::default())));
|
||||
result
|
||||
};
|
||||
let mut calls: HashMap<CodeLocation, CallId> = HashMap::new();
|
||||
let mut inferencer = Inferencer {
|
||||
top_level: ctx.as_ref(),
|
||||
defined_identifiers: identifiers.clone(),
|
||||
function_data: &mut FunctionData {
|
||||
resolver: resolver.as_ref().unwrap().clone(),
|
||||
return_type: if unifier.unioned(inst_ret, primitives_ty.none) {
|
||||
None
|
||||
} else {
|
||||
Some(inst_ret)
|
||||
},
|
||||
// NOTE: allowed type vars
|
||||
bound_variables: no_range_vars.clone(),
|
||||
},
|
||||
unifier,
|
||||
variable_mapping: {
|
||||
let mut result: HashMap<StrRef, Type> = HashMap::new();
|
||||
if let Some(self_ty) = self_type {
|
||||
result.insert("self".into(), self_ty);
|
||||
}
|
||||
result.extend(inst_args.iter().map(|x| (x.name, x.ty)));
|
||||
result
|
||||
},
|
||||
primitives: primitives_ty,
|
||||
virtual_checks: &mut Vec::new(),
|
||||
calls: &mut calls,
|
||||
in_handler: false,
|
||||
};
|
||||
|
||||
let ast::StmtKind::FunctionDef { body, decorator_list, .. } =
|
||||
ast.clone().unwrap().node
|
||||
else {
|
||||
unreachable!("must be function def ast")
|
||||
};
|
||||
|
||||
if !decorator_list.is_empty() {
|
||||
if matches!(&decorator_list[0].node, ExprKind::Name { id, .. } if id == &"extern".into())
|
||||
let ast::StmtKind::FunctionDef { body, decorator_list, .. } =
|
||||
ast.clone().unwrap().node
|
||||
else {
|
||||
unreachable!("must be function def ast")
|
||||
};
|
||||
if !decorator_list.is_empty()
|
||||
&& matches!(&decorator_list[0].node,
|
||||
ast::ExprKind::Name{ id, .. } if id == &"extern".into())
|
||||
{
|
||||
instance_to_symbol.insert(String::new(), simple_name.to_string());
|
||||
continue;
|
||||
}
|
||||
if !decorator_list.is_empty()
|
||||
&& matches!(&decorator_list[0].node,
|
||||
ast::ExprKind::Name{ id, .. } if id == &"rpc".into())
|
||||
{
|
||||
let TopLevelDef::Function { instance_to_symbol, .. } = &mut *def.write()
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
instance_to_symbol.insert(String::new(), simple_name.to_string());
|
||||
continue;
|
||||
}
|
||||
|
||||
if matches!(&decorator_list[0].node, ExprKind::Name { id, .. } if id == &"rpc".into())
|
||||
{
|
||||
let TopLevelDef::Function { instance_to_symbol, .. } = &mut *def.write()
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
instance_to_symbol.insert(String::new(), simple_name.to_string());
|
||||
continue;
|
||||
}
|
||||
|
||||
if let ExprKind::Call { func, .. } = &decorator_list[0].node {
|
||||
if matches!(&func.node, ExprKind::Name { id, .. } if id == &"rpc".into()) {
|
||||
let TopLevelDef::Function { instance_to_symbol, .. } =
|
||||
&mut *def.write()
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
instance_to_symbol.insert(String::new(), simple_name.to_string());
|
||||
continue;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let fun_body =
|
||||
body.into_iter()
|
||||
let fun_body = body
|
||||
.into_iter()
|
||||
.map(|b| inferencer.fold_stmt(b))
|
||||
.collect::<Result<Vec<_>, _>>()?;
|
||||
|
||||
let returned = inferencer.check_block(fun_body.as_slice(), &mut identifiers)?;
|
||||
{
|
||||
// check virtuals
|
||||
let defs = ctx.definitions.read();
|
||||
for (subtype, base, loc) in &*inferencer.virtual_checks {
|
||||
let base_id = {
|
||||
let base = inferencer.unifier.get_ty(*base);
|
||||
if let TypeEnum::TObj { obj_id, .. } = &*base {
|
||||
*obj_id
|
||||
} else {
|
||||
return Err(HashSet::from([format!(
|
||||
"Base type should be a class (at {loc})"
|
||||
)]));
|
||||
}
|
||||
};
|
||||
let subtype_id = {
|
||||
let ty = inferencer.unifier.get_ty(*subtype);
|
||||
if let TypeEnum::TObj { obj_id, .. } = &*ty {
|
||||
*obj_id
|
||||
} else {
|
||||
let returned = inferencer.check_block(fun_body.as_slice(), &mut identifiers)?;
|
||||
{
|
||||
// check virtuals
|
||||
let defs = ctx.definitions.read();
|
||||
for (subtype, base, loc) in &*inferencer.virtual_checks {
|
||||
let base_id = {
|
||||
let base = inferencer.unifier.get_ty(*base);
|
||||
if let TypeEnum::TObj { obj_id, .. } = &*base {
|
||||
*obj_id
|
||||
} else {
|
||||
return Err(HashSet::from([format!(
|
||||
"Base type should be a class (at {loc})"
|
||||
)]));
|
||||
}
|
||||
};
|
||||
let subtype_id = {
|
||||
let ty = inferencer.unifier.get_ty(*subtype);
|
||||
if let TypeEnum::TObj { obj_id, .. } = &*ty {
|
||||
*obj_id
|
||||
} else {
|
||||
let base_repr = inferencer.unifier.stringify(*base);
|
||||
let subtype_repr = inferencer.unifier.stringify(*subtype);
|
||||
return Err(HashSet::from([format!(
|
||||
"Expected a subtype of {base_repr}, but got {subtype_repr} (at {loc})"),
|
||||
]));
|
||||
}
|
||||
};
|
||||
let subtype_entry = defs[subtype_id.0].read();
|
||||
let TopLevelDef::Class { ancestors, .. } = &*subtype_entry else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
let m = ancestors.iter()
|
||||
.find(|kind| matches!(kind, TypeAnnotation::CustomClass { id, .. } if *id == base_id));
|
||||
if m.is_none() {
|
||||
let base_repr = inferencer.unifier.stringify(*base);
|
||||
let subtype_repr = inferencer.unifier.stringify(*subtype);
|
||||
return Err(HashSet::from([format!(
|
||||
"Expected a subtype of {base_repr}, but got {subtype_repr} (at {loc})"),
|
||||
]));
|
||||
}
|
||||
};
|
||||
let subtype_entry = defs[subtype_id.0].read();
|
||||
let TopLevelDef::Class { ancestors, .. } = &*subtype_entry else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
let m = ancestors.iter()
|
||||
.find(|kind| matches!(kind, TypeAnnotation::CustomClass { id, .. } if *id == base_id));
|
||||
if m.is_none() {
|
||||
let base_repr = inferencer.unifier.stringify(*base);
|
||||
let subtype_repr = inferencer.unifier.stringify(*subtype);
|
||||
return Err(HashSet::from([format!(
|
||||
"Expected a subtype of {base_repr}, but got {subtype_repr} (at {loc})"),
|
||||
]));
|
||||
}
|
||||
}
|
||||
}
|
||||
if !unifier.unioned(inst_ret, primitives_ty.none) && !returned {
|
||||
let def_ast_list = &definition_ast_list;
|
||||
let ret_str = unifier.internal_stringify(
|
||||
inst_ret,
|
||||
&mut |id| {
|
||||
let TopLevelDef::Class { name, .. } = &*def_ast_list[id].0.read()
|
||||
else {
|
||||
unreachable!("must be class id here")
|
||||
};
|
||||
if !unifier.unioned(inst_ret, primitives_ty.none) && !returned {
|
||||
let def_ast_list = &definition_ast_list;
|
||||
let ret_str = unifier.internal_stringify(
|
||||
inst_ret,
|
||||
&mut |id| {
|
||||
let TopLevelDef::Class { name, .. } = &*def_ast_list[id].0.read()
|
||||
else {
|
||||
unreachable!("must be class id here")
|
||||
};
|
||||
|
||||
name.to_string()
|
||||
name.to_string()
|
||||
},
|
||||
&mut |id| format!("typevar{id}"),
|
||||
&mut None,
|
||||
);
|
||||
return Err(HashSet::from([format!(
|
||||
"expected return type of `{}` in function `{}` (at {})",
|
||||
ret_str,
|
||||
name,
|
||||
ast.as_ref().unwrap().location
|
||||
)]));
|
||||
}
|
||||
|
||||
instance_to_stmt.insert(
|
||||
get_subst_key(
|
||||
unifier,
|
||||
self_type,
|
||||
&subst,
|
||||
Some(&vars.keys().copied().collect()),
|
||||
),
|
||||
FunInstance {
|
||||
body: Arc::new(fun_body),
|
||||
unifier_id: 0,
|
||||
calls: Arc::new(calls),
|
||||
subst,
|
||||
},
|
||||
&mut |id| format!("typevar{id}"),
|
||||
&mut None,
|
||||
);
|
||||
return Err(HashSet::from([format!(
|
||||
"expected return type of `{}` in function `{}` (at {})",
|
||||
ret_str,
|
||||
name,
|
||||
ast.as_ref().unwrap().location
|
||||
)]));
|
||||
}
|
||||
|
||||
let TopLevelDef::Function { instance_to_stmt, .. } = &mut *def.write() else {
|
||||
unreachable!()
|
||||
};
|
||||
instance_to_stmt.insert(
|
||||
get_subst_key(
|
||||
unifier,
|
||||
self_type,
|
||||
&subst,
|
||||
Some(&vars.keys().copied().collect()),
|
||||
),
|
||||
FunInstance {
|
||||
body: Arc::new(fun_body),
|
||||
unifier_id: 0,
|
||||
calls: Arc::new(calls),
|
||||
subst,
|
||||
},
|
||||
);
|
||||
}
|
||||
|
||||
Ok(())
|
||||
};
|
||||
|
||||
for (id, (def, ast)) in self.definition_ast_list.iter().enumerate().skip(self.builtin_num) {
|
||||
if ast.is_none() {
|
||||
continue;
|
||||
|
|
@ -2264,59 +2156,4 @@ impl TopLevelComposer {
|
|||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Step 6. Analyze and populate the types of global variables.
|
||||
fn analyze_top_level_variables(&mut self) -> Result<(), HashSet<String>> {
|
||||
let def_list = &self.definition_ast_list;
|
||||
let temp_def_list = self.extract_def_list();
|
||||
let unifier = &mut self.unifier;
|
||||
let primitives_store = &self.primitives_ty;
|
||||
|
||||
let mut analyze = |variable_def: &Arc<RwLock<TopLevelDef>>| -> Result<_, HashSet<String>> {
|
||||
let TopLevelDef::Variable { ty: dummy_ty, ty_decl, resolver, loc, .. } =
|
||||
&*variable_def.read()
|
||||
else {
|
||||
// not top level variable def, skip
|
||||
return Ok(());
|
||||
};
|
||||
|
||||
let resolver = &**resolver.as_ref().unwrap();
|
||||
|
||||
if let Some(ty_decl) = ty_decl {
|
||||
let ty_annotation = parse_ast_to_type_annotation_kinds(
|
||||
resolver,
|
||||
&temp_def_list,
|
||||
unifier,
|
||||
primitives_store,
|
||||
ty_decl,
|
||||
HashMap::new(),
|
||||
)?;
|
||||
let ty_from_ty_annotation = get_type_from_type_annotation_kinds(
|
||||
&temp_def_list,
|
||||
unifier,
|
||||
primitives_store,
|
||||
&ty_annotation,
|
||||
&mut None,
|
||||
)?;
|
||||
|
||||
unifier.unify(*dummy_ty, ty_from_ty_annotation).map_err(|e| {
|
||||
HashSet::from([e.at(Some(loc.unwrap())).to_display(unifier).to_string()])
|
||||
})?;
|
||||
}
|
||||
|
||||
Ok(())
|
||||
};
|
||||
|
||||
let mut errors = HashSet::new();
|
||||
for (variable_def, _) in def_list.iter().skip(self.builtin_num) {
|
||||
if let Err(e) = analyze(variable_def) {
|
||||
errors.extend(e);
|
||||
}
|
||||
}
|
||||
if !errors.is_empty() {
|
||||
return Err(errors);
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,15 +1,13 @@
|
|||
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 nac3parser::ast::{Constant, Location};
|
||||
use strum::IntoEnumIterator;
|
||||
use strum_macros::EnumIter;
|
||||
|
||||
use nac3parser::ast::{Constant, ExprKind, Location};
|
||||
|
||||
use super::{numpy::unpack_ndarray_var_tys, *};
|
||||
use crate::{
|
||||
symbol_resolver::SymbolValue,
|
||||
typecheck::typedef::{into_var_map, iter_type_vars, Mapping, TypeVarId, VarMap},
|
||||
};
|
||||
use super::*;
|
||||
|
||||
/// All primitive types and functions in nac3core.
|
||||
#[derive(Clone, Copy, Debug, EnumIter, PartialEq, Eq)]
|
||||
|
|
@ -53,6 +51,17 @@ pub enum PrimDef {
|
|||
FunNpArray,
|
||||
FunNpEye,
|
||||
FunNpIdentity,
|
||||
FunNpArange,
|
||||
|
||||
// NumPy view functions
|
||||
FunNpBroadcastTo,
|
||||
FunNpReshape,
|
||||
FunNpTranspose,
|
||||
|
||||
// NumPy NDArray property getters
|
||||
FunNpSize,
|
||||
FunNpShape,
|
||||
FunNpStrides,
|
||||
|
||||
// Miscellaneous NumPy & SciPy functions
|
||||
FunNpRound,
|
||||
|
|
@ -101,8 +110,6 @@ pub enum PrimDef {
|
|||
FunNpLdExp,
|
||||
FunNpHypot,
|
||||
FunNpNextAfter,
|
||||
FunNpTranspose,
|
||||
FunNpReshape,
|
||||
|
||||
// Linalg functions
|
||||
FunNpDot,
|
||||
|
|
@ -239,6 +246,17 @@ impl PrimDef {
|
|||
PrimDef::FunNpArray => fun("np_array", None),
|
||||
PrimDef::FunNpEye => fun("np_eye", None),
|
||||
PrimDef::FunNpIdentity => fun("np_identity", None),
|
||||
PrimDef::FunNpArange => fun("np_arange", None),
|
||||
|
||||
// NumPy view functions
|
||||
PrimDef::FunNpBroadcastTo => fun("np_broadcast_to", None),
|
||||
PrimDef::FunNpReshape => fun("np_reshape", None),
|
||||
PrimDef::FunNpTranspose => fun("np_transpose", None),
|
||||
|
||||
// NumPy NDArray property getters
|
||||
PrimDef::FunNpSize => fun("np_size", None),
|
||||
PrimDef::FunNpShape => fun("np_shape", None),
|
||||
PrimDef::FunNpStrides => fun("np_strides", None),
|
||||
|
||||
// Miscellaneous NumPy & SciPy functions
|
||||
PrimDef::FunNpRound => fun("np_round", None),
|
||||
|
|
@ -287,8 +305,6 @@ 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),
|
||||
|
|
@ -389,9 +405,6 @@ impl TopLevelDef {
|
|||
r
|
||||
}
|
||||
),
|
||||
TopLevelDef::Variable { name, ty, .. } => {
|
||||
format!("Variable {{ name: {name:?}, ty: {:?} }}", unifier.stringify(*ty),)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -593,18 +606,6 @@ impl TopLevelComposer {
|
|||
}
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn make_top_level_variable_def(
|
||||
name: String,
|
||||
simple_name: StrRef,
|
||||
ty: Type,
|
||||
ty_decl: Option<Expr>,
|
||||
resolver: Option<Arc<dyn SymbolResolver + Send + Sync>>,
|
||||
loc: Option<Location>,
|
||||
) -> TopLevelDef {
|
||||
TopLevelDef::Variable { name, simple_name, ty, ty_decl, resolver, loc }
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn make_class_method_name(mut class_name: String, method_name: &str) -> String {
|
||||
class_name.push('.');
|
||||
|
|
@ -750,16 +751,7 @@ impl TopLevelComposer {
|
|||
)
|
||||
}
|
||||
|
||||
/// This function returns the fields that have been initialized in the `__init__` function of a class
|
||||
/// The function takes as input:
|
||||
/// * `class_id`: The `object_id` of the class whose function is being evaluated (check `TopLevelDef::Class`)
|
||||
/// * `definition_ast_list`: A list of ast definitions and statements defined in `TopLevelComposer`
|
||||
/// * `stmts`: The body of function being parsed. Each statment is analyzed to check varaible initialization statements
|
||||
pub fn get_all_assigned_field(
|
||||
class_id: usize,
|
||||
definition_ast_list: &Vec<DefAst>,
|
||||
stmts: &[Stmt<()>],
|
||||
) -> Result<HashSet<StrRef>, HashSet<String>> {
|
||||
pub fn get_all_assigned_field(stmts: &[Stmt<()>]) -> Result<HashSet<StrRef>, HashSet<String>> {
|
||||
let mut result = HashSet::new();
|
||||
for s in stmts {
|
||||
match &s.node {
|
||||
|
|
@ -795,138 +787,30 @@ impl TopLevelComposer {
|
|||
// TODO: do not check for For and While?
|
||||
ast::StmtKind::For { body, orelse, .. }
|
||||
| ast::StmtKind::While { body, orelse, .. } => {
|
||||
result.extend(Self::get_all_assigned_field(
|
||||
class_id,
|
||||
definition_ast_list,
|
||||
body.as_slice(),
|
||||
)?);
|
||||
result.extend(Self::get_all_assigned_field(
|
||||
class_id,
|
||||
definition_ast_list,
|
||||
orelse.as_slice(),
|
||||
)?);
|
||||
result.extend(Self::get_all_assigned_field(body.as_slice())?);
|
||||
result.extend(Self::get_all_assigned_field(orelse.as_slice())?);
|
||||
}
|
||||
ast::StmtKind::If { body, orelse, .. } => {
|
||||
let inited_for_sure = Self::get_all_assigned_field(
|
||||
class_id,
|
||||
definition_ast_list,
|
||||
body.as_slice(),
|
||||
)?
|
||||
.intersection(&Self::get_all_assigned_field(
|
||||
class_id,
|
||||
definition_ast_list,
|
||||
orelse.as_slice(),
|
||||
)?)
|
||||
.copied()
|
||||
.collect::<HashSet<_>>();
|
||||
let inited_for_sure = Self::get_all_assigned_field(body.as_slice())?
|
||||
.intersection(&Self::get_all_assigned_field(orelse.as_slice())?)
|
||||
.copied()
|
||||
.collect::<HashSet<_>>();
|
||||
result.extend(inited_for_sure);
|
||||
}
|
||||
ast::StmtKind::Try { body, orelse, finalbody, .. } => {
|
||||
let inited_for_sure = Self::get_all_assigned_field(
|
||||
class_id,
|
||||
definition_ast_list,
|
||||
body.as_slice(),
|
||||
)?
|
||||
.intersection(&Self::get_all_assigned_field(
|
||||
class_id,
|
||||
definition_ast_list,
|
||||
orelse.as_slice(),
|
||||
)?)
|
||||
.copied()
|
||||
.collect::<HashSet<_>>();
|
||||
let inited_for_sure = Self::get_all_assigned_field(body.as_slice())?
|
||||
.intersection(&Self::get_all_assigned_field(orelse.as_slice())?)
|
||||
.copied()
|
||||
.collect::<HashSet<_>>();
|
||||
result.extend(inited_for_sure);
|
||||
result.extend(Self::get_all_assigned_field(
|
||||
class_id,
|
||||
definition_ast_list,
|
||||
finalbody.as_slice(),
|
||||
)?);
|
||||
result.extend(Self::get_all_assigned_field(finalbody.as_slice())?);
|
||||
}
|
||||
ast::StmtKind::With { body, .. } => {
|
||||
result.extend(Self::get_all_assigned_field(
|
||||
class_id,
|
||||
definition_ast_list,
|
||||
body.as_slice(),
|
||||
)?);
|
||||
}
|
||||
// Variables Initialized in function calls
|
||||
ast::StmtKind::Expr { value, .. } => {
|
||||
let ExprKind::Call { func, .. } = &value.node else {
|
||||
continue;
|
||||
};
|
||||
let ExprKind::Attribute { value, attr, .. } = &func.node else {
|
||||
continue;
|
||||
};
|
||||
let ExprKind::Name { id, .. } = &value.node else {
|
||||
continue;
|
||||
};
|
||||
// Need to consider the two cases:
|
||||
// Case 1) Call to class function i.e. id = `self`
|
||||
// Case 2) Call to class ancestor function i.e. id = ancestor_name
|
||||
// We leave checking whether function in case 2 belonged to class ancestor or not to type checker
|
||||
//
|
||||
// According to current handling of `self`, function definition are fixed and do not change regardless
|
||||
// of which object is passed as `self` i.e. virtual polymorphism is not supported
|
||||
// Therefore, we change class id for case 2 to reflect behavior of our compiler
|
||||
|
||||
let class_name = if *id == "self".into() {
|
||||
let ast::StmtKind::ClassDef { name, .. } =
|
||||
&definition_ast_list[class_id].1.as_ref().unwrap().node
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
name
|
||||
} else {
|
||||
id
|
||||
};
|
||||
|
||||
let parent_method = definition_ast_list.iter().find_map(|def| {
|
||||
let (
|
||||
class_def,
|
||||
Some(ast::Located {
|
||||
node: ast::StmtKind::ClassDef { name, body, .. },
|
||||
..
|
||||
}),
|
||||
) = &def
|
||||
else {
|
||||
return None;
|
||||
};
|
||||
let TopLevelDef::Class { object_id: class_id, .. } = &*class_def.read()
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
if name == class_name {
|
||||
body.iter().find_map(|m| {
|
||||
let ast::StmtKind::FunctionDef { name, body, .. } = &m.node else {
|
||||
return None;
|
||||
};
|
||||
if *name == *attr {
|
||||
return Some((body.clone(), class_id.0));
|
||||
}
|
||||
None
|
||||
})
|
||||
} else {
|
||||
None
|
||||
}
|
||||
});
|
||||
|
||||
// If method body is none then method does not exist
|
||||
if let Some((method_body, class_id)) = parent_method {
|
||||
result.extend(Self::get_all_assigned_field(
|
||||
class_id,
|
||||
definition_ast_list,
|
||||
method_body.as_slice(),
|
||||
)?);
|
||||
} else {
|
||||
return Err(HashSet::from([format!(
|
||||
"{}.{} not found in class {class_name} at {}",
|
||||
*id, *attr, value.location
|
||||
)]));
|
||||
}
|
||||
result.extend(Self::get_all_assigned_field(body.as_slice())?);
|
||||
}
|
||||
ast::StmtKind::Pass { .. }
|
||||
| ast::StmtKind::Assert { .. }
|
||||
| ast::StmtKind::AnnAssign { .. } => {}
|
||||
| ast::StmtKind::Expr { .. } => {}
|
||||
|
||||
_ => {
|
||||
unimplemented!()
|
||||
|
|
@ -1134,3 +1018,23 @@ 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)
|
||||
}
|
||||
|
|
|
|||
|
|
@ -6,36 +6,37 @@ use std::{
|
|||
sync::Arc,
|
||||
};
|
||||
|
||||
use inkwell::values::BasicValueEnum;
|
||||
use itertools::Itertools;
|
||||
use parking_lot::RwLock;
|
||||
|
||||
use nac3parser::ast::{self, Expr, Location, Stmt, StrRef};
|
||||
|
||||
use super::codegen::CodeGenContext;
|
||||
use super::typecheck::type_inferencer::PrimitiveStore;
|
||||
use super::typecheck::typedef::{
|
||||
FunSignature, FuncArg, SharedUnifier, Type, TypeEnum, Unifier, VarMap,
|
||||
};
|
||||
use crate::{
|
||||
codegen::{CodeGenContext, CodeGenerator},
|
||||
codegen::CodeGenerator,
|
||||
symbol_resolver::{SymbolResolver, ValueEnum},
|
||||
typecheck::{
|
||||
type_inferencer::{CodeLocation, PrimitiveStore},
|
||||
typedef::{
|
||||
CallId, FunSignature, FuncArg, SharedUnifier, Type, TypeEnum, TypeVarId, Unifier,
|
||||
VarMap,
|
||||
},
|
||||
type_inferencer::CodeLocation,
|
||||
typedef::{CallId, TypeVarId},
|
||||
},
|
||||
};
|
||||
use composer::*;
|
||||
use type_annotation::*;
|
||||
use inkwell::values::BasicValueEnum;
|
||||
use itertools::Itertools;
|
||||
use nac3parser::ast::{self, Location, Stmt, StrRef};
|
||||
use parking_lot::RwLock;
|
||||
|
||||
#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Copy, Hash, Debug)]
|
||||
pub struct DefinitionId(pub usize);
|
||||
|
||||
pub mod builtins;
|
||||
pub mod composer;
|
||||
pub mod helper;
|
||||
pub mod numpy;
|
||||
pub mod option;
|
||||
pub mod type_annotation;
|
||||
use composer::*;
|
||||
use type_annotation::*;
|
||||
#[cfg(test)]
|
||||
mod test;
|
||||
pub mod type_annotation;
|
||||
|
||||
#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Copy, Hash, Debug)]
|
||||
pub struct DefinitionId(pub usize);
|
||||
|
||||
type GenCallCallback = dyn for<'ctx, 'a> Fn(
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
|
|
@ -130,14 +131,14 @@ pub enum TopLevelDef {
|
|||
/// Function instance to symbol mapping
|
||||
///
|
||||
/// * Key: String representation of type variable values, sorted by variable ID in ascending
|
||||
/// order, including type variables associated with the class.
|
||||
/// order, including type variables associated with the class.
|
||||
/// * Value: Function symbol name.
|
||||
instance_to_symbol: HashMap<String, String>,
|
||||
/// Function instances to annotated AST mapping
|
||||
///
|
||||
/// * Key: String representation of type variable values, sorted by variable ID in ascending
|
||||
/// order, including type variables associated with the class. Excluding rigid type
|
||||
/// variables.
|
||||
/// order, including type variables associated with the class. Excluding rigid type
|
||||
/// variables.
|
||||
///
|
||||
/// Rigid type variables that would be substituted when the function is instantiated.
|
||||
instance_to_stmt: HashMap<String, FunInstance>,
|
||||
|
|
@ -148,25 +149,6 @@ pub enum TopLevelDef {
|
|||
/// Definition location.
|
||||
loc: Option<Location>,
|
||||
},
|
||||
Variable {
|
||||
/// Qualified name of the global variable, should be unique globally.
|
||||
name: String,
|
||||
|
||||
/// Simple name, the same as in method/function definition.
|
||||
simple_name: StrRef,
|
||||
|
||||
/// Type of the global variable.
|
||||
ty: Type,
|
||||
|
||||
/// The declared type of the global variable, or [`None`] if no type annotation is provided.
|
||||
ty_decl: Option<Expr>,
|
||||
|
||||
/// Symbol resolver of the module defined the class.
|
||||
resolver: Option<Arc<dyn SymbolResolver + Send + Sync>>,
|
||||
|
||||
/// Definition location.
|
||||
loc: Option<Location>,
|
||||
},
|
||||
}
|
||||
|
||||
pub struct TopLevelContext {
|
||||
|
|
|
|||
|
|
@ -1,17 +1,18 @@
|
|||
use itertools::Itertools;
|
||||
|
||||
use super::helper::PrimDef;
|
||||
use crate::typecheck::{
|
||||
type_inferencer::PrimitiveStore,
|
||||
typedef::{Type, TypeEnum, TypeVarId, Unifier, VarMap},
|
||||
use crate::{
|
||||
toplevel::helper::PrimDef,
|
||||
typecheck::{
|
||||
type_inferencer::PrimitiveStore,
|
||||
typedef::{Type, TypeEnum, TypeVarId, Unifier, VarMap},
|
||||
},
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
/// Creates a `ndarray` [`Type`] with the given type arguments.
|
||||
///
|
||||
/// * `dtype` - The element type of the `ndarray`, or [`None`] if the type variable is not
|
||||
/// specialized.
|
||||
/// specialized.
|
||||
/// * `ndims` - The number of dimensions of the `ndarray`, or [`None`] if the type variable is not
|
||||
/// specialized.
|
||||
/// specialized.
|
||||
pub fn make_ndarray_ty(
|
||||
unifier: &mut Unifier,
|
||||
primitives: &PrimitiveStore,
|
||||
|
|
@ -24,9 +25,9 @@ pub fn make_ndarray_ty(
|
|||
/// Substitutes type variables in `ndarray`.
|
||||
///
|
||||
/// * `dtype` - The element type of the `ndarray`, or [`None`] if the type variable is not
|
||||
/// specialized.
|
||||
/// specialized.
|
||||
/// * `ndims` - The number of dimensions of the `ndarray`, or [`None`] if the type variable is not
|
||||
/// specialized.
|
||||
/// specialized.
|
||||
pub fn subst_ndarray_tvars(
|
||||
unifier: &mut Unifier,
|
||||
ndarray: Type,
|
||||
|
|
|
|||
|
|
@ -0,0 +1,46 @@
|
|||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
toplevel::helper::PrimDef,
|
||||
typecheck::{
|
||||
type_inferencer::PrimitiveStore,
|
||||
typedef::{Type, TypeEnum, Unifier, VarMap},
|
||||
},
|
||||
};
|
||||
|
||||
// TODO: This entire module is duplicated code (numpy.rs also has these kinds of things)
|
||||
|
||||
/// Creates a `option` [`Type`] with the given type arguments.
|
||||
///
|
||||
/// * `dtype` - The element type of the `option`, or [`None`] if the type variable is not
|
||||
/// specialized.
|
||||
/// * `ndims` - The number of dimensions of the `option`, or [`None`] if the type variable is not
|
||||
/// specialized.
|
||||
pub fn make_option_ty(
|
||||
unifier: &mut Unifier,
|
||||
primitives: &PrimitiveStore,
|
||||
dtype: Option<Type>,
|
||||
) -> Type {
|
||||
subst_option_tvars(unifier, primitives.option, dtype)
|
||||
}
|
||||
|
||||
/// Substitutes type variables in `option`.
|
||||
///
|
||||
/// * `dtype` - The element type of the `option`, or [`None`] if the type variable is not
|
||||
/// specialized.
|
||||
pub fn subst_option_tvars(unifier: &mut Unifier, option: Type, dtype: Option<Type>) -> Type {
|
||||
let TypeEnum::TObj { obj_id, params, .. } = &*unifier.get_ty_immutable(option) else {
|
||||
panic!("Expected `option` to be TObj, but got {}", unifier.stringify(option))
|
||||
};
|
||||
debug_assert_eq!(*obj_id, PrimDef::Option.id());
|
||||
|
||||
let tvar_ids = params.iter().map(|(obj_id, _)| *obj_id).collect_vec();
|
||||
debug_assert_eq!(tvar_ids.len(), 1);
|
||||
|
||||
let mut tvar_subst = VarMap::new();
|
||||
if let Some(dtype) = dtype {
|
||||
tvar_subst.insert(tvar_ids[0], dtype);
|
||||
}
|
||||
|
||||
unifier.subst(option, &tvar_subst).unwrap_or(option)
|
||||
}
|
||||
|
|
@ -1,23 +1,21 @@
|
|||
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::{helper::PrimDef, DefinitionId, *};
|
||||
use super::*;
|
||||
use crate::toplevel::helper::PrimDef;
|
||||
use crate::typecheck::typedef::into_var_map;
|
||||
use crate::{
|
||||
codegen::CodeGenContext,
|
||||
symbol_resolver::{SymbolResolver, ValueEnum},
|
||||
toplevel::DefinitionId,
|
||||
typecheck::{
|
||||
type_inferencer::PrimitiveStore,
|
||||
typedef::{into_var_map, Type, Unifier},
|
||||
typedef::{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>>,
|
||||
|
|
@ -64,7 +62,6 @@ impl SymbolResolver for Resolver {
|
|||
&self,
|
||||
_: StrRef,
|
||||
_: &mut CodeGenContext<'ctx, '_>,
|
||||
_: &mut dyn CodeGenerator,
|
||||
) -> Option<ValueEnum<'ctx>> {
|
||||
unimplemented!()
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,12 +1,9 @@
|
|||
use strum::IntoEnumIterator;
|
||||
|
||||
use super::*;
|
||||
use crate::symbol_resolver::SymbolValue;
|
||||
use crate::toplevel::helper::{PrimDef, PrimDefDetails};
|
||||
use crate::typecheck::typedef::VarMap;
|
||||
use nac3parser::ast::Constant;
|
||||
|
||||
use super::{
|
||||
helper::{PrimDef, PrimDefDetails},
|
||||
*,
|
||||
};
|
||||
use crate::{symbol_resolver::SymbolValue, typecheck::typedef::VarMap};
|
||||
use strum::IntoEnumIterator;
|
||||
|
||||
#[derive(Clone, Debug)]
|
||||
pub enum TypeAnnotation {
|
||||
|
|
@ -67,9 +64,9 @@ impl TypeAnnotation {
|
|||
/// Parses an AST expression `expr` into a [`TypeAnnotation`].
|
||||
///
|
||||
/// * `locked` - A [`HashMap`] containing the IDs of known definitions, mapped to a [`Vec`] of all
|
||||
/// generic variables associated with the definition.
|
||||
/// generic variables associated with the definition.
|
||||
/// * `type_var` - The type variable associated with the type argument currently being parsed. Pass
|
||||
/// [`None`] when this function is invoked externally.
|
||||
/// [`None`] when this function is invoked externally.
|
||||
pub fn parse_ast_to_type_annotation_kinds<T, S: std::hash::BuildHasher + Clone>(
|
||||
resolver: &(dyn SymbolResolver + Send + Sync),
|
||||
top_level_defs: &[Arc<RwLock<TopLevelDef>>],
|
||||
|
|
|
|||
|
|
@ -1,19 +1,13 @@
|
|||
use std::{
|
||||
collections::{HashMap, HashSet},
|
||||
iter::once,
|
||||
};
|
||||
use crate::toplevel::helper::PrimDef;
|
||||
|
||||
use super::type_inferencer::Inferencer;
|
||||
use super::typedef::{Type, TypeEnum};
|
||||
use nac3parser::ast::{
|
||||
self, Constant, Expr, ExprKind,
|
||||
Operator::{LShift, RShift},
|
||||
Stmt, StmtKind, StrRef,
|
||||
};
|
||||
|
||||
use super::{
|
||||
type_inferencer::{DeclarationSource, IdentifierInfo, Inferencer},
|
||||
typedef::{Type, TypeEnum},
|
||||
};
|
||||
use crate::toplevel::helper::PrimDef;
|
||||
use std::{collections::HashSet, iter::once};
|
||||
|
||||
impl<'a> Inferencer<'a> {
|
||||
fn should_have_value(&mut self, expr: &Expr<Option<Type>>) -> Result<(), HashSet<String>> {
|
||||
|
|
@ -27,29 +21,15 @@ impl<'a> Inferencer<'a> {
|
|||
fn check_pattern(
|
||||
&mut self,
|
||||
pattern: &Expr<Option<Type>>,
|
||||
defined_identifiers: &mut HashMap<StrRef, IdentifierInfo>,
|
||||
defined_identifiers: &mut HashSet<StrRef>,
|
||||
) -> 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 `id` refers to a declared symbol, reject this assignment if it is used in the
|
||||
// context of an (implicit) global variable
|
||||
if let Some(id_info) = defined_identifiers.get(id) {
|
||||
if matches!(
|
||||
id_info.source,
|
||||
DeclarationSource::Global { is_explicit: Some(false) }
|
||||
) {
|
||||
return Err(HashSet::from([format!(
|
||||
"cannot access local variable '{id}' before it is declared (at {})",
|
||||
pattern.location
|
||||
)]));
|
||||
}
|
||||
}
|
||||
|
||||
if !defined_identifiers.contains_key(id) {
|
||||
defined_identifiers.insert(*id, IdentifierInfo::default());
|
||||
if !defined_identifiers.contains(id) {
|
||||
defined_identifiers.insert(*id);
|
||||
}
|
||||
self.should_have_value(pattern)?;
|
||||
Ok(())
|
||||
|
|
@ -89,7 +69,7 @@ impl<'a> Inferencer<'a> {
|
|||
fn check_expr(
|
||||
&mut self,
|
||||
expr: &Expr<Option<Type>>,
|
||||
defined_identifiers: &mut HashMap<StrRef, IdentifierInfo>,
|
||||
defined_identifiers: &mut HashSet<StrRef>,
|
||||
) -> Result<(), HashSet<String>> {
|
||||
// there are some cases where the custom field is None
|
||||
if let Some(ty) = &expr.custom {
|
||||
|
|
@ -100,7 +80,7 @@ impl<'a> Inferencer<'a> {
|
|||
return Err(HashSet::from([format!(
|
||||
"expected concrete type at {} but got {}",
|
||||
expr.location,
|
||||
self.unifier.get_ty(*ty).get_type_name()
|
||||
self.unifier.stringify(*ty)
|
||||
)]));
|
||||
}
|
||||
}
|
||||
|
|
@ -110,7 +90,7 @@ impl<'a> Inferencer<'a> {
|
|||
return Ok(());
|
||||
}
|
||||
self.should_have_value(expr)?;
|
||||
if !defined_identifiers.contains_key(id) {
|
||||
if !defined_identifiers.contains(id) {
|
||||
match self.function_data.resolver.get_symbol_type(
|
||||
self.unifier,
|
||||
&self.top_level.definitions.read(),
|
||||
|
|
@ -118,22 +98,7 @@ impl<'a> Inferencer<'a> {
|
|||
*id,
|
||||
) {
|
||||
Ok(_) => {
|
||||
let is_global = self.is_id_global(*id);
|
||||
|
||||
defined_identifiers.insert(
|
||||
*id,
|
||||
IdentifierInfo {
|
||||
source: match is_global {
|
||||
Some(true) => {
|
||||
DeclarationSource::Global { is_explicit: Some(false) }
|
||||
}
|
||||
Some(false) => {
|
||||
DeclarationSource::Global { is_explicit: None }
|
||||
}
|
||||
None => DeclarationSource::Local,
|
||||
},
|
||||
},
|
||||
);
|
||||
self.defined_identifiers.insert(*id);
|
||||
}
|
||||
Err(e) => {
|
||||
return Err(HashSet::from([format!(
|
||||
|
|
@ -206,7 +171,9 @@ impl<'a> Inferencer<'a> {
|
|||
let mut defined_identifiers = defined_identifiers.clone();
|
||||
for arg in &args.args {
|
||||
// TODO: should we check the types here?
|
||||
defined_identifiers.entry(arg.node.arg).or_default();
|
||||
if !defined_identifiers.contains(&arg.node.arg) {
|
||||
defined_identifiers.insert(arg.node.arg);
|
||||
}
|
||||
}
|
||||
self.check_expr(body, &mut defined_identifiers)?;
|
||||
}
|
||||
|
|
@ -245,23 +212,19 @@ impl<'a> Inferencer<'a> {
|
|||
/// This is a workaround preventing the caller from using a variable `alloca`-ed in the body, which
|
||||
/// is freed when the function returns.
|
||||
fn check_return_value_ty(&mut self, ret_ty: Type) -> bool {
|
||||
if cfg!(feature = "no-escape-analysis") {
|
||||
true
|
||||
} else {
|
||||
match &*self.unifier.get_ty_immutable(ret_ty) {
|
||||
TypeEnum::TObj { .. } => [
|
||||
self.primitives.int32,
|
||||
self.primitives.int64,
|
||||
self.primitives.uint32,
|
||||
self.primitives.uint64,
|
||||
self.primitives.float,
|
||||
self.primitives.bool,
|
||||
]
|
||||
.iter()
|
||||
.any(|allowed_ty| self.unifier.unioned(ret_ty, *allowed_ty)),
|
||||
TypeEnum::TTuple { ty, .. } => ty.iter().all(|t| self.check_return_value_ty(*t)),
|
||||
_ => false,
|
||||
}
|
||||
match &*self.unifier.get_ty_immutable(ret_ty) {
|
||||
TypeEnum::TObj { .. } => [
|
||||
self.primitives.int32,
|
||||
self.primitives.int64,
|
||||
self.primitives.uint32,
|
||||
self.primitives.uint64,
|
||||
self.primitives.float,
|
||||
self.primitives.bool,
|
||||
]
|
||||
.iter()
|
||||
.any(|allowed_ty| self.unifier.unioned(ret_ty, *allowed_ty)),
|
||||
TypeEnum::TTuple { ty, .. } => ty.iter().all(|t| self.check_return_value_ty(*t)),
|
||||
_ => false,
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -269,7 +232,7 @@ impl<'a> Inferencer<'a> {
|
|||
fn check_stmt(
|
||||
&mut self,
|
||||
stmt: &Stmt<Option<Type>>,
|
||||
defined_identifiers: &mut HashMap<StrRef, IdentifierInfo>,
|
||||
defined_identifiers: &mut HashSet<StrRef>,
|
||||
) -> Result<bool, HashSet<String>> {
|
||||
match &stmt.node {
|
||||
StmtKind::For { target, iter, body, orelse, .. } => {
|
||||
|
|
@ -295,11 +258,9 @@ 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.keys() {
|
||||
if !defined_identifiers.contains_key(ident)
|
||||
&& orelse_identifiers.contains_key(ident)
|
||||
{
|
||||
defined_identifiers.insert(*ident, IdentifierInfo::default());
|
||||
for ident in &body_identifiers {
|
||||
if !defined_identifiers.contains(ident) && orelse_identifiers.contains(ident) {
|
||||
defined_identifiers.insert(*ident);
|
||||
}
|
||||
}
|
||||
Ok(body_returned && orelse_returned)
|
||||
|
|
@ -330,7 +291,7 @@ 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, IdentifierInfo::default());
|
||||
defined_identifiers.insert(*name);
|
||||
}
|
||||
self.check_block(body, &mut defined_identifiers)?;
|
||||
}
|
||||
|
|
@ -394,44 +355,6 @@ impl<'a> Inferencer<'a> {
|
|||
}
|
||||
Ok(true)
|
||||
}
|
||||
StmtKind::Global { names, .. } => {
|
||||
for id in names {
|
||||
if let Some(id_info) = defined_identifiers.get(id) {
|
||||
if id_info.source == DeclarationSource::Local {
|
||||
return Err(HashSet::from([format!(
|
||||
"name '{id}' is referenced prior to global declaration at {}",
|
||||
stmt.location,
|
||||
)]));
|
||||
}
|
||||
|
||||
continue;
|
||||
}
|
||||
|
||||
match self.function_data.resolver.get_symbol_type(
|
||||
self.unifier,
|
||||
&self.top_level.definitions.read(),
|
||||
self.primitives,
|
||||
*id,
|
||||
) {
|
||||
Ok(_) => {
|
||||
defined_identifiers.insert(
|
||||
*id,
|
||||
IdentifierInfo {
|
||||
source: DeclarationSource::Global { is_explicit: Some(true) },
|
||||
},
|
||||
);
|
||||
}
|
||||
Err(e) => {
|
||||
return Err(HashSet::from([format!(
|
||||
"type error at identifier `{}` ({}) at {}",
|
||||
id, e, stmt.location
|
||||
)]))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Ok(false)
|
||||
}
|
||||
// break, raise, etc.
|
||||
_ => Ok(false),
|
||||
}
|
||||
|
|
@ -440,7 +363,7 @@ impl<'a> Inferencer<'a> {
|
|||
pub fn check_block(
|
||||
&mut self,
|
||||
block: &[Stmt<Option<Type>>],
|
||||
defined_identifiers: &mut HashMap<StrRef, IdentifierInfo>,
|
||||
defined_identifiers: &mut HashSet<StrRef>,
|
||||
) -> Result<bool, HashSet<String>> {
|
||||
let mut ret = false;
|
||||
for stmt in block {
|
||||
|
|
|
|||
|
|
@ -1,21 +1,17 @@
|
|||
use std::{cmp::max, collections::HashMap, rc::Rc};
|
||||
|
||||
use itertools::{iproduct, Itertools};
|
||||
use strum::IntoEnumIterator;
|
||||
|
||||
use nac3parser::ast::{Cmpop, Operator, StrRef, Unaryop};
|
||||
|
||||
use super::{
|
||||
use crate::symbol_resolver::SymbolValue;
|
||||
use crate::toplevel::helper::{arraylike_flatten_element_type, arraylike_get_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 crate::{
|
||||
symbol_resolver::SymbolValue,
|
||||
toplevel::{
|
||||
helper::PrimDef,
|
||||
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
|
||||
},
|
||||
};
|
||||
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;
|
||||
|
||||
/// The variant of a binary operator.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
|
|
@ -524,53 +520,41 @@ pub fn typeof_binop(
|
|||
}
|
||||
|
||||
Operator::MatMult => {
|
||||
// NOTE: NumPy matmul's LHS and RHS must both be ndarrays. Scalars are not allowed.
|
||||
match (&*unifier.get_ty(lhs), &*unifier.get_ty(rhs)) {
|
||||
(
|
||||
TypeEnum::TObj { obj_id: lhs_obj_id, .. },
|
||||
TypeEnum::TObj { obj_id: rhs_obj_id, .. },
|
||||
) if *lhs_obj_id == primitives.ndarray.obj_id(unifier).unwrap()
|
||||
&& *rhs_obj_id == primitives.ndarray.obj_id(unifier).unwrap() =>
|
||||
{
|
||||
// LHS and RHS have valid types
|
||||
}
|
||||
_ => {
|
||||
let lhs_str = unifier.stringify(lhs);
|
||||
let rhs_str = unifier.stringify(rhs);
|
||||
return Err(format!("ndarray.__matmul__ only accepts ndarray operands, but left operand has type {lhs_str}, and right operand has type {rhs_str}"));
|
||||
}
|
||||
let lhs_dtype = arraylike_flatten_element_type(unifier, lhs);
|
||||
let rhs_dtype = arraylike_flatten_element_type(unifier, rhs);
|
||||
|
||||
let lhs_ndims = arraylike_get_ndims(unifier, lhs);
|
||||
let rhs_ndims = arraylike_get_ndims(unifier, rhs);
|
||||
|
||||
if !(unifier.unioned(lhs_dtype, primitives.float)
|
||||
&& unifier.unioned(rhs_dtype, primitives.float))
|
||||
{
|
||||
return Err(format!(
|
||||
"ndarray.__matmul__ only supports float64 operations, but LHS has type {} and RHS has type {}",
|
||||
unifier.stringify(lhs),
|
||||
unifier.stringify(rhs)
|
||||
));
|
||||
}
|
||||
|
||||
let (_, 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()
|
||||
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()
|
||||
}
|
||||
_ => unreachable!(),
|
||||
(1, 1) => 0,
|
||||
(1, _) => rhs_ndims - 1,
|
||||
(_, 1) => lhs_ndims - 1,
|
||||
(m, n) => max(m, n),
|
||||
};
|
||||
|
||||
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"
|
||||
))
|
||||
}
|
||||
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))
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -701,7 +685,6 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
|
|||
bool: bool_t,
|
||||
uint32: uint32_t,
|
||||
uint64: uint64_t,
|
||||
str: str_t,
|
||||
list: list_t,
|
||||
ndarray: ndarray_t,
|
||||
..
|
||||
|
|
@ -747,9 +730,6 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
|
|||
impl_sign(unifier, store, bool_t, Some(int32_t));
|
||||
impl_eq(unifier, store, bool_t, &[bool_t, ndarray_bool_t], None);
|
||||
|
||||
/* str ========= */
|
||||
impl_cmpop(unifier, store, str_t, &[str_t], &[Cmpop::Eq, Cmpop::NotEq], Some(bool_t));
|
||||
|
||||
/* list ======== */
|
||||
impl_binop(unifier, store, list_t, &[list_t], Some(list_t), &[Operator::Add]);
|
||||
impl_binop(unifier, store, list_t, &[int32_t, int64_t], Some(list_t), &[Operator::Mult]);
|
||||
|
|
@ -773,7 +753,7 @@ 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_t], Some(ndarray_t));
|
||||
impl_matmul(unifier, store, ndarray_t, &[ndarray_unsized_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);
|
||||
|
|
|
|||
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Reference in New Issue